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        "author": "David McDougall <dam1784[at]rit.edu>",
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        "description": "Common Graph Algorithms Library\r\n\r\nLibrary of graph algorithms which operate directly on python data structures.\r\n\r\nThis library uses a novel API for representing graphs.  Graph vertexes can be \r\nany hashable python value and the connectivity between vertexes is\r\nrepresented with a callback function.  This callback is named the 'adjacent' \r\nfunction.  The adjacent function has the following form:\r\n\r\ndef adjacent(vertex):\r\n    '''\r\n    This function returns all vertexes which the given vertex is connected to.\r\n    '''\r\n    return iterable-of-neighboring-vertexes\r\n\r\n\r\n\r\nContents:\r\n\r\ndepth_first_traversal()\r\n    A lazy depth first traversal\r\n\r\ndepth_first_search()\r\n    A depth first search\r\n\r\niterative_deepening_depth_first_search()\r\n    Searching infinite graphs\r\n\r\na_star()\r\n    Fast optimal pathfinding\r\n\r\ntopological_sort()\r\n    Dependency resolution.\r\n\r\nstrongly_connected_components()\r\n    Determines which areas of the graph can reach which other areas.\r\n\r\nIn the future I would like to implement more algorithms:\r\n- Minimum Spanning Tree\r\n- Min-cut/Max-flow\r\n- Substructure Search\r\n\r\n\r\nInstallation note:\r\nThis package optionally uses numpy.\r\nNumpy is used by some unit tests.\r\nNumpy is used to calculate A-stars effective branching factor (EBF).\r\nIf numpy is not available then EBF is not reported.\r\n\r\n\r\nComments and feedback are welcome\r\nSend to David McDougall  email: dam1784[at]rit.edu\r\n",
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