/usr/lib/python2.7/dist-packages/matplotlib/tests/test_colors.py is in python-matplotlib 1.4.2-3.1.
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unicode_literals)
import six
from nose.tools import assert_raises
import numpy as np
from numpy.testing.utils import assert_array_equal, assert_array_almost_equal
import matplotlib.colors as mcolors
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from matplotlib.testing.decorators import image_comparison, cleanup
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 = cm.get_cmap("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()
#print(anative.dtype.isnative, aforeign.dtype.isnative)
assert_array_equal(cmap(anative), cmap(aforeign))
def test_BoundaryNorm():
"""
Github issue #1258: interpolation was failing with numpy
1.7 pre-release.
"""
# TODO: expand this into a more general test of BoundaryNorm.
boundaries = [0, 1.1, 2.2]
vals = [-1, 0, 2, 2.2, 4]
expected = [-1, 0, 2, 3, 3]
# ncolors != len(boundaries) - 1 triggers interpolation
ncolors = len(boundaries)
bn = mcolors.BoundaryNorm(boundaries, ncolors)
assert_array_equal(bn(vals), expected)
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_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=np.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=np.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_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
def test_Normalize():
norm = mcolors.Normalize()
vals = np.arange(-10, 10, 1, dtype=np.float)
_inverse_tester(norm, vals)
_scalar_tester(norm, vals)
_mask_tester(norm, vals)
def test_SymLogNorm():
"""
Test SymLogNorm behavior
"""
norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2)
vals = np.array([-30, -1, 2, 6], dtype=np.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)
normed_vals = norm(vals)
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(baseline_images=['levels_and_colors'],
extensions=['png'])
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)
for lab in ax.get_xticklabels() + ax.get_yticklabels():
lab.set_visible(False)
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.colorConverter.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))
assert_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)))
@cleanup
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()
def test_colors_no_float():
# Gray must be a string to distinguish 3-4 grays from RGB or RGBA.
def gray_from_float_rgb():
return mcolors.colorConverter.to_rgb(0.4)
def gray_from_float_rgba():
return mcolors.colorConverter.to_rgba(0.4)
assert_raises(ValueError, gray_from_float_rgb)
assert_raises(ValueError, gray_from_float_rgba)
def test_light_source_shading_color_range():
# see also
#http://matplotlib.org/examples/pylab_examples/shading_example.html
from matplotlib.colors import LightSource
from matplotlib.colors import Normalize
refinput = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
norm = Normalize(vmin=0, vmax=50)
ls = LightSource(azdeg=0, altdeg=65)
testoutput = ls.shade(refinput, plt.cm.jet, norm=norm)
refoutput = np.array([
[[0., 0., 0.58912656, 1.],
[0., 0., 0.67825312, 1.],
[0., 0., 0.76737968, 1.],
[0., 0., 0.85650624, 1.]],
[[0., 0., 0.9456328, 1.],
[0., 0., 1., 1.],
[0., 0.04901961, 1., 1.],
[0., 0.12745098, 1., 1.]],
[[0., 0.22156863, 1., 1.],
[0., 0.3, 1., 1.],
[0., 0.37843137, 1., 1.],
[0., 0.45686275, 1., 1.]]
])
assert_array_almost_equal(refoutput, testoutput)
if __name__ == '__main__':
import nose
nose.runmodule(argv=['-s', '--with-doctest'], exit=False)
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