parallel_development_controller.py

#!/usr/bin/env python3
"""
Parallel Development Controller
==============================

Coordinates theory refinement and experimental prototyping in parallel.
Enforces strict go/no-go criteria for full demonstrator transition.

Only proceeds to full demonstrator when BOTH:
- best_anec_2d ≤ -1e5 J·s·m⁻³  
- best_rate_2d ≥ 0.5 (50%)

Until then: PARALLEL_DEVELOPMENT mode

Usage:
    python parallel_development_controller.py
"""

import sys
import os
import time

# Add paths for imports
current_dir = os.path.dirname(os.path.abspath(__file__))
sys.path.append(current_dir)
sys.path.append(os.path.join(current_dir, 'src', 'prototype'))

try:
    from iterative_theory_refinement import check_scan_results, assess_readiness, theory_iteration_loop
    from src.prototype.combined_prototype import combined_prototype_demonstration, UnifiedVacuumGenerator
    from src.prototype.integrated_derisking_suite import integrated_derisking_assessment
except ImportError:
    # Fallback for demo purposes
    print("⚠️ Import paths not found - using mock functions for demonstration")
    
    def check_scan_results():
        return {'best_anec_2d': -2.09e-6, 'best_rate_2d': 0.42, 'scan_iteration': 1}
    
    def assess_readiness(results):
        return {
            'anec_met': False, 'rate_met': False, 
            'anec_gap': 47846889952, 'rate_gap': 0.08,
            'best_anec_2d': results['best_anec_2d'],
            'best_rate_2d': results['best_rate_2d']
        }
    
    def theory_iteration_loop(max_iter):
        return {'status': 'PARALLEL_DEVELOPMENT', 'iterations': 3}
    
    def combined_prototype_demonstration():
        return {
            'readiness': {
                'casimir': {'ready': True, 'readiness_pct': 85},
                'dynamic': {'ready': False, 'readiness_pct': 60}, 
                'squeezed': {'ready': False, 'readiness_pct': 45},
                'metamaterial': {'ready': True, 'readiness_pct': 75},
                'overall': {'ready_components': 2, 'total_components': 4, 'overall_ready': False, 'readiness_pct': 50}
            }
        }
    
    class UnifiedVacuumGenerator:
        def assess_prototype_readiness(self):
            return combined_prototype_demonstration()['readiness']
        def run_parallel_optimization_scans(self):
            return {'casimir': {}, 'dynamic': {}, 'squeezed': {}, 'metamaterial': {}}
        def breakthrough_optimization_suite(self):
            return {
                'status': 'success',
                'combined_analysis': {
                    'combined_improvement': 10,
                    'remaining_gap': 50
                }
            }
    
    def integrated_derisking_assessment():
        return {
            'overall_assessment': {
                'risk_level': 'LOW-MEDIUM',
                'recommendation': 'Proceed with prototyping'
            }
        }

def check_full_demonstrator_readiness():
    """Check if ready for full demonstrator based on strict criteria."""
    
    print("🚦 FULL DEMONSTRATOR READINESS CHECK")
    print("=" * 36)
    print()
    
    # Check theory readiness
    results = check_scan_results()
    status = assess_readiness(results)
    
    print("📊 THEORY STATUS:")
    print(f"  ANEC: {results['best_anec_2d']:.2e} J·s·m⁻³")
    print(f"  Rate: {results['best_rate_2d']*100:.1f}%")
    print()
    
    anec_status = "✅ MET" if status['anec_met'] else "❌ NOT MET"
    rate_status = "✅ MET" if status['rate_met'] else "❌ NOT MET"
    
    print("🎯 CRITERIA EVALUATION:")
    print(f"  ANEC ≤ -1e5: {anec_status}")
    print(f"  Rate ≥ 50%: {rate_status}")
    print()
    
    # Check experimental readiness
    generator = UnifiedVacuumGenerator()
    readiness = generator.assess_prototype_readiness()
    
    print("🔬 EXPERIMENTAL STATUS:")
    experimental_ready = readiness['overall']['overall_ready']
    exp_status = "✅ READY" if experimental_ready else "🔄 DEVELOPING"
    print(f"  Prototypes: {exp_status} ({readiness['overall']['ready_components']}/4 ready)")
    print()
    
    # Overall decision
    theory_ready = status['anec_met'] and status['rate_met']
    full_ready = theory_ready and experimental_ready
    
    print("🚦 FINAL DECISION:")
    if full_ready:
        decision = "🟢 READY FOR FULL DEMONSTRATOR"
        mode = "FULL_DEMONSTRATOR"
        print(f"   {decision}")
        print("   ✅ Both theory and experimental criteria satisfied")
        print("   🚀 Proceed with large-scale integration")
    else:
        decision = "🟡 CONTINUE PARALLEL DEVELOPMENT"
        mode = "PARALLEL_DEVELOPMENT"
        print(f"   {decision}")
        if not theory_ready:
            print("   ⚠️ Theory targets not yet achieved")
            if not status['anec_met']:
                print(f"      • ANEC gap: {status['anec_gap']:.1e}× improvement needed")
            if not status['rate_met']:
                print(f"      • Rate gap: {status['rate_gap']*100:.1f} percentage points")
        if not experimental_ready:
            print("   ⚠️ Experimental modules need further development")
    
    print()
    
    return {
        'mode': mode,
        'theory_ready': theory_ready,
        'experimental_ready': experimental_ready,
        'full_ready': full_ready,
        'theory_status': status,
        'experimental_status': readiness
    }

def run_parallel_development_cycle():
    """Run one cycle of parallel theory-experiment development."""
    
    print("🔄 PARALLEL DEVELOPMENT CYCLE")
    print("=" * 30)
    print()
    
    results = {}
    
    # 1. Theory refinement iteration
    print("🧮 THEORY TRACK")
    print("-" * 13)
    from iterative_theory_refinement import refine_theoretical_model, update_theory_results
    
    current_results = check_scan_results()
    refinement = refine_theoretical_model(current_results.get('scan_iteration', 1))
    updated_results = update_theory_results(current_results, refinement)
    
    results['theory'] = {
        'previous': current_results,
        'refinement': refinement,
        'updated': updated_results
    }
    print()
    
    # 2. Experimental optimization
    print("🔬 EXPERIMENT TRACK")
    print("-" * 16)
    
    generator = UnifiedVacuumGenerator()
    scan_results = generator.run_parallel_optimization_scans()
    readiness = generator.assess_prototype_readiness()
    
    results['experiments'] = {
        'scan_results': scan_results,
        'readiness': readiness
    }
    
    # 3. Risk assessment
    print("🛡️ VALIDATION TRACK")
    print("-" * 17)
    
    risk_assessment = integrated_derisking_assessment()
    
    results['validation'] = risk_assessment
    print()
    
    return results

def parallel_development_loop(max_cycles=10):
    """Main parallel development loop."""
    
    print("🎯 PARALLEL DEVELOPMENT COORDINATION")
    print("=" * 37)
    print()
    print("Coordinating theory refinement + experimental prototyping")
    print("until both ANEC and violation rate targets are achieved.")
    print()
    
    cycle = 1
    
    while cycle <= max_cycles:
        print(f"🔄 DEVELOPMENT CYCLE {cycle}")
        print("=" * 20)
        
        # Check current readiness
        readiness_check = check_full_demonstrator_readiness()
        
        if readiness_check['full_ready']:
            print("🎉 BREAKTHROUGH ACHIEVED!")
            print("Both theory and experimental targets met!")
            print("🚀 Proceeding to full demonstrator construction...")
            return {
                'status': 'READY',
                'cycles_completed': cycle,
                'final_readiness': readiness_check
            }
        
        print(f"Continuing parallel development (cycle {cycle})...")
        print()
        
        # Run development cycle
        cycle_results = run_parallel_development_cycle()
        
        # Summary of cycle progress
        theory_progress = cycle_results['theory']['refinement']
        exp_progress = cycle_results['experiments']['readiness']['overall']
        
        print("📈 CYCLE PROGRESS SUMMARY:")
        print(f"  Theory: {theory_progress['strategy']}")
        print(f"  Expected ANEC improvement: {theory_progress['anec_improvement']:.2f}×")
        print(f"  Expected rate improvement: +{theory_progress['rate_improvement']*100:.1f}%")
        print(f"  Experimental readiness: {exp_progress['readiness_pct']:.0f}%")
        print()
        
        cycle += 1
    
    # Max cycles reached
    print("⏰ Maximum development cycles reached")
    print("🟡 Continuing in PARALLEL_DEVELOPMENT mode")
    
    final_check = check_full_demonstrator_readiness()
    return {
        'status': 'PARALLEL_DEVELOPMENT',
        'cycles_completed': max_cycles,
        'final_readiness': final_check
    }

def show_development_status():
    """Show comprehensive development status across all tracks."""
    
    print("📊 COMPREHENSIVE DEVELOPMENT STATUS")
    print("=" * 35)
    print()
    
    # Theory status
    results = check_scan_results()
    status = assess_readiness(results)
    
    print("🧮 THEORY TRACK STATUS:")
    print(f"  Current ANEC: {results['best_anec_2d']:.2e} J·s·m⁻³")
    print(f"  Current rate: {results['best_rate_2d']*100:.1f}%")
    print(f"  ANEC gap: {status['anec_gap']:.0f}× improvement needed")
    print(f"  Rate gap: {status['rate_gap']*100:.1f} percentage points") 
    print(f"  Theory ready: {'✅ YES' if status['anec_met'] and status['rate_met'] else '❌ NO'}")
    print()
    
    # Experimental status
    demo_results = combined_prototype_demonstration()
    readiness = demo_results['readiness']
    
    print("🔬 EXPERIMENTAL TRACK STATUS:")
    for component, assessment in readiness.items():
        if component == 'overall':
            continue
        status_icon = "✅" if assessment['ready'] else "⚠️"
        print(f"  {status_icon} {component.capitalize()}: {assessment['readiness_pct']:.0f}% ready")
    
    overall = readiness['overall']
    print(f"  Overall: {overall['ready_components']}/{overall['total_components']} modules ready")
    print()
    
    # Risk status
    print("🛡️ VALIDATION TRACK STATUS:")
    risk_assessment = integrated_derisking_assessment()
    overall_risk = risk_assessment['overall_assessment']['risk_level']
    recommendation = risk_assessment['overall_assessment']['recommendation']
    
    print(f"  Overall risk: {overall_risk}")
    print(f"  Recommendation: {recommendation}")
    print()
    
    # Coordination status
    print("🎯 COORDINATION STATUS:")
    readiness_check = check_full_demonstrator_readiness()
    print(f"  Development mode: {readiness_check['mode']}")
    
    if readiness_check['mode'] == 'PARALLEL_DEVELOPMENT':
        print("  🔄 Continue theory + experiment in parallel")
        print("  🎯 Work toward both ANEC ≤ -1e5 AND rate ≥ 50%")
    else:
        print("  🚀 Ready for full demonstrator!")
    
    print()

def main():
    """Main parallel development controller."""
    
    print("🎛️ PARALLEL DEVELOPMENT CONTROLLER")
    print("=" * 35)
    print()
    
    # Show current comprehensive status
    show_development_status()
    
    # Check readiness for full demonstrator
    readiness_check = check_full_demonstrator_readiness()
    
    if readiness_check['full_ready']:
        print("🎉 READY FOR FULL DEMONSTRATOR!")
        print("Both theory and experimental criteria satisfied.")
        return readiness_check
    
    # Run development cycles
    print("🔄 Starting parallel development cycles...")
    print("(Limited to 3 cycles for demonstration)")
    print()
    
    result = parallel_development_loop(max_cycles=3)
    
    print("=" * 35)
    print("🏁 DEVELOPMENT SUMMARY")
    print("=" * 35)
    print(f"Status: {result['status']}")
    print(f"Cycles completed: {result['cycles_completed']}")
    
    if result['status'] == 'READY':
        print("🚀 Ready for full demonstrator construction!")
    else:
        print("🔄 Continue parallel development")
        print("🎯 Focus: Close theory gaps while optimizing prototypes")
    
    return result

def run_breakthrough_coordination():
        """
        Coordinate all breakthrough approaches with theory and prototyping.
        
        This implements the complete negative energy advancement strategy:
        1. Run theory refinement for ANEC/violation rate targets
        2. Run breakthrough optimization suite (ansatz, quantum, metamaterial, ML)
        3. Run prototype optimization with enhanced modules
        4. Assess combined progress toward "easy" large-volume negative energy
        
        Returns comprehensive coordination results.
        """
        print("🚀 BREAKTHROUGH COORDINATION PROTOCOL")
        print("=" * 41)
        print()
        print("Coordinating all four breakthrough approaches:")
        print("🔬 Theory refinement + advanced methods")
        print("🧪 Prototype optimization + vacuum engineering")
        print("🤖 ML-driven discovery + automated optimization")
        print("📊 Combined assessment + readiness evaluation")
        print()
        
        results = {'coordination_timestamp': time.time()}
        
        # 1. Theory refinement with advanced methods
        print("1️⃣ ADVANCED THEORY REFINEMENT")
        print("-" * 30)
        theory_results = self.run_theory_refinement()
        results['theory'] = theory_results
        
        best_anec = theory_results['best_anec_2d']
        best_rate = theory_results['best_rate_2d']
        
        print(f"Current ANEC: {best_anec:.3e} J·s·m⁻³")
        print(f"Current rate: {best_rate:.3f}")
        print(f"ANEC gap: {abs(-1e5 / best_anec):.0f}× remaining")
        print(f"Rate gap: {abs(0.5 / best_rate):.1f}× remaining")
        print()
        
        # 2. Breakthrough optimization suite
        print("2️⃣ BREAKTHROUGH OPTIMIZATION SUITE")
        print("-" * 34)
        
        try:
            # Import and run unified vacuum generator with breakthrough optimization
            from prototype.combined_prototype import UnifiedVacuumGenerator
            
            generator = UnifiedVacuumGenerator()
            breakthrough_results = generator.breakthrough_optimization_suite()
            results['breakthrough'] = breakthrough_results
            
            # Extract key metrics
            if 'combined_analysis' in breakthrough_results:
                analysis = breakthrough_results['combined_analysis']
                combined_improvement = analysis.get('combined_improvement', 1)
                remaining_gap = analysis.get('remaining_gap', float('inf'))
                
                print(f"Combined improvement: {combined_improvement:.1f}×")
                print(f"Remaining theory gap: {remaining_gap:.0f}×")
                
                # Check if breakthrough approaches can close theory gap
                if remaining_gap < 100:
                    print("✅ BREAKTHROUGH: Theory gap nearly closed!")
                elif remaining_gap < 1000:
                    print("🎯 MAJOR PROGRESS: Significant advancement")
                else:
                    print("🔬 INCREMENTAL: Continue optimization")
            
        except ImportError as e:
            print(f"⚠️ Breakthrough modules unavailable: {e}")
            print("Running simplified coordination...")
            results['breakthrough'] = {'status': 'simplified', 'error': str(e)}
        
        print()
        
        # 3. Integrated prototype optimization
        print("3️⃣ INTEGRATED PROTOTYPE OPTIMIZATION")
        print("-" * 37)
        prototype_results = run_prototype_optimization()
        results['prototypes'] = prototype_results
        
        prototype_readiness = prototype_results.get('overall_readiness', 0)
        print(f"Prototype readiness: {prototype_readiness:.1f}%")
        print()
        
        # 4. Comprehensive risk assessment
        print("4️⃣ COMPREHENSIVE RISK ASSESSMENT")
        print("-" * 33)
        risk_results = run_comprehensive_risk_assessment()
        results['risk'] = risk_results
        
        risk_level = risk_results.get('overall_risk_level', 'UNKNOWN')
        technical_readiness = risk_results.get('technical_readiness', 0)
        print(f"Risk level: {risk_level}")
        print(f"Technical readiness: {technical_readiness:.1f}%")
        print()
        
        # 5. Development phase determination with breakthrough integration
        print("5️⃣ DEVELOPMENT PHASE DETERMINATION")
        print("-" * 35)
        
        # Enhanced readiness criteria including breakthrough potential
        anec_met = best_anec <= -1e5
        rate_met = best_rate >= 0.5
        breakthrough_promising = False
        
        if 'breakthrough' in results and 'combined_analysis' in results['breakthrough']:
            remaining_gap = results['breakthrough']['combined_analysis'].get('remaining_gap', float('inf'))
            breakthrough_promising = remaining_gap < 500  # Promising if gap < 500×
        
        high_prototype_readiness = prototype_readiness >= 75
        low_risk = risk_level in ['LOW', 'MEDIUM-LOW']
        
        # Determine phase
        if anec_met and rate_met:
            phase = 'FULL_DEMONSTRATOR'
            print("🚀 FULL DEMONSTRATOR: All theory targets met!")
        elif breakthrough_promising and high_prototype_readiness and low_risk:
            phase = 'BREAKTHROUGH_ACCELERATION'
            print("⚡ BREAKTHROUGH ACCELERATION: Promising breakthrough path identified!")
        elif high_prototype_readiness and low_risk:
            phase = 'ENHANCED_PARALLEL_DEVELOPMENT'
            print("🔬 ENHANCED PARALLEL DEVELOPMENT: Strong experimental foundation")
        else:
            phase = 'PARALLEL_DEVELOPMENT'
            print("🔄 PARALLEL DEVELOPMENT: Continue theory + experiment coordination")
        
        results['development_phase'] = phase
        print()
        
        # 6. Strategic recommendations
        print("6️⃣ STRATEGIC RECOMMENDATIONS")
        print("-" * 29)
        
        recommendations = []
        
        if not anec_met:
            anec_gap = abs(-1e5 / best_anec)
            if anec_gap > 1000:
                recommendations.append("🎯 Priority: Advanced ansatz design + ML discovery")
            elif anec_gap > 100:
                recommendations.append("🔬 Focus: Three-loop corrections + polymer effects")
            else:
                recommendations.append("⚡ Push: Final ANEC optimization")
        
        if not rate_met:
            rate_gap = abs(0.5 / best_rate)
            if rate_gap > 2:
                recommendations.append("📊 Critical: Violation rate methodology refinement")
            else:
                recommendations.append("📈 Optimize: Fine-tune violation rate calculations")
        
        if breakthrough_promising:
            recommendations.append("🚀 Accelerate: Scale up most promising breakthrough approach")
        
        if prototype_readiness < 50:
            recommendations.append("🔧 Strengthen: Prototype optimization and validation")
        
        if risk_level in ['HIGH', 'MEDIUM-HIGH']:
            recommendations.append("🛡️ Address: Risk mitigation and safety protocols")
        
        if not recommendations:
            recommendations.append("✅ Proceed: Execute full demonstrator construction")
        
        for rec in recommendations:
            print(f"  {rec}")
        
        results['recommendations'] = recommendations
        print()
        
        # 7. Next cycle planning
        print("7️⃣ NEXT CYCLE PLANNING")
        print("-" * 23)
        
        if phase == 'FULL_DEMONSTRATOR':
            print("🏗️ Begin full demonstrator construction")
            print("📊 Establish production-scale metrics")
            print("🌟 Plan practical applications")
        elif phase == 'BREAKTHROUGH_ACCELERATION':
            print("⚡ Focus resources on breakthrough approach")
            print("🔬 Accelerate most promising pathway")
            print("📈 Target rapid gap closure")
        else:
            print("🔄 Continue coordinated development:")
            print("  • Theory: Advanced refinement methods")
            print("  • Experiments: Enhanced prototype optimization")
            print("  • Integration: Breakthrough approach synergy")
            print("  • Validation: Comprehensive risk management")
        
        print()
        
        return results