fix minor bug

src/calculation_time_constrains/service.py

@@ -47,27 +47,7 @@ def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int,numEquipment
 #     results = np.where(np.isfinite(results), results, 0)
 #     return results
 
-# def get_corrective_cost_time_chart(material_cost: float, service_cost: float, days: int) -> Tuple[np.ndarray, np.ndarray]:
+def get_corrective_cost_time_chart(material_cost: float, service_cost: float, days: 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
-
-#     # Calculate daily failure rate using sigmoid function
-#     daily_failure_rate = base_rate / (1 + np.exp(-acceleration * (day_points - grace_period)/days))
-
-#     # 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
-
-#     return corrective_costs, daily_failure_rate
-
-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
@@ -78,11 +58,9 @@ def get_corrective_cost_time_chart(material_cost: float, service_cost: float, da
     # 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
+    noise = np.random.normal(0.0, 0.05, days)  # Mean 0.0, Std Dev 0.05
-    random_noise = np.random.normal(0.0, 0.05, (numEquipments, days))  # Mean 0.0, Std Dev 0.05
+    daily_failure_rate += noise
-    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)
 
@@ -92,6 +70,31 @@ def get_corrective_cost_time_chart(material_cost: float, service_cost: float, da
 
     return corrective_costs, daily_failure_rate
 
+# 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
+
+#     # 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
+
+#     # 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
+
+#     return corrective_costs, daily_failure_rate
+
 
 async def create_param_and_data(*, db_session: DbSession, calculation_param_in: CalculationTimeConstrainsParametersCreate, created_by: str, parameter_id: Optional[UUID] = None):
     """Creates a new document."""
@@ -257,8 +260,7 @@ async def create_calculation_result_service(
         corrective_costs, daily_failures = get_corrective_cost_time_chart(
             material_cost=eq.material_cost,
             service_cost=eq.service_cost,
-            days=days,
+            days=days
-            numEquipments=len(equipments)
         )
         
         overhaul_cost_points = get_overhaul_cost_by_time_chart(