import heapq
import math


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 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.
    """
    min_node = None
    min_value = None

    for node in nodes:
        length = length_haversine(source_node, node)
        if (min_node is None or length < min_value):
            min_node = node
            min_value = length

    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