2025 Undergraduate Research Showcase

Document Type

Student Presentation

Presentation Date

4-15-2025

Faculty Sponsor

Dr. Min Long

Abstract

This research focuses on translating a critical scientific code from Fortran to Python to improve accessibility, maintainability, and future development in Extended X-ray Absorption Fine Structure (EXAFS) data analysis. The original code performs self-absorption correction as part of the Real Space X-ray Absorption Package (RSXAP), a suite of tools known for its rigorous treatment of Extended X-ray Absorption Fine Structure (EXAFS) and XANES data. The legacy code, written in a mix of Fortran 77 and Fortran 90, posed challenges for integration with modern data science tools and workflows. A complete manual translation was undertaken, with careful validation to ensure the Python version reproduced results consistent with the original implementation. This translation modernizes an essential step in EXAFS analysis, enabling wider use by researchers without Fortran expertise and opening the door for future enhancements such as interactive visualization, automated fitting pipelines, and integration with modern machine learning libraries. This research demonstrates the importance of software modernization in scientific research and contributes to the long-term sustainability of specialized computational methods in physics, chemistry, materials science, and many other fields.

Comments

This research was supported by the National Science Foundation (award #2213494).

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