import pandas as pd
import numpy as np

base_dir = '/home/robert/projekte/planner/export'

id_header = ['Ebene' + str(i) for i in range(1, 11)]
# values2_header = ['VJ', 'AJ', 'FC', 'Plan_ori', 'Plan_Prozent', 'Stk', 'VAK', 'BE_Prozent', 'Plan_VJ', 'Plan_Stk_VJ', 'Plan',
#                   'Jan', 'Feb', 'Mar', 'Apr', 'Mai', 'Jun', 'Jul', 'Aug', 'Sep', 'Okt', 'Nov', 'Dez', 'Periode13']
values2_header = ['Plan', 'Jan', 'Feb', 'Mar', 'Apr', 'Mai', 'Jun', 'Jul', 'Aug', 'Sep', 'Okt', 'Nov', 'Dez', 'Periode13',
                  'Plan_ori', 'Plan_Prozent', 'Stk', 'VAK', 'BE_Prozent', 'frei', 
                  'VJ', 'VJ_Stk', 'AJ', 'AJ_Stk', 'AJ_Okt', 'AJ_Okt_Stk', 'FC', 'FC_Stk', 'Plan_VJ', 'Plan_VJ_Stk']
season_header = ['Jan', 'Feb', 'Mar', 'Apr', 'Mai', 'Jun', 'Jul', 'Aug', 'Sep', 'Okt', 'Nov', 'Dez']
info_header = ['text', 'costcenter', 'department']
header = info_header + id_header + values2_header
season_export_header = info_header + season_header

source_header = ['department', 'text', 'costcenter', 'Ebene1', 'Plan', 'Periode13']
export_header = ['Betrieb Nr', 'Zeile mit Bez', 'Bereich', 'Vstufe 1', 'Gesamt', 'Periode13']    # 'Version', 'Konto', 'Jahr']


def expand(df, header, values_label):
    for i, key in enumerate(header):
        df[key] = df[values_label].str[i]
    return df


def apply_season(df):
    df['Saison'] = df['Ebene1'].str.contains('Umsatzerlöse|Materialaufwand|Verkaufsabh. Kosten')
    df['saison_sum'] = df[season_header].sum(axis=1)
    for i, key in enumerate(season_header):
        df['temp'] = np.where((df['Saison']) & (df[key + '_2'] != 8.3333), df['Plan'] * df[key + '_2'] / 100, df['Plan'] / 12)
        df[key] = np.where(df['saison_sum'] == 0, df['temp'], df[key] * df['Minus1'])
    df['Dez'] = df['Plan'] - df[season_header].sum(axis=1) + df['Dez']
    return df


def data_cleansing(filename):
    df = pd.read_json(filename)
    df['values2'] = df['values2'].apply(lambda v: list(v.items()))
    df = df.explode('values2')
    df['department'], df['values2'] = zip(*df['values2'])

    df['id'] = df['id'].str.split(';')
    df = expand(df, id_header, 'id')
    df = expand(df, values2_header, 'values2')
    return df


def export_plan(filename, version, target_year, amount_value):
    df = data_cleansing(f'{base_dir}/{filename}.json')
    season = df[(df['level'] == 2) & (df['Ebene1'] == 'Umsatzerlöse')].copy()
    season['Dez'] = (100 - season[season_header].sum(axis=1) + season['Dez']).round(4)
    season[season_export_header].to_csv(f'{base_dir}/Planner_{target_year}_{version}_Saison.csv',
                                        encoding='latin_1', sep=';', decimal=',', index=False)

    df['Minus1'] = np.where(df['Ebene1'] != 'Umsatzerlöse', -1, 1)
    df['Plan'] = df[amount_value] * df['Minus1']
    if amount_value == 'Plan':
        df['Periode13'] = df['Periode13'] * df['Minus1']
    else:
        df['Periode13'] = 0
    plan = df[df['planlevel'] == True]

    plan = pd.merge(plan, season, how='left', on=['Ebene2', 'department'], suffixes=('', '_2'))
    plan = apply_season(plan)
    plan = plan[source_header + season_header].rename(columns=dict(zip(source_header, export_header)))

    # Reisacher Spezialbedingungen
    plan['Zeile'] = plan['Zeile mit Bez'].str.slice(stop=4)
    plan['Zeile'] = np.where(plan['Zeile mit Bez'].isin(['BMW aus Leasingrücklauf BFS', 'BMW aus Leasingrücklauf Alphabet']),
                             '3040', plan['Zeile'])
    plan['Zeile'] = np.where(plan['Zeile mit Bez'].isin(['BMW an Wiederverkäufer BFS', 'BMW an Wiederverkäufer Alphabet']),
                             '3120', plan['Zeile'])

    desciption = pd.read_csv(f'{base_dir}/../data/Planner_Zeilen_Bez.csv', sep=';', encoding='latin-1', dtype={0: str, 1: str})
    plan = pd.merge(plan, desciption, how='left', on=['Zeile'], suffixes=['', '_3'])
    plan['Zeile mit Bez'] = plan['Zeile mit Bez_3']
    plan.drop(['Zeile mit Bez_3'], axis=1, inplace=True)

    if amount_value == 'Stk':
        plan = plan[plan['Vstufe 1'] == 'Umsatzerlöse']
        plan['Vstufe 1'] = 'Verk. Stückzahlen'

    plan['Version'] = version
    plan['Konto'] = ''
    plan['Jahr'] = target_year

    plan.to_csv(f'{base_dir}/Planner_{target_year}_{version}_{amount_value}.csv', encoding='latin_1',
                sep=';', decimal=',', index=False)


if __name__ == '__main__':
    # export_plan('V3', '2021', 'Plan')
    # export_plan('V3', '2021', 'Stk')
    filename = '../save/2023_V1_20230214225753'
    export_plan(filename, 'V1', '2023', 'Plan')
    export_plan(filename, 'V1', '2023', 'Stk')