/colorcet

A set of useful perceptually uniform colormaps for plotting scientific data

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Colorcet: Collection of perceptually uniform colormaps
Build Status Linux/MacOS Build Status
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Latest dev release Github tag dev-site
Latest release Github release PyPI version colorcet version conda-forge version defaults version
Python Python support
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What is it?

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.

Installation

Colorcet supports Python 3.7 and greater on Linux, Windows, or Mac and can be installed with conda:

conda install colorcet

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:

jupyter-lab

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.

Learning more

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.

Samples

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.