def corresponding_chromaticities_prediction_Fairchild1990(experiment=1,
**kwargs):
"""
Returns the corresponding chromaticities prediction for Fairchild (1990)
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2089528677990308, 0.47240345174230519)),
((0.449, 0.511), (0.43756528098582792, 0.51210303139041924)),
((0.263, 0.505), (0.26213623665658092, 0.49725385033264224)),
((0.322, 0.545), (0.3235312762825191, 0.54756652922585702)),
((0.316, 0.537), (0.3151390992740366, 0.53983332031574016)),
((0.265, 0.553), (0.26347459238415272, 0.55443357809543037)),
((0.221, 0.538), (0.22115956537655593, 0.53244703908294599)),
((0.135, 0.532), (0.13969494108553854, 0.52072342107668024)),
((0.145, 0.472), (0.1512288710743511, 0.45330415352961834)),
((0.163, 0.331), (0.17156913711903982, 0.30262647410866889)),
((0.176, 0.431), (0.18257922398137369, 0.40778921192793854)),
((0.244, 0.349), (0.24189049501108895, 0.34134012046930529))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_r = XYZ_to_xy(XYZ_r)
Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_Fairchild1990(
XYZ_1, XYZ_n, XYZ_r, Y_n)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_Fairchild1990(experiment=1,
**kwargs):
"""
Returns the corresponding chromaticities prediction for Fairchild (1990)
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2089528677990308, 0.47240345174230519)),
((0.449, 0.511), (0.43756528098582792, 0.51210303139041924)),
((0.263, 0.505), (0.26213623665658092, 0.49725385033264224)),
((0.322, 0.545), (0.3235312762825191, 0.54756652922585702)),
((0.316, 0.537), (0.3151390992740366, 0.53983332031574016)),
((0.265, 0.553), (0.26347459238415272, 0.55443357809543037)),
((0.221, 0.538), (0.22115956537655593, 0.53244703908294599)),
((0.135, 0.532), (0.13969494108553854, 0.52072342107668024)),
((0.145, 0.472), (0.1512288710743511, 0.45330415352961834)),
((0.163, 0.331), (0.17156913711903982, 0.30262647410866889)),
((0.176, 0.431), (0.18257922398137369, 0.40778921192793854)),
((0.244, 0.349), (0.24189049501108895, 0.34134012046930529))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_r = XYZ_to_xy(XYZ_r)
Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_Fairchild1990(
XYZ_1, XYZ_n, XYZ_r, Y_n)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1, **kwargs):
"""
Returns the corresponding chromaticities prediction for CMCCAT2000
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.20832101929657834, 0.47271680534693694)),
((0.449, 0.511), (0.44592707020371486, 0.50777351504395707)),
((0.263, 0.505), (0.26402624712986333, 0.4955361681706304)),
((0.322, 0.545), (0.33168840090358015, 0.54315801981008516)),
((0.316, 0.537), (0.32226245779851387, 0.53576245377085929)),
((0.265, 0.553), (0.27107058097430181, 0.5501997842556422)),
((0.221, 0.538), (0.22618269421847523, 0.52947407170848704)),
((0.135, 0.532), (0.14396930475660724, 0.51909841743126817)),
((0.145, 0.472), (0.14948357434418671, 0.45567605010224305)),
((0.163, 0.331), (0.15631720730028753, 0.31641514460738623)),
((0.176, 0.431), (0.17631993066748047, 0.41275893424542082)),
((0.244, 0.349), (0.22876382018951744, 0.3499324084859976))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CMCCAT2000(
XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
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), (0.20820148430638033, 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_CMCCAT2000(experiment=1, **kwargs):
"""
Returns the corresponding chromaticities prediction for CMCCAT2000
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.20832101929657834, 0.47271680534693694)),
((0.449, 0.511), (0.44592707020371486, 0.50777351504395707)),
((0.263, 0.505), (0.26402624712986333, 0.4955361681706304)),
((0.322, 0.545), (0.33168840090358015, 0.54315801981008516)),
((0.316, 0.537), (0.32226245779851387, 0.53576245377085929)),
((0.265, 0.553), (0.27107058097430181, 0.5501997842556422)),
((0.221, 0.538), (0.22618269421847523, 0.52947407170848704)),
((0.135, 0.532), (0.14396930475660724, 0.51909841743126817)),
((0.145, 0.472), (0.14948357434418671, 0.45567605010224305)),
((0.163, 0.331), (0.15631720730028753, 0.31641514460738623)),
((0.176, 0.431), (0.17631993066748047, 0.41275893424542082)),
((0.244, 0.349), (0.22876382018951744, 0.3499324084859976))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CMCCAT2000(
XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
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_CIE1994(experiment=1):
"""
Returns the corresponding chromaticities prediction for CIE 1994
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2133909..., 0.4939794...)),
((0.449, 0.511), (0.4450345..., 0.5120939...)),
((0.263, 0.505), (0.2693262..., 0.5083212...)),
((0.322, 0.545), (0.3308593..., 0.5443940...)),
((0.316, 0.537), (0.3225195..., 0.5377826...)),
((0.265, 0.553), (0.2709737..., 0.5513666...)),
((0.221, 0.538), (0.2280786..., 0.5351592...)),
((0.135, 0.532), (0.1439436..., 0.5303576...)),
((0.145, 0.472), (0.1500743..., 0.4842895...)),
((0.163, 0.331), (0.1559955..., 0.3772379...)),
((0.176, 0.431), (0.1806318..., 0.4518475...)),
((0.244, 0.349), (0.2454445..., 0.4018004...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
# :math:`Y_o` is set to an arbitrary value in domain [18, 100].
Y_o = 18
E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CIE1994(
XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
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_CMCCAT2000(experiment=1):
"""
Returns the corresponding chromaticities prediction for CMCCAT2000
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2083210..., 0.4727168...)),
((0.449, 0.511), (0.4459270..., 0.5077735...)),
((0.263, 0.505), (0.2640262..., 0.4955361...)),
((0.322, 0.545), (0.3316884..., 0.5431580...)),
((0.316, 0.537), (0.3222624..., 0.5357624...)),
((0.265, 0.553), (0.2710705..., 0.5501997...)),
((0.221, 0.538), (0.2261826..., 0.5294740...)),
((0.135, 0.532), (0.1439693..., 0.5190984...)),
((0.145, 0.472), (0.1494835..., 0.4556760...)),
((0.163, 0.331), (0.1563172..., 0.3164151...)),
((0.176, 0.431), (0.1763199..., 0.4127589...)),
((0.244, 0.349), (0.2287638..., 0.3499324...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CMCCAT2000(
XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
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_Fairchild1990(experiment=1):
"""
Returns the corresponding chromaticities prediction for Fairchild (1990)
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2089528..., 0.4724034...)),
((0.449, 0.511), (0.4375652..., 0.5121030...)),
((0.263, 0.505), (0.2621362..., 0.4972538...)),
((0.322, 0.545), (0.3235312..., 0.5475665...)),
((0.316, 0.537), (0.3151390..., 0.5398333...)),
((0.265, 0.553), (0.2634745..., 0.5544335...)),
((0.221, 0.538), (0.2211595..., 0.5324470...)),
((0.135, 0.532), (0.1396949..., 0.5207234...)),
((0.145, 0.472), (0.1512288..., 0.4533041...)),
((0.163, 0.331), (0.1715691..., 0.3026264...)),
((0.176, 0.431), (0.1825792..., 0.4077892...)),
((0.244, 0.349), (0.2418904..., 0.3413401...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_r = XYZ_to_xy(XYZ_r)
Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_Fairchild1990(
XYZ_1, XYZ_n, XYZ_r, Y_n)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_CIE1994(experiment=1):
"""
Returns the corresponding chromaticities prediction for CIE 1994
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2133909..., 0.4939794...)),
((0.449, 0.511), (0.4450345..., 0.5120939...)),
((0.263, 0.505), (0.2693262..., 0.5083212...)),
((0.322, 0.545), (0.3308593..., 0.5443940...)),
((0.316, 0.537), (0.3225195..., 0.5377826...)),
((0.265, 0.553), (0.2709737..., 0.5513666...)),
((0.221, 0.538), (0.2280786..., 0.5351592...)),
((0.135, 0.532), (0.1439436..., 0.5303576...)),
((0.145, 0.472), (0.1500743..., 0.4842895...)),
((0.163, 0.331), (0.1559955..., 0.3772379...)),
((0.176, 0.431), (0.1806318..., 0.4518475...)),
((0.244, 0.349), (0.2454445..., 0.4018004...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
# :math:`Y_o` is set to an arbitrary value in domain [18, 100].
Y_o = 18
E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1,
E_o2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
prediction.append(
CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
result.uvp_m, uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1):
"""
Returns the corresponding chromaticities prediction for CMCCAT2000
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2083210..., 0.4727168...)),
((0.449, 0.511), (0.4459270..., 0.5077735...)),
((0.263, 0.505), (0.2640262..., 0.4955361...)),
((0.322, 0.545), (0.3316884..., 0.5431580...)),
((0.316, 0.537), (0.3222624..., 0.5357624...)),
((0.265, 0.553), (0.2710705..., 0.5501997...)),
((0.221, 0.538), (0.2261826..., 0.5294740...)),
((0.135, 0.532), (0.1439693..., 0.5190984...)),
((0.145, 0.472), (0.1494835..., 0.4556760...)),
((0.163, 0.331), (0.1563172..., 0.3164151...)),
((0.176, 0.431), (0.1763199..., 0.4127589...)),
((0.244, 0.349), (0.2287638..., 0.3499324...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1,
L_A2)
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_Fairchild1990(experiment=1):
"""
Returns the corresponding chromaticities prediction for Fairchild (1990)
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2089528..., 0.4724034...)),
((0.449, 0.511), (0.4375652..., 0.5121030...)),
((0.263, 0.505), (0.2621362..., 0.4972538...)),
((0.322, 0.545), (0.3235312..., 0.5475665...)),
((0.316, 0.537), (0.3151390..., 0.5398333...)),
((0.265, 0.553), (0.2634745..., 0.5544335...)),
((0.221, 0.538), (0.2211595..., 0.5324470...)),
((0.135, 0.532), (0.1396949..., 0.5207234...)),
((0.145, 0.472), (0.1512288..., 0.4533041...)),
((0.163, 0.331), (0.1715691..., 0.3026264...)),
((0.176, 0.431), (0.1825792..., 0.4077892...)),
((0.244, 0.349), (0.2418904..., 0.3413401...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_r = XYZ_to_xy(XYZ_r)
Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_n, XYZ_r, Y_n)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
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_CIE1994(experiment=1, **kwargs):
"""
Returns the corresponding chromaticities prediction for CIE 1994
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.21339093279517196, 0.49397945742298016)),
((0.449, 0.511), (0.4450345313098153, 0.5120939085633327)),
((0.263, 0.505), (0.26932620724691858, 0.50832124608390727)),
((0.322, 0.545), (0.33085939370840811, 0.54439408389253441)),
((0.316, 0.537), (0.3225195584183046, 0.53778269440789594)),
((0.265, 0.553), (0.2709737181087471, 0.5513666373694861)),
((0.221, 0.538), (0.22807869730753863, 0.53515923458385406)),
((0.135, 0.532), (0.14394366662060523, 0.53035769204585748)),
((0.145, 0.472), (0.15007438031976222, 0.48428958620888679)),
((0.163, 0.331), (0.15599555781959967, 0.37723798698131394)),
((0.176, 0.431), (0.18063180902005657, 0.45184759430042898)),
((0.244, 0.349), (0.24544456656434688, 0.40180048388092021))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
# :math:`Y_o` is set to an arbitrary value in domain [18, 100].
Y_o = 18
E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CIE1994(
XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
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), (0.20820148430638033, 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_CIE1994(experiment=1, **kwargs):
"""
Returns the corresponding chromaticities prediction for CIE 1994
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.21339093279517196, 0.49397945742298016)),
((0.449, 0.511), (0.4450345313098153, 0.5120939085633327)),
((0.263, 0.505), (0.26932620724691858, 0.50832124608390727)),
((0.322, 0.545), (0.33085939370840811, 0.54439408389253441)),
((0.316, 0.537), (0.3225195584183046, 0.53778269440789594)),
((0.265, 0.553), (0.2709737181087471, 0.5513666373694861)),
((0.221, 0.538), (0.22807869730753863, 0.53515923458385406)),
((0.135, 0.532), (0.14394366662060523, 0.53035769204585748)),
((0.145, 0.472), (0.15007438031976222, 0.48428958620888679)),
((0.163, 0.331), (0.15599555781959967, 0.37723798698131394)),
((0.176, 0.431), (0.18063180902005657, 0.45184759430042898)),
((0.244, 0.349), (0.24544456656434688, 0.40180048388092021))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
# :math:`Y_o` is set to an arbitrary value in domain [18, 100].
Y_o = 18
E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CIE1994(
XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
