def test_domain_range_scale_XYZ_to_RLAB(self):
"""
Tests :func:`colour.appearance.rlab.XYZ_to_RLAB` definition domain and
range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_n = np.array([109.85, 100, 35.58])
Y_n = 31.83
sigma = RLAB_VIEWING_CONDITIONS['Average']
D = RLAB_D_FACTOR['Hard Copy Images']
specification = XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D)[:4]
d_r = (
('reference', 1, 1),
(1, 0.01, np.array([1, 1, 1 / 360, 1])),
(100, 1, np.array([1, 1, 100 / 360, 1])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_RLAB(XYZ * factor_a, XYZ_n * factor_a, Y_n, sigma,
D)[:4],
specification * factor_b,
decimal=7)
def test_n_dimensional_XYZ_to_RLAB(self):
"""
Test :func:`colour.appearance.rlab.XYZ_to_RLAB` definition
n-dimensional support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_n = np.array([95.05, 100.00, 108.88])
Y_n = 318.31
sigma = 0.4347
specification = XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma)
XYZ = np.tile(XYZ, (6, 1))
specification = np.tile(specification, (6, 1))
np.testing.assert_almost_equal(XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
specification,
decimal=7)
XYZ_n = np.tile(XYZ_n, (6, 1))
np.testing.assert_almost_equal(XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
specification,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_n = np.reshape(XYZ_n, (2, 3, 3))
specification = np.reshape(specification, (2, 3, 7))
np.testing.assert_almost_equal(XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
specification,
decimal=7)
def test_domain_range_scale_XYZ_to_RLAB(self):
"""
Test :func:`colour.appearance.rlab.XYZ_to_RLAB` definition domain and
range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_n = np.array([109.85, 100, 35.58])
Y_n = 31.83
sigma = VIEWING_CONDITIONS_RLAB["Average"]
D = D_FACTOR_RLAB["Hard Copy Images"]
specification = XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D)
d_r = (
("reference", 1, 1),
("1", 0.01, np.array([1, 1, 1 / 360, 1, np.nan, 1, 1])),
("100", 1, np.array([1, 1, 100 / 360, 1, np.nan, 1, 1])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_RLAB(XYZ * factor_a, XYZ_n * factor_a, Y_n, sigma,
D),
as_float_array(specification) * factor_b,
decimal=7,
)
def test_XYZ_to_RLAB(self):
"""
Test :func:`colour.appearance.rlab.XYZ_to_RLAB` 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_n = np.array([95.05, 100.00, 108.88])
Y_n = 318.31
sigma = 0.4347
np.testing.assert_allclose(
XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
np.array([49.67, 0.01, 270, 0, np.nan, 0, -0.01]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([57.06, 43.06, 31.96])
Y_n = 31.83
np.testing.assert_allclose(
XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
np.array([69.33, 49.74, 21.3, 0.72, np.nan, 46.33, 18.09]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([3.53, 6.56, 2.14])
XYZ_n = np.array([109.85, 100.00, 35.58])
Y_n = 318.31
np.testing.assert_allclose(
XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
np.array([30.78, 41.02, 176.9, 1.33, np.nan, -40.96, 2.25]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([19.01, 20.00, 21.78])
Y_n = 31.83
np.testing.assert_allclose(
XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma),
np.array([49.83, 54.87, 286.5, 1.1, np.nan, 15.57, -52.61]),
rtol=0.01,
atol=0.01,
)
def output_specification_from_data(self, data):
"""
Returns the RLAB colour appearance model output specification
from given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
RLAB_Specification
RLAB colour appearance model specification.
"""
XYZ = tstack((data['X'], data['Y'], data['Z']))
XYZ_n = tstack((data['X_n'], data['Y_n'], data['Z_n']))
specification = XYZ_to_RLAB(XYZ,
XYZ_n,
data['Y_n2'],
data['sigma'],
data['D'])
return specification
def test_nan_XYZ_to_RLAB(self):
"""
Tests :func:`colour.appearance.rlab.XYZ_to_RLAB` 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_n = np.array(case)
Y_n = case[0]
sigma = case[0]
D = case[0]
XYZ_to_RLAB(XYZ, XYZ_n, Y_n, sigma, D)
