snapshot...

modules/deeplearning/cloud_opd_srcnn_abi.py

@@ -56,7 +56,7 @@ 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_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
 sub_fields = ['temp_stddev3x3_ch31', 'refl_stddev3x3_ch01']
 
 label_idx_i = params_i.index(label_param)
@@ -207,7 +207,7 @@ 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 = 5
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans))
 
@@ -272,7 +272,8 @@ 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)
-            if param != 'refl_substddev_ch01':
+            # 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)