cmuseo 1.0.1

Perceptually uniform colormaps for scientific visualization

cmuseo

cmuseo is a Python package that provides perceptually uniform colormaps, ensuring accurate gradients and accessible data interpretation for scientific visualization, with palette names reflecting the key colors of each colormap.

• vivian transitions smoothly from midnight violet through refreshing teal-green (viridian) to bright pale canary yellow, forming a vibrant palette well-suited for clear, engaging visualizations in data science and research.

• indira moves from deep indigo through plum purple and brilliant orange into light straw yellow, offering a luminous, warm palette ideal for visualizations requiring energy and contrast.

• elsa progresses from dark elderberry purple through deep blue and pastel sapphire to frosty mint and pale jade, creating a cool, crystalline palette suited for visualizations with an icy aesthetic and arctic clarity.

Features

• Unique RGB colormaps designed with perceptual uniformity in mind
• Easy integration with matplotlib
• Openly licensed under GNU GPL v3.0

Installation

pip install cmuseo

Usage

Basic usage

import cmuseo

Example: Previewing colormaps

import matplotlib.pyplot as plt
import numpy as np
import cmuseo

colormaps = ['vivian', 'indira', 'elsa']

fig, axs = plt.subplots(len(colormaps), 1, figsize=(8, 0.8*len(colormaps)))

gradient = np.linspace(0, 1, 256).reshape(1, -1)

for ax, cmap_name in zip(axs, colormaps):
    ax.imshow(gradient, aspect='auto', cmap=cmap_name)
    ax.text(-0.02, 0.5, cmap_name, va='center', ha='right', transform=ax.transAxes)
    ax.set_axis_off()

plt.tight_layout()
plt.show()