Pre-Processing XY Data from Experimental Methods
alkahest: Pre-Processing XY Data from Experimental Methods
Asymmetric Least Squares Smoothing
Linear Baseline Estimation
4S Peak Filling
Polynomial Baseline Estimation
Rolling Ball Baseline Estimation
Rubberband Baseline Estimation
SNIP Baseline Estimation
Rectangle Rule
Trapezoidal Rule
Strip XRD ka2
Find Peaks
Half-Width at Half-Maximum
Replace Negative Values
Replace Values Below a Given Threshold
Bin
Downsample
Linearly Interpolate
Normalize intensities by AUC
Rescales intensities to have specified minimum and maximum
Standard Normal Variate (SNV) Transformation
Rescale intensities to sum to a specified value
Transform Intensities
Bind
Baseline Correction
Drift Intensities
Mean Intensities
Shift the X Scale
Penalized Likelihood Smoothing
Loess Smoothing
Rectangular Smoothing
Savitzky-Golay Filter
Triangular Smoothing
Whittaker Smoothing
Subset
Sliding Windows
Tumbling Windows
A lightweight, dependency-free toolbox for pre-processing XY data from experimental methods (i.e. any signal that can be measured along a continuous variable). This package provides methods for baseline estimation and correction, smoothing, normalization, integration and peaks detection. Baseline correction methods includes polynomial fitting as described in Lieber and Mahadevan-Jansen (2003) <doi:10.1366/000370203322554518>, Rolling Ball algorithm after Kneen and Annegarn (1996) <doi:10.1016/0168-583X(95)00908-6>, SNIP algorithm after Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, 4S Peak Filling after Liland (2015) <doi:10.1016/j.mex.2015.02.009> and more.
Useful links