def test_XYZ_to_plotting_colourspace(self):
"""
Tests :func:`colour.plotting.common.XYZ_to_plotting_colourspace`
definition.
"""
XYZ = np.random.random(3)
np.testing.assert_almost_equal(
XYZ_to_sRGB(XYZ), XYZ_to_plotting_colourspace(XYZ), decimal=7)
def CCT_D_uv_to_plotting_colourspace(CCT_D_uv):
"""
Convert given *uv* chromaticity coordinates to the default plotting
colourspace.
"""
return normalise_maximum(
XYZ_to_plotting_colourspace(
xy_to_XYZ(UCS_uv_to_xy(CCT_to_uv(CCT_D_uv,
"Robertson 1968")))),
axis=-1,
)
def 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 plot_simple_rainbow(ax=None,
wavelength=None,
flux=None,
cmfs="CIE 1931 2 Degree Standard Observer",
**kwargs):
"""
Plot a simple horizontal rainbow,
based off colour's plot_single_sd.
Parameters
----------
ax : matplotlib.axes._subplots.AxesSubplot
The ax into which the rainbow should be drawn.
cmfs : string
The color matching function(s?) to use.
Returns
-------
ax : matplotlib.axes._subplots.AxesSubplot
The ax into which the rainbow was drawn.
"""
if ax is None:
ax = plt.gca()
# pull out the CMFss
cmfs = first_item(filter_cmfs(cmfs).values())
if wavelength is None:
# create a grid of wavelengths (at which CMFss are useful)
wavelength = cmfs.wavelengths
ok = (wavelength >= np.min(cmfs.wavelengths)) & (wavelength <= np.max(
cmfs.wavelengths))
# create colors at theose wavelengths
colours = XYZ_to_plotting_colourspace(
wavelength_to_XYZ(wavelength[ok], cmfs),
CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["E"],
)
# normalize the colors to their maximum?
colours = CONSTANTS_COLOUR_STYLE.colour.colourspace.cctf_encoding(
normalise_maximum(colours))
# create y values that will be plotted (these could be spectrum)
if flux is None:
flux = np.ones_like(wavelength)
x_min, x_max = min(wavelength), max(wavelength)
y_min, y_max = 0, max(flux) + max(flux) * 0.05
# create a polygon to define the top of the bars?
polygon = Polygon(
np.vstack([
(x_min, 0),
tstack([wavelength[ok], flux[ok]]),
(x_max, 0),
]),
facecolor="none",
edgecolor="none",
)
ax.add_patch(polygon)
# draw bars, with the colors at each vertical stripe
padding = 0.2
ax.bar(
x=wavelength[ok] - padding / 2,
height=max(flux[ok]),
width=1 + padding,
color=colours,
align="edge",
)
return ax
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_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_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_hull_section_contour(
hull: trimesh.Trimesh, # type: ignore[name-defined] # noqa
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,
contour_colours: Optional[Union[ArrayLike, str]] = None,
contour_opacity: Floating = 1,
convert_kwargs: Optional[Dict] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the section contour of given *trimesh* hull along given axis and
origin.
Parameters
----------
hull
*Trimesh* hull.
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.
contour_colours
Colours of the hull section contour, if ``contour_colours`` is set to
*RGB*, the colours will be computed according to the corresponding
coordinates.
contour_opacity
Opacity of the hull section contour.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour.models import RGB_COLOURSPACE_sRGB
>>> from colour.utilities import is_trimesh_installed
>>> vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
>>> XYZ_vertices = RGB_to_XYZ(
... vertices['position'] + 0.5,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
... )
>>> if is_trimesh_installed:
... import trimesh
... hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
... plot_hull_section_contour(hull, contour_colours='RGB')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Hull_Section_Contour.png
:align: center
:alt: plot_hull_section_contour
"""
hull = hull.copy()
contour_colours = cast(
Union[ArrayLike, str],
optional(contour_colours, CONSTANTS_COLOUR_STYLE.colour.dark),
)
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
convert_kwargs = optional(convert_kwargs, {})
# Luminance / Lightness is re-ordered along "z-up" axis.
with suppress_warnings(python_warnings=True):
ijk_vertices = colourspace_model_axis_reorder(
convert(hull.vertices, "CIE XYZ", model, **convert_kwargs), model)
ijk_vertices = np.nan_to_num(ijk_vertices)
ijk_vertices *= COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[
model]
hull.vertices = ijk_vertices
plane = MAPPING_AXIS_TO_PLANE[axis]
padding = 0.1 * np.mean(
COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[model])
min_x = np.min(ijk_vertices[..., plane[0]]) - padding
max_x = np.max(ijk_vertices[..., plane[0]]) + padding
min_y = np.min(ijk_vertices[..., plane[1]]) - padding
max_y = np.max(ijk_vertices[..., plane[1]]) + padding
extent = (min_x, max_x, min_y, max_y)
use_RGB_contour_colours = str(contour_colours).upper() == "RGB"
section = hull_section(hull, axis, origin, normalise)
if use_RGB_contour_colours:
ijk_section = section / (
COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[model])
XYZ_section = convert(
colourspace_model_axis_reorder(ijk_section, model, "Inverse"),
model,
"CIE XYZ",
**convert_kwargs,
)
contour_colours = np.clip(XYZ_to_plotting_colourspace(XYZ_section), 0,
1)
section = np.reshape(section[..., plane], (-1, 1, 2))
line_collection = LineCollection(
np.concatenate([section[:-1], section[1:]], axis=1),
colors=contour_colours,
alpha=contour_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
)
axes.add_collection(line_collection)
settings = {
"axes": axes,
"bounding_box": extent,
}
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_multi_sds(
sds: Union[Sequence[Union[SpectralDistribution,
MultiSpectralDistributions]],
MultiSpectralDistributions, ],
plot_kwargs: Optional[Union[Dict, List[Dict]]] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given spectral distributions.
Parameters
----------
sds
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.
plot_kwargs
Keyword arguments for the :func:`matplotlib.pyplot.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 the 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`` : 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`` : 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`` : Whether to normalise the computed
spectral distributions colours. The default is *True*.
- ``use_sd_colours`` : Whether to use the computed spectral
distributions colours under the plotting colourspace illuminant.
Alternatively, it is possible to use the
:func:`matplotlib.pyplot.plot` definition ``color`` argument with
pre-computed values. The default is *True*.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> 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_kwargs = [
... {'use_sd_colours': True},
... {'use_sd_colours': True, 'linestyle': 'dashed'},
... ]
>>> plot_multi_sds([sd_1, sd_2], plot_kwargs=plot_kwargs)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Multi_SDS.png
:align: center
:alt: plot_multi_sds
"""
_figure, axes = artist(**kwargs)
sds_converted = sds_and_msds_to_sds(sds)
plot_settings_collection = [{
"label":
f"{sd.strict_name}",
"zorder":
CONSTANTS_COLOUR_STYLE.zorder.midground_line,
"cmfs":
"CIE 1931 2 Degree Standard Observer",
"illuminant":
SDS_ILLUMINANTS[
CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint_name],
"use_sd_colours":
False,
"normalise_sd_colours":
False,
} for sd in sds_converted]
if plot_kwargs is not None:
update_settings_collection(plot_settings_collection, plot_kwargs,
len(sds_converted))
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for i, sd in enumerate(sds_converted):
plot_settings = plot_settings_collection[i]
cmfs = cast(
MultiSpectralDistributions,
first_item(filter_cmfs(plot_settings.pop("cmfs")).values()),
)
illuminant = cast(
SpectralDistribution,
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")
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_sd_colours:
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
if normalise_sd_colours:
XYZ /= XYZ[..., 1]
plot_settings["color"] = np.clip(XYZ_to_plotting_colourspace(XYZ),
0, 1)
axes.plot(wavelengths, values, **plot_settings)
bounding_box = (
min(x_limit_min),
max(x_limit_max),
min(y_limit_min),
max(y_limit_max) + np.max(y_limit_max) * 0.05,
)
settings: Dict[str, Any] = {
"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_with_rainbow_fill(ax=None,
wavelength=None,
flux=None,
cmfs="CIE 1931 2 Degree Standard Observer",
rainbowtop=np.inf,
**kwargs):
"""
(This is still *real* blarg-y*.)
Plot a spectrum, with a rainbow underneath.
Parameters
----------
ax : matplotlib.axes._subplots.AxesSubplot
The ax into which the rainbow should be drawn.
cmfs : string
The color matching function(s?) to use.
Returns
-------
ax : matplotlib.axes._subplots.AxesSubplot
The ax into which the rainbow was drawn.
"""
if ax is None:
ax = plt.gca()
if wavelength is None:
# create a grid of wavelengths (at which CMFss are useful)
wavelength = CMFs.wavelengths
# create y values that will be plotted (these could be spectrum)
if flux is None:
flux = np.ones_like(wavelength)
# pull out only the values that *can* be converted to colors
ok = (wavelength >= np.min(CMFs.wavelengths)) & (wavelength <= np.max(
CMFs.wavelengths))
w, f = wavelength[ok], flux[ok]
# clip the top of the box?
f = np.minimum(f, rainbowtop)
XYZ = wavelength_to_XYZ(w)
# create colors at theose wavelengths
colours = XYZ_to_plotting_colourspace(XYZ)
# normalize the colors to their maximum?
# colours = CONSTANTS_COLOUR_STYLE.colour.colourspace.cctf_encoding(
# normalise_maximum(colours))
colours = np.maximum(0, colours / np.max(colours))
x_min, x_max = min(w), max(w)
y_min, y_max = 0, max(f) + max(f) * 0.05
# create a polygon to define the top of the bars?
polygon = Polygon(
np.vstack([
(x_min, 0),
tstack([w, f]),
(x_max, 0),
]),
facecolor="none",
edgecolor="none",
)
ax.add_patch(polygon)
# draw bars, with the colors at each vertical stripe
padding = 0.0
dw = np.mean(np.diff(w))
ax.bar(
x=w,
height=f,
width=dw,
color=colours,
clip_path=polygon,
align="edge",
clip_on=True,
)
return ax
def plot_blackbody_colours(
shape: SpectralShape = SpectralShape(150, 12500, 50),
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot blackbody colours.
Parameters
----------
shape
Spectral shape to use as plot boundaries.
cmfs
Standard observer colour matching functions used for computing the
blackbody 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.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_blackbody_colours(SpectralShape(150, 12500, 50))
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Colours.png
:align: center
:alt: plot_blackbody_colours
"""
_figure, axes = artist(**kwargs)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
RGB = []
temperatures = []
for temperature in shape:
sd = sd_blackbody(temperature, cmfs.shape)
with domain_range_scale("1"):
XYZ = sd_to_XYZ(sd, cmfs)
RGB.append(normalise_maximum(XYZ_to_plotting_colourspace(XYZ)))
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=RGB,
align="edge",
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
settings: Dict[str, Any] = {
"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_single_sd(
sd: SpectralDistribution,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
out_of_gamut_clipping: Boolean = True,
modulate_colours_with_sd_amplitude: Boolean = False,
equalize_sd_amplitude: Boolean = False,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given spectral distribution.
Parameters
----------
sd
Spectral distribution to plot.
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.
out_of_gamut_clipping
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
Whether to modulate the colours with the spectral distribution
amplitude.
equalize_sd_amplitude
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.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
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>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Single_SD.png
:align: center
:alt: plot_single_sd
"""
_figure, axes = artist(**kwargs)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
sd = cast(SpectralDistribution, 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 = as_float_array(sd[wavelengths])
RGB = XYZ_to_plotting_colourspace(
wavelength_to_XYZ(wavelengths, cmfs),
CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["E"],
apply_cctf_encoding=False,
)
if not out_of_gamut_clipping:
RGB += np.abs(np.min(RGB))
RGB = normalise_maximum(RGB)
if modulate_colours_with_sd_amplitude:
RGB *= (values / np.max(values))[..., np.newaxis]
RGB = CONSTANTS_COLOUR_STYLE.colour.colourspace.cctf_encoding(RGB)
if equalize_sd_amplitude:
values = 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",
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
axes.add_patch(polygon)
padding = 0.1
axes.bar(
x=wavelengths - padding,
height=max(values),
width=1 + padding,
color=RGB,
align="edge",
clip_path=polygon,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
axes.plot(
wavelengths,
values,
color=CONSTANTS_COLOUR_STYLE.colour.dark,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
settings: Dict[str, Any] = {
"axes": axes,
"bounding_box": (x_min, x_max, y_min, y_max),
"title": f"{sd.strict_name} - {cmfs.strict_name}",
"x_label": "Wavelength $\\lambda$ (nm)",
"y_label": "Spectral Distribution",
}
settings.update(kwargs)
return render(**settings)
def plot_sds_in_chromaticity_diagram(
sds: Union[Sequence[Union[SpectralDistribution,
MultiSpectralDistributions]],
MultiSpectralDistributions, ],
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
chromaticity_diagram_callable: Callable = plot_chromaticity_diagram,
method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
str] = "CIE 1931",
annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None,
plot_kwargs: Optional[Union[Dict, List[Dict]]] = None,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given spectral distribution chromaticity coordinates into the
*Chromaticity Diagram* using given method.
Parameters
----------
sds
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
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 responsible for drawing the *Chromaticity Diagram*.
method
*Chromaticity Diagram* method.
annotate_kwargs
Keyword arguments for the :func:`matplotlib.pyplot.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`` : Whether to annotate the spectral distributions.
plot_kwargs
Keyword arguments for the :func:`matplotlib.pyplot.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 the 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`` : 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`` : 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`` : Whether to normalise the computed
spectral distributions colours. The default is *True*.
- ``use_sd_colours`` : Whether to use the computed spectral
distributions colours under the plotting colourspace illuminant.
Alternatively, it is possible to use the
:func:`matplotlib.pyplot.plot` definition ``color`` argument with
pre-computed values. The default is *True*.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.diagrams.plot_chromaticity_diagram`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`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
"""
method = validate_method(method,
["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])
sds_converted = sds_and_msds_to_sds(sds)
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
settings.update({
"axes": axes,
"standalone": False,
"method": method,
"cmfs": cmfs,
})
chromaticity_diagram_callable(**settings)
if method == "cie 1931":
def XYZ_to_ij(XYZ: NDArray) -> NDArray:
"""
Convert 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: NDArray) -> NDArray:
"""
Convert 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: NDArray) -> NDArray:
"""
Convert 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)
annotate_settings_collection = [{
"annotate":
True,
"xytext": (-50, 30),
"textcoords":
"offset points",
"arrowprops":
CONSTANTS_ARROW_STYLE,
"zorder":
CONSTANTS_COLOUR_STYLE.zorder.midground_annotation,
} for _ in range(len(sds_converted))]
if annotate_kwargs is not None:
update_settings_collection(annotate_settings_collection,
annotate_kwargs, len(sds_converted))
plot_settings_collection = [{
"color":
CONSTANTS_COLOUR_STYLE.colour.brightest,
"label":
f"{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),
"zorder":
CONSTANTS_COLOUR_STYLE.zorder.midground_line,
"cmfs":
cmfs,
"illuminant":
SDS_ILLUMINANTS[
CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint_name],
"use_sd_colours":
False,
"normalise_sd_colours":
False,
} for sd in sds_converted]
if plot_kwargs is not None:
update_settings_collection(plot_settings_collection, plot_kwargs,
len(sds_converted))
for i, sd in enumerate(sds_converted):
plot_settings = plot_settings_collection[i]
cmfs = cast(
MultiSpectralDistributions,
first_item(filter_cmfs(plot_settings.pop("cmfs")).values()),
)
illuminant = cast(
SpectralDistribution,
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: Integer = 256,
diagram_colours: Optional[Union[ArrayLike, str]] = None,
diagram_opacity: Floating = 1,
diagram_clipping_path: Optional[ArrayLike] = None,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
str] = "CIE 1931",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the *Chromaticity Diagram* colours according to given method.
Parameters
----------
samples
Samples count on one axis when computing the *Chromaticity Diagram*
colours.
diagram_colours
Colours of the *Chromaticity Diagram*, if ``diagram_colours`` is set
to *RGB*, the colours will be computed according to the corresponding
coordinates.
diagram_opacity
Opacity of the *Chromaticity Diagram*.
diagram_clipping_path
Path of points used to clip the *Chromaticity Diagram* colours.
cmfs
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
method
*Chromaticity Diagram* method.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_chromaticity_diagram_colours(diagram_colours='RGB')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Chromaticity_Diagram_Colours.png
:align: center
:alt: plot_chromaticity_diagram_colours
"""
method = validate_method(method,
["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
diagram_colours = cast(
ArrayLike,
optional(diagram_colours,
HEX_to_RGB(CONSTANTS_COLOUR_STYLE.colour.average)),
)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
if method == "cie 1931":
spectral_locus = XYZ_to_xy(cmfs.values, illuminant)
elif method == "cie 1960 ucs":
spectral_locus = UCS_to_uv(XYZ_to_UCS(cmfs.values))
elif method == "cie 1976 ucs":
spectral_locus = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant),
illuminant)
use_RGB_diagram_colours = str(diagram_colours).upper() == "RGB"
if use_RGB_diagram_colours:
ii, jj = np.meshgrid(np.linspace(0, 1, samples),
np.linspace(1, 0, samples))
ij = tstack([ii, jj])
# NOTE: Various values in the grid have potential to generate
# zero-divisions, they could be avoided by perturbing the grid, e.g.
# adding a small epsilon. It was decided instead to disable warnings.
with suppress_warnings(python_warnings=True):
if method == "cie 1931":
XYZ = xy_to_XYZ(ij)
elif method == "cie 1960 ucs":
XYZ = xy_to_XYZ(UCS_uv_to_xy(ij))
elif method == "cie 1976 ucs":
XYZ = xy_to_XYZ(Luv_uv_to_xy(ij))
diagram_colours = normalise_maximum(XYZ_to_plotting_colourspace(
XYZ, illuminant),
axis=-1)
polygon = Polygon(
spectral_locus
if diagram_clipping_path is None else diagram_clipping_path,
facecolor="none" if use_RGB_diagram_colours else np.hstack(
[diagram_colours, diagram_opacity]),
edgecolor="none" if use_RGB_diagram_colours else np.hstack(
[diagram_colours, diagram_opacity]),
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
axes.add_patch(polygon)
if use_RGB_diagram_colours:
# Preventing bounding box related issues as per
# https://github.com/matplotlib/matplotlib/issues/10529
image = axes.imshow(
diagram_colours,
interpolation="bilinear",
extent=(0, 1, 0, 1),
clip_path=None,
alpha=diagram_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
image.set_clip_path(polygon)
settings = {"axes": axes}
settings.update(kwargs)
return render(**kwargs)
def plot_spectral_locus(
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
spectral_locus_colours: Optional[Union[ArrayLike, str]] = None,
spectral_locus_opacity: Floating = 1,
spectral_locus_labels: Optional[Sequence] = None,
method: Union[Literal["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"],
str] = "CIE 1931",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the *Spectral Locus* according to given method.
Parameters
----------
cmfs
Standard observer colour matching functions used for computing the
spectral locus boundaries. ``cmfs`` can be of any type or form
supported by the :func:`colour.plotting.filter_cmfs` definition.
spectral_locus_colours
Colours of the *Spectral Locus*, if ``spectral_locus_colours`` is set
to *RGB*, the colours will be computed according to the corresponding
chromaticity coordinates.
spectral_locus_opacity
Opacity of the *Spectral Locus*.
spectral_locus_labels
Array of wavelength labels used to customise which labels will be drawn
around the spectral locus. Passing an empty array will result in no
wavelength labels being drawn.
method
*Chromaticity Diagram* method.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> plot_spectral_locus(spectral_locus_colours='RGB') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Spectral_Locus.png
:align: center
:alt: plot_spectral_locus
"""
method = validate_method(method,
["CIE 1931", "CIE 1960 UCS", "CIE 1976 UCS"])
spectral_locus_colours = optional(spectral_locus_colours,
CONSTANTS_COLOUR_STYLE.colour.dark)
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
illuminant = CONSTANTS_COLOUR_STYLE.colour.colourspace.whitepoint
wavelengths = list(cmfs.wavelengths)
equal_energy = np.array([1 / 3] * 2)
if method == "cie 1931":
ij = XYZ_to_xy(cmfs.values, illuminant)
labels = cast(
Tuple,
optional(
spectral_locus_labels,
(
390,
460,
470,
480,
490,
500,
510,
520,
540,
560,
580,
600,
620,
700,
),
),
)
elif method == "cie 1960 ucs":
ij = UCS_to_uv(XYZ_to_UCS(cmfs.values))
labels = cast(
Tuple,
optional(
spectral_locus_labels,
(
420,
440,
450,
460,
470,
480,
490,
500,
510,
520,
530,
540,
550,
560,
570,
580,
590,
600,
610,
620,
630,
645,
680,
),
),
)
elif method == "cie 1976 ucs":
ij = Luv_to_uv(XYZ_to_Luv(cmfs.values, illuminant), illuminant)
labels = cast(
Tuple,
optional(
spectral_locus_labels,
(
420,
440,
450,
460,
470,
480,
490,
500,
510,
520,
530,
540,
550,
560,
570,
580,
590,
600,
610,
620,
630,
645,
680,
),
),
)
pl_ij = np.reshape(
tstack([
np.linspace(ij[0][0], ij[-1][0], 20),
np.linspace(ij[0][1], ij[-1][1], 20),
]),
(-1, 1, 2),
)
sl_ij = np.copy(ij).reshape(-1, 1, 2)
purple_line_colours: Optional[Union[ArrayLike, str]]
if str(spectral_locus_colours).upper() == "RGB":
spectral_locus_colours = normalise_maximum(XYZ_to_plotting_colourspace(
cmfs.values),
axis=-1)
if method == "cie 1931":
XYZ = xy_to_XYZ(pl_ij)
elif method == "cie 1960 ucs":
XYZ = xy_to_XYZ(UCS_uv_to_xy(pl_ij))
elif method == "cie 1976 ucs":
XYZ = xy_to_XYZ(Luv_uv_to_xy(pl_ij))
purple_line_colours = normalise_maximum(XYZ_to_plotting_colourspace(
np.reshape(XYZ, (-1, 3))),
axis=-1)
else:
purple_line_colours = spectral_locus_colours
for slp_ij, slp_colours in (
(pl_ij, purple_line_colours),
(sl_ij, spectral_locus_colours),
):
line_collection = LineCollection(
np.concatenate([slp_ij[:-1], slp_ij[1:]], axis=1),
colors=slp_colours,
alpha=spectral_locus_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_scatter,
)
axes.add_collection(line_collection)
wl_ij = dict(zip(wavelengths, ij))
for label in labels:
ij_l = wl_ij.get(label)
if ij_l is None:
continue
ij_l = as_float_array([ij_l])
i, j = tsplit(ij_l)
index = bisect.bisect(wavelengths, label)
left = wavelengths[index - 1] if index >= 0 else wavelengths[index]
right = (wavelengths[index]
if index < len(wavelengths) else wavelengths[-1])
dx = wl_ij[right][0] - wl_ij[left][0]
dy = wl_ij[right][1] - wl_ij[left][1]
direction = np.array([-dy, dx])
normal = (np.array([-dy, dx]) if np.dot(
normalise_vector(ij_l - equal_energy),
normalise_vector(direction),
) > 0 else np.array([dy, -dx]))
normal = normalise_vector(normal) / 30
label_colour = (
spectral_locus_colours if is_string(spectral_locus_colours) else
spectral_locus_colours[index] # type: ignore[index]
)
axes.plot(
(i, i + normal[0] * 0.75),
(j, j + normal[1] * 0.75),
color=label_colour,
alpha=spectral_locus_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
)
axes.plot(
i,
j,
"o",
color=label_colour,
alpha=spectral_locus_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_line,
)
axes.text(
i + normal[0],
j + normal[1],
label,
clip_on=True,
ha="left" if normal[0] >= 0 else "right",
va="center",
fontdict={"size": "small"},
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_label,
)
settings = {"axes": axes}
settings.update(kwargs)
return render(**kwargs)
def plot_colour_quality_bars(specifications,
labels=True,
hatching=None,
hatching_repeat=2,
**kwargs):
"""
Plots the colour quality data of given illuminants or light sources colour
quality specifications.
Parameters
----------
specifications : array_like
Array of illuminants or light sources colour quality specifications.
labels : bool, optional
Add labels above bars.
hatching : bool or None, optional
Use hatching for the bars.
hatching_repeat : int, optional
Hatching pattern repeat.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.quality.plot_colour_quality_bars`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> from colour import (ILLUMINANTS_SDS,
... LIGHT_SOURCES_SDS, SpectralShape)
>>> illuminant = ILLUMINANTS_SDS['FL2']
>>> light_source = LIGHT_SOURCES_SDS['Kinoton 75P']
>>> light_source = light_source.copy().align(SpectralShape(360, 830, 1))
>>> cqs_i = colour_quality_scale(illuminant, additional_data=True)
>>> cqs_l = colour_quality_scale(light_source, additional_data=True)
>>> plot_colour_quality_bars([cqs_i, cqs_l]) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Colour_Quality_Bars.png
:align: center
:alt: plot_colour_quality_bars
"""
settings = {'uniform': True}
settings.update(kwargs)
_figure, axes = artist(**settings)
bar_width = 0.5
y_ticks_interval = 10
count_s, count_Q_as = len(specifications), 0
patterns = cycle(COLOUR_STYLE_CONSTANTS.hatch.patterns)
if hatching is None:
hatching = False if count_s == 1 else True
for i, specification in enumerate(specifications):
Q_a, Q_as, colorimetry_data = (specification.Q_a, specification.Q_as,
specification.colorimetry_data)
count_Q_as = len(Q_as)
colours = ([[1] * 3] + [
np.clip(XYZ_to_plotting_colourspace(x.XYZ), 0, 1)
for x in colorimetry_data[0]
])
x = (i + np.arange(0, (count_Q_as + 1) * (count_s + 1), (count_s + 1),
dtype=DEFAULT_FLOAT_DTYPE)) * bar_width
y = [s[1].Q_a for s in sorted(Q_as.items(), key=lambda s: s[0])]
y = np.array([Q_a] + list(y))
bars = axes.bar(x,
np.abs(y),
color=colours,
width=bar_width,
edgecolor=COLOUR_STYLE_CONSTANTS.colour.dark,
label=specification.name)
hatches = ([next(patterns) * hatching_repeat] *
(count_Q_as + 1) if hatching else np.where(
y < 0, next(patterns), None).tolist())
for j, bar in enumerate(bars.patches):
bar.set_hatch(hatches[j])
if labels:
label_rectangles(
['{0:.1f}'.format(y_v) for y_v in y],
bars,
rotation='horizontal' if count_s == 1 else 'vertical',
offset=(0 if count_s == 1 else 3 / 100 * count_s + 65 / 1000,
0.025),
text_size=-5 / 7 * count_s + 12.5,
axes=axes)
axes.axhline(y=100,
color=COLOUR_STYLE_CONSTANTS.colour.dark,
linestyle='--')
axes.set_xticks(
(np.arange(0, (count_Q_as + 1) * (count_s + 1), (count_s + 1),
dtype=DEFAULT_FLOAT_DTYPE) * bar_width +
(count_s * bar_width / 2)), ['Qa'] +
['Q{0}'.format(index + 1) for index in range(0, count_Q_as + 1, 1)])
axes.set_yticks(range(0, 100 + y_ticks_interval, y_ticks_interval))
aspect = 1 / (120 / (bar_width + len(Q_as) + bar_width * 2))
bounding_box = (-bar_width, ((count_Q_as + 1) * (count_s + 1)) / 2, 0, 120)
settings = {
'axes': axes,
'aspect': aspect,
'bounding_box': bounding_box,
'legend': hatching,
'title': 'Colour Quality',
}
settings.update(kwargs)
return render(**settings)
def plot_blackbody_spectral_radiance(
temperature: Floating = 3500,
cmfs: Union[MultiSpectralDistributions, str, Sequence[Union[
MultiSpectralDistributions,
str]], ] = "CIE 1931 2 Degree Standard Observer",
blackbody: str = "VY Canis Major",
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot given blackbody spectral radiance.
Parameters
----------
temperature
Blackbody temperature.
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.
blackbody
Blackbody name.
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_blackbody_spectral_radiance(3500, blackbody='VY Canis Major')
... # doctest: +ELLIPSIS
(<Figure size ... with 2 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Blackbody_Spectral_Radiance.png
:align: center
:alt: plot_blackbody_spectral_radiance
"""
figure = plt.figure()
figure.subplots_adjust(hspace=CONSTANTS_COLOUR_STYLE.geometry.short / 2)
cmfs = cast(MultiSpectralDistributions,
first_item(filter_cmfs(cmfs).values()))
sd = sd_blackbody(temperature, cmfs.shape)
axes = figure.add_subplot(211)
settings: Dict[str, Any] = {
"axes": axes,
"title": f"{blackbody} - Spectral Radiance",
"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": f"{blackbody} - Colour",
"x_label": f"{temperature}K",
"y_label": "",
"x_ticker": False,
"y_ticker": False,
}
settings.update(kwargs)
settings["standalone"] = False
figure, axes = plot_single_colour_swatch(RGB, **settings)
settings = {"axes": axes, "standalone": True}
settings.update(kwargs)
return render(**settings)
def plot_multi_colour_checkers(
colour_checkers: Union[ColourChecker, str, Sequence[Union[ColourChecker,
str]]],
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot and compares given colour checkers.
Parameters
----------
colour_checkers
Color checker to plot, count must be less than or equal to 2.
``colour_checkers`` elements can be of any type or form supported by
the :func:`colour.plotting.filter_colour_checkers` definition.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
: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_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Multi_Colour_Checkers.png
:align: center
:alt: plot_multi_colour_checkers
"""
filtered_colour_checkers = list(
filter_colour_checkers(colour_checkers).values())
attest(
len(filtered_colour_checkers) <= 2,
"Only two colour checkers can be compared at a time!",
)
_figure, axes = artist(**kwargs)
compare_swatches = len(filtered_colour_checkers) == 2
colour_swatches = []
colour_checker_names = []
for colour_checker in filtered_colour_checkers:
colour_checker_names.append(colour_checker.name)
for label, xyY in colour_checker.data.items():
XYZ = xyY_to_XYZ(xyY)
RGB = XYZ_to_plotting_colourspace(XYZ, colour_checker.illuminant)
colour_swatches.append(
ColourSwatch(np.clip(np.ravel(RGB), 0, 1), label.title()))
if compare_swatches:
colour_swatches = [
swatch for pairs in zip(
colour_swatches[0:len(colour_swatches) // 2],
colour_swatches[len(colour_swatches) // 2:],
) for swatch in pairs
]
background_colour = "0.1"
width = height = 1.0
spacing = 0.25
columns = 6
settings: Dict[str, Any] = {
"axes": axes,
"width": width,
"height": height,
"spacing": spacing,
"columns": columns,
"direction": "-y",
"text_kwargs": {
"size": 8
},
"background_colour": background_colour,
"compare_swatches": "Stacked" if compare_swatches else None,
}
settings.update(kwargs)
settings["standalone"] = False
plot_multi_colour_swatches(colour_swatches, **settings)
axes.text(
0.5,
0.005,
(f"{', '.join(colour_checker_names)} - "
f"{CONSTANTS_COLOUR_STYLE.colour.colourspace.name} - "
f"Colour Rendition Chart"),
transform=axes.transAxes,
color=CONSTANTS_COLOUR_STYLE.colour.bright,
ha="center",
va="bottom",
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_label,
)
settings.update({
"axes": axes,
"standalone": True,
"title": ", ".join(colour_checker_names),
})
return render(**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_colour_quality_bars(
specifications: Sequence[
Union[
ColourRendering_Specification_CQS,
ColourRendering_Specification_CRI,
]
],
labels: Boolean = True,
hatching: Optional[Boolean] = None,
hatching_repeat: Integer = 2,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the colour quality data of given illuminants or light sources colour
quality specifications.
Parameters
----------
specifications
Array of illuminants or light sources colour quality specifications.
labels
Add labels above bars.
hatching
Use hatching for the bars.
hatching_repeat
Hatching pattern repeat.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.quality.plot_colour_quality_bars`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour import (SDS_ILLUMINANTS,
... SDS_LIGHT_SOURCES, SpectralShape)
>>> illuminant = SDS_ILLUMINANTS['FL2']
>>> light_source = SDS_LIGHT_SOURCES['Kinoton 75P']
>>> light_source = light_source.copy().align(SpectralShape(360, 830, 1))
>>> cqs_i = colour_quality_scale(illuminant, additional_data=True)
>>> cqs_l = colour_quality_scale(light_source, additional_data=True)
>>> plot_colour_quality_bars([cqs_i, cqs_l]) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Colour_Quality_Bars.png
:align: center
:alt: plot_colour_quality_bars
"""
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
bar_width = 0.5
y_ticks_interval = 10
count_s, count_Q_as = len(specifications), 0
patterns = cycle(CONSTANTS_COLOUR_STYLE.hatch.patterns)
if hatching is None:
hatching = False if count_s == 1 else True
for i, specification in enumerate(specifications):
Q_a, Q_as, colorimetry_data = (
specification.Q_a,
specification.Q_as,
specification.colorimetry_data,
)
count_Q_as = len(Q_as)
RGB = [[1] * 3] + [
np.clip(XYZ_to_plotting_colourspace(x.XYZ), 0, 1)
for x in colorimetry_data[0]
]
x = (
as_float_array(
i
+ np.arange(
0,
(count_Q_as + 1) * (count_s + 1),
(count_s + 1),
dtype=DEFAULT_FLOAT_DTYPE,
)
)
* bar_width
)
y = as_float_array(
[Q_a]
+ [
s[1].Q_a # type: ignore[attr-defined]
for s in sorted(Q_as.items(), key=lambda s: s[0])
]
)
bars = axes.bar(
x,
np.abs(y),
color=RGB,
width=bar_width,
edgecolor=CONSTANTS_COLOUR_STYLE.colour.dark,
label=specification.name,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
hatches = (
[next(patterns) * hatching_repeat] * (count_Q_as + 1)
if hatching
else list(
np.where(y < 0, next(patterns), None) # type: ignore[call-overload]
)
)
for j, bar in enumerate(bars.patches):
bar.set_hatch(hatches[j])
if labels:
label_rectangles(
[f"{y_v:.1f}" for y_v in y],
bars,
rotation="horizontal" if count_s == 1 else "vertical",
offset=(
0 if count_s == 1 else 3 / 100 * count_s + 65 / 1000,
0.025,
),
text_size=-5 / 7 * count_s + 12.5,
axes=axes,
)
axes.axhline(
y=100,
color=CONSTANTS_COLOUR_STYLE.colour.dark,
linestyle="--",
zorder=CONSTANTS_COLOUR_STYLE.zorder.midground_line,
)
axes.set_xticks(
(
np.arange(
0,
(count_Q_as + 1) * (count_s + 1),
(count_s + 1),
dtype=DEFAULT_FLOAT_DTYPE,
)
- bar_width
)
* bar_width
+ (count_s * bar_width / 2)
)
axes.set_xticklabels(
["Qa"] + [f"Q{index + 1}" for index in range(0, count_Q_as, 1)]
)
axes.set_yticks(range(0, 100 + y_ticks_interval, y_ticks_interval))
aspect = 1 / (120 / (bar_width + len(Q_as) + bar_width * 2))
bounding_box = (
-bar_width,
((count_Q_as + 1) * (count_s + 1)) / 2 - bar_width,
0,
120,
)
settings = {
"axes": axes,
"aspect": aspect,
"bounding_box": bounding_box,
"legend": hatching,
"title": "Colour Quality",
}
settings.update(kwargs)
return render(**settings)
def plot_multi_colour_checkers(colour_checkers=None, **kwargs):
"""
Plots and compares given colour checkers.
Parameters
----------
colour_checkers : array_like, optional
Color checker names, must be less than or equal to 2 names.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
: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_multi_colour_checkers(['ColorChecker 1976', 'ColorChecker 2005'])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_Colour_Checkers.png
:align: center
:alt: plot_multi_colour_checkers
"""
if colour_checkers is None:
colour_checkers = ['ColorChecker 1976', 'ColorChecker 2005']
else:
assert len(colour_checkers) <= 2, (
'Only two colour checkers can be compared at a time!')
colour_checkers = filter_colour_checkers(colour_checkers).values()
_figure, axes = artist(**kwargs)
compare_swatches = len(colour_checkers) == 2
colour_swatches = []
colour_checker_names = []
for colour_checker in colour_checkers:
colour_checker_names.append(colour_checker.name)
for label, xyY in colour_checker.data.items():
XYZ = xyY_to_XYZ(xyY)
RGB = XYZ_to_plotting_colourspace(XYZ, colour_checker.illuminant)
colour_swatches.append(
ColourSwatch(label.title(), np.clip(np.ravel(RGB), 0, 1)))
if compare_swatches:
colour_swatches = [
swatch
for pairs in zip(colour_swatches[0:len(colour_swatches) // 2],
colour_swatches[len(colour_swatches) // 2:])
for swatch in pairs
]
background_colour = '0.1'
width = height = 1.0
spacing = 0.25
columns = 6
settings = {
'axes': axes,
'width': width,
'height': height,
'spacing': spacing,
'columns': columns,
'text_parameters': {
'size': 8
},
'background_colour': background_colour,
'compare_swatches': 'Stacked' if compare_swatches else None,
}
settings.update(kwargs)
settings['standalone'] = False
plot_multi_colour_swatches(colour_swatches, **settings)
axes.text(0.5,
0.005,
'{0} - {1} - Colour Rendition Chart'.format(
', '.join(colour_checker_names),
COLOUR_STYLE_CONSTANTS.colour.colourspace.name),
transform=axes.transAxes,
color=COLOUR_STYLE_CONSTANTS.colour.bright,
ha='center',
va='bottom')
settings.update({
'axes': axes,
'standalone': True,
'title': ', '.join(colour_checker_names),
})
return render(**settings)
def plot_hull_section_colours(
hull: trimesh.Trimesh, # type: ignore[name-defined] # noqa
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,
section_colours: Optional[Union[ArrayLike, str]] = None,
section_opacity: Floating = 1,
convert_kwargs: Optional[Dict] = None,
samples: Integer = 256,
**kwargs: Any,
) -> Tuple[plt.Figure, plt.Axes]:
"""
Plot the section colours of given *trimesh* hull along given axis and
origin.
Parameters
----------
hull
*Trimesh* hull.
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.
section_colours
Colours of the hull section, if ``section_colours`` is set to *RGB*,
the colours will be computed according to the corresponding
coordinates.
section_opacity
Opacity of the hull section colours.
convert_kwargs
Keyword arguments for the :func:`colour.convert` definition.
samples
Samples count on one axis when computing the hull section colours.
Other Parameters
----------------
kwargs
{:func:`colour.plotting.artist`,
:func:`colour.plotting.render`},
See the documentation of the previously listed definitions.
Returns
-------
:class:`tuple`
Current figure and axes.
Examples
--------
>>> from colour.models import RGB_COLOURSPACE_sRGB
>>> from colour.utilities import is_trimesh_installed
>>> vertices, faces, _outline = primitive_cube(1, 1, 1, 64, 64, 64)
>>> XYZ_vertices = RGB_to_XYZ(
... vertices['position'] + 0.5,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.whitepoint,
... RGB_COLOURSPACE_sRGB.matrix_RGB_to_XYZ,
... )
>>> if is_trimesh_installed:
... import trimesh
... hull = trimesh.Trimesh(XYZ_vertices, faces, process=False)
... plot_hull_section_colours(hull, section_colours='RGB')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Hull_Section_Colours.png
:align: center
:alt: plot_hull_section_colours
"""
axis = validate_method(
axis,
["+z", "+x", "+y"],
'"{0}" axis is invalid, it must be one of {1}!',
)
hull = hull.copy()
settings: Dict[str, Any] = {"uniform": True}
settings.update(kwargs)
_figure, axes = artist(**settings)
section_colours = cast(
ArrayLike,
optional(section_colours,
HEX_to_RGB(CONSTANTS_COLOUR_STYLE.colour.average)),
)
convert_kwargs = optional(convert_kwargs, {})
# Luminance / Lightness reordered along "z" axis.
with suppress_warnings(python_warnings=True):
ijk_vertices = colourspace_model_axis_reorder(
convert(hull.vertices, "CIE XYZ", model, **convert_kwargs), model)
ijk_vertices = np.nan_to_num(ijk_vertices)
ijk_vertices *= COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[
model]
hull.vertices = ijk_vertices
if axis == "+x":
index_origin = 0
elif axis == "+y":
index_origin = 1
elif axis == "+z":
index_origin = 2
plane = MAPPING_AXIS_TO_PLANE[axis]
section = hull_section(hull, axis, origin, normalise)
padding = 0.1 * np.mean(
COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[model])
min_x = np.min(ijk_vertices[..., plane[0]]) - padding
max_x = np.max(ijk_vertices[..., plane[0]]) + padding
min_y = np.min(ijk_vertices[..., plane[1]]) - padding
max_y = np.max(ijk_vertices[..., plane[1]]) + padding
extent = (min_x, max_x, min_y, max_y)
use_RGB_section_colours = str(section_colours).upper() == "RGB"
if use_RGB_section_colours:
ii, jj = np.meshgrid(
np.linspace(min_x, max_x, samples),
np.linspace(max_y, min_y, samples),
)
ij = tstack([ii, jj])
ijk_section = full((samples, samples, 3),
np.median(section[..., index_origin]))
ijk_section[..., plane] = ij
ijk_section /= COLOURSPACE_MODELS_DOMAIN_RANGE_SCALE_1_TO_REFERENCE[
model]
XYZ_section = convert(
colourspace_model_axis_reorder(ijk_section, model, "Inverse"),
model,
"CIE XYZ",
**convert_kwargs,
)
RGB_section = XYZ_to_plotting_colourspace(XYZ_section)
else:
section_colours = np.hstack([section_colours, section_opacity])
facecolor = "none" if use_RGB_section_colours else section_colours
polygon = Polygon(
section[..., plane],
facecolor=facecolor,
edgecolor="none",
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
axes.add_patch(polygon)
if use_RGB_section_colours:
image = axes.imshow(
np.clip(RGB_section, 0, 1),
interpolation="bilinear",
extent=extent,
clip_path=None,
alpha=section_opacity,
zorder=CONSTANTS_COLOUR_STYLE.zorder.background_polygon,
)
image.set_clip_path(polygon)
settings = {
"axes": axes,
"bounding_box": extent,
}
settings.update(kwargs)
return render(**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_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 plot_multi_sds(sds, plot_kwargs=None, **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.
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.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_kwargs = [
... {'use_sd_colours': True},
... {'use_sd_colours': True, 'linestyle': 'dashed'},
... ]
>>> plot_multi_sds([sd_1, sd_2], plot_kwargs=plot_kwargs)
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, <...AxesSubplot...>)
.. image:: ../_static/Plotting_Plot_Multi_SDS.png
:align: center
:alt: plot_multi_sds
"""
handle_arguments_deprecation(
{'ArgumentRemoved': ['normalise_sd_colours', 'use_sds_colours']},
**kwargs)
_figure, axes = artist(**kwargs)
sds = sds_and_msds_to_sds(sds)
plot_settings_collection = [{
'label':
'{0}'.format(sd.strict_name),
'cmfs':
'CIE 1931 2 Degree Standard Observer',
'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))
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
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')
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_sd_colours:
with domain_range_scale('1'):
XYZ = sd_to_XYZ(sd, cmfs, illuminant)
if normalise_sd_colours:
XYZ /= XYZ[..., 1]
plot_settings['color'] = np.clip(XYZ_to_plotting_colourspace(XYZ),
0, 1)
axes.plot(wavelengths, values, **plot_settings)
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_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_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_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_as_slit_rainbow(ax=None,
wavelength=None,
flux=None,
cmfs="CIE 1931 2 Degree Standard Observer",
**kwargs):
"""
(This is still *real* blarg-y*.)
Plot a spectrum as a light source would be seen through
a slit spectrometer, with vertical bands of light that
are brighter or fainter depending on the intensity
of the spectrum at that particular wavelength.
Parameters
----------
ax : matplotlib.axes._subplots.AxesSubplot
The ax into which the rainbow should be drawn.
wavelength : array
The wavelengths to include in the spectrum.
In units of nm, but not as astropy units.
flux : array
The fluxes to include in the spectrum.
In units of whatever, but not as astropy units.
cmfs : string
The color matching function(s?) to use.
vector : bool
Returns
-------
ax : matplotlib.axes._subplots.AxesSubplot
The ax into which the rainbow was drawn.
"""
# make sure our plotting ax is defined
if ax is None:
ax = plt.gca()
# make sure we have a grid of wavelengths defined
if wavelength is None:
# create a grid of wavelengths (at which CMFss are useful)
wavelength = CMFs.wavelengths
# create y values that will be plotted (these could be spectrum)
if flux is None:
flux = np.ones_like(wavelength)
# pull out only the values that *can* be converted to colors
ok = (wavelength >= np.min(CMFs.wavelengths)) & (wavelength <= np.max(
CMFs.wavelengths))
w, f = wavelength[ok], flux[ok]
# get the XYZ for the wavelengths
XYZ = wavelength_to_XYZ(w)
# create colors at those wavelengths
colours = XYZ_to_plotting_colourspace(XYZ)
# normalize the colors to their maximum?
# colours = CONSTANTS_COLOUR_STYLE.colour.colourspace.cctf_encoding(
# normalise_maximum(colours))
# normalize the brightness by the flux
colours *= f[:, np.newaxis]
# normalize to the brightest line
colours = np.maximum(0, colours / np.max(colours))
# draw as an RGB color image with imshow
ax.imshow(
colours[np.newaxis, :, :],
aspect="auto",
extent=[np.min(w), np.max(w), 0, 1],
interpolation="nearest",
)
return ax
