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 | |
| # | |
| # 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() |