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erp_data_generation.py
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# Generation of fake customer or supplier data (company name, address, employee data...)
# purpose example: populate ERP database for tests
# helpful literature:
# https://zetcode.com/python/faker/
# https://towardsdatascience.com/how-to-create-fake-data-with-faker-a835e5b7a9d9
# https://medium.com/district-data-labs/a-practical-guide-to-anonymizing-datasets-with-python-faker-ecf15114c9be
import csv
import random
import re
from decode_import_file import read_structure
from erp_basic_tools import CreateFile
from faker import Faker
faker = Faker('de_DE') # locale for local sounding Names, Companies, Addresses
class GeneratorBase(CreateFile):
def __init__(self, outputfile, outputfile_structure, count, **kwrest):
super().__init__(outputfile, outputfile_structure, **kwrest)
self.count = count
@staticmethod
def split_address(address):
street, postcode_town = address.splitlines()
postcode, town = postcode_town.split(' ', 1)
return [street, postcode, town]
class GenerateCompanyData(GeneratorBase):
def generate(self):
company_name_and_address = []
for nn in range(self.count):
company_name = faker.company()
company_address = faker.address()
address_parts = self.split_address(company_address)
status_customer = random.randint(0, 3)
status_supplier = random.randint(0, 1)
company_name_and_address.append([company_name, company_address])
self.output(company_name, address_parts, status_customer, status_supplier)
return company_name_and_address
def output(self, company_name, address_parts, status_customer, status_supplier):
if not self.testmode:
company_dict = {'company_name': company_name, 'address_parts\[0\]': address_parts[0],
'address_parts\[1\]': address_parts[1], 'address_parts\[2\]': address_parts[2],
'status_customer': str(status_customer), 'status_supplier': str(status_supplier)}
self.output_csv(company_dict)
else:
self.output_test(company_name, address_parts, status_customer, status_supplier)
@staticmethod
def output_test(company_name, address_parts, status_customer, status_supplier):
print(f'Firma: {company_name}')
print(f'Straße, Hausnummer: {address_parts[0]}')
print(f'PLZ: {address_parts[1]}')
print(f'Ort: {address_parts[2]}')
print('+++++++++++++++')
class GeneratePersonData(GeneratorBase):
def __init__(self, outputfile, outputfile_structure, count, **kwrest):
super().__init__(outputfile, outputfile_structure, count, **kwrest)
company = kwrest.get('company', '') #to enable person contacts belonging to a company
company_address = kwrest.get('company_address', '') #to enable person address equal to company address
self.company = company
self.company_address = company_address
def generate(self):
nampart = [] # contains patterns to remove prename like elments like Prof.Dr., Frau, Herr, ...
nampart.append(r"(.*\w+\.)+\s") # search for title at beginning, indentifier: "." (detects Prof.Dr. ...)
#nampart.append(r"(^[Prof|Dr]+\.)+\s") # search for title at beginning, indentifier: "." (detects Prof.Dr. ...)
nampart.append(r"Frau |Herr ") # search for "Frau" or "Herr" at beginning
for nn in range(self.count):
if self.company == '': # if company name is empty, a name is being created, but no other company data
company_name = faker.company()
else:
company_name = self.company
prename = [] # reset prename variable
person_name = faker.name()
# print(f'raw Person Name: {person_name}')
# remove prename elements
for pattern in nampart:
mat = re.match(pattern, person_name)
if mat:
prename.append(mat.group())
person_name = re.sub(pattern, "", person_name)
try: # it occurs that remaining name consists only of one word, so split function will raise ValueError
first_name, last_name = person_name.split(' ', 1) # split in first and last name. ToDo: handle double first name
except(ValueError):
print(f'Value Error - person name before split: "{person_name}", prename: "{prename}"')
last_name = person_name
first_name = ''
email = faker.ascii_company_email()
if self.company_address == '': # if company_address is empty, a company address is being created
person_address = faker.address()
else:
person_address = self.company_address
address_parts = self.split_address(person_address)
self.output(company_name, first_name, last_name, address_parts, email)
def output(self, company_name, first_name, last_name, address_parts, email):
if not self.testmode:
person_dict = {'company_name': company_name, 'address_parts\[0\]': address_parts[0],
'address_parts\[1\]': address_parts[1], 'address_parts\[2\]': address_parts[2],
'first_name': first_name, 'last_name': last_name, 'email': email}
self.output_csv(person_dict)
else:
self.output_test(company_name, first_name, last_name, address_parts, email)
@staticmethod
def output_test(company_name, first_name, last_name, address_parts, email):
# print(f'name: {person_name}')
# print(f'mat: {mat}')
# print(f'prename: {prename}')
print(f'nname: {last_name}')
print(f'vname: {first_name}')
print(f'email: {email}')
# print(f'address: {person_address}')
print(f'Straße, Hausnummer: {address_parts[0]}')
# print(f'PLZ Ort: {plz_stadt}')
print(f'PLZ: {address_parts[1]}')
print(f'Ort: {address_parts[2]}')
print(f'Firma: {company_name}')
print('------------------')
def generate_persondata_and_companydata(count_company, count_person, outputfile_company, outputfile_person,
data_structure, **kwrest):
test = kwrest.get('test', False) # in testmode, output is written to display instead of file
CG = GenerateCompanyData(outputfile_company, data_structure[0], count_company, test=test)
company_name_and_address = CG.generate()
for nn in range(count_company):
for mm in range(random.randint(1, count_person)):
PG = GeneratePersonData(outputfile_person, data_structure[1], 1, company=company_name_and_address[nn][0],
company_address=company_name_and_address[nn][1], test=test)
PG.generate()
if __name__ == '__main__':
GePartnerDatei_Template = '/home/hhhans/Lokal/Labor/Dolibarr/Datenimport/Beispiel_Import_Datei_societe_1.orig.V13.csv'
GeKontaktDatei_Template = '/home/hhhans/Lokal/Labor/Dolibarr/Datenimport/Beispiel_Import_Datei_societe_2.orig.V13.csv'
# GePartnerDatei='~/Lokal/Labor/Dolibarr/Datenimport/Beispiel_Import_Datei_societe_1.csv'
# GeKontaktDatei = '~/Lokal/Labor/Dolibarr/Datenimport/Beispiel_Import_Datei_societe_2.csv'
GePartnerDatei = 'Beispiel_Import_Datei_societe_1.csv'
GeKontaktDatei = 'Beispiel_Import_Datei_societe_2.csv'
mapping1 = {'s.nom': 'company_name', 's.client': 'status_customer', 's.fournisseur': 'status_supplier',
's.status': '1', 's.code_client': 'auto', 's.code_fournisseur': 'auto', 's.address': 'address_parts[0]',
's.zip': 'address_parts[1]', 's.town': 'address_parts[2]'}
mapping2 = {'s.fk_soc': 'company_name', 's.firstname': 'first_name', 's.lastname': 'last_name',
's.address': 'address_parts[0]', 's.zip': 'address_parts[1]', 's.town': 'address_parts[2]',
's.email': 'email'}
data_structure = []
data_structure.append(read_structure(GePartnerDatei_Template, mapping1))
# PG = GeneratePersonData(GeKontaktDatei, data_structure, 3)
# PG.generate()
data_structure.append(read_structure(GeKontaktDatei_Template, mapping2))
# CG = GenerateCompanyData(GePartnerDatei, data_structure[0], 3)
# CG.generate()
generate_persondata_and_companydata(50, 20, GePartnerDatei, GeKontaktDatei, data_structure, test=False)