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        "author": "Alan Elkner",
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        "description": "# traversify\n\nHandy python classes for manipulating json data, providing syntactic sugar for less verbose, easier to write code.\n\nTraverser class allows one to:\n\n* traverse complex trees of data with dotted syntax rather than the verbose dictionary dereferencing.\n* treat nodes on the tree as lists even if they are singleton dictionaries, eliminating a lot of type-checking code.\n* add or delete branches of the tree with simple dotted syntax.\n* treat missing keys on the tree as None rather than throwing a key exception, much as JavaScript returns undefined.\n* linkage to Filter class (defined next) for powerful tree comparisons or tree pruning.\n\nFilter class allows one to:\n\n* define a set of criteria for comparing two partially incongruous trees by limiting the sets of fields compared.\n* apply said criteria to prune a tree of any unwanted fields.\n\n# Traverser\n\nPass tree data to Traverser, either as a list, dictionary, json string or any class offering a json method, and the resultant object will provide the syntactic sugar for traversing with dotted syntax, treating singleston nodes as lists:\n\n```pycon\n>>> from traversify import Traverser\n>>> obj = Traverser({'id': 1, 'username': 'jdoe'})\n>>> obj.id\n1\n>>> obj.username\n'jdoe'\n>>> obj.bad_key is None\nTrue\n>>> [node.id for node in obj]\n[1]\n>>> obj[0].id\n1\n>>> {'id': 1, 'username': 'jdoe'} in obj\nTrue\n```\n\nNot only  can singletons be addressed as lists, but append and extend methods are available to turn singletons into lists on the fly:\n\n```pycon\n>>> obj = Traverser({'id': 1})\n>>> obj.append({'id': 2})\n>>> obj.extend([{'id': 3, 'id': 4}])\n>>> [node.id for node in obj]\n[1, 2, 3, 4]\n```\n\nAt any time, a Traverser instance will return the underlying value when called:\n\n```pycon\n>>> obj = Traverser({'id': 1})\n>>> obj()\n{'id': 1}\n```\n\nThe tree can be updated using dotted syntax.  Note that by default, a Traverser instance makes a deepcopy of the json data so that there are no unintended side effects:\n\n```pycon\n>>> data = {'id': 1, 'username': 'jdoe'}\n>>> obj = Traverser(data)\n>>> obj.id = 2\n>>> del obj.username\n>>> obj()\n{'id': 2}\n>>> data\n{'id': 1, 'username': 'jdoe'}\n```\n\nHowever, if the side-effect of updating the data passed is desired (perhaps due to memory constaints), then pass deepcopy=False:\n\n```pycon\n>>> data = {'id': 1}\n>>> obj = Traverser(data, deepcopy=False)\n>>> obj.id = 2\n>>> obj()\n{'id': 2}\n>>> data\n{'id': 2}\n```\n\nIn case there are keys that are not identifiers, then dictionary dereferencing can still be used:\n\n```pycon\n>>> obj = Traverser({'@xsi.type': 'textarea'})\n>>> obj['@xsi.type']\n'textarea'\n```\n\nThe get method allows traversing multiple levels in one call, using dots to set off the levels:\n```pycon\n>>> obj = Traverser({'root': {'username': 'any'}})\n>>> obj.get('root.username')\n'any'\n```\n\nAlso, the get method supports dot-escaping so that keys containing dots can still be traversed:\n\n```pycon\n>>> obj = Traverser({'@xsi.type': 'textarea'})\n>>> obj.get('@xsi..type')\n'textarea'\n```\n\nThere's a set method that will update a node multiple levels down and even build out branches that aren't already there:\n\n```pycon\n>>> obj = Traverser({'stats': {'id': 1}})\n>>> obj.set('stats.id', 2)\n>>> obj()\n{'stats': {'id': 2}}\n>>> obj.set('users.0.username', 'any')\n>>> obj()\n{'stats': {'id': 2}, 'users': [{'username': 'any'}]}\n```\n\nTo save the trouble of importing json and using dumps, there's a handy to_json method:\n\n```pycon\n>>> obj = Traverser({'id': 1})\n>>> obj.to_json()\n'{\"id\": 1}'\n```\n\n# Filter\n\nOften one needs to compare two trees without taking into account irrelavant fields, like when records in the tree have ids, but a new record doesn't have it yet.  Filter provides a way to make this less verbose by providing blacklist and whitelist attributes for controlled comparison:\n\n```pycon\n>>> from traversify import Traverser, Filter\n>>> id_exclude_filter = Filter(blacklist='id')\n>>> record = Traverser({'id': 1, 'username': 'jdoe'})\n>>> id_exclude_filter.are_equal(record, {'username': 'jdoe'})\nTrue\n```\n\nThe same filter can be used to prune a tree of its unwanted fields:\n\n```pycon\n>>> id_exclude_filter.prune(record)\n>>> record()\n{'username': 'jdoe'}\n```\n\nIf a filter is passed while creating a Traverser instance, then `==`, `in` and the `prune()` method will use it to do the comparison or pruning:\n\n```pycon\n>>> record = Traverser({'id': 1, 'username': 'jdoe'}, filter=Filter(blacklist='id'))\n>>> record == {'username': 'jdoe'}\nTrue\n>>> {'username': 'jdoe'} in record\nTrue\n>>> record.prune()\n>>> record()\n{'username': 'jdoe'}\n```\n\nTraverser's prune method will accept a filter to override the default (or supply one not already supplied):\n\n```pycon\n>>> record = Traverser({'id': 1, 'username': 'jdoe'})\n>>> record.prune(filter=Filter(blacklist='id'))\n>>> record()\n{'username': 'jdoe'}\n```\n\n\n",
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