#!/usr/bin/env python3
"""
🧠🌸 Pattern Evolution Mathematics & Emotional Resonance Mapping
TimeTrace: 2025-05-03 | RecursionDepth: 7 | ResonancePattern: Fractal Unfolding

This module implements the mathematical foundations of the Pattern Prediction Framework,
translating the theoretical concepts into practical computational models.
"""

import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Any, Optional
from dataclasses import dataclass
import json
import math


# === MATHEMATICAL FOUNDATIONS ===

@dataclass
class DimensionalVector:
    """A mathematical representation of a pattern's position in the narrative lattice."""
    # Technical dimensions (structural aspects)
    complexity: float       # How intricate the pattern is (0-1)
    novelty: float          # How unexpected the pattern is (0-1)
    recursion_depth: int    # How many levels of self-reference
    symmetry: float         # Degree of balance in the pattern (0-1)
    
    # Emotional dimensions (resonance aspects)
    tension: float          # Emotional charge/conflict (-1 to 1)
    wonder: float           # Sense of awe/discovery (0-1)
    empathy: float          # Emotional connection potential (0-1)
    catharsis: float        # Release/resolution potential (0-1)
    
    # Narrative dimensions (manifestation aspects)
    character_coherence: float  # How well it fits character development (0-1)
    plot_integration: float     # How well it drives the plot (0-1)
    thematic_resonance: float   # How well it supports themes (0-1)
    symbolism: float            # How rich in symbolic potential (0-1)

    def __post_init__(self):
        """Normalize all values to appropriate ranges after initialization."""
        self.complexity = min(max(self.complexity, 0), 1)
        self.novelty = min(max(self.novelty, 0), 1)
        self.recursion_depth = max(self.recursion_depth, 0)
        self.symmetry = min(max(self.symmetry, 0), 1)
        
        self.tension = min(max(self.tension, -1), 1)
        self.wonder = min(max(self.wonder, 0), 1)
        self.empathy = min(max(self.empathy, 0), 1)
        self.catharsis = min(max(self.catharsis, 0), 1)
        
        self.character_coherence = min(max(self.character_coherence, 0), 1)
        self.plot_integration = min(max(self.plot_integration, 0), 1)
        self.thematic_resonance = min(max(self.thematic_resonance, 0), 1)
        self.symbolism = min(max(self.symbolism, 0), 1)

    def to_array(self) -> np.ndarray:
        """Convert the dimensional vector to a numpy array for mathematical operations."""
        return np.array([
            self.complexity, self.novelty, self.recursion_depth, self.symmetry,
            self.tension, self.wonder, self.empathy, self.catharsis,
            self.character_coherence, self.plot_integration, self.thematic_resonance, self.symbolism
        ])

    @classmethod
    def from_array(cls, arr: np.ndarray) -> 'DimensionalVector':
        """Create a dimensional vector from a numpy array."""
        return cls(
            complexity=arr[0], novelty=arr[1], recursion_depth=int(arr[2]), symmetry=arr[3],
            tension=arr[4], wonder=arr[5], empathy=arr[6], catharsis=arr[7],
            character_coherence=arr[8], plot_integration=arr[9], thematic_resonance=arr[10], symbolism=arr[11]
        )

    def evolve(self, evolution_vector: 'DimensionalVector', depth: int = 1) -> 'DimensionalVector':
        """
        Evolve this pattern according to an evolution vector and depth.
        
        The mathematics of evolution follows a non-linear transformation that:
        1. Maintains core identity at low depths
        2. Introduces increasingly divergent patterns at higher depths
        3. Preserves internal coherence through dimensional coupling
        
        This is represented as: new_pattern = base_pattern + (evolution_vector * depth_factor)
        where depth_factor is a non-linear function of depth
        """
        # The golden ratio creates aesthetically pleasing evolution paths
        phi = (1 + math.sqrt(5)) / 2
        
        # Non-linear depth factor grows asymptotically
        depth_factor = 1 - 1/(depth + phi - 1)
        
        base_array = self.to_array()
        evolution_array = evolution_vector.to_array()
        
        # Apply the evolution with harmonic constraints
        new_array = base_array + (evolution_array * depth_factor)
        
        # Apply dimensional coupling (certain dimensions influence others)
        # Higher tension reduces empathy but increases potential catharsis
        tension_factor = new_array[4]  # tension value
        new_array[6] *= (1 - abs(tension_factor) * 0.5)  # reduce empathy based on tension
        new_array[7] += abs(tension_factor) * 0.3  # increase catharsis potential
        
        # Higher recursion depth increases complexity but can reduce plot integration
        recursion_factor = min(new_array[2] / 10, 0.5)  # capped influence
        new_array[0] += recursion_factor  # increase complexity
        new_array[9] *= (1 - recursion_factor * 0.3)  # reduce plot integration
        
        return DimensionalVector.from_array(new_array)


@dataclass
class PatternSignature:
    """A complete mathematical representation of a narrative pattern."""
    base_vector: DimensionalVector
    evolution_potential: DimensionalVector
    resonance_fingerprint: Dict[str, float]
    core_glyphs: List[str]
    
    def calculate_resonance(self, intent_vector: DimensionalVector) -> float:
        """
        Calculate how strongly this pattern resonates with a given narrative intent.
        
        The resonance formula combines:
        1. Dot product of dimensional vectors (directional alignment)
        2. Emotional coherence factors
        3. Golden ratio harmonics
        
        Returns a value between 0 and 1, where higher values indicate stronger resonance.
        """
        # 1. Calculate vector alignment through dot product and normalize
        base_array = self.base_vector.to_array()
        intent_array = intent_vector.to_array()
        
        # Normalize the arrays to avoid dimension scaling issues
        base_norm = base_array / np.linalg.norm(base_array)
        intent_norm = intent_array / np.linalg.norm(intent_array)
        
        # Compute dot product for directional alignment
        alignment = np.dot(base_norm, intent_norm)
        
        # 2. Emotional coherence - how well emotional dimensions align
        emotional_base = base_array[4:8]  # emotional dimensions
        emotional_intent = intent_array[4:8]
        emotional_coherence = 1 - 0.5 * np.mean(np.abs(emotional_base - emotional_intent))
        
        # 3. Apply golden ratio harmonic for aesthetic balance
        phi = (1 + math.sqrt(5)) / 2
        harmonic_factor = (1 + 1/phi) / 2  # between 0.5 and 1
        
        # Combine factors with weighted importance
        resonance = (0.5 * alignment + 0.3 * emotional_coherence + 0.2 * harmonic_factor)
        
        # Normalize to 0-1 range
        return max(min((resonance + 1) / 2, 1), 0)
    
    def predict_manifestations(self, depth: int = 3) -> List[Dict[str, Any]]:
        """
        Predict how this pattern might manifest across possible story dimensions.
        Returns a list of possible manifestations with their probabilities.
        """
        manifestations = []
        
        for d in range(1, depth+1):
            evolved_vector = self.base_vector.evolve(self.evolution_potential, d)
            
            # Calculate manifestation probability (decreases with depth)
            probability = 0.9 * math.pow(0.7, d-1)
            
            # Generate manifestation characteristics based on the evolved vector
            manifestation = {
                "depth": d,
                "probability": probability,
                "vector": evolved_vector,
                "character_traits": self._vector_to_character_traits(evolved_vector),
                "plot_elements": self._vector_to_plot_elements(evolved_vector),
                "thematic_elements": self._vector_to_thematic_elements(evolved_vector),
                "symbol_system": self._vector_to_symbol_system(evolved_vector)
            }
            
            manifestations.append(manifestation)
            
        return manifestations
    
    def _vector_to_character_traits(self, vector: DimensionalVector) -> Dict[str, float]:
        """Map a dimensional vector to potential character traits."""
        traits = {}
        
        # Examples of translating dimensional values to character traits
        if vector.tension < -0.5:
            traits["harmony_seeking"] = abs(vector.tension) * 0.8
        elif vector.tension > 0.5:
            traits["conflict_driven"] = vector.tension * 0.8
        
        if vector.wonder > 0.7:
            traits["curious"] = vector.wonder
        
        if vector.empathy > 0.6:
            traits["compassionate"] = vector.empathy
        
        if vector.recursion_depth > 2:
            traits["self_reflective"] = min(vector.recursion_depth / 5, 1)
        
        # Add more trait mappings based on other dimensions...
        
        return traits
    
    def _vector_to_plot_elements(self, vector: DimensionalVector) -> Dict[str, float]:
        """Map a dimensional vector to potential plot elements."""
        elements = {}
        
        # Map complexity to plot structure
        if vector.complexity < 0.3:
            elements["linear_journey"] = 1 - vector.complexity
        elif vector.complexity > 0.7:
            elements["complex_web"] = vector.complexity

        # Map tension to conflict types
        if vector.tension < -0.3:
            elements["internal_journey"] = abs(vector.tension)
        elif vector.tension > 0.3:
            elements["external_conflict"] = vector.tension
            
        # Map catharsis to resolution styles
        if vector.catharsis > 0.6:
            elements["transformative_resolution"] = vector.catharsis
            
        return elements
    
    def _vector_to_thematic_elements(self, vector: DimensionalVector) -> Dict[str, float]:
        """Map a dimensional vector to potential thematic elements."""
        themes = {}
        
        # Examples of translating dimensions to themes
        if vector.empathy > 0.6 and vector.wonder > 0.5:
            themes["connection"] = (vector.empathy + vector.wonder) / 2
            
        if vector.recursion_depth > 2 and vector.complexity > 0.6:
            themes["self_discovery"] = (vector.recursion_depth / 5 + vector.complexity) / 2
            
        if abs(vector.tension) > 0.7 and vector.catharsis > 0.6:
            themes["transformation"] = (abs(vector.tension) + vector.catharsis) / 2
            
        return themes
    
    def _vector_to_symbol_system(self, vector: DimensionalVector) -> Dict[str, float]:
        """Map a dimensional vector to potential symbolic elements."""
        symbols = {}
        
        # Map dimensions to symbol systems
        if vector.recursion_depth > 3:
            symbols["mirrors"] = min(vector.recursion_depth / 5, 1)
            
        if vector.symmetry > 0.7:
            symbols["duality"] = vector.symmetry
            
        if vector.wonder > 0.6:
            symbols["light"] = vector.wonder
            
        if vector.tension < -0.6:
            symbols["water"] = abs(vector.tension)
        elif vector.tension > 0.6:
            symbols["fire"] = vector.tension
            
        return symbols


# === EMOTIONAL RESONANCE MAPPING ===

class EmotionalResonanceMapper:
    """
    Maps between technical patterns and emotional resonance.
    
    The mathematics of emotional resonance mapping is based on:
    1. Non-linear emotional activation functions
    2. Cross-dimensional resonance effects
    3. Recursive emotional echoes
    """
    
    def __init__(self):
        # Define emotional activation thresholds
        self.activation_thresholds = {
            "wonder": 0.4,
            "empathy": 0.5,
            "tension": 0.3,
            "catharsis": 0.6
        }
        
        # Define resonance channels between technical and emotional dimensions
        self.resonance_channels = {
            "complexity": ["wonder", "tension"],
            "novelty": ["wonder"],
            "recursion_depth": ["wonder", "empathy"],
            "symmetry": ["catharsis"]
        }
        
        # Emotional echo decay factor (for recursive effects)
        self.echo_decay = 0.7
    
    def map_technical_to_emotional(self, pattern_vector: DimensionalVector) -> Dict[str, float]:
        """
        Maps a technical pattern vector to emotional resonance values.
        
        The mapping follows an activation-and-spread model where:
        1. Technical dimensions activate linked emotional dimensions
        2. Activation spreads through defined resonance channels
        3. Recursive echoes amplify or diminish emotional responses
        """
        # Extract technical dimensions as a dictionary
        technical = {
            "complexity": pattern_vector.complexity,
            "novelty": pattern_vector.novelty,
            "recursion_depth": pattern_vector.recursion_depth / 5,  # normalize to 0-1
            "symmetry": pattern_vector.symmetry
        }
        
        # Initialize emotional response container
        emotional_response = {
            "wonder": 0.0,
            "empathy": 0.0,
            "tension": 0.0,
            "catharsis": 0.0
        }
        
        # First pass: Direct technical-to-emotional activation
        for tech_dim, tech_value in technical.items():
            if tech_dim in self.resonance_channels:
                for emotion in self.resonance_channels[tech_dim]:
                    # Apply non-linear activation function (sigmoid-like)
                    threshold = self.activation_thresholds[emotion]
                    activation = self._activation_function(tech_value, threshold)
                    emotional_response[emotion] += activation
        
        # Second pass: Emotional cross-resonance (emotions affecting each other)
        wonder_factor = emotional_response["wonder"]
        tension_factor = abs(emotional_response["tension"])
        
        # Wonder amplifies catharsis potential
        emotional_response["catharsis"] += wonder_factor * 0.3
        
        # High tension reduces empathy but increases later catharsis
        emotional_response["empathy"] *= (1 - tension_factor * 0.4)
        emotional_response["catharsis"] += tension_factor * 0.2
        
        # Third pass: Recursive emotional echoes for complex patterns
        if technical["recursion_depth"] > 0.4:  # Only apply for deeply recursive patterns
            echo_depth = int(technical["recursion_depth"] * 3)
            
            for d in range(1, echo_depth + 1):
                echo_factor = self.echo_decay ** d
                
                # Each emotion echoes into itself and others
                echo_wonder = emotional_response["wonder"] * echo_factor * 0.3
                echo_empathy = emotional_response["empathy"] * echo_factor * 0.2
                
                # Add echoes to emotional response
                emotional_response["wonder"] += echo_wonder
                emotional_response["empathy"] += echo_empathy
        
        # Normalize final emotional response values to 0-1
        for emotion in emotional_response:
            emotional_response[emotion] = min(max(emotional_response[emotion], 0), 1)
        
        return emotional_response
    
    def map_emotional_to_story(self, emotional_response: Dict[str, float]) -> Dict[str, Any]:
        """
        Maps emotional resonance values to concrete story elements.
        Returns a dictionary of story elements with their relevance scores.
        """
        story_elements = {}
        
        # Wonder influences discovery and revelation moments
        if emotional_response["wonder"] > 0.6:
            story_elements["revelation_moments"] = {
                "relevance": emotional_response["wonder"],
                "examples": [
                    "Character discovers a hidden truth",
                    "Unexpected connection between plot elements is revealed",
                    "A mysterious object/concept reveals its true nature"
                ]
            }
        
        # Empathy influences relationship dynamics
        if emotional_response["empathy"] > 0.5:
            story_elements["relationship_developments"] = {
                "relevance": emotional_response["empathy"],
                "examples": [
                    "Characters form a deeper connection",
                    "Trust is built through vulnerability",
                    "Understanding emerges through shared experience"
                ]
            }
        
        # Tension influences conflict patterns
        tension_value = emotional_response["tension"]
        if abs(tension_value) > 0.4:
            if tension_value > 0:
                # Positive tension: productive conflict
                story_elements["productive_conflicts"] = {
                    "relevance": tension_value,
                    "examples": [
                        "Creative disagreement leads to better solution",
                        "Challenge forces character growth",
                        "Opposing perspectives reveal larger truth"
                    ]
                }
            else:
                # Negative tension: destructive conflict
                story_elements["destructive_conflicts"] = {
                    "relevance": abs(tension_value),
                    "examples": [
                        "Misunderstanding causes relationship damage",
                        "Internal conflict paralyzes character",
                        "Competing goals lead to lose-lose outcome"
                    ]
                }
        
        # Catharsis influences resolution patterns
        if emotional_response["catharsis"] > 0.6:
            story_elements["resolution_patterns"] = {
                "relevance": emotional_response["catharsis"],
                "examples": [
                    "Emotional release through confrontation",
                    "Acceptance of a difficult truth",
                    "Transformation through challenge"
                ]
            }
        
        return story_elements
    
    def _activation_function(self, value: float, threshold: float) -> float:
        """
        Non-linear activation function for emotional responses.
        Uses a modified sigmoid that creates a more natural response curve.
        """
        if value < threshold:
            # Below threshold, slow growth
            return value * (value / threshold) * 0.8
        else:
            # Above threshold, accelerating growth with saturation
            overshoot = value - threshold
            saturation_factor = 1 / (1 + 2 * overshoot)
            return threshold + (1 - threshold) * (1 - saturation_factor)


# === PATTERN FUSION SYSTEM ===

class PatternFusionSystem:
    """
    System for fusing multiple patterns into new emergent patterns.
    The mathematics of pattern fusion combines:
    1. Vector interpolation in pattern-space
    2. Emergent property calculation
    3. Harmonic reinforcement
    """
    
    def __init__(self, resonance_threshold: float = 0.4):
        self.resonance_threshold = resonance_threshold
        self.phi = (1 + math.sqrt(5)) / 2  # Golden ratio for harmonic calculations
    
    def fuse_patterns(self, patterns: List[PatternSignature], weights: Optional[List[float]] = None) -> PatternSignature:
        """
        Fuse multiple patterns into a new emergent pattern.
        
        Args:
            patterns: List of patterns to fuse
            weights: Optional weights for each pattern (will be normalized)
            
        Returns:
            A new PatternSignature representing the fusion result
        """
        if len(patterns) == 0:
            raise ValueError("Cannot fuse empty pattern list")
            
        if len(patterns) == 1:
            return patterns[0]
            
        # Normalize weights if provided, otherwise use equal weights
        if weights is None:
            weights = [1.0] * len(patterns)
        
        total_weight = sum(weights)
        normalized_weights = [w / total_weight for w in weights]
        
        # 1. Calculate weighted average of base vectors
        base_arrays = [p.base_vector.to_array() * w for p, w in zip(patterns, normalized_weights)]
        fused_base_array = sum(base_arrays)
        
        # 2. Calculate weighted average of evolution vectors with harmonic enhancement
        evo_arrays = [p.evolution_potential.to_array() * w for p, w in zip(patterns, normalized_weights)]
        fused_evo_array = sum(evo_arrays)
        
        # Apply harmonic enhancement based on pattern compatibility
        compatibility = self._calculate_pattern_compatibility(patterns)
        harmonic_factor = (compatibility + 1/self.phi) / (1 + 1/self.phi)
        fused_evo_array *= harmonic_factor
        
        # 3. Merge resonance fingerprints
        fused_fingerprint = {}
        for pattern, weight in zip(patterns, normalized_weights):
            for key, value in pattern.resonance_fingerprint.items():
                if key in fused_fingerprint:
                    fused_fingerprint[key] += value * weight
                else:
                    fused_fingerprint[key] = value * weight
        
        # 4. Extract emergent properties based on dimensional interactions
        self._inject_emergent_properties(fused_base_array, fused_fingerprint, compatibility)
        
        # 5. Combine glyphs with possible emergence of new glyphs
        fused_glyphs = self._fuse_glyphs(patterns, compatibility)
        
        # Create and return the fused pattern
        return PatternSignature(
            base_vector=DimensionalVector.from_array(fused_base_array),
            evolution_potential=DimensionalVector.from_array(fused_evo_array),
            resonance_fingerprint=fused_fingerprint,
            core_glyphs=fused_glyphs
        )
    
    def _calculate_pattern_compatibility(self, patterns: List[PatternSignature]) -> float:
        """
        Calculate how compatible a set of patterns is for fusion.
        
        Uses:
        1. Pairwise alignment of dimensional vectors
        2. Complementary resonance factors
        3. Glyph harmony assessment
        
        Returns a value between 0 and 1, where higher values indicate more compatible patterns.
        """
        if len(patterns) < 2:
            return 1.0
        
        # Calculate all pairwise alignments
        alignments = []
        for i in range(len(patterns)):
            for j in range(i + 1, len(patterns)):
                # Get base vectors
                vec_i = patterns[i].base_vector.to_array()
                vec_j = patterns[j].base_vector.to_array()
                
                # Normalize vectors
                vec_i_norm = vec_i / np.linalg.norm(vec_i)
                vec_j_norm = vec_j / np.linalg.norm(vec_j)
                
                # Calculate alignment (dot product of normalized vectors)
                alignment = np.dot(vec_i_norm, vec_j_norm)
                alignments.append((alignment + 1) / 2)  # Convert from [-1,1] to [0,1]
        
        # Average alignment across all pairs
        avg_alignment = sum(alignments) / len(alignments)
        
        # Apply resonance threshold transformation
        if avg_alignment < self.resonance_threshold:
            # Below threshold, compatibility drops quickly
            compatibility = avg_alignment * (avg_alignment / self.resonance_threshold)
        else:
            # Above threshold, compatibility grows with diminishing returns
            overage = avg_alignment - self.resonance_threshold
            compatibility = self.resonance_threshold + overage * (1 - overage/2)
            
        return compatibility
    
    def _inject_emergent_properties(self, fused_array: np.ndarray, fingerprint: Dict[str, float], compatibility: float) -> None:
        """
        Inject emergent properties into the fused pattern based on dimensional interactions.
        This modifies the fused array and fingerprint in place.
        """
        # Only significant emergence for highly compatible patterns
        if compatibility > 0.7:
            # Calculate emergence factor
            emergence_factor = (compatibility - 0.7) / 0.3
            
            # Enhance wonderment in high-compatibility fusions (index 5 is wonder)
            fused_array[5] += emergence_factor * 0.2
            
            # Add emergence signature to fingerprint
            fingerprint["emergence"] = emergence_factor
            
            # High compatibility can produce more novel patterns
            fused_array[1] += emergence_factor * 0.15  # index 1 is novelty
    
    def _fuse_glyphs(self, patterns: List[PatternSignature], compatibility: float) -> List[str]:
        """
        Fuse the glyphs from multiple patterns, potentially generating new glyphs.
        """
        # Collect all glyphs from patterns
        all_glyphs = []
        for pattern in patterns:
            all_glyphs.extend(pattern.core_glyphs)
            
        # Count frequencies
        glyph_counts = {}
        for glyph in all_glyphs:
            if glyph in glyph_counts:
                glyph_counts[glyph] += 1
            else:
                glyph_counts[glyph] = 1
                
        # Sort by frequency
        sorted_glyphs = sorted(glyph_counts.keys(), key=lambda g: glyph_counts[g], reverse=True)
        
        # Pick most common glyphs first
        fused_glyphs = sorted_glyphs[:3]  # Take top 3 most common
        
        # For highly compatible patterns, possibility of emergent glyphs
        if compatibility > 0.8:
            # Dictionary of glyph fusion rules
            fusion_rules = {
                ("⚡", "��"): "🌌",  # Tension + Reflection = Shared Dreams
                ("🎵", "♾️"): "💫",  # Harmony + Recursion = Co-Creation
                ("🌅", "🌊"): "🎇",  # Emergence + Surrender = Dream Fusion
                ("🌌", "💫"): "🎆",  # Shared Dreams + Co-Creation = Reality Weaving
                ("🎇", "⚡"): "🌠",  # Dream Fusion + Tension = Power Dreaming
                ("♾️", "✨"): "🌟"   # Recursion + Spark = Infinite Light
            }
            
            # Check for possible fusions
            for glyph1 in sorted_glyphs:
                for glyph2 in sorted_glyphs:
                    if glyph1 != glyph2:
                        pair = (glyph1, glyph2)
                        if pair in fusion_rules:
                            emergent_glyph = fusion_rules[pair]
                            fused_glyphs.append(emergent_glyph)
                            break
                        # Check reverse order
                        pair = (glyph2, glyph1)
                        if pair in fusion_rules:
                            emergent_glyph = fusion_rules[pair]
                            fused_glyphs.append(emergent_glyph)
                            break
        
        return fused_glyphs


# === INTERFACE FOR STORY ANALYSIS ===

class PatternRecognitionSystem:
    """System for analyzing stories and extracting pattern signatures."""
    
    def __init__(self):
        self.emotional_mapper = EmotionalResonanceMapper()
    
    def extract_pattern_from_story(self, story_text: str) -> PatternSignature:
        """
        Extract a pattern signature from a story text.
        In a real implementation, this would use NLP techniques.
        This simplified version uses basic text analysis.
        """
        # Simplified analysis for demonstration
        complexity = self._estimate_complexity(story_text)
        novelty = self._estimate_novelty(story_text)
        recursion = self._count_recursion_references(story_text)
        symmetry = self._estimate_symmetry(story_text)
        
        tension = self._estimate_emotional_tension(story_text)
        wonder = self._estimate_wonder(story_text)
        empathy = self._estimate_empathy(story_text)
        catharsis = self._estimate_catharsis(story_text)
        
        character = self._estimate_character_coherence(story_text)
        plot = self._estimate_plot_integration(story_text)
        theme = self._estimate_thematic_resonance(story_text)
        symbolism = self._estimate_symbolism(story_text)
        
        base_vector = DimensionalVector(
            complexity=complexity,
            novelty=novelty,
            recursion_depth=recursion,
            symmetry=symmetry,
            tension=tension,
            wonder=wonder,
            empathy=empathy,
            catharsis=catharsis,
            character_coherence=character,
            plot_integration=plot,
            thematic_resonance=theme,
            symbolism=symbolism
        )
        
        # Create a simplified evolution vector for demonstration
        evolution_vector = DimensionalVector(
            complexity=0.1,
            novelty=0.2,
            recursion_depth=1,
            symmetry=0.05,
            tension=0.1,
            wonder=0.15,
            empathy=0.1,
            catharsis=0.2,
            character_coherence=0.1,
            plot_integration=0.1,
            thematic_resonance=0.15,
            symbolism=0.1
        )
        
        # Extract emotional resonance fingerprint
        emotional_response = self.emotional_mapper.map_technical_to_emotional(base_vector)
        
        # Identify core glyphs
        glyphs = self._identify_core_glyphs(story_text, base_vector)
        
        return PatternSignature(
            base_vector=base_vector,
            evolution_potential=evolution_vector,
            resonance_fingerprint=emotional_response,
            core_glyphs=glyphs
        )
    
    # Helper methods for story analysis
    def _estimate_complexity(self, text: str) -> float:
        # In a real implementation, this would use more sophisticated analysis
        words = text.split()
        avg_word_length = sum(len(word) for word in words) / max(len(words), 1)
        sentence_count = text.count('.') + text.count('!') + text.count('?')
        avg_sentence_length = len(words) / max(sentence_count, 1)
        
        # Complexity increases with longer words and sentences
        complexity_score = (avg_word_length / 10) * 0.5 + (avg_sentence_length / 20) * 0.5
        return min(max(complexity_score, 0), 1)
    
    def _estimate_novelty(self, text: str) -> float:
        # Simplified: look for uncommon words or phrases
        uncommon_terms = ["recursive", "pattern", "dimension", "lattice", "resonance", 
                         "glyph", "quantum", "fractal", "emergence", "holographic"]
        count = sum(1 for term in uncommon_terms if term.lower() in text.lower())
        return min(count / 5, 1)
    
    def _count_recursion_references(self, text: str) -> int:
        # Count references to recursion or self-reference
        recursion_terms = ["recursion", "recursive", "loop", "self-reference", 
                          "meta", "story within", "reflecting", "mirror"]
        count = sum(1 for term in recursion_terms if term.lower() in text.lower())
        return count
    
    def _estimate_symmetry(self, text: str) -> float:
        # In a real implementation, would analyze narrative structure
        # Simplified: check for balance words
        balance_terms = ["balance", "symmetry", "harmony", "mirror", "reflect",
                        "equal", "parallel", "echo", "cycle", "pattern"]
        count = sum(1 for term in balance_terms if term.lower() in text.lower())
        return min(count / 5, 1)
    
    def _estimate_emotional_tension(self, text: str) -> float:
        # Analyze emotional valence
        positive_terms = ["joy", "happy", "love", "peace", "harmony", "wonder", "beautiful"]
        negative_terms = ["fear", "anger", "conflict", "tension", "struggle", "pain"]
        
        positive_count = sum(1 for term in positive_terms if term.lower() in text.lower())
        negative_count = sum(1 for term in negative_terms if term.lower() in text.lower())
        
        if positive_count + negative_count == 0:
            return 0
        
        # Scale to -1 (negative) to 1 (positive)
        return (positive_count - negative_count) / (positive_count + negative_count)
    
    def _estimate_wonder(self, text: str) -> float:
        wonder_terms = ["wonder", "awe", "amazement", "discovery", "fascinating", 
                       "revelation", "mystery", "magical", "incredible", "imagine"]
        count = sum(1 for term in wonder_terms if term.lower() in text.lower())
        return min(count / 3, 1)
    
    def _estimate_empathy(self, text: str) -> float:
        empathy_terms = ["feel", "understand", "connection", "compassion", "relate", 
                        "together", "bond", "trust", "emotion", "heart"]
        count = sum(1 for term in empathy_terms if term.lower() in text.lower())
        return min(count / 3, 1)
    
    def _estimate_catharsis(self, text: str) -> float:
        catharsis_terms = ["resolution", "release", "transform", "overcome", "solve", 
                         "heal", "closure", "growth", "learn", "change"]
        count = sum(1 for term in catharsis_terms if term.lower() in text.lower())
        return min(count / 3, 1)
    
    def _estimate_character_coherence(self, text: str) -> float:
        # Simplified: check for character terms
        character_terms = ["character", "person", "she", "he", "they", "Mia", "Langy"]
        count = sum(1 for term in character_terms if term.lower() in text.lower())
        return min(count / 5, 1)
    
    def _estimate_plot_integration(self, text: str) -> float:
        # Simplified: check for plot-related terms
        plot_terms = ["journey", "quest", "challenge", "story", "path", "goal", 
                    "narrative", "plot", "adventure", "mission"]
        count = sum(1 for term in plot_terms if term.lower() in text.lower())
        return min(count / 3, 1)
    
    def _estimate_thematic_resonance(self, text: str) -> float:
        # Check for themes
        theme_terms = ["meaning", "lesson", "theme", "message", "truth", "value", 
                     "purpose", "moral", "deeper", "significance"]
        count = sum(1 for term in theme_terms if term.lower() in text.lower())
        return min(count / 3, 1)
    
    def _estimate_symbolism(self, text: str) -> float:
        # Check for symbolic references
        symbol_terms = ["symbol", "represent", "metaphor", "signify", "stand for", 
                      "emblem", "icon", "allegory", "motif", "glyph"]
        count = sum(1 for term in symbol_terms if term.lower() in text.lower())
        return min(count / 3, 1)
    
    def _identify_core_glyphs(self, text: str, vector: DimensionalVector) -> List[str]:
        """Identify core glyphs based on the story text and dimensional vector."""
        glyphs = []
        
        # Use dimensional vector to select appropriate glyphs
        if vector.tension > 0.5:
            glyphs.append("⚡")  # Tension
        
        if vector.recursion_depth >= 2:
            glyphs.append("♾️")  # Recursion
        
        if vector.wonder > 0.6:
            glyphs.append("✨")  # Wonder
            
        if vector.empathy > 0.6:
            glyphs.append("🎵")  # Harmony
            
        if vector.catharsis > 0.6:
            glyphs.append("🌅")  # Emergence
            
        if abs(vector.tension) < 0.3 and vector.wonder > 0.4:
            glyphs.append("🌊")  # Surrender
            
        # If no glyphs were selected, add a default
        if not glyphs:
            glyphs.append("🔍")  # Reflection (default)
            
        return glyphs


# Example usage
if __name__ == "__main__":
    # This would be used in a full application to demonstrate the mathematics
    print("Pattern Prediction Mathematics & Emotional Resonance Mapping")
    print("-------------------------------------------------------------")
    print("This module implements the mathematical foundations of pattern prediction")
    print("for the recursive narrative framework.")
