Method sel

xarray/indexes/nd_point_index.py:327–374

(
        self, labels: dict[Any, Any], method=None, tolerance=None
    )

        return dim_indexers

    def sel(
        self, labels: dict[Any, Any], method=None, tolerance=None
    ) -> IndexSelResult:
        if method != "nearest":
            raise ValueError(
                "NDPointIndex only supports selection with method='nearest'"
            )

        missing_labels = set(self._coord_names) - set(labels)
        if missing_labels:
            missing_labels_str = ",".join([f"{name}" for name in missing_labels])
            raise ValueError(f"missing labels for coordinate(s): {missing_labels_str}.")

        # maybe convert labels into xarray DataArray objects
        xr_labels: dict[Any, DataArray] = {}

        for name, lbl in labels.items():
            if isinstance(lbl, DataArray):
                xr_labels[name] = lbl
            elif isinstance(lbl, Variable):
                xr_labels[name] = DataArray(lbl)
            elif is_scalar(lbl):
                xr_labels[name] = DataArray(lbl, dims=())
            elif np.asarray(lbl).ndim == len(self._dims):
                xr_labels[name] = DataArray(lbl, dims=self._dims)
            else:
                raise ValueError(
                    "invalid label value. NDPointIndex only supports advanced (point-wise) indexing "
                    "with the following label value kinds:\n"
                    "- xarray.DataArray or xarray.Variable objects\n"
                    "- scalar values\n"
                    "- unlabelled array-like objects with the same number of dimensions "
                    f"than the {self._coord_names} coordinate variables ({len(self._dims)})"
                )

        # broadcast xarray labels against one another and determine labels shape and dimensions
        broadcasted = broadcast(*xr_labels.values())
        label_dims = broadcasted[0].dims
        label_shape = broadcasted[0].shape
        xr_labels = dict(zip(xr_labels, broadcasted, strict=True))

        # get and return dimension indexers
        points = get_points(xr_labels[name] for name in self._coord_names)
        _, indices = self._tree_obj.query(points)

        dim_indexers = self._get_dim_indexers(indices, label_dims, label_shape)

        return IndexSelResult(dim_indexers=dim_indexers)

    def rename(
        self,