def test_n_dimensional_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition n-dimensional arrays support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
XYZ_a = chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
XYZ = np.tile(XYZ, (6, 1))
XYZ_w = np.tile(XYZ_w, (6, 1))
XYZ_wr = np.tile(XYZ_wr, (6, 1))
XYZ_a = np.tile(XYZ_a, (6, 1))
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_w = np.reshape(XYZ_w, (2, 3, 3))
XYZ_wr = np.reshape(XYZ_wr, (2, 3, 3))
XYZ_a = np.reshape(XYZ_a, (2, 3, 3))
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
def test_n_dimensional_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition n-dimensional arrays support.
"""
XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
XYZ_a = np.array([0.08397461, 0.11413219, 0.28625545])
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
XYZ = np.tile(XYZ, (6, 1))
XYZ_w = np.tile(XYZ_w, (6, 1))
XYZ_wr = np.tile(XYZ_wr, (6, 1))
XYZ_a = np.tile(XYZ_a, (6, 1))
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_w = np.reshape(XYZ_w, (2, 3, 3))
XYZ_wr = np.reshape(XYZ_wr, (2, 3, 3))
XYZ_a = np.reshape(XYZ_a, (2, 3, 3))
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
def test_n_dimensional_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition n-dimensional arrays support.
"""
XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
XYZ_w = np.array([1.09846607, 1.00000000, 0.35582280])
XYZ_wr = np.array([0.95042855, 1.00000000, 1.08890037])
XYZ_a = np.array([0.08397461, 0.11413219, 0.28625545])
np.testing.assert_almost_equal(chromatic_adaptation_VonKries(
XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
XYZ = np.tile(XYZ, (6, 1))
XYZ_w = np.tile(XYZ_w, (6, 1))
XYZ_wr = np.tile(XYZ_wr, (6, 1))
XYZ_a = np.tile(XYZ_a, (6, 1))
np.testing.assert_almost_equal(chromatic_adaptation_VonKries(
XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_w = np.reshape(XYZ_w, (2, 3, 3))
XYZ_wr = np.reshape(XYZ_wr, (2, 3, 3))
XYZ_a = np.reshape(XYZ_a, (2, 3, 3))
np.testing.assert_almost_equal(chromatic_adaptation_VonKries(
XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
def test_n_dimensional_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition n-dimensional arrays support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
XYZ_a = chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
XYZ = np.tile(XYZ, (6, 1))
XYZ_w = np.tile(XYZ_w, (6, 1))
XYZ_wr = np.tile(XYZ_wr, (6, 1))
XYZ_a = np.tile(XYZ_a, (6, 1))
np.testing.assert_almost_equal(chromatic_adaptation_VonKries(
XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
XYZ_w = np.reshape(XYZ_w, (2, 3, 3))
XYZ_wr = np.reshape(XYZ_wr, (2, 3, 3))
XYZ_a = np.reshape(XYZ_a, (2, 3, 3))
np.testing.assert_almost_equal(chromatic_adaptation_VonKries(
XYZ, XYZ_w, XYZ_wr),
XYZ_a,
decimal=7)
def test_nan_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
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_wr = np.array(case)
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
def test_nan_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
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_wr = np.array(case)
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for Von Kries
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
transform : unicode, optional
{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild,
'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'},
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.20820148430638033, 0.47229226819364528)),
((0.449, 0.511), (0.44891022948064191, 0.50716028901449561)),
((0.263, 0.505), (0.26435459360846608, 0.49596314494922683)),
((0.322, 0.545), (0.33487309037107632, 0.54712207251983425)),
((0.316, 0.537), (0.32487581236911361, 0.53905899356457776)),
((0.265, 0.553), (0.27331050571632376, 0.55550280647813977)),
((0.221, 0.538), (0.22714800102072819, 0.53313179748041983)),
((0.135, 0.532), (0.14427303768336433, 0.52268044497913713)),
((0.145, 0.472), (0.14987451889726533, 0.45507852741116867)),
((0.163, 0.331), (0.15649757464732098, 0.31487959772753954)),
((0.176, 0.431), (0.17605936460371163, 0.41037722722471409)),
((0.244, 0.349), (0.22598059059292835, 0.34652914678030416))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def vs_colorimetry_data(spd_test,
spd_reference,
spds_vs,
cmfs,
chromatic_adaptation=False):
XYZ_t = spectral_to_XYZ(spd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
spd_vs = spds_vs.get(value)
XYZ_vs = spectral_to_XYZ(spd_vs, cmfs, spd_test)
XYZ_vs /= 100
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(spd_vs.name, XYZ_vs, Lab_vs, C_vs))
return vs_data
def CCT_factor(reference_data, XYZ_r):
"""
Returns the correlated colour temperature factor penalizing lamps with
extremely low correlated colour temperatures.
Parameters
----------
reference_data : VS_ColorimetryData
Reference colorimetry data.
XYZ_r : array_like
*CIE XYZ* tristimulus values for reference.
Returns
-------
numeric
Correlated colour temperature factor.
"""
xy_w = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
XYZ_w = xy_to_XYZ(xy_w)
Labs = []
for vs_colorimetry_data_ in reference_data:
_name, XYZ, _Lab, _C = vs_colorimetry_data_
XYZ_a = chromatic_adaptation_VonKries(
XYZ, XYZ_r, XYZ_w, transform='CMCCAT2000')
Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w)
Labs.append(Lab)
G_r = gamut_area(Labs) / D65_GAMUT_AREA
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for *Von Kries*
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2082014..., 0.4722922...)),
((0.449, 0.511), (0.4489102..., 0.5071602...)),
((0.263, 0.505), (0.2643545..., 0.4959631...)),
((0.322, 0.545), (0.3348730..., 0.5471220...)),
((0.316, 0.537), (0.3248758..., 0.5390589...)),
((0.265, 0.553), (0.2733105..., 0.5555028...)),
((0.221, 0.538), (0.2271480..., 0.5331317...)),
((0.135, 0.532), (0.1442730..., 0.5226804...)),
((0.145, 0.472), (0.1498745..., 0.4550785...)),
((0.163, 0.331), (0.1564975..., 0.3148795...)),
((0.176, 0.431), (0.1760593..., 0.4103772...)),
((0.244, 0.349), (0.2259805..., 0.3465291...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(
CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
result.uvp_m, uvp))
return tuple(prediction)
def chromatically_adapted_primaries(
primaries: ArrayLike,
whitepoint_t: ArrayLike,
whitepoint_r: ArrayLike,
chromatic_adaptation_transform: Union[Literal["Bianco 2010",
"Bianco PC 2010", "Bradford",
"CAT02 Brill 2008", "CAT02",
"CAT16", "CMCCAT2000",
"CMCCAT97", "Fairchild",
"Sharp", "Von Kries",
"XYZ Scaling", ],
str, ] = "CAT02",
) -> NDArray:
"""
Chromatically adapt given *primaries* :math:`xy` chromaticity coordinates
from test ``whitepoint_t`` to reference ``whitepoint_r``.
Parameters
----------
primaries
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform
*Chromatic adaptation* transform.
Returns
-------
:class:`numpy.ndarray`
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> w_t = np.array([0.31270, 0.32900])
>>> w_r = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries(p, w_t, w_r,
... chromatic_adaptation_transform)
... # doctest: +ELLIPSIS
array([[ 0.6484414..., 0.3308533...],
[ 0.3211951..., 0.5978443...],
[ 0.1558932..., 0.0660492...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(
xy_to_XYZ(primaries),
xy_to_XYZ(whitepoint_t),
xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform,
)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def test_domain_range_scale_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition domain and range scale support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
XYZ_a = chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
d_r = (('reference', 1), (1, 1), (100, 0.01))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(chromatic_adaptation_VonKries(
XYZ * factor, XYZ_w * factor, XYZ_wr * factor),
XYZ_a * factor,
decimal=7)
def test_domain_range_scale_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition domain and range scale support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
XYZ_w = np.array([0.95045593, 1.00000000, 1.08905775])
XYZ_wr = np.array([0.96429568, 1.00000000, 0.82510460])
XYZ_a = chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
d_r = (('reference', 1), (1, 1), (100, 0.01))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(XYZ * factor, XYZ_w * factor,
XYZ_wr * factor),
XYZ_a * factor,
decimal=7)
def vs_colorimetry_data(
sd_test: SpectralDistribution,
sd_reference: SpectralDistribution,
sds_vs: Dict[str, SpectralDistribution],
cmfs: MultiSpectralDistributions,
chromatic_adaptation: Boolean = False,
) -> Tuple[VS_ColorimetryData, ...]:
"""
Return the *VS test colour samples* colorimetry data.
Parameters
----------
sd_test
Test spectral distribution.
sd_reference
Reference spectral distribution.
sds_vs
*VS test colour samples* spectral distributions.
cmfs
Standard observer colour matching functions.
chromatic_adaptation
Whether to perform chromatic adaptation.
Returns
-------
:class:`tuple`
*VS test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(INDEXES_TO_NAMES_VS.items()):
sd_vs = sds_vs[value]
with domain_range_scale("1"):
XYZ_vs = sd_to_XYZ(sd_vs, cmfs, sd_test)
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform="CMCCAT2000")
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(sd_vs.name, XYZ_vs, Lab_vs, C_vs))
return tuple(vs_data)
def vs_colorimetry_data(spd_test,
spd_reference,
spds_vs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *VS test colour samples* colorimetry data.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
spd_reference : SpectralPowerDistribution
Reference spectral power distribution.
spds_vs : dict
*VS test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*VS test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
spd_vs = spds_vs.get(value)
XYZ_vs = spectral_to_XYZ(spd_vs, cmfs, spd_test)
XYZ_vs /= 100
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(
VS_ColorimetryData(spd_vs.name,
XYZ_vs,
Lab_vs,
C_vs))
return vs_data
def chromatically_adapted_primaries(primaries,
whitepoint_t,
whitepoint_r,
chromatic_adaptation_transform='CAT02'):
"""
Chromatically adapts given *primaries* :math:`xy` chromaticity coordinates
from test `whitepoint_t` to reference `whitepoint_r`.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t : array_like
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r : array_like
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> whitepoint_t = np.array([0.31271, 0.32902])
>>> whitepoint_r = np.array([0.34567, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries( # doctest: +ELLIPSIS
... p,
... whitepoint_t,
... whitepoint_r,
... chromatic_adaptation_transform)
array([[ 0.6484318..., 0.3308548...],
[ 0.3211603..., 0.5978620...],
[ 0.1558860..., 0.0660431...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(
xy_to_XYZ(primaries),
xy_to_XYZ(whitepoint_t),
xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def vs_colorimetry_data(sd_test,
sd_reference,
sds_vs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *VS test colour samples* colorimetry data.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
sd_reference : SpectralDistribution
Reference spectral distribution.
sds_vs : dict
*VS test colour samples* spectral distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*VS test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
sd_vs = sds_vs[value]
with domain_range_scale('1'):
XYZ_vs = sd_to_XYZ(sd_vs, cmfs, sd_test)
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(sd_vs.name, XYZ_vs, Lab_vs, C_vs))
return vs_data
def chromatically_adapted_primaries(primaries,
whitepoint_t,
whitepoint_r,
chromatic_adaptation_transform='CAT02'):
"""
Chromatically adapts given *primaries* :math:`xy` chromaticity coordinates
from test ``whitepoint_t`` to reference ``whitepoint_r``.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t : array_like
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r : array_like
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02 Brill 2008',
'Bianco 2010', 'Bianco PC 2010'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> w_t = np.array([0.31270, 0.32900])
>>> w_r = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries(p, w_t, w_r,
... chromatic_adaptation_transform)
... # doctest: +ELLIPSIS
array([[ 0.6484414..., 0.3308533...],
[ 0.3211951..., 0.5978443...],
[ 0.1558932..., 0.0660492...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(xy_to_XYZ(primaries),
xy_to_XYZ(whitepoint_t),
xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def chromatically_adapted_primaries(primaries,
whitepoint_t,
whitepoint_r,
chromatic_adaptation_transform='CAT02'):
"""
Chromatically adapts given *primaries* :math:`xy` chromaticity coordinates
from test ``whitepoint_t`` to reference ``whitepoint_r``.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t : array_like
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r : array_like
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> w_t = np.array([0.31270, 0.32900])
>>> w_r = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries(p, w_t, w_r,
... chromatic_adaptation_transform)
... # doctest: +ELLIPSIS
array([[ 0.6484414..., 0.3308533...],
[ 0.3211951..., 0.5978443...],
[ 0.1558932..., 0.0660492...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(
xy_to_XYZ(primaries), xy_to_XYZ(whitepoint_t), xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def test_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition.
"""
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460])),
np.array([0.21638819, 0.12570000, 0.03847494]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.14222010, 0.23042768, 0.10495772]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([1.09846607, 1.00000000, 0.35582280])),
np.array([0.18673833, 0.23111171, 0.03285972]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07818780, 0.06157201, 0.28099326]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.99144661, 1.00000000, 0.67315942])),
np.array([0.06385467, 0.05509729, 0.17506386]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460]),
transform='XYZ Scaling'),
np.array([0.20954755, 0.12197225, 0.03891917]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460]),
transform='Bradford'),
np.array([0.21666003, 0.12604777, 0.03855068]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460]),
transform='Von Kries'),
np.array([0.21394049, 0.12262315, 0.03891917]),
decimal=7)
def test_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition.
"""
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.10080000, 0.09558313]),
np.array([1.09846607, 1.00000000, 0.35582280]),
np.array([0.95042855, 1.00000000, 1.08890037])),
np.array([0.08397461, 0.11413219, 0.28625545]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.47097710, 0.34950000, 0.11301649]),
np.array([0.99092745, 1.00000000, 0.85313273]),
np.array([1.01679082, 1.00000000, 0.67610122])),
np.array([0.49253636, 0.35555812, 0.08860435]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.25506814, 0.19150000, 0.08849752]),
np.array([0.98070597, 1.00000000, 1.18224949]),
np.array([0.92833635, 1.00000000, 1.03664720])),
np.array([0.24731314, 0.19137674, 0.07734837]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.10080000, 0.09558313]),
np.array([1.09846607, 1.00000000, 0.35582280]),
np.array([0.95042855, 1.00000000, 1.08890037]),
transform='XYZ Scaling'),
np.array([0.06099486, 0.10080000, 0.29250657]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.10080000, 0.09558313]),
np.array([1.09846607, 1.00000000, 0.35582280]),
np.array([0.95042855, 1.00000000, 1.08890037]),
transform='Bradford'),
np.array([0.08540328, 0.11401229, 0.29721491]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.10080000, 0.09558313]),
np.array([1.09846607, 1.00000000, 0.35582280]),
np.array([0.95042855, 1.00000000, 1.08890037]),
transform='Von Kries'),
np.array([0.08357823, 0.10214289, 0.29250657]),
decimal=7)
def test_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonrkies.chromatic_adaptation_VonKries`
definition.
"""
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.1008, 0.09558313]),
np.array([1.09846607, 1., 0.3558228]),
np.array([0.95042855, 1., 1.08890037])),
np.array([0.08397461, 0.11413219, 0.28625545]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.4709771, 0.3495, 0.11301649]),
np.array([0.99092745, 1., 0.85313273]),
np.array([1.01679082, 1., 0.67610122])),
np.array([0.49253636, 0.35555812, 0.08860435]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.25506814, 0.1915, 0.08849752]),
np.array([0.98070597, 1., 1.18224949]),
np.array([0.92833635, 1., 1.0366472])),
np.array([0.24731314, 0.19137674, 0.07734837]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.1008, 0.09558313]),
np.array([1.09846607, 1., 0.3558228]),
np.array([0.95042855, 1., 1.08890037]),
transform='XYZ Scaling'),
np.array([0.06099486, 0.1008, 0.29250657]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.1008, 0.09558313]),
np.array([1.09846607, 1., 0.3558228]),
np.array([0.95042855, 1., 1.08890037]),
transform='Bradford'),
np.array([0.08540328, 0.11401229, 0.29721491]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07049534, 0.1008, 0.09558313]),
np.array([1.09846607, 1., 0.3558228]),
np.array([0.95042855, 1., 1.08890037]),
transform='Von Kries'),
np.array([0.08357823, 0.10214289, 0.29250657]),
decimal=7)
def test_chromatic_adaptation_VonKries(self):
"""
Tests :func:`colour.adaptation.vonkries.chromatic_adaptation_VonKries`
definition.
"""
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460])),
np.array([0.21638819, 0.12570000, 0.03847494]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.14222010, 0.23042768, 0.10495772]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([1.09846607, 1.00000000, 0.35582280])),
np.array([0.18673833, 0.23111171, 0.03285972]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.07818780, 0.06157201, 0.28099326]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.99144661, 1.00000000, 0.67315942])),
np.array([0.06385467, 0.05509729, 0.17506386]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460]),
transform='XYZ Scaling'),
np.array([0.20954755, 0.12197225, 0.03891917]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460]),
transform='Bradford'),
np.array([0.21666003, 0.12604777, 0.03855068]),
decimal=7)
np.testing.assert_almost_equal(
chromatic_adaptation_VonKries(
np.array([0.20654008, 0.12197225, 0.05136952]),
np.array([0.95045593, 1.00000000, 1.08905775]),
np.array([0.96429568, 1.00000000, 0.82510460]),
transform='Von Kries'),
np.array([0.21394049, 0.12262315, 0.03891917]),
decimal=7)
def CCT_factor(reference_data, XYZ_r):
xy_w = ILLUMINANTS.get('CIE 1931 2 Degree Standard Observer').get('D65')
XYZ_w = xy_to_XYZ(xy_w)
Labs = []
for vs_colorimetry_data_ in reference_data:
_name, XYZ, _Lab, _C = vs_colorimetry_data_
XYZ_a = chromatic_adaptation_VonKries(XYZ,
XYZ_r,
XYZ_w,
transform='CMCCAT2000')
Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w)
Labs.append(Lab)
G_r = gamut_area(Labs) / D65_GAMUT_AREA
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def CCT_factor(reference_data: Tuple[VS_ColorimetryData, ...],
XYZ_r: ArrayLike) -> Floating:
"""
Return the correlated colour temperature factor penalizing lamps with
extremely low correlated colour temperatures.
Parameters
----------
reference_data
Reference colorimetry data.
XYZ_r
*CIE XYZ* tristimulus values for reference.
Returns
-------
:class:`numpy.floating`
Correlated colour temperature factor.
"""
xy_w = CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"]
XYZ_w = xy_to_XYZ(xy_w)
Lab = XYZ_to_Lab(
chromatic_adaptation_VonKries(
[colorimetry_data.XYZ for colorimetry_data in reference_data],
XYZ_r,
XYZ_w,
transform="CMCCAT2000",
),
illuminant=xy_w,
)
G_r = gamut_area(Lab) / GAMUT_AREA_D65
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def CCT_factor(reference_data, XYZ_r):
"""
Returns the correlated colour temperature factor penalizing lamps with
extremely low correlated colour temperatures.
Parameters
----------
reference_data : VS_ColorimetryData
Reference colorimetry data.
XYZ_r : array_like
*CIE XYZ* tristimulus values for reference.
Returns
-------
numeric
Correlated colour temperature factor.
"""
xy_w = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
XYZ_w = xy_to_XYZ(xy_w)
Labs = []
for vs_colorimetry_data_ in reference_data:
_name, XYZ, _Lab, _C = vs_colorimetry_data_
XYZ_a = chromatic_adaptation_VonKries(XYZ,
XYZ_r,
XYZ_w,
transform='CMCCAT2000')
Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w)
Labs.append(Lab)
G_r = gamut_area(Labs) / D65_GAMUT_AREA
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for Von Kries
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.20820148430638033, 0.47229226819364528)),
((0.449, 0.511), (0.44891022948064191, 0.50716028901449561)),
((0.263, 0.505), (0.26435459360846608, 0.49596314494922683)),
((0.322, 0.545), (0.33487309037107632, 0.54712207251983425)),
((0.316, 0.537), (0.32487581236911361, 0.53905899356457776)),
((0.265, 0.553), (0.27331050571632376, 0.55550280647813977)),
((0.221, 0.538), (0.22714800102072819, 0.53313179748041983)),
((0.135, 0.532), (0.14427303768336433, 0.52268044497913713)),
((0.145, 0.472), (0.14987451889726533, 0.45507852741116867)),
((0.163, 0.331), (0.15649757464732098, 0.31487959772753954)),
((0.176, 0.431), (0.17605936460371163, 0.41037722722471409)),
((0.244, 0.349), (0.22598059059292835, 0.34652914678030416))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for Von Kries
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2082014..., 0.4722922...)),
((0.449, 0.511), (0.4489102..., 0.5071602...)),
((0.263, 0.505), (0.2643545..., 0.4959631...)),
((0.322, 0.545), (0.3348730..., 0.5471220...)),
((0.316, 0.537), (0.3248758..., 0.5390589...)),
((0.265, 0.553), (0.2733105..., 0.5555028...)),
((0.221, 0.538), (0.2271480..., 0.5331317...)),
((0.135, 0.532), (0.1442730..., 0.5226804...)),
((0.145, 0.472), (0.1498745..., 0.4550785...)),
((0.163, 0.331), (0.1564975..., 0.3148795...)),
((0.176, 0.431), (0.1760593..., 0.4103772...)),
((0.244, 0.349), (0.2259805..., 0.3465291...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for *Von Kries*
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer or CorrespondingColourDataset, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Fairchild2013t`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2082014..., 0.4722922...])),
(array([ 0.449, 0.511]), array([ 0.4489102..., 0.5071602...])),
(array([ 0.263, 0.505]), array([ 0.2643545..., 0.4959631...])),
(array([ 0.322, 0.545]), array([ 0.3348730..., 0.5471220...])),
(array([ 0.316, 0.537]), array([ 0.3248758..., 0.5390589...])),
(array([ 0.265, 0.553]), array([ 0.2733105..., 0.5555028...])),
(array([ 0.221, 0.538]), array([ 0.227148 ..., 0.5331318...)),
(array([ 0.135, 0.532]), array([ 0.1442730..., 0.5226804...])),
(array([ 0.145, 0.472]), array([ 0.1498745..., 0.4550785...])),
(array([ 0.163, 0.331]), array([ 0.1564975..., 0.3148796...])),
(array([ 0.176, 0.431]), array([ 0.1760593..., 0.4103772...])),
(array([ 0.244, 0.349]), array([ 0.2259805..., 0.3465291...]))]
"""
experiment_results = (convert_experiment_results_Breneman1987(experiment)
if is_numeric(experiment) else experiment)
with domain_range_scale(1):
XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
return tuple([
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t))
])
def corresponding_chromaticities_prediction_VonKries(
experiment: Union[Literal[1, 2, 3, 4, 6, 8, 9, 11, 12],
CorrespondingColourDataset] = 1,
transform: Union[Literal["Bianco 2010", "Bianco PC 2010", "Bradford",
"CAT02 Brill 2008", "CAT02", "CAT16",
"CMCCAT2000", "CMCCAT97", "Fairchild", "Sharp",
"Von Kries", "XYZ Scaling", ], str, ] = "CAT02",
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for *Von Kries*
chromatic adaptation model using given transform.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
transform
Chromatic adaptation transform.
Returns
-------
:class:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Fairchild2013t`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2082014..., 0.4722922...])),
(array([ 0.449, 0.511]), array([ 0.4489102..., 0.5071602...])),
(array([ 0.263, 0.505]), array([ 0.2643545..., 0.4959631...])),
(array([ 0.322, 0.545]), array([ 0.3348730..., 0.5471220...])),
(array([ 0.316, 0.537]), array([ 0.3248758..., 0.5390589...])),
(array([ 0.265, 0.553]), array([ 0.2733105..., 0.5555028...])),
(array([ 0.221, 0.538]), array([ 0.227148 ..., 0.5331318...)),
(array([ 0.135, 0.532]), array([ 0.1442730..., 0.5226804...])),
(array([ 0.145, 0.472]), array([ 0.1498745..., 0.4550785...])),
(array([ 0.163, 0.331]), array([ 0.1564975..., 0.3148796...])),
(array([ 0.176, 0.431]), array([ 0.1760593..., 0.4103772...])),
(array([ 0.244, 0.349]), array([ 0.2259805..., 0.3465291...]))]
"""
experiment_results = (experiment if isinstance(
experiment, CorrespondingColourDataset) else
convert_experiment_results_Breneman1987(experiment))
with domain_range_scale("1"):
XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
return tuple(
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t)))
