"""
Spectral Generation
===================
Define various objects performing spectral generation:
- :func:`colour.sd_constant`
- :func:`colour.sd_zeros`
- :func:`colour.sd_ones`
- :func:`colour.msds_constant`
- :func:`colour.msds_zeros`
- :func:`colour.msds_ones`
- :func:`colour.colorimetry.sd_gaussian_normal`
- :func:`colour.colorimetry.sd_gaussian_fwhm`
- :attr:`colour.SD_GAUSSIAN_METHODS`
- :func:`colour.sd_gaussian`
- :func:`colour.colorimetry.sd_single_led_Ohno2005`
- :attr:`colour.SD_SINGLE_LED_METHODS`
- :func:`colour.sd_single_led`
- :func:`colour.colorimetry.sd_multi_leds_Ohno2005`
- :attr:`colour.SD_MULTI_LEDS_METHODS`
- :func:`colour.sd_multi_leds`
References
----------
- :cite:`Ohno2005` : Ohno, Yoshi. (2005). Spectral design considerations for
white LED color rendering. Optical Engineering, 44(11), 111302.
doi:10.1117/1.2130694
- :cite:`Ohno2008a` : Ohno, Yoshiro, & Davis, W. (2008). NIST CQS simulation
(Version 7.4) [Computer software].
https://drive.google.com/file/d/1PsuU6QjUJjCX6tQyCud6ul2Tbs8rYWW9/view?\
usp=sharing
"""
from __future__ import annotations
import numpy as np
from colour.algebra.interpolation import LinearInterpolator
from colour.colorimetry import (
SPECTRAL_SHAPE_DEFAULT,
MultiSpectralDistributions,
SpectralDistribution,
SpectralShape,
)
from colour.hints import (
Any,
ArrayLike,
Literal,
NDArrayFloat,
Sequence,
)
from colour.utilities import (
CanonicalMapping,
as_float_array,
full,
ones,
validate_method,
)
__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__ = [
"sd_constant",
"sd_zeros",
"sd_ones",
"msds_constant",
"msds_zeros",
"msds_ones",
"sd_gaussian_normal",
"sd_gaussian_fwhm",
"SD_GAUSSIAN_METHODS",
"sd_gaussian",
"sd_single_led_Ohno2005",
"SD_SINGLE_LED_METHODS",
"sd_single_led",
"sd_multi_leds_Ohno2005",
"SD_MULTI_LEDS_METHODS",
"sd_multi_leds",
]
[docs]
def sd_constant(
k: float, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any
) -> SpectralDistribution:
"""
Return a spectral distribution of given spectral shape filled with
constant :math:`k` values.
Parameters
----------
k
Constant :math:`k` to fill the spectral distribution with.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Constant :math:`k` filled spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
- The interpolator is set to :class:`colour.LinearInterpolator` class.
Examples
--------
>>> sd = sd_constant(100)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[400]
100.0
"""
settings = {"name": f"{k} Constant", "interpolator": LinearInterpolator}
settings.update(kwargs)
values = full(len(shape.wavelengths), k)
return SpectralDistribution(values, shape.wavelengths, **settings)
[docs]
def sd_zeros(
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any
) -> SpectralDistribution:
"""
Return a spectral distribution of given spectral shape filled with zeros.
Parameters
----------
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:func:`colour.sd_constant`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.SpectralDistribution`
Zeros filled spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
- The interpolator is set to :class:`colour.LinearInterpolator` class.
Examples
--------
>>> sd = sd_zeros()
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[400]
0.0
"""
return sd_constant(0, shape, **kwargs)
[docs]
def sd_ones(
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any
) -> SpectralDistribution:
"""
Return a spectral distribution of given spectral shape filled with ones.
Parameters
----------
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:func:`colour.sd_constant`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.SpectralDistribution`
Ones filled spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
- The interpolator is set to :class:`colour.LinearInterpolator` class.
Examples
--------
>>> sd = sd_ones()
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[400]
1.0
"""
return sd_constant(1, shape, **kwargs)
[docs]
def msds_constant(
k: float,
labels: Sequence,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> MultiSpectralDistributions:
"""
Return the multi-spectral distributions with given labels and given
spectral shape filled with constant :math:`k` values.
Parameters
----------
k
Constant :math:`k` to fill the multi-spectral distributions with.
labels
Names to use for the :class:`colour.SpectralDistribution` class
instances.
shape
Spectral shape used to create the multi-spectral distributions.
Other Parameters
----------------
kwargs
{:class:`colour.MultiSpectralDistributions`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.MultiSpectralDistributions`
Constant :math:`k` filled multi-spectral distributions.
Notes
-----
- By default, the multi-spectral distributions will use the shape given
by :attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
- The interpolator is set to :class:`colour.LinearInterpolator` class.
Examples
--------
>>> msds = msds_constant(100, labels=["a", "b", "c"])
>>> msds.shape
SpectralShape(360.0, 780.0, 1.0)
>>> msds[400]
array([ 100., 100., 100.])
>>> msds.labels # doctest: +SKIP
['a', 'b', 'c']
"""
settings = {"name": f"{k} Constant", "interpolator": LinearInterpolator}
settings.update(kwargs)
wavelengths = shape.wavelengths
values = full((len(wavelengths), len(labels)), k)
return MultiSpectralDistributions(values, wavelengths, labels=labels, **settings)
[docs]
def msds_zeros(
labels: Sequence,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> MultiSpectralDistributions:
"""
Return the multi-spectral distributionss with given labels and given
spectral shape filled with zeros.
Parameters
----------
labels
Names to use for the :class:`colour.SpectralDistribution` class
instances.
shape
Spectral shape used to create the multi-spectral distributions.
Other Parameters
----------------
kwargs
{:func:`colour.msds_constant`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.MultiSpectralDistributions`
Zeros filled multi-spectral distributions.
Notes
-----
- By default, the multi-spectral distributions will use the shape given
by :attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
- The interpolator is set to :class:`colour.LinearInterpolator` class.
Examples
--------
>>> msds = msds_zeros(labels=["a", "b", "c"])
>>> msds.shape
SpectralShape(360.0, 780.0, 1.0)
>>> msds[400]
array([ 0., 0., 0.])
>>> msds.labels # doctest: +SKIP
['a', 'b', 'c']
"""
return msds_constant(0, labels, shape, **kwargs)
[docs]
def msds_ones(
labels: Sequence,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> MultiSpectralDistributions:
"""
Return the multi-spectral distributionss with given labels and given
spectral shape filled with ones.
Parameters
----------
labels
Names to use for the :class:`colour.SpectralDistribution` class
instances.
shape
Spectral shape used to create the multi-spectral distributions.
Other Parameters
----------------
kwargs
{:func:`colour.msds_constant`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.MultiSpectralDistributions`
Ones filled multi-spectral distributions.
Notes
-----
- By default, the multi-spectral distributions will use the shape given
by :attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
- The interpolator is set to :class:`colour.LinearInterpolator` class.
Examples
--------
>>> msds = msds_ones(labels=["a", "b", "c"])
>>> msds.shape
SpectralShape(360.0, 780.0, 1.0)
>>> msds[400]
array([ 1., 1., 1.])
>>> msds.labels # doctest: +SKIP
['a', 'b', 'c']
"""
return msds_constant(1, labels, shape, **kwargs)
[docs]
def sd_gaussian_normal(
mu: float,
sigma: float,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape at
given mean wavelength :math:`\\mu` and standard deviation :math:`sigma`.
Parameters
----------
mu
Mean wavelength :math:`\\mu` the gaussian spectral distribution will
peak at.
sigma
Standard deviation :math:`sigma` of the gaussian spectral distribution.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian_normal(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +SKIP
1.0
>>> sd[530] # doctest: +ELLIPSIS
0.6065306...
"""
settings = {"name": f"{mu}nm - {sigma} Sigma - Gaussian"}
settings.update(kwargs)
values = np.exp(-((shape.wavelengths - mu) ** 2) / (2 * sigma**2))
return SpectralDistribution(values, shape.wavelengths, **settings)
[docs]
def sd_gaussian_fwhm(
peak_wavelength: float,
fwhm: float,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape at given
peak wavelength and full width at half maximum.
Parameters
----------
peak_wavelength
Wavelength the gaussian spectral distribution will peak at.
fwhm
Full width at half maximum, i.e., width of the gaussian spectral
distribution measured between those points on the *y* axis which are
half the maximum amplitude.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian_fwhm(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +SKIP
1.0
>>> sd[530] # doctest: +ELLIPSIS
0.062...
"""
settings = {"name": f"{peak_wavelength}nm - {fwhm} FWHM - Gaussian"}
settings.update(kwargs)
mu, sigma = peak_wavelength, fwhm / (2 * np.sqrt(2 * np.log(2)))
values = np.exp(-((shape.wavelengths - mu) ** 2) / (2 * sigma**2))
return SpectralDistribution(values, shape.wavelengths, **settings)
SD_GAUSSIAN_METHODS: CanonicalMapping = CanonicalMapping(
{"Normal": sd_gaussian_normal, "FWHM": sd_gaussian_fwhm}
)
SD_GAUSSIAN_METHODS.__doc__ = """
Supported gaussian spectral distribution computation methods.
"""
[docs]
def sd_gaussian(
mu_peak_wavelength: float,
sigma_fwhm: float,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
method: Literal["Normal", "FWHM"] | str = "Normal",
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a gaussian spectral distribution of given spectral shape using
given method.
Parameters
----------
mu_peak_wavelength
Mean wavelength :math:`\\mu` the gaussian spectral distribution will
peak at.
sigma_fwhm
Standard deviation :math:`sigma` of the gaussian spectral distribution
or full width at half maximum, i.e., width of the gaussian spectral
distribution measured between those points on the *y* axis which are
half the maximum amplitude.
shape
Spectral shape used to create the spectral distribution.
method
Computation method.
Other Parameters
----------------
kwargs
{:func:`colour.colorimetry.sd_gaussian_normal`,
:func:`colour.colorimetry.sd_gaussian_fwhm`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`colour.SpectralDistribution`
Gaussian spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
Examples
--------
>>> sd = sd_gaussian(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +SKIP
1.0
>>> sd[530] # doctest: +ELLIPSIS
0.6065306...
>>> sd = sd_gaussian(555, 25, method="FWHM")
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +SKIP
1.0
>>> sd[530] # doctest: +ELLIPSIS
0.062...
"""
method = validate_method(method, tuple(SD_GAUSSIAN_METHODS))
return SD_GAUSSIAN_METHODS[method](mu_peak_wavelength, sigma_fwhm, shape, **kwargs)
[docs]
def sd_single_led_Ohno2005(
peak_wavelength: float,
half_spectral_width: float,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a single *LED* spectral distribution of given spectral shape at
given peak wavelength and half spectral width :math:`\\Delta\\lambda_{0.5}`
according to *Ohno (2005)* method.
Parameters
----------
peak_wavelength
Wavelength the single *LED* spectral distribution will peak at.
half_spectral_width
Half spectral width :math:`\\Delta\\lambda_{0.5}`.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:class:`colour.SpectralDistribution`},
See the documentation of the previously listed class.
Returns
-------
:class:`colour.SpectralDistribution`
Single *LED* spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
References
----------
:cite:`Ohno2005`, :cite:`Ohno2008a`
Examples
--------
>>> sd = sd_single_led_Ohno2005(555, 25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +ELLIPSIS
1...
"""
settings = {
"name": f"{peak_wavelength}nm - {half_spectral_width} "
f"Half Spectral Width LED - Ohno (2005)"
}
settings.update(kwargs)
values = np.exp(
-(((shape.wavelengths - peak_wavelength) / half_spectral_width) ** 2)
)
values = (values + 2 * values**5) / 3
return SpectralDistribution(values, shape.wavelengths, **settings)
SD_SINGLE_LED_METHODS: CanonicalMapping = CanonicalMapping(
{
"Ohno 2005": sd_single_led_Ohno2005,
}
)
SD_SINGLE_LED_METHODS.__doc__ = """
Supported single *LED* spectral distribution computation methods.
"""
[docs]
def sd_single_led(
peak_wavelength: float,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
method: Literal["Ohno 2005"] | str = "Ohno 2005",
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a single *LED* spectral distribution of given spectral shape at
given peak wavelength according to given method.
Parameters
----------
peak_wavelength
Wavelength the single *LED* spectral distribution will peak at.
shape
Spectral shape used to create the spectral distribution.
method
Computation method.
Other Parameters
----------------
kwargs
{:func:`colour.colorimetry.sd_single_led_Ohno2005`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.SpectralDistribution`
Single *LED* spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
References
----------
:cite:`Ohno2005`, :cite:`Ohno2008a`
Examples
--------
>>> sd = sd_single_led(555, half_spectral_width=25)
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[555] # doctest: +ELLIPSIS
1...
"""
method = validate_method(method, tuple(SD_SINGLE_LED_METHODS))
kwargs["shape"] = shape
return SD_SINGLE_LED_METHODS[method](peak_wavelength, **kwargs)
[docs]
def sd_multi_leds_Ohno2005(
peak_wavelengths: ArrayLike,
half_spectral_widths: ArrayLike,
peak_power_ratios: ArrayLike | None = None,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a multi *LED* spectral distribution of given spectral shape at
given peak wavelengths, half spectral widths :math:`\\Delta\\lambda_{0.5}`
and peak power ratios according to *Ohno (2005)* method.
The multi *LED* spectral distribution is generated using many single *LED*
spectral distributions generated with :func:`colour.sd_single_led_Ohno2005`
definition.
Parameters
----------
peak_wavelengths
Wavelengths the multi *LED* spectral distribution will peak at, i.e.,
the peaks for each generated single *LED* spectral distributions.
half_spectral_widths
Half spectral widths :math:`\\Delta\\lambda_{0.5}`.
peak_power_ratios
Peak power ratios for each generated single *LED* spectral
distributions.
shape
Spectral shape used to create the spectral distribution.
Other Parameters
----------------
kwargs
{:func:`colour.colorimetry.sd_single_led_Ohno2005`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.SpectralDistribution`
Multi *LED* spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
References
----------
:cite:`Ohno2005`, :cite:`Ohno2008a`
Examples
--------
>>> sd = sd_multi_leds_Ohno2005(
... np.array([457, 530, 615]),
... np.array([20, 30, 20]),
... np.array([0.731, 1.000, 1.660]),
... )
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[500] # doctest: +ELLIPSIS
0.1295132...
"""
peak_wavelengths = as_float_array(peak_wavelengths)
half_spectral_widths = np.resize(half_spectral_widths, peak_wavelengths.shape)
if peak_power_ratios is None:
peak_power_ratios = ones(peak_wavelengths.shape)
else:
peak_power_ratios = np.resize(peak_power_ratios, peak_wavelengths.shape)
sd = sd_zeros(shape)
for peak_wavelength, half_spectral_width, peak_power_ratio in zip(
peak_wavelengths, half_spectral_widths, peak_power_ratios
):
sd += (
sd_single_led_Ohno2005(peak_wavelength, half_spectral_width, **kwargs)
* peak_power_ratio
)
def _format_array(a: NDArrayFloat) -> str:
"""Format given array :math:`a`."""
return ", ".join([str(e) for e in a])
sd.name = (
f"{_format_array(peak_wavelengths)}nm - "
f"{_format_array(half_spectral_widths)} FWHM - "
f"{_format_array(peak_power_ratios)} Peak Power Ratios - "
f"LED - Ohno (2005)"
)
return sd
SD_MULTI_LEDS_METHODS: CanonicalMapping = CanonicalMapping(
{
"Ohno 2005": sd_multi_leds_Ohno2005,
}
)
SD_MULTI_LEDS_METHODS.__doc__ = """
Supported multi *LED* spectral distribution computation methods.
"""
[docs]
def sd_multi_leds(
peak_wavelengths: ArrayLike,
shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT,
method: Literal["Ohno 2005"] | str = "Ohno 2005",
**kwargs: Any,
) -> SpectralDistribution:
"""
Return a multi *LED* spectral distribution of given spectral shape at
given peak wavelengths.
Parameters
----------
peak_wavelengths
Wavelengths the multi *LED* spectral distribution will peak at, i.e.,
the peaks for each generated single *LED* spectral distributions.
shape
Spectral shape used to create the spectral distribution.
method
Computation method.
Other Parameters
----------------
kwargs
{:func:`colour.colorimetry.sd_multi_leds_Ohno2005`},
See the documentation of the previously listed definition.
Returns
-------
:class:`colour.SpectralDistribution`
Multi *LED* spectral distribution.
Notes
-----
- By default, the spectral distribution will use the shape given by
:attr:`colour.SPECTRAL_SHAPE_DEFAULT` attribute.
References
----------
:cite:`Ohno2005`, :cite:`Ohno2008a`
Examples
--------
>>> sd = sd_multi_leds(
... np.array([457, 530, 615]),
... half_spectral_widths=np.array([20, 30, 20]),
... peak_power_ratios=np.array([0.731, 1.000, 1.660]),
... )
>>> sd.shape
SpectralShape(360.0, 780.0, 1.0)
>>> sd[500] # doctest: +ELLIPSIS
0.1295132...
"""
method = validate_method(method, tuple(SD_MULTI_LEDS_METHODS))
kwargs["shape"] = shape
return SD_MULTI_LEDS_METHODS[method](peak_wavelengths, **kwargs)
