Over a year in the making, this release integrates most of the GSoC 2021 work from Cédric (@villirion), all the code from Geetansh (@SGeetansh) and the remaining GSoC 2020 code from Nishant (@njwardhan). We would like to thank them again for their great contributions!

Python 2.7 support has been dropped and the minimal version is Python 3.8 as per https://scientific-python.org/. The following minimal dependency versions are also required:

imageio >= 2, < 3
numpy >= 1.19, < 2
scipy >= 1.5, < 2

The highlights of this release are as follows:

Colour now runs on iOS and iPadOS with Pyto.
The import of colour is now 3.6 times faster.
Typing annotations have been added and the codebase is checked with Mypy.
The documentation has been updated and uses the pydata-sphinx-theme and has better compliance with PEP257.
The code formatter is now Black,
Many Python 3 features such as f-Strings or the dataclass decorator have been adopted.
The plotting API has been improved to be more consistent when setting the colours of some figures, e.g. spectral or planckian locus.
New colour appearance models:
- Zhai and Luo (2018) chromatic adaptation model.
- Kim, Weyrich and Kautz (2009) colour appearance model.
- ZCAM colour appearance model.
- Helmholtz-Kohlrausch effect estimation.
New colour models:
- Oklab colour model.
- Hanbury (2003) IHLS (Improved HLS) colourspace.
- DIN99b, DIN99c, and DIN99d refined formulas.
- ProLab colourspace.
- Sarifuddin and Missaoui (2005) HCL colourspace.
New RGB colourspaces and transfer functions:
- Nikon N-Gamut colourspace and the N-Log log encoding.
- Blackmagic Wide Gamut colourspace and the associated Blackmagic Film Generation 5 OETF.
- DaVinci Intermediate OETF.
- RED Log3G10 encoding and decoding curves with linear extension.
Other notable features:
- Huang et al. (2015) power-functions.
- LUT 1D, LUT 3x1D and LUT 3D inversion.
- UPRTek and Sekonic spectral data parsers.
- SPImtx LUT input and output.
- R'G'B' to Y'CbCr matrices computation.
- Gamut ring/section plotting.
- Rösch-MacAdam colour solid hue lines.
- Support for OpenColorIO processor.
  - Note that the optional dependency is not specified in the pyproject.toml, see AcademySoftwareFoundation/OpenColorIO#1573 for more information.

Thanks again to all the contributors to this release!

Features

Typing

The API has been fully annotated with typing annotations, a new colour.hints sub-package exporting all the hints has been created.

Performance

Import time has been reduced to ~1.5secs from 5.5secs on @KelSolaar's MacBook Pro (Retina, 13-inch, Mid 2014) by using a lazy load mechanism for the spectral data and delaying various imports in the colour.plotting.tm3018 module. See PR #840 for more information.
Repetitive spectral computations are now cached and a small cache management API has been created, see colour.utilities.CACHE_REGISTRY attribute for more information.
The colour.sd_to_XYZ and colour.msds_to_XYZ definitions now use the same n-dimensional code under the hood. Some minor numerical differences are expected as the explicit summations and multiplications have been replaced with np.dot.

`colour.adaptation`

GSoC - 2021

Implement support for Zhai and Luo (2018) chromatic adaptation model with colour.adaptation.chromatic_adaptation_Zhai2018 definition. (@villirion, @KelSolaar)

`colour.appearance`

GSoC - 2021

Implement support for Kim, Weyrich and Kautz (2009) colour appearance model with colour.XYZ_to_Kim2009 and colour.Kim2009_to_XYZ definitions. (@villirion, @KelSolaar)

Implement support for ZCAM colour appearance model with the colour.XYZ_to_ZCAM and colour.ZCAM_to_XYZ definitions. (@KelSolaar)
Implement support for Helmholtz-Kohlrausch effect estimation with colour.HelmholtzKohlrausch_effect_object_Nayatani1997 and colour.HelmholtzKohlrausch_effect_luminous_Nayatani1997 definitions. (@ilia3101, @KelSolaar)
Colour appearance models now use a dataclass-like class output instead of the namedtuple and thus are mutable.

`colour.characterisation`

Various ACES Input Device Transform computation objects have been updated to generate additional data and support more features for the Academy Input Device Transform (IDT) calculator. (@KelSolaar, @aforsythe)
Add colour.camera_RGB_to_ACES2065_1 definition. (@KelSolaar, @aforsythe)

`colour.colorimetry`

Implement support for spectral uniformity computation with colour.spectral_uniformity definition. (@KelSolaar)
Add colour.TVS_ILLUMINANTS attribute providing a reference for the CIE XYZ tristimulus values of the CIE illuminants. (@KelSolaar)
Ensure that colour.SpectralShape and dict KeysView class instances can be passed as domain to colour.SpectralDistribution and colour.MutliSpectralDistributions classes. (@KelSolaar)
The colour.colorimetry.yellowness_ASTME313 definition has been updated to use the recommended Yellowness Index equation as given by ASTME313. The alternative method has been renamed to colour.colorimetry.yellowness_ASTME313_alternative. (@KelSolaar, @romanovar)

`colour.difference`

Implement support for STRESS index computation according to García et al. (2007) method with colour.index_stress definition and colour.INDEX_STRESS_METHODS attribute. (@KelSolaar)
Implement support for Huang et al. (2015) power-functions improving colour-difference formulas with colour.difference.power_function_Huang2015 definition. (@KelSolaar)

`colour.io`

GSoC - 2020

Implement support for LUT 1D, LUT 3x1D and LUT 3D inversion with the colour.LUT1D.invert, colour.LUT3x1D.invert and colour.LUT3D.invert methods. (@njwardhan, @KelSolaar)

GSoC - 2021

Implement support for UPRTek and Sekonic spectral data parsers with the colour.SpectralDistribution_UPRTek and colour.SpectralDistribution_Sekonic classes. (@SGeetansh, @KelSolaar)

Implement support for OpenColorIO processor with colour.io.process_image_OpenColorIO definition. (@KelSolaar)
Implement support for SPImtx LUT input and output with new colour.io.read_LUT_SonySPImtx and colour.io.write_LUT_SonySPImtx definitions and colour.io.LUTOperatorMatrix support class. (@nick-shaw, @KelSolaar, @zachlewis)
The colour.io.tm2714.Header_IESTM2714 class can now be hashed and compared for equality. (@JGoldstone)

`colour.models`

GSoC - 2021

Implement support for Hanbury (2003) IHLS (Improved HLS) colourspace with colour.RGB_to_IHLS and colour.IHLS_to_RGB definitions. (@SGeetansh, @KelSolaar)
Implement support for DIN99b, DIN99c, and DIN99d refined formulas in colour.Lab_to_DIN99 and colour.DIN99_to_Lab definitions. (@SGeetansh)
Implement support for ProLab colourspace with colour.ProLab_to_XYZ and colour.XYZ_to_ProLab definitions. (@SGeetansh, @KelSolaar)

Implement support for Sarifuddin and Missaoui (2005) HCL colourspace with colour.RGB_to_HCL and colour.HCL_to_RGB definitions. (@Saransh-cpp, @KelSolaar)
Implement wrapper colour.XYZ_to_ICTCP and colour.ICTCP_to_XYZ definitions. (@KelSolaar)
Implement wrapper colour.XYZ_to_CAM02LCD, colour.CAM02LCD_to_XYZ, colour.XYZ_to_CAM02SCD, colour.CAM02SCD_to_XYZ, colour.XYZ_to_CAM02UCS and
colour.CAM02UCS_to_XYZ definitions. (@KelSolaar)
Implement wrapper colour.XYZ_to_CAM16LCD, colour.CAM16LCD_to_XYZ, colour.XYZ_to_CAM16SCD, colour.CAM16SCD_to_XYZ, colour.XYZ_to_CAM16UCS and
colour.CAM16UCS_to_XYZ definitions. (@KelSolaar)
Implement support for R'G'B' to Y'CbCr matrices computation with colour.matrix_YCbCr and colour.offset_YCbCr definitions. (@KelSolaar, @nick-shaw)
Implement support for Oklab colour model with colour.XYZ_to_Oklab and colour.Oklab_to_XYZ definitions. (@KelSolaar)
Implement support for Nikon N-Gamut colourspace and the N-Log log encoding and decoding curves with colour.models.RGB_COLOURSPACE_N_GAMUT class and colour.models.log_encoding_NLOG and colour.models.log_decoding_NLOG definitions. (@sobotka, @KelSolaar)
Implement support for RED Log3G10 encoding and decoding curves that uses a linear extension as given in the final version of White Paper on REDWideGamutRGB and Log3G10. (@jedypod)
Implement support for Blackmagic Wide Gamut colourspace and the associated Blackmagic Film Generation 5 OETF and its inverse with colour.models.RGB_COLOURSPACE_BLACKMAGIC_WIDE_GAMUT attribute, colour.models.oetf_BlackmagicFilmGeneration5 and colour.models.oetf_inverse_BlackmagicFilmGeneration5 definitions. (@KelSolaar)
Implement support for DaVinci Intermediate OETF and its inverse with colour.models.oetf_DaVinciIntermediate and colour.models.oetf_inverse_DaVinciIntermediate definitions. (@fredsavoir, @KelSolaar)

`colour.plotting`

Implement support for gamut ring/section plotting with the colour.plotting.plot_visible_spectrum_section and colour.plotting.plot_RGB_colourspace_section definitions. (@KelSolaar)

figure, axes = plot_visible_spectrum_section(
    model='DIN99', origin=0.5, section_opacity=0.15, standalone=False)

bounding_box = [
    axes.get_xlim()[0],
    axes.get_xlim()[1],
    axes.get_ylim()[0],
    axes.get_ylim()[1]
]

section_colours = colour.notation.HEX_to_RGB(
    colour.plotting.CONSTANTS_COLOUR_STYLE.colour.cycle[:4])

origins = []
legend_lines = []
for i, RGB in zip(np.arange(0.5, 0.9, 0.1), section_colours):
    origins.append(i * 100)
    plot_RGB_colourspace_section(
        colourspace='sRGB',
        model='DIN99',
        origin=i,
        section_colours=RGB,
        section_opacity=0.15,
        contour_colours=RGB,
        axes=axes,
        standalone=False)
    legend_lines.append(
        Line2D([0], [0], color=RGB, label='{0}%'.format(i * 100)))

axes.legend(handles=legend_lines)

colour.plotting.render(
    title='Visible Spectrum - 50% - sRGB Sections - {0}% -  DIN99'.format(
        origins),
    axes=axes,
    bounding_box=bounding_box)

Improve zorder across the colour.plotting sub-package and implement various new parameters to customize better figures such as the Chromaticity Diagram.

`colour.volume`

Implement support to generate the Rösch-MacAdam colour solid hue lines with the colour.volume.XYZ_outer_surface/colour.volume.solid_RoschMacAdam definition. (@KelSolaar)

import colour

figure, axes = colour.plotting.plot_chromaticity_diagram_CIE1931(
    standalone=False)

bins = len(colour.volume.spectrum.SPECTRAL_SHAPE_OUTER_SURFACE_XYZ.range())

xy = colour.XYZ_to_xy(
    colour.volume.solid_RoschMacAdam(
        point_order='Pulse Wave Width', filter_jagged_points=True)[1:-1])

for i, j in enumerate(range(0, xy.shape[0], (bins - 1) // 2)):
    hue_lines = np.vstack([xy[j:j + (bins - 1) // 2, :], xy[-1]])
    axes.plot(hue_lines[:, 0], hue_lines[:, 1], c='k')

colour.plotting.render()

Fixes

`colour`

Ensure that colour runs on iOS and iPadOS. (@KelSolaar)

`colour.colorimetry`

Ensure that the cache used by colour.sd_to_XYZ definition returns a copy of the data. (@KelSolaar)

`colour.plotting`

Fix IES TM-30-18 Colour Rendition Report Local Color Fidelity sub-plot values. (@KelSolaar, @Paul-Sims)

Changes

`colour.algebra`

Object	Signature	Author
`colour.algebra.lerp`	`lerp(x: FloatingOrArrayLike, a: FloatingOrArrayLike = 0, b: FloatingOrArrayLike = 1, clip: Boolean = False) -> NDArray`	@KelSolaar

`colour.biochemistry`

Object	Name	Author
`colour.biochemistry.reaction_rate_MichealisMenten`	`reaction_rate_MichaelisMenten`	@KelSolaar
`colour.biochemistry.substrate_concentration_MichealisMenten`	`substrate_concentration_MichaelisMenten`	...

`colour.characterisation`

Object	Signature	Author
`colour.characterisation.training_data_sds_to_XYZ`	`training_data_sds_to_XYZ(training_data: MultiSpectralDistributions, cmfs: MultiSpectralDistributions, illuminant: SpectralDistribution, chromatic_adaptation_transform: Union[Literal["Bianco 2010", "Bianco PC 2010", "Bradford", "CAT02 Brill 2008", "CAT02", "CAT16", "CMCCAT2000", "CMCCAT97", "Fairchild", "Sharp", "Von Kries", "XYZ Scaling", ], str, ] = "CAT02") -> NDArray`	@KelSolaar
`colour.matrix_idt`	matrix_idt(sensitivities: RGB_CameraSensitivities, illuminant: SpectralDistribution, training_data: Optional[MultiSpectralDistributions] = None, cmfs: Optional[MultiSpectralDistributions] = None, optimisation_factory: Callable = optimisation_factory_rawtoaces_v1, optimisation_kwargs: Optional[Dict] = None, chromatic_adaptation_transform: Union[Literal["Bianco 2010", "Bianco PC 2010", "Bradford", "CAT02 Brill 2008", "CAT02", "CAT16", "CMCCAT2000", "CMCCAT97", "Fairchild", "Sharp", "Von Kries", "XYZ Scaling", ], str, ] = "CAT02", additional_data: Boolean = False) -> Union[Tuple[NDArray, NDArray, NDArray, NDArray], Tuple[NDArray, NDArray]]	...
`colour.sd_to_aces_relative_exposure_values`	`sd_to_aces_relative_exposure_values(sd: SpectralDistribution, illuminant: Optional[SpectralDistribution] = None, apply_chromatic_adaptation: Boolean = False, chromatic_adaptation_transform: Union[Literal["Bianco 2010", "Bianco PC 2010", "Bradford", "CAT02 Brill 2008", "CAT02", "CAT16", "CMCCAT2000", "CMCCAT97", "Fairchild", "Sharp", "Von Kries", "XYZ Scaling", ], str, ] = "CAT02") -> NDArray`	...

`colour.colorimetry`

colour.SpectralShape class must always be initialised with the start, end and interval parameters. (@KelSolaar)

Object	Signature	Author
`colour.colorimetric_purity`	`colorimetric_purity(xy: ArrayLike, xy_n: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None) -> FloatingOrNDArray`	@KelSolaar
`colour.colorimetry.msds_to_XYZ_ASTME308`	`msds_to_XYZ_ASTME308(msds: MultiSpectralDistributions, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, use_practice_range: Boolean = True, mi_5nm_omission_method: Boolean = True, mi_20nm_interpolation_method: Boolean = True, k: Optional[Number] = None) -> NDArray`	...
`colour.colorimetry.msds_to_XYZ_integration`	`msds_to_XYZ_integration(msds: Union[ArrayLike, SpectralDistribution, MultiSpectralDistributions], cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, k: Optional[Number] = None, shape: Optional[SpectralShape] = None) -> NDArray`	...
`colour.colorimetry.sd_gaussian_fwhm`	`sd_gaussian_fwhm(peak_wavelength: Floating, fwhm: Floating, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.colorimetry.sd_gaussian_normal`	`sd_gaussian_normal(mu: Floating, sigma: Floating, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.colorimetry.sd_single_led_Ohno2005`	`sd_single_led_Ohno2005(peak_wavelength: Floating, fwhm: Floating, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.colorimetry.sd_to_XYZ_ASTME308`	`sd_to_XYZ_ASTME308(sd: SpectralDistribution, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, use_practice_range: Boolean = True, mi_5nm_omission_method: Boolean = True, mi_20nm_interpolation_method: Boolean = True, k: Optional[Number] = None) -> NDArray`	...
`colour.colorimetry.sd_to_XYZ_integration`	`sd_to_XYZ_integration(sd: Union[ArrayLike, SpectralDistribution, MultiSpectralDistributions], cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, k: Optional[Number] = None, shape: Optional[SpectralShape] = None) -> NDArray`	...
`colour.colorimetry.sd_to_XYZ_tristimulus_weighting_factors_ASTME308`	`sd_to_XYZ_tristimulus_weighting_factors_ASTME308(sd: SpectralDistribution, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, k: Optional[Number] = None) -> NDArray`	...
`colour.colorimetry.yellowness_ASTME313`	`yellowness_ASTME313(XYZ: ArrayLike, C_XZ: ArrayLike = YELLOWNESS_COEFFICIENTS_ASTME313["CIE 1931 2 Degree Standard Observer" ]["D65"]) -> FloatingOrNDArray`	...
`colour.complementary_wavelength`	`complementary_wavelength(xy: ArrayLike, xy_n: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None) -> Tuple[NDArray, NDArray, NDArray]`	...
`colour.dominant_wavelength`	`dominant_wavelength(xy: ArrayLike, xy_n: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, inverse: bool = False) -> Tuple[NDArray, NDArray, NDArray]`	...
`colour.excitation_purity`	`excitation_purity(xy: ArrayLike, xy_n: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None) -> FloatingOrNDArray`	...
`colour.luminous_efficacy`	`luminous_efficacy(sd: SpectralDistribution, lef: Optional[SpectralDistribution] = None) -> Floating`	...
`colour.luminous_efficiency`	`luminous_efficiency(sd: SpectralDistribution, lef: Optional[SpectralDistribution] = None) -> Floating`	...
`colour.luminous_flux`	`luminous_flux(sd: SpectralDistribution, lef: Optional[SpectralDistribution] = None, K_m: Floating = CONSTANT_K_M) -> Floating`	...
`colour.mesopic_weighting_function`	`mesopic_weighting_function(wavelength: FloatingOrArrayLike, L_p: Floating, source: Union[Literal["Blue Heavy", "Red Heavy"], str] = "Blue Heavy", method: Union[Literal["MOVE", "LRC"], str] = "MOVE", photopic_lef: Optional[SpectralDistribution] = None, scotopic_lef: Optional[SpectralDistribution] = None) -> FloatingOrNDArray`	...
`colour.msds_constant`	`msds_constant(k: Floating, labels: Sequence, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> MultiSpectralDistributions`	...
`colour.msds_ones`	`msds_ones(labels: Sequence, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> MultiSpectralDistributions`	...
`colour.msds_to_XYZ`	`msds_to_XYZ(msds: Union[ArrayLike, SpectralDistribution, MultiSpectralDistributions], cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, k: Optional[Number] = None, method: Union[Literal["ASTM E308", "Integration"], str] = "ASTM E308", **kwargs: Any) -> NDArray`	...
`colour.msds_zeros`	`msds_zeros(labels: Sequence, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> MultiSpectralDistributions`	...
`colour.sd_constant`	`sd_constant(k: Floating, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.sd_gaussian`	`sd_gaussian(mu_peak_wavelength: Floating, sigma_fwhm: Floating, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, method: Union[Literal["Normal", "FWHM"], str] = "Normal", **kwargs: Any) -> SpectralDistribution`	...
`colour.sd_mesopic_luminous_efficiency_function`	`sd_mesopic_luminous_efficiency_function(L_p: Floating, source: Union[Literal["Blue Heavy", "Red Heavy"], str] = "Blue Heavy", method: Union[Literal["MOVE", "LRC"], str] = "MOVE", photopic_lef: Optional[SpectralDistribution] = None, scotopic_lef: Optional[SpectralDistribution] = None) -> SpectralDistribution`	...
`colour.sd_multi_leds_Ohno2005`	`sd_multi_leds_Ohno2005(peak_wavelengths: ArrayLike, fwhm: ArrayLike, peak_power_ratios: Optional[ArrayLike] = None, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.sd_multi_leds`	`sd_multi_leds(peak_wavelengths: ArrayLike, fwhm: ArrayLike, peak_power_ratios: Optional[ArrayLike] = None, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, method: Union[Literal["Ohno 2005"], str] = "Ohno 2005", **kwargs: Any) -> SpectralDistribution`	...
`colour.sd_ones`	`sd_ones(shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.sd_single_led`	`sd_single_led(peak_wavelength: Floating, fwhm: Floating, shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, method: Union[Literal["Ohno 2005"], str] = "Ohno 2005", **kwargs: Any) -> SpectralDistribution`	...
`colour.sd_to_XYZ`	`sd_to_XYZ(sd: Union[ArrayLike, SpectralDistribution, MultiSpectralDistributions], cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, k: Optional[Number] = None, method: Union[Literal["ASTM E308", "Integration"], str] = "ASTM E308", **kwargs: Any) -> NDArray`	...
`colour.sd_zeros`	`sd_zeros(shape: SpectralShape = SPECTRAL_SHAPE_DEFAULT, **kwargs: Any) -> SpectralDistribution`	...
`colour.wavelength_to_XYZ`	`wavelength_to_XYZ(wavelength: FloatingOrNDArray, cmfs: Optional[MultiSpectralDistributions] = None) -> NDArray`	...
`colour.yellowness`	`yellowness(XYZ: ArrayLike, method: Union[ Literal["ASTM D1925", "ASTM E313", "ASTM E313 Alternative"], str ] = "ASTM E313", **kwargs: Any) -> FloatingOrNDArray`	...

`colour.geometry`

Object	Name	Author
`colour.geometry.PLANE_TO_AXIS_MAPPING`	`MAPPING_PLANE_TO_AXIS`	@KelSolaar

Object	Signature	Author
`colour.geometry.primitive_grid`	`primitive_grid(width: Floating = 1, height: Floating = 1, width_segments: Integer = 1, height_segments: Integer = 1, axis: Literal[ "-x", "+x", "-y", "+y", "-z", "+z", "xy", "xz", "yz", "yx", "zx", "zy" ] = "+z", dtype_vertices: Optional[Type[DTypeFloating]] = None, dtype_indexes: Optional[Type[DTypeInteger]] = None) -> Tuple[NDArray, NDArray, NDArray]`	@KelSolaar
`colour.geometry.primitive_cube`	`primitive_cube(width: Floating = 1, height: Floating = 1, depth: Floating = 1, width_segments: Integer = 1, height_segments: Integer = 1, depth_segments: Integer = 1, planes: Optional[ Literal[ "-x", "+x", "-y", "+y", "-z", "+z", "xy", "xz", "yz", "yx", "zx", "zy", ] ] = None, dtype_vertices: Optional[Type[DTypeFloating]] = None, dtype_indexes: Optional[Type[DTypeInteger]] = None) -> Tuple[NDArray, NDArray, NDArray]`	...

`colour.io`

Object	Name	Author
`colour.LUT1D.as_LUT`	`convert`	@KelSolaar
`colour.LUT3x1D.as_LUT`	`convert`	...
`colour.LUT3D.as_LUT`	`convert`	...

Object	Signature	Author
`colour.read_spectral_data_from_csv_file`	`read_spectral_data_from_csv_file(path: str, **kwargs: Any) -> Dict[str, NDArray]`	@KelSolaar
`colour.read_sds_from_csv_file`	`read_sds_from_csv_file(path: str, **kwargs: Any) -> Dict[str, SpectralDistribution]`	...
`colour.write_sds_to_csv_file`	`write_sds_to_csv_file(sds: Dict[str, SpectralDistribution], path: str) -> Boolean`	...

`colour.models`

Object	Signature	Author
`colour.Lab_to_DIN99`	`Lab_to_DIN99(Lab: ArrayLike, k_E: Floating = 1, k_CH: Floating = 1, method: Union[ Literal["ASTMD2244-07", "DIN99", "DIN99b", "DIN99c", "DIN99d"], str ] = "DIN99") -> NDArray`	@SGeetansh
`colour.DIN99_to_Lab`	`DIN99_to_Lab(Lab_99: ArrayLike, k_E: Floating = 1, k_CH: Floating = 1, method: Union[ Literal["ASTMD2244-07", "DIN99", "DIN99b", "DIN99c", "DIN99d"], str ] = "DIN99") -> NDArray`	@SGeetansh

Object	Name	Author
`colour.RGB_to_ICTCP`	`RGB_to_ICtCp`	@KelSolaar
`colour.ICTCP_to_RGB`	`ICtCp_to_RGB`	...
`colour.RGB_to_IGPGTG`	`RGB_to_IgPgTg`	...
`colour.IGPGTG_to_RGB`	`IgPgTg_to_RGB`	...
`colour.XYZ_to_JzAzBz`	`XYZ_to_Jzazbz`	...
`colour.JzAzBz_to_XYZ`	`Jzazbz_to_XYZ`	...

`colour.plotting`

Object	Signature	Author
`colour.plotting.plot_constant_hue_loci`	`plot_constant_hue_loci(data: ArrayLike, model: Union[ Literal[ "CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD", "CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY", "CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99", "Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT", "IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB", "hdr-IPT", ], str, ] = "CIE Lab", scatter_kwargs: Optional[Dict] = None, convert_kwargs: Optional[Dict] = None, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]`	@KelSolaar
`colour.plotting.plot_corresponding_chromaticities_prediction`	`plot_corresponding_chromaticities_prediction(experiment: Union[ Literal[1, 2, 3, 4, 6, 8, 9, 11, 12], CorrespondingColourDataset ] = 1, model: Union[ Literal[ "CIE 1994", "CMCCAT2000", "Fairchild 1990", "Von Kries", "Zhai 2018", ], str, ] = "Von Kries", corresponding_chromaticities_prediction_kwargs: Optional[Dict] = None, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]`	...
`colour.plotting.plot_RGB_colourspaces_gamuts`	plot_RGB_colourspaces_gamuts(colourspaces: Union[ RGB_Colourspace, str, Sequence[Union[RGB_Colourspace, str]] ], reference_colourspace: Union[ Literal[ "CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD", "CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY", "CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99", "Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT", "IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB", "hdr-IPT", ], str, ] = "CIE xyY", segments: Integer = 8, show_grid: Boolean = True, grid_segments: Integer = 10, show_spectral_locus: Boolean = False, spectral_locus_colour: Optional[Union[ArrayLike, str]] = None, cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", chromatically_adapt: Boolean = False, convert_kwargs: Optional[Dict] = None, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]	...
`colour.plotting.plot_RGB_scatter`	plot_RGB_scatter(RGB: ArrayLike, colourspace: Union[ RGB_Colourspace, str, Sequence[Union[RGB_Colourspace, str]] ], reference_colourspace: Union[ Literal[ "CAM02LCD", "CAM02SCD", "CAM02UCS", "CAM16LCD", "CAM16SCD", "CAM16UCS", "CIE XYZ", "CIE xyY", "CIE Lab", "CIE Luv", "CIE UCS", "CIE UVW", "DIN99", "Hunter Lab", "Hunter Rdab", "ICaCb", "ICtCp", "IPT", "IgPgTg", "Jzazbz", "OSA UCS", "Oklab", "hdr-CIELAB", "hdr-IPT", ], str, ] = "CIE xyY", colourspaces: Optional[ Union[RGB_Colourspace, str, Sequence[Union[RGB_Colourspace, str]]] ] = None, segments: Integer = 8, show_grid: Boolean = True, grid_segments: Integer = 10, show_spectral_locus: Boolean = False, spectral_locus_colour: Optional[Union[ArrayLike, str]] = None, points_size: Floating = 12, cmfs: Union[ MultiSpectralDistributions, str, Sequence[Union[MultiSpectralDistributions, str]], ] = "CIE 1931 2 Degree Standard Observer", chromatically_adapt: Boolean = False, convert_kwargs: Optional[Dict] = None, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]	...
`colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1931`	`plot_planckian_locus_in_chromaticity_diagram_CIE1931(illuminants: Union[str, Sequence[str]], chromaticity_diagram_callable_CIE1931: Callable = plot_chromaticity_diagram_CIE1931, annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None, plot_kwargs: Optional[Union[Dict, List[Dict]]] = None, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]`	...
`colour.plotting.plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS`	`plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(illuminants: Union[str, Sequence[str]], chromaticity_diagram_callable_CIE1960UCS: Callable = plot_chromaticity_diagram_CIE1960UCS, annotate_kwargs: Optional[Union[Dict, List[Dict]]] = None, plot_kwargs: Optional[Union[Dict, List[Dict]]] = None, **kwargs: Any) -> Tuple[plt.Figure, plt.Axes]`	...

`colour.recovery`

Object	Signature	Author
`colour.recovery.find_coefficients_Jakob2019`	`find_coefficients_Jakob2019(XYZ: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, coefficients_0: ArrayLike = zeros(3), max_error: Floating = JND_CIE1976 / 100, dimensionalise: Boolean = True) -> Tuple[NDArray, Floating]`	@KelSolaar
`colour.recovery.spectral_primary_decomposition_Mallett2019`	`spectral_primary_decomposition_Mallett2019(colourspace: RGB_Colourspace, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, metric: Callable = np.linalg.norm, metric_args: Tuple = tuple(), optimisation_kwargs: Optional[Dict] = None) -> MultiSpectralDistributions`	...
`colour.recovery.XYZ_to_sd_Jakob2019`	`XYZ_to_sd_Jakob2019(XYZ: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, optimisation_kwargs: Optional[Dict] = None, additional_data: Boolean = False) -> Union[Tuple[SpectralDistribution, Floating], SpectralDistribution]`	...
`colour.recovery.XYZ_to_sd_Meng2015`	`XYZ_to_sd_Meng2015(XYZ: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, optimisation_kwargs: Optional[Dict] = None) -> SpectralDistribution`	...
`colour.recovery.XYZ_to_sd_Otsu2018`	`XYZ_to_sd_Otsu2018(XYZ: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, dataset: Dataset_Otsu2018 = DATASET_REFERENCE_OTSU2018, clip: Boolean = True) -> SpectralDistribution`	...

`colour.temperature`

Object	Access	Author
`colour.temperature.CCT_to_uv_Ohno2013`	`CCT_to_uv_Ohno2013(CCT_D_uv: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None) -> NDArray`	@KelSolaar
`colour.temperature.uv_to_CCT_Ohno2013`	`uv_to_CCT_Ohno2013(uv: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, start: Floating = CCT_MINIMAL, end: Floating = CCT_MAXIMAL, count: Integer = CCT_SAMPLES, iterations: Integer = CCT_CALCULATION_ITERATIONS) -> NDArray`	...

`colour.utilities`

Object	Access	Author
`colour.utilities.lerp`	`colour.algebra.lerp`	@KelSolaar
`colour.utilities.linear_conversion`	`colour.algebra.linear_conversion`	...
`colour.utilities.matrix_dot`	`colour.algebra.matrix_dot`	...
`colour.utilities.normalise_maximum`	`colour.algebra.normalise_maximum`	...
`colour.utilities.vector_dot`	`colour.algebra.vector_dot`	...

Object	Name	Author
`colour.utilities.set_int _precision`	`set_default_int_dtype`	@KelSolaar
`colour.utilities.set_float_precision`	`set_default_float_dtype`	...

`colour.volume`

Object	Signature	Author
`colour.is_within_visible_spectrum`	`is_within_visible_spectrum(XYZ: ArrayLike, cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, tolerance: Optional[Floating] = None, **kwargs: Any) -> NDArray`	@KelSolaar
`colour.RGB_colourspace_limits`	`RGB_colourspace_limits(colourspace: RGB_Colourspace) -> NDArray`	@KelSolaar, @ramparvathaneni
`colour.volume.generate_pulse_waves`	`generate_pulse_waves(bins: Integer, pulse_order: Union[Literal["Bins", "Pulse Wave Width"], str] = "Bins", filter_jagged_pulses: Boolean = False) -> NDArray`	...
`colour.volume.XYZ_outer_surface`	`XYZ_outer_surface(cmfs: Optional[MultiSpectralDistributions] = None, illuminant: Optional[SpectralDistribution] = None, point_order: Union[Literal["Bins", "Pulse Wave Width"], str] = "Bins", filter_jagged_points: Boolean = False, **kwargs: Any) -> NDArray`	...

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Colour 0.4.0

Features

Typing

Performance

`colour.adaptation`

GSoC - 2021

`colour.appearance`

GSoC - 2021

`colour.characterisation`

`colour.colorimetry`

`colour.difference`

`colour.io`

GSoC - 2020

GSoC - 2021

`colour.models`

GSoC - 2021

`colour.plotting`

`colour.volume`

Fixes

`colour`

`colour.colorimetry`

`colour.plotting`

Changes

`colour.algebra`

`colour.biochemistry`

`colour.characterisation`

`colour.colorimetry`

`colour.geometry`

`colour.io`

`colour.models`

`colour.plotting`

`colour.recovery`

`colour.temperature`

`colour.utilities`

`colour.volume`

Contributors