new interface and setting

torchhydro/datasets/data_sets.py

@@ -1,7 +1,7 @@
 """
 Author: Wenyu Ouyang
 Date: 2024-04-08 18:16:53
-LastEditTime: 2024-05-27 17:05:14
+LastEditTime: 2024-05-27 17:48:17
 LastEditors: Wenyu Ouyang
 Description: A pytorch dataset class; references to https://github.com/neuralhydrology/neuralhydrology
 FilePath: \torchhydro\torchhydro\datasets\data_sets.py
@@ -141,7 +141,7 @@ def nt(self):
         """
         if isinstance(self.t_s_dict["t_final_range"][0], tuple):
             trange_type_num = len(self.t_s_dict["t_final_range"])
-            if trange_type_num != self.ngrid:
+            if trange_type_num not in [self.ngrid, 1]:
                 raise ValueError(
                     "The number of time ranges should be equal to the number of basins "
                     "if you choose different time ranges for different basins"
@@ -191,7 +191,7 @@ def times(self):
         if isinstance(self.t_s_dict["t_final_range"][0], tuple):
             times_ = []
             trange_type_num = len(self.t_s_dict["t_final_range"])
-            if trange_type_num != self.ngrid:
+            if trange_type_num not in [self.ngrid, 1]:
                 raise ValueError(
                     "The number of time ranges should be equal to the number of basins "
                     "if you choose different time ranges for different basins"
@@ -426,7 +426,7 @@ def __len__(self):
 
 class DplDataset(BaseDataset):
     """pytorch dataset for Differential parameter learning"""
-
+    # TODO: USE NUMPY ARRAY INSTEAD OF DATAARRAY FOR GET_ITEM
     def __init__(self, data_cfgs: dict, is_tra_val_te: str):
         """
         Parameters
@@ -1131,7 +1131,8 @@ def _prepare_forcing(self):
         var_subset_list = []
         for start_date, end_date in t_range:
             adjusted_start_date = (
-                datetime.strptime(start_date, "%Y-%m-%d-%H") - timedelta(hours=self.rho)
+                datetime.strptime(start_date, "%Y-%m-%d-%H")
+                - timedelta(hours=self.rho * self.data_cfgs["min_time_interval"])
             ).strftime("%Y-%m-%d-%H")
             adjusted_end_date = (
                 datetime.strptime(end_date, "%Y-%m-%d-%H")
@@ -1161,12 +1162,19 @@ def _prepare_target(self):
         for start_date, end_date in t_range:
             adjusted_start_date = (
                 datetime.strptime(start_date, "%Y-%m-%d-%H")
-                - timedelta(hours=(self.data_cfgs["prec_window"]))
+                - timedelta(
+                    hours=(
+                        self.data_cfgs["prec_window"]
+                        * self.data_cfgs["min_time_interval"]
+                    )
+                )
             ).strftime("%Y-%m-%d-%H")
 
             adjusted_end_date = (
                 datetime.strptime(end_date, "%Y-%m-%d-%H")
-                + timedelta(hours=self.forecast_length)
+                + timedelta(
+                    hours=self.forecast_length * self.data_cfgs["min_time_interval"]
+                )
             ).strftime("%Y-%m-%d-%H")
             subset = data.sel(time=slice(adjusted_start_date, adjusted_end_date))
             subset_list.append(subset)