gpd-compare-experiment

What problem does it solve?

Systematically quantify the agreement between theoretical predictions and experimental or observational data, including robust handling of units, uncertainties, and discrepancy analysis to guide scientific conclusions.

Core Features & Use Cases

• Systematic theory-data comparisons with quantified metrics (chi-squared, p-values, pulls) to determine compatibility.
• Load predictions from workspace artifacts and data sources, perform unit/convention checks, and generate standardized comparison reports and figures.
• Use cases include validating models against experiments, benchmarking between predictions, and producing publication-ready summaries for papers or debugging.

Quick Start

Ask me to compare a specific theoretical prediction with a dataset by providing the target name or data path.