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Implement enhanced VAL metrics with canonical structural indicators
Co-authored-by: fermga <203334638+fermga@users.noreply.github.com>
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‎examples/val_enhanced_metrics_demo.py‎

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"""Demo of enhanced VAL (Expansion) metrics (Issue #2724).
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This script demonstrates the comprehensive telemetry metrics for the VAL
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operator including bifurcation risk, coherence preservation, fractality
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indicators, network impact, and structural stability.
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"""
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from tnfr.alias import set_attr
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from tnfr.constants.aliases import ALIAS_DNFR, ALIAS_D2EPI, ALIAS_THETA
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from tnfr.operators.definitions import Expansion
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from tnfr.structural import create_nfr
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def print_metrics_section(title: str, metrics: dict, keys: list[str]) -> None:
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"""Print a section of metrics."""
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print(f"\n{title}")
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print("=" * len(title))
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for key in keys:
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if key in metrics:
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value = metrics[key]
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if isinstance(value, float):
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print(f" {key}: {value:.4f}")
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elif isinstance(value, bool):
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print(f" {key}: {'✓' if value else '✗'}")
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else:
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print(f" {key}: {value}")
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def demo_healthy_expansion():
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"""Demonstrate healthy expansion with all indicators positive."""
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print("\n" + "=" * 60)
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print("SCENARIO 1: Healthy Expansion")
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print("=" * 60)
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G, node = create_nfr("healthy_node", epi=0.4, vf=1.0)
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# Add network context
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for i in range(3):
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neighbor = f"neighbor_{i}"
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G.add_node(neighbor)
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set_attr(G.nodes[neighbor], ALIAS_THETA, 0.3 + i * 0.1)
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G.add_edge(node, neighbor)
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# Set healthy conditions
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G.graph["COLLECT_OPERATOR_METRICS"] = True
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set_attr(G.nodes[node], ALIAS_DNFR, 0.2) # Positive, stable
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set_attr(G.nodes[node], ALIAS_D2EPI, 0.1) # Below threshold
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set_attr(G.nodes[node], ALIAS_THETA, 0.35) # Aligned with neighbors
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# Apply expansion
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Expansion()(G, node)
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# Get metrics
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metrics = G.graph["operator_metrics"][-1]
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# Display metrics
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print_metrics_section(
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"Core Metrics",
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metrics,
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["vf_increase", "delta_epi", "expansion_factor"],
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)
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print_metrics_section(
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"Structural Stability",
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metrics,
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["dnfr_final", "dnfr_positive", "dnfr_stable"],
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)
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print_metrics_section(
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"Bifurcation Risk",
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metrics,
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["d2epi", "bifurcation_risk", "bifurcation_magnitude"],
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)
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print_metrics_section(
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"Coherence",
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metrics,
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["coherence_local", "coherence_preserved"],
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)
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print_metrics_section(
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"Fractality",
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metrics,
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["epi_growth_rate", "vf_growth_rate", "growth_ratio", "fractal_preserved"],
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)
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print_metrics_section(
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"Network Impact",
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metrics,
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["neighbor_count", "phase_coherence_neighbors", "network_coupled"],
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)
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print_metrics_section(
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"Overall Health",
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metrics,
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["expansion_healthy"],
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)
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def demo_bifurcation_risk():
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"""Demonstrate expansion at bifurcation threshold."""
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print("\n" + "=" * 60)
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print("SCENARIO 2: Expansion at Bifurcation Threshold")
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print("=" * 60)
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G, node = create_nfr("bifurcating_node", epi=0.4, vf=1.0)
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# Set conditions for bifurcation risk
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G.graph["COLLECT_OPERATOR_METRICS"] = True
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G.graph["VAL_BIFURCATION_THRESHOLD"] = 0.3
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set_attr(G.nodes[node], ALIAS_DNFR, 0.2)
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set_attr(G.nodes[node], ALIAS_D2EPI, 0.5) # HIGH - above threshold
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# Apply expansion
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Expansion()(G, node)
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# Get metrics
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metrics = G.graph["operator_metrics"][-1]
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print_metrics_section(
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"Bifurcation Indicators",
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metrics,
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[
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"d2epi",
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"bifurcation_threshold",
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"bifurcation_risk",
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"bifurcation_magnitude",
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],
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)
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print_metrics_section(
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"Overall Health",
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metrics,
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["expansion_healthy"],
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)
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if metrics["bifurcation_risk"]:
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print("\n⚠️ WARNING: Bifurcation risk detected!")
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print(
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f" d²EPI/dt² = {metrics['d2epi']:.3f} > threshold = {metrics['bifurcation_threshold']:.3f}"
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)
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print(" Consider applying IL (Coherence) or THOL (Self-organization)")
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def demo_coherence_degradation():
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"""Demonstrate expansion with coherence concerns."""
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print("\n" + "=" * 60)
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print("SCENARIO 3: Expansion with Low Coherence")
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print("=" * 60)
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G, node = create_nfr("low_coherence_node", epi=0.3, vf=1.0)
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# Set conditions
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G.graph["COLLECT_OPERATOR_METRICS"] = True
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G.graph["VAL_MIN_COHERENCE"] = 0.5
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set_attr(G.nodes[node], ALIAS_DNFR, 0.2)
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set_attr(G.nodes[node], ALIAS_D2EPI, 0.1)
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# Apply expansion
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Expansion()(G, node)
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# Get metrics
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metrics = G.graph["operator_metrics"][-1]
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print_metrics_section(
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"Coherence Metrics",
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metrics,
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["coherence_local", "coherence_preserved"],
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)
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print_metrics_section(
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"Overall Health",
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metrics,
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["expansion_healthy"],
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)
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if not metrics.get("coherence_preserved", True):
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print("\n⚠️ WARNING: Coherence below threshold!")
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print(f" C_local = {metrics['coherence_local']:.3f}")
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print(" Consider applying IL (Coherence) to stabilize")
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def demo_network_impact():
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"""Demonstrate network coupling analysis."""
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print("\n" + "=" * 60)
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print("SCENARIO 4: Network Coupling Analysis")
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print("=" * 60)
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G, node = create_nfr("network_node", epi=0.4, vf=1.0)
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# Add neighbors with varying phase alignment
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neighbors_info = [
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("aligned_1", 0.50),
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("aligned_2", 0.52),
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("misaligned", 2.0), # Far from node's phase
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]
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for neighbor, phase in neighbors_info:
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G.add_node(neighbor)
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set_attr(G.nodes[neighbor], ALIAS_THETA, phase)
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G.add_edge(node, neighbor)
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# Set conditions
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G.graph["COLLECT_OPERATOR_METRICS"] = True
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set_attr(G.nodes[node], ALIAS_DNFR, 0.2)
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set_attr(G.nodes[node], ALIAS_D2EPI, 0.1)
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set_attr(G.nodes[node], ALIAS_THETA, 0.51) # Mostly aligned
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# Apply expansion
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Expansion()(G, node)
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# Get metrics
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metrics = G.graph["operator_metrics"][-1]
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print_metrics_section(
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"Network Metrics",
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metrics,
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[
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"neighbor_count",
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"phase_coherence_neighbors",
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"network_coupled",
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"theta_final",
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],
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)
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print("\nNetwork Analysis:")
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if metrics["network_coupled"]:
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print(" ✓ Node is well-coupled to network")
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else:
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print(" ✗ Node has weak network coupling")
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print(
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f" Phase coherence: {metrics['phase_coherence_neighbors']:.2%} "
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f"({metrics['neighbor_count']} neighbors)"
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)
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def main():
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"""Run all demonstration scenarios."""
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print("\n" + "=" * 60)
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print("Enhanced VAL (Expansion) Metrics Demo")
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print("Issue #2724: Canonical Structural Indicators")
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print("=" * 60)
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demo_healthy_expansion()
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demo_bifurcation_risk()
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demo_coherence_degradation()
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demo_network_impact()
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print("\n" + "=" * 60)
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print("Demo Complete!")
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print("=" * 60)
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print(
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"\nThese metrics enable:\n"
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" 1. Early detection of bifurcation events\n"
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" 2. Validation of coherence preservation\n"
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" 3. Analysis of fractal self-similarity\n"
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" 4. Monitoring of network coupling\n"
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" 5. Overall structural health assessment"
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)
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if __name__ == "__main__":
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main()

‎src/tnfr/config/defaults_core.py‎

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VAL_CHECK_NETWORK_CAPACITY: bool = False # Optional network capacity validation
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VAL_MAX_NETWORK_SIZE: int = 1000 # Maximum network size if capacity checking enabled
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# VAL (Expansion) metric thresholds (Issue #2724)
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VAL_BIFURCATION_THRESHOLD: float = 0.3 # Threshold for |∂²EPI/∂t²| bifurcation detection
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VAL_MIN_COHERENCE: float = 0.5 # Minimum local coherence for healthy expansion
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VAL_FRACTAL_RATIO_MIN: float = 0.5 # Minimum vf_growth/epi_growth ratio for fractality
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VAL_FRACTAL_RATIO_MAX: float = 2.0 # Maximum vf_growth/epi_growth ratio for fractality
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@dataclass(frozen=True, slots=True)
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class RemeshDefaults:

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