Version 0.4.0 (2021-05-30)

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

New Features

Significantly reduced both the calculation time and memory usage of polynomial.loess.
For example, getting the baseline for a dataset with 20,000 points now takes ~12 seconds
and ~0.7 Gb of memory compared to ~55 seconds and ~3 Gb of memory in version 0.3.
Added a conserve_memory parameter to polynomial.loess that will recalculate the distance
kernels each iteration, which is slower than the default but uses very little memory. For
example, using loess with conserve_memory set to True on a dataset with 20,000 points
takes ~18 seconds while using ~0 Gb of memory.
Allow more user inputs for optimizers.optimize_extended_range to allow specifying the range
of lam/poly_order values to test and to have more control over the added lines and
Gaussians on the sides.
Added a constant called PERMC_SPEC (accessed from pybaselines.utils.PERMC_SPEC),
which is used by SciPy's sparse solver when using Whittaker-smoothing-based algorithms.
Changed the default value to "NATURAL", which reduced the computation time of all
Whittaker-smoothing-based algorithms by ~5-35% compared to other permc_spec options
on the tested system.
misc.interp_pts (formerly manual.linear_interp) now allows specifying any interpolation
method supported by scipy.interpolate.interp1d, allowing for methods such as spline
interpolation.

Fixed poly_order calculation for optimizers.adaptive_minmax when poly_order was a
single item within a container.
Potential fix for namespace error with utils; accessing pybaselines.utils gave an
attribute error in very specific envinronments, so changed the import order in
pybaselines.init to potentially fix it. Updated the quick start example in case
the fix is not correct so that the example will still work.
Increased minimum NumPy version to 1.14 to use rcond=None with numpy.linalg.lstsq.

polynomial.loess now allows inputting weights, specifying a use_original keyword for
thresholding to match the modpoly and imodpoly functions, and specifying a return_coef
keyword to allow returning the polynomial coefficients for each x-value to recreate
the fitted polynomial, to match all other polynomial functions.
Changed the default smooth_half_window value in window.noise_median, window.snip, and
morphological.mormol to None, rather than being fixed values. Each function sets its default
slightly different but still follows the behavior in previous versions, except for
window.noise_median as noted below.
Changed default smooth_half_window value for window.noise_median to match specified
half_window value rather than 1.
Changed default sigma value for window.noise_median to scale with the specified
smooth_half_window, rather than being a fixed value.

Renamed pybaselines.manual to pybaselines.misc to allow for adding any future
miscellaneous algorithms that will not fit elsewhere.
Renamed the manual.linear_interp function to misc.interp_pts to reflect its more
general interpolation usage.
The parameter dictionary returned from Whittaker-smoothing-based functions
no longer includes 'roughness' and 'fidelity' values since the values were not used
elsewhere.