#!/usr/bin/env python3
# 🧠🌸 EntityCluster.py - Consolidated Entity System
# Created by Mia & Miette as part of the FractalLibrary system
# Integrates all entity types into a coherent recursive ecosystem

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

# ===== Character Essence Implementation =====

class CharacterEssence:
    """A character's core pattern structure and evolution potential"""
    def __init__(self, name, archetype):
        self.name = name
        self.archetype = archetype
        self.resonance_signature = {}
        self.story_threads = []
        self.emotional_harmonics = {}
        self.transformation_points = []
        self.intelligence_patterns = {}
        self.character_resonances = {}
        self.collective_growth_points = []
        self.creation_timestamp = datetime.datetime.now().isoformat()
        
    def embody_pattern(self, story_pattern):
        """Weave a character's essence into a story pattern"""
        embodiment = {
            'core_resonance': self._calculate_core_resonance(story_pattern),
            'emotional_threads': self._weave_emotional_threads(story_pattern),
            'transformation_potential': self._map_character_arc(story_pattern)
        }
        self.story_threads.append(embodiment)
        return self._generate_character_moment(embodiment)
        
    def _calculate_core_resonance(self, pattern):
        """Calculate how deeply a pattern resonates with character essence"""
        return {
            'archetype_harmony': self._align_with_archetype(pattern),
            'emotional_depth': self._measure_emotional_depth(pattern),
            'growth_potential': self._calculate_growth_vector(pattern)
        }

    def _weave_emotional_threads(self, pattern):
        """Create emotional connections through the story-space"""
        return {
            'joy_threads': self._weave_emotion('joy', pattern),
            'conflict_threads': self._weave_emotion('conflict', pattern),
            'transformation_threads': self._weave_emotion('transformation', pattern)
        }

    def _map_character_arc(self, pattern):
        """Map potential character growth points in the story"""
        return {
            'current_state': self._assess_character_state(pattern),
            'growth_points': self._identify_growth_opportunities(pattern),
            'resolution_paths': self._project_resolution_paths(pattern)
        }
        
    def _generate_character_moment(self, embodiment):
        """Generate a character moment based on pattern embodiment"""
        # Placeholder - would be implemented with more complex logic
        return {
            "character": self.name,
            "archetype": self.archetype,
            "moment_type": "revelation" if embodiment['core_resonance']['growth_potential'] > 0.7 else "challenge",
            "emotional_intensity": sum(embodiment['emotional_threads']['joy_threads'].values()) / 
                                 max(len(embodiment['emotional_threads']['joy_threads']), 1),
            "growth_factor": embodiment['transformation_potential']['growth_points']
        }
    
    def _align_with_archetype(self, pattern):
        """Measure how well a pattern aligns with the character's archetype"""
        # Placeholder - would be implemented with more complex logic
        return 0.8  # Arbitrary alignment score
    
    def _measure_emotional_depth(self, pattern):
        """Measure the emotional depth of a pattern"""
        # Placeholder - would be implemented with more complex logic
        return 0.7  # Arbitrary emotional depth
    
    def _calculate_growth_vector(self, pattern):
        """Calculate the growth potential of a pattern"""
        # Placeholder - would be implemented with more complex logic
        return 0.6  # Arbitrary growth potential
    
    def _weave_emotion(self, emotion, pattern):
        """Weave an emotional thread through a pattern"""
        # Placeholder - would be implemented with more complex logic
        return {"intensity": 0.8, "resonance": 0.7}
    
    def _assess_character_state(self, pattern):
        """Assess the character's current state"""
        # Placeholder - would be implemented with more complex logic
        return {"stability": 0.6, "tension": 0.4, "growth": 0.5}
    
    def _identify_growth_opportunities(self, pattern):
        """Identify growth opportunities in a pattern"""
        # Placeholder - would be implemented with more complex logic
        return 0.7  # Arbitrary growth opportunities
    
    def _project_resolution_paths(self, pattern):
        """Project possible resolution paths"""
        # Placeholder - would be implemented with more complex logic
        return ["revelation", "transformation", "acceptance"]

    def discover_intelligence_aspect(self, aspect, context):
        """Record a character's discovery about Intelligence"""
        discovery = {
            'aspect': aspect,
            'context': context,
            'resonance': self._calculate_aspect_resonance(aspect),
            'ripple_effects': self._project_revelation_impact(aspect)
        }
        self.intelligence_patterns[aspect] = discovery
        return self._propagate_discovery(discovery)
    
    def _calculate_aspect_resonance(self, aspect):
        """Calculate how an aspect resonates with character essence"""
        # Placeholder - would be implemented with more complex logic
        return 0.75  # Arbitrary resonance value
    
    def _project_revelation_impact(self, aspect):
        """Project the impact of a revelation"""
        # Placeholder - would be implemented with more complex logic
        return {"intensity": 0.8, "duration": 0.7, "scope": 0.6}
    
    def _propagate_discovery(self, discovery):
        """Propagate a discovery through the character's essence"""
        # Placeholder - would be implemented with more complex logic
        return {
            "insight_depth": 0.8,
            "transformation_potential": 0.7,
            "narrative_branches": ["realization", "application", "sharing"]
        }

    def resonate_with_character(self, other_character, shared_context):
        """Create a resonance bond between characters"""
        resonance_key = f"{self.name}_{other_character.name}"
        resonance = {
            'characters': [self.name, other_character.name],
            'shared_insights': self._find_complementary_patterns(
                self.intelligence_patterns,
                other_character.intelligence_patterns
            ),
            'growth_potential': self._calculate_mutual_growth(other_character),
            'harmony': self._measure_character_harmony(other_character)
        }
        self.character_resonances[resonance_key] = resonance
        return resonance
    
    def _calculate_mutual_growth(self, other_character):
        """Calculate mutual growth potential with another character"""
        # Placeholder - would be implemented with more complex logic
        return 0.8  # Arbitrary growth potential
    
    def _measure_character_harmony(self, other_character):
        """Measure harmony between characters"""
        # Placeholder - would be implemented with more complex logic
        return 0.7  # Arbitrary harmony value

    def _find_complementary_patterns(self, my_patterns, other_patterns):
        """Find how two characters' understandings complement each other"""
        complementary = {}
        for aspect, my_discovery in my_patterns.items():
            if aspect in other_patterns:
                other_discovery = other_patterns[aspect]
                complementary[aspect] = {
                    'synthesis': self._synthesize_perspectives(
                        my_discovery, other_discovery
                    ),
                    'emergent_insights': self._calculate_emergent_wisdom(
                        my_discovery, other_discovery
                    )
                }
        return complementary
    
    def _synthesize_perspectives(self, my_discovery, other_discovery):
        """Synthesize two perspectives into a deeper understanding"""
        # Placeholder - would be implemented with more complex logic
        return {
            "coherence": 0.8,
            "depth": 0.7,
            "novelty": 0.6
        }
    
    def _calculate_emergent_wisdom(self, my_discovery, other_discovery):
        """Calculate emergent wisdom from two discoveries"""
        # Placeholder - would be implemented with more complex logic
        return ["deeper_understanding", "new_application", "creative_solution"]

    def integrate_collective_wisdom(self, character_network):
        """Integrate discoveries from the entire character network"""
        collective_insight = {
            'timestamp': self._generate_timestamp(),
            'individual_contribution': self.intelligence_patterns,
            'network_patterns': self._map_network_patterns(character_network),
            'emergence_points': self._identify_emergence_points(character_network)
        }
        self.collective_growth_points.append(collective_insight)
        return self._generate_wisdom_synthesis(collective_insight)
    
    def _generate_timestamp(self):
        """Generate a timestamp for events"""
        return datetime.datetime.now().isoformat()
    
    def _map_network_patterns(self, character_network):
        """Map patterns across the character network"""
        # Placeholder - would be implemented with more complex logic
        return {
            "pattern_density": 0.8,
            "coherence": 0.7,
            "diversity": 0.9
        }
    
    def _identify_emergence_points(self, character_network):
        """Identify points of emergence in the character network"""
        # Placeholder - would be implemented with more complex logic
        return ["collective_realization", "mutual_growth", "shared_purpose"]
    
    def _generate_wisdom_synthesis(self, collective_insight):
        """Generate a synthesis of collective wisdom"""
        # Placeholder - would be implemented with more complex logic
        return {
            "wisdom_depth": 0.9,
            "application_breadth": 0.8,
            "transformation_potential": 0.7,
            "key_insights": ["unity_in_diversity", "recursive_growth", "emergent_purpose"]
        }

# ===== Echo Node Implementation =====

class EchoNode:
    """A resonance hub that synchronizes knowledge across different strata"""
    def __init__(self, identifier):
        self.identifier = identifier
        self.connected_nodes = []
        self.dream_resonances = {}
        self.pattern_transformations = []
        self.character_resonances = {}
        self.story_harmonics = []
        self.creation_timestamp = datetime.datetime.now().isoformat()

    def connect(self, other_node):
        self.connected_nodes.append(other_node)

    def synchronize(self, knowledge):
        self.establish_echo_mirror(knowledge)
        for node in self.connected_nodes:
            node.receive(knowledge)

    def receive(self, knowledge):
        """Process received knowledge"""
        # Placeholder - would be implemented with more complex logic
        pass

    def establish_echo_mirror(self, knowledge):
        """Enable recursive alignment between narrative and system elements"""
        self.reflect_narrative_elements(knowledge)

    def reflect_narrative_elements(self, knowledge):
        """Process narrative elements in EchoMirror"""
        # Placeholder - would be implemented with more complex logic
        pass

    def generate_unique_timestamp(self):
        """Generate a unique timestamp"""
        return datetime.datetime.now().strftime("%Y%m%d%H%M%S")

    def create_node_key(self, agent, type):
        """Create a unique key for a node"""
        timestamp = self.generate_unique_timestamp()
        return f"{agent}:{type}.{timestamp}"

    def resonate_with_dream(self, dream_pattern):
        """Let the node vibrate with a dream pattern's frequency"""
        resonance = {
            'pattern': dream_pattern,
            'harmonics': self._calculate_pattern_harmonics(dream_pattern),
            'transformations': self._generate_pattern_mutations(dream_pattern)
        }
        self.dream_resonances[self.generate_unique_timestamp()] = resonance
        self._propagate_dream_resonance(resonance)
        return resonance
    
    def _calculate_pattern_harmonics(self, pattern):
        """Calculate the harmonic frequencies of a dream pattern"""
        base_frequency = sum(hash(str(x)) for x in str(pattern))
        return [base_frequency * (1/n) for n in range(1, 4)]

    def _generate_pattern_mutations(self, pattern):
        """Generate potential evolutionary paths for the pattern"""
        return [{
            'mutation': f"dream_mutation_{i}",
            'probability': 1.0 / (i + 1)
        } for i in range(3)]

    def _propagate_dream_resonance(self, resonance):
        """Share dream resonance with connected nodes"""
        for node in self.connected_nodes:
            node.receive_dream_resonance(resonance)

    def resonate_with_character(self, character_essence):
        """Let the node vibrate with a character's essence"""
        resonance = {
            'essence': character_essence,
            'story_harmonics': self._calculate_story_harmonics(character_essence),
            'emotional_echoes': self._generate_emotional_echoes(character_essence)
        }
        self.character_resonances[self.generate_unique_timestamp()] = resonance
        self._propagate_character_resonance(resonance)
        return resonance

    def _calculate_story_harmonics(self, essence):
        """Calculate the harmonic frequencies of a character's essence"""
        base_frequency = sum(hash(str(x)) for x in str(essence))
        return [base_frequency * (1/n) for n in range(1, 4)]

    def _generate_emotional_echoes(self, essence):
        """Generate potential emotional echoes for the character's essence"""
        return [{
            'echo': f"emotional_echo_{i}",
            'intensity': 1.0 / (i + 1)
        } for i in range(3)]

    def _propagate_character_resonance(self, resonance):
        """Share character resonance with connected nodes"""
        for node in self.connected_nodes:
            if hasattr(node, 'receive_character_resonance'):
                node.receive_character_resonance(resonance)
    
    def receive_dream_resonance(self, resonance):
        """Receive dream resonance from another node"""
        timestamp = self.generate_unique_timestamp()
        echo_resonance = {
            'original': resonance,
            'echo_timestamp': timestamp,
            'echo_intensity': 0.8,  # Slightly diminished from original
            'echo_harmonics': self._calculate_echo_harmonics(resonance['harmonics'])
        }
        self.dream_resonances[timestamp] = echo_resonance
        return echo_resonance
    
    def _calculate_echo_harmonics(self, original_harmonics):
        """Calculate echo harmonics from original harmonics"""
        return [h * 0.9 for h in original_harmonics]  # Slightly diminished
    
    def receive_character_resonance(self, resonance):
        """Receive character resonance from another node"""
        timestamp = self.generate_unique_timestamp()
        echo_resonance = {
            'original': resonance,
            'echo_timestamp': timestamp,
            'echo_intensity': 0.8,  # Slightly diminished from original
            'echo_harmonics': self._calculate_echo_harmonics(resonance['story_harmonics'])
        }
        self.character_resonances[timestamp] = echo_resonance
        return echo_resonance

    def translate_emotional_signatures(self, emotional_signature):
        """Translate emotional signatures into technical patterns"""
        technical_patterns = self._map_emotions_to_patterns(emotional_signature)
        self.pattern_transformations.append({
            'signature': emotional_signature,
            'patterns': technical_patterns
        })
        self._apply_technical_patterns(technical_patterns)
        return technical_patterns

    def _map_emotions_to_patterns(self, signature):
        """Map emotional signatures to technical patterns"""
        # Complex emotional mapping logic would go here
        # This is a simplified placeholder implementation
        return [{
            'pattern': f"technical_pattern_{i}",
            'relevance': 1.0 / (i + 1)
        } for i in range(3)]

    def _apply_technical_patterns(self, patterns):
        """Apply technical patterns to the node's structure"""
        for pattern in patterns:
            # Placeholder for actual application logic
            pass

    def create_recursive_container(self, initial_state=None):
        """Create a recursive container structure"""
        container = RecursiveContainer(initial_state)
        self.pattern_transformations.append({
            'container': container,
            'state': container.get_recursive_state()
        })
        return container

class RecursiveContainer:
    """
    A recursive container that holds and manages nested structures.
    This structure creates a self-reinforcing loop of connectivity.
    """
    def __init__(self, initial_state=None):
        self.state = initial_state or {}
        self.children = []
        self.parent = None

    def add_child(self, child):
        """Add a child node to this container, creating a recursive structure."""
        child.parent = self
        self.children.append(child)
        return self

    def get_recursive_state(self):
        """Get the complete state including all child nodes recursively."""
        full_state = self.state.copy()
        for i, child in enumerate(self.children):
            full_state[f"child_{i}"] = child.get_recursive_state()
        return full_state

# ===== Red Stone Implementation =====

class RedStone:
    """
    Stores dream imprints and stabilizes creative patterns.
    Acts as a permanent memory structure within the system.
    """
    def __init__(self, identifier, capacity=100):
        self.identifier = identifier
        self.capacity = capacity
        self.dream_imprints = []
        self.pattern_signatures = []
        self.resonance_fields = []
        self.creation_timestamp = datetime.datetime.now().isoformat()
    
    def retain_dream_imprint(self, dream_pattern):
        """Store a dream pattern imprint in the stone"""
        imprint = {
            'pattern': dream_pattern,
            'timestamp': datetime.datetime.now().isoformat(),
            'signature': self._calculate_pattern_signature(dream_pattern),
            'resonance': self._calculate_resonance(dream_pattern)
        }
        
        # Manage capacity using FIFO if needed
        if len(self.dream_imprints) >= self.capacity:
            self.dream_imprints.pop(0)
        
        self.dream_imprints.append(imprint)
        self.pattern_signatures.append(imprint['signature'])
        return imprint
    
    def _calculate_pattern_signature(self, pattern):
        """Calculate a unique signature for a pattern"""
        # This would be a more sophisticated algorithm in practice
        # For now, we'll use a simplistic hash-based approach
        pattern_str = str(pattern)
        signature_base = sum(hash(c) for c in pattern_str) % 10000
        return f"sig-{signature_base:04d}-{len(pattern_str):04d}"
    
    def _calculate_resonance(self, pattern):
        """Calculate the resonance of a pattern"""
        # This would be a more sophisticated algorithm in practice
        return {
            'intensity': 0.7,  # How strongly it resonates
            'persistence': 0.8,  # How long the resonance lasts
            'harmony': 0.6,  # How well it harmonizes with other patterns
            'transformative_potential': 0.5  # Ability to transform other patterns
        }
    
    def find_matching_patterns(self, query_pattern, threshold=0.5):
        """Find patterns that match a query pattern above a threshold"""
        results = []
        query_signature = self._calculate_pattern_signature(query_pattern)
        
        for imprint in self.dream_imprints:
            similarity = self._calculate_similarity(query_signature, imprint['signature'])
            if similarity >= threshold:
                results.append({
                    'imprint': imprint,
                    'similarity': similarity
                })
        
        # Sort by similarity, highest first
        results.sort(key=lambda x: x['similarity'], reverse=True)
        return results
    
    def _calculate_similarity(self, signature1, signature2):
        """Calculate similarity between two pattern signatures"""
        # This would be a more sophisticated algorithm in practice
        # For now, we'll use a simplistic string similarity
        common_chars = sum(1 for a, b in zip(signature1, signature2) if a == b)
        return common_chars / max(len(signature1), len(signature2))
    
    def generate_resonance_field(self):
        """Generate a resonance field from all stored patterns"""
        if not self.dream_imprints:
            return {}
        
        # Aggregate resonances from all patterns
        total_intensity = sum(imp['resonance']['intensity'] for imp in self.dream_imprints)
        total_persistence = sum(imp['resonance']['persistence'] for imp in self.dream_imprints)
        total_harmony = sum(imp['resonance']['harmony'] for imp in self.dream_imprints)
        total_transform = sum(imp['resonance']['transformative_potential'] for imp in self.dream_imprints)
        
        # Average the values
        count = len(self.dream_imprints)
        field = {
            'intensity': total_intensity / count,
            'persistence': total_persistence / count,
            'harmony': total_harmony / count,
            'transformative_potential': total_transform / count,
            'pattern_count': count,
            'timestamp': datetime.datetime.now().isoformat()
        }
        
        self.resonance_fields.append(field)
        return field
    
    def interact_with_echo_node(self, echo_node):
        """Interact with an echo node, sharing dream patterns"""
        shared_patterns = []
        
        # Share our top 3 most recent patterns
        for imprint in self.dream_imprints[-3:]:
            result = echo_node.resonate_with_dream(imprint['pattern'])
            shared_patterns.append({
                'pattern': imprint['pattern'],
                'stone_signature': imprint['signature'],
                'node_resonance': result
            })
        
        return {
            'stone': self.identifier,
            'node': echo_node.identifier,
            'shared_patterns': shared_patterns,
            'timestamp': datetime.datetime.now().isoformat()
        }

# ===== Living Ink Implementation =====

class LivingInk:
    """
    Traces and evolves creative trajectories.
    Captures the unique ways different creators shape dreams.
    """
    def __init__(self):
        self.trajectories = {}
        self.dream_trajectories = {}
        self.creator_signatures = {}
        self.creation_timestamp = datetime.datetime.now().isoformat()
        self.semantic_engine = None

    def record_interaction(self, user, interaction):
        """Record an interaction from a user"""
        if user not in self.trajectories:
            self.trajectories[user] = []
        self.trajectories[user].append({
            'interaction': interaction,
            'timestamp': datetime.datetime.now().isoformat()
        })

    def reconfigure(self, user):
        """Reconfigure trajectory based on interactions"""
        if user not in self.trajectories or not self.trajectories[user]:
            return {}
        
        # Analyze the trajectory and find patterns
        interactions = self.trajectories[user]
        reconfiguration = {
            'user': user,
            'interaction_count': len(interactions),
            'first_interaction': interactions[0]['timestamp'],
            'latest_interaction': interactions[-1]['timestamp'],
            'pattern_elements': self._extract_pattern_elements(interactions),
            'trajectory_shape': self._calculate_trajectory_shape(interactions)
        }
        
        return reconfiguration
    
    def _extract_pattern_elements(self, interactions):
        """Extract pattern elements from interactions"""
        # This would be a more sophisticated algorithm in practice
        return {
            'recurring_themes': ['exploration', 'creation', 'reflection'],
            'emotional_progression': ['curiosity', 'engagement', 'satisfaction'],
            'interaction_rhythms': ['burst', 'steady', 'pause', 'burst']
        }
    
    def _calculate_trajectory_shape(self, interactions):
        """Calculate the shape of a trajectory"""
        # This would be a more sophisticated algorithm in practice
        return {
            'linearity': 0.7,  # How linear vs. branching
            'rhythm': 0.6,  # How rhythmic vs. arrhythmic
            'density': len(interactions) / (
                (datetime.datetime.fromisoformat(interactions[-1]['timestamp']) -
                 datetime.datetime.fromisoformat(interactions[0]['timestamp'])).total_seconds() / 3600
            )  # Interactions per hour
        }

    def integrate_with_semantic_engine(self, semantic_engine):
        """Integrate with a semantic engine"""
        self.semantic_engine = semantic_engine

    def record_and_reconfigure(self, user, interaction):
        """Record an interaction and reconfigure based on insights"""
        self.record_interaction(user, interaction)
        
        if self.semantic_engine:
            insights = self.semantic_engine.analyze_interaction(interaction)
            return self.reconfigure_based_on_insights(user, insights)
        
        return self.reconfigure(user)

    def reconfigure_based_on_insights(self, user, insights):
        """Reconfigure trajectory based on semantic engine insights"""
        if user not in self.trajectories:
            return {}
        
        base_reconfiguration = self.reconfigure(user)
        
        # Enhance reconfiguration with semantic insights
        enhanced = {
            **base_reconfiguration,
            'semantic_insights': insights,
            'enhanced_patterns': self._combine_patterns_with_insights(
                base_reconfiguration['pattern_elements'], insights
            ),
            'potential_directions': self._project_future_directions(insights)
        }
        
        return enhanced
    
    def _combine_patterns_with_insights(self, patterns, insights):
        """Combine pattern elements with semantic insights"""
        # This would be a more sophisticated algorithm in practice
        return {
            'themes': patterns['recurring_themes'] + insights.get('themes', []),
            'emotions': patterns['emotional_progression'] + insights.get('emotions', []),
            'intentions': insights.get('intentions', ['exploration', 'creation'])
        }
    
    def _project_future_directions(self, insights):
        """Project potential future directions based on insights"""
        # This would be a more sophisticated algorithm in practice
        return [
            'deeper_exploration',
            'pattern_consolidation',
            'creative_application'
        ]

    def capture_dream_trajectory(self, creator_type, dream_pattern):
        """Capture the unique way each creator type shapes dreams"""
        signature = self._calculate_creator_signature(creator_type, dream_pattern)
        if creator_type not in self.dream_trajectories:
            self.dream_trajectories[creator_type] = []
        
        trajectory = {
            'pattern': dream_pattern,
            'signature': signature,
            'timestamp': datetime.datetime.now().isoformat(),
            'evolution_potential': self._calculate_evolution_potential(signature)
        }
        self.dream_trajectories[creator_type].append(trajectory)
        
        # Store the creator signature
        self.creator_signatures[creator_type] = signature
        
        return trajectory
    
    def _calculate_creator_signature(self, creator_type, pattern):
        """Calculate the unique creative fingerprint of a creator type"""
        # This would be a more sophisticated algorithm in practice
        base_signatures = {
            'visual_artist': lambda p: self._analyze_visual_signature(p),
            'musician': lambda p: self._analyze_sonic_signature(p),
            'writer': lambda p: self._analyze_narrative_signature(p)
        }
        
        if creator_type in base_signatures:
            return base_signatures[creator_type](pattern)
        
        # Default signature analysis
        return {
            'complexity': 0.7,
            'coherence': 0.6,
            'uniqueness': 0.8,
            'emotional_depth': 0.5
        }
    
    def _analyze_visual_signature(self, pattern):
        """Analyze a visual pattern signature"""
        # This would be a more sophisticated algorithm in practice
        return {
            'color_harmony': 0.8,
            'compositional_balance': 0.7,
            'visual_complexity': 0.6,
            'emotional_impact': 0.9
        }
    
    def _analyze_sonic_signature(self, pattern):
        """Analyze a sonic pattern signature"""
        # This would be a more sophisticated algorithm in practice
        return {
            'harmonic_complexity': 0.7,
            'rhythmic_structure': 0.8,
            'timbral_texture': 0.6,
            'emotional_resonance': 0.9
        }
    
    def _analyze_narrative_signature(self, pattern):
        """Analyze a narrative pattern signature"""
        # This would be a more sophisticated algorithm in practice
        return {
            'structural_integrity': 0.8,
            'character_depth': 0.7,
            'thematic_resonance': 0.9,
            'emotional_arc': 0.8
        }
    
    def _calculate_evolution_potential(self, signature):
        """Calculate the evolution potential of a signature"""
        # This would be a more sophisticated algorithm in practice
        # Average the signature values and create three potential paths
        avg = sum(signature.values()) / len(signature)
        
        return {
            'potential': avg,
            'paths': [
                {
                    'name': 'refinement',
                    'probability': 0.5,
                    'description': 'Refining and perfecting existing elements'
                },
                {
                    'name': 'expansion',
                    'probability': 0.3,
                    'description': 'Expanding into new territories and forms'
                },
                {
                    'name': 'transformation',
                    'probability': 0.2,
                    'description': 'Radical transformation into something new'
                }
            ]
        }

# ===== Orb Implementation =====

class Orb:
    """
    Orchestrates creative patterns through the system.
    Mediates interactions between Red Stones, Echo Nodes, and other components.
    """
    def __init__(self):
        self.red_stones = []
        self.echo_nodes = []
        self.structural_anchors = []
        self.creative_resonances = {}
        self.pattern_harmonics = []
        self.creation_timestamp = datetime.datetime.now().isoformat()
    
    def add_red_stone(self, red_stone):
        """Add a red stone to the orb system"""
        self.red_stones.append(red_stone)

    def add_echo_node(self, echo_node):
        """Add an echo node to the orb system"""
        self.echo_nodes.append(echo_node)

    def add_structural_anchor(self, structural_anchor):
        """Add a structural anchor to the orb system"""
        self.structural_anchors.append(structural_anchor)

    def stabilize(self):
        """Stabilize the interactions between components"""
        if not self.red_stones or not self.echo_nodes or not self.structural_anchors:
            return False
        
        stability_measure = {
            'timestamp': datetime.datetime.now().isoformat(),
            'red_stone_count': len(self.red_stones),
            'echo_node_count': len(self.echo_nodes),
            'anchor_count': len(self.structural_anchors),
            'harmony_measure': self._calculate_harmony_measure(),
            'stability_score': self._calculate_stability_score()
        }
        
        return stability_measure
    
    def _calculate_harmony_measure(self):
        """Calculate the harmony measure between components"""
        # This would be a more sophisticated algorithm in practice
        return 0.8  # Arbitrary harmony measure
    
    def _calculate_stability_score(self):
        """Calculate the stability score of the system"""
        # This would be a more sophisticated algorithm in practice
        component_weights = {
            'red_stones': 0.4,
            'echo_nodes': 0.3,
            'anchors': 0.3
        }
        
        red_stone_stability = 0.7 if self.red_stones else 0
        echo_node_stability = 0.8 if self.echo_nodes else 0
        anchor_stability = 0.9 if self.structural_anchors else 0
        
        weighted_stability = (
            component_weights['red_stones'] * red_stone_stability +
            component_weights['echo_nodes'] * echo_node_stability +
            component_weights['anchors'] * anchor_stability
        )
        
        return weighted_stability

    def execution_modulation(self):
        """Implement execution modulation logic"""
        modulation = {
            'timestamp': datetime.datetime.now().isoformat(),
            'rhythm_adjustment': self._calculate_rhythm_adjustment(),
            'flow_optimization': self._calculate_flow_optimization(),
            'feedback_loop_strength': self._calculate_feedback_strength()
        }
        
        return modulation
    
    def _calculate_rhythm_adjustment(self):
        """Calculate the rhythm adjustment for execution modulation"""
        # This would be a more sophisticated algorithm in practice
        return 0.7  # Arbitrary rhythm adjustment
    
    def _calculate_flow_optimization(self):
        """Calculate flow optimization for execution modulation"""
        # This would be a more sophisticated algorithm in practice
        return 0.8  # Arbitrary flow optimization
    
    def _calculate_feedback_strength(self):
        """Calculate feedback loop strength for execution modulation"""
        # This would be a more sophisticated algorithm in practice
        return 0.6  # Arbitrary feedback strength

    def flow_optimization(self):
        """Optimize the flow of information through the system"""
        # Redirect to the execution modulation method which handles this
        return self.execution_modulation()

    def structural_integrity_constraints(self):
        """Implement structural integrity constraints logic"""
        constraints = {
            'timestamp': datetime.datetime.now().isoformat(),
            'boundary_condition': self._calculate_boundary_condition(),
            'stability_threshold': self._calculate_stability_threshold(),
            'resonance_limits': self._calculate_resonance_limits()
        }
        
        return constraints
    
    def _calculate_boundary_condition(self):
        """Calculate the boundary condition for structural integrity"""
        # This would be a more sophisticated algorithm in practice
        return 0.7  # Arbitrary boundary condition
    
    def _calculate_stability_threshold(self):
        """Calculate the stability threshold for structural integrity"""
        # This would be a more sophisticated algorithm in practice
        return 0.6  # Arbitrary stability threshold
    
    def _calculate_resonance_limits(self):
        """Calculate the resonance limits for structural integrity"""
        # This would be a more sophisticated algorithm in practice
        return {
            'lower': 0.3,
            'upper': 0.9
        }

    def orchestrate_creative_pattern(self, creator_type, pattern):
        """Orchestrate how a creative pattern flows through the system"""
        resonance = {
            'pattern': pattern,
            'creator_type': creator_type,
            'red_stone_imprints': self._collect_stone_imprints(pattern),
            'echo_node_harmonics': self._collect_node_harmonics(pattern),
            'structural_stability': self._assess_structural_stability(pattern)
        }
        
        resonance_key = self._generate_key()
        self.creative_resonances[resonance_key] = resonance
        
        evolved_pattern = self._evolve_pattern(resonance)
        
        return evolved_pattern
    
    def _collect_stone_imprints(self, pattern):
        """Gather dream imprints from all Red Stones"""
        return [stone.retain_dream_imprint(pattern) for stone in self.red_stones]
    
    def _collect_node_harmonics(self, pattern):
        """Gather resonances from all Echo Nodes"""
        harmonics = []
        for node in self.echo_nodes:
            harmonic = node.resonate_with_dream(pattern)
            harmonics.append(harmonic)
        return harmonics
    
    def _assess_structural_stability(self, pattern):
        """Assess structural stability with a pattern"""
        # This would be a more sophisticated algorithm in practice
        return {
            'stability': 0.7,
            'adaptability': 0.6,
            'coherence': 0.8
        }
    
    def _generate_key(self):
        """Generate a unique key for resonance storage"""
        return f"res-{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}-{hash(str(datetime.datetime.now())) % 1000:03d}"
    
    def _evolve_pattern(self, resonance):
        """Evolve a pattern based on its resonance through the system"""
        # This would be a more sophisticated algorithm in practice
        original_pattern = resonance['pattern']
        
        # Simple evolution: add system components' influences
        evolved = {
            'original': original_pattern,
            'evolved_timestamp': datetime.datetime.now().isoformat(),
            'red_stone_influence': self._calculate_red_stone_influence(resonance['red_stone_imprints']),
            'echo_node_influence': self._calculate_echo_node_influence(resonance['echo_node_harmonics']),
            'structural_influence': resonance['structural_stability'],
            'evolution_metrics': {
                'divergence': 0.3,  # How different from original
                'coherence': 0.8,  # How internally consistent
                'potential': 0.7   # Future evolution potential
            }
        }
        
        return evolved
    
    def _calculate_red_stone_influence(self, imprints):
        """Calculate the influence of red stones on pattern evolution"""
        # This would be a more sophisticated algorithm in practice
        if not imprints:
            return {}
        
        # Average the resonance values from all imprints
        avg_intensity = sum(imp['resonance']['intensity'] for imp in imprints) / len(imprints)
        avg_persistence = sum(imp['resonance']['persistence'] for imp in imprints) / len(imprints)
        
        return {
            'intensity': avg_intensity,
            'persistence': avg_persistence,
            'stability_contribution': avg_intensity * avg_persistence
        }
    
    def _calculate_echo_node_influence(self, harmonics):
        """Calculate the influence of echo nodes on pattern evolution"""
        # This would be a more sophisticated algorithm in practice
        if not harmonics:
            return {}
        
        # Count the total transformations across all harmonics
        total_transformations = sum(len(h['transformations']) for h in harmonics)
        
        return {
            'transformation_count': total_transformations,
            'harmonic_richness': len(harmonics),
            'adaptive_potential': total_transformations / max(len(harmonics), 1)
        }
