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Update hard-coded outputs in artists tutorial.
We've added several custom reprs to objects, so these hard-coded results are no longer in sync with what anyone would see.
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‎tutorials/intermediate/artists.py‎

Lines changed: 103 additions & 41 deletions
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@@ -91,10 +91,10 @@ class in the matplotlib API, and the one you will be working with most
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.. sourcecode:: ipython
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In [101]: ax.lines[0]
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Out[101]: <matplotlib.lines.Line2D instance at 0x19a95710>
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Out[101]: <matplotlib.lines.Line2D at 0x19a95710>
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In [102]: line
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Out[102]: <matplotlib.lines.Line2D instance at 0x19a95710>
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Out[102]: <matplotlib.lines.Line2D at 0x19a95710>
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If you make subsequent calls to ``ax.plot`` (and the hold state is "on"
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which is the default) then additional lines will be added to the list.
@@ -107,7 +107,7 @@ class in the matplotlib API, and the one you will be working with most
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The Axes also has helper methods to configure and decorate the x-axis
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and y-axis tick, tick labels and axis labels::
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xtext = ax.set_xlabel('my xdata') # returns a Text instance
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xtext = ax.set_xlabel('my xdata') # returns a Text instance
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ytext = ax.set_ylabel('my ydata')
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When you call :meth:`ax.set_xlabel <matplotlib.axes.Axes.set_xlabel>`,
@@ -210,31 +210,49 @@ class in the matplotlib API, and the one you will be working with most
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# .. sourcecode:: ipython
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#
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# In [149]: matplotlib.artist.getp(fig.patch)
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# alpha = 1.0
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# animated = False
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# antialiased or aa = True
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# axes = None
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# clip_box = None
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# clip_on = False
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# clip_path = None
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# contains = None
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# edgecolor or ec = w
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# facecolor or fc = 0.75
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# figure = Figure(8.125x6.125)
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# fill = 1
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# hatch = None
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# height = 1
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# label =
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# linewidth or lw = 1.0
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# picker = None
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# transform = <Affine object at 0x134cca84>
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# verts = ((0, 0), (0, 1), (1, 1), (1, 0))
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# visible = True
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# width = 1
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# window_extent = <Bbox object at 0x134acbcc>
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# x = 0
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# y = 0
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# zorder = 1
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# agg_filter = None
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# alpha = None
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# animated = False
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# antialiased or aa = False
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# bbox = Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0)
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# capstyle = butt
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# children = []
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# clip_box = None
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# clip_on = True
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# clip_path = None
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# contains = None
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# data_transform = BboxTransformTo( TransformedBbox( Bbox...
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# edgecolor or ec = (1.0, 1.0, 1.0, 1.0)
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# extents = Bbox(x0=0.0, y0=0.0, x1=640.0, y1=480.0)
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# facecolor or fc = (1.0, 1.0, 1.0, 1.0)
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# figure = Figure(640x480)
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# fill = True
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# gid = None
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# hatch = None
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# height = 1
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# in_layout = False
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# joinstyle = miter
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# label =
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# linestyle or ls = solid
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# linewidth or lw = 0.0
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# patch_transform = CompositeGenericTransform( BboxTransformTo( ...
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# path = Path(array([[0., 0.], [1., 0.], [1.,...
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# path_effects = []
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# picker = None
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# rasterized = None
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# sketch_params = None
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# snap = None
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# transform = CompositeGenericTransform( CompositeGenericTra...
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# transformed_clip_path_and_affine = (None, None)
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# url = None
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# verts = [[ 0. 0.] [640. 0.] [640. 480.] [ 0. 480....
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# visible = True
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# width = 1
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# window_extent = Bbox(x0=0.0, y0=0.0, x1=640.0, y1=480.0)
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# x = 0
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# xy = (0, 0)
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# y = 0
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# zorder = 1
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#
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# The docstrings for all of the classes also contain the ``Artist``
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# properties, so you can consult the interactive "help" or the
@@ -284,11 +302,10 @@ class in the matplotlib API, and the one you will be working with most
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# In [158]: ax2 = fig.add_axes([0.1, 0.1, 0.7, 0.3])
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#
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# In [159]: ax1
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# Out[159]: <matplotlib.axes.Subplot instance at 0xd54b26c>
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# Out[159]: <AxesSubplot:>
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#
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# In [160]: print(fig.axes)
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# [<matplotlib.axes.Subplot instance at 0xd54b26c>,
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# <matplotlib.axes.Axes instance at 0xd3f0b2c>]
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# [<AxesSubplot:>, <matplotlib.axes._axes.Axes object at 0x7f0768702be0>]
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#
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# Because the figure maintains the concept of the "current axes" (see
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# :meth:`Figure.gca <matplotlib.figure.Figure.gca>` and
@@ -391,7 +408,7 @@ class in the matplotlib API, and the one you will be working with most
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# .. sourcecode:: ipython
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#
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# In [229]: print(ax.lines)
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# [<matplotlib.lines.Line2D instance at 0xd378b0c>]
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# [<matplotlib.lines.Line2D at 0xd378b0c>]
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#
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# Similarly, methods that create patches, like
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# :meth:`~matplotlib.axes.Axes.bar` creates a list of rectangles, will
@@ -403,9 +420,10 @@ class in the matplotlib API, and the one you will be working with most
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# In [233]: n, bins, rectangles = ax.hist(np.random.randn(1000), 50)
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#
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# In [234]: rectangles
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# Out[234]: <a list of 50 Patch objects>
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# Out[234]: <BarContainer object of 50 artists>
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#
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# In [235]: print(len(ax.patches))
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# Out[235]: 50
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#
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# You should not add objects directly to the ``Axes.lines`` or
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# ``Axes.patches`` lists unless you know exactly what you are doing,
@@ -433,8 +451,8 @@ class in the matplotlib API, and the one you will be working with most
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# None
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#
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# # and the transformation instance is set to the "identity transform"
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# In [265]: print(rect.get_transform())
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# <Affine object at 0x13695544>
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# In [265]: print(rect.get_data_transform())
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# IdentityTransform()
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#
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# # now we add the Rectangle to the Axes
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# In [266]: ax.add_patch(rect)
@@ -445,12 +463,56 @@ class in the matplotlib API, and the one you will be working with most
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# Axes(0.125,0.1;0.775x0.8)
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#
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# # and the transformation has been set too
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# In [268]: print(rect.get_transform())
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# <Affine object at 0x15009ca4>
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# In [268]: print(rect.get_data_transform())
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# CompositeGenericTransform(
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# TransformWrapper(
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# BlendedAffine2D(
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# IdentityTransform(),
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# IdentityTransform())),
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# CompositeGenericTransform(
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# BboxTransformFrom(
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# TransformedBbox(
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# Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0),
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# TransformWrapper(
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# BlendedAffine2D(
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# IdentityTransform(),
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# IdentityTransform())))),
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# BboxTransformTo(
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# TransformedBbox(
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# Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88),
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# BboxTransformTo(
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# TransformedBbox(
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# Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8),
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# Affine2D(
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# [[100. 0. 0.]
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# [ 0. 100. 0.]
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# [ 0. 0. 1.]])))))))
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#
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# # the default axes transformation is ax.transData
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# In [269]: print(ax.transData)
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# <Affine object at 0x15009ca4>
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# CompositeGenericTransform(
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# TransformWrapper(
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# BlendedAffine2D(
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# IdentityTransform(),
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# IdentityTransform())),
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# CompositeGenericTransform(
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# BboxTransformFrom(
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# TransformedBbox(
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# Bbox(x0=0.0, y0=0.0, x1=1.0, y1=1.0),
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# TransformWrapper(
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# BlendedAffine2D(
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# IdentityTransform(),
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# IdentityTransform())))),
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# BboxTransformTo(
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# TransformedBbox(
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# Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=0.88),
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# BboxTransformTo(
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# TransformedBbox(
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# Bbox(x0=0.0, y0=0.0, x1=6.4, y1=4.8),
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# Affine2D(
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# [[100. 0. 0.]
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# [ 0. 100. 0.]
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# [ 0. 0. 1.]])))))))
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#
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# # notice that the xlimits of the Axes have not been changed
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# In [270]: print(ax.get_xlim())
@@ -463,12 +525,12 @@ class in the matplotlib API, and the one you will be working with most
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# # we can manually invoke the auto-scaling machinery
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# In [272]: ax.autoscale_view()
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#
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# # and now the xlim are updated to encompass the rectangle
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# # and now the xlim are updated to encompass the rectangle, plus margins
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# In [273]: print(ax.get_xlim())
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# (1.0, 6.0)
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# (0.75, 6.25)
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#
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# # we have to manually force a figure draw
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# In [274]: ax.figure.canvas.draw()
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# In [274]: fig.canvas.draw()
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#
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#
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# There are many, many ``Axes`` helper methods for creating primitive

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