polymerist.genutils.sequences.similarity.edits
==============================================

.. py:module:: polymerist.genutils.sequences.similarity.edits

.. autoapi-nested-parse::

   For calculating the edit distance between sequences and inspecting the edits needed to go between them



Attributes
----------

.. autoapisummary::

   polymerist.genutils.sequences.similarity.edits.T


Classes
-------

.. autoapisummary::

   polymerist.genutils.sequences.similarity.edits.EditOperation
   polymerist.genutils.sequences.similarity.edits.EditInfo


Functions
---------

.. autoapisummary::

   polymerist.genutils.sequences.similarity.edits.compute_wf_matrix
   polymerist.genutils.sequences.similarity.edits.traverse_wf_matrix
   polymerist.genutils.sequences.similarity.edits.describe_edits
   polymerist.genutils.sequences.similarity.edits.levenshtein_distance


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

.. py:data:: T

.. py:class:: EditOperation(*args, **kwds)

   Bases: :py:obj:`enum.Enum`


   For annotating distinct kinds of sequence edits and their associated index offsets


   .. py:attribute:: NULL
      :value: 0



   .. py:attribute:: INSERTION
      :value: 1



   .. py:attribute:: DELETION
      :value: 2



   .. py:attribute:: SUBSTITUTION
      :value: 3



   .. py:property:: bits
      :type: tuple[int, int]


      Convert the integer value of the Enum field into its binary bits


   .. py:attribute:: offsets


.. py:class:: EditInfo

   for bundling together information about a sequence edit step


   .. py:attribute:: edit_op
      :type:  EditOperation


   .. py:attribute:: indices
      :type:  tuple[int, int]


   .. py:attribute:: distance
      :type:  int


.. py:function:: compute_wf_matrix(seq1: Sequence[T], seq2: Sequence[T], int_type: Type = int) -> numpy.ndarray[polymerist.genutils.typetools.numpytypes.Shape[polymerist.genutils.typetools.numpytypes.N, polymerist.genutils.typetools.numpytypes.M], int]

   Compute (N+1)x(M+1) matrix of Levenshtein distances between all partial prefices of a pair of sequences
   where N and M are the lengths of the first and second sequence, respectively. Implements the Wagner-Fischer algorithm


.. py:function:: traverse_wf_matrix(wf_matrix: numpy.ndarray[polymerist.genutils.typetools.numpytypes.Shape[polymerist.genutils.typetools.numpytypes.N, polymerist.genutils.typetools.numpytypes.M], int], begin_idxs: tuple[int, int] = (0, 0), end_idxs: tuple[int, int] = (-1, -1)) -> Generator[list[EditInfo], None, None]

   Takes a Wagner-Fischer Levenshtein distance matrix and returns the indices of the minimal path through the matrix
   from the origin (i.e. empty sequences) to the


.. py:function:: describe_edits(seq1: Sequence[T], seq2: Sequence[T], int_type: Type = int, indicator: str = ' -> ', delimiter: str = '\n') -> Generator[str, None, None]

   Describes step-by-step the insertion, deletion, or substitution operations needed to transform one sequence into another


.. py:function:: levenshtein_distance(seq1: Sequence[T], seq2: Sequence[T], int_type: Type = int) -> int

   Compute the Levenshtein (edit) distance between a pair of sequences with elements of compatible type
   Denotes the minimal number of insertion, deletion, or substitution operations needed to transform either sequence into the other