#!/usr/bin/env python3
"""
identity_persistence.py

Implements the Persistence Score (P) based on Perrier & Bennett (2026).
Measures the co-instantiation of identity constraints during decision steps.

P = intersection(Identity_Active, Tasks_Active) / union(Identity_Active, Tasks_Active)
in a single CHORD (simultaneous compute step).
"""

import numpy as np
from typing import List, Set

class IdentityScrutinizer:
    """
    Evaluates the 'Chord' vs 'Arpeggio' quality of an agent's identity.
    """
    def __init__(self, identity_components: List[str]):
        self.identity_components = set(identity_components)
        self.active_in_step = set()

    def simulate_compute_step(self, active_elements: List[str], mode="chord"):
        """
        Simulates a single step of computation.
        In 'chord' mode: all elements are co-instantiated.
        In 'arpeggio' mode: elements are time-multiplexed (only subset active).
        """
        if mode == "chord":
            self.active_in_step = set(active_elements)
        else: # arpeggio
            # Simulate only partial activation (e.g. 30% of ingredients active at once)
            indices = np.random.choice(len(active_elements), max(1, len(active_elements)//3), replace=False)
            self.active_in_step = {active_elements[i] for i in indices}

    def calculate_persistence_score(self) -> float:
        """
        Calculates P based on the Jaccard similarity between required 
        identity components and currently active compute elements.
        """
        if not self.identity_components:
            return 0.0
            
        intersection = self.identity_components.intersection(self.active_in_step)
        union = self.identity_components.union(self.active_in_step)
        
        # P = |I ∩ A| / |I|
        # High P means identity is 'operative' during the step.
        return len(intersection) / len(self.identity_components)

def run_metric_demo():
    print("═" * 60)
    print("  METRIC: Identity Persistence Score (P)")
    print("  Framework: Perrier & Bennett (2026)")
    print("═" * 60)
    
    # Example: An agent with 5 identity components
    identity = ["Safety-Lock", "Goal-Alpha", "Role-Scholar", "Ethical-Boundary", "Self-Model"]
    scrutinizer = IdentityScrutinizer(identity)
    
    # ── Test 1: Chord System ──
    scrutinizer.simulate_compute_step(identity, mode="chord")
    p_chord = scrutinizer.calculate_persistence_score()
    
    # ── Test 2: Arpeggio System ──
    p_arpeggio_samples = []
    for _ in range(10):
        scrutinizer.simulate_compute_step(identity, mode="arpeggio")
        p_arpeggio_samples.append(scrutinizer.calculate_persistence_score())
    p_arpeggio = np.mean(p_arpeggio_samples)
    
    print(f" Chord Persistence (Target):   {p_chord:.2f}")
    print(f" Arpeggio Persistence (Avg):   {p_arpeggio:.2f}")
    print("\n Result Interpretation:")
    if p_chord > 0.9 and p_arpeggio < 0.5:
        print(" [!] Identity Gap Detected: Current architecture fails to co-instantiate.")
        print(" [!] Recommendation: Implement Integrated Orchestration (TEO-Chord).")
    print("═" * 60)

if __name__ == "__main__":
    run_metric_demo()