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 3.9 and greater on Linux, Windows, or Mac and can be installed with conda:
or with pip:
python -m pip install colorcet
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:
If you want to try out the latest features between releases, you can get the latest dev release by installing:
conda install -c pyviz/label/dev colorcet
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.
Some of the Colorcet colormaps that have short, memorable names (which are probably the most useful ones) are visible here:
But the complete set of 100+ is shown in the User Guide.
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