colour.temperature.uv_to_CCT_Ohno2013#

colour.temperature.uv_to_CCT_Ohno2013(uv: ArrayLike, cmfs: MultiSpectralDistributions | None = None, start: float | None = None, end: float | None = None, count: int | None = None, iterations: int | None = None) → ndarray[source]#

Return the correlated colour temperature \(T_{cp}\) and \(\Delta_{uv}\) from given CIE UCS colourspace uv chromaticity coordinates, colour matching functions and temperature range using Ohno (2013) method.

The iterations parameter defines the calculations’ precision: The higher its value, the more planckian tables will be generated through cascade expansion in order to converge to the exact solution.

Parameters:

  • uv (ArrayLike) – CIE UCS colourspace uv chromaticity coordinates.

  • cmfs (MultiSpectralDistributions | None) – Standard observer colour matching functions, default to the CIE 1931 2 Degree Standard Observer.

  • start (float | None) – Temperature range start in kelvin degrees, default to 1000.

  • end (float | None) – Temperature range end in kelvin degrees, default to 100000.

  • count (int | None) – Temperatures count/samples in the planckian tables, default to 10.

  • iterations (int | None) – Number of planckian tables to generate, default to 6.

Returns:

Correlated colour temperature \(T_{cp}\), \(\Delta_{uv}\).

Return type:

numpy.ndarray

References

[Ohn14]

Examples

>>> from pprint import pprint
>>> from colour import MSDS_CMFS, SPECTRAL_SHAPE_DEFAULT
>>> cmfs = (
...     MSDS_CMFS["CIE 1931 2 Degree Standard Observer"]
...     .copy()
...     .align(SPECTRAL_SHAPE_DEFAULT)
... )
>>> uv = np.array([0.1978, 0.3122])
>>> uv_to_CCT_Ohno2013(uv, cmfs)  
array([  6.50747...e+03,   3.22334...e-03])