import random
import pylab
import numpy as np


def initialize_model_matrix():
    matrix = np.zeros((100, 100))
    return matrix


def set_cells(matrix, count):
    cells_count = count
    random_dict = {}
    for i in range(100):
        random_dict[i] = []
    while cells_count > 0:
        random_row = random.randint(0, 99)
        random_col = random.randint(0, 99)
        if random_col not in random_dict[random_row]:
            matrix[random_row, random_col] = 1
            random_dict[random_row].append(random_col)
            cells_count -= 1


def check_cells(matrix):
    for i in range(100):
        for j in range(100):
            if matrix[i][j] in [1, 2]:
                return True
    return False


def count_cells(matrix):
    count = 0
    for i in range(100):
        for j in range(100):
            if matrix[i][j] in [1, 2]:
                count = count + 1
    return count


def model_cells(matrix, q, p):
    q_cells = [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]
    iterations_result = 0
    while check_cells(matrix):
        for i in range(100):
            for j in range(100):
                if matrix[i][j] == 0:
                    q_count = q
                    if (i in [0, 99]) and (j in [0, 99]) and (q_count > 3):
                        q_count = 3
                    if ((((i in [0, 99]) and (j in [x for x in range(1, 99, 1)])) or
                         ((i in [x for x in range(1, 99, 1)]) and (j in [0, 99]))) and (q_count > 5)):
                        q_count = 5
                    used_indexes = []
                    selected_cells = []
                    while q_count > 0:
                        random_index = random.randint(0, 7)
                        if ((random_index not in used_indexes) and (100 > i + q_cells[random_index][0] > -1) and
                                (100 > j + q_cells[random_index][1] > -1)):
                            used_indexes.append(random_index)
                            selected_cells.append(q_cells[random_index])
                            q_count -= 1
                    for cell in selected_cells:
                        if matrix[i + cell[0]][j + cell[1]] in [1, 2]:
                            random_p_num = random.random()
                            if random_p_num < p:
                                matrix[i][j] = 1
                    continue
                if matrix[i][j] == 1:
                    matrix[i][j] = 2
                    continue
                if matrix[i][j] == 2:
                    matrix[i][j] = 3
                    continue
        if count_cells(matrix) > 50:
            iterations_result += 1
    return iterations_result


model_matrix = initialize_model_matrix()
set_cells(model_matrix, 10)
q_parameters = []
time = []
for q in range(1, 9, 1):
    current_matrix = np.copy(model_matrix)
    time.append(model_cells(current_matrix, q, 0.9))
    q_parameters.append(q)
pylab.plot(q_parameters, time)
pylab.show()