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test_colors.py
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test_colors.py
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import copy
import itertools
import unittest.mock
from io import BytesIO
import numpy as np
from PIL import Image
import pytest
import base64
from numpy.testing import assert_array_equal, assert_array_almost_equal
from matplotlib import cbook, cm, cycler
import matplotlib
import matplotlib as mpl
import matplotlib.colors as mcolors
import matplotlib.colorbar as mcolorbar
import matplotlib.pyplot as plt
import matplotlib.scale as mscale
from matplotlib.testing.decorators import image_comparison, check_figures_equal
@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)
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)
lc = mcolors.ListedColormap(colorlist)
# Set some bad values for testing too
for cmap in [lsc, lc]:
cmap.set_under('r')
cmap.set_over('g')
cmap.set_bad('b')
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_register_cmap():
new_cm = mpl.colormaps["viridis"]
target = "viridis2"
with pytest.warns(
PendingDeprecationWarning,
match=r"matplotlib\.colormaps\.register\(name\)"
):
cm.register_cmap(target, new_cm)
assert mpl.colormaps[target] == new_cm
with pytest.raises(ValueError,
match="Arguments must include a name or a Colormap"):
with pytest.warns(
PendingDeprecationWarning,
match=r"matplotlib\.colormaps\.register\(name\)"
):
cm.register_cmap()
with pytest.warns(
PendingDeprecationWarning,
match=r"matplotlib\.colormaps\.unregister\(name\)"
):
cm.unregister_cmap(target)
with pytest.raises(ValueError,
match=f'{target!r} is not a valid value for name;'):
with pytest.warns(
PendingDeprecationWarning,
match=r"matplotlib\.colormaps\[name\]"
):
cm.get_cmap(target)
with pytest.warns(
PendingDeprecationWarning,
match=r"matplotlib\.colormaps\.unregister\(name\)"
):
# test that second time is error free
cm.unregister_cmap(target)
with pytest.raises(TypeError, match="'cmap' must be"):
with pytest.warns(
PendingDeprecationWarning,
match=r"matplotlib\.colormaps\.register\(name\)"
):
cm.register_cmap('nome', cmap='not a cmap')
def test_ensure_cmap():
cr = mpl.colormaps
new_cm = mcolors.ListedColormap(cr["viridis"].colors, name='v2')
# check None, str, and Colormap pass
assert cr.get_cmap('plasma') == cr["plasma"]
assert cr.get_cmap(cr["magma"]) == cr["magma"]
# check default default
assert cr.get_cmap(None) == cr[mpl.rcParams['image.cmap']]
bad_cmap = 'AardvarksAreAwkward'
with pytest.raises(KeyError, match=bad_cmap):
cr.get_cmap(bad_cmap)
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
)
with pytest.raises(ValueError, match='A colormap named "viridis"'):
with pytest.warns():
cm.register_cmap(name, mpl.colormaps[name])
with pytest.warns(UserWarning):
# TODO is warning more than once!
cm.register_cmap(name, mpl.colormaps[name], override_builtin=True)
def test_unregister_builtin_cmap():
name = "viridis"
match = f'cannot unregister {name!r} which is a builtin colormap.'
with pytest.raises(ValueError, match=match):
with pytest.warns():
cm.unregister_cmap(name)
def test_colormap_copy():
cmap = plt.cm.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)
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.cm.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)
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
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 not equal
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().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), np.array([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_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'])
cmref.set_over('black')
cmref.set_under('white')
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'])
cmref.set_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'])
cmref.set_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 double halfrange
norm.vcenter = 1
assert norm.vmin == -1.0
assert norm.vmax == 3.0
assert norm.halfrange == 2.0
@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():
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 = [0, 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]
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
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
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
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]
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_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(np.asarray(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]))
assert_array_almost_equal(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([1, 2, 3, 5])
assert norm.vmin == 0
assert norm.vmax == 5
def test_TwoSlopeNorm_scaleout_center_max():
# test the vmax never goes below vcenter
norm = mcolors.TwoSlopeNorm(vcenter=0)
norm([-1, -2, -3, -5])
assert norm.vmax == 0
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'])
def test_cmap_and_norm_from_levels_and_colors():
# Remove this line when this test image is regenerated.
plt.rcParams['pcolormesh.snap'] = False
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(baseline_images=['boundarynorm_and_colorbar'],
extensions=['png'], tol=1.0)
def test_boundarynorm_and_colorbarbase():
# Remove this line when this test image is regenerated.
plt.rcParams['pcolormesh.snap'] = False
# 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.set_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],
'Wih extend={0!r} and data '
'value={1!r}'.format(extend, d_val))
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_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'])
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', np_load=True)
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.cm.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]]