Taking subarrays from numpy array with given stride/stepsize

Question

Lets say I have a Python Numpy array a.

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

I want to create a matrix of sub sequences from this array of length 5 with stride 3. The results matrix hence will look as follows:

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

One possible way of implementing this would be using a for-loop.

result_matrix = np.zeros((3, 5)) for i in range(0, len(a), 3):   result_matrix[i] = a[i:i+5] 

Is there a cleaner way to implement this in Numpy?

Answer 1

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]]) 

Answer 2

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]])