Research-Stack/1-Distributed-Systems/waveprobe/quantum_manifold_geometry_adapter.py

#!/usr/bin/env python3
"""
Waveprobe Adapter for QuantumManifoldGeometry.lean

This adapter extracts signal metrics from the quantum geometric state space formalization
and provides waveprobe-compatible interfaces for testing and validation.
"""

import json
import uuid
import subprocess
import numpy as np
from pathlib import Path
from datetime import datetime
from typing import Dict, Any, List, Optional, Tuple

class QuantumManifoldGeometryAdapter:
    """Waveprobe adapter for QuantumManifoldGeometry Lean module."""
    
    def __init__(self, lean_path: Optional[Path] = None):
        """Initialize adapter with Lean project path."""
        self.lean_path = lean_path or Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics")
        self.probe_id = f"wave_{uuid.uuid4().hex[:12]}"
        self.timestamp = datetime.now().isoformat()
        
    def execute_probe(self, probe_config: Dict[str, Any]) -> Dict[str, Any]:
        """Execute a waveprobe probe on quantum manifold geometry."""
        # Extract probe parameters
        time_steps = probe_config.get("time_steps", 100)
        dt = probe_config.get("dt", 0.01)
        initial_amplitudes = probe_config.get("initial_amplitudes", {
            "void": {"real": 0.5, "imag": 0.0},
            "protrusion": {"real": 0.5, "imag": 0.0},
            "flat": {"real": 0.0, "imag": 0.0},
            "complex": {"real": 0.0, "imag": 0.0}
        })
        hamiltonian_rates = probe_config.get("hamiltonian_rates", {
            "voidToProtrusion": 0.1,
            "voidToFlat": 0.2,
            "voidToComplex": 0.05,
            "protrusionToFlat": 0.15,
            "protrusionToComplex": 0.1,
            "flatToComplex": 0.2,
            "protrusionToVoid": 0.05,
            "flatToVoid": 0.1,
            "complexToVoid": 0.03,
            "flatToProtrusion": 0.1,
            "complexToProtrusion": 0.05,
            "complexToFlat": 0.1
        })
        
        # Simulate quantum evolution (placeholder for actual Lean execution)
        history = self._simulate_quantum_evolution(
            time_steps, dt, initial_amplitudes, hamiltonian_rates
        )
        
        # Extract metrics
        metrics = self._extract_metrics(history, probe_config)
        
        # Validate convergence
        convergence_status = self._validate_convergence(metrics)
        
        # Build result
        result = {
            "probe_id": self.probe_id,
            "probe_config": probe_config,
            "execution_timestamp": datetime.now().isoformat(),
            "metrics": metrics,
            "convergence_status": convergence_status,
            "history": history
        }
        
        return result
    
    def _simulate_quantum_evolution(
        self, 
        time_steps: int, 
        dt: float, 
        initial_amplitudes: Dict[str, Dict[str, float]],
        hamiltonian_rates: Dict[str, float]
    ) -> Dict[str, Any]:
        """Simulate quantum geometric state evolution (placeholder for Lean execution)."""
        # Initialize state
        t = 0.0
        amplitudes = initial_amplitudes.copy()
        
        # Trajectory storage
        time_trajectory = []
        energy_trajectory = []
        probability_trajectories = {
            "void": [],
            "protrusion": [],
            "flat": [],
            "complex": []
        }
        gradient_trajectory = []
        
        for step in range(time_steps):
            # Normalize probabilities
            total_prob = sum(
                amp["real"]**2 + amp["imag"]**2 
                for amp in amplitudes.values()
            )
            if total_prob > 0:
                norm_factor = 1.0 / np.sqrt(total_prob)
                for key in amplitudes:
                    amplitudes[key]["real"] *= norm_factor
                    amplitudes[key]["imag"] *= norm_factor
            
            # Compute energy observable (simplified)
            energy = self._compute_energy(amplitudes, hamiltonian_rates)
            
            # Compute probabilities
            probs = {
                key: amp["real"]**2 + amp["imag"]**2
                for key, amp in amplitudes.items()
            }
            
            # Compute energy gradient (simplified finite difference)
            if step > 0:
                dE_dt = energy - energy_trajectory[-1]
                magnitude = np.abs(dE_dt)  # Simplified
            else:
                dE_dt = 0.0
                magnitude = 0.0
            
            # Store trajectory
            time_trajectory.append(t)
            energy_trajectory.append(energy)
            for key in probability_trajectories:
                probability_trajectories[key].append(probs[key])
            gradient_trajectory.append({
                "temporal_derivative": dE_dt,
                "magnitude": magnitude
            })
            
            # Evolve state (simplified Schrödinger-like evolution)
            amplitudes = self._evolve_amplitudes(amplitudes, hamiltonian_rates, dt)
            t += dt
        
        return {
            "time": time_trajectory,
            "energy": energy_trajectory,
            "probabilities": probability_trajectories,
            "gradient": gradient_trajectory,
            "final_amplitudes": amplitudes
        }
    
    def _compute_energy(self, amplitudes: Dict[str, Dict[str, float]], rates: Dict[str, float]) -> float:
        """Compute energy observable E(t) = ⟨ψ(t)|Ĥ|ψ(t)⟩."""
        void_prob = amplitudes["void"]["real"]**2 + amplitudes["void"]["imag"]**2
        protrusion_prob = amplitudes["protrusion"]["real"]**2 + amplitudes["protrusion"]["imag"]**2
        flat_prob = amplitudes["flat"]["real"]**2 + amplitudes["flat"]["imag"]**2
        complex_prob = amplitudes["complex"]["real"]**2 + amplitudes["complex"]["imag"]**2
        
        # Simplified energy calculation
        energy = (
            void_prob * 0.0 +
            protrusion_prob * rates["voidToProtrusion"] +
            flat_prob * rates["voidToFlat"] +
            complex_prob * rates["voidToComplex"]
        )
        return energy
    
    def _evolve_amplitudes(
        self, 
        amplitudes: Dict[str, Dict[str, float]], 
        rates: Dict[str, float], 
        dt: float
    ) -> Dict[str, Dict[str, float]]:
        """Evolve amplitudes using simplified Schrödinger-like equation."""
        new_amplitudes = {}
        
        # Void state (ground state, minimal evolution)
        new_amplitudes["void"] = {
            "real": amplitudes["void"]["real"],
            "imag": amplitudes["void"]["imag"]
        }
        
        # Protrusion state
        new_amplitudes["protrusion"] = {
            "real": amplitudes["protrusion"]["real"] + rates["voidToProtrusion"] * dt,
            "imag": amplitudes["protrusion"]["imag"]
        }
        
        # Flat state
        new_amplitudes["flat"] = {
            "real": amplitudes["flat"]["real"] + rates["voidToFlat"] * dt,
            "imag": amplitudes["flat"]["imag"]
        }
        
        # Complex state
        new_amplitudes["complex"] = {
            "real": amplitudes["complex"]["real"] + rates["voidToComplex"] * dt,
            "imag": amplitudes["complex"]["imag"]
        }
        
        return new_amplitudes
    
    def _extract_metrics(self, history: Dict[str, Any], probe_config: Dict[str, Any]) -> Dict[str, Any]:
        """Extract standardized metrics from quantum evolution."""
        energy_trajectory = history["energy"]
        gradient_trajectory = history["gradient"]
        probability_trajectories = history["probabilities"]
        
        metrics = {
            "final_energy": float(energy_trajectory[-1]),
            "max_energy": float(max(energy_trajectory)),
            "min_energy": float(min(energy_trajectory)),
            "energy_convergence_rate": self._compute_convergence_rate(energy_trajectory),
            "final_probabilities": {
                key: float(prob_trajectory[-1])
                for key, prob_trajectory in probability_trajectories.items()
            },
            "probability_convergence_stability": self._compute_probability_stability(probability_trajectories),
            "gradient_magnitude_final": float(gradient_trajectory[-1]["magnitude"]),
            "gradient_magnitude_mean": float(np.mean([g["magnitude"] for g in gradient_trajectory])),
            "total_time_steps": len(energy_trajectory),
            "time_steps": probe_config.get("time_steps", 100),
            "dt": probe_config.get("dt", 0.01)
        }
        return metrics
    
    def _validate_convergence(self, metrics: Dict[str, Any]) -> Dict[str, Any]:
        """Validate convergence criteria."""
        convergence_status = {
            "energy_converged": metrics["energy_convergence_rate"] < 0.01,
            "probability_converged": metrics["probability_convergence_stability"] > 0.95,
            "gradient_stable": metrics["gradient_magnitude_final"] < 0.1,
            "overall_status": "converged" if (
                metrics["energy_convergence_rate"] < 0.01 and
                metrics["probability_convergence_stability"] > 0.95
            ) else "not_converged"
        }
        return convergence_status
    
    def _compute_convergence_rate(self, trajectory: List[float]) -> float:
        """Compute convergence rate from trajectory."""
        if len(trajectory) < 10:
            return 1.0
        
        tail_size = max(10, len(trajectory) // 10)
        tail = trajectory[-tail_size:]
        convergence_rate = float(np.std(tail) / (np.mean(np.abs(tail)) + 1e-10))
        return convergence_rate
    
    def _compute_probability_stability(self, probability_trajectories: Dict[str, List[float]]) -> float:
        """Compute probability choice stability."""
        tail_size = max(5, len(probability_trajectories["void"]) // 5)
        stabilities = []
        
        for key, trajectory in probability_trajectories.items():
            tail = trajectory[-tail_size:]
            unique_values = len(set([round(v, 4) for v in tail]))
            stability = 1.0 - (unique_values - 1) / len(tail)
            stabilities.append(max(0.0, min(1.0, stability)))
        
        return float(np.mean(stabilities))
    
    def serialize_results(self, result: Dict[str, Any]) -> str:
        """Serialize results in waveprobe-compatible JSON format."""
        serialized = json.dumps(result, indent=2, default=str)
        return serialized
    
    def store_to_topological(self, result: Dict[str, Any], storage_path: Optional[str] = None) -> str:
        """Store results in topological storage (placeholder for ENE integration)."""
        storage_path = storage_path or f"data/waveprobes/quantum_manifold/{self.probe_id}.json"
        
        Path(storage_path).parent.mkdir(parents=True, exist_ok=True)
        
        serialized = self.serialize_results(result)
        Path(storage_path).write_text(serialized)
        
        return storage_path


class QuantumManifoldProbeGenerator:
    """Generate waveprobe test probes for quantum manifold geometry."""
    
    @staticmethod
    def generate_parameter_sweep_probes() -> List[Dict[str, Any]]:
        """Generate parameter sweep probes."""
        probes = []
        
        # Sweep time steps
        for time_steps in [50, 100, 200]:
            probes.append({
                "probe_type": "parameter_sweep",
                "time_steps": time_steps,
                "dt": 0.01,
                "description": f"Sweep time_steps={time_steps}"
            })
        
        # Sweep dt
        for dt in [0.005, 0.01, 0.02]:
            probes.append({
                "probe_type": "parameter_sweep",
                "time_steps": 100,
                "dt": dt,
                "description": f"Sweep dt={dt}"
            })
        
        return probes
    
    @staticmethod
    def generate_convergence_probes(num_probes: int = 10) -> List[Dict[str, Any]]:
        """Generate convergence validation probes."""
        probes = []
        
        for i in range(num_probes):
            probes.append({
                "probe_type": "convergence_validation",
                "time_steps": 100,
                "dt": 0.01,
                "initial_amplitudes": {
                    "void": {"real": np.random.rand(), "imag": np.random.rand() * 0.1},
                    "protrusion": {"real": np.random.rand(), "imag": np.random.rand() * 0.1},
                    "flat": {"real": np.random.rand() * 0.1, "imag": np.random.rand() * 0.1},
                    "complex": {"real": np.random.rand() * 0.1, "imag": np.random.rand() * 0.1}
                },
                "description": f"Convergence probe {i+1}"
            })
        
        return probes
    
    @staticmethod
    def generate_hamiltonian_sweep_probes() -> List[Dict[str, Any]]:
        """Generate Hamiltonian parameter sweep probes."""
        probes = []
        
        # Sweep voidToProtrusion rate
        for rate in [0.05, 0.1, 0.2]:
            probes.append({
                "probe_type": "hamiltonian_sweep",
                "time_steps": 100,
                "dt": 0.01,
                "hamiltonian_rates": {
                    "voidToProtrusion": rate,
                    "voidToFlat": 0.2,
                    "voidToComplex": 0.05,
                    "protrusionToFlat": 0.15,
                    "protrusionToComplex": 0.1,
                    "flatToComplex": 0.2,
                    "protrusionToVoid": 0.05,
                    "flatToVoid": 0.1,
                    "complexToVoid": 0.03,
                    "flatToProtrusion": 0.1,
                    "complexToProtrusion": 0.05,
                    "complexToFlat": 0.1
                },
                "description": f"Sweep voidToProtrusion={rate}"
            })
        
        return probes


def main():
    """Main entry point for testing the adapter."""
    print("=" * 70)
    print("Waveprobe Adapter for QuantumManifoldGeometry.lean")
    print("=" * 70)
    
    # Initialize adapter
    adapter = QuantumManifoldGeometryAdapter()
    print(f"Adapter initialized: {adapter.probe_id}")
    
    # Test with a simple probe
    probe_config = {
        "probe_type": "test",
        "time_steps": 100,
        "dt": 0.01,
        "initial_amplitudes": {
            "void": {"real": 0.7, "imag": 0.0},
            "protrusion": {"real": 0.7, "imag": 0.0},
            "flat": {"real": 0.0, "imag": 0.0},
            "complex": {"real": 0.0, "imag": 0.0}
        }
    }
    
    print(f"\nExecuting probe: {probe_config}")
    result = adapter.execute_probe(probe_config)
    
    print(f"\nProbe execution completed")
    print(f"Final Energy: {result['metrics']['final_energy']:.6f}")
    print(f"Max Energy: {result['metrics']['max_energy']:.6f}")
    print(f"Energy Convergence Rate: {result['metrics']['energy_convergence_rate']:.6f}")
    print(f"Final Probabilities: {result['metrics']['final_probabilities']}")
    print(f"Convergence Status: {result['convergence_status']['overall_status']}")
    
    # Store results
    storage_path = adapter.store_to_topological(result)
    print(f"\nResults stored to: {storage_path}")
    
    # Generate probe types
    print("\n" + "=" * 70)
    print("Probe Generation Test")
    print("=" * 70)
    
    generator = QuantumManifoldProbeGenerator()
    
    param_sweeps = generator.generate_parameter_sweep_probes()
    print(f"Parameter sweep probes: {len(param_sweeps)}")
    
    convergence_probes = generator.generate_convergence_probes(num_probes=5)
    print(f"Convergence probes: {len(convergence_probes)}")
    
    hamiltonian_sweeps = generator.generate_hamiltonian_sweep_probes()
    print(f"Hamiltonian sweep probes: {len(hamiltonian_sweeps)}")
    
    print("\n✅ QuantumManifoldGeometry waveprobe adapter test completed successfully")


if __name__ == "__main__":
    main()