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pybaselines
Library of algorithms for baseline correction of experimental data
...The aim of the project is to provide a semi-unified API to allow quick testing and comparing multiple baseline correction algorithms to find the best one for a set of data. pybaselines has 50+ baseline correction algorithms. These include popular algorithms, such as AsLS, airPLS, ModPoly, and SNIP, as well as many lesser-known algorithms. Most algorithms are adapted directly from literature, although there are a few that are unique to pybaselines, such as penalized spline versions of Whittaker-smoothing-based algorithms. The full list of implemented algorithms can be found in the documentation.
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