:mod:`neuron_morphology.feature_extractor.marked_feature`
=========================================================

.. py:module:: neuron_morphology.feature_extractor.marked_feature


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

Classes
~~~~~~~

.. autoapisummary::

   neuron_morphology.feature_extractor.marked_feature.MarkedFeature



Functions
~~~~~~~~~

.. autoapisummary::

   neuron_morphology.feature_extractor.marked_feature.specialize
   neuron_morphology.feature_extractor.marked_feature.nested_specialize
   neuron_morphology.feature_extractor.marked_feature.marked


.. data:: FeatureFn
   

   

.. data:: M
   

   

.. py:class:: MarkedFeature(marks: Set[Type[Mark]], feature: Feature, name: Optional[str] = None, preserve_marks: bool = True)

   .. attribute:: __slots__
      :annotation: = ['marks', 'feature', 'name']

      

   .. attribute:: __name__
      

      

   .. method:: __repr__(self)

      Return repr(self).
















      ..
          !! processed by numpydoc !!


   .. method:: __hash__(self)

      Return hash(self).
















      ..
          !! processed by numpydoc !!


   .. method:: add_mark(self, mark: Type[Mark])

      Assign an additional mark to this feature
















      ..
          !! processed by numpydoc !!


   .. method:: __call__(self, *args, **kwargs)

      Execute the underlying feature, passing along all arguments
















      ..
          !! processed by numpydoc !!


   .. method:: deepcopy(self, **kwargs)

      Make a deep copy of this marked feature
















      ..
          !! processed by numpydoc !!


   .. method:: partial(self, *args, **kwargs)

      Fix one or more parameters on this feature's callable  
















      ..
          !! processed by numpydoc !!


   .. method:: specialize(self, option: SpecializationOption)

      Apply a specialization option to this feature. This binds 
      parameters on the feature's __call__ method, sets 0 or more additional 
      marks, and namespaces the feature's name.


      :Parameters:

          **option** : The specialization option with which to specialize this
              feature.

      :Returns:

          a deep copy of this feature with updated callable, marks and name
              ..













      ..
          !! processed by numpydoc !!


   .. classmethod:: ensure(cls: Type[M], feature: Feature)

      If a function is not a MarkedFeature, convert it.


      :Parameters:

          **feature** : the feature to be converted
              ..

      :Returns:

          Either a marked feature generated from the input, or the input
              marked feature.













      ..
          !! processed by numpydoc !!



.. data:: Feature
   

   

.. function:: specialize(feature: Feature, specialization_set: SpecializationSet) -> Dict[str, MarkedFeature]

   Bind some of a feature's keyword arguments, using provided 
   specialization options.


   :Parameters:

       **feature** : will be used as a basis for specialization
           ..

       **specialization_set** : each element defines a particular specialization (i.e
           a set of keyword argument values and marks) to be applied to the
           feature

   :Returns:

       A dictionary mapping (namespaced) feature names to specialized features.
           ..

       Note that names are formatted as "specialization_name.base_feature_name"
           ..













   ..
       !! processed by numpydoc !!


.. function:: nested_specialize(feature: Feature, specialization_sets: SpecializationSets) -> Dict[str, MarkedFeature]

   Apply specializations hierarchically to a base feature. Generating a
   new collection of specialized features.


   :Parameters:

       **feature** : will be used as a basis for specialization
           ..

       **specialization_sets** : each element describes a set of specialization
           options. The output will have one specialization for each element of the 
           cartesian product of these sets.

   :Returns:

       A dictionary mapping namespaced feature names to specialized features.
           ..








   .. rubric:: Notes

   Specializations are applied from the start of the specialization_sets to 
   the end. This means that the generated names are structures like:
       "last_spec.middle_spec.first_spec.base_feature_name"





   ..
       !! processed by numpydoc !!


.. function:: marked(mark: Type[Mark])

   Decorator for adding a mark to a function.


   :Parameters:

       **mark** : the mark to be applied
           ..











   .. rubric:: Examples

   @marked(RequiresA)
   @marked(RequiresB)
   def some_feature_requiring_a_and_b(...):
       ...



   ..
       !! processed by numpydoc !!