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/dev/null +++ b/joss.05868/10.21105.joss.05868.crossref.xml @@ -0,0 +1,153 @@ + + + + 20240415T170816-8be31b052abd735ef3ad2e4d1708214fd0d5c617 + 20240415170816 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 04 + 2024 + + + 9 + + 96 + + + + Fitspy: A Python package for spectral +decomposition + + + + Patrick + Quéméré + https://orcid.org/0009-0008-6936-1249 + + + + 04 + 15 + 2024 + + + 5868 + + + 10.21105/joss.05868 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.10812332 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/5868 + + + + 10.21105/joss.05868 + https://joss.theoj.org/papers/10.21105/joss.05868 + + + https://joss.theoj.org/papers/10.21105/joss.05868.pdf + + + + + + Fityk: a general-purpose peak fitting +program + Wojdyr + Journal of Applied +Crystallography + 43 + 10.1107/S0021889810030499 + 2010 + Wojdyr, M. (2010). Fityk: a +general-purpose peak fitting program. In Journal of Applied +Crystallography (5 Part 1; Vol. 43, pp. 1126–1128). +https://doi.org/10.1107/S0021889810030499 + + + PRISMA: A robust and intuitive tool for +high-throughput processing of chemical spectra + Flores + Chemistry–Methods + 2 + 10.1002/cmtd.202100094 + 2022 + Flores, E., Mozhzhukhina, N., Li, X., +Norby, P., Matic, A., & Vegge, T. (2022). PRISMA: A robust and +intuitive tool for high-throughput processing of chemical spectra. In +Chemistry–Methods (No. 10; Vol. 2). +https://doi.org/10.1002/cmtd.202100094 + + + LMFIT: Non-linear least-square minimization +and curve-fitting for python + Newville + 10.5281/zenodo.11813 + 2014 + Newville, M., Stensitzki, T., Allen, +D. B., & Ingargiola, A. (2014). LMFIT: Non-linear least-square +minimization and curve-fitting for python (Version 0.8.0). Zenodo. +https://doi.org/10.5281/zenodo.11813 + + + Scipy/scipy: SciPy 1.11.1 + Gommers + 10.5281/zenodo.8092679 + 2023 + Gommers, R., Virtanen, P., Burovski, +E., Haberland, M., Weckesser, W., Oliphant, T. E., Reddy, T., +Cournapeau, D., alexbrc, Nelson, A., Peterson, P., Roy, P., Wilson, J., +Polat, I., endolith, Mayorov, N., Walt, S. van der, Brett, M., Laxalde, +D., … Kai. (2023). Scipy/scipy: SciPy 1.11.1 (Version v1.11.1). Zenodo. +https://doi.org/10.5281/zenodo.8092679 + + + Full wafer-scale characterization method for +2D materials. 10th International Conference on Advanced Applied Raman +Spectroscopy + Meyer + 2023 + Meyer, T., Quéméré, P., Brunet, P., +Rolland, E., Cadot, S., & Le Van-Jodin, L. (2023). Full wafer-scale +characterization method for 2D materials. 10th International Conference +on Advanced Applied Raman Spectroscopy. +https://www.ramanfestconf.com/2023/Abstracts/2023_Meyer_Thibaut_52.pdf + + + + + + From 90e939c02085c59abeeed7069122c687bf7a4179 Mon Sep 17 00:00:00 2001 From: The Open Journals editorial robot <89919391+editorialbot@users.noreply.github.com> Date: Mon, 15 Apr 2024 18:08:37 +0100 Subject: [PATCH 3/5] Creating 10.21105.joss.05868.jats --- joss.05868/10.21105.joss.05868.jats | 416 ++++++++++++++++++++++++++++ 1 file changed, 416 insertions(+) create mode 100644 joss.05868/10.21105.joss.05868.jats diff --git a/joss.05868/10.21105.joss.05868.jats b/joss.05868/10.21105.joss.05868.jats new file mode 100644 index 0000000000..4173f39c8e --- /dev/null +++ b/joss.05868/10.21105.joss.05868.jats @@ -0,0 +1,416 @@ + + +

+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +5868 +10.21105/joss.05868 + +Fitspy: A Python package for spectral +decomposition + + + +https://orcid.org/0009-0008-6936-1249 + +Quéméré +Patrick + + + + + +Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, +France + + + + +11 +7 +2023 + +9 +96 +5868 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +spectrum +spectra +decomposition +fit + + + + + + Summary +

Fitspy is a dedicated tool to + fit spectra in python. Spectrum + decomposition (also known as fitting) is the choice of a linear + combination of peak models to best represent an experimental spectrum. + Fitspy currently has implementations for + Gaussian, Lorentzian, related asymmetric functions and the Pseudovoigt + as peak models. User-defined models can also be added. For the + fitting, the model parameters can be subject to bounds and + constraints. From a practical point of view, the + Fitspy GUI (see + [fig:GUI]) is designed + to be as simple and intuitive to use as possible.

+ +

The Fitpsy GUI featuring + application options on the right-hand side, and in the central area, + the current experimental spectrum displayed in black, the + corresponding fit in blue, and the various modeled peaks shown in + different colors. +

+ +
+
+ + Statement of need +

The analysis of spectra in many areas of physics, from materials + characterisation to astrophysics, often requires their decomposition + into more or less complex models to estimate the composition of the + subject being analysed. To carry out these decompositions, research + communities can rely on spectral decomposition tools. While many + open-source tools for spectral decomposition exist, most of them have, + however, been designed for specific application domains, offering a + broad range of services beyond mere spectral fitting. Consequently, + these tools can prove challenging to use, especially for less + experienced individuals.

+

In the vein of generic tools like Fityk + (Wojdyr, + 2010) or PRISMA + (Flores + et al., 2022), Fitspy is a dedicated + tool for spectral fitting — and only spectral fitting — with the + following characteristics or functionalities:

+ + +

Agnostic Nature: Fitspy is + not tied to any specific physical quantity or database. It is + designed to process spectra regardless of their x-support and + y-intensity without any prior knowledge.

+
+ +

Python Implementation: + Fitspy is coded in Python. As a result, for + individuals with basic knowledge of the language, spectra can be + easily processed by scripts.

+
+ +

User Models: Fitspy allows + the user to input their own models either in the form of literal + expressions (for simple models) or through Python scripts (for + more complex models).

+
+ +

2D Maps: Fitspy has been + designed to handle spectra derived from 2D acquisitions. Note that + “2D” can encompass time or any other dimension. When dealing with + 2D data, an interactive map in the Fitspy + GUI allows the user to locate and select spectra of interest + easily.

+
+ +

Multiprocessing Capabilities: + Fitspy enables spectral fit processing on + multiple processors, enhancing efficiency.

+
+ +

Constrained Parameters: Leveraging the + lmfit library + (Newville + et al., 2014), Fitspy empowers the + user to impose constraints on parameter ranges or establish + constraints between parameters using literal expressions.

+
+ +

Simple GUI: Fitspy has been + designed to be as intuitive and simple to use as possible + (subjective criterion).

+
+
+

To the author’s knowledge, although other open-source software + packages are much more advanced in certain aspects mentioned, none of + them seem to encompass all the functionalities described above. + Therefore, the features of Fitspy make it an + ideal tool for quickly fitting a few spectra through its GUI or for + fitting several thousand of spectra (or more) by Python batches, as + can occur in the context of large-scale parametric studies + (Meyer + et al., 2023).

+
+ + <monospace>Fitspy</monospace> workflow short + description +

Fitspy accepts at the time of writing two + types of input data file formats:

+ + +

a first format where each spectrum is stored individually in a + .txt file. The input data file consists of + a two-column base format, with the first column containing the + physical support of the spectrum (typically wavelengths) and the + second to the corresponding spectrum intensity.

+
+ +

the second format is associated with 2D-maps of spectra + acquisitions. The related spectra are stored in a single file in + which each spectrum is identified by its grid coordinates + (X,Y).

+
+
+

Once loaded in the GUI via a files selection widget, the spectra + can be processed one by one or in groups (depending on spectra + selected with the cursor in the file selection widget), or as a whole + dataset.

+

Firstly, the user can choose to reduce the physical support + (wavelength axis) to a range of interest for subsequent processing. + They can also activate the Attractors + capability, which automatically returns the local maxima of spectra + obtained with the signal.find_peaks() function of the + scipy library + (Gommers + et al., 2023). These attractors can be used subsequently to + normalise spectra or to select points associated with the baseline and + peaks to be modelled (see below).

+

Once the range of interest and the spectra are (optionally) + normalised, the user can define a baseline so that the + spectra have a common, flat zero-intensity level. This baseline can be + defined in an automatic way or by the user, who positions + characteristic points on the figure with the cursor. These points can + be left at their initial positions or attached to the spectrum profile + (Attached mode). In the latter case, the points + can be attached either to the raw spectrum or to an averaged spectrum + if the user wishes to attenuate the effects of noise. The baseline is + then approximated over the entire spectrum range using either + piecewise linear interpolation or an n-order + polynomial approximation.

+

Once the baseline has been subtracted, the next step consists of + defining the peaks of interest for the decomposition. + This can be done either directly in the figure by clicking, or by an + Auto mode which consists of an iterative + process to automatically determine the main peak locations. Each peak + is associated with a model, which can be either a predefined + Fitspy model (Gaussian model, Lorentzian model, + their asymmetric variants, or a Pseudovoigt model) or a user-defined + model issued from literal expressions in a .txt file or from a Python + script in a .py file. These models rely essentially on 3 main + parameters: a position, an amplitude and a width (full-width + half-maximum, FWHM) which is divided into left and + right component for an asymmetric model.

+

For the fit, an additional background model can be added. Similar + to peak models, background models can be defined using pre-defined + models provided by Fitspy (Constant, Linear, + Parabolic or Exponential) or by loading user-defined models from .txt + or .py files.

+

Bounds can be imposed for each of the parameters as + well as associated constraints. If required, these + constraints can be used to link model parameters with each other, for + instance in the case where a physical ratio between the amplitudes of + two peaks is expected.

+

Finally, the parameters can be free to evolve during the fit + processing or maintained fixed at their initial values.

+

Several fit options are also proposed, such as:

+ + +

taking into account only the positive values of the + spectra.

+
+ +

a (limited) choice of minimisation algorithms

+
+ +

the maximum number of fit iterations

+
+ +

the number of CPUs to be used during the fit processing.

+
+
+

At the end of the fit, the fit parameters and the statistics + returned by lmfit are displayed in related + widgets and can be exported in .csv and .txt files respectively.

+

All the processing steps previously described constitute a model in + the sense of the Fitspy application that can be + saved (in a .json file) and easily replayed as-is or applied to other + spectra datasets.

+

In terms of visualisation, the GUI allows the user to display all + of the spectra simultaneously (Show All mode) + or, when a spectrum is selected, to display the corresponding fit + decomposition and its residual. Figure titles and axis names can be + customised and peak models labeled.

+

All the actions described above can be performed through Python + scripts without using the GUI. In practice, when performing repeated + analyses, users familiar with Python may find it helpful to use + Fitspy as follows:

+ + +

create a Fitspy model with the GUI

+
+ +

save the model

+
+ +

apply the model to several datasets using a Python script

+
+
+

This approach has recently been used by Meyer + (Meyer + et al., 2023) to process tens of thousands of PhotoLuminescence + and Raman spectra acquired on wafers, taking advantage of the + parallelism capabilities offered by Fitpsy (see + [fig:wafer]).

+ +

Example of a Fitpsy application + used in photoluminescence to characterise exciton intensities on a + wafer + (Meyer + et al., + 2023).

+ +
+
+ + Acknowledgements +

This work, carried out on the Platform for Nanocharacterisation + (PFNC), was supported by the “Recherche Technologique de Base” program + of the French National Research Agency (ANR).

+
+ + + + + + + WojdyrMarcin + + Fityk: a general-purpose peak fitting program + Journal of Applied Crystallography + 201010 + 43 + https://doi.org/10.1107/S0021889810030499 + 10.1107/S0021889810030499 + 1126 + 1128 + + + + + + FloresEibar + MozhzhukhinaNataliia + LiXinyu + NorbyPoul + MaticAleksandar + VeggeTejs + + PRISMA: A robust and intuitive tool for high-throughput processing of chemical spectra + Chemistry–Methods + 2022 + 2 + https://chemistry-europe.onlinelibrary.wiley.com/doi/abs/10.1002/cmtd.202100094 + 10.1002/cmtd.202100094 + + + + + + NewvilleMatthew + StensitzkiTill + AllenDaniel B. + IngargiolaAntonino + + LMFIT: Non-linear least-square minimization and curve-fitting for python + Zenodo + 201409 + https://doi.org/10.5281/zenodo.11813 + 10.5281/zenodo.11813 + + + + + + GommersRalf + VirtanenPauli + BurovskiEvgeni + HaberlandMatt + WeckesserWarren + OliphantTravis E. + ReddyTyler + CournapeauDavid + alexbrc + NelsonAndrew + PetersonPearu + RoyPamphile + WilsonJosh + PolatIlhan + endolith + MayorovNikolay + WaltStefan van der + BrettMatthew + LaxaldeDenis + LarsonEric + MillmanJarrod + SakaiAtsushi + Lars + peterbell10 + MulbregtPaul van + CareyCJ + eric-jones + McKibbenNicholas + KernRobert + Kai + + Scipy/scipy: SciPy 1.11.1 + Zenodo + 202306 + https://doi.org/10.5281/zenodo.8092679 + 10.5281/zenodo.8092679 + + + + + + MeyerThibaut + QuéméréPatrick + BrunetPaul + RollandEmmanuel + CadotStéphane + Le Van-JodinLucie + + Full wafer-scale characterization method for 2D materials. 10th International Conference on Advanced Applied Raman Spectroscopy + RamanFest 2023 conference + Paris + 202311 + https://www.ramanfestconf.com/2023/Abstracts/2023_Meyer_Thibaut_52.pdf + + + + +
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