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:

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

Workflow

1. Data Inventory

First, understand what data is available:

List available fields and timesteps

python scripts/field_extractor.py --input results/ --list --json

2. Field Extraction

Extract spatial field data at specific timesteps:

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:

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:

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

5. Statistical Analysis

Compute statistics over field data:

Global statistics

python scripts/statistical_analyzer.py
--input results/field_0100.json
--field concentration
--json

Statistics in specific region

python scripts/statistical_analyzer.py
--input results/field_0100.json
--field phi
--region "x>0.3 and x<0.7"
--json

Distribution analysis

python scripts/statistical_analyzer.py
--input results/field_0100.json
--field phi
--histogram
--bins 50
--json

6. Derived Quantities

Calculate physical quantities from raw data:

Compute interface area

python scripts/derived_quantities.py
--input results/field_0100.json
--quantity interface_area
--threshold 0.5
--json

Compute gradient magnitude

python scripts/derived_quantities.py
--input results/field_0100.json
--quantity gradient_magnitude
--field phi
--json

Compute volume fractions

python scripts/derived_quantities.py
--input results/field_0100.json
--quantity volume_fraction
--field phi
--threshold 0.5
--json

Compute flux through boundary

python scripts/derived_quantities.py
--input results/field_0100.json
--quantity boundary_flux
--field concentration
--boundary "x=0"
--json

7. Comparison with Reference

Compare simulation results to reference data:

Compare to analytical solution

python scripts/comparison_tool.py
--simulation results/profile.json
--reference reference/analytical.json
--metric l2_error
--json

Compare to experimental data

python scripts/comparison_tool.py
--simulation results/history.json
--reference experimental_data.csv
--metric rmse
--interpolate
--json

Compare two simulations

python scripts/comparison_tool.py
--simulation results_fine/field.json
--reference results_coarse/field.json
--metric max_difference
--json

8. Report Generation

Generate automated reports:

Generate summary report

python scripts/report_generator.py
--input results/
--output report.json
--json

Generate with specific sections

python scripts/report_generator.py
--input results/
--sections "summary,statistics,convergence"
--output report.json
--json

Typical Post-Processing Pipeline

For a complete simulation analysis:

Step 1: Inventory available data

python scripts/field_extractor.py --input results/ --list --json

Step 2: Extract final state statistics

python scripts/statistical_analyzer.py
--input results/field_final.json
--field phi
--json

Step 3: Analyze convergence history

python scripts/time_series_analyzer.py
--input results/history.json
--quantity residual
--detect-steady-state
--json

Step 4: Compute derived quantities

python scripts/derived_quantities.py
--input results/field_final.json
--quantity volume_fraction
--field phi
--json

Step 5: Compare to reference (if available)

python scripts/comparison_tool.py
--simulation results/profile.json
--reference benchmark/expected.json
--metric l2_error
--json

Step 6: Generate summary report

python scripts/report_generator.py
--input results/
--output analysis_report.json
--json

Interpretation Guidelines

Time Series Analysis

• Monotonic decrease in energy: System approaching equilibrium

• Oscillations in residual: May indicate time step too large

• Plateau in quantities: Steady state reached

• Sudden jumps: Possible numerical instability

Statistical Analysis

• Bimodal distribution of order parameter: Two-phase mixture

• High variance: Heterogeneous microstructure

• Skewed distribution: Asymmetric phase fractions

Comparison Metrics

Metric Interpretation

L2 error < 1% Excellent agreement

L2 error 1-5% Good agreement

L2 error 5-10% Moderate agreement

L2 error > 10% Poor agreement, investigate

Output Format

All scripts support --json flag for machine-readable output:

{ "script": "field_extractor", "version": "1.0.0", "input_file": "results/field_0100.json", "field": "concentration", "data": { "shape": [100, 100], "min": 0.1, "max": 0.9, "mean": 0.5 }, "values": [[...], [...]] }

References

For detailed information, see:

• references/data_formats.md

• Supported input/output formats

• references/statistical_methods.md

• Statistical analysis methods

• references/derived_quantities_guide.md

• Physical quantity calculations

• references/comparison_metrics.md

• Error metrics and interpretation

Requirements

• Python 3.8+

• NumPy (for numerical operations)

• No other external dependencies for core functionality

Version History

• v1.0.0 (2024-12-24): Initial release