#!/usr/bin/env python3
"""
FORENSIC ANALYSIS FRAMEWORK
Quartz Optical Computing Chip Dossier
Cairo University Submission Validation

Author: Claude (Forensic Analysis for Marcel)
Date: February 2026
"""

import numpy as np
import matplotlib.pyplot as plt
import json
from typing import Dict, List, Tuple, Optional
import warnings

class ForensicAnalysis:
    """Complete forensic validation framework for optical computing chip dossier"""
    
    def __init__(self):
        self.validation_results = {}
        self.critical_issues = []
        self.recommendations = []
        
    def analyze_mathematical_foundation(self) -> Dict:
        """Analyze the mathematical completeness and consistency"""
        results = {
            "normalization_condition": self._validate_normalization(),
            "boundary_physics": self._validate_boundary_physics(),
            "convergence_theory": self._validate_convergence(),
            "field_equations": self._validate_field_equations()
        }
        return results
    
    def _validate_normalization(self) -> Dict:
        """Validate the intensity normalization condition: ∑I_i = 1"""
        # Test case: 6-sector boundary
        N = 6
        I = np.random.random(N)
        I_normalized = I / np.sum(I)
        
        normalization_check = abs(np.sum(I_normalized) - 1.0) < 1e-12
        
        return {
            "valid": normalization_check,
            "error": abs(np.sum(I_normalized) - 1.0),
            "test_sectors": N,
            "status": "PASS" if normalization_check else "FAIL"
        }
    
    def _validate_boundary_physics(self) -> Dict:
        """Validate phase continuity along optical boundary"""
        N = 18  # Fine discretization
        theta = np.linspace(0, 2*np.pi, N, endpoint=False)
        
        # Test phase function: should be continuous
        phase = np.sin(3*theta) + 0.5*np.cos(5*theta)
        
        # Check for discontinuities
        phase_diff = np.diff(phase)
        max_discontinuity = np.max(np.abs(phase_diff))
        
        # Per dossier: no discontinuities exceeding π/20
        threshold = np.pi / 20
        valid = max_discontinuity < threshold
        
        return {
            "valid": valid,
            "max_discontinuity": max_discontinuity,
            "threshold": threshold,
            "test_points": N,
            "status": "PASS" if valid else "CRITICAL_FAIL"
        }
    
    def _validate_convergence(self) -> Dict:
        """Validate discrete-to-continuous convergence O(1/N²)"""
        results = []
        N_values = [6, 12, 18, 36, 72]
        
        # Test function for Laplacian convergence
        def test_function(theta):
            return np.sin(2*theta) + 0.3*np.cos(4*theta)
        
        def analytical_laplacian(theta):
            return -4*np.sin(2*theta) - 4.8*np.cos(4*theta)
        
        for N in N_values:
            theta = np.linspace(0, 2*np.pi, N, endpoint=False)
            f_discrete = test_function(theta)
            
            # Discrete Laplacian (circular boundary)
            laplacian_discrete = np.zeros(N)
            for i in range(N):
                im1 = (i - 1) % N
                ip1 = (i + 1) % N
                laplacian_discrete[i] = f_discrete[ip1] - 2*f_discrete[i] + f_discrete[im1]
            
            # Scale by (N/(2π))² for proper discretization
            laplacian_discrete *= (N / (2*np.pi))**2
            
            # Analytical reference
            laplacian_analytical = analytical_laplacian(theta)
            
            # Compute error
            error = np.sqrt(np.mean((laplacian_discrete - laplacian_analytical)**2))
            expected_error = 1.0 / N**2  # O(1/N²) scaling
            
            results.append({
                "N": N,
                "error": error,
                "expected_error": expected_error,
                "ratio": error / expected_error if expected_error > 0 else float('inf')
            })
        
        # Check if error scaling follows O(1/N²)
        convergence_valid = True
        for i in range(1, len(results)):
            ratio = results[i-1]["error"] / results[i]["error"]
            expected_ratio = (N_values[i] / N_values[i-1])**2
            if abs(ratio - expected_ratio) > 0.5 * expected_ratio:
                convergence_valid = False
                break
        
        return {
            "valid": convergence_valid,
            "test_results": results,
            "status": "PASS" if convergence_valid else "FAIL"
        }
    
    def _validate_field_equations(self) -> Dict:
        """Validate field evolution equations"""
        # Implement the diffusion equation from Section 7
        N = 18
        alpha = 0.1  # Diffusion coefficient
        dt = 0.01
        
        # Initial condition: localized intensity
        I = np.zeros(N)
        I[0] = 1.0  # All intensity in one sector
        
        # Evolution step
        def evolve_step(I_current, alpha, dt):
            I_new = np.copy(I_current)
            for i in range(N):
                im1 = (i - 1) % N
                ip1 = (i + 1) % N
                laplacian = I_current[ip1] - 2*I_current[i] + I_current[im1]
                I_new[i] = I_current[i] + alpha * dt * laplacian
            return I_new
        
        # Run simulation
        steps = 100
        I_history = [np.copy(I)]
        for step in range(steps):
            I = evolve_step(I, alpha, dt)
            I_history.append(np.copy(I))
        
        # Check conservation (normalization)
        final_sum = np.sum(I)
        conservation_valid = abs(final_sum - 1.0) < 1e-10
        
        # Check for equilibrium approach
        equilibrium = np.ones(N) / N  # Uniform distribution
        final_error = np.sqrt(np.mean((I - equilibrium)**2))
        
        return {
            "conservation_valid": conservation_valid,
            "final_sum": final_sum,
            "equilibrium_error": final_error,
            "simulation_steps": steps,
            "status": "PASS" if conservation_valid else "CRITICAL_FAIL"
        }

class FabricationAnalysis:
    """Forensic analysis of fabrication specifications"""
    
    def __init__(self):
        self.spec_validation = {}
    
    def analyze_material_specs(self) -> Dict:
        """Analyze material specifications for feasibility"""
        quartz_specs = {
            "purity": 99.999,  # percent
            "crystal_cut": "Z-cut",
            "optical_axis_deviation": 0.01,  # degrees
            "birefringence_uniformity": 1e-6,  # Δn across active region
        }
        
        # Validate against industry standards
        validation = {
            "purity_achievable": quartz_specs["purity"] <= 99.9999,  # Commercially available
            "z_cut_standard": quartz_specs["crystal_cut"] == "Z-cut",  # Standard cut
            "axis_precision": quartz_specs["optical_axis_deviation"] >= 0.005,  # Realistic tolerance
            "birefringence_spec": quartz_specs["birefringence_uniformity"] >= 1e-7,  # Achievable
        }
        
        all_valid = all(validation.values())
        
        return {
            "specifications": quartz_specs,
            "validation": validation,
            "overall_feasible": all_valid,
            "status": "PASS" if all_valid else "REVIEW_REQUIRED"
        }
    
    def analyze_fabrication_tolerances(self) -> Dict:
        """Analyze fabrication tolerances for manufacturability"""
        tolerances = {
            "wafer_thickness": 10e-6,  # ±10 μm
            "surface_roughness_ra": 0.2e-9,  # Ra ≤ 0.2 nm
            "surface_roughness_rq": 0.3e-9,  # Rq ≤ 0.3 nm
            "flatness": 633e-9 / 10,  # λ/10 at 633 nm
            "trench_depth": 10e-9,  # ±10 nm
        }
        
        # Assess manufacturability
        assessment = {
            "thickness_achievable": tolerances["wafer_thickness"] >= 5e-6,
            "roughness_challenging": tolerances["surface_roughness_ra"] <= 0.5e-9,
            "flatness_standard": tolerances["flatness"] <= 100e-9,
            "depth_precision": tolerances["trench_depth"] >= 5e-9,
        }
        
        return {
            "tolerances": tolerances,
            "assessment": assessment,
            "manufacturing_risk": "LOW" if all(assessment.values()) else "MEDIUM"
        }

class OpticalSimulation:
    """Simulation validation of optical computing principles"""
    
    def __init__(self, N: int = 18):
        self.N = N
        self.theta = np.linspace(0, 2*np.pi, N, endpoint=False)
    
    def simulate_boundary_evolution(self, steps: int = 1000, alpha: float = 0.1) -> Dict:
        """Simulate the boundary field evolution"""
        dt = 0.001
        
        # Initial condition: asymmetric distribution
        I = np.exp(-((self.theta - np.pi)**2) / 0.5)
        I = I / np.sum(I)  # Normalize
        
        # Store evolution
        I_history = [np.copy(I)]
        energy_history = []
        
        for step in range(steps):
            # Compute energy (quadratic form)
            energy = 0.5 * np.sum(I**2)
            energy_history.append(energy)
            
            # Evolution step (discrete diffusion)
            I_new = np.copy(I)
            for i in range(self.N):
                im1 = (i - 1) % self.N
                ip1 = (i + 1) % self.N
                laplacian = I[ip1] - 2*I[i] + I[im1]
                I_new[i] = I[i] + alpha * dt * laplacian
            
            I = I_new
            
            # Renormalize to maintain ∑I = 1
            I = I / np.sum(I)
            I_history.append(np.copy(I))
        
        return {
            "initial_distribution": I_history[0],
            "final_distribution": I_history[-1],
            "energy_evolution": energy_history,
            "converged_to_uniform": np.allclose(I_history[-1], 1.0/self.N, atol=1e-6),
            "conservation_maintained": all(abs(np.sum(I_hist) - 1.0) < 1e-12 for I_hist in I_history),
            "steps": steps
        }
    
    def analyze_information_capacity(self) -> Dict:
        """Analyze information processing capacity"""
        # Shannon entropy calculation
        def entropy(I):
            # Remove zeros to avoid log(0)
            I_clean = I[I > 1e-15]
            return -np.sum(I_clean * np.log2(I_clean))
        
        # Test different initial distributions
        distributions = {
            "uniform": np.ones(self.N) / self.N,
            "localized": np.zeros(self.N),
            "binary": np.zeros(self.N),
            "random": np.random.random(self.N)
        }
        
        distributions["localized"][0] = 1.0
        distributions["binary"][[0, self.N//2]] = 0.5
        distributions["random"] /= np.sum(distributions["random"])
        
        entropies = {}
        for name, dist in distributions.items():
            entropies[name] = entropy(dist)
        
        return {
            "max_entropy": np.log2(self.N),  # Theoretical maximum
            "distribution_entropies": entropies,
            "information_capacity_bits": np.log2(self.N),
            "distinguishable_states": 2**self.N  # Upper bound
        }

def generate_forensic_report():
    """Generate complete forensic analysis report"""
    print("=" * 80)
    print("FORENSIC ANALYSIS REPORT")
    print("Quartz Optical Computing Chip Dossier")
    print("Cairo University Submission")
    print("=" * 80)
    print()
    
    # Mathematical analysis
    print("1. MATHEMATICAL FOUNDATION ANALYSIS")
    print("-" * 40)
    
    math_analyzer = ForensicAnalysis()
    math_results = math_analyzer.analyze_mathematical_foundation()
    
    for test, result in math_results.items():
        print(f"{test.upper()}: {result['status']}")
        if result['status'] == "CRITICAL_FAIL":
            print(f"   ⚠️  CRITICAL ISSUE DETECTED")
        elif result['status'] == "FAIL":
            print(f"   ❌ ISSUE DETECTED")
        else:
            print(f"   ✅ VALIDATED")
    print()
    
    # Fabrication analysis
    print("2. FABRICATION FEASIBILITY ANALYSIS")
    print("-" * 40)
    
    fab_analyzer = FabricationAnalysis()
    material_results = fab_analyzer.analyze_material_specs()
    tolerance_results = fab_analyzer.analyze_fabrication_tolerances()
    
    print(f"MATERIAL SPECIFICATIONS: {material_results['status']}")
    print(f"MANUFACTURING TOLERANCES: {tolerance_results['manufacturing_risk']} RISK")
    print()
    
    # Optical simulation
    print("3. OPTICAL SIMULATION VALIDATION")
    print("-" * 40)
    
    sim = OpticalSimulation(N=18)
    sim_results = sim.simulate_boundary_evolution(steps=1000)
    info_results = sim.analyze_information_capacity()
    
    print(f"BOUNDARY EVOLUTION: {'✅ CONVERGED' if sim_results['converged_to_uniform'] else '❌ DIVERGED'}")
    print(f"CONSERVATION: {'✅ MAINTAINED' if sim_results['conservation_maintained'] else '❌ VIOLATED'}")
    print(f"INFORMATION CAPACITY: {info_results['information_capacity_bits']:.2f} bits")
    print()
    
    return {
        "mathematical": math_results,
        "fabrication_material": material_results,
        "fabrication_tolerance": tolerance_results,
        "optical_simulation": sim_results,
        "information_analysis": info_results
    }

if __name__ == "__main__":
    results = generate_forensic_report()
    
    # Save detailed results
    with open('/home/claude/forensic_analysis_results.json', 'w') as f:
        json.dump(results, f, indent=2, default=str)
    
    print("✅ Complete forensic analysis saved to forensic_analysis_results.json")