import numpy as np
from scipy import sparse

if __name__ == "__main__":
    # Aufgabe 1b
    input_matrix = [[3, 0, -2, 11],
                    [0, 0, 9, 0],
                    [0, 7, 0, 0],
                    [0, 0, 0, 0]]
    sparse_matrix = []
    for row in range(len(input_matrix)):
        for column in range(len(input_matrix[0])):
            if input_matrix[row][column] != 0:
                # Aufgabe 1d
                print("Index: ["+str(row)+"]"+"["+str(column)+"] = "+str(input_matrix[row][column]))
                triplet = [row, column, input_matrix[row][column]]  # saving index of not null values
                sparse_matrix.append(triplet)

    # Aufgabe 1c
    print("\nThe sparce matrix computed without numpy\n" + str(sparse_matrix))

    # faster way with numpy
    input_matrix = np.array(input_matrix)
    rows, columns = input_matrix.shape
    sparse_matrix = sparse.csr_matrix(input_matrix)
    print("\nThe sparce matrix computed with numpy\n" + str(sparse_matrix))