cmcrameri
Perceptually uniform colormaps by Fabio Crameri
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
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 · maintenance · safety
permissive license (permissive) — 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.
last release 2026-08-04 (10 days) · last repo commit 2026-08-10 · 230 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 111,890 downloads/mo, #12,391 on PyPI
Alternatives
Verify before relying
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()- Whether colormaps are tested or validated for colorblind-friendly perception beyond Crameri's original design claims.
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 on this page. Verify before relying on it.
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
cmcrameri on PyPI
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 | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesmatplotlibnumpypackaging |
| Maintenance | Actively maintained 10 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 111,890 / month, #12,391 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: cmcrameri-1.10-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “perceptually uniform colormaps”
- cmcrameriProvides perceptually uniform colormaps by Fabio Crameri for…
- colorcetColorcet provides a collection of perceptually uniform colormaps…
- cmoceanProvides perceptually uniform colormaps designed for oceanographic…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also cmap · cmocean · colorcet · cmweather · colorspacious · colourmap · distinctipy · fabio · brewer2mpl · matplotlib-scalebar