def plot_cvd_simulation_Machado2009(RGB,
deficiency='Protanomaly',
severity=0.5,
M_a=None,
**kwargs):
"""
Performs colour vision deficiency simulation on given *RGB* colourspace
array using *Machado et al. (2009)* model.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
deficiency : unicode, optional
{'Protanomaly', 'Deuteranomaly', 'Tritanomaly'}
Colour blindness / vision deficiency type.
severity : numeric, optional
Severity of the colour vision deficiency in domain [0, 1].
M_a : array_like, optional
Anomalous trichromacy matrix to use instead of Machado (2010)
pre-computed matrix.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.plot_image`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Notes
-----
- Input *RGB* array is expected to be linearly encoded.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> import numpy as np
>>> RGB = np.random.rand(32, 32, 3)
>>> plot_cvd_simulation_Machado2009(RGB) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_CVD_Simulation_Machado2009.png
:align: center
:alt: plot_cvd_simulation_Machado2009
"""
if M_a is None:
M_a = cvd_matrix_Machado2009(deficiency, severity)
text = 'Deficiency: {0} - Severity: {1}'.format(deficiency, severity)
settings = {'text_parameters': {'text': None if M_a is None else text}}
settings.update(kwargs)
return plot_image(
COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf(
dot_vector(M_a, RGB)), **settings)
def plot_cvd_simulation_Machado2009(RGB,
deficiency='Protanomaly',
severity=0.5,
M_a=None,
**kwargs):
"""
Performs colour vision deficiency simulation on given *RGB* colourspace
array using *Machado et al. (2009)* model.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
deficiency : unicode, optional
{'Protanomaly', 'Deuteranomaly', 'Tritanomaly'}
Colour blindness / vision deficiency type.
severity : numeric, optional
Severity of the colour vision deficiency in domain [0, 1].
M_a : array_like, optional
Anomalous trichromacy matrix to use instead of Machado (2010)
pre-computed matrix.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.plot_image`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Notes
-----
- Input *RGB* array is expected to be linearly encoded.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> import numpy as np
>>> RGB = np.random.rand(32, 32, 3)
>>> plot_cvd_simulation_Machado2009(RGB) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_CVD_Simulation_Machado2009.png
:align: center
:alt: plot_cvd_simulation_Machado2009
"""
if M_a is None:
M_a = cvd_matrix_Machado2009(deficiency, severity)
text = 'Deficiency: {0} - Severity: {1}'.format(deficiency, severity)
settings = {'text_parameters': {'text': None if M_a is None else text}}
settings.update(kwargs)
return plot_image(
COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf(
dot_vector(M_a, RGB)), **settings)
def test_plot_image(self):
"""Test :func:`colour.plotting.common.plot_image` definition."""
path = os.path.join(colour.__path__[0], "..", "docs", "_static",
"Logo_Medium_001.png")
# Distribution does not ship the documentation thus we are skipping
# this unit test if the image does not exist.
if not os.path.exists(path): # pragma: no cover
return
figure, axes = plot_image(read_image(path))
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
from colour.utilities.verbose import message_box
RESOURCES_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resources')
colour_style()
ISHIHARA_CBT_3_IMAGE = colour.oetf_reverse(colour.read_image(
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'),
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])
colour_style()
ISHIHARA_CBT_3_IMAGE = colour.cctf_decoding(
colour.read_image(
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(
colour.cctf_encoding(ISHIHARA_CBT_3_IMAGE),
text_kwargs={
"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={
RESOURCES_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resources')
colour_style()
ISHIHARA_CBT_3_IMAGE = colour.cctf_decoding(colour.read_image(
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(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'
})
def plot_colour_vector_graphic(
specification: ColourQuality_Specification_ANSIIESTM3018,
**kwargs: Any) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot *Color Vector Graphic* according to
*ANSI/IES TM-30-18 Colour Rendition Report*.
Parameters
----------
specification
*ANSI/IES TM-30-18 Colour Rendition Report* specification.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> from colour.quality import colour_fidelity_index_ANSIIESTM3018
>>> sd = SDS_ILLUMINANTS['FL2']
>>> specification = colour_fidelity_index_ANSIIESTM3018(sd, True)
>>> plot_colour_vector_graphic(specification)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
"""
settings: Dict[str, Any] = dict(kwargs)
settings["standalone"] = False
# Background
background_image = read_image(
os.path.join(RESOURCES_DIRECTORY_ANSIIESTM3018, "CVG_Background.jpg"))
_figure, axes = plot_image(
background_image,
imshow_kwargs={"extent": [-1.5, 1.5, -1.5, 1.5]},
**settings,
)
# Lines dividing the hues in 16 equal parts along with bin numbers.
axes.plot(0, 0, "+", color="#A6A6A6")
for i in range(16):
angle = 2 * np.pi * i / 16
dx = np.cos(angle)
dy = np.sin(angle)
axes.plot(
(0.15 * dx, 1.5 * dx),
(0.15 * dy, 1.5 * dy),
"--",
color="#A6A6A6",
lw=0.75,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
angle = 2 * np.pi * (i + 0.5) / 16
axes.annotate(
str(i + 1),
color="#A6A6A6",
ha="center",
va="center",
xy=(1.41 * np.cos(angle), 1.41 * np.sin(angle)),
weight="bold",
size=9,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation,
)
# Circles.
circle = plt.Circle(
(0, 0),
1,
color="black",
lw=1.25,
fill=False,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon,
)
axes.add_artist(circle)
for radius in [0.8, 0.9, 1.1, 1.2]:
circle = plt.Circle(
(0, 0),
radius,
color="white",
lw=0.75,
fill=False,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_polygon,
)
axes.add_artist(circle)
# -/+20% marks near the white circles.
props = dict(ha="right", color="white", size=7)
axes.annotate(
"-20%",
xy=(0, -0.8),
va="bottom",
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation,
**props,
)
axes.annotate(
"+20%",
xy=(0, -1.2),
va="top",
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation,
**props,
)
# Average "CAM02" h correlate for each bin, in radians.
average_hues = np.radians([
np.mean(
as_float_array([
cast(
Floating,
specification.colorimetry_data[1][i].CAM.h,
) for i in specification.bins[j]
])) for j in range(16)
])
xy_reference = np.transpose(
np.vstack([np.cos(average_hues),
np.sin(average_hues)]))
# Arrow offsets as defined by the standard.
offsets = (specification.averages_test - specification.averages_reference
) / specification.average_norms[:, np.newaxis]
xy_test = xy_reference + offsets
# Arrows.
for i in range(16):
axes.arrow(
xy_reference[i, 0],
xy_reference[i, 1],
offsets[i, 0],
offsets[i, 1],
length_includes_head=True,
width=0.005,
head_width=0.04,
linewidth=None,
color=_COLOURS_BIN_ARROW[i],
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_annotation,
)
# Red (test) gamut shape.
loop = np.append(xy_test, xy_test[0, np.newaxis], axis=0)
axes.plot(
loop[:, 0],
loop[:, 1],
"-",
color="#F05046",
lw=2,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
def corner_label_and_text(label: str, text: str, ha: str, va: str):
"""Draw a label and text in given corner."""
x = -1.45 if ha == "left" else 1.45
y = 1.45 if va == "top" else -1.45
y_text = -15 if va == "top" else 15
axes.annotate(
text,
xy=(x, y),
color="black",
ha=ha,
va=va,
weight="bold",
size="larger",
zorder=CONSTANTS_COLOUR_STYLE.zorder.foreground_label,
)
axes.annotate(
label,
xy=(x, y),
color="black",
xytext=(0, y_text),
textcoords="offset points",
ha=ha,
va=va,
size="small",
zorder=CONSTANTS_COLOUR_STYLE.zorder.foreground_label,
)
corner_label_and_text("$R_f$", f"{specification.R_f:.0f}", "left", "top")
corner_label_and_text("$R_g$", f"{specification.R_g:.0f}", "right", "top")
corner_label_and_text("CCT", f"{specification.CCT:.0f} K", "left",
"bottom")
corner_label_and_text("$D_{uv}$", f"{specification.D_uv:.4f}", "right",
"bottom")
settings = {"standalone": True}
settings.update(kwargs)
return render(**settings)
def plot_colour_vector_graphic(specification, **kwargs):
"""
Plots *Color Vector Graphic* according to
*ANSI/IES TM-30-18 Colour Rendition Report*.
Parameters
----------
specification : ColourQuality_Specification_ANSIIESTM3018
*ANSI/IES TM-30-18 Colour Rendition Report* specification.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> from colour.quality import colour_fidelity_index_ANSIIESTM3018
>>> sd = SDS_ILLUMINANTS['FL2']
>>> specification = colour_fidelity_index_ANSIIESTM3018(sd, True)
>>> plot_colour_vector_graphic(specification)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
"""
settings = kwargs.copy()
settings['standalone'] = False
# Background
background_image = read_image(
os.path.join(RESOURCES_DIRECTORY_ANSIIESTM3018, 'CVG_Background.jpg'))
_figure, axes = plot_image(
background_image,
imshow_kwargs={'extent': [-1.5, 1.5, -1.5, 1.5]},
**settings)
# Lines dividing the hues in 16 equal parts along with bin numbers.
axes.plot(0, 0, '+', color='#A6A6A6')
for i in range(16):
angle = 2 * np.pi * i / 16
dx = np.cos(angle)
dy = np.sin(angle)
axes.plot((0.15 * dx, 1.5 * dx), (0.15 * dy, 1.5 * dy),
'--',
color='#A6A6A6',
lw=0.75)
angle = 2 * np.pi * (i + 0.5) / 16
axes.annotate(str(i + 1),
color='#A6A6A6',
ha='center',
va='center',
xy=(1.41 * np.cos(angle), 1.41 * np.sin(angle)),
weight='bold',
size=9)
# Circles.
circle = plt.Circle((0, 0), 1, color='black', lw=1.25, fill=False)
axes.add_artist(circle)
for radius in [0.8, 0.9, 1.1, 1.2]:
circle = plt.Circle((0, 0), radius, color='white', lw=0.75, fill=False)
axes.add_artist(circle)
# -/+20% marks near the white circles.
props = dict(ha='right', color='white', size=7)
axes.annotate('-20%', xy=(0, -0.8), va='bottom', **props)
axes.annotate('+20%', xy=(0, -1.2), va='top', **props)
# Average "CAM02" h correlate for each bin, in radians.
average_hues = as_float_array([
np.mean([
specification.colorimetry_data[1][i].CAM.h
for i in specification.bins[j]
]) for j in range(16)
]) / 180 * np.pi
xy_reference = np.transpose(
np.vstack([np.cos(average_hues),
np.sin(average_hues)]))
# Arrow offsets as defined by the standard.
offsets = (
(specification.averages_test - specification.averages_reference) /
specification.average_norms[:, np.newaxis])
xy_test = xy_reference + offsets
# Arrows.
for i in range(16):
axes.arrow(xy_reference[i, 0],
xy_reference[i, 1],
offsets[i, 0],
offsets[i, 1],
length_includes_head=True,
width=0.005,
head_width=0.04,
linewidth=None,
color=_BIN_ARROW_COLOURS[i])
# Red (test) gamut shape.
loop = np.append(xy_test, xy_test[0, np.newaxis], axis=0)
axes.plot(loop[:, 0], loop[:, 1], '-', color='#F05046', lw=2)
def corner_label_and_text(label, text, ha, va):
"""
Draws a label and text in given corner.
"""
x = -1.45 if ha == 'left' else 1.45
y = 1.45 if va == 'top' else -1.45
y_text = -15 if va == 'top' else 15
axes.annotate(text,
xy=(x, y),
color='black',
ha=ha,
va=va,
weight='bold',
size='larger')
axes.annotate(label,
xy=(x, y),
color='black',
xytext=(0, y_text),
textcoords='offset points',
ha=ha,
va=va,
size='small')
corner_label_and_text('$R_f$', '{0:.0f}'.format(specification.R_f), 'left',
'top')
corner_label_and_text('$R_g$', '{0:.0f}'.format(specification.R_g),
'right', 'top')
corner_label_and_text('CCT', '{0:.0f} K'.format(specification.CCT), 'left',
'bottom')
corner_label_and_text('$D_{uv}$', '{0:.4f}'.format(specification.D_uv),
'right', 'bottom')
settings = {'standalone': True}
settings.update(kwargs)
return render(**settings)
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]
)
RESOURCES_DIRECTORY = os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'resources')
colour_style()
ISHIHARA_CBT_3_IMAGE = colour.oetf_reverse(
colour.read_image(
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(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'],
