def visible_spectrum_plot(cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
**kwargs):
"""
Plots the visible colours spectrum using given standard observer *CIE XYZ*
colour matching functions.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
out_of_gamut_clipping : bool, optional
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother.
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.
References
----------
- :cite:`Spiker2015a`
Examples
--------
>>> visible_spectrum_plot() # doctest: +SKIP
"""
settings = {'y_label': None, 'y_ticker': False, 'standalone': False}
single_spd_plot(ones_spd(DEFAULT_SPECTRAL_SHAPE),
cmfs=cmfs,
out_of_gamut_clipping=out_of_gamut_clipping,
**settings)
cmfs = get_cmfs(cmfs)
settings = {
'title': 'The Visible Spectrum - {0}'.format(cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
'standalone': True
}
settings.update(kwargs)
return render(**settings)
def test_spectral_to_aces_relative_exposure_values(self):
"""
Tests :func:`colour.models.rgb.aces_it.
spectral_to_aces_relative_exposure_values` definition.
"""
shape = ACES_RICD.shape
grey_reflector = constant_spd(0.18, shape)
np.testing.assert_almost_equal(
spectral_to_aces_relative_exposure_values(grey_reflector),
np.array([0.18, 0.18, 0.18]))
perfect_reflector = ones_spd(shape)
np.testing.assert_almost_equal(
spectral_to_aces_relative_exposure_values(perfect_reflector),
np.array([0.97783784, 0.97783784, 0.97783784]))
dark_skin = (COLOURCHECKERS_SPDS['ColorChecker N Ohta']['dark skin'])
np.testing.assert_almost_equal(
spectral_to_aces_relative_exposure_values(dark_skin),
np.array([0.11876978, 0.08708666, 0.0589442]))
def test_spectral_to_aces_relative_exposure_values(self):
"""
Tests :func:`colour.models.rgb.aces_it.
spectral_to_aces_relative_exposure_values` definition.
"""
shape = ACES_RICD.shape
grey_reflector = constant_spd(0.18, shape)
np.testing.assert_almost_equal(
spectral_to_aces_relative_exposure_values(grey_reflector),
np.array([0.18, 0.18, 0.18]))
perfect_reflector = ones_spd(shape)
np.testing.assert_almost_equal(
spectral_to_aces_relative_exposure_values(perfect_reflector),
np.array([0.97783784, 0.97783784, 0.97783784]))
dark_skin = (
COLOURCHECKERS_SPDS.get('ColorChecker N Ohta').get('dark skin'))
np.testing.assert_almost_equal(
spectral_to_aces_relative_exposure_values(dark_skin),
np.array([0.11876978, 0.08708666, 0.0589442]))
def spectral_to_XYZ(
spd,
cmfs=STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'],
illuminant=ones_spd(ASTME30815_PRACTISE_SHAPE),
method='ASTM E308-15',
**kwargs):
"""
Converts given spectral power distribution to *CIE XYZ* tristimulus values
using given colour matching functions, illuminant and method.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
Illuminant spectral power distribution.
method : unicode, optional
**{'ASTM E308-15', 'Integration'}**,
Computation method.
Other Parameters
----------------
use_practice_range : bool, optional
{:func:`spectral_to_XYZ_ASTME30815`},
Practise *ASTM E308-15* working wavelengths range is [360, 780],
if `True` this argument will trim the colour matching functions
appropriately.
mi_5nm_omission_method : bool, optional
{:func:`spectral_to_XYZ_ASTME30815`},
5 nm measurement intervals spectral power distribution conversion to
tristimulus values will use a 5 nm version of the colour matching
functions instead of a table of tristimulus weighting factors.
mi_20nm_interpolation_method : bool, optional
{:func:`spectral_to_XYZ_ASTME30815`},
20 nm measurement intervals spectral power distribution conversion to
tristimulus values will use a dedicated interpolation method instead
of a table of tristimulus weighting factors.
Returns
-------
ndarray, (3,)
*CIE XYZ* tristimulus values.
Warning
-------
The output range of that definition is non standard!
Notes
-----
- Output *CIE XYZ* tristimulus values are in range [0, 100].
Examples
--------
>>> from colour import (
... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> data = {
... 400: 0.0641,
... 420: 0.0645,
... 440: 0.0562,
... 460: 0.0537,
... 480: 0.0559,
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360,
... 620: 0.1511,
... 640: 0.1688,
... 660: 0.1996,
... 680: 0.2397,
... 700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
>>> spectral_to_XYZ( # doctest: +ELLIPSIS
... spd, cmfs, illuminant)
array([ 11.5290265..., 9.9502091..., 4.7098882...])
>>> spectral_to_XYZ( # doctest: +ELLIPSIS
... spd, cmfs, illuminant, use_practice_range=False)
array([ 11.5291275..., 9.9502369..., 4.7098811...])
>>> spectral_to_XYZ( # doctest: +ELLIPSIS
... spd, cmfs, illuminant, method='Integration')
array([ 11.5296285..., 9.9499467..., 4.7066079...])
"""
function = SPECTRAL_TO_XYZ_METHODS[method]
filter_kwargs(function, **kwargs)
return function(spd, cmfs, illuminant, **kwargs)
def spectral_to_XYZ_ASTME30815(
spd,
cmfs=STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'],
illuminant=ones_spd(ASTME30815_PRACTISE_SHAPE),
use_practice_range=True,
mi_5nm_omission_method=True,
mi_20nm_interpolation_method=True):
"""
Converts given spectral power distribution to *CIE XYZ* tristimulus values
using given colour matching functions and illuminant accordingly to
practise *ASTM E308-15* method [2]_.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
Illuminant spectral power distribution.
use_practice_range : bool, optional
Practise *ASTM E308-15* working wavelengths range is [360, 780],
if `True` this argument will trim the colour matching functions
appropriately.
mi_5nm_omission_method : bool, optional
5 nm measurement intervals spectral power distribution conversion to
tristimulus values will use a 5 nm version of the colour matching
functions instead of a table of tristimulus weighting factors.
mi_20nm_interpolation_method : bool, optional
20 nm measurement intervals spectral power distribution conversion to
tristimulus values will use a dedicated interpolation method instead
of a table of tristimulus weighting factors.
Returns
-------
ndarray, (3,)
*CIE XYZ* tristimulus values.
Warning
-------
- The tables of tristimulus weighting factors are cached in
:attr:`_TRISTIMULUS_WEIGHTING_FACTORS_CACHE` attribute. Their
identifier key is defined by the colour matching functions and
illuminant names along the current shape such as:
`CIE 1964 10 Degree Standard Observer, A, (360.0, 830.0, 10.0)`
Considering the above, one should be mindful that using similar colour
matching functions and illuminant names but with different spectral
data will lead to unexpected behaviour.
- The output range of that definition is non standard!
Notes
-----
- Output *CIE XYZ* tristimulus values are in range [0, 100].
Examples
--------
>>> from colour import (
... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> data = {
... 400: 0.0641,
... 420: 0.0645,
... 440: 0.0562,
... 460: 0.0537,
... 480: 0.0559,
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360,
... 620: 0.1511,
... 640: 0.1688,
... 660: 0.1996,
... 680: 0.2397,
... 700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
>>> spectral_to_XYZ_ASTME30815(
... spd, cmfs, illuminant) # doctest: +ELLIPSIS
array([ 11.5290265..., 9.9502091..., 4.7098882...])
"""
if spd.shape.interval not in (1, 5, 10, 20):
raise ValueError(
'Tristimulus values conversion from spectral data accordingly to '
'practise "ASTM E308-15" should be performed on spectral data '
'with measurement interval of 1, 5, 10 or 20nm!')
if use_practice_range:
cmfs = cmfs.clone().trim_wavelengths(ASTME30815_PRACTISE_SHAPE)
method = spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815
if spd.shape.interval == 1:
method = spectral_to_XYZ_integration
elif spd.shape.interval == 5 and mi_5nm_omission_method:
if cmfs.shape.interval != 5:
cmfs = cmfs.clone().interpolate(SpectralShape(interval=5))
method = spectral_to_XYZ_integration
elif spd.shape.interval == 20 and mi_20nm_interpolation_method:
spd = spd.clone()
if spd.shape.boundaries != cmfs.shape.boundaries:
warning(
'Trimming "{0}" spectral power distribution shape to "{1}" '
'colour matching functions shape.'.format(illuminant, cmfs))
spd.trim_wavelengths(cmfs.shape)
# Extrapolation of additional 20nm padding intervals.
spd.align(SpectralShape(spd.shape.start - 20, spd.shape.end + 20, 10))
for i in range(2):
spd[spd.wavelengths[i]] = (3 * spd.values[i + 2] -
3 * spd.values[i + 4] +
spd.values[i + 6])
i_e = len(spd) - 1 - i
spd[spd.wavelengths[i_e]] = (spd.values[i_e - 6] -
3 * spd.values[i_e - 4] +
3 * spd.values[i_e - 2])
# Interpolating every odd numbered values.
# TODO: Investigate code vectorisation.
for i in range(3, len(spd) - 3, 2):
spd[spd.wavelengths[i]] = (-0.0625 * spd.values[i - 3] +
0.5625 * spd.values[i - 1] +
0.5625 * spd.values[i + 1] -
0.0625 * spd.values[i + 3])
# Discarding the additional 20nm padding intervals.
spd.trim_wavelengths(SpectralShape(spd.shape.start + 20,
spd.shape.end - 20,
10))
XYZ = method(spd, cmfs, illuminant)
return XYZ
def spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
spd,
cmfs=STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'],
illuminant=ones_spd(ASTME30815_PRACTISE_SHAPE)):
"""
Converts given spectral power distribution to *CIE XYZ* tristimulus values
using given colour matching functions and illuminant using a table
of tristimulus weighting factors accordingly to practise
*ASTM E308-15* method [2]_.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
Illuminant spectral power distribution.
Returns
-------
ndarray, (3,)
*CIE XYZ* tristimulus values.
Warning
-------
The output range of that definition is non standard!
Notes
-----
- Output *CIE XYZ* tristimulus values are in range [0, 100].
Examples
--------
>>> from colour import (
... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> data = {
... 400: 0.0641,
... 420: 0.0645,
... 440: 0.0562,
... 460: 0.0537,
... 480: 0.0559,
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360,
... 620: 0.1511,
... 640: 0.1688,
... 660: 0.1996,
... 680: 0.2397,
... 700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
>>> spectral_to_XYZ_tristimulus_weighting_factors_ASTME30815(
... spd, cmfs, illuminant) # doctest: +ELLIPSIS
array([ 11.5296311..., 9.9505845..., 4.7098037...])
"""
if illuminant.shape != cmfs.shape:
warning('Aligning "{0}" illuminant shape to "{1}" colour matching '
'functions shape.'.format(illuminant, cmfs))
illuminant = illuminant.clone().align(cmfs.shape)
if spd.shape.boundaries != cmfs.shape.boundaries:
warning('Trimming "{0}" spectral power distribution shape to "{1}" '
'colour matching functions shape.'.format(illuminant, cmfs))
spd = spd.clone().trim_wavelengths(cmfs.shape)
W = tristimulus_weighting_factors_ASTME202211(
cmfs, illuminant, SpectralShape(
cmfs.shape.start, cmfs.shape.end, spd.shape.interval))
start_w = cmfs.shape.start
end_w = cmfs.shape.start + spd.shape.interval * (W.shape[0] - 1)
W = adjust_tristimulus_weighting_factors_ASTME30815(
W, SpectralShape(start_w, end_w, spd.shape.interval), spd.shape)
R = spd.values
XYZ = np.sum(W * R[..., np.newaxis], axis=0)
return XYZ
def spectral_to_XYZ_integration(
spd,
cmfs=STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer'],
illuminant=ones_spd(
STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer'].shape)):
"""
Converts given spectral power distribution to *CIE XYZ* tristimulus values
using given colour matching functions and illuminant accordingly to
classical integration method.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
Illuminant spectral power distribution.
Returns
-------
ndarray, (3,)
*CIE XYZ* tristimulus values.
Warning
-------
The output range of that definition is non standard!
Notes
-----
- Output *CIE XYZ* tristimulus values are in range [0, 100].
References
----------
.. [3] Wyszecki, G., & Stiles, W. S. (2000). Integration Replace by
Summation. In Color Science: Concepts and Methods, Quantitative
Data and Formulae (pp. 158–163). Wiley. ISBN:978-0471399186
Examples
--------
>>> from colour import (
... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> data = {
... 400: 0.0641,
... 420: 0.0645,
... 440: 0.0562,
... 460: 0.0537,
... 480: 0.0559,
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360,
... 620: 0.1511,
... 640: 0.1688,
... 660: 0.1996,
... 680: 0.2397,
... 700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
>>> spectral_to_XYZ_integration( # doctest: +ELLIPSIS
... spd, cmfs, illuminant)
array([ 11.5296285..., 9.9499467..., 4.7066079...])
"""
if illuminant.shape != cmfs.shape:
warning('Aligning "{0}" illuminant shape to "{1}" colour matching '
'functions shape.'.format(illuminant, cmfs))
illuminant = illuminant.clone().align(cmfs.shape)
if spd.shape != cmfs.shape:
warning('Aligning "{0}" spectral power distribution shape to "{1}" '
'colour matching functions shape.'.format(spd, cmfs))
spd = spd.clone().align(cmfs.shape)
S = illuminant.values
x_bar, y_bar, z_bar = tsplit(cmfs.values)
R = spd.values
dw = cmfs.shape.interval
k = 100 / (np.sum(y_bar * S) * dw)
X_p = R * x_bar * S * dw
Y_p = R * y_bar * S * dw
Z_p = R * z_bar * S * dw
XYZ = k * np.sum(np.array([X_p, Y_p, Z_p]), axis=-1)
return XYZ
def spectral_to_XYZ(spd,
cmfs=STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer'),
illuminant=ones_spd(
STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer').shape),
method='ASTM E308–15',
**kwargs):
"""
Converts given spectral power distribution to *CIE XYZ* tristimulus values
using given colour matching functions, illuminant and method.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
Illuminant spectral power distribution.
method : unicode, optional
**{'ASTM E308–15', 'Integration'}**,
Computation method.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray, (3,)
*CIE XYZ* tristimulus values.
Warning
-------
The output range of that definition is non standard!
Notes
-----
- Output *CIE XYZ* tristimulus values are in range [0, 100].
Examples
--------
>>> from colour import (
... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
>>> data = {
... 400: 0.0641,
... 420: 0.0645,
... 440: 0.0562,
... 460: 0.0537,
... 480: 0.0559,
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360,
... 620: 0.1511,
... 640: 0.1688,
... 660: 0.1996,
... 680: 0.2397,
... 700: 0.2852}
>>> spd = SpectralPowerDistribution('Sample', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D50')
>>> spectral_to_XYZ( # doctest: +ELLIPSIS
... spd, cmfs, illuminant)
array([ 11.5290265..., 9.9502091..., 4.7098882...])
>>> spectral_to_XYZ( # doctest: +ELLIPSIS
... spd, cmfs, illuminant, use_practice_range=False)
array([ 11.5291275..., 9.9502369..., 4.7098811...])
>>> spectral_to_XYZ( # doctest: +ELLIPSIS
... spd, cmfs, illuminant, method='Integration')
array([ 11.5296285..., 9.9499467..., 4.7066079...])
"""
function = SPECTRAL_TO_XYZ_METHODS[method]
filter_kwargs(function, **kwargs)
return function(spd, cmfs, illuminant, **kwargs)
def XYZ_to_spectral_Meng2015(
XYZ,
cmfs=STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'],
interval=5,
tolerance=1e-10,
maximum_iterations=2000):
"""
Recovers the spectral power distribution of given *CIE XYZ* tristimulus
values using *Meng et alii (2015)* method.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* tristimulus values to recover the spectral power distribution
from.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
interval : numeric, optional
Wavelength :math:`\lambda_{i}` range interval in nm. The smaller
``interval`` is, the longer the computations will be.
tolerance : numeric, optional
Tolerance for termination. The lower ``tolerance`` is, the smoother
the recovered spectral power distribution will be.
maximum_iterations : int, optional
Maximum number of iterations to perform.
Returns
-------
SpectralPowerDistribution
Recovered spectral power distribution.
Notes
-----
- The definition used to convert spectrum to *CIE XYZ* tristimulus
values is :func:`colour.colorimetry.spectral_to_XYZ_integration`
definition because it processes any measurement interval opposed to
:func:`colour.colorimetry.spectral_to_XYZ_ASTME30815` definition that
handles only measurement interval of 1, 5, 10 or 20nm.
References
----------
- :cite:`Meng2015c`
Examples
--------
>>> from colour.utilities import numpy_print_options
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> spd = XYZ_to_spectral_Meng2015(XYZ, interval=10)
>>> with numpy_print_options(suppress=True):
... spd # doctest: +ELLIPSIS
SpectralPowerDistribution([[ 360. , 0.0788075...],
[ 370. , 0.0788543...],
[ 380. , 0.0788825...],
[ 390. , 0.0788714...],
[ 400. , 0.0788911...],
[ 410. , 0.07893 ...],
[ 420. , 0.0797471...],
[ 430. , 0.0813339...],
[ 440. , 0.0840145...],
[ 450. , 0.0892826...],
[ 460. , 0.0965359...],
[ 470. , 0.1053176...],
[ 480. , 0.1150921...],
[ 490. , 0.1244252...],
[ 500. , 0.1326083...],
[ 510. , 0.1390282...],
[ 520. , 0.1423548...],
[ 530. , 0.1414636...],
[ 540. , 0.1365195...],
[ 550. , 0.1277319...],
[ 560. , 0.1152622...],
[ 570. , 0.1004513...],
[ 580. , 0.0844187...],
[ 590. , 0.0686863...],
[ 600. , 0.0543013...],
[ 610. , 0.0423486...],
[ 620. , 0.0333861...],
[ 630. , 0.0273558...],
[ 640. , 0.0233407...],
[ 650. , 0.0211208...],
[ 660. , 0.0197248...],
[ 670. , 0.0187157...],
[ 680. , 0.0181510...],
[ 690. , 0.0179691...],
[ 700. , 0.0179247...],
[ 710. , 0.0178665...],
[ 720. , 0.0178005...],
[ 730. , 0.0177570...],
[ 740. , 0.0177090...],
[ 750. , 0.0175743...],
[ 760. , 0.0175058...],
[ 770. , 0.0174492...],
[ 780. , 0.0174984...],
[ 790. , 0.0175667...],
[ 800. , 0.0175657...],
[ 810. , 0.0175319...],
[ 820. , 0.0175184...],
[ 830. , 0.0175390...]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={...})
>>> spectral_to_XYZ_integration(spd) / 100 # doctest: +ELLIPSIS
array([ 0.0705100..., 0.1007987..., 0.0956738...])
"""
XYZ = np.asarray(XYZ)
shape = SpectralShape(cmfs.shape.start, cmfs.shape.end, interval)
cmfs = cmfs.copy().align(shape)
illuminant = ones_spd(shape)
spd = ones_spd(shape)
def function_objective(a):
"""
Objective function.
"""
return np.sum(np.diff(a) ** 2)
def function_constraint(a):
"""
Function defining the constraint.
"""
spd[:] = a
return spectral_to_XYZ_integration(
spd, cmfs=cmfs, illuminant=illuminant) - XYZ
wavelengths = spd.wavelengths
bins = wavelengths.size
constraints = {'type': 'eq', 'fun': function_constraint}
bounds = np.tile(np.array([0, 1000]), (bins, 1))
result = minimize(
function_objective,
spd.values,
method='SLSQP',
constraints=constraints,
bounds=bounds,
options={'ftol': tolerance,
'maxiter': maximum_iterations})
if not result.success:
raise RuntimeError(
'Optimization failed for {0} after {1} iterations: "{2}".'.format(
XYZ, result.nit, result.message))
return SpectralPowerDistribution(
dict(zip(wavelengths, result.x * 100)),
name='Meng (2015) - {0}'.format(XYZ))
def spectral_to_XYZ(spd,
cmfs=STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer'),
illuminant=None):
"""
Converts given spectral power distribution to *CIE XYZ* tristimulus values
using given colour matching functions and illuminant.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
*Illuminant* spectral power distribution.
Returns
-------
ndarray, (3,)
*CIE XYZ* tristimulus values.
Warning
-------
The output domain of that definition is non standard!
Notes
-----
- Output *CIE XYZ* tristimulus values are in domain [0, 100].
References
----------
.. [1] Wyszecki, G., & Stiles, W. S. (2000). Integration Replace by
Summation. In Color Science: Concepts and Methods, Quantitative
Data and Formulae (pp. 158–163). Wiley. ISBN:978-0471399186
Examples
--------
>>> from colour import (
... CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution)
>>> cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
>>> data = {380: 0.0600, 390: 0.0600}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D50')
>>> spectral_to_XYZ(spd, cmfs, illuminant) # doctest: +ELLIPSIS
array([ 4.5764852...e-04, 1.2964866...e-05, 2.1615807...e-03])
"""
shape = cmfs.shape
if spd.shape != cmfs.shape:
spd = spd.clone().zeros(shape)
if illuminant is None:
illuminant = ones_spd(shape)
else:
if illuminant.shape != cmfs.shape:
illuminant = illuminant.clone().zeros(shape)
spd = spd.values
x_bar, y_bar, z_bar = (cmfs.x_bar.values,
cmfs.y_bar.values,
cmfs.z_bar.values)
illuminant = illuminant.values
x_products = spd * x_bar * illuminant
y_products = spd * y_bar * illuminant
z_products = spd * z_bar * illuminant
normalising_factor = 100 / np.sum(y_bar * illuminant)
XYZ = np.array([normalising_factor * np.sum(x_products),
normalising_factor * np.sum(y_products),
normalising_factor * np.sum(z_products)])
return XYZ
def spectral_to_XYZ(spd,
cmfs=STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer'),
illuminant=None):
"""
Converts given spectral power distribution to *CIE XYZ* colourspace using
given colour matching functions and illuminant.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
*Illuminant* spectral power distribution.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Warning
-------
The output domain of that definition is non standard!
Notes
-----
- Output *CIE XYZ* colourspace matrix is in domain [0, 100].
References
----------
.. [1] **Wyszecki & Stiles**,
*Color Science - Concepts and Methods Data and Formulae -
Second Edition*,
Wiley Classics Library Edition, published 2000,
ISBN-10: 0-471-39918-3,
page 158.
Examples
--------
>>> from colour import CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution # noqa
>>> cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
>>> data = {380: 0.0600, 390: 0.0600}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D50')
>>> spectral_to_XYZ(spd, cmfs, illuminant) # doctest: +ELLIPSIS
array([ 4.5764852...e-04, 1.2964866...e-05, 2.1615807...e-03])
"""
shape = cmfs.shape
if spd.shape != cmfs.shape:
spd = spd.clone().zeros(shape)
if illuminant is None:
illuminant = ones_spd(shape)
else:
if illuminant.shape != cmfs.shape:
illuminant = illuminant.clone().zeros(shape)
illuminant = illuminant.values
spd = spd.values
x_bar, y_bar, z_bar = (cmfs.x_bar.values,
cmfs.y_bar.values,
cmfs.z_bar.values)
x_products = spd * x_bar * illuminant
y_products = spd * y_bar * illuminant
z_products = spd * z_bar * illuminant
normalising_factor = 100 / np.sum(y_bar * illuminant)
XYZ = np.array([normalising_factor * np.sum(x_products),
normalising_factor * np.sum(y_products),
normalising_factor * np.sum(z_products)])
return XYZ
def spectral_to_XYZ(spd,
cmfs=STANDARD_OBSERVERS_CMFS.get(
'CIE 1931 2 Degree Standard Observer'),
illuminant=None):
"""
Converts given spectral power distribution to *CIE XYZ* colourspace using
given colour matching functions and illuminant.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
illuminant : SpectralPowerDistribution, optional
*Illuminant* spectral power distribution.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Warning
-------
The output domain of that definition is non standard!
Notes
-----
- Output *CIE XYZ* colourspace matrix is in domain [0, 100].
References
----------
.. [1] **Wyszecki & Stiles**,
*Color Science - Concepts and Methods Data and Formulae -
Second Edition*,
Wiley Classics Library Edition, published 2000,
ISBN-10: 0-471-39918-3,
page 158.
Examples
--------
>>> from colour import CMFS, ILLUMINANTS_RELATIVE_SPDS, SpectralPowerDistribution # noqa
>>> cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
>>> data = {380: 0.0600, 390: 0.0600}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D50')
>>> spectral_to_XYZ(spd, cmfs, illuminant) # doctest: +ELLIPSIS
array([ 4.5764852...e-04, 1.2964866...e-05, 2.1615807...e-03])
"""
shape = cmfs.shape
if spd.shape != cmfs.shape:
spd = spd.clone().zeros(shape)
if illuminant is None:
illuminant = ones_spd(shape)
else:
if illuminant.shape != cmfs.shape:
illuminant = illuminant.clone().zeros(shape)
illuminant = illuminant.values
spd = spd.values
x_bar, y_bar, z_bar = (cmfs.x_bar.values, cmfs.y_bar.values,
cmfs.z_bar.values)
x_products = spd * x_bar * illuminant
y_products = spd * y_bar * illuminant
z_products = spd * z_bar * illuminant
normalising_factor = 100 / np.sum(y_bar * illuminant)
XYZ = np.array([
normalising_factor * np.sum(x_products),
normalising_factor * np.sum(y_products),
normalising_factor * np.sum(z_products)
])
return XYZ
