feat: fix minor

src/calculation_time_constrains/service.py

@@ -59,84 +59,93 @@ from .utils import get_months_between
 #     results = np.where(np.isfinite(results), results, 0)
 #     return results
 
+async def get_corrective_cost_time_chart(
+    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
 
-# 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]:
+    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')}"
 
-#     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)
+    try:
+        response = requests.get(
+            url,
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {token}",
+            },
+        )
+        data = response.json()
+        latest_num = data["data"][-1]["num_fail"]
 
-#         latest_num = data["data"][-1]["num_fail"]
+        if not latest_num:
+            latest_num = 1
 
-#         if not latest_num:
-#             latest_num = 1
+        # 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(days_difference)]
 
-#         # 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"]
+        }
 
-#         # Create a dictionary of existing data
-#         data_dict = {
-#             datetime.datetime.strptime(item["date"], "%d %b %Y"): item["num_fail"]
-#             for item in data["data"]
-#         }
+        # Initialize all months in the range with 0
+        monthly_data = {}
+        current_date = start_date.replace(day=1)
+        while current_date <= end_date:
+            monthly_data[current_date] = 0
+            # Move to next month
+            if current_date.month == 12:
+                current_date = datetime.datetime(current_date.year + 1, 1, 1)
+            else:
+                current_date = datetime.datetime(current_date.year, current_date.month + 1, 1)
 
-#         # Fill in missing dates with nearest available value
-#         complete_data = []
-#         last_known_value = 0  # Default value if no data is available
-#         not_full_data = []
+        # Get the last day's value for each month
+        for date in data_dict.keys():
+            month_key = datetime.datetime(date.year, date.month, 1)
+            if month_key in monthly_data and data_dict[date] is not None:
+                # Update only if the value is higher (to get the last day's value)
+                monthly_data[month_key] = max(monthly_data[month_key], data_dict[date])
 
-#         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)
+        # Convert to list maintaining chronological order
+        complete_data = []
+        for month in sorted(monthly_data.keys()):
+            complete_data.append(monthly_data[month])
 
-#         hourly_failure = np.repeat(daily_failure, 24) / 24
+        # Convert to numpy array
+        monthly_failure = np.array(complete_data)
 
-#         # 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")
 
-#         # Calculate corrective costs
-#         cost_per_failure = (material_cost + service_cost) / latest_num
+        corrective_costs = monthly_failure * cost_per_failure
 
-#         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
+        return corrective_costs, monthly_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
 
-async def get_corrective_cost_time_chart(
-    material_cost: float,
-    service_cost: float,
-    location_tag: str,
-    token,
-    start_date: datetime.datetime,
-    end_date: datetime.datetime
-) -> Tuple[np.ndarray, np.ndarray]:
+
+# async def get_corrective_cost_time_chart(
+#     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
 
     today = datetime.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
@@ -159,8 +168,11 @@ async def get_corrective_cost_time_chart(
             )
             history_data = response.json()
 
+
              # Process historical data - accumulate failures by month
             history_dict = {}
+            monthly_failures = {}
+
             for item in history_data["data"]:
                 date = datetime.datetime.strptime(item["date"], "%d %b %Y")
                 month_key = datetime.datetime(date.year, date.month, 1)
@@ -173,10 +185,22 @@ async def get_corrective_cost_time_chart(
                 if item["num_fail"] is not None:
                     history_dict[month_key] += item["num_fail"]
 
-            # Update monthly_data with historical data
+            # Sort months chronologically
+            sorted_months = sorted(monthly_failures.keys())
+
+            # Calculate cumulative failures
+            running_total = 0
+            for month in sorted_months:
+                running_total += monthly_failures[month]
+                history_dict[month] = running_total
+
+
+            # Update monthly_data with cumulative historical data
             monthly_data.update(history_dict)
         except Exception as e:
-            print(f"Error fetching historical data: {e}")
+            # print(f"Error fetching historical data: {e}")
+            raise Exception(e)
+
 
     latest_num = 1