def _CCT_to_uv_Ohno2013(
CCT_D_uv,
cmfs=MSDS_CMFS_STANDARD_OBSERVER['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_D_uv : ndarray
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
"""
CCT, D_uv = tsplit(CCT_D_uv)
cmfs = cmfs.copy().trim(SPECTRAL_SHAPE_DEFAULT)
shape = cmfs.shape
delta = 0.01
sd = sd_blackbody(CCT, shape)
XYZ = sd_to_XYZ(sd, 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:
sd = sd_blackbody(CCT + delta, shape)
XYZ = sd_to_XYZ(sd, 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 _CCT_to_uv_Ohno2013(
CCT_D_uv,
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_D_uv : ndarray
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
"""
CCT, D_uv = tsplit(CCT_D_uv)
cmfs = cmfs.copy().trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
delta = 0.01
sd = sd_blackbody(CCT, shape)
XYZ = sd_to_XYZ(sd, 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:
sd = sd_blackbody(CCT + delta, shape)
XYZ = sd_to_XYZ(sd, 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 _CCT_to_uv_Ohno2013(
CCT_D_uv: ArrayLike,
cmfs: Optional[MultiSpectralDistributions] = None) -> NDArray:
"""
Return 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_D_uv
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
cmfs
Standard observer colour matching functions, default to the
*CIE 1931 2 Degree Standard Observer*.
Returns
-------
:class:`numpy.ndarray`
*CIE UCS* colourspace *uv* chromaticity coordinates.
"""
CCT, D_uv = tsplit(CCT_D_uv)
cmfs, _illuminant = handle_spectral_arguments(cmfs)
delta = 0.01
sd = sd_blackbody(CCT, cmfs.shape)
XYZ = sd_to_XYZ(sd, 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:
sd = sd_blackbody(CCT + delta, cmfs.shape)
XYZ = sd_to_XYZ(sd, 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 test_sd_reference_illuminant(self):
"""
Tests :func:`colour.quality.CIE2017.sd_reference_illuminant`
definition.
"""
for sd, shape in [
(SD_SAMPLE_5NM, SD_SAMPLE_5NM.shape),
(SD_SAMPLE_1NM, SD_SAMPLE_1NM.shape),
]:
CCT, _D_uv = CCT_reference_illuminant(sd)
sd_reference = sd_reference_illuminant(CCT, shape)
np.testing.assert_allclose(sd_reference.values,
sd_blackbody(3288, shape).values,
rtol=0.005)
def planckian_table(
uv: ArrayLike,
cmfs: MultiSpectralDistributions,
start: Floating,
end: Floating,
count: Integer,
) -> List[PlanckianTableRow]:
"""
Return a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using *Ohno (2013)* method.
Parameters
----------
uv
*uv* chromaticity coordinates.
cmfs
Standard observer colour matching functions.
start
Temperature range start in kelvin degrees.
end
Temperature range end in kelvin degrees.
count
Temperatures count in the planckian table.
Returns
-------
:class:`list`
Planckian table.
Examples
--------
>>> from colour import MSDS_CMFS, SPECTRAL_SHAPE_DEFAULT
>>> cmfs = (
... MSDS_CMFS['CIE 1931 2 Degree Standard Observer'].
... copy().align(SPECTRAL_SHAPE_DEFAULT)
... )
>>> uv = np.array([0.1978, 0.3122])
>>> pprint(planckian_table(uv, cmfs, 1000, 1010, 10))
... # doctest: +SKIP
[PlanckianTableRow(Ti=1000.0, ui=0.4479628..., \
vi=0.3546296..., di=0.2537355...),
PlanckianTableRow(Ti=1001.1111111..., ui=0.4477030..., \
vi=0.3546521..., di=0.2534831...),
PlanckianTableRow(Ti=1002.2222222..., ui=0.4474434..., \
vi=0.3546746..., di=0.2532310...),
PlanckianTableRow(Ti=1003.3333333..., ui=0.4471842..., \
vi=0.3546970..., di=0.2529792...),
PlanckianTableRow(Ti=1004.4444444..., ui=0.4469252..., \
vi=0.3547194..., di=0.2527277...),
PlanckianTableRow(Ti=1005.5555555..., ui=0.4466666..., \
vi=0.3547417..., di=0.2524765...),
PlanckianTableRow(Ti=1006.6666666..., ui=0.4464083..., \
vi=0.3547640..., di=0.2522256...),
PlanckianTableRow(Ti=1007.7777777..., ui=0.4461502..., \
vi=0.3547862..., di=0.2519751...),
PlanckianTableRow(Ti=1008.8888888..., ui=0.4458925..., \
vi=0.3548084..., di=0.2517248...),
PlanckianTableRow(Ti=1010.0, ui=0.4456351..., \
vi=0.3548306..., di=0.2514749...)]
"""
ux, vx = tsplit(uv)
table = []
for Ti in np.linspace(start, end, count):
sd = sd_blackbody(Ti, cmfs.shape)
XYZ = sd_to_XYZ(sd, 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(PlanckianTableRow(Ti, ui, vi, di))
return table
def colour_quality_scale(sd_test,
additional_data=False,
method='NIST CQS 9.0'):
"""
Returns the *Colour Quality Scale* (CQS) of given spectral distribution
using given method.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
method : unicode, optional
**{'NIST CQS 9.0', 'NIST CQS 7.4'}**,
Computation method.
Returns
-------
numeric or CQS_Specification
Color quality scale.
References
----------
:cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_quality_scale(sd) # doctest: +ELLIPSIS
64.0172835...
"""
method = method.lower()
assert method.lower() in [
m.lower() for m in COLOUR_QUALITY_SCALE_METHODS
], ('"{0}" method is invalid, must be one of {1}!'.format(
method, COLOUR_QUALITY_SCALE_METHODS))
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(DEFAULT_SPECTRAL_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
vs_sds = {
sd.name: sd.copy().align(shape)
for sd in VS_SDS[method].values()
}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(sd_test,
sd_reference,
vs_sds,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
sd_reference, sd_reference, vs_sds, cmfs)
if method == 'nist cqs 9.0':
CCT_f = 1
scaling_f = 3.2
else:
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
scaling_f = 3.104
Q_as = colour_quality_scales(test_vs_colorimetry_data,
reference_vs_colorimetry_data, scaling_f,
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, scaling_f)
if method == 'nist cqs 9.0':
scaling_f = 2.93 * 1.0343
else:
scaling_f = 2.928
Q_f = scale_conversion(D_E_RMS, CCT_f, scaling_f)
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
if method == 'nist cqs 9.0':
Q_d = Q_p = None
else:
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)
Q_d = G_t / G_r * CCT_f * 100
if additional_data:
return CQS_Specification(
sd_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_quality_scale(sd_test, additional_data=False,
method='NIST CQS 9.0'):
"""
Returns the *Colour Quality Scale* (CQS) of given spectral distribution
using given method.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
method : unicode, optional
**{'NIST CQS 9.0', 'NIST CQS 7.4'}**,
Computation method.
Returns
-------
numeric or CQS_Specification
Color quality scale.
References
----------
:cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_quality_scale(sd) # doctest: +ELLIPSIS
64.0172835...
"""
method = method.lower()
assert method.lower() in [
m.lower() for m in COLOUR_QUALITY_SCALE_METHODS
], ('"{0}" method is invalid, must be one of {1}!'.format(
method, COLOUR_QUALITY_SCALE_METHODS))
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
vs_sds = {
sd.name: sd.copy().align(shape)
for sd in VS_SDS[method].values()
}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(
sd_test, sd_reference, vs_sds, cmfs, chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
sd_reference, sd_reference, vs_sds, cmfs)
if method == 'nist cqs 9.0':
CCT_f = 1
scaling_f = 3.2
else:
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
scaling_f = 3.104
Q_as = colour_quality_scales(test_vs_colorimetry_data,
reference_vs_colorimetry_data, scaling_f,
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, scaling_f)
if method == 'nist cqs 9.0':
scaling_f = 2.93 * 1.0343
else:
scaling_f = 2.928
Q_f = scale_conversion(D_E_RMS, CCT_f, scaling_f)
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
if method == 'nist cqs 9.0':
Q_d = Q_p = None
else:
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)
Q_d = G_t / G_r * CCT_f * 100
if additional_data:
return CQS_Specification(
sd_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(
sd_test: SpectralDistribution, additional_data: Boolean = False
) -> Union[Floating, ColourRendering_Specification_CRI]:
"""
Return the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test
Test spectral distribution.
additional_data
Whether to output additional data.
Returns
-------
:class:`numpy.floating` or \
:class:`colour.quality.ColourRendering_Specification_CRI`
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.2337241...
"""
# pylint: disable=E1102
cmfs = reshape_msds(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SPECTRAL_SHAPE_DEFAULT,
)
shape = cmfs.shape
sd_test = reshape_sd(sd_test, shape)
tcs_sds = {sd.name: reshape_sd(sd, shape) for sd in SDS_TCS.values()}
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
sd_test, sd_reference, tcs_sds, cmfs, chromatic_adaptation=True
)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, cmfs
)
Q_as = colour_rendering_indexes(
test_tcs_colorimetry_data, reference_tcs_colorimetry_data
)
Q_a = as_float_scalar(
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 ColourRendering_Specification_CRI(
sd_test.name,
Q_a,
Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data),
)
else:
return Q_a
def plot_blackbody_spectral_radiance(
temperature: Floating = 3500,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
blackbody: str = "VY Canis Major",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given blackbody spectral radiance.
Parameters
----------
temperature
Blackbody temperature.
cmfs
Standard observer colour matching functions used for computing the
spectrum domain and colours. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
blackbody
Blackbody name.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_blackbody_spectral_radiance(3500, blackbody='VY Canis Major')
... # doctest: +ELLIPSIS
(<Figure size ... with 2 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Spectral_Radiance.png
:align: center
:alt: plot_blackbody_spectral_radiance
"""
figure = plt.figure()
figure.subplots_adjust(hspace=CONSTANTS_COLOUR_STYLE.geometry.short / 2)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
sd = sd_blackbody(temperature, cmfs.shape)
axes = figure.add_subplot(211)
settings: Dict[str, Any] = {
"axes": axes,
"title": f"{blackbody} - Spectral Radiance",
"y_label": "W / (sr m$^2$) / m",
}
settings.update(kwargs)
settings["standalone"] = False
plot_single_sd(sd, cmfs.name, **settings)
axes = figure.add_subplot(212)
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
settings = {
"axes": axes,
"aspect": None,
"title": f"{blackbody} - Colour",
"x_label": f"{temperature}K",
"y_label": "",
"x_ticker": False,
"y_ticker": False,
}
settings.update(kwargs)
settings["standalone"] = False
figure, axes = plot_single_colour_swatch(RGB, **settings)
settings = {"axes": axes, "standalone": True}
settings.update(kwargs)
return render(**settings)
def sd_reference_illuminant(CCT: Floating,
shape: SpectralShape) -> SpectralDistribution:
"""
Compute the reference illuminant for a given correlated colour temperature
:math:`T_{cp}` for use in *CIE 2017 Colour Fidelity Index* (CFI)
computation.
Parameters
----------
CCT
Correlated colour temperature :math:`T_{cp}`.
shape
Desired shape of the returned spectral distribution.
Returns
-------
:class:`colour.SpectralDistribution`
Reference illuminant for *CIE 2017 Colour Fidelity Index* (CFI)
computation.
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> with numpy_print_options(suppress=True):
... sd_reference_illuminant( # doctest: +ELLIPSIS
... 4224.469705295263300, SpectralShape(380, 780, 20))
SpectralDistribution([[ 380. , 0.0034089...],
[ 400. , 0.0044208...],
[ 420. , 0.0053260...],
[ 440. , 0.0062857...],
[ 460. , 0.0072767...],
[ 480. , 0.0080207...],
[ 500. , 0.0086590...],
[ 520. , 0.0092242...],
[ 540. , 0.0097686...],
[ 560. , 0.0101444...],
[ 580. , 0.0104475...],
[ 600. , 0.0107642...],
[ 620. , 0.0110439...],
[ 640. , 0.0112535...],
[ 660. , 0.0113922...],
[ 680. , 0.0115185...],
[ 700. , 0.0113155...],
[ 720. , 0.0108192...],
[ 740. , 0.0111582...],
[ 760. , 0.0101299...],
[ 780. , 0.0105638...]],
interpolator=SpragueInterpolator,
interpolator_kwargs={},
extrapolator=Extrapolator,
extrapolator_kwargs={...})
"""
if CCT <= 5000:
sd_planckian = sd_blackbody(CCT, shape)
if CCT >= 4000:
xy = CCT_to_xy_CIE_D(CCT)
sd_daylight = sd_CIE_illuminant_D_series(xy).align(shape)
if CCT < 4000:
sd_reference = sd_planckian
elif 4000 <= CCT <= 5000:
# Planckian and daylight illuminant must be normalised so that the
# mixture isn't biased.
sd_planckian /= sd_to_XYZ(sd_planckian)[1] # type: ignore[misc]
sd_daylight /= sd_to_XYZ(sd_daylight)[1] # type: ignore[misc]
# Mixture: 4200K should be 80% Planckian, 20% CIE Illuminant D Series.
m = (CCT - 4000) / 1000
values = linstep_function(m, sd_planckian.values, sd_daylight.values)
name = (f"{as_int_scalar(CCT)}K "
f"Blackbody & CIE Illuminant D Series Mixture - "
f"{as_float_scalar(100 * m):.1f}%")
sd_reference = SpectralDistribution(values, shape.range(), name=name)
elif CCT > 5000:
sd_reference = sd_daylight
return sd_reference
def plot_blackbody_spectral_radiance(
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.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_spectral_radiance(3500, blackbody='VY Canis Major')
... # doctest: +ELLIPSIS
(<Figure size ... with 2 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Spectral_Radiance.png
:align: center
:alt: plot_blackbody_spectral_radiance
"""
figure = plt.figure()
figure.subplots_adjust(hspace=COLOUR_STYLE_CONSTANTS.geometry.short / 2)
cmfs = first_item(filter_cmfs(cmfs).values())
sd = sd_blackbody(temperature, cmfs.shape)
axes = figure.add_subplot(211)
settings = {
'axes': axes,
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': 'W / (sr m$^2$) / m',
}
settings.update(kwargs)
settings['standalone'] = False
plot_single_sd(sd, cmfs.name, **settings)
axes = figure.add_subplot(212)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
settings = {
'axes': axes,
'aspect': None,
'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'x_ticker': False,
'y_ticker': False,
}
settings.update(kwargs)
settings['standalone'] = False
figure, axes = plot_single_colour_swatch(ColourSwatch(name='', RGB=RGB),
**settings)
settings = {'axes': axes, 'standalone': True}
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
sd = sd_blackbody(CCT, shape)
XYZ = sd_to_XYZ(sd, 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:
sd = sd_blackbody(CCT + delta, shape)
XYZ = sd_to_XYZ(sd, 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 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):
sd = sd_blackbody(Ti, shape)
XYZ = sd_to_XYZ(sd, 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 plot_blackbody_spectral_radiance(
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.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_spectral_radiance(3500, blackbody='VY Canis Major')
... # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Blackbody_Spectral_Radiance.png
:align: center
:alt: plot_blackbody_spectral_radiance
"""
figure = plt.figure()
figure.subplots_adjust(hspace=COLOUR_STYLE_CONSTANTS.geometry.short / 2)
cmfs = first_item(filter_cmfs(cmfs).values())
sd = sd_blackbody(temperature, cmfs.shape)
axes = figure.add_subplot(211)
settings = {
'axes': axes,
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': 'W / (sr m$^2$) / m',
}
settings.update(kwargs)
settings['standalone'] = False
plot_single_sd(sd, cmfs.name, **settings)
axes = figure.add_subplot(212)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
settings = {
'axes': axes,
'aspect': None,
'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'x_ticker': False,
'y_ticker': False,
}
settings.update(kwargs)
settings['standalone'] = False
figure, axes = plot_single_colour_swatch(
ColourSwatch(name='', RGB=RGB), **settings)
settings = {'axes': axes, 'standalone': True}
settings.update(kwargs)
return render(**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.colorimetry import (
... SPECTRAL_SHAPE_DEFAULT, MSDS_CMFS_STANDARD_OBSERVER)
>>> from pprint import pprint
>>> cmfs = (
... MSDS_CMFS_STANDARD_OBSERVER['CIE 1931 2 Degree Standard Observer'].
... copy().align(SPECTRAL_SHAPE_DEFAULT)
... )
>>> 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(SPECTRAL_SHAPE_DEFAULT)
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
sd = sd_blackbody(Ti, shape)
XYZ = sd_to_XYZ(sd, 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 generate_documentation_plots(output_directory: str):
"""
Generate documentation plots.
Parameters
----------
output_directory
Output directory.
"""
filter_warnings()
colour_style()
np.random.seed(0)
# *************************************************************************
# "README.rst"
# *************************************************************************
filename = os.path.join(
output_directory, "Examples_Colour_Automatic_Conversion_Graph.png"
)
plot_automatic_colour_conversion_graph(filename)
arguments = {
"tight_layout": True,
"transparent_background": True,
"filename": os.path.join(
output_directory, "Examples_Plotting_Visible_Spectrum.png"
),
}
plt.close(
plot_visible_spectrum(
"CIE 1931 2 Degree Standard Observer", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Illuminant_F1_SD.png"
)
plt.close(plot_single_illuminant_sd("FL1", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Blackbodies.png"
)
blackbody_sds = [
sd_blackbody(i, SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plt.close(
plot_multi_sds(
blackbody_sds,
y_label="W / (sr m$^2$) / m",
plot_kwargs={"use_sd_colours": True, "normalise_sd_colours": True},
legend_location="upper right",
bounding_box=(0, 1250, 0, 2.5e6),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Cone_Fundamentals.png"
)
plt.close(
plot_single_cmfs(
"Stockman & Sharpe 2 Degree Cone Fundamentals",
y_label="Sensitivity",
bounding_box=(390, 870, 0, 1.1),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Luminous_Efficiency.png"
)
plt.close(
plot_multi_sds(
(
sd_mesopic_luminous_efficiency_function(0.2),
SDS_LEFS_PHOTOPIC["CIE 1924 Photopic Standard Observer"],
SDS_LEFS_SCOTOPIC["CIE 1951 Scotopic Standard Observer"],
),
y_label="Luminous Efficiency",
legend_location="upper right",
y_tighten=True,
margins=(0, 0, 0, 0.1),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_BabelColor_Average.png"
)
plt.close(
plot_multi_sds(
SDS_COLOURCHECKERS["BabelColor Average"].values(),
plot_kwargs={"use_sd_colours": True},
title=("BabelColor Average - " "Spectral Distributions"),
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_ColorChecker_2005.png"
)
plt.close(
plot_single_colour_checker(
"ColorChecker 2005", text_kwargs={"visible": False}, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Chromaticities_Prediction.png"
)
plt.close(
plot_corresponding_chromaticities_prediction(
2, "Von Kries", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png",
)
RGB = np.random.random((32, 32, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB,
"ITU-R BT.709",
colourspaces=["ACEScg", "S-Gamut"],
show_pointer_gamut=True,
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_CRI.png"
)
plt.close(
plot_single_sd_colour_rendering_index_bars(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Colour_Rendition_Report.png"
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Plot_Visible_Spectrum_Section.png"
)
plt.close(
plot_visible_spectrum_section(
section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Examples_Plotting_Plot_RGB_Colourspace_Section.png"
)
plt.close(
plot_RGB_colourspace_section(
"sRGB", section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
["A", "B", "C"], **arguments
)[0]
)
# *************************************************************************
# Documentation
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_CVD_Simulation_Machado2009.png"
)
plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Colour_Checker.png"
)
plt.close(plot_single_colour_checker("ColorChecker 2005", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Colour_Checkers.png"
)
plt.close(
plot_multi_colour_checkers(
["ColorChecker 1976", "ColorChecker 2005"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_SD.png"
)
data = {
500: 0.0651,
520: 0.0705,
540: 0.0772,
560: 0.0870,
580: 0.1128,
600: 0.1360,
}
sd = SpectralDistribution(data, name="Custom")
plt.close(plot_single_sd(sd, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_SDS.png"
)
data_1 = {
500: 0.004900,
510: 0.009300,
520: 0.063270,
530: 0.165500,
540: 0.290400,
550: 0.433450,
560: 0.594500,
}
data_2 = {
500: 0.323000,
510: 0.503000,
520: 0.710000,
530: 0.862000,
540: 0.954000,
550: 0.994950,
560: 0.995000,
}
spd1 = SpectralDistribution(data_1, name="Custom 1")
spd2 = SpectralDistribution(data_2, name="Custom 2")
plt.close(plot_multi_sds([spd1, spd2], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_CMFS.png"
)
plt.close(
plot_single_cmfs("CIE 1931 2 Degree Standard Observer", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_CMFS.png"
)
cmfs = (
"CIE 1931 2 Degree Standard Observer",
"CIE 1964 10 Degree Standard Observer",
)
plt.close(plot_multi_cmfs(cmfs, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Illuminant_SD.png"
)
plt.close(plot_single_illuminant_sd("A", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Illuminant_SDS.png"
)
plt.close(plot_multi_illuminant_sds(["A", "B", "C"], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Visible_Spectrum.png"
)
plt.close(plot_visible_spectrum(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Lightness_Function.png"
)
plt.close(plot_single_lightness_function("CIE 1976", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Lightness_Functions.png"
)
plt.close(
plot_multi_lightness_functions(
["CIE 1976", "Wyszecki 1963"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Luminance_Function.png"
)
plt.close(plot_single_luminance_function("CIE 1976", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Luminance_Functions.png"
)
plt.close(
plot_multi_luminance_functions(
["CIE 1976", "Newhall 1943"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Blackbody_Spectral_Radiance.png"
)
plt.close(
plot_blackbody_spectral_radiance(
3500, blackbody="VY Canis Major", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Blackbody_Colours.png"
)
plt.close(
plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Colour_Swatch.png"
)
RGB = ColourSwatch((0.45620519, 0.03081071, 0.04091952))
plt.close(plot_single_colour_swatch(RGB, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Colour_Swatches.png"
)
RGB_1 = ColourSwatch((0.45293517, 0.31732158, 0.26414773))
RGB_2 = ColourSwatch((0.77875824, 0.57726450, 0.50453169))
plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Function.png"
)
plt.close(plot_single_function(lambda x: x ** (1 / 2.2), **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Functions.png"
)
functions = {
"Gamma 2.2": lambda x: x ** (1 / 2.2),
"Gamma 2.4": lambda x: x ** (1 / 2.4),
"Gamma 2.6": lambda x: x ** (1 / 2.6),
}
plt.close(plot_multi_functions(functions, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Image.png"
)
path = os.path.join(
colour.__path__[0],
"examples",
"plotting",
"resources",
"Ishihara_Colour_Blindness_Test_Plate_3.png",
)
plt.close(plot_image(read_image(str(path)), **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Corresponding_Chromaticities_Prediction.png",
)
plt.close(
plot_corresponding_chromaticities_prediction(
1, "Von Kries", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Spectral_Locus.png"
)
plt.close(
plot_spectral_locus(spectral_locus_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_Colours.png"
)
plt.close(
plot_chromaticity_diagram_colours(diagram_colours="RGB", **arguments)[
0
]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram.png"
)
plt.close(plot_chromaticity_diagram(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1931.png"
)
plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png"
)
plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png"
)
plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_SDS_In_Chromaticity_Diagram.png"
)
A = SDS_ILLUMINANTS["A"]
D65 = SDS_ILLUMINANTS["D65"]
plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Pointer_Gamut.png"
)
plt.close(plot_pointer_gamut(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Colourspaces_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_" "Chromaticity_Diagram.png",
)
RGB = np.random.random((128, 128, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_RGB_Chromaticities_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(
RGB, "ITU-R BT.709", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(**arguments)[
0
]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(
**arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Ellipses_MacAdam1942_In_"
"Chromaticity_Diagram_CIE1976UCS.png",
)
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS(
**arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_CCTF.png"
)
plt.close(plot_single_cctf("ITU-R BT.709", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_CCTFs.png"
)
plt.close(plot_multi_cctfs(["ITU-R BT.709", "sRGB"], **arguments)[0])
data = np.array(
[
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.40920000, 0.28120000, 0.30600000]),
np.array(
[
[0.02495100, 0.01908600, 0.02032900],
[0.10944300, 0.06235900, 0.06788100],
[0.27186500, 0.18418700, 0.19565300],
[0.48898900, 0.40749400, 0.44854600],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.30760000, 0.48280000, 0.42770000]),
np.array(
[
[0.02108000, 0.02989100, 0.02790400],
[0.06194700, 0.11251000, 0.09334400],
[0.15255800, 0.28123300, 0.23234900],
[0.34157700, 0.56681300, 0.47035300],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.39530000, 0.28120000, 0.18450000]),
np.array(
[
[0.02436400, 0.01908600, 0.01468800],
[0.10331200, 0.06235900, 0.02854600],
[0.26311900, 0.18418700, 0.12109700],
[0.43158700, 0.40749400, 0.39008600],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.20510000, 0.18420000, 0.57130000]),
np.array(
[
[0.03039800, 0.02989100, 0.06123300],
[0.08870000, 0.08498400, 0.21843500],
[0.18405800, 0.18418700, 0.40111400],
[0.32550100, 0.34047200, 0.50296900],
[0.53826100, 0.56681300, 0.80010400],
]
),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.35770000, 0.28120000, 0.11250000]),
np.array(
[
[0.03678100, 0.02989100, 0.01481100],
[0.17127700, 0.11251000, 0.01229900],
[0.30080900, 0.28123300, 0.21229800],
[0.52976000, 0.40749400, 0.11720000],
]
),
None,
],
]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Constant_Hue_Loci.png"
)
plt.close(plot_constant_hue_loci(data, "IPT", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_Munsell_Value_Function.png"
)
plt.close(plot_single_munsell_value_function("ASTM D1535", **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Multi_Munsell_Value_Functions.png"
)
plt.close(
plot_multi_munsell_value_functions(
["ASTM D1535", "McCamy 1987"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Single_SD_Rayleigh_Scattering.png"
)
plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_The_Blue_Sky.png"
)
plt.close(plot_the_blue_sky(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Colour_Quality_Bars.png"
)
illuminant = SDS_ILLUMINANTS["FL2"]
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
light_source = light_source.copy().align(SpectralShape(360, 830, 1))
cqs_i = colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour_quality_scale(light_source, additional_data=True)
plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0])
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png",
)
illuminant = SDS_ILLUMINANTS["FL2"]
plt.close(
plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png",
)
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
plt.close(
plot_multi_sds_colour_rendering_indexes_bars(
[illuminant, light_source], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png",
)
illuminant = SDS_ILLUMINANTS["FL2"]
plt.close(
plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png",
)
light_source = SDS_LIGHT_SOURCES["Kinoton 75P"]
plt.close(
plot_multi_sds_colour_quality_scales_bars(
[illuminant, light_source], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Hull_Section_Colours.png"
)
vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
XYZ_vertices = RGB_to_XYZ(
vertices["position"] + 0.5,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.whitepoint,
RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
)
hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
plt.close(
plot_hull_section_colours(hull, section_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Hull_Section_Contour.png"
)
plt.close(
plot_hull_section_contour(hull, section_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Visible_Spectrum_Section.png"
)
plt.close(
plot_visible_spectrum_section(
section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspace_Section.png"
)
plt.close(
plot_RGB_colourspace_section(
"sRGB", section_colours="RGB", section_opacity=0.15, **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_Planckian_Locus.png"
)
plt.close(
plot_planckian_locus(planckian_locus_colours="RGB", **arguments)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1931(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png",
)
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
["A", "B", "C"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Full.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Intermediate.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], "Intermediate", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory,
"Plotting_Plot_Single_SD_Colour_Rendition_Report_Simple.png",
)
plt.close(
plot_single_sd_colour_rendition_report(
SDS_ILLUMINANTS["FL2"], "Simple", **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png"
)
plt.close(
plot_RGB_colourspaces_gamuts(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Colourspaces_Gamuts.png"
)
plt.close(
plot_RGB_colourspaces_gamuts(
["ITU-R BT.709", "ACEScg", "S-Gamut"], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Plotting_Plot_RGB_Scatter.png"
)
plt.close(plot_RGB_scatter(RGB, "ITU-R BT.709", **arguments)[0])
filename = os.path.join(
output_directory, "Plotting_Plot_Colour_Automatic_Conversion_Graph.png"
)
plot_automatic_colour_conversion_graph(filename)
# *************************************************************************
# "tutorial.rst"
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Visible_Spectrum.png"
)
plt.close(plot_visible_spectrum(**arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Sample_SD.png"
)
sample_sd_data = {
380: 0.048,
385: 0.051,
390: 0.055,
395: 0.060,
400: 0.065,
405: 0.068,
410: 0.068,
415: 0.067,
420: 0.064,
425: 0.062,
430: 0.059,
435: 0.057,
440: 0.055,
445: 0.054,
450: 0.053,
455: 0.053,
460: 0.052,
465: 0.052,
470: 0.052,
475: 0.053,
480: 0.054,
485: 0.055,
490: 0.057,
495: 0.059,
500: 0.061,
505: 0.062,
510: 0.065,
515: 0.067,
520: 0.070,
525: 0.072,
530: 0.074,
535: 0.075,
540: 0.076,
545: 0.078,
550: 0.079,
555: 0.082,
560: 0.087,
565: 0.092,
570: 0.100,
575: 0.107,
580: 0.115,
585: 0.122,
590: 0.129,
595: 0.134,
600: 0.138,
605: 0.142,
610: 0.146,
615: 0.150,
620: 0.154,
625: 0.158,
630: 0.163,
635: 0.167,
640: 0.173,
645: 0.180,
650: 0.188,
655: 0.196,
660: 0.204,
665: 0.213,
670: 0.222,
675: 0.231,
680: 0.242,
685: 0.251,
690: 0.261,
695: 0.271,
700: 0.282,
705: 0.294,
710: 0.305,
715: 0.318,
720: 0.334,
725: 0.354,
730: 0.372,
735: 0.392,
740: 0.409,
745: 0.420,
750: 0.436,
755: 0.450,
760: 0.462,
765: 0.465,
770: 0.448,
775: 0.432,
780: 0.421,
}
sd = SpectralDistribution(sample_sd_data, name="Sample")
plt.close(plot_single_sd(sd, **arguments)[0])
arguments["filename"] = os.path.join(
output_directory, "Tutorial_SD_Interpolation.png"
)
sd_copy = sd.copy()
sd_copy.interpolate(SpectralShape(400, 770, 1))
plt.close(
plot_multi_sds(
[sd, sd_copy], bounding_box=[730, 780, 0.25, 0.5], **arguments
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Sample_Swatch.png"
)
sd = SpectralDistribution(sample_sd_data)
cmfs = MSDS_CMFS_STANDARD_OBSERVER["CIE 1931 2 Degree Standard Observer"]
illuminant = SDS_ILLUMINANTS["D65"]
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(
ColourSwatch(RGB, "Sample"),
text_kwargs={"size": "x-large"},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Neutral5.png"
)
patch_name = "neutral 5 (.70 D)"
patch_sd = SDS_COLOURCHECKERS["ColorChecker N Ohta"][patch_name]
with domain_range_scale("1"):
XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(
ColourSwatch(RGB, patch_name.title()),
text_kwargs={"size": "x-large"},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_Colour_Checker.png"
)
plt.close(
plot_single_colour_checker(
colour_checker="ColorChecker 2005",
text_kwargs={"visible": False},
**arguments,
)[0]
)
arguments["filename"] = os.path.join(
output_directory, "Tutorial_CIE_1931_Chromaticity_Diagram.png"
)
xy = XYZ_to_xy(XYZ)
plot_chromaticity_diagram_CIE1931(standalone=False)
x, y = xy
plt.plot(x, y, "o-", color="white")
# Annotating the plot.
plt.annotate(
patch_sd.name.title(),
xy=xy,
xytext=(-50, 30),
textcoords="offset points",
arrowprops=dict(arrowstyle="->", connectionstyle="arc3, rad=-0.2"),
)
plt.close(
render(
standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True,
**arguments,
)[0]
)
# *************************************************************************
# "basics.rst"
# *************************************************************************
arguments["filename"] = os.path.join(
output_directory, "Basics_Logo_Small_001_CIE_XYZ.png"
)
RGB = read_image(os.path.join(output_directory, "Logo_Small_001.png"))[
..., 0:3
]
XYZ = sRGB_to_XYZ(RGB)
plt.close(
plot_image(XYZ, text_kwargs={"text": "sRGB to XYZ"}, **arguments)[0]
)
def colour_quality_scale(sd_test, additional_data=False):
"""
Returns the *Colour Quality Scale* (CQS) of given spectral
distribution.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
Returns
-------
numeric or CQS_Specification
Color quality scale.
References
----------
:cite:`Davis2010a`, :cite:`Ohno2008a`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_quality_scale(sd) # doctest: +ELLIPSIS
64.6863391...
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
vs_sds = {sd.name: sd.copy().align(shape) for sd in VS_SDS.values()}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(sd_test,
sd_reference,
vs_sds,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
sd_reference, sd_reference, vs_sds, cmfs)
XYZ_r = sd_to_XYZ(sd_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(
sd_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 generate_documentation_plots(output_directory):
"""
Generates documentation plots.
Parameters
----------
output_directory : unicode
Output directory.
"""
filter_warnings()
colour_style()
np.random.seed(0)
# *************************************************************************
# "README.rst"
# *************************************************************************
filename = os.path.join(output_directory,
'Examples_Colour_Automatic_Conversion_Graph.png')
plot_automatic_colour_conversion_graph(filename)
arguments = {
'tight_layout':
True,
'transparent_background':
True,
'filename':
os.path.join(output_directory,
'Examples_Plotting_Visible_Spectrum.png')
}
plt.close(
plot_visible_spectrum('CIE 1931 2 Degree Standard Observer',
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Illuminant_F1_SD.png')
plt.close(plot_single_illuminant_sd('FL1', **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Examples_Plotting_Blackbodies.png')
blackbody_sds = [
sd_blackbody(i, SpectralShape(0, 10000, 10))
for i in range(1000, 15000, 1000)
]
plt.close(
plot_multi_sds(blackbody_sds,
y_label='W / (sr m$^2$) / m',
use_sds_colours=True,
normalise_sds_colours=True,
legend_location='upper right',
bounding_box=(0, 1250, 0, 2.5e15),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Cone_Fundamentals.png')
plt.close(
plot_single_cmfs('Stockman & Sharpe 2 Degree Cone Fundamentals',
y_label='Sensitivity',
bounding_box=(390, 870, 0, 1.1),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Luminous_Efficiency.png')
plt.close(
plot_multi_sds((sd_mesopic_luminous_efficiency_function(0.2),
PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']),
y_label='Luminous Efficiency',
legend_location='upper right',
y_tighten=True,
margins=(0, 0, 0, .1),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_BabelColor_Average.png')
plt.close(
plot_multi_sds(COLOURCHECKERS_SDS['BabelColor Average'].values(),
use_sds_colours=True,
title=('BabelColor Average - '
'Spectral Distributions'),
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_ColorChecker_2005.png')
plt.close(
plot_single_colour_checker('ColorChecker 2005',
text_parameters={'visible': False},
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Examples_Plotting_Chromaticities_Prediction.png')
plt.close(
plot_corresponding_chromaticities_prediction(2, 'Von Kries', 'Bianco',
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
['A', 'B', 'C'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png')
RGB = np.random.random((32, 32, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB,
'ITU-R BT.709',
colourspaces=['ACEScg', 'S-Gamut'],
show_pointer_gamut=True,
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Examples_Plotting_CRI.png')
plt.close(
plot_single_sd_colour_rendering_index_bars(ILLUMINANTS_SDS['FL2'],
**arguments)[0])
# *************************************************************************
# Documentation
# *************************************************************************
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_CVD_Simulation_Machado2009.png')
plt.close(plot_cvd_simulation_Machado2009(RGB, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Colour_Checker.png')
plt.close(plot_single_colour_checker('ColorChecker 2005', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Colour_Checkers.png')
plt.close(
plot_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_SD.png')
data = {
500: 0.0651,
520: 0.0705,
540: 0.0772,
560: 0.0870,
580: 0.1128,
600: 0.1360
}
sd = SpectralDistribution(data, name='Custom')
plt.close(plot_single_sd(sd, **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_SDS.png')
data_1 = {
500: 0.004900,
510: 0.009300,
520: 0.063270,
530: 0.165500,
540: 0.290400,
550: 0.433450,
560: 0.594500
}
data_2 = {
500: 0.323000,
510: 0.503000,
520: 0.710000,
530: 0.862000,
540: 0.954000,
550: 0.994950,
560: 0.995000
}
spd1 = SpectralDistribution(data_1, name='Custom 1')
spd2 = SpectralDistribution(data_2, name='Custom 2')
plt.close(plot_multi_sds([spd1, spd2], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_CMFS.png')
plt.close(
plot_single_cmfs('CIE 1931 2 Degree Standard Observer',
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_CMFS.png')
cmfs = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
plt.close(plot_multi_cmfs(cmfs, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Illuminant_SD.png')
plt.close(plot_single_illuminant_sd('A', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Illuminant_SDS.png')
plt.close(plot_multi_illuminant_sds(['A', 'B', 'C'], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Visible_Spectrum.png')
plt.close(plot_visible_spectrum(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Lightness_Function.png')
plt.close(plot_single_lightness_function('CIE 1976', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Lightness_Functions.png')
plt.close(
plot_multi_lightness_functions(['CIE 1976', 'Wyszecki 1963'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Luminance_Function.png')
plt.close(plot_single_luminance_function('CIE 1976', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Luminance_Functions.png')
plt.close(
plot_multi_luminance_functions(['CIE 1976', 'Newhall 1943'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Blackbody_Spectral_Radiance.png')
plt.close(
plot_blackbody_spectral_radiance(3500,
blackbody='VY Canis Major',
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Blackbody_Colours.png')
plt.close(
plot_blackbody_colours(SpectralShape(150, 12500, 50), **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Colour_Swatch.png')
RGB = ColourSwatch(RGB=(0.45620519, 0.03081071, 0.04091952))
plt.close(plot_single_colour_swatch(RGB, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Colour_Swatches.png')
RGB_1 = ColourSwatch(RGB=(0.45293517, 0.31732158, 0.26414773))
RGB_2 = ColourSwatch(RGB=(0.77875824, 0.57726450, 0.50453169))
plt.close(plot_multi_colour_swatches([RGB_1, RGB_2], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_Function.png')
plt.close(plot_single_function(lambda x: x**(1 / 2.2), **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_Functions.png')
functions = {
'Gamma 2.2': lambda x: x**(1 / 2.2),
'Gamma 2.4': lambda x: x**(1 / 2.4),
'Gamma 2.6': lambda x: x**(1 / 2.6),
}
plt.close(plot_multi_functions(functions, **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Image.png')
path = os.path.join(output_directory, 'Logo_Medium_001.png')
plt.close(plot_image(read_image(str(path)), **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Corresponding_Chromaticities_Prediction.png')
plt.close(
plot_corresponding_chromaticities_prediction(1, 'Von Kries', 'CAT02',
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Spectral_Locus.png')
plt.close(
plot_spectral_locus(spectral_locus_colours='RGB', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_Colours.png')
plt.close(plot_chromaticity_diagram_colours(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram.png')
plt.close(plot_chromaticity_diagram(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1931.png')
plt.close(plot_chromaticity_diagram_CIE1931(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1960UCS.png')
plt.close(plot_chromaticity_diagram_CIE1960UCS(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Chromaticity_Diagram_CIE1976UCS.png')
plt.close(plot_chromaticity_diagram_CIE1976UCS(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_SDS_In_Chromaticity_Diagram.png')
A = ILLUMINANTS_SDS['A']
D65 = ILLUMINANTS_SDS['D65']
plt.close(plot_sds_in_chromaticity_diagram([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_sds_in_chromaticity_diagram_CIE1931([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_sds_in_chromaticity_diagram_CIE1960UCS([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_SDS_In_Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_sds_in_chromaticity_diagram_CIE1976UCS([A, D65], **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Pointer_Gamut.png')
plt.close(plot_pointer_gamut(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_RGB_Colourspaces_In_Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS(
['ITU-R BT.709', 'ACEScg', 'S-Gamut'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram.png')
RGB = np.random.random((128, 128, 3))
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Chromaticities_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(
RGB, 'ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Ellipses_MacAdam1942_In_Chromaticity_Diagram.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931(
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS(
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Ellipses_MacAdam1942_In_'
'Chromaticity_Diagram_CIE1976UCS.png')
plt.close(
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS(
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Single_CCTF.png')
plt.close(plot_single_cctf('ITU-R BT.709', **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Multi_CCTFs.png')
plt.close(plot_multi_cctfs(['ITU-R BT.709', 'sRGB'], **arguments)[0])
data = np.array([
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.40920000, 0.28120000, 0.30600000]),
np.array([
[0.02495100, 0.01908600, 0.02032900],
[0.10944300, 0.06235900, 0.06788100],
[0.27186500, 0.18418700, 0.19565300],
[0.48898900, 0.40749400, 0.44854600],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.30760000, 0.48280000, 0.42770000]),
np.array([
[0.02108000, 0.02989100, 0.02790400],
[0.06194700, 0.11251000, 0.09334400],
[0.15255800, 0.28123300, 0.23234900],
[0.34157700, 0.56681300, 0.47035300],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.39530000, 0.28120000, 0.18450000]),
np.array([
[0.02436400, 0.01908600, 0.01468800],
[0.10331200, 0.06235900, 0.02854600],
[0.26311900, 0.18418700, 0.12109700],
[0.43158700, 0.40749400, 0.39008600],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.20510000, 0.18420000, 0.57130000]),
np.array([
[0.03039800, 0.02989100, 0.06123300],
[0.08870000, 0.08498400, 0.21843500],
[0.18405800, 0.18418700, 0.40111400],
[0.32550100, 0.34047200, 0.50296900],
[0.53826100, 0.56681300, 0.80010400],
]),
None,
],
[
None,
np.array([0.95010000, 1.00000000, 1.08810000]),
np.array([0.35770000, 0.28120000, 0.11250000]),
np.array([
[0.03678100, 0.02989100, 0.01481100],
[0.17127700, 0.11251000, 0.01229900],
[0.30080900, 0.28123300, 0.21229800],
[0.52976000, 0.40749400, 0.11720000],
]),
None,
],
])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Constant_Hue_Loci.png')
plt.close(plot_constant_hue_loci(data, 'IPT', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_Munsell_Value_Function.png')
plt.close(plot_single_munsell_value_function('ASTM D1535', **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Multi_Munsell_Value_Functions.png')
plt.close(
plot_multi_munsell_value_functions(['ASTM D1535', 'McCamy 1987'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Single_SD_Rayleigh_Scattering.png')
plt.close(plot_single_sd_rayleigh_scattering(**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_The_Blue_Sky.png')
plt.close(plot_the_blue_sky(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Colour_Quality_Bars.png')
illuminant = ILLUMINANTS_SDS['FL2']
light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
light_source = light_source.copy().align(SpectralShape(360, 830, 1))
cqs_i = colour_quality_scale(illuminant, additional_data=True)
cqs_l = colour_quality_scale(light_source, additional_data=True)
plt.close(plot_colour_quality_bars([cqs_i, cqs_l], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Single_SD_Colour_Rendering_Index_Bars.png')
illuminant = ILLUMINANTS_SDS['FL2']
plt.close(
plot_single_sd_colour_rendering_index_bars(illuminant, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Multi_SDS_Colour_Rendering_Indexes_Bars.png')
light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
plt.close(
plot_multi_sds_colour_rendering_indexes_bars(
[illuminant, light_source], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Single_SD_Colour_Quality_Scale_Bars.png')
illuminant = ILLUMINANTS_SDS['FL2']
plt.close(
plot_single_sd_colour_quality_scale_bars(illuminant, **arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Multi_SDS_Colour_Quality_Scales_Bars.png')
light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
plt.close(
plot_multi_sds_colour_quality_scales_bars([illuminant, light_source],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_Planckian_Locus.png')
plt.close(plot_planckian_locus(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Planckian_Locus_CIE1931.png')
plt.close(plot_planckian_locus_CIE1931(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_Planckian_Locus_CIE1960UCS.png')
plt.close(plot_planckian_locus_CIE1960UCS(**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram(['A', 'B', 'C'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1931.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1931(['A', 'B', 'C'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory,
'Plotting_Plot_Planckian_Locus_In_Chromaticity_Diagram_CIE1960UCS.png')
plt.close(
plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(
['A', 'B', 'C'], **arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png')
plt.close(
plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'],
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Plotting_Plot_RGB_Colourspaces_Gamuts.png')
plt.close(
plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Plotting_Plot_RGB_Scatter.png')
plt.close(plot_RGB_scatter(RGB, 'ITU-R BT.709', **arguments)[0])
filename = os.path.join(
output_directory,
'Plotting_Plot_Colour_Automatic_Conversion_Graph.png')
plot_automatic_colour_conversion_graph(filename)
# *************************************************************************
# "tutorial.rst"
# *************************************************************************
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Visible_Spectrum.png')
plt.close(plot_visible_spectrum(**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Sample_SD.png')
sample_sd_data = {
380: 0.048,
385: 0.051,
390: 0.055,
395: 0.060,
400: 0.065,
405: 0.068,
410: 0.068,
415: 0.067,
420: 0.064,
425: 0.062,
430: 0.059,
435: 0.057,
440: 0.055,
445: 0.054,
450: 0.053,
455: 0.053,
460: 0.052,
465: 0.052,
470: 0.052,
475: 0.053,
480: 0.054,
485: 0.055,
490: 0.057,
495: 0.059,
500: 0.061,
505: 0.062,
510: 0.065,
515: 0.067,
520: 0.070,
525: 0.072,
530: 0.074,
535: 0.075,
540: 0.076,
545: 0.078,
550: 0.079,
555: 0.082,
560: 0.087,
565: 0.092,
570: 0.100,
575: 0.107,
580: 0.115,
585: 0.122,
590: 0.129,
595: 0.134,
600: 0.138,
605: 0.142,
610: 0.146,
615: 0.150,
620: 0.154,
625: 0.158,
630: 0.163,
635: 0.167,
640: 0.173,
645: 0.180,
650: 0.188,
655: 0.196,
660: 0.204,
665: 0.213,
670: 0.222,
675: 0.231,
680: 0.242,
685: 0.251,
690: 0.261,
695: 0.271,
700: 0.282,
705: 0.294,
710: 0.305,
715: 0.318,
720: 0.334,
725: 0.354,
730: 0.372,
735: 0.392,
740: 0.409,
745: 0.420,
750: 0.436,
755: 0.450,
760: 0.462,
765: 0.465,
770: 0.448,
775: 0.432,
780: 0.421
}
sd = SpectralDistribution(sample_sd_data, name='Sample')
plt.close(plot_single_sd(sd, **arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_SD_Interpolation.png')
sd_copy = sd.copy()
sd_copy.interpolate(SpectralShape(400, 770, 1))
plt.close(
plot_multi_sds([sd, sd_copy],
bounding_box=[730, 780, 0.25, 0.5],
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Sample_Swatch.png')
sd = SpectralDistribution(sample_sd_data)
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
illuminant = ILLUMINANTS_SDS['D65']
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(ColourSwatch('Sample', RGB),
text_parameters={'size': 'x-large'},
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Neutral5.png')
patch_name = 'neutral 5 (.70 D)'
patch_sd = COLOURCHECKERS_SDS['ColorChecker N Ohta'][patch_name]
with domain_range_scale('1'):
XYZ = sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = XYZ_to_sRGB(XYZ)
plt.close(
plot_single_colour_swatch(ColourSwatch(patch_name.title(), RGB),
text_parameters={'size': 'x-large'},
**arguments)[0])
arguments['filename'] = os.path.join(output_directory,
'Tutorial_Colour_Checker.png')
plt.close(
plot_single_colour_checker(colour_checker='ColorChecker 2005',
text_parameters={'visible': False},
**arguments)[0])
arguments['filename'] = os.path.join(
output_directory, 'Tutorial_CIE_1931_Chromaticity_Diagram.png')
xy = XYZ_to_xy(XYZ)
plot_chromaticity_diagram_CIE1931(standalone=False)
x, y = xy
plt.plot(x, y, 'o-', color='white')
# Annotating the plot.
plt.annotate(patch_sd.name.title(),
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
plt.close(
render(standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True,
**arguments)[0])
# *************************************************************************
# "basics.rst"
# *************************************************************************
arguments['filename'] = os.path.join(output_directory,
'Basics_Logo_Small_001_CIE_XYZ.png')
RGB = read_image(os.path.join(output_directory, 'Logo_Small_001.png'))[...,
0:3]
XYZ = sRGB_to_XYZ(RGB)
plt.close(
plot_image(XYZ, text_parameters={'text': 'sRGB to XYZ'},
**arguments)[0])
def colour_quality_scale(
sd_test: SpectralDistribution,
additional_data: Boolean = False,
method: Union[Literal["NIST CQS 7.4", "NIST CQS 9.0"],
str] = "NIST CQS 9.0",
) -> Union[Floating, ColourRendering_Specification_CQS]:
"""
Return the *Colour Quality Scale* (CQS) of given spectral distribution
using given method.
Parameters
----------
sd_test
Test spectral distribution.
additional_data
Whether to output additional data.
method
Computation method.
Returns
-------
:class:`numpy.floating` or \
:class:`colour.quality.ColourRendering_Specification_CQS`
*Colour Quality Scale* (CQS).
References
----------
:cite:`Davis2010a`, :cite:`Ohno2008a`, :cite:`Ohno2013`
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> colour_quality_scale(sd) # doctest: +ELLIPSIS
64.1117031...
"""
method = validate_method(method, COLOUR_QUALITY_SCALE_METHODS)
# pylint: disable=E1102
cmfs = reshape_msds(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"],
SPECTRAL_SHAPE_DEFAULT,
)
shape = cmfs.shape
sd_test = reshape_sd(sd_test, shape)
vs_sds = {sd.name: reshape_sd(sd, shape) for sd in SDS_VS[method].values()}
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Ohno2013(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_vs_colorimetry_data = vs_colorimetry_data(sd_test,
sd_reference,
vs_sds,
cmfs,
chromatic_adaptation=True)
reference_vs_colorimetry_data = vs_colorimetry_data(
sd_reference, sd_reference, vs_sds, cmfs)
CCT_f: Floating
if method == "nist cqs 9.0":
CCT_f = 1
scaling_f = 3.2
else:
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
CCT_f = CCT_factor(reference_vs_colorimetry_data, XYZ_r)
scaling_f = 3.104
Q_as = colour_quality_scales(
test_vs_colorimetry_data,
reference_vs_colorimetry_data,
scaling_f,
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, scaling_f)
if method == "nist cqs 9.0":
scaling_f = 2.93 * 1.0343
else:
scaling_f = 2.928
Q_f = scale_conversion(D_E_RMS, CCT_f, scaling_f)
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 / GAMUT_AREA_D65 * 100
if method == "nist cqs 9.0":
Q_p = Q_d = None
else:
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 = as_float_scalar(100 - 3.6 * (D_Ep_RMS - p_delta_C))
Q_d = as_float_scalar(G_t / G_r * CCT_f * 100)
if additional_data:
return ColourRendering_Specification_CQS(
sd_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(sd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(DEFAULT_SPECTRAL_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
tcs_sds = {sd.name: sd.copy().align(shape) for sd in TCS_SDS.values()}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(sd_test,
sd_reference,
tcs_sds,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, 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(
sd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data))
else:
return Q_a
def generate_illuminants_rawtoaces_v1() -> CaseInsensitiveMapping:
"""
Generate a series of illuminants according to *RAW to ACES* v1:
- *CIE Illuminant D Series* in range [4000, 25000] kelvin degrees.
- *Blackbodies* in range [1000, 3500] kelvin degrees.
- A.M.P.A.S. variant of *ISO 7589 Studio Tungsten*.
Returns
-------
:class:`colour.utilities.CaseInsensitiveMapping`
Series of illuminants.
Notes
-----
- This definition introduces a few differences compared to
*RAW to ACES* v1: *CIE Illuminant D Series* are computed in range
[4002.15, 7003.77] kelvin degrees and the :math:`C_2` change is not
used in *RAW to ACES* v1.
References
----------
:cite:`Dyer2017`
Examples
--------
>>> list(sorted(generate_illuminants_rawtoaces_v1().keys()))
['1000K Blackbody', '1500K Blackbody', '2000K Blackbody', \
'2500K Blackbody', '3000K Blackbody', '3500K Blackbody', 'D100', 'D105', \
'D110', 'D115', 'D120', 'D125', 'D130', 'D135', 'D140', 'D145', 'D150', \
'D155', 'D160', 'D165', 'D170', 'D175', 'D180', 'D185', 'D190', 'D195', \
'D200', 'D205', 'D210', 'D215', 'D220', 'D225', 'D230', 'D235', 'D240', \
'D245', 'D250', 'D40', 'D45', 'D50', 'D55', 'D60', 'D65', 'D70', 'D75', \
'D80', 'D85', 'D90', 'D95', 'iso7589']
"""
global _ILLUMINANTS_RAWTOACES_V1
if _ILLUMINANTS_RAWTOACES_V1 is not None:
illuminants = _ILLUMINANTS_RAWTOACES_V1
else:
illuminants = CaseInsensitiveMapping()
# CIE Illuminants D Series from 4000K to 25000K.
for i in np.arange(4000, 25000 + 500, 500):
CCT = i * 1.4388 / 1.4380
xy = CCT_to_xy_CIE_D(CCT)
sd = sd_CIE_illuminant_D_series(xy)
sd.name = f"D{int(CCT / 100):d}"
illuminants[sd.name] = sd.align(SPECTRAL_SHAPE_RAWTOACES)
# TODO: Remove when removing the "colour.sd_blackbody" definition
# warning.
with suppress_warnings(colour_usage_warnings=True):
# Blackbody from 1000K to 4000K.
for i in np.arange(1000, 4000, 500):
sd = sd_blackbody(i, SPECTRAL_SHAPE_RAWTOACES)
illuminants[sd.name] = sd
# A.M.P.A.S. variant of ISO 7589 Studio Tungsten.
sd = read_sds_from_csv_file(
os.path.join(RESOURCES_DIRECTORY_RAWTOACES,
"AMPAS_ISO_7589_Tungsten.csv"))["iso7589"]
illuminants.update({sd.name: sd})
_ILLUMINANTS_RAWTOACES_V1 = illuminants
return illuminants
def plot_blackbody_colours(
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.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_colours(SpectralShape(150, 12500, 50)) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Blackbody_Colours.png
:align: center
:alt: plot_blackbody_colours
"""
_figure, axes = artist(**kwargs)
cmfs = first_item(filter_cmfs(cmfs).values())
colours = []
temperatures = []
for temperature in shape:
sd = sd_blackbody(temperature, cmfs.shape)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
colours.append(RGB)
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
padding = 0.1
axes.bar(
x=np.array(temperatures) - padding,
height=1,
width=shape.interval + (padding * shape.interval),
color=colours,
align='edge')
settings = {
'axes': axes,
'bounding_box': (x_min, x_max, y_min, y_max),
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': None,
}
settings.update(kwargs)
return render(**settings)
def colour_rendering_index(sd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
tcs_sds = {sd.name: sd.copy().align(shape) for sd in TCS_SDS.values()}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
sd_test, sd_reference, tcs_sds, cmfs, chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, 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(
sd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data))
else:
return Q_a
def plot_blackbody_colours(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.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_colours(SpectralShape(150, 12500, 50))
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Colours.png
:align: center
:alt: plot_blackbody_colours
"""
_figure, axes = artist(**kwargs)
cmfs = first_item(filter_cmfs(cmfs).values())
colours = []
temperatures = []
for temperature in shape:
sd = sd_blackbody(temperature, cmfs.shape)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
colours.append(RGB)
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
padding = 0.1
axes.bar(x=np.array(temperatures) - padding,
height=1,
width=shape.interval + (padding * shape.interval),
color=colours,
align='edge')
settings = {
'axes': axes,
'bounding_box': (x_min, x_max, y_min, y_max),
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': None,
}
settings.update(kwargs)
return render(**settings)
def plot_blackbody_colours(
shape: SpectralShape = SpectralShape(150, 12500, 50),
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot blackbody colours.
Parameters
----------
shape
Spectral shape to use as plot boundaries.
cmfs
Standard observer colour matching functions used for computing the
blackbody colours. ``cmfs`` can be of any type or form supported by the
:func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_blackbody_colours(SpectralShape(150, 12500, 50))
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Colours.png
:align: center
:alt: plot_blackbody_colours
"""
_figure, axes = artist(**kwargs)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
RGB = []
temperatures = []
for temperature in shape:
sd = sd_blackbody(temperature, cmfs.shape)
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs)
RGB.append(normalise_maximum(XYZ_to_plotting_colourspace(XYZ)))
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
padding = 0.1
axes.bar(
x=np.array(temperatures) - padding,
height=1,
width=shape.interval + (padding * shape.interval),
color=RGB,
align="edge",
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
settings: Dict[str, Any] = {
"axes": axes,
"bounding_box": (x_min, x_max, y_min, y_max),
"title": "Blackbody Colours",
"x_label": "Temperature K",
"y_label": None,
}
settings.update(kwargs)
return render(**settings)