Testing Patterns

Structure pytest tests using a clear tests/ layout with file/class/method naming conventions and conftest.py for shared fixtures. This organization standardizes testing practices to reduce brittle tests and speed up delivery for analysis modules.

Test Numba-optimized functions using warmup_jit patterns before execution. Use xfail markers for timing variability, and test with both small correctness datasets and large performance datasets to validate Numba-optimized functions properly.

Apply TDD workflows by writing a focused failing pytest reproducing the pivot detection or harmonic pattern recognition bug first. Then implement the minimal fix following Red-Green-Refactor iteration before running the test suite to ensure coverage meets the threshold.

Enforce a 63% CI coverage threshold and use pytest markers to categorize slow or performance tests. Apply plain assert statements, pytest.approx for floats, and consistent markers to maintain test quality across analysis modules.

Yes, use plain assert statements and pytest.approx for float comparisons when testing Python analysis modules. These assertion patterns are recommended best practices to ensure test reliability and reduce brittleness in pivot detection and harmonic pattern recognition testing.

Pytest coverage fails when test coverage drops below the enforced 63% CI threshold. Ensure all pivot detection, harmonic pattern recognition, and Numba-optimized functions have corresponding tests using proper naming conventions and fixture usage to meet this requirement.