@classmethod
def summary(cls, gid, excl_fpath, gen_fpath, tm_dset, gen_index, excl_dict=
    None, res_class_dset=None, res_class_bin=None, excl_area=0.0081,
    power_density=None, cf_dset='cf_mean-means', lcoe_dset='lcoe_fcr-means',
    resolution=64, exclusion_shape=None, close=False, offshore_flags=None,
    friction_layer=None, args=None, data_layers=None):
    """Get a summary dictionary of a single supply curve point.

        Parameters
        ----------
        gid : int
            gid for supply curve point to analyze.
        excl_fpath : str
            Filepath to exclusions h5.
        gen_fpath : str
            Filepath to .h5 reV generation output results.
        tm_dset : str
            Dataset name in the techmap file containing the
            exclusions-to-resource mapping data.
        gen_index : np.ndarray
            Array of generation gids with array index equal to resource gid.
            Array value is -1 if the resource index was not used in the
            generation run.
        excl_dict : dict | None
            Dictionary of exclusion LayerMask arugments {layer: {kwarg: value}}
            None if excl input is pre-initialized.
        res_class_dset : str | np.ndarray | None
            Dataset in the generation file dictating resource classes.
            Can be pre-extracted resource data in np.ndarray.
            None if no resource classes.
        res_class_bin : list | None
            Two-entry lists dictating the single resource class bin.
            None if no resource classes.
        excl_area : float
            Area of an exclusion cell (square km).
        power_density : float | None | pd.DataFrame
            Constant power density float, None, or opened dataframe with
            (resource) "gid" and "power_density columns".
        cf_dset : str | np.ndarray
            Dataset name from gen containing capacity factor mean values.
            Can be pre-extracted generation output data in np.ndarray.
        lcoe_dset : str | np.ndarray
            Dataset name from gen containing LCOE mean values.
            Can be pre-extracted generation output data in np.ndarray.
        resolution : int | None
            SC resolution, must be input in combination with gid.
        exclusion_shape : tuple
            Shape of the exclusions extent (rows, cols). Inputing this will
            speed things up considerably.
        close : bool
            Flag to close object file handlers on exit.
        offshore_flags : np.ndarray | None
            Array of offshore boolean flags if available from wind generation
            data. None if offshore flag is not available.
        friction_layer : None | FrictionMask
            Friction layer with scalar friction values if valid friction inputs
            were entered. Otherwise, None to not apply friction layer.
        args : tuple | list, optional
            List of summary arguments to include. None defaults to all
            available args defined in the class attr, by default None
        data_layers : dict, optional
            Aggregation data layers. Must be a dictionary keyed by data label
            name. Each value must be another dictionary with "dset", "method",
            and "fpath", by default None

        Returns
        -------
        summary : dict
            Dictionary of summary outputs for this sc point.
        """
    kwargs = {'excl_dict': excl_dict, 'res_class_dset': res_class_dset,
        'res_class_bin': res_class_bin, 'excl_area': excl_area,
        'power_density': power_density, 'cf_dset': cf_dset, 'lcoe_dset':
        lcoe_dset, 'resolution': resolution, 'exclusion_shape':
        exclusion_shape, 'close': close, 'offshore_flags': offshore_flags,
        'friction_layer': friction_layer}
    with cls(gid, excl_fpath, gen_fpath, tm_dset, gen_index, **kwargs
        ) as point:
        summary = point.summarize(args=args, data_layers=data_layers)
    return summary
