def single_rayleigh_scattering_spd_plot(
CO2_concentration=STANDARD_CO2_CONCENTRATION,
temperature=STANDARD_AIR_TEMPERATURE,
pressure=AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude=DEFAULT_LATITUDE,
altitude=DEFAULT_ALTITUDE,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots a single *Rayleigh* scattering spectral power distribution.
Parameters
----------
CO2_concentration : numeric, optional
:math:`CO_2` concentration in parts per million (ppm).
temperature : numeric, optional
Air temperature :math:`T[K]` in kelvin degrees.
pressure : numeric
Surface pressure :math:`P` of the measurement site.
latitude : numeric, optional
Latitude of the site in degrees.
altitude : numeric, optional
Altitude of the site in meters.
cmfs : unicode, optional
Standard observer colour matching functions.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`boundaries`, :func:`canvas`, :func:`decorate`,
:func:`display`},
Please refer to the documentation of the previously listed definitions.
out_of_gamut_clipping : bool, optional
{:func:`single_spd_plot`},
Whether to clip out of gamut colours otherwise, the colours will be
offset by the absolute minimal colour leading to a rendering on
gray background, less saturated and smoother. [1]_
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> single_rayleigh_scattering_spd_plot() # doctest: +SKIP
"""
title = 'Rayleigh Scattering'
cmfs = get_cmfs(cmfs)
settings = {'title': title, 'y_label': 'Optical Depth'}
settings.update(kwargs)
spd = rayleigh_scattering_spd(cmfs.shape, CO2_concentration, temperature,
pressure, latitude, altitude)
return single_spd_plot(spd, **settings)
def single_rayleigh_scattering_spd_plot(
CO2_concentration=STANDARD_CO2_CONCENTRATION,
temperature=STANDARD_AIR_TEMPERATURE,
pressure=AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude=DEFAULT_LATITUDE,
altitude=DEFAULT_ALTITUDE,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots a single rayleigh scattering spectral power distribution.
Parameters
----------
CO2_concentration : numeric, optional
:math:`CO_2` concentration in parts per million (ppm).
temperature : numeric, optional
Air temperature :math:`T[K]` in kelvin degrees.
pressure : numeric
Surface pressure :math:`P` of the measurement site.
latitude : numeric, optional
Latitude of the site in degrees.
altitude : numeric, optional
Altitude of the site in meters.
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> single_rayleigh_scattering_spd_plot() # doctest: +SKIP
True
"""
title = 'Rayleigh Scattering'
cmfs = get_cmfs(cmfs)
settings = {
'title': title,
'y_label': 'Optical Depth'}
settings.update(kwargs)
spd = rayleigh_scattering_spd(cmfs.shape,
CO2_concentration,
temperature,
pressure,
latitude,
altitude)
return single_spd_plot(spd, **settings)
def single_rayleigh_scattering_spd_plot(
CO2_concentration=STANDARD_CO2_CONCENTRATION,
temperature=STANDARD_AIR_TEMPERATURE,
pressure=AVERAGE_PRESSURE_MEAN_SEA_LEVEL,
latitude=DEFAULT_LATITUDE,
altitude=DEFAULT_ALTITUDE,
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots a single rayleigh scattering spectral power distribution.
Parameters
----------
CO2_concentration : numeric, optional
:math:`CO_2` concentration in parts per million (ppm).
temperature : numeric, optional
Air temperature :math:`T[K]` in kelvin degrees.
pressure : numeric
Surface pressure :math:`P` of the measurement site.
latitude : numeric, optional
Latitude of the site in degrees.
altitude : numeric, optional
Altitude of the site in meters.
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> single_rayleigh_scattering_spd_plot() # doctest: +SKIP
"""
title = 'Rayleigh Scattering'
cmfs = get_cmfs(cmfs)
settings = {
'title': title,
'y_label': 'Optical Depth'}
settings.update(kwargs)
spd = rayleigh_scattering_spd(cmfs.shape,
CO2_concentration,
temperature,
pressure,
latitude,
altitude)
return single_spd_plot(spd, **settings)
def the_blue_sky_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
\**kwargs : dict, optional
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.clone()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title':
'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label':
'',
'aspect':
None,
'standalone':
False
}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_plot(ColourParameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
780: 84.19,
782: 84.19,
784: 84.21,
786: 84.22,
788: 84.25,
790: 84.29,
792: 84.29,
794: 84.31,
796: 84.31,
798: 84.34,
800: 84.34,
820: 84.47
}
message_box('Plotting various single spectral power distributions.')
single_spd_plot(
colour.SpectralPowerDistribution(sample_spd_data, name='Custom'))
single_spd_plot(
colour.SpectralPowerDistribution(galvanized_steel_metal_spd_data,
name='Galvanized Steel Metal'))
print('\n')
message_box('Plotting multiple spectral power distributions.')
multi_spd_plot(
(colour.SpectralPowerDistribution(galvanized_steel_metal_spd_data,
name='Galvanized Steel Metal'),
colour.SpectralPowerDistribution(white_marble_spd_data,
name='White Marble')))
print('\n')
def the_blue_sky_plot(
cmfs='CIE 1931 2 Degree Standard Observer',
**kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
\*\*kwargs : \*\*
Keywords arguments.
Returns
-------
bool
Definition success.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
True
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.clone()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title': 'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label': '',
'aspect': None,
'standalone': False}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_plot(colour_parameter('', normalise(blue_sky_color)),
**settings)
settings = {'standalone': True}
settings.update(kwargs)
boundaries(**settings)
decorate(**settings)
return display(**settings)
seed(20180501)
from iminuit import Minuit, describe, Struct
from iminuit.util import make_func_code # lo utiliza Chi2Functor
from math import pi, exp, sqrt
#import colour.colorimetry as colorimetry
"""
Conviete valores RGB cualquiera, dado como constante, a xy y lo grafica como un punto dentro chromaticity diagram standar
"""
from colour.plotting import (
colour_plotting_defaults, chromaticity_diagram_plot_CIE1931,render,single_spd_plot)
# Defining a sample spectral power distribution data.
sample_spd_data = {380+i*5: random() for i in range(80)}
spd = colour.SpectralPowerDistribution(sample_spd_data)
single_spd_plot(spd)
cmfs = colour.STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
XYZ = colour.spectral_to_XYZ(spd, cmfs)
#ch1=colour.XYZ_to_spectral(XYZ,Method='Meng 2015')
ch1=colour.XYZ_to_spectral(XYZ,Method='Smits 1999')
single_spd_plot(ch1)
#spd_rec=ch1.values
y=ch1.values
x=[360+i*5 for i in range(95)]
#for i, v in enumerate(spd_rec):
# x=360+i*5
# y=v
#this is very useful if you want to build a generic cost functor
#this is actually how probfit is implemented
#x=sorted(sample_spd_data.keys())
#y=sorted(sample_spd_data.values())
def the_blue_sky_plot(cmfs='CIE 1931 2 Degree Standard Observer', **kwargs):
"""
Plots the blue sky.
Parameters
----------
cmfs : unicode, optional
Standard observer colour matching functions.
Other Parameters
----------------
\**kwargs : dict, optional
{:func:`colour.plotting.render`},
Please refer to the documentation of the previously listed definition.
Returns
-------
Figure
Current figure or None.
Examples
--------
>>> the_blue_sky_plot() # doctest: +SKIP
"""
canvas(**kwargs)
cmfs, name = get_cmfs(cmfs), cmfs
ASTM_G_173_spd = ASTM_G_173_ETR.copy()
rayleigh_spd = rayleigh_scattering_spd()
ASTM_G_173_spd.align(rayleigh_spd.shape)
spd = rayleigh_spd * ASTM_G_173_spd
matplotlib.pyplot.subplots_adjust(hspace=0.4)
matplotlib.pyplot.figure(1)
matplotlib.pyplot.subplot(211)
settings = {
'title': 'The Blue Sky - Synthetic Spectral Power Distribution',
'y_label': u'W / m-2 / nm-1',
'standalone': False
}
settings.update(kwargs)
single_spd_plot(spd, name, **settings)
matplotlib.pyplot.subplot(212)
settings = {
'title':
'The Blue Sky - Colour',
'x_label': ('The sky is blue because molecules in the atmosphere '
'scatter shorter wavelengths more than longer ones.\n'
'The synthetic spectral power distribution is computed as '
'follows: '
'(ASTM G-173 ETR * Standard Air Rayleigh Scattering).'),
'y_label':
'',
'aspect':
None,
'standalone':
False
}
blue_sky_color = XYZ_to_sRGB(spectral_to_XYZ(spd))
single_colour_swatch_plot(
ColourSwatch('', normalise_maximum(blue_sky_color)), **settings)
settings = {'standalone': True}
settings.update(kwargs)
return render(**settings)
def index():
cmfs = CMFS['CIE 1931 2 Degree Standard Observer']
with open('test.txt') as f:
data = pd.read_csv(f, sep="\t" or ' ' or ',', header=None)
f.close()
w = [i[0] for i in data.values]
s = [i[1] for i in data.values]
data_formated = dict(zip(w, s))
spd = SpectralPowerDistribution('Sample', data_formated)
b = single_spd_plot(spd,
standalone=False,
figure_size=(5, 5),
title='Spectrum')
figfile_b = StringIO()
b.savefig(figfile_b, format='svg')
figfile_b.seek(0)
figdata_svg_b = '<svg' + figfile_b.getvalue().split('<svg')[1]
b.clf()
plot.close(b)
illuminant = ILLUMINANTS_RELATIVE_SPDS['D50']
XYZ = spectral_to_XYZ(spd, cmfs, illuminant)
xy = XYZ_to_xy(XYZ)
print(xy)
cct = xy_to_CCT(xy)
print(cct)
cri = colour_rendering_index(spd, additional_data=True)
print(cri.Q_a)
Q_as = cri.Q_as
y = [s[1].Q_a for s in sorted(Q_as.items(), key=lambda s: s[0])]
print(y)
single_spd_colour_rendering_index_bars_plot(spd,
standalone=False,
figure_size=(7, 7),
title='Colour rendering index')
c = plot.gcf()
figfile_c = StringIO()
c.savefig(figfile_c, format='svg')
figfile_c.seek(0)
figdata_svg_c = '<svg' + figfile_c.getvalue().split('<svg')[1]
c.clf()
plot.close(c)
CIE_1931_chromaticity_diagram_plot(standalone=False,
figure_size=(6, 5),
grid=False,
title='CIE 1931 Chromaticity Diagram',
bounding_box=(-0.1, 0.9, -0.05, 0.95))
x, y = xy
pylab.plot(x, y, 'o-', color='white')
pylab.annotate((("%.4f" % x), ("%.4f" % y)),
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->',
connectionstyle='arc3, rad=-0.2'))
a = plot.gcf()
figfile = StringIO()
a.savefig(figfile, format='svg')
figfile.seek(0)
figdata_svg = '<svg' + figfile.getvalue().split('<svg')[1]
a.clf()
plot.close(a)
del a, b, c
# pprint.pprint(figdata_svg)
return render_template('index.html',
spd=figdata_svg_b,
result=figdata_svg,
colour_rendering_index=figdata_svg_c)
def spd_plot(self):
cpt.single_spd_plot(self.spd)
