also import modelsensor when importing while training

src/train_db.py

@@ -132,6 +132,7 @@ class DBObjective:
         # Save metrics to optuna
         model_path = str(Path(trainer.log_dir).resolve())
         trial.set_user_attr("model_path", model_path)
+        trial.set_user_attr("external_fcst", data_module.external_fcst)
         
         for metric in ["hp/val_nse", "hp/val_mae", "hp/val_mae_flood"]:
             for i in [23, 47]:
@@ -141,6 +142,7 @@ class DBObjective:
         if self.config["general"]["save_all_to_db"]:
             model_id = dbt.add_model(self.engine,self.config,trainer.log_dir)
             if model_id != -1:
+                dbt.add_model_sensor(self.engine,model_id,data_module.sensors,data_module.external_fcst)
                 dbt.add_metrics(self.engine,model_id,my_callback.metrics,data_module.time_ranges)
             #dbt.add_metrics(self.engine,self.config,trainer.log_dir,key,value)
 
@@ -248,11 +250,14 @@ def main():
             #TODO mark the model somehow maybe?
         else:
             model_path = best_trial.user_attrs["model_path"]
+            external_fcst = best_trial.user_attrs["external_fcst"]
             hparams = hp.load_settings_model(Path(model_path))
             time_ranges =  {s: {a : pd.to_datetime(hparams[f"{s}_{a}"]) for a in ["start","end"]} for s in ["train","val","test"]}
 
             model_id = dbt.add_model(engine,config,model_path)
             if model_id != -1:
+                dbt.add_model_sensor(engine,model_id,hparams["scaler"].feature_names_in_,external_fcst)
+
                 reader = SummaryReader(model_path,pivot=True)
                 df =reader.scalars
                 loss_cols = df.columns[df.columns.str.contains("loss")]
@@ -262,8 +267,6 @@ def main():
                     metrics[k] = torch.tensor(v)
 
                 dbt.add_metrics(engine,model_id,metrics,time_ranges)
-                #dbt.add_metrics(self.engine,self.config,trainer.log_dir,key,value)
-
 
     con.close()
 

src/utils/db_tools/db_tools.py

@@ -1014,6 +1014,28 @@ def add_model(engine,config,model_path,in_size=144) -> int:
         logging.info("Added model %s to database",model_obj)
     return model_obj.id
 
+def add_model_sensor(engine,model_id,sensor_names,external_fcst) -> None:
+    with Session(engine) as session:
+
+
+        for i,col in enumerate(sensor_names):
+            if col != "d1":
+                if col in external_fcst:
+                    vhs_gebiet = session.scalars(select(InputForecastsMeta.vhs_gebiet).where(InputForecastsMeta.sensor_name == col)).first()
+                    if vhs_gebiet is None:
+                        raise LookupError(f"Sensor {col} not found in INPUT_FORECASTS_META")
+                else:
+                    vhs_gebiet = None
+
+            model_sensor = ModellSensor(
+                modell_id = model_id,
+                sensor_name = col,
+                vhs_gebiet = vhs_gebiet,
+                ix = i
+            )
+            session.add(model_sensor)
+        session.commit()
+        
 def add_metrics(engine,model_id,metrics,time_ranges) -> None:
     metric_list = []
     for key,value in metrics.items():