def visible_spectrum_plot(cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
**kwargs):
"""
Plots the visible colours spectrum using given standard observer *CIE XYZ*
colour matching functions.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
out_of_gamut_clipping : bool, optional
Out of gamut colours will be clipped if *True* otherwise, the colours
will be offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother. [1]_
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> visible_spectrum_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
cmfs = cmfs.clone().align(DEFAULT_SPECTRAL_SHAPE)
wavelengths = cmfs.shape.range()
colours = XYZ_to_sRGB(
wavelength_to_XYZ(wavelengths, cmfs),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_OECF=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_OECF(normalise(colours))
settings = {
'title': 'The Visible Spectrum - {0}'.format(cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'x_tighten': True
}
settings.update(kwargs)
return colour_parameters_plot([
ColourParameter(x=x[0], RGB=x[1])
for x in tuple(zip(wavelengths, colours))
], **settings)
def visible_spectrum_plot(cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
**kwargs):
"""
Plots the visible colours spectrum using given standard observer *CIE XYZ*
colour matching functions.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
out_of_gamut_clipping : bool, optional
Out of gamut colours will be clipped if *True* otherwise, the colours
will be offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother. [1]_
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> visible_spectrum_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
cmfs = cmfs.clone().align(DEFAULT_SPECTRAL_SHAPE)
wavelengths = cmfs.shape.range()
colours = XYZ_to_sRGB(
wavelength_to_XYZ(wavelengths, cmfs),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_OECF=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_OECF(normalise(colours))
settings = {
'title': 'The Visible Spectrum - {0}'.format(cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'x_tighten': True}
settings.update(kwargs)
return colour_parameters_plot([ColourParameter(x=x[0], RGB=x[1])
for x in tuple(zip(wavelengths, colours))],
**settings)
def blackbody_colours_plot(shape=SpectralShape(150, 12500, 50),
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots blackbody colours.
Parameters
----------
shape : SpectralShape, optional
Spectral shape to use as plot boundaries.
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_colours_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
colours = []
temperatures = []
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': '',
'x_tighten': True,
'y_ticker': False
}
settings.update(kwargs)
return colour_parameters_plot([
ColourParameter(x=x[0], RGB=x[1])
for x in tuple(zip(temperatures, colours))
], **settings)
def blackbody_colours_plot(shape=SpectralShape(150, 12500, 50),
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots blackbody colours.
Parameters
----------
shape : SpectralShape, optional
Spectral shape to use as plot boundaries.
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_colours_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
colours = []
temperatures = []
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': '',
'x_tighten': True,
'x_ticker': True,
'y_ticker': False}
settings.update(kwargs)
return colour_parameters_plot([colour_parameter(x=x[0], RGB=x[1])
for x in tuple(zip(temperatures, colours))],
**settings)
def single_spd_plot(spd, cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given spectral power distribution.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution to plot.
cmfs : unicode
Standard observer colour matching functions used for spectrum creation.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> single_spd_plot(spd) # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
shape = cmfs.shape
spd = spd.clone().interpolate(shape, 'Linear')
wavelengths = spd.wavelengths
values = spd.values
colours = XYZ_to_sRGB(wavelength_to_XYZ(wavelengths, cmfs))
y1 = values
colours = normalise(colours)
settings = {
'title': '{0} - {1}'.format(spd.title, cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Power Distribution',
'x_tighten': True,
'x_ticker': True,
'y_ticker': True}
settings.update(kwargs)
return colour_parameters_plot(
[colour_parameter(x=x[0], y1=x[1], RGB=x[2])
for x in tuple(zip(wavelengths, y1, colours))],
**settings)
def test_normalise(self):
"""
Tests :func:`colour.utilities.array.normalise` definition.
"""
np.testing.assert_almost_equal(normalise(
np.array([0.1151847498, 0.1008000000, 0.0508937252])),
np.array([1., 0.87511585, 0.4418443]),
decimal=7)
np.testing.assert_almost_equal(
normalise(
np.array([[0.1151847498, 0.1008000000, 0.0508937252],
[0.0704953400, 0.1008000000, 0.0955831300],
[0.1750135800, 0.3881879500, 0.3216195500]])),
np.array([[0.29672418, 0.25966803, 0.13110589],
[0.18160105, 0.25966803, 0.246229],
[0.45084753, 1., 0.82851503]]),
decimal=7)
np.testing.assert_almost_equal(normalise(np.array(
[[0.1151847498, 0.1008000000, 0.0508937252],
[0.0704953400, 0.1008000000, 0.0955831300],
[0.1750135800, 0.3881879500, 0.3216195500]]),
axis=-1),
np.array([[1., 0.87511585, 0.4418443],
[0.69935853, 1., 0.94824534],
[0.45084753, 1.,
0.82851503]]),
decimal=7)
np.testing.assert_almost_equal(normalise(np.array(
[0.1151847498, 0.1008000000, 0.0508937252]),
factor=10),
np.array([10., 8.75115848, 4.418443]),
decimal=7)
np.testing.assert_almost_equal(normalise(
np.array([-0.1151847498, -0.1008000000, 0.0508937252])),
np.array([0., 0., 1.]),
decimal=7)
np.testing.assert_almost_equal(normalise(np.array(
[-0.1151847498, -0.1008000000, 0.0508937252]),
clip=False),
np.array([-2.26324069, -1.9805978, 1.]),
decimal=7)
def test_normalise(self):
"""
Tests :func:`colour.utilities.array.normalise` definition.
"""
np.testing.assert_almost_equal(
normalise(np.array([0.1151847498, 0.1008000000, 0.0508937252])),
np.array([1., 0.87511585, 0.4418443]),
decimal=7)
np.testing.assert_almost_equal(
normalise(np.array([[0.1151847498, 0.1008000000, 0.0508937252],
[0.0704953400, 0.1008000000, 0.0955831300],
[0.1750135800, 0.3881879500, 0.3216195500]])),
np.array([[0.29672418, 0.25966803, 0.13110589],
[0.18160105, 0.25966803, 0.246229],
[0.45084753, 1., 0.82851503]]),
decimal=7)
np.testing.assert_almost_equal(
normalise(np.array([[0.1151847498, 0.1008000000, 0.0508937252],
[0.0704953400, 0.1008000000, 0.0955831300],
[0.1750135800, 0.3881879500, 0.3216195500]]),
axis=-1),
np.array([[1., 0.87511585, 0.4418443],
[0.69935853, 1., 0.94824534],
[0.45084753, 1., 0.82851503]]),
decimal=7)
np.testing.assert_almost_equal(
normalise(np.array([0.1151847498, 0.1008000000, 0.0508937252]),
factor=10),
np.array([10., 8.75115848, 4.418443]),
decimal=7)
np.testing.assert_almost_equal(
normalise(np.array([-0.1151847498, -0.1008000000, 0.0508937252])),
np.array([0., 0., 1.]),
decimal=7)
np.testing.assert_almost_equal(
normalise(np.array([-0.1151847498, -0.1008000000, 0.0508937252]),
clip=False),
np.array([-2.26324069, -1.9805978, 1.]),
decimal=7)
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 : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs = get_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': 'W / (sr m$^2$) / m',
'standalone': False}
settings.update(kwargs)
single_spd_plot(spd, cmfs.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(ColourParameter(name='', RGB=RGB), **settings)
settings = {
'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**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
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 : dict, optional
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
"""
canvas(**kwargs)
cmfs = get_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(*spd.items))
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))
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.title,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.title, linewidth=2)
settings = {
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Power Distribution',
'x_tighten': True,
'legend': True,
'legend_location': 'upper left',
'limits': (min(x_limit_min), max(x_limit_max),
min(y_limit_min), max(y_limit_max))}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1931_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 = CHROMATICITY_DIAGRAM_DEFAULT_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise(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({
'no_ticks': True,
'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 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 : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
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(ColourParameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def single_spd_plot(spd,
cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
**kwargs):
"""
Plots given spectral power distribution.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution to plot.
out_of_gamut_clipping : bool, optional
Out of gamut colours will be clipped if *True* otherwise, the colours
will be offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother. [1]_
cmfs : unicode
Standard observer colour matching functions used for spectrum creation.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> single_spd_plot(spd) # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
shape = cmfs.shape
spd = spd.clone().interpolate(shape, 'Linear')
wavelengths = spd.wavelengths
values = spd.values
y1 = values
colours = XYZ_to_sRGB(
wavelength_to_XYZ(wavelengths, cmfs),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_OECF=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_OECF(normalise(colours))
settings = {
'title': '{0} - {1}'.format(spd.title, cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Power Distribution',
'x_tighten': True
}
settings.update(kwargs)
return colour_parameters_plot([
ColourParameter(x=x[0], y1=x[1], RGB=x[2])
for x in tuple(zip(wavelengths, y1, colours))
], **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 : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs = get_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': 'W / (sr m$^2$) / m',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, cmfs.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(ColourParameter(name='', RGB=RGB), **settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**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
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 : dict, optional
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
"""
canvas(**kwargs)
cmfs = get_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(*spd.items))
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))
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.title,
linewidth=2)
else:
pylab.plot(wavelengths, values, label=spd.title, linewidth=2)
settings = {
'x_label':
'Wavelength $\\lambda$ (nm)',
'y_label':
'Spectral Power Distribution',
'x_tighten':
True,
'legend':
True,
'legend_location':
'upper left',
'limits': (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max))
}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def single_spd_plot(spd,
cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
**kwargs):
"""
Plots given spectral power distribution.
Parameters
----------
spd : SpectralPowerDistribution
Spectral power distribution to plot.
out_of_gamut_clipping : bool, optional
Out of gamut colours will be clipped if *True* otherwise, the colours
will be offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother. [1]_
cmfs : unicode
Standard observer colour matching functions used for spectrum creation.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data = {400: 0.0641, 420: 0.0645, 440: 0.0562}
>>> spd = SpectralPowerDistribution('Custom', data)
>>> single_spd_plot(spd) # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
shape = cmfs.shape
spd = spd.clone().interpolate(shape, 'Linear')
wavelengths = spd.wavelengths
values = spd.values
y1 = values
colours = XYZ_to_sRGB(
wavelength_to_XYZ(wavelengths, cmfs),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_OECF=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_OECF(normalise(colours))
settings = {
'title': '{0} - {1}'.format(spd.title, cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Power Distribution',
'x_tighten': True}
settings.update(kwargs)
return colour_parameters_plot(
[ColourParameter(x=x[0], y1=x[1], RGB=x[2])
for x in tuple(zip(wavelengths, y1, colours))],
**settings)
def CIE_1931_chromaticity_diagram_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> CIE_1931_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(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise(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 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 : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
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(ColourParameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def colour_quality_bars_plot(specification, **kwargs):
"""
Plots the colour quality data of given illuminant or light source colour
quality specification.
Parameters
----------
specification : CRI_Specification or VS_ColourQualityScaleData
Illuminant or light source specification colour quality specification.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> illuminant = ILLUMINANTS_RELATIVE_SPDS.get('F2')
>>> colour_quality_bars_plot(illuminant) # doctest: +SKIP
True
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
axis = matplotlib.pyplot.gca()
Q_a, Q_as, colorimetry_data = (specification.Q_a,
specification.Q_as,
specification.colorimetry_data)
colours = ([[1] * 3] + [normalise(XYZ_to_sRGB(x.XYZ / 100))
for x in colorimetry_data[0]])
x, y = tuple(zip(*[(x[0], x[1].Q_a) for x in sorted(Q_as.items(),
key=lambda x: x[0])]))
x, y = np.array([0] + list(x)), np.array([Q_a] + 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,
['Qa'] + ['Q{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.update({
'title': 'Colour Quality',
'grid': True,
'grid_axis': 'y',
'x_tighten': True,
'y_tighten': True,
'limits': (-width,
len(Q_as) + width * 2,
0 if positive else -110,
110),
'aspect': 1 / ((110 if positive else 220) /
(width + len(Q_as) + width * 2))})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
