astar 1.1

generic A-Star path searching algorithm

python-astar

This is a simple implementation of the a-star path finding algorithm in python

Documentation

The astar module defines the AStar class, which has to be inherited from and completed with the implementation of several methods.

The functions take/return _node_ objects. The astar library only requires the following property from these objects:

• They must be hashable (i.e. implement __hash__).

For the default implementation of is_goal_reached, the objects must be comparable for same-ness (i.e. implement __eq__).

The computation of the hash may be implemented by several means :

• base types like strings, floats, integers, tuples. already implement __hash__

• dataclass https://docs.python.org/3/library/dataclasses.html#dataclasses.dataclass ` objects declared with `@dataclass(frozen=True) directly implement __hash__ if possible.

• by overriding the __hash__() https://docs.python.org/3/reference/datamodel.html#object.__hash__ method as a combination of the fields that you consider relevant for your object.

neighbors

@abstractmethod
def neighbors(self, node)

For a given node, returns (or yields) the list of its neighbors.

This is the method that one would provide in order to give to the algorithm the description of the graph to use during for computation.

Alternately, your override method may be named “path_neighbors”. Instead of your node, this method receives a “SearchNode” object whose “came_from” attribute points to the previous node; your node is in its “data” attribute. You might want to use this if your path is directional, like the track of a train that can’t do 90° turns.

One of these methods must be implemented in a subclass.

distance_between

@abstractmethod
def distance_between(self, n1, n2)

Gives the real distance/cost between two adjacent nodes n1 and n2 (i.e n2 belongs to the list of n1’s neighbors). n2 is guaranteed to belong to the list returned by a call to neighbors(n1).

Alternately, you may override “path_distance_between”. The arguments will be a “SearchNode”, as in “path_neighbors”. You might want to use this if your distance measure should include the path’s attainable speed, the kind and number of turns on it, or similar. You can use the nodes’ “cache” attributes to store some data, to speed up calculation.

One of these methods must be implemented in a subclass.

heuristic_cost_estimate

@abstractmethod
def heuristic_cost_estimate(self, current, goal)

Computes the estimated (rough) distance/cost between a node and the goal. The first argument is the start node, or any node that have been returned by a call to the neighbors() method.

This method is used to give to the algorithm an hint about the node it may try next during search.

This method must be implemented in a subclass.

is_goal_reached

def is_goal_reached(self, current, goal)

This method shall return a truthy value when the goal is ‘reached’. By default it checks that current == goal.

“Functional” API.

If you dislike to have to inherit from the AStar class and create an instance in order to run the algorithm, the module also provides a “find_path” function, which takes functions as parameters and provides reasonnable defaults for some of them.

See <https://github.com/jrialland/python-astar/blob/master/tests/basic/test_basic.py>

def find_path(
    start,
    goal,
    neighbors_fnct,
    reversePath=False,
    heuristic_cost_estimate_fnct = lambda a, b: Infinite,
    distance_between_fnct = lambda a, b: 1.0,
    is_goal_reached_fnct = lambda a, b: a == b
    )

Examples

Maze solver

This script generates an ascii maze, and finds the path between the upper left corner and the bottom right

PYTHONPATH=. python tests/maze/test_maze.py

+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+
|####    |     |              |        |              |     |
+--+# +  +  +  +  +--+--+--+  +  +--+  +--+--+--+  +--+  +  +
| ### |  |  |  |  |        |  |     |     |        |     |  |
+ #+--+--+  +  +  +--+  +--+  +  +--+--+  +  +--+--+  +--+  +
| #|        |  |  |     |     |  |        |  |     |  |     |
+ #+  +--+--+  +  +  +--+  +--+  +  +--+--+  +  +  +  +  +--+
| #|        |  |  |     |     |  |     |        |     |     |
+ #+--+--+  +  +  +--+  +--+  +  +--+--+  +--+--+--+--+--+  +
| #      |  |  |  |        |     | ### |  |     |        |  |
+ #+--+--+  +  +  +  +--+--+--+--+ #+# +  +--+  +  +--+  +  +
| #         |     |       ####| ####|# |  |     |     |  |  |
+ #+--+--+--+--+--+--+--+ #+ #+ #+--+# +  +  +  +--+  +  +  +
| #|    ####|       #######| ####| ### |     |     |  |     |
+ #+--+ #+ #+--+--+ #+--+--+--+--+ #+--+  +--+--+--+  +--+--+
| ####| #| ##########|           | ### |  | ###### |        |
+--+ #+ #+--+--+--+--+  +--+--+  +--+# +--+ #+--+# +--+--+  +
|  | ####|        |     |           |########|  |##| ### |  |
+  +--+--+  +--+  +  +--+  +--+--+  +--+--+--+  + #+ #+# +  +
|        |     |  |  |     |                    | ####|#### |
+  +--+--+--+  +  +  +  +--+  +--+--+--+--+--+  +--+--+--+# +
|  |           |     |     |     | ####|     |     | ###### |
+  +  +--+--+--+--+--+  +  +--+--+##+ #+--+  +--+  + #+--+--+
|     |  |           |  |  | ###### | ####|        | ### |  |
+  +--+  +  +--+--+  +--+  + #+--+--+--+ #+--+--+--+--+# +  +
|        |  |     |        | ###### |  | ############ |# |  |
+--+--+--+  +  +  +--+--+  +--+--+# +  +--+--+--+--+# +# +  +
|           |  |  |        | ###### | ##########|  |#### |  |
+  +--+  +--+--+  +  +--+--+ #+--+--+ #+--+--+ #+  +--+--+  +
|  |     |     |        | ####|     | #######| ############ |
+  +--+--+  +  +--+  +--+ #+--+--+  +  +--+ #+--+--+--+--+# +
|        |  |     |  | ####| ####|        | #| ### |     |##|
+--+--+  +  +--+  +  + #+--+ #+ #+--+--+  + #+ #+# +  +  + #+
|        |  |     |  | #######| ####|     | #| #|# |  |  | #|
+  +--+  +  +  +--+--+--+--+--+--+ #+--+--+ #+ #+# +--+  + #+
|  |     |  |  |                 | #| ####| ####|# |     | #|
+  +  +--+  +  +  +--+--+--+--+  + #+ #+ #+--+--+# +  +  + #+
|  |  |     |  |        |     |  | ####| ######### |  |  | #|
+  +--+  +--+  +--+--+  +  +  +  +--+--+--+--+--+--+  +--+ #+
|           |              |  |                            #|
+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+

London Underground

This script finds the shortest path between two underground stations, based on a list of London’s stations

PYTHONPATH=. python tests/london/test_london_underground.py Chesham Beckton

Chesham
Chalfont & Latimer
Chorleywood
Rickmansworth
Moor Park
Northwood
Northwood Hills
Pinner
North Harrow
Harrow-on-the-Hill
Northwick Park
Preston Road
Wembley Park
Finchley Road
Baker Street
Bond Street
Oxford Circus
Tottenham Court Road
Holborn
Chancery Lane
St. Paul's
Bank
Shadwell
Limehouse
Westferry
Poplar
Blackwall
East India
Canning Town
Royal Victoria
Custom House
Prince Regent
Royal Albert
Beckton Park
Cyprus
Gallions Reach
Beckton

TAN Network

A solution for a codingame’s puzzle (https://www.codingame.com/training/hard/tan-network)

PYTHONPATH=. python tests/tan_network/test_tan_network_5.py

.
----------------------------------------------------------------------
Ran 1 test in 0.010s

OK