How does it handle floating-point comparisons?

The skill implements patterns using pytest.approx for scalars and numpy.testing.assert_allclose for complex arrays, ensuring tests are resilient to minor precision differences.

Can it help me set up a new test directory?

Absolutely. It follows standard scientific package layouts, including src layouts and pyproject.toml configuration for pytest to ensure proper test discovery.

Does it follow specific community standards?

Yes, it is built around the Scientific Python community guidelines for robust and maintainable research software engineering.

Does this skill support NumPy and Pandas?

Yes, it specifically focuses on scientific libraries, including best practices for validating NumPy arrays and handling floating-point precision issues using pytest.approx and numpy.testing.

Scientific Python Testing

Name: Scientific Python Testing
Author: uw-ssec

byuw-ssec

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Security & Testing

Implements robust testing for scientific Python packages using pytest best practices and community-standard guidelines.

About

This skill provides specialized guidance for writing maintainable and effective tests for research software and scientific computations. It leverages the pytest framework and adheres to Scientific Python community standards, offering deep support for numerical validation using tools like NumPy testing and floating-point comparisons. Whether you are building data analysis pipelines, validating simulations, or ensuring the reproducibility of research code, this skill helps you set up modern testing infrastructure, manage fixtures, and implement comprehensive test suites that guarantee the correctness of complex scientific algorithms.

Key Features

Advanced numerical validation with NumPy and floating-point comparisons
Adherence to Scientific Python Community testing guidelines
Automated test configuration and directory structure setup
Expertise in pytest fixtures, parametrization, and custom markers
5 GitHub stars
Implementation of 'Outside-In' testing strategies for research software

Use Cases

Migrating legacy scientific codebases to modern pytest standards
Validating complex numerical algorithms and scientific simulations
Setting up CI/CD test suites for open-source research packages

About

Key Features

Advanced numerical validation with NumPy and floating-point comparisons
Adherence to Scientific Python Community testing guidelines
Automated test configuration and directory structure setup
Expertise in pytest fixtures, parametrization, and custom markers
5 GitHub stars
Implementation of 'Outside-In' testing strategies for research software

Use Cases

Migrating legacy scientific codebases to modern pytest standards
Validating complex numerical algorithms and scientific simulations
Setting up CI/CD test suites for open-source research packages