text: """County level agg."""
  
  from pyiem.util import get_dbconn
  from pyiem.plot.geoplot import MapPlot
  from geopandas import read_postgis
  from pandas.io.sql import read_sql
  
  
  def main():
      """Go Main Go."""
      years = 12.0  # 2008 - 2019
      pgconn = get_dbconn("idep")
      postgis = get_dbconn("postgis")
  
      # Get the initial geometries
      df = read_postgis(
          """
          SELECT ugc, name, geom from ugcs WHERE end_ts is null and
          substr(ugc, 1, 3) = 'IAC'
          """,
          postgis,
          index_col="ugc",
          crs="EPSG:4326",
      )
      scenario = 0
      df2 = read_sql(
          """WITH data as (
      SELECT r.huc_12,
      sum(avg_loss) * 4.163 / %s as detach,
      sum(avg_delivery) * 4.163 / %s as delivery,
      sum(avg_runoff) / 25.4 / %s as runoff
      from results_by_huc12 r
      , huc12 h WHERE r.huc_12 = h.huc_12 and h.states ~* 'IA'
      and r.scenario = %s and h.scenario = 0 and r.valid < '2020-01-01'
      and r.valid > '2008-01-01'
      GROUP by r.huc_12)
  
      SELECT ugc, avg(detach) as detach, avg(delivery) as delivery,
      avg(runoff) as runoff from data d JOIN huc12 h on (d.huc_12 = h.huc_12)
      WHERE h.scenario = 0 GROUP by ugc ORDER by delivery desc
      """,
          pgconn,
          params=(years, years, years, scenario),
          index_col="ugc",
      )
      newcols = {
          "detach": "det%s" % (0,),
          "delivery": "del%s" % (0,),
          "runoff": "run%s" % (0,),
      }
      for key, val in newcols.items():
          df[val] = df2[key]
      df = df.sort_values("del0", ascending=False)
      print(df.head(10))
  
      mp = MapPlot(
          title="2008-2019 DEP Top 10 Erosive Counties", logo="dep", caption=""
      )
      df2 = df.head(10)
      mp.fill_ugcs(df2["del0"].to_dict())
      mp.postprocess(filename="test.png")
  
  
  if __name__ == "__main__":
      main()
  