cfasp

import numpy as np
import pandas as pd

# Define constants for the space dimensions
TIME_DIMENSION = 0
SEVERITY_DIMENSION = 1
LOCATION_DIMENSION = 2
TYPE_DIMENSION = 3

# Function to create a 4D space for crime incidents
def create_crime_space(incidents):
    """ Create a 4D space representation of crime incidents. """
    space_shape = (10, 10, 10, 10)  # Example dimensions for time, severity, location, type
    crime_space = np.zeros(space_shape)

    for incident in incidents:
        time_index = min(int(incident['time']), space_shape[TIME_DIMENSION] - 1)
        severity_index = min(int(incident['severity']), space_shape[SEVERITY_DIMENSION] - 1)
        location_index = min(int(incident['location']), space_shape[LOCATION_DIMENSION] - 1)
        type_index = min(int(incident['type']), space_shape[TYPE_DIMENSION] - 1)

        crime_space[time_index][severity_index][location_index][type_index] += 1

    return crime_space

# Class to manage crime data
class CrimeDataManager:
    def __init__(self):
        self.incidents = []

    def add_incident(self, time, severity, location, crime_type):
        self.incidents.append({'time': time, 'severity': severity, 'location': location, 'type': crime_type})

    def analyze_space(self):
        """ Analyze the crime incidents within the 4D space. """
        crime_space = create_crime_space(self.incidents)
        return crime_space

    def point_of_absence_analysis(self, crime_space):
        """ Analyze points of absence in the crime space. """
        # Example logic to identify areas with zero incidents
        absence_points = np.argwhere(crime_space == 0)
        return absence_points

    def free_association_effervescence(self, crime_space):
        """ Explore relationships between crime values in the space. """
        associations = []
        # Analyzing associations based on the space structure
        for i in range(crime_space.shape[TIME_DIMENSION]):
            for j in range(crime_space.shape[SEVERITY_DIMENSION]):
                for k in range(crime_space.shape[LOCATION_DIMENSION]):
                    if crime_space[i][j][k].sum() > 0:
                        associations.append((i, j, k, crime_space[i][j][k].sum()))
        return associations

# Example usage
def main():
    crime_manager = CrimeDataManager()

    # Add sample incidents (time, severity, location, type)
    crime_manager.add_incident(time=1, severity=7, location=2, crime_type=0)  # Example values
    crime_manager.add_incident(time=2, severity=5, location=1, crime_type=1)
    crime_manager.add_incident(time=3, severity=9, location=3, crime_type=0)
    crime_manager.add_incident(time=1, severity=6, location=1, crime_type=2)
    crime_manager.add_incident(time=2, severity=8, location=0, crime_type=3)

    # Analyze the space
    crime_space = crime_manager.analyze_space()

    # Analyze points of absence
    absence_points = crime_manager.point_of_absence_analysis(crime_space)
    print("Points of Absence (zero incidents):")
    for point in absence_points:
        print(f"Time: {point[0]}, Severity: {point[1]}, Location: {point[2]}, Type: {point[3]}")

    # Explore free association of effervescence
    associations = crime_manager.free_association_effervescence(crime_space)
    print("\nAssociations within the Crime Space:")
    for association in associations:
        print(f"Time: {association[0]}, Severity: {association[1]}, Location: {association[2]}, Count: {association[3]}")

if __name__ == "__main__":
    main()