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?

## Answers:

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### Method 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]])

### Method 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]])

### Method 3

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 array a - 1D array framelen - Samples per frame frameadv - Samples between starts of consecutive frames Set to framelen for non-overlaping consecutive frames Modified from Divakar's 10/17/16 11:20 solution: https://stackoverflow.com/questions/40084931/taking-subarrays-from-numpy-array-with-given-stride-stepsize CAVEATS: Assumes array is contiguous Output 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") # Output nrows = ((a.size-framelen)//frameadv)+1 oshape = (nrows, framelen) # Size of each element in a n = a.strides[0] # Indexing in the new object will advance by frameadv * element size ostrides = (frameadv*n, n) return np.lib.stride_tricks.as_strided(a, shape=oshape, strides=ostrides, writeable=False)

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0