Source code for colour.temperature.mccamy1992

"""
McCamy (1992) Correlated Colour Temperature
===========================================

Define the *McCamy (1992)* correlated colour temperature :math:`T_{cp}`
computation objects.

-   :func:`colour.temperature.xy_to_CCT_McCamy1992`: Compute correlated
    colour temperature :math:`T_{cp}` from specified *CIE xy* chromaticity
    coordinates using the *McCamy (1992)* method.
-   :func:`colour.temperature.CCT_to_xy_McCamy1992`: Compute *CIE xy*
    chromaticity coordinates from specified correlated colour temperature
    :math:`T_{cp}` using the *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 annotations

import typing

import numpy as np

from colour.algebra import sdiv, sdiv_mode
from colour.colorimetry import CCS_ILLUMINANTS

if typing.TYPE_CHECKING:
    from colour.hints import ArrayLike, DTypeFloat, NDArrayFloat

from colour.utilities import as_float, as_float_array, required, tsplit, usage_warning

__author__ = "Colour Developers"
__copyright__ = "Copyright 2013 Colour Developers"
__license__ = "BSD-3-Clause - 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",
]




@required("SciPy")
def xy_to_CCT_McCamy1992(xy: ArrayLike) -> NDArrayFloat:
    """
    Compute the correlated colour temperature :math:`T_{cp}` from the
    specified *CIE xy* chromaticity coordinates using the *McCamy (1992)*
    method.

    Parameters
    ----------
    xy
        *CIE xy* chromaticity coordinates.

    Returns
    -------
    :class:`numpy.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
    np.float64(6505.0805913...)
    """

    x, y = tsplit(xy)

    with sdiv_mode():
        n = sdiv(x - 0.3320, y - 0.1858)

    CCT = -449 * n**3 + 3525 * n**2 - 6823.3 * n + 5520.33

    return as_float(CCT)





def CCT_to_xy_McCamy1992(
    CCT: ArrayLike, optimisation_kwargs: dict | None = None
) -> NDArrayFloat:
    """
    Compute the *CIE xy* chromaticity coordinates from the specified
    correlated colour temperature :math:`T_{cp}` using the *McCamy (1992)*
    method.

    Parameters
    ----------
    CCT
        Correlated colour temperature :math:`T_{cp}`.
    optimisation_kwargs
        Parameters for :func:`scipy.optimize.minimize` definition.

    Returns
    -------
    :class:`numpy.ndarray`
        *CIE xy* chromaticity coordinates.

    Warnings
    --------
    The *McCamy (1992)* method for computing *CIE xy* chromaticity coordinates
    from the specified correlated colour temperature is not a bijective
    function and might produce unexpected results. It is provided for
    consistency with other correlated colour temperature computation methods
    but should be avoided for practical applications. The current
    implementation relies on optimisation 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...])
    """

    from scipy.optimize import minimize  # noqa: PLC0415

    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(np.reshape(CCT, (-1, 1)))

    def objective_function(xy: NDArrayFloat, CCT: NDArrayFloat) -> DTypeFloat:
        """Objective function."""

        objective = np.linalg.norm(xy_to_CCT_McCamy1992(xy) - CCT)

        return as_float(objective)

    optimisation_settings = {
        "method": "Nelder-Mead",
        "options": {
            "fatol": 1e-10,
        },
    }
    if optimisation_kwargs is not None:
        optimisation_settings.update(optimisation_kwargs)

    xy = as_float_array(
        [
            minimize(
                objective_function,
                x0=CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"],
                args=(CCT_i,),
                **optimisation_settings,
            ).x
            for CCT_i in CCT
        ]
    )

    return np.reshape(xy, ([*shape, 2]))