snapshot...

modules/util/viirs_l1b_l2.py

@@ -51,8 +51,8 @@ emis_params = ['temp_10_4um_nom', 'temp_11_0um_nom', 'temp_12_0um_nom', 'temp_13
 # data_params = refl_params + emis_params
 # data_params = emis_params
 
-l2_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'cloud_fraction']
-# l2_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
+# l2_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'cloud_fraction']
+l2_params = ['temp_11_0um_nom', 'temp_12_0um_nom', 'refl_0_65um_nom', 'cloud_fraction']
 
 label_params = l2_params
 data_params = l2_params
@@ -126,11 +126,12 @@ def run_all(directory, out_directory, day_night='ANY'):
             except Exception as e:
                 print(e)
                 data_h5f.close()
-                #label_h5f.close()
+                # label_h5f.close()
                 continue
+            print(data_f)
 
             data_h5f.close()
-            #label_h5f.close()
+            # label_h5f.close()
 
             # if len(data_tiles) == 0 or len(label_tiles) == 0:
             #     continue
@@ -153,25 +154,26 @@ def run_all(directory, out_directory, day_night='ANY'):
             if f_cnt == 5:
                 f_cnt = 0
 
-                # label_valid = np.stack(label_valid_tiles)
+                num_valid_samples = 0
+                if len(data_valid_tiles) > 0:
+                    # label_valid = np.stack(label_valid_tiles)
+                    data_valid = np.stack(data_valid_tiles)
+                    np.save(out_directory + 'data_valid_' + str(cnt), data_valid)
+                    # np.save(out_directory+'label_valid_' + str(cnt), label_valid)
+                    num_valid_samples = data_valid.shape[0]
+
                 # label_train = np.stack(label_train_tiles)
-                data_valid = np.stack(data_valid_tiles)
+                # np.save(out_directory+'label_train_' + str(cnt), label_train)
                 data_train = np.stack(data_train_tiles)
-
                 np.save(out_directory+'data_train_' + str(cnt), data_train)
-                np.save(out_directory+'data_valid_' + str(cnt), data_valid)
-                # np.save(out_directory+'label_train_' + str(cnt), label_train)
-                # np.save(out_directory+'label_valid_' + str(cnt), label_valid)
+                num_train_samples = data_train.shape[0]
 
                 label_valid_tiles = []
                 label_train_tiles = []
                 data_valid_tiles = []
                 data_train_tiles = []
 
-                num_train_samples = data_train.shape[0]
-                num_valid_samples = data_valid.shape[0]
-                print('   file # done: ', cnt)
-                print('num_train_samples, num_valid_samples: ', num_train_samples, num_valid_samples)
+                print('  num_train_samples, num_valid_samples: ', num_train_samples, num_valid_samples)
                 total_num_train_samples += num_train_samples
                 total_num_valid_samples += num_valid_samples