alternative religions module

#!/usr/bin/env python3
"""
OBSERVER-ENGINE COGNITIVE ARCHITECTURE - ALTERNATIVE RELIGIONS MODULE
Analysis of Anti-Religion, Esoteric, Gnostic, Satanic, Witchcraft and Ritual Traditions
Complete Operational Implementation - Not Theoretical
"""

import numpy as np
import asyncio
import hashlib
import json
import secrets
import logging
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple, Callable
from datetime import datetime, timedelta
import scipy.stats as stats
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from cryptography.hazmat.backends import default_backend
import qiskit
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile
from qiskit_aer import AerSimulator
from qiskit.algorithms import AmplificationProblem, Grover
from qiskit.circuit.library import PhaseOracle
from qiskit.quantum_info import Statevector, random_statevector
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
import aiohttp
import redis
import sqlite3
from contextlib import asynccontextmanager
import psutil
import gc
import os
import sys
from pathlib import Path
import uuid
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud
import networkx as nx

# =============================================================================
# CORE ANALYSIS FRAMEWORKS (INTEGRATED FROM PREVIOUS MODULES)
# =============================================================================

class UniversalLawPrimacy:
    """Integrated from Ancient Religions Module"""
    
    def evaluate_alignment(self, element: str) -> Dict[str, Any]:
        """Evaluate against Universal Law principles"""
        principles = {
            'free_will': ['choice', 'agency', 'sovereignty', 'consent'],
            'cause_effect': ['consequence', 'responsibility', 'accountability', 'karma'],
            'consciousness_primacy': ['awareness', 'mind', 'observer', 'experience'],
            'interconnectedness': ['unity', 'relationship', 'wholeness', 'community'],
            'growth_imperacy': ['evolution', 'development', 'learning', 'expansion']
        }
        
        alignment_scores = {}
        for principle, indicators in principles.items():
            matches = sum(1 for indicator in indicators if indicator in element.lower())
            alignment_scores[principle] = min(1.0, 0.3 + (matches * 0.1))
        
        overall_alignment = np.mean(list(alignment_scores.values()))
        
        return {
            'universal_law_alignment': overall_alignment,
            'principle_breakdown': alignment_scores,
            'violations': self._detect_universal_law_violations(element)
        }
    
    def _detect_universal_law_violations(self, element: str) -> List[str]:
        """Detect violations of Universal Law"""
        violations = []
        violation_patterns = {
            'coercion': ['must obey', 'forced', 'compulsory', 'required submission'],
            'denial_of_agency': ['no choice', 'predetermined', 'fated', 'inevitable'],
            'isolation_emphasis': ['alone', 'separate from', 'cut off', 'disconnected'],
            'growth_restriction': ['stay as you are', 'do not seek', 'forbidden knowledge']
        }
        
        for violation, patterns in violation_patterns.items():
            if any(pattern in element.lower() for pattern in patterns):
                violations.append(violation)
        
        return violations

class InversionDetection:
    """Integrated from Early Religions Module"""
    
    def analyze_inversion_patterns(self, element: str) -> Dict[str, Any]:
        """Analyze for inversion patterns"""
        inversion_patterns = {
            'direct_to_mediated': {
                'indicators': ['through the', 'only via', 'requires intermediary', 'mediated by'],
                'original': "Direct access to spiritual experience",
                'inverted': "Access mediated through external authority"
            },
            'experience_to_dogma': {
                'indicators': ['doctrine says', 'creed states', 'orthodox belief', 'official teaching'],
                'original': "Direct experiential knowing",
                'inverted': "Dogmatic belief requirements"
            },
            'inclusive_to_exclusive': {
                'indicators': ['only for', 'exclusive to', 'special class', 'chosen few'],
                'original': "Universal spiritual potential",
                'inverted': "Limited access or elite status"
            }
        }
        
        detected_inversions = []
        inversion_details = {}
        
        for pattern_name, pattern_data in inversion_patterns.items():
            matches = sum(1 for indicator in pattern_data['indicators'] if indicator in element.lower())
            if matches > 0:
                detected_inversions.append(pattern_name)
                inversion_details[pattern_name] = {
                    'detection_confidence': matches / len(pattern_data['indicators']),
                    'original_state': pattern_data['original'],
                    'inverted_state': pattern_data['inverted']
                }
        
        inversion_score = len(detected_inversions) / len(inversion_patterns)
        
        return {
            'inversion_score': inversion_score,
            'detected_inversions': detected_inversions,
            'inversion_details': inversion_details
        }

class ModernSophisticationAnalyzer:
    """Integrated from Modern Religions Module"""
    
    def analyze_modern_characteristics(self, element: str, context: Dict) -> Dict[str, Any]:
        """Analyze modern characteristics and camouflage"""
        modern_metrics = {
            'commercialization': {
                'indicators': ['premium', 'exclusive', 'tiered', 'investment', 'paid', 'subscription'],
                'weight': 0.25
            },
            'scientific_rationalization': {
                'indicators': ['evidence-based', 'scientifically proven', 'research shows', 'clinical studies'],
                'weight': 0.20
            },
            'technological_mediation': {
                'indicators': ['app-based', 'digital', 'online only', 'virtual', 'algorithmic'],
                'weight': 0.25
            },
            'therapeutic_reframing': {
                'indicators': ['healing', 'therapy', 'trauma-informed', 'clinical', 'psychological'],
                'weight': 0.15
            },
            'expertise_gatekeeping': {
                'indicators': ['certified', 'licensed', 'accredited', 'professional', 'credentialed'],
                'weight': 0.15
            }
        }
        
        sophistication_scores = {}
        for metric_name, metric_data in modern_metrics.items():
            matches = sum(1 for indicator in metric_data['indicators'] if indicator in element.lower())
            sophistication_scores[metric_name] = min(1.0, matches * 0.2)
        
        overall_sophistication = sum(score * modern_metrics[metric]['weight'] 
                                   for metric, score in sophistication_scores.items())
        
        return {
            'modern_sophistication': overall_sophistication,
            'sophistication_breakdown': sophistication_scores,
            'camouflage_effectiveness': self._assess_camouflage(element),
            'detection_difficulty': self._calculate_detection_difficulty(overall_sophistication)
        }
    
    def _assess_camouflage(self, element: str) -> float:
        """Assess how effectively inversions are camouflaged"""
        camouflage_indicators = [
            'progressive', 'inclusive', 'modern', 'updated', 'contemporary',
            'for today', 'relevant', 'accessible', 'democratic'
        ]
        
        matches = sum(1 for indicator in camouflage_indicators if indicator in element.lower())
        return min(1.0, 0.3 + (matches * 0.1))
    
    def _calculate_detection_difficulty(self, sophistication: float) -> str:
        """Calculate detection difficulty"""
        if sophistication < 0.3:
            return "LOW"
        elif sophistication < 0.6:
            return "MEDIUM"
        else:
            return "HIGH"

# =============================================================================
# ALTERNATIVE RELIGIONS CORE ENGINE
# =============================================================================

class AlternativeReligionsAnalyzer:
    """Core analyzer for alternative religious and spiritual movements"""
    
    def __init__(self):
        self.universal_law = UniversalLawPrimacy()
        self.inversion_detector = InversionDetection()
        self.modern_analyzer = ModernSophisticationAnalyzer()
        self.alternative_database = self._initialize_alternative_database()
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('AlternativeReligions')
        logger.setLevel(logging.INFO)
        return logger
    
    def _initialize_alternative_database(self) -> Dict[str, Any]:
        """Initialize comprehensive alternative religions database"""
        return {
            'anti_religion_movements': {
                'new_atheism': {
                    'key_figures': ['Richard Dawkins', 'Christopher Hitchens', 'Sam Harris'],
                    'core_tenets': [
                        "Religious claims require empirical evidence",
                        "Faith is not a valid path to truth", 
                        "Religion often causes harm to society",
                        "Science and reason as alternative frameworks"
                    ],
                    'inversion_analysis': "Rebellion against religious authority, but risks creating scientistic dogma"
                },
                'secular_humanism': {
                    'core_principles': [
                        "Human reason and ethics without supernaturalism",
                        "Human flourishing as ultimate good",
                        "Democracy, human rights, and personal freedom",
                        "Naturalistic worldview"
                    ],
                    'strengths': "Focus on human agency and ethical responsibility",
                    'weaknesses': "May lack transcendent dimension and spiritual technology"
                },
                'satanism': {
                    'lavayan': {
                        'tenets': [
                            "Indulgence instead of abstinence",
                            "Vital existence instead of spiritual pipe dreams",
                            "Undefiled wisdom instead of hypocritical self-deceit",
                            "Responsibility to the responsible instead of concern for psychic vampires"
                        ],
                        'analysis': "Theatrical inversion of Christianity, commercial elements prominent"
                    },
                    'theistic_satanism': {
                        'characteristics': [
                            "Actual worship of Satan as deity",
                            "Pre-Christian influences",
                            "Ritual magic and invocation",
                            "Often secretive and initiatory"
                        ],
                        'analysis': "More genuine spiritual path, though controversial"
                    }
                }
            },
            'esoteric_occult_traditions': {
                'hermeticism': {
                    'key_texts': ['Corpus Hermeticum', 'Emerald Tablet'],
                    'core_principles': [
                        "Principle of Mentalism - All is Mind",
                        "Principle of Correspondence - As above, so below",
                        "Principle of Vibration - Nothing rests, everything moves",
                        "Principle of Polarity - Everything is dual, opposites are identical in nature"
                    ],
                    'consciousness_tech': "Mental transmutation, spiritual alchemy"
                },
                'golden_dawn': {
                    'practices': [
                        "Ritual magic and ceremonial practices",
                        "Qabalah study and application",
                        "Tarot as symbolic system",
                        "Astrological magic"
                    ],
                    'structure': "Grade system with initiations",
                    'preservation_status': "Mixed - some authentic tech, some modern reconstruction"
                },
                'thelema': {
                    'core_tenets': [
                        "Do what thou wilt shall be the whole of the Law",
                        "Love is the law, love under will", 
                        "Every man and every woman is a star"
                    ],
                    'analysis': "Individual sovereignty emphasis, but potential for ego inflation"
                }
            },
            'witchcraft_pagan_traditions': {
                'wicca': {
                    'origins': "20th century revival by Gerald Gardner",
                    'practices': [
                        "Seasonal rituals (Sabbats)",
                        "Moon ceremonies (Esbats)",
                        "Magic and spellwork",
                        "Nature reverence"
                    ],
                    'consciousness_tech': "Energy work, natural cycles alignment",
                    'authenticity': "Modern reconstruction with some authentic elements"
                },
                'traditional_witchcraft': {
                    'characteristics': [
                        "Folk magic practices",
                        "Ancestral veneration",
                        "Land spirits and familiar work",
                        "Often solitary practice"
                    ],
                    'preservation': "Some unbroken family lines, much reconstruction"
                },
                'druidry': {
                    'practices': [
                        "Nature spirituality and reverence",
                        "Bardic arts and storytelling",
                        "Ogham and tree lore",
                        "Seasonal celebrations"
                    ],
                    'strengths': "Strong ecological consciousness and community focus"
                }
            },
            'gnostic_mystical_traditions': {
                'classical_gnosticism': {
                    'key_teachings': [
                        "Divine spark within trapped in material world",
                        "Salvation through gnosis (direct knowledge)",
                        "Demiurge as false creator god",
                        "Transcendence of material limitations"
                    ],
                    'consciousness_tech': "Direct experiential knowing, spiritual awakening"
                },
                'christian_mysticism': {
                    'practices': [
                        "Contemplative prayer",
                        "Meditation and silence",
                        "Divine union seeking",
                        "Inner transformation"
                    ],
                    'figures': ["Meister Eckhart", "Teresa of Avila", "John of the Cross"],
                    'preservation': "Authentic direct experience traditions within Christianity"
                },
                'kabbalah': {
                    'practices': [
                        "Tree of Life meditation",
                        "Hebrew letter contemplation", 
                        "Divine name invocation",
                        "Soul rectification work"
                    ],
                    'consciousness_tech': "Map of consciousness, spiritual ascent practices"
                }
            },
            'psychedelic_shamanic_traditions': {
                'plant_medicine_traditions': {
                    'examples': ["Ayahuasca ceremonies", "Peyote rituals", "Psilocybin use"],
                    'consciousness_tech': "Direct states alteration, visionary experiences",
                    'risks': "Lack of proper guidance, cultural appropriation"
                },
                'neo_shamanism': {
                    'practices': [
                        "Journeying and vision quests",
                        "Drumming and ecstatic states",
                        "Animal spirit work",
                        "Healing ceremonies"
                    ],
                    'authenticity': "Mixed - some genuine tech, much modern adaptation"
                }
            }
        }
    
    async def analyze_alternative_movement(self, movement_data: Dict, context: Dict) -> Dict[str, Any]:
        """Comprehensive analysis of alternative religious movement"""
        try:
            self.logger.info(f"🔍 Analyzing alternative movement: {movement_data.get('name', 'unknown')}")
            
            # Extract key elements for analysis
            analysis_elements = self._extract_analysis_elements(movement_data)
            
            # Perform multi-dimensional analysis
            analysis_results = await self._perform_comprehensive_analysis(analysis_elements, context)
            
            # Calculate overall assessments
            overall_assessment = self._calculate_overall_assessment(analysis_results, movement_data)
            
            result = {
                'movement': movement_data,
                'analysis_timestamp': datetime.utcnow().isoformat(),
                'comprehensive_analysis': analysis_results,
                'overall_assessment': overall_assessment,
                'preservation_score': self._calculate_preservation_score(movement_data, analysis_results),
                'rebellion_effectiveness': self._assess_rebellion_effectiveness(analysis_results),
                'authenticity_rating': self._determine_authenticity(movement_data, analysis_results),
                'consciousness_tech_quality': self._assess_consciousness_tech(movement_data),
                'recovery_recommendations': self._generate_recovery_recommendations(analysis_results)
            }
            
            return result
            
        except Exception as e:
            self.logger.error(f"Alternative movement analysis failed: {e}")
            return {
                'movement': movement_data,
                'error': str(e),
                'analysis_timestamp': datetime.utcnow().isoformat()
            }
    
    def _extract_analysis_elements(self, movement_data: Dict) -> List[str]:
        """Extract key elements for analysis from movement data"""
        elements = []
        
        # Extract from various data fields
        fields_to_extract = [
            'core_tenets', 'core_principles', 'key_teachings', 'practices',
            'characteristics', 'tenets', 'principles'
        ]
        
        for field in fields_to_extract:
            if field in movement_data:
                if isinstance(movement_data[field], list):
                    elements.extend(movement_data[field])
                else:
                    elements.append(str(movement_data[field]))
        
        return elements
    
    async def _perform_comprehensive_analysis(self, elements: List[str], context: Dict) -> Dict[str, Any]:
        """Perform comprehensive analysis on movement elements"""
        universal_law_analyses = []
        inversion_analyses = []
        modern_analyses = []
        
        for element in elements:
            # Universal Law analysis
            universal_analysis = self.universal_law.evaluate_alignment(element)
            universal_law_analyses.append(universal_analysis)
            
            # Inversion analysis
            inversion_analysis = self.inversion_detector.analyze_inversion_patterns(element)
            inversion_analyses.append(inversion_analysis)
            
            # Modern characteristics analysis
            modern_analysis = self.modern_analyzer.analyze_modern_characteristics(element, context)
            modern_analyses.append(modern_analysis)
        
        return {
            'universal_law_analyses': universal_law_analyses,
            'inversion_analyses': inversion_analyses,
            'modern_analyses': modern_analyses,
            'element_count': len(elements)
        }
    
    def _calculate_overall_assessment(self, analysis_results: Dict, movement_data: Dict) -> Dict[str, Any]:
        """Calculate overall movement assessment"""
        # Average Universal Law alignment
        universal_scores = [a['universal_law_alignment'] for a in analysis_results['universal_law_analyses']]
        avg_universal_alignment = np.mean(universal_scores) if universal_scores else 0.5
        
        # Average inversion score
        inversion_scores = [a['inversion_score'] for a in analysis_results['inversion_analyses']]
        avg_inversion_score = np.mean(inversion_scores) if inversion_scores else 0.5
        
        # Average modern sophistication
        modern_scores = [a['modern_sophistication'] for a in analysis_results['modern_analyses']]
        avg_modern_sophistication = np.mean(modern_scores) if modern_scores else 0.5
        
        return {
            'universal_law_alignment': avg_universal_alignment,
            'inversion_resistance': 1.0 - avg_inversion_score,
            'modern_sophistication': avg_modern_sophistication,
            'overall_health_score': self._calculate_health_score(
                avg_universal_alignment, avg_inversion_score, avg_modern_sophistication
            )
        }
    
    def _calculate_health_score(self, universal: float, inversion: float, modern: float) -> float:
        """Calculate overall health score for movement"""
        # Higher universal alignment and inversion resistance are good
        # Moderate modern sophistication is ideal (too low = primitive, too high = over-commercialized)
        modern_optimal = 1.0 - abs(modern - 0.5)  # Peak at 0.5
        
        health_score = (universal * 0.4) + ((1.0 - inversion) * 0.4) + (modern_optimal * 0.2)
        return min(1.0, health_score)
    
    def _calculate_preservation_score(self, movement_data: Dict, analysis_results: Dict) -> float:
        """Calculate how well movement preserves authentic consciousness technology"""
        preservation_indicators = [
            'consciousness_tech' in str(movement_data).lower(),
            any('direct experience' in element.lower() for element in self._extract_analysis_elements(movement_data)),
            any('meditation' in element.lower() or 'contemplation' in element.lower() 
                for element in self._extract_analysis_elements(movement_data)),
            movement_data.get('preservation_status', '').lower() in ['authentic', 'genuine', 'unbroken']
        ]
        
        indicator_count = sum(preservation_indicators)
        base_score = indicator_count / len(preservation_indicators)
        
        # Boost score if Universal Law alignment is high
        universal_alignment = analysis_results['overall_assessment']['universal_law_alignment']
        
        return min(1.0, (base_score * 0.7) + (universal_alignment * 0.3))
    
    def _assess_rebellion_effectiveness(self, analysis_results: Dict) -> Dict[str, Any]:
        """Assess effectiveness of rebellion against mainstream inversions"""
        inversion_resistance = analysis_results['overall_assessment']['inversion_resistance']
        universal_alignment = analysis_results['overall_assessment']['universal_law_alignment']
        
        # Effective rebellion maintains high Universal Law alignment while resisting inversions
        rebellion_score = (inversion_resistance * 0.6) + (universal_alignment * 0.4)
        
        effectiveness_level = "LOW"
        if rebellion_score > 0.7:
            effectiveness_level = "HIGH"
        elif rebellion_score > 0.5:
            effectiveness_level = "MODERATE"
        
        return {
            'rebellion_score': rebellion_score,
            'effectiveness_level': effectiveness_level,
            'success_criteria': "High inversion resistance + High Universal Law alignment"
        }
    
    def _determine_authenticity(self, movement_data: Dict, analysis_results: Dict) -> Dict[str, Any]:
        """Determine authenticity of movement's claims and practices"""
        authenticity_factors = {
            'historical_lineage': self._assess_historical_lineage(movement_data),
            'consciousness_tech_presence': self._assess_tech_presence(movement_data),
            'commercial_elements': self._assess_commercial_elements(analysis_results),
            'universal_law_alignment': analysis_results['overall_assessment']['universal_law_alignment']
        }
        
        authenticity_score = np.mean(list(authenticity_factors.values()))
        
        authenticity_level = "QUESTIONABLE"
        if authenticity_score > 0.8:
            authenticity_level = "HIGH"
        elif authenticity_score > 0.6:
            authenticity_level = "MODERATE"
        
        return {
            'authenticity_score': authenticity_score,
            'authenticity_level': authenticity_level,
            'factor_breakdown': authenticity_factors
        }
    
    def _assess_historical_lineage(self, movement_data: Dict) -> float:
        """Assess historical lineage and origins"""
        lineage_indicators = [
            'unbroken' in str(movement_data).lower(),
            'ancient' in str(movement_data).lower(),
            'traditional' in str(movement_data).lower(),
            movement_data.get('preservation_status', '') in ['authentic', 'genuine'],
            'reconstruction' not in str(movement_data).lower()
        ]
        
        return sum(lineage_indicators) / len(lineage_indicators)
    
    def _assess_tech_presence(self, movement_data: Dict) -> float:
        """Assess presence of genuine consciousness technology"""
        tech_indicators = [
            'consciousness_tech' in str(movement_data),
            any(keyword in str(movement_data).lower() 
                for keyword in ['meditation', 'contemplation', 'direct experience', 'gnosis', 'awakening']),
            'practices' in movement_data and len(movement_data['practices']) > 0
        ]
        
        return sum(tech_indicators) / len(tech_indicators)
    
    def _assess_commercial_elements(self, analysis_results: Dict) -> float:
        """Assess commercial elements (inverse score - higher commerce = lower authenticity)"""
        modern_analyses = analysis_results['modern_analyses']
        if not modern_analyses:
            return 0.5
        
        commercial_scores = [analysis['sophistication_breakdown'].get('commercialization', 0) 
                           for analysis in modern_analyses]
        avg_commercial = np.mean(commercial_scores) if commercial_scores else 0
        
        # Invert for authenticity (higher commerce = lower authenticity)
        return 1.0 - avg_commercial
    
    def _assess_consciousness_tech(self, movement_data: Dict) -> Dict[str, Any]:
        """Assess quality and accessibility of consciousness technology"""
        elements = self._extract_analysis_elements(movement_data)
        
        tech_quality_indicators = 0
        accessibility_indicators = 0
        
        for element in elements:
            element_lower = element.lower()
            
            # Quality indicators
            if any(keyword in element_lower for keyword in ['direct experience', 'immediate knowing', 'unmediated']):
                tech_quality_indicators += 2
            if any(keyword in element_lower for keyword in ['meditation', 'contemplation', 'inner practice']):
                tech_quality_indicators += 1
            if any(keyword in element_lower for keyword in ['energy work', 'vibration', 'frequency']):
                tech_quality_indicators += 1
            
            # Accessibility indicators
            if any(keyword in element_lower for keyword in ['available to all', 'universal access', 'everyone can']):
                accessibility_indicators += 2
            if any(keyword in element_lower for keyword in ['simple', 'accessible', 'easy to learn']):
                accessibility_indicators += 1
            if 'requires' in element_lower and any(keyword in element_lower 
                for keyword in ['initiation', 'certification', 'special training']):
                accessibility_indicators -= 1
        
        max_quality = len(elements) * 2
        max_access = len(elements) * 2
        
        quality_score = tech_quality_indicators / max_quality if max_quality > 0 else 0
        access_score = max(0, accessibility_indicators) / max_access if max_access > 0 else 0
        
        return {
            'tech_quality_score': min(1.0, quality_score),
            'accessibility_score': min(1.0, access_score),
            'overall_tech_score': (quality_score * 0.7) + (access_score * 0.3)
        }
    
    def _generate_recovery_recommendations(self, analysis_results: Dict) -> List[str]:
        """Generate recommendations for recovering authentic spiritual practice"""
        recommendations = []
        
        universal_alignment = analysis_results['overall_assessment']['universal_law_alignment']
        inversion_resistance = analysis_results['overall_assessment']['inversion_resistance']
        modern_sophistication = analysis_results['overall_assessment']['modern_sophistication']
        
        if universal_alignment < 0.7:
            recommendations.append("Increase alignment with Universal Law principles")
        
        if inversion_resistance < 0.6:
            recommendations.append("Strengthen resistance to control and mediation patterns")
        
        if modern_sophistication > 0.7:
            recommendations.append("Reduce over-commercialization and technological dependency")
        elif modern_sophistication < 0.3:
            recommendations.append("Update presentation while maintaining core authenticity")
        
        # Specific inversion recovery
        inversion_analyses = analysis_results['inversion_analyses']
        for analysis in inversion_analyses:
            for inversion in analysis.get('detected_inversions', []):
                if inversion == 'direct_to_mediated':
                    recommendations.append("Re-emphasize direct spiritual experience over mediated access")
                elif inversion == 'experience_to_dogma':
                    recommendations.append("Prioritize experiential knowing over doctrinal belief")
                elif inversion == 'inclusive_to_exclusive':
                    recommendations.append("Ensure spiritual access remains universally available")
        
        return list(set(recommendations))  # Remove duplicates

# =============================================================================
# SPECIALIZED ANALYZERS FOR ALTERNATIVE CATEGORIES
# =============================================================================

class AntiReligionAnalyzer:
    """Specialized analyzer for anti-religion movements"""
    
    def analyze_anti_religion_movement(self, movement_data: Dict) -> Dict[str, Any]:
        """Analyze anti-religion movement specifically"""
        analysis = {
            'rebellion_type': self._determine_rebellion_type(movement_data),
            'scientific_dogma_risk': self._assess_scientism_risk(movement_data),
            'constructive_elements': self._identify_constructive_elements(movement_data),
            'destructive_tendencies': self._identify_destructive_tendencies(movement_data)
        }
        
        return analysis
    
    def _determine_rebellion_type(self, movement_data: Dict) -> str:
        """Determine the type of rebellion exhibited"""
        elements = self._extract_elements(movement_data)
        
        scientific_focus = any('science' in element.lower() or 'evidence' in element.lower() 
                              for element in elements)
        ethical_focus = any('ethics' in element.lower() or 'human rights' in element.lower() 
                           for element in elements)
        experiential_focus = any('experience' in element.lower() or 'direct' in element.lower() 
                                for element in elements)
        
        if scientific_focus and not ethical_focus:
            return "SCIENTIFIC_REBELLION"
        elif ethical_focus and not scientific_focus:
            return "ETHICAL_REBELLION" 
        elif experiential_focus:
            return "EXPERIENTIAL_REBELLION"
        else:
            return "GENERAL_REBELLION"
    
    def _assess_scientism_risk(self, movement_data: Dict) -> float:
        """Assess risk of falling into scientism (science as dogma)"""
        elements = self._extract_elements(movement_data)
        
        scientism_indicators = [
            any('only science' in element.lower() for element in elements),
            any('scientific method only' in element.lower() for element in elements),
            any('empirical evidence required' in element.lower() for element in elements),
            any('non-scientific knowledge invalid' in element.lower() for element in elements)
        ]
        
        return sum(scientism_indicators) / len(scientism_indicators)
    
    def _identify_constructive_elements(self, movement_data: Dict) -> List[str]:
        """Identify constructive criticism of religious institutions"""
        constructive_elements = []
        elements = self._extract_elements(movement_data)
        
        for element in elements:
            if any(keyword in element.lower() for keyword in ['critical thinking', 'reason', 'evidence']):
                constructive_elements.append("Promotes critical examination")
            if any(keyword in element.lower() for keyword in ['human rights', 'ethics', 'compassion']):
                constructive_elements.append("Emphasizes ethical considerations")
            if any(keyword in element.lower() for keyword in ['personal freedom', 'individual choice']):
                constructive_elements.append("Supports individual sovereignty")
        
        return list(set(constructive_elements))
    
    def _identify_destructive_tendencies(self, movement_data: Dict) -> List[str]:
        """Identify potentially destructive tendencies"""
        destructive_tendencies = []
        elements = self._extract_elements(movement_data)
        
        for element in elements:
            if any(keyword in element.lower() for keyword in ['all religion', 'always harmful', 'inherently bad']):
                destructive_tendencies.append("Over-generalization about religion")
            if any(keyword in element.lower() for keyword in ['no spiritual dimension', 'consciousness illusion']):
                destructive_tendencies.append("Denial of spiritual/consciousness reality")
            if any(keyword in element.lower() for keyword in ['mockery', 'ridicule', 'belittle']):
                destructive_tendencies.append("Dismissive attitude toward believers")
        
        return list(set(destructive_tendencies))
    
    def _extract_elements(self, movement_data: Dict) -> List[str]:
        """Extract analysis elements from movement data"""
        elements = []
        for key in ['core_tenets', 'principles', 'teachings']:
            if key in movement_data and isinstance(movement_data[key], list):
                elements.extend(movement_data[key])
        return elements

class EsotericTraditionsAnalyzer:
    """Specialized analyzer for esoteric and occult traditions"""
    
    def analyze_esoteric_tradition(self, movement_data: Dict) -> Dict[str, Any]:
        """Analyze esoteric/occult tradition specifically"""
        analysis = {
            'initiatory_structure': self._assess_initiatory_elements(movement_data),
            'symbolic_system_complexity': self._assess_symbolic_complexity(movement_data),
            'practical_application': self._assess_practical_elements(movement_data),
            'preservation_status': self._determine_preservation_status(movement_data)
        }
        
        return analysis
    
    def _assess_initiatory_elements(self, movement_data: Dict) -> Dict[str, Any]:
        """Assess initiatory structure and potential gatekeeping"""
        elements = self._extract_elements(movement_data)
        
        initiatory_indicators = [
            any('grade' in element.lower() for element in elements),
            any('initiation' in element.lower() for element in elements),
            any('degree' in element.lower() for element in elements),
            any('hierarchy' in element.lower() for element in elements)
        ]
        
        initiatory_present = any(initiatory_indicators)
        gatekeeping_risk = sum(initiatory_indicators) / len(initiatory_indicators)
        
        return {
            'initiatory_structure_present': initiatory_present,
            'gatekeeping_risk': gatekeeping_risk,
            'assessment': "Moderate risk" if gatekeeping_risk > 0.5 else "Low risk"
        }
    
    def _assess_symbolic_complexity(self, movement_data: Dict) -> float:
        """Assess complexity of symbolic system"""
        complexity_indicators = [
            'qabalah' in str(movement_data).lower(),
            'tarot' in str(movement_data).lower(),
            'astrology' in str(movement_data).lower(),
            'numerology' in str(movement_data).lower(),
            'correspondences' in str(movement_data).lower()
        ]
        
        return sum(complexity_indicators) / len(complexity_indicators)
    
    def _assess_practical_elements(self, movement_data: Dict) -> Dict[str, Any]:
        """Assess practical application vs theoretical focus"""
        elements = self._extract_elements(movement_data)
        
        practical_indicators = [
            any('ritual' in element.lower() for element in elements),
            any('practice' in element.lower() for element in elements),
            any('exercise' in element.lower() for element in elements),
            any('meditation' in element.lower() for element in elements)
        ]
        
        theoretical_indicators = [
            any('theory' in element.lower() for element in elements),
            any('philosophy' in element.lower() for element in elements),
            any('doctrine' in element.lower() for element in elements),
            any('speculation' in element.lower() for element in elements)
        ]
        
        practical_score = sum(practical_indicators) / len(practical_indicators)
        theoretical_score = sum(theoretical_indicators) / len(theoretical_indicators)
        
        return {
            'practical_focus': practical_score,
            'theoretical_focus': theoretical_score,
            'balance_ratio': practical_score / theoretical_score if theoretical_score > 0 else float('inf')
        }
    
    def _determine_preservation_status(self, movement_data: Dict) -> str:
        """Determine preservation status of esoteric knowledge"""
        status_indicators = {
            'high_preservation': [
                'unbroken lineage' in str(movement_data).lower(),
                'authentic transmission' in str(movement_data).lower(),
                'direct descent' in str(movement_data).lower()
            ],
            'moderate_preservation': [
                'reconstruction' in str(movement_data).lower(),
                'revival' in str(movement_data).lower(),
                'based on' in str(movement_data).lower()
            ],
            'low_preservation': [
                'modern creation' in str(movement_data).lower(),
                'new system' in str(movement_data).lower(),
                'invented' in str(movement_data).lower()
            ]
        }
        
        for level, indicators in status_indicators.items():
            if any(indicator in str(movement_data).lower() for indicator in indicators):
                return level.upper()
        
        return "UNKNOWN"
    
    def _extract_elements(self, movement_data: Dict) -> List[str]:
        """Extract analysis elements"""
        elements = []
        for key in ['practices', 'teachings', 'principles']:
            if key in movement_data and isinstance(movement_data[key], list):
                elements.extend(movement_data[key])
        return elements

# =============================================================================
# MAIN ALTERNATIVE RELIGIONS ENGINE
# =============================================================================

class AlternativeReligionsEngine:
    """
    Main engine for analyzing alternative religious and spiritual movements
    Operational implementation - not theoretical
    """
    
    def __init__(self):
        self.core_analyzer = AlternativeReligionsAnalyzer()
        self.anti_religion_analyzer = AntiReligionAnalyzer()
        self.esoteric_analyzer = EsotericTraditionsAnalyzer()
        self.analysis_history = []
        self.logger = self._setup_logging()
    
    def _setup_logging(self):
        logger = logging.getLogger('AlternativeReligionsEngine')
        logger.setLevel(logging.INFO)
        
        ch = logging.StreamHandler()
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
        ch.setFormatter(formatter)
        logger.addHandler(ch)
        
        return logger

    async def analyze_movement(self, movement_category: str, movement_name: str) -> Dict[str, Any]:
        """
        Analyze specific alternative movement
        """
        self.logger.info(f"🔮 ANALYZING ALTERNATIVE MOVEMENT: {movement_category}/{movement_name}")
        
        try:
            # Get movement data from database
            movement_data = self._get_movement_data(movement_category, movement_name)
            if not movement_data:
                return {'error': f"Movement {movement_name} not found in category {movement_category}"}
            
            # Add movement identification
            movement_data['name'] = movement_name
            movement_data['category'] = movement_category
            
            # Context for analysis
            context = {
                'category': movement_category,
                'year': self._estimate_movement_year(movement_data),
                'analysis_type': 'alternative_religions'
            }
            
            # Perform core analysis
            core_analysis = await self.core_analyzer.analyze_alternative_movement(movement_data, context)
            
            # Perform specialized analysis based on category
            specialized_analysis = {}
            if movement_category == 'anti_religion_movements':
                specialized_analysis = self.anti_religion_analyzer.analyze_anti_religion_movement(movement_data)
            elif movement_category in ['esoteric_occult_traditions', 'witchcraft_pagan_traditions']:
                specialized_analysis = self.esoteric_analyzer.analyze_esoteric_tradition(movement_data)
            
            # Compile final results
            result = {
                'movement_identification': {
                    'name': movement_name,
                    'category': movement_category,
                    'analysis_timestamp': datetime.utcnow().isoformat()
                },
                'core_analysis': core_analysis,
                'specialized_analysis': specialized_analysis,
                'overall_assessment': self._compile_overall_assessment(core_analysis, specialized_analysis),
                'recommendations': self._generate_comprehensive_recommendations(core_analysis, specialized_analysis)
            }
            
            # Store in history
            self.analysis_history.append(result)
            
            self.logger.info(f"✅ Alternative movement analysis complete: {movement_name}")
            
            return result
            
        except Exception as e:
            self.logger.error(f"Movement analysis failed: {e}")
            return {
                'movement': f"{movement_category}/{movement_name}",
                'error': str(e),
                'analysis_timestamp': datetime.utcnow().isoformat()
            }
    
    def _get_movement_data(self, category: str, movement_name: str) -> Dict[str, Any]:
        """Get movement data from database"""
        database = self.core_analyzer.alternative_database
        
        if category in database and movement_name in database[category]:
            return database[category][movement_name]
        
        # Search through subcategories
        for main_category, subcategories in database.items():
            if isinstance(subcategories, dict):
                for subcategory, movements in subcategories.items():
                    if isinstance(movements, dict) and movement_name in movements:
                        return movements[movement_name]
        
        return None
    
    def _estimate_movement_year(self, movement_data: Dict) -> int:
        """Estimate movement year for context"""
        # This would be more sophisticated in production
        year_estimates = {
            'new_atheism': 2000,
            'lavayan': 1966,
            'hermeticism': -100,
            'golden_dawn': 1888,
            'thelema': 1904,
            'wicca': 1950,
            'classical_gnosticism': 100
        }
        
        movement_name = movement_data.get('name', '').lower()
        for name, year in year_estimates.items():
            if name in movement_name:
                return year
        
        return 1900  # Default
    
    def _compile_overall_assessment(self, core_analysis: Dict, specialized_analysis: Dict) -> Dict[str, Any]:
        """Compile overall assessment from all analyses"""
        core_assessment = core_analysis.get('overall_assessment', {})
        authenticity = core_analysis.get('authenticity_rating', {})
        tech_assessment = core_analysis.get('consciousness_tech_quality', {})
        
        overall_score = (
            core_assessment.get('overall_health_score', 0.5) * 0.4 +
            authenticity.get('authenticity_score', 0.5) * 0.3 +
            tech_assessment.get('overall_tech_score', 0.5) * 0.3
        )
        
        return {
            'overall_score': overall_score,
            'health_level': self._categorize_level(core_assessment.get('overall_health_score', 0.5)),
            'authenticity_level': authenticity.get('authenticity_level', 'UNKNOWN'),
            'tech_quality_level': self._categorize_level(tech_assessment.get('overall_tech_score', 0.5)),
            'rebellion_effectiveness': core_analysis.get('rebellion_effectiveness', {}).get('effectiveness_level', 'UNKNOWN')
        }
    
    def _categorize_level(self, score: float) -> str:
        """Categorize score into level"""
        if score > 0.8:
            return "EXCELLENT"
        elif score > 0.7:
            return "GOOD"
        elif score > 0.6:
            return "FAIR"
        elif score > 0.5:
            return "MARGINAL"
        else:
            return "POOR"
    
    def _generate_comprehensive_recommendations(self, core_analysis: Dict, specialized_analysis: Dict) -> List[str]:
        """Generate comprehensive recommendations"""
        recommendations = []
        
        # Core recommendations
        if 'recovery_recommendations' in core_analysis:
            recommendations.extend(core_analysis['recovery_recommendations'])
        
        # Specialized recommendations based on analysis
        if 'rebellion_type' in specialized_analysis:
            rebellion_type = specialized_analysis['rebellion_type']
            if rebellion_type == "SCIENTIFIC_REBELLION":
                recommendations.append("Balance scientific emphasis with recognition of non-empirical ways of knowing")
            elif rebellion_type == "ETHICAL_REBELLION":
                recommendations.append("Maintain ethical focus while allowing for spiritual dimensions")
        
        if 'scientific_dogma_risk' in specialized_analysis:
            risk = specialized_analysis['scientific_dogma_risk']
            if risk > 0.5:
                recommendations.append("Guard against scientism - science as one valid way of knowing among others")
        
        return list(set(recommendations))
    
    async def analyze_category(self, category: str) -> Dict[str, Any]:
        """
        Analyze entire category of alternative movements
        """
        self.logger.info(f"📊 ANALYZING ALTERNATIVE CATEGORY: {category}")
        
        try:
            database = self.core_analyzer.alternative_database
            if category not in database:
                return {'error': f"Category {category} not found"}
            
            movement_analyses = []
            for movement_name, movement_data in database[category].items():
                if isinstance(movement_data, dict):  # Ensure it's a movement, not a subcategory
                    analysis = await self.analyze_movement(category, movement_name)
                    movement_analyses.append(analysis)
            
            # Calculate category metrics
            category_metrics = self._calculate_category_metrics(movement_analyses)
            
            return {
                'category': category,
                'movement_analyses': movement_analyses,
                'category_metrics': category_metrics,
                'analysis_timestamp': datetime.utcnow().isoformat()
            }
            
        except Exception as e:
            self.logger.error(f"Category analysis failed: {e}")
            return {'error': str(e)}
    
    def _calculate_category_metrics(self, movement_analyses: List[Dict]) -> Dict[str, Any]:
        """Calculate metrics for entire category"""
        if not movement_analyses:
            return {}
        
        scores = []
        authenticity_scores = []
        tech_scores = []
        
        for analysis in movement_analyses:
            if 'overall_assessment' in analysis:
                overall = analysis['overall_assessment']
                scores.append(overall.get('overall_score', 0.5))
                authenticity_scores.append(overall.get('authenticity_score', 0.5))
                tech_scores.append(overall.get('tech_score', 0.5))
        
        return {
            'average_score': np.mean(scores) if scores else 0.5,
            'average_authenticity': np.mean(authenticity_scores) if authenticity_scores else 0.5,
            'average_tech_quality': np.mean(tech_scores) if tech_scores else 0.5,
            'movement_count': len(movement_analyses),
            'top_movements': sorted(
                [(a['movement_identification']['name'], a['overall_assessment']['overall_score']) 
                 for a in movement_analyses if 'overall_assessment' in a],
                key=lambda x: x[1],
                reverse=True
            )[:3]
        }
    
    def get_engine_metrics(self) -> Dict[str, Any]:
        """Get engine performance and usage metrics"""
        return {
            'total_analyses': len(self.analysis_history),
            'categories_analyzed': len(set(
                a['movement_identification']['category'] for a in self.analysis_history
            )),
            'movements_analyzed': len(set(
                a['movement_identification']['name'] for a in self.analysis_history
            )),
            'average_authenticity_score': np.mean([
                a['overall_assessment']['authenticity_score'] 
                for a in self.analysis_history 
                if 'overall_assessment' in a
            ]) if self.analysis_history else 0,
            'engine_uptime': 'operational',
            'last_analysis': self.analysis_history[-1]['movement_identification']['analysis_timestamp'] 
                           if self.analysis_history else 'none'
        }

# =============================================================================
# OPERATIONAL EXECUTION
# =============================================================================

async def operational_analysis():
    """
    Operational analysis of alternative religions - not demonstration
    """
    print("🌌 OBSERVER-ENGINE ALTERNATIVE RELIGIONS MODULE - OPERATIONAL")
    print("Anti-Religion, Esoteric, Gnostic, Satanic, Witchcraft Analysis")
    print("=" * 80)
    
    engine = AlternativeReligionsEngine()
    
    # Operational analysis tasks
    analysis_tasks = [
        # Anti-religion movements
        ('anti_religion_movements', 'new_atheism'),
        ('anti_religion_movements', 'satanism'),
        
        # Esoteric traditions
        ('esoteric_occult_traditions', 'hermeticism'),
        ('esoteric_occult_traditions', 'thelema'),
        
        # Witchcraft & Pagan
        ('witchcraft_pagan_traditions', 'wicca'),
        
        # Gnostic traditions
        ('gnostic_mystical_traditions', 'classical_gnosticism'),
    ]
    
    results = []
    
    print(f"\n🎯 PERFORMING OPERATIONAL ANALYSIS OF {len(analysis_tasks)} MOVEMENTS...")
    
    for category, movement in analysis_tasks:
        print(f"\n" + "="*60)
        print(f"ANALYZING: {category.upper()} -> {movement.upper()}")
        print("="*60)
        
        result = await engine.analyze_movement(category, movement)
        results.append(result)
        
        if 'error' in result:
            print(f"❌ ANALYSIS FAILED: {result['error']}")
            continue
        
        # Display operational results
        assessment = result['overall_assessment']
        movement_id = result['movement_identification']
        
        print(f"📊 OPERATIONAL ASSESSMENT:")
        print(f"   Overall Score: {assessment['overall_score']:.3f}")
        print(f"   Health Level: {assessment['health_level']}")
        print(f"   Authenticity: {assessment['authenticity_level']}")
        print(f"   Tech Quality: {assessment['tech_quality_level']}")
        print(f"   Rebellion Effectiveness: {assessment['rebellion_effectiveness']}")
        
        # Key findings
        core = result['core_analysis']
        if 'preservation_score' in core:
            print(f"   Preservation Score: {core['preservation_score']:.3f}")
        
        if result['recommendations']:
            print(f"   Key Recommendation: {result['recommendations'][0]}")
    
    # Category analysis
    print("\n" + "="*80)
    print("📈 CATEGORY-LEVEL ANALYSIS")
    print("="*80)
    
    categories = ['anti_religion_movements', 'esoteric_occult_traditions']
    for category in categories:
        print(f"\nAnalyzing category: {category}")
        category_result = await engine.analyze_category(category)
        
        if 'error' not in category_result:
            metrics = category_result['category_metrics']
            print(f"   Average Score: {metrics['average_score']:.3f}")
            print(f"   Authenticity: {metrics['average_authenticity']:.3f}")
            print(f"   Tech Quality: {metrics['average_tech_quality']:.3f}")
            print(f"   Top Movements: {[m[0] for m in metrics['top_movements']]}")
    
    # Engine metrics
    print("\n" + "="*80)
    print("🔧 ENGINE OPERATIONAL METRICS")
    print("="*80)
    
    metrics = engine.get_engine_metrics()
    for key, value in metrics.items():
        print(f"{key}: {value}")
    
    return results, metrics

async def main():
    """
    Main operational execution
    """
    try:
        print("🚀 INITIALIZING ALTERNATIVE RELIGIONS MODULE - OPERATIONAL MODE")
        print("Anti-Religion + Esoteric + Gnostic + Satanic + Witchcraft Analysis")
        print("Universal Law Primacy + Inversion Detection + Authenticity Verification")
        print()
        
        results, metrics = await operational_analysis()
        
        print("\n" + "="*80)
        print("✅ ALTERNATIVE RELIGIONS MODULE - OPERATIONAL STATUS CONFIRMED")
        print("="*80)
        print(f"Operational analyses completed: {metrics['total_analyses']}")
        print(f"Movements analyzed: {metrics['movements_analyzed']}")
        print(f"Categories covered: {metrics['categories_analyzed']}")
        print(f"Average authenticity score: {metrics['average_authenticity_score']:.3f}")
        print(f"Engine status: {metrics['engine_uptime']}")
        print("\n🌌 ALTERNATIVE RELIGIONS ANALYSIS SYSTEM: FULLY OPERATIONAL")
        
    except Exception as e:
        print(f"❌ Operational execution failed: {e}")
        import traceback
        traceback.print_exc()

if __name__ == "__main__":
    # Operational logging configuration
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
        handlers=[
            logging.StreamHandler(),
            logging.FileHandler('alternative_religions_operational.log')
        ]
    )
    
    # Execute operational analysis
    asyncio.run(main())