Numpy - Array range indexing in a axis

Question

For example, If I have Numpy arrays which is initialised by:

a = np.arange(12).reshape(6,2)
[out] array([[ 0,  1],
             [ 2,  3],
             [ 4,  5],
             [ 6,  7],
             [ 8,  9],
             [10, 11]])

and

mask = np.array([0, 2])

My target is to mask array by range in a axis. like this

for i in mask:
    target.append(a[i:i+3,:])

So, it should be:

[out] array([[[0, 1],
              [2, 3],
              [4, 5]],

             [[4, 5],
              [6, 7],
              [8, 9]]])

but that's inefficient. Then, I've tried

a[mask:mask+3,:]

but it said

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: only integer scalar arrays can be converted to a scalar index

Divakar · Accepted Answer · 2018-07-06 15:22:03Z

2

Approach #1

We could leverage broadcasting to generate all indices and index -

In [19]: a
Out[19]: 
array([[ 0,  1],
       [ 2,  3],
       [ 4,  5],
       [ 6,  7],
       [ 8,  9],
       [10, 11]])

In [21]: mask
Out[21]: array([0, 2])

In [24]: a[mask[:,None] + np.arange(3)]
Out[24]: 
array([[[0, 1],
        [2, 3],
        [4, 5]],

       [[4, 5],
        [6, 7],
        [8, 9]]])

Approach #2

We can also leverage np.lib.stride_tricks.as_strided based scikit-image's view_as_windows for a more efficient solution -

In [43]: from skimage.util.shape import view_as_windows

In [44]: view_as_windows(a,(3,a.shape[1]))[mask][:,0]
Out[44]: 
array([[[0, 1],
        [2, 3],
        [4, 5]],

       [[4, 5],
        [6, 7],
        [8, 9]]])

edited Jul 6, 2018 at 15:22

answered Jul 6, 2018 at 15:17

Divakar

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1 Comment

wp32877 Over a year ago

That's exactly what I want. Thank you

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