colour.dominant_wavelength#
- colour.dominant_wavelength(xy: ArrayLike, xy_n: ArrayLike, cmfs: MultiSpectralDistributions | None = None, inverse: bool = False) Tuple[NDArrayFloat, NDArrayFloat, NDArrayFloat] [source]#
Return the dominant wavelength \(\lambda_d\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus.
In the eventuality where the \(xy_wl\) first intersection coordinates are on the line of purples, the complementary wavelength will be computed in lieu.
The complementary wavelength is indicated by a negative sign and the \(xy_{cw}\) second intersection coordinates which are set by default to the same value as \(xy_wl\) first intersection coordinates will be set to the complementary dominant wavelength intersection coordinates with the spectral locus.
- Parameters:
xy (ArrayLike) – Colour stimulus CIE xy chromaticity coordinates.
xy_n (ArrayLike) – Achromatic stimulus CIE xy chromaticity coordinates.
cmfs (MultiSpectralDistributions | None) – Standard observer colour matching functions, default to the CIE 1931 2 Degree Standard Observer.
inverse (bool) – Inverse the computation direction to retrieve the complementary wavelength.
- Returns:
Dominant wavelength, first intersection point CIE xy chromaticity coordinates, second intersection point CIE xy chromaticity coordinates.
- Return type:
References
[CIET14804b], [Erdb]
Examples
Dominant wavelength computation:
>>> from colour.colorimetry import MSDS_CMFS >>> from pprint import pprint >>> cmfs = MSDS_CMFS["CIE 1931 2 Degree Standard Observer"] >>> xy = np.array([0.54369557, 0.32107944]) >>> xy_n = np.array([0.31270000, 0.32900000]) >>> pprint(dominant_wavelength(xy, xy_n, cmfs)) (array(616...), array([ 0.6835474..., 0.3162840...]), array([ 0.6835474..., 0.3162840...]))
Complementary dominant wavelength is returned if the first intersection is located on the line of purples:
>>> xy = np.array([0.37605506, 0.24452225]) >>> pprint(dominant_wavelength(xy, xy_n)) (array(-509.0), array([ 0.4572314..., 0.1362814...]), array([ 0.0104096..., 0.7320745...]))