types
=====

.. py:module:: quantify_scheduler.structure.types 

.. autoapi-nested-parse::

   Types that support validation in Pydantic.

   Pydantic recognizes magic method ``__get_validators__`` to receive additional
   validators, that can be used, i.e., for custom serialization and deserialization.
   We implement several custom types here to tune behavior of our models.

   See `Pydantic documentation`_ for more information about implementing new types.

   .. _Pydantic documentation: https://docs.pydantic.dev/latest/usage/types/custom/



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   quantify_scheduler.structure.types.NDArray
   quantify_scheduler.structure.types.Graph




.. py:class:: NDArray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)


   Bases: :py:obj:`numpy.ndarray`

   Pydantic-compatible version of :class:`numpy.ndarray`.

   Serialization is implemented using custom methods :meth:`.to_dict` and
   :meth:`.from_dict`. Data array is encoded in Base64.

   .. py:method:: to_dict() -> dict[str, Any]

      Convert the array to JSON-compatible dictionary.


   .. py:method:: from_dict(serialized: collections.abc.Mapping[str, Any]) -> NDArray
      :classmethod:

      Construct an instance from a dictionary generated by :meth`to_dict`.

      :param serialized: Dictionary that has ``"data"``, ``"shape"`` and ``"dtype"`` keys.",
                         where data is a base64-encoded bytes array, shape is a tuple and dtype is
                         a string representation of a Numpy data type.


   .. py:method:: validate(v: Any) -> NDArray
      :classmethod:

      Validate the data and cast from all known representations.



.. py:class:: Graph(incoming_graph_data=None, **attr)


   Bases: :py:obj:`networkx.Graph`

   Pydantic-compatible version of :class:`networkx.Graph`.

   .. py:method:: validate(v: Any) -> Graph
      :classmethod:

      Validate the data and cast from all known representations.