switch to total column prediction for now - need more R&D

modules/icing/pirep_goes.py

@@ -1657,6 +1657,31 @@ def spatial_filter(icing_dict):
     return grd_bins
 
 
+# def spatial_filter2(icing_dict):
+#     keys = icing_dict.keys()
+#     grd_x_hi = lon_space.shape[0] - 1
+#     grd_y_hi = lat_space.shape[0] - 1
+#
+#     grd_bins = np.full((lat_space.shape[0], lon_space.shape[0]), 0)
+#
+#     for key in keys:
+#             tup = icing_dict.get(key)
+#             lat = tup[0]
+#             lon = tup[1]
+#
+#             lon_idx = np.searchsorted(lon_space, lon)
+#             lat_idx = np.searchsorted(lat_space, lat)
+#
+#             if lon_idx < 0 or lon_idx > grd_x_hi:
+#                 continue
+#             if lat_idx < 0 or lat_idx > grd_y_hi:
+#                 continue
+#
+#             grd_bins[lat_idx, lon_idx] += 1
+#
+#     return grd_bins
+
+
 # dt_str_0: start datetime string in format YYYY-MM-DD_HH:MM (default)
 # dt_str_1: end datetime string in format YYYY-MM-DD_HH:MM (default)
 # format_code: Python Datetime format code, default: '%Y-%m-%d_%H:%M'
@@ -2015,7 +2040,8 @@ def run_icing_predict(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', output_dir=h
             lons_2d, lats_2d, x_rad, y_rad = get_lon_lat_2d_mesh(nav, ll, cc)
 
         preds_2d_dct, probs_2d_dct = run_evaluate_static_new(data_dct, num_lines, num_elems, day_night=day_night,
-                                                             ckpt_dir_s_path=model_path, prob_thresh=prob_thresh)
+                                                             ckpt_dir_s_path=model_path, prob_thresh=prob_thresh,
+                                                             flight_levels=[0])
         flt_lvls = list(preds_2d_dct.keys())
         for flvl in flt_lvls:
             preds = preds_2d_dct[flvl]