import collections
import numpy as np
from scipy import sparse


class Matrix:
    def __init__(self):
        self.__matrix = collections.defaultdict(dict)

    def set(self, row, column, value):
        if value != 0:
            self.__matrix[row][column] = value
        else:
            if self.__matrix.get(row) and self.__matrix.get(row).get(column):
                del self.__matrix[row][column]

    def get(self, row, column):
        if self.__matrix.get(row) and self.__matrix.get(row).get(column):
            return self.__matrix[row][column]
        else:
            return 0

    def print(self):
        print(self.__matrix)


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 = Matrix()
    #
    # for row in range(len(input_matrix)):
    #     for column in range(len(input_matrix[0])):
    #         sparse_matrix.set(row, column, input_matrix[row][column])

    sparce_matrix = {}
    rows = len(input_matrix)
    columns = len(input_matrix[0])

    for row in range(rows):
        for column in range(columns):
            if input_matrix[row][column] != 0:
                sparce_matrix[(row, column)] = input_matrix[row][column]

    # Aufgabe 1c
    print("\n The sparce matrix without numpy\n" + str(sparce_matrix))
    # sparse_matrix.print()
    # 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))