import numpy as np
import pandas as pd
from Levenshtein import distance


def compare_data(new, sys, count_test, path, school):
    print(f"\nEinträge in System Liste: {len(sys)}")
    print(f"Einträge in Import Liste: {len(new)}")

    bool_class = 'klasse' in new.columns

    if bool_class:
        if 'index' in new.columns:
            new = new.drop('index', axis=1)
        unique_classes(new, school)
        unique_classes(sys, school)
        sys = sys.drop(columns=['klasse'])
        merged_df = pd.merge(new[['name', 'vorname', 'klasse']], sys, on=['name', 'vorname'], how='outer', indicator=True)
        matches = pd.merge(new, sys, on=['name', 'vorname'])

    else:
        merged_df = pd.merge(new[['name', 'vorname']], sys, on=['name', 'vorname'], how='outer', indicator=True)
        matches = pd.merge(new, sys, on=['name', 'vorname'])

    # Subsets für Zeilen erstellen, die nur in einem der DataFrames vorhanden sind
    only_new = merged_df[merged_df['_merge'] == 'left_only'].drop(columns=['_merge'])
    only_sys = merged_df[merged_df['_merge'] == 'right_only'].drop(columns=['_merge'])

    print_status(matches, only_new, only_sys, count_test, path)
    search_typos(only_new[['name', 'vorname']], only_sys[['name', 'vorname']])

    return matches, only_new


def search_typos(new, sys):
    typos = []
    col1 = 'vorname'
    col2 = 'name'
    for idx1, row1 in new.iterrows():
        for idx2, row2 in sys.iterrows():
            if distance(row1[col1], row2[col1]) <= 2 and distance(row1[col2], row2[col2]) <= 2:
                typos.append(([row1[col1], row1[col2]], [row2[col1], row2[col2]]))
    typo_df = pd.DataFrame(typos, columns=['Import', 'System'])
    if len(typo_df) > 0:
        print('Mögliche Tippfehler:', len(typo_df), '\n', typo_df)
    else:
        print('Mögliche Tippfehler: keine Fehler gefunden!')


def unique_classes(df, school):
    df['klasse'] = df['klasse'].str.split(',')
    df = df.explode('klasse')
    eindeutige_klassen = df['klasse'].unique()
    with open(f'./Data/{school}/gen_klassen.txt', 'a') as file:
        file.write(np.array_str(eindeutige_klassen))
    # print(eindeutige_klassen)


def print_status(matches, new, old, count_test, path):
    print("\nAnzahl neuer Nutzer:", len(new))
    print("Anzahl Übereinstimmungen:", len(matches) + count_test)
    print("Anzahl veralteter Nutzer:", len(old) - count_test)
    with pd.ExcelWriter(path, engine='openpyxl') as writer:

        matches.to_excel(writer, sheet_name='Matches', index=False)
        new.to_excel(writer, sheet_name='Neu', index=False)
        old.to_excel(writer, sheet_name='Alt', index=False)