fix start end date

src/calculation_time_constrains/service.py

@@ -10,7 +10,7 @@ from sqlalchemy.orm import joinedload
 
 from src.database.core import DbSession
 from src.overhaul_activity.service import get_all_by_session_id
-from src.overhaul_scope.service import get as get_scope
+from src.overhaul_scope.service import get as get_scope, get_prev_oh
 from src.utils import get_latest_numOfFail
 from src.workorder.model import MasterWorkOrder
 
@@ -138,6 +138,16 @@ async def get_corrective_cost_time_chart(
         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,
@@ -148,6 +158,159 @@ async def get_corrective_cost_time_chart(
 ) -> 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)
+    tomorrow = today + datetime.timedelta(days=1)
+
+    # Initialize monthly data dictionary
+    monthly_data = {}
+    latest_num = 1
+
+    # Handle historical data (any portion before or including today)
+    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')}"
+
+        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())
+
+            if sorted_months:
+                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)
+
+                # Get the latest number for predictions if we have historical data
+                if failures.size > 0:
+                    latest_num = max(1, failures[-1])  # Use the last month's failures, minimum 1
+
+        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 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(
+                url_prediction,
+                headers={
+                    "Content-Type": "application/json",
+                    "Authorization": f"Bearer {token}",
+                },
+            )
+            prediction_data = response.json()
+
+            # Process prediction data
+            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]
+
+        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)
+    end_month = datetime.datetime(end_date.year, end_date.month, 1)
+
+    while current_date <= end_month:
+        if current_date not in monthly_data:
+            # Try to find the most recent month with data
+            prev_months = [m for m in monthly_data.keys() if m < current_date]
+
+            if prev_months:
+                # Use the most recent previous month's data
+                latest_month = max(prev_months)
+                monthly_data[current_date] = monthly_data[latest_month]
+            else:
+                # If no previous months exist, look for future months
+                future_months = [m for m in monthly_data.keys() if m > current_date]
+
+                if future_months:
+                    # Use the earliest future month's data
+                    earliest_future = min(future_months)
+                    monthly_data[current_date] = monthly_data[earliest_future]
+                else:
+                    # No data available at all, use default
+                    monthly_data[current_date] = latest_num
+
+        # 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")
+
+    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')}"
@@ -389,8 +552,19 @@ async def get_calculation_result(db_session: DbSession, calculation_id: str):
             status_code=status.HTTP_404_NOT_FOUND,
             detail="A data with this id does not exist.",
         )
-    start_date = datetime.datetime.combine(scope_overhaul.start_date, datetime.time.min)
-    end_date = datetime.datetime.combine(scope_overhaul.end_date, datetime.time.min)
+
+    prev_oh_scope = await get_prev_oh(db_session=db_session, overhaul_session=scope_overhaul)
+
+    # Set the date range for the calculation
+    if prev_oh_scope:
+        # Start date is the day after the previous scope's end date
+        start_date = datetime.datetime.combine(prev_oh_scope.end_date + datetime.timedelta(days=1), datetime.time.min)
+        # End date is the start date of the current scope
+        end_date = datetime.datetime.combine(scope.start_date, datetime.time.min)
+    else:
+        # If there's no previous scope, use the start and end dates from the current scope
+        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)
 
@@ -517,11 +691,19 @@ async def create_calculation_result_service(
     db_session: DbSession, calculation: CalculationData, token: str
 ) -> CalculationTimeConstrainsRead:
 
-
     scope = await get_scope(db_session=db_session, overhaul_session_id=calculation.overhaul_session_id)
-
-    start_date = datetime.datetime.combine(scope.start_date, datetime.time.min)
-    end_date = datetime.datetime.combine(scope.end_date, datetime.time.min)
+    prev_oh_scope = await get_prev_oh(db_session=db_session, overhaul_session=scope)
+
+    # Set the date range for the calculation
+    if prev_oh_scope:
+        # Start date is the day after the previous scope's end date
+        start_date = datetime.datetime.combine(prev_oh_scope.end_date + datetime.timedelta(days=1), datetime.time.min)
+        # End date is the start date of the current scope
+        end_date = datetime.datetime.combine(scope.start_date, datetime.time.min)
+    else:
+        # If there's no previous scope, use the start and end dates from the current scope
+        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)
 
@@ -561,6 +743,7 @@ async def create_calculation_result_service(
         )
 
 
+
         # Calculate individual equipment optimum points
         equipment_total_cost = corrective_costs + overhaul_cost_points
         equipment_optimum_index = np.argmin(equipment_total_cost)

src/overhaul_scope/service.py

@@ -22,6 +22,12 @@ async def get(
     result = await db_session.execute(query)
     return result.scalars().one_or_none()
 
+async def get_prev_oh(*, db_session: DbSession, overhaul_session: OverhaulScope) -> Optional[OverhaulScope]:
+    """Returns a document based on the given document id."""
+    result = Select(OverhaulScope).where(OverhaulScope.end_date < overhaul_session.end_date).order_by(OverhaulScope.end_date.desc()).limit(1)
+    result = await db_session.execute(result)
+    return result.scalars().one_or_none()
+
 
 async def get_all(*, common, scope_name: Optional[str] = None):
     """Returns all documents."""