"""
Corresponding Chromaticities Prediction
=======================================
Defines the objects to compute corresponding chromaticities prediction.
References
----------
- :cite:`Breneman1987b` : Breneman, E. J. (1987). Corresponding
chromaticities for different states of adaptation to complex visual fields.
Journal of the Optical Society of America A, 4(6), 1115.
doi:10.1364/JOSAA.4.001115
- :cite:`CIETC1-321994b` : CIE TC 1-32. (1994). CIE 109-1994 A Method of
Predicting Corresponding Colours under Different Chromatic and Illuminance
Adaptations. Commission Internationale de l'Eclairage.
ISBN:978-3-900734-51-0
- :cite:`Fairchild1991a` : Fairchild, M. D. (1991). Formulation and testing
of an incomplete-chromatic-adaptation model. Color Research & Application,
16(4), 243-250. doi:10.1002/col.5080160406
- :cite:`Fairchild2013s` : Fairchild, M. D. (2013). FAIRCHILD'S 1990 MODEL.
In Color Appearance Models (3rd ed., pp. 4418-4495). Wiley. ISBN:B00DAYO8E2
- :cite:`Fairchild2013t` : Fairchild, M. D. (2013). Chromatic Adaptation
Models. In Color Appearance Models (3rd ed., pp. 4179-4252). Wiley.
ISBN:B00DAYO8E2
- :cite:`Li2002a` : Li, C., Luo, M. R., Rigg, B., & Hunt, R. W. G. (2002).
CMC 2000 chromatic adaptation transform: CMCCAT2000. Color Research &
Application, 27(1), 49-58. doi:10.1002/col.10005
- :cite:`Luo1999` : Luo, M. Ronnier, & Rhodes, P. A. (1999).
Corresponding-colour datasets. Color Research & Application, 24(4),
295-296. doi:10.1002/(SICI)1520-6378(199908)24:4<295::AID-COL10>3.0.CO;2-K
- :cite:`Westland2012k` : Westland, S., Ripamonti, C., & Cheung, V. (2012).
CMCCAT2000. In Computational Colour Science Using MATLAB (2nd ed., pp.
83-86). ISBN:978-0-470-66569-5
- :cite:`Zhai2018` : Zhai, Q., & Luo, M. R. (2018). Study of chromatic
adaptation via neutral white matches on different viewing media. Optics
Express, 26(6), 7724. doi:10.1364/OE.26.007724
"""
from __future__ import annotations
from collections import namedtuple
import numpy as np
from colour.adaptation import (
chromatic_adaptation_CIE1994,
chromatic_adaptation_CMCCAT2000,
chromatic_adaptation_Fairchild1990,
chromatic_adaptation_VonKries,
chromatic_adaptation_Zhai2018,
)
from colour.corresponding import (
BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES,
BRENEMAN_EXPERIMENTS,
)
from colour.hints import (
Any,
ArrayLike,
Literal,
LiteralChromaticAdaptationTransform,
Tuple,
)
from colour.models import (
Luv_to_uv,
Luv_uv_to_xy,
XYZ_to_Luv,
XYZ_to_xy,
xy_to_XYZ,
xyY_to_XYZ,
)
from colour.utilities import (
CanonicalMapping,
as_float_scalar,
attest,
domain_range_scale,
filter_kwargs,
full,
)
__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - https://opensource.org/licenses/BSD-3-Clause"
__maintainer__ = "Colour Developers"
__email__ = "colour-developers@colour-science.org"
__status__ = "Production"
__all__ = [
"CorrespondingColourDataset",
"CorrespondingChromaticitiesPrediction",
"convert_experiment_results_Breneman1987",
"corresponding_chromaticities_prediction_Fairchild1990",
"corresponding_chromaticities_prediction_CIE1994",
"corresponding_chromaticities_prediction_CMCCAT2000",
"corresponding_chromaticities_prediction_VonKries",
"corresponding_chromaticities_prediction_Zhai2018",
"CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS",
"corresponding_chromaticities_prediction",
]
[docs]
class CorrespondingColourDataset(
namedtuple(
"CorrespondingColourDataset",
(
"name",
"XYZ_r",
"XYZ_t",
"XYZ_cr",
"XYZ_ct",
"Y_r",
"Y_t",
"B_r",
"B_t",
"metadata",
),
)
):
"""
Define a corresponding colour dataset.
Parameters
----------
name
Corresponding colour dataset name.
XYZ_r
*CIE XYZ* tristimulus values of the reference illuminant.
XYZ_t
*CIE XYZ* tristimulus values of the test illuminant.
XYZ_cr
Corresponding *CIE XYZ* tristimulus values under the reference
illuminant.
XYZ_ct
Corresponding *CIE XYZ* tristimulus values under the test illuminant.
Y_r
Reference white luminance :math:`Y_r` in :math:`cd/m^2`.
Y_t
Test white luminance :math:`Y_t` in :math:`cd/m^2`.
B_r
Luminance factor :math:`B_r` of reference achromatic background as
percentage.
B_t
Luminance factor :math:`B_t` of test achromatic background as
percentage.
metadata
Dataset metadata.
Notes
-----
- This class is compatible with *Luo and Rhodes (1999)*
*Corresponding-Colour Datasets* datasets.
References
----------
:cite:`Luo1999`
"""
[docs]
class CorrespondingChromaticitiesPrediction(
namedtuple(
"CorrespondingChromaticitiesPrediction",
("name", "uv_t", "uv_m", "uv_p"),
)
):
"""
Define a chromatic adaptation model prediction.
Parameters
----------
name
Test colour name.
uv_t
Chromaticity coordinates :math:`uv_t^p` of test colour.
uv_m
Chromaticity coordinates :math:`uv_m^p` of matching colour.
uv_p
Chromaticity coordinates :math:`uv_p^p` of predicted colour.
"""
def convert_experiment_results_Breneman1987(
experiment: Literal[1, 2, 3, 4, 6, 8, 9, 11, 12],
) -> CorrespondingColourDataset:
"""
Convert *Breneman (1987)* experiment results to a
:class:`colour.CorrespondingColourDataset` class instance.
Parameters
----------
experiment
*Breneman (1987)* experiment number.
Returns
-------
:class:`colour.CorrespondingColourDataset`
:class:`colour.CorrespondingColourDataset` class instance.
Examples
--------
>>> from pprint import pprint
>>> pprint(tuple(convert_experiment_results_Breneman1987(2)))
... # doctest: +ELLIPSIS
(2,
array([ 0.9582463..., 1. , 0.9436325...]),
array([ 0.9587332..., 1. , 0.4385796...]),
array([[ 388.125 , 405. , 345.625 ],
[ 266.8957925..., 135. , 28.5983365...],
[ 474.5717821..., 405. , 222.75 ...],
[ 538.3899082..., 405. , 24.8944954...],
[ 178.7430167..., 135. , 19.6089385...],
[ 436.6749547..., 405. , 26.5483725...],
[ 124.7746282..., 135. , 36.1965613...],
[ 77.0794172..., 135. , 60.5850563...],
[ 279.9390889..., 405. , 455.8395127...],
[ 149.5808157..., 135. , 498.7046827...],
[ 372.1113689..., 405. , 669.9883990...],
[ 212.3638968..., 135. , 414.6704871...]]),
array([[ 400.1039651..., 405. , 191.7287234...],
[ 271.0384615..., 135. , 13.5 ...],
[ 495.4705323..., 405. , 119.7290874...],
[ 580.7967033..., 405. , 6.6758241...],
[ 190.1933701..., 135. , 7.4585635...],
[ 473.7184115..., 405. , 10.2346570...],
[ 135.4936014..., 135. , 20.2376599...],
[ 86.4689781..., 135. , 35.2281021...],
[ 283.5396281..., 405. , 258.1775929...],
[ 119.7044335..., 135. , 282.6354679...],
[ 359.9532224..., 405. , 381.0031185...],
[ 181.8271461..., 135. , 204.0661252...]]),
1500.0,
1500.0,
0.3,
0.3,
{})
"""
valid_experiment_results = [1, 2, 3, 4, 6, 8, 9, 11, 12]
attest(
experiment in valid_experiment_results,
f'"Breneman (1987)" experiment result is invalid, it must be one of '
f'"{valid_experiment_results}"!',
)
samples_luminance = [
0.270,
0.090,
0.270,
0.270,
0.090,
0.270,
0.090,
0.090,
0.270,
0.090,
0.270,
0.090,
]
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminant_chromaticities = experiment_results.pop(0)
Y_r = Y_t = as_float_scalar(
BRENEMAN_EXPERIMENT_PRIMARIES_CHROMATICITIES[experiment].Y
)
B_r = B_t = 0.3
XYZ_t, XYZ_r = (
xy_to_XYZ(
np.hstack(
[
Luv_uv_to_xy(illuminant_chromaticities[1:3]),
full((2, 1), Y_r),
]
)
)
/ Y_r
)
xyY_cr, xyY_ct = [], []
for i, experiment_result in enumerate(experiment_results):
xyY_cr.append(
np.hstack(
[
Luv_uv_to_xy(experiment_result[2]),
samples_luminance[i] * Y_r,
]
)
)
xyY_ct.append(
np.hstack(
[
Luv_uv_to_xy(experiment_result[1]),
samples_luminance[i] * Y_t,
]
)
)
XYZ_cr = xyY_to_XYZ(xyY_cr)
XYZ_ct = xyY_to_XYZ(xyY_ct)
return CorrespondingColourDataset(
experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct, Y_r, Y_t, B_r, B_t, {}
)
[docs]
def corresponding_chromaticities_prediction_Fairchild1990(
experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for *Fairchild (1990)*
chromatic adaptation model.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
Returns
-------
:class:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2089528..., 0.4724034...])),
(array([ 0.449, 0.511]), array([ 0.4375652..., 0.5121030...])),
(array([ 0.263, 0.505]), array([ 0.2621362..., 0.4972538...])),
(array([ 0.322, 0.545]), array([ 0.3235312..., 0.5475665...])),
(array([ 0.316, 0.537]), array([ 0.3151391..., 0.5398333...])),
(array([ 0.265, 0.553]), array([ 0.2634745..., 0.5544335...])),
(array([ 0.221, 0.538]), array([ 0.2211595..., 0.5324470...])),
(array([ 0.135, 0.532]), array([ 0.1396949..., 0.5207234...])),
(array([ 0.145, 0.472]), array([ 0.1512288..., 0.4533041...])),
(array([ 0.163, 0.331]), array([ 0.1715691..., 0.3026264...])),
(array([ 0.176, 0.431]), array([ 0.1825792..., 0.4077892...])),
(array([ 0.244, 0.349]), array([ 0.2418905..., 0.3413401...]))]
"""
experiment_results = (
experiment
if isinstance(experiment, CorrespondingColourDataset)
else convert_experiment_results_Breneman1987(experiment)
)
with domain_range_scale("1"):
XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_t, xy_r = XYZ_to_xy([XYZ_t, XYZ_r])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_t), xy_t)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_r), xy_r)
Y_n = experiment_results.Y_t
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_t, XYZ_r, Y_n)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
return tuple(
CorrespondingChromaticitiesPrediction(
experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
)
for i in range(len(uv_t))
)
[docs]
def corresponding_chromaticities_prediction_CIE1994(
experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for *CIE 1994*
chromatic adaptation model.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
Returns
-------
:class:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`CIETC1-321994b`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2273130..., 0.5267609...])),
(array([ 0.449, 0.511]), array([ 0.4612181..., 0.5191849...])),
(array([ 0.263, 0.505]), array([ 0.2872404..., 0.5306938...])),
(array([ 0.322, 0.545]), array([ 0.3489822..., 0.5454398...])),
(array([ 0.316, 0.537]), array([ 0.3371612..., 0.5421567...])),
(array([ 0.265, 0.553]), array([ 0.2889416..., 0.5534074...])),
(array([ 0.221, 0.538]), array([ 0.2412195..., 0.5464301...])),
(array([ 0.135, 0.532]), array([ 0.1530344..., 0.5488239...])),
(array([ 0.145, 0.472]), array([ 0.1568709..., 0.5258835...])),
(array([ 0.163, 0.331]), array([ 0.1499762..., 0.4401747...])),
(array([ 0.176, 0.431]), array([ 0.1876711..., 0.5039627...])),
(array([ 0.244, 0.349]), array([ 0.2560012..., 0.4546263...]))]
"""
experiment_results = (
experiment
if isinstance(experiment, CorrespondingColourDataset)
else convert_experiment_results_Breneman1987(experiment)
)
with domain_range_scale("1"):
XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_o1, xy_o2 = XYZ_to_xy([XYZ_t, XYZ_r])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_o1), xy_o1)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_o2), xy_o2)
Y_r = experiment_results.B_r
E_o1, E_o2 = experiment_results.Y_t, experiment_results.Y_r
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_r, E_o1, E_o2)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
return tuple(
CorrespondingChromaticitiesPrediction(
experiment_results.name, uv_t[i], uv_m[i], uv_p[i]
)
for i in range(len(uv_t))
)
[docs]
def corresponding_chromaticities_prediction_CMCCAT2000(
experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for *CMCCAT2000*
chromatic adaptation model.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
Returns
-------
:class:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Li2002a`, :cite:`Westland2012k`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2083210..., 0.4727168...])),
(array([ 0.449, 0.511]), array([ 0.4459270..., 0.5077735...])),
(array([ 0.263, 0.505]), array([ 0.2640262..., 0.4955361...])),
(array([ 0.322, 0.545]), array([ 0.3316884..., 0.5431580...])),
(array([ 0.316, 0.537]), array([ 0.3222624..., 0.5357624...])),
(array([ 0.265, 0.553]), array([ 0.2710705..., 0.5501997...])),
(array([ 0.221, 0.538]), array([ 0.2261826..., 0.5294740...])),
(array([ 0.135, 0.532]), array([ 0.1439693..., 0.5190984...])),
(array([ 0.145, 0.472]), array([ 0.1494835..., 0.4556760...])),
(array([ 0.163, 0.331]), array([ 0.1563172..., 0.3164151...])),
(array([ 0.176, 0.431]), array([ 0.1763199..., 0.4127589...])),
(array([ 0.244, 0.349]), array([ 0.2287638..., 0.3499324...]))]
"""
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)
L_A1 = experiment_results.Y_t
L_A2 = experiment_results.Y_r
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
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))
)
[docs]
def corresponding_chromaticities_prediction_VonKries(
experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
transform: LiteralChromaticAdaptationTransform | 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))
)
def corresponding_chromaticities_prediction_Zhai2018(
experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
D_b: ArrayLike = 1,
D_d: ArrayLike = 1,
XYZ_wo: ArrayLike = np.array([1, 1, 1]),
transform: Literal["CAT02", "CAT16"] | str = "CAT02",
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for
*Zhai and Luo (2018)* chromatic adaptation model using given transform.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
D_b
Degree of adaptation :math:`D_{\\beta}` of input illuminant
:math:`\\beta`.
D_d
Degree of adaptation :math:`D_{\\delta}` of output illuminant
:math:`\\delta`.
XYZ_wo
Baseline illuminant (:math:`BI`) :math:`o`.
transform
Chromatic adaptation transform.
Returns
-------
:class:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Zhai2018`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Zhai2018(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2082238..., 0.4727943...])),
(array([ 0.449, 0.511]), array([ 0.4474691..., 0.5076681...])),
(array([ 0.263, 0.505]), array([ 0.2640379..., 0.4954003...])),
(array([ 0.322, 0.545]), array([ 0.3336937..., 0.5435500...])),
(array([ 0.316, 0.537]), array([ 0.3238490..., 0.5359889...])),
(array([ 0.265, 0.553]), array([ 0.2724846..., 0.5506939...])),
(array([ 0.221, 0.538]), array([ 0.2267596..., 0.5295259...])),
(array([ 0.135, 0.532]), array([ 0.1443208..., 0.5190035...])),
(array([ 0.145, 0.472]), array([ 0.1500723..., 0.4561352...])),
(array([ 0.163, 0.331]), array([ 0.1570902..., 0.3245137...])),
(array([ 0.176, 0.431]), array([ 0.1763887..., 0.4146000...])),
(array([ 0.244, 0.349]), array([ 0.2267005..., 0.3551480...]))]
"""
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_Zhai2018(
XYZ_1, XYZ_w, XYZ_wr, D_b, D_d, XYZ_wo, 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))
)
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS = CanonicalMapping(
{
"CIE 1994": corresponding_chromaticities_prediction_CIE1994,
"CMCCAT2000": corresponding_chromaticities_prediction_CMCCAT2000,
"Fairchild 1990": corresponding_chromaticities_prediction_Fairchild1990,
"Von Kries": corresponding_chromaticities_prediction_VonKries,
"Zhai 2018": corresponding_chromaticities_prediction_Zhai2018,
}
)
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS.__doc__ = """
Aggregated corresponding chromaticities prediction models.
References
----------
:cite:`Breneman1987b`, :cite:`CIETC1-321994b`, :cite:`Fairchild1991a`,
:cite:`Fairchild2013s`, :cite:`Fairchild2013t`, :cite:`Li2002a`,
:cite:`Westland2012k`, :cite:`Zhai2018`
Aliases:
- 'vonkries': 'Von Kries'
"""
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS[
"vonkries"
] = CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS["Von Kries"]
[docs]
def corresponding_chromaticities_prediction(
experiment: (Literal[1, 2, 3, 4, 6, 8, 9, 11, 12] | CorrespondingColourDataset) = 1,
model: (
Literal[
"CIE 1994",
"CMCCAT2000",
"Fairchild 1990",
"Von Kries",
"Zhai 2018",
]
| str
) = "Von Kries",
**kwargs: Any,
) -> Tuple[CorrespondingChromaticitiesPrediction, ...]:
"""
Return the corresponding chromaticities prediction for given chromatic
adaptation model.
Parameters
----------
experiment
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
model
Chromatic adaptation model.
Other Parameters
----------------
D_b
{:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
Degree of adaptation :math:`D_{\\beta}` of input illuminant
:math:`\\beta`.
D_d
{:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
Degree of adaptation :math:`D_{\\delta}` of output illuminant
:math:`\\delta`.
transform
{:func:`colour.corresponding.\
corresponding_chromaticities_prediction_VonKries`,
:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
Chromatic adaptation transform.
XYZ_wo
{:func:`colour.corresponding.\
corresponding_chromaticities_prediction_Zhai2018`},
Baseline illuminant (:math:`BI`) :math:`o`.
Returns
-------
:class:`tuple`
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`CIETC1-321994b`, :cite:`Fairchild1991a`,
:cite:`Fairchild2013s`, :cite:`Fairchild2013t`, :cite:`Li2002a`,
:cite:`Westland2012k`, :cite:`Zhai2018`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction(2, "CMCCAT2000")
>>> pr = [(p.uv_m, p.uv_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...))]
"""
function = CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS[model]
return function(experiment, **filter_kwargs(function, **kwargs))