more fixing

modules/util/viirs_l1b_l2.py

@@ -105,34 +105,34 @@ def run_all(directory, out_directory):
                 [data_valid_tiles.append(data_tiles[k]) for k in range(n_vld)]
                 [data_train_tiles.append(data_tiles[k]) for k in range(n_vld, num)]
 
-            if f_cnt == 10:
-                cnt += 1
-
-                #label_valid = np.stack(label_valid_tiles)
-                #label_train = np.stack(label_train_tiles)
-                data_valid = np.stack(data_valid_tiles)
-                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)
-
-                #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)
-                total_num_train_samples += num_train_samples
-                total_num_test_samples += num_valid_samples
-
-                f_cnt = 0
-            else:
-                f_cnt += 1
+                if f_cnt == 10:
+                    cnt += 1
+
+                    #label_valid = np.stack(label_valid_tiles)
+                    #label_train = np.stack(label_train_tiles)
+                    data_valid = np.stack(data_valid_tiles)
+                    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)
+
+                    #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)
+                    total_num_train_samples += num_train_samples
+                    total_num_test_samples += num_valid_samples
+
+                    f_cnt = 0
+                else:
+                    f_cnt += 1
 
         # if len(label_train_tiles) == 0 or len(data_train_tiles) == 0:
         #     continue