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DOC: change all sphinx-gallery section separators to # %%
This is so that IDEs will auto detect "cells" and make (re) running sub sections of the code easier for development and debugging.
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‎examples/animation/animated_histogram.py

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@@ -21,7 +21,7 @@
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data = np.random.randn(1000)
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n, _ = np.histogram(data, HIST_BINS)
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###############################################################################
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# %%
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# To animate the histogram, we need an ``animate`` function, which generates
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# a random set of numbers and updates the heights of rectangles. We utilize a
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# python closure to track an instance of `.BarContainer` whose `.Rectangle`
@@ -39,7 +39,7 @@ def animate(frame_number):
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return bar_container.patches
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return animate
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###############################################################################
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# %%
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# Using :func:`~matplotlib.pyplot.hist` allows us to get an instance of
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# `.BarContainer`, which is a collection of `.Rectangle` instances. Calling
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# ``prepare_animation`` will define ``animate`` function working with supplied

‎examples/animation/multiple_axes.py

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@@ -70,7 +70,7 @@ def animate(i):
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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/axes_grid1/demo_axes_hbox_divider.py

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plt.show()
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###############################################################################
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# %%
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# Using a `.VBoxDivider` to arrange subplots.
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#
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# Note that both axes' location are adjusted so that they have

‎examples/axes_grid1/demo_fixed_size_axes.py

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from mpl_toolkits.axes_grid1 import Divider, Size
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###############################################################################
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# %%
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fig = plt.figure(figsize=(6, 6))
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ax.plot([1, 2, 3])
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###############################################################################
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# %%
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fig = plt.figure(figsize=(6, 6))

‎examples/axes_grid1/inset_locator_demo.py

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"""
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8-
###############################################################################
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# %%
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# The `.inset_locator`'s `~.inset_locator.inset_axes` allows
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# easily placing insets in the corners of the axes by specifying a width and
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# height and optionally a location (loc) that accepts locations as codes,
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plt.show()
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###############################################################################
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# %%
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# The parameters *bbox_to_anchor* and *bbox_transform* can be used for a more
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# fine-grained control over the inset position and size or even to position
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# the inset at completely arbitrary positions.
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plt.show()
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###############################################################################
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# %%
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# In the above the axes transform together with 4-tuple bounding boxes has been
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# used as it mostly is useful to specify an inset relative to the axes it is
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# an inset to. However, other use cases are equally possible. The following

‎examples/axes_grid1/make_room_for_ylabel_using_axesgrid.py

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make_axes_area_auto_adjustable(ax)
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###############################################################################
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# %%
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fig = plt.figure()
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ax1 = fig.add_axes([0, 0, 1, 0.5])
@@ -31,7 +31,7 @@
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make_axes_area_auto_adjustable(ax1, pad=0.1, use_axes=[ax1, ax2])
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make_axes_area_auto_adjustable(ax2, pad=0.1, use_axes=[ax1, ax2])
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###############################################################################
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# %%
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fig = plt.figure()
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ax1 = fig.add_axes([0, 0, 1, 1])

‎examples/axes_grid1/scatter_hist_locatable_axes.py

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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/axes_grid1/simple_axes_divider1.py

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@@ -18,7 +18,7 @@ def label_axes(ax, text):
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left=False, labelleft=False)
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##############################################################################
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# %%
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# Fixed axes sizes; fixed paddings.
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fig = plt.figure(figsize=(6, 6))
@@ -42,7 +42,7 @@ def label_axes(ax, text):
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ax4 = fig.add_axes(rect, axes_locator=div.new_locator(nx=2, nx1=4, ny=0))
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label_axes(ax4, "nx=2, nx1=4, ny=0")
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45-
##############################################################################
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# %%
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# Axes sizes that scale with the figure size; fixed paddings.
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fig = plt.figure(figsize=(6, 6))

‎examples/axisartist/demo_floating_axes.py

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return ax1, aux_ax
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##########################################################
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# %%
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fig = plt.figure(figsize=(8, 4))
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fig.subplots_adjust(wspace=0.3, left=0.05, right=0.95)
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‎examples/color/color_by_yvalue.py

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ax.plot(t, smiddle, t, slower, t, supper)
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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/color_cycle_default.py

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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/color_demo.py

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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/colorbar_basics.py

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cbar.minorticks_on()
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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/colormap_reference.py

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plot_color_gradients(cmap_category, cmap_list)
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###############################################################################
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# %%
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# .. _reverse-cmap:
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#
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# Reversed colormaps
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# The built-in reversed colormaps are generated using `.Colormap.reversed`.
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# For an example, see :ref:`reversing-colormap`
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/custom_cmap.py

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Z = np.cos(X) * np.sin(Y) * 10
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###############################################################################
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# %%
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# Colormaps from a list
113113
# ---------------------
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126126
fig.colorbar(im, ax=ax)
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###############################################################################
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# %%
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# Custom colormaps
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# ----------------
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}
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###############################################################################
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# %%
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# Now we will use this example to illustrate 2 ways of
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# handling custom colormaps.
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# First, the most direct and explicit:
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blue_red1 = LinearSegmentedColormap('BlueRed1', cdict1)
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###############################################################################
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# %%
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# Second, create the map explicitly and register it.
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# Like the first method, this method works with any kind
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# of Colormap, not just
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mpl.colormaps.register(LinearSegmentedColormap('BlueRed3', cdict3))
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mpl.colormaps.register(LinearSegmentedColormap('BlueRedAlpha', cdict4))
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# %%
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# Make the figure, with 4 subplots:
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fig, axs = plt.subplots(2, 2, figsize=(6, 9))
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plt.show()
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#############################################################################
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# %%
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#
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# .. admonition:: References
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#

‎examples/color/named_colors.py

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return fig
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# %%
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# -----------
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# Base colors
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# -----------
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plot_colortable(mcolors.BASE_COLORS, ncols=3, sort_colors=False)
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# %%
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# ---------------
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# Tableau Palette
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# ---------------
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plot_colortable(mcolors.TABLEAU_COLORS, ncols=2, sort_colors=False)
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# %%
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# ----------
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# CSS Colors
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# ----------
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plot_colortable(mcolors.CSS4_COLORS)
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plt.show()
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# %%
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# -----------
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# XKCD Colors
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# -----------

‎examples/event_handling/cursor_demo.py

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fig.canvas.mpl_connect('motion_notify_event', cursor.on_mouse_move)
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##############################################################################
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# %%
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# Faster redrawing using blitting
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# """""""""""""""""""""""""""""""
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# This technique stores the rendered plot as a background image. Only the
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153153
fig.canvas.mpl_connect('motion_notify_event', blitted_cursor.on_mouse_move)
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##############################################################################
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# %%
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# Snapping to data points
158158
# """""""""""""""""""""""
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# The following cursor snaps its position to the data points of a `.Line2D`

‎examples/event_handling/ginput_manual_clabel_sgskip.py

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plt.title(s, fontsize=16)
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plt.draw()
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##################################################
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# %%
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# Define a triangle by clicking three points
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p.remove()
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# %%
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# Now contour according to distance from triangle
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# corners - just an example
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tellme('Use mouse to select contour label locations, middle button to finish')
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CL = plt.clabel(CS, manual=True)
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# %%
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# Now do a zoom
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tellme('Now do a nested zoom, click to begin')

‎examples/event_handling/pick_event_demo.py

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np.random.seed(19680801)
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# %%
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# Simple picking, lines, rectangles and text
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# ------------------------------------------
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fig.canvas.mpl_connect('pick_event', onpick1)
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# %%
121121
# Picking with a custom hit test function
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# ---------------------------------------
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# You can define custom pickers by setting picker to a callable function. The
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163163
fig.canvas.mpl_connect('pick_event', onpick2)
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# %%
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# Picking on a scatter plot
168168
# -------------------------
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# A scatter plot is backed by a `~matplotlib.collections.PathCollection`.
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181181
fig.canvas.mpl_connect('pick_event', onpick3)
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# %%
185185
# Picking images
186186
# --------------
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# Images plotted using `.Axes.imshow` are `~matplotlib.image.AxesImage`

‎examples/images_contours_and_fields/affine_image.py

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plt.show()
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# %%
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#
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# .. admonition:: References
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#

‎examples/images_contours_and_fields/barb_demo.py

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masked_u[4] = 1000 # Bad value that should not be plotted when masked
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masked_u[4] = np.ma.masked
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# %%
5151
# Identical plot to panel 2 in the first figure, but with the point at
5252
# (0.5, 0.25) missing (masked)
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fig2, ax2 = plt.subplots()
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ax2.barbs(data['x'], data['y'], masked_u, data['v'], length=8, pivot='middle')
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plt.show()
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# %%
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#
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# .. admonition:: References
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#

‎examples/images_contours_and_fields/barcode_demo.py

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interpolation='nearest')
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plt.show()
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# %%
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#
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# .. admonition:: References
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#

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