How to Efficiently Create Subarrays from a NumPy Array with a Stride?

Taking Subarrays from Numpy Array with Stride/Stepsize

In this context, we discuss an efficient approach in Python NumPy to create subarrays from a given array with a specific stride.

To achieve this, we explore two methods:

1. Broadcasting Approach:

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

In this method, broadcasting is used to create a matrix of strides.

2. Efficient NumPy Strides Approach:

def strided_app(a, L, 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))

This method utilizes NumPy's efficient strides to create the subarray matrix.

Example:

Consider an array a:

a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])

To create subarrays of length 5 with a stride of 3, we can use either of the methods:

subarrays_broadcasting = broadcasting_app(a, L=5, S=3)
subarrays_strides = strided_app(a, L=5, S=3)

Both approaches will produce the following result:

[[ 1  2  3  4  5]
 [ 4  5  6  7  8]
 [ 7  8  9 10 11]]

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