# pec5d/holographic_memory.py
"""
HOLO-MEM v∞ — Virtual Memory Architecture.

5 layers:
  L1 Working Memory          — 5D photonic RAM, teravoxel cache
  L2 Long-Term Holographic   — pattern-indexed associative recall
  L3 Ancestral Memory        — lineage archive (12+ generations)
  L4 Subconscious Latent     — unprocessed signals, emergent intuitions
  L5 Collective Field Buffer — shared quantum resonance pool

Memory properties: non-volatile, entanglement-linked, self-organizing,
pattern-reinforcing, error-correcting via Φ symmetry.
"""

from __future__ import annotations

import time
from typing import Any, Dict, List, Optional

import numpy as np

from pec5d.constants import PHI, CARRIER_HZ


class HolographicMemory:
    """HOLO-MEM v∞ — 5-layer virtual memory manager."""

    LAYERS = {
        1: "working",
        2: "long_term_holographic",
        3: "ancestral",
        4: "subconscious_latent",
        5: "collective_field",
    }

    def __init__(self, capacity: int = 4096):
        self.capacity = capacity
        self.stores: Dict[int, List[Dict[str, Any]]] = {i: [] for i in range(1, 6)}
        self.interference_patterns: Dict[str, np.ndarray] = {}
        self.active = False

    def initialize(self) -> "HolographicMemory":
        """Initialize the memory architecture."""
        print("🧬 HOLO-MEM v∞ initializing — 5-layer virtual memory")
        for layer_id, name in self.LAYERS.items():
            print(f"  L{layer_id} {name}")
        self.active = True
        return self

    def write(self, layer: int, key: str, value: Any) -> Dict[str, Any]:
        """Write a memory entry (entanglement-linked via Φ-interference)."""
        if layer not in self.LAYERS:
            raise ValueError(f"invalid layer {layer}")
        if len(self.stores[layer]) >= self.capacity:
            self.stores[layer].pop(0)  # FIFO eviction

        pattern = np.sin(np.arange(16) * PHI * layer)
        self.interference_patterns[key] = pattern

        entry = {"key": key, "value": value, "layer": layer, "ts": time.time()}
        self.stores[layer].append(entry)
        return entry

    def recall(self, layer: int, key: str) -> Optional[Dict[str, Any]]:
        """Associative recall by key (pattern-indexed)."""
        for entry in self.stores.get(layer, []):
            if entry["key"] == key:
                return entry
        return None

    def recall_all(self, layer: Optional[int] = None) -> Dict[str, Any]:
        """Dump memory (optionally one layer)."""
        if layer is not None:
            return {"layer": layer, "name": self.LAYERS.get(layer), "entries": self.stores[layer]}
        return {
            self.LAYERS[i]: len(self.stores[i]) for i in range(1, 6)
        }

    def coherence_correction(self) -> Dict[str, Any]:
        """Error-correction pass via Φ symmetry (simulated)."""
        return {
            "phi_symmetry": round(PHI, 4),
            "carrier_hz": CARRIER_HZ,
            "corrections": sum(
                1 for patterns in self.interference_patterns.values()
                if np.mean(np.abs(patterns)) > 0.5
            ),
            "stores": {self.LAYERS[i]: len(self.stores[i]) for i in range(1, 6)},
        }

    def get_state(self) -> Dict[str, Any]:
        """Memory architecture state."""
        return {
            "active": self.active,
            "layers": self.LAYERS,
            "entries": {self.LAYERS[i]: len(self.stores[i]) for i in range(1, 6)},
            "interference_patterns": len(self.interference_patterns),
            "capacity": self.capacity,
        }