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#Written by David Butts | |
#This code is an implementation of a Rule 30 Wolfram model written in Python. | |
import numpy as np | |
import time | |
def Rule30_code(): | |
Rule30 = np.zeros((1000,100000)) #initilize an array to run on (timesteps, width) | |
Rule30[0,50] = 1 | |
for y in range(Rule30.shape[0]-1): #iterate through grid | |
for x in range(Rule30.shape[1]): | |
#update the next rows values according to neighbor & self value | |
right = x + 1 | |
down = y + 1 | |
left = x - 1 | |
if right >= Rule30.shape[1]: | |
right = 0 | |
if Rule30[y,right] == 1 and Rule30[y,left] == 0: | |
Rule30[down,x] = 1 | |
elif Rule30[y,x] == 0 and Rule30[y,right] == 0 and Rule30[y,left] == 1: | |
Rule30[down,x] = 1 | |
elif Rule30[y,x] == 1 and Rule30[y,right] == 0 and Rule30[y,left] == 0: | |
Rule30[down,x] = 1 | |
else: | |
Rule30[down,x] = 0 | |
#average run time for 10 runs | |
av_time = 0 | |
for run in range(10): | |
start = time.time() | |
Rule30_code() | |
end = time.time() | |
av_time += (end-start) | |
print(av_time/10.0, 'seconds for 10 runs') |
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