colour.recovery.find_coefficients_Jakob2019¶
- colour.recovery.find_coefficients_Jakob2019(XYZ, cmfs=XYZ_ColourMatchingFunctions(name='CIE 1931 2 Degree Standard Observer', ...), illuminant=SpectralDistribution(name='D65', ...), coefficients_0=array([ 0., 0., 0.]), max_error=0.023, dimensionalise=True)[source]¶
Computes the coefficients for Jakob and Hanika (2019) reflectance spectral model.
- Parameters
XYZ (array_like, (3,)) – CIE XYZ tristimulus values to find the coefficients for.
cmfs (XYZ_ColourMatchingFunctions) – Standard observer colour matching functions.
illuminant (SpectralDistribution) – Illuminant spectral distribution.
coefficients_0 (array_like, (3,), optional) – Starting coefficients for the solver.
max_error (float, optional) – Maximal acceptable error. Set higher to save computational time. If None, the solver will keep going until it is very close to the minimum. The default is
ACCEPTABLE_DELTA_E
.dimensionalise (bool, optional) – If True, returned coefficients are dimensionful and will not work correctly if fed back as
coefficients_0
. The default is True.
- Returns
coefficients (ndarray, (3,)) – Computed coefficients that best fit the given colour.
error (float) – \(\Delta E_{76}\) between the target colour and the colour corresponding to the computed coefficients.
References
[]
Examples
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) >>> find_coefficients_Jakob2019(XYZ) (array([ 1.3723791...e-04, -1.3514399...e-01, 3.0838973...e+01]), 0.0141941...)