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        "author": "Michael V. DePalatis",
        "author_email": "mike@depalatis.net",
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        "description": "traitschema\n===========\n\n.. image:: https://travis-ci.org/mivade/traitschema.svg?branch=master\n    :target: https://travis-ci.org/mivade/traitschema\n\n.. image:: https://readthedocs.org/projects/traitschema/badge/?version=latest\n    :target: http://traitschema.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n.. image:: https://codecov.io/gh/mivade/traitschema/branch/master/graph/badge.svg\n    :target: https://codecov.io/gh/mivade/traitschema\n\n.. image:: https://img.shields.io/github/release/mivade/traitschema.svg\n    :target: https://github.com/mivade/traitschema\n\nCreate serializable, type-checked schema using traits_ and Numpy. A typical use\ncase involves saving several Numpy arrays of varying shape and type.\n\n.. _traits: http://docs.enthought.com/traits/\n\n\nDefining schema\n---------------\n\n.. note::\n\n    The following assumes a basic familiarity with the ``traits`` package. See\n    its `documentation <http://docs.enthought.com/traits/>`_ for details.\n\nIn order to be able to properly serialize data, non-scalar traits should be\ndeclared as a ``traits.api.Array`` type. Example:\n\n.. code-block:: python\n\n    import numpy as np\n    from traits.api import Array, String\n    from traitschema import Schema\n\n    class NamedMatrix(Schema):\n        name = String()\n        data = Array(dtype=np.float64)\n\n    matrix = NamedMatrix(name=\"name\", data=np.random.random((8, 8)))\n\nFor other demos, see the ``demos`` directory.\n\n\nSaving and loading\n------------------\n\nData can be stored in the following formats:\n\n* HDF5 via ``h5py``\n* JSON via the standard library ``json`` module\n* Numpy ``npz`` format\n\nMultiple schema can be saved at once to a zip file via\n:func:`traitschema.bundle_schema` and loaded with\n:func:`traitschema.load_bundle`.\n\n\n",
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