Conway's Game of Life

Load Packages

import random
import numpy as np
from scipy.signal import convolve2d

Generate initial random matrix

def create_random_matrix(rows, cols, fraction, seed=None):
    # Set the random seed if provided
    if seed is not None:
        random.seed(str(seed))
    else:
        seed = str(random.randint(0,2000000))
        print("Seed: " + seed)
        random.seed(seed)

    # Calculate the total number of elements in the matrix
    total_elements = rows * cols

    # Calculate the number of 1's based on the fraction
    num_ones = int(total_elements * fraction)

    # Create a matrix of zeros
    matrix = np.zeros((rows, cols))

    # Set random positions to 1
    positions = random.sample(range(total_elements), num_ones)
    for pos in positions:
        row = pos // cols
        col = pos % cols
        matrix[row, col] = 1

    return matrix

Action CGL steps using convolution

def game_of_life(matrix, steps):
    kernel = np.array([[1, 1, 1],
                       [1, 0, 1],
                       [1, 1, 1]])

    for _ in range(steps):
        # Perform convolution with the kernel
        neighbors_count = convolve2d(matrix, kernel, mode='same', boundary='wrap')

        # Apply the rules of the Game of Life
        matrix = np.logical_or(np.logical_and(matrix, neighbors_count == 2),
                               neighbors_count == 3)

    return matrix

Print the matrix

def render_matrix(matrix):
    for row in matrix:
        for cell in row:
            if cell:
                print("█", end="")
            else:
                print(" ", end="")
        print()

Example

render_matrix(game_of_life(create_random_matrix(25,150,0.1005,554874),1))

                                                      █                                           █      █                                           █
                                                      ███              ██                               █                                    █ █      
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                 █          ██                                  ██ █   █                                                                              
               ███          █                                      █         █   ██                                                                   
               ██ █                                                █         ███                               █                                      
             █ █                              █                                                          ██                                           
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█ █                                    ██                                         █                                    █               █ █       █    
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