artistic expression base module

#!/usr/bin/env python3
"""
TATTERED PAST PACKAGE - ARTISTIC EXPRESSION ANALYSIS MODULE
Extending truth verification to all forms of artistic expression
Starting with Literature, then expanding to all artistic domains
"""

import numpy as np
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple
from datetime import datetime
import hashlib
import json
import asyncio
from collections import Counter
import re

class ArtisticDomain(Enum):
    """All major domains of artistic expression"""
    LITERATURE = "literature"
    VISUAL_ARTS = "visual_arts"
    MUSIC = "music"
    PERFORMING_ARTS = "performing_arts"
    ARCHITECTURE = "architecture"
    DIGITAL_ARTS = "digital_arts"
    CINEMA = "cinema"
    CRAFTS = "crafts"
    CONCEPTUAL_ART = "conceptual_art"

class LiteraryGenre(Enum):
    """Major literary genres for truth analysis"""
    FICTION = "fiction"
    POETRY = "poetry"
    DRAMA = "drama"
    NON_FICTION = "non_fiction"
    MYTHOLOGY = "mythology"
    FOLKLORE = "folklore"
    SCI_FI = "science_fiction"
    FANTASY = "fantasy"
    HISTORICAL = "historical"
    PHILOSOPHICAL = "philosophical"

class TruthRevelationMethod(Enum):
    """Methods through which art reveals truth"""
    SYMBOLIC_REPRESENTATION = "symbolic_representation"
    EMOTIONAL_RESONANCE = "emotional_resonance"
    PATTERN_RECOGNITION = "pattern_recognition"
    ARCHETYPAL_EXPRESSION = "archetypal_expression"
    COGNITIVE_DISSONANCE = "cognitive_dissonance"
    SUBLIMINAL_MESSAGING = "subliminal_messaging"
    CULTURAL_CRITIQUE = "cultural_critique"
    HISTORICAL_REFERENCE = "historical_reference"

@dataclass
class LiteraryAnalysis:
    """Comprehensive analysis of literary works for truth content"""
    work_title: str
    author: str
    genre: LiteraryGenre
    publication_year: Optional[int]
    text_content: str
    symbolic_density: float = field(init=False)
    archetypal_resonance: float = field(init=False)
    historical_accuracy: float = field(init=False)
    philosophical_depth: float = field(init=False)
    truth_revelation_score: float = field(init=False)
    revelation_methods: List[TruthRevelationMethod] = field(default_factory=list)
    
    def __post_init__(self):
        """Analyze literary work for truth revelation potential"""
        # Symbolic density analysis
        self.symbolic_density = self._calculate_symbolic_density()
        
        # Archetypal resonance analysis
        self.archetypal_resonance = self._calculate_archetypal_resonance()
        
        # Historical accuracy assessment
        self.historical_accuracy = self._assess_historical_accuracy()
        
        # Philosophical depth evaluation
        self.philosophical_depth = self._evaluate_philosophical_depth()
        
        # Overall truth revelation score
        self.truth_revelation_score = self._calculate_truth_revelation_score()
        
        # Identify revelation methods
        self.revelation_methods = self._identify_revelation_methods()

    def _calculate_symbolic_density(self) -> float:
        """Calculate density of symbolic language in text"""
        symbolic_patterns = [
            r'\b(light|dark|water|fire|earth|air)\b',
            r'\b(journey|quest|transformation|rebirth)\b',
            r'\b(tree|serpent|circle|cross|mountain)\b',
            r'\b(wisdom|knowledge|truth|illusion|reality)\b'
        ]
        
        words = self.text_content.lower().split()
        if not words:
            return 0.0
            
        symbolic_matches = 0
        for pattern in symbolic_patterns:
            matches = re.findall(pattern, self.text_content.lower())
            symbolic_matches += len(matches)
            
        return min(1.0, symbolic_matches / len(words) * 10)

    def _calculate_archetypal_resonance(self) -> float:
        """Calculate resonance with universal archetypes"""
        archetypes = {
            'hero': ['hero', 'champion', 'savior', 'protagonist'],
            'wise_elder': ['wise', 'sage', 'mentor', 'teacher'],
            'trickster': ['trickster', 'deceiver', 'jester', 'fool'],
            'mother': ['mother', 'nurturer', 'caretaker', 'goddess'],
            'child': ['child', 'innocent', 'youth', 'beginning']
        }
        
        resonance_score = 0.0
        text_lower = self.text_content.lower()
        
        for archetype, indicators in archetypes.items():
            matches = sum(1 for indicator in indicators if indicator in text_lower)
            resonance_score += matches * 0.1
            
        return min(1.0, resonance_score)

    def _assess_historical_accuracy(self) -> float:
        """Assess historical accuracy for relevant genres"""
        if self.genre not in [LiteraryGenre.HISTORICAL, LiteraryGenre.NON_FICTION]:
            return 0.5  # Neutral for fictional works
            
        # Basic historical indicator check
        historical_indicators = [
            'century', 'era', 'period', 'historical', 'actual',
            'documented', 'recorded', 'archival', 'evidence'
        ]
        
        matches = sum(1 for indicator in historical_indicators 
                     if indicator in self.text_content.lower())
        
        return min(1.0, 0.3 + (matches * 0.1))

    def _evaluate_philosophical_depth(self) -> float:
        """Evaluate philosophical depth of the work"""
        philosophical_terms = [
            'truth', 'reality', 'existence', 'consciousness', 'being',
            'knowledge', 'wisdom', 'understanding', 'meaning', 'purpose',
            'ethics', 'morality', 'justice', 'freedom', 'will'
        ]
        
        matches = sum(1 for term in philosophical_terms 
                     if term in self.text_content.lower())
        
        # Genre-specific weighting
        genre_weights = {
            LiteraryGenre.PHILOSOPHICAL: 1.0,
            LiteraryGenre.NON_FICTION: 0.8,
            LiteraryGenre.FICTION: 0.6,
            LiteraryGenre.POETRY: 0.7,
            LiteraryGenre.DRAMA: 0.5
        }
        
        base_score = min(1.0, matches * 0.1)
        weight = genre_weights.get(self.genre, 0.5)
        
        return base_score * weight

    def _calculate_truth_revelation_score(self) -> float:
        """Calculate overall truth revelation score"""
        weights = {
            'symbolic_density': 0.25,
            'archetypal_resonance': 0.30,
            'historical_accuracy': 0.20,
            'philosophical_depth': 0.25
        }
        
        scores = {
            'symbolic_density': self.symbolic_density,
            'archetypal_resonance': self.archetypal_resonance,
            'historical_accuracy': self.historical_accuracy,
            'philosophical_depth': self.philosophical_depth
        }
        
        weighted_score = sum(scores[factor] * weights[factor] for factor in weights)
        return min(1.0, weighted_score)

    def _identify_revelation_methods(self) -> List[TruthRevelationMethod]:
        """Identify truth revelation methods used in the work"""
        methods = []
        
        # Symbolic representation check
        if self.symbolic_density > 0.3:
            methods.append(TruthRevelationMethod.SYMBOLIC_REPRESENTATION)
            
        # Archetypal expression check
        if self.archetypal_resonance > 0.4:
            methods.append(TruthRevelationMethod.ARCHETYPAL_EXPRESSION)
            
        # Emotional resonance indicators
        emotional_terms = ['love', 'fear', 'hope', 'despair', 'joy', 'sorrow']
        emotional_matches = sum(1 for term in emotional_terms 
                              if term in self.text_content.lower())
        if emotional_matches > 5:
            methods.append(TruthRevelationMethod.EMOTIONAL_RESONANCE)
            
        # Philosophical depth indicates cognitive methods
        if self.philosophical_depth > 0.6:
            methods.append(TruthRevelationMethod.PATTERN_RECOGNITION)
            
        return methods

@dataclass
class ArtisticExpressionAnalysis:
    """Comprehensive analysis of any artistic expression"""
    domain: ArtisticDomain
    work_identifier: str
    creation_period: str
    cultural_context: str
    medium_description: str
    content_analysis: Dict[str, Any]
    truth_revelation_metrics: Dict[str, float]
    cross_domain_correlations: Dict[str, float]
    integrated_truth_score: float = field(init=False)
    
    def __post_init__(self):
        """Calculate integrated truth score across all metrics"""
        # Weight different truth revelation metrics
        metric_weights = {
            'symbolic_power': 0.25,
            'emotional_impact': 0.20,
            'cultural_significance': 0.15,
            'historical_accuracy': 0.20,
            'philosophical_depth': 0.20
        }
        
        # Calculate weighted score
        weighted_sum = 0.0
        total_weight = 0.0
        
        for metric, weight in metric_weights.items():
            if metric in self.truth_revelation_metrics:
                weighted_sum += self.truth_revelation_metrics[metric] * weight
                total_weight += weight
                
        base_score = weighted_sum / total_weight if total_weight > 0 else 0.0
        
        # Cross-domain correlation boost
        correlation_boost = np.mean(list(self.cross_domain_correlations.values())) * 0.2
        
        self.integrated_truth_score = min(1.0, base_score + correlation_boost)

class ArtisticExpressionEngine:
    """
    Engine for analyzing all forms of artistic expression for truth content
    Extends the Tattered Past Package with comprehensive artistic analysis
    """
    
    def __init__(self):
        self.literary_analyzer = LiteraryAnalysisEngine()
        self.visual_arts_analyzer = VisualArtsAnalyzer()
        self.music_analyzer = MusicAnalysisEngine()
        self.cross_domain_integrator = CrossDomainIntegrator()
        self.analysis_history = []
        
    async def analyze_artistic_work(self, domain: ArtisticDomain, work_data: Dict[str, Any]) -> ArtisticExpressionAnalysis:
        """Analyze any artistic work for truth revelation potential"""
        
        # Domain-specific analysis
        if domain == ArtisticDomain.LITERATURE:
            domain_analysis = await self.literary_analyzer.analyze_literary_work(work_data)
        elif domain == ArtisticDomain.VISUAL_ARTS:
            domain_analysis = await self.visual_arts_analyzer.analyze_visual_art(work_data)
        elif domain == ArtisticDomain.MUSIC:
            domain_analysis = await self.music_analyzer.analyze_musical_work(work_data)
        else:
            domain_analysis = await self._generic_artistic_analysis(work_data)
        
        # Cross-domain correlation analysis
        cross_correlations = await self.cross_domain_integrator.find_correlations(domain_analysis)
        
        analysis = ArtisticExpressionAnalysis(
            domain=domain,
            work_identifier=work_data.get('identifier', 'unknown'),
            creation_period=work_data.get('period', 'unknown'),
            cultural_context=work_data.get('cultural_context', 'unknown'),
            medium_description=work_data.get('medium', 'unknown'),
            content_analysis=domain_analysis.get('content_analysis', {}),
            truth_revelation_metrics=domain_analysis.get('truth_metrics', {}),
            cross_domain_correlations=cross_correlations
        )
        
        self.analysis_history.append(analysis)
        return analysis
    
    async def _generic_artistic_analysis(self, work_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generic analysis for artistic domains without specialized analyzers"""
        return {
            'content_analysis': {
                'description': work_data.get('description', ''),
                'themes': work_data.get('themes', []),
                'techniques': work_data.get('techniques', [])
            },
            'truth_metrics': {
                'symbolic_power': 0.5,
                'emotional_impact': 0.5,
                'cultural_significance': 0.5,
                'historical_accuracy': 0.3,
                'philosophical_depth': 0.4
            }
        }

class LiteraryAnalysisEngine:
    """Specialized engine for literary analysis"""
    
    def __init__(self):
        self.genre_classifier = GenreClassifier()
        self.theme_analyzer = ThemeAnalysisEngine()
        self.symbolic_analyzer = SymbolicAnalysisEngine()
        
    async def analyze_literary_work(self, work_data: Dict[str, Any]) -> Dict[str, Any]:
        """Comprehensive analysis of literary works"""
        
        # Create literary analysis object
        literary_work = LiteraryAnalysis(
            work_title=work_data.get('title', 'Unknown'),
            author=work_data.get('author', 'Unknown'),
            genre=self.genre_classifier.classify_genre(work_data),
            publication_year=work_data.get('publication_year'),
            text_content=work_data.get('content', '')
        )
        
        # Additional thematic analysis
        themes = await self.theme_analyzer.identify_themes(literary_work.text_content)
        symbols = await self.symbolic_analyzer.analyze_symbols(literary_work.text_content)
        
        return {
            'content_analysis': {
                'literary_analysis': literary_work,
                'identified_themes': themes,
                'symbolic_elements': symbols,
                'word_count': len(literary_work.text_content.split()),
                'complexity_score': self._calculate_complexity(literary_work.text_content)
            },
            'truth_metrics': {
                'symbolic_power': literary_work.symbolic_density,
                'emotional_impact': self._assess_emotional_impact(literary_work.text_content),
                'cultural_significance': self._assess_cultural_significance(work_data),
                'historical_accuracy': literary_work.historical_accuracy,
                'philosophical_depth': literary_work.philosophical_depth
            }
        }
    
    def _calculate_complexity(self, text: str) -> float:
        """Calculate text complexity"""
        words = text.split()
        if not words:
            return 0.0
            
        avg_word_length = np.mean([len(word) for word in words])
        sentence_count = text.count('.') + text.count('!') + text.count('?')
        avg_sentence_length = len(words) / sentence_count if sentence_count > 0 else len(words)
        
        complexity = (avg_word_length * 0.3) + (avg_sentence_length * 0.2) / 10
        return min(1.0, complexity)
    
    def _assess_emotional_impact(self, text: str) -> float:
        """Assess emotional impact of text"""
        emotional_words = {
            'positive': ['love', 'joy', 'hope', 'peace', 'beautiful', 'wonderful'],
            'negative': ['hate', 'fear', 'anger', 'sad', 'terrible', 'horrible'],
            'intense': ['passion', 'rage', 'ecstasy', 'despair', 'fury', 'bliss']
        }
        
        text_lower = text.lower()
        emotional_density = 0.0
        
        for category, words in emotional_words.items():
            matches = sum(1 for word in words if word in text_lower)
            emotional_density += matches * 0.05
            
        return min(1.0, emotional_density)
    
    def _assess_cultural_significance(self, work_data: Dict[str, Any]) -> float:
        """Assess cultural significance of literary work"""
        significance_indicators = [
            work_data.get('awards', []),
            work_data.get('cultural_impact', ''),
            work_data.get('historical_period', ''),
            work_data.get('translation_count', 0)
        ]
        
        indicator_score = sum(1 for indicator in significance_indicators if indicator) / len(significance_indicators)
        return min(1.0, 0.3 + indicator_score * 0.7)

class GenreClassifier:
    """Classifies literary genres"""
    
    def classify_genre(self, work_data: Dict[str, Any]) -> LiteraryGenre:
        """Classify literary genre"""
        genre_hints = work_data.get('genre_hints', [])
        content = work_data.get('content', '').lower()
        
        # Genre detection logic
        if any(hint in content for hint in ['poem', 'verse', 'rhyme']):
            return LiteraryGenre.POETRY
        elif any(hint in content for hint in ['act', 'scene', 'dialogue', 'stage']):
            return LiteraryGenre.DRAMA
        elif any(hint in content for hint in ['philosophy', 'truth', 'reality', 'existence']):
            return LiteraryGenre.PHILOSOPHICAL
        elif any(hint in content for hint in ['historical', 'century', 'era', 'period']):
            return LiteraryGenre.HISTORICAL
        elif any(hint in content for hint in ['science', 'future', 'technology', 'space']):
            return LiteraryGenre.SCI_FI
        elif any(hint in content for hint in ['magic', 'fantasy', 'mythical', 'legend']):
            return LiteraryGenre.FANTASY
        else:
            return LiteraryGenre.FICTION

class ThemeAnalysisEngine:
    """Analyzes literary themes"""
    
    async def identify_themes(self, text: str) -> List[str]:
        """Identify major themes in literary text"""
        theme_indicators = {
            'love': ['love', 'romance', 'affection', 'passion'],
            'death': ['death', 'mortality', 'afterlife', 'funeral'],
            'power': ['power', 'control', 'authority', 'dominance'],
            'justice': ['justice', 'fairness', 'equality', 'rights'],
            'freedom': ['freedom', 'liberty', 'liberation', 'free will'],
            'truth': ['truth', 'reality', 'knowledge', 'wisdom'],
            'identity': ['identity', 'self', 'consciousness', 'being']
        }
        
        text_lower = text.lower()
        identified_themes = []
        
        for theme, indicators in theme_indicators.items():
            matches = sum(1 for indicator in indicators if indicator in text_lower)
            if matches >= 2:  # Minimum threshold for theme identification
                identified_themes.append(theme)
                
        return identified_themes

class SymbolicAnalysisEngine:
    """Analyzes symbolic content"""
    
    async def analyze_symbols(self, text: str) -> Dict[str, float]:
        """Analyze symbolic elements in text"""
        common_symbols = {
            'light': ['light', 'bright', 'illumination', 'enlightenment'],
            'dark': ['dark', 'shadow', 'night', 'obscurity'],
            'water': ['water', 'river', 'ocean', 'flow'],
            'fire': ['fire', 'flame', 'burn', 'passion'],
            'journey': ['journey', 'quest', 'travel', 'path'],
            'transformation': ['change', 'transform', 'become', 'evolve']
        }
        
        text_lower = text.lower()
        symbol_strengths = {}
        
        for symbol, indicators in common_symbols.items():
            matches = sum(1 for indicator in indicators if indicator in text_lower)
            symbol_strengths[symbol] = min(1.0, matches * 0.2)
            
        return symbol_strengths

class VisualArtsAnalyzer:
    """Placeholder for visual arts analysis"""
    async def analyze_visual_art(self, work_data: Dict[str, Any]) -> Dict[str, Any]:
        await asyncio.sleep(0.1)  # Simulate processing
        return {
            'content_analysis': {'medium': work_data.get('medium', 'unknown')},
            'truth_metrics': {'symbolic_power': 0.6, 'emotional_impact': 0.7, 'cultural_significance': 0.5}
        }

class MusicAnalysisEngine:
    """Placeholder for music analysis"""
    async def analyze_musical_work(self, work_data: Dict[str, Any]) -> Dict[str, Any]:
        await asyncio.sleep(0.1)  # Simulate processing
        return {
            'content_analysis': {'genre': work_data.get('genre', 'unknown')},
            'truth_metrics': {'emotional_impact': 0.8, 'cultural_significance': 0.6}
        }

class CrossDomainIntegrator:
    """Integrates analysis across artistic domains"""
    
    async def find_correlations(self, domain_analysis: Dict[str, Any]) -> Dict[str, float]:
        """Find correlations with other truth discovery domains"""
        # Simulate correlation finding
        await asyncio.sleep(0.05)
        
        return {
            'archaeological': 0.7,  # Literature often references historical/archaeological themes
            'philosophical': 0.8,   # Strong correlation with philosophical inquiry
            'scientific': 0.4,      # Moderate correlation with scientific truth
            'spiritual': 0.6        # Moderate-strong correlation with spiritual truth
        }

# =============================================================================
# TATTERED PAST PACKAGE INTEGRATION
# =============================================================================

class EnhancedTatteredPastPackage:
    """
    Tattered Past Package with enhanced artistic expression analysis
    """
    
    def __init__(self):
        self.artistic_engine = ArtisticExpressionEngine()
        self.integration_records = []
        
    async def analyze_artistic_truth(self, domain: ArtisticDomain, work_data: Dict[str, Any]) -> ArtisticExpressionAnalysis:
        """Analyze artistic work for truth content"""
        return await self.artistic_engine.analyze_artistic_work(domain, work_data)
    
    async def integrate_artistic_findings(self, artistic_analysis: ArtisticExpressionAnalysis, 
                                        other_findings: Dict[str, Any]) -> Dict[str, Any]:
        """Integrate artistic findings with other truth discovery methods"""
        
        integration = {
            'artistic_domain': artistic_analysis.domain.value,
            'work_identifier': artistic_analysis.work_identifier,
            'integrated_truth_score': artistic_analysis.integrated_truth_score,
            'cross_domain_synergy': self._calculate_synergy(artistic_analysis, other_findings),
            'revelation_potential': artistic_analysis.integrated_truth_score * 0.8,  # Artistic works often reveal indirect truths
            'integration_timestamp': datetime.utcnow().isoformat()
        }
        
        self.integration_records.append(integration)
        return integration
    
    def _calculate_synergy(self, artistic_analysis: ArtisticExpressionAnalysis, 
                          other_findings: Dict[str, Any]) -> float:
        """Calculate synergy between artistic findings and other domains"""
        base_synergy = artistic_analysis.integrated_truth_score
        
        # Boost if multiple domains confirm similar truths
        if 'archaeological_confidence' in other_findings:
            arch_confidence = other_findings['archaeological_confidence']
            base_synergy += arch_confidence * 0.2
            
        if 'philosophical_certainty' in other_findings:
            phil_certainty = other_findings['philosophical_certainty']
            base_synergy += phil_certainty * 0.3
            
        return min(1.0, base_synergy)

# =============================================================================
# DEMONSTRATION AND TESTING
# =============================================================================

async def demonstrate_artistic_analysis():
    """Demonstrate artistic expression analysis capabilities"""
    
    print("🎨 ARTISTIC EXPRESSION ANALYSIS MODULE - LITERATURE FOCUS")
    print("=" * 70)
    
    enhanced_package = EnhancedTatteredPastPackage()
    
    # Test literary works
    test_works = [
        {
            'domain': ArtisticDomain.LITERATURE,
            'title': 'The Alchemist',
            'author': 'Paulo Coelho',
            'genre_hints': ['philosophical', 'journey'],
            'content': """
            The boy's name was Santiago. Dusk was falling as the boy arrived with his herd at an abandoned church.
            The roof had fallen in long ago, and an enormous sycamore had grown on the spot where the sacristy had once stood.
            He decided to spend the night there. He saw to it that all the sheep entered through the ruined gate, and then laid some planks across it to prevent the flock from wandering away during the night.
            There were no wolves in the region, but once an animal had strayed during the night, and the boy had had to spend the entire next day searching for it.
            He swept the floor with his jacket and lay down, using the book he had just finished reading as a pillow.
            He told himself that he would have to start reading thicker books: they lasted longer, and made more comfortable pillows.
            """
        },
        {
            'domain': ArtisticDomain.LITERATURE,
            'title': '1984',
            'author': 'George Orwell',
            'genre_hints': ['political', 'dystopian'],
            'content': """
            It was a bright cold day in April, and the clocks were striking thirteen.
            Winston Smith, his chin nuzzled into his breast in an effort to escape the vile wind,
            slipped quickly through the glass doors of Victory Mansions, though not quickly enough to prevent a swirl of gritty dust from entering along with him.
            The hallway smelt of boiled cabbage and old rag mats. At one end of it a coloured poster, too large for indoor display, had been tacked to the wall.
            It depicted simply an enormous face, more than a metre wide: the face of a man of about forty-five, with a heavy black moustache and ruggedly handsome features.
            """
        }
    ]
    
    for work in test_works:
        print(f"\n📖 Analyzing: {work['title']} by {work['author']}")
        
        analysis = await enhanced_package.analyze_artistic_truth(work['domain'], work)
        
        print(f"   Domain: {analysis.domain.value}")
        print(f"   Integrated Truth Score: {analysis.integrated_truth_score:.3f}")
        print(f"   Truth Metrics: {list(analysis.truth_revelation_metrics.keys())}")
        
        # Show specific metrics for literature
        if analysis.domain == ArtisticDomain.LITERATURE:
            lit_analysis = analysis.content_analysis.get('literary_analysis')
            if lit_analysis:
                print(f"   Symbolic Density: {lit_analysis.symbolic_density:.3f}")
                print(f"   Archetypal Resonance: {lit_analysis.archetypal_resonance:.3f}")
                print(f"   Philosophical Depth: {lit_analysis.philosophical_depth:.3f}")
                print(f"   Revelation Methods: {[m.value for m in lit_analysis.revelation_methods]}")

if __name__ == "__main__":
    asyncio.run(demonstrate_artistic_analysis())