Color Palettes

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha c8c1fc3a2b13 · 19 Oct 2026Report an issue or request a re-scan

• PublisherGPTomics GitHub organizationOwner of the repository this skill is read from
• Source folderGPTomics/bioSkills/data-visualization/color-palettesThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdictc8c1fc3a2b13A new audit runs when the source changes
• Repository statusArchivedLast push 65 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps,…

---
name: bio-data-visualization-color-palettes
description: Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
tool_type: mixed
primary_tool: viridis
goal_approach_exempt: true
---

## Version Compatibility

Reference examples tested with: viridis 0.6+, RColorBrewer 1.1+, scico 1.5+ (Crameri colormaps in R), khroma 1.12+ (Tol/Crameri palettes in R), matplotlib 3.8+, colorcet 3.0+, ggsci 3.0+, colorspace 2.1+.

Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

# Color Palettes for Scientific Visualization

**"Pick a color palette"** -> Choose a colormap that (a) is perceptually uniform along the relevant data axis, (b) remains interpretable under common color-vision deficiencies, (c) prints correctly to grayscale, and (d) matches the data type — sequential, diverging, cyclic, or qualitative.

- R: `viridis::viridis`, `scico::scale_color_scico`, `khroma::color`, `RColorBrewer::brewer.pal`
- Python: `matplotlib.colormaps`, `colorcet`, `seaborn.color_palette`, `cmcrameri.cm`

## The Three Modern Standards

1. **Perceptual uniformity** -- equal data steps produce equal perceived color steps. viridis (van der Walt 2015), cividis (Nuñez 2018), and the Crameri family (batlow, roma, vik) are designed for this. Jet, rainbow, and red->green are not.

2. **Color vision deficiency safety** -- ~6% of males have deuteranopia / protanopia (red-green deficiency). cividis was explicitly designed to be near-identical under normal and CVD viewing (Nuñez 2018 *PLOS ONE* 13:e0199239). The Okabe-Ito 8-color qualitative palette (popularized in Wong 2011 *Nat Methods* 8:441) is the CVD-safe categorical default.

3. **Grayscale monotonicity** -- a perceptually-uniform sequential colormap has monotonically increasing luminance. Convert the figure to grayscale; if the order is still readable, the colormap is luminance-monotonic. This is the single most actionable test.

## Palette Type by Data Type

| Data type | Use | Avoid |
|-----------|-----|-------|
| Sequential (expression, coverage, density) | viridis, magma, cividis, batlow, lipari | jet, rainbow, hsv |
| Diverging (log fold change, z-score, signed correlation) | vik, roma, RdBu, BrBG, PiYG | jet, rainbow |
| Cyclic (phase, time-of-day, angle) | romaO, vikO, twilight | linear sequential (wrap creates artifactual jump) |
| Categorical (≤8 groups) | Okabe-Ito (Wong 2011), Tol bright, Dark2 | rainbow with N=20, Set1 if CVD matters |
| Categorical (9-20 groups) | tab20, Paired, Polychrome | too-many categorical hues -- consider faceting |
| Categorical (>20) | None -- reconsider design | More colors will not help |

## The Crameri Scientific Colormaps

Crameri 2020 *Nat Commun* 11:5444 documented the prevalence of misleading palettes (rainbow, red-green) across published science and released a family of perceptually-uniform CVD-safe colormaps via Zenodo (doi:10.5281/zenodo.8409685). Key entries:

| Crameri name | Type | Use case |
|--------------|------|----------|
| `batlow` | sequential | Default jet replacement; runs through dark-blue -> ochre -> light-yellow |
| `lipari` | sequential | Higher-saturation alternative; better for projection |
| `vik` | diverging | Blue -> white -> red equivalent, perceptually uniform |
| `roma` | diverging | Slightly warmer than vik |
| `bam` | diverging | Brown -> white -> green |
| `romaO` | cyclic | Phase, time-of-day, angle data |
| `vikO` | cyclic | Diverging cyclic |

```r
library(scico)
# Sequential
ggplot(df, aes(x, y, fill = value)) + geom_tile() +
    scale_fill_scico(palette = 'batlow')
# Diverging
ggplot(df, aes(x, y, fill = lfc)) + geom_tile() +
    scale_fill_scico(palette = 'vik', midpoint = 0)
```

```python
from cmcrameri import cm
import matplotlib.pyplot as plt
plt.imshow(data, cmap=cm.batlow)         # sequential
plt.imshow(data, cmap=cm.vik, vmin=-vmax, vmax=vmax)   # diverging, symmetric
```

## viridis Family (matplotlib default since 3.0)

```r
library(viridis)
scale_color_viridis_c(option = 'viridis')   # default: dark blue -> yellow
scale_color_viridis_c(option = 'magma')     # black -> red -> yellow
scale_color_viridis_c(option = 'inferno')   # black -> purple -> yellow
scale_color_viridis_c(option = 'plasma')    # purple -> pink -> yellow
scale_color_viridis_c(option = 'cividis')   # CVD-optimized
scale_color_viridis_c(option = 'turbo')     # jet-like but perceptually uniform
```

```python
plt.imshow(data, cmap='viridis')   # 'magma', 'inferno', 'plasma', 'cividis', 'turbo'
```

**cividis is the only viridis-family colormap optimized for CVD.** Use it for any figure intended to remain interpretable under deuteranopia/protanopia.

## Okabe-Ito Categorical Palette (Wong 2011)

The 8-color CVD-safe categorical palette. Memorize the hexes:

```r
okabe_ito <- c(
    '#E69F00',  # orange
    '#56B4E9',  # sky blue
    '#009E73',  # bluish green
    '#F0E442',  # yellow
    '#0072B2',  # blue
    '#D55E00',  # vermilion
    '#CC79A7',  # reddish purple
    '#000000'   # black
)
scale_color_manual(values = okabe_ito)
```

Available as `palette.colors(8, 'Okabe-Ito')` in R 4.0+, `scale_color_manual(values = palette.colors(8, 'Okabe-Ito'))`. In matplotlib, `colorblind` style or manual hex list.

For DE plots, the canonical assignment is Up = `#D55E00` (vermilion), Down = `#0072B2` (blue), NS = `#999999` 

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

pip install matplotlib colorcet cmcrameri colorspacious

git clone https://github.com/GPTomics/bioSkills
git -C bioSkills checkout c8c1fc3a2b13

Works in

1. 2026-10-19
   ✓SAFE B 89 / 100

   first audit · source c8c1fc3a2b13

Questions about Color Palettes

• github.com/GPTomics/bioSkills/tree/main/data-visualization/color — the audited source at commit c8c1fc3a2b13 and every finding aboveObserved · 19 Oct 2026

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