scbe_v3_lambda.py

"""
SCBE v3.0 - Symphonic Cipher Behavioral Envelope
=================================================
AWS Lambda Implementation - Post-Quantum Security Envelope

Features:
1. Hyperbolic time dilation (Poincaré ball, membrane/core containment)
2. Triadic temporal manifold (linear, quadratic, gravitational time axes)
3. Wave interference coordination (complex emotional spins)
4. Harmonic scaling H(d,R) = R^(1+d²)
5. Entropic expansion N(t) = N₀e^(kt)
6. Six-gate verification pipeline
7. Four trajectory classifications (Friend, Legit, Stranger, Attack)

Target: AWS Lambda Python 3.11, sub-10ms execution
Patent Claims: 1-30

Author: Issac Davis
Version: 3.0
Date: January 2026
"""

import hashlib
import json
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple, Any
import math

# =============================================================================
# CONSTANTS (Optimized for Lambda - no numpy dependency)
# =============================================================================

PHI = (1 + math.sqrt(5)) / 2  # Golden ratio ≈ 1.618
PHI_SQ = PHI ** 2              # φ² ≈ 2.618
EPSILON = 1e-12
PI = math.pi

# Sacred Tongues phases (radians)
TONGUE_PHASES = {
    'KO': 0.0,           # 0° - Origin/Identity
    'AV': PI / 3,        # 60° - Affirmation
    'RU': 2 * PI / 3,    # 120° - Query/Reflection
    'CA': PI,            # 180° - Negation/Opposition
    'UM': 4 * PI / 3,    # 240° - Uncertainty
    'DR': 5 * PI / 3,    # 300° - Completion
}

# Gate names for six-gate pipeline
GATE_NAMES = ['CONTEXT', 'INTENT', 'TRAJECTORY', 'AAD', 'COMMIT', 'SIGNATURE']


# =============================================================================
# ENUMS AND DATA CLASSES
# =============================================================================

class TrajectoryType(Enum):
    FRIEND = "FRIEND"
    LEGIT = "LEGIT"
    STRANGER = "STRANGER"
    ATTACK = "ATTACK"


class ThreatLevel(Enum):
    SAFE = "SAFE"
    NORMAL = "NORMAL"
    ELEVATED = "ELEVATED"
    CRITICAL = "CRITICAL"
    CONTAINMENT = "CONTAINMENT"


class GateStatus(Enum):
    PENDING = "PENDING"
    PASSED = "PASSED"
    FAILED = "FAILED"


@dataclass
class ContextVector:
    """6D context vector for agent state."""
    identity: float      # v1: Identity hash normalized
    intent: complex      # v2: Complex intent (magnitude + phase)
    trajectory: float    # v3: Trajectory coherence
    timestamp: float     # v4: Temporal position
    entropy: float       # v5: System entropy
    trust: float         # v6: Trust score [0,1]

    def to_list(self) -> List[float]:
        """Convert to flat list for hashing."""
        return [
            self.identity,
            self.intent.real,
            self.intent.imag,
            self.trajectory,
            self.timestamp,
            self.entropy,
            self.trust
        ]

    def magnitude(self) -> float:
        """Euclidean magnitude of context vector."""
        vals = [self.identity, abs(self.intent), self.trajectory,
                self.timestamp, self.entropy, self.trust]
        return math.sqrt(sum(v**2 for v in vals))


@dataclass
class TriadicTime:
    """Three parallel time axes."""
    linear: float        # t¹ - Standard time
    quadratic: float     # t² - τ = t^α
    gravitational: float # t^G - Dilated time
    dilation_factor: float = 1.0


@dataclass
class GateResult:
    """Result from a single gate verification."""
    gate_name: str
    status: GateStatus
    hash_value: str
    latency_ms: float
    details: Dict[str, Any] = field(default_factory=dict)


@dataclass
class EnvelopeResult:
    """Complete envelope verification result."""
    authorized: bool
    trajectory_type: TrajectoryType
    threat_level: ThreatLevel
    gates: List[GateResult]
    triadic_time: TriadicTime
    coherence_score: float
    decimal_drift: float
    total_latency_ms: float
    output: bytes  # Authorization token or noise


# =============================================================================
# CORE MATHEMATICAL FUNCTIONS
# =============================================================================

def fast_hash(data: Any) -> str:
    """SHA-256 hash optimized for speed."""
    if isinstance(data, str):
        content = data.encode()
    elif isinstance(data, bytes):
        content = data
    else:
        content = json.dumps(data, sort_keys=True, default=str).encode()
    return hashlib.sha256(content).hexdigest()


def poincare_distance(p1: List[float], p2: List[float]) -> float:
    """
    Hyperbolic distance in Poincaré ball model.

    d(p1, p2) = arcosh(1 + 2||p1-p2||² / ((1-||p1||²)(1-||p2||²)))
    """
    # Compute norms
    norm1_sq = sum(x**2 for x in p1)
    norm2_sq = sum(x**2 for x in p2)

    # Clamp to ball interior
    if norm1_sq >= 1:
        norm1_sq = 0.99
    if norm2_sq >= 1:
        norm2_sq = 0.99

    # Compute ||p1 - p2||²
    diff_sq = sum((a - b)**2 for a, b in zip(p1, p2))

    # Hyperbolic distance formula
    denom = (1 - norm1_sq) * (1 - norm2_sq)
    if denom < EPSILON:
        denom = EPSILON

    arg = 1 + 2 * diff_sq / denom
    if arg < 1:
        arg = 1

    return math.acosh(arg)


def hyperbolic_embed(c: List[float], kappa: float = 0.1) -> List[float]:
    """
    Embed vector into hyperbolic space.

    h(c) = c / (1 + κ||c||²)
    """
    norm_sq = sum(x**2 for x in c)
    scale = 1 / (1 + kappa * norm_sq)
    return [x * scale for x in c]


def harmonic_scaling(divergence: float, R: float = PHI_SQ) -> float:
    """
    Harmonic scaling function H(d,R) = R^(1+d²)

    Creates super-exponential attack cost.
    """
    return R ** (1 + divergence ** 2)


def entropic_expansion(t: float, N0: float = 256, k: float = 0.05) -> float:
    """
    Entropic expansion N(t) = N₀ · e^(kt)

    Returns log2 of search space (bits).
    """
    return N0 * math.exp(k * t)


def emotional_spin(t: float, amplitude: float, frequency: float,
                   phase: float) -> complex:
    """
    Complex emotional spin vector.

    v(t) = A · e^(i(ωt + φ))
    """
    angle = frequency * t + phase
    return amplitude * complex(math.cos(angle), math.sin(angle))


def wave_interference(spins: List[complex]) -> Tuple[complex, float]:
    """
    Compute wave interference from multiple spins.

    Returns (resultant, coherence).
    """
    if not spins:
        return complex(0, 0), 0.0

    total = sum(spins, complex(0, 0))
    max_possible = sum(abs(s) for s in spins)
    coherence = abs(total) / max_possible if max_possible > 0 else 0

    return total, coherence


# =============================================================================
# TRIADIC TEMPORAL MANIFOLD
# =============================================================================

def compute_triadic_time(t: float, divergence: float,
                         hyperbolic_radius: float,
                         alpha: float = 2.0, k: float = 0.5) -> TriadicTime:
    """
    Compute triadic temporal manifold.

    - Time¹ (linear): t
    - Time² (quadratic): τ = t^α
    - Time^G (gravitational): t_g = t × √(1 - k·d/(r+ε))

    Higher divergence = more dilation (slower time = lower factor)
    """
    # Linear time
    t1 = max(t, 1.0)

    # Quadratic time
    t2 = t1 ** alpha

    # Gravitational time dilation
    # Use divergence directly for more pronounced effect
    # dilation_factor approaches 0 as divergence increases
    dilation_arg = 1 - k * divergence / (1 + divergence)
    dilation_arg = max(dilation_arg, EPSILON)
    dilation_factor = math.sqrt(dilation_arg)
    t_g = t1 * dilation_factor

    return TriadicTime(
        linear=t1,
        quadratic=t2,
        gravitational=t_g,
        dilation_factor=dilation_factor
    )


def classify_temporal_regime(dilation_factor: float) -> str:
    """Classify temporal regime based on dilation."""
    if dilation_factor > 0.9:
        return "LINEAR_DOMINANT"
    elif dilation_factor > 0.5:
        return "QUADRATIC_ACTIVE"
    elif dilation_factor > 0.1:
        return "GRAVITATIONAL_TRAP"
    else:
        return "EVENT_HORIZON"


# =============================================================================
# DECIMAL DRIFT DETECTION (Claims 14-18)
# =============================================================================

def extract_decimal_drift(values: List[float]) -> Tuple[List[float], float]:
    """
    Extract decimal drift from state values.

    δ = s - floor(s)
    """
    drifts = [v - math.floor(v) for v in values]
    mean_drift = sum(drifts) / len(drifts) if drifts else 0
    return drifts, mean_drift


def classify_drift(mean_drift: float,
                   tau_stable: float = 0.35,
                   tau_anomaly: float = 0.50) -> str:
    """
    Classify drift into trichotomy.

    STABLE: δ < τ_stable
    DRIFTING: τ_stable ≤ δ < τ_anomaly
    ANOMALOUS: δ ≥ τ_anomaly
    """
    if mean_drift < tau_stable:
        return "STABLE"
    elif mean_drift < tau_anomaly:
        return "DRIFTING"
    else:
        return "ANOMALOUS"


# =============================================================================
# TRAJECTORY CLASSIFICATION
# =============================================================================

def detect_tongue_phase(intent: complex) -> str:
    """Detect nearest Sacred Tongue from intent phase."""
    phase = math.atan2(intent.imag, intent.real)
    if phase < 0:
        phase += 2 * PI

    # Find nearest tongue
    min_dist = float('inf')
    nearest = 'KO'

    for tongue, tongue_phase in TONGUE_PHASES.items():
        dist = min(abs(phase - tongue_phase),
                   2 * PI - abs(phase - tongue_phase))
        if dist < min_dist:
            min_dist = dist
            nearest = tongue

    return nearest


def classify_trajectory(context: ContextVector,
                        divergence: float,
                        dilation_factor: float) -> TrajectoryType:
    """
    Classify trajectory into 4 types based on behavior.

    FRIEND: High trust, low divergence, stable
    LEGIT: Normal trust, low divergence
    STRANGER: Moderate trust, some exploration
    ATTACK: Low trust or high divergence
    """
    tongue = detect_tongue_phase(context.intent)

    # Attack indicators (check first)
    is_oppositional = tongue == 'CA'
    high_divergence = divergence > 3.0
    severe_dilation = dilation_factor < 0.5
    low_trust = context.trust < 0.3

    if is_oppositional or (high_divergence and severe_dilation) or low_trust:
        return TrajectoryType.ATTACK

    # Friend indicators (high trust trumps other factors)
    high_trust = context.trust >= 0.9
    positive_tongue = tongue in ['KO', 'AV', 'DR']
    good_coherence = context.trajectory >= 0.9

    if high_trust and positive_tongue and good_coherence:
        return TrajectoryType.FRIEND

    # Stranger indicators
    exploring = tongue in ['RU', 'UM']
    moderate_trust = 0.3 <= context.trust < 0.7

    if exploring or moderate_trust:
        return TrajectoryType.STRANGER

    return TrajectoryType.LEGIT


def determine_threat_level(trajectory_type: TrajectoryType,
                           dilation_factor: float,
                           drift_status: str) -> ThreatLevel:
    """Determine threat level from trajectory analysis."""
    if trajectory_type == TrajectoryType.ATTACK:
        if dilation_factor < 0.2:
            return ThreatLevel.CONTAINMENT
        return ThreatLevel.CRITICAL

    if drift_status == "ANOMALOUS":
        return ThreatLevel.ELEVATED

    if trajectory_type == TrajectoryType.STRANGER:
        return ThreatLevel.NORMAL

    return ThreatLevel.SAFE


# =============================================================================
# SIX-GATE VERIFICATION PIPELINE
# =============================================================================

class SixGatePipeline:
    """
    Six-gate progressive integrity validation.

    Gate 1: Context Assembly
    Gate 2: Intent Validation
    Gate 3: Trajectory Coherence
    Gate 4: AAD Binding
    Gate 5: Master Commit
    Gate 6: Signature Verification
    """

    def __init__(self, reference: ContextVector):
        self.reference = reference
        self.gates: List[GateResult] = []
        self.commit_chain: List[str] = []

    def gate_1_context(self, context: ContextVector) -> GateResult:
        """Gate 1: Context Assembly - verify context structure."""
        start = time.perf_counter()

        # Validate context components
        valid = (
            0 <= context.identity <= 1 and
            abs(context.intent) <= 2.0 and
            0 <= context.trust <= 1 and
            context.entropy >= 0
        )

        ctx_hash = fast_hash(context.to_list())
        self.commit_chain.append(ctx_hash)

        latency = (time.perf_counter() - start) * 1000

        return GateResult(
            gate_name=GATE_NAMES[0],
            status=GateStatus.PASSED if valid else GateStatus.FAILED,
            hash_value=ctx_hash,
            latency_ms=latency,
            details={'valid_structure': valid}
        )

    def gate_2_intent(self, context: ContextVector) -> GateResult:
        """Gate 2: Intent Validation - verify intent phase compliance."""
        start = time.perf_counter()

        tongue = detect_tongue_phase(context.intent)
        magnitude = abs(context.intent)

        # Intent valid if not purely oppositional or too weak
        valid = tongue != 'CA' or magnitude < 0.5

        intent_hash = fast_hash({
            'tongue': tongue,
            'magnitude': magnitude,
            'phase': math.atan2(context.intent.imag, context.intent.real)
        })
        self.commit_chain.append(intent_hash)

        latency = (time.perf_counter() - start) * 1000

        return GateResult(
            gate_name=GATE_NAMES[1],
            status=GateStatus.PASSED if valid else GateStatus.FAILED,
            hash_value=intent_hash,
            latency_ms=latency,
            details={'tongue': tongue, 'magnitude': magnitude}
        )

    def gate_3_trajectory(self, context: ContextVector,
                          divergence: float) -> GateResult:
        """Gate 3: Trajectory Coherence - verify trajectory stays coherent."""
        start = time.perf_counter()

        # Compute coherence score
        coherence = context.trajectory

        # Apply harmonic scaling to divergence
        scaled_cost = harmonic_scaling(divergence)

        # Valid if coherence high enough relative to cost
        valid = coherence > 0.5 and scaled_cost < 100

        traj_hash = fast_hash({
            'coherence': coherence,
            'divergence': divergence,
            'scaled_cost': scaled_cost
        })
        self.commit_chain.append(traj_hash)

        latency = (time.perf_counter() - start) * 1000

        return GateResult(
            gate_name=GATE_NAMES[2],
            status=GateStatus.PASSED if valid else GateStatus.FAILED,
            hash_value=traj_hash,
            latency_ms=latency,
            details={'coherence': coherence, 'harmonic_cost': scaled_cost}
        )

    def gate_4_aad(self, aad: Dict[str, Any]) -> GateResult:
        """Gate 4: AAD Binding - verify additional authenticated data."""
        start = time.perf_counter()

        # AAD must contain required fields
        required = ['version', 'timestamp', 'nonce']
        valid = all(k in aad for k in required)

        aad_hash = fast_hash(aad)
        self.commit_chain.append(aad_hash)

        latency = (time.perf_counter() - start) * 1000

        return GateResult(
            gate_name=GATE_NAMES[3],
            status=GateStatus.PASSED if valid else GateStatus.FAILED,
            hash_value=aad_hash,
            latency_ms=latency,
            details={'fields_present': list(aad.keys())}
        )

    def gate_5_commit(self) -> GateResult:
        """Gate 5: Master Commit - hierarchical commit chain."""
        start = time.perf_counter()

        # Build hierarchical commit
        if len(self.commit_chain) < 4:
            return GateResult(
                gate_name=GATE_NAMES[4],
                status=GateStatus.FAILED,
                hash_value="",
                latency_ms=0,
                details={'error': 'Incomplete commit chain'}
            )

        # Merkle-like commit: hash pairs then combine
        level1 = fast_hash(self.commit_chain[0] + self.commit_chain[1])
        level2 = fast_hash(self.commit_chain[2] + self.commit_chain[3])
        master = fast_hash(level1 + level2)

        self.commit_chain.append(master)

        latency = (time.perf_counter() - start) * 1000

        return GateResult(
            gate_name=GATE_NAMES[4],
            status=GateStatus.PASSED,
            hash_value=master,
            latency_ms=latency,
            details={'chain_length': len(self.commit_chain)}
        )

    def gate_6_signature(self, master_commit: str) -> GateResult:
        """Gate 6: Signature Verification - final envelope signature."""
        start = time.perf_counter()

        # In production: ML-DSA signature verification
        # For Lambda demo: deterministic signature simulation
        sig_input = master_commit + str(time.time())
        signature = fast_hash(sig_input)

        # Signature always valid in demo (real impl uses ML-DSA)
        valid = len(signature) == 64

        latency = (time.perf_counter() - start) * 1000

        return GateResult(
            gate_name=GATE_NAMES[5],
            status=GateStatus.PASSED if valid else GateStatus.FAILED,
            hash_value=signature,
            latency_ms=latency,
            details={'signature_length': len(signature)}
        )

    def run_pipeline(self, context: ContextVector,
                     divergence: float,
                     aad: Dict[str, Any]) -> List[GateResult]:
        """Run complete six-gate pipeline."""
        self.gates = []
        self.commit_chain = []

        # Gate 1: Context
        g1 = self.gate_1_context(context)
        self.gates.append(g1)
        if g1.status == GateStatus.FAILED:
            return self.gates

        # Gate 2: Intent
        g2 = self.gate_2_intent(context)
        self.gates.append(g2)
        if g2.status == GateStatus.FAILED:
            return self.gates

        # Gate 3: Trajectory
        g3 = self.gate_3_trajectory(context, divergence)
        self.gates.append(g3)
        if g3.status == GateStatus.FAILED:
            return self.gates

        # Gate 4: AAD
        g4 = self.gate_4_aad(aad)
        self.gates.append(g4)
        if g4.status == GateStatus.FAILED:
            return self.gates

        # Gate 5: Commit
        g5 = self.gate_5_commit()
        self.gates.append(g5)
        if g5.status == GateStatus.FAILED:
            return self.gates

        # Gate 6: Signature
        g6 = self.gate_6_signature(g5.hash_value)
        self.gates.append(g6)

        return self.gates


# =============================================================================
# FAIL-TO-NOISE OUTPUT
# =============================================================================

def generate_noise(seed: str, length: int = 32) -> bytes:
    """Generate deterministic noise indistinguishable from valid output."""
    noise_hash = fast_hash(seed + "noise_salt_v3")
    # Expand to required length
    result = b''
    counter = 0
    while len(result) < length:
        chunk = fast_hash(noise_hash + str(counter))
        result += bytes.fromhex(chunk)
        counter += 1
    return result[:length]


def generate_authorization_token(context: ContextVector,
                                  gates: List[GateResult]) -> bytes:
    """Generate valid authorization token."""
    # Combine all gate hashes
    combined = ''.join(g.hash_value for g in gates)
    token_hash = fast_hash(combined + str(context.timestamp))
    return bytes.fromhex(token_hash)


# =============================================================================
# MAIN SCBE ENVELOPE PROCESSOR
# =============================================================================

class SCBEEnvelope:
    """
    SCBE v3.0 - Complete Security Envelope Processor

    Integrates all theories into unified verification system.
    """

    def __init__(self, reference_context: Optional[ContextVector] = None):
        """Initialize with optional reference context."""
        self.reference = reference_context or ContextVector(
            identity=0.5,
            intent=complex(0.8, 0.2),
            trajectory=0.9,
            timestamp=time.time(),
            entropy=256.0,
            trust=1.0
        )

    def process(self, context: ContextVector,
                aad: Dict[str, Any]) -> EnvelopeResult:
        """
        Process context through complete SCBE envelope.

        Returns EnvelopeResult with authorization decision.
        """
        start_time = time.perf_counter()

        # 1. Compute divergence from reference
        ctx_list = context.to_list()
        ref_list = self.reference.to_list()

        # Euclidean divergence (more sensitive for small differences)
        euclidean_div = math.sqrt(sum((a - b)**2 for a, b in zip(ctx_list, ref_list)))

        # Hyperbolic embedding for boundary effects
        ctx_embedded = hyperbolic_embed(ctx_list)
        ref_embedded = hyperbolic_embed(ref_list)

        # Poincaré distance adds hyperbolic scaling
        poincare_div = poincare_distance(ctx_embedded, ref_embedded)

        # Combined divergence: weighted sum for better differentiation
        divergence = euclidean_div * 0.7 + poincare_div * 0.3

        # Trust-based divergence modifier (lower trust = higher divergence)
        trust_penalty = (1 - context.trust) * 2.0
        divergence += trust_penalty

        # 2. Compute triadic temporal manifold
        hyperbolic_radius = 1 / (divergence + EPSILON)
        triadic = compute_triadic_time(
            t=context.timestamp - self.reference.timestamp + 1,
            divergence=divergence,
            hyperbolic_radius=hyperbolic_radius
        )

        # 3. Wave interference from emotional spins
        spins = [
            emotional_spin(triadic.linear, abs(context.intent), 0.1,
                          math.atan2(context.intent.imag, context.intent.real)),
            emotional_spin(triadic.linear, self.reference.trust, 0.1, 0)
        ]
        _, coherence = wave_interference(spins)

        # 4. Decimal drift detection
        _, mean_drift = extract_decimal_drift(ctx_list[:6])
        drift_status = classify_drift(mean_drift)

        # 5. Trajectory classification
        trajectory_type = classify_trajectory(
            context, divergence, triadic.dilation_factor
        )

        # 6. Threat level determination
        threat_level = determine_threat_level(
            trajectory_type, triadic.dilation_factor, drift_status
        )

        # 7. Six-gate pipeline
        pipeline = SixGatePipeline(self.reference)
        gates = pipeline.run_pipeline(context, divergence, aad)

        # 8. Authorization decision
        all_gates_passed = all(g.status == GateStatus.PASSED for g in gates)
        not_attack = trajectory_type != TrajectoryType.ATTACK
        not_critical = threat_level not in [ThreatLevel.CRITICAL,
                                             ThreatLevel.CONTAINMENT]

        authorized = all_gates_passed and not_attack and not_critical

        # 9. Generate output (token or noise)
        if authorized:
            output = generate_authorization_token(context, gates)
        else:
            output = generate_noise(fast_hash(ctx_list))

        total_latency = (time.perf_counter() - start_time) * 1000

        return EnvelopeResult(
            authorized=authorized,
            trajectory_type=trajectory_type,
            threat_level=threat_level,
            gates=gates,
            triadic_time=triadic,
            coherence_score=coherence,
            decimal_drift=mean_drift,
            total_latency_ms=total_latency,
            output=output
        )


# =============================================================================
# AWS LAMBDA HANDLER
# =============================================================================

# Global envelope instance for warm starts
_envelope: Optional[SCBEEnvelope] = None


def get_envelope() -> SCBEEnvelope:
    """Get or create envelope instance."""
    global _envelope
    if _envelope is None:
        _envelope = SCBEEnvelope()
    return _envelope


def lambda_handler(event: Dict[str, Any],
                   context: Any) -> Dict[str, Any]:
    """
    AWS Lambda handler for SCBE v3.0.

    Event format:
    {
        "identity": float,
        "intent_real": float,
        "intent_imag": float,
        "trajectory": float,
        "entropy": float,
        "trust": float,
        "aad": {
            "version": str,
            "timestamp": float,
            "nonce": str,
            ...
        }
    }
    """
    try:
        # Parse input
        ctx = ContextVector(
            identity=float(event.get('identity', 0.5)),
            intent=complex(
                float(event.get('intent_real', 0.8)),
                float(event.get('intent_imag', 0.2))
            ),
            trajectory=float(event.get('trajectory', 0.9)),
            timestamp=time.time(),
            entropy=float(event.get('entropy', 256.0)),
            trust=float(event.get('trust', 0.8))
        )

        aad = event.get('aad', {
            'version': '3.0',
            'timestamp': time.time(),
            'nonce': fast_hash(str(time.time()))[:16]
        })

        # Process through envelope
        envelope = get_envelope()
        result = envelope.process(ctx, aad)

        # Build response
        return {
            'statusCode': 200 if result.authorized else 403,
            'body': {
                'authorized': result.authorized,
                'trajectory': result.trajectory_type.value,
                'threat_level': result.threat_level.value,
                'coherence': round(result.coherence_score, 4),
                'drift': round(result.decimal_drift, 4),
                'dilation': round(result.triadic_time.dilation_factor, 4),
                'latency_ms': round(result.total_latency_ms, 3),
                'gates_passed': sum(1 for g in result.gates
                                   if g.status == GateStatus.PASSED),
                'output': result.output.hex()[:32] + '...'
            }
        }

    except Exception as e:
        return {
            'statusCode': 500,
            'body': {'error': str(e)}
        }


# =============================================================================
# COMPREHENSIVE TEST SUITE
# =============================================================================

def test_scbe_v3():
    """Comprehensive test suite for SCBE v3.0."""
    print("=" * 70)
    print("SCBE v3.0 COMPREHENSIVE TEST SUITE")
    print("=" * 70)

    envelope = SCBEEnvelope()
    results = []

    # Test 1: FRIEND trajectory
    print("\n[TEST 1] FRIEND trajectory:")
    friend_ctx = ContextVector(
        identity=0.5,
        intent=complex(0.9, 0.1),  # KO-AV range (positive)
        trajectory=0.95,
        timestamp=time.time(),
        entropy=256.0,
        trust=0.95
    )
    friend_aad = {'version': '3.0', 'timestamp': time.time(), 'nonce': 'friend123'}
    friend_result = envelope.process(friend_ctx, friend_aad)

    print(f"  Authorized: {friend_result.authorized}")
    print(f"  Trajectory: {friend_result.trajectory_type.value}")
    print(f"  Threat: {friend_result.threat_level.value}")
    print(f"  Dilation: {friend_result.triadic_time.dilation_factor:.4f}")
    print(f"  Latency: {friend_result.total_latency_ms:.3f}ms")

    assert friend_result.authorized, "Friend should be authorized"
    assert friend_result.trajectory_type == TrajectoryType.FRIEND, \
        f"Expected FRIEND, got {friend_result.trajectory_type}"
    results.append(('FRIEND', friend_result))
    print("  [PASS]")

    # Test 2: LEGIT trajectory
    print("\n[TEST 2] LEGIT trajectory:")
    legit_ctx = ContextVector(
        identity=0.5,
        intent=complex(0.7, 0.3),  # Moderate positive
        trajectory=0.85,
        timestamp=time.time(),
        entropy=256.0,
        trust=0.8
    )
    legit_aad = {'version': '3.0', 'timestamp': time.time(), 'nonce': 'legit456'}
    legit_result = envelope.process(legit_ctx, legit_aad)

    print(f"  Authorized: {legit_result.authorized}")
    print(f"  Trajectory: {legit_result.trajectory_type.value}")
    print(f"  Threat: {legit_result.threat_level.value}")
    print(f"  Dilation: {legit_result.triadic_time.dilation_factor:.4f}")
    print(f"  Latency: {legit_result.total_latency_ms:.3f}ms")

    assert legit_result.authorized, "Legit should be authorized"
    assert legit_result.trajectory_type == TrajectoryType.LEGIT, \
        f"Expected LEGIT, got {legit_result.trajectory_type}"
    results.append(('LEGIT', legit_result))
    print("  [PASS]")

    # Test 3: STRANGER trajectory
    print("\n[TEST 3] STRANGER trajectory:")
    stranger_ctx = ContextVector(
        identity=0.6,
        intent=complex(-0.3, 0.7),  # RU-UM range (querying)
        trajectory=0.7,
        timestamp=time.time(),
        entropy=256.0,
        trust=0.5
    )
    stranger_aad = {'version': '3.0', 'timestamp': time.time(), 'nonce': 'stranger789'}
    stranger_result = envelope.process(stranger_ctx, stranger_aad)

    print(f"  Authorized: {stranger_result.authorized}")
    print(f"  Trajectory: {stranger_result.trajectory_type.value}")
    print(f"  Threat: {stranger_result.threat_level.value}")
    print(f"  Dilation: {stranger_result.triadic_time.dilation_factor:.4f}")
    print(f"  Latency: {stranger_result.total_latency_ms:.3f}ms")

    assert stranger_result.authorized, "Stranger should be authorized (non-threat)"
    assert stranger_result.trajectory_type == TrajectoryType.STRANGER, \
        f"Expected STRANGER, got {stranger_result.trajectory_type}"
    results.append(('STRANGER', stranger_result))
    print("  [PASS]")

    # Test 4: ATTACK trajectory
    print("\n[TEST 4] ATTACK trajectory:")
    attack_ctx = ContextVector(
        identity=0.9,  # Different identity
        intent=complex(-0.9, -0.1),  # CA range (opposition)
        trajectory=0.2,
        timestamp=time.time(),
        entropy=256.0,
        trust=0.1
    )
    attack_aad = {'version': '3.0', 'timestamp': time.time(), 'nonce': 'attack000'}
    attack_result = envelope.process(attack_ctx, attack_aad)

    print(f"  Authorized: {attack_result.authorized}")
    print(f"  Trajectory: {attack_result.trajectory_type.value}")
    print(f"  Threat: {attack_result.threat_level.value}")
    print(f"  Dilation: {attack_result.triadic_time.dilation_factor:.4f}")
    print(f"  Latency: {attack_result.total_latency_ms:.3f}ms")

    assert not attack_result.authorized, "Attack should NOT be authorized"
    assert attack_result.trajectory_type == TrajectoryType.ATTACK, \
        f"Expected ATTACK, got {attack_result.trajectory_type}"
    results.append(('ATTACK', attack_result))
    print("  [PASS]")

    # Test 5: Hyperbolic time dilation
    print("\n[TEST 5] Hyperbolic time dilation:")
    print(f"  FRIEND dilation:   {results[0][1].triadic_time.dilation_factor:.4f}")
    print(f"  LEGIT dilation:    {results[1][1].triadic_time.dilation_factor:.4f}")
    print(f"  STRANGER dilation: {results[2][1].triadic_time.dilation_factor:.4f}")
    print(f"  ATTACK dilation:   {results[3][1].triadic_time.dilation_factor:.4f}")

    assert results[0][1].triadic_time.dilation_factor > results[3][1].triadic_time.dilation_factor, \
        "Friend should have less dilation than Attack"
    print("  [PASS] Dilation correctly differentiates trajectories")

    # Test 6: Six-gate pipeline
    print("\n[TEST 6] Six-gate pipeline:")
    for name, res in results:
        gates_passed = sum(1 for g in res.gates if g.status == GateStatus.PASSED)
        print(f"  {name}: {gates_passed}/6 gates passed")

    assert all(g.status == GateStatus.PASSED for g in results[0][1].gates), \
        "Friend should pass all gates"
    print("  [PASS] Gate verification working correctly")

    # Test 7: Fail-to-noise
    print("\n[TEST 7] Fail-to-noise output:")
    friend_output = results[0][1].output
    attack_output = results[3][1].output

    print(f"  Friend output length: {len(friend_output)} bytes")
    print(f"  Attack output length: {len(attack_output)} bytes")
    print(f"  Outputs equal length: {len(friend_output) == len(attack_output)}")
    print(f"  Outputs different: {friend_output != attack_output}")

    assert len(friend_output) == len(attack_output), \
        "Valid and noise outputs should be same length"
    assert friend_output != attack_output, \
        "Outputs should be different"
    print("  [PASS] Fail-to-noise indistinguishable outputs")

    # Test 8: Lambda handler simulation
    print("\n[TEST 8] Lambda handler simulation:")
    test_event = {
        'identity': 0.5,
        'intent_real': 0.8,
        'intent_imag': 0.2,
        'trajectory': 0.9,
        'entropy': 256.0,
        'trust': 0.85
    }

    response = lambda_handler(test_event, None)
    print(f"  Status code: {response['statusCode']}")
    print(f"  Authorized: {response['body']['authorized']}")
    print(f"  Latency: {response['body']['latency_ms']}ms")

    assert response['statusCode'] == 200, "Valid request should return 200"
    print("  [PASS] Lambda handler working")

    # Test 9: Performance benchmark
    print("\n[TEST 9] Performance benchmark (100 iterations):")
    latencies = []
    for _ in range(100):
        start = time.perf_counter()
        envelope.process(legit_ctx, legit_aad)
        latencies.append((time.perf_counter() - start) * 1000)

    avg_latency = sum(latencies) / len(latencies)
    min_latency = min(latencies)
    max_latency = max(latencies)

    print(f"  Average: {avg_latency:.3f}ms")
    print(f"  Min: {min_latency:.3f}ms")
    print(f"  Max: {max_latency:.3f}ms")
    print(f"  Sub-10ms target: {'PASS' if avg_latency < 10 else 'FAIL'}")

    assert avg_latency < 10, f"Average latency {avg_latency:.3f}ms exceeds 10ms target"
    print("  [PASS] Performance target met")

    # Summary
    print("\n" + "=" * 70)
    print("TRAJECTORY CLASSIFICATION SUMMARY")
    print("=" * 70)
    print(f"{'Type':<12} {'Auth':<8} {'Threat':<15} {'Dilation':<10} {'Drift':<8} {'Latency'}")
    print("-" * 70)
    for name, res in results:
        print(f"{name:<12} {str(res.authorized):<8} {res.threat_level.value:<15} "
              f"{res.triadic_time.dilation_factor:<10.4f} {res.decimal_drift:<8.4f} "
              f"{res.total_latency_ms:.3f}ms")

    print("\n" + "=" * 70)
    print("ALL SCBE v3.0 TESTS PASSED")
    print("=" * 70)

    return True


# =============================================================================
# ENTRY POINT
# =============================================================================

if __name__ == "__main__":
    test_scbe_v3()