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453 lines
16 KiB
Python
453 lines
16 KiB
Python
from typing import List
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from typing import Optional
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from typing import Tuple
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from uuid import UUID
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import numpy as np
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from fastapi import HTTPException
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from fastapi import status
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from sqlalchemy import and_
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from sqlalchemy import case
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from sqlalchemy import func
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from sqlalchemy import select
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from sqlalchemy import update
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from sqlalchemy.orm import joinedload
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from src.database.core import DbSession
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from src.overhaul_activity.service import get_all_by_session_id
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from src.overhaul_scope.service import get as get_scope
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from src.workorder.model import MasterWorkOrder
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from .model import CalculationData
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from .model import CalculationEquipmentResult
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from .model import CalculationResult
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from .schema import CalculationResultsRead
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from .schema import CalculationTimeConstrainsParametersCreate
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from .schema import CalculationTimeConstrainsRead
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from .schema import OptimumResult
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from .schema import CalculationSelectedEquipmentUpdate
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import requests
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import datetime
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def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int,numEquipments:int ,decay_base: float = 1.01) -> np.ndarray:
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if overhaul_cost < 0:
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raise ValueError("Overhaul cost cannot be negative")
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if days <= 0:
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raise ValueError("Days must be positive")
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exponents = np.arange(0, days)
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cost_per_equipment = overhaul_cost / numEquipments
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# Using a slower decay base to spread the budget depletion over more days
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results = cost_per_equipment / (decay_base ** exponents)
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results = np.where(np.isfinite(results), results, 0)
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return results
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# def get_overhaul_cost_by_time_chart(overhaul_cost: float, days: int, numEquipments: int, decay_base: float = 1.1) -> np.ndarray:
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# if overhaul_cost < 0:
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# raise ValueError("Overhaul cost cannot be negative")
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# if days <= 0:
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# raise ValueError("Days must be positive")
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# exponents = np.arange(0, days)
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# cost_per_equipment = overhaul_cost / numEquipments
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# # Introduce randomness by multiplying with a random factor
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# random_factors = np.random.normal(1.0, 0.1, numEquipments) # Mean 1.0, Std Dev 0.1
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# results = np.array([cost_per_equipment * factor / (decay_base ** exponents) for factor in random_factors])
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# results = np.where(np.isfinite(results), results, 0)
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# return results
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async def get_corrective_cost_time_chart(material_cost: float, service_cost: float, location_tag: str, token) -> Tuple[np.ndarray, np.ndarray]:
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"""
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Fetch failure data from API and calculate corrective costs, ensuring 365 days of data.
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Args:
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material_cost: Cost of materials per failure
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service_cost: Cost of service per failure
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location_tag: Location tag of the equipment
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token: Authorization token
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Returns:
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Tuple of (corrective_costs, daily_failure_rate)
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"""
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url = f'http://192.168.1.82:8000/reliability/main/number-of-failures/{location_tag}/2024-01-01/2024-12-31'
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try:
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response = requests.get(
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url,
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headers={
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'Content-Type': 'application/json',
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'Authorization': f'Bearer {token}'
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},
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)
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data = response.json()
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# Create a complete date range for 2024
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start_date = datetime.datetime(2024, 1, 1)
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date_range = [start_date + datetime.timedelta(days=x) for x in range(365)]
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# Create a dictionary of existing data
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data_dict = {
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datetime.datetime.strptime(item['date'], '%d %b %Y'): item['num_fail']
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for item in data['data']
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}
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# Fill in missing dates with nearest available value
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complete_data = []
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last_known_value = 0 # Default value if no data is available
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for date in date_range:
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if date in data_dict:
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if data_dict[date] is not None:
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last_known_value = data_dict[date]
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complete_data.append(last_known_value)
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else:
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complete_data.append(last_known_value)
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# Convert to numpy array
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daily_failure = np.array(complete_data)
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# Calculate corrective costs
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cost_per_failure = material_cost + service_cost
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corrective_costs = daily_failure * cost_per_failure
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return corrective_costs, daily_failure
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except Exception as e:
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print(f"Error fetching or processing data: {str(e)}")
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raise
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# def get_corrective_cost_time_chart(material_cost: float, service_cost: float, days: int, numEquipments: int) -> Tuple[np.ndarray, np.ndarray]:
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# day_points = np.arange(0, days)
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# # Parameters for failure rate
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# base_rate = 0.2 # Base failure rate per day
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# acceleration = 2.4 # How quickly failure rate increases
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# grace_period = 170 # Days before failures start increasing significantly
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# # Calculate daily failure rate using sigmoid function
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# daily_failure_rate = base_rate / (1 + np.exp(-acceleration * (day_points - grace_period)/days))
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# # Introduce randomness in the failure rate
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# random_noise = np.random.normal(0.0, 0.05, (numEquipments, days)) # Mean 0.0, Std Dev 0.05
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# daily_failure_rate = daily_failure_rate + random_noise
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# daily_failure_rate = np.clip(daily_failure_rate, 0, None) # Ensure failure rate is non-negative
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# # Calculate cumulative failures
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# failure_counts = np.cumsum(daily_failure_rate)
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# # Calculate corrective costs based on cumulative failures and combined costs
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# cost_per_failure = material_cost + service_cost
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# corrective_costs = failure_counts * cost_per_failure
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# return corrective_costs, daily_failure_rate
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async def create_param_and_data(*, db_session: DbSession, calculation_param_in: CalculationTimeConstrainsParametersCreate, created_by: str, parameter_id: Optional[UUID] = None):
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"""Creates a new document."""
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if calculation_param_in.ohSessionId is None:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="overhaul_session_id is required"
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)
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calculationData = await CalculationData.create_with_param(
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db=db_session,
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overhaul_session_id=calculation_param_in.ohSessionId,
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avg_failure_cost=calculation_param_in.costPerFailure,
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overhaul_cost=calculation_param_in.overhaulCost,
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created_by=created_by,
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params_id=parameter_id
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)
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return calculationData
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async def get_calculation_result(db_session: DbSession, calculation_id: str):
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days=365
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scope_calculation = await get_calculation_data_by_id(db_session=db_session, calculation_id=calculation_id)
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if not scope_calculation:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="A data with this id does not exist.",
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)
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scope_overhaul = await get_scope(db_session=db_session, overhaul_session_id=scope_calculation.overhaul_session_id)
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if not scope_overhaul:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="A data with this id does not exist.",
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)
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calculation_results = []
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for i in range(days):
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result = {
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"overhaul_cost": 0,
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"corrective_cost": 0,
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"num_failures": 0,
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"day": i + 1
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}
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for eq in scope_calculation.equipment_results:
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if not eq.is_included:
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continue
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result["corrective_cost"] += float(eq.corrective_costs[i])
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result["overhaul_cost"] += float(eq.overhaul_costs[i])
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result["num_failures"] += int(eq.daily_failures[i])
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calculation_results.append(CalculationResultsRead(**result))
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# Check if calculation already exist
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return CalculationTimeConstrainsRead(
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id=scope_calculation.id,
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reference=scope_calculation.overhaul_session_id,
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scope=scope_overhaul.type,
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results=calculation_results,
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optimum_oh=scope_calculation.optimum_oh_day,
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equipment_results=scope_calculation.equipment_results
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)
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async def get_calculation_data_by_id(db_session: DbSession, calculation_id) -> CalculationData:
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stmt = select(CalculationData).filter(
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CalculationData.id == calculation_id
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).options(
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joinedload(CalculationData.equipment_results), joinedload(CalculationData.parameter)
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)
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result = await db_session.execute(stmt)
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return result.unique().scalar()
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# async def create_calculation_result_service(db_session: DbSession, calculation_id: UUID, costPerFailure: Optional[float] = None):
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# days = 360
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# calculation = await get_calculation_data_by_id(db_session=db_session, calculation_id=calculation_id)
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# # reference = await get_by_assetnum(db_session=db_session, assetnum=calculation.reference_id) if calculation.overhaul_reference_type == OverhaulReferenceType.ASSET else await get(db_session=db_session, scope_id=calculation.reference_id)
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# # Multiple Eequipment
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# equipments_scope = get_all_by_session_id(db_session=db_session, overhaul_session_id=calculation.overhaul_session_id)
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# # Parameter
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# overhaulCost = calculation.parameter.overhaul_cost
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# costPerFailure = costPerFailure if costPerFailure else calculation.parameter.avg_failure_cost
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# overhaul_cost_points = get_overhaul_cost_by_time_chart(
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# overhaulCost, days=days)
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# for eq in equipments_scope:
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# corrective_cost_points, dailyNumberOfFailure = get_corrective_cost_time_chart(
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# costPerFailure, days)
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# total_cost = overhaul_cost_points + corrective_cost_points
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# optimumOHIndex = np.argmin(total_cost)
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# numbersOfFailure = sum(dailyNumberOfFailure[:optimumOHIndex])
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# optimum = {
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# "overhaulCost": float(overhaul_cost_points[optimumOHIndex]),
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# "correctiveCost": float(corrective_cost_points[optimumOHIndex]),
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# "numOfFailures": int(numbersOfFailure),
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# "days": int(optimumOHIndex+1)
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# }
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# calculation_results = []
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# for i in range(days):
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# result = CalculationResult(
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# parameter_id=calculation.parameter_id,
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# calculation_data_id=calculation.id,
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# day=(i + 1),
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# corrective_cost=float(corrective_cost_points[i]),
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# overhaul_cost=float(overhaul_cost_points[i]),
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# num_failures=int(dailyNumberOfFailure[i]),
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# )
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# calculation_results.append(result)
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# calculation.optimum_oh_day = int(optimumOHIndex+1)
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# db_session.add_all(calculation_results)
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# await db_session.commit()
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# return CalculationTimeConstrainsRead(
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# id=calculation.id,
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# name=reference.scope_name if hasattr(
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# reference, "scope_name") else reference.master_equipment.name,
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# reference=reference.assetnum if hasattr(
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# reference, "assetnum") else reference.scope_name,
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# results=calculation_results,
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# optimumOh=optimum
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# )
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async def create_calculation_result_service(
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db_session: DbSession,
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calculation: CalculationData,
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token: str
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) -> CalculationTimeConstrainsRead:
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days = 365 # Changed to 365 days as per requirement
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# Get all equipment for this calculation session
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equipments = await get_all_by_session_id(db_session=db_session, overhaul_session_id=calculation.overhaul_session_id)
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scope = await get_scope(db_session=db_session, overhaul_session_id=calculation.overhaul_session_id)
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calculation_data = await get_calculation_data_by_id(db_session=db_session, calculation_id=calculation.id)
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# Store results for each equipment
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equipment_results: List[CalculationEquipmentResult] = []
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total_corrective_costs = np.zeros(days)
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total_daily_failures = np.zeros(days)
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# Calculate for each equipment
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for eq in equipments:
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corrective_costs, daily_failures = await get_corrective_cost_time_chart(
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material_cost=eq.material_cost,
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service_cost=eq.service_cost,
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token=token,
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location_tag=eq.equipment.location_tag
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)
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overhaul_cost_points = get_overhaul_cost_by_time_chart(
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calculation_data.parameter.overhaul_cost,
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days=len(corrective_costs),
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numEquipments=len(equipments)
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)
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# Calculate individual equipment optimum points
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equipment_total_cost = corrective_costs + overhaul_cost_points
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equipment_optimum_index = np.argmin(equipment_total_cost)
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equipment_failure_sum = sum(daily_failures[:equipment_optimum_index])
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equipment_results.append(CalculationEquipmentResult(
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corrective_costs=corrective_costs.tolist(),
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overhaul_costs=overhaul_cost_points.tolist(),
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daily_failures=daily_failures.tolist(),
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assetnum=eq.assetnum,
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material_cost=eq.material_cost,
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service_cost=eq.service_cost,
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optimum_day=int(equipment_optimum_index + 1),
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calculation_data_id=calculation.id,
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master_equipment=eq.equipment
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))
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# Add to totals
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total_corrective_costs += corrective_costs
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total_daily_failures += daily_failures
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db_session.add_all(equipment_results)
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# Calculate optimum points using total costs
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total_cost = total_corrective_costs + overhaul_cost_points
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optimum_oh_index = np.argmin(total_cost)
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numbers_of_failure = sum(total_daily_failures[:optimum_oh_index])
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optimum = OptimumResult(
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overhaul_cost=float(overhaul_cost_points[optimum_oh_index]),
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corrective_cost=float(total_corrective_costs[optimum_oh_index]),
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num_failures=int(numbers_of_failure),
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days=int(optimum_oh_index + 1)
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)
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# # Create calculation results for database
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# calculation_results = []
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# for i in range(days):
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# result = CalculationResult(
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# parameter_id=calculation.parameter_id,
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# calculation_data_id=calculation.id,
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# day=(i + 1),
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# corrective_cost=float(total_corrective_costs[i]),
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# overhaul_cost=float(overhaul_cost_points[i]),
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# num_failures=int(total_daily_failures[i]),
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# )
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# calculation_results.append(result)
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# Update calculation with optimum day
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calculation.optimum_oh_day = optimum.days
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await db_session.commit()
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# Return results including individual equipment data
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return CalculationTimeConstrainsRead(
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id=calculation.id,
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reference=calculation.overhaul_session_id,
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scope=scope.type,
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results=[],
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optimum_oh=optimum,
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equipment_results=equipment_results
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)
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async def get_calculation_by_reference_and_parameter(*, db_session: DbSession, calculation_reference_id, parameter_id):
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stmt = select(CalculationData).filter(and_(
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CalculationData.reference_id == calculation_reference_id,
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CalculationData.parameter_id == parameter_id,
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))
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result = await db_session.execute(stmt)
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return result.scalar()
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async def get_calculation_result_by_day(*, db_session: DbSession, calculation_id, simulation_day):
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stmt = select(CalculationResult).filter(and_(
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CalculationResult.day == simulation_day,
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CalculationResult.calculation_data_id == calculation_id
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))
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result = await db_session.execute(stmt)
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return result.scalar()
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async def get_avg_cost_by_asset(*, db_session: DbSession, assetnum: str):
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stmt = (
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select(func.avg(MasterWorkOrder.total_cost_max).label('average_cost'))
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.where(MasterWorkOrder.assetnum == assetnum)
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)
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result = await db_session.execute(stmt)
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return result.scalar_one_or_none()
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async def bulk_update_equipment(*, db: DbSession, selected_equipments: List[CalculationSelectedEquipmentUpdate], calculation_data_id: UUID):
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# Create a dictionary mapping assetnum to is_included status
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case_mappings = {
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asset.assetnum: asset.is_included
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for asset in selected_equipments
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}
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# Get all assetnums that need to be updated
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assetnums = list(case_mappings.keys())
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# Create a list of when clauses for the case statement
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when_clauses = [
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(CalculationEquipmentResult.assetnum == assetnum, is_included)
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for assetnum, is_included in case_mappings.items()
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]
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# Build the update statement
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stmt = (
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update(CalculationEquipmentResult)
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.where(CalculationEquipmentResult.calculation_data_id == calculation_data_id)
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.where(CalculationEquipmentResult.assetnum.in_(assetnums))
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.values({
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"is_included": case(*when_clauses) # Unpack the when clauses as separate arguments
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})
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)
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await db.execute(stmt)
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await db.commit()
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return assetnums
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