92 lines
3.7 KiB
Python
92 lines
3.7 KiB
Python
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import pytest
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import numpy as np
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from numpy.testing import assert_, assert_array_equal, assert_allclose
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try:
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import matplotlib
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matplotlib.rcParams['backend'] = 'Agg'
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import matplotlib.pyplot as plt
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has_matplotlib = True
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except Exception:
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has_matplotlib = False
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from scipy.spatial import \
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delaunay_plot_2d, voronoi_plot_2d, convex_hull_plot_2d, \
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Delaunay, Voronoi, ConvexHull
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@pytest.mark.skipif(not has_matplotlib, reason="Matplotlib not available")
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class TestPlotting:
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points = [(0,0), (0,1), (1,0), (1,1)]
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def test_delaunay(self):
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# Smoke test
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fig = plt.figure()
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obj = Delaunay(self.points)
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s_before = obj.simplices.copy()
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r = delaunay_plot_2d(obj, ax=fig.gca())
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assert_array_equal(obj.simplices, s_before) # shouldn't modify
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assert_(r is fig)
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delaunay_plot_2d(obj, ax=fig.gca())
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def test_voronoi(self):
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# Smoke test
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fig = plt.figure()
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obj = Voronoi(self.points)
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r = voronoi_plot_2d(obj, ax=fig.gca())
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assert_(r is fig)
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voronoi_plot_2d(obj)
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voronoi_plot_2d(obj, show_vertices=False)
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def test_convex_hull(self):
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# Smoke test
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fig = plt.figure()
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tri = ConvexHull(self.points)
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r = convex_hull_plot_2d(tri, ax=fig.gca())
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assert_(r is fig)
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convex_hull_plot_2d(tri)
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def test_gh_19653(self):
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# aspect ratio sensitivity of voronoi_plot_2d
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# infinite Voronoi edges
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points = np.array([[245.059986986012, 10.971011721360075],
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[320.49044143557785, 10.970258360366753],
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[239.79023081978914, 13.108487516946218],
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[263.38325791238833, 12.93241352743668],
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[219.53334398353175, 13.346107628161008]])
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vor = Voronoi(points)
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fig = voronoi_plot_2d(vor)
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ax = fig.gca()
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infinite_segments = ax.collections[1].get_segments()
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expected_segments = np.array([[[282.77256, -254.76904],
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[282.729714, -4544.744698]],
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[[282.77256014, -254.76904029],
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[430.08561382, 4032.67658742]],
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[[229.26733285, -20.39957514],
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[-168.17167404, -4291.92545966]],
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[[289.93433364, 5151.40412217],
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[330.40553385, 9441.18887532]]])
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assert_allclose(infinite_segments, expected_segments)
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def test_gh_19653_smaller_aspect(self):
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# reasonable behavior for less extreme aspect
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# ratio
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points = np.array([[24.059986986012, 10.971011721360075],
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[32.49044143557785, 10.970258360366753],
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[23.79023081978914, 13.108487516946218],
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[26.38325791238833, 12.93241352743668],
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[21.53334398353175, 13.346107628161008]])
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vor = Voronoi(points)
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fig = voronoi_plot_2d(vor)
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ax = fig.gca()
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infinite_segments = ax.collections[1].get_segments()
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expected_segments = np.array([[[28.274979, 8.335027],
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[28.270463, -42.19763338]],
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[[28.27497869, 8.33502697],
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[43.73223829, 56.44555501]],
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[[22.51805823, 11.8621754],
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[-12.09266506, -24.95694485]],
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[[29.53092448, 78.46952378],
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[33.82572726, 128.81934455]]])
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assert_allclose(infinite_segments, expected_segments)
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