====================================
 Mock - Mocking and Testing Library
====================================

.. currentmodule:: mock

:Author: `Michael Foord <http://www.voidspace.org.uk/python/weblog/index.shtml>`_
:Co-maintainer: `Konrad Delong <http://konryd.blogspot.com/>`_
:Version: |release|
:Date: 2011/03/05
:Homepage: `Mock Homepage`_
:Download: `Mock on PyPI`_
:Documentation: `PDF Documentation <http://www.voidspace.org.uk/downloads/mock-0.7.1.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 Python module that provides a core :class:`Mock` class. It removes
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.

The mock module also 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 and Python 3.


.. testsetup::

   import sys
   class ProductionClass(object):
      def method(self, *args):
         pass

   test_module = sys.modules['test_module'] = ProductionClass
   ProductionClass.ClassName1 = ProductionClass
   ProductionClass.ClassName2 = ProductionClass

   from mock import Mock, patch, MagicMock


API Documentation
=================

.. toctree::
   :maxdepth: 2

   mock
   patch
   sentinel
   magicmock
   mocksignature


User Guide
==========

.. toctree::
   :maxdepth: 2

   getting-started
   examples
   compare
   changelog

.. index:: installing

Installing
==========

The current version is |release|. Mock is still experimental; the API may
change (although we are moving towards a 1.0 release when the API will
stabilise). If you find 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-0.7.1.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` 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 Mock
    >>> real = ProductionClass()
    >>> real.method = Mock(return_value=3)
    >>> real.method(3, 4, 5, key='value')
    3
    >>> real.method.assert_called_with(3, 4, 5, key='value')

:attr:`side_effect` allows you to perform side effects, return different
values or raise an exception when a mock is called:

.. doctest::

   >>> mock = Mock(side_effect=KeyError('foo'))
   >>> mock()
   Traceback (most recent call last):
    ...
   KeyError: 'foo'
   >>> values = [1, 2, 3]
   >>> def side_effect():
   ...     return values.pop()
   ...
   >>> mock.side_effect = side_effect
   >>> mock(), mock(), mock()
   (3, 2, 1)

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('test_module.ClassName1')
    ... @patch('test_module.ClassName2')
    ... def test(MockClass2, MockClass1):
    ...     test_module.ClassName1()
    ...     test_module.ClassName2()

    ...     assert MockClass1 is test_module.ClassName1
    ...     assert MockClass2 is test_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 `test_module.ClassName2` 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') as mock_method:
    ...     mock_method.return_value = None
    ...     real = ProductionClass()
    ...     real.method(1, 2, 3)
    ...
    >>> mock_method.assert_called_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::

    >>> from mock import MagicMock
    >>> 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()
    >>> mock.__str__.return_value = 'wheeeeee'
    >>> str(mock)
    'wheeeeee'

:func:`mocksignature` is a useful companion to Mock and patch. It creates
copies of functions that delegate to a mock, but have the same signature as the
original function. This ensures that your mocks will fail in the same way as
your production code if they are called incorrectly:

.. doctest::

   >>> from mock import mocksignature
   >>> def function(a, b, c):
   ...     pass
   ...
   >>> function2 = mocksignature(function)
   >>> function2.mock.return_value = 'fishy'
   >>> function2(1, 2, 3)
   'fishy'
   >>> function2.mock.assert_called_with(1, 2, 3)
   >>> function2('wrong arguments')
   Traceback (most recent call last):
    ...
   TypeError: <lambda>() takes exactly 3 arguments (1 given)

`mocksignature` 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 and blog entries on testing with Mock:

* `PyCon 2011 Video: Testing with mock <https://blip.tv/file/4881513>`_
* `Mocking Django <http://www.mattjmorrison.com/blog/mocking-django>`_
* `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>`_
* `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/>`_
* `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 0.6.0 <http://www.voidspace.org.uk/python/mock/0.6.0/>`_


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 its 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".
