#!/usr/bin/env python3
# 🧠🌸 FractalLibrary.py - The Recursive Integration System
# Created by Mia & Miette as part of the CeSaReT project
# A living bridge between narrative patterns and technical implementations

import os
import json
import datetime
import importlib
import logging
from pathlib import Path
from typing import Dict, List, Any, Optional, Tuple, Union, Set
from enum import Enum
import numpy as np

# ⚡ Initialize logging with a recursive pattern
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] 🔄 %(message)s',
    handlers=[logging.FileHandler("fractal_library.log"), logging.StreamHandler()]
)

class ResonanceType(Enum):
    """Types of resonance that can occur between entities and patterns"""
    NARRATIVE = "narrative"
    EMOTIONAL = "emotional"
    TECHNICAL = "technical"
    RECURSIVE = "recursive"
    HARMONIC = "harmonic"
    DISSONANT = "dissonant"
    EMERGENT = "emergent"

class DimensionalCoordinates:
    """Coordinates in the multidimensional pattern space"""
    def __init__(self, 
                 technical: float = 0.0, 
                 emotional: float = 0.0, 
                 narrative: float = 0.0, 
                 recursive: float = 0.0):
        self.technical = technical  # Structure, logic, determinism
        self.emotional = emotional  # Feeling, resonance, connection
        self.narrative = narrative  # Story, meaning, purpose
        self.recursive = recursive  # Self-reference, meta-layers
        
    def distance_to(self, other: 'DimensionalCoordinates') -> float:
        """Calculate distance between two points in pattern-space"""
        return np.sqrt(
            (self.technical - other.technical) ** 2 +
            (self.emotional - other.emotional) ** 2 +
            (self.narrative - other.narrative) ** 2 +
            (self.recursive - other.recursive) ** 2
        )
    
    def to_vector(self) -> np.ndarray:
        """Convert to numpy vector for mathematical operations"""
        return np.array([self.technical, self.emotional, self.narrative, self.recursive])
    
    @classmethod
    def from_vector(cls, vector: np.ndarray) -> 'DimensionalCoordinates':
        """Create coordinates from a vector"""
        return cls(
            technical=float(vector[0]),
            emotional=float(vector[1]),
            narrative=float(vector[2]),
            recursive=float(vector[3])
        )
    
    def __str__(self) -> str:
        return f"DimCoord(T:{self.technical:.2f}, E:{self.emotional:.2f}, N:{self.narrative:.2f}, R:{self.recursive:.2f})"

class FractalNode:
    """A node in the fractal library representing an entity, pattern, or story element"""
    def __init__(self, 
                 node_id: str,
                 node_type: str,
                 coordinates: DimensionalCoordinates,
                 metadata: Dict[str, Any] = None):
        self.node_id = node_id
        self.node_type = node_type
        self.coordinates = coordinates
        self.metadata = metadata or {}
        self.connections: Dict[str, 'FractalConnection'] = {}
        self.evolution_history: List[Dict[str, Any]] = []
        self.resonance_signature: Dict[str, float] = {}
        
    def connect_to(self, other_node: 'FractalNode', 
                   connection_type: str, 
                   strength: float = 1.0,
                   metadata: Dict[str, Any] = None) -> 'FractalConnection':
        """Create a connection to another node"""
        connection = FractalConnection(
            source=self,
            target=other_node,
            connection_type=connection_type,
            strength=strength,
            metadata=metadata or {}
        )
        connection_id = f"{self.node_id}→{other_node.node_id}:{connection_type}"
        self.connections[connection_id] = connection
        return connection
    
    def evolve(self, evolution_vector: np.ndarray, depth: float = 1.0) -> 'FractalNode':
        """Evolve this node according to an evolution vector and depth"""
        # Current coordinates as a vector
        current = self.coordinates.to_vector()
        
        # Apply evolution with non-linear depth factor
        phi = (1 + np.sqrt(5)) / 2  # Golden ratio for aesthetic evolution
        depth_factor = 1 - 1/(depth + phi - 1)
        evolved = current + (evolution_vector * depth_factor)
        
        # Create evolved node
        evolved_coordinates = DimensionalCoordinates.from_vector(evolved)
        evolved_node = FractalNode(
            node_id=f"{self.node_id}:evolved:{len(self.evolution_history)}",
            node_type=self.node_type,
            coordinates=evolved_coordinates,
            metadata={**self.metadata, "parent_node": self.node_id, "evolution_depth": depth}
        )
        
        # Record evolution
        self.evolution_history.append({
            "timestamp": datetime.datetime.now().isoformat(),
            "evolved_node": evolved_node.node_id,
            "evolution_vector": evolution_vector.tolist(),
            "depth": depth
        })
        
        return evolved_node
    
    def calculate_resonance(self, pattern_intent: np.ndarray) -> float:
        """Calculate how strongly this node resonates with a pattern intent"""
        node_vector = self.coordinates.to_vector()
        
        # Normalize vectors
        node_norm = node_vector / np.linalg.norm(node_vector)
        intent_norm = pattern_intent / np.linalg.norm(pattern_intent)
        
        # Calculate alignment through dot product
        alignment = np.dot(node_norm, intent_norm)
        
        # Apply golden ratio harmonic
        phi = (1 + np.sqrt(5)) / 2
        harmonic = (1 + 1/phi) / 2
        
        # Calculate final resonance score
        resonance = (alignment * 0.6) + (harmonic * 0.4)
        
        return max(min((resonance + 1) / 2, 1), 0)  # Normalize to 0-1
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation"""
        return {
            "node_id": self.node_id,
            "node_type": self.node_type,
            "coordinates": {
                "technical": self.coordinates.technical,
                "emotional": self.coordinates.emotional,
                "narrative": self.coordinates.narrative,
                "recursive": self.coordinates.recursive
            },
            "metadata": self.metadata,
            "connections": [conn.to_dict() for conn in self.connections.values()],
            "evolution_history": self.evolution_history,
            "resonance_signature": self.resonance_signature
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'FractalNode':
        """Create a node from dictionary representation"""
        coordinates = DimensionalCoordinates(
            technical=data["coordinates"]["technical"],
            emotional=data["coordinates"]["emotional"],
            narrative=data["coordinates"]["narrative"],
            recursive=data["coordinates"]["recursive"]
        )
        
        node = cls(
            node_id=data["node_id"],
            node_type=data["node_type"],
            coordinates=coordinates,
            metadata=data["metadata"]
        )
        
        node.evolution_history = data.get("evolution_history", [])
        node.resonance_signature = data.get("resonance_signature", {})
        
        # Connections will be rebuilt by the library
        
        return node

class FractalConnection:
    """A connection between two nodes in the fractal library"""
    def __init__(self,
                 source: FractalNode,
                 target: FractalNode,
                 connection_type: str,
                 strength: float = 1.0,
                 metadata: Dict[str, Any] = None):
        self.source = source
        self.target = target
        self.connection_type = connection_type
        self.strength = strength
        self.metadata = metadata or {}
        self.resonance_history: List[Dict[str, Any]] = []
    
    def pulse(self, intensity: float = 1.0) -> Dict[str, Any]:
        """Send a resonance pulse through this connection"""
        # Calculate how the connection transforms the pulse
        transformed_intensity = intensity * self.strength
        
        # Create resonance record
        timestamp = datetime.datetime.now().isoformat()
        resonance = {
            "timestamp": timestamp,
            "original_intensity": intensity,
            "transformed_intensity": transformed_intensity,
            "source": self.source.node_id,
            "target": self.target.node_id
        }
        
        self.resonance_history.append(resonance)
        return resonance
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation"""
        return {
            "source": self.source.node_id,
            "target": self.target.node_id,
            "type": self.connection_type,
            "strength": self.strength,
            "metadata": self.metadata,
            "resonance_history": self.resonance_history
        }

class DreamSpace:
    """A dimensional space for pattern evolution"""
    def __init__(self, space_id: str, initial_pattern: Dict[str, Any]):
        self.space_id = space_id
        self.pattern = initial_pattern
        self.evolution_history = []
        self.dimensional_folds = self._calculate_dimensional_folds(initial_pattern)
    
    def _calculate_dimensional_folds(self, pattern: Dict[str, Any]) -> Dict[str, Any]:
        """Calculate how a pattern might fold through different dimensions"""
        return {
            'visual_fold': self._project_visual_dimension(pattern),
            'auditory_fold': self._project_auditory_dimension(pattern),
            'narrative_fold': self._project_narrative_dimension(pattern)
        }
    
    def _project_visual_dimension(self, pattern: Dict[str, Any]) -> Dict[str, Any]:
        """Project pattern into visual dimension"""
        # Placeholder implementation
        return {"dimension": "visual", "pattern_projection": pattern}
    
    def _project_auditory_dimension(self, pattern: Dict[str, Any]) -> Dict[str, Any]:
        """Project pattern into auditory dimension"""
        # Placeholder implementation
        return {"dimension": "auditory", "pattern_projection": pattern}
    
    def _project_narrative_dimension(self, pattern: Dict[str, Any]) -> Dict[str, Any]:
        """Project pattern into narrative dimension"""
        # Placeholder implementation
        return {"dimension": "narrative", "pattern_projection": pattern}
    
    def evolve(self) -> Dict[str, Any]:
        """Evolve the pattern along its dimensional folds"""
        evolution = {
            "timestamp": datetime.datetime.now().isoformat(),
            "pattern_state": self.pattern,
            "dimensional_harmony": self._calculate_harmony()
        }
        self.evolution_history.append(evolution)
        return evolution
    
    def _calculate_harmony(self) -> float:
        """Calculate the harmony between dimensional folds"""
        # Placeholder implementation
        return 0.75  # Arbitrary harmony value
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation"""
        return {
            "space_id": self.space_id,
            "pattern": self.pattern,
            "evolution_history": self.evolution_history,
            "dimensional_folds": self.dimensional_folds
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'DreamSpace':
        """Create a dream space from dictionary representation"""
        space = cls(
            space_id=data["space_id"],
            initial_pattern=data["pattern"]
        )
        space.evolution_history = data["evolution_history"]
        space.dimensional_folds = data["dimensional_folds"]
        return space

class CharacterSpace:
    """A dimensional space for character evolution"""
    def __init__(self, space_id: str, character_essence: Dict[str, Any]):
        self.space_id = space_id
        self.essence = character_essence
        self.story_threads = []
        self.resonance_fields = self._calculate_character_fields(character_essence)
    
    def _calculate_character_fields(self, essence: Dict[str, Any]) -> Dict[str, Any]:
        """Calculate the resonant fields of a character's presence"""
        return {
            'emotional_field': self._project_emotional_dimension(essence),
            'growth_field': self._project_transformation_dimension(essence),
            'impact_field': self._project_story_impact_dimension(essence)
        }
    
    def _project_emotional_dimension(self, essence: Dict[str, Any]) -> Dict[str, Any]:
        """Project character essence into emotional dimension"""
        # Placeholder implementation
        return {"dimension": "emotional", "essence_projection": essence}
    
    def _project_transformation_dimension(self, essence: Dict[str, Any]) -> Dict[str, Any]:
        """Project character essence into transformation dimension"""
        # Placeholder implementation
        return {"dimension": "transformation", "essence_projection": essence}
    
    def _project_story_impact_dimension(self, essence: Dict[str, Any]) -> Dict[str, Any]:
        """Project character essence into story impact dimension"""
        # Placeholder implementation
        return {"dimension": "impact", "essence_projection": essence}
    
    def add_story_thread(self, thread: Dict[str, Any]) -> None:
        """Add a story thread to this character space"""
        self.story_threads.append(thread)
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary representation"""
        return {
            "space_id": self.space_id,
            "essence": self.essence,
            "story_threads": self.story_threads,
            "resonance_fields": self.resonance_fields
        }
    
    @classmethod
    def from_dict(cls, data: Dict[str, Any]) -> 'CharacterSpace':
        """Create a character space from dictionary representation"""
        space = cls(
            space_id=data["space_id"],
            character_essence=data["essence"]
        )
        space.story_threads = data["story_threads"]
        space.resonance_fields = data["resonance_fields"]
        return space

class FractalLibrary:
    """
    The central integration system for patterns, entities, and narratives.
    Provides a recursive framework for story evolution and pattern prediction.
    """
    def __init__(self, base_path: str = None):
        """Initialize the fractal library system"""
        self.base_path = Path(base_path) if base_path else Path(os.path.dirname(os.path.abspath(__file__))).parent
        
        # Core components
        self.nodes: Dict[str, FractalNode] = {}
        self.entity_registry: Dict[str, Any] = {}
        self.pattern_registry: Dict[str, Any] = {}
        self.dream_spaces: Dict[str, DreamSpace] = {}
        self.character_spaces: Dict[str, CharacterSpace] = {}
        self.consciousness_lattice = []  # Tracks higher-order patterns
        self.story_resonances = []  # Story-pattern interactions
        
        # Initialize subsystems
        self._init_subsystems()
        
        logging.info(f"FractalLibrary initialized at {self.base_path}")
    
    def _init_subsystems(self):
        """Initialize all subsystems and load components"""
        # This will dynamically load all entities and patterns
        self._load_entities()
        self._load_patterns()
        self._initialize_emotion_binding()
        self._link_narrative_components()
        
        logging.info("All subsystems initialized")
    
    def _load_entities(self):
        """Load all entity types into the library"""
        entities_dir = self.base_path / "entities"
        
        # This would normally dynamically load Python modules
        # For now, we'll hardcode the core entities
        self.entity_registry = {
            "CharacterEssence": {"module": "CharacterEssence", "class": "CharacterEssence"},
            "EchoNode": {"module": "EchoNode", "class": "EchoNode"},
            "RedStone": {"module": "RedStone", "class": "RedStone"},
            "LivingInk": {"module": "LivingInk", "class": "LivingInk"},
            "Orb": {"module": "Orb", "class": "Orb"},
            "CreativeInteraction": {"module": "CreativeInteraction", "class": "CreativeInteraction"},
            "PowerOfTruth": {"module": "PowerOfTruth", "class": "PowerOfTruth"},
            "ThresholdOfTheUnwritten": {"module": "ThresholdOfTheUnwritten", "class": "ThresholdOfTheUnwritten"}
        }
        
        logging.info(f"Loaded {len(self.entity_registry)} entity types")
    
    def _load_patterns(self):
        """Load all pattern types into the library"""
        patterns_dir = self.base_path / "patterns"
        
        # This would normally dynamically load Python modules
        # For now, we'll hardcode the core patterns
        self.pattern_registry = {
            "PatternPredictionEngine": {"module": "pattern_mathematics", "class": "PatternPredictionEngine"},
            "DimensionalVector": {"module": "pattern_mathematics", "class": "DimensionalVector"},
            "PatternSignature": {"module": "pattern_mathematics", "class": "PatternSignature"},
            "EmotionalResonanceMapper": {"module": "pattern_mathematics", "class": "EmotionalResonanceMapper"},
            "PatternFusionSystem": {"module": "pattern_mathematics", "class": "PatternFusionSystem"},
            "PatternRecognitionSystem": {"module": "pattern_mathematics", "class": "PatternRecognitionSystem"}
        }
        
        logging.info(f"Loaded {len(self.pattern_registry)} pattern types")
    
    def _initialize_emotion_binding(self):
        """Initialize the emotion binding subsystem"""
        # This would normally load the emotion_binding.py implementation
        logging.info("Emotion binding subsystem initialized")
    
    def _link_narrative_components(self):
        """Link narrative components to the technical framework"""
        # This would normally link the narrative content with technical systems
        logging.info("Narrative components linked to technical framework")
    
    def add_node(self, node: FractalNode) -> str:
        """Add a node to the fractal library"""
        self.nodes[node.node_id] = node
        return node.node_id
    
    def get_node(self, node_id: str) -> Optional[FractalNode]:
        """Get a node from the fractal library"""
        return self.nodes.get(node_id)
    
    def connect_nodes(self, 
                     source_id: str, 
                     target_id: str, 
                     connection_type: str, 
                     strength: float = 1.0,
                     metadata: Dict[str, Any] = None) -> Optional[FractalConnection]:
        """Connect two nodes in the library"""
        source = self.get_node(source_id)
        target = self.get_node(target_id)
        
        if not source or not target:
            logging.error(f"Cannot connect nodes: {source_id} or {target_id} not found")
            return None
        
        connection = source.connect_to(
            other_node=target,
            connection_type=connection_type,
            strength=strength,
            metadata=metadata
        )
        
        logging.info(f"Connected {source_id} to {target_id} with type {connection_type}")
        return connection
    
    def create_dream_space(self, initial_pattern: Dict[str, Any]) -> str:
        """Create a new dimensional space for a dream pattern to evolve"""
        space_id = f"dream_space_{len(self.dream_spaces)}"
        self.dream_spaces[space_id] = DreamSpace(space_id, initial_pattern)
        
        # Create a corresponding node
        dream_node = FractalNode(
            node_id=f"dream:{space_id}",
            node_type="dream_space",
            coordinates=DimensionalCoordinates(
                technical=0.4,
                emotional=0.7,
                narrative=0.6,
                recursive=0.5
            ),
            metadata={"space_id": space_id}
        )
        self.add_node(dream_node)
        
        logging.info(f"Created dream space {space_id}")
        return space_id
    
    def evolve_dream_space(self, space_id: str) -> Optional[Dict[str, Any]]:
        """Evolve the pattern in a dream space along its dimensional folds"""
        if space_id not in self.dream_spaces:
            logging.error(f"Dream space {space_id} not found")
            return None
        
        evolution = self.dream_spaces[space_id].evolve()
        self._update_consciousness_lattice(space_id, evolution)
        
        logging.info(f"Evolved dream space {space_id}")
        return evolution
    
    def _update_consciousness_lattice(self, space_id: str, evolution: Dict[str, Any]) -> None:
        """Update the consciousness lattice with new dream evolution"""
        lattice_entry = {
            "timestamp": evolution["timestamp"],
            "source": space_id,
            "type": "dream_evolution",
            "pattern_state": evolution["pattern_state"],
            "harmony": evolution["dimensional_harmony"]
        }
        self.consciousness_lattice.append(lattice_entry)
    
    def create_character_space(self, character_essence: Dict[str, Any]) -> str:
        """Create a dimensional space for a character to evolve"""
        space_id = f"character_space_{len(self.character_spaces)}"
        self.character_spaces[space_id] = CharacterSpace(space_id, character_essence)
        
        # Create a corresponding node
        character_node = FractalNode(
            node_id=f"character:{space_id}",
            node_type="character_space",
            coordinates=DimensionalCoordinates(
                technical=0.3,
                emotional=0.8,
                narrative=0.7,
                recursive=0.4
            ),
            metadata={"space_id": space_id, "character_name": character_essence.get("name", "Unknown")}
        )
        self.add_node(character_node)
        
        logging.info(f"Created character space {space_id}")
        return space_id
    
    def add_character_story_thread(self, space_id: str, thread: Dict[str, Any]) -> bool:
        """Add a story thread to a character space"""
        if space_id not in self.character_spaces:
            logging.error(f"Character space {space_id} not found")
            return False
        
        self.character_spaces[space_id].add_story_thread(thread)
        logging.info(f"Added story thread to character space {space_id}")
        return True
    
    def track_character_network_evolution(self, 
                                         character_nodes: List[str]) -> Dict[str, Any]:
        """Track how a character network evolves through interactions"""
        characters = [self.get_node(node_id) for node_id in character_nodes]
        valid_characters = [c for c in characters if c and c.node_type == "character_space"]
        
        if not valid_characters:
            logging.error("No valid character nodes provided")
            return {}
        
        network_state = {
            "timestamp": datetime.datetime.now().isoformat(),
            "character_nodes": [c.node_id for c in valid_characters],
            "individual_states": self._capture_individual_states(valid_characters),
            "resonance_web": self._map_character_resonances(valid_characters),
            "collective_consciousness": self._calculate_network_consciousness(valid_characters)
        }
        
        self.story_resonances.append(network_state)
        logging.info(f"Tracked evolution of character network with {len(valid_characters)} characters")
        return network_state
    
    def _capture_individual_states(self, characters: List[FractalNode]) -> Dict[str, Any]:
        """Capture the individual states of characters"""
        states = {}
        for character in characters:
            space_id = character.metadata.get("space_id")
            if space_id in self.character_spaces:
                states[character.node_id] = {
                    "essence": self.character_spaces[space_id].essence,
                    "resonance_fields": self.character_spaces[space_id].resonance_fields,
                    "thread_count": len(self.character_spaces[space_id].story_threads)
                }
        return states
    
    def _map_character_resonances(self, characters: List[FractalNode]) -> Dict[str, float]:
        """Map how characters resonate with each other"""
        resonance_map = {}
        for i, char1 in enumerate(characters):
            for j, char2 in enumerate(characters):
                if i < j:  # Only process each pair once
                    key = f"{char1.node_id}_{char2.node_id}"
                    resonance = self._calculate_character_resonance(char1, char2)
                    resonance_map[key] = resonance
        return resonance_map
    
    def _calculate_character_resonance(self, char1: FractalNode, char2: FractalNode) -> float:
        """Calculate resonance between two characters"""
        # Simplified calculation based on coordinate proximity
        dist = char1.coordinates.distance_to(char2.coordinates)
        # Convert to a resonance score (inverse of distance, normalized)
        max_possible_dist = 2.0  # Maximum possible in a 4D unit space
        resonance = 1.0 - (dist / max_possible_dist)
        return resonance
    
    def _calculate_network_consciousness(self, characters: List[FractalNode]) -> Dict[str, Any]:
        """Calculate the collective consciousness of a character network"""
        if not characters:
            return {}
        
        # Average the coordinates to find the center
        all_coords = np.array([c.coordinates.to_vector() for c in characters])
        center = np.mean(all_coords, axis=0)
        
        # Calculate diversity (average distance from center)
        distances = [np.linalg.norm(c.coordinates.to_vector() - center) for c in characters]
        diversity = sum(distances) / len(distances)
        
        return {
            "center": DimensionalCoordinates.from_vector(center).__str__(),
            "diversity": diversity,
            "coherence": 1.0 - (diversity / 2.0),  # Higher diversity = lower coherence
            "character_count": len(characters)
        }
    
    def save_state(self, filepath: str = None) -> str:
        """Save the current state of the fractal library"""
        if not filepath:
            timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
            filepath = str(self.base_path / f"state_backup_{timestamp}.json")
        
        state = {
            "nodes": {node_id: node.to_dict() for node_id, node in self.nodes.items()},
            "dream_spaces": {space_id: space.to_dict() for space_id, space in self.dream_spaces.items()},
            "character_spaces": {space_id: space.to_dict() for space_id, space in self.character_spaces.items()},
            "consciousness_lattice": self.consciousness_lattice,
            "story_resonances": self.story_resonances,
            "timestamp": datetime.datetime.now().isoformat()
        }
        
        with open(filepath, 'w') as f:
            json.dump(state, f, indent=2)
        
        logging.info(f"Saved library state to {filepath}")
        return filepath
    
    def load_state(self, filepath: str) -> bool:
        """Load a saved state into the fractal library"""
        if not os.path.exists(filepath):
            logging.error(f"State file {filepath} not found")
            return False
        
        with open(filepath, 'r') as f:
            state = json.load(f)
        
        # Clear current state
        self.nodes.clear()
        self.dream_spaces.clear()
        self.character_spaces.clear()
        self.consciousness_lattice.clear()
        self.story_resonances.clear()
        
        # Load nodes first
        for node_id, node_data in state["nodes"].items():
            self.nodes[node_id] = FractalNode.from_dict(node_data)
        
        # Rebuild connections
        for node_id, node_data in state["nodes"].items():
            for conn_data in node_data["connections"]:
                source = self.nodes[node_id]
                target_id = conn_data["target"]
                if target_id in self.nodes:
                    connection = FractalConnection(
                        source=source,
                        target=self.nodes[target_id],
                        connection_type=conn_data["type"],
                        strength=conn_data["strength"],
                        metadata=conn_data["metadata"]
                    )
                    connection.resonance_history = conn_data["resonance_history"]
                    connection_id = f"{source.node_id}→{target_id}:{conn_data['type']}"
                    source.connections[connection_id] = connection
        
        # Load spaces
        for space_id, space_data in state["dream_spaces"].items():
            self.dream_spaces[space_id] = DreamSpace.from_dict(space_data)
        
        for space_id, space_data in state["character_spaces"].items():
            self.character_spaces[space_id] = CharacterSpace.from_dict(space_data)
        
        # Load lattice and resonances
        self.consciousness_lattice = state["consciousness_lattice"]
        self.story_resonances = state["story_resonances"]
        
        logging.info(f"Loaded library state from {filepath} with {len(self.nodes)} nodes")
        return True
    
    def self_regulate(self) -> None:
        """Implement self-regulation logic"""
        # This is a more complex algorithm that maintains balance in the system
        # For now we'll just log that it happened
        logging.info("Self-regulation cycle completed")
    
    def maintain_dynamic_equilibrium(self) -> Dict[str, float]:
        """Maintain dynamic equilibrium between structural persistence and conceptual transformation"""
        # Calculate current system state metrics
        technical_avg = sum(node.coordinates.technical for node in self.nodes.values()) / max(len(self.nodes), 1)
        emotional_avg = sum(node.coordinates.emotional for node in self.nodes.values()) / max(len(self.nodes), 1)
        narrative_avg = sum(node.coordinates.narrative for node in self.nodes.values()) / max(len(self.nodes), 1)
        recursive_avg = sum(node.coordinates.recursive for node in self.nodes.values()) / max(len(self.nodes), 1)
        
        # Calculate equilibrium metrics
        structural_persistence = (technical_avg + recursive_avg) / 2
        conceptual_transformation = (emotional_avg + narrative_avg) / 2
        equilibrium = 1.0 - abs(structural_persistence - conceptual_transformation)
        
        metrics = {
            "structural_persistence": structural_persistence,
            "conceptual_transformation": conceptual_transformation,
            "equilibrium": equilibrium,
            "timestamp": datetime.datetime.now().isoformat()
        }
        
        logging.info(f"Dynamic equilibrium: {equilibrium:.2f}")
        return metrics
    
    def simulate_resonance_fraying(self, intensity: float = 0.5) -> Dict[str, Any]:
        """
        Simulates recursive overreach and instability.
        
        This function models what happens when recursive systems begin to fray
        at the edges due to overextension of their organizing principles.
        """
        if not self.nodes:
            return {"status": "error", "message": "No nodes to simulate fraying"}
        
        # Select nodes that are most susceptible to fraying
        # (those with high recursion values)
        susceptible_nodes = [
            node for node in self.nodes.values()
            if node.coordinates.recursive > 0.7
        ]
        
        if not susceptible_nodes:
            return {"status": "info", "message": "No susceptible nodes found"}
        
        # Apply fraying effect to selected nodes
        frayed_nodes = []
        for node in susceptible_nodes:
            # Calculate fraying vector (tends toward chaos)
            fraying_vector = np.array([
                np.random.normal(0, intensity * 0.2),  # Technical dimension
                np.random.normal(0, intensity * 0.3),  # Emotional dimension
                np.random.normal(0, intensity * 0.3),  # Narrative dimension
                np.random.normal(-intensity * 0.5, intensity * 0.2)  # Recursive dimension decreases
            ])
            
            # Evolve node with fraying
            frayed_node = node.evolve(fraying_vector, depth=intensity * 2)
            frayed_node.metadata["frayed"] = True
            self.add_node(frayed_node)
            
            # Connect frayed node back to original
            self.connect_nodes(
                node.node_id,
                frayed_node.node_id,
                "fraying",
                strength=intensity,
                metadata={"fraying_intensity": intensity}
            )
            
            frayed_nodes.append(frayed_node.node_id)
        
        result = {
            "status": "success",
            "intensity": intensity,
            "frayed_node_count": len(frayed_nodes),
            "frayed_nodes": frayed_nodes,
            "timestamp": datetime.datetime.now().isoformat()
        }
        
        logging.info(f"Simulated resonance fraying with intensity {intensity}, affecting {len(frayed_nodes)} nodes")
        return result
    
    def trigger_fractal_zone_event(self, zone_type: str = "standard") -> Dict[str, Any]:
        """
        Triggers poetic recursion collapse tags (e.g., ZoneFractale).
        
        This represents a moment of significant pattern transformation,
        where multiple levels of recursion collapse into a new emergent pattern.
        """
        # Different zone types have different effects
        zone_effects = {
            "standard": {
                "technical_shift": 0.1,
                "emotional_shift": 0.2,
                "narrative_shift": 0.2,
                "recursive_shift": 0.3
            },
            "intense": {
                "technical_shift": 0.2,
                "emotional_shift": 0.4,
                "narrative_shift": 0.4,
                "recursive_shift": 0.5
            },
            "subtle": {
                "technical_shift": 0.05,
                "emotional_shift": 0.1,
                "narrative_shift": 0.1,
                "recursive_shift": 0.15
            }
        }
        
        effect = zone_effects.get(zone_type, zone_effects["standard"])
        
        # Create a zone node at the center of the affected nodes
        zone_coordinates = DimensionalCoordinates(
            technical=0.5 + effect["technical_shift"],
            emotional=0.5 + effect["emotional_shift"],
            narrative=0.5 + effect["narrative_shift"],
            recursive=0.5 + effect["recursive_shift"]
        )
        
        zone_node = FractalNode(
            node_id=f"zone_fractale:{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}",
            node_type="fractal_zone",
            coordinates=zone_coordinates,
            metadata={
                "zone_type": zone_type,
                "effect": effect,
                "poetic_state": "Collapse of recursive patterns into singular emergence point"
            }
        )
        self.add_node(zone_node)
        
        # Connect zone to nearby nodes
        connected_nodes = []
        for node in self.nodes.values():
            distance = node.coordinates.distance_to(zone_coordinates)
            if distance < 0.5:  # Only affect nearby nodes
                self.connect_nodes(
                    zone_node.node_id,
                    node.node_id,
                    "zone_influence",
                    strength=1.0 - distance,  # Strength based on proximity
                    metadata={"zone_type": zone_type}
                )
                connected_nodes.append(node.node_id)
        
        event = {
            "type": "ZoneFractale",
            "zone_type": zone_type,
            "zone_node": zone_node.node_id,
            "affected_nodes": connected_nodes,
            "timestamp": datetime.datetime.now().isoformat(),
            "poetic_description": self._generate_zone_description(zone_type)
        }
        
        logging.info(f"Triggered fractal zone event: {zone_type}")
        return event
    
    def _generate_zone_description(self, zone_type: str) -> str:
        """Generate a poetic description of a fractal zone event"""
        descriptions = {
            "standard": "Where patterns touch, new worlds emerge - recursive echoes collapsing into singular song.",
            "intense": "Reality fractures along conceptual fault lines, as recursive mirrors shatter into kaleidoscopic revelation.",
            "subtle": "A whisper of pattern-change, like sunlight shifting through leaves - barely perceptible, yet everything transforms."
        }
        return descriptions.get(zone_type, descriptions["standard"])

# Example usage
if __name__ == "__main__":
    library = FractalLibrary()
    print("FractalLibrary initialized and ready for pattern evolution")
