pybaselines - Release v1.2.1

Version 1.2.1 (2025-08-10)

This is a minor patch with bug fixes, minor changes, and documentation improvements.

Bug Fixes

• Allow quant_reg with tol=inf to only perform one fit, since it was previously impossible to recreate the output of quant_reg by inputting weights since it required one inner loop.
• Allow a smooth_half_window of 0 for mormol to skip smoothing rather than raising an exception in order to be consistent with other methods.

Other Changes

• Set the default of normalize_weights to False for airpls to match its literature implementation; will deprecate the keyword in version 1.3.
• For 2D Whittaker-smoothing algorithms, a ValueError is now raised when num_eigens is less than or equal to diff_order, which has the same effect as inputting lam=0 and is also not allowed. Previously just emitted a warning.
• Slightly sped up several functions used for setting up and solving penalized splines.
• Only calculate method signatures when their corresponding functions (eg. pybaselines.polynomial.modpoly) are called rather than immediately upon importing pybaselines. Should slightly improve import timings.

Documentation/Examples

• Added an example for using masking with the various types of algorithms in pybaselines.
• Made all example programs in the documentation able to be ran without cloning the repository.
• Added back API documentation which was removed in version 1.2.0 for the functional interface of pybaselines. The functional interface is now marked as legacy rather than pending deprecation.

- Python
Published by derb12 7 months ago

pybaselines - Release v1.2.0

Version 1.2.0 (2025-03-17)

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

New Features

• Added the locally symmetric reweighted penalized least squares (lsrpls) Whittaker smoothing algorithm and its penalized spline version pspline_lsrpls.
• Added the Bayesian reweighted penalized least squares (brpls) Whittaker smoothing algorithm and its penalized spline version pspline_brpls.
• Added the 4S peak filling (peak_filling) algorithm, which truncates the data and then iteratively selects the minimum of a directional moving average and the current data point.
• Baseline and Baseline2D objects keep the computed pseudo-inverse of the Vandermonde for polynomial methods if weights are not given, which speeds up most polynomial methods for repeated fits.

Bug Fixes

• All methods of Baseline and Baseline2D are now thread-safe as long as non-data arguments are the same for each method call.
• Fixed incorrect indexing in rubberband.
• Removed using copy=False for numpy.array calls since it raised an error in Numpy versions 2.0 or later if a copy had to be made.
• Fixed an issue converting sparse matrices to banded matrices when solving penalized splines where a column could be omitted if the last diagonal value was zero. Only relevant if Numba is not installed and using SciPy versions 1.15 and newer.
• Corrected airpls weighting; the weighting equation for airpls was misprinted in its journal article, so changed to the correct weighting scheme.
• Improved overflow avoidance for iarpls, airpls, and drpls.
• Removed internal parallel processing for loess since it was problematic for both threaded and multiprocessing uses.
• Fixed an issue when flattening 3D arrays with shape (N, M, 1) to (N, M) where the shape would be output instead of the flattened array.

Other Changes

• Officially list Python 3.13 as supported, as well as the experimental free-threaded Python 3.13 build.
• Updated lowest supported Python version to 3.9
• Updated lowest supported dependency versions: NumPy 1.20, SciPy 1.6, pentapy 1.1, and Numba 0.53
• Allow inputting assymetric_coef for aspls and pspline_aspls to modify shape of the weighting curve.
• Added normalize_weights for airpls to normalize weights between 0 and 1, which is set to True by default. The new, corrected airpls weighting makes all negative residuals have weights greater than 1, so this option can help to avoid numerical issues or overflow. Set to False to ensure matching the literature implementation.

Deprecations/Breaking Changes

• All optimizer algorithms other than Baseline2D.individual_axes now return the parameter dictionary from the underlying method within the method_params key in order to avoid key overlap between the optimizer's parameters and the method parameters.
• The default delta for loess was changed from 0 to 0.01 * (max(x_data) - min(x_data)).
• The delta parameter for loess is now used on the actual input x_data rather than the x_data after scaling to the domain [-1, 1] to make it easier to use.
• The optimal_parameter key for the optimize_extended_range method no longer returns the log10 of the optimal value when fitting a non polynomial method. For example, it now returns 10000 rather than 4 if the optimal lam value was 10000.
• Deprecated passing tol and max_iter to mpls and pspline_mpls since the keywords were not internally used. The keywords will be removed in version 1.4.
• Deprecated the pentapy_solver attribute of Baseline and Baseline2D in favor of the banded_solver attribute to control the solvers used for banded linear systems. The attribute will be removed in version 1.4.
• Deprecated passing additional keyword arguments for padding to multiple methods, and will remove the functionality in version 1.4. Keyword arguments for padding should now be grouped into the pad_kwargs parameter instead.
• Deprecated passing additional keyword arguments for estimating the half-window parameter if none was given for morphological methods, and will remove the functionality in version 1.4. Keyword arguments for estimating the half-window should now be grouped into the window_kwargs parameter instead.
• Deprecated the min_rmse key from the parameter dictionary output of optimize_extended_range in favor of returning all calculated root mean square values from the fittings through the new rmse key. The min_rmse key will be removed in version 1.4.
• Pending Deprecation: The functional interface of pybaselines will be deprecated in version 1.3, and will be removed in version 2.0. For example, user code using whittaker.arpls(...) should migrate to Baseline.arpls(...). The only items that will be kept under the main pybaselines namespace will be Baseline, Baseline2D, and utils.

Documentation/Examples

• Added new examples to the documentation.
• Added a page describing best practices for fitting multiple datasets with pybaselines.
• Render each method on its own page in the documentation.

- Python
Published by derb12 11 months ago

pybaselines - Release v1.1.0

Version 1.1.0 (2024-02-18)

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

New Features

• Added two dimensional versions of various baseline correction algorithms, with the focus on Whittaker-smoothing-based, spline, and polynomial methods. These can be accessed using the new Baseline2D class.
• Added the Baseline2D.individual_axes method, which allows fitting each row and/or column in two dimensional data with any one dimensional method in pybaselines.
• Added a version of the rubberband method to pybaselines.misc which allows fitting individual segments within data to better fit concave-shaped data.
• Added the Customized Baseline Correction (custom_bc) method to pybaselines.optimizers, which allows fitting baselines with controllable levels of stiffness in different regions.
• Added a penalized version of mpls (pspline_mpls) to pybaselines.spline.
• Updated spline.mixture_model to use expectation-maximization rather than the previous nieve approach of fitting the histogram of the residuals with the probability density function. Should reduce calculation times.
• Added a function for penalized spline (P-spline) smoothing to pybaselines.utils, pybaselines.utils.pspline_smooth, which will return a tuple of the smoothed input and the knots, spline coefficients, and spline degree for any further use with SciPy's BSpline.

Other Changes

• Officially list Python 3.12 as supported.
• Updated lowest supported Python version to 3.8
• Updated lowest supported dependency versions: NumPy 1.20, SciPy 1.5, pentapy 1.1, and Numba 0.49
• Use SciPy's sparse arrays when the installed SciPy version is 1.12 or newer. This only affects user codes if using functions from the pybaselines.utils module.
• Vendor SciPy's cwt and ricker functions, which were deprecated from SciPy in version 1.12.

Deprecations/Breaking Changes

• Deprecated passing num_bins to spline.mixture_model. The keyword argument will be removed in version 1.3.
• Removed the pybaselines.config module, which was simply used to set the pentapy solver. The same behavior can be done by setting the pentapy_solver attribute of a Baseline object after initialization.

Documentation/Examples

• Added a section of the documentation explaining the extension of baseline correction for two dimensional data.
• Added new examples for 2D baseline correction and for custom_bc.

- Python
Published by derb12 about 2 years ago

pybaselines - Release v1.0.0

Version 1.0.0 (2022-10-26)

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

New Features

• Added a new class-based api for all algorithms, which can be accessed by using the pybaselines.api.Baseline class. All algorithms are available as methods of the Baseline class. The functional api from earlier versions is also maintained for backwards compatibility.
• All functions now allow inputting an x_data keyword, even if it is not used within the function, to allow for a more consistent api. Likewise, pybaselines.misc.interp_pts added an unused data keyword. Now, all algorithms can be called with the signature baseline_algorithm(data=y_data, x_data=x_data, ...).
• Added a function for Whittaker smoothing to pybaselines.utils, pybaselines.utils.whittaker_smooth.
• whittaker.iasls and spline.pslineiasls now allow inputting a `difforder` parameter.

Bug Fixes

• Fixed the addition of the penalty difference diagonals in spline.pspline_drpls, which was incorrectly treating the penalty diagonals as lower banded rather than fully banded.

Other Changes

• Officially list Python 3.11 as supported.
• Added default half_window values for snip and noise_median.
• collabpls accomodates alpha for aspls and psplineaspls; the alpha parameter is calculated for the entire dataset in the same way as the weights and is then fixed when fitting each of the individual data entries.
• Improved input validation.
• Improved testing base classes to reduce copied code and improve test coverage.
• Improved code handling for banded systems and penalized splines to simplify internal code.

Deprecations/Breaking Changes

• Removed the ability to pass addtional keyword arguments to algorithms in pybaselines.optimizers, which was deprecated in version 0.8.0.
• Removed the deprecated pybaselines.window module, which was formally deprecated in version 0.8.
• Moved the PENTAPY_SOLVER constant from pybaselines.utils to the new pybaselines.config module.

Documentation/Examples

• Added citation guidelines to make it easier to cite pybaselines.
• Added new examples showing how to use the new Baseline class.
• Added a new example examining the beads algorithm.

- Python
Published by derb12 over 3 years ago

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.mixturemodel 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.mixturemodel without numba is ~50-90%.
• Added penalized spline versions of all Whittaker-smoothing-based algorithms (psplineasls, psplineiasls, psplineairpls, psplinearpls, psplinedrpls, psplineiarpls, psplineaspls, psplinepsalsa, 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.optimizeextendedrange are now truncated and sorted before returning to match the original order and length of the input x-values.
• smooth.noisemedian now works with a `smoothhalf_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.adaptiveminmax now allows separate `constrainedfractionand 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 optimizeextendedrange 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.

- Python
Published by derb12 about 4 years ago

pybaselines - 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.optimizeextendedrange, 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.optimizeextendedrange to use spline (mixturemodel, irsqr) and classification (dietrich, cwtbr, fabc) functions.
• Allow optimizers.collabpls to use spline functions (mixturemodel, 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.optimizeextendedrange 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.optimizeextendedrange 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.padedges, or an `extrapolatewindow` of 0 or 1 for utils.getedges (called by optimizers.optimizeextendedrange).

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.optimizeextendedrange, 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.optimizeextendedrange since the pad_kwargs parameter is used by both the optimizeextendedrange 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.rollingball. 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 `halfwindow` 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.

- Python
Published by derb12 over 4 years ago

pybaselines - Release v0.6.0

Version 0.6.0 (2021-09-09)

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

New Features

• Added goldindec to pybaselines.polynomial, which uses a non-quadratic cost function with a shrinking threshold to fit the baseline.
• Added the morphological penalized spline (mpspline) algorithm to pybaselines.morphological, which uses morphology to identify baseline points and then fits the points using a penalized spline.
• Added the derivative peak-screening asymmetric least squares algorithm (derpsalsa) to pybaselines.whittaker, which includes additional weights based on the first and second derivatives of the data.
• Added the fastchrom algorithm to pybaselines.classification, which identifies baseline points as where the rolling standard deviation is less than the specified threshold.
• Added the module pybaselines.spline, which contains algorithms that use splines to create the baseline.
• Added the mixture model algorithm (mixture_model) to pybaselines.spline, which uses a weighted penalized spline to fit the baseline, where weights are calculated based on the probability each point belongs to the noise.
• Added iterative reweighted spline quantile regression (irsqr) to pybaselines.spline, which uses penalized splines and iterative reweighted least squares to perform quantile regression on the data.
• Added the corner-cutting algorithm (corner_cutting) to pybaselines.spline, which iteratively removes corner points and then fits a quadratic Bezier spline with the remaining points.

Bug Fixes

• Fixed an issue with utils.padedges when mode was "extrapolate" and `extrapolatewindow` was 1.

Other Changes

• Increased the minimum SciPy version to 0.17 in order to use bounds with scipy.optimize.curve_fit.
• Changed the default extrapolate_window value in pybaselines.utils.pad_edges to the input window length, rather than 2 * window length + 1.
• Slightly sped up pybaselines.optimizers.adaptiveminmax when `polyorder` is None by using the numpy array's min and max methods rather than the built-in functions.

Deprecations/Breaking Changes

• Renamed pybaselines.window to pybaselines.smooth to make its usage more clear. Using pybaselines.window will still work for now, but will begin emitting a DeprecationWarning in a later version (maybe version 0.8 or 0.9) and will be removed shortly thereafter.
• Removed the constant utils.PERMC_SPEC that was deprecated in version 0.4.1.
• Deprecated the function pybaselines.morphological.optimizewindow, which will be removed in version 0.8.0. Use pybaselines.utils.optimizewindow instead.

Documentation/Examples

• Fixed the plot for morphological.mpls in the documentation.
• Fixed the weighting formula for whittaker.arpls in the documentation.
• Fixed a typo for the cost function in the docstring of misc.beads.
• Updated the example program for all of the newly added algorithms.

- Python
Published by derb12 over 4 years ago

pybaselines - Release v0.5.1

Version 0.5.1 (2021-08-10)

This is a minor patch with bug fixes and minor changes.

Bug Fixes

• Added classification to the main pybaselines namespace so that calling pybaselines.classification works correctly.

Other Changes

• Changed the default tol for pybaselines.polynomial.quant_reg to 1e-6 to get better results.
• Directly use the input eps value for pybaselines.polynomial.quant_reg rather than its square.

- Python
Published by derb12 over 4 years ago

pybaselines - 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.collabpls is now 'averageweights' 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.

- Python
Published by derb12 over 4 years ago

pybaselines - Release v0.4.1

Version 0.4.1 (2021-06-10)

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

New Features

• Switched to using banded solvers for all Whittaker-smoothing-based algorithms (all functions in pybaselines.whittaker as well as pybaselines.morphological.mpls), which reduced their computation time by ~60-85% compared to version 0.4.0.
• Added pentapy as an optional dependency. All Whittaker-smoothing-based functions will use pentapy's solver, which is faster than SciPy's solvebanded and solvehbanded functions, if pentapy is installed and the system is pentadiagonal (diff_order is 2). All Whittaker functions with pentapy installed take ~80-95% less time compared to pybaselines version 0.4.0.

Bug Fixes

• The alpha item in the dictionary output of whittaker.aspls is now the full alpha array rather than a single value.
• The weighting for several Whittaker-smoothing-based functions was made more robust and less likely to create nan weights.

Other Changes

• Increased the default max_iter for whittaker.aspls to 100.

Deprecations/Breaking Changes

• The constant pybaselines.utils.PERMC_SPEC is no longer used. It will be removed in version 0.6.0.

- Python
Published by derb12 over 4 years ago

pybaselines - Release v0.4.0

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.optimizeextendedrange 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 PERMCSPEC (accessed from pybaselines.utils.PERMCSPEC), 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.interppts (formerly manual.linearinterp) now allows specifying any interpolation method supported by scipy.interpolate.interp1d, allowing for methods such as spline interpolation.

Bug Fixes

• Fixed polyorder calculation for optimizers.adaptiveminmax 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.

Other Changes

• 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.noisemedian, 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.noisemedian as noted below.
• Changed default smooth_half_window value for window.noisemedian to match specified `halfwindow` value rather than 1.
• Changed default sigma value for window.noisemedian to scale with the specified `smoothhalf_window`, rather than being a fixed value.

Deprecations/Breaking Changes

• Renamed pybaselines.manual to pybaselines.misc to allow for adding any future miscellaneous algorithms that will not fit elsewhere.
• Renamed the manual.linearinterp function to misc.interppts 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.

- Python
Published by derb12 over 4 years ago

pybaselines - Release v0.3.0

Version 0.3.0 (2021-04-29)

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

New Features

• Added the small-window moving average (swima) baseline to pybaselines.window, which iteratively smooths the data with a moving average to eliminate peaks and obtain the baseline.
• Added the rolling_ball function to pybaselines.morphological, which applies a minimum and then maximum moving window, and subsequently smooths the result, giving a baseline that resembles rolling a ball across the data. Also allows giving an array of half-window values to allow the ball to change size as it moves across the data.
• Added the adaptive_minmax algorithm to pybaselines.optimizers, which uses the modpoly or imodpoly functions and performs polynomial fits with two different orders and two different weighting schemes and then uses the maximum values of all the baselines.
• Added the Peaked Signal's Asymmetric Least Squares Algorithm (psalsa) function to pybaselines.whittaker, which uses exponentially decaying weighting to better fit noisy data.
• The imodpoly and loess functions in pybaselines.polynomial now use num_std to specify the number of standard deviations to use when thresholding.
• The pybaselines.polynomial.penalized_poly function now allows weights to be used. Also made the default threshold value scale with the data better.
• Added higher order filters for pybaselines.window.snip to allow for more complicated baselines. Also allow inputting a sequence of ints for max_half_window to better fit asymmetric peaks.

Bug Fixes

• Fixed a bug that would not allow even morphological half windows, since it is not needed for the half windows, only the full windows.
• Fixed the thresholding for pybaselines.polynomial.imodpoly, which was incorrectly not adding the standard deviation to the baseline when thresholding.
• Fixed weighting for pybaselines.whittaker.airpls so that weights no longer get values greater than 1.
• Removed the append and prepend keywords for np.diff in the pybaselines.morphological.mpls function, since the keywords were not added until numpy version 1.16, which is higher than the minimum stated version for pybaselines.

Other Changes

• Allow utils.padedges to work with a padlength of 0 (no padding).
• Added a 'minhalfwindow' parameter for pybaselines.morphological.optimize_window so that small window sizes can be skipped to speed up the calculation.
• Changed the default method from 'aspls' to 'asls' for optimizers.optimizeextendedrange.

Deprecations/Breaking Changes

• Removed the 'smooth' keyword argument for pybaselines.window.snip. Smoothing is now performed if the given smooth half window is greater than 0.
• pybaselines.polynomial.loess no longer has an include_stdev keyword argument. Equivalent behavior can be obtained by setting num_std to 0.

Documentation/Examples

• Updated the documentation to include simple explanations for some techniques.

- Python
Published by derb12 almost 5 years ago

pybaselines - Release v0.2.0

Version 0.2.0 (2021-04-02)

New Features

• Added the morphological and mollified (mormol) function to pybaselines.morphological, which uses a combination of morphology for baseline estimation and mollification for smoothing.
• Added the loess function to pybaselines.polynomial, which does local robust polynomial fitting. Allows using symmetric or asymmetric weighting, or using thresholding, similar to the modpoly and imodpoly functions.
• Added the penalized_poly function to pybaselines.polynomial, which fits a polynomial baseline using a non=quadratic cost function. The non=quadratic cost functions include huber, truncated=quadratic, and indec, and can be either symmetric or asymmetric.
• Added options for padding data when doing convolution or window=based operations to reduce edge effects and give better results.

Bug Fixes

• Fixed the mollification kernel used for the morphological.iamor (now amormol) function.
• Fixed a miscalculation with the weighting for whittaker.aspls.

Other Changes

• Slightly sped up several functions in whittaker.py by precomputing terms.
• Added tests for all baseline algorithms

Deprecations/Breaking Changes

• Renamed morphology.iamor to morphology.amormol (averaging morphological and mollified baseline) to make it more clear that mormol and amormol are similar methods.
• Renamed penalizedleastsquares.py to whittaker.py, to be more specific, since other techniques also use penalized least squares for polynomial fitting.

Documentation/Examples

• Updated the example program to match the changes to pybaselines.
• Setup initial documentation.

- Python
Published by derb12 almost 5 years ago

pybaselines - Release v0.1.0

Version 0.1.0 (2021-03-22)

• Initial release to pypi.

- Python
Published by derb12 almost 5 years ago