Date of Award

2020

Document Type

Thesis

Degree Name

Bachelors

Department

Natural Sciences

First Advisor

Shipman, Steven

Area of Concentration

Chemistry

Abstract

As technology have improved, so has the ability to collect spectra of increasingly complex molecules. Being able to understand these more complex spectra is important for expanding the field of microwave spectroscopy; however, these spectra are often time consuming to analyze by traditional methods. Many spectral fitting programs have been developed to automate part of this process. In this thesis, the progress achieved in the integrationofRAARRintoAUTOFITisdescribed. RAARRwasportedfromMATLAB to Python with SMOP (Small MATLAB/Octave to Python compiler) and then further ported by hand. The ported code was tested in Visual Studio Code and was evaluated with test spectra provided in RAARR. The outputs for pattern finding for scaffolds were compared in the RAARR and Python version. Future prospects for this work and plans for further integration of RAARR into AUTOFIT are discussed.

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