Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/matplotlib/tests/test_lines.py

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