test.py

from colour.plotting import *
import colour

from pprint import pprint

import colour.plotting

from pprint import pprint
import colour.colorimetry as colorimetry

import colour.colorimetry.dataset as dataset



# Defining a sample spectral power distribution data.
sample_spd_data = {
      406: 0.356748826,
   420: 0.433866837,
   436: 0.442941741,
   449:	0.382922535,
   457:	0.29693662,
   464:	0.213380282,
   470:	0.136663732,
   480:	0.039276569,
   491:	0.083054577,
   497:	0.173635563,
   501:	0.262676056,
   506:	0.34278169,
   513:	0.429295775,
   520:	0.515070423,
   528:	0.598943662,
   541:	0.67956146,
   556:	0.740396927,
   572:	0.77299936,
   587:	0.80459347,
   603:	0.822071063,
   619:	0.829129321,
   634:	0.831482074,
   650:	0.840556978,
   666:	0.855345711,
   681:	0.862403969,
   697:	0.867445583,
   713:	0.868453905,
   728:	0.86778169,
   742:	0.866549296}

spd = colour.SpectralPowerDistribution('Sample', sample_spd_data)
print(spd)



# Cloning the sample spectral power distribution.
clone_spd = spd.clone()

# Interpolating the cloned sample spectral power distribution.   

# Methos should be spline. 

clone_spd.interpolate(colour.SpectralShape(406, 742, 1),method='spline')


# Check the value at 407 nm. 
clone_spd[407]



# Extrapolating the cloned sample spectral power distribution.
clone_spd.extrapolate(colour.SpectralShape(340, 830))
clone_spd[340], clone_spd[830]








# Make sample data as clone data.


spd = colour.SpectralPowerDistribution('Sample', spd)
cmfs = colour.STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
illuminant = colour.ILLUMINANTS_RELATIVE_SPDS['D65']

# Calculating the sample spectral power distribution *CIE XYZ* tristimulus values.
XYZ = colour.spectral_to_XYZ(spd, cmfs, illuminant)
print(XYZ)





# The output domain of *colour.spectral_to_XYZ* is [0, 100] and the input 
# domain of *colour.XYZ_to_sRGB* is [0, 1]. We need to take it in account and 
# rescale the input *CIE XYZ* colourspace matrix.
RGB = colour.XYZ_to_sRGB(XYZ / 100)
print(RGB)






# Plotting the *sRGB* colourspace colour of the *Sample* spectral power distribution.
single_colour_plot(ColourParameter('Sample', RGB), text_size=32)





# Computing *xy* chromaticity coordinates for the *neutral 5 (.70 D)* patch.
xy =  colour.XYZ_to_xy(XYZ)
print(xy)




import pylab

# Plotting the *CIE 1931 Chromaticity Diagram*.
# The argument *standalone=False* is passed so that the plot doesn't get displayed
# and can be used as a basis for other plots.
CIE_1931_chromaticity_diagram_plot(standalone=False)

# Plotting the *xy* chromaticity coordinates.
x, y = xy
pylab.plot(x, y, 'o-', color='white')

# Annotating the plot.
pylab.annotate(spd.name.title(),
               xy=xy,
               xytext=(-50, 30),
               textcoords='offset points',
               arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2'))

# Displaying the plot.
display(standalone=True)