snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -58,8 +58,8 @@ params = ['temp_11_0um_nom', 'refl_0_65um_nom', 'refl_submin_ch01', 'refl_submax
 params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_stddev3x3_ch01', label_param]
 data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
 data_params_full = ['refl_0_65um_nom']
-# sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
-sub_fields = ['refl_stddev3x3_ch01']
+sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
+# sub_fields = ['refl_stddev3x3_ch01']
 
 label_idx_i = params_i.index(label_param)
 label_idx = params.index(label_param)
@@ -209,7 +209,7 @@ class SRCNN:
         self.test_label_files = None
 
         # self.n_chans = len(data_params_half) + len(data_params_full) + 1
-        self.n_chans = 4
+        self.n_chans = 6
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
 
@@ -265,38 +265,40 @@ class SRCNN:
             tmp = np.where(np.isnan(tmp), 0.0, tmp)
             tmp = tmp[:, self.slc_y_m, self.slc_x_m]
             tmp = self.upsample(tmp)
+            tmp = smooth_2d(tmp)
             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 = np.where(np.isnan(tmp), 0.0, tmp)
-            tmp = tmp[:, self.slc_y_m, self.slc_x_m]
-            tmp = self.upsample(tmp)
-            # if param != 'refl_substddev_ch01':
-            if False:
-                tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
-            else:
-                tmp = np.where(np.isnan(tmp), 0.0, tmp)
-            data_norm.append(tmp)
-
         # for param in sub_fields:
         #     idx = params.index(param)
         #     tmp = input_data[:, idx, :, :]
-        #     tmp = upsample_nearest(tmp)
-        #     tmp = tmp[:, self.slc_y, self.slc_x]
-        #     if param != 'refl_substddev_ch01':
+        #     tmp = np.where(np.isnan(tmp), 0.0, tmp)
+        #     tmp = tmp[:, self.slc_y_m, self.slc_x_m]
+        #     tmp = self.upsample(tmp)
+        #     # if param != 'refl_substddev_ch01':
+        #     if False:
         #         tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
         #     else:
-        #         tmp = np.where(np.isnan(tmp), 0, tmp)
+        #         tmp = np.where(np.isnan(tmp), 0.0, tmp)
         #     data_norm.append(tmp)
+
+        for param in sub_fields:
+            idx = params.index(param)
+            tmp = input_data[:, idx, :, :]
+            tmp = upsample_nearest(tmp)
+            tmp = tmp[:, self.slc_y, self.slc_x]
+            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)
         # ---------------------------------------------------
         tmp = input_label[:, label_idx_i, ::2, ::2]
         tmp = tmp.copy()
         tmp = np.where(np.isnan(tmp), 0.0, tmp)
         tmp = tmp[:, self.slc_y_2, self.slc_x_2]
         tmp = self.upsample(tmp)
+        tmp = smooth_2d(tmp)
         tmp = normalize(tmp, label_param, mean_std_dct)
         data_norm.append(tmp)
         # ---------