refactor clim

pcmdi_metrics/sea_ice/lib/sea_ice_lib.py

@@ -222,17 +222,22 @@ def get_total_extent(data, ds_area):
  return total_extent, te_mean
 
 
-def get_clim(total_extent, ds, ds_var):
- try:
- clim = to_ice_con_ds(total_extent, ds, ds_var).temporal.climatology(
- ds_var, freq="month"
- )
- except IndexError: # Issue with time bounds
- tmp = to_ice_con_ds(total_extent, ds, ds_var)
- tbkey = xcdat_dataset_io.get_time_bounds_key(tmp)
- tmp = tmp.drop_vars(tbkey)
- tmp = tmp.bounds.add_missing_bounds()
- clim = tmp.temporal.climatology(ds_var, freq="month")
+def get_clim(total_extent, ds_var, ds=None):
+ # ds is a dataset that contains the dimensions
+ # needed to turn total_extent into a dataset
+ if ds is None:
+ clim = total_extent.temporal.climatology(ds_var, freq="month")
+ else:
+ try:
+ clim = to_ice_con_ds(total_extent, ds, ds_var).temporal.climatology(
+ ds_var, freq="month"
+ )
+ except IndexError: # Issue with time bounds
+ tmp = to_ice_con_ds(total_extent, ds, ds_var)
+ tbkey = xcdat_dataset_io.get_time_bounds_key(tmp)
+ tmp = tmp.drop_vars(tbkey)
+ tmp = tmp.bounds.add_missing_bounds()
+ clim = tmp.temporal.climatology(ds_var, freq="month")
  return clim
 
 
@@ -245,7 +250,7 @@ def process_by_region(ds, ds_var, ds_area, pole):
  yvar = find_lat(ds)
  data = choose_region(region, ds, ds_var, xvar, yvar, pole)
  total_extent, te_mean = get_total_extent(data, ds_area)
- clim = get_clim(total_extent, ds, ds_var)
+ clim = get_clim(total_extent, ds_var, ds)
  clims[region] = clim
  means[region] = te_mean
  del data