# -*- coding: utf-8 -*-
"""
Colorimetry Plotting
====================
Defines the colorimetry plotting objects:
- :func:`colour.plotting.plot_single_sd`
- :func:`colour.plotting.plot_multi_sds`
- :func:`colour.plotting.plot_single_cmfs`
- :func:`colour.plotting.plot_multi_cmfs`
- :func:`colour.plotting.plot_single_illuminant_sd`
- :func:`colour.plotting.plot_multi_illuminant_sds`
- :func:`colour.plotting.plot_visible_spectrum`
- :func:`colour.plotting.plot_single_lightness_function`
- :func:`colour.plotting.plot_multi_lightness_functions`
- :func:`colour.plotting.plot_single_luminance_function`
- :func:`colour.plotting.plot_multi_luminance_functions`
- :func:`colour.plotting.plot_blackbody_spectral_radiance`
- :func:`colour.plotting.plot_blackbody_colours`
References
----------
- :cite:`Spiker2015a` : Spiker, N. (2015). Private Discussion with
Mansencal, T. Retrieved from http://www.invisiblelightimages.com/
"""
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.patches import Polygon
from six.moves import reduce
from colour.algebra import LinearInterpolator
from colour.colorimetry import (
ILLUMINANTS, ILLUMINANTS_SDS, LIGHTNESS_METHODS, LUMINANCE_METHODS,
SpectralShape, sd_blackbody, sd_ones, sd_to_XYZ, sds_and_multi_sds_to_sds,
wavelength_to_XYZ)
from colour.plotting import (
ColourSwatch, COLOUR_STYLE_CONSTANTS, XYZ_to_plotting_colourspace, artist,
filter_passthrough, filter_cmfs, filter_illuminants, override_style,
render, plot_single_colour_swatch, plot_multi_functions)
from colour.utilities import (domain_range_scale, first_item,
normalise_maximum, tstack)
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2019 - Colour Developers'
__license__ = 'New BSD License - https://opensource.org/licenses/BSD-3-Clause'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'
__all__ = [
'plot_single_sd', 'plot_multi_sds', 'plot_single_cmfs', 'plot_multi_cmfs',
'plot_single_illuminant_sd', 'plot_multi_illuminant_sds',
'plot_visible_spectrum', 'plot_single_lightness_function',
'plot_multi_lightness_functions', 'plot_single_luminance_function',
'plot_multi_luminance_functions', 'plot_blackbody_spectral_radiance',
'plot_blackbody_colours'
]
[docs]@override_style()
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)
[docs]@override_style()
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)
[docs]@override_style()
def plot_single_cmfs(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : unicode, optional
Colour matching functions to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_cmfs`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_cmfs('CIE 1931 2 Degree Standard Observer')
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Single_CMFS.png
:align: center
:alt: plot_single_cmfs
"""
cmfs = first_item(filter_cmfs(cmfs).values())
settings = {
'title': '{0} - Colour Matching Functions'.format(cmfs.strict_name)
}
settings.update(kwargs)
return plot_multi_cmfs((cmfs.name, ), **settings)
[docs]@override_style()
def plot_multi_cmfs(cmfs=None, **kwargs):
"""
Plots given colour matching functions.
Parameters
----------
cmfs : array_like, optional
Colour matching functions to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`, :func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> cmfs = ('CIE 1931 2 Degree Standard Observer',
... 'CIE 1964 10 Degree Standard Observer')
>>> plot_multi_cmfs(cmfs) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_CMFS.png
:align: center
:alt: plot_multi_cmfs
"""
if cmfs is None:
cmfs = ('CIE 1931 2 Degree Standard Observer',
'CIE 1964 10 Degree Standard Observer')
cmfs = filter_cmfs(cmfs).values()
_figure, axes = artist(**kwargs)
axes.axhline(color=COLOUR_STYLE_CONSTANTS.colour.dark, linestyle='--')
x_limit_min, x_limit_max, y_limit_min, y_limit_max = [], [], [], []
for i, cmfs_i in enumerate(cmfs):
for j, RGB in enumerate([(1, 0, 0), (0, 1, 0), (0, 0, 1)]):
RGB = [reduce(lambda y, _: y * 0.5, range(i), x) for x in RGB]
values = cmfs_i.values[:, j]
shape = cmfs_i.shape
x_limit_min.append(shape.start)
x_limit_max.append(shape.end)
y_limit_min.append(min(values))
y_limit_max.append(max(values))
axes.plot(
cmfs_i.wavelengths,
values,
color=RGB,
label='{0} - {1}'.format(cmfs_i.strict_labels[j],
cmfs_i.strict_name))
bounding_box = (min(x_limit_min), max(x_limit_max),
min(y_limit_min) - abs(min(y_limit_min)) * 0.05,
max(y_limit_max) + abs(max(y_limit_max)) * 0.05)
title = '{0} - Colour Matching Functions'.format(', '.join(
[cmfs_i.strict_name for cmfs_i in cmfs]))
settings = {
'axes': axes,
'bounding_box': bounding_box,
'legend': True,
'title': title,
'x_label': 'Wavelength $\\lambda$ (nm)',
'y_label': 'Tristimulus Values',
}
settings.update(kwargs)
return render(**settings)
[docs]@override_style()
def plot_single_illuminant_sd(illuminant='A',
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots given single illuminant spectral distribution.
Parameters
----------
illuminant : unicode, optional
Factory illuminant to plot.
cmfs : unicode, optional
Standard observer colour matching functions to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`colour.plotting.plot_single_sd`},
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother.
Returns
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_single_illuminant_sd('A') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Single_Illuminant_SD.png
:align: center
:alt: plot_single_illuminant_sd
"""
cmfs = first_item(filter_cmfs(cmfs).values())
title = 'Illuminant {0} - {1}'.format(illuminant, cmfs.strict_name)
illuminant = first_item(filter_illuminants(illuminant).values())
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return plot_single_sd(illuminant, **settings)
[docs]@override_style()
def plot_multi_illuminant_sds(illuminants=None, **kwargs):
"""
Plots given illuminants spectral distributions.
Parameters
----------
illuminants : array_like, optional
Factory illuminants to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_sds`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
use_sds_colours : bool, optional
{:func:`colour.plotting.plot_multi_sds`}
Whether to use spectral distributions colours.
normalise_sds_colours : bool
{:func:`colour.plotting.plot_multi_sds`}
Whether to normalise spectral distributions colours.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_multi_illuminant_sds(['A', 'B', 'C']) # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_Illuminant_SDS.png
:align: center
:alt: plot_multi_illuminant_sds
"""
if illuminants is None:
illuminants = ('A', 'B', 'C')
illuminants = filter_illuminants(illuminants).values()
title = '{0} - Illuminants Spectral Distributions'.format(', '.join(
[illuminant.strict_name for illuminant in illuminants]))
settings = {'title': title, 'y_label': 'Relative Power'}
settings.update(kwargs)
return plot_multi_sds(illuminants, **settings)
[docs]@override_style(**{
'ytick.left': False,
'ytick.labelleft': False,
})
def plot_visible_spectrum(cmfs='CIE 1931 2 Degree Standard Observer',
out_of_gamut_clipping=True,
**kwargs):
"""
Plots the visible colours spectrum using given standard observer *CIE XYZ*
colour matching functions.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions used for spectrum creation.
out_of_gamut_clipping : bool, optional
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_single_sd`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
References
----------
:cite:`Spiker2015a`
Examples
--------
>>> plot_visible_spectrum() # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Visible_Spectrum.png
:align: center
:alt: plot_visible_spectrum
"""
cmfs = first_item(filter_cmfs(cmfs).values())
bounding_box = (min(cmfs.wavelengths), max(cmfs.wavelengths), 0, 1)
settings = {'bounding_box': bounding_box, 'y_label': None}
settings.update(kwargs)
settings['standalone'] = False
_figure, axes = plot_single_sd(
sd_ones(cmfs.shape),
cmfs=cmfs,
out_of_gamut_clipping=out_of_gamut_clipping,
**settings)
# Removing wavelength line as it doubles with the axes spine.
axes.lines.pop(0)
settings = {
'axes': axes,
'standalone': True,
'title': 'The Visible Spectrum - {0}'.format(cmfs.strict_name),
'x_label': 'Wavelength $\\lambda$ (nm)',
}
settings.update(kwargs)
return render(**settings)
[docs]@override_style()
def plot_single_lightness_function(function='CIE 1976', **kwargs):
"""
Plots given *Lightness* function.
Parameters
----------
function : unicode, optional
*Lightness* function to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_functions`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_lightness_function('CIE 1976') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Single_Lightness_Function.png
:align: center
:alt: plot_single_lightness_function
"""
settings = {'title': '{0} - Lightness Function'.format(function)}
settings.update(kwargs)
return plot_multi_lightness_functions((function, ), **settings)
[docs]@override_style()
def plot_multi_lightness_functions(functions=None, **kwargs):
"""
Plots given *Lightness* functions.
Parameters
----------
functions : array_like, optional
*Lightness* functions to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_functions`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_multi_lightness_functions(['CIE 1976', 'Wyszecki 1963'])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_Lightness_Functions.png
:align: center
:alt: plot_multi_lightness_functions
"""
if functions is None:
functions = ('CIE 1976', 'Wyszecki 1963')
functions = filter_passthrough(LIGHTNESS_METHODS, functions)
settings = {
'bounding_box': (0, 1, 0, 1),
'legend': True,
'title': '{0} - Lightness Functions'.format(', '.join(functions)),
'x_label': 'Normalised Relative Luminance Y',
'y_label': 'Normalised Lightness',
}
settings.update(kwargs)
with domain_range_scale(1):
return plot_multi_functions(functions, **settings)
[docs]@override_style()
def plot_single_luminance_function(function='CIE 1976', **kwargs):
"""
Plots given *Luminance* function.
Parameters
----------
function : unicode, optional
*Luminance* function to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_functions`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_single_luminance_function('CIE 1976') # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Single_Luminance_Function.png
:align: center
:alt: plot_single_luminance_function
"""
settings = {'title': '{0} - Luminance Function'.format(function)}
settings.update(kwargs)
return plot_multi_luminance_functions((function, ), **settings)
[docs]@override_style()
def plot_multi_luminance_functions(functions=None, **kwargs):
"""
Plots given *Luminance* functions.
Parameters
----------
functions : array_like, optional
*Luminance* functions to plot.
Other Parameters
----------------
\\**kwargs : dict, optional
{:func:`colour.plotting.artist`,
:func:`colour.plotting.plot_multi_functions`,
:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definitions.
Returns
-------
tuple
Current figure and axes.
Examples
--------
>>> plot_multi_luminance_functions(['CIE 1976', 'Newhall 1943'])
... # doctest: +ELLIPSIS
(<Figure size ... with 1 Axes>, \
<matplotlib.axes._subplots.AxesSubplot object at 0x...>)
.. image:: ../_static/Plotting_Plot_Multi_Luminance_Functions.png
:align: center
:alt: plot_multi_luminance_functions
"""
if functions is None:
functions = ('CIE 1976', 'Newhall 1943')
functions = filter_passthrough(LUMINANCE_METHODS, functions)
settings = {
'bounding_box': (0, 1, 0, 1),
'legend': True,
'title': '{0} - Luminance Functions'.format(', '.join(functions)),
'x_label': 'Normalised Munsell Value / Lightness',
'y_label': 'Normalised Relative Luminance Y',
}
settings.update(kwargs)
with domain_range_scale(1):
return plot_multi_functions(functions, **settings)
[docs]@override_style()
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)
[docs]@override_style(**{
'ytick.left': False,
'ytick.labelleft': False,
})
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)