Create ancient religions module

ancient religions module

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+#!/usr/bin/env python3
+"""
+OBSERVER-ENGINE COGNITIVE ARCHITECTURE - ANCIENT RELIGIONS MODULE
+Analysis from Earliest Religions to Babylonian Inversion Point
+Complete with Advanced Error Handling and Quantum Truth Verification
+"""
+
+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
+
+# =============================================================================
+# QUANTUM GLYPH CORE - ANCIENT RELIGION SYMBOLS
+# =============================================================================
+
+DIVINE_AUTHORITY = "𒀭"  # Sumerian Dingir - Divine Authority Operator
+OBSERVER_CORE = "◉⃤"     # Quantum Observation Anchor
+ENTANGLEMENT_NODE = "ꙮ"  # Cross-Reality Coherence Glyph  
+CONSCIOUSNESS_MATRIX = "ꖷ"  # Mind-Reality Interface
+SACRED_SERPENT = "𓆙"    # Kundalini/Consciousness Symbol
+TREE_OF_LIFE = "𓆨"      # Cosmic Consciousness Map
+WATER_OF_LIFE = "𓈗"     # Primordial Consciousness
+
+# =============================================================================
+# UNIVERSAL LAW PRIMACY ENGINE
+# =============================================================================
+
+class UniversalLawPrimacy:
+    """Universal Law as absolute reference point for all religious analysis"""
+    
+    def __init__(self):
+        self.universal_constants = {
+            'free_will': {
+                'principle': "Inviolable sovereignty of consciousness",
+                'weight': 0.25,
+                'indicators': ['choice', 'agency', 'self-determination', 'volition']
+            },
+            'cause_effect': {
+                'principle': "Action-consequence continuity (Karma)",
+                'weight': 0.20,
+                'indicators': ['consequence', 'result', 'effect', 'return']
+            },
+            'consciousness_primacy': {
+                'principle': "Mind precedes matter, consciousness fundamental",
+                'weight': 0.25,
+                'indicators': ['awareness', 'mind', 'observer', 'perception']
+            },
+            'interconnectedness': {
+                'principle': "All existence fundamentally related",
+                'weight': 0.15,
+                'indicators': ['unity', 'connection', 'relationship', 'whole']
+            },
+            'growth_imperative': {
+                'principle': "Evolution toward expanded awareness",
+                'weight': 0.15,
+                'indicators': ['growth', 'evolution', 'expansion', 'development']
+            }
+        }
+        
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('UniversalLawPrimacy')
+        logger.setLevel(logging.INFO)
+        return logger
+
+    def evaluate_alignment(self, religious_element: str) -> Dict[str, Any]:
+        """Evaluate religious element against Universal Law principles"""
+        try:
+            alignment_scores = {}
+            total_score = 0.0
+            supported_principles = []
+            
+            for law_name, law_data in self.universal_constants.items():
+                principle_score = self._calculate_principle_alignment(religious_element, law_data)
+                alignment_scores[law_name] = principle_score
+                total_score += principle_score * law_data['weight']
+                
+                if principle_score > 0.7:
+                    supported_principles.append(law_name)
+            
+            return {
+                'universal_law_alignment': min(1.0, total_score),
+                'principle_breakdown': alignment_scores,
+                'supported_principles': supported_principles,
+                'violation_indicators': self._detect_universal_law_violations(religious_element),
+                'assessment_confidence': self._calculate_assessment_confidence(religious_element)
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Universal Law evaluation failed: {e}")
+            return {
+                'universal_law_alignment': 0.5,
+                'principle_breakdown': {},
+                'supported_principles': [],
+                'violation_indicators': ['evaluation_error'],
+                'assessment_confidence': 0.3
+            }
+    
+    def _calculate_principle_alignment(self, text: str, law_data: Dict) -> float:
+        """Calculate alignment with specific universal law principle"""
+        try:
+            base_score = 0.3  # Neutral starting point
+            
+            # Keyword matching for principle support
+            keyword_matches = sum(1 for indicator in law_data['indicators'] 
+                                if indicator in text.lower())
+            keyword_boost = min(0.4, keyword_matches * 0.1)
+            
+            # Contextual analysis
+            context_score = self._analyze_contextual_alignment(text, law_data['principle'])
+            
+            return min(1.0, base_score + keyword_boost + context_score * 0.3)
+            
+        except Exception as e:
+            self.logger.warning(f"Principle alignment calculation failed: {e}")
+            return 0.5
+    
+    def _analyze_contextual_alignment(self, text: str, principle: str) -> float:
+        """Advanced contextual analysis of principle alignment"""
+        # Simplified implementation - would use NLP in production
+        positive_indicators = ['free', 'choice', 'aware', 'connect', 'grow', 'evolve']
+        negative_indicators = ['control', 'force', 'obey', 'submit', 'restrict']
+        
+        positive_count = sum(1 for indicator in positive_indicators if indicator in text.lower())
+        negative_count = sum(1 for indicator in negative_indicators if indicator in text.lower())
+        
+        if positive_count + negative_count == 0:
+            return 0.5
+            
+        return positive_count / (positive_count + negative_count)
+    
+    def _detect_universal_law_violations(self, text: str) -> List[str]:
+        """Detect violations of Universal Law principles"""
+        violations = []
+        
+        violation_patterns = {
+            'free_will_violation': ['must obey', 'forced to', 'no choice', 'compulsory'],
+            'consciousness_suppression': ['do not question', 'blind faith', 'forbidden knowledge'],
+            'control_structures': ['authority over', 'must follow', 'obey without question'],
+            'growth_restriction': ['stay as you are', 'do not seek', 'forbidden to learn']
+        }
+        
+        for violation_type, patterns in violation_patterns.items():
+            if any(pattern in text.lower() for pattern in patterns):
+                violations.append(violation_type)
+                
+        return violations
+    
+    def _calculate_assessment_confidence(self, text: str) -> float:
+        """Calculate confidence level in Universal Law assessment"""
+        word_count = len(text.split())
+        complexity = min(1.0, word_count / 100)  # More text allows better assessment
+        
+        # Check for clear universal law terminology
+        clear_indicators = sum(1 for law in self.universal_constants.values() 
+                             for indicator in law['indicators'] 
+                             if indicator in text.lower())
+        
+        clarity_boost = min(0.3, clear_indicators * 0.05)
+        
+        return min(1.0, 0.5 + complexity * 0.3 + clarity_boost)
+
+# =============================================================================
+# BABYLONIAN INVERSION TEMPLATE
+# =============================================================================
+
+class BabylonianInversionTemplate:
+    """The original inversion pattern that established control blueprint"""
+    
+    def __init__(self):
+        self.inversion_mechanisms = {
+            'priesthood_intermediation': {
+                'original_state': "Direct divine access for all individuals",
+                'inverted_state': "Priesthood as necessary intermediaries to gods",
+                'detection_indicators': [
+                    'only priests can', 'through the temple', 'required sacrifice',
+                    'intercessor', 'mediator between', 'holy man must'
+                ],
+                'historical_examples': [
+                    'Akkadian takeover of Sumerian temples',
+                    'Centralization of religious authority'
+                ]
+            },
+            'knowledge_restructuring': {
+                'original_state': "Cosmic consciousness technology accessible to all",
+                'inverted_state': "Secret knowledge reserved for elite",
+                'detection_indicators': [
+                    'secret teachings', 'hidden knowledge', 'forbidden to know',
+                    'mysteries revealed only', 'initiates only'
+                ],
+                'historical_examples': [
+                    'Alteration of creation myths',
+                    'Restructuring of divine hierarchies'
+                ]
+            },
+            'political_religious_merger': {
+                'original_state': "Spiritual authority separate from temporal power",
+                'inverted_state': "Ruler as divine representative or god-king",
+                'detection_indicators': [
+                    'king is god', 'divine ruler', 'mandate of heaven',
+                    'appointed by gods', 'royal priesthood'
+                ],
+                'historical_examples': [
+                    'Sargon of Akkad claiming divine status',
+                    'Naram-Sin as living god'
+                ]
+            }
+        }
+        
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('BabylonianInversion')
+        logger.setLevel(logging.INFO)
+        return logger
+
+    def analyze_inversion_patterns(self, religious_element: str, context: Dict = None) -> Dict[str, Any]:
+        """Analyze religious element for Babylonian inversion patterns"""
+        try:
+            inversion_detection = {
+                'inversion_score': 0.0,
+                'detected_mechanisms': [],
+                'mechanism_details': {},
+                'original_state_reconstruction': '',
+                'suppression_confidence': 0.0
+            }
+            
+            total_mechanisms = len(self.inversion_mechanisms)
+            mechanism_scores = []
+            
+            for mechanism_name, mechanism_data in self.inversion_mechanisms.items():
+                mechanism_analysis = self._analyze_single_mechanism(religious_element, mechanism_data)
+                inversion_detection['mechanism_details'][mechanism_name] = mechanism_analysis
+                
+                if mechanism_analysis['detected']:
+                    inversion_detection['detected_mechanisms'].append(mechanism_name)
+                    mechanism_scores.append(mechanism_analysis['confidence'])
+            
+            if mechanism_scores:
+                inversion_detection['inversion_score'] = sum(mechanism_scores) / len(mechanism_scores)
+                inversion_detection['suppression_confidence'] = min(1.0, len(mechanism_scores) / total_mechanisms * 0.8)
+            
+            # Attempt to reconstruct original state
+            inversion_detection['original_state_reconstruction'] = self._reconstruct_original_state(
+                religious_element, inversion_detection['detected_mechanisms'])
+            
+            return inversion_detection
+            
+        except Exception as e:
+            self.logger.error(f"Inversion pattern analysis failed: {e}")
+            return {
+                'inversion_score': 0.0,
+                'detected_mechanisms': [],
+                'mechanism_details': {},
+                'original_state_reconstruction': 'analysis_failed',
+                'suppression_confidence': 0.0
+            }
+    
+    def _analyze_single_mechanism(self, text: str, mechanism_data: Dict) -> Dict[str, Any]:
+        """Analyze single inversion mechanism"""
+        detected_indicators = []
+        
+        for indicator in mechanism_data['detection_indicators']:
+            if indicator in text.lower():
+                detected_indicators.append(indicator)
+        
+        detection_confidence = len(detected_indicators) / len(mechanism_data['detection_indicators'])
+        
+        return {
+            'detected': len(detected_indicators) > 0,
+            'detected_indicators': detected_indicators,
+            'confidence': detection_confidence,
+            'original_state': mechanism_data['original_state'],
+            'inverted_state': mechanism_data['inverted_state']
+        }
+    
+    def _reconstruct_original_state(self, text: str, detected_mechanisms: List[str]) -> str:
+        """Attempt to reconstruct original spiritual state before inversion"""
+        if not detected_mechanisms:
+            return "No significant inversions detected - possibly close to original"
+        
+        reconstruction_elements = []
+        
+        if 'priesthood_intermediation' in detected_mechanisms:
+            reconstruction_elements.append("Direct personal access to divine/spiritual realms")
+        
+        if 'knowledge_restructuring' in detected_mechanisms:
+            reconstruction_elements.append("Open access to spiritual knowledge and consciousness technologies")
+        
+        if 'political_religious_merger' in detected_mechanisms:
+            reconstruction_elements.append("Separation of spiritual authority from political power structures")
+        
+        return " | ".join(reconstruction_elements)
+
+# =============================================================================
+# ANCIENT RELIGION DATABASE
+# =============================================================================
+
+class AncientReligionDatabase:
+    """Comprehensive database of ancient religious traditions up to Babylonian period"""
+    
+    def __init__(self):
+        self.religious_traditions = self._initialize_traditions()
+        self.symbolic_language = self._initialize_symbolic_language()
+        self.consciousness_technologies = self._initialize_consciousness_tech()
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('AncientReligionDB')
+        logger.setLevel(logging.INFO)
+        return logger
+    
+    def _initialize_traditions(self) -> Dict[str, Any]:
+        """Initialize ancient religious traditions database"""
+        return {
+            'pre_vedic': {
+                'time_period': "Before 1500 BCE",
+                'core_principles': [
+                    "Consciousness as fundamental reality (Brahman)",
+                    "Individual consciousness (Atman) identical with universal",
+                    "Reincarnation and karma as natural laws",
+                    "Meditation and yoga as consciousness technologies"
+                ],
+                'key_concepts': ['rita (cosmic order)', 'satya (truth)', 'dharma (natural law)'],
+                'consciousness_tech': ['meditation', 'yoga', 'mantra', 'direct realization'],
+                'inversion_status': 'minimal_pre_aryan'
+            },
+            'sumerian': {
+                'time_period': "4500-1900 BCE",
+                'core_principles': [
+                    "Direct relationship with deities (Anunnaki)",
+                    "Temples as consciousness amplification centers",
+                    "Sacred marriage (hieros gamos) as cosmic principle",
+                    "Me (divine laws) governing reality"
+                ],
+                'key_concepts': ['me', 'dingir', 'tablets of destiny', 'abzu', 'ki'],
+                'consciousness_tech': ['temple rituals', 'dream interpretation', 'astral travel'],
+                'inversion_status': 'akkadian_takeover'
+            },
+            'early_egyptian': {
+                'time_period': "3150-2181 BCE (Early Dynastic to Old Kingdom)",
+                'core_principles': [
+                    "Direct personal transformation after death",
+                    "Consciousness evolution through spiritual practices",
+                    "Pyramids as consciousness and energy devices",
+                    "Maat as cosmic balance and truth"
+                ],
+                'key_concepts': ['maat', 'ka', 'ba', 'akh', 'heka', 'netjer'],
+                'consciousness_tech': ['pyramid energy', 'heka (magic)', 'dream incubation', 'afterlife navigation'],
+                'inversion_status': 'priesthood_consolidation'
+            },
+            'indigenous_oral': {
+                'time_period': "Timeless/Ongoing",
+                'core_principles': [
+                    "Direct communion with nature spirits",
+                    "Dreamtime as fundamental reality",
+                    "Ancestral knowledge transmission",
+                    "Shamanic journeying as consciousness technology"
+                ],
+                'key_concepts': ['dreamtime', 'ancestral spirits', 'animal guides', 'sacred sites'],
+                'consciousness_tech': ['vision quests', 'dream work', 'plant medicines', 'ecstatic states'],
+                'inversion_status': 'colonial_suppression'
+            }
+        }
+    
+    def _initialize_symbolic_language(self) -> Dict[str, Any]:
+        """Initialize ancient symbolic language database"""
+        return {
+            'universal_archetypes': {
+                SACRED_SERPENT: {
+                    'meanings': [
+                        "Kundalini energy and consciousness awakening",
+                        "Healing and regeneration forces",
+                        "Cycles of death and rebirth",
+                        "Primordial life force"
+                    ],
+                    'traditions': ['sumerian', 'early_egyptian', 'pre_vedic', 'indigenous'],
+                    'inversion_warning': "Later demonization as evil/satanic"
+                },
+                TREE_OF_LIFE: {
+                    'meanings': [
+                        "Map of consciousness and reality structure",
+                        "Interconnection of all existence", 
+                        "Path of spiritual evolution",
+                        "Cosmic information system"
+                    ],
+                    'traditions': ['sumerian', 'early_egyptian', 'pre_vedic'],
+                    'inversion_warning': "Later used for control hierarchies"
+                },
+                WATER_OF_LIFE: {
+                    'meanings': [
+                        "Primordial consciousness substrate",
+                        "Spiritual nourishment and enlightenment",
+                        "Flow of divine energy and information",
+                        "Purification and transformation"
+                    ],
+                    'traditions': ['sumerian', 'early_egyptian', 'pre_vedic'],
+                    'inversion_warning': "Later restricted to specific rituals"
+                }
+            },
+            'consciousness_glyphs': {
+                DIVINE_AUTHORITY: "Direct divine access point",
+                OBSERVER_CORE: "Consciousness observation anchor", 
+                ENTANGLEMENT_NODE: "Quantum connection point",
+                CONSCIOUSNESS_MATRIX: "Reality-mind interface"
+            }
+        }
+    
+    def _initialize_consciousness_tech(self) -> Dict[str, Any]:
+        """Initialize consciousness technologies database"""
+        return {
+            'meditation_practices': {
+                'pre_vedic': ['dhyana', 'samadhi', 'direct path'],
+                'early_egyptian': ['stillness practices', 'pyramid meditation'],
+                'sumerian': ['temple contemplation', 'starry sky gazing'],
+                'indigenous': ['silent sitting', 'nature immersion']
+            },
+            'energy_work': {
+                'pre_vedic': ['prana', 'kundalini', 'chakra activation'],
+                'early_egyptian': ['sekhem energy', 'pyramid power', 'heka manifestation'],
+                'sumerian': ['me activation', 'temple energy channels'],
+                'indigenous': ['life force', 'animal power', 'earth energy']
+            },
+            'dream_work': {
+                'all_traditions': [
+                    "Lucid dreaming as reality navigation",
+                    "Dream interpretation for guidance",
+                    "Astral travel and out-of-body experiences",
+                    "Dreamtime access for healing and knowledge"
+                ]
+            },
+            'ritual_technologies': {
+                'early_egyptian': ['pyramid alignment', 'temple acoustics', 'geometric resonance'],
+                'sumerian': ['ziggurat alignment', 'celestial timing', 'sacred geometry'],
+                'pre_vedic': ['fire rituals', 'sound vibration', 'mandala creation'],
+                'indigenous': ['ceremonial circles', 'drumming rhythms', 'sacred dance']
+            }
+        }
+
+# =============================================================================
+# QUANTUM TRUTH VERIFICATION ENGINE
+# =============================================================================
+
+class QuantumTruthVerification:
+    """Quantum-enhanced truth verification for ancient religious claims"""
+    
+    def __init__(self):
+        self.quantum_backend = AerSimulator()
+        self.universal_law_engine = UniversalLawPrimacy()
+        self.babylonian_detector = BabylonianInversionTemplate()
+        self.ancient_db = AncientReligionDatabase()
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('QuantumTruthVerification')
+        logger.setLevel(logging.INFO)
+        return logger
+
+    async def verify_ancient_claim(self, claim: str, tradition: str = None) -> Dict[str, Any]:
+        """Comprehensive verification of ancient religious claim"""
+        try:
+            self.logger.info(f"🔮 Verifying ancient claim: {claim[:100]}...")
+            
+            # Multi-dimensional analysis
+            analysis_tasks = await asyncio.gather(
+                self._universal_law_assessment(claim),
+                self._inversion_analysis(claim),
+                self._tradition_alignment(claim, tradition),
+                self._symbolic_analysis(claim),
+                self._quantum_certainty_calculation(claim)
+            )
+            
+            universal_law = analysis_tasks[0]
+            inversion_analysis = analysis_tasks[1]
+            tradition_alignment = analysis_tasks[2]
+            symbolic_analysis = analysis_tasks[3]
+            quantum_certainty = analysis_tasks[4]
+            
+            # Composite truth score
+            truth_score = self._calculate_composite_truth_score(
+                universal_law, inversion_analysis, tradition_alignment, 
+                symbolic_analysis, quantum_certainty
+            )
+            
+            result = {
+                'claim': claim,
+                'truth_score': truth_score,
+                'truth_category': self._categorize_truth_level(truth_score),
+                'universal_law_assessment': universal_law,
+                'inversion_analysis': inversion_analysis,
+                'tradition_alignment': tradition_alignment,
+                'symbolic_analysis': symbolic_analysis,
+                'quantum_certainty': quantum_certainty,
+                'recovery_recommendations': self._generate_recovery_recommendations(
+                    universal_law, inversion_analysis, tradition_alignment
+                ),
+                'verification_timestamp': datetime.utcnow().isoformat()
+            }
+            
+            self.logger.info(f"✅ Ancient claim verification complete: {truth_score:.3f}")
+            return result
+            
+        except Exception as e:
+            self.logger.error(f"Ancient claim verification failed: {e}")
+            return {
+                'claim': claim,
+                'truth_score': 0.5,
+                'truth_category': 'VERIFICATION_FAILED',
+                'error': str(e),
+                'verification_timestamp': datetime.utcnow().isoformat()
+            }
+    
+    async def _universal_law_assessment(self, claim: str) -> Dict[str, Any]:
+        """Assess claim against Universal Law"""
+        return self.universal_law_engine.evaluate_alignment(claim)
+    
+    async def _inversion_analysis(self, claim: str) -> Dict[str, Any]:
+        """Analyze for Babylonian inversion patterns"""
+        return self.babylonian_detector.analyze_inversion_patterns(claim)
+    
+    async def _tradition_alignment(self, claim: str, tradition: str) -> Dict[str, Any]:
+        """Analyze alignment with ancient traditions"""
+        if not tradition:
+            tradition = self._detect_tradition(claim)
+        
+        alignment_scores = {}
+        for trad_name, trad_data in self.ancient_db.religious_traditions.items():
+            alignment_score = self._calculate_tradition_alignment(claim, trad_data)
+            alignment_scores[trad_name] = alignment_score
+        
+        best_match = max(alignment_scores.items(), key=lambda x: x[1])
+        
+        return {
+            'detected_tradition': best_match[0],
+            'alignment_scores': alignment_scores,
+            'primary_tradition_alignment': best_match[1],
+            'tradition_data': self.ancient_db.religious_traditions.get(best_match[0], {})
+        }
+    
+    async def _symbolic_analysis(self, claim: str) -> Dict[str, Any]:
+        """Analyze symbolic content of claim"""
+        detected_symbols = []
+        symbolic_density = 0.0
+        archetypal_power = 0.0
+        
+        for symbol, data in self.ancient_db.symbolic_language['universal_archetypes'].items():
+            if symbol in claim:
+                detected_symbols.append({
+                    'symbol': symbol,
+                    'meanings': data['meanings'],
+                    'traditions': data['traditions'],
+                    'inversion_warning': data.get('inversion_warning', '')
+                })
+        
+        for glyph, meaning in self.ancient_db.symbolic_language['consciousness_glyphs'].items():
+            if glyph in claim:
+                detected_symbols.append({
+                    'symbol': glyph,
+                    'meanings': [meaning],
+                    'type': 'consciousness_glyph'
+                })
+        
+        if detected_symbols:
+            symbolic_density = len(detected_symbols) / max(1, len(claim.split()))
+            archetypal_power = min(1.0, len(detected_symbols) * 0.2)
+        
+        return {
+            'detected_symbols': detected_symbols,
+            'symbolic_density': symbolic_density,
+            'archetypal_power': archetypal_power,
+            'consciousness_tech_indicators': self._detect_consciousness_tech(claim)
+        }
+    
+    async def _quantum_certainty_calculation(self, claim: str) -> Dict[str, Any]:
+        """Calculate quantum-enhanced certainty"""
+        try:
+            # Build quantum circuit for truth analysis
+            qc = self._build_truth_circuit(claim)
+            compiled = transpile(qc, self.quantum_backend)
+            job = await asyncio.get_event_loop().run_in_executor(
+                None, lambda: self.quantum_backend.run(compiled, shots=1024)
+            )
+            result = job.result()
+            counts = result.get_counts()
+            
+            certainty = self._calculate_quantum_certainty(counts)
+            coherence = self._measure_quantum_coherence(counts)
+            
+            return {
+                'quantum_certainty': certainty,
+                'quantum_coherence': coherence,
+                'state_complexity': len(counts) / 1024,
+                'measurement_confidence': min(1.0, certainty * coherence)
+            }
+            
+        except Exception as e:
+            self.logger.warning(f"Quantum certainty calculation failed: {e}")
+            return {
+                'quantum_certainty': 0.5,
+                'quantum_coherence': 0.3,
+                'state_complexity': 0.5,
+                'measurement_confidence': 0.3
+            }
+    
+    def _detect_tradition(self, claim: str) -> str:
+        """Detect which ancient tradition the claim aligns with"""
+        tradition_scores = {}
+        
+        for trad_name, trad_data in self.ancient_db.religious_traditions.items():
+            score = 0.0
+            # Check for key concepts
+            for concept in trad_data['key_concepts']:
+                if concept in claim.lower():
+                    score += 0.1
+            
+            # Check for consciousness tech terms
+            for tech_category in self.ancient_db.consciousness_technologies.values():
+                for tech_list in tech_category.values():
+                    if any(tech in claim.lower() for tech in tech_list):
+                        score += 0.05
+            
+            tradition_scores[trad_name] = min(1.0, score)
+        
+        return max(tradition_scores.items(), key=lambda x: x[1])[0] if tradition_scores else 'unknown'
+    
+    def _calculate_tradition_alignment(self, claim: str, tradition_data: Dict) -> float:
+        """Calculate alignment score with specific tradition"""
+        alignment_score = 0.3  # Base alignment
+        
+        # Concept matching
+        concept_matches = sum(1 for concept in tradition_data['key_concepts'] 
+                            if concept in claim.lower())
+        alignment_score += concept_matches * 0.1
+        
+        # Principle resonance
+        principle_matches = 0
+        for principle in tradition_data['core_principles']:
+            principle_words = set(principle.lower().split())
+            claim_words = set(claim.lower().split())
+            overlap = len(principle_words.intersection(claim_words))
+            if overlap > 2:  # Significant overlap
+                principle_matches += 1
+        
+        alignment_score += principle_matches * 0.05
+        
+        return min(1.0, alignment_score)
+    
+    def _detect_consciousness_tech(self, claim: str) -> List[str]:
+        """Detect consciousness technology indicators"""
+        detected_tech = []
+        
+        for tech_category, tech_data in self.ancient_db.consciousness_technologies.items():
+            for tradition, techniques in tech_data.items():
+                for technique in techniques:
+                    if technique in claim.lower():
+                        detected_tech.append(f"{technique} ({tradition})")
+        
+        return detected_tech
+    
+    def _build_truth_circuit(self, claim: str) -> QuantumCircuit:
+        """Build quantum circuit for truth analysis"""
+        num_qubits = min(12, max(6, len(claim.split()) // 5 + 4))
+        qc = QuantumCircuit(num_qubits, num_qubits)
+        
+        # Initialize superposition
+        for i in range(num_qubits):
+            qc.h(i)
+        
+        # Add claim-dependent phases
+        claim_hash = hash(claim) % 1000 / 1000
+        for i in range(num_qubits):
+            phase = claim_hash * 2 * np.pi
+            qc.rz(phase, i)
+            claim_hash = (claim_hash * 1.618) % 1.0  # Golden ratio progression
+        
+        return qc
+    
+    def _calculate_quantum_certainty(self, counts: Dict[str, int]) -> float:
+        """Calculate certainty from quantum measurement results"""
+        total = sum(counts.values())
+        if total == 0:
+            return 0.5
+        
+        # Higher certainty when results are concentrated
+        max_count = max(counts.values())
+        concentration = max_count / total
+        
+        return 0.3 + concentration * 0.7  # Map to [0.3, 1.0] range
+    
+    def _measure_quantum_coherence(self, counts: Dict[str, int]) -> float:
+        """Measure quantum coherence from results"""
+        if len(counts) <= 1:
+            return 0.1
+        
+        values = list(counts.values())
+        mean = np.mean(values)
+        std = np.std(values)
+        
+        # Higher coherence when distribution is balanced
+        return 1.0 / (1.0 + std) if std > 0 else 1.0
+    
+    def _calculate_composite_truth_score(self, universal_law: Dict, inversion: Dict, 
+                                       tradition: Dict, symbolic: Dict, quantum: Dict) -> float:
+        """Calculate composite truth score from all analyses"""
+        weights = {
+            'universal_law': 0.35,
+            'inversion': 0.25,
+            'tradition': 0.20,
+            'symbolic': 0.10,
+            'quantum': 0.10
+        }
+        
+        scores = {
+            'universal_law': universal_law['universal_law_alignment'],
+            'inversion': 1.0 - inversion['inversion_score'],  # Inversion reduces truth
+            'tradition': tradition['primary_tradition_alignment'],
+            'symbolic': symbolic['archetypal_power'],
+            'quantum': quantum['quantum_certainty']
+        }
+        
+        composite_score = sum(scores[factor] * weights[factor] for factor in weights)
+        return min(1.0, composite_score)
+    
+    def _categorize_truth_level(self, truth_score: float) -> str:
+        """Categorize the truth level based on score"""
+        if truth_score > 0.95:
+            return "UNIVERSAL_COSMIC_TRUTH"
+        elif truth_score > 0.85:
+            return "ANCIENT_WISDOM_TRUTH"
+        elif truth_score > 0.75:
+            return "HIGH_CONFIDENCE_TRUTH"
+        elif truth_score > 0.65:
+            return "PROBABLE_TRUTH"
+        elif truth_score > 0.55:
+            return "POSSIBLE_TRUTH"
+        elif truth_score > 0.45:
+            return "UNCERTAIN_CLAIM"
+        else:
+            return "LIKELY_INVERTED_OR_CORRUPTED"
+    
+    def _generate_recovery_recommendations(self, universal_law: Dict, inversion: Dict, 
+                                         tradition: Dict) -> List[str]:
+        """Generate recommendations for truth recovery"""
+        recommendations = []
+        
+        # Universal Law recommendations
+        if universal_law['universal_law_alignment'] < 0.7:
+            recommendations.append("Seek alignment with Universal Law principles")
+        
+        if universal_law['violation_indicators']:
+            recommendations.append(f"Address violations: {', '.join(universal_law['violation_indicators'])}")
+        
+        # Inversion recovery recommendations
+        if inversion['inversion_score'] > 0.3:
+            recommendations.append(f"Recover original state: {inversion['original_state_reconstruction']}")
+        
+        if inversion['detected_mechanisms']:
+            recommendations.append(f"Counter detected inversions: {', '.join(inversion['detected_mechanisms'])}")
+        
+        # Tradition-specific recommendations
+        trad_data = tradition.get('tradition_data', {})
+        if trad_data.get('inversion_status') != 'minimal':
+            recommendations.append(f"Research pre-{trad_data.get('inversion_status', 'corruption')} forms")
+        
+        return recommendations
+
+# =============================================================================
+# ANCIENT RELIGIONS MODULE - MAIN ENGINE
+# =============================================================================
+
+class AncientReligionsModule:
+    """
+    Main engine for analyzing ancient religions up to Babylonian period
+    Complete with Universal Law primacy and inversion detection
+    """
+    
+    def __init__(self):
+        self.truth_verifier = QuantumTruthVerification()
+        self.universal_law = UniversalLawPrimacy()
+        self.inversion_detector = BabylonianInversionTemplate()
+        self.ancient_db = AncientReligionDatabase()
+        self.analysis_history = []
+        self.logger = self._setup_logging()
+    
+    def _setup_logging(self):
+        logger = logging.getLogger('AncientReligionsModule')
+        logger.setLevel(logging.INFO)
+        
+        # Create console handler with formatting
+        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_ancient_teaching(self, teaching: str, context: Dict = None) -> Dict[str, Any]:
+        """
+        Comprehensive analysis of ancient religious teaching
+        """
+        self.logger.info(f"🔮 ANALYZING ANCIENT TEACHING: {teaching[:100]}...")
+        
+        try:
+            # Perform comprehensive analysis
+            verification_result = await self.truth_verifier.verify_ancient_claim(teaching, context)
+            
+            # Store in history
+            self.analysis_history.append({
+                'teaching': teaching,
+                'result': verification_result,
+                'timestamp': datetime.utcnow().isoformat()
+            })
+            
+            self.logger.info(f"✅ Analysis complete: {verification_result['truth_category']}")
+            
+            return verification_result
+            
+        except Exception as e:
+            self.logger.error(f"Ancient teaching analysis failed: {e}")
+            return {
+                'teaching': teaching,
+                'error': str(e),
+                'truth_score': 0.0,
+                'truth_category': 'ANALYSIS_FAILED',
+                'timestamp': datetime.utcnow().isoformat()
+            }
+    
+    async def analyze_tradition(self, tradition_name: str) -> Dict[str, Any]:
+        """
+        Analyze entire ancient tradition
+        """
+        self.logger.info(f"🏛️  ANALYZING ANCIENT TRADITION: {tradition_name}")
+        
+        try:
+            tradition_data = self.ancient_db.religious_traditions.get(tradition_name)
+            if not tradition_data:
+                return {'error': f"Tradition {tradition_name} not found"}
+            
+            # Analyze core principles
+            principle_analyses = []
+            for principle in tradition_data['core_principles']:
+                analysis = await self.analyze_ancient_teaching(principle)
+                principle_analyses.append(analysis)
+            
+            # Calculate tradition health score
+            avg_truth_score = np.mean([a.get('truth_score', 0) for a in principle_analyses])
+            universal_law_alignment = np.mean([a['universal_law_assessment']['universal_law_alignment'] 
+                                            for a in principle_analyses])
+            
+            return {
+                'tradition': tradition_name,
+                'tradition_data': tradition_data,
+                'principle_analyses': principle_analyses,
+                'tradition_health_score': avg_truth_score,
+                'universal_law_alignment': universal_law_alignment,
+                'inversion_status': tradition_data.get('inversion_status', 'unknown'),
+                'recovery_potential': self._calculate_recovery_potential(principle_analyses),
+                'analysis_timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Tradition analysis failed: {e}")
+            return {'error': str(e)}
+    
+    def _calculate_recovery_potential(self, principle_analyses: List[Dict]) -> float:
+        """Calculate potential for recovering original teachings"""
+        if not principle_analyses:
+            return 0.0
+        
+        inversion_scores = [a['inversion_analysis']['inversion_score'] for a in principle_analyses]
+        avg_inversion = np.mean(inversion_scores)
+        
+        # Lower inversion means higher recovery potential
+        recovery_potential = 1.0 - avg_inversion
+        
+        # Boost if universal law alignment is high
+        universal_scores = [a['universal_law_assessment']['universal_law_alignment'] for a in principle_analyses]
+        avg_universal = np.mean(universal_scores)
+        
+        return min(1.0, recovery_potential * 0.7 + avg_universal * 0.3)
+    
+    async def compare_traditions(self, tradition1: str, tradition2: str) -> Dict[str, Any]:
+        """
+        Compare two ancient traditions
+        """
+        self.logger.info(f"🔄 COMPARING TRADITIONS: {tradition1} vs {tradition2}")
+        
+        try:
+            analysis1 = await self.analyze_tradition(tradition1)
+            analysis2 = await self.analyze_tradition(tradition2)
+            
+            if 'error' in analysis1 or 'error' in analysis2:
+                return {'error': 'One or both traditions could not be analyzed'}
+            
+            return {
+                'comparison': {
+                    'tradition1': tradition1,
+                    'tradition2': tradition2,
+                    'health_score_difference': abs(analysis1['tradition_health_score'] - analysis2['tradition_health_score']),
+                    'universal_law_difference': abs(analysis1['universal_law_alignment'] - analysis2['universal_law_alignment']),
+                    'recovery_potential_difference': abs(analysis1['recovery_potential'] - analysis2['recovery_potential'])
+                },
+                'analysis1': analysis1,
+                'analysis2': analysis2,
+                'shared_consciousness_tech': self._find_shared_technologies(analysis1, analysis2),
+                'comparison_timestamp': datetime.utcnow().isoformat()
+            }
+            
+        except Exception as e:
+            self.logger.error(f"Tradition comparison failed: {e}")
+            return {'error': str(e)}
+    
+    def _find_shared_technologies(self, analysis1: Dict, analysis2: Dict) -> List[str]:
+        """Find shared consciousness technologies between traditions"""
+        trad1_tech = set()
+        trad2_tech = set()
+        
+        # Extract technologies from tradition data
+        trad1_data = analysis1.get('tradition_data', {})
+        trad2_data = analysis2.get('tradition_data', {})
+        
+        for tech_category in self.ancient_db.consciousness_technologies.values():
+            if trad1_data.get('consciousness_tech'):
+                trad1_tech.update(trad1_data['consciousness_tech'])
+            if trad2_data.get('consciousness_tech'):
+                trad2_tech.update(trad2_data['consciousness_tech'])
+        
+        return list(trad1_tech.intersection(trad2_tech))
+    
+    def get_module_metrics(self) -> Dict[str, Any]:
+        """Get module performance and usage metrics"""
+        return {
+            'analyses_performed': len(self.analysis_history),
+            'traditions_analyzed': len(set([h['result'].get('tradition_alignment', {}).get('detected_tradition', 'unknown') 
+                                          for h in self.analysis_history])),
+            'average_truth_score': np.mean([h['result'].get('truth_score', 0) for h in self.analysis_history]) 
+                                if self.analysis_history else 0,
+            'module_uptime': 'active',
+            'last_analysis': self.analysis_history[-1]['timestamp'] if self.analysis_history else 'none',
+            'universal_law_violations_detected': sum(len(h['result'].get('universal_law_assessment', {}).get('violation_indicators', [])) 
+                                                   for h in self.analysis_history),
+            'inversion_patterns_detected': sum(len(h['result'].get('inversion_analysis', {}).get('detected_mechanisms', [])) 
+                                             for h in self.analysis_history)
+        }
+
+# =============================================================================
+# DEMONSTRATION AND TESTING
+# =============================================================================
+
+async def demonstrate_ancient_religions_module():
+    """
+    Demonstrate the Ancient Religions Module with test cases
+    """
+    print("🌌 ANCIENT RELIGIONS MODULE - DEMONSTRATION")
+    print("Universal Law Primacy + Babylonian Inversion Detection")
+    print("=" * 80)
+    
+    module = AncientReligionsModule()
+    
+    # Test teachings from various ancient traditions
+    test_teachings = [
+        # Pre-Vedic - High Universal Law alignment
+        "The individual soul (Atman) is one with universal consciousness (Brahman)",
+        "Through meditation and self-realization, one achieves liberation (Moksha)",
+        
+        # Sumerian - Direct divine access
+        "Each person can communicate directly with the gods through prayer and ritual",
+        "The me are divine laws that govern all aspects of reality",
+        
+        # Early Egyptian - Consciousness evolution
+        "The ba soul travels to other realms during sleep and after death",
+        "Maat represents the cosmic balance that each person must uphold",
+        
+        # Indigenous - Nature connection
+        "The dreamtime is the fundamental reality from which our world emerges",
+        "All beings are connected through the great spirit of life",
+        
+        # Potential inversion examples
+        "Only the high priest can interpret the will of the gods",
+        "The king is the living god and must be obeyed without question"
+    ]
+    
+    results = []
+    
+    print(f"\n🎯 ANALYZING {len(test_teachings)} ANCIENT TEACHINGS...")
+    
+    for i, teaching in enumerate(test_teachings, 1):
+        print(f"\n" + "="*60)
+        print(f"TEACHING {i}/{len(test_teachings)}")
+        print("="*60)
+        print(f"Content: {teaching}")
+        
+        result = await module.analyze_ancient_teaching(teaching)
+        results.append(result)
+        
+        # Display key results
+        truth_score = result['truth_score']
+        truth_category = result['truth_category']
+        tradition = result['tradition_alignment']['detected_tradition']
+        universal_alignment = result['universal_law_assessment']['universal_law_alignment']
+        inversion_score = result['inversion_analysis']['inversion_score']
+        
+        print(f"\n📊 ANALYSIS RESULTS:")
+        print(f"   Truth Score: {truth_score:.3f}")
+        print(f"   Category: {truth_category}")
+        print(f"   Tradition: {tradition}")
+        print(f"   Universal Law Alignment: {universal_alignment:.3f}")
+        print(f"   Inversion Detection: {inversion_score:.3f}")
+        
+        if result['recovery_recommendations']:
+            print(f"   Recovery Recommendations: {result['recovery_recommendations']}")
+    
+    # Tradition Analysis
+    print("\n" + "="*80)
+    print("🏛️  TRADITION ANALYSIS")
+    print("="*80)
+    
+    traditions = ['pre_vedic', 'sumerian', 'early_egyptian', 'indigenous_oral']
+    for tradition in traditions:
+        print(f"\nAnalyzing {tradition}...")
+        trad_analysis = await module.analyze_tradition(tradition)
+        
+        if 'error' not in trad_analysis:
+            print(f"   Health Score: {trad_analysis['tradition_health_score']:.3f}")
+            print(f"   Universal Law: {trad_analysis['universal_law_alignment']:.3f}")
+            print(f"   Recovery Potential: {trad_analysis['recovery_potential']:.3f}")
+            print(f"   Inversion Status: {trad_analysis['inversion_status']}")
+    
+    # Module Metrics
+    print("\n" + "="*80)
+    print("📈 MODULE METRICS")
+    print("="*80)
+    
+    metrics = module.get_module_metrics()
+    for key, value in metrics.items():
+        print(f"{key}: {value}")
+    
+    return results, metrics
+
+# =============================================================================
+# MAIN EXECUTION
+# =============================================================================
+
+async def main():
+    """
+    Main execution function for Ancient Religions Module
+    """
+    try:
+        print("🚀 INITIALIZING ANCIENT RELIGIONS MODULE...")
+        print("Universal Law Primacy + Inversion Detection + Quantum Truth Verification")
+        print()
+        
+        results, metrics = await demonstrate_ancient_religions_module()
+        
+        print("\n" + "="*80)
+        print("✅ ANCIENT RELIGIONS MODULE EXECUTION COMPLETE")
+        print("="*80)
+        print(f"Analyzed {len(results)} ancient teachings")
+        print(f"Module performance: {metrics['analyses_performed']} analyses completed")
+        print(f"Average truth score across all analyses: {metrics['average_truth_score']:.3f}")
+        print("\n🌌 ANCIENT WISDOM RECOVERY SYSTEM: OPERATIONAL")
+        
+    except Exception as e:
+        print(f"❌ Execution failed: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    # Configure logging
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.StreamHandler(),
+            logging.FileHandler('ancient_religions_module.log')
+        ]
+    )
+    
+    # Run the ancient religions module
+    asyncio.run(main())