add id

src/calculation_time_constrains/service.py

@@ -20,6 +20,7 @@ from .schema import (CalculationResultsRead,
                      CalculationTimeConstrainsParametersCreate,
                      CalculationTimeConstrainsRead, OptimumResult)
 
+from src.utils import get_latest_numOfFail
 
 def get_overhaul_cost_by_time_chart(
     overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.01
@@ -29,10 +30,10 @@ def get_overhaul_cost_by_time_chart(
     if days <= 0:
         raise ValueError("Days must be positive")
 
-    exponents = np.arange(0, days)
+    exponents = np.arange(1, days+1)
     cost_per_equipment = overhaul_cost / numEquipments
     # Using a slower decay base to spread the budget depletion over more days
-    results = cost_per_equipment / (decay_base**exponents)
+    results = cost_per_equipment - ((cost_per_equipment / days) * exponents)
     results = np.where(np.isfinite(results), results, 0)
     return results
 
@@ -55,7 +56,7 @@ def get_overhaul_cost_by_time_chart(
 
 
 async def get_corrective_cost_time_chart(
-    material_cost: float, service_cost: float, location_tag: str, token
+    material_cost: float, service_cost: float, location_tag: str, token, max_days: int
 ) -> Tuple[np.ndarray, np.ndarray]:
     """
     Fetch failure data from API and calculate corrective costs, ensuring 365 days of data.
@@ -69,7 +70,11 @@ async def get_corrective_cost_time_chart(
     Returns:
         Tuple of (corrective_costs, daily_failure_rate)
     """
-    url = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/2024-01-01/2024-12-31"
+    start_date = datetime.datetime(2025, 1, 1)
+
+# Calculate end date (667 days after start date)
+    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(
@@ -81,9 +86,17 @@ 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
-        start_date = datetime.datetime(2024, 1, 1)
+        start_date = datetime.datetime(2025, 1, 1)
-        date_range = [start_date + datetime.timedelta(days=x) for x in range(365)]
+        date_range = [start_date + datetime.timedelta(days=x) for x in range(max_days)]
 
         # Create a dictionary of existing data
         data_dict = {
@@ -91,6 +104,7 @@ 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
@@ -102,7 +116,6 @@ async def get_corrective_cost_time_chart(
                     last_known_value = data_dict[date]
                 complete_data.append(last_known_value)
             else:
-                not_full_data.append(location_tag)
                 complete_data.append(0)
         # Convert to numpy array
         daily_failure = np.array(complete_data)
@@ -110,7 +123,11 @@ async def get_corrective_cost_time_chart(
         # failure_counts = np.cumsum(daily_failure)
 
         # Calculate corrective costs
-        cost_per_failure = material_cost + service_cost
+        cost_per_failure = (material_cost + service_cost) / latest_num
+
+        if cost_per_failure == 0:
+            raise ValueError("Cost per failure cannot be zero")
+
         corrective_costs = daily_failure * cost_per_failure
 
         return corrective_costs, daily_failure
@@ -173,7 +190,7 @@ async def create_param_and_data(
 
 
 async def get_calculation_result(db_session: DbSession, calculation_id: str):
-    days = 365
+    days = 667
     scope_calculation = await get_calculation_data_by_id(
         db_session=db_session, calculation_id=calculation_id
     )
@@ -299,7 +316,7 @@ async def get_calculation_data_by_id(
 async def create_calculation_result_service(
     db_session: DbSession, calculation: CalculationData, token: str
 ) -> CalculationTimeConstrainsRead:
-    days = 365  # Changed to 365 days as per requirement
+    days = 667  # Changed to 365 days as per requirement
 
     # Get all equipment for this calculation session
     equipments = await get_all_by_session_id(
@@ -325,6 +342,7 @@ async def create_calculation_result_service(
             service_cost=eq.service_cost,
             token=token,
             location_tag=eq.equipment.location_tag,
+            max_days=667
         )
         # corrective_costs, daily_failures = get_corrective_cost_time_chart(
         #     material_cost=eq.material_cost,

src/utils.py

@@ -88,3 +88,31 @@ def parse_date_string(date_str: str) -> Optional[datetime]:
 
 def time_now():
     return datetime.now(pytz.timezone(TIMEZONE))
+
+import requests
+
+def get_latest_numOfFail(location_tag, token)-> float:
+    today = datetime.today().strftime("%Y-%m-%d")
+    url_today = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/2016-01-01/{today}"
+
+    try:
+        response = requests.get(url_today, {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {token}",
+        })
+        data = response.json()
+
+        raise Exception(data)
+
+        latest_num = data['data'][-1]['num_fail']
+
+        raise Exception(latest_num)
+
+        if not latest_num:
+            latest_num = 0
+
+
+        return latest_num
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching data: {e}")
+        return 0