colour.adaptation.chromatic_adaptation_CIE1994

colour.adaptation.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2, n=1)[source]

Adapts given stimulus CIE XYZ_1 tristimulus values from test viewing conditions to reference viewing conditions using CIE 1994 chromatic adaptation model.

Parameters:
  • XYZ_1 (array_like) – CIE XYZ tristimulus values of test sample / stimulus in domain [0, 100].
  • xy_o1 (array_like) – Chromaticity coordinates \(x_{o1}\) and \(y_{o1}\) of test illuminant and background.
  • xy_o2 (array_like) – Chromaticity coordinates \(x_{o2}\) and \(y_{o2}\) of reference illuminant and background.
  • Y_o (numeric) – Luminance factor \(Y_o\) of achromatic background as percentage in domain [18, 100].
  • E_o1 (numeric) – Test illuminance \(E_{o1}\) in \(cd/m^2\).
  • E_o2 (numeric) – Reference illuminance \(E_{o2}\) in \(cd/m^2\).
  • n (numeric, optional) – Noise component in fundamental primary system.
Returns:

Adapted CIE XYZ_2 tristimulus values of test stimulus.
Return type:

ndarray

Warning

The input domain and output range of that definition are non standard!

Notes

  • Input CIE XYZ_1 tristimulus values are in domain [0, 100].
  • Output CIE XYZ_2 tristimulus values are in range [0, 100].

References

Examples

>>> XYZ_1 = np.array([28.00, 21.26, 5.27])
>>> xy_o1 = np.array([0.4476, 0.4074])
>>> xy_o2 = np.array([0.3127, 0.3290])
>>> Y_o = 20
>>> E_o1 = 1000
>>> E_o2 = 1000
>>> chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
... 
array([ 24.0337952...,  21.1562121...,  17.6430119...])