colour.recovery.find_coefficients_Jakob2019#
- colour.recovery.find_coefficients_Jakob2019(XYZ: ArrayLike, cmfs: MultiSpectralDistributions | None = None, illuminant: SpectralDistribution | None = None, coefficients_0: ArrayLike = (0, 0, 0), max_error: float = JND_CIE1976 / 100, dimensionalise: bool = True) Tuple[NDArrayFloat, float] [source]#
Compute the coefficients for Jakob and Hanika (2019) reflectance spectral model.
- Parameters:
XYZ (ArrayLike) – CIE XYZ tristimulus values to find the coefficients for.
cmfs (MultiSpectralDistributions | None) – Standard observer colour matching functions, default to the CIE 1931 2 Degree Standard Observer.
illuminant (SpectralDistribution | None) – Illuminant spectral distribution, default to CIE Standard Illuminant D65.
coefficients_0 (ArrayLike) – Starting coefficients for the solver.
max_error (float) – 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) – If True, returned coefficients are dimensionful and will not work correctly if fed back as
coefficients_0
. The default is True.
- Returns:
Tuple of computed coefficients that best fit the given colour and \(\Delta E_{76}\) between the target colour and the colour corresponding to the computed coefficients.
- Return type:
References
[JH19]
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...)