Class ScipyArrayWrapper

xarray/backends/scipy_.py:74–122

class ScipyArrayWrapper(BackendArray):
    datastore: ScipyDataStore
    variable_name: str
    shape: tuple[int, ...]
    dtype: np.dtype

    def __init__(self, variable_name: str, datastore: ScipyDataStore) -> None:
        self.datastore = datastore
        self.variable_name = variable_name
        array = self.get_variable().data
        self.shape = array.shape
        self.dtype = np.dtype(array.dtype.kind + str(array.dtype.itemsize))

    def get_variable(self, needs_lock: bool = True) -> scipy.io.netcdf_variable:
        ds = self.datastore._manager.acquire(needs_lock)
        return ds.variables[self.variable_name]

    def _getitem(self, key):
        with self.datastore.lock:
            data = self.get_variable(needs_lock=False).data
            return data[key]

    def __getitem__(self, key):
        data = indexing.explicit_indexing_adapter(
            key, self.shape, indexing.IndexingSupport.OUTER_1VECTOR, self._getitem
        )
        # Copy data if the source file is mmapped. This makes things consistent
        # with the netCDF4 library by ensuring we can safely read arrays even
        # after closing associated files.
        copy: bool | None = self.datastore.ds.use_mmap

        # adapt handling of copy-kwarg to numpy 2.0
        # see https://github.com/numpy/numpy/issues/25916
        # and https://github.com/numpy/numpy/pull/25922
        copy = None if HAS_NUMPY_2_0 and copy is False else copy

        return np.array(data, dtype=self.dtype, copy=copy)

    def __setitem__(self, key, value):
        with self.datastore.lock:
            data = self.get_variable(needs_lock=False)
            try:
                data[key] = value
            except TypeError:
                if key is Ellipsis:
                    # workaround for GH: scipy/scipy#6880
                    data[:] = value
                else:
                    raise


# This is a dirty workaround to allow pickling of the flush_only_netcdf_file class.