### view env/lib/python3.9/site-packages/networkx/algorithms/shortest_paths/astar.py @ 0:4f3585e2f14bdraftdefaulttip

author shellac Mon, 22 Mar 2021 18:12:50 +0000
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"""Shortest paths and path lengths using the A* ("A star") algorithm.
"""
from heapq import heappush, heappop
from itertools import count

import networkx as nx
from networkx.algorithms.shortest_paths.weighted import _weight_function

__all__ = ["astar_path", "astar_path_length"]

def astar_path(G, source, target, heuristic=None, weight="weight"):
"""Returns 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 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 * 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)]

--------
shortest_path, dijkstra_path

"""
if source not in G or target not in G:
msg = f"Either source {source} or target {target} is not in G"
raise nx.NodeNotFound(msg)

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
weight = _weight_function(G, weight)

# 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 guaranteed 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:
# Do not override the parent of starting node
if explored[curnode] is None:
continue

# Skip bad paths that were enqueued before finding a better one
qcost, h = enqueued[curnode]
if qcost < dist:
continue

explored[curnode] = parent

for neighbor, w in G[curnode].items():
ncost = dist + weight(curnode, neighbor, w)
if neighbor in enqueued:
qcost, h = enqueued[neighbor]
# if qcost <= ncost, a less costly path from the
# neighbor to the source was already determined.
# Therefore, we won't attempt to push this neighbor
# to the queue
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(f"Node {target} not reachable from {source}")

def astar_path_length(G, source, target, heuristic=None, weight="weight"):
"""Returns 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.