snapshot...

modules/deeplearning/cloud_opd_fcn_abi.py

@@ -71,8 +71,8 @@ label_param = 'cld_opd_dcomp'
 
 params = ['temp_11_0um_nom', 'refl_0_65um_nom', 'refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01', 'cloud_probability', label_param]
 params_i = ['temp_11_0um_nom', 'refl_0_65um_nom', 'cloud_probability', label_param]
-# data_params_half = ['temp_11_0um_nom']
-data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
+data_params_half = ['temp_11_0um_nom']
+# data_params_half = ['temp_11_0um_nom', 'refl_0_65um_nom']
 sub_fields = ['refl_submin_ch01', 'refl_submax_ch01', 'refl_substddev_ch01']
 data_params_full = ['refl_0_65um_nom']
 
@@ -289,7 +289,7 @@ class SRCNN:
         self.test_label_files = None
 
         # self.n_chans = len(data_params_half) + len(data_params_full) + 1
-        self.n_chans = 5
+        self.n_chans = 6
 
         self.X_img = tf.keras.Input(shape=(None, None, self.n_chans + 1))
 
@@ -319,12 +319,13 @@ class SRCNN:
         input_label = np.concatenate(label_s)
 
         data_norm = []
-        # for param in data_params_half:
-        #     idx = params.index(param)
-        #     tmp = input_data[:, idx, :, :]
-        #     tmp = tmp[:, slc_y, slc_x]
+        for param in data_params_half:
+            idx = params.index(param)
+            tmp = input_data[:, idx, :, :]
+            tmp = tmp[:, slc_y, slc_x]
             # tmp = normalize(tmp, param, mean_std_dct)
-        #     data_norm.append(tmp)
+            tmp = scale(tmp, param, mean_std_dct)
+            data_norm.append(tmp)
 
         tmp = input_label[:, params_i.index('cloud_probability'), :, :]
         cld_prob = tmp.copy()
@@ -931,12 +932,13 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
         np.save(out_file,
                 [labels[:, :, :, 0],
                  preds[:, :, :, 0],
-                 inputs[:, 1:y_hi, 1:x_hi, 0],
-                 descale(inputs[:, 1:y_hi, 1:x_hi, 1], 'refl_0_65um_nom', mean_std_dct),
+                 descale(inputs[:, 1:y_hi, 1:x_hi, 0], 'temp_11_0um_nom', mean_std_dct),
+                 inputs[:, 1:y_hi, 1:x_hi, 1],
                  descale(inputs[:, 1:y_hi, 1:x_hi, 2], 'refl_0_65um_nom', mean_std_dct),
-                 inputs[:, 1:y_hi, 1:x_hi, 3],
-                 descale(inputs[:, 1:y_hi, 1:x_hi, 4], label_param, mean_std_dct),
-                 inputs[:, 1:y_hi, 1:x_hi, 5]])
+                 descale(inputs[:, 1:y_hi, 1:x_hi, 3], 'refl_0_65um_nom', mean_std_dct),
+                 inputs[:, 1:y_hi, 1:x_hi, 4],
+                 descale(inputs[:, 1:y_hi, 1:x_hi, 5], label_param, mean_std_dct),
+                 inputs[:, 1:y_hi, 1:x_hi, 6]])
 
 
 def run_evaluate_static(in_file, out_file, ckpt_dir):