User Guide#

Choose the right colormap type#

Use sequential colormaps for ordered values, diverging colormaps for values around a midpoint, and qualitative colormaps for categories.

If your figure shows directional or cyclic variables (phase, angle), use circular colormaps.

Assess a colormap before using it#

Use assess_cmap to inspect lightness progression, chroma behavior, and colorblind rendering.

import matplotlib.pyplot as plt
import scicomap as sc

jet = plt.get_cmap("jet")
cmap = sc.ScicoMiscellaneous(cmap=jet)
cmap.assess_cmap(figsize=(14, 6))
Jet assessment view with non-uniformity and artifacts.

Jet/rainbow often introduces false contrast and non-linear lightness changes.#

Uniformize a colormap#

When a colormap contains visible artifacts, apply uniformization and reassess.

import scicomap as sc

cmap = sc.ScicoSequential(cmap="hawaii")
cmap.unif_sym_cmap(lift=None, bitonic=False, diffuse=True)
cmap.assess_cmap(figsize=(14, 6))
Baseline assessment for hawaii before uniformization.

Before correction.#

Assessment for hawaii after uniformization.

After correction. Uniformization reduces visible artifacts in practical rendering tests.#

Practical workflow#

  1. Start with a colormap family that matches your data semantics.

  2. Assess lightness and colorblind behavior.

  3. Apply uniformization only when needed.

  4. Validate with your real data, not only synthetic examples.

CLI profiles#

Use profile defaults to reduce option tuning in report and wizard:

  • quick-look: fast diagnosis with minimal outputs.

  • publication: quality-first defaults (improve + fix + CVD checks).

  • presentation: publication defaults with a brighter lift bias.

  • cvd-safe: accessibility-first diagnostics, CVD checks enforced.

  • agent: deterministic machine mode (JSON output, non-interactive).

Example:

scicomap report --profile publication --cmap hawaii
scicomap report --profile cvd-safe --cmap thermal --format json

Next steps from this guide#