8000 Fix containment test with nonlinear transforms. by anntzer · Pull Request #7844 · matplotlib/matplotlib · GitHub
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Fix containment test with nonlinear transforms. #7844

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Merged
merged 2 commits into from
Mar 19, 2017
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Fix containment test with nonlinear transforms. #7844

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15eb7e7
Fix containment test with nonlinear transforms.
anntzer Jan 16, 2017
76173e1
Test path containment w/ nonlin. transforms; group containment tests.
anntzer Jan 28, 2017
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Test path containment w/ nonlin. transforms; group containment tests.
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@anntzer
anntzer committed Mar 19, 2017
commit 76173e1aae1a6f92e0e0deba00cd156edfa6ba83
30 changes: 21 additions & 9 deletions lib/matplotlib/tests/test_path.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,27 @@ def test_contains_points_negative_radius():
assert np.all(result == expected)


def test_point_in_path_nan():
box = np.array([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])
p = Path(box)
test = np.array([[np.nan, 0.5]])
contains = p.contains_points(test)
assert len(contains) == 1
assert not contains[0]


def test_nonlinear_containment():
fig, ax = plt.subplots()
ax.set(xscale="log", ylim=(0, 1))
polygon = ax.axvspan(1, 10)
assert polygon.get_path().contains_point(
ax.transData.transform_point((5, .5)), ax.transData)
assert not polygon.get_path().contains_point(
ax.transData.transform_point((.5, .5)), ax.transData)
assert not polygon.get_path().contains_point(
ax.transData.transform_point((50, .5)), ax.transData)

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May also be worth checking that the points (0.5, 0.5) and (20, 0.5) are not inside polygon

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done


@image_comparison(baseline_images=['path_clipping'],
extensions=['svg'], remove_text=True)
def test_path_clipping():
Expand All @@ -66,15 +87,6 @@ def test_path_clipping():
xy, facecolor='none', edgecolor='red', closed=True))


def test_point_in_path_nan():
box = np.array([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])
p = Path(box)
test = np.array([[np.nan, 0.5]])
contains = p.contains_points(test)
assert len(contains) == 1
assert not contains[0]


@image_comparison(baseline_images=['semi_log_with_zero'], extensions=['png'])
def test_log_transform_with_zero():
x = np.arange(-10, 10)
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0