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Merge pull request #11268 from ImportanceOfBeingErnest/sanitize-some-doc-links
DOC: Sanitize some internal documentation links
2 parents ec66f36 + 2f78a1e commit 4814755

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‎examples/lines_bars_and_markers/markevery_prop_cycle.py

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This example demonstrates a working solution to issue #8576, providing full
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support of the markevery property for axes.prop_cycle assignments through
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rcParams. Makes use of the same list of markevery cases from
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https://matplotlib.org/examples/pylab_examples/markevery_demo.html
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rcParams. Makes use of the same list of markevery cases from the
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:doc:`markevery demo
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</gallery/lines_bars_and_markers/markevery_demo>`.
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Renders a plot with shifted-sine curves along each column with
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a unique markevery value for each sine curve.

‎examples/text_labels_and_annotations/annotation_demo.py

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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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`tutorial on annotation <http://matplotlib.org/users/annotations.html>`_.
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:doc:`tutorial on annotation </tutorials/text/annotations>`.
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"""
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import matplotlib.pyplot as plt

‎examples/text_labels_and_annotations/tex_demo.py

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=================================
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You can use TeX to render all of your matplotlib text if the rc
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parameter text.usetex is set. This works currently on the agg and ps
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parameter ``text.usetex`` is set. This works currently on the agg and ps
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backends, and requires that you have tex and the other dependencies
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described a 10000 t http://matplotlib.org/users/usetex.html
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described in the :doc:`/tutorials/text/usetex` tutorial
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properly installed on your system. The first time you run a script
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you will see a lot of output from tex and associated tools. The next
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time, the run may be silent, as a lot of the information is cached.

‎examples/ticks_and_spines/custom_ticker1.py

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==============
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The new ticker code was designed to explicitly support user customized
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ticking. The documentation
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http://matplotlib.org/api/ticker_api.html#module-matplotlib.ticker details this
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ticking. The documentation of :mod:`matplotlib.ticker` details this
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process. That code defines a lot of preset tickers but was primarily
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designed to be user extensible.
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In this example a user defined function is used to format the ticks in
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millions of dollars on the y axis
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millions of dollars on the y axis.
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"""
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from matplotlib.ticker import FuncFormatter
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import matplotlib.pyplot as plt

‎tutorials/colors/colorbar_only.py

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will be used. Then create the colorbar by calling
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:class:`~matplotlib.colorbar.ColorbarBase` and specify axis, colormap, norm
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and orientation as parameters. Here we create a basic continuous colorbar
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with ticks and labels. More information on the colorbar API can be found
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`here <https://matplotlib.org/api/colorbar_api.html>`_.
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with ticks and labels. For more information see the
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:mod:`~matplotlib.colorbar` API.
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"""
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import matplotlib.pyplot as plt

‎tutorials/colors/colors.py

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All string specifications of color, other than "CN", are case-insensitive.
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For more information on colors in matplotlib see
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* the :doc:`/gallery/color/color_demo` example;
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* the `matplotlib.colors` API;
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* the :doc:`/gallery/color/named_colors` example.
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"CN" color selection
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--------------------
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‎tutorials/introductory/customizing.py

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------------------
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The ``style`` package adds support for easy-to-switch plotting "styles" with
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the same parameters as a matplotlibrc_ file (which is read at startup to
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configure matplotlib).
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the same parameters as a
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:ref:`matplotlib rc <customizing-with-matplotlibrc-files>` file (which is read
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at startup to configure matplotlib).
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There are a number of pre-defined styles provided by matplotlib. For
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example, there's a pre-defined style called "ggplot", which emulates the
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# .. literalinclude:: ../../../matplotlibrc.template
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#
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#
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# .. _matplotlibrc: http://matplotlib.org/users/customizing.html
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# .. _ggplot: http://ggplot2.org/
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# .. _R: https://www.r-project.org/

‎tutorials/introductory/images.py

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settings. The alternative is the object-oriented interface, which is also
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very powerful, and generally more suitable for large application
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development. If you'd like to learn about the object-oriented
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interface, a great place to start is our `FAQ on usage
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<http://matplotlib.org/faq/usage_faq.html>`_. For now, let's get on
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interface, a great place to start is our :doc:`Usage guide
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</tutorials/introductory/usage>`. For now, let's get on
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with the imperative-style approach:
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"""
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‎tutorials/introductory/pyplot.py

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# the current figure and plotting area, and the plotting
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# functions are directed to the current axes (please note that "axes" here
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# and in most places in the documentation refers to the *axes*
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# `part of a figure <http://matplotlib.org/faq/usage_faq.html#parts-of-a-figure>`__
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# :ref:`part of a figure <figure_parts>`
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# and not the strict mathematical term for more than one axis).
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#
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# .. note::

‎tutorials/text/pgf.py

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`tex.stackexchange.com <http://tex.stackexchange.com/questions/7953>`_.
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Another way would be to "rasterize" parts of the graph causing problems
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using either the ``rasterized=True`` keyword, or ``.set_rasterized(True)`` as per
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`this example <http://matplotlib.org/examples/misc/rasterization_demo.html>`_.
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:doc:`this example </gallery/misc/rasterization_demo>`.
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* If you still need help, please see :ref:`reporting-problems`
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‎tutorials/text/text_intro.py

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The user has a great deal of control over text properties (font size, font
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weight, text location and color, etc.) with sensible defaults set in
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the `rc file <http://matplotlib.org/users/customizing.html>`.
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the :doc:`rc file </tutorials/introductory/customizing>`.
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And significantly, for those interested in mathematical
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or scientific figures, matplotlib implements a large number of TeX
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math symbols and commands, supporting :ref:`mathematical expressions
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<sphx_glr_tutorials_text_mathtext.py>` anywhere in your figure.
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math symbols and commands, supporting :doc:`mathematical expressions
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</tutorials/text/mathtext>` anywhere in your figure.
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Basic text commands

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