The core algorithmic steps employed by hplc-py are diagrammed in Figure i) Estimation of and correction for a variable baseline. A common challenge in the analysis of HPLC data is the identification and removal of spurious background signal. While the physicochemical basis for baseline variance is complex , numerous methods have been developed for their correction . In hplc-py, this is implemented using the Sensitive Nonlinear Iterative Peak (SNIP) method originally developed for smoothing of spectroscopic data .