:mod:`heat.indexing`
=========================
.. py:module:: heat.core.indexing

.. autoapi-nested-parse::

   Functions relating to indices of items within DNDarrays, i.e. `where()`



Module Contents
---------------


.. function:: nonzero(x: heat.core.dndarray.DNDarray) -> heat.core.dndarray.DNDarray

   Return a :class:`~heat.core.dndarray.DNDarray` containing the indices of the elements that are non-zero (using ``torch.nonzero``).
   If ``x`` is split then the result is split in the first dimension. However, this :class:`~heat.core.dndarray.DNDarray`
   can be UNBALANCED as it contains the indices of the non-zero elements on each node.
   Returns an array with one entry for each dimension of ``x``, containing the indices of the non-zero elements in that dimension.
   The values in ``x`` are always tested and returned in row-major, C-style order.
   The corresponding non-zero values can be obtained with: ``x[nonzero(x)]``.

   :param x: Input array
   :type x: DNDarray

   .. rubric:: Examples

   >>> import heat as ht
   >>> x = ht.array([[3, 0, 0], [0, 4, 1], [0, 6, 0]], split=0)
   >>> ht.nonzero(x)
   DNDarray([[0, 0],
             [1, 1],
             [1, 2],
             [2, 1]], dtype=ht.int64, device=cpu:0, split=0)
   >>> y = ht.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], split=0)
   >>> y > 3
   DNDarray([[False, False, False],
             [ True,  True,  True],
             [ True,  True,  True]], dtype=ht.bool, device=cpu:0, split=0)
   >>> ht.nonzero(y > 3)
   DNDarray([[1, 0],
             [1, 1],
             [1, 2],
             [2, 0],
             [2, 1],
             [2, 2]], dtype=ht.int64, device=cpu:0, split=0)
   >>> y[ht.nonzero(y > 3)]
   DNDarray([4, 5, 6, 7, 8, 9], dtype=ht.int64, device=cpu:0, split=0)


.. function:: where(cond: heat.core.dndarray.DNDarray, x: Union[None, int, float, heat.core.dndarray.DNDarray] = None, y: Union[None, int, float, heat.core.dndarray.DNDarray] = None) -> heat.core.dndarray.DNDarray

   Return a :class:`~heat.core.dndarray.DNDarray` containing elements chosen from ``x`` or ``y`` depending on condition.
   Result is a :class:`~heat.core.dndarray.DNDarray` with elements from ``x`` where cond is ``True``,
   and elements from ``y`` elsewhere (``False``).

   :param cond: Condition of interest, where true yield ``x`` otherwise yield ``y``
   :type cond: DNDarray
   :param x: Values from which to choose. ``x``, ``y`` and condition need to be broadcastable to some shape.
   :type x: DNDarray or int or float, optional
   :param y: Values from which to choose. ``x``, ``y`` and condition need to be broadcastable to some shape.
   :type y: DNDarray or int or float, optional

   :raises NotImplementedError: if splits of the two input :class:`~heat.core.dndarray.DNDarray` differ
   :raises TypeError: if only x or y is given or both are not DNDarrays or numerical scalars

   .. rubric:: Notes

   When only condition is provided, this function is a shorthand for :func:`nonzero`.

   .. rubric:: Examples

   >>> import heat as ht
   >>> x = ht.arange(10, split=0)
   >>> ht.where(x < 5, x, 10 * x)
   DNDarray([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90], dtype=ht.int64, device=cpu:0, split=0)
   >>> y = ht.array([[0, 1, 2], [0, 2, 4], [0, 3, 6]])
   >>> ht.where(y < 4, y, -1)
   DNDarray([[ 0,  1,  2],
             [ 0,  2, -1],
             [ 0,  3, -1]], dtype=ht.int64, device=cpu:0, split=None)