05-pytest.md

layout	page
title	Testing
subtitle	Running Tests with pytest
minutes	10

Learning Objectives {.objectives}

• Understand how to run a test suite using the pytest framework
• Understand how to read the output of a pytest test suite

We created a suite of tests for our mean function, but it was annoying to run them one at a time. It would be a lot better if there were some way to run them all at once, just reporting which tests fail and which succeed.

Thankfully, that exists. Recall our tests:

from mean import *

def test_ints():
    num_list = [1,2,3,4,5]
    obs = mean(num_list)
    exp = 3
    assert obs == exp

def test_zero():
    num_list=[0,2,4,6]
    obs = mean(num_list)
    exp = 3
    assert obs == exp

def test_double():
    # This one will fail in Python 2
    num_list=[1,2,3,4]
    obs = mean(num_list)
    exp = 2.5
    assert obs == exp

def test_long():
    big = 100000000
    obs = mean(range(1,big))
    exp = big/2.0
    assert obs == exp

def test_complex():
    # given that complex numbers are an unordered field
    # the arithmetic mean of complex numbers is meaningless
    num_list = [2 + 3j, 3 + 4j, -32 - 2j]
    obs = mean(num_list)
    exp = NotImplemented
    assert obs == exp

Once these tests are written in a file called test_mean.py, the command py.test can be called from the directory containing the tests:

$ py.test

collected 5 items

test_mean.py ....F

================================== FAILURES ===================================
________________________________ test_complex _________________________________

    def test_complex():
        # given that complex numbers are an unordered field
        # the arithmetic mean of complex numbers is meaningless
        num_list = [2 + 3j, 3 + 4j, -32 - 2j]
        obs = mean(num_list)
        exp = NotImplemented
>       assert obs == exp
E       assert (-9+1.6666666666666667j) == NotImplemented

test_mean.py:34: AssertionError
===================== 1 failed, 4 passed in 2.71 seconds ======================

In the above case, the pytest package 'sniffed-out' the tests in the directory and ran them together to produce a report of the sum of the files and functions matching the regular expression [Tt]est[-_]*.

The major boon a testing framework provides is exactly that, a utility to find and run the tests automatically. With pytest, this is the command-line tool called py.test. When py.test is run, it will search all directories below where it was called, find all of the Python files in these directories whose names start or end with test, import them, and run all of the functions and classes whose names start with test or Test. This automatic registration of test code saves tons of human time and allows us to focus on what is important: writing more tests.

When you run py.test, it will print a dot (.) on the screen for every test that passes, an F for every test that fails or where there was an unexpected error. In rarer situations you may also see an s indicating a skipped tests (because the test is not applicable on your system) or a x for a known failure (because the developers could not fix it promptly). After the dots, pytest will print summary information.

Fix The Failing Code {.challenge}

Without changing the tests, alter the mean.py file from the previous section until it passes. When it passes, py.test will produce results like the following:

$ py.test

collected 5 items

test_mean.py .....

========================== 5 passed in 2.68 seconds ===========================

As we write more code, we would write more tests, and pytest would produce more dots. Each passing test is a small, satisfying reward for having written quality scientific software. Now that you know how to write tests, let's go into what can go wrong.