def test_n_dimensional_XYZ_to_xy(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xy` definition n-dimensions
support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
illuminant = np.array([0.31270, 0.32900])
xy = np.array([0.54369557, 0.32107944])
np.testing.assert_almost_equal(
XYZ_to_xy(XYZ, illuminant), xy, decimal=7)
XYZ = np.tile(XYZ, (6, 1))
xy = np.tile(xy, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_xy(XYZ, illuminant), xy, decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(
XYZ_to_xy(XYZ, illuminant), xy, decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
illuminant = np.reshape(xy, (2, 3, 2))
xy = np.reshape(xy, (2, 3, 2))
np.testing.assert_almost_equal(
XYZ_to_xy(XYZ, illuminant), xy, decimal=7)
def test_n_dimensional_XYZ_to_xy(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xy` definition n-dimensions
support.
"""
XYZ = np.array([0.69935853, 1.00000000, 0.94824534])
illuminant = np.array([0.34570, 0.35850])
xy = np.array([0.26414772, 0.37770001])
np.testing.assert_almost_equal(XYZ_to_xy(XYZ, illuminant),
xy,
decimal=7)
XYZ = np.tile(XYZ, (6, 1))
xy = np.tile(xy, (6, 1))
np.testing.assert_almost_equal(XYZ_to_xy(XYZ, illuminant),
xy,
decimal=7)
illuminant = np.tile(illuminant, (6, 1))
np.testing.assert_almost_equal(XYZ_to_xy(XYZ, illuminant),
xy,
decimal=7)
XYZ = np.reshape(XYZ, (2, 3, 3))
illuminant = np.reshape(xy, (2, 3, 2))
xy = np.reshape(xy, (2, 3, 2))
np.testing.assert_almost_equal(XYZ_to_xy(XYZ, illuminant),
xy,
decimal=7)
def test_XYZ_to_xy(self):
"""Test :func:`colour.models.cie_xyy.XYZ_to_xy` definition."""
np.testing.assert_almost_equal(
XYZ_to_xy(np.array([0.20654008, 0.12197225, 0.05136952])),
np.array([0.54369557, 0.32107944]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xy(np.array([0.14222010, 0.23042768, 0.10495772])),
np.array([0.29777735, 0.48246446]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xy(np.array([0.07818780, 0.06157201, 0.28099326])),
np.array([0.18582823, 0.14633764]),
decimal=7,
)
np.testing.assert_almost_equal(
XYZ_to_xy(np.array([0.00000000, 0.00000000, 0.00000000])),
np.array([0.31270000, 0.32900000]),
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 : 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 test_domain_range_scale_XYZ_to_xy(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xy` definition domain and
range scale support.
"""
XYZ = np.array([0.20654008, 0.12197225, 0.05136952])
xy = XYZ_to_xy(XYZ)
XYZ = np.tile(XYZ, (6, 1)).reshape(2, 3, 3)
xy = np.tile(xy, (6, 1)).reshape(2, 3, 2)
d_r = (('reference', 1), (1, 1), (100, 1))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(
XYZ_to_xy(XYZ * factor), xy, decimal=7)
def setUp(self):
"""Initialise the common tests attributes."""
self._shape = SPECTRAL_SHAPE_OTSU2018
self._cmfs, self._sd_D65 = handle_spectral_arguments(
shape_default=self._shape
)
self._reflectances = sds_and_msds_to_msds(
list(SDS_COLOURCHECKERS["ColorChecker N Ohta"].values())
+ list(SDS_COLOURCHECKERS["BabelColor Average"].values())
)
self._tree = Tree_Otsu2018(
self._reflectances, self._cmfs, self._sd_D65
)
self._XYZ_D65 = sd_to_XYZ(self._sd_D65)
self._xy_D65 = XYZ_to_xy(self._XYZ_D65)
self._temporary_directory = tempfile.mkdtemp()
self._path = os.path.join(
self._temporary_directory, "Test_Otsu2018.npz"
)
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 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 get_secondaries(name='ITU-R BT.2020'):
"""
secondary color の座標を求める
Parameters
----------
name : str
a name of the color space.
Returns
-------
array_like
secondaries. the order is magenta, yellow, cyan.
"""
secondary_rgb = np.array([[1.0, 0.0, 1.0],
[1.0, 1.0, 0.0],
[0.0, 1.0, 1.0]])
illuminant_XYZ = D65_WHITE
illuminant_RGB = D65_WHITE
chromatic_adaptation_transform = 'CAT02'
rgb_to_xyz_matrix = get_rgb_to_xyz_matrix(name)
large_xyz = RGB_to_XYZ(secondary_rgb, illuminant_RGB,
illuminant_XYZ, rgb_to_xyz_matrix,
chromatic_adaptation_transform)
xy = XYZ_to_xy(large_xyz, illuminant_XYZ)
return xy, secondary_rgb.reshape((3, 3))
def vs_colorimetry_data(spd_test,
spd_reference,
spds_vs,
cmfs,
chromatic_adaptation=False):
XYZ_t = spectral_to_XYZ(spd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = spectral_to_XYZ(spd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
spd_vs = spds_vs.get(value)
XYZ_vs = spectral_to_XYZ(spd_vs, cmfs, spd_test)
XYZ_vs /= 100
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(spd_vs.name, XYZ_vs, Lab_vs, C_vs))
return vs_data
def XYZ_to_ij(XYZ):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return XYZ_to_xy(XYZ)
def intermediate_values(XYZ_n):
"""
Returns the intermediate values :math:`\\xi`, :math:`\eta`, :math:`\zeta`.
Parameters
----------
XYZ_n : array_like, (3,)
*CIE XYZ* colourspace matrix of reference white in domain [0, 100].
Returns
-------
ndarray, (3,)
Intermediate values :math:`\\xi`, :math:`\eta`, :math:`\zeta`.
Examples
--------
>>> XYZ_n = np.array([95.05, 100, 108.88])
>>> intermediate_values(XYZ_n) # doctest: +ELLIPSIS
array([ 1.0000421..., 0.9999800..., 0.9997579...])
"""
# Illuminant chromaticity coordinates.
x_o, y_o = XYZ_to_xy(XYZ_n)
# Computing :math:`\xi`, :math:`\eta`, :math:`\zeta` values.
xi = (0.48105 * x_o + 0.78841 * y_o - 0.08081) / y_o
eta = (-0.27200 * x_o + 1.11962 * y_o + 0.04570) / y_o
zeta = (0.91822 * (1 - x_o - y_o)) / y_o
return np.array([xi, eta, zeta])
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 RGB_chromaticity_coordinates_CIE_1931_chromaticity_diagram_plot(
RGB,
colourspace,
**kwargs):
"""
Plots given *RGB* colourspace array in *CIE 1931 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_1931_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_1931_chromaticity_diagram_plot(**settings)
alpha_p, colour_p = 0.85, 'black'
xy = XYZ_to_xy(RGB_to_XYZ(RGB,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix),
colourspace.whitepoint)
pylab.scatter(xy[..., 0],
xy[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
settings.update({'standalone': True})
boundaries(**settings)
decorate(**settings)
return display(**settings)
def chromaticity_diagram_colours_CIE1931(
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
antialiasing=True):
"""
Plots the *CIE 1931 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_CIE1931() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, 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(xy)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
return np.dstack([RGB, mask])
def setUp(self):
"""Initialise the common tests attributes."""
self._shape = SPECTRAL_SHAPE_OTSU2018
self._cmfs, self._sd_D65 = handle_spectral_arguments(
shape_default=self._shape
)
self._XYZ_D65 = sd_to_XYZ(self._sd_D65)
self._xy_D65 = XYZ_to_xy(self._XYZ_D65)
def test_nan_XYZ_to_xy(self):
"""Test :func:`colour.models.cie_xyy.XYZ_to_xy` 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:
XYZ = np.array(case)
illuminant = np.array(case[0:2])
XYZ_to_xy(XYZ, illuminant)
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 primaries_whitepoint(npm):
"""
Returns the *primaries* and *whitepoint* :math:`xy` chromaticity
coordinates using given *normalised primary matrix*.
Parameters
----------
npm : array_like, (3, 3)
*Normalised primary matrix*.
Returns
-------
tuple
*Primaries* and *whitepoint* :math:`xy` chromaticity coordinates.
References
----------
.. [2] Trieu, T. (2015). Private Discussion with Mansencal, T.
Examples
--------
>>> npm = np.array([[9.52552396e-01, 0.00000000e+00, 9.36786317e-05],
... [3.43966450e-01, 7.28166097e-01, -7.21325464e-02],
... [0.00000000e+00, 0.00000000e+00, 1.00882518e+00]])
>>> p, w = primaries_whitepoint(npm)
>>> p # doctest: +ELLIPSIS
array([[ 7.3470000...e-01, 2.6530000...e-01],
[ 0.0000000...e+00, 1.0000000...e+00],
[ 1.0000000...e-04, -7.7000000...e-02]])
>>> w # doctest: +ELLIPSIS
array([ 0.32168, 0.33767])
"""
npm = npm.reshape((3, 3))
primaries = XYZ_to_xy(
np.transpose(np.dot(npm, np.identity(3))))
whitepoint = np.squeeze(XYZ_to_xy(
np.transpose(np.dot(npm, np.ones((3, 1))))))
# TODO: Investigate if we return an ndarray here with primaries and
# whitepoint stacked together.
return primaries, whitepoint
def setUp(self):
"""Initialise the common tests attributes."""
self._xy_s = XYZ_to_xy(
MSDS_CMFS["CIE 1931 2 Degree Standard Observer"].values
)
self._xy_D65 = CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"][
"D65"
]
def vs_colorimetry_data(
sd_test: SpectralDistribution,
sd_reference: SpectralDistribution,
sds_vs: Dict[str, SpectralDistribution],
cmfs: MultiSpectralDistributions,
chromatic_adaptation: Boolean = False,
) -> Tuple[VS_ColorimetryData, ...]:
"""
Return the *VS test colour samples* colorimetry data.
Parameters
----------
sd_test
Test spectral distribution.
sd_reference
Reference spectral distribution.
sds_vs
*VS test colour samples* spectral distributions.
cmfs
Standard observer colour matching functions.
chromatic_adaptation
Whether to perform chromatic adaptation.
Returns
-------
:class:`tuple`
*VS test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(INDEXES_TO_NAMES_VS.items()):
sd_vs = sds_vs[value]
with domain_range_scale("1"):
XYZ_vs = sd_to_XYZ(sd_vs, cmfs, sd_test)
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform="CMCCAT2000")
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(sd_vs.name, XYZ_vs, Lab_vs, C_vs))
return tuple(vs_data)
def primaries_whitepoint(npm: ArrayLike) -> Tuple[NDArray, NDArray]:
"""
Compute the *primaries* and *whitepoint* :math:`xy` chromaticity
coordinates using given *Normalised Primary Matrix* (NPM).
Parameters
----------
npm
*Normalised Primary Matrix*.
Returns
-------
:class:`tuple`
*Primaries* and *whitepoint* :math:`xy` chromaticity coordinates.
References
----------
:cite:`Trieu2015a`
Examples
--------
>>> npm = np.array([[9.52552396e-01, 0.00000000e+00, 9.36786317e-05],
... [3.43966450e-01, 7.28166097e-01, -7.21325464e-02],
... [0.00000000e+00, 0.00000000e+00, 1.00882518e+00]])
>>> p, w = primaries_whitepoint(npm)
>>> p # doctest: +ELLIPSIS
array([[ 7.3470000...e-01, 2.6530000...e-01],
[ 0.0000000...e+00, 1.0000000...e+00],
[ 1.0000000...e-04, -7.7000000...e-02]])
>>> w # doctest: +ELLIPSIS
array([ 0.32168, 0.33767])
"""
npm = np.reshape(npm, (3, 3))
primaries = XYZ_to_xy(np.transpose(np.dot(npm, np.identity(3))))
whitepoint = np.squeeze(XYZ_to_xy(np.transpose(np.dot(npm, ones((3, 1))))))
# TODO: Investigate if we return an ndarray here with primaries and
# whitepoint stacked together.
return primaries, whitepoint
def vs_colorimetry_data(sd_test,
sd_reference,
sds_vs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *VS test colour samples* colorimetry data.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
sd_reference : SpectralDistribution
Reference spectral distribution.
sds_vs : dict
*VS test colour samples* spectral distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*VS test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_test, cmfs)
XYZ_t /= XYZ_t[1]
XYZ_r = sd_to_XYZ(sd_reference, cmfs)
XYZ_r /= XYZ_r[1]
xy_r = XYZ_to_xy(XYZ_r)
vs_data = []
for _key, value in sorted(VS_INDEXES_TO_NAMES.items()):
sd_vs = sds_vs[value]
with domain_range_scale('1'):
XYZ_vs = sd_to_XYZ(sd_vs, cmfs, sd_test)
if chromatic_adaptation:
XYZ_vs = chromatic_adaptation_VonKries(XYZ_vs,
XYZ_t,
XYZ_r,
transform='CMCCAT2000')
Lab_vs = XYZ_to_Lab(XYZ_vs, illuminant=xy_r)
_L_vs, C_vs, _Hab = Lab_to_LCHab(Lab_vs)
vs_data.append(VS_ColorimetryData(sd_vs.name, XYZ_vs, Lab_vs, C_vs))
return vs_data
def test_XYZ_to_xy(self):
"""
Tests :func:`colour.models.cie_xyy.XYZ_to_xy` definition.
"""
np.testing.assert_almost_equal(XYZ_to_xy(
np.array([0.07049534, 0.10080000, 0.09558313])),
np.array([0.26414772, 0.37770001]),
decimal=7)
np.testing.assert_almost_equal(XYZ_to_xy(
np.array([0.47097710, 0.34950000, 0.11301649])),
np.array([0.50453169, 0.37440000]),
decimal=7)
np.testing.assert_almost_equal(XYZ_to_xy(
np.array([0.25506814, 0.19150000, 0.08849752])),
np.array([0.47670437, 0.35790000]),
decimal=7)
np.testing.assert_almost_equal(XYZ_to_xy(
np.array([0.00000000, 0.00000000, 0.00000000])),
np.array([0.34570000, 0.35850000]),
decimal=7)
def _get_cmfs_xy():
"""
xy色度図のプロットのための馬蹄形の外枠のxy値を求める。
Returns
-------
array_like
xy coordinate for chromaticity diagram
"""
# 基本パラメータ設定
# ------------------
cmf = CMFS.get(CMFS_NAME)
d65_white = D65_WHITE
# 馬蹄形のxy値を算出
# --------------------------
cmf_xy = XYZ_to_xy(cmf.values, d65_white)
return cmf_xy
def spds_CIE_1931_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1931 Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
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_1931_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_1931_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1931_chromaticity_diagram_plot(cmfs=cmfs, **settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
xy = XYZ_to_xy(XYZ)
pylab.plot(xy[0], xy[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=xy,
xytext=(50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.9, -0.1, 0.9),
'standalone': True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def 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 CIE_1931_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
**kwargs):
"""
Plots the *CIE 1931 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_1931_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_1931_Chromaticity_Diagram_{0}.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation=None, extent=(0, 1, 0, 1))
labels = (390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600, 620,
700)
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
xy = XYZ_to_xy(cmfs.values, illuminant)
wavelengths_chromaticity_coordinates = dict(tuple(zip(wavelengths, xy)))
pylab.plot(xy[..., 0], xy[..., 1], color='black', linewidth=2)
pylab.plot((xy[-1][0], xy[0][0]), (xy[-1][1], xy[0][1]),
color='black',
linewidth=2)
for label in labels:
x, y = wavelengths_chromaticity_coordinates[label]
pylab.plot(x, y, '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])
xy = np.array([x, y])
direction = np.array([-dy, dx])
normal = (np.array([
-dy, dx
]) if np.dot(normalise_vector(xy - equal_energy),
normalise_vector(direction)) > 0 else np.array([dy, -dx]))
normal = normalise_vector(normal)
normal /= 25
pylab.plot((x, x + normal[0] * 0.75), (y, y + normal[1] * 0.75),
color='black',
linewidth=1.5)
pylab.text(x + normal[0],
y + 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 1931 Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label':
'CIE x',
'y_label':
'CIE y',
'grid':
True,
'bounding_box': (0, 1, 0, 1)
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def dominant_wavelength(xy,
xy_n,
cmfs=CMFS['CIE 1931 2 Degree Standard Observer'],
inverse=False):
"""
Returns the *dominant wavelength* :math:`\\lambda_d` for given colour
stimulus :math:`xy` and the related :math:`xy_wl` first and :math:`xy_{cw}`
second intersection coordinates with the spectral locus.
In the eventuality where the :math:`xy_wl` first intersection coordinates
are on the line of purples, the *complementary wavelength* will be
computed in lieu.
The *complementary wavelength* is indicated by a negative sign
and the :math:`xy_{cw}` second intersection coordinates which are set by
default to the same value than :math:`xy_wl` first intersection coordinates
will be set to the *complementary dominant wavelength* intersection
coordinates with the spectral locus.
Parameters
----------
xy : array_like
Colour stimulus *CIE xy* chromaticity coordinates.
xy_n : array_like
Achromatic stimulus *CIE xy* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
inverse : bool, optional
Inverse the computation direction to retrieve the
*complementary wavelength*.
Returns
-------
tuple
*Dominant wavelength*, first intersection point *CIE xy* chromaticity
coordinates, second intersection point *CIE xy* chromaticity
coordinates.
References
----------
:cite:`CIETC1-482004o`, :cite:`Erdogana`
Examples
--------
*Dominant wavelength* computation:
>>> from pprint import pprint
>>> xy = np.array([0.54369557, 0.32107944])
>>> xy_n = np.array([0.31270000, 0.32900000])
>>> cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(616...),
array([ 0.6835474..., 0.3162840...]),
array([ 0.6835474..., 0.3162840...]))
*Complementary dominant wavelength* is returned if the first intersection
is located on the line of purples:
>>> xy = np.array([0.37605506, 0.24452225])
>>> pprint(dominant_wavelength(xy, xy_n, cmfs)) # doctest: +ELLIPSIS
(array(-509.0),
array([ 0.4572314..., 0.1362814...]),
array([ 0.0104096..., 0.7320745...]))
"""
xy = as_float_array(xy)
xy_n = np.resize(xy_n, xy.shape)
xy_s = XYZ_to_xy(cmfs.values)
i_wl, xy_wl = closest_spectral_locus_wavelength(xy, xy_n, xy_s, inverse)
xy_cwl = xy_wl
wl = cmfs.wavelengths[i_wl]
xy_e = (extend_line_segment(xy, xy_n) if inverse else extend_line_segment(
xy_n, xy))
intersect = intersect_line_segments(np.concatenate((xy_n, xy_e), -1),
np.hstack([xy_s[0],
xy_s[-1]])).intersect
intersect = np.reshape(intersect, wl.shape)
i_wl_r, xy_cwl_r = closest_spectral_locus_wavelength(
xy, xy_n, xy_s, not inverse)
wl_r = -cmfs.wavelengths[i_wl_r]
wl = np.where(intersect, wl_r, wl)
xy_cwl = np.where(intersect[..., np.newaxis], xy_cwl_r, xy_cwl)
return wl, np.squeeze(xy_wl), np.squeeze(xy_cwl)
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)
