fix oh sim

.env

@@ -1,6 +1,6 @@
 ENV=development
 LOG_LEVEL=ERROR
-PORT=3020
+PORT=3021
 HOST=0.0.0.0
 
 DATABASE_HOSTNAME=192.168.1.85

src/calculation_time_constrains/service.py

@@ -1,6 +1,7 @@
 import datetime
 from typing import Coroutine, List, Optional, Tuple
 from uuid import UUID
+import calendar
 
 import numpy as np
 import requests
@@ -59,93 +60,84 @@ 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
-
-    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')}"
+# 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
 
+#     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()
-        latest_num = data["data"][-1]["num_fail"]
 
-        if not latest_num:
-            latest_num = 1
+#     try:
+#         response = requests.get(
+#             url,
+#             headers={
+#                 "Content-Type": "application/json",
+#                 "Authorization": f"Bearer {token}",
+#             },
+#         )
+#         data = response.json()
+#         latest_num = data["data"][-1]["num_fail"]
 
-        # 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)]
+#         if not latest_num:
+#             latest_num = 1
 
-        # 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 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)]
 
-        # 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)
+#         # Create a dictionary of existing data
+#         data_dict = {
+#             datetime.datetime.strptime(item["date"], "%d %b %Y"): item["num_fail"]
+#             for item in data["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])
+#         # 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)
 
-        # Convert to list maintaining chronological order
-        complete_data = []
-        for month in sorted(monthly_data.keys()):
-            complete_data.append(monthly_data[month])
+#         # 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])
 
-        # Convert to numpy array
-        monthly_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])
 
-        # 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")
+#         # Convert to numpy array
+#         monthly_failure = np.array(complete_data)
 
-        corrective_costs = monthly_failure * cost_per_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 = monthly_failure * cost_per_failure
 
 
-        return corrective_costs, monthly_failure
 
-    except Exception as e:
-        print(f"Error fetching or processing data: {str(e)}")
-        raise
+#         return corrective_costs, monthly_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,
-#     start_date: datetime.datetime,
-#     end_date: datetime.datetime
-# ) -> Tuple[np.ndarray, np.ndarray]:
 
 
 async def get_corrective_cost_time_chart(
@@ -169,6 +161,7 @@ async def get_corrective_cost_time_chart(
     historical_start = start_date if start_date <= today else None
     historical_end = min(today, end_date)
 
+
     if historical_start and historical_start <= historical_end:
         url_history = f"http://192.168.1.82:8000/reliability/main/failures/{location_tag}/{historical_start.strftime('%Y-%m-%d')}/{historical_end.strftime('%Y-%m-%d')}"
 
@@ -198,6 +191,9 @@ async def get_corrective_cost_time_chart(
                 if item["num_fail"] is not None:
                     history_dict[month_key] += item["num_fail"]
 
+
+
+
             # Sort months chronologically
             sorted_months = sorted(history_dict.keys())
 
@@ -218,11 +214,13 @@ async def get_corrective_cost_time_chart(
         except Exception as e:
             raise Exception(f"Error fetching historical data: {e}")
 
-    # Handle prediction data (any portion after today)
-    prediction_start = max(tomorrow, start_date)
+    if location_tag == '3TR-TF005':
+        raise Exception("tes",monthly_data)
+
+
+    if end_date >= start_date:
+        url_prediction = 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')}"
 
-    if end_date >= tomorrow:
-        url_prediction = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/{prediction_start.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
 
         try:
             response = requests.get(
@@ -234,33 +232,27 @@ async def get_corrective_cost_time_chart(
             )
             prediction_data = response.json()
 
-            # Process prediction data
+            # Process prediction data - but only use it for future dates
             if prediction_data["data"]:
-                # Use the last prediction value for future months
-                latest_prediction = prediction_data["data"][-1]["num_fail"]
-
-                # Ensure the value is at least 1 and rounded
-                latest_num = max(1, round(latest_prediction)) if latest_prediction is not None else 1
-
-                # Create prediction dictionary
-                prediction_dict = {}
                 for item in prediction_data["data"]:
                     date = datetime.datetime.strptime(item["date"], "%d %b %Y")
-                    month_key = datetime.datetime(date.year, date.month, 1)
-                    prediction_dict[month_key] = round(item["num_fail"]) if item["num_fail"] is not None else latest_num
 
-                # Update monthly_data with prediction data
-                for key in prediction_dict:
-                    monthly_data[key] = prediction_dict[key]
+                    # Only apply prediction data for dates after today
+                    if date > today:
+                        month_key = datetime.datetime(date.year, date.month, 1)
+
+                        monthly_data[month_key] =  item["num_fail"] if item["num_fail"] is not None else 0
+
+
+                # Update latest_num with the last prediction if available
+                last_prediction = prediction_data["data"][-1]["num_fail"]
+                if last_prediction is not None:
+                    latest_num = max(1, round(last_prediction))
 
         except Exception as e:
             print(f"Error fetching prediction data: {e}")
-            # If we can't get prediction data but the range is in the future,
-            # we need to at least populate with our best estimate
-            if not monthly_data:
-                # Create a default entry for the start month
-                start_month_key = datetime.datetime(start_date.year, start_date.month, 1)
-                monthly_data[start_month_key] = latest_num
+
+
 
     # Fill in any missing months in the range
     current_date = datetime.datetime(start_date.year, start_date.month, 1)
@@ -298,170 +290,175 @@ async def get_corrective_cost_time_chart(
     for month in sorted(monthly_data.keys()):
         complete_data.append(monthly_data[month])
 
-    # Convert to numpy array
-    monthly_failure = np.array(complete_data)
-    cost_per_failure = (material_cost + service_cost) / latest_num
-
-    if cost_per_failure == 0:
-        raise ValueError("Cost per failure cannot be zero")
-
-    corrective_costs = monthly_failure * cost_per_failure
-
-    return corrective_costs, monthly_failure
-
-    days_difference = (end_date - start_date).days
-
-    today = datetime.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
-    tomorrow = today + datetime.timedelta(days=1)
-    url_prediction = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/{tomorrow.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
-    url_history = f"http://192.168.1.82:8000/reliability/main/failures/{location_tag}/{start_date.strftime('%Y-%m-%d')}/{today.strftime('%Y-%m-%d')}"
-
-     # Initialize monthly data dictionary
-    monthly_data = {}
-
-    # Get historical data (start_date to today)
-    if start_date <= today:
-        try:
-            response = requests.get(
-                url_history,
-                headers={
-                    "Content-Type": "application/json",
-                    "Authorization": f"Bearer {token}",
-                },
-            )
-            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)
-
-                # Initialize if first occurrence of this month
-                if month_key not in history_dict:
-                    history_dict[month_key] = 0
-
-                # Accumulate failures for this month
-                if item["num_fail"] is not None:
-                    history_dict[month_key] += item["num_fail"]
-
-            # Sort months chronologically
-            sorted_months = sorted(history_dict.keys())
-
-            failures = np.array([history_dict[month] for month in sorted_months])
-            cum_failure = np.cumsum(failures)
-
-            for month_key in sorted_months:
-                monthly_failures[month_key] = int(cum_failure[sorted_months.index(month_key)])
-
-
-            # Update monthly_data with cumulative historical data
-            monthly_data.update(monthly_failures)
-        except Exception as e:
-            # print(f"Error fetching historical data: {e}")
-            raise Exception(e)
-
-
-    latest_num = 1
-
-    # Get prediction data (today+1 to end_date)
-    if end_date > today:
-        try:
-            response = requests.get(
-                url_prediction,
-                headers={
-                    "Content-Type": "application/json",
-                    "Authorization": f"Bearer {token}",
-                },
-            )
-            prediction_data = response.json()
-
-            # Use the last prediction value for future months
-            # Get the latest number from prediction data
-            latest_num = prediction_data["data"][-1]["num_fail"]
-
-            # Ensure the value is at least 1
-            if not latest_num or latest_num < 1:
-                latest_num = 1
-            else:
-                 # Round the number to the nearest integer
-                latest_num = round(latest_num)
-
-            # Create prediction dictionary
-            prediction_dict = {}
-            for item in prediction_data["data"]:
-                date = datetime.datetime.strptime(item["date"], "%d %b %Y")
-                month_key = datetime.datetime(date.year, date.month, 1)
-                prediction_dict[month_key] = round(item["num_fail"])
-
-            # Update monthly_data with prediction data
-            for key in prediction_dict:
-                if key not in monthly_data:  # Don't overwrite historical data
-                    monthly_data[key] = prediction_dict[key]
-        except Exception as e:
-            print(f"Error fetching prediction data: {e}")
-
-     # Create a complete date range covering all months from start to end
-    current_date = datetime.datetime(start_date.year, start_date.month, 1)
-    while current_date <= end_date:
-        if current_date not in monthly_data:
-             # Initialize to check previous months
-            previous_month = current_date.replace(day=1) - datetime.timedelta(days=1)
-            # Now previous_month is the last day of the previous month
-            # Convert back to first day of previous month for consistency
-            previous_month = previous_month.replace(day=1)
-
-            # Keep going back until we find data or run out of months to check
-            month_diff = (current_date.year - start_date.year) * 12 + (current_date.month - start_date.month)
-            max_attempts = max(1, month_diff)  # Ensure at least 1 attempt
-            attempts = 0
-
-            while previous_month not in monthly_data and attempts < max_attempts:
-                # Move to the previous month (last day of the month before)
-                previous_month = previous_month.replace(day=1) - datetime.timedelta(days=1)
-                # Convert to first day of month
-                previous_month = previous_month.replace(day=1)
-                attempts += 1
-
-            # Use the found value or default to 0 if no previous month with data exists
-            if previous_month in monthly_data:
-                monthly_data[current_date] = monthly_data[previous_month]
-            else:
-                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)
-
-
-    # # Convert to list maintaining chronological order
-    complete_data = []
-    for month in sorted(monthly_data.keys()):
-        complete_data.append(monthly_data[month])
-
+    if latest_num < 1:
+        raise ValueError("Number of failures cannot be negative", latest_num)
 
     # Convert to numpy array
     monthly_failure = np.array(complete_data)
     cost_per_failure = (material_cost + service_cost) / latest_num
-    if cost_per_failure == 0:
-        raise ValueError("Cost per failure cannot be zero")
-
-    # if location_tag == "3TR-TF005":
-    #     raise Exception(cost_per_failure, latest_num)
 
-    corrective_costs = monthly_failure * cost_per_failure
+    raise Exception(monthly_data, location_tag)
 
+    try:
+        corrective_costs = monthly_failure * cost_per_failure
+    except Exception as e:
+        raise Exception(f"Error calculating corrective costs: {monthly_failure}", location_tag)
 
     return corrective_costs, monthly_failure
 
-    # except Exception as e:
-    #     print(f"Error fetching or processing data: {str(e)}")
-    #     raise
+    # days_difference = (end_date - start_date).days
+
+    # today = datetime.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
+    # tomorrow = today + datetime.timedelta(days=1)
+    # url_prediction = f"http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/{tomorrow.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
+    # url_history = f"http://192.168.1.82:8000/reliability/main/failures/{location_tag}/{start_date.strftime('%Y-%m-%d')}/{today.strftime('%Y-%m-%d')}"
+
+    #  # Initialize monthly data dictionary
+    # monthly_data = {}
+
+    # # Get historical data (start_date to today)
+    # if start_date <= today:
+    #     try:
+    #         response = requests.get(
+    #             url_history,
+    #             headers={
+    #                 "Content-Type": "application/json",
+    #                 "Authorization": f"Bearer {token}",
+    #             },
+    #         )
+    #         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)
+
+    #             # Initialize if first occurrence of this month
+    #             if month_key not in history_dict:
+    #                 history_dict[month_key] = 0
+
+    #             # Accumulate failures for this month
+    #             if item["num_fail"] is not None:
+    #                 history_dict[month_key] += item["num_fail"]
+
+    #         # Sort months chronologically
+    #         sorted_months = sorted(history_dict.keys())
+
+    #         failures = np.array([history_dict[month] for month in sorted_months])
+    #         cum_failure = np.cumsum(failures)
+
+    #         for month_key in sorted_months:
+    #             monthly_failures[month_key] = int(cum_failure[sorted_months.index(month_key)])
+
+
+    #         # Update monthly_data with cumulative historical data
+    #         monthly_data.update(monthly_failures)
+    #     except Exception as e:
+    #         # print(f"Error fetching historical data: {e}")
+    #         raise Exception(e)
+
+
+    # latest_num = 1
+
+    # # Get prediction data (today+1 to end_date)
+    # if end_date > today:
+    #     try:
+    #         response = requests.get(
+    #             url_prediction,
+    #             headers={
+    #                 "Content-Type": "application/json",
+    #                 "Authorization": f"Bearer {token}",
+    #             },
+    #         )
+    #         prediction_data = response.json()
+
+    #         # Use the last prediction value for future months
+    #         # Get the latest number from prediction data
+    #         latest_num = prediction_data["data"][-1]["num_fail"]
+
+    #         # Ensure the value is at least 1
+    #         if not latest_num or latest_num < 1:
+    #             latest_num = 1
+    #         else:
+    #              # Round the number to the nearest integer
+    #             latest_num = round(latest_num)
+
+    #         # Create prediction dictionary
+    #         prediction_dict = {}
+    #         for item in prediction_data["data"]:
+    #             date = datetime.datetime.strptime(item["date"], "%d %b %Y")
+    #             month_key = datetime.datetime(date.year, date.month, 1)
+    #             prediction_dict[month_key] = round(item["num_fail"])
+
+    #         # Update monthly_data with prediction data
+    #         for key in prediction_dict:
+    #             if key not in monthly_data:  # Don't overwrite historical data
+    #                 monthly_data[key] = prediction_dict[key]
+    #     except Exception as e:
+    #         print(f"Error fetching prediction data: {e}")
+
+    #  # Create a complete date range covering all months from start to end
+    # current_date = datetime.datetime(start_date.year, start_date.month, 1)
+    # while current_date <= end_date:
+    #     if current_date not in monthly_data:
+    #          # Initialize to check previous months
+    #         previous_month = current_date.replace(day=1) - datetime.timedelta(days=1)
+    #         # Now previous_month is the last day of the previous month
+    #         # Convert back to first day of previous month for consistency
+    #         previous_month = previous_month.replace(day=1)
+
+    #         # Keep going back until we find data or run out of months to check
+    #         month_diff = (current_date.year - start_date.year) * 12 + (current_date.month - start_date.month)
+    #         max_attempts = max(1, month_diff)  # Ensure at least 1 attempt
+    #         attempts = 0
+
+    #         while previous_month not in monthly_data and attempts < max_attempts:
+    #             # Move to the previous month (last day of the month before)
+    #             previous_month = previous_month.replace(day=1) - datetime.timedelta(days=1)
+    #             # Convert to first day of month
+    #             previous_month = previous_month.replace(day=1)
+    #             attempts += 1
+
+    #         # Use the found value or default to 0 if no previous month with data exists
+    #         if previous_month in monthly_data:
+    #             monthly_data[current_date] = monthly_data[previous_month]
+    #         else:
+    #             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)
+
+
+    # # # Convert to list maintaining chronological order
+    # 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)
+    # cost_per_failure = (material_cost + service_cost) / latest_num
+    # if cost_per_failure == 0:
+    #     raise ValueError("Cost per failure cannot be zero")
+
+    # # if location_tag == "3TR-TF005":
+    # #     raise Exception(cost_per_failure, latest_num)
+
+    # corrective_costs = monthly_failure * cost_per_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
@@ -687,13 +684,135 @@ async def get_calculation_by_assetnum(
 #     )
 
 
+async def get_number_of_failures(location_tag, start_date, end_date, token, max_interval=24):
+    url_prediction = (
+        f"http://192.168.1.82:8000/reliability/main/number-of-failures/"
+        f"{location_tag}/{start_date.strftime('%Y-%m-%d')}/{end_date.strftime('%Y-%m-%d')}"
+    )
+
+    results = {}
+
+    try:
+        response = requests.get(
+            url_prediction,
+            headers={
+                "Content-Type": "application/json",
+                "Authorization": f"Bearer {token}",
+            },
+            timeout=10
+        )
+        response.raise_for_status()
+        prediction_data = response.json()
+    except (requests.RequestException, ValueError) as e:
+        raise Exception(f"Failed to fetch or parse prediction data: {e}")
+
+    if not prediction_data or "data" not in prediction_data or not isinstance(prediction_data["data"], list):
+        raise Exception("Invalid or empty prediction data format.")
+
+    last_data = prediction_data["data"][-1]
+    last_data_date = datetime.datetime.strptime(last_data["date"], "%d %b %Y")
+    results[datetime.date(last_data_date.year, last_data_date.month, last_data_date.day)] = round(last_data["num_fail"]) if last_data["num_fail"] is not None else 0
+
+
+    # Parse prediction data
+    for item in prediction_data["data"]:
+        try:
+            date = datetime.datetime.strptime(item["date"], "%d %b %Y")
+            last_day = calendar.monthrange(date.year, date.month)[1]
+            if date.day == last_day:
+                results[date.date()] = round(item.get("num_fail", 0))
+
+        except (KeyError, ValueError):
+            continue  # skip invalid items
+
+    # Fill missing months with 0
+    current = start_date.replace(day=1)
+    for _ in range(max_interval):
+        last_day = calendar.monthrange(current.year, current.month)[1]
+        last_day_date = datetime.date(current.year, current.month, last_day)
+        if last_day_date not in results:
+            results[last_day_date] = 0
+        # move to next month
+        if current.month == 12:
+            current = current.replace(year=current.year + 1, month=1)
+        else:
+            current = current.replace(month=current.month + 1)
+
+    # Sort results by date
+    results = dict(sorted(results.items()))
+
+
+    return results
+
+# Function to simulate overhaul strategy for a single equipment
+def simulate_equipment_overhaul(equipment, preventive_cost,predicted_num_failures, interval_months ,total_months=24):
+    """
+    Simulates overhaul strategy for a specific piece of equipment
+    and returns the associated costs.
+    """
+    total_preventive_cost = 0
+    total_corrective_cost = 0
+    months_since_overhaul = 0
+
+    failures_by_month = {i: val for i, (date, val) in enumerate(sorted(predicted_num_failures.items()))}
+
+    cost_per_failure = equipment.material_cost + equipment.service_cost
+
+    # Simulate for the total period
+    for month in range(total_months):
+        # If it's time for overhaul
+        if months_since_overhaul >= interval_months:
+            # Perform preventive overhaul
+            total_preventive_cost += preventive_cost
+            months_since_overhaul = 0
+
+        if months_since_overhaul == 0:
+             # Calculate failures for this month based on time since last overhaul
+            expected_failures = 0
+            failure_cost = expected_failures * cost_per_failure
+            total_corrective_cost += failure_cost
+
+        else:
+            # Calculate failures for this month based on time since last overhaul
+            expected_failures = failures_by_month.get(months_since_overhaul, 0)
+            failure_cost = expected_failures * cost_per_failure
+            total_corrective_cost += failure_cost
+
+        # Increment time since overhaul
+        months_since_overhaul += 1
+
+    # Calculate costs per month (to normalize for comparison)
+    monthly_preventive_cost = total_preventive_cost / total_months
+    monthly_corrective_cost = total_corrective_cost / total_months
+    monthly_total_cost = monthly_preventive_cost + monthly_corrective_cost
+
+    return {
+        'interval': interval_months,
+        'preventive_cost': monthly_preventive_cost,
+        'corrective_cost': monthly_corrective_cost,
+        'total_cost': monthly_total_cost
+    }
+
+
+
 async def create_calculation_result_service(
     db_session: DbSession, calculation: CalculationData, token: str
 ) -> CalculationTimeConstrainsRead:
 
+     # Get all equipment for this calculation session
+    equipments = await get_all_by_session_id(
+        db_session=db_session, overhaul_session_id=calculation.overhaul_session_id
+    )
+
     scope = await get_scope(db_session=db_session, overhaul_session_id=calculation.overhaul_session_id)
+
     prev_oh_scope = await get_prev_oh(db_session=db_session, overhaul_session=scope)
 
+
+    calculation_data = await get_calculation_data_by_id(
+        db_session=db_session, calculation_id=calculation.id
+    )
+
     # Set the date range for the calculation
     if prev_oh_scope:
         # Start date is the day after the previous scope's end date
@@ -705,81 +824,117 @@ async def create_calculation_result_service(
         start_date = datetime.datetime.combine(scope.start_date, datetime.time.min)
         end_date = datetime.datetime.combine(scope.end_date, datetime.time.min)
 
-    months_num = get_months_between(start_date, end_date)
-
-    # Get all equipment for this calculation session
-    equipments = await get_all_by_session_id(
-        db_session=db_session, overhaul_session_id=calculation.overhaul_session_id
-    )
-    scope = await get_scope(
-        db_session=db_session, overhaul_session_id=calculation.overhaul_session_id
-    )
-
-    calculation_data = await get_calculation_data_by_id(
-        db_session=db_session, calculation_id=calculation.id
-    )
+    max_interval = get_months_between(start_date, end_date)
+    overhaul_cost = calculation_data.parameter.overhaul_cost / len(equipments)
 
     # Store results for each equipment
-    equipment_results: List[CalculationEquipmentResult] = []
-    total_corrective_costs = np.zeros(months_num)
-    total_overhaul_cost = np.zeros(months_num)
-    total_daily_failures = np.zeros(months_num)
+    results = []
+
+    total_corrective_costs = np.zeros(max_interval)
+    total_overhaul_costs = np.zeros(max_interval)
+    total_daily_failures = np.zeros(max_interval)
+    total_costs = np.zeros(max_interval)
 
     # Calculate for each equipment
     for eq in equipments:
-        corrective_costs, daily_failures = await get_corrective_cost_time_chart(
-            material_cost=eq.material_cost,
-            service_cost=eq.service_cost,
-            token=token,
+        equipment_results = []
+        corrective_costs = []
+        overhaul_costs = []
+        total = []
+
+        predicted_num_failures = await get_number_of_failures(
             location_tag=eq.equipment.location_tag,
             start_date=start_date,
-            end_date=end_date
+            end_date=end_date,
+            token=token
         )
 
-        overhaul_cost_points = get_overhaul_cost_by_time_chart(
-            calculation_data.parameter.overhaul_cost,
-            months_num=months_num,
-            numEquipments=len(equipments),
-        )
+        for interval in range(1, max_interval+1):
+            result = simulate_equipment_overhaul(eq, overhaul_cost, predicted_num_failures, interval,total_months=max_interval)
+            corrective_costs.append(result['corrective_cost'])
+            overhaul_costs.append(result['preventive_cost'])
+            total.append(result['total_cost'])
+            equipment_results.append(result)
+
+        optimal_result = min(equipment_results, key=lambda x: x['total_cost'])
+
+        results.append(
+                CalculationEquipmentResult(
+                    corrective_costs=corrective_costs,
+                    overhaul_costs=overhaul_costs,
+                    daily_failures=[failure for _, failure in predicted_num_failures.items()],
+                    assetnum=eq.assetnum,
+                    material_cost=eq.material_cost,
+                    service_cost=eq.service_cost,
+                    optimum_day=optimal_result['interval'],
+                    calculation_data_id=calculation.id,
+                    master_equipment=eq.equipment,
+                )
+            )
 
+        if len(predicted_num_failures.values()) < max_interval:
+            raise Exception(eq.equipment.assetnum)
 
 
-        # Calculate individual equipment optimum points
-        equipment_total_cost = corrective_costs + overhaul_cost_points
-        equipment_optimum_index = np.argmin(equipment_total_cost)
-        equipment_failure_sum = sum(daily_failures[:equipment_optimum_index])
+        total_corrective_costs += np.array(corrective_costs)
+        total_overhaul_costs += np.array(overhaul_costs)
+        total_daily_failures += np.array([failure for _, failure in predicted_num_failures.items()])
+        total_costs += np.array(total_costs)
 
 
-        equipment_results.append(
-            CalculationEquipmentResult(
-                corrective_costs=corrective_costs.tolist(),
-                overhaul_costs=overhaul_cost_points.tolist(),
-                daily_failures=daily_failures.tolist(),
-                assetnum=eq.assetnum,
-                material_cost=eq.material_cost,
-                service_cost=eq.service_cost,
-                optimum_day=int(equipment_optimum_index + 1),
-                calculation_data_id=calculation.id,
-                master_equipment=eq.equipment,
-            )
-        )
+        # 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,
+        #     start_date=start_date,
+        #     end_date=end_date
+        # )
 
-        # Add to totals
-        total_corrective_costs += corrective_costs
-        total_overhaul_cost += overhaul_cost_points
-        total_daily_failures += daily_failures
+        # overhaul_cost_points = get_overhaul_cost_by_time_chart(
+        #     calculation_data.parameter.overhaul_cost,
+        #     months_num=months_num,
+        #     numEquipments=len(equipments),
+        # )
 
-    db_session.add_all(equipment_results)
 
-    # Calculate optimum points using total costs
-    total_cost = total_corrective_costs + total_overhaul_cost
-    optimum_oh_index = np.argmin(total_cost)
 
+        # # Calculate individual equipment optimum points
+        # equipment_total_cost = corrective_costs + overhaul_cost_points
+        # equipment_optimum_index = np.argmin(equipment_total_cost)
+        # equipment_failure_sum = sum(daily_failures[:equipment_optimum_index])
+
+
+        # equipment_results.append(
+        #     CalculationEquipmentResult(
+        #         corrective_costs=corrective_costs.tolist(),
+        #         overhaul_costs=overhaul_cost_points.tolist(),
+        #         daily_failures=daily_failures.tolist(),
+        #         assetnum=eq.assetnum,
+        #         material_cost=eq.material_cost,
+        #         service_cost=eq.service_cost,
+        #         optimum_day=int(equipment_optimum_index + 1),
+        #         calculation_data_id=calculation.id,
+        #         master_equipment=eq.equipment,
+        #     )
+        # )
+
+        # # Add to totals
+        # total_corrective_costs += corrective_costs
+        # total_overhaul_cost += overhaul_cost_points
+        # total_daily_failures += daily_failures
+
+    db_session.add_all(results)
+
+    total_costs_point = total_corrective_costs + total_overhaul_costs
+
+    # Calculate optimum points using total costs
+    optimum_oh_index = np.argmin(total_costs_point)
 
     numbers_of_failure = sum(total_daily_failures[:optimum_oh_index])
 
     optimum = OptimumResult(
-        overhaul_cost=float(overhaul_cost_points[optimum_oh_index]),
+        overhaul_cost=float(total_overhaul_costs[optimum_oh_index]),
         corrective_cost=float(total_corrective_costs[optimum_oh_index]),
         num_failures=int(numbers_of_failure),
         days=int(optimum_oh_index + 1),
@@ -810,7 +965,7 @@ async def create_calculation_result_service(
         scope=scope.type,
         results=[],
         optimum_oh=optimum,
-        equipment_results=equipment_results,
+        equipment_results=results,
     )