snapshot...

modules/util/viirs_l1b_l2.py

@@ -140,26 +140,26 @@ def run_all(directory, out_directory, day_night='ANY', start=10):
             if len(data_train_tiles) == 0:
                 continue
 
-            # if (f_cnt % 5) == 0:
-            #     num_valid_samples = 0
-            #     if len(data_valid_tiles) > 0:
-            #         data_valid = np.stack(data_valid_tiles)
-            #         np.save(out_directory + 'data_valid_' + str(cnt), data_valid)
-            #         num_valid_samples = data_valid.shape[0]
-            #
-            #     data_train = np.stack(data_train_tiles)
-            #     np.save(out_directory+'data_train_' + str(cnt), data_train)
-            #     num_train_samples = data_train.shape[0]
-            #
-            #     data_valid_tiles = []
-            #     data_train_tiles = []
-            #
-            #     print('  num_train_samples, num_valid_samples, progress % : ', num_train_samples, num_valid_samples, int((f_cnt/num_files)*100))
-            #     total_num_train_samples += num_train_samples
-            #     total_num_valid_samples += num_valid_samples
-            #     print('total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)
-            #
-            #     cnt += 1
+            if (f_cnt % 5) == 0:
+                num_valid_samples = 0
+                if len(data_valid_tiles) > 0:
+                    data_valid = np.stack(data_valid_tiles)
+                    np.save(out_directory + 'data_valid_' + str(cnt), data_valid)
+                    num_valid_samples = data_valid.shape[0]
+
+                data_train = np.stack(data_train_tiles)
+                np.save(out_directory+'data_train_' + str(cnt), data_train)
+                num_train_samples = data_train.shape[0]
+
+                data_valid_tiles = []
+                data_train_tiles = []
+
+                print('  num_train_samples, num_valid_samples, progress % : ', num_train_samples, num_valid_samples, int((f_cnt/num_files)*100))
+                total_num_train_samples += num_train_samples
+                total_num_valid_samples += num_valid_samples
+                print('total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)
+
+                cnt += 1
 
     print('** total_num_train_samples, total_num_valid_samples: ', total_num_train_samples, total_num_valid_samples)