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        "author": "Johannes Baiter",
        "author_email": "johannes.baiter@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Topic :: Text Processing :: Indexing"
        ],
        "description": "python-rust-fst\r\n===============\r\n\r\n| |appveyor|\r\n| |travis|\r\n\r\n| Python bindings for `burntsushi's <http://burntsushi.net>`__ `fst\r\n  crate <https://github.com/BurntSushi/fst>`__\r\n  (`rustdocs <http://burntsushi.net/rustdoc/fst/>`__)\r\n| for FST-backed sets and maps.\r\n\r\n| For reasons why you might want to consider using it, see BurntSushi's\r\n  great\r\n| article on `\"Index[ing] 1,600,000,000 Keys with Automata and\r\n  Rust\" <http://blog.burntsushi.net/transducers/>`__.\r\n\r\n**tl;dr**:\r\n\r\n-  Work with larger-than-memory sets\r\n-  Perform fuzzy search using Levenshtein automata\r\n\r\nInstallation\r\n------------\r\n\r\n-  You will need:\r\n\r\n   -  Python >= 3.3, Python or PyPy >= 2.7 with development headers\r\n      installed\r\n   -  Rust nightly (install via `rustup <https://www.rustup.rs/>`__)\r\n\r\n-  Clone the repository. Installation with ``pip install git+...`` does\r\n   not work\r\n   currently\r\n-  Run ``rustup override add nightly`` to add an override for rustup to\r\n   use the\r\n   nightly channel for the repository\r\n-  Run ``python setup.py bdist_wheel`` to generate a wheel\r\n-  Install the wheel with\r\n   ``pip install dist/rust_fst-0.1-py3-none-any.whl``\r\n\r\nStatus\r\n------\r\n\r\nThe package exposes almost all functionality of the ``fst`` crate,\r\nexcept for:\r\n\r\n-  Combining the results of slicing, ``search`` and ``search_re`` with\r\n   set operations\r\n-  Using raw transducers\r\n\r\nExamples\r\n--------\r\n\r\n.. code:: python\r\n\r\n    from rust_fst import Map, Set\r\n\r\n    # Building a set in memory\r\n    keys = [\"fa\", \"fo\", \"fob\", \"focus\", \"foo\", \"food\", \"foul\"]\r\n    s = Set.from_iter(keys)\r\n\r\n    # Fuzzy searches on the set\r\n    matches = list(s.search(term=\"foo\", max_dist=1))\r\n    assert matches == [\"fo\", \"fob\", \"foo\", \"food\"]\r\n\r\n    # Searching with a regular expression\r\n    matches = list(s.search_re(r'f\\w{2}'))\r\n    assert matches == [\"fob\", \"foo\"]\r\n\r\n    # Store map on disk, requiring only constant memory for querying\r\n    items = [(\"bruce\", 1), (\"clarence\", 2), (\"stevie\", 3)]\r\n    m = Map.from_iter(items, path=\"/tmp/map.fst\")\r\n\r\n    # Find all items whose key is greater or equal (in lexicographical sense) to\r\n    # 'clarence'\r\n    matches = dict(m['clarence':])\r\n    assert matches == {'clarence': 2, 'stevie': 3}\r\n\r\nDocumentation\r\n-------------\r\n\r\nHead over to `readthedocs.org <https://rust-fst.readthedocs.org/>`__ for\r\nthe API documentation.\r\n\r\n| If you want to know more about performance characteristics, memory\r\n  usage\r\n| and about the implementation details, please head over to the\r\n| `documentation for the Rust\r\n  crate <https://github.com/BurntSushi/fst>`__\r\n\r\n.. |appveyor| image:: https://ci.appveyor.com/api/projects/status/github/jbaiter/python-rust-fst\r\n   :target: https://ci.appveyor.com/project/jbaiter/python-rust-fst\r\n.. |travis| image:: https://travis-ci.org/jbaiter/python-rust-fst.svg\r\n   :target: https://travis-ci.org/jbaiter/python-rust-fst",
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