Merge pull request matplotlib#8899 from tonyyli/sep-multiplot

Separating examples with multiple plots into separate blocks

Author: phobson

examples/animation/basic_example.py

@@ -16,6 +16,8 @@ def update_line(num, data, line):
     line.set_data(data[..., :num])
     return line,
 
+###############################################################################
+
 fig1 = plt.figure()
 
 # Fixing random state for reproducibility
@@ -32,6 +34,8 @@ def update_line(num, data, line):
 
 # To save the animation, use the command: line_ani.save('lines.mp4')
 
+###############################################################################
+
 fig2 = plt.figure()
 
 x = np.arange(-9, 10)

examples/api/filled_step.py

@@ -191,7 +191,9 @@ def stack_hist(ax, stacked_data, sty_cycle, bottoms=None,
 stack_data = np.random.randn(4, 12250)
 dict_data = OrderedDict(zip((c['label'] for c in label_cycle), stack_data))
 
-# work with plain arrays
+###############################################################################
+# Work with plain arrays
+
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4.5), tight_layout=True)
 arts = stack_hist(ax1, stack_data, color_cycle + label_cycle + hatch_cycle,
                   hist_func=hist_func)
@@ -204,7 +206,8 @@ def stack_hist(ax, stacked_data, sty_cycle, bottoms=None,
 ax2.set_xlabel('counts')
 ax2.set_ylabel('x')
 
-# work with labeled data
+###############################################################################
+# Work with labeled data
 
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 4.5),
                                tight_layout=True, sharey=True)

examples/api/sankey_basics.py

@@ -11,31 +11,42 @@
 from matplotlib.sankey import Sankey
 
 
+###############################################################################
 # Example 1 -- Mostly defaults
+#
 # This demonstrates how to create a simple diagram by implicitly calling the
 # Sankey.add() method and by appending finish() to the call to the class.
+
 Sankey(flows=[0.25, 0.15, 0.60, -0.20, -0.15, -0.05, -0.50, -0.10],
        labels=['', '', '', 'First', 'Second', 'Third', 'Fourth', 'Fifth'],
        orientations=[-1, 1, 0, 1, 1, 1, 0, -1]).finish()
 plt.title("The default settings produce a diagram like this.")
+
+###############################################################################
 # Notice:
-#   1. Axes weren't provided when Sankey() was instantiated, so they were
-#      created automatically.
-#   2. The scale argument wasn't necessary since the data was already
-#      normalized.
-#   3. By default, the lengths of the paths are justified.
+#
+# 1. Axes weren't provided when Sankey() was instantiated, so they were
+#    created automatically.
+# 2. The scale argument wasn't necessary since the data was already
+#    normalized.
+# 3. By default, the lengths of the paths are justified.
 
+
+###############################################################################
 # Example 2
+#
 # This demonstrates:
-#   1. Setting one path longer than the others
-#   2. Placing a label in the middle of the diagram
-#   3. Using the scale argument to normalize the flows
-#   4. Implicitly passing keyword arguments to PathPatch()
-#   5. Changing the angle of the arrow heads
-#   6. Changing the offset between the tips of the paths and their labels
-#   7. Formatting the numbers in the path labels and the associated unit
-#   8. Changing the appearance of the patch and the labels after the figure is
-#      created
+#
+# 1. Setting one path longer than the others
+# 2. Placing a label in the middle of the diagram
+# 3. Using the scale argument to normalize the flows
+# 4. Implicitly passing keyword arguments to PathPatch()
+# 5. Changing the angle of the arrow heads
+# 6. Changing the offset between the tips of the paths and their labels
+# 7. Formatting the numbers in the path labels and the associated unit
+# 8. Changing the appearance of the patch and the labels after the figure is
+#    created
+
 fig = plt.figure()
 ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[],
                      title="Flow Diagram of a Widget")
@@ -51,18 +62,26 @@
 diagrams = sankey.finish()
 diagrams[0].texts[-1].set_color('r')
 diagrams[0].text.set_fontweight('bold')
+
+###############################################################################
 # Notice:
-#   1. Since the sum of the flows is nonzero, the width of the trunk isn't
-#      uniform.  If verbose.level is helpful (in matplotlibrc), a message is
-#      given in the terminal window.
-#   2. The second flow doesn't appear because its value is zero.  Again, if
-#      verbose.level is helpful, a message is given in the terminal window.
+#
+# 1. Since the sum of the flows is nonzero, the width of the trunk isn't
+#    uniform.  If verbose.level is helpful (in matplotlibrc), a message is
+#    given in the terminal window.
+# 2. The second flow doesn't appear because its value is zero.  Again, if
+#    verbose.level is helpful, a message is given in the terminal window.
+
 
+###############################################################################
 # Example 3
+#
 # This demonstrates:
-#   1. Connecting two systems
-#   2. Turning off the labels of the quantities
-#   3. Adding a legend
+#
+# 1. Connecting two systems
+# 2. Turning off the labels of the quantities
+# 3. Adding a legend
+
 fig = plt.figure()
 ax = fig.add_subplot(1, 1, 1, xticks=[], yticks=[], title="Two Systems")
 flows = [0.25, 0.15, 0.60, -0.10, -0.05, -0.25, -0.15, -0.10, -0.35]
@@ -74,6 +93,8 @@
 diagrams = sankey.finish()
 diagrams[-1].patch.set_hatch('/')
 plt.legend(loc='best')
+
+###############################################################################
 # Notice that only one connection is specified, but the systems form a
 # circuit since: (1) the lengths of the paths are justified and (2) the
 # orientation and ordering of the flows is mirrored.

examples/event_handling/figure_axes_enter_leave.py

@@ -33,6 +33,8 @@ def leave_figure(event):
     event.canvas.figure.patch.set_facecolor('grey')
     event.canvas.draw()
 
+###############################################################################
+
 fig1, (ax, ax2) = plt.subplots(2, 1)
 fig1.suptitle('mouse hover over figure or axes to trigger events')
 
@@ -41,6 +43,8 @@ def leave_figure(event):
 fig1.canvas.mpl_connect('axes_enter_event', enter_axes)
 fig1.canvas.mpl_connect('axes_leave_event', leave_axes)
 
+###############################################################################
+
 fig2, (ax, ax2) = plt.subplots(2, 1)
 fig2.suptitle('mouse hover over figure or axes to trigger events')
 

examples/images_contours_and_fields/contour_demo.py

@@ -26,36 +26,43 @@
 # difference of Gaussians
 Z = 10.0 * (Z2 - Z1)
 
-
+###############################################################################
 # Create a simple contour plot with labels using default colors.  The
 # inline argument to clabel will control whether the labels are draw
 # over the line segments of the contour, removing the lines beneath
 # the label
+
 plt.figure()
 CS = plt.contour(X, Y, Z)
 plt.clabel(CS, inline=1, fontsize=10)
 plt.title('Simplest default with labels')
 
 
+###############################################################################
 # contour labels can be placed manually by providing list of positions
 # (in data coordinate). See ginput_manual_clabel.py for interactive
 # placement.
+
 plt.figure()
 CS = plt.contour(X, Y, Z)
 manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]
 plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)
 plt.title('labels at selected locations')
 
 
+###############################################################################
 # You can force all the contours to be the same color.
+
 plt.figure()
 CS = plt.contour(X, Y, Z, 6,
                  colors='k',  # negative contours will be dashed by default
                  )
 plt.clabel(CS, fontsize=9, inline=1)
 plt.title('Single color - negative contours dashed')
 
+###############################################################################
 # You can set negative contours to be solid instead of dashed:
+
 matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
 plt.figure()
 CS = plt.contour(X, Y, Z, 6,
@@ -65,7 +72,9 @@
 plt.title('Single color - negative contours solid')
 
 
+###############################################################################
 # And you can manually specify the colors of the contour
+
 plt.figure()
 CS = plt.contour(X, Y, Z, 6,
                  linewidths=np.arange(.5, 4, .5),
@@ -75,8 +84,10 @@
 plt.title('Crazy lines')
 
 
+###############################################################################
 # Or you can use a colormap to specify the colors; the default
 # colormap will be used for the contour lines
+
 plt.figure()
 im = plt.imshow(Z, interpolation='bilinear', origin='lower',
                 cmap=cm.gray, extent=(-3, 3, -2, 2))

examples/images_contours_and_fields/contour_label_demo.py

@@ -18,8 +18,9 @@
 matplotlib.rcParams['xtick.direction'] = 'out'
 matplotlib.rcParams['ytick.direction'] = 'out'
 
-##################################################
+###############################################################################
 # Define our surface
+
 delta = 0.025
 x = np.arange(-3.0, 3.0, delta)
 y = np.arange(-2.0, 2.0, delta)
@@ -29,9 +30,10 @@
 # difference of Gaussians
 Z = 10.0 * (Z2 - Z1)
 
-##################################################
+###############################################################################
 # Make contour labels using creative float classes
 # Follows suggestion of Manuel Metz
+
 plt.figure()
 
 # Basic contour plot
@@ -59,9 +61,9 @@ def __repr__(self):
     fmt = '%r %%'
 plt.clabel(CS, CS.levels, inline=True, fmt=fmt, fontsize=10)
 
-##################################################
-# Label contours with arbitrary strings using a
-# dictionary
+###############################################################################
+# Label contours with arbitrary strings using a dictionary
+
 plt.figure()
 
 # Basic contour plot
@@ -75,6 +77,7 @@ def __repr__(self):
 # Label every other level using strings
 plt.clabel(CS, CS.levels[::2], inline=True, fmt=fmt, fontsize=10)
 
+###############################################################################
 # Use a Formatter
 
 plt.figure()

examples/images_contours_and_fields/contourf_hatching.py

@@ -8,7 +8,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-
 # invent some numbers, turning the x and y arrays into simple
 # 2d arrays, which make combining them together easier.
 x = np.linspace(-3, 5, 150).reshape(1, -1)
@@ -18,22 +17,19 @@
 # we no longer need x and y to be 2 dimensional, so flatten them.
 x, y = x.flatten(), y.flatten()
 
+###############################################################################
+# Plot 1: the simplest hatched plot with a colorbar
 
-# ---------------------------------------------
-# |                 Plot #1                   |
-# ---------------------------------------------
-# the simplest hatched plot with a colorbar
 fig = plt.figure()
 cs = plt.contourf(x, y, z, hatches=['-', '/', '\\', '//'],
                   cmap=plt.get_cmap('gray'),
                   extend='both', alpha=0.5
                   )
 plt.colorbar()
 
-# ---------------------------------------------
-# |                 Plot #2                   |
-# ---------------------------------------------
-# a plot of hatches without color with a legend
+###############################################################################
+# Plot 2: a plot of hatches without color with a legend
+
 plt.figure()
 n_levels = 6
 plt.contour(x, y, z, n_levels, colors='black', linestyles='-')

examples/images_contours_and_fields/quiver_demo.py

@@ -16,12 +16,16 @@
 U = np.cos(X)
 V = np.sin(Y)
 
+###############################################################################
+
 plt.figure()
 plt.title('Arrows scale with plot width, not view')
 Q = plt.quiver(X, Y, U, V, units='width')
 qk = plt.quiverkey(Q, 0.9, 0.9, 2, r'$2 \frac{m}{s}$', labelpos='E',
                    coordinates='figure')
 
+###############################################################################
+
 plt.figure()
 plt.title("pivot='mid'; every third arrow; units='inches'")
 Q = plt.quiver(X[::3, ::3], Y[::3, ::3], U[::3, ::3], V[::3, ::3],
@@ -30,6 +34,8 @@
                    coordinates='figure')
 plt.scatter(X[::3, ::3], Y[::3, ::3], color='r', s=5)
 
+###############################################################################
+
 plt.figure()
 plt.title("pivot='tip'; scales with x view")
 M = np.hypot(U, V)

examples/images_contours_and_fields/tricontour_demo.py

@@ -10,6 +10,7 @@
 import numpy as np
 import math
 
+###############################################################################
 # Creating a Triangulation without specifying the triangles results in the
 # Delaunay triangulation of the points.
 
@@ -36,14 +37,17 @@
 mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
 triang.set_mask(mask)
 
+###############################################################################
 # pcolor plot.
+
 plt.figure()
 plt.gca().set_aspect('equal')
 plt.tricontourf(triang, z)
 plt.colorbar()
 plt.tricontour(triang, z, colors='k')
 plt.title('Contour plot of Delaunay triangulation')
 
+###############################################################################
 # You can specify your own triangulation rather than perform a Delaunay
 # triangulation of the points, where each triangle is given by the indices of
 # the three points that make up the triangle, ordered in either a clockwise or
@@ -93,10 +97,12 @@
     [42, 41, 40], [72, 33, 31], [32, 31, 33], [39, 38, 72], [33, 72, 38],
     [33, 38, 34], [37, 35, 38], [34, 38, 35], [35, 37, 36]])
 
+###############################################################################
 # Rather than create a Triangulation object, can simply pass x, y and triangles
 # arrays to tripcolor directly.  It would be better to use a Triangulation
 # object if the same triangulation was to be used more than once to save
 # duplicated calculations.
+
 plt.figure()
 plt.gca().set_aspect('equal')
 plt.tricontourf(x, y, triangles, z)