colour.XYZ_to_sd¶
-
colour.XYZ_to_sd(XYZ, method='Meng 2015', **kwargs)[source]¶ Recovers the spectral distribution of given CIE XYZ tristimulus values using given method.
- Parameters
XYZ (array_like) – CIE XYZ tristimulus values to recover the spectral distribution from.
method (unicode, optional) – {‘Meng 2015’, ‘Smits 1999’}, Computation method.
- Other Parameters
cmfs (XYZ_ColourMatchingFunctions) – {
colour.recovery.XYZ_to_sd_Meng2015()}, Standard observer colour matching functions.interval (numeric, optional) – {
colour.recovery.XYZ_to_sd_Meng2015()}, Wavelength \(\lambda_{i}\) range interval in nm. The smallerintervalis, the longer the computations will be.optimisation_parameters (dict_like, optional) – {
colour.recovery.XYZ_to_sd_Meng2015()}, Parameters forscipy.optimize.minimize()definition.
- Returns
Recovered spectral distribution.
- Return type
Notes
Domain
Scale - Reference
Scale - 1
XYZ[0, 1]
[0, 1]
Smits (1999) method will internally convert given CIE XYZ tristimulus values to RGB colourspace array assuming equal energy illuminant E.
References
Examples
Meng (2015) reflectance recovery:
>>> import numpy as np >>> from colour.utilities import numpy_print_options >>> from colour.colorimetry import ( ... STANDARD_OBSERVERS_CMFS, SpectralShape, sd_to_XYZ_integration) >>> XYZ = np.array([0.21781186, 0.12541048, 0.04697113]) >>> cmfs = ( ... STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']. ... copy().align(SpectralShape(360, 780, 10)) ... ) >>> sd = XYZ_to_sd(XYZ, cmfs=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([ 0.2178545..., 0.1254141..., 0.0470095...])
Smits (1999) reflectance recovery:
>>> sd = XYZ_to_sd(XYZ, method='Smits 1999') >>> with numpy_print_options(suppress=True): ... sd SpectralDistribution([[ 380. , 0.07691923], [ 417.7778 , 0.0587005 ], [ 455.5556 , 0.03943195], [ 493.3333 , 0.03024978], [ 531.1111 , 0.02750692], [ 568.8889 , 0.02808645], [ 606.6667 , 0.34298985], [ 644.4444 , 0.41185795], [ 682.2222 , 0.41185795], [ 720. , 0.41180754]], interpolator=LinearInterpolator, interpolator_args={}, extrapolator=Extrapolator, extrapolator_args={...}) >>> sd_to_XYZ_integration(sd) / 100 array([ 0.2004523..., 0.1105627..., 0.0420964...])