def planckian_table(uv, cmfs, start, end, count):
"""
Returns a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using Ohno (2013) method.
Parameters
----------
uv : array_like
*uv* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
start : numeric
Temperature range start in kelvins.
end : numeric
Temperature range end in kelvins.
count : int
Temperatures count in the planckian table.
Returns
-------
list
Planckian table.
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> from pprint import pprint
>>> cmfs = 'CIE 1931 2 Degree Standard Observer'
>>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)
>>> uv = np.array([0.1978, 0.3122])
>>> pprint(planckian_table(uv, cmfs, 1000, 1010, 10)) # noqa # doctest: +ELLIPSIS
[PlanckianTable_Tuvdi(Ti=1000.0, ui=0.4480108..., vi=0.3546249..., di=0.2537821...),
PlanckianTable_Tuvdi(Ti=1001.1111111..., ui=0.4477508..., vi=0.3546475..., di=0.2535294...),
PlanckianTable_Tuvdi(Ti=1002.2222222..., ui=0.4474910..., vi=0.3546700..., di=0.2532771...),
PlanckianTable_Tuvdi(Ti=1003.3333333..., ui=0.4472316..., vi=0.3546924..., di=0.2530251...),
PlanckianTable_Tuvdi(Ti=1004.4444444..., ui=0.4469724..., vi=0.3547148..., di=0.2527734...),
PlanckianTable_Tuvdi(Ti=1005.5555555..., ui=0.4467136..., vi=0.3547372..., di=0.2525220...),
PlanckianTable_Tuvdi(Ti=1006.6666666..., ui=0.4464550..., vi=0.3547595..., di=0.2522710...),
PlanckianTable_Tuvdi(Ti=1007.7777777..., ui=0.4461968..., vi=0.3547817..., di=0.2520202...),
PlanckianTable_Tuvdi(Ti=1008.8888888..., ui=0.4459389..., vi=0.3548040..., di=0.2517697...),
PlanckianTable_Tuvdi(Ti=1010.0, ui=0.4456812..., vi=0.3548261..., di=0.2515196...)]
"""
ux, vx = uv
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
spd = blackbody_spd(Ti, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
ui, vi = UCS_to_uv(UVW)
di = np.sqrt((ux - ui) ** 2 + (vx - vi) ** 2)
table.append(PLANCKIAN_TABLE_TUVD(Ti, ui, vi, di))
return table
def planckian_table(uv, cmfs, start, end, count):
"""
Returns a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using *Yoshi Ohno (2013)* method.
Parameters
----------
uv : array_like
*uv* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
start : numeric
Temperature range start in kelvins.
end : numeric
Temperature range end in kelvins.
count : int
Temperatures count in the planckian table.
Returns
-------
list
Planckian table.
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> from pprint import pprint
>>> cmfs = 'CIE 1931 2 Degree Standard Observer'
>>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)
>>> pprint(planckian_table((0.1978, 0.3122), cmfs, 1000, 1010, 10)) # noqa # doctest: +ELLIPSIS
[PlanckianTable_Tuvdi(Ti=1000.0, ui=0.4480108..., vi=0.3546249..., di=0.2537821...),
PlanckianTable_Tuvdi(Ti=1001.1111111..., ui=0.4477508..., vi=0.3546475..., di=0.2535294...),
PlanckianTable_Tuvdi(Ti=1002.2222222..., ui=0.4474910..., vi=0.3546700..., di=0.2532771...),
PlanckianTable_Tuvdi(Ti=1003.3333333..., ui=0.4472316..., vi=0.3546924..., di=0.2530251...),
PlanckianTable_Tuvdi(Ti=1004.4444444..., ui=0.4469724..., vi=0.3547148..., di=0.2527734...),
PlanckianTable_Tuvdi(Ti=1005.5555555..., ui=0.4467136..., vi=0.3547372..., di=0.2525220...),
PlanckianTable_Tuvdi(Ti=1006.6666666..., ui=0.4464550..., vi=0.3547595..., di=0.2522710...),
PlanckianTable_Tuvdi(Ti=1007.7777777..., ui=0.4461968..., vi=0.3547817..., di=0.2520202...),
PlanckianTable_Tuvdi(Ti=1008.8888888..., ui=0.4459389..., vi=0.3548040..., di=0.2517697...),
PlanckianTable_Tuvdi(Ti=1010.0, ui=0.4456812..., vi=0.3548261..., di=0.2515196...)]
"""
ux, vx = uv
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
spd = blackbody_spd(Ti, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
ui, vi = UCS_to_uv(UVW)
di = math.sqrt((ux - ui)**2 + (vx - vi)**2)
table.append(PLANCKIAN_TABLE_TUVD(Ti, ui, vi, di))
return table
def colour_quality_scale(spd_test, T, additional_data=False):
cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
CCT, _D_uv = T
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(spd_test,
spd_reference,
VS_SPDS,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
spd_reference, spd_reference, VS_SPDS, cmfs)
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
Q_as = colour_quality_scales(test_vs_colorimetry_data,
reference_vs_colorimetry_data, CCT_f)
D_E_RMS = delta_E_RMS(Q_as, 'D_E_ab')
D_Ep_RMS = delta_E_RMS(Q_as, 'D_Ep_ab')
Q_a = scale_conversion(D_Ep_RMS, CCT_f)
Q_f = scale_conversion(D_E_RMS, CCT_f, 2.928)
p_delta_C = np.average([
sample_data.D_C_ab if sample_data.D_C_ab > 0 else 0
for sample_data in Q_as.values()
])
Q_p = 100 - 3.6 * (D_Ep_RMS - p_delta_C)
G_t = gamut_area(
[vs_CQS_data.Lab for vs_CQS_data in test_vs_colorimetry_data])
G_r = gamut_area(
[vs_CQS_data.Lab for vs_CQS_data in reference_vs_colorimetry_data])
Q_g = G_t / D65_GAMUT_AREA * 100
Q_d = G_t / G_r * CCT_f * 100
if additional_data:
return CQS_Specification(
spd_test.name, Q_a, Q_f, Q_p, Q_g, Q_d, Q_as,
(test_vs_colorimetry_data, reference_vs_colorimetry_data))
else:
return Q_a
def blackbody_colours_plot(shape=SpectralShape(150, 12500, 50),
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots blackbody colours.
Parameters
----------
shape : SpectralShape, optional
Spectral shape to use as plot boundaries.
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_colours_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
colours = []
temperatures = []
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': '',
'x_tighten': True,
'x_ticker': True,
'y_ticker': False
}
settings.update(kwargs)
return colour_parameters_plot([
colour_parameter(x=x[0], RGB=x[1])
for x in tuple(zip(temperatures, colours))
], **settings)
def blackbody_colours_plot(shape=SpectralShape(150, 12500, 50),
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots blackbody colours.
Parameters
----------
shape : SpectralShape, optional
Spectral shape to use as plot boundaries.
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_colours_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
colours = []
temperatures = []
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': '',
'x_tighten': True,
'x_ticker': True,
'y_ticker': False}
settings.update(kwargs)
return colour_parameters_plot([colour_parameter(x=x[0], RGB=x[1])
for x in tuple(zip(temperatures, colours))],
**settings)
def planckian_table(uv, cmfs, start, end, count):
"""
Returns a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using *Ohno (2013)* method.
Parameters
----------
uv : array_like
*uv* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
start : numeric
Temperature range start in kelvins.
end : numeric
Temperature range end in kelvins.
count : int
Temperatures count in the planckian table.
Returns
-------
list
Planckian table.
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> from pprint import pprint
>>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> uv = np.array([0.1978, 0.3122])
>>> pprint(planckian_table(uv, cmfs, 1000, 1010, 10))
... # doctest: +ELLIPSIS
[PlanckianTable_Tuvdi(Ti=1000.0, \
ui=0.4479628..., vi=0.3546296..., di=0.2537355...),
PlanckianTable_Tuvdi(Ti=1001.1111111..., \
ui=0.4477030..., vi=0.3546521..., di=0.2534831...),
PlanckianTable_Tuvdi(Ti=1002.2222222..., \
ui=0.4474434..., vi=0.3546746..., di=0.2532310...),
PlanckianTable_Tuvdi(Ti=1003.3333333..., \
ui=0.4471842..., vi=0.3546970..., di=0.2529792...),
PlanckianTable_Tuvdi(Ti=1004.4444444..., \
ui=0.4469252..., vi=0.3547194..., di=0.2527277...),
PlanckianTable_Tuvdi(Ti=1005.5555555..., \
ui=0.4466666..., vi=0.3547417..., di=0.2524765...),
PlanckianTable_Tuvdi(Ti=1006.6666666..., \
ui=0.4464083..., vi=0.3547640..., di=0.2522256...),
PlanckianTable_Tuvdi(Ti=1007.7777777..., \
ui=0.4461502..., vi=0.3547862..., di=0.2519751...),
PlanckianTable_Tuvdi(Ti=1008.8888888..., \
ui=0.4458925..., vi=0.3548084..., di=0.2517248...),
PlanckianTable_Tuvdi(Ti=1010.0, \
ui=0.4456351..., vi=0.3548306..., di=0.2514749...)]
"""
ux, vx = uv
cmfs = cmfs.copy().trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
spd = blackbody_spd(Ti, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ /= np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
ui, vi = UCS_to_uv(UVW)
di = np.hypot(ux - ui, vx - vi)
table.append(PLANCKIAN_TABLE_TUVD(Ti, ui, vi, di))
return table
def blackbody_spectral_radiance_plot(
temperature=3500,
cmfs='CIE 1931 2 Degree Standard Observer',
blackbody='VY Canis Major',
**kwargs):
"""
Plots given blackbody spectral radiance.
Parameters
----------
temperature : numeric, optional
Blackbody temperature.
cmfs : unicode, optional
Standard observer colour matching functions.
blackbody : unicode, optional
Blackbody name.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
"""
canvas(**kwargs)
cmfs = get_cmfs(cmfs)
matplotlib.pyplot.subplots_adjust(hspace=0.4)
spd = blackbody_spd(temperature, cmfs.shape)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': 'W / (sr m$^2$) / m',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, cmfs.name, **settings)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ / 100))
matplotlib.pyplot.subplot(212)
settings = {
'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'aspect': None,
'standalone': False
}
single_colour_plot(ColourParameter(name='', RGB=RGB), **settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def blackbody_spectral_radiance_plot(
temperature=3500,
cmfs='CIE 1931 2 Degree Standard Observer',
blackbody='VY Canis Major',
**kwargs):
"""
Plots given blackbody spectral radiance.
Parameters
----------
temperature : numeric, optional
Blackbody temperature.
cmfs : unicode, optional
Standard observer colour matching functions.
blackbody : unicode, optional
Blackbody name.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs = get_cmfs(cmfs)
matplotlib.pyplot.subplots_adjust(hspace=0.4)
spd = blackbody_spd(temperature, cmfs.shape)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': 'W / (sr m$^2$) / m',
'standalone': False}
settings.update(kwargs)
single_spd_plot(spd, cmfs.name, **settings)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
matplotlib.pyplot.subplot(212)
settings = {'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'aspect': None,
'standalone': False}
single_colour_plot(ColourParameter(name='', RGB=RGB), **settings)
settings = {
'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def colour_quality_scale(spd_test, additional_data=False):
"""
Returns the *Colour Quality Scale* (CQS) of given spectral power
distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CQS_Specification
Color quality scale.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS['F2']
>>> colour_quality_scale(spd) # doctest: +ELLIPSIS
64.6864169...
"""
cmfs = STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer'].clone().trim_wavelengths(
ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
spd_test = spd_test.clone().align(shape)
vs_spds = {spd.name: spd.clone().align(shape) for spd in VS_SPDS.values()}
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(spd_test,
spd_reference,
vs_spds,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
spd_reference, spd_reference, vs_spds, cmfs)
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
Q_as = colour_quality_scales(test_vs_colorimetry_data,
reference_vs_colorimetry_data, CCT_f)
D_E_RMS = delta_E_RMS(Q_as, 'D_E_ab')
D_Ep_RMS = delta_E_RMS(Q_as, 'D_Ep_ab')
Q_a = scale_conversion(D_Ep_RMS, CCT_f)
Q_f = scale_conversion(D_E_RMS, CCT_f, 2.928)
p_delta_C = np.average(
[sample_data.D_C_ab if sample_data.D_C_ab > 0 else 0
for sample_data in Q_as.values()]) # yapf: disable
Q_p = 100 - 3.6 * (D_Ep_RMS - p_delta_C)
G_t = gamut_area(
[vs_CQS_data.Lab for vs_CQS_data in test_vs_colorimetry_data])
G_r = gamut_area(
[vs_CQS_data.Lab for vs_CQS_data in reference_vs_colorimetry_data])
Q_g = G_t / D65_GAMUT_AREA * 100
Q_d = G_t / G_r * CCT_f * 100
if additional_data:
return CQS_Specification(
spd_test.name, Q_a, Q_f, Q_p, Q_g, Q_d, Q_as,
(test_vs_colorimetry_data, reference_vs_colorimetry_data))
else:
return Q_a
def colour_rendering_index(spd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
power distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS['F2']
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer'].clone().trim_wavelengths(
ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
spd_test = spd_test.clone().align(shape)
tcs_spds = {
spd.name: spd.clone().align(shape)
for spd in TCS_SPDS.values()
}
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
spd_test, spd_reference, tcs_spds, cmfs, chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
spd_reference, spd_reference, tcs_spds, cmfs)
Q_as = colour_rendering_indexes(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data)
Q_a = np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(spd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data))
else:
return Q_a
def colour_rendering_index(test_spd, additional_data=False):
"""
Returns the *colour rendering index* of given spectral power distribution.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or (numeric, dict)
Colour rendering index, Tsc data.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1507331...
"""
cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
test_spd = test_spd.clone().align(shape)
tcs_spds = {}
for index, tcs_spd in sorted(TCS_SPDS.items()):
tcs_spds[index] = tcs_spd.clone().align(shape)
XYZ = spectral_to_XYZ(test_spd, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, Duv = uv_to_CCT_robertson1968(uv)
if CCT < 5000:
reference_spd = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_illuminant_D(CCT)
reference_spd = D_illuminant_relative_spd(xy)
reference_spd.align(shape)
test_tcs_colorimetry_data = _tcs_colorimetry_data(
test_spd, reference_spd, tcs_spds, cmfs, chromatic_adaptation=True)
reference_tcs_colorimetry_data = _tcs_colorimetry_data(
reference_spd, reference_spd, tcs_spds, cmfs)
colour_rendering_indexes = _colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data)
colour_rendering_index = np.average([
v for k, v in colour_rendering_indexes.items()
if k in (1, 2, 3, 4, 5, 6, 7, 8)
])
if additional_data:
return (colour_rendering_index, colour_rendering_indexes,
[test_tcs_colorimetry_data, reference_tcs_colorimetry_data])
else:
return colour_rendering_index
def planckian_table(uv, cmfs, start, end, count):
"""
Returns a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using Ohno (2013) method.
Parameters
----------
uv : array_like
*uv* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
start : numeric
Temperature range start in kelvins.
end : numeric
Temperature range end in kelvins.
count : int
Temperatures count in the planckian table.
Returns
-------
list
Planckian table.
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> from pprint import pprint
>>> cmfs = 'CIE 1931 2 Degree Standard Observer'
>>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)
>>> uv = np.array([0.1978, 0.3122])
>>> pprint(planckian_table( # doctest: +ELLIPSIS
... uv, cmfs, 1000, 1010, 10))
[PlanckianTable_Tuvdi(Ti=1000.0, \
ui=0.4479628..., vi=0.3546296..., di=0.2537355...),
PlanckianTable_Tuvdi(Ti=1001.1111111..., \
ui=0.4477030..., vi=0.3546521..., di=0.2534831...),
PlanckianTable_Tuvdi(Ti=1002.2222222..., \
ui=0.4474434..., vi=0.3546746..., di=0.2532310...),
PlanckianTable_Tuvdi(Ti=1003.3333333..., \
ui=0.4471842..., vi=0.3546970..., di=0.2529792...),
PlanckianTable_Tuvdi(Ti=1004.4444444..., \
ui=0.4469252..., vi=0.3547194..., di=0.2527277...),
PlanckianTable_Tuvdi(Ti=1005.5555555..., \
ui=0.4466666..., vi=0.3547417..., di=0.2524765...),
PlanckianTable_Tuvdi(Ti=1006.6666666..., \
ui=0.4464083..., vi=0.3547640..., di=0.2522256...),
PlanckianTable_Tuvdi(Ti=1007.7777777..., \
ui=0.4461502..., vi=0.3547862..., di=0.2519751...),
PlanckianTable_Tuvdi(Ti=1008.8888888..., \
ui=0.4458925..., vi=0.3548084..., di=0.2517248...),
PlanckianTable_Tuvdi(Ti=1010.0, \
ui=0.4456351..., vi=0.3548306..., di=0.2514749...)]
"""
ux, vx = uv
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
spd = blackbody_spd(Ti, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
ui, vi = UCS_to_uv(UVW)
di = np.sqrt((ux - ui) ** 2 + (vx - vi) ** 2)
table.append(PLANCKIAN_TABLE_TUVD(Ti, ui, vi, di))
return table
def colour_rendering_index(spd_test,T, additional_data=False):
"""
Returns the *colour rendering index* :math:`Q_a` of given spectral power
distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CRI_Specification
Colour rendering index.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1495478...
"""
cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
CCT, _D_uv = T[0],T[1]
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
spd_test,
spd_reference,
TCS_SPDS,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
spd_reference,
spd_reference,
TCS_SPDS,
cmfs)
Q_as = colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data)
Q_a = np.average([v.Q_a for k, v in Q_as.items()
if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(spd_test.name,
Q_a,
Q_as,
(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data))
else:
return Q_a
def blackbody_colours_plot(shape=SpectralShape(150, 12500, 50),
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots blackbody colours.
Parameters
----------
shape : SpectralShape, optional
Spectral shape to use as plot boundaries.
cmfs : unicode, optional
Standard observer colour matching functions.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> blackbody_colours_plot() # doctest: +SKIP
"""
axes = canvas(**kwargs).gca()
cmfs = get_cmfs(cmfs)
colours = []
temperatures = []
with suppress_warnings():
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
axes.bar(x=temperatures,
height=1,
width=shape.interval,
color=colours,
align='edge')
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': None,
'limits': (x_min, x_max, y_min, y_max),
'x_tighten': True,
'y_tighten': True,
'y_ticker': False
}
settings.update(kwargs)
return render(**settings)
def CCT_to_uv_Ohno2013(
CCT,
D_uv=0,
cmfs=STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']):
"""
Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\Delta_{uv}` and
colour matching functions using *Ohno (2013)* method.
Parameters
----------
CCT : numeric
Correlated colour temperature :math:`T_{cp}`.
D_uv : numeric, optional
:math:`\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
References
----------
- :cite:`Ohno2014a`
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> CCT = 6507.4342201047066
>>> D_uv = 0.003223690901513
>>> CCT_to_uv_Ohno2013(CCT, D_uv, cmfs) # doctest: +ELLIPSIS
array([ 0.1977999..., 0.3122004...])
"""
cmfs = cmfs.copy().trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
delta = 0.01
spd = blackbody_spd(CCT, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u0, v0 = UCS_to_uv(UVW)
if D_uv == 0:
return np.array([u0, v0])
else:
spd = blackbody_spd(CCT + delta, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u1, v1 = UCS_to_uv(UVW)
du = u0 - u1
dv = v0 - v1
u = u0 - D_uv * (dv / np.hypot(du, dv))
v = v0 + D_uv * (du / np.hypot(du, dv))
return np.array([u, v])
def colour_quality_scale(spd_test, additional_data=False):
"""
Returns the *colour quality scale* of given spectral power distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CQS_Specification
Color quality scale.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_quality_scale(spd) # doctest: +ELLIPSIS
64.6781117...
"""
cmfs = STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(
spd_test,
spd_reference,
VS_SPDS,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
spd_reference,
spd_reference,
VS_SPDS,
cmfs)
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
Q_as = colour_quality_scales(
test_vs_colorimetry_data, reference_vs_colorimetry_data, CCT_f)
D_E_RMS = delta_E_RMS(Q_as, 'D_E_ab')
D_Ep_RMS = delta_E_RMS(Q_as, 'D_Ep_ab')
Q_a = scale_conversion(D_Ep_RMS, CCT_f)
Q_f = scale_conversion(D_E_RMS, CCT_f, 2.928)
p_delta_C = np.average(
[sample_data.D_C_ab if sample_data.D_C_ab > 0 else 0
for sample_data in
Q_as.values()])
Q_p = 100 - 3.6 * (D_Ep_RMS - p_delta_C)
G_t = gamut_area([vs_CQS_data.Lab
for vs_CQS_data in test_vs_colorimetry_data])
G_r = gamut_area([vs_CQS_data.Lab
for vs_CQS_data in reference_vs_colorimetry_data])
Q_g = G_t / D65_GAMUT_AREA * 100
Q_d = G_t / G_r * CCT_f * 100
if additional_data:
return CQS_Specification(spd_test.name,
Q_a,
Q_f,
Q_p,
Q_g,
Q_d,
Q_as,
(test_vs_colorimetry_data,
reference_vs_colorimetry_data))
else:
return Q_a
def CCT_to_uv_Ohno2013(
CCT,
D_uv=0,
cmfs=STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')):
"""
Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\Delta_{uv}` and
colour matching functions using Ohno (2013) method.
Parameters
----------
CCT : numeric
Correlated colour temperature :math:`T_{cp}`.
D_uv : numeric, optional
:math:`\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
References
----------
.. [4] Ohno, Y. (2014). Practical Use and Calculation of CCT and Duv.
LEUKOS, 10(1), 47–55. doi:10.1080/15502724.2014.839020
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = 'CIE 1931 2 Degree Standard Observer'
>>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)
>>> CCT = 6507.4342201047066
>>> D_uv = 0.003223690901512735
>>> CCT_to_uv_Ohno2013(CCT, D_uv, cmfs) # doctest: +ELLIPSIS
array([ 0.1978003..., 0.3122005...])
"""
shape = cmfs.shape
delta = 0.01
spd = blackbody_spd(CCT, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u0, v0 = UCS_to_uv(UVW)
if D_uv == 0:
return np.array([u0, v0])
else:
spd = blackbody_spd(CCT + delta, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u1, v1 = UCS_to_uv(UVW)
du = u0 - u1
dv = v0 - v1
u = u0 - D_uv * (dv / np.sqrt(du**2 + dv**2))
v = v0 + D_uv * (du / np.sqrt(du**2 + dv**2))
return np.array([u, v])
def colour_rendering_index(spd_test, additional_data=False):
"""
Returns the *colour rendering index* :math:`Q_a` of given spectral power
distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CRI_Specification
Colour rendering index.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1507331...
"""
cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
spd_test = spd_test.clone().align(shape)
tcs_spds = {}
for index, tcs_spd in TCS_SPDS.items():
tcs_spds[index] = tcs_spd.clone().align(shape)
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
spd_test,
spd_reference,
tcs_spds,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
spd_reference,
spd_reference,
tcs_spds,
cmfs)
Q_as = colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data)
Q_a = np.average([v.Q_a for k, v in Q_as.items()
if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(spd_test.name,
Q_a,
Q_as,
(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data))
else:
return Q_a
def colour_rendering_index(test_spd, additional_data=False):
"""
Returns the *colour rendering index* of given spectral power distribution.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or (numeric, dict)
Colour rendering index, Tsc data.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1507331...
"""
cmfs = STANDARD_OBSERVERS_CMFS.get('CIE 1931 2 Degree Standard Observer')
shape = cmfs.shape
test_spd = test_spd.clone().align(shape)
tcs_spds = {}
for index, tcs_spd in sorted(TCS_SPDS.items()):
tcs_spds[index] = tcs_spd.clone().align(shape)
XYZ = spectral_to_XYZ(test_spd, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, Duv = uv_to_CCT_robertson1968(uv)
if CCT < 5000:
reference_spd = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_illuminant_D(CCT)
reference_spd = D_illuminant_relative_spd(xy)
reference_spd.align(shape)
test_tcs_colorimetry_data = _tcs_colorimetry_data(
test_spd,
reference_spd,
tcs_spds,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = _tcs_colorimetry_data(
reference_spd,
reference_spd,
tcs_spds, cmfs)
colour_rendering_indexes = _colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data)
colour_rendering_index = np.average(
[v for k, v in colour_rendering_indexes.items()
if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return (colour_rendering_index,
colour_rendering_indexes,
[test_tcs_colorimetry_data, reference_tcs_colorimetry_data])
else:
return colour_rendering_index
def CCT_to_uv_Ohno2013(CCT,
D_uv=0,
cmfs=STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer')):
"""
Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\Delta_{uv}` and
colour matching functions using Ohno (2013) method.
Parameters
----------
CCT : numeric
Correlated colour temperature :math:`T_{cp}`.
D_uv : numeric, optional
:math:`\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
References
----------
.. [4] Ohno, Y. (2014). Practical Use and Calculation of CCT and Duv.
LEUKOS, 10(1), 47–55. doi:10.1080/15502724.2014.839020
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = 'CIE 1931 2 Degree Standard Observer'
>>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)
>>> CCT = 6507.4342201047066
>>> D_uv = 0.003223690901512735
>>> CCT_to_uv_Ohno2013(CCT, D_uv, cmfs) # doctest: +ELLIPSIS
array([ 0.1978003..., 0.3122005...])
"""
shape = cmfs.shape
delta = 0.01
spd = blackbody_spd(CCT, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u0, v0 = UCS_to_uv(UVW)
if D_uv == 0:
return np.array([u0, v0])
else:
spd = blackbody_spd(CCT + delta, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u1, v1 = UCS_to_uv(UVW)
du = u0 - u1
dv = v0 - v1
u = u0 - D_uv * (dv / np.sqrt(du ** 2 + dv ** 2))
v = v0 + D_uv * (du / np.sqrt(du ** 2 + dv ** 2))
return np.array([u, v])
def blackbody_colours_plot(shape=SpectralShape(150, 12500, 50),
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots blackbody colours.
Parameters
----------
shape : SpectralShape, optional
Spectral shape to use as plot boundaries.
cmfs : unicode, optional
Standard observer colour matching functions.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
y0_plot : bool, optional
{:func:`colour_parameters_plot`},
Whether to plot *y0* line.
y1_plot : bool, optional
{:func:`colour_parameters_plot`},
Whether to plot *y1* line.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> blackbody_colours_plot() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
colours = []
temperatures = []
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': '',
'x_tighten': True,
'y_tighten': True,
'y_ticker': False
}
settings.update(kwargs)
return colour_parameters_plot([
ColourParameter(x=x[0], RGB=x[1])
for x in tuple(zip(temperatures, colours))
], **settings)
