A simple Python matplotlib implementation of Conway's Game of Life.

readme.md

conway-game-of-life-python numpy

Vectorized implementation of Conway's game of life in Python Numpy

This is a vectorized implementation of conway's game of life written in Numpy with a single loop where the full grid is evaluated at once.

It implements toroidal boundary conditions and a random initial state.

conway.py

################################################################################
# conway.py
#
# Original author: electronut.in
# Added vectorization by: David Contreras
# 
# Description:
#
# A vectorized Python/matplotlib implementation of Conway's Game of Life.
################################################################################

import numpy as np
import matplotlib.pyplot as plt 
import matplotlib.animation as animation

# Define number of rows and columns

N = 100
M = 120

# Making and array of rows and columns
rows = np.arange(0,N)
columns = np.arange(0,M)

# Define rows and columns for toroidal boundary conditions - x and y wrap around 
top_rows = np.mod(rows-1, N)
bottom_rows = np.mod(rows+1, N)
left_columns = np.mod(columns-1, M)
right_columns = np.mod(columns+1, M)

# populate grid with random alive / dead - more dead than alive
grid = np.random.choice([0,1], size=(N,M), p = [0.85, 0.15])

def update(data):
  global grid
  total = np.sum(
    np.stack(
      [
        grid[np.ix_(top_rows, left_columns)],     # top left
        grid[np.ix_(top_rows, columns)],          # top
        grid[np.ix_(top_rows, right_columns)],    # top right
        grid[np.ix_(rows, left_columns)],         # left
        grid[np.ix_(rows, right_columns)],        # right
        grid[np.ix_(bottom_rows, left_columns)],  # bottom left
        grid[np.ix_(bottom_rows, columns)],       # bottom
        grid[np.ix_(bottom_rows, right_columns)], # bottom right
      ]),
      axis=0)
  
  newGrid = np.zeros_like(grid)
  # apply Conway's rules
  newGrid[(grid & (total == 2)) | total == 3] = 1
  # update data
  mat.set_data(newGrid)
  grid = newGrid
  return [mat]

# set up animation
fig, ax = plt.subplots()
mat = ax.matshow(grid)
ani = animation.FuncAnimation(fig, update, interval=50, save_count=50)
plt.show()