def test_domain_range_scale_XYZ_to_ATD95(self):
"""
Tests :func:`colour.appearance.atd95.XYZ_to_ATD95` definition domain
and range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_0 = np.array([95.05, 100.00, 108.88])
Y_0 = 318.31
k_1 = 0.0
k_2 = 50.0
specification = XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2)
d_r = (
('reference', 1, 1),
(1, 0.01, np.array([1 / 360, 1, 1, 1, 1, 1, 1, 1, 1])),
(100, 1, np.array([100 / 360, 1, 1, 1, 1, 1, 1, 1, 1])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_ATD95(XYZ * factor_a, XYZ_0 * factor_a, Y_0, k_1,
k_2),
specification * factor_b,
decimal=7)
def test_n_dimensional_XYZ_to_ATD95(self):
"""
Test :func:`colour.appearance.atd95.XYZ_to_ATD95` definition
n-dimensional support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_0 = np.array([95.05, 100.00, 108.88])
Y_02 = 318.31
K_1 = 0
K_2 = 50
sigma = 300
specification = XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma)
XYZ = np.tile(XYZ, (6, 1))
specification = np.tile(specification, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
specification,
decimal=7,
)
XYZ_0 = np.tile(XYZ_0, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
specification,
decimal=7,
)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_0 = np.reshape(XYZ_0, (2, 3, 3))
specification = np.reshape(specification, (2, 3, 9))
np.testing.assert_almost_equal(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
specification,
decimal=7,
)
def test_nan_XYZ_to_ATD95(self):
"""
Tests :func:`colour.appearance.atd95.XYZ_to_ATD95` definition nan
support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=3))
for case in cases:
XYZ = np.array(case)
XYZ_0 = np.array(case)
Y_0 = np.array(case[0])
k_1 = np.array(case[0])
k_2 = np.array(case[0])
XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2)
def output_specification_from_data(self, data):
"""
Returns the *ATD (1995)* colour vision model output specification from
given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
ATD95_Specification
*ATD (1995)* colour vision model specification.
"""
XYZ = np.array([data['X'], data['Y'], data['Z']])
XYZ_0 = np.array([data['X_0'], data['Y_0'], data['Z_0']])
specification = XYZ_to_ATD95(XYZ, XYZ_0, data['Y_02'], data['K_1'],
data['K_2'], data['sigma'])
return specification
def test_XYZ_to_ATD95(self):
"""
Test :func:`colour.appearance.atd95.XYZ_to_ATD95` definition.
Notes
-----
- The test values have been generated from data of the following file
by *Fairchild (2013)*:
http://rit-mcsl.org/fairchild//files/AppModEx.xls
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_0 = np.array([95.05, 100.00, 108.88])
Y_02 = 318.31
K_1 = 0
K_2 = 50
sigma = 300
np.testing.assert_allclose(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
np.array([
1.91,
1.206,
0.1814,
0.1788,
0.0287,
0.0108,
0.0192,
0.0205,
0.0108,
]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([57.06, 43.06, 31.96])
Y_02 = 31.83
np.testing.assert_allclose(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
np.array([
63.96,
1.371,
0.2142,
0.2031,
0.068,
0.0005,
0.0224,
0.0308,
0.0005,
]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([3.53, 6.56, 2.14])
XYZ_0 = np.array([109.85, 100.00, 35.58])
Y_02 = 318.31
np.testing.assert_allclose(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
np.array([
-0.31,
0.436,
0.1075,
0.1068,
-0.011,
0.0044,
0.0106,
-0.0014,
0.0044,
]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([19.01, 20.00, 21.78])
Y_02 = 31.83
np.testing.assert_allclose(
XYZ_to_ATD95(XYZ, XYZ_0, Y_02, K_1, K_2, sigma),
np.array([
0.79,
1.091,
0.1466,
0.146,
0.0007,
0.013,
0.0152,
0.0102,
0.013,
]),
rtol=0.01,
atol=0.01,
)