@classmethod
def simple(cls, sc_points, trans_table, fcr, sc_features=None,
    transmission_costs=None, sort_on='total_lcoe', columns=('trans_gid',
    'trans_type', 'lcot', 'total_lcoe', 'trans_cap_cost'), max_workers=None,
    wind_dirs=None, n_dirs=2, downwind=False):
    """
        Run simple supply curve by connecting to the cheapest tranmission
        feature.

        Parameters
        ----------
        sc_points : str | pandas.DataFrame
            Path to .csv or .json or DataFrame containing supplcy curve
            point summary
        trans_table : str | pandas.DataFrame
            Path to .csv or .json or DataFrame containing supply curve
            transmission mapping
        fcr : float
            Fixed charge rate, used to compute LCOT
        sc_features : str | pandas.DataFrame
            Path to .csv or .json or DataFrame containing additional supply
            curve features, e.g. transmission multipliers, regions
        transmission_costs : str | dict
            Transmission feature costs to use with TransmissionFeatures
            handler: line_tie_in_cost, line_cost, station_tie_in_cost,
            center_tie_in_cost, sink_tie_in_cost
        sort_on : str
            Column label to sort the Supply Curve table on. This affects the
            build priority - connections with the lowest value in this column
            will be built first.
        columns : list | tuple
            Columns to preserve in output supply curve dataframe.
        max_workers : int | NoneType
            Number of workers to use to compute lcot, if > 1 run in parallel.
            None uses all available cpu's.
        wind_dirs : pandas.DataFrame | str
            path to .csv or reVX.wind_dirs.wind_dirs.WindDirs output with
            the neighboring supply curve point gids and power-rose value at
            each cardinal direction
        n_dirs : int, optional
            Number of prominent directions to use, by default 2
        downwind : bool, optional
            Flag to remove downwind neighbors as well as upwind neighbors

        Returns
        -------
        supply_curve : pandas.DataFrame
            Updated sc_points table with transmission connections, LCOT
            and LCOE+LCOT
        """
    sc = cls(sc_points, trans_table, fcr, sc_features=sc_features,
        transmission_costs=transmission_costs, connectable=False,
        max_workers=max_workers)
    supply_curve = sc.simple_sort(sort_on=sort_on, columns=columns,
        wind_dirs=wind_dirs, n_dirs=n_dirs, downwind=downwind)
    return supply_curve
