IMPROVE MRBTS INFOS

queries/process_gsm.py

@@ -2,7 +2,7 @@ import pandas as pd
 
 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.config_band import bcf_band, config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.kml_creator import generate_kml_from_df
 from utils.utils_vars import GsmAnalysisData, UtilsVars, get_physical_db
@@ -159,11 +159,13 @@ def process_gsm_data(file_path: str):
 
     # create band dataframe
     df_band = config_band(df_bts)
+    df_bcf_band = bcf_band(df_bts)
 
     # Merge dataframes
     df_bts_bcf = pd.merge(df_bcf, df_bts, on="ID_BCF", how="left")
     df_2g = pd.merge(df_bts_bcf, df_trx, on="ID_BTS", how="left")
     df_2g = pd.merge(df_2g, df_band, on="code", how="left")
+    df_2g = pd.merge(df_2g, df_bcf_band, on="ID_BCF", how="left")
     df_2g = pd.merge(df_2g, df_mal, on="ID_MAL", how="left")
     df_2g["TRX_TCH_VS_MAL"] = df_2g.apply(
         lambda row: compare_trx_tch_versus_mal(row["TRX_TCH"], row["MAL_TCH"]), axis=1

queries/process_lte.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 
-from utils.config_band import config_band
+from utils.config_band import config_band, lte_mrbts_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.kml_creator import generate_kml_from_df
 from utils.utils_vars import (
@@ -177,6 +177,7 @@ def process_lte_data(file_path: str):
 
     # create band dataframe
     df_band = config_band(df_lncel)
+    df_mrbts_band = lte_mrbts_band(df_lncel)
 
     # Process LNBTS data
     df_lnbts = dfs["LNBTS"]
@@ -190,6 +191,7 @@ def process_lte_data(file_path: str):
     # Merge dataframes
     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_lncel_lnbts = pd.merge(df_lncel_lnbts, df_mrbts_band, on="MRBTS", how="left")
 
     df_physical_db = get_physical_db()
     df_lncel_lnbts = pd.merge(

queries/process_mrbts.py

@@ -2,10 +2,12 @@ import pandas as pd
 
 from utils.convert_to_excel import convert_dfs
 from utils.extract_code import extract_code_from_mrbts
-from utils.utils_vars import UtilsVars
+from utils.utils_vars import UtilsVars, clean_bands
 
 
-def process_mrbts_data(file_path: str) -> pd.DataFrame:
+def process_mrbts_data(
+    file_path: str,
+) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
     """
     Process data from the specified file path.
 
@@ -14,11 +16,12 @@ def process_mrbts_data(file_path: str) -> pd.DataFrame:
     """
     dfs = pd.read_excel(
         file_path,
-        sheet_name=["MRBTS"],
+        sheet_name=["MRBTS", "GNBCF", "WNBTS", "ALD"],
         engine="calamine",
         skiprows=[0],
     )
 
+    # MRBTS
     df_mrbts = dfs["MRBTS"]
     df_mrbts.columns = df_mrbts.columns.str.replace(r"[ ]", "", regex=True)
 
@@ -29,9 +32,67 @@ def process_mrbts_data(file_path: str) -> pd.DataFrame:
     df_mrbts.loc[:, "code"] = df_mrbts["MRBTS"].apply(extract_code_from_mrbts)
     df_mrbts = df_mrbts[["MRBTS", "code", "name", "btsName"]]
 
+    # GNBCF
+    df_gnbcf = dfs["GNBCF"]
+    df_gnbcf.columns = df_gnbcf.columns.str.replace(r"[ ]", "", regex=True)
+    df_gnbcf = df_gnbcf[["MRBTS", "bscId", "bcfId"]]
+    df_gnbcf["ID_BCF"] = (
+        (df_gnbcf[["bscId", "bcfId"]]).astype(str).apply("_".join, axis=1)
+    )
+
+    df_gnbcf = df_gnbcf[["MRBTS", "ID_BCF"]]
+
+    # WNBTS
+    df_wnbts = dfs["WNBTS"]
+    df_wnbts.columns = df_wnbts.columns.str.replace(r"[ ]", "", regex=True)
+    df_wnbts = df_wnbts[["MRBTS", "wbtsId"]]
+    df_wnbts = df_wnbts.rename(columns={"wbtsId": "WBTS"})
+
+    # ALD
+    df_ald = dfs["ALD"]
+    df_ald.columns = df_ald.columns.str.replace(r"[ ]", "", regex=True)
+    df_ald = df_ald[["MRBTS", "productCode"]]
+    df_ald = df_ald.drop_duplicates(subset=["MRBTS"], keep="first")
+
+    df_mrbts = pd.merge(df_mrbts, df_gnbcf, on="MRBTS", how="left")
+    df_mrbts = pd.merge(df_mrbts, df_wnbts, on="MRBTS", how="left")
+    df_mrbts = pd.merge(df_mrbts, df_ald, on="MRBTS", how="left")
+
+    ##################################################################################"""
+    ###################################################################################
+
+    gsm_df: pd.DataFrame = UtilsVars.all_db_dfs[0]
+    wcdma_df: pd.DataFrame = UtilsVars.all_db_dfs[3]
+    lte_fdd_df: pd.DataFrame = UtilsVars.all_db_dfs[4]
+    lte_tdd_df: pd.DataFrame = UtilsVars.all_db_dfs[5]
+
+    gsm_df = gsm_df[["ID_BCF", "site_name", "number_trx_per_bcf", "bcf_config_band"]]
+    gsm_df = gsm_df.drop_duplicates(subset=["ID_BCF"], keep="first")
+    gsm_df = gsm_df.rename(columns={"site_name": "gsm_name"})
+
+    wcdma_df = wcdma_df[["WBTS", "site_name", "wbts_config_band"]]
+    wcdma_df = wcdma_df.drop_duplicates(subset=["WBTS"], keep="first")
+    wcdma_df = wcdma_df.rename(columns={"site_name": "wcdma_name"})
+
+    lte_fdd_df = lte_fdd_df[["MRBTS", "lnbts_name", "lte_config_band"]]
+    lte_tdd_df = lte_tdd_df[["MRBTS", "lnbts_name", "lte_config_band"]]
+    lte_df = pd.concat([lte_fdd_df, lte_tdd_df], ignore_index=True)
+    lte_df = lte_df.drop_duplicates(subset=["MRBTS"], keep="first")
+
+    df_mrbts = pd.merge(df_mrbts, gsm_df, on="ID_BCF", how="left")
+    df_mrbts = pd.merge(df_mrbts, wcdma_df, on="WBTS", how="left")
+    df_mrbts = pd.merge(df_mrbts, lte_df, on="MRBTS", how="left")
+
+    df_mrbts["mrbts_config_band"] = (
+        df_mrbts[["bcf_config_band", "wbts_config_band", "lte_config_band"]]
+        .astype(str)
+        .apply("/".join, axis=1)
+    )
+    df_mrbts["mrbts_config_band"] = df_mrbts["mrbts_config_band"].apply(clean_bands)
+
     UtilsVars.all_db_dfs.append(df_mrbts)
     UtilsVars.all_db_dfs_names.append("MRBTS")
-    return df_mrbts
+    return df_mrbts, df_gnbcf, df_wnbts
 
 
 def process_mrbts_data_to_excel(file_path: str) -> None:
@@ -41,5 +102,7 @@ def process_mrbts_data_to_excel(file_path: str) -> None:
     Args:
         file_path (str): The path to the file.
     """
-    mrbts_df = process_mrbts_data(file_path)
-    UtilsVars.final_mrbts_database = convert_dfs([mrbts_df], ["MRBTS"])
+    mrbts_df, gnbcf_df, wnbts_df = process_mrbts_data(file_path)
+    UtilsVars.final_mrbts_database = convert_dfs(
+        [mrbts_df, gnbcf_df, wnbts_df], ["MRBTS", "GNBCF", "WNBTS"]
+    )

queries/process_site_db.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from utils.utils_vars import SiteAnalysisData, UtilsVars
+from utils.utils_vars import SiteAnalysisData, UtilsVars, clean_bands
 
 GSM_COLUMNS = [
     "code",
@@ -57,13 +57,6 @@ CODE_COLUMNS = [
 ]
 
 
-def clean_bands(bands):
-    if pd.isna(bands):
-        return None
-    parts = [p for p in bands.split("/") if p != "nan"]
-    return "/".join(parts) if parts else None
-
-
 def site_db():
     gsm_df: pd.DataFrame = UtilsVars.all_db_dfs[0]
     wcdma_df: pd.DataFrame = UtilsVars.all_db_dfs[3]

queries/process_wcdma.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from utils.config_band import config_band
+from utils.config_band import config_band, wbts_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.extract_code import extract_code_from_mrbts
 from utils.kml_creator import generate_kml_from_df
@@ -145,6 +145,7 @@ def process_wcdma_data(file_path: str):
 
     # create config_band dataframe
     df_band = config_band(df_wcel)
+    df_wbts_band = wbts_band(df_wcel)
 
     # Process WBTS data
     df_wbts = dfs["WBTS"]
@@ -171,6 +172,7 @@ def process_wcdma_data(file_path: str):
 
     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_wbts_band, on="WBTS", how="left")
     df_3g = pd.merge(df_3g, df_physical_db, on="Code_Sector", how="left")
     # Save dataframes
     # save_dataframe(df_wcel, "wcel")

utils/config_band.py

@@ -30,3 +30,96 @@ def config_band(df: pd.DataFrame) -> pd.DataFrame:
     df_band.rename(columns={"band": "site_config_band"}, inplace=True)
 
     return df_band
+
+
+def bcf_band(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Create a dataframe that contains the bcf configuration band for each bcf ID.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe containing the bcf information, with columns "ID" and "band"
+
+    Returns
+    -------
+    pd.DataFrame
+        The dataframe containing the bcf configuration band for each bcf ID, with columns "ID" and "bcf_config_band"
+    """
+    df_band = df[["ID_BCF", "band"]].copy()
+    df_band["ID"] = df_band[["ID_BCF", "band"]].astype(str).apply("_".join, axis=1)
+    # remove duplicates ID
+    df_band = df_band.drop_duplicates(subset=["ID"])
+    df_band = df_band[["ID_BCF", "band"]]
+    df_band["band"] = df_band["band"].fillna("empty")
+    df_band = (
+        df_band.groupby("ID_BCF")["band"]
+        .apply(lambda x: "/".join(sorted(x)))
+        .reset_index()
+    )
+    # rename band to config
+    df_band.rename(columns={"band": "bcf_config_band"}, inplace=True)
+
+    return df_band
+
+
+def wbts_band(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Create a dataframe that contains the wbts configuration band for each wbts ID.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe containing the wbts information, with columns "ID" and "band"
+
+    Returns
+    -------
+    pd.DataFrame
+        The dataframe containing the wbts configuration band for each wbts ID, with columns "ID" and "wbts_config_band"
+    """
+    df_band = df[["WBTS", "band"]].copy()
+    df_band["ID"] = df_band[["WBTS", "band"]].astype(str).apply("_".join, axis=1)
+    # remove duplicates ID
+    df_band = df_band.drop_duplicates(subset=["ID"])
+    df_band = df_band[["WBTS", "band"]]
+    df_band["band"] = df_band["band"].fillna("empty")
+    df_band = (
+        df_band.groupby("WBTS")["band"]
+        .apply(lambda x: "/".join(sorted(x)))
+        .reset_index()
+    )
+    # rename band to config
+    df_band.rename(columns={"band": "wbts_config_band"}, inplace=True)
+
+    return df_band
+
+
+def lte_mrbts_band(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Create a dataframe that contains the mrbts configuration band for each mrbts ID.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe containing the mrbts information, with columns "ID" and "band"
+
+    Returns
+    -------
+    pd.DataFrame
+        The dataframe containing the mrbts configuration band for each mrbts ID, with columns "ID" and "mrbts_config_band"
+    """
+    df_band = df[["MRBTS", "band"]].copy()
+    df_band["ID"] = df_band[["MRBTS", "band"]].astype(str).apply("_".join, axis=1)
+    # remove duplicates ID
+    df_band = df_band.drop_duplicates(subset=["ID"])
+    df_band = df_band[["MRBTS", "band"]]
+    df_band["band"] = df_band["band"].fillna("empty")
+    df_band = (
+        df_band.groupby("MRBTS")["band"]
+        .apply(lambda x: "/".join(sorted(x)))
+        .reset_index()
+    )
+    # rename band to config
+    df_band.rename(columns={"band": "lte_config_band"}, inplace=True)
+
+    return df_band

utils/utils_vars.py

@@ -139,6 +139,13 @@ def get_band(text):
     return np.nan  # or return None
 
 
+def clean_bands(bands):
+    if pd.isna(bands):
+        return None
+    parts = [p for p in bands.split("/") if p != "nan"]
+    return "/".join(parts) if parts else None
+
+
 class GsmAnalysisData:
     total_number_of_bsc = 0
     total_number_of_cell = 0