colour.recovery.XYZ_to_sd_Jakob2019¶
- colour.recovery.XYZ_to_sd_Jakob2019(XYZ: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, optimisation_kwargs: Optional[Dict] = None, additional_data: Boolean = False) Union[Tuple[SpectralDistribution, Floating], SpectralDistribution][source]¶
Recover the spectral distribution of given RGB colourspace array using Jakob and Hanika (2019) method.
- Parameters
XYZ (ArrayLike) – CIE XYZ tristimulus values to recover the spectral distribution from.
cmfs (Optional[MultiSpectralDistributions]) – Standard observer colour matching functions, default to the CIE 1931 2 Degree Standard Observer.
illuminant (Optional[SpectralDistribution]) – Illuminant spectral distribution, default to CIE Standard Illuminant D65.
optimisation_kwargs (Optional[Dict]) – Parameters for
colour.recovery.find_coefficients_Jakob2019()definition.additional_data (Boolean) – If True,
errorwill be returned alongside the recovered spectral distribution.
- Returns
Tuple of recovered spectral distribution and \(\Delta E_{76}\) between the target colour and the colour corresponding to the computed coefficients or recovered spectral distribution.
- Return type
References
[JH19]
Examples
>>> from colour import ( ... CCS_ILLUMINANTS, MSDS_CMFS, SDS_ILLUMINANTS, XYZ_to_sRGB) >>> 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['D65'].copy().align(cmfs.shape) >>> sd = XYZ_to_sd_Jakob2019(XYZ, cmfs, illuminant) >>> with numpy_print_options(suppress=True): ... sd 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...]], interpolator=SpragueInterpolator, interpolator_kwargs={}, extrapolator=Extrapolator, extrapolator_kwargs={...}) >>> sd_to_XYZ_integration(sd, cmfs, illuminant) / 100 array([ 0.2066217..., 0.1220128..., 0.0513958...])