networkx.algorithms.shortest_paths.weighted.single_source_dijkstra¶

single_source_dijkstra
(G, source, target=None, cutoff=None, weight='weight')[source]¶ Find shortest weighted paths and lengths from a source node.
Compute the shortest path length between source and all other reachable nodes for a weighted graph.
Uses Dijkstra’s algorithm to compute shortest paths and lengths between a source and all other reachable nodes in a weighted graph.
 Parameters
G (NetworkX graph)
source (node label) – Starting node for path
target (node label, optional) – Ending node for path
cutoff (integer or float, optional) – Depth to stop the search. Only return paths with length <= cutoff.
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
tov
will beG.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.
 Returns
distance, path – If target is None, paths and lengths to all nodes are computed. The return value is a tuple of two dictionaries keyed by target nodes. The first dictionary stores distance to each target node. The second stores the path to each target node. If target is not None, returns a tuple (distance, path), where distance is the distance from source to target and path is a list representing the path from source to target.
 Return type
pair of dictionaries, or numeric and list.
 Raises
NodeNotFound – If
source
is not inG
.
Examples
>>> G = nx.path_graph(5) >>> length, path = nx.single_source_dijkstra(G, 0) >>> print(length[4]) 4 >>> for node in [0, 1, 2, 3, 4]: ... print('{}: {}'.format(node, length[node])) 0: 0 1: 1 2: 2 3: 3 4: 4 >>> path[4] [0, 1, 2, 3, 4] >>> length, path = nx.single_source_dijkstra(G, 0, 1) >>> length 1 >>> path [0, 1]
Notes
Edge weight attributes must be numerical. Distances are calculated as sums of weighted edges traversed.
The weight function can be used to hide edges by returning None. So
weight = lambda u, v, d: 1 if d['color']=="red" else None
will find the shortest red path.Based on the Python cookbook recipe (119466) at http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/119466
This algorithm is not guaranteed to work if edge weights are negative or are floating point numbers (overflows and roundoff errors can cause problems).