def test_n_dimensional_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition n-dimensional
support.
"""
Luv = np.array([41.52787529, 96.83626054, 17.75210149])
illuminant = np.array([0.31270, 0.32900])
uv = Luv_to_uv(Luv, illuminant)
Luv = np.tile(Luv, (6, 1))
uv = np.tile(uv, (6, 1))
np.testing.assert_almost_equal(Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
Luv = np.reshape(Luv, (2, 3, 3))
illuminant = np.reshape(illuminant, (2, 3, 2))
uv = np.reshape(uv, (2, 3, 2))
np.testing.assert_almost_equal(Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
def test_n_dimensional_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition n-dimensions
support.
"""
Luv = np.array([37.98562910, -28.80219593, -1.35800706])
illuminant = np.array([0.34570, 0.35850])
uv = np.array([0.15085310, 0.48532971])
np.testing.assert_almost_equal(
Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
Luv = np.tile(Luv, (6, 1))
uv = np.tile(uv, (6, 1))
np.testing.assert_almost_equal(
Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(
Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
Luv = np.reshape(Luv, (2, 3, 3))
illuminant = np.reshape(illuminant, (2, 3, 2))
uv = np.reshape(uv, (2, 3, 2))
np.testing.assert_almost_equal(
Luv_to_uv(Luv, illuminant),
uv,
decimal=7)
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 : dict, optional
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 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 : dict, optional
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_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.
References
----------
:cite:`Breneman1987b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`
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...))]
"""
with domain_range_scale(1):
experiment_results = list(BRENEMAN_EXPERIMENTS[experiment])
illuminants = experiment_results.pop(0)
XYZ_n = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_t))
XYZ_r = xy_to_XYZ(Luv_uv_to_xy(illuminants.uvp_m))
xy_r = XYZ_to_xy(XYZ_r)
Y_n = 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_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 RGB_chromaticity_coordinates_CIE_1976_UCS_chromaticity_diagram_plot(
RGB,
colourspace,
**kwargs):
"""
Plots given *RGB* colourspace array in *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
RGB : array_like
*RGB* colourspace array.
colourspace : RGB_Colourspace
*RGB* colourspace of the *RGB* array.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> RGB = np.random.random((10, 10, 3))
>>> c = 'Rec. 709'
>>> RGB_chromaticity_coordinates_CIE_1976_UCS_chromaticity_diagram_plot(
... RGB, c) # doctest: +SKIP
True
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
settings['colourspaces'] = (
[colourspace.name] + settings.get('colourspaces', []))
RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(**settings)
alpha_p, colour_p = 0.85, 'black'
uv = Luv_to_uv(XYZ_to_Luv(RGB_to_XYZ(RGB,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix),
colourspace.whitepoint),
colourspace.whitepoint)
pylab.scatter(uv[..., 0],
uv[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
settings.update({'standalone': True})
boundaries(**settings)
decorate(**settings)
return display(**settings)
def XYZ_to_ij(XYZ):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return Luv_to_uv(XYZ_to_Luv(XYZ))
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), (0.2082014..., 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 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 test_domain_range_scale_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition
domain and range scale support.
"""
Luv = np.array([41.52787529, 96.83626054, 17.75210149])
illuminant = np.array([0.31270, 0.32900])
uv = Luv_to_uv(Luv, illuminant)
d_r = (('reference', 1), (1, 0.01), (100, 1))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(Luv_to_uv(
Luv * factor, illuminant),
uv,
decimal=7)
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 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 test_nan_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition nan support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=3))
for case in cases:
Luv = np.array(case)
illuminant = np.array(case[0:2])
Luv_to_uv(Luv, illuminant)
def test_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition.
"""
np.testing.assert_almost_equal(
Luv_to_uv(np.array([100, -11.27488915, -29.36041662])),
(0.20048615433157738, 0.4654903849082484),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([100, 331.44911128, 72.55258319])),
(0.46412000081619281, 0.54388500014670993),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([100, -36.17788915, -111.00091702])),
(0.18133000048542355, 0.40268999998517152),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([100, -97.02442861, -158.08546907]),
(0.44757, 0.40745)), (0.18133000048503745, 0.40268999998707306),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([100, -37.95520989, -92.29247371]),
(1 / 3, 1 / 3)), (0.18133000048947367, 0.40268999998016192),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([100, -21.44928374, -85.33481874]),
(0.31271, 0.32902)), (0.1813300004870092, 0.4026899999798475),
decimal=7)
def test_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition.
"""
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -28.79229446, -1.35581950])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([65.70971880, 87.21709531, 27.01490816])),
np.array([0.31125983, 0.51970032]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([50.86223896, 60.52359443, 13.14030896])),
np.array([0.30069387, 0.50794847]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -51.90523525, -19.24118281]),
np.array([0.44757, 0.40745])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -23.19754103, 8.39360940]),
np.array([0.31271, 0.32902])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -34.23840374, -7.09461715]),
np.array([0.37208, 0.37529])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -34.23840374, -7.09461715]),
np.array([0.37208, 0.37529, 0.10080])),
np.array([0.15085310, 0.48532971]),
decimal=7)
def test_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition.
"""
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -28.80219593, -1.35800706])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([65.70971880, 87.19996716, 27.01112399])),
np.array([0.31125983, 0.51970032]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([50.86223896, 60.51033649, 13.13737985])),
np.array([0.30069387, 0.50794847]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -51.90523525, -19.24118281]),
np.array([0.44757, 0.40745])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -23.19781615, 8.39962073]),
np.array([0.31270, 0.32900])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -34.23840374, -7.09461715]),
np.array([0.37208, 0.37529])),
np.array([0.15085310, 0.48532971]),
decimal=7)
np.testing.assert_almost_equal(
Luv_to_uv(np.array([37.98562910, -34.23840374, -7.09461715]),
np.array([0.37208, 0.37529, 0.10080])),
np.array([0.15085310, 0.48532971]),
decimal=7)
def test_Luv_to_uv(self):
"""
Tests :func:`colour.models.cie_luv.Luv_to_uv` definition.
"""
np.testing.assert_almost_equal(Luv_to_uv(
np.array([41.52787529, 96.83626054, 17.75210149])),
np.array([0.37720213, 0.50120264]),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([55.11636304, -37.59308176, 44.13768458])),
np.array([0.14536327, 0.52992069]),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([29.80565520, -10.96316802, -65.06751860])),
np.array([0.16953603, 0.30039234]),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([41.52787529, 65.45180940, -12.46626977]),
np.array([0.44757, 0.40745])),
np.array([0.37720213, 0.50120264]),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([41.52787529, 90.70925962, 7.08455273]),
np.array([0.34570, 0.35850])),
np.array([0.37720213, 0.50120264]),
decimal=7)
np.testing.assert_almost_equal(Luv_to_uv(
np.array([41.52787529, 90.70925962, 7.08455273]),
np.array([0.34570, 0.35850, 1.00000])),
np.array([0.37720213, 0.50120264]),
decimal=7)
def CIE_1976_UCS_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1976 UCS 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_1976_UCS_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(Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
illuminant),
qhull_options='QJ Qf')
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(Luv_uv_to_xy(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 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 or CorrespondingColourDataset, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
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.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2082014..., 0.4722922...])),
(array([ 0.449, 0.511]), array([ 0.4489102..., 0.5071602...])),
(array([ 0.263, 0.505]), array([ 0.2643545..., 0.4959631...])),
(array([ 0.322, 0.545]), array([ 0.3348730..., 0.5471220...])),
(array([ 0.316, 0.537]), array([ 0.3248758..., 0.5390589...])),
(array([ 0.265, 0.553]), array([ 0.2733105..., 0.5555028...])),
(array([ 0.221, 0.538]), array([ 0.227148 ..., 0.5331318...)),
(array([ 0.135, 0.532]), array([ 0.1442730..., 0.5226804...])),
(array([ 0.145, 0.472]), array([ 0.1498745..., 0.4550785...])),
(array([ 0.163, 0.331]), array([ 0.1564975..., 0.3148796...])),
(array([ 0.176, 0.431]), array([ 0.1760593..., 0.4103772...])),
(array([ 0.244, 0.349]), array([ 0.2259805..., 0.3465291...]))]
"""
experiment_results = (convert_experiment_results_Breneman1987(experiment)
if is_numeric(experiment) else experiment)
with domain_range_scale(1):
XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_VonKries(XYZ_1, XYZ_w, XYZ_wr, transform)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
return tuple([
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t))
])
def corresponding_chromaticities_prediction_CMCCAT2000(experiment=1):
"""
Returns the corresponding chromaticities prediction for *CMCCAT2000*
chromatic adaptation model.
Parameters
----------
experiment : integer or CorrespondingColourDataset, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
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.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2083210..., 0.4727168...])),
(array([ 0.449, 0.511]), array([ 0.4459270..., 0.5077735...])),
(array([ 0.263, 0.505]), array([ 0.2640262..., 0.4955361...])),
(array([ 0.322, 0.545]), array([ 0.3316884..., 0.5431580...])),
(array([ 0.316, 0.537]), array([ 0.3222624..., 0.5357624...])),
(array([ 0.265, 0.553]), array([ 0.2710705..., 0.5501997...])),
(array([ 0.221, 0.538]), array([ 0.2261826..., 0.5294740...])),
(array([ 0.135, 0.532]), array([ 0.1439693..., 0.5190984...])),
(array([ 0.145, 0.472]), array([ 0.1494835..., 0.4556760...])),
(array([ 0.163, 0.331]), array([ 0.1563172..., 0.3164151...])),
(array([ 0.176, 0.431]), array([ 0.1763199..., 0.4127589...])),
(array([ 0.244, 0.349]), array([ 0.2287638..., 0.3499324...]))]
"""
experiment_results = (convert_experiment_results_Breneman1987(experiment)
if is_numeric(experiment) else experiment)
with domain_range_scale(1):
XYZ_w, XYZ_wr = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_w, xy_wr = XYZ_to_xy([XYZ_w, XYZ_wr])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_w), xy_w)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_wr), xy_wr)
L_A1 = experiment_results.Y_t
L_A2 = experiment_results.Y_r
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_CMCCAT2000(XYZ_1, XYZ_w, XYZ_wr, L_A1,
L_A2)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_wr), xy_wr)
return tuple([
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t))
])
def corresponding_chromaticities_prediction_CIE1994(experiment=1):
"""
Returns the corresponding chromaticities prediction for *CIE 1994*
chromatic adaptation model.
Parameters
----------
experiment : integer or CorrespondingColourDataset, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance. Returns
-------
tuple
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`CIETC1-321994b`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_CIE1994(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2273130..., 0.5267609...])),
(array([ 0.449, 0.511]), array([ 0.4612181..., 0.5191849...])),
(array([ 0.263, 0.505]), array([ 0.2872404..., 0.5306938...])),
(array([ 0.322, 0.545]), array([ 0.3489822..., 0.5454398...])),
(array([ 0.316, 0.537]), array([ 0.3371612..., 0.5421567...])),
(array([ 0.265, 0.553]), array([ 0.2889416..., 0.5534074...])),
(array([ 0.221, 0.538]), array([ 0.2412195..., 0.5464301...])),
(array([ 0.135, 0.532]), array([ 0.1530344..., 0.5488239...])),
(array([ 0.145, 0.472]), array([ 0.1568709..., 0.5258835...])),
(array([ 0.163, 0.331]), array([ 0.1499762..., 0.4401747...])),
(array([ 0.176, 0.431]), array([ 0.1876711..., 0.5039627...])),
(array([ 0.244, 0.349]), array([ 0.2560012..., 0.4546263...]))]
"""
experiment_results = (convert_experiment_results_Breneman1987(experiment)
if is_numeric(experiment) else experiment)
with domain_range_scale(1):
XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_o1, xy_o2 = XYZ_to_xy([XYZ_t, XYZ_r])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_o1), xy_o1)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_o2), xy_o2)
Y_r = experiment_results.B_r
E_o1, E_o2 = experiment_results.Y_t, experiment_results.Y_r
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_r, E_o1,
E_o2)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_o2), xy_o2)
return tuple([
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t))
])
def corresponding_chromaticities_prediction_Fairchild1990(experiment=1):
"""
Returns the corresponding chromaticities prediction for *Fairchild (1990)*
chromatic adaptation model.
Parameters
----------
experiment : integer or CorrespondingColourDataset, optional
{1, 2, 3, 4, 6, 8, 9, 11, 12}
*Breneman (1987)* experiment number or
:class:`colour.CorrespondingColourDataset` class instance.
Returns
-------
tuple
Corresponding chromaticities prediction.
References
----------
:cite:`Breneman1987b`, :cite:`Fairchild1991a`, :cite:`Fairchild2013s`
Examples
--------
>>> from pprint import pprint
>>> pr = corresponding_chromaticities_prediction_Fairchild1990(2)
>>> pr = [(p.uv_m, p.uv_p) for p in pr]
>>> pprint(pr) # doctest: +ELLIPSIS
[(array([ 0.207, 0.486]), array([ 0.2089528..., 0.4724034...])),
(array([ 0.449, 0.511]), array([ 0.4375652..., 0.5121030...])),
(array([ 0.263, 0.505]), array([ 0.2621362..., 0.4972538...])),
(array([ 0.322, 0.545]), array([ 0.3235312..., 0.5475665...])),
(array([ 0.316, 0.537]), array([ 0.3151391..., 0.5398333...])),
(array([ 0.265, 0.553]), array([ 0.2634745..., 0.5544335...])),
(array([ 0.221, 0.538]), array([ 0.2211595..., 0.5324470...])),
(array([ 0.135, 0.532]), array([ 0.1396949..., 0.5207234...])),
(array([ 0.145, 0.472]), array([ 0.1512288..., 0.4533041...])),
(array([ 0.163, 0.331]), array([ 0.1715691..., 0.3026264...])),
(array([ 0.176, 0.431]), array([ 0.1825792..., 0.4077892...])),
(array([ 0.244, 0.349]), array([ 0.2418905..., 0.3413401...]))]
"""
experiment_results = (convert_experiment_results_Breneman1987(experiment)
if is_numeric(experiment) else experiment)
with domain_range_scale(1):
XYZ_t, XYZ_r = experiment_results.XYZ_t, experiment_results.XYZ_r
xy_t, xy_r = XYZ_to_xy([XYZ_t, XYZ_r])
uv_t = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_ct, xy_t), xy_t)
uv_m = Luv_to_uv(XYZ_to_Luv(experiment_results.XYZ_cr, xy_r), xy_r)
Y_n = experiment_results.Y_t
XYZ_1 = experiment_results.XYZ_ct
XYZ_2 = chromatic_adaptation_Fairchild1990(XYZ_1, XYZ_t, XYZ_r, Y_n)
uv_p = Luv_to_uv(XYZ_to_Luv(XYZ_2, xy_r), xy_r)
return tuple([
CorrespondingChromaticitiesPrediction(experiment_results.name,
uv_t[i], uv_m[i], uv_p[i])
for i in range(len(uv_t))
])
def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):
"""
Converts from *CIE XYZ* tristimulus values to given colourspace model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like
*CIE XYZ* tristimulus values *illuminant* *xy* chromaticity
coordinates.
model : unicode
**{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',
'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW',
'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz, 'OSA UCS',
'hdr-CIELAB', 'hdr-IPT'}**,
Colourspace model to convert the *CIE XYZ* tristimulus values to.
Other Parameters
----------------
\\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray
Colourspace model values.
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> W = np.array([0.31270, 0.32900])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE XYZ')
array([ 0.2065400..., 0.1219722..., 0.0513695...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xyY')
array([ 0.5436955..., 0.3210794..., 0.1219722...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xy')
array([ 0.5436955..., 0.3210794...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Lab')
array([ 0.4152787..., 0.5263858..., 0.2692317...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHab')
array([ 0.4152787..., 0.5912425..., 0.0752458...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv')
array([ 0.4152787..., 0.9683626..., 0.1775210...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv uv')
array([ 0.3772021..., 0.5012026...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHuv')
array([ 0.4152787..., 0.9844997..., 0.0288560...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS uv')
array([ 0.3772021..., 0.3341350...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UVW')
array([ 0.9455035..., 0.1155536..., 0.4054757...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'DIN 99')
array([ 0.5322822..., 0.2841634..., 0.0389839...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Lab')
array([ 0.3492452..., 0.4703302..., 0.1439330...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Rdab')
array([ 0.1219722..., 0.5709032..., 0.1747109...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'IPT')
array([ 0.3842619..., 0.3848730..., 0.1888683...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'JzAzBz')
array([ 0.0053504..., 0.0092430..., 0.0052600...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'OSA UCS')
array([-0.0300499..., 0.0299713..., -0.0966784...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-CIELAB')
array([ 0.5187002..., 0.6047633..., 0.3214551...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-IPT')
array([ 0.4839376..., 0.4244990..., 0.2201954...])
"""
with domain_range_scale(1):
values = None
if model == 'CIE XYZ':
values = XYZ
elif model == 'CIE xyY':
values = XYZ_to_xyY(XYZ, illuminant)
elif model == 'CIE xy':
values = XYZ_to_xy(XYZ, illuminant)
elif model == 'CIE Lab':
values = XYZ_to_Lab(XYZ, illuminant)
elif model == 'CIE LCHab':
values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))
elif model == 'CIE Luv':
values = XYZ_to_Luv(XYZ, illuminant)
elif model == 'CIE Luv uv':
values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
elif model == 'CIE LCHuv':
values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))
elif model == 'CIE UCS':
values = XYZ_to_UCS(XYZ)
elif model == 'CIE UCS uv':
values = UCS_to_uv(XYZ_to_UCS(XYZ))
elif model == 'CIE UVW':
values = XYZ_to_UVW(XYZ, illuminant)
elif model == 'DIN 99':
values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))
elif model == 'Hunter Lab':
values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))
elif model == 'Hunter Rdab':
values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))
elif model == 'IPT':
values = XYZ_to_IPT(XYZ)
elif model == 'JzAzBz':
values = XYZ_to_JzAzBz(XYZ)
elif model == 'OSA UCS':
values = XYZ_to_OSA_UCS(XYZ)
elif model == 'hdr-CIELAB':
values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)
elif model == 'hdr-IPT':
values = XYZ_to_hdr_IPT(XYZ, **kwargs)
if values is None:
raise ValueError(
'"{0}" not found in colourspace models: "{1}".'.format(
model, ', '.join(COLOURSPACE_MODELS)))
return values
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 or XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
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: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. 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 = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
ii, jj = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(1, 0, samples))
ij = tstack([ii, jj])
# NOTE: Various values in the grid have potential to generate
# zero-divisions, they could be avoided by perturbing the grid, e.g. adding
# a small epsilon. It was decided instead to disable warnings.
with suppress_warnings(python_warnings=True):
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 _full_report(spec, source, date, manufacturer, model, notes=None):
figure = plt.figure(figsize=(8.27, 11.69))
figure.text(0.5,
0.97,
'TM-30-18 Color Rendition Report',
ha='center',
size='x-large')
figure.text(0.250,
0.935,
'Source: ',
ha='right',
size='large',
weight='bold')
figure.text(0.250, 0.935, source, size='large')
figure.text(0.250,
0.907,
'Date: ',
ha='right',
size='large',
weight='bold')
figure.text(0.250, 0.907, date, size='large')
figure.text(0.700,
0.935,
'Manufacturer: ',
ha='right',
size='large',
weight='bold')
figure.text(0.700, 0.935, manufacturer, ha='left', size='large')
figure.text(0.700,
0.907,
'Model: ',
ha='right',
size='large',
weight='bold')
figure.text(0.700, 0.907, model, size='large')
ax = figure.add_axes((0.057, 0.767, 0.385, 0.112))
plot_spectra_TM_30_18(ax, spec)
ax = figure.add_axes((0.036, 0.385, 0.428, 0.333))
plot_color_vector_graphic(ax, spec)
ax = figure.add_axes((0.554, 0.736, 0.409, 0.141))
plot_local_chroma_shifts(ax, spec)
ax = figure.add_axes((0.554, 0.576, 0.409, 0.141))
plot_local_hue_shifts(ax, spec)
ax = figure.add_axes((0.554, 0.401, 0.409, 0.141))
plot_local_color_fidelities(ax, spec)
ax = figure.add_axes((0.094, 0.195, 0.870, 0.161))
plot_colour_fidelity_indexes(ax, spec)
if notes:
figure.text(0.139,
0.114,
'Notes: ',
ha='right',
size='large',
weight='bold')
figure.text(0.139, 0.114, notes, size='large')
XYZ = sd_to_XYZ(spec.sd_test)
xy = XYZ_to_xy(XYZ)
Luv = XYZ_to_Luv(XYZ, xy)
up, vp = Luv_to_uv(Luv, xy)
figure.text(0.712, 0.111, '$x$ {:.4f}'.format(xy[0]), ha='center')
figure.text(0.712, 0.091, '$y$ {:.4f}'.format(xy[1]), ha='center')
figure.text(0.712, 0.071, '$u\'$ {:.4f}'.format(up), ha='center')
figure.text(0.712, 0.051, '$v\'$ {:.4f}'.format(vp), ha='center')
rect = plt.Rectangle((0.814, 0.035),
0.144,
0.096,
color='black',
fill=False)
figure.add_artist(rect)
CRI_spec = colour_rendering_index(spec.sd_test, additional_data=True)
figure.text(0.886, 0.111, 'CIE 13.31-1995', ha='center')
figure.text(0.886, 0.091, '(CRI)', ha='center')
figure.text(0.886,
0.071,
'$R_a$ {:.0f}'.format(CRI_spec.Q_a),
ha='center',
weight='bold')
figure.text(0.886,
0.051,
'$R_9$ {:.0f}'.format(CRI_spec.Q_as[8].Q_a),
ha='center',
weight='bold')
figure.text(0.500,
0.010,
'Created with Colour ' + colour.__version__,
ha='center')
def CIE_1976_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
**kwargs):
"""
Plots the *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
show_diagram_colours : bool, optional
Whether to display the chromaticity diagram background colours.
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_1976_UCS_chromaticity_diagram_plot() # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
if show_diagram_colours:
image = matplotlib.image.imread(
os.path.join(
PLOTTING_RESOURCES_DIRECTORY,
'CIE_1976_UCS_Chromaticity_Diagram_{0}.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation=None, extent=(0, 1, 0, 1))
labels = (420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 680)
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
uv = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
wavelengths_chromaticity_coordinates = dict(zip(wavelengths, uv))
pylab.plot(uv[..., 0], uv[..., 1], color='black', linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]), (uv[-1][1], uv[0][1]),
color='black',
linewidth=2)
for label in labels:
u, v = wavelengths_chromaticity_coordinates[label]
pylab.plot(u, v, 'o', color='black', linewidth=2)
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 = (wavelengths_chromaticity_coordinates[right][0] -
wavelengths_chromaticity_coordinates[left][0])
dy = (wavelengths_chromaticity_coordinates[right][1] -
wavelengths_chromaticity_coordinates[left][1])
uv = np.array([u, v])
direction = np.array([-dy, dx])
normal = (np.array([
-dy, dx
]) if np.dot(normalise_vector(uv - equal_energy),
normalise_vector(direction)) > 0 else np.array([dy, -dx]))
normal = normalise_vector(normal)
normal /= 25
pylab.plot((u, u + normal[0] * 0.75), (v, v + normal[1] * 0.75),
color='black',
linewidth=1.5)
pylab.text(u + normal[0],
v + normal[1],
label,
color='black',
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
ticks = np.arange(-10, 10, 0.1)
pylab.xticks(ticks)
pylab.yticks(ticks)
settings.update({
'title':
'CIE 1976 UCS Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label':
'CIE u\'',
'y_label':
'CIE v\'',
'grid':
True,
'bounding_box': (0, 1, 0, 1)
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def spds_CIE_1976_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
show_diagram_colours : bool, optional
{:func:`CIE_1976_UCS_chromaticity_diagram_plot`},
Whether to display the chromaticity diagram background colours.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1976_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1976_UCS_chromaticity_diagram_plot(cmfs=cmfs, **settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
uv = Luv_to_uv(XYZ_to_Luv(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.1, 0.7),
'standalone': True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs):
"""
Converts from *CIE XYZ* tristimulus values to given colourspace model.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like
Reference *illuminant* *CIE xy* chromaticity coordinates or *CIE xyY*
colourspace array.
model : unicode
**{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',
'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW',
'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz, 'OSA UCS',
'hdr-CIELAB', 'hdr-IPT'}**,
Colourspace model to convert the *CIE XYZ* tristimulus values to.
Other Parameters
----------------
\\**kwargs : dict, optional
Keywords arguments.
Returns
-------
ndarray
Colourspace model values.
Warnings
--------
This definition is is deprecated and will be removed in a future release.
:func:`colour.convert` definition should be used instead.
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
>>> W = np.array([0.31270, 0.32900])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE XYZ')
array([ 0.2065400..., 0.1219722..., 0.0513695...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xyY')
array([ 0.5436955..., 0.3210794..., 0.1219722...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE xy')
array([ 0.5436955..., 0.3210794...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Lab')
array([ 0.4152787..., 0.5263858..., 0.2692317...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHab')
array([ 0.4152787..., 0.5912425..., 0.0752458...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv')
array([ 0.4152787..., 0.9683626..., 0.1775210...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE Luv uv')
array([ 0.3772021..., 0.5012026...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE LCHuv')
array([ 0.4152787..., 0.9844997..., 0.0288560...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS')
array([ 0.1376933..., 0.1219722..., 0.1053731...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UCS uv')
array([ 0.3772021..., 0.3341350...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'CIE UVW')
array([ 0.9455035..., 0.1155536..., 0.4054757...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'DIN 99')
array([ 0.5322822..., 0.2841634..., 0.0389839...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Lab')
array([ 0.3492452..., 0.4703302..., 0.1439330...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'Hunter Rdab')
array([ 0.1219722..., 0.5709032..., 0.1747109...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'IPT')
array([ 0.3842619..., 0.3848730..., 0.1888683...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'JzAzBz')
array([ 0.0053504..., 0.0092430..., 0.0052600...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'OSA UCS')
array([-0.0300499..., 0.0299713..., -0.0966784...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-CIELAB')
array([ 0.5187002..., 0.6047633..., 0.3214551...])
>>> XYZ_to_colourspace_model( # doctest: +ELLIPSIS
... XYZ, W, 'hdr-IPT')
array([ 0.4839376..., 0.4244990..., 0.2201954...])
>>> try:
... XYZ_to_colourspace_model(XYZ, W, 'Undefined')
... except ValueError as error:
... print(error)
"Undefined" not found in colourspace models: "CIE XYZ, CIE xyY, CIE Lab, \
CIE LCHab, CIE Luv, CIE Luv uv, CIE LCHuv, CIE UCS, CIE UCS uv, CIE UVW, \
DIN 99, Hunter Lab, Hunter Rdab, IPT, JzAzBz, OSA UCS, hdr-CIELAB, hdr-IPT".
"""
from colour.models import (
Lab_to_DIN99, Lab_to_LCHab, Luv_to_LCHuv, Luv_to_uv, UCS_to_uv,
XYZ_to_IPT, XYZ_to_Hunter_Lab, XYZ_to_Hunter_Rdab, XYZ_to_Lab,
XYZ_to_Luv, XYZ_to_OSA_UCS, XYZ_to_UCS, XYZ_to_UVW, XYZ_to_hdr_CIELab,
XYZ_to_hdr_IPT, XYZ_to_JzAzBz, XYZ_to_xy, XYZ_to_xyY, xy_to_XYZ)
with domain_range_scale(1):
values = None
if model == 'CIE XYZ':
values = XYZ
elif model == 'CIE xyY':
values = XYZ_to_xyY(XYZ, illuminant)
elif model == 'CIE xy':
values = XYZ_to_xy(XYZ, illuminant)
elif model == 'CIE Lab':
values = XYZ_to_Lab(XYZ, illuminant)
elif model == 'CIE LCHab':
values = Lab_to_LCHab(XYZ_to_Lab(XYZ, illuminant))
elif model == 'CIE Luv':
values = XYZ_to_Luv(XYZ, illuminant)
elif model == 'CIE Luv uv':
values = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
elif model == 'CIE LCHuv':
values = Luv_to_LCHuv(XYZ_to_Luv(XYZ, illuminant))
elif model == 'CIE UCS':
values = XYZ_to_UCS(XYZ)
elif model == 'CIE UCS uv':
values = UCS_to_uv(XYZ_to_UCS(XYZ))
elif model == 'CIE UVW':
values = XYZ_to_UVW(XYZ, illuminant)
elif model == 'DIN 99':
values = Lab_to_DIN99(XYZ_to_Lab(XYZ, illuminant))
elif model == 'Hunter Lab':
values = XYZ_to_Hunter_Lab(XYZ, xy_to_XYZ(illuminant))
elif model == 'Hunter Rdab':
values = XYZ_to_Hunter_Rdab(XYZ, xy_to_XYZ(illuminant))
elif model == 'IPT':
values = XYZ_to_IPT(XYZ)
elif model == 'JzAzBz':
values = XYZ_to_JzAzBz(XYZ)
elif model == 'OSA UCS':
values = XYZ_to_OSA_UCS(XYZ)
elif model == 'hdr-CIELAB':
values = XYZ_to_hdr_CIELab(XYZ, illuminant, **kwargs)
elif model == 'hdr-IPT':
values = XYZ_to_hdr_IPT(XYZ, **kwargs)
if values is None:
raise ValueError(
'"{0}" not found in colourspace models: "{1}".'.format(
model, ', '.join(COLOURSPACE_MODELS)))
return values
