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Strip out unneeded roles from rst markup.
This was done by running. sed -i -E 's/:(mod|class|func|data|meth):`/`/g' **/*.py The current default_role setting for sphinx is `obj`, which means that backticks-wrapped terms automatically look up documented Python objects.
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‎doc/utils/pylab_names.py

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if mod is not None:
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if mod.startswith('matplotlib'):
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if k[0].isupper():
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k = ':class:`~%s.%s`'%(mod, k)
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k = '`~%s.%s`'%(mod, k)
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else:
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k = ':func:`~%s.%s`'%(mod, k)
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mod = ':mod:`%s`'%mod
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k = '`~%s.%s`'%(mod, k)
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mod = '`%s`'%mod
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elif mod.startswith('numpy'):
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#k = '`%s <%s>`_'%(k, 'http://scipy.org/Numpy_Example_List_With_Doc#%s'%k)
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k = '`%s <%s>`_'%(k, 'http://sd-2116.dedibox.fr/pydocweb/doc/%s.%s'%(mod, k))

‎examples/api/span_regions.py

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Illustrate some helper functions for shading regions where a logical
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mask is True
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See :meth:`matplotlib.collections.BrokenBarHCollection.span_where`
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See `matplotlib.collections.BrokenBarHCollection.span_where`
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"""
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import numpy as np
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import matplotlib.pyplot as plt

‎examples/misc/keyword_plotting.py

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There are some instances where you have data in a format that lets you
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access particular variables with strings. For example, with
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:class:`numpy.recarray` or :class:`pandas.DataFrame`.
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`numpy.recarray` or `pandas.DataFrame`.
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Matplotlib allows you provide such an object with the ``data`` keyword
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argument. If provided, then you may generate plots with the strings

‎examples/pie_and_polar_charts/nested_pie.py

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###############################################################################
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# The most straightforward way to build a pie chart is to use the
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# :meth:`pie method <matplotlib.axes.Axes.pie>`
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# `pie method <matplotlib.axes.Axes.pie>`
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#
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# In this case, pie takes values corresponding to counts in a group.
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# We'll first generate some fake data, corresponding to three groups.

‎examples/recipes/common_date_problems.py

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# What we'd like is for the location in the toolbar to have
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# a higher degree of precision, e.g., giving us the exact date out mouse is
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# hovering over. To fix the first problem, we can use
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# :func:`matplotlib.figure.Figure.autofmt_xdate` and to fix the second
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# `matplotlib.figure.Figure.autofmt_xdate` and to fix the second
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# problem we can use the ``ax.fmt_xdata`` attribute which can be set to
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# any function that takes a scalar and returns a string. matplotlib has
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# a number of date formatters built in, so we'll use one of those.

‎examples/recipes/create_subplots.py

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###############################################################################
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# Fernando Perez has provided a nice top level method to create in
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# :func:`~matplotlib.pyplots.subplots` (note the "s" at the end)
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# `~matplotlib.pyplots.subplots` (note the "s" at the end)
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# everything at once, and turn on x and y sharing for the whole bunch.
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# You can either unpack the axes individually::
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‎examples/recipes/fill_between_alpha.py

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Fill Between and Alpha
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======================
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The :meth:`~matplotlib.axes.Axes.fill_between` function generates a
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The `~matplotlib.axes.Axes.fill_between` function generates a
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shaded region between a min and max boundary that is useful for
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illustrating ranges. It has a very handy ``where`` argument to
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combine filling with logical ranges, e.g., to just fill in a curve over
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###############################################################################
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# Another handy use of filled regions is to highlight horizontal or
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# vertical spans of an axes -- for that matplotlib has some helper
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# functions :meth:`~matplotlib.axes.Axes.axhspan` and
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# :meth:`~matplotlib.axes.Axes.axvspan` and example
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# functions `~matplotlib.axes.Axes.axhspan` and
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# `~matplotlib.axes.Axes.axvspan` and example
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# :ref:`sphx_glr_gallery_subplots_axes_and_figures_axhspan_demo.py`.

‎examples/recipes/placing_text_boxes.py

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the text in axes coordinates (see :ref:`sphx_glr_tutorials_advanced_transforms_tutorial.py`), so the
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text doesn't move around with changes in x or y limits. You can also
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use the ``bbox`` property of text to surround the text with a
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:class:`~matplotlib.patches.Patch` instance -- the ``bbox`` keyword
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`~matplotlib.patches.Patch` instance -- the ``bbox`` keyword
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argument takes a dictionary with keys that are Patch properties.
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"""
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‎examples/recipes/share_axis_lims_views.py

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subplots with time as a common axis. When you pan and zoom around on
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one, you want the other to move around with you. To facilitate this,
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matplotlib Axes support a ``sharex`` and ``sharey`` attribute. When
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you create a :func:`~matplotlib.pyplot.subplot` or
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:func:`~matplotlib.pyplot.axes` instance, you can pass in a keyword
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you create a `~matplotlib.pyplot.subplot` or
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`~matplotlib.pyplot.axes` instance, you can pass in a keyword
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indicating what axes you want to share with
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"""
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‎examples/shapes_and_collections/line_collection.py

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Plotting lines with Matplotlib.
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:class:`matplotlib.collections.LineCollection` allows one to plot multiple
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`matplotlib.collections.LineCollection` allows one to plot multiple
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lines on a figure. Below we show off some of its properties.
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"""
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import matplotlib.pyplot as plt
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# solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq)
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# where onoffseq is an even length tuple of on and off ink in points.
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# If linestyle is omitted, 'solid' is used
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# See :class:`matplotlib.collections.LineCollection` for more information
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# See `matplotlib.collections.LineCollection` for more information
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colors = [mcolors.to_rgba(c)
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for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
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# solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq)
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# where onoffseq is an even length tuple of on and off ink in points.
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# If linestyle is omitted, 'solid' is used
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# See :class:`matplotlib.collections.LineCollection` for more information
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# See `matplotlib.collections.LineCollection` for more information
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# Make a sequence of x,y pairs
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line_segments = LineCollection([list(zip(x, y)) for y in ys],

‎examples/text_labels_and_annotations/annotation_demo.py

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The following examples show how it is possible to annotate plots in matplotlib.
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This includes highlighting specific points of interest and using various
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visual tools to call attention to this point. For a more complete and in-depth
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description of the annotation and text tools in :mod:`matplotlib`, see the
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description of the annotation and text tools in `matplotlib`, see the
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`tutorial on annotation <http://matplotlib.org/users/annotations.html>`_.
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"""
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‎examples/widgets/lasso_selector_demo_sgskip.py

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@@ -27,10 +27,10 @@ class SelectFromCollection(object):
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Parameters
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----------
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ax : :class:`~matplotlib.axes.Axes`
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ax : `~matplotlib.axes.Axes`
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Axes to interact with.
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collection : :class:`matplotlib.collections.Collection` subclass
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collection : `matplotlib.collections.Collection` subclass
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Collection you want to select from.
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alpha_other : 0 <= float <= 1

‎examples/widgets/polygon_selector_demo.py

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Parameters
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----------
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ax : :class:`~matplotlib.axes.Axes`
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ax : `~matplotlib.axes.Axes`
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Axes to interact with.
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collection : :class:`matplotlib.collections.Collection` subclass
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collection : `matplotlib.collections.Collection` subclass
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Collection you want to select from.
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alpha_other : 0 <= float <= 1

‎lib/matplotlib/__init__.py

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For the most part, direct use of the object-oriented library is
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encouraged when programming; pyplot is primarily for working
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interactively. The
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exceptions are the pyplot commands :func:`~matplotlib.pyplot.figure`,
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:func:`~matplotlib.pyplot.subplot`,
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:func:`~matplotlib.pyplot.subplots`, and
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:func:`~pyplot.savefig`, which can greatly simplify scripting.
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exceptions are the pyplot commands `~matplotlib.pyplot.figure`,
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`~matplotlib.pyplot.subplot`,
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`~matplotlib.pyplot.subplots`, and
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`~pyplot.savefig`, which can greatly simplify scripting.
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Modules include:
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:mod:`matplotlib.axes`
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defines the :class:`~matplotlib.axes.Axes` class. Most pylab
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commands are wrappers for :class:`~matplotlib.axes.Axes`
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`matplotlib.axes`
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defines the `~matplotlib.axes.Axes` class. Most pylab
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commands are wrappers for `~matplotlib.axes.Axes`
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methods. The axes module is the highest level of OO access to
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the library.
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:mod:`matplotlib.figure`
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defines the :class:`~matplotlib.figure.Figure` class.
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`matplotlib.figure`
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defines the `~matplotlib.figure.Figure` class.
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:mod:`matplotlib.artist`
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defines the :class:`~matplotlib.artist.Artist` base class for
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`matplotlib.artist`
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defines the `~matplotlib.artist.Artist` base class for
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all classes that draw things.
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:mod:`matplotlib.lines`
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defines the :class:`~matplotlib.lines.Line2D` class for
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`matplotlib.lines`
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defines the `~matplotlib.lines.Line2D` class for
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drawing lines and markers
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:mod:`matplotlib.patches`
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`matplotlib.patches`
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defines classes for drawing polygons
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:mod:`matplotlib.text`
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defines the :class:`~matplotlib.text.Text`,
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:class:`~matplotlib.text.TextWithDash`, and
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:class:`~matplotlib.text.Annotate` classes
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`matplotlib.text`
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defines the `~matplotlib.text.Text`,
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`~matplotlib.text.TextWithDash`, and
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`~matplotlib.text.Annotate` classes
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:mod:`matplotlib.image`
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defines the :class:`~matplotlib.image.AxesImage` and
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:class:`~matplotlib.image.FigureImage` classes
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`matplotlib.image`
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defines the `~matplotlib.image.AxesImage` and
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`~matplotlib.image.FigureImage` classes
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:mod:`matplotlib.collections`
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`matplotlib.collections`
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classes for efficient drawing of groups of lines or polygons
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:mod:`matplotlib.colors`
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`matplotlib.colors`
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classes for interpreting color specifications and for making
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colormaps
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:mod:`matplotlib.cm`
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colormaps and the :class:`~matplotlib.image.ScalarMappable`
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`matplotlib.cm`
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colormaps and the `~matplotlib.image.ScalarMappable`
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mixin class for providing color mapping functionality to other
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classes
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:mod:`matplotlib.ticker`
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`matplotlib.ticker`
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classes for calculating tick mark locations and for formatting
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tick labels
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:mod:`matplotlib.backends`
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`matplotlib.backends`
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a subpackage with modules for various gui libraries and output
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formats
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The base matplotlib namespace includes:
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:data:`~matplotlib.rcParams`
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`~matplotlib.rcParams`
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a global dictionary of default configuration settings. It is
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initialized by code which may be overridded by a matplotlibrc
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file.
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:func:`~matplotlib.rc`
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`~matplotlib.rc`
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a function for setting groups of rcParams values
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:func:`~matplotlib.use`
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`~matplotlib.use`
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a function for setting the matplotlib backend. If used, this
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function must be called immediately after importing matplotlib
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for the first time. In particular, it must be called
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A dictionary object including validation
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validating functions are defined and associated with rc parameters in
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:mod:`matplotlib.rcsetup`
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`matplotlib.rcsetup`
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"""
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validate = dict((key, converter) for key, (default, converter) in
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870870
def find_all(self, pattern):
871871
"""
872872
Return the subset of this RcParams dictionary whose keys match,
873-
using :func:`re.search`, the given ``pattern``.
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using `re.search`, the given ``pattern``.
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.. note::
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def rc_params(fail_on_error=False):
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"""Return a :class:`matplotlib.RcParams` instance from the
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"""Return a `matplotlib.RcParams` instance from the
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default matplotlib rc file.
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"""
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fname = matplotlib_fname()
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def _rc_params_in_file(fname, fail_on_error=False):
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"""Return :class:`matplotlib.RcParams` from the contents of the given file.
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"""Return `matplotlib.RcParams` from the contents of the given file.
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940940
Unlike `rc_params_from_file`, the configuration class only contains the
941941
parameters specified in the file (i.e. default values are not filled in).
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10141014

10151015
def rc_params_from_file(fname, fail_on_error=False, use_default_template=True):
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"""Return :class:`matplotlib.RcParams` from the contents of the given file.
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"""Return `matplotlib.RcParams` from the contents of the given file.
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Parameters
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----------
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11341134
rc('font', **font) # pass in the font dict as kwargs
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11361136
This enables you to easily switch between several configurations. Use
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``matplotlib.style.use('default')`` or :func:`~matplotlib.rcdefaults` to
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``matplotlib.style.use('default')`` or `~matplotlib.rcdefaults` to
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restore the default rc params after changes.
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"""
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@@ -1262,12 +1262,12 @@ def use(arg, warn=True, force=False):
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before importing matplotlib.backends. If warn is True, a warning
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is issued if you try and call this after pylab or pyplot have been
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loaded. In certain black magic use cases, e.g.
1265-
:func:`pyplot.switch_backend`, we are doing the reloading necessary to
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`pyplot.switch_backend`, we are doing the reloading necessary to
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make the backend switch work (in some cases, e.g., pure image
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backends) so one can set warn=False to suppress the warnings.
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To find out which backend is currently set, see
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:func:`matplotlib.get_backend`.
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`matplotlib.get_backend`.
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"""
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# Lets determine the proper backend name first

‎lib/matplotlib/_cm.py

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def cubehelix(gamma=1.0, s=0.5, r=-1.5, h=1.0):
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"""Return custom data dictionary of (r,g,b) conversion functions, which
63-
can be used with :func:`register_cmap`, for the cubehelix color scheme.
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can be used with `register_cmap`, for the cubehelix color scheme.
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Unlike most other color schemes cubehelix was designed by D.A. Green to
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be monotonically increasing in terms of perceived brightness.

‎lib/matplotlib/afm.py

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Parse the optional fields for kern pair data and composites
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return value is a (*kernDict*, *compositeDict*) which are the
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return values from :func:`_parse_kern_pairs`, and
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:func:`_parse_composites` if the data exists, or empty dicts
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return values from `_parse_kern_pairs`, and
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`_parse_composites` if the data exists, or empty dicts
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otherwise
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"""
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optional = {
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"""
312312
Parse the Adobe Font Metics file in file handle *fh*. Return value
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is a (*dhead*, *dcmetrics*, *dkernpairs*, *dcomposite*) tuple where
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*dhead* is a :func:`_parse_header` dict, *dcmetrics* is a
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:func:`_parse_composites` dict, *dkernpairs* is a
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:func:`_parse_kern_pairs` dict (possibly {}), and *dcomposite* is a
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:func:`_parse_composites` dict (possibly {})
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*dhead* is a `_parse_header` dict, *dcmetrics* is a
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`_parse_composites` dict, *dkernpairs* is a
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`_parse_kern_pairs` dict (possibly {}), and *dcomposite* is a
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`_parse_composites` dict (possibly {})
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"""
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_sanity_check(fh)
320320
dhead = _parse_header(fh)

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