ii) Peak identification and separation of the chromatogram into region windows. After any variable background has been identified and corrected, peak-filled regions of the chromatogram are identified through the application of topographic prominence thresholds, a method common in the signal processing of neuron action potentials . With peak locations identified, the chromatogram is further clipped into windowsregions in time where chemical species co-elute and therefore overlap. iii) Fitting a mixture of amplitude-weighted skew-normal distributions to each peak window. For an assigned peak window with N peaks, hplc-py fits a convolution of N amplitude-weighted skew-normal distributions to the observed signal S within that window. A weighted skew-normal distribution is parameterized by an amplitude A, location and scale parameters τ and σ, and a skew parameter α and has the form