Function as_compatible_data

xarray/core/variable.py:245–318

Prepare and wrap data to put in a Variable. - If data does not have the necessary attributes, convert it to ndarray. - If it's a pandas.Timestamp, convert it to datetime64. - If data is already a pandas or xarray object (other than an Index), just use the values. Finally, wra

(
    data: T_DuckArray | np.typing.ArrayLike, fastpath: bool = False
)

def as_compatible_data(
    data: T_DuckArray | np.typing.ArrayLike, fastpath: bool = False
) -> T_DuckArray:
    """Prepare and wrap data to put in a Variable.

    - If data does not have the necessary attributes, convert it to ndarray.
    - If it's a pandas.Timestamp, convert it to datetime64.
    - If data is already a pandas or xarray object (other than an Index), just
      use the values.

    Finally, wrap it up with an adapter if necessary.
    """
    if fastpath and getattr(data, "ndim", None) is not None:
        return cast("T_DuckArray", data)

    from xarray.core.dataarray import DataArray

    # TODO: do this uwrapping in the Variable/NamedArray constructor instead.
    if isinstance(data, Variable):
        return cast("T_DuckArray", data._data)

    # TODO: do this uwrapping in the DataArray constructor instead.
    if isinstance(data, DataArray):
        return cast("T_DuckArray", data._variable._data)

    def convert_non_numpy_type(data):
        return cast("T_DuckArray", _maybe_wrap_data(data))

    if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
        return convert_non_numpy_type(data)

    if isinstance(data, tuple):
        data = utils.to_0d_object_array(data)

    # we don't want nested self-described arrays
    if isinstance(data, pd.Series | pd.DataFrame):
        if (
            isinstance(data, pd.Series)
            and is_allowed_extension_array(data.array)
            # Some datetime types are not allowed as well as backing Variable types
            and not isinstance(data.array, UNSUPPORTED_EXTENSION_ARRAY_TYPES)
        ):
            pandas_data = data.array
        else:
            pandas_data = data.values  # type: ignore[assignment]
        if isinstance(pandas_data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
            return convert_non_numpy_type(pandas_data)
        else:
            data = pandas_data

    if isinstance(data, np.ma.MaskedArray) or (
        isinstance(data, array_type("dask"))
        and isinstance(getattr(data, "_meta", None), np.ma.MaskedArray)
    ):
        mask = duck_array_ops.getmaskarray(data)
        data = duck_array_ops.where_method(data, ~mask)

    if isinstance(data, np.matrix):