Colour is an open-source Python package providing a comprehensive number of algorithms and datasets for colour science.
It is freely available under the New BSD License terms.
Colour is an affiliated project of NumFOCUS, a 501(c)(3) nonprofit in the United States.
Table of Contents
- 1 Draft Release Notes
- 2 Sponsors
- 3 Features
- 4 Installation
- 5 Documentation
- 5.1 Tutorial
- 5.2 How-To Guide
- 5.3 API Reference
- 5.4 Jupyter Notebooks
- 5.5 Examples
- 5.5.1 Automatic Colour Conversion Graph -
colour.graph
- 5.5.2 Chromatic Adaptation -
colour.adaptation
- 5.5.3 Algebra -
colour.algebra
- 5.5.4 Colour Appearance Models -
colour.appearance
- 5.5.5 Colour Blindness -
colour.blindness
- 5.5.6 Colour Correction -
colour characterisation
- 5.5.7 Colorimetry -
colour.colorimetry
- 5.5.8 Contrast Sensitivity Function -
colour.contrast
- 5.5.9 Colour Difference -
colour.difference
- 5.5.10 IO -
colour.io
- 5.5.11 Colour Models -
colour.models
- 5.5.12 Colour Notation Systems -
colour.notation
- 5.5.13 Optical Phenomena -
colour.phenomena
- 5.5.14 Light Quality -
colour.quality
- 5.5.15 Spectral Up-sampling & Reflectance Recovery -
colour.recovery
- 5.5.16 Correlated Colour Temperature Computation Methods -
colour.temperature
- 5.5.17 Colour Volume -
colour.volume
- 5.5.18 Geometry Primitives Generation -
colour.geometry
- 5.5.19 Plotting -
colour.plotting
- 5.5.1 Automatic Colour Conversion Graph -
- 6 Contributing
- 7 Changes
- 8 Bibliography
- 9 See Also
- 10 Code of Conduct
- 11 Thank You!
- 12 About
The draft release notes of the develop branch are available at this url.
We are grateful 💖 for the support of our sponsors. If you'd like to join them, please consider becoming a sponsor on OpenCollective.
Colour features a rich dataset and collection of objects, please see the features page for more information.
Colour can be easily installed from the Python Package Index by issuing this command in a shell:
$ pip install colour-science
Colour is also available for Anaconda from Continuum Analytics via conda-forge:
$ conda install -c conda-forge colour-science
The detailed installation procedure is described in the Installation Guide.
The static tutorial provides an introduction to Colour. An interactive version is available via Google Colab.
The How-To guide for Colour shows various techniques to solve specific problems and highlights some interesting use cases.
The main technical reference for Colour and its API is the Colour Manual.
Jupyter Notebooks are available and designed to provide an historical perspective of colour science via Colour usage.
Most of the objects are available from the colour
namespace:
>>> import colour
Starting with version 0.3.14, Colour implements an automatic colour conversion graph enabling easier colour conversions.
>>> sd = colour.COLOURCHECKER_SDS['ColorChecker N Ohta']['dark skin']
>>> convert(sd, 'Spectral Distribution', 'sRGB', verbose={'mode': 'Short'})
=============================================================================== * * * [ Conversion Path ] * * * * "sd_to_XYZ" --> "XYZ_to_sRGB" * * * =============================================================================== array([ 0.45675795, 0.30986982, 0.24861924])
>>> illuminant = colour.ILLUMINANT_SDS['FL2']
>>> convert(sd, 'Spectral Distribution', 'sRGB', sd_to_XYZ={'illuminant': illuminant})
array([ 0.47924575, 0.31676968, 0.17362725])
>>> XYZ = [0.20654008, 0.12197225, 0.05136952]
>>> D65 = colour.ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['D65']
>>> A = colour.ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['A']
>>> colour.chromatic_adaptation(
... XYZ, colour.xy_to_XYZ(D65), colour.xy_to_XYZ(A))
array([ 0.2533053 , 0.13765138, 0.01543307])
>>> sorted(colour.CHROMATIC_ADAPTATION_METHODS.keys())
['CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries']
>>> 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])
>>> 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])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
>>> 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=34.434525727858997, C=67.365010921125915, h=22.279164147957076, s=62.814855853327131, Q=177.47124941102123, M=70.024939419291385, H=2.689608534423904, HC=None)
>>> 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.DISPLAY_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]])
>>> 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())
['Cheung 2004', 'Finlayson 2015', 'Vandermonde']
>>> colour.sd_to_XYZ(colour.LIGHT_SOURCE_SDS['Neodimium Incandescent'])
array([ 36.94726204, 32.62076174, 13.0143849 ])
>>> sorted(colour.SPECTRAL_TO_XYZ_METHODS.keys())
['ASTM E308', 'Integration', 'astm2015']
>>> msds = 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_sds_to_XYZ(msds, cmfs, illuminant, method='Integration',
... shape=colour.SpectralShape(400, 700, 60)))
[[[ 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]
[ 48.58635054 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())
['ASTM E308', 'Integration', 'astm2015']
>>> colour.sd_blackbody(5000)
SpectralDistribution([[ 3.60000000e 02, 6.65427827e 12],
[ 3.61000000e 02, 6.70960528e 12],
[ 3.62000000e 02, 6.76482512e 12],
...
[ 7.78000000e 02, 1.06068004e 13],
[ 7.79500000e 02, 1.05903327e 13],
[ 7.80000000e 02, 1.05738520e 13]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={'right': None, 'method': 'Constant', 'left': None})
>>> xy = [0.54369557, 0.32107944]
>>> xy_n = [0.31270000, 0.32950000]
>>> colour.dominant_wavelength(xy, xy_n)
(array(616.0),
array([ 0.68354746, 0.31628409]),
array([ 0.68354746, 0.31628409]))
>>> colour.lightness(12.19722535)
41.527875844653451
>>> sorted(colour.LIGHTNESS_METHODS.keys())
['CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Glasser 1958',
'Lstar1976',
'Wyszecki 1963']
>>> colour.luminance(41.52787585)
12.197225353400775
>>> sorted(colour.LUMINANCE_METHODS.keys())
['ASTM D1535',
'CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Newhall 1943',
'astm2008',
'cie1976']
>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> XYZ_0 = [94.80966767, 100.00000000, 107.30513595]
>>> colour.whiteness(XYZ, XYZ_0)
array([ 93.756 , -1.33000001])
>>> sorted(colour.WHITENESS_METHODS.keys())
['ASTM E313',
'Berger 1959',
'CIE 2004',
'Ganz 1979',
'Stensby 1968',
'Taube 1960',
'cie2004']
>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> colour.yellowness(XYZ)
11.065000000000003
>>> sorted(colour.YELLOWNESS_METHODS.keys())
['ASTM D1925', 'ASTM E313']
>>> sd = colour.LIGHT_SOURCE_SDS['Neodimium Incandescent']
>>> colour.luminous_flux(sd)
23807.655527367202
>>> sd = colour.LIGHT_SOURCE_SDS['Neodimium Incandescent']
>>> colour.luminous_efficiency(sd)
0.19943935624521045
>>> sd = colour.LIGHT_SOURCE_SDS['Neodimium Incandescent']
>>> colour.luminous_efficacy(sd)
136.21708031547874
>>> colour.contrast_sensitivity_function(u=4, X_0=60, E=65)
358.51180789884984
>>> sorted(colour.CONTRAST_SENSITIVITY_METHODS.keys())
['Barten 1999']
>>> Lab_1 = [100.00000000, 21.57210357, 272.22819350]
>>> Lab_2 = [100.00000000, 426.67945353, 72.39590835]
>>> colour.delta_E(Lab_1, Lab_2)
94.035649026686385
>>> 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']
>>> RGB = colour.read_image('Ishihara_Colour_Blindness_Test_Plate_3.png')
>>> RGB.shape
(276, 281, 3)
>>> LUT = colour.read_LUT('ACES_Proxy_10_to_ACES.cube')
>>> print(LUT)
LUT3x1D - 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])
>>> colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])
array([ 0.54369557, 0.32107944, 0.12197225])
>>> colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 52.63858304, 26.92317922])
>>> colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 96.83626054, 17.75210149])
>>> colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])
array([ 0.13769339, 0.12197225, 0.1053731 ])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_UVW(XYZ)
array([ 94.55035725, 11.55536523, 40.54757405])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_Hunter_Lab(XYZ)
array([ 34.92452577, 47.06189858, 14.38615107])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_Hunter_Rdab(XYZ)
array([ 12.197225 , 57.12537874, 17.46241341])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> XYZ_w = [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([ 47.16899898, 38.72623785, 15.8663383 ])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> XYZ_w = [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([ 46.55542238, 40.22460974, 14.25288392]
>>> colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 0.38426191, 0.38487306, 0.18886838])
>>> Lab = [41.52787529, 52.63858304, 26.92317922]
>>> colour.Lab_to_DIN99(Lab)
array([ 53.22821988, 28.41634656, 3.89839552])
>>> colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])
array([ 51.87002062, 60.4763385 , 32.14551912])
>>> colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 25.18261761, -22.62111297, 3.18511729])
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_OSA_UCS(XYZ)
array([-3.0049979 , 2.99713697, -9.66784231])
>>> colour.XYZ_to_JzAzBz([0.20654008, 0.12197225, 0.05136952])
array([ 0.00535048, 0.00924302, 0.00526007])
>>> colour.RGB_to_YCbCr([1.0, 1.0, 1.0])
array([ 0.92156863, 0.50196078, 0.50196078])
>>> colour.RGB_to_YCoCg([0.75, 0.75, 0.0])
array([ 0.5625, 0.375 , 0.1875])
>>> colour.RGB_to_ICTCP([0.45620519, 0.03081071, 0.04091952])
array([ 0.07351364, 0.00475253, 0.09351596])
>>> colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])
array([ 0.99603944, 0.93246304, 0.45620519])
>>> colour.RGB_to_Prismatic([0.25, 0.50, 0.75])
array([ 0.75 , 0.16666667, 0.33333333, 0.5 ])
>>> XYZ = [0.21638819, 0.12570000, 0.03847493]
>>> 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, 1.05699863]]
>>> colour.XYZ_to_RGB(
XYZ,
illuminant_XYZ,
illuminant_RGB,
XYZ_to_RGB_matrix,
chromatic_adaptation_transform)
array([ 0.45595571, 0.03039702, 0.04087245])
>>> 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]])
>>> sorted(colour.RGB_COLOURSPACES.keys())
['ACES2065-1',
'ACEScc',
'ACEScct',
'ACEScg',
'ACESproxy',
'ALEXA Wide Gamut',
'Adobe RGB (1998)',
'Adobe Wide Gamut RGB',
'Apple RGB',
'Best RGB',
'Beta RGB',
'CIE RGB',
'Cinema Gamut',
'ColorMatch RGB',
'DCDM XYZ',
'DCI-P3',
'DCI-P3 ',
'Display P3',
'DJI D-Gamut',
'DRAGONcolor',
'DRAGONcolor2',
'Don RGB 4',
'ECI RGB v2',
'ERIMM RGB',
'Ekta Space PS 5',
'F-Gamut',
'FilmLight E-Gamut',
'ITU-R BT.2020',
'ITU-R BT.470 - 525',
'ITU-R BT.470 - 625',
'ITU-R BT.709',
'Max RGB',
'NTSC (1953)',
'NTSC (1987)',
'P3-D65',
'Pal/Secam',
'ProPhoto RGB',
'Protune Native',
'REDWideGamutRGB',
'REDcolor',
'REDcolor2',
'REDcolor3',
'REDcolor4',
'RIMM RGB',
'ROMM RGB',
'Russell RGB',
'S-Gamut',
'S-Gamut3',
'S-Gamut3.Cine',
'SMPTE 240M',
'SMPTE C',
'Sharp RGB',
'V-Gamut',
'Xtreme RGB',
'aces',
'adobe1998',
'prophoto',
'sRGB']
>>> sorted(colour.OETFS.keys())
['ARIB STD-B67',
'ITU-R BT.2020',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ITU-R BT.601',
'ITU-R BT.709',
'SMPTE 240M']
>>> sorted(colour.OETF_INVERSES.keys())
['ARIB STD-B67',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ITU-R BT.601',
'ITU-R BT.709']
>>> sorted(colour.EOTFS.keys())
['DCDM',
'DICOM GSDF',
'ITU-R BT.1886',
'ITU-R BT.2020',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'SMPTE 240M',
'ST 2084',
'sRGB']
>>> sorted(colour.EOTF_INVERSES.keys())
['DCDM',
'DICOM GSDF',
'ITU-R BT.1886',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ST 2084',
'sRGB']
>>> sorted(colour.OOTFS.keys())
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
>>> sorted(colour.OOTF_INVERSES.keys())
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
>>> sorted(colour.LOG_ENCODINGS.keys())
['ACEScc',
'ACEScct',
'ACESproxy',
'ALEXA Log C',
'Canon Log',
'Canon Log 2',
'Canon Log 3',
'Cineon',
'D-Log',
'ERIMM RGB',
'F-Log',
'Filmic Pro 6',
'Log3G10',
'Log3G12',
'PLog',
'Panalog',
'Protune',
'REDLog',
'REDLogFilm',
'S-Log',
'S-Log2',
'S-Log3',
'T-Log',
'V-Log',
'ViperLog']
>>> sorted(colour.CCTF_ENCODINGS.keys())
['ACEScc',
'ACEScct',
'ACESproxy',
'ALEXA Log C',
'ARIB STD-B67',
'Canon Log',
'Canon Log 2',
'Canon Log 3',
'Cineon',
'D-Log',
'DCDM',
'DICOM GSDF',
'ERIMM RGB',
'F-Log',
'Filmic Pro 6',
'Gamma 2.2',
'Gamma 2.4',
'Gamma 2.6',
'ITU-R BT.1886',
'ITU-R BT.2020',
'ITU-R BT.2100 HLG',
'ITU-R BT.2100 PQ',
'ITU-R BT.601',
'ITU-R BT.709',
'Log3G10',
'Log3G12',
'PLog',
'Panalog',
'ProPhoto RGB',
'Protune',
'REDLog',
'REDLogFilm',
'RIMM RGB',
'ROMM RGB',
'S-Log',
'S-Log2',
'S-Log3',
'SMPTE 240M',
'ST 2084',
'T-Log',
'V-Log',
'ViperLog',
'sRGB']
>>> colour.munsell_value(12.23634268)
4.0824437076525664
>>> sorted(colour.MUNSELL_VALUE_METHODS.keys())
['ASTM D1535',
'Ladd 1955',
'McCamy 1987',
'Moon 1943',
'Munsell 1933',
'Priest 1920',
'Saunderson 1944',
'astm2008']
>>> colour.xyY_to_munsell_colour([0.38736945, 0.35751656, 0.59362000])
'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.rayleigh_scattering_sd()
SpectralDistribution([[ 3.60000000e 02, 5.99101337e-01],
[ 3.61000000e 02, 5.92170690e-01],
[ 3.62000000e 02, 5.85341006e-01],
...
[ 7.78000000e 02, 2.55208377e-02],
[ 7.79500000e 02, 2.53887969e-02],
[ 7.80000000e 02, 2.52576106e-02]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={'right': None, 'method': 'Constant', 'left': None})
>>> colour.colour_quality_scale(colour.ILLUMINANT_SDS['FL2'])
64.017283509280588
>>> colour.COLOUR_QUALITY_SCALE_METHODS
('NIST CQS 7.4', 'NIST CQS 9.0')
>>> colour.colour_rendering_index(colour.ILLUMINANT_SDS['FL2'])
64.151520202968015
>>> colour.spectral_similarity_index(colour.ILLUMINANT_SDS['C'], colour.ILLUMINANT_SDS['D65'])
94.0
>>> colour.XYZ_to_sd([0.20654008, 0.12197225, 0.05136952])
SpectralDistribution([[ 3.60000000e 02, 7.73462151e-02],
[ 3.65000000e 02, 7.73632975e-02],
[ 3.70000000e 02, 7.74299705e-02],
...
[ 8.20000000e 02, 3.93126353e-01],
[ 8.25000000e 02, 3.93158148e-01],
[ 8.30000000e 02, 3.93163548e-01]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={'right': None, 'method': 'Constant', 'left': None})
>>> sorted(colour.REFLECTANCE_RECOVERY_METHODS.keys())
['Meng 2015', 'Smits 1999']
>>> colour.uv_to_CCT([0.1978, 0.3122])
array([ 6.50751282e 03, 3.22335875e-03])
>>> sorted(colour.UV_TO_CCT_METHODS.keys())
['Krystek 1985', 'Ohno 2013', 'Robertson 1968', 'ohno2013', 'robertson1968']
>>> sorted(colour.XY_TO_CCT_METHODS.keys())
['CIE Illuminant D Series', 'Hernandez 1999', 'Kang 2002', 'McCamy 1992', 'daylight', 'hernandez1999', 'kang2002', 'mccamy1992']
>>> colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE['sRGB'])
821958.30000000005
>>> colour.primitive('Grid')
(array([ ([-0.5, 0.5, 0. ], [ 0., 1.], [ 0., 0., 1.], [ 0., 1., 0., 1.]),
([ 0.5, 0.5, 0. ], [ 1., 1.], [ 0., 0., 1.], [ 1., 1., 0., 1.]),
([-0.5, -0.5, 0. ], [ 0., 0.], [ 0., 0., 1.], [ 0., 0., 0., 1.]),
([ 0.5, -0.5, 0. ], [ 1., 0.], [ 0., 0., 1.], [ 1., 0., 0., 1.])],
dtype=[('position', '<f4', (3,)), ('uv', '<f4', (2,)), ('normal', '<f4', (3,)), ('colour', '<f4', (4,))]), array([[0, 2, 1],
[2, 3, 1]], dtype=uint32), array([[0, 2],
[2, 3],
[3, 1],
[1, 0]], dtype=uint32))
>>> sorted(colour.PRIMITIVE_METHODS.keys())
['Cube', 'Grid']
>>> colour.primitive_vertices('Quad MPL')
array([[ 0., 0., 0.],
[ 1., 0., 0.],
[ 1., 1., 0.],
[ 0., 1., 0.]])
>>> sorted(colour.PRIMITIVE_VERTICES_METHODS.keys())
['Cube MPL', 'Grid MPL', 'Quad MPL', 'Sphere']
Most of the objects are available from the colour.plotting
namespace:
>>> from colour.plotting import *
>>> colour_style()
>>> plot_visible_spectrum('CIE 1931 2 Degree Standard Observer')
>>> plot_single_illuminant_sd('FL1')
>>> blackbody_sds = [
... colour.sd_blackbody(i, colour.SpectralShape(0, 10000, 10))
... for i in range(1000, 15000, 1000)
... ]
>>> plot_multi_sds(
... blackbody_sds,
... y_label='W / (sr m$^2$) / m',
... use_sds_colours=True,
... normalise_sds_colours=True,
... legend_location='upper right',
... bounding_box=(0, 1250, 0, 2.5e15))
>>> plot_single_cmfs(
... 'Stockman & Sharpe 2 Degree Cone Fundamentals',
... y_label='Sensitivity',
... bounding_box=(390, 870, 0, 1.1))
>>> sd_mesopic_luminous_efficiency_function = (
... colour.sd_mesopic_luminous_efficiency_function(0.2))
>>> plot_multi_sds(
... (sd_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))
>>> from colour.characterisation.dataset.colour_checkers.sds import (
... COLOURCHECKER_INDEXES_TO_NAMES_MAPPING)
>>> plot_multi_sds(
... [
... colour.COLOURCHECKER_SDS['BabelColor Average'][value]
... for key, value in sorted(
... COLOURCHECKER_INDEXES_TO_NAMES_MAPPING.items())
... ],
... use_sds_colours=True,
... title=('BabelColor Average - '
... 'Spectral Distributions'))
>>> plot_single_colour_checker('ColorChecker 2005', text_parameters={'visible': False})
>>> plot_corresponding_chromaticities_prediction(2, 'Von Kries', 'Bianco')
>>> plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(['A', 'B', 'C'])
>>> import numpy as np
>>> RGB = np.random.random((32, 32, 3))
>>> plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
... RGB, 'ITU-R BT.709', colourspaces=['ACEScg', 'S-Gamut', 'Pointer Gamut'])
>>> plot_single_sd_colour_rendering_index_bars(
... colour.ILLUMINANT_SDS['FL2'])
If you would like to contribute to Colour, please refer to the following Contributing guide.
The changes are viewable on the Releases page.
The bibliography is available on the Bibliography page.
It is also viewable directly from the repository in BibTeX format.
Here is a list of notable colour science packages sorted by languages:
Python
- Colorio by Schlömer, N.
- ColorPy by Kness, M.
- Colorspacious by Smith, N. J., et al.
- python-colormath by Taylor, G., et al.
Go
- go-colorful by Beyer, L., et al.
.NET
- Colourful by Pažourek, T., et al.
Julia
- Colors.jl by Holy, T., et al.
Matlab & Octave
- COLORLAB by Malo, J., et al.
- Psychtoolbox by Brainard, D., et al.
- The Munsell and Kubelka-Munk Toolbox by Centore, P.
The Code of Conduct, adapted from the Contributor Covenant 1.4, is available on the Code of Conduct page.