fix

src/calculation_time_constrains/router.py

@@ -85,12 +85,12 @@ async def get_calculation_parameters(
 @router.get(
     "/{calculation_id}", response_model=StandardResponse[CalculationTimeConstrainsRead]
 )
-async def get_calculation_results(db_session: DbSession, calculation_id, collector_db_session:CollectorDbSession):
+async def get_calculation_results(db_session: DbSession, calculation_id, token:Token):
     if calculation_id == 'default':
         calculation_id = DEFAULT_TC_ID
     
     results = await get_calculation_result(
-        db_session=db_session, calculation_id=calculation_id, collector_db_session=collector_db_session
+        db_session=db_session, calculation_id=calculation_id, token=token
     )
 
     return StandardResponse(

src/calculation_time_constrains/service.py

@@ -68,7 +68,7 @@ class OptimumCostModelWithSpareparts:
         self.planned_oh_months = self._get_months_between(last_oh_date, next_oh_date)
         
         # Set analysis time window (default: 1.5x planned interval)
-        self.time_window_months = time_window_months or int(self.planned_oh_months * 1.3)
+        self.time_window_months = time_window_months or int(self.planned_oh_months * 1.2)
         
         # Pre-calculate date range for API calls
         self.date_range = self._generate_date_range()
@@ -350,10 +350,8 @@ class OptimumCostModelWithSpareparts:
                 for imp in importance_results["calc_result"]
             }
             
-            loss_production_permonth = {
-                imp['aeros_node']['node_name']: (imp['ideal_production'] - imp['production']) / 60
-                for imp in importance_results["calc_result"]
-            }
+            loss_production = importance_results["plant_result"]['total_downtime'] * 660
+        
         except Exception as e:
             self.logger.error(f"Failed to get simulation results: {e}")
             equipment_birnbaum = {}
@@ -377,12 +375,11 @@ class OptimumCostModelWithSpareparts:
         cumulative_loss_money = np.cumsum(plant_capacity_loss_money)
     
         
+ 
         for equipment in equipments:
             location_tag = equipment.location_tag
             contribution_factor = equipment_birnbaum.get(location_tag, 0.0)
             ecs = ecs_tags.get(location_tag, None)
-            loss_production = loss_production_permonth.get(location_tag, 0) * 960000
-            
             
             # try:
             # # Get failure predictions
@@ -433,6 +430,8 @@ class OptimumCostModelWithSpareparts:
             individual_results, equipments, equipment_birnbaum, simulation_id
         )
         
+        
+        
         # Phase 3: Generate final results and database objects
         fleet_results = []
         total_corrective_costs = np.zeros(max_interval) + cumulative_loss_money[0:max_interval]
@@ -763,7 +762,7 @@ async def create_param_and_data(
     return calculationData
 
 
-async def get_calculation_result(db_session: DbSession, calculation_id: str, collector_db_session):
+async def get_calculation_result(db_session: DbSession, calculation_id: str, token):
     """
     Get calculation results with improved error handling, performance, and sparepart details
     """
@@ -825,7 +824,12 @@ async def get_calculation_result(db_session: DbSession, calculation_id: str, col
         # seeing the implementation of the code, it is not possible to determine exactly what it is
         # doing. It could be used to store monthly metrics for a plant, calculate metrics, or perform
         # some other operation related to plant data.
-        # plant_monthly_metrics = await plant_simulation_metrics(simulation_id=scope_calculation.rbd_simulation_id, location_tag="plant", use_location_tag=0, token=token, last_oh_date=prev_oh_scope.end_date, max_interval=scope_calculation.max_interval)
+        plant_monthly_metrics = await plant_simulation_metrics(simulation_id=scope_calculation.rbd_simulation_id, location_tag="plant", use_location_tag=0, token=token, last_oh_date=prev_oh_scope.end_date, max_interval=scope_calculation.max_interval)
+        
+        
+        loss_production_per_month = np.arange(0, scope_calculation.max_interval)
+        k = 4
+        loss_exp = (plant_monthly_metrics['total_downtime'] * 660 * 500_000) * (np.exp(k * (loss_production_per_month / scope_calculation.max_interval)) - 1) / (np.exp(k) - 1)
         
         # REFERENCE_CAPACITY = 630  # or 550
         # COST_PER_MWH = 1_000_000  # rupiah
@@ -839,7 +843,7 @@ async def get_calculation_result(db_session: DbSession, calculation_id: str, col
         for month_index in range(data_num):
             month_result = {
                 "overhaul_cost": 0.0,
-                "corrective_cost": 0.0,
+                "corrective_cost": float(loss_exp[month_index]),
                 "procurement_cost": 0.0,
                 "num_failures": 0.0,
                 "day": month_index + 1,

src/calculation_time_constrains/utils.py

@@ -107,11 +107,11 @@ import pandas as pd
 
 async def plant_simulation_metrics(simulation_id: str, location_tag: str, max_interval, token, last_oh_date, use_location_tag: int = 1):
         """Get failure predictions for equipment from simulation service"""
-        plot_result_url = f"{RBD_SERVICE_API}/aeros/simulation/result/plot/{simulation_id}/{location_tag}?use_location_tag={use_location_tag}"
+        calc_result_url = f"{RBD_SERVICE_API}/aeros/simulation/result/calc/{simulation_id}/{location_tag}"
         
         try:
             response = requests.get(
-                plot_result_url,
+                calc_result_url,
                 headers={
                     "Content-Type": "application/json",
                     "Authorization": f"Bearer {token}",
@@ -119,19 +119,12 @@ async def plant_simulation_metrics(simulation_id: str, location_tag: str, max_in
                 timeout=30
             )
             response.raise_for_status()
-            prediction_data = response.json()
+            prediction_data = response.json()['data']
         except (requests.RequestException, ValueError) as e:
             raise Exception(str(e))
 
-        plot_data = prediction_data.get('data', {}).get('timestamp_outs') if prediction_data.get("data") else None
         
-        if not plot_data:
-            raise Exception(str("no data"))
-        
-        time_series = create_time_series_data(plot_data, (max_interval * 24 * 31))
-        monthly_data = analyze_monthly_metrics(time_series, last_oh_date)
-        
-        return monthly_data
+        return prediction_data
     
 def analyze_monthly_metrics(timestamp_outs, start_date, max_flow_rate: float = 550):
     if not timestamp_outs:

src/sparepart/service.py

@@ -792,7 +792,7 @@ async def load_sparepart_data_from_db(scope, prev_oh_scope, db_session, analysis
     # prev_oh_scope = await get_prev_oh(db_session=db_session, overhaul_session=scope)
     
     analysis_start_date = prev_oh_scope.end_date
-    analysis_window_months = int(((scope.start_date - prev_oh_scope.end_date).days / 30) * 1.3) if not analysis_window_months else analysis_window_months
+    analysis_window_months = int(((scope.start_date - prev_oh_scope.end_date).days / 30) * 1.2) if not analysis_window_months else analysis_window_months
     
     sparepart_manager = SparepartManager(analysis_start_date, analysis_window_months)
     start_date = prev_oh_scope.end_date