def test_n_dimensional_UCS_uv_to_xy(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_uv_to_xy` definition
n-dimensional arrays support.
"""
uv = np.array([0.15085309, 0.32355314])
xy = np.array([0.26414771, 0.37770001])
np.testing.assert_almost_equal(UCS_uv_to_xy(uv), xy, decimal=7)
uv = np.tile(uv, (6, 1))
xy = np.tile(xy, (6, 1))
np.testing.assert_almost_equal(UCS_uv_to_xy(uv), xy, decimal=7)
uv = np.reshape(uv, (2, 3, 2))
xy = np.reshape(xy, (2, 3, 2))
np.testing.assert_almost_equal(UCS_uv_to_xy(uv), xy, decimal=7)
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 test_UCS_uv_to_xy(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_uv_to_xy` definition.
"""
np.testing.assert_almost_equal(UCS_uv_to_xy(
np.array([0.37720213, 0.33413508])),
np.array([0.54369555, 0.32107941]),
decimal=7)
np.testing.assert_almost_equal(UCS_uv_to_xy(
np.array([0.14536327, 0.35328046])),
np.array([0.29777734, 0.48246445]),
decimal=7)
np.testing.assert_almost_equal(UCS_uv_to_xy(
np.array([0.16953602, 0.20026156])),
np.array([0.18582823, 0.14633764]),
decimal=7)
def test_UCS_uv_to_xy(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_uv_to_xy` definition.
"""
np.testing.assert_almost_equal(
UCS_uv_to_xy(np.array([0.15085309, 0.32355314])),
np.array([0.26414771, 0.37770001]),
decimal=7)
np.testing.assert_almost_equal(
UCS_uv_to_xy(np.array([0.31125983, 0.34646688])),
np.array([0.50453169, 0.37440000]),
decimal=7)
np.testing.assert_almost_equal(
UCS_uv_to_xy(np.array([0.30069388, 0.33863231])),
np.array([0.47670437, 0.35789998]),
decimal=7)
def test_UCS_uv_to_xy(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_uv_to_xy` definition.
"""
np.testing.assert_almost_equal(UCS_uv_to_xy(
(0.2033733344733139,
0.3140500001549052)), (0.32207410281368043, 0.33156550013623537),
decimal=7)
np.testing.assert_almost_equal(UCS_uv_to_xy(
(0.20873418102926322,
0.32457285063327812)), (0.3439000000209443, 0.35650000010917804),
decimal=7)
np.testing.assert_almost_equal(UCS_uv_to_xy(
(0.25585459629500179,
0.34952813701502972)), (0.4474327628361858, 0.40749796251018744),
decimal=7)
def test_nan_UCS_uv_to_xy(self):
"""
Tests :func:`colour.models.cie_ucs.UCS_uv_to_xy` definition nan
support.
"""
cases = [-1.0, 0.0, 1.0, -np.inf, np.inf, np.nan]
cases = set(permutations(cases * 3, r=2))
for case in cases:
uv = np.array(case)
UCS_uv_to_xy(uv)
def CCT_D_uv_to_plotting_colourspace(CCT_D_uv):
"""
Convert given *uv* chromaticity coordinates to the default plotting
colourspace.
"""
return normalise_maximum(
XYZ_to_plotting_colourspace(
xy_to_XYZ(UCS_uv_to_xy(CCT_to_uv(CCT_D_uv,
"Robertson 1968")))),
axis=-1,
)
def 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 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.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1960_UCS_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
if is_scipy_installed(raise_exception=True):
from scipy.spatial import Delaunay
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(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),
'bbox_inches': 'tight',
'pad_inches': 0
})
settings.update(kwargs)
ax = matplotlib.pyplot.gca()
matplotlib.pyplot.setp(ax, frame_on=False)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def planckian_locus_CIE_1931_chromaticity_diagram_plot(
illuminants=None,
**kwargs):
"""
Plots the planckian locus and given illuminants in
*CIE 1931 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', 'B', 'C']
>>> planckian_locus_CIE_1931_chromaticity_diagram_plot(
... ils) # doctest: +SKIP
"""
if illuminants is None:
illuminants = ('A', 'B', 'C')
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
settings = {
'title': ('{0} Illuminants - Planckian Locus\n'
'CIE 1931 Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer').format(
', '.join(illuminants))
if illuminants else
('Planckian Locus\nCIE 1931 Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer'),
'standalone': False}
settings.update(kwargs)
CIE_1931_chromaticity_diagram_plot(**settings)
start, end = 1667, 100000
xy = np.array([UCS_uv_to_xy(CCT_to_uv(x, 0, method='Robertson 1968'))
for x in np.arange(start, end + 250, 250)])
pylab.plot(xy[..., 0], xy[..., 1], color='black', linewidth=2)
for i in (1667, 2000, 2500, 3000, 4000, 6000, 10000):
x0, y0 = UCS_uv_to_xy(CCT_to_uv(i, -0.025, method='Robertson 1968'))
x1, y1 = UCS_uv_to_xy(CCT_to_uv(i, 0.025, method='Robertson 1968'))
pylab.plot((x0, x1), (y0, y1), color='black', linewidth=2)
pylab.annotate('{0}K'.format(i),
xy=(x0, y0),
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())))
pylab.plot(xy[0], xy[1], 'o', color='white', linewidth=2)
pylab.annotate(illuminant,
xy=(xy[0], xy[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, 0.9, -0.1, 0.9),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def UVW_to_XYZ(
UVW,
illuminant=ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']):
"""
Converts *CIE 1964 U\\*V\\*W\\** colourspace to *CIE XYZ* tristimulus
values.
Parameters
----------
UVW : array_like
*CIE 1964 U\\*V\\*W\\** colourspace array.
illuminant : array_like, optional
Reference *illuminant* *xy* chromaticity coordinates or *CIE xyY*
colourspace array.
Returns
-------
ndarray
*CIE XYZ* tristimulus values.
Warning
-------
The input domain and output range of that definition are non standard!
Notes
-----
+----------------+-----------------------+-----------------+
| **Domain** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``UVW`` | ``U`` : [-100, 100] | ``U`` : [-1, 1] |
| | | |
| | ``V`` : [-100, 100] | ``V`` : [-1, 1] |
| | | |
| | ``W`` : [0, 100] | ``W`` : [0, 1] |
+----------------+-----------------------+-----------------+
| ``illuminant`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
+----------------+-----------------------+-----------------+
| **Range** | **Scale - Reference** | **Scale - 1** |
+================+=======================+=================+
| ``XYZ`` | [0, 1] | [0, 1] |
+----------------+-----------------------+-----------------+
References
----------
:cite:`Wikipedia2008a`
Examples
--------
>>> import numpy as np
>>> UVW = np.array([94.55035725, 11.55536523, 40.54757405])
>>> UVW_to_XYZ(UVW)
array([ 20.654008, 12.197225, 5.136952])
"""
U, V, W = tsplit(to_domain_100(UVW))
u_0, v_0 = tsplit(xy_to_UCS_uv(xyY_to_xy(illuminant)))
Y = ((W + 17) / 25)**3
u = U / (13 * W) + u_0
v = V / (13 * W) + v_0
x, y = tsplit(UCS_uv_to_xy(tstack([u, v])))
XYZ = xyY_to_XYZ(tstack([x, y, Y]))
return from_range_100(XYZ)
def plot_spectral_locus(
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
spectral_locus_colours: Optional[Union[ArrayLike, str]] = None,
spectral_locus_opacity: Floating = 1,
spectral_locus_labels: Optional[Sequence] = None,
method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
str] = "CIE 1931",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the *Spectral Locus* according to given method.
Parameters
----------
cmfs
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.
spectral_locus_colours
Colours of the *Spectral Locus*, if ``spectral_locus_colours`` is set
to *RGB*, the colours will be computed according to the corresponding
chromaticity coordinates.
spectral_locus_opacity
Opacity of the *Spectral Locus*.
spectral_locus_labels
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
*Chromaticity Diagram* method.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_spectral_locus(spectral_locus_colours='RGB') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Spectral_Locus.png
:align: center
:alt: plot_spectral_locus
"""
method = validate_method(method,
["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])
spectral_locus_colours = optional(spectral_locus_colours,
CONSTANTS_COLOUR_STYLE.colour.dark)
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
wavelengths = list(cmfs.wavelengths)
equal_energy = np.array([1 / 3] * 2)
if method == "cie 1931":
ij = XYZ_to_xy(cmfs.values, illuminant)
labels = cast(
Tuple,
optional(
spectral_locus_labels,
(
390,
460,
470,
480,
490,
500,
510,
520,
540,
560,
580,
600,
620,
700,
),
),
)
elif method == "cie 1960 ucs":
ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
labels = cast(
Tuple,
optional(
spectral_locus_labels,
(
420,
440,
450,
460,
470,
480,
490,
500,
510,
520,
530,
540,
550,
560,
570,
580,
590,
600,
610,
620,
630,
645,
680,
),
),
)
elif method == "cie 1976 ucs":
ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
labels = cast(
Tuple,
optional(
spectral_locus_labels,
(
420,
440,
450,
460,
470,
480,
490,
500,
510,
520,
530,
540,
550,
560,
570,
580,
590,
600,
610,
620,
630,
645,
680,
),
),
)
pl_ij = np.reshape(
tstack([
np.linspace(ij[0][0], ij[-1][0], 20),
np.linspace(ij[0][1], ij[-1][1], 20),
]),
(-1, 1, 2),
)
sl_ij = np.copy(ij).reshape(-1, 1, 2)
purple_line_colours: Optional[Union[ArrayLike, str]]
if str(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(
np.reshape(XYZ, (-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,
alpha=spectral_locus_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_scatter,
)
axes.add_collection(line_collection)
wl_ij = dict(zip(wavelengths, ij))
for label in labels:
ij_l = wl_ij.get(label)
if ij_l is None:
continue
ij_l = as_float_array([ij_l])
i, j = tsplit(ij_l)
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]
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx]) if np.dot(
normalise_vector(ij_l - 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] # type: ignore[index]
)
axes.plot(
(i, i + normal[0] * 0.75),
(j, j + normal[1] * 0.75),
color=label_colour,
alpha=spectral_locus_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
)
axes.plot(
i,
j,
"o",
color=label_colour,
alpha=spectral_locus_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
)
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"},
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_label,
)
settings = {"axes": axes}
settings.update(kwargs)
return render(**kwargs)
def plot_chromaticity_diagram_colours(
samples: Integer = 256,
diagram_colours: Optional[Union[ArrayLike, str]] = None,
diagram_opacity: Floating = 1,
diagram_clipping_path: Optional[ArrayLike] = None,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
str] = "CIE 1931",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the *Chromaticity Diagram* colours according to given method.
Parameters
----------
samples
Samples count on one axis when computing the *Chromaticity Diagram*
colours.
diagram_colours
Colours of the *Chromaticity Diagram*, if ``diagram_colours`` is set
to *RGB*, the colours will be computed according to the corresponding
coordinates.
diagram_opacity
Opacity of the *Chromaticity Diagram*.
diagram_clipping_path
Path of points used to clip the *Chromaticity Diagram* colours.
cmfs
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
*Chromaticity Diagram* method.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram_colours(diagram_colours='RGB')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
:align: center
:alt: plot_chromaticity_diagram_colours
"""
method = validate_method(method,
["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
diagram_colours = cast(
ArrayLike,
optional(diagram_colours,
HEX_to_RGB(CONSTANTS_COLOUR_STYLE.colour.average)),
)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
if method == "cie 1931":
spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
elif method == "cie 1960 ucs":
spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
elif method == "cie 1976 ucs":
spectral_locus = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
illuminant)
use_RGB_diagram_colours = str(diagram_colours).upper() == "RGB"
if use_RGB_diagram_colours:
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)
elif method == "cie 1960 ucs":
XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
elif method == "cie 1976 ucs":
XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
diagram_colours = 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" if use_RGB_diagram_colours else np.hstack(
[diagram_colours, diagram_opacity]),
edgecolor="none" if use_RGB_diagram_colours else np.hstack(
[diagram_colours, diagram_opacity]),
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
axes.add_patch(polygon)
if use_RGB_diagram_colours:
# Preventing bounding box related issues as per
# https://github.com/matplotlib/matplotlib/issues/10529
image = axes.imshow(
diagram_colours,
interpolation="bilinear",
extent=(0, 1, 0, 1),
clip_path=None,
alpha=diagram_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
image.set_clip_path(polygon)
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 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 CIE_1960_UCS_chromaticity_diagram_colours_plot(
surface=1.25,
spacing=0.00075,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1960 UCS Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
spacing : numeric, optional
Spacing between markers.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1960_UCS_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
illuminant = ILLUMINANTS.get('CIE 1931 2 Degree Standard Observer').get(
'E')
UVWs = [XYZ_to_UCS(value) for key, value in cmfs]
u, v = tuple(zip(*([UCS_to_uv(x) for x in UVWs])))
path = matplotlib.path.Path(tuple(zip(u, v)))
x_dot, y_dot, colours = [], [], []
for i in np.arange(0, 1, spacing):
for j in np.arange(0, 1, spacing):
if path.contains_path(matplotlib.path.Path([[i, j], [i, j]])):
x_dot.append(i)
y_dot.append(j)
XYZ = xy_to_XYZ(UCS_uv_to_xy((i, j)))
RGB = normalise(XYZ_to_sRGB(XYZ, illuminant))
colours.append(RGB)
pylab.scatter(x_dot, y_dot, color=colours, s=surface)
settings = {
'no_ticks': True,
'bounding_box': [0, 1, 0, 1],
'bbox_inches': 'tight',
'pad_inches': 0
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**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 planckian_locus_CIE_1931_chromaticity_diagram_plot(
illuminants=None,
**kwargs):
"""
Plots the planckian locus and given illuminants in
*CIE 1931 Chromaticity Diagram*.
Parameters
----------
illuminants : array_like, optional
Factory illuminants to plot.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If one of the given illuminant is not found in the factory illuminants.
Examples
--------
>>> ils = ['A', 'B', 'C']
>>> planckian_locus_CIE_1931_chromaticity_diagram_plot(ils) # noqa # doctest: +SKIP
True
"""
if illuminants is None:
illuminants = ('A', 'B', 'C')
cmfs = CMFS.get('CIE 1931 2 Degree Standard Observer')
settings = {
'title': ('{0} Illuminants - Planckian Locus\n'
'CIE 1931 Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer').format(
', '.join(illuminants))
if illuminants else
('Planckian Locus\nCIE 1931 Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer'),
'standalone': False}
settings.update(kwargs)
if not CIE_1931_chromaticity_diagram_plot(**settings):
return
start, end = 1667, 100000
x, y = tuple(zip(*[UCS_uv_to_xy(CCT_to_uv(x, 0, cmfs=cmfs))
for x in np.arange(start, end + 250, 250)]))
pylab.plot(x, y, color='black', linewidth=2)
for i in [1667, 2000, 2500, 3000, 4000, 6000, 10000]:
x0, y0 = UCS_uv_to_xy(CCT_to_uv(i, -0.025, cmfs=cmfs))
x1, y1 = UCS_uv_to_xy(CCT_to_uv(i, 0.025, cmfs=cmfs))
pylab.plot([x0, x1], [y0, y1], color='black', linewidth=2)
pylab.annotate('{0}K'.format(i),
xy=(x0, y0),
xytext=(0, -10),
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())))
pylab.plot(xy[0], xy[1], 'o', color='white', linewidth=2)
pylab.annotate(illuminant,
xy=(xy[0], xy[1]),
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
settings.update({'standalone': True})
return display(**settings)
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 planckian_locus_chromaticity_diagram_plot_CIE1931(
illuminants=None,
chromaticity_diagram_callable_CIE1931=(
chromaticity_diagram_plot_CIE1931),
**kwargs):
"""
Plots the planckian locus and given illuminants in
*CIE 1931 Chromaticity Diagram*.
Parameters
----------
illuminants : array_like, optional
Factory illuminants to plot.
chromaticity_diagram_callable_CIE1931 : callable, optional
Callable responsible for drawing the *CIE 1931 Chromaticity Diagram*.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
show_diagram_colours : bool, optional
{:func:`colour.plotting.chromaticity_diagram_plot_CIE1931`},
Whether to display the chromaticity diagram background colours.
use_cached_diagram_colours : bool, optional
{:func:`colour.plotting.chromaticity_diagram_plot_CIE1931`},
Whether to used the cached chromaticity diagram background colours
image.
Returns
-------
Figure
Current figure or None.
Raises
------
KeyError
If one of the given illuminant is not found in the factory illuminants.
Examples
--------
>>> planckian_locus_chromaticity_diagram_plot_CIE1931(['A', 'B', 'C'])
... # doctest: +SKIP
"""
if illuminants is None:
illuminants = ('A', 'B', 'C')
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
settings = {
'title': ('{0} Illuminants - Planckian Locus\n'
'CIE 1931 Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer').format(
', '.join(illuminants)) if illuminants else
('Planckian Locus\nCIE 1931 Chromaticity Diagram - '
'CIE 1931 2 Degree Standard Observer'),
'standalone':
False
}
settings.update(kwargs)
chromaticity_diagram_callable_CIE1931(**settings)
start, end = 1667, 100000
xy = np.array(
[UCS_uv_to_xy(CCT_to_uv(x, 'Robertson 1968', D_uv=0))
for x in np.arange(start, end + 250, 250)]) # yapf: disable
pylab.plot(xy[..., 0], xy[..., 1], color='black', linewidth=1)
for i in (1667, 2000, 2500, 3000, 4000, 6000, 10000):
x0, y0 = UCS_uv_to_xy(CCT_to_uv(i, 'Robertson 1968', D_uv=-0.025))
x1, y1 = UCS_uv_to_xy(CCT_to_uv(i, 'Robertson 1968', D_uv=0.025))
pylab.plot((x0, x1), (y0, y1), color='black', linewidth=1)
pylab.annotate('{0}K'.format(i),
xy=(x0, y0),
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[cmfs.name].keys())))
pylab.plot(xy[0], xy[1], 'o', color='white', linewidth=1)
pylab.annotate(illuminant,
xy=(xy[0], xy[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, 0.9, -0.1, 0.9),
'standalone': True
})
settings.update(kwargs)
return render(**settings)
