Installation Guide

Primary Dependencies

• python>=2.7 or python>=3.5

• imageio

• scipy

• six

Optional Dependencies

• networkx

• openimageio

• pandas

Plotting Dependencies

• matplotlib

• graphviz

• pygraphviz

Development Dependencies

• backports.functools_lru_cache

• biblib-simple

• coverage

• coveralls

• flake8

• invoke

• jupyter

• mock

• nbformat>=4

• nose

• pre-commit

• pytest

• restructuredtext-lint

• sphinx

• sphinx-rtd-theme

• sphinxcontrib-bibtex

• twine

• yapf==0.23.0

Pypi

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

$ pip install --user colour-science

This asciicast demonstrates how to generate a pristine Python VirtualEnv environment for Colour:

The optional features dependencies are installed as follows:

$ pip install --user 'colour-science[optional]'

The development dependencies are installed as follows:

$ pip install --user 'colour-science[development]'

The figures plotting dependencies are installed as follows:

$ pip install --user 'colour-science[plotting]'

If you wish to read OpenEXR files, you will need to install the FreeImage plugin for Imageio as follows:

$ python -c "import imageio;imageio.plugins.freeimage.download()"

Continuum Analytics Anaconda

Colour is also available for Anaconda from Continuum Analytics via conda-forge:

$ conda install -c conda-forge colour-science

This asciicast demonstrates how to generate a pristine Python conda environment for Colour:

Github

Alternatively, you can also install directly from Github source repository:

$ git clone git://github.com/colour-science/colour.git
$ cd colour
$ pip install --user .

Poetry

Colour adopts Poetry to help managing its dependencies, this is the recommended way to get started with Colour development.

Assuming Python>=3.5 is available on your system, the development dependencies are installed with Poetry as follows:

$ git clone git://github.com/colour-science/colour.git
$ cd colour
$ poetry install --extras "optional plotting"

$ sed -i.bak 's/python = "~2.7 || ^3.5"/python = "^3.6"/g' pyproject.toml
$ sed -i.bak 's/matplotlib = { version = "\*"/matplotlib = { version = "^3.1"/g' pyproject.toml
$ git diff --unified=1
diff --git a/pyproject.toml b/pyproject.toml
index 93088d8c..c2b282cf 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -46,3 +46,3 @@ classifiers = [
 [tool.poetry.dependencies]
-python = "~2.7 || ^3.5"
+python = "^3.6"
 imageio = "*"
@@ -58,3 +58,3 @@ invoke = { version = "*", optional = true }  # Development dependency.
 jupyter = { version = "*", optional = true }  # Development dependency.
-matplotlib = { version = "*", optional = true }
+matplotlib = { version = "^3.1", optional = true }
 mock = { version = "*", optional = true }  # Development dependency.

If Graphviz is available on your system, you might issue the following commands instead of the aforementioned ones:

$ git clone git://github.com/colour-science/colour.git
$ cd colour
$ poetry install --extras "graphviz optional plotting"

Those commands will create a Virtual Environment in which all the required python packages will be installed.

Tools can then be run as follows:

$ poetry run invoke -l

or alternatively:

$ source $(poetry env info -p)/bin/activate
$ invoke -l

Vagrant

An easy way to get all the pre-requisites at once is to use our colour-vagrant environment for Vagrant.

Please refer to the dedicated blog post for more details about its deployment: PyCharm, Vagrant, Ansible & Poetry