plot

Generate publication-ready matplotlib figures with consistent styling and save them to figs/.

32|5|Updated Nov 11, 2024

Search Tags:#plotting #matplotlib #data-visualization #figure-styling #grouped-bar-chart #stacked-bar-chart

Ready to run

npx skills add https://github.com/thejaminator/latteries --skill plot-thejaminator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: plot
Source: https://github.com/thejaminator/latteries/tree/main/.claude/skills/plot
Command: npx skills add https://github.com/thejaminator/latteries --skill plot-thejaminator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creating publication-quality matplotlib figures that adhere to project conventions, saving time on styling and ensuring consistent visuals across experiments.

Core Features & Use Cases

Automates consistent styling for common chart types, including grouped and stacked bar charts.
Enforces a shared color palette, font sizes, grid settings, and layout guidelines for readability.
Saves figures to a figs/ subdirectory and provides templates and usage examples for typical plotting tasks.

Quick Start

Use this skill to generate a publication-ready grouped bar chart from your experiment results and save it to figs/.

Frequently Asked Questions about plot

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I create publication-ready matplotlib figures with consistent styling?▼

Publication-ready matplotlib figures are generated using enforced shared color palettes, font sizes, and grid settings to ensure visual consistency and readability across experiments.

What's the best way to style grouped and stacked bar charts in matplotlib?▼

Grouped and stacked bar charts are styled using automated layout guidelines and shared color palettes, ensuring proper labeling and readability for data visualization.

How do I save matplotlib figures to a specific subdirectory for reproducibility?▼

Matplotlib figures are automatically saved to a figs/ subdirectory, providing templates and usage examples that ensure reproducibility across different projects.

Can I apply project-specific color palettes and font settings to my data visualization plots?▼

Shared color palettes and font settings are applied as styling defaults to your data visualization plots, enforcing layout guidelines and consistent visual conventions.

Does this matplotlib figure styling approach work for experimental result visualization?▼

Experimental results are visualized through publication-ready matplotlib figures, applying consistent styling defaults and saving outputs to maintain readability across various experiments.