def sel_points(self, dim='points', method=None, tolerance=None,
                   **indexers):
        """Returns a new dataset with each array indexed pointwise by tick
        labels along the specified dimension(s).

        In contrast to `Dataset.isel_points`, indexers for this method should
        use labels instead of integers.

        In contrast to `Dataset.sel`, this method selects points along the
        diagonal of multi-dimensional arrays, not the intersection.

        Parameters
        ----------
        dim : str or DataArray or pandas.Index or other list-like object, optional
            Name of the dimension to concatenate along. If dim is provided as a
            string, it must be a new dimension name, in which case it is added
            along axis=0. If dim is provided as a DataArray or Index or
            list-like object, its name, which must not be present in the
            dataset, is used as the dimension to concatenate along and the
            values are added as a coordinate.
        method : {None, 'nearest', 'pad'/'ffill', 'backfill'/'bfill'}, optional
            Method to use for inexact matches (requires pandas>=0.16):

            * None (default): only exact matches
            * pad / ffill: propagate last valid index value forward
            * backfill / bfill: propagate next valid index value backward
            * nearest: use nearest valid index value
        tolerance : optional
            Maximum distance between original and new labels for inexact
            matches. The values of the index at the matching locations must
            satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
            Requires pandas>=0.17.
        **indexers : {dim: indexer, ...}
            Keyword arguments with names matching dimensions and values given
            by array-like objects. All indexers must be the same length and
            1 dimensional.

        Returns
        -------
        obj : Dataset
            A new Dataset with the same contents as this dataset, except each
            array and dimension is indexed by the appropriate indexers. With
            pointwise indexing, the new Dataset will always be a copy of the
            original.

        See Also
        --------
        Dataset.sel
        Dataset.isel
        Dataset.isel_points
        DataArray.sel_points
        """  # noqa
        warnings.warn('Dataset.sel_points is deprecated: use Dataset.sel()'
                      'instead.', DeprecationWarning, stacklevel=2)

        pos_indexers, _ = indexing.remap_label_indexers(
            self, indexers, method=method, tolerance=tolerance
        )
        return self.isel_points(dim=dim, **pos_indexers)