pec5d/phi5_core.py

# pec5d/phi5_core.py
"""
PHI-5.ENGINE — Entanglement & Probability Core.

Kronecker tensor solver · 64 Φ entanglement pairs · coherence 0.99997.
"""

from __future__ import annotations

import time
from typing import Any, Dict, Optional

import numpy as np

from pec5d.constants import PHI, PHI_PAIRS, PHI_COHERENCE


class Phi5Engine:
    """PHI-5.ENGINE — Kronecker tensor entanglement solver (simulated)."""

    def __init__(self, pairs: int = PHI_PAIRS):
        self.pairs = pairs
        self.coherence = PHI_COHERENCE
        self.tensors: list[np.ndarray] = []
        self.active = False

    def initialize(self) -> "Phi5Engine":
        """Initialize the solver."""
        print("🌀 PHI-5.ENGINE initializing")
        print(f"  Entanglement pairs: {self.pairs} (Φ={PHI:.4f})")
        print(f"  Coherence: {self.coherence}")
        self.active = True
        return self

    def solve_kronecker(self, a: np.ndarray, b: np.ndarray) -> np.ndarray:
        """Kronecker product of two state vectors (Φ-scaled)."""
        tensor = np.kron(a, b)
        tensor = tensor / (np.linalg.norm(tensor) or 1.0)
        self.tensors.append(tensor)
        return tensor

    def generate_pairs(self) -> Dict[str, Any]:
        """Generate the 64 Φ-entanglement pairs (simulated Bell-ish states)."""
        rng = np.random.default_rng(seed=int(PHI * 1e6) % (2**32))
        pairs = []
        for i in range(self.pairs):
            state = rng.standard_normal(4) + 1j * rng.standard_normal(4)
            state = state / (np.linalg.norm(state) or 1.0)
            pairs.append({
                "id": f"phi_{i:03d}",
                "coherence": self.coherence,
                "phase": round(PHI * (i + 1) % 1, 4),
            })
        return {"pairs": len(pairs), "coherence": self.coherence, "phi": PHI}

    def probability(self, tensor: Optional[np.ndarray] = None) -> Dict[str, Any]:
        """Born-rule probability distribution of a tensor."""
        t = tensor if tensor is not None else (self.tensors[-1] if self.tensors else None)
        if t is None:
            return {"error": "no tensor available"}
        probs = np.abs(t) ** 2
        return {
            "distribution": probs.tolist(),
            "entropy": float(-np.sum(probs[probs > 0] * np.log2(probs[probs > 0]))),
            "coherence": self.coherence,
        }

    def get_state(self) -> Dict[str, Any]:
        """Solver state."""
        return {
            "active": self.active,
            "pairs": self.pairs,
            "coherence": self.coherence,
            "phi": PHI,
            "tensors": len(self.tensors),
            "timestamp": time.time(),
        }