Metadata-Version: 1.0
Name: datrie
Version: 0.2
Summary: Super-fast, efficiently stored Trie for Python
Home-page: https://github.com/kmike/datrie/
Author: Mikhail Korobov
Author-email: kmike84@gmail.com
License: UNKNOWN
Description: datrie
        ======
        
        Super-fast, efficiently stored Trie for Python (2.x and 3.x).
        Uses `libdatrie`_.
        
        .. _libdatrie: http://linux.thai.net/~thep/datrie/datrie.html
        
        Installation
        ============
        
        ::
        
            pip install datrie
        
        Usage
        =====
        
        Create a new trie capable of storing items with lower-case ascii keys::
        
            >>> import string
            >>> import datrie
            >>> trie = datrie.new(string.ascii_lowercase)
        
        ``trie`` variable is a dict-like object that can have unicode keys of
        certain ranges and Python objects as values.
        
        In addition to implementing the mapping interface, tries facilitate
        finding the items for a given prefix, and vice versa, finding the
        items whose keys are prefixes of a given string. As a common special
        case, finding the longest-prefix item is also supported.
        
        .. warning::
        
            For efficiency you must define allowed character range(s) while
            creating trie. ``datrie`` doesn't check if keys are in allowed
            ranges at runtime, so be careful! Invalid keys are OK at lookup time
            but values won't be stored correctly for such keys.
        
        Add some values to it (datrie keys must be unicode; the examples
        are for Python 2.x)::
        
            >>> trie[u'foo'] = 5
            >>> trie[u'foobar'] = 10
            >>> trie[u'bar'] = 'bar value'
            >>> trie.setdefault(u'foobar', 15)
            10
        
        Check if u'foo' is in trie::
        
            >>> u'foo' in trie
            True
        
        Get a value::
        
            >>> trie[u'foo']
            5
        
        Find all prefixes of a word::
        
            >>> trie.prefixes(u'foobarbaz')
            [u'foo', u'foobar']
        
            >>> trie.prefix_items(u'foobarbaz')
            [(u'foo', 5), (u'foobar', 10)]
        
            >>> trie.iter_prefixes(u'foobarbaz')
            <generator object ...>
        
            >>> trie.iter_prefix_items(u'foobarbaz')
            <generator object ...>
        
        Find the longest prefix of a word::
        
            >>> trie.longest_prefix(u'foo')
            u'foo'
        
            >>> trie.longest_prefix(u'foobarbaz')
            u'foobar'
        
            >>> trie.longest_prefix(u'gaz')
            KeyError: u'gaz'
        
            >>> trie.longest_prefix(u'gaz', default=u'vasia')
            u'vasia'
        
            >>> trie.longest_prefix_item(u'foobarbaz')
            (u'foobar', 10)
        
        Check if the trie has keys with a given prefix::
        
            >>> trie.has_keys_with_prefix(u'fo')
            True
        
            >>> trie.has_keys_with_prefix(u'FO')
            False
        
        Get all items with a given prefix from a trie::
        
            >>> trie.keys(u'fo')
            [u'foo', u'foobar']
        
            >>> trie.items(u'ba')
            [(u'bar', 'bar value')]
        
            >>> trie.values(u'foob')
            [10]
        
        Save & load a trie (values must be picklable)::
        
            >>> trie.save('my.trie')
            >>> trie2 = datrie.Trie.load('my.trie')
        
        
        Trie and BaseTrie
        =================
        
        There are two Trie classes in datrie package: ``datrie.Trie`` and
        ``datrie.BaseTrie``. ``datrie.BaseTrie`` is slightly faster and uses less
        memory but it can store only integer numbers 0 <= x <= 2147483647.
        ``datrie.Trie`` is a bit slower but can store any Python object as a value;
        it is the trie created by ``datrie.new`` by default.
        
        If you don't need values or integer values are OK then use ``datrie.BaseTrie``::
        
            import datrie
            import string
            alpha_map = datrie.AlphaMap(alphabet=string.ascii_lowercase)
            trie = datrie.BaseTrie(alpha_map=alpha_map)
        
        Performance
        ===========
        
        Performance is measured for ``datrie.Trie`` against Python's dict with
        100k unique unicode words (English and Russian) as keys and '1' numbers
        as values.
        
        ``datrie.Trie`` uses about 5M memory for 100k words; Python's dict
        uses about 22M for this according to my unscientific tests.
        
        This trie implementation is 2-6 times slower than python's dict
        on __getitem__. Benchmark results (macbook air i5 1.7GHz,
        "1.000M ops/sec" == "1 000 000 operations per second")::
        
            Python 2.6:
        
            dict __getitem__: 6.024M ops/sec
            trie __getitem__: 2.272M ops/sec
        
            Python 2.7:
            dict __getitem__: 6.693M ops/sec
            trie __getitem__: 2.357M ops/sec
        
            Python 3.2:
            dict __getitem__: 3.628M ops/sec
            trie __getitem__: 1.980M ops/sec
        
        Looking for prefixes of a given word is almost as fast as
        __getitem__ (results are for Python 3.2, they are even faster under
        Python 2.x on my machine)::
        
            trie.iter_prefix_items (hits):      0.431M ops/sec
            trie.prefix_items (hits):           0.685M ops/sec
            trie.prefix_items loop (hits):      0.601M ops/sec
            trie.iter_prefixes (hits):          0.814M ops/sec
            trie.iter_prefixes (misses):        1.565M ops/sec
            trie.iter_prefixes (mixed):         1.461M ops/sec
            trie.has_keys_with_prefix (hits):   1.945M ops/sec
            trie.has_keys_with_prefix (misses): 2.625M ops/sec
            trie.longest_prefix (hits):         1.750M ops/sec
            trie.longest_prefix (misses):       1.569M ops/sec
            trie.longest_prefix (mixed):        1.662M ops/sec
            trie.longest_prefix_item (hits):    1.075M ops/sec
            trie.longest_prefix_item (misses):  1.058M ops/sec
            trie.longest_prefix_item (mixed):   1.083M ops/sec
        
        Looking for all words starting with a given prefix is mostly limited
        by overall result count (this can be improved in future because a
        lot of time is spent decoding strings from utf_32_le to Python's
        unicode)::
        
            trie.items(prefix="xxx"), avg_len(res)==415:        0.690K ops/sec
            trie.keys(prefix="xxx"), avg_len(res)==415:         0.721K ops/sec
            trie.values(prefix="xxx"), avg_len(res)==415:       2.151K ops/sec
            trie.items(prefix="xxxxx"), avg_len(res)==17:       15.841K ops/sec
            trie.keys(prefix="xxxxx"), avg_len(res)==17:        16.829K ops/sec
            trie.values(prefix="xxxxx"), avg_len(res)==17:      43.930K ops/sec
            trie.items(prefix="xxxxxxxx"), avg_len(res)==3:     71.620K ops/sec
            trie.keys(prefix="xxxxxxxx"), avg_len(res)==3:      77.067K ops/sec
            trie.values(prefix="xxxxxxxx"), avg_len(res)==3:    157.464K ops/sec
            trie.items(prefix="xxxxx..xx"), avg_len(res)==1.4:  116.869K ops/sec
            trie.keys(prefix="xxxxx..xx"), avg_len(res)==1.4:   128.392K ops/sec
            trie.values(prefix="xxxxx..xx"), avg_len(res)==1.4: 194.388K ops/sec
            trie.items(prefix="xxx"), NON_EXISTING:             1753.472K ops/sec
            trie.keys(prefix="xxx"), NON_EXISTING:              1797.559K ops/sec
            trie.values(prefix="xxx"), NON_EXISTING:            1705.695K ops/sec
        
        Build time is worse than dict's; updates are quite fast::
        
            dict __setitem__ (updates): 3.489M ops/sec
            trie __setitem__ (updates): 1.862M ops/sec
            dict __setitem__ (inserts): 3.628M ops/sec
            trie __setitem__ (inserts): 0.050M ops/sec
            dict setdefault (updates):  2.575M ops/sec
            trie setdefault (updates):  1.600M ops/sec
            dict setdefault (inserts):  2.596M ops/sec
            trie setdefault (inserts):  0.050M ops/sec
        
        
        Please take this benchmark results with a grain of salt; this
        is a very simple benchmark and may not cover your use case.
        
        Current Limitations
        ===================
        
        * keys must be unicode (no implicit conversion for byte strings
          under Python 2.x, sorry);
        * there are no iterator versions of keys/values/items (this is a current
          limitation of libdatrie);
        * it doesn't work under pypy+MacOS X (some obscure error which I don't
          understand);
        * library is not tested with narrow Python builds.
        
        Contributing
        ============
        
        Development happens at github and bitbucket:
        
        * https://github.com/kmike/datrie
        * https://bitbucket.org/kmike/datrie
        
        The main issue tracker is at github.
        
        Feel free to submit ideas, bugs, pull requests (git or hg) or
        regular patches.
        
        Running tests and benchmarks
        ----------------------------
        
        Make sure `tox`_ is installed and run
        
        ::
        
            $ tox
        
        from the source checkout. Tests should pass under python 2.6, 2.7
        and 3.2.
        
        ::
        
            $ tox -c tox-bench.ini
        
        runs benchmarks.
        
        If you've changed anything in the source code then
        make sure `cython`_ is installed and run
        
        ::
        
            $ update_c.sh
        
        before each ``tox`` command.
        
        Please note that benchmarks are not included in the release
        tar.gz's because benchmark data is large and this
        saves a lot of bandwidth; use source checkouts from
        github or bitbucket for the benchmarks.
        
        .. _cython: http://cython.org
        .. _tox: http://tox.testrun.org
        
        Authors & Contributors
        ----------------------
        
        * Mikhail Korobov <kmike84@gmail.com>
        
        This module is based on `libdatrie`_ C library and is inspired by
        `fast_trie`_ Ruby bindings, `PyTrie`_ pure Python implementation
        and `Tree::Trie`_ Perl implementation; some docs and API ideas are
        borrowed from these projects.
        
        .. _fast_trie: https://github.com/tyler/trie
        .. _PyTrie: https://bitbucket.org/gsakkis/pytrie
        .. _Tree::Trie: http://search.cpan.org/~avif/Tree-Trie-1.9/Trie.pm
        
        License
        =======
        
        Licensed under LGPL v2.1.
        
        CHANGES
        =======
        
        0.1.1 (2012-07-13)
        ------------------
        
        * Windows support (upstream libdatrie changes are merged);
        * license is changed from LGPL v3 to LGPL v2.1 to match the libdatrie license.
        
        0.1 (2012-07-12)
        ----------------
        
        Initial release.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+)
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Text Processing :: Linguistic
