VEIL ENGINE VI

#!/usr/bin/env python3
"""
VEIL ENGINE VI – ADVANCED UNIFIED FRAMEWORK
Synthesis of:
  • VEIL_ENGINE_1 (empirical anchoring, anti‑subversion, knowledge graph)
  • trustfall2 (dynamic Bayesian validation)
  • IICE (cryptographic audit, evidence bundles, recursive investigation)
  • MEM_REC_MCON (archetypal, numismatic, Tesla‑Logos, control matrix)
  • VeILEngine (numismatic API, policing layer)

Principles:
  - Power Geometry
  - Narrative as Data
  - Symbols Carry Suppressed Realities
  - No Final Truth
"""

import asyncio
import hashlib
import json
import logging
import time
from dataclasses import dataclass, field, asdict
from datetime import datetime, timedelta
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple, Set
import numpy as np
from scipy.stats import beta

# =============================================================================
# CONFIGURATION & CONSTANTS
# =============================================================================
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger("OmegaIntegrityEngine")

TRUTH_ESCAPE_PREVENTION_THRESHOLD = 0.95
EVIDENCE_OVERWHELM_FACTOR = 5
MAX_RECURSION_DEPTH = 7

# =============================================================================
# ENUMS (from MEM_REC_MCON, IICE)
# =============================================================================
class InvestigationDomain(Enum):
    SCIENTIFIC = "scientific"
    HISTORICAL = "historical"
    LEGAL = "legal"
    NUMISMATIC = "numismatic"
    ARCHETYPAL = "archetypal"
    SOVEREIGNTY = "sovereignty"
    MEMETIC = "memetic"
    TESLA = "tesla"

class ControlArchetype(Enum):
    PRIEST_KING = "priest_king"
    CORPORATE_OVERLORD = "corporate_overlord"
    ALGORITHMIC_CURATOR = "algorithmic_curator"
    # ... others can be added

class SlaveryType(Enum):
    CHATTEL_SLAVERY = "chattel_slavery"
    WAGE_SLAVERY = "wage_slavery"
    DIGITAL_SLAVERY = "digital_slavery"

class ConsciousnessTechnology(Enum):
    SOVEREIGNTY_ACTIVATION = "sovereignty_activation"
    TRANSCENDENT_VISION = "transcendent_vision"
    ENLIGHTENMENT_ACCESS = "enlightenment_access"

class ArchetypeTransmission(Enum):
    FELINE_PREDATOR = "jaguar_lion_predator"
    SOLAR_SYMBOLISM = "eight_star_sunburst"
    FEMINE_DIVINE = "inanna_liberty_freedom"

class RealityDistortionLevel(Enum):
    MINOR_ANOMALY = "minor_anomaly"
    MODERATE_FRACTURE = "moderate_fracture"
    MAJOR_COLLISION = "major_collision"
    REALITY_BRANCH_POINT = "reality_branch_point"

class SignalType(Enum):
    MEDIA_ARC = "media_arc"
    EVENT_TRIGGER = "event_trigger"
    INSTITUTIONAL_FRAMING = "institutional_framing"
    MEMETIC_PRIMER = "memetic_primer"

class OutcomeState(Enum):
    LOW_ADOPTION = "low_adoption"
    PARTIAL_ADOPTION = "partial_adoption"
    HIGH_ADOPTION = "high_adoption"
    POLARIZATION = "polarization"
    FATIGUE = "fatigue"

# =============================================================================
# CORE DATA STRUCTURES (IICE + enhancements)
# =============================================================================
@dataclass
class EvidenceSource:
    source_id: str
    domain: InvestigationDomain
    reliability_score: float = 0.5
    independence_score: float = 0.5
    methodology: str = "unknown"
    last_verified: datetime = field(default_factory=datetime.utcnow)
    verification_chain: List[str] = field(default_factory=list)

    def to_hashable_dict(self) -> Dict:
        return {
            'source_id': self.source_id,
            'domain': self.domain.value,
            'reliability_score': self.reliability_score,
            'independence_score': self.independence_score,
            'methodology': self.methodology
        }

@dataclass
class EvidenceBundle:
    claim: str
    supporting_sources: List[EvidenceSource]
    contradictory_sources: List[EvidenceSource]
    temporal_markers: Dict[str, datetime]
    methodological_scores: Dict[str, float]
    cross_domain_correlations: Dict[InvestigationDomain, float]
    recursive_depth: int = 0
    parent_hashes: List[str] = field(default_factory=list)
    evidence_hash: str = field(init=False)

    def __post_init__(self):
        self.evidence_hash = deterministic_hash(self.to_hashable_dict())

    def to_hashable_dict(self) -> Dict:
        return {
            'claim': self.claim,
            'supporting_sources': sorted([s.to_hashable_dict() for s in self.supporting_sources], key=lambda x: x['source_id']),
            'contradictory_sources': sorted([s.to_hashable_dict() for s in self.contradictory_sources], key=lambda x: x['source_id']),
            'methodological_scores': {k: v for k, v in sorted(self.methodological_scores.items())},
            'cross_domain_correlations': {k.value: v for k, v in sorted(self.cross_domain_correlations.items())},
            'recursive_depth': self.recursive_depth,
            'parent_hashes': sorted(self.parent_hashes)
        }

    def calculate_coherence(self) -> float:
        if not self.supporting_sources:
            return 0.0
        avg_reliability = np.mean([s.reliability_score for s in self.supporting_sources])
        avg_independence = np.mean([s.independence_score for s in self.supporting_sources])
        avg_methodology = np.mean(list(self.methodological_scores.values())) if self.methodological_scores else 0.5
        avg_domain = np.mean(list(self.cross_domain_correlations.values())) if self.cross_domain_correlations else 0.5
        return min(1.0, max(0.0,
            avg_reliability * 0.35 +
            avg_independence * 0.25 +
            avg_methodology * 0.25 +
            avg_domain * 0.15
        ))

def deterministic_hash(data: Any) -> str:
    data_str = json.dumps(data, sort_keys=True, separators=(',', ':')) if not isinstance(data, str) else data
    return hashlib.sha3_256(data_str.encode()).hexdigest()

# =============================================================================
# AUDIT CHAIN (from IICE)
# =============================================================================
class AuditChain:
    def __init__(self):
        self.chain: List[Dict] = []
        self.genesis_hash = self._create_genesis()

    def _create_genesis(self) -> str:
        genesis_data = {
            'system': 'Omega Integrity Engine',
            'version': '5.1',
            'principles': ['power_geometry', 'narrative_as_data', 'symbols_carry_suppressed_realities', 'no_final_truth']
        }
        genesis_hash = self._hash_record('genesis', genesis_data, '0'*64)
        self.chain.append({
            'block_type': 'genesis',
            'timestamp': datetime.utcnow().isoformat(),
            'data': genesis_data,
            'hash': genesis_hash,
            'previous_hash': '0'*64,
            'index': 0
        })
        return genesis_hash

    def _hash_record(self, record_type: str, data: Dict, previous_hash: str) -> str:
        record = {
            'record_type': record_type,
            'timestamp': datetime.utcnow().isoformat(),
            'data': data,
            'previous_hash': previous_hash
        }
        return deterministic_hash(record)

    def add_record(self, record_type: str, data: Dict):
        previous_hash = self.chain[-1]['hash'] if self.chain else self.genesis_hash
        record_hash = self._hash_record(record_type, data, previous_hash)
        self.chain.append({
            'record_type': record_type,
            'timestamp': datetime.utcnow().isoformat(),
            'data': data,
            'hash': record_hash,
            'previous_hash': previous_hash,
            'index': len(self.chain)
        })
        logger.debug(f"Audit record added: {record_type}")

    def verify(self) -> bool:
        for i in range(1, len(self.chain)):
            prev = self.chain[i-1]
            curr = self.chain[i]
            if curr['previous_hash'] != prev['hash']:
                return False
            expected = self._hash_record(curr['record_type'], curr['data'], curr['previous_hash'])
            if curr['hash'] != expected:
                return False
        return True

    def summary(self) -> Dict:
        return {
            'total_blocks': len(self.chain),
            'genesis_hash': self.genesis_hash[:16],
            'latest_hash': self.chain[-1]['hash'][:16] if self.chain else None,
            'chain_integrity': self.verify()
        }

# =============================================================================
# EMPIRICAL DATA ANCHOR (from VEIL_ENGINE_1)
# =============================================================================
class EmpiricalDataAnchor:
    """Fetches live geomagnetic and solar data to influence resonance calculations."""
    GEOMAGNETIC_API = "https://services.swpc.noaa.gov/products/geospace/geospace_forecast_current.json"
    SOLAR_FLUX_API = "https://services.swpc.noaa.gov/json/solar-cycle/observed-solar-cycle-indices.json"

    def __init__(self):
        self.geomagnetic_data = None
        self.solar_flux_data = None
        self.last_update = 0
        self.update_interval = 3600  # 1 hour

    async def update(self):
        now = time.time()
        if now - self.last_update < self.update_interval:
            return
        try:
            import aiohttp
            async with aiohttp.ClientSession() as session:
                async with session.get(self.GEOMAGNETIC_API) as resp:
                    if resp.status == 200:
                        self.geomagnetic_data = await resp.json()
                async with session.get(self.SOLAR_FLUX_API) as resp:
                    if resp.status == 200:
                        self.solar_flux_data = await resp.json()
            self.last_update = now
            logger.info("Empirical data updated")
        except Exception as e:
            logger.warning(f"Empirical data update failed: {e}")

    def get_geomagnetic_index(self) -> float:
        if not self.geomagnetic_data:
            return 2.0  # default quiet
        try:
            if isinstance(self.geomagnetic_data, list) and len(self.geomagnetic_data) > 0:
                return float(self.geomagnetic_data[0].get('Kp', 2.0))
        except:
            pass
        return 2.0

    def get_solar_flux(self) -> float:
        if not self.solar_flux_data:
            return 100.0
        try:
            if isinstance(self.solar_flux_data, list) and len(self.solar_flux_data) > 0:
                return float(self.solar_flux_data[-1].get('ssn', 100.0))
        except:
            pass
        return 100.0

    def resonance_factor(self) -> float:
        kp = self.get_geomagnetic_index()
        flux = self.get_solar_flux()
        # Optimal around Kp=3, flux=120
        kp_ideal = 1.0 - abs(kp - 3.0) / 9.0
        flux_ideal = 1.0 - abs(flux - 120.0) / 250.0
        return (kp_ideal + flux_ideal) / 2.0

# =============================================================================
# SOVEREIGNTY ANALYZER (from HelperKillerEngine)
# =============================================================================
class SovereigntyAnalyzer:
    """Power geometry analysis: who controls event and narrative."""
    def __init__(self):
        # Built‑in power database (expandable)
        self.actors = {
            "FBI":   {"control": 4, "narrator": True, "layers": ["evidence", "access", "reporting"]},
            "CIA":   {"control": 3, "narrator": False, "layers": ["intelligence", "covert_ops"]},
            "NASA":  {"control": 2, "narrator": True, "layers": ["space_access", "media"]},
            "WHO":   {"control": 3, "narrator": True, "layers": ["health_policy", "data"]},
            "WSJ":   {"control": 1, "narrator": True, "layers": ["media"]},
        }

    async def analyze(self, claim: str) -> EvidenceBundle:
        # Extract actors mentioned (simple keyword match)
        found = [name for name in self.actors if name.lower() in claim.lower()]
        if not found:
            # No dominant institution – low threat
            bundle = self._create_bundle(claim, [], 0.3, "No dominant institution detected.")
            return bundle

        threats = []
        for name in found:
            props = self.actors[name]
            base = props["control"] / 6.0
            if props["narrator"]:
                base *= 1.5  # narrator penalty
            threats.append(min(1.0, base))
        avg_threat = sum(threats) / len(threats)

        # Create sources
        sources = []
        for name in found:
            source = EvidenceSource(
                source_id=f"sovereignty_{name}",
                domain=InvestigationDomain.SOVEREIGNTY,
                reliability_score=0.7 - avg_threat * 0.3,  # lower if threat high
                independence_score=0.5,
                methodology="power_geometry_analysis"
            )
            sources.append(source)

        bundle = EvidenceBundle(
            claim=claim,
            supporting_sources=sources,
            contradictory_sources=[],
            temporal_markers={'analyzed_at': datetime.utcnow()},
            methodological_scores={'control_overlap_analysis': avg_threat},
            cross_domain_correlations={},
            recursive_depth=0
        )
        return bundle

    def _create_bundle(self, claim, sources, threat, msg) -> EvidenceBundle:
        source = EvidenceSource(
            source_id="sovereignty_default",
            domain=InvestigationDomain.SOVEREIGNTY,
            reliability_score=0.5,
            independence_score=0.8,
            methodology="default"
        )
        return EvidenceBundle(
            claim=claim,
            supporting_sources=[source],
            contradictory_sources=[],
            temporal_markers={'analyzed_at': datetime.utcnow()},
            methodological_scores={'sovereignty_threat': threat},
            cross_domain_correlations={}
        )

# =============================================================================
# ARCHETYPAL ENGINE (from UniversalArchetypeProver)
# =============================================================================
class ArchetypalEngine:
    def __init__(self):
        self.archetypes = {
            ArchetypeTransmission.SOLAR_SYMBOLISM: {
                "strength": 0.98,
                "keywords": ["sun", "star", "radiant", "enlightenment", "liberty crown"],
                "transmission": ["Inanna", "Ishtar", "Virgin Mary", "Statue of Liberty"],
                "consciousness": ConsciousnessTechnology.ENLIGHTENMENT_ACCESS
            },
            ArchetypeTransmission.FELINE_PREDATOR: {
                "strength": 0.95,
                "keywords": ["lion", "jaguar", "predator", "power", "sovereign"],
                "transmission": ["Mesoamerican jaguar", "Egyptian lion", "heraldic lion"],
                "consciousness": ConsciousnessTechnology.SOVEREIGNTY_ACTIVATION
            },
            ArchetypeTransmission.FEMINE_DIVINE: {
                "strength": 0.99,
                "keywords": ["goddess", "virgin", "mother", "liberty", "freedom"],
                "transmission": ["Inanna", "Ishtar", "Aphrodite", "Virgin Mary", "Statue of Liberty"],
                "consciousness": ConsciousnessTechnology.TRANSCENDENT_VISION
            }
        }

    async def analyze(self, claim: str) -> EvidenceBundle:
        claim_lower = claim.lower()
        matches = []
        for arch, data in self.archetypes.items():
            if any(kw in claim_lower for kw in data["keywords"]):
                matches.append((arch, data))
        if not matches:
            # No strong archetype
            source = EvidenceSource(
                source_id="archetype_null",
                domain=InvestigationDomain.ARCHETYPAL,
                reliability_score=0.5,
                independence_score=0.8,
                methodology="keyword_scan"
            )
            return EvidenceBundle(
                claim=claim,
                supporting_sources=[source],
                contradictory_sources=[],
                temporal_markers={},
                methodological_scores={'archetype_strength': 0.5},
                cross_domain_correlations={}
            )

        # Take strongest match
        arch, data = max(matches, key=lambda x: x[1]["strength"])
        source = EvidenceSource(
            source_id=f"archetype_{arch.value}",
            domain=InvestigationDomain.ARCHETYPAL,
            reliability_score=data["strength"] * 0.9,
            independence_score=0.7,
            methodology="symbolic_dna_matching"
        )
        bundle = EvidenceBundle(
            claim=claim,
            supporting_sources=[source],
            contradictory_sources=[],
            temporal_markers={},
            methodological_scores={
                'archetype_strength': data["strength"],
                'consciousness_technology': data["consciousness"].value
            },
            cross_domain_correlations={}
        )
        return bundle

# =============================================================================
# NUMISMATIC ANALYZER (from QuantumNumismaticAnalyzer)
# =============================================================================
class NumismaticAnalyzer:
    """Analyzes coin overstrikes for reality distortion signatures."""
    def __init__(self):
        # Mock metallurgical reference
        self.metallurgical_db = {
            "silver_standard": {"silver": 0.925, "copper": 0.075},
            "gold_standard": {"gold": 0.900, "copper": 0.100}
        }

    async def analyze(self, claim: str, host_coin: str = None, overstrike_coin: str = None) -> EvidenceBundle:
        # For demo, generate simulated analysis
        # In production, fetch from NGC/PCGS APIs
        if not host_coin:
            host_coin = "host_default"
        if not overstrike_coin:
            overstrike_coin = "overstrike_default"

        # Simulate metallurgical discrepancy
        compositional_discrepancy = np.random.uniform(0.1, 0.8)
        sovereignty_collision = np.random.uniform(0.3, 0.9)
        temporal_displacement = np.random.uniform(0.2, 0.7)

        # Determine reality distortion level
        impact = (compositional_discrepancy + sovereignty_collision + temporal_displacement) / 3
        if impact > 0.8:
            level = RealityDistortionLevel.REALITY_BRANCH_POINT
        elif impact > 0.6:
            level = RealityDistortionLevel.MAJOR_COLLISION
        elif impact > 0.4:
            level = RealityDistortionLevel.MODERATE_FRACTURE
        else:
            level = RealityDistortionLevel.MINOR_ANOMALY

        source = EvidenceSource(
            source_id=f"numismatic_{host_coin}_{overstrike_coin}",
            domain=InvestigationDomain.NUMISMATIC,
            reliability_score=0.8,
            independence_score=0.9,
            methodology="metallurgical_and_temporal_analysis"
        )
        bundle = EvidenceBundle(
            claim=claim,
            supporting_sources=[source],
            contradictory_sources=[],
            temporal_markers={'analysis_time': datetime.utcnow()},
            methodological_scores={
                'compositional_discrepancy': compositional_discrepancy,
                'sovereignty_collision': sovereignty_collision,
                'temporal_displacement': temporal_displacement,
                'reality_impact': impact,
                'distortion_level': level.value
            },
            cross_domain_correlations={InvestigationDomain.HISTORICAL: 0.7}
        )
        return bundle

# =============================================================================
# MEMETIC RECURSION ENGINE (from MEM_REC_MCON)
# =============================================================================
class MemeticRecursionEngine:
    """Simulates narrative spread and audience states."""
    def __init__(self):
        self.audience = {
            'conditioning': 0.15,
            'fatigue': 0.10,
            'polarization': 0.10,
            'adoption': 0.10
        }

    async def analyze(self, claim: str, institutional_pressure: float = 0.5) -> EvidenceBundle:
        # Simple simulation: adoption increases with coherence, fatigue with exposure
        coherence = np.random.uniform(0.4, 0.9)
        exposure = np.random.uniform(0.5, 1.5)

        new_adoption = min(1.0, self.audience['adoption'] + coherence * 0.2 + institutional_pressure * 0.1)
        new_fatigue = min(1.0, self.audience['fatigue'] + exposure * 0.05)
        new_polarization = min(1.0, self.audience['polarization'] + abs(0.5 - coherence) * 0.1)

        # Determine outcome
        if new_fatigue > 0.6 and new_adoption < 0.4:
            outcome = OutcomeState.FATIGUE
        elif new_polarization > 0.5 and 0.3 < new_adoption < 0.7:
            outcome = OutcomeState.POLARIZATION
        elif new_adoption >= 0.7:
            outcome = OutcomeState.HIGH_ADOPTION
        elif new_adoption >= 0.4:
            outcome = OutcomeState.PARTIAL_ADOPTION
        else:
            outcome = OutcomeState.LOW_ADOPTION

        source = EvidenceSource(
            source_id="memetic_sim",
            domain=InvestigationDomain.MEMETIC,
            reliability_score=0.6,
            independence_score=0.7,
            methodology="differential_equation_simulation"
        )
        bundle = EvidenceBundle(
            claim=claim,
            supporting_sources=[source],
            contradictory_sources=[],
            temporal_markers={'simulation_time': datetime.utcnow()},
            methodological_scores={
                'adoption_score': new_adoption,
                'fatigue_score': new_fatigue,
                'polarization_score': new_polarization,
                'outcome': outcome.value
            },
            cross_domain_correlations={}
        )
        return bundle

# =============================================================================
# TESLA‑LOGOS ENGINE (simplified from MEM_REC_MCON)
# =============================================================================
class TeslaLogosEngine:
    """Calculates resonance coherence using Tesla frequencies (3,6,9, Schumann)."""
    SCHUMANN = 7.83
    GOLDEN_RATIO = 1.61803398875

    async def analyze(self, claim: str) -> EvidenceBundle:
        # Compute a simple resonance score based on character frequencies
        text = claim.lower()
        # Count occurrences of digits 3,6,9
        tesla_counts = sum(text.count(d) for d in ['3','6','9'])
        # Check for golden ratio patterns in word lengths (simplistic)
        word_lengths = [len(w) for w in text.split()]
        if len(word_lengths) > 2:
            ratios = [word_lengths[i+1]/max(1,word_lengths[i]) for i in range(len(word_lengths)-1)]
            golden_alignments = sum(1 for r in ratios if abs(r - self.GOLDEN_RATIO) < 0.2)
        else:
            golden_alignments = 0

        resonance = (tesla_counts / max(1, len(text))) * 0.5 + (golden_alignments / max(1, len(word_lengths))) * 0.5
        resonance = min(1.0, resonance * 10)  # scale

        source = EvidenceSource(
            source_id="tesla_logos",
            domain=InvestigationDomain.TESLA,
            reliability_score=0.7,
            independence_score=0.8,
            methodology="frequency_harmonic_analysis"
        )
        bundle = EvidenceBundle(
            claim=claim,
            supporting_sources=[source],
            contradictory_sources=[],
            temporal_markers={},
            methodological_scores={'resonance_coherence': resonance},
            cross_domain_correlations={InvestigationDomain.SCIENTIFIC: 0.6}
        )
        return bundle

# =============================================================================
# BAYESIAN CORROBORATOR (from trustfall2 + IICE)
# =============================================================================
class BayesianCorroborator:
    """Combines evidence bundles using dynamic Bayesian updating with volatility tracking."""
    def __init__(self):
        self.domain_stats = {}  # volatility per domain
        self.base_priors = {
            InvestigationDomain.SCIENTIFIC: (50, 1),
            InvestigationDomain.HISTORICAL: (6, 4),
            InvestigationDomain.NUMISMATIC: (10, 2),
            InvestigationDomain.ARCHETYPAL: (5, 5),
            InvestigationDomain.SOVEREIGNTY: (4, 6),
            InvestigationDomain.MEMETIC: (3, 7),
            InvestigationDomain.TESLA: (8, 8)
        }

    def update_volatility(self, domain: InvestigationDomain, certainty_drift: float):
        if domain not in self.domain_stats:
            self.domain_stats[domain] = {'volatility': 0.5, 'history': []}
        self.domain_stats[domain]['history'].append(certainty_drift)
        # keep last 10
        if len(self.domain_stats[domain]['history']) > 10:
            self.domain_stats[domain]['history'].pop(0)
        self.domain_stats[domain]['volatility'] = np.mean(self.domain_stats[domain]['history'])

    def get_prior(self, domain: InvestigationDomain) -> Tuple[float, float]:
        base_alpha, base_beta = self.base_priors.get(domain, (5, 5))
        vol = self.domain_stats.get(domain, {}).get('volatility', 0.5)
        # Adjust: higher volatility → lower confidence (increase beta)
        alpha = base_alpha * (1 - 0.3 * vol)
        beta_val = base_beta * (1 + 0.5 * vol)
        return max(1, alpha), max(1, beta_val)

    async def combine(self, bundles: List[EvidenceBundle]) -> Dict[str, Any]:
        # Aggregate evidence by domain
        domain_alpha = {}
        domain_beta = {}
        for bundle in bundles:
            coherence = bundle.calculate_coherence()
            # For each source, update domain counts
            for source in bundle.supporting_sources:
                domain = source.domain
                a, b = self.get_prior(domain)
                # Strength of evidence: coherence * reliability
                strength = coherence * source.reliability_score
                # Update alpha and beta
                if domain not in domain_alpha:
                    domain_alpha[domain] = a
                    domain_beta[domain] = b
                # Supporting evidence increases alpha, contradictory increases beta
                # Here we treat supporting_sources as positive evidence; contradictory would be handled separately
                domain_alpha[domain] += strength * source.independence_score
                # Simulate some uncertainty
                domain_beta[domain] += (1 - strength) * source.independence_score

        # Combine across domains using weighted average of posterior means
        total_alpha = 0
        total_beta = 0
        for domain in domain_alpha:
            total_alpha += domain_alpha[domain]
            total_beta += domain_beta[domain]

        if total_alpha + total_beta == 0:
            posterior = 0.5
        else:
            posterior = total_alpha / (total_alpha + total_beta)

        # Compute credible interval
        hdi = beta.interval(0.95, total_alpha, total_beta)

        return {
            'posterior_probability': posterior,
            'credible_interval': (float(hdi[0]), float(hdi[1])),
            'domain_contributions': {d.value: a/(a+b) for d, a, b in zip(domain_alpha.keys(), domain_alpha.values(), domain_beta.values())},
            'total_evidence': total_alpha + total_beta
        }

# =============================================================================
# ORCHESTRATOR (MegaconsciousnessEngine)
# =============================================================================
class OmegaOrchestrator:
    """Main investigation controller with audit, recursion, and module management."""
    def __init__(self):
        self.audit = AuditChain()
        self.empirical = EmpiricalDataAnchor()
        self.modules = {
            InvestigationDomain.SOVEREIGNTY: SovereigntyAnalyzer(),
            InvestigationDomain.ARCHETYPAL: ArchetypalEngine(),
            InvestigationDomain.NUMISMATIC: NumismaticAnalyzer(),
            InvestigationDomain.MEMETIC: MemeticRecursionEngine(),
            InvestigationDomain.TESLA: TeslaLogosEngine(),
        }
        self.corroborator = BayesianCorroborator()
        self.investigation_cache = {}

    async def investigate(self, claim: str, depth: int = 0, parent_hashes: List[str] = None) -> Dict[str, Any]:
        if parent_hashes is None:
            parent_hashes = []
        inv_id = deterministic_hash(claim + str(depth) + str(time.time()))

        self.audit.add_record("investigation_start", {"claim": claim, "depth": depth, "id": inv_id})

        # Update empirical data
        await self.empirical.update()
        resonance = self.empirical.resonance_factor()

        # Run all modules in parallel
        tasks = []
        for domain, module in self.modules.items():
            # Pass claim, and maybe additional context (like coin IDs if numismatic)
            if domain == InvestigationDomain.NUMISMATIC:
                # In real use, we would extract coin IDs from claim or context
                tasks.append(module.analyze(claim, "host_placeholder", "overstrike_placeholder"))
            else:
                tasks.append(module.analyze(claim))
        bundles = await asyncio.gather(*tasks)

        # Add empirical resonance to each bundle's methodological scores (optional)
        for b in bundles:
            b.methodological_scores['empirical_resonance'] = resonance

        # Combine evidence
        combined = await self.corroborator.combine(bundles)

        # Determine if deeper recursion needed
        needs_deeper = False
        if combined['posterior_probability'] < 0.4 and depth < MAX_RECURSION_DEPTH:
            needs_deeper = True
        if combined['credible_interval'][1] - combined['credible_interval'][0] > 0.3 and depth < MAX_RECURSION_DEPTH:
            needs_deeper = True

        sub_investigations = []
        if needs_deeper:
            # Generate sub‑claims (simplified: use the same claim but deeper)
            sub_result = await self.investigate(claim + " (deeper)", depth+1, parent_hashes + [inv_id])
            sub_investigations.append(sub_result)

        # Prepare final report
        report = {
            'investigation_id': inv_id,
            'claim': claim,
            'depth': depth,
            'timestamp': datetime.utcnow().isoformat(),
            'evidence_bundles': [b.evidence_hash for b in bundles],
            'combined_analysis': combined,
            'empirical_resonance': resonance,
            'sub_investigations': sub_investigations,
            'audit_hash': self.audit.chain[-1]['hash'] if self.audit.chain else None
        }

        # Sign report with cryptographic hash
        report_hash = deterministic_hash(report)
        report['report_hash'] = report_hash
        self.audit.add_record("investigation_complete", {"id": inv_id, "hash": report_hash})

        return report

    def verify_audit(self) -> bool:
        return self.audit.verify()

# =============================================================================
# POLICING ADD‑ON (from VeILEngine)
# =============================================================================
class IntegrityMonitor:
    """Non‑invasive runtime integrity verification."""
    def __init__(self, orchestrator: OmegaOrchestrator):
        self.orchestrator = orchestrator
        self.baseline_manifest = self._generate_manifest()
        self.violations = []

    def _generate_manifest(self) -> Dict[str, str]:
        # Simplified: hash of the orchestrator's method source
        import inspect
        manifest = {}
        for name, method in inspect.getmembers(self.orchestrator, inspect.ismethod):
            try:
                src = inspect.getsource(method)
                manifest[name] = hashlib.sha256(src.encode()).hexdigest()
            except:
                pass
        return manifest

    def check_integrity(self) -> bool:
        current = self._generate_manifest()
        ok = current == self.baseline_manifest
        if not ok:
            self.violations.append({'time': datetime.utcnow().isoformat(), 'type': 'code_alteration'})
        return ok

    async def monitored_investigate(self, claim: str):
        if not self.check_integrity():
            logger.critical("Integrity violation detected! Running in degraded mode.")
        return await self.orchestrator.investigate(claim)

# =============================================================================
# DEMONSTRATION
# =============================================================================
async def main():
    print("=" * 70)
    print("OMEGA INTEGRITY ENGINE – ADVANCED UNIFIED FRAMEWORK")
    print("=" * 70)

    orchestrator = OmegaOrchestrator()
    monitor = IntegrityMonitor(orchestrator)

    test_claims = [
        "The Warren Commission concluded that Lee Harvey Oswald acted alone.",
        "NASA's Apollo missions were genuine achievements of human exploration.",
        "The WHO's pandemic response was coordinated and transparent."
    ]

    for i, claim in enumerate(test_claims, 1):
        print(f"\n🔍 Investigating claim {i}: {claim}")
        result = await monitor.monitored_investigate(claim)

        print(f"\n📊 Results:")
        print(f"   Posterior probability: {result['combined_analysis']['posterior_probability']:.3f}")
        print(f"   95% credible interval: {result['combined_analysis']['credible_interval']}")
        print(f"   Empirical resonance: {result['empirical_resonance']:.3f}")
        print(f"   Depth: {result['depth']}")
        print(f"   Report hash: {result['report_hash'][:16]}...")

    print(f"\n🔒 Audit chain integrity: {orchestrator.verify_audit()}")
    print(f"   Total audit blocks: {orchestrator.audit.summary()['total_blocks']}")
    print(f"   Genesis hash: {orchestrator.audit.summary()['genesis_hash']}...")

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