# Taking subarrays from numpy array with given stride/stepsize

Approach #1 : Using broadcasting -

def broadcasting_app(a, L, S ):  # Window len = L, Stride len/stepsize = S
nrows = ((a.size-L)//S)+1
return a[S*np.arange(nrows)[:,None] + np.arange(L)]


Approach #2 : Using more efficient NumPy strides -

def strided_app(a, L, S ):  # Window len = L, Stride len/stepsize = S
nrows = ((a.size-L)//S)+1
n = a.strides[0]
return np.lib.stride_tricks.as_strided(a, shape=(nrows,L), strides=(S*n,n))


Sample run -

In [143]: a
Out[143]: array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

In [144]: broadcasting_app(a, L = 5, S = 3)
Out[144]:
array([[ 1,  2,  3,  4,  5],
[ 4,  5,  6,  7,  8],
[ 7,  8,  9, 10, 11]])

In [145]: strided_app(a, L = 5, S = 3)
Out[145]:
array([[ 1,  2,  3,  4,  5],
[ 4,  5,  6,  7,  8],
[ 7,  8,  9, 10, 11]])


Starting in Numpy 1.20, we can make use of the new sliding_window_view to slide/roll over windows of elements.

And coupled with a stepping [::3], it simply becomes:

from numpy.lib.stride_tricks import sliding_window_view

# values = np.array([1,2,3,4,5,6,7,8,9,10,11])
sliding_window_view(values, window_shape = 5)[::3]
# array([[ 1,  2,  3,  4,  5],
#        [ 4,  5,  6,  7,  8],
#        [ 7,  8,  9, 10, 11]])


where the intermediate result of the sliding is:

sliding_window_view(values, window_shape = 5)
# array([[ 1,  2,  3,  4,  5],
#        [ 2,  3,  4,  5,  6],
#        [ 3,  4,  5,  6,  7],
#        [ 4,  5,  6,  7,  8],
#        [ 5,  6,  7,  8,  9],
#        [ 6,  7,  8,  9, 10],
#        [ 7,  8,  9, 10, 11]])