# PyFasma Documentation

**PyFasma** is a [free](https://www.gnu.org/philosophy/free-sw.en.html) and [open source](https://opensource.com/resources/what-open-source) Python package for preprocessing and analyzing Raman spectra. It offers a complete set of tools for:

- Preprocessing Raman spectra  
- Deconvoluting complex Raman bands  
- Applying multivariate analysis, including Principal Components Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA)  
- Generating publication-ready plots

The package depends on the following Python libraries:

- [LMFIT 1.3+](https://lmfit.github.io/lmfit-py/)  
- [Matplotlib 3.8+](https://matplotlib.org/)  
- [NumPy 2.0+](https://numpy.org/)  
- [pandas 2.2+](https://pandas.pydata.org/)  
- [pybaselines 1.1+](http://pybaselines.readthedocs.io/)  
- [Seaborn 0.13+](https://seaborn.pydata.org/)  
- [SciPy 1.14+](https://scipy.org/)  
- [scikit-learn 1.5+](https://scikit-learn.org/stable/index.html)  
- [A fork of rohanisaac's spc](https://github.com/rohanisaac/spc)

PyFasma follows a modular structure with seven modules: `helpers`, `fileio`, `numpyfuncs`, `dffuncs`, `plotting`, `modeling`, and `deconvolution`. Each is tailored to support a specific step in a typical Raman spectroscopy workflow, as illustrated below.

![PyFasma Structure](./_static/pyfasma_structure.png)

The source code and issue tracker are hosted on GitLab:  
[PyFasma GitLab Repository](https://gitlab.com/lepa22/pyfasma)

```{toctree}
:hidden:
:maxdepth: 4
:caption: Contents:

installation
examples
modules
how_to_cite
license
```