import heapq import math import util from debug_draw import DebugDraw def length_haversine(p1, p2): lat1 = p1.lat lng1 = p1.lng lat2 = p2.lat lng2 = p2.lng lat1, lng1, lat2, lng2 = map(math.radians, [lat1, lng1, lat2, lng2]) dlat = lat2 - lat1 dlng = lng2 - lng1 a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlng / 2) ** 2 c = 2 * math.asin(math.sqrt(a)) return 6372797.560856 * c # return the distance in meters def get_closest_node_id(nodes, source_node): """ Search through all nodes and return the id of the node that is closest to 'source_node'. """ min_node = None min_value = None for node_id, node in nodes.items(): length = length_haversine(source_node, node) if min_node is None or length < min_value: min_node = node_id min_value = length grid_p = nodes[min_node].coord_tuple() DebugDraw.add_square(util.to_grid(grid_p), util.to_grid(grid_p, +1)) return min_node def find_shortest_path(nodes, source_id, target_id): """ Return the shortest path using Dijkstra's algortihm. """ # queue contains multiple (walk_dist, (node_0, node_1, ... node_n))-tuples # where (node_0, node_1, ... node_n) is a walk to node_n # and walk_dist is the total length of the walk in meters queue = [(0, (source_id,))] visited = set() while queue: # consider an unchecked walk walk_dist, walk = heapq.heappop(queue) walk_end = walk[-1] if walk_end == target_id: # you have reached your destination return walk if walk_end in visited: # there exists a shorter walk to walk_end continue # otherwise this is the shortest walk to walk_end visited.add(walk_end) # consider all our neighbours for neighbour in nodes[walk_end].neighbours: if neighbour in visited: # there exists a shorter walk to neighbour continue # otherwise this MIGHT be the shortest walk to neighbour # so put it in the queue new_dist = walk_dist + length_haversine(nodes[walk_end], neighbour) new_walk = walk + (neighbour.id,) heapq.heappush(queue, (new_dist, new_walk)) # no path found return None