Attempts to build my own moving average background subtractor for pims

moving_average.py

import numpy as np

def moving_average(a, n=5) :
    """ creates moving average along the first axis of the
    three dimensional numpy array, aka stack of images
    """
    ret = np.cumsum(a, dtype=float, axis=0)
    ret[n:,:,:] = ret[n:,:,:] - ret[:-n,:,:]
    return ret[n - 1:,:,:] / n


from itertools import islice
def window(seq, n=2):
    "Returns a sliding window (of width n) over data from the iterable"
    "   s -> (s0,s1,...s[n-1]), (s1,s2,...,sn), ...                   "
    it = iter(seq)
    result = tuple(islice(it, n))
    if len(result) == n:
        yield result    
    for elem in it:
        result = result[1:] + (elem,)
        yield result

# Martijn's version:
def moving_averages_SO(values, size):
    for selection in window(values, size):
        yield sum(selection) / size


from itertools import tee, islice
def nwise(iterable, n):
    ts = tee(iterable, n)
    for c, t in enumerate(ts):
        next(islice(t, c, c), None)
    return zip(*ts)

def moving_averages_nw(iterable, n):
    yield from (sum(x)/n for x in nwise(iterable, n))