remove physical DB utilsVars

queries/process_gsm.py

@@ -4,7 +4,7 @@ from queries.process_mal import process_mal_data, process_mal_with_bts_name
 from queries.process_trx import process_trx_data, process_trx_with_bts_name
 from utils.config_band import config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
-from utils.utils_vars import GsmAnalysisData, UtilsVars
+from utils.utils_vars import GsmAnalysisData, UtilsVars, get_physical_db
 
 BTS_COLUMNS = [
     "ID_BCF",
@@ -148,7 +148,7 @@ def process_gsm_data(file_path: str):
         lambda row: compare_trx_tch_versus_mal(row["TRX_TCH"], row["MAL_TCH"]), axis=1
     )
 
-    df_physical_db = UtilsVars.physisal_db
+    df_physical_db = get_physical_db()
     df_2g = pd.merge(df_2g, df_physical_db, on="Code_Sector", how="left")
 
     # Save dataframes

queries/process_lte.py

@@ -3,7 +3,13 @@ import pandas as pd
 
 from utils.config_band import config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
-from utils.utils_vars import LteFddAnalysisData, LteTddAnalysisData, UtilsVars, get_band
+from utils.utils_vars import (
+    LteFddAnalysisData,
+    LteTddAnalysisData,
+    UtilsVars,
+    get_band,
+    get_physical_db,
+)
 
 LNCEL_COLUMNS = [
     "ID_LNBTS",
@@ -169,10 +175,7 @@ def process_lte_data(file_path: str):
     df_lncel_lnbts = pd.merge(df_lncel, df_lnbts, on="ID_LNBTS", how="left")
     df_lncel_lnbts = pd.merge(df_lncel_lnbts, df_band, on="code", how="left")
 
-    df_physical_db = UtilsVars.physisal_db
-    df_physical_db = df_physical_db[
-        ["Code_Sector", "Azimut", "Longitude", "Latitude", "Hauteur"]
-    ]
+    df_physical_db = get_physical_db()
     df_lncel_lnbts = pd.merge(
         df_lncel_lnbts, df_physical_db, on="Code_Sector", how="left"
     )

queries/process_wcdma.py

@@ -3,7 +3,7 @@ import pandas as pd
 from utils.config_band import config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.extract_code import extract_code_from_mrbts
-from utils.utils_vars import UtilsVars, WcdmaAnalysisData
+from utils.utils_vars import UtilsVars, WcdmaAnalysisData, get_physical_db
 
 WCEL_COLUMNS = [
     "ID_WBTS",
@@ -153,7 +153,7 @@ def process_wcdma_data(file_path: str):
 
     df_3g = df_3g[WCEL_COLUMNS]
 
-    df_physical_db = UtilsVars.physisal_db
+    df_physical_db = get_physical_db()
     df_3g = pd.merge(df_3g, df_band, on="code", how="left")
     df_3g = pd.merge(df_3g, df_physical_db, on="Code_Sector", how="left")
     # Save dataframes

utils/utils_vars.py

@@ -65,20 +65,7 @@ class UtilsVars:
     final_all_database = None
     neighbors_database = ""
     file_path = ""
-    physisal_db = get_physical_db()
-
-
-# BSC	name
-# 403698	MBSCTST
-# 403699	MBSC01
-# 403701	MBSC04
-# 403702	MBSC03
-# 403703	MBSC02
-# 406283	MBSKTL01
-# 406284	MBSSEG01
-# 406308	MBSSK0S1
-
-# print(UtilsVars.physisal_db)
+    # physisal_db = get_physical_db()
 
 
 def get_band(text):