def corresponding_chromaticities_prediction_Fairchild1990(experiment=1,
**kwargs):
"""
Returns the corresponding chromaticities prediction for Fairchild (1990)
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2089528677990308, 0.47240345174230519)),
((0.449, 0.511), (0.43756528098582792, 0.51210303139041924)),
((0.263, 0.505), (0.26213623665658092, 0.49725385033264224)),
((0.322, 0.545), (0.3235312762825191, 0.54756652922585702)),
((0.316, 0.537), (0.3151390992740366, 0.53983332031574016)),
((0.265, 0.553), (0.26347459238415272, 0.55443357809543037)),
((0.221, 0.538), (0.22115956537655593, 0.53244703908294599)),
((0.135, 0.532), (0.13969494108553854, 0.52072342107668024)),
((0.145, 0.472), (0.1512288710743511, 0.45330415352961834)),
((0.163, 0.331), (0.17156913711903982, 0.30262647410866889)),
((0.176, 0.431), (0.18257922398137369, 0.40778921192793854)),
((0.244, 0.349), (0.24189049501108895, 0.34134012046930529))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_r = XYZ_to_xy(XYZ_r)
Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_Fairchild1990(
XYZ_1, XYZ_n, XYZ_r, Y_n)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def test_n_dimensional_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition n-dimensions
support.
"""
xy = np.array([0.26414772236966133, 0.37770000704815188])
XYZ = np.array([0.69935853, 1., 0.94824534])
np.testing.assert_almost_equal(
xy_to_XYZ(xy),
XYZ,
decimal=7)
xy = np.tile(xy, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(
xy_to_XYZ(xy),
XYZ,
decimal=7)
xy = np.reshape(xy, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(
xy_to_XYZ(xy),
XYZ,
decimal=7)
def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1, **kwargs):
"""
Returns the corresponding chromaticities prediction for CMCCAT2000
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.20832101929657834, 0.47271680534693694)),
((0.449, 0.511), (0.44592707020371486, 0.50777351504395707)),
((0.263, 0.505), (0.26402624712986333, 0.4955361681706304)),
((0.322, 0.545), (0.33168840090358015, 0.54315801981008516)),
((0.316, 0.537), (0.32226245779851387, 0.53576245377085929)),
((0.265, 0.553), (0.27107058097430181, 0.5501997842556422)),
((0.221, 0.538), (0.22618269421847523, 0.52947407170848704)),
((0.135, 0.532), (0.14396930475660724, 0.51909841743126817)),
((0.145, 0.472), (0.14948357434418671, 0.45567605010224305)),
((0.163, 0.331), (0.15631720730028753, 0.31641514460738623)),
((0.176, 0.431), (0.17631993066748047, 0.41275893424542082)),
((0.244, 0.349), (0.22876382018951744, 0.3499324084859976))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CMCCAT2000(
XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for Von Kries
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
transform : unicode, optional
{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp', 'Fairchild,
'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco', 'Bianco PC'},
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.20820148430638033, 0.47229226819364528)),
((0.449, 0.511), (0.44891022948064191, 0.50716028901449561)),
((0.263, 0.505), (0.26435459360846608, 0.49596314494922683)),
((0.322, 0.545), (0.33487309037107632, 0.54712207251983425)),
((0.316, 0.537), (0.32487581236911361, 0.53905899356457776)),
((0.265, 0.553), (0.27331050571632376, 0.55550280647813977)),
((0.221, 0.538), (0.22714800102072819, 0.53313179748041983)),
((0.135, 0.532), (0.14427303768336433, 0.52268044497913713)),
((0.145, 0.472), (0.14987451889726533, 0.45507852741116867)),
((0.163, 0.331), (0.15649757464732098, 0.31487959772753954)),
((0.176, 0.431), (0.17605936460371163, 0.41037722722471409)),
((0.244, 0.349), (0.22598059059292835, 0.34652914678030416))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1):
"""
Returns the corresponding chromaticities prediction for CMCCAT2000
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2083210..., 0.4727168...)),
((0.449, 0.511), (0.4459270..., 0.5077735...)),
((0.263, 0.505), (0.2640262..., 0.4955361...)),
((0.322, 0.545), (0.3316884..., 0.5431580...)),
((0.316, 0.537), (0.3222624..., 0.5357624...)),
((0.265, 0.553), (0.2710705..., 0.5501997...)),
((0.221, 0.538), (0.2261826..., 0.5294740...)),
((0.135, 0.532), (0.1439693..., 0.5190984...)),
((0.145, 0.472), (0.1494835..., 0.4556760...)),
((0.163, 0.331), (0.1563172..., 0.3164151...)),
((0.176, 0.431), (0.1763199..., 0.4127589...)),
((0.244, 0.349), (0.2287638..., 0.3499324...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CMCCAT2000(
XYZ_1, XYZ_w, XYZ_wr, L_A1, L_A2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_Fairchild1990(experiment=1):
"""
Returns the corresponding chromaticities prediction for Fairchild (1990)
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2089528..., 0.4724034...)),
((0.449, 0.511), (0.4375652..., 0.5121030...)),
((0.263, 0.505), (0.2621362..., 0.4972538...)),
((0.322, 0.545), (0.3235312..., 0.5475665...)),
((0.316, 0.537), (0.3151390..., 0.5398333...)),
((0.265, 0.553), (0.2634745..., 0.5544335...)),
((0.221, 0.538), (0.2211595..., 0.5324470...)),
((0.135, 0.532), (0.1396949..., 0.5207234...)),
((0.145, 0.472), (0.1512288..., 0.4533041...)),
((0.163, 0.331), (0.1715691..., 0.3026264...)),
((0.176, 0.431), (0.1825792..., 0.4077892...)),
((0.244, 0.349), (0.2418904..., 0.3413401...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t)) * 100
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m)) * 100
xy_r = XYZ_to_xy(XYZ_r)
Y_n = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_Fairchild1990(
XYZ_1, XYZ_n, XYZ_r, Y_n)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def test_nan_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=2))
for case in cases:
xy = np.array(case)
xy_to_XYZ(xy)
def RGB_to_RGB(RGB,
input_colourspace,
output_colourspace,
chromatic_adaptation_transform='CAT02'):
"""
Converts from given input *RGB* colourspace to output *RGB* colourspace
using given *chromatic adaptation* method.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild, 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
*RGB* colourspace array.
Notes
-----
- *RGB* colourspace arrays are in domain [0, 1].
Examples
--------
>>> from colour import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB = np.array([0.01103604, 0.12734466, 0.11631037])
>>> RGB_to_RGB(
... RGB,
... sRGB_COLOURSPACE,
... PROPHOTO_RGB_COLOURSPACE) # doctest: +ELLIPSIS
array([ 0.0643338..., 0.1157362..., 0.1157614...])
"""
cat = chromatic_adaptation_matrix_VonKries(
xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_transform)
M = dot_matrix(cat, input_colourspace.RGB_to_XYZ_matrix)
M = dot_matrix(output_colourspace.XYZ_to_RGB_matrix, M)
RGB = dot_vector(M, RGB)
return RGB
def chromatically_adapted_primaries(primaries,
whitepoint_t,
whitepoint_r,
chromatic_adaptation_transform='CAT02'):
"""
Chromatically adapts given *primaries* :math:`xy` chromaticity coordinates
from test ``whitepoint_t`` to reference ``whitepoint_r``.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint_t : array_like
Test illuminant / whitepoint :math:`xy` chromaticity coordinates.
whitepoint_r : array_like
Reference illuminant / whitepoint :math:`xy` chromaticity coordinates.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Chromatically adapted primaries :math:`xy` chromaticity coordinates.
Examples
--------
>>> p = np.array([0.64, 0.33, 0.30, 0.60, 0.15, 0.06])
>>> w_t = np.array([0.31270, 0.32900])
>>> w_r = np.array([0.34570, 0.35850])
>>> chromatic_adaptation_transform = 'Bradford'
>>> chromatically_adapted_primaries(p, w_t, w_r,
... chromatic_adaptation_transform)
... # doctest: +ELLIPSIS
array([[ 0.6484414..., 0.3308533...],
[ 0.3211951..., 0.5978443...],
[ 0.1558932..., 0.0660492...]])
"""
primaries = np.reshape(primaries, (3, 2))
XYZ_a = chromatic_adaptation_VonKries(
xy_to_XYZ(primaries), xy_to_XYZ(whitepoint_t), xy_to_XYZ(whitepoint_r),
chromatic_adaptation_transform)
P_a = XYZ_to_xyY(XYZ_a)[..., 0:2]
return P_a
def RGB_to_RGB_matrix(input_colourspace,
output_colourspace,
chromatic_adaptation_transform='CAT02'):
"""
Computes the matrix :math:`M` converting from given input *RGB*
colourspace to output *RGB* colourspace using given *chromatic
adaptation* method.
Parameters
----------
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC', None}**,
*Chromatic adaptation* transform, if *None* no chromatic adaptation is
performed.
Returns
-------
ndarray
Conversion matrix :math:`M`.
Examples
--------
>>> from colour.models import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB_to_RGB_matrix(sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE)
... # doctest: +ELLIPSIS
array([[ 0.5288241..., 0.3340609..., 0.1373616...],
[ 0.0975294..., 0.8790074..., 0.0233981...],
[ 0.0163599..., 0.1066124..., 0.8772485...]])
"""
M = input_colourspace.RGB_to_XYZ_matrix
if chromatic_adaptation_transform is not None:
M_CAT = chromatic_adaptation_matrix_VonKries(
xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_transform)
M = dot_matrix(M_CAT, input_colourspace.RGB_to_XYZ_matrix)
M = dot_matrix(output_colourspace.XYZ_to_RGB_matrix, M)
return M
def test_n_dimensional_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition n-dimensional
support.
"""
xy = np.array([0.54369557, 0.32107944])
XYZ = xy_to_XYZ(xy)
xy = np.tile(xy, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
xy = np.reshape(xy, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
def CCT_factor(reference_data, XYZ_r):
"""
Returns the correlated colour temperature factor penalizing lamps with
extremely low correlated colour temperatures.
Parameters
----------
reference_data : VS_ColorimetryData
Reference colorimetry data.
XYZ_r : array_like
*CIE XYZ* tristimulus values for reference.
Returns
-------
numeric
Correlated colour temperature factor.
"""
xy_w = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
XYZ_w = xy_to_XYZ(xy_w)
Labs = []
for vs_colorimetry_data_ in reference_data:
_name, XYZ, _Lab, _C = vs_colorimetry_data_
XYZ_a = chromatic_adaptation_VonKries(
XYZ, XYZ_r, XYZ_w, transform='CMCCAT2000')
Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w)
Labs.append(Lab)
G_r = gamut_area(Labs) / D65_GAMUT_AREA
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def test_n_dimensional_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition n-dimensional
support.
"""
xy = np.array([0.54369557, 0.32107944])
XYZ = xy_to_XYZ(xy)
xy = np.tile(xy, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
xy = np.reshape(xy, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
def xy_to_ij(xy):
"""
Converts given *CIE xy* chromaticity coordinates to *ij*
chromaticity coordinates.
"""
return UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
def RGB_to_RGB(RGB,
input_colourspace,
output_colourspace,
chromatic_adaptation_method='CAT02'):
"""
Converts from given input *RGB* colourspace to output *RGB* colourspace
using given *chromatic adaptation* method.
Parameters
----------
RGB : array_like, (3,)
*RGB* colourspace matrix.
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_method : unicode, optional
('XYZ Scaling', 'Bradford', 'Von Kries', 'Fairchild', 'CAT02')
*Chromatic adaptation* method.
ndarray, (3,)
*RGB* colourspace matrix.
Notes
-----
- *RGB* colourspace matrices are in domain [0, 1].
Examples
--------
>>> from colour import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB = np.array([0.35521588, 0.41, 0.24177934])
>>> RGB_to_RGB(
... RGB,
... sRGB_COLOURSPACE,
... PROPHOTO_RGB_COLOURSPACE) # doctest: +ELLIPSIS
array([ 0.3579334..., 0.4007138..., 0.2615704...])
"""
cat = chromatic_adaptation_matrix(
xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_method)
trs_matrix = np.dot(output_colourspace.to_RGB,
np.dot(cat, input_colourspace.to_XYZ))
return np.dot(trs_matrix, RGB)
def chromaticity_diagram_colours_CIE1976UCS(
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
antialiasing=True):
"""
Plots the *CIE 1976 UCS Chromaticity Diagram* colours.
Parameters
----------
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
antialiasing : bool, optional
Whether to apply anti-aliasing to the image.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> chromaticity_diagram_colours_CIE1976UCS() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
illuminant),
qhull_options='Qu QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(1, 0, samples))
xy = tstack((xx, yy))
mask = (triangulation.find_simplex(xy) < 0).astype(DEFAULT_FLOAT_DTYPE)
if antialiasing:
kernel = np.array([
[0, 1, 0],
[1, 2, 1],
[0, 1, 0],
]).astype(DEFAULT_FLOAT_DTYPE)
kernel /= np.sum(kernel)
mask = convolve(mask, kernel)
mask = 1 - mask[:, :, np.newaxis]
XYZ = xy_to_XYZ(Luv_uv_to_xy(xy))
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
return np.dstack([RGB, mask])
def corresponding_chromaticities_prediction_CIE1994(experiment=1):
"""
Returns the corresponding chromaticities prediction for CIE 1994
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2133909..., 0.4939794...)),
((0.449, 0.511), (0.4450345..., 0.5120939...)),
((0.263, 0.505), (0.2693262..., 0.5083212...)),
((0.322, 0.545), (0.3308593..., 0.5443940...)),
((0.316, 0.537), (0.3225195..., 0.5377826...)),
((0.265, 0.553), (0.2709737..., 0.5513666...)),
((0.221, 0.538), (0.2280786..., 0.5351592...)),
((0.135, 0.532), (0.1439436..., 0.5303576...)),
((0.145, 0.472), (0.1500743..., 0.4842895...)),
((0.163, 0.331), (0.1559955..., 0.3772379...)),
((0.176, 0.431), (0.1806318..., 0.4518475...)),
((0.244, 0.349), (0.2454445..., 0.4018004...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
# :math:`Y_o` is set to an arbitrary value in domain [18, 100].
Y_o = 18
E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CIE1994(
XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
prediction.append(CorrespondingChromaticitiesPrediction(
result.name,
result.uvp_t,
result.uvp_m,
uvp))
return tuple(prediction)
def RGB_to_RGB(RGB,
input_colourspace,
output_colourspace,
chromatic_adaptation_method='CAT02'):
"""
Converts from given input *RGB* colourspace to output *RGB* colourspace
using given *chromatic adaptation* method.
Parameters
----------
RGB : array_like, (3,)
*RGB* colourspace matrix.
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_method : unicode, optional
('XYZ Scaling', 'Bradford', 'Von Kries', 'Fairchild', 'CAT02')
*Chromatic adaptation* method.
ndarray, (3,)
*RGB* colourspace matrix.
Notes
-----
- *RGB* colourspace matrices are in domain [0, 1].
Examples
--------
>>> from colour import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB = np.array([0.35521588, 0.41, 0.24177934])
>>> RGB_to_RGB(
... RGB,
... sRGB_COLOURSPACE,
... PROPHOTO_RGB_COLOURSPACE) # doctest: +ELLIPSIS
array([ 0.3579334..., 0.4007138..., 0.2615704...])
"""
cat = chromatic_adaptation_matrix(xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_method)
trs_matrix = np.dot(output_colourspace.to_RGB,
np.dot(cat, input_colourspace.to_XYZ))
return np.dot(trs_matrix, RGB)
def test_n_dimensional_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition n-dimensions
support.
"""
xy = np.array([0.26414772, 0.37770001])
XYZ = np.array([0.69935852, 1.00000000, 0.94824533])
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
xy = np.tile(xy, (6, 1))
XYZ = np.tile(XYZ, (6, 1))
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
xy = np.reshape(xy, (2, 3, 2))
XYZ = np.reshape(XYZ, (2, 3, 3))
np.testing.assert_almost_equal(xy_to_XYZ(xy), XYZ, decimal=7)
def RGB_to_RGB_matrix(input_colourspace,
output_colourspace,
chromatic_adaptation_transform='CAT02'):
"""
Computes the matrix :math:`M` converting from given input *RGB*
colourspace to output *RGB* colourspace using given *chromatic
adaptation* method.
Parameters
----------
input_colourspace : RGB_Colourspace
*RGB* input colourspace.
output_colourspace : RGB_Colourspace
*RGB* output colourspace.
chromatic_adaptation_transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
*Chromatic adaptation* transform.
Returns
-------
ndarray
Conversion matrix :math:`M`.
Examples
--------
>>> from colour import sRGB_COLOURSPACE, PROPHOTO_RGB_COLOURSPACE
>>> RGB = np.array([0.01103742, 0.12734226, 0.11632971])
>>> RGB_to_RGB_matrix(
... sRGB_COLOURSPACE,
... PROPHOTO_RGB_COLOURSPACE) # doctest: +ELLIPSIS
array([[ 0.5288241..., 0.3340609..., 0.1373616...],
[ 0.0975294..., 0.8790074..., 0.0233981...],
[ 0.0163599..., 0.1066124..., 0.8772485...]])
"""
cat = chromatic_adaptation_matrix_VonKries(
xy_to_XYZ(input_colourspace.whitepoint),
xy_to_XYZ(output_colourspace.whitepoint),
chromatic_adaptation_transform)
M = dot_matrix(cat, input_colourspace.RGB_to_XYZ_matrix)
M = dot_matrix(output_colourspace.XYZ_to_RGB_matrix, M)
return M
def corresponding_chromaticities_prediction_CIE1994(experiment=1):
"""
Returns the corresponding chromaticities prediction for CIE 1994
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
Breneman (1987) experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2133909..., 0.4939794...)),
((0.449, 0.511), (0.4450345..., 0.5120939...)),
((0.263, 0.505), (0.2693262..., 0.5083212...)),
((0.322, 0.545), (0.3308593..., 0.5443940...)),
((0.316, 0.537), (0.3225195..., 0.5377826...)),
((0.265, 0.553), (0.2709737..., 0.5513666...)),
((0.221, 0.538), (0.2280786..., 0.5351592...)),
((0.135, 0.532), (0.1439436..., 0.5303576...)),
((0.145, 0.472), (0.1500743..., 0.4842895...)),
((0.163, 0.331), (0.1559955..., 0.3772379...)),
((0.176, 0.431), (0.1806318..., 0.4518475...)),
((0.244, 0.349), (0.2454445..., 0.4018004...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS.get(experiment))
illuminants = experiment_results.pop(0)
xy_o1 = Luv_uv_to_xy(illuminants.uvp_t)
xy_o2 = Luv_uv_to_xy(illuminants.uvp_m)
# :math:`Y_o` is set to an arbitrary value in domain [18, 100].
Y_o = 18
E_o1 = E_o2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES.get(
experiment).Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t)) * 100
XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1,
E_o2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
prediction.append(
CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
result.uvp_m, uvp))
return tuple(prediction)
def test_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xy_to_XYZ((0.32207410281368043, 0.3315655001362353)),
np.array([0.97137399, 1., 1.04462134]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ((0.32174206617150575, 0.337609723160027)),
np.array([0.953, 1.000, 1.009]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ((0.4474327628361859, 0.4074979625101875)),
np.array([1.098, 1.000, 0.356]),
decimal=7)
def test_domain_range_scale_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition domain and
range scale support.
"""
xy = np.array([0.54369557, 0.32107944, 0.12197225])
XYZ = xy_to_XYZ(xy)
xy = np.tile(xy, (6, 1)).reshape(2, 3, 3)
XYZ = np.tile(XYZ, (6, 1)).reshape(2, 3, 3)
d_r = (('reference', 1, 1), (1, 1, 1), (
100,
np.array([1, 1, 100]),
100,
))
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
xy_to_XYZ(xy * factor_a), XYZ * factor_b, decimal=7)
def test_domain_range_scale_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition domain and
range scale support.
"""
xy = np.array([0.54369557, 0.32107944, 0.12197225])
XYZ = xy_to_XYZ(xy)
xy = np.tile(xy, (6, 1)).reshape(2, 3, 3)
XYZ = np.tile(XYZ, (6, 1)).reshape(2, 3, 3)
d_r = (('reference', 1, 1), (1, 1, 1), (
100,
np.array([1, 1, 100]),
100,
))
for scale, factor_a, factor_b in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
xy_to_XYZ(xy * factor_a), XYZ * factor_b, decimal=7)
def XYZ_to_Lab(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* tristimulus values to *CIE Lab* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray
*CIE Lab* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *Lightness* :math:`L^*` is in domain [0, 100].
References
----------
.. [2] Lindbloom, B. (2003). XYZ to Lab. Retrieved February 24, 2014,
from http://www.brucelindbloom.com/Eqn_XYZ_to_Lab.html
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Lab(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -23.6230288..., -4.4141703...])
"""
XYZ = np.asarray(XYZ)
XYZ_r = xy_to_XYZ(illuminant)
XYZ_f = XYZ / XYZ_r
XYZ_f = np.where(XYZ_f > CIE_E,
np.power(XYZ_f, 1 / 3),
(CIE_K * XYZ_f + 16) / 116)
X_f, Y_f, Z_f = tsplit(XYZ_f)
L = 116 * Y_f - 16
a = 500 * (X_f - Y_f)
b = 200 * (Y_f - Z_f)
Lab = tstack((L, a, b))
return Lab
def test_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.26414772, 0.37770001])), np.array([0.69935852, 1.0, 0.94824533]), decimal=7
)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.50453169, 0.37440000])), np.array([1.34757396, 1.0, 0.32336621]), decimal=7
)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.47670437, 0.35790000])), np.array([1.33194851, 1.0, 0.46212805]), decimal=7
)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.34567, 0.35850])), np.array([0.96421199, 1.0, 0.82518828]), decimal=7
)
def Luv_to_XYZ(Luv,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Luv* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Luv : array_like
*CIE Luv* colourspace array.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input :math:`L^*` is in domain [0, 100].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *CIE XYZ* tristimulus values are in domain [0, 1].
References
----------
.. [3] Lindbloom, B. (2003). Luv to XYZ. Retrieved February 24, 2014,
from http://brucelindbloom.com/Eqn_Luv_to_XYZ.html
Examples
--------
>>> Luv = np.array([37.98562910, -28.79229446, -1.35581950])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, u, v = tsplit(Luv)
X_r, Y_r, Z_r = tsplit(xy_to_XYZ(illuminant))
Y = np.where(L > CIE_E * CIE_K, ((L + 16) / 116) ** 3, L / CIE_K)
a = 1 / 3 * ((52 * L / (u + 13 * L *
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L *
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
XYZ = tstack((X, Y, Z))
return XYZ
def CCT_D_uv_to_plotting_colourspace(CCT_D_uv):
"""
Convert given *uv* chromaticity coordinates to the default plotting
colourspace.
"""
return normalise_maximum(
XYZ_to_plotting_colourspace(
xy_to_XYZ(UCS_uv_to_xy(CCT_to_uv(CCT_D_uv,
"Robertson 1968")))),
axis=-1,
)
def Luv_to_XYZ(Luv,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Luv* colourspace to *CIE XYZ* colourspace.
Parameters
----------
Luv : array_like, (3,)
*CIE Luv* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Notes
-----
- Input :math:`L^*` is in domain [0, 100].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *CIE XYZ* colourspace matrix is in domain [0, 1].
References
----------
.. [3] Lindbloom, B. (2003). Luv to XYZ. Retrieved February 24, 2014,
from http://brucelindbloom.com/Eqn_Luv_to_XYZ.html
Examples
--------
>>> Luv = np.array([37.9856291, -28.79229446, -1.3558195])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, u, v = np.ravel(Luv)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
Y = ((L + 16) / 116) ** 3 if L > CIE_E * CIE_K else L / CIE_K
a = 1 / 3 * ((52 * L / (u + 13 * L *
(4 * Xr / (Xr + 15 * Yr + 3 * Zr)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L *
(9 * Yr / (Xr + 15 * Yr + 3 * Zr))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
return np.array([X, Y, Z])
def Lab_to_XYZ(Lab,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Lab* colourspace to *CIE XYZ* colourspace.
Parameters
----------
Lab : array_like, (3,)
*CIE Lab* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Notes
-----
- Input *Lightness* :math:`L^*` is in domain [0, 100].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *CIE XYZ* colourspace matrix is in domain [0, 1].
References
----------
.. [3] http://www.brucelindbloom.com/Eqn_Lab_to_XYZ.html
(Last accessed 24 February 2014)
Examples
--------
>>> Lab = np.array([100, -7.41787844, -15.85742105])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.9219310..., 1. , 1.0374424...])
"""
L, a, b = np.ravel(Lab)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
fy = (L + 16) / 116
fx = a / 500 + fy
fz = fy - b / 200
xr = fx**3 if fx**3 > CIE_E else (116 * fx - 16) / CIE_K
yr = ((L + 16) / 116)**3 if L > CIE_K * CIE_E else L / CIE_K
zr = fz**3 if fz**3 > CIE_E else (116 * fz - 16) / CIE_K
X = xr * Xr
Y = yr * Yr
Z = zr * Zr
return np.array([X, Y, Z])
def Luv_to_XYZ(Luv,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Luv* colourspace to *CIE XYZ* colourspace.
Parameters
----------
Luv : array_like, (3,)
*CIE Luv* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Notes
-----
- Input :math:`L^*` is in domain [0, 100].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *CIE XYZ* colourspace matrix is in domain [0, 1].
References
----------
.. [3] http://brucelindbloom.com/Eqn_Luv_to_XYZ.html
(Last accessed 24 February 2014)
Examples
--------
>>> Luv = np.array([100, -20.04304247, -19.81676035])
>>> Luv_to_XYZ(Luv) # doctest: +ELLIPSIS
array([ 0.9219310..., 1. , 1.0374424...])
"""
L, u, v = np.ravel(Luv)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
Y = ((L + 16) / 116) ** 3 if L > CIE_E * CIE_K else L / CIE_K
a = 1 / 3 * ((52 * L / (u + 13 * L *
(4 * Xr / (Xr + 15 * Yr + 3 * Zr)))) - 1)
b = -5 * Y
c = -1 / 3.0
d = Y * (39 * L / (v + 13 * L *
(9 * Yr / (Xr + 15 * Yr + 3 * Zr))) - 5)
X = (d - b) / (a - c)
Z = X * a + b
return np.array([X, Y, Z])
def Lab_to_XYZ(Lab,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Lab* colourspace to *CIE XYZ* colourspace.
Parameters
----------
Lab : array_like, (3,)
*CIE Lab* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE XYZ* colourspace matrix.
Notes
-----
- Input *Lightness* :math:`L^*` is in domain [0, 100].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *CIE XYZ* colourspace matrix is in domain [0, 1].
References
----------
.. [3] http://www.brucelindbloom.com/Eqn_Lab_to_XYZ.html
(Last accessed 24 February 2014)
Examples
--------
>>> Lab = np.array([100, -7.41787844, -15.85742105])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.9219310..., 1. , 1.0374424...])
"""
L, a, b = np.ravel(Lab)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
fy = (L + 16) / 116
fx = a / 500 + fy
fz = fy - b / 200
xr = fx ** 3 if fx ** 3 > CIE_E else (116 * fx - 16) / CIE_K
yr = ((L + 16) / 116) ** 3 if L > CIE_K * CIE_E else L / CIE_K
zr = fz ** 3 if fz ** 3 > CIE_E else (116 * fz - 16) / CIE_K
X = xr * Xr
Y = yr * Yr
Z = zr * Zr
return np.array([X, Y, Z])
def get_rgb_to_xyz_matrix(name):
"""
RGB to XYZ の Matrix を求める。
DCI-P3 で D65 の係数を返せるように内部関数化した。
"""
if name != "DCI-P3":
rgb_to_xyz_matrix = RGB_COLOURSPACES[name].RGB_to_XYZ_matrix
else:
rgb_to_xyz_matrix\
= calc_rgb_to_xyz_matrix(RGB_COLOURSPACES[DCI_P3].primaries,
xy_to_XYZ(ILLUMINANTS[CMFS_NAME]['D65']))
return rgb_to_xyz_matrix
def XYZ_to_Lab(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE Lab* colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE Lab* colourspace matrix.
Notes
-----
- Input *CIE XYZ* is in domain [0, 1].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *Lightness* :math:`L^*` is in domain [0, 100].
References
----------
.. [2] http://www.brucelindbloom.com/Eqn_XYZ_to_Lab.html
(Last accessed 24 February 2014)
Examples
--------
>>> XYZ_to_Lab(np.array([0.92193107, 1, 1.03744246])) # doctest: +ELLIPSIS
array([ 100. , -7.4178784..., -15.8574210...])
"""
X, Y, Z = np.ravel(XYZ)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
xr = X / Xr
yr = Y / Yr
zr = Z / Zr
fx = xr**(1 / 3) if xr > CIE_E else (CIE_K * xr + 16) / 116
fy = yr**(1 / 3) if yr > CIE_E else (CIE_K * yr + 16) / 116
fz = zr**(1 / 3) if zr > CIE_E else (CIE_K * zr + 16) / 116
L = 116 * fy - 16
a = 500 * (fx - fy)
b = 200 * (fy - fz)
return np.array([L, a, b])
def XYZ_to_Lab(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE Lab* colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE Lab* colourspace matrix.
Notes
-----
- Input *CIE XYZ* is in domain [0, 1].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *Lightness* :math:`L^*` is in domain [0, 100].
References
----------
.. [2] http://www.brucelindbloom.com/Eqn_XYZ_to_Lab.html
(Last accessed 24 February 2014)
Examples
--------
>>> XYZ_to_Lab(np.array([0.92193107, 1, 1.03744246])) # doctest: +ELLIPSIS
array([ 100. , -7.4178784..., -15.8574210...])
"""
X, Y, Z = np.ravel(XYZ)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
xr = X / Xr
yr = Y / Yr
zr = Z / Zr
fx = xr ** (1 / 3) if xr > CIE_E else (CIE_K * xr + 16) / 116
fy = yr ** (1 / 3) if yr > CIE_E else (CIE_K * yr + 16) / 116
fz = zr ** (1 / 3) if zr > CIE_E else (CIE_K * zr + 16) / 116
L = 116 * fy - 16
a = 500 * (fx - fy)
b = 200 * (fy - fz)
return np.array([L, a, b])
def XYZ_to_Luv(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* tristimulus values to *CIE Luv* colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray
*CIE Luv* colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 1].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output :math:`L^*` is in domain [0, 100].
References
----------
.. [2] Lindbloom, B. (2003). XYZ to Luv. Retrieved February 24, 2014,
from http://brucelindbloom.com/Eqn_XYZ_to_Luv.html
Examples
--------
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313])
>>> XYZ_to_Luv(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -28.7922944..., -1.3558195...])
"""
X, Y, Z = tsplit(XYZ)
X_r, Y_r, Z_r = tsplit(xy_to_XYZ(illuminant))
y_r = Y / Y_r
L = np.where(y_r > CIE_E, 116 * y_r ** (1 / 3) - 16, CIE_K * y_r)
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * X_r / (X_r + 15 * Y_r + 3 * Z_r))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Y_r / (X_r + 15 * Y_r + 3 * Z_r))))
Luv = tstack((L, u, v))
return Luv
def XYZ_to_UVW(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* tristimulus values to *CIE 1964 U\*V\*W\**
colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray
*CIE 1964 U\*V\*W\** colourspace array.
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Output *CIE UVW* colourspace array is in domain [0, 100].
Warning
-------
The input / output domains of that definition are non standard!
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313]) * 100
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([-28.0483277..., -0.8805242..., 37.0041149...])
"""
xyY = XYZ_to_xyY(XYZ, illuminant)
_x, y, Y = tsplit(xyY)
u, v = tsplit(UCS_to_uv(XYZ_to_UCS(XYZ)))
u_0, v_0 = tsplit(UCS_to_uv(XYZ_to_UCS(
xy_to_XYZ(illuminant))))
W = 25 * Y ** (1 / 3) - 17
U = 13 * W * (u - u_0)
V = 13 * W * (v - v_0)
UVW = tstack((U, V, W))
return UVW
def get_xyz_to_rgb_matrix(name):
"""
XYZ to RGB の Matrix を求める。
DCI-P3 で D65 の係数を返せるように内部関数化した。
"""
if name != "DCI-P3":
xyz_to_rgb_matrix = RGB_COLOURSPACES[name].XYZ_to_RGB_matrix
else:
rgb_to_xyz_matrix\
= calc_rgb_to_xyz_matrix(RGB_COLOURSPACES[DCI_P3].primaries,
xy_to_XYZ(ILLUMINANTS[CMFS_NAME]['D65']))
xyz_to_rgb_matrix = linalg.inv(rgb_to_xyz_matrix)
return xyz_to_rgb_matrix
def Lab_to_XYZ(Lab,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE Lab* colourspace to *CIE XYZ* tristimulus values.
Parameters
----------
Lab : array_like
*CIE Lab* colourspace array.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Notes
-----
- Input *Lightness* :math:`L^*` is in domain [0, 100].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output *CIE XYZ* tristimulus values are in domain [0, 1].
References
----------
.. [3] Lindbloom, B. (2008). Lab to XYZ. Retrieved February 24, 2014,
from http://www.brucelindbloom.com/Eqn_Lab_to_XYZ.html
Examples
--------
>>> Lab = np.array([37.98562910, -23.62302887, -4.41417036])
>>> Lab_to_XYZ(Lab) # doctest: +ELLIPSIS
array([ 0.0704953..., 0.1008 , 0.0955831...])
"""
L, a, b = tsplit(Lab)
XYZ_r = xy_to_XYZ(illuminant)
f_y = (L + 16) / 116
f_x = a / 500 + f_y
f_z = f_y - b / 200
x_r = np.where(f_x ** 3 > CIE_E, f_x ** 3, (116 * f_x - 16) / CIE_K)
y_r = np.where(L > CIE_K * CIE_E, ((L + 16) / 116) ** 3, L / CIE_K)
z_r = np.where(f_z ** 3 > CIE_E, f_z ** 3, (116 * f_z - 16) / CIE_K)
XYZ = tstack((x_r, y_r, z_r)) * XYZ_r
return XYZ
def xy_to_rgb(xy, name='ITU-R BT.2020', normalize='maximum', specific=None):
"""
xy値からRGB値を算出する。
いい感じに正規化もしておく。
Parameters
----------
xy : array_like
xy value.
name : string
color space name.
normalize : string
normalize method. You can select 'maximum', 'specific' or None.
Returns
-------
array_like
rgb value. the value is normalized.
"""
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
large_xyz_to_rgb_matrix = get_xyz_to_rgb_matrix(name)
if normalize == 'specific':
xyY = xy_to_xyY(xy)
xyY[..., 2] = specific
large_xyz = xyY_to_XYZ(xyY)
else:
large_xyz = xy_to_XYZ(xy)
rgb = XYZ_to_RGB(large_xyz, illuminant_XYZ, illuminant_RGB,
large_xyz_to_rgb_matrix,
chromatic_adaptation_transform)
"""
そのままだとビデオレベルが低かったりするので、
各ドット毎にRGB値を正規化＆最大化する。必要であれば。
"""
if normalize == 'maximum':
rgb = normalise_maximum(rgb, axis=-1)
else:
if(np.sum(rgb > 1.0) > 0):
print("warning: over flow has occured at xy_to_rgb")
if(np.sum(rgb < 0.0) > 0):
print("warning: under flow has occured at xy_to_rgb")
rgb[rgb < 0] = 0
rgb[rgb > 1.0] = 1.0
return rgb
def test_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.26414772, 0.37770001])),
np.array([0.69935852, 1.00000000, 0.94824533]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.50453169, 0.37440000])),
np.array([1.34757396, 1.00000000, 0.32336621]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.47670437, 0.35790000])),
np.array([1.33194851, 1.00000000, 0.46212805]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.34570000, 0.35850000])),
np.array([0.96429568, 1.00000000, 0.82510460]),
decimal=7)
def test_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.54369557, 0.32107944])),
np.array([1.69333661, 1.00000000, 0.42115742]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.29777735, 0.48246446])),
np.array([0.61720059, 1.00000000, 0.45549094]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.18582823, 0.14633764])),
np.array([1.26985942, 1.00000000, 4.56365245]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.31270000, 0.32900000])),
np.array([0.95045593, 1.00000000, 1.08905775]),
decimal=7)
def test_xy_to_XYZ(self):
"""
Tests :func:`colour.models.cie_xyy.xy_to_XYZ` definition.
"""
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.54369557, 0.32107944])),
np.array([1.69333661, 1.00000000, 0.42115742]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.29777735, 0.48246446])),
np.array([0.61720059, 1.00000000, 0.45549094]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.18582823, 0.14633764])),
np.array([1.26985942, 1.00000000, 4.56365245]),
decimal=7)
np.testing.assert_almost_equal(
xy_to_XYZ(np.array([0.31270000, 0.32900000])),
np.array([0.95045593, 1.00000000, 1.08905775]),
decimal=7)
def XYZ_to_Luv(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE Luv* colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE Luv* colourspace matrix.
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 1].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output :math:`L^*` is in domain [0, 100].
References
----------
.. [2] Lindbloom, B. (2003). XYZ to Luv. Retrieved February 24, 2014,
from http://brucelindbloom.com/Eqn_XYZ_to_Luv.html
Examples
--------
>>> XYZ = np.array([0.07049534, 0.1008, 0.09558313])
>>> XYZ_to_Luv(XYZ) # doctest: +ELLIPSIS
array([ 37.9856291..., -28.7922944..., -1.3558195...])
"""
X, Y, Z = np.ravel(XYZ)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
yr = Y / Yr
L = 116 * yr ** (1 / 3) - 16 if yr > CIE_E else CIE_K * yr
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * Xr / (Xr + 15 * Yr + 3 * Zr))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Yr / (Xr + 15 * Yr + 3 * Zr))))
return np.array([L, u, v])
def XYZ_to_Luv(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE Luv* colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE Luv* colourspace matrix.
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 1].
- Input *illuminant* chromaticity coordinates are in domain [0, 1].
- Output :math:`L^*` is in domain [0, 100].
References
----------
.. [2] http://brucelindbloom.com/Eqn_XYZ_to_Luv.html
(Last accessed 24 February 2014)
Examples
--------
>>> XYZ_to_Luv(np.array([0.92193107, 1, 1.03744246])) # doctest: +ELLIPSIS
array([ 100. , -20.0430424..., -19.8167603...])
"""
X, Y, Z = np.ravel(XYZ)
Xr, Yr, Zr = np.ravel(xy_to_XYZ(illuminant))
yr = Y / Yr
L = 116 * yr ** (1 / 3) - 16 if yr > CIE_E else CIE_K * yr
u = (13 * L * ((4 * X / (X + 15 * Y + 3 * Z)) -
(4 * Xr / (Xr + 15 * Yr + 3 * Zr))))
v = (13 * L * ((9 * Y / (X + 15 * Y + 3 * Z)) -
(9 * Yr / (Xr + 15 * Yr + 3 * Zr))))
return np.array([L, u, v])
def normalised_primary_matrix(primaries: ArrayLike,
whitepoint: ArrayLike) -> NDArray:
"""
Compute the *Normalised Primary Matrix* (NPM) converting a *RGB*
colourspace array to *CIE XYZ* tristimulus values using given *primaries*
and *whitepoint* :math:`xy` chromaticity coordinates.
Parameters
----------
primaries
Primaries :math:`xy` chromaticity coordinates.
whitepoint
Illuminant / whitepoint :math:`xy` chromaticity coordinates.
Returns
-------
:class:`numpy.ndarray`
*Normalised Primary Matrix* (NPM).
References
----------
:cite:`SocietyofMotionPictureandTelevisionEngineers1993a`
Examples
--------
>>> p = np.array([0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700])
>>> w = np.array([0.32168, 0.33767])
>>> normalised_primary_matrix(p, w) # doctest: +ELLIPSIS
array([[ 9.5255239...e-01, 0.0000000...e+00, 9.3678631...e-05],
[ 3.4396645...e-01, 7.2816609...e-01, -7.2132546...e-02],
[ 0.0000000...e+00, 0.0000000...e+00, 1.0088251...e+00]])
"""
primaries = np.reshape(primaries, (3, 2))
z = as_float_array(xy_to_z(primaries))[..., np.newaxis]
primaries = np.transpose(np.hstack([primaries, z]))
whitepoint = xy_to_XYZ(whitepoint)
coefficients = np.dot(np.linalg.inv(primaries), whitepoint)
coefficients = np.diagflat(coefficients)
npm = np.dot(primaries, coefficients)
return npm
def normalised_primary_matrix(primaries, whitepoint):
"""
Returns the *normalised primary matrix* using given *primaries* and
*whitepoint* :math:`xy` chromaticity coordinates.
Parameters
----------
primaries : array_like, (3, 2)
Primaries :math:`xy` chromaticity coordinates.
whitepoint : array_like
Illuminant / whitepoint :math:`xy` chromaticity coordinates.
Returns
-------
ndarray, (3, 3)
*Normalised primary matrix*.
References
----------
:cite:`SocietyofMotionPictureandTelevisionEngineers1993a`
Examples
--------
>>> p = np.array([0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700])
>>> w = np.array([0.32168, 0.33767])
>>> normalised_primary_matrix(p, w) # doctest: +ELLIPSIS
array([[ 9.5255239...e-01, 0.0000000...e+00, 9.3678631...e-05],
[ 3.4396645...e-01, 7.2816609...e-01, -7.2132546...e-02],
[ 0.0000000...e+00, 0.0000000...e+00, 1.0088251...e+00]])
"""
primaries = np.reshape(primaries, (3, 2))
z = xy_to_z(primaries)[..., np.newaxis]
primaries = np.transpose(np.hstack([primaries, z]))
whitepoint = xy_to_XYZ(whitepoint)
coefficients = np.dot(np.linalg.inv(primaries), whitepoint)
coefficients = np.diagflat(coefficients)
npm = np.dot(primaries, coefficients)
return npm
def XYZ_to_UVW(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE 1964 U\*V*\W\** colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE 1964 U\*V*\W\** colourspace matrix.
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 100].
- Output *CIE UVW* colourspace matrix is in domain [0, 100].
Warning
-------
The input / output domains of that definition are non standard!
Examples
--------
>>> XYZ = np.array([11.80583421, 10.34, 5.15089229])
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([ 24.2543371..., 7.2205484..., 37.4645000...])
"""
x, y, Y = np.ravel(XYZ_to_xyY(XYZ, illuminant))
u, v = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ)))
u0, v0 = np.ravel(UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(illuminant))))
W = 25 * Y**(1 / 3) - 17
U = 13 * W * (u - u0)
V = 13 * W * (v - v0)
return np.array([U, V, W])
def CCT_factor(reference_data, XYZ_r):
xy_w = ILLUMINANTS.get('CIE 1931 2 Degree Standard Observer').get('D65')
XYZ_w = xy_to_XYZ(xy_w)
Labs = []
for vs_colorimetry_data_ in reference_data:
_name, XYZ, _Lab, _C = vs_colorimetry_data_
XYZ_a = chromatic_adaptation_VonKries(XYZ,
XYZ_r,
XYZ_w,
transform='CMCCAT2000')
Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w)
Labs.append(Lab)
G_r = gamut_area(Labs) / D65_GAMUT_AREA
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def CCT_factor(reference_data, XYZ_r):
"""
Returns the correlated colour temperature factor penalizing lamps with
extremely low correlated colour temperatures.
Parameters
----------
reference_data : VS_ColorimetryData
Reference colorimetry data.
XYZ_r : array_like
*CIE XYZ* tristimulus values for reference.
Returns
-------
numeric
Correlated colour temperature factor.
"""
xy_w = ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
XYZ_w = xy_to_XYZ(xy_w)
Labs = []
for vs_colorimetry_data_ in reference_data:
_name, XYZ, _Lab, _C = vs_colorimetry_data_
XYZ_a = chromatic_adaptation_VonKries(XYZ,
XYZ_r,
XYZ_w,
transform='CMCCAT2000')
Lab = XYZ_to_Lab(XYZ_a, illuminant=xy_w)
Labs.append(Lab)
G_r = gamut_area(Labs) / D65_GAMUT_AREA
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def CCT_factor(reference_data: Tuple[VS_ColorimetryData, ...],
XYZ_r: ArrayLike) -> Floating:
"""
Return the correlated colour temperature factor penalizing lamps with
extremely low correlated colour temperatures.
Parameters
----------
reference_data
Reference colorimetry data.
XYZ_r
*CIE XYZ* tristimulus values for reference.
Returns
-------
:class:`numpy.floating`
Correlated colour temperature factor.
"""
xy_w = CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"]
XYZ_w = xy_to_XYZ(xy_w)
Lab = XYZ_to_Lab(
chromatic_adaptation_VonKries(
[colorimetry_data.XYZ for colorimetry_data in reference_data],
XYZ_r,
XYZ_w,
transform="CMCCAT2000",
),
illuminant=xy_w,
)
G_r = gamut_area(Lab) / GAMUT_AREA_D65
CCT_f = 1 if G_r > 1 else G_r
return CCT_f
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> CIE_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
"""
settings = {'figure_size': (64, 64)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
x_dot, y_dot = tsplit(xy)
pylab.scatter(x_dot, y_dot, color=RGB, s=surface)
settings.update({
'x_ticker': False,
'y_ticker': False,
'bounding_box': (0, 1, 0, 1)
})
settings.update(kwargs)
ax = matplotlib.pyplot.gca()
matplotlib.pyplot.setp(ax, frame_on=False)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def plot_spectral_locus(cmfs='CIE 1931 2 Degree Standard Observer',
spectral_locus_colours=None,
spectral_locus_labels=None,
method='CIE 1931',
**kwargs):
"""
Plots the *Spectral Locus* according to given method.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions defining the
*Spectral Locus*.
spectral_locus_colours : array_like or unicode, optional
*Spectral Locus* colours, if ``spectral_locus_colours`` is set to
*RGB*, the colours will be computed according to the corresponding
chromaticity coordinates.
spectral_locus_labels : array_like, optional
Array of wavelength labels used to customise which labels will be drawn
around the spectral locus. Passing an empty array will result in no
wavelength labels being drawn.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
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_spectral_locus(spectral_locus_colours='RGB') # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Spectral_Locus.png
:align: center
:alt: plot_spectral_locus
"""
if spectral_locus_colours is None:
spectral_locus_colours = COLOUR_STYLE_CONSTANTS.colour.dark
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
if method == 'CIE 1931':
ij = XYZ_to_xy(cmfs.values, illuminant)
labels = ((390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600,
620, 700)
if spectral_locus_labels is None else spectral_locus_labels)
elif method == 'CIE 1960 UCS':
ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
if spectral_locus_labels is None else spectral_locus_labels)
elif method == 'CIE 1976 UCS':
ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
if spectral_locus_labels is None else spectral_locus_labels)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
pl_ij = tstack([
np.linspace(ij[0][0], ij[-1][0], 20),
np.linspace(ij[0][1], ij[-1][1], 20)
]).reshape(-1, 1, 2)
sl_ij = np.copy(ij).reshape(-1, 1, 2)
if spectral_locus_colours.upper() == 'RGB':
spectral_locus_colours = normalise_maximum(
XYZ_to_plotting_colourspace(cmfs.values), axis=-1)
if method == 'CIE 1931':
XYZ = xy_to_XYZ(pl_ij)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij))
purple_line_colours = normalise_maximum(
XYZ_to_plotting_colourspace(XYZ.reshape(-1, 3)), axis=-1)
else:
purple_line_colours = spectral_locus_colours
for slp_ij, slp_colours in ((pl_ij, purple_line_colours),
(sl_ij, spectral_locus_colours)):
line_collection = LineCollection(
np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1),
colors=slp_colours)
axes.add_collection(line_collection)
wl_ij = dict(tuple(zip(wavelengths, ij)))
for label in labels:
i, j = wl_ij[label]
index = bisect.bisect(wavelengths, label)
left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
right = (wavelengths[index]
if index < len(wavelengths) else wavelengths[-1])
dx = wl_ij[right][0] - wl_ij[left][0]
dy = wl_ij[right][1] - wl_ij[left][1]
ij = np.array([i, j])
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx]) if np.dot(
normalise_vector(ij - equal_energy), normalise_vector(direction)) >
0 else np.array([dy, -dx]))
normal = normalise_vector(normal) / 30
label_colour = (spectral_locus_colours
if is_string(spectral_locus_colours) else
spectral_locus_colours[index])
axes.plot(
(i, i + normal[0] * 0.75), (j, j + normal[1] * 0.75),
color=label_colour)
axes.plot(i, j, 'o', color=label_colour)
axes.text(
i + normal[0],
j + normal[1],
label,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def plot_chromaticity_diagram_colours(
samples=256,
diagram_opacity=1.0,
diagram_clipping_path=None,
cmfs='CIE 1931 2 Degree Standard Observer',
method='CIE 1931',
**kwargs):
"""
Plots the *Chromaticity Diagram* colours according to given method.
Parameters
----------
samples : numeric, optional
Samples count on one axis.
diagram_opacity : numeric, optional
Opacity of the *Chromaticity Diagram* colours.
diagram_clipping_path : array_like, optional
Path of points used to clip the *Chromaticity Diagram* colours.
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
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_chromaticity_diagram_colours() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
:align: center
:alt: plot_chromaticity_diagram_colours
"""
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
ii, jj = np.meshgrid(
np.linspace(0, 1, samples), np.linspace(1, 0, samples))
ij = tstack([ii, jj])
if method == 'CIE 1931':
XYZ = xy_to_XYZ(ij)
spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
spectral_locus = Luv_to_uv(
XYZ_to_Luv(cmfs.values, illuminant), illuminant)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
RGB = normalise_maximum(
XYZ_to_plotting_colourspace(XYZ, illuminant), axis=-1)
polygon = Polygon(
spectral_locus
if diagram_clipping_path is None else diagram_clipping_path,
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
# Preventing bounding box related issues as per
# https://github.com/matplotlib/matplotlib/issues/10529
image = axes.imshow(
RGB,
interpolation='bilinear',
extent=(0, 1, 0, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def convert_experiment_results_Breneman1987(experiment):
"""
Converts *Breneman (1987)* experiment results to a
:class:`colour.CorrespondingColourDataset` class instance.
Parameters
----------
experiment : integer
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number.
Returns
-------
CorrespondingColourDataset
:class:`colour.CorrespondingColourDataset` class instance.
Examples
--------
>>> from pprint import pprint
>>> pprint(tuple(convert_experiment_results_Breneman1987(2)))
... # doctest: +ELLIPSIS
(2,
array([ 0.9582463..., 1. , 0.9436325...]),
array([ 0.9587332..., 1. , 0.4385796...]),
array([[ 388.125 , 405. , 345.625 ],
[ 266.8957925..., 135. , 28.5983365...],
[ 474.5717821..., 405. , 222.75 ...],
[ 538.3899082..., 405. , 24.8944954...],
[ 178.7430167..., 135. , 19.6089385...],
[ 436.6749547..., 405. , 26.5483725...],
[ 124.7746282..., 135. , 36.1965613...],
[ 77.0794172..., 135. , 60.5850563...],
[ 279.9390889..., 405. , 455.8395127...],
[ 149.5808157..., 135. , 498.7046827...],
[ 372.1113689..., 405. , 669.9883990...],
[ 212.3638968..., 135. , 414.6704871...]]),
array([[ 400.1039651..., 405. , 191.7287234...],
[ 271.0384615..., 135. , 13.5 ...],
[ 495.4705323..., 405. , 119.7290874...],
[ 580.7967033..., 405. , 6.6758241...],
[ 190.1933701..., 135. , 7.4585635...],
[ 473.7184115..., 405. , 10.2346570...],
[ 135.4936014..., 135. , 20.2376599...],
[ 86.4689781..., 135. , 35.2281021...],
[ 283.5396281..., 405. , 258.1775929...],
[ 119.7044335..., 135. , 282.6354679...],
[ 359.9532224..., 405. , 381.0031185...],
[ 181.8271461..., 135. , 204.0661252...]]),
array(1500),
array(1500),
0.3,
0.3,
{})
"""
valid_experiment_results = (1, 2, 3, 4, 6, 8, 9, 11, 12)
assert experiment in valid_experiment_results, (
'"Breneman (1987)" experiment result must be one of "{0}"!'.format(
valid_experiment_results))
samples_luminance = [
0.270,
0.090,
0.270,
0.270,
0.090,
0.270,
0.090,
0.090,
0.270,
0.090,
0.270,
0.090,
]
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminant_chromaticities = experiment_results.pop(0)
Y_r = Y_t = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES[experiment].Y
B_r = B_t = 0.3
XYZ_t, XYZ_r = xy_to_XYZ(
np.hstack([
Luv_uv_to_xy(illuminant_chromaticities[1:3]),
np.full([2, 1], Y_r)
])) / Y_r
xyY_cr, xyY_ct = [], []
for i, experiment_result in enumerate(experiment_results):
xyY_cr.append(
np.hstack([
Luv_uv_to_xy(experiment_result[2]), samples_luminance[i] * Y_r
]))
xyY_ct.append(
np.hstack([
Luv_uv_to_xy(experiment_result[1]), samples_luminance[i] * Y_t
]))
XYZ_cr = xyY_to_XYZ(xyY_cr)
XYZ_ct = xyY_to_XYZ(xyY_ct)
return CorrespondingColourDataset(experiment, XYZ_r, XYZ_t, XYZ_cr, XYZ_ct,
Y_r, Y_t, B_r, B_t, {})
def planckian_locus_CIE_1960_UCS_chromaticity_diagram_plot(
illuminants=None,
**kwargs):
"""
Plots the planckian locus and given illuminants in
*CIE 1960 UCS Chromaticity Diagram*.
Parameters
----------
illuminants : array_like, optional
Factory illuminants to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Raises
------
KeyError
If one of the given illuminant is not found in the factory illuminants.
Examples
--------
>>> ils = ['A', 'C', 'E']
>>> planckian_locus_CIE_1960_UCS_chromaticity_diagram_plot(
... ils) # doctest: +SKIP
"""
if illuminants is None:
illuminants = ('A', 'C', 'E')
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
settings = {
'title': ('{0} Illuminants - Planckian Locus\n'
'CIE 1960 UCS Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer').format(
', '.join(illuminants))
if illuminants else
('Planckian Locus\nCIE 1960 UCS Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer'),
'standalone': False}
settings.update(kwargs)
CIE_1960_UCS_chromaticity_diagram_plot(**settings)
start, end = 1667, 100000
uv = np.array([CCT_to_uv(x, 0, method='Robertson 1968')
for x in np.arange(start, end + 250, 250)])
pylab.plot(uv[..., 0], uv[..., 1], color='black', linewidth=2)
for i in (1667, 2000, 2500, 3000, 4000, 6000, 10000):
u0, v0 = CCT_to_uv(i, -0.05, method='Robertson 1968')
u1, v1 = CCT_to_uv(i, 0.05, method='Robertson 1968')
pylab.plot((u0, u1), (v0, v1), color='black', linewidth=2)
pylab.annotate('{0}K'.format(i),
xy=(u0, v0),
xytext=(0, -10),
color='black',
textcoords='offset points',
size='x-small')
for illuminant in illuminants:
xy = ILLUMINANTS.get(cmfs.name).get(illuminant)
if xy is None:
raise KeyError(
('Illuminant "{0}" not found in factory illuminants: '
'"{1}".').format(illuminant,
sorted(ILLUMINANTS.get(cmfs.name).keys())))
uv = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
pylab.plot(uv[0], uv[1], 'o', color='white', linewidth=2)
pylab.annotate(illuminant,
xy=(uv[0], uv[1]),
xytext=(-50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.2, 0.6),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def corresponding_chromaticities_prediction_VonKries(experiment=1,
transform='CAT02'):
"""
Returns the corresponding chromaticities prediction for *Von Kries*
chromatic adaptation model using given transform.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number.
transform : unicode, optional
**{'CAT02', 'XYZ Scaling', 'Von Kries', 'Bradford', 'Sharp',
'Fairchild', 'CMCCAT97', 'CMCCAT2000', 'CAT02_BRILL_CAT', 'Bianco',
'Bianco PC'}**,
Chromatic adaptation transform.
Returns
-------
tuple
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Fairchild2013t`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_VonKries(2, 'Bradford')
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2082014..., 0.4722922...)),
((0.449, 0.511), (0.4489102..., 0.5071602...)),
((0.263, 0.505), (0.2643545..., 0.4959631...)),
((0.322, 0.545), (0.3348730..., 0.5471220...)),
((0.316, 0.537), (0.3248758..., 0.5390589...)),
((0.265, 0.553), (0.2733105..., 0.5555028...)),
((0.221, 0.538), (0.2271480..., 0.5331317...)),
((0.135, 0.532), (0.1442730..., 0.5226804...)),
((0.145, 0.472), (0.1498745..., 0.4550785...)),
((0.163, 0.331), (0.1564975..., 0.3148795...)),
((0.176, 0.431), (0.1760593..., 0.4103772...)),
((0.244, 0.349), (0.2259805..., 0.3465291...))]
"""
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(
CorrespondingChromaticitiesPrediction(result.name, result.uvp_t,
result.uvp_m, uvp))
return tuple(prediction)
def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1):
"""
Returns the corresponding chromaticities prediction for *CMCCAT2000*
chromatic adaptation model.
Parameters
----------
experiment : integer, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number.
Returns
-------
tuple
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Li2002a`, :cite:`Westland2012k`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CMCCAT2000(2)
>>> pr = [(p.uvp_m, p.uvp_p) for p in pr]
>>> pprint(pr) # doctest: +SKIP
[((0.207, 0.486), (0.2083210..., 0.4727168...)),
((0.449, 0.511), (0.4459270..., 0.5077735...)),
((0.263, 0.505), (0.2640262..., 0.4955361...)),
((0.322, 0.545), (0.3316884..., 0.5431580...)),
((0.316, 0.537), (0.3222624..., 0.5357624...)),
((0.265, 0.553), (0.2710705..., 0.5501997...)),
((0.221, 0.538), (0.2261826..., 0.5294740...)),
((0.135, 0.532), (0.1439693..., 0.5190984...)),
((0.145, 0.472), (0.1494835..., 0.4556760...)),
((0.163, 0.331), (0.1563172..., 0.3164151...)),
((0.176, 0.431), (0.1763199..., 0.4127589...)),
((0.244, 0.349), (0.2287638..., 0.3499324...))]
"""
with domain_range_scale(1):
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminants = experiment_results.pop(0)
XYZ_w = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_wr = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_wr = XYZ_to_xy(XYZ_wr)
L_A1 = L_A2 = BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES[
experiment].Y
prediction = []
for result in experiment_results:
XYZ_1 = xy_to_XYZ(Luv_uv_to_xy(result.uvp_t))
XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1,
L_A2)
uvp = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
prediction.append(
CorrespondingChromaticitiesPrediction(
result.name, result.uvp_t, result.uvp_m, uvp))
return tuple(prediction)
"""
Default automatic colour conversion graph illuminant spectral distribution.
_DEFAULT_ILLUMINANT_SD : SpectralDistribution
"""
_DEFAULT_ILLUMINANT_XY = ILLUMINANTS['CIE 1931 2 Degree Standard Observer'][
_DEFAULT_ILLUMINANT]
"""
Default automatic colour conversion graph illuminant *CIE xy* chromaticity
coordinates.
_DEFAULT_ILLUMINANT_XY : ndarray
"""
_DEFAULT_ILLUMINANT_XYZ = xy_to_XYZ(_DEFAULT_ILLUMINANT_XY)
"""
Default automatic colour conversion graph illuminant *CIE XYZ* tristimulus
values.
_DEFAULT_ILLUMINANT_XYZ : ndarray
"""
_DEFAULT_RGB_COLOURSPACE = sRGB_COLOURSPACE
"""
Default automatic colour conversion graph *RGB* colourspace.
_DEFAULT_RGB_COLOURSPACE : RGB_COLOURSPACE
"""
CONVERSION_SPECIFICATIONS_DATA = [
