def generate_documentation_plots(output_directory):
"""
Generates documentation plots.
Parameters
----------
output_directory : unicode
Output directory.
"""
colour.utilities.filter_warnings()
colour_style()
np.random.seed(0)
# *************************************************************************
# "README.rst"
# *************************************************************************
arguments = {
'tight_layout':
True,
'transparent_background':
True,
'filename':
os.path.join(output_directory,
'Examples_Plotting_Visible_Spectrum.png')
}
plot_visible_spectrum('CIE 1931 2 Degree Standard Observer', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Illuminant_F1_SD.png')
plot_single_illuminant_sd('FL1', **arguments)
arguments['filename'] = os.path.join(output_directory,
'Examples_Plotting_Blackbodies.png')
blackbody_sds = [
colour.sd_blackbody(i, colour.SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plot_multi_sds(
blackbody_sds,
y_label='W / (sr m$^2$) / m',
use_sds_colours=True,
normalise_sds_colours=True,
legend_location='upper right',
bounding_box=(0, 1250, 0, 2.5e15),
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Cone_Fundamentals.png')
plot_single_cmfs(
'Stockman & Sharpe 2 Degree Cone Fundamentals',
y_label='Sensitivity',
bounding_box=(390, 870, 0, 1.1),
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Luminous_Efficiency.png')
sd_mesopic_luminous_efficiency_function = (
colour.sd_mesopic_luminous_efficiency_function(0.2))
plot_multi_sds(
(sd_mesopic_luminous_efficiency_function,
colour.PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
colour.SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']),
y_label='Luminous Efficiency',
legend_location='upper right',
y_tighten=True,
margins=(0, 0, 0, .1),
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_BabelColor_Average.png')
plot_multi_sds(
colour.COLOURCHECKERS_SDS['BabelColor Average'].values(),
use_sds_colours=True,
title=('BabelColor Average - '
'Spectral Distributions'),
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_ColorChecker_2005.png')
plot_single_colour_checker(
'ColorChecker 2005', text_parameters={'visible': False}, **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Chromaticities_Prediction.png')
plot_corresponding_chromaticities_prediction(2, 'Von Kries', 'Bianco',
**arguments)
arguments['filename'] = os.path.join(
output_directory,
'Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png')
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'B', 'C'],
**arguments)
arguments['filename'] = os.path.join(
output_directory,
'Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png')
RGB = np.random.random((32, 32, 3))
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB,
'ITU-R BT.709',
colourspaces=['ACEScg', 'S-Gamut'],
show_pointer_gamut=True,
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Examples_Plotting_CRI.png')
plot_single_sd_colour_rendering_index_bars(colour.ILLUMINANTS_SDS['FL2'],
**arguments)
# *************************************************************************
# Documentation
# *************************************************************************
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_CVD_Simulation_Machado2009.png')
plot_cvd_simulation_Machado2009(RGB, **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Colour_Checker.png')
plot_single_colour_checker('ColorChecker 2005', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Colour_Checkers.png')
plot_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'],
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_SD.png')
data = {
500: 0.0651,
520: 0.0705,
540: 0.0772,
560: 0.0870,
580: 0.1128,
600: 0.1360
}
sd = colour.SpectralDistribution(data, name='Custom')
plot_single_sd(sd, **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_SDs.png')
data_1 = {
500: 0.004900,
510: 0.009300,
520: 0.063270,
530: 0.165500,
540: 0.290400,
550: 0.433450,
560: 0.594500
}
data_2 = {
500: 0.323000,
510: 0.503000,
520: 0.710000,
530: 0.862000,
540: 0.954000,
550: 0.994950,
560: 0.995000
}
spd1 = colour.SpectralDistribution(data_1, name='Custom 1')
spd2 = colour.SpectralDistribution(data_2, name='Custom 2')
plot_multi_sds([spd1, spd2], **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_CMFS.png')
plot_single_cmfs('CIE 1931 2 Degree Standard Observer', **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_CMFS.png')
cmfs = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
plot_multi_cmfs(cmfs, **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Illuminant_SD.png')
plot_single_illuminant_sd('A', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Illuminant_SDs.png')
plot_multi_illuminant_sds(['A', 'B', 'C'], **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Visible_Spectrum.png')
plot_visible_spectrum(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Lightness_Function.png')
plot_single_lightness_function('CIE 1976', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Lightness_Functions.png')
plot_multi_lightness_functions(['CIE 1976', 'Wyszecki 1963'], **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Luminance_Function.png')
plot_single_luminance_function('CIE 1976', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Luminance_Functions.png')
plot_multi_luminance_functions(['CIE 1976', 'Newhall 1943'], **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Blackbody_Spectral_Radiance.png')
plot_blackbody_spectral_radiance(
3500, blackbody='VY Canis Major', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Blackbody_Colours.png')
plot_blackbody_colours(colour.SpectralShape(150, 12500, 50), **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Colour_Swatch.png')
RGB = ColourSwatch(RGB=(0.32315746, 0.32983556, 0.33640183))
plot_single_colour_swatch(RGB, **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Colour_Swatches.png')
RGB_1 = ColourSwatch(RGB=(0.45293517, 0.31732158, 0.26414773))
RGB_2 = ColourSwatch(RGB=(0.77875824, 0.57726450, 0.50453169))
plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_Function.png')
plot_single_function(lambda x: x ** (1 / 2.2), **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_Functions.png')
functions = {
'Gamma 2.2': lambda x: x ** (1 / 2.2),
'Gamma 2.4': lambda x: x ** (1 / 2.4),
'Gamma 2.6': lambda x: x ** (1 / 2.6),
}
plot_multi_functions(functions, **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Image.png')
path = os.path.join(colour.__path__[0], '..', 'docs', '_static',
'Logo_Medium_001.png')
plot_image(colour.read_image(str(path)), **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Corresponding_Chromaticities_Prediction.png')
plot_corresponding_chromaticities_prediction(1, 'Von Kries', 'CAT02',
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Spectral_Locus.png')
plot_spectral_locus(spectral_locus_colours='RGB', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_Colours.png')
plot_chromaticity_diagram_colours(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram.png')
plot_chromaticity_diagram(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1931.png')
plot_chromaticity_diagram_CIE1931(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png')
plot_chromaticity_diagram_CIE1960UCS(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png')
plot_chromaticity_diagram_CIE1976UCS(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_SDs_In_Chromaticity_Diagram.png')
A = colour.ILLUMINANTS_SDS['A']
D65 = colour.ILLUMINANTS_SDS['D65']
plot_sds_in_chromaticity_diagram([A, D65], **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDs_In_Chromaticity_Diagram_CIE1931.png')
plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDs_In_Chromaticity_Diagram_CIE1960UCS.png')
plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDs_In_Chromaticity_Diagram_CIE1976UCS.png')
plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Pointer_Gamut.png')
plot_pointer_gamut(**arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png')
plot_RGB_colourspaces_in_chromaticity_diagram(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png')
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_Plot.png')
RGB = np.random.random((128, 128, 3))
plot_RGB_chromaticities_in_chromaticity_diagram(RGB, 'ITU-R BT.709',
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1931.png')
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB, 'ITU-R BT.709', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(
RGB, 'ITU-R BT.709', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(
RGB, 'ITU-R BT.709', **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png')
plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1931.png')
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS(**arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_CCTF.png')
plot_single_cctf('ITU-R BT.709', **arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_CCTFs.png')
plot_multi_cctfs(['ITU-R BT.709', 'sRGB'], **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Munsell_Value_Function.png')
plot_single_munsell_value_function('ASTM D1535-08', **arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Munsell_Value_Functions.png')
plot_multi_munsell_value_functions(['ASTM D1535-08', 'McCamy 1987'],
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_SD_Rayleigh_Scattering.png')
plot_single_sd_rayleigh_scattering(**arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_The_Blue_Sky.png')
plot_the_blue_sky(**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Colour_Quality_Bars.png')
illuminant = colour.ILLUMINANTS_SDS['FL2']
light_source = colour.LIGHT_SOURCES_SDS['Kinoton 75P']
light_source = light_source.copy().align(colour.SpectralShape(360, 830, 1))
cqs_i = colour.colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour.colour_quality_scale(light_source, additional_data=True)
plot_colour_quality_bars([cqs_i, cqs_l], **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png')
illuminant = colour.ILLUMINANTS_SDS['FL2']
plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Multi_SDs_Colour_Rendering_Indexes_Bars.png')
light_source = colour.LIGHT_SOURCES_SDS['Kinoton 75P']
plot_multi_sds_colour_rendering_indexes_bars([illuminant, light_source],
**arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png')
illuminant = colour.ILLUMINANTS_SDS['FL2']
plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Multi_SDs_Colour_Quality_Scales_Bars.png')
light_source = colour.LIGHT_SOURCES_SDS['Kinoton 75P']
plot_multi_sds_colour_quality_scales_bars([illuminant, light_source],
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Planckian_Locus.png')
plot_planckian_locus(**arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png')
plot_planckian_locus_in_chromaticity_diagram(['A', 'B', 'C'], **arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png')
plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', 'B', 'C'],
**arguments)
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png')
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'B', 'C'],
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png')
plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'],
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png')
plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'],
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_RGB_Scatter.png')
plot_RGB_scatter(RGB, 'ITU-R BT.709', **arguments)
# *************************************************************************
# "tutorial.rst"
# *************************************************************************
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Visible_Spectrum.png')
plot_visible_spectrum(**arguments)
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Sample_SD.png')
sample_sd_data = {
380: 0.048,
385: 0.051,
390: 0.055,
395: 0.060,
400: 0.065,
405: 0.068,
410: 0.068,
415: 0.067,
420: 0.064,
425: 0.062,
430: 0.059,
435: 0.057,
440: 0.055,
445: 0.054,
450: 0.053,
455: 0.053,
460: 0.052,
465: 0.052,
470: 0.052,
475: 0.053,
480: 0.054,
485: 0.055,
490: 0.057,
495: 0.059,
500: 0.061,
505: 0.062,
510: 0.065,
515: 0.067,
520: 0.070,
525: 0.072,
530: 0.074,
535: 0.075,
540: 0.076,
545: 0.078,
550: 0.079,
555: 0.082,
560: 0.087,
565: 0.092,
570: 0.100,
575: 0.107,
580: 0.115,
585: 0.122,
590: 0.129,
595: 0.134,
600: 0.138,
605: 0.142,
610: 0.146,
615: 0.150,
620: 0.154,
625: 0.158,
630: 0.163,
635: 0.167,
640: 0.173,
645: 0.180,
650: 0.188,
655: 0.196,
660: 0.204,
665: 0.213,
670: 0.222,
675: 0.231,
680: 0.242,
685: 0.251,
690: 0.261,
695: 0.271,
700: 0.282,
705: 0.294,
710: 0.305,
715: 0.318,
720: 0.334,
725: 0.354,
730: 0.372,
735: 0.392,
740: 0.409,
745: 0.420,
750: 0.436,
755: 0.450,
760: 0.462,
765: 0.465,
770: 0.448,
775: 0.432,
780: 0.421
}
sd = colour.SpectralDistribution(sample_sd_data, name='Sample')
plot_single_sd(sd, **arguments)
arguments['filename'] = os.path.join(output_directory,
'Tutorial_SD_Interpolation.png')
sd_copy = sd.copy()
sd_copy.interpolate(colour.SpectralShape(400, 770, 1))
plot_multi_sds(
[sd, sd_copy], bounding_box=[730, 780, 0.25, 0.5], **arguments)
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Sample_Swatch.png')
sd = colour.SpectralDistribution(sample_sd_data)
cmfs = colour.STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer']
illuminant = colour.ILLUMINANTS_SDS['D65']
with domain_range_scale('1'):
XYZ = colour.sd_to_XYZ(sd, cmfs, illuminant)
RGB = colour.XYZ_to_sRGB(XYZ)
plot_single_colour_swatch(
ColourSwatch('Sample', RGB),
text_parameters={'size': 'x-large'},
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Neutral5.png')
patch_name = 'neutral 5 (.70 D)'
patch_sd = colour.COLOURCHECKERS_SDS['ColorChecker N Ohta'][patch_name]
with domain_range_scale('1'):
XYZ = colour.sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = colour.XYZ_to_sRGB(XYZ)
plot_single_colour_swatch(
ColourSwatch(patch_name.title(), RGB),
text_parameters={'size': 'x-large'},
**arguments)
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Colour_Checker.png')
plot_single_colour_checker(
colour_checker='ColorChecker 2005',
text_parameters={'visible': False},
**arguments)
arguments['filename'] = os.path.join(
output_directory, 'Tutorial_CIE_1931_Chromaticity_Diagram.png')
xy = colour.XYZ_to_xy(XYZ)
plot_chromaticity_diagram_CIE1931(standalone=False)
x, y = xy
plt.plot(x, y, 'o-', color='white')
# Annotating the plot.
plt.annotate(
patch_sd.name.title(),
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2'))
render(
standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True,
**arguments)
# *************************************************************************
# "basics.rst"
# *************************************************************************
arguments['filename'] = os.path.join(output_directory,
'Basics_Logo_Small_001_CIE_XYZ.png')
RGB = colour.read_image(
os.path.join(output_directory, 'Logo_Small_001.png'))[..., 0:3]
XYZ = colour.sRGB_to_XYZ(RGB)
colour.plotting.plot_image(
XYZ, text_parameters={'text': 'sRGB to XYZ'}, **arguments)
# -*- coding: utf-8 -*-
"""
Showcases blackbody / planckian radiator computations.
"""
import colour
from colour.utilities import message_box
message_box('Blackbody / Planckian Radiator Computations')
message_box(('Computing the spectral 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_sd = colour.sd_blackbody(5500, cmfs.shape)
print(blackbody_sd)
XYZ = colour.sd_to_XYZ(blackbody_sd, cmfs)
print(XYZ)
print('\n')
message_box(('Computing the spectral radiance of a blackbody at wavelength '
'500 nm and temperature 5500 kelvin degrees.'))
print(colour.colorimetry.blackbody_spectral_radiance(500 * 1e-9, 5500))
print(colour.colorimetry.planck_law(500 * 1e-9, 5500))
plot_blackbody_spectral_radiance(
temperature=3500, blackbody='VY Canis Majoris')
plot_blackbody_spectral_radiance(temperature=5778, blackbody='The Sun')
plot_blackbody_spectral_radiance(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.sd_to_XYZ` definition.
ASTM_G_173_sd = ASTM_G_173_ETR.copy() * 1.37905559e+13
ASTM_G_173_sd.interpolate(
colour.SpectralShape(interval=5), interpolator=colour.LinearInterpolator)
blackbody_sd = colour.sd_blackbody(5778, ASTM_G_173_sd.shape)
blackbody_sd.name = 'The Sun - 5778K'
plot_multi_sds((ASTM_G_173_sd, blackbody_sd), y_label='W / (sr m$^2$) / m')
print('\n')
message_box('Plotting various "blackbody" spectral distributions.')
blackbody_sds = [
colour.sd_blackbody(i, colour.SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plot_multi_sds(
blackbody_sds,
y_label='W / (sr m$^2$) / m',
message_box('Plotting various blackbody spectral radiance.')
plot_blackbody_spectral_radiance(temperature=3500,
blackbody='VY Canis Majoris')
plot_blackbody_spectral_radiance(temperature=5778, blackbody='The Sun')
plot_blackbody_spectral_radiance(temperature=12130, blackbody='Rigel')
print('\n')
message_box('Comparing theoretical and measured "Sun" spectral distributions.')
sd_ASTMG173 = SD_ASTMG173_ETR.copy()
sd_ASTMG173.interpolate(colour.SpectralShape(interval=5),
interpolator=colour.LinearInterpolator)
sd_blackbody = colour.sd_blackbody(5778, sd_ASTMG173.shape)
sd_blackbody.name = 'The Sun - 5778K'
sd_blackbody /= colour.sd_to_XYZ(sd_blackbody)[1]
sd_blackbody *= colour.sd_to_XYZ(sd_ASTMG173)[1]
plot_multi_sds([sd_ASTMG173, sd_blackbody], y_label='W / (sr m$^2$) / m')
print('\n')
message_box('Plotting various "blackbody" spectral distributions.')
sds_blackbody = [
colour.sd_blackbody(i, colour.SpectralShape(100, 1500, 10))
for i in range(1000, 11000, 1000)
]
plot_blackbody_spectral_radiance(
temperature=3500, blackbody='VY Canis Majoris')
plot_blackbody_spectral_radiance(temperature=5778, blackbody='The Sun')
plot_blackbody_spectral_radiance(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.sd_to_XYZ` definition.
ASTM_G_173_sd = ASTM_G_173_ETR.copy() * 1.37905559e+13
ASTM_G_173_sd.interpolate(
colour.SpectralShape(interval=5), interpolator=colour.LinearInterpolator)
blackbody_sd = colour.sd_blackbody(5778, ASTM_G_173_sd.shape)
blackbody_sd.name = 'The Sun - 5778K'
plot_multi_sds((ASTM_G_173_sd, blackbody_sd), y_label='W / (sr m$^2$) / m')
print('\n')
message_box('Plotting various "blackbody" spectral distributions.')
blackbody_sds = [
colour.sd_blackbody(i, colour.SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plot_multi_sds(
blackbody_sds,
y_label='W / (sr m$^2$) / m',