o 3ήcN@sTdZddlmZmZddlmZddlZddlm Z ddgZ d d dZ d d dZ dS) zCShortest paths and path lengths using the A* ("A star") algorithm. )heappopheappush)countN)_weight_function astar_pathastar_path_lengthweightcCs||vs||vrd|d|d}t||durdd}t}t}t||}t}dt||ddfg} i} i} | r|| \} } }}}||krc|g}|}|dur]||| |}|dusP||S|| vry| |durnq8| |\}}||kryq8|| |<|| D]5\}}|||||}|| vr| |\}}||krqn|||}||f| |<|| ||t||||fq| s:t d|d |) abReturns a list of nodes in a shortest path between source and target using the A* ("A-star") algorithm. There may be more than one shortest path. This returns only one. Parameters ---------- G : NetworkX graph source : node Starting node for path target : node Ending node for path heuristic : function A function to evaluate the estimate of the distance from the a node to the target. The function takes two nodes arguments and must return a number. If the heuristic is inadmissible (if it might overestimate the cost of reaching the goal from a node), the result may not be a shortest path. The algorithm does not support updating heuristic values for the same node due to caching the first heuristic calculation per node. weight : string or function If this is a string, then edge weights will be accessed via the edge attribute with this key (that is, the weight of the edge joining `u` to `v` will be ``G.edges[u, v][weight]``). If no such edge attribute exists, the weight of the edge is assumed to be one. If this is a function, the weight of an edge is the value returned by the function. The function must accept exactly three positional arguments: the two endpoints of an edge and the dictionary of edge attributes for that edge. The function must return a number. Raises ------ NetworkXNoPath If no path exists between source and target. Examples -------- >>> G = nx.path_graph(5) >>> print(nx.astar_path(G, 0, 4)) [0, 1, 2, 3, 4] >>> G = nx.grid_graph(dim=[3, 3]) # nodes are two-tuples (x,y) >>> nx.set_edge_attributes(G, {e: e[1][0] * 2 for e in G.edges()}, "cost") >>> def dist(a, b): ... (x1, y1) = a ... (x2, y2) = b ... return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5 >>> print(nx.astar_path(G, (0, 0), (2, 2), heuristic=dist, weight="cost")) [(0, 0), (0, 1), (0, 2), (1, 2), (2, 2)] See Also -------- shortest_path, dijkstra_path Either source or target is not in GNcSsdS)Nr)uvr r O/tmp/pip-target-vg8gfxp4/lib/python/networkx/algorithms/shortest_paths/astar.py heuristicRszastar_path..heuristicrzNode z not reachable from ) nx NodeNotFoundrrrrnextappendreverseitemsNetworkXNoPath)Gsourcetargetrrmsgpushpopcqueueenqueuedexplored___curnodedistparentpathnodeqcosthneighborwncostr r rr sT@        (cst|vs|vrd|d|d}t|tt|||}tfddt|dd|ddDS) aReturns the length of the shortest path between source and target using the A* ("A-star") algorithm. Parameters ---------- G : NetworkX graph source : node Starting node for path target : node Ending node for path heuristic : function A function to evaluate the estimate of the distance from the a node to the target. The function takes two nodes arguments and must return a number. If the heuristic is inadmissible (if it might overestimate the cost of reaching the goal from a node), the result may not be a shortest path. The algorithm does not support updating heuristic values for the same node due to caching the first heuristic calculation per node. weight : string or function If this is a string, then edge weights will be accessed via the edge attribute with this key (that is, the weight of the edge joining `u` to `v` will be ``G.edges[u, v][weight]``). If no such edge attribute exists, the weight of the edge is assumed to be one. If this is a function, the weight of an edge is the value returned by the function. The function must accept exactly three positional arguments: the two endpoints of an edge and the dictionary of edge attributes for that edge. The function must return a number. Raises ------ NetworkXNoPath If no path exists between source and target. See Also -------- astar_path r r r c3s(|]\}}||||VqdS)Nr ).0r rrrr r s&z$astar_path_length..N)rrrrsumzip)rrrrrrr'r r/rrs .  .)Nr) __doc__heapqrr itertoolsrnetworkxr+networkx.algorithms.shortest_paths.weightedr__all__rrr r r rs