401 lines
14 KiB
Python
401 lines
14 KiB
Python
import datetime
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import platform
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import re
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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import matplotlib as mpl
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from matplotlib.testing.decorators import image_comparison
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from matplotlib import pyplot as plt, rc_context
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from matplotlib.colors import LogNorm
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import pytest
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def test_contour_shape_1d_valid():
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x = np.arange(10)
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y = np.arange(9)
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z = np.random.random((9, 10))
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fig, ax = plt.subplots()
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ax.contour(x, y, z)
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def test_contour_shape_2d_valid():
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x = np.arange(10)
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y = np.arange(9)
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xg, yg = np.meshgrid(x, y)
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z = np.random.random((9, 10))
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fig, ax = plt.subplots()
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ax.contour(xg, yg, z)
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@pytest.mark.parametrize("args, message", [
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((np.arange(9), np.arange(9), np.empty((9, 10))),
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'Length of x (9) must match number of columns in z (10)'),
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((np.arange(10), np.arange(10), np.empty((9, 10))),
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'Length of y (10) must match number of rows in z (9)'),
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((np.empty((10, 10)), np.arange(10), np.empty((9, 10))),
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'Number of dimensions of x (2) and y (1) do not match'),
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((np.arange(10), np.empty((10, 10)), np.empty((9, 10))),
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'Number of dimensions of x (1) and y (2) do not match'),
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((np.empty((9, 9)), np.empty((9, 10)), np.empty((9, 10))),
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'Shapes of x (9, 9) and z (9, 10) do not match'),
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((np.empty((9, 10)), np.empty((9, 9)), np.empty((9, 10))),
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'Shapes of y (9, 9) and z (9, 10) do not match'),
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((np.empty((3, 3, 3)), np.empty((3, 3, 3)), np.empty((9, 10))),
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'Inputs x and y must be 1D or 2D, not 3D'),
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((np.empty((3, 3, 3)), np.empty((3, 3, 3)), np.empty((3, 3, 3))),
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'Input z must be 2D, not 3D'),
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(([[0]],), # github issue 8197
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'Input z must be at least a (2, 2) shaped array, but has shape (1, 1)'),
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(([0], [0], [[0]]),
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'Input z must be at least a (2, 2) shaped array, but has shape (1, 1)'),
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])
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def test_contour_shape_error(args, message):
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fig, ax = plt.subplots()
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with pytest.raises(TypeError, match=re.escape(message)):
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ax.contour(*args)
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def test_contour_empty_levels():
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x = np.arange(9)
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z = np.random.random((9, 9))
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fig, ax = plt.subplots()
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with pytest.warns(UserWarning) as record:
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ax.contour(x, x, z, levels=[])
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assert len(record) == 1
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def test_contour_Nlevels():
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# A scalar levels arg or kwarg should trigger auto level generation.
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# https://github.com/matplotlib/matplotlib/issues/11913
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z = np.arange(12).reshape((3, 4))
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fig, ax = plt.subplots()
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cs1 = ax.contour(z, 5)
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assert len(cs1.levels) > 1
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cs2 = ax.contour(z, levels=5)
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assert (cs1.levels == cs2.levels).all()
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def test_contour_badlevel_fmt():
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# Test edge case from https://github.com/matplotlib/matplotlib/issues/9742
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# User supplied fmt for each level as a dictionary, but Matplotlib changed
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# the level to the minimum data value because no contours possible.
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# This was fixed in https://github.com/matplotlib/matplotlib/pull/9743
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x = np.arange(9)
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z = np.zeros((9, 9))
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fig, ax = plt.subplots()
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fmt = {1.: '%1.2f'}
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with pytest.warns(UserWarning) as record:
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cs = ax.contour(x, x, z, levels=[1.])
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ax.clabel(cs, fmt=fmt)
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assert len(record) == 1
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def test_contour_uniform_z():
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x = np.arange(9)
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z = np.ones((9, 9))
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fig, ax = plt.subplots()
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with pytest.warns(UserWarning) as record:
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ax.contour(x, x, z)
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assert len(record) == 1
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@image_comparison(['contour_manual_labels'], remove_text=True, style='mpl20')
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def test_contour_manual_labels():
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x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
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z = np.max(np.dstack([abs(x), abs(y)]), 2)
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plt.figure(figsize=(6, 2), dpi=200)
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cs = plt.contour(x, y, z)
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pts = np.array([(1.0, 3.0), (1.0, 4.4), (1.0, 6.0)])
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plt.clabel(cs, manual=pts)
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pts = np.array([(2.0, 3.0), (2.0, 4.4), (2.0, 6.0)])
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plt.clabel(cs, manual=pts, fontsize='small', colors=('r', 'g'))
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@image_comparison(['contour_manual_colors_and_levels.png'], remove_text=True)
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def test_given_colors_levels_and_extends():
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# Remove this line when this test image is regenerated.
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plt.rcParams['pcolormesh.snap'] = False
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_, axs = plt.subplots(2, 4)
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data = np.arange(12).reshape(3, 4)
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colors = ['red', 'yellow', 'pink', 'blue', 'black']
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levels = [2, 4, 8, 10]
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for i, ax in enumerate(axs.flat):
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filled = i % 2 == 0.
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extend = ['neither', 'min', 'max', 'both'][i // 2]
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if filled:
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# If filled, we have 3 colors with no extension,
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# 4 colors with one extension, and 5 colors with both extensions
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first_color = 1 if extend in ['max', 'neither'] else None
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last_color = -1 if extend in ['min', 'neither'] else None
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c = ax.contourf(data, colors=colors[first_color:last_color],
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levels=levels, extend=extend)
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else:
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# If not filled, we have 4 levels and 4 colors
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c = ax.contour(data, colors=colors[:-1],
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levels=levels, extend=extend)
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plt.colorbar(c, ax=ax)
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@image_comparison(['contour_datetime_axis.png'],
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remove_text=False, style='mpl20')
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def test_contour_datetime_axis():
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fig = plt.figure()
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fig.subplots_adjust(hspace=0.4, top=0.98, bottom=.15)
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base = datetime.datetime(2013, 1, 1)
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x = np.array([base + datetime.timedelta(days=d) for d in range(20)])
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y = np.arange(20)
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z1, z2 = np.meshgrid(np.arange(20), np.arange(20))
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z = z1 * z2
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plt.subplot(221)
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plt.contour(x, y, z)
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plt.subplot(222)
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plt.contourf(x, y, z)
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x = np.repeat(x[np.newaxis], 20, axis=0)
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y = np.repeat(y[:, np.newaxis], 20, axis=1)
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plt.subplot(223)
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plt.contour(x, y, z)
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plt.subplot(224)
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plt.contourf(x, y, z)
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for ax in fig.get_axes():
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for label in ax.get_xticklabels():
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label.set_ha('right')
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label.set_rotation(30)
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@image_comparison(['contour_test_label_transforms.png'],
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remove_text=True, style='mpl20',
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tol=0 if platform.machine() == 'x86_64' else 0.08)
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def test_labels():
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# Adapted from pylab_examples example code: contour_demo.py
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# see issues #2475, #2843, and #2818 for explanation
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delta = 0.025
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x = np.arange(-3.0, 3.0, delta)
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y = np.arange(-2.0, 2.0, delta)
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X, Y = np.meshgrid(x, y)
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Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
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Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
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(2 * np.pi * 0.5 * 1.5))
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# difference of Gaussians
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Z = 10.0 * (Z2 - Z1)
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fig, ax = plt.subplots(1, 1)
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CS = ax.contour(X, Y, Z)
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disp_units = [(216, 177), (359, 290), (521, 406)]
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data_units = [(-2, .5), (0, -1.5), (2.8, 1)]
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CS.clabel()
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for x, y in data_units:
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CS.add_label_near(x, y, inline=True, transform=None)
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for x, y in disp_units:
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CS.add_label_near(x, y, inline=True, transform=False)
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@image_comparison(['contour_corner_mask_False.png',
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'contour_corner_mask_True.png'],
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remove_text=True)
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def test_corner_mask():
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n = 60
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mask_level = 0.95
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noise_amp = 1.0
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np.random.seed([1])
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x, y = np.meshgrid(np.linspace(0, 2.0, n), np.linspace(0, 2.0, n))
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z = np.cos(7*x)*np.sin(8*y) + noise_amp*np.random.rand(n, n)
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mask = np.random.rand(n, n) >= mask_level
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z = np.ma.array(z, mask=mask)
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for corner_mask in [False, True]:
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plt.figure()
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plt.contourf(z, corner_mask=corner_mask)
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def test_contourf_decreasing_levels():
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# github issue 5477.
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z = [[0.1, 0.3], [0.5, 0.7]]
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plt.figure()
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with pytest.raises(ValueError):
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plt.contourf(z, [1.0, 0.0])
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def test_contourf_symmetric_locator():
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# github issue 7271
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z = np.arange(12).reshape((3, 4))
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locator = plt.MaxNLocator(nbins=4, symmetric=True)
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cs = plt.contourf(z, locator=locator)
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assert_array_almost_equal(cs.levels, np.linspace(-12, 12, 5))
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@pytest.mark.parametrize("args, cls, message", [
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((), TypeError,
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'function takes exactly 6 arguments (0 given)'),
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((1, 2, 3, 4, 5, 6), ValueError,
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'Expected 2-dimensional array, got 0'),
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(([[0]], [[0]], [[]], None, True, 0), ValueError,
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'x, y and z must all be 2D arrays with the same dimensions'),
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(([[0]], [[0]], [[0]], None, True, 0), ValueError,
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'x, y and z must all be at least 2x2 arrays'),
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((*[np.arange(4).reshape((2, 2))] * 3, [[0]], True, 0), ValueError,
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'If mask is set it must be a 2D array with the same dimensions as x.'),
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])
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def test_internal_cpp_api(args, cls, message): # Github issue 8197.
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from matplotlib import _contour # noqa: ensure lazy-loaded module *is* loaded.
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with pytest.raises(cls, match=re.escape(message)):
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mpl._contour.QuadContourGenerator(*args)
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def test_internal_cpp_api_2():
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from matplotlib import _contour # noqa: ensure lazy-loaded module *is* loaded.
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arr = [[0, 1], [2, 3]]
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qcg = mpl._contour.QuadContourGenerator(arr, arr, arr, None, True, 0)
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with pytest.raises(
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ValueError, match=r'filled contour levels must be increasing'):
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qcg.create_filled_contour(1, 0)
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def test_circular_contour_warning():
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# Check that almost circular contours don't throw a warning
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x, y = np.meshgrid(np.linspace(-2, 2, 4), np.linspace(-2, 2, 4))
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r = np.hypot(x, y)
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plt.figure()
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cs = plt.contour(x, y, r)
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plt.clabel(cs)
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@pytest.mark.parametrize("use_clabeltext, contour_zorder, clabel_zorder",
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[(True, 123, 1234), (False, 123, 1234),
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(True, 123, None), (False, 123, None)])
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def test_clabel_zorder(use_clabeltext, contour_zorder, clabel_zorder):
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x, y = np.meshgrid(np.arange(0, 10), np.arange(0, 10))
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z = np.max(np.dstack([abs(x), abs(y)]), 2)
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fig, (ax1, ax2) = plt.subplots(ncols=2)
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cs = ax1.contour(x, y, z, zorder=contour_zorder)
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cs_filled = ax2.contourf(x, y, z, zorder=contour_zorder)
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clabels1 = cs.clabel(zorder=clabel_zorder, use_clabeltext=use_clabeltext)
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clabels2 = cs_filled.clabel(zorder=clabel_zorder,
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use_clabeltext=use_clabeltext)
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if clabel_zorder is None:
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expected_clabel_zorder = 2+contour_zorder
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else:
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expected_clabel_zorder = clabel_zorder
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for clabel in clabels1:
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assert clabel.get_zorder() == expected_clabel_zorder
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for clabel in clabels2:
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assert clabel.get_zorder() == expected_clabel_zorder
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@image_comparison(['contour_log_extension.png'],
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remove_text=True, style='mpl20')
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def test_contourf_log_extension():
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# Remove this line when this test image is regenerated.
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plt.rcParams['pcolormesh.snap'] = False
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# Test that contourf with lognorm is extended correctly
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fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
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fig.subplots_adjust(left=0.05, right=0.95)
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# make data set with large range e.g. between 1e-8 and 1e10
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data_exp = np.linspace(-7.5, 9.5, 1200)
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data = np.power(10, data_exp).reshape(30, 40)
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# make manual levels e.g. between 1e-4 and 1e-6
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levels_exp = np.arange(-4., 7.)
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levels = np.power(10., levels_exp)
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# original data
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c1 = ax1.contourf(data,
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norm=LogNorm(vmin=data.min(), vmax=data.max()))
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# just show data in levels
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c2 = ax2.contourf(data, levels=levels,
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norm=LogNorm(vmin=levels.min(), vmax=levels.max()),
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extend='neither')
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# extend data from levels
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c3 = ax3.contourf(data, levels=levels,
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norm=LogNorm(vmin=levels.min(), vmax=levels.max()),
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extend='both')
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cb = plt.colorbar(c1, ax=ax1)
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assert cb.ax.get_ylim() == (1e-8, 1e10)
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cb = plt.colorbar(c2, ax=ax2)
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assert cb.ax.get_ylim() == (1e-4, 1e6)
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cb = plt.colorbar(c3, ax=ax3)
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assert_array_almost_equal(
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cb.ax.get_ylim(), [3.162277660168379e-05, 3162277.660168383], 2)
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@image_comparison(['contour_addlines.png'],
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remove_text=True, style='mpl20', tol=0.03)
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# tolerance is because image changed minutely when tick finding on
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# colorbars was cleaned up...
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def test_contour_addlines():
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# Remove this line when this test image is regenerated.
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plt.rcParams['pcolormesh.snap'] = False
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fig, ax = plt.subplots()
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np.random.seed(19680812)
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X = np.random.rand(10, 10)*10000
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pcm = ax.pcolormesh(X)
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# add 1000 to make colors visible...
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cont = ax.contour(X+1000)
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cb = fig.colorbar(pcm)
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cb.add_lines(cont)
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assert_array_almost_equal(cb.ax.get_ylim(), [114.3091, 9972.30735], 3)
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@image_comparison(baseline_images=['contour_uneven'],
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extensions=['png'], remove_text=True, style='mpl20')
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def test_contour_uneven():
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# Remove this line when this test image is regenerated.
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plt.rcParams['pcolormesh.snap'] = False
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z = np.arange(24).reshape(4, 6)
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fig, axs = plt.subplots(1, 2)
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ax = axs[0]
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cs = ax.contourf(z, levels=[2, 4, 6, 10, 20])
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fig.colorbar(cs, ax=ax, spacing='proportional')
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ax = axs[1]
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cs = ax.contourf(z, levels=[2, 4, 6, 10, 20])
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fig.colorbar(cs, ax=ax, spacing='uniform')
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@pytest.mark.parametrize(
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"rc_lines_linewidth, rc_contour_linewidth, call_linewidths, expected", [
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(1.23, None, None, 1.23),
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(1.23, 4.24, None, 4.24),
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(1.23, 4.24, 5.02, 5.02)
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])
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def test_contour_linewidth(
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rc_lines_linewidth, rc_contour_linewidth, call_linewidths, expected):
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with rc_context(rc={"lines.linewidth": rc_lines_linewidth,
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"contour.linewidth": rc_contour_linewidth}):
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fig, ax = plt.subplots()
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X = np.arange(4*3).reshape(4, 3)
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cs = ax.contour(X, linewidths=call_linewidths)
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assert cs.tlinewidths[0][0] == expected
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@pytest.mark.backend("pdf")
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def test_label_nonagg():
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# This should not crash even if the canvas doesn't have a get_renderer().
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plt.clabel(plt.contour([[1, 2], [3, 4]]))
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