def single_cmfs_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : unicode, optional
Colour matching functions to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> single_cmfs_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
settings = {
'title': '{0} - Colour Matching Functions'.format(cmfs.title)}
settings.update(kwargs)
return multi_cmfs_plot((cmfs.name, ), **settings)
def single_cmfs_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : unicode, optional
Colour matching functions to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> single_cmfs_plot() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
settings = {'title': '{0} - Colour Matching Functions'.format(cmfs.title)}
settings.update(kwargs)
return multi_cmfs_plot((cmfs.name, ), **settings)
def single_cmfs_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : unicode, optional
Colour matching functions to plot.
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
--------
>>> single_cmfs_plot() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
settings = {
'title': '{0} - Colour Matching Functions'.format(cmfs.strict_name)
}
settings.update(kwargs)
return multi_cmfs_plot((cmfs.name, ), **settings)
def single_illuminant_relative_spd_plot(
illuminant='A', cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given single illuminant relative spectral power distribution.
Parameters
----------
illuminant : unicode, optional
Factory illuminant to plot.
cmfs : unicode, optional
Standard observer colour matching functions to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> single_illuminant_relative_spd_plot() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.title)
illuminant = get_illuminant(illuminant)
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return single_spd_plot(illuminant, **settings)
def single_rayleigh_scattering_spd_plot(
CO2_concentration=STANDARD_CO2_CONCENTRATION,
temperature=STANDARD_AIR_TEMPERATURE,
pressure=AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude=DEFAULT_LATITUDE,
altitude=DEFAULT_ALTITUDE,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots a single *Rayleigh* scattering spectral power distribution.
Parameters
----------
CO2_concentration : numeric, optional
:math:`CO_2` concentration in parts per million (ppm).
temperature : numeric, optional
Air temperature :math:`T[K]` in kelvin degrees.
pressure : numeric
Surface pressure :math:`P` of the measurement site.
latitude : numeric, optional
Latitude of the site in degrees.
altitude : numeric, optional
Altitude of the site in meters.
cmfs : unicode, optional
Standard observer colour matching functions.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`single_spd_plot`},
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother. [1]_
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> single_rayleigh_scattering_spd_plot() # doctest: +SKIP
"""
title = 'Rayleigh Scattering'
cmfs = get_cmfs(cmfs)
settings = {'title': title, 'y_label': 'Optical Depth'}
settings.update(kwargs)
spd = rayleigh_scattering_spd(cmfs.shape, CO2_concentration, temperature,
pressure, latitude, altitude)
return single_spd_plot(spd, **settings)
def chromaticity_diagram_colours_CIE1976UCS(
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
antialiasing=True):
"""
Plots the *CIE 1976 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_CIE1976UCS() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
illuminant),
qhull_options='Qu QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(1, 0, samples))
xy = tstack((xx, yy))
mask = (triangulation.find_simplex(xy) < 0).astype(DEFAULT_FLOAT_DTYPE)
if antialiasing:
kernel = np.array([
[0, 1, 0],
[1, 2, 1],
[0, 1, 0],
]).astype(DEFAULT_FLOAT_DTYPE)
kernel /= np.sum(kernel)
mask = convolve(mask, kernel)
mask = 1 - mask[:, :, np.newaxis]
XYZ = xy_to_XYZ(Luv_uv_to_xy(xy))
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
return np.dstack([RGB, mask])
def chromaticity_diagram_construction_visual(
cmfs='CIE 1931 2 Degree Standard Observer',
width=2.0,
method='gl',
parent=None):
"""
Returns a :class:`vispy.scene.visuals.Node` class instance representing
the chromaticity diagram construction with the spectral locus.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used to draw the spectral
locus.
width : numeric, optional
Line width.
method : unicode, optional
**{'gl', 'agg'}**,
Line drawing method.
parent : Node, optional
Parent of the spectral locus visual in the `SceneGraph`.
Returns
-------
Node
Chromaticity diagram construction visual.
"""
from colour_analysis.visuals import Primitive
node = Node(parent=parent)
simplex_p = np.array([(1, 0, 0), (0, 1, 0), (0, 0, 1)])
simplex_f = np.array([(0, 1, 2)])
simplex_c = np.array([(1, 1, 1), (1, 1, 1), (1, 1, 1)])
Primitive(simplex_p,
simplex_f,
uniform_opacity=0.5,
vertex_colours=simplex_c,
parent=node)
simplex_f = np.array([(0, 1, 2), (1, 2, 0), (2, 0, 1)])
Primitive(simplex_p,
simplex_f,
uniform_opacity=1.0,
vertex_colours=simplex_c,
wireframe=True,
parent=node)
lines = []
for XYZ in get_cmfs(cmfs).values:
lines.append(XYZ * 1.75)
lines.append((0, 0, 0))
lines = np.array(lines)
Line(lines, (0, 0, 0), width=width, method=method, parent=node)
return node
def chromaticity_diagram_visual(
samples=256,
cmfs='CIE 1931 2 Degree Standard Observer',
transformation='CIE 1931',
parent=None):
"""
Creates a chromaticity diagram visual based on
:class:`colour_analysis.visuals.Primitive` class.
Parameters
----------
samples : int, optional
Inner samples count used to construct the chromaticity diagram
triangulation.
cmfs : unicode, optional
Standard observer colour matching functions used for the chromaticity
diagram boundaries.
transformation : unicode, optional
{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}
Chromaticity diagram transformation.
parent : Node, optional
Parent of the chromaticity diagram in the `SceneGraph`.
Returns
-------
Primitive
Chromaticity diagram visual.
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
XYZ_to_ij = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['XYZ_to_ij'])
ij_to_XYZ = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['ij_to_XYZ'])
ij_c = XYZ_to_ij(cmfs.values, illuminant)
triangulation = Delaunay(ij_c, qhull_options='QJ')
samples = np.linspace(0, 1, samples)
ii, jj = np.meshgrid(samples, samples)
ij = tstack((ii, jj))
ij = np.vstack((ij_c, ij[triangulation.find_simplex(ij) > 0]))
ij_p = np.hstack((ij, np.full((ij.shape[0], 1), 0)))
triangulation = Delaunay(ij, qhull_options='QJ')
RGB = normalise(XYZ_to_sRGB(ij_to_XYZ(ij, illuminant), illuminant),
axis=-1)
diagram = Primitive(vertices=ij_p,
faces=triangulation.simplices,
vertex_colours=RGB,
parent=parent)
return diagram
def spds_CIE_1976_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
spds : list, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1976_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
True
"""
CIE_1976_UCS_chromaticity_diagram_plot(standalone=False,
**kwargs)
cmfs = get_cmfs(cmfs)
cmfs_shape = cmfs.shape
for spd in spds:
spd = spd.clone().align(cmfs_shape)
XYZ = spectral_to_XYZ(spd) / 100
uv = Luv_to_uv(XYZ_to_Luv(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
display(standalone=True)
def chromaticity_diagram_visual(samples=256,
cmfs='CIE 1931 2 Degree Standard Observer',
transformation='CIE 1931',
parent=None):
"""
Creates a chromaticity diagram visual based on
:class:`colour_analysis.visuals.Primitive` class.
Parameters
----------
samples : int, optional
Inner samples count used to construct the chromaticity diagram
triangulation.
cmfs : unicode, optional
Standard observer colour matching functions used for the chromaticity
diagram boundaries.
transformation : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
Chromaticity diagram transformation.
parent : Node, optional
Parent of the chromaticity diagram in the `SceneGraph`.
Returns
-------
Primitive
Chromaticity diagram visual.
"""
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
XYZ_to_ij = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['XYZ_to_ij'])
ij_to_XYZ = (
CHROMATICITY_DIAGRAM_TRANSFORMATIONS[transformation]['ij_to_XYZ'])
ij_c = XYZ_to_ij(cmfs.values, illuminant)
triangulation = Delaunay(ij_c, qhull_options='QJ')
samples = np.linspace(0, 1, samples)
ii, jj = np.meshgrid(samples, samples)
ij = tstack((ii, jj))
ij = np.vstack((ij_c, ij[triangulation.find_simplex(ij) > 0]))
ij_p = np.hstack((ij, np.full((ij.shape[0], 1), 0, DEFAULT_FLOAT_DTYPE)))
triangulation = Delaunay(ij, qhull_options='QJ')
RGB = normalise_maximum(
XYZ_to_sRGB(ij_to_XYZ(ij, illuminant), illuminant), axis=-1)
diagram = Primitive(
vertices=ij_p,
faces=triangulation.simplices,
vertex_colours=RGB,
parent=parent)
return diagram
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
-------
Figure
Current figure or None.
Examples
--------
>>> visible_spectrum_plot() # doctest: +SKIP
"""
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_encoding_cctf=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_ENCODING_CCTF(normalise_maximum(colours))
settings = {
'title': 'The Visible Spectrum - {0}'.format(cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': False,
'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(wavelengths, colours))
], **settings)
def single_rayleigh_scattering_spd_plot(
CO2_concentration=STANDARD_CO2_CONCENTRATION,
temperature=STANDARD_AIR_TEMPERATURE,
pressure=AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude=DEFAULT_LATITUDE,
altitude=DEFAULT_ALTITUDE,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots a single rayleigh scattering spectral power distribution.
Parameters
----------
CO2_concentration : numeric, optional
:math:`CO_2` concentration in parts per million (ppm).
temperature : numeric, optional
Air temperature :math:`T[K]` in kelvin degrees.
pressure : numeric
Surface pressure :math:`P` of the measurement site.
latitude : numeric, optional
Latitude of the site in degrees.
altitude : numeric, optional
Altitude of the site in meters.
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> single_rayleigh_scattering_spd_plot() # doctest: +SKIP
True
"""
title = 'Rayleigh Scattering'
cmfs = get_cmfs(cmfs)
settings = {
'title': title,
'y_label': 'Optical Depth'}
settings.update(kwargs)
spd = rayleigh_scattering_spd(cmfs.shape,
CO2_concentration,
temperature,
pressure,
latitude,
altitude)
return single_spd_plot(spd, **settings)
def single_rayleigh_scattering_spd_plot(
CO2_concentration=STANDARD_CO2_CONCENTRATION,
temperature=STANDARD_AIR_TEMPERATURE,
pressure=AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude=DEFAULT_LATITUDE,
altitude=DEFAULT_ALTITUDE,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots a single rayleigh scattering spectral power distribution.
Parameters
----------
CO2_concentration : numeric, optional
:math:`CO_2` concentration in parts per million (ppm).
temperature : numeric, optional
Air temperature :math:`T[K]` in kelvin degrees.
pressure : numeric
Surface pressure :math:`P` of the measurement site.
latitude : numeric, optional
Latitude of the site in degrees.
altitude : numeric, optional
Altitude of the site in meters.
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> single_rayleigh_scattering_spd_plot() # doctest: +SKIP
"""
title = 'Rayleigh Scattering'
cmfs = get_cmfs(cmfs)
settings = {
'title': title,
'y_label': 'Optical Depth'}
settings.update(kwargs)
spd = rayleigh_scattering_spd(cmfs.shape,
CO2_concentration,
temperature,
pressure,
latitude,
altitude)
return single_spd_plot(spd, **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
-------
Figure
Current figure or None.
Examples
--------
>>> visible_spectrum_plot() # doctest: +SKIP
"""
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_encoding_cctf=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_ENCODING_CCTF(normalise_maximum(colours))
settings = {
'title': 'The Visible Spectrum - {0}'.format(cmfs.title),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': False,
'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(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
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother.
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.
References
----------
- :cite:`Spiker2015a`
Examples
--------
>>> visible_spectrum_plot() # doctest: +SKIP
"""
settings = {'y_label': None, 'y_ticker': False, 'standalone': False}
single_spd_plot(ones_spd(DEFAULT_SPECTRAL_SHAPE),
cmfs=cmfs,
out_of_gamut_clipping=out_of_gamut_clipping,
**settings)
cmfs = get_cmfs(cmfs)
settings = {
'title': 'The Visible Spectrum - {0}'.format(cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
'standalone': True
}
settings.update(kwargs)
return render(**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 : 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 single_illuminant_relative_spd_plot(
illuminant='A', cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given single illuminant relative spectral power distribution.
Parameters
----------
illuminant : unicode, optional
Factory illuminant to plot.
cmfs : unicode, optional
Standard observer colour matching functions to plot.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.single_spd_plot`},
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother.
Returns
-------
Figure
Current figure or None.
References
----------
- :cite:`Spiker2015a`
Examples
--------
>>> single_illuminant_relative_spd_plot() # doctest: +SKIP
"""
cmfs = get_cmfs(cmfs)
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.strict_name)
illuminant = get_illuminant(illuminant)
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return single_spd_plot(illuminant, **settings)
def single_illuminant_relative_spd_plot(
illuminant='A',
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given single illuminant relative spectral power distribution.
Parameters
----------
illuminant : unicode, optional
Factory illuminant to plot.
cmfs : unicode, optional
Standard observer colour matching functions to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> single_illuminant_relative_spd_plot() # doctest: +SKIP
True
"""
cmfs = get_cmfs(cmfs)
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.title)
illuminant = get_illuminant(illuminant)
settings = {
'title': title,
'y_label': 'Relative Spectral Power Distribution'}
settings.update(kwargs)
return single_spd_plot(illuminant, **settings)
def RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(
... c) # doctest: +SKIP
True
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
illuminant = DEFAULT_PLOTTING_ILLUMINANT
settings = {
'title': '{0} - {1} - CIE 1976 UCS Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone': False}
settings.update(kwargs)
CIE_1976_UCS_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.1], [0.7]
settings = {'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
uv = Luv_to_uv(XYZ_to_Luv(xy_to_XYZ(
POINTER_GAMUT_BOUNDARIES), illuminant), illuminant)
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(uv[..., 0],
uv[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]),
(uv[-1][1], uv[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
uv = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
pylab.scatter(uv[..., 0],
uv[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
# RGB colourspaces such as *ACES2065-1* have primaries with
# chromaticity coordinates set to 0 thus we prevent nan from being
# yield by zero division in later colour transformations.
primaries = np.where(colourspace.primaries == 0,
EPSILON,
colourspace.primaries)
primaries = Luv_to_uv(XYZ_to_Luv(xy_to_XYZ(
primaries), illuminant), illuminant)
whitepoint = Luv_to_uv(XYZ_to_Luv(xy_to_XYZ(
colourspace.whitepoint), illuminant), illuminant)
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]) - 0.1)
y_limit_min.append(np.amin(primaries[..., 1]) - 0.1)
x_limit_max.append(np.amax(primaries[..., 0]) + 0.1)
y_limit_max.append(np.amax(primaries[..., 1]) + 0.1)
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)),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
show_diagram_colours : bool, optional
Display the chromaticity diagram background colours.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> CIE_1931_chromaticity_diagram_plot() # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
if show_diagram_colours:
image = matplotlib.image.imread(
os.path.join(PLOTTING_RESOURCES_DIRECTORY,
'CIE_1931_Chromaticity_Diagram_{0}.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation=None, extent=(0, 1, 0, 1))
labels = (
390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600, 620, 700)
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
xy = XYZ_to_xy(cmfs.values, illuminant)
wavelengths_chromaticity_coordinates = dict(tuple(zip(wavelengths, xy)))
pylab.plot(xy[..., 0], xy[..., 1], color='black', linewidth=2)
pylab.plot((xy[-1][0], xy[0][0]),
(xy[-1][1], xy[0][1]),
color='black',
linewidth=2)
for label in labels:
x, y = wavelengths_chromaticity_coordinates.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])
xy = np.array([x, y])
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx])
if np.dot(normalise_vector(xy - equal_energy),
normalise_vector(direction)) > 0 else
np.array([dy, -dx]))
normal = normalise_vector(normal)
normal /= 25
pylab.plot((x, x + normal[0] * 0.75),
(y, y + normal[1] * 0.75),
color='black',
linewidth=1.5)
pylab.text(x + normal[0],
y + normal[1],
label,
color='black',
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
ticks = np.arange(-10, 10, 0.1)
pylab.xticks(ticks)
pylab.yticks(ticks)
settings.update({
'title': 'CIE 1931 Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label': 'CIE x',
'y_label': 'CIE y',
'grid': True,
'bounding_box': (0, 1, 0, 1)})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1960_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the *CIE 1960 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_1960_UCS_chromaticity_diagram_plot() # doctest: +SKIP
True
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
image = matplotlib.image.imread(
os.path.join(PLOTTING_RESOURCES_DIRECTORY,
'CIE_1960_UCS_Chromaticity_Diagram_{0}_Large.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)
uv = np.array([UCS_to_uv(XYZ_to_UCS(XYZ)) for XYZ in cmfs.values])
wavelengths_chromaticity_coordinates = dict(tuple(zip(wavelengths, uv)))
pylab.plot(uv[:, 0], uv[:, 1], color='black', linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]),
(uv[-1][1], uv[0][1]),
color='black',
linewidth=2)
for label in labels:
u, v = wavelengths_chromaticity_coordinates.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.update({
'title': 'CIE 1960 UCS Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label': 'CIE u',
'y_label': 'CIE v',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'bounding_box': [-0.075, 0.675, -0.15, 0.6],
'bbox_inches': 'tight',
'pad_inches': 0})
settings.update(kwargs)
boundaries(**settings)
decorate(**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
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = CHROMATICITY_DIAGRAM_DEFAULT_ILLUMINANT
image = matplotlib.image.imread(
os.path.join(PLOTTING_RESOURCES_DIRECTORY,
'CIE_1931_Chromaticity_Diagram_{0}.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation=None, extent=(0, 1, 0, 1))
labels = (
390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600, 620, 700)
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
xy = XYZ_to_xy(cmfs.values, illuminant)
wavelengths_chromaticity_coordinates = dict(tuple(zip(wavelengths, xy)))
pylab.plot(xy[..., 0], xy[..., 1], color='black', linewidth=2)
pylab.plot((xy[-1][0], xy[0][0]),
(xy[-1][1], xy[0][1]),
color='black',
linewidth=2)
for label in labels:
x, y = wavelengths_chromaticity_coordinates.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'})
ticks = np.arange(-10, 10, 0.1)
pylab.xticks(ticks)
pylab.yticks(ticks)
settings.update({
'title': 'CIE 1931 Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label': 'CIE x',
'y_label': 'CIE y',
'x_ticker': True,
'y_ticker': True,
'grid': True,
'bounding_box': (0, 1, 0, 1),
'bbox_inches': 'tight',
'pad_inches': 0})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def multi_cmfs_plot(cmfs=None, **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : array_like, optional
Colour matching functions to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> cmfs = [
... 'CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer']
>>> multi_cmfs_plot(cmfs) # doctest: +SKIP
True
"""
canvas(**kwargs)
if cmfs is None:
cmfs = ('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_i in enumerate(cmfs):
cmfs_i = get_cmfs(cmfs_i)
rgb = [reduce(lambda y, _: y * 0.5, range(i), x) # noqa
for x in rgb]
wavelengths, values = tuple(
zip(*[(key, value) for key, value in getattr(cmfs_i, axis)]))
shape = cmfs_i.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_i.labels.get(axis), cmfs_i.title),
linewidth=2)
settings = {
'title': '{0} - Colour Matching Functions'.format(', '.join(
[get_cmfs(c).title for c in cmfs])),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Tristimulus Values',
'x_tighten': True,
'legend': True,
'legend_location': 'upper right',
'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)
boundaries(**settings)
decorate(**settings)
return display(**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.
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
--------
>>> blackbody_colours_plot() # doctest: +SKIP
"""
axes = canvas(**kwargs).gca()
cmfs = get_cmfs(cmfs)
colours = []
temperatures = []
with suppress_warnings():
for temperature in shape:
spd = blackbody_spd(temperature, cmfs.shape)
XYZ = spectral_to_XYZ(spd, cmfs)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ / 100))
colours.append(RGB)
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
axes.bar(x=temperatures,
height=1,
width=shape.interval,
color=colours,
align='edge')
settings = {
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': None,
'limits': (x_min, x_max, y_min, y_max),
'x_tighten': True,
'y_tighten': True,
'y_ticker': False
}
settings.update(kwargs)
return render(**settings)
def RGB_colourspaces_gamuts_plot(colourspaces=None,
reference_colourspace='CIE xyY',
segments=8,
display_grid=True,
grid_segments=10,
spectral_locus=False,
spectral_locus_colour=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces gamuts in given reference colourspace.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot the gamuts.
reference_colourspace : unicode, optional
**{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',
'IPT'}**,
Reference colourspace to plot the gamuts into.
segments : int, optional
Edge segments count for each *RGB* colourspace cubes.
display_grid : bool, optional
Display a grid at the bottom of the *RGB* colourspace cubes.
grid_segments : bool, optional
Edge segments count for the grid.
spectral_locus : bool, optional
Is spectral locus line plotted.
spectral_locus_colour : array_like, optional
Spectral locus line colour.
cmfs : unicode, optional
Standard observer colour matching functions used for spectral locus.
\**kwargs : dict, optional
**{'face_colours', 'edge_colours', 'edge_alpha', 'face_alpha'}**,
Arguments for each given colourspace where each key has an array_like
value such as: ``{ 'face_colours': (None, (0.5, 0.5, 1.0)),
'edge_colours': (None, (0.5, 0.5, 1.0)), 'edge_alpha': (0.5, 1.0),
'face_alpha': (0.0, 1.0)}``
**{'grid_face_colours', 'grid_edge_colours', 'grid_face_alpha',
'grid_edge_alpha', 'x_axis_colour', 'y_axis_colour', 'x_ticks_colour',
'y_ticks_colour', 'x_label_colour', 'y_label_colour',
'ticks_and_label_location'}**,
Arguments for the nadir grid such as ``{'grid_face_colours':
(0.25, 0.25, 0.25), 'grid_edge_colours': (0.50, 0.50, 0.50),
'grid_face_alpha': 0.1, 'grid_edge_alpha': 0.5, 'x_axis_colour':
(0.0, 0.0, 0.0, 1.0), 'y_axis_colour': (0.0, 0.0, 0.0, 1.0),
'x_ticks_colour': (0.0, 0.0, 0.0, 0.85), 'y_ticks_colour':
(0.0, 0.0, 0.0, 0.85), 'x_label_colour': (0.0, 0.0, 0.0, 0.85),
'y_label_colour': (0.0, 0.0, 0.0, 0.85), 'ticks_and_label_location':
('-x', '-y')}``
Returns
-------
bool
Definition success.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_gamuts_plot(c) # doctest: +SKIP
True
"""
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg')
count_c = len(colourspaces)
settings = Structure(
**{'face_colours': [None] * count_c,
'edge_colours': [None] * count_c,
'face_alpha': [1] * count_c,
'edge_alpha': [1] * count_c,
'title': '{0} - {1} Reference Colourspace'.format(
', '.join(colourspaces), reference_colourspace)})
settings.update(kwargs)
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
illuminant = DEFAULT_PLOTTING_ILLUMINANT
points = np.zeros((4, 3))
if spectral_locus:
cmfs = get_cmfs(cmfs)
XYZ = cmfs.values
points = XYZ_to_reference_colourspace(XYZ,
illuminant,
reference_colourspace)
points[np.isnan(points)] = 0
c = ((0.0, 0.0, 0.0, 0.5)
if spectral_locus_colour is None else
spectral_locus_colour)
pylab.plot(points[..., 0],
points[..., 1],
points[..., 2],
color=c,
linewidth=2,
zorder=1)
pylab.plot((points[-1][0], points[0][0]),
(points[-1][1], points[0][1]),
(points[-1][2], points[0][2]),
color=c,
linewidth=2,
zorder=1)
quads, RGB_f, RGB_e = [], [], []
for i, colourspace in enumerate(colourspaces):
colourspace = get_RGB_colourspace(colourspace)
quads_c, RGB = RGB_identity_cube(width_segments=segments,
height_segments=segments,
depth_segments=segments)
XYZ = RGB_to_XYZ(
quads_c,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.RGB_to_XYZ_matrix)
quads.extend(XYZ_to_reference_colourspace(XYZ,
colourspace.whitepoint,
reference_colourspace))
if settings.face_colours[i] is not None:
RGB = np.ones(RGB.shape) * settings.face_colours[i]
RGB_f.extend(np.hstack(
(RGB, np.full((RGB.shape[0], 1, np.float_),
settings.face_alpha[i]))))
if settings.edge_colours[i] is not None:
RGB = np.ones(RGB.shape) * settings.edge_colours[i]
RGB_e.extend(np.hstack(
(RGB, np.full((RGB.shape[0], 1, np.float_),
settings.edge_alpha[i]))))
quads = np.asarray(quads)
quads[np.isnan(quads)] = 0
if quads.size != 0:
for i, axis in enumerate('xyz'):
min_a = np.min(np.vstack((quads[..., i], points[..., i])))
max_a = np.max(np.vstack((quads[..., i], points[..., i])))
getattr(axes, 'set_{}lim'.format(axis))((min_a, max_a))
labels = REFERENCE_COLOURSPACES_TO_LABELS[reference_colourspace]
for i, axis in enumerate('xyz'):
getattr(axes, 'set_{}label'.format(axis))(labels[i])
if display_grid:
if reference_colourspace == 'CIE Lab':
limits = np.array([[-450, 450], [-450, 450]])
elif reference_colourspace == 'CIE Luv':
limits = np.array([[-650, 650], [-650, 650]])
elif reference_colourspace == 'CIE UVW':
limits = np.array([[-850, 850], [-850, 850]])
else:
limits = np.array([[-1.5, 1.5], [-1.5, 1.5]])
quads_g, RGB_gf, RGB_ge = nadir_grid(
limits, grid_segments, labels, axes, **settings)
quads = np.vstack((quads_g, quads))
RGB_f = np.vstack((RGB_gf, RGB_f))
RGB_e = np.vstack((RGB_ge, RGB_e))
collection = Poly3DCollection(quads)
collection.set_facecolors(RGB_f)
collection.set_edgecolors(RGB_e)
axes.add_collection3d(collection)
settings.update({
'camera_aspect': 'equal',
'no_axes3d': True})
settings.update(kwargs)
camera(**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 RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
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.
show_diagram_colours : bool, optional
{:func:`CIE_1976_UCS_chromaticity_diagram_plot`},
Whether to display the chromaticity diagram background colours.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1976_UCS_chromaticity_diagram_plot(
... c) # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
illuminant = DEFAULT_PLOTTING_ILLUMINANT
settings = {
'title':
'{0} - {1} - CIE 1976 UCS Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone':
False
}
settings.update(kwargs)
CIE_1976_UCS_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.1], [0.7]
settings = {
'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)
}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
uv = Luv_to_uv(
XYZ_to_Luv(xy_to_XYZ(POINTER_GAMUT_BOUNDARIES), illuminant),
illuminant)
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(uv[..., 0],
uv[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]), (uv[-1][1], uv[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
uv = Luv_to_uv(XYZ_to_Luv(XYZ, illuminant), illuminant)
pylab.scatter(uv[..., 0],
uv[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
# RGB colourspaces such as *ACES2065-1* have primaries with
# chromaticity coordinates set to 0 thus we prevent nan from being
# yield by zero division in later colour transformations.
P = np.where(colourspace.primaries == 0, EPSILON,
colourspace.primaries)
P = Luv_to_uv(XYZ_to_Luv(xy_to_XYZ(P), illuminant), illuminant)
W = Luv_to_uv(
XYZ_to_Luv(xy_to_XYZ(colourspace.whitepoint), illuminant),
illuminant)
pylab.plot((W[0], W[0]), (W[1], W[1]),
color=(r, g, b),
label=colourspace.name,
linewidth=2)
pylab.plot((W[0], W[0]), (W[1], W[1]),
'o',
color=(r, g, b),
linewidth=2)
pylab.plot((P[0, 0], P[1, 0]), (P[0, 1], P[1, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((P[1, 0], P[2, 0]), (P[1, 1], P[2, 1]),
'o-',
color=(r, g, b),
linewidth=2)
pylab.plot((P[2, 0], P[0, 0]), (P[2, 1], P[0, 1]),
'o-',
color=(r, g, b),
linewidth=2)
x_limit_min.append(np.amin(P[..., 0]) - 0.1)
y_limit_min.append(np.amin(P[..., 1]) - 0.1)
x_limit_max.append(np.amax(P[..., 0]) + 0.1)
y_limit_max.append(np.amax(P[..., 1]) + 0.1)
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)),
'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
-------
Figure
Current figure or None.
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
"""
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_encoding_cctf=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_ENCODING_CCTF(normalise_maximum(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 RGB_colourspaces_CIE_1931_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1931 Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1931_chromaticity_diagram_plot(
... c) # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
settings = {
'title': '{0} - {1} - CIE 1931 Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone': False}
settings.update(kwargs)
CIE_1931_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.9]
y_limit_min, y_limit_max = [-0.1], [0.9]
settings = {'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
xy = np.asarray(POINTER_GAMUT_BOUNDARIES)
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(xy[..., 0],
xy[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((xy[-1][0], xy[0][0]),
(xy[-1][1], xy[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
xy = XYZ_to_xy(XYZ, POINTER_GAMUT_ILLUMINANT)
pylab.scatter(xy[..., 0],
xy[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
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]) - 0.1)
y_limit_min.append(np.amin(primaries[..., 1]) - 0.1)
x_limit_max.append(np.amax(primaries[..., 0]) + 0.1)
y_limit_max.append(np.amax(primaries[..., 1]) + 0.1)
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)),
'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
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother.
cmfs : unicode
Standard observer colour matching functions used for spectrum creation.
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.
References
----------
- :cite:`Spiker2015a`
Examples
--------
>>> from colour import SpectralPowerDistribution
>>> data = {
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360
... }
>>> spd = SpectralPowerDistribution(data, name='Custom')
>>> single_spd_plot(spd) # doctest: +SKIP
"""
axes = canvas(**kwargs).gca()
cmfs = get_cmfs(cmfs)
spd = spd.copy()
spd.interpolator = LinearInterpolator
wavelengths = cmfs.wavelengths[np.logical_and(
cmfs.wavelengths >= max(min(cmfs.wavelengths), min(spd.wavelengths)),
cmfs.wavelengths <= min(max(cmfs.wavelengths), max(spd.wavelengths)),
)]
values = spd[wavelengths]
colours = XYZ_to_sRGB(
wavelength_to_XYZ(wavelengths, cmfs),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_encoding_cctf=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = DEFAULT_PLOTTING_ENCODING_CCTF(normalise_maximum(colours))
x_min, x_max = min(wavelengths), max(wavelengths)
y_min, y_max = 0, max(values)
polygon = Polygon(np.vstack([
(x_min, 0),
tstack((wavelengths, values)),
(x_max, 0),
]),
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
axes.bar(x=wavelengths,
height=max(values),
width=1,
color=colours,
align='edge',
clip_path=polygon)
axes.plot(wavelengths, values, color='black', linewidth=1)
settings = {
'title': '{0} - {1}'.format(spd.strict_name, cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Power Distribution',
'limits': (x_min, x_max, y_min, y_max),
'x_tighten': True,
'y_tighten': True
}
settings.update(kwargs)
return render(**settings)
def spectral_locus_visual(
reference_colourspace="CIE xyY",
cmfs="CIE 1931 2 Degree Standard Observer",
width=2.0,
uniform_colour=None,
uniform_opacity=1.0,
method="gl",
parent=None,
):
"""
Returns a :class:`vispy.scene.visuals.Line` class instance representing
the spectral locus.
Parameters
----------
reference_colourspace : unicode, optional
{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',
'IPT'}
Reference colourspace to use for colour conversions / transformations.
cmfs : unicode, optional
Standard observer colour matching functions used to draw the spectral
locus.
width : numeric, optional
Line width.
edge_size : numeric, optional
Symbol edge size.
uniform_colour : array_like, optional
Uniform symbol colour.
uniform_opacity : numeric, optional
Uniform symbol opacity.
method : unicode, optional
{'gl', 'agg'}
Line drawing method.
parent : Node, optional
Parent of the spectral locus visual in the `SceneGraph`.
Returns
-------
Line
Spectral locus visual.
"""
cmfs = get_cmfs(cmfs)
XYZ = cmfs.values
XYZ = np.vstack((XYZ, XYZ[0, ...]))
illuminant = DEFAULT_PLOTTING_ILLUMINANT
points = XYZ_to_reference_colourspace(XYZ, illuminant, reference_colourspace)
points[np.isnan(points)] = 0
if uniform_colour is None:
RGB = normalise(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
RGB = np.hstack((RGB, np.full((RGB.shape[0], 1), uniform_opacity)))
else:
RGB = ColorArray(uniform_colour, alpha=uniform_opacity).rgba
line = Line(points, np.clip(RGB, 0, 1), width=width, method=method, parent=parent)
return line
def CIE_1976_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
**kwargs):
"""
Plots the *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
show_diagram_colours : bool, optional
Display the chromaticity diagram background colours.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> CIE_1976_UCS_chromaticity_diagram_plot() # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
if show_diagram_colours:
image = matplotlib.image.imread(
os.path.join(PLOTTING_RESOURCES_DIRECTORY,
'CIE_1976_UCS_Chromaticity_Diagram_{0}.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation=None, extent=(0, 1, 0, 1))
labels = (420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,
540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 680)
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
uv = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
wavelengths_chromaticity_coordinates = dict(zip(wavelengths, uv))
pylab.plot(uv[..., 0], uv[..., 1], color='black', linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]),
(uv[-1][1], uv[0][1]),
color='black',
linewidth=2)
for label in labels:
u, v = wavelengths_chromaticity_coordinates.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])
uv = np.array([u, v])
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx])
if np.dot(normalise_vector(uv - equal_energy),
normalise_vector(direction)) > 0 else
np.array([dy, -dx]))
normal = normalise_vector(normal)
normal /= 25
pylab.plot((u, u + normal[0] * 0.75),
(v, v + normal[1] * 0.75),
color='black',
linewidth=1.5)
pylab.text(u + normal[0],
v + normal[1],
label,
color='black',
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
ticks = np.arange(-10, 10, 0.1)
pylab.xticks(ticks)
pylab.yticks(ticks)
settings.update({
'title': 'CIE 1976 UCS Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label': 'CIE u\'',
'y_label': 'CIE v\'',
'grid': True,
'bounding_box': (0, 1, 0, 1)})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def 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 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
-------
Figure
Current figure or None.
Examples
--------
>>> CIE_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
"""
settings = {'figure_size': (64, 64)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
x_dot, y_dot = tsplit(xy)
pylab.scatter(x_dot, y_dot, color=RGB, s=surface)
settings.update({
'x_ticker': False,
'y_ticker': False,
'bounding_box': (0, 1, 0, 1)})
settings.update(kwargs)
ax = matplotlib.pyplot.gca()
matplotlib.pyplot.setp(ax, frame_on=False)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def 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 multi_cmfs_plot(cmfs=None, **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : array_like, optional
Colour matching functions to plot.
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
--------
>>> cmfs = [
... 'CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer']
>>> multi_cmfs_plot(cmfs) # doctest: +SKIP
"""
canvas(**kwargs)
if cmfs is None:
cmfs = ('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 i, rgb in enumerate([(1, 0, 0), (0, 1, 0), (0, 0, 1)]):
for j, cmfs_i in enumerate(cmfs):
cmfs_i = get_cmfs(cmfs_i)
rgb = [reduce(lambda y, _: y * 0.5, range(j), x) for x in rgb]
values = cmfs_i.values[:, i]
shape = cmfs_i.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(cmfs_i.wavelengths,
values,
color=rgb,
label=u'{0} - {1}'.format(cmfs_i.labels[i],
cmfs_i.strict_name),
linewidth=1)
settings = {
'title':
'{0} - Colour Matching Functions'.format(', '.join(
[get_cmfs(c).strict_name for c in cmfs])),
'x_label':
'Wavelength $\\lambda$ (nm)',
'y_label':
'Tristimulus Values',
'x_tighten':
True,
'y_tighten':
True,
'legend':
True,
'legend_location':
'upper right',
'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)
return render(**settings)
def RGB_colourspaces_CIE_1960_UCS_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1960 UCS Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1960_UCS_chromaticity_diagram_plot(
... c) # doctest: +SKIP
True
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
settings = {
'title': '{0} - {1} - CIE 1960 UCS Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone': False}
settings.update(kwargs)
CIE_1960_UCS_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.2], [0.6]
settings = {'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
uv = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(POINTER_GAMUT_BOUNDARIES)))
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(uv[..., 0],
uv[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]),
(uv[-1][1], uv[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
pylab.scatter(uv[..., 0],
uv[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
# RGB colourspaces such as *ACES2065-1* have primaries with
# chromaticity coordinates set to 0 thus we prevent nan from being
# yield by zero division in later colour transformations.
primaries = np.where(colourspace.primaries == 0,
EPSILON,
colourspace.primaries)
primaries = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(primaries)))
whitepoint = UCS_to_uv(XYZ_to_UCS(xy_to_XYZ(
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]) - 0.1)
y_limit_min.append(np.amin(primaries[..., 1]) - 0.1)
x_limit_max.append(np.amax(primaries[..., 0]) + 0.1)
y_limit_max.append(np.amax(primaries[..., 1]) + 0.1)
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)),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1976_UCS_chromaticity_diagram_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
**kwargs):
"""
Plots the *CIE 1976 UCS Chromaticity Diagram*.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
show_diagram_colours : bool, optional
Whether to display the chromaticity diagram background colours.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> CIE_1976_UCS_chromaticity_diagram_plot() # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
if show_diagram_colours:
image = matplotlib.image.imread(
os.path.join(
PLOTTING_RESOURCES_DIRECTORY,
'CIE_1976_UCS_Chromaticity_Diagram_{0}.png'.format(
cmfs.name.replace(' ', '_'))))
pylab.imshow(image, interpolation=None, extent=(0, 1, 0, 1))
labels = (420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 680)
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
uv = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
wavelengths_chromaticity_coordinates = dict(zip(wavelengths, uv))
pylab.plot(uv[..., 0], uv[..., 1], color='black', linewidth=2)
pylab.plot((uv[-1][0], uv[0][0]), (uv[-1][1], uv[0][1]),
color='black',
linewidth=2)
for label in labels:
u, v = wavelengths_chromaticity_coordinates[label]
pylab.plot(u, v, 'o', color='black', linewidth=2)
index = bisect.bisect(wavelengths, label)
left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
right = (wavelengths[index]
if index < len(wavelengths) else wavelengths[-1])
dx = (wavelengths_chromaticity_coordinates[right][0] -
wavelengths_chromaticity_coordinates[left][0])
dy = (wavelengths_chromaticity_coordinates[right][1] -
wavelengths_chromaticity_coordinates[left][1])
uv = np.array([u, v])
direction = np.array([-dy, dx])
normal = (np.array([
-dy, dx
]) if np.dot(normalise_vector(uv - equal_energy),
normalise_vector(direction)) > 0 else np.array([dy, -dx]))
normal = normalise_vector(normal)
normal /= 25
pylab.plot((u, u + normal[0] * 0.75), (v, v + normal[1] * 0.75),
color='black',
linewidth=1.5)
pylab.text(u + normal[0],
v + normal[1],
label,
color='black',
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
ticks = np.arange(-10, 10, 0.1)
pylab.xticks(ticks)
pylab.yticks(ticks)
settings.update({
'title':
'CIE 1976 UCS Chromaticity Diagram - {0}'.format(cmfs.title),
'x_label':
'CIE u\'',
'y_label':
'CIE v\'',
'grid':
True,
'bounding_box': (0, 1, 0, 1)
})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1976_UCS_chromaticity_diagram_colours_plot(
surface=1.25,
spacing=0.00075,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1976 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_1976_UCS_chromaticity_diagram_colours_plot() # doctest: +SKIP
True
"""
settings = {'figure_size': (32, 32)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = ILLUMINANTS.get(
'CIE 1931 2 Degree Standard Observer').get('D50')
uv = np.array([Luv_to_uv(XYZ_to_Luv(XYZ, illuminant))
for XYZ in cmfs.values])
path = matplotlib.path.Path(uv)
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(Luv_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.update({
'no_ticks': True,
'bounding_box': [0, 1, 0, 1],
'bbox_inches': 'tight',
'pad_inches': 0})
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
-------
Figure
Current figure or None.
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
"""
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_maximum(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 multi_cmfs_plot(cmfs=None, **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : array_like, optional
Colour matching functions to plot.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> cmfs = [
... 'CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer']
>>> multi_cmfs_plot(cmfs) # doctest: +SKIP
"""
canvas(**kwargs)
if cmfs is None:
cmfs = ('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_i in enumerate(cmfs):
cmfs_i = get_cmfs(cmfs_i)
rgb = [
reduce(lambda y, _: y * 0.5, range(i), x) # noqa
for x in rgb
]
wavelengths, values = tuple(
zip(*[(key, value) for key, value in getattr(cmfs_i, axis)]))
shape = cmfs_i.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_i.labels.get(axis),
cmfs_i.title),
linewidth=2)
settings = {
'title':
'{0} - Colour Matching Functions'.format(', '.join(
[get_cmfs(c).title for c in cmfs])),
'x_label':
'Wavelength $\\lambda$ (nm)',
'y_label':
'Tristimulus Values',
'x_tighten':
True,
'legend':
True,
'legend_location':
'upper right',
'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)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def RGB_colourspaces_gamuts_plot(colourspaces=None,
reference_colourspace='CIE xyY',
segments=8,
display_grid=True,
grid_segments=10,
spectral_locus=False,
spectral_locus_colour=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces gamuts in given reference colourspace.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot the gamuts.
reference_colourspace : unicode, optional
**{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',
'IPT', 'Hunter Lab', 'Hunter Rdab'}**,
Reference colourspace to plot the gamuts into.
segments : int, optional
Edge segments count for each *RGB* colourspace cubes.
display_grid : bool, optional
Display a grid at the bottom of the *RGB* colourspace cubes.
grid_segments : bool, optional
Edge segments count for the grid.
spectral_locus : bool, optional
Is spectral locus line plotted.
spectral_locus_colour : array_like, optional
Spectral locus line colour.
cmfs : unicode, optional
Standard observer colour matching functions used for spectral locus.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`nadir_grid`},
Please refer to the documentation of the previously listed definitions.
face_colours : array_like, optional
Face colours array such as `face_colours = (None, (0.5, 0.5, 1.0))`.
edge_colours : array_like, optional
Edge colours array such as `edge_colours = (None, (0.5, 0.5, 1.0))`.
face_alpha : numeric, optional
Face opacity value such as `face_alpha = (0.5, 1.0)`.
edge_alpha : numeric, optional
Edge opacity value such as `edge_alpha = (0.0, 1.0)`.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_gamuts_plot(c) # doctest: +SKIP
"""
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg')
count_c = len(colourspaces)
settings = Structure(
**{
'face_colours': [None] * count_c,
'edge_colours': [None] * count_c,
'face_alpha': [1] * count_c,
'edge_alpha': [1] * count_c,
'title':
'{0} - {1} Reference Colourspace'.format(', '.join(colourspaces),
reference_colourspace)
})
settings.update(kwargs)
figure = matplotlib.pyplot.figure()
axes = figure.add_subplot(111, projection='3d')
illuminant = DEFAULT_PLOTTING_ILLUMINANT
points = np.zeros((4, 3))
if spectral_locus:
cmfs = get_cmfs(cmfs)
XYZ = cmfs.values
points = common_colourspace_model_axis_reorder(
XYZ_to_colourspace_model(XYZ, illuminant, reference_colourspace),
reference_colourspace)
points[np.isnan(points)] = 0
c = ((0.0, 0.0, 0.0,
0.5) if spectral_locus_colour is None else spectral_locus_colour)
pylab.plot(points[..., 0],
points[..., 1],
points[..., 2],
color=c,
linewidth=2,
zorder=1)
pylab.plot((points[-1][0], points[0][0]),
(points[-1][1], points[0][1]),
(points[-1][2], points[0][2]),
color=c,
linewidth=2,
zorder=1)
quads, RGB_f, RGB_e = [], [], []
for i, colourspace in enumerate(colourspaces):
colourspace = get_RGB_colourspace(colourspace)
quads_c, RGB = RGB_identity_cube(width_segments=segments,
height_segments=segments,
depth_segments=segments)
XYZ = RGB_to_XYZ(quads_c, colourspace.whitepoint,
colourspace.whitepoint, colourspace.RGB_to_XYZ_matrix)
quads.extend(
common_colourspace_model_axis_reorder(
XYZ_to_colourspace_model(XYZ, colourspace.whitepoint,
reference_colourspace),
reference_colourspace))
if settings.face_colours[i] is not None:
RGB = np.ones(RGB.shape) * settings.face_colours[i]
RGB_f.extend(
np.hstack((RGB,
np.full((RGB.shape[0], 1), settings.face_alpha[i],
np.float_))))
if settings.edge_colours[i] is not None:
RGB = np.ones(RGB.shape) * settings.edge_colours[i]
RGB_e.extend(
np.hstack((RGB,
np.full((RGB.shape[0], 1), settings.edge_alpha[i],
np.float_))))
quads = np.asarray(quads)
quads[np.isnan(quads)] = 0
if quads.size != 0:
for i, axis in enumerate('xyz'):
min_a = np.min(np.vstack((quads[..., i], points[..., i])))
max_a = np.max(np.vstack((quads[..., i], points[..., i])))
getattr(axes, 'set_{}lim'.format(axis))((min_a, max_a))
labels = COLOURSPACE_MODELS_LABELS[reference_colourspace]
for i, axis in enumerate('xyz'):
getattr(axes, 'set_{}label'.format(axis))(labels[i])
if display_grid:
if reference_colourspace == 'CIE Lab':
limits = np.array([[-450, 450], [-450, 450]])
elif reference_colourspace == 'CIE Luv':
limits = np.array([[-650, 650], [-650, 650]])
elif reference_colourspace == 'CIE UVW':
limits = np.array([[-850, 850], [-850, 850]])
elif reference_colourspace in ('Hunter Lab', 'Hunter Rdab'):
limits = np.array([[-250, 250], [-250, 250]])
else:
limits = np.array([[-1.5, 1.5], [-1.5, 1.5]])
quads_g, RGB_gf, RGB_ge = nadir_grid(limits, grid_segments, labels,
axes, **settings)
quads = np.vstack((quads_g, quads))
RGB_f = np.vstack((RGB_gf, RGB_f))
RGB_e = np.vstack((RGB_ge, RGB_e))
collection = Poly3DCollection(quads)
collection.set_facecolors(RGB_f)
collection.set_edgecolors(RGB_e)
axes.add_collection3d(collection)
settings.update({'camera_aspect': 'equal', 'no_axes': True})
settings.update(kwargs)
camera(**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.
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
--------
>>> the_blue_sky_plot() # doctest: +SKIP
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.copy()
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_swatch_plot(
ColourSwatch('', normalise_maximum(blue_sky_color)), **settings)
settings = {'standalone': True}
settings.update(kwargs)
return render(**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
Whether to use spectral power distributions colours.
normalise_spds_colours : bool
Whether to normalise spectral power distributions colours.
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
--------
>>> from colour import SpectralPowerDistribution
>>> data_1 = {
... 500: 0.004900,
... 510: 0.009300,
... 520: 0.063270,
... 530: 0.165500,
... 540: 0.290400,
... 550: 0.433450,
... 560: 0.594500
... }
>>> data_2 = {
... 500: 0.323000,
... 510: 0.503000,
... 520: 0.710000,
... 530: 0.862000,
... 540: 0.954000,
... 550: 0.994950,
... 560: 0.995000
... }
>>> spd1 = SpectralPowerDistribution(data_1, name='Custom 1')
>>> spd2 = SpectralPowerDistribution(data_2, name='Custom 2')
>>> multi_spd_plot([spd1, spd2]) # doctest: +SKIP
"""
canvas(**kwargs)
cmfs = get_cmfs(cmfs)
if use_spds_colours:
illuminant = ILLUMINANTS_RELATIVE_SPDS['D65']
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for spd in spds:
wavelengths, values = spd.wavelengths, spd.values
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_maximum(XYZ, clip=False)
RGB = np.clip(XYZ_to_sRGB(XYZ), 0, 1)
pylab.plot(wavelengths,
values,
color=RGB,
label=spd.strict_name,
linewidth=1)
else:
pylab.plot(wavelengths, values, label=spd.strict_name, linewidth=1)
settings = {
'x_label':
'Wavelength $\\lambda$ (nm)',
'y_label':
'Spectral Power Distribution',
'x_tighten':
True,
'y_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)
return render(**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 spds_CIE_1960_UCS_chromaticity_diagram_plot(
spds,
cmfs='CIE 1931 2 Degree Standard Observer',
annotate=True,
**kwargs):
"""
Plots given spectral power distribution chromaticity coordinates into the
*CIE 1960 UCS Chromaticity Diagram*.
Parameters
----------
spds : array_like, optional
Spectral power distributions to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
annotate : bool
Should resulting chromaticity coordinates annotated with their
respective spectral power distribution names.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> from colour import ILLUMINANTS_RELATIVE_SPDS
>>> A = ILLUMINANTS_RELATIVE_SPDS['A']
>>> D65 = ILLUMINANTS_RELATIVE_SPDS['D65']
>>> spds_CIE_1960_UCS_chromaticity_diagram_plot([A, D65]) # doctest: +SKIP
True
"""
settings = {}
settings.update(kwargs)
settings.update({'standalone': False})
CIE_1960_UCS_chromaticity_diagram_plot(**settings)
cmfs = get_cmfs(cmfs)
cmfs_shape = cmfs.shape
for spd in spds:
spd = spd.clone().align(cmfs_shape)
XYZ = spectral_to_XYZ(spd) / 100
uv = UCS_to_uv(XYZ_to_UCS(XYZ))
pylab.plot(uv[0], uv[1], 'o', color='white')
if spd.name is not None and annotate:
pylab.annotate(spd.name,
xy=uv,
xytext=(50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=0.2'))
settings.update({
'x_tighten': True,
'y_tighten': True,
'limits': (-0.1, 0.7, -0.2, 0.6),
'standalone': True})
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def RGB_colourspaces_CIE_1931_chromaticity_diagram_plot(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given *RGB* colourspaces in *CIE 1931 Chromaticity Diagram*.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
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.
show_diagram_colours : bool, optional
{:func:`CIE_1931_chromaticity_diagram_plot`},
Whether to display the chromaticity diagram background colours.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> c = ['Rec. 709', 'ACEScg', 'S-Gamut']
>>> RGB_colourspaces_CIE_1931_chromaticity_diagram_plot(
... c) # doctest: +SKIP
"""
settings = {'figure_size': (DEFAULT_FIGURE_WIDTH, DEFAULT_FIGURE_WIDTH)}
settings.update(kwargs)
canvas(**settings)
if colourspaces is None:
colourspaces = ('Rec. 709', 'ACEScg', 'S-Gamut', 'Pointer Gamut')
cmfs, name = get_cmfs(cmfs), cmfs
settings = {
'title':
'{0} - {1} - CIE 1931 Chromaticity Diagram'.format(
', '.join(colourspaces), name),
'standalone':
False
}
settings.update(kwargs)
CIE_1931_chromaticity_diagram_plot(**settings)
x_limit_min, x_limit_max = [-0.1], [0.9]
y_limit_min, y_limit_max = [-0.1], [0.9]
settings = {
'colour_cycle_map': 'rainbow',
'colour_cycle_count': len(colourspaces)
}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
if colourspace == 'Pointer Gamut':
xy = np.asarray(POINTER_GAMUT_BOUNDARIES)
alpha_p, colour_p = 0.85, '0.95'
pylab.plot(xy[..., 0],
xy[..., 1],
label='Pointer\'s Gamut',
color=colour_p,
alpha=alpha_p,
linewidth=2)
pylab.plot((xy[-1][0], xy[0][0]), (xy[-1][1], xy[0][1]),
color=colour_p,
alpha=alpha_p,
linewidth=2)
XYZ = Lab_to_XYZ(LCHab_to_Lab(POINTER_GAMUT_DATA),
POINTER_GAMUT_ILLUMINANT)
xy = XYZ_to_xy(XYZ, POINTER_GAMUT_ILLUMINANT)
pylab.scatter(xy[..., 0],
xy[..., 1],
alpha=alpha_p / 2,
color=colour_p,
marker='+')
else:
colourspace, name = get_RGB_colourspace(colourspace), colourspace
r, g, b, _a = next(cycle)
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]) - 0.1)
y_limit_min.append(np.amin(primaries[..., 1]) - 0.1)
x_limit_max.append(np.amax(primaries[..., 0]) + 0.1)
y_limit_max.append(np.amax(primaries[..., 1]) + 0.1)
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)),
'standalone':
True
})
settings.update(kwargs)
boundaries(**settings)
decorate(**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 : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> blackbody_spectral_radiance_plot() # doctest: +SKIP
"""
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_maximum(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 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 : \*\*
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(colour_parameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**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 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 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_colours_plot(
surface=1,
samples=4096,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the *CIE 1931 Chromaticity Diagram* colours.
Parameters
----------
surface : numeric, optional
Generated markers surface.
samples : numeric, optional
Samples count on one axis.
cmfs : unicode, optional
Standard observer colour matching functions used for diagram bounds.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> CIE_1931_chromaticity_diagram_colours_plot() # doctest: +SKIP
"""
settings = {'figure_size': (64, 64)}
settings.update(kwargs)
canvas(**settings)
cmfs = get_cmfs(cmfs)
illuminant = DEFAULT_PLOTTING_ILLUMINANT
triangulation = Delaunay(XYZ_to_xy(cmfs.values, illuminant),
qhull_options='QJ')
xx, yy = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(0, 1, samples))
xy = tstack((xx, yy))
xy = xy[triangulation.find_simplex(xy) > 0]
XYZ = xy_to_XYZ(xy)
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
x_dot, y_dot = tsplit(xy)
pylab.scatter(x_dot, y_dot, color=RGB, s=surface)
settings.update({
'x_ticker': False,
'y_ticker': False,
'bounding_box': (0, 1, 0, 1)
})
settings.update(kwargs)
ax = matplotlib.pyplot.gca()
matplotlib.pyplot.setp(ax, frame_on=False)
boundaries(**settings)
decorate(**settings)
return display(**settings)
def CIE_1931_chromaticity_diagram_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)