Metadata-Version: 2.1
Name: GMMClusteringAlgorithms
Version: 0.0.1
Summary: A data analysis package for PI-ICR Mass Spectroscopy
Home-page: https://pypi.org/project/GMMClusteringAlgorithms
Author: Colin Weber
Author-email: colin.weber.27@gmail.com
License: UNKNOWN
Description: # GMMClusteringAlgorithms
        
        A package for implementing Gaussian Mixture Models as a data 
        analysis tool in PI-ICR Mass Spectroscopy experiments. It was
        first developed in the Fall of 2020 to be used in PI-ICR 
        experiments at Argonne National Laboratory (Lemont, IL, U.S.).
        At its core is a modified version of the ['mixture' module 
        from the package scikit-learn.](https://scikit-learn.org/stable/modules/mixture.html)
        The modified version retains all the same components as the
        original version. In addition, it contains two classes with 
        restricted fitting algorithms: a GMM fit where the phase 
        dimension of the component means is _not_ a parameter, and a
        BGM fit where the number of components is _not_ a parameter.
        The rest of the gmm_clustering_algorithms package facilitates
        quick, intuitive use of the GMM algorithms through the use 
        of 4 classes.
        1. DataFrame
            * This class is responsible for processing the .lmf 
            file and phase shifts. As attributes, it holds the 
            processed data for easy access, as well as any data 
            cuts.
        2. GaussianMixtureModel
            * This class fits Gaussian Mixture Models to the 
            DataFrame object. As parameters, it takes:
                1. Cartesian/Polar coordinates
                2. Number of components to use
                3. Covariance matrix type
                4. Information criterion
            * Allows for 'strict' fits, i.e. fits where the number
            of components is specified.
        3. BayesianGaussianModel
            * Exact same as the GaussianMixtureModel class, but 
            uses the BayesianGaussianModel class from scikit-learn
            instead of the GaussianMixtureModel class.
        4. PhaseFirstGaussianModel
            * Implements a fit where the phase dimension is fit to
            first, followed by a GMM fit to both spatial dimensions
            in which the phase dimension of the component means is
            fixed. This type of fit was found to work especially 
            well with data sets in which there were many species, 
            like the 168Ho data.
            * Only works with Polar coordinates
        
        Each model class also includes the ability to visualize
        results in several ways (clustering results, One-dimensional
        histograms, Probability density function) and the ability to
        copy fit results to the clipboard for pasting into an Excel
        spreadsheet.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
