Python-Projekte/gcstruct/gchr.py

import pandas as pd
import numpy as np
import xml.etree.ElementTree as ET
import csv
from xml.dom import minidom
from datetime import datetime
import logging


ACCOUNT_INFO = ['Account', 'Make', 'Site', 'Origin', 'SalesChannel', 'CostCarrier', 'CostAccountingString']


class GCHR:
    def __init__(self) -> None:
        self.base_dir = '/home/robert/projekte/python/gcstruct/Luchtenberg/'

        self.account_translation = self.base_dir + 'Kontenrahmen_uebersetzt.csv'
        self.account_bookings = self.base_dir + 'GuV_Bilanz_Salden.csv'
        self.first_month_of_financial_year = '01'

        pd.set_option('display.max_rows', 500)
        pd.set_option('display.float_format', lambda x: '%.2f' % x)

    def set_bookkeep_period(self, year, month):
        self.current_year = year
        self.current_month = month
        period = f'{year}-{month}'
        prot_file = self.base_dir + f'protokoll_{period}.log'
        logging.basicConfig(
            filename=prot_file,
            filemode='w',
            encoding='utf-8',
            level=logging.DEBUG
        )
        self.account_ignored = self.base_dir + f'ignoriert_{period}.csv'
        self.account_invalid = self.base_dir + f'ungueltig_{period}.csv'

        self.last_year = str(int(self.current_year) - 1)
        self.last_year2 = str(int(self.current_year) - 2)
        self.next_year = str(int(self.current_year) + 1)

    def header(self, makes, sites):
        return {
            'Country': 'DE',
            'MainBmCode': sites[0]['Marke_HBV'],
            'Month': self.current_month,
            'Year': self.current_year,
            'Currency': 'EUR',
            'NumberOfMakes': len(makes),
            'NumberOfSites': len(sites),
            'ExtractionDate': datetime.now().strftime('%d.%m.%Y'),
            'ExtractionTime': datetime.now().strftime('%H:%M:%S'),
            'BeginFiscalYear': self.first_month_of_financial_year
        }

    def bookkeep_filter(self):
        period = [self.current_year + str(i).zfill(2) for i in range(1, 13)]
        if self.first_month_of_financial_year != '01':
            if self.first_month_of_financial_year > self.current_month:
                period = [self.last_year + str(i).zfill(2) for i in range(1, 13)] + period
            else:
                period = period + [self.next_year + str(i).zfill(2) for i in range(1, 13)]
            fm = int(self.first_month_of_financial_year)
            period = period[fm - 1:fm + 12]
        rename_to = ['Period' + str(i).zfill(2) for i in range(1, 13)]
        return dict(zip(period, rename_to))

    def extract_acct_info(self, df: pd.DataFrame):
        acct_info = ['Marke', 'Standort', 'Konto_Nr', 'Kostenstelle', 'Absatzkanal', 'Kostenträger']
        df['Konto_Nr_SKR51'] = df.index
        df[acct_info] = df['Konto_Nr_SKR51'].str.split('-', 6, expand=True)
        return df

    def export_period(self, year, month):
        self.set_bookkeep_period(year, month)
        # Übersetzungstabelle laden
        df_translate = pd.read_csv(self.account_translation, decimal=',', sep=';', encoding='latin-1',
                                   converters={i: str for i in range(0, 200)})
        logging.info(df_translate.shape)
        df_translate['duplicated'] = df_translate.duplicated()
        logging.info(df_translate[df_translate['duplicated']])
        df_translate.drop(columns=['duplicated'], inplace=True)
        df_translate.drop_duplicates(inplace=True)
        df_translate.set_index('Konto_Nr_Händler')

        # Kontensalden laden
        df_bookings = pd.read_csv(self.account_bookings, decimal=',', sep=';', encoding='latin-1', converters={0: str, 1: str})
        # Kontensalden auf gegebenen Monat filtern
        filter_from = self.current_year + self.first_month_of_financial_year
        filter_prev = self.last_year + self.first_month_of_financial_year

        if self.first_month_of_financial_year > self.current_month:
            filter_from = self.last_year + self.first_month_of_financial_year
            filter_prev = self.last_year2 + self.first_month_of_financial_year
        filter_to = self.current_year + self.current_month

        df_opening_balance = df_bookings[(df_bookings['Bookkeep Period'] >= filter_prev) & (df_bookings['Bookkeep Period'] <= filter_from)]
        df_opening_balance = df_opening_balance.merge(df_translate, how='inner', on='Konto_Nr_Händler')
        df_opening_balance = df_opening_balance[(df_opening_balance['Konto_Nr'].str.match(r'^[013]'))]
        df_opening_balance['amount'] = (df_opening_balance['Debit Amount'] + df_opening_balance['Credit Amount']).round(2)
        df_opening_balance.drop(columns=['Debit Amount', 'Credit Amount', 'Debit Quantity', 'Credit Quantity'], inplace=True)
        df_opening_balance = df_opening_balance.groupby(['Marke', 'Standort']).sum()
        logging.info('Gewinn/Verlustvortrag')
        logging.info(df_opening_balance)
        logging.info(df_opening_balance.sum())

        df_bookings = df_bookings[(df_bookings['Bookkeep Period'] >= filter_from) & (df_bookings['Bookkeep Period'] <= filter_to)]
        df_bookings['amount'] = (df_bookings['Debit Amount'] + df_bookings['Credit Amount']).round(2)
        df_stats = df_bookings.copy()
        # df_stats = df_stats[df_stats['Konto_Nr_Händler'].str.match(r'-[24578]\d\d\d-')]
        df_stats['Konto_Nr_Händler'] = df_stats['Konto_Nr_Händler'].str.replace(r'-(\d\d\d+)-', r'-\1_STK-', regex=True)
        df_stats['amount'] = (df_bookings['Debit Quantity'] + df_bookings['Credit Quantity']).round(2)

        df_bookings = pd.concat([df_bookings, df_stats])
        df_bookings = df_bookings[df_bookings['amount'] != 0.00]

        bk_filter = self.bookkeep_filter()
        period_no = list(bk_filter.keys()).index(filter_to) + 1

        # Spalten konvertieren
        df_bookings['period'] = df_bookings['Bookkeep Period'].apply(lambda x: bk_filter[x])

        logging.info('df_bookings: ' + str(df_bookings.shape))
        # Join auf Übersetzung
        df_combined = df_bookings.merge(df_translate, how='inner', on='Konto_Nr_Händler')
        logging.info('df_combined: ' + str(df_combined.shape))

        makes = df_combined[['Marke', 'Marke_HBV']].drop_duplicates().sort_values(by=['Marke'])
        sites = df_combined[['Marke', 'Standort', 'Marke_HBV']].drop_duplicates().sort_values(by=['Marke', 'Standort'])

        # df_combined.to_csv(account_invalid, decimal=',', sep=';', encoding='latin-1', index=False)
        # Gruppieren
        # df_grouped = df_combined.groupby(['Konto_Nr_SKR51', 'period']).sum()
        df = df_combined.pivot_table(index=['Konto_Nr_SKR51'], columns=['period'], values='amount',
                                     aggfunc=np.sum, margins=True, margins_name='CumulatedYear')

        logging.info('df_pivot: ' + str(df.shape))
        df = self.extract_acct_info(df)
        # df = df_translate.reset_index(drop=True).drop(columns=['Kostenträger_Ebene']).drop_duplicates()
        logging.info(df.shape)
        logging.info(df.columns)
        logging.info(df.head())
        # df = df.merge(df_translate, how='inner', on='Konto_Nr_SKR51')
        logging.info('df: ' + str(df.shape))
        df['Bilanz'] = (df['Konto_Nr'].str.match(r'^[013]'))
        df['Kontoart'] = np.where(df['Bilanz'], '1', '2')
        df['Kontoart'] = np.where(df['Konto_Nr'].str.contains('_STK'), '3', df['Kontoart'])
        df['Kontoart'] = np.where(df['Konto_Nr'].str.match(r'^[9]'), '3', df['Kontoart'])
        df['Konto_1'] = (df['Konto_Nr'].str.slice(0, 1))

        df_debug = df.drop(columns=['Bilanz'])
        logging.info(df_debug.groupby(['Kontoart']).sum())
        logging.info(df_debug.groupby(['Kontoart', 'Konto_1']).sum())
        logging.info(df_debug.groupby(['Konto_Nr']).sum())
        df_debug.groupby(['Konto_Nr']).sum().to_csv(self.base_dir + 'debug.csv', decimal=',', sep=';', encoding='latin-1')

        # Bereinigung GW-Kostenträger
        df['GW_Verkauf_1'] = (df['Konto_Nr'].str.match(r'^[78]0')) & (df['Kostenstelle'].str.match(r'^[^1]\d'))
        df['Kostenstelle'] = np.where(df['GW_Verkauf_1'] == True, '11', df['Kostenstelle'])

        df['GW_Verkauf_2'] = (df['Konto_Nr'].str.match(r'^[78]1')) & (df['Kostenstelle'].str.match(r'^[^2]\d'))
        df['Kostenstelle'] = np.where(df['GW_Verkauf_2'] == True, '21', df['Kostenstelle'])

        df['GW_Verkauf_3'] = (df['Konto_Nr'].str.match(r'^[78]3')) & (df['Kostenstelle'].str.match(r'^[^3]\d'))
        df['Kostenstelle'] = np.where(df['GW_Verkauf_3'] == True, '31', df['Kostenstelle'])

        df['GW_Verkauf_4'] = (df['Konto_Nr'].str.match(r'^[78]4')) & (df['Kostenstelle'].str.match(r'^[^4]\d'))
        df['Kostenstelle'] = np.where(df['GW_Verkauf_4'] == True, '41', df['Kostenstelle'])

        df['GW_Verkauf_5'] = (df['Konto_Nr'].str.match(r'^[78]5')) & (df['Kostenstelle'].str.match(r'^[^5]\d'))
        df['Kostenstelle'] = np.where(df['GW_Verkauf_5'] == True, '51', df['Kostenstelle'])

        df['GW_Verkauf_50'] = (df['Konto_Nr'].str.match(r'^[78]')) & (df['Kostenstelle'].str.match(r'^2'))
        df['Kostenträger'] = np.where(df['GW_Verkauf_50'] == True, '52', df['Kostenträger'])
        df['Kostenträger'] = np.where((df['GW_Verkauf_50'] == True) & (df['Marke'] == '01'), '50', df['Kostenträger'])

        df['GW_Stk_50'] = (df['Konto_Nr'].str.match(r'^9130')) & (df['Kostenstelle'].str.match(r'^2'))
        df['Kostenträger'] = np.where(df['GW_Stk_50'] == True, '52', df['Kostenträger'])
        df['Kostenträger'] = np.where((df['GW_Stk_50'] == True) & (df['Marke'] == '01'), '50', df['Kostenträger'])

        df['Kostenträger'] = np.where(df['Bilanz'] == True, '00', df['Kostenträger'])

        df['Konto_5er'] = (df['Konto_Nr'].str.match('^5'))
        df['Absatzkanal'] = np.where(df['Konto_5er'] == True, '99', df['Absatzkanal'])

        df['Teile_30_60'] = (df['Konto_Nr'].str.match(r'^[578]')) & \
                            (df['Kostenstelle'].str.match(r'^[3]')) & \
                            (df['Kostenträger'].str.match(r'^[^6]'))
        df['Kostenträger'] = np.where(df['Teile_30_60'] == True, '60', df['Kostenträger'])

        df['Service_40_70'] = (df['Konto_Nr'].str.match(r'^[578]')) & \
                              (df['Kostenstelle'].str.match(r'^[4]')) & \
                              (df['Kostenträger'].str.match(r'^[^7]'))
        df['Kostenträger'] = np.where(df['Service_40_70'] == True, '70', df['Kostenträger'])

        from_label = ['Marke', 'Standort', 'Konto_Nr', 'Kostenstelle', 'Absatzkanal', 'Kostenträger']
        to_label = ['Make', 'Site', 'Account', 'Origin', 'SalesChannel', 'CostCarrier']
        df = df.rename(columns=dict(zip(from_label, to_label)))
        makes = makes.rename(columns=dict(zip(from_label, to_label))).to_dict(orient='records')
        sites = sites.rename(columns=dict(zip(from_label, to_label))).to_dict(orient='records')

        df['CostAccountingString'] = df['Make'] + df['Site'] + df['Origin'] + \
            df['SalesChannel'] + df['CostCarrier']
        df['IsNumeric'] = (df['CostAccountingString'].str.isdigit()) & (df['Account'].str.isdigit()) & (df['Account'].str.len() == 4)

        df_invalid = df[df['IsNumeric'] == False]
        df_invalid.to_csv(self.account_invalid + '.2.csv', decimal=',', sep=';', encoding='latin-1', index=False)
        export_csv = self.base_dir + 'export_' + self.current_year + '-' + self.current_month + '.csv'
        xmlfile = export_csv[:-4] + '.xml'

        df.to_csv(export_csv, decimal=',', sep=';', encoding='latin-1', index=False)
        df = df[df['IsNumeric'] != False].groupby(ACCOUNT_INFO, as_index=False).sum()
        # Infos ergänzen
        df['Decimals'] = 2
        df['OpeningBalance'] = 0.0
        logging.info(df.shape)
        export_file = self.export_xml(df.to_dict(orient='records'), bk_filter, period_no, makes, sites, xmlfile)

        # Join auf Übersetzung - nicht zugeordnet
        df_ignored = df_bookings.merge(df_translate, how='left', on='Konto_Nr_Händler')
        df_ignored = df_ignored[df_ignored['Konto_Nr_SKR51'].isna()]    # [['Konto_Nr_Händler', 'Bookkeep Period', 'amount', 'quantity']]
        if not df_ignored.empty:
            df_ignored = df_ignored.pivot_table(index=['Konto_Nr_Händler'], columns=['period'], values='amount',
                                                aggfunc=np.sum, margins=True, margins_name='CumulatedYear')
            df_ignored.to_csv(self.account_ignored, decimal=',', sep=';', encoding='latin-1')
        return export_file

    def export_xml(self, records, bk_filter, period_no, makes, sites, export_file):
        record_elements = ACCOUNT_INFO + ['Decimals', 'OpeningBalance'] + \
            list(bk_filter.values())[:period_no] + ['CumulatedYear']
        root = ET.Element('HbvData')
        h = ET.SubElement(root, 'Header')
        for k, v in self.header(makes, sites).items():
            ET.SubElement(h, k).text = str(v)

        make_list = ET.SubElement(root, 'MakeList')
        for m in makes:
            e = ET.SubElement(make_list, 'MakeListEntry')
            ET.SubElement(e, 'Make').text = m['Make']
            ET.SubElement(e, 'MakeCode').text = m['Marke_HBV']

        bm_code_list = ET.SubElement(root, 'BmCodeList')
        for s in sites:
            e = ET.SubElement(bm_code_list, 'BmCodeEntry')
            ET.SubElement(e, 'Make').text = s['Make']
            ET.SubElement(e, 'Site').text = s['Site']
            ET.SubElement(e, 'BmCode').text = s['Marke_HBV']

        record_list = ET.SubElement(root, 'RecordList')
        for row in records:
            record = ET.SubElement(record_list, 'Record')
            for e in record_elements:
                child = ET.SubElement(record, e)
                field = row.get(e, 0.0)
                if str(field) == 'nan':
                    field = '0'
                elif type(field) is float:
                    field = '{:.0f}'.format(field * 100)
                child.text = str(field)

        with open(export_file, 'w', encoding='utf-8') as fwh:
            fwh.write(minidom.parseString(ET.tostring(root)).toprettyxml(indent='  '))
        # with open(export_file, 'wb') as fwh:
            # fwh.write(ET.tostring(root))
        return export_file

    def convert_to_row(self, node):
        return [child.text for child in node]

    def convert_xml_to_csv(self, xmlfile, csvfile):
        record_list = ET.parse(xmlfile).getroot().find('RecordList')
        header = [child.tag for child in record_list.find('Record')]
        bookings = [self.convert_to_row(node) for node in record_list.findall('Record')]
        with open(csvfile, 'w') as fwh:
            cwh = csv.writer(fwh, delimiter=';')
            cwh.writerow(header)
            cwh.writerows(bookings)
        return True

    def convert_csv_to_xml(self, csvfile, xmlfile):
        makes = [{'Make': '01', 'Marke_HBV': '1844'}]
        sites = [{'Make': '01', 'Site': '01', 'Marke_HBV': '1844'}]
        with open(csvfile, 'r', encoding='latin-1') as frh:
            csv_reader = csv.DictReader(frh, delimiter=';')
            self.export_xml(csv_reader, self.bookkeep_filter(), 1, makes, sites, xmlfile)


if __name__ == '__main__':
    gchr = GCHR()
    export_file = gchr.export_period('2022', '01')
    # convert_xml_to_csv(export_file, export_file[:-4] + '.csv')
    # convert_xml_to_csv('DE2119_2105_150621_080944.xml', 'DE2119_2105_150621_080944.csv')
    # convert_csv_to_xml(base_dir + '../SKR51/maximum.csv', base_dir + '../maximum_2022-01.xml')