How to find neighbors of a 2D list in python?

Question

I have a 2D list of only 1's and 0's:

Boundaries = [
[0,0,0,0,0],
[0,1,1,1,0],
[0,1,1,1,1],
[0,1,1,1,0],
[0,0,1,0,0]]

I need to test this list to check if there are any 1's surrounded by 8 other 1's (such as the middle 1 in this list). If there is a 1 surrounded by 1's as neighbours it should then be changed into a 0 such that after running the program the list above would return as something like this:

[
[0,0,0,0,0],
[0,1,1,1,0],
[0,1,0,1,1],
[0,1,1,1,0],
[0,0,1,0,0]]

I'm trying to only use one parameter (the matrix of 1's and 0's). For some reason this is an incredibly difficult thing to wrap my head around. So far my code looks something like this:

def tempBoundaries(matrixC):
    for i in matrixC:
        for j in i:
            if j == 1:
                try:
                    if matrixC[i-1]==1 or matrixC[i+1]==1:
                     .......

This is a real struggle for whatever reason and I seem to be incapable of figuring out what to do, any tips or help would be greatly appreciated! Thanks.

Answer 1

Using scipy you'd do something like the following

import numpy

boundaries = numpy.array([
                         [0,0,0,0,0],
                         [0,1,1,1,0],
                         [0,1,1,1,1],
                         [0,1,1,1,0],
                         [0,0,1,0,0]])

counts = scipy.signal.convolve2d(boundaries, numpy.ones((3,3)), mode='same')

# which gives you a matrix with counts of the number of 1s around each point
array([[ 1.,  2.,  3.,  2.,  1.],
       [ 2.,  4.,  6.,  5.,  3.],
       [ 3.,  6.,  9.,  7.,  4.],
       [ 2.,  5.,  7.,  6.,  3.],
       [ 1.,  3.,  4.,  3.,  1.]])

 # so then you just find the points where it's == 9
 counts == 9
 array([[False, False, False, False, False],
        [False, False, False, False, False],
        [False, False,  True, False, False],
        [False, False, False, False, False],
        [False, False, False, False, False]], dtype=bool)

 # so you can update those positions
 boundaries[counts == 9] = 0

So the whole operation is simply:

boundaries = numpy.array(Boundaries)
counts = scipy.signal.convolve2d(boundaries, numpy.ones((3,3)), mode='same')
boundaries[counts == 9] = 0