snapshot...

modules/util/util.py

@@ -776,7 +776,8 @@ def write_icing_file(clvrx_str_time, output_dir, preds_dct, probs_dct, x, y, lon
 
 
 def write_icing_file_nc4(clvrx_str_time, output_dir, preds_dct, probs_dct,
-                         x, y, lons, lats, elems, lines, satellite='GOES16', domain='CONUS'):
+                         x, y, lons, lats, elems, lines, satellite='GOES16', domain='CONUS',
+                         has_time=False):
     outfile_name = output_dir + 'icing_prediction_'+clvrx_str_time+'.nc'
     rootgrp = Dataset(outfile_name, 'w', format='NETCDF4')
 
@@ -795,40 +796,53 @@ def write_icing_file_nc4(clvrx_str_time, output_dir, preds_dct, probs_dct,
     tvar[0] = get_timestamp(clvrx_str_time)
     tvar.units = 'seconds since 1970-01-01 00:00:00'
 
+    if not has_time:
+        var_dim_list = [dim_1_name, dim_0_name]
+    else:
+        var_dim_list = [time_dim_name, dim_1_name, dim_0_name]
+
     prob_s = []
 
     flt_lvls = list(preds_dct.keys())
     for flvl in flt_lvls:
         preds = preds_dct[flvl]
 
-        icing_pred_ds = rootgrp.createVariable('icing_prediction_level_'+flt_level_ranges_str[flvl], 'i2', [dim_1_name, dim_0_name])
+        icing_pred_ds = rootgrp.createVariable('icing_prediction_level_'+flt_level_ranges_str[flvl], 'i2', var_dim_list)
         icing_pred_ds.setncattr('coordinates', geo_coords)
         icing_pred_ds.setncattr('grid_mapping', 'Projection')
         icing_pred_ds.setncattr('missing', -1)
+        if has_time:
+            preds = preds.reshap((1, y.shape[0], x.shape[0]))
         icing_pred_ds[:,] = preds
 
     for flvl in flt_lvls:
         probs = probs_dct[flvl]
         prob_s.append(probs)
 
-        icing_prob_ds = rootgrp.createVariable('icing_probability_level_'+flt_level_ranges_str[flvl], 'f4', [dim_1_name, dim_0_name])
+        icing_prob_ds = rootgrp.createVariable('icing_probability_level_'+flt_level_ranges_str[flvl], 'f4', var_dim_list)
         icing_prob_ds.setncattr('coordinates', geo_coords)
         icing_prob_ds.setncattr('grid_mapping', 'Projection')
         icing_prob_ds.setncattr('missing', -1.0)
+        if has_time:
+            probs = probs.reshap((1, y.shape[0], x.shape[0]))
         icing_prob_ds[:,] = probs
 
     prob_s = np.stack(prob_s, axis=-1)
     max_prob = np.max(prob_s, axis=2)
+    if has_time:
+        max_prob = max_prob.reshape(1, y.shape[0], x.shape[0])
 
-    icing_prob_ds = rootgrp.createVariable('max_icing_probability_column', 'f4', [dim_1_name, dim_0_name])
+    icing_prob_ds = rootgrp.createVariable('max_icing_probability_column', 'f4', var_dim_list)
     icing_prob_ds.setncattr('coordinates', geo_coords)
     icing_prob_ds.setncattr('grid_mapping', 'Projection')
     icing_prob_ds.setncattr('missing', -1.0)
     icing_prob_ds[:,] = max_prob
 
     max_lvl = np.argmax(prob_s, axis=2)
+    if has_time:
+        max_lvl = max_lvl.reshap((1, y.shape[0], x.shape[0]))
 
-    icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', [dim_1_name, dim_0_name])
+    icing_pred_ds = rootgrp.createVariable('max_icing_probability_level', 'i2', var_dim_list)
     icing_pred_ds.setncattr('coordinates', geo_coords)
     icing_pred_ds.setncattr('grid_mapping', 'Projection')
     icing_pred_ds.setncattr('missing', -1)