colour.recovery.XYZ_to_sd_Meng2015¶
-
colour.recovery.
XYZ_to_sd_Meng2015
(XYZ, cmfs=XYZ_ColourMatchingFunctions(name='CIE 1931 2 Degree Standard Observer', ...), illuminant=SpectralDistribution(name='1 Constant', ...), optimisation_parameters=None)[source]¶ Recovers the spectral distribution of given CIE XYZ tristimulus values using Meng et al. (2015) method.
- Parameters
XYZ (array_like, (3,)) – CIE XYZ tristimulus values to recover the spectral distribution from.
cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions. The wavelength \(\lambda_{i}\) range interval of the colour matching functions affects directly the time the computations take. The current default interval of 5 is a good compromise between precision and time spent.
illuminant (SpectralDistribution, optional) – Illuminant spectral distribution.
optimisation_parameters (dict_like, optional) – Parameters for
scipy.optimize.minimize()
definition.
- Returns
Recovered spectral distribution.
- Return type
Notes
Domain
Scale - Reference
Scale - 1
XYZ
[0, 1]
[0, 1]
The definition used to convert spectrum to CIE XYZ tristimulus values is
colour.colorimetry.spectral_to_XYZ_integration()
definition because it processes any measurement interval opposed tocolour.colorimetry.sd_to_XYZ_ASTME308()
definition that handles only measurement interval of 1, 5, 10 or 20nm.
References
Examples
>>> from colour.utilities import numpy_print_options >>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> cmfs = ( ... STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']. ... copy().align(SpectralShape(360, 780, 10)) ... ) >>> sd = XYZ_to_sd_Meng2015(XYZ, cmfs) >>> with numpy_print_options(suppress=True): ... # Doctests skip for Python 2.x compatibility. ... sd SpectralDistribution([[ 360. , 0.0780114...], [ 370. , 0.0780316...], [ 380. , 0.0780471...], [ 390. , 0.0780351...], [ 400. , 0.0779702...], [ 410. , 0.0778033...], [ 420. , 0.0770958...], [ 430. , 0.0748008...], [ 440. , 0.0693230...], [ 450. , 0.0601136...], [ 460. , 0.0477407...], [ 470. , 0.0334964...], [ 480. , 0.0193352...], [ 490. , 0.0074858...], [ 500. , 0.0001225...], [ 510. , 0. ...], [ 520. , 0. ...], [ 530. , 0. ...], [ 540. , 0.0124896...], [ 550. , 0.0389831...], [ 560. , 0.0775105...], [ 570. , 0.1247947...], [ 580. , 0.1765339...], [ 590. , 0.2281918...], [ 600. , 0.2751347...], [ 610. , 0.3140115...], [ 620. , 0.3433561...], [ 630. , 0.3635777...], [ 640. , 0.3765428...], [ 650. , 0.3841726...], [ 660. , 0.3883633...], [ 670. , 0.3905415...], [ 680. , 0.3916742...], [ 690. , 0.3922554...], [ 700. , 0.3925427...], [ 710. , 0.3926783...], [ 720. , 0.3927330...], [ 730. , 0.3927586...], [ 740. , 0.3927548...], [ 750. , 0.3927681...], [ 760. , 0.3927813...], [ 770. , 0.3927840...], [ 780. , 0.3927536...]], interpolator=SpragueInterpolator, interpolator_args={}, extrapolator=Extrapolator, extrapolator_args={...}) >>> sd_to_XYZ_integration(sd) / 100 array([ 0.2065812..., 0.1219752..., 0.0514132...])