# -----------------------------------------------------------------------------
# From Numpy to Python
# Copyright (2017) Nicolas P. Rougier - BSD license
# More information at https://github.com/rougier/numpy-book
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation


#
# The update function is called periodically from the animation library.  For convenience, it access
# all of its "arguments" through global variables.
#
def update(*args):
    global Z, M

    N = (Z[0:-2, 0:-2] + Z[0:-2, 1:-1] + Z[0:-2, 2:] +
         Z[1:-1, 0:-2]                 + Z[1:-1, 2:] +
         Z[2:  , 0:-2] + Z[2:  , 1:-1] + Z[2:  , 2:])
    birth = (N == 3) & (Z[1:-1, 1:-1] == 0)
    survive = ((N == 2) | (N == 3)) & (Z[1:-1, 1:-1] == 1)
    Z[:,:] = 0
    Z[1:-1, 1:-1][birth | survive] = 1

    # The Z array has the current state of the game.  The M array retains some history; cells that were recently live (but aren't now) have a decaying
    # fractional value shown in the visualization as a lighter color.
    # Show past activities
    M[M>0.25] = 0.25
    M *= 0.995
    M[Z==1] = 1
    # Direct activity
    # M[...] = Z
    im.set_data(M)

Z = np.zeros((300, 600))
Z[1:-1,1:-1] = np.random.randint(0,2,np.shape(Z[1:-1,1:-1]))

# Z = np.random.randint(0, 2, (300, 600))
M = np.zeros(Z.shape)

#
# The rest of this is visualization stuff we don't need to concern ourselves with.
#
size = np.array(Z.shape)
dpi = 80.0
figsize = size[1]/float(dpi), size[0]/float(dpi)
fig = plt.figure(figsize=figsize, dpi=dpi)
fig.add_axes([0.0, 0.0, 1.0, 1.0], frameon=False)
im = plt.imshow(M, interpolation='nearest', cmap=plt.cm.gray_r, vmin=0, vmax=1)
plt.xticks([]), plt.yticks([])

animation = FuncAnimation(fig, update, interval=10, frames=2000)
plt.show()