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import pandas as pd
from geopy.distance import geodesic # Imported but not used — consider removing
from queries.process_gsm import process_gsm_data
from queries.process_lte import process_lte_data
from queries.process_wcdma import process_wcdma_data
from utils.config_band import adjl_band
from utils.convert_to_excel import convert_dfs, save_dataframe
from utils.utils_vars import UtilsVars
# -------------------------------
# Constants
# -------------------------------
ADJL_GSM_COLUMNS = ["BSC", "BCF", "BTS", "ADJL", "earfcn", "lteAdjCellTac"]
ADJL_WCDMA_COLUMNS = ["RNC", "WBTS", "WCEL", "ADJL", "AdjLEARFCN"]
BTS_COLUMNS = ["ID_BTS", "name", "Code_Sector"]
WCEL_COLUMNS = ["ID_WCEL", "name", "Code_Sector"]
LTE_COLUMNS_CONFIG = ["Code_Sector", "site_config_band"]
LTE_COLUMNS_TAC = ["Code_Sector", "tac", "band"]
LTE_COLUMNS_ADJL = ["Code_Sector", "site_config_band", "tac", "band"]
# -------------------------------
# Helper functions
# -------------------------------
def check_bands(row: pd.Series) -> bool:
"""
Verify whether all configured site bands exist in ADJL created bands.
"""
site_bands = (
set(str(row["site_config_band"]).split("/"))
if pd.notna(row["site_config_band"])
else set()
)
adjl_bands = (
set(str(row["adjl_created_band"]).split("/"))
if pd.notna(row["adjl_created_band"])
else set()
)
return site_bands.issubset(adjl_bands)
def missing_bands(row: pd.Series) -> str | None:
"""
Return missing bands from ADJL compared to site configuration.
"""
site_bands = (
set(str(row["site_config_band"]).split("/"))
if pd.notna(row["site_config_band"])
else set()
)
adjl_bands = (
set(str(row["adjl_created_band"]).split("/"))
if pd.notna(row["adjl_created_band"])
else set()
)
diff = site_bands - adjl_bands
return ",".join(diff) if diff else None
# -------------------------------
# Main Processing
# -------------------------------
def process_adjl_data(file_path: str) -> list[pd.DataFrame]:
"""
Process ADJL data from an Excel file and return structured DataFrames.
Args:
file_path (str): Path to the input Excel file.
Returns:
list[pd.DataFrame]: [GSM_ADJL, WCDMA_ADJL, BTS, WCEL, LTE]
"""
# Read Excel sheets
dfs = pd.read_excel(
file_path,
sheet_name=["ADJL", "BTS", "WCEL"],
engine="calamine",
skiprows=[0],
)
# ------------------- BTS -------------------
df_bts = process_gsm_data(file_path)[BTS_COLUMNS]
# ------------------- WCEL -------------------
df_wcel = process_wcdma_data(file_path)[WCEL_COLUMNS]
# df_wcel["ID_WCEL"] = (
# df_wcel[["RNC", "WBTS", "WCEL"]].astype(str).agg("_".join, axis=1)
# )
# ------------------- LTE -------------------
lte_fdd_df, lte_tdd_df = process_lte_data(file_path)
lte_tdd_df = lte_tdd_df.rename(columns={"earfcn": "earfcnDL"})
lte_df = pd.concat([lte_fdd_df, lte_tdd_df], ignore_index=True)[LTE_COLUMNS_ADJL]
# Config & TAC references
lte_df_config = lte_df[LTE_COLUMNS_CONFIG]
lte_df_global_tac = (
lte_df[["Code_Sector", "tac"]]
.drop_duplicates(subset=["Code_Sector"], keep="first")
.rename(columns={"tac": "global_tac"})
)
lte_df_band_tac = lte_df[LTE_COLUMNS_TAC].copy()
lte_df_band_tac["Code_Sector_band"] = (
lte_df_band_tac[["Code_Sector", "band"]].astype(str).agg("_".join, axis=1)
)
lte_df_band_tac = lte_df_band_tac.drop(columns=["Code_Sector"])
# ------------------- ADJL -------------------
df_adjl = dfs["ADJL"]
df_adjl.columns = df_adjl.columns.str.replace(r"[ ]", "", regex=True)
gsm_adjl_df = df_adjl[ADJL_GSM_COLUMNS]
wcdma_adjl_df = df_adjl[ADJL_WCDMA_COLUMNS]
# --- GSM ADJL ---
# Filter invalid rows
gsm_adjl_df = gsm_adjl_df[
gsm_adjl_df["BSC"].notna()
& gsm_adjl_df["BCF"].notna()
& gsm_adjl_df["BTS"].notna()
].reset_index(drop=True)
# Build IDs and bands
gsm_adjl_df["ID_BTS"] = (
gsm_adjl_df[["BSC", "BCF", "BTS"]].astype(str).agg("_".join, axis=1)
)
gsm_adjl_df["ID_BTS"] = gsm_adjl_df["ID_BTS"].str.replace(".0", "", regex=False)
gsm_adjl_df["adjl_band"] = gsm_adjl_df["earfcn"].map(UtilsVars.lte_band)
# Merge BTS info
gsm_adjl_df = pd.merge(gsm_adjl_df, df_bts, on="ID_BTS", how="left")
# Aggregate ADJL band info
gsm_adjl_df_band = adjl_band(gsm_adjl_df, "ID_BTS", "adjl_band")
gsm_adjl_df = pd.merge(gsm_adjl_df, gsm_adjl_df_band, on="ID_BTS", how="left")
# Build Code_Sector_band
gsm_adjl_df["Code_Sector_band"] = (
gsm_adjl_df[["Code_Sector", "adjl_band"]].astype(str).agg("_".join, axis=1)
)
# Merge LTE references
gsm_adjl_df = gsm_adjl_df.merge(lte_df_config, on="Code_Sector", how="left")
gsm_adjl_df = gsm_adjl_df.merge(lte_df_band_tac, on="Code_Sector_band", how="left")
gsm_adjl_df = gsm_adjl_df.merge(lte_df_global_tac, on="Code_Sector", how="left")
# Final TAC
gsm_adjl_df["final_tac"] = gsm_adjl_df["tac"].fillna(gsm_adjl_df["global_tac"])
# Validations
gsm_adjl_df["check_bands"] = gsm_adjl_df.apply(check_bands, axis=1)
gsm_adjl_df["missing_bands"] = gsm_adjl_df.apply(missing_bands, axis=1)
gsm_adjl_df["check_tac"] = gsm_adjl_df["lteAdjCellTac"] == gsm_adjl_df["final_tac"]
# Drop intermediate columns
gsm_adjl_df = gsm_adjl_df.drop(
columns=["Code_Sector_band", "tac", "band", "global_tac"]
)
# Mark existing BTS
df_bts["adjl_exists"] = df_bts["ID_BTS"].isin(gsm_adjl_df["ID_BTS"])
# --- WCDMA ADJL ---
# Filter invalid rows
wcdma_adjl_df = wcdma_adjl_df[
wcdma_adjl_df["RNC"].notna()
& wcdma_adjl_df["WBTS"].notna()
& wcdma_adjl_df["WCEL"].notna()
].reset_index(drop=True)
# Build IDs and bands
wcdma_adjl_df["ID_WCEL"] = (
wcdma_adjl_df[["RNC", "WBTS", "WCEL"]].astype(str).agg("_".join, axis=1)
)
wcdma_adjl_df["ID_WCEL"] = wcdma_adjl_df["ID_WCEL"].str.replace(
".0", "", regex=False
)
wcdma_adjl_df["adjl_band"] = wcdma_adjl_df["AdjLEARFCN"].map(UtilsVars.lte_band)
# Merge WCEL info
wcdma_adjl_df = pd.merge(wcdma_adjl_df, df_wcel, on="ID_WCEL", how="left")
# Aggregate ADJL band info
wcdma_adjl_df_band = adjl_band(wcdma_adjl_df, "ID_WCEL", "adjl_band")
wcdma_adjl_df = pd.merge(
wcdma_adjl_df, wcdma_adjl_df_band, on="ID_WCEL", how="left"
)
# Build Code_Sector_band
wcdma_adjl_df["Code_Sector_band"] = (
wcdma_adjl_df[["Code_Sector", "adjl_band"]].astype(str).agg("_".join, axis=1)
)
# Merge LTE references
wcdma_adjl_df = wcdma_adjl_df.merge(lte_df_config, on="Code_Sector", how="left")
# Validations
wcdma_adjl_df["check_bands"] = wcdma_adjl_df.apply(check_bands, axis=1)
wcdma_adjl_df["missing_bands"] = wcdma_adjl_df.apply(missing_bands, axis=1)
# Mark existing WCEL
df_wcel["adjl_exists"] = df_wcel["ID_WCEL"].isin(wcdma_adjl_df["ID_WCEL"])
return [gsm_adjl_df, wcdma_adjl_df, df_bts, df_wcel, lte_df]
def process_adjl_data_to_excel(file_path: str) -> None:
"""
Process ADJL data and save the result into an Excel-like format via UtilsVars.
"""
adjl_dfs = process_adjl_data(file_path)
UtilsVars.adjl_database = convert_dfs(
adjl_dfs, ["GSM_ADJL", "WCDMA_ADJL", "BTS", "WCEL", "LTE"]
)
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