def thread2(G, carData, roadData, returndict2):
finalPath = []
for carNum in range(int(len(carData) / 2), len(carData)):
# items = algorithms.ksp_yen(G, '51', '3', 5)
items = algorithms.ksp_yen(G, str(carData[carNum][1]),
str(carData[carNum][2]), 3)
for path in items:
carRoute = path['path']
length = len(carRoute)
carRoute.reverse()
print("2222222222222222222222222")
# for i in range(len(carRoute)):
# f.write(carRoute[i])
# if i != len(carRoute) - 1:
# f.write(',')
# f.write('\n')
carRouteTmp = []
for i in range(1, length):
for j in range(len(roadData)):
if ((roadData[j][-3] == int(carRoute[length - i])
and roadData[j][-2] == int(carRoute[length - i - 1]))
or
(roadData[j][-2] == int(carRoute[length - i]) and
roadData[j][-3] == int(carRoute[length - i - 1]))):
carRouteTmp.append(roadData[j][0])
finalPath.append(carRouteTmp)
returndict2["result"] = finalPath
def thread1(G, carData, roadData, returndict1):
finalPath = []
for carNum in range(0, int(len(carData) / 2)):
# items = algorithms.ksp_yen(G, '51', '3', 5)
items = algorithms.ksp_yen(G, str(carData[carNum][1]),
str(carData[carNum][2]), 3)
for path in items:
# print(str(carNum) + "Cost:%s\t%s" % (path['cost'], "->".join(path['path'])))
carRoute = path['path']
length = len(carRoute)
carRoute.reverse()
print("1111111111111111111111111")
carRouteTmp = []
for i in range(1, length):
for j in range(len(roadData)):
if ((roadData[j][-3] == int(carRoute[length - i])
and roadData[j][-2] == int(carRoute[length - i - 1]))
or
(roadData[j][-2] == int(carRoute[length - i]) and
roadData[j][-3] == int(carRoute[length - i - 1]))):
carRouteTmp.append(roadData[j][0])
finalPath.append(carRouteTmp)
returndict1["result"] = finalPath
def returnsingle():
source = request.GET.get('source')
sink = request.GET.get('sink')
rank = request.GET.get('rank')
# Load the graph
G = DiGraph("net5")
# Get the painting object and set its properties.
paint = G.painter()
paint.set_source_sink(source, sink)
# Generate the graph using the painter we configured.
G.export(False, paint)
i=int(rank)
iRank = i
# Get shortest paths from the graph.
items = algorithms.ksp_yen(G, source, sink, i)
for path in items:
sCost = path['cost']
sPath = ", ".join(path['path'])
response.content_type='application/json'
response.headers['Access-Control-Allow-Origin']='*'
bus_Stop, bus_No, stop_Name, route_Id = result.busChanger(source,sink,iRank)
if i==1:
return { "BusStop": bus_Stop, "BusNo": bus_No, "StopName": stop_Name, "RouteNo ": route_Id}
else:
i=i-1
continue
def getDirections(start, end):
# Load the graph
G = DiGraph("shortestPath")
# Get 3 shortest paths from the graph.
items = algorithms.ksp_yen(G, start, end, 3)
return items
def getDirections(start, end):
# Load the graph
G = DiGraph("shortestPath")
# Get 3 shortest paths from the graph.
items = algorithms.ksp_yen(G, start, end, 3)
return items
def main():
# Load the graph
G = DiGraph("net5")
# Get the painting object and set its properties.
#paint = G.painter()
#paint.set_source_sink("C", "H")
#paint.set_rank_same(['C', 'D', 'F'])
#paint.set_rank_same(['E', 'G', 'H'])
# Generate the graph using the painter we configured.
#G.export(False, paint)
# Get 30 shortest paths from the graph.
items = algorithms.ksp_yen(G, "C", "H", 30)
for path in items:
print "Cost:%s\t%s" % (path['cost'], "->".join(path['path']))
return 0
def main():
# Load the graph
G = DiGraph("net5")
# Get the painting object and set its properties.
paint = G.painter()
paint.set_source_sink("C", "H")
paint.set_rank_same(['C', 'D', 'F'])
paint.set_rank_same(['E', 'G', 'H'])
# Generate the graph using the painter we configured.
G.export(False, paint)
# Get 30 shortest paths from the graph.
items = algorithms.ksp_yen(G, "C", "H", 30)
for path in items:
print "Cost:%s\t%s" % (path['cost'], "->".join(path['path']))
return 0
from graph import DiGraph
import algorithms
source = "s175"
sink = "s555"
rank = "13"
# Load the graph
G = DiGraph("net5")
# Get the painting object and set its properties.
paint = G.painter()
paint.set_source_sink(source, sink)
# Generate the graph using the painter we configured.
G.export(False, paint)
i=int(rank)
iRank = i
# Get shortest paths from the graph.
items = algorithms.ksp_yen(G, source, sink, i)
print(items)
#for path in items:
# sCost = path['cost']
# sPath = ", ".join(path['path'])
# response.content_type='application/json'
# if i==1:
# return { "Cost": sCost, "Path": sPath, "BusChange": result.busChanger(source,sink,iRank) }
# else:
# i=i-1
# continue
def adjust_route(topology, remove_file, routes, link_order):
# get remove_links
f = open(remove_file)
links = []
line = f.readline()
while line:
s = line.rstrip().split(' ')
s = [int(i) for i in s]
ls = s[0]
ld = s[1]
if ld < ls:
tmp = ls
ls = ld
ld = tmp
links.append((ls,ld))
line = f.readline()
f.close()
# get final topology
g = DiGraph()
f = open(topology)
line = f.readline()
line = f.readline()
while line:
s = line.rstrip().split(' ')
lls = int(s[0])
lld = int(s[1])
#g.add_nodes([int(s[0]), int(s[1])])
g.add_edge(lls, lld, link_order[lls,lld][1] / link_order[lls,lld][0])
g.add_edge(lld, lls, link_order[lls,lld][1] / link_order[lls,lld][0])
line = f.readline()
f.close()
def sort_path(paths):
for i in range(len(paths)):
for j in range(i, len(paths)):
if paths[i][1] < paths[j][1]:
tmp = paths[i]
paths[i] = paths[j]
paths[j] = tmp
def calculate_cf(paths, link_order):
for p in paths:
cf = 100000
for i in range(len(p[0]) - 1):
tmp_cf = 0
s = p[0][i]
d = p[0][i+1]
if s < d:
tmp_cf = link_order[s,d][0] / link_order[s,d][1]
else:
tmp_cf = link_order[d,s][0] / link_order[d,s][1]
if tmp_cf < cf:
cf = tmp_cf
p[1] = cf
sort_path(paths)
for ls,ld in links:
#print(ls,ld)
for s,d in routes:
route_paths = routes[s,d]
remove_route = []
remove_weight = 0
for path, weight in route_paths:
for i in range(len(path) - 1):
if (path[i] == ls and path[i+1] == ld) or (path[i] == ld and path[i+1] == ls):
remove_route.append([path, weight])
remove_weight += weight
# remove weight from all the link
for pi in range(len(path) - 1):
ps = path[pi]
pd = path[pi+1]
if pd < ps:
tmp = pd
pd = ps
ps = tmp
link_order[ps,pd][1] -= weight
break
if remove_weight != 0:
# Find k-shortest-paths between s,d
tmp_paths = algorithms.ksp_yen(g, s, d, 20)
yen_paths = [[yen_path['path'], 0, 0] for yen_path in tmp_paths]
# make sure the proportion of every yen_path
steps = 10.0
gap = remove_weight / steps
calculate_cf(yen_paths, link_order)
for step in range(int(steps)):
for yi in range(len(yen_paths[0][0]) - 1):
ys = yen_paths[0][0][yi]
yd = yen_paths[0][0][yi + 1]
if yd < ys:
tmp = ys
ys = yd
yd = tmp
link_order[ys,yd][1] += gap
yen_paths[0][2] += 1 / steps
calculate_cf(yen_paths, link_order)
# remain routes
for rr in remove_route:
route_paths.remove(rr)
# merge k-s-p into routes
for yen_path, flows, proportion in yen_paths:
if proportion == 0:
continue
exist = False
for rp in route_paths:
if rp[0] == yen_path:
rp[1] += remove_weight * proportion
exist = True
if not exist:
route_paths.append([yen_path, remove_weight * proportion])
routes[s,d] = route_paths
return routes
def main():
# Load the graph
G = DiGraph("net5")
# Get the painting object and set its properties.
paint = G.painter()
#paint.set_source_sink('ada', 'bob')
paint.set_source_sink('s5', 's7')
# load the graph dictionary
das_ergebnis = joblib.load(
"/Users/aditimiglani/Downloads/das_ergebnis.pkl")
list_of_edges = das_ergebnis['edges']
list_of_nodes = das_ergebnis['nodes']
for edge in list_of_edges:
source = str(edge['source'])
target = str(edge['target'])
load = float(edge['load'])
G.add_edge(source, target, load)
G.add_edge(target, source, load)
#G.add_edge('ada', 'sue', 3)
#G.add_edge('ada', 'john', 2)
#G.add_edge('sue', 'bob', 1)
#G.add_edge('a', 'b', 1)
#G.add_edge('sue', 'john', 1)
#G.add_edge('john', 'bob', 1)
# Generate the graph using the painter we configured.
G.export(False, paint)
# Get 5 shortest paths from the graph.
items = algorithms.ksp_yen(G, 's5', 's7', 5)
all_paths = []
#items = algorithms.ksp_yen(G, 'ada', 'bob', 5)
for path in items:
print "Cost:%s\t%s" % (path['cost'], "->".join(path['path']))
all_paths.append(path['path'])
print all_paths
sub_das_ergebnis = {}
sub_das_ergebnis['nodes'] = []
sub_das_ergebnis['edges'] = []
for sub_dict in list_of_nodes:
for path in all_paths:
for i in range(len(path)):
if path[i] == sub_dict['id']:
if sub_dict not in sub_das_ergebnis['nodes']:
if i == 0 or i == len(path) - 1:
sub_dict['root'] = True
sub_das_ergebnis['nodes'].append(sub_dict)
else:
sub_dict['root'] = False
sub_das_ergebnis['nodes'].append(sub_dict)
for sub_dict in list_of_edges:
for path in all_paths:
for i in range(len(path)):
if i < len(path) - 1:
if (path[i] == sub_dict['source']
or path[i] == sub_dict['target']) and (
path[i + 1] == sub_dict['source']
or path[i + 1] == sub_dict['target']):
if sub_dict not in sub_das_ergebnis['edges']:
sub_das_ergebnis['edges'].append(sub_dict)
joblib.dump(sub_das_ergebnis, 'sub_das_ergebnis.pkl')
print sub_das_ergebnis
print 'loading'
loaded = joblib.load('sub_das_ergebnis.pkl')
print loaded
return 0
def GenerateGraph(person1,person2):
person1_found = 'N'
person2_found = 'N'
#u'phillip.k': u's2625'
print person1
print person2
try:
node1 =person_node_mapping[person1.lower()]
person1_found = 'Y'
except:
print "Person 1 Not found"
node1 =u's78' #Janel Guerrero
try:
node2 =person_node_mapping[person2.lower()]
person2_found = 'Y'
except:
print "Person 2 Not found"
node2 = u's30018'
G = DiGraph("net5")
# Get the painting object and set its properties.
paint = G.painter()
#paint.set_source_sink('ada', 'bob')
paint.set_source_sink(node1, node2)
# load the graph dictionary
list_of_edges = das_ergebnis['edges']
list_of_nodes = das_ergebnis['nodes']
for edge in list_of_edges:
source = str(edge['source'])
target = str(edge['target'])
load = float(edge['load'])
G.add_edge(source, target, load)
G.add_edge(target, source, load)
#G.add_edge('ada', 'sue', 3)
#G.add_edge('ada', 'john', 2)
#G.add_edge('sue', 'bob', 1)
#G.add_edge('a', 'b', 1)
#G.add_edge('sue', 'john', 1)
#G.add_edge('john', 'bob', 1)
# Generate the graph using the painter we configured.
#G.export(False, paint)
# Get 5 shortest paths from the graph.
items = algorithms.ksp_yen(G, node1, node2, 5)
all_paths = []
#items = algorithms.ksp_yen(G, 'ada', 'bob', 5)
for path in items:
print "Cost:%s\t%s" % (path['cost'], "->".join(path['path']))
all_paths.append(path['path'])
print all_paths
sub_das_ergebnis = {}
sub_das_ergebnis['nodes'] = []
sub_das_ergebnis['edges'] = []
for sub_dict in list_of_nodes:
for path in all_paths:
for i in range(len(path)):
if path[i] == sub_dict['id']:
if sub_dict not in sub_das_ergebnis['nodes']:
if i == 0 or i == len(path) - 1:
sub_dict['root'] = True
sub_das_ergebnis['nodes'].append(sub_dict)
else:
sub_dict['root'] = False
sub_das_ergebnis['nodes'].append(sub_dict)
for sub_dict in list_of_edges:
for path in all_paths:
for i in range(len(path)):
if i < len(path)-1:
if (path[i] == sub_dict['source'] or path[i] == sub_dict['target']) and (path[i+1] == sub_dict['source'] or path[i+1] == sub_dict['target']):
if sub_dict not in sub_das_ergebnis['edges']:
sub_das_ergebnis['edges'].append(sub_dict)
#joblib.dump(sub_das_ergebnis, 'sub_das_ergebnis.pkl')
nodes =sub_das_ergebnis['nodes']
edges =sub_das_ergebnis['edges']
node_list = []
count =0
for node in nodes:
if node['caption'] in enron_table:
word = enron_table[node['caption']][1]
org = enron_table[node['caption']][0]
node_list.append([word,org,node['id']])
count+=1
elif node['caption'] in other_org_table:
word = other_org_table[node['caption']][1]
org = other_org_table[node['caption']][0]
node_list.append([word,org,node['id']])
count+=1
else:
continue
edge_list = []
connection_exist =[]
for edge in edges:
source = edge['source']
target = edge['target']
value = edge['load']
value = value/4
value1 = int(value*10)
value2 = int(value*200)
connection_exist.append(edge['source'])
connection_exist.append(edge['target'])
edge_list.append([edge['source'],edge['target'],value1,value2])
F = open("WorldCup2014.js","w")
F.write("var nodes = [")
nodes =[]
for node in node_list:
num = 1
if node[0] ==person1:
num = 2
if node[0] ==person2:
num = 3
string = "{id: " + str(node[2][1:]) + ", label: '" + str(node[0].title()) + "', value: " + str(20) +", group: " + str(num)+"},"
nodes.append(string)
#print string
F.write(string)
F.write("];")
F.write("var edges = [")
edges =[]
for edge in edge_list:
string = "{from: %s, to: %s, value: %s, title: 'Relationship score: %s'}," %(edge[0][1:],edge[1][1:],edge[2],edge[3])
edges.append(string)
F.write(string)
F.write("];")
F.close()
return nodes,edges
def adjust_route(topology, remove_file, routes, link_order):
# get remove_links
f = open(remove_file)
links = []
line = f.readline()
while line:
s = line.rstrip().split(' ')
s = [int(i) for i in s]
ls = s[0]
ld = s[1]
if ld < ls:
tmp = ls
ls = ld
ld = tmp
links.append((ls, ld))
line = f.readline()
f.close()
# get final topology
g = DiGraph()
f = open(topology)
line = f.readline()
line = f.readline()
while line:
s = line.rstrip().split(' ')
lls = int(s[0])
lld = int(s[1])
#g.add_nodes([int(s[0]), int(s[1])])
g.add_edge(lls, lld, link_order[lls, lld][1] / link_order[lls, lld][0])
g.add_edge(lld, lls, link_order[lls, lld][1] / link_order[lls, lld][0])
line = f.readline()
f.close()
def sort_path(paths):
for i in range(len(paths)):
for j in range(i, len(paths)):
if paths[i][1] < paths[j][1]:
tmp = paths[i]
paths[i] = paths[j]
paths[j] = tmp
def calculate_cf(paths, link_order):
for p in paths:
cf = 100000
for i in range(len(p[0]) - 1):
tmp_cf = 0
s = p[0][i]
d = p[0][i + 1]
if s < d:
tmp_cf = link_order[s, d][0] / link_order[s, d][1]
else:
tmp_cf = link_order[d, s][0] / link_order[d, s][1]
if tmp_cf < cf:
cf = tmp_cf
p[1] = cf
sort_path(paths)
for ls, ld in links:
#print(ls,ld)
for s, d in routes:
route_paths = routes[s, d]
remove_route = []
remove_weight = 0
for path, weight in route_paths:
for i in range(len(path) - 1):
if (path[i] == ls
and path[i + 1] == ld) or (path[i] == ld
and path[i + 1] == ls):
remove_route.append([path, weight])
remove_weight += weight
# remove weight from all the link
for pi in range(len(path) - 1):
ps = path[pi]
pd = path[pi + 1]
if pd < ps:
tmp = pd
pd = ps
ps = tmp
link_order[ps, pd][1] -= weight
break
if remove_weight != 0:
# Find k-shortest-paths between s,d
tmp_paths = algorithms.ksp_yen(g, s, d, 20)
yen_paths = [[yen_path['path'], 0, 0]
for yen_path in tmp_paths]
# make sure the proportion of every yen_path
steps = 10.0
gap = remove_weight / steps
calculate_cf(yen_paths, link_order)
for step in range(int(steps)):
for yi in range(len(yen_paths[0][0]) - 1):
ys = yen_paths[0][0][yi]
yd = yen_paths[0][0][yi + 1]
if yd < ys:
tmp = ys
ys = yd
yd = tmp
link_order[ys, yd][1] += gap
yen_paths[0][2] += 1 / steps
calculate_cf(yen_paths, link_order)
# remain routes
for rr in remove_route:
route_paths.remove(rr)
# merge k-s-p into routes
for yen_path, flows, proportion in yen_paths:
if proportion == 0:
continue
exist = False
for rp in route_paths:
if rp[0] == yen_path:
rp[1] += remove_weight * proportion
exist = True
if not exist:
route_paths.append(
[yen_path, remove_weight * proportion])
routes[s, d] = route_paths
return routes
def generate_small_graph(num_cols=5, num_rows=5, num_routes_per_od_pair=2):
# we have n*m nodes, (((of which a 5*5 grid is for Caltec and a 5*5 grid
# is for the streets (for now) --> imagine it as a 5 rows, 10 columns
# grid, indexed like a matrix)))
sensors = []
# Generate directed road network
G = nx.DiGraph()
n = num_cols
m = num_rows
r = num_routes_per_od_pair
for j in range(0, n):
for k in range(0, m):
G.add_node(k * n + j, pos = (j, -k))
for j in range(0, n-1):
for k in range(0, m-1):
G.add_edge(k * n + j, k * n + j + 1, weight=1)
sensors.append((k * n + j, k * n + j + 1))
G.add_edge(k * n + j, (k+1) * n + j, weight=1)
sensors.append((k * n + j, (k+1) * n + j))
# Manually get the last row
for j in range(0, n-1):
G.add_edge((m-1) * n + j,(m-1) * n + j + 1, weight=6)
sensors.append(((m-1) * n + j, (m-1) * n + j + 1))
# had to do this for this small node case to get at least 2 highways...
# Manually set the last column
for k in range(0, m-1):
G.add_edge(k * n + n-1, (k+1) * n + n-1, weight=1)
sensors.append((k * n + n-1, (k+1) * n + n-1));
# Set bigger road weights
for k in range(0, m-1):
for j in range(0, n-1):
# Highways
if k % 4 == 0:
G.edge[k*n + j][k*n + j+1]['weight'] = 6
# sensors.append((k*n + j,k*n + j+1))
# Big streets
if k % 4 == 2:
G.edge[k*n + j][k*n + j+1]['weight'] = 3
# Philipp: Hungry for sensors
# if j % 3 == 0:
# sensors.append((k*n + j,k*n + j+1))
# Half big streets
if j % 2 == 0:
G.edge[k*n + j][(k+1)*n + j]['weight'] = 2
# oh my gosh, I want more sensors
# sensors.append((k*n + j,(k+1)*n + j))
for (u, v, data) in G.edges(data=True):
G.add_edge(v, u, weight = data['weight'])
# Invert graph weights
H = graph.DiGraph()
for (u, v) in G.edges():
H.add_edge(u, v, cost = 1./G.edge[u][v]['weight'])
# Add opposite sensors
sensors_opposite = [(v,u) for (u,v) in sensors]
sensors.extend(sensors_opposite)
# Find k shortest routes between all 2 nodes
routes = []
for pair in itertools.product(range(0, n*m), repeat=2):
u = pair[0]
v = pair[1]
if u == v:
continue
# dijkstra would be routes.append(nx.shortest_path(G,source=v,target=w))
k_shortest = algorithms.ksp_yen(H, u, v, max_k = r)
paths = map(lambda x: x['path'], k_shortest)
if len(paths) > 0 and not(len(paths[0]) == 0):
routes.extend(paths)
return G, routes, sensors
def main(carData, roadData):
# Load the graph
G = DiGraph("net5")
# Get the painting object and set its properties.
# paint = G.painter()
# paint.set_source_sink("C", "H")
# paint.set_source_sink("C", "H")
# paint.set_rank_same(['C', 'D', 'F'])
# paint.set_rank_same(['E', 'G', 'H'])
# Generate the graph using the painter we configured.
# G.export(False, paint)
# Get 30 shortest paths from the graph.
# items = algorithms.ksp_yen(G, "E", "H", 10)
start = datetime.now()
with open('./answer.txt', 'w') as f:
for carNum in range(len(carData)):
# items = algorithms.ksp_yen(G, '51', '3', 5)
f.write(str(carData[carNum][0]))
f.write('\n')
items = algorithms.ksp_yen(G, str(carData[carNum][1]),
str(carData[carNum][2]), 2)
finalPath = []
for path in items:
print(
str(carNum) + "Cost:%s\t%s" %
(path['cost'], "->".join(path['path'])))
carRoute = path['path']
length = len(carRoute)
carRoute.reverse()
# for i in range(len(carRoute)):
# f.write(carRoute[i])
# if i != len(carRoute) - 1:
# f.write(',')
# f.write('\n')
carRouteTmp = []
for i in range(1, length):
for j in range(len(roadData)):
if ((roadData[j][-3] == int(carRoute[length - i]) and
roadData[j][-2] == int(carRoute[length - i - 1]))
or
(roadData[j][-2] == int(carRoute[length - i])
and roadData[j][-3] == int(
carRoute[length - i - 1]))):
carRouteTmp.append(roadData[j][0])
finalPath.append(carRouteTmp)
for i in range(len(finalPath)):
for j in range(len(finalPath[i])):
f.write(str(finalPath[i][j]))
if j != len(finalPath[i]) - 1:
f.write(',')
f.write('\n')
f.close()
end = datetime.now()
print((end - start).seconds)
return 0
fd.close()
for i in range(0, len(host_node)):
host_node[i] = host_node[i].encode(CODEC)
switch_path = "switches.json"
fd = open(switch_path, 'r')
switch_node = json.loads(fd.read())
fd.close()
for i in range(0, len(switch_node)):
switch_node[i] = switch_node[i].encode(CODEC)
mynet = "200h100r.json"
g = DiGraph(mynet)
for i in range(0, 100):
terminal.append(host_node[i])
max_count = 0
avg_count = 0
for sw in range(0, 100):
temp = 0
for h in range(0, 200):
path = ksp_yen(g, switch_node[sw], host_node[h], 2)
if len(path) > 1:
if path[0].get('cost') == path[1].get('cost'):
temp = temp + 1
if temp > max_count:
max_count = temp
print max_count
avg_count = avg_count + temp
print max_count, avg_count / float(100)
