Metadata-Version: 2.1
Name: concentrationMetrics
Version: 0.6.0
Summary: A python library for the computation of various concentration, inequality and diversity indices
Home-page: https://github.com/open-risk/concentrationMetrics
Author: Open Risk
Author-email: info@openrisk.eu
License: The MIT License (MIT)
Download-URL: https://github.com/open-risk/concentrationMetrics/archive/v_0.5.0.tar.gz
Description: The concentrationMetrics Library
        ================================
        
        concentrationMetrics is an MIT-licensed `Python <http://www.python.org>`_ package aimed at becoming a reference implementation of indexes used in the analysis of concentration, inequality and diversity measures.
        
        Overview of Main Features
        =========================
        
        * exhaustive collection of concentration and inequality indexes and metrics
        * supports file input/output in both json and csv formats
        * detailed mathematical documentation
        * computation of confidence intervals via bootstraping
        * visualization using matplotlib
        
        
        Usage
        ===============================
        
        Using the library is quite straightforward. For example calculating the Gini and the HHI indexes
        on randomly generated portfolio data:
        
        .. code:: python
        
            import numpy as np
            from concentrationMetrics import Index
            
            # Create some data
            a = 1.7
            portfolio = np.random.zipf(a, 100)
        
            # Calculate the desired indexes
            indices = Index()
            hhi = indices.hhi(portfolio)
            gini = indices.gini(portfolio)
        
            # Compute the confidence interval around the HHI index value
            lower_bound, val, upper_bound = indices.compute(portfolio, index='hhi', ci=0.95, samples=10000)
        
            # Calculate indexes on a dataframe
            BCI = pd.read_json(dataset_path + "BCI.json")
            y = BCI.values
            myGroupIndex = cm.Index(data=y, index='simpson')
            myGroupIndex.print(6)
        
        
        Many more examples and tests are available in the examples and test directories.
        
        
        File structure
        ==============
        
        * `concentrationMetrics/model.py` The library module
        * `datasets/` Contains a variety of datasets useful for getting started with the ConcentrationMetrics
        * `examples/` Variety of usage examples
        * `docs/` Sphinx generated documentation
        * `tests/` testing the implementation
        
        All indexes are currently implemented in concentrationMetrics/model.py as methods of the Index object.
        
        Dependencies
        ============
        The main dependencies are the standard python datascience stack (numpy, pandas etc) and networkx. The full list is in requirements.txt
        
        - matplotlib
        - numpy
        - pandas
        - scipy
        - networkx
        
        Datasets
        ========
        Version 0.5.0 includes datasets used primarily for testing and comparison with R implementations:
        
        - hhbudget.csv
        - Ilocos.csv
        - BCI.json
        
        
        Comparison with R packages
        =================================
        -   atkinson\_test.py compares the Atkinson function with the IC2/Atk function
        
Keywords: concentration,diversity,inequality,index
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Provides: concentrationMetrics
Description-Content-Type: text/x-rst
