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Rukkha1024
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aggregated-signal-figure

Generate and refactor signal visualization scripts with Polars and configuration-driven layouts.

Updated Aug 27, 2026
Search Tags:#data analysis#visualization#signal processing#plotting#matplotlib#polars
One-click install
npx skills add https://github.com/Rukkha1024/replace_V3D --skill aggregated-signal-figure
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: aggregated-signal-figure
Source: https://github.com/Rukkha1024/replace_V3D/tree/main/.claude/skills/aggregated-signal-figure
Command: npx skills add https://github.com/Rukkha1024/replace_V3D --skill aggregated-signal-figure

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires polars, matplotlib, PyYAML, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the creation and refinement of complex signal visualizations, ensuring clarity and consistency in data representation across different plot types and layouts.

Core Features & Use Cases

  • Config-Driven Plotting: Generates various plots (channel grids, summary plots) based on a configuration file.
  • Data Processing: Utilizes Polars for efficient data I/O and manipulation.
  • Consistent Styling: Enforces standardized visual parameters for plots and legends.
  • Use Case: Visualize EMG signals across multiple channels in a grid layout, or create summary plots of event onsets, all driven by a single configuration file.

Quick Start

Use the aggregated-signal-figure skill to create a summary grid plot for onset data using the provided config.yaml file.

Frequently Asked Questions about aggregated-signal-figure

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

FAQPage Schema
How do I visualize complex multi-channel signals in a consistent grid layout?▼

You can visualize complex signals by defining channel grids and summary plots in a configuration file. The tool enforces consistent styling and legend management to ensure standardized visual parameters across different plot types.

What is the best way to manage plot styling for signal visualization across multiple charts?▼

Config-driven plotting manages signal visualization styling by enforcing standardized visual parameters. Defining layouts and legends in a configuration file ensures consistent representation across all generated charts.

Can I use Polars for data processing when generating matplotlib visualizations?▼

Yes, Polars supports data processing for matplotlib visualizations. This Skill utilizes Polars for efficient data I/O and manipulation to prepare signal data before creating standardized plots.

How do I create a summary plot for event onsets using a configuration file?▼

You create a summary plot for event onsets by defining visualization parameters in a config.yaml file. The Skill processes this configuration to generate plots with consistent styling and managed legends.

Does configuration-driven signal visualization require specific data input formats?▼

Configuration-driven signal visualization requires data formats compatible with Polars for efficient I/O and manipulation. The tool enforces specific rules for data input and output formatting to generate consistent plots.

Why use a configuration file for refactoring signal visualization scripts?▼

A configuration file streamlines signal visualization script refactoring by separating plotting logic from layout parameters. This enforces consistent styling and manages legends across different plot types.

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