442 lines
12 KiB
Python
442 lines
12 KiB
Python
# coding: utf-8
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"""
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Provide a report and downloadable CSV according to the German DATEV format.
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- Query report showing only the columns that contain data, formatted nicely for
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dispay to the user.
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- CSV download functionality `download_datev_csv` that provides a CSV file with
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all required columns. Used to import the data into the DATEV Software.
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"""
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from __future__ import unicode_literals
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import datetime
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import json
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from six import string_types
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import frappe
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from frappe import _
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import pandas as pd
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def execute(filters=None):
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"""Entry point for frappe."""
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validate_filters(filters)
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result = get_gl_entries(filters, as_dict=0)
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columns = get_columns()
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return columns, result
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def validate_filters(filters):
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"""Make sure all mandatory filters are present."""
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if not filters.get('company'):
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frappe.throw(_('{0} is mandatory').format(_('Company')))
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if not filters.get('from_date'):
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frappe.throw(_('{0} is mandatory').format(_('From Date')))
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if not filters.get('to_date'):
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frappe.throw(_('{0} is mandatory').format(_('To Date')))
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def get_columns():
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"""Return the list of columns that will be shown in query report."""
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columns = [
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{
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"label": "Umsatz (ohne Soll/Haben-Kz)",
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"fieldname": "Umsatz (ohne Soll/Haben-Kz)",
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"fieldtype": "Currency",
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},
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{
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"label": "Soll/Haben-Kennzeichen",
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"fieldname": "Soll/Haben-Kennzeichen",
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"fieldtype": "Data",
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},
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{
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"label": "Kontonummer",
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"fieldname": "Kontonummer",
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"fieldtype": "Data",
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},
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{
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"label": "Gegenkonto (ohne BU-Schlüssel)",
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"fieldname": "Gegenkonto (ohne BU-Schlüssel)",
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"fieldtype": "Data",
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},
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{
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"label": "Belegdatum",
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"fieldname": "Belegdatum",
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"fieldtype": "Date",
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},
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{
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"label": "Buchungstext",
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"fieldname": "Buchungstext",
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"fieldtype": "Text",
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},
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{
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"label": "Beleginfo - Art 1",
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"fieldname": "Beleginfo - Art 1",
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"fieldtype": "Data",
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},
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{
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"label": "Beleginfo - Inhalt 1",
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"fieldname": "Beleginfo - Inhalt 1",
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"fieldtype": "Data",
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},
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{
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"label": "Beleginfo - Art 2",
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"fieldname": "Beleginfo - Art 2",
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"fieldtype": "Data",
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},
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{
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"label": "Beleginfo - Inhalt 2",
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"fieldname": "Beleginfo - Inhalt 2",
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"fieldtype": "Data",
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}
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]
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return columns
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def get_gl_entries(filters, as_dict):
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"""
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Get a list of accounting entries.
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Select GL Entries joined with Account and Party Account in order to get the
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account numbers. Returns a list of accounting entries.
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Arguments:
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filters -- dict of filters to be passed to the sql query
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as_dict -- return as list of dicts [0,1]
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"""
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gl_entries = frappe.db.sql("""
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select
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/* either debit or credit amount; always positive */
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case gl.debit when 0 then gl.credit else gl.debit end as 'Umsatz (ohne Soll/Haben-Kz)',
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/* 'H' when credit, 'S' when debit */
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case gl.debit when 0 then 'H' else 'S' end as 'Soll/Haben-Kennzeichen',
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/* account number or, if empty, party account number */
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coalesce(acc.account_number, acc_pa.account_number) as 'Kontonummer',
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/* against number or, if empty, party against number */
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coalesce(acc_against.account_number, acc_against_pa.account_number) as 'Gegenkonto (ohne BU-Schlüssel)',
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gl.posting_date as 'Belegdatum',
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gl.remarks as 'Buchungstext',
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gl.voucher_type as 'Beleginfo - Art 1',
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gl.voucher_no as 'Beleginfo - Inhalt 1',
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gl.against_voucher_type as 'Beleginfo - Art 2',
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gl.against_voucher as 'Beleginfo - Inhalt 2'
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from `tabGL Entry` gl
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/* Statistisches Konto (Debitoren/Kreditoren) */
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left join `tabParty Account` pa
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on gl.against = pa.parent
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and gl.company = pa.company
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/* Kontonummer */
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left join `tabAccount` acc
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on gl.account = acc.name
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/* Gegenkonto-Nummer */
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left join `tabAccount` acc_against
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on gl.against = acc_against.name
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/* Statistische Kontonummer */
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left join `tabAccount` acc_pa
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on pa.account = acc_pa.name
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/* Statistische Gegenkonto-Nummer */
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left join `tabAccount` acc_against_pa
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on pa.account = acc_against_pa.name
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where gl.company = %(company)s
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and DATE(gl.posting_date) >= %(from_date)s
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and DATE(gl.posting_date) <= %(to_date)s
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order by 'Belegdatum', gl.voucher_no""", filters, as_dict=as_dict)
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return gl_entries
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def get_datev_csv(data, filters):
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"""
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Fill in missing columns and return a CSV in DATEV Format.
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For automatic processing, DATEV requires the first line of the CSV file to
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hold meta data such as the length of account numbers oder the category of
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the data.
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Arguments:
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data -- array of dictionaries
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filters -- dict
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"""
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header = [
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# A = DATEV format
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# DTVF = created by DATEV software,
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# EXTF = created by other software
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"EXTF",
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# B = version of the DATEV format
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# 141 = 1.41,
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# 510 = 5.10,
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# 720 = 7.20
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"510",
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# C = Data category
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# 21 = Transaction batch (Buchungsstapel),
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# 67 = Buchungstextkonstanten,
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# 16 = Debitors/Creditors,
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# 20 = Account names (Kontenbeschriftungen)
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"21",
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# D = Format name
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# Buchungsstapel,
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# Buchungstextkonstanten,
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# Debitoren/Kreditoren,
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# Kontenbeschriftungen
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"Buchungsstapel",
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# E = Format version (regarding format name)
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"",
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# F = Generated on
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datetime.datetime.now().strftime("%Y%m%d"),
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# G = Imported on -- stays empty
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"",
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# H = Origin (SV = other (?), RE = KARE)
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"SV",
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# I = Exported by
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frappe.session.user,
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# J = Imported by -- stays empty
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"",
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# K = Tax consultant number (Beraternummer)
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frappe.get_value("DATEV Settings", filters.get("company"), "consultant_number") or "",
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"",
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# L = Tax client number (Mandantennummer)
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frappe.get_value("DATEV Settings", filters.get("company"), "client_number") or "",
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"",
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# M = Start of the fiscal year (Wirtschaftsjahresbeginn)
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frappe.utils.formatdate(frappe.defaults.get_user_default("year_start_date"), "yyyyMMdd"),
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# N = Length of account numbers (Sachkontenlänge)
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"4",
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# O = Transaction batch start date (YYYYMMDD)
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frappe.utils.formatdate(filters.get('from_date'), "yyyyMMdd"),
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# P = Transaction batch end date (YYYYMMDD)
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frappe.utils.formatdate(filters.get('to_date'), "yyyyMMdd"),
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# Q = Description (for example, "January - February 2019 Transactions")
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"{} - {} Buchungsstapel".format(
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frappe.utils.formatdate(filters.get('from_date'), "MMMM yyyy"),
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frappe.utils.formatdate(filters.get('to_date'), "MMMM yyyy")
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),
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# R = Diktatkürzel
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"",
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# S = Buchungstyp
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# 1 = Transaction batch (Buchungsstapel),
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# 2 = Annual financial statement (Jahresabschluss)
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"1",
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# T = Rechnungslegungszweck
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"",
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# U = Festschreibung
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"",
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# V = Kontoführungs-Währungskennzeichen des Geldkontos
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frappe.get_value("Company", filters.get("company"), "default_currency")
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]
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columns = [
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# All possible columns must tbe listed here, because DATEV requires them to
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# be present in the CSV.
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# ---
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# Umsatz
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"Umsatz (ohne Soll/Haben-Kz)",
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"Soll/Haben-Kennzeichen",
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"WKZ Umsatz",
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"Kurs",
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"Basis-Umsatz",
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"WKZ Basis-Umsatz",
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# Konto/Gegenkonto
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"Kontonummer",
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"Gegenkonto (ohne BU-Schlüssel)",
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"BU-Schlüssel",
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# Datum
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"Belegdatum",
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# Belegfelder
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"Belegfeld 1",
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"Belegfeld 2",
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# Weitere Felder
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"Skonto",
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"Buchungstext",
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# OPOS-Informationen
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"Postensperre",
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"Diverse Adressnummer",
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"Geschäftspartnerbank",
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"Sachverhalt",
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"Zinssperre",
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# Digitaler Beleg
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"Beleglink",
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# Beleginfo
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"Beleginfo - Art 1",
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"Beleginfo - Inhalt 1",
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"Beleginfo - Art 2",
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"Beleginfo - Inhalt 2",
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"Beleginfo - Art 3",
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"Beleginfo - Inhalt 3",
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"Beleginfo - Art 4",
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"Beleginfo - Inhalt 4",
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"Beleginfo - Art 5",
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"Beleginfo - Inhalt 5",
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"Beleginfo - Art 6",
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"Beleginfo - Inhalt 6",
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"Beleginfo - Art 7",
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"Beleginfo - Inhalt 7",
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"Beleginfo - Art 8",
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"Beleginfo - Inhalt 8",
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# Kostenrechnung
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"Kost 1 - Kostenstelle",
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"Kost 2 - Kostenstelle",
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"Kost-Menge",
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# Steuerrechnung
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"EU-Land u. UStID",
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"EU-Steuersatz",
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"Abw. Versteuerungsart",
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# L+L Sachverhalt
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"Sachverhalt L+L",
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"Funktionsergänzung L+L",
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# Funktion Steuerschlüssel 49
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"BU 49 Hauptfunktionstyp",
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"BU 49 Hauptfunktionsnummer",
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"BU 49 Funktionsergänzung",
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# Zusatzinformationen
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"Zusatzinformation - Art 1",
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"Zusatzinformation - Inhalt 1",
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"Zusatzinformation - Art 2",
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"Zusatzinformation - Inhalt 2",
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"Zusatzinformation - Art 3",
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"Zusatzinformation - Inhalt 3",
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"Zusatzinformation - Art 4",
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"Zusatzinformation - Inhalt 4",
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"Zusatzinformation - Art 5",
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"Zusatzinformation - Inhalt 5",
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"Zusatzinformation - Art 6",
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"Zusatzinformation - Inhalt 6",
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"Zusatzinformation - Art 7",
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"Zusatzinformation - Inhalt 7",
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"Zusatzinformation - Art 8",
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"Zusatzinformation - Inhalt 8",
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"Zusatzinformation - Art 9",
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"Zusatzinformation - Inhalt 9",
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"Zusatzinformation - Art 10",
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"Zusatzinformation - Inhalt 10",
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"Zusatzinformation - Art 11",
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"Zusatzinformation - Inhalt 11",
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"Zusatzinformation - Art 12",
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"Zusatzinformation - Inhalt 12",
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"Zusatzinformation - Art 13",
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"Zusatzinformation - Inhalt 13",
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"Zusatzinformation - Art 14",
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"Zusatzinformation - Inhalt 14",
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"Zusatzinformation - Art 15",
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"Zusatzinformation - Inhalt 15",
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"Zusatzinformation - Art 16",
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"Zusatzinformation - Inhalt 16",
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"Zusatzinformation - Art 17",
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"Zusatzinformation - Inhalt 17",
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"Zusatzinformation - Art 18",
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"Zusatzinformation - Inhalt 18",
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"Zusatzinformation - Art 19",
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"Zusatzinformation - Inhalt 19",
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"Zusatzinformation - Art 20",
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"Zusatzinformation - Inhalt 20",
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# Mengenfelder LuF
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"Stück",
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"Gewicht",
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# Forderungsart
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"Zahlweise",
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"Forderungsart",
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"Veranlagungsjahr",
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"Zugeordnete Fälligkeit",
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# Weitere Felder
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"Skontotyp",
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# Anzahlungen
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"Auftragsnummer",
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"Buchungstyp",
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"USt-Schlüssel (Anzahlungen)",
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"EU-Land (Anzahlungen)",
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"Sachverhalt L+L (Anzahlungen)",
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"EU-Steuersatz (Anzahlungen)",
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"Erlöskonto (Anzahlungen)",
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# Stapelinformationen
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"Herkunft-Kz",
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# Technische Identifikation
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"Buchungs GUID",
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# Kostenrechnung
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"Kost-Datum",
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# OPOS-Informationen
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"SEPA-Mandatsreferenz",
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"Skontosperre",
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# Gesellschafter und Sonderbilanzsachverhalt
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"Gesellschaftername",
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"Beteiligtennummer",
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"Identifikationsnummer",
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"Zeichnernummer",
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# OPOS-Informationen
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"Postensperre bis",
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# Gesellschafter und Sonderbilanzsachverhalt
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"Bezeichnung SoBil-Sachverhalt",
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"Kennzeichen SoBil-Buchung",
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# Stapelinformationen
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"Festschreibung",
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# Datum
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"Leistungsdatum",
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"Datum Zuord. Steuerperiode",
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# OPOS-Informationen
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"Fälligkeit",
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# Konto/Gegenkonto
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"Generalumkehr (GU)",
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# Steuersatz für Steuerschlüssel
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"Steuersatz",
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"Land"
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]
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empty_df = pd.DataFrame(columns=columns)
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data_df = pd.DataFrame.from_records(data)
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result = empty_df.append(data_df)
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result['Belegdatum'] = pd.to_datetime(result['Belegdatum'])
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header = ';'.join(header).encode('latin_1')
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data = result.to_csv(
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sep=b';',
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# European decimal seperator
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decimal=',',
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# Windows "ANSI" encoding
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encoding='latin_1',
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# format date as DDMM
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date_format='%d%m',
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# Windows line terminator
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line_terminator=b'\r\n',
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# Do not number rows
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index=False,
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# Use all columns defined above
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columns=columns
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)
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return header + b'\r\n' + data
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@frappe.whitelist()
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def download_datev_csv(filters=None):
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"""
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Provide accounting entries for download in DATEV format.
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Validate the filters, get the data, produce the CSV file and provide it for
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download. Can be called like this:
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GET /api/method/erpnext.regional.report.datev.datev.download_datev_csv
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Arguments / Params:
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filters -- dict of filters to be passed to the sql query
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"""
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if isinstance(filters, string_types):
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filters = json.loads(filters)
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validate_filters(filters)
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data = get_gl_entries(filters, as_dict=1)
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frappe.response['result'] = get_datev_csv(data, filters)
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frappe.response['doctype'] = 'EXTF_Buchungsstapel'
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frappe.response['type'] = 'csv'
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