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Jun 26, 2020
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Update hard-coded results in artist tutorial #17761

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6b1f1f7
Capitalize Matplotlib in artists tutorial.
QuLogic Jun 26, 2020
4d5a295
Update hard-coded outputs in artists tutorial.
QuLogic Jun 26, 2020
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152 changes: 107 additions & 45 deletions tutorials/intermediate/artists.py
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Original file line number Diff line number Diff line change
Expand Up @@ -91,10 +91,10 @@ class in the matplotlib API, and the one you will be working with most
.. sourcecode:: ipython

In [101]: ax.lines[0]
Out[101]: <matplotlib.lines.Line2D instance at 0x19a95710>
Out[101]: <matplotlib.lines.Line2D at 0x19a95710>

In [102]: line
Out[102]: <matplotlib.lines.Line2D instance at 0x19a95710>
Out[102]: <matplotlib.lines.Line2D at 0x19a95710>

If you make subsequent calls to ``ax.plot`` (and the hold state is "on"
which is the default) then additional lines will be added to the list.
Expand All @@ -107,7 +107,7 @@ class in the matplotlib API, and the one you will be working with most
The Axes also has helper methods to configure and decorate the x-axis
and y-axis tick, tick labels and axis labels::

xtext = ax.set_xlabel('my xdata') # returns a Text instance
xtext = ax.set_xlabel('my xdata') # returns a Text instance
ytext = ax.set_ylabel('my ydata')

When you call :meth:`ax.set_xlabel <matplotlib.axes.Axes.set_xlabel>`,
Expand Down Expand Up @@ -149,20 +149,20 @@ class in the matplotlib API, and the one you will be working with most
# Customizing your objects
# ========================
#
# Every element in the figure is represented by a matplotlib
# Every element in the figure is represented by a Matplotlib
# :class:`~matplotlib.artist.Artist`, and each has an extensive list of
# properties to configure its appearance. The figure itself contains a
# :class:`~matplotlib.patches.Rectangle` exactly the size of the figure,
# which you can use to set the background color and transparency of the
# figures. Likewise, each :class:`~matplotlib.axes.Axes` bounding box
# (the standard white box with black edges in the typical matplotlib
# (the standard white box with black edges in the typical Matplotlib
# plot, has a ``Rectangle`` instance that determines the color,
# transparency, and other properties of the Axes. These instances are
# stored as member variables :attr:`Figure.patch
# <matplotlib.figure.Figure.patch>` and :attr:`Axes.patch
# <matplotlib.axes.Axes.patch>` ("Patch" is a name inherited from
# MATLAB, and is a 2D "patch" of color on the figure, e.g., rectangles,
# circles and polygons). Every matplotlib ``Artist`` has the following
# circles and polygons). Every Matplotlib ``Artist`` has the following
# properties
#
# ========== =================================================================
Expand Down Expand Up @@ -210,31 +210,49 @@ class in the matplotlib API, and the one you will be working with most
# .. sourcecode:: ipython
#
# In [149]: matplotlib.artist.getp(fig.patch)
# alpha = 1.0
# animated = False
# antialiased or aa = True
# axes = None
# clip_box = None
# clip_on = False
# clip_path = None
# contains = None
# edgecolor or ec = w
# facecolor or fc = 0.75
# figure = Figure(8.125x6.125)
# fill = 1
# hatch = None
# height = 1
# label =
# linewidth or lw = 1.0
# picker = None
# transform = <Affine object at 0x134cca84>
# verts = ((0, 0), (0, 1), (1, 1), (1, 0))
# visible = True
# width = 1
# window_extent = <Bbox object at 0x134acbcc>
# x = 0
# y = 0
# zorder = 1
# agg_filter = None
# alpha = None
# animated = False
# antialiased or aa = False
# bbox = Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0)
# capstyle = butt
# children = []
# clip_box = None
# clip_on = True
# clip_path = None
# contains = None
# data_transform = BboxTransformTo( TransformedBbox( Bbox...
# edgecolor or ec = (1.0, 1.0, 1.0, 1.0)
# extents = Bbox(x0=0.0, y0=0.0, x1=640.0, y1=480.0)
# facecolor or fc = (1.0, 1.0, 1.0, 1.0)
# figure = Figure(640x480)
# fill = True
# gid = None
# hatch = None
# height = 1
# in_layout = False
# joinstyle = miter
# label =
# linestyle or ls = solid
# linewidth or lw = 0.0
# patch_transform = CompositeGenericTransform( BboxTransformTo( ...
# path = Path(array([[0., 0.], [1., 0.], [1.,...
# path_effects = []
# picker = None
# rasterized = None
# sketch_params = None
# snap = None
# transform = CompositeGenericTransform( CompositeGenericTra...
# transformed_clip_path_and_affine = (None, None)
# url = None
# verts = [[ 0. 0.] [640. 0.] [640. 480.] [ 0. 480....
# visible = True
# width = 1
# window_extent = Bbox(x0=0.0, y0=0.0, x1=640.0, y1=480.0)
# x = 0
# xy = (0, 0)
# y = 0
# zorder = 1
#
# The docstrings for all of the classes also contain the ``Artist``
# properties, so you can consult the interactive "help" or the
Expand Down Expand Up @@ -284,11 +302,10 @@ class in the matplotlib API, and the one you will be working with most
# In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3])
#
# In [159]: ax1
# Out[159]: <matplotlib.axes.Subplot instance at 0xd54b26c>
# Out[159]: <AxesSubplot:>
#
# In [160]: print(fig.axes)
# [<matplotlib.axes.Subplot instance at 0xd54b26c>,
# <matplotlib.axes.Axes instance at 0xd3f0b2c>]
# [<AxesSubplot:>, <matplotlib.axes._axes.Axes object at 0x7f0768702be0>]
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Maybe we should add a __repr__ to Axes, too.

#
# Because the figure maintains the concept of the "current axes" (see
# :meth:`Figure.gca <matplotlib.figure.Figure.gca>` and
Expand Down Expand Up @@ -354,7 +371,7 @@ class in the matplotlib API, and the one you will be working with most
# Axes container
# --------------
#
# The :class:`matplotlib.axes.Axes` is the center of the matplotlib
# The :class:`matplotlib.axes.Axes` is the center of the Matplotlib
# universe -- it contains the vast majority of all the ``Artists`` used
# in a figure with many helper methods to create and add these
# ``Artists`` to itself, as well as helper methods to access and
Expand Down Expand Up @@ -391,7 +408,7 @@ class in the matplotlib API, and the one you will be working with most
# .. sourcecode:: ipython
#
# In [229]: print(ax.lines)
# [<matplotlib.lines.Line2D instance at 0xd378b0c>]
# [<matplotlib.lines.Line2D at 0xd378b0c>]
#
# Similarly, methods that create patches, like
# :meth:`~matplotlib.axes.Axes.bar` creates a list of rectangles, will
Expand All @@ -403,9 +420,10 @@ class in the matplotlib API, and the one you will be working with most
# In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50)
#
# In [234]: rectangles
# Out[234]: <a list of 50 Patch objects>
# Out[234]: <BarContainer object of 50 artists>
#
# In [235]: print(len(ax.patches))
# Out[235]: 50
#
# You should not add objects directly to the ``Axes.lines`` or
# ``Axes.patches`` lists unless you know exactly what you are doing,
Expand Down Expand Up @@ -433,8 +451,8 @@ class in the matplotlib API, and the one you will be working with most
# None
#
# # and the transformation instance is set to the "identity transform"
# In [265]: print(rect.get_transform())
# <Affine object at 0x13695544>
# In [265]: print(rect.get_data_transform())
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Note, get_transform is wrong, because it shows the re-scaling of a unit rectangle to the expected size, and not the identity transform that the tutorial is talking about here.

# IdentityTransform()
#
# # now we add the Rectangle to the Axes
# In [266]: ax.add_patch(rect)
Expand All @@ -445,12 +463,56 @@ class in the matplotlib API, and the one you will be working with most
# Axes(0.125,0.1;0.775x0.8)
#
# # and the transformation has been set too
# In [268]: print(rect.get_transform())
# <Affine object at 0x15009ca4>
# In [268]: print(rect.get_data_transform())
# CompositeGenericTransform(
# TransformWrapper(
# BlendedAffine2D(
# IdentityTransform(),
# IdentityTransform())),
# CompositeGenericTransform(
# BboxTransformFrom(
# TransformedBbox(
# Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0),
# TransformWrapper(
# BlendedAffine2D(
# IdentityTransform(),
# IdentityTransform())))),
# BboxTransformTo(
# TransformedBbox(
# Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88),
# BboxTransformTo(
# TransformedBbox(
# Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8),
# Affine2D(
# [[100. 0. 0.]
# [ 0. 100. 0.]
# [ 0. 0. 1.]])))))))
#
# # the default axes transformation is ax.transData
# In [269]: print(ax.transData)
# <Affine object at 0x15009ca4>
# CompositeGenericTransform(
# TransformWrapper(
# BlendedAffine2D(
# IdentityTransform(),
# IdentityTransform())),
# CompositeGenericTransform(
# BboxTransformFrom(
# TransformedBbox(
# Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0),
# TransformWrapper(
# BlendedAffine2D(
# IdentityTransform(),
# IdentityTransform())))),
# BboxTransformTo(
# TransformedBbox(
# Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88),
# BboxTransformTo(
# TransformedBbox(
# Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8),
# Affine2D(
# [[100. 0. 0.]
# [ 0. 100. 0.]
# [ 0. 0. 1.]])))))))
#
# # notice that the xlimits of the Axes have not been changed
# In [270]: print(ax.get_xlim())
Expand All @@ -463,12 +525,12 @@ class in the matplotlib API, and the one you will be working with most
# # we can manually invoke the auto-scaling machinery
# In [272]: ax.autoscale_view()
#
# # and now the xlim are updated to encompass the rectangle
# # and now the xlim are updated to encompass the rectangle, plus margins
# In [273]: print(ax.get_xlim())
# (1.0, 6.0)
# (0.75, 6.25)
#
# # we have to manually force a figure draw
# In [274]: ax.figure.canvas.draw()
# In [274]: fig.canvas.draw()
#
#
# There are many, many ``Axes`` helper methods for creating primitive
Expand Down