Source code for colour.temperature.mccamy1992

# -*- coding: utf-8 -*-
"""
McCamy (1992) Correlated Colour Temperature
===========================================

Defines *McCamy (1992)* correlated colour temperature :math:`T_{cp}`
computations objects:

-   :func:`colour.temperature.xy_to_CCT_McCamy1992`: Correlated colour
    temperature :math:`T_{cp}` computation of given *CIE xy* chromaticity
    coordinates using *McCamy (1992)* method.
-   :func:`colour.temperature.xy_to_CCT_McCamy1992`: *CIE xy* chromaticity
    coordinates computation of given correlated colour temperature
    :math:`T_{cp}` using *McCamy (1992)* method.

References
----------
-   :cite:`Wikipedia2001` : Wikipedia. (2001). Approximation. Retrieved June
    28, 2014, from http://en.wikipedia.org/wiki/Color_temperature#Approximation
"""

from __future__ import division, unicode_literals

import numpy as np
from scipy.optimize import minimize

from colour.colorimetry import ILLUMINANTS
from colour.utilities import as_float_array, as_numeric, tsplit, 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__ = 'colour-developers@colour-science.org'
__status__ = 'Production'

__all__ = ['xy_to_CCT_McCamy1992', 'CCT_to_xy_McCamy1992']


[docs]def xy_to_CCT_McCamy1992(xy): """ Returns the correlated colour temperature :math:`T_{cp}` from given *CIE xy* chromaticity coordinates using *McCamy (1992)* method. Parameters ---------- xy : array_like *CIE xy* chromaticity coordinates. Returns ------- numeric or ndarray Correlated colour temperature :math:`T_{cp}`. References ---------- :cite:`Wikipedia2001` Examples -------- >>> import numpy as np >>> xy = np.array([0.31270, 0.32900]) >>> xy_to_CCT_McCamy1992(xy) # doctest: +ELLIPSIS 6505.0805913... """ x, y = tsplit(xy) n = (x - 0.3320) / (y - 0.1858) CCT = -449 * n ** 3 + 3525 * n ** 2 - 6823.3 * n + 5520.33 return CCT
[docs]def CCT_to_xy_McCamy1992(CCT, optimisation_kwargs=None, **kwargs): """ Returns the *CIE xy* chromaticity coordinates from given correlated colour temperature :math:`T_{cp}` using *McCamy (1992)* method. Parameters ---------- CCT : numeric or array_like Correlated colour temperature :math:`T_{cp}`. optimisation_kwargs : dict_like, optional Parameters for :func:`scipy.optimize.minimize` definition. Other Parameters ---------------- \\**kwargs : dict, optional Keywords arguments for deprecation management. Returns ------- ndarray *CIE xy* chromaticity coordinates. Warnings -------- *McCamy (1992)* method for computing *CIE xy* chromaticity coordinates from given correlated colour temperature is a bijective function and might produce unexpected results. It is given for consistency with other correlated colour temperature computation methods but should be avoided for practical applications.The current implementation relies on optimization using :func:`scipy.optimize.minimize` definition and thus has reduced precision and poor performance. References ---------- :cite:`Wikipedia2001` Examples -------- >>> CCT_to_xy_McCamy1992(6505.0805913074782) # doctest: +ELLIPSIS array([ 0.3127..., 0.329...]) """ optimisation_kwargs = handle_arguments_deprecation({ 'ArgumentRenamed': [['optimisation_parameters', 'optimisation_kwargs'] ], }, **kwargs).get('optimisation_kwargs', optimisation_kwargs) usage_warning('"*McCamy (1992)" method for computing "CIE xy" ' 'chromaticity coordinates from given correlated colour ' 'temperature is not a bijective function and might produce ' 'unexpected results. It is given for consistency with other ' 'correlated colour temperature computation methods but ' 'should be avoided for practical applications.') CCT = as_float_array(CCT) shape = list(CCT.shape) CCT = np.atleast_1d(CCT.reshape([-1, 1])) def objective_function(xy, CCT): """ Objective function. """ objective = np.linalg.norm(xy_to_CCT_McCamy1992(xy) - CCT) 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=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65'], args=(CCT_i, ), **optimisation_settings).x for CCT_i in CCT ]) return as_numeric(CCT.reshape(shape + [2]))