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import heapq
import math
from store import get_relevant_neighbours
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
<<<<<<< HEAD
def grid_search(grid, source_node):
"""
Finds closest node to source node by comparing distance to nodes within
nearest tiles in the grid.
"""
closest_nodes = []
source_key = (int(round(source_node.lat, 3) * 1000), int(round(
source_node.lng, 3) * 1000))
closest_tiles = find_nodes(0, grid, source_key)
for tile_nodes in closest_tiles:
closest_nodes.append(get_closest_node(tile_nodes, source_node))
closest_node_id = get_closest_node(closest_nodes, source_node).id
return closest_node_id
def find_nodes(offset, grid, source_key):
"""
Searches a grid in an outward "spiral" from source node fetching all tiles
which contain nodes.
"""
tiles = None
while not tiles:
tiles = look_for_nodes(offset, grid, source_key)
offset += 1
return tiles + look_for_nodes(offset + 1, grid, source_key)
def look_for_nodes(offset, grid, source_key):
"""Search for nearest tile containing nodes in an outward spiral."""
tiles = []
for i in range(-offset, offset + 1):
for j in range(-offset, offset + 1):
if i in (-offset, offset) or j in (-offset, offset):
key = (source_key[0] + j, source_key[1] + i)
if key in grid.keys():
tiles.append(grid[key])
return tiles
def get_closest_node(nodes, source_node):
"""
Searches through all nodes in a specified grid and return node
closes to source node.
"""
def get_closest_node_id(nodes, source_node, transport_mode):
""" 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 in nodes:
length = length_haversine(source_node, node)
relevant_neighbours = get_relevant_neighbours(node, transport_mode)
if (min_node is None or length < min_value) and relevant_neighbours:
min_node = node
min_value = length
return min_node
def find_shortest_path(nodes, source_id, target_id, transport_mode: str):
""" 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
end_node = nodes[walk_end]
relevant_neighbours = get_relevant_neighbours(end_node, transport_mode)
for neighbour in relevant_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
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