%matplotlib inline

from colour.plotting import *

visible_spectrum_plot()

import colour

print(colour.__all__[:5] + ['...'])

from pprint import pprint

import colour.plotting

for sub_package in ('adaptation', 'algebra', 'appearance', 'characterisation',
                    'colorimetry', 'constants', 'corresponding', 'difference', 
                    'io', 'models', 'notation', 'phenomenons', 'plotting', 
                    'quality', 'recovery', 'temperature', 'utilities',
                    'volume'):
    print(sub_package.title())
    pprint(getattr(colour, sub_package).__all__)
    print('\n')

print(colour.CCT_to_uv_Ohno2013.__doc__)

from pprint import pprint
import colour.colorimetry as colorimetry

pprint(colorimetry.__all__)

import colour.colorimetry.dataset as dataset

pprint(dataset.__all__)

# Defining a sample spectral power distribution data.
sample_spd_data = {
    380: 0.048,
    385: 0.051,
    390: 0.055,
    395: 0.060,
    400: 0.065,
    405: 0.068,
    410: 0.068,
    415: 0.067,
    420: 0.064,
    425: 0.062,
    430: 0.059,
    435: 0.057,
    440: 0.055,
    445: 0.054,
    450: 0.053,
    455: 0.053,
    460: 0.052,
    465: 0.052,
    470: 0.052,
    475: 0.053,
    480: 0.054,
    485: 0.055,
    490: 0.057,
    495: 0.059,
    500: 0.061,
    505: 0.062,
    510: 0.065,
    515: 0.067,
    520: 0.070,
    525: 0.072,
    530: 0.074,
    535: 0.075,
    540: 0.076,
    545: 0.078,
    550: 0.079,
    555: 0.082,
    560: 0.087,
    565: 0.092,
    570: 0.100,
    575: 0.107,
    580: 0.115,
    585: 0.122,
    590: 0.129,
    595: 0.134,
    600: 0.138,
    605: 0.142,
    610: 0.146,
    615: 0.150,
    620: 0.154,
    625: 0.158,
    630: 0.163,
    635: 0.167,
    640: 0.173,
    645: 0.180,
    650: 0.188,
    655: 0.196,
    660: 0.204,
    665: 0.213,
    670: 0.222,
    675: 0.231,
    680: 0.242,
    685: 0.251,
    690: 0.261,
    695: 0.271,
    700: 0.282,
    705: 0.294,
    710: 0.305,
    715: 0.318,
    720: 0.334,
    725: 0.354,
    730: 0.372,
    735: 0.392,
    740: 0.409,
    745: 0.420,
    750: 0.436,
    755: 0.450,
    760: 0.462,
    765: 0.465,
    770: 0.448,
    775: 0.432,
    780: 0.421}

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

# Plotting the sample spectral power distribution.
single_spd_plot(spd)

# Displaying the sample spectral power distribution shape.
print(spd.shape)

repr(spd.shape)

# Using *colour.SpectralShape* with iteration.
shape = colour.SpectralShape(start=0, end=10, steps=1)
for wavelength in shape:
    print(wavelength)

# *colour.SpectralShape.range* method is providing the complete range of values. 
shape = colour.SpectralShape(0, 10, 0.5)
shape.range()

# Defining a constant spectral power distribution.
constant_spd = colour.constant_spd(100)
print('"Constant Spectral Power Distribution"')
print(constant_spd.shape)
print(constant_spd[400])

# Defining a zeros filled spectral power distribution.
print('\n"Zeros Filled Spectral Power Distribution"')
zeros_spd = colour.zeros_spd()
print(zeros_spd.shape)
print(zeros_spd[400])

# Defining a ones filled spectral power distribution.
print('\n"Ones Filled Spectral Power Distribution"')
ones_spd = colour.ones_spd()
print(ones_spd.shape)
print(ones_spd[400])

print(repr(colour.DEFAULT_SPECTRAL_SHAPE))

colour.ones_spd(colour.SpectralShape(400, 700, 5))[450]

spd1 = colour.ones_spd()
print('"Ones Filled Spectral Power Distribution"')
print(spd1[400])

print('\n"x2 Constant Multiplied"')
print((spd1 * 2)[400])

print('\n"+ Spectral Power Distribution"')
print((spd1 + colour.ones_spd())[400])

# Checking the sample spectral power distribution uniformity.
print(spd.is_uniform())

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

# Interpolating the cloned sample spectral power distribution.
clone_spd.interpolate(colour.SpectralShape(400, 770, 1))
clone_spd[401]

# Comparing the interpolated spectral power distribution with the original one.
multi_spd_plot([spd, clone_spd], bounding_box=[730,780, 0.1, 0.5])

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

# Extrapolating the cloned sample spectral power distribution with *Linear* method.
# We first trim the spectral power distribution using the interpolation method,
# the steps being the same, no interpolation will actually occur.
clone_spd.interpolate(colour.SpectralShape(400, 700))  
clone_spd.extrapolate(colour.SpectralShape(340, 830), method='Linear', right=0)
clone_spd[340], clone_spd[830]

# Aligning the cloned sample spectral power distribution.
# We first trim the spectral power distribution as above.
clone_spd.interpolate(colour.SpectralShape(400, 700))  
clone_spd.align(colour.SpectralShape(340, 830, 5))
clone_spd[340], clone_spd[830]

spd = colour.SpectralPowerDistribution('Sample', {410: 0.50, 420: 0.75, 430: 1., 440: 0.75, 450: 0.50})
print((spd.clone() + 1).values)
print((spd.clone() * 2).values)
print((spd * [0.35, 1.55, 0.75, 2.55, 0.95]).values)
print((spd * colour.constant_spd(2, spd.shape) * colour.constant_spd(3, spd.shape)).values)

print(spd.normalise().values)
print(spd.normalise(100).values)

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

# Displaying objects interacting directly with the *CIE XYZ* colourspace.
pprint([name for name in colour.__all__ if name.startswith('XYZ_to')])

# 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(colour_parameter('Sample', RGB), text_size=32)

# Colour rendition charts chromaticity coordinates.
print(sorted(colour.characterisation.COLOURCHECKERS.keys()))

# Colour rendition charts spectral power distributions.
print(sorted(colour.characterisation.COLOURCHECKERS_SPDS.keys()))

# Plotting the *sRGB* colourspace colour of *neutral 5 (.70 D)* patch.
patch_name = 'neutral 5 (.70 D)'
patch_spd = colour.COLOURCHECKERS_SPDS['ColorChecker N Ohta'][patch_name]
XYZ = colour.spectral_to_XYZ(patch_spd, cmfs, illuminant)
RGB = colour.XYZ_to_sRGB(XYZ / 100)

single_colour_plot(colour_parameter(patch_name.title(), RGB), text_size=32)

colour_checker_plot(colour_checker='ColorChecker 2005', text_display=False)

# 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(patch_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)