doc: fix Patch.contains_point docstring example

[hassec]

PR summary

See the docstring of Patch.contains_point:

matplotlib/lib/matplotlib/patches.py

Lines 174 to 197 in 09eab5b

	Notes
	-----
	The proper use of this method depends on the transform of the patch.
	Isolated patches do not have a transform. In this case, the patch
	creation coordinates and the point coordinates match. The following
	example checks that the center of a circle is within the circle
	>>> center = 0, 0
	>>> c = Circle(center, radius=1)
	>>> c.contains_point(center)
	True
	The convention of checking against the transformed patch stems from
	the fact that this method is predominantly used to check if display
	coordinates (e.g. from mouse events) are within the patch. If you want
	to do the above check with data coordinates, you have to properly
	transform them first:
	>>> center = 0, 0
	>>> c = Circle(center, radius=1)
	>>> plt.gca().add_patch(c)
	>>> transformed_center = c.get_transform().transform(center)
	>>> c.contains_point(transformed_center)
	True

It explains that one has to use data coordinates when using contains_point but then provides an example that uses Circle and get_transform which isn't correct. This has lead to confusion in the past, see #23178.

The proposed changes in this PR will move the example to use get_data_transform and uses values which would fail if one were to use get_transform.

IMHO this closes #23178.

PR checklist

• "closes #0000" is in the body of the PR description to link the related issue
• new and changed code is tested
• Documentation complies with general and docstring guidelines

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[timhoffm]

To convince me and others that this change is correct, I used the following. Note that the only difference between the two plots is get_transform() vs get_data_transform() in the last two lines.

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Circle

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))

c1 = Circle((0, 0), radius=3)
c2 = Circle((0, 0), radius=3)
ax1.add_patch(c1)
ax2.add_patch(c2)
points = 8* (np.random.random((200, 2)) - 0.5)
ax1.scatter(points[:, 0], points[:, 1], c=[c1.contains_point(c1.get_transform().transform(p)) for p in points])
ax2.scatter(points[:, 0], points[:, 1], c=[c1.contains_point(c1.get_data_transform().transform(p)) for p in points])

[timhoffm]

Thanks @hassec for figuring this out. Congratulations on your first contribution to Matplotlib! We'd love to see you back.