346 lines
11 KiB
Python
346 lines
11 KiB
Python
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import base64
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import io
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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import pytest
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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 patches, transforms
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from matplotlib.path import Path
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# NOTE: All of these tests assume that path.simplify is set to True
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# (the default)
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@image_comparison(['clipping'], remove_text=True)
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def test_clipping():
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t = np.arange(0.0, 2.0, 0.01)
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s = np.sin(2*np.pi*t)
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fig, ax = plt.subplots()
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ax.plot(t, s, linewidth=1.0)
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ax.set_ylim((-0.20, -0.28))
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@image_comparison(['overflow'], remove_text=True)
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def test_overflow():
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x = np.array([1.0, 2.0, 3.0, 2.0e5])
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y = np.arange(len(x))
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fig, ax = plt.subplots()
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ax.plot(x, y)
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ax.set_xlim(2, 6)
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@image_comparison(['clipping_diamond'], remove_text=True)
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def test_diamond():
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x = np.array([0.0, 1.0, 0.0, -1.0, 0.0])
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y = np.array([1.0, 0.0, -1.0, 0.0, 1.0])
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fig, ax = plt.subplots()
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ax.plot(x, y)
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ax.set_xlim(-0.6, 0.6)
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ax.set_ylim(-0.6, 0.6)
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def test_noise():
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np.random.seed(0)
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x = np.random.uniform(size=50000) * 50
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fig, ax = plt.subplots()
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p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)
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# Ensure that the path's transform takes the new axes limits into account.
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fig.canvas.draw()
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path = p1[0].get_path()
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transform = p1[0].get_transform()
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path = transform.transform_path(path)
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simplified = path.cleaned(simplify=True)
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assert simplified.vertices.size == 25512
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def test_antiparallel_simplification():
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def _get_simplified(x, y):
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fig, ax = plt.subplots()
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p1 = ax.plot(x, y)
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path = p1[0].get_path()
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transform = p1[0].get_transform()
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path = transform.transform_path(path)
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simplified = path.cleaned(simplify=True)
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simplified = transform.inverted().transform_path(simplified)
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return simplified
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# test ending on a maximum
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x = [0, 0, 0, 0, 0, 1]
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y = [.5, 1, -1, 1, 2, .5]
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simplified = _get_simplified(x, y)
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assert_array_almost_equal([[0., 0.5],
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[0., -1.],
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[0., 2.],
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[1., 0.5]],
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simplified.vertices[:-2, :])
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# test ending on a minimum
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x = [0, 0, 0, 0, 0, 1]
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y = [.5, 1, -1, 1, -2, .5]
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simplified = _get_simplified(x, y)
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assert_array_almost_equal([[0., 0.5],
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[0., 1.],
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[0., -2.],
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[1., 0.5]],
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simplified.vertices[:-2, :])
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# test ending in between
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x = [0, 0, 0, 0, 0, 1]
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y = [.5, 1, -1, 1, 0, .5]
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simplified = _get_simplified(x, y)
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assert_array_almost_equal([[0., 0.5],
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[0., 1.],
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[0., -1.],
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[0., 0.],
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[1., 0.5]],
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simplified.vertices[:-2, :])
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# test no anti-parallel ending at max
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x = [0, 0, 0, 0, 0, 1]
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y = [.5, 1, 2, 1, 3, .5]
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simplified = _get_simplified(x, y)
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assert_array_almost_equal([[0., 0.5],
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[0., 3.],
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[1., 0.5]],
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simplified.vertices[:-2, :])
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# test no anti-parallel ending in middle
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x = [0, 0, 0, 0, 0, 1]
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y = [.5, 1, 2, 1, 1, .5]
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simplified = _get_simplified(x, y)
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assert_array_almost_equal([[0., 0.5],
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[0., 2.],
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[0., 1.],
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[1., 0.5]],
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simplified.vertices[:-2, :])
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# Only consider angles in 0 <= angle <= pi/2, otherwise
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# using min/max will get the expected results out of order:
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# min/max for simplification code depends on original vector,
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# and if angle is outside above range then simplification
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# min/max will be opposite from actual min/max.
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@pytest.mark.parametrize('angle', [0, np.pi/4, np.pi/3, np.pi/2])
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@pytest.mark.parametrize('offset', [0, .5])
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def test_angled_antiparallel(angle, offset):
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scale = 5
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np.random.seed(19680801)
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# get 15 random offsets
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# TODO: guarantee offset > 0 results in some offsets < 0
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vert_offsets = (np.random.rand(15) - offset) * scale
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# always start at 0 so rotation makes sense
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vert_offsets[0] = 0
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# always take the first step the same direction
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vert_offsets[1] = 1
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# compute points along a diagonal line
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x = np.sin(angle) * vert_offsets
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y = np.cos(angle) * vert_offsets
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# will check these later
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x_max = x[1:].max()
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x_min = x[1:].min()
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y_max = y[1:].max()
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y_min = y[1:].min()
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if offset > 0:
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p_expected = Path([[0, 0],
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[x_max, y_max],
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[x_min, y_min],
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[x[-1], y[-1]],
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[0, 0]],
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codes=[1, 2, 2, 2, 0])
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else:
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p_expected = Path([[0, 0],
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[x_max, y_max],
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[x[-1], y[-1]],
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[0, 0]],
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codes=[1, 2, 2, 0])
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p = Path(np.vstack([x, y]).T)
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p2 = p.cleaned(simplify=True)
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assert_array_almost_equal(p_expected.vertices,
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p2.vertices)
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assert_array_equal(p_expected.codes, p2.codes)
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def test_sine_plus_noise():
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np.random.seed(0)
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x = (np.sin(np.linspace(0, np.pi * 2.0, 50000)) +
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np.random.uniform(size=50000) * 0.01)
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fig, ax = plt.subplots()
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p1 = ax.plot(x, solid_joinstyle='round', linewidth=2.0)
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# Ensure that the path's transform takes the new axes limits into account.
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fig.canvas.draw()
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path = p1[0].get_path()
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transform = p1[0].get_transform()
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path = transform.transform_path(path)
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simplified = path.cleaned(simplify=True)
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assert simplified.vertices.size == 25240
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@image_comparison(['simplify_curve'], remove_text=True)
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def test_simplify_curve():
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pp1 = patches.PathPatch(
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Path([(0, 0), (1, 0), (1, 1), (np.nan, 1), (0, 0), (2, 0), (2, 2),
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(0, 0)],
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[Path.MOVETO, Path.CURVE3, Path.CURVE3, Path.CURVE3, Path.CURVE3,
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Path.CURVE3, Path.CURVE3, Path.CLOSEPOLY]),
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fc="none")
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fig, ax = plt.subplots()
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ax.add_patch(pp1)
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ax.set_xlim((0, 2))
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ax.set_ylim((0, 2))
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@image_comparison(['hatch_simplify'], remove_text=True)
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def test_hatch():
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fig, ax = plt.subplots()
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ax.add_patch(plt.Rectangle((0, 0), 1, 1, fill=False, hatch="/"))
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ax.set_xlim((0.45, 0.55))
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ax.set_ylim((0.45, 0.55))
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@image_comparison(['fft_peaks'], remove_text=True)
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def test_fft_peaks():
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fig, ax = plt.subplots()
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t = np.arange(65536)
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p1 = ax.plot(abs(np.fft.fft(np.sin(2*np.pi*.01*t)*np.blackman(len(t)))))
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# Ensure that the path's transform takes the new axes limits into account.
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fig.canvas.draw()
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path = p1[0].get_path()
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transform = p1[0].get_transform()
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path = transform.transform_path(path)
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simplified = path.cleaned(simplify=True)
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assert simplified.vertices.size == 36
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def test_start_with_moveto():
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# Should be entirely clipped away to a single MOVETO
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data = b"""
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ZwAAAAku+v9UAQAA+Tj6/z8CAADpQ/r/KAMAANlO+v8QBAAAyVn6//UEAAC6ZPr/2gUAAKpv+v+8
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BgAAm3r6/50HAACLhfr/ewgAAHyQ+v9ZCQAAbZv6/zQKAABepvr/DgsAAE+x+v/lCwAAQLz6/7wM
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AAAxx/r/kA0AACPS+v9jDgAAFN36/zQPAAAF6Pr/AxAAAPfy+v/QEAAA6f36/5wRAADbCPv/ZhIA
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AMwT+/8uEwAAvh77//UTAACwKfv/uRQAAKM0+/98FQAAlT/7/z0WAACHSvv//RYAAHlV+/+7FwAA
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bGD7/3cYAABea/v/MRkAAFF2+//pGQAARIH7/6AaAAA3jPv/VRsAACmX+/8JHAAAHKL7/7ocAAAP
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rfv/ah0AAAO4+/8YHgAA9sL7/8QeAADpzfv/bx8AANzY+/8YIAAA0OP7/78gAADD7vv/ZCEAALf5
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+/8IIgAAqwT8/6kiAACeD/z/SiMAAJIa/P/oIwAAhiX8/4QkAAB6MPz/HyUAAG47/P+4JQAAYkb8
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/1AmAABWUfz/5SYAAEpc/P95JwAAPmf8/wsoAAAzcvz/nCgAACd9/P8qKQAAHIj8/7cpAAAQk/z/
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QyoAAAWe/P/MKgAA+aj8/1QrAADus/z/2isAAOO+/P9eLAAA2Mn8/+AsAADM1Pz/YS0AAMHf/P/g
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LQAAtur8/10uAACr9fz/2C4AAKEA/f9SLwAAlgv9/8ovAACLFv3/QDAAAIAh/f+1MAAAdSz9/ycx
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AABrN/3/mDEAAGBC/f8IMgAAVk39/3UyAABLWP3/4TIAAEFj/f9LMwAANm79/7MzAAAsef3/GjQA
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ACKE/f9+NAAAF4/9/+E0AAANmv3/QzUAAAOl/f+iNQAA+a/9/wA2AADvuv3/XDYAAOXF/f+2NgAA
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29D9/w83AADR2/3/ZjcAAMfm/f+7NwAAvfH9/w44AACz/P3/XzgAAKkH/v+vOAAAnxL+//04AACW
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Hf7/SjkAAIwo/v+UOQAAgjP+/905AAB5Pv7/JDoAAG9J/v9pOgAAZVT+/606AABcX/7/7zoAAFJq
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/v8vOwAASXX+/207AAA/gP7/qjsAADaL/v/lOwAALZb+/x48AAAjof7/VTwAABqs/v+LPAAAELf+
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/788AAAHwv7/8TwAAP7M/v8hPQAA9df+/1A9AADr4v7/fT0AAOLt/v+oPQAA2fj+/9E9AADQA///
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+T0AAMYO//8fPgAAvRn//0M+AAC0JP//ZT4AAKsv//+GPgAAojr//6U+AACZRf//wj4AAJBQ///d
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PgAAh1v///c+AAB+Zv//Dz8AAHRx//8lPwAAa3z//zk/AABih///TD8AAFmS//9dPwAAUJ3//2w/
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AABHqP//ej8AAD6z//+FPwAANb7//48/AAAsyf//lz8AACPU//+ePwAAGt///6M/AAAR6v//pj8A
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AAj1//+nPwAA/////w=="""
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verts = np.frombuffer(base64.decodebytes(data), dtype='<i4')
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verts = verts.reshape((len(verts) // 2, 2))
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path = Path(verts)
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segs = path.iter_segments(transforms.IdentityTransform(),
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clip=(0.0, 0.0, 100.0, 100.0))
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segs = list(segs)
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assert len(segs) == 1
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assert segs[0][1] == Path.MOVETO
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def test_throw_rendering_complexity_exceeded():
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plt.rcParams['path.simplify'] = False
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xx = np.arange(200000)
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yy = np.random.rand(200000)
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yy[1000] = np.nan
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fig, ax = plt.subplots()
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ax.plot(xx, yy)
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with pytest.raises(OverflowError):
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fig.savefig(io.BytesIO())
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@image_comparison(['clipper_edge'], remove_text=True)
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def test_clipper():
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dat = (0, 1, 0, 2, 0, 3, 0, 4, 0, 5)
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fig = plt.figure(figsize=(2, 1))
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fig.subplots_adjust(left=0, bottom=0, wspace=0, hspace=0)
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ax = fig.add_axes((0, 0, 1.0, 1.0), ylim=(0, 5), autoscale_on=False)
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ax.plot(dat)
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ax.xaxis.set_major_locator(plt.MultipleLocator(1))
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ax.yaxis.set_major_locator(plt.MultipleLocator(1))
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ax.xaxis.set_ticks_position('bottom')
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ax.yaxis.set_ticks_position('left')
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ax.set_xlim(5, 9)
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@image_comparison(['para_equal_perp'], remove_text=True)
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def test_para_equal_perp():
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x = np.array([0, 1, 2, 1, 0, -1, 0, 1] + [1] * 128)
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y = np.array([1, 1, 2, 1, 0, -1, 0, 0] + [0] * 128)
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fig, ax = plt.subplots()
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ax.plot(x + 1, y + 1)
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ax.plot(x + 1, y + 1, 'ro')
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@image_comparison(['clipping_with_nans'])
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def test_clipping_with_nans():
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x = np.linspace(0, 3.14 * 2, 3000)
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y = np.sin(x)
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x[::100] = np.nan
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fig, ax = plt.subplots()
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ax.plot(x, y)
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ax.set_ylim(-0.25, 0.25)
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def test_clipping_full():
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p = Path([[1e30, 1e30]] * 5)
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simplified = list(p.iter_segments(clip=[0, 0, 100, 100]))
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assert simplified == []
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|
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p = Path([[50, 40], [75, 65]], [1, 2])
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simplified = list(p.iter_segments(clip=[0, 0, 100, 100]))
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assert ([(list(x), y) for x, y in simplified] ==
|
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|
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[([50, 40], 1), ([75, 65], 2)])
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|
|
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|
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p = Path([[50, 40]], [1])
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||
|
|
simplified = list(p.iter_segments(clip=[0, 0, 100, 100]))
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|
|
assert ([(list(x), y) for x, y in simplified] ==
|
||
|
|
[([50, 40], 1)])
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