"""

Process ADICAP codes

Thésaurus de la codification ADICAP - Index raisonné des lésions

source : https://smt.esante.gouv.fr/terminologie-adicap/

"""

import gzip

import json

import re

from pathlib import Path

import pandas as pd

import typer

def parse_each_dict(df, dictionaryCode: str):

    d_spec = df.query(f"dictionaryCode=='{dictionaryCode}'")

    d_spec.fillna("", inplace=True)

    decode_d_spec = {}

    for code, label, anatomyCode in d_spec[["code", "label", "anatomyCode"]].values:

        if dictionaryCode == "D5":

            if re.match(r"[0-9]{4}", code) is None:

                decode_d_spec[str(anatomyCode) + str(code)] = label

        else:

            decode_d_spec[str(anatomyCode) + str(code)] = label

    d_value = decode_d_spec.pop(dictionaryCode)

    return dict(label=d_value, codes=decode_d_spec)

def get_decode_dict(df, dict_keys=["D1", "D2", "D3", "D4", "D5", "D6", "D7"]):

    decode_dict = {}

    for key in dict_keys:

        decode_dict[key] = parse_each_dict(df, dictionaryCode=key)

    return decode_dict

def run(

    raw: Path = typer.Argument(..., help="Path to the raw file"),

    output: Path = typer.Option(

        "edsnlp/resources/adicap.json.gz", help="Path to the output CSV table."

    ),

) -> None:

    """

    Convenience script to automatically process the ADICAP codes

    into a processable file.

    """

    df = pd.read_excel(

        raw,

        sheet_name="rawdatas",

        header=0,

    )

    decode_dict = get_decode_dict(df)

    typer.echo(f"Saving to {output}")

    with gzip.open(output, "w") as f:

        f.write(json.dumps(decode_dict).encode("utf-8"))

    typer.echo("Done !")

if __name__ == "__main__":

    typer.run(run)