def generate_accounts(amount):
known_accounts = []
for person in range(0, amount):
accounts = []
first_name, last_name = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
address_id = str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
birth_day = gen_data.create_birthday()
street_address = gen_data.create_street()
email_address = gen_data.create_email(name=(first_name, last_name))
print_instance(get_random_uuid_uri(), "nco:PersonContact")
print_property("nco:fullname", str.join(' ', [first_name, last_name]))
print_property("nco:nameGiven", first_name)
print_property("nco:nameFamily", last_name)
for j in range(0, random.randint(0, 4)):
account_data = get_random_in_list(ACCOUNTS)
user_account = str.join(
'', [account_data[2], str(j), email_address])
print_property("nco:hasIMAccount", user_account, t="uri")
accounts.append((user_account, account_data))
known_accounts.insert(0, user_account)
print_property("nco:birthDate", str(birth_day), final=True)
return known_accounts
def generate_accounts(amount):
known_accounts = []
for person in range(0, amount):
accounts = []
first_name, last_name = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
address_id = str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
birth_day = gen_data.create_birthday()
street_address = gen_data.create_street()
email_address = gen_data.create_email(name=(first_name, last_name))
print_instance(get_random_uuid_uri(), "nco:PersonContact")
print_property("nco:fullname", str.join(" ", [first_name, last_name]))
print_property("nco:nameGiven", first_name)
print_property("nco:nameFamily", last_name)
for j in range(0, random.randint(0, 4)):
account_data = get_random_in_list(ACCOUNTS)
user_account = str.join("", [account_data[2], str(j), email_address])
print_property("nco:hasIMAccount", user_account, t="uri")
accounts.append((user_account, account_data))
known_accounts.insert(0, user_account)
print_property("nco:birthDate", str(birth_day), final=True)
return known_accounts
def get_rows():
i=501
#line = input("Enter a row (python dict) into the table: ")
while i < 1000:
fake = Faker()
#Pick an account number and store it in acct
#if the account hasn't been already generated then generate a record with all fields
i=i+1
line = "{'rownum':"+str(i)+",'dunno':"+str(10)+",'CC':"+str(gen_data.cc_number())+",'Employer':"+str(gen_data.create_company_name())+\
",'Custemail':"+str(gen_data.create_email())+",'name':"+\
str(gen_data.create_name())+",'occupation':"+str(gen_data.create_job_title())+",'address_street':"+\
str(gen_data.create_city_state_zip())+",'DOB':"+str(gen_data.create_birthday(min_age=2, max_age=85))+\
",'previous_address_city_state_zip':"+str(gen_data.create_city_state_zip())+",'altcustomer_name':"+str(fake.name())+\
",'altcustomer_occupation':"+str(gen_data.create_job_title())+",'altcustomer_dob':"+str(gen_data.create_birthday(min_age=2, max_age=85))+\
",'ssn':"+str((randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1)))+",'phone':"+\
str((randrange(101,1000,1),randrange(101,999,1),randrange(1000,10000,1)))+ \
",'AccountID':"+str(randrange(100000,100000000,1))+",'PepFlag':"+str(max((randrange(0,101,1)-99,0)))+",'altcustomerssn':"+\
str((randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1)))+",'demarketed_customer_flag':"+\
str(max((randrange(0,101,1)-99),0))+\
",'SAR_flag':"+str(max((randrange(0,101,1)-99),0))+",'nolonger_a_customer':"+str(max((randrange(0,101,1)-99),0))+\
",'closed_account'"+str(max((randrange(0,101,1)-90),0))+",'High_risk_flag':"+str(max((randrange(0,101,1)-99),0))+\
",'Risk_rating':"+str(max((randrange(0,101,1)-99),0))+"}"
yield ast.literal_eval(line)
def generate_vCard():
gender_initial = gender_vcard_list[random.randint(0, 4)]
gender = None
if gender_initial == 'M':
gender = 'Male'
elif gender_initial == 'F':
gender = 'Female'
(first_name, last_name) = gen_data.create_name(gender=gender)
adr = gen_data.create_street()
zip, city, state = gen_data.create_city_state_zip()
properties = []
properties.append('FN:{} {}\r\n'.format(first_name, last_name))
if random.randint(0, 1):
properties.append('N:{};{};;;\r\n'.format(last_name, first_name))
if random.randint(0, 1):
properties.append('TEL:tel:{}\r\n'.format(gen_data.create_phone()))
if random.randint(0, 1):
properties.append('GENDER:{}\r\n'.format(gender_initial))
if random.randint(0, 1):
properties.append('EMAIL:{}\r\n'.format(
gen_data.create_email(name=(first_name, last_name)).lower()))
if random.randint(0, 1):
properties.append('IMPP:sip:{}@{}\r\n'.format(first_name.lower(),
'sip.linphone.org'))
if random.randint(0, 1):
properties.append('ADR:;;{};{};{};{};\r\n'.format(
adr, city, state, zip))
if random.randint(0, 1):
properties.append('NOTE:{}\r\n'.format(gen_data.create_sentence()))
if random.randint(0, 1):
properties.append('ORG:{}\r\n'.format(gen_data.create_company_name()))
if random.randint(0, 1):
properties.append('BDAY:{0:%Y%m%d}\r\n'.format(
gen_data.create_birthday()))
shuffle(properties)
vCard = 'BEGIN:VCARD\r\n'
vCard += 'VERSION:4.0\r\n'
for property in properties:
vCard += property
vCard += 'END:VCARD\r\n'
return vCard
def fake_user():
f = open('test_user.txt', 'w')
lat = 40.4365
lng = -99.3925
jobs = [gen_data.create_job_title() for i in xrange(200)]
for i in xrange(100):
lat += 0.001
lng -= 0.001
zipcode, city, state = gen_data.create_city_state_zip()
f.write(fake.first_name() + '|' + fake.last_name() + '|' +
gen_data.create_street() + '|' + city + '|' + state + '|' +
zipcode + '|' + fake.date() + '|' + str(sample(jobs, 1)[0]) +
'|' + str(i) + '|' + fake.email() + '|' +
fake.password(length=6,
special_chars=True,
digits=True,
upper_case=True,
lower_case=True) + '|' + str(lat) + '|' +
str(lng) + '\n')
f.close()
def generate_vCard():
gender_initial = gender_vcard_list[random.randint(0, 4)]
gender = None
if gender_initial == 'M':
gender = 'Male'
elif gender_initial == 'F':
gender = 'Female'
(first_name, last_name) = gen_data.create_name(gender=gender)
adr = gen_data.create_street()
zip, city, state = gen_data.create_city_state_zip()
properties = []
properties.append('FN:{} {}\r\n'.format(first_name, last_name))
if random.randint(0, 1):
properties.append('N:{};{};;;\r\n'.format(last_name, first_name))
if random.randint(0, 1):
properties.append('TEL:tel:{}\r\n'.format(gen_data.create_phone()))
if random.randint(0, 1):
properties.append('GENDER:{}\r\n'.format(gender_initial))
if random.randint(0, 1):
properties.append('EMAIL:{}\r\n'.format(gen_data.create_email(name=(first_name, last_name)).lower()))
if random.randint(0, 1):
properties.append('IMPP:sip:{}@{}\r\n'.format(first_name.lower(), 'sip.linphone.org'))
if random.randint(0, 1):
properties.append('ADR:;;{};{};{};{};\r\n'.format(adr, city, state, zip))
if random.randint(0, 1):
properties.append('NOTE:{}\r\n'.format(gen_data.create_sentence()))
if random.randint(0, 1):
properties.append('ORG:{}\r\n'.format(gen_data.create_company_name()))
if random.randint(0, 1):
properties.append('BDAY:{0:%Y%m%d}\r\n'.format(gen_data.create_birthday()))
shuffle(properties)
vCard = 'BEGIN:VCARD\r\n'
vCard += 'VERSION:4.0\r\n'
for property in properties:
vCard += property
vCard += 'END:VCARD\r\n'
return vCard
from random import random
from random import shuffle
from faker import Faker
from barnum import gen_data
import csv
fake = Faker()
with open('large.csv','w') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow(['rownum'] +['dunno'] + ['CC'] + ['Employer'] + ['Custemail'] + ['name'] \
+ ['occupation'] + ['address_street'] + ['DOB']+['previous address_city_state_zip']+ ['altcustomer_name'] \
+ ['altcustomer_occupation'] + ['altcustomer_dob'] + ['ssn'] + ['phone'] + \
['AccountID'] + ['PepFlag'] + ['altcustomerssn'] + ['demarketed_customer_flag'] + \
['SAR_flag'] + ['nolonger_a_customer'] + ['closed_account'] +['High_risk_flag'] +['Risk_rating'])
for i in range(50000000):
row = [i] + [10] + [gen_data.cc_number()]+[gen_data.create_company_name()] + \
[gen_data.create_email()]+[gen_data.create_name()] +[gen_data.create_job_title()] + \
[gen_data.create_city_state_zip()] + [gen_data.create_birthday(min_age=2, max_age=85)] + \
[gen_data.create_city_state_zip()] + [fake.name()] + [gen_data.create_job_title()] + \
[gen_data.create_birthday(min_age=2, max_age=85)] +\
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[(randrange(101,1000,1),randrange(101,999,1),randrange(1000,10000,1))] + \
[randrange(100000,100000000,1)] + \
[max((randrange(0,101,1)-99),0)] + \
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-90),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)]
writer.writerow(row)
from barnum import gen_data
import csv
with open('demographic.csv','w') as csvfile:
csvwriter =csv.writer(csvfile, delimiter=' ')
for i in range (0,100):
name=gen_data.create_name()
job_title=gen_data.create_job_title()
phone=gen_data.create_phone()
address=gen_data.create_city_state_zip()
csvwriter.writerow([name,job_title,phone,address])
csvfile.close()
f1,
delimiter=',',
lineterminator='\n',
)
writer.writerow(['rownum'] +['dunno'] + ['CC'] + ['Employer'] + ['Custemail'] + ['name'] \
+ ['occupation'] + ['address_street'] + ['DOB']+['previous address_city_state_zip']+ ['altcustomer_name'] \
+ ['altcustomer_occupation'] + ['altcustomer_dob'] + ['ssn'] + ['phone'] + \
['AccountID'] + ['PepFlag'] + ['altcustomerssn'] + ['demarketed_customer_flag'] + \
['SAR_flag'] + ['nolonger_a_customer'] + ['closed_account'] +['High_risk_flag'] +['Risk_rating'])
while i < 50000000:
#Pick an account number and store it in acct
acct = randrange(100000, 100000000, 1)
#if the account hasn't been already generated then generate a record with all fields
if d.has_key(str(acct)) == False:
row = [i] + [10] + [gen_data.cc_number()]+[gen_data.create_company_name()] + \
[gen_data.create_email()]+[gen_data.create_name()] +[gen_data.create_job_title()] + \
[gen_data.create_city_state_zip()] + [gen_data.create_birthday(min_age=2, max_age=85)] + \
[gen_data.create_city_state_zip()] + [fake.name()] + [gen_data.create_job_title()] + \
[gen_data.create_birthday(min_age=2, max_age=85)] +\
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[(randrange(101,1000,1),randrange(101,999,1),randrange(1000,10000,1))] + \
[acct] + \
[max((randrange(0,101,1)-99),0)] + \
[(randrange(101,1000,1),randrange(10,100,1),randrange(1000,10000,1))] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-90),0)] + \
[max((randrange(0,101,1)-99),0)] + [max((randrange(0,101,1)-99),0)]
d[str(acct)] = acct
i = i + 1
writer.writerow(row)
#date of transaction a day later
date2 = date1-timedelta(days=1)
row.extend([country,date1,date2,tranType,cr_dbt,limit,tmpAmt,Balance,CCs[j],
CCTypes[j],usecase,Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j]])
count = count + 1
checkin=''
checkout=''
transDetail=''
#Add details or Hotel Transactions
if((cat_desc=='Hotels/Motels/Inns/Resorts' or cat_desc=='Hotels, Motels, and Resorts') and (UseCase[j]=='28' or UseCase[j]=='29')):
if (maxCheckin == ''):
checkin=maxDate+timedelta(days=randrange(365,389,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
maxCheckin=checkin
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
else:
checkin=maxCheckin + timedelta(days=randrange(2,5,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
maxCheckin=checkin
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
if((cat_desc=='Hotels/Motels/Inns/Resorts' or cat_desc=='Hotels, Motels, and Resorts') and UseCase[j]=='30'):
checkin=maxDate+timedelta(days=randrange(30,200,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
def create_customers(n):
# List for client whose net worth is over $500K
HighNetWorth = ['Yes'] + ['No'] * 30
# List for type of account
Related_Type = ['Primary', 'Secondary', 'Joint']
# List for how the account was opened
Party_Type = ['Person', 'Non-Person']
# List for a BMO customer
Party_Relation = ['Customer', 'Non-Customer']
# List for random Yes/No Flag
Yes_No = ['Yes'] + ['No'] * 12
# List for random Yes/No Consent
Yes_No_Consent = ['Yes'] + ['No'] * 4
# List for equal Yes/No Flag
Yes_No_50 = ['Yes', 'No']
# List for official language
Official_Lang = ['English'] * 3 + ['French']
# List for method of communication
Preffered_Channel = ['Direct Mail', 'Telemarketing', 'Email', 'SMS']
# List for status of customer
# customer_status = ['Prospect','Inactive Customer','Past Customer'] + ['Active Customer'] * 56
# List for LOB Segment Type
Seg_Model_Type = ['LOB Specific', 'Profitability', 'Geographical', 'Behavioral', 'Risk Tolerance']
# List for Model ID
Model_ID = ['01', '02', '03', '04', '05']
# List for Model Name
Seg_Model_Name = ['IRRI', 'CRS Risk Score', 'Geo Risk', 'Financial Behavior Risk', 'CM Risk']
# List for Model Score
Seg_Model_Score = ['200', '300', '400', '100', '500']
# List for Model Group
Seg_Model_Group = ['Group 1'] * 2 + ['Group 2', 'Group 3', 'Group 4']
# List for Model Description
Seg_Model_Description = ['High Risk Tier', 'Mid Risk Tier', 'Low Risk Tier', 'Vertical Risk', 'Geographical Risk']
# List for random Arms Dealer flag
Arms_Manufacturer = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random auction flag
Auction = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random Cash Intensive flag
CashIntensive_Business = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random Casino?Gaming flag
Casino_Gambling = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random Client Onboarding flag
Channel_Onboarding = ['E-mail', 'In Person', 'In person - In Branch/Bank Office',
'In person - Offsite/Client Location', 'Mail', 'Online', 'Phone',
'Request for Proposal (RFP)'] + ['Not Applicable'] * 10
# List for random Transaction flag
Channel_Ongoing_Transactions = ['ATM', 'E-mail', 'Fax', 'Mail', 'Not Applicable', 'OTC Communication System',
'Phone'] + ['Online'] * 4 + ['In Person'] * 31
# List for random HI_Vehicle flag
Complex_HI_Vehicle = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random Metals flag
Dealer_Precious_Metal = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random Arms Dealer flag
Digital_PM_Operator = ['Yes'] + ['No'] * 2 + [''] * 392
# List for random Embassy flag
Embassy_Consulate = ['Yes'] + ['No'] * 2 + [''] * 392
# Sets variable to Embassy flag
Exchange_Currency = Embassy_Consulate
# Sets variable to Embassy flag
Foreign_Financial_Institution = Embassy_Consulate
# Sets variable to Embassy flag
Foreign_Government = Embassy_Consulate
# Sets variable to Embassy flag
Foreign_NonBank_Financial_Institution = Embassy_Consulate
# Sets variable to Embassy flag
Internet_Gambling = Embassy_Consulate
# Sets variable to Embassy flag
Medical_Marijuana_Dispensary = Embassy_Consulate
# Sets variable to Embassy flag
Money_Service_Business = Embassy_Consulate
# Sets variable to Embassy flag
NonRegulated_Financial_Institution = Embassy_Consulate
# Sets variable to Embassy flag
Not_Profit = Embassy_Consulate
# List for random occupation
Occupation = ['11-1011 Chief Executives', \
'11-3011 Administrative services Managers', \
'11-3031 Financial Managers', \
'11-3061 Purchasing Managers', \
'13-1011 Agents and Business Managers of Artists Performers and Athletes', \
'13-1031 Claims Adjusters Examiners, and Investigators', \
'13-1199 Business Operations Specialists, All Other', \
'13-2099 Financial Specialists All Other', \
'17-1011 Architects Except Landscape and Naval', \
'23-1011 Lawyers', \
'23-1023 Judges, Magistrate Judges and Magistrates', \
'25-2012 Kindergarten Teachers Except Special Education', \
'25-2021 Elementary School Teachers Except Special Education', \
'29-1041 Optometrists', \
'29-2054 Respiratory Therapy Technicians', \
'33-2011 Firefighters', \
'37-1012 First-Line Supervisors of Landscaping Lawn Service and Groundskeeping Workers', \
'39-1011 Gaming Supervisors', \
'39-2011 Animal Trainers', \
'41-1011 First-Line Supervisors of Retail Sales Workers', \
'41-1012 First-Line Supervisors of Non-Retail Sales Workers', \
'41-2011 Cashiers', \
'41-2031 Retail Salespersons', \
'43-3021 Billing and Posting Clerks', \
'45-1011 First-Line Supervisors of Farming, Fishing, and Forestry Workers', \
'49-2011 Computer Automated Teller and Office Machine Repairers', \
'53-3021 Bus Drivers Transit and Intercity', \
'53-4031 Railroad Conductors and Yardmasters', \
'55-1011 Air Crew Officers', \
'55-1012 Aircraft Launch and Recovery Officers', \
'55-1013 Armored Assault Vehicle Officers', \
]
# Sets variable to Embassy flag
Privately_ATM_Operator = Embassy_Consulate
# List for random products
Products = ['Certificate of Deposit', \
'Checking Account', \
'Credit Card', \
'Custodial and Investment Agency - Institutional', \
'Custodial and Investment Agency - Personal', \
'Custodial/Trust Outsourcing services (BTOS)', \
'Custody Accounts (PTIM)', \
'Custody Accounts (RSTC)', \
'DTF (BHFA)', \
'Investment Agency - Personal', \
'Investment Management Account (PTIM)', \
'Lease', \
'Loan / Letter of Credit', \
'Money Market', \
'Mortgage / Bond / Debentures', \
'None', \
'Savings Account', \
'Trust Administration - Irrevocable and Revocable (PTIM)', \
'Trust Administration - Irrevocable and Revocable Trusts (BDTC)', \
] + ['Nondeposit Investment products'] * 14 + ['Investment Agency - Institutional'] * 5
# Sets variable to Embassy flag
Sales_Used_Vehicles = Embassy_Consulate
# Dictionary for random services
Services = ['Benefit Payment services', \
'Domestic Wires and Direct Deposit / ACH', \
'Family Office services (FOS)', \
'Fiduciary services', \
'International Wires and IAT', \
'Investment Advisory services (IAS)', \
'Investment services', \
'None', \
'Online / Mobile Banking', \
'Payroll', \
'Short Term Cash Management', \
'Trust services', \
'Trustee services', \
'Vault Cash services', \
] + ['Financial Planning'] * 6 + ['Retirement Plans'] * 19
# Dictionary for random sic_code
SIC_Code = ['6021 National Commercial Banks', \
'6211 Security Brokers Dealers and Flotation Companies', \
'6282 Investment Advice', \
'6311 Life Insurance', \
'6733 Trusts Except Educational Religious and Charitable', \
'8999 services NEC', \
] + ['6722 Management Investment Offices Open-End'] * 12
# Dictionary for random Market Listing
Stock_Market_Listing = ['Australian Stock Exchange', \
'Brussels Stock Exchange', \
'Montreal Stock Exchange', \
'Tiers 1 and 2 of the TSX Venture Exchange (also known as Tiers 1 and 2 of the Canadian Venture Exchange)', \
'Toronto Stock Exchange', \
] + ['Not Found'] * 30
# Sets variable to Embassy flag
Third_Party_Payment_Processor = Embassy_Consulate
# Sets variable to Embassy flag
Transacting_Provider = Embassy_Consulate
# Dictionary for random Low Net Worth
LowNet = [1, 2] + [0] * 5
# Dictionary for Consumer vs Business
Acct_Type = ['B'] + ['C'] * 5
# Dictionary for random number of credits cards per account
Number_CC = [1] * 7 + [2] * 11 + [3] * 3 + [4]
# Dictionary for Account list set to blank
acct_list = []
# Dictionary for CreditCard list set to blank
CC_list = []
# Dictionary for random Wolfsberg scenario
Use_Case = [1, 4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 39] * 4 + [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35,
38] * 7 + [3, 6, 9, 12, 15, 18, 21, 24, 27, 30,
33, 36] * 65 + [37] * 73 + [40,
41] * 2
refrating = ['1', '1', '1', '2', '3', '4', '2', '4', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5', '5']
fake = Faker()
global liSSNMaster
start = 10786147
acct_list = []
risk_flags = {'PEP': 'No', 'SAR': 'No', 'Clsd': 'No', 'high_risk': 'No', 'hr_rating': 'No', 'demarket': ''}
liCSV = []
for i in xrange(n):
# Initiate High Risk Flags
# Politically Exposed Person
PEP = 'No'
# Customer with a Suspicous Activity Report
SAR = 'No'
# Customer with a closed account
Clsd = 'No'
# High risk customer flag
high_risk = 'No'
# High Risk Rating
hr_rating = ''
# Customer that was demarketed by the bank
demarket = 'No'
dem_date = ''
# generate closed acct flag
if (max((randrange(0, 98, 1) - 96), 0) == 1):
Clsd = 'Yes'
# Random choice for number of credit card users per account number
No_CCs = random.choice(Number_CC)
# Generate account number
acct = start + 1 + randrange(1, 10, 1)
start = acct
# Randomly generate customer name + middle name in tmp
name = fake.name()
tmp = gen_data.create_name()
# Adds account number to account dictionary
acct_list.extend([acct])
# Creates a new row and adds data elements
row = [i] + [acct] + [random.choice(Acct_Type)] + [No_CCs] + [name] + [tmp[0]] + [liSSNMaster[i]]
# Dictionary for names list set to blank
names = []
# Dictionary for Social Security Number list set to blank
ssn = []
# Middle Name to reduce name dups
mdl = []
for j in range(No_CCs - 1):
names.insert(j, fake.name())
tmp2 = gen_data.create_name()
mdl.insert(j, tmp2[0])
##Pull from SSN Master list
randInt = randrange(1, len(liSSNMaster), 1)
if randInt != i:
ssn.insert(j, liSSNMaster[randInt])
else:
ssn.insert(j, liSSNMaster[randInt - 1])
# Name and SSN is set to blank if less than 4 customers on an account
for k in range(4 - No_CCs):
names.insert(No_CCs + k, '')
ssn.insert(No_CCs + k, '')
mdl.insert(No_CCs, '')
# Sets CC_NO to a random credit card number
CC_NO = gen_data.create_cc_number()
# Extract CC_Number from the tuple returned by CC_Number then scramble to ensure uniqueness...Tuple contains CC Number and Type
CC_TRANS = CC_NO[1][0]
dt = str(datetime.now())
clean = re.sub('\W', '', dt)
printCC = str(CC_TRANS[-4:]) + str(clean[-12:-3]) + str(randrange(1111, 9999, randrange(1, 10, 1)))
# Add data elements to current csv row
row.extend([names[0], mdl[0], ssn[0], names[1], mdl[1], ssn[1], names[2], mdl[2], ssn[2], printCC, CC_NO[0],
gen_data.create_company_name() + ' ' + tmp[1], gen_data.create_email(),
gen_data.create_job_title()])
# Create Current Address
zip = random.choice(zips.zip)
addr = gen_data.create_city_state_zip([zip])
# Create Previous address
zip2 = random.choice(zips.zip)
addr2 = gen_data.create_city_state_zip([zip2])
# Add additional data elements to current csv row
lrg_cash_ex = random.choice(Yes_No)
# Condition for SARs and Demarketed Clients
if (Clsd == 'Yes'):
# 1% of closed accounts are demarketed but never had a SAR filed
if (max((randrange(0, 101, 1) - 99), 0) == 1 and SAR == 'No'):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if (max((randrange(0, 11, 1) - 9), 0) == 1 and demarket == 'No'):
# 10% of closed accounts have SARs
SAR = 'Yes'
# 90% of closed accounts with SARs are demarketed
if (max((randrange(0, 11, 1) - 9), 0) == 0):
demarket = 'Yes'
dem_date = gen_data.create_date(past=True)
if max((randrange(0, 101, 1) - 99), 0) == 1:
PEP = 'Yes'
row.extend([addr[0], addr[1], zip, 'US', addr2[0], addr2[1], zip2, 'US',
gen_data.create_birthday(min_age=2, max_age=85), PEP, SAR, Clsd])
# Start Generating related accounts from account list once 10,000 accounts are generated - to avoid duplicating accounts in the beginning
if i > 10000:
rel = int(random.choice(acct_list)) * max((randrange(0, 10001, 1) - 9999), 0)
if rel <> 0:
row.append(rel)
row.append(random.choice(Related_Type))
else:
row.append('')
row.append('')
else:
row.append('')
row.append('')
# Randomly generates account start date
party_start = gen_data.create_date(past=True)
# Randomly selects consent option for sharing info
Consent_Share = random.choice(Yes_No_Consent)
# Add additional data elements to current csv row
row.extend(
[random.choice(Party_Type), random.choice(Party_Relation), party_start, gen_data.create_date(past=True), \
lrg_cash_ex, demarket, dem_date, randrange(0, 100, 1), random.choice(Official_Lang)])
# Add data element preferred methond of contact for yes to share info...if not then blank to current row
if Consent_Share == 'Yes':
row.extend(['Yes', random.choice(Preffered_Channel)])
else:
row.extend(['No', ''])
row.extend([zip, randrange(0, 5, 1)])
# Generate Segment ID then add additional Segment data based on the selection to the current csv row
Segment_ID = randrange(0, 5, 1) % 5
if Segment_ID == 0:
row.extend([Model_ID[0], Seg_Model_Type[0], Seg_Model_Name[0], Seg_Model_Group[0], Seg_Model_Description[0],
Seg_Model_Score[0]])
if Segment_ID == 1:
row.extend([Model_ID[1], Seg_Model_Type[1], Seg_Model_Name[1], Seg_Model_Group[1], Seg_Model_Description[1],
Seg_Model_Score[1]])
if Segment_ID == 2:
row.extend([Model_ID[2], Seg_Model_Type[2], Seg_Model_Name[2], Seg_Model_Group[2], Seg_Model_Description[2],
Seg_Model_Score[2]])
if Segment_ID == 3:
row.extend([Model_ID[3], Seg_Model_Type[3], Seg_Model_Name[3], Seg_Model_Group[3], Seg_Model_Description[3],
Seg_Model_Score[3]])
if Segment_ID == 4:
row.extend([Model_ID[4], Seg_Model_Type[4], Seg_Model_Name[4], Seg_Model_Group[4], Seg_Model_Description[4],
Seg_Model_Score[4]])
# Add additional data elements to current csv row
hr0 = random.choice(Arms_Manufacturer)
hr01 = random.choice(Auction)
hr02 = random.choice(CashIntensive_Business)
hr03 = random.choice(Casino_Gambling)
hr04 = random.choice(Channel_Onboarding)
hr05 = random.choice(Channel_Ongoing_Transactions)
row.extend([hr0, hr01, hr02, hr03, hr04, hr05])
# Randomly select whether customer has a High Net Worth
HighNetWorthFlag = random.choice(HighNetWorth)
# Randomly Generate customer net worth based on the above flag
if HighNetWorthFlag == 'Yes':
row.append(max(max((randrange(0, 101, 1) - 99), 0) * randrange(1000000, 25000000, 1),
randrange(1000000, 5000000, 1)))
else:
flag = random.choice(LowNet)
if flag == 0:
row.append(randrange(-250000, 600000, 1))
else:
if flag == 1:
row.append(randrange(149000, 151000, 1))
else:
row.append(randrange(40000, 50000, 1))
# Add data elements to current csv row
hr1 = random.choice(Complex_HI_Vehicle)
hr2 = random.choice(Dealer_Precious_Metal)
hr3 = random.choice(Digital_PM_Operator)
hr4 = random.choice(Embassy_Consulate)
hr5 = random.choice(Exchange_Currency)
hr6 = random.choice(Foreign_Financial_Institution)
hr7 = random.choice(Foreign_Government)
hr8 = random.choice(Foreign_NonBank_Financial_Institution)
hr9 = random.choice(Internet_Gambling)
hr10 = random.choice(Medical_Marijuana_Dispensary)
hr11 = random.choice(Money_Service_Business)
hr12 = random.choice(NAICS.NAICS_Code)
hr13 = random.choice(NonRegulated_Financial_Institution)
hr14 = random.choice(Not_Profit)
# hr15=random.choice(occupation) - added before through gen_data
hr16 = random.choice(Privately_ATM_Operator)
hr17 = random.choice(Products)
hr18 = random.choice(Sales_Used_Vehicles)
hr19 = random.choice(Services)
hr20 = random.choice(SIC_Code)
hr21 = random.choice(Stock_Market_Listing)
hr22 = random.choice(Third_Party_Payment_Processor)
hr23 = random.choice(Transacting_Provider)
if (PEP == 'Yes' or SAR == 'Yes' or lrg_cash_ex == 'Yes' or demarket == 'Yes' or hr0 == 'Yes'
or hr01 == 'Yes' or hr02 == 'Yes' or hr03 == 'Yes' or hr1 == 'Yes' or hr2 == 'Yes' or hr3 == 'Yes' or hr4 == 'Yes' or
hr5 == 'Yes' or hr6 == 'Yes' or hr7 == 'Yes' or hr8 == 'Yes' or hr9 == 'Yes' or hr10 == 'Yes' or hr11 == 'Yes' or hr13 == 'Yes' or hr14 == 'Yes' or
hr16 == 'Yes' or hr17 == 'Yes' or hr18 == 'Yes' or hr22 == 'Yes' or hr23 == 'Yes' or HighNetWorthFlag == 'Yes'):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (SAR == 'No' and high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (PEP == 'No' and high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
if (high_risk == 'No'):
if (max((randrange(0, 101, 1) - 99), 0) == 1):
high_risk = 'Yes'
hr_rating = random.choice(refrating)
row.extend(
[hr1, hr2, hr3, hr4, hr5, hr6, hr7, hr8, hr9, hr10, hr11, hr12, hr13, hr14, hr16, hr17, hr18, hr19, hr20,
hr21, hr22, hr23,
HighNetWorthFlag, high_risk, hr_rating, random.choice(Use_Case)])
liCSV.append(row)
return liCSV
+ ["Risk_rating"]
)
while i < 50000000:
# Pick an account number and store it in acct
acct = randrange(100000, 100000000, 1)
# if the account hasn't been already generated then generate a record with all fields
if d.has_key(str(acct)) == False:
row = (
[i]
+ [10]
+ [gen_data.cc_number()]
+ [gen_data.create_company_name()]
+ [gen_data.create_email()]
+ [gen_data.create_name()]
+ [gen_data.create_job_title()]
+ [gen_data.create_city_state_zip()]
+ [gen_data.create_birthday(min_age=2, max_age=85)]
+ [gen_data.create_city_state_zip()]
+ [fake.name()]
+ [gen_data.create_job_title()]
+ [gen_data.create_birthday(min_age=2, max_age=85)]
+ [(randrange(101, 1000, 1), randrange(10, 100, 1), randrange(1000, 10000, 1))]
+ [(randrange(101, 1000, 1), randrange(101, 999, 1), randrange(1000, 10000, 1))]
+ [acct]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [(randrange(101, 1000, 1), randrange(10, 100, 1), randrange(1000, 10000, 1))]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [max((randrange(0, 101, 1) - 99), 0)]
+ [max((randrange(0, 101, 1) - 90), 0)]
+ [max((randrange(0, 101, 1) - 99), 0)]
def gen_tran(MCC_credits,MCC_debits,Tran_Country_Credits,Tran_Country_Debits,Tran_Type_C,Tran_Type_D,Upper_Limit,Delta,count,j,usecase):
liTrans = []
#Initiate start date for transactions
startDate=date(2015,01,01)
#Pick out account based on counter
acct=ACCTs[j]
#Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(max((randrange(1,101,1)-99),0)* randrange(25000,50000,1000),randrange(1000,25000,1000))
#local Amt variable to calculate customer total usage
usedAmt = 0
tmpAmt = 0
Balance = limit
maxDate= startDate
#Random number generator for transactions per customer
NoTrans = randrange(100,150,1)
desc=''
flag=0
maxCheckin=''
maxBook=''
#loop to generate NoTrans transactions per customer
for k in range(NoTrans):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cr_dbt='D'
tranType = ''
country=[]
cat_desc=''
flag=0
#If Balance is within the credit limit, generate credits/debits
if(Balance>0 and Balance<=limit*1.2):
#Probability of credits (tmpAmt>0) and debits (tmpAmt==0) is driven by parameters Upper_Limit and Delta
tmpAmt = max((randrange(1,Upper_Limit,1)+Delta),0)*randrange(1,Balance+1,1)
flag=1
#Define time delta for next transaction
tdelta = timedelta(days=randrange(1,4,1))
row = [str(count)+'_'+dt] + [acct]
#If we have credit or debit within balance
if tmpAmt == 0 and flag==1:
tmpAmt=random.randrange(1,Balance+1,1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch=gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Debits)
else:
if tmpAmt > 0 and flag==1:
cr_dbt='C'
tranType=random.choice(Tran_Type_C)
Balance = Balance + tmpAmt
merch=''
cat = random.choice(MCC_credits)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
if(tranType=='Merchant Credit'):
merch=gen_data.create_company_name()
cat=random.choice(Merchant_Category.Green)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
if(tranType=='Refund'):
cat='0000'
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Credits)
#If we need to make a payment or get credit then assign codes
if Balance > limit and flag==0:
tmpAmt=random.randrange(1,Balance-limit+1,1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch=gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Debits)
else:
if ((Balance < 0 or Balance==0)and flag==0):
cr_dbt='C'
tranType='Payment'
tmpAmt = random.randrange(1,limit/2,1)
Balance = Balance + tmpAmt
merch = ''
cat = '1111'
cat_desc=python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country=random.choice(Tran_Country_Credits)
#date posted
date1 = maxDate+tdelta
maxDate = date1
#date of transaction a day later
date2 = date1-timedelta(days=1)
row.extend([country,date1,date2,tranType,cr_dbt,limit,tmpAmt,Balance,CCs[j],
CCTypes[j],usecase,Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j]])
count = count + 1
checkin=''
checkout=''
transDetail=''
#Add details or Hotel Transactions
if((cat_desc=='Hotels/Motels/Inns/Resorts' or cat_desc=='Hotels, Motels, and Resorts') and (UseCase[j]=='28' or UseCase[j]=='29')):
if (maxCheckin == ''):
checkin=maxDate+timedelta(days=randrange(365,389,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
maxCheckin=checkin
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
else:
checkin=maxCheckin + timedelta(days=randrange(2,5,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
maxCheckin=checkin
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
if((cat_desc=='Hotels/Motels/Inns/Resorts' or cat_desc=='Hotels, Motels, and Resorts') and UseCase[j]=='30'):
checkin=maxDate+timedelta(days=randrange(30,200,1))
checkout=checkin+timedelta(days=randrange(4,11,1))
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
hotel=tmp2[1]+' Hotels; '+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]
transDetail='Checkin: '+str(checkin)+'; Checkout: '+str(checkout)+'; Hotel: '+hotel
#Add details or Airline Transactions
if(cat_desc=='Airlines' and (UseCase[j]=='31' or UseCase[j]=='32')):
if (maxBook == ''):
booking=maxDate+timedelta(days=randrange(1,15,1))
maxBook=booking
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
transDetail='Date Booked: '+str(booking)+'; Name Booked: '+tmp2[0]+tmp2[1]+'; Address: '+addr[1]+' '+addr[2]+', '+addr[0]+'; Source :'+random.choice(Airport_Code)+'; Destination:'+random.choice(Airport_Code)
else:
booking=maxBook + timedelta(days=randrange(1,15,1))
maxBook=booking
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
transDetail='Date Booked: '+str(booking)+'; Name Booked: '+ tmp2[0] + tmp2[1] + '; Address: '+ addr[1] + ' ' + addr[2]+', '+addr[0]+'; Source :'+random.choice(Airport_Code)+'; Destination:'+random.choice(Airport_Code)
if(cat_desc=='Airlines' and UseCase[j]=='33'):
booking=maxDate + timedelta(days=randrange(1,15,1))
tmp2=gen_data.create_name()
addr=gen_data.create_city_state_zip()
transDetail='Date Booked: '+str(booking)+'; Name Booked: '+ tmp2[0] + tmp2[1] + '; Address: '+addr[1]+' '+addr[2]+', '+addr[0]+'; Source :'+random.choice(Airport_Code)+'; Destination:'+random.choice(Airport_Code)
row.append(transDetail)
writer.writerow(row)
#post generating all transactions, check account balance - if overpaid - refund $ and add a refund transaction
if Balance > limit:
row = [str(count)+'_'+dt]+ [acct]+['Uber Bank']+['0000']+['Refund to Customer from Bank']+[random.choice(Tran_Country_Debits)]
date1=maxDate+timedelta(days=90)
date2=date1-timedelta(days=1)
row.extend([date1, date2, 'Credit Balance Refund','D',limit,Balance-limit,limit,CCs[j],CCTypes[j],
usecase,Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j],''])
count = count + 1
usedAmt = 0
maxDate= datetime(0001,01,01)
else:
date1 = maxDate+tdelta
maxDate = date1
#date of transaction a day later
date2 = date1-timedelta(days=1)
row = [str(count)+'_'+dt]+[acct]+['Customer Payment']+['1111']+['Customer Payment']+[random.choice(Tran_Country_Credits)]
row.extend([date1, date2, 'Payment','C',limit,limit-Balance,limit,CCs[j],CCTypes[j],usecase,
Holders[j],CCsCount[j],Cities[j],States[j],ZIPs[j],Countries[j],''])
count = count + 1
usedAmt = 0
writer.writerow(row)
sys.stdout.write('\tnco:hasPhoneNumber <tel:+11111111111>.\n')
sys.stdout.write('\n')
sys.stdout.write('<tel:+11111111111> a nco:PhoneNumber; \n')
sys.stdout.write('\tnco:phoneNumber "(111) 111-1111".\n')
sys.stdout.write('\n')
#TODO need to create some email folders
myOwnPhoneNumberURI = "tel:+11111111111"
previousContacts = []
previousEmailAddresses = []
previousIMAccounts = []
allchars = string.maketrans('', '')
for dummy in range(0, count):
firstName, lastName = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
postalAddressID = str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
phoneNumber = gen_data.create_phone()
phoneUri = 'tel:+1' + phoneNumber.translate(allchars, ' -()')
birthDay = gen_data.create_birthday()
streetAddress = gen_data.create_street()
emailAddress = gen_data.create_email(name=(firstName, lastName))
xmppAddress = str(firstName + "." + lastName + "@gmail.com").lower()
hasIMAccount = False
hasPhoneNumber = False
jobTitle = gen_data.create_job_title()
generatePostalAddress()
generateEmailAddress()
import random
from barnum import gen_data
import addressbook_pb2
# Barnum generates US data but that's ok for the example
names = [gen_data.create_name() for _ in range(0, 15)]
phones = [gen_data.create_phone() for _ in range(0, 30)]
postcodes = [gen_data.create_city_state_zip() for _ in range(0, 15)]
streets = [gen_data.create_street() for _ in range(0, 30)]
contacts = []
for name in names:
address = {}
# Simulate the fact that postcode are optionals
if random.choice([True, False]):
address['postcode'] = random.choice(postcodes)[0]
address['address_lines'] = random.sample(streets, random.randint(0, 2))
phone_numbers = []
for _ in range(0, random.randint(0, 2)):
phone_numbers.append({
'type': random.choice(['MOBILE', 'LANDLINE']),
'number': random.choice(phones)
})
contacts.append({
'first_name': name[0],
'last_name': name[1],
'address': address,
'phone_numbers': phone_numbers
comments_count = "0"
community_id = "1"
created_at = str(datetime.datetime.now())[0:-7]
currency = "USD"
deleted = "0"
delta = "1"
description = "shipping"
destination = "NULL"
language = "NULL"
last_modified = "NULL"
listing_shape_id = "1"
listing_type_old = "NULL"
old_category_id = "NULL"
_open = "1"
organization_id = "NULL"
origin = gen_data.create_city_state_zip()[0]
pickup_enabled = "0"
price_cents = str(random.randint(0, 10000))
privacy = "private"
quantity = "NULL"
quantity_selector = "NULL"
require_shipping_address = "1"
shape_name_tr_key = get_random_tr_key()
share_type_id = "NULL"
share_type_old = "NULL"
#INSERT INTO `listings` (`action_button_tr_key`, `author_id`, `category_id`, `category_old`,
#`comments_count`, `community_id`, `created_at`, `currency`, `deleted`, `delta`, `description`, `destination`,
#`language`, `last_modified`, `listing_shape_id`, `listing_type_old`, `old_category_id`, `open`, `organization_id`,
#`origin`, `pickup_enabled`, `price_cents`, `privacy`, `quantity`, `quantity_selector`, `require_shipping_address`,
#`shape_name_tr_key`, `share_type_id`, `share_type_old`, `shipping_price_additional_cents`,
import random
from barnum import gen_data
import addressbook_pb2
# Barnum generates US data but that's ok for the example
names = [gen_data.create_name() for _ in range(0, 15)]
phones = [gen_data.create_phone() for _ in range(0, 30)]
postcodes = [gen_data.create_city_state_zip() for _ in range(0, 15)]
streets = [gen_data.create_street() for _ in range(0, 30)]
contacts = []
for name in names:
address = {}
# Simulate the fact that postcode are optionals
if random.choice([True, False]):
address['postcode'] = random.choice(postcodes)[0]
address['address_lines'] = random.sample(streets, random.randint(0, 2))
phone_numbers = []
for _ in range(0, random.randint(0, 2)):
phone_numbers.append({
'type': random.choice(['MOBILE', 'LANDLINE']),
'number': random.choice(phones)
})
contacts.append({
'first_name': name[0],
'last_name': name[1],
'address': address,
import random
from random import shuffle
from faker import Faker
from barnum import gen_data
import csv
fake = Faker()
ON_US_INDICATOR = ['ON','OFF']
DebitCredit = ['CR','DR']
bank= ['0','8','9']
select = '1'
#ATM_FLAG = ['1','0','0','0','0','0','0','0','0','0','0','0','0','0','0','0']
ATM_FLAG = ['1','0']
with open('largetrns.csv','w') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow(['rownum'] +['TXN_ROUTING_TRANSIT'] + ['DEBIT_CREDIT_INDICATOR'] +['Bank']+ \
['AccountID'] + ['Amount'] + ['ON_US_INDICATOR'] + \
['COMPANY_NAME'] + ['DATE_POSTED']+ ['City_State_Zip'] + ['ATM_FLAG'] +['RDCFLAG'])
for i in range(500):
row = [i] + [random.choice(Python_aba_transit_numbers.aba)] + \
[DebitCredit[random.randint(0,len(DebitCredit)-1)]] + \
[random.choice(bank)] + \
[random.choice(python_account_ID.accountid)] + \
[max ( max((randrange(0,101,1)-99),0)* randrange(20000,500000,1),randrange(5,35000,1))] + \
[ON_US_INDICATOR[random.randint(0,len(ON_US_INDICATOR)-1)]] + \
[gen_data.create_company_name()] + \
[gen_data.create_birthday(min_age=1, max_age=8)] + \
[gen_data.create_city_state_zip()] + \
[random.choice(ATM_FLAG)* max((randrange(0,101,1)-99),0)] + [random.choice(ATM_FLAG)* max((randrange(0,101,1)-99),0)]
writer.writerow(row)
sys.stdout.write('\tnco:hasPhoneNumber <tel:+11111111111>.\n')
sys.stdout.write('\n')
sys.stdout.write('<tel:+11111111111> a nco:PhoneNumber; \n')
sys.stdout.write('\tnco:phoneNumber "(111) 111-1111".\n')
sys.stdout.write('\n')
#TODO need to create some email folders
myOwnPhoneNumberURI = "tel:+11111111111"
previousContacts = []
previousEmailAddresses = []
previousIMAccounts = []
allchars = string.maketrans('','')
for dummy in range (0, count):
firstName, lastName = gen_data.create_name()
zip, city, state = gen_data.create_city_state_zip()
postalAddressID=str(random.randint(0, sys.maxint))
UID = str(random.randint(0, sys.maxint))
phoneNumber = gen_data.create_phone()
phoneUri = 'tel:+1' + phoneNumber.translate(allchars,' -()')
birthDay = gen_data.create_birthday()
streetAddress = gen_data.create_street()
emailAddress = gen_data.create_email(name=(firstName, lastName))
xmppAddress = str(firstName+"." + lastName + "@gmail.com").lower()
hasIMAccount = False
hasPhoneNumber = False
jobTitle = gen_data.create_job_title()
generatePostalAddress()
generateEmailAddress()
def pop_transDetail(cat_desc, maxDate, j, maxBook, maxCheckin, randomrange,
randomchoice):
checkin = date(2000, 1, 1)
checkout = date(2000, 1, 1)
booking = date(2000, 1, 1)
transDetail = ''
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
#Add details or Hotel Transactions
if (cat_desc == 'Hotels/Motels/Inns/Resorts'
or cat_desc == 'Hotels, Motels, and Resorts'):
if (UseCase[j] == '28' or UseCase[j] == '29'):
if (maxCheckin == ''):
checkin = maxDate + timedelta(days=randomrange(365, 389, 1))
else:
checkin = maxCheckin + timedelta(days=randomrange(2, 5, 1))
maxCheckin = checkin
elif UseCase[j] == '30':
checkin = maxDate + timedelta(days=randomrange(30, 200, 1))
checkout = checkin + timedelta(days=randomrange(4, 11, 1))
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[1] + ' ' + addr[
2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
#Add details or Airline Transactions
elif cat_desc == 'Airlines':
if (UseCase[j] == '31' or UseCase[j] == '32'):
if (maxBook == ''):
booking = maxDate + timedelta(days=randomrange(1, 15, 1))
else:
booking = maxBook + timedelta(days=randomrange(1, 15, 1))
maxBook = booking
elif UseCase[j] == '33':
booking = maxDate + timedelta(days=randomrange(1, 15, 1))
Airport_Code = [
'0AK', '16A', '1G4', '2A3', '2A9', '3A5', '3T7', '3W2', '6R7',
'74S', 'A61', 'A85', 'ABE', 'ABI', 'ABQ', 'ABR', 'ABY', 'ACB',
'ACK', 'ACT', 'ACV', 'ACY', 'ADK', 'ADQ', 'AEX', 'AFM', 'AGC',
'AGN', 'AGS', 'AHN', 'AIA', 'AID', 'AIY', 'AIZ', 'AKN', 'AKP',
'AKW', 'ALB', 'ALM', 'ALN', 'ALO', 'ALS', 'ALW', 'AMA', 'ANB',
'ANC', 'AND', 'ANI', 'AOO', 'APF', 'APN', 'AQH', 'AQT', 'ART',
'ASE', 'ASN', 'AST', 'ATK', 'ATL', 'ATW', 'ATY', 'AUG', 'AUK',
'AUS', 'AVL', 'AVP', 'AWI', 'AXN', 'AZO', 'BAF', 'BAK', 'BCE',
'BDE', 'BDL', 'BDR', 'BED', 'BEH', 'BET', 'BFD', 'BFF', 'BFI',
'BFL', 'BGM', 'BGR', 'BHB', 'BHM', 'BID', 'BIG', 'BIL', 'BIS',
'BJI', 'BKL', 'BKW', 'BKX', 'BLI', 'BLM', 'BLV', 'BMG', 'BMI',
'BNA', 'BOI', 'BOS', 'BPK', 'BPT', 'BQK', 'BQN', 'BRD', 'BRL',
'BRO', 'BRW', 'BTI', 'BTL', 'BTM', 'BTR', 'BTV', 'BUF', 'BUR',
'BVK', 'BWG', 'BWI', 'BZN', 'CAE', 'CAK', 'CCR', 'CDB', 'CDC',
'CDV', 'CDW', 'CEC', 'CEF', 'CEZ', 'CFK', 'CGA', 'CGF', 'CGI',
'CGX', 'CHA', 'CHO', 'CHS', 'CIC', 'CID', 'CIU', 'CKB', 'CLE',
'CLL', 'CLM', 'CLT', 'CMH', 'CMI', 'CMX', 'CNM', 'CNY', 'COD',
'COE', 'COS', 'COU', 'CPR', 'CPX', 'CRP', 'CRQ', 'CRW', 'CSG',
'CVG', 'CVO', 'CVX', 'CWA', 'CWI', 'CYS', 'D76', 'DAB', 'DAL',
'DAN', 'DAY', 'DBQ', 'DCA', 'DDC', 'DDH', 'DEC', 'DEN', 'DET',
'DFW', 'DHN', 'DIK', 'DLG', 'DLH', 'DNV', 'DRO', 'DRT', 'DSM',
'DTW', 'DUJ', 'DUT', 'DUY', 'DVL', 'DVT', 'DXR', 'EAR', 'EAT',
'EAU', 'EEK', 'EEN', 'EFD', 'EFK', 'EGE', 'EKM', 'EKO', 'ELI',
'ELM', 'ELO', 'ELP', 'ELY', 'ENA', 'ENM', 'ENW', 'ERI', 'ESC',
'ESF', 'EUG', 'EVV', 'EWB', 'EWN', 'EWR', 'EWU', 'EYW', 'FAI',
'FAQ', 'FAR', 'FAT', 'FAY', 'FHR', 'FHU', 'FKL', 'FLG', 'FLL',
'FLO', 'FMN', 'FNL', 'FNT', 'FOD', 'FOE', 'FRG', 'FRM', 'FSD',
'FSM', 'FTW', 'FWA', 'FYU', 'FYV', 'GAL', 'GAM', 'GBD', 'GBH',
'GCC', 'GCK', 'GCN', 'GED', 'GEG', 'GFK', 'GFL', 'GGG', 'GGV',
'GGW', 'GJT', 'GKN', 'GLD', 'GLH', 'GLR', 'GLS', 'GNV', 'GON',
'GPI', 'GPT', 'GPZ', 'GRB', 'GRI', 'GRK', 'GRO', 'GRR', 'GSN',
'GSO', 'GSP', 'GST', 'GTF', 'GTR', 'GUC', 'GUM', 'GUP', 'GYH',
'GYR', 'GYY', 'HDN', 'HFD', 'HGR', 'HIB', 'HII', 'HKS', 'HKY',
'HLA', 'HLN', 'HND', 'HNH', 'HNL', 'HNM', 'HNS', 'HOB', 'HOM',
'HON', 'HOT', 'HOU', 'HPB', 'HPN', 'HRL', 'HRO', 'HSL', 'HSV',
'HTS', 'HUF', 'HUT', 'HVN', 'HXD', 'HYA', 'HYL', 'HYS', 'IAD',
'IAH', 'IAN', 'ICT', 'IDA', 'IFP', 'IGM', 'IIK', 'ILE', 'ILG',
'ILI', 'ILL', 'ILM', 'IMT', 'IND', 'INL', 'INT', 'IPL', 'IPT',
'IRK', 'ISN', 'ISO', 'ISP', 'ITH', 'ITO', 'IWA', 'IWD', 'IXD',
'IYK', 'JAC', 'JAN', 'JAX', 'JBR', 'JEF', 'JFK', 'JHW', 'JLN',
'JMS', 'JNU', 'JRB', 'JST', 'JVL', 'JXN', 'KAE', 'KAL', 'KDK',
'KEB', 'KKA', 'KLG', 'KOA', 'KSM', 'KTB', 'KTN', 'KVC', 'KVL',
'KWT', 'LAA', 'LAF', 'LAL', 'LAN', 'LAR', 'LAS', 'LAW', 'LAX',
'LBB', 'LBE', 'LBF', 'LBL', 'LBX', 'LCH', 'LCK', 'LEB', 'LEX',
'LFT', 'LGA', 'LGB', 'LHD', 'LIH', 'LIT', 'LMT', 'LNK', 'LNS',
'LNY', 'LPR', 'LRD', 'LRU', 'LSE', 'LUK', 'LWB', 'LWS', 'LYH',
'MAF', 'MAZ', 'MBA', 'MBL', 'MBS', 'MCC', 'MCE', 'MCG', 'MCI',
'MCK', 'MCN', 'MCO', 'MCW', 'MDH', 'MDM', 'MDT', 'MDW', 'MDY',
'MEI', 'MEM', 'MFD', 'MFE', 'MFR', 'MGM', 'MGW', 'MHE', 'MHK',
'MHT', 'MIA', 'MIE', 'MIV', 'MJX', 'MKC', 'MKE', 'MKG', 'MKK',
'MKL', 'MKT', 'MLB', 'MLI', 'MLL', 'MLU', 'MMH', 'MMU', 'MMV',
'MNM', 'MOB', 'MOD', 'MOT', 'MOU', 'MPV', 'MQI', 'MQJ', 'MQY',
'MRI', 'MRY', 'MSL', 'MSN', 'MSO', 'MSP', 'MSS', 'MSV', 'MSY',
'MTH', 'MTJ', 'MTM', 'MTO', 'MUE', 'MVL', 'MVN', 'MVY', 'MWA',
'MWH', 'MYR', 'MZJ', 'N93', 'NEW', 'NQA', 'NUL', 'OAJ', 'OAK',
'OCF', 'OFK', 'OGD', 'OGG', 'OGS', 'OKC', 'OLM', 'OMA', 'OME',
'ONP', 'ONT', 'OOK', 'OQU', 'ORD', 'ORF', 'ORH', 'ORI', 'ORS',
'ORV', 'OSH', 'OSU', 'OTG', 'OTH', 'OTM', 'OTZ', 'OWB', 'OXC',
'OXR', 'PAE', 'PAH', 'PBI', 'PCW', 'PDT', 'PDX', 'PFN', 'PGA',
'PGD', 'PGM', 'PGV', 'PHF', 'PHL', 'PHO', 'PHX', 'PIA', 'PIB',
'PIE', 'PIH', 'PIR', 'PIT', 'PKB', 'PLB', 'PLK', 'PLN', 'PMD',
'PNC', 'PNS', 'POU', 'PPC', 'PPG', 'PQI', 'PQL', 'PRB', 'PRC',
'PSC', 'PSE', 'PSG', 'PSM', 'PSP', 'PTH', 'PTK', 'PUB', 'PUW',
'PVC', 'PVD', 'PVU', 'PWM', 'PWT', 'RAP', 'RDD', 'RDG', 'RDM',
'RDU', 'RFD', 'RHI', 'RIC', 'RIW', 'RKD', 'RKS', 'RME', 'RMG',
'RNO', 'ROA', 'ROC', 'ROW', 'RSH', 'RST', 'RSW', 'RUT', 'RWI',
'SAF', 'SAN', 'SAT', 'SAV', 'SAW', 'SBA', 'SBD', 'SBN', 'SBP',
'SBY', 'SCC', 'SCK', 'SCM', 'SDF', 'SDP', 'SDY', 'SEA', 'SFB',
'SFO', 'SFZ', 'SGF', 'SGH', 'SGJ', 'SGU', 'SGY', 'SHD', 'SHG',
'SHH', 'SHR', 'SHV', 'SIG', 'SIT', 'SJC', 'SJT', 'SJU', 'SKX',
'SLC', 'SLE', 'SLK', 'SLN', 'SMF', 'SMX', 'SNA', 'SNP', 'SOP',
'SOV', 'SOW', 'SPI', 'SPS', 'SQI', 'SRQ', 'SRR', 'STC', 'STJ',
'STL', 'STP', 'STS', 'STT', 'STX', 'SUN', 'SUS', 'SUX', 'SVA',
'SVC', 'SWF', 'SWO', 'SYR', 'T44', 'TAL', 'TBN', 'TCL', 'TEB',
'TEX', 'TIX', 'TLH', 'TLT', 'TNI', 'TOG', 'TOL', 'TPA', 'TPL',
'TRI', 'TTN', 'TUL', 'TUP', 'TUS', 'TVC', 'TVF', 'TVL', 'TVR',
'TWF', 'TXK', 'TYR', 'TYS', 'UCA', 'UIN', 'UNK', 'UNV', 'UOX',
'UUU', 'VAK', 'VCT', 'VCV', 'VDZ', 'VGT', 'VIS', 'VLD', 'VPS',
'VPZ', 'VQQ', 'VQS', 'VRB', 'WBB', 'WDG', 'WLK', 'WNA', 'WRG',
'WRL', 'WST', 'WTK', 'WWD', 'WYS', 'X44', 'X95', 'XNA', 'YAK',
'YKM', 'YKN', 'YNG', 'YUM', 'Z08', 'Z09'
]
transDetail = 'Date Booked: ' + str(
booking
) + '; Name Booked: ' + tmp2[0] + tmp2[1] + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0] + '; Source :' + randomchoice(
Airport_Code) + '; Destination:' + randomchoice(Airport_Code)
return transDetail
from barnum import gen_data
import csv
#gen_data = gen_data()
with open('large.csv','w') as f1:
writer=csv.writer(f1, delimiter=',',lineterminator='\n',)
writer.writerow([''] + range(10))
for i in range(50000000):
row = [i] + [10] + [gen_data.cc_number()]+[gen_data.create_company_name()] +[gen_data.create_email()]+[gen_data.create_name()] +[gen_data.create_job_title()] + [gen_data.create_city_state_zip()] + [gen_data.create_birthday(min_age=2, max_age=85)]
writer.writerow(row)
#row = [i] + [10] + [fake.name()] +[fake.address()]
def gen_tran(MCC_credits, MCC_debits, Tran_Country_Credits,
Tran_Country_Debits, Tran_Type_C, Tran_Type_D, Upper_Limit, Delta,
count, j, usecase):
liTrans = []
#Initiate start date for transactions
startDate = date(2015, 01, 01)
#Pick out account based on counter
acct = ACCTs[j]
#Set customer credit limit - skew to clients with $1000-$25000 and 10% with $25K - $50K
limit = max(
max((randrange(1, 101, 1) - 99), 0) * randrange(25000, 50000, 1000),
randrange(1000, 25000, 1000))
#local Amt variable to calculate customer total usage
usedAmt = 0
tmpAmt = 0
Balance = limit
maxDate = startDate
#Random number generator for transactions per customer
NoTrans = randrange(100, 150, 1)
desc = ''
flag = 0
maxCheckin = ''
maxBook = ''
#loop to generate NoTrans transactions per customer
for k in range(NoTrans):
dt = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
cr_dbt = 'D'
tranType = ''
country = []
cat_desc = ''
flag = 0
#If Balance is within the credit limit, generate credits/debits
if (Balance > 0 and Balance <= limit * 1.2):
#Probability of credits (tmpAmt>0) and debits (tmpAmt==0) is driven by parameters Upper_Limit and Delta
tmpAmt = max(
(randrange(1, Upper_Limit, 1) + Delta), 0) * randrange(
1, Balance + 1, 1)
flag = 1
#Define time delta for next transaction
tdelta = timedelta(days=randrange(1, 4, 1))
row = [str(count) + '_' + dt] + [acct]
#If we have credit or debit within balance
if tmpAmt == 0 and flag == 1:
tmpAmt = random.randrange(1, Balance + 1, 1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch = gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Debits)
else:
if tmpAmt > 0 and flag == 1:
cr_dbt = 'C'
tranType = random.choice(Tran_Type_C)
Balance = Balance + tmpAmt
merch = ''
cat = random.choice(MCC_credits)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
if (tranType == 'Merchant Credit'):
merch = gen_data.create_company_name()
cat = random.choice(Merchant_Category.Green)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
if (tranType == 'Refund'):
cat = '0000'
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Credits)
#If we need to make a payment or get credit then assign codes
if Balance > limit and flag == 0:
tmpAmt = random.randrange(1, Balance - limit + 1, 1)
tranType = random.choice(Tran_Type_D)
cat = random.choice(MCC_debits)
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
Balance = Balance - tmpAmt
merch = gen_data.create_company_name()
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Debits)
else:
if ((Balance < 0 or Balance == 0) and flag == 0):
cr_dbt = 'C'
tranType = 'Payment'
tmpAmt = random.randrange(1, limit / 2, 1)
Balance = Balance + tmpAmt
merch = ''
cat = '1111'
cat_desc = python_merchant_cat.All_Merchant_Cat[cat]
row.append(merch)
row.append(cat)
row.append(cat_desc)
country = random.choice(Tran_Country_Credits)
#date posted
date1 = maxDate + tdelta
maxDate = date1
#date of transaction a day later
date2 = date1 - timedelta(days=1)
row.extend([
country, date1, date2, tranType, cr_dbt, limit, tmpAmt, Balance,
CCs[j], CCTypes[j], usecase, Holders[j], CCsCount[j], Cities[j],
States[j], ZIPs[j], Countries[j]
])
count = count + 1
checkin = ''
checkout = ''
transDetail = ''
#Add details or Hotel Transactions
if ((cat_desc == 'Hotels/Motels/Inns/Resorts'
or cat_desc == 'Hotels, Motels, and Resorts')
and (UseCase[j] == '28' or UseCase[j] == '29')):
if (maxCheckin == ''):
checkin = maxDate + timedelta(days=randrange(365, 389, 1))
checkout = checkin + timedelta(days=randrange(4, 11, 1))
maxCheckin = checkin
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(
checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
else:
checkin = maxCheckin + timedelta(days=randrange(2, 5, 1))
checkout = checkin + timedelta(days=randrange(4, 11, 1))
maxCheckin = checkin
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(
checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
if ((cat_desc == 'Hotels/Motels/Inns/Resorts'
or cat_desc == 'Hotels, Motels, and Resorts')
and UseCase[j] == '30'):
checkin = maxDate + timedelta(days=randrange(30, 200, 1))
checkout = checkin + timedelta(days=randrange(4, 11, 1))
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
hotel = tmp2[1] + ' Hotels; ' + '; Address: ' + addr[
1] + ' ' + addr[2] + ', ' + addr[0]
transDetail = 'Checkin: ' + str(checkin) + '; Checkout: ' + str(
checkout) + '; Hotel: ' + hotel
#Add details or Airline Transactions
if (cat_desc == 'Airlines'
and (UseCase[j] == '31' or UseCase[j] == '32')):
if (maxBook == ''):
booking = maxDate + timedelta(days=randrange(1, 15, 1))
maxBook = booking
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
transDetail = 'Date Booked: ' + str(
booking) + '; Name Booked: ' + tmp2[0] + tmp2[
1] + '; Address: ' + addr[1] + ' ' + addr[
2] + ', ' + addr[0] + '; Source :' + random.choice(
Airport_Code
) + '; Destination:' + random.choice(Airport_Code)
else:
booking = maxBook + timedelta(days=randrange(1, 15, 1))
maxBook = booking
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
transDetail = 'Date Booked: ' + str(
booking) + '; Name Booked: ' + tmp2[0] + tmp2[
1] + '; Address: ' + addr[1] + ' ' + addr[
2] + ', ' + addr[0] + '; Source :' + random.choice(
Airport_Code
) + '; Destination:' + random.choice(Airport_Code)
if (cat_desc == 'Airlines' and UseCase[j] == '33'):
booking = maxDate + timedelta(days=randrange(1, 15, 1))
tmp2 = gen_data.create_name()
addr = gen_data.create_city_state_zip()
transDetail = 'Date Booked: ' + str(
booking) + '; Name Booked: ' + tmp2[0] + tmp2[
1] + '; Address: ' + addr[1] + ' ' + addr[2] + ', ' + addr[
0] + '; Source :' + random.choice(
Airport_Code) + '; Destination:' + random.choice(
Airport_Code)
row.append(transDetail)
writer.writerow(row)
#post generating all transactions, check account balance - if overpaid - refund $ and add a refund transaction
if Balance > limit:
row = [str(count) + '_' + dt] + [acct] + ['Uber Bank'] + ['0000'] + [
'Refund to Customer from Bank'
] + [random.choice(Tran_Country_Debits)]
date1 = maxDate + timedelta(days=90)
date2 = date1 - timedelta(days=1)
row.extend([
date1, date2, 'Credit Balance Refund', 'D', limit, Balance - limit,
limit, CCs[j], CCTypes[j], usecase, Holders[j], CCsCount[j],
Cities[j], States[j], ZIPs[j], Countries[j], ''
])
count = count + 1
usedAmt = 0
maxDate = datetime(0001, 01, 01)
else:
date1 = maxDate + tdelta
maxDate = date1
#date of transaction a day later
date2 = date1 - timedelta(days=1)
row = [str(count) + '_' + dt] + [acct] + ['Customer Payment'] + [
'1111'
] + ['Customer Payment'] + [random.choice(Tran_Country_Credits)]
row.extend([
date1, date2, 'Payment', 'C', limit, limit - Balance, limit,
CCs[j], CCTypes[j], usecase, Holders[j], CCsCount[j], Cities[j],
States[j], ZIPs[j], Countries[j], ''
])
count = count + 1
usedAmt = 0
writer.writerow(row)
from barnum import gen_data
import json
f = open('Users_old.txt', 'r')
f_out = open('Users.txt', 'w')
for line in f.readlines():
line = line.strip().split('\t')
new_line = []
new_line.append(line[0])
new_line.append(line[1])
new_line.append(line[2])
info = {}
info['birthday'] = str(gen_data.create_birthday(min_age=18, max_age=60))
info['email'] = gen_data.create_email(tld="com")
info['mobile'] = gen_data.create_phone()
tmp = gen_data.create_city_state_zip()
info['city'] = tmp[1] + ', ' + tmp[2]
new_line.append(json.dumps(info))
f_out.write('\t'.join(new_line) + '\n')
f.close()
f_out.close()
