Source-linked AI summary
CMasher: Scientific colormaps for making accessible, informative and 'cmashing' plots
Ellert van der Velden
TL;DR
Scientific visualization needs colormaps that convey numerical values without misleading viewers, yet poor and non-color-vision-deficiency-friendly choices remain common. CMasher introduces a curated Python collection of perceptually uniform sequential colormaps with varied color combinations and alternatives to commonly used maps; its analysis illustrates that jet creates perceptual problems and is not color-vision-deficiency-friendly.
Problem
Despite guidance and testing tools, misleading colormaps remain common because viewers do not perceive all colors equally.
Method
CMasher provides Python colormaps designed with viscm to be perceptually uniform sequential, mostly color-vision-deficiency-friendly, and varied in color combination.
Results
The jet colormap introduces perceptual problems because green appears brightest and the map is not color-vision-deficiency-friendly.
Takeaways & Limitations
CMasher offers scientific colormap alternatives, including replacements for jet, hot, binary, and coolwarm, to support selecting a suitable map.
Abstract
from arXiv · showhide
CMasher is a Python package that provides a curated collection of scientific colormaps, showcased in the online documentation (https://cmasher.readthedocs.io). The colormaps in CMasher are all designed to be perceptually uniform sequential using the 'viscm' package; most of them are color-vision deficiency friendly; and they cover a wide range of different color combinations to accommodate for most applications. It aims to provide several alternatives to commonly used colormaps, like 'chroma' and 'rainforest' for 'jet'; 'sunburst' for 'hot'; 'neutral' for 'binary'; and 'fusion' and 'redshift' for 'coolwarm'. With CMasher, I hope to help others with picking the correct colormap for the job.
Introduction
Color enables complex scientific data to be shown in a single figure, but because it influences interpretation, selecting an appropriate colormap is crucial.
- Color allows complex scientific data to be plotted in one figure without relying on difficult-to-interpret 3D plots, subplots, online material, or interactive applications.The passage presents color as a way to increase the amount of data shown within a figure while avoiding these alternatives.
- Because color affects how visualized data is interpreted, choosing the correct colormap is crucial.
Background summary
Perceptually uniform sequential colormaps support correct data interpretation by changing uniformly in perceived lightness and saturation, yet misleading colormaps remain common despite guidance and testing tools. Jet illustrates these shortcomings: it is not CVD-friendly, overemphasizes green, and remains widely used partly for its high perceptual range.
- Colormap principles: Perceptually uniform sequential colormaps change uniformly in perceived lightness and saturation, helping viewers interpret plotted data without false information.They also often support proper conversion of plots to grey-scale.
- Motivation: Despite extensive guidance and available testing tools, bad or misleading colormaps remain commonly used.The paper identifies this persistent use as the main issue motivating its discussion.
- Jet example: Jet introduces multiple problems: green is perceived as the brightest visible color, and the colormap is absolutely not CVD-friendly.Although jet increases linearly in wavelength, that property does not ensure perceptual suitability.
- Jet example: Jet remains commonly used in scientific literature partly because its high perceptual range makes adjacent values easier to distinguish.Its perceptual range exceeds that of every CMasher colormap, including the diverging colormaps, but high range is not universally useful.
CMasher
CMasher provides perceptually uniform sequential scientific colormaps for Python visualization, with mostly CVD-friendly designs and diverse color combinations suited to many applications. Its colormaps have been used in scientific studies and can also represent qualitative data.
- CMasher: CMasher provides scientific colormaps mainly for use with Python packages and projects, especially matplotlib.The package includes viscm source files alongside its colormaps.
- CMasher: Its colormaps are perceptually uniform sequential, mostly CVD-friendly, and available in diverse color combinations.The designs were created using the viscm package to support a wide range of applications.
- CMasher: CMasher offers alternatives to commonly used colormaps, including chroma as an alternative to jet.
- Applications: CMasher has been used in studies of model emulations, galaxy kinematics, and redshift estimations for fast radio bursts.
- Applications: Its diverse color sequences and perceptually uniform sequential design also make CMasher suitable for representing qualitative data.