snapshot...

modules/deeplearning/srcnn_l1b_l2.py

@@ -49,9 +49,9 @@ mean_std_dct.update(mean_std_dct_l2)
 
 #params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
 #data_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom']
-params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
+params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cld_opd_dcomp']
 data_params = ['temp_11_0um_nom']
-label_params = ['cloud_fraction']
+label_params = ['cld_opd_dcomp']
 
 
 DO_ZERO_OUT = False
@@ -239,6 +239,8 @@ class SRCNN:
         tmp = input_data[:, label_idx, 3:131:2, 3:131:2]
         tmp = np.where(np.isnan(tmp), 0, tmp)
         tmp = resample_2d_linear(x_64, y_64, tmp, t, s)
+        if label_param != 'cloud_fraction':
+            tmp = normalize(tmp, param, mean_std_dct, add_noise=add_noise, noise_scale=noise_scale)
         data_norm.append(tmp)
         # ---------
         data = np.stack(data_norm, axis=3)