def multi_spd_colour_rendering_index_bars_plot(spds, **kwargs):
"""
Plots the *colour rendering index* 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 rendering index*.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
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_rendering_index_bars_plot( # doctest: +SKIP
... [illuminant, light_source])
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
specifications = [
colour_rendering_index(spd, additional_data=True) for spd in spds
]
# *colour rendering index* colorimetry data tristimulus values are
# computed in [0, 100] domain however `colour_quality_bars_plot` expects
# [0, 1] domain. As we want to keep `colour_quality_bars_plot` definition
# agnostic from the colour quality data, we update the test spd
# colorimetry data tristimulus values domain.
for specification in specifications:
colorimetry_data = specification.colorimetry_data
for i, c_d in enumerate(colorimetry_data[0]):
colorimetry_data[0][i] = TCS_ColorimetryData(
c_d.name, c_d.XYZ / 100, c_d.uv, c_d.UVW)
colour_quality_bars_plot(specifications, **settings)
settings = {
'title':
'Colour Rendering Index - {0}'.format(', '.join(
[spd.title for spd in spds]))
}
settings.update(kwargs)
decorate(**settings)
return display(**settings)
def multi_spd_colour_rendering_index_bars_plot(spds, **kwargs):
"""
Plots the *Colour Rendering Index* (CRI) 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 Rendering Index* (CRI).
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
labels : bool, optional
{:func:`colour_quality_bars_plot`},
Add labels above bars.
hatching : bool or None, optional
{:func:`colour_quality_bars_plot`},
Use hatching for the bars.
hatching_repeat : int, optional
{:func:`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_rendering_index_bars_plot( # doctest: +SKIP
... [illuminant, light_source])
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
specifications = [
colour_rendering_index(spd, additional_data=True) for spd in spds
]
# *colour rendering index* colorimetry data tristimulus values are
# computed in [0, 100] domain however `colour_quality_bars_plot` expects
# [0, 1] domain. As we want to keep `colour_quality_bars_plot` definition
# agnostic from the colour quality data, we update the test spd
# colorimetry data tristimulus values domain.
for specification in specifications:
colorimetry_data = specification.colorimetry_data
for i, c_d in enumerate(colorimetry_data[0]):
colorimetry_data[0][i] = TCS_ColorimetryData(
c_d.name, c_d.XYZ / 100, c_d.uv, c_d.UVW)
colour_quality_bars_plot(specifications, **settings)
settings = {
'title':
'Colour Rendering Index - {0}'.format(', '.join(
[spd.title for spd in spds]))
}
settings.update(kwargs)
decorate(**settings)
return display(**settings)
def plot_multi_sds_colour_rendering_indexes_bars(sds, **kwargs):
"""
Plots the *Colour Rendering Index* (CRI) 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.
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_rendering_indexes_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_Rendering_Indexes_Bars.png
:align: center
:alt: plot_multi_sds_colour_rendering_indexes_bars
"""
sds = sds_and_multi_sds_to_sds(sds)
settings = dict(kwargs)
settings.update({'standalone': False})
specifications = [
colour_rendering_index(sd, additional_data=True) for sd in sds
]
# *colour rendering index* colorimetry data tristimulus values are
# computed in [0, 100] domain however `plot_colour_quality_bars` expects
# [0, 1] domain. As we want to keep `plot_colour_quality_bars` definition
# agnostic from the colour quality data, we update the test sd
# colorimetry data tristimulus values domain.
for specification in specifications:
colorimetry_data = specification.colorimetry_data
for i, c_d in enumerate(colorimetry_data[0]):
colorimetry_data[0][i] = TCS_ColorimetryData(
c_d.name, c_d.XYZ / 100, c_d.uv, c_d.UVW)
_figure, axes = plot_colour_quality_bars(specifications, **settings)
title = 'Colour Rendering Index - {0}'.format(', '.join(
[sd.strict_name for sd in sds]))
settings = {'axes': axes, 'title': title}
settings.update(kwargs)
return render(**settings)
def test_colour_rendering_index(self):
"""
Tests :func:`colour.quality.cri.colour_rendering_index` definition.
"""
self.assertAlmostEqual(colour_rendering_index(SDS_ILLUMINANTS['FL1']),
75.852827992149358,
places=7)
self.assertAlmostEqual(colour_rendering_index(SDS_ILLUMINANTS['FL2']),
64.233724121664778,
places=7)
self.assertAlmostEqual(colour_rendering_index(SDS_ILLUMINANTS['A']),
99.996230290506887,
places=7)
self.assertAlmostEqual(colour_rendering_index(
SpectralDistribution(DATA_SAMPLE)),
70.815265381660197,
places=7)
specification_r = ColourRendering_Specification_CRI(
name='FL1',
Q_a=75.852827992149358,
Q_as={
1:
TCS_ColourQualityScaleData(name='TCS01',
Q_a=69.196992839933557),
2:
TCS_ColourQualityScaleData(name='TCS02',
Q_a=83.650754065677816),
3:
TCS_ColourQualityScaleData(name='TCS03',
Q_a=92.136090764490675),
4:
TCS_ColourQualityScaleData(name='TCS04',
Q_a=72.665649420972784),
5:
TCS_ColourQualityScaleData(name='TCS05',
Q_a=73.890734513517486),
6:
TCS_ColourQualityScaleData(name='TCS06',
Q_a=79.619188860052745),
7:
TCS_ColourQualityScaleData(name='TCS07',
Q_a=82.272569853644669),
8:
TCS_ColourQualityScaleData(name='TCS08',
Q_a=53.390643618905109),
9:
TCS_ColourQualityScaleData(name='TCS09',
Q_a=-47.284477750225903),
10:
TCS_ColourQualityScaleData(name='TCS10',
Q_a=61.568336431199967),
11:
TCS_ColourQualityScaleData(name='TCS11',
Q_a=67.522241168172485),
12:
TCS_ColourQualityScaleData(name='TCS12',
Q_a=74.890093866757994),
13:
TCS_ColourQualityScaleData(name='TCS13',
Q_a=72.771930354944615),
14:
TCS_ColourQualityScaleData(name='TCS14',
Q_a=94.884867470552663)
},
colorimetry_data=([
TCS_ColorimetryData(
name='TCS01',
XYZ=np.array([31.19561134, 29.74560797, 23.44190201]),
uv=np.array([0.22782766, 0.32585700]),
UVW=np.array([25.43090825, 8.72673714, 60.46061819])),
TCS_ColorimetryData(
name='TCS02',
XYZ=np.array([26.70829694, 29.25797165, 13.98804447]),
uv=np.array([0.21049132, 0.34587843]),
UVW=np.array([11.90704877, 24.68727835, 60.03499882])),
TCS_ColorimetryData(
name='TCS03',
XYZ=np.array([24.20150315, 31.45341032, 9.19170689]),
uv=np.array([0.18489328, 0.36044399]),
UVW=np.array([-8.11343024, 37.47469142, 61.91555021])),
TCS_ColorimetryData(
name='TCS04',
XYZ=np.array([20.74577359, 29.44046997, 19.40749007]),
uv=np.array([0.15940652, 0.33932233]),
UVW=np.array([-27.55686536, 19.30727375, 60.19483706])),
TCS_ColorimetryData(
name='TCS05',
XYZ=np.array([25.09405566, 30.60912259, 37.93634878]),
uv=np.array([0.16784200, 0.30709443]),
UVW=np.array([-21.30541564, -6.63859760, 61.20303996])),
TCS_ColorimetryData(
name='TCS06',
XYZ=np.array([27.43598853, 28.84467787, 55.15209308]),
uv=np.array([0.17543246, 0.27665994]),
UVW=np.array([-14.91500194, -30.51166823, 59.67054901])),
TCS_ColorimetryData(
name='TCS07',
XYZ=np.array([31.30354023, 28.49931283, 51.55875721]),
uv=np.array([0.20410821, 0.27873574]),
UVW=np.array([6.88826195, -28.65430811, 59.36332055])),
TCS_ColorimetryData(
name='TCS08',
XYZ=np.array([33.81156122, 29.92921717, 44.07459562]),
uv=np.array([0.21992203, 0.29200489]),
UVW=np.array([19.29699368, -18.57115711, 60.61967045])),
TCS_ColorimetryData(
name='TCS09',
XYZ=np.array([14.75210654, 9.07663825, 4.24056478]),
uv=np.array([0.36063567, 0.33283649]),
UVW=np.array([74.85972274, 8.59043120, 35.14928413])),
TCS_ColorimetryData(
name='TCS10',
XYZ=np.array([53.37923227, 60.57123196, 10.90035400]),
uv=np.array([0.21466565, 0.36538264]),
UVW=np.array([20.48612095, 54.66177264, 81.18130740])),
TCS_ColorimetryData(
name='TCS11',
XYZ=np.array([12.25313424, 19.77564573, 14.04738059]),
uv=np.array([0.13962494, 0.33801637]),
UVW=np.array([-36.00690822, 15.29571595, 50.60573931])),
TCS_ColorimetryData(
name='TCS12',
XYZ=np.array([5.77964943, 5.69096075, 24.73530409]),
uv=np.array([0.13981616, 0.20650602]),
UVW=np.array([-19.30689974, -39.58762581, 27.63428055])),
TCS_ColorimetryData(
name='TCS13',
XYZ=np.array([56.62340157, 57.50304691, 39.13768236]),
uv=np.array([0.21850039, 0.33284220]),
UVW=np.array([23.93135946, 18.85365757, 79.49473722])),
TCS_ColorimetryData(
name='TCS14',
XYZ=np.array([9.42270977, 12.06929274, 5.06066928]),
uv=np.array([0.18328188, 0.35214117]),
UVW=np.array([-6.11563143, 19.91896684, 40.34566797]))
], [
TCS_ColorimetryData(
name='TCS01',
XYZ=np.array([33.04774537, 29.80902109, 24.23929188]),
uv=np.array([0.23908620, 0.32348313]),
UVW=np.array([32.11891091, 8.39794012, 60.51562388])),
TCS_ColorimetryData(
name='TCS02',
XYZ=np.array([27.53677515, 28.90913487, 14.75211494]),
uv=np.array([0.21792745, 0.34318256]),
UVW=np.array([15.27177183, 23.58438306, 59.72761646])),
TCS_ColorimetryData(
name='TCS03',
XYZ=np.array([23.95706051, 30.44569936, 9.79977770]),
uv=np.array([0.18788308, 0.35815528]),
UVW=np.array([-8.23665275, 35.99767796, 61.06361523])),
TCS_ColorimetryData(
name='TCS04',
XYZ=np.array([20.43647757, 29.46748627, 21.03631859]),
uv=np.array([0.15554126, 0.33641389]),
UVW=np.array([-33.44111710, 18.47940853, 60.21844268])),
TCS_ColorimetryData(
name='TCS05',
XYZ=np.array([24.95511680, 30.81953457, 39.91407614]),
uv=np.array([0.16445149, 0.30464603]),
UVW=np.array([-26.97713986, -6.51317420, 61.38182431])),
TCS_ColorimetryData(
name='TCS06',
XYZ=np.array([28.14601546, 29.75632189, 57.16886797]),
uv=np.array([0.17427942, 0.27637560]),
UVW=np.array([-18.85053083, -28.64005452, 60.46991712])),
TCS_ColorimetryData(
name='TCS07',
XYZ=np.array([33.29720901, 29.36938555, 52.53803430]),
uv=np.array([0.21092469, 0.27906521]),
UVW=np.array([9.90110830, -26.37778265, 60.13265766])),
TCS_ColorimetryData(
name='TCS08',
XYZ=np.array([37.64974363, 31.35401774, 44.84930911]),
uv=np.array([0.23439240, 0.29279655]),
UVW=np.array([29.04479043, -16.08583648, 61.83233828])),
TCS_ColorimetryData(
name='TCS09',
XYZ=np.array([20.69443384, 11.28822434, 4.28723010]),
uv=np.array([0.40801431, 0.33384028]),
UVW=np.array([106.56664825, 10.68535426, 39.08093160])),
TCS_ColorimetryData(
name='TCS10',
XYZ=np.array([55.04099876, 59.04719161, 11.86354410]),
uv=np.array([0.22549942, 0.36286881]),
UVW=np.array([28.45432459, 52.29127793, 80.35085218])),
TCS_ColorimetryData(
name='TCS11',
XYZ=np.array([12.13069359, 20.35272336, 15.17132466]),
uv=np.array([0.13369530, 0.33646842]),
UVW=np.array([-43.02145539, 15.76573781, 51.25705062])),
TCS_ColorimetryData(
name='TCS12',
XYZ=np.array([6.18799870, 6.41132549, 27.28158667]),
uv=np.array([0.13437372, 0.20883497]),
UVW=np.array([-24.45285903, -39.79705961, 29.44325151])),
TCS_ColorimetryData(
name='TCS13',
XYZ=np.array([58.97935503, 57.14777525, 40.83450223]),
uv=np.array([0.22712769, 0.33011150]),
UVW=np.array([29.75912462, 17.83690656, 79.29560174])),
TCS_ColorimetryData(
name='TCS14',
XYZ=np.array([9.34469915, 11.70922060, 5.33442353]),
uv=np.array([0.18597686, 0.34955284]),
UVW=np.array([-6.34991066, 18.99712303, 39.76962229]))
]))
specification_t = colour_rendering_index(SDS_ILLUMINANTS['FL1'],
additional_data=True)
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 test_colour_rendering_index(self):
"""
Tests :func:`colour.quality.cri.colour_rendering_index` definition.
"""
self.assertAlmostEqual(
colour_rendering_index(ILLUMINANTS_SDS['FL1']),
75.821550491976069,
places=7)
self.assertAlmostEqual(
colour_rendering_index(ILLUMINANTS_SDS['FL2']),
64.151520202968015,
places=7)
self.assertAlmostEqual(
colour_rendering_index(ILLUMINANTS_SDS['A']),
99.996732643006169,
places=7)
self.assertAlmostEqual(
colour_rendering_index(SpectralDistribution(SAMPLE_SD_DATA)),
70.813839034481575,
places=7)
specification_r = CRI_Specification(
name='FL1',
Q_a=75.821550491976069,
Q_as={
1:
TCS_ColourQualityScaleData(
name='TCS01', Q_a=69.146993939830651),
2:
TCS_ColourQualityScaleData(
name='TCS02', Q_a=83.621259237346464),
3:
TCS_ColourQualityScaleData(
name='TCS03', Q_a=92.103176674726171),
4:
TCS_ColourQualityScaleData(
name='TCS04', Q_a=72.659091884741599),
5:
TCS_ColourQualityScaleData(
name='TCS05', Q_a=73.875477056438399),
6:
TCS_ColourQualityScaleData(
name='TCS06', Q_a=79.561558275578435),
7:
TCS_ColourQualityScaleData(
name='TCS07', Q_a=82.238690395582836),
8:
TCS_ColourQualityScaleData(
name='TCS08', Q_a=53.366156471564040),
9:
TCS_ColourQualityScaleData(
name='TCS09', Q_a=-47.429377845680563),
10:
TCS_ColourQualityScaleData(
name='TCS10', Q_a=61.459446471229136),
11:
TCS_ColourQualityScaleData(
name='TCS11', Q_a=67.488681008259860),
12:
TCS_ColourQualityScaleData(
name='TCS12', Q_a=74.912512331077920),
13:
TCS_ColourQualityScaleData(
name='TCS13', Q_a=72.752285379884341),
14:
TCS_ColourQualityScaleData(
name='TCS14', Q_a=94.874668874605561)
},
colorimetry_data=([
TCS_ColorimetryData(
name='TCS01',
XYZ=np.array([31.20331782, 29.74794094, 23.46699878]),
uv=np.array([0.22783485, 0.32581236]),
UVW=np.array([25.41292946, 8.7380799, 60.46264324])),
TCS_ColorimetryData(
name='TCS02',
XYZ=np.array([26.7148606, 29.25905082, 14.00052658]),
uv=np.array([0.21051808, 0.34585016]),
UVW=np.array([11.89933381, 24.70828029, 60.03594595])),
TCS_ColorimetryData(
name='TCS03',
XYZ=np.array([24.20882878, 31.45657446, 9.19966949]),
uv=np.array([0.18492146, 0.36042609]),
UVW=np.array([-8.1256375, 37.50367669, 61.91819636])),
TCS_ColorimetryData(
name='TCS04',
XYZ=np.array([20.75294445, 29.43766611, 19.42067879]),
uv=np.array([0.15946018, 0.33928696]),
UVW=np.array([-27.55034027, 19.3247759, 60.19238635])),
TCS_ColorimetryData(
name='TCS05',
XYZ=np.array([25.10593837, 30.60666903, 37.97182815]),
uv=np.array([0.1678986, 0.30702797]),
UVW=np.array([-21.29395798, -6.63937802, 61.20095038])),
TCS_ColorimetryData(
name='TCS06',
XYZ=np.array([27.45144596, 28.84090621, 55.21254008]),
uv=np.array([0.17549196, 0.27656177]),
UVW=np.array([-14.89855536, -30.53194048, 59.66720711])),
TCS_ColorimetryData(
name='TCS07',
XYZ=np.array([31.31828342, 28.49717267, 51.6262295]),
uv=np.array([0.20414276, 0.27863076]),
UVW=np.array([6.89004159, -28.68174855, 59.36140901])),
TCS_ColorimetryData(
name='TCS08',
XYZ=np.array([33.82293106, 29.92724727, 44.13391542]),
uv=np.array([0.21993884, 0.29190983]),
UVW=np.array([19.28664162, -18.59403459, 60.61796747])),
TCS_ColorimetryData(
name='TCS09',
XYZ=np.array([14.74628552, 9.07383027, 4.24660598]),
uv=np.array([0.36055908, 0.33279417]),
UVW=np.array([74.80963996, 8.5929104, 35.14390587])),
TCS_ColorimetryData(
name='TCS10',
XYZ=np.array([53.38914901, 60.57766887, 10.90608553]),
uv=np.array([0.21467884, 0.36537604]),
UVW=np.array([20.46081762, 54.70909846, 81.18478519])),
TCS_ColorimetryData(
name='TCS11',
XYZ=np.array([12.25740399, 19.77026703, 14.05795099]),
uv=np.array([0.13969138, 0.33796747]),
UVW=np.array([-35.99235266, 15.30093833, 50.59960948])),
TCS_ColorimetryData(
name='TCS12',
XYZ=np.array([5.7789513, 5.68760485, 24.7298429]),
uv=np.array([0.13985629, 0.20646843]),
UVW=np.array([-19.30294902, -39.56090075, 27.62550537])),
TCS_ColorimetryData(
name='TCS13',
XYZ=np.array([56.63907549, 57.50545374, 39.17805]),
uv=np.array([0.21852443, 0.33280062]),
UVW=np.array([23.92148732, 18.87040227, 79.49608348])),
TCS_ColorimetryData(
name='TCS14',
XYZ=np.array([9.42594992, 12.07064446, 5.06612415]),
uv=np.array([0.18330936, 0.35211232]),
UVW=np.array([-6.1238664, 19.9332886, 40.34780872]))
], [
TCS_ColorimetryData(
name='TCS01',
XYZ=np.array([33.04406274, 29.80722965, 24.25677014]),
uv=np.array([0.23905009, 0.32345089]),
UVW=np.array([32.11150701, 8.4025005, 60.51407102])),
TCS_ColorimetryData(
name='TCS02',
XYZ=np.array([27.53323581, 28.90793444, 14.76248032]),
uv=np.array([0.21789532, 0.34316182]),
UVW=np.array([15.26809231, 23.59761128, 59.72655443])),
TCS_ColorimetryData(
name='TCS03',
XYZ=np.array([23.95435019, 30.44568852, 9.80615507]),
uv=np.array([0.18785584, 0.35814374]),
UVW=np.array([-8.23625903, 36.01892099, 61.06360597])),
TCS_ColorimetryData(
name='TCS04',
XYZ=np.array([20.43638681, 29.46870454, 21.05101191]),
uv=np.array([0.15552214, 0.33638794]),
UVW=np.array([-33.43495759, 18.48941724, 60.21950681])),
TCS_ColorimetryData(
name='TCS05',
XYZ=np.array([24.95728556, 30.82110743, 39.94366077]),
uv=np.array([0.16443476, 0.30460412]),
UVW=np.array([-26.96894169, -6.51619473, 61.38315768])),
TCS_ColorimetryData(
name='TCS06',
XYZ=np.array([28.15012994, 29.75806707, 57.21213255]),
uv=np.array([0.17426171, 0.27632333]),
UVW=np.array([-18.84311713, -28.6517426, 60.4714316])),
TCS_ColorimetryData(
name='TCS07',
XYZ=np.array([33.29875981, 29.36956806, 52.57688943]),
uv=np.array([0.21089415, 0.27901355]),
UVW=np.array([9.89894512, -26.38829247, 60.13281743])),
TCS_ColorimetryData(
name='TCS08',
XYZ=np.array([37.64824851, 31.35309879, 44.88145951]),
uv=np.array([0.23435348, 0.29275098]),
UVW=np.array([29.03544464, -16.09146766, 61.83156811])),
TCS_ColorimetryData(
name='TCS09',
XYZ=np.array([20.68888349, 11.28578196, 4.29031293]),
uv=np.array([0.40797112, 0.33382225]),
UVW=np.array([106.54776544, 10.69454868, 39.07688665])),
TCS_ColorimetryData(
name='TCS10',
XYZ=np.array([55.03120276, 59.04411929, 11.87116937]),
uv=np.array([0.22546691, 0.36286219]),
UVW=np.array([28.44874091, 52.32328834, 80.34916371])),
TCS_ColorimetryData(
name='TCS11',
XYZ=np.array([12.13121967, 20.3541437, 15.18142797]),
uv=np.array([0.1336819, 0.33644357]),
UVW=np.array([-43.01323833, 15.77519367, 51.25863839])),
TCS_ColorimetryData(
name='TCS12',
XYZ=np.array([6.19245638, 6.41273455, 27.30815071]),
uv=np.array([0.13439371, 0.20876154]),
UVW=np.array([-24.4374163, -39.8151001, 29.44665364])),
TCS_ColorimetryData(
name='TCS13',
XYZ=np.array([58.97285728, 57.14500933, 40.86401249]),
uv=np.array([0.22709381, 0.33008264]),
UVW=np.array([29.75220364, 17.84629845, 79.29404816])),
TCS_ColorimetryData(
name='TCS14',
XYZ=np.array([9.34392028, 11.70931454, 5.33816006]),
uv=np.array([0.1859504, 0.34953505]),
UVW=np.array([-6.3492713, 19.0078073, 39.7697741]))
]))
specification_t = colour_rendering_index(
ILLUMINANTS_SDS['FL1'], additional_data=True)
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,
)