class FieldReportFactory(factory.django.DjangoModelFactory):
class Meta:
model = models.FieldReport
is_covid_report = fuzzy.FuzzyChoice([True, False])
rid = fuzzy.FuzzyText(length=100)
dtype = factory.SubFactory(disaster_type.DisasterTypeFactory)
event = factory.SubFactory(event.EventFactory)
summary = fuzzy.FuzzyText(length=500)
description = fuzzy.FuzzyText(length=200)
report_date = fuzzy.FuzzyDateTime(
datetime.datetime(2008, 1, 1, tzinfo=pytz.utc))
# start_date is now what the user explicitly sets while filling the Field Report form.
start_date = fuzzy.FuzzyDateTime(
datetime.datetime(2008, 1, 1, tzinfo=pytz.utc))
created_at = fuzzy.FuzzyDateTime(
datetime.datetime(2008, 1, 1, tzinfo=pytz.utc))
updated_at = fuzzy.FuzzyDateTime(
datetime.datetime(2008, 1, 1, tzinfo=pytz.utc))
previous_update = fuzzy.FuzzyDateTime(
datetime.datetime(2008, 1, 1, tzinfo=pytz.utc))
status = fuzzy.FuzzyInteger(0)
request_assistance = False # fuzzy.FuzzyChoice([True, False])
ns_request_assistance = False # fuzzy.FuzzyChoice([True, False])
num_injured = fuzzy.FuzzyInteger(0, 9)
num_dead = fuzzy.FuzzyInteger(0, 9)
num_missing = fuzzy.FuzzyInteger(0, 9)
num_affected = fuzzy.FuzzyInteger(0, 9)
num_displaced = fuzzy.FuzzyInteger(0, 9)
num_assisted = fuzzy.FuzzyInteger(0, 9)
num_localstaff = fuzzy.FuzzyInteger(0, 9)
num_volunteers = fuzzy.FuzzyInteger(0, 9)
num_expats_delegates = fuzzy.FuzzyInteger(0, 9)
# Early Warning fields
num_potentially_affected = fuzzy.FuzzyInteger(0, 9)
num_highest_risk = fuzzy.FuzzyInteger(0, 9)
affected_pop_centres = fuzzy.FuzzyText(length=500)
gov_num_injured = fuzzy.FuzzyInteger(0, 9)
gov_num_dead = fuzzy.FuzzyInteger(0, 9)
gov_num_missing = fuzzy.FuzzyInteger(0, 9)
gov_num_affected = fuzzy.FuzzyInteger(0, 9)
gov_num_displaced = fuzzy.FuzzyInteger(0, 9)
gov_num_assisted = fuzzy.FuzzyInteger(0, 9)
# Epidemic fields
epi_cases = fuzzy.FuzzyInteger(0, 9)
epi_suspected_cases = fuzzy.FuzzyInteger(0, 9)
epi_probable_cases = fuzzy.FuzzyInteger(0, 9)
epi_confirmed_cases = fuzzy.FuzzyInteger(0, 9)
epi_num_dead = fuzzy.FuzzyInteger(0, 9)
epi_figures_source = fuzzy.FuzzyChoice(models.EPISourceChoices)
epi_cases_since_last_fr = fuzzy.FuzzyInteger(0, 9)
epi_deaths_since_last_fr = fuzzy.FuzzyInteger(0, 9)
epi_notes_since_last_fr = fuzzy.FuzzyText(length=50)
who_num_assisted = fuzzy.FuzzyInteger(0, 9)
health_min_num_assisted = fuzzy.FuzzyInteger(0, 9)
# Early Warning fields
gov_num_potentially_affected = fuzzy.FuzzyInteger(0, 9)
gov_num_highest_risk = fuzzy.FuzzyInteger(0, 9)
gov_affected_pop_centres = fuzzy.FuzzyText(length=500)
other_num_injured = fuzzy.FuzzyInteger(0, 9)
other_num_dead = fuzzy.FuzzyInteger(0, 9)
other_num_missing = fuzzy.FuzzyInteger(0, 9)
other_num_affected = fuzzy.FuzzyInteger(0, 9)
other_num_displaced = fuzzy.FuzzyInteger(0, 9)
other_num_assisted = fuzzy.FuzzyInteger(0, 9)
# Early Warning fields
other_num_potentially_affected = fuzzy.FuzzyInteger(0, 9)
other_num_highest_risk = fuzzy.FuzzyInteger(0, 9)
other_affected_pop_centres = fuzzy.FuzzyText(length=500)
# Date of data for situation fields
sit_fields_date = fuzzy.FuzzyDateTime(
datetime.datetime(2008, 1, 1, tzinfo=pytz.utc))
# Text field for users to specify sources for where they have marked 'Other' as source.
other_sources = fuzzy.FuzzyText(length=50)
# actions taken
actions_others = fuzzy.FuzzyText(length=50)
# visibility
visibility = fuzzy.FuzzyChoice(models.VisibilityChoices)
# information
bulletin = fuzzy.FuzzyChoice(models.RequestChoices)
dref = fuzzy.FuzzyChoice(models.RequestChoices)
dref_amount = fuzzy.FuzzyInteger(0, 9999)
appeal = fuzzy.FuzzyChoice(models.RequestChoices)
appeal_amount = fuzzy.FuzzyInteger(0, 9999)
imminent_dref = fuzzy.FuzzyChoice(models.RequestChoices)
imminent_dref_amount = fuzzy.FuzzyInteger(0, 9999)
forecast_based_action = fuzzy.FuzzyChoice(models.RequestChoices) # only EW
forecast_based_action_amount = fuzzy.FuzzyInteger(0, 9999) # only EW
# disaster response
rdrt = fuzzy.FuzzyChoice(models.RequestChoices)
num_rdrt = fuzzy.FuzzyInteger(0, 9)
fact = fuzzy.FuzzyChoice(models.RequestChoices)
num_fact = fuzzy.FuzzyInteger(0, 9)
ifrc_staff = fuzzy.FuzzyChoice(models.RequestChoices)
num_ifrc_staff = fuzzy.FuzzyInteger(0, 9)
# ERU units
eru_base_camp = fuzzy.FuzzyChoice(models.RequestChoices)
eru_base_camp_units = fuzzy.FuzzyInteger(0, 9)
eru_basic_health_care = fuzzy.FuzzyChoice(models.RequestChoices)
eru_basic_health_care_units = fuzzy.FuzzyInteger(0, 9)
eru_it_telecom = fuzzy.FuzzyChoice(models.RequestChoices)
eru_it_telecom_units = fuzzy.FuzzyInteger(0, 9)
eru_logistics = fuzzy.FuzzyChoice(models.RequestChoices)
eru_logistics_units = fuzzy.FuzzyInteger(0, 9)
eru_deployment_hospital = fuzzy.FuzzyChoice(models.RequestChoices)
eru_deployment_hospital_units = fuzzy.FuzzyInteger(0, 9)
eru_referral_hospital = fuzzy.FuzzyChoice(models.RequestChoices)
eru_referral_hospital_units = fuzzy.FuzzyInteger(0, 9)
eru_relief = fuzzy.FuzzyChoice(models.RequestChoices)
eru_relief_units = fuzzy.FuzzyInteger(0, 9)
eru_water_sanitation_15 = fuzzy.FuzzyChoice(models.RequestChoices)
eru_water_sanitation_15_units = fuzzy.FuzzyInteger(0, 9)
eru_water_sanitation_40 = fuzzy.FuzzyChoice(models.RequestChoices)
eru_water_sanitation_40_units = fuzzy.FuzzyInteger(0, 9)
eru_water_sanitation_20 = fuzzy.FuzzyChoice(models.RequestChoices)
eru_water_sanitation_20_units = fuzzy.FuzzyInteger(0, 9)
notes_health = fuzzy.FuzzyText(length=50)
notes_ns = fuzzy.FuzzyText(length=50)
notes_socioeco = fuzzy.FuzzyText(length=50)
class TagFactory(DjangoModelFactory):
class Meta:
model = Tag
name = FacFaker('word', locale='zh_CN')
owner = fuzzy.FuzzyChoice(User.objects.all())
def test_unbiased(self):
options = [1, 2, 3]
d = fuzzy.FuzzyChoice(options)
res = d.evaluate(2, None, False)
self.assertIn(res, options)
def handle(self, *args, **options):
if options.get('purge'):
if options.get('interactive'): # pragma: no cover
msg = "Are you sure to delete all data?"
choice = input("%s (y/N): " % msg).strip().lower()
if choice != 'y':
return
# Deleting users only should be enough to delete all instances.
get_user_model().objects.all().delete()
BankAccount.objects.all().delete()
BankTransactionTag.objects.all().delete()
BankTransaction.objects.all().delete()
BankTransactionScheduler.objects.all().delete()
self.stdout.write('All data have been deleted.')
return
user = UserFactory(
username=options.get('username'),
password=options.get('password'),
email=options.get('email'),
user_permissions='admin',
)
bankaccount = BankAccountFactory(
label=_('Current account'),
balance=2000,
balance_initial=150,
currency=options.get('currency'),
owners=[user],
)
tag_rent = BankTransactionTagFactory(name=_('Rent'), owner=user)
tag_shopping = BankTransactionTagFactory(name=_('Shopping'),
owner=user)
tag_car = BankTransactionTagFactory(name=_('Car'), owner=user)
tag_tax = BankTransactionTagFactory(name=_('Tax'), owner=user)
tag_restaurant = BankTransactionTagFactory(name=_('Restaurant'),
owner=user)
today = datetime.date.today()
BankTransactionSchedulerFactory(
bankaccount=bankaccount,
label=_("Rent"),
amount=Decimal("-910"),
date=datetime.date(today.year, today.month, 10),
payment_method=BankTransaction.PAYMENT_METHOD_TRANSFER,
tag=tag_rent,
type=BankTransactionScheduler.TYPE_MONTHLY,
recurrence=None,
).clone()
BankTransactionSchedulerFactory(
bankaccount=bankaccount,
label=_("Council tax"),
amount=Decimal("-99.89"),
date=datetime.date(today.year, today.month, 15),
payment_method=BankTransaction.PAYMENT_METHOD_TRANSFER,
tag=tag_tax,
type=BankTransactionScheduler.TYPE_MONTHLY,
recurrence=10,
).clone()
BankTransactionSchedulerFactory(
bankaccount=bankaccount,
label=_("Wages"),
amount=Decimal("2615.78"),
date=datetime.date(today.year, today.month, 5),
payment_method=BankTransaction.PAYMENT_METHOD_TRANSFER,
tag=None,
type=BankTransactionScheduler.TYPE_MONTHLY,
recurrence=None,
).clone()
BankTransactionFactory(
bankaccount=bankaccount,
label=_("Internal transfer"),
amount=Decimal("500"),
date=today - relativedelta(months=1, day=28),
reconciled=True,
status=BankTransaction.STATUS_IGNORED,
payment_method=BankTransaction.PAYMENT_METHOD_TRANSFER_INTERNAL,
tag=None,
memo="Ineed$",
)
BankTransactionFactory(
bankaccount=bankaccount,
label=_("Scratch ticket"),
amount=Decimal("150"),
date=today,
reconciled=False,
payment_method=BankTransaction.PAYMENT_METHOD_CASH,
tag=None,
memo="Hooray!",
)
BankTransactionFactory(
bankaccount=bankaccount,
label=_("New tires"),
amount=Decimal("-189.59"),
date=today - relativedelta(days=5),
reconciled=True,
payment_method=BankTransaction.PAYMENT_METHOD_CHECK,
tag=tag_car,
memo="Love my bike!",
)
BankTransactionFactory(
bankaccount=bankaccount,
label=_("Bad stuff"),
amount=Decimal("-79.90"),
date=datetime.date(today.year, today.month, 9),
reconciled=True,
payment_method=BankTransaction.PAYMENT_METHOD_CREDIT_CARD,
tag=tag_shopping,
)
BankTransactionFactory(
bankaccount=bankaccount,
label=_("Refund"),
amount=Decimal("49.59"),
date=datetime.date(today.year, today.month, 15),
reconciled=True,
payment_method=BankTransaction.PAYMENT_METHOD_TRANSFER,
tag=tag_shopping,
)
date_start = today + relativedelta(months=-1, day=15)
date_end = today + relativedelta(months=1, day=15)
date = date_start
while date < date_end:
if date <= today or date.day % 3 == 0:
choice = [tag_shopping, tag_restaurant, None, None]
tag = fuzzy.FuzzyChoice(choice).fuzz()
BankTransactionFactory(
bankaccount=bankaccount,
label=tag.name if tag is not None else _('Something'),
amount=fuzzy.FuzzyDecimal(-100, -10),
date=date,
reconciled=date < today - relativedelta(days=-3),
status=BankTransaction.STATUS_ACTIVE,
tag=tag,
)
date += relativedelta(days=1)
self.stdout.write("Data have been generated successfully.")
class LanguageFactory(factory.DjangoModelFactory):
code = fuzzy.FuzzyChoice(choices=['en', 'fr', 'el', 'es'])
class Meta:
model = Language
def test_unbiased(self):
options = [1, 2, 3]
d = fuzzy.FuzzyChoice(options)
res = utils.evaluate_declaration(d)
self.assertIn(res, options)
class PizzaFactory(DjangoModelFactory):
size = fuzzy.FuzzyChoice(['fifty', 'thirty'])
class Meta:
model = Pizza
class IndividualFactory(BaseFactory):
birth_year = fuzzy.FuzzyInteger(1800, 2100)
center = factory.SubFactory(CenterFactory)
gender = fuzzy.FuzzyChoice(["MALE", "FEMALE", "UNKNOWN"])
species = fuzzy.FuzzyChoice(["HUMAN", "MOUSE"])
identifier = fuzzy.FuzzyText(length=12, chars=string.hexdigits)
class SampleFactory(BaseFactory):
disease = factory.SubFactory(DiseaseFactory)
individual = factory.SubFactory(IndividualFactory)
pdx_id = fuzzy.FuzzyText(length=12, chars=string.hexdigits)
category = fuzzy.FuzzyChoice(["TUMOR", "NORMAL"])
identifier = fuzzy.FuzzyText(length=12, chars=string.hexdigits)
class AssemblyFactory(BaseFactory):
name = "GRCh37"
reference_data = {}
species = fuzzy.FuzzyChoice(["HUMAN", "MOUSE"])
class TechniqueFactory(BaseFactory):
reference_data = factory.SubFactory(factory.DictFactory)
name = fuzzy.FuzzyText(length=12, chars=string.hexdigits)
method = fuzzy.FuzzyChoice(
choices=["CS", "TD", "WE", "WG", "MD", "TR", "WT"])
def populate_recipes(number):
for i in range(number):
Progress.show_progress(i / number, 'Recipes')
author = fuzzy.FuzzyChoice(User.objects.all())
RecipeFactory.create(author=author, tags=Tag.objects.all())
Progress.report_success(number, 'Recipes')
class WorkspaceFactory(factory.django.DjangoModelFactory):
title = factory.Sequence(lambda n: "workspace_%d" % n)
workspace_code = fuzzy.FuzzyChoice(COUNTRIES_LIST)
class Meta:
model = Workspace
class CountryFactory(factory.django.DjangoModelFactory):
name = fuzzy.FuzzyChoice(COUNTRY_NAMES_LIST)
class Meta:
model = Country
class CarFactory(DjangoModelFactory):
REGIONAL_PREFIXES = (
"AA",
"KA",
"AB",
"KB",
"AC",
"KC",
"AE",
"KE",
"AH",
"KH",
"AI",
"KI",
"AK",
"KK",
"AM",
"KM",
"AO",
"KO",
"AP",
"KP",
"AT",
"KT",
"AX",
"KX",
"BA",
"HA",
"BB",
"HB",
"BC",
"HC",
"BE",
"HE",
"BH",
"HH",
"BI",
"HI",
"BK",
"HK",
"BM",
"HM",
"BO",
"HO",
"BP",
"HP",
"BT",
"HT",
"BX",
"HX",
"CA",
"IA",
"CB",
"IB",
"CC",
"IC",
"CE",
"IE",
"CH",
"IH",
"II",
)
COLORS = ("white", "black", "silver", "gray", "brown", "red", "blue",
"green")
MODELS = {
"Peugeot": (
"108",
"208",
"301",
"308",
"408",
"508",
"Bipper",
"Partner",
"Expert",
"Boxer",
),
"Citroen": (
"C1",
"C3",
"C4",
"C5",
"DS 3",
"DS 4",
"DS 4S",
"DS 5",
"Berlingo",
"Jumper",
"Jumpy",
"Nemo",
),
"Suzuki": (
"Alto",
"Baleno",
"Celerio",
"Lapin",
"Spacia",
"Swift",
"Carry",
"Jimny",
"Vitara",
"Every",
"Landy",
),
"Fiat": (
"Panda",
"500",
"Uno",
"Linea",
"Tipo",
"Doblò",
"Ducato",
"Fiorino",
"Qubo",
"Talento",
),
"Honda": (
"Accord",
"Civic",
"Jazz",
"Insight",
"Inspire",
"Legend",
"Acty",
"Ridgeline",
"CR-V",
"HR-V",
"Acty",
"Freed",
"Odyssey",
),
"Ford": (
"C-Max",
"Fiesta",
"Figo",
"Fusion",
"Mondeo",
"Focus",
"Ka",
"Ranger",
"Super Duty",
"Galaxy",
"S-Max",
"Transit",
),
"Hyundai": (
"i10",
"i20",
"i30",
"Elantra",
"i40",
"Accent",
"Atos",
"Visto"
"Elantra",
"Eon",
"ix20",
"Cargo Truck",
"Porter",
"Xcient",
),
"Kia": (
"Ceed",
"Cerato",
"Optima",
"Picanto",
"Quoris",
"Ray",
"Stinger",
"Carens",
"Carnival",
"Venga",
"Besta",
"Bongo",
),
"Renault": (
"Clio",
"Fluence",
"Mégane",
"Talisman",
"Twingo",
"Scénic",
"Captur",
"Espace",
"Kadjar",
"Kangoo",
"Trafic",
),
"Dacia": ("Logan", "Sandero", "Lodgy", "Duster", "Lodgy", "Dokker"),
"Nissan": (
"Altima",
"Cima",
"Fuga",
"Almera",
"Leaf",
"Micra",
"Navara",
"Patrol",
"Juke",
"Murano",
"Pathfinder",
"Clipper",
"Cabstar",
"Atlas",
),
"Toyota": (
"Auris",
"Aygo",
"Camry",
"Corolla",
"Prius",
"Yaris",
"Hilux",
"Land Cruiser",
"C-HR",
"RAV4",
"Avanza",
"Esquire",
"Estima",
"Innova",
"Pixis",
"Sienna",
"Dyna",
"ToyoAce",
),
"SEAT":
("Toledo", "Ibiza", "León", "Mii", "Alhambra", "Arona", "Ateca"),
"Škoda":
("Citigo", "Fabia", "Octavia", "Rapid", "Superb", "Kodiaq", "Karoq"),
"Volkswagen": (
"Bora",
"Fox",
"Golf",
"Jetta",
"Passat",
"Polo",
"Up",
"Vento",
"Voyage",
"T-Roc",
"Tiguan",
"Touareg",
"Caddy",
"Sharan",
"Suran",
"Touran",
"Amarok",
"Saveiro",
"Transporter",
"Crafter",
),
}
fuel_consumption = fuzzy.FuzzyFloat(3, 10)
country = fuzzy.FuzzyChoice(COUNTRIES)
class Meta:
model = Car
exclude = ("REGIONAL_PREFIXES", "COLORS", "MODELS")
django_get_or_create = ("plates", )
@lazy_attribute
def plates(self):
return "{regional_prefix}{four_digits}{two_letters}".format(
regional_prefix=random.choice(self.REGIONAL_PREFIXES),
four_digits="".join(random.choices(string.digits, k=4)),
two_letters="".join(random.choices("ABEIKMHOPCTX", k=2)),
)
@lazy_attribute
def description(self):
manufacturer = random.choice(list(self.MODELS.keys()))
return "{color} {manufacturer} {model} {year}".format(
color=random.choice(self.COLORS).capitalize(),
manufacturer=manufacturer,
model=random.choice(self.MODELS[manufacturer]),
year=random.randrange(1990,
now().year),
)
class FakeBadgeFactory(django.DjangoModelFactory):
code = fuzzy.FuzzyChoice(dict(settings.BADGE_CODE_CHOICES).keys())
class Meta:
model = Badge
import factory
from factory import fuzzy, django
from faker import Factory as FakerFactory
from django.contrib.auth import get_user_model
from django.contrib.auth.models import Group
from django.utils.timezone import now
from ..models.mixins import UserProfileMixin
from ..models import SocialNetwork
from ..conf import settings
faker = FakerFactory.create(getattr(settings, 'FAKER_SETTINGS_LOCALE',
'en_GB'))
random_group = fuzzy.FuzzyChoice(Group.objects.all())
def fake_random_group():
return random_group.fuzz()
class FakeUserProfileMixinFactory(django.DjangoModelFactory):
class Meta:
model = UserProfileMixin
about_me = factory.LazyAttribute(lambda x: faker.text())
bio_me = factory.LazyAttribute(lambda x: faker.text())
short_me = factory.LazyAttribute(lambda x: faker.text())
location = factory.LazyAttribute(
lambda x: '{}, {}'.format(faker.city(), faker.country()))
class PlannedInterventionFactory(factory.django.DjangoModelFactory):
class Meta:
model = PlannedIntervention
title = fuzzy.FuzzyChoice(PlannedIntervention.Title)
def from_choices(field_cls):
return fuzzy.FuzzyChoice([c[0] for c in field_cls.choices])
class IdentifiedNeedFactory(factory.django.DjangoModelFactory):
class Meta:
model = IdentifiedNeed
title = fuzzy.FuzzyChoice(IdentifiedNeed.Title)
class FeatureFactory(DjangoModelFactory):
title = factory.PostGeneration(lambda feature, *args, **kwargs: f'Feature {feature.key}')
feature_type = fuzzy.FuzzyChoice(Feature.FeatureType.choices, getter=lambda c: c[0])
class Meta:
model = Feature
class NationalSocietyActionFactory(factory.django.DjangoModelFactory):
class Meta:
model = NationalSocietyAction
title = fuzzy.FuzzyChoice(NationalSocietyAction.Title)
class CarFactory(factory.DjangoModelFactory):
REGIONAL_PREFIXES = ('AA', 'KA', 'AB', 'KB', 'AC', 'KC', 'AE', 'KE', 'AH',
'KH', 'AI', 'KI', 'AK', 'KK', 'AM', 'KM', 'AO', 'KO',
'AP', 'KP', 'AT', 'KT', 'AX', 'KX', 'BA', 'HA', 'BB',
'HB', 'BC', 'HC', 'BE', 'HE', 'BH', 'HH', 'BI', 'HI',
'BK', 'HK', 'BM', 'HM', 'BO', 'HO', 'BP', 'HP', 'BT',
'HT', 'BX', 'HX', 'CA', 'IA', 'CB', 'IB', 'CC', 'IC',
'CE', 'IE', 'CH', 'IH', 'II')
COLORS = ('white', 'black', 'silver', 'gray', 'brown', 'red', 'blue',
'green')
MODELS = {
'Peugeot': ('108', '208', '301', '308', '408', '508', 'Bipper',
'Partner', 'Expert', 'Boxer'),
'Citroen': ('C1', 'C3', 'C4', 'C5', 'DS 3', 'DS 4', 'DS 4S', 'DS 5',
'Berlingo', 'Jumper', 'Jumpy', 'Nemo'),
'Suzuki': ('Alto', 'Baleno', 'Celerio', 'Lapin', 'Spacia', 'Swift',
'Carry', 'Jimny', 'Vitara', 'Every', 'Landy'),
'Fiat': ('Panda', '500', 'Uno', 'Linea', 'Tipo', 'Doblò', 'Ducato',
'Fiorino', 'Qubo', 'Talento'),
'Honda':
('Accord', 'Civic', 'Jazz', 'Insight', 'Inspire', 'Legend', 'Acty',
'Ridgeline', 'CR-V', 'HR-V', 'Acty', 'Freed', 'Odyssey'),
'Ford': ('C-Max', 'Fiesta', 'Figo', 'Fusion', 'Mondeo', 'Focus', 'Ka',
'Ranger', 'Super Duty', 'Galaxy', 'S-Max', 'Transit'),
'Hyundai':
('i10', 'i20', 'i30', 'Elantra', 'i40', 'Accent', 'Atos', 'Visto'
'Elantra', 'Eon', 'ix20', 'Cargo Truck', 'Porter', 'Xcient'),
'Kia': ('Ceed', 'Cerato', 'Optima', 'Picanto', 'Quoris', 'Ray',
'Stinger', 'Carens', 'Carnival', 'Venga', 'Besta', 'Bongo'),
'Renault':
('Clio', 'Fluence', 'Mégane', 'Talisman', 'Twingo', 'Scénic', 'Captur',
'Espace', 'Kadjar', 'Kangoo', 'Trafic'),
'Dacia': ('Logan', 'Sandero', 'Lodgy', 'Duster', 'Lodgy', 'Dokker'),
'Nissan': ('Altima', 'Cima', 'Fuga', 'Almera', 'Leaf', 'Micra',
'Navara', 'Patrol', 'Juke', 'Murano', 'Pathfinder',
'Clipper', 'Cabstar', 'Atlas'),
'Toyota':
('Auris', 'Aygo', 'Camry', 'Corolla', 'Prius', 'Yaris', 'Hilux',
'Land Cruiser', 'C-HR', 'RAV4', 'Avanza', 'Esquire', 'Estima',
'Innova', 'Pixis', 'Sienna', 'Dyna', 'ToyoAce'),
'SEAT': ('Toledo', 'Ibiza', 'León', 'Mii', 'Alhambra', 'Arona',
'Ateca'),
'Škoda': ('Citigo', 'Fabia', 'Octavia', 'Rapid', 'Superb', 'Kodiaq',
'Karoq'),
'Volkswagen':
('Bora', 'Fox', 'Golf', 'Jetta', 'Passat', 'Polo', 'Up', 'Vento',
'Voyage', 'T-Roc', 'Tiguan', 'Touareg', 'Caddy', 'Sharan', 'Suran',
'Touran', 'Amarok', 'Saveiro', 'Transporter', 'Crafter'),
}
mileage_unit = fuzzy.FuzzyChoice(k for k, _ in Car.UNITS)
fuel_consumption = fuzzy.FuzzyFloat(3, 10)
class Meta:
model = Car
exclude = ('REGIONAL_PREFIXES', 'COLORS', 'MODELS')
@factory.lazy_attribute
def plates(self):
return '{regional_prefix}{four_digits}{two_letters}'.format(
regional_prefix=random.choice(self.REGIONAL_PREFIXES),
four_digits=''.join(random.choices(string.digits, k=4)),
two_letters=''.join(random.choices('ABEIKMHOPCTX', k=2)),
)
@factory.lazy_attribute
def description(self):
manufacturer = random.choice(list(self.MODELS.keys()))
return '{color} {manufacturer} {model} {year}'.format(
color=random.choice(self.COLORS).capitalize(),
manufacturer=manufacturer,
model=random.choice(self.MODELS[manufacturer]),
year=random.randrange(1990,
now().year),
)
class FactoryRoleFactory(django.DjangoModelFactory):
class Meta:
model = Role
status = fuzzy.FuzzyChoice(dict(settings.RELATION_ROLE_CH_STATUS).keys())
class CourseFactory(factory.django.DjangoModelFactory):
class Meta:
model = Course
title = fuzzy.FuzzyChoice(NAME_CHOICES)
description = fuzzy.FuzzyText(length=256)
class PublishedAwardFinancialAssistanceFactory(factory.Factory):
class Meta:
model = stagingModels.PublishedAwardFinancialAssistance
published_award_financial_assistance_id = None
afa_generated_unique = fuzzy.FuzzyText()
action_date = fuzzy.FuzzyDate(date(2015, 1, 1), date(2015, 12, 31))
action_type = fuzzy.FuzzyText()
assistance_type = fuzzy.FuzzyText()
award_description = fuzzy.FuzzyText()
awardee_or_recipient_legal = fuzzy.FuzzyText()
awardee_or_recipient_uniqu = fuzzy.FuzzyText()
awarding_agency_code = fuzzy.FuzzyText()
awarding_agency_name = fuzzy.FuzzyText()
awarding_office_code = fuzzy.FuzzyText()
awarding_office_name = fuzzy.FuzzyText()
awarding_sub_tier_agency_c = fuzzy.FuzzyText()
awarding_sub_tier_agency_n = fuzzy.FuzzyText()
award_modification_amendme = fuzzy.FuzzyText()
business_funds_indicator = fuzzy.FuzzyText()
business_types = fuzzy.FuzzyText()
cfda_number = fuzzy.FuzzyText()
cfda_title = fuzzy.FuzzyText()
correction_late_delete_ind = fuzzy.FuzzyText()
face_value_loan_guarantee = fuzzy.FuzzyDecimal(9999)
fain = fuzzy.FuzzyText()
federal_action_obligation = fuzzy.FuzzyDecimal(9999)
fiscal_year_and_quarter_co = fuzzy.FuzzyText()
funding_agency_code = fuzzy.FuzzyText()
funding_agency_name = fuzzy.FuzzyText()
funding_office_code = fuzzy.FuzzyText()
funding_office_name = fuzzy.FuzzyText()
funding_sub_tier_agency_co = fuzzy.FuzzyText()
funding_sub_tier_agency_na = fuzzy.FuzzyText()
is_historical = fuzzy.FuzzyChoice([True, False])
legal_entity_address_line1 = fuzzy.FuzzyText()
legal_entity_address_line2 = fuzzy.FuzzyText()
legal_entity_address_line3 = fuzzy.FuzzyText()
legal_entity_city_name = fuzzy.FuzzyText()
legal_entity_city_code = fuzzy.FuzzyText()
legal_entity_country_code = fuzzy.FuzzyText()
legal_entity_country_name = fuzzy.FuzzyText()
legal_entity_county_code = fuzzy.FuzzyText()
legal_entity_county_name = fuzzy.FuzzyText()
legal_entity_foreign_city = fuzzy.FuzzyText()
legal_entity_foreign_posta = fuzzy.FuzzyText()
legal_entity_foreign_provi = fuzzy.FuzzyText()
legal_entity_congressional = fuzzy.FuzzyText()
legal_entity_state_code = fuzzy.FuzzyText()
legal_entity_state_name = fuzzy.FuzzyText()
legal_entity_zip5 = fuzzy.FuzzyText()
legal_entity_zip_last4 = fuzzy.FuzzyText()
non_federal_funding_amount = fuzzy.FuzzyDecimal(9999)
original_loan_subsidy_cost = fuzzy.FuzzyDecimal(9999)
total_funding_amount = fuzzy.FuzzyDecimal(9999)
period_of_performance_curr = fuzzy.FuzzyDate(date(2015, 1, 1),
date(2015, 12, 31))
period_of_performance_star = fuzzy.FuzzyDate(date(2015, 1, 1),
date(2015, 12, 31))
place_of_performance_code = fuzzy.FuzzyText()
place_of_performance_congr = fuzzy.FuzzyText()
place_of_perform_country_c = fuzzy.FuzzyText()
place_of_perform_country_n = fuzzy.FuzzyText()
place_of_perform_county_co = fuzzy.FuzzyText()
place_of_perform_state_nam = fuzzy.FuzzyText()
place_of_perform_county_na = fuzzy.FuzzyText()
place_of_performance_city = fuzzy.FuzzyText()
place_of_performance_forei = fuzzy.FuzzyText()
place_of_performance_zip4a = fuzzy.FuzzyText()
record_type = fuzzy.FuzzyInteger(1, 2)
sai_number = fuzzy.FuzzyText()
uri = fuzzy.FuzzyText()
modified_at = fuzzy.FuzzyDate(date(2015, 1, 1), date(2015, 12, 31))
class LanguageFactory(factory.DjangoModelFactory):
FACTORY_FOR = Language
code = fuzzy.FuzzyChoice(choices=['en', 'fr', 'el', 'es'])
class ProviderFactory(factory.django.DjangoModelFactory):
class Meta:
model = 'testapp.Provider'
name = factory.Faker('company')
score = fuzzy.FuzzyChoice('ABCDEF')
class CellAreaKeyFactory(Factory):
radio = fuzzy.FuzzyChoice([Radio.gsm, Radio.wcdma, Radio.lte])
mcc = GB_MCC
mnc = GB_MNC
lac = fuzzy.FuzzyInteger(1, 60000)
def test_getter(self):
options = [('a', 1), ('b', 2), ('c', 3)]
d = fuzzy.FuzzyChoice(options, getter=lambda x: x[1])
res = utils.evaluate_declaration(d)
self.assertIn(res, [1, 2, 3])
