dpnp.vsplit

dpnp.vsplit(ary, indices_or_sections)[source]

Split an array into multiple sub-arrays vertically (row-wise).

Please refer to the dpnp.split documentation. vsplit is equivalent to split with axis=0 (default), the array is always split along the first axis regardless of the array dimension.

For full documentation refer to numpy.vsplit.

Parameters:

ary ({dpnp.ndarray, usm_ndarray}) -- Array to be divided into sub-arrays.
indices_or_sections ({int, sequence of ints}) -- If indices_or_sections is an integer, N, the array will be divided into N equal arrays along the first axis. If such a split is not possible, an error is raised. If indices_or_sections is a sequence of sorted integers, the entries indicate where along the first axis the array is split.

Returns:

sub-arrays -- A list of sub arrays. Each array is a view of the corresponding input array.

Return type:

list of dpnp.ndarray

See also

dpnp.split: Split array into multiple sub-arrays of equal size.

Examples

>>> import dpnp as np
>>> x = np.arange(16.0).reshape(4, 4)
>>> x
array([[ 0.,  1.,  2.,  3.],
       [ 4.,  5.,  6.,  7.],
       [ 8.,  9., 10., 11.],
       [12., 13., 14., 15.]])
>>> np.vsplit(x, 2)
[array([[0., 1., 2., 3.],
        [4., 5., 6., 7.]]),
 array([[ 8.,  9., 10., 11.],
        [12., 13., 14., 15.]])]
>>> np.vsplit(x, np.array([3, 6]))
[array([[ 0.,  1.,  2.,  3.],
        [ 4.,  5.,  6.,  7.],
        [ 8.,  9., 10., 11.]]),
 array([[12., 13., 14., 15.]]),
 array([], shape=(0, 4), dtype=float64)]

With a higher dimensional array the split is still along the first axis.

>>> x = np.arange(8.0).reshape(2, 2, 2)
>>> x
array([[[0., 1.],
        [2., 3.]],
       [[4., 5.],
        [6., 7.]]])
>>> np.vsplit(x, 2)
[array([[[0., 1.],
         [2., 3.]]]),
 array([[[4., 5.],
         [6., 7.]]])]