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            "Natural Language :: English",
            "Programming Language :: Python :: 3",
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        "description": "=============================\nzerial\n=============================\n\n.. image:: https://badge.fury.io/py/zerial.png\n    :target: http://badge.fury.io/py/zerial\n\n.. image:: https://travis-ci.org/jriddy/zerial.png?branch=master\n    :target: https://travis-ci.org/jriddy/zerial\n\n.. _attrs: http://www.attrs.org/en/stable/\n\nZerial is the serialization tool that allows your model classes to be the\nZingle Zource of Truth\u2122 for your project.  Let your model classes take\nwhatever form or use whatever collection types they need, and just use metadata\nto define how that text gets serialized.  With support for variant record\ntypes, you can even evolve your data models over time, and even create\nversioned models if need be.\n\nZerial is built on top of the excellent attrs_ library, which makes class\ncreation and definition in Python very easy and very obvious.  This library\nadds abritrarily recursive serialization and de-serialization of complex\ndata classes.\n\nZerial was inspired because complex applications in spaces where requirements\nare hard to define up front call for a unique approach to modeling data.  A\nsolution has to be flexible enough to accomodate a growing and changing\nunderstanding of the underlying problem domain, while being rigorous enough\nto encapsulate these changes to data modules.  External schemas like ORM or\nJSON Schema are both inflexible and instrinsically bound to specific data\nexchange formats (SQL and JSON) that you may or may not want to actually use.\nImplicit schemas, although sufficienty flexible, ultimately fail because they\nbreak separation of concerns, requiring every bit of code that touches data\nto understand how to create a validate an entire history of model versions\nfor that data type.\n\nThis project aims to allow components to structure their data in a way that is\nconvenient for people interacting with the code, provided that it can be\ndestructured into simple types.  The combination of attrs_' rich support for\ndefaults and value factories and zerial's support of variant records,  you can\nevolve your data models over time, without breaking your client code or stored\nserialized data.\n\n\nFeatures\n--------\n\n* Stucturing and destructuring of model classes\n* Supports rich typing without any runtime dependency on stuff from the\n  ``typing`` module, which has deeply inconsistent runtime behavior\n* Model fields can be simple types or other model classes\n* Collection types can be represented as any kind of Python object, as long as\n  you can convert it to and from a list or dict with string keys.\n* Variant records permit fields that accept multiple types of data, permitting\n  extensibility.  Variants with default types allow this to be added at any\n  point in development (and even permits for *versioned* data models).\n\n\nTodo/Roadmap\n------------\n1. Optional native support for numpy arrays\n2. Export of schema definition formats from record classes\n3. Debug tools for destructure/restructure failures\n4. Wrappers around ``attr.s`` and ``attr.ib`` to make defining models cleaner\n5. Better automated checking and testing tools for serializability\n6. More extensive documentation\n\n\n\nDocumentation\n-------------\n\nThe full documentation is at http://zerial.rtfd.org.\n\n\n\nHistory\n-------\n\n0.1.0 (2018-10-21)\n++++++++++++++++++\n\n* First release on PyPI.\n\n\n",
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