Source code for numpydantic.interface.numpy

"""
Interface to numpy arrays
"""

from typing import Any, Literal

from pydantic import BaseModel, SerializationInfo

from numpydantic.interface.interface import Interface, JsonDict

try:
    import numpy as np
    from numpy import ndarray

    ENABLED = True

except ImportError:  # pragma: no cover
    ENABLED = False
    ndarray = None
    np = None




class NumpyJsonDict(JsonDict):
    """
    JSON-able roundtrip representation of numpy array
    """

    type: Literal["numpy"]
    dtype: str
    value: list
    # allow shape to be None for backwards compatibility.
    shape: tuple[int, ...] | None = None



    def to_array_input(self) -> ndarray:
        """
        Construct a numpy array
        """
        array = np.array(self.value, dtype=self.dtype)
        if self.shape is not None and array.shape != self.shape:
            array = self.reshape_input(array, self.shape)
        return array






class NumpyInterface(Interface):
    """
    Numpy :class:`~numpy.ndarray` s!
    """

    name = "numpy"
    input_types = (ndarray, list)
    return_type = ndarray
    json_model = NumpyJsonDict
    priority = -999
    """
    The numpy interface is usually the interface of last resort.
    We want to use any more specific interface that we might have,
    because the numpy interface checks for anything that could be coerced
    to a numpy array (see :meth:`.NumpyInterface.check` )
    """



    @classmethod
    def check(cls, array: Any) -> bool:
        """
        Check that this is in fact a numpy ndarray or something that can be
        coerced to one
        """
        if array is None:
            return False

        if isinstance(array, ndarray):
            return True
        elif isinstance(array, dict):
            return NumpyJsonDict.is_valid(array)
        else:
            try:
                _ = np.array(array)
                return True
            except Exception:
                return False




    def before_validation(self, array: Any) -> ndarray:
        """
        Coerce to an ndarray. We have already checked if coercion is possible
        in :meth:`.check`
        """
        if not isinstance(array, ndarray):
            array = np.array(array)

        try:
            if (
                issubclass(self.dtype, BaseModel)
                and len(array) > 0
                and isinstance(array.flat[0], dict)
            ):
                array = np.vectorize(lambda x: self.dtype(**x))(array)
        except TypeError:
            # fine, dtype isn't a type
            pass

        return array




    @classmethod
    def enabled(cls) -> bool:
        """Check that numpy is present in the environment"""
        return ENABLED




    @classmethod
    def to_json(cls, array: ndarray, info: SerializationInfo = None) -> list | JsonDict:
        """
        Convert an array of :attr:`.return_type` to a JSON-compatible format using
        base python types
        """
        if not isinstance(array, np.ndarray):  # pragma: no cover
            array = np.array(array)

        json_array = [array.tolist()] if array.ndim == 0 else array.tolist()

        if info.round_trip:
            json_array = NumpyJsonDict(
                type=cls.name,
                dtype=str(array.dtype),
                value=json_array,
                shape=array.shape,
            )
        return json_array