semvec/pypi-packages huggingface dataset:
traitschema 1.2.0
Serializable schema using traits
License: BSD

traitschema
===========

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Create serializable, type-checked schema using traits_ and Numpy. A typical use
case involves saving several Numpy arrays of varying shape and type.

.. _traits: http://docs.enthought.com/traits/


Defining schema
---------------

.. note::

    The following assumes a basic familiarity with the ``traits`` package. See
    its `documentation <http://docs.enthought.com/traits/>`_ for details.

In order to be able to properly serialize data, non-scalar traits should be
declared as a ``traits.api.Array`` type. Example:

.. code-block:: python

    import numpy as np
    from traits.api import Array, String
    from traitschema import Schema

    class NamedMatrix(Schema):
        name = String()
        data = Array(dtype=np.float64)

    matrix = NamedMatrix(name="name", data=np.random.random((8, 8)))

For other demos, see the ``demos`` directory.


Saving and loading
------------------

Data can be stored in the following formats:

* HDF5 via ``h5py``
* JSON via the standard library ``json`` module
* Numpy ``npz`` format

Multiple schema can be saved at once to a zip file via
:func:`traitschema.bundle_schema` and loaded with
:func:`traitschema.load_bundle`.