Macro X-ray fluorescence (MA-XRF) analysis has experienced an exponential increase in usage during the last decade, especially in the field of conservation sciences. This was supported, in parts, by the unceasing development in robotics and electronics, being now possible to develop in-house systems at a much lower cost than commercially available ones. Developing home-made systems entails the need for complex data analysis routines or standalone software to evaluate the large amount of data obtained (sometimes in the order of few gigabytes). The software herein proposed presents itself as an ad hoc alternative for currently existing software, providing a simple and fast way ofanalyzing multiple datasets and stitch them into a larger set if needed. Moreover, it creates a local user database for easy navigation through the datasets. The software was written in Python and its interface with the Tcl/Tk package. High-performance python routines and parallel processing were implemented to speed up calculation-intensive steps. Automated data extraction routines were written to simplify the evaluation step, dismissing a deep understanding ofthe underlying physics process.

A new software for MA-XRF data visualization / Barcellos Lins, Sergio A.; Bremmers, Boris; Gigante, Giovanni E.. - (2020), pp. 399-406. ((Intervento presentato al convegno XXIII Encontro Nacional de Modelagem Computacional e o XI Encontro de Ciência e Tecnologia de Materiais tenutosi a Palmas, Brasile.

A new software for MA-XRF data visualization

Sergio A. Barcellos Lins
Primo
;
Giovanni E. Gigante
Ultimo
2020

Abstract

Macro X-ray fluorescence (MA-XRF) analysis has experienced an exponential increase in usage during the last decade, especially in the field of conservation sciences. This was supported, in parts, by the unceasing development in robotics and electronics, being now possible to develop in-house systems at a much lower cost than commercially available ones. Developing home-made systems entails the need for complex data analysis routines or standalone software to evaluate the large amount of data obtained (sometimes in the order of few gigabytes). The software herein proposed presents itself as an ad hoc alternative for currently existing software, providing a simple and fast way ofanalyzing multiple datasets and stitch them into a larger set if needed. Moreover, it creates a local user database for easy navigation through the datasets. The software was written in Python and its interface with the Tcl/Tk package. High-performance python routines and parallel processing were implemented to speed up calculation-intensive steps. Automated data extraction routines were written to simplify the evaluation step, dismissing a deep understanding ofthe underlying physics process.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/1466294
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