cfaf.kt

import numpy as np
import pandas as pd
from scipy.spatial import distance

# Constants
PLANCKS_CONSTANT = 6.626e-34  # Planck's constant in Js
SPEED_OF_LIGHT = 3.0e8  # Speed of light in m/s
CARBON_ATOMIC_WEIGHT = 12.0  # Atomic weight of carbon in amu
THRESHOLD = 1.0  # Define an appropriate threshold for matching

# Constants for quantized points and evidence tracking
QUANTIZED_POINTS = np.array([...])  # Define quantized points based on new improvements
EVIDENCE_DATABASE = pd.DataFrame(columns=['Name', 'Description', 'Evidence'])

def calculate_wavelength(energy):
    """
    Calculate the wavelength based on energy using Planck's equation.
    """
    return PLANCKS_CONSTANT * SPEED_OF_LIGHT / energy

def polygon_properties(sides, length):
    """
    Calculate the properties of a regular polygon with given number of sides and side length.
    """
    if sides < 3:
        raise ValueError("A polygon must have at least 3 sides.")
    
    # Calculate interior angle
    interior_angle = (sides - 2) * 180 / sides
    
    # Calculate area
    area = (sides * length**2) / (4 * np.tan(np.pi / sides))
    
    return {
        'Interior Angle': interior_angle,
        'Area': area
    }

def identify_criminal(name, description):
    """
    Identify potential criminals based on name and description.
    Utilizes advanced quantization for matching.
    """
    # Generate a quantized representation of the description
    description_vector = quantize_description(description)
    
    # Match against the database
    potential_matches = []
    for index, row in EVIDENCE_DATABASE.iterrows():
        evidence_vector = quantize_description(row['Description'])
        distance_score = distance.euclidean(description_vector, evidence_vector)
        if distance_score < THRESHOLD:  # Define THRESHOLD based on new data
            potential_matches.append({
                'Name': row['Name'],
                'Description': row['Description'],
                'Evidence': row['Evidence']
            })
    
    return potential_matches

def quantize_description(description):
    """
    Convert description into a quantized vector.
    """
    # Placeholder for advanced quantization logic
    vector = np.zeros(len(QUANTIZED_POINTS))
    # Implement detailed quantization process
    return vector

def add_evidence(name, description, evidence):
    """
    Add evidence to the database.
    """
    global EVIDENCE_DATABASE
    EVIDENCE_DATABASE = EVIDENCE_DATABASE.append({
        'Name': name,
        'Description': description,
        'Evidence': evidence
    }, ignore_index=True)

def main():
    # Example energy calculation (for demonstration purposes)
    energy = 3.0e-19  # Example energy in Joules
    wavelength = calculate_wavelength(energy)
    print(f'Calculated Wavelength: {wavelength:.2e} meters')

    # Example polygon extrapolation
    sides = 6
    length = 1.0  # Side length in meters
    properties = polygon_properties(sides, length)
    print(f'Polygon Properties: {properties}')

    # Example evidence handling
    add_evidence('John Doe', 'Suspected in robbery', 'Fingerprint evidence')
    
    # Identify potential criminals
    results = identify_criminal('John Doe', 'Robbery suspect with fingerprints')
    print(f'Potential Matches: {results}')

if __name__ == "__main__":
    main()