scicomap documentation#

scicomap helps you choose, assess, and improve scientific colormaps so figures remain readable, faithful to the data, and safer for color-vision-deficient readers.

Why this matters#

Colormap type decision guide. — Pick a colormap family that matches your data semantics before styling.#

Jet introduces staircase-like artifacts in smooth data. — Non-uniform maps such as jet/rainbow can create false boundaries and visual artifacts in otherwise smooth fields.#

Who this is for#

Researchers preparing publication figures.
Data analysts and data scientists building trustworthy dashboards.
Engineers who need robust colormap defaults in Matplotlib workflows.

Quick start#

Same workflow in both interfaces:

import scicomap as sc

cmap = sc.ScicoSequential(cmap="hawaii")
cmap.assess_cmap(figsize=(14, 6))
cmap.unif_sym_cmap(lift=None, bitonic=False, diffuse=True)
cmap.draw_example()

scicomap check hawaii --type sequential
scicomap report --profile publication --cmap hawaii --type sequential
scicomap cvd hawaii --type sequential --out hawaii-cvd.png

Choose your path#

New user: Getting Started
Practical guidance: User Guide
Full tutorial notebook: Scicomap Tutorial
Interactive playground: Interactive Marimo Tutorial (open directly)
Visual family browser: Colormap Gallery
Full API details: API Reference
CLI command reference: CLI Reference

Common tasks#

Assess a colormap before publication.
Fix non-uniform lightness and chroma artifacts.
Validate colorblind accessibility.
Apply a colormap to your own image data.

Advanced and automation#

One-command workflow reports with status, artifacts, and recommendations: scicomap report ....
Profile-driven defaults for quick decisions: quick-look, publication, presentation, cvd-safe, agent.
Machine-friendly docs and JSON outputs for tooling/LLMs: LLM Access.

Documentation last change: February 18, 2026 at 17:07

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How-to Guides

User Guide