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        "author": "Martin Sosic",
        "author_email": "sosic.martin@gmail.com",
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        "description": "=====\nEdlib\n=====\n\nLightweight, super fast library for sequence alignment using edit (Levenshtein) distance.\n\n.. code:: python\n\n    edlib.align(\"hello\", \"world\")\n\nEdlib is actually a C/C++ library, and this package is it's wrapper for Python.\nPython Edlib has mostly the same API as C/C++ Edlib, so make sure to check out `C/C++ Edlib docs <http://github.com/Martinsos/edlib>`_ for more code examples, details on API and how Edlib works.\n\n--------\nFeatures\n--------\n\n* Calculates **edit distance**.\n* It can find **optimal alignment path** (instructions how to transform first sequence into the second sequence).\n* It can find just the **start and/or end locations of alignment path** - can be useful when speed is more important than having exact alignment path.\n* Supports **multiple alignment methods**: global(**NW**), prefix(**SHW**) and infix(**HW**), each of them useful for different scenarios.\n* You can **extend character equality definition**, enabling you to e.g. have wildcard characters, to have case insensitive alignment or to work with degenerate nucleotides.\n* It can easily handle small or **very large** sequences, even when finding alignment path.\n* **Super fast** thanks to Myers's bit-vector algorithm.\n\nNOTE: Size of alphabet has to be <= 256 (meaning that query and target together must have <= 256 unique values).\n\n------------\nInstallation\n------------\n::\n\n    pip install edlib\n\n---\nAPI\n---\n\nEdlib has two functions, `align()` and `getNiceAlignment()`:\n\n.. code:: python\n\n    align(query, target, [mode], [task], [k])\n\n.. code:: python\n\n    getNiceAlignment(alignResutl, query, target)\n\n\n\nTo learn more about it, type :code:`help(edlib.align)` in your python interpreter.\n\n-----\nUsage\n-----\n.. code:: python\n\n    import edlib\n\n    result = edlib.align(\"elephant\", \"telephone\")\n    print(result[\"editDistance\"])  # 3\n    print(result[\"alphabetLength\"])  # 8\n    print(result[\"locations\"])  # [(None, 8)]\n    print(result[\"cigar\"])  # None\n\n    result = edlib.align(\"ACTG\", \"CACTRT\", mode=\"HW\", task=\"path\", additionalEqualities=[(\"R\", \"A\"), (\"R\", \"G\")])\n    print(result[\"editDistance\"])  # 0\n    print(result[\"alphabetLength\"])  # 5\n    print(result[\"locations\"])  # [(1, 4)]\n    print(result[\"cigar\"])  # \"4=\"\n\n    result = edlib.align(\"elephant\", \"telephone\", task=\"path\")  ## users must use 'task=\"path\"' \n    niceAlign = edlib.getNiceAlignment(result, \"elephant\", \"telephone\")\n    print(niceAlign['query_aligned'])  # \"-elephant\"\n    print(niceAlign['matched_aligned'])  # \"-|||||.|.\"\n    print(niceAlign['target_aligned'])  # \"telephone\"\n\n\n\n\n---------\nBenchmark\n---------\n\nI run a simple benchmark on 7 Feb 2017 (using timeit, on Python3) to get a feeling of how Edlib compares to other Python libraries: `editdistance <https://pypi.python.org/pypi/editdistance>`_ and `python-Levenshtein <https://pypi.python.org/pypi/python-Levenshtein>`_.\n\nAs input data I used pairs of DNA sequences of different lengths, where each pair has about 90% similarity.\n\n::\n\n   #1: query length: 30, target length: 30\n   edlib.align(query, target): 1.88\u00b5s\n   editdistance.eval(query, target): 1.26\u00b5s\n   Levenshtein.distance(query, target): 0.43\u00b5s\n\n   #2: query length: 100, target length: 100\n   edlib.align(query, target): 3.64\u00b5s\n   editdistance.eval(query, target): 3.86\u00b5s\n   Levenshtein.distance(query, target): 14.1\u00b5s\n\n   #3: query length: 1000, target length: 1000\n   edlib.align(query, target): 0.047ms\n   editdistance.eval(query, target): 5.4ms\n   Levenshtein.distance(query, target): 1.9ms\n\n   #4: query length: 10000, target length: 10000\n   edlib.align(query, target): 0.0021s\n   editdistance.eval(query, target): 0.56s\n   Levenshtein.distance(query, target): 0.2s\n\n   #5: query length: 50000, target length: 50000\n   edlib.align(query, target): 0.031s\n   editdistance.eval(query, target): 13.8s\n   Levenshtein.distance(query, target): 5.0s\n\n----\nMore\n----\n\nCheck out `C/C++ Edlib docs <http://github.com/Martinsos/edlib>`_ for more information about Edlib!\n\n-----------\nDevelopment\n-----------\n\nRun :code:`make build` to generate an extension module as .so file. You can test it then by importing it from python interpreter :code:`import edlib` and running :code:`edlib.align(...)` (you have to be positioned in the directory where .so was built). This is useful for testing while developing.\n\nRun :code:`make sdist` to create a source distribution, but not publish it - it is a tarball in dist/ that will be uploaded to pip on `publish`. Use this to check that tarball is well structured and contains all needed files, before you publish.\nGood way to test it is to run :code:`sudo pip install dist/edlib-*.tar.gz`, which will try to install edlib from it, same way as pip will do it when it is published.\n\nRun :code:`make publish` to create a source distribution and publish it to the PyPI. Use this to publish new version of package.\nMake sure to bump the version in `setup.py` before publishing, if needed.\n\n:code:`make clean` removes all generated files.",
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        "keywords": "edit distance levenshtein align sequence bioinformatics",
        "license": "MIT",
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        "name": "edlib",
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        "summary": "Lightweight, super fast library for sequence alignment using edit (Levenshtein) distance.",
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