def test_plot_cvd_simulation_Machado2009(self):
"""
Tests :func:`colour.plotting.blindness.plot_cvd_simulation_Machado2009`
definition.
"""
figure, axes = plot_cvd_simulation_Machado2009(
np.random.rand(32, 32, 3))
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
os.path.join(RESOURCES_DIRECTORY,
'Ishihara_Colour_Blindness_Test_Plate_3.png')),
function='sRGB')
message_box('Colour Blindness Plots')
message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".')
plot_image(ISHIHARA_CBT_3_IMAGE, 'Normal Trichromat', label_colour='black')
print('\n')
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model and pre-computed matrix.')
plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE,
'Protanomaly',
0.5,
label_colour='black')
print('\n')
M_a = colour.anomalous_trichromacy_matrix_Machado2009(
colour.LMS_CMFS.get('Stockman & Sharpe 2 Degree Cone Fundamentals'),
colour.DISPLAYS_RGB_PRIMARIES['Typical CRT Brainard 1997'],
np.array([10, 0, 0]))
label = 'Average Protanomaly - 10nm'
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model using "Stockman & Sharpe 2 Degree Cone Fundamentals" and '
'"Typical CRT Brainard 1997" "RGB" display primaries.')
plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE,
M_a=M_a,
def generate_documentation_plots(output_directory):
"""
Generates documentation plots.
Parameters
----------
output_directory : unicode
Output directory.
"""
filter_warnings()
colour_style()
np.random.seed(0)
# *************************************************************************
# "README.rst"
# *************************************************************************
filename = os.path.join(output_directory,
'Examples_Colour_Automatic_Conversion_Graph.png')
plot_automatic_colour_conversion_graph(filename)
arguments = {
'tight_layout':
True,
'transparent_background':
True,
'filename':
os.path.join(output_directory,
'Examples_Plotting_Visible_Spectrum.png')
}
plt.close(
plot_visible_spectrum('CIE 1931 2 Degree Standard Observer',
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Illuminant_F1_SD.png')
plt.close(plot_single_illuminant_sd('FL1', **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Examples_Plotting_Blackbodies.png')
blackbody_sds = [
sd_blackbody(i, SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plt.close(
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)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Cone_Fundamentals.png')
plt.close(
plot_single_cmfs('Stockman & Sharpe 2 Degree Cone Fundamentals',
y_label='Sensitivity',
bounding_box=(390, 870, 0, 1.1),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Luminous_Efficiency.png')
plt.close(
plot_multi_sds((sd_mesopic_luminous_efficiency_function(0.2),
PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']),
y_label='Luminous Efficiency',
legend_location='upper right',
y_tighten=True,
margins=(0, 0, 0, .1),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_BabelColor_Average.png')
plt.close(
plot_multi_sds(COLOURCHECKERS_SDS['BabelColor Average'].values(),
use_sds_colours=True,
title=('BabelColor Average - '
'Spectral Distributions'),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_ColorChecker_2005.png')
plt.close(
plot_single_colour_checker('ColorChecker 2005',
text_parameters={'visible': False},
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Chromaticities_Prediction.png')
plt.close(
plot_corresponding_chromaticities_prediction(2, 'Von Kries', 'Bianco',
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
['A', 'B', 'C'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png')
RGB = np.random.random((32, 32, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB,
'ITU-R BT.709',
colourspaces=['ACEScg', 'S-Gamut'],
show_pointer_gamut=True,
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Examples_Plotting_CRI.png')
plt.close(
plot_single_sd_colour_rendering_index_bars(ILLUMINANTS_SDS['FL2'],
**arguments)[0])
# *************************************************************************
# Documentation
# *************************************************************************
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_CVD_Simulation_Machado2009.png')
plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Colour_Checker.png')
plt.close(plot_single_colour_checker('ColorChecker 2005', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Colour_Checkers.png')
plt.close(
plot_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'],
**arguments)[0])
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 = SpectralDistribution(data, name='Custom')
plt.close(plot_single_sd(sd, **arguments)[0])
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 = SpectralDistribution(data_1, name='Custom 1')
spd2 = SpectralDistribution(data_2, name='Custom 2')
plt.close(plot_multi_sds([spd1, spd2], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_CMFS.png')
plt.close(
plot_single_cmfs('CIE 1931 2 Degree Standard Observer',
**arguments)[0])
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')
plt.close(plot_multi_cmfs(cmfs, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Illuminant_SD.png')
plt.close(plot_single_illuminant_sd('A', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Illuminant_SDS.png')
plt.close(plot_multi_illuminant_sds(['A', 'B', 'C'], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Visible_Spectrum.png')
plt.close(plot_visible_spectrum(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Lightness_Function.png')
plt.close(plot_single_lightness_function('CIE 1976', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Lightness_Functions.png')
plt.close(
plot_multi_lightness_functions(['CIE 1976', 'Wyszecki 1963'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Luminance_Function.png')
plt.close(plot_single_luminance_function('CIE 1976', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Luminance_Functions.png')
plt.close(
plot_multi_luminance_functions(['CIE 1976', 'Newhall 1943'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Blackbody_Spectral_Radiance.png')
plt.close(
plot_blackbody_spectral_radiance(3500,
blackbody='VY Canis Major',
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Blackbody_Colours.png')
plt.close(
plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Colour_Swatch.png')
RGB = ColourSwatch(RGB=(0.45620519, 0.03081071, 0.04091952))
plt.close(plot_single_colour_swatch(RGB, **arguments)[0])
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))
plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_Function.png')
plt.close(plot_single_function(lambda x: x**(1 / 2.2), **arguments)[0])
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),
}
plt.close(plot_multi_functions(functions, **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Image.png')
path = os.path.join(output_directory, 'Logo_Medium_001.png')
plt.close(plot_image(read_image(str(path)), **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Corresponding_Chromaticities_Prediction.png')
plt.close(
plot_corresponding_chromaticities_prediction(1, 'Von Kries', 'CAT02',
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Spectral_Locus.png')
plt.close(
plot_spectral_locus(spectral_locus_colours='RGB', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_Colours.png')
plt.close(plot_chromaticity_diagram_colours(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram.png')
plt.close(plot_chromaticity_diagram(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1931.png')
plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png')
plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png')
plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_SDS_In_Chromaticity_Diagram.png')
A = ILLUMINANTS_SDS['A']
D65 = ILLUMINANTS_SDS['D65']
plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Pointer_Gamut.png')
plt.close(plot_pointer_gamut(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram.png')
RGB = np.random.random((128, 128, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS(
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_CCTF.png')
plt.close(plot_single_cctf('ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_CCTFs.png')
plt.close(plot_multi_cctfs(['ITU-R BT.709', 'sRGB'], **arguments)[0])
data = np.array([
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.40920000, 0.28120000, 0.30600000]),
np.array([
[0.02495100, 0.01908600, 0.02032900],
[0.10944300, 0.06235900, 0.06788100],
[0.27186500, 0.18418700, 0.19565300],
[0.48898900, 0.40749400, 0.44854600],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.30760000, 0.48280000, 0.42770000]),
np.array([
[0.02108000, 0.02989100, 0.02790400],
[0.06194700, 0.11251000, 0.09334400],
[0.15255800, 0.28123300, 0.23234900],
[0.34157700, 0.56681300, 0.47035300],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.39530000, 0.28120000, 0.18450000]),
np.array([
[0.02436400, 0.01908600, 0.01468800],
[0.10331200, 0.06235900, 0.02854600],
[0.26311900, 0.18418700, 0.12109700],
[0.43158700, 0.40749400, 0.39008600],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.20510000, 0.18420000, 0.57130000]),
np.array([
[0.03039800, 0.02989100, 0.06123300],
[0.08870000, 0.08498400, 0.21843500],
[0.18405800, 0.18418700, 0.40111400],
[0.32550100, 0.34047200, 0.50296900],
[0.53826100, 0.56681300, 0.80010400],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.35770000, 0.28120000, 0.11250000]),
np.array([
[0.03678100, 0.02989100, 0.01481100],
[0.17127700, 0.11251000, 0.01229900],
[0.30080900, 0.28123300, 0.21229800],
[0.52976000, 0.40749400, 0.11720000],
]),
None,
],
])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Constant_Hue_Loci.png')
plt.close(plot_constant_hue_loci(data, 'IPT', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Munsell_Value_Function.png')
plt.close(plot_single_munsell_value_function('ASTM D1535', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Munsell_Value_Functions.png')
plt.close(
plot_multi_munsell_value_functions(['ASTM D1535', 'McCamy 1987'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_SD_Rayleigh_Scattering.png')
plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_The_Blue_Sky.png')
plt.close(plot_the_blue_sky(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Colour_Quality_Bars.png')
illuminant = ILLUMINANTS_SDS['FL2']
light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
light_source = light_source.copy().align(SpectralShape(360, 830, 1))
cqs_i = colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour_quality_scale(light_source, additional_data=True)
plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png')
illuminant = ILLUMINANTS_SDS['FL2']
plt.close(
plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png')
light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
plt.close(
plot_multi_sds_colour_rendering_indexes_bars(
[illuminant, light_source], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png')
illuminant = ILLUMINANTS_SDS['FL2']
plt.close(
plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png')
light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
plt.close(
plot_multi_sds_colour_quality_scales_bars([illuminant, light_source],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Planckian_Locus.png')
plt.close(plot_planckian_locus(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Planckian_Locus_CIE1931.png')
plt.close(plot_planckian_locus_CIE1931(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Planckian_Locus_CIE1960UCS.png')
plt.close(plot_planckian_locus_CIE1960UCS(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram(['A', 'B', 'C'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', 'B', 'C'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
['A', 'B', 'C'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png')
plt.close(
plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png')
plt.close(
plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_RGB_Scatter.png')
plt.close(plot_RGB_scatter(RGB, 'ITU-R BT.709', **arguments)[0])
filename = os.path.join(
output_directory,
'Plotting_Plot_Colour_Automatic_Conversion_Graph.png')
plot_automatic_colour_conversion_graph(filename)
# *************************************************************************
# "tutorial.rst"
# *************************************************************************
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Visible_Spectrum.png')
plt.close(plot_visible_spectrum(**arguments)[0])
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 = SpectralDistribution(sample_sd_data, name='Sample')
plt.close(plot_single_sd(sd, **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_SD_Interpolation.png')
sd_copy = sd.copy()
sd_copy.interpolate(SpectralShape(400, 770, 1))
plt.close(
plot_multi_sds([sd, sd_copy],
bounding_box=[730, 780, 0.25, 0.5],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Sample_Swatch.png')
sd = SpectralDistribution(sample_sd_data)
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
illuminant = ILLUMINANTS_SDS['D65']
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(ColourSwatch('Sample', RGB),
text_parameters={'size': 'x-large'},
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Neutral5.png')
patch_name = 'neutral 5 (.70 D)'
patch_sd = COLOURCHECKERS_SDS['ColorChecker N Ohta'][patch_name]
with domain_range_scale('1'):
XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(ColourSwatch(patch_name.title(), RGB),
text_parameters={'size': 'x-large'},
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Colour_Checker.png')
plt.close(
plot_single_colour_checker(colour_checker='ColorChecker 2005',
text_parameters={'visible': False},
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Tutorial_CIE_1931_Chromaticity_Diagram.png')
xy = 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'))
plt.close(
render(standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True,
**arguments)[0])
# *************************************************************************
# "basics.rst"
# *************************************************************************
arguments['filename'] = os.path.join(output_directory,
'Basics_Logo_Small_001_CIE_XYZ.png')
RGB = read_image(os.path.join(output_directory, 'Logo_Small_001.png'))[...,
0:3]
XYZ = sRGB_to_XYZ(RGB)
plt.close(
plot_image(XYZ, text_parameters={'text': 'sRGB to XYZ'},
**arguments)[0])
"text": "Normal Trichromat",
"color": "black"
},
)
print("\n")
message_box(
'Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) model and '
"pre-computed matrix.")
plot_cvd_simulation_Machado2009(
ISHIHARA_CBT_3_IMAGE,
"Protanomaly",
0.5,
text_kwargs={
"text": "Protanomaly - 50%",
"color": "black"
},
)
print("\n")
M_a = colour.matrix_anomalous_trichromacy_Machado2009(
colour.MSDS_CMFS["Stockman & Sharpe 2 Degree Cone Fundamentals"],
colour.MSDS_DISPLAY_PRIMARIES["Typical CRT Brainard 1997"],
np.array([10, 0, 0]),
)
message_box(
'Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) model '
message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".')
plot_image(colour.cctf_encoding(ISHIHARA_CBT_3_IMAGE),
text_parameters={
'text': 'Normal Trichromat',
'color': 'black'
})
print('\n')
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model and pre-computed matrix.')
plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE,
'Protanomaly',
0.5,
text_parameters={
'text': 'Protanomaly - 50%',
'color': 'black'
})
print('\n')
M_a = colour.anomalous_trichromacy_matrix_Machado2009(
colour.LMS_CMFS.get('Stockman & Sharpe 2 Degree Cone Fundamentals'),
colour.DISPLAYS_RGB_PRIMARIES['Typical CRT Brainard 1997'],
np.array([10, 0, 0]))
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model using "Stockman & Sharpe 2 Degree Cone Fundamentals" and '
'"Typical CRT Brainard 1997" "RGB" display primaries.')
plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE,
def generate_documentation_plots(output_directory: str):
"""
Generate documentation plots.
Parameters
----------
output_directory
Output directory.
"""
filter_warnings()
colour_style()
np.random.seed(0)
# *************************************************************************
# "README.rst"
# *************************************************************************
filename = os.path.join(
output_directory, "Examples_Colour_Automatic_Conversion_Graph.png"
)
plot_automatic_colour_conversion_graph(filename)
arguments = {
"tight_layout": True,
"transparent_background": True,
"filename": os.path.join(
output_directory, "Examples_Plotting_Visible_Spectrum.png"
),
}
plt.close(
plot_visible_spectrum(
"CIE 1931 2 Degree Standard Observer", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Illuminant_F1_SD.png"
)
plt.close(plot_single_illuminant_sd("FL1", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Blackbodies.png"
)
blackbody_sds = [
sd_blackbody(i, SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plt.close(
plot_multi_sds(
blackbody_sds,
y_label="W / (sr m$^2$) / m",
plot_kwargs={"use_sd_colours": True, "normalise_sd_colours": True},
legend_location="upper right",
bounding_box=(0, 1250, 0, 2.5e6),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Cone_Fundamentals.png"
)
plt.close(
plot_single_cmfs(
"Stockman & Sharpe 2 Degree Cone Fundamentals",
y_label="Sensitivity",
bounding_box=(390, 870, 0, 1.1),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Luminous_Efficiency.png"
)
plt.close(
plot_multi_sds(
(
sd_mesopic_luminous_efficiency_function(0.2),
SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"],
SDS_LEFS_SCOTOPIC["CIE 1951 Scotopic Standard Observer"],
),
y_label="Luminous Efficiency",
legend_location="upper right",
y_tighten=True,
margins=(0, 0, 0, 0.1),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_BabelColor_Average.png"
)
plt.close(
plot_multi_sds(
SDS_COLOURCHECKERS["BabelColor Average"].values(),
plot_kwargs={"use_sd_colours": True},
title=("BabelColor Average - " "Spectral Distributions"),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_ColorChecker_2005.png"
)
plt.close(
plot_single_colour_checker(
"ColorChecker 2005", text_kwargs={"visible": False}, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Chromaticities_Prediction.png"
)
plt.close(
plot_corresponding_chromaticities_prediction(
2, "Von Kries", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png",
)
RGB = np.random.random((32, 32, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB,
"ITU-R BT.709",
colourspaces=["ACEScg", "S-Gamut"],
show_pointer_gamut=True,
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_CRI.png"
)
plt.close(
plot_single_sd_colour_rendering_index_bars(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Colour_Rendition_Report.png"
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Plot_Visible_Spectrum_Section.png"
)
plt.close(
plot_visible_spectrum_section(
section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Plot_RGB_Colourspace_Section.png"
)
plt.close(
plot_RGB_colourspace_section(
"sRGB", section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
["A", "B", "C"], **arguments
)[0]
)
# *************************************************************************
# Documentation
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_CVD_Simulation_Machado2009.png"
)
plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Colour_Checker.png"
)
plt.close(plot_single_colour_checker("ColorChecker 2005", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Colour_Checkers.png"
)
plt.close(
plot_multi_colour_checkers(
["ColorChecker 1976", "ColorChecker 2005"], **arguments
)[0]
)
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 = SpectralDistribution(data, name="Custom")
plt.close(plot_single_sd(sd, **arguments)[0])
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 = SpectralDistribution(data_1, name="Custom 1")
spd2 = SpectralDistribution(data_2, name="Custom 2")
plt.close(plot_multi_sds([spd1, spd2], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_CMFS.png"
)
plt.close(
plot_single_cmfs("CIE 1931 2 Degree Standard Observer", **arguments)[0]
)
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",
)
plt.close(plot_multi_cmfs(cmfs, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Illuminant_SD.png"
)
plt.close(plot_single_illuminant_sd("A", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Illuminant_SDS.png"
)
plt.close(plot_multi_illuminant_sds(["A", "B", "C"], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Visible_Spectrum.png"
)
plt.close(plot_visible_spectrum(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Lightness_Function.png"
)
plt.close(plot_single_lightness_function("CIE 1976", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Lightness_Functions.png"
)
plt.close(
plot_multi_lightness_functions(
["CIE 1976", "Wyszecki 1963"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Luminance_Function.png"
)
plt.close(plot_single_luminance_function("CIE 1976", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Luminance_Functions.png"
)
plt.close(
plot_multi_luminance_functions(
["CIE 1976", "Newhall 1943"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Blackbody_Spectral_Radiance.png"
)
plt.close(
plot_blackbody_spectral_radiance(
3500, blackbody="VY Canis Major", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Blackbody_Colours.png"
)
plt.close(
plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Colour_Swatch.png"
)
RGB = ColourSwatch((0.45620519, 0.03081071, 0.04091952))
plt.close(plot_single_colour_swatch(RGB, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Colour_Swatches.png"
)
RGB_1 = ColourSwatch((0.45293517, 0.31732158, 0.26414773))
RGB_2 = ColourSwatch((0.77875824, 0.57726450, 0.50453169))
plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Function.png"
)
plt.close(plot_single_function(lambda x: x ** (1 / 2.2), **arguments)[0])
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),
}
plt.close(plot_multi_functions(functions, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Image.png"
)
path = os.path.join(
colour.__path__[0],
"examples",
"plotting",
"resources",
"Ishihara_Colour_Blindness_Test_Plate_3.png",
)
plt.close(plot_image(read_image(str(path)), **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Corresponding_Chromaticities_Prediction.png",
)
plt.close(
plot_corresponding_chromaticities_prediction(
1, "Von Kries", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Spectral_Locus.png"
)
plt.close(
plot_spectral_locus(spectral_locus_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_Colours.png"
)
plt.close(
plot_chromaticity_diagram_colours(diagram_colours="RGB", **arguments)[
0
]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram.png"
)
plt.close(plot_chromaticity_diagram(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1931.png"
)
plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png"
)
plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png"
)
plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram.png"
)
A = SDS_ILLUMINANTS["A"]
D65 = SDS_ILLUMINANTS["D65"]
plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Pointer_Gamut.png"
)
plt.close(plot_pointer_gamut(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram.png",
)
RGB = np.random.random((128, 128, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(**arguments)[
0
]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(
**arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS(
**arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_CCTF.png"
)
plt.close(plot_single_cctf("ITU-R BT.709", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_CCTFs.png"
)
plt.close(plot_multi_cctfs(["ITU-R BT.709", "sRGB"], **arguments)[0])
data = np.array(
[
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.40920000, 0.28120000, 0.30600000]),
np.array(
[
[0.02495100, 0.01908600, 0.02032900],
[0.10944300, 0.06235900, 0.06788100],
[0.27186500, 0.18418700, 0.19565300],
[0.48898900, 0.40749400, 0.44854600],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.30760000, 0.48280000, 0.42770000]),
np.array(
[
[0.02108000, 0.02989100, 0.02790400],
[0.06194700, 0.11251000, 0.09334400],
[0.15255800, 0.28123300, 0.23234900],
[0.34157700, 0.56681300, 0.47035300],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.39530000, 0.28120000, 0.18450000]),
np.array(
[
[0.02436400, 0.01908600, 0.01468800],
[0.10331200, 0.06235900, 0.02854600],
[0.26311900, 0.18418700, 0.12109700],
[0.43158700, 0.40749400, 0.39008600],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.20510000, 0.18420000, 0.57130000]),
np.array(
[
[0.03039800, 0.02989100, 0.06123300],
[0.08870000, 0.08498400, 0.21843500],
[0.18405800, 0.18418700, 0.40111400],
[0.32550100, 0.34047200, 0.50296900],
[0.53826100, 0.56681300, 0.80010400],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.35770000, 0.28120000, 0.11250000]),
np.array(
[
[0.03678100, 0.02989100, 0.01481100],
[0.17127700, 0.11251000, 0.01229900],
[0.30080900, 0.28123300, 0.21229800],
[0.52976000, 0.40749400, 0.11720000],
]
),
None,
],
]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Constant_Hue_Loci.png"
)
plt.close(plot_constant_hue_loci(data, "IPT", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Munsell_Value_Function.png"
)
plt.close(plot_single_munsell_value_function("ASTM D1535", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Munsell_Value_Functions.png"
)
plt.close(
plot_multi_munsell_value_functions(
["ASTM D1535", "McCamy 1987"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_SD_Rayleigh_Scattering.png"
)
plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_The_Blue_Sky.png"
)
plt.close(plot_the_blue_sky(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Colour_Quality_Bars.png"
)
illuminant = SDS_ILLUMINANTS["FL2"]
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
light_source = light_source.copy().align(SpectralShape(360, 830, 1))
cqs_i = colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour_quality_scale(light_source, additional_data=True)
plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png",
)
illuminant = SDS_ILLUMINANTS["FL2"]
plt.close(
plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png",
)
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
plt.close(
plot_multi_sds_colour_rendering_indexes_bars(
[illuminant, light_source], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png",
)
illuminant = SDS_ILLUMINANTS["FL2"]
plt.close(
plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png",
)
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
plt.close(
plot_multi_sds_colour_quality_scales_bars(
[illuminant, light_source], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Hull_Section_Colours.png"
)
vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
XYZ_vertices = RGB_to_XYZ(
vertices["position"] + 0.5,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
)
hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
plt.close(
plot_hull_section_colours(hull, section_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Hull_Section_Contour.png"
)
plt.close(
plot_hull_section_contour(hull, section_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Visible_Spectrum_Section.png"
)
plt.close(
plot_visible_spectrum_section(
section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspace_Section.png"
)
plt.close(
plot_RGB_colourspace_section(
"sRGB", section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Planckian_Locus.png"
)
plt.close(
plot_planckian_locus(planckian_locus_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1931(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Full.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Intermediate.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], "Intermediate", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Simple.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], "Simple", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png"
)
plt.close(
plot_RGB_colourspaces_gamuts(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png"
)
plt.close(
plot_RGB_colourspaces_gamuts(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Scatter.png"
)
plt.close(plot_RGB_scatter(RGB, "ITU-R BT.709", **arguments)[0])
filename = os.path.join(
output_directory, "Plotting_Plot_Colour_Automatic_Conversion_Graph.png"
)
plot_automatic_colour_conversion_graph(filename)
# *************************************************************************
# "tutorial.rst"
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Visible_Spectrum.png"
)
plt.close(plot_visible_spectrum(**arguments)[0])
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 = SpectralDistribution(sample_sd_data, name="Sample")
plt.close(plot_single_sd(sd, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Tutorial_SD_Interpolation.png"
)
sd_copy = sd.copy()
sd_copy.interpolate(SpectralShape(400, 770, 1))
plt.close(
plot_multi_sds(
[sd, sd_copy], bounding_box=[730, 780, 0.25, 0.5], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Sample_Swatch.png"
)
sd = SpectralDistribution(sample_sd_data)
cmfs = MSDS_CMFS_STANDARD_OBSERVER["CIE 1931 2 Degree Standard Observer"]
illuminant = SDS_ILLUMINANTS["D65"]
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(
ColourSwatch(RGB, "Sample"),
text_kwargs={"size": "x-large"},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Neutral5.png"
)
patch_name = "neutral 5 (.70 D)"
patch_sd = SDS_COLOURCHECKERS["ColorChecker N Ohta"][patch_name]
with domain_range_scale("1"):
XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(
ColourSwatch(RGB, patch_name.title()),
text_kwargs={"size": "x-large"},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Colour_Checker.png"
)
plt.close(
plot_single_colour_checker(
colour_checker="ColorChecker 2005",
text_kwargs={"visible": False},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_CIE_1931_Chromaticity_Diagram.png"
)
xy = 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"),
)
plt.close(
render(
standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True,
**arguments,
)[0]
)
# *************************************************************************
# "basics.rst"
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Basics_Logo_Small_001_CIE_XYZ.png"
)
RGB = read_image(os.path.join(output_directory, "Logo_Small_001.png"))[
..., 0:3
]
XYZ = sRGB_to_XYZ(RGB)
plt.close(
plot_image(XYZ, text_kwargs={"text": "sRGB to XYZ"}, **arguments)[0]
)
'Ishihara_Colour_Blindness_Test_Plate_3.png')),
function='sRGB')
message_box('Colour Blindness Plots')
message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".')
plot_image(colour.oetf(ISHIHARA_CBT_3_IMAGE),
text_parameters={'text': 'Normal Trichromat', 'color': 'black'})
print('\n')
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model and pre-computed matrix.')
plot_cvd_simulation_Machado2009(
ISHIHARA_CBT_3_IMAGE, 'Protanomaly', 0.5,
text_parameters={'text': 'Protanomaly - 50%', 'color': 'black'})
print('\n')
M_a = colour.anomalous_trichromacy_matrix_Machado2009(
colour.LMS_CMFS.get('Stockman & Sharpe 2 Degree Cone Fundamentals'),
colour.DISPLAYS_RGB_PRIMARIES['Typical CRT Brainard 1997'],
np.array([10, 0, 0]))
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model using "Stockman & Sharpe 2 Degree Cone Fundamentals" and '
'"Typical CRT Brainard 1997" "RGB" display primaries.')
plot_cvd_simulation_Machado2009(
ISHIHARA_CBT_3_IMAGE, M_a=M_a,
text_parameters={'text': 'Average Protanomaly - 10nm', 'color': 'black'})