How does this skill handle floating-point errors in tests?

The skill provides patterns using pytest.approx for scalars and numpy.testing.assert_allclose for arrays, allowing for configurable relative and absolute tolerances.

Does this support NumPy and Pandas data structures?

Yes, it specifically includes patterns for validating NumPy arrays and suggests best practices for testing code within the scientific Python ecosystem.

What is the recommended testing approach for research code?

It advocates for an 'Outside-In' approach: starting with public interface tests to capture user behavior, followed by integration and unit tests for internal logic.

Can I use this to set up a new scientific project?

Absolutely. It provides a standard directory layout and pyproject.toml configuration to ensure your project follows community guidelines from the start.

Scientific Python Testing

Name: Scientific Python Testing
Author: uw-ssec

byuw-ssec

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보안 및 테스팅

Implements robust, community-standard testing patterns for scientific Python packages using pytest and NumPy.

소개

This skill equips Claude with specialized knowledge to write maintainable test suites for scientific software, following official Scientific Python Community guidelines. It streamlines the creation of complex numerical tests by providing patterns for floating-point comparisons, efficient data fixtures, and multi-scenario parametrization. By adhering to modern testing principles like the 'Outside-In' approach, it ensures that research code is both reproducible and correct while maintaining high standards of software quality in the scientific ecosystem.

주요 기능

Parametrized testing for multi-scenario numerical validation
Advanced floating-point comparison patterns using pytest.approx and numpy.testing
Best practices for testing exceptions, warnings, and public API interfaces
6 GitHub stars
Standardized pytest configuration for scientific projects via pyproject.toml
Reusable fixture implementations for complex scientific data structures

사용 사례

Ensuring reproducibility in data analysis and modeling workflows
Validating numerical algorithms and scientific simulations for accuracy
Implementing CI/CD pipelines for research software engineering (RSE) projects

소개

주요 기능

Parametrized testing for multi-scenario numerical validation
Advanced floating-point comparison patterns using pytest.approx and numpy.testing
Best practices for testing exceptions, warnings, and public API interfaces
6 GitHub stars
Standardized pytest configuration for scientific projects via pyproject.toml
Reusable fixture implementations for complex scientific data structures

사용 사례

Ensuring reproducibility in data analysis and modeling workflows
Validating numerical algorithms and scientific simulations for accuracy
Implementing CI/CD pipelines for research software engineering (RSE) projects