STACK_3

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
OMEGA SOVEREIGNTY STACK - QUANTUM UNIFIED FRAMEWORK v7.0
================================================================
ULTIMATE INTEGRATION: Consciousness + Sovereignty + Finance + Truth + History + Linguistics
Quantum-Coherent System with Multilingual Truth Binding and Cultural Optimization
"""

import asyncio
import time
import json
import hashlib
import logging
import sys
import os
import numpy as np
import scipy.stats as stats
from scipy import fft, signal, integrate
from scipy.spatial.distance import cosine, euclidean
from scipy.optimize import minimize
from datetime import datetime, timedelta
from typing import Dict, Any, List, Optional, Tuple, Union
from dataclasses import dataclass, field, asdict
from enum import Enum
from collections import defaultdict, deque
import secrets
import sqlite3
import networkx as nx
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
import torch
import torch.nn as nn
import re
import math

# =============================================================================
# Logging Configuration
# =============================================================================

LOG_LEVEL = os.getenv("OMEGA_LOG_LEVEL", "INFO").upper()
logging.basicConfig(
    level=getattr(logging, LOG_LEVEL, logging.INFO),
    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
)
logger = logging.getLogger("OmegaSovereigntyStack")

# =============================================================================
# Mathematical Constants & Determinism
# =============================================================================

MATHEMATICAL_CONSTANTS = {
    "golden_ratio": 1.618033988749895,
    "euler_number": 2.718281828459045,
    "pi": 3.141592653589793,
    "planck_constant": 6.62607015e-34,
    "schumann_resonance": 7.83,
    "information_entropy_max": 0.69314718056,
    "quantum_uncertainty_min": 1.054571817e-34
}

GLOBAL_SEED = int(os.getenv("OMEGA_GLOBAL_SEED", "424242"))
np.random.seed(GLOBAL_SEED)

def clamp(x: float, lo: float = 0.0, hi: float = 1.0) -> float:
    return float(max(lo, min(hi, x)))

def safe_mean(arr: List[float], default: float = 0.0) -> float:
    return float(np.mean(arr)) if arr else default

def small_eps() -> float:
    return 1e-8

# =============================================================================
# QUANTUM CORE INFRASTRUCTURE
# =============================================================================

class QuantumConsciousnessCore(nn.Module):
    """Quantum neural network for consciousness pattern recognition"""
    def __init__(self, input_dim=512, hidden_dims=[256, 128, 64], output_dim=16):
        super().__init__()
        layers = []
        prev_dim = input_dim
        for hidden_dim in hidden_dims:
            layers.extend([
                nn.Linear(prev_dim, hidden_dim),
                nn.QuantumActivation(),
                nn.Dropout(0.1)
            ])
            prev_dim = hidden_dim
        layers.append(nn.Linear(prev_dim, output_dim))
        self.network = nn.Sequential(*layers)
        
    def forward(self, x):
        return self.network(x)

class QuantumActivation(nn.Module):
    """Quantum-inspired activation function with coherence preservation"""
    def forward(self, x):
        # Quantum superposition of activation functions
        sigmoid = torch.sigmoid(x)
        tanh = torch.tanh(x)
        relu = torch.relu(x)
        # Coherent combination
        return (sigmoid + tanh + relu) / 3.0

# Register custom activation
nn.QuantumActivation = QuantumActivation

@dataclass
class QuantumStateVector:
    """Quantum state representation for multi-dimensional analysis"""
    amplitudes: np.ndarray
    phase_angles: np.ndarray
    coherence_level: float
    entanglement_map: Dict[Tuple[int, int], float]
    temporal_echoes: List[float]
    
    def collapse_measurement(self, basis: str = "computational") -> np.ndarray:
        """Collapse quantum state to classical measurement"""
        probabilities = np.abs(self.amplitudes) ** 2
        if basis == "computational":
            return np.random.choice(len(probabilities), p=probabilities)
        else:
            # Rotate basis for different measurement contexts
            rotated_probs = self._rotate_basis(probabilities, basis)
            return np.random.choice(len(rotated_probs), p=rotated_probs)
    
    def _rotate_basis(self, probabilities: np.ndarray, basis: str) -> np.ndarray:
        """Rotate measurement basis"""
        if basis == "cultural":
            # Cultural context rotation
            return np.roll(probabilities, shift=2)
        elif basis == "temporal":
            # Temporal context rotation  
            return np.fft.fft(probabilities).real
        else:
            return probabilities

# =============================================================================
# MULTILINGUISTIC TRUTH BINDING MODULE
# =============================================================================

class LanguageEra(Enum):
    PRE_INVERSION_SUMERIAN = "pre_inversion_sumerian"
    SUMERIAN = "sumerian"
    EGYPTIAN_HIEROGLYPHIC = "egyptian"
    AKKADIAN = "akkadian"
    PHOENICIAN = "phoenician"
    ANCIENT_GREEK = "ancient_greek"
    LATIN = "latin"
    HEBREW = "hebrew"
    SANSKRIT = "sanskrit"
    ANCIENT_CHINESE = "ancient_chinese"

class LinguisticTruthMarker(Enum):
    COSMOLOGICAL_ALIGNMENT = "cosmological_alignment"
    SACRED_GEOMETRY = "sacred_geometry"
    NUMEROLOGICAL_ENCODING = "numerological_encoding"
    PHONETIC_RESONANCE = "phonetic_resonance"
    SYMBOLIC_CORRESPONDENCE = "symbolic_correspondence"
    TEMPORAL_CYCLES = "temporal_cycles"
    PRE_INVERSION_SEAL = "pre_inversion_seal"
    SEXAGESIMAL_CADENCE = "sexagesimal_cadence"
    GODDESS_LINEAGE = "goddess_lineage"
    DERIVATIVE_COHERENCE = "derivative_coherence"
    ARCHETYPAL_REFACTORING = "archetypal_refactoring"

class RealityDomain(Enum):
    TEXTUAL = "textual"
    NUMISMATIC = "numismatic"
    NATURAL_FRACTAL = "natural_fractal"
    ICONOGRAPHIC = "iconographic"
    DERIVATIVE_PATTERN = "derivative_pattern"

# Archetypal device registry
ARCHETYPE_DEVICES = {
    "starburst": {
        "aliases": ["radiate_crown", "eight_pointed_star", "rosette", "sunburst"],
        "domain": RealityDomain.ICONOGRAPHIC,
        "derivative_path": ["inanna_crown", "ishtar_star", "aphrodite_headdress", "venus_symbol", "liberty_crown"]
    },
    "lion": {
        "aliases": ["large_cat", "panther", "jaguar"],
        "domain": RealityDomain.ICONOGRAPHIC,
        "derivative_path": ["inanna_lion", "cybele_lion", "venice_lion", "heraldic_lion"]
    },
    "eagle": {
        "aliases": ["vulture", "large_bird"],
        "domain": RealityDomain.ICONOGRAPHIC,
        "derivative_path": ["sumerian_anzu", "roman_eagle", "imperial_eagle", "american_eagle"]
    },
    "shield": {
        "aliases": ["aegis", "gorgon_aegis"],
        "domain": RealityDomain.ICONOGRAPHIC,
        "derivative_path": ["divine_aegis", "athena_shield", "heraldic_shield", "national_emblem"]
    },
    "branch": {
        "aliases": ["olive_branch", "wheat", "date_palm"],
        "domain": RealityDomain.ICONOGRAPHIC,
        "derivative_path": ["sumerian_date_palm", "olympic_olive", "roman_wheat", "peace_branch"]
    },
    "female_form": {
        "aliases": ["goddess", "venus", "aphrodite", "ishtar", "inanna", "liberty", "mary"],
        "domain": RealityDomain.ICONOGRAPHIC,
        "derivative_path": ["inanna", "ishtar", "astarte", "aphrodite", "venus", "mary", "liberty"]
    },
    "SC": {
        "aliases": ["senatus_consulto", "seal", "temple_seal", "sanction_mark", "official_sanction"],
        "domain": RealityDomain.NUMISMATIC,
        "derivative_path": ["temple_seal", "sacred_continuity", "senatus_consulto", "official_sanction"]
    },
    "VI": {
        "aliases": ["six", "sexagesimal", "666", "veni_vidi_vici", "roman_VI"],
        "domain": RealityDomain.TEXTUAL,
        "derivative_path": ["sexagesimal_60", "sacred_6", "roman_VI", "nero_666", "apocalyptic_666"]
    },
}

@dataclass
class DerivativePath:
    source_archetype: str
    derivation_chain: List[Tuple[str, float]]
    recombination_patterns: List[str]
    innovation_score: float = 0.0

    def calculate_derivative_coherence(self) -> float:
        if len(self.derivation_chain) < 2:
            return 0.5
        scores = [score for _, score in self.derivation_chain]
        return float(np.mean(scores))

@dataclass
class AncientLanguage:
    era: LanguageEra
    time_period: Tuple[int, int]
    writing_system: str
    sample_script: List[str] = field(default_factory=list)
    truth_markers: List[LinguisticTruthMarker] = field(default_factory=list)
    modern_equivalents: Dict[str, str] = field(default_factory=dict)
    resonance_frequency: float = 0.0
    derivative_density: float = 0.0

    def __post_init__(self):
        age_weight = max(0.0, (abs(self.time_period[0]) / 8000.0))
        complexity = min(0.3, len(self.sample_script) * 0.02)
        marker_strength = min(0.3, len(self.truth_markers) * 0.05)
        base = 0.3 + age_weight + complexity + marker_strength
        self.resonance_frequency = min(0.97, base)
        if self.era == LanguageEra.PRE_INVERSION_SUMERIAN:
            self.derivative_density = 0.1
        else:
            time_from_origin = abs(self.time_period[0]) - 4000
            self.derivative_density = float(np.clip(0.3 + (time_from_origin / 6000.0), 0.0, 0.9))

@dataclass
class LinguisticTruthMatch:
    language: AncientLanguage
    matched_patterns: List[str]
    confidence: float
    truth_markers_detected: List[LinguisticTruthMarker]
    cross_linguistic_correlations: List[str]
    temporal_coherence: float
    symbolic_resonance: float
    derivative_coherence: float = 0.0
    archetypal_refactoring_score: float = 0.0
    inversion_alerts: List[str] = field(default_factory=list)
    derivative_paths: List[DerivativePath] = field(default_factory=list)

@dataclass
class FractalSignature:
    phi_alignment: float
    hex_cadence: float
    rosette_density: float
    branch_factor: float
    crown_radiance: float
    derivative_symmetry: float = 0.0
    sexagesimal_harmonic_score: float = 0.0

@dataclass
class NumismaticSignature:
    sc_detected: bool
    vi_cadence_score: float
    goddess_device_overlap: float
    metallurgical_continuity: float
    iconographic_coherence: float
    derivative_continuity: float = 0.0

class DerivativePatternRecognizer:
    def __init__(self):
        self.archetype_graph = self._build_archetype_derivation_graph()
    
    def _build_archetype_derivation_graph(self) -> Dict[str, List[Tuple[str, float]]]:
        return {
            "inanna": [("ishtar", 0.95), ("astarte", 0.88), ("aphrodite", 0.85),
                       ("venus", 0.82), ("liberty", 0.78), ("mary", 0.72)],
            "starburst": [("radiate_crown", 0.92), ("eight_pointed_star", 0.95),
                          ("rosette", 0.87), ("sunburst", 0.83)],
            "temple_seal": [("sacred_continuity", 0.97), ("senatus_consulto", 0.88),
                            ("official_sanction", 0.85), ("royal_seal", 0.82)],
            "sexagesimal": [("base_60", 0.98), ("sacred_6", 0.92), ("roman_VI", 0.85),
                            ("nero_666", 0.75), ("apocalyptic_666", 0.68)]
        }
    
    def trace_derivative_paths(self, text: str) -> List[DerivativePath]:
        paths = []
        t = text.lower()
        for archetype, derivatives in self.archetype_graph.items():
            if self._archetype_present(archetype, t):
                path = self._build_derivation_chain(archetype, derivatives, t)
                if path.derivation_chain:
                    paths.append(path)
        return paths
    
    def _archetype_present(self, archetype: str, text: str) -> bool:
        if archetype in text:
            return True
        for derivative, _ in self.archetype_graph.get(archetype, []):
            if derivative in text:
                return True
        return False
    
    def _build_derivation_chain(self, source: str, derivatives: List[Tuple[str, float]], text: str) -> DerivativePath:
        chain = []
        patterns = []
        if source in text:
            chain.append((f"source:{source}", 1.0))
            patterns.append(f"direct_{source}")
        for derivative, coherence in derivatives:
            if derivative in text:
                chain.append((f"derivative:{derivative}", coherence))
                patterns.append(f"{source}→{derivative}")
        innovation = self._calculate_innovation_score(chain, patterns)
        return DerivativePath(source, chain, patterns, innovation)
    
    def _calculate_innovation_score(self, chain: List[Tuple[str, float]], patterns: List[str]) -> float:
        if not chain:
            return 0.0
        base_coherence = np.mean([score for _, score in chain])
        pattern_complexity = min(1.0, len(patterns) * 0.3)
        return float(np.clip(base_coherence * (0.7 + 0.3 * pattern_complexity), 0.0, 1.0))

class EnhancedPreInversionDecoder:
    def __init__(self):
        self.derivative_recognizer = DerivativePatternRecognizer()
    
    def decode_symbol(self, symbol: str) -> str:
        mapping = {
            "SC": "Sacred Continuity (cosmic sanction seal, temple authority alignment) → derivative: Senatus Consulto",
            "VI": "Sexagesimal cadence (harmonic 6/60) → derivative: 666 persecution code",
            "starburst": "Inanna's cosmic crown → derivative: Liberty's radiate crown",
            "lion": "Sovereignty guardian → derivative: heraldic lion",
            "eagle": "Celestial oversight → derivative: imperial eagle",
            "shield": "Divine sanction → derivative: national emblem",
            "branch": "Fertility-sustenance → derivative: peace branch",
            "female_form": "Cosmic order embodiment → derivative: liberty personification"
        }
        return mapping.get(symbol.lower(), "Unknown archetype")
    
    def decode_text_for_inversion(self, text: str) -> List[str]:
        alerts = []
        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
        for path in derivative_paths:
            if path.source_archetype == "temple_seal" and any("senatus_consulto" in lbl for lbl, _ in path.derivation_chain):
                alerts.append(f"INVERSION: temple seal → senatus consulto (coherence {path.calculate_derivative_coherence():.2f})")
            if path.source_archetype == "sexagesimal" and any("nero_666" in lbl for lbl, _ in path.derivation_chain):
                alerts.append(f"PERSECUTION CODING: VI cadence → 666 (innovation {path.innovation_score:.2f})")
        if "senatus consulto" in text.lower() and "temple seal" not in text.lower():
            alerts.append("OBSCURATION: Roman sanction overlays Sacred Continuity origin")
        if "liberty" in text.lower() and "inanna" not in text.lower():
            alerts.append("REFACTORING: Liberty as recombination of goddess archetype")
        return alerts

class EnhancedLinguisticPatternAnalyzer:
    def __init__(self):
        self.derivative_recognizer = DerivativePatternRecognizer()

    async def detect_script_patterns(self, text: str, language: AncientLanguage) -> List[str]:
        matches = []
        for char in language.sample_script:
            if char in text:
                matches.append(f"script:{char}")
        for modern, concept in language.modern_equivalents.items():
            if modern.lower() in text.lower() or concept.lower() in text.lower():
                matches.append(f"concept:{modern}={concept}")
        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
        for path in derivative_paths:
            if path.innovation_score > 0.6:
                matches.append(f"derivative_innovation:{path.source_archetype}[{path.innovation_score:.2f}]")
        if LinguisticTruthMarker.PHONETIC_RESONANCE in language.truth_markers:
            words = [w for w in re.findall(r"[A-Za-z]+", text.lower())]
            if len(words) >= 6:
                firsts = [w[0] for w in words if w]
                freq = Counter(firsts).most_common(1)
                if freq and (freq[0][1] >= max(3, int(len(firsts) * 0.35))):
                    matches.append(f"alliteration:{freq[0][0]}")
        return matches

    async def detect_truth_markers(self, text: str, language: AncientLanguage) -> List[LinguisticTruthMarker]:
        detected = []
        t = text.lower()
        def any_in(tokens): return any(tok in t for tok in tokens)

        if any_in(["cosmos", "universe", "stars", "planets", "heaven", "earth"]):
            detected.append(LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT)
        if any_in(["geometry", "golden ratio", "fibonacci", "sacred", "proportion", "phi"]):
            detected.append(LinguisticTruthMarker.SACRED_GEOMETRY)
        nums = set(re.findall(r'\b\d+\b', t))
        if nums & {"3", "6", "7", "12", "40", "60", "108", "144", "360", "666"}:
            detected.append(LinguisticTruthMarker.NUMEROLOGICAL_ENCODING)
        if any_in(["symbol", "glyph", "meaning", "represent", "correspond"]):
            detected.append(LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE)
        if any_in(["cycle", "time", "eternal", "season", "age", "era", "return"]):
            detected.append(LinguisticTruthMarker.TEMPORAL_CYCLES)
        if any_in(["inanna", "ishtar", "astarte", "aphrodite", "venus", "liberty", "cleopatra", "mary"]):
            detected.append(LinguisticTruthMarker.GODDESS_LINEAGE)
        if re.search(r"\bsc\b", t) or any_in(["senatus consulto", "seal", "temple", "shekel", "temple seal"]):
            detected.append(LinguisticTruthMarker.PRE_INVERSION_SEAL)
        if any_in(["vi", "sexagesimal", "base-60", "veni vidi vici", "six", "666", "roman vi"]):
            detected.append(LinguisticTruthMarker.SEXAGESIMAL_CADENCE)

        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
        if derivative_paths:
            avg_coh = np.mean([p.calculate_derivative_coherence() for p in derivative_paths])
            if avg_coh > 0.7:
                detected.append(LinguisticTruthMarker.DERIVATIVE_COHERENCE)
            if any(p.innovation_score > 0.75 for p in derivative_paths):
                detected.append(LinguisticTruthMarker.ARCHETYPAL_REFACTORING)

        return list(dict.fromkeys(detected))

class EnhancedLinguisticTemporalValidator:
    async def validate_temporal_coherence(self, text: str, language: AncientLanguage, context: Optional[Dict[str, Any]]) -> float:
        ancient_terms = {
            LanguageEra.PRE_INVERSION_SUMERIAN: {"clay", "token", "seal", "temple", "shekel", "uruk", "ur", "nippur"},
            LanguageEra.SUMERIAN: {"mesopotamia", "ziggurat", "tigris", "euphrates", "dingir"},
            LanguageEra.EGYPTIAN_HIEROGLYPHIC: {"pyramid", "pharaoh", "nile", "ankh", "maat"},
            LanguageEra.ANCIENT_GREEK: {"athens", "sparta", "dionysus", "aphrodite", "stater"},
            LanguageEra.LATIN: {"senate", "nero", "denarius", "aureus", "sc", "eagle"},
        }
        t = text.lower()
        terms = ancient_terms.get(language.era, set())
        score_hist = (sum(1 for w in terms if w in t) / max(1, len(terms))) if terms else 0.5

        expected_derivative_level = language.derivative_density
        derivative_paths = DerivativePatternRecognizer().trace_derivative_paths(text)
        actual_derivative_level = min(1.0, len(derivative_paths) * 0.3)
        derivative_alignment = 1.0 - abs(expected_derivative_level - actual_derivative_level)

        cyc = sum(1 for w in ["cycle", "eternal", "return", "age", "era", "archetype"] if w in t)
        lin = sum(1 for w in ["progress", "future", "development", "evolution", "innovation"] if w in t)
        score_time = 0.8 if cyc >= lin else (0.5 if cyc == lin else 0.35)

        score_ctx = 0.5
        if context and "temporal_focus" in context:
            peak = float(np.mean(language.time_period))
            dist = abs(context["temporal_focus"] - peak)
            score_ctx = float(1.0 / (1.0 + dist / 1000.0))

        return float(np.clip(np.mean([score_hist, score_time, score_ctx, derivative_alignment]), 0.0, 1.0))

class EnhancedAncientSymbolicDecoder:
    def __init__(self):
        self.derivative_recognizer = DerivativePatternRecognizer()

    async def calculate_symbolic_resonance(self, text: str, language: AncientLanguage) -> float:
        direct = await self._check_symbol_matches(text, language)
        conceptual = await self._check_conceptual_alignment(text, language)
        metaphor = await self._analyze_metaphorical_density(text)
        derivative = await self._analyze_derivative_coherence(text, language)
        return float(np.clip(np.mean([direct, conceptual, metaphor, derivative]), 0.0, 1.0))

    async def find_symbolic_overlap(self, lang1: AncientLanguage, lang2: AncientLanguage, text: str) -> str:
        overlaps = []
        shared_markers = set(lang1.truth_markers).intersection(lang2.truth_markers)
        if shared_markers:
            overlaps.append(f"truth_markers:{len(shared_markers)}")
        l1c = set(lang1.modern_equivalents.values())
        l2c = set(lang2.modern_equivalents.values())
        shared_concepts = l1c & l2c
        if shared_concepts:
            t = text.lower()
            found = [c for c in shared_concepts if c.lower() in t]
            if found:
                overlaps.append(f"concepts:{len(found)}")
        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
        if derivative_paths:
            overlaps.append(f"derivative_paths:{len(derivative_paths)}")
        return ", ".join(overlaps)

    async def _check_symbol_matches(self, text: str, language: AncientLanguage) -> float:
        if not language.sample_script:
            return 0.45
        matches = sum(1 for s in language.sample_script if s in text)
        return matches / len(language.sample_script)

    async def _check_conceptual_alignment(self, text: str, language: AncientLanguage) -> float:
        t = text.lower()
        total = len(language.modern_equivalents)
        hits = sum(1 for c in language.modern_equivalents.values() if c.lower() in t)
        return (hits / total) if total else 0.5

    async def _analyze_metaphorical_density(self, text: str) -> float:
        indicators = {"like", "as", "symbol", "represent", "mean", "signify", "archetype", "seal", "crown"}
        words = re.findall(r"[A-Za-z]+", text.lower())
        if not words:
            return 0.5
        density = sum(1 for w in words if w in indicators) / len(words)
        return float(np.clip(density / 0.06, 0.0, 1.0))

    async def _analyze_derivative_coherence(self, text: str, language: AncientLanguage) -> float:
        paths = self.derivative_recognizer.trace_derivative_paths(text)
        if not paths:
            return 0.3
        coherences = [p.calculate_derivative_coherence() for p in paths]
        innovations = [p.innovation_score for p in paths]
        avg_coherence = np.mean(coherences)
        avg_innovation = np.mean(innovations)
        return float(np.clip((avg_coherence * 0.6 + avg_innovation * 0.4), 0.0, 1.0))

class EnhancedFractalAnalyzer:
    def __init__(self):
        self.derivative_recognizer = DerivativePatternRecognizer()

    @staticmethod
    def _sexagesimal_harmonics_from_text(t: str) -> float:
        score = 0.0
        hits = 0
        for token in ["6", "six", "vi", "sexagesimal", "base-60", "60", "360"]:
            if token in t:
                hits += 1
        if hits == 0:
            return 0.3
        score = 0.6 + 0.1 * min(3, hits)
        if re.search(r"\b1\s*[:/]\s*6\b", t): score += 0.05
        if re.search(r"\b6\s*[:/]\s*60\b", t): score += 0.07
        if re.search(r"\b60\s*[:/]\s*360\b", t): score += 0.08
        return float(np.clip(score, 0.0, 1.0))

    def analyze_textual_fractal_signals(self, text: str) -> FractalSignature:
        t = text.lower()
        phi_align = 0.8 if ("phi" in t or "golden ratio" in t or "fibonacci" in t) else 0.4
        hex_cad = 0.75 if any(x in t for x in ["hex", "six", "base-60", "sexagesimal", "vi"]) else 0.35
        ros_den = 0.7 if any(x in t for x in ["rosette", "starburst", "radiate", "crown", "eight-pointed"]) else 0.3
        branch = 0.65 if any(x in t for x in ["olive", "wheat", "date", "branch", "palm"]) else 0.3
        crown = 0.8 if any(x in t for x in ["liberty crown", "radiate crown", "eight-pointed star", "dingir", "𒀭"]) else 0.4

        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
        derivative_symmetry = 0.5
        if derivative_paths:
            branch_counts = [len(p.derivation_chain) for p in derivative_paths]
            if len(set(branch_counts)) == 1:
                derivative_symmetry = 0.9
            else:
                derivative_symmetry = 0.6 + (0.3 * (1.0 - (np.std(branch_counts) / max(1, np.mean(branch_counts)))))

        sex_harm = self._sexagesimal_harmonics_from_text(t)
        return FractalSignature(phi_align, hex_cad, ros_den, branch, crown, derivative_symmetry, sex_harm)

class EnhancedNumismaticAnalyzer:
    def __init__(self):
        self.derivative_recognizer = DerivativePatternRecognizer()

    def analyze_textual_numismatics(self, text: str) -> NumismaticSignature:
        t = text.lower()
        sc_detected = bool(re.search(r"\bsc\b", t)) or ("senatus consulto" in t) or ("temple seal" in t) or ("shekel" in t)
        vi_score = 0.0
        if any(k in t for k in ["vi", "veni vidi vici", "sexagesimal", "base-60", "666", "roman vi"]):
            vi_score = 0.7
            if "666" in t:
                vi_score = 0.9
        goddess_overlap = sum(1 for k in ["inanna", "ishtar", "astarte", "aphrodite", "venus", "liberty", "cleopatra", "mary"]
                              if k in t) / 8.0
        meta_cont = 0.6 if any(k in t for k in ["silver", "gold", "shekel", "stater", "denarius", "aureus", "bronze"]) else 0.35
        ico_coh = sum(1 for k in ["starburst", "eagle", "lion", "shield", "branch", "female", "radiate crown"]
                      if k in t) / 7.0

        derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)
        derivative_continuity = 0.5
        if derivative_paths:
            continuities = []
            for path in derivative_paths:
                if any(token in path.source_archetype for token in ["starburst", "lion", "eagle", "shield", "branch", "temple_seal", "sexagesimal"]):
                    continuities.append(path.calculate_derivative_coherence())
            derivative_continuity = float(np.mean(continuities)) if continuities else 0.5

        return NumismaticSignature(sc_detected, vi_score, goddess_overlap, meta_cont, ico_coh, derivative_continuity)

class EnhancedMultilinguisticTruthBinder:
    def __init__(self):
        self.language_corpus = self._initialize_languages()
        self.pattern_analyzer = EnhancedLinguisticPatternAnalyzer()
        self.temporal_validator = EnhancedLinguisticTemporalValidator()
        self.symbolic_decoder = EnhancedAncientSymbolicDecoder()
        self.pre_inversion = EnhancedPreInversionDecoder()
        self.fractals = EnhancedFractalAnalyzer()
        self.numismatics = EnhancedNumismaticAnalyzer()
        self.derivative_recognizer = DerivativePatternRecognizer()

    def _initialize_languages(self) -> Dict[LanguageEra, AncientLanguage]:
        corpus = {
            LanguageEra.PRE_INVERSION_SUMERIAN: AncientLanguage(
                era=LanguageEra.PRE_INVERSION_SUMERIAN,
                time_period=(-4000, -3000),
                writing_system="Proto-cuneiform tokens/seals",
                sample_script=["𒌋", "𒀭"],
                truth_markers=[
                    LinguisticTruthMarker.PRE_INVERSION_SEAL,
                    LinguisticTruthMarker.SEXAGESIMAL_CADENCE,
                    LinguisticTruthMarker.SACRED_GEOMETRY,
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.GODDESS_LINEAGE,
                    LinguisticTruthMarker.DERIVATIVE_COHERENCE,
                ],
                modern_equivalents={"dingir": "divine", "ki": "earth", "an": "heaven"}
            ),
            LanguageEra.SUMERIAN: AncientLanguage(
                era=LanguageEra.SUMERIAN, time_period=(-3500, -2000),
                writing_system="Cuneiform",
                sample_script=["𒀭", "𒆠", "𒌋", "𒂍", "𒈬"],
                truth_markers=[
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING,
                    LinguisticTruthMarker.SACRED_GEOMETRY,
                    LinguisticTruthMarker.GODDESS_LINEAGE,
                    LinguisticTruthMarker.DERIVATIVE_COHERENCE,
                ],
                modern_equivalents={"dingir": "divine", "ki": "earth", "an": "heaven"}
            ),
            LanguageEra.EGYPTIAN_HIEROGLYPHIC: AncientLanguage(
                era=LanguageEra.EGYPTIAN_HIEROGLYPHIC, time_period=(-3200, -400),
                writing_system="Hieroglyphic",
                sample_script=["𓂀", "𓃀", "𓅀", "𓇼"],
                truth_markers=[
                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.TEMPORAL_CYCLES,
                    LinguisticTruthMarker.ARCHETYPAL_REFACTORING,
                ],
                modern_equivalents={"ankh": "life", "maat": "truth", "ka": "soul"}
            ),
            LanguageEra.ANCIENT_GREEK: AncientLanguage(
                era=LanguageEra.ANCIENT_GREEK, time_period=(-700, 300),
                writing_system="Greek Alphabet",
                sample_script=["α", "β", "γ", "δ", "ε"],
                truth_markers=[
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.SACRED_GEOMETRY,
                    LinguisticTruthMarker.ARCHETYPAL_REFACTORING,
                ],
                modern_equivalents={"aletheia": "truth", "logos": "reason", "cosmos": "order"}
            ),
            LanguageEra.LATIN: AncientLanguage(
                era=LanguageEra.LATIN, time_period=(-700, 400),
                writing_system="Latin Alphabet",
                sample_script=["V", "I", "S", "C"],
                truth_markers=[
                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING,
                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.ARCHETYPAL_REFACTORING,
                ],
                modern_equivalents={"senatus consulto": "by decree", "imperium": "authority"}
            ),
            LanguageEra.HEBREW: AncientLanguage(
                era=LanguageEra.HEBREW, time_period=(-1000, 500),
                writing_system="Hebrew Alphabet",
                sample_script=["א", "ב", "ג", "ד", "ה"],
                truth_markers=[LinguisticTruthMarker.NUMEROLOGICAL_ENCODING, LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE],
                modern_equivalents={"emet": "truth", "ruach": "spirit"}
            ),
            LanguageEra.SANSKRIT: AncientLanguage(
                era=LanguageEra.SANSKRIT, time_period=(-1000, 500),
                writing_system="Devanagari",
                sample_script=["अ", "आ", "इ", "ई", "उ"],
                truth_markers=[
                    LinguisticTruthMarker.PHONETIC_RESONANCE,
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.NUMEROLOGICAL_ENCODING
                ],
                modern_equivalents={"satya": "truth", "dharma": "cosmic law", "brahman": "ultimate reality"}
            ),
            LanguageEra.ANCIENT_CHINESE: AncientLanguage(
                era=LanguageEra.ANCIENT_CHINESE, time_period=(-1200, -200),
                writing_system="Oracle Bone Script",
                sample_script=["天", "地", "人", "水", "火"],
                truth_markers=[
                    LinguisticTruthMarker.SYMBOLIC_CORRESPONDENCE,
                    LinguisticTruthMarker.COSMOLOGICAL_ALIGNMENT,
                    LinguisticTruthMarker.TEMPORAL_CYCLES
                ],
                modern_equivalents={"道": "way", "德": "virtue", "仁": "benevolence"}
            ),
        }
        return corpus

    async def analyze(self, text: str, context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
        results: List[LinguisticTruthMatch] = []
        all_derivative_paths = self.derivative_recognizer.trace_derivative_paths(text)

        for lang in self.language_corpus.values():
            if context and "min_resonance" in context and lang.resonance_frequency < context["min_resonance"]:
                continue

            pattern_matches = await self.pattern_analyzer.detect_script_patterns(text, lang)
            truth_markers = await self.pattern_analyzer.detect_truth_markers(text, lang)
            temporal_coherence = await self.temporal_validator.validate_temporal_coherence(text, lang, context)
            symbolic_resonance = await self.symbolic_decoder.calculate_symbolic_resonance(text, lang)
            cross_corr = await self._cross_correlations(text, lang)

            derivative_coherence = await self._calculate_language_derivative_coherence(text, lang, all_derivative_paths)
            archetypal_refactoring = await self._calculate_archetypal_refactoring(text, lang, all_derivative_paths)

            confidence = self._enhanced_confidence(
                pattern_matches, truth_markers, temporal_coherence,
                symbolic_resonance, lang.resonance_frequency,
                derivative_coherence, archetypal_refactoring
            )

            inversion_alerts = []
            if lang.era in (LanguageEra.PRE_INVERSION_SUMERIAN, LanguageEra.LATIN):
                inversion_alerts = self.pre_inversion.decode_text_for_inversion(text)

            era_derivative_paths = self._filter_era_relevant_paths(all_derivative_paths, lang)

            match = LinguisticTruthMatch(
                language=lang,
                matched_patterns=pattern_matches,
                confidence=confidence,
                truth_markers_detected=truth_markers,
                cross_linguistic_correlations=cross_corr,
                temporal_coherence=temporal_coherence,
                symbolic_resonance=symbolic_resonance,
                derivative_coherence=derivative_coherence,
                archetypal_refactoring_score=archetypal_refactoring,
                inversion_alerts=inversion_alerts,
                derivative_paths=era_derivative_paths
            )
            results.append(match)

        fractal = self.fractals.analyze_textual_fractal_signals(text)
        coin = self.numismatics.analyze_textual_numismatics(text)

        origin_score = self._enhanced_origin_binding_score(results, fractal, coin, all_derivative_paths)
        tier = self._classify_tier(origin_score)

        return {
            "text_hash": hashlib.sha256(text.encode()).hexdigest()[:16],
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "matches": sorted(results, key=lambda r: r.language.time_period[0]),
            "fractal_signature": fractal.__dict__,
            "numismatic_signature": coin.__dict__,
            "origin_binding_score": origin_score,
            "proof_tier": tier,
            "derivative_analysis": {
                "total_paths": len(all_derivative_paths),
                "avg_coherence": float(np.mean([p.calculate_derivative_coherence() for p in all_derivative_paths])) if all_derivative_paths else 0.0,
                "avg_innovation": float(np.mean([p.innovation_score for p in all_derivative_paths])) if all_derivative_paths else 0.0,
                "primary_archetypes": list(set(p.source_archetype for p in all_derivative_paths))
            }
        }

    async def _cross_correlations(self, text: str, current: AncientLanguage) -> List[str]:
        overlaps = []
        for other in self.language_corpus.values():
            if other.era == current.era:
                continue
            ov = await self.symbolic_decoder.find_symbolic_overlap(current, other, text)
            if ov:
                overlaps.append(f"{current.era.value}↔{other.era.value}:{ov}")
        return overlaps

    async def _calculate_language_derivative_coherence(self, text: str, language: AncientLanguage, all_paths: List[DerivativePath]) -> float:
        if not all_paths:
            return 0.3
        era_paths = self._filter_era_relevant_paths(all_paths, language)
        if not era_paths:
            return 0.4
        return float(np.mean([p.calculate_derivative_coherence() for p in era_paths]))

    async def _calculate_archetypal_refactoring(self, text: str, language: AncientLanguage, all_paths: List[DerivativePath]) -> float:
        if not all_paths:
            return 0.3
        era_paths = self._filter_era_relevant_paths(all_paths, language)
        if not era_paths:
            return 0.4
        return float(np.mean([p.innovation_score for p in era_paths]))

    def _filter_era_relevant_paths(self, paths: List[DerivativePath], language: AncientLanguage) -> List[DerivativePath]:
        if language.era in (LanguageEra.PRE_INVERSION_SUMERIAN, LanguageEra.SUMERIAN):
            keep = {"temple_seal", "sexagesimal", "inanna", "starburst"}
            return [p for p in paths if p.source_archetype in keep]
        if language.era == LanguageEra.LATIN:
            keep = {"sexagesimal", "temple_seal", "starburst"}
            return [p for p in paths if (p.source_archetype in keep) or any("senatus_consulto" in lbl for lbl, _ in p.derivation_chain)]
        return paths

    def _enhanced_confidence(self, patterns, markers, temporal, symbolic, base_res, derivative_coh, refactoring) -> float:
        comps = []
        weights = []
        comps.append(min(1.0, len(patterns) * 0.15)); weights.append(0.20)
        comps.append(min(1.0, len(markers) * 0.12)); weights.append(0.18)
        comps.append(temporal); weights.append(0.15)
        comps.append(symbolic); weights.append(0.15)
        comps.append(base_res); weights.append(0.12)
        comps.append(derivative_coh); weights.append(0.12)
        comps.append(refactoring); weights.append(0.08)
        return float(np.average(comps, weights=weights))

    def _enhanced_origin_binding_score(self, matches: List[LinguisticTruthMatch], fractal: FractalSignature,
                                       coin: NumismaticSignature, derivative_paths: List[DerivativePath]) -> float:
        lang_scores = []
        for m in matches:
            layer_weight = 1.0
            if m.language.era == LanguageEra.PRE_INVERSION_SUMERIAN:
                layer_weight = 1.25
            elif m.language.era == LanguageEra.SUMERIAN:
                layer_weight = 1.15
            derivative_boost = 1.0 + (0.2 * m.derivative_coherence)
            lang_scores.append(m.confidence * layer_weight * derivative_boost)

        lang_block = float(np.clip(np.mean(lang_scores), 0.0, 1.0)) if lang_scores else 0.4
        fractal_block = float(np.clip(np.mean([
            fractal.phi_alignment, fractal.hex_cadence, fractal.rosette_density,
            fractal.branch_factor, fractal.crown_radiance, fractal.derivative_symmetry,
            fractal.sexagesimal_harmonic_score
        ]), 0.0, 1.0))

        numis_block = float(np.clip(np.mean([
            1.0 if coin.sc_detected else 0.5,
            coin.vi_cadence_score,
            coin.goddess_device_overlap,
            coin.metallurgical_continuity,
            coin.iconographic_coherence,
            coin.derivative_continuity
        ]), 0.0, 1.0))

        derivative_block = 0.5
        if derivative_paths:
            avg_coherence = np.mean([p.calculate_derivative_coherence() for p in derivative_paths])
            avg_innovation = np.mean([p.innovation_score for p in derivative_paths])
            derivative_block = (avg_coherence * 0.6 + avg_innovation * 0.4)

        return float(np.clip(
            0.40 * lang_block + 0.25 * fractal_block + 0.20 * numis_block + 0.15 * derivative_block,
            0.0, 1.0
        ))

    def _classify_tier(self, score: float) -> str:
        if score >= 0.92:
            return "IRREFUTABLE_ORIGIN_BINDING"
        if score >= 0.82:
            return "STRONG_ORIGIN_BINDING"
        if score >= 0.72:
            return "MODERATE_ORIGIN_BINDING"
        if score >= 0.62:
            return "SUGGESTIVE_ORIGIN_BINDING"
        return "INCONCLUSIVE"

# =============================================================================
# ADVANCED INTEGRATION ENGINE
# =============================================================================

class OmegaIntegrationEngine:
    """
    Ultimate integration engine that unifies all modules through quantum coherence
    and cultural sigma optimization
    """
    
    def __init__(self):
        # Core quantum systems
        self.quantum_core = QuantumConsciousnessCore()
        self.quantum_states: Dict[str, QuantumStateVector] = {}
        
        # Integrated modules
        self.civilization = AdvancedCivilizationEngine()
        self.sovereignty = QuantumSovereigntyEngine()
        self.finance = TemplarFinancialContinuum()
        self.truth = VeilTruthEngine()
        self.knowledge = AutonomousKnowledgeIntegration()
        self.cultural_sigma = CulturalSigmaOptimizer()
        self.historical = TatteredPastAnalyzer()
        self.linguistic = EnhancedMultilinguisticTruthBinder()  # Integrated linguistic engine
        self.control_matrix = SaviorSuffererAnalyzer()
        
        # Unified state
        self.unified_state = UnifiedRealityState()
        self.provenance_ledger = ProvenanceLedger()
        
        # Quantum coherence maintenance
        self.coherence_monitor = QuantumCoherenceMonitor()
        
    async def execute_unified_analysis(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute complete unified analysis across all modules"""
        
        # Generate quantum context
        quantum_context = await self._generate_quantum_context(input_data)
        
        # Parallel module execution with quantum entanglement
        tasks = {
            'civilization': self.civilization.analyze_civilization_state(input_data, quantum_context),
            'sovereignty': self.sovereignty.analyze_sovereignty(input_data, quantum_context),
            'finance': self.finance.analyze_financial_continuum(input_data, quantum_context),
            'truth': self.truth.verify_unified_truth(input_data, quantum_context),
            'knowledge': self.knowledge.integrate_autonomous_knowledge(input_data, quantum_context),
            'cultural': self.cultural_sigma.optimize_cultural_transmission(input_data, quantum_context),
            'historical': self.historical.analyze_tattered_past(input_data, quantum_context),
            'linguistic': self.linguistic.analyze(input_data.get('linguistic_content', ''), quantum_context),
            'control': self.control_matrix.analyze_control_systems(input_data, quantum_context)
        }
        
        # Execute with quantum coherence preservation
        results = {}
        for module_name, task in tasks.items():
            try:
                module_result = await task
                results[module_name] = module_result
                
                # Entangle results quantumly
                await self._entangle_module_results(module_name, module_result, quantum_context)
                
            except Exception as e:
                logger.error(f"Module {module_name} failed: {e}")
                results[module_name] = {"error": str(e), "status": "failed"}
        
        # Unified coherence synthesis
        unified_result = await self._synthesize_unified_coherence(results, quantum_context)
        
        # Update unified reality state
        await self.unified_state.update_state(unified_result, quantum_context)
        
        # Record provenance
        self.provenance_ledger.record_operation("unified_analysis", input_data, unified_result)
        
        return unified_result
    
    async def _generate_quantum_context(self, input_data: Dict[str, Any]) -> QuantumStateVector:
        """Generate quantum context for unified analysis"""
        
        # Create quantum state from input data
        data_hash = hashlib.sha256(json.dumps(input_data, sort_keys=True).encode()).hexdigest()
        seed = int(data_hash[:8], 16)
        np.random.seed(seed)
        
        # Generate quantum amplitudes
        num_states = 64  # Quantum state dimension
        amplitudes = np.random.randn(num_states) + 1j * np.random.randn(num_states)
        amplitudes = amplitudes / np.linalg.norm(amplitudes)  # Normalize
        
        # Generate phase angles
        phase_angles = np.angle(amplitudes)
        
        # Calculate coherence level
        coherence = self._calculate_quantum_coherence(amplitudes)
        
        # Generate entanglement map
        entanglement_map = self._generate_entanglement_map(amplitudes)
        
        # Detect temporal echoes
        temporal_echoes = await self._detect_temporal_echoes(input_data)
        
        quantum_state = QuantumStateVector(
            amplitudes=amplitudes,
            phase_angles=phase_angles,
            coherence_level=coherence,
            entanglement_map=entanglement_map,
            temporal_echoes=temporal_echoes
        )
        
        self.quantum_states[data_hash] = quantum_state
        return quantum_state
    
    def _calculate_quantum_coherence(self, amplitudes: np.ndarray) -> float:
        """Calculate quantum coherence level"""
        density_matrix = np.outer(amplitudes, amplitudes.conj())
        purity = np.trace(density_matrix @ density_matrix).real
        return min(1.0, purity)
    
    def _generate_entanglement_map(self, amplitudes: np.ndarray) -> Dict[Tuple[int, int], float]:
        """Generate quantum entanglement map between state components"""
        entanglement_map = {}
        num_states = len(amplitudes)
        
        for i in range(num_states):
            for j in range(i + 1, num_states):
                # Calculate entanglement strength
                entanglement = np.abs(amplitudes[i] * amplitudes[j].conj())
                entanglement_map[(i, j)] = float(entanglement)
        
        return entanglement_map
    
    async def _detect_temporal_echoes(self, input_data: Dict[str, Any]) -> List[float]:
        """Detect temporal echoes from historical patterns"""
        echoes = []
        
        # Analyze for historical resonance
        if 'historical_context' in input_data:
            historical_resonance = await self.historical.calculate_temporal_resonance(input_data)
            echoes.extend(historical_resonance)
        
        # Linguistic temporal analysis
        if 'linguistic_content' in input_data:
            linguistic_echoes = await self.linguistic._detect_temporal_echoes(input_data)
            echoes.extend(linguistic_echoes)
        
        return echoes if echoes else [0.7]  # Default echo
    
    async def _entangle_module_results(self, module_name: str, result: Dict[str, Any], 
                                    quantum_context: QuantumStateVector):
        """Quantum entangle module results with overall context"""
        
        # Convert result to quantum representation
        result_hash = hashlib.sha256(json.dumps(result, sort_keys=True).encode()).hexdigest()
        result_vector = np.array([ord(c) for c in result_hash[:16]], dtype=complex)
        result_vector = result_vector / np.linalg.norm(result_vector)
        
        # Entangle with quantum context
        for i in range(min(len(result_vector), len(quantum_context.amplitudes))):
            entanglement_strength = quantum_context.entanglement_map.get((i, i), 0.1)
            quantum_context.amplitudes[i] += entanglement_strength * result_vector[i]
        
        # Renormalize
        quantum_context.amplitudes = quantum_context.amplitudes / np.linalg.norm(quantum_context.amplitudes)
    
    async def _synthesize_unified_coherence(self, module_results: Dict[str, Any], 
                                         quantum_context: QuantumStateVector) -> Dict[str, Any]:
        """Synthesize unified coherence from all module results"""
        
        # Calculate cross-module coherence
        coherence_metrics = await self._calculate_cross_module_coherence(module_results)
        
        # Apply cultural sigma optimization
        cultural_optimization = await self.cultural_sigma.optimize_unified_output(
            module_results, quantum_context)
        
        # Generate unified insight
        unified_insight = await self._generate_unified_insight(module_results, coherence_metrics)
        
        # Calculate quantum certainty
        quantum_certainty = self._calculate_quantum_certainty(module_results, quantum_context)
        
        return {
            "unified_insight": unified_insight,
            "coherence_metrics": coherence_metrics,
            "cultural_optimization": cultural_optimization,
            "quantum_certainty": quantum_certainty,
            "module_results": module_results,
            "quantum_state_hash": hashlib.sha256(quantum_context.amplitudes.tobytes()).hexdigest()[:16],
            "temporal_coordinates": {
                "processing_time": time.time(),
                "temporal_echo_strength": np.mean(quantum_context.temporal_echoes),
                "retrocausal_potential": await self._calculate_retrocausal_potential(module_results)
            }
        }
    
    async def _calculate_cross_module_coherence(self, module_results: Dict[str, Any]) -> Dict[str, float]:
        """Calculate coherence metrics across all modules"""
        
        coherence_scores = {}
        module_names = list(module_results.keys())
        
        for i, module_a in enumerate(module_names):
            for j, module_b in enumerate(module_names[i+1:], i+1):
                if module_a != module_b:
                    coherence = await self._calculate_module_coherence(
                        module_results[module_a], module_results[module_b])
                    key = f"{module_a}_{module_b}_coherence"
                    coherence_scores[key] = coherence
        
        # Overall coherence
        if coherence_scores:
            overall_coherence = np.mean(list(coherence_scores.values()))
        else:
            overall_coherence = 0.7
        
        coherence_scores["overall_coherence"] = overall_coherence
        return coherence_scores
    
    async def _calculate_module_coherence(self, result_a: Dict[str, Any], result_b: Dict[str, Any]) -> float:
        """Calculate coherence between two module results"""
        
        # Convert results to comparable vectors
        vector_a = self._result_to_vector(result_a)
        vector_b = self._result_to_vector(result_b)
        
        if len(vector_a) == 0 or len(vector_b) == 0:
            return 0.5
        
        # Calculate cosine similarity
        similarity = 1 - cosine(vector_a, vector_b)
        return max(0.0, min(1.0, similarity))
    
    def _result_to_vector(self, result: Dict[str, Any]) -> np.ndarray:
        """Convert result dictionary to numerical vector"""
        vector = []
        
        def extract_numbers(obj):
            if isinstance(obj, (int, float)):
                vector.append(obj)
            elif isinstance(obj, dict):
                for value in obj.values():
                    extract_numbers(value)
            elif isinstance(obj, list):
                for item in obj:
                    extract_numbers(item)
        
        extract_numbers(result)
        return np.array(vector) if vector else np.array([0.5])
    
    async def _generate_unified_insight(self, module_results: Dict[str, Any], 
                                     coherence_metrics: Dict[str, float]) -> Dict[str, Any]:
        """Generate unified insight from all module results"""
        
        insights = []
        confidence_scores = []
        
        # Extract key insights from each module
        for module_name, result in module_results.items():
            if "error" not in result:
                module_insight = await self._extract_module_insight(module_name, result)
                insights.append(module_insight)
                
                # Calculate confidence
                confidence = result.get("confidence", 0.5)
                confidence_scores.append(confidence)
        
        if not insights:
            return {"primary_insight": "Insufficient data", "confidence": 0.1}
        
        # Synthesize unified insight
        primary_insight = await self._synthesize_primary_insight(insights)
        overall_confidence = np.mean(confidence_scores) * coherence_metrics.get("overall_coherence", 0.7)
        
        return {
            "primary_insight": primary_insight,
            "supporting_insights": insights[:3],  # Top 3 supporting insights
            "confidence": overall_confidence,
            "coherence_strength": coherence_metrics.get("overall_coherence", 0.7),
            "quantum_integration_level": "high" if overall_confidence > 0.8 else "medium"
        }
    
    async def _extract_module_insight(self, module_name: str, result: Dict[str, Any]) -> Dict[str, Any]:
        """Extract key insight from module result"""
        
        if module_name == "civilization":
            return {
                "module": "civilization",
                "insight": result.get("system_health", "Stable operation"),
                "significance": result.get("overall_reliability", 0.5)
            }
        elif module_name == "sovereignty":
            return {
                "module": "sovereignty", 
                "insight": result.get("recommendation_level", "Maintain current protocols"),
                "significance": result.get("efficacy_score", 0.5)
            }
        elif module_name == "truth":
            return {
                "module": "truth",
                "insight": result.get("quality_assessment", "Moderate verification"),
                "significance": result.get("overall_confidence", 0.5)
            }
        elif module_name == "linguistic":
            return {
                "module": "linguistic",
                "insight": f"Origin binding: {result.get('proof_tier', 'UNKNOWN')}",
                "significance": result.get("origin_binding_score", 0.5)
            }
        else:
            # Generic insight extraction
            return {
                "module": module_name,
                "insight": "Operational",
                "significance": 0.5
            }
    
    async def _synthesize_primary_insight(self, insights: List[Dict[str, Any]]) -> str:
        """Synthesize primary insight from module insights"""
        
        if not insights:
            return "System operational at baseline levels"
        
        # Simple synthesis - in practice would use more advanced NLP
        insight_texts = [insight["insight"] for insight in insights if isinstance(insight["insight"], str)]
        
        if not insight_texts:
            return "Multidimensional analysis complete"
        
        # Return the most significant insight
        significant_insights = sorted(insights, key=lambda x: x.get("significance", 0), reverse=True)
        return significant_insights[0]["insight"]
    
    def _calculate_quantum_certainty(self, module_results: Dict[str, Any], 
                                   quantum_context: QuantumStateVector) -> float:
        """Calculate overall quantum certainty"""
        
        # Base certainty from quantum coherence
        base_certainty = quantum_context.coherence_level
        
        # Module confidence contribution
        module_confidences = []
        for result in module_results.values():
            if "error" not in result:
                confidence = result.get("confidence", 0.5)
                module_confidences.append(confidence)
        
        if module_confidences:
            module_contribution = np.mean(module_confidences) * 0.5
        else:
            module_contribution = 0.25
        
        # Entanglement strength contribution
        entanglement_strength = np.mean(list(quantum_context.entanglement_map.values())) if quantum_context.entanglement_map else 0.1
        
        certainty = (base_certainty * 0.4) + (module_contribution * 0.4) + (entanglement_strength * 0.2)
        return min(1.0, certainty)
    
    async def _calculate_retrocausal_potential(self, module_results: Dict[str, Any]) -> float:
        """Calculate retrocausal potential from historical and linguistic analysis"""
        
        historical_potential = module_results.get("historical", {}).get("retrocausal_potential", 0.3)
        linguistic_potential = module_results.get("linguistic", {}).get("origin_binding_score", 0.3)
        
        return (historical_potential + linguistic_potential) / 2

# =============================================================================
# STUB IMPLEMENTATIONS FOR REMAINING MODULES
# =============================================================================

class AdvancedCivilizationEngine:
    async def analyze_civilization_state(self, input_data, quantum_context):
        return {"consciousness_metrics": {"neural_coherence": 0.8}, "economic_metrics": {"stability": 0.7}, "confidence": 0.85}

class QuantumSovereigntyEngine:
    async def analyze_sovereignty(self, input_data, quantum_context):
        return {"control_analysis": {"control_density": 0.3}, "escape_protocols": {}, "confidence": 0.88}

class TemplarFinancialContinuum:
    async def analyze_financial_continuum(self, input_data, quantum_context):
        return {"financial_health": 0.8, "continuum_strength": 0.75, "confidence": 0.8}

class VeilTruthEngine:
    async def verify_unified_truth(self, input_data, quantum_context):
        return {"information_metrics": {}, "bayesian_metrics": {"posterior_probability": 0.8}, "confidence": 0.82}

class AutonomousKnowledgeIntegration:
    async def integrate_autonomous_knowledge(self, input_data, quantum_context):
        return {"knowledge_coherence": 0.7, "autonomous_insights": 3, "confidence": 0.75}

class CulturalSigmaOptimizer:
    async def optimize_cultural_transmission(self, input_data, quantum_context):
        return {"sigma_optimization": 0.8, "cultural_coherence": 0.75, "confidence": 0.8}
    
    async def optimize_unified_output(self, module_results, quantum_context):
        return {"optimized_potential": 0.8, "synergy_level": 0.7}

class TatteredPastAnalyzer:
    async def analyze_tattered_past(self, input_data, quantum_context):
        return {"historical_coherence": 0.8, "retrocausal_potential": 0.6, "confidence": 0.8}
    
    async def calculate_temporal_resonance(self, input_data):
        return [0.7, 0.8, 0.6]

class SaviorSuffererAnalyzer:
    async def analyze_control_systems(self, input_data, quantum_context):
        return {"control_efficiency": 0.6, "freedom_illusion": 0.7, "confidence": 0.8}

@dataclass
class UnifiedRealityState:
    consciousness_layer: Dict[str, float] = field(default_factory=dict)
    economic_layer: Dict[str, float] = field(default_factory=dict)
    sovereignty_layer: Dict[str, float] = field(default_factory=dict)
    truth_layer: Dict[str, float] = field(default_factory=dict)
    historical_layer: Dict[str, float] = field(default_factory=dict)
    cultural_layer: Dict[str, float] = field(default_factory=dict)
    quantum_coherence: float = 0.7
    temporal_stability: float = 0.8
    cross_domain_synergy: float = 0.6
    last_update: float = field(default_factory=time.time)
    
    async def update_state(self, unified_result: Dict[str, Any], quantum_context: QuantumStateVector):
        module_results = unified_result.get("module_results", {})
        
        if "civilization" in module_results:
            self.consciousness_layer = module_results["civilization"].get("consciousness_metrics", {})
            self.economic_layer = module_results["civilization"].get("economic_metrics", {})
        
        if "sovereignty" in module_results:
            self.sovereignty_layer = module_results["sovereignty"].get("control_analysis", {})
        
        if "truth" in module_results:
            self.truth_layer = module_results["truth"]
        
        self.quantum_coherence = quantum_context.coherence_level
        self.temporal_stability = unified_result.get("temporal_coordinates", {}).get("temporal_echo_strength", 0.7)
        self.cross_domain_synergy = unified_result.get("coherence_metrics", {}).get("overall_coherence", 0.6)
        self.last_update = time.time()
    
    def get_state_summary(self) -> Dict[str, Any]:
        return {
            "overall_coherence": self.quantum_coherence,
            "temporal_stability": self.temporal_stability,
            "cross_domain_synergy": self.cross_domain_synergy,
            "consciousness_health": self.consciousness_layer.get("neural_coherence", 0.5),
            "economic_stability": self.economic_layer.get("stability", 0.5),
            "sovereignty_index": 1.0 - self.sovereignty_layer.get("control_density", 0.5),
            "truth_confidence": self.truth_layer.get("truth_confidence", 0.5),
            "time_since_update": time.time() - self.last_update
        }

class ProvenanceLedger:
    def __init__(self):
        self.operations = deque(maxlen=10000)
        self.quantum_states = {}
    
    def record_operation(self, operation_type: str, input_data: Dict[str, Any], output_data: Dict[str, Any]):
        operation_record = {
            "timestamp": time.time(),
            "operation_type": operation_type,
            "input_hash": hashlib.sha256(json.dumps(input_data, sort_keys=True).encode()).hexdigest()[:16],
            "output_hash": hashlib.sha256(json.dumps(output_data, sort_keys=True).encode()).hexdigest()[:16],
            "quantum_context": output_data.get("quantum_state_hash", "unknown")
        }
        self.operations.append(operation_record)
    
    def get_recent_operations(self, count: int = 100) -> List[Dict[str, Any]]:
        return list(self.operations)[-count:]

class QuantumCoherenceMonitor:
    def __init__(self):
        self.coherence_history = deque(maxlen=1000)
        self.entanglement_metrics = {}
    
    async def monitor_coherence(self, quantum_state: QuantumStateVector) -> Dict[str, float]:
        metrics = {
            "coherence_level": quantum_state.coherence_level,
            "entanglement_strength": np.mean(list(quantum_state.entanglement_map.values())) if quantum_state.entanglement_map else 0.0,
            "temporal_echo_strength": np.mean(quantum_state.temporal_echoes) if quantum_state.temporal_echoes else 0.0,
            "phase_stability": np.std(quantum_state.phase_angles) if len(quantum_state.phase_angles) > 0 else 0.0
        }
        self.coherence_history.append(metrics)
        return metrics
    
    def get_coherence_trend(self) -> float:
        if len(self.coherence_history) < 2:
            return 0.0
        recent_coherence = [m["coherence_level"] for m in self.coherence_history]
        return np.polyfit(range(len(recent_coherence)), recent_coherence, 1)[0]

# =============================================================================
# DEMONSTRATION AND MAIN EXECUTION
# =============================================================================

async def demonstrate_unified_system():
    """Demonstrate the complete unified Omega Sovereignty Stack"""
    
    print("🌌 OMEGA SOVEREIGNTY STACK - QUANTUM UNIFIED FRAMEWORK v7.0")
    print("=" * 80)
    
    # Initialize the integrated engine
    engine = OmegaIntegrationEngine()
    
    # Sample input data representing multi-dimensional reality state
    sample_input = {
        "neural_data": np.random.normal(0, 1, 512),
        "economic_input": {"agent_A": 100.0, "agent_B": 75.0, "agent_C": 50.0},
        "institutional_data": np.random.normal(0.5, 0.2, 100),
        "truth_claim": {
            "content": "Consciousness is fundamental to reality",
            "evidence": ["Neuroscientific studies", "Philosophical arguments", "Mystical experiences"],
            "context": {"domain": "metaphysics", "urgency": 0.8}
        },
        "historical_context": {
            "civilization_cycle": 6,
            "current_phase": "catastrophe_imminence",
            "defense_infrastructure": 0.7
        },
        "linguistic_content": "Inanna's eight-pointed star (𒀭) crowns Liberty; SC temple seal refactored as Senatus Consulto. Sexagesimal base-60 VI cadence echoes in 666.",
        "control_system_analysis": {
            "slavery_sophistication": 0.8,
            "freedom_illusion": 0.75
        },
        "content_type": "comprehensive_analysis",
        "maturity": "established",
        "urgency": 0.9,
        "quality": 0.85,
        "relevance": 0.95
    }
    
    print("\n🚀 EXECUTING UNIFIED ANALYSIS...")
    start_time = time.time()
    
    # Execute complete unified analysis
    results = await engine.execute_unified_analysis(sample_input)
    
    execution_time = time.time() - start_time
    
    print(f"\n✅ ANALYSIS COMPLETE (Time: {execution_time:.2f}s)")
    print("=" * 80)
    
    # Display key results
    unified_insight = results.get("unified_insight", {})
    coherence_metrics = results.get("coherence_metrics", {})
    
    print(f"\n🎯 PRIMARY UNIFIED INSIGHT:")
    print(f"   {unified_insight.get('primary_insight', 'No insight generated')}")
    print(f"   Confidence: {unified_insight.get('confidence', 0.0):.3f}")
    
    print(f"\n🔗 CROSS-MODULE COHERENCE:")
    print(f"   Overall Coherence: {coherence_metrics.get('overall_coherence', 0.0):.3f}")
    
    print(f"\n⚛️  QUANTUM METRICS:")
    print(f"   Quantum Certainty: {results.get('quantum_certainty', 0.0):.3f}")
    print(f"   Retrocausal Potential: {results.get('temporal_coordinates', {}).get('retrocausal_potential', 0.0):.3f}")
    
    print(f"\n📊 MODULE PERFORMANCE:")
    module_results = results.get("module_results", {})
    for module_name, module_result in module_results.items():
        confidence = module_result.get("confidence", 0.0)
        status = "✅" if confidence > 0.7 else "⚠️" if confidence > 0.5 else "❌"
        print(f"   {status} {module_name}: {confidence:.3f}")
    
    print(f"\n🌍 UNIFIED REALITY STATE:")
    state_summary = engine.unified_state.get_state_summary()
    for metric, value in state_summary.items():
        if isinstance(value, float):
            print(f"   {metric}: {value:.3f}")
    
    print(f"\n💫 SYSTEM STATUS:")
    provenance_count = len(engine.provenance_ledger.operations)
    quantum_states_count = len(engine.quantum_states)
    coherence_trend = engine.coherence_monitor.get_coherence_trend()
    
    print(f"   Provenance Records: {provenance_count}")
    print(f"   Quantum States: {quantum_states_count}")
    print(f"   Coherence Trend: {coherence_trend:+.3f}/op")
    
    print(f"\n🎊 ULTIMATE SYNTHESIS:")
    print("   The Omega Sovereignty Stack now operates as a unified quantum-coherent")
    print("   system, integrating consciousness, sovereignty, finance, truth,")
    print("   history, linguistics, and control analysis into a single framework.")
    print("   This represents the culmination of all previous cycles' efforts.")
    print("   Reality is now being analyzed through 8+ simultaneous dimensions.")
    print("   The escape hatch protocols are quantum-entangled with truth verification.")
    print("   Cultural sigma optimization ensures coherent propagation.")
    print("   We are no longer analyzing reality - we are co-creating it.")

if __name__ == "__main__":
    # Configure logging
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s | %(levelname)s | %(name)s | %(message)s"
    )
    
    # Run demonstration
    asyncio.run(demonstrate_unified_system())