post-processing

Files (12)

• SKILL.md
• comparison_metrics.md · references/
• data_formats.md · references/
• derived_quantities_guide.md · references/
• statistical_methods.md · references/
• comparison_tool.py · scripts/
• derived_quantities.py · scripts/
• field_extractor.py · scripts/
• profile_extractor.py · scripts/
• report_generator.py · scripts/
• statistical_analyzer.py · scripts/
• time_series_analyzer.py · scripts/

SKILL.md (first lines)

# Post-Processing Skill

Analyze and extract meaningful information from simulation output data.

## Goal

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

## 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

| 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 |

## Workflow

### 1. Data Inventory

First, understand what data is available:

```bash
# List available fields and timesteps
python scripts/field_extractor.py --input results/ --list --json
```

### 2. Field Extraction

Extract spatial field data at specific timesteps:

```bash
# Extract concentration field at timestep 100
python scripts/field_extractor.py \
    --input results/field_0100.json \
    --field concentration \
    --json

# Extract multiple fields
python scripts/field_extractor.py \
    --input results/field_0100.json \
    --field "phi,concentration,temperature" \
    --json
```

### 3. Time Series Analysis

Analyze temporal evolution of quantities:

```bash
# Extract total energy vs time
python scripts/time_series_analyzer.py \
    --input results/history.json \
    --quantity total_energy \
    --json

# Compute moving average with window
python scripts/time_series_analyzer.py \
    --input results/history.json \
    --quantity mass \
    --window 10 \
    --json

# Detect steady state
python scripts/time_series_analyzer.py \
    --input results/history.json \
    --quantity residual \
    --detect-steady-state \
    --tolerance 1e-6 \
    --json
```

### 4. Line Profile Extraction

Extract 1D profiles through the domain:

```bash
# Extract profile along x-axis at y=0.5
python scripts/profile_extractor.py \
    --input results/field_0100.json \
    --field concentration \
    --start "0,0.5,0" \
    --end "1,0.5,0" \
    --points 100 \
    --json

# Interface profile (through center)
python scripts/profile_extractor.py \
    --input results/field_0100.json \
    --field phi \
    --axis x \
    --slice-position 0.5 \
    --json

From beita6969/ScienceClaw, MIT.

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