---
id: cmcrameri
version: "1.10"
license: MIT License Colormaps in cmcrameri/cm/cmaps: Copyright (c) 2020 Fabio Crameri Python scripts and packaging: Copyright (c) 2020 Callum Rollo Permission is hereby granted, free of charge, to any person… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# cmcrameri — Perceptually uniform colormaps by Fabio Crameri

License: permissive · Maintenance: active · Downloads: 111.9K/mo

## What it is and what it does

cmcrameri is a Python wrapper that brings Fabio Crameri's scientifically designed colormaps into matplotlib. The package registers a collection of perceptually uniform colormaps—colormaps engineered to represent data changes uniformly across the full range of colors, making them suitable for scientific publication. You import the colormaps directly or by registered string name and use them with matplotlib's standard plotting functions.

The package ships the original colormap definitions and provides convenient access through tab-completable attributes. You can discretize colormaps into a specific number of levels, reverse them by appending `_r`, and access categorical variants with the `S` suffix. It requires matplotlib and numpy as runtime dependencies and supports Python 3.7 and later.

Use it for:

- Create publication-ready scientific plots where color gradients must remain perceptually uniform across print and digital media.
- Build heatmaps and contour plots in geophysics, oceanography, or climate science where standard colormaps introduce perceptual bias.
- Discretize continuous colormaps into categorical levels for segmented data visualization without losing perceptual uniformity.
- Integrate Crameri's colormaps into existing matplotlib workflows using registered string names like 'cmc.batlow'.
- Access reversed colormaps by appending `_r` to colormap names for inverted data representations.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides perceptually uniform colormaps by Fabio Crameri for matplotlib, enabling scientific visualizations designed to be readable across different media and accessible to colorblind viewers.

Yes. The package is actively maintained, has no known vulnerabilities, installs with minimal friction, and solves a real problem for scientific visualization. If you create plots for publication or need colormaps designed for perceptual uniformity, this is a straightforward addition to matplotlib. The MIT license imposes no restrictions.

## Install

pip install cmcrameri
uv add cmcrameri
poetry add cmcrameri

## Installing cmcrameri

Before you install: Low install friction with a pure Python wheel distribution. The package is actively maintained with a recent release and supports Python 3.7 through 3.14. Dependencies are minimal: matplotlib, numpy, and packaging.

License in practice: MIT license permits unrestricted use, modification, and distribution. The colormaps are copyrighted by Fabio Crameri; the Python packaging and scripts are copyrighted by Callum Rollo, both under MIT terms.

Quickstart:

pip install cmcrameri

import cmcrameri.cm as cmc
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 1, 100)[np.newaxis, :]
plt.imshow(x, aspect='auto', cmap=cmc.batlow)
plt.show()

Verify before relying:

- Whether colormaps are tested or validated for colorblind-friendly perception beyond Crameri's original design claims.

## Package facts

- License: MIT License Colormaps in cmcrameri/cm/cmaps: Copyright (c) 2020 Fabio Crameri Python scripts and packaging: Copyright (c) 2020 Callum Rollo Permission is hereby granted, free of charge, to any person… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 111.9K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

perceptually uniform colormaps, scientific colormaps matplotlib, colorblind-friendly color maps, fabio crameri colormaps, matplotlib colormap library, visualization color schemes, perceptual color mapping, visualization, scientific-computing, colormap

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