post-processing

Field extraction pulls scalar and vector fields directly from JSON or CSV simulation results. Use the field_extractor script with --input and --field parameters to isolate the data you need, then output as JSON or CSV for downstream analysis.

Time-series analysis processes simulation output across multiple timesteps to detect trends and evolution patterns. The post-processor ingests sequential field data, performs shape validation, and generates statistical summaries and visualization metadata for trend detection.

Automated reporting combines field extraction, statistical summaries, and derived quantity computation into structured outputs. The Skill orchestrates modular scripts to process 2D/3D field data, apply thresholds, and produce JSON or CSV reports with analysis metadata in a single workflow.

Line-profile generation and statistics extract cross-sectional data and compute derived metrics from field arrays. The post-processor validates input shapes, interpolates where needed, and outputs profile coordinates and summary statistics for comparative analysis.

Reference-data comparison ingests both simulation output and baseline data in JSON or CSV format, then applies validation checks and interpolation to align datasets. The Skill generates comparative statistical summaries and reports highlighting differences and derived quantities.

The post-processor handles both 2D and 3D field data in JSON and CSV formats. It performs shape validation, field extraction, and statistical analysis across spatial dimensions, with CLI-driven input of field names and output formatting options.