fix an error in data_postprocess

hydromodel/app/datapostprocess4calibrate.py

@@ -1,7 +1,7 @@
 """
 Author: Wenyu Ouyang
 Date: 2022-11-19 17:27:05
-LastEditTime: 2022-12-25 09:14:33
+LastEditTime: 2023-05-05 10:14:20
 LastEditors: Wenyu Ouyang
 Description: the script to postprocess calibrated models in hydro-model-xaj
 FilePath: \hydro-model-xaj\hydromodel\app\datapostprocess4calibrate.py
@@ -135,7 +135,7 @@ def statistics(args):
         "--exp",
         dest="exp",
         help="An exp is corresponding to one data setting",
-        default="exp61561",
+        default="example",
         type=str,
     )
     parser.add_argument(
@@ -149,7 +149,7 @@ def statistics(args):
         "--cv_fold",
         dest="cv_fold",
         help="The number of cross-validation fold",
-        default=2,
+        default=1,
         type=int,
     )
     the_args = parser.parse_args()

hydromodel/data/data_postprocess.py

@@ -136,15 +136,9 @@ def summarize_metrics(result_dir, model_info: dict):
         test_metric = hydro_utils.unserialize_json(test_metric_file)
 
         for key, value in train_metric.items():
-            if count == 0:
-                train_metrics[key] = value
-            else:
-                train_metrics[key] = train_metrics[key] + value
+            train_metrics[key] = value if count == 0 else train_metrics[key] + value
         for key, value in test_metric.items():
-            if count == 0:
-                test_metrics[key] = value
-            else:
-                test_metrics[key] = test_metrics[key] + value
+            test_metrics[key] = value if count == 0 else test_metrics[key] + value
         count = count + 1
     metric_dfs_train = pd.DataFrame(train_metrics, index=basin_ids).transpose()
     metric_dfs_test = pd.DataFrame(test_metrics, index=basin_ids).transpose()
@@ -205,35 +199,31 @@ def read_and_save_et_ouputs(result_dir, fold: int):
     model_func_param = args["model"]
     exp_dir = pathlib.Path(result_dir).parent
     data_info_train = hydro_utils.unserialize_json(
-        exp_dir.joinpath("data_info_fold" + str(fold) + "_train.json")
+        exp_dir.joinpath(f"data_info_fold{fold}_train.json")
     )
     data_info_test = hydro_utils.unserialize_json(
-        exp_dir.joinpath("data_info_fold" + str(fold) + "_test.json")
+        exp_dir.joinpath(f"data_info_fold{fold}_test.json")
     )
     train_period = data_info_train["time"]
     test_period = data_info_test["time"]
-    train_np_file = os.path.join(
-        exp_dir, "basins_lump_p_pe_q_fold" + str(fold) + "_train.npy"
-    )
-    test_np_file = os.path.join(
-        exp_dir, "basins_lump_p_pe_q_fold" + str(fold) + "_test.npy"
-    )
+    train_np_file = os.path.join(exp_dir, f"basins_lump_p_pe_q_fold{fold}_train.npy")
+    test_np_file = os.path.join(exp_dir, f"basins_lump_p_pe_q_fold{fold}_test.npy")
     train_data = np.load(train_np_file)
     test_data = np.load(test_np_file)
     es_test = []
     es_train = []
     for i in range(len(basins_id)):
         _, e_train = xaj(
-            train_data[:, :, 0:2],
+            train_data[:, i : i + 1, 0:2],
             param_values[basins_id[i]].values.reshape(1, -1),
             warmup_length=warmup_length,
-            **model_func_param
+            **model_func_param,
         )
         _, e_test = xaj(
-            test_data[:, :, 0:2],
+            test_data[:, i : i + 1, 0:2],
             param_values[basins_id[i]].values.reshape(1, -1),
             warmup_length=warmup_length,
-            **model_func_param
+            **model_func_param,
         )
         es_train.append(e_train.flatten())
         es_test.append(e_test.flatten())