I have a problem with the speed of my program. I want to calculate the average of four neighbors in a huge array. Here is a part of my code. Do you have any ideas how to change the last line? Or should I use another array?
for a in np.arange(100000):
for x in np.arange(size):
for y in np.arange(size):
if unchangeableflag[x*size+y] == 0:
vnew[x*size+y] = (v[(x+1)*size+y] + v[(x-1)*size+y] + v[x*size+y+1] + v[x*size+y-1]) / 4.0
You would not need the loop at all. Assuming v, vnew and unchangeableflag are 1-d arrays with size*size entries, you can do
v = v.reshape(size, size)
vnew = vnew.reshape(size, size)
unchangeableflag = unchangeableflag.reshape(size, size)
average = v[1:-1, 2:]
average += v[1:-1, :-2]
average += v[2:, 1:-1]
average += v[-2:, 1:-1]
average /= 4.0
vnew[1:-1, 1:-1][unchangeableflag[1:-1, 1:-1] == 0] = average
But what are you actually trying to achieve? This looks suspiciously like you could get away with some application of the discrete Laplacian.
(Note that this assumes that v contains floating point numbers. If the dtype of `v´ is sime integral type, you need a slight modification.)