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Hypothesis for Django users

Hypothesis offers a number of features specific for Django testing, available in the hypothesis[django] :doc:`extra </extras>`. This is tested against each supported series with mainstream or extended support - if you're still getting security patches, you can test with Hypothesis.

Using it is quite straightforward: All you need to do is subclass :class:`hypothesis.extra.django.TestCase` or :class:`hypothesis.extra.django.TransactionTestCase` or :class:`~hypothesis.extra.django.LiveServerTestCase` or :class:`~hypothesis.extra.django.StaticLiveServerTestCase` and you can use :func:`@given <hypothesis.given>` as normal, and the transactions will be per example rather than per test function as they would be if you used :func:`@given <hypothesis.given>` with a normal django test suite (this is important because your test function will be called multiple times and you don't want them to interfere with each other). Test cases on these classes that do not use :func:`@given <hypothesis.given>` will be run as normal.

We recommend avoiding :class:`~hypothesis.extra.django.TransactionTestCase` unless you really have to run each test case in a database transaction. Because Hypothesis runs this in a loop, the performance problems it normally has are significantly exacerbated and your tests will be really slow. If you are using :class:`~hypothesis.extra.django.TransactionTestCase`, you may need to use @settings(suppress_health_check=[HealthCheck.too_slow]) to avoid :doc:`errors due to slow example generation </healthchecks>`.

Having set up a test class, you can now pass :func:`@given <hypothesis.given>` a strategy for Django models:

.. autofunction:: hypothesis.extra.django.from_model

For example, using the trivial django project we have for testing:

>>> from hypothesis.extra.django import from_model
>>> from toystore.models import Customer
>>> c = from_model(Customer).example()
>>> c
<Customer: Customer object>
>>> c.age

Hypothesis has just created this with whatever the relevant type of data is.

Obviously the customer's age is implausible, which is only possible because we have not used (eg) :class:`~django:django.core.validators.MinValueValidator` to set the valid range for this field (or used a :class:`~django:django.db.models.PositiveSmallIntegerField`, which would only need a maximum value validator).

If you do have validators attached, Hypothesis will only generate examples that pass validation. Sometimes that will mean that we fail a :class:`~hypothesis.HealthCheck` because of the filtering, so let's explicitly pass a strategy to skip validation at the strategy level:


Inference from validators will be much more powerful when :issue:`1116` is implemented, but there will always be some edge cases that require you to pass an explicit strategy.

>>> from hypothesis.strategies import integers
>>> c = from_model(Customer, age=integers(min_value=0, max_value=120)).example()
>>> c
<Customer: Customer object>
>>> c.age
.. autofunction:: hypothesis.extra.django.from_form

Tips and tricks

Custom field types

If you have a custom Django field type you can register it with Hypothesis's model deriving functionality by registering a default strategy for it:

>>> from toystore.models import CustomishField, Customish
>>> from_model(Customish).example()
hypothesis.errors.InvalidArgument: Missing arguments for mandatory field
    customish for model Customish
>>> from hypothesis.extra.django import register_field_strategy
>>> from hypothesis.strategies import just
>>> register_field_strategy(CustomishField, just("hi"))
>>> x = from_model(Customish).example()
>>> x.customish

Note that this mapping is on exact type. Subtypes will not inherit it.

.. autofunction:: hypothesis.extra.django.register_field_strategy

.. autofunction:: hypothesis.extra.django.from_field

Generating child models

For the moment there's no explicit support in hypothesis-django for generating dependent models. i.e. a Company model will generate no Shops. However if you want to generate some dependent models as well, you can emulate this by using the flatmap function as follows:

from hypothesis.strategies import just, lists

def generate_with_shops(company):
    return lists(from_model(Shop, company=just(company))).map(lambda _: company)

company_with_shops_strategy = from_model(Company).flatmap(generate_with_shops)

Let's unpack what this is doing:

The way flatmap works is that we draw a value from the original strategy, then apply a function to it which gives us a new strategy. We then draw a value from that strategy. So in this case we're first drawing a company, and then we're drawing a list of shops belonging to that company: The just strategy is a strategy such that drawing it always produces the individual value, so from_model(Shop, company=just(company)) is a strategy that generates a Shop belonging to the original company.

So the following code would give us a list of shops all belonging to the same company:

from_model(Company).flatmap(lambda c: lists(from_model(Shop, company=just(c))))

The only difference from this and the above is that we want the company, not the shops. This is where the inner map comes in. We build the list of shops and then throw it away, instead returning the company we started for. This works because the models that Hypothesis generates are saved in the database, so we're essentially running the inner strategy purely for the side effect of creating those children in the database.

Generating primary key values

If your model includes a custom primary key that you want to generate using a strategy (rather than a default auto-increment primary key) then Hypothesis has to deal with the possibility of a duplicate primary key.

If a model strategy generates a value for the primary key field, Hypothesis will create the model instance with :meth:`~django:django.db.models.query.QuerySet.update_or_create`, overwriting any existing instance in the database for this test case with the same primary key.

On the subject of MultiValueField

Django forms feature the :class:`~django:django.forms.MultiValueField` which allows for several fields to be combined under a single named field, the default example of this is the :class:`~django:django.forms.SplitDateTimeField`.

class CustomerForm(forms.Form):
    name = forms.CharField()
    birth_date_time = forms.SplitDateTimeField()

from_form supports MultiValueField subclasses directly, however if you want to define your own strategy be forewarned that Django binds data for a MultiValueField in a peculiar way. Specifically each sub-field is expected to have its own entry in data addressed by the field name (e.g. birth_date_time) and the index of the sub-field within the MultiValueField, so form data for the example above might look like this:

    "name": "Samuel John",
    "birth_date_time_0": "2018-05-19",  # the date, as the first sub-field
    "birth_date_time_1": "15:18:00",  # the time, as the second sub-field

Thus, if you want to define your own strategies for such a field you must address your sub-fields appropriately:

from_form(CustomerForm, birth_date_time_0=just("2018-05-19"))