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name gnn-statistical-analysis
description GNN advanced statistical analysis and result aggregation. Use when performing statistical analysis on simulation results, cross-simulation aggregation, computing information-theoretic metrics, or creating analytical visualizations of pipeline outputs.

GNN Statistical Analysis (Step 16)

Purpose

Performs advanced statistical analysis on pipeline outputs including simulation results, cross-framework comparison, Active Inference metrics, and comprehensive statistical reporting.

Key Commands

# Run analysis
python src/16_analysis.py --target-dir input/gnn_files --output-dir output --verbose

# As part of pipeline
python src/main.py --only-steps 16 --verbose

API

from analysis import (
    process_analysis, perform_statistical_analysis,
    calculate_variable_statistics, calculate_connection_statistics,
    calculate_complexity_metrics, calculate_maintainability_index,
    analyze_framework_outputs, generate_framework_comparison_report,
    analyze_simulation_traces, analyze_free_energy,
    compute_shannon_entropy, compute_kl_divergence,
    compute_variational_free_energy, compute_expected_free_energy,
    generate_analysis_summary
)

# Process analysis step (used by pipeline)
process_analysis(target_dir, output_dir, verbose=True)

# Statistical analysis
stats = perform_statistical_analysis(parsed_data)

# Complexity metrics
metrics = calculate_complexity_metrics(model_data)

# Framework comparison
report = generate_framework_comparison_report(results)

# Active Inference metrics
entropy = compute_shannon_entropy(distribution)
kl_div = compute_kl_divergence(p, q)
vfe = compute_variational_free_energy(beliefs, observations)
efe = compute_expected_free_energy(policy, beliefs)

Key Exports

  • process_analysis — main pipeline processing function
  • perform_statistical_analysis — core statistical analysis
  • calculate_complexity_metrics, calculate_maintainability_index, calculate_technical_debt
  • analyze_framework_outputs, generate_framework_comparison_report
  • compute_shannon_entropy, compute_kl_divergence, compute_variational_free_energy, compute_expected_free_energy
  • analyze_simulation_traces, analyze_free_energy, analyze_policy_convergence
  • generate_analysis_summary — summary report generation

Output

  • Analysis reports in output/16_analysis_output/
  • Statistical summaries and aggregations
  • Comparative visualizations

MCP Tools

This module registers tools with the GNN MCP server (see mcp.py):

  • process_analysis
  • get_analysis_results
  • compute_complexity_metrics

References

Documentation

  • README: Module Overview
  • AGENTS: Agentic Workflows
  • SPEC: Architectural Specification
  • SKILL: Capability API