Code testing

In the research lab environment, testing Python code is crucial for ensuring accuracy, reliability, and reproducibility of scientific results. pytest is one of the most popular testing frameworks in Python due to its simplicity, flexibility, and powerful features.

Basic Test Structure

pytest automatically discovers and runs tests based on their naming conventions. Tests should be placed in files named test_*.py or *_test.py, and test functions should start with test_.

Example:

# test_calculator.py
def add(x, y):
    return x + y

def test_add():
    assert add(1, 2) == 3
    assert add(-1, 1) == 0

Run tests using the pytest command:

pytest

Writing Tests with pytest

Basic Assertions

Use assert statements to check if the code behaves as expected. pytest will report failed assertions with detailed information.

Example:

def multiply(x, y):
    return x * y

def test_multiply():
    assert multiply(2, 3) == 6
    assert multiply(0, 5) == 0

Using Fixtures

Fixtures are used to set up and tear down resources needed for tests. They are defined using the @pytest.fixture decorator and can be scoped to functions, classes, modules, or sessions.

Example:

import pytest

@pytest.fixture
def sample_data():
    return [1, 2, 3, 4, 5]

def test_sum(sample_data):
    assert sum(sample_data) == 15

Parameterized Tests

Parameterized tests allow you to run the same test function with multiple sets of inputs using the @pytest.mark.parametrize decorator.

Example:

import pytest

@pytest.mark.parametrize("a, b, expected", [
    (1, 2, 3),
    (-1, 1, 0),
    (2, 2, 4),
])
def test_add(a, b, expected):
    assert add(a, b) == expected

Testing for Exceptions

You can check for expected exceptions using the pytest.raises context manager.

Example:

import pytest

def divide(x, y):
    if y == 0:
        raise ValueError("Cannot divide by zero")
    return x / y

def test_divide():
    with pytest.raises(ValueError):
        divide(1, 0)

Customizing Test Output

pytest provides options for customizing test output, such as verbosity levels and formatting.

Example:

Run tests with verbose output:

pytest -v

Generate a test report in a JUnit-compatible format:

pytest --junitxml=report.xml

Advanced Features of pytest

Plugins

pytest supports a wide range of plugins to enhance its functionality. Some popular plugins include:

• pytest-cov: Provides code coverage reporting.
• pytest-mock: Simplifies mocking of objects and functions.
• pytest-xdist: Allows parallel test execution and distributed testing.

Install and use plugins via pip:

pip install pytest-cov pytest-mock

Example with pytest-cov:

pytest --cov=my_module

Fixtures with Scope and Autouse

Control the scope and automatic application of fixtures using the scope and autouse parameters.

Example:

import pytest

@pytest.fixture(scope="module", autouse=True)
def setup_module():
    print("\nSetting up module...")
    yield
    print("\nTearing down module...")

Integrating pytest into the Research Workflow

Continuous Integration (CI)

Integrate pytest with CI/CD pipelines to automatically run tests on code changes. Tools like GitHub Actions, GitLab CI, and Jenkins support pytest integration.

Example with GitHub Actions:

Create a .github/workflows/test.yml file:

name: Run Tests

on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'
      - name: Install dependencies
        run: |
          pip install pytest
      - name: Run tests
        run: |
          pytest

Code Coverage

Use pytest-cov to measure test coverage and ensure that critical parts of your code are tested.

Example:

pytest --cov=my_module --cov-report=html

Test Documentation

Document your tests to explain their purpose and expected outcomes, which helps new team members understand the testing strategy and rationale.

Conclusion

pytest is a powerful and flexible testing framework that enhances the reliability and maintainability of Python code in a research lab setting. By leveraging pytest’s features such as fixtures, parameterized tests, and plugins, researchers can create comprehensive test suites that ensure code quality and facilitate collaboration. Integrating pytest into your research workflow, including continuous integration and code coverage, will help maintain the integrity of your codebase and support reproducible research.

By adopting these practices, you can build a robust testing framework that contributes to more reliable and effective scientific research.

Learn more about testing here.