数据与复现统计与数据分析FreedomIntelligence/OpenClaw-Medical-Skills数据与复现
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post-processing

维护者 FreedomIntelligence · 最近更新 2026年4月1日

post-processing:提取,analyze,、 visualize 模拟 output data。 可用于field extraction,时序分析,line profiles,statistical summaries,derived quantity computation,result comparison to references,、 automated report generation ,面向 模拟 results。

OpenClawNanoClaw分析处理复现实验post-processing📊 data science & toolscomputational simulation & ontologyextract

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

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

维护者
FreedomIntelligence
许可
MIT
最近更新
2026年4月1日

技能摘要

来自 SKILL.md 的关键信息

2 min

核心说明

  • 分析 、 extract meaningful information ,面向 模拟 output data。
  • 转换 raw 模拟 output into actionable insights through field extraction,统计分析,derived quantities,visualizations,、 comparison ,支持 reference data。

原始文档

SKILL.md 摘录

Inputs to Gather

Before running post-processing scripts, collect:

  1. Output Data Location

    • Path to simulation output files (JSON, CSV, HDF5, VTK)
    • Time step/snapshot indices of interest
    • Field names to extract
  2. 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
  3. Output Requirements

    • Output format (JSON, CSV, tabular)
    • Visualization needs
    • Report format

Scripts

ScriptPurposeKey Inputs
field_extractor.pyExtract field data from output files--input, --field, --timestep
time_series_analyzer.pyAnalyze temporal evolution--input, --quantity, --window
profile_extractor.pyExtract line profiles--input, --field, --start, --end
statistical_analyzer.pyCompute field statistics--input, --field, --region
derived_quantities.pyCalculate derived quantities--input, --quantity, --params
comparison_tool.pyCompare to reference data--simulation, --reference, --metric
report_generator.pyGenerate summary reports--input, --template, --output

1. Data Inventory

First, understand what data is available:

适用场景

  • 可用于field extraction,时序分析,line profiles,statistical summaries,derived quantity computation,result comparison to references,、 automated report generation ,面向 模拟 results。

不适用场景

  • Do not rely on this catalog entry alone ,用于 installation 或 maintenance details。

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