def test_sanity_check(self):
fschema = self.data_gen.get_faker_schema()
schema_faker = FakerSchema()
data = schema_faker.generate_fake(fschema, 1) # Generate one record.
# Note at this point data[u'lo_orderdate'] is a datetime.date object while Biguery expects
# a string
self.assertIsInstance(data[u'lo_orderdate'], datetime.date)
data = self.fakerowgen.sanity_check(record=data,
fieldname=u'lo_orderdate')
# Check that the date was converted to a string
self.assertIsInstance(data[u'lo_orderdate'], unicode)
# Check that the date is in the correct format
_ = datetime.datetime.strptime(data[u'lo_orderdate'], '%Y-%m-%d')
# Check if sanity check enforces integers < data_args.max_int
data[u'lo_linenumber'] = 10**12 # Note that max_int is 10**11
data = self.fakerowgen.sanity_check(record=data,
fieldname=u'lo_linenumber')
self.assertLessEqual(data[u'lo_linenumber'], self.data_gen.max_int)
def generate_fake(self, fschema=None, key_dict=None):
"""
This method creates a single fake record based on the constraints
defined in this FakeRowGen instance's data_gen attribute.
Arguments:
fschema (dict): Contains a faker_schema (this should be
generated by DataGenerator.get_faker_schema() )
"""
schema_faker = FakerSchema()
# Drop the key columns because we do not need to randomly generate them.
if key_dict:
for key in list(key_dict.keys()):
fschema.pop(key, None)
# Generate a fake record.
data = schema_faker.generate_fake(fschema, 1) # Generate one record.
# This performs a sanity check on datatypes and parameterized
# constraints.
for col_name in data:
data = self.sanity_check(data, col_name)
if key_dict:
keys = self.convert_key_types(key_dict)
# Join the keys and the rest of the genreated data
data.update(keys)
data.pop('frequency')
return json.dumps(data)
class TestFakerSchema(unittest.TestCase):
def setUp(self):
self.faker_schema = FakerSchema(faker=MockFaker())
def test_generate_fake_flat_schema(self):
schema = {'Full Name': 'name', 'Address': 'address', 'Email': 'email'}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
def test_generate_fake_flat_schema_4_iterations(self):
schema = {'Full Name': 'name', 'Address': 'address', 'Email': 'email'}
data = self.faker_schema.generate_fake(schema, iterations=4)
self.assertIsInstance(data, list)
self.assertEqual(len(data), 4)
def test_generate_fake_nested_schema(self):
schema = {
'Full Name': 'name',
'Location': {
'Address': 'street_address',
'City': 'city',
'Country': 'country',
'Postal Code': 'postalcode'
}
}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['Location'], dict)
def test_generate_fake_schema_with_list(self):
schema = {
'Employer': 'name',
'EmployeeList': [{
'Employee1': 'name'
}, {
'Employee2': 'name'
}]
}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['EmployeeList'], list)
def generate_fake(self, fschema):
"""
This method creates a single fake record based on the constraints defined int
the FakeRowGen instance's data_gen attribute.
Args:
fschema: A dictionary containing a faker_schema (this should be generated by
DataGenerator.get_faker_schema() )
"""
# Initialize a FakerSchema object.
schema_faker = FakerSchema()
# Generate a fake record.
data = schema_faker.generate_fake(fschema, 1) # Generate one record.
# This performs a sanity check on datatypes and parameterized constraints.
for col_name in data:
data = self.sanity_check(data, col_name)
return data
def fake_data(json_file, seed=0):
"""Return faked json data
Args:
json_file: example json file with data to simulate
seed (int): Optionally seed Faker for reproducability
Returns:
dict: dict with summary and DataFrame with extracted data
"""
# get dict of visited places
places = _create_places(total=max(NPLACES.values()))
# Get json schema from json file
with open(json_file) as file_object:
json_data = json.load(file_object)
json_schema = get_json_schema(json_data)
fake = Faker('nl_NL')
fake.add_provider(geo)
faker = FakerSchema(faker=fake, locale='nl_NL')
faked_data = {}
for year in YEARS:
for month in MONTHS:
schema = get_faker_schema(
json_schema["properties"],
custom=SCHEMA_TYPES,
iterations={"timelineObjects": NACTIVITIES[year]})
data = faker.generate_fake(schema)
month_number = datetime.strptime(month[:3], '%b').month
seed += 1
json_data = _update_data(
data,
datetime(year, month_number, 1),
dict(itertools.islice(places.items(), NPLACES[year])),
seed=seed)
faked_data[(year, month)] = json_data
return faked_data
def generate(schema):
fake = Faker()
fake.add_provider(BornProvider)
faker = FakerSchema(faker=fake)
return faker.generate_fake(load_json_from_file(schema))
class TestFakerSchema(unittest.TestCase):
def setUp(self):
self.faker_schema = FakerSchema(faker=MockFaker())
def test_generate_fake_flat_schema(self):
schema = {'Full Name': 'name', 'Address': 'address', 'Email': 'email'}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
def test_generate_fake_flat_schema_4_iterations(self):
schema = {'Full Name': 'name', 'Address': 'address', 'Email': 'email'}
data = self.faker_schema.generate_fake(schema, iterations=4)
self.assertIsInstance(data, list)
self.assertEqual(len(data), 4)
def test_generate_fake_nested_schema(self):
schema = {'Full Name': 'name', 'Location': {'Address': 'street_address', 'City': 'city',
'Country': 'country', 'Postal Code': 'postalcode'}}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['Location'], dict)
def test_generate_fake_schema_with_list(self):
schema = {'Employer': 'name', 'EmployeeList': [{'Employee1': 'name'},
{'Employee2': 'name'}]}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['EmployeeList'], list)
def test_generate_fake_schema_with_choices(self):
schema = {
'Person': 'name',
'Gender': '(Male,Female)' # simliar to fuzzy choices
}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['Gender'], str)
def test_generate_fake_schema_with_date(self):
schema = {
'Person': 'name',
'Gender': '(Male,Female)',
'BirthDay': 'date_of_birth' # returns date with isoformat
}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['BirthDay'], str)
def test_generate_fake_schema_with_fake_args(self):
schema = {
'Person': 'name',
'Gender': '(Male,Female)',
'BirthDay': 'date_of_birth',
'Age': 'random_int(18,80)' # passing arguments (age betweeen 18 and 80)
}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['Age'], int)
def test_generate_fake_schema_with_fake_kargs(self):
schema = {
'Person': 'name',
'Gender': '(Male,Female)',
'BirthDay': 'date_of_birth',
'Age': 'random_int(min=18,max=80)' # passing named arguments (age betweeen min=18 and max=80)
}
data = self.faker_schema.generate_fake(schema)
self.assertIsInstance(data, dict)
self.assertIsInstance(data['Age'], int)