Further Examples — Mock v0.7.0 documentation

Mock v0.7.0 documentation

# Further Examples

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# Further Examples

Here are some more examples for some slightly more advanced scenarios than in the getting started guide.

## Mocking chained calls

Mocking chained calls is actually straightforward with mock once you understand the `Mock.return_value` attribute. When a mock is called for the first time, or you fetch its return_value before it has been called, a new Mock is created.

This means that you can see how the object returned from a call to a mocked object has been used by interrogating the return_value mock:

    >>> mock = Mock()
    >>> mock().foo(a=2, b=3)
    <mock.Mock object at 0x...>
    >>> mock.return_value.foo.assert_called_with(a=2, b=3)

From here it is a simple step to configure and then make assertions about chained calls. Of course another alternative is writing your code in a more testable way in the first place...

So, suppose we have some code that looks a little bit like this:

    >>> class Something(object):
    ...     def __init__(self):
    ...         self.backend = BackendProvider()
    ...     def method(self):
    ...         response = self.backend.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
    ...         # more code

Assuming that BackendProvider is already well tested, how do we test method()? Specifically, we want to test that the code section `# more code` uses the response object in the correct way.

As this chain of calls is made from an instance attribute we can monkey patch the backend attribute on a Something instance. In this particular case we are only interested in the return value from the final call to start_call so we don’t have much configuration to do. Let’s assume the object it returns is ‘file-like’, so we’ll ensure that our response object uses the builtin `file` as its spec.

To do this we create a mock instance as our mock backend and create a mock response object for it. To set the response as the return value for that final start_call we could do this:

> `mock_backend.get_endpoint.return_value.create_call.return_value.start_call.return_value = mock_response`.

Here’s how we might do it in a slightly nicer way. We start by creating our initial mocks:

    >>> something = Something()
    >>> mock_response = Mock(spec=file)
    >>> mock_backend = Mock()
    >>> get_endpoint = mock_backend.get_endpoint
    >>> create_call = get_endpoint.return_value.create_call
    >>> start_call = create_call.return_value.start_call
    >>> start_call.return_value = mock_response

With these we monkey patch the “mock backend” in place and can make the real call:

    >>> something.backend = mock_backend
    >>> something.method()

Keeping references to the intermediate methods makes our assertions easier, and also makes the code less ugly.

    >>> get_endpoint.assert_called_with('foobar')
    >>> create_call.assert_called_with('spam', 'eggs')
    >>> start_call.assert_called_with()
    >>> # make assertions on mock_response about how it is used

## Partial mocking

In some tests I wanted to mock out a call to datetime.date.today() to return a known date, but I didn’t want to prevent the code under test from creating new date objects. Unfortunately datetime.date is written in C, and so I couldn’t just monkey-patch out the static date.today method.

I found a simple way of doing this that involved effectively wrapping the date class with a mock, but passing through calls to the constructor to the real class (and returning real instances).

The `patch decorator` is used here to mock out the date class in the module under test. The `side_effect` attribute on the mock date class is then set to a lambda function that returns a real date. When the mock date class is called a real date will be constructed and returned by side_effect.

    >>> from datetime import date
    >>> with patch('mymodule.date') as mock_date:
    ...     mock_date.today.return_value = date(2010, 10, 8)
    ...     mock_date.side_effect = lambda *args, **kw: date(*args, **kw)
    ...
    ...     assert mymodule.date.today() == date(2010, 10, 8)
    ...     assert mymodule.date(2009, 6, 8) == date(2009, 6, 8)
    ...

Note that we don’t patch datetime.date globally, we patch date in the module that uses it. See where to patch.

When date.today() is called a known date is returned, but calls to the date(...) constructor still return normal dates. Without this you can find yourself having to calculate an expected result using exactly the same algorithm as the code under test, which is a classic testing anti-pattern.

Calls to the date constructor are recorded in the mock_date attributes (call_count and friends) which may also be useful for your tests.

## Mocking open

Using open as a context manager is a great way to ensure your file handles are closed properly and is becoming common:

    with open('/some/path', 'w') as f:
        f.write('something')

The issue is that even if you mock out the call to open it is the returned object that is used as a context manager (and has __enter__ and __exit__ called).

So first the topic of creating a mock object that can be called, with the return value able to act as a context manager. The easiest way of doing this is to use the new `MagicMock`, which is preconfigured to be able to act as a context manger. As an added bonus we’ll use the spec argument to ensure that the mocked object can only be used in the same ways a real file could be used (attempting to access a method or attribute not on the file will raise an `AttributeError`):

    >>> mock_open = Mock()
    >>> mock_open.return_value = MagicMock(spec=file)

In terms of configuring our mock this is all that needs to be done. In fact it could be constructed with a one liner: `mock_open = Mock(return_value=MagicMock(spec=file))`.

So what is the best way of patching the builtin `open` function? One way would be to globally patch `__builtin__.open`. So long as you are sure that none of the other code being called also accesses `open` this is perfectly reasonable. It does make some people nervous however. By default we can’t patch the `open` name in the module where it is used, because `open` doesn’t exist as an attribute in that namespace. `patch` refuses to patch attributes that don’t exist because that is a great way of having tests that pass but code that is horribly broken (your code can access attributes that only exist during your tests!). `patch` will however create (and then remove again) non-existent attributes if you tell it that you are really sure you know what you’re doing.

By passing `create=True` into `patch` we can just patch the `open` function in the module under test instead of patching it globally:

    >>> open_name = '%s.open' % __name__
    >>> with patch(open_name, create=True) as mock_open:
    ...     mock_open.return_value = MagicMock(spec=file)
    ...
    ...     with open('/some/path', 'w') as f:
    ...         f.write('something')
    ...
    <mock.Mock object at 0x...>
    >>> file_handle = mock_open.return_value.__enter__.return_value
    >>> file_handle.write.assert_called_with('something')

## Applying the same patch to every test method

If you want several patches in place for multiple test methods the obvious way is to apply the patch decorators to every method. This can feel like unnecessary repetition. For Python 2.6 or more recent you can use patch (in all its various forms) as a class decorator. This applies the patches to all test methods on the class. A test method is identified by methods whose names start with test:

    >>> @patch('mymodule.SomeClass')
    ... class MyTest(unittest.TestCase):
    ...
    ...     def test_one(self, MockSomeClass):
    ...         self.assertTrue(mymodule.SomeClass is MockSomeClass)
    ...
    ...     def test_two(self, MockSomeClass):
    ...         self.assertTrue(mymodule.SomeClass is MockSomeClass)
    ...
    ...     def not_a_test(self):
    ...         return 'something'
    ...
    >>> MyTest('test_one').run()
    >>> MyTest('test_two').run()
    >>> MyTest('test_two').not_a_test()
    'something'

An alternative way of managing patches is to use the start and stop methods of patch. These allow you to move the patching into your setUp and tearDown methods.

    >>> class MyTest(unittest.TestCase):
    ...     def setUp(self):
    ...         self.patcher = patch('mymodule.foo')
    ...         self.mock_foo = self.patcher.start()
    ...
    ...     def test_foo(self):
    ...         self.assertTrue(mymodule.foo is self.mock_foo)
    ...
    ...     def tearDown(self):
    ...         self.patcher.stop()
    ...
    >>> MyTest('test_foo').run()

If you use this technique you must ensure that the patching is “undone” by calling stop. This can be fiddlier than you might think, because if an exception is raised in the setUp then tearDown is not called. unittest2 cleanup functions make this simpler:

    >>> class MyTest(unittest2.TestCase):
    ...     def setUp(self):
    ...         patcher = patch('mymodule.foo')
    ...         self.addCleanup(patcher.stop)
    ...         self.mock_foo = patcher.start()
    ...
    ...     def test_foo(self):
    ...         self.assertTrue(mymodule.foo is self.mock_foo)
    ...
    >>> MyTest('test_foo').run()

## Mocking Unbound Methods

Whilst writing tests today I needed to patch an unbound method (patching the method on the class rather than on the instance). I needed self to be passed in as the first argument because I want to make asserts about which objects were calling this particular method. The issue is that you can’t patch with a mock for this, because if you replace an unbound method with a mock it doesn’t become a bound method when fetched from the instance, and so it doesn’t get self passed in. The workaround is to patch the unbound method with a real function instead. The `patch()` decorator makes it so simple to patch out methods with a mock that having to create a real function becomes a nuisance.

If you pass `mocksignature=True` to patch then it does the patching with a real function object. This function object has the same signature as the one it is replacing, but delegates to a mock under the hood. You still get your mock auto-created in exactly the same way as before. What it means though, is that if you use it to patch out an unbound method on a class the mocked function will be turned into a bound method if it is fetched from an instance. It will have self passed in as the first argument, which is exactly what I wanted:

    >>> class Foo(object):
    ...   def foo(self):
    ...     pass
    ...
    >>> with patch.object(Foo, 'foo', mocksignature=True) as mock_foo:
    ...   mock_foo.return_value = 'foo'
    ...   foo = Foo()
    ...   foo.foo()
    ...
    'foo'
    >>> mock_foo.assert_called_once_with(foo)

If we don’t use mocksignature=True then the unbound method is patched out with a Mock instance instead, and isn’t called with self.

## Mocking Properties

A few people have asked about mocking properties, specifically tracking when properties are fetched from objects or even having side effects when properties are fetched.

You can already do this by subclassing `Mock` and providing your own property. Delegating to another mock is one way to record the property being accessed whilst still able to control things like return values:

    >>> mock_foo = Mock(return_value='fish')
    >>> class MyMock(Mock):
    ...     @property
    ...     def foo(self):
    ...         return mock_foo()
    ...
    >>> mock = MyMock()
    >>> mock.foo
    'fish'
    >>> mock_foo.assert_called_once_with()

## Checking multiple calls with mock

mock has a nice API for making assertions about how your mock objects are used.

    >>> mock = Mock()
    >>> mock.foo_bar.return_value = None
    >>> mock.foo_bar('baz', spam='eggs')
    >>> mock.foo_bar.assert_called_with('baz', spam='eggs')

If your mock is only being called once you can use the `assert_called_once_with()` method that also asserts that the `call_count` is one.

    >>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
    >>> mock.foo_bar()
    >>> mock.foo_bar.assert_called_once_with('baz', spam='eggs')
    Traceback (most recent call last):
        ...
    AssertionError: Expected to be called once. Called 2 times.

Both assert_called_with and assert_called_once_with make assertions about the most recent call. If your mock is going to be called several times, and you want to make assertions about all those calls, the API is not quite so nice.

All of the calls, in order, are stored in call_args_list as tuples of (positional args, keyword args).

    >>> mock = Mock(return_value=None)
    >>> mock(1, 2, 3)
    >>> mock(4, 5, 6)
    >>> mock()
    >>> mock.call_args_list
    [((1, 2, 3), {}), ((4, 5, 6), {}), ((), {})]

Because it stores positional args and keyword args, even if they are empty, the list is overly verbose which makes for ugly tests. It turns out that I do this rarely enough that I’ve never got around to improving it. One of the new features in 0.7.0 helps with this. The tuples of (positional, keyword) arguments are now custom objects that allow for ‘soft comparisons’ (implemented by Konrad Delong). This allows you to omit empty positional or keyword arguments from tuples you compare against.

    >>> mock.call_args_list
    [((1, 2, 3), {}), ((4, 5, 6), {}), ((), {})]
    >>> expected = [((1, 2, 3),), ((4, 5, 6),), ()]
    >>> mock.call_args_list == expected
    True

This is an improvement, but still not as nice as assert_called_with. Here’s a helper function that pops the last argument of the call args list and decrements the call count. This allows you to make asserts as a series of calls to assert_called_with followed by a pop_last_call.

    >>> def pop_last_call(mock):
    ...     if not mock.call_count:
    ...         raise AssertionError("Cannot pop last call: call_count is 0")
    ...     mock.call_args_list.pop()
    ...     try:
    ...         mock.call_args = mock.call_args_list[-1]
    ...     except IndexError:
    ...         mock.call_args = None
    ...         mock.called = False
    ...     mock.call_count -=1
    ...
    >>> mock = Mock(return_value=None)
    >>> mock(1, foo='bar')
    >>> mock(2, foo='baz')
    >>> mock(3, foo='spam')
    >>> mock.assert_called_with(3, foo='spam')
    >>> pop_last_call(mock)
    >>> mock.assert_called_with(2, foo='baz')
    >>> pop_last_call(mock)
    >>> mock.assert_called_once_with(1, foo='bar')

The calls to assert_called_with are made in reverse order to the actual calls. Your final call can be a call to assert_called_once_with, that ensures there were no extra calls you weren’t expecting. You could, if you wanted, extend the function to take args and kwargs and do the assert for you.

## Coping with mutable arguments

Another situation is rare, but can bite you, is when your mock is called with mutable arguments. call_args and call_args_list store references to the arguments. If the arguments are mutated by the code under test then you can no longer make assertions about what the values were when the mock was called.

Here’s some example code that shows the problem. Imagine the following functions defined in ‘mymodule’:

    def frob(val):
        pass

    def grob(val):
        "First frob and then clear val"
        frob(val)
        val.clear()

When we try to test that grob calls frob with the correct argument look what happens:

    >>> with patch('mymodule.frob') as mock_frob:
    ...     val = set([6])
    ...     mymodule.grob(val)
    ...
    >>> val
    set([])
    >>> mock_frob.assert_called_with(set([6]))
    Traceback (most recent call last):
        ...
    AssertionError: Expected: ((set([6]),), {})
    Called with: ((set([]),), {})

One possibility would be for mock to copy the arguments you pass in. This could then cause problems if you do assertions that rely on object identity for equality.

Here’s one solution that uses the `side_effect` functionality. If you provide a side_effect function for a mock then side_effect will be called with the same args as the mock. This gives us an opportunity to copy the arguments and store them for later assertions. In this example I’m using another mock to store the arguments so that I can use the mock methods for doing the assertion. Again a helper function sets this up for me.

    >>> from copy import deepcopy
    >>> from mock import Mock, patch, DEFAULT
    >>> def copy_call_args(mock):
    ...     new_mock = Mock()
    ...     def side_effect(*args, **kwargs):
    ...         args = deepcopy(args)
    ...         kwargs = deepcopy(kwargs)
    ...         new_mock(*args, **kwargs)
    ...         return DEFAULT
    ...     mock.side_effect = side_effect
    ...     return new_mock
    ...
    >>> with patch('mymodule.frob') as mock_frob:
    ...     new_mock = copy_call_args(mock_frob)
    ...     val = set([6])
    ...     mymodule.grob(val)
    ...
    >>> new_mock.assert_called_with(set([6]))
    >>> new_mock.call_args
    ((set([6]),), {})

copy_call_args is called with the mock that will be called. It returns a new mock that we do the assertion on. The side_effect function makes a copy of the args and calls our new_mock with the copy.

Note

If your mock is only going to be used once there is an easier way of checking arguments at the point they are called. You can simply do the checking inside a side_effect function.

    >>> def side_effect(arg):
    ...     assert arg == set([6])
    ...
    >>> mock = Mock(side_effect=side_effect)
    >>> mock(set([6]))
    >>> mock(set())
    Traceback (most recent call last):
     ...
    AssertionError

## Mocking multiple calls

Handling code that needs to behave differently on subsequent calls during the test can be tricky. For example you may have a function that needs to raise an exception the first time it is called but returns a response on the second call (testing retry behaviour).

One approach is to use a `side_effect` function that replaces itself. The first time it is called the side_effect sets a new side_effect that will be used for the second call. It then raises an exception:

    >>> def side_effect(*args):
    ...   def second_call(*args):
    ...     return 'response'
    ...   mock.side_effect = second_call
    ...   raise Exception('boom')
    ...
    >>> mock = Mock(side_effect=side_effect)
    >>> mock('first')
    Traceback (most recent call last):
      ...
    Exception: boom
    >>> mock('second')
    'response'
    >>> mock.assert_called_with('second')

Another perfectly valid way would be to pop return values from a list. If the return value is an exception, raise it instead of returning it:

    >>> returns = [Exception('boom'), 'response']
    >>> def side_effect(*args):
    ...   result = returns.pop(0)
    ...   if isinstance(result, Exception):
    ...     raise result
    ...   return result
    ...
    >>> mock = Mock(side_effect=side_effect)
    >>> mock('first')
    Traceback (most recent call last):
      ...
    Exception: boom
    >>> mock('second')
    'response'
    >>> mock.assert_called_with('second')

Which approach you prefer is a matter of taste. The first approach is actually a line shorter but maybe the second approach is more readable.