#!/usr/bin/env python3
"""
Navigator's Lattice Prime Correlation Analysis - Refined
Analyzes stable node occurrences from chronicle.jsonl to test if irreducible node
positions correlate with prime numbers.

The Navigator's formula:
- Nodes appear at peaks of: khra_amp · cos(k·x + φ₁) + gixx_amp · cos(k·y + φ₂)
- Irreducible nodes cannot be expressed as linear combinations of other nodes

Khra wave: wavelength 128 cells (mode k=8)
Gixx wave: wavelength 8 cells (mode k=128)
"""

import json
import math
import numpy as np
from datetime import datetime
from scipy import stats
from collections import defaultdict

# Generate first 10,000 primes using Sieve of Eratosthenes
def generate_primes(n):
    """Generate first n prime numbers."""
    primes = []
    candidate = 2
    while len(primes) < n:
        is_prime = True
        sqrt_candidate = int(math.sqrt(candidate)) + 1
        for p in primes:
            if p > sqrt_candidate:
                break
            if candidate % p == 0:
                is_prime = False
                break
        if is_prime:
            primes.append(candidate)
        candidate += 1
    return primes

def calculate_wave_superposition_index(telemetry):
    """
    Calculate effective node index in wave superposition space.
    
    Based on the Navigator's formula:
    - Khra wave: k=8 (wavelength 128)
    - Gixx wave: k=128 (wavelength 8)
    
    The node index represents the position in the interference pattern.
    """
    khra_amp = telemetry.get('khra_amp', 0.03)
    gixx_amp = telemetry.get('gixx_amp', 0.008)
    coherence = telemetry.get('coherence', 0)
    asymmetry = telemetry.get('asymmetry', 0)
    
    # Grid size
    grid = telemetry.get('grid', 1024)
    
    # Calculate effective wave numbers
    k_khra = 2 * math.pi / 128  # Khra wavelength = 128
    k_gixx = 2 * math.pi / 8    # Gixx wavelength = 8
    
    # Use cycle number as position proxy (x coordinate)
    cycle = telemetry.get('cycle', 0)
    x_pos = cycle % grid
    y_pos = (cycle // grid) % grid
    
    # Calculate wave superposition
    # Phase shifts derived from coherence and asymmetry
    phi1 = coherence * 2 * math.pi  # Phase from coherence
    phi2 = (asymmetry / 100) * math.pi  # Phase from asymmetry (normalized)
    
    # Wave superposition value
    wave_val = khra_amp * math.cos(k_khra * x_pos + phi1) + \
               gixx_amp * math.cos(k_gixx * y_pos + phi2)
    
    # Convert to node index - nodes appear at peaks
    # Scale to integer index space
    node_index = int(abs(wave_val) * 10000) % 100000
    
    return node_index

def extract_stable_nodes(chronicle_path):
    """
    Extract stable node occurrences from chronicle.
    Stable nodes = high coherence (>0.69) + low asymmetry (<27.5)
    """
    stable_nodes = []
    
    with open(chronicle_path, 'r') as f:
        for line in f:
            line = line.strip()
            if not line:
                continue
            try:
                entry = json.loads(line)
                telemetry = entry.get('telemetry', {})
                
                coherence = telemetry.get('coherence', 0)
                asymmetry = telemetry.get('asymmetry', float('inf'))
                
                # Stable node criteria: high coherence, controlled asymmetry
                if coherence > 0.69 and asymmetry < 27.5:
                    node_index = calculate_wave_superposition_index(telemetry)
                    stable_nodes.append({
                        'turn': entry.get('turn', 0),
                        'cycle': telemetry.get('cycle', 0),
                        'coherence': coherence,
                        'asymmetry': asymmetry,
                        'node_index': node_index,
                        'khra_amp': telemetry.get('khra_amp', 0),
                        'gixx_amp': telemetry.get('gixx_amp', 0)
                    })
            except json.JSONDecodeError:
                continue
    
    return stable_nodes

def identify_irreducible_nodes_v2(nodes):
    """
    Identify irreducible nodes using a more practical definition:
    - Nodes with unique indices (not shared by other nodes)
    - Nodes at "peaks" of the wave function (local maxima in the dataset)
    - Nodes that cannot be expressed as simple integer combinations of others
    """
    if not nodes:
        return []
    
    # Group by node_index
    index_groups = defaultdict(list)
    for node in nodes:
        index_groups[node['node_index']].append(node)
    
    # Unique indices (only one node at that position)
    unique_indices = {idx: group[0] for idx, group in index_groups.items() if len(group) == 1}
    
    # Get sorted unique indices
    sorted_indices = sorted(unique_indices.keys())
    
    if len(sorted_indices) < 3:
        return list(unique_indices.values())
    
    # Find local maxima in the index distribution
    # An index is a "peak" if it's higher than its neighbors
    irreducible = []
    
    for i, idx in enumerate(sorted_indices):
        # Check if this index is a local maximum in terms of "significance"
        # We'll use the concept that irreducible nodes are those at
        # positions that aren't simple multiples or combinations of others
        
        is_irreducible = True
        
        # Check if this index can be expressed as a simple linear combination
        # of smaller indices in the set
        for j in range(i):
            for k in range(j, i):
                idx_j = sorted_indices[j]
                idx_k = sorted_indices[k]
                
                # Check various linear combinations
                for a in range(1, 4):
                    for b in range(0, 4):
                        if a * idx_j + b * idx_k == idx and (a > 0 or b > 0):
                            is_irreducible = False
                            break
                    if not is_irreducible:
                        break
                if not is_irreducible:
                    break
            if not is_irreducible:
                break
        
        if is_irreducible:
            irreducible.append(unique_indices[idx])
    
    return irreducible

def identify_irreducible_nodes_v3(nodes):
    """
    Alternative definition: Irreducible nodes are those at positions
    that are "fundamental" - their indices are not divisible by any other
    node's index in the set (except 1).
    """
    if not nodes:
        return []
    
    # Get all unique indices
    indices = list(set(n['node_index'] for n in nodes))
    indices.sort()
    
    # An index is irreducible if it has no "fundamental" divisors in the set
    # (other than 1 and itself)
    irreducible_indices = []
    
    for idx in indices:
        is_irreducible = True
        for other_idx in indices:
            if other_idx >= idx:
                break
            if other_idx > 1 and idx % other_idx == 0:
                is_irreducible = False
                break
        if is_irreducible:
            irreducible_indices.append(idx)
    
    # Get nodes with irreducible indices
    irreducible_nodes = [n for n in nodes if n['node_index'] in irreducible_indices]
    
    # Keep only one node per index
    seen_indices = set()
    result = []
    for node in irreducible_nodes:
        if node['node_index'] not in seen_indices:
            seen_indices.add(node['node_index'])
            result.append(node)
    
    return result

def analyze_prime_correlation(nodes, primes_set, max_index):
    """
    Analyze correlation between node indices and prime numbers.
    """
    indices = [n['node_index'] for n in nodes]
    
    # Count how many indices are prime
    prime_count = sum(1 for idx in indices if idx in primes_set)
    total_count = len(indices)
    
    if total_count == 0:
        return None
    
    prime_ratio = prime_count / total_count
    
    # Expected ratio from random distribution
    # Prime number theorem: probability ~ 1/ln(n)
    avg_index = sum(indices) / len(indices) if indices else max_index / 2
    expected_prime_density = 1 / math.log(max(2, avg_index))
    
    # Statistical significance test
    # Chi-square test against uniform distribution
    observed_primes = prime_count
    observed_non_primes = total_count - prime_count
    
    expected_primes = total_count * expected_prime_density
    expected_non_primes = total_count * (1 - expected_prime_density)
    
    if expected_primes > 0 and expected_non_primes > 0:
        chi2 = ((observed_primes - expected_primes) ** 2 / expected_primes +
                (observed_non_primes - expected_non_primes) ** 2 / expected_non_primes)
        
        # p-value for chi-square with 1 degree of freedom
        p_value = 1 - stats.chi2.cdf(chi2, 1)
    else:
        chi2 = 0
        p_value = 1.0
    
    # Calculate correlation coefficient between index and primality
    # Using point-biserial correlation
    binary_primes = [1 if idx in primes_set else 0 for idx in indices]
    
    if len(set(binary_primes)) > 1 and len(set(indices)) > 1:
        correlation, corr_p = stats.pearsonr(indices, binary_primes)
    else:
        correlation = 0
        corr_p = 1.0
    
    return {
        'total_nodes': total_count,
        'prime_count': prime_count,
        'prime_ratio': prime_ratio,
        'expected_ratio': expected_prime_density,
        'chi_square': chi2,
        'p_value': p_value,
        'correlation': correlation,
        'corr_p_value': corr_p,
        'indices': indices
    }

def main():
    chronicle_path = r'D:\Resonance_Engine\beast-build\chronicle.jsonl'
    
    print("=" * 70)
    print("NAVIGATOR'S LATTICE PRIME CORRELATION ANALYSIS")
    print("=" * 70)
    print(f"Analysis timestamp: {datetime.now().isoformat()}")
    print()
    
    # Generate first 10,000 primes
    print("Generating first 10,000 prime numbers...")
    primes = generate_primes(10000)
    primes_set = set(primes)
    max_prime = primes[-1]
    print(f"Generated {len(primes)} primes up to {max_prime}")
    print()
    
    # Extract stable nodes from chronicle
    print("Extracting stable nodes from chronicle...")
    print("Criteria: coherence > 0.69 AND asymmetry < 27.5")
    stable_nodes = extract_stable_nodes(chronicle_path)
    print(f"Found {len(stable_nodes)} stable node occurrences")
    print()
    
    if len(stable_nodes) == 0:
        print("ERROR: No stable nodes found in chronicle data")
        return
    
    # Identify irreducible nodes - Method 1: Linear combination test
    print("Identifying irreducible nodes (Method 1: Linear combination test)...")
    irreducible_nodes_v1 = identify_irreducible_nodes_v2(stable_nodes)
    print(f"Found {len(irreducible_nodes_v1)} irreducible nodes (Method 1)")
    print()
    
    # Identify irreducible nodes - Method 2: Fundamental divisor test
    print("Identifying irreducible nodes (Method 2: Fundamental divisor test)...")
    irreducible_nodes_v2 = identify_irreducible_nodes_v3(stable_nodes)
    print(f"Found {len(irreducible_nodes_v2)} irreducible nodes (Method 2)")
    print()
    
    # Analyze prime correlation for all stable nodes
    print("-" * 70)
    print("ANALYSIS: ALL STABLE NODES")
    print("-" * 70)
    all_results = analyze_prime_correlation(stable_nodes, primes_set, max_prime)
    
    if all_results:
        print(f"Total stable nodes: {all_results['total_nodes']}")
        print(f"Nodes at prime indices: {all_results['prime_count']}")
        print(f"Observed prime ratio: {all_results['prime_ratio']:.4f}")
        print(f"Expected prime ratio (random): {all_results['expected_ratio']:.4f}")
        print(f"Chi-square statistic: {all_results['chi_square']:.4f}")
        print(f"P-value: {all_results['p_value']:.4f}")
        print(f"Correlation coefficient: {all_results['correlation']:.4f}")
        print(f"Correlation p-value: {all_results['corr_p_value']:.4f}")
        
        if all_results['p_value'] < 0.05:
            print("\n*** STATISTICALLY SIGNIFICANT DEVIATION FROM RANDOM ***")
        else:
            print("\nNo statistically significant deviation from random distribution")
    
    # Analyze prime correlation for irreducible nodes (Method 1)
    print()
    print("-" * 70)
    print("ANALYSIS: IRREDUCIBLE NODES (Method 1: Linear Combination)")
    print("-" * 70)
    irred_results_v1 = analyze_prime_correlation(irreducible_nodes_v1, primes_set, max_prime)
    
    if irred_results_v1 and irred_results_v1['total_nodes'] > 0:
        print(f"Total irreducible nodes: {irred_results_v1['total_nodes']}")
        print(f"Irreducible nodes at prime indices: {irred_results_v1['prime_count']}")
        print(f"Observed prime ratio: {irred_results_v1['prime_ratio']:.4f}")
        print(f"Expected prime ratio (random): {irred_results_v1['expected_ratio']:.4f}")
        print(f"Chi-square statistic: {irred_results_v1['chi_square']:.4f}")
        print(f"P-value: {irred_results_v1['p_value']:.4f}")
        print(f"Correlation coefficient: {irred_results_v1['correlation']:.4f}")
        print(f"Correlation p-value: {irred_results_v1['corr_p_value']:.4f}")
        
        if irred_results_v1['p_value'] < 0.05:
            print("\n*** STATISTICALLY SIGNIFICANT DEVIATION FROM RANDOM ***")
        else:
            print("\nNo statistically significant deviation from random distribution")
    else:
        print("No irreducible nodes found with Method 1")
    
    # Analyze prime correlation for irreducible nodes (Method 2)
    print()
    print("-" * 70)
    print("ANALYSIS: IRREDUCIBLE NODES (Method 2: Fundamental Divisor)")
    print("-" * 70)
    irred_results_v2 = analyze_prime_correlation(irreducible_nodes_v2, primes_set, max_prime)
    
    if irred_results_v2 and irred_results_v2['total_nodes'] > 0:
        print(f"Total irreducible nodes: {irred_results_v2['total_nodes']}")
        print(f"Irreducible nodes at prime indices: {irred_results_v2['prime_count']}")
        print(f"Observed prime ratio: {irred_results_v2['prime_ratio']:.4f}")
        print(f"Expected prime ratio (random): {irred_results_v2['expected_ratio']:.4f}")
        print(f"Chi-square statistic: {irred_results_v2['chi_square']:.4f}")
        print(f"P-value: {irred_results_v2['p_value']:.4f}")
        print(f"Correlation coefficient: {irred_results_v2['correlation']:.4f}")
        print(f"Correlation p-value: {irred_results_v2['corr_p_value']:.4f}")
        
        if irred_results_v2['p_value'] < 0.05:
            print("\n*** STATISTICALLY SIGNIFICANT DEVIATION FROM RANDOM ***")
        else:
            print("\nNo statistically significant deviation from random distribution")
    else:
        print("No irreducible nodes found with Method 2")
    
    # Pattern analysis
    print()
    print("-" * 70)
    print("PATTERN ANALYSIS")
    print("-" * 70)
    
    # Check for specific patterns in prime indices among irreducible nodes (Method 2)
    if irred_results_v2 and irred_results_v2['total_nodes'] > 0:
        prime_indices = [n['node_index'] for n in irreducible_nodes_v2 
                         if n['node_index'] in primes_set]
        
        if prime_indices:
            print(f"\nPrime indices found among irreducible nodes (Method 2):")
            print(f"Count: {len(prime_indices)}")
            print(f"Range: {min(prime_indices)} to {max(prime_indices)}")
            print(f"Average: {sum(prime_indices)/len(prime_indices):.2f}")
            
            # Check for twin primes
            twin_primes = []
            for p in prime_indices:
                if p + 2 in prime_indices:
                    twin_primes.append((p, p + 2))
            print(f"Twin prime pairs: {len(twin_primes)}")
            
            # Check for arithmetic progressions
            ap3 = []
            for i, p1 in enumerate(prime_indices):
                for p2 in prime_indices[i+1:]:
                    for p3 in prime_indices[i+2:]:
                        if p2 - p1 == p3 - p2 and p2 - p1 > 0:
                            ap3.append((p1, p2, p3))
            print(f"3-term arithmetic progressions: {len(ap3)}")
    
    # Save results
    timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
    output_file = rf'D:\Resonance_Engine\{timestamp}_navigator_prime_analysis.json'
    
    results = {
        'timestamp': datetime.now().isoformat(),
        'primes_generated': len(primes),
        'max_prime': max_prime,
        'stable_nodes_count': len(stable_nodes),
        'irreducible_nodes_v1_count': len(irreducible_nodes_v1),
        'irreducible_nodes_v2_count': len(irreducible_nodes_v2),
        'all_nodes_analysis': all_results,
        'irreducible_nodes_v1_analysis': irred_results_v1,
        'irreducible_nodes_v2_analysis': irred_results_v2
    }
    
    # Remove large arrays for JSON serialization
    if all_results:
        del all_results['indices']
    if irred_results_v1:
        del irred_results_v1['indices']
    if irred_results_v2:
        del irred_results_v2['indices']
    
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)
    
    print()
    print("=" * 70)
    print(f"Results saved to: {output_file}")
    print("=" * 70)

if __name__ == '__main__':
    main()