Release Notes¶

Legend

: Features something new which could not be done before
: A change which improves the performance or memory usage.
: An improvement to a previously existing feature.
: Something which was not working as expected or leading to errors has been fixed.
: This will require changes in your scripts or code.

Release `v1.1.4` - September 2024¶

: Expanded sophgrid to allow for closed phi integral. (#482)
: Removed Python 3.8 support and requires Numpy 2.0

Release `v1.1.3` - July 2024¶

: Removes unnecessary files from the docs
: Improves the performance of the dipolarkernel function by 10-30% (#473), by caching the interpolation of he effective dipolar evolution time vector.
: Removes the $candidates$ input from $selregparam$ and integrates its function into $regparamrange$ .
: Adds support for Numpy 2.0
: Add support for Python 3.12

Release `v1.1.2` - November 2023¶

: Fixes an issue with sophgrid (#463).
: Fixes an error in the normalisation of the rice models (#459).
: Removes the broken three spin example (#427).
: Fixes an error in the linear solver for linearly constrained not non-negative problems.

Release `v1.1.1` - August 2023¶

: Fixes an error in the $FitResult.evaluate$ function. (#454)

Release `v1.1.0` - August 2023¶

: The definition of the dipolar time axis for RIDME has changed to match the one for 4-pulse DEER (#436).
: The doc theme has moved to furo (#432).
: Fixes errors in documentation (#429).
- Changes the file name of figures ‘modelling*.png` to $modeling*.png$ . To keep all spelling consistent with American english.
- Adds a missing $)$ in $fitting_guide$
- Corrects the time axis in $echo_crossing$ example.
- Corrects plotting in $ex_simulate_basic_4pdeer$ example.
: Fixes an errors in tests (#429).
- The test test_algorithms had an incorrect search range.
: Fixes an errors in the general Gaussian distribution model (#435).
: Removes the default font from the $fit$ function due to conflicts on some systems (#429).
: Methods of the $FitResult$ class are now defined with the class (#440).
- $FitResult$ class now in file $fitresults.py$
- $fit$ function now in file $fit.py$
: Added a RIDME example (#438).
: Masked data points are no-longer included in gof statistics in $FitResult.plot()$ .

Release `v1.0.1` - March 2023¶

: Fixes some minor bugs in the documentation.
- The file modelling_guide.rst is renamed to modeling_guide.rst to keep spelling consistency.
- The “Simulating a two-pathway 5-pulse DEER signal” and “Simulating a three-pathway 4-pulse DEER signal” examples now run correctly.
: Fixes issues with CVXOPT in tests.
- The testing will now use quadprog as the default solver. To account for the change tested values are now generated using the grid method
: Updates GitHub actions to use latest packages.
: Hardcodes out Python 3.11 support. This will remain until quadprog is fixed.
: Removes hard-wired RNG seeding

Release `v1.0.0` - December 2022¶

Introduces multi-spin dipolar EPR spectroscopy models (#385).
- Implements multi-spin models in dipolarmodel by taking into account all possible two-spin an three-spin interaction pathways in a multi-pin system. Add a new optional argument spins to specify the number of spins in a molecule. From that number and the npathways or experiment arguments, dipolarmodel automatically constructs the full set of multi-spin pathways (including permutations) within the pair-wise pathway factorization approximation. Adds a new keyword argument triangles, required for spins>3 to specify triads of indices of interspin vectors that are connected forming a triangle (must be specified by the user since it defines the geometry of the spins system).
- Adds a new argument specperm to dipolarmodel, to enable/disable the assumption of spectral permutability of the multi-spin system. This option allows to account for multi-spin system with chemically or spectrally different spin-1/2 centers.
- Adds and changes the optional argument nKnots in dipolarkernel and dipolarmodel to gridsize. Changing the specification from knots to grid points allows a finer control of the computation costs due to the highly non-linear scaling of grid points with number of knots in a spherical grid.
- Implements a new utility function sophegrid to generate SOPHE grids based on a port of the Easyspin sphgrid function.
- The multi-spin models in dipolarmodel have at the moment a hard-coded unimodal normal multi-variate distance distribution at its core. This could be adapted in the future to be modular to specify multi-modal distributions and other multi-variate basis functions.
Implements different parametrization strategies for dipolar EPR models (#409). Implements a new optional argument parametrization for dipolarmodel, which specifies the parametrization strategy of the dipolar pathway refocusing times. Can be one of the following:
- 'reftimes' - Each refocusing time is represented individually as a parameter. The current behavior.
- 'delays'` - The pulse delays are introduced as parameters from which the refocusing times are computed
- 'shift' - A time shift is introduced as a parameter to represent the variability of the refocusing times from their theoretical values.
Expands the toolset for asserting the goodness-of-fit of model estimates robustly (#388).
- Adds new option gof to FitResults.plot() method to add plots to aid goodness-of-fit assessment. Besides the plot of the data and the model fit, now adds a plot of the residuals with its mean and noise level estimates shown as lines, a plot of the normalized residual histogram compared to the standard normal distribution, and a plot of the autocorrelation in the data along the confidence region expected from white noise.
- Adds a new quantity, Residuals autocorr. to the results summary. The value of this quantity is computed as abs(2 - dDW), where dDW is the Durbin–Watson statistic. It allows a quick assessment of the autocorrelation in the fit residuals. This quantity is also returned by the fit function as FitResults.stats['autocorr']
- Remove the AIC quantity specified for each dataset from the fit’s summary.
- Add a new automatic system that colorizes the chi2red and Residuals autocorr. values depending on their severity, indicating a wrong estimate. Yellow coloring alerts the user of potential failures and red of confidently wrong estimates.
Implements a new NNLS solver function qpnnls that uses the Goldfarb/Idnani dual algorithm implemented in the quadprog package (#390). The resulting NNLS solver is more efficient than the current implementation with cvxopt without sacrificing any accuracy. Since NNLS problem-solving is the bottleneck of most applications of DeerLab, it results in a significant improvement in speed when analyzing any models with linear parameters. Since it substitutes cvxopt as the default solver engine, it removes the hard dependency on the $cvxopt$ package and its issues related to the Apple M1 chip (#407).
Implements a new, more efficient evaluation of multi-pathway dipolar models based to greatly enhance the performance of dipolar EPR spectroscopy analyses (#393). Combined with the new NNLS solver, the analysis time of, e.g., 4-pulse and 5-pulse DEER multi-pathway models, has been reduced on average about 70-80%.
Improves the interface and definition of dipolar pathways in dipolarmodel (#396).
- Labels the parameters of the different dipolar pathways included in dipolarmodel via the experiment argument based on the pathway label numbers rather than numerically by order of specification.
- Harmonizes the pathway ordering of the ex_rev5pdeer and ex_fwd_5pdeer models according to published literature.
- Adds new figures to the ex_ model functions to show the table of dipolar pathways along a schematic illustration of their intramolecular contributions. Improved the docstrings of the ex_ models in general.
Improves and expands the documentation (#397, #408).
- Adds multiple new examples, fixes errors in existing ones.
- Fixes several graphical bugs in the website.
- Thoroughly documents all public and private functions in the code.
- Adds release instructions for maintainers.
- Adds a Publications page to the documentation linking to all literature for concepts introduced by DeerLab.
Removes the subpackage deerlab.utils and makes its functions part of the main package for simpler maintenance (#408). Removes multiple unused private functions.

fit

The function now returns a full uncertainty quantification for the normalization factor of any model parameter with a normalization condition (#372).
Removes the automatic computation of the modelUncert output containing the propagated uncertainty estimate of the model’s response (#401). This significantly speeds up the runtime of the function by disabling the automatic propagation of uncertainty to the model’s response which could take from several seconds to several minutes in complex models (#391).

dipolarkernel

Implements multi-spin dipolar pathways up to three-spin interactions (#385). The function takes now a list of distance vectors [r1,r2,...,rQ] for multi-spin kernels.
Expands the function to be able to account for arbitrary experimental time coordinates (#385). Now a list of time vectors [t1,t2,...,tD] can be specified to construct a D-dimensional dipolar kernel.
: Refactors most code in the function (#385). THe code should now be more logically ordered using mathematical symbols for clearer equations.
Introduces a new and clearer syntax for defining dipolar pathways (#385). Now, instead of specifying a list of pathways, where each pathway is a list of values (being the amplitude, refocusing time, and harmonic in that order), now pathways are specified as a list of dictionaries, e.g. pathways = [{'amp':0.5}, 'reftime':0, 'harmonic':1].
Adds a new optional argument tinterp to construct a dipolar kernel for a pathway and interpolate other pathways from that one (#393).

dipolarbackground

Implements multi-spin dipolar pathways up to three-spin interactions (#385).
Expands the function to be able to account for arbitrary experimental time coordinates (#385). Now a list of time vectors [t1,t2,...,tD] can be specified to construct a D-dimensional dipolar background function.
Introduces the same new syntax for defining dipolar pathways as in dipolarkernel (#385).

correctphase

Adds a new optional argument offset to enable a numerical optimization of the phase while accounting for a non-zero imaginary component offset (#392, #395).

snlls

Adds an optional argument modeluq to enable /disable the model uncertainty propagation (#401).

Release `v0.14.5` - December 2022¶

The distribution of DeerLab through Anaconda and its conda manager has been deprecated as of this release (#400).
Fix errors in the background function plots used in the examples showing 4-pulse DEER analyses.

fit

Expose the cores option of bootstrap_analysis to parallelize bootstrap analysis from the fit function (#387).
Correct behavior of masking during fitting (#394). When using the mask option of the fit function, certain steps such as noise estimation and goodness-of-fit assessment were not taking into account the mask during the analysis.

bootstrap_analysis

Fix error prompted when analyzing scalar variables (#402).

Release `v0.14.4` - August 2022¶

The experiment model functions such as ex_4pdeer now take an additional optional keyword argument pulselength to specify the longest durations of a pulse during an experiment. The value is then used to more accurately set the boundaries of refocusing time parameters in dipolar models (#368).
The parameter table displayed when printing DeerLab models has been expanded to show the start values and the frozen values, if present (#369).
Added missing documentation for certain attributes of the UQResult objects related to the bootstrap and profile-likelihood methods (#360).
Behavior of the documention on minimized browser windows and mobile phones (#365).
Multiple issues with the incorrect dark theming of the webpage (#359)

fit

Added multiple missing optional keyword arguments to the documentation of the function (#367).

dd_randcoil

Fixed the erronously switched descriptions of the model parameters (#361).

Release `v0.14.3` - July 2022¶

Deprecated support for Python 3.6 and 3.7 (#353).
Added multiple quality of life improvements to the modelling system (#354).
- Add new method paramA.setas(paramB) for Parameter objects to copy the full metadata content from paramB into paramA.
- Expand the FitResult summary to report on the regularization parameter and penalty weights when used in the analysis.
- Improve the report of incorrect attribute requests in FitResult objects and provide close matches as suggestions.
- Improve the report of errors during the evaluation of Model objects.
Fix bug in the fit function unfreezing all frozen model parameters upon fitting if any model parameter included a normalization constraint (#348, #352).
Corrected two minor mathematical errors in the physical background models bg_homfractal and bg_homfractal_phase (#351).
Fixed display of the online documentation in browsers with an enabled dark theme that made certain menus and text sections unreadable (#349, #350). The documentation will now default to a light theme even for dark themed browser.

Release `v0.14.2` - June 2022¶

(Windows-systems only) Removed the unorthodox default installation procedure of DeerLab based on the installation of Numpy and related packages linked against MKL via the Gohlke repository (#322, #330).
- As a result the default performance of DeerLab can be affected in some Windows systems. To link the Numpy and related packages against MKL as in previous versions, an automated script upgrade_mkl.py is provided with the package.
- Fixes the error appearing during installation if the git command was not installed or available in the system (#326).
- Allows the distribution of DeerLab as wheels.
Implemented better options for automated and user-supplied noise estimates to improve bootstrapping approaches (#334, #343).
Avoid the installation of (potentially unstable) pre-release versions of Numpy in systems with fresh Python installations (#336).
Improved the robustness of several function against non-numerical values due to division-by-zero errors (#335).
Corrected the behavior of regularization parameter selection with L-curve methods (#340). Fixes the lc method in selgregparam which was seeking the optimal regularization parameter by minimizing curvature rather than by maximizing it. Prevents failure of the L-curve methods due to the appearance of non-numeric values when evaluating too large regularization parameter values.
Fixes the error when specifying a limited excitation bandwidth in dipolarmodel via the excbandwidth argument (#342).
Fixes the navigation menu on the documentation that appeared empty on mobile phones or for partially minimized windows on computers, impeding navigation through the documentation (#346).
Minor corrections to the documentation and examples.

Release `v0.14.1` - June 2022¶

Use Scipy’s eigh instead of Numpy’s to avoid convergence error numpy.linalg.LinAlgError: Eigenvalues did not converge during model uncertainty propagation (#310, #311).
Refactored the code to avoid the use of lambda and nested functions. This enables pickling DeerLab’s objects with Python’s pickle module without errors (#312).
Added two new utility functions store_pickle and read_pickle that implement pickling with the dill package to be more robust against potential lambda functions defined by the users in scripts (#312).
Fixed minor bug when printing fit results with many model parameters being frozen. The print command would return an error message (#329).
Fixed bug when propagating bootstrapped uncertainty in presence of round-off errors (#325).
Multiple minor improvements and corrections in the documentation.

Release `v0.14.0` - April 2022¶

Overall changes

Complete overhaul of the DeerLab modeling and fitting interface. Check the new documentation for help and details. (#218, #223, #228, #237, #225, #243).
- A new modeling system has been introduced. DeerLab main interface runs on a new Model object class. Models implement and provide the distinction between linear and non-linear parameters.
- Model parameters are no longer (solely) identified by their indexing inside a parameter vector, but are referenced by name. This avoids the need for a user to recall the ordering of the parameters. This is now all handled internally. For example, before paramA = parameters[idxA] is now model.paramA.
- Any model parameter is accessible from the model object and its boundaries, start values and other properties can be easily modified. For example, to change the lower boundary of a parameter: model.paramA.lb = 0.
- A new general fit function that fits arbitrary Model objects to single or multiple datasets has been implemented. The function automatically handles the selection of solvers to optimally fit the data to the model.
- Implemented a new function link to link model parameters (setting equality constraints in models).
- Implemented a new function merge to create a model returning more than one response (allowing the creation of global models).
- Implemented a new function relate to define functional relationships between model parameters.
- Implemented a new function lincombine to create a model whose response is a linear combination of the inputs’ model responses.
- Model parameters can now be frozen (set to a constant value and ignored during optimization) in the Model object and on the back-end snlls solver. For examples, to fix a parameter to a certain value: model.paramA.freeze(0.5).
- Arbitrary normalization conditions can be imposed to the linear parameters.
- Bootstrapping can now be requested directly from the fit function via the bootstrap keyword argument. The function will then return the bootstrap uncertainty quantification of all model parameters and of the model’s response instead of the covariance-based uncertainty.
- Implemented a new function dipolarmodel, which generates models based on the dipolar EPR multi-pathway theoretical model.
- Added new examples, adapted existing ones, and removed unneeded examples.
- Add many new tests and removed tests related to deprecated functionality.
- All the built-in parametric models are now pre-compiled Model objects instead of just functions.
- The function fitmodel has been deprecated and removed. The original has been substituted (and greatly expanded) by the new dipolarmodel and fit functions.
- The function fitmultimodel has been deprecated and removed. The original functionality can be easily scripted with the new modeling system. An example of has been added, describing how to script the same functionality.
Introduced the profile-likelihood methodology both for uncertainty quantification based on likelihood-confidence intervals, and for identifiability analysis (#222).
- Added a new function profile_analysis to calculate the objective function profiles from model object parameters.
- Implemented a new uncertainty quantification UQResult object type 'profile' for results obtained from profile_analysis.
Implemented a system to specify arbitrary penalty functions to be included in the non-linear part of the objective function during optimization. The penalties can be custom-defined and constructed into a Penalty object that can be passed to the fit function. Outer optimization of the penalty weights can also be included based on certain information-based criteria (#197, #218, #225).
- Implemented a new object Penalty that includes the penalty function, weight parameter (and its boundaries), and the selection functional for optimization.
- Adds new outer optimization options for the penalty weights, based on hard-coded model selection functionals. For now, the ICC, AIC, AICc, and BIC functionals are available.
- Implemented a new function dipolarpenalty that generates dipolar-EPR-specific penalties, e.g. to induce compactness and/or smoothness.
Implemented masking of datasets during optimization (#250).
Added a verbose option to display progress of the fit routines (#250).
Added support for analyzing and fitting complex-valued models and data (#127, #218).
Orientation selection in dipolar signals can now be simulated for arbitrary orientation weights distributions via the orisel keyword argument in the new dipolarmodel or the dipolarkernel functions (#183, #203).
Re-purposed the ex_ models. Each of these function represents a specific dipolar EPR experiment. These now take the experimental time delays as input, and return a new ExperimentInfo object. This can be passed to dipolarmodel via the optional keyword argument experiment to refine the boundaries and start values of the dipolar pathway refocusing times and amplitudes based on the experimental setup (#225).
Implemented masking of datasets during optimization (#250).
Overhaul of the DeerLab documentation website (#235).
- Full HTML/CSS overhaul. The new web design based on the PyData theme has a clearer design, with more readable pages and code blocks.
- Deprecates the use of the RTD theme. This removes the hard constraint of using Sphinx 1.8.0. Now the documentation builds with the latest Sphinx release.
- Add a user-guide for the new modeling and fitting system.
- Re-organize all of the website content.
- Improved the dipolar EPR starting guide, and adapted it to the new system.
- Fixed some minor errors in the examples.
- Redesigned all examples towards the use of actual experimental data files in BES3T format. Examples can now be taken and easily adapted by users to their experiment data files (#304).
Added the functionality to print a FitResult object to display a summary of the fit goodness-of-fit statistics and a table of all estimated parameters and their uncertainties (#234).
Added a new keyword argument regparamrange to snlls and fit to specify the search range of the optimal regularization parameter (#225).
Noise levels of the datasets can be optionally specified in all functions taking datasets (#213).
Added the option to include or exclude the edges of vector in regoperator via a new keyword argument includeedges (#204). The default for all functions in DeerLab has been set to includeedges=False (#205, #230).
Generalized the regularization operator. Related functions no longer take regorder (regularization operator order) as an argument. Instead they now take regop (the full regularization operator) as an argument (#216).
Generalized the regularized linear least-squares functionality. Now it can handle arbitrary bounds on linear parameters and adapts the linear LSQ solver back end accordingly (#216).
Improved performance of post-optimization model evaluation/propagation for large datasets (#200, #238).
Implemented (adaptable) memory limits for potentially memory-intense functions (#201, #239).
The function correctscale has been deprecated (#293). Its limited functionality is included in the now broader functionality provided by the new modeling and fitting system.
The functions fitregmodel and fitparamodel have been deprecated and their core functionality merged into snlls. The snlls function now handles any kind of least-squares problem and automatically employs optimal combinations of solvers to find the solution to the problems (#218).
Renamed the function bootan to bootstrap_analysis (#227).
Deprecated TV and Huber regularization. Accordingly the keyword arguments regtype, huberparameter have been removed throughout (#216).
Deprecated the nnlsbpp NNLS solver (#231).
Deprecated the regparamrange function (#232). It depended on home-written code for the GSVD, which (as shown in previous issues) was prone to LAPACK backend problems and other bugs. This function was still a derelict from DeerAnalysis methodology.
The function time2dist has been renamed to distancerange (#273, #274).- The function time2dist has been renamed to distancerange (#273, #274).
The convergence control arguments of the fit functions have now been renamed for consistency with the least_squares function of the SciPy package (#296).
Changed the name of the parameter width to std in the dd_gauss, dd_gauss2, dd_gauss3, and dd_skewgauss models (#278, #280).
When using the multistart keyword argument, no longer includes the parameter boundaries in the set of multiple start values (#218).
Fixed error (manifesting as nan values in the confidence intervals) caused by a division-by-zero in the covariance matrix estimation (#218).
Fix encoding error during installation (#252). This error could disrupt the installation in OS with default encoding different from CP-1252.
Implement a new function to ensure that estimated Jacobians are positive semi-definite matrices. This fixes the appearance of warnings and bugs when calculating confidence intervals (#216).
Corrected the scale invariance behavior of the covariance-based uncertainty estimates (#218).
Fixed multiple numpy.VisibleDeprecationWarning and RunTime warnings (#207, #212).
Corrected the math in the documentation of some distance distribution models (#215).
Corrected the behavior of dataset weights. These are no longer normalized at runtime and kept as specified by the users (#248, #250).
While testing, now skips a unit test if an error with the Tk backend of Matplotlib occurs (#211).
Fix multiple bugs and errors related to the new modeling and fitting system (#226, #233, #235, #241, #242, #244, #245, #246, #249).
Correct behavior of multistart optimization for one-sided parameter boundaries (#252).
Fix bug when globally fitting multiple datasets. The global weights were not being manipulated correctly in the estimation of the linear parameters leading to incorrect results (#302)

bootstrap_analysis

Added a new keyword argument memorylimit to specify the maximal memory used by the bootstrap analysis (by default 8GB). If the total analysis is expected to exceed the memory limit, the function will abort the execution (#200, #238).

dipolarkernel

Added a new option $complex$ to request the complex-valued dipolar kernel to simulate the out-of-phase contributions to the dipolar signals (#258).
Added a new keyword argument memorylimit to specify the maximal memory used by the dipolar kernel (by default 8GB). If the dipolar kernel is expected to exceed the memory limit, the function will abort the execution (#200, #238).
Prompts error if wrong method is selected when specifying a limited excitation bandwidth (#181, #183).

bg_models

Implemented the time-dependent phase shifts for all the built-in physical background models, namely bg_hon3d_phase, bg_hom3dex_phase, and bg_homfractal_phase (#258).
Changed the implementation of bg_hom3dex (#258). This avoids the use of tabulated pre-calculated values. Accordingly the utility functions calculate_exvolume_redfactor and load_exvolume_redfactor have been removed.
Improved the implementation and behavior of the bg_homfractal model (#258).

diststats

Fixed the behavior when dealing with distributions with arbitrary integral values

selregparam

Implemented a general LSQ solver as backend to adapt to different regularized optimization problem structures.
Generalized the linear least-squares solver. (#216).
In the brent mode, the search range is no longer selected from the min/max of regparamrange output, but from a new keyword argument searchrange set by default to [1e-8,1e2]. The default values were chosen as the statistical means of Monte-Carlo simulations of the min/max values of regparamrange’s output for typical 4-pulse DEER kernels (#232).
In the grid mode, the grid-values are passed by the pre-existing keyword argument candidates. By default, if not specified, a grid will be generated from the searchrange argument (#232).

UQResult

Ensures non-negativity of estimated parameter uncertainty probability density functions.
Improve the behavior of UQresult.propagate() for bootstrapped uncertainty results. Now, instead of propagating bootstrapped uncertainty via the estimated covariance matrix, the uncertainty is propagated by bootstrapping from the bootstrapped uncertainty distributions (#218).
Fix behavior of the bootstrap median (#254).
Suppress multiple DeprecationWarning warnings during uncertainty calculations (#255).
Fix error prompt when requesting private methods such as __deepcopy__ (#301, #303).

correctphase

Implement a fully vectorized analytical solution, resulting in a 30-150x speedup (#256, #279).
Eliminate the phase='posrealint' and phase='negrealint' options (#279).

deerload

Raise warning instead of exception when parsing lines without key-value pairs (#256). This avoid errors when trying to load BES3T files with PulseSPEL scripts edited in different OS systems.

whitegaussnoise

Renamed the argument level to std for clarity (#276).
Make the argument std a required positional argument and no longer provide a default value (#276).

Release `v0.13.2` - July 2021¶

Overall changes

Fixed an error appearing during installation in Windows systems. If during installation a python executable alias was not created, the call to the pipwin manager returned an error and the installation failed to download and install Numpy, SciPy and CVXOpt (#187).
Fixed the rendering of certain code-blocks in the documentation examples which were appearing as plain text (#179, #184).
Fixed the execution and rendering of the model examples in the documentation (#189, #190).
Fixed a bug in snlls where one of the linear least-squares solvers can return results that violate the boundary conditions due to float-point round-off errors (#177, #188).

Release `v0.13.1` - May 2021¶

Overall changes

Fixed the behavior of global weights throughout DeerLab fit functions. The keyword argument weights was not having any or the expected effect in the results in some fit functions. Also fixes the behavior of built-in plots for global fits (#168, #171).
Optimize default weights in global fitting according to the datasets noise levels (#169, #174).
Fixed a bug in snlls that was causing the confidence intervals in snlls, fitmodel and fitmultimodel to vanish for large signal scales (#165, #166).

deerload

Corrected a bug that happened in certain BES3T Bruker spectrometer files, when there are entries under the MANIPULATION HISTORY LAYER section at the end of the descriptor file. Also fixed the reading of .XGF partner files (#164).

snlls

The keyword argument extrapenalty now requires a function that takes both non-linear and linear parameters. Corrected the name of the keyword in the documentation (#175).

fitparamodel

Fixed the scaling of the output FitResult.model and FitResult.modelUncert (#173).

ex_pseudotitration_parameter_free:

Removed Ctot from second order term in the chemicalequalibrium polynomial (#163).

Release `v0.13.0` - April 2021¶

Overall changes

DeerLab is now distributed via the Anaconda repository and can be installed with the conda package manager (#12, #157). The installation instructions have been expanded to describe the Anaconda installation (#155).
DeerLab now supports Python 3.9.
The function fitsignal has been re-named to fitmodel for correctness and consistency with other functions (#102).
Added new experiment models for RIDME on systems with one to seven harmonic pathways (S=1/2 to S=7/2) to include all higher harmonics (overtones) (#79).
Bootstrapping is now embedded into fitmodel to automatically bootstrap all output quantities without the need to write additional script lines (#55). In fitmodel a new option uq allows to switch between covariance or bootstrapping uncertainty quantification (#88).
The function fitmodel now returns Vmod and Vunmod, the modulated and unmodulated contributions to the fitted dipolar signal, respectively, along their uncertainties as additional outputs (#78).
Implemented several initialization strategies in fitmultimodel for multi-model components (#67). Three different new strategies 'spread', 'split' and 'merge' will initialize the parameter values of the N-component fit based on the results of the N-1/N+1 component fit to improve quality of results and speed.
Added contribution guidelines to the documentation and automated list of DeerLab contributors.
The function snlls now accepts additional custom penalties to include in the optimization (#76, #112).
All fit functions now return the fit of the data along its uncertainty automatically as part of the FitResult object(#130, #134).
Implemented a new method UQResult.join() to merge multiple uncertainty quantification objects (#154). This permits error propagation from multiple uncertainty sources to a common function.
The performance of all fit functions has been considerably accelerated by removing call overheads in built-in DeerLab models (#100, #101, #143).
Improved robustness of the installation from PyPI (#65):
The installer no longer assumes the alias pip to be setup on the system.
The installation will now handle cases when system-wide privileges are not available (#52).
Improved robustness of the installation in Windows systems to avoid missing DLL errors (#64).
The installer will now get the latest Numpy/Scipy releases in Windows systems available at the [Gohlke repository](https://www.lfd.uci.edu/~gohlke/pythonlibs/).
Adapted piece of code leading to a VisibleDeprecationWarning visible during execution of certain DeerLab functions.
Improved interface of built-in plots in FitResult.plot(). The method now returns a Matplotlib figure object (matplotlib.figure.Figure) instead of an axes object (matplotlib.axes._subplots.AxesSubplot) which can be modified more freely to adjust graphical elements (#85). The method now takes an optional keyword FitResult.plot(show=True\False) to enable/disable rendering of the graphics upon calling the method (#87).
The fit objective values returned in FitResult.cost are now correct (previous versions had an erroneous 1/2 factor) (#80). The value is now returned as a scalar value instead of a single-element list (#81).
Removed the re-normalization conventions K(t=0,r)=1 and B(t=0)=1 and associated options renormalize and renormpaths in the dipolarkernel and dipolarbackground functions (#99) to avoid identifiability issues between dipolar pathway amplitudes and signal scales during fitting (#76).
The fit convergence criteria tol (objective function tolerance) and maxiter (iteration limit) are now exposed as keyword argument in all fit functions (#111, #112).
Multiple improvements and corrections to the documentation (#95, #96, #104, #106, #107, #115, #122)
Corrections in the metadata of multiple dd_models. The key Parameters of some models contained the wrong names.
The metadata of the built-in models is now accessible and manipulable via function attributes (e.g. dd_gauss.parameters) rather than trought a returned dictionary (e.g. dd_gauss()['Parameters']) (#143).
The keyword argument to request uncertainty quantification has been unified across all fitting functions. It is now uq (#120).
The UncertQuant class has been renamed into UQResult (#123).
Uncertainty quantification is now tested numerically against an external package (lmfit) to ensue quality and accuracy(#121).
Expanded the collection of examples in the documentation, and improved existing ones (#144, #148, #153).

deerload

Fixed behavior of the function when loading certain 2D-datasets in the BES3T format (#82, #83).
In 2D-datasets, the abscissas are now returned as a list of abscissas instead of a single 2D-matrix (#83).

fitmodel

Corrected the scaling behaviour of all outputs related to components of the dipolar signal to match the scaling of the original experimental data (#78).
The built-in plot method FitResult.plot() now plots the unmodulated component fit as well with its uncertainty (#78).
When plotting bootstrapped results with FitResult.plot(), the fit is substituted with the median of the bootstrapped distribution (#148).
Extended information included in the verbose summary (#78).
Simplified the interface for defining initial values and boundaries of parameters in fitsignal (#71). Now instead of defining, e.g., fitsignal(..., lb = [[],[50],[0.2, 0.5]]) one can define the individual vales/boundaries fitsignal(..., bg_lb = 50, ex_lb = [0.2,0.5]) by using the new keywords.
Removed the keyword argument uqanalysis=True/False. The uncertainty quantification can now be disabled via the new keyword uq=None (#98).
Corrected the behaviour of built-in start values when manually specifying boundaries (#73). If the built-in start values are outside of the user-specified boundaries the program will now automatically set the start values in the middle of the boundaries to avoid errors (#72)).
Implemented the constraint Lam0+sum(lam)<=1 to ensure the structural-identifiability of Lam0 and V0 during SNLLS optimization of experiment models with more than one modulated dipolar pathway (i.e. does not affect ex_4pdeer) (#76, #108).
The function now accepts any sequence input (lists, arrays, tuples, etc.) instead of just lists (#152).
Removed the optional keyword argument regtype (#137).
Fixed a bug in the scaling of the distance distribution uncertainty quantification (#148).

fitregmodel

Corrected the behaviour of the uncertainty quantification when disabling the non-negativity constraint (#121).

fitparamodel

Made par0 a positional argument instead of an optional keyword (#70). to avoid errors when not defined (#69).
Keyword argument rescale has been renamed to fitscale (#128, pr: $129$ ).

snlls

Corrected bug that was leading to the smoothness penalty being accounted for twice in the least-squares residual during optimization (#103).
Now returns the uncertainty quantification of linear and nonlinear parts as separate objects nonlinUncert and linUncert (#108).
Improved the covariance-based uncertainty analysis by including correlations between linear and non-linear parameters(#108).
Improved the behavior of signal scale determination (#108).
Enabled prescaling of the data to avoid scaling issues during uncertainty quantification (#132, #133).
Corrected the behaviour of the uncertainty quantification when disabling the regularization penalty (#121).

diststats

Now compatible with non-uniformly defined distance distributions (#92, #94)).
Added internal validation step to avoid non-sensical results when confounding the syntax (#91).

dipolarkernel

Now allows defining pathways without unmodulated components.
All optional keyword arguments can only be passed as named and not positional arguments (#138)).
The keyword pathways now only takes lists of pathways and not modulation depth parameters. A new separate keyword mod takes the modulation depth parameter for the simplified 4-pulse DEER kernel (#118, #138).
Renamed the background argument keyword B into bg (#138).

regparamrange

Implemented new CSD algorithm to avoid LAPACK library crashes encountered when using multiple DeerLab functions calling regparamrange internally (#68).

correctphase

Implement new keyword phase to select the criterion for optimizing the phase for correction (#114, #131).
Deprecated imaginary offset fitting (#131).
Deprecated manual phase correction. Manual correction can be done by the user and is now described in the beginner’s guide (#131).

Release `v0.12.2` - October 2020¶

Overall changes

Fit functions using the multistart option are now fully deterministic. The functions was using now a random generator to define the different start points, this is now deterministic.
Documentation UI has been re-designed for a more confortable reading. Minor errors and outdated information have been corrected throughout. Expanded reference documentation of several functions for better understanding.

regparamrange

The exception handling introduced in the previous release was still too specific. The function kept crashing due to SVD non-convergence errors during the GSVD. This has been fixed and the error will not lead to a crash. (#42).

dd_skewgauss

Corrected an error in the implementation that was leading to wrong distributions (#61).

dd_models, ex_models

Adapted numerical boundaries and start values of some built-in models to reflect better the physical reality. Afected models: dd_skewgauss, dd_triangle, dd_gengauss, ex_5pdeer, ex_ovl4pdeer.

Release `v0.12.1` - October 2020¶

Overall changes

The calculation of the Jacobian for covariance-based uncertainty analysis has been simplified providing a significant boost in performance for all fit functions (#55).
The Jacobian computation is more robust, now taking into consideration parameter boundaries (#58). This solves errors such as the ones reported in (#50).
Broken examples in the documentation have been corrected (#57).
When requesting attributes or method of a UncertQuant object under disabled uncertainty analysis (uqanalysis=False) now it will prompt an explanatory error instead of just crashing (#56).

fitsignal

Corrected the behaviour of the scaling output (fit.scale). Now all fitted dipolar signals (fit.V) have the same scaling as the input signal (#53).

regparamrange

Relaxed the exception handling to catch errors occuring under certain conditions. The function seems to crash due to LAPACK or SVD non-convergence errors during the GSVD, now these are catched and the alpha-range is estimated using simple SVD as an approximation. This function might be deprecated in a future release (#42).

Release `v0.12.0` - October 2020¶

Overall changes

Added new function diststats to calculate different statistical quantities of the distance distribution and their corresponding uncertainties (#37).
Introduced the option cores to bootan parallelize the bootstrapping using multiple CPUs (#35).
The regularization operator matrices regoperator now include the edges of the distribution (#38). Now the smoothness penalty is imposed on the distribution edges avoiding the accumulation of distribution mass at the edges of r.
The interface for defining dipolar pathways has been simplified (#41). For example, a signal with two dipolar pathways had to be defined as pathways = [[Lam0,np.nan], [lam1,T0]]. Now the unmodulated pathway must be defined by its amplitude and does not accept the use of np.nan, e.g. pathways = [Lam0, [lam1,T0]].
The project version control has been switched from the Git-flow to the GitHub-flow design. The default branch has been switched from master to main, which is now always production-ready. All new contributions are merged into main exclusively by pull requests.
The dependency on the lambda parameter has been removed from all phenomenological background models, and kept only for physical models (#43). Their interface with dipolarbackground and dipolarkernel have been updated accordingly.

bg_homfractal

Corrected behavior of the model. For d=3 the model returned wrong values, and for d~=3 the model resulted in an error.

UncertQuant

Fixed bug when propagating uncertainty to scalar functions.

deerload

Fixed UTF-8 error when loading certain spectrometer files in MacOS (#30)

fitsignal

The fitted scale of the signal is now properly calculated when fitting fully parametric signals.
Fixed error occuring when fitting a dipolar evolution function with a non-parametric distribution.

selregparam

Fixed bug occuring when requesting the lc or lr selection methods.

regparamange

An error occuring at the BLAS/LAPLACK error ocurring under certain conditions in MacOS and Ubuntu is now handled to avoid a crash.

Release `v0.11.0` - September 2020¶

Overall changes

All Gauss models (dd_gauss,etc.) now use the standard deviation sigma instead of the FWHM as the width parameter for consistency with other method such as Rice distributions (#19).
All hard-wired random seeds have been removed.
A new method plot() has been added to the FitResult class returned by all fit functions. This will create a basic plot of the fit results (#7).

snlls

Renamed option penalty as reg and improved its interface (#13).
The regularization parameter of the optimal solution is returned now (#20).

whitegaussnoise

Added a seed option to select static noise realizations.

correctzerotime

Fixed bug when zero-time is at start/end of array (#24).
Function no longer rescales the experimental data passed on to the function.

fitsignal

The regularization parameter of the optimal solution is returned now (#20).
Bug fixed when fitting dipolar evolution functions (no background and no experiment models) with a parametric distance distribution.

fitmultimodel

Start points are now spread over constrained parameter space grid instead of being randomble initiated(#22).

deerload

Now returns the time axis in microseconds instead of nanoseconds (#21).
The bug appearing when loading certain BES3T files has been fixed (#14).

fitregmodel

Now returns the fitted dipolar signal in the FitResult output

correctscale

The parameter fit ranges have been adjusted.

Release `v0.10.0` - August 2020¶

As of this version, DeerLab is based on Python in contrast to older versions based on MATLAB found [here](https://github.com/JeschkeLab/DeerLab-Matlab).

Overall changes

The following functions have been deprecated due to limited usability or due to functionality overlap with other DeerLab functions: aptkernel, backgroundstart, fitbackground, paramodel, and time2freq.
All fit functions now return a single FitResult output which will contain all results.
All functions are now compatible with non-uniformly increasing distance axes.
All fit functions are completely agnostic with respect of the abolute values of the signal amplitude. This is automatically fitted by all function and return as part of the results.
Uncertainty quantification for all fit functions is returned as a UncertQuant object from which confidence intervals, parameter distributions, etc. can be generated generalizing the uncertainty interface for all DeerLab. Uncertainty can now be propagated to arbitrary functions.

fitparamodel

The functionality has been streamlined. Function now fits arbitrary parametric models using non-linear leas-squares without consideration of whether it is a time-domain or distance-domain model. The models no longer need to take two inputs (axis+parameters) and now only tk the parameters as input.

fitregmodel

Goodness-of-fit estimators are now computed using the proper estimation the degrees of freedom.

fitmultimodel

Added internal measures to avoid situations where one or several components are suppressed by fitting zero-amplitudes making the method more stable.

uqst

The uncertainty distributions of the parameters are now returned as properly normalized probability density functions.

fftspec

Frequency axis construction has been corrected.

regoperator

Now calculates the numerically exact finite-difference matrix using Fornberg’s method.

correctphase

Now can handle 2D-datasets.

Release Notes¶

Release v1.1.4 - September 2024¶

Release v1.1.3 - July 2024¶

Release v1.1.2 - November 2023¶

Release v1.1.1 - August 2023¶

Release v1.1.0 - August 2023¶

Release v1.0.1 - March 2023¶

Release v1.0.0 - December 2022¶

Release v0.14.5 - December 2022¶

Release v0.14.4 - August 2022¶

Release v0.14.3 - July 2022¶

Release v0.14.2 - June 2022¶

Release v0.14.1 - June 2022¶

Release v0.14.0 - April 2022¶

Release v0.13.2 - July 2021¶

Release v0.13.1 - May 2021¶

Release v0.13.0 - April 2021¶

Release v0.12.2 - October 2020¶

Release v0.12.1 - October 2020¶

Release v0.12.0 - October 2020¶

Release v0.11.0 - September 2020¶

Release v0.10.0 - August 2020¶

Release `v1.1.4` - September 2024¶

Release `v1.1.3` - July 2024¶

Release `v1.1.2` - November 2023¶

Release `v1.1.1` - August 2023¶

Release `v1.1.0` - August 2023¶

Release `v1.0.1` - March 2023¶

Release `v1.0.0` - December 2022¶

Release `v0.14.5` - December 2022¶

Release `v0.14.4` - August 2022¶

Release `v0.14.3` - July 2022¶

Release `v0.14.2` - June 2022¶

Release `v0.14.1` - June 2022¶

Release `v0.14.0` - April 2022¶

Release `v0.13.2` - July 2021¶

Release `v0.13.1` - May 2021¶

Release `v0.13.0` - April 2021¶

Release `v0.12.2` - October 2020¶

Release `v0.12.1` - October 2020¶

Release `v0.12.0` - October 2020¶

Release `v0.11.0` - September 2020¶

Release `v0.10.0` - August 2020¶