266 lines
8.1 KiB
Python
266 lines
8.1 KiB
Python
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"""
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Tests specific to the lines module.
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"""
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import itertools
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import timeit
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from cycler import cycler
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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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import matplotlib
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import matplotlib.lines as mlines
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from matplotlib.markers import MarkerStyle
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from matplotlib.path import Path
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import matplotlib.pyplot as plt
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from matplotlib.testing.decorators import image_comparison, check_figures_equal
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def test_segment_hits():
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"""Test a problematic case."""
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cx, cy = 553, 902
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x, y = np.array([553., 553.]), np.array([95., 947.])
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radius = 6.94
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assert_array_equal(mlines.segment_hits(cx, cy, x, y, radius), [0])
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# Runtimes on a loaded system are inherently flaky. Not so much that a rerun
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# won't help, hopefully.
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@pytest.mark.flaky(reruns=3)
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def test_invisible_Line_rendering():
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"""
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GitHub issue #1256 identified a bug in Line.draw method
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Despite visibility attribute set to False, the draw method was not
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returning early enough and some pre-rendering code was executed
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though not necessary.
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Consequence was an excessive draw time for invisible Line instances
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holding a large number of points (Npts> 10**6)
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"""
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# Creates big x and y data:
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N = 10**7
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x = np.linspace(0, 1, N)
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y = np.random.normal(size=N)
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# Create a plot figure:
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fig = plt.figure()
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ax = plt.subplot(111)
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# Create a "big" Line instance:
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l = mlines.Line2D(x, y)
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l.set_visible(False)
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# but don't add it to the Axis instance `ax`
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# [here Interactive panning and zooming is pretty responsive]
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# Time the canvas drawing:
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t_no_line = min(timeit.repeat(fig.canvas.draw, number=1, repeat=3))
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# (gives about 25 ms)
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# Add the big invisible Line:
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ax.add_line(l)
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# [Now interactive panning and zooming is very slow]
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# Time the canvas drawing:
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t_invisible_line = min(timeit.repeat(fig.canvas.draw, number=1, repeat=3))
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# gives about 290 ms for N = 10**7 pts
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slowdown_factor = t_invisible_line / t_no_line
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slowdown_threshold = 2 # trying to avoid false positive failures
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assert slowdown_factor < slowdown_threshold
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def test_set_line_coll_dash():
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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np.random.seed(0)
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# Testing setting linestyles for line collections.
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# This should not produce an error.
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ax.contour(np.random.randn(20, 30), linestyles=[(0, (3, 3))])
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@image_comparison(['line_dashes'], remove_text=True)
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def test_line_dashes():
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax.plot(range(10), linestyle=(0, (3, 3)), lw=5)
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def test_line_colors():
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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ax.plot(range(10), color='none')
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ax.plot(range(10), color='r')
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ax.plot(range(10), color='.3')
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ax.plot(range(10), color=(1, 0, 0, 1))
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ax.plot(range(10), color=(1, 0, 0))
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fig.canvas.draw()
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def test_linestyle_variants():
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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for ls in ["-", "solid", "--", "dashed",
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"-.", "dashdot", ":", "dotted"]:
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ax.plot(range(10), linestyle=ls)
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fig.canvas.draw()
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def test_valid_linestyles():
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line = mlines.Line2D([], [])
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with pytest.raises(ValueError):
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line.set_linestyle('aardvark')
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@image_comparison(['drawstyle_variants.png'], remove_text=True)
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def test_drawstyle_variants():
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fig, axs = plt.subplots(6)
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dss = ["default", "steps-mid", "steps-pre", "steps-post", "steps", None]
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# We want to check that drawstyles are properly handled even for very long
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# lines (for which the subslice optimization is on); however, we need
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# to zoom in so that the difference between the drawstyles is actually
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# visible.
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for ax, ds in zip(axs.flat, dss):
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ax.plot(range(2000), drawstyle=ds)
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ax.set(xlim=(0, 2), ylim=(0, 2))
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def test_valid_drawstyles():
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line = mlines.Line2D([], [])
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with pytest.raises(ValueError):
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line.set_drawstyle('foobar')
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def test_set_drawstyle():
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x = np.linspace(0, 2*np.pi, 10)
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y = np.sin(x)
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fig, ax = plt.subplots()
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line, = ax.plot(x, y)
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line.set_drawstyle("steps-pre")
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assert len(line.get_path().vertices) == 2*len(x)-1
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line.set_drawstyle("default")
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assert len(line.get_path().vertices) == len(x)
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@image_comparison(['line_collection_dashes'], remove_text=True, style='mpl20')
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def test_set_line_coll_dash_image():
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fig = plt.figure()
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ax = fig.add_subplot(1, 1, 1)
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np.random.seed(0)
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ax.contour(np.random.randn(20, 30), linestyles=[(0, (3, 3))])
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@image_comparison(['marker_fill_styles.png'], remove_text=True)
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def test_marker_fill_styles():
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colors = itertools.cycle([[0, 0, 1], 'g', '#ff0000', 'c', 'm', 'y',
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np.array([0, 0, 0])])
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altcolor = 'lightgreen'
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y = np.array([1, 1])
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x = np.array([0, 9])
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fig, ax = plt.subplots()
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for j, marker in enumerate(mlines.Line2D.filled_markers):
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for i, fs in enumerate(mlines.Line2D.fillStyles):
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color = next(colors)
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ax.plot(j * 10 + x, y + i + .5 * (j % 2),
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marker=marker,
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markersize=20,
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markerfacecoloralt=altcolor,
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fillstyle=fs,
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label=fs,
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linewidth=5,
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color=color,
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markeredgecolor=color,
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markeredgewidth=2)
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ax.set_ylim([0, 7.5])
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ax.set_xlim([-5, 155])
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def test_markerfacecolor_fillstyle():
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"""Test that markerfacecolor does not override fillstyle='none'."""
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l, = plt.plot([1, 3, 2], marker=MarkerStyle('o', fillstyle='none'),
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markerfacecolor='red')
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assert l.get_fillstyle() == 'none'
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assert l.get_markerfacecolor() == 'none'
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@image_comparison(['scaled_lines'], style='default')
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def test_lw_scaling():
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th = np.linspace(0, 32)
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fig, ax = plt.subplots()
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lins_styles = ['dashed', 'dotted', 'dashdot']
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cy = cycler(matplotlib.rcParams['axes.prop_cycle'])
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for j, (ls, sty) in enumerate(zip(lins_styles, cy)):
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for lw in np.linspace(.5, 10, 10):
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ax.plot(th, j*np.ones(50) + .1 * lw, linestyle=ls, lw=lw, **sty)
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def test_nan_is_sorted():
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line = mlines.Line2D([], [])
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assert line._is_sorted(np.array([1, 2, 3]))
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assert line._is_sorted(np.array([1, np.nan, 3]))
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assert not line._is_sorted([3, 5] + [np.nan] * 100 + [0, 2])
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@check_figures_equal()
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def test_step_markers(fig_test, fig_ref):
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fig_test.subplots().step([0, 1], "-o")
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fig_ref.subplots().plot([0, 0, 1], [0, 1, 1], "-o", markevery=[0, 2])
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@check_figures_equal(extensions=('png',))
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def test_markevery(fig_test, fig_ref):
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np.random.seed(42)
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t = np.linspace(0, 3, 14)
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y = np.random.rand(len(t))
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casesA = [None, 4, (2, 5), [1, 5, 11],
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[0, -1], slice(5, 10, 2), 0.3, (0.3, 0.4),
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np.arange(len(t))[y > 0.5]]
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casesB = ["11111111111111", "10001000100010", "00100001000010",
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"01000100000100", "10000000000001", "00000101010000",
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"11011011011110", "01010011011101", "01110001110110"]
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axsA = fig_ref.subplots(3, 3)
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axsB = fig_test.subplots(3, 3)
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for ax, case in zip(axsA.flat, casesA):
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ax.plot(t, y, "-gD", markevery=case)
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for ax, case in zip(axsB.flat, casesB):
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me = np.array(list(case)).astype(int).astype(bool)
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ax.plot(t, y, "-gD", markevery=me)
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def test_marker_as_markerstyle():
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fig, ax = plt.subplots()
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line, = ax.plot([2, 4, 3], marker=MarkerStyle("D"))
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fig.canvas.draw()
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assert line.get_marker() == "D"
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# continue with smoke tests:
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line.set_marker("s")
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fig.canvas.draw()
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line.set_marker(MarkerStyle("o"))
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fig.canvas.draw()
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# test Path roundtrip
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triangle1 = Path([[-1., -1.], [1., -1.], [0., 2.], [0., 0.]], closed=True)
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line2, = ax.plot([1, 3, 2], marker=MarkerStyle(triangle1), ms=22)
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line3, = ax.plot([0, 2, 1], marker=triangle1, ms=22)
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assert_array_equal(line2.get_marker().vertices, triangle1.vertices)
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assert_array_equal(line3.get_marker().vertices, triangle1.vertices)
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@check_figures_equal()
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def test_odd_dashes(fig_test, fig_ref):
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fig_test.add_subplot().plot([1, 2], dashes=[1, 2, 3])
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fig_ref.add_subplot().plot([1, 2], dashes=[1, 2, 3, 1, 2, 3])
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