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tonyylitonyyli
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Separating examples with multiple plots into separate blocks
1 parent 63744d5 commit be4667a

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-61
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‎examples/animation/basic_example.py‎

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@@ -16,6 +16,7 @@ def update_line(num, data, line):
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line.set_data(data[..., :num])
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return line,
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###############################################################################
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fig1 = plt.figure()
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# Fixing random state for reproducibility
@@ -32,6 +33,7 @@ def update_line(num, data, line):
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# To save the animation, use the command: line_ani.save('lines.mp4')
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###############################################################################
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fig2 = plt.figure()
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x = np.arange(-9, 10)

‎examples/api/filled_step.py‎

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

‎examples/api/sankey_basics.py‎

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from matplotlib.sankey import Sankey
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###############################################################################
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# Example 1 -- Mostly defaults
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# This demonstrates how to create a simple diagram by implicitly calling the
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# Sankey.add() method and by appending finish() to the call to the class.
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Sankey(flows=[0.25, 0.15, 0.60, -0.20, -0.15, -0.05, -0.50, -0.10],
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labels=['', '', '', 'First', 'Second', 'Third', 'Fourth', 'Fifth'],
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orientations=[-1, 1, 0, 1, 1, 1, 0, -1]).finish()
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plt.title("The default settings produce a diagram like this.")
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###############################################################################
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# Notice:
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# 1. Axes weren't provided when Sankey() was instantiated, so they were
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# created automatically.
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# 2. The scale argument wasn't necessary since the data was already
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# normalized.
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# 3. By default, the lengths of the paths are justified.
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###############################################################################
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# Example 2
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# This demonstrates:
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# 1. Setting one path longer than the others
@@ -51,13 +54,15 @@
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diagrams = sankey.finish()
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diagrams[0].texts[-1].set_color('r')
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diagrams[0].text.set_fontweight('bold')
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###############################################################################
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# Notice:
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# 1. Since the sum of the flows is nonzero, the width of the trunk isn't
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# uniform. If verbose.level is helpful (in matplotlibrc), a message is
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# given in the terminal window.
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# 2. The second flow doesn't appear because its value is zero. Again, if
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# verbose.level is helpful, a message is given in the terminal window.
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###############################################################################
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# Example 3
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# This demonstrates:
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# 1. Connecting two systems
@@ -74,6 +79,7 @@
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diagrams = sankey.finish()
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diagrams[-1].patch.set_hatch('/')
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plt.legend(loc='best')
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###############################################################################
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# Notice that only one connection is specified, but the systems form a
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# circuit since: (1) the lengths of the paths are justified and (2) the
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# orientation and ordering of the flows is mirrored.

‎examples/event_handling/figure_axes_enter_leave.py‎

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

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# difference of Gaussians
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Z = 10.0 * (Z2 - Z1)
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###############################################################################
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# Create a simple contour plot with labels using default colors. The
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# inline argument to clabel will control whether the labels are draw
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# over the line segments of the contour, removing the lines beneath
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plt.title('Simplest default with labels')
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###############################################################################
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# contour labels can be placed manually by providing list of positions
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# (in data coordinate). See ginput_manual_clabel.py for interactive
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# placement.
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plt.title('labels at selected locations')
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###############################################################################
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# You can force all the contours to be the same color.
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plt.figure()
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CS = plt.contour(X, Y, Z, 6,
@@ -55,6 +57,7 @@
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plt.clabel(CS, fontsize=9, inline=1)
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plt.title('Single color - negative contours dashed')
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###############################################################################
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# You can set negative contours to be solid instead of dashed:
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matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
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plt.figure()
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plt.title('Single color - negative contours solid')
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###############################################################################
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# And you can manually specify the colors of the contour
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plt.figure()
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CS = plt.contour(X, Y, Z, 6,
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plt.title('Crazy lines')
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###############################################################################
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# Or you can use a colormap to specify the colors; the default
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# colormap will be used for the contour lines
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plt.figure()

‎examples/images_contours_and_fields/contour_label_demo.py‎

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matplotlib.rcParams['xtick.direction'] = 'out'
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matplotlib.rcParams['ytick.direction'] = 'out'
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##################################################
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###############################################################################
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# Define our surface
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delta = 0.025
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x = np.arange(-3.0, 3.0, delta)
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# difference of Gaussians
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Z = 10.0 * (Z2 - Z1)
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##################################################
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###############################################################################
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# Make contour labels using creative float classes
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# Follows suggestion of Manuel Metz
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plt.figure()
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fmt = '%r %%'
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plt.clabel(CS, CS.levels, inline=True, fmt=fmt, fontsize=10)
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##################################################
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###############################################################################
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# Label contours with arbitrary strings using a
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# dictionary
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plt.figure()
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# Label every other level using strings
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plt.clabel(CS, CS.levels[::2], inline=True, fmt=fmt, fontsize=10)
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###############################################################################
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# Use a Formatter
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plt.figure()

‎examples/images_contours_and_fields/contourf_hatching.py‎

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@@ -8,7 +8,6 @@
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import matplotlib.pyplot as plt
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import numpy as np
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# invent some numbers, turning the x and y arrays into simple
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# 2d arrays, which make combining them together easier.
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x = np.linspace(-3, 5, 150).reshape(1, -1)
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# we no longer need x and y to be 2 dimensional, so flatten them.
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x, y = x.flatten(), y.flatten()
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###############################################################################
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# ---------------------------------------------
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# | Plot #1 |
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# ---------------------------------------------
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)
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plt.colorbar()
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###############################################################################
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# ---------------------------------------------
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# | Plot #2 |
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# ---------------------------------------------

‎examples/images_contours_and_fields/quiver_demo.py‎

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

‎examples/images_contours_and_fields/tricontour_demo.py‎

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

‎examples/images_contours_and_fields/tripcolor_demo.py‎

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import numpy as np
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import math
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###############################################################################
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# Creating a Triangulation without specifying the triangles results in the
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# Delaunay triangulation of the points.
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mask = np.where(xmid * xmid + ymid * ymid < min_radius * min_radius, 1, 0)
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triang.set_mask(mask)
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40+
###############################################################################
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# tripcolor plot.
4042
plt.figure()
4143
plt.gca().set_aspect('equal')
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plt.tripcolor(triang, z, shading='flat')
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plt.colorbar()
4446
plt.title('tripcolor of Delaunay triangulation, flat shading')
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###############################################################################
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# Illustrate Gouraud shading.
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plt.figure()
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plt.gca().set_aspect('equal')
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plt.title('tripcolor of Delaunay triangulation, gouraud shading')
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###############################################################################
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# You can specify your own triangulation rather than perform a Delaunay
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# triangulation of the points, where each triangle is given by the indices of
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# the three points that make up the triangle, ordered in either a clockwise or
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103107
zfaces = np.exp(-0.01 * ((xmid - x0) * (xmid - x0) +
104108
(ymid - y0) * (ymid - y0)))
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###############################################################################
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# Rather than create a Triangulation object, can simply pass x, y and triangles
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# arrays to tripcolor directly. It would be better to use a Triangulation
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# object if the same triangulation was to be used more than once to save

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