Source code for colour.temperature.kang2002

# -*- coding: utf-8 -*-
"""
Kang, Moon, Hong, Lee, Cho and Kim (2002) Correlated Colour Temperature
=======================================================================

Defines *Kang et al. (2002)* correlated colour temperature :math:`T_{cp}`
computations objects:

-   :func:`colour.temperature.xy_to_CCT_Kang2002`: Correlated colour
    temperature :math:`T_{cp}` of given *CIE xy* chromaticity coordinates
    computation  using *Kang, Moon, Hong, Lee, Cho and Kim (2002)* method.
-   :func:`colour.temperature.CCT_to_xy_Kang2002`: *CIE xy* chromaticity
    coordinates computation of given correlated colour temperature
    :math:`T_{cp}` using *Kang, Moon, Hong, Lee, Cho and Kim (2002)* method.

References
----------
-   :cite:`Kang2002a` : Kang, B., Moon, O., Hong, C., Lee, H., Cho, B., & Kim,
    Y. (2002). Design of advanced color: Temperature control system for HDTV
    applications. Journal of the Korean Physical Society, 41(6), 865-871.
"""

from __future__ import division, unicode_literals

import numpy as np
from scipy.optimize import minimize

from colour.utilities import as_float_array, as_numeric, tstack, usage_warning
from colour.utilities.deprecation import handle_arguments_deprecation

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2020 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = '[email protected]'
__status__ = 'Production'

__all__ = ['xy_to_CCT_Kang2002', 'CCT_to_xy_Kang2002']


[docs]def xy_to_CCT_Kang2002(xy, optimisation_kwargs=None, **kwargs): """ Returns the correlated colour temperature :math:`T_{cp}` from given *CIE xy* chromaticity coordinates using *Kang et al. (2002)* method. Parameters ---------- xy : array_like *CIE xy* chromaticity coordinates. optimisation_kwargs : dict_like, optional Parameters for :func:`scipy.optimize.minimize` definition. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- ndarray Correlated colour temperature :math:`T_{cp}`. Warnings -------- *Kang et al. (2002)* does not give an analytical inverse transformation to compute the correlated colour temperature :math:`T_{cp}` from given *CIE xy* chromaticity coordinates, the current implementation relies on optimization using :func:`scipy.optimize.minimize` definition and thus has reduced precision and poor performance. References ---------- :cite:`Kang2002a` Examples -------- >>> xy_to_CCT_Kang2002(np.array([0.31342600, 0.32359597])) ... # doctest: +ELLIPSIS 6504.3893128... """ optimisation_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['optimisation_parameters', 'optimisation_kwargs'] ], }, **kwargs).get('optimisation_kwargs', optimisation_kwargs) xy = as_float_array(xy) shape = xy.shape xy = np.atleast_1d(xy.reshape([-1, 2])) def objective_function(CCT, xy): """ Objective function. """ objective = np.linalg.norm(CCT_to_xy_Kang2002(CCT) - xy) return objective optimisation_settings = { 'method': 'Nelder-Mead', 'options': { 'fatol': 1e-10, }, } if optimisation_kwargs is not None: optimisation_settings.update(optimisation_kwargs) CCT = as_float_array([ minimize( objective_function, x0=6500, args=(xy_i, ), **optimisation_settings).x for xy_i in xy ]) return as_numeric(CCT.reshape(shape[:-1]))
[docs]def CCT_to_xy_Kang2002(CCT): """ Returns the *CIE xy* chromaticity coordinates from given correlated colour temperature :math:`T_{cp}` using *Kang et al. (2002)* method. Parameters ---------- CCT : numeric or array_like Correlated colour temperature :math:`T_{cp}`. Returns ------- ndarray *CIE xy* chromaticity coordinates. Raises ------ ValueError If the correlated colour temperature is not in appropriate domain. References ---------- :cite:`Kang2002a` Examples -------- >>> CCT_to_xy_Kang2002(6504.38938305) # doctest: +ELLIPSIS array([ 0.313426 ..., 0.3235959...]) """ CCT = as_float_array(CCT) if np.any(CCT[np.asarray(np.logical_or(CCT < 1667, CCT > 25000))]): usage_warning(('Correlated colour temperature must be in domain ' '[1667, 25000], unpredictable results may occur!')) x = np.where( CCT <= 4000, -0.2661239 * 10 ** 9 / CCT ** 3 - 0.2343589 * 10 ** 6 / CCT ** 2 + 0.8776956 * 10 ** 3 / CCT + 0.179910, -3.0258469 * 10 ** 9 / CCT ** 3 + 2.1070379 * 10 ** 6 / CCT ** 2 + 0.2226347 * 10 ** 3 / CCT + 0.24039, ) cnd_l = [CCT <= 2222, np.logical_and(CCT > 2222, CCT <= 4000), CCT > 4000] i = -1.1063814 * x ** 3 - 1.34811020 * x ** 2 + 2.18555832 * x - 0.20219683 j = -0.9549476 * x ** 3 - 1.37418593 * x ** 2 + 2.09137015 * x - 0.16748867 k = 3.0817580 * x ** 3 - 5.8733867 * x ** 2 + 3.75112997 * x - 0.37001483 y = np.select(cnd_l, [i, j, k]) xy = tstack([x, y]) return xy