[](https://pypi.org/project/fitspy/)
[](https://github.com/CEA-MetroCarac/fitspy)
[](https://cea-metrocarac.github.io/fitspy/doc/index.html)
[](https://doi.org/10.5281/zenodo.10812333)
[](https://joss.theoj.org/papers/971a02868d903c0b7c0cbc3d1cd3d139)
<p align="center" width="100%">
<img align="center" width=250 src=https://cea-metrocarac.github.io/fitspy/logo.png>
</p>
**Fitspy** is a generic tool dedicated to **fit sp**ectra in **py**thon with a GUI that aims to be as simple and intuitive to use as possible.
<p align="center" width="100%">
<img align="center" width="75%" src=https://cea-metrocarac.github.io/fitspy/fitspy.png>
</p>
Processed spectra may be independent of each other or may result from 2D-maps
acquisitions.
<p align="center" width="100%">
<img align="center" width="30%" src=https://cea-metrocarac.github.io/fitspy/2d-map.png> <br>
<em>Example of fitspy 2D-map frame interacting with the main GUI.</em>
</p>
The predefined peak models considered in Fitspy are `Gaussian`, `Lorentzian`, `Asymetric Gaussian`, `Asymetric Lorentzian` and `Pseudovoigt`.
A `constant`, `linear`, `parabolic` or `exponential` background model can also be added in the fitting.
In both cases, `user-defined models` can be added.
Fitspy main features:
- Fitspy uses the [lmfit](https://github.com/lmfit/lmfit-py) library to fit the spectra
- The fit processing can be multi-threaded
- Bounds and constraints can be set on each peaks models parameter
- From an automatic noise level estimation, according to the local noise, peak models can be automatically deactivated
- Fitspy also includes automatic outlier detection to be excluded during the fitting process
All actions allowed with the GUI can be executed in script mode (see examples [here](https://github.com/CEA-MetroCarac/fitspy/tree/main/examples)).
These actions (like baseline and peaks definition, parameters constraints, ...) can be saved in a `Fitspy model` and replayed as-is or applied to other new spectra datasets.
## Installation
```
pip install fitspy
```
*(See the [documentation](https://cea-metrocarac.github.io/fitspy/doc/user_guide/intro.html#install-and-launching) for more details)*
## Tests and examples execution
```
pip install pytest
git clone https://github.com/CEA-MetroCarac/fitspy.git
cd fitspy
pytest
python examples/ex_gui_auto_decomposition.py
python examples/ex_.......
```
*(See the [documentation](https://cea-metrocarac.github.io/fitspy/doc/user_guide/intro.html#install-and-launching) for more details)*
## Quick start
Launch the application:
```
fitspy
```
Then, from the top to the bottom of the right panel:
- `Select` file(s)
- *(Optional)* Define the **X-range**
- Define the baseline to `subtract` *(left or right click on the figure to add or delete (resp.) a baseline point)*
- *(Optional)* Normalize the spectrum/spectra
- Click on the `Fitting` panel to activate it
- Select `Peak model` and add peaks *(left or right click on the figure to add or delete (resp.) a peak)*
- *(Optional)* Add a background (**BKG model**) to be fitted
- *(Optional)* Use **Parameters** to set bounds and constraints
- `Fit` the selected spectrum/spectra
- *(Optional)* **Save** the parameters in **.csv** format
- *(Optional)* **Save** the **Model** in a .json file (to be replayed later)
*(See the [documentation](https://cea-metrocarac.github.io/fitspy/doc/user_guide/workflow.html) for more details)*
## Acknowledgements
This work, carried out on the CEA - Platform for Nanocharacterisation (PFNC), was supported by the “Recherche Technologique de Base” program of the French National Research Agency (ANR).
Warm thanks to the [JOSS](https://joss.theoj.org/) reviewers ([@maurov](https://github.com/maurov) and [@FCMeng](https://github.com/FCMeng)) and editor ([@phibeck](https://github.com/phibeck)) for their contributions to enhancing Fitspy.
## Citations
In case you use the results of this code in an article, please cite:
- Quéméré P., (2024). Fitspy: A python package for spectral decomposition. Journal of Open Source Software. doi: [10.21105/joss.05868](https://doi.org/10.21105/joss.05868)
- Newville M., (2014). LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python. Zenodo. doi: [10.5281/zenodo.11813](https://doi.org/10.5281/zenodo.11813).
Raw data
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