import pandas as pd
import numpy as np
import xml.etree.ElementTree as ET
import json
import csv
import re
import chevron
# from shutil import copyfile
from bs4 import BeautifulSoup
from functools import reduce
from pathlib import Path
def get_flat(node):
result = [{
'id': node['id'],
'text': node['text'],
'children': [x['id'] for x in node['children']],
'children2': [],
'parents': node['parents'],
'accounts': node['accounts'],
'costcenter': '',
'level': node['level'],
'drilldown': node['level'] < 2, # (node['level'] != 2 and len(node['accounts']) == 0),
'form': node['form'],
'accountlevel': False,
'absolute': True,
'seasonal': True,
'status': "0",
'values': [],
'values2': {}
}]
for child in node['children']:
result += get_flat(child)
return result
def get_parents_list(p_list):
id = ';'.join(p_list) + ';' * (10 - len(p_list))
if len(p_list) > 0:
return [id] + get_parents_list(p_list[:-1])
return [';' * 9]
def structure_from_tree(node):
result = []
result.append(node['id'])
for child in node['children']:
result.extend(structure_from_tree(child))
return result
def xml_from_tree(xml_node, tree_node):
for child in tree_node['children']:
element = ET.SubElement(xml_node, 'Ebene')
element.set("Name", child['text'])
xml_from_tree(element, child)
def split_it(text, index):
try:
return re.findall(r'([^;]+) - ([^;]*);;', text)[0][index]
except Exception:
return ''
def last_layer(text):
try:
return re.findall(r'([^;]+);;', text)[0]
except Exception:
return ''
def get_default_cols(i):
return ['Ebene' + str(i) for i in range(i * 10 + 1, (i + 1) * 10 + 1)]
def get_structure_exports(s):
result = {
'files': {},
'format': {
'KontoFormat': '{0} - {1}',
'HerstellerkontoFormat': '{{Herstellerkonto_Nr}}',
'HerstellerBezeichnungFormat': '{{Herstellerkonto_Bez}}',
'NeueHerstellerkontenAnlegen': False
}
}
export_files = ['ExportStk', 'ExportStrukturenStk', 'ExportAdjazenz', 'ExportUebersetzung',
'ExportUebersetzungStk', 'ExportHerstellerKontenrahmen']
export_format = ['KontoFormat', 'HerstellerkontoFormat', 'HerstellerBezeichnungFormat', 'NeueHerstellerkontenAnlegen']
for e in export_files:
if s.find(e) is not None and s.find(e).text is not None and s.find(e).text[-4:] == '.csv':
result['files'][e] = s.find(e).text
for e in export_format:
if s.find(e) is not None and s.find(e).text != '':
result['format'][e] = s.find(e).text
result['format']['NeueHerstellerkontenAnlegen'] = (result['format']['NeueHerstellerkontenAnlegen'] == 'true')
return result
class GCStruct():
config = {
'path': 'c:/projekte/python/gcstruct',
'path2': 'c:/projekte/python/gcstruct',
'file': 'c:/projekte/python/gcstruct/config/config.xml',
'output': 'gcstruct.json',
'default': [],
'special': {},
'special2': {
'Planner': ['Kostenstelle', 'Ebene1', 'Ebene2'],
'Test': ['Ebene1', 'Ebene2']
},
'columns': ['Konto_Nr', 'Konto_Bezeichnung', 'Konto_Art', 'Konto_KST', 'Konto_STK',
'Konto_1', 'Konto_2', 'Konto_3', 'Konto_4', 'Konto_5'],
'struct': {},
'export': {}
}
json_result = {'accounts': {}, 'tree': {}, 'flat': {}, 'struct_export': {}, 'skr51_vars': {}}
structure_ids = []
translate = {'Konto_Nr': 'SKR51', 'Kostenstelle': 'KST', 'Absatzkanal': 'ABS',
'Kostenträger': 'KTR', 'Marke': 'MAR', 'Standort': 'STA', 'Marke_HBV': 'MAR', 'Standort_HBV': 'BMC'}
def __init__(self, struct_dir, export_dir=None):
self.config['path'] = struct_dir
self.config['path2'] = struct_dir + '/export' if export_dir is None else export_dir
self.config['file'] = f"{self.config['path']}/config/gcstruct.xml"
if not Path(self.config['file']).exists():
self.config['file'] = f"{self.config['path']}/config/config.xml"
cfg = ET.parse(self.config['file'])
self.config['default'] = [s.find('Name').text for s in cfg.getroot().find('Strukturdefinitionen').findall('Struktur')]
self.config['export'] = dict([(s.find('Name').text, get_structure_exports(s)) for s in
cfg.getroot().find('Strukturdefinitionen').findall('Struktur')])
struct = dict([(x, get_default_cols(i)) for (i, x) in enumerate(self.config['default'])])
struct.update(self.config['special'])
self.config['struct'] = struct
# print(self.config['struct'])
def export_header(self, filetype):
return {
'ExportStk': [],
'ExportStrukturenStk': [],
'ExportAdjazenz': [],
'ExportUebersetzung': ['Konto_Nr_Hersteller', 'Konto_Nr_Split', 'Konto_Nr_Haendler', 'Info'],
'ExportUebersetzungStk': ['Konto_Nr_Hersteller', 'Konto_Nr_Split', 'Konto_Nr_Haendler', 'Info'],
'ExportHerstellerKontenrahmen': ['Konto_Nr', 'Konto_Bezeichnung', 'Case', 'Info']
}[filetype]
def accounts_from_csv(self, struct):
max_rows = (len(self.config['default']) + 1) * 10
with open(f"{self.config['path']}/Kontenrahmen/Kontenrahmen.csv", 'r', encoding='latin-1') as f:
csv_reader = csv.reader(f, delimiter=';')
imported_csv = [row[:max_rows] for row in csv_reader]
df = pd.DataFrame.from_records(np.array(imported_csv[1:], dtype='object'), columns=imported_csv[0]).fillna(value='')
df = df.rename(columns={'Kostenstelle': 'Konto_KST', 'STK': 'Konto_STK'})
for i, (s, cols) in enumerate(struct.items()):
df[s] = reduce(lambda x, y: x + ";" + df[y], cols, '')
df[s] = df[s].apply(lambda x: x[1:])
df['LetzteEbene' + str(i + 1)] = df[s].apply(lambda x: last_layer(x))
df['LetzteEbene' + str(i + 1) + '_Nr'] = df[s].apply(lambda x: split_it(x, 0))
df['LetzteEbene' + str(i + 1) + '_Bez'] = df[s].apply(lambda x: split_it(x, 1))
df['Herstellerkonto_Nr'] = df['LetzteEbene1_Nr']
df['Herstellerkonto_Bez'] = df['LetzteEbene1_Bez']
return df
def tree_from_xml(self, struct, df):
result = {}
for (s, cols) in struct.items():
try:
tree = ET.parse(f"{self.config['path']}/Xml/{s}.xml")
result[s] = self.get_tree_root(tree.getroot(), s)
except FileNotFoundError:
print('XML-Datei fehlt')
used_entries = [x.split(";")[1:] for x in set(df[s].to_numpy())]
print(used_entries)
root = ET.Element('Ebene')
root.set('Name', s)
result[s] = self.get_tree_root(root, s)
# self.json_result["tree"][s] = get_tree_from_accounts(cols, [])
return result
def get_structure_and_tree(self):
df = self.accounts_from_csv(self.config['struct'])
self.json_result['accounts'] = df.to_dict('records')
self.structure_ids = df.melt(id_vars=['Konto_Nr'], value_vars=self.config['struct'].keys(),
var_name='Struktur', value_name='id').groupby(by=['Struktur', 'id'])
self.json_result['tree'] = self.tree_from_xml(self.config['struct'], df)
for (s, cols) in self.config['struct'].items():
self.json_result['flat'][s] = get_flat(self.json_result['tree'][s])
for (s, entries) in self.json_result['flat'].items():
cols = self.config['struct'][s]
df_temp = pd.DataFrame([x['id'].split(';') for x in entries], columns=cols)
self.json_result['struct_export'][s] = df_temp.to_dict(orient='records')
# json.dump(self.json_result, open(f"{self.config['path2']}/{self.config['output']}", 'w'), indent=2)
return df
def get_accounts(self, structure, id):
res = self.structure_ids.groups.get((structure, id))
if res is None:
return []
return res.values
# return [x['Konto_Nr'] for x in self.json_result['accounts'] if x[structure] == id]
def export(self):
for s in self.config['export'].keys():
for (filetype, filename) in self.config['export'][s]['files'].items():
with open(self.config['path2'] + '/' + filename, 'w') as fwh:
fwh.write('Konto_Nr_Hersteller;Konto_Nr_Split;Konto_Nr_Haendler;Info\n')
# 'Hersteller'Konto_Nr;Konto_Bezeichnung;Case;Info'
for a in self.json_result['accounts']:
if a['Herstellerkonto_Nr'] != '':
account = chevron.render(self.config['export']['SKR51']['format']['HerstellerkontoFormat'], a)
fwh.write(account + ';' + account + ';' + a['Konto_Nr'] + ';' + '\n') # a['Herstellerkonto_Bez']
def get_tree(self, node, parents, structure):
result = []
for child in node:
p = get_parents_list(parents)
parents.append(child.attrib['Name'])
id = ';'.join(parents) + ';' * (10 - len(parents))
result.append({
'id': id,
'text': child.attrib['Name'],
'children': self.get_tree(child, parents, structure),
'parents': p,
'accounts': self.get_accounts(structure, id),
'level': len(parents),
'form': child.attrib.get('Split', '')
})
parents.pop()
return result
def get_tree_root(self, node, structure):
id = ';' * 9
return {
'id': id,
'text': node.attrib['Name'],
'children': self.get_tree(node, [], structure),
'parents': [],
'accounts': [],
'level': 0,
'form': ''
}
def post_structure_and_tree(self):
json_post = json.load(open(f"{self.config['path']}/{self.config['output']}", 'r'))
# Kontenrahmen.csv
ebenen = ['Ebene' + str(i) for i in range(1, len(self.config['default']) * 10 + 1)]
header = ';'.join(self.config['columns'] + ebenen)
cols = self.config['columns'] + self.config['default']
with open(self.config['path'] + '/Kontenrahmen/Kontenrahmen_out.csv', 'w', encoding='latin-1') as f:
f.write(header + '\n')
for row in json_post['Kontenrahmen']:
f.write(';'.join([row[e] for e in cols]) + '\n')
# print(header)
# xml und evtl. Struktur.csv
for i, s in enumerate(self.config['default']):
with open(f"{self.config['path']}/Strukturen/Kontenrahmen.csv/{s}_out.csv", 'w', encoding='latin-1') as f:
f.write(';'.join(['Ebene' + str(i * 10 + j) for j in range(1, 11)]) + '\n')
rows = structure_from_tree({'id': ";" * 9, 'children': json_post[s]})
f.write('\n'.join(rows))
# with open(self.config['path'] + "/Strukturen/Kontenrahmen.csv/" + structure + "_2.csv", "w", encoding="latin-1") as f:
root = ET.Element('Ebene')
root.set('Name', s)
xml_from_tree(root, {'id': ";" * 9, 'children': json_post[s]})
with open(f"{self.config['path']}/Xml/{s}_out.xml", 'w', encoding='utf-8') as f:
f.write(BeautifulSoup(ET.tostring(root), 'xml').prettify())
def skr51_translate(self, accounts_combined_files):
df = self.accounts_from_csv(self.config['struct'])
df_translate = {}
for i, (t_from, t_to) in enumerate(self.translate.items()):
last = 'LetzteEbene' + str(i + 1)
from_label = ['Konto_Nr', last, last + '_Nr', last + '_Bez', 'Ebene' + str(i * 10 + 1), 'Ebene' + str(i * 10 + 2)]
to_label = [t_to, t_to + '_Ebene', t_to + '_Nr', t_to + '_Bez', 'Ebene1', 'Ebene2']
df_translate[t_from] = df[df[last + '_Nr'] != ''][from_label].rename(columns=dict(zip(from_label, to_label)))
# print(df_translate[t_to].head())
df2 = []
for ac_file in accounts_combined_files:
df2.append(pd.read_csv(ac_file, decimal=',', sep=';', encoding='latin-1',
converters={i: str for i in range(0, 200)}))
df_source = pd.concat(df2)
for t_from, t_to in self.translate.items():
if t_to == 'SKR51':
df_source['SKR51'] = df_source['Konto_Nr']
elif t_from in ['Marke_HBV']:
df_source['Marke_HBV'] = df_source['Marke']
elif t_from in ['Standort_HBV']:
df_source['Standort_HBV'] = df_source['Standort'] + '_' + df_source['Marke']
df_source['BMC'] = 'BMC_' + df_source['Standort_HBV']
elif t_to == 'KTR':
df_source['KTR'] = np.where(df_source['Kostenträger_Quelle'] == 'TZ', 'KTR_TZ_' + df_source['Kostenträger'], 'KTR_00')
df_source['KTR'] = np.where(df_source['Kostenträger_Quelle'].isin(['NW', 'SC']), 'KTR_' + df_source['Kostenträger_Quelle'] +
'_' + df_source['Marke'] + '_' + df_source['Kostenträger'], df_source['KTR'])
else:
df_source[t_to] = t_to + '_' + df_source[t_from]
df_source = df_source.merge(df_translate[t_from], how='left', on=[t_to], suffixes=(None, '_' + t_to))
df_source[t_to + '_Nr'] = np.where(df_source[t_to + '_Nr'].isna(), df_source[t_from], df_source[t_to + '_Nr'])
df_source['Konto_Nr_SKR51'] = df_source['MAR_Nr'] + '-' + df_source['STA_Nr'] + '-' + df_source['SKR51_Nr'] + '-' + \
df_source['KST_Nr'] + '-' + df_source['ABS_Nr'] + '-' + df_source['KTR_Nr']
df_source['Konto_Nr_Händler'] = df_source['Marke'] + '-' + df_source['Standort'] + '-' + df_source['Konto_Nr'] + '-' + \
df_source['Kostenstelle'] + '-' + df_source['Absatzkanal'] + '-' + df_source['Kostenträger']
# df_source.to_csv(f"{self.config['path2']}/SKR51_Uebersetzung.csv", sep=';', encoding='latin-1', index=False)
df_source['MAR_Nr_MAR'] = np.where(df_source['MAR_Nr_MAR'].isna(), '0000', df_source['MAR_Nr_MAR'])
from_label = ['MAR_Nr', 'STA_Nr', 'SKR51_Nr', 'KST_Nr', 'ABS_Nr', 'KTR_Nr', 'KTR_Ebene', 'Konto_Nr_Händler',
'Konto_Nr_SKR51', 'MAR_Nr_MAR', 'BMC_Nr']
to_label = ['Marke', 'Standort', 'Konto_Nr', 'Kostenstelle', 'Absatzkanal', 'Kostenträger',
'Kostenträger_Ebene', 'Konto_Nr_Händler', 'Konto_Nr_SKR51', 'Marke_HBV', 'Standort_HBV']
df_combined = df_source[from_label].rename(columns=dict(zip(from_label, to_label)))
df_combined.to_csv(f"{self.config['path2']}/Kontenrahmen_kombiniert.csv", sep=';', encoding='latin-1', index=False)
def skr51_translate2(self, accounts_combined_file):
df = self.accounts_from_csv(self.config['struct'])
df_list = []
for i, s in enumerate(self.config['struct'].keys()):
from_label = ['Konto_Nr', 'Ebene' + str(i * 10 + 1), 'Ebene' + str(i * 10 + 2), 'Ebene' + str(i * 10 + 3)]
to_label = ['Konto_Nr', 'key', 'value', 'value2']
df_temp = df[from_label].rename(columns=dict(zip(from_label, to_label)))
df_temp['key'] = '{' + s + '/' + df_temp['key'] + '}'
df_list.append(df_temp[df_temp['value'] != ''])
df_translate = pd.concat(df_list)
# df_translate.to_csv(f"{self.config['path2']}/SKR51_Variablen.csv", sep=';', encoding='latin-1', index=False)
df_source = pd.read_csv(accounts_combined_file, decimal=',', sep=';', encoding='latin-1',
converters={i: str for i in range(0, 200)})
df_source['Konto_Nr'] = np.where(df_source['Konto_Nr'].str.contains(r'^[4578]'), df_source['Konto_Nr'] + '_' +
df_source['Kostenstelle'].str.slice(stop=1), df_source['Konto_Nr'])
df_source['Konto_Nr'] = np.where(df_source['Konto_Nr'].str.contains(r'^5\d+_4'), df_source['Konto_Nr'] +
df_source['Kostenstelle'].str.slice(start=1, stop=2), df_source['Konto_Nr'])
df_source = df_source.merge(df, how='left', on=['Konto_Nr'])
rows = df_source.shape[0]
df_source['value'] = ''
cols = get_default_cols(0)
for t_from, t_to in self.translate.items():
if t_from in ['Marke_HBV', 'Standort_HBV']:
continue
if t_from == 'Konto_Nr':
df_source[t_to] = df_source[t_from]
else:
df_source[t_to] = t_to + '_' + df_source[t_from]
for e in cols:
df_source = df_source.merge(df_translate, how='left', left_on=[t_to, e], right_on=['Konto_Nr', 'key'],
suffixes=(None, '_' + t_to + '_' + e))
df_source[e] = np.where(df_source['value_' + t_to + '_' + e].notna(), df_source['value_' + t_to + '_' + e], df_source[e])
if df_source.shape[0] > rows:
print(t_to + '_' + e + ': ' + str(df_source.shape[0]))
# df_source.to_csv(f"{self.config['path2']}/SKR51_Variablen2.csv", sep=';', encoding='latin-1', index=False)
# df_source[t_to + '_Nr'] = np.where(df_source[t_to + '_Nr'].isna(), df_source[t_from], df_source[t_to + '_Nr'])
for e in cols:
df_source[e] = np.where(df_source[e].str.startswith('{'), df_source[e].str.extract(r'\/(.*)}', expand=False) +
' falsch', df_source[e]) # df_source[e].str.extract(r'/(.*)}') +
df_source[e] = np.where(df_source[e] == '[KTR]', df_source['Kostenträger_Ebene'], df_source[e])
# df_all[df_all['Ebene1'] == ]
# print(df_source.head())
df_source['Konto_neu'] = df_source['Marke'] + '-' + df_source['Standort'] + '-' + df_source['Konto_Nr'] + '-' + \
df_source['Kostenstelle'] + '-' + df_source['Absatzkanal'] + '-' + df_source['Kostenträger'] + ' - ' + \
df_source['Konto_Bezeichnung']
df_source['Ebene1_empty'] = df_source['Ebene1'].isna() # , df_source['Ebene1'].map(lambda x: x == ''))
df_source['Konto_neu'] = np.where(df_source['Ebene1_empty'], 'keine Zuordnung', df_source['Konto_neu'])
df_source['Ebene1'] = np.where(df_source['Ebene1_empty'], 'keine Zuordnung', df_source['Ebene1'])
df_source['Konto_Gruppe'] = df_source['Konto_Nr'] + ' - ' + df_source['Konto_Bezeichnung']
df_source['Konto_Gruppe'] = np.where(df_source['Ebene1_empty'], 'keine Zuordnung', df_source['Konto_Gruppe'])
df_amount = df_source[df_source['Ebene1'] == 'Umsatzerlöse'].reset_index()
df_amount['Ebene1'] = 'verkaufte Stückzahlen'
df_amount['Konto_neu'] = 'STK ' + df_amount['Konto_neu']
df_amount['Konto_Nr_Händler'] = df_amount['Konto_Nr_Händler'] + '_STK'
df_source = pd.concat([df_source, df_amount])
df_source['GuV'] = (df_source['Ebene71'] == 'GuV')
df_source['Ebene81'] = np.where(df_source['GuV'], df_source['Ebene72'], 'Bilanz')
df_source['Ebene82'] = np.where(df_source['GuV'], df_source['Ebene73'], '')
df_source['Ebene83'] = np.where(df_source['GuV'], df_source['Ebene74'], '')
df_source['Ebene84'] = np.where(df_source['GuV'], df_source['Ebene75'], '')
df_source['Ebene85'] = np.where(df_source['GuV'], df_source['Ebene76'], '')
df_source['Ebene86'] = np.where(df_source['GuV'], df_source['Ebene77'], '')
df_source['Ebene87'] = np.where(df_source['GuV'], df_source['Ebene78'], '')
df_source['Ebene88'] = np.where(df_source['GuV'], df_source['Ebene79'], '')
df_source['Ebene89'] = np.where(df_source['GuV'], df_source['Ebene80'], '')
df_source['Ebene90'] = ''
df_source['Ebene71'] = np.where(df_source['GuV'], 'GuV', df_source['Ebene72'])
df_source['Ebene72'] = np.where(df_source['GuV'], '', df_source['Ebene73'])
df_source['Ebene73'] = np.where(df_source['GuV'], '', df_source['Ebene74'])
df_source['Ebene74'] = np.where(df_source['GuV'], '', df_source['Ebene75'])
df_source['Ebene75'] = np.where(df_source['GuV'], '', df_source['Ebene76'])
df_source['Ebene76'] = np.where(df_source['GuV'], '', df_source['Ebene77'])
df_source['Ebene77'] = np.where(df_source['GuV'], '', df_source['Ebene78'])
df_source['Ebene78'] = np.where(df_source['GuV'], '', df_source['Ebene79'])
df_source['Ebene79'] = np.where(df_source['GuV'], '', df_source['Ebene80'])
df_source['Ebene80'] = ''
df_source = df_source[['Konto_neu', 'Konto_Nr_Händler', 'Konto_Bezeichnung', 'Konto_Art', 'Konto_KST', 'Konto_STK',
'Konto_1', 'Konto_2', 'Konto_3', 'Konto_4', 'Konto_5'] +
get_default_cols(0) + get_default_cols(7) + get_default_cols(8) +
['Konto_Gruppe']]
# from_label = cols
# to_label = get_default_cols(9)
from_label = ['Konto_neu', 'Konto_Nr_Händler', 'Konto_Art']
to_label = ['Konto', 'Acct_Nr', 'GuV_Bilanz']
df_source = df_source.rename(columns=dict(zip(from_label, to_label)))
df_source['Susa'] = df_source['Konto_Gruppe'].str.slice(stop=1)
# 'Marke', 'Standort', 'Konto_Nr', 'Kostenstelle', 'Absatzkanal', 'Kostenträger',
df_source.to_csv(f"{self.config['path2']}/SKR51_Uebersetzung.csv", sep=';', encoding='latin-1', index=False)
def skr51_vars(self):
self.get_structure_and_tree()
cols = get_default_cols(0)
df_temp = pd.read_csv(f"{self.config['path']}/Export/Kostentraeger.csv", decimal=',', sep=';',
encoding='latin-1', converters={i: str for i in range(0, 200)})
df_temp['value'] = df_temp['Ebene33']
df_temp['key'] = '[KTR]'
df_temp = df_temp[df_temp['value'].str.contains(' - ')]
df_list = [df_temp[['key', 'value']]]
for (s, entries) in self.json_result['flat'].items():
df = pd.DataFrame([x['id'].split(';') for x in entries], columns=cols)
df['key'] = df[cols[0]].apply(lambda x: '{' + s + '/' + x + '}')
df['value'] = df[cols[1]]
df_list.append(df[['key', 'value']])
df = pd.concat(df_list)
df_vars = df[df['value'] != '']
# df_vars.to_csv(f"{self.config['path2']}/SKR51_Variablen2.csv", sep=';', encoding='latin-1', index=False)
df_main = pd.DataFrame([x['id'].split(';') for x in self.json_result['flat']['SKR51']], columns=cols)
df_main['value'] = ''
for c in cols:
df_main = df_main.merge(df_vars, how='left', left_on=c, right_on='key', suffixes=(None, '_' + c))
df_main[c] = np.where(df_main['value_' + c].isna(), df_main[c], df_main['value_' + c])
df_amount = df_main[df_main['Ebene1'] == 'Umsatzerlöse'].reset_index()
df_amount['Ebene1'] = 'verkaufte Stückzahlen'
df_main = pd.concat([df_main, df_amount])
# from_label = cols
to_label = cols # get_default_cols(9)
# df_main = df_main.rename(columns=dict(zip(from_label, to_label)))
df_main[to_label].to_csv(f"{self.config['path2']}/SKR51_Struktur.csv", sep=';', encoding='latin-1', index_label='Sortierung')
def gcstruct_uebersetzung():
# base_dir = 'P:/SKR51_GCStruct/'
base_dir = Path('.').absolute()
import_dir = base_dir
if base_dir.name == 'scripts':
if base_dir.parent.parent.name == 'Portal':
base_dir = base_dir.parent.parent.parent
import_dir = base_dir.joinpath('Portal/System/IQD/Belege/Kontenrahmen')
else:
base_dir = base_dir.parent.parent
import_dir = base_dir.joinpath('System/OPTIMA/Export')
elif not base_dir.joinpath('GCStruct_Aufbereitung').exists():
base_dir = Path('//192.168.2.21/verwaltung/Kunden/Luchtenberg/1 Umstellung SKR51/')
if not base_dir.exists():
base_dir = Path('//media/fileserver1/verwaltung/Kunden/Luchtenberg/1 Umstellung SKR51/')
import_dir = base_dir
struct = GCStruct(str(base_dir.joinpath('GCStruct_Aufbereitung')))
struct.skr51_translate(import_dir.glob('Kontenrahmen_kombiniert*.csv'))
# copyfile('c:/Projekte/Python/Gcstruct/Kontenrahmen_kombiniert.csv', base_dir + 'GCStruct_Modell/Export/Kontenrahmen_kombiniert.csv')
struct2 = GCStruct(str(base_dir.joinpath('GCStruct_Modell')))
struct2.skr51_translate2(str(base_dir.joinpath('GCStruct_Aufbereitung/Export/Kontenrahmen_kombiniert.csv')))
struct2.skr51_vars()
def dresen():
struct = GCStruct('c:/projekte/GCHRStruct_Hyundai_Export')
struct.get_structure_and_tree()
struct.export()
if __name__ == '__main__':
# struct = GCStruct('c:/projekte/gcstruct_dresen')
# struct = GCStruct('c:/projekte/python/gcstruct')
# struct = GCStruct('c:/projekte/python/gcstruct_reisacher_planung')
# struct = GCStruct('X:/Robert/Planung Reisacher/GCStruct_neue_Struktur_Planung')
# print(struct.config['struct'])
# struct.post_structure_and_tree()
gcstruct_uebersetzung()
# dresen()