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This is a minor version with new features, bug fixes, and deprecations.
New Features
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* Added quantile regression (quant_reg) to pybaselines.polynomial, which uses quantile
regression to fit a polynomial to the baseline.
* Added the top-hat transformation (tophat) to pybaselines.morphological, which estimates
the baseline using the morphological opening.
* Added the moving-window minimum value (mwmv) pybaseline.morphological, which estimates the
baseline using the rolling minimum values.
* Added the baseline estimation and denoising with sparsity (beads) method to pybaselines.misc,
which decomposes the input data into baseline and pure, noise-free signal by modeling the
baseline as a low pass filter and by considering the signal and its derivatives as sparse.
* Added the module pybaselines.classification, which contains algorithms that
classify baseline and/or peak segments to create the baseline.
* Added Dietrich's classification method (dietrich) to pybaselines.classification,
which classifies baseline points by analyzing the power spectrum of the data's
derivative and then iteratively fits the points with a polynomial.
* Added Golotvin's classification method (golotvin) to pybaselines.classification,
which breaks the data into segments, uses the minimum standard deviation of all
the segments to define the standard deviation of the entire data, and then
classifies baseline points using that value.
* Added the standard deviation distribution method (std_distribution) to
pybaselines.classification, which classifies baseline segments by grouping the
rolling standard deviation values into a distribution for the baseline and a
distribution for the signal.
* Added Numba as an optional dependency. Currently, the functions pybaselines.polynomial.loess,
pybaselines.classification.std_distribution, and pybaselines.misc.beads are faster when Numba
is installed.
* When Numba is installed, the pybaselines.polynomial.loess calculation is done
in parallel, which greatly improves the speed of the calculation.
* The pybaselines.polynomial.loess function now takes a `delta` parameter, which will
use linear interpolation rather than weighted least squares fitting for all but the
last x-values that are less than `delta` from the last-fit x-value. Can significantly
reduce calculation time.
* All iterative methods now return an array of the calculated tolerance value for each iteration
in the dictionary output, which should help to pick appropriate `tol` and `max_iter` values.
Bug Fixes
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* Added checks for airpls, drpls, and iarpls functions in pybaselines.whittaker to
prevent nan or infinite weights in edge cases where too many iterations were done.
* The baseline returned from polynomial algorithms was the second-to-last iteration's baseline,
rather than the last iteration's. Now the returned baseline is the last iteration's.
* Sort input weights and y0 (if `use_original` is True) for pybaselines.polynomial.loess
after sorting the x-values, rather than leaving them unsorted.
Other Changes
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* Added a custom ParameterWarning for when a user-input parameter is valid but
outside the recommended range and could cause issues with a calculation.
* Changed the default `conserve_memory` value in polynomial.loess to True, since
it is just as fast as False when Numba is installed and is safer.
* pybaselines.optimizers.collab_pls now includes the parameters from each function
call in the dictionary output as items in lists.
Deprecations/Breaking Changes
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* The key for the averaged weights for pybaselines.optimizers.collab_pls is now
'average_weights' to avoid clashing with the 'weights' key from the called function.
Documentation/Examples
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* Most algorithms in the documentation now include several plots showing how
the algorithm fits different types of baselines.
* Added more in-depth explanations for all baseline correction algorithms.