split¶

Documentation¶

treetensor.numpy.split(ary, *args, **kwargs)[source]¶

Numpy Version Related

This documentation is based on numpy.split in numpy v1.24.4. Its arguments’ arrangements depend on the version of numpy you installed.

If some arguments listed here are not working properly, please check your numpy’s version with the following command and find its documentation.

python -c 'import numpy as np;print(np.__version__)'

The arguments and keyword arguments supported in numpy v1.24.4 is listed below.

Description From Numpy v1.24¶

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

Parameters¶

ary : ndarray
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 : list of ndarrays
A list of sub-arrays as views into ary.

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).

Examples¶

>>> x = np.arange(9.0)
>>> np.split(x, 3)
[array([0.,  1.,  2.]), array([3.,  4.,  5.]), array([6.,  7.,  8.])]

>>> x = np.arange(8.0)
>>> np.split(x, [3, 5, 6, 10])
[array([0.,  1.,  2.]),
 array([3.,  4.]),
 array([5.]),
 array([6.,  7.]),
 array([], dtype=float64)]