""" 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])