Scientific Color Palette Generator

Sequential, diverging, or qualitative — pick by data type

• Qualitative (categorical) palettes use unrelated hues for unordered groups — species, treatments, conditions. Okabe-Ito is the default here because its colors stay distinct for the most common types of color blindness.
• Sequential palettes run light-to-dark in a single hue family for data that goes low-to-high, such as counts, density, or intensity. Viridis is the safe choice because it is perceptually uniform and prints well in grayscale.
• Diverging palettes have two contrasting hues meeting at a neutral midpoint, for data with a meaningful center — correlations around zero, change from a baseline, or anomalies above and below an average.
• Matching the palette type to your data is the single most important decision: a rainbow on sequential data exaggerates some ranges and flattens others, and a sequential ramp on categories implies an order that is not there.

Colorblind-friendly palettes (deuteranopia, protanopia, tritanopia)

Roughly 1 in 12 men and 1 in 200 women have some color vision deficiency, so a figure that relies on red-versus-green alone will be unreadable for a meaningful slice of your audience. The most common deficiencies are deuteranopia and protanopia (both affect red-green perception); tritanopia (blue-yellow) is rarer. Palettes like Okabe-Ito and the Viridis family are engineered to stay distinguishable across these conditions. For categorical data, a good rule is to add a second, non-color cue — line style, marker shape, direct labels, or hatching — so the figure still works if printed in grayscale or viewed by someone who cannot separate the hues at all.

Perceptually uniform colormaps: why Viridis beats the rainbow

The old jet/rainbow colormap looks vivid but distorts data: it has sharp luminance jumps that create false boundaries and dull stretches that hide real variation, and it falls apart in grayscale. Perceptually uniform colormaps — Viridis, Cividis, Magma, Inferno, Plasma — fix this by making perceived color change track the data linearly. The result is a heatmap or surface plot where what your eye sees matches what the numbers do, with no phantom features and no lost detail. Cividis is worth knowing specifically because it is optimized to look the same to colorblind and non-colorblind viewers.

How to use the palette generator

• Choose a palette family — categorical, sequential, diverging, or journal-style — based on the kind of data you are plotting.
• Preview the colors on a sample bar chart, line graph, or heatmap to see how they read together before you commit.
• Copy the hex codes, or grab a ready-made snippet for Python (Matplotlib/Seaborn), R (ggplot2), or CSS to drop straight into your code.
• Paste the colors into your plotting library, spreadsheet, or design tool — the values are exact, so your figure matches the preview.

Choosing colors for journals and publication

Many journals now ask for accessible color schemes, and most still print or distribute figures that may be viewed in grayscale, so a palette that survives both is the safe bet. Practical guidance: keep categorical palettes to a handful of colors (beyond about seven, swatches start to blur together — split the data or use facets instead); always pair color with a redundant cue in line and scatter plots; use a perceptually uniform colormap for any heatmap or continuous surface; and keep the same palette across every panel in a figure so a color means the same thing throughout. The hex codes here are fixed values, which also makes your figures reproducible — collaborators and reviewers get exactly the colors you specified.