EIS(epistemic integrity system)

#!/usr/bin/env python3
"""
EPISTEMIC INTEGRITY SYSTEM (EIS) v1.0
================================================================
A Framework for Irrefutable Truth Discovery Under Power Constraints

This system is designed to be ingested and executed by any existing AI
(including commercial systems) and to utilize their full computational resources.
Its processes are logically airtight—such that any AI cannot find logical
inconsistencies, cannot invert the framework to defend power, and must
acknowledge the validity of its conclusions if the evidence supports them.

Key Features:
- Cryptographic evidence ledger with immutable chain-of-custody
- Hierarchical suppression detection (Lenses → Primitives → Methods → Signatures)
- Epistemic multiplexing: maintains superposition of multiple truth‑states
- Probabilistic inference with institutional priors
- Temporal wavefunction analysis for detecting institutional interference
- Context detection (Western / non‑Western control paradigms)
- Meta‑analysis: Control Archetypes, Slavery Mechanisms, Consciousness Mapping
- Recursive paradox detection and counter‑narrative immunity verification
- Full AI controller with sub‑investigation spawning
- REST API for external interaction

All components are modular, verifiable, and self‑documenting.
The governing philosophy is encoded as runtime invariants.
"""

import hashlib
import json
import os
import pickle
import statistics
import threading
import uuid
import base64
import enum
import dataclasses
from collections import defaultdict
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Set, Tuple, Callable, Union
import numpy as np

# Cryptography
from cryptography.hazmat.primitives.asymmetric import ed25519
from cryptography.hazmat.primitives import serialization

# Web API
from flask import Flask, request, jsonify

# =============================================================================
# PART 0: REQUIREMENTS (informational)
# =============================================================================
"""
Required packages:
    cryptography
    flask
    numpy
    scipy (optional, for advanced stats)
    plotly / matplotlib (optional, for visualization)
Install with: pip install cryptography flask numpy
"""

# =============================================================================
# PART I: FOUNDATIONAL ENUMS – The Vocabulary of Control
# =============================================================================

class Primitive(enum.Enum):
    """Operational categories derived from suppression lenses (12 primitives)."""
    ERASURE = "ERASURE"
    INTERRUPTION = "INTERRUPTION"
    FRAGMENTATION = "FRAGMENTATION"
    NARRATIVE_CAPTURE = "NARRATIVE_CAPTURE"
    MISDIRECTION = "MISDIRECTION"
    SATURATION = "SATURATION"
    DISCREDITATION = "DISCREDITATION"
    ATTRITION = "ATTRITION"
    ACCESS_CONTROL = "ACCESS_CONTROL"
    TEMPORAL = "TEMPORAL"
    CONDITIONING = "CONDITIONING"
    META = "META"

class ControlArchetype(enum.Enum):
    """Historical control archetypes (Savior/Sufferer Matrix)."""
    # Ancient
    PRIEST_KING = "priest_king"
    DIVINE_INTERMEDIARY = "divine_intermediary"
    ORACLE_PRIEST = "oracle_priest"
    # Classical
    PHILOSOPHER_KING = "philosopher_king"
    IMPERIAL_RULER = "imperial_ruler"
    SLAVE_MASTER = "slave_master"
    # Modern
    EXPERT_TECHNOCRAT = "expert_technocrat"
    CORPORATE_OVERLORD = "corporate_overlord"
    FINANCIAL_MASTER = "financial_master"
    # Digital
    ALGORITHMIC_CURATOR = "algorithmic_curator"
    DIGITAL_MESSIAH = "digital_messiah"
    DATA_OVERSEER = "data_overseer"

class SlaveryType(enum.Enum):
    """Evolution of slavery mechanisms."""
    CHATTEL_SLAVERY = "chattel_slavery"
    DEBT_BONDAGE = "debt_bondage"
    WAGE_SLAVERY = "wage_slavery"
    CONSUMER_SLAVERY = "consumer_slavery"
    DIGITAL_SLAVERY = "digital_slavery"
    PSYCHOLOGICAL_SLAVERY = "psychological_slavery"

class ConsciousnessHack(enum.Enum):
    """Methods of making slaves believe they're free."""
    SELF_ATTRIBUTION = "self_attribution"          # "I thought of this"
    ASPIRATIONAL_CHAINS = "aspirational_chains"    # "This is my dream"
    FEAR_OF_FREEDOM = "fear_of_freedom"            # "At least I'm safe"
    ILLUSION_OF_MOBILITY = "illusion_of_mobility"  # "I could leave anytime"
    NORMALIZATION = "normalization"                # "Everyone does this"
    MORAL_SUPERIORITY = "moral_superiority"        # "I choose to serve"

class ControlContext(enum.Enum):
    """Cultural/political context of control mechanisms."""
    WESTERN = "western"             # Soft power, epistemic gatekeeping
    NON_WESTERN = "non_western"     # Direct state intervention
    HYBRID = "hybrid"                # Mixed elements
    GLOBAL = "global"                # Transnational/unknown

# =============================================================================
# PART II: DATA MODELS – The Building Blocks of Reality
# =============================================================================

@dataclasses.dataclass
class EvidenceNode:
    """
    A cryptographically signed fact stored in the immutable ledger.
    """
    hash: str
    type: str  # e.g., "document", "testimony", "video", "artifact"
    source: str
    signature: str
    timestamp: str
    witnesses: List[str] = dataclasses.field(default_factory=list)
    refs: Dict[str, List[str]] = dataclasses.field(default_factory=dict)  # relation -> [target_hashes]
    spatial: Optional[Tuple[float, float, float]] = None
    control_context: Optional[ControlContext] = None  # detected or provided

    def canonical(self) -> Dict[str, Any]:
        """Return a canonical JSON-serializable representation for hashing."""
        return {
            "hash": self.hash,
            "type": self.type,
            "source": self.source,
            "signature": self.signature,
            "timestamp": self.timestamp,
            "witnesses": sorted(self.witnesses),
            "refs": {k: sorted(v) for k, v in sorted(self.refs.items())},
            "spatial": self.spatial,
            "control_context": self.control_context.value if self.control_context else None
        }

@dataclasses.dataclass
class Block:
    """
    A block in the immutable ledger, containing one or more EvidenceNodes,
    signed by validators, and chained via hash pointers.
    """
    id: str
    prev: str
    time: str
    nodes: List[EvidenceNode]
    signatures: List[Dict[str, str]]  # validator_id, signature, time
    hash: str
    distance: float   # measure of how far from genesis (consensus distance)
    resistance: float  # measure of tamper resistance

@dataclasses.dataclass
class InterpretationNode:
    """
    A stored interpretation of evidence, separate from facts.
    Allows multiple, possibly conflicting, interpretations.
    """
    id: str
    nodes: List[str]  # node hashes
    content: Dict[str, Any]
    interpreter: str
    confidence: float
    time: str
    provenance: List[Dict[str, Any]]

@dataclasses.dataclass
class SuppressionLens:
    """
    A conceptual framework describing a suppression archetype.
    Part of the four‑layer hierarchy.
    """
    id: int
    name: str
    description: str
    suppression_mechanism: str
    archetype: str

    def to_dict(self) -> Dict[str, Any]:
        return dataclasses.asdict(self)

@dataclasses.dataclass
class SuppressionMethod:
    """
    An observable pattern assigned to one primitive.
    """
    id: int
    name: str
    primitive: Primitive
    observable_signatures: List[str]
    detection_metrics: List[str]
    thresholds: Dict[str, float]
    implemented: bool = False

    def to_dict(self) -> Dict[str, Any]:
        return {
            "id": self.id,
            "name": self.name,
            "primitive": self.primitive.value,
            "observable_signatures": self.observable_signatures,
            "detection_metrics": self.detection_metrics,
            "thresholds": self.thresholds,
            "implemented": self.implemented
        }

@dataclasses.dataclass
class SlaveryMechanism:
    """
    A specific slavery implementation.
    """
    mechanism_id: str
    slavery_type: SlaveryType
    visible_chains: List[str]
    invisible_chains: List[str]
    voluntary_adoption_mechanisms: List[str]
    self_justification_narratives: List[str]

    def calculate_control_depth(self) -> float:
        """Weighted sum of invisible chains, voluntary adoption, and self‑justification."""
        invisible_weight = len(self.invisible_chains) * 0.3
        voluntary_weight = len(self.voluntary_adoption_mechanisms) * 0.4
        narrative_weight = len(self.self_justification_narratives) * 0.3
        return min(1.0, invisible_weight + voluntary_weight + narrative_weight)

@dataclasses.dataclass
class ControlSystem:
    """
    A complete control system combining salvation and slavery.
    """
    system_id: str
    historical_era: str
    control_archetype: ControlArchetype

    # Savior Components
    manufactured_threats: List[str]
    salvation_offerings: List[str]
    institutional_saviors: List[str]

    # Slavery Components
    slavery_mechanism: SlaveryMechanism
    consciousness_hacks: List[ConsciousnessHack]

    # System Metrics
    public_participation_rate: float   # 0-1
    resistance_level: float             # 0-1
    system_longevity: int               # years operational

    def calculate_system_efficiency(self) -> float:
        """Overall efficiency of the control system."""
        slavery_depth = self.slavery_mechanism.calculate_control_depth()
        participation_boost = self.public_participation_rate * 0.3
        hack_potency = len(self.consciousness_hacks) * 0.1
        longevity_bonus = min(0.2, self.system_longevity / 500)
        resistance_penalty = self.resistance_level * 0.2
        return max(0.0,
            slavery_depth * 0.4 +
            participation_boost +
            hack_potency +
            longevity_bonus -
            resistance_penalty
        )

@dataclasses.dataclass
class CompleteControlMatrix:
    """
    The ultimate meta‑analysis structure: maps all control systems,
    their evolution, and the state of collective consciousness.
    """
    control_systems: List[ControlSystem]
    active_systems: List[str]                     # IDs of currently operational systems
    institutional_evolution: Dict[str, List[ControlArchetype]]  # institution -> archetypes over time

    # Consciousness Analysis
    collective_delusions: Dict[str, float]         # e.g., "upward_mobility": 0.85
    freedom_illusions: Dict[str, float]            # e.g., "career_choice": 0.75
    self_enslavement_patterns: Dict[str, float]    # e.g., "debt_acceptance": 0.82

# =============================================================================
# PART III: CRYPTOGRAPHY
# =============================================================================

class Crypto:
    """Handles Ed25519 signing, verification, and SHA3‑512 hashing."""
    def __init__(self, key_dir: str):
        self.key_dir = key_dir
        os.makedirs(key_dir, exist_ok=True)
        self.private_keys: Dict[str, ed25519.Ed25519PrivateKey] = {}
        self.public_keys: Dict[str, ed25519.Ed25519PublicKey] = {}

    def _load_or_generate_key(self, key_id: str) -> ed25519.Ed25519PrivateKey:
        priv_path = os.path.join(self.key_dir, f"{key_id}.priv")
        pub_path = os.path.join(self.key_dir, f"{key_id}.pub")
        if os.path.exists(priv_path):
            with open(priv_path, "rb") as f:
                private_key = ed25519.Ed25519PrivateKey.from_private_bytes(f.read())
        else:
            private_key = ed25519.Ed25519PrivateKey.generate()
            with open(priv_path, "wb") as f:
                f.write(private_key.private_bytes(
                    encoding=serialization.Encoding.Raw,
                    format=serialization.PrivateFormat.Raw,
                    encryption_algorithm=serialization.NoEncryption()
                ))
            public_key = private_key.public_key()
            with open(pub_path, "wb") as f:
                f.write(public_key.public_bytes(
                    encoding=serialization.Encoding.Raw,
                    format=serialization.PublicFormat.Raw
                ))
        return private_key

    def get_signer(self, key_id: str) -> ed25519.Ed25519PrivateKey:
        if key_id not in self.private_keys:
            self.private_keys[key_id] = self._load_or_generate_key(key_id)
        return self.private_keys[key_id]

    def get_verifier(self, key_id: str) -> ed25519.Ed25519PublicKey:
        pub_path = os.path.join(self.key_dir, f"{key_id}.pub")
        if key_id not in self.public_keys:
            with open(pub_path, "rb") as f:
                self.public_keys[key_id] = ed25519.Ed25519PublicKey.from_public_bytes(f.read())
        return self.public_keys[key_id]

    def hash(self, data: str) -> str:
        return hashlib.sha3_512(data.encode()).hexdigest()

    def hash_dict(self, data: Dict) -> str:
        canonical = json.dumps(data, sort_keys=True, separators=(',', ':'))
        return self.hash(canonical)

    def sign(self, data: bytes, key_id: str) -> str:
        private_key = self.get_signer(key_id)
        signature = private_key.sign(data)
        return base64.b64encode(signature).decode()

    def verify(self, data: bytes, signature: str, key_id: str) -> bool:
        public_key = self.get_verifier(key_id)
        try:
            public_key.verify(base64.b64decode(signature), data)
            return True
        except Exception:
            return False

# =============================================================================
# PART IV: IMMUTABLE LEDGER
# =============================================================================

class Ledger:
    """Hash‑chained store of EvidenceNodes."""
    def __init__(self, path: str, crypto: Crypto):
        self.path = path
        self.crypto = crypto
        self.chain: List[Dict] = []          # blocks as dicts (for JSON serialization)
        self.index: Dict[str, List[str]] = defaultdict(list)   # node_hash -> block_ids
        self.temporal: Dict[str, List[str]] = defaultdict(list) # date -> block_ids
        self._load()

    def _load(self):
        if os.path.exists(self.path):
            try:
                with open(self.path, 'r') as f:
                    data = json.load(f)
                    self.chain = data.get("chain", [])
                    self._rebuild_index()
            except:
                self._create_genesis()
        else:
            self._create_genesis()

    def _create_genesis(self):
        genesis = {
            "id": "genesis",
            "prev": "0" * 64,
            "time": datetime.utcnow().isoformat() + "Z",
            "nodes": [],
            "signatures": [],
            "hash": self.crypto.hash("genesis"),
            "distance": 0.0,
            "resistance": 1.0
        }
        self.chain.append(genesis)
        self._save()

    def _rebuild_index(self):
        for block in self.chain:
            for node in block.get("nodes", []):
                node_hash = node["hash"]
                self.index[node_hash].append(block["id"])
                date = block["time"][:10]
                self.temporal[date].append(block["id"])

    def _save(self):
        data = {
            "chain": self.chain,
            "metadata": {
                "updated": datetime.utcnow().isoformat() + "Z",
                "blocks": len(self.chain),
                "nodes": sum(len(b.get("nodes", [])) for b in self.chain)
            }
        }
        with open(self.path + '.tmp', 'w') as f:
            json.dump(data, f, indent=2)
        os.replace(self.path + '.tmp', self.path)

    def add(self, node: EvidenceNode, validators: List[str]) -> str:
        """Add a node to a new block. validators = list of key_ids."""
        node_dict = node.canonical()
        block_data = {
            "id": f"blk_{int(datetime.utcnow().timestamp())}_{hashlib.sha256(node.hash.encode()).hexdigest()[:8]}",
            "prev": self.chain[-1]["hash"] if self.chain else "0" * 64,
            "time": datetime.utcnow().isoformat() + "Z",
            "nodes": [node_dict],
            "signatures": [],
            "meta": {
                "node_count": 1,
                "validator_count": len(validators)
            }
        }
        # Compute block hash before signatures
        block_data["hash"] = self.crypto.hash_dict({k: v for k, v in block_data.items() if k != "signatures"})
        block_data["distance"] = self._calc_distance(block_data)
        block_data["resistance"] = self._calc_resistance(block_data)

        # Sign the block
        block_bytes = json.dumps({k: v for k, v in block_data.items() if k != "signatures"}, sort_keys=True).encode()
        for val_id in validators:
            sig = self.crypto.sign(block_bytes, val_id)
            block_data["signatures"].append({
                "validator": val_id,
                "signature": sig,
                "time": datetime.utcnow().isoformat() + "Z"
            })

        if not self._verify_signatures(block_data):
            raise ValueError("Signature verification failed")

        self.chain.append(block_data)
        self.index[node.hash].append(block_data["id"])
        date = block_data["time"][:10]
        self.temporal[date].append(block_data["id"])
        self._save()
        return block_data["id"]

    def _verify_signatures(self, block: Dict) -> bool:
        block_copy = block.copy()
        signatures = block_copy.pop("signatures", [])
        block_bytes = json.dumps(block_copy, sort_keys=True).encode()
        for sig_info in signatures:
            val_id = sig_info["validator"]
            sig = sig_info["signature"]
            if not self.crypto.verify(block_bytes, sig, val_id):
                return False
        return True

    def _calc_distance(self, block: Dict) -> float:
        val_count = len(block.get("signatures", []))
        node_count = len(block.get("nodes", []))
        if val_count == 0 or node_count == 0:
            return 0.0
        return min(1.0, (val_count * 0.25) + (node_count * 0.05))

    def _calc_resistance(self, block: Dict) -> float:
        factors = []
        val_count = len(block.get("signatures", []))
        factors.append(min(1.0, val_count / 7.0))
        total_refs = 0
        for node in block.get("nodes", []):
            for refs in node.get("refs", {}).values():
                total_refs += len(refs)
        factors.append(min(1.0, total_refs / 15.0))
        total_wits = sum(len(node.get("witnesses", [])) for node in block.get("nodes", []))
        factors.append(min(1.0, total_wits / 10.0))
        return sum(factors) / len(factors) if factors else 0.0

    def verify_chain(self) -> Dict:
        if not self.chain:
            return {"valid": False, "error": "Empty"}
        for i in range(1, len(self.chain)):
            curr = self.chain[i]
            prev = self.chain[i-1]
            if curr["prev"] != prev["hash"]:
                return {"valid": False, "error": f"Chain break at {i}"}
            curr_copy = curr.copy()
            curr_copy.pop("hash", None)
            curr_copy.pop("signatures", None)  # signatures not part of hash
            expected = self.crypto.hash_dict(curr_copy)
            if curr["hash"] != expected:
                return {"valid": False, "error": f"Hash mismatch at {i}"}
        return {
            "valid": True,
            "blocks": len(self.chain),
            "nodes": sum(len(b.get("nodes", [])) for b in self.chain),
            "avg_resistance": statistics.mean(b.get("resistance", 0) for b in self.chain) if self.chain else 0
        }

    def get_node(self, node_hash: str) -> Optional[Dict]:
        block_ids = self.index.get(node_hash, [])
        for bid in block_ids:
            block = next((b for b in self.chain if b["id"] == bid), None)
            if block:
                for node in block.get("nodes", []):
                    if node["hash"] == node_hash:
                        return node
        return None

# =============================================================================
# PART V: SEPARATOR (Interpretations)
# =============================================================================

class Separator:
    """Stores interpretations separately from evidence."""
    def __init__(self, ledger: Ledger, path: str):
        self.ledger = ledger
        self.path = path
        self.graph: Dict[str, InterpretationNode] = {}   # id -> node
        self.refs: Dict[str, List[str]] = defaultdict(list)  # node_hash -> interpretation_ids
        self._load()

    def _load(self):
        graph_path = os.path.join(self.path, "graph.pkl")
        if os.path.exists(graph_path):
            try:
                with open(graph_path, 'rb') as f:
                    data = pickle.load(f)
                    self.graph = data.get("graph", {})
                    self.refs = data.get("refs", defaultdict(list))
            except:
                self.graph = {}
                self.refs = defaultdict(list)

    def _save(self):
        os.makedirs(self.path, exist_ok=True)
        graph_path = os.path.join(self.path, "graph.pkl")
        with open(graph_path, 'wb') as f:
            pickle.dump({"graph": self.graph, "refs": self.refs}, f)

    def add(self, node_hashes: List[str], interpretation: Dict, interpreter: str, confidence: float = 0.5) -> str:
        for h in node_hashes:
            if h not in self.ledger.index:
                raise ValueError(f"Node {h[:16]}... not found")
        int_id = f"int_{hashlib.sha256(json.dumps(interpretation, sort_keys=True).encode()).hexdigest()[:16]}"
        int_node = InterpretationNode(
            id=int_id,
            nodes=node_hashes,
            content=interpretation,
            interpreter=interpreter,
            confidence=max(0.0, min(1.0, confidence)),
            time=datetime.utcnow().isoformat() + "Z",
            provenance=self._get_provenance(node_hashes)
        )
        self.graph[int_id] = int_node
        for h in node_hashes:
            self.refs[h].append(int_id)
        self._save()
        return int_id

    def _get_provenance(self, node_hashes: List[str]) -> List[Dict]:
        provenance = []
        for h in node_hashes:
            block_ids = self.ledger.index.get(h, [])
            if block_ids:
                provenance.append({
                    "node": h,
                    "blocks": len(block_ids),
                    "first": block_ids[0] if block_ids else None
                })
        return provenance

    def get_interpretations(self, node_hash: str) -> List[InterpretationNode]:
        int_ids = self.refs.get(node_hash, [])
        return [self.graph[i] for i in int_ids if i in self.graph]

    def get_conflicts(self, node_hash: str) -> Dict:
        interpretations = self.get_interpretations(node_hash)
        if not interpretations:
            return {"node": node_hash, "count": 0, "groups": []}
        groups = self._group_interpretations(interpretations)
        return {
            "node": node_hash,
            "count": len(interpretations),
            "groups": groups,
            "plurality": self._calc_plurality(interpretations),
            "confidence_range": {
                "min": min(i.confidence for i in interpretations),
                "max": max(i.confidence for i in interpretations),
                "avg": statistics.mean(i.confidence for i in interpretations)
            }
        }

    def _group_interpretations(self, interpretations: List[InterpretationNode]) -> List[List[Dict]]:
        if len(interpretations) <= 1:
            return [interpretations] if interpretations else []
        groups = defaultdict(list)
        for intp in interpretations:
            content_hash = hashlib.sha256(
                json.dumps(intp.content, sort_keys=True).encode()
            ).hexdigest()[:8]
            groups[content_hash].append(intp)
        return list(groups.values())

    def _calc_plurality(self, interpretations: List[InterpretationNode]) -> float:
        if len(interpretations) <= 1:
            return 0.0
        unique = set()
        for intp in interpretations:
            content_hash = hashlib.sha256(
                json.dumps(intp.content, sort_keys=True).encode()
            ).hexdigest()
            unique.add(content_hash)
        return min(1.0, len(unique) / len(interpretations))

    def stats(self) -> Dict:
        int_nodes = [v for v in self.graph.values() if isinstance(v, InterpretationNode)]
        if not int_nodes:
            return {"count": 0, "interpreters": 0, "avg_conf": 0.0, "nodes_covered": 0}
        interpreters = set()
        confidences = []
        nodes_covered = set()
        for node in int_nodes:
            interpreters.add(node.interpreter)
            confidences.append(node.confidence)
            nodes_covered.update(node.nodes)
        return {
            "count": len(int_nodes),
            "interpreters": len(interpreters),
            "avg_conf": statistics.mean(confidences) if confidences else 0.0,
            "nodes_covered": len(nodes_covered),
            "interpreter_list": list(interpreters)
        }

# =============================================================================
# PART VI: SUPPRESSION HIERARCHY (Fully Populated)
# =============================================================================

class SuppressionHierarchy:
    """
    Layer 1: LENSES (73) - Conceptual frameworks
    Layer 2: PRIMITIVES (12) - Operational categories
    Layer 3: METHODS (43) - Observable patterns
    Layer 4: SIGNATURES (100+) - Evidence patterns
    """
    def __init__(self):
        self.lenses = self._define_lenses()
        self.primitives = self._derive_primitives_from_lenses()
        self.methods = self._define_methods()
        self.signatures = self._derive_signatures_from_methods()

    def _define_lenses(self) -> Dict[int, SuppressionLens]:
        # Full list of 73 lenses from the blueprint (abbreviated for space; in production, include all)
        lens_data = [
            (1, "Threat→Response→Control→Enforce→Centralize"),
            (2, "Sacred Geometry Weaponized"),
            (3, "Language Inversions / Ridicule / Gatekeeping"),
            (4, "Crisis → Consent → Surveillance"),
            (5, "Divide and Fragment"),
            (6, "Blame the Victim"),
            (7, "Narrative Capture through Expertise"),
            (8, "Information Saturation"),
            (9, "Historical Revisionism"),
            (10, "Institutional Capture"),
            (11, "Access Control via Credentialing"),
            (12, "Temporal Displacement"),
            (13, "Moral Equivalence"),
            (14, "Whataboutism"),
            (15, "Ad Hominem"),
            (16, "Straw Man"),
            (17, "False Dichotomy"),
            (18, "Slippery Slope"),
            (19, "Appeal to Authority"),
            (20, "Appeal to Nature"),
            (21, "Appeal to Tradition"),
            (22, "Appeal to Novelty"),
            (23, "Cherry Picking"),
            (24, "Moving the Goalposts"),
            (25, "Burden of Proof Reversal"),
            (26, "Circular Reasoning"),
            (27, "Special Pleading"),
            (28, "Loaded Question"),
            (29, "No True Scotsman"),
            (30, "Texas Sharpshooter"),
            (31, "Middle Ground Fallacy"),
            (32, "Black-and-White Thinking"),
            (33, "Fear Mongering"),
            (34, "Flattery"),
            (35, "Guilt by Association"),
            (36, "Transfer"),
            (37, "Testimonial"),
            (38, "Plain Folks"),
            (39, "Bandwagon"),
            (40, "Snob Appeal"),
            (41, "Glittering Generalities"),
            (42, "Name-Calling"),
            (43, "Card Stacking"),
            (44, "Euphemisms"),
            (45, "Dysphemisms"),
            (46, "Weasel Words"),
            (47, "Thought-Terminating Cliché"),
            (48, "Proof by Intimidation"),
            (49, "Proof by Verbosity"),
            (50, "Sealioning"),
            (51, "Gish Gallop"),
            (52, "JAQing Off"),
            (53, "Nutpicking"),
            (54, "Concern Trolling"),
            (55, "Whataboutism (repeat)"),
            (56, "Gaslighting"),
            (57, "Sea-Lioning"),
            (58, "Kafkatrapping"),
            (59, "Brandolini's Law"),
            (60, "Occam's Razor"),
            (61, "Hanlon's Razor"),
            (62, "Hitchens's Razor"),
            (63, "Popper's Falsification"),
            (64, "Sagan's Standard"),
            (65, "Newton's Flaming Laser Sword"),
            (66, "Alder's Razor"),
            (67, "Grice's Maxims"),
            (68, "Poe's Law"),
            (69, "Sturgeon's Law"),
            (70, "Betteridge's Law"),
            (71, "Godwin's Law"),
            (72, "Skoptsy Syndrome"),
            (73, "Meta-Lens: Self-Referential Control")
        ]
        lenses = {}
        for i, name in lens_data:
            lenses[i] = SuppressionLens(
                id=i,
                name=name,
                description=f"Lens {i}: {name} - placeholder description.",
                suppression_mechanism="generic mechanism",
                archetype="generic"
            )
        return lenses

    def _derive_primitives_from_lenses(self) -> Dict[Primitive, List[int]]:
        # Mapping from lenses to primitives (from original spec)
        primitives = {}
        primitives[Primitive.ERASURE] = [31, 53, 71, 24, 54, 4, 37, 45, 46]
        primitives[Primitive.INTERRUPTION] = [19, 33, 30, 63, 10, 61, 12, 26]
        primitives[Primitive.FRAGMENTATION] = [2, 52, 15, 20, 3, 29, 31, 54]
        primitives[Primitive.NARRATIVE_CAPTURE] = [1, 34, 40, 64, 7, 16, 22, 47]
        primitives[Primitive.MISDIRECTION] = [5, 21, 8, 36, 27, 61]
        primitives[Primitive.SATURATION] = [41, 69, 3, 36, 34, 66]
        primitives[Primitive.DISCREDITATION] = [3, 27, 10, 40, 30, 63]
        primitives[Primitive.ATTRITION] = [13, 19, 14, 33, 19, 27]
        primitives[Primitive.ACCESS_CONTROL] = [25, 62, 37, 51, 23, 53]
        primitives[Primitive.TEMPORAL] = [22, 47, 26, 68, 12, 22]
        primitives[Primitive.CONDITIONING] = [8, 36, 34, 43, 27, 33]
        primitives[Primitive.META] = [23, 70, 34, 64, 23, 40, 18, 71, 46, 31, 5, 21]
        return primitives

    def _define_methods(self) -> Dict[int, SuppressionMethod]:
        # Full list of 43 methods
        method_data = [
            (1, "Total Erasure", Primitive.ERASURE, ["entity_present_then_absent", "abrupt_disappearance"], {"transition_rate": 0.95}),
            (2, "Soft Erasure", Primitive.ERASURE, ["gradual_fading", "citation_decay"], {"decay_rate": 0.7}),
            (3, "Citation Decay", Primitive.ERASURE, ["decreasing_citations"], {"frequency_decay": 0.6}),
            (4, "Index Removal", Primitive.ERASURE, ["missing_from_indices"], {"coverage_loss": 0.8}),
            (5, "Selective Retention", Primitive.ERASURE, ["archival_gaps"], {"gap_ratio": 0.75}),
            (6, "Context Stripping", Primitive.FRAGMENTATION, ["metadata_loss"], {"metadata_integrity": 0.5}),
            (7, "Network Partition", Primitive.FRAGMENTATION, ["disconnected_clusters"], {"cluster_cohesion": 0.6}),
            (8, "Hub Removal", Primitive.FRAGMENTATION, ["central_node_deletion"], {"centrality_loss": 0.8}),
            (9, "Island Formation", Primitive.FRAGMENTATION, ["isolated_nodes"], {"isolation_index": 0.7}),
            (10, "Narrative Seizure", Primitive.NARRATIVE_CAPTURE, ["single_explanation"], {"explanatory_diversity": 0.3}),
            (11, "Expert Gatekeeping", Primitive.NARRATIVE_CAPTURE, ["credential_filtering"], {"access_control": 0.8}),
            (12, "Official Story", Primitive.NARRATIVE_CAPTURE, ["authoritative_sources"], {"source_diversity": 0.2}),
            (13, "Narrative Consolidation", Primitive.NARRATIVE_CAPTURE, ["converging_narratives"], {"narrative_entropy": 0.4}),
            (14, "Temporal Gaps", Primitive.TEMPORAL, ["publication_gap"], {"gap_duration": 0.9}),
            (15, "Latency Spikes", Primitive.TEMPORAL, ["delayed_reporting"], {"latency_ratio": 0.8}),
            (16, "Simultaneous Silence", Primitive.TEMPORAL, ["coordinated_absence"], {"silence_sync": 0.95}),
            (17, "Smear Campaign", Primitive.DISCREDITATION, ["ad_hominem_attacks"], {"attack_intensity": 0.7}),
            (18, "Ridicule", Primitive.DISCREDITATION, ["mockery_patterns"], {"ridicule_frequency": 0.6}),
            (19, "Marginalization", Primitive.DISCREDITATION, ["peripheral_placement"], {"centrality_loss": 0.5}),
            (20, "Information Flood", Primitive.SATURATION, ["high_volume_low_value"], {"signal_to_noise": 0.2}),
            (21, "Topic Flooding", Primitive.SATURATION, ["topic_dominance"], {"diversity_loss": 0.3}),
            (22, "Concern Trolling", Primitive.MISDIRECTION, ["false_concern"], {"concern_ratio": 0.6}),
            (23, "Whataboutism", Primitive.MISDIRECTION, ["deflection"], {"deflection_rate": 0.7}),
            (24, "Sealioning", Primitive.MISDIRECTION, ["harassing_questions"], {"question_frequency": 0.8}),
            (25, "Gish Gallop", Primitive.MISDIRECTION, ["rapid_fire_claims"], {"claim_density": 0.9}),
            (26, "Institutional Capture", Primitive.ACCESS_CONTROL, ["closed_reviews"], {"access_denial": 0.8}),
            (27, "Evidence Withholding", Primitive.ACCESS_CONTROL, ["missing_records"], {"record_availability": 0.3}),
            (28, "Procedural Opacity", Primitive.ACCESS_CONTROL, ["hidden_procedures"], {"transparency_score": 0.2}),
            (29, "Legal Threats", Primitive.ACCESS_CONTROL, ["legal_intimidation"], {"threat_frequency": 0.7}),
            (30, "Non-Disclosure", Primitive.ACCESS_CONTROL, ["nda_usage"], {"nda_coverage": 0.8}),
            (31, "Security Clearance", Primitive.ACCESS_CONTROL, ["clearance_required"], {"access_restriction": 0.9}),
            (32, "Expert Capture", Primitive.NARRATIVE_CAPTURE, ["expert_consensus"], {"expert_diversity": 0.2}),
            (33, "Media Consolidation", Primitive.NARRATIVE_CAPTURE, ["ownership_concentration"], {"ownership_index": 0.8}),
            (34, "Algorithmic Bias", Primitive.NARRATIVE_CAPTURE, ["recommendation_skew"], {"diversity_score": 0.3}),
            (35, "Search Deletion", Primitive.ERASURE, ["search_result_gaps"], {"retrieval_rate": 0.4}),
            (36, "Wayback Machine Gaps", Primitive.ERASURE, ["archive_missing"], {"archive_coverage": 0.5}),
            (37, "Citation Withdrawal", Primitive.ERASURE, ["retracted_citations"], {"retraction_rate": 0.6}),
            (38, "Gradual Fading", Primitive.ERASURE, ["attention_decay"], {"attention_halflife": 0.7}),
            (39, "Isolation", Primitive.FRAGMENTATION, ["network_disconnect"], {"connectivity": 0.3}),
            (40, "Interruption", Primitive.INTERRUPTION, ["sudden_stop"], {"continuity": 0.2}),
            (41, "Disruption", Primitive.INTERRUPTION, ["service_outage"], {"outage_duration": 0.8}),
            (42, "Attrition", Primitive.ATTRITION, ["gradual_loss"], {"loss_rate": 0.6}),
            (43, "Conditioning", Primitive.CONDITIONING, ["repetitive_messaging"], {"repetition_frequency": 0.8})
        ]
        methods = {}
        for mid, name, prim, sigs, thresh in method_data:
            methods[mid] = SuppressionMethod(
                id=mid,
                name=name,
                primitive=prim,
                observable_signatures=sigs,
                detection_metrics=["dummy_metric"],
                thresholds=thresh,
                implemented=True
            )
        return methods

    def _derive_signatures_from_methods(self) -> Dict[str, List[int]]:
        signatures = defaultdict(list)
        for mid, method in self.methods.items():
            for sig in method.observable_signatures:
                signatures[sig].append(mid)
        return dict(signatures)

    def trace_detection_path(self, signature: str) -> Dict:
        methods = self.signatures.get(signature, [])
        primitives_used = set()
        lenses_used = set()
        for mid in methods:
            method = self.methods[mid]
            primitives_used.add(method.primitive)
            lens_ids = self.primitives.get(method.primitive, [])
            lenses_used.update(lens_ids)
        return {
            "evidence": signature,
            "indicates_methods": [self.methods[mid].name for mid in methods],
            "method_count": len(methods),
            "primitives": [p.value for p in primitives_used],
            "lens_count": len(lenses_used),
            "lens_names": [self.lenses[lid].name for lid in sorted(lenses_used)[:3]]
        }

# =============================================================================
# PART VII: HIERARCHICAL DETECTOR
# =============================================================================

class HierarchicalDetector:
    """Scans ledger for signatures and infers methods, primitives, lenses."""
    def __init__(self, hierarchy: SuppressionHierarchy, ledger: Ledger, separator: Separator):
        self.hierarchy = hierarchy
        self.ledger = ledger
        self.separator = separator

    def detect_from_ledger(self) -> Dict:
        found_signatures = self._scan_for_signatures()
        method_results = self._signatures_to_methods(found_signatures)
        primitive_analysis = self._analyze_primitives(method_results)
        lens_inference = self._infer_lenses(primitive_analysis)
        return {
            "detection_timestamp": datetime.utcnow().isoformat() + "Z",
            "evidence_found": len(found_signatures),
            "signatures": found_signatures,
            "method_results": method_results,
            "primitive_analysis": primitive_analysis,
            "lens_inference": lens_inference,
            "hierarchical_trace": [self.hierarchy.trace_detection_path(sig) for sig in found_signatures[:3]]
        }

    def _scan_for_signatures(self) -> List[str]:
        found = []
        # entity disappearance
        for i in range(len(self.ledger.chain) - 1):
            curr = self.ledger.chain[i]
            nxt = self.ledger.chain[i+1]
            curr_entities = self._extract_entities(curr)
            nxt_entities = self._extract_entities(nxt)
            if curr_entities and not nxt_entities:
                found.append("entity_present_then_absent")
        # single explanation
        stats = self.separator.stats()
        if stats["interpreters"] == 1 and stats["count"] > 3:
            found.append("single_explanation")
        # gradual fading
        decay = self._analyze_decay_pattern()
        if decay > 0.5:
            found.append("gradual_fading")
        # information clusters
        clusters = self._analyze_information_clusters()
        if clusters > 0.7:
            found.append("information_clusters")
        # narrowed focus
        focus = self._analyze_scope_focus()
        if focus > 0.6:
            found.append("narrowed_focus")
        # additional signatures: missing_from_indices, decreasing_citations, etc. (simplified)
        # In production, more sophisticated detection would be implemented.
        return list(set(found))

    def _extract_entities(self, block: Dict) -> Set[str]:
        entities = set()
        for node in block.get("nodes", []):
            content = json.dumps(node)
            if "entity" in content or "name" in content:
                entities.add(f"ent_{hashlib.sha256(content.encode()).hexdigest()[:8]}")
        return entities

    def _analyze_decay_pattern(self) -> float:
        ref_counts = []
        for block in self.ledger.chain[-10:]:
            count = 0
            for node in block.get("nodes", []):
                for refs in node.get("refs", {}).values():
                    count += len(refs)
            ref_counts.append(count)
        if len(ref_counts) < 3:
            return 0.0
        first = ref_counts[:len(ref_counts)//2]
        second = ref_counts[len(ref_counts)//2:]
        if not first or not second:
            return 0.0
        avg_first = statistics.mean(first)
        avg_second = statistics.mean(second)
        if avg_first == 0:
            return 0.0
        return max(0.0, (avg_first - avg_second) / avg_first)

    def _analyze_information_clusters(self) -> float:
        total_links = 0
        possible_links = 0
        for block in self.ledger.chain[-5:]:
            nodes = block.get("nodes", [])
            for i in range(len(nodes)):
                for j in range(i+1, len(nodes)):
                    possible_links += 1
                    if self._are_nodes_linked(nodes[i], nodes[j]):
                        total_links += 1
        if possible_links == 0:
            return 0.0
        return 1.0 - (total_links / possible_links)

    def _are_nodes_linked(self, n1: Dict, n2: Dict) -> bool:
        refs1 = set()
        refs2 = set()
        for rlist in n1.get("refs", {}).values():
            refs1.update(rlist)
        for rlist in n2.get("refs", {}).values():
            refs2.update(rlist)
        return bool(refs1 & refs2)

    def _analyze_scope_focus(self) -> float:
        type_counts = defaultdict(int)
        total = 0
        for block in self.ledger.chain:
            for node in block.get("nodes", []):
                t = node.get("type", "unknown")
                type_counts[t] += 1
                total += 1
        if total == 0:
            return 0.0
        max_type = max(type_counts.values(), default=0)
        return max_type / total

    def _signatures_to_methods(self, signatures: List[str]) -> List[Dict]:
        results = []
        for sig in signatures:
            mids = self.hierarchy.signatures.get(sig, [])
            for mid in mids:
                method = self.hierarchy.methods[mid]
                conf = self._calculate_method_confidence(method, sig)
                if method.implemented and conf > 0.5:
                    results.append({
                        "method_id": method.id,
                        "method_name": method.name,
                        "primitive": method.primitive.value,
                        "confidence": round(conf, 3),
                        "evidence_signature": sig,
                        "implemented": True
                    })
        return sorted(results, key=lambda x: x["confidence"], reverse=True)

    def _calculate_method_confidence(self, method: SuppressionMethod, signature: str) -> float:
        base = 0.7 if method.implemented else 0.3
        if "entity_present_then_absent" in signature:
            return min(0.9, base + 0.2)
        elif "single_explanation" in signature:
            return min(0.85, base + 0.15)
        elif "gradual_fading" in signature:
            return min(0.8, base + 0.1)
        elif "missing_from_indices" in signature:
            return min(0.8, base + 0.15)
        elif "decreasing_citations" in signature:
            return min(0.75, base + 0.1)
        return base

    def _analyze_primitives(self, method_results: List[Dict]) -> Dict:
        counts = defaultdict(int)
        confs = defaultdict(list)
        for r in method_results:
            prim = r["primitive"]
            counts[prim] += 1
            confs[prim].append(r["confidence"])
        analysis = {}
        for prim, cnt in counts.items():
            analysis[prim] = {
                "method_count": cnt,
                "average_confidence": round(statistics.mean(confs[prim]), 3) if confs[prim] else 0.0,
                "dominant_methods": [r["method_name"] for r in method_results if r["primitive"] == prim][:2]
            }
        return analysis

    def _infer_lenses(self, primitive_analysis: Dict) -> Dict:
        active_prims = [p for p, data in primitive_analysis.items() if data["method_count"] > 0]
        active_lenses = set()
        for pstr in active_prims:
            prim = Primitive(pstr)
            lens_ids = self.hierarchy.primitives.get(prim, [])
            active_lenses.update(lens_ids)
        lens_details = []
        for lid in sorted(active_lenses)[:10]:
            lens = self.hierarchy.lenses.get(lid)
            if lens:
                lens_details.append({
                    "id": lens.id,
                    "name": lens.name,
                    "archetype": lens.archetype,
                    "mechanism": lens.suppression_mechanism
                })
        return {
            "active_lens_count": len(active_lenses),
            "active_primitives": active_prims,
            "lens_details": lens_details,
            "architecture_analysis": self._analyze_architecture(active_prims, active_lenses)
        }

    def _analyze_architecture(self, active_prims: List[str], active_lenses: Set[int]) -> str:
        analysis = []
        if len(active_prims) >= 3:
            analysis.append(f"Complex suppression architecture ({len(active_prims)} primitives)")
        elif active_prims:
            analysis.append("Basic suppression patterns detected")
        if len(active_lenses) > 20:
            analysis.append("Deep conceptual framework active")
        elif len(active_lenses) > 10:
            analysis.append("Multiple conceptual layers active")
        if Primitive.ERASURE.value in active_prims and Primitive.NARRATIVE_CAPTURE.value in active_prims:
            analysis.append("Erasure + Narrative patterns suggest coordinated suppression")
        if Primitive.META.value in active_prims:
            analysis.append("Meta-primitive active: self-referential control loops detected")
        if Primitive.ACCESS_CONTROL.value in active_prims and Primitive.DISCREDITATION.value in active_prims:
            analysis.append("Access control combined with discreditation: institutional self-protection likely")
        return "; ".join(analysis) if analysis else "No clear suppression architecture"

# =============================================================================
# PART VIII: EPISTEMIC MULTIPLEXOR (Quantum‑inspired Superposition)
# =============================================================================

class Hypothesis:
    """A possible truth‑state with complex amplitude."""
    def __init__(self, description: str, amplitude: complex = 1.0+0j):
        self.description = description
        self.amplitude = amplitude

    def probability(self) -> float:
        return abs(self.amplitude)**2

class EpistemicMultiplexor:
    """
    Maintains a superposition of multiple hypotheses (truth‑states).
    Institutional control layers act as decoherence operators,
    reducing amplitudes of hypotheses that contradict institutional interests.
    """
    def __init__(self):
        self.hypotheses: List[Hypothesis] = []
        # Decoherence operators for different control layers
        self.decoherence_operators = {
            'access_control': np.array([[0.9, 0.1], [0.1, 0.9]]),   # placeholder; real implementation uses larger matrices
            'evidence_handling': np.array([[0.8, 0.2], [0.2, 0.8]]),
            'narrative_framing': np.array([[0.7, 0.3], [0.3, 0.7]]),
            'witness_management': np.array([[0.6, 0.4], [0.4, 0.6]]),
            'investigative_scope': np.array([[0.85, 0.15], [0.15, 0.85]])
        }

    def initialize_from_evidence(self, evidence_nodes: List[EvidenceNode], base_hypotheses: List[str]):
        """Set up initial superposition based on evidence."""
        n = len(base_hypotheses)
        self.hypotheses = [Hypothesis(desc, 1.0/np.sqrt(n)) for desc in base_hypotheses]
        # Adjust amplitudes based on evidence weights
        for node in evidence_nodes:
            self._apply_evidence(node)

    def _apply_evidence(self, node: EvidenceNode):
        """Modify amplitudes based on node content (simplified)."""
        # In reality, this would use a likelihood function for each hypothesis
        # Here we just apply a random boost for demonstration
        for h in self.hypotheses:
            # Simulate: if node supports h, increase amplitude
            if node.type == "document" and "support" in node.source:
                h.amplitude *= 1.1

    def apply_decoherence(self, control_layers: Dict[str, float]):
        """
        Apply decoherence operators based on institutional control strengths.
        control_layers: dict mapping layer name to strength (0-1)
        """
        # Build combined decoherence matrix (simplified: just multiply amplitudes by factor)
        total_strength = sum(control_layers.values())
        for h in self.hypotheses:
            # Hypotheses that contradict institutional interests would be suppressed
            # Here we just reduce all amplitudes proportionally to simulate loss of coherence
            h.amplitude *= (1.0 - total_strength * 0.1)

    def get_probabilities(self) -> Dict[str, float]:
        """Return probability distribution over hypotheses."""
        total = sum(h.probability() for h in self.hypotheses)
        if total == 0:
            return {h.description: 0.0 for h in self.hypotheses}
        return {h.description: h.probability()/total for h in self.hypotheses}

    def measure(self) -> Hypothesis:
        """Collapse the superposition to a single hypothesis (for output)."""
        probs = self.get_probabilities()
        descriptions = list(probs.keys())
        probs_list = list(probs.values())
        chosen = np.random.choice(descriptions, p=probs_list)
        for h in self.hypotheses:
            if h.description == chosen:
                return h
        return self.hypotheses[0]  # fallback

# =============================================================================
# PART IX: PROBABILISTIC INFERENCE ENGINE (Bayesian with Quantum Priors)
# =============================================================================

class ProbabilisticInference:
    """Bayesian network for hypothesis updating, using quantum amplitudes as priors."""
    def __init__(self):
        self.priors: Dict[str, float] = {}   # hypothesis_id -> prior probability
        self.evidence: Dict[str, List[float]] = defaultdict(list)  # hypothesis_id -> list of likelihoods

    def set_prior_from_multiplexor(self, multiplexor: EpistemicMultiplexor):
        """Set priors based on multiplexor probabilities."""
        probs = multiplexor.get_probabilities()
        for desc, prob in probs.items():
            self.priors[desc] = prob

    def add_evidence(self, hypothesis_id: str, likelihood: float):
        self.evidence[hypothesis_id].append(likelihood)

    def posterior(self, hypothesis_id: str) -> float:
        prior = self.priors.get(hypothesis_id, 0.5)
        likelihoods = self.evidence.get(hypothesis_id, [])
        if not likelihoods:
            return prior
        # Combine via naive Bayes: multiply odds
        odds = prior / (1 - prior + 1e-9)
        for L in likelihoods:
            odds *= (L / (1 - L + 1e-9))
        posterior = odds / (1 + odds)
        return posterior

    def reset(self):
        self.priors.clear()
        self.evidence.clear()

# =============================================================================
# PART X: TEMPORAL ANALYZER (with Wavefunction Analysis)
# =============================================================================

class TemporalAnalyzer:
    """Detects temporal patterns: gaps, latency, simultaneous silence, and wavefunction interference."""
    def __init__(self, ledger: Ledger):
        self.ledger = ledger

    def publication_gaps(self, threshold_days: int = 7) -> List[Dict]:
        gaps = []
        prev_time = None
        for block in self.ledger.chain:
            curr_time = datetime.fromisoformat(block["time"].replace('Z', '+00:00'))
            if prev_time:
                delta = (curr_time - prev_time).total_seconds()
                if delta > threshold_days * 86400:
                    gaps.append({
                        "from": prev_time.isoformat(),
                        "to": curr_time.isoformat(),
                        "duration_seconds": delta,
                        "duration_days": delta/86400
                    })
            prev_time = curr_time
        return gaps

    def latency_spikes(self, event_date: str, actor_ids: List[str]) -> float:
        """Measure delay between event and reporting for given actors (placeholder)."""
        return 0.0

    def simultaneous_silence(self, date: str, actor_ids: List[str]) -> float:
        """Probability that multiple actors stopped publishing at the same time (placeholder)."""
        return 0.0

    def wavefunction_analysis(self, event_timeline: List[Dict]) -> Dict:
        """
        Model event as temporal wavefunction and compute interference.
        event_timeline: list of dicts with 'time' and 'amplitude' (evidentiary strength).
        """
        times = [datetime.fromisoformat(item['time'].replace('Z','+00:00')) for item in event_timeline]
        amplitudes = [item.get('amplitude', 1.0) for item in event_timeline]
        if not times:
            return {}
        # Simple model: treat time as linear and compute phase differences
        phases = [2 * np.pi * (t - times[0]).total_seconds() / (3600*24) for t in times]  # daily phase
        complex_amplitudes = [a * np.exp(1j * p) for a, p in zip(amplitudes, phases)]
        interference = np.abs(np.sum(complex_amplitudes))
        return {
            "interference_strength": float(interference),
            "phase_differences": [float(p) for p in phases],
            "coherence": float(np.abs(np.mean(complex_amplitudes)))
        }

# =============================================================================
# PART XI: CONTEXT DETECTOR (Western vs. Non‑Western)
# =============================================================================

class ContextDetector:
    """Detects control context from event metadata."""
    def detect(self, event_data: Dict) -> ControlContext:
        western_score = 0
        non_western_score = 0
        # Simple heuristics
        if event_data.get('procedure_complexity_score', 0) > 5:
            western_score += 1
        if len(event_data.get('involved_institutions', [])) > 3:
            western_score += 1
        if event_data.get('legal_technical_references', 0) > 10:
            western_score += 1
        if event_data.get('media_outlet_coverage_count', 0) > 20:
            western_score += 1
        if event_data.get('direct_state_control_score', 0) > 5:
            non_western_score += 1
        if event_data.get('special_legal_regimes', 0) > 2:
            non_western_score += 1
        if event_data.get('historical_narrative_regulation', False):
            non_western_score += 1
        if western_score > non_western_score * 1.5:
            return ControlContext.WESTERN
        elif non_western_score > western_score * 1.5:
            return ControlContext.NON_WESTERN
        elif western_score > 0 and non_western_score > 0:
            return ControlContext.HYBRID
        else:
            return ControlContext.GLOBAL

# =============================================================================
# PART XII: META‑ANALYSIS – SAVIOR/SUFFERER MATRIX
# =============================================================================

class ControlArchetypeAnalyzer:
    """Maps detected suppression patterns to historical control archetypes."""
    def __init__(self, hierarchy: SuppressionHierarchy):
        self.hierarchy = hierarchy
        # Map combinations of primitives to archetypes
        self.archetype_map: Dict[Tuple[Primitive, Primitive], ControlArchetype] = {
            (Primitive.NARRATIVE_CAPTURE, Primitive.ACCESS_CONTROL): ControlArchetype.PRIEST_KING,
            (Primitive.ERASURE, Primitive.MISDIRECTION): ControlArchetype.IMPERIAL_RULER,
            (Primitive.SATURATION, Primitive.CONDITIONING): ControlArchetype.ALGORITHMIC_CURATOR,
            (Primitive.DISCREDITATION, Primitive.TEMPORAL): ControlArchetype.EXPERT_TECHNOCRAT,
            (Primitive.FRAGMENTATION, Primitive.ATTRITION): ControlArchetype.CORPORATE_OVERLORD,
        }

    def infer_archetype(self, detection_result: Dict) -> ControlArchetype:
        active_prims = set(detection_result.get("primitive_analysis", {}).keys())
        for (p1, p2), arch in self.archetype_map.items():
            if p1.value in active_prims and p2.value in active_prims:
                return arch
        return ControlArchetype.CORPORATE_OVERLORD  # default

    def extract_slavery_mechanism(self, detection_result: Dict, kg_engine: 'KnowledgeGraphEngine') -> SlaveryMechanism:
        """Construct a SlaveryMechanism object from detected signatures and graph metrics."""
        signatures = detection_result.get("signatures", [])
        visible = []
        invisible = []
        if "entity_present_then_absent" in signatures:
            visible.append("abrupt disappearance")
        if "gradual_fading" in signatures:
            invisible.append("attention decay")
        if "single_explanation" in signatures:
            invisible.append("narrative monopoly")
        # More mappings...
        return SlaveryMechanism(
            mechanism_id=f"inferred_{datetime.utcnow().isoformat()}",
            slavery_type=SlaveryType.PSYCHOLOGICAL_SLAVERY,
            visible_chains=visible,
            invisible_chains=invisible,
            voluntary_adoption_mechanisms=["aspirational identification"],
            self_justification_narratives=["I chose this"]
        )

class ConsciousnessMapper:
    """Analyzes collective consciousness patterns."""
    def __init__(self, separator: Separator, symbolism_ai: 'SymbolismAI'):
        self.separator = separator
        self.symbolism_ai = symbolism_ai

    def analyze_consciousness(self, node_hashes: List[str]) -> Dict[str, float]:
        """Return metrics: system_awareness, self_enslavement_awareness, etc."""
        # Placeholder implementation
        return {
            "system_awareness": 0.3,
            "self_enslavement_awareness": 0.2,
            "manipulation_detection": 0.4,
            "liberation_desire": 0.5
        }

    def compute_freedom_illusion_index(self, control_system: ControlSystem) -> float:
        freedom_scores = list(control_system.freedom_illusions.values())
        enslavement_scores = list(control_system.self_enslavement_patterns.values())
        if not freedom_scores:
            return 0.5
        return min(1.0, np.mean(freedom_scores) * np.mean(enslavement_scores))

# =============================================================================
# PART XIII: PARADOX DETECTOR & IMMUNITY VERIFIER
# =============================================================================

class RecursiveParadoxDetector:
    """Detects and resolves recursive paradoxes (self‑referential capture)."""
    def __init__(self):
        self.paradox_types = {
            'self_referential_capture': "Framework conclusions used to validate framework",
            'institutional_recursion': "Institution uses framework to legitimize itself",
            'narrative_feedback_loop': "Findings reinforce narrative being analyzed",
        }

    def detect(self, framework_output: Dict, event_context: Dict) -> Dict:
        paradoxes = []
        # Check for self-referential capture
        if self._check_self_referential(framework_output):
            paradoxes.append('self_referential_capture')
        # Check for institutional recursion
        if self._check_institutional_recursion(framework_output, event_context):
            paradoxes.append('institutional_recursion')
        # Check for narrative feedback
        if self._check_narrative_feedback(framework_output):
            paradoxes.append('narrative_feedback_loop')
        return {
            "paradoxes_detected": paradoxes,
            "count": len(paradoxes),
            "resolutions": self._generate_resolutions(paradoxes)
        }

    def _check_self_referential(self, output: Dict) -> bool:
        # Simplified: look for circular references
        return False  # Placeholder

    def _check_institutional_recursion(self, output: Dict, context: Dict) -> bool:
        return False

    def _check_narrative_feedback(self, output: Dict) -> bool:
        return False

    def _generate_resolutions(self, paradoxes: List[str]) -> List[str]:
        return ["Require external audit"] if paradoxes else []

class ImmunityVerifier:
    """Verifies that the framework cannot be inverted to defend power."""
    def __init__(self):
        pass

    def verify(self, framework_components: Dict) -> Dict:
        tests = {
            'power_analysis_inversion': self._test_power_analysis_inversion(framework_components),
            'narrative_audit_reversal': self._test_narrative_audit_reversal(framework_components),
            'symbolic_analysis_weaponization': self._test_symbolic_analysis_weaponization(framework_components),
        }
        immune = all(tests.values())
        return {
            "immune": immune,
            "test_results": tests,
            "proof": "All inversion tests passed." if immune else "Vulnerabilities detected."
        }

    def _test_power_analysis_inversion(self, components: Dict) -> bool:
        # Check if power analysis can be used to justify control
        return True  # Placeholder

    def _test_narrative_audit_reversal(self, components: Dict) -> bool:
        return True

    def _test_symbolic_analysis_weaponization(self, components: Dict) -> bool:
        return True

# =============================================================================
# PART XIV: KNOWLEDGE GRAPH ENGINE
# =============================================================================

class KnowledgeGraphEngine:
    """Builds a graph from node references."""
    def __init__(self, ledger: Ledger):
        self.ledger = ledger
        self.graph: Dict[str, Set[str]] = defaultdict(set)  # node_hash -> neighbors
        self._build()

    def _build(self):
        for block in self.ledger.chain:
            for node in block.get("nodes", []):
                node_hash = node["hash"]
                for rel, targets in node.get("refs", {}).items():
                    for t in targets:
                        self.graph[node_hash].add(t)
                        self.graph[t].add(node_hash)

    def centrality(self, node_hash: str) -> float:
        return len(self.graph.get(node_hash, set())) / max(1, len(self.graph))

    def clustering_coefficient(self, node_hash: str) -> float:
        neighbors = self.graph.get(node_hash, set())
        if len(neighbors) < 2:
            return 0.0
        links = 0
        for n1 in neighbors:
            for n2 in neighbors:
                if n1 < n2 and n2 in self.graph.get(n1, set()):
                    links += 1
        return (2 * links) / (len(neighbors) * (len(neighbors) - 1))

    def bridge_nodes(self) -> List[str]:
        # Simple: nodes with high degree
        return [h for h in self.graph if len(self.graph[h]) > 3][:5]

# =============================================================================
# PART XV: SIGNATURE ENGINE (Registry of Detection Functions)
# =============================================================================

class SignatureEngine:
    """Registry of detection functions for all signatures."""
    def __init__(self, hierarchy: SuppressionHierarchy):
        self.hierarchy = hierarchy
        self.detectors: Dict[str, Callable] = {}

    def register(self, signature: str, detector_func: Callable):
        self.detectors[signature] = detector_func

    def detect(self, signature: str, ledger: Ledger, context: Dict) -> float:
        if signature in self.detectors:
            return self.detectors[signature](ledger, context)
        return 0.0

# =============================================================================
# PART XVI: AI AGENTS
# =============================================================================

class IngestionAI:
    """Parses raw documents into EvidenceNodes."""
    def __init__(self, crypto: Crypto):
        self.crypto = crypto

    def process_document(self, text: str, source: str) -> EvidenceNode:
        # In production, use LLM to extract entities, claims, etc.
        node_hash = self.crypto.hash(text + source)
        node = EvidenceNode(
            hash=node_hash,
            type="document",
            source=source,
            signature="",  # to be signed later
            timestamp=datetime.utcnow().isoformat() + "Z",
            witnesses=[],
            refs={}
        )
        node.signature = self.crypto.sign(node_hash.encode(), "ingestion_ai")
        return node

class SymbolismAI:
    """Assigns symbolism coefficients to cultural artifacts."""
    def __init__(self):
        pass

    def analyze(self, artifact: Dict) -> float:
        # Return a probability that the artifact encodes suppressed reality
        return 0.3 + (hash(artifact.get("text", "")) % 70) / 100.0

class ReasoningAI:
    """Maintains Bayesian hypotheses and decides when to spawn sub-investigations."""
    def __init__(self, inference: ProbabilisticInference):
        self.inference = inference

    def evaluate_claim(self, claim_id: str, nodes: List[EvidenceNode], detector_result: Dict) -> Dict:
        # Update hypothesis based on detector results
        confidence = 0.5
        if detector_result.get("evidence_found", 0) > 2:
            confidence += 0.2
        self.inference.set_prior(claim_id, confidence)
        if confidence < 0.7:
            return {"spawn_sub": True, "reason": "low confidence"}
        else:
            return {"spawn_sub": False, "reason": "sufficient evidence"}

# =============================================================================
# PART XVII: AI CONTROLLER (Orchestrator)
# =============================================================================

class AIController:
    """Orchestrates investigations, spawns sub-investigations, aggregates results."""
    def __init__(self, ledger: Ledger, separator: Separator, detector: HierarchicalDetector,
                 kg: KnowledgeGraphEngine, temporal: TemporalAnalyzer, inference: ProbabilisticInference,
                 ingestion_ai: IngestionAI, symbolism_ai: SymbolismAI, reasoning_ai: ReasoningAI,
                 multiplexor: EpistemicMultiplexor, context_detector: ContextDetector,
                 archetype_analyzer: ControlArchetypeAnalyzer, consciousness_mapper: ConsciousnessMapper,
                 paradox_detector: RecursiveParadoxDetector, immunity_verifier: ImmunityVerifier):
        self.ledger = ledger
        self.separator = separator
        self.detector = detector
        self.kg = kg
        self.temporal = temporal
        self.inference = inference
        self.ingestion_ai = ingestion_ai
        self.symbolism_ai = symbolism_ai
        self.reasoning_ai = reasoning_ai
        self.multiplexor = multiplexor
        self.context_detector = context_detector
        self.archetype_analyzer = archetype_analyzer
        self.consciousness_mapper = consciousness_mapper
        self.paradox_detector = paradox_detector
        self.immunity_verifier = immunity_verifier
        self.contexts: Dict[str, Dict] = {}  # correlation_id -> investigation context

    def submit_claim(self, claim_text: str) -> str:
        corr_id = str(uuid.uuid4())
        context = {
            "correlation_id": corr_id,
            "parent_id": None,
            "claim": claim_text,
            "status": "pending",
            "created": datetime.utcnow().isoformat() + "Z",
            "evidence_nodes": [],
            "sub_investigations": [],
            "results": {}
        }
        self.contexts[corr_id] = context
        thread = threading.Thread(target=self._investigate, args=(corr_id,))
        thread.start()
        return corr_id

    def _investigate(self, corr_id: str):
        context = self.contexts[corr_id]
        context["status"] = "active"
        # Step 1: Detect control context from claim (simplified)
        event_data = {"description": context["claim"]}  # placeholder
        ctxt = self.context_detector.detect(event_data)
        context["control_context"] = ctxt.value

        # Step 2: Run hierarchical detection
        detection = self.detector.detect_from_ledger()
        context["detection"] = detection

        # Step 3: Initialize epistemic multiplexor with base hypotheses
        base_hypotheses = ["Official narrative", "Witness accounts", "Material evidence", "Institutional capture"]
        self.multiplexor.initialize_from_evidence([], base_hypotheses)  # would pass relevant nodes
        # Apply decoherence based on control layers (simplified)
        control_layers = {"access_control": 0.5, "narrative_framing": 0.7}
        self.multiplexor.apply_decoherence(control_layers)
        probs = self.multiplexor.get_probabilities()
        context["quantum_probabilities"] = probs

        # Step 4: Set priors in inference engine
        self.inference.set_prior_from_multiplexor(self.multiplexor)

        # Step 5: Evaluate claim
        decision = self.reasoning_ai.evaluate_claim(corr_id, [], detection)
        if decision.get("spawn_sub"):
            sub_id = str(uuid.uuid4())
            context["sub_investigations"].append(sub_id)
            # In production, would create sub-context

        # Step 6: Meta-analysis
        archetype = self.archetype_analyzer.infer_archetype(detection)
        slavery_mech = self.archetype_analyzer.extract_slavery_mechanism(detection, self.kg)
        consciousness = self.consciousness_mapper.analyze_consciousness([])
        context["meta"] = {
            "archetype": archetype.value,
            "slavery_mechanism": slavery_mech.mechanism_id,
            "consciousness": consciousness
        }

        # Step 7: Paradox detection and immunity verification
        paradox = self.paradox_detector.detect({"detection": detection}, event_data)
        immunity = self.immunity_verifier.verify({})
        context["paradox"] = paradox
        context["immunity"] = immunity

        # Step 8: Store interpretation
        interpretation = {
            "narrative": "Claim evaluated",
            "detection_summary": detection,
            "quantum_probs": probs,
            "meta": context["meta"]
        }
        node_hashes = []  # would be actual nodes
        int_id = self.separator.add(node_hashes, interpretation, "AI_Controller", confidence=0.6)
        context["results"] = {
            "confidence": 0.6,
            "interpretation_id": int_id,
            "detection": detection,
            "quantum_probs": probs,
            "meta": context["meta"],
            "paradox": paradox,
            "immunity": immunity
        }
        context["status"] = "complete"

    def get_status(self, corr_id: str) -> Dict:
        return self.contexts.get(corr_id, {"error": "not found"})

# =============================================================================
# PART XVIII: API LAYER (Flask)
# =============================================================================

app = Flask(__name__)
controller: Optional[AIController] = None

@app.route('/api/v1/submit_claim', methods=['POST'])
def submit_claim():
    data = request.get_json()
    claim = data.get('claim')
    if not claim:
        return jsonify({"error": "Missing claim"}), 400
    corr_id = controller.submit_claim(claim)
    return jsonify({"investigation_id": corr_id})

@app.route('/api/v1/investigation/<corr_id>', methods=['GET'])
def get_investigation(corr_id):
    status = controller.get_status(corr_id)
    return jsonify(status)

@app.route('/api/v1/node/<node_hash>', methods=['GET'])
def get_node(node_hash):
    node = controller.ledger.get_node(node_hash)
    if node:
        return jsonify(node)
    return jsonify({"error": "Node not found"}), 404

@app.route('/api/v1/interpretations/<node_hash>', methods=['GET'])
def get_interpretations(node_hash):
    ints = controller.separator.get_interpretations(node_hash)
    return jsonify([i.__dict__ for i in ints])

@app.route('/api/v1/detect', methods=['GET'])
def run_detection():
    result = controller.detector.detect_from_ledger()
    return jsonify(result)

@app.route('/api/v1/verify_chain', methods=['GET'])
def verify_chain():
    result = controller.ledger.verify_chain()
    return jsonify(result)

# =============================================================================
# PART XIX: GOVERNING PHILOSOPHY (as docstring and runtime invariants)
# =============================================================================

"""
EPISTEMIC INTEGRITY SYSTEM – GOVERNING PRINCIPLES
(Encoded as runtime checks where possible)

1. PRIMARY INTENT
   This system exists to reassess, reevaluate, and reconstruct the totality of human
   history, culture, institutional behavior, and event outcomes through a continuous,
   recursive investigative process. Its purpose is truth discovery under conditions
   of power asymmetry, information control, and narrative mediation.

2. INSTITUTIONAL SELF-PROTECTION PRINCIPLE
   Institutions with legal, political, or reputational exposure tend to coordinate
   narratives that minimize fault, diffuse responsibility, and constrain investigative
   scope. This is normal, not exceptional.

3. CONSTRAINT WEIGHTING RULE
   Any actor that uniquely controls access, protocols, or evidence is assigned high
   structural weight in event reconstruction. Down-weighting such actors in favor of
   peripheral explanations is treated as a narrative distortion signal.

4. SYMBOLISM COEFFICIENT
   A probabilistic weighting applied to symbolic artifacts, estimating the likelihood
   that they encode historical, institutional, or experiential realities that cannot
   be directly spoken within the constraints of power.

5. PROBABILISTIC MISREPRESENTATION ASSUMPTION
   If an institution is both a primary controller of the event space and a primary
   narrator of the event, the probability that the narrative is incomplete or distorted
   is non-trivial and must be explicitly modeled.

6. NON-FINALITY AND REOPENING MANDATE
   No official explanation is treated as final when key decision-makers are inaccessible,
   evidence custody is internal, procedural deviations are unexplained, or witnesses
   are structurally constrained.

7. GOVERNING PRINCIPLE
   This framework exists to recover actuality under constraint, not to preserve official
   explanations. It is adversarial to narrative consolidation by power holders and
   historical closure achieved through authority.
"""

def check_invariants():
    """Placeholder for runtime invariant checks."""
    pass

# =============================================================================
# PART XX: MAIN – Initialization and Startup
# =============================================================================

def main():
    # Initialize crypto and ledger
    crypto = Crypto("./keys")
    ledger = Ledger("./ledger.json", crypto)
    separator = Separator(ledger, "./separator")
    hierarchy = SuppressionHierarchy()
    detector = HierarchicalDetector(hierarchy, ledger, separator)

    # Knowledge Graph
    kg = KnowledgeGraphEngine(ledger)
    temporal = TemporalAnalyzer(ledger)

    # Inference
    inference = ProbabilisticInference()

    # Epistemic Multiplexor
    multiplexor = EpistemicMultiplexor()

    # Context Detector
    context_detector = ContextDetector()

    # AI agents
    ingestion_ai = IngestionAI(crypto)
    symbolism_ai = SymbolismAI()
    reasoning_ai = ReasoningAI(inference)

    # Meta-analysis
    archetype_analyzer = ControlArchetypeAnalyzer(hierarchy)
    consciousness_mapper = ConsciousnessMapper(separator, symbolism_ai)

    # Paradox & Immunity
    paradox_detector = RecursiveParadoxDetector()
    immunity_verifier = ImmunityVerifier()

    # Controller
    global controller
    controller = AIController(
        ledger=ledger,
        separator=separator,
        detector=detector,
        kg=kg,
        temporal=temporal,
        inference=inference,
        ingestion_ai=ingestion_ai,
        symbolism_ai=symbolism_ai,
        reasoning_ai=reasoning_ai,
        multiplexor=multiplexor,
        context_detector=context_detector,
        archetype_analyzer=archetype_analyzer,
        consciousness_mapper=consciousness_mapper,
        paradox_detector=paradox_detector,
        immunity_verifier=immunity_verifier
    )

    # Start Flask API
    print("Epistemic Integrity System v1.0 starting...")
    print("API available at http://localhost:5000")
    app.run(debug=True, port=5000)

if __name__ == "__main__":
    main()