Color Map Advice for Scientific Visualization

This page provides advice for using colors in scientific visualization. More specifically, this page provides color maps that you can use while using pseudocoloring of a scalar field. The color maps are organized by how and where they are best used. Each color map shows some example usage and provides color tables in CSV format so that they can readily be used in rendering system textures or entered into visualization software. For simplicity, the color tables are provided in many different lengths and with colors expressed in both bytes (integers between 0 and 255) and floats (decimals between 0.0 and 1.0). Each color map also has instructions on getting these colors in the ParaView visualization application. Where applicable, IPython notebooks containing details about how each color map is generated. You can either run the code directly with the appropriate software or copy/paste scripts into your own interpreter.

This work originates from the paper "Why We Use Bad Color Maps and What You Can Do About It." Details about this paper are given below. Another related publication is "Diverging Color Maps for Scientific Visualization," which describes specifics about one particular type of color map. Details of this paper and the techniques used can be found on its companion page.

Color Maps

3D Surfaces

In general a color map should use changes in luminance (brightness) to communicate changes in value. However, in a 3D sceen, shading cues, which are themselves changes in brightness, are vital to understanding shapes. Thus, you have to avoid having the brightness changes in the color map interfear with the brightness changes in shading and vice versa. You achieve this by limiting the color map to reasonably bright colors. Because this reduces the total range of brightness in the color map, I find it most effective to use a diverging (double-ended) color map.

Smooth Cool Warm

This color map uses the techniques based on "Diverging Color Maps for Scientific Visualization" by Kenneth Moreland. It is a diverging (double-ended) color map with a smooth transition in the middle to prevent artifacts at the midpoint. There are several more color maps of a similar nature described here.


Color Tables (byte): 8 16 32 64 128 256 512 1024

Color Tables (float): 8 16 32 64 128 256 512 1024

Download all color tables.

This color map is available in ParaView as the "Cool to Warm" preset.

Python code to generate these colors.

2D Images

When pseudocoloring is applied to the flat surface of the image, you do not have to contend with 3D shading. In this case, you are free to use the entire range of brightness from completely dark to full white. These color maps take advantage of that extra range.

Black Body

The black body color map is based on colors from black-body radiation. The colors are are not exact to those of black-body radiation but are designed to have a constant increase in brightness throughout.

The black body color map based on the colors of black body radiation. Although the colors are inspired by the wavelengths of light from black body radiation, the actual colors used are designed to be perceptually uniform. Colors of the desired brightness and hue are chosen, and then the colors are adjusted such that the luminance is perceptually linear (according to the CIELAB color space).


Color Tables (byte): 8 16 32 64 128 256 512 1024

Color Tables (float): 8 16 32 64 128 256 512 1024

Download all color tables.

Download ParaView color map file.

Python code to generate these colors.

Other Resources

Color Brewer

The web-based Color Brewer tool, available at, is an excellent resource for choosing a collection of tools for a variety of uses.

CET Perceptually Uniform Color Maps

Peter Kovesi from the Centre for Exploration Targeting (CET) provides a collection of perceptually uniform color maps as well as example code used for their generation and analysis at

Sci Vis Color

The ECX project has collected some helpful tools at These tools include professionally designed color maps and other color editing tools.

Los Alamos Data Science Color Map Collection

The data science team at Los Alamos National Laboratory have posted a collection of color maps they have designed at


"Why We Use Bad Color Maps and What You Can Do About It." Kenneth Moreland. In Proceedings of Human Vision and Electronic Imaging (HVEI), February 2016. DOI 10.2352/ISSN.2470-1173.2016.16.HVEI-133.


We know the rainbow color map is terrible, and it is emphatically reviled by the visualization community, yet its use continues to persist. Why do we continue to use a this perceptual encoding with so many known flaws? Instead of focusing on why we should not use rainbow colors, this position statement explores the rational for why we do pick these colors despite their flaws. Often the decision is influenced by a lack of knowledge, but even experts that know better sometimes choose poorly. A larger issue is the expedience that we have inadvertently made the rainbow color map become. Knowing why the rainbow color map is used will help us move away from it. Education is good, but clearly not sufficient. We gain traction by making sensible color alternatives more convenient. It is not feasible to force a color map on users. Our goal is to supplant the rainbow color map as a common standard, and we will find that even those wedded to it will migrate away.

Full Paper

Why We Use Bad Color Maps and What You Can Do About It

  • "Diverging Color Maps for Scientific Visualization." Kenneth Moreland. In Proceedings of the 5th International Symposium on Visual Computing, December 2009. DOI 10.1007/978-3-642-10520-3_9.
    • Introduces the smooth diverging color maps.
  • "Face-Based Luminance Matching for Perceptual Colormap Generation." Gordon Kindlmann, Erik Reinhard, and Sarah Creem. In Proceedings of IEEE Visualization, pages 299–306, October 2002. DOI 10.1109/VISUAL.2002.1183788.
    • Originally proposed what we call the Kindlmann color map.
  • "The Rainbow is Dead... Long Live the Rainbow! - Perceptual Palettes, Part 5 - CIE LAB Linear L* Rainbow." Matteo Niccoli. MyCarta (blog post), December 2012.
    • Proposes adjusting using luminance in CIELAB space rather than human input to build the Kindlmann color map. I used the same technique to build the color map posted on this page.
  • "A Colour Scheme for the Display of Astronomical Intensity Images." D. A. Green. Bulletin of the Astronomical Society of India, 39:289–295, 2011. arXiv:1108.5083.
    • A proposed color map technique that creates a color spiral of monotonic luminance. Similar concepts were used in the Extended Kindlmann color map presented here although the techniques used are different.
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