Metadata-Version: 2.1
Name: bandicoot
Version: 0.6.0
Summary: A toolbox to analyze mobile phone metadata.
Home-page: https://github.com/computationalprivacy/bandicoot
Author: Yves-Alexandre de Montjoye
Author-email: yvesalexandre@demontjoye.com
License: MIT
Description: =========
        bandicoot
        =========
        
        .. image:: https://img.shields.io/pypi/v/bandicoot.svg
            :target: https://pypi.python.org/pypi/bandicoot
            :alt: Version
             
        .. image:: https://img.shields.io/pypi/l/bandicoot.svg
            :target: https://github.com/computationalprivacy/bandicoot/blob/master/LICENSE
            :alt: MIT License
        
        .. image:: https://img.shields.io/pypi/dm/bandicoot.svg
            :target: https://pypi.python.org/pypi/bandicoot
            :alt: PyPI downloads
        
        .. image:: https://img.shields.io/travis/computationalprivacy/bandicoot.svg
            :target: https://travis-ci.org/computationalprivacy/bandicoot
            :alt: Continuous integration
        
        .. begin
        
        **bandicoot** (http://bandicoot.mit.edu) is Python toolbox to analyze mobile phone metadata. It provides a complete, easy-to-use environment for data-scientist to analyze mobile phone metadata. With only a few lines of code, load your datasets, visualize the data, perform analyses, and export the results.
        
        .. image:: https://raw.githubusercontent.com/computationalprivacy/bandicoot/master/docs/_static/bandicoot-dashboard.png
            :alt: Bandicoot interactive visualization
        
        ---------------
        Where to get it
        ---------------
        
        The source code is currently hosted on Github at https://github.com/computationalprivacy/bandicoot. Binary installers for the latest released version are available at the Python package index:
        
            http://pypi.python.org/pypi/bandicoot/
        
        And via `easy_install`:
        
        .. code-block:: sh
        
            easy_install bandicoot
        
        or  `pip`:
        
        .. code-block:: sh
        
            pip install bandicoot
        
        ------------
        Dependencies
        ------------
        
        bandicoot has no dependencies, which allows users to easily compute indicators on a production machine. To run tests and compile the visualization, optional dependencies are needed:
        
        - `nose <http://nose.readthedocs.io/en/latest/>`_, `numpy <http://www.numpy.org/>`_, `scipy <https://www.scipy.org/>`_, and `networkx <https://networkx.github.io/>`_ for tests,
        - `npm <http://npmjs.com>`_ to compile the js and css files of the dashboard.
        
        -------
        Licence
        -------
        
        MIT
        
        -------------
        Documentation
        -------------
        
        The official documentation is hosted on http://bandicoot.mit.edu/docs. It includes a quickstart tutorial, a detailed reference for all functions, and guides on how to use and extend bandicoot. You can also check out our `interactive training notebooks <https://github.com/yvesalexandre/bandicoot-training>`_ to learn how to download your own data from your mobile phone and load it into bandicoot to visualize it or to learn how to use bandicoot indicators in *scikit-learn*.
        
Platform: UNKNOWN
Classifier: Environment :: Plugins
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Provides-Extra: tests
