types

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.

Module Contents

Classes

NDArray	Pydantic-compatible version of numpy.ndarray.
Graph	Pydantic-compatible version of networkx.Graph.

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

Bases: numpy.ndarray

Pydantic-compatible version of numpy.ndarray.

Serialization is implemented using custom methods to_dict() and from_dict(). Data array is encoded in Base64.

to_dict() → dict[str, Any]

Convert the array to JSON-compatible dictionary.

classmethod from_dict(serialized: collections.abc.Mapping[str, Any]) → NDArray

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

Parameters:

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.

classmethod validate(v: Any) → NDArray

Validate the data and cast from all known representations.

class Graph(incoming_graph_data=None, **attr)

Bases: networkx.Graph

Pydantic-compatible version of networkx.Graph.

classmethod validate(v: Any) → Graph

Validate the data and cast from all known representations.