Python标准库unittest.mock上手指南

  • Z时代
  • 2024-01-10
  • 分类：综合

python lib

3.3 新版功能.

使用 mock¶

模拟方法调用¶

使用 Mock 的常见场景：

  • 模拟函数调用

  • 记录“对象上的方法调用”

你可能需要替换一个对象上的方法，用于确认此方法被系统中的其他部分调用过，并且调用时使用了正确的参数。

>>> real=SomeClass()
>>> real.method=MagicMock(name='method')
>>> real.method(3,4,5,key='value')
<MagicMock name='method()' id='...'>

使用了 mock（本例中的 real.method）之后，它有方法和属性可以让你针对它是被如何使用的下断言。

注解

在多数示例中，Mock 与 MagicMock 两个类可以相互替换，而 MagicMock 是一个更适用的类，通常情况下，使用它就可以了。

如果 mock 被调用，它的 called 属性就会变成 True，更重要的是，我们可以使用 assert_called_with() 或者 assert_called_once_with() 方法来确认它在被调用时使用了正确的参数。

在如下的测试示例中，验证对于 ProductionClass().method 的调用会导致 something 的调用。

>>> classProductionClass:
... defmethod(self):
... self.something(1,2,3)
... defsomething(self,a,b,c):
... pass
...
>>> real=ProductionClass()
>>> real.something=MagicMock()
>>> real.method()
>>> real.something.assert_called_once_with(1,2,3)

对象上的方法调用的 mock¶

In the last example we patched a method directly on an object to check that it

was called correctly. Another common use case is to pass an object into a

method (or some part of the system under test) and then check that it is used

in the correct way.

The simple ProductionClass below has a closer method. If it is called with

an object then it calls close on it.

>>> classProductionClass:
... defcloser(self,something):
... something.close()
...

So to test it we need to pass in an object with a close method and check

that it was called correctly.

>>> real=ProductionClass()
>>> mock=Mock()
>>> real.closer(mock)
>>> mock.close.assert_called_with()

We don't have to do any work to provide the 'close' method on our mock.

Accessing close creates it. So, if 'close' hasn't already been called then

accessing it in the test will create it, but assert_called_with()

will raise a failure exception.

Mocking Classes¶

A common use case is to mock out classes instantiated by your code under test.

When you patch a class, then that class is replaced with a mock. Instances

are created by calling the class. This means you access the "mock instance"

by looking at the return value of the mocked class.

In the example below we have a function some_function that instantiates Foo

and calls a method on it. The call to patch() replaces the class Foo with a

mock. The Foo instance is the result of calling the mock, so it is configured

by modifying the mock return_value.

>>> defsome_function():
... instance=module.Foo()
... returninstance.method()
...
>>> withpatch('module.Foo')asmock:
... instance=mock.return_value
... instance.method.return_value='the result'
... result=some_function()
... assertresult=='the result'

Naming your mocks¶

It can be useful to give your mocks a name. The name is shown in the repr of

the mock and can be helpful when the mock appears in test failure messages. The

name is also propagated to attributes or methods of the mock:

>>> mock=MagicMock(name='foo')
>>> mock
<MagicMock name='foo' id='...'>
>>> mock.method
<MagicMock name='foo.method' id='...'>

Tracking all Calls¶

Often you want to track more than a single call to a method. The

mock_calls attribute records all calls

to child attributes of the mock - and also to their children.

>>> mock=MagicMock()
>>> mock.method()
<MagicMock name='mock.method()' id='...'>
>>> mock.attribute.method(10,x=53)
<MagicMock name='mock.attribute.method()' id='...'>
>>> mock.mock_calls
[call.method(), call.attribute.method(10, x=53)]

If you make an assertion about mock_calls and any unexpected methods

have been called, then the assertion will fail. This is useful because as well

as asserting that the calls you expected have been made, you are also checking

that they were made in the right order and with no additional calls:

You use the call object to construct lists for comparing with

mock_calls:

>>> expected=[call.method(),call.attribute.method(10,x=53)]
>>> mock.mock_calls==expected
True

However, parameters to calls that return mocks are not recorded, which means it is not

possible to track nested calls where the parameters used to create ancestors are important:

>>> m=Mock()
>>> m.factory(important=True).deliver()
<Mock name='mock.factory().deliver()' id='...'>
>>> m.mock_calls[-1]==call.factory(important=False).deliver()
True

Setting Return Values and Attributes¶

Setting the return values on a mock object is trivially easy:

>>> mock=Mock()
>>> mock.return_value=3
>>> mock()
3

Of course you can do the same for methods on the mock:

>>> mock=Mock()
>>> mock.method.return_value=3
>>> mock.method()
3

The return value can also be set in the constructor:

>>> mock=Mock(return_value=3)
>>> mock()
3

If you need an attribute setting on your mock, just do it:

>>> mock=Mock()
>>> mock.x=3
>>> mock.x
3

Sometimes you want to mock up a more complex situation, like for example

mock.connection.cursor().execute("SELECT1"). If we wanted this call to

return a list, then we have to configure the result of the nested call.

We can use call to construct the set of calls in a "chained call" like

this for easy assertion afterwards:

>>> mock=Mock()
>>> cursor=mock.connection.cursor.return_value
>>> cursor.execute.return_value=['foo']
>>> mock.connection.cursor().execute("SELECT 1")
['foo']
>>> expected=call.connection.cursor().execute("SELECT 1").call_list()
>>> mock.mock_calls
[call.connection.cursor(), call.connection.cursor().execute('SELECT 1')]
>>> mock.mock_calls==expected
True

It is the call to .call_list() that turns our call object into a list of

calls representing the chained calls.

Raising exceptions with mocks¶

A useful attribute is side_effect. If you set this to an

exception class or instance then the exception will be raised when the mock

is called.

>>> mock=Mock(side_effect=Exception('Boom!'))
>>> mock()
Traceback (most recent call last):
...
Exception: Boom!

Side effect functions and iterables¶

side_effect can also be set to a function or an iterable. The use case for

side_effect as an iterable is where your mock is going to be called several

times, and you want each call to return a different value. When you set

side_effect to an iterable every call to the mock returns the next value

from the iterable:

>>> mock=MagicMock(side_effect=[4,5,6])
>>> mock()
4
>>> mock()
5
>>> mock()
6

For more advanced use cases, like dynamically varying the return values

depending 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 the

function returns is what the call returns:

>>> vals={(1,2):1,(2,3):2}
>>> defside_effect(*args):
... returnvals[args]
...
>>> mock=MagicMock(side_effect=side_effect)
>>> mock(1,2)
1
>>> mock(2,3)
2

Creating a Mock from an Existing Object¶

One problem with over use of mocking is that it couples your tests to the

implementation of your mocks rather than your real code. Suppose you have a

class that implements some_method. In a test for another class, you

provide a mock of this object that also provides some_method. If later

you refactor the first class, so that it no longer has some_method - then

your tests will continue to pass even though your code is now broken!

Mock allows you to provide an object as a specification for the mock,

using the spec keyword argument. Accessing methods / attributes on the

mock that don't exist on your specification object will immediately raise an

attribute error. If you change the implementation of your specification, then

tests that use that class will start failing immediately without you having to

instantiate the class in those tests.

>>> mock=Mock(spec=SomeClass)
>>> mock.old_method()
Traceback (most recent call last):
...
AttributeError: object has no attribute 'old_method'

Using a specification also enables a smarter matching of calls made to the

mock, regardless of whether some parameters were passed as positional or

named arguments:

>>> deff(a,b,c):pass
...
>>> mock=Mock(spec=f)
>>> mock(1,2,3)
<Mock name='mock()' id='140161580456576'>
>>> mock.assert_called_with(a=1,b=2,c=3)

If you want this smarter matching to also work with method calls on the mock,

you can use auto-speccing.

If you want a spaner form of specification that prevents the setting

of arbitrary attributes as well as the getting of them then you can use

spec_set instead of spec.

Patch Decorators¶

注解

在查找对象的名称空间中修补对象使用 patch() 。使用起来很简单，阅读 在哪里打补丁 来快速上手。

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 it

is instantiated. Modules and classes are effectively global, so patching on

them has to be undone after the test or the patch will persist into other

tests and cause hard to diagnose problems.

mock provides three convenient decorators for this: patch(), patch.object() and

patch.dict(). patch takes a single string, of the form

package.module.Class.attribute to specify the attribute you are patching. It

also optionally takes a value that you want the attribute (or class or

whatever) to be replaced with. 'patch.object' takes an object and the name of

the attribute you would like patched, plus optionally the value to patch it

with.

patch.object:

>>> original=SomeClass.attribute
>>> @patch.object(SomeClass,'attribute',sentinel.attribute)
... deftest():
... assertSomeClass.attribute==sentinel.attribute
...
>>> test()
>>> assertSomeClass.attribute==original

>>> @patch('package.module.attribute',sentinel.attribute)
... deftest():
... frompackage.moduleimportattribute
... assertattributeissentinel.attribute
...
>>> test()

If you are patching a module (including builtins) then use patch()

instead of patch.object():

>>> mock=MagicMock(return_value=sentinel.file_handle)
>>> withpatch('builtins.open',mock):
... handle=open('filename','r')
...
>>> mock.assert_called_with('filename','r')
>>> asserthandle==sentinel.file_handle,"incorrect file handle returned"

The module name can be 'dotted', in the form package.module if needed:

>>> @patch('package.module.ClassName.attribute',sentinel.attribute)
... deftest():
... frompackage.moduleimportClassName
... assertClassName.attribute==sentinel.attribute
...
>>> test()

A nice pattern is to actually decorate test methods themselves:

>>> classMyTest(unittest.TestCase):
... @patch.object(SomeClass,'attribute',sentinel.attribute)
... deftest_something(self):
... self.assertEqual(SomeClass.attribute,sentinel.attribute)
...
>>> original=SomeClass.attribute
>>> MyTest('test_something').test_something()
>>> assertSomeClass.attribute==original

If you want to patch with a Mock, you can use patch() with only one argument

(or patch.object() with two arguments). The mock will be created for you and

passed into the test function / method:

>>> classMyTest(unittest.TestCase):
... @patch.object(SomeClass,'static_method')
... deftest_something(self,mock_method):
... SomeClass.static_method()
... mock_method.assert_called_with()
...
>>> MyTest('test_something').test_something()

You can stack up multiple patch decorators using this pattern:

>>> classMyTest(unittest.TestCase):
... @patch('package.module.ClassName1')
... @patch('package.module.ClassName2')
... deftest_something(self,MockClass2,MockClass1):
... self.assertIs(package.module.ClassName1,MockClass1)
... self.assertIs(package.module.ClassName2,MockClass2)
...
>>> MyTest('test_something').test_something()

When you nest patch decorators the mocks are passed in to the decorated

function in the same order they applied (the normal Python order that

decorators are applied). This means from the bottom up, so in the example

above the mock for test_module.ClassName2 is passed in first.

还有一个 patch.dict() 用于在一定范围内设置字典中的值，并在测试结束时将字典恢复为其原始状态：

>>> foo={'key':'value'}
>>> original=foo.copy()
>>> withpatch.dict(foo,{'newkey':'newvalue'},clear=True):
... assertfoo=={'newkey':'newvalue'}
...
>>> assertfoo==original

patch, patch.object and patch.dict can all be used as context managers.

Where you use patch() to create a mock for you, you can get a reference to the

mock using the "as" form of the with statement:

>>> classProductionClass:
... defmethod(self):
... pass
...
>>> withpatch.object(ProductionClass,'method')asmock_method:
... mock_method.return_value=None
... real=ProductionClass()
... real.method(1,2,3)
...
>>> mock_method.assert_called_with(1,2,3)

As an alternative patch, patch.object and patch.dict can be used as

class decorators. When used in this way it is the same as applying the

decorator individually to every method whose name starts with "test".

Further Examples¶

Here are some more examples for some slightly more advanced scenarios.

Mocking chained calls¶

Mocking chained calls is actually straightforward with mock once you

understand the 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 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:

>>> classSomething:
... def__init__(self):
... self.backend=BackendProvider()
... defmethod(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 open() 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 configure_mock()

method to directly set the return value for us:

>>> something=Something()
>>> mock_response=Mock(spec=open)
>>> 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:

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

Using 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 call.call_list() to create

this list of calls for us:

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

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 patchdecorator 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.

>>> fromdatetimeimportdate
>>> withpatch('mymodule.date')asmock_date:
... mock_date.today.return_value=date(2010,10,8)
... mock_date.side_effect=lambda*args,**kw:date(*args,**kw)
...
... assertmymodule.date.today()==date(2010,10,8)
... assertmymodule.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.

An alternative way of dealing with mocking dates, or other builtin classes,

is discussed in this blog entry.

Mocking a Generator Method¶

A Python generator is a function or method that uses the yield statement

to return a series of values when iterated over 1.

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__(), so we can

mock this using a MagicMock.

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

>>> classFoo:
... defiter(self):
... foriin[1,2,3]:
... yieldi
...
>>> 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().

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

1

There are also generator expressions and more advanced uses 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.

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')
... classMyTest(TestCase):
...
... deftest_one(self,MockSomeClass):
... self.assertIs(mymodule.SomeClass,MockSomeClass)
...
... deftest_two(self,MockSomeClass):
... self.assertIs(mymodule.SomeClass,MockSomeClass)
...
... defnot_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 patch methods: start and stop.

These allow you to move the patching into your setUp and tearDown methods.

>>> classMyTest(TestCase):
... defsetUp(self):
... self.patcher=patch('mymodule.foo')
... self.mock_foo=self.patcher.start()
...
... deftest_foo(self):
... self.assertIs(mymodule.foo,self.mock_foo)
...
... deftearDown(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.

unittest.TestCase.addCleanup() makes this easier:

>>> classMyTest(TestCase):
... defsetUp(self):
... patcher=patch('mymodule.foo')
... self.addCleanup(patcher.stop)
... self.mock_foo=patcher.start()
...
... deftest_foo(self):
... self.assertIs(mymodule.foo,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 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:

>>> classFoo:
... deffoo(self):
... pass
...
>>> withpatch.object(Foo,'foo',autospec=True)asmock_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.

>>> 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 you can use

call_args_list:

>>> 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 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:

>>> 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':

deffrob(val):
pass
defgrob(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:

>>> withpatch('mymodule.frob')asmock_frob:
... val={6}
... mymodule.grob(val)
...
>>> val
set()
>>> mock_frob.assert_called_with({6})
Traceback (most recent call last):
...
AssertionError: Expected: (({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.

>>> fromcopyimportdeepcopy
>>> fromunittest.mockimportMock,patch,DEFAULT
>>> defcopy_call_args(mock):
... new_mock=Mock()
... defside_effect(*args,**kwargs):
... args=deepcopy(args)
... kwargs=deepcopy(kwargs)
... new_mock(*args,**kwargs)
... returnDEFAULT
... mock.side_effect=side_effect
... returnnew_mock
...
>>> withpatch('mymodule.frob')asmock_frob:
... new_mock=copy_call_args(mock_frob)
... val={6}
... mymodule.grob(val)
...
>>> new_mock.assert_called_with({6})
>>> new_mock.call_args
call({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.

注解

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.

>>> defside_effect(arg):
... assertarg=={6}
...
>>> mock=Mock(side_effect=side_effect)
>>> mock({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()) the arguments.

Here's an example implementation:

>>> fromcopyimportdeepcopy
>>> classCopyingMock(MagicMock):
... def__call__(self,*args,**kwargs):
... args=deepcopy(args)
... kwargs=deepcopy(kwargs)
... returnsuper(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({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.

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:

>>> classMyTest(TestCase):
...
... deftest_foo(self):
... withpatch('mymodule.Foo')asmock_foo:
... withpatch('mymodule.Bar')asmock_bar:
... withpatch('mymodule.Spam')asmock_spam:
... assertmymodule.Fooismock_foo
... assertmymodule.Barismock_bar
... assertmymodule.Spamismock_spam
...
>>> original=mymodule.Foo
>>> MyTest('test_foo').test_foo()
>>> assertmymodule.Fooisoriginal

With unittest cleanup functions and the patch methods: 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:

>>> classMyTest(TestCase):
...
... defcreate_patch(self,name):
... patcher=patch(name)
... thing=patcher.start()
... self.addCleanup(patcher.stop)
... returnthing
...
... deftest_foo(self):
... mock_foo=self.create_patch('mymodule.Foo')
... mock_bar=self.create_patch('mymodule.Bar')
... mock_spam=self.create_patch('mymodule.Spam')
...
... assertmymodule.Fooismock_foo
... assertmymodule.Barismock_bar
... assertmymodule.Spamismock_spam
...
>>> original=mymodule.Foo
>>> MyTest('test_foo').run()
>>> assertmymodule.Fooisoriginal

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 MagicMock, which will behave like a dictionary,

and using 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

call_args_list to assert about how the dictionary was used:

>>> my_dict={'a':1,'b':2,'c':3}
>>> defgetitem(name):
... returnmy_dict[name]
...
>>> defsetitem(name,val):
... my_dict[name]=val
...
>>> mock=MagicMock()
>>> mock.__getitem__.side_effect=getitem
>>> mock.__setitem__.side_effect=setitem

注解

An alternative to using MagicMock is to use Mock and only provide

the magic methods you specifically want:

>>> mock=Mock()
>>> mock.__getitem__=Mock(side_effect=getitem)
>>> mock.__setitem__=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:

>>> classMyMock(MagicMock):
... defhas_been_called(self):
... returnself.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

2. 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 is subclassing mock to

created a Twisted adaptor.

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:

>>> classSubclass(MagicMock):
... def_get_child_mock(self,**kwargs):
... returnMagicMock(**kwargs)
...
>>> mymock=Subclass()
>>> mymock.foo
<MagicMock name='mock.foo' id='...'>
>>> assertisinstance(mymock,Subclass)
>>> assertnotisinstance(mymock.foo,Subclass)
>>> assertnotisinstance(mymock(),Subclass)

2

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 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()
>>> withpatch.dict('sys.modules',{'fooble':mock}):
... importfooble
... fooble.blob()
...
<Mock name='mock.blob()' id='...'>
>>> assert'fooble'notinsys.modules
>>> mock.blob.assert_called_once_with()

As you can see the importfooble succeeds, but on exit there is no 'fooble'

left in sys.modules.

This also works for the frommoduleimportname form:

>>> mock=Mock()
>>> withpatch.dict('sys.modules',{'fooble':mock}):
... fromfoobleimportblob
... 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}
>>> withpatch.dict('sys.modules',modules):
... frompackage.moduleimportfooble
... fooble()
...
<Mock name='mock.module.fooble()' id='...'>
>>> mock.module.fooble.assert_called_once_with()

Tracking order of calls and less verbose call assertions¶

The Mock class allows you to track the order of method calls on

your mock objects through the method_calls attribute. This

doesn't allow you to track the order of calls between separate mock objects,

however we can use 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 attach_mock() method. After

attaching calls will be recorded in mock_calls of the manager.

>>> manager=MagicMock()
>>> withpatch('mymodule.Class1')asMockClass1:
... withpatch('mymodule.Class2')asMockClass2:
... 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

assert_has_calls() method. This takes a list of calls (constructed

with the call object). If that sequence of calls are in

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 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 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:

>>> classFoo:
... 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:

>>> defcompare(self,other):
... ifnottype(self)==type(other):
... returnFalse
... ifself.a!=other.a:
... returnFalse
... ifself.b!=other.b:
... returnFalse
... returnTrue
...

And a matcher object that can use comparison functions like this for its

equality operation would look something like this:

>>> classMatcher:
... def__init__(self,compare,some_obj):
... self.compare=compare
... self.some_obj=some_obj
... def__eq__(self,other):
... returnself.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 provides similar functionality,

that may be useful here, in the form of its equality matcher

(hamcrest.library.integration.match_equality).

以上是 Python标准库unittest.mock上手指南 的全部内容， 来源链接： utcz.com/z/507854.html

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