change to month

src/calculation_time_constrains/schema.py

@@ -77,7 +77,7 @@ class CalculationTimeConstrainsRead(CalculationTimeConstrainsBase):
     reference: UUID
     scope: str
     results: List[CalculationResultsRead]
-    # equipment_results: List[EquipmentResult]
+    equipment_results: List[EquipmentResult]
     optimum_oh: Any
 
 

src/calculation_time_constrains/service.py

@@ -21,24 +21,26 @@ from .schema import (CalculationResultsRead,
                      CalculationTimeConstrainsParametersCreate,
                      CalculationTimeConstrainsRead, OptimumResult)
 
+from .utils import get_months_between
 
-def get_overhaul_cost_by_time_chart(
-    overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.01
-) -> np.ndarray:
-    if overhaul_cost < 0:
-        raise ValueError("Overhaul cost cannot be negative")
-    if days <= 0:
-        raise ValueError("Days must be positive")
 
-    hours = days * 24
+# def get_overhaul_cost_by_time_chart(
+#     overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.01
+# ) -> np.ndarray:
+#     if overhaul_cost < 0:
+#         raise ValueError("Overhaul cost cannot be negative")
+#     if days <= 0:
+#         raise ValueError("Days must be positive")
 
-    rate = np.arange(1, hours + 1)
+#     hours = days * 24
 
-    cost_per_equipment = overhaul_cost / numEquipments
+#     rate = np.arange(1, hours + 1)
 
-    results = cost_per_equipment - ((cost_per_equipment / hours) * rate)
+#     cost_per_equipment = overhaul_cost / numEquipments
 
-    return results
+#     results = cost_per_equipment - ((cost_per_equipment / hours) * rate)
+
+#     return results
 
 
 # def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.1) -> np.ndarray:
@@ -58,14 +60,88 @@ def get_overhaul_cost_by_time_chart(
 #     return results
 
 
+# async def get_corrective_cost_time_chart(
+#     material_cost: float, service_cost: float, location_tag: str, token, max_days: int
+# ) -> Tuple[np.ndarray, np.ndarray]:
+
+#     start_date = datetime.datetime(2025, 1, 1)
+#     end_date = start_date + datetime.timedelta(days=max_days)
+#     url = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/{start_date.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
+
+#     try:
+#         response = requests.get(
+#             url,
+#             headers={
+#                 "Content-Type": "application/json",
+#                 "Authorization": f"Bearer {token}",
+#             },
+#         )
+#         data = response.json()
+
+#         ## Get latest data fromdata_today
+#         # latest_num_of_fail:float = get_latest_numOfFail(location_tag=location_tag, token=token)
+
+#         latest_num = data["data"][-1]["num_fail"]
+
+#         if not latest_num:
+#             latest_num = 1
+
+#         # Create a complete date range for 2024
+#         start_date = datetime.datetime(2025, 1, 1)
+#         date_range = [start_date + datetime.timedelta(days=x) for x in range(max_days)]
+
+#         # Create a dictionary of existing data
+#         data_dict = {
+#             datetime.datetime.strptime(item["date"], "%d %b %Y"): item["num_fail"]
+#             for item in data["data"]
+#         }
+
+#         # Fill in missing dates with nearest available value
+#         complete_data = []
+#         last_known_value = 0  # Default value if no data is available
+#         not_full_data = []
+
+#         for date in date_range:
+#             if date in data_dict:
+#                 if data_dict[date] is not None:
+#                     last_known_value = data_dict[date]
+#                 complete_data.append(last_known_value)
+#             else:
+#                 complete_data.append(0)
+#         # Convert to numpy array
+#         daily_failure = np.array(complete_data)
+
+#         hourly_failure = np.repeat(daily_failure, 24) / 24
+
+#         # failure_counts = np.cumsum(daily_failure)
+
+#         # Calculate corrective costs
+#         cost_per_failure = (material_cost + service_cost) / latest_num
+
+#         if cost_per_failure == 0:
+#             raise ValueError("Cost per failure cannot be zero")
+
+#         corrective_costs = hourly_failure * cost_per_failure
+
+#         return corrective_costs, hourly_failure
+
+#     except Exception as e:
+#         print(f"Error fetching or processing data: {str(e)}")
+#         raise
+
 async def get_corrective_cost_time_chart(
-    material_cost: float, service_cost: float, location_tag: str, token, max_days: int
+    material_cost: float,
+    service_cost: float,
+    location_tag: str,
+    token,
+    start_date: datetime.datetime,
+    end_date: datetime.datetime
 ) -> Tuple[np.ndarray, np.ndarray]:
+    days_difference = (end_date - start_date).days
 
-    start_date = datetime.datetime(2025, 1, 1)
-    end_date = start_date + datetime.timedelta(days=max_days)
     url = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/{start_date.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
 
+
     try:
         response = requests.get(
             url,
@@ -75,18 +151,14 @@ async def get_corrective_cost_time_chart(
             },
         )
         data = response.json()
-
-        ## Get latest data fromdata_today
-        # latest_num_of_fail:float = get_latest_numOfFail(location_tag=location_tag, token=token)
-
         latest_num = data["data"][-1]["num_fail"]
 
         if not latest_num:
             latest_num = 1
 
-        # Create a complete date range for 2024
+        # Create a complete date range for 2025
         start_date = datetime.datetime(2025, 1, 1)
-        date_range = [start_date + datetime.timedelta(days=x) for x in range(max_days)]
+        date_range = [start_date + datetime.timedelta(days=x) for x in range(days_difference)]
 
         # Create a dictionary of existing data
         data_dict = {
@@ -94,39 +166,55 @@ async def get_corrective_cost_time_chart(
             for item in data["data"]
         }
 
-        # Fill in missing dates with nearest available value
-        complete_data = []
-        last_known_value = 0  # Default value if no data is available
-        not_full_data = []
-
+        # Group data by month
+        monthly_data = {}
         for date in date_range:
-            if date in data_dict:
-                if data_dict[date] is not None:
-                    last_known_value = data_dict[date]
-                complete_data.append(last_known_value)
-            else:
-                complete_data.append(0)
-        # Convert to numpy array
-        daily_failure = np.array(complete_data)
+            month_key = date.replace(day=1)
+            if month_key not in monthly_data:
+                monthly_data[month_key] = 0
+
+            if date in data_dict and data_dict[date] is not None:
+                monthly_data[month_key] += data_dict[date]
 
-        hourly_failure = np.repeat(daily_failure, 24) / 24
 
-        # failure_counts = np.cumsum(daily_failure)
+        # Convert monthly data to list
+        complete_data = []
+        for month in sorted(monthly_data.keys()):
+            complete_data.append(monthly_data[month])
+
+
+        # Convert to numpy array
+        monthly_failure = np.array(complete_data)
 
         # Calculate corrective costs
         cost_per_failure = (material_cost + service_cost) / latest_num
-
         if cost_per_failure == 0:
             raise ValueError("Cost per failure cannot be zero")
 
-        corrective_costs = hourly_failure * cost_per_failure
+        corrective_costs = monthly_failure * cost_per_failure
 
-        return corrective_costs, hourly_failure
+        return corrective_costs, monthly_failure
 
     except Exception as e:
         print(f"Error fetching or processing data: {str(e)}")
         raise
 
+def get_overhaul_cost_by_time_chart(
+    overhaul_cost: float, months_num: int, numEquipments: int, decay_base: float = 1.01
+) -> np.ndarray:
+    if overhaul_cost < 0:
+        raise ValueError("Overhaul cost cannot be negative")
+    if months_num <= 0:
+        raise ValueError("months_num must be positive")
+
+    rate = np.arange(1, months_num + 1)
+
+    cost_per_equipment = overhaul_cost / numEquipments
+
+    # results = cost_per_equipment - ((cost_per_equipment / hours) * rate)
+    results = cost_per_equipment / rate
+
+    return results
 
 # def get_corrective_cost_time_chart(material_cost: float, service_cost: float, days: int, numEquipments: int) -> Tuple[np.ndarray, np.ndarray]:
 #     day_points = np.arange(0, days)
@@ -181,7 +269,10 @@ async def create_param_and_data(
 
 
 async def get_calculation_result(db_session: DbSession, calculation_id: str):
-    days = 667 * 24
+    start_date = datetime.datetime(2025, 1, 1)
+    end_date = start_date + datetime.timedelta(days=677)
+
+    months_num = get_months_between(start_date, end_date)
     scope_calculation = await get_calculation_data_by_id(
         db_session=db_session, calculation_id=calculation_id
     )
@@ -201,7 +292,7 @@ async def get_calculation_result(db_session: DbSession, calculation_id: str):
         )
 
     calculation_results = []
-    for i in range(days):
+    for i in range(months_num):
         result = {
             "overhaul_cost": 0,
             "corrective_cost": 0,
@@ -225,7 +316,7 @@ async def get_calculation_result(db_session: DbSession, calculation_id: str):
         scope=scope_overhaul.type,
         results=calculation_results,
         optimum_oh=scope_calculation.optimum_oh_day,
-        # equipment_results=scope_calculation.equipment_results,
+        equipment_results=scope_calculation.equipment_results,
     )
 
 
@@ -320,7 +411,10 @@ async def get_calculation_by_assetnum(
 async def create_calculation_result_service(
     db_session: DbSession, calculation: CalculationData, token: str
 ) -> CalculationTimeConstrainsRead:
-    days = 667  # Changed to 365 days as per requirement
+    start_date = datetime.datetime(2025, 1, 1)
+    end_date = start_date + datetime.timedelta(days=677)
+
+    months_num = get_months_between(start_date, end_date)
 
     # Get all equipment for this calculation session
     equipments = await get_all_by_session_id(
@@ -336,8 +430,8 @@ async def create_calculation_result_service(
 
     # Store results for each equipment
     equipment_results: List[CalculationEquipmentResult] = []
-    total_corrective_costs = np.zeros(days * 24)
-    total_daily_failures = np.zeros(days * 24)
+    total_corrective_costs = np.zeros(months_num)
+    total_daily_failures = np.zeros(months_num)
 
     # Calculate for each equipment
     for eq in equipments:
@@ -346,12 +440,13 @@ async def create_calculation_result_service(
             service_cost=eq.service_cost,
             token=token,
             location_tag=eq.equipment.location_tag,
-            max_days=667,
+            start_date=start_date,
+            end_date=end_date
         )
 
         overhaul_cost_points = get_overhaul_cost_by_time_chart(
             calculation_data.parameter.overhaul_cost,
-            days=667,
+            months_num=months_num,
             numEquipments=len(equipments),
         )
 

src/calculation_time_constrains/utils.py

@@ -0,0 +1,9 @@
+import datetime
+
+def get_months_between(start_date: datetime.datetime, end_date: datetime.datetime) -> int:
+    """
+    Calculate number of months between two dates.
+    """
+    months = (end_date.year - start_date.year) * 12 + (end_date.month - start_date.month)
+    # Add 1 to include both start and end months
+    return months + 1