split
==========

.. currentmodule:: treetensor.numpy

    
Documentation
------------------

.. autofunction:: split

    
.. admonition:: Numpy Version Related
    :class: tip

    This documentation is based on 
    `numpy.split <https://numpy.org/doc/1.24/reference/generated/numpy.split.html>`_ 
    in `numpy v1.24.4 <https://numpy.org/doc/1.24/>`_.
    **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.
    
    .. code-block:: shell
        :linenos:
    
        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
---------------------------------

.. currentmodule:: numpy

    
.. function:: 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)]