{
    "info": {
        "author": "Jeffrey Tratner",
        "author_email": "jeffrey.tratner@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Database :: Front-Ends",
            "Topic :: Scientific/Engineering :: Information Analysis"
        ],
        "description": "============\nGraphAlchemy\n============\n\n.. contents::\n    :depth: 2\n\nGraph node-edge relationships in SQLAlchemy_ (and soon more orms) + networkx_\nintegration.\n\nThe Goal\n========\n\nSet up a generalized abstraction/interface for a graph that can be used across\nvarious platforms and within various frameworks that is easy and simple to use,\nextensible, and that easily hooks into other graphing tools (like gephi_\nnetworkx_, etc.)\n\nWhat's here\n===========\n\nA basic set of node/edge abstractions + many-to-one relationships for a graph\nrepresented in SQL with SQLAlchemy_\n\nUsing this package\n==================\n\nDocumentation_ is available at http://graphalchemy.readthedocs.org\n\nIt's very simple to use (and examples are to come). But for now the best thing to do is\nto go read the docs on SQLAlchemy_\n\nA very minimal example (using an SQLite database)::\n\n    from graphalchemy.sqlmodels import create_base_classes, sqlite_connect\n    from sqlalchemy.ext.declarative import declarative_base\n\n    Base = declarative_base()\n    Node, Edge = create_base_classes(NodeClass=\"Node\", EdgeClass=\"Edge\", Base=Base)\n    engine, session = sqlite_connect(\"database.db\", metadata=Base.metadata)\n\n    # be sure to use unicode!!\n    node1 = Node(label=u\"First node!\")\n    node2 = Node(label=u\"Second node!\")\n    node3 = Node(label=u\"Third node!\")\n    edge1 = Edge.connect_nodes(node1, node2)\n    edge2 = Edge.connect_nodes(node1, node3)\n\n    session.add_all([node1, node2, node3, edge1, edge2])\n    session.commit()\n\n\n    # now we can graph it\n\n    import networkx as nx\n    G = nx.Graph()\n    G.add_edges_from([edge1, edge2])\n\n    # now we can draw this! (if you had pylab, matplotlib, etc)\n\nAnd you'd get a picture that looked something like this (clearly, we haven't added all the traits\nand such in, but you get the idea):\n\n.. image:: https://github.com/jtratner/graphalchemy/raw/master/docs/images/readme-example.png\n\nNow, obviously this is a pretty minimal example, but it shows how you can take advantage\nof the power of SQL joins, queries, etc, but also very easily \n\nWhat's going to be here\n=======================\n\n1. networkx_ integration\n2. testing for multiple sql databases and adapters\n3. abstractions for Google App Engine, mongoalchemy_, and possibly Django ORM\n4. adapter between networkx_ and web service requests (maybe?)\n\nTesting coverage\n================\n\nBasic test suite that gets 100% line coverage for SQLAlchemy models and base\nmodels (still missing a test for Flask-SQLAlchemy). I've only run it on SQLite\nso far, but presumably it should work with other SQL databases just fine (since\nit uses SQLAlchemy's `declarative base`_)\n\n.. _sqlalchemy : http://www.sqlalchemy.org/\n.. _networkx : http://networkx.lanl.gov/\n.. _mongoalchemy : http://www.mongoalchemy.org/\n.. _gephi : http://gephi.org/\n.. _declarative base : http://docs.sqlalchemy.org/en/rel_0_7/orm/extensions/declarative.html\n.. _documentation : http://graphalchemy.readthedocs.org",
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        "license": "MIT License",
        "maintainer": null,
        "maintainer_email": null,
        "name": "graph-alchemy",
        "package_url": "https://pypi.org/project/graph-alchemy/",
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        "summary": "Basic Node/Edge model for SQLAlchemy",
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