244 lines
7.0 KiB
Python
244 lines
7.0 KiB
Python
import copy
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import numpy as np
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from numpy.testing import assert_array_equal
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import pytest
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from matplotlib.path import Path
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from matplotlib.patches import Polygon
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from matplotlib.testing.decorators import image_comparison
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import matplotlib.pyplot as plt
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from matplotlib import transforms
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def test_empty_closed_path():
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path = Path(np.zeros((0, 2)), closed=True)
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assert path.vertices.shape == (0, 2)
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assert path.codes is None
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def test_readonly_path():
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path = Path.unit_circle()
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def modify_vertices():
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path.vertices = path.vertices * 2.0
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with pytest.raises(AttributeError):
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modify_vertices()
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def test_point_in_path():
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# Test #1787
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verts2 = [(0, 0), (0, 1), (1, 1), (1, 0), (0, 0)]
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path = Path(verts2, closed=True)
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points = [(0.5, 0.5), (1.5, 0.5)]
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ret = path.contains_points(points)
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assert ret.dtype == 'bool'
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assert np.all(ret == [True, False])
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def test_contains_points_negative_radius():
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path = Path.unit_circle()
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points = [(0.0, 0.0), (1.25, 0.0), (0.9, 0.9)]
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expected = [True, False, False]
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result = path.contains_points(points, radius=-0.5)
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assert np.all(result == expected)
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def test_point_in_path_nan():
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box = np.array([[0, 0], [1, 0], [1, 1], [0, 1], [0, 0]])
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p = Path(box)
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test = np.array([[np.nan, 0.5]])
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contains = p.contains_points(test)
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assert len(contains) == 1
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assert not contains[0]
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def test_nonlinear_containment():
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fig, ax = plt.subplots()
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ax.set(xscale="log", ylim=(0, 1))
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polygon = ax.axvspan(1, 10)
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assert polygon.get_path().contains_point(
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ax.transData.transform_point((5, .5)), ax.transData)
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assert not polygon.get_path().contains_point(
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ax.transData.transform_point((.5, .5)), ax.transData)
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assert not polygon.get_path().contains_point(
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ax.transData.transform_point((50, .5)), ax.transData)
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@image_comparison(baseline_images=['path_clipping'],
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extensions=['svg'], remove_text=True)
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def test_path_clipping():
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fig = plt.figure(figsize=(6.0, 6.2))
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for i, xy in enumerate([
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[(200, 200), (200, 350), (400, 350), (400, 200)],
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[(200, 200), (200, 350), (400, 350), (400, 100)],
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[(200, 100), (200, 350), (400, 350), (400, 100)],
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[(200, 100), (200, 415), (400, 350), (400, 100)],
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[(200, 100), (200, 415), (400, 415), (400, 100)],
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[(200, 415), (400, 415), (400, 100), (200, 100)],
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[(400, 415), (400, 100), (200, 100), (200, 415)]]):
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ax = fig.add_subplot(4, 2, i+1)
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bbox = [0, 140, 640, 260]
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ax.set_xlim(bbox[0], bbox[0] + bbox[2])
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ax.set_ylim(bbox[1], bbox[1] + bbox[3])
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ax.add_patch(Polygon(
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xy, facecolor='none', edgecolor='red', closed=True))
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@image_comparison(baseline_images=['semi_log_with_zero'], extensions=['png'],
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style='mpl20')
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def test_log_transform_with_zero():
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x = np.arange(-10, 10)
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y = (1.0 - 1.0/(x**2+1))**20
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fig, ax = plt.subplots()
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ax.semilogy(x, y, "-o", lw=15, markeredgecolor='k')
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ax.set_ylim(1e-7, 1)
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ax.grid(True)
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def test_make_compound_path_empty():
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# We should be able to make a compound path with no arguments.
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# This makes it easier to write generic path based code.
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r = Path.make_compound_path()
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assert r.vertices.shape == (0, 2)
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@image_comparison(baseline_images=['xkcd'], extensions=['png'],
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remove_text=True)
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def test_xkcd():
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np.random.seed(0)
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x = np.linspace(0, 2 * np.pi, 100)
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y = np.sin(x)
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with plt.xkcd():
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fig, ax = plt.subplots()
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ax.plot(x, y)
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@image_comparison(baseline_images=['xkcd_marker'], extensions=['png'],
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remove_text=True)
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def test_xkcd_marker():
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np.random.seed(0)
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x = np.linspace(0, 5, 8)
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y1 = x
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y2 = 5 - x
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y3 = 2.5 * np.ones(8)
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with plt.xkcd():
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fig, ax = plt.subplots()
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ax.plot(x, y1, '+', ms=10)
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ax.plot(x, y2, 'o', ms=10)
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ax.plot(x, y3, '^', ms=10)
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@image_comparison(baseline_images=['marker_paths'], extensions=['pdf'],
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remove_text=True)
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def test_marker_paths_pdf():
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N = 7
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plt.errorbar(np.arange(N),
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np.ones(N) + 4,
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np.ones(N))
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plt.xlim(-1, N)
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plt.ylim(-1, 7)
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@image_comparison(baseline_images=['nan_path'], style='default',
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remove_text=True, extensions=['pdf', 'svg', 'eps', 'png'])
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def test_nan_isolated_points():
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y0 = [0, np.nan, 2, np.nan, 4, 5, 6]
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y1 = [np.nan, 7, np.nan, 9, 10, np.nan, 12]
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fig, ax = plt.subplots()
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ax.plot(y0, '-o')
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ax.plot(y1, '-o')
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def test_path_no_doubled_point_in_to_polygon():
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hand = np.array(
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[[1.64516129, 1.16145833],
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[1.64516129, 1.59375],
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[1.35080645, 1.921875],
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[1.375, 2.18229167],
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[1.68548387, 1.9375],
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[1.60887097, 2.55208333],
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[1.68548387, 2.69791667],
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[1.76209677, 2.56770833],
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[1.83064516, 1.97395833],
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[1.89516129, 2.75],
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[1.9516129, 2.84895833],
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[2.01209677, 2.76041667],
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[1.99193548, 1.99479167],
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[2.11290323, 2.63020833],
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[2.2016129, 2.734375],
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[2.25403226, 2.60416667],
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[2.14919355, 1.953125],
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[2.30645161, 2.36979167],
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[2.39112903, 2.36979167],
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[2.41532258, 2.1875],
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[2.1733871, 1.703125],
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[2.07782258, 1.16666667]])
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(r0, c0, r1, c1) = (1.0, 1.5, 2.1, 2.5)
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poly = Path(np.vstack((hand[:, 1], hand[:, 0])).T, closed=True)
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clip_rect = transforms.Bbox([[r0, c0], [r1, c1]])
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poly_clipped = poly.clip_to_bbox(clip_rect).to_polygons()[0]
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assert np.all(poly_clipped[-2] != poly_clipped[-1])
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assert np.all(poly_clipped[-1] == poly_clipped[0])
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def test_path_to_polygons():
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data = [[10, 10], [20, 20]]
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p = Path(data)
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assert_array_equal(p.to_polygons(width=40, height=40), [])
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assert_array_equal(p.to_polygons(width=40, height=40, closed_only=False),
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[data])
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assert_array_equal(p.to_polygons(), [])
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assert_array_equal(p.to_polygons(closed_only=False), [data])
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data = [[10, 10], [20, 20], [30, 30]]
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closed_data = [[10, 10], [20, 20], [30, 30], [10, 10]]
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p = Path(data)
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assert_array_equal(p.to_polygons(width=40, height=40), [closed_data])
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assert_array_equal(p.to_polygons(width=40, height=40, closed_only=False),
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[data])
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assert_array_equal(p.to_polygons(), [closed_data])
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assert_array_equal(p.to_polygons(closed_only=False), [data])
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def test_path_deepcopy():
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# Should not raise any error
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verts = [[0, 0], [1, 1]]
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codes = [Path.MOVETO, Path.LINETO]
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path1 = Path(verts)
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path2 = Path(verts, codes)
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copy.deepcopy(path1)
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copy.deepcopy(path2)
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@pytest.mark.parametrize('offset', range(-720, 361, 45))
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def test_full_arc(offset):
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low = offset
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high = 360 + offset
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path = Path.arc(low, high)
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mins = np.min(path.vertices, axis=0)
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maxs = np.max(path.vertices, axis=0)
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np.testing.assert_allclose(mins, -1)
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assert np.allclose(maxs, 1)
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