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        "author": "Panos Kittenis",
        "author_email": "22e889d8@opayq.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: GNU Lesser General Public License v2 (LGPLv2)",
            "License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "description": "NodeTrie\n==========\n\nA Trie data structure library.\n\n.. image:: https://img.shields.io/pypi/v/nodetrie.svg\n  :target: https://pypi.python.org/pypi/nodetrie\n  :alt: Latest Version\n.. image:: https://travis-ci.org/NodeTrie/NodeTrie_Py.svg?branch=master\n  :target: https://travis-ci.org/NodeTrie/NodeTrie_Py\n  :alt: CI status\n\nInstallation\n=============\n\n::\n\n  pip install nodetrie\n\nMotivation, design goals\n==========================\n\nNodeTrie is a Python extension to a native C library written for this purpose.\n\nIt came about from a lack of viable alternatives for Python. While other trie library implementations exist, they suffer from severe limitations such as\n\n* Read only structures, no insertions\n* High memory use for large trees\n* Lack of searching, particularly file mask or wild card style searching\n* Slow inserts\n\nExisting implementations on PyPi fall into these broad categories, including `Marissa-Trie <https://github.com/pytries/marisa-trie>`_ (read only) and `datrie <https://github.com/pytries/datrie>`_ (slow inserts, very high memory use for large trees).\n\nNodeTrie's C library is designed to minimize memory use as much as possible and still allow arbitrary length trees that can be searched.\n\nEach node has a name associated with it as its data, along with children list and number of children.\n\nFeatures and design notes\n==========================\n\n* NodeTrie is an n-ary tree, meaning any one node can have any number of children\n* Node children arrays are dynamically resized *as needed on insertion* on a per node basis. No fixed minimum nor maximum size\n* Node names can be of arbitrary length, available memory allowing\n* Node names from ``Node.name`` are always unicode in either Python 2/3\n* Any python string type may be used on insertion\n* Node names are implicitly decoded from unicode on insertion, if needed, with ``nodetrie.ENCODING`` (`utf-8`) default encoding which can be overridden\n* New Python ``Node`` objects are created from the underlying C pointers every time ``Node.children`` is called. There is overhead on the Python interpreter to create these objects. It is safe and better performing to keep and re-use children references instead, see examples below\n\nLimitations\n=============\n\n* Deletions are not implemented\n* The C library implementation uses pointer arrays for children to reduce search space complexity and character pointers for names to allow for arbitrary name lengths. This may lead to memory fragmentation\n* ``Node`` objects in python are read only. It is not possible to override the name of an existing ``Node`` object nor modify its attributes\n* Character encodings that allow for null characters such as UCS-2 *should not be used*\n\nExample Usage\n==============\n\n.. code-block:: python\n\n  from nodetrie import Node\n\n  # This is the root of the tree, keep a reference to it.\n  # Deleting or letting the root node go out of scope will de-allocate\n  # the entire tree\n  node = Node()\n\n  # Insert a linked tree so that a->b->c->d where -> means 'has child node'\n  node.insert_split_path(['a', 'b', 'c', 'd'])\n  node.children[0].name == 'a'\n\n  # Sub-trees can be referred to by child nodes\n  a_node = node.children[0]\n  a_node.name == 'a'\n  a_node.children[0].name == 'b'\n  a_node.is_leaf() == False\n\n  # Insertions create only new nodes\n  # Insert linked tree so that a->b->c->dd\n  node.insert_split_path(['a', 'b', 'c', 'dd'])\n\n  # Only one 'a' node\n  node.children_size == 1\n\n  # Existing references to nodes will have correct children\n  # after insertion without recreating the node object.\n  # Here, a_node is an existing object prior to more nodes\n  # being added to its sub-tree. After insertion, a's sub-tree contains newly\n  # inserted nodes as expected\n\n  # 'c' node is first child of 'b' which is first child of 'a'\n  # 'c' node has two children, 'd' and 'dd'\n  c_node = a_node.children[0].children[0]\n  c_node.children_size == 2\n  c_node.is_leaf() == False\n\n  # 'd' and 'dd' are both leaf nodes\n  leaf_nodes = [c for c in c_node.children if c.is_leaf()]\n  len(leaf_nodes) == 2\n\n.. note:: De-allocation\n\n  Tree is de-allocated when and only when root node goes out of scope or is deleted. Letting sub-tree objects go out of scope or explicitly deleting them will *not de-allocate that sub-tree*.\n\n.. note:: Sub-tree insertions\n\n  Insertions on non-root nodes work as expected. However, ``Node.insert`` does *not* check if a node is already present, unlike ``Node.insert_split_path``\n\nSearching\n----------\n\nNodeTrie supports exact name as well as file mask matching tree search.\n\n.. code-block:: python\n\n  from __future__ import print_function\n  from nodetrie import Node\n\n  node = Node()\n  for paths in [['a', 'b', 'c1', 'd1'], ['a', 'b', 'c1', 'd2'],\n                ['a', 'b', 'c2', 'd1'], ['a', 'b', 'c2', 'd2']]:\n      node.insert_split_path(paths)\n  for path, _node in node.search(node, ['a', 'b', '*', '*'], []):\n      print(path, _node)\n\nOutput\n\n.. code-block:: python\n\n  [u'a', u'b', u'c1', u'd1'] Node: 'd1'\n  [u'a', u'b', u'c1', u'd2'] Node: 'd2'\n  [u'a', u'b', u'c2', u'd1'] Node: 'd1'\n  [u'a', u'b', u'c2', u'd2'] Node: 'd2'\n\nSeparator joined node names for a matched sub-tree are returned by the query function.\n\n.. code:: python\n\n  for match in node.query('a.b.*.*'):\n      print(match)\n\n  for match in node.query('a|b|*|*', separator='|'):\n     print(match)\n\nOutput\n\n.. code:: python\n\n  (u'a.b.c1.d1', Node: 'd1')\n  (u'a.b.c1.d2', Node: 'd2')\n  (u'a.b.c2.d1', Node: 'd1')\n  (u'a.b.c2.d2', Node: 'd2')\n\n  (u'a|b|c1|d1', Node: 'd1')\n  (u'a|b|c1|d2', Node: 'd2')\n  (u'a|b|c2|d1', Node: 'd1')\n  (u'a|b|c2|d2', Node: 'd2')\n\nContributions are most welcome.",
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