def fill_persondata():
db = MySQLdb.connect(host=config.DB_HOST,
user=config.DB_USER,
passwd=config.DB_PASSWORD,
db=config.DB_NAME)
cursor = db.cursor()
# Zero client, anon
cursor.execute(
"""INSERT INTO PersonData (first_name, last_name, birth_date, email, mobile, gender) VALUES (%s, %s, %s, %s,
%s, %s);""", [
'anon', 'anon',
Datetime().date(start=1950, end=2001), None, None, None
])
for _ in range(config.N_PERSONS):
p = Person('ru')
cursor.execute(
"""INSERT INTO PersonData (first_name, last_name, birth_date, email, mobile, gender) VALUES (%s, %s, %s,
%s, %s, %s);""", [
p.name(),
p.surname(),
Datetime().date(start=1950, end=2001),
p.email(),
p.telephone('#' * 11),
'M' if p.gender(iso5218=True) == 1 else 'F'
])
db.commit()
cursor.close()
db.close()
def generatorGender():
person = Person('ru')
gend = person.gender()
if gend == 'Муж.':
return 'M'
else:
return 'F'
def create_personalData(): # function of generate new client data
passport = ''
person = Person('ru')
# Выводим исландские мужские имена.
lastname = person.last_name()
personalData['lastname'] = lastname
# print('\nфамилия:\t', personalData['lastname'])
name = person.name()
personalData['name'] = name
# print('имя:\t\t', personalData['name'])
surname = person.surname()
personalData['surname'] = surname
# print('отчество:\t', personalData['surname'])
gender = person.gender()
personalData['gender'] = gender
# print('пол:\t\t', personalData['gender'])
for _ in range(2):
varLet = random.choice(string.ascii_uppercase)
passport += varLet
passport += person.telephone('#######')
personalData['passport'] = passport
# print('паспорт:\t', personalData['passport'])
### birthday
currentDate()
personalData['age'] = age
personalData['age'][2] = str(
int(personalData['age'][2]) - person.age(16, 70))
# print('возраст:\t', personalData['age'], '\n')
###
# print('гражд-во:\t', person.get_current_locale())
# print('Нац-сть:\t',person.nationality())
### mobile
mobile = person.telephone('+37529#######')
personalData['mobile'] = mobile
# print('телефон:\t', personalData['mobile'])
###
### email
# personalData['email'] = person.email()
# print('email:\t\t', personalData['email'])
###
# print('проф-ия:\t', person.occupation(), '\n')
# print('обращение:\t', person.title())
# print('взгляды:\t', person.views_on())
# print('вера:\t\t', person.worldview(), '\n')
return personalData
def create_person():
from mimesis import Person as MimesisPerson, locales, Address
person = MimesisPerson(locales.RU)
address = Address(locales.RU)
with transaction.atomic():
first_name = person.name()
last_name = person.surname()
gender = person.gender()
if gender == 'Муж.':
patronymic = choice([
'Сергеевич', 'Николаевич', 'Артурович', 'Константинович',
'Игоревич'
])
else:
patronymic = choice([
'Александровна', 'Борисовна', 'Петровна', 'Юрьевна',
'Анатольевна'
])
birth_date = generate_birthdate()
address = '{}, {}'.format(address.federal_subject(), address.address())
passport_series = randint(1000, 7000)
passport_number = randint(100000, 999999)
pair = generate_RSA_pair()
signature_public_key = pair[1]
load_key_to_file(
'../../../person_signature_private_keys/{}_{}_{}_{}.pem'.format(
passport_series, passport_number, last_name, first_name),
pair[0])
person_info = '{} {} {} {} {} {} {} {}'.format(
first_name, last_name, patronymic, birth_date, address,
passport_series, passport_number, signature_public_key)
signature_i = sign_person_info(person_info)
Person(first_name=first_name,
last_name=last_name,
patronymic=patronymic,
birth_date=birth_date,
address=address,
passport_series=passport_series,
passport_number=passport_number,
signature_public_key=signature_public_key,
signature_i=signature_i).save()
def generate_student() -> dict:
"""Generate a student record."""
person = Person()
student = {
"academic_degree": person.academic_degree(),
"age": person.age(),
"full_name": person.full_name(),
"gender": person.gender(),
"nationality": person.nationality(),
"university": person.university(),
}
return student
def person(
cls,
*,
locale=Locales.EN,
qualification=None,
age=None,
blood_type=None,
email=None,
first_name=None,
last_name=None,
gender=None,
height=None,
id=None,
language=None,
nationality=None,
occupation=None,
phone=None,
title=None,
university=None,
weight=None,
work_experience=None,
):
'''
Create an Person Data Entity object.
All individual fields are automatically randomly generated based on locale. If provided, the corresponding values are overriden.
Note:
All individual fields are randomly generated. Don't expect correct correlation e.g. correct postal code for the generated city.
Keyword Arguments:
locale: Approprite Random.locale.<local_name> object. Default is Random.locale.EN
qualification: Educational Qualification
age: Age
blood_type: Blood type
email: Email address
first_name: First name
last_name: Last name
gender: Gender
height: Height
id: Identifier
language: Language
nationality: Nationality
occupation: Occupation
phone: Phone number
title: Title
university: University
weight: Weight
work_experience: Work Experience
'''
person = Person(locale=locale)
from arjuna.engine.data.entity.person import Person as ArjPerson
first_name = first_name is not None and first_name or person.first_name(
)
last_name = last_name is not None and last_name or person.last_name()
return ArjPerson(
qualification=qualification is not None and qualification
or person.academic_degree(),
age=age is not None and age or person.age(),
blood_type=blood_type is not None and blood_type
or person.blood_type(),
email=email is not None and email or person.email(),
first_name=first_name,
last_name=last_name,
name=first_name + " " + last_name,
gender=gender is not None and gender or person.gender(),
height=height is not None and height or person.height(),
id=id is not None and id or person.identifier(),
language=language is not None and language or person.language(),
nationality=nationality is not None and nationality
or person.nationality(),
occupation=occupation is not None and occupation
or person.occupation(),
phone=phone is not None and phone or person.telephone(),
title=title is not None and title or person.title(),
university=university is not None and university
or person.university(),
weight=weight is not None and weight or person.weight(),
work_experience=work_experience is not None and work_experience
or person.work_experience(),
)
noise = noise_ampl*np.random.normal(0.01, 1.7, df.shape[0])
gated_age = np.heaviside(df[age] - min_age, 0.5) - np.heaviside(df[age] - max_age, 0.5)
return 0.01*pow(np.abs(df[age] - 30), 2.5) + df[age] + 50*df[marketing_level]*gated_age + 2*df[account_balance] + noise
##################################################
##################################################
### Generate a DataFrame of user information
##################################################
# Generate 10,000 rows of the following:
# user_id, first_name, last_name, email, password, address,
# birth_date, credit_card_num, credit_card_exp, security_answer,
# account_balance
user_df = pd.DataFrame([[x, person.name(), person.surname(), person.gender(),
person.email(), hashed_passwd(person.password()),
address.address(), person.age(),
payment.credit_card_number(),
payment.credit_card_expiration_date(), text.word(),
account_balance(), np.random.randint(1, 11)]
for x in range(10000)])
user_df.columns = ["user_id", "first_name", "last_name",
"gender", "email", "password_hashed", "address",
"age", "credit_card_num", "credit_card_exp",
"security_answer", "account_balance",
"marketing_level"]
# Generate sales, based on a noisy linear model
user_df['sales'] = generate_sales(user_df)
##################################################
##################################################
### Generate a DataFrame of user information
##################################################
# Generate 10,000 rows of the following:
# user_id, first_name, last_name, email, password, address,
# birth_date, credit_card_num, credit_card_exp, security_answer,
# account_balance
user_df = pd.DataFrame([[
x,
person.name(),
person.surname(),
person.gender(),
person.email(),
hashed_passwd(person.password()),
address.address(),
person.age(),
payment.credit_card_number(),
payment.credit_card_expiration_date(),
text.word(),
account_balance(),
np.random.randint(1, 11)
] for x in range(10000)])
user_df.columns = [
"user_id", "first_name", "last_name", "gender", "email", "password_hashed",
"address", "age", "credit_card_num", "credit_card_exp", "security_answer",
"account_balance", "marketing_level"
