visualize_fields

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.

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.

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.

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.

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.