Merge pull request #15983 from anntzer/classless

tacaswell · web-flow · commit a429b4dc6ca0 · 2019-12-19T21:17:10.000-05:00
DOC: Fix some examples docstrings.
diff --git a/examples/images_contours_and_fields/affine_image.py b/examples/images_contours_and_fields/affine_image.py
@@ -4,15 +4,13 @@
 ============================
 
 
-Prepending an affine transformation (:class:`~.transforms.Affine2D`)
-to the :ref:`data transform <data-coords>`
-of an image allows to manipulate the image's shape and orientation.
-This is an example of the concept of
-:ref:`transform chaining <transformation-pipeline>`.
-
-For the backends that support draw_image with optional affine
-transform (e.g., agg, ps backend), the image of the output should
-have its boundary match the dashed yellow rectangle.
+Prepending an affine transformation (`~.transforms.Affine2D`) to the :ref:`data
+transform <data-coords>` of an image allows to manipulate the image's shape and
+orientation.  This is an example of the concept of :ref:`transform chaining
+<transformation-pipeline>`.
+
+The image of the output should have its boundary match the dashed yellow
+rectangle.
 """
 
 import numpy as np
diff --git a/examples/images_contours_and_fields/demo_bboximage.py b/examples/images_contours_and_fields/demo_bboximage.py
@@ -3,10 +3,9 @@
 BboxImage Demo
 ==============
 
-A :class:`~matplotlib.image.BboxImage` can be used to position
-an image according to a bounding box. This demo shows how to
-show an image inside a `.text.Text`'s bounding box as well as
-how to manually create a bounding box for the image.
+A `~matplotlib.image.BboxImage` can be used to position an image according to
+a bounding box. This demo shows how to show an image inside a `.text.Text`'s
+bounding box as well as how to manually create a bounding box for the image.
 """
 import matplotlib.pyplot as plt
 import numpy as np
diff --git a/examples/images_contours_and_fields/image_annotated_heatmap.py b/examples/images_contours_and_fields/image_annotated_heatmap.py
@@ -32,9 +32,8 @@
 # tick labels (:meth:`set_xticklabels<matplotlib.axes.Axes.set_xticklabels>`),
 # otherwise they would become out of sync. The locations are just
 # the ascending integer numbers, while the ticklabels are the labels to show.
-# Finally we can label the data itself by creating a
-# :class:`~matplotlib.text.Text` within each cell showing the value of
-# that cell.
+# Finally we can label the data itself by creating a `~matplotlib.text.Text`
+# within each cell showing the value of that cell.
 
 
 import numpy as np
@@ -263,7 +262,7 @@ def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
                  textcolors=("red", "white"))
 
 # Sometimes even the data itself is categorical. Here we use a
-# :class:`matplotlib.colors.BoundaryNorm` to get the data into classes
+# `matplotlib.colors.BoundaryNorm` to get the data into classes
 # and use this to colorize the plot, but also to obtain the class
 # labels from an array of classes.
 
@@ -286,7 +285,7 @@ def annotate_heatmap(im, data=None, valfmt="{x:.2f}",
 # We can nicely plot a correlation matrix. Since this is bound by -1 and 1,
 # we use those as vmin and vmax. We may also remove leading zeros and hide
 # the diagonal elements (which are all 1) by using a
-# :class:`matplotlib.ticker.FuncFormatter`.
+# `matplotlib.ticker.FuncFormatter`.
 
 corr_matrix = np.corrcoef(np.random.rand(6, 5))
 im, _ = heatmap(corr_matrix, vegetables, vegetables, ax=ax4,
diff --git a/examples/lines_bars_and_markers/scatter_with_legend.py b/examples/lines_bars_and_markers/scatter_with_legend.py
@@ -37,7 +37,7 @@
 # -------------------------
 #
 # Another option for creating a legend for a scatter is to use the
-# :class:`~matplotlib.collections.PathCollection`'s
+# `~matplotlib.collections.PathCollection`'s
 # :meth:`~.PathCollection.legend_elements` method.
 # It will automatically try to determine a useful number of legend entries
 # to be shown and return a tuple of handles and labels. Those can be passed
diff --git a/examples/misc/keyword_plotting.py b/examples/misc/keyword_plotting.py
@@ -4,8 +4,8 @@
 ======================
 
 There are some instances where you have data in a format that lets you
-access particular variables with strings. For example, with
-:class:`numpy.recarray` or :class:`pandas.DataFrame`.
+access particular variables with strings: for example, with
+`numpy.recarray` or `pandas.DataFrame`.
 
 Matplotlib allows you provide such an object with the ``data`` keyword
 argument. If provided, then you may generate plots with the strings
diff --git a/examples/pie_and_polar_charts/pie_and_donut_labels.py b/examples/pie_and_polar_charts/pie_and_donut_labels.py
@@ -19,16 +19,14 @@
 # automatic percentage labeling by showing absolute values; we calculate
 # the latter back from relative data and the known sum of all values.
 #
-# We then create the pie and store the returned objects for later.
-# The first returned element of the returned tuple is a list of the wedges.
-# Those are
-# :class:`matplotlib.patches.Wedge <matplotlib.patches.Wedge>` patches, which
-# can directly be used as the handles for a legend. We can use the
-# legend's ``bbox_to_anchor`` argument to position the legend outside of
-# the pie. Here we use the axes coordinates ``(1, 0, 0.5, 1)`` together
-# with the location ``"center left"``; i.e.
-# the left central point of the legend will be at the left central point of the
-# bounding box, spanning from ``(1, 0)`` to ``(1.5, 1)`` in axes coordinates.
+# We then create the pie and store the returned objects for later.  The first
+# returned element of the returned tuple is a list of the wedges.  Those are
+# `matplotlib.patches.Wedge` patches, which can directly be used as the handles
+# for a legend. We can use the legend's ``bbox_to_anchor`` argument to position
+# the legend outside of the pie. Here we use the axes coordinates ``(1, 0, 0.5,
+# 1)`` together with the location ``"center left"``; i.e. the left central
+# point of the legend will be at the left central point of the bounding box,
+# spanning from ``(1, 0)`` to ``(1.5, 1)`` in axes coordinates.
 
 import numpy as np
 import matplotlib.pyplot as plt
diff --git a/examples/recipes/placing_text_boxes.py b/examples/recipes/placing_text_boxes.py
@@ -2,12 +2,12 @@
 Placing text boxes
 ==================
 
-When decorating axes with text boxes, two useful tricks are to place
-the text in axes coordinates (see :doc:`/tutorials/advanced/transforms_tutorial`), so the
-text doesn't move around with changes in x or y limits.  You can also
-use the ``bbox`` property of text to surround the text with a
-:class:`~matplotlib.patches.Patch` instance -- the ``bbox`` keyword
-argument takes a dictionary with keys that are Patch properties.
+When decorating axes with text boxes, two useful tricks are to place the text
+in axes coordinates (see :doc:`/tutorials/advanced/transforms_tutorial`),
+so the text doesn't move around with changes in x or y limits.  You
+can also use the ``bbox`` property of text to surround the text with a
+`~matplotlib.patches.Patch` instance -- the ``bbox`` keyword argument takes a
+dictionary with keys that are Patch properties.
 """
 
 import numpy as np
diff --git a/examples/shapes_and_collections/line_collection.py b/examples/shapes_and_collections/line_collection.py
@@ -5,9 +5,10 @@
 
 Plotting lines with Matplotlib.
 
-:class:`~matplotlib.collections.LineCollection` allows one to plot multiple
+`~matplotlib.collections.LineCollection` allows one to plot multiple
 lines on a figure. Below we show off some of its properties.
 """
+
 import matplotlib.pyplot as plt
 from matplotlib.collections import LineCollection
 from matplotlib import colors as mcolors
@@ -34,12 +35,12 @@
 ax.set_xlim(x.min(), x.max())
 ax.set_ylim(ys.min(), ys.max())
 
-# colors is sequence of rgba tuples
-# linestyle is a string or dash tuple. Legal string values are
-#          solid|dashed|dashdot|dotted.  The dash tuple is (offset, onoffseq)
-#          where onoffseq is an even length tuple of on and off ink in points.
-#          If linestyle is omitted, 'solid' is used
-# See :class:`matplotlib.collections.LineCollection` for more information
+# *colors* is sequence of rgba tuples.
+# *linestyle* is a string or dash tuple. Legal string values are
+# solid|dashed|dashdot|dotted.  The dash tuple is (offset, onoffseq) where
+# onoffseq is an even length tuple of on and off ink in points.  If linestyle
+# is omitted, 'solid' is used.
+# See `matplotlib.collections.LineCollection` for more information.
 colors = [mcolors.to_rgba(c)
           for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
 
@@ -69,7 +70,7 @@
 #          solid|dashed|dashdot|dotted.  The dash tuple is (offset, onoffseq)
 #          where onoffseq is an even length tuple of on and off ink in points.
 #          If linestyle is omitted, 'solid' is used
-# See :class:`matplotlib.collections.LineCollection` for more information
+# See `matplotlib.collections.LineCollection` for more information
 
 # Make a sequence of (x, y) pairs.
 line_segments = LineCollection([np.column_stack([x, y]) for y in ys],
diff --git a/examples/shapes_and_collections/patch_collection.py b/examples/shapes_and_collections/patch_collection.py
@@ -3,8 +3,7 @@
 Circles, Wedges and Polygons
 ============================
 
-This example demonstrates how to use
-:class:`patch collections <.collections.PatchCollection>`.
+This example demonstrates how to use `.collections.PatchCollection`.
 """
 
 import numpy as np
diff --git a/examples/text_labels_and_annotations/date.py b/examples/text_labels_and_annotations/date.py
@@ -7,13 +7,13 @@
 formatters.  See :doc:`/gallery/ticks_and_spines/major_minor_demo` for more
 information on controlling major and minor ticks.
 
-All matplotlib date plotting is done by converting date instances into days
-since 0001-01-01 00:00:00 UTC plus one day (for historical reasons).  The
-conversion, tick locating and formatting is done behind the scenes so this
-is most transparent to you.  The dates module provides several converter
-functions `matplotlib.dates.date2num` and `matplotlib.dates.num2date`.
-These can convert between `datetime.datetime` objects and
-:class:`numpy.datetime64` objects.
+All Matplotlib date plotting is done by converting date instances into
+days since 0001-01-01 00:00:00 UTC plus one day (for historical reasons).
+The conversion, tick locating and formatting is done behind the scenes
+so this is most transparent to you.  The :mod:`matplotlib.dates` module
+provides the converter functions `.date2num` and `.num2date`, which convert
+`datetime.datetime` and `numpy.datetime64` objects to and from Matplotlib's
+internal representation.
 """
 
 import numpy as np
diff --git a/examples/ticks_and_spines/multiple_yaxis_with_spines.py b/examples/ticks_and_spines/multiple_yaxis_with_spines.py
@@ -1,4 +1,4 @@
-"""
+r"""
 ==========================
 Multiple Yaxis With Spines
 ==========================
@@ -8,7 +8,7 @@
 and offset its position using `~.spines.Spine.set_position`.
 
 Note that this approach uses `matplotlib.axes.Axes` and their
-:class:`Spines <matplotlib.spines.Spine>`. An alternative approach for parasite
+`~matplotlib.spines.Spine`\s. An alternative approach for parasite
 axes is shown in the :doc:`/gallery/axisartist/demo_parasite_axes` and
 :doc:`/gallery/axisartist/demo_parasite_axes2` examples.
 """
diff --git a/examples/widgets/lasso_selector_demo_sgskip.py b/examples/widgets/lasso_selector_demo_sgskip.py
@@ -30,15 +30,13 @@ class SelectFromCollection:
 
     Parameters
     ----------
-    ax : :class:`~matplotlib.axes.Axes`
+    ax : `~matplotlib.axes.Axes`
         Axes to interact with.
-
-    collection : :class:`matplotlib.collections.Collection` subclass
+    collection : `matplotlib.collections.Collection` subclass
         Collection you want to select from.
-
     alpha_other : 0 <= float <= 1
         To highlight a selection, this tool sets all selected points to an
-        alpha value of 1 and non-selected points to `alpha_other`.
+        alpha value of 1 and non-selected points to *alpha_other*.
     """
 
     def __init__(self, ax, collection, alpha_other=0.3):
diff --git a/examples/widgets/polygon_selector_demo.py b/examples/widgets/polygon_selector_demo.py
@@ -4,16 +4,17 @@
 =====================
 
 Shows how one can select indices of a polygon interactively.
-
 """
+
 import numpy as np
 
 from matplotlib.widgets import PolygonSelector
 from matplotlib.path import Path
 
 
 class SelectFromCollection:
-    """Select indices from a matplotlib collection using `PolygonSelector`.
+    """
+    Select indices from a matplotlib collection using `PolygonSelector`.
 
     Selected indices are saved in the `ind` attribute. This tool fades out the
     points that are not part of the selection (i.e., reduces their alpha
@@ -25,15 +26,13 @@ class SelectFromCollection:
 
     Parameters
     ----------
-    ax : :class:`~matplotlib.axes.Axes`
+    ax : `~matplotlib.axes.Axes`
         Axes to interact with.
-
-    collection : :class:`matplotlib.collections.Collection` subclass
+    collection : `matplotlib.collections.Collection` subclass
         Collection you want to select from.
-
     alpha_other : 0 <= float <= 1
         To highlight a selection, this tool sets all selected points to an
-        alpha value of 1 and non-selected points to `alpha_other`.
+        alpha value of 1 and non-selected points to *alpha_other*.
     """
 
     def __init__(self, ax, collection, alpha_other=0.3):

Original file line number	Diff line number	Diff line change
`@@ -37,7 +37,7 @@`
`37`	`37`	`# -------------------------`
`38`	`38`	`#`
`39`	`39`	`# Another option for creating a legend for a scatter is to use the`
`40`		-# :class:`~matplotlib.collections.PathCollection`'s
	`40`	+# `~matplotlib.collections.PathCollection`'s
`41`	`41`	# :meth:`~.PathCollection.legend_elements` method.
`42`	`42`	`# It will automatically try to determine a useful number of legend entries`
`43`	`43`	`# to be shown and return a tuple of handles and labels. Those can be passed`