modify target reliability endpoint

src/calculation_target_reliability/router.py

@@ -6,20 +6,43 @@ from fastapi.params import Query
 from src.database.core import DbSession
 from src.models import StandardResponse
 
-from .service import get_all_target_reliability
+from .service import get_eaf_timeline
 
 router = APIRouter()
 
 
+# @router.get("", response_model=StandardResponse[List[Dict]])
+# async def get_target_reliability(
+#     db_session: DbSession,
+#     scope_name: Optional[str] = Query(None),
+#     eaf_threshold: float = Query(100),
+# ):
+#     """Get all scope pagination."""
+#     results = await get_all_target_reliability(
+#         db_session=db_session, scope_name=scope_name, eaf_threshold=eaf_threshold
+#     )
+
+#     return StandardResponse(
+#         data=results,
+#         message="Data retrieved successfully",
+#     )
+
+
 @router.get("", response_model=StandardResponse[List[Dict]])
 async def get_target_reliability(
     db_session: DbSession,
-    scope_name: Optional[str] = Query(None),
-    eaf_threshold: float = Query(100),
+    oh_session_id: Optional[str] = Query(None),
+    eaf_input: float = Query(0.5),
 ):
     """Get all scope pagination."""
-    results = await get_all_target_reliability(
-        db_session=db_session, scope_name=scope_name, eaf_threshold=eaf_threshold
+    if not oh_session_id:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="oh_session_id is required",
+        )
+
+    results = get_eaf_timeline(
+        oh_session_id=oh_session_id, eaf_input=eaf_input
     )
 
     return StandardResponse(

src/calculation_target_reliability/service.py

@@ -7,87 +7,153 @@ from src.database.core import DbSession
 from src.scope_equipment.model import ScopeEquipment
 from src.scope_equipment.service import get_by_scope_name
 from src.scope_equipment_job.service import get_equipment_level_by_no
-
-
-async def get_all_target_reliability(
-    *, db_session: DbSession, scope_name: str, eaf_threshold: float = 100.0
-):
-    """Get all overhaul overview with EAF values that sum to 100%, aggregated by system."""
-    equipments = await get_by_scope_name(db_session=db_session, scope_name=scope_name)
-    equipment_system = await get_equipment_level_by_no(db_session=db_session, level=1)
-    equipment_subsystem = await get_equipment_level_by_no(
-        db_session=db_session, level=2
-    )
-
-    # If no equipments found, return empty list
-    if not equipments:
-        return []
-
-    import random
-
-    n = len(equipments)
-    base_value = 100 / n  # Even distribution as base
-
-    # Generate EAF values with ±30% variation from base
-    eaf_values = [
-        base_value + random.uniform(-0.3 * base_value, 0.3 * base_value)
-        for _ in range(n)
-    ]
-
-    # Normalize to ensure sum is 100
-    total = sum(eaf_values)
-    eaf_values = [(v * 100 / total) for v in eaf_values]
-
-    # Create result array of dictionaries
-    result = [
-        {
-            "id": equipment.id,
-            "assetnum": equipment.assetnum,
-            "location_tag": equipment.master_equipment.location_tag,
-            "name": equipment.master_equipment.name,
-            "parent_id": equipment.master_equipment.parent_id,  # Add parent_id to identify the system
-            "eaf": round(eaf, 4),  # Add EAF value
-        }
-        for equipment, eaf in zip(equipments, eaf_values)
-    ]
-
-    # Group equipment by system
-    sub_system = {
-        subsystem.id: subsystem.parent_id for subsystem in equipment_subsystem
-    }
-    systems = {
-        system.id: {"name": system.name, "total_eaf": 0, "equipments": []}
-        for system in equipment_system
-    }
-
-    for equipment in result:
-        if equipment["parent_id"] in sub_system:
-            systems[sub_system[equipment["parent_id"]]]["equipments"].append(equipment)
-            systems[sub_system[equipment["parent_id"]]]["total_eaf"] += equipment["eaf"]
-
-    # Convert the systems dictionary to a list of aggregated results
-    aggregated_result = [
-        {
-            "system_id": system_id,
-            "system_name": system_data["name"],
-            "total_eaf": round(system_data["total_eaf"], 4),
-            "equipments": system_data["equipments"],
-        }
-        for system_id, system_data in systems.items()
-    ]
-
-    # Sort the aggregated result by total_eaf in descending order
-    aggregated_result.sort(key=lambda x: x["total_eaf"], reverse=True)
-
-    # Filter systems up to the threshold
-    cumulative_eaf = 0
-    filtered_aggregated_result = []
-
-    for system in aggregated_result:
-        cumulative_eaf += system["total_eaf"]
-        filtered_aggregated_result.append(system)
-
-        if cumulative_eaf >= eaf_threshold:
-            break
-
-    return filtered_aggregated_result
+from datetime import datetime, timedelta
+import random
+from typing import List
+from .utils import generate_down_periods
+
+# async def get_all_target_reliability(
+#     *, db_session: DbSession, scope_name: str, eaf_threshold: float = 100.0
+# ):
+    # """Get all overhaul overview with EAF values that sum to 100%, aggregated by system."""
+    # equipments = await get_by_scope_name(db_session=db_session, scope_name=scope_name)
+    # equipment_system = await get_equipment_level_by_no(db_session=db_session, level=1)
+    # equipment_subsystem = await get_equipment_level_by_no(
+    #     db_session=db_session, level=2
+    # )
+
+    # # If no equipments found, return empty list
+    # if not equipments:
+    #     return []
+
+    # import random
+
+    # n = len(equipments)
+    # base_value = 100 / n  # Even distribution as base
+
+    # # Generate EAF values with ±30% variation from base
+    # eaf_values = [
+    #     base_value + random.uniform(-0.3 * base_value, 0.3 * base_value)
+    #     for _ in range(n)
+    # ]
+
+    # # Normalize to ensure sum is 100
+    # total = sum(eaf_values)
+    # eaf_values = [(v * 100 / total) for v in eaf_values]
+
+    # # Create result array of dictionaries
+    # result = [
+    #     {
+    #         "id": equipment.id,
+    #         "assetnum": equipment.assetnum,
+    #         "location_tag": equipment.master_equipment.location_tag,
+    #         "name": equipment.master_equipment.name,
+    #         "parent_id": equipment.master_equipment.parent_id,  # Add parent_id to identify the system
+    #         "eaf": round(eaf, 4),  # Add EAF value
+    #     }
+    #     for equipment, eaf in zip(equipments, eaf_values)
+    # ]
+
+    # # Group equipment by system
+    # sub_system = {
+    #     subsystem.id: subsystem.parent_id for subsystem in equipment_subsystem
+    # }
+    # systems = {
+    #     system.id: {"name": system.name, "total_eaf": 0, "equipments": []}
+    #     for system in equipment_system
+    # }
+
+    # for equipment in result:
+    #     if equipment["parent_id"] in sub_system:
+    #         systems[sub_system[equipment["parent_id"]]]["equipments"].append(equipment)
+    #         systems[sub_system[equipment["parent_id"]]]["total_eaf"] += equipment["eaf"]
+
+    # # Convert the systems dictionary to a list of aggregated results
+    # aggregated_result = [
+    #     {
+    #         "system_id": system_id,
+    #         "system_name": system_data["name"],
+    #         "total_eaf": round(system_data["total_eaf"], 4),
+    #         "equipments": system_data["equipments"],
+    #     }
+    #     for system_id, system_data in systems.items()
+    # ]
+
+    # # Sort the aggregated result by total_eaf in descending order
+    # aggregated_result.sort(key=lambda x: x["total_eaf"], reverse=True)
+
+    # # Filter systems up to the threshold
+    # cumulative_eaf = 0
+    # filtered_aggregated_result = []
+
+    # for system in aggregated_result:
+    #     cumulative_eaf += system["total_eaf"]
+    #     filtered_aggregated_result.append(system)
+
+    #     if cumulative_eaf >= eaf_threshold:
+    #         break
+
+    # return filtered_aggregated_result
+
+def get_eaf_timeline(eaf_input: float, oh_session_id: str) -> List[dict]:
+    """
+    Generate a timeline of EAF values based on input parameters.
+
+    Args:
+        eaf_input (float): EAF value to check against thresholds
+        oh_session_id (str): OH session identifier
+
+    Returns:
+        set[dict]: Set of dictionaries containing dates and their EAF values
+    """
+    # Define EAF thresholds
+    MIN_EAF = 0.3
+    MAX_EAF = 0.8
+
+    # Dummy OH session dates
+    oh_session_start = datetime(2024, 1, 1)
+    oh_session_end = datetime(2026, 7, 30)
+
+    # Initialize result set
+    results = []
+
+    # Determine date range based on EAF input
+    if MIN_EAF <= eaf_input <= MAX_EAF:
+        start_date = oh_session_start
+        end_date = oh_session_end
+    elif eaf_input < MIN_EAF:
+        # If below minimum, extend end date by 2 months weeks
+        start_date = oh_session_start
+        end_date = oh_session_end + timedelta(days=360)
+    else:  # eaf_input > MAX_EAF
+        # If above maximum, reduce end date by 1 month
+        start_date = oh_session_start
+        end_date = oh_session_end - timedelta(days=180)
+
+    # Generate random down periods
+    down_periods = generate_down_periods(start_date, end_date, 10)
+
+    # Generate daily entries
+    current_date = start_date
+    while current_date <= end_date:
+        # Convert date to string format
+        date_str = current_date.strftime('%Y-%m-%d')
+
+        # Set default EAF value to 1 (system up)
+        eaf_value = 1.0
+
+        # Check if current date is in any down period
+        for period_start, period_end in down_periods:
+            if period_start <= current_date <= period_end:
+                eaf_value = 0.2
+                break
+
+        # Add entry to timeline
+        results.append({
+            'date': date_str,
+            'eaf_value': eaf_value
+        })
+
+        current_date += timedelta(days=1)
+
+    return results

src/calculation_target_reliability/utils.py

@@ -0,0 +1,54 @@
+from datetime import datetime, timedelta
+import random
+from typing import List, Optional
+
+def generate_down_periods(start_date: datetime, end_date: datetime,
+                         num_periods: Optional[int] = None, min_duration: int = 3,
+                         max_duration: int = 7) -> list[tuple[datetime, datetime]]:
+    """
+    Generate random system down periods within a date range.
+
+    Args:
+        start_date (datetime): Start date of the overall period
+        end_date (datetime): End date of the overall period
+        num_periods (int, optional): Number of down periods to generate.
+                                   If None, generates 1-3 periods randomly
+        min_duration (int): Minimum duration of each down period in days
+        max_duration (int): Maximum duration of each down period in days
+
+    Returns:
+        list[tuple[datetime, datetime]]: List of (start_date, end_date) tuples
+                                       for each down period
+    """
+    if num_periods is None:
+        num_periods = random.randint(1, 3)
+
+    total_days = (end_date - start_date).days
+    down_periods = []
+
+    # Generate random down periods
+    for _ in range(num_periods):
+        # Random duration for this period
+        duration = random.randint(min_duration, max_duration)
+
+        # Ensure we don't exceed the total date range
+        latest_possible_start = total_days - duration
+
+        if latest_possible_start < 0:
+            continue
+
+        # Random start day within available range
+        start_day = random.randint(0, latest_possible_start)
+        period_start = start_date + timedelta(days=start_day)
+        period_end = period_start + timedelta(days=duration)
+
+        # Check for overlaps with existing periods
+        overlaps = any(
+            (p_start <= period_end and period_start <= p_end)
+            for p_start, p_end in down_periods
+        )
+
+        if not overlaps:
+            down_periods.append((period_start, period_end))
+
+    return sorted(down_periods)

src/calculation_time_constrains/service.py

@@ -199,6 +199,8 @@ async def get_corrective_cost_time_chart(
 
         corrective_costs = monthly_failure * cost_per_failure
 
+
+
         return corrective_costs, monthly_failure
 
     except Exception as e:
@@ -306,6 +308,8 @@ async def get_calculation_result(db_session: DbSession, calculation_id: str):
             "num_failures": 0,
             "day": i + 1,
         }
+        ## Add risk Cost
+        # risk cost = ((Down Time1 * MW Loss 1) + (Downtime2 * Mw 2) + .... (DowntimeN * MwN) ) * Harga listrik (Efficicency HL App)
 
         for eq in scope_calculation.equipment_results:
             if not eq.is_included:
@@ -486,6 +490,8 @@ async def create_calculation_result_service(
     # Calculate optimum points using total costs
     total_cost = total_corrective_costs + overhaul_cost_points
     optimum_oh_index = np.argmin(total_cost)
+
+    raise Exception(optimum_oh_index)
     numbers_of_failure = sum(total_daily_failures[:optimum_oh_index])
 
     optimum = OptimumResult(