Karate Club

## Name: Jaewon Son
## Date: October 29 2023
## Honor Statement: I have not given or received any unauthorized assistance on this assignment.
## Link: https://youtu.be/nPMTpUoWfac


import numpy as np


def file_load():

    """Load the text file into a Numpy array.

    Returns:
        np.ndarray: Returns a Numpy array.
    """
    
    file_path = input("Please enter the file path of the text file: ")
    text_file = np.loadtxt(file_path)   # Load the text file into a Numpy array

    print('\n========================================================================\n')
    print('The loaded interaction matrix: \n')
    print(text_file)
    print('\n========================================================================')

    return text_file


def is_symmetry(text_file):

    """Check if the Numpy array is symmetric.

    Returns:
        bool: Returns True if the array is symmetric, False otherwise.
    """

    if np.array_equal(text_file, text_file.T):
        return True
    
    else:
        return False


def result_of_analysis(text_file, is_symmetry):

    """Perform analysis and print the results.
    """
    
    if is_symmetry == False:   # Verifies symmetry of the input 2D array
        print("I can't give you answers because this text file's array is not symmetry.")
        return
    
    if is_symmetry == True:

        number_of_members = text_file.shape[0]   # Calculate the number of members in the Karate club
        print(f"\nThe number of members in the Karate club: {number_of_members} member(s)\n")
        print('========================================================================\n')

        interactions_per_member = np.sum(text_file, axis=1)   # Calculate the number of interactions for each member       
        print(f"The number of interactions each member of the club had: {interactions_per_member}\n")
        print('========================================================================\n')

        most_active_member = np.where(interactions_per_member == np.max(interactions_per_member))[0]   # Find the indices of the most active members
        most_active_interactions = np.max(interactions_per_member)   # Find the maximum number of interactions
        print(f"The indices of the most active members and the number of interactions: Indices {most_active_member} / {int(most_active_interactions)} time(s)\n")
        print('========================================================================\n')

        least_active_member = np.where(interactions_per_member == np.min(interactions_per_member))[0]   # Find the indices of the least active members
        list_active_interactions = np.min(interactions_per_member)   # Find the minimum number of interactions
        print(f"The indices of the least active members and the number of interactions: Indices {least_active_member} / {int(list_active_interactions)} time(s)\n")
        print('========================================================================\n')

        average_of_interactions = np.mean(interactions_per_member)   # Calculate the average of the number of interactions across all the members
        print(f"The average of the number of interactions: {average_of_interactions}\n")
        print('========================================================================\n')

        std_of_interactions = np.std(interactions_per_member)   # Calculate the standard deviation of the number of interactions across all the members
        print(f"The standard deviation of the number of interactions: {std_of_interactions}\n")
        print('========================================================================\n')


def main():

    """Main function.
    """

    text_file = file_load()

    true_or_false = is_symmetry(text_file)
    
    result_of_analysis(text_file, true_or_false)


if __name__ == "__main__":
    main()