dacapo.utils.affinities
=======================

.. py:module:: dacapo.utils.affinities


Attributes
----------

.. autoapisummary::

   dacapo.utils.affinities.logger


Functions
---------

.. autoapisummary::

   dacapo.utils.affinities.seg_to_affgraph
   dacapo.utils.affinities.padding


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

.. py:data:: logger

.. py:function:: seg_to_affgraph(seg: numpy.ndarray, neighborhood: List[funlib.geometry.Coordinate]) -> numpy.ndarray

   Constructs an affinity graph from a segmentation.

   :param seg: The segmentation array.
   :type seg: np.ndarray
   :param neighborhood: The list of coordinates representing the neighborhood.
   :type neighborhood: List[Coordinate]

   :returns: The affinity graph.
   :rtype: np.ndarray

   :raises RuntimeError: If the number of dimensions is not 2 or 3.

   .. rubric:: Examples

   >>> seg = np.array([[1, 1, 2], [1, 1, 2], [3, 3, 4]])
   >>> neighborhood = [Coordinate(1, 0), Coordinate(0, 1)]
   >>> seg_to_affgraph(seg, neighborhood)
   array([[[0, 0, 0],
           [0, 0, 0],
           [0, 0, 0]],
   <BLANKLINE>
          [[1, 1, 0],
           [1, 1, 0],
           [0, 0, 0]]], dtype=int32)

   .. rubric:: Notes

   The affinity graph is represented as:
   shape = (e, z, y, x)
   nhood.shape = (edges, 3)


.. py:function:: padding(neighborhood, voxel_size)

   Get the appropriate padding to make sure all provided affinities are "True"

   :param neighborhood: The list of coordinates representing the neighborhood.
   :type neighborhood: List[Coordinate]
   :param voxel_size: The voxel size.
   :type voxel_size: Coordinate

   :returns: The negative and positive padding.
   :rtype: Tuple[Coordinate, Coordinate]

   :raises RuntimeError: If the number of dimensions is not 2 or 3.

   .. rubric:: Examples

   >>> neighborhood = [Coordinate(1, 0), Coordinate(0, 1)]
   >>> voxel_size = Coordinate(1, 1)
   >>> padding(neighborhood, voxel_size)
   (Coordinate(0, 0), Coordinate(1, 1))