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Merge pull request #5774 from mdboom/style-change2
API: Style changes omnibus PR
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‎doc/users/whats_new/rcparams.rst‎

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@@ -22,6 +22,7 @@ Configuration (rcParams)
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``svg.hashsalt``
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````````````````
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If ``svg.hashsalt`` is ``None`` (which it is by default), the svg backend uses ``uuid4`` to generate the hash salt.
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If it is not ``None``, it must be a string that is used as the hash salt instead of ``uuid4``.
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This allows for deterministic SVG output.
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If ``svg.hashsalt`` is ``None`` (which it is by default), the svg
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backend uses ``uuid4`` to generate the hash salt. If it is not
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``None``, it must be a string that is used as the hash salt instead of
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``uuid4``. This allows for deterministic SVG output.

‎doc/users/whats_new/style_changes.rst‎

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Changes to the default style
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----------------------------
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The most important changes in matplotlib 2.0 are the changes to the
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default style.
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While it is impossible to select the best default for all cases, these
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are designed to work well in the most common cases.
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These changes include:
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Colors
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``````
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- The default figure background color has changed from grey to white.
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Use the rcParam ``figure.facecolor`` to control this.
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- The default cycle of colors to draw lines, markers and other content
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has been changed. It is based on the `Vega category10 palette
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<https://github.com/vega/vega/wiki/Scales#scale-range-literals>`__.
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- The default color map used for images and pcolor meshes, etc., has
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changed from ``jet`` to ``viridis``.
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- For markers, scatter plots, bar charts and pie charts, there is no
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longer a black outline around filled markers by default.
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- Grid lines are light grey solid 1pt lines. They are no longer dashed by
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default.
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Plot layout
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```````````
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- The default dpi used for on-screen is now 100, which is the same as
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the old default for saving files. Due to this, the on-screen
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display is now more what-you-see-is-what-you-get.
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- The number of ticks on an axis is now automatically determined based
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on the length of the axis.
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- The limits are scaled to exactly the dimensions of the data, plus 5%
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padding. The old behavior was to scale to the nearest "round"
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numbers. To use the old behavior, set the ``rcParam``
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``axes.autolimit_mode`` to ``round_numbers``. To control the
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margins on particular side individually, pass any of the following
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to any artist or plotting function:
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- ``top_margin=False``
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- ``bottom_margin=False``
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- ``left_margin=False``
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- ``right_margin=False``
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- Ticks now point outward by default. To have ticks pointing inward,
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use the ``rcParams`` ``xtick.direction`` and ``ytick.direction``.
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- By default, caps on the ends of errorbars are not present. Use the
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rcParam ``errorbar.capsize`` to control this.
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Images
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``````
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- The default mode for image interpolation, in the rcParam
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``image.interpolation``, is now ``nearest``.
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- The default shading mode for light source shading, in
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``matplotlib.colors.LightSource.shade``, is now ``overlay``.
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Formerly, it was ``hsv``.
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- The default value for the rcParam ``image.resample`` is now
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``True``. This will apply interpolation for both upsampling and
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downsampling of an image.
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Fonts
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`````
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- The default font has changed from "Bitstream Vera Sans" to "DejaVu
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Sans". "DejaVu Sans" is an improvement on "Bistream Vera Sans" that
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adds more international and math characters, but otherwise has the
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same appearance.
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- The default math font when using the built-in math rendering engine
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(mathtext) has changed from "Computer Modern" (i.e. LaTeX-like) to
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"DejaVu Sans". To revert to the old behavior, set the ``rcParam``
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``mathtext.fontset`` to ``cm``. This change has no effect if the
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TeX backend is used (i.e. ``text.usetex`` is ``True``).
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Dates
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`````
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- The default date formats are now all based on ISO format, i.e., with
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the slowest-moving value first. The date formatters are still
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changeable through the ``date.autoformatter.*`` rcParams. Python's
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``%x`` and ``%X`` date formats may be of particular interest to
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format dates based on the current locale.
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Legends
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```````
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- By default, the number of points displayed in a legend is now 1.
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- The default legend location is ``best``, so the legend will be
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automatically placed in a location to obscure the least amount of
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data possible.
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- The legend now has rounded corners by default.

‎examples/api/demo_affine_image.py‎

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@@ -37,7 +37,7 @@ def imshow_affine(ax, z, *kl, **kwargs):
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fig, (ax1, ax2) = plt.subplots(1, 2)
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Z = get_image()
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im1 = imshow_affine(ax1, Z, interpolation='none', cmap=cm.jet,
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im1 = imshow_affine(ax1, Z, interpolation='none',
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origin='lower',
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extent=[-2, 4, -3, 2], clip_on=True)
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@@ -48,15 +48,15 @@ def imshow_affine(ax, z, *kl, **kwargs):
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x1, x2, y1, y2 = im1.get_extent()
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x3, y3 = x2, y1
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ax1.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], "r--", lw=3,
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ax1.plot([x1, x2, x2, x1, x1], [y1, y1, y2, y2, y1], "--",
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transform=trans_data2)
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ax1.set_xlim(-3, 5)
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ax1.set_ylim(-4, 4)
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# image skew
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im2 = ax2.imshow(Z, interpolation='none', cmap=cm.jet,
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im2 = ax2.imshow(Z, interpolation='none',
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origin='lower',
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extent=[-2, 4, -3, 2], clip_on=True)
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im2._image_skew_coordinate = (3, -2)

‎examples/api/filled_step.py‎

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@@ -61,9 +61,11 @@ def filled_hist(ax, edges, values, bottoms=None, orientation='v',
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values = np.r_[values, values[-1]]
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bottoms = np.r_[bottoms, bottoms[-1]]
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if orientation == 'h':
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return ax.fill_betweenx(edges, values, bottoms, **kwargs)
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return ax.fill_betweenx(edges, values, bottoms, left_margin=False,
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**kwargs)
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elif orientation == 'v':
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return ax.fill_between(edges, values, bottoms, **kwargs)
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return ax.fill_between(edges, values, bottoms, bottom_margin=False,
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**kwargs)
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else:
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raise AssertionError("you should never be here")
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ax1.set_xlabel('counts')
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ax1.set_ylabel('x')
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ax2.set_ylabel('x')
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plt.show()

‎examples/api/histogram_path_demo.py‎

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barpath = path.Path.make_compound_path_from_polys(XY)
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# make a patch out of it
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patch = patches.PathPatch(
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barpath, facecolor='blue', edgecolor='gray', alpha=0.8)
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patch = patches.PathPatch(barpath, facecolor='blue')
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ax.add_patch(patch)
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# update the view limits

‎examples/api/image_zcoord.py‎

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X = 10*np.random.rand(5, 3)
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fig, ax = plt.subplots()
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ax.imshow(X, cmap=cm.jet, interpolation='nearest')
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ax.imshow(X, interpolation='nearest')
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numrows, numcols = X.shape
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‎examples/api/patch_collection.py‎

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patches.append(polygon)
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colors = 100*np.random.rand(len(patches))
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p = PatchCollection(patches, cmap=matplotlib.cm.jet, alpha=0.4)
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p = PatchCollection(patches, alpha=0.4)
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p.set_array(np.array(colors))
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ax.add_collection(p)
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plt.colorbar(p)

‎examples/api/power_norm_demo.py‎

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for i, gamma in enumerate(gammas):
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plt.subplot(xgrid, ygrid, i + 2)
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plt.title('Power law normalization\n$(\gamma=%1.1f)$' % gamma)
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plt.title('Power law\n$(\gamma=%1.1f)$' % gamma)
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plt.hist2d(data[:, 0], data[:, 1],
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bins=100, norm=mcolors.PowerNorm(gamma))
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plt.subplots_adjust(hspace=0.39)
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plt.tight_layout()
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plt.show()

‎examples/api/sankey_demo_basics.py‎

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orientations=[-1, 1, 0, 1, 1, 1, -1, -1, 0],
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pathlengths=[0.25, 0.25, 0.25, 0.25, 0.25, 0.6, 0.25, 0.25,
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0.25],
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patchlabel="Widget\nA",
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alpha=0.2, lw=2.0) # Arguments to matplotlib.patches.PathPatch()
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patchlabel="Widget\nA") # Arguments to matplotlib.patches.PathPatch()
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diagrams = sankey.finish()
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diagrams[0].patch.set_facecolor('#37c959')
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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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# Notice:
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sankey = Sankey(ax=ax, unit=None)
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sankey.add(flows=flows, label='one',
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orientations=[-1, 1, 0, 1, 1, 1, -1, -1, 0])
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sankey.add(flows=[-0.25, 0.15, 0.1], fc='#37c959', label='two',
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sankey.add(flows=[-0.25, 0.15, 0.1], label='two',
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orientations=[-1, -1, -1], prior=0, connect=(0, 0))
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diagrams = sankey.finish()
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diagrams[-1].patch.set_hatch('/')

‎examples/api/sankey_demo_links.py‎

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def side(sankey, n=1):
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"""Generate a side chain."""
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prior = len(sankey.diagrams)
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colors = cycle(['orange', 'b', 'g', 'r', 'c', 'm', 'y'])
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for i in range(0, 2*n, 2):
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sankey.add(flows=[1, -1], orientations=[-1, -1],
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patchlabel=str(prior + i), facecolor=next(colors),
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patchlabel=str(prior + i),
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prior=prior + i - 1, connect=(1, 0), alpha=0.5)
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sankey.add(flows=[1, -1], orientations=[1, 1],
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patchlabel=str(prior + i + 1), facecolor=next(colors),
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patchlabel=str(prior + i + 1),
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prior=prior + i, connect=(1, 0), alpha=0.5)
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