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]: aOut[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]])

Modified version of @Divakar's code with checking to ensure that memory is contiguous and that the returned array cannot be modified. (Variable names changed for my DSP application).

def frame(a, framelen, frameadv):"""frame - Frame a 1D arraya - 1D arrayframelen - Samples per frameframeadv - Samples between starts of consecutive frames   Set to framelen for non-overlaping consecutive framesModified from Divakar's 10/17/16 11:20 solution:https://stackoverflow.com/questions/40084931/taking-subarrays-from-numpy-array-with-given-stride-stepsizeCAVEATS:Assumes array is contiguousOutput is not writable as there are multiple views on the same memory"""if not isinstance(a, np.ndarray) or \   not (a.flags['C_CONTIGUOUS'] or a.flags['F_CONTIGUOUS']):    raise ValueError("Input array a must be a contiguous numpy array")# Outputnrows = ((a.size-framelen)//frameadv)+1oshape = (nrows, framelen)# Size of each element in an = a.strides[0]# Indexing in the new object will advance by frameadv * element sizeostrides = (frameadv*n, n)return np.lib.stride_tricks.as_strided(a, shape=oshape,                                       strides=ostrides, writeable=False)