"""SCRIPT TO GENERATE COMPLETENESS TABLE"""

from pathlib import Path

import pandas as pd

import pyfunc

from pyconfig import appConfig

# pylint: disable=invalid-name

def flatten_multiindex_monthly(dataframe: pd.DataFrame) -> pd.DataFrame:

"""

Flatten the multi-index of a DataFrame by

renaming the index levels and converting it to a single-level index

with a date range as the index values.

Args:

dataframe (pd.DataFrame): The DataFrame with a multi-index to be flattened.

Returns:

pd.DataFrame: The DataFrame with a flattened index, where the index values are a date range.

"""

table = dataframe.copy()

table.index = table.index.rename(["tahun", "bulan"])

flat_index = table.index.to_flat_index()

index_first, index_last = flat_index[0], flat_index[-1]

date_start = f"{index_first[0]}-{index_first[1]}"

date_end = f"{index_last[0]}-{index_last[1]}"

dt_date_start = pd.to_datetime(date_start, format="%Y-%m")

dt_date_end = pd.to_datetime(date_end, format="%Y-%m")

index_date = pd.date_range(dt_date_start, dt_date_end, freq="MS")

table = table.reset_index(drop=True)

table.index = index_date

return table

def calculate_monthly_completeness(dataframe: pd.DataFrame) -> pd.DataFrame:

"""

Calculate the monthly completeness of a given dataframe.

Parameters:

dataframe (pd.DataFrame): The input dataframe.

Returns:

pd.DataFrame: A dataframe containing the monthly completeness percentages.

"""

table = dataframe.copy()

monthly_count = table.groupby(by=[table.index.year, table.index.month]).count()

monthly_len = (

table.groupby(by=[table.index.year, table.index.month]).size().to_frame()

)

monthly_count = flatten_multiindex_monthly(monthly_count)

monthly_percent = monthly_count.div(monthly_len.values, axis="columns")

return monthly_percent.rename_axis("date")

def main():

"""Main function to generate the completeness table."""

FOLDER_RAINFALL = Path(appConfig.DATASET.RAINFALL)

FOLDER_COMPLETENESS = Path(appConfig.DATASET.COMPLETENESS)

for file_rainfall in FOLDER_RAINFALL.glob("*.h5"):

print(f"PROCESSING: {file_rainfall}")

metadata_file = pyfunc.get_metadata_file(file_rainfall)

metadata_stations = pyfunc.get_metadata_stations(file_rainfall)

filename, title = metadata_file.filename, metadata_file.title

FILENAME_COMP = f"comp_{filename}"

PATH_COMP = FOLDER_COMPLETENESS / FILENAME_COMP

_info_file = {"filename": FILENAME_COMP, "title": title}

info_file = pd.Series(_info_file)

with pd.HDFStore(PATH_COMP, mode="w", complevel=1) as store_comp, pd.HDFStore(

file_rainfall, mode="r"

) as read_rr:

print(f"WRITING: {PATH_COMP}")

store_comp.put("/metadata/file", value=info_file)

store_comp.put("/metadata/stations", value=metadata_stations)

for stat_id in metadata_stations.index:

key = metadata_stations.loc[stat_id, "key"]

_df_rainfall = read_rr.get(key)

pyfunc.replace_unmeasured_data(_df_rainfall)

_df_completeness = calculate_monthly_completeness(_df_rainfall)

_df_completeness = _df_completeness.round(3) * 100

store_comp.put(key, value=_df_completeness)

if __name__ == "__main__":

main()