Quantum entanglement prediction SDKP

[FatherTimeSDKP]

import json
import numpy as np
import math
from typing import Dict, List, Tuple, Any
from dataclasses import dataclass
import logging

@DataClass
class SimulationResults:
"""Container for simulation results"""
entanglement_values: List[float]
correlation_coefficients: List[float]
numerical_mappings: Dict[str, Any]
lambda_contributions: Dict[str, float]
polarization_data: List[Dict[str, Any]]

class QuantumEntanglementSDKPSimulator:
"""
Multi-dimensional entanglement simulator using SDKP, SD&N, VEI, and QF methodologies
"""

def __init__(self, config: Dict[str, Any]):
    self.config = config
    self.setup_logging()
    self.validate_config()
    
def setup_logging(self):
    """Initialize logging if enabled"""
    if self.config.get('parameters', {}).get('logging', False):
        # Set logging level to INFO and include time and level for clarity
        logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s: %(message)s')
        self.logger = logging.getLogger(__name__)
    else:
        self.logger = None
        
def validate_config(self):
    """Validate simulation configuration"""
    required_keys = ['polarization_angles_deg', 'numerical_signatures', 'lambda_weights']
    params = self.config.get('parameters', {})
    
    for key in required_keys:
        if key not in params:
            raise ValueError(f"Missing required parameter: {key}")
            
    # Validate lambda weights sum to 1
    weights = params['lambda_weights']
    total_weight = sum(weights.values())
    if abs(total_weight - 1.0) > 1e-6:
        self.log(f"Warning: Lambda weights sum to {total_weight:.6f}, normalizing...")
        for key in weights:
            weights[key] /= total_weight
            
    # Validate required weight keys
    required_weight_keys = ['C_SDN', 'VEI_delta', 'QF_delta']
    for key in required_weight_keys:
        if key not in weights:
            raise ValueError(f"Missing lambda weight: {key}. Ensure 'C_SDN', 'VEI_delta', 'QF_delta' are defined.")

def log(self, message: str):
    """Log message if logging is enabled"""
    if self.logger:
        self.logger.info(message)
    else:
        print(message)

def calculate_numerical_signature_value(self, signature: str) -> float:
    """
    Convert numerical signature to quantum-relevant value
    Uses digit sum and positional weighting.
    """
    # Handle non-numeric characters
    clean_signature = ''.join(filter(str.isdigit, str(signature)))
    
    if not clean_signature:
        self.log(f"Warning: No digits found in signature '{signature}', using default value 0.5 for calculation.")
        return 0.5
        
    digit_sum = sum(int(d) for d in clean_signature)
    positional_weight = sum(int(d) * (i + 1) for i, d in enumerate(clean_signature))
    
    # Enhanced normalization with signature length consideration
    # Normalize by typical length (e.g., assuming average signature length is 10 for scaling)
    length_factor = len(clean_signature) / 10.0  
    normalized_value = (digit_sum + positional_weight * 0.1 + length_factor) % 1.0
    
    return normalized_value

def calculate_mass_basis(self, dim_index: int, signature_value: float) -> float:
    """
    Calculate mass basis value based on dimensional index and signature.
    This function is currently not integrated into the main simulation flow
    but can be used for future extensions related to dimensional modeling.
    """
    model = self.config['parameters'].get('mass_basis_model', 'dim_index_dependent')
    
    if model == 'dim_index_dependent':
        return (dim_index + 1) * signature_value * 0.1
    elif model == 'uniform':
        return signature_value
    else:
        # Default fallback with enhanced scaling
        return signature_value * (1 + dim_index * 0.05)

def apply_mapping_function(self, value: float, mapping_type: str) -> float:
    """
    Apply transformation mapping to values.
    Ensures value is within [0, 1] for consistent mapping behavior.
    """
    # Ensure value is in valid range for mapping functions like sin
    value = max(0.0, min(1.0, value))
    
    if mapping_type == 'linear':
        return value
    elif mapping_type == 'nonlinear':
        # Example: A sine-squared curve that peaks at 0.5
        return np.sin(value * np.pi) ** 2
    elif mapping_type == 'hybrid':
        # Example: A blend of linear and non-linear behavior
        return 0.5 * value + 0.5 * np.sin(value * np.pi) ** 2
    else:
        self.log(f"Warning: Unknown mapping type '{mapping_type}', using linear mapping.")
        return value

def calculate_entanglement_measure(self, angle_deg: float, signature: str) -> Tuple[float, Dict[str, float]]:
    """
    Calculate entanglement measure for given angle and numerical signature.
    Combines components from SD&N (C_SDN), VEI (VEI_delta), and QF (QF_delta).
    Includes detailed logging of calculation steps.
    """
    self.log(f"\n--- Calculating Entanglement for Angle {angle_deg}° and Signature '{signature}' ---")
    
    angle_rad = math.radians(angle_deg)
    self.log(f"Step 1: Convert angle {angle_deg}° to radians: {angle_rad:.4f} rad")
    
    signature_value = self.calculate_numerical_signature_value(signature)
    self.log(f"Step 2: Calculated numerical signature value for '{signature}': {signature_value:.4f}")
    
    # Calculate individual raw components based on conceptual models
    # C_SDN (Symmetry, Duality, Negation): Cosine correlation with signature influence
    C_SDN = abs(np.cos(angle_rad)) * signature_value
    self.log(f"Step 3a: Raw C_SDN = |cos({angle_rad:.4f})| * {signature_value:.4f} = {abs(np.cos(angle_rad)):.4f} * {signature_value:.4f} = {C_SDN:.4f}")
    
    # VEI_delta (Conceptual placeholder, needs specific definition): Sine-squared dependence
    VEI_delta = np.sin(angle_rad) ** 2 * (1 - signature_value)
    self.log(f"Step 3b: Raw VEI_delta = sin({angle_rad:.4f})^2 * (1 - {signature_value:.4f}) = {np.sin(angle_rad)**2:.4f} * {(1-signature_value):.4f} = {VEI_delta:.4f}")
    
    # QF_delta (Conceptual placeholder, needs specific definition): Exponential decay with sine modulation
    QF_delta = np.exp(-signature_value) * abs(np.sin(angle_rad * 2))
    self.log(f"Step 3c: Raw QF_delta = exp(-{signature_value:.4f}) * |sin({angle_rad*2:.4f})| = {np.exp(-signature_value):.4f} * {abs(np.sin(angle_rad*2)):.4f} = {QF_delta:.4f}")
    
    # Apply mapping transformations to each component
    mapping_type = self.config['parameters'].get('mapping_type', 'linear')
    self.log(f"Step 4: Applying '{mapping_type}' mapping function to components.")
    C_SDN_mapped = self.apply_mapping_function(C_SDN, mapping_type)
    self.log(f"  Mapped C_SDN: {C_SDN:.4f} -> {C_SDN_mapped:.4f}")
    VEI_delta_mapped = self.apply_mapping_function(VEI_delta, mapping_type)
    self.log(f"  Mapped VEI_delta: {VEI_delta:.4f} -> {VEI_delta_mapped:.4f}")
    QF_delta_mapped = self.apply_mapping_function(QF_delta, mapping_type)
    self.log(f"  Mapped QF_delta: {QF_delta:.4f} -> {QF_delta_mapped:.4f}")
    
    # Weighted combination of mapped components
    weights = self.config['parameters']['lambda_weights']
    self.log(f"Step 5: Applying lambda weights: C_SDN={weights['C_SDN']:.2f}, VEI_delta={weights['VEI_delta']:.2f}, QF_delta={weights['QF_delta']:.2f}")
    
    entanglement_value = (
        weights['C_SDN'] * C_SDN_mapped + 
        weights['VEI_delta'] * VEI_delta_mapped + 
        weights['QF_delta'] * QF_delta_mapped
    )
    self.log(f"  Calculation: ({weights['C_SDN']:.2f} * {C_SDN_mapped:.4f}) + "
             f"({weights['VEI_delta']:.2f} * {VEI_delta_mapped:.4f}) + "
             f"({weights['QF_delta']:.2f} * {QF_delta_mapped:.4f})")
    self.log(f"  Final Entanglement Value: {entanglement_value:.4f}")
    
    components = {
        'C_SDN': C_SDN_mapped,
        'VEI_delta': VEI_delta_mapped,
        'QF_delta': QF_delta_mapped,
        'signature_value': signature_value,
        'raw_C_SDN': C_SDN,
        'raw_VEI_delta': VEI_delta,
        'raw_QF_delta': QF_delta
    }
    
    return entanglement_value, components

def calculate_correlation_coefficient(self, entanglement_values: List[float]) -> float:
    """
    Calculate auto-correlation coefficient at lag 1 for entanglement values.
    Handles edge cases for constant or insufficient data.
    """
    if len(entanglement_values) < 2:
        self.log("Correlation: Not enough data points (<2) for correlation calculation. Returning 0.0.")
        return 0.0
        
    # If all values are identical, it's perfect correlation
    if all(v == entanglement_values[0] for v in entanglement_values):
        self.log("Correlation: All entanglement values are identical. Returning 1.0 (perfect correlation).")
        return 1.0  
        
    try:
        # Shifted array for auto-correlation (lag 1, circular)
        shifted_values = entanglement_values[1:] + [entanglement_values[0]]
        self.log(f"Correlation: Original values: {[f'{v:.4f}' for v in entanglement_values]}")
        self.log(f"Correlation: Shifted values: {[f'{v:.4f}' for v in shifted_values]}")
        correlation_matrix = np.corrcoef(entanglement_values, shifted_values)
        
        # The correlation coefficient is at [0, 1] in a 2x2 matrix
        correlation = correlation_matrix[0, 1]
        self.log(f"Correlation: Calculated numpy correlation matrix: \n{correlation_matrix.round(4)}")
        self.log(f"Correlation: Resulting coefficient: {correlation:.4f}")
        return correlation if not np.isnan(correlation) else 0.0
    except Exception as e:
        self.log(f"Warning: Correlation calculation failed: {e}. Returning 0.0.")
        return 0.0

def run_simulation(self) -> SimulationResults:
    """
    Run the complete entanglement simulation process.
    Iterates through defined angles and numerical signatures,
    calculating entanglement and correlation measures.
    """
    self.log("\n========================================================")
    self.log("=== Starting Quantum Entanglement SDKP Simulation ===")
    self.log("========================================================")
    
    angles = self.config['parameters']['polarization_angles_deg']
    signatures = self.config['parameters']['numerical_signatures']
    threshold = self.config['parameters'].get('entanglement_threshold', 0.75)
    
    self.log(f"Simulation Configuration: Angles={angles}, Signatures={signatures}")
    self.log(f"Processing {len(angles)} angles with {len(signatures)} numerical signatures.")
    self.log(f"Total individual measurements planned: {len(angles) * len(signatures)}")
    self.log(f"Entanglement threshold for 'high entanglement': {threshold}")
    self.log(f"Mapping type: '{self.config['parameters'].get('mapping_type', 'linear')}'")
    self.log(f"Lambda Weights: {self.config['parameters']['lambda_weights']}")
    
    all_entanglement_values = []
    all_correlation_coefficients = []
    numerical_mappings = {}
    polarization_data = []
    
    for i, angle in enumerate(angles):
        angle_entanglements = []
        
        for j, signature in enumerate(signatures):
            entanglement_val, components = self.calculate_entanglement_measure(angle, signature)
            angle_entanglements.append(entanglement_val)
            
            # Store detailed numerical mapping for each specific angle-signature pair
            key = f"angle_{angle}_sig_{signature}"
            numerical_mappings[key] = {
                'entanglement': float(entanglement_val),
                'components': {k: float(v) for k, v in components.items()}, # Ensure float conversion
                'above_threshold': bool(entanglement_val > threshold) # Ensure boolean type
            }
        
        # Calculate correlation for this angle across all signatures
        self.log(f"\n--- Calculating Correlation for Angle {angle}° Entanglement Values ---")
        correlation = self.calculate_correlation_coefficient(angle_entanglements)
        all_correlation_coefficients.append(correlation)
        all_entanglement_values.extend(angle_entanglements) # Flatten list of all entanglement values
        
        # Collect aggregated statistics for the current angle
        angle_stats = {
            'angle_deg': angle,
            'entanglement_values': [float(x) for x in angle_entanglements], # Ensure float conversion
            'mean_entanglement': float(np.mean(angle_entanglements)),
            'std_entanglement': float(np.std(angle_entanglements)),
            'min_entanglement': float(np.min(angle_entanglements)),
            'max_entanglement': float(np.max(angle_entanglements)),
            'correlation': float(correlation),
            'high_entanglement_count': sum(1 for e in angle_entanglements if e > threshold)
        }
        polarization_data.append(angle_stats)
        
        self.log(f"\nSummary for Angle {angle:3.0f}°: Mean Entanglement={np.mean(angle_entanglements):.4f}, "
                f"Std Dev={np.std(angle_entanglements):.4f}, Correlation={correlation:.4f}")
        self.log("--------------------------------------------------------") # Separator for each angle summary
    
    # Calculate overall lambda contributions based on average component values
    self.log("\n--- Calculating Overall Lambda Contributions ---")
    weights = self.config['parameters']['lambda_weights']
    lambda_contributions = {}
    
    for weight_key in weights.keys():
        component_values = []
        # Extract relevant component values from all numerical mappings
        for mapping in numerical_mappings.values():
            if weight_key in mapping['components']:
                component_values.append(mapping['components'][weight_key])
        
        if component_values:
            # Calculate weighted average contribution
            mean_component = np.mean(component_values)
            lambda_contributions[weight_key] = float(weights[weight_key] * mean_component)
            self.log(f"  {weight_key}: Weight={weights[weight_key]:.2f} * Mean_Component={mean_component:.4f} = {lambda_contributions[weight_key]:.4f}")
        else:
            lambda_contributions[weight_key] = 0.0 # Default if no data for component
            self.log(f"  {weight_key}: No component values found. Contribution = 0.0.")

    self.log("--------------------------------------------------------")
    
    results = SimulationResults(
        entanglement_values=all_entanglement_values,
        correlation_coefficients=all_correlation_coefficients,
        numerical_mappings=numerical_mappings,
        lambda_contributions=lambda_contributions,
        polarization_data=polarization_data
    )
    
    self.log("\n========================================================")
    self.log("=== Simulation completed successfully! ===")
    self.log("========================================================\n")
    return results

def export_results(self, results: SimulationResults, filename: str = None) -> str:
    """
    Export simulation results to JSON format.
    Provides a comprehensive summary, lambda contributions,
    detailed polarization analysis, and a sample of detailed mappings.
    """
    # Calculate comprehensive summary statistics
    entanglement_values = results.entanglement_values
    correlation_coefficients = results.correlation_coefficients
    threshold = self.config['parameters']['entanglement_threshold']
    
    # Avoid division by zero if no measurements
    total_measurements = len(entanglement_values)
    high_entanglement_fraction = (sum(1 for e in entanglement_values if e > threshold) / total_measurements) \
                                 if total_measurements > 0 else 0.0

    export_data = {
        'simulation_name': self.config.get('simulation_name', 'Unnamed Simulation'),
        'description': self.config.get('description', 'No description provided.'),
        'simulation_config': self.config['parameters'], # Export only parameters for conciseness
        'results': {
            'summary_statistics': {
                'total_individual_measurements': total_measurements,
                'mean_entanglement_across_all': float(np.mean(entanglement_values)) if entanglement_values else 0.0,
                'std_entanglement_across_all': float(np.std(entanglement_values)) if entanglement_values else 0.0,
                'min_entanglement_across_all': float(np.min(entanglement_values)) if entanglement_values else 0.0,
                'max_entanglement_across_all': float(np.max(entanglement_values)) if entanglement_values else 0.0,
                'mean_correlation_across_angles': float(np.mean(correlation_coefficients)) if correlation_coefficients else 0.0,
                'std_correlation_across_angles': float(np.std(correlation_coefficients)) if correlation_coefficients else 0.0,
                'high_entanglement_threshold': threshold,
                'fraction_above_threshold': high_entanglement_fraction
            },
            'lambda_contributions': results.lambda_contributions, # Already float-converted
            'polarization_analysis_per_angle': results.polarization_data, # Already float-converted
            'detailed_mappings_sample': {k: v for k, v in list(results.numerical_mappings.items())[:20]} # Limit to first 20 for readability in export
        }
    }
    
    if filename:
        try:
            with open(filename, 'w') as f:
                json.dump(export_data, f, indent=4)
            self.log(f"Results successfully exported to '{filename}'")
            return f"Results exported to {filename}"
        except IOError as e:
            self.log(f"Error: Could not write to file {filename}: {e}")
            return json.dumps({"error": f"Could not write to file: {e}", "data": export_data}, indent=2)
    else:
        return json.dumps(export_data, indent=2)

Example usage and configuration

def create_default_config():
"""Create the default simulation configuration"""
return {
"simulation_name": "Quantum_Entanglement_SDKP_Study",
"description": "Configuration for multi-dimensional entanglement simulations using SDKP, SD&N, VEI, and QF.",
"parameters": {
"polarization_angles_deg": [0, 30, 45, 60, 90, 120, 135, 150, 180],
"numerical_signatures": ["7146", "1467", "4671", "6714", "999988889999", "6", "7", "ABCD123", "0"],
"lambda_weights": {
"C_SDN": 0.4,
"VEI_delta": 0.3,
"QF_delta": 0.3
},
"mapping_type": "hybrid",
"entanglement_threshold": 0.75,
"mass_basis_model": "dim_index_dependent", # This parameter is currently not used in the main simulation flow
"output_format": "json",
"logging": True,
"max_simulations": 100 # This parameter is currently not used in the simulation flow
}
}

Example simulation run

if name == "main":
# Create configuration
config = create_default_config()

# Initialize simulator
simulator = QuantumEntanglementSDKPSimulator(config)

# Run simulation
results = simulator.run_simulation()

# Export results
json_output = simulator.export_results(results, filename="entanglement_simulation_results.json")
print("\n=== SIMULATION RESULTS ===")
print(json_output)

# Example of retrieving and printing specific data
print("\n--- Example: First few detailed mappings ---")
for k, v in list(results.numerical_mappings.items())[:3]:
    print(f"  {k}: Entanglement={v['entanglement']:.4f}, Above Threshold={v['above_threshold']}")

print("\n--- Example: Lambda Contributions ---")
for k, v in results.lambda_contributions.items():
    print(f"  {k}: {v:.4f}")

print("\n--- Example: Mean Entanglement across all measurements ---")
print(f"  {np.mean(results.entanglement_values):.4f}")