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
from pathlib import Path
import os
from enum import Enum, auto


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


class GCHR:
    def __init__(self, base_dir) -> None:
        self.base_dir = base_dir

        self.account_translation = f"{self.base_dir}/data/Kontenrahmen_uebersetzt.csv"
        self.account_bookings = list(Path(self.base_dir).joinpath("data").glob("GuV_Bilanz_Salden*.csv"))
        self.first_month_of_financial_year = "09"

        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 = f"{self.export_info_dir}/protokoll_{period}.log"
        logging.basicConfig(
            filename=prot_file,
            filemode="w",
            encoding="utf-8",
            level=logging.DEBUG,
            force=True,
        )
        self.debug_file = f"{self.export_info_dir}/debug_{period}.csv"
        self.account_ignored = f"{self.export_info_dir}/ignoriert_{period}.csv"
        self.account_invalid = f"{self.export_info_dir}/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]["Standort_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]
        period = [self.current_year + "00"] + period
        rename_to = ["OpeningBalance"] + ["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(pat="-", n=6, expand=True)
        return df

    def export_all_periods(self):
        dt = datetime.now()
        prev = str(dt.year - 1)
        periods = [(prev, str(x).zfill(2)) for x in range(dt.month, 13)] + [
            (str(dt.year), str(x).zfill(2)) for x in range(1, dt.month)
        ]
        for year, month in periods:
            filename = self.export_filename_for_period(year, month)
            if not Path(filename).exists():
                os.makedirs(Path(filename).parent.joinpath("info"), exist_ok=True)
                self.export_period(year, month)

    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 = df_translate[
            [
                "Konto_Nr_Händler",
                "Konto_Nr_SKR51",
                "Marke",
                "Marke_HBV",
                "Standort",
                "Standort_HBV",
            ]
        ]

        row = (
            df_translate[["Marke", "Marke_HBV", "Standort", "Standort_HBV"]]
            .drop_duplicates()
            .sort_values(by=["Marke", "Standort"])
            .iloc[:1]
            .to_dict(orient="records")[0]
        )
        row["Konto_Nr_Händler"] = "01-01-0861-00-00-00"
        row["Konto_Nr_SKR51"] = "01-01-0861-00-00-00"

        df_translate = pd.concat([df_translate, pd.DataFrame.from_records([row])])
        # print(df_translate.tail())
        # df_translate.drop(columns=['duplicated'], inplace=True)
        df_translate.drop_duplicates(inplace=True)
        df_translate.set_index("Konto_Nr_Händler")

        # Kontensalden laden
        df2 = []
        for csv_file in self.account_bookings:
            df2.append(
                pd.read_csv(
                    csv_file,
                    decimal=",",
                    sep=";",
                    encoding="latin-1",
                    converters={0: str, 1: str},
                )
            )
        df_bookings = pd.concat(df2)
        # 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
        filter_opening = self.current_year + "00"
        filter_prev_opening = self.last_year + "00"
        prev_year_closed = True

        df_opening_balance = df_bookings[(df_bookings["Bookkeep Period"] == filter_opening)]
        if df_opening_balance.shape[0] == 0:
            df_opening_balance = df_bookings[
                (df_bookings["Bookkeep Period"] == filter_prev_opening)
                | ((df_bookings["Bookkeep Period"] >= filter_prev) & (df_bookings["Bookkeep Period"] < filter_from))
            ].copy()
            df_opening_balance["Bookkeep Period"] = filter_opening
            prev_year_closed = False
        # 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_Händler"].str.contains(r"-[013]\d\d+-"))]
        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()
        opening_balance = df_opening_balance["amount"].aggregate("sum").round(2)
        logging.info("Gewinn/Verlustvortrag")
        logging.info(opening_balance)

        if not prev_year_closed:
            row = {
                "Konto_Nr_Händler": "01-01-0861-00-00-00",
                "Bookkeep Period": filter_opening,
                "Debit Amount": opening_balance * -1,
                "Credit Amount": 0,
                "Debit Quantity": 0,
                "Credit Quantity": 0,
                "amount": opening_balance * -1,
            }
            df_opening_balance = pd.concat([df_opening_balance, pd.DataFrame.from_records([row])])

        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_opening_balance, df_bookings, df_stats])
        df_bookings = df_bookings[df_bookings["amount"] != 0.00]

        if df_bookings.shape[0] == 0:
            logging.error("ABBRUCH!!! Keine Daten vorhanden!")
            return False

        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(f"df_combined: {df_combined.shape}")

        # Hack für fehlende Markenzuordnung
        df_combined["Fremdmarke"] = df_combined["Marke_HBV"].str.match(r"^0000")
        df_combined["Marke"] = np.where(df_combined["Fremdmarke"], "99", df_combined["Marke"])

        df_combined["Standort_egal"] = df_combined["Standort_HBV"].str.match(r"^\d\d_")
        df_combined["Standort_HBV"] = np.where(
            df_combined["Fremdmarke"] | df_combined["Standort_egal"],
            "0000",
            df_combined["Standort_HBV"],
        )

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

        # Hack für fehlende Markenzuordnung
        df = df.merge(makes, how="left", on="Marke")
        df["Marke"] = np.where(df["Marke_HBV"].isna(), "99", df["Marke"])

        df_debug = df.drop(columns=["Bilanz"])
        logging.info(df_debug.groupby(["Kontoart"]).aggregate("sum"))
        logging.info(df_debug.groupby(["Kontoart", "Konto_1"]).aggregate("sum"))
        logging.info(df_debug.groupby(["Konto_Nr"]).aggregate("sum"))
        df_debug.groupby(["Konto_Nr"]).aggregate("sum").to_csv(
            self.debug_file, decimal=",", sep=";", encoding="latin-1"
        )

        # Bereinigung GW-Kostenträger
        df["NW_Verkauf_1"] = (df["Konto_Nr"].str.match(r"^[78]0")) & (df["Kostenstelle"].str.match(r"^[^1]\d"))
        df["Kostenstelle"] = np.where(df["NW_Verkauf_1"] == True, "11", df["Kostenstelle"])

        df["Konto_7010"] = df["Konto_Nr"].str.match(r"^[78]01[01]")
        df["Kostenstelle"] = np.where(df["Konto_7010"] == True, "14", 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_x420"] = df["Konto_Nr"].str.match(r"^[78]420")
        df["Kostenstelle"] = np.where(df["GW_Verkauf_x420"] == True, "42", 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["NW_Verkauf_00"] = (
            (df["Konto_Nr"].str.match(r"^[78]2"))
            & (df["Kostenstelle"].str.match(r"^1"))
            & (df["Kostenträger"].str.match(r"^[^01234]"))
        )
        df["Kostenträger"] = np.where(df["NW_Verkauf_00"] == True, "00", 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["Konto_Nr"].str.match("^9143"))
        df["Absatzkanal"] = np.where(df["Konto_5er"] == True, "99", df["Absatzkanal"])

        df["Konto_5005"] = (df["Konto_Nr"].str.match("^5005")) & (df["Kostenstelle"].str.match(r"^[^12]"))
        df["Kostenstelle"] = np.where(df["Konto_5005"] == True, "20", df["Kostenstelle"])
        df["Kostenträger"] = np.where(df["Konto_5005"] == True, "50", df["Kostenträger"])

        df["Konto_5007"] = (df["Konto_Nr"].str.match("^5007")) & (df["Kostenstelle"].str.match(r"^([^4]|42)"))
        df["Kostenstelle"] = np.where(df["Konto_5007"] == True, "41", df["Kostenstelle"])
        df["Kostenträger"] = np.where(df["Konto_5007"] == True, "70", df["Kostenträger"])

        df["Konto_914er"] = (df["Konto_Nr"].str.match("^914[34]")) & (df["Kostenträger"].str.match(r"^[^7]"))
        df["Kostenträger"] = np.where(df["Konto_914er"] == True, "70", df["Kostenträger"])

        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, decimal=",", sep=";", encoding="latin-1", index=False)
        export_csv = self.export_filename[:-4] + ".csv"

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

        # 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 self.export_filename

    @property
    def export_filename(self):
        return self.export_filename_for_period(self.current_year, self.current_month)

    @property
    def export_info_dir(self):
        return f"{self.base_dir}/Export/{self.current_year}/info/"

    def export_filename_for_period(self, year, month):
        return f"{self.base_dir}/Export/{year}/export_{year}-{month}.xml"

    def export_xml(self, records, bk_filter, period_no, makes, sites):
        record_elements = ACCOUNT_INFO + ["Decimals"] + 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["Standort_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(self.export_filename, "w", encoding="utf-8") as fwh:
            fwh.write(minidom.parseString(ET.tostring(root)).toprettyxml(indent="  "))

    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)


class Kunden(Enum):
    Altermann = auto()
    Barth_und_Frey = auto()
    Hannuschka = auto()
    Koenig_und_Partner = auto()
    Luchtenberg = auto()
    Russig_Neustadt_deop01 = auto()
    Russig_Neustadt_deop02 = auto()
    Siebrecht = auto()


def gchr_local(base_dir):
    for path in Path(base_dir).glob("*"):
        if path.is_dir():
            print(path.name)
            gchr_export(str(path))


def gchr_export(base_dir):
    gchr = GCHR(base_dir)
    gchr.export_all_periods()


if __name__ == "__main__":
    base_dir = os.getcwd() + "/gcstruct/Kunden"

    if Path(base_dir).exists():
        gchr_local(base_dir)
    else:
        gchr_export(os.getcwd())