Merge branch 'main' of https://git.reliabilityindonesia.com/CIzz22/be-lcca

src/equipment/service.py

@@ -2,7 +2,7 @@ import os
 import logging
 from typing import Optional, TypedDict, Any
 
-from sqlalchemy import Select, Delete, Float, func, cast, String, text, case
+from sqlalchemy import Select, Delete, Float, func, cast, String, text, case, asc, desc
 from sqlalchemy.orm import selectinload
 from sqlalchemy.exc import SQLAlchemyError
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -392,16 +392,16 @@ async def get_top_10_economic_life(*, db_session: DbSession, common) -> list[Equ
         query.add_columns(
             case(
                 (
-                    (current_year - Equipment.minimum_eac_year) >= 0,
-                    (current_year - Equipment.minimum_eac_year),
+                    (Equipment.minimum_eac_year - current_year) >= 0,
+                    (Equipment.minimum_eac_year - current_year),
                 ),
-                else_=0,
-            ).label("remaining_life")
-        )
-        .filter(Equipment.minimum_eac_year != None)
-        .filter((Equipment.minimum_eac != None) & (Equipment.minimum_eac != 0))
+            else_=0,
+        ).label("remaining_life")
+    )
+    .filter(Equipment.minimum_eac_year != None)
+    .filter((Equipment.minimum_eac != None) & (Equipment.minimum_eac != 0))
         # .filter((current_year - Equipment.minimum_eac_year) >= 0)
-        .order_by((current_year - Equipment.minimum_eac_year).desc())
+        .order_by(desc("remaining_life"))
         .order_by(func.abs(Equipment.minimum_eac).desc())
     )
 
@@ -424,8 +424,8 @@ async def get_top_10_replacement_priorities(*, db_session: DbSession, common) ->
         query.add_columns(
             case(
                 (
-                    (current_year - Equipment.minimum_eac_year) >= 0,
-                    (current_year - Equipment.minimum_eac_year),
+                    (Equipment.minimum_eac_year - current_year) >= 0,
+                    (Equipment.minimum_eac_year - current_year),
                 ),
                 else_=0,
             ).label("remaining_life")
@@ -433,7 +433,7 @@ async def get_top_10_replacement_priorities(*, db_session: DbSession, common) ->
         .filter(Equipment.minimum_eac_year != None)
         .filter((Equipment.minimum_eac != None) & (Equipment.minimum_eac != 0))
         # .filter((current_year - Equipment.minimum_eac_year) >= 0)
-        .order_by((current_year - Equipment.minimum_eac_year).asc())
+        .order_by(asc("remaining_life"))
         .order_by(func.abs(Equipment.minimum_eac).desc())
     )
 

src/modules/equipment/Eac.py

@@ -5,8 +5,7 @@ import json
 import sys
 import os
 
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-from config import get_connection
+from src.modules.config import get_connection
 import argparse
 
 
@@ -187,7 +186,8 @@ class Eac:
                 # final_value adalah PV pada titik proyeksi periods
                 pmt_mnt_cost = -float(npf.pmt(disc_rate, periods, final_value))
 
-                eac_disposal_cost_proyeksi = 0.07 * pmt_mnt_cost
+                eac_disposal_cost_proyeksi = -npf.pmt(disc_rate, row["seq"], 0, 0.05 * rc_total_cost_0) if row["seq"] > 0 else 0.0
+                
                 # menghitung PMT biaya akuisisi
                 # Rumus PMT: PMT = PV * [r(1 + r)^n] / [(1 + r)^n – 1]
                 # dimana PV = rc_total_cost_0, r = disc_rate, n = row["seq"]

src/modules/equipment/Prediksi.py

@@ -15,10 +15,8 @@ from dotenv import load_dotenv
 
 import sys
 import os
-from equipment.formula import rc_labor_cost, rc_lost_cost, rc_total_cost
-
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-from config import get_connection, get_production_connection
+from .formula import rc_labor_cost, rc_lost_cost, rc_total_cost
+from src.modules.config import get_connection, get_production_connection
 import json
 
 load_dotenv()
@@ -97,6 +95,7 @@ class Prediksi:
             FROM lcc_equipment_tr_data
             WHERE assetnum = %s
             and is_actual=1
+            and seq != 0
             ;
             """
             cursor.execute(query, (equipment_id,))
@@ -240,57 +239,39 @@ class Prediksi:
             #             print(f"HTTP error occurred: {e}")
             #             return {}
 
-            # Menyiapkan data untuk batch insert
-            # print(f"Data to be inserted: {data}")
+            # Menyiapkan data untuk batch insert atau update
             records_to_insert = []
+            
+            check_existence_query = """
+            SELECT id FROM lcc_equipment_tr_data 
+            WHERE assetnum = %s AND tahun = %s AND is_actual = 0
+            """
+
+            update_query = """
+            UPDATE lcc_equipment_tr_data
+            SET 
+                rc_cm_material_cost = %s,
+                rc_cm_labor_cost = %s,
+                rc_pm_material_cost = %s,
+                rc_pm_labor_cost = %s,
+                rc_oh_material_cost = %s,
+                rc_oh_labor_cost = %s,
+                rc_predictive_material_cost = %s,
+                rc_predictive_labor_cost = %s,
+                updated_by = 'Sys', 
+                updated_at = NOW()
+            WHERE id = %s
+            """
+
             for _, row in data.iterrows():
-                max_seq = max_seq + 1
-                # (token already stored before defining fetch_api_data)
-                # maintain previous cm_interval between iterations using attribute on fetch_api_data
-                # if not hasattr(fetch_api_data, "prev_cm"):
-                #     fetch_api_data.prev_cm = None
-
-                # Update values from API (current year)
-                # api_data = await fetch_api_data(equipment_id, row["year"])
-                # if api_data and "data" in api_data and isinstance(api_data["data"], list) and len(api_data["data"]) > 0:
-                #     try:
-                #         cur_cm = float(api_data["data"][0].get("num_fail", row.get("cm_interval", 1)))
-                #     except Exception:
-                #         cur_cm = float(row.get("cm_interval", 1)) if not pd.isna(row.get("cm_interval", None)) else 1.0
-                # else:
-                #     try:
-                #         val = float(row.get("cm_interval", 1))
-                #         cur_cm = val if val >= 1 else 1.0
-                #     except Exception:
-                #         cur_cm = 1.0
-
-                # Determine previous cm_interval: prefer stored prev_cm, otherwise try API for previous year, else fallback to cur_cm
-                # if fetch_api_data.prev_cm is not None:
-                #     prev_cm = float(fetch_api_data.prev_cm)
-                # else:
-                #     try:
-                #         api_prev = await fetch_api_data(equipment_id, int(row["year"]) - 1)
-                #         if api_prev and "data" in api_prev and isinstance(api_prev["data"], list) and len(api_prev["data"]) > 0:
-                #             prev_cm = float(api_prev["data"][0].get("num_fail", cur_cm))
-                #         else:
-                #             # attempt to use any available previous value from the row if present, otherwise fallback to current
-                #             prev_cm = float(row.get("cm_interval", cur_cm)) if not pd.isna(row.get("cm_interval", None)) else cur_cm
-                #     except Exception:
-                #         prev_cm = cur_cm
-
-                # compute difference: current year interval minus previous year interval
-                # try:
-                #     cm_interval_diff = float(cur_cm) - float(prev_cm)
-                # except Exception:
-                #     cm_interval_diff = 0.0
-
-                # append record using the difference for raw_cm_interval
-                records_to_insert.append(
-                    (
-                        str(uuid4()), # id
-                        int(max_seq), # seq
-                        int(row["year"]),
-                        equipment_id,
+                # Check if data exists
+                cursor.execute(check_existence_query, (equipment_id, int(row["year"])))
+                existing_record = cursor.fetchone()
+
+                if existing_record:
+                    # Update existing record
+                    record_id = existing_record[0]
+                    cursor.execute(update_query, (
                         float(row.get("rc_cm_material_cost", 0)) if not pd.isna(row.get("rc_cm_material_cost", 0)) else 0.0,
                         float(row.get("rc_cm_labor_cost", 0)) if not pd.isna(row.get("rc_cm_labor_cost", 0)) else 0.0,
                         float(row.get("rc_pm_material_cost", 0)) if not pd.isna(row.get("rc_pm_material_cost", 0)) else 0.0,
@@ -299,16 +280,36 @@ class Prediksi:
                         float(row.get("rc_oh_labor_cost", 0)) if not pd.isna(row.get("rc_oh_labor_cost", 0)) else 0.0,
                         float(row.get("rc_predictive_material_cost", 0)) if not pd.isna(row.get("rc_predictive_material_cost", 0)) else 0.0,
                         float(row.get("rc_predictive_labor_cost", 0)) if not pd.isna(row.get("rc_predictive_labor_cost", 0)) else 0.0,
+                        record_id
+                    ))
+                else:
+                    max_seq = max_seq + 1
+                    # Prepare for insert
+                    records_to_insert.append(
+                        (
+                            str(uuid4()), # id
+                            int(max_seq), # seq
+                            int(row["year"]),
+                            equipment_id,
+                            float(row.get("rc_cm_material_cost", 0)) if not pd.isna(row.get("rc_cm_material_cost", 0)) else 0.0,
+                            float(row.get("rc_cm_labor_cost", 0)) if not pd.isna(row.get("rc_cm_labor_cost", 0)) else 0.0,
+                            float(row.get("rc_pm_material_cost", 0)) if not pd.isna(row.get("rc_pm_material_cost", 0)) else 0.0,
+                            float(row.get("rc_pm_labor_cost", 0)) if not pd.isna(row.get("rc_pm_labor_cost", 0)) else 0.0,
+                            float(row.get("rc_oh_material_cost", 0)) if not pd.isna(row.get("rc_oh_material_cost", 0)) else 0.0,
+                            float(row.get("rc_oh_labor_cost", 0)) if not pd.isna(row.get("rc_oh_labor_cost", 0)) else 0.0,
+                            float(row.get("rc_predictive_material_cost", 0)) if not pd.isna(row.get("rc_predictive_material_cost", 0)) else 0.0,
+                            float(row.get("rc_predictive_labor_cost", 0)) if not pd.isna(row.get("rc_predictive_labor_cost", 0)) else 0.0,
+                        )
                     )
-                )
 
-                # store current cm for next iteration
-                # fetch_api_data.prev_cm = cur_cm
+            # Eksekusi batch insert jika ada data baru
+            if records_to_insert:
+                cursor.executemany(insert_query, records_to_insert)
             
-            # Eksekusi batch insert
-            cursor.executemany(insert_query, records_to_insert)
             connection.commit()
-            # print("Data proyeksi berhasil dimasukkan ke database.")
+            
+            # Recalculate total costs and update asset criticality
+            self.__update_data_lcc(equipment_id)
 
         except Exception as e:
             print(f"Error saat menyimpan data ke database: {e}")
@@ -713,7 +714,6 @@ class Prediksi:
             if df is None:
                 print("Data tidak tersedia untuk prediksi.")
                 return
-
             # Mendapatkan tahun proyeksi dari DB
             par_tahun_target = self.__get_param(assetnum)
 
@@ -771,19 +771,14 @@ class Prediksi:
                 try:
                     # Case untuk kolom yang terkait dengan corrective maintenance (cm)
                     if "cm" in col_lower:
-                        # Tentukan jumlah baris recent yang dianggap actual jika kolom is_actual ada
-                        if "is_actual" in df.columns:
-                            recent_df = df[df["is_actual"] == 1]
-                            recent_n = recent_df.shape[0]
-                            avg_recent = recent_df[column].mean()
-                            print(f"avg_recent: {avg_recent}")
-                        else:
-                            recent_df = df
-                            recent_n = df.shape[0]
+
+                        recent_df = df
+                        recent_n = df.shape[0]
 
                         recent_n = max(1, recent_n)
                         recent_vals = (
-                            recent_df.sort_values("year", ascending=True)
+                            recent_df
+                            .sort_values("year", ascending=True)
                             .head(recent_n)[column]
                             .dropna()
                         )
@@ -796,18 +791,11 @@ class Prediksi:
                         if recent_vals.empty:
                             avg = 0.0
                         else:
-                            # Pastikan numeric; jika gagal, pakai mean dari yang bisa dikonversi
-                            try:
-                                avg = float(np.nanmean(recent_vals.astype(float)))
-                            except Exception:
-                                # jika conversion gagal gunakan mean pandas (objek mungkin numeric-like)
-                                avg = float(recent_vals.mean())
-
-                        if "interval" in col_lower:
-                            avg = max(0.0, avg)
+                            avg = pd.to_numeric(recent_vals, errors="coerce").fillna(0).mean()
+                            avg = 0.0 if pd.isna(avg) else float(avg)
 
                         preds = np.repeat(float(avg), n_future)
-                        print(preds)
+                        # print(preds)
                     else:
                         # Untuk kolom non-cm, gunakan nilai dari last actual year bila ada,
                         # jika tidak ada gunakan last available non-NA value, jika tidak ada pakai 0.0
@@ -944,7 +932,7 @@ class Prediksi:
             predictions_df = pd.DataFrame(predictions)
             # print(predictions_df)
             # Hapus data prediksi yang ada sebelumnya
-            self.__delete_predictions_from_db(assetnum)
+            # self.__delete_predictions_from_db(assetnum)
 
             # Insert hasil prediksi ke database
             try:

src/modules/equipment/insert_actual_data.py

@@ -8,10 +8,8 @@ from datetime import datetime
 import sys
 import os
 import httpx
-from where_query_sql import get_where_query_sql
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-from config import get_connection, get_production_connection
-
+from src.modules.config import get_connection, get_production_connection
+from .where_query_sql import get_where_query_sql
 
 async def fetch_api_data(
     assetnum: str, year: int, RELIABILITY_APP_URL: str, token: str
@@ -110,22 +108,13 @@ def get_recursive_query(cursor, assetnum, worktype="CM"):
 
     query = f"""
     select 
-    DATE_PART('year', a.reportdate) as tahun,
-    COUNT(DISTINCT a.wonum) as raw_{worktype.lower()}_interval, 
-    sum(a.actmatcost) as raw_{worktype.lower()}_material_cost,
-    (
-        ROUND(SUM(EXTRACT(EPOCH FROM (a.actfinish - a.actstart)) / 3600), 2)
-    ) AS raw_{worktype.lower()}_labor_time,
-    CASE 
-        WHEN COUNT(DISTINCT b.laborcode) = 0 THEN 3 
-        ELSE COUNT(DISTINCT b.laborcode) 
-    END AS raw_{worktype.lower()}_labor_human
-from public.wo_maximo as a 
-LEFT JOIN public.wo_maximo_labtrans AS b 
-    ON b.wonum = a.wonum
-    {where_query}
-group by DATE_PART('year', a.reportdate);
-"""
+        DATE_PART('year', a.reportdate) as tahun,
+        COUNT(DISTINCT a.wonum) as raw_{worktype.lower()}_interval, 
+        sum(a.actmatcost) as raw_{worktype.lower()}_material_cost
+    from public.wo_maximo as a 
+        {where_query}
+    group by DATE_PART('year', a.reportdate);
+    """
     # Eksekusi query dan fetch hasil
     cursor.execute(query)
     return cursor.fetchall()
@@ -459,7 +448,7 @@ async def insert_ms_equipment_data():
     try:
         connection, connection_wo_db = get_connection()
         cursor_db_app = connection.cursor(cursor_factory=DictCursor)
-        query_main = "SELECT DISTINCT(assetnum) FROM ms_equipment_master"
+        query_main = f"SELECT DISTINCT(assetnum) FROM ms_equipment_master WHERE assetnum = 'A27860'"
         cursor_db_app.execute(query_main)
         results = cursor_db_app.fetchall()
 
@@ -570,7 +559,7 @@ async def insert_lcca_maximo_corrective_data():
             data_corrective_maintenance = get_recursive_query(
                 cursor_production, assetnum, worktype="CM"
             )
-            print(data_corrective_maintenance)
+            # print(data_corrective_maintenance)
             start_year = 2015
             end_year = 2056
             seq = 0
@@ -870,7 +859,7 @@ async def insert_acquisition_cost_data():
             except Exception:
                 pass
 
-async def query_data():
+async def query_data(target_assetnum: str = None):
     connection = None
     connection_wo_db = None
     connection_production_wo = None
@@ -950,7 +939,11 @@ async def query_data():
         # Query untuk mendapatkan semua data dari tabel `lcc_ms_equipment_data`
         # query_main = "SELECT * FROM lcc_ms_equipment_data"
         query_main = "SELECT DISTINCT(assetnum) FROM ms_equipment_master"
-        cursor.execute(query_main)
+        if target_assetnum:
+            query_main += " WHERE assetnum = %s"
+            cursor.execute(query_main, (target_assetnum,))
+        else:
+            cursor.execute(query_main)
 
         # Fetch semua hasil query
         results = cursor.fetchall()
@@ -1046,7 +1039,6 @@ async def query_data():
                     year=year,
                     labour_cost_lookup=labour_cost_lookup,
                 )
-
                 if not data_exists:
                     cursor.execute(
                         insert_query,

src/modules/equipment/run.py

@@ -1,17 +1,10 @@
 import asyncio
 import time
 
-# prefer package-relative imports, but allow running this file directly as a script
-try:
-    from src.modules.equipment.insert_actual_data import query_data, insert_lcca_maximo_corrective_data, insert_ms_equipment_data, insert_acquisition_cost_data
-    from src.modules.equipment.Prediksi import Prediksi, main as predict_run
-    from src.modules.equipment.Eac import Eac, main as eac_run
-except ImportError:
-    # fallback when there's no parent package (e.g., python run.py)
-    from insert_actual_data import query_data, insert_lcca_maximo_corrective_data, insert_ms_equipment_data, insert_acquisition_cost_data
-    from Prediksi import Prediksi, main as predict_run
-    from Eac import Eac, main as eac_run
-
+# clean absolute imports
+from src.modules.equipment.insert_actual_data import query_data
+from src.modules.equipment.Prediksi import Prediksi, main as predict_run
+from src.modules.equipment.Eac import Eac, main as eac_run
 
 # Panggil fungsi
 async def main():

src/modules/equipment/where_query_sql.py

@@ -14,7 +14,7 @@ def get_where_query_sql(assetnum, worktype):
         )
         )
     """
-
+    # EM = Emergency Maintainance 
     return where_query
 
 def get_where_query_sql_all_worktype(assetnum):