def test_n_dimensional_UCS_to_uv(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_to_uv` definition n-dimensions
support.
"""
UCS = np.array([0.04699689, 0.10080000, 0.16374390])
uv = np.array([0.15085309, 0.32355314])
np.testing.assert_almost_equal(
UCS_to_uv(UCS),
uv,
decimal=7)
UCS = np.tile(UCS, (6, 1))
uv = np.tile(uv, (6, 1))
np.testing.assert_almost_equal(
UCS_to_uv(UCS),
uv,
decimal=7)
UCS = np.reshape(UCS, (2, 3, 3))
uv = np.reshape(uv, (2, 3, 2))
np.testing.assert_almost_equal(
UCS_to_uv(UCS),
uv,
decimal=7)
def XYZ_to_UVW(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D50']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE 1964 U\*V\*W\**
colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE 1964 U\*V\*W\** colourspace array.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
- Input *CIE XYZ* tristimulus values are in domain [0, 100].
- Input *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array are in domain [0, :math:`\infty`].
- Output *CIE 1964 U\*V\*W\** colourspace array is in range [0, 100].
References
----------
- :cite:`Wikipediacj`
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.07049534, 0.10080000, 0.09558313]) * 100
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([-28.0579733..., -0.8819449..., 37.0041149...])
"""
xyY = XYZ_to_xyY(XYZ, xyY_to_xy(illuminant))
_x, _y, Y = tsplit(xyY)
u, v = tsplit(UCS_to_uv(XYZ_to_UCS(XYZ)))
u_0, v_0 = tsplit(UCS_to_uv(XYZ_to_UCS(xyY_to_XYZ(xy_to_xyY(illuminant)))))
W = 25 * Y**(1 / 3) - 17
U = 13 * W * (u - u_0)
V = 13 * W * (v - v_0)
UVW = tstack((U, V, W))
return UVW
def _CCT_to_uv_Ohno2013(
CCT_D_uv,
cmfs=MSDS_CMFS_STANDARD_OBSERVER['CIE 1931 2 Degree Standard Observer']
):
"""
Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}` and
colour matching functions using *Ohno (2013)* method.
Parameters
----------
CCT_D_uv : ndarray
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
"""
CCT, D_uv = tsplit(CCT_D_uv)
cmfs = cmfs.copy().trim(SPECTRAL_SHAPE_DEFAULT)
shape = cmfs.shape
delta = 0.01
sd = sd_blackbody(CCT, shape)
XYZ = sd_to_XYZ(sd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u0, v0 = UCS_to_uv(UVW)
if D_uv == 0:
return np.array([u0, v0])
else:
sd = sd_blackbody(CCT + delta, shape)
XYZ = sd_to_XYZ(sd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u1, v1 = UCS_to_uv(UVW)
du = u0 - u1
dv = v0 - v1
u = u0 - D_uv * (dv / np.hypot(du, dv))
v = v0 + D_uv * (du / np.hypot(du, dv))
return np.array([u, v])
def _CCT_to_uv_Ohno2013(
CCT_D_uv: ArrayLike,
cmfs: Optional[MultiSpectralDistributions] = None) -> NDArray:
"""
Return the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}` and
colour matching functions using *Ohno (2013)* method.
Parameters
----------
CCT_D_uv
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
cmfs
Standard observer colour matching functions, default to the
*CIE 1931 2 Degree Standard Observer*.
Returns
-------
:class:`numpy.ndarray`
*CIE UCS* colourspace *uv* chromaticity coordinates.
"""
CCT, D_uv = tsplit(CCT_D_uv)
cmfs, _illuminant = handle_spectral_arguments(cmfs)
delta = 0.01
sd = sd_blackbody(CCT, cmfs.shape)
XYZ = sd_to_XYZ(sd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u0, v0 = UCS_to_uv(UVW)
if D_uv == 0:
return np.array([u0, v0])
else:
sd = sd_blackbody(CCT + delta, cmfs.shape)
XYZ = sd_to_XYZ(sd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u1, v1 = UCS_to_uv(UVW)
du = u0 - u1
dv = v0 - v1
u = u0 - D_uv * (dv / np.hypot(du, dv))
v = v0 + D_uv * (du / np.hypot(du, dv))
return np.array([u, v])
def test_domain_range_scale_UCS_to_uv(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_to_uv` definition domain and
range scale support.
"""
UCS = np.array([0.0469968933, 0.1008000000, 0.1637438950])
uv = UCS_to_uv(UCS)
d_r = (('reference', 1), (1, 1), (100, 100))
for scale, factor in d_r:
with domain_range_scale(scale):
np.testing.assert_almost_equal(UCS_to_uv(UCS * factor),
uv,
decimal=7)
def test_n_dimensional_UCS_to_uv(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_to_uv` definition n-dimensional
support.
"""
UCS = np.array([0.13769339, 0.12197225, 0.10537310])
uv = UCS_to_uv(UCS)
UCS = np.tile(UCS, (6, 1))
uv = np.tile(uv, (6, 1))
np.testing.assert_almost_equal(UCS_to_uv(UCS), uv, decimal=7)
UCS = np.reshape(UCS, (2, 3, 3))
uv = np.reshape(uv, (2, 3, 2))
np.testing.assert_almost_equal(UCS_to_uv(UCS), uv, decimal=7)
def XYZ_to_ij(XYZ):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return UCS_to_uv(XYZ_to_UCS(XYZ))
def xy_to_ij(xy):
"""
Converts given *CIE xy* chromaticity coordinates to *ij*
chromaticity coordinates.
"""
return UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
def CCT_reference_illuminant(sd: SpectralDistribution) -> NDArray:
"""
Compute the reference illuminant correlated colour temperature
:math:`T_{cp}` and :math:`\\Delta_{uv}` for given test spectral
distribution using *Ohno (2013)* method.
Parameters
----------
sd
Test spectral distribution.
Returns
-------
:class:`numpy.ndarray`
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> CCT_reference_illuminant(sd) # doctest: +ELLIPSIS
array([ 4.2244697...e+03, 1.7871111...e-03])
"""
XYZ = sd_to_XYZ(sd)
return uv_to_CCT_Ohno2013(UCS_to_uv(XYZ_to_UCS(XYZ)))
def CCT_reference_illuminant(sd):
"""
Computes the reference illuminant correlated colour temperature
:math:`T_{cp}` and :math:`\\Delta_{uv}` for given test spectral
distribution using *Ohno (2013)* method.
Parameters
----------
sd : SpectralDistribution
Test spectral distribution.
Returns
-------
ndarray
Correlated colour temperature :math:`T_{cp}`, :math:`\\Delta_{uv}`.
Examples
--------
>>> from colour import SDS_ILLUMINANTS
>>> sd = SDS_ILLUMINANTS['FL2']
>>> CCT_reference_illuminant(sd) # doctest: +ELLIPSIS
(4224.4697052..., 0.0017871...)
"""
XYZ = sd_to_XYZ(sd)
CCT, D_uv = uv_to_CCT_Ohno2013(UCS_to_uv(XYZ_to_UCS(XYZ)))
return CCT, D_uv
def test_nan_UCS_to_uv(self):
"""Test :func:`colour.models.cie_ucs.UCS_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:
UCS = np.array(case)
UCS_to_uv(UCS)
def chromaticity_diagram_colours_CIE1960UCS(
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
antialiasing=True):
"""
Plots the *CIE 1960 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_CIE1960UCS() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(UCS_to_uv(XYZ_to_UCS(cmfs.values)),
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(UCS_uv_to_xy(xy))
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
return np.dstack([RGB, mask])
def planckian_table(uv, cmfs, start, end, count):
"""
Returns a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using *Yoshi Ohno (2013)* method.
Parameters
----------
uv : array_like
*uv* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
start : numeric
Temperature range start in kelvins.
end : numeric
Temperature range end in kelvins.
count : int
Temperatures count in the planckian table.
Returns
-------
list
Planckian table.
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> from pprint import pprint
>>> cmfs = 'CIE 1931 2 Degree Standard Observer'
>>> cmfs = STANDARD_OBSERVERS_CMFS.get(cmfs)
>>> pprint(planckian_table((0.1978, 0.3122), cmfs, 1000, 1010, 10)) # noqa # doctest: +ELLIPSIS
[PlanckianTable_Tuvdi(Ti=1000.0, ui=0.4480108..., vi=0.3546249..., di=0.2537821...),
PlanckianTable_Tuvdi(Ti=1001.1111111..., ui=0.4477508..., vi=0.3546475..., di=0.2535294...),
PlanckianTable_Tuvdi(Ti=1002.2222222..., ui=0.4474910..., vi=0.3546700..., di=0.2532771...),
PlanckianTable_Tuvdi(Ti=1003.3333333..., ui=0.4472316..., vi=0.3546924..., di=0.2530251...),
PlanckianTable_Tuvdi(Ti=1004.4444444..., ui=0.4469724..., vi=0.3547148..., di=0.2527734...),
PlanckianTable_Tuvdi(Ti=1005.5555555..., ui=0.4467136..., vi=0.3547372..., di=0.2525220...),
PlanckianTable_Tuvdi(Ti=1006.6666666..., ui=0.4464550..., vi=0.3547595..., di=0.2522710...),
PlanckianTable_Tuvdi(Ti=1007.7777777..., ui=0.4461968..., vi=0.3547817..., di=0.2520202...),
PlanckianTable_Tuvdi(Ti=1008.8888888..., ui=0.4459389..., vi=0.3548040..., di=0.2517697...),
PlanckianTable_Tuvdi(Ti=1010.0, ui=0.4456812..., vi=0.3548261..., di=0.2515196...)]
"""
ux, vx = uv
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
spd = blackbody_spd(Ti, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
ui, vi = UCS_to_uv(UVW)
di = math.sqrt((ux - ui)**2 + (vx - vi)**2)
table.append(PLANCKIAN_TABLE_TUVD(Ti, ui, vi, di))
return table
def RGB_chromaticity_coordinates_CIE_1960_UCS_chromaticity_diagram_plot(
RGB,
colourspace,
**kwargs):
"""
Plots given *RGB* colourspace array in *CIE 1960 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_1960_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_1960_UCS_chromaticity_diagram_plot(**settings)
alpha_p, colour_p = 0.85, 'black'
uv = UCS_to_uv(XYZ_to_UCS(RGB_to_XYZ(RGB,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix)))
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 test_UCS_to_uv(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_to_uv` definition.
"""
np.testing.assert_almost_equal(UCS_to_uv(
np.array([0.13769339, 0.12197225, 0.10537310])),
np.array([0.37720213, 0.33413508]),
decimal=7)
np.testing.assert_almost_equal(UCS_to_uv(
np.array([0.09481340, 0.23042768, 0.32701033])),
np.array([0.14536327, 0.35328046]),
decimal=7)
np.testing.assert_almost_equal(UCS_to_uv(
np.array([0.05212520, 0.06157201, 0.19376075])),
np.array([0.16953602, 0.20026156]),
decimal=7)
def XYZ_to_UVW(XYZ,
illuminant=ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')):
"""
Converts from *CIE XYZ* colourspace to *CIE 1964 U\*V*\W\** colourspace.
Parameters
----------
XYZ : array_like, (3,)
*CIE XYZ* colourspace matrix.
illuminant : array_like, optional
Reference *illuminant* chromaticity coordinates.
Returns
-------
ndarray, (3,)
*CIE 1964 U\*V*\W\** colourspace matrix.
Notes
-----
- Input *CIE XYZ* colourspace matrix is in domain [0, 100].
- Output *CIE UVW* colourspace matrix is in domain [0, 100].
Warning
-------
The input / output domains of that definition are non standard!
Examples
--------
>>> XYZ = np.array([11.80583421, 10.34, 5.15089229])
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([ 24.2543371..., 7.2205484..., 37.4645000...])
"""
x, y, Y = np.ravel(XYZ_to_xyY(XYZ, illuminant))
u, v = np.ravel(UCS_to_uv(XYZ_to_UCS(XYZ)))
u0, v0 = np.ravel(UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(illuminant))))
W = 25 * Y**(1 / 3) - 17
U = 13 * W * (u - u0)
V = 13 * W * (v - v0)
return np.array([U, V, W])
def test_UCS_to_uv(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_to_uv` definition.
"""
np.testing.assert_almost_equal(
UCS_to_uv(np.array([7.87055614, 10.34, 12.18252904])),
(0.25895877609618834, 0.34020896328103534),
decimal=7)
np.testing.assert_almost_equal(
UCS_to_uv(np.array([2.05793361, 3.2, 4.60117812])),
(0.20873418076173886, 0.32457285074301517),
decimal=7)
np.testing.assert_almost_equal(
UCS_to_uv(np.array([0.64604821, 1, 1.57635992])),
(0.20048615319251942, 0.31032692311386395),
decimal=7)
def test_UCS_to_uv(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_to_uv` definition.
"""
np.testing.assert_almost_equal(
UCS_to_uv(np.array([0.04699689, 0.10080000, 0.16374390])),
np.array([0.15085309, 0.32355314]),
decimal=7)
np.testing.assert_almost_equal(
UCS_to_uv(np.array([0.31398473, 0.34950000, 0.34526969])),
np.array([0.31125983, 0.34646688]),
decimal=7)
np.testing.assert_almost_equal(
UCS_to_uv(np.array([0.17004543, 0.19150000, 0.20396469])),
np.array([0.30069388, 0.33863231]),
decimal=7)
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 planckian_locus_CIE_1960_UCS_chromaticity_diagram_plot(
illuminants=None,
**kwargs):
"""
Plots the planckian locus and given illuminants in
*CIE 1960 UCS Chromaticity Diagram*.
Parameters
----------
illuminants : array_like, optional
Factory illuminants to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Raises
------
KeyError
If one of the given illuminant is not found in the factory illuminants.
Examples
--------
>>> ils = ['A', 'C', 'E']
>>> planckian_locus_CIE_1960_UCS_chromaticity_diagram_plot(
... ils) # doctest: +SKIP
"""
if illuminants is None:
illuminants = ('A', 'C', 'E')
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
settings = {
'title': ('{0} Illuminants - Planckian Locus\n'
'CIE 1960 UCS Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer').format(
', '.join(illuminants))
if illuminants else
('Planckian Locus\nCIE 1960 UCS Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer'),
'standalone': False}
settings.update(kwargs)
CIE_1960_UCS_chromaticity_diagram_plot(**settings)
start, end = 1667, 100000
uv = np.array([CCT_to_uv(x, 0, method='Robertson 1968')
for x in np.arange(start, end + 250, 250)])
pylab.plot(uv[..., 0], uv[..., 1], color='black', linewidth=2)
for i in (1667, 2000, 2500, 3000, 4000, 6000, 10000):
u0, v0 = CCT_to_uv(i, -0.05, method='Robertson 1968')
u1, v1 = CCT_to_uv(i, 0.05, method='Robertson 1968')
pylab.plot((u0, u1), (v0, v1), color='black', linewidth=2)
pylab.annotate('{0}K'.format(i),
xy=(u0, v0),
xytext=(0, -10),
color='black',
textcoords='offset points',
size='x-small')
for illuminant in illuminants:
xy = ILLUMINANTS.get(cmfs.name).get(illuminant)
if xy is None:
raise KeyError(
('Illuminant "{0}" not found in factory illuminants: '
'"{1}".').format(illuminant,
sorted(ILLUMINANTS.get(cmfs.name).keys())))
uv = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(xy)))
pylab.plot(uv[0], uv[1], 'o', color='white', linewidth=2)
pylab.annotate(illuminant,
xy=(uv[0], uv[1]),
xytext=(-50, 30),
color='black',
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.2, 0.6),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def colour_rendering_index(sd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
distribution.
Parameters
----------
sd_test : SpectralDistribution
Test spectral distribution.
additional_data : bool, optional
Whether to output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
References
----------
:cite:`Ohno2008a`
Examples
--------
>>> from colour import ILLUMINANTS_SDS
>>> sd = ILLUMINANTS_SDS['FL2']
>>> colour_rendering_index(sd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer'].copy(
).trim(DEFAULT_SPECTRAL_SHAPE)
shape = cmfs.shape
sd_test = sd_test.copy().align(shape)
tcs_sds = {sd.name: sd.copy().align(shape) for sd in TCS_SDS.values()}
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
sd_reference = sd_blackbody(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
sd_reference = sd_CIE_illuminant_D_series(xy)
sd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(sd_test,
sd_reference,
tcs_sds,
cmfs,
chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
sd_reference, sd_reference, tcs_sds, cmfs)
Q_as = colour_rendering_indexes(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data)
Q_a = np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(
sd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data, reference_tcs_colorimetry_data))
else:
return Q_a
def tcs_colorimetry_data(sd_t,
sd_r,
sds_tcs,
cmfs,
chromatic_adaptation=False):
"""
Returns the *test colour samples* colorimetry data.
Parameters
----------
sd_t : SpectralDistribution
Test spectral distribution.
sd_r : SpectralDistribution
Reference spectral distribution.
sds_tcs : dict
*Test colour samples* spectral distributions.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
chromatic_adaptation : bool, optional
Perform chromatic adaptation.
Returns
-------
list
*Test colour samples* colorimetry data.
"""
XYZ_t = sd_to_XYZ(sd_t, cmfs)
uv_t = UCS_to_uv(XYZ_to_UCS(XYZ_t))
u_t, v_t = uv_t[0], uv_t[1]
XYZ_r = sd_to_XYZ(sd_r, cmfs)
uv_r = UCS_to_uv(XYZ_to_UCS(XYZ_r))
u_r, v_r = uv_r[0], uv_r[1]
tcs_data = []
for _key, value in sorted(TCS_INDEXES_TO_NAMES.items()):
sd_tcs = sds_tcs[value]
XYZ_tcs = sd_to_XYZ(sd_tcs, cmfs, sd_t)
xyY_tcs = XYZ_to_xyY(XYZ_tcs)
uv_tcs = UCS_to_uv(XYZ_to_UCS(XYZ_tcs))
u_tcs, v_tcs = uv_tcs[0], uv_tcs[1]
if chromatic_adaptation:
def c(x, y):
"""
Computes the :math:`c` term.
"""
return (4 - x - 10 * y) / y
def d(x, y):
"""
Computes the :math:`d` term.
"""
return (1.708 * y + 0.404 - 1.481 * x) / y
c_t, d_t = c(u_t, v_t), d(u_t, v_t)
c_r, d_r = c(u_r, v_r), d(u_r, v_r)
tcs_c, tcs_d = c(u_tcs, v_tcs), d(u_tcs, v_tcs)
u_tcs = (
(10.872 + 0.404 * c_r / c_t * tcs_c - 4 * d_r / d_t * tcs_d) /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
v_tcs = (5.52 /
(16.518 + 1.481 * c_r / c_t * tcs_c - d_r / d_t * tcs_d))
W_tcs = 25 * spow(xyY_tcs[-1], 1 / 3) - 17
U_tcs = 13 * W_tcs * (u_tcs - u_r)
V_tcs = 13 * W_tcs * (v_tcs - v_r)
tcs_data.append(
TCS_ColorimetryData(sd_tcs.name, XYZ_tcs, uv_tcs,
np.array([U_tcs, V_tcs, W_tcs])))
return tcs_data
def CIE_1960_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
**kwargs):
"""
Plots the *CIE 1960 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_1960_UCS_chromaticity_diagram_plot() # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
if show_diagram_colours:
image = matplotlib.image.imread(
os.path.join(
PLOTTING_RESOURCES_DIRECTORY,
'CIE_1960_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 = UCS_to_uv(XYZ_to_UCS(cmfs.values))
wavelengths_chromaticity_coordinates = dict(tuple(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 1960 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 colour_rendering_index(spd_test, additional_data=False):
"""
Returns the *Colour Rendering Index* (CRI) :math:`Q_a` of given spectral
power distribution.
Parameters
----------
spd_test : SpectralPowerDistribution
Test spectral power distribution.
additional_data : bool, optional
Output additional data.
Returns
-------
numeric or CRI_Specification
*Colour Rendering Index* (CRI).
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> spd = ILLUMINANTS_RELATIVE_SPDS['F2']
>>> colour_rendering_index(spd) # doctest: +ELLIPSIS
64.1515202...
"""
cmfs = STANDARD_OBSERVERS_CMFS[
'CIE 1931 2 Degree Standard Observer'].clone().trim_wavelengths(
ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
spd_test = spd_test.clone().align(shape)
tcs_spds = {
spd.name: spd.clone().align(shape)
for spd in TCS_SPDS.values()
}
XYZ = spectral_to_XYZ(spd_test, cmfs)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
CCT, _D_uv = uv_to_CCT_Robertson1968(uv)
if CCT < 5000:
spd_reference = blackbody_spd(CCT, shape)
else:
xy = CCT_to_xy_CIE_D(CCT)
spd_reference = D_illuminant_relative_spd(xy)
spd_reference.align(shape)
test_tcs_colorimetry_data = tcs_colorimetry_data(
spd_test, spd_reference, tcs_spds, cmfs, chromatic_adaptation=True)
reference_tcs_colorimetry_data = tcs_colorimetry_data(
spd_reference, spd_reference, tcs_spds, cmfs)
Q_as = colour_rendering_indexes(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data)
Q_a = np.average(
[v.Q_a for k, v in Q_as.items() if k in (1, 2, 3, 4, 5, 6, 7, 8)])
if additional_data:
return CRI_Specification(spd_test.name, Q_a, Q_as,
(test_tcs_colorimetry_data,
reference_tcs_colorimetry_data))
else:
return Q_a
def XYZ_to_UVW(
XYZ,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts from *CIE XYZ* tristimulus values to *CIE 1964 U\\*V\\*W\\**
colourspace.
Parameters
----------
XYZ : array_like
*CIE XYZ* tristimulus values.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE 1964 U\\*V\\*W\\** colourspace array.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``UVW`` | ``U`` : [-100, 100] | ``U`` : [-1, 1] |
| | | |
| | ``V`` : [-100, 100] | ``V`` : [-1, 1] |
| | | |
| | ``W`` : [0, 100] | ``W`` : [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`Wikipedia2008a`
Examples
--------
>>> import numpy as np
>>> XYZ = np.array([0.20654008, 0.12197225, 0.05136952]) * 100
>>> XYZ_to_UVW(XYZ) # doctest: +ELLIPSIS
array([ 94.5503572..., 11.5553652..., 40.5475740...])
"""
XYZ = to_domain_100(XYZ)
xy = xyY_to_xy(illuminant)
xyY = XYZ_to_xyY(XYZ, xy)
_x, _y, Y = tsplit(xyY)
u, v = tsplit(UCS_to_uv(XYZ_to_UCS(XYZ)))
u_0, v_0 = tsplit(xy_to_UCS_uv(xy))
W = 25 * spow(Y, 1 / 3) - 17
U = 13 * W * (u - u_0)
V = 13 * W * (v - v_0)
UVW = tstack([U, V, W])
return from_range_100(UVW)
def CIE_1960_UCS_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1960 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_1960_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(UCS_to_uv(XYZ_to_UCS(cmfs.values)),
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(UCS_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 CCT_to_uv_Ohno2013(
CCT,
D_uv=0,
cmfs=STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']):
"""
Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\Delta_{uv}` and
colour matching functions using *Ohno (2013)* method.
Parameters
----------
CCT : numeric
Correlated colour temperature :math:`T_{cp}`.
D_uv : numeric, optional
:math:`\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
References
----------
- :cite:`Ohno2014a`
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> CCT = 6507.4342201047066
>>> D_uv = 0.003223690901513
>>> CCT_to_uv_Ohno2013(CCT, D_uv, cmfs) # doctest: +ELLIPSIS
array([ 0.1977999..., 0.3122004...])
"""
cmfs = cmfs.copy().trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
delta = 0.01
spd = blackbody_spd(CCT, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u0, v0 = UCS_to_uv(UVW)
if D_uv == 0:
return np.array([u0, v0])
else:
spd = blackbody_spd(CCT + delta, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ *= 1 / np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
u1, v1 = UCS_to_uv(UVW)
du = u0 - u1
dv = v0 - v1
u = u0 - D_uv * (dv / np.hypot(du, dv))
v = v0 + D_uv * (du / np.hypot(du, dv))
return np.array([u, v])
def spds_CIE_1960_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 1960 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_1960_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_1960_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1960_UCS_chromaticity_diagram_plot(cmfs=cmfs, **settings)
for spd in spds:
XYZ = spectral_to_XYZ(spd) / 100
uv = UCS_to_uv(XYZ_to_UCS(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, -0.2, 0.6),
'standalone': True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def planckian_table(uv, cmfs, start, end, count):
"""
Returns a planckian table from given *CIE UCS* colourspace *uv*
chromaticity coordinates, colour matching functions and temperature range
using *Ohno (2013)* method.
Parameters
----------
uv : array_like
*uv* chromaticity coordinates.
cmfs : XYZ_ColourMatchingFunctions
Standard observer colour matching functions.
start : numeric
Temperature range start in kelvins.
end : numeric
Temperature range end in kelvins.
count : int
Temperatures count in the planckian table.
Returns
-------
list
Planckian table.
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> from pprint import pprint
>>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> uv = np.array([0.1978, 0.3122])
>>> pprint(planckian_table(uv, cmfs, 1000, 1010, 10))
... # doctest: +ELLIPSIS
[PlanckianTable_Tuvdi(Ti=1000.0, \
ui=0.4479628..., vi=0.3546296..., di=0.2537355...),
PlanckianTable_Tuvdi(Ti=1001.1111111..., \
ui=0.4477030..., vi=0.3546521..., di=0.2534831...),
PlanckianTable_Tuvdi(Ti=1002.2222222..., \
ui=0.4474434..., vi=0.3546746..., di=0.2532310...),
PlanckianTable_Tuvdi(Ti=1003.3333333..., \
ui=0.4471842..., vi=0.3546970..., di=0.2529792...),
PlanckianTable_Tuvdi(Ti=1004.4444444..., \
ui=0.4469252..., vi=0.3547194..., di=0.2527277...),
PlanckianTable_Tuvdi(Ti=1005.5555555..., \
ui=0.4466666..., vi=0.3547417..., di=0.2524765...),
PlanckianTable_Tuvdi(Ti=1006.6666666..., \
ui=0.4464083..., vi=0.3547640..., di=0.2522256...),
PlanckianTable_Tuvdi(Ti=1007.7777777..., \
ui=0.4461502..., vi=0.3547862..., di=0.2519751...),
PlanckianTable_Tuvdi(Ti=1008.8888888..., \
ui=0.4458925..., vi=0.3548084..., di=0.2517248...),
PlanckianTable_Tuvdi(Ti=1010.0, \
ui=0.4456351..., vi=0.3548306..., di=0.2514749...)]
"""
ux, vx = uv
cmfs = cmfs.copy().trim(ASTME30815_PRACTISE_SHAPE)
shape = cmfs.shape
table = []
for Ti in np.linspace(start, end, count):
spd = blackbody_spd(Ti, shape)
XYZ = spectral_to_XYZ(spd, cmfs)
XYZ /= np.max(XYZ)
UVW = XYZ_to_UCS(XYZ)
ui, vi = UCS_to_uv(UVW)
di = np.hypot(ux - ui, vx - vi)
table.append(PLANCKIAN_TABLE_TUVD(Ti, ui, vi, di))
return table