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post-processing

Maintainer FreedomIntelligence · Last updated April 1, 2026

Extract, analyze, and visualize simulation output data. Use for field extraction, time series analysis, line profiles, statistical summaries, derived quantity computation, result comparison to references, and automated report generation from simulation results.

OpenClawNanoClawAnalysisReproductionpost-processing📊 data science & toolscomputational simulation & ontologyextract

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/post-processing

Maintainer: FreedomIntelligence
License: MIT
Last updated: April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

Analyze and extract meaningful information from simulation output data.
Transform raw simulation output into actionable insights through field extraction, statistical analysis, derived quantities, visualizations, and comparison with reference data.

Source Doc

Excerpt From SKILL.md

Inputs to Gather

Before running post-processing scripts, collect:

Output Data Location
- Path to simulation output files (JSON, CSV, HDF5, VTK)
- Time step/snapshot indices of interest
- Field names to extract
Analysis Type
- Field extraction (spatial data at specific times)
- Time series (temporal evolution of quantities)
- Line profiles (1D cuts through domain)
- Statistical summary (mean, std, distributions)
- Derived quantities (gradients, integrals, fluxes)
- Comparison to reference data
Output Requirements
- Output format (JSON, CSV, tabular)
- Visualization needs
- Report format

Scripts

Script	Purpose	Key Inputs
`field_extractor.py`	Extract field data from output files	--input, --field, --timestep
`time_series_analyzer.py`	Analyze temporal evolution	--input, --quantity, --window
`profile_extractor.py`	Extract line profiles	--input, --field, --start, --end
`statistical_analyzer.py`	Compute field statistics	--input, --field, --region
`derived_quantities.py`	Calculate derived quantities	--input, --quantity, --params
`comparison_tool.py`	Compare to reference data	--simulation, --reference, --metric
`report_generator.py`	Generate summary reports	--input, --template, --output

1. Data Inventory

First, understand what data is available:

Use cases

Use for field extraction, time series analysis, line profiles, statistical summaries, derived quantity computation, result comparison to references, and automated report generation from simulation results.

Not for

Do not rely on this catalog entry alone for installation or maintenance details.

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