Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
doc
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 



Colorcet: Collection of perceptually uniform colormaps

Build Status Linux/MacOS Build Status
Coverage codecov
Latest dev release Github tag dev-site
Latest release Github release PyPI version colorcet version conda-forge version defaults version
Docs gh-pages 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 2.7, 3.5, 3.6, 3.7, 3.8 and 3.9 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.

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

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.