GOL with numpy & scipy convolve2d

conwaysGOL_py5_numpy.py

# based on https://www.jpytr.com/post/game_of_life/
# previous version calculated neighbours twice at each step... it could be useful to color cells but I removed it.

import py5
import numpy as np
from scipy.signal import convolve2d

board_img = None

def setup():
    global status
    py5.size(600, 600)
    py5.no_smooth()
    status = get_init_status((py5.width, py5.height))

def draw():
    global status, board_img
    status = apply_conways_game_of_life_rules(status)
    board_img = py5.create_image_from_numpy(status * 255, 'L', dst=board_img)
    py5.image(board_img, 0, 0)
        
def get_init_status(size=(50, 50), initial_prob_life=0.5):
    status = np.random.uniform(0, 1, size=size) <= initial_prob_life
    return status

def apply_conways_game_of_life_rules(status):
    """Applies Conway's Game of Life rules given the current status of the game"""
    live_neighbors = count_live_neighbors(status)
    survive_underpopulation = live_neighbors >= 2
    survive_overpopulation = live_neighbors <= 3
    survive = status * survive_underpopulation * survive_overpopulation
    new_status = np.where(live_neighbors==3, True, survive)  # Born
    return new_status 

def count_live_neighbors(status):
    """Counts the number of neighboring live cells"""
    kernel = np.array(
        [[1, 1, 1],
         [1, 0, 1],
         [1, 1, 1]])
    return convolve2d(status, kernel, mode='same', boundary="wrap")

py5.run_sketch()

Very cool, thanks @hx2A !

I have used the second one at times, but I have missed the Py5Image method used in the first one, nice to know both!

You are welcome, @villares!

I thought you would be interested to see some of the other useful approaches for this kind of work.