def vs_colorimetry_data(spd_test,
spd_reference,
spds_vs,
cmfs,
chromatic_adaptation=False):
XYZ_t = spectral_to_XYZ(spd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
spd_vs = spds_vs.get(value)
XYZ_vs = spectral_to_XYZ(spd_vs, cmfs, spd_test)
XYZ_vs /= 100
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(spd_vs.name, XYZ_vs, Lab_vs, C_vs))
return vs_data
def test_spectral_to_XYZ(self):
"""
Tests :func:`colour.colorimetry.tristimulus.spectral_to_XYZ`
definition.
"""
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
np.testing.assert_almost_equal(
spectral_to_XYZ(
RELATIVE_SPD_DATA.zeros(cmfs.shape), cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('A').clone().zeros(cmfs.shape)),
np.array([14.46371626, 10.85832347, 2.04664796]),
decimal=7)
cmfs = CMFS.get('CIE 1964 10 Degree Standard Observer')
np.testing.assert_almost_equal(
spectral_to_XYZ(
RELATIVE_SPD_DATA.zeros(cmfs.shape), cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('C').clone().zeros(cmfs.shape)),
np.array([10.7704252, 9.44870313, 6.62742289]),
decimal=7)
np.testing.assert_almost_equal(
spectral_to_XYZ(
RELATIVE_SPD_DATA.zeros(cmfs.shape), cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('F2').clone().zeros(cmfs.shape)),
np.array([11.57830745, 9.98744967, 3.95396539]),
decimal=7)
def test_spectral_to_XYZ(self):
"""
Tests :func:`colour.colorimetry.tristimulus.spectral_to_XYZ`
definition.
"""
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
np.testing.assert_almost_equal(
spectral_to_XYZ(
RELATIVE_SPD_DATA.zeros(cmfs.shape),
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('A').clone().zeros(cmfs.shape)),
np.array([14.46371626, 10.85832347, 2.04664796]),
decimal=7)
cmfs = CMFS.get('CIE 1964 10 Degree Standard Observer')
np.testing.assert_almost_equal(
spectral_to_XYZ(
RELATIVE_SPD_DATA.zeros(cmfs.shape),
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('C').clone().zeros(cmfs.shape)),
np.array([10.7704252, 9.44870313, 6.62742289]),
decimal=7)
np.testing.assert_almost_equal(
spectral_to_XYZ(
RELATIVE_SPD_DATA.zeros(cmfs.shape),
cmfs,
ILLUMINANTS_RELATIVE_SPDS.get('F2').clone().zeros(cmfs.shape)),
np.array([11.57830745, 9.98744967, 3.95396539]),
decimal=7)
def vs_colorimetry_data(spd_test,
spd_reference,
spds_vs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *VS test colour samples* colorimetry data.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
spd_reference : SpectralPowerDistribution
Reference spectral power distribution.
spds_vs : dict
*VS test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*VS test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
spd_vs = spds_vs.get(value)
XYZ_vs = spectral_to_XYZ(spd_vs, cmfs, spd_test)
XYZ_vs /= 100
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(
VS_ColorimetryData(spd_vs.name,
XYZ_vs,
Lab_vs,
C_vs))
return vs_data
def vs_colorimetry_data(spd_test,
spd_reference,
spds_vs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *VS test colour samples* colorimetry data.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
spd_reference : SpectralPowerDistribution
Reference spectral power distribution.
spds_vs : dict
*VS test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*VS test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
spd_vs = spds_vs[value]
XYZ_vs = spectral_to_XYZ(spd_vs, cmfs, spd_test)
XYZ_vs /= 100
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(spd_vs.name, XYZ_vs, Lab_vs, C_vs))
return vs_data
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 spds_CIE_1976_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
spds : list, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1976_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
True
"""
CIE_1976_UCS_chromaticity_diagram_plot(standalone=False,
**kwargs)
cmfs = get_cmfs(cmfs)
cmfs_shape = cmfs.shape
for spd in spds:
spd = spd.clone().align(cmfs_shape)
XYZ = spectral_to_XYZ(spd) / 100
uv = Luv_to_uv(XYZ_to_Luv(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
display(standalone=True)
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 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 spds_CIE_1976_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1976_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1976_UCS_chromaticity_diagram_plot(cmfs=cmfs, **settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
uv = Luv_to_uv(XYZ_to_Luv(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.1, 0.7),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def multi_spd_plot(spds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_spds_colours=False,
normalise_spds_colours=False,
**kwargs):
"""
Plots given spectral power distributions.
Parameters
----------
spds : list
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_spds_colours : bool, optional
Use spectral power distributions colours.
normalise_spds_colours : bool
Should spectral power distributions colours normalised.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data1 = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> data2 = {400: 0.134, 420: 0.789, 440: 1.289}
>>> spd1 = SpectralPowerDistribution('Custom1', data1)
>>> spd2 = SpectralPowerDistribution('Custom2', data2)
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs = get_cmfs(cmfs)
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D65')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = tuple(zip(*spd.items))
shape = spd.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
if use_spds_colours:
XYZ = spectral_to_XYZ(spd, cmfs, illuminant) / 100
if normalise_spds_colours:
XYZ = normalise(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths, values, color=RGB, label=spd.title,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.title, linewidth=2)
settings = {
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Power Distribution',
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'limits': (min(x_limit_min), max(x_limit_max),
min(y_limit_min), max(y_limit_max))}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def the_blue_sky_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.clone()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title':
'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label':
'',
'aspect':
None,
'standalone':
False
}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_plot(ColourParameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def multi_spd_plot(spds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_spds_colours=False,
normalise_spds_colours=False,
**kwargs):
"""
Plots given spectral power distributions.
Parameters
----------
spds : list
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_spds_colours : bool, optional
Whether to use spectral power distributions colours.
normalise_spds_colours : bool
Whether to normalise spectral power distributions colours.
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
--------
>>> from colour import SpectralPowerDistribution
>>> 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 = SpectralPowerDistribution(data_1, name='Custom 1')
>>> spd2 = SpectralPowerDistribution(data_2, name='Custom 2')
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
"""
canvas(**kwargs)
cmfs = get_cmfs(cmfs)
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS['D65']
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = spd.wavelengths, spd.values
shape = spd.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
if use_spds_colours:
XYZ = spectral_to_XYZ(spd, cmfs, illuminant) / 100
if normalise_spds_colours:
XYZ = normalise_maximum(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths,
values,
color=RGB,
label=spd.strict_name,
linewidth=1)
else:
pylab.plot(wavelengths, values, label=spd.strict_name, linewidth=1)
settings = {
'x_label':
'Wavelength $\\lambda$ (nm)',
'y_label':
'Spectral Power Distribution',
'x_tighten':
True,
'y_tighten':
True,
'legend':
True,
'legend_location':
'upper left',
'limits': (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max))
}
settings.update(kwargs)
return render(**settings)
def _tcs_colorimetry_data(test_spd,
reference_spd,
tsc_spds,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
reference_spd : SpectralPowerDistribution
Reference spectral power distribution.
tsc_spds : dict
Test colour samples.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
test_XYZ = spectral_to_XYZ(test_spd, cmfs)
test_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(test_XYZ)))
test_u, test_v = test_uv[0], test_uv[1]
reference_XYZ = spectral_to_XYZ(reference_spd, cmfs)
reference_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(reference_XYZ)))
reference_u, reference_v = reference_uv[0], reference_uv[1]
tcs_data = []
for key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
tcs_spd = tsc_spds.get(value)
tcs_XYZ = spectral_to_XYZ(tcs_spd, cmfs, test_spd)
tcs_xyY = np.ravel(XYZ_to_xyY(tcs_XYZ))
tcs_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(tcs_XYZ)))
tcs_u, tcs_v = tcs_uv[0], tcs_uv[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
test_c, test_d = c(test_u, test_v), d(test_u, test_v)
reference_c, reference_d = (c(reference_u, reference_v),
d(reference_u, reference_v))
tcs_c, tcs_d = c(tcs_u, tcs_v), d(tcs_u, tcs_v)
tcs_u = ((10.872 + 0.404 * reference_c / test_c * tcs_c -
4 * reference_d / test_d * tcs_d) /
(16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_v = (5.52 / (16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_W = 25 * tcs_xyY[-1]**(1 / 3) - 17
tcs_U = 13 * tcs_W * (tcs_u - reference_u)
tcs_V = 13 * tcs_W * (tcs_v - reference_v)
tcs_data.append(
TSC_COLORIMETRY_DATA_NXYZUVUVW(tcs_spd.name, tcs_XYZ, tcs_uv,
np.array([tcs_U, tcs_V, tcs_W])))
return tcs_data
def tcs_colorimetry_data(spd_t,
spd_r,
spds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
spd_t : SpectralPowerDistribution
Test spectral power distribution.
spd_r : SpectralPowerDistribution
Reference spectral power distribution.
spds_tcs : dict
*Test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_t, cmfs)
uv_t = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_t)))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = spectral_to_XYZ(spd_r, cmfs)
uv_r = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_r)))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
spd_tcs = spds_tcs.get(value)
XYZ_tcs = spectral_to_XYZ(spd_tcs, cmfs, spd_t)
xyY_tcs = np.ravel(XYZ_to_xyY(XYZ_tcs))
uv_tcs = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ_tcs)))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = (c(u_r, v_r),
d(u_r, v_r))
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = ((10.872 + 0.404 * c_r / c_t * tcs_c - 4 *
d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c -
d_r / d_t * tcs_d))
v_tcs = (5.52 / (16.518 + 1.481 * c_r / c_t * tcs_c -
d_r / d_t * tcs_d))
W_tcs = 25 * xyY_tcs[-1] ** (1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(spd_tcs.name,
XYZ_tcs,
uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
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 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.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 the_blue_sky_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.clone()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title': 'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label': '',
'aspect': None,
'standalone': False}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_plot(colour_parameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**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 = '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 spds_CIE_1976_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1976_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
True
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1976_UCS_chromaticity_diagram_plot(**settings)
cmfs = get_cmfs(cmfs)
cmfs_shape = cmfs.shape
for spd in spds:
spd = spd.clone().align(cmfs_shape)
XYZ = spectral_to_XYZ(spd) / 100
uv = Luv_to_uv(XYZ_to_Luv(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.1, 0.7),
'standalone': True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def the_blue_sky_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the blue sky.
Parameters
----------
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
--------
>>> the_blue_sky_plot() # doctest: +SKIP
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.copy()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title':
'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label':
'',
'aspect':
None,
'standalone':
False
}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_swatch_plot(
ColourSwatch('', normalise_maximum(blue_sky_color)), **settings)
settings = {'standalone': True}
settings.update(kwargs)
return render(**settings)
def multi_spd_plot(spds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_spds_colours=False,
normalise_spds_colours=False,
**kwargs):
"""
Plots given spectral power distributions.
Parameters
----------
spds : list
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_spds_colours : bool, optional
Use spectral power distributions colours.
normalise_spds_colours : bool
Should spectral power distributions colours normalised.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data1 = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> data2 = {400: 0.134, 420: 0.789, 440: 1.289}
>>> spd1 = SpectralPowerDistribution('Custom1', data1)
>>> spd2 = SpectralPowerDistribution('Custom2', data2)
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
"""
canvas(**kwargs)
cmfs = get_cmfs(cmfs)
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D65')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = tuple(zip(*spd.items))
shape = spd.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
if use_spds_colours:
XYZ = spectral_to_XYZ(spd, cmfs, illuminant) / 100
if normalise_spds_colours:
XYZ = normalise_maximum(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths,
values,
color=RGB,
label=spd.title,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.title, linewidth=2)
settings = {
'x_label':
'Wavelength $\\lambda$ (nm)',
'y_label':
'Spectral Power Distribution',
'x_tighten':
True,
'legend':
True,
'legend_location':
'upper left',
'limits': (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max))
}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
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 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 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 = 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 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 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 _tcs_colorimetry_data(test_spd,
reference_spd,
tsc_spds,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
test_spd : SpectralPowerDistribution
Test spectral power distribution.
reference_spd : SpectralPowerDistribution
Reference spectral power distribution.
tsc_spds : dict
Test colour samples.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
test_XYZ = spectral_to_XYZ(test_spd, cmfs)
test_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(test_XYZ)))
test_u, test_v = test_uv[0], test_uv[1]
reference_XYZ = spectral_to_XYZ(reference_spd, cmfs)
reference_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(reference_XYZ)))
reference_u, reference_v = reference_uv[0], reference_uv[1]
tcs_data = []
for key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
tcs_spd = tsc_spds.get(value)
tcs_XYZ = spectral_to_XYZ(tcs_spd, cmfs, test_spd)
tcs_xyY = np.ravel(XYZ_to_xyY(tcs_XYZ))
tcs_uv = np.ravel(UCS_to_uv(XYZ_to_UCS(tcs_XYZ)))
tcs_u, tcs_v = tcs_uv[0], tcs_uv[1]
if chromatic_adaptation:
c = lambda x, y: (4 - x - 10 * y) / y
d = lambda x, y: (1.708 * y + 0.404 - 1.481 * x) / y
test_c, test_d = c(test_u, test_v), d(test_u, test_v)
reference_c, reference_d = (c(reference_u, reference_v),
d(reference_u, reference_v))
tcs_c, tcs_d = c(tcs_u, tcs_v), d(tcs_u, tcs_v)
tcs_u = ((10.872 + 0.404 * reference_c / test_c * tcs_c - 4 *
reference_d / test_d * tcs_d) /
(16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_v = (5.52 / (16.518 + 1.481 * reference_c / test_c * tcs_c -
reference_d / test_d * tcs_d))
tcs_W = 25 * tcs_xyY[-1] ** (1 / 3) - 17
tcs_U = 13 * tcs_W * (tcs_u - reference_u)
tcs_V = 13 * tcs_W * (tcs_v - reference_v)
tcs_data.append(
TSC_COLORIMETRY_DATA_NXYZUVUVW(tcs_spd.name,
tcs_XYZ,
tcs_uv,
np.array([tcs_U, tcs_V, tcs_W])))
return tcs_data
def tcs_colorimetry_data(spd_t,
spd_r,
spds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
spd_t : SpectralPowerDistribution
Test spectral power distribution.
spd_r : SpectralPowerDistribution
Reference spectral power distribution.
spds_tcs : dict
*Test colour samples* spectral power distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = spectral_to_XYZ(spd_t, cmfs)
uv_t = UCS_to_uv(XYZ_to_UCS(XYZ_t))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = spectral_to_XYZ(spd_r, cmfs)
uv_r = UCS_to_uv(XYZ_to_UCS(XYZ_r))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
spd_tcs = spds_tcs[value]
XYZ_tcs = spectral_to_XYZ(spd_tcs, cmfs, spd_t)
xyY_tcs = XYZ_to_xyY(XYZ_tcs)
uv_tcs = UCS_to_uv(XYZ_to_UCS(XYZ_tcs))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
def c(x, y):
"""
Computes the :math:`c` term.
"""
return (4 - x - 10 * y) / y
def d(x, y):
"""
Computes the :math:`d` term.
"""
return (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = c(u_r, v_r), d(u_r, v_r)
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = ((
10.872 + 0.404 * c_r / c_t * tcs_c - 4 * d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
v_tcs = (5.52 /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
W_tcs = 25 * xyY_tcs[-1] ** (1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(spd_tcs.name, XYZ_tcs, uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
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 multi_spd_plot(spds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_spds_colours=False,
normalise_spds_colours=False,
**kwargs):
"""
Plots given spectral power distributions.
Parameters
----------
spds : list, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_spds_colours : bool, optional
Use spectral power distributions colours.
normalise_spds_colours : bool
Should spectral power distributions colours normalised.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data1 = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> data2 = {400: 0.134, 420: 0.789, 440: 1.289}
>>> spd1 = SpectralPowerDistribution('Custom1', data1)
>>> spd2 = SpectralPowerDistribution('Custom2', data2)
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D65')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = tuple(zip(*[(key, value) for key, value in spd]))
shape = spd.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
matplotlib.pyplot.rc("axes", color_cycle=["r", "g", "b", "y"])
if use_spds_colours:
XYZ = spectral_to_XYZ(spd, cmfs, illuminant) / 100
if normalise_spds_colours:
XYZ = normalise(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths,
values,
color=RGB,
label=spd.name,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.name, linewidth=2)
settings = {
'x_label':
u'Wavelength λ (nm)',
'y_label':
'Spectral Power Distribution',
'x_tighten':
True,
'legend':
True,
'legend_location':
'upper left',
'x_ticker':
True,
'y_ticker':
True,
'limits': [
min(x_limit_min),
max(x_limit_max),
min(y_limit_min),
max(y_limit_max)
]
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def spds_chromaticity_diagram_plot_CIE1931(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
chromaticity_diagram_callable_CIE1931=(
chromaticity_diagram_plot_CIE1931),
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1931 Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
chromaticity_diagram_callable_CIE1931 : callable, optional
Callable responsible for drawing the *CIE 1931 Chromaticity Diagram*.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
show_diagram_colours : bool, optional
{:func:`colour.plotting.chromaticity_diagram_plot_CIE1931`},
Whether to display the chromaticity diagram background colours.
use_cached_diagram_colours : bool, optional
{:func:`colour.plotting.chromaticity_diagram_plot_CIE1931`},
Whether to used the cached chromaticity diagram background colours
image.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_chromaticity_diagram_plot_CIE1931([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'cmfs': cmfs, 'standalone': False})
chromaticity_diagram_callable_CIE1931(**settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
xy = XYZ_to_xy(XYZ)
pylab.plot(xy[0], xy[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=xy,
xytext=(50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.9, -0.1, 0.9),
'standalone': True
})
settings.update(kwargs)
return render(**settings)
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 spds_CIE_1931_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1931 Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1931_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1931_chromaticity_diagram_plot(cmfs=cmfs, **settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
xy = XYZ_to_xy(XYZ)
pylab.plot(xy[0], xy[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=xy,
xytext=(50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.9, -0.1, 0.9),
'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 spds_CIE_1931_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1931 Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1931_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
True
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1931_chromaticity_diagram_plot(**settings)
cmfs = get_cmfs(cmfs)
cmfs_shape = cmfs.shape
for spd in spds:
spd = spd.clone().align(cmfs_shape)
XYZ = spectral_to_XYZ(spd) / 100
xy = XYZ_to_xy(XYZ)
pylab.plot(xy[0], xy[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=xy,
xytext=(50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.9, -0.1, 0.9),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def spds_CIE_1976_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
show_diagram_colours : bool, optional
{:func:`CIE_1976_UCS_chromaticity_diagram_plot`},
Whether to display the chromaticity diagram background colours.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1976_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1976_UCS_chromaticity_diagram_plot(cmfs=cmfs, **settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
uv = Luv_to_uv(XYZ_to_Luv(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.1, 0.7),
'standalone': True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**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.
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)
