dacapo.experiments.tasks.hot_distance_task_config
=================================================

.. py:module:: dacapo.experiments.tasks.hot_distance_task_config


Classes
-------

.. autoapisummary::

   dacapo.experiments.tasks.hot_distance_task_config.HotDistanceTaskConfig


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

.. py:class:: HotDistanceTaskConfig



   Class for generating TaskConfigs for the HotDistanceTask, which predicts one hot encodings of classes, as well as signed distance transforms of those classes.

   .. attribute:: task_type

      A reference to the Hot Distance Task class.

   .. attribute:: channels

      A list of channel names.

      :type: List[str]

   .. attribute:: clip_distance

      Maximum distance to consider for false positive/negatives.

      :type: float

   .. attribute:: tol_distance

      Tolerance distance for counting false positives/negatives.

      :type: float

   .. attribute:: scale_factor

      The amount by which to scale distances before applying
      a tanh normalization. Defaults to 1.

      :type: float

   .. attribute:: mask_distances

      Whether or not to mask out regions where the true distance to
      object boundary cannot be known. Defaults to False

      :type: bool

   .. method:: verify(self) -> Tuple[bool, str]

      This method verifies the HotDistanceTaskConfig object.

   .. note::

      Generating distance transforms over regular affinities provides you with a denser
      signal, i.e., one misclassified pixel in an affinity prediction can merge 2
      otherwise very distinct objects, a situation that cannot happen with distances.


   .. py:attribute:: task_type


   .. py:attribute:: channels
      :type:  List[str]


   .. py:attribute:: clip_distance
      :type:  float


   .. py:attribute:: tol_distance
      :type:  float


   .. py:attribute:: scale_factor
      :type:  float


   .. py:attribute:: mask_distances
      :type:  bool