Post Processing

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

   Read field names from the file, never assume them. Before extracting, open the output file (or run field_extractor.py --input <file> --list --json) and use only the field names that actually appear under fields. Do not invent fields such as temperature if they are not present, and do not assume a grid size — read the real shape/count from the data.

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

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 (relative-variation test; best for physical quantities)
python scripts/time_series_analyzer.py \
    --input results/history.json \
    --quantity residual \
    --detect-steady-state \
    --tolerance 1e-6 \
    --json

# Convergence by absolute threshold (physically correct test for residuals)
python scripts/time_series_analyzer.py \
    --input results/history.json \
    --quantity residual \
    --absolute-threshold 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 a specific spatial region (1D/2D fields only).
# Coordinates are derived from the field shape and grid spacing
# (explicit dx/dy, or Lx/Ly via dx = Lx/(nx-1)); only the variables
# x, y, z compared against numbers, joined by and/or, are allowed.
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:

python scripts/field_extractor.py --input results/ --list --json                                             # 1. inventory
python scripts/statistical_analyzer.py --input results/field_final.json --field phi --json                    # 2. final-state stats
python scripts/time_series_analyzer.py --input results/history.json --quantity residual --detect-steady-state --json   # 3. convergence
python scripts/derived_quantities.py --input results/field_final.json --quantity volume_fraction --field phi --json    # 4. derived quantities
python scripts/comparison_tool.py --simulation results/profile.json --reference benchmark/expected.json --metric l2_error --json   # 5. compare to reference
python scripts/report_generator.py --input results/ --output analysis_report.json --json                      # 6. summary report

Interpretation Guidelines

Time Series Analysis

Interpret convergence differently depending on the quantity type, because the two signals the analyzer reports (convergence.{rate,type} and steady_state.reached) answer different questions and can legitimately disagree.

Residual / error quantities (e.g. residual, error):

• Judge convergence by absolute magnitude vs a tolerance: a residual at or below the target tolerance (e.g. 1e-6) is converged. Use --absolute-threshold <tol> to get the convergence_threshold block, which is the physically correct test for residuals.
• The relative --detect-steady-state test answers "has the residual stopped changing?", not "has it converged?". For a still-decreasing residual, steady_state.reached = false is expected and not a failure.
• A plateau of a residual means a stalled solver (convergence.type = "stalled"), not steady state.
• Reconcile the signals: if convergence.type is fast/linear and the final residual is small (or convergence_threshold.reached = true), report the run as converged even when steady_state.reached = false.

Physical quantities (e.g. energy, volume_fraction, mass, interface_area):

• Monotonic decrease in energy: system approaching equilibrium.
• Plateau (steady_state.reached = true): steady state reached.
• Oscillations: may indicate the time step is too large.
• 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

Output Format

All scripts support the --json flag for machine-readable output. Most scripts emit a flat top-level object whose keys depend on the script. For example, field_extractor.py --include-data on a single field emits:

{
    "field": "concentration",
    "found": true,
    "data": [[0.1, 0.9], [0.3, 0.6]],
    "shape": [2, 2],
    "min": 0.1,
    "max": 0.9,
    "mean": 0.475,
    "count": 4,
    "source_file": "results/field_0100.json",
    "timestep_info": {"timestep": 100, "time": 1.5}
}

Notes on envelope shapes (they are not uniform across scripts):

• field_extractor.py, statistical_analyzer.py, time_series_analyzer.py, profile_extractor.py, and comparison_tool.py emit a flat object with a source_file key plus script-specific result keys.
• derived_quantities.py wraps its payload in an { "inputs": {...}, "results": {...} } envelope.
• report_generator.py emits top-level report_version and generator keys plus the requested report sections.

No script emits top-level script, version, or input_file keys, and field statistics (min/max/mean/count) appear at the top level, not nested under a data object.

Verification checklist

Before trusting or reporting a post-processing result, produce and record the concrete evidence below (tied to this skill's scripts and output keys):

• Listed the real fields first. Ran field_extractor.py --list --json and recorded the actual fields names and shape; every later --field argument is one that appears in that list (no assumed temperature, no guessed grid size).
• Used the right convergence test for the quantity type. For a residual/error, recorded convergence_threshold.reached and final_value from --absolute-threshold <tol> (the |x_final| <= tol test) rather than relying on steady_state.reached; for a physical quantity, recorded steady_state.{reached,relative_variation,value} from --detect-steady-state.
• Reconciled the two convergence signals. Logged convergence.type and convergence.rate alongside steady_state.reached, and confirmed a convergence.type = "stalled" is read as a stalled solver (not steady state), and a still-decreasing residual with steady_state.reached = false is not reported as a failure.
• Checked conservation against a tolerance. Recorded the conserved integral (derived_quantities.py --quantity mass / integral, or volume_fraction) at the first and last timestep and confirmed the drift is within the documented tolerance for the dynamics (≈0 for Cahn-Hilliard conserved order parameter; expected to change for Allen-Cahn).
• Confirmed grid spacing is physical. Recorded the spacing block (dx/dy/dz) echoed by derived_quantities.py and verified it came from explicit dx/dy or the correct Lx/(nx-1) derivation — and that no WARNING: explicit dx ... inconsistent with Lx line was emitted to stderr.
• Verified no non-finite values corrupted the result. Confirmed derived-quantity scripts did not raise Field contains non-finite value (NaN/Inf) and that reported min/max are physically plausible (e.g. an order parameter stays within its expected bounds).
• Qualified comparison error against the documented bands. Recorded the comparison_tool.py metric value (e.g. l2_error) and mapped it to the agreement band in this skill (<1% excellent ... >10% poor, investigate), confirming the simulation and reference were aligned/interpolated onto the same axis first.

Common pitfalls & rationalizations

Security

Input Validation

• User-provided field names are validated against [a-zA-Z_][a-zA-Z0-9_.-]* to prevent injection via crafted field names
• statistical_analyzer.py validates --region conditions against a strict allowlist before use: only the coordinate variables x, y, z compared (< <= > >= == !=) against numeric literals and joined by and/or are accepted; anything else exits with code 2. The parsed condition is applied as a real coordinate mask (no eval/exec), so the reported statistics describe the requested region
• profile_extractor.py validates the field name against the same pattern and point coordinates as finite numbers with max 3 dimensions
• --metric values in comparison_tool.py are validated against a fixed allowlist (l1_error, l2_error, linf_error, rmse, mae, max_difference, correlation, r_squared); unknown metrics return an error
• --sections in report_generator.py are validated against the known section names (summary, statistics, convergence, validation, files, parameters, all); unknown sections exit with code 2
• --bins (statistical_analyzer), --points (profile_extractor), and --window (time_series_analyzer) are validated as positive integers with upper bounds; out-of-range values exit with code 2

File Access

• All JSON and CSV loading functions reject files exceeding 500 MB before parsing
• Loaded JSON files must have an object (dict) as root element
• report_generator.py caps directory listing at 10,000 entries to prevent resource exhaustion
• Scripts read user-specified simulation output files (JSON, CSV) but do not traverse directories beyond what is explicitly provided
• Output goes to stdout (JSON) unless the agent uses Write to save reports

Tool Restrictions

• Read: Used to inspect script source, references, and simulation output files
• Write: Used to save analysis results, comparison reports, or generated summaries; writes are scoped to the user's working directory
• Grep/Glob: Used to locate simulation output files and search references
• The skill's allowed-tools excludes Bash to prevent the agent from executing arbitrary commands when processing untrusted simulation output files

Safety Measures

• No eval(), exec(), or dynamic code generation — region parsing uses regex matching, never code evaluation
• All subprocess calls use explicit argument lists (no shell=True)
• Reduced tool surface (no Bash) limits the agent to read/write operations only
• Field names and region expressions are sanitized before use to prevent injection

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 3.10+
• NumPy (for numerical operations)
• No other external dependencies for core functionality

Version History

See CHANGELOG.md for the authoritative record.

• v1.1.3 (2026-06-24): Added a "Verification checklist" (evidence-based, tied to the scripts' real output keys: field listing, residual-vs-physical convergence, signal reconciliation, conservation drift, grid-spacing sanity, non-finite guards, comparison error bands) and a "Common pitfalls & rationalizations" table before the Security section. Documentation only; no script behavior change.
• v1.1.2 (2026-06-23): Made the eval suite self-contained and discriminating — copied the real fixtures into evals/files/ (only phi/concentration fields on a 10x10 grid; residual series ending at 5e-6), rewrote every eval prompt to reference those exact files, and added deterministic script_checks pinning the verified script outputs (including the correct verdict that the 1e-6 absolute residual threshold is NOT reached). Added guidance to read field names from the output file rather than assuming them.
• v1.1.1 (2026-06-23): Implemented real coordinate-based --region filtering in statistical_analyzer.py; fixed report_generator.py to read nested fields.*.values output; gave explicit dx/dy/dz precedence in derived_quantities.py grid spacing; added an --absolute-threshold convergence mode and residual-vs-physical interpretation guidance; corrected the Output Format example and version metadata; added --bins/--window bounds validation.
• v1.1.0 (2026-03-26): Optimized description, evaluation suite, security review, standardized metadata, CHANGELOG.
• v1.0.0 (2026-02-25): Initial release.