"""
References
----------
- :cite:`Jakob2019` : Jakob, W., & Hanika, J. (2019). A Low-Dimensional
Function Space for Efficient Spectral Upsampling. Computer Graphics Forum,
38(2), 147-155. doi:10.1111/cgf.13626
- :cite:`Mallett2019` : Mallett, I., & Yuksel, C. (2019). Spectral Primary
Decomposition for Rendering with sRGB Reflectance. Eurographics Symposium
on Rendering - DL-Only and Industry Track, 7 pages. doi:10.2312/SR.20191216
- :cite:`Meng2015c` : Meng, J., Simon, F., Hanika, J., & Dachsbacher, C.
(2015). Physically Meaningful Rendering using Tristimulus Colours. Computer
Graphics Forum, 34(4), 31-40. doi:10.1111/cgf.12676
- :cite:`Otsu2018` : Otsu, H., Yamamoto, M., & Hachisuka, T. (2018).
Reproducing Spectral Reflectances From Tristimulus Colours. Computer
Graphics Forum, 37(6), 370-381. doi:10.1111/cgf.13332
- :cite:`Smits1999a` : Smits, B. (1999). An RGB-to-Spectrum Conversion for
Reflectances. Journal of Graphics Tools, 4(4), 11-22.
doi:10.1080/10867651.1999.10487511
"""
from __future__ import annotations
import typing
if typing.TYPE_CHECKING:
from colour.colorimetry import SpectralDistribution
from colour.hints import Any, ArrayLike, Literal, NDArrayFloat
from colour.utilities import (
CanonicalMapping,
as_float_array,
filter_kwargs,
validate_method,
)
from . import datasets
from .datasets import * # noqa: F403
from .gaussian import (
CCS_WHITEPOINT_GAUSSIAN,
EXPONENT_GAUSSIAN_BASIS,
FWHM_GAUSSIAN_BASIS,
MSDS_GAUSSIAN_BASIS,
PEAK_WAVELENGTHS_GAUSSIAN_BASIS,
PRIMARIES_GAUSSIAN,
RGB_COLOURSPACE_GAUSSIAN,
WHITEPOINT_NAME_GAUSSIAN,
RGB_to_msds_Gaussian,
RGB_to_sd_Gaussian,
XYZ_to_RGB_Gaussian,
generate_gaussian_basis,
optimise_gaussian_basis_parameters,
)
from .jakob2019 import (
LUT3D_Jakob2019,
XYZ_to_sd_Jakob2019,
find_coefficients_Jakob2019,
sd_Jakob2019,
)
from .jiang2013 import (
PCA_Jiang2013,
RGB_to_msds_camera_sensitivities_Jiang2013,
RGB_to_sd_camera_sensitivity_Jiang2013,
)
from .mallett2019 import (
RGB_to_sd_Mallett2019,
spectral_primary_decomposition_Mallett2019,
)
from .meng2015 import XYZ_to_sd_Meng2015
from .otsu2018 import (
Dataset_Otsu2018,
Tree_Otsu2018,
XYZ_to_sd_Otsu2018,
)
from .smits1999 import (
RGB_to_msds_Smits1999,
RGB_to_sd_Smits1999,
)
__all__ = datasets.__all__
__all__ += [
"LUT3D_Jakob2019",
"XYZ_to_sd_Jakob2019",
"find_coefficients_Jakob2019",
"sd_Jakob2019",
]
__all__ += [
"PCA_Jiang2013",
"RGB_to_msds_camera_sensitivities_Jiang2013",
"RGB_to_sd_camera_sensitivity_Jiang2013",
]
__all__ += [
"RGB_to_sd_Mallett2019",
"spectral_primary_decomposition_Mallett2019",
]
__all__ += [
"XYZ_to_sd_Meng2015",
]
__all__ += [
"Dataset_Otsu2018",
"Tree_Otsu2018",
"XYZ_to_sd_Otsu2018",
]
__all__ += [
"RGB_to_msds_Smits1999",
"RGB_to_sd_Smits1999",
]
__all__ += [
"CCS_WHITEPOINT_GAUSSIAN",
"EXPONENT_GAUSSIAN_BASIS",
"FWHM_GAUSSIAN_BASIS",
"MSDS_GAUSSIAN_BASIS",
"PEAK_WAVELENGTHS_GAUSSIAN_BASIS",
"PRIMARIES_GAUSSIAN",
"RGB_COLOURSPACE_GAUSSIAN",
"RGB_to_msds_Gaussian",
"RGB_to_sd_Gaussian",
"WHITEPOINT_NAME_GAUSSIAN",
"XYZ_to_RGB_Gaussian",
"generate_gaussian_basis",
"optimise_gaussian_basis_parameters",
]
XYZ_TO_SD_METHODS: CanonicalMapping = CanonicalMapping(
{
"Gaussian": RGB_to_sd_Gaussian,
"Jakob 2019": XYZ_to_sd_Jakob2019,
"Mallett 2019": RGB_to_sd_Mallett2019,
"Meng 2015": XYZ_to_sd_Meng2015,
"Otsu 2018": XYZ_to_sd_Otsu2018,
"Smits 1999": RGB_to_sd_Smits1999,
}
)
XYZ_TO_SD_METHODS.__doc__ = """
Supported spectral distribution recovery methods.
References
----------
:cite:`Jakob2019`, :cite:`Mallett2019`, :cite:`Meng2015c`,
:cite:`Smits1999a`
"""
[docs]
def XYZ_to_sd(
XYZ: ArrayLike,
method: (
Literal[
"Gaussian",
"Jakob 2019",
"Mallett 2019",
"Meng 2015",
"Otsu 2018",
"Smits 1999",
]
| str
) = "Meng 2015",
**kwargs: Any,
) -> SpectralDistribution:
"""
Recover the spectral distribution of the specified *CIE XYZ* tristimulus
values using the specified method.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values to recover the spectral distribution
from.
method
Computation method.
Other Parameters
----------------
additional_data
{:func:`colour.recovery.XYZ_to_sd_Jakob2019`},
If *True*, ``error`` will be returned alongside the recovered
spectral distribution.
basis_functions
{:func:`colour.recovery.RGB_to_sd_Mallett2019`},
Basis functions for the method. The default is to use the built-in
*sRGB* basis functions, i.e.,
:attr:`colour.recovery.MSDS_BASIS_FUNCTIONS_sRGB_MALLETT2019`.
clip
{:func:`colour.recovery.XYZ_to_sd_Otsu2018`},
If *True*, the default, values below zero and above unity in the
recovered spectral distributions will be clipped. This ensures that
the returned reflectance is physical and conserves energy, but will
cause noticeable colour differences in case of very saturated
colours.
cmfs
{:func:`colour.recovery.XYZ_to_sd_Meng2015`},
Standard observer colour matching functions.
dataset
{:func:`colour.recovery.XYZ_to_sd_Otsu2018`},
Dataset to use for reconstruction. The default is to use the
published data.
illuminant
{:func:`colour.recovery.XYZ_to_sd_Jakob2019`,
:func:`colour.recovery.XYZ_to_sd_Meng2015`},
Illuminant spectral distribution, default to
*CIE Standard Illuminant D65*.
optimisation_kwargs
{:func:`colour.recovery.XYZ_to_sd_Jakob2019`,
:func:`colour.recovery.XYZ_to_sd_Meng2015`},
Parameters for :func:`scipy.optimize.minimize` and
:func:`colour.recovery.find_coefficients_Jakob2019` definitions.
Returns
-------
:class:`colour.SpectralDistribution`
Recovered spectral distribution.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | 1 | 1 |
+------------+-----------------------+---------------+
- *Gaussian* and *Smits (1999)* methods will internally convert
specified *CIE XYZ* tristimulus values to *RGB* colourspace array
assuming *sRGB* primaries and equal energy illuminant *E*.
References
----------
:cite:`Jakob2019`, :cite:`Mallett2019`, :cite:`Meng2015c`,
:cite:`Otsu2018`, :cite:`Smits1999a`
Examples
--------
*Gaussian* reflectance recovery:
>>> import numpy as np
>>> from colour import MSDS_CMFS, SDS_ILLUMINANTS, SpectralShape
>>> from colour.colorimetry import sd_to_XYZ_integration
>>> from colour.utilities import numpy_print_options
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SpectralShape(360, 780, 10))
... )
>>> illuminant = SDS_ILLUMINANTS["E"].copy().align(cmfs.shape)
>>> sd = XYZ_to_sd(XYZ, method="Gaussian").align(SpectralShape(360, 780, 10))
>>> with numpy_print_options(suppress=True):
... sd # doctest: +ELLIPSIS
SpectralDistribution([[360. , 0.04502009],
[370. , 0.04501988],
[380. , 0.04501914],
[390. , 0.0450168 ],
[400. , 0.04500998],
[410. , 0.04499185],
[420. , 0.0449477 ],
[430. , 0.0448493 ],
[440. , 0.04464874],
[450. , 0.04427526],
[460. , 0.04364072],
[470. , 0.04265916],
[480. , 0.04128144],
[490. , 0.03953613],
[500. , 0.03755869],
[510. , 0.03558836],
[520. , 0.03392457],
[530. , 0.03287194],
[540. , 0.03279225],
[550. , 0.03461775],
[560. , 0.04157706],
[570. , 0.06103056],
[580. , 0.10398511],
[590. , 0.17707942],
[600. , 0.26899683],
[610. , 0.34699756],
[620. , 0.37718472],
[630. , 0.37785039],
[640. , 0.37824584],
[650. , 0.37846014],
[660. , 0.37856623],
[670. , 0.37861426],
[680. , 0.37863415],
[690. , 0.3786417 ],
[700. , 0.37864431],
[710. , 0.37864515],
[720. , 0.37864539],
[730. , 0.37864545],
[740. , 0.37864547],
[750. , 0.37864547],
[760. , 0.37864547],
[770. , 0.37864547],
[780. , 0.37864547]],
SpragueInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS
array([0.19324..., 0.11592..., 0.04332...])
*Jakob and Hanika (2019)* reflectance recovery:
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SpectralShape(360, 780, 10))
... )
>>> illuminant = SDS_ILLUMINANTS["D65"].copy().align(cmfs.shape)
>>> sd = XYZ_to_sd(XYZ, method="Jakob 2019", cmfs=cmfs, illuminant=illuminant)
>>> with numpy_print_options(suppress=True):
... sd # doctest: +ELLIPSIS
SpectralDistribution([[360. , 0.4893773...],
[370. , 0.3258214...],
[380. , 0.2147792...],
[390. , 0.1482413...],
[400. , 0.1086169...],
[410. , 0.0841255...],
[420. , 0.0683114...],
[430. , 0.0577144...],
[440. , 0.0504267...],
[450. , 0.0453552...],
[460. , 0.0418520...],
[470. , 0.0395259...],
[480. , 0.0381430...],
[490. , 0.0375741...],
[500. , 0.0377685...],
[510. , 0.0387432...],
[520. , 0.0405871...],
[530. , 0.0434783...],
[540. , 0.0477225...],
[550. , 0.0538256...],
[560. , 0.0626314...],
[570. , 0.0755869...],
[580. , 0.0952675...],
[590. , 0.1264265...],
[600. , 0.1779272...],
[610. , 0.2649393...],
[620. , 0.4039779...],
[630. , 0.5832105...],
[640. , 0.7445440...],
[650. , 0.8499970...],
[660. , 0.9094792...],
[670. , 0.9425378...],
[680. , 0.9616376...],
[690. , 0.9732481...],
[700. , 0.9806562...],
[710. , 0.9855873...],
[720. , 0.9889903...],
[730. , 0.9914117...],
[740. , 0.9931801...],
[750. , 0.9945009...],
[760. , 0.9955066...],
[770. , 0.9962855...],
[780. , 0.9968976...]],
SpragueInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS
array([0.2066217..., 0.1220128..., 0.0513958...])
*Mallett and Yuksel (2019)* reflectance recovery:
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SPECTRAL_SHAPE_sRGB_MALLETT2019)
... )
>>> illuminant = SDS_ILLUMINANTS["D65"].copy().align(cmfs.shape)
>>> sd = XYZ_to_sd(XYZ, method="Mallett 2019")
>>> with numpy_print_options(suppress=True):
... sd # doctest: +ELLIPSIS
SpectralDistribution([[380. , 0.1735531...],
[385. , 0.1720357...],
[390. , 0.1677721...],
[395. , 0.1576605...],
[400. , 0.1372829...],
[405. , 0.1170849...],
[410. , 0.0895694...],
[415. , 0.0706232...],
[420. , 0.0585765...],
[425. , 0.0523959...],
[430. , 0.0497598...],
[435. , 0.0476057...],
[440. , 0.0465079...],
[445. , 0.0460337...],
[450. , 0.0455839...],
[455. , 0.0452872...],
[460. , 0.0450981...],
[465. , 0.0448895...],
[470. , 0.0449257...],
[475. , 0.0448987...],
[480. , 0.0446834...],
[485. , 0.0441372...],
[490. , 0.0417137...],
[495. , 0.0373832...],
[500. , 0.0357657...],
[505. , 0.0348263...],
[510. , 0.0341953...],
[515. , 0.0337683...],
[520. , 0.0334979...],
[525. , 0.0332991...],
[530. , 0.0331909...],
[535. , 0.0332181...],
[540. , 0.0333387...],
[545. , 0.0334970...],
[550. , 0.0337381...],
[555. , 0.0341847...],
[560. , 0.0346447...],
[565. , 0.0353993...],
[570. , 0.0367367...],
[575. , 0.0392007...],
[580. , 0.0445902...],
[585. , 0.0625633...],
[590. , 0.2965381...],
[595. , 0.4215576...],
[600. , 0.4347139...],
[605. , 0.4385134...],
[610. , 0.4385184...],
[615. , 0.4385249...],
[620. , 0.4374694...],
[625. , 0.4384672...],
[630. , 0.4368251...],
[635. , 0.4340867...],
[640. , 0.4303219...],
[645. , 0.4243257...],
[650. , 0.4159482...],
[655. , 0.4057443...],
[660. , 0.3919874...],
[665. , 0.3742784...],
[670. , 0.3518421...],
[675. , 0.3240127...],
[680. , 0.2955145...],
[685. , 0.2625658...],
[690. , 0.2343423...],
[695. , 0.2174830...],
[700. , 0.2060461...],
[705. , 0.1977437...],
[710. , 0.1916846...],
[715. , 0.1861020...],
[720. , 0.1823908...],
[725. , 0.1807923...],
[730. , 0.1795571...],
[735. , 0.1785623...],
[740. , 0.1775758...],
[745. , 0.1771614...],
[750. , 0.1767431...],
[755. , 0.1764319...],
[760. , 0.1762597...],
[765. , 0.1762209...],
[770. , 0.1761803...],
[775. , 0.1761195...],
[780. , 0.1760763...]],
SpragueInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100
... # doctest: +ELLIPSIS
array([0.2065436..., 0.1219996..., 0.0513764...])
*Meng (2015)* reflectance recovery:
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SpectralShape(360, 780, 10))
... )
>>> illuminant = SDS_ILLUMINANTS["D65"].copy().align(cmfs.shape)
>>> sd = XYZ_to_sd(XYZ, method="Meng 2015", cmfs=cmfs, illuminant=illuminant)
>>> with numpy_print_options(suppress=True):
... sd # doctest: +SKIP
SpectralDistribution([[ 360. , 0.0762005...],
[ 370. , 0.0761792...],
[ 380. , 0.0761363...],
[ 390. , 0.0761194...],
[ 400. , 0.0762539...],
[ 410. , 0.0761671...],
[ 420. , 0.0754649...],
[ 430. , 0.0731519...],
[ 440. , 0.0676701...],
[ 450. , 0.0577800...],
[ 460. , 0.0441993...],
[ 470. , 0.0285064...],
[ 480. , 0.0138728...],
[ 490. , 0.0033585...],
[ 500. , 0. ...],
[ 510. , 0. ...],
[ 520. , 0. ...],
[ 530. , 0. ...],
[ 540. , 0.0055767...],
[ 550. , 0.0317581...],
[ 560. , 0.0754491...],
[ 570. , 0.1314115...],
[ 580. , 0.1937649...],
[ 590. , 0.2559311...],
[ 600. , 0.3123173...],
[ 610. , 0.3584966...],
[ 620. , 0.3927335...],
[ 630. , 0.4159458...],
[ 640. , 0.4306660...],
[ 650. , 0.4391040...],
[ 660. , 0.4439497...],
[ 670. , 0.4463618...],
[ 680. , 0.4474625...],
[ 690. , 0.4479868...],
[ 700. , 0.4482116...],
[ 710. , 0.4482800...],
[ 720. , 0.4483472...],
[ 730. , 0.4484251...],
[ 740. , 0.4484633...],
[ 750. , 0.4485071...],
[ 760. , 0.4484969...],
[ 770. , 0.4484853...],
[ 780. , 0.4485134...]],
SpragueInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS
array([0.2065400..., 0.1219722..., 0.0513695...])
*Otsu, Yamamoto and Hachisuka (2018)* reflectance recovery:
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SPECTRAL_SHAPE_OTSU2018)
... )
>>> illuminant = SDS_ILLUMINANTS["D65"].copy().align(cmfs.shape)
>>> sd = XYZ_to_sd(XYZ, method="Otsu 2018", cmfs=cmfs, illuminant=illuminant)
>>> with numpy_print_options(suppress=True):
... sd # doctest: +ELLIPSIS
SpectralDistribution([[380. , 0.0601939...],
[390. , 0.0568063...],
[400. , 0.0517429...],
[410. , 0.0495841...],
[420. , 0.0502007...],
[430. , 0.0506489...],
[440. , 0.0510020...],
[450. , 0.0493782...],
[460. , 0.0468046...],
[470. , 0.0437132...],
[480. , 0.0416957...],
[490. , 0.0403783...],
[500. , 0.0405197...],
[510. , 0.0406031...],
[520. , 0.0416912...],
[530. , 0.0430956...],
[540. , 0.0444474...],
[550. , 0.0459336...],
[560. , 0.0507631...],
[570. , 0.0628967...],
[580. , 0.0844661...],
[590. , 0.1334277...],
[600. , 0.2262428...],
[610. , 0.3599330...],
[620. , 0.4885571...],
[630. , 0.5752546...],
[640. , 0.6193023...],
[650. , 0.6450744...],
[660. , 0.6610548...],
[670. , 0.6688673...],
[680. , 0.6795426...],
[690. , 0.6887933...],
[700. , 0.7003469...],
[710. , 0.7084128...],
[720. , 0.7154674...],
[730. , 0.7234334...]],
SpragueInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS
array([0.2065494..., 0.1219712..., 0.0514002...])
*Smits (1999)* reflectance recovery:
>>> cmfs = (
... MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
... .copy()
... .align(SpectralShape(360, 780, 10))
... )
>>> illuminant = SDS_ILLUMINANTS["E"].copy().align(cmfs.shape)
>>> sd = XYZ_to_sd(XYZ, method="Smits 1999")
>>> with numpy_print_options(suppress=True):
... sd # doctest: +ELLIPSIS
SpectralDistribution([[380. , 0.0787830...],
[417.7778 , 0.0622018...],
[455.5556 , 0.0446206...],
[493.3333 , 0.0352220...],
[531.1111 , 0.0324149...],
[568.8889 , 0.0330105...],
[606.6667 , 0.3207115...],
[644.4444 , 0.3836164...],
[682.2222 , 0.3836164...],
[720. , 0.3835649...]],
LinearInterpolator,
{},
Extrapolator,
{'method': 'Constant', 'left': None, 'right': None})
>>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 # doctest: +ELLIPSIS
array([0.1894770..., 0.1126470..., 0.0474420...])
"""
a = as_float_array(XYZ)
method = validate_method(method, tuple(XYZ_TO_SD_METHODS))
function = XYZ_TO_SD_METHODS[method]
if function is RGB_to_sd_Gaussian:
a = XYZ_to_RGB_Gaussian(XYZ)
elif function is RGB_to_sd_Smits1999:
from colour.recovery.smits1999 import XYZ_to_RGB_Smits1999 # noqa: PLC0415
a = XYZ_to_RGB_Smits1999(XYZ)
elif function is RGB_to_sd_Mallett2019:
from colour.models import XYZ_to_sRGB # noqa: PLC0415
a = XYZ_to_sRGB(XYZ, apply_cctf_encoding=False)
return function(a, **filter_kwargs(function, **kwargs))
__all__ += [
"XYZ_TO_SD_METHODS",
"XYZ_to_sd",
]
XYZ_TO_MSDS_METHODS: CanonicalMapping = CanonicalMapping(
{
"Gaussian": RGB_to_msds_Gaussian,
"Smits 1999": RGB_to_msds_Smits1999,
}
)
XYZ_TO_MSDS_METHODS.__doc__ = """
Supported multi-spectral distributions recovery methods.
References
----------
:cite:`Smits1999a`
"""
[docs]
def XYZ_to_msds(
XYZ: ArrayLike,
method: (Literal["Gaussian", "Smits 1999"] | str) = "Gaussian",
) -> NDArrayFloat:
"""
Recover spectral values from the specified *CIE XYZ* tristimulus values
using the specified method.
Parameters
----------
XYZ
*CIE XYZ* tristimulus values to recover the spectral values from.
The last dimension must be size 3.
method
Computation method.
Returns
-------
:class:`numpy.ndarray`
Recovered spectral values with shape ``(*XYZ.shape[:-1], wavelengths)``.
Notes
-----
+------------+-----------------------+---------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+============+=======================+===============+
| ``XYZ`` | 1 | 1 |
+------------+-----------------------+---------------+
- Both methods will internally convert specified *CIE XYZ* tristimulus
values to *RGB* colourspace array assuming *sRGB* primaries and equal
energy illuminant *E*.
References
----------
:cite:`Smits1999a`
Examples
--------
*Gaussian* reflectance recovery:
>>> import numpy as np
>>> XYZ = np.array(
... [
... [0.20654008, 0.12197225, 0.05136952],
... [0.14223761, 0.23042375, 0.10498415],
... [0.07820260, 0.06157595, 0.28106183],
... ]
... )
>>> XYZ_to_msds(XYZ, method="Gaussian").shape
(3, 421)
>>> float(XYZ_to_msds(XYZ, method="Gaussian")[0, 300]) # doctest: +ELLIPSIS
0.3785...
*Smits (1999)* reflectance recovery:
>>> XYZ_to_msds(XYZ, method="Smits 1999").shape
(3, 10)
>>> float(XYZ_to_msds(XYZ, method="Smits 1999")[0, 6]) # doctest: +ELLIPSIS
0.3207...
"""
method = validate_method(method, tuple(XYZ_TO_MSDS_METHODS))
function = XYZ_TO_MSDS_METHODS[method]
if function is RGB_to_msds_Gaussian:
a = XYZ_to_RGB_Gaussian(XYZ)
else: # RGB_to_msds_Smits1999
from colour.recovery.smits1999 import XYZ_to_RGB_Smits1999 # noqa: PLC0415
a = XYZ_to_RGB_Smits1999(XYZ)
return function(a)
__all__ += [
"XYZ_TO_MSDS_METHODS",
"XYZ_to_msds",
]
