8000 Fix some examples docstrings. · matplotlib/matplotlib@bda5d20 · GitHub
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Fix some examples docstrings.
Remove extraneous :class:. Additionally reword the docs for affine_image.py (all backends support affine transforms on images) and date.py (wrt. date2num and num2date).
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‎examples/images_contours_and_fields/affine_image.py

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============================
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Prepending an affine transformation (:class:`~.transforms.Affine2D`)
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to the :ref:`data transform <data-coords>`
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of an image allows to manipulate the image's shape and orientation.
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This is an example of the concept of
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:ref:`transform chaining <transformation-pipeline>`.
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For the backends that support draw_image with optional affine
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transform (e.g., agg, ps backend), the image of the output should
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have its boundary match the dashed yellow rectangle.
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Prepending an affine transformation (`~.transforms.Affine2D`) to the :ref:`data
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transform <data-coords>` of an image allows to manipulate the image's shape and
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orientation. This is an example of the concept of :ref:`transform chaining
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<transformation-pipeline>`.
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The image of the output should have its boundary match the dashed yellow
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rectangle.
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"""
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import numpy as np

‎examples/images_contours_and_fields/demo_bboximage.py

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BboxImage Demo
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==============
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A :class:`~matplotlib.image.BboxImage` can be used to position
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an image according to a bounding box. This demo shows how to
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show an image inside a `.text.Text`'s bounding box as well as
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how to manually create a bounding box for the image.
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A `~matplotlib.image.BboxImage` can be used to position an image according to
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a bounding box. This demo shows how to show an image inside a `.text.Text`'s
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bounding box as well as how to manually create a bounding box for the image.
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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/image_annotated_heatmap.py

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# tick labels (:meth:`set_xticklabels<matplotlib.axes.Axes.set_xticklabels>`),
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# otherwise they would become out of sync. The locations are just
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# the ascending integer numbers, while the ticklabels are the labels to show.
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# Finally we can label the data itself by creating a
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# :class:`~matplotlib.text.Text` within each cell showing the value of
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# that cell.
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# Finally we can label the data itself by creating a `~matplotlib.text.Text`
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# within each cell showing the value of that cell.
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import numpy as np
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textcolors=("red", "white"))
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# Somet 9E81 imes even the data itself is categorical. Here we use a
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# :class:`matplotlib.colors.BoundaryNorm` to get the data into classes
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# `matplotlib.colors.BoundaryNorm` to get the data into classes
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# and use this to colorize the plot, but also to obtain the class
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# labels from an array of classes.
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# We can nicely plot a correlation matrix. Since this is bound by -1 and 1,
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# we use those as vmin and vmax. We may also remove leading zeros and hide
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# the diagonal elements (which are all 1) by using a
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# :class:`matplotlib.ticker.FuncFormatter`.
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# `matplotlib.ticker.FuncFormatter`.
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corr_matrix = np.corrcoef(np.random.rand(6, 5))
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im, _ = heatmap(corr_matrix, vegetables, vegetables, ax=ax4,

‎examples/lines_bars_and_markers/scatter_with_legend.py

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# -------------------------
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#
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# Another option for creating a legend for a scatter is to use the
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# :class:`~matplotlib.collections.PathCollection`'s
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# `~matplotlib.collections.PathCollection`'s
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# :meth:`~.PathCollection.legend_elements` method.
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# It will automatically try to determine a useful number of legend entries
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# to be shown and return a tuple of handles and labels. Those can be passed

‎examples/misc/keyword_plotting.py

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======================
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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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access particular variables with strings: for example, with
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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/pie_and_donut_labels.py

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# automatic percentage labeling by showing absolute values; we calculate
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# the latter back from relative data and the known sum of all values.
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#
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# We then create the pie and store the returned objects for later.
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# The first returned element of the returned tuple is a list of the wedges.
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# Those are
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# :class:`matplotlib.patches.Wedge <matplotlib.patches.Wedge>` patches, which
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# can directly be used as the handles for a legend. We can use the
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# legend's ``bbox_to_anchor`` argument to position the legend outside of
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# the pie. Here we use the axes coordinates ``(1, 0, 0.5, 1)`` together
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# with the location ``"center left"``; i.e.
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# the left central point of the legend will be at the left central point of the
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# bounding box, spanning from ``(1, 0)`` to ``(1.5, 1)`` in axes coordinates.
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# We then create the pie and store the returned objects for later. The first
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# returned element of the returned tuple is a list of the wedges. Those are
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# `matplotlib.patches.Wedge` patches, which can directly be used as the handles
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# for a legend. We can use the legend's ``bbox_to_anchor`` argument to position
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# the legend outside of the pie. Here we use the axes coordinates ``(1, 0, 0.5,
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# 1)`` together with the location ``"center left"``; i.e. the left central
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# point of the legend will be at the left central point of the bounding box,
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# spanning from ``(1, 0)`` to ``(1.5, 1)`` in axes coordinates.
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import numpy as np
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import matplotlib.pyplot as plt

‎examples/recipes/placing_text_boxes.py

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Placing text boxes
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==================
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When decorating axes with text boxes, two useful tricks are to place
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the text in axes coordinates (see :doc:`/tutorials/advanced/transforms_tutorial`), 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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argument takes a dictionary with keys that are Patch properties.
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When decorating axes with text boxes, two useful tricks are to place the text
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in axes coordinates (see :doc:`/tutorials/advanced/transforms_tutorial`),
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so the text doesn't move around with changes in x or y limits. You
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can also use the ``bbox`` property of text to surround the text with a
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`~matplotlib.patches.Patch` instance -- the ``bbox`` keyword argument takes a
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dictionary with keys that are Patch properties.
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"""
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import numpy as np

‎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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from matplotlib.collections import LineCollection
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from matplotlib import colors as mcolors
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ax.set_xlim(x.min(), x.max())
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ax.set_ylim(ys.min(), ys.max())
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# colors is sequence of rgba tuples
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# linestyle is a string or dash tuple. Legal string values are
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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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# *colors* is sequence of rgba tuples.
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# *linestyle* is a string or dash tuple. Legal string values are
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# solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq) where
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# onoffseq is an even length tuple of on and off ink in points. If linestyle
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# is omitted, 'solid' is used.
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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([np.column_stack([x, y]) for y in ys],

‎examples/shapes_and_collections/patch_collection.py

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Circles, Wedges and Polygons
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============================
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This example demonstrates how to use
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:class:`patch collections <.collections.PatchCollection>`.
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This example demonstrates how to use `.collections.PatchCollection`.
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"""
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import numpy as np

‎examples/text_labels_and_annotations/date.py

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formatters. See :doc:`/gallery/ticks_and_spines/major_minor_demo` for more
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information on controlling major and minor ticks.
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All matplotlib date plotting is done by converting date instances into days
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since 0001-01-01 00:00:00 UTC plus one day (for historical reasons). The
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conversion, tick locating and formatting is done behind the scenes so this
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is most transparent to you. 8330 The dates module provides several converter
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functions `matplotlib.dates.date2num` and `matplotlib.dates.num2date`.
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These can convert between `datetime.datetime` objects and
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:class:`numpy.datetime64` objects.
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All Matplotlib date plotting is done by converting date instances into
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days since 0001-01-01 00:00:00 UTC plus one day (for historical reasons).
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The conversion, tick locating and formatting is done behind the scenes
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so this is most transparent to you. The :mod:`matplotlib.dates` module
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provides the converter functions `.date2num` and `.num2date`, which convert
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`datetime.datetime` and `numpy.datetime64` objects to and from Matplotlib's
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internal representation.
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"""
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import numpy as np

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