The Tor Browser: python/mock-1.0.0/docs/index.txt@925c144e1f1f (annotated)

python/mock-1.0.0/docs/index.txt@925c144e1f1f (annotated)

python/mock-1.0.0/docs/index.txt

Fri, 16 Jan 2015 18:13:44 +0100

author: Michael Schloh von Bennewitz <michael@schloh.com>
date: Fri, 16 Jan 2015 18:13:44 +0100
branch: TOR_BUG_9701
changeset 14: 925c144e1f1f
permissions: -rw-r--r--

Integrate suggestion from review to improve consistency with existing code.

 ====================================
  Mock - Mocking and Testing Library
 ====================================
 .. currentmodule:: mock
 :Author: `Michael Foord
  <http://www.voidspace.org.uk/python/weblog/index.shtml>`_
 :Version: |release|
 :Date: 2012/10/07
 :Homepage: `Mock Homepage`_
 :Download: `Mock on PyPI`_
 :Documentation: `PDF Documentation
  <http://www.voidspace.org.uk/downloads/mock-1.0.0.pdf>`_
 :License: `BSD License`_
 :Support: `Mailing list (testing-in-python@lists.idyll.org)
  <http://lists.idyll.org/listinfo/testing-in-python>`_
 :Issue tracker: `Google code project
  <http://code.google.com/p/mock/issues/list>`_
 .. _Mock Homepage: http://www.voidspace.org.uk/python/mock/
 .. _BSD License: http://www.voidspace.org.uk/python/license.shtml
 .. currentmodule:: mock
 .. module:: mock
    :synopsis: Mock object and testing library.
 .. index:: introduction
 mock is a library for testing in Python. It allows you to replace parts of
 your system under test with mock objects and make assertions about how they
 have been used.
 mock is now part of the Python standard library, available as `unittest.mock
 <http://docs.python.org/py3k/library/unittest.mock.html#module-unittest.mock>`_
 in Python 3.3 onwards.
 mock provides a core :class:`Mock` class removing the need to create a host
 of stubs throughout your test suite. After performing an action, you can make
 assertions about which methods / attributes were used and arguments they were
 called with. You can also specify return values and set needed attributes in
 the normal way.
 Additionally, mock provides a :func:`patch` decorator that handles patching
 module and class level attributes within the scope of a test, along with
 :const:`sentinel` for creating unique objects. See the `quick guide`_ for
 some examples of how to use :class:`Mock`, :class:`MagicMock` and
 :func:`patch`.
 Mock is very easy to use and is designed for use with
 `unittest <http://pypi.python.org/pypi/unittest2>`_. Mock is based on
 the 'action -> assertion' pattern instead of `'record -> replay'` used by many
 mocking frameworks.
 mock is tested on Python versions 2.4-2.7, Python 3 plus the latest versions of
 Jython and PyPy.
 .. testsetup::
    class ProductionClass(object):
       def method(self, *args):
          pass
    module = sys.modules['module'] = ProductionClass
    ProductionClass.ClassName1 = ProductionClass
    ProductionClass.ClassName2 = ProductionClass
 API Documentation
 =================
 .. toctree::
    :maxdepth: 2
    mock
    patch
    helpers
    sentinel
    magicmock
 User Guide
 ==========
 .. toctree::
    :maxdepth: 2
    getting-started
    examples
    compare
    changelog
 .. index:: installing
 Installing
 ==========
 The current version is |release|. Mock is stable and widely used. If you do
 find any bugs, or have suggestions for improvements / extensions
 then please contact us.
 * `mock on PyPI <http://pypi.python.org/pypi/mock>`_
 * `mock documentation as PDF
   <http://www.voidspace.org.uk/downloads/mock-1.0.0.pdf>`_
 * `Google Code Home & Mercurial Repository <http://code.google.com/p/mock/>`_
 .. index:: repository
 .. index:: hg
 You can checkout the latest development version from the Google Code Mercurial
 repository with the following command:
     ``hg clone https://mock.googlecode.com/hg/ mock``
 .. index:: pip
 .. index:: easy_install
 .. index:: setuptools
 If you have pip, setuptools or distribute you can install mock with:
     | ``easy_install -U mock``
     | ``pip install -U mock``
 Alternatively you can download the mock distribution from PyPI and after
 unpacking run:
    ``python setup.py install``
 Quick Guide
 ===========
 :class:`Mock` and :class:`MagicMock` objects create all attributes and
 methods as you access them and store details of how they have been used. You
 can configure them, to specify return values or limit what attributes are
 available, and then make assertions about how they have been used:
 .. doctest::
     >>> from mock import MagicMock
     >>> thing = ProductionClass()
     >>> thing.method = MagicMock(return_value=3)
     >>> thing.method(3, 4, 5, key='value')
 
     >>> thing.method.assert_called_with(3, 4, 5, key='value')
 :attr:`side_effect` allows you to perform side effects, including raising an
 exception when a mock is called:
 .. doctest::
    >>> mock = Mock(side_effect=KeyError('foo'))
    >>> mock()
    Traceback (most recent call last):
     ...
    KeyError: 'foo'
    >>> values = {'a': 1, 'b': 2, 'c': 3}
    >>> def side_effect(arg):
    ...     return values[arg]
    ...
    >>> mock.side_effect = side_effect
    >>> mock('a'), mock('b'), mock('c')
    (1, 2, 3)
    >>> mock.side_effect = [5, 4, 3, 2, 1]
    >>> mock(), mock(), mock()
    (5, 4, 3)
 Mock has many other ways you can configure it and control its behaviour. For
 example the `spec` argument configures the mock to take its specification
 from another object. Attempting to access attributes or methods on the mock
 that don't exist on the spec will fail with an `AttributeError`.
 The :func:`patch` decorator / context manager makes it easy to mock classes or
 objects in a module under test. The object you specify will be replaced with a
 mock (or other object) during the test and restored when the test ends:
 .. doctest::
     >>> from mock import patch
     >>> @patch('module.ClassName2')
     ... @patch('module.ClassName1')
     ... def test(MockClass1, MockClass2):
     ...     module.ClassName1()
     ...     module.ClassName2()
     ...     assert MockClass1 is module.ClassName1
     ...     assert MockClass2 is module.ClassName2
     ...     assert MockClass1.called
     ...     assert MockClass2.called
     ...
     >>> test()
 .. note::
    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 `module.ClassName1` is passed in first.
    With `patch` it matters that you patch objects in the namespace where they
    are looked up. This is normally straightforward, but for a quick guide
    read :ref:`where to patch <where-to-patch>`.
 As well as a decorator `patch` can be used as a context manager in a with
 statement:
 .. doctest::
     >>> with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
     ...     thing = ProductionClass()
     ...     thing.method(1, 2, 3)
     ...
     >>> mock_method.assert_called_once_with(1, 2, 3)
 There is also :func:`patch.dict` for setting values in a dictionary just
 during a scope and restoring the dictionary to its original state when the test
 ends:
 .. doctest::
    >>> foo = {'key': 'value'}
    >>> original = foo.copy()
    >>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
    ...     assert foo == {'newkey': 'newvalue'}
    ...
    >>> assert foo == original
 Mock supports the mocking of Python :ref:`magic methods <magic-methods>`. The
 easiest way of using magic methods is with the :class:`MagicMock` class. It
 allows you to do things like:
 .. doctest::
     >>> mock = MagicMock()
     >>> mock.__str__.return_value = 'foobarbaz'
     >>> str(mock)
     'foobarbaz'
     >>> mock.__str__.assert_called_with()
 Mock allows you to assign functions (or other Mock instances) to magic methods
 and they will be called appropriately. The `MagicMock` class is just a Mock
 variant that has all of the magic methods pre-created for you (well, all the
 useful ones anyway).
 The following is an example of using magic methods with the ordinary Mock
 class:
 .. doctest::
     >>> mock = Mock()
     >>> mock.__str__ = Mock(return_value='wheeeeee')
     >>> str(mock)
     'wheeeeee'
 For ensuring that the mock objects in your tests have the same api as the
 objects they are replacing, you can use :ref:`auto-speccing <auto-speccing>`.
 Auto-speccing can be done through the `autospec` argument to patch, or the
 :func:`create_autospec` function. Auto-speccing creates mock objects that
 have the same attributes and methods as the objects they are replacing, and
 any functions and methods (including constructors) have the same call
 signature as the real object.
 This ensures that your mocks will fail in the same way as your production
 code if they are used incorrectly:
 .. doctest::
    >>> from mock import create_autospec
    >>> def function(a, b, c):
    ...     pass
    ...
    >>> mock_function = create_autospec(function, return_value='fishy')
    >>> mock_function(1, 2, 3)
    'fishy'
    >>> mock_function.assert_called_once_with(1, 2, 3)
    >>> mock_function('wrong arguments')
    Traceback (most recent call last):
     ...
    TypeError: <lambda>() takes exactly 3 arguments (1 given)
 `create_autospec` can also be used on classes, where it copies the signature of
 the `__init__` method, and on callable objects where it copies the signature of
 the `__call__` method.
 .. index:: references
 .. index:: articles
 References
 ==========
 Articles, blog entries and other stuff related to testing with Mock:
 * `Imposing a No DB Discipline on Django unit tests
   <https://github.com/carljm/django-testing-slides/blob/master/models/30_no_database.md>`_
 * `mock-django: tools for mocking the Django ORM and models
   <https://github.com/dcramer/mock-django>`_
 * `PyCon 2011 Video: Testing with mock <https://blip.tv/file/4881513>`_
 * `Mock objects in Python
   <http://noopenblockers.com/2012/01/06/mock-objects-in-python/>`_
 * `Python: Injecting Mock Objects for Powerful Testing
   <http://blueprintforge.com/blog/2012/01/08/python-injecting-mock-objects-for-powerful-testing/>`_
 * `Python Mock: How to assert a substring of logger output
   <http://www.michaelpollmeier.com/python-mock-how-to-assert-a-substring-of-logger-output/>`_
 * `Mocking Django <http://www.mattjmorrison.com/2011/09/mocking-django.html>`_
 * `Mocking dates and other classes that can't be modified
   <http://williamjohnbert.com/2011/07/how-to-unit-testing-in-django-with-mocking-and-patching/>`_
 * `Mock recipes <http://konryd.blogspot.com/2010/06/mock-recipies.html>`_
 * `Mockity mock mock - some love for the mock module
   <http://konryd.blogspot.com/2010/05/mockity-mock-mock-some-love-for-mock.html>`_
 * `Coverage and Mock (with django)
   <http://mattsnider.com/python/mock-and-coverage/>`_
 * `Python Unit Testing with Mock <http://www.insomnihack.com/?p=194>`_
 * `Getting started with Python Mock
   <http://myadventuresincoding.wordpress.com/2011/02/26/python-python-mock-cheat-sheet/>`_
 * `Smart Parameter Checks with mock
   <http://tobyho.com/2011/03/24/smart-parameter-checks-in/>`_
 * `Python mock testing techniques and tools
   <http://agiletesting.blogspot.com/2009/07/python-mock-testing-techniques-and.html>`_
 * `How To Test Django Template Tags
   <http://techblog.ironfroggy.com/2008/10/how-to-test.html>`_
 * `A presentation on Unit Testing with Mock
   <http://pypap.blogspot.com/2008/10/newbie-nugget-unit-testing-with-mock.html>`_
 * `Mocking with Django and Google AppEngine
   <http://michael-a-nelson.blogspot.com/2008/09/mocking-with-django-and-google-app.html>`_
 .. index:: tests
 .. index:: unittest2
 Tests
 =====
 Mock uses `unittest2 <http://pypi.python.org/pypi/unittest2>`_ for its own
 test suite. In order to run it, use the `unit2` script that comes with
 `unittest2` module on a checkout of the source repository:
    `unit2 discover`
 If you have `setuptools <http://pypi.python.org/pypi/distribute>`_ as well as
 unittest2 you can run:
    ``python setup.py test``
 On Python 3.2 you can use ``unittest`` module from the standard library.
    ``python3.2 -m unittest discover``
 .. index:: Python 3
 On Python 3 the tests for unicode are skipped as they are not relevant. On
 Python 2.4 tests that use the with statements are skipped as the with statement
 is invalid syntax on Python 2.4.
 .. index:: older versions
 Older Versions
 ==============
 Documentation for older versions of mock:
 * `mock 0.8 <http://www.voidspace.org.uk/python/mock/0.8/>`_
 * `mock 0.7 <http://www.voidspace.org.uk/python/mock/0.7/>`_
 * `mock 0.6 <http://www.voidspace.org.uk/python/mock/0.6.0/>`_
 Docs from the in-development version of `mock` can be found at
 `mock.readthedocs.org <http://mock.readthedocs.org>`_.
 Terminology
 ===========
 Terminology for objects used to replace other ones can be confusing. Terms
 like double, fake, mock, stub, and spy are all used with varying meanings.
 In `classic mock terminology
 <http://xunitpatterns.com/Mocks,%20Fakes,%20Stubs%20and%20Dummies.html>`_
 :class:`mock.Mock` is a `spy <http://xunitpatterns.com/Test%20Spy.html>`_ that
 allows for *post-mortem* examination. This is what I call the "action ->
 assertion" [#]_ pattern of testing.
 I'm not however a fan of this "statically typed mocking terminology"
 promulgated by `Martin Fowler
 <http://martinfowler.com/articles/mocksArentStubs.html>`_. It confuses usage
 patterns with implementation and prevents you from using natural terminology
 when discussing mocking.
 I much prefer duck typing, if an object used in your test suite looks like a
 mock object and quacks like a mock object then it's fine to call it a mock, no
 matter what the implementation looks like.
 This terminology is perhaps more useful in less capable languages where
 different usage patterns will *require* different implementations.
 `mock.Mock()` is capable of being used in most of the different roles
 described by Fowler, except (annoyingly / frustratingly / ironically) a Mock
 itself!
 How about a simpler definition: a "mock object" is an object used to replace a
 real one in a system under test.
 .. [#] This pattern is called "AAA" by some members of the testing community;
    "Arrange - Act - Assert".

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