blackbody_spectral_radiance_plot(temperature=3500,
blackbody='VY Canis Majoris')
blackbody_spectral_radiance_plot(temperature=5778, blackbody='The Sun')
blackbody_spectral_radiance_plot(temperature=12130, blackbody='Rigel')
print('\n')
message_box('Comparing theoretical and measured "Sun" spectral distributions.')
# Arbitrary ASTM_G_173_ETR scaling factor calculated with
# :func:`colour.spectral_to_XYZ` definition.
ASTM_G_173_spd = ASTM_G_173_ETR.copy() * 1.37905559e+13
ASTM_G_173_spd.interpolate(colour.SpectralShape(interval=5),
interpolator=colour.LinearInterpolator)
blackbody_spd = colour.blackbody_spd(5778, ASTM_G_173_spd.shape)
blackbody_spd.name = 'The Sun - 5778K'
multi_spd_plot((ASTM_G_173_spd, blackbody_spd),
y_label='W / (sr m$^2$) / m',
legend_location='upper right')
print('\n')
message_box('Plotting various "blackbody" spectral power distributions.')
blackbody_spds = [
colour.blackbody_spd(i, colour.SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
multi_spd_plot(blackbody_spds,
message_box("Plotting various blackbody spectral radiance.")
blackbody_spectral_radiance_plot(temperature=3500, blackbody="VY Canis Majoris")
blackbody_spectral_radiance_plot(temperature=5778, blackbody="The Sun")
blackbody_spectral_radiance_plot(temperature=12130, blackbody="Rigel")
print("\n")
message_box('Comparing theoretical and measured "Sun" spectral distributions.')
# Arbitrary ASTM_G_173_ETR scaling factor calculated with
# :def:`colour.spectral_to_XYZ` definition.
ASTM_G_173_spd = ASTM_G_173_ETR.clone() * 1.37905559e13
ASTM_G_173_spd.interpolate(colour.SpectralShape(steps=5), method="Linear")
blackbody_spd = colour.blackbody_spd(5778, ASTM_G_173_spd.shape)
blackbody_spd.name = "The Sun - 5778K"
multi_spd_plot([ASTM_G_173_spd, blackbody_spd], y_label=u"W / (sr m²) / m", legend_location="upper right")
print("\n")
message_box('Plotting various "blackbody" spectral power distributions.')
blackbody_spds = [colour.blackbody_spd(i, colour.SpectralShape(0, 10000, 10)) for i in range(1000, 15000, 1000)]
multi_spd_plot(
blackbody_spds,
y_label=u"W / (sr m²) / m",
use_spds_colours=True,
normalise_spds_colours=True,
legend_location="upper right",
blackbody_spectral_radiance_plot(temperature=3500,
blackbody='VY Canis Majoris')
blackbody_spectral_radiance_plot(temperature=5778, blackbody='The Sun')
blackbody_spectral_radiance_plot(temperature=12130, blackbody='Rigel')
print('\n')
message_box('Comparing theoretical and measured "Sun" spectral distributions.')
# Arbitrary ASTM_G_173_ETR scaling factor calculated with
# :func:`colour.spectral_to_XYZ` definition.
ASTM_G_173_spd = ASTM_G_173_ETR.clone() * 1.37905559e+13
ASTM_G_173_spd.interpolate(colour.SpectralShape(steps=5), method='Linear')
blackbody_spd = colour.blackbody_spd(
5778,
ASTM_G_173_spd.shape)
blackbody_spd.name = 'The Sun - 5778K'
multi_spd_plot((ASTM_G_173_spd, blackbody_spd),
y_label='W / (sr m$^2$) / m',
legend_location='upper right')
print('\n')
message_box('Plotting various "blackbody" spectral power distributions.')
blackbody_spds = [
colour.blackbody_spd(i, colour.SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)]
multi_spd_plot(blackbody_spds,
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Showcases blackbody / planckian radiator computations.
"""
import colour
from colour.utilities.verbose import message_box
message_box('Blackbody / Planckian Radiator Computations')
message_box(('Computing the spectral radiance of a blackbody at wavelength '
'500 nm and temperature 5500 kelvin degrees.'))
print(colour.planck_law(500 * 1e-9, 5500))
print('\n')
message_box(('Computing the spectral power distribution of a blackbody at '
'temperature 5500 kelvin degrees and converting to "CIE XYZ" '
'tristimulus values.'))
cmfs = colour.STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
blackbody_spd = colour.blackbody_spd(5500, cmfs.shape)
print(blackbody_spd)
XYZ = colour.spectral_to_XYZ(blackbody_spd, cmfs)
print(XYZ)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Showcases blackbody / planckian radiator computations.
"""
from __future__ import division, unicode_literals
import colour
from colour.utilities.verbose import message_box
message_box('Blackbody / Planckian Radiator Computations')
message_box(('Computing the spectral radiance of a blackbody at wavelength '
'500 nm and temperature 5500 kelvin degrees.'))
print(colour.planck_law(500 * 1e-9, 5500))
print('\n')
message_box(('Computing the spectral power distribution of a blackbody at '
'temperature 5500 kelvin degrees and converting to "CIE XYZ" '
'tristimulus values.'))
cmfs = colour.STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer')
blackbody_spd = colour.blackbody_spd(5500, cmfs.shape)
print(blackbody_spd)
XYZ = colour.spectral_to_XYZ(blackbody_spd, cmfs)
print(XYZ)
def LED_3D(l_red, l_green, l_blue, T, h, spd3_name, image):
def aprocsimate_CIA(l0, l05):
sample_spd_data = {}
for lamb in np.arange(200, 800, 0.9):
g = np.exp(-((lamb - l0) / l05)**2)
Phi_led = (g + 2 * g**5) / 3
sample_spd_data[lamb] = float(Phi_led)
return sample_spd_data
def colour_spectr(sample_spd_data, name):
spd = colour.SpectralPowerDistribution(name, sample_spd_data)
spd.interpolate(colour.SpectralShape(200, 800, 1))
return spd
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_red = colour_spectr(spd_red0, 'red')
spd_green = colour_spectr(spd_green0, 'green')
spd_blue = colour_spectr(spd_blue0, 'blue')
blackbody_spd = colour.blackbody_spd(T)
cmfs = colour.STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer']
# # # # Calculating the sample spectral power distribution *CIE XYZ* tristimulus values.
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)
#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)
abc = abc / max(abc)
def spectr_LED(abc, spd_red0, spd_green0, spd_blue0):
sample_spd_data = {}
a, b, c = abc[0], abc[1], abc[2]
for lamb in np.arange(200, 800, 0.9):
chip_3d = a * spd_red0[lamb] + b * spd_green0[
lamb] + c * spd_blue0[lamb]
sample_spd_data[lamb] = float(chip_3d)
spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)
spd.interpolate(colour.SpectralShape(200, 800, 1))
return spd
spd_3 = [spd_red, spd_green, spd_blue]
spd_3d = spectr_LED(abc, spd_red0, spd_green0, spd_blue0)
b1, b2, b3 = abc[0], abc[1], abc[2]
c1 = 1
c2 = (b2 / b1) * (l_red[0] / l_green[0])
c3 = (b3 / b1) * (l_red[0] / l_blue[0])
h1, h2, h3 = h[0], h[1], h[2]
h_sum = (c1 * h1 + c2 * h2 + c3 * h3) / (c1 + c2 + c3)
multi_spd_plot(spd_3, bounding_box=[300, 800, 0, 1], filename=spd3_name)
image.append(spd3_name)
return spd_3, spd_3d, h_sum, image, abc
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
