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Commit 844b28c

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anntzeranntzer
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Some manual fixes.
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‎examples/color/color_cycle_default.py

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====================================
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Display the colors from the default prop_cycle, which is obtained from the
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:ref:`rc parameters</tutorials/introductory/customizing>`.
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:doc:`rc parameters</tutorials/introductory/customizing>`.
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"""
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import numpy as np
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import matplotlib.pyplot as plt

‎examples/color/color_cycler.py

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This example demonstrates two different APIs:
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1. Setting the default
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:doc:`rc parameter</tutorials/introductory/customizing>`
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specifying the property cycle. This affects all subsequent axes
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(but not axes already created).
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2. Setting the property cycle for a single pair of axes.
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1. Setting the default :doc:`rc parameter</tutorials/introductory/customizing>`
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specifying the property cycle. This affects all subsequent axes (but not
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axes already created).
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2. Setting the property cycle for a single pair of axes.
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"""
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from cycler import cycler
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import numpy as np

‎examples/images_contours_and_fields/contour_demo.py

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Illustrate simple contour plotting, contours on an image with
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a colorbar for the contours, and labelled contours.
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See also the
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:ref:`contour image example
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See also the :doc:`contour image example
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</gallery/images_contours_and_fields/contour_image>`.
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"""
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import matplotlib

‎examples/images_contours_and_fields/contour_image.py

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=============
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Test combinations of contouring, filled contouring, and image plotting.
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For contour labelling, see See also the
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:ref:`contour demo example
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For contour labelling, see also the :doc:`contour demo example
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</gallery/images_contours_and_fields/contour_demo>`.
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The emphasis in this demo is on showing how to make contours register
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correctly on images, and on how to get both of them oriented as
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desired. In particular, note the usage of the
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:ref:`"origin" and "extent"
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</tutorials/intermediate/imshow_extent>`
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keyword arguments to imshow and contour.
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correctly on images, and on how to get both of them oriented as desired.
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In particular, note the usage of the :doc:`"origin" and "extent"
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</tutorials/intermediate/imshow_extent>` keyword arguments to imshow and
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contour.
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"""
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import matplotlib.pyplot as plt
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import numpy as np

‎examples/images_contours_and_fields/contour_label_demo.py

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Illustrate some of the more advanced things that one can do with
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contour labels.
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See also the :ref:`contour demo example
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See also the :doc:`contour demo example
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</gallery/images_contours_and_fields/contour_demo>`.
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"""
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‎examples/images_contours_and_fields/image_demo.py

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# x[0,0] in the upper left or lower right by using the origin parameter.
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# You can also control the default setting image.origin in your
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# :ref:`matplotlibrc file <customizing-with-matplotlibrc-files>`. For more on
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# this topic see the :ref:`complete guide on origin and extent
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# this topic see the :doc:`complete guide on origin and extent
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# </tutorials/intermediate/imshow_extent>`.
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x = np.arange(120).reshape((10, 12))

‎examples/images_contours_and_fields/quiver_demo.py

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Demonstration of advanced quiver and quiverkey functions
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========================================================
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Demonstrates some more advanced options for `~.axes.Axes.quiver`.
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For a simple example refer to
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:doc:`/gallery/images_contours_and_fields/quiver_simple_demo`.
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Demonstrates some more advanced options for `~.axes.Axes.quiver`. For a simple
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example refer to :doc:`/gallery/images_contours_and_fields/quiver_simple_demo`.
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Known problem: the plot autoscaling does not take into account
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the arrows, so those on the boundaries are often out of the picture.
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This is *not* an easy problem to solve in a perfectly general way.
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The workaround is to manually expand the Axes objects.
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Known problem: the plot autoscaling does not take into account the arrows, so
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those on the boundaries are often out of the picture. This is *not* an easy
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problem to solve in a perfectly general way. The workaround is to manually
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expand the Axes objects.
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"""
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import matplotlib.pyplot as plt
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import numpy as np

‎examples/images_contours_and_fields/quiver_simple_demo.py

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Quiver Simple Demo
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==================
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A simple example of a `~.axes.Axes.quiver` plot with a
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`~.axes.Axes.quiverkey`.
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A simple example of a `~.axes.Axes.quiver` plot with a `~.axes.Axes.quiverkey`.
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For more advanced options refer to
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For more advanced options refer to
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:doc:`/gallery/images_contours_and_fields/quiver_demo`.
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"""
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import matplotlib.pyplot as plt

‎examples/shapes_and_collections/ellipse_demo.py

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============
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Draw many ellipses. Here individual ellipses are drawn. Compare this
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to the :ref:`Ellipse collection example
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to the :doc:`Ellipse collection example
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</gallery/shapes_and_collections/ellipse_collection>`.
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"""
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import matplotlib.pyplot as plt

‎lib/matplotlib/legend.py

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.. important::
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It is unlikely that you would ever create a Legend instance manually.
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Most users would normally create a legend via the
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It is unlikely that you would ever create a Legend instance
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manually. Most users would normally create a legend via the
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:meth:`~matplotlib.axes.Axes.legend` function. For more details on legends
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there is also a :ref:`legend guide
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</tutorials/intermediate/legend_guide>`.
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The Legend class can be considered as a container of legend handles
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and legend texts. Creation of corresponding legend handles from the
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plot elements in the axes or figures (e.g., lines, patches, etc.) are
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specified by the handler map, which defines the mapping between the
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plot elements and the legend handlers to be used (the default legend
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handlers are defined in the :mod:`~matplotlib.legend_handler` module).
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Note that not all kinds of artist are supported by the legend yet by default
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but it is possible to extend the legend handler's capabilities to support
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arbitrary objects. See the :ref:`legend guide
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</tutorials/intermediate/legend_guide>` for more information.
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there is also a :doc:`legend guide </tutorials/intermediate/legend_guide>`.
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The Legend class can be considered as a container of legend handles and
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legend texts. Creation of corresponding legend handles from the plot elements
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in the axes or figures (e.g., lines, patches, etc.) are specified by the
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handler map, which defines the mapping between the plot elements and the
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legend handlers to be used (the default legend handlers are defined in the
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:mod:`~matplotlib.legend_handler` module). Note that not all kinds of
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artist are supported by the legend yet by default but it is possible to
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extend the legend handler's capabilities to support arbitrary objects. See
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the :doc:`legend guide </tutorials/intermediate/legend_guide>` for more
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information.
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"""
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import logging

‎lib/matplotlib/legend_handler.py

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"""
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This module defines default legend handlers.
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It is strongly encouraged to have read the :ref:`legend guide
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It is strongly encouraged to have read the :doc:`legend guide
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</tutorials/intermediate/legend_guide>` before this documentation.
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Legend handlers are expected to be a callable object with a following

‎lib/matplotlib/widgets.py

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if the mouse click is within `vertex_select_radius` pixels of the
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vertex. The default radius is 15 pixels.
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See Also
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Examples
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--------
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:doc:`/gallery/widgets/polygon_selector_demo`
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"""

‎tutorials/introductory/lifecycle.py

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interface. In this case, we utilize an instance of :class:`axes.Axes`
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in order to render visualizations on an instance of :class:`figure.Figure`.
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The second is based on MATLAB and uses
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a state-based interface. This is encapsulated in the :mod:`pyplot`
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module. See the :ref:`pyplot tutorials
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</tutorials/introductory/pyplot>`
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for a more in-depth look at the pyplot interface.
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The second is based on MATLAB and uses a state-based interface. This is
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encapsulated in the :mod:`pyplot` module. See the :doc:`pyplot tutorials
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</tutorials/introductory/pyplot>` for a more in-depth look at the pyplot
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interface.
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Most of the terms are straightforward but the main thing to remember
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is that:
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# .. note::
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#
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# Figures can have multiple axes on them. For information on how to do this,
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# see the :ref:`Tight Layout tutorial
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# see the :doc:`Tight Layout tutorial
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# </tutorials/intermediate/tight_layout_guide>`.
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fig, ax = plt.subplots()

‎tutorials/text/mathtext.py

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#
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# have special meaning outside of math mode in TeX. Therefore, these
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# characters will behave differently depending on the rcParam
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# ``text.usetex`` flag. See the :ref:`usetex tutorial
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# ``text.usetex`` flag. See the :doc:`usetex tutorial
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# </tutorials/text/usetex>` for more information.
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#
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# Subscripts and superscripts

‎tutorials/text/text_props.py

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# font.sans-serif: Source Han Sans TW, Arial, sans-serif
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#
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# To control the font used on per-artist basis use the ``'name'``,
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# ``'fontname'`` or ``'fontproperties'`` kwargs documented :ref:`above
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# ``'fontname'`` or ``'fontproperties'`` kwargs documented :doc:`above
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# </tutorials/text/text_props>`.
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#
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#

‎tutorials/text/usetex.py

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* PS
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* PDF
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The LaTeX option is activated by setting ``text.usetex : True`` in
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your rc settings. Text handling with matplotlib's LaTeX support is
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slower than matplotlib's very capable :ref:`mathtext
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</tutorials/text/mathtext>`, but is more flexible, since different LaTeX
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packages (font packages, math packages, etc.) can be used. The
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results can be striking, especially when you take care to use the same
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fonts in your figures as in the main document.
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The LaTeX option is activated by setting ``text.usetex : True`` in your rc
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settings. Text handling with matplotlib's LaTeX support is slower than
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matplotlib's very capable :doc:`mathtext </tutorials/text/mathtext>`, but is
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more flexible, since different LaTeX packages (font packages, math packages,
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etc.) can be used. The results can be striking, especially when you take care
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to use the same fonts in your figures as in the main document.
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Matplotlib's LaTeX support requires a working LaTeX_ installation, dvipng_
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(which may be included with your LaTeX installation), and Ghostscript_
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(GPL Ghostscript 8.60 or later is recommended). The executables for these
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external dependencies must all be located on your :envvar:`PATH`.
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There are a couple of options to mention, which can be changed using :ref:`rc
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settings </tutorials/introductory/customizing>`. Here is an example matplotlibrc file::
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There are a couple of options to mention, which can be changed using
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:doc:`rc settings </tutorials/introductory/customizing>`. Here is an example
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matplotlibrc file::
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font.family : serif
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font.serif : Times, Palatino, New Century Schoolbook, Bookman, Computer Modern Roman

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