Merge remote-tracking branch 'origin/main'

README.rst

@@ -0,0 +1,111 @@
+|docs|_  |github|_  |pypi|_ 
+
+.. |docs| image:: https://img.shields.io/badge/%F0%9F%95%AE-docs-green.svg
+.. _docs: https://cea-metrocarac.github.io/fitspy/doc/index.html
+
+.. |github| image:: https://img.shields.io/badge/GitHub-GPL--3.0-informational
+.. _github: https://github.dev/CEA-MetroCarac/fitspy
+
+.. |pypi| image:: https://badge.fury.io/py/libertem.svg
+.. _pypi: https://pypi.org/project/fitspy/
+
+
+.. raw:: html
+
+    <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.
+
+.. raw:: html
+
+    <p align="center" width="100%">
+    <img align="center" width="90%" src=https://cea-metrocarac.github.io/fitspy/fitspy.png>
+    </p>
+    </br>
+
+Processed spectra may be independent of each other or may result from 2D-maps
+acquisitions.
+
+.. raw:: html
+
+    <p align="center" width="100%">
+    <img align="center" width="40%" 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  :code:`Gaussian`, :code:`Lorentzian`, :code:`Asymetric Gaussian`, :code:`Asymetric Lorentzian` and :code:`Pseudovoigt`.
+
+A :code:`constant`, :code:`linear`, :code:`parabolic` or :code:`exponential` background model can also be added in the fitting.
+
+In both cases, :code:`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
+------------
+
+.. code-block::
+
+    pip install fitspy
+
+
+Tests and examples execution
+----------------------------
+
+.. code-block::
+
+    pip install pytest
+    git clone https://github.com/CEA-MetroCarac/fitspy.git
+    cd fitspy
+    pytest
+    python example/ex_gui_auto_decomposition.py
+    ...
+
+
+Quick start
+-----------
+
+Launch the application:
+
+.. code-block::
+
+    fitspy
+
+Then, from the top to the bottom of the right panel:
+
+- :code:`Select` file(s)
+- *(Optional)* Define the **X-range**
+- Define the baseline to :code:`subtract` *(left or right click on the figure to add or delete (resp.) a baseline point)*
+- *(Optional)* Normalize the spectrum/spectra
+- Click on the :code:`Fitting` panel to activate it
+- Select :code:`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
+- :code:`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/index.html>`_ for more details.
+
+
+Authors information
+-------------------
+
+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. (submitted)*
+
+- Newville M., (2014). LMFIT: Non-Linear Least-Square Minimization and Curve-Fitting for Python. Zenodo. doi: 10.5281/zenodo.11813.