def test_plot_colour_quality_bars(self):
"""
Tests :func:`colour.plotting.quality.plot_colour_quality_bars`
definition.
"""
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)
figure, axes = plot_colour_quality_bars([cqs_i, cqs_l])
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
def colour_quality_scale_bars_plot(spd, **kwargs):
"""
Plots the *colour quality scale* of given illuminant or light source.
Parameters
----------
spd : SpectralPowerDistribution
Illuminant or light source to plot the *colour quality scale*.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_quality_scale_bars_plot(illuminant) # doctest: +SKIP
True
"""
if colour_quality_bars_plot(
colour_quality_scale(
spd,
additional_data=True),
standalone=False):
settings = {
'title': 'Colour Quality Scale - {0}'.format(spd.title)}
decorate(**settings)
return display(**settings)
def test_plot_colour_quality_bars(self):
"""
Test :func:`colour.plotting.quality.plot_colour_quality_bars`
definition.
"""
illuminant = SDS_ILLUMINANTS["FL2"]
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
light_source = reshape_sd(light_source, SpectralShape(360, 830, 1))
cqs_i = colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour_quality_scale(light_source, additional_data=True)
figure, axes = plot_colour_quality_bars([cqs_i, cqs_l])
self.assertIsInstance(figure, Figure)
self.assertIsInstance(axes, Axes)
def multi_spd_colour_quality_scale_bars_plot(spds, **kwargs):
"""
Plots the *Colour Quality Scale* (CQS) of given illuminants or light
sources spectral power distributions.
Parameters
----------
spds : array_like
Array of illuminants or light sources spectral power distributions to
plot the *Colour Quality Scale* (CQS).
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
labels : bool, optional
{:func:`colour.plotting.quality.colour_quality_bars_plot`},
Add labels above bars.
hatching : bool or None, optional
{:func:`colour.plotting.quality.colour_quality_bars_plot`},
Use hatching for the bars.
hatching_repeat : int, optional
{:func:`colour.plotting.quality.colour_quality_bars_plot`},
Hatching pattern repeat.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import (ILLUMINANTS_RELATIVE_SPDS,
... LIGHT_SOURCES_RELATIVE_SPDS)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['F2']
>>> light_source = LIGHT_SOURCES_RELATIVE_SPDS['Kinoton 75P']
>>> multi_spd_colour_quality_scale_bars_plot([illuminant, light_source])
... # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
specifications = [
colour_quality_scale(spd, additional_data=True) for spd in spds
]
colour_quality_bars_plot(specifications, **settings)
settings = {
'title':
'Colour Quality Scale - {0}'.format(', '.join(
[spd.strict_name for spd in spds]))
}
settings.update(kwargs)
return render(with_boundaries=False, **settings)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(
colour_quality_scale(ILLUMINANTS_SDS['FL1']),
75.332008182589348,
places=7)
self.assertAlmostEqual(
colour_quality_scale(ILLUMINANTS_SDS['FL2']),
64.686339173112856,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['Neodimium Incandescent']),
87.655035241231985,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['F32T8/TL841 (Triphosphor)']),
83.179881092827671,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['H38HT-100 (Mercury)']),
22.860610106043985,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['Luxeon WW 2880']),
84.879524259605077,
places=7)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F1')),
75.342060410089019,
places=7)
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F2')),
64.686058037115245,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')),
87.659381222366406,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('F32T8/TL841 (Triphosphor)')),
83.179667074336621,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('H38HT-100 (Mercury)')),
22.869936010810584,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Luxeon WW 2880')),
84.883777827678131,
places=7)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F1')),
75.334361226715345,
places=7)
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F2')),
64.678111793396397,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')),
87.655549804699419,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('F32T8/TL841 (Triphosphor)')),
83.175799064274571,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('H38HT-100 (Mercury)')),
22.847928690340929,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Luxeon WW 2880')),
84.880575409680162,
places=7)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS['F1']),
75.342427844068794,
places=7)
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS['F2']),
64.686416902222192,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent']),
87.655035241231985,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['F32T8/TL841 (Triphosphor)']),
83.179847129624022,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['H38HT-100 (Mercury)']),
22.869154075340347,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['Luxeon WW 2880']),
84.880324578632809,
places=7)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS['F1']),
75.342591389578701,
places=7)
self.assertAlmostEqual(colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS['F2']),
64.686339173112856,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['Neodimium Incandescent']),
87.655035241231985,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['F32T8/TL841 (Triphosphor)']),
83.179881092827671,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['H38HT-100 (Mercury)']),
22.870604734960732,
places=7)
self.assertAlmostEqual(colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS['Luxeon WW 2880']),
84.879524259605077,
places=7)
def multi_spd_colour_quality_scale_bars_plot(spds, **kwargs):
"""
Plots the *colour quality scale* of given illuminants or light sources
spectral power distributions.
Parameters
----------
spds : array_like
Array of illuminants or light sources spectral power distributions to
plot the *colour quality scale*.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import (
... ILLUMINANTS_RELATIVE_SPDS,
... LIGHT_SOURCES_RELATIVE_SPDS)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> light_source = LIGHT_SOURCES_RELATIVE_SPDS.get('Kinoton 75P')
>>> multi_spd_colour_quality_scale_bars_plot( # doctest: +SKIP
... [illuminant, light_source])
True
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
specifications = [
colour_quality_scale(spd, additional_data=True) for spd in spds
]
colour_quality_bars_plot(specifications, **settings)
settings = {
'title':
'Colour Quality Scale - {0}'.format(', '.join(
[spd.title for spd in spds]))
}
settings.update(kwargs)
decorate(**settings)
return display(**settings)
def multi_spd_colour_quality_scale_bars_plot(spds, **kwargs):
"""
Plots the *colour quality scale* of given illuminants or light sources
spectral power distributions.
Parameters
----------
spds : array_like
Array of illuminants or light sources spectral power distributions to
plot the *colour quality scale*.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import (
... ILLUMINANTS_RELATIVE_SPDS,
... LIGHT_SOURCES_RELATIVE_SPDS)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> light_source = LIGHT_SOURCES_RELATIVE_SPDS.get('Kinoton 75P')
>>> multi_spd_colour_quality_scale_bars_plot( # doctest: +SKIP
... [illuminant, light_source])
True
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
specifications = [colour_quality_scale(spd, additional_data=True)
for spd in spds]
colour_quality_bars_plot(specifications, **settings)
settings = {'title': 'Colour Quality Scale - {0}'.format(', '.join(
[spd.title for spd in spds]))}
settings.update(kwargs)
decorate(**settings)
return display(**settings)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F1')),
75.342060410089019,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F2')),
64.686058037115245,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')),
87.659381222366406,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('F32T8/TL841 (Triphosphor)')),
83.179667074336621,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('H38HT-100 (Mercury)')),
22.869936010810584,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Luxeon WW 2880')),
84.883777827678131,
places=7)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F1')),
75.334361226715345,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_RELATIVE_SPDS.get('F2')),
64.678111793396397,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Neodimium Incandescent')),
87.655549804699419,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('F32T8/TL841 (Triphosphor)')),
83.175799064274571,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('H38HT-100 (Mercury)')),
22.847928690340929,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_RELATIVE_SPDS.get('Luxeon WW 2880')),
84.880575409680162,
places=7)
def plot_multi_sds_colour_quality_scales_bars(sds, **kwargs):
"""
Plots the *Colour Quality Scale* (CQS) of given illuminants or light
sources spectral distributions.
Parameters
----------
sds : array_like
Array of illuminants or light sources spectral distributions to
plot the *Colour Quality Scale* (CQS).
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
labels : bool, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Add labels above bars.
hatching : bool or None, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Use hatching for the bars.
hatching_repeat : int, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Hatching pattern repeat.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> from colour import (ILLUMINANTS_SDS,
... LIGHT_SOURCES_SDS)
>>> illuminant = ILLUMINANTS_SDS['F2']
>>> light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
>>> plot_multi_sds_colour_quality_scales_bars([illuminant, light_source])
... # doctest: +SKIP
.. image:: ../_static/Plotting_\
Plot_Multi_SDs_Colour_Quality_Scales_Bars.png
:align: center
:alt: plot_multi_sds_colour_quality_scales_bars
"""
settings = dict(kwargs)
settings.update({'standalone': False})
specifications = [
colour_quality_scale(sd, additional_data=True) for sd in sds
]
figure, axes = plot_colour_quality_bars(specifications, **settings)
title = 'Colour Quality Scale - {0}'.format(', '.join(
[sd.strict_name for sd in sds]))
settings = {'axes': axes, 'title': title}
settings.update(kwargs)
return render(**settings)
def plot_multi_sds_colour_quality_scales_bars(
sds: Union[
Sequence[Union[SpectralDistribution, MultiSpectralDistributions]],
MultiSpectralDistributions,
],
method: Union[
Literal["NIST CQS 7.4", "NIST CQS 9.0"], str
] = "NIST CQS 9.0",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the *Colour Quality Scale* (CQS) of given illuminants or light
sources spectral distributions.
Parameters
----------
sds
Spectral distributions or multi-spectral distributions to
plot. `sds` can be a single
:class:`colour.MultiSpectralDistributions` class instance, a list
of :class:`colour.MultiSpectralDistributions` class instances or a
list of :class:`colour.SpectralDistribution` class instances.
method
*Colour Quality Scale* (CQS) computation method.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.quality.plot_colour_quality_bars`,
: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,
... SDS_LIGHT_SOURCES)
>>> illuminant = SDS_ILLUMINANTS['FL2']
>>> light_source = SDS_LIGHT_SOURCES['Kinoton 75P']
>>> plot_multi_sds_colour_quality_scales_bars([illuminant, light_source])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_\
Plot_Multi_SDS_Colour_Quality_Scales_Bars.png
:align: center
:alt: plot_multi_sds_colour_quality_scales_bars
"""
method = validate_method(method, COLOUR_QUALITY_SCALE_METHODS)
sds_converted = sds_and_msds_to_sds(sds)
settings: Dict[str, Any] = dict(kwargs)
settings.update({"standalone": False})
specifications = cast(
List[ColourRendering_Specification_CQS],
[colour_quality_scale(sd, True, method) for sd in sds_converted],
)
_figure, axes = plot_colour_quality_bars(specifications, **settings)
title = (
f"Colour Quality Scale - "
f"{', '.join([sd.strict_name for sd in sds_converted])}"
)
settings = {"axes": axes, "title": title}
settings.update(kwargs)
return render(**settings)
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])
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(
colour_quality_scale(ILLUMINANTS_SDS['FL1']),
74.933405395713180,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_SDS['FL1'], method='NIST CQS 7.4'),
75.332008182589348,
places=7)
self.assertAlmostEqual(
colour_quality_scale(ILLUMINANTS_SDS['FL2']),
64.017283509280588,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_SDS['FL2'], method='NIST CQS 7.4'),
64.686339173112856,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['Neodimium Incandescent']),
89.693921013642381,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['Neodimium Incandescent'],
method='NIST CQS 7.4'),
87.655035241231985,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['F32T8/TL841 (Triphosphor)']),
84.878441814420910,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['F32T8/TL841 (Triphosphor)'],
method='NIST CQS 7.4'),
83.179881092827671,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['H38HT-100 (Mercury)']),
19.836071708638958,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['H38HT-100 (Mercury)'],
method='NIST CQS 7.4'),
22.860610106043985,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['Luxeon WW 2880']),
86.491761709787994,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['Luxeon WW 2880'], method='NIST CQS 7.4'),
84.879524259605077,
places=7)
specification_r = CQS_Specification(
name='FL1',
Q_a=75.332008182589348,
Q_f=76.339439008799872,
Q_p=74.235391056498486,
Q_g=84.222748330112054,
Q_d=84.126957016581841,
Q_as={
1:
VS_ColourQualityScaleData(
name='VS1',
Q_a=77.680854801718439,
D_C_ab=-1.411454116288503,
D_E_ab=7.191809380490093,
D_Ep_ab=7.191809380490093),
2:
VS_ColourQualityScaleData(
name='VS2',
Q_a=98.449964521835454,
D_C_ab=2.416949235948934,
D_E_ab=2.468031951275495,
D_Ep_ab=0.499537889816771),
3:
VS_ColourQualityScaleData(
name='VS3',
Q_a=85.128488974188656,
D_C_ab=2.051673516490851,
D_E_ab=5.212485950404579,
D_Ep_ab=4.791726783206154),
4:
VS_ColourQualityScaleData(
name='VS4',
Q_a=75.929101405967231,
D_C_ab=-1.544244803829976,
D_E_ab=7.756423964663330,
D_Ep_ab=7.756423964663330),
5:
VS_ColourQualityScaleData(
name='VS5',
Q_a=76.560036127768228,
D_C_ab=-5.962959343458696,
D_E_ab=7.553059580932679,
D_Ep_ab=7.553059580932679),
6:
VS_ColourQualityScaleData(
name='VS6',
Q_a=75.674875949078682,
D_C_ab=-7.785240663276603,
D_E_ab=7.838368326299190,
D_Ep_ab=7.838368326299190),
7:
VS_ColourQualityScaleData(
name='VS7',
Q_a=74.856012003857145,
D_C_ab=-6.773721961614541,
D_E_ab=8.102319790027652,
D_Ep_ab=8.102319790027652),
8:
VS_ColourQualityScaleData(
name='VS8',
Q_a=85.007243427918965,
D_C_ab=-0.684332522268967,
D_E_ab=4.830795743950247,
D_Ep_ab=4.830795743950247),
9:
VS_ColourQualityScaleData(
name='VS9',
Q_a=95.600642739549912,
D_C_ab=3.245002214218772,
D_E_ab=3.541112179773836,
D_Ep_ab=1.417545801537952),
10:
VS_ColourQualityScaleData(
name='VS10',
Q_a=84.897394198279201,
D_C_ab=3.2779983437733051,
D_E_ab=5.867290950207070,
D_Ep_ab=4.866192551944616),
11:
VS_ColourQualityScaleData(
name='VS11',
Q_a=76.652664070814652,
D_C_ab=0.889135877229947,
D_E_ab=7.575563455510737,
D_Ep_ab=7.523204042181251),
12:
VS_ColourQualityScaleData(
name='VS12',
Q_a=74.392341352838727,
D_C_ab=-1.950461685183292,
D_E_ab=8.251784061375552,
D_Ep_ab=8.251784061375552),
13:
VS_ColourQualityScaleData(
name='VS13',
Q_a=71.733157403665416,
D_C_ab=-5.748914457790455,
D_E_ab=9.109057119322612,
D_Ep_ab=9.109057119322612),
14:
VS_ColourQualityScaleData(
name='VS14',
Q_a=53.322108325530110,
D_C_ab=-14.642967562714134,
D_E_ab=15.053589749938459,
D_Ep_ab=15.053589749938459),
15:
VS_ColourQualityScaleData(
name='VS15',
Q_a=62.611167146423085,
D_C_ab=-10.071676496831763,
D_E_ab=12.051527779341935,
D_Ep_ab=12.051527779341935)
},
colorimetry_data=([
VS_ColorimetryData(
name='VS1',
XYZ=np.array([0.13184562, 0.09885158, 0.22533524]),
Lab=np.array([37.63552369, 27.62126274, -26.23567072]),
C=38.095204070389400),
VS_ColorimetryData(
name='VS2',
XYZ=np.array([0.13066767, 0.10335651, 0.30768635]),
Lab=np.array([38.43822294, 23.38828386, -37.84013645]),
C=44.484691170961092),
VS_ColorimetryData(
name='VS3',
XYZ=np.array([0.10087152, 0.09197794, 0.32255009]),
Lab=np.array([36.36236009, 11.00844158, -43.50665998]),
C=44.877781233091532),
VS_ColorimetryData(
name='VS4',
XYZ=np.array([0.13339874, 0.15690626, 0.38428052]),
Lab=np.array([46.56595565, -9.85969969, -33.955993]),
C=35.358494572170223),
VS_ColorimetryData(
name='VS5',
XYZ=np.array([0.18662805, 0.24704107, 0.40046308]),
Lab=np.array([56.78597632, -23.14117043, -18.29881674]),
C=29.501872194883592),
VS_ColorimetryData(
name='VS6',
XYZ=np.array([0.15841746, 0.24152639, 0.26931454]),
Lab=np.array([56.24029681, -36.23772023, -1.42733778]),
C=36.265819453020534),
VS_ColorimetryData(
name='VS7',
XYZ=np.array([0.14989922, 0.24926212, 0.138238]),
Lab=np.array([57.003456, -44.54988402, 24.99942371]),
C=51.084864212342254),
VS_ColorimetryData(
name='VS8',
XYZ=np.array([0.26139991, 0.36817885, 0.11429053]),
Lab=np.array([67.14017484, -33.23131783, 48.67413212]),
C=58.936335329004450),
VS_ColorimetryData(
name='VS9',
XYZ=np.array([0.42407924, 0.5285824, 0.11437796]),
Lab=np.array([77.791228, -22.23481066, 67.01388225]),
C=70.606283139833707),
VS_ColorimetryData(
name='VS10',
XYZ=np.array([0.553588, 0.62024037, 0.09664934]),
Lab=np.array([82.9262011, -8.87753163, 81.03696819]),
C=81.521781025372348),
VS_ColorimetryData(
name='VS11',
XYZ=np.array([0.39748223, 0.39523993, 0.0573943]),
Lab=np.array([69.1291429, 6.94195834, 71.52497069]),
C=71.861061901342111),
VS_ColorimetryData(
name='VS12',
XYZ=np.array([0.43749683, 0.38968504, 0.08635639]),
Lab=np.array([68.72844451, 20.81629234, 59.85617607]),
C=63.372548004889417),
VS_ColorimetryData(
name='VS13',
XYZ=np.array([0.34652079, 0.27550838, 0.08903238]),
Lab=np.array([59.48075943, 31.8153513, 43.0302754]),
C=53.514681906739007),
VS_ColorimetryData(
name='VS14',
XYZ=np.array([0.14262899, 0.09104011, 0.04951868]),
Lab=np.array([36.18378646, 40.75048676, 18.33518283]),
C=44.685356670534020),
VS_ColorimetryData(
name='VS15',
XYZ=np.array([0.13589052, 0.092129, 0.11599991]),
Lab=np.array([36.39100967, 35.60560293, -4.81131533]),
C=35.929204214045100)
], [
VS_ColorimetryData(
name='VS1',
XYZ=np.array([0.15204948, 0.10842571, 0.21637094]),
Lab=np.array([39.31404365, 32.98305053, -21.74613572]),
C=39.506658186677903),
VS_ColorimetryData(
name='VS2',
XYZ=np.array([0.13188419, 0.10619651, 0.29958251]),
Lab=np.array([38.93233648, 22.05678627, -35.82168464]),
C=42.067741935012158),
VS_ColorimetryData(
name='VS3',
XYZ=np.array([0.10125948, 0.09854418, 0.32974284]),
Lab=np.array([37.57986983, 6.06363078, -42.39466811]),
C=42.826107716600681),
VS_ColorimetryData(
name='VS4',
XYZ=np.array([0.13147324, 0.16804591, 0.39334981]),
Lab=np.array([48.01287105, -17.35257125, -32.5683964]),
C=36.902739376000198),
VS_ColorimetryData(
name='VS5',
XYZ=np.array([0.18148174, 0.25847203, 0.41337803]),
Lab=np.array([57.89173056, -30.60333248, -17.92178331]),
C=35.464831538342288),
VS_ColorimetryData(
name='VS6',
XYZ=np.array([0.15185555, 0.25077644, 0.28171886]),
Lab=np.array([57.15099451, -44.01579207, -1.76236936]),
C=44.051060116297137),
VS_ColorimetryData(
name='VS7',
XYZ=np.array([0.1395656, 0.25329693, 0.1447564]),
Lab=np.array([57.39525164, -52.59411951, 24.11378416]),
C=57.858586173956795),
VS_ColorimetryData(
name='VS8',
XYZ=np.array([0.24671064, 0.36211041, 0.11980564]),
Lab=np.array([66.68086148, -37.45951248, 46.3832832]),
C=59.620667851273417),
VS_ColorimetryData(
name='VS9',
XYZ=np.array([0.4081725, 0.508617, 0.11898838]),
Lab=np.array([76.59515888, -21.91694258, 63.69607363]),
C=67.361280925614935),
VS_ColorimetryData(
name='VS10',
XYZ=np.array([0.56031473, 0.60567807, 0.10172838]),
Lab=np.array([82.14584971, -3.82960002, 78.15000763]),
C=78.243782681599043),
VS_ColorimetryData(
name='VS11',
XYZ=np.array([0.40536278, 0.38001713, 0.05756101]),
Lab=np.array([68.02187691, 14.16935995, 69.5431055]),
C=70.971926024112165),
VS_ColorimetryData(
name='VS12',
XYZ=np.array([0.45402541, 0.37917641, 0.08624766]),
Lab=np.array([67.95987006, 28.93047228, 58.56725509]),
C=65.323009690072709),
VS_ColorimetryData(
name='VS13',
XYZ=np.array([0.37202687, 0.27411307, 0.08885905]),
Lab=np.array([59.35311972, 40.9220464, 42.86677002]),
C=59.263596364529462),
VS_ColorimetryData(
name='VS14',
XYZ=np.array([0.19305452, 0.11049115, 0.04885402]),
Lab=np.array([39.66307016, 53.96236134, 24.65590426]),
C=59.328324233248154),
VS_ColorimetryData(
name='VS15',
XYZ=np.array([0.17304703, 0.10699393, 0.11285076]),
Lab=np.array([39.0694885, 45.99591907, 0.67561493]),
C=46.000880710876864)
]))
specification_t = colour_quality_scale(
ILLUMINANTS_SDS['FL1'],
additional_data=True,
method='NIST CQS 7.4')
np.testing.assert_almost_equal(
[
data.Q_a
for _index, data in sorted(specification_r.Q_as.items())
],
[
data.Q_a
for _index, data in sorted(specification_t.Q_as.items())
],
decimal=7,
)
specification_r = CQS_Specification(
name='FL1',
Q_a=74.933405395713152,
Q_f=75.895019131814692,
Q_p=None,
Q_g=83.859802969126989,
Q_d=None,
Q_as={
1:
VS_ColourQualityScaleData(
name='VS1',
Q_a=51.884484734774539,
D_C_ab=-14.642964823527400,
D_E_ab=15.053586922553365,
D_Ep_ab=15.053586922553365),
2:
VS_ColourQualityScaleData(
name='VS2',
Q_a=70.859386580835093,
D_C_ab=-5.748912901584454,
D_E_ab=9.109057753686301,
D_Ep_ab=9.109057753686301),
3:
VS_ColourQualityScaleData(
name='VS3',
Q_a=74.615079552044733,
D_C_ab=-0.199055128982607,
D_E_ab=7.934584371287040,
D_Ep_ab=7.934584371287040),
4:
VS_ColourQualityScaleData(
name='VS4',
Q_a=85.470454126502858,
D_C_ab=2.620284036804321,
D_E_ab=5.242841277796065,
D_Ep_ab=4.541089762450278),
5:
VS_ColourQualityScaleData(
name='VS5',
Q_a=95.464568465261436,
D_C_ab=3.244999121708176,
D_E_ab=3.541109287718627,
D_Ep_ab=1.417545656294811),
6:
VS_ColourQualityScaleData(
name='VS6',
Q_a=84.543583902150289,
D_C_ab=-0.684333146377760,
D_E_ab=4.830795633277699,
D_Ep_ab=4.830795633277699),
7:
VS_ColourQualityScaleData(
name='VS7',
Q_a=74.078633444044030,
D_C_ab=-6.773727982269591,
D_E_ab=8.102322827150674,
D_Ep_ab=8.102322827150674),
8:
VS_ColourQualityScaleData(
name='VS8',
Q_a=74.922760174879087,
D_C_ab=-7.785252182726744,
D_E_ab=7.838379721335357,
D_Ep_ab=7.838379721335357),
9:
VS_ColourQualityScaleData(
name='VS9',
Q_a=75.835254560528938,
D_C_ab=-5.962964833116001,
D_E_ab=7.553073243258186,
D_Ep_ab=7.553073243258186),
10:
VS_ColourQualityScaleData(
name='VS10',
Q_a=75.184803574188905,
D_C_ab=-1.544240934167505,
D_E_ab=7.756446084611037,
D_Ep_ab=7.756446084611037),
11:
VS_ColourQualityScaleData(
name='VS11',
Q_a=78.089782546668729,
D_C_ab=1.745770231264103,
D_E_ab=7.067228869124241,
D_Ep_ab=6.848212189193262),
12:
VS_ColourQualityScaleData(
name='VS12',
Q_a=91.955688469006077,
D_C_ab=2.855869216256835,
D_E_ab=3.804866911398577,
D_Ep_ab=2.514164519893699),
13:
VS_ColourQualityScaleData(
name='VS13',
Q_a=81.468184220490429,
D_C_ab=0.030402986778803,
D_E_ab=5.792177528304779,
D_Ep_ab=5.792097735518090),
14:
VS_ColourQualityScaleData(
name='VS14',
Q_a=76.482628265932789,
D_C_ab=-5.080750395110528,
D_E_ab=7.350669246766205,
D_Ep_ab=7.350669246766205),
15:
VS_ColourQualityScaleData(
name='VS15',
Q_a=65.370026142948518,
D_C_ab=-9.509238816066890,
D_E_ab=10.826397690468854,
D_Ep_ab=10.826397690468854)
},
colorimetry_data=([
VS_ColorimetryData(
name='VS1',
XYZ=np.array([0.14262899, 0.09104012, 0.04951869]),
Lab=np.array([36.18378647, 40.75048793, 18.33517831]),
C=44.685355882758152),
VS_ColorimetryData(
name='VS2',
XYZ=np.array([0.3465208, 0.27550838, 0.08903238]),
Lab=np.array([59.48075944, 31.81535155, 43.03027426]),
C=53.514681133664730),
VS_ColorimetryData(
name='VS3',
XYZ=np.array([0.41042219, 0.38688299, 0.06618968]),
Lab=np.array([68.5248742, 13.56049316, 66.82043633]),
C=68.182532115437340),
VS_ColorimetryData(
name='VS4',
XYZ=np.array([0.45420723, 0.50405653, 0.08194422]),
Lab=np.array([76.31757819, -7.04196646, 74.43516148]),
C=74.767523404153394),
VS_ColorimetryData(
name='VS5',
XYZ=np.array([0.42407924, 0.5285824, 0.11437796]),
Lab=np.array([77.79122799, -22.23481106, 67.01388407]),
C=70.606284995201392),
VS_ColorimetryData(
name='VS6',
XYZ=np.array([0.26139991, 0.36817885, 0.11429053]),
Lab=np.array([67.14017484, -33.23131787, 48.67413226]),
C=58.936335472298275),
VS_ColorimetryData(
name='VS7',
XYZ=np.array([0.14989922, 0.24926212, 0.13823798]),
Lab=np.array([57.00345599, -44.54988535, 24.99942632]),
C=51.084866649348065),
VS_ColorimetryData(
name='VS8',
XYZ=np.array([0.15841745, 0.24152639, 0.2693145]),
Lab=np.array([56.24029678, -36.23772472, -1.42733192]),
C=36.265823711788478),
VS_ColorimetryData(
name='VS9',
XYZ=np.array([0.18662804, 0.24704107, 0.40046303]),
Lab=np.array([56.78597628, -23.14117544, -18.29881115]),
C=29.501872653031871),
VS_ColorimetryData(
name='VS10',
XYZ=np.array([0.13339873, 0.15690626, 0.38428046]),
Lab=np.array([46.56595559, -9.85970727, -33.95598605]),
C=35.358490015776226),
VS_ColorimetryData(
name='VS11',
XYZ=np.array([0.09902437, 0.09951361, 0.32046526]),
Lab=np.array([37.75499466, 3.55194161, -40.81669619]),
C=40.970952848102279),
VS_ColorimetryData(
name='VS12',
XYZ=np.array([0.11579654, 0.09612863, 0.31943786]),
Lab=np.array([37.1384526, 18.80505207, -41.7368833]),
C=45.777695561949436),
VS_ColorimetryData(
name='VS13',
XYZ=np.array([0.20977187, 0.16844709, 0.37299873]),
Lab=np.array([48.06376988, 26.00174066, -29.97180426]),
C=39.678704214193381),
VS_ColorimetryData(
name='VS14',
XYZ=np.array([0.32294822, 0.2416386, 0.3623672]),
Lab=np.array([56.25148226, 37.44488177, -14.50799142]),
C=40.157203412534642),
VS_ColorimetryData(
name='VS15',
XYZ=np.array([0.22031646, 0.15367587, 0.17564225]),
Lab=np.array([46.13360422, 39.30141636, -2.12353664]),
C=39.358744081490819)
], [
VS_ColorimetryData(
name='VS1',
XYZ=np.array([0.19305453, 0.11049115, 0.04885406]),
Lab=np.array([39.6630702, 53.96236472, 24.65588837]),
C=59.328320706285552),
VS_ColorimetryData(
name='VS2',
XYZ=np.array([0.37202688, 0.27411307, 0.08885906]),
Lab=np.array([59.35311972, 40.92204723, 42.86676601]),
C=59.263594035249184),
VS_ColorimetryData(
name='VS3',
XYZ=np.array([0.42075427, 0.37269638, 0.06621283]),
Lab=np.array([67.47883546, 21.21408713, 65.00772248]),
C=68.381587244419947),
VS_ColorimetryData(
name='VS4',
XYZ=np.array([0.4619684, 0.49480415, 0.08591535]),
Lab=np.array([75.74923125, -2.37840511, 72.10802547]),
C=72.147239367349073),
VS_ColorimetryData(
name='VS5',
XYZ=np.array([0.40817249, 0.508617, 0.11898836]),
Lab=np.array([76.59515887, -21.91694373, 63.69607847]),
C=67.361285873493216),
VS_ColorimetryData(
name='VS6',
XYZ=np.array([0.24671064, 0.36211041, 0.11980563]),
Lab=np.array([66.68086148, -37.45951274, 46.38328397]),
C=59.620668618676035),
VS_ColorimetryData(
name='VS7',
XYZ=np.array([0.1395656, 0.25329693, 0.14475636]),
Lab=np.array([57.39525162, -52.59412462, 24.11379331]),
C=57.858594631617656),
VS_ColorimetryData(
name='VS8',
XYZ=np.array([0.15185552, 0.25077644, 0.28171872]),
Lab=np.array([57.15099443, -44.01580867, -1.76234921]),
C=44.051075894515222),
VS_ColorimetryData(
name='VS9',
XYZ=np.array([0.1814817, 0.25847202, 0.41337786]),
Lab=np.array([57.89173046, -30.60335063, -17.92176409]),
C=35.464837486147871),
VS_ColorimetryData(
name='VS10',
XYZ=np.array([0.1314732, 0.16804591, 0.39334961]),
Lab=np.array([48.0128709, -17.35259857, -32.5683723]),
C=36.902730949943731),
VS_ColorimetryData(
name='VS11',
XYZ=np.array([0.0972772, 0.10656633, 0.32348741]),
Lab=np.array([38.99602774, -3.18915863, -39.09532221]),
C=39.225182616838175),
VS_ColorimetryData(
name='VS12',
XYZ=np.array([0.11499913, 0.09966411, 0.31524145]),
Lab=np.array([37.78207842, 15.46056944, -40.04065396]),
C=42.921826345692601),
VS_ColorimetryData(
name='VS13',
XYZ=np.array([0.23126112, 0.17972713, 0.36052054]),
Lab=np.array([49.46299499, 29.95421157, -25.97562318]),
C=39.648301227414578),
VS_ColorimetryData(
name='VS14',
XYZ=np.array([0.35886253, 0.2560872, 0.35532873]),
Lab=np.array([57.66377054, 43.8380947, -11.16664308]),
C=45.237953807645169),
VS_ColorimetryData(
name='VS15',
XYZ=np.array([0.26550827, 0.17191964, 0.17308227]),
Lab=np.array([48.50100521, 48.80442473, 2.49156155]),
C=48.86798289755771)
]))
specification_t = colour_quality_scale(
ILLUMINANTS_SDS['FL1'],
additional_data=True,
method='NIST CQS 9.0')
np.testing.assert_almost_equal(
[
data.Q_a
for _index, data in sorted(specification_r.Q_as.items())
],
[
data.Q_a
for _index, data in sorted(specification_t.Q_as.items())
],
decimal=7,
)
def plot_multi_sds_colour_quality_scales_bars(sds,
method='NIST CQS 9.0',
**kwargs):
"""
Plots the *Colour Quality Scale* (CQS) of given illuminants or light
sources spectral distributions.
Parameters
----------
sds : array_like or MultiSpectralDistributions
Spectral distributions or multi-spectral distributions to
plot. `sds` can be a single
:class:`colour.MultiSpectralDistributions` class instance, a list
of :class:`colour.MultiSpectralDistributions` class instances or a
list of :class:`colour.SpectralDistribution` class instances.
method : unicode, optional
**{NIST CQS 7.4'}**,
*Colour Quality Scale* (CQS) computation method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.quality.plot_colour_quality_bars`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
labels : bool, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Add labels above bars.
hatching : bool or None, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Use hatching for the bars.
hatching_repeat : int, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Hatching pattern repeat.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> from colour import (ILLUMINANTS_SDS,
... LIGHT_SOURCES_SDS)
>>> illuminant = ILLUMINANTS_SDS['FL2']
>>> light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
>>> plot_multi_sds_colour_quality_scales_bars([illuminant, light_source])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_\
Plot_Multi_SDS_Colour_Quality_Scales_Bars.png
:align: center
:alt: plot_multi_sds_colour_quality_scales_bars
"""
sds = sds_and_multi_sds_to_sds(sds)
settings = dict(kwargs)
settings.update({'standalone': False})
specifications = [colour_quality_scale(sd, True, method) for sd in sds]
_figure, axes = plot_colour_quality_bars(specifications, **settings)
title = 'Colour Quality Scale - {0}'.format(', '.join(
[sd.strict_name for sd in sds]))
settings = {'axes': axes, 'title': title}
settings.update(kwargs)
return render(**settings)
def plot_multi_sds_colour_quality_scales_bars(sds,
method='NIST CQS 7.4',
**kwargs):
"""
Plots the *Colour Quality Scale* (CQS) of given illuminants or light
sources spectral distributions.
Parameters
----------
sds : array_like
Array of illuminants or light sources spectral distributions to
plot the *Colour Quality Scale* (CQS).
method : unicode, optional
**{NIST CQS 7.4'}**,
*Colour Quality Scale* (CQS) computation method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.quality.plot_colour_quality_bars`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
labels : bool, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Add labels above bars.
hatching : bool or None, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Use hatching for the bars.
hatching_repeat : int, optional
{:func:`colour.plotting.quality.plot_colour_quality_bars`},
Hatching pattern repeat.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> from colour import (ILLUMINANTS_SDS,
... LIGHT_SOURCES_SDS)
>>> illuminant = ILLUMINANTS_SDS['FL2']
>>> light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
>>> plot_multi_sds_colour_quality_scales_bars([illuminant, light_source])
... # doctest: +SKIP
.. image:: ../_static/Plotting_\
Plot_Multi_SDs_Colour_Quality_Scales_Bars.png
:align: center
:alt: plot_multi_sds_colour_quality_scales_bars
"""
settings = dict(kwargs)
settings.update({'standalone': False})
specifications = [colour_quality_scale(sd, True, method) for sd in sds]
_figure, axes = plot_colour_quality_bars(specifications, **settings)
title = 'Colour Quality Scale - {0}'.format(', '.join(
[sd.strict_name for sd in sds]))
settings = {'axes': axes, 'title': title}
settings.update(kwargs)
return render(**settings)
def test_colour_quality_scale(self):
"""
Tests :func:`colour.quality.cqs.colour_quality_scale` definition.
"""
self.assertAlmostEqual(
colour_quality_scale(ILLUMINANTS_SDS['FL1']),
74.933405395713180,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_SDS['FL1'], method='NIST CQS 7.4'),
75.332008182589348,
places=7)
self.assertAlmostEqual(
colour_quality_scale(ILLUMINANTS_SDS['FL2']),
64.017283509280588,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
ILLUMINANTS_SDS['FL2'], method='NIST CQS 7.4'),
64.686339173112856,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['Neodimium Incandescent']),
89.693921013642381,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['Neodimium Incandescent'],
method='NIST CQS 7.4'),
87.655035241231985,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['F32T8/TL841 (Triphosphor)']),
84.878441814420910,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['F32T8/TL841 (Triphosphor)'],
method='NIST CQS 7.4'),
83.179881092827671,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['H38HT-100 (Mercury)']),
19.836071708638958,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['H38HT-100 (Mercury)'],
method='NIST CQS 7.4'),
22.860610106043985,
places=7)
self.assertAlmostEqual(
colour_quality_scale(LIGHT_SOURCES_SDS['Luxeon WW 2880']),
86.491761709787994,
places=7)
self.assertAlmostEqual(
colour_quality_scale(
LIGHT_SOURCES_SDS['Luxeon WW 2880'], method='NIST CQS 7.4'),
84.879524259605077,
places=7)
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]
)
