Getting Started#

In five minutes, you should be able to pick a colormap, assess it, and run a safe default improvement workflow.

Install#

uv add scicomap

or

pip install -U scicomap

Quickstart#

The same starter workflow is available in Python and CLI forms.

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()

Expected result:

- A diagnostics status (good/caution/fix-recommended)
- A report directory containing summary.txt and report.json
- A colorblind preview image at hawaii-cvd.png
Example output panels for hawaii before correction.

Typical visual output from assessment-style workflows.#

Simple usage#

Use these commands and APIs first if you are new to scicomap.

import scicomap as sc

cmap = sc.ScicoSequential(cmap="hawaii")
cmap.assess_cmap(figsize=(14, 6))

Choose a colormap family#

sc_map = sc.SciCoMap()
sc_map.get_ctype()

Typical output:

dict_keys(['diverging', 'sequential', 'multi-sequential', 'circular', 'miscellaneous', 'qualitative'])

Get a Matplotlib colormap object#

plt_cmap_obj = cmap.get_mpl_color_map()

Advanced next steps#

Use profiles and guided workflows when you want repeatable quality checks.

scicomap wizard --profile quick-look --type sequential --cmap thermal --no-interactive
scicomap report --profile cvd-safe --cmap thermal --format json
Example output panels for hawaii after correction.

After correction, transitions and gradients are typically more stable across test images.#

Where to go next#