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Merge pull request #17798 from anntzer/indent
Fix overindented bullet/enumerated lists.
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doc/devel/MEP/MEP11.rst

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@@ -127,21 +127,21 @@ dateutil_.
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For binary installations, there are a number of alternatives (here
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ordered from best/hardest to worst/easiest):
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1. The distutils wininst installer allows a post-install script to
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run. It might be possible to get this script to run pip_ to
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install the other dependencies. (See `this thread
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<http://grokbase.com/t/python/distutils-sig/109bdnfhp4/distutils-ann-setuptools-post-install-script-for-bdist-wininst>`_
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for someone who has trod that ground before).
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2. Continue to ship dateutil_, pytz_, six_ and pyparsing_ in
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our installer, but use the post-install-script to install them
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*only* if they can not already be found.
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3. Move all of these packages inside a (new) ``matplotlib.extern``
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namespace so it is clear for outside users that these are
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external packages. Add some conditional imports in the core
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matplotlib codebase so dateutil_ (at the top-level) is tried
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first, and failing that ``matplotlib.extern.dateutil`` is used.
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1. The distutils wininst installer allows a post-install script to
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run. It might be possible to get this script to run pip_ to
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install the other dependencies. (See `this thread
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<http://grokbase.com/t/python/distutils-sig/109bdnfhp4/distutils-ann-setuptools-post-install-script-for-bdist-wininst>`_
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for someone who has trod that ground before).
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2. Continue to ship dateutil_, pytz_, six_ and pyparsing_ in
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our installer, but use the post-install-script to install them
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*only* if they can not already be found.
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3. Move all of these packages inside a (new) ``matplotlib.extern``
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namespace so it is clear for outside users that these are
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external packages. Add some conditional imports in the core
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matplotlib codebase so dateutil_ (at the top-level) is tried
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first, and failing that ``matplotlib.extern.dateutil`` is used.
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2 and 3 are undesirable as they still require maintaining copies of
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these packages in our tree -- and this is exacerbated by the fact that

examples/misc/custom_projection.py

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@@ -76,13 +76,13 @@ def _set_lim_and_transforms(self):
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# There are three important coordinate spaces going on here:
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#
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# 1. Data space: The space of the data itself
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# 1. Data space: The space of the data itself
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#
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# 2. Axes space: The unit rectangle (0, 0) to (1, 1)
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# covering the entire plot area.
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# 2. Axes space: The unit rectangle (0, 0) to (1, 1)
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# covering the entire plot area.
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#
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# 3. Display space: The coordinates of the resulting image,
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# often in pixels or dpi/inch.
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# 3. Display space: The coordinates of the resulting image,
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# often in pixels or dpi/inch.
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# This function makes heavy use of the Transform classes in
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# ``lib/matplotlib/transforms.py.`` For more information, see

examples/pie_and_polar_charts/pie_features.py

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In addition to the basic pie chart, this demo shows a few optional features:
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* slice labels
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* auto-labeling the percentage
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* offsetting a slice with "explode"
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* drop-shadow
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* custom start angle
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* slice labels
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* auto-labeling the percentage
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* offsetting a slice with "explode"
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* drop-shadow
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* custom start angle
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Note about the custom start angle:
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examples/shapes_and_collections/arrow_guide.py

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Here we show three use cases for plotting arrows, depending on whether the
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head or anchor points need to be fixed in data or display space:
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1. Head shape fixed in display space, anchor points fixed in data space
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2. Head shape and anchor points fixed in display space
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3. Entire patch fixed in data space
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1. Head shape fixed in display space, anchor points fixed in data space
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2. Head shape and anchor points fixed in display space
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3. Entire patch fixed in data space
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Below each use case is presented in turn.
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"""

examples/showcase/integral.py

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Although this is a simple example, it demonstrates some important tweaks:
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* A simple line plot with custom color and line width.
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* A shaded region created using a Polygon patch.
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* A text label with mathtext rendering.
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* figtext calls to label the x- and y-axes.
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* Use of axis spines to hide the top and right spines.
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* Custom tick placement and labels.
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* A simple line plot with custom color and line width.
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* A shaded region created using a Polygon patch.
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* A text label with mathtext rendering.
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* figtext calls to label the x- and y-axes.
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* Use of axis spines to hide the top and right spines.
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* Custom tick placement and labels.
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"""
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import numpy as np
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import matplotlib.pyplot as plt

examples/statistics/histogram_multihist.py

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Plot histogram with multiple sample sets and demonstrate:
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* Use of legend with multiple sample sets
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* Stacked bars
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* Step curve with no fill
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* Data sets of different sample sizes
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* Use of legend with multiple sample sets
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* Stacked bars
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* Step curve with no fill
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* Data sets of different sample sizes
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Selecting different bin counts and sizes can significantly affect the
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shape of a histogram. The Astropy docs have a great section on how to

lib/matplotlib/tests/test_ticker.py

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"""
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Test the formatting of EngFormatter for various values of the 'places'
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argument, in several cases:
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0. without a unit symbol but with a (default) space separator;
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1. with both a unit symbol and a (default) space separator;
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2. with both a unit symbol and some non default separators;
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3. without a unit symbol but with some non default separators.
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0. without a unit symbol but with a (default) space separator;
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1. with both a unit symbol and a (default) space separator;
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2. with both a unit symbol and some non default separators;
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3. without a unit symbol but with some non default separators.
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Note that cases 2. and 3. are looped over several separator strings.
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"""
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lib/mpl_toolkits/mplot3d/art3d.py

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----------
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zdir : {'x', 'y', 'z', None, 3-tuple}
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The direction. Possible values are:
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- 'x': equivalent to (1, 0, 0)
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- 'y': equivalent to (0, 1, 0)
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- 'z': equivalent to (0, 0, 1)
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x, y, z : array-like
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The direction vector. This is either a numpy.array or *zdir* itself if
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*zdir* is already a length-3 iterable.
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"""
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if zdir == 'x':
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return np.array((1, 0, 0))

lib/mpl_toolkits/mplot3d/axes3d.py

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modifying the object and adding z coordinate information.
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Supported are:
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- PolyCollection
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- LineCollection
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- PatchCollection
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- PolyCollection
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- LineCollection
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- PatchCollection
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"""
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zvals = np.atleast_1d(zs)
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zsortval = (np.min(zvals) if zvals.size
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When coloring the faces of the boxes specifically, this is
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the order of the coloring:
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1. -Z (bottom of box)
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2. +Z (top of box)
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3. -Y
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4. +Y
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5. -X
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6. +X
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1. -Z (bottom of box)
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2. +Z (top of box)
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3. -Y
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4. +Y
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5. -X
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6. +X
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zsort : str, optional
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The z-axis sorting scheme passed onto `~.art3d.Poly3DCollection`
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facecolors, edgecolors : array-like, optional
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The color to draw the faces and edges of the voxels. Can only be
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passed as keyword arguments.
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This parameter can be:
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- A single color value, to color all voxels the same color. This
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can be either a string, or a 1D rgb/rgba array
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- ``None``, the default, to use a single color for the faces, and
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the style default for the edges.
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- A 3D ndarray of color names, with each item the color for the
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corresponding voxel. The size must match the voxels.
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- A 4D ndarray of rgb/rgba data, with the components along the
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last axis.
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These parameters can be:
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- A single color value, to color all voxels the same color. This
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can be either a string, or a 1D rgb/rgba array
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- ``None``, the default, to use a single color for the faces, and
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the style default for the edges.
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- A 3D ndarray of color names, with each item the color for the
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corresponding voxel. The size must match the voxels.
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- A 4D ndarray of rgb/rgba data, with the components along the
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last axis.
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shade : bool, default: True
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Whether to shade the facecolors. Shading is always disabled when

tutorials/intermediate/legend_guide.py

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# appropriate :class:`~matplotlib.legend_handler.HandlerBase` subclass.
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# The choice of handler subclass is determined by the following rules:
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#
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# 1. Update :func:`~matplotlib.legend.Legend.get_legend_handler_map`
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# with the value in the ``handler_map`` keyword.
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# 2. Check if the ``handle`` is in the newly created ``handler_map``.
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# 3. Check if the type of ``handle`` is in the newly created
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# ``handler_map``.
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# 4. Check if any of the types in the ``handle``'s mro is in the newly
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# created ``handler_map``.
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# 1. Update :func:`~matplotlib.legend.Legend.get_legend_handler_map`
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# with the value in the ``handler_map`` keyword.
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# 2. Check if the ``handle`` is in the newly created ``handler_map``.
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# 3. Check if the type of ``handle`` is in the newly created ``handler_map``.
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# 4. Check if any of the types in the ``handle``'s mro is in the newly
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# created ``handler_map``.
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#
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# For completeness, this logic is mostly implemented in
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# :func:`~matplotlib.legend.Legend.get_legend_handler`.

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