Does this skill support accessibility for colorblind users?

Yes, it includes specific implementation patterns for Deuteranopia, Protanopia, and Tritanopia-safe palettes, as well as grayscale-safe options for print and high-contrast needs.

Can I apply these styles to interactive dashboards?

Absolutely. The skill provides patterns for Panel themes and global HoloViews options, allowing you to maintain consistent aesthetics across complex interactive web applications.

Why should I use Colorcet instead of default matplotlib colormaps?

Colorcet provides perceptually uniform colormaps, meaning equal changes in data values result in equal perceived changes in color, preventing visual artifacts and misinterpretation common in maps like 'jet' or 'rainbow'.

Which plotting libraries are compatible with this skill?

It is optimized for the HoloViz ecosystem, specifically supporting HoloViews, Panel, Bokeh, and hvPlot, ensuring seamless color management across these tools.

Colormaps & Visual Styling

Name: Colormaps & Visual Styling
Author: uw-ssec

byuw-ssec

•

数据科学与机器学习

Master color management and accessible visual styling using perceptually uniform Colorcet palettes for scientific data visualizations.

This skill empowers Claude to implement professional-grade color management and visual aesthetics within the HoloViz ecosystem. By leveraging Colorcet, it ensures that data visualizations are not only aesthetically pleasing but also scientifically accurate through perceptually uniform colormaps where data changes correspond linearly to visual changes. It provides specific implementation patterns for sequential, diverging, categorical, and cyclic data types, while prioritizing accessibility through colorblind-safe palettes and consistent application-wide styling for Panel dashboards and Bokeh plots.

主要功能

01Standardized application-wide themes and CSS customization for interactive dashboards

02Perceptually uniform colormaps for accurate scientific data representation

03Specialized mappings for sequential, diverging, categorical, and cyclic data

0418 GitHub stars

05Seamless integration with HoloViews, Panel, and Bokeh plotting libraries

06Built-in accessibility with colorblind-friendly and grayscale-safe palettes

使用场景

01Developing accessible data dashboards that comply with WCAG color contrast standards

02Applying consistent corporate branding and custom themes to Python-based web applications

03Creating high-precision heatmaps and density plots for scientific research data

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add uw-ssec/rse-plugins colormaps-styling

For use in Claude.ai and ChatGPT

Download Skill