Colormaps

Colormaps are a tool used in data visualization to map a range of data values to colors. They enable the visual encoding of quantitative, ordinal, or categorical information in images, charts, and scientific visuals. A colormap can be applied to two-dimensional arrays (as in heatmaps) or to one-dimensional data along an axis.

Colormaps are commonly categorized as sequential, diverging, or qualitative. Sequential colormaps progress through a single hue from light to dark, suitable for representing ordered data. Diverging colormaps use two contrasting hues joined at a neutral midpoint, suitable when a central value is meaningful. Qualitative colormaps use distinct colors for categories and do not imply order. Many modern colormaps are designed for perceptual uniformity—equal data increments should correspond to roughly equal perceptual changes in color. They are often constructed in perceptual color spaces (for example CIELAB or CIELUV) and then mapped to display-referred RGB spaces. Commonly recommended palettes include viridis, plasma, inferno, magma, and cividis; some systems avoid the older jet palette due to nonuniform perceptual changes and biases for color-vision deficiencies.

Discrete versus continuous: a colormap can be sampled into a finite set of colors for discrete steps or interpolated to produce a smooth gradient. Implementation is typically through a mapping function that translates normalized data values to color values, often with optional alpha transparency and gamma correction to account for display characteristics.

Applications span heatmaps, terrain and medical imaging, and any visualization where color conveys data. Considerations include accessibility for colorblind users, printer reproduction, and consistency across devices and software environments.