Introduction#

scicomap helps you build scientific visualizations with perceptually safer colormaps.

Many default colormaps can create false boundaries and hide important structure. The problem gets worse for readers with color-vision deficiency. scicomap gives you tools to inspect these issues and correct them.

Choose by data semantics#

Diagram explaining when to choose sequential, diverging, circular, or qualitative colormaps.

Choosing the right colormap family is the first high-impact decision.#

Why jet/rainbow is problematic#

Assessment panel showing jet has non-uniform lightness and chroma artifacts.

Jet is not perceptually uniform and tends to amplify false visual structure.#

Comparison showing staircase artifacts created by jet.

Artifact-heavy rendering can add boundaries that are not present in data.#

Color-vision accessibility#

Color-vision deficiency illustration.

Around 8% of Caucasian male readers are affected by color-vision deficiencies, so accessibility checks are essential for trustworthy figures.#

Perceptual uniformity in practice#

Baseline assessment of the hawaii colormap.

Baseline colormap assessment before correction.#

Corrected hawaii colormap after uniformization.

After uniformization, lightness and chroma behavior are typically smoother and less artifact-prone.#

What you can do with scicomap#

  • Browse colormaps by purpose (sequential, diverging, circular, qualitative).

  • Assess lightness, chroma symmetry, and colorblind accessibility.

  • Uniformize and symmetrize existing colormaps.

  • Generate examples that make artifacts easy to spot.

Who this project is for#

  • Researchers and engineers preparing figures for publications.

  • Data scientists building dashboards where color meaning must stay clear.

  • Anyone who needs better colormap defaults in Matplotlib workflows.

Next step#

Go to Getting Started for a copy-paste quickstart.

For guided examples#