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        "author": "Benjamin F. Maier",
        "author_email": "bfmaier@physik.hu-berlin.de",
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        "description": "# Radial Distance Layout\r\n\r\nGenerates a radial layout for trees whose nodes are associated with a distance to the root, similar to how it has been done in [1]. You can choose the basic method or a more sophisticated version which makes a more efficient use of space.\r\n\r\n[1] *The Hidden Geometry of Complex, Network-Driven Contagion Phenomena*, D Brockmann, D Helbing, Science Vol. 342, Issue 6164, pp. 1337-1342 (2013)\r\n\r\n## Install \r\n\r\n    $ sudo python setup.py install\r\n\r\n## Example\r\n\r\n    $ python example.py\r\n\r\nor look here:\r\n\r\n```\r\n#!python\r\nfrom radial_distance_layout import radial_distance_layout\r\nimport matplotlib.pyplot as pl\r\nimport networkx as nx\r\n\r\npaths  = [ [ 'a','b','c'] ]\r\npaths += [ [ 'a','b','d'] ]\r\npaths += [ [ 'a','e','f','g'] ]\r\npaths += [ [ 'a','e','f','h'] ]\r\npaths += [ [ 'a','e','i'] ]\r\npaths += [ [ 'a','j','k'] ]\r\npaths += [ [ 'a','j','l'] ]\r\n\r\ndists = {'a': 0,\r\n         'b':1.1, 'e': 1.2, 'j': 1.4,\r\n         'c':2.1, 'd': 2.2, 'f': 2.1, 'i': 2.34, 'k':3.8, 'l':2.5,\r\n         'g': 3.9, 'h': 3.8}\r\n\r\n#The Tree has to be a DiGraph! The root is always the one with distance 0.\r\nT = nx.DiGraph()\r\n\r\nfor p in paths:\r\n    T.add_path(p)\r\n\r\nkeystr = 'dist'\r\n\r\nnx.set_node_attributes(T,keystr,dists)\r\n\r\nfig,ax = pl.subplots(1,2,figsize=(15,8))\r\n\r\npos = radial_distance_layout(T,keystr,mode='soph')\r\nnx.draw_networkx(T,pos,ax=ax[0])\r\npos = radial_distance_layout(T,keystr,mode='normal')\r\nnx.draw_networkx(T,pos,ax=ax[1])\r\npl.show()\r\n\r\n```",
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