minor add

src/calculation_time_constrains/service.py

@@ -1,5 +1,5 @@
 import datetime
-from typing import List, Optional, Tuple
+from typing import Coroutine, List, Optional, Tuple
 from uuid import UUID
 
 import numpy as np
@@ -54,93 +54,93 @@ 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
-) -> Tuple[np.ndarray, np.ndarray]:
-    """
-    Fetch failure data from API and calculate corrective costs, ensuring 365 days of data.
-
-    Args:
-        material_cost: Cost of materials per failure
-        service_cost: Cost of service per failure
-        location_tag: Location tag of the equipment
-        token: Authorization token
-
-    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"
-
-    try:
-        response = requests.get(
-            url,
-            headers={
-                "Content-Type": "application/json",
-                "Authorization": f"Bearer {token}",
-            },
-        )
-        data = response.json()
+# async def get_corrective_cost_time_chart(
+#     material_cost: float, service_cost: float, location_tag: str, token
+# ) -> Tuple[np.ndarray, np.ndarray]:
+#     """
+#     Fetch failure data from API and calculate corrective costs, ensuring 365 days of data.
+
+#     Args:
+#         material_cost: Cost of materials per failure
+#         service_cost: Cost of service per failure
+#         location_tag: Location tag of the equipment
+#         token: Authorization token
+
+#     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"
+
+#     try:
+#         response = requests.get(
+#             url,
+#             headers={
+#                 "Content-Type": "application/json",
+#                 "Authorization": f"Bearer {token}",
+#             },
+#         )
+#         data = response.json()
 
-        # Create a complete date range for 2024
-        start_date = datetime.datetime(2024, 1, 1)
-        date_range = [start_date + datetime.timedelta(days=x) for x in range(365)]
+#         # Create a complete date range for 2024
+#         start_date = datetime.datetime(2024, 1, 1)
+#         date_range = [start_date + datetime.timedelta(days=x) for x in range(365)]
 
-        # Create a dictionary of existing data
-        data_dict = {
-            datetime.datetime.strptime(item["date"], "%d %b %Y"): item["num_fail"]
-            for item in data["data"]
-        }
+#         # 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
+#         # Fill in missing dates with nearest available value
+#         complete_data = []
+#         last_known_value = 0  # Default value if no data is available
 
-        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(last_known_value)
+#         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(last_known_value)
 
-        # Convert to numpy array
-        daily_failure = np.array(complete_data)
+#         # Convert to numpy array
+#         daily_failure = np.array(complete_data)
 
-        # Calculate corrective costs
-        cost_per_failure = material_cost + service_cost
-        corrective_costs = daily_failure * cost_per_failure
+#         # Calculate corrective costs
+#         cost_per_failure = material_cost + service_cost
+#         corrective_costs = daily_failure * cost_per_failure
 
-        return corrective_costs, daily_failure
+#         return corrective_costs, daily_failure
 
-    except Exception as e:
-        print(f"Error fetching or processing data: {str(e)}")
-        raise
+#     except Exception as e:
+#         print(f"Error fetching or processing data: {str(e)}")
+#         raise
 
 
-# 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)
+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)
 
-#     # Parameters for failure rate
-#     base_rate = 0.2  # Base failure rate per day
-#     acceleration = 2.4  # How quickly failure rate increases
-#     grace_period = 170  # Days before failures start increasing significantly
+    # Parameters for failure rate
+    base_rate = 0.04  # Base failure rate per day
+    acceleration = 0.7  # How quickly failure rate increases
+    grace_period = 49  # Days before failures start increasing significantly
 
-#     # Calculate daily failure rate using sigmoid function
-#     daily_failure_rate = base_rate / (1 + np.exp(-acceleration * (day_points - grace_period)/days))
+    # Calculate daily failure rate using sigmoid function
+    daily_failure_rate = base_rate / (1 + np.exp(-acceleration * (day_points - grace_period)/days))
 
-#     # Introduce randomness in the failure rate
-#     random_noise = np.random.normal(0.0, 0.05, (numEquipments, days))  # Mean 0.0, Std Dev 0.05
-#     daily_failure_rate = daily_failure_rate + random_noise
-#     daily_failure_rate = np.clip(daily_failure_rate, 0, None)  # Ensure failure rate is non-negative
+    # Introduce randomness in the failure rate
+    random_noise = np.random.normal(0.0, 0.05, (numEquipments, days))  # Mean 0.0, Std Dev 0.05
+    daily_failure_rate = daily_failure_rate + random_noise
+    daily_failure_rate = np.clip(daily_failure_rate, 0, None)  # Ensure failure rate is non-negative
 
-#     # Calculate cumulative failures
-#     failure_counts = np.cumsum(daily_failure_rate)
+    # Calculate cumulative failures
+    failure_counts = np.cumsum(daily_failure_rate)
 
-#     # Calculate corrective costs based on cumulative failures and combined costs
-#     cost_per_failure = material_cost + service_cost
-#     corrective_costs = failure_counts * cost_per_failure
+    # Calculate corrective costs based on cumulative failures and combined costs
+    cost_per_failure = material_cost + service_cost
+    corrective_costs = failure_counts * cost_per_failure
 
-#     return corrective_costs, daily_failure_rate
+    return corrective_costs, daily_failure_rate
 
 
 async def create_param_and_data(
@@ -317,11 +317,17 @@ async def create_calculation_result_service(
 
     # Calculate for each equipment
     for eq in equipments:
-        corrective_costs, daily_failures = await get_corrective_cost_time_chart(
+        # corrective_costs, daily_failures = await get_corrective_cost_time_chart(
+        #     material_cost=eq.material_cost,
+        #     service_cost=eq.service_cost,
+        #     token=token,
+        #     location_tag=eq.equipment.location_tag,
+        # )
+        corrective_costs, daily_failures = get_corrective_cost_time_chart(
             material_cost=eq.material_cost,
             service_cost=eq.service_cost,
-            token=token,
-            location_tag=eq.equipment.location_tag,
+            days=days,
+            numEquipments=len(equipments),
         )
 
         overhaul_cost_points = get_overhaul_cost_by_time_chart(

src/overhaul_job/router.py

@@ -9,7 +9,7 @@ from src.models import StandardResponse
 
 from .schema import (OverhaulJobBase, OverhaulJobCreate, OverhaulJobPagination,
                      OverhaulJobRead)
-from .service import create, get_all
+from .service import create, get_all, delete
 
 router = APIRouter()
 
@@ -45,6 +45,15 @@ async def create_overhaul_equipment_jobs(
         message="Data created successfully",
     )
 
+@router.delete("/{overhaul_job_id}", response_model=StandardResponse[None])
+async def delete_overhaul_equipment_job(db_session: DbSession,overhaul_job_id):
+    await delete(db_session=db_session, overhaul_job_id=overhaul_job_id)
+
+    return StandardResponse(
+        data=None,
+        message="Data deleted successfully",
+    )
+
 
 # @router.post("", response_model=StandardResponse[List[str]])
 # async def create_scope(db_session: DbSession, scope_in: OverhaulJobCreate):

src/overhaul_job/service.py

@@ -52,6 +52,12 @@ async def create(
     return overhaul_job_in.job_ids
 
 
+async def delete(*, db_session: DbSession, overhaul_job_id):
+    """Deletes a document."""
+    activity = await db_session.get(OverhaulJob, overhaul_job_id)
+    await db_session.delete(activity)
+    await db_session.commit()
+
 # async def update(*, db_session: DbSession, scope: OverhaulScope, scope_in: ScopeUpdate):
 #     """Updates a document."""
 #     data = scope_in.model_dump()

src/scope_equipment_job/router.py

@@ -42,5 +42,4 @@ async def create_scope_equipment_jobs(
 
 @router.delete("/{assetnum}", response_model=StandardResponse[None])
 async def delete_scope_equipment_job(db_session: DbSession, assetnum, scope_job_id):
-
     await delete(db_session=db_session, assetnum=assetnum, scope_job_id=scope_job_id)