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        "author": "Max Shinn",
        "author_email": "maxwell.shinn@yale.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Medical Science Apps.",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "# Versatility\n\nThis package implements versatility [(Shinn et al.,\n2017)](https://www.nature.com/articles/s41598-017-03394-5), which\ndescribes how closely affiliated a node is with a network community\nstructure.  It is written in Python3, and can only be guaranteed to\nwork there.  (This MAY work in Python2 if you `import __future__` but\nthis is untested... see code for details.)\n\nInstall with:\n\n    pip3 install versatility\n\nAlternatively, clone the git repo and install with:\n\n    python3 setup.py install\n\nDependencies:\n\n- Python3\n- [networkx](https://networkx.github.io/)\n- [Scipy](https://www.scipy.org/) (including numpy and matplotlib)\n- [bctpy](https://github.com/aestrivex/bctpy): The module \"bct\" is\n  bctpy, a port of the Brain Connectivity Toolbox to Python.  The\n  latest version supports Python3, and can be installed most easily\n  with \"`pip install bcpty`\".\n\nSee function help for full documentation, but the most useful\nfunctions are:\n\n- `find_nodal_versatility` - Compute the versatility of each node in a\n  graph using a specific community detection algorithm.\n- `find_nodal_mean_versatility` - Compute the versatility of each node\n  across a spectrum of community detection algorithm parameters (most\n  notably the resolution parameter) and find the average.\n- `find_optimal_gamma_curve` - Find the mean and standard error of\n  versatility across a spectrum of resolution parameters and\n  (optionally) plot the result.  This is most useful for finding the\n  best resolution parameter, e.g. in [Figure 3c of the original\n  paper](https://www.nature.com/articles/s41598-017-03394-5/figures/3).\n\nHere is a quick example to get you started:\n\n    import networkx\n    from versatility import *\n    G = networkx.karate_club_graph()\n    find_nodal_mean_versatility(G, find_communities_louvain, processors=2)\n    find_nodal_versatility(G, find_communities_louvain, algargs={\"gamma\" : 0.5})\n\nIf you use this code, please cite:\n\n    Shinn, M., Romero-Garcia, R., Seidlitz, J., Vasa, F., Vertes, P.,\n    Bullmore, E. (2017). Versatility of nodal affiliation to\n    communities. Scientific Reports 7: 4273.\n    doi:10.1038/s41598-017-03394-5\n\nCopyright 2016-2019 Maxwell Shinn (maxwell.shinn@yale.edu)\nAvailable under the GNU GPLv3.\n\n\n",
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