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        "author": "John Reese",
        "author_email": "john@noswap.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Topic :: Software Development :: Libraries"
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        "description": "ent\n===\n\n.. image:: https://travis-ci.org/jreese/ent.svg?branch=master\n    :target: https://travis-ci.org/jreese/ent\n\n\n``ent`` is a basic framework for generating attribute-based data structures from\ndictionary-like data sources::\n\n    > from ent import Ent\n\n    > obj = Ent(foo='foo', bar=None)\n\n    > obj.foo\n    u'foo'\n\n    > obj.bar\n    None\n\n``ent`` can contain arbitrarily-nested hierarchies, as long as every node/leaf\nis either a primitive type, or an ent::\n\n    > obj = Ent({\n        'foo': 'bar',\n        'list': [1, 2, 3, 4],\n        'inner': {\n            'foo': 'baz',\n            'bar': 'bang',\n            },\n        'ent': Ent(foo='bar'),\n        })\n\n    > obj.foo\n    u'bar'\n\n    > obj.list[2]\n    3\n\n    > obj.inner.foo\n    u'baz'\n\n``ent`` can be merged and diffed, and it will even enforce types when merging\nkeys that are shared::\n\n    > ent1 = Ent(foo=1, bar=True)\n\n    > ent2 = Ent(foo='hi', bar=False, goo='win')\n\n    > Ent.merge(ent1, ent2)\n    <Ent {'foo': 1, 'bar': False}>\n\n    > Ent.diff(ent1, ent2)\n    <Ent {'bar': False}>\n\n``ent`` even provides a stand-in for the ``json`` and ``yaml`` modules to\nautomatically convert to and from Ent objects and raw JSON/YAML::\n\n    > from ent import json, yaml\n\n    > json.loads('{\"foo\": true, \"bar\": null}')\n    <Ent {'foo': True, 'bar': None}>\n\n    > json.dumps(Ent(foo=True, bar=None))\n    u'{\"foo\": true, \"bar\": null}'\n\n    > yaml.safe_load('bar: null\\nfoo: true\\n')\n    <Ent {'foo': True, 'bar': None}>\n\n    > yaml.dump(Ent(foo=True, bar=None))\n    u'baz: 1\\nfoo: bar\\n'\n\n\nwhy\n---\n\nWhen working with configs, or other content pulled from sources like JSON, it\ncan be really annoying to need to constantly use brackets to access nested\ndata structures.\n\nLet's say we have a small JSON file containing my personal profile::\n\n    {\n        \"name\": \"John Reese\",\n        \"urls\": {\n            \"blog\": \"https://noswap.com\",\n            \"github\": \"https://github.com/jreese\",\n            \"facebook\": \"https://www.facebook.com/nucleareclipse\"\n        }\n    }\n\nNow let's read in that data and print some of it out using the standard\n``json`` module::\n\n    import json\n\n    with open(...) as f:\n        data = json.load(f)\n\n    name = data['name']\n    url = data['urls']['github']\n\nWe can do better with ``ent``::\n\n    from ent import json\n\n    with open(...) as f:\n        data = json.load(f)\n\n    name = data.name\n    url = data.urls.github\n\n\ninstall\n-------\n\nent is compatible with Python 2.7+ and Python 3.3+.\nYou can install it from PyPI with the following command::\n\n    $ pip install ent\n\n\nlicense\n-------\n\nent is copyright 2015 John Reese, and is licensed under the MIT license.\n",
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