snapshot..

modules/aeolus/aeolus_amv.py

@@ -444,9 +444,9 @@ def create_amv_to_aeolus_match_file(aeolus_files_dir, amv_files_dir, outfile=Non
 # match_file: pathname for the product file
 # dt_str_0: start time (YYYY-MM-DD_HH:MM)
 # dt_str_1: end time (YYYY-MM-DD_HH:MM)
-# returns:
+# returns: Xarray.DataArray
 # amvs[nprofs, max_num_amvs_per_prof, num_of_params], profs[nprofs, max_num_levs_per_prof, num_of_params],
-# prof_times_locs[nprofs, (time, lon, lat)
+# prof_locs[nprofs, (lon, lat)
 def subset_by_time(match_file, dt_str_0, dt_str_1):
     rootgrp = Dataset(match_file, 'r', format='NETCDF4')
     all_dims = rootgrp.dimensions
@@ -525,9 +525,7 @@ def subset_by_time(match_file, dt_str_0, dt_str_1):
         accum_amv += n_amvs
         accum_prf += n_levs
 
-    prof_times_locs = np.column_stack([times[time_idxs], lons[time_idxs], lats[time_idxs]])
-
-    coords = {'num_profs' : times[time_idxs], 'num_params' : ['speed', 'azimuth', 'layer_bot', 'layer_top']}
+    coords = {'num_profs': times[time_idxs], 'num_params': ['speed', 'azimuth', 'layer_bot', 'layer_top']}
     prof_da = xr.DataArray(profs, coords=coords, dims=['num_profs', 'max_num_levels', 'num_params'])
 
     coords = {'num_profs': times[time_idxs], 'num_params': ['speed', 'azimuth', 'pressure', 'distance']}