python mock side_effect or return_value dependent on call_count

Question:

To test a polling function I want to mock the calling of a sub function so that the first time it is called it will fail, and the second time it is called it will succeed. Here’s a very simplified version of it:

poll_function(var1):
    value = sub_function(var1)  # First call will return None
    while not value:
        time.sleep(POLLING_INTERVAL)  
        value = sub_function(var1) # A subsequent call will return a string, e.g "data"
    return value

Is this possible to do with a Mock object from the mock framework? I know Mock objects have a call_count attribute I should be able to use somehow.

Right now I’ve solved it by creating a custom mock object that I use to monkey patch sub_function(), but I feel there should be a better less verbose way of doing it:

def test_poll():
    class MyMock(object):                                                      

        def __init__(self, *args):                                             
            self.call_count = 0                                                

        def sub_function(self, *args, **kwargs):                             
            if self.call_count > 1:                                            
                return "data"            
            else:                                                              
                self.call_count += 1                                           
                return None  

    my_mock = MyMock()                                                         
    with patch('sub_function', my_mock.sub_function):           
        ok_(poll_function())         
Asked By: imolit

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Answers:

If I understand your question correctly, you do it by setting side_effect to an iterable. For your simple case:

>>> mock_poll = Mock(side_effect=[None, 'data'])
>>> mock_poll()
None
>>> mock_poll()
'data'

If you want to allow for an unlimited number of calls, use the itertools cycle and chain functions:

>>> mock_poll = Mock(side_effect=chain(['first'], cycle(['others'])))
Answered By: aquavitae
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