7. Quality Policy & Research Integrity

The PyEQSP project maintains a strict quality policy to ensure that researchers can rely on its mathematical outputs and visualizations for high-fidelity scientific work.

7.1. Research Integrity Standards

We adhere to the following benchmarks for every release:

  • 100% Test Coverage: Every mathematical logic path, coordinate transform, and property metric is exercised by our test suite. This ensures that no “silent” numerical drifts are introduced during updates.

  • Pylint 10.00/10 Compliance: We use deep static analysis to enforce strict code quality. While we allow standard mathematical notation (like N_values or Ns), we enforce consistent variable naming and structural integrity across the entire codebase.

  • Reproducibility: The library includes a full “Reproduction Kit” to verify results against the canonical PhD thesis baseline (Leopardi, 2007).

7.2. High-Fidelity Verification

We utilize a multi-layered verification system (“Defense in Depth”) to maintain numerical and structural integrity. This strategy deploys automated local hooks for every commit, comprehensive project-wide verification suites, and a robust CI pipeline monitoring all changes across multiple platforms. Beyond standard Python lints, we enforce project-specific guardrails for documentation quality and research terminology to maintain professional scientific standards.

7.3. Verification Baselines

Our hybrid testing approach combines:

  1. Unit Tests: Comparing results against known-good values from the original MATLAB implementation.

  2. Doctests: Ensuring that every example shown in the documentation is live-tested and correct.

  3. Property Assertions: Verifying that partitions always meet the “Equal Area” and “Small Diameter” requirements.

For technical details on running the test suite, coverage audits, and performance benchmarks, see the Technical Testing & Verification guide in the Maintenance Guide.