colour

Colour Science for Python

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Quick Overview

Colour is a comprehensive colour science package for Python. It provides a wide range of algorithms and datasets for colour science, including colour appearance models, colour difference equations, colour space conversions, and more. The library aims to be a one-stop solution for colour-related computations in various fields such as computer graphics, image processing, and colorimetry.

Pros

Extensive collection of colour science algorithms and datasets
Well-documented with detailed API references and examples
Actively maintained with regular updates and improvements
Supports a wide range of colour spaces and models

Cons

Steep learning curve for beginners due to the complexity of colour science
Large package size due to comprehensive functionality
Some advanced features may require additional dependencies
Performance may be slower compared to specialized libraries for specific tasks

Code Examples

Example 1: Converting RGB to XYZ colour space

import colour

RGB = [0.45620519, 0.03081071, 0.04091952]
XYZ = colour.sRGB_to_XYZ(RGB)
print(XYZ)

Example 2: Calculating colour difference using CIEDE2000

import colour

Lab_1 = [100.00000000, 21.57210357, 272.22819350]
Lab_2 = [100.00000000, 426.67945353, 72.39590835]
delta_E = colour.delta_E(Lab_1, Lab_2, method='CIEDE2000')
print(delta_E)

Example 3: Applying a colour appearance model (CAM16)

import colour

XYZ = [19.01, 20.00, 21.78]
XYZ_w = [95.05, 100.00, 108.88]
L_A = 318.31
Y_b = 20.0
surround = colour.VIEWING_CONDITIONS_CAM16['Average']
specification = colour.CAM16(XYZ, XYZ_w, L_A, Y_b, surround)
print(specification)

Getting Started

To get started with Colour, follow these steps:

Install the library using pip:
```
pip install colour-science
```
Import the library in your Python script:
```
import colour
```

Start using Colour's functions. For example, to convert RGB to XYZ:

RGB = [0.45620519, 0.03081071, 0.04091952]
XYZ = colour.sRGB_to_XYZ(RGB)
print(XYZ)

For more detailed information and examples, refer to the official documentation at https://colour.readthedocs.io/.

Competitor Comparisons

scikit-image

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Pros of scikit-image

Broader scope, covering general image processing tasks
Larger community and more frequent updates
Extensive documentation and tutorials

Cons of scikit-image

Less specialized for color science applications
May require additional libraries for advanced color operations
Steeper learning curve for color-specific tasks

Code Comparison

scikit-image:

from skimage import color
rgb_image = ...  # Your RGB image
lab_image = color.rgb2lab(rgb_image)

colour-science:

import colour
rgb_image = ...  # Your RGB image
lab_image = colour.RGB_to_Lab(rgb_image)

Summary

scikit-image is a comprehensive image processing library with a wide range of functionalities, while colour-science focuses specifically on color science applications. scikit-image offers more general-purpose tools and has a larger community, but colour-science provides more specialized color-related functions and may be easier to use for specific color science tasks. The choice between the two depends on the project's requirements and the user's familiarity with color science concepts.

opencv

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Open Source Computer Vision Library

Pros of OpenCV

Extensive functionality for computer vision and image processing
Large community and widespread adoption in industry and academia
Optimized performance with hardware acceleration support

Cons of OpenCV

Steeper learning curve due to its broad scope and complexity
Larger codebase and dependencies, potentially increasing project size

Code Comparison

OpenCV:

import cv2

img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 100, 200)

Colour:

import colour

RGB = colour.read_image('image.jpg')
XYZ = colour.RGB_to_XYZ(RGB)
Lab = colour.XYZ_to_Lab(XYZ)

OpenCV focuses on general image processing and computer vision tasks, while Colour specializes in color science and colorimetry. OpenCV offers a wider range of functionalities but may be more complex for specific color-related tasks. Colour provides a more focused and intuitive approach to color science applications but has a narrower scope compared to OpenCV's broad capabilities.

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Pros of Pillow

Broader image processing capabilities, including resizing, filtering, and drawing
Extensive file format support for reading and writing various image types
Larger community and more frequent updates

Cons of Pillow

Limited color science functionality compared to Colour
Less focus on color space transformations and colorimetry
Not specifically designed for scientific color computations

Code Comparison

Pillow example (image resizing):

from PIL import Image

img = Image.open("input.jpg")
resized_img = img.resize((300, 200))
resized_img.save("output.jpg")

Colour example (color space conversion):

import colour

RGB = [0.18, 0.18, 0.18]
XYZ = colour.sRGB_to_XYZ(RGB)
Lab = colour.XYZ_to_Lab(XYZ)

Pillow is more suitable for general image processing tasks, while Colour specializes in color science operations. Pillow offers a wide range of image manipulation functions, whereas Colour provides advanced color space transformations and colorimetry calculations. Choose Pillow for everyday image handling and Colour for precise color-related computations in scientific or professional applications.

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Pros of matplotlib

Widely adopted and extensively documented plotting library
Supports a vast array of plot types and customization options
Integrates seamlessly with NumPy and other scientific Python libraries

Cons of matplotlib

Steeper learning curve for complex visualizations
Can be slower for large datasets compared to specialized libraries
Default styles may require more tweaking for publication-ready plots

Code Comparison

matplotlib:

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
plt.plot(x, np.sin(x))
plt.show()

colour:

import colour
import numpy as np

RGB = np.array([0.45620519, 0.03081071, 0.04091952])
XYZ = colour.sRGB_to_XYZ(RGB)
print(XYZ)

matplotlib focuses on creating various types of plots and visualizations, while colour specializes in color science computations and transformations. matplotlib is more suitable for general-purpose plotting, whereas colour is tailored for specific color-related tasks and calculations.

labelme

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Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

Pros of labelme

User-friendly graphical interface for image annotation
Supports multiple annotation types (polygons, rectangles, points)
Easy integration with machine learning workflows

Cons of labelme

Limited color science functionality
Focused primarily on image annotation, not color analysis
May require additional tools for comprehensive color processing

Code Comparison

labelme:

from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0, 'person': 1}
lbl = utils.shapes_to_label(img.shape, shapes, label_name_to_value)

colour:

import colour
RGB = colour.read_image('image.png')
XYZ = colour.RGB_to_XYZ(RGB)
Lab = colour.XYZ_to_Lab(XYZ)

Summary

labelme is primarily focused on image annotation and labeling, making it ideal for preparing datasets for machine learning tasks. It offers a user-friendly interface and supports various annotation types. However, it lacks advanced color science capabilities.

colour, on the other hand, is a comprehensive color science library that provides a wide range of color-related functions and algorithms. It's more suitable for tasks involving color analysis, conversion, and manipulation, but doesn't offer image annotation features.

The choice between these libraries depends on the specific requirements of your project. If you need image annotation capabilities, labelme is the better option. For color science and analysis tasks, colour would be more appropriate.

scikit-learn

59,384

scikit-learn: machine learning in Python

Pros of scikit-learn

Broader scope: Covers a wide range of machine learning algorithms and tools
Larger community: More contributors, frequent updates, and extensive documentation
Integration: Seamlessly works with other popular data science libraries like NumPy and pandas

Cons of scikit-learn

Steeper learning curve: Due to its extensive feature set
Less specialized: Not focused on color science specifically
Heavier package: Larger installation size and potentially slower import times

Code Comparison

scikit-learn (simple linear regression):

from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X, y)
predictions = model.predict(X_test)

colour (color conversion):

import colour
RGB = [0.45620519, 0.03081071, 0.04091952]
XYZ = colour.sRGB_to_XYZ(RGB)

Both libraries offer powerful functionality in their respective domains. scikit-learn provides a comprehensive suite of machine learning tools, while colour focuses specifically on color science computations. The choice between them depends on the specific needs of your project.

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README

.. begin-trim-long-description

.. raw:: html

<picture>
    <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/colour-science/colour-branding/master/images/Colour_Logo_Dark_001.svg">
    <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/colour-science/colour-branding/master/images/Colour_Logo_001.svg">
    <img style="background:rgb(0, 0, 0, 0) !important;" src="https://raw.githubusercontent.com/colour-science/colour-branding/master/images/Colour_Logo_001.svg">
</picture>

.. end-trim-long-description

.. start-badges

.. |NumFOCUS| image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat-square&colorA=E1523D&colorB=007D8A :target: http://numfocus.org :alt: Powered by NumFOCUS .. |actions| image:: https://img.shields.io/github/actions/workflow/status/colour-science/colour/.github/workflows/continuous-integration-quality-unit-tests.yml?branch=develop&style=flat-square :target: https://github.com/colour-science/colour/actions :alt: Develop Build Status .. |coveralls| image:: http://img.shields.io/coveralls/colour-science/colour/develop.svg?style=flat-square :target: https://coveralls.io/r/colour-science/colour :alt: Coverage Status .. |codacy| image:: https://img.shields.io/codacy/grade/1f3b8d3bba7440ba9ebc1170589628b1/develop.svg?style=flat-square :target: https://app.codacy.com/gh/colour-science/colour :alt: Code Grade .. |version| image:: https://img.shields.io/pypi/v/colour-science.svg?style=flat-square :target: https://pypi.org/project/colour-science :alt: Package Version .. |zenodo| image:: https://img.shields.io/badge/DOI-10.5281/zenodo.10396329-blue.svg?style=flat-square :target: https://dx.doi.org/10.5281/zenodo.10396329 :alt: DOI

.. end-badges

Colour <https://github.com/colour-science/colour>__ is an open-source Python <https://www.python.org>__ package providing a comprehensive number of algorithms and datasets for colour science.

It is freely available under the BSD-3-Clause <https://opensource.org/licenses/BSD-3-Clause>__ terms.

Colour is an affiliated project of NumFOCUS <https://numfocus.org>__, a 501(c)(3) nonprofit in the United States.

.. contents:: Table of Contents :backlinks: none :depth: 2

.. sectnum::

Draft Release Notes

The draft release notes of the develop <https://github.com/colour-science/colour/tree/develop>__ branch are available at this url <https://gist.github.com/KelSolaar/4a6ebe9ec3d389f0934b154fec8df51d>__.

Features

Most of the objects are available from the colour namespace:

.. code-block:: python

import colour

Automatic Colour Conversion Graph - colour.graph


Starting with version *0.3.14*, **Colour** implements an automatic colour
conversion graph enabling easier colour conversions.

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Colour_Automatic_Conversion_Graph.png

.. code-block:: python

    sd = colour.SDS_COLOURCHECKERS["ColorChecker N Ohta"]["dark skin"]
    colour.convert(sd, "Spectral Distribution", "sRGB", verbose={"mode": "Short"})

.. code-block:: text

    ===============================================================================
    *                                                                             *
    *   [ Conversion Path ]                                                       *
    *                                                                             *
    *   "sd_to_XYZ" --> "XYZ_to_sRGB"                                             *
    *                                                                             *
    ===============================================================================
    array([ 0.45675795,  0.30986982,  0.24861924])

.. code-block:: python

    illuminant = colour.SDS_ILLUMINANTS["FL2"]
    colour.convert(
        sd,
        "Spectral Distribution",
        "sRGB",
        sd_to_XYZ={"illuminant": illuminant},
    )

.. code-block:: text

    array([ 0.47924575,  0.31676968,  0.17362725])

Chromatic Adaptation - ``colour.adaptation``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    XYZ = [0.20654008, 0.12197225, 0.05136952]
    D65 = colour.CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["D65"]
    A = colour.CCS_ILLUMINANTS["CIE 1931 2 Degree Standard Observer"]["A"]
    colour.chromatic_adaptation(XYZ, colour.xy_to_XYZ(D65), colour.xy_to_XYZ(A))

.. code-block:: text

    array([ 0.2533053 ,  0.13765138,  0.01543307])


.. code-block:: python

    sorted(colour.CHROMATIC_ADAPTATION_METHODS)
.. code-block:: text

    ['CIE 1994', 'CMCCAT2000', 'Fairchild 1990', 'Von Kries', 'Zhai 2018', 'vK20']

Algebra - ``colour.algebra``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Kernel Interpolation
********************

.. code-block:: python

    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])

.. code-block:: text

    array([  6.18062083,   8.08238488,  57.85783403])

Sprague (1880) Interpolation
****************************

.. code-block:: python

    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])

.. code-block:: text

    array([  6.72951612,   7.81406251,  43.77379185])

Colour Appearance Models - ``colour.appearance``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    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)

.. code-block:: text

    CAM_Specification_CIECAM02(J=34.434525727858997, C=67.365010921125943, h=22.279164147957065, s=62.81485585332716, Q=177.47124941102123, M=70.024939419291414, H=2.6896085344238898, HC=None)

.. code-block:: python

    colour.XYZ_to_CIECAM16(XYZ, XYZ_w, L_A, Y_b)

.. code-block:: text

    CAM_Specification_CIECAM16(J=34.434525727858997, C=67.365010921125943, h=22.279164147957065, s=62.81485585332716, Q=177.47124941102123, M=70.024939419291414, H=2.6896085344238898, HC=None)

.. code-block:: python

    colour.XYZ_to_CAM16(XYZ, XYZ_w, L_A, Y_b)

.. code-block:: text

    CAM_Specification_CAM16(J=33.880368498111686, C=69.444353357408033, h=19.510887327451748, s=64.03612114840314, Q=176.03752758512178, M=72.18638534116765, H=399.52975599115319, HC=None)

.. code-block:: python

    colour.XYZ_to_Hellwig2022(XYZ, XYZ_w, L_A)

.. code-block:: text

    CAM_Specification_Hellwig2022(J=33.880368498111686, C=40.347043294550311, h=19.510887327451748, s=117.38555017188679, Q=45.34489577734751, M=53.228355383108031, H=399.52975599115319, HC=None)

.. code-block:: python

    colour.XYZ_to_Kim2009(XYZ, XYZ_w, L_A)

.. code-block:: text

    CAM_Specification_Kim2009(J=19.879918542450902, C=55.839055250876946, h=22.013388165090046, s=112.97979354939129, Q=36.309026130161449, M=46.346415858227864, H=2.3543198369639931, HC=None)

.. code-block:: python

    colour.XYZ_to_ZCAM(XYZ, XYZ_w, L_A, Y_b)

.. code-block:: text

    CAM_Specification_ZCAM(J=38.347186278956357, C=21.12138989208518, h=33.711578931095197, s=81.444585609489536, Q=76.986725284523772, M=42.403805833900506, H=0.45779200212219573, HC=None, V=43.623590687423544, K=43.20894953152817, W=34.829588380192149)

Colour Blindness - ``colour.blindness``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    import numpy as np

    cmfs = colour.LMS_CMFS["Stockman & Sharpe 2 Degree Cone Fundamentals"]
    colour.msds_cmfs_anomalous_trichromacy_Machado2009(cmfs, np.array([15, 0, 0]))[450]

.. code-block:: text

    array([ 0.08912884,  0.0870524 ,  0.955393  ])

.. code-block:: python

    primaries = colour.MSDS_DISPLAY_PRIMARIES["Apple Studio Display"]
    d_LMS = (15, 0, 0)
    colour.matrix_anomalous_trichromacy_Machado2009(cmfs, primaries, d_LMS)

.. code-block:: text

    array([[-0.27774652,  2.65150084, -1.37375432],
           [ 0.27189369,  0.20047862,  0.52762768],
           [ 0.00644047,  0.25921579,  0.73434374]])

Colour Correction - ``colour characterisation``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    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)

.. code-block:: text

    array([ 0.1806237 ,  0.07234791,  0.07848845])

.. code-block:: python

    sorted(colour.COLOUR_CORRECTION_METHODS)

.. code-block:: text

    ['Cheung 2004', 'Finlayson 2015', 'Vandermonde']

ACES Input Transform - ``colour characterisation``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    sensitivities = colour.MSDS_CAMERA_SENSITIVITIES["Nikon 5100 (NPL)"]
    illuminant = colour.SDS_ILLUMINANTS["D55"]
    colour.matrix_idt(sensitivities, illuminant)

.. code-block:: text

    (array([[ 0.59368175,  0.30418371,  0.10213454],
           [ 0.00457979,  1.14946003, -0.15403982],
           [ 0.03552213, -0.16312291,  1.12760077]]), array([ 1.58214188,  1.        ,  1.28910346]))

Colorimetry - ``colour.colorimetry``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Spectral Computations
*********************

.. code-block:: python

    colour.sd_to_XYZ(colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"])

.. code-block:: text

    array([ 36.94726204,  32.62076174,  13.0143849 ])

.. code-block:: python

    sorted(colour.SPECTRAL_TO_XYZ_METHODS)

.. code-block:: text

    ['ASTM E308', 'Integration', 'astm2015']


Multi-Spectral Computations
***************************

.. code-block:: python

    msds = np.array(
        [
            [
                [
                    0.01367208,
                    0.09127947,
                    0.01524376,
                    0.02810712,
                    0.19176012,
                    0.04299992,
                ],
                [
                    0.00959792,
                    0.25822842,
                    0.41388571,
                    0.22275120,
                    0.00407416,
                    0.37439537,
                ],
                [
                    0.01791409,
                    0.29707789,
                    0.56295109,
                    0.23752193,
                    0.00236515,
                    0.58190280,
                ],
            ],
            [
                [
                    0.01492332,
                    0.10421912,
                    0.02240025,
                    0.03735409,
                    0.57663846,
                    0.32416266,
                ],
                [
                    0.04180972,
                    0.26402685,
                    0.03572137,
                    0.00413520,
                    0.41808194,
                    0.24696727,
                ],
                [
                    0.00628672,
                    0.11454948,
                    0.02198825,
                    0.39906919,
                    0.63640803,
                    0.01139849,
                ],
            ],
            [
                [
                    0.04325933,
                    0.26825359,
                    0.23732357,
                    0.05175860,
                    0.01181048,
                    0.08233768,
                ],
                [
                    0.02484169,
                    0.12027161,
                    0.00541695,
                    0.00654612,
                    0.18603799,
                    0.36247808,
                ],
                [
                    0.03102159,
                    0.16815442,
                    0.37186235,
                    0.08610666,
                    0.00413520,
                    0.78492409,
                ],
            ],
            [
                [
                    0.11682307,
                    0.78883040,
                    0.74468607,
                    0.83375293,
                    0.90571451,
                    0.70054168,
                ],
                [
                    0.06321812,
                    0.41898224,
                    0.15190357,
                    0.24591440,
                    0.55301750,
                    0.00657664,
                ],
                [
                    0.00305180,
                    0.11288624,
                    0.11357290,
                    0.12924391,
                    0.00195315,
                    0.21771573,
                ],
            ],
        ]
    )
    colour.msds_to_XYZ(
        msds,
        method="Integration",
        shape=colour.SpectralShape(400, 700, 60),
    )

.. code-block:: text

    array([[[  7.68544647,   4.09414317,   8.49324254],
            [ 17.12567298,  27.77681821,  25.52573685],
            [ 19.10280411,  34.45851476,  29.76319628]],
           [[ 18.03375827,   8.62340812,   9.71702574],
            [ 15.03110867,   6.54001068,  24.53208465],
            [ 37.68269495,  26.4411103 ,  10.66361816]],
           [[  8.09532373,  12.75333339,  25.79613956],
            [  7.09620297,   2.79257389,  11.15039854],
            [  8.933163  ,  19.39985815,  17.14915636]],
           [[ 80.00969553,  80.39810464,  76.08184429],
            [ 33.27611427,  24.38947838,  39.34919287],
            [  8.89425686,  11.05185138,  10.86767594]]])

.. code-block:: python

    sorted(colour.MSDS_TO_XYZ_METHODS)

.. code-block:: text

    ['ASTM E308', 'Integration', 'astm2015']

Blackbody Spectral Radiance Computation
***************************************

.. code-block:: python

    colour.sd_blackbody(5000)

.. code-block:: text

    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
**************************************************************

.. code-block:: python

    xy = [0.54369557, 0.32107944]
    xy_n = [0.31270000, 0.32900000]
    colour.dominant_wavelength(xy, xy_n)

.. code-block:: text

    (array(616.0),
     array([ 0.68354746,  0.31628409]),
     array([ 0.68354746,  0.31628409]))

Lightness Computation
*********************

.. code-block:: python

    colour.lightness(12.19722535)

.. code-block:: text

    41.527875844653451

.. code-block:: python

    sorted(colour.LIGHTNESS_METHODS)

.. code-block:: text

    ['Abebe 2017',
     'CIE 1976',
     'Fairchild 2010',
     'Fairchild 2011',
     'Glasser 1958',
     'Lstar1976',
     'Wyszecki 1963']

Luminance Computation
*********************

.. code-block:: python

    colour.luminance(41.52787585)

.. code-block:: text

    12.197225353400775

.. code-block:: python

    sorted(colour.LUMINANCE_METHODS)

.. code-block:: text

    ['ASTM D1535',
     'CIE 1976',
     'Fairchild 2010',
     'Fairchild 2011',
     'Newhall 1943',
     'astm2008',
     'cie1976']

Whiteness Computation
*********************

.. code-block:: python

    XYZ = [95.00000000, 100.00000000, 105.00000000]
    XYZ_0 = [94.80966767, 100.00000000, 107.30513595]
    colour.whiteness(XYZ, XYZ_0)

.. code-block:: text

    array([ 93.756     ,  -1.33000001])

.. code-block:: python

    sorted(colour.WHITENESS_METHODS)

.. code-block:: text

    ['ASTM E313',
     'Berger 1959',
     'CIE 2004',
     'Ganz 1979',
     'Stensby 1968',
     'Taube 1960',
     'cie2004']

Yellowness Computation
**********************

.. code-block:: python

    XYZ = [95.00000000, 100.00000000, 105.00000000]
    colour.yellowness(XYZ)

.. code-block:: text

    4.3400000000000034

.. code-block:: python

    sorted(colour.YELLOWNESS_METHODS)

.. code-block:: text

    ['ASTM D1925', 'ASTM E313', 'ASTM E313 Alternative']

Luminous Flux, Efficiency & Efficacy Computation
************************************************

.. code-block:: python

    sd = colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"]
    colour.luminous_flux(sd)

.. code-block:: text

    23807.655527367202

.. code-block:: python

    sd = colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"]
    colour.luminous_efficiency(sd)

.. code-block:: text

    0.19943935624521045

.. code-block:: python

    sd = colour.SDS_LIGHT_SOURCES["Neodimium Incandescent"]
    colour.luminous_efficacy(sd)

.. code-block:: text

    136.21708031547874

Contrast Sensitivity Function - ``colour.contrast``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    colour.contrast_sensitivity_function(u=4, X_0=60, E=65)

.. code-block:: text

    358.51180789884984

.. code-block:: python

    sorted(colour.CONTRAST_SENSITIVITY_METHODS)

.. code-block:: text

    ['Barten 1999']

Colour Difference - ``colour.difference``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    Lab_1 = [100.00000000, 21.57210357, 272.22819350]
    Lab_2 = [100.00000000, 426.67945353, 72.39590835]
    colour.delta_E(Lab_1, Lab_2)

.. code-block:: text

    94.035649026659485

.. code-block:: python

    sorted(colour.DELTA_E_METHODS)

.. code-block:: text

    ['CAM02-LCD',
     'CAM02-SCD',
     'CAM02-UCS',
     'CAM16-LCD',
     'CAM16-SCD',
     'CAM16-UCS',
     'CIE 1976',
     'CIE 1994',
     'CIE 2000',
     'CMC',
     'DIN99',
     'ITP',
     'cie1976',
     'cie1994',
     'cie2000']

IO - ``colour.io``
~~~~~~~~~~~~~~~~~~

Images
******

.. code-block:: python

    RGB = colour.read_image("Ishihara_Colour_Blindness_Test_Plate_3.png")
    RGB.shape

.. code-block:: text

    (276, 281, 3)

Spectral Images - Fichet et al. (2021)
**************************************

.. code-block:: python

    components = colour.read_spectral_image_Fichet2021("Polarised.exr")
    list(components.keys())

.. code-block:: text

    ['S0', 'S1', 'S2', 'S3']

Look Up Table (LUT) Data
************************

.. code-block:: python

    LUT = colour.read_LUT("ACES_Proxy_10_to_ACES.cube")
    print(LUT)

.. code-block:: text

    LUT3x1D - ACES Proxy 10 to ACES
    -------------------------------
    Dimensions : 2
    Domain     : [[0 0 0]
                  [1 1 1]]
    Size       : (32, 3)

.. code-block:: python

    RGB = [0.17224810, 0.09170660, 0.06416938]
    LUT.apply(RGB)

.. code-block:: text

    array([ 0.00575674,  0.00181493,  0.00121419])

Colour Models - ``colour.models``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

CIE xyY Colourspace
*******************

.. code-block:: python

    colour.XYZ_to_xyY([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.54369557,  0.32107944,  0.12197225])

CIE L*a*b* Colourspace
**********************

.. code-block:: python

    colour.XYZ_to_Lab([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 41.52787529,  52.63858304,  26.92317922])

CIE L*u*v* Colourspace
**********************

.. code-block:: python

    colour.XYZ_to_Luv([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 41.52787529,  96.83626054,  17.75210149])

CIE 1960 UCS Colourspace
************************

.. code-block:: python

    colour.XYZ_to_UCS([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.13769339,  0.12197225,  0.1053731 ])

CIE 1964 U*V*W* Colourspace
***************************

.. code-block:: python

    XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
    colour.XYZ_to_UVW(XYZ)

.. code-block:: text

    array([ 94.55035725,  11.55536523,  40.54757405])

CAM02-LCD, CAM02-SCD, and CAM02-UCS Colourspaces - Luo, Cui and Li (2006)
*************************************************************************

.. code-block:: python

    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.VIEWING_CONDITIONS_CIECAM02["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)

.. code-block:: text

    array([ 47.16899898,  38.72623785,  15.8663383 ])

.. code-block:: python

    XYZ = [0.20654008, 0.12197225, 0.05136952]
    XYZ_w = [95.05 / 100, 100.00 / 100, 108.88 / 100]
    colour.XYZ_to_CAM02UCS(XYZ, XYZ_w=XYZ_w, L_A=L_A, Y_b=Y_b)

.. code-block:: text

    array([ 47.16899898,  38.72623785,  15.8663383 ])

CAM16-LCD, CAM16-SCD, and CAM16-UCS Colourspaces - Li et al. (2017)
*******************************************************************

.. code-block:: python

    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.VIEWING_CONDITIONS_CAM16["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)

.. code-block:: text

    array([ 46.55542238,  40.22460974,  14.25288392])

.. code-block:: python

    XYZ = [0.20654008, 0.12197225, 0.05136952]
    XYZ_w = [95.05 / 100, 100.00 / 100, 108.88 / 100]
    colour.XYZ_to_CAM16UCS(XYZ, XYZ_w=XYZ_w, L_A=L_A, Y_b=Y_b)

.. code-block:: text

    array([ 46.55542238,  40.22460974,  14.25288392])

DIN99 Colourspace and DIN99b, DIN99c, DIN99d Refined Formulas
*************************************************************

.. code-block:: python

    Lab = [41.52787529, 52.63858304, 26.92317922]
    colour.Lab_to_DIN99(Lab)

.. code-block:: text

    array([ 53.22821988,  28.41634656,   3.89839552])

ICaCb Colourspace
******************

.. code-block:: python

    XYZ_to_ICaCb(np.array([0.20654008, 0.12197225, 0.05136952]))

.. code-block:: text

    array([ 0.06875297,  0.05753352,  0.02081548])

IgPgTg Colourspace
******************

.. code-block:: python

    colour.XYZ_to_IgPgTg([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.42421258,  0.18632491,  0.10689223])

IPT Colourspace
***************

.. code-block:: python

    colour.XYZ_to_IPT([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.38426191,  0.38487306,  0.18886838])

Jzazbz Colourspace
******************

.. code-block:: python

    colour.XYZ_to_Jzazbz([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.00535048,  0.00924302,  0.00526007])

hdr-CIELAB Colourspace
**********************

.. code-block:: python

    colour.XYZ_to_hdr_CIELab([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 51.87002062,  60.4763385 ,  32.14551912])

hdr-IPT Colourspace
*******************

.. code-block:: python

    colour.XYZ_to_hdr_IPT([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 25.18261761, -22.62111297,   3.18511729])

Hunter L,a,b Colour Scale
*************************

.. code-block:: python

    XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
    colour.XYZ_to_Hunter_Lab(XYZ)

.. code-block:: text

    array([ 34.92452577,  47.06189858,  14.38615107])

Hunter Rd,a,b Colour Scale
**************************

.. code-block:: python

    XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
    colour.XYZ_to_Hunter_Rdab(XYZ)

.. code-block:: text

    array([ 12.197225  ,  57.12537874,  17.46241341])

Oklab Colourspace
*****************

.. code-block:: python

    colour.XYZ_to_Oklab([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.51634019,  0.154695  ,  0.06289579])

OSA UCS Colourspace
*******************

.. code-block:: python

    XYZ = [0.20654008 * 100, 0.12197225 * 100, 0.05136952 * 100]
    colour.XYZ_to_OSA_UCS(XYZ)

.. code-block:: text

    array([-3.0049979 ,  2.99713697, -9.66784231])

ProLab Colourspace
******************

.. code-block:: python

    colour.XYZ_to_ProLab([0.51634019, 0.15469500, 0.06289579])

.. code-block:: text

    array([1.24610688, 2.39525236, 0.41902126])

Ragoo and Farup (2021) Optimised IPT Colourspace
************************************************

.. code-block:: python

    colour.XYZ_to_IPT_Ragoo2021([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.42248243,  0.2910514 ,  0.20410663])

Yrg Colourspace - Kirk (2019)
*****************************

.. code-block:: python

    colour.XYZ_to_Yrg([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

    array([ 0.13137801,  0.49037645,  0.37777388])

Y'CbCr Colour Encoding
**********************

.. code-block:: python

    colour.RGB_to_YCbCr([1.0, 1.0, 1.0])

.. code-block:: text

    array([ 0.92156863,  0.50196078,  0.50196078])

YCoCg Colour Encoding
*********************

.. code-block:: python

    colour.RGB_to_YCoCg([0.75, 0.75, 0.0])

.. code-block:: text

    array([ 0.5625,  0.375 ,  0.1875])

ICtCp Colour Encoding
*********************

.. code-block:: python

    colour.RGB_to_ICtCp([0.45620519, 0.03081071, 0.04091952])

.. code-block:: text

    array([ 0.07351364,  0.00475253,  0.09351596])

HSV Colourspace
***************

.. code-block:: python

    colour.RGB_to_HSV([0.45620519, 0.03081071, 0.04091952])

.. code-block:: text

    array([ 0.99603944,  0.93246304,  0.45620519])

IHLS Colourspace
****************

.. code-block:: python

    colour.RGB_to_IHLS([0.45620519, 0.03081071, 0.04091952])

.. code-block:: text

    array([ 6.26236117,  0.12197943,  0.42539448])

Prismatic Colourspace
*********************

.. code-block:: python

    colour.RGB_to_Prismatic([0.25, 0.50, 0.75])

.. code-block:: text

    array([ 0.75      ,  0.16666667,  0.33333333,  0.5       ])

RGB Colourspace and Transformations
***********************************

.. code-block:: python

    XYZ = [0.21638819, 0.12570000, 0.03847493]
    illuminant_XYZ = [0.34570, 0.35850]
    illuminant_RGB = [0.31270, 0.32900]
    chromatic_adaptation_transform = "Bradford"
    matrix_XYZ_to_RGB = [
        [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,
        matrix_XYZ_to_RGB,
        chromatic_adaptation_transform,
    )

.. code-block:: text

    array([ 0.45595571,  0.03039702,  0.04087245])

RGB Colourspace Derivation
**************************

.. code-block:: python

    p = [0.73470, 0.26530, 0.00000, 1.00000, 0.00010, -0.07700]
    w = [0.32168, 0.33767]
    colour.normalised_primary_matrix(p, w)

.. code-block:: text

    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]])

RGB Colourspaces
****************

.. code-block:: python

    sorted(colour.RGB_COLOURSPACES)

.. code-block:: text

    ['ACES2065-1',
     'ACEScc',
     'ACEScct',
     'ACEScg',
     'ACESproxy',
     'ARRI Wide Gamut 3',
     'ARRI Wide Gamut 4',
     'Adobe RGB (1998)',
     'Adobe Wide Gamut RGB',
     'Apple RGB',
     'Best RGB',
     'Beta RGB',
     'Blackmagic Wide Gamut',
     'CIE RGB',
     'Cinema Gamut',
     'ColorMatch RGB',
     'DCDM XYZ',
     'DCI-P3',
     'DCI-P3-P',
     'DJI D-Gamut',
     'DRAGONcolor',
     'DRAGONcolor2',
     'DaVinci Wide Gamut',
     'Display P3',
     'Don RGB 4',
     'EBU Tech. 3213-E',
     'ECI RGB v2',
     'ERIMM RGB',
     'Ekta Space PS 5',
     'F-Gamut',
     'FilmLight E-Gamut',
     'ITU-R BT.2020',
     'ITU-R BT.470 - 525',
     'ITU-R BT.470 - 625',
     'ITU-R BT.709',
     'ITU-T H.273 - 22 Unspecified',
     'ITU-T H.273 - Generic Film',
     'Max RGB',
     'N-Gamut',
     'NTSC (1953)',
     'NTSC (1987)',
     'P3-D65',
     'PLASA ANSI E1.54',
     'Pal/Secam',
     'ProPhoto RGB',
     'Protune Native',
     'REDWideGamutRGB',
     'REDcolor',
     'REDcolor2',
     'REDcolor3',
     'REDcolor4',
     'RIMM RGB',
     'ROMM RGB',
     'Russell RGB',
     'S-Gamut',
     'S-Gamut3',
     'S-Gamut3.Cine',
     'SMPTE 240M',
     'SMPTE C',
     'Sharp RGB',
     'V-Gamut',
     'Venice S-Gamut3',
     'Venice S-Gamut3.Cine',
     'Xtreme RGB',
     'aces',
     'adobe1998',
     'prophoto',
     'sRGB']


OETFs
*****

.. code-block:: python

    sorted(colour.OETFS)

.. code-block:: text

    ['ARIB STD-B67',
     'Blackmagic Film Generation 5',
     'DaVinci Intermediate',
     'ITU-R BT.2020',
     'ITU-R BT.2100 HLG',
     'ITU-R BT.2100 PQ',
     'ITU-R BT.601',
     'ITU-R BT.709',
     'ITU-T H.273 IEC 61966-2',
     'ITU-T H.273 Log',
     'ITU-T H.273 Log Sqrt',
     'SMPTE 240M']


EOTFs
*****

.. code-block:: python

    sorted(colour.EOTFS)

.. code-block:: text

    ['DCDM',
     'DICOM GSDF',
     'ITU-R BT.1886',
     'ITU-R BT.2100 HLG',
     'ITU-R BT.2100 PQ',
     'ITU-T H.273 ST.428-1',
     'SMPTE 240M',
     'ST 2084',
     'sRGB']

OOTFs
*****

.. code-block:: python

    sorted(colour.OOTFS)

.. code-block:: text

    ['ITU-R BT.2100 HLG', 'ITU-R BT.2100 PQ']


Log Encoding / Decoding
***********************

.. code-block:: python

    sorted(colour.LOG_ENCODINGS)

.. code-block:: text

    ['ACEScc',
     'ACEScct',
     'ACESproxy',
     'Apple Log Profile',
     'ARRI LogC3',
     'ARRI LogC4',
     'Canon Log',
     'Canon Log 2',
     'Canon Log 3',
     'Cineon',
     'D-Log',
     'ERIMM RGB',
     'F-Log',
     'F-Log2',
     'Filmic Pro 6',
     'L-Log',
     'Log2',
     'Log3G10',
     'Log3G12',
     'N-Log',
     'PLog',
     'Panalog',
     'Protune',
     'REDLog',
     'REDLogFilm',
     'S-Log',
     'S-Log2',
     'S-Log3',
     'T-Log',
     'V-Log',
     'ViperLog']

CCTFs Encoding / Decoding
*************************

.. code-block:: python

    sorted(colour.CCTF_ENCODINGS)

.. code-block:: text

    ['ACEScc',
     'ACEScct',
     'ACESproxy',
     'Apple Log Profile',
     'ARRI LogC3',
     'ARRI LogC4',
     'ARIB STD-B67',
     'Canon Log',
     'Canon Log 2',
     'Canon Log 3',
     'Cineon',
     'D-Log',
     'DCDM',
     'DICOM GSDF',
     'ERIMM RGB',
     'F-Log',
     'F-Log2',
     'Filmic Pro 6',
     'Gamma 2.2',
     'Gamma 2.4',
     'Gamma 2.6',
     'ITU-R BT.1886',
     'ITU-R BT.2020',
     'ITU-R BT.2100 HLG',
     'ITU-R BT.2100 PQ',
     'ITU-R BT.601',
     'ITU-R BT.709',
     'Log2',
     'Log3G10',
     'Log3G12',
     'PLog',
     'Panalog',
     'ProPhoto RGB',
     'Protune',
     'REDLog',
     'REDLogFilm',
     'RIMM RGB',
     'ROMM RGB',
     'S-Log',
     'S-Log2',
     'S-Log3',
     'SMPTE 240M',
     'ST 2084',
     'T-Log',
     'V-Log',
     'ViperLog',
     'sRGB']

Recommendation ITU-T H.273 Code points for Video Signal Type Identification
***************************************************************************

.. code-block:: python

    colour.COLOUR_PRIMARIES_ITUTH273.keys()

.. code-block:: text

    dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 22, 23])

.. code-block:: python

    colour.models.describe_video_signal_colour_primaries(1)

.. code-block:: text

    ===============================================================================
    *                                                                             *
    *   Colour Primaries: 1                                                       *
    *   -------------------                                                       *
    *                                                                             *
    *   Primaries        : [[ 0.64  0.33]                                         *
    *                       [ 0.3   0.6 ]                                         *
    *                       [ 0.15  0.06]]                                        *
    *   Whitepoint       : [ 0.3127  0.329 ]                                      *
    *   Whitepoint Name  : D65                                                    *
    *   NPM              : [[ 0.4123908   0.35758434  0.18048079]                 *
    *                       [ 0.21263901  0.71516868  0.07219232]                 *
    *                       [ 0.01933082  0.11919478  0.95053215]]                *
    *   NPM -1           : [[ 3.24096994 -1.53738318 -0.49861076]                 *
    *                       [-0.96924364  1.8759675   0.04155506]                 *
    *                       [ 0.05563008 -0.20397696  1.05697151]]                *
    *   FFmpeg Constants : ['AVCOL_PRI_BT709', 'BT709']                           *
    *                                                                             *
    ===============================================================================

.. code-block:: python

    colour.TRANSFER_CHARACTERISTICS_ITUTH273.keys()

.. code-block:: text

    dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

.. code-block:: python

    colour.models.describe_video_signal_transfer_characteristics(1)

.. code-block:: text

    ===============================================================================
    *                                                                             *
    *   Transfer Characteristics: 1                                               *
    *   ---------------------------                                               *
    *                                                                             *
    *   Function         : <function oetf_BT709 at 0x165bb3550>                   *
    *   FFmpeg Constants : ['AVCOL_TRC_BT709', 'BT709']                           *
    *                                                                             *
    ===============================================================================

.. code-block:: python

    colour.MATRIX_COEFFICIENTS_ITUTH273.keys()

.. code-block:: text

    dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])

.. code-block:: python

    colour.models.describe_video_signal_matrix_coefficients(1)

.. code-block:: text

    ===============================================================================
    *                                                                             *
    *   Matrix Coefficients: 1                                                    *
    *   ----------------------                                                    *
    *                                                                             *
    *   Matrix Coefficients : [ 0.2126  0.0722]                                   *
    *   FFmpeg Constants    : ['AVCOL_SPC_BT709', 'BT709']                        *
    *                                                                             *
    ===============================================================================

Colour Notation Systems - ``colour.notation``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Munsell Value
*************

.. code-block:: python

    colour.munsell_value(12.23634268)

.. code-block:: text

    4.0824437076525664

.. code-block:: python

    sorted(colour.MUNSELL_VALUE_METHODS)

.. code-block:: text

    ['ASTM D1535',
     'Ladd 1955',
     'McCamy 1987',
     'Moon 1943',
     'Munsell 1933',
     'Priest 1920',
     'Saunderson 1944',
     'astm2008']

Munsell Colour
**************

.. code-block:: python

    colour.xyY_to_munsell_colour([0.38736945, 0.35751656, 0.59362000])

.. code-block:: text

    '4.2YR 8.1/5.3'

.. code-block:: python

    colour.munsell_colour_to_xyY("4.2YR 8.1/5.3")

.. code-block:: text

    array([ 0.38736945,  0.35751656,  0.59362   ])

Optical Phenomena - ``colour.phenomena``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    colour.rayleigh_scattering_sd()

.. code-block:: text

    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.quality``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Colour Fidelity Index
*********************

.. code-block:: python

    colour.colour_fidelity_index(colour.SDS_ILLUMINANTS["FL2"])

.. code-block:: text

    70.120825477833037

.. code-block:: python

    sorted(colour.COLOUR_FIDELITY_INDEX_METHODS)

.. code-block:: text

    ['ANSI/IES TM-30-18', 'CIE 2017']

Colour Quality Scale
********************

.. code-block:: python

    colour.colour_quality_scale(colour.SDS_ILLUMINANTS["FL2"])

.. code-block:: text

    64.111703163816699

.. code-block:: python

    sorted(colour.COLOUR_QUALITY_SCALE_METHODS)

.. code-block:: text

    ['NIST CQS 7.4', 'NIST CQS 9.0']

Colour Rendering Index
**********************

.. code-block:: python

    colour.colour_rendering_index(colour.SDS_ILLUMINANTS["FL2"])

.. code-block:: text

    64.233724121664807

Academy Spectral Similarity Index (SSI)
***************************************

.. code-block:: python

    colour.spectral_similarity_index(
        colour.SDS_ILLUMINANTS["C"], colour.SDS_ILLUMINANTS["D65"]
    )

.. code-block:: text

    94.0

Spectral Up-Sampling & Recovery - ``colour.recovery``

Reflectance Recovery

.. code-block:: python

colour.XYZ_to_sd([0.20654008, 0.12197225, 0.05136952])

.. code-block:: text

SpectralDistribution([[  3.60000000e+02,   8.40144095e-02],
                      [  3.65000000e+02,   8.41264236e-02],
                      [  3.70000000e+02,   8.40057597e-02],
                      ...
                      [  7.70000000e+02,   4.46743012e-01],
                      [  7.75000000e+02,   4.46817187e-01],
                      [  7.80000000e+02,   4.46857696e-01]],
                     SpragueInterpolator,
                     {},
                     Extrapolator,
                     {'method': 'Constant', 'left': None, 'right': None})

.. code-block:: python

sorted(colour.REFLECTANCE_RECOVERY_METHODS)

.. code-block:: text

['Jakob 2019', 'Mallett 2019', 'Meng 2015', 'Otsu 2018', 'Smits 1999']

Camera RGB Sensitivities Recovery

.. code-block:: python

illuminant = colour.colorimetry.SDS_ILLUMINANTS["D65"]
sensitivities = colour.characterisation.MSDS_CAMERA_SENSITIVITIES["Nikon 5100 (NPL)"]
reflectances = [
    sd.copy().align(colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017)
    for sd in colour.characterisation.SDS_COLOURCHECKERS["BabelColor Average"].values()
]
reflectances = colour.colorimetry.sds_and_msds_to_msds(reflectances)
RGB = colour.colorimetry.msds_to_XYZ(
    reflectances,
    method="Integration",
    cmfs=sensitivities,
    illuminant=illuminant,
    k=0.01,
    shape=colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017,
)
colour.recovery.RGB_to_msds_camera_sensitivities_Jiang2013(
    RGB,
    illuminant,
    reflectances,
    colour.recovery.BASIS_FUNCTIONS_DYER2017,
    colour.recovery.SPECTRAL_SHAPE_BASIS_FUNCTIONS_DYER2017,
)

.. code-block:: text

RGB_CameraSensitivities([[  4.00000000e+02,   7.22815777e-03,   9.22506480e-03,
                           -9.88368972e-03],
                         [  4.10000000e+02,  -8.50457609e-03,   1.12777480e-02,
                            3.86248655e-03],
                         [  4.20000000e+02,   4.58191132e-02,   7.15520948e-02,
                            4.04068293e-01],
                         ...
                         [  6.80000000e+02,   4.08276173e-02,   5.55290476e-03,
                            1.39907862e-03],
                         [  6.90000000e+02,  -3.71437574e-03,   2.50935640e-03,
                            3.97652622e-04],
                         [  7.00000000e+02,  -5.62256563e-03,   1.56433970e-03,
                            5.84726936e-04]],
                        ['red', 'green', 'blue'],
                        SpragueInterpolator,
                        {},
                        Extrapolator,
                        {'method': 'Constant', 'left': None, 'right': None})

Correlated Colour Temperature Computation Methods - colour.temperature


.. code-block:: python

    colour.uv_to_CCT([0.1978, 0.3122])

.. code-block:: text

    array([  6.50751282e+03,   3.22335875e-03])

.. code-block:: python

    sorted(colour.UV_TO_CCT_METHODS)

.. code-block:: text

    ['Krystek 1985', 'Ohno 2013', 'Planck 1900', 'Robertson 1968', 'ohno2013', 'robertson1968']

.. code-block:: python

    sorted(colour.XY_TO_CCT_METHODS)

.. code-block:: text

    ['CIE Illuminant D Series',
     'Hernandez 1999',
     'Kang 2002',
     'McCamy 1992',
     'daylight',
     'hernandez1999',
     'kang2002',
     'mccamy1992']

Colour Volume - ``colour.volume``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    colour.RGB_colourspace_volume_MonteCarlo(colour.RGB_COLOURSPACE_RGB["sRGB"])

.. code-block:: text

    821958.30000000005

Geometry Primitives Generation - ``colour.geometry``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    colour.primitive("Grid")

.. code-block:: text

 (array([ ([-0.5,  0.5,  0. ], [ 0.,  1.], [ 0.,  0.,  1.], [ 0.,  1.,  0.,  1.]),
           ([ 0.5,  0.5,  0. ], [ 1.,  1.], [ 0.,  0.,  1.], [ 1.,  1.,  0.,  1.]),
           ([-0.5, -0.5,  0. ], [ 0.,  0.], [ 0.,  0.,  1.], [ 0.,  0.,  0.,  1.]),
           ([ 0.5, -0.5,  0. ], [ 1.,  0.], [ 0.,  0.,  1.], [ 1.,  0.,  0.,  1.])],
          dtype=[('position', '<f4', (3,)), ('uv', '<f4', (2,)), ('normal', '<f4', (3,)), ('colour', '<f4', (4,))]), array([[0, 2, 1],
           [2, 3, 1]], dtype=uint32), array([[0, 2],
           [2, 3],
           [3, 1],
           [1, 0]], dtype=uint32))

.. code-block:: python

    sorted(colour.PRIMITIVE_METHODS)

.. code-block:: text

    ['Cube', 'Grid']

.. code-block:: python

    colour.primitive_vertices("Quad MPL")

.. code-block:: text

    array([[ 0.,  0.,  0.],
           [ 1.,  0.,  0.],
           [ 1.,  1.,  0.],
           [ 0.,  1.,  0.]])
    sorted(colour.PRIMITIVE_VERTICES_METHODS)
    ['Cube MPL', 'Grid MPL', 'Quad MPL', 'Sphere']

Plotting - ``colour.plotting``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

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

.. code-block:: python

    from colour.plotting import *

    colour_style()

Visible Spectrum
****************

.. code-block:: python

    plot_visible_spectrum("CIE 1931 2 Degree Standard Observer")

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Visible_Spectrum.png

Spectral Distribution
*********************

.. code-block:: python

    plot_single_illuminant_sd("FL1")

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Illuminant_F1_SD.png

Blackbody
*********

.. code-block:: python

    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",
        plot_kwargs={"use_sd_colours": True, "normalise_sd_colours": True},
        legend_location="upper right",
        bounding_box=(0, 1250, 0, 2.5e6),
    )

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Blackbodies.png

Colour Matching Functions
*************************

.. code-block:: python

    plot_single_cmfs(
        "Stockman & Sharpe 2 Degree Cone Fundamentals",
        y_label="Sensitivity",
        bounding_box=(390, 870, 0, 1.1),
    )

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Cone_Fundamentals.png

Luminous Efficiency
*******************

.. code-block:: python

    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, 0.1),
    )

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Luminous_Efficiency.png

Colour Checker
**************

.. code-block:: python

    from colour.characterisation.dataset.colour_checkers.sds import (
        COLOURCHECKER_INDEXES_TO_NAMES_MAPPING,
    )

    plot_multi_sds(
        [
            colour.SDS_COLOURCHECKERS["BabelColor Average"][value]
            for key, value in sorted(COLOURCHECKER_INDEXES_TO_NAMES_MAPPING.items())
        ],
        plot_kwargs={
            "use_sd_colours": True,
        },
        title=("BabelColor Average - " "Spectral Distributions"),
    )

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_BabelColor_Average.png

.. code-block:: python

    plot_single_colour_checker("ColorChecker 2005", text_kwargs={"visible": False})

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_ColorChecker_2005.png

Chromaticities Prediction
*************************

.. code-block:: python

    plot_corresponding_chromaticities_prediction(2, "Von Kries", "Bianco 2010")

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Chromaticities_Prediction.png

Chromaticities
**************

.. code-block:: python

    import numpy as np

    RGB = np.random.random((32, 32, 3))
    plot_RGB_chromaticities_in_chromaticity_diagram_CIE1931(
        RGB,
        "ITU-R BT.709",
        colourspaces=["ACEScg", "S-Gamut", "Pointer Gamut"],
    )

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Chromaticities_CIE_1931_Chromaticity_Diagram.png

Colour Rendering Index Bars
***************************

.. code-block:: python

    plot_single_sd_colour_rendering_index_bars(colour.SDS_ILLUMINANTS["FL2"])

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_CRI.png

ANSI/IES TM-30-18 Colour Rendition Report
*****************************************

.. code-block:: python

    plot_single_sd_colour_rendition_report(colour.SDS_ILLUMINANTS["FL2"])

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Colour_Rendition_Report.png

Gamut Section
*************

.. code-block:: python

    plot_visible_spectrum_section(section_colours="RGB", section_opacity=0.15)

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Plot_Visible_Spectrum_Section.png

.. code-block:: python

    plot_RGB_colourspace_section("sRGB", section_colours="RGB", section_opacity=0.15)

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_Plot_RGB_Colourspace_Section.png

Colour Temperature
******************

.. code-block:: python

    plot_planckian_locus_in_chromaticity_diagram_CIE1960UCS(["A", "B", "C"])

..  image:: https://colour.readthedocs.io/en/develop/_static/Examples_Plotting_CCT_CIE_1960_UCS_Chromaticity_Diagram.png

User Guide
----------

Installation
~~~~~~~~~~~~

**Colour** and its primary dependencies can be easily installed from the
`Python Package Index <https://pypi.org/project/colour-science>`__
by issuing this command in a shell:

.. code-block:: bash

    $ pip install --user colour-science

The detailed installation procedure for the secondary dependencies is
described in the `Installation Guide <https://www.colour-science.org/installation-guide>`__.

**Colour** is also available for `Anaconda <https://www.anaconda.com/download>`__
from *Continuum Analytics* via `conda-forge <https://conda-forge.org>`__:

.. code-block:: bash

    $ conda install -c conda-forge colour-science

Tutorial
~~~~~~~~

The `static tutorial <https://colour.readthedocs.io/en/develop/tutorial.html>`__
provides an introduction to **Colour**. An interactive version is available via
`Google Colab <https://colab.research.google.com/notebook#fileId=1Im9J7or9qyClQCv5sPHmKdyiQbG4898K&offline=true&sandboxMode=true>`__.

How-To
~~~~~~

The `Google Colab How-To <https://colab.research.google.com/notebook#fileId=1NRcdXSCshivkwoU2nieCvC3y14fx1X4X&offline=true&sandboxMode=true>`__
guide for **Colour** shows various techniques to solve specific problems and
highlights some interesting use cases.

Contributing
~~~~~~~~~~~~

If you would like to contribute to **Colour**, please refer to the following
`Contributing <https://www.colour-science.org/contributing>`__ guide.

Changes
~~~~~~~

The changes are viewable on the `Releases <https://github.com/colour-science/colour/releases>`__ page.

Bibliography
~~~~~~~~~~~~

The bibliography is available on the `Bibliography <https://www.colour-science.org/bibliography>`__ page.

It is also viewable directly from the repository in
`BibTeX <https://github.com/colour-science/colour/blob/develop/BIBLIOGRAPHY.bib>`__
format.

API Reference
-------------

The main technical reference for **Colour** is the *API Reference*:

- `Release <https://colour.readthedocs.io/en/master/reference.html>`__.
- `Develop <https://colour.readthedocs.io/en/latest/reference.html>`__.

See Also
--------

Software
~~~~~~~~

**Python**

- `ColorPy <http://markkness.net/colorpy/ColorPy.html>`__ by Kness, M.
- `Colorspacious <https://colorspacious.readthedocs.io>`__ by Smith, N. J., et al.
- `python-colormath <https://python-colormath.readthedocs.io>`__ by Taylor, G., et al.

**Go**

- `go-colorful <https://github.com/lucasb-eyer/go-colorful>`__  by Beyer, L., et al.

**.NET**

- `Colourful <https://github.com/tompazourek/Colourful>`__ by PaÅ¾ourek, T., et al.

**Julia**

- `Colors.jl <https://github.com/JuliaGraphics/Colors.jl>`__ by Holy, T., et al.

**Matlab & Octave**

- `COLORLAB <https://www.uv.es/vista/vistavalencia/software/colorlab.html>`__ by Malo, J., et al.
- `Psychtoolbox <http://psychtoolbox.org>`__ by Brainard, D., et al.
- `The Munsell and Kubelka-Munk Toolbox <http://www.munsellcolourscienceforpainters.com/MunsellAndKubelkaMunkToolbox/MunsellAndKubelkaMunkToolbox.html>`__ by Centore, P.

Code of Conduct
---------------

The *Code of Conduct*, adapted from the `Contributor Covenant 1.4 <https://www.contributor-covenant.org/version/1/4/code-of-conduct.html>`__,
is available on the `Code of Conduct <https://www.colour-science.org/code-of-conduct>`__ page.

Contact & Social
----------------

The *Colour Developers* can be reached via different means:

- `Email <mailto:colour-developers@colour-science.org>`__
- `Facebook <https://www.facebook.com/python.colour.science>`__
- `Github Discussions <https://github.com/colour-science/colour/discussions>`__
- `Gitter <https://gitter.im/colour-science/colour>`__
- `Twitter <https://twitter.com/colour_science>`__

.. begin-trim-long-description

Thank You!
----------

.. raw:: html

    <h3 align="center">Coffee Sponsors</h3>

.. raw:: html

    <table>
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.. end-trim-long-description

About
-----

| **Colour** by Colour Developers
| Copyright 2013 Colour Developers â `colour-developers@colour-science.org <colour-developers@colour-science.org>`__
| This software is released under terms of BSD-3-Clause: https://opensource.org/licenses/BSD-3-Clause
| `https://github.com/colour-science/colour <https://github.com/colour-science/colour>`__

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