Post originally published on http://krzysztofzuraw.com/. Republished with author’s permission.
Introduction
In this post I will look into the essential part of testing — mocks.
First of all, what I want to accomplish here is to give you basic examples of how to mock data using two tools — mock and pytest monkeypatch.
Why bother mocking?
Some of the parts of our application may have dependencies for other libraries or objects. To isolate the behaviour of our parts, we need to substitute external dependencies. Here comes the mocking. We mock an external API to check certain behaviours, such as proper return values, that we previously defined.
Mocking function
Let’s say we have a module called function.py
:
def square(value):
return value ** 2
def cube(value):
return value ** 3
def main(value):
return square(value) + cube(value)
Then let’s see how these functions are mocked using the mock
library:
try:
import mock
except ImportError:
from unittest import mock
import unittest
from function import square, main
class TestNotMockedFunction(unittest.TestCase):
@mock.patch('__main__.square', return_value=1)
def test_function(self, mocked_square):
# because you need to patch in exact place where function that has to be mocked is called
self.assertEquals(square(5), 1)
@mock.patch('function.square')
@mock.patch('function.cube')
def test_main_function(self, mocked_square, mocked_cube):
# underling function are mocks so calling main(5) will return mock
mocked_square.return_value = 1
mocked_cube.return_value = 0
self.assertEquals(main(5), 1)
mocked_square.assert_called_once_with(5)
mocked_cube.assert_called_once_with(5)
if __name__ == '__main__':
unittest.main()
What is happening here? Lines 1-4 are for making this code compatible between Python 2 and 3. In Python 3, mock is part of the standard library, whereas in Python 2 you need to install it by pip install mock
.
In line 13, I patched the square
function. You have to remember to patch it in the same place you use it. For instance, I’m calling square(5)
in the test itself so I need to patch it in __main__
. This is the case if I’m running this by using python tests/test_function.py
. If I’m using pytest for that, I need to patch it as test_function.square
.
In lines 18-19, I patch the square
and cube
functions in their module because they are used in the main
function. The last two asserts come from the mock library, and are there to make sure that mock was called with proper values.
The same can be accomplished using mokeypatching
for py.test:
from function import square, main
def test_function(monkeypatch):
monkeypatch.setattr(“test_function_pytest.square”, lambda x: 1)
assert square(5) == 1
def test_main_function(monkeypatch):
monkeypatch.setattr(‘function.square’, lambda x: 1)
monkeypatch.setattr(‘function.cube’, lambda x: 0)
assert main(5) == 1
As you can see, I’m using monkeypatch.setattr
for setting up a return value for given functions. I still need to monkeypatch it in proper places — test_function_pytest
and function
.
Mocking classes
I have a module called square
:
import math
class Square(object):
def __init__(radius):
self.radius = radius
def calculate_area(self):
return math.sqrt(self.radius) * math.pi
and mocks using standard lib:
try:
import mock
except ImportError:
from unittest import mock
import unittest
from square import Square
class TestClass(unittest.TestCase):
@mock.patch('__main__.Square') # depends in witch from is run
def test_mocking_instance(self, mocked_instance):
mocked_instance = mocked_instance.return_value
mocked_instance.calculate_area.return_value = 1
sq = Square(100)
self.assertEquals(sq.calculate_area(), 1)
def test_mocking_classes(self):
sq = Square
sq.calculate_area = mock.MagicMock(return_value=1)
self.assertEquals(sq.calculate_area(), 1)
@mock.patch.object(Square, 'calculate_area')
def test_mocking_class_methods(self, mocked_method):
mocked_method.return_value = 20
self.assertEquals(Square.calculate_area(), 20)
if __name__ == ‘__main__’:
unittest.main()
At line 13, I patch the class Square
. Lines 15 and 16 present a mocking instance. mocked_instance
is a mock object which returns another mock by default, and to these mock.calculate_area
I add return_value
1. In line 23, I’m using MagicMock
, which is a normal mock class, except in that it also retrieves magic methods from the given object. Lastly, I use patch.object
to mock the method in the Square
class.
The same using pytest:
try:
from mock import MagicMock
except ImportError:
from unittest.mock import MagicMock
from square import Square
def test_mocking_class_methods(monkeypatch):
monkeypatch.setattr('test_class_pytest.Square.calculate_area', lambda: 1)
assert Square.calculate_area() == 1
def test_mocking_classes(monkeypatch):
monkeypatch.setattr('test_class_pytest.Square', MagicMock(Square))
sq = Square
sq.calculate_area.return_value = 1
assert sq.calculate_area() == 1
The issue here is with test_mocking_class_methods
, which works well in Python 3, but not in Python 2.
All examples can be found in this repo.
If you have any questions and comments, feel free to leave them in the section below.
I feel that this article assumes that the reader has more knowledge of monkeypatching than it ought to given it’s introductory level. The author uses the phrase ” I still need to monkeypatch it in proper places — test_function_pytest and function.” Which assumes the reader understands what it means to “monkeypatch” without explaining in any further detail.
I chose this article hoping for help with understanding that very concept and have left without any further knowledge on the subject.