def test_n_dimensional_XYZ_to_LLAB(self):
"""
Test :func:`colour.appearance.llab.XYZ_to_LLAB` definition
n-dimensional support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_0 = np.array([95.05, 100.00, 108.88])
Y_b = 20
L = 318.31
surround = surround = VIEWING_CONDITIONS_LLAB[
"Reference Samples & Images, Average Surround, Subtending < 4"]
specification = XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround)
XYZ = np.tile(XYZ, (6, 1))
specification = np.tile(specification, (6, 1))
np.testing.assert_almost_equal(XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L,
surround),
specification,
decimal=7)
XYZ_0 = np.tile(XYZ_0, (6, 1))
np.testing.assert_almost_equal(XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L,
surround),
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, 8))
np.testing.assert_almost_equal(XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L,
surround),
specification,
decimal=7)
def test_XYZ_to_LLAB(self):
"""
Test :func:`colour.appearance.llab.XYZ_to_LLAB` 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
"""
with mock.patch(
"colour.appearance.llab.MATRIX_RGB_TO_XYZ_LLAB",
np.around(np.linalg.inv(llab.MATRIX_XYZ_TO_RGB_LLAB),
decimals=4),
):
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_0 = np.array([95.05, 100.00, 108.88])
Y_b = 20
L = 318.31
surround = VIEWING_CONDITIONS_LLAB[
"Reference Samples & Images, Average Surround, Subtending < 4"]
np.testing.assert_allclose(
XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround),
np.array([37.37, 0.01, 229.5, 0, 0.02, np.nan, -0.01, -0.01]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([57.06, 43.06, 31.96])
L = 31.83
np.testing.assert_allclose(
XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround),
np.array(
[61.26, 30.51, 22.3, 0.5, 56.55, np.nan, 52.33, 21.43]),
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])
L = 318.31
np.testing.assert_allclose(
XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround),
np.array(
[16.25, 30.43, 173.8, 1.87, 53.83, np.nan, -53.51, 5.83]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([19.01, 20.00, 21.78])
L = 31.83
np.testing.assert_allclose(
XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround),
np.array(
[39.82, 29.34, 271.9, 0.74, 54.59, np.nan, 1.76, -54.56]),
rtol=0.01,
atol=0.01,
)
def test_domain_range_scale_XYZ_to_LLAB(self):
"""
Test :func:`colour.appearance.llab.XYZ_to_LLAB` 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_b = 20
L = 318.31
surround = VIEWING_CONDITIONS_LLAB["ref_average_4_minus"]
specification = XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround)
d_r = (
("reference", 1, 1),
("1", 0.01, np.array([1, 1, 1 / 360, 1, 1, np.nan, 1, 1])),
("100", 1, np.array([1, 1, 100 / 360, 1, 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_LLAB(XYZ * factor_a, XYZ_0 * factor_a, Y_b, L,
surround),
as_float_array(specification) * factor_b,
decimal=7,
)
def test_domain_range_scale_XYZ_to_LLAB(self):
"""
Tests :func:`colour.appearance.llab.XYZ_to_LLAB` 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_b = 20.0
L = 318.31
surround = LLAB_VIEWING_CONDITIONS['ref_average_4_minus']
specification = XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround)[:5]
d_r = (
('reference', 1, 1),
(1, 0.01, np.array([1, 1, 1 / 360, 1, 1])),
(100, 1, np.array([1, 1, 100 / 360, 1, 1])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_LLAB(XYZ * factor_a, XYZ_0 * factor_a, Y_b, L,
surround)[:5],
specification * factor_b,
decimal=7)
def test_nan_XYZ_to_LLAB(self):
"""
Tests :func:`colour.appearance.llab.XYZ_to_LLAB` 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_b = case[0]
L = case[0]
surround = LLAB_InductionFactors(1, case[0], case[0], case[0])
XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L, surround)
def output_specification_from_data(self, data):
"""
Returns the *LLAB(l:c)* colour appearance model output specification
from given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
LLAB_Specification
*LLAB(L:c)* colour appearance 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_LLAB(
XYZ, XYZ_0, data['Y_b'], data['L'],
LLAB_InductionFactors(1, data['F_S'], data['F_L'], data['F_C']))
return specification
