Release v0.7.0

Version 0.7.0 (2021-10-28)

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

Notice: beginning in version 0.8.0, a DeprecationWarning will be emitted
when using any function from the pybaselines.window module. Use the
pybaselines.smooth module instead.

New Features

• Added the range independent algorithm (ria) to pybaselines.smooth, which extends
  the left and/or right edges, similar to optimizers.optimize_extended_range, and
  iteratively smooths until the area of the extended regions is recovered.
• Added the joint baseline correction and denoising algorithm (jbcd) to
  pybaselines.morphological, which uses regularized least-squares fitting combined
  with morphological operations to simultaneously obtain the baseline and denoised signal.
• Added the iterative polynomial smoothing algorithm (ipsa) to pybaselines.smooth, which
  iteratively smooths the input data using a second-order Savitzky–Golay filter.
• Added the continuous wavelet transform baseline recognition algorithm (cwt_br) to
  pybaselines.classification, which uses a continuous wavelet transform to classify
  the baseline points and iterative polynomial fitting to create the baseline.
• Added the fully automatic baseline correction algorithm (fabc) to
  pybaselines.classification, which is very similar to classification.dietrich, except
  that it uses a continuous wavelet transform to estimate the derivative and fits the
  baseline using Whittaker smoothing.
• Added a min_length parameter to most classification algorithms, which allows
  discarding any values in the baseline mask where the number of consecutive points
  designated as baseline is less than min_length, making the algorithms more robust.
• The threshold for polynomial.fastchrom can now be a Callable to allow the user to
  define their own thresholding functions based on the rolling standard deviation
  distribution.
• Allow optimizers.optimize_extended_range to use spline (mixture_model, irsqr)
  and classification (dietrich, cwt_br, fabc) functions.
• Allow optimizers.collab_pls to use spline functions (mixture_model, irsqr).

Bug Fixes

• Increased the minimum scipy version to 1.0 in order to use the BLAS function
  gbmv (dot product of a banded matrix and vector) for misc.beads.
• Use stable sorting when sorting the x-values for polynomial.loess and
  optimizers.optimize_extended_range to ensure that the sorting is correct.
• Fixed an issue when specifying output with scipy.ndimage.uniform_filter1d in scipy
  versions before version 1.1.0.
• Fixed an issue using dtype with numpy.arange in a numba jit wrapped function, which
  was not introduced until numba version 0.47.
• Fixed an indexing error in spline.corner_cutting which would give an erroneous index
  at which the maximum area removal occurred.
• Fixed an issue that occurred when inputting weights into spline.mixture_model.
• If weights are input into optimizers.optimize_extended_range as keyword arguments,
  the weights are now correctly sorted to match the sorting of the x-values and padded
  to account for the added portions on the left and/or right edges before using in the
  fitting function.
• Fixed the output of utils.padded_convolve when the kernel was even shaped (which
  never happens in actual application in pybaselines) or larger than the data.
• Fixed an issue caused by using an extrapolate_window of 1 for utils.pad_edges,
  or an extrapolate_window of 0 or 1 for utils._get_edges (called by
  optimizers.optimize_extended_range).

Other Changes

• Use scipy's expit function for whittaker.arpls and aspls, which does not emit the
  warning for exponential overflow. The warning was not needed since the overflow
  ultimately makes weights of 0 for the two functions.
• Use np.gradient for the computed derivatives in derpsalsa and dietrich, which gives
  slightly less noisy derivatives than the finite difference used by np.diff.
• Only sort x-values if they are given for polynomial.loess and
  optimizers.optimize_extended_range, which saves a little time otherwise.
• Made whittaker.airpls error handling more robust in order to catch errors from the
  solvers as well, which should catch any errors not prevented by checking the residual's
  length.
• Allow the mode for utils.pad_edges to be a callable padding function,
  matching numpy.pad's behavior.
• Added tol_history to the output parameters of classification.dietrich.
• Switched to using Scipy's convolve over Numpy's. Scipy's convolve can choose between
  the direct convolution, which is always used by Numpy, or an FFT based convolution,
  which is significantly faster for large arrays.
• Added testing for the minimum supported versions of all dependencies to
  the project's continuous integration in order to ensure that the minimum
  stated dependencies actually work.
• Allow specifying two separate extrapolate windows when padding using
  utils.pad_edges to allow better flexibility for fitting the edges.

Deprecations/Breaking Changes

• Deprecated allowing passing additional keyword arguments to optimizers.optimize_extended_range
  since the pad_kwargs parameter is used by both the optimize_extended_range function
  and the internal functions it supports. Now, all keyword arguments should be placed in
  the method_kwargs dictionary. Passing additional keyword arguments will raise
  an error starting in version 0.9.0.
• Deprecated allowing an array for the half_window or smooth_half_window parameters in
  morphological.rolling_ball. While the array-based moving min/max functions were valid,
  when combined for the morphological opening, the output would produce invalid results
  where the opening values were greater than the input data, which should not be allowed by
  the actual morphological opening. Using an array half_window will raise an error in
  version 0.8.0.

Documentation/Examples

• Added several new examples that explore different aspects of pybaselines.
• Use sphinx-gallery to display the example programs' code and outputs within
  the documentation.