Colorcet

3.1.0 · active · verified Sat Apr 11

Colorcet is a collection of perceptually uniform and distinct colormaps for use with Python plotting programs like Matplotlib, Bokeh, HoloViews, and Datashader. It provides 256-color continuous colormaps based on Peter Kovesi's work and perceptually distinct categorical colormaps. The library is actively maintained, with the current version being 3.1.0, and receives updates for new colormaps and compatibility with the Python ecosystem.

Warnings

breaking The `colorcet examples` command-line interface (CLI) was removed in version 3.1.0.
Fix: Refer to the official documentation and GitHub examples for usage demonstrations and plotting commands, rather than the removed CLI.
breaking Support for Python 2.7 and 3.6 was officially dropped in version 3.1.0. Older Python versions are no longer tested or maintained.
Fix: Upgrade your Python environment to 3.7 or newer. Python 3.7+ is supported, with 3.9+ being recommended by some sources.
deprecated The `inferno` colormap was renamed to `bmy` in version 1.0.0 to avoid confusion with Matplotlib's built-in `inferno`.
Fix: If migrating from pre-1.0.0 code using `inferno`, update calls to `bmy`. For new code, use `bmy` directly.
gotcha Colorcet colormaps can be accessed in several ways: `cc.cm.<name>` (Matplotlib colormap object), `cc.palette.<name>` or `cc.<name>` (Bokeh-style hex string list), or via Matplotlib's registry using names like `cet_<name>` (e.g., `plt.get_cmap('cet_fire')`). Choosing the correct access method depends on the plotting library and desired output format.
Fix: Consult the User Guide for detailed explanations on accessing continuous and categorical colormaps in different formats for various plotting backends.
gotcha While Colorcet's continuous colormaps are perceptually uniform, its categorical colormaps are perceptually *distinct* but not necessarily uniform. Also, not all colormaps are guaranteed to be colorblind-safe or perceptually uniform in their monochrome representation, unlike some Matplotlib-native colormaps.
Fix: For scientific visualization, understand the characteristics of the chosen colormap (continuous vs. categorical, colorblind safety) and verify its suitability for your data and audience.
gotcha The functions in `colorcet.plotting` (e.g., `swatch`, `swatches`) require `holoviews` to be installed and the appropriate `holoviews` extension loaded (e.g., `hv.extension('matplotlib')` or `hv.extension('bokeh')`).
Fix: Install `holoviews` (`pip install holoviews`) and ensure the correct `hv.extension()` is called before using `colorcet.plotting` functions.

Install

pip install colorcet Install with pip

Imports

colorcet
```
import colorcet as cc
```
The common and recommended alias for Colorcet.
continuous_colormap
```
cc.cm.fire
```
For Matplotlib `cmap` arguments, access colormaps via `cc.cm.<name>`. `cc.<name>` directly provides the Bokeh-style hex list palette. Matplotlib also registers colormaps under names like `cet_<name>` (e.g., `plt.get_cmap('cet_fire')`).
categorical_palette
```
cc.glasbey
```
Access hex-string palettes directly as `cc.<name>` or `cc.palette.<name>`. For Matplotlib, these might need to be wrapped in `matplotlib.colors.ListedColormap` for some plotting functions.

Quickstart

This quickstart demonstrates how to import `colorcet` and apply one of its continuous colormaps to a Matplotlib plot. It also shows how to access a categorical palette as a list of hex strings.

import numpy as np
import matplotlib.pyplot as plt
import colorcet as cc

# Create some dummy data
xs, _ = np.meshgrid(np.linspace(0, 1, 80), np.linspace(0, 1, 10))

# Use a continuous colormap from colorcet with Matplotlib
plt.figure(figsize=(8, 1))
plt.imshow(xs, cmap=cc.cm.fire, aspect='auto')
plt.title('Using colorcet.cm.fire')
plt.axis('off')
plt.show()

# Example of a categorical palette (prints hex codes)
print(f"First 5 colors of glasbey palette: {cc.glasbey[:5]}")

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