pathfinding3d.finder.msp module

class pathfinding3d.finder.msp.MinimumSpanningTree(*args, **kwargs)[source]

Bases: Finder

Minimum Spanning Tree implementation by Brad Beattie (see https://github.com/brean/python-pathfinding/issues/18)

The wikipedia page has a nice description about MSP: https://en.wikipedia.org/wiki/Minimum_spanning_tree

__init__(*args, **kwargs)[source]

Find shortest path

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)

  • weighted (the algorithm supports weighted nodes) – (should be True for A* and Dijkstra)

  • 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.

tree(grid, start)[source]

Returns a list of nodes that are part of the minimum spanning tree of the grid.

Parameters:

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

  • start (GridNode) – start node

Return type:

List[GridNode]

itertree(grid, start)[source]

Returns a generator that yields nodes that are part of the minimum spanning tree of the grid.

Parameters:

  • grid (Grid) – grid that stores all possible steps/tiles as 3D-list

  • start (GridNode) – start node

find_path(start, end, grid)[source]

Find a path from start to end node on grid using the Minimum Spanning Tree 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]

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

check_neighbors(start, end, grid, open_list, open_value=1, backtrace_by=None)

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]]

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]

keep_running()

Check, if we run into time or iteration constrains.

Raises:
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)