How to Efficiently Extract Subarrays with Strides in NumPy?

Subarray Extraction with Strides in Numpy Arrays

Consider a Python Numpy array a:

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

We aim to extract subarrays of length 5 with a stride of 3. This results in a matrix with the following content:

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

Cleaner Implementation

While a for-loop approach is viable, Numpy provides more efficient methods:

Approach 1: Broadcasting

This approach takes advantage of 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: Strides Optimization

This method utilizes Numpy's efficient 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))

Usage Example:

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

broadcasting_app(a, L = 5, S = 3)
# [[ 1  2  3  4  5]
#  [ 4  5  6  7  8]
#  [ 7  8  9 10 11]]

strided_app(a, L = 5, S = 3)
# [[ 1  2  3  4  5]
#  [ 4  5  6  7  8]
#  [ 7  8  9 10 11]]

These approaches offer more efficient and optimized solutions for extracting subarrays with strides in Numpy arrays.

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