888 lines
33 KiB
ReStructuredText
888 lines
33 KiB
ReStructuredText
.. _further-examples:
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:mod:`unittest.mock` --- further examples
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=========================================
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.. module:: unittest.mock
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:synopsis: Mock object library.
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.. moduleauthor:: Michael Foord <michael@python.org>
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.. currentmodule:: unittest.mock
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.. versionadded:: 3.3
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Here are some more examples for some slightly more advanced scenarios than in
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the :ref:`getting started <getting-started>` guide.
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Mocking chained calls
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---------------------
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Mocking chained calls is actually straightforward with mock once you
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understand the :attr:`~Mock.return_value` attribute. When a mock is called for
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the first time, or you fetch its `return_value` before it has been called, a
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new `Mock` is created.
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This means that you can see how the object returned from a call to a mocked
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object has been used by interrogating the `return_value` mock:
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>>> mock = Mock()
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>>> mock().foo(a=2, b=3)
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<Mock name='mock().foo()' id='...'>
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>>> mock.return_value.foo.assert_called_with(a=2, b=3)
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From here it is a simple step to configure and then make assertions about
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chained calls. Of course another alternative is writing your code in a more
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testable way in the first place...
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So, suppose we have some code that looks a little bit like this:
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>>> class Something(object):
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... def __init__(self):
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... self.backend = BackendProvider()
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... def method(self):
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... response = self.backend.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
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... # more code
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Assuming that `BackendProvider` is already well tested, how do we test
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`method()`? Specifically, we want to test that the code section `# more
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code` uses the response object in the correct way.
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As this chain of calls is made from an instance attribute we can monkey patch
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the `backend` attribute on a `Something` instance. In this particular case
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we are only interested in the return value from the final call to
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`start_call` so we don't have much configuration to do. Let's assume the
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object it returns is 'file-like', so we'll ensure that our response object
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uses the builtin `file` as its `spec`.
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To do this we create a mock instance as our mock backend and create a mock
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response object for it. To set the response as the return value for that final
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`start_call` we could do this:
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`mock_backend.get_endpoint.return_value.create_call.return_value.start_call.return_value = mock_response`.
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We can do that in a slightly nicer way using the :meth:`~Mock.configure_mock`
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method to directly set the return value for us:
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>>> something = Something()
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>>> mock_response = Mock(spec=file)
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>>> mock_backend = Mock()
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>>> config = {'get_endpoint.return_value.create_call.return_value.start_call.return_value': mock_response}
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>>> mock_backend.configure_mock(**config)
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With these we monkey patch the "mock backend" in place and can make the real
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call:
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>>> something.backend = mock_backend
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>>> something.method()
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Using :attr:`~Mock.mock_calls` we can check the chained call with a single
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assert. A chained call is several calls in one line of code, so there will be
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several entries in `mock_calls`. We can use :meth:`call.call_list` to create
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this list of calls for us:
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>>> chained = call.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
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>>> call_list = chained.call_list()
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>>> assert mock_backend.mock_calls == call_list
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Partial mocking
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---------------
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In some tests I wanted to mock out a call to `datetime.date.today()
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<http://docs.python.org/library/datetime.html#datetime.date.today>`_ to return
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a known date, but I didn't want to prevent the code under test from
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creating new date objects. Unfortunately `datetime.date` is written in C, and
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so I couldn't just monkey-patch out the static `date.today` method.
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I found a simple way of doing this that involved effectively wrapping the date
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class with a mock, but passing through calls to the constructor to the real
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class (and returning real instances).
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The :func:`patch decorator <patch>` is used here to
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mock out the `date` class in the module under test. The :attr:`side_effect`
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attribute on the mock date class is then set to a lambda function that returns
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a real date. When the mock date class is called a real date will be
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constructed and returned by `side_effect`.
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>>> from datetime import date
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>>> with patch('mymodule.date') as mock_date:
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... mock_date.today.return_value = date(2010, 10, 8)
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... mock_date.side_effect = lambda *args, **kw: date(*args, **kw)
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...
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... assert mymodule.date.today() == date(2010, 10, 8)
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... assert mymodule.date(2009, 6, 8) == date(2009, 6, 8)
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...
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Note that we don't patch `datetime.date` globally, we patch `date` in the
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module that *uses* it. See :ref:`where to patch <where-to-patch>`.
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When `date.today()` is called a known date is returned, but calls to the
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`date(...)` constructor still return normal dates. Without this you can find
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yourself having to calculate an expected result using exactly the same
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algorithm as the code under test, which is a classic testing anti-pattern.
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Calls to the date constructor are recorded in the `mock_date` attributes
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(`call_count` and friends) which may also be useful for your tests.
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An alternative way of dealing with mocking dates, or other builtin classes,
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is discussed in `this blog entry
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<http://williamjohnbert.com/2011/07/how-to-unit-testing-in-django-with-mocking-and-patching/>`_.
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Mocking a Generator Method
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--------------------------
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A Python generator is a function or method that uses the `yield statement
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<http://docs.python.org/reference/simple_stmts.html#the-yield-statement>`_ to
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return a series of values when iterated over [#]_.
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A generator method / function is called to return the generator object. It is
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the generator object that is then iterated over. The protocol method for
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iteration is `__iter__
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<http://docs.python.org/library/stdtypes.html#container.__iter__>`_, so we can
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mock this using a `MagicMock`.
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Here's an example class with an "iter" method implemented as a generator:
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>>> class Foo(object):
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... def iter(self):
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... for i in [1, 2, 3]:
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... yield i
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...
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>>> foo = Foo()
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>>> list(foo.iter())
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[1, 2, 3]
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How would we mock this class, and in particular its "iter" method?
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To configure the values returned from the iteration (implicit in the call to
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`list`), we need to configure the object returned by the call to `foo.iter()`.
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>>> mock_foo = MagicMock()
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>>> mock_foo.iter.return_value = iter([1, 2, 3])
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>>> list(mock_foo.iter())
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[1, 2, 3]
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.. [#] There are also generator expressions and more `advanced uses
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<http://www.dabeaz.com/coroutines/index.html>`_ of generators, but we aren't
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concerned about them here. A very good introduction to generators and how
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powerful they are is: `Generator Tricks for Systems Programmers
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<http://www.dabeaz.com/generators/>`_.
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Applying the same patch to every test method
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--------------------------------------------
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If you want several patches in place for multiple test methods the obvious way
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is to apply the patch decorators to every method. This can feel like unnecessary
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repetition. For Python 2.6 or more recent you can use `patch` (in all its
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various forms) as a class decorator. This applies the patches to all test
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methods on the class. A test method is identified by methods whose names start
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with `test`:
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>>> @patch('mymodule.SomeClass')
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... class MyTest(TestCase):
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...
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... def test_one(self, MockSomeClass):
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... self.assertTrue(mymodule.SomeClass is MockSomeClass)
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...
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... def test_two(self, MockSomeClass):
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... self.assertTrue(mymodule.SomeClass is MockSomeClass)
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...
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... def not_a_test(self):
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... return 'something'
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...
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>>> MyTest('test_one').test_one()
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>>> MyTest('test_two').test_two()
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>>> MyTest('test_two').not_a_test()
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'something'
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An alternative way of managing patches is to use the :ref:`start-and-stop`.
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These allow you to move the patching into your `setUp` and `tearDown` methods.
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>>> class MyTest(TestCase):
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... def setUp(self):
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... self.patcher = patch('mymodule.foo')
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... self.mock_foo = self.patcher.start()
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...
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... def test_foo(self):
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... self.assertTrue(mymodule.foo is self.mock_foo)
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...
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... def tearDown(self):
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... self.patcher.stop()
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...
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>>> MyTest('test_foo').run()
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If you use this technique you must ensure that the patching is "undone" by
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calling `stop`. This can be fiddlier than you might think, because if an
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exception is raised in the setUp then tearDown is not called.
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:meth:`unittest.TestCase.addCleanup` makes this easier:
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>>> class MyTest(TestCase):
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... def setUp(self):
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... patcher = patch('mymodule.foo')
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... self.addCleanup(patcher.stop)
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... self.mock_foo = patcher.start()
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...
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... def test_foo(self):
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... self.assertTrue(mymodule.foo is self.mock_foo)
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...
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>>> MyTest('test_foo').run()
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Mocking Unbound Methods
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-----------------------
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Whilst writing tests today I needed to patch an *unbound method* (patching the
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method on the class rather than on the instance). I needed self to be passed
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in as the first argument because I want to make asserts about which objects
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were calling this particular method. The issue is that you can't patch with a
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mock for this, because if you replace an unbound method with a mock it doesn't
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become a bound method when fetched from the instance, and so it doesn't get
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self passed in. The workaround is to patch the unbound method with a real
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function instead. The :func:`patch` decorator makes it so simple to
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patch out methods with a mock that having to create a real function becomes a
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nuisance.
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If you pass `autospec=True` to patch then it does the patching with a
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*real* function object. This function object has the same signature as the one
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it is replacing, but delegates to a mock under the hood. You still get your
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mock auto-created in exactly the same way as before. What it means though, is
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that if you use it to patch out an unbound method on a class the mocked
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function will be turned into a bound method if it is fetched from an instance.
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It will have `self` passed in as the first argument, which is exactly what I
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wanted:
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>>> class Foo(object):
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... def foo(self):
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... pass
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...
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>>> with patch.object(Foo, 'foo', autospec=True) as mock_foo:
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... mock_foo.return_value = 'foo'
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... foo = Foo()
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... foo.foo()
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...
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'foo'
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>>> mock_foo.assert_called_once_with(foo)
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If we don't use `autospec=True` then the unbound method is patched out
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with a Mock instance instead, and isn't called with `self`.
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Checking multiple calls with mock
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---------------------------------
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mock has a nice API for making assertions about how your mock objects are used.
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>>> mock = Mock()
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>>> mock.foo_bar.return_value = None
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>>> mock.foo_bar('baz', spam='eggs')
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>>> mock.foo_bar.assert_called_with('baz', spam='eggs')
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If your mock is only being called once you can use the
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:meth:`assert_called_once_with` method that also asserts that the
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:attr:`call_count` is one.
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>>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
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>>> mock.foo_bar()
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>>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
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Traceback (most recent call last):
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...
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AssertionError: Expected to be called once. Called 2 times.
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Both `assert_called_with` and `assert_called_once_with` make assertions about
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the *most recent* call. If your mock is going to be called several times, and
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you want to make assertions about *all* those calls you can use
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:attr:`~Mock.call_args_list`:
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>>> mock = Mock(return_value=None)
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>>> mock(1, 2, 3)
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>>> mock(4, 5, 6)
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>>> mock()
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>>> mock.call_args_list
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[call(1, 2, 3), call(4, 5, 6), call()]
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The :data:`call` helper makes it easy to make assertions about these calls. You
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can build up a list of expected calls and compare it to `call_args_list`. This
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looks remarkably similar to the repr of the `call_args_list`:
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>>> expected = [call(1, 2, 3), call(4, 5, 6), call()]
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>>> mock.call_args_list == expected
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True
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Coping with mutable arguments
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-----------------------------
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Another situation is rare, but can bite you, is when your mock is called with
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mutable arguments. `call_args` and `call_args_list` store *references* to the
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arguments. If the arguments are mutated by the code under test then you can no
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longer make assertions about what the values were when the mock was called.
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Here's some example code that shows the problem. Imagine the following functions
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defined in 'mymodule'::
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def frob(val):
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pass
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def grob(val):
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"First frob and then clear val"
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frob(val)
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val.clear()
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When we try to test that `grob` calls `frob` with the correct argument look
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what happens:
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>>> with patch('mymodule.frob') as mock_frob:
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... val = set([6])
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... mymodule.grob(val)
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...
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>>> val
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set([])
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>>> mock_frob.assert_called_with(set([6]))
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Traceback (most recent call last):
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...
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AssertionError: Expected: ((set([6]),), {})
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Called with: ((set([]),), {})
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One possibility would be for mock to copy the arguments you pass in. This
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could then cause problems if you do assertions that rely on object identity
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for equality.
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Here's one solution that uses the :attr:`side_effect`
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functionality. If you provide a `side_effect` function for a mock then
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`side_effect` will be called with the same args as the mock. This gives us an
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opportunity to copy the arguments and store them for later assertions. In this
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example I'm using *another* mock to store the arguments so that I can use the
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mock methods for doing the assertion. Again a helper function sets this up for
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me.
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>>> from copy import deepcopy
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>>> from unittest.mock import Mock, patch, DEFAULT
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>>> def copy_call_args(mock):
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... new_mock = Mock()
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... def side_effect(*args, **kwargs):
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... args = deepcopy(args)
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... kwargs = deepcopy(kwargs)
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... new_mock(*args, **kwargs)
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... return DEFAULT
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... mock.side_effect = side_effect
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... return new_mock
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...
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>>> with patch('mymodule.frob') as mock_frob:
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... new_mock = copy_call_args(mock_frob)
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... val = set([6])
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... mymodule.grob(val)
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...
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>>> new_mock.assert_called_with(set([6]))
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>>> new_mock.call_args
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call(set([6]))
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`copy_call_args` is called with the mock that will be called. It returns a new
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mock that we do the assertion on. The `side_effect` function makes a copy of
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the args and calls our `new_mock` with the copy.
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.. note::
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If your mock is only going to be used once there is an easier way of
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checking arguments at the point they are called. You can simply do the
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checking inside a `side_effect` function.
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>>> def side_effect(arg):
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... assert arg == set([6])
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...
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>>> mock = Mock(side_effect=side_effect)
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>>> mock(set([6]))
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>>> mock(set())
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Traceback (most recent call last):
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...
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AssertionError
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An alternative approach is to create a subclass of `Mock` or `MagicMock` that
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copies (using :func:`copy.deepcopy`) the arguments.
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Here's an example implementation:
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>>> from copy import deepcopy
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>>> class CopyingMock(MagicMock):
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... def __call__(self, *args, **kwargs):
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... args = deepcopy(args)
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... kwargs = deepcopy(kwargs)
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... return super(CopyingMock, self).__call__(*args, **kwargs)
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...
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>>> c = CopyingMock(return_value=None)
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>>> arg = set()
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>>> c(arg)
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>>> arg.add(1)
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>>> c.assert_called_with(set())
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>>> c.assert_called_with(arg)
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Traceback (most recent call last):
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...
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AssertionError: Expected call: mock(set([1]))
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Actual call: mock(set([]))
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>>> c.foo
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<CopyingMock name='mock.foo' id='...'>
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When you subclass `Mock` or `MagicMock` all dynamically created attributes,
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and the `return_value` will use your subclass automatically. That means all
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children of a `CopyingMock` will also have the type `CopyingMock`.
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Multiple calls with different effects
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-------------------------------------
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Handling code that needs to behave differently on subsequent calls during the
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test can be tricky. For example you may have a function that needs to raise
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an exception the first time it is called but returns a response on the second
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call (testing retry behaviour).
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One approach is to use a :attr:`side_effect` function that replaces itself. The
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first time it is called the `side_effect` sets a new `side_effect` that will
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be used for the second call. It then raises an exception:
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>>> def side_effect(*args):
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... def second_call(*args):
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... return 'response'
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... mock.side_effect = second_call
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... raise Exception('boom')
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...
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>>> mock = Mock(side_effect=side_effect)
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>>> mock('first')
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Traceback (most recent call last):
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...
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Exception: boom
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>>> mock('second')
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'response'
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>>> mock.assert_called_with('second')
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Another perfectly valid way would be to pop return values from a list. If the
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return value is an exception, raise it instead of returning it:
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>>> returns = [Exception('boom'), 'response']
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>>> def side_effect(*args):
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... result = returns.pop(0)
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... if isinstance(result, Exception):
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... raise result
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... return result
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...
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>>> mock = Mock(side_effect=side_effect)
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>>> mock('first')
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Traceback (most recent call last):
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...
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Exception: boom
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>>> mock('second')
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'response'
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>>> mock.assert_called_with('second')
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Which approach you prefer is a matter of taste. The first approach is actually
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a line shorter but maybe the second approach is more readable.
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Nesting Patches
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---------------
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Using patch as a context manager is nice, but if you do multiple patches you
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can end up with nested with statements indenting further and further to the
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right:
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>>> class MyTest(TestCase):
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...
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... def test_foo(self):
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... with patch('mymodule.Foo') as mock_foo:
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... with patch('mymodule.Bar') as mock_bar:
|
|
... with patch('mymodule.Spam') as mock_spam:
|
|
... assert mymodule.Foo is mock_foo
|
|
... assert mymodule.Bar is mock_bar
|
|
... assert mymodule.Spam is mock_spam
|
|
...
|
|
>>> original = mymodule.Foo
|
|
>>> MyTest('test_foo').test_foo()
|
|
>>> assert mymodule.Foo is original
|
|
|
|
With unittest `cleanup` functions and the :ref:`start-and-stop` we can
|
|
achieve the same effect without the nested indentation. A simple helper
|
|
method, `create_patch`, puts the patch in place and returns the created mock
|
|
for us:
|
|
|
|
>>> class MyTest(TestCase):
|
|
...
|
|
... def create_patch(self, name):
|
|
... patcher = patch(name)
|
|
... thing = patcher.start()
|
|
... self.addCleanup(patcher.stop)
|
|
... return thing
|
|
...
|
|
... def test_foo(self):
|
|
... mock_foo = self.create_patch('mymodule.Foo')
|
|
... mock_bar = self.create_patch('mymodule.Bar')
|
|
... mock_spam = self.create_patch('mymodule.Spam')
|
|
...
|
|
... assert mymodule.Foo is mock_foo
|
|
... assert mymodule.Bar is mock_bar
|
|
... assert mymodule.Spam is mock_spam
|
|
...
|
|
>>> original = mymodule.Foo
|
|
>>> MyTest('test_foo').run()
|
|
>>> assert mymodule.Foo is original
|
|
|
|
|
|
Mocking a dictionary with MagicMock
|
|
-----------------------------------
|
|
|
|
You may want to mock a dictionary, or other container object, recording all
|
|
access to it whilst having it still behave like a dictionary.
|
|
|
|
We can do this with :class:`MagicMock`, which will behave like a dictionary,
|
|
and using :data:`~Mock.side_effect` to delegate dictionary access to a real
|
|
underlying dictionary that is under our control.
|
|
|
|
When the `__getitem__` and `__setitem__` methods of our `MagicMock` are called
|
|
(normal dictionary access) then `side_effect` is called with the key (and in
|
|
the case of `__setitem__` the value too). We can also control what is returned.
|
|
|
|
After the `MagicMock` has been used we can use attributes like
|
|
:data:`~Mock.call_args_list` to assert about how the dictionary was used:
|
|
|
|
>>> my_dict = {'a': 1, 'b': 2, 'c': 3}
|
|
>>> def getitem(name):
|
|
... return my_dict[name]
|
|
...
|
|
>>> def setitem(name, val):
|
|
... my_dict[name] = val
|
|
...
|
|
>>> mock = MagicMock()
|
|
>>> mock.__getitem__.side_effect = getitem
|
|
>>> mock.__setitem__.side_effect = setitem
|
|
|
|
.. note::
|
|
|
|
An alternative to using `MagicMock` is to use `Mock` and *only* provide
|
|
the magic methods you specifically want:
|
|
|
|
>>> mock = Mock()
|
|
>>> mock.__setitem__ = Mock(side_effect=getitem)
|
|
>>> mock.__getitem__ = Mock(side_effect=setitem)
|
|
|
|
A *third* option is to use `MagicMock` but passing in `dict` as the `spec`
|
|
(or `spec_set`) argument so that the `MagicMock` created only has
|
|
dictionary magic methods available:
|
|
|
|
>>> mock = MagicMock(spec_set=dict)
|
|
>>> mock.__getitem__.side_effect = getitem
|
|
>>> mock.__setitem__.side_effect = setitem
|
|
|
|
With these side effect functions in place, the `mock` will behave like a normal
|
|
dictionary but recording the access. It even raises a `KeyError` if you try
|
|
to access a key that doesn't exist.
|
|
|
|
>>> mock['a']
|
|
1
|
|
>>> mock['c']
|
|
3
|
|
>>> mock['d']
|
|
Traceback (most recent call last):
|
|
...
|
|
KeyError: 'd'
|
|
>>> mock['b'] = 'fish'
|
|
>>> mock['d'] = 'eggs'
|
|
>>> mock['b']
|
|
'fish'
|
|
>>> mock['d']
|
|
'eggs'
|
|
|
|
After it has been used you can make assertions about the access using the normal
|
|
mock methods and attributes:
|
|
|
|
>>> mock.__getitem__.call_args_list
|
|
[call('a'), call('c'), call('d'), call('b'), call('d')]
|
|
>>> mock.__setitem__.call_args_list
|
|
[call('b', 'fish'), call('d', 'eggs')]
|
|
>>> my_dict
|
|
{'a': 1, 'c': 3, 'b': 'fish', 'd': 'eggs'}
|
|
|
|
|
|
Mock subclasses and their attributes
|
|
------------------------------------
|
|
|
|
There are various reasons why you might want to subclass `Mock`. One reason
|
|
might be to add helper methods. Here's a silly example:
|
|
|
|
>>> class MyMock(MagicMock):
|
|
... def has_been_called(self):
|
|
... return self.called
|
|
...
|
|
>>> mymock = MyMock(return_value=None)
|
|
>>> mymock
|
|
<MyMock id='...'>
|
|
>>> mymock.has_been_called()
|
|
False
|
|
>>> mymock()
|
|
>>> mymock.has_been_called()
|
|
True
|
|
|
|
The standard behaviour for `Mock` instances is that attributes and the return
|
|
value mocks are of the same type as the mock they are accessed on. This ensures
|
|
that `Mock` attributes are `Mocks` and `MagicMock` attributes are `MagicMocks`
|
|
[#]_. So if you're subclassing to add helper methods then they'll also be
|
|
available on the attributes and return value mock of instances of your
|
|
subclass.
|
|
|
|
>>> mymock.foo
|
|
<MyMock name='mock.foo' id='...'>
|
|
>>> mymock.foo.has_been_called()
|
|
False
|
|
>>> mymock.foo()
|
|
<MyMock name='mock.foo()' id='...'>
|
|
>>> mymock.foo.has_been_called()
|
|
True
|
|
|
|
Sometimes this is inconvenient. For example, `one user
|
|
<https://code.google.com/p/mock/issues/detail?id=105>`_ is subclassing mock to
|
|
created a `Twisted adaptor
|
|
<http://twistedmatrix.com/documents/11.0.0/api/twisted.python.components.html>`_.
|
|
Having this applied to attributes too actually causes errors.
|
|
|
|
`Mock` (in all its flavours) uses a method called `_get_child_mock` to create
|
|
these "sub-mocks" for attributes and return values. You can prevent your
|
|
subclass being used for attributes by overriding this method. The signature is
|
|
that it takes arbitrary keyword arguments (`**kwargs`) which are then passed
|
|
onto the mock constructor:
|
|
|
|
>>> class Subclass(MagicMock):
|
|
... def _get_child_mock(self, **kwargs):
|
|
... return MagicMock(**kwargs)
|
|
...
|
|
>>> mymock = Subclass()
|
|
>>> mymock.foo
|
|
<MagicMock name='mock.foo' id='...'>
|
|
>>> assert isinstance(mymock, Subclass)
|
|
>>> assert not isinstance(mymock.foo, Subclass)
|
|
>>> assert not isinstance(mymock(), Subclass)
|
|
|
|
.. [#] An exception to this rule are the non-callable mocks. Attributes use the
|
|
callable variant because otherwise non-callable mocks couldn't have callable
|
|
methods.
|
|
|
|
|
|
Mocking imports with patch.dict
|
|
-------------------------------
|
|
|
|
One situation where mocking can be hard is where you have a local import inside
|
|
a function. These are harder to mock because they aren't using an object from
|
|
the module namespace that we can patch out.
|
|
|
|
Generally local imports are to be avoided. They are sometimes done to prevent
|
|
circular dependencies, for which there is *usually* a much better way to solve
|
|
the problem (refactor the code) or to prevent "up front costs" by delaying the
|
|
import. This can also be solved in better ways than an unconditional local
|
|
import (store the module as a class or module attribute and only do the import
|
|
on first use).
|
|
|
|
That aside there is a way to use `mock` to affect the results of an import.
|
|
Importing fetches an *object* from the `sys.modules` dictionary. Note that it
|
|
fetches an *object*, which need not be a module. Importing a module for the
|
|
first time results in a module object being put in `sys.modules`, so usually
|
|
when you import something you get a module back. This need not be the case
|
|
however.
|
|
|
|
This means you can use :func:`patch.dict` to *temporarily* put a mock in place
|
|
in `sys.modules`. Any imports whilst this patch is active will fetch the mock.
|
|
When the patch is complete (the decorated function exits, the with statement
|
|
body is complete or `patcher.stop()` is called) then whatever was there
|
|
previously will be restored safely.
|
|
|
|
Here's an example that mocks out the 'fooble' module.
|
|
|
|
>>> mock = Mock()
|
|
>>> with patch.dict('sys.modules', {'fooble': mock}):
|
|
... import fooble
|
|
... fooble.blob()
|
|
...
|
|
<Mock name='mock.blob()' id='...'>
|
|
>>> assert 'fooble' not in sys.modules
|
|
>>> mock.blob.assert_called_once_with()
|
|
|
|
As you can see the `import fooble` succeeds, but on exit there is no 'fooble'
|
|
left in `sys.modules`.
|
|
|
|
This also works for the `from module import name` form:
|
|
|
|
>>> mock = Mock()
|
|
>>> with patch.dict('sys.modules', {'fooble': mock}):
|
|
... from fooble import blob
|
|
... blob.blip()
|
|
...
|
|
<Mock name='mock.blob.blip()' id='...'>
|
|
>>> mock.blob.blip.assert_called_once_with()
|
|
|
|
With slightly more work you can also mock package imports:
|
|
|
|
>>> mock = Mock()
|
|
>>> modules = {'package': mock, 'package.module': mock.module}
|
|
>>> with patch.dict('sys.modules', modules):
|
|
... from package.module import fooble
|
|
... fooble()
|
|
...
|
|
<Mock name='mock.module.fooble()' id='...'>
|
|
>>> mock.module.fooble.assert_called_once_with()
|
|
|
|
|
|
Tracking order of calls and less verbose call assertions
|
|
--------------------------------------------------------
|
|
|
|
The :class:`Mock` class allows you to track the *order* of method calls on
|
|
your mock objects through the :attr:`~Mock.method_calls` attribute. This
|
|
doesn't allow you to track the order of calls between separate mock objects,
|
|
however we can use :attr:`~Mock.mock_calls` to achieve the same effect.
|
|
|
|
Because mocks track calls to child mocks in `mock_calls`, and accessing an
|
|
arbitrary attribute of a mock creates a child mock, we can create our separate
|
|
mocks from a parent one. Calls to those child mock will then all be recorded,
|
|
in order, in the `mock_calls` of the parent:
|
|
|
|
>>> manager = Mock()
|
|
>>> mock_foo = manager.foo
|
|
>>> mock_bar = manager.bar
|
|
|
|
>>> mock_foo.something()
|
|
<Mock name='mock.foo.something()' id='...'>
|
|
>>> mock_bar.other.thing()
|
|
<Mock name='mock.bar.other.thing()' id='...'>
|
|
|
|
>>> manager.mock_calls
|
|
[call.foo.something(), call.bar.other.thing()]
|
|
|
|
We can then assert about the calls, including the order, by comparing with
|
|
the `mock_calls` attribute on the manager mock:
|
|
|
|
>>> expected_calls = [call.foo.something(), call.bar.other.thing()]
|
|
>>> manager.mock_calls == expected_calls
|
|
True
|
|
|
|
If `patch` is creating, and putting in place, your mocks then you can attach
|
|
them to a manager mock using the :meth:`~Mock.attach_mock` method. After
|
|
attaching calls will be recorded in `mock_calls` of the manager.
|
|
|
|
>>> manager = MagicMock()
|
|
>>> with patch('mymodule.Class1') as MockClass1:
|
|
... with patch('mymodule.Class2') as MockClass2:
|
|
... manager.attach_mock(MockClass1, 'MockClass1')
|
|
... manager.attach_mock(MockClass2, 'MockClass2')
|
|
... MockClass1().foo()
|
|
... MockClass2().bar()
|
|
...
|
|
<MagicMock name='mock.MockClass1().foo()' id='...'>
|
|
<MagicMock name='mock.MockClass2().bar()' id='...'>
|
|
>>> manager.mock_calls
|
|
[call.MockClass1(),
|
|
call.MockClass1().foo(),
|
|
call.MockClass2(),
|
|
call.MockClass2().bar()]
|
|
|
|
If many calls have been made, but you're only interested in a particular
|
|
sequence of them then an alternative is to use the
|
|
:meth:`~Mock.assert_has_calls` method. This takes a list of calls (constructed
|
|
with the :data:`call` object). If that sequence of calls are in
|
|
:attr:`~Mock.mock_calls` then the assert succeeds.
|
|
|
|
>>> m = MagicMock()
|
|
>>> m().foo().bar().baz()
|
|
<MagicMock name='mock().foo().bar().baz()' id='...'>
|
|
>>> m.one().two().three()
|
|
<MagicMock name='mock.one().two().three()' id='...'>
|
|
>>> calls = call.one().two().three().call_list()
|
|
>>> m.assert_has_calls(calls)
|
|
|
|
Even though the chained call `m.one().two().three()` aren't the only calls that
|
|
have been made to the mock, the assert still succeeds.
|
|
|
|
Sometimes a mock may have several calls made to it, and you are only interested
|
|
in asserting about *some* of those calls. You may not even care about the
|
|
order. In this case you can pass `any_order=True` to `assert_has_calls`:
|
|
|
|
>>> m = MagicMock()
|
|
>>> m(1), m.two(2, 3), m.seven(7), m.fifty('50')
|
|
(...)
|
|
>>> calls = [call.fifty('50'), call(1), call.seven(7)]
|
|
>>> m.assert_has_calls(calls, any_order=True)
|
|
|
|
|
|
More complex argument matching
|
|
------------------------------
|
|
|
|
Using the same basic concept as :data:`ANY` we can implement matchers to do more
|
|
complex assertions on objects used as arguments to mocks.
|
|
|
|
Suppose we expect some object to be passed to a mock that by default
|
|
compares equal based on object identity (which is the Python default for user
|
|
defined classes). To use :meth:`~Mock.assert_called_with` we would need to pass
|
|
in the exact same object. If we are only interested in some of the attributes
|
|
of this object then we can create a matcher that will check these attributes
|
|
for us.
|
|
|
|
You can see in this example how a 'standard' call to `assert_called_with` isn't
|
|
sufficient:
|
|
|
|
>>> class Foo(object):
|
|
... def __init__(self, a, b):
|
|
... self.a, self.b = a, b
|
|
...
|
|
>>> mock = Mock(return_value=None)
|
|
>>> mock(Foo(1, 2))
|
|
>>> mock.assert_called_with(Foo(1, 2))
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError: Expected: call(<__main__.Foo object at 0x...>)
|
|
Actual call: call(<__main__.Foo object at 0x...>)
|
|
|
|
A comparison function for our `Foo` class might look something like this:
|
|
|
|
>>> def compare(self, other):
|
|
... if not type(self) == type(other):
|
|
... return False
|
|
... if self.a != other.a:
|
|
... return False
|
|
... if self.b != other.b:
|
|
... return False
|
|
... return True
|
|
...
|
|
|
|
And a matcher object that can use comparison functions like this for its
|
|
equality operation would look something like this:
|
|
|
|
>>> class Matcher(object):
|
|
... def __init__(self, compare, some_obj):
|
|
... self.compare = compare
|
|
... self.some_obj = some_obj
|
|
... def __eq__(self, other):
|
|
... return self.compare(self.some_obj, other)
|
|
...
|
|
|
|
Putting all this together:
|
|
|
|
>>> match_foo = Matcher(compare, Foo(1, 2))
|
|
>>> mock.assert_called_with(match_foo)
|
|
|
|
The `Matcher` is instantiated with our compare function and the `Foo` object
|
|
we want to compare against. In `assert_called_with` the `Matcher` equality
|
|
method will be called, which compares the object the mock was called with
|
|
against the one we created our matcher with. If they match then
|
|
`assert_called_with` passes, and if they don't an `AssertionError` is raised:
|
|
|
|
>>> match_wrong = Matcher(compare, Foo(3, 4))
|
|
>>> mock.assert_called_with(match_wrong)
|
|
Traceback (most recent call last):
|
|
...
|
|
AssertionError: Expected: ((<Matcher object at 0x...>,), {})
|
|
Called with: ((<Foo object at 0x...>,), {})
|
|
|
|
With a bit of tweaking you could have the comparison function raise the
|
|
`AssertionError` directly and provide a more useful failure message.
|
|
|
|
As of version 1.5, the Python testing library `PyHamcrest
|
|
<http://pypi.python.org/pypi/PyHamcrest>`_ provides similar functionality,
|
|
that may be useful here, in the form of its equality matcher
|
|
(`hamcrest.library.integration.match_equality
|
|
<http://packages.python.org/PyHamcrest/integration.html#hamcrest.library.integration.match_equality>`_).
|