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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def ts(self, data, lon_cyclic=True, lon_str=LON_STR, lat_str=LAT_STR, land_mask_str=LAND_MASK_STR, sfc_area_str=SFC_AREA_STR):
 """Create yearly time-series of region-averaged data. Parameters data : xarray.DataArray The array to create the regional timeseries of lon_cyclic : { None, True, False }, optional (default True) Whether or not the longitudes of ``data`` span the whole globe, meaning that they should be wrapped around as necessary to cover the Region's full width. lat_str, lon_str, land_mask_str, sfc_area_str : str, optional The name of the latitude, longitude, land mask, and surface area coordinates, respectively, in ``data``. Defaults are the corresponding values in ``aospy.internal_names``. Returns ------- xarray.DataArray The timeseries of values averaged within the region and within each year, one value per year. """

        data_masked = self.mask_var(data, lon_cyclic=lon_cyclic,
                                    lon_str=lon_str, lat_str=lat_str)
        sfc_area = data[sfc_area_str]
        sfc_area_masked = self.mask_var(sfc_area, lon_cyclic=lon_cyclic,
                                        lon_str=lon_str, lat_str=lat_str)
        land_mask = _get_land_mask(data, self.do_land_mask,
                                   land_mask_str=land_mask_str)
        weights = sfc_area_masked * land_mask
        # Mask weights where data values are initially invalid in addition
        # to applying the region mask.
        weights = weights.where(np.isfinite(data))
        weights_reg_sum = weights.sum(lon_str).sum(lat_str)
        data_reg_sum = (data_masked * sfc_area_masked *
                        land_mask).sum(lat_str).sum(lon_str)
        return data_reg_sum / weights_reg_sum