Maze Generation by Cellular Automaton

# ca_maze.py
import numpy as np
from scipy.signal import convolve2d
import matplotlib.pyplot as plt

# Create a maze using the cellular automaton approach described at              
# https://scipython.com/blog/maze-generation-by-cellular-automaton/             
# The frames for animation of the growth of the maze are saved to               
# the subdirectory ca_frames/.                                                  
# Christian Hill, January 2018.

def ca_step(X):
    """Evolve the maze by a single CA step."""

    K = np.ones((3, 3))
    n = convolve2d(X, K, mode='same', boundary='wrap') - X
    return (n == 3) | (X & ((n > 0) & (n < 6)))

# Maze size
nx, ny = 200, 150
X = np.zeros((ny, nx), dtype=np.bool)
# Size of initial random area (must be even numbers)
mx, my = 20, 16

# Initialize a patch with a random mx x my region
r = np.random.random((my, mx)) > 0.75
X[ny//2-my//2:ny//2+my//2, nx//2-mx//2:nx//2+mx//2] = r

# Total number of iterations
nit = 400
# Make an image every ipf iterations
ipf = 10

# Figure dimensions (pixels) and resolution (dpi)
width, height, dpi = 600, 450, 10
fig = plt.figure(figsize=(width/dpi, height/dpi), dpi=dpi)
ax = fig.add_subplot(111)

for i in range(nit):
    X = ca_step(X)
    if not i % ipf:
        print('{}/{}'.format(i,nit))
        im = ax.imshow(X, cmap=plt.cm.binary, interpolation='nearest')
        plt.axis('off')
        plt.savefig('ca_frames/_img{:04d}.png'.format(i), dpi=dpi)
        plt.cla()