visualize_fields

Visualize multi-dimensional field data with coordinate transformation and axis diagnostics.

1|Updated Dec 17, 2025

Search Tags:#visualization #plotting #data-visualization #multidimensional #field #axis-diagnostics #gwexpy

Ready to run

npx skills add https://github.com/tatsuki-washimi/gwexpy --skill visualize-fields

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: visualize_fields
Source: https://github.com/tatsuki-washimi/gwexpy/tree/main/.agent/skills/visualize_fields
Command: npx skills add https://github.com/tatsuki-washimi/gwexpy --skill visualize-fields

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured toolkit to visualize multi-dimensional field data (e.g., 3D, 4D, matrices) and to diagnose and adjust plot axes, ticks, and display ranges to ensure physical accuracy and clarity.

Core Features & Use Cases

Infrastructure layer: separates physics-to-index mapping from plotting code, enabling reusable coordinate utilities.
Extraction API Layer: adds methods to data types to generate visualization-ready subsets without mutating the original data.
Plotting API Layer: provides user-facing plotting methods named consistently (e.g., plot_map2d, plot_timeseries_points).
Diagnostic workflows: built-in checks for unit propagation, coordinate accuracy, and proper scaling to prevent misinterpretation.

Quick Start

Construct a basic 2D map from a 4D field and render it with the plotting API to verify axis scales.

Frequently Asked Questions about visualize_fields

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

FAQPage Schema

How do I plot multi-dimensional field data accurately in Python?▼

To plot multi-dimensional field data accurately, you need a structured toolkit that separates physics-to-index mapping from plotting code, ensuring unit propagation and coordinate accuracy. This Skill standardizes unit handling and scale defaults to prevent misinterpretation across 3D and 4D datasets.

How do I extract a 2D map from a 4D field for visualization?▼

You can extract a 2D map from a 4D field by using an extraction API layer that generates visualization-ready subsets without mutating the original data. This ensures the extracted coordinates remain physically accurate for rendering with the plotting API.

Can I use this for GWpy-style time-series visualization tasks?▼

Yes, this approach applies to GWpy-style time-series visualization tasks, including coordinate transformation and data extraction for plots. It provides user-facing plotting methods like plot_timeseries_points to render time-series data accurately.

Why does my multi-dimensional plot have incorrect axis scales and units?▼

Incorrect axis scales and units in multi-dimensional plots often result from missing coordinate accuracy and proper scaling diagnostics. Built-in diagnostic workflows check unit propagation and display ranges to enforce physical accuracy and clarity.

What is the best way to diagnose and adjust plot axes for 3D datasets?▼

The best way to diagnose and adjust plot axes for 3D datasets is using a modular architecture with built-in checks for unit propagation and coordinate accuracy. This enforces standard scale defaults and separates infrastructure from plotting code.