summaryrefslogtreecommitdiffstats
path: root/python/mock-1.0.0/html/_sources/examples.txt
blob: ecb994b156ab6f59cc33aba79a7cfdec7c0b0677 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
.. _further-examples:

==================
 Further Examples
==================

.. currentmodule:: mock

.. testsetup::

    from datetime import date

    BackendProvider = Mock()
    sys.modules['mymodule'] = mymodule = Mock(name='mymodule')

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

    mymodule.frob = lambda val: val
    mymodule.grob = grob
    mymodule.date = date

    class TestCase(unittest2.TestCase):
        def run(self):
            result = unittest2.TestResult()
            out = unittest2.TestCase.run(self, result)
            assert result.wasSuccessful()

    from mock import inPy3k



For comprehensive examples, see the unit tests included in the full source
distribution.

Here are some more examples for some slightly more advanced scenarios than in
the :ref:`getting started <getting-started>` guide.


Mocking chained calls
=====================

Mocking chained calls is actually straightforward with mock once you
understand the :attr:`~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:

.. doctest::

    >>> mock = Mock()
    >>> mock().foo(a=2, b=3)
    <Mock name='mock().foo()' id='...'>
    >>> 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:

.. doctest::

    >>> 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`.

We can do that in a slightly nicer way using the :meth:`~Mock.configure_mock`
method to directly set the return value for us:

.. doctest::

    >>> something = Something()
    >>> mock_response = Mock(spec=file)
    >>> mock_backend = Mock()
    >>> config = {'get_endpoint.return_value.create_call.return_value.start_call.return_value': mock_response}
    >>> mock_backend.configure_mock(**config)

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

.. doctest::

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

Using :attr:`~Mock.mock_calls` we can check the chained call with a single
assert. A chained call is several calls in one line of code, so there will be
several entries in `mock_calls`. We can use :meth:`call.call_list` to create
this list of calls for us:

.. doctest::

    >>> chained = call.get_endpoint('foobar').create_call('spam', 'eggs').start_call()
    >>> call_list = chained.call_list()
    >>> assert mock_backend.mock_calls == call_list


Partial mocking
===============

In some tests I wanted to mock out a call to `datetime.date.today()
<http://docs.python.org/library/datetime.html#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 :func:`patch decorator <patch>` is used here to
mock out the `date` class in the module under test. The :attr:`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`.

.. doctest::

    >>> 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 :ref:`where to patch <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.

An alternative way of dealing with mocking dates, or other builtin classes,
is discussed in `this blog entry
<http://williamjohnbert.com/2011/07/how-to-unit-testing-in-django-with-mocking-and-patching/>`_.


Mocking a Generator Method
==========================

A Python generator is a function or method that uses the `yield statement
<http://docs.python.org/reference/simple_stmts.html#the-yield-statement>`_ to
return a series of values when iterated over [#]_.

A generator method / function is called to return the generator object. It is
the generator object that is then iterated over. The protocol method for
iteration is `__iter__
<http://docs.python.org/library/stdtypes.html#container.__iter__>`_, so we can
mock this using a `MagicMock`.

Here's an example class with an "iter" method implemented as a generator:

.. doctest::

    >>> class Foo(object):
    ...     def iter(self):
    ...         for i in [1, 2, 3]:
    ...             yield i
    ...
    >>> foo = Foo()
    >>> list(foo.iter())
    [1, 2, 3]


How would we mock this class, and in particular its "iter" method?

To configure the values returned from the iteration (implicit in the call to
`list`), we need to configure the object returned by the call to `foo.iter()`.

.. doctest::

    >>> mock_foo = MagicMock()
    >>> mock_foo.iter.return_value = iter([1, 2, 3])
    >>> list(mock_foo.iter())
    [1, 2, 3]

.. [#] There are also generator expressions and more `advanced uses
    <http://www.dabeaz.com/coroutines/index.html>`_ of generators, but we aren't
    concerned about them here. A very good introduction to generators and how
    powerful they are is: `Generator Tricks for Systems Programmers
    <http://www.dabeaz.com/generators/>`_.


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`:

.. doctest::

    >>> @patch('mymodule.SomeClass')
    ... class MyTest(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').test_one()
    >>> MyTest('test_two').test_two()
    >>> MyTest('test_two').not_a_test()
    'something'

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

.. doctest::

    >>> class MyTest(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
<http://pypi.python.org/pypi/unittest2>`_ cleanup functions make this simpler:


.. doctest::

    >>> class MyTest(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 :func:`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 `autospec=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:

.. doctest::

    >>> class Foo(object):
    ...   def foo(self):
    ...     pass
    ...
    >>> with patch.object(Foo, 'foo', autospec=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 `autospec=True` then the unbound method is patched out
with a Mock instance instead, and isn't called with `self`.


Checking multiple calls with mock
=================================

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

.. doctest::

    >>> 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
:meth:`assert_called_once_with` method that also asserts that the
:attr:`call_count` is one.

.. doctest::

    >>> 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 you can use
:attr:`~Mock.call_args_list`:

.. doctest::

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

The :data:`call` helper makes it easy to make assertions about these calls. You
can build up a list of expected calls and compare it to `call_args_list`. This
looks remarkably similar to the repr of the `call_args_list`:

.. doctest::

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


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:

.. doctest::

    >>> 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 :attr:`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.

.. doctest::

    >>> 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
    call(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.

    .. doctest::

        >>> 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

An alternative approach is to create a subclass of `Mock` or `MagicMock` that
copies (using `copy.deepcopy
<http://docs.python.org/library/copy.html#copy.deepcopy>`_) the arguments.
Here's an example implementation:

.. doctest::

    >>> from copy import deepcopy
    >>> class CopyingMock(MagicMock):
    ...     def __call__(self, *args, **kwargs):
    ...         args = deepcopy(args)
    ...         kwargs = deepcopy(kwargs)
    ...         return super(CopyingMock, self).__call__(*args, **kwargs)
    ...
    >>> c = CopyingMock(return_value=None)
    >>> arg = set()
    >>> c(arg)
    >>> arg.add(1)
    >>> c.assert_called_with(set())
    >>> c.assert_called_with(arg)
    Traceback (most recent call last):
        ...
    AssertionError: Expected call: mock(set([1]))
    Actual call: mock(set([]))
    >>> c.foo
    <CopyingMock name='mock.foo' id='...'>

When you subclass `Mock` or `MagicMock` all dynamically created attributes,
and the `return_value` will use your subclass automatically. That means all
children of a `CopyingMock` will also have the type `CopyingMock`.


Raising exceptions on attribute access
======================================

You can use :class:`PropertyMock` to mimic the behaviour of properties. This
includes raising exceptions when an attribute is accessed.

Here's an example raising a `ValueError` when the 'foo' attribute is accessed:

.. doctest::

    >>> m = MagicMock()
    >>> p = PropertyMock(side_effect=ValueError)
    >>> type(m).foo = p
    >>> m.foo
    Traceback (most recent call last):
    ....
    ValueError

Because every mock object has its own type, a new subclass of whichever mock
class you're using, all mock objects are isolated from each other. You can
safely attach properties (or other descriptors or whatever you want in fact)
to `type(mock)` without affecting other mock objects.


Multiple calls with different effects
=====================================

.. note::

    In mock 1.0 the handling of iterable `side_effect` was changed. Any
    exceptions in the iterable will be raised instead of returned.

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 :attr:`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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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.


Nesting Patches
===============

Using patch as a context manager is nice, but if you do multiple patches you
can end up with nested with statements indenting further and further to the
right:

.. doctest::

    >>> class MyTest(TestCase):
    ...
    ...     def test_foo(self):
    ...         with patch('mymodule.Foo') as mock_foo:
    ...             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 unittest2_ `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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

    .. doctest::

        >>> 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:

    .. doctest::

        >>> 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.

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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.

.. doctest::

    >>> 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:

.. doctest::

    >>> 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.

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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.

.. doctest::

    >>> 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.

.. doctest::

    >>> 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`:

.. doctest::

    >>> 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 `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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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:

.. doctest::

    >>> 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>`_).


Less verbose configuration of mock objects
==========================================

This recipe, for easier configuration of mock objects, is now part of `Mock`.
See the :meth:`~Mock.configure_mock` method.


Matching any argument in assertions
===================================

This example is now built in to mock. See :data:`ANY`.


Mocking Properties
==================

This example is now built in to mock. See :class:`PropertyMock`.


Mocking open
============

This example is now built in to mock. See :func:`mock_open`.


Mocks without some attributes
=============================

This example is now built in to mock. See :ref:`deleting-attributes`.