#!/usr/bin/env python3
# flake8: noqa E501 (line length)

import pandas as pd
import config
import unicodedata
import re
pd.options.mode.chained_assignment = None


def is_hospital_affiliation(affiliation):
    affiliation_lower = affiliation.lower()
    for aff in config.acronyms:
        if aff in affiliation_lower:
            return True
    return False


def remove_accents(city_name):
    """correction of the cities : remove accents"""
    normalized_text = unicodedata.normalize("NFD", city_name)
    text_with_no_accent = re.sub("[\u0300-\u036f]", "", normalized_text)
    return text_with_no_accent


def remove_department_numbers(city_name):
    """correction of the cities : remove the department numbers"""
    # Grenoble - 38 => Grenoble
    sep = " -"
    stripped = city_name.split(sep, 1)[0]
    stripped = stripped.replace("-", " ")

    return stripped


def convert_acronyms(city_name):
    """correction of the cities: convert acronyms"""
    stripped = city_name.replace("Saint ", "St ")
    stripped = stripped.replace("Mont ", "Mt ")

    return stripped


def is_city_in_affiliation(city, affiliation):
    return city.lower() in affiliation.lower()


affiliations_dataframe = pd.read_csv("hospital_affiliations_1.csv", sep=";")
acronyms = config.acronyms

for acronym in acronyms:
    # standarize original dataframe
    affiliations_dataframe["contains_acronyms"] = affiliations_dataframe["Affiliation"].apply(
        is_hospital_affiliation)
    affiliations_dataframe["standardized_city"] = affiliations_dataframe["Ville_canonique_Dpt"].apply(
        remove_department_numbers).apply(convert_acronyms).apply(remove_accents)
    # print(affiliations_dataframe)

    normalized_dataframe = affiliations_dataframe[affiliations_dataframe["contains_acronyms"] == True]  # noqa: E712
    if len(normalized_dataframe) == 0:  # noqa: E712
        continue

    normalized_dataframe["standardized_hospital"] = affiliations_dataframe["Hospital"]

normalized_dataframe = normalized_dataframe.drop(
    normalized_dataframe.columns[[1, 2, 3]], axis=1)
normalized_dataframe.to_csv(
    'hospital_affiliation_normalized.csv', sep=';', encoding='utf-8', index=False)