def multi_transfer_function_plot(colourspaces=None,
inverse=False, **kwargs):
"""
Plots given colourspaces transfer functions.
Parameters
----------
colourspaces : list, optional
Colourspaces transfer functions to plot.
inverse : bool
Plot inverse transfer functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> multi_transfer_function_plot(['sRGB', 'Rec. 709']) # doctest: +SKIP
True
"""
if colourspaces is None:
colourspaces = ['sRGB', 'Rec. 709']
samples = np.linspace(0, 1, 1000)
for i, colourspace in enumerate(colourspaces):
colourspace, name = get_RGB_colourspace(colourspace), colourspace
RGBs = np.array([colourspace.inverse_transfer_function(x)
if inverse else
colourspace.transfer_function(x)
for x in samples])
pylab.plot(samples,
RGBs,
label=u'{0}'.format(colourspace.name),
linewidth=2)
settings = {
'title': '{0} - Transfer Functions'.format(
', '.join(colourspaces)),
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'limits': [0, 1, 0, 1]}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_munsell_value_function_plot(
functions=None,
**kwargs):
"""
Plots given *Munsell* value functions.
Parameters
----------
functions : array_like, optional
*Munsell* value functions to plot.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If one of the given *Munsell* value function is not found in the
factory *Munsell* value functions.
Examples
--------
>>> fs = ('ASTM D1535-08', 'McCamy 1987')
>>> multi_munsell_value_function_plot(fs) # doctest: +SKIP
True
"""
if functions is None:
functions = ('ASTM D1535-08',
'McCamy 1987')
samples = np.linspace(0, 100, 1000)
for i, function in enumerate(functions):
function, name = MUNSELL_VALUE_METHODS.get(function), function
if function is None:
raise KeyError(
('"{0}" "Munsell" value function not found in '
'factory "Munsell" value functions: "{1}".').format(
name, sorted(MUNSELL_VALUE_METHODS.keys())))
pylab.plot(samples,
[function(x) for x in samples],
label=u'{0}'.format(name),
linewidth=2)
settings = {
'title': '{0} - Munsell Functions'.format(', '.join(functions)),
'x_label': 'Luminance Y',
'y_label': 'Munsell Value V',
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'limits': [0, 100, 0, 100]}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def blackbody_spectral_radiance_plot(
temperature=3500,
cmfs='CIE 1931 2 Degree Standard Observer',
blackbody='VY Canis Major',
**kwargs):
"""
Plots given blackbody spectral radiance.
Parameters
----------
temperature : numeric, optional
Blackbody temperature.
cmfs : unicode, optional
Standard observer colour matching functions.
blackbody : unicode, optional
Blackbody name.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
matplotlib.pyplot.subplots_adjust(hspace=0.4)
spd = blackbody_spd(temperature, cmfs.shape)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': u'W / (sr m²) / m',
'standalone': False}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
matplotlib.pyplot.subplot(212)
settings = {'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'aspect': None,
'standalone': False}
single_colour_plot(colour_parameter(name='', RGB=RGB), **settings)
settings = {'standalone': True}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_lightness_function_plot(functions=None, **kwargs):
"""
Plots given *Lightness* functions.
Parameters
----------
functions : array_like, optional
*Lightness* functions to plot.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If one of the given *Lightness* function is not found in the factory
*Lightness* functions.
Examples
--------
>>> fs = ('CIE 1976', 'Wyszecki 1964')
>>> multi_lightness_function_plot(fs) # doctest: +SKIP
True
"""
if functions is None:
functions = ('CIE 1976', 'Wyszecki 1964')
samples = np.linspace(0, 100, 1000)
for i, function in enumerate(functions):
function, name = LIGHTNESS_METHODS.get(function), function
if function is None:
raise KeyError(
('"{0}" "Lightness" function not found in factory '
'"Lightness" functions: "{1}".').format(
name, sorted(LIGHTNESS_METHODS.keys())))
pylab.plot(samples,
[function(x) for x in samples],
label=u'{0}'.format(name),
linewidth=2)
settings = {
'title': '{0} - Lightness Functions'.format(', '.join(functions)),
'x_label': 'Luminance Y',
'y_label': 'Lightness L*',
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'limits': [0, 100, 0, 100]}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def colour_rendering_index_bars_plot(illuminant, **kwargs):
"""
Plots the *colour rendering index* of given illuminant.
Parameters
----------
illuminant : SpectralPowerDistribution
Illuminant to plot the *colour rendering index*.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index_bars_plot(illuminant) # doctest: +SKIP
True
"""
figure, axis = matplotlib.pyplot.subplots()
cri, colour_rendering_indexes, additional_data = \
colour_rendering_index(illuminant, additional_data=True)
colours = (
[[1] * 3] +
[normalise(XYZ_to_sRGB(x.XYZ / 100)) for x in additional_data[0]])
x, y = tuple(
zip(*sorted(colour_rendering_indexes.items(), key=lambda x: x[0])))
x, y = np.array([0] + list(x)), np.array([cri] + list(y))
positive = True if np.sign(min(y)) in (0, 1) else False
width = 0.5
bars = pylab.bar(x, y, color=colours, width=width)
y_ticks_steps = 10
pylab.yticks(
range(0 if positive else -100, 100 + y_ticks_steps, y_ticks_steps))
pylab.xticks(x + width / 2,
['Ra'] + ['R{0}'.format(index) for index in x[1:]])
def label_bars(bars):
"""
Add labels above given bars.
"""
for bar in bars:
y = bar.get_y()
height = bar.get_height()
value = height if np.sign(y) in (0, 1) else -height
axis.text(bar.get_x() + bar.get_width() / 2,
0.025 * height + height + y,
'{0:.1f}'.format(value),
ha='center',
va='bottom')
label_bars(bars)
settings = {
'title': 'Colour Rendering Index - {0}'.format(illuminant.name),
'grid': True,
'x_tighten': True,
'y_tighten': True,
'limits': [-width, 14 + width * 2, -10 if positive else -110, 110]
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def colourspaces_CIE_1931_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given colourspaces in *CIE 1931 Chromaticity Diagram*.
Parameters
----------
colourspaces : list, optional
Colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> csps = ['sRGB', 'ACES RGB']
>>> colourspaces_CIE_1931_chromaticity_diagram_plot(csps) # doctest: +SKIP
True
"""
if colourspaces is None:
colourspaces = ('sRGB', 'ACES RGB', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
settings = {'title': '{0} - {1}'.format(', '.join(colourspaces), name),
'standalone': False}
settings.update(kwargs)
if not CIE_1931_chromaticity_diagram_plot(**settings):
return
x_limit_min, x_limit_max = [-0.1], [0.9]
y_limit_min, y_limit_max = [-0.1], [0.9]
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
x, y = tuple(zip(*POINTER_GAMUT_DATA))
pylab.plot(x,
y,
label='Pointer Gamut',
color='0.95',
linewidth=2)
pylab.plot([x[-1],
x[0]],
[y[-1],
y[0]],
color='0.95',
linewidth=2)
else:
colourspace, name = get_RGB_colourspace(
colourspace), colourspace
random_colour = lambda: float(random.randint(64, 224)) / 255
r, g, b = random_colour(), random_colour(), random_colour()
primaries = colourspace.primaries
whitepoint = colourspace.whitepoint
pylab.plot([whitepoint[0], whitepoint[0]],
[whitepoint[1], whitepoint[1]],
color=(r, g, b),
label=colourspace.name,
linewidth=2)
pylab.plot([whitepoint[0], whitepoint[0]],
[whitepoint[1], whitepoint[1]],
'o',
color=(r, g, b),
linewidth=2)
pylab.plot([primaries[0, 0], primaries[1, 0]],
[primaries[0, 1], primaries[1, 1]],
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot([primaries[1, 0], primaries[2, 0]],
[primaries[1, 1], primaries[2, 1]],
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot([primaries[2, 0], primaries[0, 0]],
[primaries[2, 1], primaries[0, 1]],
'o-',
color=(r, g, b),
linewidth=2)
x_limit_min.append(np.amin(primaries[:, 0]))
y_limit_min.append(np.amin(primaries[:, 1]))
x_limit_max.append(np.amax(primaries[:, 0]))
y_limit_max.append(np.amax(primaries[:, 1]))
settings.update({'legend': True,
'legend_location': 'upper right',
'x_tighten': True,
'y_tighten': True,
'limits': [min(x_limit_min), max(x_limit_max),
min(y_limit_min), max(y_limit_max)],
'margins': [-0.05, 0.05, -0.05, 0.05],
'standalone': True})
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1.25,
spacing=0.00075,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 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_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
illuminant = ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('E')
XYZs = [value for key, value in cmfs]
x, y = tuple(zip(*([XYZ_to_xy(x) for x in XYZs])))
path = matplotlib.path.Path(tuple(zip(x, y)))
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((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 CIE_1931_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1931_chromaticity_diagram_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
image = matplotlib.image.imread(
os.path.join(PLOTTING_RESOURCES_DIRECTORY,
'CIE_1931_Chromaticity_Diagram_{0}_Small.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation='nearest', 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)
XYZs = [value for key, value in cmfs]
x, y = tuple(zip(*([XYZ_to_xy(x) for x in XYZs])))
wavelengths_chromaticity_coordinates = dict(
tuple(zip(wavelengths, tuple(zip(x, y)))))
pylab.plot(x, y, color='black', linewidth=2)
pylab.plot((x[-1], x[0]), (y[-1], y[0]), color='black', linewidth=2)
for label in labels:
x, y = wavelengths_chromaticity_coordinates.get(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.get(right)[0] -
wavelengths_chromaticity_coordinates.get(left)[0])
dy = (wavelengths_chromaticity_coordinates.get(right)[1] -
wavelengths_chromaticity_coordinates.get(left)[1])
norme = lambda x: x / np.linalg.norm(x)
xy = np.array([x, y])
direction = np.array((-dy, dx))
normal = (np.array((-dy, dx))
if np.dot(norme(xy - equal_energy),
norme(direction)) > 0 else
np.array((dy, -dx)))
normal = norme(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,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {
'title': 'CIE 1931 Chromaticity Diagram - {0}'.format(name),
'x_label': 'CIE x',
'y_label': 'CIE y',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'bounding_box': [-0.1, 0.9, -0.1, 0.9],
'bbox_inches': 'tight',
'pad_inches': 0}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_cmfs_plot(cmfss=None, **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfss : array_like, optional
Colour matching functions to plot.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> cmfss = [
... 'CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer']
>>> multi_cmfs_plot(cmfss) # doctest: +SKIP
True
"""
if cmfss is None:
cmfss = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for axis, rgb in (('x', [1, 0, 0]), ('y', [0, 1, 0]), ('z', [0, 0, 1])):
for i, cmfs in enumerate(cmfss):
cmfs, name = get_cmfs(cmfs), cmfs
rgb = [reduce(lambda y, _: y * 0.5, range(i), x) for x in rgb]
wavelengths, values = tuple(
zip(*[(key, value) for key, value in getattr(cmfs, axis)]))
shape = cmfs.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
pylab.plot(wavelengths,
values,
color=rgb,
label=u'{0} - {1}'.format(cmfs.labels.get(axis),
cmfs.name),
linewidth=2)
settings = {
'title':
'{0} - Colour Matching Functions'.format(', '.join(cmfss)),
'x_label':
u'Wavelength λ (nm)',
'y_label':
'Tristimulus Values',
'x_tighten':
True,
'legend':
True,
'legend_location':
'upper right',
'x_ticker':
True,
'y_ticker':
True,
'grid':
True,
'y_axis_line':
True,
'limits': [
min(x_limit_min),
max(x_limit_max),
min(y_limit_min),
max(y_limit_max)
]
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_spd_plot(spds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_spds_colours=False,
normalise_spds_colours=False,
**kwargs):
"""
Plots given spectral power distributions.
Parameters
----------
spds : list, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_spds_colours : bool, optional
Use spectral power distributions colours.
normalise_spds_colours : bool
Should spectral power distributions colours normalised.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data1 = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> data2 = {400: 0.134, 420: 0.789, 440: 1.289}
>>> spd1 = SpectralPowerDistribution('Custom1', data1)
>>> spd2 = SpectralPowerDistribution('Custom2', data2)
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D65')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = tuple(zip(*[(key, value) for key, value in spd]))
shape = spd.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
matplotlib.pyplot.rc("axes", color_cycle=["r", "g", "b", "y"])
if use_spds_colours:
XYZ = spectral_to_XYZ(spd, cmfs, illuminant) / 100
if normalise_spds_colours:
XYZ = normalise(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths,
values,
color=RGB,
label=spd.name,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.name, linewidth=2)
settings = {
'x_label':
u'Wavelength λ (nm)',
'y_label':
'Spectral Power Distribution',
'x_tighten':
True,
'legend':
True,
'legend_location':
'upper left',
'x_ticker':
True,
'y_ticker':
True,
'limits': [
min(x_limit_min),
max(x_limit_max),
min(y_limit_min),
max(y_limit_max)
]
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1.25,
spacing=0.00075,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 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_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
illuminant = ILLUMINANTS.get('CIE 1931 2 Degree Standard Observer').get(
'E')
XYZs = [value for key, value in cmfs]
x, y = tuple(zip(*([XYZ_to_xy(x) for x in XYZs])))
path = matplotlib.path.Path(tuple(zip(x, y)))
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((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 CIE_1976_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1976_UCS_chromaticity_diagram_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
image = matplotlib.image.imread(
os.path.join(
PLOTTING_RESOURCES_DIRECTORY,
'CIE_1976_UCS_Chromaticity_Diagram_{0}_Small.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation='nearest', 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)
illuminant = ILLUMINANTS.get('CIE 1931 2 Degree Standard Observer').get(
'D50')
Luvs = [XYZ_to_Luv(value, illuminant) for key, value in cmfs]
u, v = tuple(zip(*([Luv_to_uv(x) for x in Luvs])))
wavelengths_chromaticity_coordinates = dict(
zip(wavelengths, tuple(zip(u, v))))
pylab.plot(u, v, color='black', linewidth=2)
pylab.plot((u[-1], u[0]), (v[-1], v[0]), color='black', linewidth=2)
for label in labels:
u, v = wavelengths_chromaticity_coordinates.get(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.get(right)[0] -
wavelengths_chromaticity_coordinates.get(left)[0])
dy = (wavelengths_chromaticity_coordinates.get(right)[1] -
wavelengths_chromaticity_coordinates.get(left)[1])
norme = lambda x: x / np.linalg.norm(x)
uv = np.array([u, v])
direction = np.array((-dy, dx))
normal = (np.array(
(-dy,
dx)) if np.dot(norme(uv - equal_energy), norme(direction)) > 0
else np.array((dy, -dx)))
normal = norme(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,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {
'title': 'CIE 1976 UCS Chromaticity Diagram - {0}'.format(name),
'x_label': 'CIE u"',
'y_label': 'CIE v"',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'bounding_box': [-0.1, .7, -.1, .7],
'bbox_inches': 'tight',
'pad_inches': 0
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1931_chromaticity_diagram_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
image = matplotlib.image.imread(
os.path.join(
PLOTTING_RESOURCES_DIRECTORY,
'CIE_1931_Chromaticity_Diagram_{0}_Small.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation='nearest', 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)
XYZs = [value for key, value in cmfs]
x, y = tuple(zip(*([XYZ_to_xy(x) for x in XYZs])))
wavelengths_chromaticity_coordinates = dict(
tuple(zip(wavelengths, tuple(zip(x, y)))))
pylab.plot(x, y, color='black', linewidth=2)
pylab.plot((x[-1], x[0]), (y[-1], y[0]), color='black', linewidth=2)
for label in labels:
x, y = wavelengths_chromaticity_coordinates.get(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.get(right)[0] -
wavelengths_chromaticity_coordinates.get(left)[0])
dy = (wavelengths_chromaticity_coordinates.get(right)[1] -
wavelengths_chromaticity_coordinates.get(left)[1])
norme = lambda x: x / np.linalg.norm(x)
xy = np.array([x, y])
direction = np.array((-dy, dx))
normal = (np.array(
(-dy,
dx)) if np.dot(norme(xy - equal_energy), norme(direction)) > 0
else np.array((dy, -dx)))
normal = norme(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,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {
'title': 'CIE 1931 Chromaticity Diagram - {0}'.format(name),
'x_label': 'CIE x',
'y_label': 'CIE y',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'bounding_box': [-0.1, 0.9, -0.1, 0.9],
'bbox_inches': 'tight',
'pad_inches': 0
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def colour_checker_plot(colour_checker='ColorChecker 2005', **kwargs):
"""
Plots given colour checker.
Parameters
----------
colour_checker : unicode, optional
Color checker name.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If the given colour rendition chart is not found in the factory colour
rendition charts.
Examples
--------
>>> colour_checker_plot() # doctest: +SKIP
True
"""
colour_checker, name = COLOURCHECKERS.get(colour_checker), colour_checker
if colour_checker is None:
raise KeyError(('Colour checker "{0}" not found in '
'factory colour checkers: "{1}".').format(
name, sorted(COLOURCHECKERS.keys())))
_, data, illuminant = colour_checker
colour_parameters = []
for index, label, x, y, Y in data:
XYZ = xyY_to_XYZ((x, y, Y))
RGB = XYZ_to_sRGB(XYZ, illuminant)
colour_parameters.append(
colour_parameter(label.title(), np.clip(np.ravel(RGB), 0, 1)))
background_colour = '0.1'
matplotlib.pyplot.gca().patch.set_facecolor(background_colour)
width = height = 1.0
spacing = 0.25
across = 6
settings = {
'standalone': False,
'width': width,
'height': height,
'spacing': spacing,
'across': across,
'text_size': 8,
'margins': [-0.125, 0.125, -0.5, 0.125]
}
settings.update(kwargs)
multi_colour_plot(colour_parameters, **settings)
text_x = width * (across / 2) + (across * (spacing / 2)) - spacing / 2
text_y = -(len(colour_parameters) / across + spacing / 2)
pylab.text(text_x,
text_y,
'{0} - {1} - Colour Rendition Chart'.format(
name,
RGB_COLOURSPACES.get('sRGB').name),
color='0.95',
clip_on=True,
ha='center')
settings.update({
'title': name,
'facecolor': background_colour,
'edgecolor': None,
'standalone': True
})
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def the_blue_sky_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.clone()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title':
'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label':
'',
'aspect':
None,
'standalone':
False
}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_plot(colour_parameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def colour_checker_plot(colour_checker='ColorChecker 2005', **kwargs):
"""
Plots given colour checker.
Parameters
----------
colour_checker : unicode, optional
Color checker name.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If the given colour rendition chart is not found in the factory colour
rendition charts.
Examples
--------
>>> colour_checker_plot() # doctest: +SKIP
True
"""
colour_checker, name = COLOURCHECKERS.get(colour_checker), colour_checker
if colour_checker is None:
raise KeyError(
('Colour checker "{0}" not found in '
'factory colour checkers: "{1}".').format(
name, sorted(COLOURCHECKERS.keys())))
_, data, illuminant = colour_checker
colour_parameters = []
for index, label, x, y, Y in data:
XYZ = xyY_to_XYZ((x, y, Y))
RGB = XYZ_to_sRGB(XYZ, illuminant)
colour_parameters.append(
colour_parameter(label.title(), np.clip(np.ravel(RGB), 0, 1)))
background_colour = '0.1'
matplotlib.pyplot.gca().patch.set_facecolor(background_colour)
width = height = 1.0
spacing = 0.25
across = 6
settings = {'standalone': False,
'width': width,
'height': height,
'spacing': spacing,
'across': across,
'text_size': 8,
'margins': [-0.125, 0.125, -0.5, 0.125]}
settings.update(kwargs)
multi_colour_plot(colour_parameters, **settings)
text_x = width * (across / 2) + (across * (spacing / 2)) - spacing / 2
text_y = -(len(colour_parameters) / across + spacing / 2)
pylab.text(text_x,
text_y,
'{0} - {1} - Colour Rendition Chart'.format(
name, RGB_COLOURSPACES.get('sRGB').name),
color='0.95',
clip_on=True,
ha='center')
settings.update({'title': name,
'facecolor': background_colour,
'edgecolor': None,
'standalone': True})
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_lightness_function_plot(functions=None, **kwargs):
"""
Plots given *Lightness* functions.
Parameters
----------
functions : array_like, optional
*Lightness* functions to plot.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Raises
------
KeyError
If one of the given *Lightness* function is not found in the factory
*Lightness* functions.
Examples
--------
>>> fs = ('CIE 1976', 'Wyszecki 1964')
>>> multi_lightness_function_plot(fs) # doctest: +SKIP
True
"""
if functions is None:
functions = ('CIE 1976', 'Wyszecki 1964')
samples = np.linspace(0, 100, 1000)
for i, function in enumerate(functions):
function, name = LIGHTNESS_METHODS.get(function), function
if function is None:
raise KeyError(('"{0}" "Lightness" function not found in factory '
'"Lightness" functions: "{1}".').format(
name, sorted(LIGHTNESS_METHODS.keys())))
pylab.plot(samples, [function(x) for x in samples],
label=u'{0}'.format(name),
linewidth=2)
settings = {
'title': '{0} - Lightness Functions'.format(', '.join(functions)),
'x_label': 'Luminance Y',
'y_label': 'Lightness L*',
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'limits': [0, 100, 0, 100]
}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def CIE_1976_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1976_UCS_chromaticity_diagram_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
image = matplotlib.image.imread(
os.path.join(PLOTTING_RESOURCES_DIRECTORY,
'CIE_1976_UCS_Chromaticity_Diagram_{0}_Small.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation='nearest', 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)
illuminant = ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')
Luvs = [XYZ_to_Luv(value, illuminant) for key, value in cmfs]
u, v = tuple(zip(*([Luv_to_uv(x) for x in Luvs])))
wavelengths_chromaticity_coordinates = dict(zip(wavelengths,
tuple(zip(u, v))))
pylab.plot(u, v, color='black', linewidth=2)
pylab.plot((u[-1], u[0]), (v[-1], v[0]), color='black', linewidth=2)
for label in labels:
u, v = wavelengths_chromaticity_coordinates.get(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.get(right)[0] -
wavelengths_chromaticity_coordinates.get(left)[0])
dy = (wavelengths_chromaticity_coordinates.get(right)[1] -
wavelengths_chromaticity_coordinates.get(left)[1])
norme = lambda x: x / np.linalg.norm(x)
uv = np.array([u, v])
direction = np.array((-dy, dx))
normal = (np.array((-dy, dx))
if np.dot(norme(uv - equal_energy),
norme(direction)) > 0 else
np.array((dy, -dx)))
normal = norme(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,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {
'title': 'CIE 1976 UCS Chromaticity Diagram - {0}'.format(name),
'x_label': 'CIE u"',
'y_label': 'CIE v"',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'bounding_box': [-0.1, .7, -.1, .7],
'bbox_inches': 'tight',
'pad_inches': 0}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def blackbody_spectral_radiance_plot(
temperature=3500,
cmfs='CIE 1931 2 Degree Standard Observer',
blackbody='VY Canis Major',
**kwargs):
"""
Plots given blackbody spectral radiance.
Parameters
----------
temperature : numeric, optional
Blackbody temperature.
cmfs : unicode, optional
Standard observer colour matching functions.
blackbody : unicode, optional
Blackbody name.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
matplotlib.pyplot.subplots_adjust(hspace=0.4)
spd = blackbody_spd(temperature, cmfs.shape)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': u'W / (sr m²) / m',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
matplotlib.pyplot.subplot(212)
settings = {
'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'aspect': None,
'standalone': False
}
single_colour_plot(colour_parameter(name='', RGB=RGB), **settings)
settings = {'standalone': True}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_spd_plot(spds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_spds_colours=False,
normalise_spds_colours=False,
**kwargs):
"""
Plots given spectral power distributions.
Parameters
----------
spds : list, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_spds_colours : bool, optional
Use spectral power distributions colours.
normalise_spds_colours : bool
Should spectral power distributions colours normalised.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data1 = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> data2 = {400: 0.134, 420: 0.789, 440: 1.289}
>>> spd1 = SpectralPowerDistribution('Custom1', data1)
>>> spd2 = SpectralPowerDistribution('Custom2', data2)
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS.get('D65')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = tuple(zip(*[(key, value) for key, value in spd]))
shape = spd.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
matplotlib.pyplot.rc("axes", color_cycle=["r", "g", "b", "y"])
if use_spds_colours:
XYZ = spectral_to_XYZ(spd, cmfs, illuminant) / 100
if normalise_spds_colours:
XYZ = normalise(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths, values, color=RGB, label=spd.name,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.name, linewidth=2)
settings = {'x_label': u'Wavelength λ (nm)',
'y_label': 'Spectral Power Distribution',
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'x_ticker': True,
'y_ticker': True,
'limits': [min(x_limit_min), max(x_limit_max),
min(y_limit_min), max(y_limit_max)]}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def multi_cmfs_plot(cmfss=None, **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfss : array_like, optional
Colour matching functions to plot.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> cmfss = [
... 'CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer']
>>> multi_cmfs_plot(cmfss) # doctest: +SKIP
True
"""
if cmfss is None:
cmfss = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for axis, rgb in (('x', [1, 0, 0]),
('y', [0, 1, 0]),
('z', [0, 0, 1])):
for i, cmfs in enumerate(cmfss):
cmfs, name = get_cmfs(cmfs), cmfs
rgb = [reduce(lambda y, _: y * 0.5, range(i), x) for x in rgb]
wavelengths, values = tuple(
zip(*[(key, value) for key, value in getattr(cmfs, axis)]))
shape = cmfs.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
pylab.plot(wavelengths,
values,
color=rgb,
label=u'{0} - {1}'.format(
cmfs.labels.get(axis), cmfs.name),
linewidth=2)
settings = {
'title': '{0} - Colour Matching Functions'.format(', '.join(cmfss)),
'x_label': u'Wavelength λ (nm)',
'y_label': 'Tristimulus Values',
'x_tighten': True,
'legend': True,
'legend_location': 'upper right',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'y_axis_line': True,
'limits': [min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max)]}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def colour_rendering_index_bars_plot(illuminant, **kwargs):
"""
Plots the *colour rendering index* of given illuminant.
Parameters
----------
illuminant : SpectralPowerDistribution
Illuminant to plot the *colour rendering index*.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_rendering_index_bars_plot(illuminant) # doctest: +SKIP
True
"""
figure, axis = matplotlib.pyplot.subplots()
cri, colour_rendering_indexes, additional_data = \
colour_rendering_index(illuminant, additional_data=True)
colours = ([[1] * 3] + [normalise(XYZ_to_sRGB(x.XYZ / 100))
for x in additional_data[0]])
x, y = tuple(zip(*sorted(colour_rendering_indexes.items(),
key=lambda x: x[0])))
x, y = np.array([0] + list(x)), np.array(
[cri] + list(y))
positive = True if np.sign(min(y)) in (0, 1) else False
width = 0.5
bars = pylab.bar(x, y, color=colours, width=width)
y_ticks_steps = 10
pylab.yticks(range(0 if positive else -100,
100 + y_ticks_steps,
y_ticks_steps))
pylab.xticks(x + width / 2,
['Ra'] + ['R{0}'.format(index) for index in x[1:]])
def label_bars(bars):
"""
Add labels above given bars.
"""
for bar in bars:
y = bar.get_y()
height = bar.get_height()
value = height if np.sign(y) in (0, 1) else -height
axis.text(bar.get_x() + bar.get_width() / 2,
0.025 * height + height + y,
'{0:.1f}'.format(value),
ha='center', va='bottom')
label_bars(bars)
settings = {
'title': 'Colour Rendering Index - {0}'.format(illuminant.name),
'grid': True,
'x_tighten': True,
'y_tighten': True,
'limits': [-width, 14 + width * 2, -10 if positive else -110,
110]}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
def the_blue_sky_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.clone()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title': 'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label': '',
'aspect': None,
'standalone': False}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_plot(colour_parameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
bounding_box(**settings)
aspect(**settings)
return display(**settings)
