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        "author": "Philip Schleihauf",
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        "description": "kale\n====\n[![Build Status](https://travis-ci.org/Calama/kale.png?branch=master)](https://travis-ci.org/Calama/kale)\n\nA convenient superclass and some helpers for stuff you want to keep in mongodb.\n\n\n**Note**: The master branch should be considered unstable. Releases are tagged,\nso checkout a tag or use a release published on pypi for production.\n\n\nMotivation\n----------\n\nPyMongo is awesome. Object-oriented data through model classes is awesome. kale\ntries to bridge those two, and get out of your way.\n\n\n### Why not just use PyMongo?\n\nYou should! It's awesome, and perfectly useable stand-alone. It keeps you\nconnected to your data, and to mongo itself, and I think that's important.\n\nKale does not try to stand as a layer to hide PyMongo from you. It simply\nchanges a couple things around to make more sense in the Model paradigm, and\ngive you something consistent to build your models on. It extends PyMongo.\n\n\n### blah blah blah\n\nabout the paradigm, why I don't like other ORMs. explicit++; schema\nvalidation--.\n\n\nQuick, Start!\n-------------\n\nThis is not a tutorial on PyMongo. There's a decent chance that PyMongo alone\nis enough for you. Start there.\n\n\n```python\nPython 2.7.3 (default, Sep 26 2012, 21:51:14) \n[GCC 4.7.2] on linux2\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> from pymongo import MongoClient\n>>> from kale import Model\n>>> \n>>> def super_insecure_hash(to_hash):\n...     hashed = \"\".join(str(ord(c)) for c in to_hash)\n...     return hashed\n... \n>>> class User(Model):\n...     _collection_name = 'users'\n...     _database = MongoClient().test_database\n...     \n...     def __init__(self, username, password):\n...         self.username = username\n...         self.set_password(password)\n...     \n...     def set_password(self, password):\n...         pw_hash = super_insecure_hash(password)\n...         self.pw_hash = pw_hash\n...     \n...     def check_password(self, password_challenge):\n...         hashed_challenge = super_insecure_hash(password_challenge)\n...         return hashed_challenge == self.pw_hash  # true if they match\n... \n>>> alice = User('alice', 'abc123')\n>>> alice.save()\nObjectId('513a4a99360e2e1697b81f15')\n>>> alice\n<User: {'username': 'alice', 'pw_hash': '979899495051', '_id': ObjectId('513a4a99360e2e1697b81f15')}>\n>>> del alice\n>>> \n>>> def login(username, password):\n...     requested_user = User.collection.find_one({'username': username})\n...     if requested_user.check_password(password):\n...         return requested_user\n...     else:\n...         return 'Bad login!'\n... \n>>> faker = login('alice', '123456')\n>>> faker\n'Bad login!'\n>>> \n>>> real_alice = login('alice', 'abc123')\n>>> real_alice\n<User: {u'username': u'alice', u'pw_hash': u'979899495051', u'_id': ObjectId('513a4a99360e2e1697b81f15')}>\n>>> real_alice.set_password('password')\n>>> real_alice.save()\nObjectId('513a4a99360e2e1697b81f15')\n>>> \n>>> User.collection.drop()\n>>> \n```\n\n\nkale provides you with a base class for your own models. This base class\nsubclasses python's `dict`, so it can be directly saved to Mongo.\n\nYou need to define two things in your models:\n\n 1. `_collection_name`, a string specifying the collection where instances of\n    your models should be saved to and loaded from.\n\n 2. '_database', a PyMongo database instance.\n\nYour model will be provided with four attributes you should know about:\n\n 1. `Model.save`,\n 2. `Model.insert`,\n 3. `Model.remove`: These functions map\n    almost directly to `PyMongo`'s `Collection.save`, etc. However, they are\n    inteded for use on _instances of your model_. So you don't need to pass\n    anything to them. If you have an instance of something, you can just call\n    `save()` on it, and it'll be saved.\n\n 4. `Model.collection`: This attribute gives you a special version of\n    `PyMongo`'s `Collection` object, tied to the model's collection (specified\n    with `Model._collection_name`!). The special part is that any documents\n    retrieved from mongo will be instantiations of the Model.\n\n    The `Model.collection.raw()` method will give you access to `PyMongo`'s\n    `Collection` for the model, unaltered.\n\n\nNotes\n-----\n\n * Collection-level operations are accessible though the `.collection`,\n   eg. `MyModel.collection.find_one()`.\n\n * All documents returned through the collection will be instantiated as\n   models. To get the raw json document, use `raw()`, eg.\n   `MyModel.collection.raw().find_one()`.\n\n * Document-level operations are ported down directly to the model, eg.\n   `MyModel().save()`. The model's `_id` will be passed in where appropriate.\n\n * There is no model-level `update`, since it clashes with `dict`'s `update`.\n   Use `save`, or `Model.collection.update(instance, ...)`.\n\n * The model-level `remove` is restricted to only remove the model's document.\n\n * No special ref support... yet.\n\n * Feedback and tests welcome!\n\n * Kale does its best to cast dicts to `kale.AttrDict` recursively when you\n   instantiate a `kale.Model`, but it can't do magic -- If you do\n   `my_model_instance.listproperty.append({'some': 'dict'})`, it will be a\n   dict, not an `AttrDict`. However, if a document with this structure is\n   retrieved from the database, `dict`s in iterables _will_ be cast to\n   `AttrDict`s (as of v0.2.1).\n\n\nChangelog\n---------\n\n### v0.3\n\n * Added live-instance registry that ensure only one instance of a document's\n   model exists in the program.\n * Added `collectionmethod` decorator.\n\n\n### v0.2.2\n * fix some casting stuff\n\n### v0.2.1\n\n * bugfix for casting dicts in iterables to AttrDict.",
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