Additional packages

Hypothesis itself does not have any dependencies, but there are some packages that need additional things installed in order to work.

You can install these dependencies using the setuptools extra feature as e.g. pip install hypothesis[django]. This will check installation of compatible versions.

You can also just install hypothesis into a project using them, ignore the version constraints, and hope for the best.

In general “Which version is Hypothesis compatible with?” is a hard question to answer and even harder to regularly test. Hypothesis is always tested against the latest compatible version and each package will note the expected compatibility range. If you run into a bug with any of these please specify the dependency version.

hypothesis[datetime]

As might be expected, this provides strategies for which generating instances of objects from the datetime module: datetimes, dates, and times. It depends on pytz to work.

It should work with just about any version of pytz. pytz has a very stable API and Hypothesis works around a bug or two in older versions.

It lives in the hypothesis.extra.datetime package.

datetimes(allow_naive=None, timezones=None, min_year=None, max_year=None)

This strategy generates datetime objects. For example:

>>> from hypothesis.extra.datetime import datetimes
>>> datetimes().example()
datetime.datetime(1705, 1, 20, 0, 32, 0, 973139, tzinfo=<DstTzInfo 'Israel...
>>> datetimes().example()
datetime.datetime(7274, 6, 9, 23, 0, 31, 75498, tzinfo=<DstTzInfo 'America...

As you can see, it produces years from quite a wide range. If you want to narrow it down you can ask for a more specific range of years:

>>> datetimes(min_year=2001, max_year=2010).example()
datetime.datetime(2010, 7, 7, 0, 15, 0, 614034, tzinfo=<DstTzInfo 'Pacif...
>>> datetimes(min_year=2001, max_year=2010).example()
datetime.datetime(2006, 9, 26, 22, 0, 0, 220365, tzinfo=<DstTzInfo 'Asia...

You can also specify timezones:

>>> import pytz
>>> pytz.all_timezones[:3]
['Africa/Abidjan', 'Africa/Accra', 'Africa/Addis_Ababa']
>>> datetimes(timezones=pytz.all_timezones[:3]).example()
datetime.datetime(6257, 8, 21, 13, 6, 24, 8751, tzinfo=<DstTzInfo 'Africa/Accra' GMT0:00:00 STD>)
>>> datetimes(timezones=pytz.all_timezones[:3]).example()
datetime.datetime(7851, 2, 3, 0, 0, 0, 767400, tzinfo=<DstTzInfo 'Africa/Accra' GMT0:00:00 STD>)
>>> datetimes(timezones=pytz.all_timezones[:3]).example()
datetime.datetime(8262, 6, 22, 16, 0, 0, 154235, tzinfo=<DstTzInfo 'Africa/Abidjan' GMT0:00:00 STD>)

If the set of timezones is empty you will get a naive datetime:

>>> datetimes(timezones=[]).example()
datetime.datetime(918, 11, 26, 2, 0, 35, 916439)

You can also explicitly get a mix of naive and non-naive datetimes if you want:

>>> datetimes(allow_naive=True).example()
datetime.datetime(2433, 3, 20, 0, 0, 44, 460383, tzinfo=<DstTzInfo 'Asia/Hovd' HOVT+7:00:00 STD>)
>>> datetimes(allow_naive=True).example()
datetime.datetime(7003, 1, 22, 0, 0, 52, 401259)
dates(min_year=None, max_year=None)

This strategy generates date objects. For example:

>>> from hypothesis.extra.datetime import dates
>>> dates().example()
datetime.date(1687, 3, 23)
>>> dates().example()
datetime.date(9565, 5, 2)

Again, you can restrict the range with the min_year and max_year arguments.

times(allow_naive=None, timezones=None)

This strategy generates time objects. For example:

>>> from hypothesis.extra.datetime import times
>>> times().example()
datetime.time(0, 15, 55, 188712, tzinfo=<DstTzInfo 'US/Hawaii' LMT-1 day, 13:29:00 STD>)
>>> times().example()
datetime.time(9, 0, 47, 959374, tzinfo=<DstTzInfo 'Pacific/Bougainville' BST+11:00:00 STD>)

The allow_naive and timezones arguments act the same as the datetimes strategy.

hypothesis[fakefactory]

Fake-factory is another Python library for data generation. hypothesis.extra.fakefactory is a package which lets you use fake-factory generators to parametrize tests.

The fake-factory API is extremely unstable, even between patch releases, and Hypothesis’s support for it is unlikely to work with anything except the exact version it has been tested against.

hypothesis.extra.fakefactory defines a function fake_factory which returns a strategy for producing text data from any FakeFactory provider.

So for example the following will parametrize a test by an email address:

>>> fake_factory('email').example()
'tnader@prosacco.info'

>>> fake_factory('name').example()
'Zbyněk Černý CSc.'

You can explicitly specify the locale (otherwise it uses any of the available locales), either as a single locale or as several:

>>> fake_factory('name', locale='en_GB').example()
'Antione Gerlach'
>>> fake_factory('name', locales=['en_GB', 'cs_CZ']).example()
'Miloš Šťastný'
>>> fake_factory('name', locales=['en_GB', 'cs_CZ']).example()
'Harm Sanford'

If you want to your own FakeFactory providers you can do that too, passing them in as a providers argument:

>>> from faker.providers import BaseProvider
>>> class KittenProvider(BaseProvider):
...     def meows(self):
...             return 'meow %d' % (self.random_number(digits=10),)
...
>>> fake_factory('meows', providers=[KittenProvider]).example()
'meow 9139348419'

Generally you probably shouldn’t do this unless you’re reusing a provider you already have - Hypothesis’s facilities for strategy generation are much more powerful and easier to use. Consider using something like BasicStrategy instead if you want to write a strategy from scratch. This is only here to provide easy reuse of things you already have.

hypothesis[django]

hypothesis.extra.django adds support for testing your Django models with Hypothesis.

It is tested extensively against all versions of Django in mainstream or extended support, including LTS releases. It may be compatible with earlier versions too, but there’s no support from us either and you really should update to get security patches.

It’s large enough that it is documented elsewhere.

hypothesis[numpy]

hypothesis.extra.numpy adds support for testing your Numpy code with Hypothesis.

This includes generating arrays, array shapes, and both scalar or compound dtypes.

Like the Django extra, Numpy has it’s own page.