Package: fake-gen
fake-genA simple package that generates data for tests.fake_gen provides the basic Factory and DictFactory classes that generate content.
it also provides many more specialized factories that provide extended functionality.
every Factory instance knows how many elements its going to generate, this enables us to generate statistical results.The DictFactory is especially useful if you want to generate data that you will later input to your NoSQL, Document based
databaseIn addition, using the DictFactory and the DependentField factories allows us to create factorys that depend on the results
of other factories. (see Examples for more information).fake_gen isn't bound to a specifc database, but does include database specfic modules inside, like - extra.mongodb.py)
but it will always be clean of database related dependencies.Installationpip install fake-genExamplesWe integrate the awsome faker package to generate data using FakeDataFactory,
this allows us to generate all sorts of content like:
* Names (First, last, full names)
* companies
* addresses
* emails
* urls
* and much much morelets create a very simple factory that generates Users:importfake_genclassUsers(fake_gen.DictFactory):id=fake_gen.CountingFactory(10)firstname=fake_gen.FakeDataFactory('firstName')lastname=fake_gen.FakeDataFactory('lastName')address=fake_gen.FakeDataFactory('address')age=fake_gen.RandomInteger(10,30)gender=fake_gen.RandomSelection(['female','male'])foruserinUsers().generate(10):# let say we only want 10 usersprintuser# {'firstname': 'Toni', 'lastname': 'Schaden', 'gender': 'female', 'age': 18, 'address': '0641 Homenick Hills\nSouth Branson, RI 70388', 'id': 10}# {'firstname': 'Gene', 'lastname': 'Greenfelder', 'gender': 'male', 'age': 17, 'address': '292 Loy Lights Suite 328\nFritzfort, IN 73914', 'id': 11}# or more likely you'd want to insert them into your favorite database (MongoDB, ElasticSearch, ..)When creating our own subclasses for DictFactory, we can make some fields dependent on other fields.
for example:classExampleFactory(DictFactory):a=CountingFactory(10)b=ClonedField("a")# b will have the same value as field 'a'foreinExampleFactory().generate(100):printe# {'a': 10, 'b': 10}# {'a': 11, 'b': 11}# ...Lets say we want to generate something like events data, we want events to have
a start time, and an end time that will be 20 minutes in the future.
In addition, we want the event's start_time will be 12 minutes apart.importfake_genEVENT_TYPES=["USER_DISCONNECT","USER_CONNECTED","USER_LOGIN","USER_LOGOUT"]classEventsFactory(fake_gen.DictFactory):start_time=fake_gen.DateIntervalFactory(datetime.datetime.now(),datetime.timedelta(minutes=12))end_time=fake_gen.RelativeToDatetimeField("start_time",datetime.timedelta(minutes=20))event_code=fake_gen.RandomSelection(EVENT_TYPES)foreventinEventFactory().generate(100):printevent# {'start_time': datetime.datetime(2013, 12, 23, 13, 37, 1, 591878), 'end_time': datetime.datetime(2013, 12, 23, 13, 57, 1, 591878), 'event_code': 'USER_CONNECTED'}# {'start_time': datetime.datetime(2013, 12, 23, 13, 49, 1, 591878), 'end_time': datetime.datetime(2013, 12, 23, 14, 9, 1, 591878), 'event_code': 'USER_LOGIN'}# {'start_time': datetime.datetime(2013, 12, 23, 14, 1, 1, 591878), 'end_time': datetime.datetime(2013, 12, 23, 14, 21, 1, 591878), 'event_code': 'USER_DISCONNECT'}We also have factories that allow us to generate different data distributed by different percentage, for example,
lets say we want to create a 'Job', that will have an assigned user field, a state field and a description field.
We want the state to be 'pending' in 90% of dictionaries and 'error' in the rest of them. In addition, we want that if the 'state' field is
'error' the assigned user will be 'support', or else it should be 'admin'.classJob(fake_gen.DictFactory):state=fake_gen.StatisticalValuesFactory([('pending',90),('error',10)])assigned_user=fake_gen.ConditionalValueField('state',{'error':'support'},'admin')description=fake_gen.RandomLengthStringFactory()foriinJob().generate(10):printi# {'state': 'error', 'assigned_user': 'support', 'description': 'jUlyFByPxPdFlBPBfPaGaTPPuajFSHXKkyewzrQ'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'tOzkgmBBnxQZhSYEjVduyXGdLrtqeTZqRxmHNXbaJBfpdNxuLKWyTDxkCZgiZTLHeiKEswvIyDzAnuuOLtXmVWhjvazaOYuu'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'TIDVuvZRUBLLTtG'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'RgcSaFzmMrhwCAZjLofikmXJhtqkVOTsWHnqTXjgrxgzTKH'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'tLkSEkCbYDvlcDBDWUBGMmidEdOxeiLDBADDKnqGqWLnxUBqzOXFXnBxkiGTymuGNbUnmxyawzLGsiummCiwxNSw'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'tUyYLofuZpceaWYKkiRvksQLqFHGOiwACuPIvRxMIuftJPsObSqCBcrQnOkOhqAukfMwrY'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'JbFrUxrERMObfwhEtCQGcxEbimvoTFwJriSfRFLFkBpyemqEfqUCGKmVlgSlVoZrrnetEnLCgbfobFbTMQOZ'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'lqatAwdcQuMMOPiYdVMRyyQgEIzOlcoozijjdCfXsVoZnnTtQjPSGBFZQGSkPblJrTIYLAotiZoyYRFrlncevwuNcqfOmeXeCPD'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'VYxnhydWtIUFiOEPszVQHuxYBIUGDyAefZiPIgkWHCMmophiueXbixXtdwKQkuvWImuErMOOOcwevQHGApXkolhjAq'}# {'state': 'pending', 'assigned_user': 'admin', 'description': 'RcawgTkQggchdHppSyQxnbDdNxqkGqbQWnQMSlorqnAQLdAqyWnKtGpXaZuVdxcGQBImzVPQsYAbIFUIpqvDzwTDdRpleBrc'}In version 1.0.5 we extended the DictFactory to support passing additional factories, or overriding existing factories, for a specific instance.
Lets take for example our 'User' example from the begining.importfake_genclassUsers(fake_gen.DictFactory):id=fake_gen.CountingFactory(10)firstname=fake_gen.FakeDataFactory('firstName')lastname=fake_gen.FakeDataFactory('lastName')address=fake_gen.FakeDataFactory('address')age=fake_gen.RandomInteger(10,30)gender=fake_gen.RandomSelection(['female','male'])But lets override it so the 'firstname' always returns John, and make the age be a random integer between 40 and 60 and add an 'email' field.foruserinUsers(firstname=fake_gen.Constant('John'),age=fake_gen.RandomInteger(40,60),email=fake_gen.FakeDataFactory('email')).generate(10):# let say we only want 10 usersprintuser#{'firstname': 'John', 'gender': 'male', 'age': 54, 'email': 'hazle.wehner@brekke.com', 'lastname': 'Willms', 'address': '245 Pfeffer Light Apt. 309\nEast Audieside, IN 11931', 'id': 10}#{'firstname': 'John', 'gender': 'male', 'age': 47, 'email': 'mariam25@gmail.com', 'lastname': 'Ratke', 'address': '98710 Freddy Gateway\nDelilahborough, GU 50849', 'id': 11}#{'firstname': 'John', 'gender': 'male', 'age': 55, 'email': 'tyler22@yahoo.com', 'lastname': 'Cormier', 'address': '432 Block Locks Apt. 547\nNew Estel, NJ 54026', 'id': 12}# or more likely you'd want to insert them into your favorite database (MongoDB, ElasticSearch, ..)FactoriesSee the Factorie's Docstrings for more examples and doctests.BasesFactory ClassDescriptionFactoryThe base class of all the factories.DictFactoryA very powerful base class. allows sub classing to create factories that generate dicts with a specific schema (see [Examples][#Examples]).ListFactoryA factory that returns on each iteration a list ofelements_per_listitems returned from calls to the given factory.CallableGets a callable object as an argument and returns the result of calling the object on every iterationDependentCallableGets a callable object as an argument and returns the result of calling the object passing the defined fields as arguments on every iterationClonedFieldA factory that copies the value of another factory.DatesFactory ClassDescriptionRandomDateFactoryGenerates random dates (python's datetime) between 2 datesDateIntervalFactoryGenerates datetime objects starting frombasewhile addingdeltato it each iteration.RelativeToDatetimeFieldGenerates datetime object relative to another datetime field, like if you havestart_timewhich is a RandomDateFactory field, and want anend_timefield that is always 15 minutes later.And MUCH MUCH more..TodosAdd usage documentation for each factory (using doctest maybe?)Add more testsAdd GeoLocationFactories to generates Location and distance related data (for example, random points near a central point).Add MORE Statistical Factoriesmore ideas welcome!