Call Recorder V2.3.6 Patched Apk
Teodosio Shepperd
Once the mock has been called its called attribute is set toTrue. More importantly we can use the assert_called_with() orassert_called_once_with() method to check that it was called withthe correct arguments.
In the last example we patched a method directly on an object to check that itwas called correctly. Another common use case is to pass an object into amethod (or some part of the system under test) and then check that it is usedin the correct way.
In the example below we have a function some_function that instantiates Fooand calls a method on it. The call to patch() replaces the class Foo with amock. The Foo instance is the result of calling the mock, so it is configuredby modifying the mock return_value.
It can be useful to give your mocks a name. The name is shown in the repr ofthe mock and can be helpful when the mock appears in test failure messages. Thename is also propagated to attributes or methods of the mock:
If you make an assertion about mock_calls and any unexpected methodshave been called, then the assertion will fail. This is useful because as wellas asserting that the calls you expected have been made, you are also checkingthat they were made in the right order and with no additional calls:
Sometimes you want to mock up a more complex situation, like for examplemock.connection.cursor().execute("SELECT 1"). If we wanted this call toreturn a list, then we have to configure the result of the nested call.
side_effect can also be set to a function or an iterable. The use case forside_effect as an iterable is where your mock is going to be called severaltimes, and you want each call to return a different value. When you setside_effect to an iterable every call to the mock returns the next valuefrom the iterable:
For more advanced use cases, like dynamically varying the return valuesdepending on what the mock is called with, side_effect can be a function.The function will be called with the same arguments as the mock. Whatever thefunction returns is what the call returns:
One problem with over use of mocking is that it couples your tests to theimplementation of your mocks rather than your real code. Suppose you have aclass that implements some_method. In a test for another class, youprovide a mock of this object that also provides some_method. If lateryou refactor the first class, so that it no longer has some_method - thenyour tests will continue to pass even though your code is now broken!
A common need in tests is to patch a class attribute or a module attribute,for example patching a builtin or patching a class in a module to test that itis instantiated. Modules and classes are effectively global, so patching onthem has to be undone after the test or the patch will persist into othertests and cause hard to diagnose problems.
When you nest patch decorators the mocks are passed in to the decoratedfunction in the same order they applied (the normal python order thatdecorators are applied). This means from the bottom up, so in the exampleabove the mock for test_module.ClassName2 is passed in first.
Mocking chained calls is actually straightforward with mock once youunderstand the return_value attribute. When a mock is called forthe first time, or you fetch its return_value before it has been called, anew Mock is created.
Using mock_calls we can check the chained call with a singleassert. A chained call is several calls in one line of code, so there will beseveral entries in mock_calls. We can use call.call_list() to createthis list of calls for us:
The patch decorator is used here tomock out the date class in the module under test. The side_effectattribute on the mock date class is then set to a lambda function that returnsa real date. When the mock date class is called a real date will beconstructed and returned by side_effect.
When date.today() is called a known date is returned, but calls to thedate(...) constructor still return normal dates. Without this you can findyourself having to calculate an expected result using exactly the samealgorithm as the code under test, which is a classic testing anti-pattern.
A generator method / function is called to return the generator object. It isthe generator object that is then iterated over. The protocol method foriteration is __iter__(), so we canmock this using a MagicMock.
If you want several patches in place for multiple test methods the obvious wayis to apply the patch decorators to every method. This can feel like unnecessaryrepetition. For Python 2.6 or more recent you can use patch() (in all itsvarious forms) as a class decorator. This applies the patches to all testmethods on the class. A test method is identified by methods whose names startwith test:
If you pass autospec=True to patch then it does the patching with areal function object. This function object has the same signature as the oneit is replacing, but delegates to a mock under the hood. You still get yourmock auto-created in exactly the same way as before. What it means though, isthat if you use it to patch out an unbound method on a class the mockedfunction 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 Iwanted:
Both assert_called_with and assert_called_once_with make assertions aboutthe most recent call. If your mock is going to be called several times, andyou want to make assertions about all those calls you can usecall_args_list:
The call helper makes it easy to make assertions about these calls. Youcan build up a list of expected calls and compare it to call_args_list. Thislooks remarkably similar to the repr of the call_args_list:
Another situation is rare, but can bite you, is when your mock is called withmutable arguments. call_args and call_args_list store references to thearguments. If the arguments are mutated by the code under test then you can nolonger make assertions about what the values were when the mock was called.
When you subclass Mock or MagicMock all dynamically created attributes,and the return_value will use your subclass automatically. That means allchildren of a CopyingMock will also have the type CopyingMock.
With unittest cleanup functions and the patch methods: start and stop we canachieve the same effect without the nested indentation. A simple helpermethod, create_patch, puts the patch in place and returns the created mockfor us:
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 itfetches an object, which need not be a module. Importing a module for thefirst time results in a module object being put in sys.modules, so usuallywhen you import something you get a module back. This need not be the casehowever.
This means you can use patch.dict() to temporarily put a mock in placein sys.modules. Any imports whilst this patch is active will fetch the mock.When the patch is complete (the decorated function exits, the with statementbody is complete or patcher.stop() is called) then whatever was therepreviously will be restored safely.
Because mocks track calls to child mocks in mock_calls, and accessing anarbitrary attribute of a mock creates a child mock, we can create our separatemocks from a parent one. Calls to those child mock will then all be recorded,in order, in the mock_calls of the parent:
Sometimes a mock may have several calls made to it, and you are only interestedin asserting about some of those calls. You may not even care about theorder. In this case you can pass any_order=True to assert_has_calls:
Suppose we expect some object to be passed to a mock that by defaultcompares equal based on object identity (which is the Python default for userdefined classes). To use assert_called_with() we would need to passin the exact same object. If we are only interested in some of the attributesof this object then we can create a matcher that will check these attributesfor us.
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