Release Notes

Version 2024.5 (September 4, 2024)

New features:

Enhancements:

The progressbar (with the number of CPUS used during the calculations) has been integrated in the processing windows.
The spectra input data formats have been extended to .dm3, .dm4, .emd, .hspy, .nxs files and many other formats thanks to the rosettasciio readers integration

Bug fixes:

Allow fit processing without any peak or background model (just for baselines visualization for instance)

New features:

noise is now estimated and a noise level criteria enables to automatically deactivate peak models in noisy regions.
optional outlier detection has been added, enabling the disregarding of outliers during baseline calculation and fitting processes.
‘xtol’ fitting parameter has been added (associated with ‘leastsq’ and ‘least_square’ fitting methods).
all fitted parameters from all loaded spectra can now be saved in a single file named ‘results.csv’.

Enhancements:

multi-threaded calculations return now the complete fit report.
a new boolean argument, ‘reinit_guess’, has been added in Spectrum.fit(), enabling the adjustment of initial values for ‘ampli’ and ‘fwhm’ to the current spectrum. This helps circumvent “ill-conditioned” peak models (with fwhm~0) resulting from previous ‘Fitspy’ model calculations.
lists are now written to .json files on a single line.
parameters and statistics displaying have been separated.

Bug fixes:

the index count used to label the models was not reset to 1 when reloading a model
2d-maps with a single X or Y coordinate were not supported
the Lorentzian model could previously return Nan values when dealing with fwhm=0
the threads execution used by apply_model() to display the different progress bars did not quit correctly in some cases

New features:

Add a progressbar in the terminal and in the GUI to follow the calculation progression during the fit processing
Enable the loading of user-defined models through dedicated ‘Load’ buttons in the GUI.

Enhancements:

Display the models parameters on the figure whatever the fit status
Highlight/reduce the peak model curve linewidth according to the fit success status
Reproduce the same view when reloading a model, considering the fit success status
Take into account the ‘fit_kws’ through the ‘kwargs’ of Spectrum.fit() to be passed to the lmfit Model.fit()

Code changes:

Baseline format/use revisited to disable multiple baselines subtract capability (the ‘baseline_history’ spectrum attributes has been removed)
Spectrum attributes has benn changed from:
- ‘peaks’ to ‘attractors’
- ‘peaks_params’ to ‘attractors_params’
- ‘models’ to ‘peak_models’
- ‘models_labels’ to ‘peak_labels’
- ‘models_index’ to ‘peak_index’

Bug fixes:

Fixed bug when reloading ‘old’ model.json in which bkg_model was absent
Fixed bug when using user-defined model in multithreading
Fixed bug in bkg_model plotting
Fixed bug in removing ‘old’ Spectra 2D-map when reloading a ‘new’ one (the ‘old’ Spectra 2D-map figures are now closed)

New features:

Add user-defined model capability from external files (in a ‘.txt’ file located in %HOMEUSER%/Fitspy for models creation from literal expressions or in a ‘.py’ file from python scripting)
Add Fitspy static HTML Sphinx documentation in https://cea-metrocarac.github.io/fitspy/doc
in 2D-map, in addition to intensity, the model parameters can now be displayed and the corresponding 2D field can be exported in a .csv file
Spectra, Spectrum and SpectraMap class have now their own dedicated .py modules

Enhancements:

Add background visualization and display the corresponding parameters in the tabview
Display only the used parameters models in the tabview
Add xmin and xmax (optional) arguments to spectrum.load_profile() to ease the x-range setting when loading profiles by python scripts

Bug fixes:

First releases.