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Merge pull request matplotlib#27268 from anntzer/d
Copy-edit various examples.
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‎galleries/examples/color/README.txt

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Color
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=====
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For more in-depth information about the colormaps available in matplotlib
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as well as a description of their properties,
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For a description of the colormaps available in Matplotlib,
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see the :ref:`colormaps tutorial <tutorials-colors>`.

‎galleries/examples/color/named_colors.py

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List of named colors
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====================
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This plots a list of the named colors supported in matplotlib.
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This plots a list of the named colors supported by Matplotlib.
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For more information on colors in matplotlib see
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* the :ref:`colors_def` tutorial;

‎galleries/examples/images_contours_and_fields/image_demo.py

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"""
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==========
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Image demo
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==========
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Many ways to plot images in Matplotlib.
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========================
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Many ways to plot images
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========================
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The most common way to plot images in Matplotlib is with
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`~.axes.Axes.imshow`. The following examples demonstrate much of the

‎galleries/examples/images_contours_and_fields/pcolor_demo.py

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"""
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===========
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Pcolor demo
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===========
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=============
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pcolor images
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=============
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Generating images with `~.axes.Axes.pcolor`.
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Pcolor allows you to generate 2D image-style plots. Below we will show how
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to do so in Matplotlib.
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`~.Axes.pcolor` generates 2D image-style plots, as illustrated below.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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‎galleries/examples/text_labels_and_annotations/accented_text.py

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r"""
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=================================
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Using accented text in Matplotlib
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=================================
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=============
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Accented text
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=============
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Matplotlib supports accented characters via TeX mathtext or Unicode.
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‎galleries/examples/text_labels_and_annotations/annotation_demo.py

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Annotating Plots
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================
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The following examples show how it is possible to annotate plots in Matplotlib.
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The following examples show ways 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 Matplotlib, see the

‎galleries/examples/text_labels_and_annotations/font_file.py

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r"""
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===================================
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Using a ttf font file in Matplotlib
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===================================
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====================
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Using ttf font files
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====================
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Although it is usually not a good idea to explicitly point to a single ttf file
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for a font instance, you can do so by passing a `pathlib.Path` instance as the

‎galleries/examples/text_labels_and_annotations/legend_demo.py

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Legend Demo
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===========
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Plotting legends in Matplotlib.
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There are many ways to create and customize legends in Matplotlib. Below
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we'll show a few examples for how to do so.
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‎galleries/users_explain/axes/constrainedlayout_guide.py

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.. warning::
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Calling ``plt.tight_layout()`` will turn off *constrained layout*!
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Calling `~.pyplot.tight_layout` will turn off *constrained layout*!
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Simple example
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==============
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In Matplotlib, the location of Axes (including subplots) are specified in
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normalized figure coordinates. It can happen that your axis labels or titles
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(or sometimes even ticklabels) go outside the figure area, and are thus
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clipped.
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With the default Axes positioning, the axes title, axis labels, or tick labels
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can sometimes go outside the figure area, and thus get clipped.
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"""
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# sphinx_gallery_thumbnail_number = 18

‎galleries/users_explain/axes/legend_guide.py

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Legend guide
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============
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Generating legends flexibly in Matplotlib.
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.. currentmodule:: matplotlib.pyplot
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This legend guide is an extension of the documentation available at
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:func:`~matplotlib.pyplot.legend` - please ensure you are familiar with
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contents of that documentation before proceeding with this guide.
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This legend guide extends the `~.Axes.legend` docstring -
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please read it before proceeding with this guide.
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This guide makes use of some common terms, which are documented here for
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clarity:
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line_down, = ax.plot([3, 2, 1], label='Line 1')
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ax.legend(handles=[line_up, line_down])
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In some cases, it is not possible to set the label of the handle, so it is
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possible to pass through the list of labels to :func:`legend`::
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In the rare case where the labels cannot directly be set on the handles, they
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can also be directly passed to :func:`legend`::
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fig, ax = plt.subplots()
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line_up, = ax.plot([1, 2, 3], label='Line 2')

‎galleries/users_explain/axes/tight_layout_guide.py

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An alternative to *tight_layout* is :ref:`constrained_layout
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<constrainedlayout_guide>`.
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Simple Example
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Simple example
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==============
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In matplotlib, the location of axes (including subplots) are specified in
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normalized figure coordinates. It can happen that your axis labels or
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titles (or sometimes even ticklabels) go outside the figure area, and are thus
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clipped.
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With the default Axes positioning, the axes title, axis labels, or tick labels
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can sometimes go outside the figure area, and thus get clipped.
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"""
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# sphinx_gallery_thumbnail_number = 7
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# %%
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# You may provide an optional *rect* parameter, which specifies the bounding
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# box that the subplots will be fit inside. The coordinates must be in
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# normalized figure coordinates and the default is (0, 0, 1, 1).
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# box that the subplots will be fit inside. The coordinates are in
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# normalized figure coordinates and default to (0, 0, 1, 1) (the whole figure).
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fig = plt.figure()
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# Use with AxesGrid1
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# ==================
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#
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# While limited, :mod:`mpl_toolkits.axes_grid1` is also supported.
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# Limited support for :mod:`mpl_toolkits.axes_grid1` is provided.
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from mpl_toolkits.axes_grid1 import Grid
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‎galleries/users_explain/colors/colormap-manipulation.py

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.. _colormap-manipulation:
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********************************
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Creating Colormaps in Matplotlib
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********************************
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******************
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Creating Colormaps
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******************
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Matplotlib has a number of built-in colormaps accessible via
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`.matplotlib.colormaps`. There are also external libraries like
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palettable_ that have many extra colormaps.
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.. _palettable: https://jiffyclub.github.io/palettable/
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However, we often want to create or manipulate colormaps in Matplotlib.
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However, we may also want to create or manipulate our own colormaps.
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This can be done using the class `.ListedColormap` or
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`.LinearSegmentedColormap`.
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Seen from the outside, both colormap classes map values between 0 and 1 to
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a bunch of colors. There are, however, slight differences, some of which are
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shown in the following.
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Both colormap classes map values between 0 and 1 to colors. There are however
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differences, as explained below.
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Before manually creating or manipulating colormaps, let us first see how we
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can obtain colormaps and their colors from existing colormap classes.
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Getting colormaps and accessing their values
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============================================
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which returns a colormap object. The length of the list of colors used
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internally to define the colormap can be adjusted via `.Colormap.resampled`.
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Below we use a modest value of 8 so there are not a lot of values to look at.
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"""
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import matplotlib.pyplot as plt

‎galleries/users_explain/colors/colormaps.py

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.. _colormaps:
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********************************
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Choosing Colormaps in Matplotlib
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********************************
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******************
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Choosing Colormaps
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******************
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Matplotlib has a number of built-in colormaps accessible via
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`.matplotlib.colormaps`. There are also external libraries that

‎galleries/users_explain/text/text_intro.py

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.. _text_intro:
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========================
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Text in Matplotlib Plots
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========================
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==================
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Text in Matplotlib
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==================
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Introduction to plotting and working with text in Matplotlib.
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