Optimizer Baselines

Algorithms

collab_pls (Collaborative Penalized Least Squares)

collab_pls(): explanation for the algorithm. There is no figure showing a fit for for this method since it requires multiple sets of data.

adaptive_minmax (Adaptive MinMax)

adaptive_minmax(): explanation for the algorithm.

(Source code)

(png)

(png)

individual_axes (1D Baseline Correction Along Individual Axes)

individual_axes() is the single unique 2D baseline correction algorithm that is not available as a 1D algorithm, and it applies the specified 1D baseline algorithm along each row and/or column of the measured data. This is useful if the axes of the data are not correlated such that no information is lost by fitting each axis separately, or when baselines only exist along one axis.

Note that one limitation of individual_axes() is that it does not handle array-like method_kwargs, such as when different input weights are desired for each dataset along the rows and/or columns. However, this is an extremely niche situation, and could be handled by simply using a for-loop to do one dimensional baseline correction instead.

(Source code)

(png)

(png)