nums.numpy.split

nums.numpy.split(ary, indices_or_sections, axis=0)[source]

Split an array into multiple sub-arrays as views into ary.

This docstring was copied from numpy.split.

Some inconsistencies with the NumS version may exist.

Parameters

ary (BlockArray) – Array to be divided into sub-arrays.
indices_or_sections (int or 1-D array) –
If indices_or_sections is an integer, N, the array will be divided into N equal arrays along axis. If such a split is not possible, an error is raised.

If indices_or_sections is a 1-D array of sorted integers, the entries indicate where along axis the array is split. For example, [2, 3] would, for axis=0, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along axis, an empty sub-array is returned correspondingly.
axis (int, optional) – The axis along which to split, default is 0.

Returns

sub-arrays – A list of sub-arrays as views into ary.

Return type

list of BlockArrays

Raises

ValueError – If indices_or_sections is given as an integer, but a split does not result in equal division.

See also

array_split: Split an array into multiple sub-arrays of equal or near-equal size. Does not raise an exception if an equal division cannot be made.
hsplit: Split array into multiple sub-arrays horizontally (column-wise).
vsplit: Split array into multiple sub-arrays vertically (row wise).
dsplit: Split array into multiple sub-arrays along the 3rd axis (depth).
concatenate: Join a sequence of arrays along an existing axis.
stack: Join a sequence of arrays along a new axis.
hstack: Stack arrays in sequence horizontally (column wise).
vstack: Stack arrays in sequence vertically (row wise).
dstack: Stack arrays in sequence depth wise (along third dimension).

Notes

Split currently supports integers only.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> x = nps.arange(9.0)  
>>> [a.get() for a in  nps.split(x, 3)]  
[array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]

nums.numpy.concatenate

nums.numpy.identity