pathfinding3d.finder.a_star module

class pathfinding3d.finder.a_star.AStarFinder(heuristic=None, weight=1, diagonal_movement=DiagonalMovement.never, time_limit=TIME_LIMIT, max_runs=MAX_RUNS)[source]

Bases: Finder

Parameters:

heuristic (Callable | None) –
weight (int) –
diagonal_movement (int) –
time_limit (float) –
max_runs (int | float) –

__init__(heuristic=None, weight=1, diagonal_movement=DiagonalMovement.never, time_limit=TIME_LIMIT, max_runs=MAX_RUNS)[source]

Find shortest path using A* algorithm

Parameters:

heuristic (Callable) – heuristic used to calculate distance of 2 points
weight (int) – weight for the edges
diagonal_movement (int) – if diagonal movement is allowed (see enum in diagonal_movement)
time_limit (float) – max. runtime in seconds
max_runs (int) – max. amount of tries until we abort the search (optional, only if we enter huge grids and have time constrains) <=0 means there are no constrains and the code might run on any large map.

check_neighbors(start, end, grid, open_list, open_value=1, backtrace_by=None)[source]

Find next path segment based on given node (or return path if we found the end)

Parameters:

start (GridNode) – start node
end (GridNode) – end node
grid (Grid) – grid that stores all possible steps/tiles as 3D-list
open_list (List) – stores nodes that will be processed next
open_value (int) –

Returns:

path

Return type:

Optional[List[GridNode]]

apply_heuristic(node_a, node_b, heuristic=None)

Helper function to apply heuristic

Parameters:

node_a (GridNode) – first node
node_b (GridNode) – second node
heuristic (Callable) – heuristic used to calculate distance of 2 points

Returns:

heuristic value

Return type:

float

find_neighbors(grid, node, diagonal_movement=None)

Find neighbor, same for Djikstra, A*, Bi-A*, IDA*

Parameters:

grid (Grid) – grid that stores all possible steps/tiles as 3D-list
node (GridNode) – node to find neighbors for
diagonal_movement (int) – if diagonal movement is allowed (see enum in diagonal_movement)

Returns:

list of neighbors

Return type:

List[GridNode]

find_path(start, end, grid)[source]

Find a path from start to end node on grid using the A* algorithm

Parameters:

start (GridNode) – start node
end (GridNode) – end node
grid (Grid) – grid that stores all possible steps/tiles as 3D-list

Returns:

path, number of iterations

Return type:

Tuple[List, int]

keep_running()

Check, if we run into time or iteration constrains.

Raises:

ExecutionTimeException – if we run into a time constrain
ExecutionRunsException – if we run into a iteration constrain

process_node(grid, node, parent, end, open_list, open_value=1)

We check if the given node is part of the path by calculating its cost and add or remove it from our path

Parameters:

grid (Grid) – grid that stores all possible steps/tiles as 3D-list
node (GridNode) – the node we like to test (the neighbor in A* or jump-node in JumpPointSearch)
parent (GridNode) – the parent node (of the current node we like to test)
end (GridNode) – the end point to calculate the cost of the path
open_list (List) – the list that keeps track of our current path
open_value (bool) – needed if we like to set the open list to something else than True (used for bi-directional algorithms)