Metadata-Version: 2.1
Name: lmfitxps
Version: 2.4.0
Summary: This package contains additional models for the lmfit package for fitting XPS spectra.
Author-email: Julian Hochhaus <julian.hochhaus@tu-dortmund.de>
License: MIT License
        
        Copyright (c) 2023 Julian-Hochhaus
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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        Some code has been taken from the lmfit library whose licence is below.
        
        BSD-3
        
        Copyright 2022 Matthew Newville, The University of Chicago
                       Renee Otten, Brandeis University
                       Till Stensitzki, Freie Universitat Berlin
                       A. R. J. Nelson, Australian Nuclear Science and Technology Organisation
                       Antonino Ingargiola, University of California, Los Angeles
                       Daniel B. Allen, Johns Hopkins University
                       Michal Rawlik, Eidgenossische Technische Hochschule, Zurich
        
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Project-URL: homepage, https://github.com/Julian-Hochhaus/lmfitxps
Project-URL: documentation, https://lmfitxps.readthedocs.io/en/latest/
Description-Content-Type: text/markdown
License-File: LICENSE

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  <img src="src/logos/logo_large.png" alt="lmfitxps">
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## Introduction
Welcome to lmfitxps, a small Python package designed as an extension for the popular lmfit package , specifically tailored for X-ray Photoelectron Spectroscopy (XPS) data analysis.

While lmfit provides simple tools to build complex fitting models for non-linear least-squares problems and applies these models to real data, as well as introduces several built-in models, lmfitxps acts as an extension to lmfit designed for XPS data analysis. lmfitxps provides a comprehensive set of functions and models that facilitate the fitting of XPS spectra. In particular, lmfitxps provides several models, which use the convolution of a gaussian with model functions of the lmfit-package.

In addition to models for fittig signals in XPS data, lmfitxps introduces several background models which can be included in the fit model for fitting the data rather then substracting a precalculated background. This is the so-called active approach as suggested by A. Herrera-Gomez and generally leads to better fit results.
For further details, please refer to the documentation of [lmfitxps](https://lmfitxps.readthedocs.io/en/latest/index.html) and [lmfit](https://lmfit.github.io/lmfit-py/index.html)! 

## Installation
To install lmfitxps, simply use pip:

 `pip install lmfitxps`

 ## How to cite
 [![DOI](https://zenodo.org/badge/642726930.svg)](https://zenodo.org/badge/latestdoi/642726930)


