fix

src/calculation_time_constrains/service.py

@@ -2,6 +2,9 @@ import datetime
 from typing import Coroutine, List, Optional, Tuple,Dict
 from uuid import UUID
 import calendar
+
+import httpx
+from src.calculation_target_reliability.service import RBD_SERVICE_API
 from src.config import REALIBILITY_SERVICE_API
 import numpy as np
 import requests
@@ -24,7 +27,7 @@ from .schema import (CalculationResultsRead,
                      CalculationTimeConstrainsParametersCreate,
                      CalculationTimeConstrainsRead, OptimumResult)
 
-from .utils import get_months_between
+from .utils import calculate_failures_per_month, create_time_series_data, get_months_between
 from src.equipment_sparepart.model import ScopeEquipmentPart
 import copy
 import random
@@ -38,7 +41,9 @@ import aiohttp
 from datetime import datetime, date
 import asyncio
 import json
-from src.utils import save_to_pastebin
+# from src.utils import save_to_pastebin
+
+client = httpx.AsyncClient(timeout=300.0)
 
 # class ReliabilityService:
 #     """Service class for handling reliability API calls"""
@@ -1033,62 +1038,30 @@ class OptimumCostModel:
 
         return fr
 
-    def _calculate_costs_vectorized(self, failure_rate: Dict[datetime, float],
+    def _calculate_costs_vectorized(self, failures_prediction: List[Dict],
                               preventive_cost: float, failure_replacement_cost: float) -> List[Dict]:
-        valid_data = [(date, rel) for date, rel in failure_rate.items() if rel is not None]
-        if not valid_data:
-            return []
-
-        # Sort by date (past to future)
-        valid_data.sort(key=lambda x: x[0])
-        dates, reliability_values = zip(*valid_data)
-
-        dates = np.array(dates)
-        reliability_values = np.array(reliability_values)
-
-        # Calculate days from last OH
-        days_from_last_oh = np.array([(date - self.last_oh_date).days for date in dates])
-        date_point = np.array([i+1 for i in range(len(dates))])
     
-        # Calculate failure probabilities
-        failure_probs = 1 - reliability_values
+        # Extract data from failures_prediction
+        months = [item['month'] for item in failures_prediction]
+        failure_counts = [item['failures'] for item in failures_prediction]
         
-        # Calculate expected operating times using trapezoidal integration
-        # This is the denominator: ∫₀ᵀ R(t) dt for each T
-        # expected_operating_times = np.zeros_like(days_from_last_oh, dtype=float)
-        # for i in range(len(days_from_last_oh)):
-        #     # Time points from 0 to current point T
-        #     time_points = [(d - self.last_oh_date).days for d in dates[:i+1]]
-        #     # Reliability values (assuming reliability at time 0 is 1.0)
-        #     rel_values = reliability_values[:i+1]
-        #     # Calculate expected operating time up to this point
-        #     expected_operating_times[i] = np.trapz(rel_values, time_points)
-
-
-        # Calculate costs according to the formula
-        # Failure cost = (1-R(T)) × IDRu / ∫₀ᵀ R(t) dt
-        # failure_costs = (failure_rate * failure_replacement_cost * expected_operating_times)
-        # # Preventive cost = R(T) × IDRp / ∫₀ᵀ R(t) dt
-        # preventive_costs = (reliability_values * preventive_cost) / expected_operating_times
-
-        failure_costs = reliability_values * failure_replacement_cost * days_from_last_oh
-        preventive_costs = preventive_cost / date_point
+        # Calculate failure costs for each month
+        failure_costs = [count * failure_replacement_cost for count in failure_counts]
     
         
+        # Calculate preventive costs (distributed equally across months)
+        num_months = np.array([i+1 for i in range(len(failures_prediction))])
+        preventive_costs = preventive_cost/num_months
         
         # Total cost = Failure cost + Preventive cost
-        total_costs = failure_costs + preventive_costs
+        total_costs = [failure_costs[i] + preventive_costs[i] for i in range(len(num_months))]
         
         # Convert back to list of dictionaries
         results = []
-        for i in range(len(dates)):
+        for i in range(len(failures_prediction)):
             results.append({
-                'date': dates[i],
-                'days_from_last_oh': days_from_last_oh[i],
-                'failure_rate': reliability_values[i],
-                'failure_probability': failure_probs[i],
-                'number_of_failure': round(reliability_values[i] * days_from_last_oh[i]),
-                'expected_operating_time': days_from_last_oh[i],
+                'month': months[i],
+                'number_of_failure': failure_counts[i],
                 'failure_replacement_cost': failure_costs[i],
                 'preventive_replacement_cost': preventive_costs[i],
                 'total_cost': total_costs[i],
@@ -1112,14 +1085,12 @@ class OptimumCostModel:
 
         return {
             'optimal_index': min_idx,
-            'optimal_date': optimal_result['date'],
-            'days_from_last_oh': optimal_result['days_from_last_oh'],
-            'failure_rate': optimal_result['failure_rate'],
+            'optimal_month': optimal_result['month'],
             'number_of_failure': optimal_result['number_of_failure'],
             'failure_cost': optimal_result['failure_replacement_cost'],
             'preventive_cost': optimal_result['preventive_replacement_cost'],
             'total_cost': optimal_result['total_cost'],
-            'procurement_cost': 0
+            'procurement_cost': optimal_result['procurement_cost']
         }
 
     def _get_number_of_failure_after_last_oh(self, target_date, token, location_tag):
@@ -1139,6 +1110,41 @@ class OptimumCostModel:
         return data['data']['value']
 
     
+    async def get_failures_prediction(self, simulation_id: str, location_tag):
+        # calc_result_url = f"{RBD_SERVICE_API}/aeros/simulation/result/calc/{simulation_id}?nodetype=RegularNode"
+        plot_result_url = f"{RBD_SERVICE_API}/aeros/simulation/result/plot/{simulation_id}/{location_tag}?use_location_tag=1"
+        # calc_plant_result = f"{RBD_SERVICE_API}/aeros/simulation/result/calc/{simulation_id}/plant"
+
+        
+        try:
+            response = requests.get(
+                plot_result_url,
+                headers={
+                    "Content-Type": "application/json",
+                    "Authorization": f"Bearer {self.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["data"]:
+            results = []
+            for i in range(1, 33):
+                results.append({
+                    "month": i,
+                    "failures": 0
+                })
+            return results        
+    
+        plot_data = prediction_data['data']['timestamp_outs']
+        time_series = create_time_series_data(plot_data)
+        
+        return calculate_failures_per_month(time_series)
+    
+
     async def calculate_cost_all_equipment(
         self,
         db_session,  # DbSession type
@@ -1160,8 +1166,7 @@ class OptimumCostModel:
             print(f"Starting reliability data fetch for {len(equipments)} equipment...")
 
             # Fetch all reliability data concurrently
-            all_reliabilities = await self._get_reliability_equipment_batch(location_tags)
-
+            # all_reliabilities = await self._get_reliability_equipment_batch(location_tags)
 
             print("Processing cost calculations...")
 
@@ -1179,15 +1184,21 @@ class OptimumCostModel:
                 overhaul_cost = equipment.overhaul_cost
                 service_cost = equipment.service_cost
                 
-                # Get pre-fetched reliability data
-                failure_rate = all_reliabilities.get(location_tag, {})
-                # failure_rate = self._get_equipment_fr(location_tag, self.token)
+                failures_prediction = await self.get_failures_prediction(
+                    simulation_id = "864f1558-79cc-40b1-8d1f-8e7339d9d8ce",
+                    location_tag=location_tag
+                )
+
+                # # Get pre-fetched reliability data
+                # failure_rate = all_reliabilities.get(location_tag, {})
+                # # failure_rate = self._get_equipment_fr(location_tag, self.token)
+                
 
                 # Calculate costs using vectorized operations
                 predicted_costs = self._calculate_costs_vectorized(
                     preventive_cost=overhaul_cost + service_cost,
                     failure_replacement_cost=cost_per_failure,
-                    failure_rate=failure_rate
+                    failures_prediction=failures_prediction
                 )
 
                 if not predicted_costs:

src/calculation_time_constrains/utils.py

@@ -7,3 +7,84 @@ def get_months_between(start_date: datetime.datetime, end_date: datetime.datetim
     months = (end_date.year - start_date.year) * 12 + (end_date.month - start_date.month)
     # Add 1 to include both start and end months
     return months
+
+
+def create_time_series_data(chart_data, max_hours=24096):
+     # Filter out data points with currentEvent = "ON_OH"
+    filtered_data = [data for data in chart_data if data['currentEvent'] != 'ON_OH']
+    
+    # Sort filtered data by cumulative time
+    sorted_data = sorted(filtered_data, key=lambda x: x['cumulativeTime'])
+    
+    if not sorted_data:
+        return []
+    
+    hourly_data = []
+    current_state_index = 0
+    current_flow_rate = sorted_data[0]['flowRate']
+    current_eq_status = sorted_data[0]['currentEQStatus']
+    
+    for hour in range(1, max_hours + 1):
+        # Check if we need to advance to the next state
+        while (current_state_index < len(sorted_data) - 1 and 
+               hour >= int(sorted_data[current_state_index + 1]['cumulativeTime'])):
+            current_state_index += 1
+            current_flow_rate = sorted_data[current_state_index]['flowRate']
+            current_eq_status = sorted_data[current_state_index]['currentEQStatus']
+        
+        # Add hourly data point
+        hourly_data.append({
+            'hour': hour,
+            'flowrate': current_flow_rate,
+            'currentEQStatus': current_eq_status
+        })
+    
+    return hourly_data
+
+
+def calculate_failures_per_month(hourly_data):
+    """
+    Calculate the cumulative number of failures up to each month from hourly data.
+    A failure is defined as when currentEQStatus = "OoS".
+    Only counts the start of each failure period (transition from "Svc" to "OoS").
+    
+    Args:
+        hourly_data: List of dicts with 'hour', 'flowrate', and 'currentEQStatus' keys
+    
+    Returns:
+        List of dicts with 'month' and 'failures' keys (cumulative count)
+    """
+    total_failures = 0
+    previous_eq_status = None
+    monthly_data = {}
+    
+    for data_point in hourly_data:
+        hour = data_point['hour']
+        current_eq_status = data_point['currentEQStatus']
+        
+        # Calculate which month this hour belongs to (1-based)
+        # Assuming 30 days per month = 720 hours per month
+        month = ((hour - 1) // 720) + 1
+        
+        # Check if this is the start of a failure (transition to "OoS")
+        if current_eq_status == "OoS" and previous_eq_status is not None and previous_eq_status != "OoS":
+            total_failures += 1
+        # Special case: if the very first data point is a failure
+        elif current_eq_status == "OoS" and previous_eq_status is None:
+            total_failures += 1
+            
+        # Store the current cumulative count for this month
+        monthly_data[month] = total_failures
+        previous_eq_status = current_eq_status
+    
+    # Convert to list format
+    result = []
+    if monthly_data:
+        max_month = max(monthly_data.keys())
+        for month in range(1, max_month + 1):
+            result.append({
+                'month': month,
+                'failures': monthly_data.get(month, monthly_data.get(month-1, 0))
+            })
+    
+    return result