Release v0.5.0

Version 0.5.0 (2021-08-02)

This is a minor version with new features, bug fixes, and deprecations.

New Features

• 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

• 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

• 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

• 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

• 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.