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Colorcet is a collection of perceptually uniform colormaps for use with Python plotting programs like bokeh, matplotlib, holoviews, and datashader based on the set of perceptually uniform colormaps created by Peter Kovesi at the Center for Exploration Targeting.
Colorcet supports Python 2.7, 3.5, 3.6 and 3.7 on Linux, Windows, or Mac and can be installed with conda:
conda install colorcet
or with pip:
pip install colorcet
Once installed you can copy the examples into the current directory using the colorcet command and run them using the Jupyter notebook:
colorcet examples
cd colorcet-examples
jupyter notebook
(Here colorcet examples is a shorthand for colorcet copy-examples --path colorcet-examples && colorcet fetch-data --path colorcet-examples.)
To work with JupyterLab you will also need the PyViz JupyterLab extension:
conda install -c conda-forge jupyterlab
jupyter labextension install @pyviz/jupyterlab_pyviz
Once you have installed JupyterLab and the extension launch it with:
jupyter-lab
If you want to try out the latest features between releases, you can get the latest dev release by specifying -c pyviz/label/dev in place of -c pyviz.
For more information take a look at Getting Started.
You can see all the details about the methods used to create these colormaps in Peter Kovesi's 2015 arXiv paper. Other useful background is available in a 1996 paper from IBM.
The Matplotlib project also has a number of relevant resources, including an excellent 2015 SciPy talk, the viscm tool for creating maps like the four in mpl, the cmocean site collecting a set of maps created by viscm, and the discussion of how the mpl maps were created.
All the Colorcet colormaps that have short, memorable names (which are probably the most useful ones) are visible here:
But the complete set of 50+ is shown in the User Guide.