derb12/pybaselines: Release v0.8.0

Version 0.8.0 (2021-12-07) This is a minor version with new features, bug fixes, deprecations, and documentation improvements. New Features Added more efficient ways for creating the spline basis, and now solve penalized spline equations as a banded system rather than as a sparse system. Compared to version 0.7.0, spline.mixture_model, spline.irsqr, and morphological.mpspline are ~60-90% faster when numba is installed and ~10-70% faster without numba. Made several calculations in spline.mixture_model more efficient, further reducing the time by ~60-70% compared to the timings above without numba. The total time reduction from version 0.7.0 for spline.mixture_model without numba is ~50-90%. Added penalized spline versions of all Whittaker-smoothing-based algorithms (pspline_asls, pspline_iasls, pspline_airpls, pspline_arpls, pspline_drpls, pspline_iarpls, pspline_aspls, pspline_psalsa, and pspline_derpsalsa) to pybaselines.spline. Bug Fixes Was not multiplying the penalty in whittaker.iasls by lam_1. The output weights for polynomial.quant_reg and polynomial.loess are now squared before returning since the square root of the weights are used internally. The output weights and polynomial coefficients (if return_coef is True) for polynomial.loess are now sorted to match the original order of the input x-values. The output weights for optimizers.optimize_extended_range are now truncated and sorted before returning to match the original order and length of the input x-values. smooth.noise_median now works with a smooth_half_window value of 0 to give no smoothing. Other Changes Officially list Python 3.10 as supported. pybaselines is now available to install using conda from the conda-forge channel. Changed a factor in the weighting for whittaker.aspls to better match the implementation in literature. Allow inputting x-values for all penalized spline functions rather than assuming evenly spaced measurements. optimizers.adaptive_minmax now allows separate constrained_fraction and constrained_weight values for for the left and right edges. The error raised by optimizers.collab_pls if the input data is not 2-dimensional is now more explicit. Deprecations/Breaking Changes No longer allow negative or array-like values for the penalty multipliers in Whittaker-smoothing-based functions, penalized spline functions, morphological.jbcd, or misc.beads. Array-like penalty values are technically valid; however, they change the symmetry of the banded linear system, so additional code will have to be added in a later version to ensure the setup is correct before re-allowing array-like values. Deprecated passing keyword arguments to all functions in pybaselines.optimizers. Passing additional keyword arguments will raise an error starting in version 0.10.0 or 1.0.0, whichever comes first (the same deprecation for optimize_extended_range made in version 0.7.0 is also pushed back to 0.10.0 or 1.0.0). For spline algorithms, the min and max x-values are now included as inner knots when creating the spline basis rather than counting them as the first outer knots. To match the number of knots from previous versions, the num_knots parameter should add 2 to the num_knots used in previous versions. Formally deprecated pybaselines.window, which was replaced by pybaselines.smooth in version 0.6.0. pybaselines.window will be removed in version 1.0. Removed optimize_window from pybaselines.morphological, which was deprecated in version 0.6.0 Removed the code for allowing array-like half_window or smooth_half_window values for morphological.rolling_ball, which was deprecated in version 0.7.0. Documentation/Examples Added more examples to the documentation for fitting noisy data and exploring penalized spline parameters. Added an introduction for the splines category in the algorithms section of the documentation