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Last update: 28/04/2026

colorcet

A set of useful perceptually uniform colormaps for plotting scientific data

Latest version: 3.1.1a1 registry icon
Maintenance score
56
Safety score
100
Popularity score
30
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Security
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Version Suggest Low Medium High Critical
3.1.1a1 0 0 0 0 0
3.1.0rc1 0 0 0 0 0
3.1.0 0 0 0 0 0
3.0.1 0 0 0 0 0
3.0.1a0 0 0 0 0 0
3.0.0a8 0 0 0 0 0
3.0.0a7 0 0 0 0 0
3.0.0a6 0 0 0 0 0
3.0.0a5 0 0 0 0 0
3.0.0a4 0 0 0 0 0
3.0.0a3 0 0 0 0 0
3.0.0a2 0 0 0 0 0
3.0.0a1 0 0 0 0 0
3.0.0a0 0 0 0 0 0
3.0.0 0 0 0 0 0
2.0.7a2 0 0 0 0 0
2.0.6 0 0 0 0 0
2.0.5 0 0 0 0 0
2.0.2 0 0 0 0 0
2.0.1 0 0 0 0 0
2.0.0 0 0 0 0 0
1.0.1 0 0 0 0 0
0.9.1 0 0 0 0 0
0.9.0 0 0 0 0 0

Stability
Latest release:

3.1.1a1 - This version is safe to use because it has no known security vulnerabilities at this time. Find out if your coding project uses this component and get notified of any reported security vulnerabilities with Meterian-X Open Source Security Platform

Licensing

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CC-BY-4.0   -   Creative Commons Attribution 4.0 International

Not a wildcard

Not proprietary

OSI Compliant


Colorcet: Collection of perceptually uniform colormaps
Latest release Github release PyPI version colorcet version conda-forge version defaults version
Python Python support
Build Status Linux/MacOS Build Status
Coverage codecov
Latest dev release Github tag dev-site
Documentation site

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 3.10 and greater on Linux, Windows, and Mac and can be installed with conda:

conda install colorcet

or with pip:

python -m pip install colorcet

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.