(Brooks Smith) When academic engineering tools remain trapped in Matlab while the industry relies on Excel, Python offers a unique opportunity to bridge this computational divide. While academic software packages for the efficient design of light-gauge steel have been available for several years, they effectively require highly specialised expertise to use and understand—challenging the material’s adoption beyond highly specialised buildings. This talk presents a case study of transforming CUFSM, a trusted but highly technical Matlab package for the finite strip analysis of light-gauge steel, into pyCUFSM—a performant and open-source Python implementation deployable to AWS Lambda.
The migration revealed and addressed several key scientific Python challenges. Moving from Matlab’s matrix-oriented syntax to idiomatic Python required not only converting every index from 1-based to 0-based, but also careful architectural decisions around NumPy array handling and SciPy linear algebra operations. Performance bottlenecks were addressed through strategic use of Cython compilation and aggressive pre-allocation and reuse of data structures, achieving a p90 duration of 3.9 seconds for real-world usage.
However, technical performance alone wasn’t sufficient. In regulated industries where engineers face personal liability for design failures, trust and transparency are paramount. Not only must the interpretation of inputs and outputs not leave any doubt, but the calculations themselves must be validatable by the user - without having to read or understand code. By coupling this Python analysis package with a React-based frontend, a calculator was developed which requires only 4 inputs, shows all intermediate steps with full rendered equations, and still allows access to advanced parameters for those who want it. Extensively validated, such an implementation is showing significant gains in adoption by the wider structural engineering community. The final system demonstrates how Python's strengths—from scientific libraries to cloud deployment—can bring academic innovation into wider use in regulated industries where reliability, ease of use, and user trust are essential.
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Fri Sep 12 12:20:00 2025 at Ballroom 2
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