def digraph_factory(spec):
graph1 = Digraph()
graph1.add_vertex('a')
graph1.add_vertex('b')
graph1.add_vertex('c')
graph2 = Digraph()
graph2.add_vertex('a')
graph2.add_vertex('b')
graph2.add_vertex('c')
graph2.add_edge('a', 'b')
graph2.add_edge('b', 'c')
spec_dict = {'no_edge': graph1,
'with_edges': graph2}
return spec_dict[spec]
class Server:
def __init__(self):
# contents: { vertex_id: ( lat, long ) }
# lat and long values are stored in 100,000th of degrees
self.vertex_locations = {}
# contents: { ( v1, v2 ): name }
self.edges = {}
self.graph = Digraph()
def import_file(self, filename):
"""
Builds the graph and associated information from specified text file.
"""
print('Importing {}...'.format(filename))
scale = 100000
count = 0
for line in open(filename, 'r'):
count += 1
split_line = line.rstrip().split(',')
line_type = split_line[0]
if line_type == 'V':
v = split_line[1]
self.graph.add_vertex(v)
self.vertex_locations[v] = (float(split_line[2]) * scale, float(split_line[3]) * scale)
if line_type == 'E':
t = (split_line[1], split_line[2])
self.graph.add_edge(t)
self.edges[t] = split_line[3]
print('{} lines processed. Ready for input.'.format(count))
def cost_distance(self, e):
"""
cost_distance returns the straight-line distance between the two
vertices at the endpoints of the edge e.
>>> s = Server()
>>> s.vertex_locations[29577354] = (400,-400)
>>> s.vertex_locations[29770958] = (500,-500)
>>> tcompare(100 * math.sqrt(2), s.cost_distance((29577354, 29770958)))
True
"""
#print('cost_distance: e = {}'.format(e))
v1 = self.vertex_locations[e[0]]
v2 = self.vertex_locations[e[1]]
#print('cost_distance: v1 = {}; v2 = {}'.format(v1, v2))
return euclidean_distance(v1, v2)
def closest_vertex(self, lat, long):
"""
Returns the id of the closest vertex to the specified lat and long
>>> s = Server()
>>> s.vertex_locations[29577354] = (5343099.6,-11349133.1)
>>> s.vertex_locations[29770958] = (5357142.9,-11362729.9)
>>> s.closest_vertex(5343099,-11349133)
29577354
"""
coords = (lat, long)
closest = min(self.vertex_locations.items(), key=lambda x: euclidean_distance(coords, x[1]))
return closest[0]
def find_shortest_path(self, lat1, long1, lat2, long2):
"""
Returns a least cost path of coordinates from (lat1, long1) to (lat1, long1)
"""
v1 = self.closest_vertex(lat1, long1)
v2 = self.closest_vertex(lat2, long2)
#print('Closest vertices: {} and {}'.format(v1, v2))
vertex_path = least_cost_path(self.graph, v1, v2, self.cost_distance)
#print('vertex_path: {}'.format(vertex_path))
path = []
if vertex_path is not None:
for v in vertex_path:
path.append(self.vertex_locations[v])
return path
def test_add_vertex(self):
dg = Digraph()
dg.add_vertex('a')
self.assertTrue('a' in dg.vertex_names)
def read_graph(input_file):
"""
Read in Digraph data from a file, and return it as a tuple containing a Digraph
as well as a map of metadata with vertex positions and edge names.
Returns:
(digraph, { (node id or edge tuple): (position or edge name) })
"""
# # graph and metadata to populate
# graph = Digraph()
# metadata = {}
# # set of vertices that we have read in. Used to check that
# # we aren't adding verts through |add_edge| that won't have any
# # location metadata.
# vert_set = set()
# # process each line in the file
# for line in input_file:
# # strip all trailing whitespace
# line = line.rstrip()
# fields = line.split(",")
# type = fields[0]
# if type == 'V':
# # got a vertex record
# (id,lat,long) = fields[1:]
# # vertex id's should be ints
# id=int(id)
# # lat and long are floats
# lat=float(lat)
# long=float(long)
# vert_set.add(id)
# graph.add_vertex(id)
# metadata[id] = (lat,long)
# elif type == 'E':
# # got an edge record
# (start,stop,name) = fields[1:]
# # vertices are ints
# start=int(start)
# stop=int(stop)
# e = (start,stop)
# if start not in vert_set:
# raise Exception("Vertex %d is an endpoint for an edge but has no metadata" % start)
# if stop not in vert_set:
# raise Exception("Vertex %d is an endpoint for an edge but has no metadata" % stop)
# # get rid of leading and trailing quote " chars around name
# name = name.strip('"')
# graph.add_edge(e)
# metadata[e] = name
# else:
# # weird input
# raise Exception("Error: weird line |{}|".format(line))
vert_map = {} # vert_id => {at = (lat,lon), ine = set(), oute = set()}
edge_map = {} # (id,id) => name
for line in input_file:
# strip all trailing whitespace
line = line.rstrip()
fields = line.split(",")
type = fields[0]
if type == "V":
# got a vertex record
(id, lat, long) = fields[1:]
# vertex id's should be ints
id = int(id)
# lat and long are floats
lat = float(lat)
long = float(long)
# todo
vert_map[id] = {"at": (lat, long), "id": id, "ine": set(), "oute": set()}
elif type == "E":
# got an edge record
(start, stop, name) = fields[1:]
# vertices are ints
start = int(start)
stop = int(stop)
e = (start, stop)
# get rid of leading and trailing quote " chars around name
name = name.strip('"')
# todo
edge_map[(start, stop)] = name
else:
# weird input
raise Exception("Error: weird line |{}|".format(line))
graph = Digraph()
cached_aux_verts = {}
def get_aux_verts(e):
if e not in cached_aux_verts:
aux_vert = {}
# first assign ine and oute on verts
for e in edge_map:
vert_map[e[0]]["oute"].add(e)
vert_map[e[1]]["ine"].add(e)
# metadata
metadata = {}
# now, make the aux vert set
aux_vert_map = {} # {vert_id => {adjacent_id => aux_vert_id}}
aux_vert_current_id = 1
for v_id in vert_map:
my_aux_verts = {}
vdat = vert_map[v_id]
for e in vdat["ine"]:
my_aux_verts[e[0]] = aux_vert_current_id
aux_vert_current_id += 1
for e in vdat["oute"]:
my_aux_verts[e[1]] = aux_vert_current_id
aux_vert_current_id += 1
for id in my_aux_verts.values():
metadata[id] = vdat["at"]
graph.add_vertex(id)
aux_vert_map[v_id] = my_aux_verts
# aux verts have been created, add the main edges
for (a, b) in edge_map:
graph.add_edge((aux_vert_map[a][b], aux_vert_map[b][a]))
metadata[(aux_vert_map[a][b], aux_vert_map[b][a])] = 0
# and finally, we need to add the junction virtual edges
for v_id in vert_map:
vdat = vert_map[v_id]
# for each incomming edge, join it to each outgoing edge
for (a, b) in vdat["ine"]:
in_aux_vert_id = aux_vert_map[v_id][a]
for (b, c) in vdat["oute"]:
out_aux_vert_id = aux_vert_map[v_id][c]
# calculate cost
cost = 0
if len(vdat["ine"]) == 1 and len(vdat["oute"]) == 1:
# ignore turn cost for nodes that just represent curves
cost = 0
else:
# use the angle
at_a = vert_map[a]["at"]
at_b = vert_map[b]["at"]
at_c = vert_map[c]["at"]
dir_1 = (at_b[0] - at_a[0], at_b[1] - at_a[1])
dir_2 = (at_c[0] - at_b[0], at_c[1] - at_b[1])
theta = math.acos(
(dir_1[0] * dir_2[0] + dir_1[1] * dir_2[1])
/ ((dir_1[0] ** 2 + dir_1[1] ** 2) ** 0.5 * (dir_2[0] ** 2 + dir_2[1] ** 2) ** 0.5)
* 0.9999
)
cost = theta * 10
# add
graph.add_edge((in_aux_vert_id, out_aux_vert_id))
metadata[(in_aux_vert_id, out_aux_vert_id)] = cost
# return the data
return (graph, metadata)
