Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data

Abstract

Color vision deficiency (CVD) affects more than 4% of the population and leads to a different visual perception of colors. Though this has been known for decades, colormaps with many colors across the visual spectra are often used to represent data, leading to the potential for misinterpretation or difficulty with interpretation by someone with this deficiency. Until the creation of the module presented here, there were no colormaps mathematically optimized for CVD using modern color appearance models. While there have been some attempts to make aesthetically pleasing or subjectively tolerable colormaps for those with CVD, our goal was to make optimized colormaps for the most accurate perception of scientific data by as many viewers as possible. We developed a Python module, cmaputil, to create CVD-optimized colormaps, which imports colormaps and modifies them to be perceptually uniform in CVD-safe colorspace while linearizing and maximizing the brightness range. The module is made available to the science community to enable others to easily create their own CVD-optimized colormaps. Here, we present an example CVD-optimized colormap created with this module that is optimized for viewing by those without a CVD as well as those with red-green colorblindness. This colormap, cividis, enables nearly-identical visual-data interpretation to both groups, is perceptually uniform in hue and brightness, and increases in brightness linearly.

Environmental variables that ameliorate extinction learning deficits in the 129S1/SvlmJ mouse strain

“…Subsequent statistical data analyses and plotting were performed in R Studio 43 using opensource packages [44][45][46][47] and custom-written scripts. To assess discriminative freezing between the CS+ and CS−, a discrimination ratio was calculated as freezing to CS+ divided by the sum of freezing to CS+ and CS−.…”

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“…The dashed line indicates the tenth percentile redshift. The 'cividis' colormap used is described byNuñez et al (2018).dictions of their own model. However, Andrews et al used a slightly different version of theLacey et al (2016) model than in this work, as the one we use here has been recalibrated for implementation in merger trees from the P-Millennium dark matter simulation.…”

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“…Although this colormap performs well in gray-scale, it can cause problems when viewed by someone with CVD, as demonstrated by Nuñez et al (2018). Additionally, we felt that viridis did not allow for enough fine structure details.…”

“…Therefore, we decided to do something similar to the work done by Kindlmann et al (2002), but instead of using the six major colors of the rainbow, we wanted to improve the existing "standards". By using the Python package cmaputil (Nuñez et al 2018), we converted the aforementioned jet and hot colormaps into versions that are CVD-proof and work well in gray-scale (see Fig. 11).…”

“…These colormaps, named rainforest and blaze, are used by default for making Prism's projections. 8 Note that while the colormaps should allow for everybody to interpret the data correctly, those with CVD will see the colormaps differently from those without (unlike the cividis colormap introduced by Nuñez et al 2018).…”