def datastream():
record = {
"customer": str(barnum.create_name()[0]),
"saleid": str(uuid.uuid4())[24:],
"timestamp": str(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))),
"coffee": random.choice(["Flat White","Americano","Macchiato","Cappuccino","Latte","Mocha","Cold Brew"]),
"milk": random.choice(["Full Cream","Skinny","Soy","Almond","Oat"]),
"size":random.choice(["Small","Regular","Large"]),
"qty": random.choice([1, 1, 2, 2, 3, 4])
}
print("Sales Id:", record["saleid"], " Timestamp :", record["timestamp"], " Customer:",record["customer"], " Coffee:",
record["coffee"], " Milk:", record["milk"], " Size:",record["size"], "Qty:", record["qty"])
return record
def test_register(self):
allow_flash(self.driver, yaml_utils.get("constant.yaml", "host"))
username = barnum.create_name(False).lower()
email = barnum.create_email()
self.register_page.get(url_regerister)
self.register_page.read_law()
self.register_page.input_info(username, "123456", email)
self.register_page.submit()
self.register_page.upload_icon(autoitExe_file)
self.register_page.skip()
self.register_page.skip()
self.register_page.skip()
self.register_page.toProfile()
result = self.register_page.get_name()
self.assertEqual(result, username)
def generate():
record_list = []
meter_list = load_db()
if len(meter_list) == 0:
print('ERROR - empty file!!!!!!!')
else:
for meter in meter_list:
# Fullname
name_tuple = barnum.create_name()
fullname = name_tuple[0] + ' ' + name_tuple[1]
# Zip, city, state
zip_tuple = barnum.create_city_state_zip()
zipcode = zip_tuple[0]
city = zip_tuple[1]
state = zip_tuple[2]
# House no. and street
street = barnum.create_street()
# Phone no.
phone = barnum.create_phone()
# create and print cis data record
cis_data_row = [
str(uuid.uuid4()), fullname, zipcode, city, state, street,
phone, meter
]
print(cis_data_row)
record_list.append(cis_data_row)
write('cis_data.csv', record_list)
try:
(zipcode, city, state) = barnum.create_city_state_zip()
for j in range(0, 5):
location = geolocator.geocode("%s, %s %s, USA" %
(city, state, zipcode))
if location != None:
rlocation = geolocator.reverse(
"%s, %s" % (location.latitude, location.longitude))
print rlocation.address[-3:]
if rlocation.address[-3:] == "USA":
break
print ': %s, %s, %s' % (zipcode, city, state)
print ': %s' % rlocation
print "."
if location != None:
name = barnum.create_name()
now = datetime.utcnow()
then = now - timedelta(2 * 365)
when = random_date(then, now)
birthday = barnum.create_birthday()
document = {
"_id": create_object_id(when),
"Name": ' '.join(name),
"Address": {
"Street": barnum.create_street(),
"City": city,
"State": state,
"Zip": zipcode,
"Location": {
"type":
"Point",
def generate_patients(count=1):
random.seed()
patients = []
for x in range(0, count):
sex = 'Male' if random.randint(0, 1) % 2 else 'Female'
fname, lname = barnum.create_name(gender=sex)
mname = barnum.create_name(False,
sex) if random_truth(0.27) == 1 else ''
street, city, state, postal_code = generate_address()
dob = barnum.create_birthday(1, 100)
patient = {
'title':
generate_title(sex),
'language':
'',
'financial':
'',
'fname':
fname,
'lname':
lname,
'mname':
mname,
'DOB':
dob.strftime("%Y-%m-%d"),
'street':
street,
'postal_code':
postal_code,
'city':
city,
'state':
state,
'country_code':
'US',
'drivers_license':
random_drivers_license(lname[0], int(dob.strftime("%y"))),
'ss':
random.randint(100000000, 999999999),
'occupation':
barnum.create_job_title(),
'phone_home':
barnum.create_phone(postal_code),
'phone_biz':
barnum.create_phone(postal_code),
'phone_contact':
barnum.create_phone(postal_code),
'phone_cell':
barnum.create_phone(postal_code),
'pharmacy_id':
1,
'status':
'',
'contact_relationship':
'',
'date':
barnum.create_date(past=True).strftime("%Y-%m-%d"),
'sex':
sex,
'referrer':
'',
'referrerID':
'',
'providerID':
0,
'ref_providerID':
0,
'email':
barnum.create_email(name=(fname, lname)),
'email_direct':
'',
'ethnoracial':
'',
'race':
'',
'ethnicity':
'',
'religion':
'',
'interpretter':
'',
'migrantseasonal':
'',
'family_size':
random.randint(1, 8),
'monthly_income':
'',
'billing_note':
'',
'homeless':
'',
'financial_review':
barnum.create_date(past=True).strftime("%Y-%m-%d"),
'pubpid':
'',
'pid':
str(random.randint(1, 99999999999)),
'hipaa_mail':
'yes' if random_truth(0.90) == 1 else 'no',
'hipaa_voice':
'yes' if random_truth(0.75) == 1 else 'no',
'hipaa_notice':
'yes' if random_truth(0.93) == 1 else 'no',
'hipaa_message':
'yes' if random_truth(0.90) == 1 else 'no',
'hipaa_allowsms':
'yes' if random_truth(0.50) == 1 else 'no',
'hipaa_allowemail':
'yes' if random_truth(0.70) == 1 else 'no',
}
patients.append(patient)
return patients
business_accounts.append(account)
current_time = date_bank_created
accounts = []
transactions = []
compromised_indexes = []
compromiseable_attributes = ['identification', 'phone', 'location']
while len(transactions) < T:
current_time += random.random() * date_bank_created / T
# ######################### Personal Accounts ########################
if len(accounts) < P and random.random() < 0.1:
try:
account = Account(
type_='personal',
created=current_time,
name=' '.join(barnum.create_name()),
coordinates=coordinates,
)
except Exception: # barnum has internal bugs
continue
account.location = random.choice(business_accounts).location
d = 0.4 # geographical coordinates maximal offset
account.location[-1] += random.random() * d - d / 2
account.location[-2] += random.random() * d - d / 2
# modify created account with a compromised field
if compromised_indexes and random.random() < 0.05:
attribute = random.choice(compromiseable_attributes)
compromised_index = random.choice(compromised_indexes)
compromised_account = accounts[compromised_index]
into = 30000
city = []
name = []
surname = []
df = pd.DataFrame({
'ID': [],
'IDbussines': [],
'Name': [],
'Surname': [],
'Age': [],
'Fanpages': [],
'Origin': []
})
for i in range(viewers):
zip, city, state = barnum.create_city_state_zip()
name, surname = barnum.create_name()
df = df.append(
{
'ID': '%.12g' % i,
'IDbussines': '%.12g' % rnd.randint(10000000000, 99999999999),
'Name': name,
'Surname': surname,
'Age': barnum.create_birthday(10, 80),
'Fanpages': '%.12g' % rnd.randint(0, 5),
'Origin': city
},
ignore_index=True)
df.to_csv("viewers.csv", index=False)
'''df = pd.DataFrame({'ID': [], 'ViewerID': [], 'PostID': [], 'Like': [], 'Comment':[], 'Share': []})
for i in range(activities):