cfa

import math

# Define criminal data with simplified descriptions
criminal_data = {
    "John Doe": {"crime": "robbery", "location": {"latitude": 40.7128, "longitude": -74.0060}},
    "Jane Smith": {"crime": "vandalism", "location": {"latitude": 34.0522, "longitude": -118.2437}},
    # Add more criminal data as needed
}

# Simplified crime descriptions
descriptions = {
    "robbery": "Robbery at a store.",
    "vandalism": "Cars vandalized in a parking lot.",
    # Add more simplified descriptions as needed
}

# Function to calculate quantized points based on descriptions
def calculate_quantized_points(description):
    points = len(description.split())  # Number of words in the description
    return points

# Function to associate quantized points with names and locations
def associate_quantized_points(names):
    associated_names = []
    for name in names:
        if name in criminal_data:
            crime = criminal_data[name]["crime"]
            location = criminal_data[name]["location"]
            description = descriptions[crime]
            points_description = calculate_quantized_points(description)
            points_location = math.sqrt(location["latitude"]**2 + location["longitude"]**2)  # Euclidean distance
            associated_names.append((name, crime, f"{location['latitude']}, {location['longitude']}"))
    return associated_names

# Get user input for names (mass input)
names_input = input("Enter names separated by commas: ").split(",")

# Call the function to associate quantized points with the input names
associated_names = associate_quantized_points(names_input)

# Print the associated names in the desired format
if associated_names:
    print("Associated Names:")
    for name, crime, location in associated_names:
        print(f"Name: {name}, Crime: {crime}, Location: {location}")
else:
    print("No associated names found in criminal data.")