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import pandas as pd
from utils.convert_to_excel import convert_invunit_dfs, save_dataframe
from utils.extract_code import extract_code_from_mrbts
from utils.utils_vars import UtilsVars
RF_UNIT = [
"AHDA",
"AHEGB",
"AHEGC",
"AHEGHA",
"AHGA",
"AHMA",
"AHPMDA",
"AHPMDG",
"AHPMDI",
"ARDA",
"AREA",
"ARGA",
"ARMA",
"AZNA",
"FHDB",
"FHEB",
"FHEL",
"FRGU",
"FRGY",
"FRMB",
"FRMF",
"FXDB",
"FXED",
"FZNI",
]
BB_UNIT_LIST = [
"FBBA",
"FBBC",
"FSMF",
"ABIA",
"total_number_of_subunit",
]
BB_UNIT_VALUES = {
"FBBA": 6,
"FBBC": 6,
"FSMF": 5.5,
"ABIA": 8,
}
def create_invunit_summary(df: pd.DataFrame) -> pd.DataFrame:
"""
Creates a summary string column in the given DataFrame by concatenating non-NaN values of all columns except the first one (MRBTS) into a single string with '/' as separator.
Args:
df (pd.DataFrame): The DataFrame to process.
Returns:
pd.DataFrame: The DataFrame with the added "invunit_summary" column.
"""
def process_row(row):
values = []
for col in df.columns[1:]: # Exclude 'MRBTS'
if pd.notna(row[col]): # Check if value is not NaN
values.append(f"{int(row[col])} {col}") # Format as 'count column_name'
return "/".join(values) if values else ""
df["invunit_summary"] = df.apply(process_row, axis=1)
return df
def process_invunit_data(file_path: str) -> pd.DataFrame:
"""
Process data from the specified file path.
Args:
file_path (str): The path to the file.
"""
dfs = pd.read_excel(
file_path,
sheet_name=["INVUNIT"],
engine="calamine",
skiprows=[0],
)
# Parse INVUNIT
df_invunit = dfs["INVUNIT"]
df_invunit.columns = df_invunit.columns.str.replace(r"[ ]", "", regex=True)
df_invunit = df_invunit[df_invunit["MRBTS"].apply(lambda x: str(x).isnumeric())]
df_invunit["code"] = df_invunit["MRBTS"].apply(extract_code_from_mrbts)
df_invunit = df_invunit[["MRBTS", "inventoryUnitType"]]
df_invunit = (
df_invunit.groupby(["MRBTS", "inventoryUnitType"])
.size()
.unstack(fill_value=None)
.reset_index()
)
# Rename columns
df_invunit = df_invunit.rename(
columns={
"ABIA AirScale Capacity": "ABIA",
"AMIA AirScale Indoor Subrack": "AMIA",
"AMOB AirScale Outdoor Subrack": "AMOB",
"ASIA AirScale Common": "ASIA",
"ASIB AirScale Common": "ASIB",
"BB Extension Outdoor Sub-Module FBBA": "FBBA",
"CORE_ASIA AirScale Common": "CORE_ASIA",
"CORE_ASIB AirScale Common": "CORE_ASIB",
"CORE_Flexi System Module Outdoor FSMF": "CORE_FSMF",
"CORE_SMOD": "CORE_SMOD",
"Flexi Baseband Sub-Module FBBC": "FBBC",
"Flexi System Module Outdoor FSMF": "FSMF",
"Not available": "NOT_AVAILABLE",
"SingleAntennaDevice": "SAD",
}
)
df_invunit = create_invunit_summary(df_invunit)
df_invunit["code"] = df_invunit["MRBTS"].apply(extract_code_from_mrbts)
# Compute total_number_of_subunit
df_invunit["total_number_of_subunit"] = sum(
df_invunit[col].fillna(0) * val for col, val in BB_UNIT_VALUES.items()
)
# Start order with "MRBTS", "code", "invunit_summary" follow by bb_unit , rf_unit and then all other columns
df_invunit = df_invunit[
["MRBTS", "code", "invunit_summary"]
+ BB_UNIT_LIST
+ RF_UNIT
+ df_invunit.columns.difference(
["MRBTS", "code", "invunit_summary"] + BB_UNIT_LIST + RF_UNIT
).tolist()
]
UtilsVars.all_db_dfs.append(df_invunit)
UtilsVars.all_db_dfs_names.append("INVUNIT")
return df_invunit
def process_invunit_data_to_excel(file_path: str) -> None:
"""
Process data from the specified file path and save it to a excel file.
Args:
file_path (str): The path to the file.
"""
invunit_df = process_invunit_data(file_path)
UtilsVars.final_invunit_database = convert_invunit_dfs([invunit_df], ["INVUNIT"])
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