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Data factory is a simple data generator easily coupled with your web project. It's main use is to generate data for your orm during tests.

All data generated with data-factory is pseudo-random. That means a factory method does not always return the same value, but it always return the same "kind of value" you expect. This is very important because this way your tests became fuzzy.

If you have a field in your model that can receive a integer of any kind and you always give it the same number for testing purpose, your field will ONLY be tested for that particular number.

If you test your model field with data from a range of numbers (like the field domain), like [1,2,3,4,5] even if only one of these numbers is randomly picked each time, your field test is safe for the whole range of numbers (theoretically). That's fuzzy testing!

DataFactory is not a object factory for ORM's, like ModelMommy or DynamicFixture. It only creates the right raw fuzzy data that you expect. However, DataFactory could be easily used to power up a object factory as it tries to give you just the right range for the most common types.

• None (with or_null)
• Boolean
• Choices (pick from list)
• DateTime
• Tiny integer (and positive tiny integer)
• Small integer (and positive small integer)
• Integer (and positive integer)
• Big integer (and positive big integer)
• Decimal (and positive decimal)
• Real
• Double
• Binary
• String
• ASCII String
• Unicode String
• Slug

>>> from data_factory import make_integer, make_small_integer
>>> random_integer = make_integer()  # gets you a 32bits random integer
>>> random_small_integer = make_small_integer()  # gets you a 16bits random integer

This section cover some useful information about generated data.

• Positive integer methods were made, mostly, to generate proper values for database unsigned types.
• Positive decimal data is mostly used for monetary values which, usually, can not be negative.