Metadata-Version: 2.1
Name: seq-alignment
Version: 1.0.0
Summary: A package to compute edit distance measurements and sequence alignments.
Home-page: https://github.com/ryanlstevens/seq_alignment
Author: Ryan Stevens and James Nesbit
Author-email: ryan.louis.stevens@gmail.com
License: UNKNOWN
Description: # seq_alignment : A python tool for sequence alignment and edit distances 
        [![Build Status](https://travis-ci.com/ryanlstevens/py_string_matchers.svg?token=x6iEoqTBFHFvu6oqChJH&branch=main)](https://travis-ci.com/ryanlstevens/py_string_matchers) ![Git Build](https://github.com/ryanlstevens/seq_alignment/workflows/Git%20Build/badge.svg)
        
        Module to determine distances between two strings (or two lists of strings). The primary use case of this package is to allow users to both compute distance measures using different distance algorithms. Additionally, we provide additional functionality to both visualize the alignment of the two strings. This allows users to determine how one string is transformed into another.
        
        For reference on differences between global and local alignment, see Chapter 11 and 12 of [Algorithms on Strings, Trees, and Sequences](https://www.amazon.com/Algorithms-Strings-Trees-Sequences-Computational/dp/0521585198).
        
        # Installation
        
        Project is available on pypi, installation via pip:
        
        ```bash
        
        pip install seq_alignment
        
        ```
        
        # Algorithms Available
        
        ## Global Alignment (Wagner Fischer)
        
        To run the matching, you import `global_similarity` class, initialize with the strings you want to compare (accepts strings or list of strings), and then run your algorithm.
        
        The main options available are whether to compute back-trace, to not compute backtrace and return on edit distance (or string similarity) values set `backtrace = False` when initalizing the class.
        
        ```python
        ## Run algo getting ONLY edit distance 
        ## 
        ## This runs fast due to not having to store
        ## backtrace values
        
        from seq_alignment import global_similarity
        
        # Test values
        str1=['v','i','n','e']
        str2=['v','i','n']
        
        # Initalize matching class function
        sim_init = global_similarity(str1,str2,False)
        
        # Run matching 
        sim_init.run()
        
        # Get edit distance
        print("EDIT DISTANCE")
        print(sim_init.match_distance)
        ```
        
        ## Local Alignment (Smith Waterman)
        
        Same syntax to running global alignment, but now you import the `local_similarity` class.
        
        ```python
        ## Run algo getting ONLY edit distance 
        ## 
        ## This runs fast due to not having to store
        ## backtrace values
        
        from seq_alignment import local_similarity
        
        # Test values
        str1=['v','i','n','e']
        str2=['v','i','n']
        
        # Initalize matching class function
        sim_init = local_similarity(str1,str2,False)
        
        # Run matching 
        sim_init.run()
        
        # Get edit distance
        print("EDIT DISTANCE")
        print(sim_init.match_distance)
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
