Pacific Northwest National Laboratory's cividis adjusts viridis to compensate for color vision deficiencies. Viridis shares similarities with (but is noticeably different from) MATLAB's parula (which is not freely available). Although none of the colors in viridis reach black, the bottom end of the scale does get dark, so map might need to be shortened for some 3D applications. Viridis is one of the matplotlib color maps, originally contributed by Eric Firing. Viridis is a perceptually uniform color map with monotonically increasing luminance and a pleasant smooth arc through blue, green, and yellow hues. This color map is derivative of the cool to warm and might be covered under ParaView's BSD license. This makes for less washed out colors in the middle, but also creates an artifact at the midpoint. This is a similar color map to the previous except that the luminance is interpolated linearly with a sharp bend in the middle. This color map was first featured in ParaView and is covered by its BSD license. This color map is available in ParaView as the "Cool to Warm" preset. Although not isoluminant, this color map avoids dark colors to allow shading cues throughout. There are several more color maps of a similar nature described here. It is a diverging (double-ended) color map with a smooth transition in the middle to prevent artifacts at the midpoint. This color map uses the techniques based on " Diverging Color Maps for Scientific Visualization" by Kenneth Moreland. 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. You achieve this by limiting the color map to reasonably bright colors. Thus, you have to avoid having the brightness changes in the color map interfere with the brightness changes in shading and vice versa. However, in a 3D scene, shading cues, which are themselves changes in brightness, are vital to understanding shapes. In general a color map should use changes in luminance (brightness) to communicate changes in value. I make my best effort to identify the source of any borrowed or derivative work and the respective license. I have avoided works where someone has exercised private intellectual property rights and kept to open work, but some of these color maps might be covered under other licensing agreements. However, please note that much of this work is borrowed or derivative. I waive all copyrights and related rights so that these color maps may be used freely. All original color maps on this page are released as public domain (CC0). Color Mapsīelow is a collection of color maps that you can use to apply in scientific visualization.Ī note about license. Details of this paper and the techniques used can be found on its companion page. Another related publication is " Diverging Color Maps for Scientific Visualization," which describes specifics about one particular type of color map. 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. The data for both can be downloaded here. Each color map below is demonstrated on a 2D heat map and 3D surface. You can either run the code directly with the appropriate software or copy/paste scripts into your own interpreter. Where applicable, Jupyter Python notebooks containing details about how each color map is generated. Each color map also has instructions on getting these colors in the ParaView visualization application. 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 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. The color maps are organized by how and where they are best used. More specifically, this page provides color maps that you can use while using pseudocoloring of a scalar field. This page provides advice for using colors in scientific visualization.
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