@classmethod
def run(cls, excl_fpath, tm_dset, sc_point_method, excl_dict=None,
    area_filter_kernel='queen', min_area=None, check_excl_layers=False,
    resolution=64, gids=None, args=None, kwargs=None, max_workers=None,
    chunk_point_len=1000):
    """Get the supply curve points aggregation summary.

        Parameters
        ----------
        excl_fpath : str
            Filepath to exclusions h5 with techmap dataset.
        tm_dset : str
            Dataset name in the techmap file containing the
            exclusions-to-resource mapping data.
        sc_point_method : method
            Supply Curve Point Method to operate on a single SC point.
        excl_dict : dict | None
            Dictionary of exclusion LayerMask arugments {layer: {kwarg: value}}
        area_filter_kernel : str
            Contiguous area filter method to use on final exclusions mask
        min_area : float | None
            Minimum required contiguous area filter in sq-km
        check_excl_layers : bool
            Run a pre-flight check on each exclusion layer to ensure they
            contain un-excluded values
        resolution : int | None
            SC resolution, must be input in combination with gid. Prefered
            option is to use the row/col slices to define the SC point instead.
        gids : list | None
            List of gids to get summary for (can use to subset if running in
            parallel), or None for all gids in the SC extent.
        sc_point_method : method
            Supply Curve Point Method to operate on a single SC point.
        args : list | None
            List of positional args for sc_point_method
        kwargs : dict | None
            Dict of kwargs for sc_point_method
        max_workers : int | None
            Number of cores to run summary on. None is all
            available cpus.
        chunk_point_len : int
            Number of SC points to process on a single parallel worker.

        Returns
        -------
        summary : DataFrame
            Summary of the SC points.
        """
    agg = cls(excl_fpath, tm_dset, excl_dict=excl_dict, area_filter_kernel=
        area_filter_kernel, min_area=min_area, check_excl_layers=
        check_excl_layers, resolution=resolution, gids=gids)
    aggregation = agg.aggregate(sc_point_method, args=args, kwargs=kwargs,
        max_workers=max_workers, chunk_point_len=chunk_point_len)
    return aggregation
