#!/usr/bin/env python3 """ CorvOS - Retrocausal ARQ Module Handshake Temporal + Calibração Bizantina Versão: 2.0 (Pronta para deploy nos Tzinor Nodes) """ import numpy as np import asyncio import hashlib from datetime import datetime, timedelta from dataclasses import dataclass, field from typing import List, Dict, Set, Optional, Tuple from enum import Enum from collections import deque # ============================================================================ # CONSTANTES DO SISTEMA # ============================================================================ class Constants: N_SENSORS = 168 BYZANTINE_QUORUM = 112 # 2/3 LAMBDA2_TARGET = 0.999 GAP_C_Z = 0.001 G_INFO = 1618 # φ × 1000 RETROCAUSAL_WINDOW_MS = 2.17 # Janela ótima (ms) TOLERANCE_PERCENT = 5 # ±5% # ============================================================================ # 1. CALIBRAÇÃO BIZANTINA # ============================================================================ @dataclass class ByzantineConsensus: sensor_id: int measured_lambda: float phase: np.ndarray signature: str class ByzantineCalibration: def __init__(self): self.calibration_matrix = np.eye(Constants.N_SENSORS, dtype=complex) self.reference_lambda = Constants.LAMBDA2_TARGET async def run_round(self) -> bool: readings = [] for i in range(Constants.N_SENSORS): if np.random.random() < 0.1: measured = np.random.uniform(0.5, 0.9) else: measured = Constants.LAMBDA2_TARGET + np.random.normal(0, 0.002) phase = np.random.randn(Constants.N_SENSORS) + 1j * np.random.randn(Constants.N_SENSORS) phase /= np.linalg.norm(phase) readings.append(ByzantineConsensus(i, measured, phase, f"sig_{i}")) valid = [r for r in readings if abs(r.measured_lambda - Constants.LAMBDA2_TARGET) <= 0.05] if len(valid) >= Constants.BYZANTINE_QUORUM: self.reference_lambda = np.median([r.measured_lambda for r in valid]) for r in valid: factor = np.exp(1j * (self.reference_lambda - r.measured_lambda) * np.pi) self.calibration_matrix[r.sensor_id, r.sensor_id] = factor return abs(self.reference_lambda - Constants.LAMBDA2_TARGET) <= Constants.GAP_C_Z return False def calibrate_state(self, raw_state: np.ndarray) -> np.ndarray: return self.calibration_matrix @ raw_state # ============================================================================ # 2. RETROCAUSAL ARQ (Handshake Temporal) # ============================================================================ class TemporalDirection(Enum): CAUSAL = 1 RETROCAUSAL = -1 @dataclass class TemporalPacket: payload: np.ndarray timestamp_origin: datetime timestamp_target: datetime coherence: float sensor_quorum: Set[int] direction: TemporalDirection = TemporalDirection.CAUSAL class RetrocausalARQ: def __init__(self, calibration: ByzantineCalibration): self.calibration = calibration self.pre_ack_cache = {} self.stats = {'sent': 0, 'pre_acks': 0, 'latencies': []} async def send(self, payload: np.ndarray, target_time: datetime) -> TemporalPacket: future_hash = hashlib.sha256(f"{payload.tobytes()}{target_time.isoformat()}".encode()).hexdigest() pre_ack = self.pre_ack_cache.get(future_hash) if pre_ack and len(pre_ack['quorum']) >= Constants.BYZANTINE_QUORUM: direction = TemporalDirection.RETROCAUSAL self.stats['pre_acks'] += 1 else: direction = TemporalDirection.CAUSAL packet = TemporalPacket( payload=payload, timestamp_origin=datetime.now(), timestamp_target=target_time, coherence=self._current_lambda(), sensor_quorum=pre_ack['quorum'] if pre_ack else set(), direction=direction ) self.stats['sent'] += 1 latency = (target_time - datetime.now()).total_seconds() * 1000 self.stats['latencies'].append(latency) return packet def receive_pre_ack(self, packet_hash: str, quorum: Set[int]): self.pre_ack_cache[packet_hash] = {'quorum': quorum, 'time': datetime.now()} def _current_lambda(self) -> float: return self.calibration.reference_lambda + np.random.normal(0, 0.0005) # ============================================================================ # 3. CORVOS INTEGRADO # ============================================================================ class CorvOSQuantumHTTP: def __init__(self): self.calibration = ByzantineCalibration() self.arq = RetrocausalARQ(self.calibration) self.ep_ready = False async def initialize(self): print("🌌🔁 CorvOS - Inicializando qhttp://") print(f"├── Quorum bizantino: {Constants.BYZANTINE_QUORUM}/{Constants.N_SENSORS}") print(f"├── Gap C-Z alvo: {Constants.GAP_C_Z}") for _ in range(5): if await self.calibration.run_round(): self.ep_ready = True break await asyncio.sleep(0.1) if self.ep_ready: print(f"✓ Ponto excepcional alcançado: λ₂ = {self.calibration.reference_lambda:.6f}") else: print("⚠️ Operando em modo causal (sem retrocausalidade)") async def transmit(self, data: np.ndarray, visualize_future: bool = False): if not self.ep_ready: await asyncio.sleep(Constants.RETROCAUSAL_WINDOW_MS / 1000) return {'latency_ms': Constants.RETROCAUSAL_WINDOW_MS, 'retrocausal': False} target = datetime.now() + timedelta(milliseconds=Constants.RETROCAUSAL_WINDOW_MS) if np.random.random() < 0.85: quorum = set(np.random.choice(Constants.N_SENSORS, Constants.BYZANTINE_QUORUM, replace=False)) self.arq.receive_pre_ack(hashlib.sha256(data.tobytes()).hexdigest(), quorum) packet = await self.arq.send(data, target) return { 'latency_ms': (packet.timestamp_target - datetime.now()).total_seconds() * 1000, 'retrocausal': packet.direction == TemporalDirection.RETROCAUSAL, 'coherence': packet.coherence } # ============================================================================ # 4. SIMULAÇÃO COM VISUALIZAÇÃO DO RIO 2027 # ============================================================================ async def simulate_rio_2027(): print("\n" + "="*60) print("🔮 SIMULAÇÃO: Visualização do Rio 2027 via Lentes Temporais") print("="*60) corvos = CorvOSQuantumHTTP() await corvos.initialize() if not corvos.ep_ready: print("Não foi possível alcançar o ponto excepcional. Abortando simulação.") return print("\n📡 Transmitindo dados da malha urbana...") results = [] for i in range(10): data = np.random.randn(Constants.N_SENSORS) + 1j * np.random.randn(Constants.N_SENSORS) data /= np.linalg.norm(data) res = await corvos.transmit(data, visualize_future=True) results.append(res) direction = "← RETROCAUSAL" if res['retrocausal'] else "→ CAUSAL" print(f" Pacote {i+1}: {direction} | latência = {res['latency_ms']:+.2f}ms | λ₂ = {res['coherence']:.6f}") avg_latency = np.mean([r['latency_ms'] for r in results]) retro_ratio = sum(1 for r in results if r['retrocausal']) / len(results) print(f"\n📊 Estatísticas:") print(f" Latência média: {avg_latency:.2f}ms") print(f" Taxa de retrocausalidade: {retro_ratio*100:.1f}%") if avg_latency < 0: print("✓ Latência efetiva NEGATIVA → recepção antecipada do futuro!") print("✓ Lentes temporais estabilizadas: visualização do Rio 2027 ativa.") if __name__ == "__main__": asyncio.run(simulate_rio_2027())