def calculate_path(self, start):
g = Dijkstra.Graph()
nodes = {}
i = 0
for t in self.white_tiles:
nodes[i] = t.id
i += 1
for n in self.white_tiles:
l = -1
vertex = {}
for k in range(0, len(nodes)):
if nodes[k] == n.id:
l = k
break
for i in range(1, len(n.neighbour_ids) + 1):
for j in range(0, len(nodes)):
if n.neighbour_ids[i - 1][0] == nodes[j][
0] and n.neighbour_ids[i - 1][1] == nodes[j][1]:
tmp = str(j)
vertex[tmp] = n.neighbour_distances[i]
break
l = str(l)
g.add_vertex(l, vertex)
s, t = 0, 0
for i in range(0, len(nodes)):
if nodes[i] == start.id:
s = str(i)
if nodes[i] == self.target.id:
t = str(i)
tmp_path = g.shortest_path(s, t)
path = []
for v in tmp_path:
path.append(nodes.get(int(v)))
return path
zipcode_ID[j] = int(i['source'])
j = j + 1
if int(i['destination']) not in zipcode_ID.values():
zipcode_ID[j] = int(i['destination'])
j = j + 1
#print (zipcode_ID)
# reserve ID and map to use them again in final result:
for k in zipcode_ID:
value = zipcode_ID[k]
ID_zipcode[value] = k
#print (ID_zipcode)
#print ('values',zipcode_ID.values())
#--------------------------------------------------------------------
number_of_zipcodes = len(ID_zipcode)
graph = Dijkstra.Graph(number_of_zipcodes)
for i in previous_node:
source_zipcode = int(i['source'])
destination_zipcode = int(i['destination'])
source_id = ID_zipcode[source_zipcode]
destination_id = ID_zipcode[destination_zipcode]
distance = int(i['distance'])
graph.addEdge(source_id, destination_id, distance)
#graph.addEdge(int(i['i']), int(i['destination']), int(i['distance']))
#graph.addEdge(int(edg['source']), int(edg['destination']), int(edg['distance']))
#print(edg['source'], edg['destination'], edg['distance'])
# read csv files for vehicles and request and put them in dictionary:---------------------
vehicles_file = csv.DictReader(open("vehicles_case.csv"))
vehicles = []
Dijkstra.graph = Dijkstra.Graph([
("0", "1", d_p(targets[0], targets[1]), 0),
("0", "9", d_p(targets[0], targets[9]), 0),
("0", "10", d_p(targets[0], targets[10]), 0),
("1", "0", d_p(targets[1], targets[0]), 0),
("1", "10", d_p(targets[1], targets[10]), 2),
("2", "10", d_p(targets[2], targets[10]), 2),
("2", "3", d_p(targets[2], targets[3]), 2),
("3", "2", d_p(targets[3], targets[2]), 2),
("3", "4", d_p(targets[3], targets[4]), 2),
("3", "10", d_p(targets[3], targets[10]), 2),
("3", "11", d_p(targets[3], targets[11]), 0),
("4", "3", d_p(targets[4], targets[3]), 2),
("4", "5", d_p(targets[4], targets[5]), 2),
("4", "10", d_p(targets[4], targets[10]), 2),
("4", "11", d_p(targets[4], targets[11]), 0),
("4", "12", d_p(targets[4], targets[12]), 2),
("5", "4", d_p(targets[5], targets[4]), 2),
("5", "6", d_p(targets[5], targets[6]), 2),
("5", "11", d_p(targets[5], targets[11]), 0),
("5", "12", d_p(targets[5], targets[12]), 2),
("6", "5", d_p(targets[6], targets[5]), 2),
("6", "12", d_p(targets[6], targets[12]), 2),
("7", "8", d_p(targets[7], targets[8]), 0),
("7", "12", d_p(targets[7], targets[12]), 2),
("8", "7", d_p(targets[8], targets[7]), 0),
("8", "9", d_p(targets[8], targets[9]), 0),
("8", "12", d_p(targets[8], targets[12]), 0),
("9", "0", d_p(targets[9], targets[0]), 0),
("9", "8", d_p(targets[9], targets[8]), 0),
("9", "11", d_p(targets[9], targets[11]), 0),
("10", "0", d_p(targets[10], targets[0]), 0),
("10", "1", d_p(targets[10], targets[1]), 2),
("10", "2", d_p(targets[10], targets[2]), 2),
("10", "3", d_p(targets[10], targets[3]), 2),
("10", "4", d_p(targets[10], targets[4]), 2),
("11", "3", d_p(targets[11], targets[3]), 0),
("11", "4", d_p(targets[11], targets[4]), 0),
("11", "5", d_p(targets[11], targets[5]), 0),
("11", "9", d_p(targets[11], targets[9]), 0),
("12", "4", d_p(targets[12], targets[4]), 2),
("12", "5", d_p(targets[12], targets[5]), 2),
("12", "6", d_p(targets[12], targets[6]), 2),
("12", "7", d_p(targets[12], targets[7]), 2),
("12", "8", d_p(targets[12], targets[8]), 0),
])