`snapshot...`

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -59,8 +59,7 @@ params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'temp_stddev3x3_ch31', 'refl_s
 # data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
 data_params_half = ['temp_11_0um_nom']
 data_params_full = ['refl_0_65um_nom']
-# sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
-sub_fields = ['refl_substddev_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)
@@ -211,7 +210,7 @@ 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 = 3
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
 
@@ -274,21 +273,22 @@ class SRCNN:
             data_norm.append(tmp)
 
         # High res refectance ----------
-        # idx = params_i.index('refl_0_65um_nom')
-        # tmp = input_label[:, idx, :, :]
-        # tmp = np.where(np.isnan(tmp), 0, tmp)
-        # tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
-        # data_norm.append(tmp[:, self.slc_y, self.slc_x])
-
         idx = params_i.index('refl_0_65um_nom')
         tmp = input_label[:, idx, :, :]
-        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)
+        tmp = np.where(np.isnan(tmp), 0, tmp)
+        tmp = normalize(tmp, 'refl_0_65um_nom', mean_std_dct)
+        data_norm.append(tmp[:, self.slc_y, self.slc_x])
+
+        # High res reflectance down 2 ---------
+        # idx = params_i.index('refl_0_65um_nom')
+        # tmp = input_label[:, idx, :, :]
+        # 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)
 
         tmp = input_label[:, label_idx_i, :, :]
         tmp = tmp.copy()
@@ -312,16 +312,16 @@ class SRCNN:
         #         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)
+        # 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)
         # ---------------------------------------------------
 
         data = np.stack(data_norm, axis=3)