{"id":4728,"date":"2026-02-03T08:49:38","date_gmt":"2026-02-03T07:49:38","guid":{"rendered":"https:\/\/lookingatnothing.com\/?p=4728"},"modified":"2026-02-03T09:28:33","modified_gmt":"2026-02-03T08:28:33","slug":"mcsas3gui-article-preprint-available","status":"publish","type":"post","link":"https:\/\/lookingatnothing.com\/index.php\/archives\/4728","title":{"rendered":"McSAS3+GUI article preprint available"},"content":{"rendered":"
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McSAS3 and McSAS3GUI<\/a>* are a fitting engine and user interface for analysing scattering patterns easily with Monte Carlo methods. This combination is the successor to the McSAS program, typically used to get size distributions from data of practical samples and sample series. It’s been out for a while (as mentioned before here<\/a>), but I hadn’t had the chance to publish about it. <\/p>\n\n\n\n

Several advance users of McSAS3 have asked me for a reference to the paper to cite in theirs, so I wrote a brief manuscript highlighting the difference to the original and showing off its capabilities using three examples. The preprint is now available on the ArXiv here: https:\/\/arxiv.org\/abs\/2601.18659<\/a>, and the manuscript has been submitted to the regular outfit. I hope you like it. For me, it was a good opportunity to see what wild applications McSAS has been used for over the last decade, and with over 200 citations, it tells me that the effort has been quite worthwhile! <\/p>\n\n\n\n

*) for those wanting to have a go at the software, just pip install mcsas3gui in a recent python environment, and run with python3 -m mcsas3gui. The first launch may take a minute, after that it’ll be quicker. The installation will automatically install McSAS3 as a dependency. A video playlist going through the installation, examples and background is available here: <\/p>\n\n\n\n

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