snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -200,7 +200,7 @@ class SRCNN:
         self.test_label_files = None
 
         # self.n_chans = len(data_params_half) + len(data_params_full) + 1
-        self.n_chans = 6
+        self.n_chans = 3
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
 
@@ -259,16 +259,16 @@ class SRCNN:
             tmp = normalize(tmp, param, mean_std_dct)
             data_norm.append(tmp)
 
-        for param in sub_fields:
-            idx = params.index(param)
-            tmp = input_data[:, idx, :, :]
-            tmp = tmp[:, self.slc_y_m, self.slc_x_m]
-            tmp = upsample_nearest(tmp)
-            if param != 'refl_substddev_ch01':
-                tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
-            else:
-                tmp = np.where(np.isnan(tmp), 0, tmp)
-            data_norm.append(tmp)
+        # for param in sub_fields:
+        #     idx = params.index(param)
+        #     tmp = input_data[:, idx, :, :]
+        #     tmp = tmp[:, self.slc_y_m, self.slc_x_m]
+        #     tmp = upsample_nearest(tmp)
+        #     if param != 'refl_substddev_ch01':
+        #         tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+        #     else:
+        #         tmp = np.where(np.isnan(tmp), 0, tmp)
+        #     data_norm.append(tmp)
 
         # for param in data_params_full:
         #     idx = params_i.index(param)