python-testing

# Scientific Python Testing with pytest

A comprehensive guide to writing effective tests for scientific Python packages using pytest, following the [Scientific Python Community guidelines](https://learn.scientific-python.org/development/guides/pytest/) and [testing tutorial](https://learn.scientific-python.org/development/tutorials/test/). This skill focuses on modern testing patterns, fixtures, parametrization, and best practices specific to scientific computing.

## Quick Reference Card

**Common Testing Tasks - Quick Decisions:**

```python
# 1. Basic test → Use simple assert
def test_function():
    assert result == expected

# 2. Floating-point comparison → Use approx
from pytest import approx
assert result == approx(0.333, rel=1e-6)

# 3. Testing exceptions → Use pytest.raises
with pytest.raises(ValueError, match="must be positive"):
    function(-1)

# 4. Multiple inputs → Use parametrize
@pytest.mark.parametrize("input,expected", [(1,1), (2,4), (3,9)])
def test_square(input, expected):
    assert input**2 == expected

# 5. Reusable setup → Use fixture
@pytest.fixture
def sample_data():
    return np.array([1, 2, 3, 4, 5])

# 6. NumPy arrays → Use approx or numpy.testing
assert np.mean(data) == approx(3.0)
```

**Decision Tree:**
- Need multiple test cases with same logic? → **Parametrize**
- Need reusable test data/setup? → **Fixture**
- Testing floating-point results? → **pytest.approx**
- Testing exceptions/warnings? → **pytest.raises / pytest.warns**
- Complex numerical arrays? → **numpy.testing.assert_allclose**
- Organizing by speed? → **Markers and separate directories**

## When to Use This Skill

- Writing tests for scientific Python packages and libraries
- Testing numerical algorithms and scientific computations
- Setting up test infrastructure for research software
- Implementing continuous integration for scientific code
- Testing data analysis pipelines and workflows
- Validating scientific simulations and models
- Ensuring reproducibility and