colorcet
A Python library providing a collection of perceptually uniform colormaps for scientific plotting.
What is colorcet?
Colorcet is a Python library that provides a collection of perceptually uniform colormaps for plotting scientific data. It solves the problem of misleading visual representations in data visualization by ensuring colors change uniformly in perceptual space, making plots more accurate and interpretable. The colormaps are based on research from the Center for Exploration Targeting and are designed for use with tools like Matplotlib, Bokeh, and HoloViews.
Data scientists, researchers, and engineers who create visualizations for scientific data in fields like geophysics, astronomy, biology, and medical imaging, using Python plotting libraries.
Developers choose Colorcet because it offers rigorously tested, perceptually accurate colormaps that prevent common visualization pitfalls, ensuring data integrity. Its seamless integration with major Python plotting tools and foundation in color science research make it a trusted choice over default or ad-hoc colormaps.
Overview
A set of useful perceptually uniform colormaps for plotting scientific data
Use Cases
Best For
- Visualizing geophysical data like seismic surveys or elevation maps
- Creating accurate heatmaps for medical or biological imaging
- Plotting astronomical data where perceptual uniformity is critical
- Generating scientific publications with reliable color representations
- Enhancing data dashboards built with Bokeh or HoloViews
- Replacing default Matplotlib colormaps with perceptually sound alternatives
Not Ideal For
- Data visualizations where aesthetic or brand colors are prioritized over perceptual accuracy, such as marketing dashboards or design-heavy infographics
- Projects using programming languages other than Python, as Colorcet is a Python-specific library with no cross-language support
- Applications requiring dynamic, real-time colormap generation or extensive customization, since Colorcet offers a fixed, curated set of colormaps
Pros & Cons
Pros
Based on Peter Kovesi's peer-reviewed research, the colormaps ensure uniform perceptual steps, preventing data misinterpretation in scientific plots like heatmaps and elevation maps.
Offers over 100 colormaps, including short-named, memorable options shown in the README samples, catering to diverse visualization needs from geophysics to medical imaging.
Works directly with popular Python plotting libraries like Bokeh, Matplotlib, and HoloViews, as highlighted in the installation and usage sections, easing adoption in existing workflows.
Founded on methods detailed in arXiv papers and research from the Center for Exploration Targeting, providing trusted, reliable colormaps for academic and professional use.
Cons
Requires installing the PyViz JupyterLab extension for full functionality in JupyterLab, adding complexity compared to drop-in libraries with no extra dependencies.
As a Python-specific library, it's unsuitable for projects in other languages like JavaScript or R, restricting its use in multi-language or web-focused environments.
Lacks built-in tools for creating custom perceptually uniform colormaps; users are confined to the provided collection, which may not cover all niche requirements.
Frequently Asked Questions
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