Why should I use Colorcet instead of standard matplotlib colormaps?

Colorcet colormaps are perceptually uniform, meaning changes in data correspond to proportional visual changes, which prevents the introduction of false features in your data visualization.

What libraries are required for this skill?

This skill requires Colorcet (>= 3.1.0), HoloViews (>= 1.18.0), Panel (>= 1.3.0), and Bokeh (>= 3.0.0).

Does this skill help with colorblind accessibility?

Yes, it provides specific implementation patterns for deuteranopia, protanopia, and tritanopia safe palettes, as well as grayscale-safe options.

Can this skill help with dashboard themes?

Yes, it includes patterns for applying Material Design, dark mode, and custom CSS themes across Panel templates and application components.

HoloViz Colormaps & Styling

Name: HoloViz Colormaps & Styling
Author: robertpelloni

byrobertpelloni

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Data Science & ML

Masters perceptual color management and accessible visual styling for scientific visualizations using Colorcet and the HoloViz ecosystem.

About

This skill equips Claude with specialized knowledge for implementing perceptually uniform colormaps and sophisticated visual styling in Python. It focuses on the Colorcet library to ensure that data visualizations are scientifically accurate, avoiding common pitfalls like the 'jet' colormap. Users can leverage this skill to create accessible, colorblind-friendly charts, apply consistent themes across Panel dashboards, and manage complex styling options for HoloViews and Bokeh plots. It is particularly useful for researchers and data scientists who need to represent intricate data structures—such as cyclic, diverging, or categorical data—with professional-grade aesthetics.

Key Features

Selection of perceptually uniform colormaps for scientific accuracy
Advanced customization of colorbars, responsive legends, and CSS-based app branding
Global theme configuration for Panel dashboards and HoloViews plots
Automated styling patterns for sequential, diverging, and cyclic data
3 GitHub stars
Implementation of colorblind-safe and accessibility-compliant palettes

Use Cases

Creating accessible scientific visualizations for publications and reports
Visualizing complex periodic or angular data using specialized cyclic colormaps
Building professional-grade data dashboards with consistent corporate branding

About

Key Features

Selection of perceptually uniform colormaps for scientific accuracy
Advanced customization of colorbars, responsive legends, and CSS-based app branding
Global theme configuration for Panel dashboards and HoloViews plots
Automated styling patterns for sequential, diverging, and cyclic data
3 GitHub stars
Implementation of colorblind-safe and accessibility-compliant palettes

Use Cases

Creating accessible scientific visualizations for publications and reports
Visualizing complex periodic or angular data using specialized cyclic colormaps
Building professional-grade data dashboards with consistent corporate branding