Colour Science for Python

_images/Logo_Medium_001.png

Colour is a Python colour science package implementing a comprehensive number of colour theory transformations and algorithms.

It is open source and freely available under the New BSD License terms.

Features

Colour features a rich dataset and collection of objects, please see the features page for more information.

Installation

Anaconda from Continuum Analytics is the Python distribution we use to develop Colour: it ships all the scientific dependencies we require and is easily deployed cross-platform:

$ conda create -y -n python-colour
$ source activate python-colour
$ conda install -y -c conda-forge colour-science

Colour can be easily installed from the Python Package Index by issuing this command in a shell:

$ pip install colour-science

The detailed installation procedure is described in the Installation Guide.

Usage

The two main references for Colour usage are the Colour Manual and the Jupyter Notebooks with detailed historical and theoretical context and images.

Examples

Most of the objects are available from the colour namespace:

>>> import colour

Chromatic Adaptation

>>> XYZ = [0.07049534, 0.10080000, 0.09558313]
>>> A = colour.ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['A']
>>> D65 = colour.ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
>>> colour.chromatic_adaptation(
...     XYZ, colour.xy_to_XYZ(A), colour.xy_to_XYZ(D65))
array([ 0.08398225,  0.11413379,  0.28629643])
>>> sorted(colour.CHROMATIC_ADAPTATION_METHODS.keys())
['CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries']

Algebra

Kernel Interpolation
>>> y = [5.9200, 9.3700, 10.8135, 4.5100, 69.5900, 27.8007, 86.0500]
>>> x = range(len(y))
>>> colour.KernelInterpolator(x, y)([0.25, 0.75, 5.50])
array([  6.18062083,   8.08238488,  57.85783403])
Sprague (1880) Interpolation
>>> y = [5.9200, 9.3700, 10.8135, 4.5100, 69.5900, 27.8007, 86.0500]
>>> x = range(len(y))
>>> colour.SpragueInterpolator(x, y)([0.25, 0.75, 5.50])
array([  6.72951612,   7.81406251,  43.77379185])

Spectral Computations

>>> colour.spectral_to_XYZ(colour.LIGHT_SOURCES_SPDS['Neodimium Incandescent'])
array([ 36.94726204,  32.62076174,  13.0143849 ])
>>> sorted(colour.SPECTRAL_TO_XYZ_METHODS.keys())
[u'ASTM E308-15', u'Integration', u'astm2015']

Multi-Spectral Computations

>>> msa = np.array([
...     [[0.01367208, 0.09127947, 0.01524376, 0.02810712, 0.19176012, 0.04299992],
...      [0.00959792, 0.25822842, 0.41388571, 0.22275120, 0.00407416, 0.37439537],
...      [0.01791409, 0.29707789, 0.56295109, 0.23752193, 0.00236515, 0.58190280]],
...     [[0.01492332, 0.10421912, 0.02240025, 0.03735409, 0.57663846, 0.32416266],
...      [0.04180972, 0.26402685, 0.03572137, 0.00413520, 0.41808194, 0.24696727],
...      [0.00628672, 0.11454948, 0.02198825, 0.39906919, 0.63640803, 0.01139849]],
...     [[0.04325933, 0.26825359, 0.23732357, 0.05175860, 0.01181048, 0.08233768],
...      [0.02484169, 0.12027161, 0.00541695, 0.00654612, 0.18603799, 0.36247808],
...      [0.03102159, 0.16815442, 0.37186235, 0.08610666, 0.00413520, 0.78492409]],
...     [[0.11682307, 0.78883040, 0.74468607, 0.83375293, 0.90571451, 0.70054168],
...      [0.06321812, 0.41898224, 0.15190357, 0.24591440, 0.55301750, 0.00657664],
...      [0.00305180, 0.11288624, 0.11357290, 0.12924391, 0.00195315, 0.21771573]],
... ])
>>> colour.multi_spectral_to_XYZ(msa, colour.SpectralShape(400, 700, 60),
...                              cmfs, illuminant))
[[[  9.73192501   5.02105851   3.22790699]
  [ 16.08032168  24.47303359  10.28681006]
  [ 17.73513774  29.61865582  12.10713449]]
 [[ 25.69298792  11.72611193   3.70187275]
  [ 18.51208526   8.03720984   9.30361825]
  [ 48.55945054  32.30885571   4.09223401]]
 [[  5.7743232   10.10692925  10.08461311]
  [  8.81306527   3.65394599   4.20783881]
  [  8.06007398  15.87077693   7.02551086]]
 [[ 90.88877129  81.82966846  29.86765971]
  [ 38.64801062  26.70860262  15.08396538]
  [  8.77151115  10.56330761   4.28940206]]]
>>> sorted(colour.MULTI_SPECTRAL_TO_XYZ_METHODS.keys())
[u'Integration']

Blackbody Spectral Radiance Computation

>>> colour.blackbody_spd(5000)
SpectralPowerDistribution([[  3.60000000e+02,   6.65427827e+12],
                           [  3.61000000e+02,   6.70960528e+12],
                           [  3.62000000e+02,   6.76482512e+12],
                           ...
                           [  7.78000000e+02,   1.06068004e+13],
                           [  7.79000000e+02,   1.05903327e+13],
                           [  7.80000000e+02,   1.05738520e+13]],
                          interpolator=SpragueInterpolator,
                          interpolator_args={},
                          extrapolator=Extrapolator,
                          extrapolator_args={u'right': None, u'method': u'Constant', u'left': None})

Dominant, Complementary Wavelength & Colour Purity Computation

>>> xy = [0.26415, 0.37770]
>>> xy_n = [0.31270, 0.32900]
>>> colour.dominant_wavelength(xy, xy_n)
(array(504.0),
 array([ 0.00369694,  0.63895775]),
 array([ 0.00369694,  0.63895775]))

Lightness Computation

>>> colour.lightness(10.08)
24.902290269546651
>>> sorted(colour.LIGHTNESS_METHODS.keys())
[u'CIE 1976',
 u'Fairchild 2010',
 u'Fairchild 2011',
 u'Glasser 1958',
 u'Lstar1976',
 u'Wyszecki 1963']

Luminance Computation

>>> colour.luminance(37.98562910)
10.080000001314646
>>> sorted(colour.LUMINANCE_METHODS.keys())
[u'ASTM D1535-08',
 u'CIE 1976',
 u'Fairchild 2010',
 u'Fairchild 2011',
 u'Newhall 1943',
 u'astm2008',
 u'cie1976']

Whiteness Computation

>>> colour.whiteness(xy=[0.3167, 0.3334], Y=100, xy_n=[0.3139, 0.3311])
array([ 93.85 ,  -1.305])
>>> sorted(colour.WHITENESS_METHODS.keys())
[u'ASTM E313',
 u'Berger 1959',
 u'CIE 2004',
 u'Ganz 1979',
 u'Stensby 1968',
 u'Taube 1960',
 u'cie2004']

Yellowness Computation

>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> colour.yellowness(XYZ)
11.065000000000003
>>> sorted(colour.YELLOWNESS_METHODS.keys())
[u'ASTM D1925', u'ASTM E313']

Luminous Flux, Efficiency & Efficacy Computation

Luminous Flux
>>> spd = colour.LIGHT_SOURCES_SPDS['Neodimium Incandescent']
>>> colour.luminous_flux(spd)
3807.655527367202
Luminous Efficiency
>>> spd = colour.LIGHT_SOURCES_SPDS['Neodimium Incandescent']
>>> colour.luminous_efficiency(spd)
0.19943935624521045
Luminous Efficacy
>>> spd = colour.LIGHT_SOURCES_SPDS['Neodimium Incandescent']
>>> colour.luminous_efficacy(spd)
136.21708031547874

Colour Models

CIE xyY Colourspace
>>> colour.XYZ_to_xyY([0.07049534, 0.10080000, 0.09558313])
array([ 0.26414772,  0.37770001,  0.1008    ])
CIE L*a*b* Colourspace
>>> colour.XYZ_to_Lab([0.07049534, 0.10080000, 0.09558313])
array([ 37.9856291 , -23.62907688,  -4.41746615])
CIE L*u*v* Colourspace
>>> colour.XYZ_to_Luv([0.07049534, 0.10080000, 0.09558313])
array([ 37.9856291 , -28.80219593,  -1.35800706])
CIE 1960 UCS Colourspace
>>> colour.XYZ_to_UCS([0.07049534, 0.10080000, 0.09558313])
array([ 0.04699689,  0.1008    ,  0.1637439 ])
CIE 1964 U*V*W* Colourspace
>>> colour.XYZ_to_UVW([7.04953400, 10.08000000, 9.55831300])
array([-28.05797333,  -0.88194493,  37.00411491])
Hunter L,a,b Colour Scale
>>> colour.XYZ_to_Hunter_Lab([7.049534, 10.080000, 9.558313])
array([ 31.74901573, -15.11462629,  -2.78660758])
Hunter Rd,a,b Colour Scale
>>> colour.XYZ_to_Hunter_Rdab([7.049534, 10.080000, 9.558313])
array([ 10.08      , -18.67653764,  -3.44329925])
CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> Y_b = 20.0
>>> surround = colour.CIECAM02_VIEWING_CONDITIONS['Average']
>>> specification = colour.XYZ_to_CIECAM02(
        XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> colour.JMh_CIECAM02_to_CAM02UCS(JMh)
array([ 54.90433134,  -0.08442362,  -0.06848314])
CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)
>>> XYZ = np.array([19.01, 20.00, 21.78])
>>> XYZ_w = np.array([95.05, 100.00, 108.88])
>>> L_A = 318.31
>>> Y_b = 20.0
>>> surround = colour.CAM16_VIEWING_CONDITIONS['Average']
>>> specification = colour.XYZ_to_CAM16(
        XYZ, XYZ_w, L_A, Y_b, surround)
>>> JMh = (specification.J, specification.M, specification.h)
>>> colour.JMh_CAM16_to_CAM16UCS(JMh)
array([ 54.90445024,  -0.08562125,  -0.0646796 ])
IPT Colourspace
>>> colour.XYZ_to_IPT([0.07049534, 0.10080000, 0.09558313])
array([ 0.36571124, -0.11114798,  0.01594746])
hdr-CIELAB Colourspace
>>> colour.XYZ_to_hdr_CIELab([0.07049534, 0.10080000, 0.09558313])
array([ 24.90206646, -46.83127607, -10.14274843])
hdr-IPT Colourspace
>>> colour.XYZ_to_hdr_IPT([0.07049534, 0.10080000, 0.09558313])
array([ 25.18261761, -22.62111297,   3.18511729])
OSA UCS Colourspace
>>> colour.XYZ_to_OSA_UCS([7.04953400, 10.08000000, 9.55831300])
array([-4.4900683 ,  0.70305936,  3.03463664])
RGB Colourspace and Transformations
>>> XYZ = [0.07049534, 0.10080000, 0.09558313]
>>> illuminant_XYZ = [0.34570, 0.35850]
>>> illuminant_RGB = [0.31270, 0.32900]
>>> chromatic_adaptation_transform = 'Bradford'
>>> XYZ_to_RGB_matrix = [
         [3.24062548, -1.53720797, -0.49862860],
         [-0.96893071, 1.87575606, 0.04151752],
         [0.05571012, -0.20402105, 1.05699594]]
>>> colour.XYZ_to_RGB(
         XYZ,
         illuminant_XYZ,
         illuminant_RGB,
         XYZ_to_RGB_matrix,
         chromatic_adaptation_transform)
array([ 0.01100154,  0.12735048,  0.11632713])
RGB Colourspace Derivation
>>> p = [0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700]
>>> w = [0.32168, 0.33767]
>>> colour.normalised_primary_matrix(p, w)
array([[  9.52552396e-01,   0.00000000e+00,   9.36786317e-05],
       [  3.43966450e-01,   7.28166097e-01,  -7.21325464e-02],
       [  0.00000000e+00,   0.00000000e+00,   1.00882518e+00]])
Y’CbCr Colour Encoding
>>> colour.RGB_to_YCbCr([1.0, 1.0, 1.0])
array([ 0.92156863,  0.50196078,  0.50196078])
YCoCg Colour Encoding
>>> colour.RGB_to_YCoCg([0.75, 0.75, 0.0])
array([ 0.5625,  0.375 ,  0.1875])
ICTCP Colour Encoding
>>> colour.RGB_to_ICTCP([0.35181454, 0.26934757, 0.21288023])
array([ 0.09554079, -0.00890639,  0.01389286])
JzAzBz Colourspace
>>> colour.XYZ_to_JzAzBz(XYZ)
array([ 0.00357804, -0.00295507,  0.00038998])
HSV Colourspace
>>> colour.RGB_to_HSV([0.49019608, 0.98039216, 0.25098039])
array([ 0.27867383,  0.744     ,  0.98039216])
Prismatic Colourspace
>>> colour.RGB_to_Prismatic([0.25, 0.50, 0.75])
array([ 0.75      ,  0.16666667,  0.33333333,  0.5       ])

RGB Colourspaces

>>> sorted(colour.RGB_COLOURSPACES.keys())
[u'ACES2065-1',
 u'ACEScc',
 u'ACEScct',
 u'ACEScg',
 u'ACESproxy',
 u'ALEXA Wide Gamut',
 u'Adobe RGB (1998)',
 u'Adobe Wide Gamut RGB',
 u'Apple RGB',
 u'Best RGB',
 u'Beta RGB',
 u'CIE RGB',
 u'Cinema Gamut',
 u'ColorMatch RGB',
 u'DCI-P3',
 u'DCI-P3+',
 u'DRAGONcolor',
 u'DRAGONcolor2',
 u'Don RGB 4',
 u'ECI RGB v2',
 u'ERIMM RGB',
 u'Ekta Space PS 5',
 u'ITU-R BT.2020',
 u'ITU-R BT.470 - 525',
 u'ITU-R BT.470 - 625',
 u'ITU-R BT.709',
 u'Max RGB',
 u'NTSC',
 u'Pal/Secam',
 u'ProPhoto RGB',
 u'Protune Native',
 u'REDWideGamutRGB',
 u'REDcolor',
 u'REDcolor2',
 u'REDcolor3',
 u'REDcolor4',
 u'RIMM RGB',
 u'ROMM RGB',
 u'Russell RGB',
 u'S-Gamut',
 u'S-Gamut3',
 u'S-Gamut3.Cine',
 u'SMPTE 240M',
 u'V-Gamut',
 u'Xtreme RGB',
 'aces',
 'adobe1998',
 'prophoto',
 u'sRGB']

OETFs

>>> sorted(colour.OETFS.keys())
['ARIB STD-B67',
 'DCI-P3',
 'DICOM GSDF',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ITU-R BT.601',
 'ITU-R BT.709',
 'ProPhoto RGB',
 'RIMM RGB',
 'ROMM RGB',
 'SMPTE 240M',
 'ST 2084',
 'sRGB']

EOTFs

>>> sorted(colour.EOTFS.keys())
['DCI-P3',
 'DICOM GSDF',
 'ITU-R BT.1886',
 'ITU-R BT.2020',
 'ITU-R BT.2100 HLG',
 'ITU-R BT.2100 PQ',
 'ProPhoto RGB',
 'RIMM RGB',
 'ROMM RGB',
 'SMPTE 240M',
 'ST 2084']

OOTFs

>>> sorted(colour.OOTFS.keys())
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']

Log Encoding / Decoding Curves

>>> sorted(colour.LOG_ENCODING_CURVES.keys())
['ACEScc',
 'ACEScct',
 'ACESproxy',
 'ALEXA Log C',
 'Canon Log',
 'Canon Log 2',
 'Canon Log 3',
 'Cineon',
 'ERIMM RGB',
 'Log3G10',
 'Log3G12',
 'PLog',
 'Panalog',
 'Protune',
 'REDLog',
 'REDLogFilm',
 'S-Log',
 'S-Log2',
 'S-Log3',
 'V-Log',
 'ViperLog']

Chromatic Adaptation Models

>>> XYZ = [0.07049534, 0.10080000, 0.09558313]
>>> XYZ_w = [1.09846607, 1.00000000, 0.35582280]
>>> XYZ_wr = [0.95042855, 1.00000000, 1.08890037]
>>> colour.chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
array([ 0.08397461,  0.11413219,  0.28625545])

Colour Appearance Models

>>> XYZ = [19.01, 20.00, 21.78]
>>> XYZ_w = [95.05, 100.00, 108.88]
>>> L_A = 318.31
>>> Y_b = 20.0
>>> colour.XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b)
CIECAM02_Specification(J=41.731091132513917, C=0.10470775717103062, h=219.04843265831178, s=2.3603053739196032, Q=195.37132596607671, M=0.10884217566914849, H=278.06073585667758, HC=None)

Colour Difference

>>> Lab_1 = [100.00000000, 21.57210357, 272.22819350]
>>> Lab_2 = [100.00000000, 426.67945353, 72.39590835]
>>> colour.delta_E(Lab_1, Lab_2)
94.035649026659485
>>> sorted(colour.DELTA_E_METHODS.keys())
['CAM02-LCD',
 'CAM02-SCD',
 'CAM02-UCS',
 'CAM16-LCD',
 'CAM16-SCD',
 'CAM16-UCS',
 'CIE 1976',
 'CIE 1994',
 'CIE 2000',
 'CMC',
 'DIN99',
 'cie1976',
 'cie1994',
 'cie2000']

Colour Correction

>>> import numpy as np
>>> RGB = [0.17224810, 0.09170660, 0.06416938]
>>> M_T = np.random.random((24, 3))
>>> M_R = M_T + (np.random.random((24, 3)) - 0.5) * 0.5
>>> colour.colour_correction(RGB, M_T, M_R)
array([ 0.15205429,  0.08974029,  0.04141435])
>>> sorted(colour.COLOUR_CORRECTION_METHODS.keys())
[u'Cheung 2004', u'Finlayson 2015', u'Vandermonde']

Colour Notation Systems

Munsell Value
>>> colour.munsell_value(10.1488096782)
3.7462971142584354
>>> sorted(colour.MUNSELL_VALUE_METHODS.keys())
[u'ASTM D1535-08',
 u'Ladd 1955',
 u'McCamy 1987',
 u'Moon 1943',
 u'Munsell 1933',
 u'Priest 1920',
 u'Saunderson 1944',
 u'astm2008']
Munsell Colour
>>> colour.xyY_to_munsell_colour([0.38736945, 0.35751656, 0.59362000])
u'4.2YR 8.1/5.3'
>>> colour.munsell_colour_to_xyY('4.2YR 8.1/5.3')
array([ 0.38736945,  0.35751656,  0.59362   ])

Colour Blindness

>>> import colour
>>> cmfs = colour.LMS_CMFS['Stockman & Sharpe 2 Degree Cone Fundamentals']
>>> colour.anomalous_trichromacy_cmfs_Machado2009(cmfs, np.array([15, 0, 0]))[450]
array([ 0.08912884,  0.0870524 ,  0.955393  ])
>>> primaries = colour.DISPLAYS_RGB_PRIMARIES['Apple Studio Display']
>>> d_LMS = (15, 0, 0)
>>> colour.anomalous_trichromacy_matrix_Machado2009(cmfs, primaries, d_LMS)
array([[-0.27774652,  2.65150084, -1.37375432],
       [ 0.27189369,  0.20047862,  0.52762768],
       [ 0.00644047,  0.25921579,  0.73434374]])

Optical Phenomena

>>> colour.rayleigh_scattering_spd()
SpectralPowerDistribution([[  3.60000000e+02,   5.99101337e-01],
                           [  3.61000000e+02,   5.92170690e-01],
                           [  3.62000000e+02,   5.85341006e-01],
                           ...
                           [  7.78000000e+02,   2.55208377e-02],
                           [  7.79000000e+02,   2.53887969e-02],
                           [  7.80000000e+02,   2.52576106e-02]],
                          interpolator=SpragueInterpolator,
                          interpolator_args={},
                          extrapolator=Extrapolator,
                          extrapolator_args={u'right': None, u'method': u'Constant', u'left': None})

Light Quality

Colour Rendering Index
>>> colour.colour_quality_scale(colour.ILLUMINANTS_SPDS['F2'])
64.686416902221609
Colour Quality Scale
>>> colour.colour_rendering_index(colour.ILLUMINANTS_SPDS['F2'])
64.151520202968015

Reflectance Recovery

>>> colour.XYZ_to_spectral([0.07049534, 0.10080000, 0.09558313])
SpectralPowerDistribution([[  3.60000000e+02,   7.96361498e-04],
                           [  3.65000000e+02,   7.96489667e-04],
                           [  3.70000000e+02,   7.96543669e-04],
                           ...
                           [  8.20000000e+02,   1.71014294e-04],
                           [  8.25000000e+02,   1.71621924e-04],
                           [  8.30000000e+02,   1.72026883e-04]],
                          interpolator=SpragueInterpolator,
                          interpolator_args={},
                          extrapolator=Extrapolator,
                          extrapolator_args={u'right': None, u'method': u'Constant', u'left': None})
>>> sorted(colour.REFLECTANCE_RECOVERY_METHODS.keys())
['Meng 2015', 'Smits 1999']

Correlated Colour Temperature Computation Methods

>>> colour.uv_to_CCT([0.1978, 0.3122])
array([  6.50751282e+03,   3.22335875e-03])
>>> sorted(colour.UV_TO_CCT_METHODS.keys())
[u'Ohno 2013', u'Robertson 1968', u'ohno2013', u'robertson1968']
>>> sorted(colour.UV_TO_CCT_METHODS.keys())
[u'Krystek 1985',
 u'Ohno 2013',
 u'Robertson 1968',
 u'ohno2013',
 u'robertson1968']
 >>> sorted(colour.XY_TO_CCT_METHODS.keys())
 [u'Hernandez 1999', u'McCamy 1992', u'hernandez1999', u'mccamy1992']
 >>> sorted(colour.CCT_TO_XY_METHODS.keys())
 [u'CIE Illuminant D Series', u'Kang 2002', su'cie_d', u'kang2002']

Volume

>>> colour.RGB_colourspace_volume_MonteCarlo(colour.sRGB_COLOURSPACE)
857011.5

IO

Look Up Table (LUT) Data
>>> LUT = colour.read_LUT('ACES_Proxy_10_to_ACES.cube')
>>> print(LUT)
LUT2D - ACES Proxy 10 to ACES
-----------------------------
Dimensions : 2
Domain     : [[0 0 0]
              [1 1 1]]
Size       : (32, 3)

>>> RGB = [0.17224810, 0.09170660, 0.06416938]
>>> LUT.apply(RGB)
array([ 0.00575674,  0.00181493,  0.00121419])

Plotting

Most of the objects are available from the colour.plotting namespace:

>>> from colour.plotting import *
>>> colour_style()
Visible Spectrum
>>> visible_spectrum_plot('CIE 1931 2 Degree Standard Observer')
_images/Examples_Plotting_Visible_Spectrum.png
Spectral Power Distribution
>>> single_illuminant_spd_plot('F1')
_images/Examples_Plotting_Illuminant_F1_SPD.png
Blackbody
>>> blackbody_spds = [
...     colour.blackbody_spd(i, colour.SpectralShape(0, 10000, 10))
...     for i in range(1000, 15000, 1000)
... ]
>>> multi_spd_plot(
...     blackbody_spds,
...     y_label='W / (sr m$^2$) / m',
...     use_spds_colours=True,
...     normalise_spds_colours=True,
...     legend_location='upper right',
...     bounding_box=(0, 1250, 0, 2.5e15))
_images/Examples_Plotting_Blackbodies.png
Colour Matching Functions
>>> single_cmfs_plot(
...     'Stockman & Sharpe 2 Degree Cone Fundamentals',
...     y_label='Sensitivity',
...     bounding_box=(390, 870, 0, 1.1))
_images/Examples_Plotting_Cone_Fundamentals.png
Luminous Efficiency
>>> mesopic_luminous_efficiency_function = (
...     colour.mesopic_luminous_efficiency_function(0.2))
>>> multi_spd_plot(
...     (mesopic_luminous_efficiency_function,
...      colour.PHOTOPIC_LEFS['CIE 1924 Photopic Standard Observer'],
...      colour.SCOTOPIC_LEFS['CIE 1951 Scotopic Standard Observer']),
...     y_label='Luminous Efficiency',
...     legend_location='upper right',
...     y_tighten=True,
...     margins=(0, 0, 0, .1))
_images/Examples_Plotting_Luminous_Efficiency.png
Colour Checker
>>> from colour.characterisation.dataset.colour_checkers.spds import (
...     COLOURCHECKER_INDEXES_TO_NAMES_MAPPING)
>>> multi_spd_plot(
...     [
...         colour.COLOURCHECKERS_SPDS['BabelColor Average'][value]
...         for key, value in sorted(
...             COLOURCHECKER_INDEXES_TO_NAMES_MAPPING.items())
...     ],
...     use_spds_colours=True,
...     title=('BabelColor Average - '
...            'Spectral Power Distributions'))
_images/Examples_Plotting_BabelColor_Average.png
>>> colour_checker_plot('ColorChecker 2005', text_parameters={'visible': False})
_images/Examples_Plotting_ColorChecker_2005.png
Chromaticities Prediction
>>> corresponding_chromaticities_prediction_plot(2, 'Von Kries', 'Bianco')
_images/Examples_Plotting_Chromaticities_Prediction.png
Colour Temperature
>>> planckian_locus_chromaticity_diagram_plot_CIE1960UCS(['A', 'B', 'C'])
_images/Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png
Chromaticities
>>> import numpy as np
>>> RGB = np.random.random((32, 32, 3))
>>> RGB_chromaticity_coordinates_chromaticity_diagram_plot_CIE1931(
...     RGB, 'ITU-R BT.709', colourspaces=['ACEScg', 'S-Gamut'], show_pointer_gamut=True)
_images/Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png
Colour Rendering Index
>>> single_spd_colour_rendering_index_bars_plot(
...     colour.ILLUMINANTS_SPDS['F2'])
_images/Examples_Plotting_CRI.png

Contributing

If you would like to contribute to Colour, please refer to the following Contributing guide.

Changes

The changes are viewable on the Releases page.

Bibliography

The bibliography is available on the Bibliography page.

It is also viewable directly from the repository in BibTeX format.

See Also

Here is a list of notable colour science packages sorted by languages:

Python

.NET

Julia

Matlab & Octave

About

Colour by Colour Developers - 2013-2018
Copyright © 2013-2018 – Colour Developers – colour-science@googlegroups.com
This software is released under terms of New BSD License: http://opensource.org/licenses/BSD-3-Clause