Informace o publikaci




Rok publikování 2018
Druh Konferenční abstrakty
Fakulta / Pracoviště MU

Přírodovědecká fakulta

Popis With increasing amount of data that are being acquired in elemental analysis arises a question what is the best way do work with them. Using regular spreadsheet programmes can become annoyingly time consuming, assuming the programme is able to handle that amount of data. Considering elemental distribution maps, we have shifted to a highly multidimensional space, which we simply cannot easily write into one table. Python is an open source programming language offering large number of libraries specifically written to work with data or for scientific computing. This work is focusing on the wide advantages of python that could be used for data evaluation in elemental analysis by LA-ICP-MS. Main goal is to offer a way how to shorten the time of data evaluation, so that scientists could spend more time in a laboratory instead of spending hours extracting the results out of an endless matrix of numbers. Moreover, python offers many ways to visualise results. Libraries that were used as a part of our research include pandas (high performance, easy to use data structures), scikit-image (algorithms for image processing), scikit-learn (algorithms and tools for machine learning) and seaborn, which is built on top of matplotlib (plotting library which produces publication quality figures), supports pandas data structures and offers informative statistical graphics. In our research, we use python on daily basis. First, script for automatic evaluation of LA-ICP-MS file in CSV format can shorten the time needed for evaluation. The total sum of content correction for archaeological glass samples, or internal standard correction for geological samples we use, are also written in python. Additionally, quantification using NIST standard reference materials is also automated using python. When visualisation and data exploration is concerned, python has a rather broad range of capabilities. We used python for exploratory analysis of archaeological glass samples and classification such as principal component analysis or multidimensional scaling. Different types of data normalisation are also easy to calculate in python even for large data sets. Comparison of elemental distribution images obtained from 2 different methods, LA-ICP-MS and EPMA were also done in python. Since it is a programming language, using loops can be quite convenient to create large number of figures at the same time. To conclude, python is an easy to learn, open source, widely used programming language for general purpose. It combines remarkable power with very clear syntax. In this work, we showed its use in elemental analysis, its advantages and possibilities.
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