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        "description": "Schematec\n=========\n\n.. image:: https://travis-ci.org/mylokin/redisext.svg?branch=master\n   :target: https://travis-ci.org/mylokin/redisext\n\nSchematec is a set of tools that makes input data validation easier.\nThe purpose of this code is attempt to bring simplicity to applications\nlogics using separation of data validation and actual data processing.\n\nQuickstart\n----------\n\n.. code:: python\n\n   import schematec as s\n\n   schema = s.dictionary(\n      id=s.integer & s.required,\n      name=s.string,\n      tags=s.array(s.string),\n   )\n\n.. code:: python\n\n   >>> data = {\n   ...     'id': '1',\n   ...     'name': 'Red Hot Chili Peppers',\n   ...     'tags': ['funk', 'rock'],\n   ...     'rank': '1',\n   ... }\n   >>> schema(data)\n   {'id': 1, 'name': u'Red Hot Chili Peppers', 'tags': [u'funk', u'rock']}\n\n\nConcepts\n--------\n\nSchematec module is based on three basic concepts:\n\n* Schema\n* Validator\n* Converter\n\nSchema\n^^^^^^\n\nTerm \"schema\" is used to describe complex data struct such as dictionary(hashmap)\nor array(list). Schemas has two different types of validation (it is not related to\narray schemas):\n\n* Default - Only values with required validator are required, other values are optional\n* Weak - All values are optional\n\n`schematec.exc.SchemaError` is raised in case provided data is incorrect.\n\nOrder of schema validations:\n\n#. Unbound Validators\n#. Schemas(inner)\n#. Converters\n#. Bound Validators\n\nValidator\n^^^^^^^^^\n\nTerm \"validator\" describes callable objects that perform different types of checks.\nThere are two types of validators in schematec:\n\n* Bound - type related, for example \"max length\" validator is bound to sized type.\n* Unbound - universal, for example \"required\" validator.\n\nRaises `schematec.exc.ValidationError`.\n\nSchematec provides following validators:\n\nrequired\n   check if value is provided\n\nlength\n   check iterable for max length\n\nregex\n   check if given value is valid\n\nConverter\n^^^^^^^^^\n\nTerm \"converter\" is used to describe cast functions. Schematec supports subset of JSON\ndata types.\n\nBasic types:\n\n- integer(int)\n- string(str)\n- boolean(bool)\n\nContainers:\n\n- array(list)\n- dictionary(dict)\n\nRaises `schematec.exc.ConvertationError`.\n\nConvertation rules\n=================\n\ninteger\n-------\n\n#. Any int or long value\n#. Any suitable string/unicode\n#. Boolean value\n\nnumber\n-------\n\n#. Any float or int or long value\n#. Any suitable string/unicode\n#. Boolean value\n\nstring\n------\n\n#. Any suitable string/unicode\n#. Any int or long value\n\nboolean\n-------\n\n#. Boolean value\n#. 0 or 1\n#. '0' or '1'\n#. u'0' or u'1'\n\ndictionary\n----------\n\n#. Any mapping value(collections.Mapping)\n\narray\n-----\n\n#. Any iterable value(collections.Iterable), but not a mapping\n\nComplex Descriptors\n===================\n\n\"Schema\", \"validator\" and \"converter\" are internally referenced as \"descriptors\". Common task is\ncreation of complex validation rules for a field(or \"complex descriptors\"). To do this use bitwise\n\"and\" operator on descriptors:\n\n.. code:: python\n\n   >>> import schematec\n   >>> schematec.integer & schematec.required\n   <schematec.abc.ComplexDescriptor object at 0x10b05a0d0>\n\nSugar Schema\n============\n\nSchematec supports additional \"magic\" way to define your schemas. You can use simple dicts and lists\nto describe your data. 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