The pytest Package

Prerequisite: Automated Testing

Reference:

• https://github.com/pytest-dev/pytest/

• https://docs.pytest.org/en/latest/

• https://docs.pytest.org/en/latest/getting-started.html#our-first-test-run

• https://docs.pytest.org/en/latest/goodpractices.html

• https://docs.pytest.org/en/latest/usage.html#dropping-to-pdb-python-debugger-on-failures

• http://python-guide-pt-br.readthedocs.io/en/latest/writing/tests/#py-test

• https://docs.pytest.org/en/latest/capture.html

Installation

If you are using Pip to manage software packages (recommended), install Pytest, as necessary:

pip install pytest

Otherwise, if you are using Pipenv, you will want to first navigate inside your repository's root directory before installing Pytest:

cd path/to/my-repo/
pipenv install pytest --dev # NOTE: the --dev flag denotes this package will be used in development only

Usage

The Pytest package is generally used as a command-line utility for running pre-defined files of "test" code. Follow the "Testing 1,2,3" Exercise to get acclimated with Pytest.

Example invocations:

pytest

pytest --disable-pytest-warnings # disables warnings

pytest  -s #> see print statements

pytest test/parser_test.py -k 'test_my_thing' # test a certain file / function

Expecting Errors

The Pytest package can be imported to facilitate assertions that errors will be raised:

import pytest

def test_divide_by_zero():
    with pytest.raises(Exception) as e_info:
        result = 2 / 0 # we expect this code will raise the error (division by zero)

Fixtures

The Pytest package can be imported to facilitate the construction of test fixtures (for example, to be placed in the "conftest.py" file):

import pytest

# prevents unnecessary or duplicative language model loading
# fixture only loaded when a specific test needs it
# module-level fixture only invoked once for all tests
@pytest.fixture(scope="module")
def nlp():
    import spacy
    print("LOADING THE LANGUAGE MODEL...")
    return spacy.load("en_core_web_md")

def test_my_thing(nlp):
    doc = nlp(reconstructed_text)
    # etc