Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.shortest_paths.astar

# -*- coding: utf-8 -*-
"""Shortest paths and path lengths using A* ("A star") algorithm.
"""

#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>

from heapq import heappush, heappop
from itertools import count
from networkx import NetworkXError
import networkx as nx

"Matteo Dell'Amico <matteodellamico@gmail.com>"])
__all__ = ['astar_path', 'astar_path_length']

[docs]def astar_path(G, source, target, heuristic=None, weight='weight'):
"""Return 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.

weight: string, optional (default='weight')
Edge data key corresponding to the edge weight.

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)
>>> 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),dist))
[(0, 0), (0, 1), (1, 1), (1, 2), (2, 2)]

--------
shortest_path, dijkstra_path

"""
if G.is_multigraph():
raise NetworkXError("astar_path() not implemented for Multi(Di)Graphs")

if heuristic is None:
# The default heuristic is h=0 - same as Dijkstra's algorithm
def heuristic(u, v):
return 0

push = heappush
pop = heappop

# The queue stores priority, node, cost to reach, and parent.
# Uses Python heapq to keep in priority order.
# Add a counter to the queue to prevent the underlying heap from
# attempting to compare the nodes themselves. The hash breaks ties in the
# priority and is guarenteed unique for all nodes in the graph.
c = count()
queue = [(0, next(c), source, 0, None)]

# Maps enqueued nodes to distance of discovered paths and the
# computed heuristics to target. We avoid computing the heuristics
# more than once and inserting the node into the queue too many times.
enqueued = {}
# Maps explored nodes to parent closest to the source.
explored = {}

while queue:
# Pop the smallest item from queue.
_, __, curnode, dist, parent = pop(queue)

if curnode == target:
path = [curnode]
node = parent
while node is not None:
path.append(node)
node = explored[node]
path.reverse()
return path

if curnode in explored:
continue

explored[curnode] = parent

for neighbor, w in G[curnode].items():
if neighbor in explored:
continue
ncost = dist + w.get(weight, 1)
if neighbor in enqueued:
qcost, h = enqueued[neighbor]
# if qcost < ncost, a longer path to neighbor remains
# enqueued. Removing it would need to filter the whole
# queue, it's better just to leave it there and ignore
# it when we visit the node a second time.
if qcost <= ncost:
continue
else:
h = heuristic(neighbor, target)
enqueued[neighbor] = ncost, h
push(queue, (ncost + h, next(c), neighbor, ncost, curnode))

raise nx.NetworkXNoPath("Node %s not reachable from %s" % (source, target))

[docs]def astar_path_length(G, source, target, heuristic=None, weight='weight'):
"""Return 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.

Raises
------
NetworkXNoPath
If no path exists between source and target.