Metadata-Version: 2.1
Name: seqsim
Version: 0.3
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/evotext/seqsim/actions/workflows/main.yml/badge.svg)](https://github.com/evotext/seqsim/actions/workflows/main.yml)
        [![Documentation Status](https://readthedocs.org/projects/seqsim/badge/?version=latest)](https://seqsim.readthedocs.io/en/latest/?badge=latest)
        
        Python library for computing measures of distance and similarity for sequences of hashable data types.
        
        ![scriptorium](https://raw.githubusercontent.com/evotext/seqsim/main/docs/scriptorium_small.jpg)
        
        While developed as a general-purpose library, `seqsim` is mostly designed for usage
        in research within the field of cultural evolution, and particularly of the
        cultural evolution of textual traditions. Some methods act as a thin-wrapper
        to either the standard Python library or of to other libraries such as
        [textdistance](https://github.com/life4/textdistance). 
        
        ## 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 arbitrary hashable elements.
        It is possible to mix sequence and element types.
        
        Full documentation is offered at [ReadTheDocs](https://seqsim.readthedocs.io/en/latest/?badge=latest) and
        code with almost complete coverage is offered in the
        [tests](https://github.com/evotext/seqsim/tree/main/tests). For most common usages,
        a wrapper `.distance()` function can be used.
        
        ```python
        >>> import seqsim
        >>> seqsim.edit.levenshtein_dist("kitten", "string")
        5
        >>> seqsim.edit.levenshtein_dist("kitten", "string", normal=True)
        >>> 0.8333333333333334
        >>> seqsim.sequence.ratcliff_obershelp([1,2,3,4], [2,4,3,5])
        0.5
        >>> seqsim.compression.entropy_ncd([1,2,3,4], [2,4,3,5])
        0.08333333333333333
        ```
        
        ## Demonstration
        
        The core of the library are the metrics for sequence distance/similarity on
        arbitrary data types, as in the table below.
        
        | Method                       |   "kitten" / "sitting" |   (1, 2, 3, 4) / (3, 4, 2, 1) |
        |------------------------------|------------------------|-------------------------------|
        | arith_ncd                    |               1.25     |                      0.888889 |
        | arith_ncd_normal             |               1.25     |                      0.888889 |
        | birnbaum_simil               |              10        |                      5        |
        | birnbaum_simil_normal        |               0.3125   |                      0.5      |
        | birnbaun                     |               0.565217 |                      0.5      |
        | birnbaun_normal              |               0.565217 |                      0.5      |
        | bulk_delete                  |               3        |                      3        |
        | bulk_delete_normal           |               0.428571 |                      0.75     |
        | damerau                      |               3        |                      4        |
        | damerau_normal               |               0.428571 |                      1        |
        | entropy                      |               0.101341 |                      0        |
        | entropy_normal               |               0.101341 |                      0        |
        | fast_birnbaum                |               0.666667 |                      0.7      |
        | fast_birnbaum_normal         |               0.666667 |                      0.7      |
        | fast_birnbaum_simil          |               7        |                      3        |
        | fast_birnbaum_simil_normal   |               0.25     |                      0.3      |
        | fragile_ends_simil           |               3        |                      3.5      |
        | fragile_ends_simil_normal    |               0.5      |                      1        |
        | jaccard                      |               0.7      |                      0        |
        | jaccard_normal               |               0.7      |                      0        |
        | jaro                         |               0.253968 |                      0.5      |
        | jaro_normal                  |               0.253968 |                      0.5      |
        | jaro_winkler                 |               0.253968 |                      0.5      |
        | jaro_winkler_normal          |               0.253968 |                      0.5      |
        | levenshtein                  |               3        |                      4        |
        | levenshtein_normal           |               0.428571 |                      1        |
        | mmcwpa                       |               0.538462 |                      0.387628 |
        | mmcwpa_normal                |               0.538462 |                      0.387628 |
        | ratcliff_obershelp           |               0.384615 |                      0.5      |
        | ratcliff_obershelp_normal    |               0.384615 |                      0.5      |
        | sorensen                     |               0.384615 |                      0        |
        | sorensen_normal              |               0.384615 |                      0        |
        | stemmatological_simil        |               3        |                      3        |
        | stemmatological_simil_normal |               0.428571 |                      0.75     |
        | subseq_jaccard               |               0.751556 |                      0.547008 |
        | subseq_jaccard_normal        |               0.751556 |                      0.547008 |
        
        
        ## Changelog
        
        Version 0.3:
        
          - Improvements to code quality, documentation, and references
          - Added new methods and scaffolding for future expansions
        
        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 authors 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.
        
        ## Authors and citation
        
        The library is developed in the context of "[Cultural Evolution of Text](https://www.evotext.se)",
        project, with funding from the Riksbankens Jubileumsfond (grant agreement ID:
        [MXM19-1087:1](https://www.rj.se/en/anslag/2019/cultural-evolution-of-texts/)).
        
        If you use `seqsim`, please cite it as:
        
        > Tresoldi, Tiago; Maurits, Luke; Dunn, Michael. (2021). seqsim, a library
        > for computing measures of distance and similarity for sequences of hashable data
        > types. Version 0.3. Uppsala: Uppsala universitet.
        > Available at: https://github.com/evotext/seqsim
        
        In BibTeX:
        
        ```
        @misc{Tresoldi2021seqsim,
          author = {Tresoldi, Tiago; Maurits, Luke; Dunn, Michael},
          title = {seqsim, a library for computing measures of distance and similarity for sequences of hashable data types. Version 0.3},
          howpublished = {\url{https://github.com/evotext/seqsim}},
          address = {Uppsala},
          publisher = {Uppsala universitet},
          year = {2021},
        }
        ```
        
        ## References
        
        The image at the top of this file is derived from Yves de Saint-Denis, *Vie et martyre de saint
        Denis et de ses compagnons, versions latine et française*. It is available in high
        resolution from [Bibliothèque nationale de France, Département des Manuscrits, Français 2090,
        fol. 12v.](http://gallica.bnf.fr/ark:/12148/btv1b8447296x/f30.item)
        
        References to the various implementation are available in the source code comments and in
        the [online documentation](https://seqsim.readthedocs.io/en/latest/?badge=latest).
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
Provides-Extra: dev
Provides-Extra: test
