Metadata-Version: 1.2
Name: emgfit
Version: 0.3.4
Summary: Fitting of time-of-flight mass spectra with Hyper-EMG models
Home-page: https://github.com/RobbenRoll/emgfit
Author: Stefan Paul
Author-email: stefan.paul@triumf.ca
License: BSD (3-clause)
Description: ======
        emgfit
        ======
        
        .. image:: https://github.com/RobbenRoll/emgfit/workflows/CI%20tests/badge.svg?branch=master
                :target: https://github.com/RobbenRoll/emgfit/actions?query=workflow%3A%22CI+tests%22
        
        .. image:: https://img.shields.io/pypi/v/emgfit.svg
                :target: https://pypi.python.org/pypi/emgfit
        
        
        Fitting of time-of-flight mass spectra with Hyper-EMG models
        
        * Free software: 3-clause BSD license
        * Online documentation: https://RobbenRoll.github.io/emgfit.
        * Source code: https://github.com/RobbenRoll/emgfit
        
        `emgfit` is a Python package for peak fitting of time-of-flight (TOF) mass
        spectra with hyper-exponentially modified Gaussian (Hyper-EMG_ [1]_) model
        functions. `emgfit` is a wrapper around the `lmfit`_ [2]_ curve fitting package
        and uses many of lmfit's user-friendly high-level features. Experience with
        `lmfit` can be helpful but is not an essential prerequisite for using `emgfit`
        since the `lmfit` features stay largely 'hidden under the hood'. `emgfit` is
        designed to be user-friendly and offers automation features whenever reasonable
        while also supporting a large amount of flexibility and control for the user.
        Depending on the user's preferences an entire spectrum can be rapidly analyzed
        with only a few lines of code. Alternatively, various optional features are
        available to aid the user in a more rigorous analysis. The model functions and
        statistical methods provided by emgfit could be useful for analyses of
        spectroscopic data from a variety of other fields.
        
        Amongst other features, the `emgfit` toolbox includes:
        
        * Automatic and sensitive peak detection
        * Automatic import of relevant literature values from the AME2016_ [3]_ database
        * Automatic selection of the best suited peak-shape model
        * Fitting of low-statistics peaks with a binned maximum likelihood method
        * Simultaneous fitting of an entire spectrum with a large number of peaks
        * Export of all relevant fit results including fit statistics and plots to an
          EXCEL output file for convenient post-processing
        
        `emgfit` is designed to be used within Jupyter Notebooks which have become a
        standard tool in the data science community. The usage and capabilities of
        `emgfit` are best explored by looking at the tutorial. The tutorial and more
        details can be found in the `documentation of emgfit`_.
        
        .. _`lmfit`: https://lmfit.github.io/lmfit-py/
        .. _AME2016: http://amdc.in2p3.fr/web/masseval.html
        .. _Hyper-EMG: https://www.sciencedirect.com/science/article/abs/pii/S1387380616302913
        .. _documentation of emgfit: https://RobbenRoll.github.io/emgfit
        
        References
        ----------
        .. [1] Purushothaman, S., et al. "Hyper-EMG: A new probability distribution
           function composed of Exponentially Modified Gaussian distributions to analyze
           asymmetric peak shapes in high-resolution time-of-flight mass spectrometry."
           International Journal of Mass Spectrometry 421 (2017): 245-254.
        .. [2] Newville, M., et al. "LMFIT: Non-linear least-square minimization and
           curve-fitting for Python." Astrophysics Source Code Library (2016):
           ascl-1606.
        .. [3] Wang, M., et al. "The AME2016 atomic mass evaluation (II). Tables, graphs
           and references." Chinese Physics C 41.3 (2017): 030003.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.7
