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+~~NOTOC~~
+{{ :wiki:logo.png?nolink|}}
 ====== FitPy ======
-A Python framework for advanced fitting of models to data, using approaches such as semi-stochastic algorithms and global analysis.
+FitPy -- A Python framework for advanced fitting of models to spectroscopic data.
+[[https://doi.org/10.5281/zenodo.5920380|{{zenodo.5920380.png}}]]
+FitPy is a Python framework for the **advanced fitting of models to spectroscopic data** focussing on **reproducibility**. Supported are semi-stochastic sampling of starting conditions, global fitting of several datasets at once, and fitting several concurrent models to one dataset. FitPy builds upon and extends the [[https://www.aspecd.de/|ASpecD framework]]. At the same time, it relies on the [[https://www.scipy.org/|SciPy software stack]] and on [[https://lmfit.github.io/lmfit-py/|lmfit]] for its fitting capabilities.
+Making use of the concept of recipe-driven data analysis, actual fitting **no longer requires programming skills**, but is as simple as writing a text file defining both, the model and the fitting parameters in an organised way. Curious? Have a look at [[https://docs.fitpy.de/|its documentation]].
+----
+<WRAP group centeralign>
+<WRAP third column>
+[[https://pypi.org/project/fitpy/|{{fa>cube?48}}]]
+[[https://pypi.org/project/fitpy/|Package (pypi.org)]]
+</WRAP>
+<WRAP third column>
+[[https://docs.fitpy.de/|{{fa>book?48}}]]
+[[https://docs.fitpy.de/|Documentation]]
+</WRAP>
+<WRAP third column>
+[[https://github.com/tillbiskup/fitpy|{{fa>github?48}}]]
+[[https://github.com/tillbiskup/fitpy|Source (GitHub)]]
+</WRAP>
+</WRAP>
+----
+===== License and citation =====
+FitPy is **free and open source** licensed under a [[.:license|BSD 2-clause license]]. However, if you use FitPy for your own research, please cite it appropriately:
+  * Till Biskup. FitPy (2024). [[https://doi.org/10.5281/zenodo.5920380|doi:10.5281/zenodo.5920380]]
+As FitPy is based on the SciPy and lmfit packages, you are highly encouraged to cite these two packages as well: SciPy: [[https://doi.org/10.1038/s41592-019-0686-2|doi:10.1038/s41592-019-0686-2]], lmfit: [[https://doi.org/10.5281/zenodo.598352|doi:10.5281/zenodo.598352]].
+===== News =====
+Note: The most up-to-date information can be found in the [[https://docs.fitpy.de/changelog.html|changelog]] included in the [[https://docs.fitpy.de/|package documentation]].
+  * 2024-12-01: **FitPy v0.1.2 released**
+    * [[https://github.com/tillbiskup/fitpy/releases/tag/v0.1.2|Changelog (GitHub)]]
+  * 2024-01-15: **FitPy v0.1.1 released**
+    * [[https://github.com/tillbiskup/fitpy/releases/tag/v0.1.1|Changelog (GitHub)]]
+  * 2022-01-30: **FitPy v0.1.0 released**
+    * [[https://github.com/tillbiskup/fitpy/releases/tag/v0.1.0|Changelog (GitHub)]]
+----
-FitPy is designed with spectroscopic data in mind, but will be generally applicable to all kinds of data models should be fitted to.
+**A note on the logo:** The logo shows a Latin square, usually attributed to Leonhard Euler. In the context of statistical sampling, a Latin square consists of only one sample in each row and each column. Its //n//-dimensional generalisation, the Latin hypercube, is used to generate a near-random sample of parameter values from a multidimensional distribution in statistics, //e.g.//, for obtaining sets of starting parameters for minimisation and fitting tasks. The logo shows a snake (obviously a Python) distributed such over a 4x4 square that it visits each row and each column only once. The copyright of the logo belongs to J. Popp.