I wrote this Python code to do a particular computation in a bigger project and it works fine for smaller values of
N but it doesn't scale up very well for large values and even though I ran it for a number of hours to collect the data, I was wondering if there was a way to speed this up
import numpy as np def FillArray(arr): while(0 in arr): ind1 = np.random.randint(0,N) if(arr[ind1]==0): if(ind1==0): arr[ind1] = 1 arr[ind1+1] = 2 elif(ind1==len(arr)-1): arr[ind1] = 1 arr[ind1-1] = 2 else: arr[ind1] = 1 arr[ind1+1] = 2 arr[ind1-1] = 2 else: continue return arr N=50000 dist =  for i in range(1000): arr = [0 for x in range(N)] dist.append(Fillarr(arr).count(2))
N = 50,000, it currently takes slightly over a minute on my computer for one iteration to fill the array. So if I want to simulate this, lets say, a 1000 times, it takes many hours. Is there something I can do to speed this up?
Edit 1: I forgot to mention what it actually does. I have a list of length
N and I initialize it by having zeros in each entry. Then I pick a random number between
N and if that index of the list has a zero, I replace it by
1 and its neighboring indices by
2 to indicate they are not filled by
1 but they can't be filled again. I keep doing this till I populate the whole list by
2 and then I count how many of the entries contain
2 which is the result of this computation. Thus I want to find out if I fill an array randomly with this constraint, how many entries will not be filled.
Obviously I do not claim that this is the most efficient way find this number so I am hoping that perhaps there is a better alternative way if this code can't be speeded up.