:py:mod:`kwarray.util_misc`
===========================

.. py:module:: kwarray.util_misc


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

Classes
~~~~~~~

.. autoapisummary::

   kwarray.util_misc.FlatIndexer




.. py:class:: FlatIndexer(lens)

   Bases: :py:obj:`ubelt.NiceRepr`

   Creates a flat "view" of a jagged nested indexable object.
   Only supports one offset level.

   :Parameters: **lens** (*list*) -- a list of the lengths of the nested objects.

   Doctest:
       >>> self = FlatIndexer([1, 2, 3])
       >>> len(self)
       >>> self.unravel(4)
       >>> self.ravel(2, 1)

   .. py:method:: fromlist(cls, items)
      :classmethod:

      Convenience method to create a :class:`FlatIndexer` from the list of
      items itself instead of the array of lengths.

      :Parameters: **items** (*List[list]*) -- a list of the lists you want to flat index over

      :returns: FlatIndexer


   .. py:method:: __len__(self)


   .. py:method:: unravel(self, index)

      :Parameters: **index** -- raveled index

      :returns: outer and inner indices
      :rtype: Tuple[int, int]

      .. rubric:: Example

      >>> import kwarray
      >>> rng = kwarray.ensure_rng(0)
      >>> items = [rng.rand(rng.randint(0, 10)) for _ in range(10)]
      >>> self = kwarray.FlatIndexer.fromlist(items)
      >>> index = np.arange(0, len(self))
      >>> outer, inner = self.unravel(index)
      >>> recon = self.ravel(outer, inner)
      >>> # This check is only possible because index is an arange
      >>> check1 = np.hstack(list(map(sorted, kwarray.group_indices(outer)[1])))
      >>> check2 = np.hstack(kwarray.group_consecutive_indices(inner))
      >>> assert np.all(check1 == index)
      >>> assert np.all(check2 == index)
      >>> assert np.all(index == recon)


   .. py:method:: ravel(self, outer, inner)

      :Parameters: * **outer** -- index into outer list
                   * **inner** -- index into the list referenced by outer

      :returns: the raveled index
      :rtype: index