Metadata-Version: 2.1
Name: seqsim
Version: 0.2
Summary: Library for computing measures of similarity for sequences of hashable data types
Home-page: https://github.com/tresoldi/seqsim
Author: Tiago Tresoldi
Author-email: tiago.tresoldi@lingfil.uu.se
License: MIT
Description: # seqsim
        
        [![PyPI](https://img.shields.io/pypi/v/seqsim.svg)](https://pypi.org/project/seqsim)
        [![CI](https://github.com/tresoldi/seqsim/actions/workflows/main.yml/badge.svg)](https://github.com/tresoldi/seqsim/actions/workflows/main.yml)
        
        Python library for computing measures of similarity for sequences of hashable data types
        
        ## Installation
        
        In any standard Python environment, `seqsim` can be installed with:
        
        ```bash
        $ pip install seqsim
        ```
        
        ## Usage
        
        The library offers different methods to compare sequences of hashable Python elements
        (not only characters in a string).
        
        ```python
        >> import seqsim
        >> test1_seq_a = "kitten"
        >> test1_seq_b = "sitting"
        >> test2_seq_a = (1, 2, 3)
        >> test2_seq_b = [1, 2, 3]
        >> test3_seq_a = (1, 2, 3, 4, 5)
        >> test3_seq_b = (1, 2, 4, 3, 6, 7)
        >> test4_seq_a = (1, 2, 3)
        >> test4_seq_b = ["a", "b", "c", "d"]
        >> seqsim.distance.edit_distance(test1_seq_a, test1_seq_b)
        3.0
        >> seqsim.distance.jaccard_distance(test3_seq_a,test3_seq_b)
        0.4285714285714286
        >> seqsim.distance.mmcwpa_distance(test3_seq_a, test3_seq_b)
        0.5546382285848768
        ```
        
        Full documentation will be offered in future releases. For the moment, the library
        usage is illustrated by set of tests in the `tests/` directory.
        
        
        ## Changelog
        
        Version 0.2:
        
          - First release for new roadmap supporting sequences of any hashable Python
            datatype, importing code from other projects (mostly from `titivillus`)
            
        ## Community guidelines
        
        While the author can be contacted directly for support, it is recommended that third 
        parties use GitHub standard features, such as issues and pull requests, to contribute, 
        report problems, or seek support.
        
        Contributing guidelines, including a code of conduct, can be found in the
        `CONTRIBUTING.md` file.
        
        ## Author and citation
        
        The library is developed by Tiago Tresoldi (tiago.tresoldi@lingfil.uu.se).
        
        If you use `seqsim`, please cite it as:
        
        > Tresoldi, Tiago (2021). seqsim, a library for computing measures of similarity for
        > sequences of hashable data types. Version 0.2. Uppsala: Uppsala Universitet.
        > Available at: https://github.com/tresoldi/seqsim
        
        In BibTeX:
        
        ```
        @misc{Tresoldi2021titivillus,
          author = {Tresoldi, Tiago},
          title = {seqsim, a library for computing measures of similarity for sequences of hashable data types},
          howpublished = {\url{https://github.com/tresoldi/seqsim}},
          address = {Uppsala},
          publisher = {Uppsala Universitet},
          year = {2021},
        }
        
Keywords: sequence similarity,sequence distance,string similarity,string distance
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.6
Description-Content-Type: text/markdown
