def test_domain_range_scale_XYZ_to_Hunt(self):
"""
Tests :func:`colour.appearance.hunt.XYZ_to_Hunt` definition domain
and range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
XYZ_b = np.array([95.05, 100.00, 108.88])
L_A = 318.31
surround = HUNT_VIEWING_CONDITIONS['Normal Scenes']
CCT_w = 6504.0
specification = XYZ_to_Hunt(XYZ,
XYZ_w,
XYZ_b,
L_A,
surround,
CCT_w=CCT_w)[:-2]
d_r = (
('reference', 1, 1),
(1, 0.01, np.array([1, 1, 1 / 360, 1, 1, 1])),
(100, 1, np.array([1, 1, 100 / 360, 1, 1, 1])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(XYZ_to_Hunt(XYZ * factor_a,
XYZ_w * factor_a,
XYZ_b * factor_a,
L_A,
surround,
CCT_w=CCT_w)[:-2],
specification * factor_b,
decimal=7)
def test_raise_exception_CIECAM02_to_XYZ(self):
"""
Tests :func:`colour.appearance.hunt.XYZ_to_Hunt` definition raised
exception.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
XYZ_b = np.array([95.05, 100.00, 108.88])
L_A = 318.31
surround = HUNT_VIEWING_CONDITIONS['Normal Scenes']
CCT_w = 6504.0
S = S_w = 0.5
try:
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround)
except ValueError:
pass
try:
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w, S=S)
except ValueError:
pass
try:
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w, S_w=S_w)
except ValueError:
pass
def test_XYZ_to_Hunt(self):
"""
Test :func:`colour.appearance.hunt.XYZ_to_Hunt` 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_w = np.array([95.05, 100.00, 108.88])
XYZ_b = XYZ_w * np.array([1, 0.2, 1])
L_A = 318.31
surround = VIEWING_CONDITIONS_HUNT["Normal Scenes"]
CCT_w = 6504.0
np.testing.assert_allclose(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
np.array([42.12, 0.16, 269.3, 0.03, 31.92, 0.16, np.nan, np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([57.06, 43.06, 31.96])
L_A = 31.83
np.testing.assert_allclose(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
np.array(
[66.76, 63.89, 18.6, 153.36, 31.22, 58.28, np.nan, np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([3.53, 6.56, 2.14])
XYZ_w = np.array([109.85, 100.00, 35.58])
L_A = 318.31
CCT_w = 2856
np.testing.assert_allclose(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
np.array([19.56, 74.58, 178.3, 245.4, 18.9, 76.33, np.nan,
np.nan]),
rtol=0.01,
atol=0.01,
)
XYZ = np.array([19.01, 20.00, 21.78])
L_A = 31.83
np.testing.assert_allclose(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
np.array(
[40.27, 73.84, 262.8, 209.29, 22.15, 67.35, np.nan, np.nan]),
rtol=0.01,
atol=0.01,
)
def output_specification_from_data(self, data):
"""
Returns the *Hunt* colour appearance model output specification from
given data.
Parameters
----------
data : list
Fixture data.
Returns
-------
Hunt_Specification
Hunt colour appearance model specification.
"""
XYZ = tstack((data['X'], data['Y'], data['Z']))
XYZ_w = tstack((data['X_w'], data['Y_w'], data['Z_w']))
XYZ_b = tstack((data['X_w'], 0.2 * data['Y_w'], data['Z_w']))
specification = XYZ_to_Hunt(
XYZ,
XYZ_w,
XYZ_b,
data['L_A'],
Hunt_InductionFactors(data['N_c'], data['N_b']),
CCT_w=data['T'])
return specification
def test_XYZ_p_CIECAM02_to_XYZ(self):
"""
Tests :func:`colour.appearance.hunt.XYZ_to_Hunt` definition *XYZ_p*
argument handling.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
XYZ_b = XYZ_p = np.array([95.05, 100.00, 108.88])
L_A = 318.31
surround = HUNT_VIEWING_CONDITIONS['Normal Scenes']
CCT_w = 6504.0
np.testing.assert_almost_equal(XYZ_to_Hunt(
XYZ,
XYZ_w,
XYZ_b,
L_A,
surround,
XYZ_p=XYZ_p,
CCT_w=CCT_w,
)[:-2],
np.array([
30.046267861960700,
0.121050839936350,
269.273759446144600,
0.019909320692942,
22.209765491265024,
0.123896438259997,
]),
decimal=7)
def test_n_dimensional_XYZ_to_Hunt(self):
"""
Test :func:`colour.appearance.hunt.XYZ_to_Hunt` definition
n-dimensional support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
XYZ_b = XYZ_w * np.array([1, 0.2, 1])
L_A = 318.31
surround = VIEWING_CONDITIONS_HUNT["Normal Scenes"]
CCT_w = 6504.0
specification = XYZ_to_Hunt(XYZ,
XYZ_w,
XYZ_b,
L_A,
surround,
CCT_w=CCT_w)
XYZ = np.tile(XYZ, (6, 1))
specification = np.tile(specification, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
specification,
decimal=7,
)
XYZ_w = np.tile(XYZ_w, (6, 1))
XYZ_b = np.tile(XYZ_b, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
specification,
decimal=7,
)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_w = np.reshape(XYZ_w, (2, 3, 3))
XYZ_b = np.reshape(XYZ_b, (2, 3, 3))
specification = np.reshape(specification, (2, 3, 8))
np.testing.assert_almost_equal(
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w),
specification,
decimal=7,
)
def test_domain_range_scale_XYZ_to_Hunt(self):
"""
Test :func:`colour.appearance.hunt.XYZ_to_Hunt` definition domain
and range scale support.
"""
XYZ = np.array([19.01, 20.00, 21.78])
XYZ_w = np.array([95.05, 100.00, 108.88])
XYZ_b = np.array([95.05, 100.00, 108.88])
L_A = 318.31
surround = VIEWING_CONDITIONS_HUNT["Normal Scenes"]
CCT_w = 6504.0
specification = XYZ_to_Hunt(XYZ,
XYZ_w,
XYZ_b,
L_A,
surround,
CCT_w=CCT_w)
d_r = (
("reference", 1, 1),
("1", 0.01, np.array([1, 1, 1 / 360, 1, 1, 1, np.nan, np.nan])),
("100", 1, np.array([1, 1, 100 / 360, 1, 1, 1, np.nan, np.nan])),
)
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_Hunt(
XYZ * factor_a,
XYZ_w * factor_a,
XYZ_b * factor_a,
L_A,
surround,
CCT_w=CCT_w,
),
as_float_array(specification) * factor_b,
decimal=7,
)
def test_nan_XYZ_to_Hunt(self):
"""
Tests :func:`colour.appearance.hunt.XYZ_to_Hunt` 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_w = np.array(case)
XYZ_b = np.array(case)
L_A = case[0]
surround = Hunt_InductionFactors(case[0], case[0])
CCT_w = case[0]
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w)
