@classmethod
def sc_mean(cls, gid, excl, tm_dset, data, excl_dict=None, resolution=64,
    exclusion_shape=None, close=True):
    """
        Compute exclusions weight mean for the sc point from data

        Parameters
        ----------
        gid : int
            gid for supply curve point to analyze.
        excl : str | ExclusionMask
            Filepath to exclusions h5 or ExclusionMask file handler.
        tm_dset : str
            Dataset name in the exclusions file containing the
            exclusions-to-resource mapping data.
        data : ndarray | ResourceDataset
            Array of data or open dataset handler to apply exclusions too
        excl_dict : dict | None
            Dictionary of exclusion LayerMask arugments {layer: {kwarg: value}}
            None if excl input is pre-initialized.
        resolution : int
            Number of exclusion points per SC point along an axis.
            This number**2 is the total number of exclusion points per
            SC point.
        exclusion_shape : tuple
            Shape of the full exclusions extent (rows, cols). Inputing this
            will speed things up considerably.
        offshore_flags : np.ndarray | None
            Array of offshore boolean flags if available from wind generation
            data. None if offshore flag is not available.
        close : bool
            Flag to close object file handlers on exit

        Returns
        -------
        ndarray
            Exclusions weighted means of data for supply curve point
        """
    kwargs = {'excl_dict': excl_dict, 'resolution': resolution,
        'exclusion_shape': exclusion_shape, 'close': close}
    with cls(gid, excl, tm_dset, **kwargs) as point:
        means = point.exclusion_weighted_mean(data)
    return means
