Metadata-Version: 2.1
Name: PyUnfold
Version: 0.1
Summary: Python package for iterative unfolding
Home-page: https://github.com/jrbourbeau/pyunfold
Author: James Bourbeau
Author-email: james@jamesbourbeau.com
License: MIT
Description: # PyUnfold
        
        [![Build Status](https://travis-ci.org/jrbourbeau/pyunfold.svg?branch=master)](https://travis-ci.org/jrbourbeau/pyunfold)
        [![codecov](https://codecov.io/gh/jrbourbeau/pyunfold/branch/master/graph/badge.svg)](https://codecov.io/gh/jrbourbeau/pyunfold)
        ![pypi version](https://img.shields.io/pypi/v/pyunfold.svg 'pypi version')
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        ![license](https://img.shields.io/pypi/l/pyunfold.svg 'license')
        
        
        PyUnfold is a Python implementation the D’Agostini iterative unfolding method outlined in
        
        > G. D'Agostini, “A Multidimensional unfolding method based on Bayes' theorem”, Nucl. Instrum. Meth. A **362** (1995) 487
        
        
        ## Installation
        
        The latest development version of PyUnfold can be installed directly from GitHub
        
        ```
        pip install git+https://github.com/jrbourbeau/pyunfold.git
        ```
        
        or you can [fork](https://guides.github.com/activities/forking/) the [GitHub repository](https://github.com/jrbourbeau/pyunfold) and install PyUnfold on your local machine via
        
        ```
        git clone https://github.com/<your-username>/pyunfold.git
        pip install pyunfold
        ```
        
        ## Quickstart
        
        ```python
        from pyunfold import iterative_unfold
        
        # Counts distributions
        data = [100, 150]
        data_err = [10, 12.2]
        # Response matrix
        response = [[0.9, 0.1],
                    [0.1, 0.9]]
        response_err = [[0.01, 0.01],
                        [0.01, 0.01]]
        # Detection efficiencies
        efficiencies = [1, 1]
        efficiencies_err = [0.01, 0.01]
        # Perform iterative unfolding
        unfolded = iterative_unfold(data, data_err,
                                    response, response_err,
                                    efficiencies, efficiencies_err)
        ```
        The returned unfolded result is a dictionary containing:
        - `unfolded`: Unfolded cause distribution
        - `stat_err`: Statistical (Poisson) errors
        - `sys_err`: Systematic errors associated with limited statistics in the response matrix
        
        ```python
        print(unfolded)             
        {'unfolded': array([ 94.48002622, 155.51997378]),
         'sys_err': array([0.66204237, 0.6620424 ]),
         'stat_err': array([11.2351567 , 13.75617997])}
        ```
        
        ## License
        
        [MIT License](LICENSE)
        
        Copyright (c) 2018 James Bourbeau, Zigfried Hampel-Arias
        
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