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import pandas as pd
from utils.convert_to_excel import convert_dfs, save_dataframe
from utils.utils_vars import UtilsVars
TRX_COLUMNS = [
"ID_BTS",
"trxRfPower",
"BCCH",
"TCH",
"number_trx_per_cell",
"number_trx_per_site",
]
def process_trx_data(file_path: str):
"""
Process data from the specified file path.
Args:
file_path (str): The path to the file.
"""
# Read the specific sheet into a DataFrame
dfs = pd.read_excel(
file_path,
sheet_name=["BTS", "BCF", "TRX"],
engine="calamine",
skiprows=[0],
)
# Process TRX data
df_trx = dfs["TRX"]
df_trx.columns = df_trx.columns.str.replace(r"[ ]", "", regex=True)
df_trx["ID_BTS"] = df_trx[["BSC", "BCF", "BTS"]].astype(str).apply("_".join, axis=1)
df_trx["ID_BCF"] = df_trx[["BSC", "BCF"]].astype(str).apply("_".join, axis=1)
df_trx["number_trx_per_cell"] = df_trx.groupby("ID_BTS")["ID_BTS"].transform(
"count"
)
df_trx["number_trx_per_site"] = df_trx.groupby("ID_BCF")["ID_BCF"].transform(
"count"
)
bcch = df_trx[df_trx["channel0Type"] == 4]
tch = df_trx[df_trx["channel0Type"] == 3][["ID_BTS", "initialFrequency"]]
tch = tch.pivot_table(
index="ID_BTS",
values="initialFrequency",
aggfunc=lambda x: " ".join(map(str, x)),
)
tch = tch.reset_index()
# rename the columns
tch.columns = ["ID_BTS", "TCH"]
# Merge dataframes
df_trx = pd.merge(bcch, tch, on="ID_BTS", how="left")
# rename "initialFrequency" to "BCCH"
df_trx = df_trx.rename(columns={"initialFrequency": "BCCH"})
df_trx = df_trx[TRX_COLUMNS]
# Save dataframes
# save_dataframe(df_trx, "trx")
# df_2g2 = save_dataframe(df_2g, "2g")
# UtilsVars.final_gsm_database = convert_dfs([df_2g], ["GSM"])
return df_trx
# process_trx_data(r"data2\20240805_5810_05082024_Dump.xml.gz.xlsb")
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