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shboost-cmd-visualization

Generate static CMD images and looping population animations from large stellar datasets.

4|1|Updated Apr 11, 2026
Search Tags:#astronomy#animation#parquet#datashader#cmd-visualization#stellar-datasets
One-click install
npx skills add https://github.com/arm2arm/AstroAgentAssistant --skill shboost-cmd-visualization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: shboost-cmd-visualization
Source: https://github.com/arm2arm/AstroAgentAssistant/tree/main/data-science/shboost-cmd-visualization
Command: npx skills add https://github.com/arm2arm/AstroAgentAssistant --skill shboost-cmd-visualization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Generate publication-ready color-magnitude diagram (CMD) visualizations and animations from very large stellar datasets, with caching to speed future runs.

Core Features & Use Cases

  • Static CMD plots using hexbin or datashader for large datasets with log-scaled density.
  • Animated visualization cycling through stellar populations with annotations.
  • Caching and local/parquet data source support to accelerate repeat analyses.
  • Use cases include astronomers analyzing Gaia-like catalogs, producing figures for papers and outreach.

Quick Start

Run the shboost_plot_cached.py script to generate the CMD PNG and the population GIF using the default sample.

Frequently Asked Questions about shboost-cmd-visualization

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

FAQPage Schema
How do I visualize a color-magnitude diagram from a very large stellar parquet dataset?▼

To visualize a color-magnitude diagram from large stellar parquet datasets, this Skill uses a Datashader-based rendering path to handle tens of millions of rows and outputs a static density-scaled CMD image.

Can I generate an animated stellar population cycle from Gaia-like catalogs?▼

Yes, you can generate an animated stellar population cycle from Gaia-like catalogs. The Skill produces a looping population animation with annotations, and an optional ffmpeg path enables MP4 output.

Does CMD visualization with Datashader work with smaller star samples?▼

CMD visualization with Datashader works with smaller star samples by automatically switching to a Matplotlib rendering path. This ensures appropriate hexbin density plotting for datasets with fewer rows.

What Python packages do I need to plot CMDs from S3 parquet data?▼

To plot CMDs from S3 parquet data, you need Python 3.12+ with pandas, dask[dataframe], numpy, matplotlib, and seaborn installed. These packages support the data loading and visualization pipeline.

How do I speed up repeat CMD visualizations for large astronomical datasets?▼

To speed up repeat CMD visualizations for large astronomical datasets, the Skill supports caching mechanisms. This accelerates future runs by storing intermediate results when analyzing the same S3 or local parquet sources.

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