Colour Science for Python¶
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.
Colour Manual¶
Tutorial¶
Colour spreads over various domains of Colour Science, from colour models to optical phenomena, this tutorial does not give a complete overview of the API but is a good introduction to the main concepts.
Note
A directory with examples is available at this path in Colour installation: colour/examples. It can be explored directly on Github: https://github.com/colour-science/colour/tree/master/colour/examples
from colour.plotting import *
colour_style()
plot_visible_spectrum()
Overview¶
Colour is organised around various sub-packages:
- adaptation: Chromatic adaptation models and transformations.
- algebra: Algebra utilities.
- appearance: Colour appearance models.
- biochemistry: Biochemistry computations.
- blindness: Colour vision deficiency models.
- continuous: Base objects for continuous data representation.
- contrast: Objects for contrast sensitivity computation.
- characterisation: Colour fitting and camera characterisation.
- colorimetry: Core objects for colour computations.
- constants: CIE and CODATA constants.
- corresponding: Corresponding colour chromaticities computations.
- difference: Colour difference computations.
- examples: Examples for the sub-packages.
- io: Input / output objects for reading and writing data.
- models: Colour models.
- notation: Colour notation systems.
- phenomena: Computation of various optical phenomena.
- plotting: Diagrams, figures, etc…
- quality: Colour quality computation.
- recovery: Reflectance recovery.
- temperature: Colour temperature and correlated colour temperature computation.
- utilities: Various utilities and data structures.
- volume: Colourspace volumes computation and optimal colour stimuli.
Most of the public API is available from the root colour namespace:
import colour
print(colour.__all__[:5] + ['...'])
['domain_range_scale', 'get_domain_range_scale', 'set_domain_range_scale', 'CHROMATIC_ADAPTATION_METHODS', 'CHROMATIC_ADAPTATION_TRANSFORMS', '...']
The various sub-packages also expose their public API:
from pprint import pprint
import colour.plotting
for sub_package in ('adaptation', 'algebra', 'appearance', 'biochemistry',
'blindness', 'characterisation', 'colorimetry',
'constants', 'continuous', 'contrast', 'corresponding',
'difference', 'io', 'models', 'notation', 'phenomena',
'plotting', 'quality', 'recovery', 'temperature',
'utilities', 'volume'):
print(sub_package.title())
pprint(getattr(colour, sub_package).__all__[:5] + ['...'])
print('\n')
Adaptation
['CHROMATIC_ADAPTATION_TRANSFORMS',
'XYZ_SCALING_CAT',
'VON_KRIES_CAT',
'BRADFORD_CAT',
'SHARP_CAT',
'...']
Algebra
['cartesian_to_spherical',
'spherical_to_cartesian',
'cartesian_to_polar',
'polar_to_cartesian',
'cartesian_to_cylindrical',
'...']
Appearance
['Hunt_InductionFactors',
'HUNT_VIEWING_CONDITIONS',
'Hunt_Specification',
'XYZ_to_Hunt',
'ATD95_Specification',
'...']
Biochemistry
['reaction_rate_MichealisMenten',
'substrate_concentration_MichealisMenten',
'...']
Blindness
['CVD_MATRICES_MACHADO2010',
'anomalous_trichromacy_cmfs_Machado2009',
'anomalous_trichromacy_matrix_Machado2009',
'cvd_matrix_Machado2009',
'...']
Characterisation
['RGB_SpectralSensitivities',
'RGB_DisplayPrimaries',
'CAMERAS_RGB_SPECTRAL_SENSITIVITIES',
'COLOURCHECKERS',
'ColourChecker',
'...']
Colorimetry
['SpectralShape',
'DEFAULT_SPECTRAL_SHAPE',
'SpectralDistribution',
'MultiSpectralDistribution',
'sd_blackbody',
'...']
Constants
['K_M', 'KP_M', 'AVOGADRO_CONSTANT', 'BOLTZMANN_CONSTANT', 'LIGHT_SPEED', '...']
Continuous
['AbstractContinuousFunction', 'Signal', 'MultiSignal', '...']
Contrast
['optical_MTF_Barten1999',
'pupil_diameter_Barten1999',
'sigma_Barten1999',
'retinal_illuminance_Barten1999',
'maximum_angular_size_Barten1999',
'...']
Corresponding
['BRENEMAN_EXPERIMENTS',
'BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES',
'corresponding_chromaticities_prediction_CIE1994',
'corresponding_chromaticities_prediction_CMCCAT2000',
'corresponding_chromaticities_prediction_Fairchild1990',
'...']
Difference
['delta_E_CAM02LCD',
'delta_E_CAM02SCD',
'delta_E_CAM02UCS',
'delta_E_CAM16LCD',
'delta_E_CAM16SCD',
'...']
Io
['SpectralDistribution_IESTM2714',
'AbstractLUTSequenceOperator',
'LUT1D',
'LUT3x1D',
'LUT3D',
'...']
Models
['JMh_CIECAM02_to_CAM02LCD',
'CAM02LCD_to_JMh_CIECAM02',
'JMh_CIECAM02_to_CAM02SCD',
'CAM02SCD_to_JMh_CIECAM02',
'JMh_CIECAM02_to_CAM02UCS',
'...']
Notation
['MUNSELL_COLOURS_ALL',
'MUNSELL_COLOURS_1929',
'MUNSELL_COLOURS_REAL',
'MUNSELL_COLOURS',
'munsell_value',
'...']
Phenomena
['scattering_cross_section',
'rayleigh_optical_depth',
'rayleigh_scattering',
'sd_rayleigh_scattering',
'...']
Plotting
['ASTM_G_173_ETR',
'ASTM_G_173_GLOBAL_TILT',
'ASTM_G_173_DIRECT_CIRCUMSOLAR',
'COLOUR_STYLE_CONSTANTS',
'COLOUR_ARROW_STYLE',
'...']
Quality
['TCS_SDS',
'VS_SDS',
'CRI_Specification',
'colour_rendering_index',
'CQS_Specification',
'...']
Recovery
['SMITS_1999_SDS',
'XYZ_to_sd_Meng2015',
'RGB_to_sd_Smits1999',
'XYZ_TO_SD_METHODS',
'XYZ_to_sd',
'...']
Temperature
['CCT_TO_UV_METHODS',
'UV_TO_CCT_METHODS',
'CCT_to_uv',
'CCT_to_uv_Ohno2013',
'CCT_to_uv_Robertson1968',
'...']
Utilities
['Lookup',
'Structure',
'CaseInsensitiveMapping',
'handle_numpy_errors',
'ignore_numpy_errors',
'...']
Volume
['ILLUMINANTS_OPTIMAL_COLOUR_STIMULI',
'is_within_macadam_limits',
'is_within_mesh_volume',
'is_within_pointer_gamut',
'generate_pulse_waves',
'...']
The codebase is documented and most docstrings have usage examples:
print(colour.temperature.CCT_to_uv_Ohno2013.__doc__)
Returns the *CIE UCS* colourspace *uv* chromaticity coordinates from given
correlated colour temperature :math:`T_{cp}`, :math:`\Delta_{uv}` and
colour matching functions using *Ohno (2013)* method.
Parameters
----------
CCT : numeric
Correlated colour temperature :math:`T_{cp}`.
D_uv : numeric, optional
:math:`\Delta_{uv}`.
cmfs : XYZ_ColourMatchingFunctions, optional
Standard observer colour matching functions.
Returns
-------
ndarray
*CIE UCS* colourspace *uv* chromaticity coordinates.
References
----------
:cite:`Ohno2014a`
Examples
--------
>>> from colour import STANDARD_OBSERVERS_CMFS
>>> cmfs = STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
>>> CCT = 6507.4342201047066
>>> D_uv = 0.003223690901513
>>> CCT_to_uv_Ohno2013(CCT, D_uv, cmfs) # doctest: +ELLIPSIS
array([ 0.1977999..., 0.3122004...])
At the core of Colour is
the colour.colorimetry sub-package, it defines the objects needed
for spectral computations and many others:
import colour.colorimetry as colorimetry
pprint(colorimetry.__all__)
['SpectralShape',
'DEFAULT_SPECTRAL_SHAPE',
'SpectralDistribution',
'MultiSpectralDistribution',
'sd_blackbody',
'blackbody_spectral_radiance',
'planck_law',
'LMS_ConeFundamentals',
'RGB_ColourMatchingFunctions',
'XYZ_ColourMatchingFunctions',
'CMFS',
'LMS_CMFS',
'RGB_CMFS',
'STANDARD_OBSERVERS_CMFS',
'ILLUMINANTS',
'D_ILLUMINANTS_S_SDS',
'HUNTERLAB_ILLUMINANTS',
'ILLUMINANTS_SDS',
'LIGHT_SOURCES',
'LIGHT_SOURCES_SDS',
'LEFS',
'PHOTOPIC_LEFS',
'SCOTOPIC_LEFS',
'sd_constant',
'sd_zeros',
'sd_ones',
'SD_GAUSSIAN_METHODS',
'sd_gaussian',
'sd_gaussian_normal',
'sd_gaussian_fwhm',
'SD_SINGLE_LED_METHODS',
'sd_single_led',
'sd_single_led_Ohno2005',
'SD_MULTI_LEDS_METHODS',
'sd_multi_leds',
'sd_multi_leds_Ohno2005',
'SD_TO_XYZ_METHODS',
'MULTI_SD_TO_XYZ_METHODS',
'sd_to_XYZ',
'multi_sds_to_XYZ',
'ASTME30815_PRACTISE_SHAPE',
'lagrange_coefficients_ASTME202211',
'tristimulus_weighting_factors_ASTME202211',
'adjust_tristimulus_weighting_factors_ASTME30815',
'sd_to_XYZ_integration',
'sd_to_XYZ_tristimulus_weighting_factors_ASTME30815',
'sd_to_XYZ_ASTME30815',
'multi_sds_to_XYZ_integration',
'wavelength_to_XYZ',
'BANDPASS_CORRECTION_METHODS',
'bandpass_correction',
'bandpass_correction_Stearns1988',
'sd_CIE_standard_illuminant_A',
'sd_CIE_illuminant_D_series',
'daylight_locus_function',
'sd_mesopic_luminous_efficiency_function',
'mesopic_weighting_function',
'LIGHTNESS_METHODS',
'lightness',
'lightness_Glasser1958',
'lightness_Wyszecki1963',
'lightness_CIE1976',
'lightness_Fairchild2010',
'lightness_Fairchild2011',
'intermediate_lightness_function_CIE1976',
'LUMINANCE_METHODS',
'luminance',
'luminance_Newhall1943',
'luminance_ASTMD153508',
'luminance_CIE1976',
'luminance_Fairchild2010',
'luminance_Fairchild2011',
'intermediate_luminance_function_CIE1976',
'dominant_wavelength',
'complementary_wavelength',
'excitation_purity',
'colorimetric_purity',
'luminous_flux',
'luminous_efficiency',
'luminous_efficacy',
'RGB_10_degree_cmfs_to_LMS_10_degree_cmfs',
'RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs',
'RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs',
'LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs',
'LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs',
'WHITENESS_METHODS',
'whiteness',
'whiteness_Berger1959',
'whiteness_Taube1960',
'whiteness_Stensby1968',
'whiteness_ASTME313',
'whiteness_Ganz1979',
'whiteness_CIE2004',
'YELLOWNESS_METHODS',
'yellowness',
'yellowness_ASTMD1925',
'yellowness_ASTME313']
Colour computations
leverage a comprehensive dataset available in most sub-packages, for example
the colour.colorimetry.dataset defines the following components:
import colour.colorimetry.dataset as dataset
pprint(dataset.__all__)
['CMFS',
'LMS_CMFS',
'RGB_CMFS',
'STANDARD_OBSERVERS_CMFS',
'ILLUMINANTS',
'D_ILLUMINANTS_S_SDS',
'HUNTERLAB_ILLUMINANTS',
'ILLUMINANTS_SDS',
'LIGHT_SOURCES',
'LIGHT_SOURCES_SDS',
'LEFS',
'PHOTOPIC_LEFS',
'SCOTOPIC_LEFS']
From Spectral Distribution¶
Whether it be a sample spectral distribution, colour matching functions or
illuminants, spectral data is manipulated using an object built with the
colour.SpectralDistribution class or based on it:
# Defining a sample spectral distribution data.
sample_sd_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}
sd = colour.SpectralDistribution(sample_sd_data, name='Sample')
print(repr(sd))
SpectralDistribution([[ 3.80000000e+02, 4.80000000e-02],
[ 3.85000000e+02, 5.10000000e-02],
[ 3.90000000e+02, 5.50000000e-02],
[ 3.95000000e+02, 6.00000000e-02],
[ 4.00000000e+02, 6.50000000e-02],
[ 4.05000000e+02, 6.80000000e-02],
[ 4.10000000e+02, 6.80000000e-02],
[ 4.15000000e+02, 6.70000000e-02],
[ 4.20000000e+02, 6.40000000e-02],
[ 4.25000000e+02, 6.20000000e-02],
[ 4.30000000e+02, 5.90000000e-02],
[ 4.35000000e+02, 5.70000000e-02],
[ 4.40000000e+02, 5.50000000e-02],
[ 4.45000000e+02, 5.40000000e-02],
[ 4.50000000e+02, 5.30000000e-02],
[ 4.55000000e+02, 5.30000000e-02],
[ 4.60000000e+02, 5.20000000e-02],
[ 4.65000000e+02, 5.20000000e-02],
[ 4.70000000e+02, 5.20000000e-02],
[ 4.75000000e+02, 5.30000000e-02],
[ 4.80000000e+02, 5.40000000e-02],
[ 4.85000000e+02, 5.50000000e-02],
[ 4.90000000e+02, 5.70000000e-02],
[ 4.95000000e+02, 5.90000000e-02],
[ 5.00000000e+02, 6.10000000e-02],
[ 5.05000000e+02, 6.20000000e-02],
[ 5.10000000e+02, 6.50000000e-02],
[ 5.15000000e+02, 6.70000000e-02],
[ 5.20000000e+02, 7.00000000e-02],
[ 5.25000000e+02, 7.20000000e-02],
[ 5.30000000e+02, 7.40000000e-02],
[ 5.35000000e+02, 7.50000000e-02],
[ 5.40000000e+02, 7.60000000e-02],
[ 5.45000000e+02, 7.80000000e-02],
[ 5.50000000e+02, 7.90000000e-02],
[ 5.55000000e+02, 8.20000000e-02],
[ 5.60000000e+02, 8.70000000e-02],
[ 5.65000000e+02, 9.20000000e-02],
[ 5.70000000e+02, 1.00000000e-01],
[ 5.75000000e+02, 1.07000000e-01],
[ 5.80000000e+02, 1.15000000e-01],
[ 5.85000000e+02, 1.22000000e-01],
[ 5.90000000e+02, 1.29000000e-01],
[ 5.95000000e+02, 1.34000000e-01],
[ 6.00000000e+02, 1.38000000e-01],
[ 6.05000000e+02, 1.42000000e-01],
[ 6.10000000e+02, 1.46000000e-01],
[ 6.15000000e+02, 1.50000000e-01],
[ 6.20000000e+02, 1.54000000e-01],
[ 6.25000000e+02, 1.58000000e-01],
[ 6.30000000e+02, 1.63000000e-01],
[ 6.35000000e+02, 1.67000000e-01],
[ 6.40000000e+02, 1.73000000e-01],
[ 6.45000000e+02, 1.80000000e-01],
[ 6.50000000e+02, 1.88000000e-01],
[ 6.55000000e+02, 1.96000000e-01],
[ 6.60000000e+02, 2.04000000e-01],
[ 6.65000000e+02, 2.13000000e-01],
[ 6.70000000e+02, 2.22000000e-01],
[ 6.75000000e+02, 2.31000000e-01],
[ 6.80000000e+02, 2.42000000e-01],
[ 6.85000000e+02, 2.51000000e-01],
[ 6.90000000e+02, 2.61000000e-01],
[ 6.95000000e+02, 2.71000000e-01],
[ 7.00000000e+02, 2.82000000e-01],
[ 7.05000000e+02, 2.94000000e-01],
[ 7.10000000e+02, 3.05000000e-01],
[ 7.15000000e+02, 3.18000000e-01],
[ 7.20000000e+02, 3.34000000e-01],
[ 7.25000000e+02, 3.54000000e-01],
[ 7.30000000e+02, 3.72000000e-01],
[ 7.35000000e+02, 3.92000000e-01],
[ 7.40000000e+02, 4.09000000e-01],
[ 7.45000000e+02, 4.20000000e-01],
[ 7.50000000e+02, 4.36000000e-01],
[ 7.55000000e+02, 4.50000000e-01],
[ 7.60000000e+02, 4.62000000e-01],
[ 7.65000000e+02, 4.65000000e-01],
[ 7.70000000e+02, 4.48000000e-01],
[ 7.75000000e+02, 4.32000000e-01],
[ 7.80000000e+02, 4.21000000e-01]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={u'right': None, u'method': u'Constant', u'left': None})
The sample spectral distribution can be easily plotted against the visible spectrum:
# Plotting the sample spectral distribution.
plot_single_sd(sd)
With the sample spectral distribution defined, its shape is retrieved as follows:
# Displaying the sample spectral distribution shape.
print(sd.shape)
(380.0, 780.0, 5.0)
The returned shape is an instance of the colour.SpectralShape class:
repr(sd.shape)
'SpectralShape(380.0, 780.0, 5.0)'
colour.SpectralShape is used throughout
Colour to define
spectral dimensions and is instantiated as follows:
# Using *colour.SpectralShape* with iteration.
shape = colour.SpectralShape(start=0, end=10, interval=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()
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ,
4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. , 8.5,
9. , 9.5, 10. ])
Colour defines three convenient objects to create constant spectral distributions:
colour.sd_constantcolour.sd_zeroscolour.sd_ones
# Defining a constant spectral distribution.
sd_constant = colour.sd_constant(100)
print('"Constant Spectral Distribution"')
print(sd_constant.shape)
print(sd_constant[400])
# Defining a zeros filled spectral distribution.
print('\n"Zeros Filled Spectral Distribution"')
sd_zeros = colour.sd_zeros()
print(sd_zeros.shape)
print(sd_zeros[400])
# Defining a ones filled spectral distribution.
print('\n"Ones Filled Spectral Distribution"')
sd_ones = colour.sd_ones()
print(sd_ones.shape)
print(sd_ones[400])
"Constant Spectral Distribution"
(360.0, 780.0, 1.0)
100.0
"Zeros Filled Spectral Distribution"
(360.0, 780.0, 1.0)
0.0
"Ones Filled Spectral Distribution"
(360.0, 780.0, 1.0)
1.0
By default the shape used by colour.sd_constant,
colour.sd_zeros and colour.sd_ones is the one defined by the
colour.DEFAULT_SPECTRAL_SHAPE attribute and based on ASTM E308-15
practise shape.
print(repr(colour.DEFAULT_SPECTRAL_SHAPE))
SpectralShape(360, 780, 1)
A custom shape can be passed to construct a constant spectral distribution with user defined dimensions:
colour.sd_ones(colour.SpectralShape(400, 700, 5))[450]
1.0
The colour.SpectralDistribution class supports the following
arithmetical operations:
- addition
- subtraction
- multiplication
- division
- exponentiation
spd1 = colour.sd_ones()
print('"Ones Filled Spectral Distribution"')
print(spd1[400])
print('\n"x2 Constant Multiplied"')
print((spd1 * 2)[400])
print('\n"+ Spectral Distribution"')
print((spd1 + colour.sd_ones())[400])
"Ones Filled Spectral Distribution"
1.0
"x2 Constant Multiplied"
2.0
"+ Spectral Distribution"
2.0
Often interpolation of the spectral distribution is required, this is achieved
with the colour.SpectralDistribution.interpolate method. Depending on the
wavelengths uniformity, the default interpolation method will differ.
Following CIE 167:2005 recommendation: The method developed by
Sprague (1880) should be used for interpolating functions having a uniformly
spaced independent variable and a Cubic Spline method for non-uniformly spaced
independent variable [CIET13805a].
The uniformity of the sample spectral distribution is assessed as follows:
# Checking the sample spectral distribution uniformity.
print(sd.is_uniform())
True
In this case, since the sample spectral distribution is uniform the
interpolation defaults to the colour.SpragueInterpolator interpolator.
Note
Interpolation happens in place and may alter the original data, use the
colour.SpectralDistribution.copy method to generate a copy of the
spectral distribution before interpolation.
# Copying the sample spectral distribution.
sd_copy = sd.copy()
# Interpolating the copied sample spectral distribution.
sd_copy.interpolate(colour.SpectralShape(400, 770, 1))
sd_copy[401]
0.065809599999999996
# Comparing the interpolated spectral distribution with the original one.
plot_multi_sds([sd, sd_copy], bounding_box=[730,780, 0.25, 0.5])
Extrapolation although dangerous can be used to help aligning two spectral distributions together. CIE publication CIE 15:2004 “Colorimetry” recommends that unmeasured values may be set equal to the nearest measured value of the appropriate quantity in truncation [CIET14804d]:
# Extrapolating the copied sample spectral distribution.
sd_copy.extrapolate(colour.SpectralShape(340, 830))
sd_copy[340], sd_copy[830]
(0.065000000000000002, 0.44800000000000018)
The underlying interpolator can be swapped for any of the Colour interpolators.
pprint([
export for export in colour.algebra.interpolation.__all__
if 'Interpolator' in export
])
[u'KernelInterpolator',
u'LinearInterpolator',
u'SpragueInterpolator',
u'CubicSplineInterpolator',
u'PchipInterpolator',
u'NullInterpolator']
# Changing interpolator while trimming the copied spectral distribution.
sd_copy.interpolate(
colour.SpectralShape(400, 700, 10), interpolator=colour.LinearInterpolator)
SpectralDistribution([[ 4.00000000e+02, 6.50000000e-02],
[ 4.10000000e+02, 6.80000000e-02],
[ 4.20000000e+02, 6.40000000e-02],
[ 4.30000000e+02, 5.90000000e-02],
[ 4.40000000e+02, 5.50000000e-02],
[ 4.50000000e+02, 5.30000000e-02],
[ 4.60000000e+02, 5.20000000e-02],
[ 4.70000000e+02, 5.20000000e-02],
[ 4.80000000e+02, 5.40000000e-02],
[ 4.90000000e+02, 5.70000000e-02],
[ 5.00000000e+02, 6.10000000e-02],
[ 5.10000000e+02, 6.50000000e-02],
[ 5.20000000e+02, 7.00000000e-02],
[ 5.30000000e+02, 7.40000000e-02],
[ 5.40000000e+02, 7.60000000e-02],
[ 5.50000000e+02, 7.90000000e-02],
[ 5.60000000e+02, 8.70000000e-02],
[ 5.70000000e+02, 1.00000000e-01],
[ 5.80000000e+02, 1.15000000e-01],
[ 5.90000000e+02, 1.29000000e-01],
[ 6.00000000e+02, 1.38000000e-01],
[ 6.10000000e+02, 1.46000000e-01],
[ 6.20000000e+02, 1.54000000e-01],
[ 6.30000000e+02, 1.63000000e-01],
[ 6.40000000e+02, 1.73000000e-01],
[ 6.50000000e+02, 1.88000000e-01],
[ 6.60000000e+02, 2.04000000e-01],
[ 6.70000000e+02, 2.22000000e-01],
[ 6.80000000e+02, 2.42000000e-01],
[ 6.90000000e+02, 2.61000000e-01],
[ 7.00000000e+02, 2.82000000e-01]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={u'right': None, u'method': u'Constant', u'left': None})
The extrapolation behaviour can be changed for Linear method instead
of the Constant default method or even use arbitrary constant left
and right values:
# Extrapolating the copied sample spectral distribution with *Linear* method.
sd_copy.extrapolate(
colour.SpectralShape(340, 830),
extrapolator_args={'method': 'Linear',
'right': 0})
sd_copy[340], sd_copy[830]
(0.046999999999999348, 0.0)
Aligning a spectral distribution is a convenient way to first interpolates the current data within its original bounds, then, if required, extrapolate any missing values to match the requested shape:
# Aligning the cloned sample spectral distribution.
# The spectral distribution is first trimmed as above.
sd_copy.interpolate(colour.SpectralShape(400, 700))
sd_copy.align(colour.SpectralShape(340, 830, 5))
sd_copy[340], sd_copy[830]
(0.065000000000000002, 0.28199999999999975)
The colour.SpectralDistribution class also supports various arithmetic
operations like addition, subtraction, multiplication, division or
exponentiation with numeric and array_like variables or other
colour.SpectralDistribution class instances:
sd = colour.SpectralDistribution({
410: 0.25,
420: 0.50,
430: 0.75,
440: 1.0,
450: 0.75,
460: 0.50,
480: 0.25
})
print((sd.copy() + 1).values)
print((sd.copy() * 2).values)
print((sd * [0.35, 1.55, 0.75, 2.55, 0.95, 0.65, 0.15]).values)
print((sd * colour.sd_constant(2, sd.shape) * colour.sd_constant(3, sd.shape)).values)
[ 1.25 1.5 1.75 2. 1.75 1.5 1.25]
[ 0.5 1. 1.5 2. 1.5 1. 0.5]
[ 0.0875 0.775 0.5625 2.55 0.7125 0.325 0.0375]
[ 1.5 3. 4.5 6. 4.5 3. nan 1.5]
The spectral distribution can be normalised with an arbitrary factor:
print(sd.normalise().values)
print(sd.normalise(100).values)
[ 0.25 0.5 0.75 1. 0.75 0.5 0.25]
[ 25. 50. 75. 100. 75. 50. 25.]
A the heart of the colour.SpectralDistribution class is the
colour.continuous.Signal class which implements the
colour.continuous.Signal.function method.
Evaluating the function for any independent domain \(x \in \mathbb{R}\) variable returns a corresponding range \(y \in \mathbb{R}\) variable.
It adopts an interpolating function encapsulated inside an extrapolating
function. The resulting function independent domain, stored as discrete
values in the colour.continuous.Signal.domain attribute corresponds
with the function dependent and already known range stored in the
colour.continuous.Signal.range attribute.
Describing the colour.continuous.Signal class is beyond the scope of
this tutorial but the core capability can be described.
import numpy as np
range_ = np.linspace(10, 100, 10)
signal = colour.continuous.Signal(range_)
print(repr(signal))
Signal([[ 0., 10.],
[ 1., 20.],
[ 2., 30.],
[ 3., 40.],
[ 4., 50.],
[ 5., 60.],
[ 6., 70.],
[ 7., 80.],
[ 8., 90.],
[ 9., 100.]],
interpolator=KernelInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={u'right': nan, u'method': u'Constant', u'left': nan})
# Returning the corresponding range *y* variable for any arbitrary independent domain *x* variable.
signal[np.random.uniform(0, 9, 10)]
array([ 55.91309735, 65.4172615 , 65.54495059, 88.17819416,
61.88860248, 10.53878826, 55.25130534, 46.14659783,
86.41406136, 84.59897703])
Convert to Tristimulus Values¶
From a given spectral distribution, CIE XYZ tristimulus values can be calculated:
sd = colour.SpectralDistribution(sample_sd_data)
cmfs = colour.STANDARD_OBSERVERS_CMFS['CIE 1931 2 Degree Standard Observer']
illuminant = colour.ILLUMINANTS_SDS['D65']
# Calculating the sample spectral distribution *CIE XYZ* tristimulus values.
XYZ = colour.sd_to_XYZ(sd, cmfs, illuminant)
print(XYZ)
[ 10.97085572 9.70278591 6.05562778]
From CIE XYZ Colourspace¶
CIE XYZ is the central colourspace for Colour Science from which many computations are available, expanding to even more computations:
# Displaying objects interacting directly with the *CIE XYZ* colourspace.
pprint([name for name in colour.__all__ if name.startswith('XYZ_to')])
['XYZ_to_ATD95',
'XYZ_to_CAM16',
'XYZ_to_CIECAM02',
'XYZ_to_Hunt',
'XYZ_to_LLAB',
'XYZ_to_Nayatani95',
'XYZ_to_RLAB',
'XYZ_to_Hunter_Lab',
'XYZ_to_Hunter_Rdab',
'XYZ_to_IPT',
'XYZ_to_JzAzBz',
'XYZ_to_K_ab_HunterLab1966',
'XYZ_to_Lab',
'XYZ_to_Luv',
'XYZ_to_OSA_UCS',
'XYZ_to_RGB',
'XYZ_to_UCS',
'XYZ_to_UVW',
'XYZ_to_hdr_CIELab',
'XYZ_to_hdr_IPT',
'XYZ_to_sRGB',
'XYZ_to_xy',
'XYZ_to_xyY',
'XYZ_to_sd']
Convert to Screen Colours¶
CIE XYZ tristimulus values can be converted into sRGB colourspace RGB values in order to display them on screen:
# The output domain of *colour.sd_to_XYZ* is [0, 100] and the input
# domain of *colour.XYZ_to_sRGB* is [0, 1]. It needs to be accounted for,
# thus the input *CIE XYZ* tristimulus values are scaled.
RGB = colour.XYZ_to_sRGB(XYZ / 100)
print(RGB)
[ 0.45675795 0.30986982 0.24861924]
# Plotting the *sRGB* colourspace colour of the *Sample* spectral distribution.
plot_single_colour_swatch(
ColourSwatch('Sample', RGB),
text_parameters={'size': 'x-large'})
Generate Colour Rendition Charts¶
Likewise, colour values from a colour rendition chart sample can be computed.
Note
This is useful for render time checks in the VFX industry, where a synthetic colour chart can be inserted into a render to ensure the colour management is acting as expected.
The colour.characterisation sub-package contains the dataset for
various colour rendition charts:
# Colour rendition charts chromaticity coordinates.
print(sorted(colour.characterisation.COLOURCHECKERS.keys()))
# Colour rendition charts spectral distributions.
print(sorted(colour.characterisation.COLOURCHECKERS_SDS.keys()))
['BabelColor Average', 'ColorChecker 1976', 'ColorChecker 2005', 'ColorChecker24 - After November 2014', 'ColorChecker24 - Before November 2014', 'babel_average', 'cc2005', 'cca2014', 'ccb2014']
['BabelColor Average', 'ColorChecker N Ohta', 'babel_average', 'cc_ohta']
Note
The above cc2005, babel_average and cc_ohta keys are
convenient aliases for respectively ColorChecker 2005, BabelColor Average
and ColorChecker N Ohta keys.
# Plotting the *sRGB* colourspace colour of *neutral 5 (.70 D)* patch.
patch_name = 'neutral 5 (.70 D)'
patch_sd = colour.COLOURCHECKERS_SDS['ColorChecker N Ohta'][patch_name]
XYZ = colour.sd_to_XYZ(patch_sd, cmfs, illuminant)
RGB = colour.XYZ_to_sRGB(XYZ / 100)
plot_single_colour_swatch(
ColourSwatch(patch_name.title(), RGB),
text_parameters={'size': 'x-large'})
Colour defines a convenient plotting object to draw synthetic colour rendition charts figures:
plot_single_colour_checker(colour_checker='ColorChecker 2005', text_parameters={'visible': False})
Convert to Chromaticity Coordinates¶
Given a spectral distribution, chromaticity coordinates xy can be computed
using the colour.XYZ_to_xy definition:
# Computing *xy* chromaticity coordinates for the *neutral 5 (.70 D)* patch.
xy = colour.XYZ_to_xy(XYZ)
print(xy)
[ 0.31259787 0.32870029]
Chromaticity coordinates xy can be plotted into the CIE 1931 Chromaticity Diagram:
import matplotlib.pyplot as plt
# 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.
plot_chromaticity_diagram_CIE1931(standalone=False)
# Plotting the *xy* chromaticity coordinates.
x, y = xy
plt.plot(x, y, 'o-', color='white')
# Annotating the plot.
plt.annotate(patch_sd.name.title(),
xy=xy,
xytext=(-50, 30),
textcoords='offset points',
arrowprops=dict(arrowstyle='->', connectionstyle='arc3, rad=-0.2'))
# Displaying the plot.
render(
standalone=True,
limits=(-0.1, 0.9, -0.1, 0.9),
x_tighten=True,
y_tighten=True)
And More…¶
With the hope that this small introduction was useful and gave envy to see more, a good place to explore the API further more is the Jupyter Notebooks page.
Basics¶
N-Dimensional Arrays Support¶
Most of Colour definitions are fully vectorised and support n-dimensional arrays by leveraging Numpy.
While it is recommended to use ndarrays as input for the API objects, it is possible to use tuples or lists:
import colour
xyY = (0.4316, 0.3777, 0.1008)
colour.xyY_to_XYZ(xyY)
array([ 0.11518475, 0.1008 , 0.05089373])
xyY = [0.4316, 0.3777, 0.1008]
colour.xyY_to_XYZ(xyY)
array([ 0.11518475, 0.1008 , 0.05089373])
xyY = [
(0.4316, 0.3777, 0.1008),
(0.4316, 0.3777, 0.1008),
(0.4316, 0.3777, 0.1008),
]
colour.xyY_to_XYZ(xyY)
array([[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373]])
As shown in the above example, there is widespread support for n-dimensional arrays:
import numpy as np
xyY = np.array([0.4316, 0.3777, 0.1008])
xyY = np.tile(xyY, (6, 1))
colour.xyY_to_XYZ(xyY)
array([[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373]])
colour.xyY_to_XYZ(xyY.reshape((2, 3, 3)))
array([[[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373]],
[[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373],
[ 0.11518475, 0.1008 , 0.05089373]]])
Which enables image processing:
import colour.plotting
RGB = colour.read_image('_static/Logo_Small_001.png')
RGB = RGB[..., 0:3] # Discarding alpha channel.
XYZ = colour.sRGB_to_XYZ(RGB)
colour.plotting.plot_image(XYZ, text_parameters={'text': 'sRGB to XYZ'})
Domain-Range Scales¶
Note
This section has important information.
Colour adopts 4 main input domains and output ranges:
- Scalars usually in domain-range [0, 1] (or [0, 10] for Munsell Value).
- Percentages usually in domain-range [0, 100].
- Degrees usually in domain-range [0, 360].
- Integers usually in domain-range [0, 2**n -1] where n is the bit depth.
It is error prone but it is also a direct consequence of the inconsistency of the colour science field itself. We have discussed at length about this and we were leaning toward normalisation of the whole API to domain-range [0, 1], we never committed for reasons highlighted by the following points:
- Colour Scientist performing computations related to Munsell Renotation System would be very surprised if the output Munsell Value was in range [0, 1] or [0, 100].
- A Visual Effect Industry artist would be astonished to find out that conversion from CIE XYZ to sRGB was yielding values in range [0, 100].
However benefits of having a consistent and predictable domain-range scale are numerous thus with Colour 0.3.12 we have introduced a mechanism to allow users to work within one of the two available domain-range scales.
Scale - Reference¶
‘Reference’ is the default domain-range scale of Colour, objects adopt the implemented reference, i.e. paper, publication, etc.., domain-range scale.
The ‘Reference’ domain-range scale is inconsistent, e.g. colour appearance
models, spectral conversions are typically in domain-range [0, 100] while RGB
models will operate in domain-range [0, 1]. Some objects, e.g.
colour.colorimetry.lightness_Fairchild2011() definition have mismatched
domain-range: input domain [0, 1] and output range [0, 100].
Scale - 1¶
‘1’ is a domain-range scale converting all the relevant objects from Colour public API to domain-range [0, 1]:
- Scalars in domain-range [0, 10], e.g Munsell Value are scaled by 10.
- Percentages in domain-range [0, 100] are scaled by 100.
- Degrees in domain-range [0, 360] are scaled by 100.
- Integers in domain-range [0, 2**n -1] where n is the bit depth are scaled by 2**n -1.
Warning
The conversion to ‘1’ domain-range scale is a soft normalisation and similarly to the ‘Reference’ domain-range scale it is normal that you encounter values exceeding 1, e.g. High Dynamic Range Imagery (HDRI) or negative values, e.g. out-of-gamut RGB colourspace values.
Understanding the Domain-Range Scale of an Object¶
Using colour.adaptation.chromatic_adaptation_CIE1994() definition
docstring as an example, the Notes section features two tables.
The first table is for the domain, and lists the input arguments affected by the two domain-range scales and which normalisation they should adopt depending the domain-range scale in use:
| Domain | Scale - Reference | Scale - 1 |
|---|---|---|
XYZ_1 |
[0, 100] | [0, 1] |
Y_o |
[0, 100] | [0, 1] |
The second table is for the range and lists the return value of the definition:
| Range | Scale - Reference | Scale - 1 |
|---|---|---|
XYZ_2 |
[0, 100] | [0, 1] |
Working with the Domain-Range Scales¶
The current domain-range scale is returned with the
colour.get_domain_range_scale() definition:
import colour
colour.get_domain_range_scale()
u'reference'
Changing from the ‘Reference’ default domain-range scale to ‘1’ is done
with the colour.set_domain_range_scale() definition:
XYZ_1 = [28.00, 21.26, 5.27]
xy_o1 = [0.4476, 0.4074]
xy_o2 = [0.3127, 0.3290]
Y_o = 20
E_o1 = 1000
E_o2 = 1000
colour.adaptation.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
array([ 24.03379521, 21.15621214, 17.64301199])
colour.set_domain_range_scale('1')
XYZ_1 = [0.2800, 0.2126, 0.0527]
Y_o = 0.2
colour.adaptation.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
array([ 0.24033795, 0.21156212, 0.17643012])
The output tristimulus values with the ‘1’ domain-range scale are equal to those from ‘Reference’ default domain-range scale divided by 100.
Passing incorrectly scaled values to the
colour.adaptation.chromatic_adaptation_CIE1994() definition
would result in unexpected values and a warning in that case:
colour.set_domain_range_scale('Reference')
colour.adaptation.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
File "<ipython-input-...>", line 4, in <module>
E_o2)
File "/colour-science/colour/colour/adaptation/cie1994.py", line 134, in chromatic_adaptation_CIE1994
warning(('"Y_o" luminance factor must be in [18, 100] domain, '
/colour-science/colour/colour/utilities/verbose.py:207: ColourWarning: "Y_o" luminance factor must be in [18, 100] domain, unpredictable results may occur!
warn(*args, **kwargs)
array([ 0.17171825, 0.13731098, 0.09972054])
Setting the ‘1’ domain-range scale has the following effect on the
colour.adaptation.chromatic_adaptation_CIE1994() definition:
As it expects values in domain [0, 100], scaling occurs and the
relevant input values, i.e. the values listed in the domain table, XYZ_1
and Y_o are converted from domain [0, 1] to domain [0, 100] by
colour.utilities.to_domain_100() definition and conversely
return value XYZ_2 is converted from range [0, 100] to range [0, 1] by
colour.utilities.from_range_100() definition.
A convenient alternative to the colour.set_domain_range_scale()
definition is the colour.domain_range_scale context manager and
decorator. It temporarily overrides Colour
domain-range scale with given scale value:
with colour.domain_range_scale('1'):
colour.adaptation.chromatic_adaptation_CIE1994(XYZ_1, xy_o1, xy_o2, Y_o, E_o1, E_o2)
[ 0.24033795 0.21156212 0.17643012]
Reference¶
Colour¶
Chromatic Adaptation¶
colour
chromatic_adaptation(XYZ, XYZ_w, XYZ_wr[, …]) |
Adapts given stimulus from test viewing conditions to reference viewing conditions. |
CHROMATIC_ADAPTATION_METHODS |
Supported chromatic adaptation methods. |
CMCCAT2000_VIEWING_CONDITIONS |
Reference CMCCAT2000 chromatic adaptation model viewing conditions. |
Dataset
colour
CHROMATIC_ADAPTATION_TRANSFORMS |
Supported chromatic adaptation transforms. |
colour.adaptation
chromatic_adaptation_Fairchild1990(XYZ_1, …) |
Adapts given stimulus CIE XYZ_1 tristimulus values from test viewing conditions to reference viewing conditions using Fairchild (1990) chromatic adaptation model. |
colour.adaptation
chromatic_adaptation_CIE1994(XYZ_1, xy_o1, …) |
Adapts given stimulus CIE XYZ_1 tristimulus values from test viewing conditions to reference viewing conditions using CIE 1994 chromatic adaptation model. |
colour.adaptation
chromatic_adaptation_CMCCAT2000(XYZ, XYZ_w, …) |
Adapts given stimulus CIE XYZ tristimulus values using given viewing conditions. |
CMCCAT2000_VIEWING_CONDITIONS |
Reference CMCCAT2000 chromatic adaptation model viewing conditions. |
Ancillary Objects
colour.adaptation
chromatic_adaptation_forward_CMCCAT2000(XYZ, …) |
Adapts given stimulus CIE XYZ tristimulus values from test viewing conditions to reference viewing conditions using CMCCAT2000 forward chromatic adaptation model. |
chromatic_adaptation_reverse_CMCCAT2000(…) |
Adapts given stimulus corresponding colour CIE XYZ tristimulus values from reference viewing conditions to test viewing conditions using CMCCAT2000 reverse chromatic adaptation model. |
CMCCAT2000_InductionFactors |
CMCCAT2000 chromatic adaptation model induction factors. |
colour.adaptation
chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr) |
Adapts given stimulus from test viewing conditions to reference viewing conditions. |
CHROMATIC_ADAPTATION_TRANSFORMS |
Supported chromatic adaptation transforms. |
Dataset
colour.adaptation
BRADFORD_CAT |
|
BS_CAT |
|
BS_PC_CAT |
|
CAT02_BRILL_CAT |
|
CAT02_CAT |
|
CMCCAT2000_CAT |
|
CMCCAT97_CAT |
|
FAIRCHILD_CAT |
|
SHARP_CAT |
|
VON_KRIES_CAT |
|
XYZ_SCALING_CAT |
Ancillary Objects
colour.adaptation
chromatic_adaptation_matrix_VonKries(XYZ_w, …) |
Computes the chromatic adaptation matrix from test viewing conditions to reference viewing conditions. |
Algebra¶
colour
Extrapolator([interpolator, method, left, …]) |
Extrapolates the 1-D function of given interpolator. |
colour
KernelInterpolator(x, y[, window, kernel, …]) |
Kernel based interpolation of a 1-D function. |
NearestNeighbourInterpolator(*args, **kwargs) |
A nearest-neighbour interpolator. |
LinearInterpolator(x, y[, dtype]) |
Linearly interpolates a 1-D function. |
NullInterpolator(x, y[, absolute_tolerance, …]) |
Performs 1-D function null interpolation, i.e. |
PchipInterpolator(x, y, *args, **kwargs) |
Interpolates a 1-D function using Piecewise Cubic Hermite Interpolating Polynomial interpolation. |
SpragueInterpolator(x, y[, dtype]) |
Constructs a fifth-order polynomial that passes through \(y\) dependent variable. |
lagrange_coefficients(r[, n]) |
Computes the Lagrange Coefficients at given point \(r\) for degree \(n\). |
TABLE_INTERPOLATION_METHODS |
Supported table interpolation methods. |
table_interpolation(V_xyz, table[, method]) |
Performs interpolation of given \(V_{xyz}\) values using given interpolation table. |
Interpolation Kernels
colour
kernel_nearest_neighbour(x) |
Returns the nearest-neighbour kernel evaluated at given samples. |
kernel_linear(x) |
Returns the linear kernel evaluated at given samples. |
kernel_sinc(x[, a]) |
Returns the sinc kernel evaluated at given samples. |
kernel_lanczos(x[, a]) |
Returns the lanczos kernel evaluated at given samples. |
kernel_cardinal_spline(x[, a, b]) |
Returns the cardinal spline kernel evaluated at given samples. |
Ancillary Objects
colour.algebra
table_interpolation_trilinear(V_xyz, table) |
Performs trilinear interpolation of given \(V_{xyz}\) values using given interpolation table. |
table_interpolation_tetrahedral(V_xyz, table) |
Performs tetrahedral interpolation of given \(V_{xyz}\) values using given interpolation table. |
colour.algebra
cartesian_to_spherical(a) |
Transforms given Cartesian coordinates array \(xyz\) to Spherical coordinates array \(\rho\theta\phi\) (radial distance, inclination or elevation and azimuth). |
spherical_to_cartesian(a) |
Transforms given Spherical coordinates array \(\rho\theta\phi\) (radial distance, inclination or elevation and azimuth) to Cartesian coordinates array \(xyz\). |
cartesian_to_polar(a) |
Transforms given Cartesian coordinates array \(xy\) to Polar coordinates array \(\rho\phi\) (radial coordinate, angular coordinate). |
polar_to_cartesian(a) |
Transforms given Polar coordinates array \(\rho\phi\) (radial coordinate, angular coordinate) to Cartesian coordinates array \(xy\). |
cartesian_to_cylindrical(a) |
Transforms given Cartesian coordinates array \(xyz\) to Cylindrical coordinates array \(\rho\phi z\) (azimuth, radial distance and height). |
cylindrical_to_cartesian(a) |
Transforms given Cylindrical coordinates array \(\rho\phi z\) (azimuth, radial distance and height) to Cartesian coordinates array \(xyz\). |
colour.algebra
normalise_vector(a) |
Normalises given vector \(a\). |
euclidean_distance(a, b) |
Returns the euclidean distance between point arrays \(a\) and \(b\). |
extend_line_segment(a, b[, distance]) |
Extends the line segment defined by point arrays \(a\) and \(b\) by given distance and return the new end point. |
intersect_line_segments(l_1, l_2) |
Computes \(l_1\) line segments intersections with \(l_2\) line segments. |
ellipse_coefficients_general_form(coefficients) |
Returns the general form ellipse coefficients from given canonical form ellipse coefficients. |
ellipse_coefficients_canonical_form(coefficients) |
Returns the canonical form ellipse coefficients from given general form ellipse coefficients. |
point_at_angle_on_ellipse(phi, coefficients) |
Returns the coordinates of the point at angle \(\phi\) in degrees on the ellipse with given canonical form coefficients. |
ELLIPSE_FITTING_METHODS |
Supported ellipse fitting methods. |
ellipse_fitting(a[, method]) |
Returns the coefficients of the implicit second-order polynomial/quadratic curve that fits given point array \(a\) using given method. |
Ancillary Objects
colour.algebra
LineSegmentsIntersections_Specification |
Defines the specification for intersection of line segments \(l_1\) and \(l_2\) returned by colour.algebra.intersect_line_segments() definition. |
ellipse_fitting_Halir1998(a) |
Returns the coefficients of the implicit second-order polynomial/quadratic curve that fits given point array \(a\) using Halir and Flusser (1998) method. |
colour.algebra
random_triplet_generator(size[, limits, …]) |
Returns a generator yielding random triplets. |
colour.algebra
least_square_mapping_MoorePenrose(y, x) |
Computes the least-squares mapping from dependent variable \(y\) to independent variable \(x\) using Moore-Penrose inverse. |
colour.algebra
is_spow_enabled() |
Returns whether Colour safe / symmetrical power function is enabled. |
set_spow_enable(enable) |
Sets Colour safe / symmetrical power function enabled state. |
spow_enable(enable) |
A context manager and decorator temporarily setting Colour safe / symmetrical power function enabled state. |
spow(a, p) |
Raises given array \(a\) to the power \(p\) as follows: \(sign(a) * |a|^p\). |
Colour Appearance Models¶
colour
XYZ_to_ATD95(XYZ, XYZ_0, Y_0, k_1, k_2[, sigma]) |
Computes the ATD (1995) colour vision model correlates. |
ATD95_Specification |
Defines the ATD (1995) colour vision model specification. |
colour
XYZ_to_CIECAM02(XYZ, XYZ_w, L_A, Y_b[, …]) |
Computes the CIECAM02 colour appearance model correlates from given CIE XYZ tristimulus values. |
CIECAM02_to_XYZ(CIECAM02_specification, …) |
Converts CIECAM02 specification to CIE XYZ tristimulus values. |
CIECAM02_Specification |
Defines the CIECAM02 colour appearance model specification. |
CIECAM02_VIEWING_CONDITIONS |
Reference CIECAM02 colour appearance model viewing conditions. |
Ancillary Objects
colour.appearance
CIECAM02_InductionFactors |
CIECAM02 colour appearance model induction factors. |
colour
XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b[, …]) |
Computes the CAM16 colour appearance model correlates from given CIE XYZ tristimulus values. |
CAM16_to_XYZ(CAM16_specification, XYZ_w, …) |
Converts CAM16 specification to CIE XYZ tristimulus values. |
CAM16_Specification |
Defines the CAM16 colour appearance model specification. |
CAM16_VIEWING_CONDITIONS |
Reference CAM16 colour appearance model viewing conditions. |
Ancillary Objects
colour.appearance
CAM16_InductionFactors |
CAM16 colour appearance model induction factors. |
colour
XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A[, …]) |
Computes the Hunt colour appearance model correlates. |
Hunt_Specification |
Defines the Hunt colour appearance model specification. |
HUNT_VIEWING_CONDITIONS |
Reference Hunt colour appearance model viewing conditions. |
colour
XYZ_to_LLAB(XYZ, XYZ_0, Y_b, L[, surround, …]) |
Computes the :math:`LLAB(l:c)` colour appearance model correlates. |
LLAB_Specification |
Defines the :math:`LLAB(l:c)` colour appearance model specification. |
LLAB_VIEWING_CONDITIONS |
Reference \(LLAB(l:c)\) colour appearance model viewing conditions. |
Ancillary Objects
colour.appearance
LLAB_InductionFactors |
:math:`LLAB(l:c)` colour appearance model induction factors. |
colour
XYZ_to_Nayatani95(XYZ, XYZ_n, Y_o, E_o, E_or) |
Computes the Nayatani (1995) colour appearance model correlates. |
Nayatani95_Specification |
Defines the Nayatani (1995) colour appearance model specification. |
colour
XYZ_to_RLAB(XYZ, XYZ_n, Y_n[, sigma, D]) |
Computes the RLAB model color appearance correlates. |
RLAB_D_FACTOR |
RLAB colour appearance model Discounting-the-Illuminant factor values. |
RLAB_Specification |
Defines the RLAB colour appearance model specification. |
RLAB_VIEWING_CONDITIONS |
Reference RLAB colour appearance model viewing conditions. |
Biochemistry¶
colour.biochemistry
reaction_rate_MichealisMenten(S, V_max, K_m) |
Describes the rate of enzymatic reactions, by relating reaction rate \(v\) to concentration of a substrate \(S\). |
substrate_concentration_MichealisMenten(v, …) |
Describes the rate of enzymatic reactions, by relating concentration of a substrate \(S\) to reaction rate \(v\). |
Colour Vision Deficiency¶
colour
anomalous_trichromacy_cmfs_Machado2009(cmfs, …) |
Shifts given LMS cone fundamentals colour matching functions with given \(\Delta_{LMS}\) shift amount in nanometers to simulate anomalous trichromacy using Machado et al.(2009) method.. |
anomalous_trichromacy_matrix_Machado2009(…) |
Computes Machado et al.(2009) CVD matrix for given LMS cone fundamentals colour matching functions and display primaries tri-spectral distributions with given \(\Delta_{LMS}\) shift amount in nanometers to simulate anomalous trichromacy.. |
cvd_matrix_Machado2009(deficiency, severity) |
Computes Machado et al.(2009) CVD matrix for given deficiency and severity using the pre-computed matrices dataset.. |
Dataset
colour
CVD_MATRICES_MACHADO2010 |
Machado (2010) Simulation matrices \(\Phi_{CVD}\). |
Colour Characterisation¶
colour
POLYNOMIAL_EXPANSION_METHODS |
Supported polynomial expansion methods. |
polynomial_expansion(a[, method]) |
Performs polynomial expansion of given \(a\) array. |
COLOUR_CORRECTION_MATRIX_METHODS |
Supported colour correction matrix methods. |
colour_correction_matrix(M_T, M_R[, method]) |
Computes a colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array. |
COLOUR_CORRECTION_METHODS |
Supported colour correction methods. |
colour_correction(RGB, M_T, M_R[, method]) |
Performs colour correction of given RGB colourspace array using the colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array. |
Ancillary Objects
colour.characterisation
augmented_matrix_Cheung2004(RGB[, terms]) |
Performs polynomial expansion of given RGB colourspace array using Cheung et al.(2004) method.. |
polynomial_expansion_Finlayson2015(RGB[, …]) |
Performs polynomial expansion of given RGB colourspace array using Finlayson et al.(2015) method.. |
polynomial_expansion_Vandermonde(a[, degree]) |
Performs polynomial expansion of given \(a\) array using Vandermonde method. |
colour_correction_matrix_Cheung2004(M_T, M_R) |
Computes a colour correction from given \(M_T\) colour array to \(M_R\) colour array using Cheung et al.(2004) method.. |
colour_correction_matrix_Finlayson2015(M_T, M_R) |
Computes a colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array using Finlayson et al.(2015) method.. |
colour_correction_matrix_Vandermonde(M_T, M_R) |
Computes a colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array using Vandermonde method. |
colour_correction_Cheung2004(RGB, M_T, M_R) |
Performs colour correction of given RGB colourspace array using the colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array using Cheung et al.(2004) method.. |
colour_correction_Finlayson2015(RGB, M_T, M_R) |
Performs colour correction of given RGB colourspace array using the colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array using Finlayson et al.(2015) method.. |
colour_correction_Vandermonde(RGB, M_T, M_R) |
Performs colour correction of given RGB colourspace array using the colour correction matrix from given \(M_T\) colour array to \(M_R\) colour array using Vandermonde method. |
Dataset
colour
COLOURCHECKERS |
Aggregated ColourCheckers chromaticity coordinates. |
COLOURCHECKERS_SDS |
Aggregated ColourCheckers spectral distributions. |
Ancillary Objects
colour.characterisation
ColourChecker |
ColourChecker data. |
colour.characterisation
RGB_SpectralSensitivities([data, domain, labels]) |
Implements support for a camera RGB spectral sensitivities. |
Dataset
colour
CAMERAS_RGB_SPECTRAL_SENSITIVITIES |
Cameras RGB spectral sensitivities. |
colour.characterisation
RGB_DisplayPrimaries([data, domain, labels]) |
Implements support for a RGB display (such as a CRT or LCD) primaries multi-spectral distributions. |
Dataset
colour
DISPLAYS_RGB_PRIMARIES |
Displays RGB primaries multi-spectral distributions. |
Colorimetry¶
- Spectral Data Structure
- Spectral Data Generation
- Conversion to Tristimulus Values
- Spectral Bandpass Dependence Correction
- Colour Matching Functions
- Colour Matching Functions Transformations
- Illuminants and Light Sources
- Dominant Wavelength and Purity
- Luminous Efficiency Functions
- Lightness Computation
- Luminance Computation
- Whiteness Computation
- Yellowness Computation
colour
SpectralShape([start, end, interval]) |
Defines the base object for spectral distribution shape. |
SpectralDistribution([data, domain]) |
Defines the spectral distribution: the base object for spectral computations. |
MultiSpectralDistribution([data, domain, labels]) |
Defines multi-spectral distribution: the base object for multi spectral computations. |
DEFAULT_SPECTRAL_SHAPE |
(360, 780, 1). |
ASTME30815_PRACTISE_SHAPE |
(360, 780, 1). |
colour
sd_CIE_standard_illuminant_A([shape]) |
CIE Standard Illuminant A is intended to represent typical, domestic, tungsten-filament lighting. |
sd_CIE_illuminant_D_series(xy[, M1_M2_rounding]) |
Returns the spectral distribution of given CIE Illuminant D Series using given xy chromaticity coordinates. |
sd_blackbody(temperature[, shape, c1, c2, n]) |
Returns the spectral distribution of the planckian radiator for given temperature \(T[K]\). |
sd_constant(k[, shape, dtype]) |
Returns a spectral distribution of given spectral shape filled with constant \(k\) values. |
sd_ones([shape]) |
Returns a spectral distribution of given spectral shape filled with ones. |
sd_zeros([shape]) |
Returns a spectral distribution of given spectral shape filled with zeros. |
SD_GAUSSIAN_METHODS |
Supported gaussian spectral distribution computation methods. |
sd_gaussian(mu_peak_wavelength, sigma_fwhm) |
Returns a gaussian spectral distribution of given spectral shape using given method. |
SD_SINGLE_LED_METHODS |
Supported single LED spectral distribution computation methods. |
sd_single_led(peak_wavelength, fwhm[, …]) |
Returns a single LED spectral distribution of given spectral shape at given peak wavelength and full width at half maximum according to given method. |
SD_MULTI_LEDS_METHODS |
Supported multi LED spectral distribution computation methods. |
sd_multi_leds(peak_wavelengths, fwhm[, …]) |
Returns a multi LED spectral distribution of given spectral shape at given peak wavelengths and full widths at half maximum according to given method. |
colour.colorimetry
blackbody_spectral_radiance(wavelength, …) |
Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\). |
daylight_locus_function(x_D) |
Returns the daylight locus as xy chromaticity coordinates. |
planck_law(wavelength, temperature[, c1, c2, n]) |
Returns the spectral radiance of a blackbody at thermodynamic temperature \(T[K]\) in a medium having index of refraction \(n\). |
sd_gaussian_normal(mu, sigma[, shape]) |
Returns a gaussian spectral distribution of given spectral shape at given mean wavelength \(\mu\) and standard deviation \(sigma\). |
sd_gaussian_fwhm(peak_wavelength, fwhm[, shape]) |
Returns a gaussian spectral distribution of given spectral shape at given peak wavelength and full width at half maximum. |
sd_single_led_Ohno2005(peak_wavelength, fwhm) |
Returns a single LED spectral distribution of given spectral shape at given peak wavelength and full width at half maximum according to Ohno (2005) method. |
sd_multi_leds_Ohno2005(peak_wavelengths, fwhm) |
Returns a multi LED spectral distribution of given spectral shape at given peak wavelengths and full widths at half maximum according to Ohno (2005) method. |
colour
sd_to_XYZ(sd[, cmfs, illuminant, k, method]) |
Converts given spectral distribution to CIE XYZ tristimulus values using given colour matching functions, illuminant and method. |
SD_TO_XYZ_METHODS |
Supported spectral distribution to CIE XYZ tristimulus values conversion methods. |
multi_sds_to_XYZ(msd[, shape, cmfs, …]) |
Converts given multi-spectral distribution array \(msd\) with given spectral shape to CIE XYZ tristimulus values using given colour matching functions and illuminant. |
MULTI_SD_TO_XYZ_METHODS |
Supported multi-spectral array to CIE XYZ tristimulus values conversion methods. |
wavelength_to_XYZ(wavelength[, cmfs]) |
Converts given wavelength \(\lambda\) to CIE XYZ tristimulus values using given colour matching functions. |
colour.colorimetry
sd_to_XYZ_ASTME30815(sd[, cmfs, illuminant, …]) |
Converts given spectral distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant according to practise ASTM E308-15 method. |
Ancillary Objects
colour.colorimetry
sd_to_XYZ_tristimulus_weighting_factors_ASTME30815(sd) |
Converts given spectral distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant using a table of tristimulus weighting factors according to practise ASTM E308-15 method. |
adjust_tristimulus_weighting_factors_ASTME30815(W, …) |
Adjusts given table of tristimulus weighting factors to account for a shorter wavelengths range of the test spectral shape compared to the reference spectral shape using practise ASTM E308-15 method: Weights at the wavelengths for which data are not available are added to the weights at the shortest and longest wavelength for which spectral data are available. |
lagrange_coefficients_ASTME202211([…]) |
Computes the Lagrange Coefficients for given interval size using practise ASTM E2022-11 method. |
tristimulus_weighting_factors_ASTME202211(…) |
Returns a table of tristimulus weighting factors for given colour matching functions and illuminant using practise ASTM E2022-11 method. |
colour.colorimetry
sd_to_XYZ_integration(sd[, cmfs, illuminant, k]) |
Converts given spectral distribution to CIE XYZ tristimulus values using given colour matching functions and illuminant according to classical integration method. |
multi_sds_to_XYZ_integration(msd, shape[, …]) |
Converts given multi-spectral distribution array \(msd\) with given spectral shape to CIE XYZ tristimulus values using given colour matching functions and illuminant. |
colour
bandpass_correction(sd[, method]) |
Implements spectral bandpass dependence correction on given spectral distribution using given method. |
BANDPASS_CORRECTION_METHODS |
Supported spectral bandpass dependence correction methods. |
colour.colorimetry
bandpass_correction_Stearns1988(sd) |
Implements spectral bandpass dependence correction on given spectral distribution using Stearns and Stearns (1988) method. |
colour.colorimetry
LMS_ConeFundamentals([data, domain, labels]) |
Implements support for the Stockman and Sharpe LMS cone fundamentals colour matching functions. |
RGB_ColourMatchingFunctions([data, domain, …]) |
Implements support for the CIE RGB colour matching functions. |
XYZ_ColourMatchingFunctions([data, domain, …]) |
Implements support for the CIE Standard Observers XYZ colour matching functions. |
Dataset
colour
CMFS |
Aggregated colour matching functions. |
LMS_CMFS |
LMS colour matching functions. |
RGB_CMFS |
CIE RGB colour matching functions. |
STANDARD_OBSERVERS_CMFS |
CIE Standard Observers XYZ colour matching functions. |
Ancillary Objects
colour.colorimetry
RGB_2_degree_cmfs_to_XYZ_2_degree_cmfs(…) |
Converts Wright & Guild 1931 2 Degree RGB CMFs colour matching functions into the CIE 1931 2 Degree Standard Observer colour matching functions. |
RGB_10_degree_cmfs_to_XYZ_10_degree_cmfs(…) |
Converts Stiles & Burch 1959 10 Degree RGB CMFs colour matching functions into the CIE 1964 10 Degree Standard Observer colour matching functions. |
RGB_10_degree_cmfs_to_LMS_10_degree_cmfs(…) |
Converts Stiles & Burch 1959 10 Degree RGB CMFs colour matching functions into the Stockman & Sharpe 10 Degree Cone Fundamentals spectral sensitivity functions. |
LMS_2_degree_cmfs_to_XYZ_2_degree_cmfs(…) |
Converts Stockman & Sharpe 2 Degree Cone Fundamentals colour matching functions into the CIE 2012 2 Degree Standard Observer colour matching functions. |
LMS_10_degree_cmfs_to_XYZ_10_degree_cmfs(…) |
Converts Stockman & Sharpe 10 Degree Cone Fundamentals colour matching functions into the CIE 2012 10 Degree Standard Observer colour matching functions. |
Dataset
colour
ILLUMINANTS |
Aggregated CIE illuminants chromaticity coordinates. |
ILLUMINANTS_SDS |
CIE illuminants spectral distributions. |
HUNTERLAB_ILLUMINANTS |
Aggregated Hunter L,a,b illuminant dataset. |
LIGHT_SOURCES |
Aggregated light sources chromaticity coordinates. |
LIGHT_SOURCES_SDS |
Aggregated light sources spectral distributions. |
colour
dominant_wavelength(xy, xy_n[, cmfs, reverse]) |
Returns the dominant wavelength \(\lambda_d\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus. |
complementary_wavelength(xy, xy_n[, cmfs]) |
Returns the complementary wavelength \(\lambda_c\) for given colour stimulus \(xy\) and the related \(xy_wl\) first and \(xy_{cw}\) second intersection coordinates with the spectral locus. |
excitation_purity(xy, xy_n[, cmfs]) |
Returns the excitation purity \(P_e\) for given colour stimulus \(xy\). |
colorimetric_purity(xy, xy_n[, cmfs]) |
Returns the colorimetric purity \(P_c\) for given colour stimulus \(xy\). |
colour
luminous_efficacy(sd[, lef]) |
Returns the luminous efficacy in \(lm\cdot W^{-1}\) of given spectral distribution using given luminous efficiency function. |
luminous_efficiency(sd[, lef]) |
Returns the luminous efficiency of given spectral distribution using given luminous efficiency function. |
luminous_flux(sd[, lef, K_m]) |
Returns the luminous flux for given spectral distribution using given luminous efficiency function. |
sd_mesopic_luminous_efficiency_function(Lp) |
Returns the mesopic luminous efficiency function \(V_m(\lambda)\) for given photopic luminance \(L_p\). |
Dataset
colour
LEFS |
Aggregated luminous efficiency functions. |
PHOTOPIC_LEFS |
Photopic luminous efficiency functions. |
SCOTOPIC_LEFS |
Scotopic luminous efficiency functions. |
colour
lightness(Y[, method]) |
Returns the Lightness \(L\) of given luminance \(Y\) using given method. |
LIGHTNESS_METHODS |
Supported Lightness computations methods. |
colour.colorimetry
lightness_Glasser1958(Y) |
Returns the Lightness \(L\) of given luminance \(Y\) using Glasser et al.(1958) method.. |
colour.colorimetry
lightness_Wyszecki1963(Y) |
Returns the Lightness \(W\) of given luminance \(Y\) using Wyszecki (1963) method. |
colour.colorimetry
lightness_CIE1976(Y[, Y_n]) |
Returns the Lightness \(L^*\) of given luminance \(Y\) using given reference white luminance \(Y_n\) as per CIE 1976 recommendation. |
intermediate_lightness_function_CIE1976(Y[, Y_n]) |
Returns the intermediate value \(f(Y/Yn)\) in the Lightness \(L^*\) computation for given luminance \(Y\) using given reference white luminance \(Y_n\) as per CIE 1976 recommendation. |
colour.colorimetry
lightness_Fairchild2010(Y[, epsilon]) |
Computes Lightness \(L_{hdr}\) of given luminance \(Y\) using Fairchild and Wyble (2010) method according to Michealis-Menten kinetics. |
colour.colorimetry
lightness_Fairchild2011(Y[, epsilon, method]) |
Computes Lightness \(L_{hdr}\) of given luminance \(Y\) using Fairchild and Chen (2011) method according to Michealis-Menten kinetics. |
colour
luminance(LV[, method]) |
Returns the luminance \(Y\) of given Lightness \(L^*\) or given Munsell value \(V\). |
LUMINANCE_METHODS |
Supported luminance computations methods. |
colour.colorimetry
luminance_Newhall1943(V) |
Returns the luminance \(R_Y\) of given Munsell value \(V\) using Newhall et al.(1943) method.. |
colour.colorimetry
luminance_CIE1976(L_star[, Y_n]) |
Returns the luminance \(Y\) of given Lightness \(L^*\) with given reference white luminance \(Y_n\). |
intermediate_luminance_function_CIE1976(f_Y_Y_n) |
Returns the luminance \(Y\) in the luminance \(Y\) computation for given intermediate value \(f(Y/Yn)\) using given reference white luminance \(Y_n\) as per CIE 1976 recommendation. |
colour.colorimetry
luminance_ASTMD153508(V) |
Returns the luminance \(Y\) of given Munsell value \(V\) using ASTM D1535-08e1 method. |
colour.colorimetry
luminance_Fairchild2010(L_hdr[, epsilon]) |
Computes luminance \(Y\) of given Lightness \(L_{hdr}\) using Fairchild and Wyble (2010) method according to Michealis-Menten kinetics. |
colour.colorimetry
luminance_Fairchild2011(L_hdr[, epsilon, method]) |
Computes luminance \(Y\) of given Lightness \(L_{hdr}\) using Fairchild and Chen (2011) method according to Michealis-Menten kinetics. |
colour
whiteness([method]) |
Returns the whiteness \(W\) using given method. |
WHITENESS_METHODS |
Supported whiteness computations methods. |
colour.colorimetry
whiteness_Berger1959(XYZ, XYZ_0) |
Returns the whiteness index \(WI\) of given sample CIE XYZ tristimulus values using Berger (1959) method. |
colour.colorimetry
whiteness_Taube1960(XYZ, XYZ_0) |
Returns the whiteness index \(WI\) of given sample CIE XYZ tristimulus values using Taube (1960) method. |
colour.colorimetry
whiteness_Stensby1968(Lab) |
Returns the whiteness index \(WI\) of given sample CIE L*a*b* colourspace array using Stensby (1968) method. |
colour.colorimetry
whiteness_ASTME313(XYZ) |
Returns the whiteness index \(WI\) of given sample CIE XYZ tristimulus values using ASTM E313 method. |
colour.colorimetry
whiteness_Ganz1979(xy, Y) |
Returns the whiteness index \(W\) and tint \(T\) of given sample xy chromaticity coordinates using Ganz and Griesser (1979) method. |
colour.colorimetry
whiteness_CIE2004(xy, Y, xy_n[, observer]) |
Returns the whiteness \(W\) or \(W_{10}\) and tint \(T\) or \(T_{10}\) of given sample xy chromaticity coordinates using CIE 2004 method. |
colour
yellowness(XYZ[, method]) |
Returns the yellowness \(W\) using given method. |
YELLOWNESS_METHODS |
Supported yellowness computations methods. |
colour.colorimetry
yellowness_ASTMD1925(XYZ) |
Returns the yellowness index \(YI\) of given sample CIE XYZ tristimulus values using ASTM D1925 method. |
colour.colorimetry
yellowness_ASTME313(XYZ) |
Returns the yellowness index \(YI\) of given sample CIE XYZ tristimulus values using ASTM E313 method. |
Constants¶
colour.constants
K_M |
Rounded maximum photopic luminous efficiency \(K_m\) value in \(lm\cdot W^{-1}\). |
KP_M |
Rounded maximum scotopic luminous efficiency \(K^{\prime}_m\) value in \(lm\cdot W^{-1}\). |
colour.constants
AVOGADRO_CONSTANT |
Avogadro constant. |
BOLTZMANN_CONSTANT |
Boltzmann constant. |
LIGHT_SPEED |
Speed of light in vacuum. |
PLANCK_CONSTANT |
Planck constant. |
colour.constants
DEFAULT_FLOAT_DTYPE |
alias of numpy.float64 |
EPSILON |
|
FLOATING_POINT_NUMBER_PATTERN |
str(object=’‘) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str |
INTEGER_THRESHOLD |
Integer threshold value. |
Contrast Sensitivity¶
colour
contrast_sensitivity_function([method]) |
Returns the contrast sensitivity \(S\) of the human eye according to the contrast sensitivity function (CSF) described by given method. |
CONTRAST_SENSITIVITY_METHODS |
Supported contrast sensitivity methods. |
colour.contrast
contrast_sensitivity_function_Barten1999(u) |
Returns the contrast sensitivity \(S\) of the human eye according to the contrast sensitivity function (CSF) described by Barten (1999). |
Ancillary Objects
colour.contrast
optical_MTF_Barten1999(u[, sigma]) |
Returns the optical modulation transfer function (MTF) \(M_{opt}\) of the eye using Barten (1999) method. |
pupil_diameter_Barten1999(L[, X_0, Y_0]) |
Returns the pupil diameter for given luminance and object or stimulus angular size using Barten (1999) method. |
sigma_Barten1999([sigma_0, C_ab, d]) |
Returns the standard deviation \(\sigma\) of the line-spread function resulting from the convolution of the different elements of the convolution process using Barten (1999) method. |
retinal_illuminance_Barten1999(L[, d, …]) |
Returns the retinal illuminance \(E\) in Trolands for given average luminance \(L\) and pupil diameter \(d\) using Barten (1999) method. |
maximum_angular_size_Barten1999(u[, X_0, …]) |
Returns the maximum angular size \(X\) of the object considered using Barten (1999) method. |
Continuous Signal¶
colour.continuous
AbstractContinuousFunction([name]) |
Defines the base class for abstract continuous function. |
Signal([data, domain]) |
Defines the base class for continuous signal. |
MultiSignal([data, domain, labels]) |
Defines the base class for multi-continuous signal, a container for multiple colour.continuous.Signal sub-class instances. |
Corresponding Chromaticities¶
colour
corresponding_chromaticities_prediction([…]) |
Returns the corresponding chromaticities prediction for given chromatic adaptation model. |
CORRESPONDING_CHROMATICITIES_PREDICTION_MODELS |
Aggregated corresponding chromaticities prediction models. |
Dataset
colour
BRENEMAN_EXPERIMENTS |
Breneman (1987) experiments. |
BRENEMAN_EXPERIMENTS_PRIMARIES_CHROMATICITIES |
Breneman (1987) experiments primaries chromaticities. |
colour.corresponding
corresponding_chromaticities_prediction_Fairchild1990([…]) |
Returns the corresponding chromaticities prediction for Fairchild (1990) chromatic adaptation model. |
colour.corresponding
corresponding_chromaticities_prediction_CIE1994([…]) |
Returns the corresponding chromaticities prediction for CIE 1994 chromatic adaptation model. |
colour.corresponding
corresponding_chromaticities_prediction_CMCCAT2000([…]) |
Returns the corresponding chromaticities prediction for CMCCAT2000 chromatic adaptation model. |
colour.corresponding
corresponding_chromaticities_prediction_VonKries([…]) |
Returns the corresponding chromaticities prediction for Von Kries chromatic adaptation model using given transform. |
Colour Difference¶
colour
delta_E(a, b[, method]) |
Returns the difference \(\Delta E_{ab}\) between two given CIE L*a*b* or \(J'a'b'\) colourspace arrays using given method. |
DELTA_E_METHODS |
Supported \(\Delta E_{ab}\) computations methods. |
colour.difference
delta_E_CIE1976(Lab_1, Lab_2) |
Returns the difference \(\Delta E_{76}\) between two given CIE L*a*b* colourspace arrays using CIE 1976 recommendation. |
colour.difference
delta_E_CIE1994(Lab_1, Lab_2[, textiles]) |
Returns the difference \(\Delta E_{94}\) between two given CIE L*a*b* colourspace arrays using CIE 1994 recommendation. |
colour.difference
delta_E_CIE2000(Lab_1, Lab_2[, textiles]) |
Returns the difference \(\Delta E_{00}\) between two given CIE L*a*b* colourspace arrays using CIE 2000 recommendation. |
colour.difference
delta_E_CMC(Lab_1, Lab_2[, l, c]) |
Returns the difference \(\Delta E_{CMC}\) between two given CIE L*a*b* colourspace arrays using Colour Measurement Committee recommendation. |
colour.difference
delta_E_CAM02LCD(Jpapbp_1, Jpapbp_2) |
Returns the difference \(\Delta E'\) between two given Li et al.(2017) CAM16-LCD colourspaces \(J'a'b'\) arrays.. |
delta_E_CAM02SCD(Jpapbp_1, Jpapbp_2) |
Returns the difference \(\Delta E'\) between two given Li et al.(2017) CAM16-SCD colourspaces \(J'a'b'\) arrays.. |
delta_E_CAM02UCS(Jpapbp_1, Jpapbp_2) |
Returns the difference \(\Delta E'\) between two given Li et al.(2017) CAM16-UCS colourspaces \(J'a'b'\) arrays.. |
colour.difference
delta_E_CAM16LCD(Jpapbp_1, Jpapbp_2) |
Returns the difference \(\Delta E'\) between two given Li et al.(2017) CAM16-LCD colourspaces \(J'a'b'\) arrays.. |
delta_E_CAM16SCD(Jpapbp_1, Jpapbp_2) |
Returns the difference \(\Delta E'\) between two given Li et al.(2017) CAM16-SCD colourspaces \(J'a'b'\) arrays.. |
delta_E_CAM16UCS(Jpapbp_1, Jpapbp_2) |
Returns the difference \(\Delta E'\) between two given Li et al.(2017) CAM16-UCS colourspaces \(J'a'b'\) arrays.. |
colour.difference
delta_E_DIN99(Lab_1, Lab_2[, textiles]) |
Returns the difference \(\Delta E_{DIN99}\) between two given CIE L*a*b* colourspace arrays using DIN99 formula. |
Input and Output¶
colour
read_image(path[, bit_depth, attributes]) |
Reads given image using OpenImageIO. |
write_image(image, path[, bit_depth, attributes]) |
Writes given image using OpenImageIO. |
Ancillary Objects
colour.io
ImageAttribute_Specification |
Defines the an image specification attribute. |
colour
LUT1D([table, name, domain, size, comments]) |
Defines the base class for a 1D LUT. |
LUT3x1D([table, name, domain, size, comments]) |
Defines the base class for a 3x1D LUT. |
LUT3D([table, name, domain, size, comments]) |
Defines the base class for a 3D LUT. |
LUTSequence(*args) |
Defines the base class for a LUT sequence, i.e. |
read_LUT(path[, method]) |
Reads given LUT file using given method. |
write_LUT(LUT, path[, decimals, method]) |
Writes given LUT to given file using given method. |
Ancillary Objects
colour.io
AbstractLUTSequenceOperator |
Defines the base class for LUT sequence operators. |
LUT_to_LUT(LUT, cls[, force_conversion]) |
Converts given LUT to given cls class instance. |
read_LUT_Cinespace(path) |
Reads given Cinespace .csp LUT file. |
write_LUT_Cinespace(LUT, path[, decimals]) |
Writes given LUT to given Cinespace .csp LUT file. |
read_LUT_IridasCube(path) |
Reads given Iridas .cube LUT file. |
write_LUT_IridasCube(LUT, path[, decimals]) |
Writes given LUT to given Iridas .cube LUT file. |
read_LUT_SonySPI1D(path) |
Reads given Sony .spi1d LUT file. |
write_LUT_SonySPI1D(LUT, path[, decimals]) |
Writes given LUT to given Sony .spi1d LUT file. |
read_LUT_SonySPI3D(path) |
Reads given Sony .spi3d LUT file. |
write_LUT_SonySPI3D(LUT, path[, decimals]) |
Writes given LUT to given Sony .spi3d LUT file. |
colour
read_sds_from_csv_file(path[, delimiter, …]) |
Reads the spectral data from given CSV file and return its content as an OrderedDict of colour.SpectralDistribution classes. |
read_spectral_data_from_csv_file(path[, …]) |
Reads the spectral data from given CSV file in the following form: |
write_sds_to_csv_file(sds, path[, …]) |
Writes the given spectral distributions to given CSV file. |
colour
SpectralDistribution_IESTM2714([path, …]) |
Defines a IES TM-27-14 spectral distribution. |
colour
read_sds_from_xrite_file(path) |
Reads the spectral data from given X-Rite file and returns it as an OrderedDict of colour.SpectralDistribution classes. |
Colour Models¶
- Tristimulus Values, CIE xyY Colourspace and Chromaticity Coordinates
- CIE L*a*b* Colourspace
- CIE L*u*v* Colourspace
- CIE 1960 UCS Colourspace
- CIE 1964 U*V*W* Colourspace
- Hunter L,a,b Colour Scale
- Hunter Rd,a,b Colour Scale
- DIN99 Colourspace
- CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)
- CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)
- IPT Colourspace
- hdr-CIELAB Colourspace
- hdr-IPT Colourspace
- OSA UCS Colourspace
- \(JzAzBz\) Colourspace
- RGB Colourspace and Transformations
- Pointer’s Gamut
colour
XYZ_to_xyY(XYZ[, illuminant]) |
Converts from CIE XYZ tristimulus values to CIE xyY colourspace and reference illuminant. |
xyY_to_XYZ(xyY) |
Converts from CIE xyY colourspace to CIE XYZ tristimulus values. |
XYZ_to_xy(XYZ[, illuminant]) |
Returns the xy chromaticity coordinates from given CIE XYZ tristimulus values. |
xy_to_XYZ(xy) |
Returns the CIE XYZ tristimulus values from given xy chromaticity coordinates. |
xyY_to_xy(xyY) |
Converts from CIE xyY colourspace to xy chromaticity coordinates. |
xy_to_xyY(xy[, Y]) |
Converts from xy chromaticity coordinates to CIE xyY colourspace by extending the array last dimension with \(Y\) Luminance. |
colour
XYZ_to_Lab(XYZ[, illuminant]) |
Converts from CIE XYZ tristimulus values to CIE L*a*b* colourspace. |
Lab_to_XYZ(Lab[, illuminant]) |
Converts from CIE L*a*b* colourspace to CIE XYZ tristimulus values. |
Lab_to_LCHab(Lab) |
Converts from CIE L*a*b* colourspace to CIE L*C*Hab colourspace. |
LCHab_to_Lab(LCHab) |
Converts from CIE L*C*Hab colourspace to CIE L*a*b* colourspace. |
colour
XYZ_to_Luv(XYZ[, illuminant]) |
Converts from CIE XYZ tristimulus values to CIE L*u*v* colourspace. |
Luv_to_XYZ(Luv[, illuminant]) |
Converts from CIE L*u*v* colourspace to CIE XYZ tristimulus values. |
Luv_to_LCHuv(Luv) |
Converts from CIE L*u*v* colourspace to CIE L*C*Huv colourspace. |
LCHuv_to_Luv(LCHuv) |
Converts from CIE L*C*Huv colourspace to CIE L*u*v* colourspace. |
Luv_to_uv(Luv[, illuminant]) |
Returns the \(uv^p\) chromaticity coordinates from given CIE L*u*v* colourspace array. |
Luv_uv_to_xy(uv) |
Returns the xy chromaticity coordinates from given CIE L*u*v* colourspace \(uv^p\) chromaticity coordinates. |
xy_to_Luv_uv(xy) |
Returns the CIE L*u*v* colourspace \(uv^p\) chromaticity coordinates from given xy chromaticity coordinates. |
colour
XYZ_to_UCS(XYZ) |
Converts from CIE XYZ tristimulus values to CIE 1960 UCS colourspace. |
UCS_to_XYZ(UVW) |
Converts from CIE 1960 UCS colourspace to CIE XYZ tristimulus values. |
UCS_to_uv(UVW) |
Returns the uv chromaticity coordinates from given CIE 1960 UCS colourspace array. |
UCS_uv_to_xy(uv) |
Returns the xy chromaticity coordinates from given CIE 1960 UCS colourspace uv chromaticity coordinates. |
xy_to_UCS_uv(xy) |
Returns the CIE 1960 UCS colourspace uv chromaticity coordinates from given xy chromaticity coordinates. |
colour
XYZ_to_UVW(XYZ[, illuminant]) |
Converts from CIE XYZ tristimulus values to CIE 1964 U*V*W* colourspace. |
UVW_to_XYZ(UVW[, illuminant]) |
Converts CIE 1964 U*V*W* colourspace to CIE XYZ tristimulus values. |
colour
XYZ_to_Hunter_Lab(XYZ[, XYZ_n, K_ab]) |
Converts from CIE XYZ tristimulus values to Hunter L,a,b colour scale. |
Hunter_Lab_to_XYZ(Lab[, XYZ_n, K_ab]) |
Converts from Hunter L,a,b colour scale to CIE XYZ tristimulus values. |
XYZ_to_K_ab_HunterLab1966(XYZ) |
Converts from whitepoint CIE XYZ tristimulus values to Hunter L,a,b \(K_{a}\) and \(K_{b}\) chromaticity coefficients. |
colour
XYZ_to_Hunter_Rdab(XYZ[, XYZ_n, K_ab]) |
Converts from CIE XYZ tristimulus values to Hunter Rd,a,b colour scale. |
Hunter_Rdab_to_XYZ(R_d_ab[, XYZ_n, K_ab]) |
Converts from Hunter Rd,a,b colour scale to CIE XYZ tristimulus values. |
colour
Lab_to_DIN99(Lab[, k_E, k_CH]) |
Converts from CIE L*a*b* colourspace to DIN99 colourspace. |
DIN99_to_Lab(Lab_99[, k_E, k_CH]) |
Converts from DIN99 colourspace to CIE L*a*b* colourspace. |
colour
JMh_CIECAM02_to_CAM02LCD(JMh) |
Converts from CIECAM02 \(JMh\) correlates array to Luo et al.(2006) CAM02-LCD colourspace \(J'a'b'\) array.. |
CAM02LCD_to_JMh_CIECAM02(Jpapbp) |
Converts from Luo et al.(2006) CAM02-LCD colourspace \(J'a'b'\) array to CIECAM02 \(JMh\) correlates array.. |
JMh_CIECAM02_to_CAM02SCD(JMh) |
Converts from CIECAM02 \(JMh\) correlates array to Luo et al.(2006) CAM02-SCD colourspace \(J'a'b'\) array.. |
CAM02SCD_to_JMh_CIECAM02(Jpapbp) |
Converts from Luo et al.(2006) CAM02-SCD colourspace \(J'a'b'\) array to CIECAM02 \(JMh\) correlates array.. |
JMh_CIECAM02_to_CAM02UCS(JMh) |
Converts from CIECAM02 \(JMh\) correlates array to Luo et al.(2006) CAM02-UCS colourspace \(J'a'b'\) array.. |
CAM02UCS_to_JMh_CIECAM02(Jpapbp) |
Converts from Luo et al.(2006) CAM02-UCS colourspace \(J'a'b'\) array to CIECAM02 \(JMh\) correlates array.. |
colour
JMh_CAM16_to_CAM16LCD(JMh, *[, …]) |
Converts from CAM16 \(JMh\) correlates array to Li et al.(2017) CAM16-LCD colourspace \(J'a'b'\) array.. |
CAM16LCD_to_JMh_CAM16(Jpapbp, *[, …]) |
Converts from Li et al.(2017) CAM16-LCD colourspace \(J'a'b'\) array to CAM16 \(JMh\) correlates array.. |
JMh_CAM16_to_CAM16SCD(JMh, *[, …]) |
Converts from CAM16 \(JMh\) correlates array to Li et al.(2017) CAM16-SCD colourspace \(J'a'b'\) array.. |
CAM16SCD_to_JMh_CAM16(Jpapbp, *[, …]) |
Converts from Li et al.(2017) CAM16-SCD colourspace \(J'a'b'\) array to CAM16 \(JMh\) correlates array.. |
JMh_CAM16_to_CAM16UCS(JMh, *[, …]) |
Converts from CAM16 \(JMh\) correlates array to Li et al.(2017) CAM16-UCS colourspace \(J'a'b'\) array.. |
CAM16UCS_to_JMh_CAM16(Jpapbp, *[, …]) |
Converts from Li et al.(2017) CAM16-UCS colourspace \(J'a'b'\) array to CAM16 \(JMh\) correlates array.. |
colour
XYZ_to_IPT(XYZ) |
Converts from CIE XYZ tristimulus values to IPT colourspace. |
IPT_to_XYZ(IPT) |
Converts from IPT colourspace to CIE XYZ tristimulus values. |
IPT_hue_angle(IPT) |
Computes the hue angle in degrees from IPT colourspace. |
colour
XYZ_to_hdr_CIELab(XYZ[, illuminant, Y_s, …]) |
Converts from CIE XYZ tristimulus values to hdr-CIELAB colourspace. |
hdr_CIELab_to_XYZ(Lab_hdr[, illuminant, …]) |
Converts from hdr-CIELAB colourspace to CIE XYZ tristimulus values. |
HDR_CIELAB_METHODS |
Supported hdr-CIELAB colourspace computation methods. |
colour
XYZ_to_hdr_IPT(XYZ[, Y_s, Y_abs, method]) |
Converts from CIE XYZ tristimulus values to hdr-IPT colourspace. |
hdr_IPT_to_XYZ(IPT_hdr[, Y_s, Y_abs, method]) |
Converts from hdr-IPT colourspace to CIE XYZ tristimulus values. |
HDR_IPT_METHODS |
Supported hdr-IPT colourspace computation methods. |
colour
XYZ_to_OSA_UCS(XYZ) |
Converts from CIE XYZ tristimulus values under the CIE 1964 10 Degree Standard Observer to OSA UCS colourspace. |
OSA_UCS_to_XYZ(Ljg[, optimisation_parameters]) |
Converts from OSA UCS colourspace to CIE XYZ tristimulus values under the CIE 1964 10 Degree Standard Observer. |
colour
XYZ_to_JzAzBz(XYZ_D65[, constants]) |
Converts from CIE XYZ tristimulus values to \(J_zA_zB_z\) colourspace. |
JzAzBz_to_XYZ(JzAzBz[, constants]) |
Converts from \(J_zA_zB_z\) colourspace to CIE XYZ tristimulus values. |
colour
XYZ_to_RGB(XYZ, illuminant_XYZ, …[, …]) |
Converts from CIE XYZ tristimulus values to RGB colourspace array. |
RGB_to_XYZ(RGB, illuminant_RGB, …[, …]) |
Converts given RGB colourspace array to CIE XYZ tristimulus values. |
RGB_to_RGB(RGB, input_colourspace, …[, …]) |
Converts given RGB colourspace array from given input RGB colourspace to output RGB colourspace using given chromatic adaptation method. |
RGB_to_RGB_matrix(input_colourspace, …[, …]) |
Computes the matrix \(M\) converting from given input RGB colourspace to output RGB colourspace using given chromatic adaptation method. |
Ancillary Objects
colour
XYZ_to_sRGB(XYZ[, illuminant, …]) |
Converts from CIE XYZ tristimulus values to sRGB colourspace. |
sRGB_to_XYZ(RGB[, illuminant, …]) |
Converts from sRGB colourspace to CIE XYZ tristimulus values. |
colour
normalised_primary_matrix(primaries, whitepoint) |
Returns the normalised primary matrix using given primaries and whitepoint \(xy\) chromaticity coordinates. |
chromatically_adapted_primaries(primaries, …) |
Chromatically adapts given primaries \(xy\) chromaticity coordinates from test whitepoint_t to reference whitepoint_r. |
primaries_whitepoint(npm) |
Returns the primaries and whitepoint \(xy\) chromaticity coordinates using given normalised primary matrix. |
RGB_luminance(RGB, primaries, whitepoint) |
Returns the luminance \(Y\) of given RGB components from given primaries and whitepoint. |
RGB_luminance_equation(primaries, whitepoint) |
Returns the luminance equation from given primaries and whitepoint. |
colour
RGB_Colourspace(name, primaries, whitepoint) |
Implements support for the RGB colourspaces dataset from colour.models.dataset.aces_rgb, etc…. |
RGB_COLOURSPACES |
Aggregated RGB colourspaces. |
colour.models
ACES_2065_1_COLOURSPACE |
ACES2065-1 colourspace, base encoding, used for exchange of full fidelity images and archiving. |
ACES_CC_COLOURSPACE |
ACEScc colourspace, a working space for color correctors, target for ASC-CDL values created on-set. |
ACES_CCT_COLOURSPACE |
ACEScct colourspace, an alternative working space for colour correctors, intended to be transient and internal to software or hardware systems, and is specifically not intended for interchange or archiving. |
ACES_PROXY_COLOURSPACE |
ACESproxy colourspace, a lightweight encoding for transmission over HD-SDI (or other production transmission schemes), onset look management. |
ACES_CG_COLOURSPACE |
ACEScg colourspace, a working space for paint/compositor applications that don’t support ACES2065-1 or ACEScc. |
ADOBE_RGB_1998_COLOURSPACE |
Adobe RGB (1998) colourspace. |
ADOBE_WIDE_GAMUT_RGB_COLOURSPACE |
Adobe Wide Gamut RGB colourspace. |
ALEXA_WIDE_GAMUT_COLOURSPACE |
ALEXA Wide Gamut colourspace. |
APPLE_RGB_COLOURSPACE |
Apple RGB colourspace. |
BEST_RGB_COLOURSPACE |
Best RGB colourspace. |
BETA_RGB_COLOURSPACE |
Beta RGB colourspace. |
BT470_525_COLOURSPACE |
ITU-R BT.470 - 525 colourspace. |
BT470_625_COLOURSPACE |
ITU-R BT.470 - 625 colourspace. |
BT709_COLOURSPACE |
ITU-R BT.709 colourspace. |
BT2020_COLOURSPACE |
ITU-R BT.2020 colourspace. |
CIE_RGB_COLOURSPACE |
CIE RGB colourspace. |
CINEMA_GAMUT_COLOURSPACE |
Cinema Gamut colourspace. |
COLOR_MATCH_RGB_COLOURSPACE |
ColorMatch RGB colourspace. |
DCDM_XYZ_COLOURSPACE |
DCDM XYZ colourspace. |
DCI_P3_COLOURSPACE |
DCI-P3 colourspace. |
DCI_P3_P_COLOURSPACE |
DCI-P3+ colourspace. |
DON_RGB_4_COLOURSPACE |
Don RGB 4 colourspace. |
ECI_RGB_V2_COLOURSPACE |
ECI RGB v2 colourspace. |
EKTA_SPACE_PS_5_COLOURSPACE |
Ekta Space PS 5 colourspace. |
PROTUNE_NATIVE_COLOURSPACE |
Protune Native colourspace. |
MAX_RGB_COLOURSPACE |
Max RGB colourspace. |
NTSC_COLOURSPACE |
NTSC colourspace. |
P3_D65_COLOURSPACE |
P3-D65 colourspace. |
PAL_SECAM_COLOURSPACE |
Pal/Secam colourspace. |
RED_COLOR_COLOURSPACE |
REDcolor colourspace. |
RED_COLOR_2_COLOURSPACE |
REDcolor2 colourspace. |
RED_COLOR_3_COLOURSPACE |
REDcolor3 colourspace. |
RED_COLOR_4_COLOURSPACE |
REDcolor4 colourspace. |
RED_WIDE_GAMUT_RGB_COLOURSPACE |
REDWideGamutRGB colourspace. |
DRAGON_COLOR_COLOURSPACE |
DRAGONcolor colourspace. |
DRAGON_COLOR_2_COLOURSPACE |
DRAGONcolor2 colourspace. |
ROMM_RGB_COLOURSPACE |
ROMM RGB colourspace. |
RIMM_RGB_COLOURSPACE |
RIMM RGB colourspace. |
ERIMM_RGB_COLOURSPACE |
ERIMM RGB colourspace. |
PROPHOTO_RGB_COLOURSPACE |
ProPhoto RGB colourspace, an alias colourspace for ROMM RGB. |
RUSSELL_RGB_COLOURSPACE |
Russell RGB colourspace. |
SMPTE_240M_COLOURSPACE |
SMPTE 240M colourspace. |
S_GAMUT_COLOURSPACE |
S-Gamut colourspace. |
S_GAMUT3_COLOURSPACE |
S-Gamut3 colourspace. |
S_GAMUT3_CINE_COLOURSPACE |
S-Gamut3.Cine colourspace. |
sRGB_COLOURSPACE |
sRGB colourspace. |
V_GAMUT_COLOURSPACE |
V-Gamut colourspace. |
XTREME_RGB_COLOURSPACE |
Xtreme RGB colourspace. |
colour
encoding_cctf(value[, function]) |
Encodes linear \(RGB\) values to non linear \(R'G'B'\) values using given encoding colour component transfer function (Encoding CCTF). |
ENCODING_CCTFS |
Supported encoding colour component transfer functions (Encoding CCTFs), a collection of the functions defined by colour.LOG_ENCODING_CURVES, colour.OETFS, colour.EOTFS_REVERSE attributes and 3 gamma encoding functions (1 / 2.2, 1 / 2.4, 1 / 2.6). |
decoding_cctf(value[, function]) |
Decodes non-linear \(R'G'B'\) values to linear \(RGB\) values using given decoding colour component transfer function (Decoding CCTF). |
DECODING_CCTFS |
Supported decoding colour component transfer functions (Decoding CCTFs), a collection of the functions defined by colour.LOG_DECODING_CURVES, colour.EOTFS, colour.OETFS_REVERSE attributes and 3 gamma decoding functions (2.2, 2.4, 2.6). |
colour
oetf(value[, function]) |
Encodes estimated tristimulus values in a scene to \(R'G'B'\) video component signal value using given opto-electronic transfer function (OETF / OECF). |
OETFS |
Supported opto-electrical transfer functions (OETFs / OECFs). |
oetf_reverse(value[, function]) |
Decodes \(R'G'B'\) video component signal value to tristimulus values at the display using given reverse opto-electronic transfer function (OETF / OECF). |
OETFS_REVERSE |
Supported reverse opto-electrical transfer functions (OETFs / OECFs). |
colour.models
oetf_ARIBSTDB67(E[, r, constants]) |
Defines ARIB STD-B67 (Hybrid Log-Gamma) opto-electrical transfer function (OETF / OECF). |
oetf_reverse_ARIBSTDB67(E_p[, r, constants]) |
Defines ARIB STD-B67 (Hybrid Log-Gamma) reverse opto-electrical transfer function (OETF / OECF). |
oetf_DICOMGSDF(L[, out_int]) |
Defines the DICOM - Grayscale Standard Display Function opto-electronic transfer function (OETF / OECF). |
oetf_BT2020(E[, is_12_bits_system, constants]) |
Defines Recommendation ITU-R BT.2020 opto-electrical transfer function (OETF / OECF). |
oetf_BT2100_HLG(E) |
Defines Recommendation ITU-R BT.2100 Reference HLG opto-electrical transfer function (OETF / OECF). |
oetf_reverse_BT2100_HLG(E) |
Defines Recommendation ITU-R BT.2100 Reference HLG reverse opto-electrical transfer function (OETF / OECF). |
oetf_BT2100_PQ(E) |
Defines Recommendation ITU-R BT.2100 Reference PQ opto-electrical transfer function (OETF / OECF). |
oetf_reverse_BT2100_PQ(E_p) |
Defines Recommendation ITU-R BT.2100 Reference PQ reverse opto-electrical transfer function (OETF / OECF). |
oetf_BT601(L) |
Defines Recommendation ITU-R BT.601-7 opto-electronic transfer function (OETF / OECF). |
oetf_reverse_BT601(E) |
Defines Recommendation ITU-R BT.601-7 reverse opto-electronic transfer function (OETF / OECF). |
oetf_BT709(L) |
Defines Recommendation ITU-R BT.709-6 opto-electronic transfer function (OETF / OECF). |
oetf_reverse_BT709(V) |
Defines Recommendation ITU-R BT.709-6 reverse opto-electronic transfer function (OETF / OECF). |
oetf_ProPhotoRGB(X[, bit_depth, out_int]) |
Defines the ROMM RGB encoding opto-electronic transfer function (OETF / OECF). |
oetf_RIMMRGB(X[, bit_depth, out_int, E_clip]) |
Defines the RIMM RGB encoding opto-electronic transfer function (OETF / OECF). |
oetf_ROMMRGB(X[, bit_depth, out_int]) |
Defines the ROMM RGB encoding opto-electronic transfer function (OETF / OECF). |
oetf_SMPTE240M(L_c) |
Defines SMPTE 240M opto-electrical transfer function (OETF / OECF). |
oetf_ST2084(C[, L_p, constants]) |
Defines SMPTE ST 2084:2014 optimised perceptual opto-electronic transfer function (OETF / OECF). |
oetf_sRGB(L) |
Defines the sRGB colourspace opto-electronic transfer function (OETF / OECF). |
oetf_reverse_sRGB(V) |
Defines the sRGB colourspace reverse opto-electronic transfer function (OETF / OECF). |
Ancillary Objects
colour
gamma_function(a[, exponent, …]) |
Defines a typical gamma encoding / decoding function. |
linear_function(a) |
Defines a typical linear encoding / decoding function, essentially a pass-through function. |
colour
eotf(value[, function]) |
Decodes \(R'G'B'\) video component signal value to tristimulus values at the display using given electro-optical transfer function (EOTF / EOCF). |
EOTFS |
Supported electro-optical transfer functions (EOTFs / EOCFs). |
eotf_reverse(value[, function]) |
Encodes estimated tristimulus values in a scene to \(R'G'B'\) video component signal value using given reverse electro-optical transfer function (EOTF / EOCF). |
EOTFS_REVERSE |
Supported reverse electro-optical transfer functions (EOTFs / EOCFs). |
colour.models
eotf_DCDM(XYZ_p[, in_int]) |
Defines the DCDM electro-optical transfer function (EOTF / EOCF). |
eotf_reverse_DCDM(XYZ[, out_int]) |
Defines the DCDM reverse electro-optical transfer function (EOTF / EOCF). |
eotf_DICOMGSDF(J[, in_int]) |
Defines the DICOM - Grayscale Standard Display Function electro-optical transfer function (EOTF / EOCF). |
eotf_BT1886(V[, L_B, L_W]) |
Defines Recommendation ITU-R BT.1886 electro-optical transfer function (EOTF / EOCF). |
eotf_reverse_BT1886(L[, L_B, L_W]) |
Defines Recommendation ITU-R BT.1886 reverse electro-optical transfer function (EOTF / EOCF). |
eotf_BT2020(E_p[, is_12_bits_system, constants]) |
Defines Recommendation ITU-R BT.2020 electro-optical transfer function (EOTF / EOCF). |
eotf_BT2100_HLG(E_p[, L_B, L_W, gamma]) |
Defines Recommendation ITU-R BT.2100 Reference HLG electro-optical transfer function (EOTF / EOCF). |
eotf_reverse_BT2100_HLG(F_D[, L_B, L_W, gamma]) |
Defines Recommendation ITU-R BT.2100 Reference HLG reverse electro-optical transfer function (EOTF / EOCF). |
eotf_BT2100_PQ(E_p) |
Defines Recommendation ITU-R BT.2100 Reference PQ electro-optical transfer function (EOTF / EOCF). |
eotf_reverse_BT2100_PQ(F_D) |
Defines Recommendation ITU-R BT.2100 Reference PQ reverse electro-optical transfer function (EOTF / EOCF). |
eotf_ProPhotoRGB(X_p[, bit_depth, in_int]) |
Defines the ROMM RGB encoding electro-optical transfer function (EOTF / EOCF). |
eotf_RIMMRGB(X_p[, bit_depth, in_int, E_clip]) |
Defines the RIMM RGB encoding electro-optical transfer function (EOTF / EOCF). |
eotf_ROMMRGB(X_p[, bit_depth, in_int]) |
Defines the ROMM RGB encoding electro-optical transfer function (EOTF / EOCF). |
eotf_SMPTE240M(V_r) |
Defines SMPTE 240M electro-optical transfer function (EOTF / EOCF). |
eotf_ST2084(N[, L_p, constants]) |
Defines SMPTE ST 2084:2014 optimised perceptual electro-optical transfer function (EOTF / EOCF). |
colour
ootf(value[, function]) |
Maps relative scene linear light to display linear light using given opto-optical transfer function (OOTF / OOCF). |
OOTFS |
Supported opto-optical transfer functions (OOTFs / OOCFs). |
ootf_reverse(value[, function]) |
Maps relative display linear light to scene linear light using given reverse opto-optical transfer function (OOTF / OOCF). |
OOTFS_REVERSE |
Supported reverse opto-optical transfer functions (OOTFs / OOCFs). |
colour.models
ootf_BT2100_HLG(E[, L_B, L_W, gamma]) |
Defines Recommendation ITU-R BT.2100 Reference HLG opto-optical transfer function (OOTF / OOCF). |
ootf_reverse_BT2100_HLG(F_D[, L_B, L_W, gamma]) |
Defines Recommendation ITU-R BT.2100 Reference HLG reverse opto-optical transfer function (OOTF / OOCF). |
ootf_BT2100_PQ(E) |
Defines Recommendation ITU-R BT.2100 Reference PQ opto-optical transfer function (OOTF / OOCF). |
ootf_reverse_BT2100_PQ(F_D) |
Defines Recommendation ITU-R BT.2100 Reference PQ reverse opto-optical transfer function (OOTF / OOCF). |
colour
log_encoding_curve(value[, curve]) |
Encodes linear-light values to \(R'G'B'\) video component signal value using given log curve. |
LOG_ENCODING_CURVES |
Supported log encoding curves. |
log_decoding_curve(value[, curve]) |
Decodes \(R'G'B'\) video component signal value to linear-light values using given log curve. |
LOG_DECODING_CURVES |
Supported log decoding curves. |
colour.models
log_encoding_ACEScc(lin_AP1) |
Defines the ACEScc colourspace log encoding / opto-electronic transfer function. |
log_decoding_ACEScc(ACEScc) |
Defines the ACEScc colourspace log decoding / electro-optical transfer function. |
log_encoding_ACEScct(lin_AP1[, constants]) |
Defines the ACEScct colourspace log encoding / opto-electronic transfer function. |
log_decoding_ACEScct(ACEScct[, constants]) |
Defines the ACEScct colourspace log decoding / electro-optical transfer function. |
log_encoding_ACESproxy(lin_AP1[, bit_depth, …]) |
Defines the ACESproxy colourspace log encoding curve / opto-electronic transfer function. |
log_decoding_ACESproxy(ACESproxy[, …]) |
Defines the ACESproxy colourspace log decoding curve / electro-optical transfer function. |
log_encoding_ALEXALogC(x[, firmware, method, EI]) |
Defines the ALEXA Log C log encoding curve / opto-electronic transfer function. |
log_decoding_ALEXALogC(t[, firmware, method, EI]) |
Defines the ALEXA Log C log decoding curve / electro-optical transfer function. |
log_encoding_CanonLog2(x[, bit_depth, …]) |
Defines the Canon Log 2 log encoding curve / opto-electronic transfer function. |
log_decoding_CanonLog2(clog2[, bit_depth, …]) |
Defines the Canon Log 2 log decoding curve / electro-optical transfer function. |
log_encoding_CanonLog3(x[, bit_depth, …]) |
Defines the Canon Log 3 log encoding curve / opto-electronic transfer function. |
log_decoding_CanonLog3(clog3[, bit_depth, …]) |
Defines the Canon Log 3 log decoding curve / electro-optical transfer function. |
log_encoding_CanonLog(x[, bit_depth, …]) |
Defines the Canon Log log encoding curve / opto-electronic transfer function. |
log_decoding_CanonLog(clog[, bit_depth, …]) |
Defines the Canon Log log decoding curve / electro-optical transfer function. |
log_encoding_Cineon(x[, black_offset]) |
Defines the Cineon log encoding curve / opto-electronic transfer function. |
log_decoding_Cineon(y[, black_offset]) |
Defines the Cineon log decoding curve / electro-optical transfer function. |
log_encoding_ERIMMRGB(X[, bit_depth, …]) |
Defines the ERIMM RGB log encoding curve / opto-electronic transfer function (OETF / OECF). |
log_decoding_ERIMMRGB(X_p[, bit_depth, …]) |
Defines the ERIMM RGB log decoding curve / electro-optical transfer function (EOTF / EOCF). |
log_encoding_Log3G10(x[, legacy_curve]) |
Defines the Log3G10 log encoding curve / opto-electronic transfer function. |
log_decoding_Log3G10(y[, legacy_curve]) |
Defines the Log3G10 log decoding curve / electro-optical transfer function. |
log_encoding_Log3G12(x) |
Defines the Log3G12 log encoding curve / opto-electronic transfer function. |
log_decoding_Log3G12(y) |
Defines the Log3G12 log decoding curve / electro-optical transfer function. |
log_encoding_Panalog(x[, black_offset]) |
Defines the Panalog log encoding curve / opto-electronic transfer function. |
log_decoding_Panalog(y[, black_offset]) |
Defines the Panalog log decoding curve / electro-optical transfer function. |
log_encoding_PivotedLog(x[, log_reference, …]) |
Defines the Josh Pines style Pivoted Log log encoding curve / opto-electronic transfer function. |
log_decoding_PivotedLog(y[, log_reference, …]) |
Defines the Josh Pines style Pivoted Log log decoding curve / electro-optical transfer function. |
log_encoding_Protune(x) |
Defines the Protune log encoding curve / opto-electronic transfer function. |
log_decoding_Protune(y) |
Defines the Protune log decoding curve / electro-optical transfer function. |
log_encoding_REDLog(x[, black_offset]) |
Defines the REDLog log encoding curve / opto-electronic transfer function. |
log_decoding_REDLog(y[, black_offset]) |
Defines the REDLog log decoding curve / electro-optical transfer function. |
log_encoding_REDLogFilm(x[, black_offset]) |
Defines the REDLogFilm log encoding curve / opto-electronic transfer function. |
log_decoding_REDLogFilm(y[, black_offset]) |
Defines the REDLogFilm log decoding curve / electro-optical transfer function. |
log_encoding_SLog(x[, bit_depth, out_legal, …]) |
Defines the Sony S-Log log encoding curve / opto-electronic transfer function. |
log_decoding_SLog(y[, bit_depth, in_legal, …]) |
Defines the Sony S-Log log decoding curve / electro-optical transfer function. |
log_encoding_SLog2(x[, bit_depth, …]) |
Defines the Sony S-Log2 log encoding curve / opto-electronic transfer function. |
log_decoding_SLog2(y[, bit_depth, in_legal, …]) |
Defines the Sony S-Log2 log decoding curve / electro-optical transfer function. |
log_encoding_SLog3(x[, bit_depth, …]) |
Defines the Sony S-Log3 log encoding curve / opto-electronic transfer function. |
log_decoding_SLog3(y[, bit_depth, in_legal, …]) |
Defines the Sony S-Log3 log decoding curve / electro-optical transfer function. |
log_encoding_VLog(L_in[, bit_depth, …]) |
Defines the Panasonic V-Log log encoding curve / opto-electronic transfer function. |
log_decoding_VLog(V_out[, bit_depth, …]) |
Defines the Panasonic V-Log log decoding curve / electro-optical transfer function. |
log_encoding_ViperLog(x) |
Defines the Viper Log log encoding curve / opto-electronic transfer function. |
log_decoding_ViperLog(y) |
Defines the Viper Log log decoding curve / electro-optical transfer function. |
colour
sd_to_aces_relative_exposure_values(sd[, …]) |
Converts given spectral distribution to ACES2065-1 colourspace relative exposure values. |
Ancillary Objects
colour.models
ACES_RICD |
Implements support for the CIE RGB colour matching functions. |
colour
RGB_to_YCbCr(RGB[, K, in_bits, in_legal, …]) |
Converts an array of R’G’B’ values to the corresponding Y’CbCr colour encoding values array. |
YCbCr_to_RGB(YCbCr[, K, in_bits, in_legal, …]) |
Converts an array of Y’CbCr colour encoding values to the corresponding R’G’B’ values array. |
YCBCR_WEIGHTS |
Implements a case-insensitive mutable mapping / dict object. |
RGB_to_YcCbcCrc(RGB[, out_bits, out_legal, …]) |
Converts an array of RGB linear values to the corresponding Yc’Cbc’Crc’ colour encoding values array. |
YcCbcCrc_to_RGB(YcCbcCrc[, in_bits, …]) |
Converts an array of Yc’Cbc’Crc’ colour encoding values to the corresponding RGB array of linear values. |
Ancillary Objects
colour
full_to_legal(CV[, bit_depth, in_int, out_int]) |
Converts given code value \(CV\) or float equivalent of a code value at a given bit depth from full range (full swing) to legal range (studio swing). |
legal_to_full(CV[, bit_depth, in_int, out_int]) |
Converts given code value \(CV\) or float equivalent of a code value at a given bit depth from legal range (studio swing) to full range (full swing). |
CV_range([bit_depth, is_legal, is_int]) |
Returns the code value \(CV\) range for given bit depth, range legality and representation. |
colour
RGB_to_YCoCg(RGB) |
Converts an array of R’G’B’ values to the corresponding YCoCg colour encoding values array. |
YCoCg_to_RGB(YCoCg) |
Converts an array of YCoCg colour encoding values to the corresponding R’G’B’ values array. |
colour
RGB_to_ICTCP(RGB[, L_p]) |
Converts from ITU-R BT.2020 colourspace to \(IC_TC_P\) colour encoding. |
ICTCP_to_RGB(ICTCP[, L_p]) |
Converts from \(IC_TC_P\) colour encoding to ITU-R BT.2020 colourspace. |
colour
RGB_to_Prismatic(RGB) |
Converts from RGB colourspace to Prismatic \(L\rho\gamma\beta\) colourspace array. |
Prismatic_to_RGB(Lrgb) |
Converts from Prismatic \(L\rho\gamma\beta\) colourspace array to RGB colourspace. |
colour
RGB_to_HSV(RGB) |
Converts from RGB colourspace to HSV colourspace. |
HSV_to_RGB(HSV) |
Converts from HSV colourspace to RGB colourspace. |
colour
RGB_to_HSL(RGB) |
Converts from RGB colourspace to HSL colourspace. |
HSL_to_RGB(HSL) |
Converts from HSL colourspace to RGB colourspace. |
colour
RGB_to_CMY(RGB) |
Converts from RGB colourspace to CMY colourspace. |
CMY_to_RGB(CMY) |
Converts from CMY colourspace to CMY colourspace. |
CMY_to_CMYK(CMY) |
Converts from CMY colourspace to CMYK colourspace. |
CMYK_to_CMY(CMYK) |
Converts from CMYK colourspace to CMY colourspace. |
Colour Notation Systems¶
colour
munsell_colour_to_xyY(munsell_colour) |
Converts given Munsell colour to CIE xyY colourspace. |
xyY_to_munsell_colour(xyY[, hue_decimals, …]) |
Converts from CIE xyY colourspace to Munsell colour. |
Dataset
colour
MUNSELL_COLOURS |
Aggregated Munsell colours. |
colour
munsell_value(Y[, method]) |
Returns the Munsell value \(V\) of given luminance \(Y\) using given method. |
MUNSELL_VALUE_METHODS |
Supported Munsell value computations methods. |
colour.notation
munsell_value_Priest1920(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using Priest et al.(1920) method.. |
colour.notation
munsell_value_Munsell1933(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using Munsell et al.(1933) method.. |
colour.notation
munsell_value_Moon1943(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using Moon and Spencer (1943) method. |
colour.notation
munsell_value_Saunderson1944(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using Saunderson and Milner (1944) method. |
colour.notation
munsell_value_Ladd1955(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using Ladd and Pinney (1955) method. |
colour.notation
munsell_value_McCamy1987(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using McCamy (1987) method. |
colour.notation
munsell_value_ASTMD153508(Y) |
Returns the Munsell value \(V\) of given luminance \(Y\) using a reverse lookup table from ASTM D1535-08e1 method. |
colour.notation
RGB_to_HEX(RGB) |
Converts from RGB colourspace to hexadecimal triplet representation. |
HEX_to_RGB(HEX) |
Converts from hexadecimal triplet representation to RGB colourspace. |
Optical Phenomena¶
colour
rayleigh_scattering(wavelength[, …]) |
Returns the Rayleigh optical depth \(T_r(\lambda)\) as function of wavelength \(\lambda\) in centimeters (cm). |
sd_rayleigh_scattering([shape, …]) |
Returns the Rayleigh spectral distribution for given spectral shape. |
scattering_cross_section(wavelength[, …]) |
Returns the scattering cross section per molecule \(\sigma\) of dry air as function of wavelength \(\lambda\) in centimeters (cm) using given \(CO_2\) concentration in parts per million (ppm) and temperature \(T[K]\) in kelvin degrees following Van de Hulst (1957) method. |
colour.phenomena
rayleigh_optical_depth(wavelength[, …]) |
Returns the Rayleigh optical depth \(T_r(\lambda)\) as function of wavelength \(\lambda\) in centimeters (cm). |
Plotting¶
colour.plotting
colour_style([use_style]) |
Returns Colour plotting style. |
colour_cycle(**kwargs) |
Returns a colour cycle iterator using given colour map. |
artist(**kwargs) |
Returns the current figure and its axes or creates a new one. |
camera(**kwargs) |
Sets the camera settings. |
render(**kwargs) |
Renders the current figure while adjusting various settings such as the bounding box, the title or background transparency. |
label_rectangles(labels, rectangles[, …]) |
Add labels above given rectangles. |
uniform_axes3d(axes) |
Sets equal aspect ratio to given 3d axes. |
plot_single_colour_swatch(colour_swatch, …) |
Plots given colour swatch. |
plot_multi_colour_swatches(colour_swatches) |
Plots given colours swatches. |
plot_single_function(function[, samples, …]) |
Plots given function. |
plot_multi_functions(functions[, samples, …]) |
Plots given functions. |
plot_image(image[, text_parameters, …]) |
Plots given image. |
colour.plotting
plot_single_sd(sd[, cmfs, out_of_gamut_clipping]) |
Plots given spectral distribution. |
plot_multi_sds(sds[, cmfs, use_sds_colours, …]) |
Plots given spectral distributions. |
plot_single_cmfs([cmfs]) |
Plots given colour matching functions. |
plot_multi_cmfs([cmfs]) |
Plots given colour matching functions. |
plot_single_illuminant_sd([illuminant, cmfs]) |
Plots given single illuminant spectral distribution. |
plot_multi_illuminant_sds([illuminants]) |
Plots given illuminants spectral distributions. |
plot_visible_spectrum([cmfs, …]) |
Plots the visible colours spectrum using given standard observer CIE XYZ colour matching functions. |
plot_single_lightness_function([function]) |
Plots given Lightness function. |
plot_multi_lightness_functions([functions]) |
Plots given Lightness functions. |
plot_single_luminance_function([function]) |
Plots given Luminance function. |
plot_multi_luminance_functions([functions]) |
Plots given Luminance functions. |
plot_blackbody_spectral_radiance([…]) |
Plots given blackbody spectral radiance. |
plot_blackbody_colours([shape, cmfs]) |
Plots blackbody colours. |
colour.plotting
plot_cvd_simulation_Machado2009(RGB[, …]) |
Performs colour vision deficiency simulation on given RGB colourspace array using Machado et al.(2009) model.. |
colour.plotting
plot_single_colour_checker([colour_checker]) |
Plots given colour checker. |
plot_multi_colour_checkers([colour_checkers]) |
Plots and compares given colour checkers. |
colour.plotting
plot_corresponding_chromaticities_prediction([…]) |
Plots given chromatic adaptation model corresponding chromaticities prediction. |
colour.plotting
plot_chromaticity_diagram_CIE1931([cmfs, …]) |
Plots the CIE 1931 Chromaticity Diagram. |
plot_chromaticity_diagram_CIE1960UCS([cmfs, …]) |
Plots the CIE 1960 UCS Chromaticity Diagram. |
plot_chromaticity_diagram_CIE1976UCS([cmfs, …]) |
Plots the CIE 1976 UCS Chromaticity Diagram. |
plot_sds_in_chromaticity_diagram_CIE1931(sds) |
Plots given spectral distribution chromaticity coordinates into the CIE 1931 Chromaticity Diagram. |
plot_sds_in_chromaticity_diagram_CIE1960UCS(sds) |
Plots given spectral distribution chromaticity coordinates into the CIE 1960 UCS Chromaticity Diagram. |
plot_sds_in_chromaticity_diagram_CIE1976UCS(sds) |
Plots given spectral distribution chromaticity coordinates into the CIE 1976 UCS Chromaticity Diagram. |
Ancillary Objects
colour.plotting.diagrams
plot_spectral_locus([cmfs, …]) |
Plots the Spectral Locus according to given method. |
plot_chromaticity_diagram_colours([samples, …]) |
Plots the Chromaticity Diagram colours according to given method. |
plot_chromaticity_diagram([cmfs, …]) |
Plots the Chromaticity Diagram according to given method. |
plot_sds_in_chromaticity_diagram(sds[, …]) |
Plots given spectral distribution chromaticity coordinates into the Chromaticity Diagram using given method. |
colour.plotting
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1931([…]) |
Plots given RGB colourspaces in the CIE 1931 Chromaticity Diagram. |
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1960UCS([…]) |
Plots given RGB colourspaces in the CIE 1960 UCS Chromaticity Diagram. |
plot_RGB_colourspaces_in_chromaticity_diagram_CIE1976UCS([…]) |
Plots given RGB colourspaces in the CIE 1976 UCS Chromaticity Diagram. |
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(RGB) |
Plots given RGB colourspace array in the CIE 1931 Chromaticity Diagram. |
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1960UCS(RGB) |
Plots given RGB colourspace array in the CIE 1960 UCS Chromaticity Diagram. |
plot_RGB_chromaticities_in_chromaticity_diagram_CIE1976UCS(RGB) |
Plots given RGB colourspace array in the CIE 1976 UCS Chromaticity Diagram. |
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1931([…]) |
Plots MacAdam (1942) Ellipses (Observer PGN) in the CIE 1931 Chromaticity Diagram. |
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1960UCS([…]) |
Plots MacAdam (1942) Ellipses (Observer PGN) in the CIE 1960 UCS Chromaticity Diagram. |
plot_ellipses_MacAdam1942_in_chromaticity_diagram_CIE1976UCS([…]) |
Plots MacAdam (1942) Ellipses (Observer PGN) in the CIE 1976 UCS Chromaticity Diagram. |
plot_single_cctf([cctf, decoding_cctf]) |
Plots given colourspace colour component transfer function. |
plot_multi_cctfs([cctfs, decoding_cctf]) |
Plots given colour component transfer functions. |
Ancillary Objects
colour.plotting.models
plot_pointer_gamut([method]) |
Plots Pointer’s Gamut according to given method. |
plot_RGB_colourspaces_in_chromaticity_diagram([…]) |
Plots given RGB colourspaces in the Chromaticity Diagram according to given method. |
plot_RGB_chromaticities_in_chromaticity_diagram(RGB) |
Plots given RGB colourspace array in the Chromaticity Diagram according to given method. |
colour.plotting
plot_single_munsell_value_function([function]) |
Plots given Lightness function. |
plot_multi_munsell_value_functions([functions]) |
Plots given Munsell value functions. |
colour.plotting
plot_single_sd_rayleigh_scattering([…]) |
Plots a single Rayleigh scattering spectral distribution. |
plot_the_blue_sky([cmfs]) |
Plots the blue sky. |
colour.plotting
plot_single_sd_colour_rendering_index_bars(sd, …) |
Plots the Colour Rendering Index (CRI) of given illuminant or light source spectral distribution. |
plot_multi_sds_colour_rendering_indexes_bars(…) |
Plots the Colour Rendering Index (CRI) of given illuminants or light sources spectral distributions. |
plot_single_sd_colour_quality_scale_bars(sd, …) |
Plots the Colour Quality Scale (CQS) of given illuminant or light source spectral distribution. |
plot_multi_sds_colour_quality_scales_bars(…) |
Plots the Colour Quality Scale (CQS) of given illuminants or light sources spectral distributions. |
Ancillary Objects
colour.plotting.quality
plot_colour_quality_bars(specifications[, …]) |
Plots the colour quality data of given illuminants or light sources colour quality specifications. |
colour.plotting
plot_RGB_colourspaces_gamuts([colourspaces, …]) |
Plots given RGB colourspaces gamuts in given reference colourspace. |
plot_RGB_scatter(RGB, colourspace[, …]) |
Plots given RGB colourspace array in a scatter plot. |
colour.plotting
quad([plane, origin, width, height, depth]) |
Returns the vertices of a quad geometric element in counter-clockwise order. |
grid([plane, origin, width, height, depth, …]) |
Returns the vertices of a grid made of quads. |
cube([plane, origin, width, height, depth, …]) |
Returns the vertices of a cube made of grids. |
Colour Quality¶
colour
colour_rendering_index(sd_test[, …]) |
Returns the Colour Rendering Index (CRI) \(Q_a\) of given spectral distribution. |
colour.quality
CRI_Specification |
Defines the Colour Rendering Index (CRI) colour quality specification. |
colour
colour_quality_scale(sd_test[, additional_data]) |
Returns the Colour Quality Scale (CQS) of given spectral distribution. |
colour.quality
CQS_Specification |
Defines the Colour Quality Scale (CQS) colour quality specification. |
Reflectance Recovery¶
colour
XYZ_to_sd(XYZ[, method]) |
Recovers the spectral distribution of given CIE XYZ tristimulus values using given method. |
XYZ_TO_SD_METHODS |
Supported spectral distribution recovery methods. |
colour.recovery
RGB_to_sd_Smits1999(RGB) |
Recovers the spectral distribution of given RGB colourspace array using Smits (1999) method. |
SMITS_1999_SDS |
Smits (1999) spectral distributions. |
colour.recovery
XYZ_to_sd_Meng2015(XYZ[, cmfs, interval, …]) |
Recovers the spectral distribution of given CIE XYZ tristimulus values using Meng et al.(2015) method.. |
Colour Temperature¶
Utilities¶
colour
domain_range_scale(scale) |
A context manager and decorator temporarily setting Colour domain-range scale. |
get_domain_range_scale() |
Returns the current Colour domain-range scale. |
set_domain_range_scale([scale]) |
Sets the current Colour domain-range scale. |
colour.utilities
handle_numpy_errors(**kwargs) |
Decorator for handling Numpy errors. |
ignore_numpy_errors(function) |
Wrapper for given function. |
raise_numpy_errors(function) |
Wrapper for given function. |
print_numpy_errors(function) |
Wrapper for given function. |
warn_numpy_errors(function) |
Wrapper for given function. |
ignore_python_warnings(function) |
Decorator for ignoring Python warnings. |
batch(iterable[, k]) |
Returns a batch generator from given iterable. |
is_openimageio_installed([raise_exception]) |
Returns if OpenImageIO is installed and available. |
is_pandas_installed([raise_exception]) |
Returns if Pandas is installed and available. |
is_iterable(a) |
Returns if given \(a\) variable is iterable. |
is_string(a) |
Returns if given \(a\) variable is a string like variable. |
is_numeric(a) |
Returns if given \(a\) variable is a number. |
is_integer(a) |
Returns if given \(a\) variable is an integer under given threshold. |
is_sibling(element, mapping) |
Returns whether given element type is present in given mapping types. |
filter_kwargs(function, **kwargs) |
Filters keyword arguments incompatible with the given function signature. |
filter_mapping(mapping, filterers[, …]) |
Filters given mapping with given filterers. |
first_item(a) |
Return the first item of an iterable. |
to_domain_1(a[, scale_factor, dtype]) |
Scales given array \(a\) to domain ‘1’. |
to_domain_10(a[, scale_factor, dtype]) |
Scales given array \(a\) to domain ‘10’, used by Munsell Renotation System. |
to_domain_100(a[, scale_factor, dtype]) |
Scales given array \(a\) to domain ‘100’. |
to_domain_degrees(a[, scale_factor, dtype]) |
Scales given array \(a\) to degrees domain. |
to_domain_int(a[, bit_depth, dtype]) |
Scales given array \(a\) to int domain. |
from_range_1(a[, scale_factor]) |
Scales given array \(a\) from range ‘1’. |
from_range_10(a[, scale_factor]) |
Scales given array \(a\) from range ‘10’, used by Munsell Renotation System. |
from_range_100(a[, scale_factor]) |
Scales given array \(a\) from range ‘100’. |
from_range_degrees(a[, scale_factor]) |
Scales given array \(a\) from degrees range. |
from_range_int(a[, bit_depth, dtype]) |
Scales given array \(a\) from int range. |
colour.utilities
as_array(a[, dtype]) |
Converts given \(a\) variable to ndarray with given type. |
as_int_array(a) |
Converts given \(a\) variable to ndarray using the type defined by colour.constant.DEFAULT_INT_DTYPE attribute. |
as_float_array(a) |
Converts given \(a\) variable to ndarray using the type defined by colour.constant.DEFAULT_FLOAT_DTYPE attribute. |
as_numeric(a[, dtype]) |
Converts given \(a\) variable to numeric. |
as_int(a) |
Converts given \(a\) variable to numeric using the type defined by colour.constant.DEFAULT_INT_DTYPE attribute. |
as_float(a) |
Converts given \(a\) variable to numeric using the type defined by colour.constant.DEFAULT_FLOAT_DTYPE attribute. |
as_namedtuple(a, named_tuple) |
Converts given \(a\) variable to given namedtuple class instance. |
closest_indexes(a, b) |
Returns the \(a\) variable closest element indexes to reference \(b\) variable elements. |
closest(a, b) |
Returns the \(a\) variable closest elements to reference \(b\) variable elements. |
normalise_maximum(a[, axis, factor, clip]) |
Normalises given array_like \(a\) variable values by \(a\) variable maximum value and optionally clip them between. |
interval(distribution[, unique]) |
Returns the interval size of given distribution. |
is_uniform(distribution) |
Returns if given distribution is uniform. |
in_array(a, b[, tolerance]) |
Tests whether each element of an array is also present in a second array within given tolerance. |
tstack(a[, dtype]) |
Stacks arrays in sequence along the last axis (tail). |
tsplit(a[, dtype]) |
Splits arrays in sequence along the last axis (tail). |
row_as_diagonal(a) |
Returns the per row diagonal matrices of the given array. |
dot_vector(m, v) |
Convenient wrapper around np.einsum() with the following subscripts: ‘…ij,…j->…i’. |
dot_matrix(a, b) |
Convenient wrapper around np.einsum() with the following subscripts: ‘…ij,…jk->…ik’. |
orient(a, orientation) |
Orient given array according to given orientation value. |
centroid(a) |
Computes the centroid indexes of given \(a\) array. |
linear_conversion(a, old_range, new_range) |
Performs a simple linear conversion of given array between the old and new ranges. |
lerp(a, b, c) |
Performs a simple linear interpolation between given array \(a\) and array \(b\) using \(c\) value. |
fill_nan(a[, method, default]) |
Fills given array NaNs according to given method. |
ndarray_write(a) |
A context manager setting given array writeable to perform an operation and then read-only. |
colour.utilities
metric_mse(a, b) |
Computes the mean squared error (MSE) or mean squared deviation (MSD) between given array_like \(a\) and \(b\) variables. |
metric_psnr(a, b[, max_a]) |
Computes the peak signal-to-noise ratio (PSNR) between given array_like \(a\) and \(b\) variables. |
colour.utilities
CaseInsensitiveMapping([data]) |
Implements a case-insensitive mutable mapping / dict object. |
Lookup |
Extends dict type to provide a lookup by value(s). |
Structure(*args, **kwargs) |
Defines an object similar to C/C++ structured type. |
colour.utilities
message_box(message[, width, padding, …]) |
Prints a message inside a box. |
warning(*args, **kwargs) |
Issues a warning. |
filter_warnings([state, colour_warnings, …]) |
Filters Colour and also optionally overall Python warnings. |
suppress_warnings([colour_warnings, …]) |
A context manager filtering Colour and also optionally overall Python warnings. |
numpy_print_options(*args, **kwargs) |
A context manager implementing context changes to Numpy print behaviour. |
describe_environment([runtime_packages, …]) |
Describes Colour running environment, i.e. |
Ancillary Objects
colour.utilities
ColourWarning |
This is the base class of Colour warnings. |
ColourUsageWarning |
This is the base class of Colour usage warnings. |
ColourRuntimeWarning |
This is the base class of Colour runtime warnings. |
Colour Volume¶
colour
is_within_macadam_limits(xyY, illuminant[, …]) |
Returns if given CIE xyY colourspace array is within MacAdam limits of given illuminant. |
ILLUMINANTS_OPTIMAL_COLOUR_STIMULI |
Illuminants Optimal Colour Stimuli. |
colour
is_within_mesh_volume(points, mesh[, tolerance]) |
Returns if given points are within given mesh volume using Delaunay triangulation. |
colour
is_within_pointer_gamut(XYZ[, tolerance]) |
Returns if given CIE XYZ tristimulus values are within Pointer’s Gamut volume. |
colour
RGB_colourspace_limits(colourspace[, illuminant]) |
Computes given RGB colourspace volume limits in Lab colourspace. |
RGB_colourspace_pointer_gamut_coverage_MonteCarlo(…) |
Returns given RGB colourspace percentage coverage of Pointer’s Gamut volume using Monte Carlo method. |
RGB_colourspace_visible_spectrum_coverage_MonteCarlo(…) |
Returns given RGB colourspace percentage coverage of visible spectrum volume using Monte Carlo method. |
RGB_colourspace_volume_MonteCarlo(colourspace) |
Performs given RGB colourspace volume computation using Monte Carlo method and multiprocessing. |
RGB_colourspace_volume_coverage_MonteCarlo(…) |
Returns given RGB colourspace percentage coverage of an arbitrary volume. |
colour
is_within_visible_spectrum(XYZ[, interval, …]) |
Returns if given CIE XYZ tristimulus values are within visible spectrum volume / given colour matching functions volume. |
Ancillary Objects
colour.volume
generate_pulse_waves(bins) |
Generates the pulse waves of given number of bins necessary to totally stimulate the colour matching functions. |
XYZ_outer_surface([interval, cmfs, illuminant]) |
Generates the CIE XYZ colourspace outer surface for given colour matching functions using multi-spectral conversion of pulse waves to CIE XYZ tristimulus values. |
Indices and tables¶
Bibliography¶
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| [TheAoMPAaSciencesScienceaTCouncilAcademyCESACESPSubcommittee14a] | The Academy of Motion Picture Arts and Sciences, Science and Technology Council, and Academy Color Encoding System (ACES) Project Subcommittee. Specification S-2013-001 - ACESproxy, an Integer Log Encoding of ACES Image Data. 2014. URL: https://github.com/ampas/aces-dev/tree/master/documents. |
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Indirect References¶
Some extra references used in the codebase but not directly part of the public api:
Examples¶
Most of the objects are available from the colour namespace:
>>> import colour
Chromatic Adaptation¶
>>> 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']
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.sd_to_XYZ(colour.LIGHT_SOURCES_SDS['Neodimium Incandescent'])
array([ 36.94726204, 32.62076174, 13.0143849 ])
>>> sorted(colour.SPECTRAL_TO_XYZ_METHODS.keys())
['ASTM E308-15', 'Integration', 'astm2015']
Multi-Spectral Computations¶
>>> msd = 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(msd, 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())
['Integration']
Blackbody Spectral Radiance Computation¶
>>> 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],
[ 7.79000000e+02, 1.05903327e+13],
[ 7.80000000e+02, 1.05738520e+13]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={'right': None, 'method': 'Constant', 'left': None})
Dominant, Complementary Wavelength & Colour Purity Computation¶
>>> xy = [0.54369557, 0.32107944]
>>> xy_n = [0.31270000, 0.32900000]
>>> colour.dominant_wavelength(xy, xy_n)
(array(616.0),
array([ 0.68354746, 0.31628409]),
array([ 0.68354746, 0.31628409]))
Lightness Computation¶
>>> colour.lightness(12.19722535)
41.527875844653451
>>> sorted(colour.LIGHTNESS_METHODS.keys())
['CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Glasser 1958',
'Lstar1976',
'Wyszecki 1963']
Luminance Computation¶
>>> colour.luminance(41.52787585)
12.197225353400775
>>> sorted(colour.LUMINANCE_METHODS.keys())
['ASTM D1535-08',
'CIE 1976',
'Fairchild 2010',
'Fairchild 2011',
'Newhall 1943',
'astm2008',
'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())
['ASTM E313',
'Berger 1959',
'CIE 2004',
'Ganz 1979',
'Stensby 1968',
'Taube 1960',
'cie2004']
Yellowness Computation¶
>>> XYZ = [95.00000000, 100.00000000, 105.00000000]
>>> colour.yellowness(XYZ)
11.065000000000003
>>> sorted(colour.YELLOWNESS_METHODS.keys())
['ASTM D1925', 'ASTM E313']
Luminous Flux, Efficiency & Efficacy Computation¶
Luminous Flux¶
>>> sd = colour.LIGHT_SOURCES_SDS['Neodimium Incandescent']
>>> colour.luminous_flux(sd)
23807.655527367202
Luminous Efficiency¶
>>> sd = colour.LIGHT_SOURCES_SDS['Neodimium Incandescent']
>>> colour.luminous_efficiency(sd)
0.19943935624521045
Luminous Efficacy¶
>>> sd = colour.LIGHT_SOURCES_SDS['Neodimium Incandescent']
>>> colour.luminous_efficacy(sd)
136.21708031547874
Colour Models¶
CIE xyY Colourspace¶
>>> colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])
array([ 0.54369557, 0.32107944, 0.12197225])
CIE L*a*b* Colourspace¶
>>> colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 52.63858304, 26.92317922])
CIE L*u*v* Colourspace¶
>>> colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])
array([ 41.52787529, 96.83626054, 17.75210149])
CIE 1960 UCS Colourspace¶
>>> colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])
array([ 0.13769339, 0.12197225, 0.1053731 ])
CIE 1964 U*V*W* Colourspace¶
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_UVW(XYZ)
array([ 94.55035725, 11.55536523, 40.54757405])
Hunter L,a,b Colour Scale¶
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_Hunter_Lab(XYZ)
array([ 34.92452577, 47.06189858, 14.38615107])
Hunter Rd,a,b Colour Scale¶
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_Hunter_Rdab(XYZ)
array([ 12.197225 , 57.12537874, 17.46241341])
CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)¶
>>> 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 ])
CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)¶
>>> 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]
IPT Colourspace¶
>>> colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 0.38426191, 0.38487306, 0.18886838])
DIN99 Colourspace¶
>>> Lab = [41.52787529, 52.63858304, 26.92317922]
>>> colour.Lab_to_DIN99(Lab)
array([ 53.22821988, 28.41634656, 3.89839552])
hdr-CIELAB Colourspace¶
>>> colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])
array([ 51.87002062, 60.4763385 , 32.14551912])
hdr-IPT Colourspace¶
>>> colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])
array([ 25.18261761, -22.62111297, 3.18511729])
OSA UCS Colourspace¶
>>> XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952* 100]
>>> colour.XYZ_to_OSA_UCS(XYZ)
array([-3.0049979 , 2.99713697, -9.66784231])
JzAzBz Colourspace¶
>>> colour.XYZ_to_JzAzBz([0.20654008, 0.12197225, 0.05136952])
array([ 0.00535048, 0.00924302, 0.00526007])
RGB Colourspace and Transformations¶
>>> 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, 1.05699594]]
>>> colour.XYZ_to_RGB(
XYZ,
illuminant_XYZ,
illuminant_RGB,
XYZ_to_RGB_matrix,
chromatic_adaptation_transform)
array([ 0.45595571, 0.03039702, 0.04087245])
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([0.45620519, 0.03081071, 0.04091952])
array([ 0.07351364, 0.00475253, 0.09351596])
HSV Colourspace¶
>>> colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])
array([ 0.99603944, 0.93246304, 0.45620519])
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())
['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+',
'DJI D-Gamut',
'DRAGONcolor',
'DRAGONcolor2',
'Don RGB 4',
'ECI RGB v2',
'ERIMM RGB',
'Ekta Space PS 5',
'FilmLight E-Gamut',
'ITU-R BT.2020',
'ITU-R BT.470 - 525',
'ITU-R BT.470 - 625',
'ITU-R BT.709',
'Max RGB',
'NTSC',
'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',
'Sharp RGB',
'V-Gamut',
'Xtreme RGB',
'aces',
'adobe1998',
'prophoto',
'sRGB']
OETFs¶
>>> sorted(colour.OETFS.keys())
['ARIB STD-B67',
'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']
OETFs Reverse¶
>>> sorted(colour.OETFS_REVERSE.keys())
['ARIB STD-B67',
'ITU-R BT.2100 HLD',
'ITU-R BT.2100 PQ',
'ITU-R BT.601',
'ITU-R BT.709',
'sRGB']
EOTFs¶
>>> 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',
'ProPhoto RGB',
'RIMM RGB',
'ROMM RGB',
'SMPTE 240M',
'ST 2084']
EOTFs Reverse¶
>>> sorted(colour.EOTFS_REVERSE.keys())
['DCDM', 'ITU-R BT.1886', 'ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
OOTFs¶
>>> sorted(colour.OOTFS.keys())
['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']
OOTFs Reverse¶
>>> sorted(colour.OOTFs_REVERSE.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',
'D-Log',
'ERIMM RGB',
'Filmic Pro 6',
'Log3G10',
'Log3G12',
'PLog',
'Panalog',
'Protune',
'REDLog',
'REDLogFilm',
'S-Log',
'S-Log2',
'S-Log3',
'T-Log',
'V-Log',
'ViperLog']
Chromatic Adaptation Models¶
>>> XYZ = [0.20654008, 0.12197225, 0.05136952]
>>> XYZ_w = [0.95045593, 1.00000000, 1.08905775]
>>> XYZ_wr = [1.09846607, 1.00000000, 0.35582280]
>>> colour.chromatic_adaptation_VonKries(XYZ, XYZ_w, XYZ_wr)
array([ 0.2533053 , 0.13765138, 0.01543307])
>>> sorted(colour.CHROMATIC_ADAPTATION_METHODS.keys())
['CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries']
Colour Appearance Models¶
>>> 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)
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())
['Cheung 2004', 'Finlayson 2015', 'Vandermonde']
Colour Notation Systems¶
Munsell Value¶
>>> colour.munsell_value(12.23634268)
4.0824437076525664
>>> sorted(colour.MUNSELL_VALUE_METHODS.keys())
['ASTM D1535-08',
'Ladd 1955',
'McCamy 1987',
'Moon 1943',
'Munsell 1933',
'Priest 1920',
'Saunderson 1944',
'astm2008']
Munsell Colour¶
>>> 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 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_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],
[ 7.79000000e+02, 2.53887969e-02],
[ 7.80000000e+02, 2.52576106e-02]],
interpolator=SpragueInterpolator,
interpolator_args={},
extrapolator=Extrapolator,
extrapolator_args={'right': None, 'method': 'Constant', 'left': None})
Light Quality¶
Colour Rendering Index¶
>>> colour.colour_quality_scale(colour.ILLUMINANTS_SDS['FL2'])
64.686416902221609
Colour Quality Scale¶
>>> colour.colour_rendering_index(colour.ILLUMINANTS_SDS['FL2'])
64.151520202968015
Reflectance Recovery¶
>>> 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']
Volume¶
>>> colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE['sRGB'])
821958.30000000005
Contrast Sensitivity Function¶
>>> colour.contrast_sensitivity_function(u=4, X_0=60, E=65)
358.51180789884984
>>> sorted(colour.CONTRAST_SENSITIVITY_METHODS.keys())
['Barten 1999']
IO¶
Images¶
>>> RGB = colour.read_image('Ishihara_Colour_Blindness_Test_Plate_3.png')
>>> RGB.shape
(276, 281, 3)
Look Up Table (LUT) Data¶
>>> 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])
Plotting¶
Most of the objects are available from the colour.plotting namespace:
>>> from colour.plotting import *
>>> colour_style()
Blackbody¶
>>> 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))
Colour Matching Functions¶
>>> plot_single_cmfs(
... 'Stockman & Sharpe 2 Degree Cone Fundamentals',
... y_label='Sensitivity',
... bounding_box=(390, 870, 0, 1.1))
Luminous Efficiency¶
>>> 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))
Colour Checker¶
>>> from colour.characterisation.dataset.colour_checkers.sds import (
... COLOURCHECKER_INDEXES_TO_NAMES_MAPPING)
>>> plot_multi_sds(
... [
... colour.COLOURCHECKERS_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})
Chromaticities Prediction¶
>>> plot_corresponding_chromaticities_prediction(2, 'Von Kries', 'Bianco')
Chromaticities¶
>>> 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'], show_pointer_gamut=True)
Colour Rendering Index¶
>>> plot_single_sd_colour_rendering_index_bars(
... colour.ILLUMINANTS_SDS['FL2'])
Contributing¶
If you would like to contribute to Colour, please refer to the following Contributing guide.
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
- ColorPy by Kness, M.
- Colorspacious by Smith, N. J., et al.
- python-colormath by Taylor, G., 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.
About¶
Colour by Colour Developers - 2013-2019
Copyright © 2013-2019 – Colour Developers – colour-science@googlegroups.com
This software is released under terms of New BSD License: http://opensource.org/licenses/BSD-3-Clause