import copy import itertools import unittest.mock from io import BytesIO import numpy as np from PIL import Image import pytest import base64 import platform from numpy.lib import recfunctions as rfn from numpy.testing import assert_array_equal, assert_array_almost_equal from matplotlib import cbook, cm import matplotlib import matplotlib as mpl import matplotlib.colors as mcolors import matplotlib.colorbar as mcolorbar import matplotlib.colorizer as mcolorizer import matplotlib.pyplot as plt import matplotlib.scale as mscale from matplotlib.rcsetup import cycler from matplotlib.testing.decorators import image_comparison, check_figures_equal from matplotlib.colors import is_color_like, to_rgba_array, ListedColormap @pytest.mark.parametrize('N, result', [ (5, [1, .6, .2, .1, 0]), (2, [1, 0]), (1, [0]), ]) def test_create_lookup_table(N, result): data = [(0.0, 1.0, 1.0), (0.5, 0.2, 0.2), (1.0, 0.0, 0.0)] assert_array_almost_equal(mcolors._create_lookup_table(N, data), result) @pytest.mark.parametrize("dtype", [np.uint8, int, np.float16, float]) def test_index_dtype(dtype): # We use subtraction in the indexing, so need to verify that uint8 works cm = mpl.colormaps["viridis"] assert_array_equal(cm(dtype(0)), cm(0)) def test_resampled(): """ GitHub issue #6025 pointed to incorrect ListedColormap.resampled; here we test the method for LinearSegmentedColormap as well. """ n = 101 colorlist = np.empty((n, 4), float) colorlist[:, 0] = np.linspace(0, 1, n) colorlist[:, 1] = 0.2 colorlist[:, 2] = np.linspace(1, 0, n) colorlist[:, 3] = 0.7 lsc = mcolors.LinearSegmentedColormap.from_list( 'lsc', colorlist, under='red', over='green', bad='blue') lc = mcolors.ListedColormap(colorlist, under='red', over='green', bad='blue') lsc3 = lsc.resampled(3) lc3 = lc.resampled(3) expected = np.array([[0.0, 0.2, 1.0, 0.7], [0.5, 0.2, 0.5, 0.7], [1.0, 0.2, 0.0, 0.7]], float) assert_array_almost_equal(lsc3([0, 0.5, 1]), expected) assert_array_almost_equal(lc3([0, 0.5, 1]), expected) # Test over/under was copied properly assert_array_almost_equal(lsc(np.inf), lsc3(np.inf)) assert_array_almost_equal(lsc(-np.inf), lsc3(-np.inf)) assert_array_almost_equal(lsc(np.nan), lsc3(np.nan)) assert_array_almost_equal(lc(np.inf), lc3(np.inf)) assert_array_almost_equal(lc(-np.inf), lc3(-np.inf)) assert_array_almost_equal(lc(np.nan), lc3(np.nan)) def test_monochrome(): assert mcolors.ListedColormap(["red"]).monochrome assert mcolors.ListedColormap(["red"] * 5).monochrome assert not mcolors.ListedColormap(["red", "green"]).monochrome def test_colormaps_get_cmap(): cr = mpl.colormaps # check str, and Colormap pass assert cr.get_cmap('plasma') == cr["plasma"] assert cr.get_cmap(cr["magma"]) == cr["magma"] # check default assert cr.get_cmap(None) == cr[mpl.rcParams['image.cmap']] # check ValueError on bad name bad_cmap = 'AardvarksAreAwkward' with pytest.raises(ValueError, match=bad_cmap): cr.get_cmap(bad_cmap) # check TypeError on bad type with pytest.raises(TypeError, match='object'): cr.get_cmap(object()) def test_double_register_builtin_cmap(): name = "viridis" match = f"Re-registering the builtin cmap {name!r}." with pytest.raises(ValueError, match=match): matplotlib.colormaps.register(mpl.colormaps[name], name=name, force=True) def test_colormap_copy(): cmap = plt.colormaps["Reds"] copied_cmap = copy.copy(cmap) with np.errstate(invalid='ignore'): ret1 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf]) cmap2 = copy.copy(copied_cmap) with pytest.warns(PendingDeprecationWarning): cmap2.set_bad('g') with np.errstate(invalid='ignore'): ret2 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf]) assert_array_equal(ret1, ret2) # again with the .copy method: cmap = plt.colormaps["Reds"] copied_cmap = cmap.copy() with np.errstate(invalid='ignore'): ret1 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf]) cmap2 = copy.copy(copied_cmap) with pytest.warns(PendingDeprecationWarning): cmap2.set_bad('g') with np.errstate(invalid='ignore'): ret2 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf]) assert_array_equal(ret1, ret2) def test_colormap_equals(): cmap = mpl.colormaps["plasma"] cm_copy = cmap.copy() # different object id's assert cm_copy is not cmap # But the same data should be equal assert cm_copy == cmap # Change the copy with pytest.warns(PendingDeprecationWarning): cm_copy.set_bad('y') assert cm_copy != cmap # Make sure we can compare different sizes without failure cm_copy._lut = cm_copy._lut[:10, :] assert cm_copy != cmap # Test different names are equal if the lookup table is the same cm_copy = cmap.copy() cm_copy.name = "Test" assert cm_copy == cmap # Test colorbar extends cm_copy = cmap.copy() cm_copy.colorbar_extend = not cmap.colorbar_extend assert cm_copy != cmap def test_colormap_endian(): """ GitHub issue #1005: a bug in putmask caused erroneous mapping of 1.0 when input from a non-native-byteorder array. """ cmap = mpl.colormaps["jet"] # Test under, over, and invalid along with values 0 and 1. a = [-0.5, 0, 0.5, 1, 1.5, np.nan] for dt in ["f2", "f4", "f8"]: anative = np.ma.masked_invalid(np.array(a, dtype=dt)) aforeign = anative.byteswap().view(anative.dtype.newbyteorder()) assert_array_equal(cmap(anative), cmap(aforeign)) def test_colormap_invalid(): """ GitHub issue #9892: Handling of nan's were getting mapped to under rather than bad. This tests to make sure all invalid values (-inf, nan, inf) are mapped respectively to (under, bad, over). """ cmap = mpl.colormaps["plasma"] x = np.array([-np.inf, -1, 0, np.nan, .7, 2, np.inf]) expected = np.array([[0.050383, 0.029803, 0.527975, 1.], [0.050383, 0.029803, 0.527975, 1.], [0.050383, 0.029803, 0.527975, 1.], [0., 0., 0., 0.], [0.949217, 0.517763, 0.295662, 1.], [0.940015, 0.975158, 0.131326, 1.], [0.940015, 0.975158, 0.131326, 1.]]) assert_array_equal(cmap(x), expected) # Test masked representation (-inf, inf) are now masked expected = np.array([[0., 0., 0., 0.], [0.050383, 0.029803, 0.527975, 1.], [0.050383, 0.029803, 0.527975, 1.], [0., 0., 0., 0.], [0.949217, 0.517763, 0.295662, 1.], [0.940015, 0.975158, 0.131326, 1.], [0., 0., 0., 0.]]) assert_array_equal(cmap(np.ma.masked_invalid(x)), expected) # Test scalar representations assert_array_equal(cmap(-np.inf), cmap(0)) assert_array_equal(cmap(np.inf), cmap(1.0)) assert_array_equal(cmap(np.nan), [0., 0., 0., 0.]) def test_colormap_return_types(): """ Make sure that tuples are returned for scalar input and that the proper shapes are returned for ndarrays. """ cmap = mpl.colormaps["plasma"] # Test return types and shapes # scalar input needs to return a tuple of length 4 assert isinstance(cmap(0.5), tuple) assert len(cmap(0.5)) == 4 # input array returns an ndarray of shape x.shape + (4,) x = np.ones(4) assert cmap(x).shape == x.shape + (4,) # multi-dimensional array input x2d = np.zeros((2, 2)) assert cmap(x2d).shape == x2d.shape + (4,) def test_ListedColormap_bad_under_over(): cmap = mcolors.ListedColormap(["r", "g", "b"], bad="c", under="m", over="y") assert mcolors.same_color(cmap.get_bad(), "c") assert mcolors.same_color(cmap.get_under(), "m") assert mcolors.same_color(cmap.get_over(), "y") def test_LinearSegmentedColormap_bad_under_over(): cdict = { 'red': [(0., 0., 0.), (0.5, 1., 1.), (1., 1., 1.)], 'green': [(0., 0., 0.), (0.25, 0., 0.), (0.75, 1., 1.), (1., 1., 1.)], 'blue': [(0., 0., 0.), (0.5, 0., 0.), (1., 1., 1.)], } cmap = mcolors.LinearSegmentedColormap("lsc", cdict, bad="c", under="m", over="y") assert mcolors.same_color(cmap.get_bad(), "c") assert mcolors.same_color(cmap.get_under(), "m") assert mcolors.same_color(cmap.get_over(), "y") def test_LinearSegmentedColormap_from_list_bad_under_over(): cmap = mcolors.LinearSegmentedColormap.from_list( "lsc", ["r", "g", "b"], bad="c", under="m", over="y") assert mcolors.same_color(cmap.get_bad(), "c") assert mcolors.same_color(cmap.get_under(), "m") assert mcolors.same_color(cmap.get_over(), "y") def test_colormap_with_alpha(): cmap = ListedColormap(["red", "green", ("blue", 0.8)]) cmap2 = cmap.with_alpha(0.5) # color is the same: vals = [0, 0.5, 1] # numeric positions that map to the listed colors assert_array_equal(cmap(vals)[:, :3], cmap2(vals)[:, :3]) # alpha of cmap2 is changed: assert_array_equal(cmap(vals)[:, 3], [1, 1, 0.8]) assert_array_equal(cmap2(vals)[:, 3], [0.5, 0.5, 0.5]) def test_BoundaryNorm(): """ GitHub issue #1258: interpolation was failing with numpy 1.7 pre-release. """ boundaries = [0, 1.1, 2.2] vals = [-1, 0, 1, 2, 2.2, 4] # Without interpolation expected = [-1, 0, 0, 1, 2, 2] ncolors = len(boundaries) - 1 bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # ncolors != len(boundaries) - 1 triggers interpolation expected = [-1, 0, 0, 2, 3, 3] ncolors = len(boundaries) bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # with a single region and interpolation expected = [-1, 1, 1, 1, 3, 3] bn = mcolors.BoundaryNorm([0, 2.2], ncolors) assert_array_equal(bn(vals), expected) # more boundaries for a third color boundaries = [0, 1, 2, 3] vals = [-1, 0.1, 1.1, 2.2, 4] ncolors = 5 expected = [-1, 0, 2, 4, 5] bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # a scalar as input should not trigger an error and should return a scalar boundaries = [0, 1, 2] vals = [-1, 0.1, 1.1, 2.2] bn = mcolors.BoundaryNorm(boundaries, 2) expected = [-1, 0, 1, 2] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # same with interp bn = mcolors.BoundaryNorm(boundaries, 3) expected = [-1, 0, 2, 3] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # Clipping bn = mcolors.BoundaryNorm(boundaries, 3, clip=True) expected = [0, 0, 2, 2] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # Masked arrays boundaries = [0, 1.1, 2.2] vals = np.ma.masked_invalid([-1., np.nan, 0, 1.4, 9]) # Without interpolation ncolors = len(boundaries) - 1 bn = mcolors.BoundaryNorm(boundaries, ncolors) expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0]) assert_array_equal(bn(vals), expected) # With interpolation bn = mcolors.BoundaryNorm(boundaries, len(boundaries)) expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0]) assert_array_equal(bn(vals), expected) # Non-trivial masked arrays vals = np.ma.masked_invalid([np.inf, np.nan]) assert np.all(bn(vals).mask) vals = np.ma.masked_invalid([np.inf]) assert np.all(bn(vals).mask) # Incompatible extend and clip with pytest.raises(ValueError, match="not compatible"): mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True) # Too small ncolors argument with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 2) with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 3, extend='min') with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 4, extend='both') # Testing extend keyword, with interpolation (large cmap) bounds = [1, 2, 3] cmap = mpl.colormaps['viridis'] mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both') refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N) x = np.random.randn(100) * 10 + 2 ref = refnorm(x) ref[ref == 0] = -1 ref[ref == cmap.N - 1] = cmap.N assert_array_equal(mynorm(x), ref) # Without interpolation cmref = mcolors.ListedColormap(['blue', 'red'], under='white', over='black') cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black']) assert mcolors.same_color(cmref.get_over(), 'black') assert mcolors.same_color(cmref.get_under(), 'white') refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax assert mynorm(bounds[0] - 0.1) == -1 # under assert mynorm(bounds[0] + 0.1) == 1 # first bin -> second color assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2 # next-to-last color assert mynorm(bounds[-1] + 0.1) == cmshould.N # over x = [-1, 1.2, 2.3, 9.6] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) # Just min cmref = mcolors.ListedColormap(['blue', 'red'], under='white') cmshould = mcolors.ListedColormap(['white', 'blue', 'red']) assert mcolors.same_color(cmref.get_under(), 'white') assert cmref.N == 2 assert cmshould.N == 3 refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax x = [-1, 1.2, 2.3] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) # Just max cmref = mcolors.ListedColormap(['blue', 'red'], over='black') cmshould = mcolors.ListedColormap(['blue', 'red', 'black']) assert mcolors.same_color(cmref.get_over(), 'black') assert cmref.N == 2 assert cmshould.N == 3 refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='max') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax x = [1.2, 2.3, 4] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) def test_CenteredNorm(): np.random.seed(0) # Assert equivalence to symmetrical Normalize. x = np.random.normal(size=100) x_maxabs = np.max(np.abs(x)) norm_ref = mcolors.Normalize(vmin=-x_maxabs, vmax=x_maxabs) norm = mcolors.CenteredNorm() assert_array_almost_equal(norm_ref(x), norm(x)) # Check that vcenter is in the center of vmin and vmax # when vcenter is set. vcenter = int(np.random.normal(scale=50)) norm = mcolors.CenteredNorm(vcenter=vcenter) norm.autoscale_None([1, 2]) assert norm.vmax + norm.vmin == 2 * vcenter # Check that halfrange can be set without setting vcenter and that it is # not reset through autoscale_None. norm = mcolors.CenteredNorm(halfrange=1.0) norm.autoscale_None([1, 3000]) assert norm.halfrange == 1.0 # Check that halfrange input works correctly. x = np.random.normal(size=10) norm = mcolors.CenteredNorm(vcenter=0.5, halfrange=0.5) assert_array_almost_equal(x, norm(x)) norm = mcolors.CenteredNorm(vcenter=1, halfrange=1) assert_array_almost_equal(x, 2 * norm(x)) # Check that halfrange input works correctly and use setters. norm = mcolors.CenteredNorm() norm.vcenter = 2 norm.halfrange = 2 assert_array_almost_equal(x, 4 * norm(x)) # Check that prior to adding data, setting halfrange first has same effect. norm = mcolors.CenteredNorm() norm.halfrange = 2 norm.vcenter = 2 assert_array_almost_equal(x, 4 * norm(x)) # Check that manual change of vcenter adjusts halfrange accordingly. norm = mcolors.CenteredNorm() assert norm.vcenter == 0 # add data norm(np.linspace(-1.0, 0.0, 10)) assert norm.vmax == 1.0 assert norm.halfrange == 1.0 # set vcenter to 1, which should move the center but leave the # halfrange unchanged norm.vcenter = 1 assert norm.vmin == 0 assert norm.vmax == 2 assert norm.halfrange == 1 # Check setting vmin directly updates the halfrange and vmax, but # leaves vcenter alone norm.vmin = -1 assert norm.halfrange == 2 assert norm.vmax == 3 assert norm.vcenter == 1 # also check vmax updates norm.vmax = 2 assert norm.halfrange == 1 assert norm.vmin == 0 assert norm.vcenter == 1 @pytest.mark.parametrize("vmin,vmax", [[-1, 2], [3, 1]]) def test_lognorm_invalid(vmin, vmax): # Check that invalid limits in LogNorm error norm = mcolors.LogNorm(vmin=vmin, vmax=vmax) with pytest.raises(ValueError): norm(1) with pytest.raises(ValueError): norm.inverse(1) def test_LogNorm(): """ LogNorm ignored clip, now it has the same behavior as Normalize, e.g., values > vmax are bigger than 1 without clip, with clip they are 1. """ ln = mcolors.LogNorm(clip=True, vmax=5) assert_array_equal(ln([1, 6]), [0, 1.0]) def test_LogNorm_inverse(): """ Test that lists work, and that the inverse works """ norm = mcolors.LogNorm(vmin=0.1, vmax=10) assert_array_almost_equal(norm([0.5, 0.4]), [0.349485, 0.30103]) assert_array_almost_equal([0.5, 0.4], norm.inverse([0.349485, 0.30103])) assert_array_almost_equal(norm(0.4), [0.30103]) assert_array_almost_equal([0.4], norm.inverse([0.30103])) def test_PowerNorm(): # Check an exponent of 1 gives same results as a normal linear # normalization. Also implicitly checks that vmin/vmax are # automatically initialized from first array input. a = np.array([0, 0.5, 1, 1.5], dtype=float) pnorm = mcolors.PowerNorm(1) norm = mcolors.Normalize() assert_array_almost_equal(norm(a), pnorm(a)) a = np.array([-0.5, 0, 2, 4, 8], dtype=float) expected = [-1/16, 0, 1/16, 1/4, 1] pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8) assert_array_almost_equal(pnorm(a), expected) assert pnorm(a[0]) == expected[0] assert pnorm(a[2]) == expected[2] # Check inverse a_roundtrip = pnorm.inverse(pnorm(a)) assert_array_almost_equal(a, a_roundtrip) # PowerNorm inverse adds a mask, so check that is correct too assert_array_equal(a_roundtrip.mask, np.zeros(a.shape, dtype=bool)) # Clip = True a = np.array([-0.5, 0, 1, 8, 16], dtype=float) expected = [0, 0, 0, 1, 1] # Clip = True when creating the norm pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True) assert_array_almost_equal(pnorm(a), expected) assert pnorm(a[0]) == expected[0] assert pnorm(a[-1]) == expected[-1] # Clip = True at call time pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False) assert_array_almost_equal(pnorm(a, clip=True), expected) assert pnorm(a[0], clip=True) == expected[0] assert pnorm(a[-1], clip=True) == expected[-1] # Check clip=True preserves masked values a = np.ma.array([5, 2], mask=[True, False]) out = pnorm(a, clip=True) assert_array_equal(out.mask, [True, False]) def test_PowerNorm_translation_invariance(): a = np.array([0, 1/2, 1], dtype=float) expected = [0, 1/8, 1] pnorm = mcolors.PowerNorm(vmin=0, vmax=1, gamma=3) assert_array_almost_equal(pnorm(a), expected) pnorm = mcolors.PowerNorm(vmin=-2, vmax=-1, gamma=3) assert_array_almost_equal(pnorm(a - 2), expected) def test_powernorm_cbar_limits(): fig, ax = plt.subplots() vmin, vmax = 300, 1000 data = np.arange(10*10).reshape(10, 10) + vmin im = ax.imshow(data, norm=mcolors.PowerNorm(gamma=0.2, vmin=vmin, vmax=vmax)) cbar = fig.colorbar(im) assert cbar.ax.get_ylim() == (vmin, vmax) def test_Normalize(): norm = mcolors.Normalize() vals = np.arange(-10, 10, 1, dtype=float) _inverse_tester(norm, vals) _scalar_tester(norm, vals) _mask_tester(norm, vals) # Handle integer input correctly (don't overflow when computing max-min, # i.e. 127-(-128) here). vals = np.array([-128, 127], dtype=np.int8) norm = mcolors.Normalize(vals.min(), vals.max()) assert_array_equal(norm(vals), [0, 1]) # Don't lose precision on longdoubles (float128 on Linux): # for array inputs... vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble) norm = mcolors.Normalize(vals[0], vals[1]) assert norm(vals).dtype == np.longdouble assert_array_equal(norm(vals), [0, 1]) # and for scalar ones. eps = np.finfo(np.longdouble).resolution norm = plt.Normalize(1, 1 + 100 * eps) # This returns exactly 0.5 when longdouble is extended precision (80-bit), # but only a value close to it when it is quadruple precision (128-bit). assert_array_almost_equal(norm(1 + 50 * eps), 0.5, decimal=3) def test_FuncNorm(): def forward(x): return (x**2) def inverse(x): return np.sqrt(x) norm = mcolors.FuncNorm((forward, inverse), vmin=0, vmax=10) expected = np.array([0, 0.25, 1]) input = np.array([0, 5, 10]) assert_array_almost_equal(norm(input), expected) assert_array_almost_equal(norm.inverse(expected), input) def forward(x): return np.log10(x) def inverse(x): return 10**x norm = mcolors.FuncNorm((forward, inverse), vmin=0.1, vmax=10) lognorm = mcolors.LogNorm(vmin=0.1, vmax=10) assert_array_almost_equal(norm([0.2, 5, 10]), lognorm([0.2, 5, 10])) # use assert_allclose here for rtol on large numbers np.testing.assert_allclose(norm.inverse([0.2, 5, 10]), lognorm.inverse([0.2, 5, 10])) def test_TwoSlopeNorm_autoscale(): norm = mcolors.TwoSlopeNorm(vcenter=20) norm.autoscale([10, 20, 30, 40]) assert norm.vmin == 10. assert norm.vmax == 40. def test_TwoSlopeNorm_autoscale_None_vmin(): norm = mcolors.TwoSlopeNorm(2, vmin=0, vmax=None) norm.autoscale_None([1, 2, 3, 4, 5]) assert norm(5) == 1 assert norm.vmax == 5 def test_TwoSlopeNorm_autoscale_None_vmax(): norm = mcolors.TwoSlopeNorm(2, vmin=None, vmax=10) norm.autoscale_None([1, 2, 3, 4, 5]) assert norm(1) == 0 assert norm.vmin == 1 def test_TwoSlopeNorm_scale(): norm = mcolors.TwoSlopeNorm(2) assert norm.scaled() is False norm([1, 2, 3, 4]) assert norm.scaled() is True def test_TwoSlopeNorm_scaleout_center(): # test the vmin never goes above vcenter norm = mcolors.TwoSlopeNorm(vcenter=0) norm([0, 1, 2, 3, 5]) assert norm.vmin == -5 assert norm.vmax == 5 def test_TwoSlopeNorm_scaleout_center_max(): # test the vmax never goes below vcenter norm = mcolors.TwoSlopeNorm(vcenter=0) norm([0, -1, -2, -3, -5]) assert norm.vmax == 5 assert norm.vmin == -5 def test_TwoSlopeNorm_Even(): norm = mcolors.TwoSlopeNorm(vmin=-1, vcenter=0, vmax=4) vals = np.array([-1.0, -0.5, 0.0, 1.0, 2.0, 3.0, 4.0]) expected = np.array([0.0, 0.25, 0.5, 0.625, 0.75, 0.875, 1.0]) assert_array_equal(norm(vals), expected) def test_TwoSlopeNorm_Odd(): norm = mcolors.TwoSlopeNorm(vmin=-2, vcenter=0, vmax=5) vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0]) expected = np.array([0.0, 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) assert_array_equal(norm(vals), expected) def test_TwoSlopeNorm_VminEqualsVcenter(): with pytest.raises(ValueError): mcolors.TwoSlopeNorm(vmin=-2, vcenter=-2, vmax=2) def test_TwoSlopeNorm_VmaxEqualsVcenter(): with pytest.raises(ValueError): mcolors.TwoSlopeNorm(vmin=-2, vcenter=2, vmax=2) def test_TwoSlopeNorm_VminGTVcenter(): with pytest.raises(ValueError): mcolors.TwoSlopeNorm(vmin=10, vcenter=0, vmax=20) def test_TwoSlopeNorm_TwoSlopeNorm_VminGTVmax(): with pytest.raises(ValueError): mcolors.TwoSlopeNorm(vmin=10, vcenter=0, vmax=5) def test_TwoSlopeNorm_VcenterGTVmax(): with pytest.raises(ValueError): mcolors.TwoSlopeNorm(vmin=10, vcenter=25, vmax=20) def test_TwoSlopeNorm_premature_scaling(): norm = mcolors.TwoSlopeNorm(vcenter=2) with pytest.raises(ValueError): norm.inverse(np.array([0.1, 0.5, 0.9])) def test_SymLogNorm(): """ Test SymLogNorm behavior """ norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2, base=np.e) vals = np.array([-30, -1, 2, 6], dtype=float) normed_vals = norm(vals) expected = [0., 0.53980074, 0.826991, 1.02758204] assert_array_almost_equal(normed_vals, expected) _inverse_tester(norm, vals) _scalar_tester(norm, vals) _mask_tester(norm, vals) # Ensure that specifying vmin returns the same result as above norm = mcolors.SymLogNorm(3, vmin=-30, vmax=5, linscale=1.2, base=np.e) normed_vals = norm(vals) assert_array_almost_equal(normed_vals, expected) # test something more easily checked. norm = mcolors.SymLogNorm(1, vmin=-np.e**3, vmax=np.e**3, base=np.e) nn = norm([-np.e**3, -np.e**2, -np.e**1, -1, 0, 1, np.e**1, np.e**2, np.e**3]) xx = np.array([0., 0.109123, 0.218246, 0.32737, 0.5, 0.67263, 0.781754, 0.890877, 1.]) assert_array_almost_equal(nn, xx) norm = mcolors.SymLogNorm(1, vmin=-10**3, vmax=10**3, base=10) nn = norm([-10**3, -10**2, -10**1, -1, 0, 1, 10**1, 10**2, 10**3]) xx = np.array([0., 0.121622, 0.243243, 0.364865, 0.5, 0.635135, 0.756757, 0.878378, 1.]) assert_array_almost_equal(nn, xx) def test_SymLogNorm_colorbar(): """ Test un-called SymLogNorm in a colorbar. """ norm = mcolors.SymLogNorm(0.1, vmin=-1, vmax=1, linscale=1, base=np.e) fig = plt.figure() mcolorbar.ColorbarBase(fig.add_subplot(), norm=norm) plt.close(fig) def test_SymLogNorm_single_zero(): """ Test SymLogNorm to ensure it is not adding sub-ticks to zero label """ fig = plt.figure() norm = mcolors.SymLogNorm(1e-5, vmin=-1, vmax=1, base=np.e) cbar = mcolorbar.ColorbarBase(fig.add_subplot(), norm=norm) ticks = cbar.get_ticks() assert np.count_nonzero(ticks == 0) <= 1 plt.close(fig) class TestAsinhNorm: """ Tests for `~.colors.AsinhNorm` """ def test_init(self): norm0 = mcolors.AsinhNorm() assert norm0.linear_width == 1 norm5 = mcolors.AsinhNorm(linear_width=5) assert norm5.linear_width == 5 def test_norm(self): norm = mcolors.AsinhNorm(2, vmin=-4, vmax=4) vals = np.arange(-3.5, 3.5, 10) normed_vals = norm(vals) asinh2 = np.arcsinh(2) expected = (2 * np.arcsinh(vals / 2) + 2 * asinh2) / (4 * asinh2) assert_array_almost_equal(normed_vals, expected) def _inverse_tester(norm_instance, vals): """ Checks if the inverse of the given normalization is working. """ assert_array_almost_equal(norm_instance.inverse(norm_instance(vals)), vals) def _scalar_tester(norm_instance, vals): """ Checks if scalars and arrays are handled the same way. Tests only for float. """ scalar_result = [norm_instance(float(v)) for v in vals] assert_array_almost_equal(scalar_result, norm_instance(vals)) def _mask_tester(norm_instance, vals): """ Checks mask handling """ masked_array = np.ma.array(vals) masked_array[0] = np.ma.masked assert_array_equal(masked_array.mask, norm_instance(masked_array).mask) @image_comparison(['levels_and_colors.png'], style='mpl20') def test_cmap_and_norm_from_levels_and_colors(): data = np.linspace(-2, 4, 49).reshape(7, 7) levels = [-1, 2, 2.5, 3] colors = ['red', 'green', 'blue', 'yellow', 'black'] extend = 'both' cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend) ax = plt.axes() m = plt.pcolormesh(data, cmap=cmap, norm=norm) plt.colorbar(m) # Hide the axes labels (but not the colorbar ones, as they are useful) ax.tick_params(labelleft=False, labelbottom=False) @image_comparison(['boundarynorm_and_colorbar.png'], style='_classic_test') def test_boundarynorm_and_colorbarbase(): # Make a figure and axes with dimensions as desired. fig = plt.figure() ax1 = fig.add_axes((0.05, 0.80, 0.9, 0.15)) ax2 = fig.add_axes((0.05, 0.475, 0.9, 0.15)) ax3 = fig.add_axes((0.05, 0.15, 0.9, 0.15)) # Set the colormap and bounds bounds = [-1, 2, 5, 7, 12, 15] cmap = mpl.colormaps['viridis'] # Default behavior norm = mcolors.BoundaryNorm(bounds, cmap.N) cb1 = mcolorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, extend='both', orientation='horizontal', spacing='uniform') # New behavior norm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both') cb2 = mcolorbar.ColorbarBase(ax2, cmap=cmap, norm=norm, orientation='horizontal') # User can still force to any extend='' if really needed norm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both') cb3 = mcolorbar.ColorbarBase(ax3, cmap=cmap, norm=norm, extend='neither', orientation='horizontal') def test_cmap_and_norm_from_levels_and_colors2(): levels = [-1, 2, 2.5, 3] colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)] clr = mcolors.to_rgba_array(colors) bad = (0.1, 0.1, 0.1, 0.1) no_color = (0.0, 0.0, 0.0, 0.0) masked_value = 'masked_value' # Define the test values which are of interest. # Note: levels are lev[i] <= v < lev[i+1] tests = [('both', None, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: clr[4], 3.5: clr[4], masked_value: bad}), ('min', -1, {-2: clr[0], -1: clr[1], 2: clr[2], 2.25: clr[2], 3: no_color, 3.5: no_color, masked_value: bad}), ('max', -1, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: clr[3], 3.5: clr[3], masked_value: bad}), ('neither', -2, {-2: no_color, -1: clr[0], 2: clr[1], 2.25: clr[1], 3: no_color, 3.5: no_color, masked_value: bad}), ] for extend, i1, cases in tests: cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1], extend=extend) cmap = cmap.with_extremes(bad=bad) for d_val, expected_color in cases.items(): if d_val == masked_value: d_val = np.ma.array([1], mask=True) else: d_val = [d_val] assert_array_equal(expected_color, cmap(norm(d_val))[0], f'With extend={extend!r} and data ' f'value={d_val!r}') with pytest.raises(ValueError): mcolors.from_levels_and_colors(levels, colors) def test_rgb_hsv_round_trip(): for a_shape in [(500, 500, 3), (500, 3), (1, 3), (3,)]: np.random.seed(0) tt = np.random.random(a_shape) assert_array_almost_equal( tt, mcolors.hsv_to_rgb(mcolors.rgb_to_hsv(tt))) assert_array_almost_equal( tt, mcolors.rgb_to_hsv(mcolors.hsv_to_rgb(tt))) def test_rgb_to_hsv_int(): # Test that int rgb values (still range 0-1) are processed correctly. assert_array_equal(mcolors.rgb_to_hsv((0, 1, 0)), (1/3, 1, 1)) # green def test_autoscale_masked(): # Test for #2336. Previously fully masked data would trigger a ValueError. data = np.ma.masked_all((12, 20)) plt.pcolor(data) plt.draw() @image_comparison(['light_source_shading_topo.png'], style='_classic_test') def test_light_source_topo_surface(): """Shades a DEM using different v.e.'s and blend modes.""" dem = cbook.get_sample_data('jacksboro_fault_dem.npz') elev = dem['elevation'] dx, dy = dem['dx'], dem['dy'] # Get the true cellsize in meters for accurate vertical exaggeration # Convert from decimal degrees to meters dx = 111320.0 * dx * np.cos(dem['ymin']) dy = 111320.0 * dy ls = mcolors.LightSource(315, 45) cmap = cm.gist_earth fig, axs = plt.subplots(nrows=3, ncols=3) for row, mode in zip(axs, ['hsv', 'overlay', 'soft']): for ax, ve in zip(row, [0.1, 1, 10]): rgb = ls.shade(elev, cmap, vert_exag=ve, dx=dx, dy=dy, blend_mode=mode) ax.imshow(rgb) ax.set(xticks=[], yticks=[]) def test_light_source_shading_default(): """ Array comparison test for the default "hsv" blend mode. Ensure the default result doesn't change without warning. """ y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j] z = 10 * np.cos(x**2 + y**2) cmap = plt.colormaps["copper"] ls = mcolors.LightSource(315, 45) rgb = ls.shade(z, cmap) # Result stored transposed and rounded for more compact display... expect = np.array( [[[0.00, 0.45, 0.90, 0.90, 0.82, 0.62, 0.28, 0.00], [0.45, 0.94, 0.99, 1.00, 1.00, 0.96, 0.65, 0.17], [0.90, 0.99, 1.00, 1.00, 1.00, 1.00, 0.94, 0.35], [0.90, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.49], [0.82, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.41], [0.62, 0.96, 1.00, 1.00, 1.00, 1.00, 0.90, 0.07], [0.28, 0.65, 0.94, 1.00, 1.00, 0.90, 0.35, 0.01], [0.00, 0.17, 0.35, 0.49, 0.41, 0.07, 0.01, 0.00]], [[0.00, 0.28, 0.59, 0.72, 0.62, 0.40, 0.18, 0.00], [0.28, 0.78, 0.93, 0.92, 0.83, 0.66, 0.39, 0.11], [0.59, 0.93, 0.99, 1.00, 0.92, 0.75, 0.50, 0.21], [0.72, 0.92, 1.00, 0.99, 0.93, 0.76, 0.51, 0.18], [0.62, 0.83, 0.92, 0.93, 0.87, 0.68, 0.42, 0.08], [0.40, 0.66, 0.75, 0.76, 0.68, 0.52, 0.23, 0.02], [0.18, 0.39, 0.50, 0.51, 0.42, 0.23, 0.00, 0.00], [0.00, 0.11, 0.21, 0.18, 0.08, 0.02, 0.00, 0.00]], [[0.00, 0.18, 0.38, 0.46, 0.39, 0.26, 0.11, 0.00], [0.18, 0.50, 0.70, 0.75, 0.64, 0.44, 0.25, 0.07], [0.38, 0.70, 0.91, 0.98, 0.81, 0.51, 0.29, 0.13], [0.46, 0.75, 0.98, 0.96, 0.84, 0.48, 0.22, 0.12], [0.39, 0.64, 0.81, 0.84, 0.71, 0.31, 0.11, 0.05], [0.26, 0.44, 0.51, 0.48, 0.31, 0.10, 0.03, 0.01], [0.11, 0.25, 0.29, 0.22, 0.11, 0.03, 0.00, 0.00], [0.00, 0.07, 0.13, 0.12, 0.05, 0.01, 0.00, 0.00]], [[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]] ]).T assert_array_almost_equal(rgb, expect, decimal=2) def test_light_source_shading_empty_mask(): y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j] z0 = 10 * np.cos(x**2 + y**2) z1 = np.ma.array(z0) cmap = plt.colormaps["copper"] ls = mcolors.LightSource(315, 45) rgb0 = ls.shade(z0, cmap) rgb1 = ls.shade(z1, cmap) assert_array_almost_equal(rgb0, rgb1) # Numpy 1.9.1 fixed a bug in masked arrays which resulted in # additional elements being masked when calculating the gradient thus # the output is different with earlier numpy versions. def test_light_source_masked_shading(): """ Array comparison test for a surface with a masked portion. Ensures that we don't wind up with "fringes" of odd colors around masked regions. """ y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j] z = 10 * np.cos(x**2 + y**2) z = np.ma.masked_greater(z, 9.9) cmap = plt.colormaps["copper"] ls = mcolors.LightSource(315, 45) rgb = ls.shade(z, cmap) # Result stored transposed and rounded for more compact display... expect = np.array( [[[0.00, 0.46, 0.91, 0.91, 0.84, 0.64, 0.29, 0.00], [0.46, 0.96, 1.00, 1.00, 1.00, 0.97, 0.67, 0.18], [0.91, 1.00, 1.00, 1.00, 1.00, 1.00, 0.96, 0.36], [0.91, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.51], [0.84, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.44], [0.64, 0.97, 1.00, 1.00, 1.00, 1.00, 0.94, 0.09], [0.29, 0.67, 0.96, 1.00, 1.00, 0.94, 0.38, 0.01], [0.00, 0.18, 0.36, 0.51, 0.44, 0.09, 0.01, 0.00]], [[0.00, 0.29, 0.61, 0.75, 0.64, 0.41, 0.18, 0.00], [0.29, 0.81, 0.95, 0.93, 0.85, 0.68, 0.40, 0.11], [0.61, 0.95, 1.00, 0.78, 0.78, 0.77, 0.52, 0.22], [0.75, 0.93, 0.78, 0.00, 0.00, 0.78, 0.54, 0.19], [0.64, 0.85, 0.78, 0.00, 0.00, 0.78, 0.45, 0.08], [0.41, 0.68, 0.77, 0.78, 0.78, 0.55, 0.25, 0.02], [0.18, 0.40, 0.52, 0.54, 0.45, 0.25, 0.00, 0.00], [0.00, 0.11, 0.22, 0.19, 0.08, 0.02, 0.00, 0.00]], [[0.00, 0.19, 0.39, 0.48, 0.41, 0.26, 0.12, 0.00], [0.19, 0.52, 0.73, 0.78, 0.66, 0.46, 0.26, 0.07], [0.39, 0.73, 0.95, 0.50, 0.50, 0.53, 0.30, 0.14], [0.48, 0.78, 0.50, 0.00, 0.00, 0.50, 0.23, 0.12], [0.41, 0.66, 0.50, 0.00, 0.00, 0.50, 0.11, 0.05], [0.26, 0.46, 0.53, 0.50, 0.50, 0.11, 0.03, 0.01], [0.12, 0.26, 0.30, 0.23, 0.11, 0.03, 0.00, 0.00], [0.00, 0.07, 0.14, 0.12, 0.05, 0.01, 0.00, 0.00]], [[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]], ]).T assert_array_almost_equal(rgb, expect, decimal=2) def test_light_source_hillshading(): """ Compare the current hillshading method against one that should be mathematically equivalent. Illuminates a cone from a range of angles. """ def alternative_hillshade(azimuth, elev, z): illum = _sph2cart(*_azimuth2math(azimuth, elev)) illum = np.array(illum) dy, dx = np.gradient(-z) dy = -dy dz = np.ones_like(dy) normals = np.dstack([dx, dy, dz]) normals /= np.linalg.norm(normals, axis=2)[..., None] intensity = np.tensordot(normals, illum, axes=(2, 0)) intensity -= intensity.min() intensity /= np.ptp(intensity) return intensity y, x = np.mgrid[5:0:-1, :5] z = -np.hypot(x - x.mean(), y - y.mean()) for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)): ls = mcolors.LightSource(az, elev) h1 = ls.hillshade(z) h2 = alternative_hillshade(az, elev, z) assert_array_almost_equal(h1, h2) def test_light_source_planar_hillshading(): """ Ensure that the illumination intensity is correct for planar surfaces. """ def plane(azimuth, elevation, x, y): """ Create a plane whose normal vector is at the given azimuth and elevation. """ theta, phi = _azimuth2math(azimuth, elevation) a, b, c = _sph2cart(theta, phi) z = -(a*x + b*y) / c return z def angled_plane(azimuth, elevation, angle, x, y): """ Create a plane whose normal vector is at an angle from the given azimuth and elevation. """ elevation = elevation + angle if elevation > 90: azimuth = (azimuth + 180) % 360 elevation = (90 - elevation) % 90 return plane(azimuth, elevation, x, y) y, x = np.mgrid[5:0:-1, :5] for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)): ls = mcolors.LightSource(az, elev) # Make a plane at a range of angles to the illumination for angle in range(0, 105, 15): z = angled_plane(az, elev, angle, x, y) h = ls.hillshade(z) assert_array_almost_equal(h, np.cos(np.radians(angle))) def test_color_names(): assert mcolors.to_hex("blue") == "#0000ff" assert mcolors.to_hex("xkcd:blue") == "#0343df" assert mcolors.to_hex("tab:blue") == "#1f77b4" def _sph2cart(theta, phi): x = np.cos(theta) * np.sin(phi) y = np.sin(theta) * np.sin(phi) z = np.cos(phi) return x, y, z def _azimuth2math(azimuth, elevation): """ Convert from clockwise-from-north and up-from-horizontal to mathematical conventions. """ theta = np.radians((90 - azimuth) % 360) phi = np.radians(90 - elevation) return theta, phi def test_pandas_iterable(pd): # Using a list or series yields equivalent # colormaps, i.e the series isn't seen as # a single color lst = ['red', 'blue', 'green'] s = pd.Series(lst) cm1 = mcolors.ListedColormap(lst) cm2 = mcolors.ListedColormap(s) assert_array_equal(cm1.colors, cm2.colors) @pytest.mark.parametrize('name', sorted(mpl.colormaps())) def test_colormap_reversing(name): """ Check the generated _lut data of a colormap and corresponding reversed colormap if they are almost the same. """ cmap = mpl.colormaps[name] cmap_r = cmap.reversed() if not cmap_r._isinit: cmap._init() cmap_r._init() assert_array_almost_equal(cmap._lut[:-3], cmap_r._lut[-4::-1]) # Test the bad, over, under values too assert_array_almost_equal(cmap(-np.inf), cmap_r(np.inf)) assert_array_almost_equal(cmap(np.inf), cmap_r(-np.inf)) assert_array_almost_equal(cmap(np.nan), cmap_r(np.nan)) def test_has_alpha_channel(): assert mcolors._has_alpha_channel((0, 0, 0, 0)) assert mcolors._has_alpha_channel([1, 1, 1, 1]) assert mcolors._has_alpha_channel('#fff8') assert mcolors._has_alpha_channel('#0f0f0f80') assert mcolors._has_alpha_channel(('r', 0.5)) assert mcolors._has_alpha_channel(([1, 1, 1, 1], None)) assert not mcolors._has_alpha_channel('blue') # 4-char string! assert not mcolors._has_alpha_channel('0.25') assert not mcolors._has_alpha_channel('r') assert not mcolors._has_alpha_channel((1, 0, 0)) assert not mcolors._has_alpha_channel('#fff') assert not mcolors._has_alpha_channel('#0f0f0f') assert not mcolors._has_alpha_channel(('r', None)) assert not mcolors._has_alpha_channel(([1, 1, 1], None)) def test_cn(): matplotlib.rcParams['axes.prop_cycle'] = cycler('color', ['blue', 'r']) assert mcolors.to_hex("C0") == '#0000ff' assert mcolors.to_hex("C1") == '#ff0000' matplotlib.rcParams['axes.prop_cycle'] = cycler('color', ['xkcd:blue', 'r']) assert mcolors.to_hex("C0") == '#0343df' assert mcolors.to_hex("C1") == '#ff0000' assert mcolors.to_hex("C10") == '#0343df' assert mcolors.to_hex("C11") == '#ff0000' matplotlib.rcParams['axes.prop_cycle'] = cycler('color', ['8e4585', 'r']) assert mcolors.to_hex("C0") == '#8e4585' # if '8e4585' gets parsed as a float before it gets detected as a hex # colour it will be interpreted as a very large number. # this mustn't happen. assert mcolors.to_rgb("C0")[0] != np.inf def test_conversions(): # to_rgba_array("none") returns a (0, 4) array. assert_array_equal(mcolors.to_rgba_array("none"), np.zeros((0, 4))) assert_array_equal(mcolors.to_rgba_array([]), np.zeros((0, 4))) # a list of grayscale levels, not a single color. assert_array_equal( mcolors.to_rgba_array([".2", ".5", ".8"]), np.vstack([mcolors.to_rgba(c) for c in [".2", ".5", ".8"]])) # alpha is properly set. assert mcolors.to_rgba((1, 1, 1), .5) == (1, 1, 1, .5) assert mcolors.to_rgba(".1", .5) == (.1, .1, .1, .5) # builtin round differs between py2 and py3. assert mcolors.to_hex((.7, .7, .7)) == "#b2b2b2" # hex roundtrip. hex_color = "#1234abcd" assert mcolors.to_hex(mcolors.to_rgba(hex_color), keep_alpha=True) == \ hex_color def test_conversions_masked(): x1 = np.ma.array(['k', 'b'], mask=[True, False]) x2 = np.ma.array([[0, 0, 0, 1], [0, 0, 1, 1]]) x2[0] = np.ma.masked assert mcolors.to_rgba(x1[0]) == (0, 0, 0, 0) assert_array_equal(mcolors.to_rgba_array(x1), [[0, 0, 0, 0], [0, 0, 1, 1]]) assert_array_equal(mcolors.to_rgba_array(x2), mcolors.to_rgba_array(x1)) def test_to_rgba_array_single_str(): # single color name is valid assert_array_equal(mcolors.to_rgba_array("red"), [(1, 0, 0, 1)]) # single char color sequence is invalid with pytest.raises(ValueError, match="'rgb' is not a valid color value."): array = mcolors.to_rgba_array("rgb") def test_to_rgba_array_2tuple_str(): expected = np.array([[0, 0, 0, 1], [1, 1, 1, 1]]) assert_array_equal(mcolors.to_rgba_array(("k", "w")), expected) def test_to_rgba_array_alpha_array(): with pytest.raises(ValueError, match="The number of colors must match"): mcolors.to_rgba_array(np.ones((5, 3), float), alpha=np.ones((2,))) alpha = [0.5, 0.6] c = mcolors.to_rgba_array(np.ones((2, 3), float), alpha=alpha) assert_array_equal(c[:, 3], alpha) c = mcolors.to_rgba_array(['r', 'g'], alpha=alpha) assert_array_equal(c[:, 3], alpha) def test_to_rgba_array_accepts_color_alpha_tuple(): assert_array_equal( mcolors.to_rgba_array(('black', 0.9)), [[0, 0, 0, 0.9]]) def test_to_rgba_array_explicit_alpha_overrides_tuple_alpha(): assert_array_equal( mcolors.to_rgba_array(('black', 0.9), alpha=0.5), [[0, 0, 0, 0.5]]) def test_to_rgba_array_accepts_color_alpha_tuple_with_multiple_colors(): color_array = np.array([[1., 1., 1., 1.], [0., 0., 1., 0.]]) assert_array_equal( mcolors.to_rgba_array((color_array, 0.2)), [[1., 1., 1., 0.2], [0., 0., 1., 0.2]]) color_sequence = [[1., 1., 1., 1.], [0., 0., 1., 0.]] assert_array_equal( mcolors.to_rgba_array((color_sequence, 0.4)), [[1., 1., 1., 0.4], [0., 0., 1., 0.4]]) def test_to_rgba_array_error_with_color_invalid_alpha_tuple(): with pytest.raises(ValueError, match="'alpha' must be between 0 and 1,"): mcolors.to_rgba_array(('black', 2.0)) @pytest.mark.parametrize('rgba_alpha', [('white', 0.5), ('#ffffff', 0.5), ('#ffffff00', 0.5), ((1.0, 1.0, 1.0, 1.0), 0.5)]) def test_to_rgba_accepts_color_alpha_tuple(rgba_alpha): assert mcolors.to_rgba(rgba_alpha) == (1, 1, 1, 0.5) def test_to_rgba_explicit_alpha_overrides_tuple_alpha(): assert mcolors.to_rgba(('red', 0.1), alpha=0.9) == (1, 0, 0, 0.9) def test_to_rgba_error_with_color_invalid_alpha_tuple(): with pytest.raises(ValueError, match="'alpha' must be between 0 and 1"): mcolors.to_rgba(('blue', 2.0)) @pytest.mark.parametrize("bytes", (True, False)) def test_scalarmappable_to_rgba(bytes): sm = cm.ScalarMappable() alpha_1 = 255 if bytes else 1 # uint8 RGBA x = np.ones((2, 3, 4), dtype=np.uint8) expected = x.copy() if bytes else x.astype(np.float32)/255 np.testing.assert_almost_equal(sm.to_rgba(x, bytes=bytes), expected) # uint8 RGB expected[..., 3] = alpha_1 np.testing.assert_almost_equal(sm.to_rgba(x[..., :3], bytes=bytes), expected) # uint8 masked RGBA xm = np.ma.masked_array(x, mask=np.zeros_like(x)) xm.mask[0, 0, 0] = True expected = x.copy() if bytes else x.astype(np.float32)/255 expected[0, 0, 3] = 0 np.testing.assert_almost_equal(sm.to_rgba(xm, bytes=bytes), expected) # uint8 masked RGB expected[..., 3] = alpha_1 expected[0, 0, 3] = 0 np.testing.assert_almost_equal(sm.to_rgba(xm[..., :3], bytes=bytes), expected) # float RGBA x = np.ones((2, 3, 4), dtype=float) * 0.5 expected = (x * 255).astype(np.uint8) if bytes else x.copy() np.testing.assert_almost_equal(sm.to_rgba(x, bytes=bytes), expected) # float RGB expected[..., 3] = alpha_1 np.testing.assert_almost_equal(sm.to_rgba(x[..., :3], bytes=bytes), expected) # float masked RGBA xm = np.ma.masked_array(x, mask=np.zeros_like(x)) xm.mask[0, 0, 0] = True expected = (x * 255).astype(np.uint8) if bytes else x.copy() expected[0, 0, 3] = 0 np.testing.assert_almost_equal(sm.to_rgba(xm, bytes=bytes), expected) # float masked RGB expected[..., 3] = alpha_1 expected[0, 0, 3] = 0 np.testing.assert_almost_equal(sm.to_rgba(xm[..., :3], bytes=bytes), expected) @pytest.mark.parametrize("bytes", (True, False)) def test_scalarmappable_nan_to_rgba(bytes): sm = cm.ScalarMappable() # RGBA x = np.ones((2, 3, 4), dtype=float) * 0.5 x[0, 0, 0] = np.nan expected = x.copy() expected[0, 0, :] = 0 if bytes: expected = (expected * 255).astype(np.uint8) np.testing.assert_almost_equal(sm.to_rgba(x, bytes=bytes), expected) assert np.any(np.isnan(x)) # Input array should not be changed # RGB expected[..., 3] = 255 if bytes else 1 expected[0, 0, 3] = 0 np.testing.assert_almost_equal(sm.to_rgba(x[..., :3], bytes=bytes), expected) assert np.any(np.isnan(x)) # Input array should not be changed # Out-of-range fail x[1, 0, 0] = 42 with pytest.raises(ValueError, match=r'\[0,1\] range'): sm.to_rgba(x[..., :3], bytes=bytes) def test_failed_conversions(): with pytest.raises(ValueError): mcolors.to_rgba('5') with pytest.raises(ValueError): mcolors.to_rgba('-1') with pytest.raises(ValueError): mcolors.to_rgba('nan') with pytest.raises(ValueError): mcolors.to_rgba('unknown_color') with pytest.raises(ValueError): # Gray must be a string to distinguish 3-4 grays from RGB or RGBA. mcolors.to_rgba(0.4) def test_grey_gray(): color_mapping = mcolors._colors_full_map for k in color_mapping.keys(): if 'grey' in k: assert color_mapping[k] == color_mapping[k.replace('grey', 'gray')] if 'gray' in k: assert color_mapping[k] == color_mapping[k.replace('gray', 'grey')] def test_tableau_order(): dflt_cycle = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'] assert list(mcolors.TABLEAU_COLORS.values()) == dflt_cycle def test_ndarray_subclass_norm(): # Emulate an ndarray subclass that handles units # which objects when adding or subtracting with other # arrays. See #6622 and #8696 class MyArray(np.ndarray): def __isub__(self, other): # type: ignore[misc] raise RuntimeError def __add__(self, other): raise RuntimeError data = np.arange(-10, 10, 1, dtype=float).reshape((10, 2)) mydata = data.view(MyArray) for norm in [mcolors.Normalize(), mcolors.LogNorm(), mcolors.SymLogNorm(3, vmax=5, linscale=1, base=np.e), mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()), mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max(), base=np.e), mcolors.PowerNorm(1)]: assert_array_equal(norm(mydata), norm(data)) fig, ax = plt.subplots() ax.imshow(mydata, norm=norm) fig.canvas.draw() # Check that no warning is emitted. def test_same_color(): assert mcolors.same_color('k', (0, 0, 0)) assert not mcolors.same_color('w', (1, 1, 0)) assert mcolors.same_color(['red', 'blue'], ['r', 'b']) assert mcolors.same_color('none', 'none') assert not mcolors.same_color('none', 'red') with pytest.raises(ValueError): mcolors.same_color(['r', 'g', 'b'], ['r']) with pytest.raises(ValueError): mcolors.same_color(['red', 'green'], 'none') def test_hex_shorthand_notation(): assert mcolors.same_color("#123", "#112233") assert mcolors.same_color("#123a", "#112233aa") def test_repr_png(): cmap = mpl.colormaps['viridis'] png = cmap._repr_png_() assert len(png) > 0 img = Image.open(BytesIO(png)) assert img.width > 0 assert img.height > 0 assert 'Title' in img.text assert 'Description' in img.text assert 'Author' in img.text assert 'Software' in img.text def test_repr_html(): cmap = mpl.colormaps['viridis'] html = cmap._repr_html_() assert len(html) > 0 png = cmap._repr_png_() assert base64.b64encode(png).decode('ascii') in html assert cmap.name in html assert html.startswith('') def test_get_under_over_bad(): cmap = mpl.colormaps['viridis'] assert_array_equal(cmap.get_under(), cmap(-np.inf)) assert_array_equal(cmap.get_over(), cmap(np.inf)) assert_array_equal(cmap.get_bad(), cmap(np.nan)) @pytest.mark.parametrize('kind', ('over', 'under', 'bad')) def test_non_mutable_get_values(kind): cmap = copy.copy(mpl.colormaps['viridis']) init_value = getattr(cmap, f'get_{kind}')() with pytest.warns(PendingDeprecationWarning): getattr(cmap, f'set_{kind}')('k') black_value = getattr(cmap, f'get_{kind}')() assert np.all(black_value == [0, 0, 0, 1]) assert not np.all(init_value == black_value) def test_colormap_alpha_array(): cmap = mpl.colormaps['viridis'] vals = [-1, 0.5, 2] # under, valid, over with pytest.raises(ValueError, match="alpha is array-like but"): cmap(vals, alpha=[1, 1, 1, 1]) alpha = np.array([0.1, 0.2, 0.3]) c = cmap(vals, alpha=alpha) assert_array_equal(c[:, -1], alpha) c = cmap(vals, alpha=alpha, bytes=True) assert_array_equal(c[:, -1], (alpha * 255).astype(np.uint8)) def test_colormap_bad_data_with_alpha(): cmap = mpl.colormaps['viridis'] c = cmap(np.nan, alpha=0.5) assert c == (0, 0, 0, 0) c = cmap([0.5, np.nan], alpha=0.5) assert_array_equal(c[1], (0, 0, 0, 0)) c = cmap([0.5, np.nan], alpha=[0.1, 0.2]) assert_array_equal(c[1], (0, 0, 0, 0)) c = cmap([[np.nan, 0.5], [0, 0]], alpha=0.5) assert_array_equal(c[0, 0], (0, 0, 0, 0)) c = cmap([[np.nan, 0.5], [0, 0]], alpha=np.full((2, 2), 0.5)) assert_array_equal(c[0, 0], (0, 0, 0, 0)) def test_2d_to_rgba(): color = np.array([0.1, 0.2, 0.3]) rgba_1d = mcolors.to_rgba(color.reshape(-1)) rgba_2d = mcolors.to_rgba(color.reshape((1, -1))) assert rgba_1d == rgba_2d def test_set_dict_to_rgba(): # downstream libraries do this... # note we can't test this because it is not well-ordered # so just smoketest: colors = {(0, .5, 1), (1, .2, .5), (.4, 1, .2)} res = mcolors.to_rgba_array(colors) palette = {"red": (1, 0, 0), "green": (0, 1, 0), "blue": (0, 0, 1)} res = mcolors.to_rgba_array(palette.values()) exp = np.eye(3) np.testing.assert_array_almost_equal(res[:, :-1], exp) def test_norm_deepcopy(): norm = mcolors.LogNorm() norm.vmin = 0.0002 norm2 = copy.deepcopy(norm) assert norm2.vmin == norm.vmin assert isinstance(norm2._scale, mscale.LogScale) norm = mcolors.Normalize() norm.vmin = 0.0002 norm2 = copy.deepcopy(norm) assert norm2._scale is None assert norm2.vmin == norm.vmin def test_set_clim_emits_single_callback(): data = np.array([[1, 2], [3, 4]]) fig, ax = plt.subplots() image = ax.imshow(data, cmap='viridis') callback = unittest.mock.Mock() image.norm.callbacks.connect('changed', callback) callback.assert_not_called() # Call set_clim() to update the limits image.set_clim(1, 5) # Assert that only one "changed" callback is sent after calling set_clim() callback.assert_called_once() def test_norm_callback(): increment = unittest.mock.Mock(return_value=None) norm = mcolors.Normalize() norm.callbacks.connect('changed', increment) # Haven't updated anything, so call count should be 0 assert increment.call_count == 0 # Now change vmin and vmax to test callbacks norm.vmin = 1 assert increment.call_count == 1 norm.vmax = 5 assert increment.call_count == 2 # callback shouldn't be called if setting to the same value norm.vmin = 1 assert increment.call_count == 2 norm.vmax = 5 assert increment.call_count == 2 # We only want autoscale() calls to send out one update signal increment.reset_mock() norm.autoscale([0, 1, 2]) assert increment.call_count == 1 def test_scalarmappable_norm_update(): norm = mcolors.Normalize() sm = matplotlib.cm.ScalarMappable(norm=norm, cmap='plasma') # sm doesn't have a stale attribute at first, set it to False sm.stale = False # The mappable should be stale after updating vmin/vmax norm.vmin = 5 assert sm.stale sm.stale = False norm.vmax = 5 assert sm.stale sm.stale = False norm.clip = True assert sm.stale # change to the CenteredNorm and TwoSlopeNorm to test those # Also make sure that updating the norm directly and with # set_norm both update the Norm callback norm = mcolors.CenteredNorm() sm.norm = norm sm.stale = False norm.vcenter = 1 assert sm.stale norm = mcolors.TwoSlopeNorm(vcenter=0, vmin=-1, vmax=1) sm.set_norm(norm) sm.stale = False norm.vcenter = 1 assert sm.stale @check_figures_equal() def test_norm_update_figs(fig_test, fig_ref): ax_ref = fig_ref.add_subplot() ax_test = fig_test.add_subplot() z = np.arange(100).reshape((10, 10)) ax_ref.imshow(z, norm=mcolors.Normalize(10, 90)) # Create the norm beforehand with different limits and then update # after adding to the plot norm = mcolors.Normalize(0, 1) ax_test.imshow(z, norm=norm) # Force initial draw to make sure it isn't already stale fig_test.canvas.draw() norm.vmin, norm.vmax = 10, 90 def test_make_norm_from_scale_name(): logitnorm = mcolors.make_norm_from_scale( mscale.LogitScale, mcolors.Normalize) assert logitnorm.__name__ == logitnorm.__qualname__ == "LogitScaleNorm" def test_color_sequences(): # basic access assert plt.color_sequences is matplotlib.color_sequences # same registry assert list(plt.color_sequences) == [ 'tab10', 'tab20', 'tab20b', 'tab20c', 'Pastel1', 'Pastel2', 'Paired', 'Accent', 'okabe_ito', 'Dark2', 'Set1', 'Set2', 'Set3', 'petroff6', 'petroff8', 'petroff10'] assert len(plt.color_sequences['tab10']) == 10 assert len(plt.color_sequences['tab20']) == 20 tab_colors = [ 'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'] for seq_color, tab_color in zip(plt.color_sequences['tab10'], tab_colors): assert mcolors.same_color(seq_color, tab_color) # registering with pytest.raises(ValueError, match="reserved name"): plt.color_sequences.register('tab10', ['r', 'g', 'b']) with pytest.raises(ValueError, match="not a valid color specification"): plt.color_sequences.register('invalid', ['not a color']) rgb_colors = ['r', 'g', 'b'] plt.color_sequences.register('rgb', rgb_colors) assert plt.color_sequences['rgb'] == ['r', 'g', 'b'] # should not affect the registered sequence because input is copied rgb_colors.append('c') assert plt.color_sequences['rgb'] == ['r', 'g', 'b'] # should not affect the registered sequence because returned list is a copy plt.color_sequences['rgb'].append('c') assert plt.color_sequences['rgb'] == ['r', 'g', 'b'] # unregister plt.color_sequences.unregister('rgb') with pytest.raises(KeyError): plt.color_sequences['rgb'] # rgb is gone plt.color_sequences.unregister('rgb') # multiple unregisters are ok with pytest.raises(ValueError, match="Cannot unregister builtin"): plt.color_sequences.unregister('tab10') def test_cm_set_cmap_error(): sm = cm.ScalarMappable() # Pick a name we are pretty sure will never be a colormap name bad_cmap = 'AardvarksAreAwkward' with pytest.raises(ValueError, match=bad_cmap): sm.set_cmap(bad_cmap) def test_set_cmap_mismatched_name(): cmap = matplotlib.colormaps["viridis"].with_extremes(over='r') # register it with different names cmap.name = "test-cmap" matplotlib.colormaps.register(name='wrong-cmap', cmap=cmap) plt.set_cmap("wrong-cmap") cmap_returned = plt.get_cmap("wrong-cmap") assert cmap_returned == cmap assert cmap_returned.name == "wrong-cmap" def test_cmap_alias_names(): assert matplotlib.colormaps["gray"].name == "gray" # original assert matplotlib.colormaps["grey"].name == "grey" # alias def test_to_rgba_array_none_color_with_alpha_param(): # effective alpha for color "none" must always be 0 to achieve a vanishing color # even explicit alpha must be ignored c = ["blue", "none"] alpha = [1, 1] assert_array_equal( to_rgba_array(c, alpha), [[0., 0., 1., 1.], [0., 0., 0., 0.]] ) @pytest.mark.parametrize('input, expected', [('red', True), (('red', 0.5), True), (('red', 2), False), (['red', 0.5], False), (('red', 'blue'), False), (['red', 'blue'], False), ('C3', True), (('C3', 0.5), True)]) def test_is_color_like(input, expected): assert is_color_like(input) is expected def test_colorizer_vmin_vmax(): ca = mcolorizer.Colorizer() assert ca.vmin is None assert ca.vmax is None ca.vmin = 1 ca.vmax = 3 assert ca.vmin == 1.0 assert ca.vmax == 3.0 assert ca.norm.vmin == 1.0 assert ca.norm.vmax == 3.0 def test_LinearSegmentedColormap_from_list_color_alpha_tuple(): """ GitHub issue #29042: A bug in 'from_list' causes an error when passing a tuple (str, float) where the string is a color name or grayscale value and float is an alpha value. """ colors = [("red", 0.3), ("0.42", 0.1), "green"] cmap = mcolors.LinearSegmentedColormap.from_list("lsc", colors, N=3) assert_array_almost_equal(cmap([.0, 0.5, 1.]), to_rgba_array(colors)) @pytest.mark.parametrize("colors", [[(0.42, "blue"), (.1, .1, .1, .1)], ["blue", (0.42, "red")], ["blue", (.1, .1, .1, .1), ("red", 2)], [(0, "red"), (1.1, "blue")], [(0.52, "red"), (0.42, "blue")]]) def test_LinearSegmentedColormap_from_list_invalid_inputs(colors): with pytest.raises(ValueError): mcolors.LinearSegmentedColormap.from_list("lsc", colors) def test_LinearSegmentedColormap_from_list_value_color_tuple(): value_color_tuples = [(0, "red"), (0.6, "blue"), (1, "green")] cmap = mcolors.LinearSegmentedColormap.from_list("lsc", value_color_tuples, N=11) assert_array_almost_equal( cmap([value for value, _ in value_color_tuples]), to_rgba_array([color for _, color in value_color_tuples]), ) @image_comparison(['test_norm_abc.png'], remove_text=True, style='_classic_test', tol=0 if platform.machine() == 'x86_64' else 0.05) def test_norm_abc(): class CustomHalfNorm(mcolors.Norm): def __init__(self): super().__init__() @property def vmin(self): return 0 @property def vmax(self): return 1 @property def clip(self): return False def __call__(self, value, clip=None): return value / 2 def inverse(self, value): return 2 * value def autoscale(self, A): pass def autoscale_None(self, A): pass def scaled(self): return True @property def n_components(self): return 1 fig, axes = plt.subplots(2,2) r = np.linspace(-1, 3, 16*16).reshape((16,16)) norm = CustomHalfNorm() colorizer = mpl.colorizer.Colorizer(cmap='viridis', norm=norm) c = axes[0,0].imshow(r, colorizer=colorizer) axes[0,1].pcolor(r, colorizer=colorizer) axes[1,0].contour(r, colorizer=colorizer) axes[1,1].contourf(r, colorizer=colorizer) def test_close_error_name(): with pytest.raises( KeyError, match=( r"'grays' is not a valid value for colormap\. " r"Did you mean one of: 'gray', 'Grays', 'gray_r'\?")): matplotlib.colormaps["grays"] with pytest.raises( KeyError, match=( "'set' is not a valid value for sequence_name. " "Did you mean one of: 'Set3', 'Set2', 'Set1'?")): matplotlib.color_sequences["set"] def test_multi_norm_creation(): # tests for mcolors.MultiNorm # test wrong input with pytest.raises(ValueError, match="MultiNorm must be assigned an iterable"): mcolors.MultiNorm("linear") with pytest.raises(ValueError, match="MultiNorm must be assigned at least one"): mcolors.MultiNorm([]) with pytest.raises(ValueError, match="MultiNorm must be assigned an iterable"): mcolors.MultiNorm(None) with pytest.raises(ValueError, match="not a valid"): mcolors.MultiNorm(["linear", "bad_norm_name"]) with pytest.raises(ValueError, match="Each norm assigned to MultiNorm"): mcolors.MultiNorm(["linear", object()]) norm = mpl.colors.MultiNorm(['linear', 'linear']) def test_multi_norm_call_vmin_vmax(): # test get vmin, vmax norm = mpl.colors.MultiNorm(['linear', 'log']) norm.vmin = (1, 1) norm.vmax = (2, 2) assert norm.vmin == (1, 1) assert norm.vmax == (2, 2) with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm.vmin = 1 with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm.vmax = 1 with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm.vmin = (1, 2, 3) with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm.vmax = (1, 2, 3) def test_multi_norm_call_clip_inverse(): # test get vmin, vmax norm = mpl.colors.MultiNorm(['linear', 'log']) norm.vmin = (1, 1) norm.vmax = (2, 2) # test call with clip assert_array_equal(norm([3, 3], clip=[False, False]), [2.0, 1.584962500721156]) assert_array_equal(norm([3, 3], clip=[True, True]), [1.0, 1.0]) assert_array_equal(norm([3, 3], clip=[True, False]), [1.0, 1.584962500721156]) norm.clip = [False, False] assert_array_equal(norm([3, 3]), [2.0, 1.584962500721156]) norm.clip = [True, True] assert_array_equal(norm([3, 3]), [1.0, 1.0]) norm.clip = [True, False] assert_array_equal(norm([3, 3]), [1.0, 1.584962500721156]) norm.clip = [True, True] with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm.clip = True with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm.clip = [True, False, True] with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm([3, 3], clip=True) with pytest.raises(ValueError, match="Expected an iterable of length 2"): norm([3, 3], clip=[True, True, True]) # test inverse assert_array_almost_equal(norm.inverse([0.5, 0.5849625007211562]), [1.5, 1.5]) def test_multi_norm_autoscale(): norm = mpl.colors.MultiNorm(['linear', 'log']) # test autoscale norm.autoscale([[0, 1, 2, 3], [0.1, 1, 2, 3]]) assert_array_equal(norm.vmin, [0, 0.1]) assert_array_equal(norm.vmax, [3, 3]) # test autoscale_none norm0 = mcolors.TwoSlopeNorm(2, vmin=0, vmax=None) norm = mcolors.MultiNorm([norm0, 'linear'], vmax=[None, 50]) norm.autoscale_None([[1, 2, 3, 4, 5], [-50, 1, 0, 1, 500]]) assert_array_equal(norm([5, 0]), [1, 0.5]) assert_array_equal(norm.vmin, (0, -50)) assert_array_equal(norm.vmax, (5, 50)) def test_mult_norm_call_types(): mn = mpl.colors.MultiNorm(['linear', 'linear']) mn.vmin = (-2, -2) mn.vmax = (2, 2) vals = np.arange(6).reshape((3,2)) target = np.ma.array([(0.5, 0.75), (1., 1.25), (1.5, 1.75)]) # test structured array as input from_mn = mn(rfn.unstructured_to_structured(vals)) assert_array_almost_equal(from_mn, target.T) # test list of arrays as input assert_array_almost_equal(mn(list(vals.T)), list(target.T)) # test list of floats as input assert_array_almost_equal(mn(list(vals[0])), list(target[0])) # test tuple of arrays as input assert_array_almost_equal(mn(tuple(vals.T)), list(target.T)) # np.arrays of shapes that are compatible assert_array_almost_equal(mn(np.zeros(2)), 0.5*np.ones(2)) assert_array_almost_equal(mn(np.zeros((2, 3))), 0.5*np.ones((2, 3))) assert_array_almost_equal(mn(np.zeros((2, 3, 4))), 0.5*np.ones((2, 3, 4))) # test with NoNorm, list as input mn_no_norm = mpl.colors.MultiNorm(['linear', mcolors.NoNorm()]) no_norm_out = mn_no_norm(list(vals.T)) assert_array_almost_equal(no_norm_out, [[0., 0.5, 1.], [1, 3, 5]]) assert no_norm_out[0].dtype == np.dtype('float64') assert no_norm_out[1].dtype == vals.dtype # test with NoNorm, structured array as input mn_no_norm = mpl.colors.MultiNorm(['linear', mcolors.NoNorm()]) no_norm_out = mn_no_norm(rfn.unstructured_to_structured(vals)) assert_array_almost_equal(no_norm_out, [[0., 0.5, 1.], [1, 3, 5]]) # test single int as input with pytest.raises(ValueError, match="component as input, but got 1 instead"): mn(1) # test list of incompatible size with pytest.raises(ValueError, match="but got a sequence with 3 elements"): mn([3, 2, 1]) # last axis matches, len(data.shape) > 2 with pytest.raises(ValueError, match=(r"`data_as_list = \[data\[..., i\] for i in " r"range\(data.shape\[-1\]\)\]`")): mn(np.zeros((3, 3, 2))) # last axis matches, len(data.shape) == 2 with pytest.raises(ValueError, match=r"You can use `data_transposed = data.T` to convert"): mn(np.zeros((3, 2))) # incompatible arrays where no relevant axis matches for data in [np.zeros(3), np.zeros((3, 2, 3))]: with pytest.raises(ValueError, match=r"but got a sequence with 3 elements"): mn(data) # test incompatible class with pytest.raises(ValueError, match="but got 5, data) mdata = mcolorizer._ensure_multivariate_data(data, 2) assert np.all(mdata["f0"].mask[:2] == 0) assert np.all(mdata["f0"].mask[2:] == 1) assert np.all(mdata["f1"].mask[:2] == 0) assert np.all(mdata["f1"].mask[2:] == 1) # test tuple of data data = [0, 1] mdata = mcolorizer._ensure_multivariate_data(data, 2) assert mdata.shape == () # test wrong input size data = [[0, 1]] with pytest.raises(ValueError, match="must contain complex numbers"): mcolorizer._ensure_multivariate_data(data, 2) data = [[0, 1]] with pytest.raises(ValueError, match="have a first dimension 3"): mcolorizer._ensure_multivariate_data(data, 3) # test input of ints as list of lists data = [[0, 0, 0], [1, 1, 1]] mdata = mcolorizer._ensure_multivariate_data(data, 2) assert mdata.shape == (3,) assert mdata.dtype.fields['f0'][0] == np.int_ assert mdata.dtype.fields['f1'][0] == np.int_ # test input of floats, ints as tuple of lists data = ([0.0, 0.0], [1, 1]) mdata = mcolorizer._ensure_multivariate_data(data, 2) assert mdata.shape == (2,) assert mdata.dtype.fields['f0'][0] == np.float64 assert mdata.dtype.fields['f1'][0] == np.int_ # test input of array of floats data = np.array([[0.0, 0, 0], [1, 1, 1]]) mdata = mcolorizer._ensure_multivariate_data(data, 2) assert mdata.shape == (3,) assert mdata.dtype.fields['f0'][0] == np.float64 assert mdata.dtype.fields['f1'][0] == np.float64 # test more input dims data = np.zeros((3, 4, 5, 6)) mdata = mcolorizer._ensure_multivariate_data(data, 3) assert mdata.shape == (4, 5, 6) def test_colorizer_multinorm_implicit(): ca = mcolorizer.Colorizer('BiOrangeBlue') ca.vmin = (0, 0) ca.vmax = (1, 1) # test call with two single values data = [0.1, 0.2] res = (0.098039, 0.149020, 0.2, 1.0) assert_array_almost_equal(ca.to_rgba(data), res) # test call with two 1d arrays data = [[0.1, 0.2], [0.3, 0.4]] res = [[0.09803922, 0.19803922, 0.29803922, 1.], [0.2, 0.3, 0.4, 1.]] assert_array_almost_equal(ca.to_rgba(data), res) # test call with two 2d arrays data = [np.linspace(0, 1, 12).reshape(3, 4), np.linspace(1, 0, 12).reshape(3, 4)] res = np.array([[[0., 0.5, 1., 1.], [0.09019608, 0.5, 0.90980392, 1.], [0.18039216, 0.5, 0.81960784, 1.], [0.27058824, 0.5, 0.72941176, 1.]], [[0.36470588, 0.5, 0.63529412, 1.], [0.45490196, 0.5, 0.54509804, 1.], [0.54509804, 0.5, 0.45490196, 1.], [0.63529412, 0.5, 0.36470588, 1.]], [[0.72941176, 0.5, 0.27058824, 1.], [0.81960784, 0.5, 0.18039216, 1.], [0.90980392, 0.5, 0.09019608, 1.], [1., 0.5, 0., 1.]]]) assert_array_almost_equal(ca.to_rgba(data), res) with pytest.raises(ValueError, match=("This MultiNorm has 2 components, " "but got a sequence with 3 elements")): ca.to_rgba([0.1, 0.2, 0.3]) with pytest.raises(ValueError, match=("This MultiNorm has 2 components, " "but got a sequence with 1 elements")): ca.to_rgba([[0.1]]) # test multivariate ca = mcolorizer.Colorizer('3VarAddA') ca.vmin = (-0.1, -0.2, -0.3) ca.vmax = (0.1, 0.2, 0.3) data = [0.1, 0.1, 0.1] res = (0.712612, 0.896847, 0.954494, 1.0) assert_array_almost_equal(ca.to_rgba(data), res) def test_colorizer_multinorm_explicit(): with pytest.raises(ValueError, match="MultiNorm must be assigned"): ca = mcolorizer.Colorizer('BiOrangeBlue', 'linear') with pytest.raises(TypeError, match=("'norm' must be an instance of matplotlib.colors.Norm" ", str or None, not a list")): ca = mcolorizer.Colorizer('viridis', ['linear', 'linear']) with pytest.raises(ValueError, match=("Invalid norm for multivariate colormap with 2 inputs")): ca = mcolorizer.Colorizer('BiOrangeBlue', ['linear', 'linear', 'log']) # valid explicit construction ca = mcolorizer.Colorizer('BiOrangeBlue', [mcolors.Normalize(), 'log']) ca.vmin = (0, 0.01) ca.vmax = (1, 1) # test call with two single values data = [0.1, 0.2] res = (0.098039, 0.374510, 0.65098, 1.) assert_array_almost_equal(ca.to_rgba(data), res) def test_invalid_cmap_n_components_zero(): class CustomColormap(mcolors.Colormap): def __init__(self): super().__init__("custom") self.n_variates = 0 with pytest.raises(ValueError, match='`n_variates` >= 1'): ca = mcolorizer.Colorizer(CustomColormap()) def test_colorizer_bivar_cmap(): ca = mcolorizer.Colorizer('BiOrangeBlue', [mcolors.Normalize(), 'log']) with pytest.raises(ValueError, match='The colormap viridis'): ca.cmap = 'viridis' cartist = mcolorizer.ColorizingArtist(ca) cartist.set_array(np.zeros((2, 4, 4))) with pytest.raises(ValueError, match='Invalid data entry for multivariate'): cartist.set_array(np.zeros((3, 4, 4))) dt = np.dtype([('x', 'f4'), ('', 'object')]) with pytest.raises(TypeError, match='converted to a sequence of floats'): cartist.set_array(np.zeros((2, 4, 4), dtype=dt)) with pytest.raises(ValueError, match='all variates must have same shape'): cartist.set_array((np.zeros(3), np.zeros(4))) # ensure masked value is propagated from input a = np.arange(3) cartist.set_array((a, np.ma.masked_where(a > 1, a))) assert np.all(cartist.get_array()['f0'].mask == np.array([0, 0, 0], dtype=bool)) assert np.all(cartist.get_array()['f1'].mask == np.array([0, 0, 1], dtype=bool)) # test clearing data cartist.set_array(None) cartist.get_array() is None def test_colorizer_multivar_cmap(): ca = mcolorizer.Colorizer('3VarAddA', [mcolors.Normalize(), mcolors.Normalize(), 'log']) cartist = mcolorizer.ColorizingArtist(ca) cartist.set_array(np.zeros((3, 5, 5))) with pytest.raises(ValueError, match='Complex numbers are incompatible with'): cartist.set_array(np.zeros((5, 5), dtype='complex128'))