Docs build of a1937d488997169991c0c82edfcf04c2f7307613

Author: MatplotlibCircleBot

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: fee21aba0ad35d7353946075a94d4ae9
+config: 557cc0acf1b07daed2c7eaeb647fd9d9
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_downloads/2dcollections3d.ipynb

@@ -1,52 +1,52 @@
 {
   "metadata": {
     "language_info": {
+      "version": "3.5.3",
       "codemirror_mode": {
         "version": 3,
         "name": "ipython"
       },
-      "name": "python",
-      "pygments_lexer": "ipython3",
       "file_extension": ".py",
-      "mimetype": "text/x-python",
+      "name": "python",
       "nbconvert_exporter": "python",
-      "version": "3.5.2"
+      "mimetype": "text/x-python",
+      "pygments_lexer": "ipython3"
     },
     "kernelspec": {
       "display_name": "Python 3",
-      "name": "python3",
-      "language": "python"
+      "language": "python",
+      "name": "python3"
     }
   },
   "cells": [
     {
-      "source": [
-        "%matplotlib inline"
-      ],
+      "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
-      "outputs": [],
-      "cell_type": "code",
-      "execution_count": null
+      "source": [
+        "%matplotlib inline"
+      ],
+      "outputs": []
     },
     {
+      "metadata": {},
+      "cell_type": "markdown",
       "source": [
         "\n# Plot 2D data on 3D plot\n\n\nDemonstrates using ax.plot's zdir keyword to plot 2D data on\nselective axes of a 3D plot.\n\n"
-      ],
-      "metadata": {},
-      "cell_type": "markdown"
+      ]
     },
     {
-      "source": [
-        "from mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nax = fig.gca(projection='3d')\n\n# Plot a sin curve using the x and y axes.\nx = np.linspace(0, 1, 100)\ny = np.sin(x * 2 * np.pi) / 2 + 0.5\nax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')\n\n# Plot scatterplot data (20 2D points per colour) on the x and z axes.\ncolors = ('r', 'g', 'b', 'k')\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nx = np.random.sample(20 * len(colors))\ny = np.random.sample(20 * len(colors))\nc_list = []\nfor c in colors:\n    c_list.extend([c] * 20)\n# By using zdir='y', the y value of these points is fixed to the zs value 0\n# and the (x,y) points are plotted on the x and z axes.\nax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')\n\n# Make legend, set axes limits and labels\nax.legend()\nax.set_xlim(0, 1)\nax.set_ylim(0, 1)\nax.set_zlim(0, 1)\nax.set_xlabel('X')\nax.set_ylabel('Y')\nax.set_zlabel('Z')\n\n# Customize the view angle so it's easier to see that the scatter points lie\n# on the plane y=0\nax.view_init(elev=20., azim=-35)\n\nplt.show()"
-      ],
+      "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
-      "outputs": [],
-      "cell_type": "code",
-      "execution_count": null
+      "source": [
+        "from mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfig = plt.figure()\nax = fig.gca(projection='3d')\n\n# Plot a sin curve using the x and y axes.\nx = np.linspace(0, 1, 100)\ny = np.sin(x * 2 * np.pi) / 2 + 0.5\nax.plot(x, y, zs=0, zdir='z', label='curve in (x,y)')\n\n# Plot scatterplot data (20 2D points per colour) on the x and z axes.\ncolors = ('r', 'g', 'b', 'k')\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\nx = np.random.sample(20 * len(colors))\ny = np.random.sample(20 * len(colors))\nc_list = []\nfor c in colors:\n    c_list.extend([c] * 20)\n# By using zdir='y', the y value of these points is fixed to the zs value 0\n# and the (x,y) points are plotted on the x and z axes.\nax.scatter(x, y, zs=0, zdir='y', c=c_list, label='points in (x,z)')\n\n# Make legend, set axes limits and labels\nax.legend()\nax.set_xlim(0, 1)\nax.set_ylim(0, 1)\nax.set_zlim(0, 1)\nax.set_xlabel('X')\nax.set_ylabel('Y')\nax.set_zlabel('Z')\n\n# Customize the view angle so it's easier to see that the scatter points lie\n# on the plane y=0\nax.view_init(elev=20., azim=-35)\n\nplt.show()"
+      ],
+      "outputs": []
     }
   ],
   "nbformat": 4,

_downloads/3D.ipynb

@@ -1,52 +1,52 @@
 {
   "metadata": {
     "language_info": {
+      "version": "3.5.3",
       "codemirror_mode": {
         "version": 3,
         "name": "ipython"
       },
-      "name": "python",
-      "pygments_lexer": "ipython3",
       "file_extension": ".py",
-      "mimetype": "text/x-python",
+      "name": "python",
       "nbconvert_exporter": "python",
-      "version": "3.5.2"
+      "mimetype": "text/x-python",
+      "pygments_lexer": "ipython3"
     },
     "kernelspec": {
       "display_name": "Python 3",
-      "name": "python3",
-      "language": "python"
+      "language": "python",
+      "name": "python3"
     }
   },
   "cells": [
     {
-      "source": [
-        "%matplotlib inline"
-      ],
+      "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
-      "outputs": [],
-      "cell_type": "code",
-      "execution_count": null
+      "source": [
+        "%matplotlib inline"
+      ],
+      "outputs": []
     },
     {
+      "metadata": {},
+      "cell_type": "markdown",
       "source": [
         "\n# Frontpage 3D example\n\n\nThis example reproduces the frontpage 3D example.\n\n\n"
-      ],
-      "metadata": {},
-      "cell_type": "markdown"
+      ]
     },
     {
-      "source": [
-        "from mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cbook\nfrom matplotlib import cm\nfrom matplotlib.colors import LightSource\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfilename = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)\nwith np.load(filename) as dem:\n    z = dem['elevation']\n    nrows, ncols = z.shape\n    x = np.linspace(dem['xmin'], dem['xmax'], ncols)\n    y = np.linspace(dem['ymin'], dem['ymax'], nrows)\n    x, y = np.meshgrid(x, y)\n\nregion = np.s_[5:50, 5:50]\nx, y, z = x[region], y[region], z[region]\n\nfig, ax = plt.subplots(subplot_kw=dict(projection='3d'))\n\nls = LightSource(270, 45)\n# To use a custom hillshading mode, override the built-in shading and pass\n# in the rgb colors of the shaded surface calculated from \"shade\".\nrgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft')\nsurf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,\n                       linewidth=0, antialiased=False, shade=False)\nax.set_xticks([])\nax.set_yticks([])\nax.set_zticks([])\nfig.savefig(\"surface3d_frontpage.png\", dpi=25)  # results in 160x120 px image"
-      ],
+      "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
-      "outputs": [],
-      "cell_type": "code",
-      "execution_count": null
+      "source": [
+        "from mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cbook\nfrom matplotlib import cm\nfrom matplotlib.colors import LightSource\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfilename = cbook.get_sample_data('jacksboro_fault_dem.npz', asfileobj=False)\nwith np.load(filename) as dem:\n    z = dem['elevation']\n    nrows, ncols = z.shape\n    x = np.linspace(dem['xmin'], dem['xmax'], ncols)\n    y = np.linspace(dem['ymin'], dem['ymax'], nrows)\n    x, y = np.meshgrid(x, y)\n\nregion = np.s_[5:50, 5:50]\nx, y, z = x[region], y[region], z[region]\n\nfig, ax = plt.subplots(subplot_kw=dict(projection='3d'))\n\nls = LightSource(270, 45)\n# To use a custom hillshading mode, override the built-in shading and pass\n# in the rgb colors of the shaded surface calculated from \"shade\".\nrgb = ls.shade(z, cmap=cm.gist_earth, vert_exag=0.1, blend_mode='soft')\nsurf = ax.plot_surface(x, y, z, rstride=1, cstride=1, facecolors=rgb,\n                       linewidth=0, antialiased=False, shade=False)\nax.set_xticks([])\nax.set_yticks([])\nax.set_zticks([])\nfig.savefig(\"surface3d_frontpage.png\", dpi=25)  # results in 160x120 px image"
+      ],
+      "outputs": []
     }
   ],
   "nbformat": 4,

_downloads/3d_bars.ipynb

@@ -1,8 +1,27 @@
 {
+  "metadata": {
+    "language_info": {
+      "version": "3.5.3",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      },
+      "file_extension": ".py",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "mimetype": "text/x-python",
+      "pygments_lexer": "ipython3"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
   "cells": [
     {
-      "cell_type": "code",
       "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
@@ -12,15 +31,15 @@
       "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {},
+      "cell_type": "markdown",
       "source": [
         "\n# Demo of 3D bar charts\n\n\nA basic demo of how to plot 3D bars with and without\nshading.\n\n\n"
       ]
     },
     {
-      "cell_type": "code",
       "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
@@ -30,25 +49,6 @@
       "outputs": []
     }
   ],
-  "nbformat_minor": 0,
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "version": "3.5.2",
-      "nbconvert_exporter": "python",
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "version": 3,
-        "name": "ipython"
-      },
-      "file_extension": ".py",
-      "pygments_lexer": "ipython3",
-      "name": "python"
-    }
-  },
-  "nbformat": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

_downloads/accented_text.ipynb

@@ -1,8 +1,27 @@
 {
+  "metadata": {
+    "language_info": {
+      "version": "3.5.3",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      },
+      "file_extension": ".py",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "mimetype": "text/x-python",
+      "pygments_lexer": "ipython3"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
   "cells": [
     {
-      "cell_type": "code",
       "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
@@ -12,15 +31,15 @@
       "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {},
+      "cell_type": "markdown",
       "source": [
         "\n# Using accented text in matplotlib\n\n\nMatplotlib supports accented characters via TeX mathtext or unicode.\n\nUsing mathtext, the following accents are provided: \\hat, \\breve, \\grave, \\bar,\n\\acute, \\tilde, \\vec, \\dot, \\ddot.  All of them have the same syntax,\ne.g., to make an overbar you do \\bar{o} or to make an o umlaut you do\n\\ddot{o}.  The shortcuts are also provided, e.g.,: \\\"o \\'e \\`e \\~n \\.x\n\\^y\n\n\n"
       ]
     },
     {
-      "cell_type": "code",
       "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
@@ -30,25 +49,6 @@
       "outputs": []
     }
   ],
-  "nbformat_minor": 0,
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "version": "3.5.2",
-      "nbconvert_exporter": "python",
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "version": 3,
-        "name": "ipython"
-      },
-      "file_extension": ".py",
-      "pygments_lexer": "ipython3",
-      "name": "python"
-    }
-  },
-  "nbformat": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }

_downloads/advanced_hillshading.ipynb

@@ -1,8 +1,27 @@
 {
+  "metadata": {
+    "language_info": {
+      "version": "3.5.3",
+      "codemirror_mode": {
+        "version": 3,
+        "name": "ipython"
+      },
+      "file_extension": ".py",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "mimetype": "text/x-python",
+      "pygments_lexer": "ipython3"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
   "cells": [
     {
-      "cell_type": "code",
       "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
@@ -12,15 +31,15 @@
       "outputs": []
     },
     {
-      "cell_type": "markdown",
       "metadata": {},
+      "cell_type": "markdown",
       "source": [
         "\n# Hillshading\n\n\nDemonstrates a few common tricks with shaded plots.\n\n"
       ]
     },
     {
-      "cell_type": "code",
       "execution_count": null,
+      "cell_type": "code",
       "metadata": {
         "collapsed": false
       },
@@ -30,25 +49,6 @@
       "outputs": []
     }
   ],
-  "nbformat_minor": 0,
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "version": "3.5.2",
-      "nbconvert_exporter": "python",
-      "mimetype": "text/x-python",
-      "codemirror_mode": {
-        "version": 3,
-        "name": "ipython"
-      },
-      "file_extension": ".py",
-      "pygments_lexer": "ipython3",
-      "name": "python"
-    }
-  },
-  "nbformat": 4
+  "nbformat": 4,
+  "nbformat_minor": 0
 }