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 first_item(filter_cmfs(cmfs).values()).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 plot_single_illuminant_sd(illuminant,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given single illuminant spectral distribution.
Parameters
----------
illuminant : unicode or LMS_ConeFundamentals or \
RGB_ColourMatchingFunctions or XYZ_ColourMatchingFunctions, optional
Illuminant to plot. ``illuminant`` can be of any type or form supported
by the :func:`colour.plotting.filter_illuminants` definition.
cmfs : unicode or XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions used for computing the
spectrum domain and colours. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.plot_single_sd`},
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
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_single_illuminant_sd('A') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Single_Illuminant_SD.png
:align: center
:alt: plot_single_illuminant_sd
"""
cmfs = first_item(filter_cmfs(cmfs).values())
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.strict_name)
illuminant = first_item(filter_illuminants(illuminant).values())
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return plot_single_sd(illuminant, **settings)
def plot_single_illuminant_sd(
illuminant: Union[SpectralDistribution, str],
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given single illuminant spectral distribution.
Parameters
----------
illuminant
Illuminant to plot. ``illuminant`` can be of any type or form supported
by the :func:`colour.plotting.filter_illuminants` definition.
cmfs
Standard observer colour matching functions used for computing the
spectrum domain and colours. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_single_illuminant_sd('A') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Single_Illuminant_SD.png
:align: center
:alt: plot_single_illuminant_sd
"""
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
title = f"Illuminant {illuminant} - {cmfs.strict_name}"
illuminant = first_item(filter_illuminants(illuminant).values())
settings: Dict[str, Any] = {"title": title, "y_label": "Relative Power"}
settings.update(kwargs)
return plot_single_sd(illuminant, **settings)
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 = first_item(filter_cmfs(cmfs).values())
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 plot_single_illuminant_sd(illuminant='A',
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given single illuminant spectral 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.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.plot_single_sd`},
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
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_single_illuminant_sd('A') # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_Illuminant_SD.png
:align: center
:alt: plot_single_illuminant_sd
"""
cmfs = first_item(filter_cmfs(cmfs).values())
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.strict_name)
illuminant = first_item(filter_illuminants(illuminant).values())
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return plot_single_sd(illuminant, **settings)
def plot_single_illuminant_sd(illuminant='A',
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given single illuminant spectral 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.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.plot_single_sd`},
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
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_single_illuminant_sd('A') # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_Illuminant_SD.png
:align: center
:alt: plot_single_illuminant_sd
"""
cmfs = first_item(filter_cmfs(cmfs).values())
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.strict_name)
illuminant = first_item(filter_illuminants(illuminant).values())
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return plot_single_sd(illuminant, **settings)
def test_filter_cmfs(self):
"""
Tests :func:`colour.plotting.common.filter_cmfs` definition.
"""
self.assertListEqual(
sorted(filter_cmfs(['.*2 Degree.*']).keys()), [
'CIE 1931 2 Degree Standard Observer',
'CIE 2012 2 Degree Standard Observer',
'Stiles & Burch 1955 2 Degree RGB CMFs',
'Stockman & Sharpe 2 Degree Cone Fundamentals',
'Wright & Guild 1931 2 Degree RGB CMFs'
])
def add_rainbow(axis, wavelengths, values, opacity=100):
# sanity check:
if not hasattr(axis, 'plot') and not hasattr(axis, 'add_patch'):
raise Exception("ERROR:\tFirst argument needs to have method \"plot\".")
from colour.plotting import XYZ_to_plotting_colourspace, filter_cmfs, CONSTANTS_COLOUR_STYLE
from colour.colorimetry import CCS_ILLUMINANTS, wavelength_to_XYZ
from colour.utilities import first_item, normalise_maximum
from matplotlib.patches import Polygon
col_map_f = "CIE 1931 2 Degree Standard Observer"
cmfs = first_item(filter_cmfs(col_map_f).values())
wlen_cmfs = [n for n in wavelengths if n > cmfs.shape.start and n < cmfs.shape.end]
clr = XYZ_to_plotting_colourspace(
wavelength_to_XYZ(wlen_cmfs, cmfs),
CCS_ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_cctf_encoding=False)
clr = normalise_maximum(clr)
clr = CONSTANTS_COLOUR_STYLE.colour.colourspace.cctf_encoding(clr)
polygon = Polygon(
np.vstack([
[min(wavelengths), 0],
np.array([wavelengths, values]).T.tolist(),
[max(wavelengths), 0],
]),
facecolor='none',
edgecolor='none')
axis.add_patch(polygon)
if opacity < 100:
padding = 0
else:
padding = 0.1
for dom, col in [(wavelengths - padding, 'black'), (wlen_cmfs, clr)]:
axis.bar(
x=dom,
height=max(values),
width=1 + padding,
color=col,
align='edge',
alpha=opacity/100,
clip_path=polygon
)
pass
def test_filter_cmfs(self):
"""Test :func:`colour.plotting.common.filter_cmfs` definition."""
self.assertListEqual(
sorted(cmfs.name
for cmfs in filter_cmfs([".*2 Degree.*"]).values()),
[
"CIE 1931 2 Degree Standard Observer",
"CIE 2012 2 Degree Standard Observer",
"Stiles & Burch 1955 2 Degree RGB CMFs",
"Stockman & Sharpe 2 Degree Cone Fundamentals",
"Wright & Guild 1931 2 Degree RGB CMFs",
],
)
def plot_single_cmfs(
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given colour matching functions.
Parameters
----------
cmfs
Colour matching functions to plot. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_cmfs`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_single_cmfs('CIE 1931 2 Degree Standard Observer')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Single_CMFS.png
:align: center
:alt: plot_single_cmfs
"""
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
settings: Dict[str, Any] = {
"title": f"{cmfs.strict_name} - Colour Matching Functions"
}
settings.update(kwargs)
return plot_multi_cmfs((cmfs, ), **settings)
def spectral_locus_visual(colourspace=PRIMARY_COLOURSPACE,
colourspace_model=COLOURSPACE_MODEL,
cmfs='CIE 1931 2 Degree Standard Observer'):
"""
Returns the spectral locus visual geometry formatted as *JSON*.
Parameters
----------
colourspace : unicode, optional
RGB colourspace used to generate the visual geometry.
colourspace_model : unicode, optional
Colourspace model used to generate the visual geometry.
cmfs : unicode, optional
Standard observer colour matching functions used to draw the spectral
locus.
Returns
-------
unicode
Spectral locus visual geometry formatted as *JSON*.
"""
colourspace = first_item(
filter_RGB_colourspaces(re.escape(colourspace)).values())
cmfs = first_item(filter_cmfs(cmfs).values())
XYZ = cmfs.values
XYZ = np.vstack([XYZ, XYZ[0, ...]])
vertices = colourspace_model_axis_reorder(
XYZ_to_colourspace_model(
XYZ,
colourspace.whitepoint,
colourspace_model,
), colourspace_model)
RGB = normalise_maximum(XYZ_to_RGB(
XYZ,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.matrix_XYZ_to_RGB,
),
axis=-1)
return buffer_geometry(position=vertices, color=RGB)
def plot_single_cmfs(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : unicode or LMS_ConeFundamentals or \
RGB_ColourMatchingFunctions or XYZ_ColourMatchingFunctions, optional
Colour matching functions to plot. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_cmfs`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_cmfs('CIE 1931 2 Degree Standard Observer')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Single_CMFS.png
:align: center
:alt: plot_single_cmfs
"""
cmfs = first_item(filter_cmfs(cmfs).values())
settings = {
'title': '{0} - Colour Matching Functions'.format(cmfs.strict_name)
}
settings.update(kwargs)
return plot_multi_cmfs((cmfs, ), **settings)
def plot_single_cmfs(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.artist`,
:func:`colour.plotting.plot_multi_cmfs`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_cmfs('CIE 1931 2 Degree Standard Observer')
... # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_CMFS.png
:align: center
:alt: plot_single_cmfs
"""
cmfs = first_item(filter_cmfs(cmfs).values())
settings = {
'title': '{0} - Colour Matching Functions'.format(cmfs.strict_name)
}
settings.update(kwargs)
return plot_multi_cmfs((cmfs.name, ), **settings)
def plot_single_cmfs(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.artist`,
:func:`colour.plotting.plot_multi_cmfs`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_cmfs('CIE 1931 2 Degree Standard Observer')
... # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_CMFS.png
:align: center
:alt: plot_single_cmfs
"""
cmfs = first_item(filter_cmfs(cmfs).values())
settings = {
'title': '{0} - Colour Matching Functions'.format(cmfs.strict_name)
}
settings.update(kwargs)
return plot_multi_cmfs((cmfs.name, ), **settings)
def plot_spectral_locus(cmfs='CIE 1931 2 Degree Standard Observer',
spectral_locus_colours=None,
spectral_locus_labels=None,
method='CIE 1931',
**kwargs):
"""
Plots the *Spectral Locus* according to given method.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions defining the
*Spectral Locus*.
spectral_locus_colours : array_like or unicode, optional
*Spectral Locus* colours, if ``spectral_locus_colours`` is set to
*RGB*, the colours will be computed according to the corresponding
chromaticity coordinates.
spectral_locus_labels : array_like, optional
Array of wavelength labels used to customise which labels will be drawn
around the spectral locus. Passing an empty array will result in no
wavelength labels being drawn.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_spectral_locus(spectral_locus_colours='RGB') # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Spectral_Locus.png
:align: center
:alt: plot_spectral_locus
"""
if spectral_locus_colours is None:
spectral_locus_colours = COLOUR_STYLE_CONSTANTS.colour.dark
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
if method == 'CIE 1931':
ij = XYZ_to_xy(cmfs.values, illuminant)
labels = ((390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600,
620, 700)
if spectral_locus_labels is None else spectral_locus_labels)
elif method == 'CIE 1960 UCS':
ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
if spectral_locus_labels is None else spectral_locus_labels)
elif method == 'CIE 1976 UCS':
ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
if spectral_locus_labels is None else spectral_locus_labels)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
pl_ij = tstack([
np.linspace(ij[0][0], ij[-1][0], 20),
np.linspace(ij[0][1], ij[-1][1], 20)
]).reshape(-1, 1, 2)
sl_ij = np.copy(ij).reshape(-1, 1, 2)
if spectral_locus_colours.upper() == 'RGB':
spectral_locus_colours = normalise_maximum(
XYZ_to_plotting_colourspace(cmfs.values), axis=-1)
if method == 'CIE 1931':
XYZ = xy_to_XYZ(pl_ij)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij))
purple_line_colours = normalise_maximum(
XYZ_to_plotting_colourspace(XYZ.reshape(-1, 3)), axis=-1)
else:
purple_line_colours = spectral_locus_colours
for slp_ij, slp_colours in ((pl_ij, purple_line_colours),
(sl_ij, spectral_locus_colours)):
line_collection = LineCollection(
np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1),
colors=slp_colours)
axes.add_collection(line_collection)
wl_ij = dict(tuple(zip(wavelengths, ij)))
for label in labels:
i, j = wl_ij[label]
index = bisect.bisect(wavelengths, label)
left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
right = (wavelengths[index]
if index < len(wavelengths) else wavelengths[-1])
dx = wl_ij[right][0] - wl_ij[left][0]
dy = wl_ij[right][1] - wl_ij[left][1]
ij = np.array([i, j])
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx]) if np.dot(
normalise_vector(ij - equal_energy), normalise_vector(direction)) >
0 else np.array([dy, -dx]))
normal = normalise_vector(normal) / 30
label_colour = (spectral_locus_colours
if is_string(spectral_locus_colours) else
spectral_locus_colours[index])
axes.plot(
(i, i + normal[0] * 0.75), (j, j + normal[1] * 0.75),
color=label_colour)
axes.plot(i, j, 'o', color=label_colour)
axes.text(
i + normal[0],
j + normal[1],
label,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def plot_chromaticity_diagram_colours(
samples=256,
diagram_opacity=1.0,
diagram_clipping_path=None,
cmfs='CIE 1931 2 Degree Standard Observer',
method='CIE 1931',
**kwargs):
"""
Plots the *Chromaticity Diagram* colours according to given method.
Parameters
----------
samples : numeric, optional
Samples count on one axis.
diagram_opacity : numeric, optional
Opacity of the *Chromaticity Diagram* colours.
diagram_clipping_path : array_like, optional
Path of points used to clip the *Chromaticity Diagram* colours.
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram_colours() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
:align: center
:alt: plot_chromaticity_diagram_colours
"""
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
ii, jj = np.meshgrid(
np.linspace(0, 1, samples), np.linspace(1, 0, samples))
ij = tstack([ii, jj])
if method == 'CIE 1931':
XYZ = xy_to_XYZ(ij)
spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
spectral_locus = Luv_to_uv(
XYZ_to_Luv(cmfs.values, illuminant), illuminant)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
RGB = normalise_maximum(
XYZ_to_plotting_colourspace(XYZ, illuminant), axis=-1)
polygon = Polygon(
spectral_locus
if diagram_clipping_path is None else diagram_clipping_path,
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
# Preventing bounding box related issues as per
# https://github.com/matplotlib/matplotlib/issues/10529
image = axes.imshow(
RGB,
interpolation='bilinear',
extent=(0, 1, 0, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def plot_blackbody_spectral_radiance(
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.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_spectral_radiance(3500, blackbody='VY Canis Major')
... # doctest: +ELLIPSIS
(<Figure size ... with 2 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Spectral_Radiance.png
:align: center
:alt: plot_blackbody_spectral_radiance
"""
figure = plt.figure()
figure.subplots_adjust(hspace=COLOUR_STYLE_CONSTANTS.geometry.short / 2)
cmfs = first_item(filter_cmfs(cmfs).values())
sd = sd_blackbody(temperature, cmfs.shape)
axes = figure.add_subplot(211)
settings = {
'axes': axes,
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': 'W / (sr m$^2$) / m',
}
settings.update(kwargs)
settings['standalone'] = False
plot_single_sd(sd, cmfs.name, **settings)
axes = figure.add_subplot(212)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
settings = {
'axes': axes,
'aspect': None,
'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'x_ticker': False,
'y_ticker': False,
}
settings.update(kwargs)
settings['standalone'] = False
figure, axes = plot_single_colour_swatch(ColourSwatch(name='', RGB=RGB),
**settings)
settings = {'axes': axes, 'standalone': True}
settings.update(kwargs)
return render(**settings)
def plot_visible_spectrum(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.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_visible_spectrum() # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Visible_Spectrum.png
:align: center
:alt: plot_visible_spectrum
"""
cmfs = first_item(filter_cmfs(cmfs).values())
bounding_box = (min(cmfs.wavelengths), max(cmfs.wavelengths), 0, 1)
settings = {'bounding_box': bounding_box, 'y_label': None}
settings.update(kwargs)
settings['standalone'] = False
_figure, axes = plot_single_sd(sd_ones(cmfs.shape),
cmfs=cmfs,
out_of_gamut_clipping=out_of_gamut_clipping,
**settings)
# Removing wavelength line as it doubles with the axes spine.
axes.lines.pop(0)
settings = {
'axes': axes,
'standalone': True,
'title': 'The Visible Spectrum - {0}'.format(cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
}
settings.update(kwargs)
return render(**settings)
def plot_multi_sds(sds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_sds_colours=False,
normalise_sds_colours=False,
**kwargs):
"""
Plots given spectral distributions.
Parameters
----------
sds : array_like or MultiSpectralDistributions
Spectral distributions or multi-spectral distributions to
plot. `sds` can be a single
:class:`colour.MultiSpectralDistributions` class instance, a list
of :class:`colour.MultiSpectralDistributions` class instances or a
list of :class:`colour.SpectralDistribution` class instances.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_sds_colours : bool, optional
Whether to use spectral distributions colours.
normalise_sds_colours : bool
Whether to normalise spectral distributions colours.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> from colour import SpectralDistribution
>>> 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
... }
>>> sd_1 = SpectralDistribution(data_1, name='Custom 1')
>>> sd_2 = SpectralDistribution(data_2, name='Custom 2')
>>> plot_multi_sds([sd_1, sd_2]) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_SDS.png
:align: center
:alt: plot_multi_sds
"""
_figure, axes = artist(**kwargs)
sds = sds_and_multi_sds_to_sds(sds)
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = ILLUMINANTS_SDS[
COLOUR_STYLE_CONSTANTS.colour.colourspace.illuminant]
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for sd in sds:
wavelengths, values = sd.wavelengths, sd.values
shape = sd.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_sds_colours:
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
if normalise_sds_colours:
XYZ = normalise_maximum(XYZ, clip=False)
RGB = np.clip(XYZ_to_plotting_colourspace(XYZ), 0, 1)
axes.plot(wavelengths, values, color=RGB, label=sd.strict_name)
else:
axes.plot(wavelengths, values, label=sd.strict_name)
bounding_box = (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max) + max(y_limit_max) * 0.05)
settings = {
'axes': axes,
'bounding_box': bounding_box,
'legend': True,
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Distribution',
}
settings.update(kwargs)
return render(**settings)
def plot_chromaticity_diagram(cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
show_spectral_locus=True,
method='CIE 1931',
**kwargs):
"""
Plots the *Chromaticity Diagram* according to given method.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
show_diagram_colours : bool, optional
Whether to display the *Chromaticity Diagram* background colours.
show_spectral_locus : bool, optional
Whether to display the *Spectral Locus*.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.diagrams.plot_spectral_locus`,
:func:`colour.plotting.diagrams.plot_chromaticity_diagram_colours`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram.png
:align: center
:alt: plot_chromaticity_diagram
"""
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
if show_diagram_colours:
settings = {'axes': axes, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
plot_chromaticity_diagram_colours(**settings)
if show_spectral_locus:
settings = {'axes': axes, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
plot_spectral_locus(**settings)
if method == 'CIE 1931':
x_label, y_label = 'CIE x', 'CIE y'
elif method == 'CIE 1960 UCS':
x_label, y_label = 'CIE u', 'CIE v'
elif method == 'CIE 1976 UCS':
x_label, y_label = 'CIE u\'', 'CIE v\'',
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}}'.format(
method))
title = '{0} Chromaticity Diagram - {1}'.format(method, cmfs.strict_name)
settings.update({
'axes': axes,
'standalone': True,
'bounding_box': (0, 1, 0, 1),
'title': title,
'x_label': x_label,
'y_label': y_label,
})
settings.update(kwargs)
return render(**settings)
def plot_multi_cmfs(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.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> cmfs = ('CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer')
>>> plot_multi_cmfs(cmfs) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Multi_CMFS.png
:align: center
:alt: plot_multi_cmfs
"""
if cmfs is None:
cmfs = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
cmfs = filter_cmfs(cmfs).values()
_figure, axes = artist(**kwargs)
axes.axhline(color=COLOUR_STYLE_CONSTANTS.colour.dark, linestyle='--')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for i, cmfs_i in enumerate(cmfs):
for j, RGB in enumerate([(1, 0, 0), (0, 1, 0), (0, 0, 1)]):
RGB = [reduce(lambda y, _: y * 0.5, range(i), x) for x in RGB]
values = cmfs_i.values[:, j]
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))
axes.plot(
cmfs_i.wavelengths,
values,
color=RGB,
label='{0} - {1}'.format(cmfs_i.strict_labels[j],
cmfs_i.strict_name))
bounding_box = (min(x_limit_min), max(x_limit_max),
min(y_limit_min) - abs(min(y_limit_min)) * 0.05,
max(y_limit_max) + abs(max(y_limit_max)) * 0.05)
title = '{0} - Colour Matching Functions'.format(', '.join(
[cmfs_i.strict_name for cmfs_i in cmfs]))
settings = {
'axes': axes,
'bounding_box': bounding_box,
'legend': True,
'title': title,
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Tristimulus Values',
}
settings.update(kwargs)
return render(**settings)
def plot_chromaticity_diagram_colours(
samples=256,
diagram_opacity=1.0,
diagram_clipping_path=None,
cmfs='CIE 1931 2 Degree Standard Observer',
method='CIE 1931',
**kwargs):
"""
Plots the *Chromaticity Diagram* colours according to given method.
Parameters
----------
samples : numeric, optional
Samples count on one axis.
diagram_opacity : numeric, optional
Opacity of the *Chromaticity Diagram* colours.
diagram_clipping_path : array_like, optional
Path of points used to clip the *Chromaticity Diagram* colours.
cmfs : unicode or XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram_colours() # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
:align: center
:alt: plot_chromaticity_diagram_colours
"""
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
ii, jj = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(1, 0, samples))
ij = tstack([ii, jj])
# NOTE: Various values in the grid have potential to generate
# zero-divisions, they could be avoided by perturbing the grid, e.g. adding
# a small epsilon. It was decided instead to disable warnings.
with suppress_warnings(python_warnings=True):
if method == 'CIE 1931':
XYZ = xy_to_XYZ(ij)
spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
spectral_locus = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
illuminant)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'[\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\']'.format(
method))
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ, illuminant),
axis=-1)
polygon = Polygon(spectral_locus if diagram_clipping_path is None else
diagram_clipping_path,
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
# Preventing bounding box related issues as per
# https://github.com/matplotlib/matplotlib/issues/10529
image = axes.imshow(RGB,
interpolation='bilinear',
extent=(0, 1, 0, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def plot_single_sd_rayleigh_scattering(
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 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:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.plot_single_sd`},
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
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_sd_rayleigh_scattering() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_SD_Rayleigh_Scattering.png
:align: center
:alt: plot_single_sd_rayleigh_scattering
"""
title = 'Rayleigh Scattering'
cmfs = first_item(filter_cmfs(cmfs).values())
settings = {'title': title, 'y_label': 'Optical Depth'}
settings.update(kwargs)
sd = sd_rayleigh_scattering(cmfs.shape, CO2_concentration, temperature,
pressure, latitude, altitude)
return plot_single_sd(sd, **settings)
def plot_the_blue_sky(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.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.plot_multi_colour_swatches`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_the_blue_sky() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_The_Blue_Sky.png
:align: center
:alt: plot_the_blue_sky
"""
figure = plt.figure()
figure.subplots_adjust(hspace=COLOUR_STYLE_CONSTANTS.geometry.short / 2)
cmfs = first_item(filter_cmfs(cmfs).values())
ASTM_G_173_sd = ASTM_G_173_ETR.copy()
rayleigh_sd = sd_rayleigh_scattering()
ASTM_G_173_sd.align(rayleigh_sd.shape)
sd = rayleigh_sd * ASTM_G_173_sd
axes = figure.add_subplot(211)
settings = {
'axes': axes,
'title': 'The Blue Sky - Synthetic Spectral Distribution',
'y_label': u'W / m-2 / nm-1',
}
settings.update(kwargs)
settings['standalone'] = False
plot_single_sd(sd, cmfs, **settings)
axes = figure.add_subplot(212)
x_label = ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).')
settings = {
'axes': axes,
'aspect': None,
'title': 'The Blue Sky - Colour',
'x_label': x_label,
'y_label': '',
'x_ticker': False,
'y_ticker': False,
}
settings.update(kwargs)
settings['standalone'] = False
blue_sky_color = XYZ_to_plotting_colourspace(sd_to_XYZ(sd))
figure, axes = plot_single_colour_swatch(
ColourSwatch('', normalise_maximum(blue_sky_color)), **settings)
settings = {'axes': axes, 'standalone': True}
settings.update(kwargs)
return render(**settings)
def plot_single_sd_rayleigh_scattering(
CO2_concentration:
FloatingOrArrayLike = CONSTANT_STANDARD_CO2_CONCENTRATION,
temperature: FloatingOrArrayLike = CONSTANT_STANDARD_AIR_TEMPERATURE,
pressure: FloatingOrArrayLike = CONSTANT_AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude: FloatingOrArrayLike = CONSTANT_DEFAULT_LATITUDE,
altitude: FloatingOrArrayLike = CONSTANT_DEFAULT_ALTITUDE,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot a single *Rayleigh* scattering spectral distribution.
Parameters
----------
CO2_concentration
:math:`CO_2` concentration in parts per million (ppm).
temperature
Air temperature :math:`T[K]` in kelvin degrees.
pressure
Surface pressure :math:`P` of the measurement site.
latitude
Latitude of the site in degrees.
altitude
Altitude of the site in meters.
cmfs
Standard observer colour matching functions used for computing the
spectrum domain and colours. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_single_sd_rayleigh_scattering() # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Single_SD_Rayleigh_Scattering.png
:align: center
:alt: plot_single_sd_rayleigh_scattering
"""
title = "Rayleigh Scattering"
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
settings: Dict[str, Any] = {"title": title, "y_label": "Optical Depth"}
settings.update(kwargs)
sd = sd_rayleigh_scattering(
cmfs.shape,
CO2_concentration,
temperature,
pressure,
latitude,
altitude,
)
return plot_single_sd(sd, **settings)
def plot_the_blue_sky(
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the blue sky.
Parameters
----------
cmfs
Standard observer colour matching functions used for computing the
spectrum domain and colours. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.plot_multi_colour_swatches`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_the_blue_sky() # doctest: +ELLIPSIS
(<Figure size ... with 2 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_The_Blue_Sky.png
:align: center
:alt: plot_the_blue_sky
"""
figure = plt.figure()
figure.subplots_adjust(hspace=CONSTANTS_COLOUR_STYLE.geometry.short / 2)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
ASTMG173_sd = cast(SpectralDistribution, SD_ASTMG173_ETR.copy())
rayleigh_sd = sd_rayleigh_scattering()
ASTMG173_sd.align(rayleigh_sd.shape)
sd = rayleigh_sd * ASTMG173_sd
axes = figure.add_subplot(211)
settings: Dict[str, Any] = {
"axes": axes,
"title": "The Blue Sky - Synthetic Spectral Distribution",
"y_label": "W / m-2 / nm-1",
}
settings.update(kwargs)
settings["standalone"] = False
plot_single_sd(sd, cmfs, **settings)
axes = figure.add_subplot(212)
x_label = ("The sky is blue because molecules in the atmosphere "
"scatter shorter wavelengths more than longer ones.\n"
"The synthetic spectral distribution is computed as "
"follows: "
"(ASTM G-173 ETR * Standard Air Rayleigh Scattering).")
settings = {
"axes": axes,
"aspect": None,
"title": "The Blue Sky - Colour",
"x_label": x_label,
"y_label": "",
"x_ticker": False,
"y_ticker": False,
}
settings.update(kwargs)
settings["standalone"] = False
blue_sky_color = XYZ_to_plotting_colourspace(
sd_to_XYZ(cast(SpectralDistribution, sd)))
figure, axes = plot_single_colour_swatch(
ColourSwatch(normalise_maximum(blue_sky_color)), **settings)
settings = {"axes": axes, "standalone": True}
settings.update(kwargs)
return render(**settings)
def plot_spectral_locus(cmfs='CIE 1931 2 Degree Standard Observer',
spectral_locus_colours=None,
spectral_locus_labels=None,
method='CIE 1931',
**kwargs):
"""
Plots the *Spectral Locus* according to given method.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions defining the
*Spectral Locus*.
spectral_locus_colours : array_like or unicode, optional
*Spectral Locus* colours, if ``spectral_locus_colours`` is set to
*RGB*, the colours will be computed according to the corresponding
chromaticity coordinates.
spectral_locus_labels : array_like, optional
Array of wavelength labels used to customise which labels will be drawn
around the spectral locus. Passing an empty array will result in no
wavelength labels being drawn.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_spectral_locus(spectral_locus_colours='RGB') # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Spectral_Locus.png
:align: center
:alt: plot_spectral_locus
"""
if spectral_locus_colours is None:
spectral_locus_colours = COLOUR_STYLE_CONSTANTS.colour.dark
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
wavelengths = cmfs.wavelengths
equal_energy = np.array([1 / 3] * 2)
if method == 'CIE 1931':
ij = XYZ_to_xy(cmfs.values, illuminant)
labels = ((390, 460, 470, 480, 490, 500, 510, 520, 540, 560, 580, 600,
620, 700)
if spectral_locus_labels is None else spectral_locus_labels)
elif method == 'CIE 1960 UCS':
ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
if spectral_locus_labels is None else spectral_locus_labels)
elif method == 'CIE 1976 UCS':
ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
labels = ((420, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530, 540,
550, 560, 570, 580, 590, 600, 610, 620, 630, 645, 680)
if spectral_locus_labels is None else spectral_locus_labels)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}}'.format(
method))
pl_ij = tstack([
np.linspace(ij[0][0], ij[-1][0], 20),
np.linspace(ij[0][1], ij[-1][1], 20)
]).reshape(-1, 1, 2)
sl_ij = np.copy(ij).reshape(-1, 1, 2)
if spectral_locus_colours.upper() == 'RGB':
spectral_locus_colours = normalise_maximum(
XYZ_to_plotting_colourspace(cmfs.values), axis=-1)
if method == 'CIE 1931':
XYZ = xy_to_XYZ(pl_ij)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij))
purple_line_colours = normalise_maximum(
XYZ_to_plotting_colourspace(XYZ.reshape(-1, 3)), axis=-1)
else:
purple_line_colours = spectral_locus_colours
for slp_ij, slp_colours in ((pl_ij, purple_line_colours),
(sl_ij, spectral_locus_colours)):
line_collection = LineCollection(
np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1),
colors=slp_colours)
axes.add_collection(line_collection)
wl_ij = dict(tuple(zip(wavelengths, ij)))
for label in labels:
i, j = wl_ij[label]
index = bisect.bisect(wavelengths, label)
left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
right = (wavelengths[index]
if index < len(wavelengths) else wavelengths[-1])
dx = wl_ij[right][0] - wl_ij[left][0]
dy = wl_ij[right][1] - wl_ij[left][1]
ij = np.array([i, j])
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx]) if np.dot(
normalise_vector(ij - equal_energy), normalise_vector(direction)) >
0 else np.array([dy, -dx]))
normal = normalise_vector(normal) / 30
label_colour = (spectral_locus_colours
if is_string(spectral_locus_colours) else
spectral_locus_colours[index])
axes.plot(
(i, i + normal[0] * 0.75), (j, j + normal[1] * 0.75),
color=label_colour)
axes.plot(i, j, 'o', color=label_colour)
axes.text(
i + normal[0],
j + normal[1],
label,
clip_on=True,
ha='left' if normal[0] >= 0 else 'right',
va='center',
fontdict={'size': 'small'})
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def plot_RGB_colourspaces_in_chromaticity_diagram(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
chromaticity_diagram_callable=plot_chromaticity_diagram,
method='CIE 1931',
show_whitepoints=True,
show_pointer_gamut=False,
**kwargs):
"""
Plots given *RGB* colourspaces in the *Chromaticity Diagram* according
to given method.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
chromaticity_diagram_callable : callable, optional
Callable responsible for drawing the *Chromaticity Diagram*.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
show_whitepoints : bool, optional
Whether to display the *RGB* colourspaces whitepoints.
show_pointer_gamut : bool, optional
Whether to display the *Pointer's Gamut*.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.diagrams.plot_chromaticity_diagram`,
:func:`colour.plotting.plot_pointer_gamut`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_RGB_colourspaces_in_chromaticity_diagram(
... ['ITU-R BT.709', 'ACEScg', 'S-Gamut'])
... # doctest: +SKIP
.. image:: ../_static/Plotting_\
Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png
:align: center
:alt: plot_RGB_colourspaces_in_chromaticity_diagram
"""
if colourspaces is None:
colourspaces = ['ITU-R BT.709', 'ACEScg', 'S-Gamut']
colourspaces = filter_RGB_colourspaces(colourspaces).values()
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
title = '{0}\n{1} - {2} Chromaticity Diagram'.format(
', '.join([colourspace.name for colourspace in colourspaces]),
cmfs.name, method)
settings = {'axes': axes, 'title': title, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
chromaticity_diagram_callable(**settings)
if show_pointer_gamut:
settings = {'axes': axes, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
plot_pointer_gamut(**settings)
if method == 'CIE 1931':
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy
x_limit_min, x_limit_max = [-0.1], [0.9]
y_limit_min, y_limit_max = [-0.1], [0.9]
elif method == 'CIE 1960 UCS':
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy_to_UCS_uv(xy)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.2], [0.6]
elif method == 'CIE 1976 UCS':
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy_to_Luv_uv(xy)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.1], [0.7]
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
settings = {'colour_cycle_count': len(colourspaces)}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
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 = xy_to_ij(P)
W = xy_to_ij(colourspace.whitepoint)
axes.plot((W[0], W[0]), (W[1], W[1]),
color=(R, G, B),
label=colourspace.name)
if show_whitepoints:
axes.plot((W[0], W[0]), (W[1], W[1]), 'o', color=(R, G, B))
axes.plot((P[0, 0], P[1, 0]), (P[0, 1], P[1, 1]),
'o-',
color=(R, G, B))
axes.plot((P[1, 0], P[2, 0]), (P[1, 1], P[2, 1]),
'o-',
color=(R, G, B))
axes.plot((P[2, 0], P[0, 0]), (P[2, 1], P[0, 1]),
'o-',
color=(R, G, B))
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)
bounding_box = (
min(x_limit_min),
max(x_limit_max),
min(y_limit_min),
max(y_limit_max),
)
settings.update({
'standalone': True,
'legend': True,
'bounding_box': bounding_box,
})
settings.update(kwargs)
return render(**settings)
def plot_visible_spectrum(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.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_visible_spectrum() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Visible_Spectrum.png
:align: center
:alt: plot_visible_spectrum
"""
cmfs = first_item(filter_cmfs(cmfs).values())
bounding_box = (min(cmfs.wavelengths), max(cmfs.wavelengths), 0, 1)
settings = {'bounding_box': bounding_box, 'y_label': None}
settings.update(kwargs)
settings['standalone'] = False
_figure, axes = plot_single_sd(
sd_ones(cmfs.shape),
cmfs=cmfs,
out_of_gamut_clipping=out_of_gamut_clipping,
**settings)
# Removing wavelength line as it doubles with the axes spine.
axes.lines.pop(0)
settings = {
'axes': axes,
'standalone': True,
'title': 'The Visible Spectrum - {0}'.format(cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
}
settings.update(kwargs)
return render(**settings)
def plot_blackbody_spectral_radiance(
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.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_spectral_radiance(3500, blackbody='VY Canis Major')
... # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Blackbody_Spectral_Radiance.png
:align: center
:alt: plot_blackbody_spectral_radiance
"""
figure = plt.figure()
figure.subplots_adjust(hspace=COLOUR_STYLE_CONSTANTS.geometry.short / 2)
cmfs = first_item(filter_cmfs(cmfs).values())
sd = sd_blackbody(temperature, cmfs.shape)
axes = figure.add_subplot(211)
settings = {
'axes': axes,
'title': '{0} - Spectral Radiance'.format(blackbody),
'y_label': 'W / (sr m$^2$) / m',
}
settings.update(kwargs)
settings['standalone'] = False
plot_single_sd(sd, cmfs.name, **settings)
axes = figure.add_subplot(212)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
settings = {
'axes': axes,
'aspect': None,
'title': '{0} - Colour'.format(blackbody),
'x_label': '{0}K'.format(temperature),
'y_label': '',
'x_ticker': False,
'y_ticker': False,
}
settings.update(kwargs)
settings['standalone'] = False
figure, axes = plot_single_colour_swatch(
ColourSwatch(name='', RGB=RGB), **settings)
settings = {'axes': axes, 'standalone': True}
settings.update(kwargs)
return render(**settings)
def plot_sds_in_chromaticity_diagram(
sds,
cmfs='CIE 1931 2 Degree Standard Observer',
chromaticity_diagram_callable=plot_chromaticity_diagram,
method='CIE 1931',
annotate_kwargs=None,
plot_kwargs=None,
**kwargs):
"""
Plots given spectral distribution chromaticity coordinates into the
*Chromaticity Diagram* using given method.
Parameters
----------
sds : array_like or MultiSpectralDistributions
Spectral distributions or multi-spectral distributions to
plot. `sds` can be a single
:class:`colour.MultiSpectralDistributions` class instance, a list
of :class:`colour.MultiSpectralDistributions` class instances or a
list of :class:`colour.SpectralDistribution` class instances.
cmfs : unicode or XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
chromaticity_diagram_callable : callable, optional
Callable responsible for drawing the *Chromaticity Diagram*.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
annotate_kwargs : dict or array_like, optional
Keyword arguments for the :func:`plt.annotate` definition, used to
annotate the resulting chromaticity coordinates with their respective
spectral distribution names. ``annotate_kwargs`` can be either a single
dictionary applied to all the arrows with same settings or a sequence
of dictionaries with different settings for each spectral distribution.
The following special keyword arguments can also be used:
- *annotate* : bool, whether to annotate the spectral distributions.
plot_kwargs : dict or array_like, optional
Keyword arguments for the :func:`plt.plot` definition, used to control
the style of the plotted spectral distributions. ``plot_kwargs`` can be
either a single dictionary applied to all the plotted spectral
distributions with same settings or a sequence of dictionaries with
different settings for each plotted spectral distributions.
The following special keyword arguments can also be used:
- *illuminant* : unicode or :class:`colour.SpectralDistribution`, the
illuminant used to compute the spectral distributions colours. The
default is the illuminant associated with the whitepoint of the
default plotting colourspace. ``illuminant`` can be of any type or
form supported by the :func:`colour.plotting.filter_cmfs`
definition.
- *cmfs* : unicode, the standard observer colour matching functions
used for computing the spectral distributions colours. ``cmfs`` can
be of any type or form supported by the
:func:`colour.plotting.filter_cmfs` definition.
- *normalise_sd_colours* : bool, whether to normalise the computed
spectral distributions colours. The default is *True*.
- *use_sd_colours* : bool, whether to use the computed spectral
distributions colours under the plotting colourspace illuminant.
Alternatively, it is possible to use the :func:`plt.plot`
definition ``color`` argument with pre-computed values. The default
is *True*.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.diagrams.plot_chromaticity_diagram`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Also handles keywords arguments for deprecation management.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> A = SDS_ILLUMINANTS['A']
>>> D65 = SDS_ILLUMINANTS['D65']
>>> annotate_kwargs = [
... {'xytext': (-25, 15), 'arrowprops':{'arrowstyle':'-'}},
... {}
... ]
>>> plot_kwargs = [
... {
... 'illuminant': SDS_ILLUMINANTS['E'],
... 'markersize' : 15,
... 'normalise_sd_colours': True,
... 'use_sd_colours': True
... },
... {'illuminant': SDS_ILLUMINANTS['E']},
... ]
>>> plot_sds_in_chromaticity_diagram(
... [A, D65], annotate_kwargs=annotate_kwargs, plot_kwargs=plot_kwargs)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_SDS_In_Chromaticity_Diagram.png
:align: center
:alt: plot_sds_in_chromaticity_diagram
"""
annotate_kwargs = handle_arguments_deprecation(
{
'ArgumentRenamed': [['annotate_parameters', 'annotate_kwargs']],
}, **kwargs).get('annotate_kwargs', annotate_kwargs)
sds = sds_and_msds_to_sds(sds)
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
method = method.upper()
settings.update({
'axes': axes,
'standalone': False,
'method': method,
'cmfs': cmfs,
})
chromaticity_diagram_callable(**settings)
if method == 'CIE 1931':
def XYZ_to_ij(XYZ):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return XYZ_to_xy(XYZ)
bounding_box = (-0.1, 0.9, -0.1, 0.9)
elif method == 'CIE 1960 UCS':
def XYZ_to_ij(XYZ):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return UCS_to_uv(XYZ_to_UCS(XYZ))
bounding_box = (-0.1, 0.7, -0.2, 0.6)
elif method == 'CIE 1976 UCS':
def XYZ_to_ij(XYZ):
"""
Converts given *CIE XYZ* tristimulus values to *ij* chromaticity
coordinates.
"""
return Luv_to_uv(XYZ_to_Luv(XYZ))
bounding_box = (-0.1, 0.7, -0.1, 0.7)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'[\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\']'.format(
method))
annotate_settings_collection = [{
'annotate': True,
'xytext': (-50, 30),
'textcoords': 'offset points',
'arrowprops': CONSTANTS_ARROW_STYLE,
} for _ in range(len(sds))]
if annotate_kwargs is not None:
update_settings_collection(annotate_settings_collection,
annotate_kwargs, len(sds))
plot_settings_collection = [{
'color':
CONSTANTS_COLOUR_STYLE.colour.brightest,
'label':
'{0}'.format(sd.strict_name),
'marker':
'o',
'markeredgecolor':
CONSTANTS_COLOUR_STYLE.colour.dark,
'markeredgewidth':
CONSTANTS_COLOUR_STYLE.geometry.short * 0.75,
'markersize': (CONSTANTS_COLOUR_STYLE.geometry.short * 6 +
CONSTANTS_COLOUR_STYLE.geometry.short * 0.75),
'cmfs':
cmfs,
'illuminant':
SDS_ILLUMINANTS[
CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint_name],
'use_sd_colours':
False,
'normalise_sd_colours':
False,
} for sd in sds]
if plot_kwargs is not None:
update_settings_collection(plot_settings_collection, plot_kwargs,
len(sds))
for i, sd in enumerate(sds):
plot_settings = plot_settings_collection[i]
cmfs = first_item(filter_cmfs(plot_settings.pop('cmfs')).values())
illuminant = first_item(
filter_illuminants(plot_settings.pop('illuminant')).values())
normalise_sd_colours = plot_settings.pop('normalise_sd_colours')
use_sd_colours = plot_settings.pop('use_sd_colours')
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
if use_sd_colours:
if normalise_sd_colours:
XYZ /= XYZ[..., 1]
plot_settings['color'] = np.clip(XYZ_to_plotting_colourspace(XYZ),
0, 1)
ij = XYZ_to_ij(XYZ)
axes.plot(ij[0], ij[1], **plot_settings)
if (sd.name is not None
and annotate_settings_collection[i]['annotate']):
annotate_settings = annotate_settings_collection[i]
annotate_settings.pop('annotate')
axes.annotate(sd.name, xy=ij, **annotate_settings)
settings.update({'standalone': True, 'bounding_box': bounding_box})
settings.update(kwargs)
return render(**settings)
def plot_chromaticity_diagram_colours(
samples=256,
diagram_opacity=1.0,
diagram_clipping_path=None,
cmfs='CIE 1931 2 Degree Standard Observer',
method='CIE 1931',
**kwargs):
"""
Plots the *Chromaticity Diagram* colours according to given method.
Parameters
----------
samples : numeric, optional
Samples count on one axis.
diagram_opacity : numeric, optional
Opacity of the *Chromaticity Diagram* colours.
diagram_clipping_path : array_like, optional
Path of points used to clip the *Chromaticity Diagram* colours.
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram_colours() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
:align: center
:alt: plot_chromaticity_diagram_colours
"""
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
ii, jj = np.meshgrid(
np.linspace(0, 1, samples), np.linspace(1, 0, samples))
ij = tstack([ii, jj])
# Avoiding zero division in later colour transformations.
ij = np.where(ij == 0, EPSILON, ij)
if method == 'CIE 1931':
XYZ = xy_to_XYZ(ij)
spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
elif method == 'CIE 1960 UCS':
XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
elif method == 'CIE 1976 UCS':
XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
spectral_locus = Luv_to_uv(
XYZ_to_Luv(cmfs.values, illuminant), illuminant)
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}}'.format(
method))
RGB = normalise_maximum(
XYZ_to_plotting_colourspace(XYZ, illuminant), axis=-1)
polygon = Polygon(
spectral_locus
if diagram_clipping_path is None else diagram_clipping_path,
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
# Preventing bounding box related issues as per
# https://github.com/matplotlib/matplotlib/issues/10529
image = axes.imshow(
RGB,
interpolation='bilinear',
extent=(0, 1, 0, 1),
clip_path=None,
alpha=diagram_opacity)
image.set_clip_path(polygon)
settings = {'axes': axes}
settings.update(kwargs)
return render(**kwargs)
def plot_single_sd(sd,
cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
modulate_colours_with_sd_amplitude=False,
equalize_sd_amplitude=False,
**kwargs):
"""
Plots given spectral distribution.
Parameters
----------
sd : SpectralDistribution
Spectral 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.
modulate_colours_with_sd_amplitude : bool, optional
Whether to modulate the colours with the spectral distribution
amplitude.
equalize_sd_amplitude : bool, optional
Whether to equalize the spectral distribution amplitude.
Equalization occurs after the colours modulation thus setting both
arguments to *True* will generate a spectrum strip where each
wavelength colour is modulated by the spectral distribution amplitude.
The usual 5% margin above the spectral distribution is also omitted.
cmfs : unicode
Standard observer colour matching functions used for spectrum creation.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> from colour import SpectralDistribution
>>> data = {
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360
... }
>>> sd = SpectralDistribution(data, name='Custom')
>>> plot_single_sd(sd) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_SD.png
:align: center
:alt: plot_single_sd
"""
_figure, axes = artist(**kwargs)
cmfs = first_item(filter_cmfs(cmfs).values())
sd = sd.copy()
sd.interpolator = LinearInterpolator
wavelengths = cmfs.wavelengths[np.logical_and(
cmfs.wavelengths >= max(min(cmfs.wavelengths), min(sd.wavelengths)),
cmfs.wavelengths <= min(max(cmfs.wavelengths), max(sd.wavelengths)),
)]
values = sd[wavelengths]
colours = XYZ_to_plotting_colourspace(
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 = normalise_maximum(colours)
if modulate_colours_with_sd_amplitude:
colours *= (values / np.max(values))[..., np.newaxis]
colours = COLOUR_STYLE_CONSTANTS.colour.colourspace.encoding_cctf(colours)
if equalize_sd_amplitude:
values = np.ones(values.shape)
margin = 0 if equalize_sd_amplitude else 0.05
x_min, x_max = min(wavelengths), max(wavelengths)
y_min, y_max = 0, max(values) + max(values) * margin
polygon = Polygon(
np.vstack([
(x_min, 0),
tstack([wavelengths, values]),
(x_max, 0),
]),
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
padding = 0.1
axes.bar(
x=wavelengths - padding,
height=max(values),
width=1 + padding,
color=colours,
align='edge',
clip_path=polygon)
axes.plot(wavelengths, values, color=COLOUR_STYLE_CONSTANTS.colour.dark)
settings = {
'axes': axes,
'bounding_box': (x_min, x_max, y_min, y_max),
'title': '{0} - {1}'.format(sd.strict_name, cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Distribution',
}
settings.update(kwargs)
return render(**settings)
def plot_RGB_colourspaces_in_chromaticity_diagram(
colourspaces=None,
cmfs='CIE 1931 2 Degree Standard Observer',
chromaticity_diagram_callable=plot_chromaticity_diagram,
method='CIE 1931',
show_whitepoints=True,
show_pointer_gamut=False,
**kwargs):
"""
Plots given *RGB* colourspaces in the *Chromaticity Diagram* according
to given method.
Parameters
----------
colourspaces : array_like, optional
*RGB* colourspaces to plot.
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
chromaticity_diagram_callable : callable, optional
Callable responsible for drawing the *Chromaticity Diagram*.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
show_whitepoints : bool, optional
Whether to display the *RGB* colourspaces whitepoints.
show_pointer_gamut : bool, optional
Whether to display the *Pointer's Gamut*.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.diagrams.plot_chromaticity_diagram`,
:func:`colour.plotting.plot_pointer_gamut`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_RGB_colourspaces_in_chromaticity_diagram(
... ['ITU-R BT.709', 'ACEScg', 'S-Gamut'])
... # doctest: +SKIP
.. image:: ../_static/Plotting_\
Plot_RGB_Colourspaces_In_Chromaticity_Diagram.png
:align: center
:alt: plot_RGB_colourspaces_in_chromaticity_diagram
"""
if colourspaces is None:
colourspaces = ['ITU-R BT.709', 'ACEScg', 'S-Gamut']
colourspaces = filter_RGB_colourspaces(colourspaces).values()
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
title = '{0}\n{1} - {2} Chromaticity Diagram'.format(
', '.join([colourspace.name for colourspace in colourspaces]),
cmfs.name, method)
settings = {'axes': axes, 'title': title, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
chromaticity_diagram_callable(**settings)
if show_pointer_gamut:
settings = {'axes': axes, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
plot_pointer_gamut(**settings)
if method == 'CIE 1931':
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy
x_limit_min, x_limit_max = [-0.1], [0.9]
y_limit_min, y_limit_max = [-0.1], [0.9]
elif method == 'CIE 1960 UCS':
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy_to_UCS_uv(xy)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.2], [0.6]
elif method == 'CIE 1976 UCS':
def xy_to_ij(xy):
"""
Converts given *xy* chromaticity coordinates to *ij* chromaticity
coordinates.
"""
return xy_to_Luv_uv(xy)
x_limit_min, x_limit_max = [-0.1], [0.7]
y_limit_min, y_limit_max = [-0.1], [0.7]
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}}'.format(
method))
settings = {'colour_cycle_count': len(colourspaces)}
settings.update(kwargs)
cycle = colour_cycle(**settings)
for colourspace in colourspaces:
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 = xy_to_ij(P)
W = xy_to_ij(colourspace.whitepoint)
axes.plot(
(W[0], W[0]), (W[1], W[1]),
color=(R, G, B),
label=colourspace.name)
if show_whitepoints:
axes.plot((W[0], W[0]), (W[1], W[1]), 'o', color=(R, G, B))
axes.plot(
(P[0, 0], P[1, 0]), (P[0, 1], P[1, 1]), 'o-', color=(R, G, B))
axes.plot(
(P[1, 0], P[2, 0]), (P[1, 1], P[2, 1]), 'o-', color=(R, G, B))
axes.plot(
(P[2, 0], P[0, 0]), (P[2, 1], P[0, 1]), 'o-', color=(R, G, B))
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)
bounding_box = (
min(x_limit_min),
max(x_limit_max),
min(y_limit_min),
max(y_limit_max),
)
settings.update({
'standalone': True,
'legend': True,
'bounding_box': bounding_box,
})
settings.update(kwargs)
return render(**settings)
def plot_blackbody_colours(
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.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_colours(SpectralShape(150, 12500, 50)) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Blackbody_Colours.png
:align: center
:alt: plot_blackbody_colours
"""
_figure, axes = artist(**kwargs)
cmfs = first_item(filter_cmfs(cmfs).values())
colours = []
temperatures = []
for temperature in shape:
sd = sd_blackbody(temperature, cmfs.shape)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
colours.append(RGB)
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
padding = 0.1
axes.bar(
x=np.array(temperatures) - padding,
height=1,
width=shape.interval + (padding * shape.interval),
color=colours,
align='edge')
settings = {
'axes': axes,
'bounding_box': (x_min, x_max, y_min, y_max),
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': None,
}
settings.update(kwargs)
return render(**settings)
def plot_RGB_colourspaces_gamuts(
colourspaces: Union[RGB_Colourspace, str, Sequence[Union[RGB_Colourspace,
str]]],
reference_colourspace: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS",
"CAM16LCD", "CAM16SCD", "CAM16UCS",
"CIE XYZ", "CIE xyY", "CIE Lab",
"CIE Luv", "CIE UCS", "CIE UVW",
"DIN99", "Hunter Lab", "Hunter Rdab",
"ICaCb", "ICtCp", "IPT", "IgPgTg",
"Jzazbz", "OSA UCS", "Oklab",
"hdr-CIELAB", "hdr-IPT", ],
str, ] = "CIE xyY",
segments: Integer = 8,
show_grid: Boolean = True,
grid_segments: Integer = 10,
show_spectral_locus: Boolean = False,
spectral_locus_colour: Optional[Union[ArrayLike, str]] = None,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
chromatically_adapt: Boolean = False,
convert_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given *RGB* colourspaces gamuts in given reference colourspace.
Parameters
----------
colourspaces
*RGB* colourspaces to plot the gamuts. ``colourspaces`` elements
can be of any type or form supported by the
:func:`colour.plotting.filter_RGB_colourspaces` definition.
reference_colourspace
Reference colourspace model to plot the gamuts into, see
:attr:`colour.COLOURSPACE_MODELS` attribute for the list of supported
colourspace models.
segments
Edge segments count for each *RGB* colourspace cubes.
show_grid
Whether to show a grid at the bottom of the *RGB* colourspace cubes.
grid_segments
Edge segments count for the grid.
show_spectral_locus
Whether to show the spectral locus.
spectral_locus_colour
Spectral locus colour.
cmfs
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
chromatically_adapt
Whether to chromatically adapt the *RGB* colourspaces given in
``colourspaces`` to the whitepoint of the default plotting colourspace.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
Other Parameters
----------------
edge_colours
Edge colours array such as `edge_colours = (None, (0.5, 0.5, 1.0))`.
edge_alpha
Edge opacity value such as `edge_alpha = (0.0, 1.0)`.
face_alpha
Face opacity value such as `face_alpha = (0.5, 1.0)`.
face_colours
Face colours array such as `face_colours = (None, (0.5, 0.5, 1.0))`.
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.volume.nadir_grid`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...Axes3DSubplot...>)
.. image:: ../_static/Plotting_Plot_RGB_Colourspaces_Gamuts.png
:align: center
:alt: plot_RGB_colourspaces_gamuts
"""
colourspaces = cast(
List[RGB_Colourspace],
list(filter_RGB_colourspaces(colourspaces).values()),
)
convert_kwargs = optional(convert_kwargs, {})
count_c = len(colourspaces)
title = (
f"{', '.join([colourspace.name for colourspace in colourspaces])} "
f"- {reference_colourspace} Reference Colourspace")
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
convert_settings = {"illuminant": illuminant}
convert_settings.update(convert_kwargs)
settings = Structure(
**{
"face_colours": [None] * count_c,
"edge_colours": [None] * count_c,
"face_alpha": [1] * count_c,
"edge_alpha": [1] * count_c,
"title": title,
})
settings.update(kwargs)
figure = plt.figure()
axes = figure.add_subplot(111, projection="3d")
points = zeros((4, 3))
if show_spectral_locus:
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
XYZ = cmfs.values
points = colourspace_model_axis_reorder(
convert(XYZ, "CIE XYZ", reference_colourspace, **convert_settings),
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)
axes.plot(
points[..., 0],
points[..., 1],
points[..., 2],
color=c,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
axes.plot(
(points[-1][0], points[0][0]),
(points[-1][1], points[0][1]),
(points[-1][2], points[0][2]),
color=c,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
plotting_colourspace = CONSTANTS_COLOUR_STYLE.colour.colourspace
quads_c: List = []
RGB_cf: List = []
RGB_ce: List = []
for i, colourspace in enumerate(colourspaces):
if chromatically_adapt and not np.array_equal(
colourspace.whitepoint, plotting_colourspace.whitepoint):
colourspace = colourspace.chromatically_adapt(
plotting_colourspace.whitepoint,
plotting_colourspace.whitepoint_name,
)
quads_cb, RGB = RGB_identity_cube(
width_segments=segments,
height_segments=segments,
depth_segments=segments,
)
XYZ = RGB_to_XYZ(
quads_cb,
colourspace.whitepoint,
colourspace.whitepoint,
colourspace.matrix_RGB_to_XYZ,
)
convert_settings = {"illuminant": colourspace.whitepoint}
convert_settings.update(convert_kwargs)
quads_c.extend(
colourspace_model_axis_reorder(
convert(XYZ, "CIE XYZ", reference_colourspace,
**convert_settings),
reference_colourspace,
))
if settings.face_colours[i] is not None:
RGB = ones(RGB.shape) * settings.face_colours[i]
RGB_cf.extend(
np.hstack([RGB,
full((RGB.shape[0], 1), settings.face_alpha[i])]))
if settings.edge_colours[i] is not None:
RGB = ones(RGB.shape) * settings.edge_colours[i]
RGB_ce.extend(
np.hstack([RGB,
full((RGB.shape[0], 1), settings.edge_alpha[i])]))
quads = as_float_array(quads_c)
RGB_f = as_float_array(RGB_cf)
RGB_e = as_float_array(RGB_ce)
quads[np.isnan(quads)] = 0
if quads.size != 0:
for i, axis in enumerate("xyz"):
min_a = np.minimum(np.min(quads[..., i]), np.min(points[..., i]))
max_a = np.maximum(np.max(quads[..., i]), np.max(points[..., i]))
getattr(axes, f"set_{axis}lim")((min_a, max_a))
labels = np.array(
COLOURSPACE_MODELS_AXIS_LABELS[reference_colourspace])[as_int_array(
colourspace_model_axis_reorder([0, 1, 2], reference_colourspace))]
for i, axis in enumerate("xyz"):
getattr(axes, f"set_{axis}label")(labels[i])
if show_grid:
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({
"axes": axes,
"axes_visible": False,
"camera_aspect": "equal"
})
settings.update(kwargs)
return render(**settings)
def plot_multi_cmfs(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.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> cmfs = ('CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer')
>>> plot_multi_cmfs(cmfs) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_CMFS.png
:align: center
:alt: plot_multi_cmfs
"""
if cmfs is None:
cmfs = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
cmfs = filter_cmfs(cmfs).values()
_figure, axes = artist(**kwargs)
axes.axhline(color=COLOUR_STYLE_CONSTANTS.colour.dark, linestyle='--')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for i, cmfs_i in enumerate(cmfs):
for j, RGB in enumerate([(1, 0, 0), (0, 1, 0), (0, 0, 1)]):
RGB = [reduce(lambda y, _: y * 0.5, range(i), x) for x in RGB]
values = cmfs_i.values[:, j]
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))
axes.plot(cmfs_i.wavelengths,
values,
color=RGB,
label='{0} - {1}'.format(cmfs_i.strict_labels[j],
cmfs_i.strict_name))
bounding_box = (min(x_limit_min), max(x_limit_max),
min(y_limit_min) - abs(min(y_limit_min)) * 0.05,
max(y_limit_max) + abs(max(y_limit_max)) * 0.05)
title = '{0} - Colour Matching Functions'.format(', '.join(
[cmfs_i.strict_name for cmfs_i in cmfs]))
settings = {
'axes': axes,
'bounding_box': bounding_box,
'legend': True,
'title': title,
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Tristimulus Values',
}
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', 'Hunter Lab', 'Hunter Rdab'}**,
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.
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 = first_item(filter_cmfs(cmfs).values())
XYZ = cmfs.values
XYZ = np.vstack([XYZ, XYZ[0, ...]])
illuminant = DEFAULT_PLOTTING_ILLUMINANT
points = common_colourspace_model_axis_reorder(
XYZ_to_colourspace_model(XYZ, illuminant, reference_colourspace),
reference_colourspace)
points[np.isnan(points)] = 0
if uniform_colour is None:
RGB = normalise_maximum(XYZ_to_sRGB(XYZ, illuminant), axis=-1)
RGB = np.hstack([RGB,
np.full((RGB.shape[0], 1), uniform_opacity,
DEFAULT_FLOAT_DTYPE)])
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 plot_single_sd(sd,
cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
modulate_colours_with_sd_amplitude=False,
equalize_sd_amplitude=False,
**kwargs):
"""
Plots given spectral distribution.
Parameters
----------
sd : SpectralDistribution
Spectral 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.
modulate_colours_with_sd_amplitude : bool, optional
Whether to modulate the colours with the spectral distribution
amplitude.
equalize_sd_amplitude : bool, optional
Whether to equalize the spectral distribution amplitude.
Equalization occurs after the colours modulation thus setting both
arguments to *True* will generate a spectrum strip where each
wavelength colour is modulated by the spectral distribution amplitude.
The usual 5% margin above the spectral distribution is also omitted.
cmfs : unicode
Standard observer colour matching functions used for spectrum creation.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> from colour import SpectralDistribution
>>> data = {
... 500: 0.0651,
... 520: 0.0705,
... 540: 0.0772,
... 560: 0.0870,
... 580: 0.1128,
... 600: 0.1360
... }
>>> sd = SpectralDistribution(data, name='Custom')
>>> plot_single_sd(sd) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Single_SD.png
:align: center
:alt: plot_single_sd
"""
_figure, axes = artist(**kwargs)
cmfs = first_item(filter_cmfs(cmfs).values())
sd = sd.copy()
sd.interpolator = LinearInterpolator
wavelengths = cmfs.wavelengths[np.logical_and(
cmfs.wavelengths >= max(min(cmfs.wavelengths), min(sd.wavelengths)),
cmfs.wavelengths <= min(max(cmfs.wavelengths), max(sd.wavelengths)),
)]
values = sd[wavelengths]
colours = XYZ_to_plotting_colourspace(
wavelength_to_XYZ(wavelengths, cmfs),
ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],
apply_cctf_encoding=False)
if not out_of_gamut_clipping:
colours += np.abs(np.min(colours))
colours = normalise_maximum(colours)
if modulate_colours_with_sd_amplitude:
colours *= (values / np.max(values))[..., np.newaxis]
colours = COLOUR_STYLE_CONSTANTS.colour.colourspace.cctf_encoding(colours)
if equalize_sd_amplitude:
values = np.ones(values.shape)
margin = 0 if equalize_sd_amplitude else 0.05
x_min, x_max = min(wavelengths), max(wavelengths)
y_min, y_max = 0, max(values) + max(values) * margin
polygon = Polygon(np.vstack([
(x_min, 0),
tstack([wavelengths, values]),
(x_max, 0),
]),
facecolor='none',
edgecolor='none')
axes.add_patch(polygon)
padding = 0.1
axes.bar(x=wavelengths - padding,
height=max(values),
width=1 + padding,
color=colours,
align='edge',
clip_path=polygon)
axes.plot(wavelengths, values, color=COLOUR_STYLE_CONSTANTS.colour.dark)
settings = {
'axes': axes,
'bounding_box': (x_min, x_max, y_min, y_max),
'title': '{0} - {1}'.format(sd.strict_name, cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Distribution',
}
settings.update(kwargs)
return render(**settings)
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 first_item(filter_cmfs(cmfs).values()).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 plot_blackbody_colours(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.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_blackbody_colours(SpectralShape(150, 12500, 50))
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Colours.png
:align: center
:alt: plot_blackbody_colours
"""
_figure, axes = artist(**kwargs)
cmfs = first_item(filter_cmfs(cmfs).values())
colours = []
temperatures = []
for temperature in shape:
sd = sd_blackbody(temperature, cmfs.shape)
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs)
RGB = normalise_maximum(XYZ_to_plotting_colourspace(XYZ))
colours.append(RGB)
temperatures.append(temperature)
x_min, x_max = min(temperatures), max(temperatures)
y_min, y_max = 0, 1
padding = 0.1
axes.bar(x=np.array(temperatures) - padding,
height=1,
width=shape.interval + (padding * shape.interval),
color=colours,
align='edge')
settings = {
'axes': axes,
'bounding_box': (x_min, x_max, y_min, y_max),
'title': 'Blackbody Colours',
'x_label': 'Temperature K',
'y_label': None,
}
settings.update(kwargs)
return render(**settings)
def plot_visible_spectrum_section(
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
illuminant: Union[SpectralDistribution, str] = "D65",
model: Union[Literal["CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD",
"CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY",
"CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99",
"Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT",
"IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB",
"hdr-IPT", ], str, ] = "CIE xyY",
axis: Union[Literal["+z", "+x", "+y"], str] = "+z",
origin: Floating = 0.5,
normalise: Boolean = True,
show_section_colours: Boolean = True,
show_section_contour: Boolean = True,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the visible spectrum volume, i.e. *Rösch-MacAdam* colour solid,
section colours along given axis and origin.
Parameters
----------
cmfs
Standard observer colour matching functions, default to the
*CIE 1931 2 Degree Standard Observer*. ``cmfs`` can be of any type or
form supported by the :func:`colour.plotting.filter_cmfs` definition.
illuminant
Illuminant spectral distribution, default to *CIE Illuminant D65*.
``illuminant`` can be of any type or form supported by the
:func:`colour.plotting.filter_illuminants` definition.
model
Colourspace model, see :attr:`colour.COLOURSPACE_MODELS` attribute for
the list of supported colourspace models.
axis
Axis the hull section will be normal to.
origin
Coordinate along ``axis`` at which to plot the hull section.
normalise
Whether to normalise ``axis`` to the extent of the hull along it.
show_section_colours
Whether to show the hull section colours.
show_section_contour
Whether to show the hull section contour.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.render`,
:func:`colour.plotting.section.plot_hull_section_colours`
:func:`colour.plotting.section.plot_hull_section_contour`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour.utilities import is_trimesh_installed
>>> if is_trimesh_installed:
... plot_visible_spectrum_section(
... section_colours='RGB', section_opacity=0.15)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Visible_Spectrum_Section.png
:align: center
:alt: plot_visible_spectrum_section
"""
import trimesh
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
# pylint: disable=E1102
cmfs = reshape_msds(first_item(filter_cmfs(cmfs).values()),
SpectralShape(360, 780, 1))
illuminant = cast(
SpectralDistribution,
first_item(filter_illuminants(illuminant).values()),
)
vertices = solid_RoschMacAdam(
cmfs,
illuminant,
point_order="Pulse Wave Width",
filter_jagged_points=True,
)
mesh = trimesh.Trimesh(vertices)
hull = trimesh.convex.convex_hull(mesh)
if show_section_colours:
settings = {"axes": axes}
settings.update(kwargs)
settings["standalone"] = False
plot_hull_section_colours(hull, model, axis, origin, normalise,
**settings)
if show_section_contour:
settings = {"axes": axes}
settings.update(kwargs)
settings["standalone"] = False
plot_hull_section_contour(hull, model, axis, origin, normalise,
**settings)
title = (f"Visible Spectrum Section - "
f"{f'{origin * 100}%' if normalise else origin} - "
f"{model} - "
f"{cmfs.strict_name}")
plane = MAPPING_AXIS_TO_PLANE[axis]
labels = np.array(COLOURSPACE_MODELS_AXIS_LABELS[model])[as_int_array(
colourspace_model_axis_reorder([0, 1, 2], model))]
x_label, y_label = labels[plane[0]], labels[plane[1]]
settings.update({
"axes": axes,
"standalone": True,
"title": title,
"x_label": x_label,
"y_label": y_label,
})
settings.update(kwargs)
return render(**settings)
def plot_chromaticity_diagram(cmfs='CIE 1931 2 Degree Standard Observer',
show_diagram_colours=True,
show_spectral_locus=True,
method='CIE 1931',
**kwargs):
"""
Plots the *Chromaticity Diagram* according to given method.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for
*Chromaticity Diagram* bounds.
show_diagram_colours : bool, optional
Whether to display the *Chromaticity Diagram* background colours.
show_spectral_locus : bool, optional
Whether to display the *Spectral Locus*.
method : unicode, optional
**{'CIE 1931', 'CIE 1960 UCS', 'CIE 1976 UCS'}**,
*Chromaticity Diagram* method.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.diagrams.plot_spectral_locus`,
:func:`colour.plotting.diagrams.plot_chromaticity_diagram_colours`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram.png
:align: center
:alt: plot_chromaticity_diagram
"""
settings = {'uniform': True}
settings.update(kwargs)
figure, axes = artist(**settings)
method = method.upper()
cmfs = first_item(filter_cmfs(cmfs).values())
if show_diagram_colours:
settings = {'axes': axes, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
plot_chromaticity_diagram_colours(**settings)
if show_spectral_locus:
settings = {'axes': axes, 'method': method}
settings.update(kwargs)
settings['standalone'] = False
plot_spectral_locus(**settings)
if method == 'CIE 1931':
x_label, y_label = 'CIE x', 'CIE y'
elif method == 'CIE 1960 UCS':
x_label, y_label = 'CIE u', 'CIE v'
elif method == 'CIE 1976 UCS':
x_label, y_label = 'CIE u\'', 'CIE v\'',
else:
raise ValueError(
'Invalid method: "{0}", must be one of '
'{\'CIE 1931\', \'CIE 1960 UCS\', \'CIE 1976 UCS\'}'.format(
method))
title = '{0} Chromaticity Diagram - {1}'.format(method, cmfs.strict_name)
settings.update({
'axes': axes,
'standalone': True,
'bounding_box': (0, 1, 0, 1),
'title': title,
'x_label': x_label,
'y_label': y_label,
})
settings.update(kwargs)
return render(**settings)
def plot_multi_sds(sds,
cmfs='CIE 1931 2 Degree Standard Observer',
use_sds_colours=False,
normalise_sds_colours=False,
**kwargs):
"""
Plots given spectral distributions.
Parameters
----------
sds : array_like or MultiSpectralDistribution
Spectral distributions or multi-spectral distributions to
plot. `sds` can be a single
:class:`colour.MultiSpectralDistribution` class instance, a list
of :class:`colour.MultiSpectralDistribution` class instances or a
list of :class:`colour.SpectralDistribution` class instances.
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
use_sds_colours : bool, optional
Whether to use spectral distributions colours.
normalise_sds_colours : bool
Whether to normalise spectral distributions colours.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> from colour import SpectralDistribution
>>> 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 = SpectralDistribution(data_1, name='Custom 1')
>>> spd2 = SpectralDistribution(data_2, name='Custom 2')
>>> plot_multi_sds([spd1, spd2]) # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Multi_SDs.png
:align: center
:alt: plot_multi_sds
"""
_figure, axes = artist(**kwargs)
if isinstance(sds, MultiSpectralDistribution):
sds = sds.to_sds()
else:
sds = list(sds)
for i, sd in enumerate(sds[:]):
if isinstance(sd, MultiSpectralDistribution):
sds.remove(sd)
sds[i:i] = sd.to_sds()
cmfs = first_item(filter_cmfs(cmfs).values())
illuminant = ILLUMINANTS_SDS[
COLOUR_STYLE_CONSTANTS.colour.colourspace.illuminant]
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for sd in sds:
wavelengths, values = sd.wavelengths, sd.values
shape = sd.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_sds_colours:
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
if normalise_sds_colours:
XYZ = normalise_maximum(XYZ, clip=False)
RGB = np.clip(XYZ_to_plotting_colourspace(XYZ), 0, 1)
axes.plot(wavelengths, values, color=RGB, label=sd.strict_name)
else:
axes.plot(wavelengths, values, label=sd.strict_name)
bounding_box = (min(x_limit_min), max(x_limit_max), min(y_limit_min),
max(y_limit_max) + max(y_limit_max) * 0.05)
settings = {
'axes': axes,
'bounding_box': bounding_box,
'legend': True,
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Spectral Distribution',
}
settings.update(kwargs)
return render(**settings)
def plot_RGB_colourspaces_gamuts(colourspaces=None,
reference_colourspace='CIE xyY',
segments=8,
show_grid=True,
grid_segments=10,
show_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 xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',
'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW',
'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',
'hdr-CIELAB', 'hdr-IPT'}**,
Reference colourspace to plot the gamuts into.
segments : int, optional
Edge segments count for each *RGB* colourspace cubes.
show_grid : bool, optional
Whether to show a grid at the bottom of the *RGB* colourspace cubes.
grid_segments : bool, optional
Edge segments count for the grid.
show_spectral_locus : bool, optional
Whether to show the spectral locus.
spectral_locus_colour : array_like, optional
Spectral locus colour.
cmfs : unicode, optional
Standard observer colour matching functions used for spectral locus.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.volume.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
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_RGB_colourspaces_gamuts(['ITU-R BT.709', 'ACEScg', 'S-Gamut'])
... # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_RGB_Colourspaces_Gamuts.png
:align: center
:alt: plot_RGB_colourspaces_gamuts
"""
if colourspaces is None:
colourspaces = ('ITU-R BT.709', 'ACEScg')
colourspaces = filter_RGB_colourspaces(colourspaces).values()
count_c = len(colourspaces)
title = '{0} - {1} Reference Colourspace'.format(
', '.join([colourspace.name for colourspace in colourspaces]),
reference_colourspace,
)
settings = Structure(
**{
'face_colours': [None] * count_c,
'edge_colours': [None] * count_c,
'face_alpha': [1] * count_c,
'edge_alpha': [1] * count_c,
'title': title,
})
settings.update(kwargs)
figure = plt.figure()
axes = figure.add_subplot(111, projection='3d')
illuminant = COLOUR_STYLE_CONSTANTS.colour.colourspace.whitepoint
points = np.zeros((4, 3))
if show_spectral_locus:
cmfs = first_item(filter_cmfs(cmfs).values())
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)
axes.plot(
points[..., 0], points[..., 1], points[..., 2], color=c, zorder=1)
axes.plot(
(points[-1][0], points[0][0]), (points[-1][1], points[0][1]),
(points[-1][2], points[0][2]),
color=c,
zorder=1)
quads, RGB_f, RGB_e = [], [], []
for i, colourspace in enumerate(colourspaces):
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],
DEFAULT_FLOAT_DTYPE)
]))
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],
DEFAULT_FLOAT_DTYPE)
]))
quads = as_float_array(quads)
quads[np.isnan(quads)] = 0
if quads.size != 0:
for i, axis in enumerate('xyz'):
min_a = min(np.min(quads[..., i]), np.min(points[..., i]))
max_a = max(np.max(quads[..., i]), np.max(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 show_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({
'axes': axes,
'axes_visible': False,
'camera_aspect': 'equal'
})
settings.update(kwargs)
return render(**settings)
def plot_single_sd_rayleigh_scattering(
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 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:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.plot_single_sd`},
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
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_sd_rayleigh_scattering() # doctest: +SKIP
.. image:: ../_static/Plotting_Plot_Single_SD_Rayleigh_Scattering.png
:align: center
:alt: plot_single_sd_rayleigh_scattering
"""
title = 'Rayleigh Scattering'
cmfs = first_item(filter_cmfs(cmfs).values())
settings = {'title': title, 'y_label': 'Optical Depth'}
settings.update(kwargs)
sd = sd_rayleigh_scattering(cmfs.shape, CO2_concentration, temperature,
pressure, latitude, altitude)
return plot_single_sd(sd, **settings)