{ "info": { "author": "Wyatt Baldwin", "author_email": "self@wyattbaldwin.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6" ], "description": "Dijkstar\n++++++++\n\nDijkstar is an implementation of Dijkstra's single-source shortest-paths\nalgorithm. If a destination node is given, the algorithm halts when that\nnode is reached; otherwise it continues until paths from the source node\nto all other nodes are found.\n\nAccepts an optional cost (or \"weight\") function that will be called on\nevery iteration.\n\nAlso accepts an optional heuristic function that is used to push the\nalgorithm toward a destination instead of fanning out in every\ndirection. Using such a heuristic function converts Dijkstra to A* (and\nthis is where the name \"Dijkstar\" comes from).\n\nPerformance is decent on a graph with 100,000+ nodes. Runs in around .5\nseconds on average .\n\nExample::\n\n >>> from dijkstar import Graph, find_path\n >>> graph = Graph()\n >>> graph.add_edge(1, 2, {'cost': 1})\n >>> graph.add_edge(2, 3, {'cost': 2})\n >>> cost_func = lambda u, v, e, prev_e: e['cost']\n >>> find_path(graph, 1, 2, cost_func=cost_func)\n\nThe cost function is passed the current node (u), a neighbor (v) of the\ncurrent node, the edge that connects u to v, and the edge that was\ntraversed previously to get to the current node. 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