Conway's game of life

now that we have a working evolve function, we can carry out longer-term simulations of the game of life. (if you haven't gotten evolve to work yet, we provide it for this step.)

consider a wrapper loop that will simulate a glider traveling for nt time steps:

import numpy as np
import matplotlib. pyplot as plt

glider = np. zeros( ( 24,24 ) )
glider[ 10,10 ] = 1
glider[ 11,10 ] = 1
glider[ 12,10 ] = 1
glider[ 12,11 ] = 1
glider[ 11,12 ] = 1

nt = 10
for t in range( nt ):
cells = evolve( cells )
plt. imshow( cells )
plt. show()
write a program which implements a loop evolving pentadecathlon for thirty time steps. you should implement a list of ndarrays named pd_list, each containing a single generation of pentadecathlon's evolution.

pentadecathlon = np. zeros( ( 24,24 ) )
pentadecathlon[ 10: 20,10 ] = 1
pentadecathlon[ 12, 9 ] = 1
pentadecathlon[ 12,10 ] = 0
pentadecathlon[ 12,11 ] = 1
pentadecathlon[ 17, 9 ] = 1
pentadecathlon[ 17,10 ] = 0
pentadecathlon[ 17,11 ] = 1
your submission for this part should include the list of ndarrays named pd_list. if you wish to see the results at each time step, move plt. show() into the loop.