snapshot...

modules/icing/util.py

@@ -1483,19 +1483,11 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
                                 pirep_file='/Users/tomrink/data/pirep/pireps_202109200000_202109232359.csv',
                                 obs_lons=None, obs_lats=None, obs_times=None, obs_alt=None, flight_level=None, obs_intensity=None):
 
-    # import deeplearning.icing_fcn as icing_fcn
-    # model_module = icing_fcn
-    #
-    # if day_model_path is not None:
-    #     day_model = model_module.load_model(day_model_path, day_night='DAY', l1b_andor_l2=l1b_andor_l2,
-    #                                         use_flight_altitude=use_flight_altitude)
-    # if night_model_path is not None:
-    #     night_model = model_module.load_model(night_model_path, day_night='NIGHT', l1b_andor_l2=l1b_andor_l2,
-    #                                           use_flight_altitude=use_flight_altitude)
 
     # load parameter stats and model from disk
     stdSclr_day = joblib.load('/home/rink/stdSclr_4_day.pkl')
     day_model = joblib.load('/home/rink/icing_gbm_day.pkl')
+
     stdSclr_nght = joblib.load('/home/rink/stdSclr_4_nght.pkl')
     nght_model = joblib.load('/home/rink/icing_gbm_nght.pkl')
 
@@ -1513,8 +1505,8 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
     if flight_level is not None:
         alt_lo, alt_hi = flt_level_ranges[flight_level]
 
-    # day_train_params, _, _ = get_training_parameters(day_night='DAY', l1b_andor_l2=l1b_andor_l2)
-    # nght_train_params, _, _ = get_training_parameters(day_night='NIGHT', l1b_andor_l2=l1b_andor_l2)
+    day_train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp']
+    nght_train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
     #
     # if day_night == 'AUTO':
     #     train_params = list(set(day_train_params + nght_train_params))
@@ -1522,8 +1514,6 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
     #     train_params = day_train_params
     # elif day_night == 'NIGHT':
     #     train_params = nght_train_params
-    day_train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_dcomp', 'cld_opd_dcomp']
-    nght_train_params = ['cld_temp_acha', 'supercooled_cloud_fraction', 'cld_reff_acha', 'cld_opd_acha']
 
     if satellite == 'H08':
         clvrx_ds = CLAVRx_H08(clvrx_dir)
@@ -1571,9 +1561,9 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
         num_nght_tiles = np.sum(nght_idxs)
         print('num nght tiles: ', num_nght_tiles)
 
-        cldy_idxs = cldmsk >= 2
-        num_cldy_tiles = np.sum(cldy_idxs)
-        print('num cloudy tiles: ', num_cldy_tiles)
+        clr_idxs = cldmsk < 2
+        num_clr_tiles = np.sum(clr_idxs)
+        print('num clear tiles: ', num_clr_tiles)
 
         fd_preds = np.zeros(num_lines * num_elems, dtype=np.int8)
         fd_probs = np.zeros(num_lines * num_elems, dtype=np.float32)
@@ -1581,18 +1571,17 @@ def run_icing_predict_image_1x1(clvrx_dir='/Users/tomrink/data/clavrx/RadC/', ou
         fd_probs[:] = -1.0
 
         if (day_night == 'AUTO' or day_night == 'DAY') and num_day_tiles > 0:
-
             varX_std = stdSclr_day.transform(varX_day)
             varX_std = np.where(np.isnan(varX_std), 0, varX_std)
             probs = day_model.predict_proba(varX_std)[:, 1]
             fd_probs[day_idxs] = probs[day_idxs]
 
         if (day_night == 'AUTO' or day_night == 'NIGHT') and num_nght_tiles > 0:
-
             varX_std = stdSclr_nght.transform(varX_ngth)
             varX_std = np.where(np.isnan(varX_std), 0, varX_std)
             probs = nght_model.predict_proba(varX_std)[:, 1]
-            fd_probs[nght_idxs] = probs[nght_idxs]
+            fd_probs[np.invert(day_idxs)] = probs[np.invert(day_idxs)]
+        fd_probs[clr_idxs] = 0.0
 
         max_prob = fd_probs.reshape((num_lines, num_elems))