def luminous_efficiency(
spd, lef=PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer')):
"""
Returns the *luminous efficiency* of given spectral power distribution
using given luminous efficiency function.
Parameters
----------
spd : SpectralPowerDistribution
test spectral power distribution
lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` luminous efficiency function.
Returns
-------
numeric
Luminous efficiency.
Examples
--------
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')
>>> luminous_efficiency(spd) # doctest: +ELLIPSIS
0.1994393...
"""
lef = lef.clone().align(spd.shape,
extrapolation_left=0,
extrapolation_right=0)
spd = spd.clone()
efficiency = (np.trapz(lef.values * spd.values, spd.wavelengths) /
np.trapz(spd.values, spd.wavelengths))
return efficiency
def luminous_efficacy(spd,
lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer')):
"""
Returns the *luminous efficacy* in :math:`lm\cdot W^{-1}` of given spectral
power distribution using given luminous efficiency function.
Parameters
----------
spd : SpectralPowerDistribution
test spectral power distribution
lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` luminous efficiency function.
Returns
-------
numeric
Luminous efficacy in :math:`lm\cdot W^{-1}`.
Examples
--------
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')
>>> luminous_efficacy(spd) # doctest: +ELLIPSIS
136.2170803...
"""
efficacy = K_M * luminous_efficiency(spd, lef)
return efficacy
def luminous_flux(spd,
lef=PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer'),
K_m=K_M):
"""
Returns the *luminous flux* for given spectral power distribution using
the given luminous efficiency function.
Parameters
----------
spd : SpectralPowerDistribution
test spectral power distribution
lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` luminous efficiency function.
K_m : numeric, optional
:math:`lm\cdot W^{-1}` maximum photopic luminous efficiency
Returns
-------
numeric
Luminous flux
Examples
--------
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')
>>> luminous_flux(spd) # doctest: +ELLIPSIS
23807.6555273...
"""
lef = lef.clone().align(spd.shape, left=0, right=0)
spd = spd.clone() * lef
flux = K_m * np.trapz(spd.values, spd.wavelengths)
return flux
def luminous_efficacy(
spd, lef=PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer')):
"""
Returns the *luminous efficacy* in :math:`lm\cdot W^{-1}` of given spectral
power distribution using given luminous efficiency function.
Parameters
----------
spd : SpectralPowerDistribution
test spectral power distribution
lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` luminous efficiency function.
Returns
-------
numeric
Luminous efficacy in :math:`lm\cdot W^{-1}`.
Examples
--------
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')
>>> luminous_efficacy(spd) # doctest: +ELLIPSIS
136.2170803...
"""
efficacy = K_M * luminous_efficiency(spd, lef)
return efficacy
def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'CIE 1951 Scotopic Standard Observer')):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
{'Blue Heavy', 'Red Heavy'},
Light source colour temperature.
method : unicode, optional
{'MOVE', 'LRC'},
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.steps, scotopic_lef_shape.steps))
spd_data = dict((i,
mesopic_weighting_function(
i,
Lp,
source,
method,
photopic_lef,
scotopic_lef))
for i in shape)
spd = SpectralPowerDistribution(
'{0} Lp Mesopic Luminous Efficiency Function'.format(Lp),
spd_data)
return spd.normalise()
def luminous_efficacy(spd,
lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer')):
"""
Returns the *luminous efficacy* for given spectral power distribution using
the given luminous efficiency function.
Parameters
----------
spd : SpectralPowerDistribution
test spectral power distribution
lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` luminous efficiency function.
Returns
-------
numeric
Luminous efficacy
Examples
--------
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')
>>> luminous_efficacy(spd) # doctest: +ELLIPSIS
0.1994393...
"""
lef = lef.clone().align(spd.shape, left=0, right=0)
spd = spd.clone()
efficacy = (np.trapz(lef.values * spd.values, spd.wavelengths) /
np.trapz(spd.values, spd.wavelengths))
return efficacy
def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get('CIE 1951 Scotopic Standard Observer')):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
**{'Blue Heavy', 'Red Heavy'}**,
Light source colour temperature.
method : unicode, optional
**{'MOVE', 'LRC'}**,
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> mesopic_luminous_efficiency_function(0.2) # doctest: +ELLIPSIS
<colour.colorimetry.spectrum.SpectralPowerDistribution object at 0x...>
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.steps, scotopic_lef_shape.steps))
wavelengths = shape.range()
spd_data = dict(
zip(
wavelengths,
mesopic_weighting_function(wavelengths, Lp, source, method,
photopic_lef, scotopic_lef)))
spd = SpectralPowerDistribution(
'{0} Lp Mesopic Luminous Efficiency Function'.format(Lp), spd_data)
return spd.normalise()
def mesopic_weighting_function(wavelength,
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'CIE 1951 Scotopic Standard Observer')):
"""
Calculates the mesopic weighting function factor at given wavelength
:math:`\lambda` using the photopic luminance :math:`L_p`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` to calculate the mesopic weighting function
factor.
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
{'Blue Heavy', 'Red Heavy'},
Light source colour temperature.
method : unicode, optional
{'MOVE', 'LRC'},
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
numeric or ndarray
Mesopic weighting function factor.
Examples
--------
>>> mesopic_weighting_function(500, 0.2) # doctest: +ELLIPSIS
0.7052200...
"""
mesopic_x_luminance_values = sorted(MESOPIC_X_DATA.keys())
index = mesopic_x_luminance_values.index(
closest(mesopic_x_luminance_values, Lp))
x = MESOPIC_X_DATA.get(
mesopic_x_luminance_values[index]).get(source).get(method)
Vm = ((1 - x) *
scotopic_lef.get(wavelength) + x * photopic_lef.get(wavelength))
return Vm
def mesopic_weighting_function(wavelength,
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'CIE 1951 Scotopic Standard Observer')):
"""
Calculates the mesopic weighting function factor at given wavelength
:math:`\lambda` using the photopic luminance :math:`L_p`.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` to calculate the mesopic weighting function
factor.
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
**{'Blue Heavy', 'Red Heavy'}**,
Light source colour temperature.
method : unicode, optional
**{'MOVE', 'LRC'}**,
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
numeric or ndarray
Mesopic weighting function factor.
Examples
--------
>>> mesopic_weighting_function(500, 0.2) # doctest: +ELLIPSIS
0.7052200...
"""
mesopic_x_luminance_values = sorted(MESOPIC_X_DATA.keys())
index = mesopic_x_luminance_values.index(
closest(mesopic_x_luminance_values, Lp))
x = MESOPIC_X_DATA.get(
mesopic_x_luminance_values[index]).get(source).get(method)
Vm = ((1 - x) *
scotopic_lef.get(wavelength) + x * photopic_lef.get(wavelength))
return Vm
def luminous_flux(spd,
lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
K_m=K_M):
"""
Returns the *luminous flux* for given spectral power distribution using
given luminous efficiency function.
Parameters
----------
spd : SpectralPowerDistribution
test spectral power distribution
lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` luminous efficiency function.
K_m : numeric, optional
:math:`lm\cdot W^{-1}` maximum photopic luminous efficiency
Returns
-------
numeric
Luminous flux.
Examples
--------
>>> from colour import LIGHT_SOURCES_RELATIVE_SPDS
>>> spd = LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')
>>> luminous_flux(spd) # doctest: +ELLIPSIS
23807.6555273...
"""
lef = lef.clone().align(spd.shape,
extrapolation_left=0,
extrapolation_right=0)
spd = spd.clone() * lef
flux = K_m * np.trapz(spd.values, spd.wavelengths)
return flux
def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions
into the *CIE 1931 2 Degree Standard Observer* colour matching functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 1931 2 Degree Standard Observer* spectral tristimulus values.
See Also
--------
:attr:`colour.colorimetry.dataset.cmfs.RGB_CMFS`
Notes
-----
- Data for the *CIE 1931 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [1] Wyszecki, G., & Stiles, W. S. (2000). Table 1(3.3.3). In Color
Science: Concepts and Methods, Quantitative Data and Formulae
(pp. 138–139). Wiley. ISBN:978-0471399186
Examples
--------
>>> RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0113577..., 0.004102 , 0. ])
"""
cmfs = RGB_CMFS.get('Wright & Guild 1931 2 Degree RGB CMFs')
rgb_bar = cmfs.get(wavelength)
rgb = rgb_bar / np.sum(rgb_bar)
M1 = np.array([[0.49000, 0.31000, 0.20000],
[0.17697, 0.81240, 0.01063],
[0.00000, 0.01000, 0.99000]])
M2 = np.array([[0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063]])
xyz = dot_vector(M1, rgb)
xyz /= dot_vector(M2, rgb)
x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2]
V = PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer').clone()
V.align(cmfs.shape)
L = V.get(wavelength)
x_bar = x / y * L
y_bar = L
z_bar = z / y * L
xyz_bar = tstack((x_bar, y_bar, z_bar))
return xyz_bar
def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions
into the *CIE 1931 2 Degree Standard Observer* colour matching functions.
Parameters
----------
wavelength : numeric or array_like
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray
*CIE 1931 2 Degree Standard Observer* spectral tristimulus values.
See Also
--------
:attr:`colour.colorimetry.dataset.cmfs.RGB_CMFS`
Notes
-----
- Data for the *CIE 1931 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [1] Wyszecki, G., & Stiles, W. S. (2000). Table 1(3.3.3). In Color
Science: Concepts and Methods, Quantitative Data and Formulae
(pp. 138–139). Wiley. ISBN:978-0471399186
Examples
--------
>>> RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0113577..., 0.004102 , 0. ])
"""
cmfs = RGB_CMFS.get('Wright & Guild 1931 2 Degree RGB CMFs')
rgb_bar = cmfs.get(wavelength)
rgb = rgb_bar / np.sum(rgb_bar)
M1 = np.array([[0.49000, 0.31000, 0.20000], [0.17697, 0.81240, 0.01063],
[0.00000, 0.01000, 0.99000]])
M2 = np.array([[0.66697, 1.13240, 1.20063], [0.66697, 1.13240, 1.20063],
[0.66697, 1.13240, 1.20063]])
xyz = dot_vector(M1, rgb)
xyz /= dot_vector(M2, rgb)
x, y, z = xyz[..., 0], xyz[..., 1], xyz[..., 2]
V = PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer').clone()
V.align(cmfs.shape)
L = V.get(wavelength)
x_bar = x / y * L
y_bar = L
z_bar = z / y * L
xyz_bar = tstack((x_bar, y_bar, z_bar))
return xyz_bar
def mesopic_luminous_efficiency_function(
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'CIE 1951 Scotopic Standard Observer')):
"""
Returns the mesopic luminous efficiency function :math:`V_m(\lambda)` for
given photopic luminance :math:`L_p`.
Parameters
----------
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
**{'Blue Heavy', 'Red Heavy'}**,
Light source colour temperature.
method : unicode, optional
**{'MOVE', 'LRC'}**,
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
SpectralPowerDistribution
Mesopic luminous efficiency function :math:`V_m(\lambda)`.
Examples
--------
>>> print(mesopic_luminous_efficiency_function(0.2))
SpectralPowerDistribution(\
'0.2 Lp Mesopic Luminous Efficiency Function', (380.0, 780.0, 1.0))
"""
photopic_lef_shape = photopic_lef.shape
scotopic_lef_shape = scotopic_lef.shape
shape = SpectralShape(
max(photopic_lef_shape.start, scotopic_lef_shape.start),
min(photopic_lef_shape.end, scotopic_lef_shape.end),
max(photopic_lef_shape.interval, scotopic_lef_shape.interval))
wavelengths = shape.range()
spd_data = dict(zip(wavelengths,
mesopic_weighting_function(
wavelengths,
Lp,
source,
method,
photopic_lef,
scotopic_lef)))
spd = SpectralPowerDistribution(
'{0} Lp Mesopic Luminous Efficiency Function'.format(Lp),
spd_data)
return spd.normalise()
def LED_4D(l_red, l_green, l_blue, l_yellow, T, a, h, spd4_name, image, Fi_4d,
Fi_red, Fi_green, Fi_blue, Fi_yellow):
spd_red0 = aprocsimate_CIA(l_red[0], l_red[1])
spd_green0 = aprocsimate_CIA(l_green[0], l_green[1])
spd_blue0 = aprocsimate_CIA(l_blue[0], l_blue[1])
spd_yellow0 = aprocsimate_CIA(l_yellow[0], l_yellow[1])
spd_red = colour_spectr(spd_red0, 'red')
spd_green = colour_spectr(spd_green0, 'green')
spd_blue = colour_spectr(spd_blue0, 'blue')
spd_yellow = colour_spectr(spd_yellow0, 'yellow')
blackbody_spd = colour.blackbody_spd(T)
#single_spd_plot(blackbody_spd)
cmfs = colour.STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer']
# # # # Calculating the sample spectral power distribution *CIE XYZ* tristimulus values.
blackbody_spd.normalise()
XYZ_blackbody = colour.spectral_to_XYZ(blackbody_spd, cmfs)
XYZ_red = colour.spectral_to_XYZ(spd_red, cmfs)
XYZ_green = colour.spectral_to_XYZ(spd_green, cmfs)
XYZ_blue = colour.spectral_to_XYZ(spd_blue, cmfs)
XYZ_yellow = colour.spectral_to_XYZ(spd_yellow, cmfs)
XYZ_blackbody[0] = XYZ_blackbody[0] - XYZ_yellow[0] * a
XYZ_blackbody[1] = XYZ_blackbody[1] - XYZ_yellow[1] * a
XYZ_blackbody[2] = XYZ_blackbody[2] - XYZ_yellow[2] * a
#print(XYZ_blackbody,XYZ_red,XYZ_green,XYZ_blue)
XYZ = np.vstack((XYZ_red, XYZ_green, XYZ_blue))
XYZ = XYZ.transpose()
XYZ = np.linalg.inv(XYZ)
XYZ_blackbody = XYZ_blackbody.transpose()
abc = np.dot(XYZ, XYZ_blackbody)
abca = [abc[0], abc[1], abc[2], a]
#abca=abca/max(abca)
spd_4 = [spd_red, spd_green, spd_blue, spd_yellow]
spd_4d = spectr_LED4(abca, spd_red0, spd_green0, spd_blue0, spd_yellow0)
b1, b2, b3, b4 = abca[0], abca[1], abca[2], abca[3]
c1 = 1
c2 = (b2 / b1) * (l_red[0] / l_green[0])
c3 = (b3 / b1) * (l_red[0] / l_blue[0])
c4 = (b4 / b1) * (l_red[0] / l_yellow[0])
h1, h2, h3, h4 = h[0], h[1], h[2], h[3]
h_sum = (c1 * h1 + c2 * h2 + c3 * h3 + c4 * h4) / (c1 + c2 + c3 + c4)
multi_spd_plot(spd_4, bounding_box=[300, 800, 0, 1], filename=spd4_name)
image.append(spd4_name)
from colour.colorimetry import PHOTOPIC_LEFS
lef = PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer')
lef_4d = lef.clone().align(spd_4d.shape,
extrapolation_left=0,
extrapolation_right=0)
lef_red = lef.clone().align(spd_red.shape,
extrapolation_left=0,
extrapolation_right=0)
lef_green = lef.clone().align(spd_green.shape,
extrapolation_left=0,
extrapolation_right=0)
lef_blue = lef.clone().align(spd_blue.shape,
extrapolation_left=0,
extrapolation_right=0)
lef_yellow = lef.clone().align(spd_yellow.shape,
extrapolation_left=0,
extrapolation_right=0)
#import numpy as np
#spd_4d
Fi_max_4d = (683 *
np.trapz(lef_4d.values * spd_4d.values, spd_4d.wavelengths))
k_4d = Fi_4d / Fi_max_4d
Fe_4d = (k_4d * np.trapz(spd_4d.values, spd_4d.wavelengths))
F_4d = [Fi_max_4d, k_4d, Fe_4d, Fi_4d]
#spd_red
Fi_max_red = (
683 * np.trapz(lef_red.values * spd_red.values, spd_red.wavelengths))
k_red = Fi_red / Fi_max_red
Fe_red = (k_red * np.trapz(spd_red.values, spd_red.wavelengths))
F_red = [Fi_max_red, k_red, Fe_red, Fi_red]
#spd_green
Fi_max_green = (
683 *
np.trapz(lef_green.values * spd_green.values, spd_green.wavelengths))
k_green = Fi_green / Fi_max_green
Fe_green = (k_green * np.trapz(spd_green.values, spd_green.wavelengths))
F_green = [Fi_max_green, k_green, Fe_green, Fi_green]
#spd_blue
Fi_max_blue = (
683 *
np.trapz(lef_blue.values * spd_blue.values, spd_blue.wavelengths))
k_blue = Fi_blue / Fi_max_blue
Fe_blue = (k_blue * np.trapz(spd_blue.values, spd_blue.wavelengths))
F_blue = [Fi_max_blue, k_blue, Fe_blue, Fi_blue]
#spd_yellow
Fi_max_yellow = (683 * np.trapz(lef_yellow.values * spd_yellow.values,
spd_yellow.wavelengths))
k_yellow = Fi_yellow / Fi_max_yellow
Fe_yellow = (k_yellow *
np.trapz(spd_yellow.values, spd_yellow.wavelengths))
F_yellow = [Fi_max_yellow, k_yellow, Fe_yellow, Fi_yellow]
return spd_4, spd_4d, h_sum, image, abca, F_4d, F_red, F_green, F_blue, F_yellow
def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions
into the *CIE 1931 2 Degree Standard Observer* colour matching functions.
Parameters
----------
wavelength : numeric
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray, (3,)
*CIE 1931 2 Degree Standard Observer* spectral tristimulus values.
Raises
------
KeyError
If wavelength :math:`\lambda` is not available in the colour matching
functions.
See Also
--------
:attr:`colour.colorimetry.dataset.cmfs.RGB_CMFS`
Notes
-----
- Data for the *CIE 1931 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [1] **Wyszecki & Stiles**,
*Color Science - Concepts and Methods Data and Formulae -
Second Edition*,
Wiley Classics Library Edition, published 2000,
ISBN-10: 0-471-39918-3,
pages 138, 139.
Examples
--------
>>> RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0113577..., 0.004102 , 0. ])
"""
cmfs = RGB_CMFS.get('Wright & Guild 1931 2 Degree RGB CMFs')
r_bar, g_bar, b_bar = cmfs.r_bar.get(wavelength), cmfs.g_bar.get(
wavelength), cmfs.b_bar.get(wavelength)
if None in (r_bar, g_bar, b_bar):
raise KeyError(('"{0} nm" wavelength not available in "{1}" colour '
'matching functions with "{2}" shape!').format(
wavelength, cmfs.name, cmfs.shape))
r = r_bar / (r_bar + g_bar + b_bar)
g = g_bar / (r_bar + g_bar + b_bar)
b = b_bar / (r_bar + g_bar + b_bar)
x = ((0.49000 * r + 0.31000 * g + 0.20000 * b) /
(0.66697 * r + 1.13240 * g + 1.20063 * b))
y = ((0.17697 * r + 0.81240 * g + 0.01063 * b) /
(0.66697 * r + 1.13240 * g + 1.20063 * b))
z = ((0.00000 * r + 0.01000 * g + 0.99000 * b) /
(0.66697 * r + 1.13240 * g + 1.20063 * b))
V = PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer').clone()
V.align(cmfs.shape)
L = V.get(wavelength)
x_bar = x / y * L
y_bar = L
z_bar = z / y * L
return np.array([x_bar, y_bar, z_bar])
def RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(wavelength):
"""
Converts *Wright & Guild 1931 2 Degree RGB CMFs* colour matching functions
into the *CIE 1931 2 Degree Standard Observer* colour matching functions.
Parameters
----------
wavelength : numeric
Wavelength :math:`\lambda` in nm.
Returns
-------
ndarray, (3,)
*CIE 1931 2 Degree Standard Observer* spectral tristimulus values.
Raises
------
KeyError
If wavelength :math:`\lambda` is not available in the colour matching
functions.
See Also
--------
:attr:`colour.colorimetry.dataset.cmfs.RGB_CMFS`
Notes
-----
- Data for the *CIE 1931 2 Degree Standard Observer* already exists,
this definition is intended for educational purpose.
References
----------
.. [1] Wyszecki, G., & Stiles, W. S. (2000). Table 1(3.3.3). In Color
Science: Concepts and Methods, Quantitative Data and Formulae
(pp. 138–139). Wiley. ISBN:978-0471399186
Examples
--------
>>> RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(700) # doctest: +ELLIPSIS
array([ 0.0113577..., 0.004102 , 0. ])
"""
cmfs = RGB_CMFS.get('Wright & Guild 1931 2 Degree RGB CMFs')
r_bar, g_bar, b_bar = cmfs.r_bar.get(wavelength), cmfs.g_bar.get(
wavelength), cmfs.b_bar.get(wavelength)
if None in (r_bar, g_bar, b_bar):
raise KeyError(('"{0} nm" wavelength not available in "{1}" colour '
'matching functions with "{2}" shape!').format(
wavelength, cmfs.name, cmfs.shape))
r = r_bar / (r_bar + g_bar + b_bar)
g = g_bar / (r_bar + g_bar + b_bar)
b = b_bar / (r_bar + g_bar + b_bar)
x = ((0.49000 * r + 0.31000 * g + 0.20000 * b) /
(0.66697 * r + 1.13240 * g + 1.20063 * b))
y = ((0.17697 * r + 0.81240 * g + 0.01063 * b) /
(0.66697 * r + 1.13240 * g + 1.20063 * b))
z = ((0.00000 * r + 0.01000 * g + 0.99000 * b) /
(0.66697 * r + 1.13240 * g + 1.20063 * b))
V = PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer').clone()
V.align(cmfs.shape)
L = V.get(wavelength)
x_bar = x / y * L
y_bar = L
z_bar = z / y * L
return np.array([x_bar, y_bar, z_bar])
def mesopic_weighting_function(wavelength,
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get(
'CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get(
'CIE 1951 Scotopic Standard Observer')):
"""
Calculates the mesopic weighting function factor at given wavelength
:math:`\lambda` using the photopic luminance :math:`L_p`.
Parameters
----------
wavelength : numeric
Wavelength :math:`\lambda` to calculate the mesopic weighting function
factor.
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode, optional
{'Blue Heavy', 'Red Heavy'},
Light source colour temperature.
method : unicode, optional
{'MOVE', 'LRC'},
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution, optional
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution, optional
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
numeric
Mesopic weighting function factor.
Raises
------
KeyError
If wavelength :math:`\lambda` is not available in either luminous
efficiency function.
Examples
--------
>>> mesopic_weighting_function(500, 0.2) # doctest: +ELLIPSIS
0.7052200...
"""
for function in (photopic_lef, scotopic_lef):
if function.get(wavelength) is None:
raise KeyError(
('"{0} nm" wavelength not available in "{1}" '
'luminous efficiency function with "{2}" shape!').format(
wavelength, function.name, function.shape))
mesopic_x_luminance_values = sorted(MESOPIC_X_DATA.keys())
index = mesopic_x_luminance_values.index(
closest(mesopic_x_luminance_values, Lp))
x = MESOPIC_X_DATA.get(
mesopic_x_luminance_values[index]).get(source).get(method)
Vm = ((1 - x) *
scotopic_lef.get(wavelength) + x * photopic_lef.get(wavelength))
return Vm
def mesopic_weighting_function(
wavelength,
Lp,
source='Blue Heavy',
method='MOVE',
photopic_lef=PHOTOPIC_LEFS.get('CIE 1924 Photopic Standard Observer'),
scotopic_lef=SCOTOPIC_LEFS.get('CIE 1951 Scotopic Standard Observer')):
"""
Calculates the mesopic weighting function factor at given wavelength
:math:`\lambda` using the photopic luminance :math:`L_p`.
Parameters
----------
wavelength : numeric
Wavelength :math:`\lambda` to calculate the mesopic weighting function
factor.
Lp : numeric
Photopic luminance :math:`L_p`.
source : unicode
('Blue Heavy', 'Red Heavy'),
Light source colour temperature.
method : unicode
('MOVE', 'LRC'),
Method to calculate the weighting factor.
photopic_lef : SpectralPowerDistribution
:math:`V(\lambda)` photopic luminous efficiency function.
scotopic_lef : SpectralPowerDistribution
:math:`V^\prime(\lambda)` scotopic luminous efficiency function.
Returns
-------
numeric
Mesopic weighting function factor.
Raises
------
KeyError
If wavelength :math:`\lambda` is not available in either luminous
efficiency function.
Examples
--------
>>> mesopic_weighting_function(500, 0.2) # doctest: +ELLIPSIS
0.7052200...
"""
for function in (photopic_lef, scotopic_lef):
if function.get(wavelength) is None:
raise KeyError(
('"{0} nm" wavelength not available in "{1}" '
'luminous efficiency function with "{2}" shape!').format(
wavelength, function.name, function.shape))
mesopic_x_luminance_values = sorted(MESOPIC_X_DATA.keys())
index = mesopic_x_luminance_values.index(
closest(mesopic_x_luminance_values, Lp))
x = MESOPIC_X_DATA.get(
mesopic_x_luminance_values[index]).get(source).get(method)
Vm = ((1 - x) * scotopic_lef.get(wavelength) +
x * photopic_lef.get(wavelength))
return Vm
