def test_astar_undirected2(self):
XG3 = nx.Graph()
edges = [(0, 1, 2), (1, 2, 12), (2, 3, 1), (3, 4, 5), (4, 5, 1),
(5, 0, 10)]
XG3.add_weighted_edges_from(edges)
assert_equal(nx.astar_path(XG3, 0, 3), [0, 1, 2, 3])
assert_equal(nx.astar_path_length(XG3, 0, 3), 15)
def test_astar_w1(self):
G = nx.DiGraph()
G.add_edges_from([('s', 'u'), ('s', 'x'), ('u', 'v'), ('u', 'x'),
('v', 'y'), ('x', 'u'), ('x', 'w'), ('w', 'v'),
('x', 'y'), ('y', 's'), ('y', 'v')])
assert_equal(nx.astar_path(G, 's', 'v'), ['s', 'u', 'v'])
assert_equal(nx.astar_path_length(G, 's', 'v'), 2)
def hyperEmbed(g):
locs = {x: randomPoint() for x in g.nodes()}
rev_locs = {locs[x]: x for x in locs.keys()}
MAX_iter = 100
for i in range(0, MAX_iter):
print(i)
overlay = HyperfastDGVH(rev_locs.keys())
newlocs = {}
for p in g.nodes():
ploc = locs[p]
force = [0.0, 0.0]
total_hDist = 0.0
total_realdist = 0.0
weight = 0.0
for locq in overlay.nodes():
q = rev_locs[locq]
if p == q:
continue
qloc = locs[q]
dist = hDist(ploc, qloc)
ideal_dist = nx.astar_path_length(g, p, q) * 0.5
w = eDist((0, 0), qloc)
delta_f = (ideal_dist - dist) * 0.1 * (
1.0 - float(i) / MAX_iter) * w
weight += w
force[0] -= (qloc[0] - ploc[0]) * delta_f
force[1] -= (qloc[1] - ploc[1]) * delta_f
force[0] /= weight
force[1] /= weight
newlocs[p] = hyperDelta(ploc, force)
locs = newlocs
rev_locs = {locs[x]: x for x in locs.keys()}
print(isGreedy(HyperfastDGVH(rev_locs.keys()), hDist))
return locs
def total_exit_time(graph, start_points_list, exit_points_list):
"""
Calculates the total exit time as the longest time it takes any soldier to reach the exit
Satisfies TB033
"""
## Initialize to impossible value
max_exit_time = -1.0
for p_i in start_points_list:
assert p_i in graph, "Specified start point not walkable"
for p_e in exit_points_list:
assert p_e in graph, "Specified exit point not walkable"
try:
dist_to_exit = nx.astar_path_length(graph,
source=p_i,
target=p_e,
weight='weight',
heuristic=a_star_heuristic)
max_exit_time = max(max_exit_time, dist_to_exit)
except (KeyError, nx.NetworkXNoPath):
# If points are in graph but no path exists, log a message
logging.info("No path found between voxel indices {} and {}".format(p_i, p_e))
if max_exit_time == -1.0:
raise ValueError("No exit paths were found")
return max_exit_time
def test_astar_undirected3(self):
XG4 = nx.Graph()
edges = [(0, 1, 2), (1, 2, 2), (2, 3, 1), (3, 4, 1), (4, 5, 1),
(5, 6, 1), (6, 7, 1), (7, 0, 1)]
XG4.add_weighted_edges_from(edges)
assert_equal(nx.astar_path(XG4, 0, 2), [0, 1, 2])
assert_equal(nx.astar_path_length(XG4, 0, 2), 4)
def test_astar_undirected(self):
GG = self.XG.to_undirected()
# make sure we get lower weight
# to_undirected might choose either edge with weight 2 or weight 3
GG["u"]["x"]["weight"] = 2
GG["y"]["v"]["weight"] = 2
assert_equal(nx.astar_path(GG, "s", "v"), ["s", "x", "u", "v"])
assert_equal(nx.astar_path_length(GG, "s", "v"), 8)
def test_astar_undirected(self):
GG = self.XG.to_undirected()
# make sure we get lower weight
# to_undirected might choose either edge with weight 2 or weight 3
GG['u']['x']['weight'] = 2
GG['y']['v']['weight'] = 2
assert_equal(nx.astar_path(GG, 's', 'v'), ['s', 'x', 'u', 'v'])
assert_equal(nx.astar_path_length(GG, 's', 'v'), 8)
def BA_sp(m, N_range):
number_of_trials = 100
#D=1
myfile = open('BA_shortest_path', 'w')
myfile.write('N' + '\t' + 'average shortest path'+ '\t'+'std err'+'\n')
for N in N_range:
sp_list = []
comparison_sp_list = []
for trial in range(number_of_trials):
model = models.barabasi_albert_graph(N,m)
# model = models.box_model(D, N)
G = model[0]
extremes = model[1]
#tr_DAG = tr.trans_red(G)
sp_length = nx.astar_path_length(G, extremes[1], extremes[0])
# sp_length =
sp_list.append(sp_length)
average_in_degree = float(m)*float((N-1))/(float(N))
comparison_model = oneD_random_comparison(N,average_in_degree)
comparison_G = comparison_model[0]
comparison_extremes = comparison_model[1]
if nx.has_path(comparison_G, comparison_extremes[1], comparison_extremes[0]):
comparison_sp_length = nx.astar_path_length(comparison_G, comparison_extremes[1], comparison_extremes[0])
# comparison_sp_length = comparison_sp[2]
else: comparison_sp_length=0
comparison_sp_list.append(comparison_sp_length)
statistics = stats.list_stats(sp_list)
sp_av = statistics[3]
sp_std_err = statistics[0]
comparison_stats = stats.list_stats(comparison_sp_list)
comparison_sp_av = comparison_stats[3]
comparison_sp_std_err = comparison_stats[0]
print "done ", N
myfile.write(str(N) + '\t' + str(sp_av) + '\t' + str(sp_std_err) + '\t' + str(N) + '\t' + str(comparison_sp_av) + '\t' + str(comparison_sp_std_err) + '\n')
return
def findMaxScorePath(CandidateNodesList,SelectNodesGraph, i): CanPath = [] for j in CandidateNodesList[i[0]]: for k in CandidateNodesList[i[-1]]: CanPath.append((nx.astar_path(SelectNodesGraph,j,k),nx.astar_path_length(SelectNodesGraph,j,k))) MaxScore = min([j[1] for j in CanPath]) for j in CanPath: if MaxScore==j[1]: MaxScorePath = j[0] return MaxScorePath
def test_astar_undirected2(self):
XG3=nx.Graph()
XG3.add_edges_from([ [0,1,{'weight':2}],
[1,2,{'weight':12}],
[2,3,{'weight':1}],
[3,4,{'weight':5}],
[4,5,{'weight':1}],
[5,0,{'weight':10}] ])
assert nx.astar_path(XG3,0,3)==[0, 1, 2, 3]
assert nx.astar_path_length(XG3,0,3)==15
def closestStation(self, graph, node): #Returns the closest station and the distance from it
if (len(self.stations) == 0): return 0,999999
bestDistance = 99999999
bestStation = self.stations[0]
for station in self.stations:
dist = nx.astar_path_length(graph,station,node)
if (dist == 0): return node, 1
if (dist < bestDistance):
bestDistance = dist
bestStation = station
return bestStation, bestDistance
def test_astar_undirected3(self):
XG4=nx.Graph()
XG4.add_edges_from([ [0,1,{'weight':2}],
[1,2,{'weight':2}],
[2,3,{'weight':1}],
[3,4,{'weight':1}],
[4,5,{'weight':1}],
[5,6,{'weight':1}],
[6,7,{'weight':1}],
[7,0,{'weight':1}] ])
assert nx.astar_path(XG4,0,2)==[0, 1, 2]
assert nx.astar_path_length(XG4,0,2)==4
def pathadddistance(patharray,GGG):
#global allnodedist
for p in patharray:
nodepeer = zip(p[::1], p[1::1]) #参考高手的代码。将list的元素两两一组，重叠组成tuple。下面也有同样代码，最后的数字是2。
ttlong = 0 #累加路径长度。
for np in nodepeer:
dd = 0 #缓存清零。保证数值方便排障。
dd = nx.astar_path_length(GGG,np[0],np[1])#各个算法都尝试过了，结果一致。再观察。
#dd = nx.dijkstra_path_length(GGG,np[0],np[1])
#dd = allnodedist[np[0]][np[1]] #路径上每2个节点间的最短距离。其实不是最短距离，这个全局变量给点值很疑惑。
ttlong += dd #在节点直连字典中查找两点之间的距离，并累加。
p.insert(p.index(np[1]),dd) #在路径上每两个节点之间插入距离数值。
p.append(ttlong)
return patharray
def test_astar_w1(self):
G = nx.DiGraph()
G.add_edges_from(
[
("s", "u"),
("s", "x"),
("u", "v"),
("u", "x"),
("v", "y"),
("x", "u"),
("x", "w"),
("w", "v"),
("x", "y"),
("y", "s"),
("y", "v"),
]
)
assert nx.astar_path(G, "s", "v") == ["s", "u", "v"]
assert nx.astar_path_length(G, "s", "v") == 2
def getPairwiseDistanceWithinGraphOfChosenMonkey(self, graph=None, chosenMonkeyIDDict=None):
"""
2012.11.27
"""
sys.stderr.write("Getting list of pairwise distance of all pairs in graph for %s chosen monkeys ..."%\
(len(chosenMonkeyIDDict)))
distance_ls = []
chosenMonkeyIDList = list(chosenMonkeyIDDict)
for i in xrange(len(chosenMonkeyIDList)):
monkey1ID = chosenMonkeyIDList[i]
for j in xrange(i+1, len(chosenMonkeyIDList)):
monkey2ID = chosenMonkeyIDList[j]
try:
distance = nx.astar_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
except:
distance = -5
distance_ls.append(distance)
#distance_ls.append(lengthStructure[monkey1ID][monkey2ID])
sys.stderr.write("%s pairs .\n"%(len(distance_ls)))
return distance_ls
def updateNewMonkeyToChosenSetDistanceVector(self, graph=None, oldShortestDistanceVectorData=None, \
newlyChosenMonkeyID=None, minShortestDistance=0.4):
"""
2012.11.26
supplement to constructNewMonkeyToChosenSetDistanceVector()
"""
oldShortestDistanceToChosenSet_monkeyID_ls = oldShortestDistanceVectorData.shortestDistanceToChosenSet_monkeyID_ls
sys.stderr.write("Updating the shortest-distance to chosen set vector (%s elements) because monkeys %s has been added into the chosen set ..."%\
(len(oldShortestDistanceToChosenSet_monkeyID_ls), newlyChosenMonkeyID))
returnData = PassingData(shortestDistanceToChosenSet_monkeyID_ls = [])
counter = 0
real_counter = 0
spanStartPos = 0
monkey2ID = newlyChosenMonkeyID
for element in oldShortestDistanceToChosenSet_monkeyID_ls:
shortestDistance, monkey1ID, shortestDistanceToThisChosenMonkey = element[:3]
if monkey1ID!=monkey2ID: #skip the chosen monkey
counter += 1
#get the shortest path
#short path in graph, sum all the edge weights, A* star algorithm seems to be much faster (>100%) than default shortest_path.
#distance = nx.shortest_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
distance = nx.astar_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
if distance<shortestDistance:
shortestDistance = distance
shortestDistanceToThisChosenMonkey = monkey2ID
element = (shortestDistance, monkey1ID, shortestDistanceToThisChosenMonkey)
real_counter += 1
if shortestDistance>=minShortestDistance:
returnData.shortestDistanceToChosenSet_monkeyID_ls.append(element)
spanStartPos += shortestDistance #increase the distance.
returnData.totalDistance = spanStartPos
sys.stderr.write("%s (out of %s) monkeys changed their shortest distance to the chosen set (%s elements). \n\
total distance to the chosen set is %s.\n"%\
(real_counter, counter, len(returnData.shortestDistanceToChosenSet_monkeyID_ls), spanStartPos))
return returnData
def simulate_random_walk (G, damping, max_jumps):
""" Random walk simulation on weighted graph G with damping factor d
and max_jumps as maximum number of jumps"""
results = []
nodes = [] # keep nodes
current_node = random.randrange(N)
while not G.has_node(current_node):
current_node = random.randrange(N)
j = 0
while (j < max_jumps):
previous_node = current_node
jump_decision = random.uniform(0, 1)
if jump_decision < damping or G.out_degree(current_node) == 0:
# make a jump
current_node = random.randrange(N)
while not G.has_node(current_node):
current_node = random.randrange(N)
j += 1
try:
distance = nx.astar_path_length(G, previous_node, \
current_node, weight = 'weight')
# distance intervals 1h traveling
results.append(distance)
nodes.append(previous_node)
except nx.NetworkXNoPath: continue
else:
# move to neighbor node
incident = G.out_edges([current_node], data = False)
distribution = [ G.get_edge_data(e[0], e[1])['transition'] for e in incident ]
xk = np.arange(len(incident))
generator = stats.rv_discrete(values = (xk, distribution))
current_node = incident[generator.rvs()][1]
return results, nodes
def stitch(local_graph, global_graph, kd_tree, tolerance, target, rename_string):
""" stitch local unconstrained graph with global graph,
as long as the distance to nearest global graph node is within certain
tolerance. Requires pre-generated kd-tree for the global graph. """
path_candidates = []
for node, d in local_graph.nodes(data=True):
node_pos = d['pos']
dist, indexes = kd_tree.query([node_pos])
if dist[0] < tolerance:
#find astar path to the selected close proximity node
#TODO: compute node path here, and save it, extract length like this:
# path = astar_path(G, source, target, heuristic)
# length = sum(G[u][v].get(weight, 1) for u, v in zip(path[:-1], path[1:]))
path_length = nx.astar_path_length(local_graph, target, node)
entry_node = indexes[0]
path_candidates.append((path_length, target, node, entry_node))
#chose best way to join to global graph
path_candidates.sort()
best_candidate = path_candidates[0]
(path_length, target, node, entry_node) = best_candidate
astar_path = nx.astar_path(local_graph, target, node)
h = local_graph.subgraph(astar_path)
route = h.to_directed()
# because local_graphs have the same naming, aka (1,2) we have to rename
# to join properly
global_graph = nx.union(global_graph, route, rename=(None, rename_string))
#connect two graphs
global_graph.add_edge(rename_string + str(node), entry_node)
global_graph.add_edge( entry_node, rename_string + str(node))
return global_graph
def main82():
g = g82(matrix)
N = len(matrix)
M = len(matrix[0])
print "(%d) Nodes, (%d) Edges" % (len(g.nodes()), len(g.edges()))
#print nx.dijkstra_path_length(g, (0,0), (N-1,M-1)) + g.node[(N-1, M-1)].get('value')
minimum = 1e6
minimum2 = 1e6
for r in xrange(N):
for r1 in xrange(M):
try:
source = (r,0)
destiny = (r1, M-1)
l = nx.dijkstra_path_length(g, source, destiny)
l += g.node[destiny].get('value')
l2 = nx.astar_path_length(g, source, destiny, dist)
l2 += g.node[destiny].get('value')
if l < minimum:
minimum = l
if l2 < minimum2:
minimum2 = l2
except:
continue
print minimum, minimum2
def constructNewMonkeyToChosenSetDistanceVector(self, graph=None, preChosenMonkeyIDSet=None, minShortestDistance=0.4):
"""
2012.11.26
each monkey is assigned a probability mass based on its geographic distance to the closest monkey
in the preChosenMonkeyIDSet.
"""
sys.stderr.write("Constructing distance vector from new monkey to %s chosen monkeys (%s total monkeys) ..."%\
(len(preChosenMonkeyIDSet), len(graph)))
counter = 0
real_counter = 0
spanStartPos = 0
unChosenMonkeyIDSet = set(graph.nodes()) - preChosenMonkeyIDSet
preChosenAndInGraphMonkeyIDSet = set(graph.nodes()) - unChosenMonkeyIDSet
returnData = PassingData(shortestDistanceToChosenSet_monkeyID_ls = [])
for monkey1ID in unChosenMonkeyIDSet:
counter += 1
shortestDistance = None
shortestDistanceToThisChosenMonkey = None
for monkey2ID in preChosenAndInGraphMonkeyIDSet:
#get the shortest path
#distance = nx.shortest_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
distance = nx.astar_path_length(graph, source=monkey1ID, target=monkey2ID, weight='weight')
#short path in graph, sum all the edge weights
if shortestDistance is None or distance<shortestDistance:
shortestDistance = distance
shortestDistanceToThisChosenMonkey = monkey2ID
if shortestDistance is not None and shortestDistance>=minShortestDistance: #ignore monkeys from same sites
returnData.shortestDistanceToChosenSet_monkeyID_ls.append((shortestDistance, monkey1ID, shortestDistanceToThisChosenMonkey))
real_counter += 1
spanStartPos += shortestDistance #increase the distance.
returnData.totalDistance = spanStartPos
sys.stderr.write("%s (out of %s candidate) monkeys added into candidate vector, total distance to the chosen set is %s.\n"%\
(real_counter, counter, spanStartPos))
return returnData
import networkx as nx
orbits = open('006.txt', 'r').readlines()
edges = [o.strip().split(')') for o in orbits]
G = nx.DiGraph()
G.add_edges_from(edges)
ancestors = sum([len(nx.ancestors(G, n)) for n in G.nodes()])
print('direct + indirect', ancestors)
G = G.to_undirected()
shortest_path = nx.astar_path_length(G, source='YOU', target='SAN') - 2
print('path', shortest_path)
def test_astar_multigraph(self):
G = nx.MultiDiGraph(self.XG)
G.add_weighted_edges_from((u, v, 1000) for (u, v) in list(G.edges()))
assert nx.astar_path(G, 's', 'v') == ['s', 'x', 'u', 'v']
assert nx.astar_path_length(G, 's', 'v') == 9
def test_astar_directed(self):
assert nx.astar_path(self.XG, "s", "v") == ["s", "x", "u", "v"]
assert nx.astar_path_length(self.XG, "s", "v") == 9
def test_astar_w1(self):
G=nx.DiGraph()
G.add_edges_from([('s','u'), ('s','x'), ('u','v'), ('u','x'), ('v','y'), ('x','u'), ('x','v'), ('x','y'), ('y','s'), ('y','v')])
assert nx.astar_path(G,'s','v')==['s', 'u', 'v']
assert nx.astar_path_length(G,'s','v')== 2
def update_target_order(self, game):
dist_list = [(x, nx.astar_path_length(game.board, self.home_node, x))
for x in self.remaining_objectives]
dist_list.sort(key=lambda x: x[1])
self.target_order = [x[0] for x in dist_list if x[1] > 0]
def main():
# Festlegen der Standarddatei
file_name = os.path.join(os.path.dirname(__file__), "lisarennt3.txt")
if len(sys.argv) == 2:
file_name = os.path.join(os.path.dirname(__file__), sys.argv[1])
# Einlesen der Datei
input_file = open(file_name, 'r')
str_data = input_file.read().splitlines()
input_file.close()
str_data_split = [list(map(int, string.split(' '))) for string in str_data]
poly_data = str_data_split[1:len(str_data_split) - 1]
raw_polygons = []
all_points = []
# Erstellen der Polygone
for data_line in poly_data:
vertices = []
for _ in range(1, len(data_line) - 1, 2):
vertices.append((data_line[_], data_line[_ + 1]))
raw_polygons.append(Polygon(vertices))
all_points += vertices
start_point = (str_data_split[-1][0], str_data_split[-1][1])
all_points.append(start_point)
# Vereinen von Polygonen, die einander beruehren
polygons = []
for poly1 in raw_polygons:
for poly2 in raw_polygons:
if poly1 is not poly2 and poly1.poly_obj.intersects(poly2.poly_obj):
poly1 = poly1.poly_obj.union(poly2.poly_obj)
# Aktualisieren der Liste der Ecken des neuen kombinierten Polygons
poly1.vertices = [(round(p[0]), round(p[1])) for p in
geometry.mapping(poly1.poly_obj)['coordinates'][0]]
raw_polygons.remove(poly2)
polygons.append(poly1)
# Erstellen eines Graphen mit allen bisherigen Punkten
graph = nx.Graph()
graph.add_nodes_from(all_points)
# Erstellen von erlaubten Verbindungen innerhalb des Graphen
checked_connections = []
for polygon in polygons:
for p1 in polygon.vertices:
for p2 in all_points:
if p1 != p2 and (p2, p1) not in checked_connections:
valid_connection, connecting_line = check_edge_valid(p1, p2, polygons, polygon)
if valid_connection:
graph.add_edge(p1, p2, weight=connecting_line.length)
checked_connections.append((p1, p2))
# Hinzufuegen moeglicher Endpunkte an der Gerade x = 0
destinations = []
for p1 in all_points:
p2 = calculate_ideal_finish(p1)
valid_connection, connecting_line = check_edge_valid(p1, p2, polygons)
if valid_connection:
graph.add_node(p2)
graph.add_edge(p1, p2, weight=connecting_line.length)
destinations.append((p2, nx.astar_path_length(graph, start_point, p2,
lambda p, q: geometry.LineString([p, q]).length)))
# Ausrechnen der Guete jedes Endpunktes
early_start = []
for point, distance in destinations:
early_start.append(distance / RUN_SPEED - point[1] / BUS_SPEED)
best_destination = destinations[early_start.index(min(early_start))]
best_path = nx.astar_path(graph, start_point, best_destination[0], lambda p, q: geometry.LineString([p, q]).length)
# Ausgabe von Routeninformationen
start_time = (datetime.datetime(year=1, month=1, day=1, hour=7, minute=30)
- datetime.timedelta(seconds=min(early_start)))
print("Laufdistanz: " + str(best_destination[1]) + " m")
print("Laufzeit: " + str(best_destination[1] / RUN_SPEED) + " s")
print("Startzeit: " + start_time.time().strftime('%H:%M:%S.%f'))
print("Ankunftszeit: " + (start_time + datetime.timedelta(seconds=best_destination[1] / RUN_SPEED))
.time().strftime('%H:%M:%S.%f'))
print("Treffpunkt mit Bus: " + str(best_destination[0]))
print()
print("Route:")
# Ausgabe der Route, Feststellen der jeweiligen Polygon-ID
for i in range(len(best_path)):
point = best_path[i]
point_tag = ""
if i == 0:
point_tag = "L"
elif i == (len(best_path) - 1):
point_tag = "Bus"
else:
for j in range(len(raw_polygons)):
if raw_polygons[j].poly_obj.intersects(geometry.Point(point)):
point_tag = "P" + str(j + 1)
break
print(str(i + 1) + ". " + str((point[0], point[1], point_tag)))
# Zeichnen des Weges
plot_polygons(polygons)
plot_line(best_path, 'black')
plt.axvline(x=0, color='grey')
plt.scatter(best_path[0][0], best_path[0][1], color='green', label='Start')
plt.scatter(best_path[-1][0], best_path[-1][1], color='red', label='Ende')
plt.legend()
plt.axis('equal')
plt.tight_layout()
plt.show()
def test_astar_grid():
graph = nx.grid_graph(dim=[5,5])
assert nx.astar_path_length(graph,(0,0),(4,4)) == len(nx.astar_path(graph, (0,0),(4,4)))-1
def changepoint_path(wav_fn, length, graph=None, markers=None,
sim_mat=None, avg_duration=None, APP_PATH=None, nchangepoints=4,
min_start=None):
"""wave filename and graph from that wav, length in seconds"""
# generate changepoints
try:
cpraw = subprocess.check_output([
APP_PATH + 'music_changepoints/novelty',
wav_fn, '64', 'rms', 'euc', str(nchangepoints) * 3])
tmp_changepoints = [float(c) for c in cpraw.split('\n') if len(c) > 0]
changepoints = []
cp_idx = 0
while len(changepoints) < nchangepoints:
if min_start is None:
changepoints.append(tmp_changepoints[cp_idx])
elif tmp_changepoints[cp_idx] >= min_start:
changepoints.append(tmp_changepoints[cp_idx])
cp_idx += 1
except:
changepoints = novelty(wav_fn, k=64, nchangepoints=nchangepoints)
print "Change points", changepoints
if graph is not None:
edge_lens = [graph[e[0]][e[1]]["duration"]
for e in graph.edges_iter()]
avg_duration = N.mean(edge_lens)
nodes = sorted(graph.nodes(), key=float)
else:
nodes = map(str, markers)
node_count = int(float(length) / avg_duration)
closest_nodes = []
node_to_cp = {}
for cp in changepoints:
closest_nodes.append(
N.argmin([N.abs(float(node) - float(cp)) for node in nodes]))
node_to_cp[str(closest_nodes[-1])] = cp
out = []
for pair in itertools.permutations(closest_nodes, r=2):
# print "Finding path for pair", pair, "of length", node_count
avoid_nodes = [cn for cn in closest_nodes if cn != pair[1]]
# avoid_nodes = closest_nodes
if graph is not None:
try:
shortest_path = nx.astar_path_length(graph,
nodes[pair[0]], nodes[pair[1]])
# print "# shortest path:", shortest_path
if shortest_path <= node_count:
pf = PathFinder(graph=graph, start=pair[0],
end=pair[1], length=node_count)
res, cost = pf.find(avoid=avoid_nodes)
if res is not None:
out.append((res, cost))
break
except:
pass
else:
pf = PathFinder(start=pair[0],
sim_mat=sim_mat.copy(),
end=pair[1],
nodes=nodes,
length=node_count)
res, cost = pf.find(avoid=avoid_nodes)
if res is not None:
out.append((res, cost, map(lambda x: node_to_cp[str(x)], pair)))
return out, changepoints
def test_astar_directed(self):
assert_equal(nx.astar_path(self.XG, 's', 'v'), ['s', 'x', 'u', 'v'])
assert_equal(nx.astar_path_length(self.XG, 's', 'v'), 9)
def astar_len(graph, width, height, startx, starty, targetx, targety):
adj = adjecent_2DGridWorld(graph, width, height)
G = nx.from_numpy_matrix(adj)
return nx.astar_path_length(G, starty*width+startx, targety*width+targetx)
def distance(node, target):
try:
# return networkx.dijkstra_path_length(graph, node, target)
return networkx.astar_path_length(graph, node, target, heuristic=heuristic)
except networkx.NetworkXNoPath:
return float('inf')
def main():
file = open('obfuscated_term_enron_astar.txt', 'w')
file_1 = open('avg_mean_enron_Astar.txt', 'w')
G=create_graph()
#print(from_nodes)
#read the sentences from a given file.
with open('sentence.txt') as f:
lines = f.read().splitlines()
for each_line in lines:
#print(each_line)
each_line=each_line.replace('\'', '')
for c in string.punctuation:
each_line= each_line.replace(c," ")
line = ''.join([i for i in each_line if not i.isdigit()])
line = re.sub("[A-Z]{2,}",'',line).replace(" ", " ")
line=re.sub(r'\s+', ' ', line)
line=line.lower()
#print(line)
sentence=line.split(" ")
tag=find_tag(sentence)
# print(tag)
bag_of_words=[]
bw=[]
pos=[]
list_of_mean_values=[]
for i in range (0, len(tag)):
#print(tag[i][0], tag[i][1])
if tag[i][1] in ('NN', 'NNS', 'JJ', 'JJR', 'JJS', 'RBR', 'RBS'):
if len(tag[i][0])>=2:
bw.append(tag[i][0])
pos.append(tag[i][1])
#print(pos)
#print(tag[i][1])
#print('before lemma', bw)
#pos={'NN':'n','JJ':'a','VB':'v','RB':'r', 'NNP':'n', 'NNS':'n', 'NNPS':'n', 'JJR':'a', 'JJS':'a','RBR':'r','RBS':'r', 'VBD':'v', 'VBG':'v', 'VBN':'v'}
#print (pos)
lmtzr = WordNetLemmatizer()
for p in range(0, len(bw)):
val=pos[p]
# print(bw[p], val)
if(val in ('NN','NNP','NNS','NNPS')):
pos_wordnet='n'
elif(val in ('JJ', 'JJR', 'JJS')):
pos_wordnet='a'
elif(val in ('RB', 'RBR', 'RBS')):
pos_wordnet='r'
#elif(val is ('VB', 'VBD', 'VBG', 'VBN')):
else:
pos_wordnet='v'
# print(bw[p], pos_wordnet)
lemma=lmtzr.lemmatize((bw[p]), pos_wordnet)
#print(lemma)
bag_of_words.append(lemma)
#bag_of_words=['please', 'let', 'know', 'men']
#bag of words of one sentence
#should come under loop of each line in lines.
#print('bag of words', bag_of_words)
length=len(bag_of_words)
#print(length)
if length>1:
for i in range (0, length):
sen_round_i=bag_of_words.copy()
#print('orig',sen_round_i)
sen_round_i.pop(i)
mean_i=[]
len_sen_round_i=len(sen_round_i)
#print("round", i, ": ", sen_round_i)
k=0
for j in range (0, len_sen_round_i-1):
for k in range (j, len_sen_round_i):
if j != k:
#seq1=nx.dijkstra_path(G, '/c/en/'+sen_round_i[k], '/c/en/'+sen_round_i[j])
#seq2=nx.dijkstra_path(G, '/c/en/'+sen_round_i[j], '/c/en/'+sen_round_i[k])
item1='/c/en/'+sen_round_i[j]
item2='/c/en/'+sen_round_i[k]
if ((item1 in from_nodes) or (item1 in to_nodes)) and ((item2 in from_nodes) or (item2 in to_nodes)):
path_one=nx.astar_path_length(G, item1, item2)
#nx.shortest_path_length()
path_two=nx.astar_path_length(G, item2, item1)
avg_score=(path_one+path_two)/2
# print (item1, item2, 'are here')
else:
#print("not in data: ", item1, item2)
avg_score=4
#print('node is not here');
#print(item1, item2, avg_score)
#nx.shortest_path_length()
#path_one=nx.dijkstra_path_length(G, '/c/en/'+sen_round_i[k], '/c/en/'+sen_round_i[j])
#path_two=nx.dijkstra_path_length(G, '/c/en/'+sen_round_i[j], '/c/en/'+sen_round_i[k])
#avg_score=(path_one+path_two)/2
#print(sen_round_i[j],',', sen_round_i[k],'>>')
#print(seq1)
#print(seq2)
#print('path: ', path_one, ' reverse path: ', path_two, ' average: ', avg_score)
mean_i.append(avg_score)
k=k+1;
#print(avg_score)
mean_for_one_word=np.mean(mean_i)
file_1.write(str(mean_for_one_word))
file_1.write(", ")
#mean_for_one_word=mean_i/len_sen_round_i
#print('mean value: ',mean_for_one_word)
list_of_mean_values.append(mean_for_one_word)
sen_round_i=bag_of_words
#index, value = max(enumerate(list_of_mean_values), key=operator.itemgetter(1))
# print('Mean Average Conceptual Similarity: ',list_of_mean_values)
min_value=min(list_of_mean_values)
file_1.write('\n')
file_1.write('\t')
file_1.write(str(min_value))
file_1.write('\n\n')
index=list_of_mean_values.index(min_value)
ot=bag_of_words[index]
file.write(ot)
#file.write(' ')
file.write('\n')
#print('obfuscated term: ', bag_of_words[index])
else:
file.write("NA")
#file.write(' ')
file.write('\n')
file_1.write("NA")
#file.write(' ')
file_1.write('\n')
def _transmission_probability(self):
"""
Returns:
"""
i = 1
while i < len(self.candidates):
# j and k
j = i - 1
if len(self.candidates[i]) == 0:
k = i + 1
while k < len(self.candidates) and len(
self.candidates[k]) == 0:
k += 1
if k == len(self.candidates):
break
else:
k = i
d = dist(angle2radian(self.trajectory[j][2]),
angle2radian(self.trajectory[j][1]),
angle2radian(self.trajectory[k][2]),
angle2radian(
self.trajectory[k][1])) # great circle distance
for edge_j, dct_j in self.candidates[j].items():
for edge_k, dct_k in self.candidates[k].items():
brng_jk = init_bearing(angle2radian(self.trajectory[j][2]),
angle2radian(self.trajectory[j][1]),
angle2radian(self.trajectory[k][2]),
angle2radian(self.trajectory[k][1]))
brng_edge_j = init_bearing(
angle2radian(self.rd_nwk.nodes[edge_j[0]]['lat']),
angle2radian(self.rd_nwk.nodes[edge_j[0]]['lon']),
angle2radian(self.rd_nwk.nodes[edge_j[1]]['lat']),
angle2radian(self.rd_nwk.nodes[edge_j[1]]['lon']),
)
try:
if dct_j['node'] is not None and dct_k[
'node'] is not None:
dt = abs(d -
nx.astar_path_length(self.rd_nwk,
dct_j['node'],
dct_k['node'],
weight='distance'))
elif dct_j['node'] is not None:
nd2_origin = edge_k[0]
lon, lat = self.rd_nwk.nodes[nd2_origin][
'lon'], self.rd_nwk.nodes[nd2_origin]['lat']
path_len = nx.astar_path_length(self.rd_nwk,
dct_j['node'],
nd2_origin,
weight='distance')
path_len += math.sqrt(
math.fabs(
dist(angle2radian(self.trajectory[k][2]),
angle2radian(self.trajectory[k][1]),
angle2radian(lat), angle2radian(lon))
**2 - dct_k['distance']**2))
if edge_j[1] == dct_j['edge']:
path_len += self.rd_nwk[edge_j[0]][
edge_j[1]]['distance'] * 2
dt = abs(d - path_len)
elif dct_k['node'] is not None:
nd1_destination = edge_j[1]
lon, lat = self.rd_nwk.nodes[nd1_destination][
'lon'], self.rd_nwk.nodes[nd1_destination][
'lat']
path_len = nx.astar_path_length(self.rd_nwk,
nd1_destination,
dct_k['node'],
weight='distance')
path_len += math.sqrt(
math.fabs(
dist(angle2radian(self.trajectory[j][2]),
angle2radian(self.trajectory[j][1]),
angle2radian(lat), angle2radian(lon))
**2 - dct_j['distance']**2))
if edge_k[1] == dct_k['node']:
path_len += self.rd_nwk[edge_k[0]][
edge_k[1]]['distance'] * 2
dt = abs(d - path_len)
else:
if edge_j == edge_k and math.fabs(brng_edge_j -
brng_jk) < 90:
dt = 1
else:
nd1_destination = edge_j[1]
lon1, lat1 = self.rd_nwk.nodes[nd1_destination]['lon'], \
self.rd_nwk.nodes[nd1_destination]['lat']
nd2_origin = edge_k[0]
lon2, lat2 = self.rd_nwk.nodes[nd2_origin][
'lon'], self.rd_nwk.nodes[nd2_origin][
'lat']
dt = abs(d - (
nx.astar_path_length(self.rd_nwk,
nd1_destination,
nd2_origin,
weight='distance') +
math.sqrt(
math.fabs(
dist(
angle2radian(self.trajectory[j]
[2]),
angle2radian(self.trajectory[
j][1]), angle2radian(lat1),
angle2radian(lon1))**2 -
dct_j['distance']**2)) +
math.sqrt(
math.fabs(
dist(
angle2radian(self.trajectory[k]
[2]),
angle2radian(self.trajectory[
k][1]), angle2radian(lat2),
angle2radian(lon2))**2 -
dct_k['distance']**2))))
result = 1 / self.beta * math.exp(-dt / self.beta)
if 'V' in dct_j.keys():
dct_j['V'][edge_k] = min(result, 1)
else:
dct_j['V'] = {edge_k: min(result, 1)}
except:
if 'V' in dct_j.keys():
dct_j['V'][edge_k] = 0
else:
dct_j['V'] = {edge_k: 0}
i += 1
def test_astar_undirected(self):
GG = self.XG.to_undirected()
GG['y']['v']['weight'] = 2
assert nx.astar_path(GG, 's', 'v') == ['s', 'x', 'u', 'v']
assert nx.astar_path_length(GG, 's', 'v') == 8
def test_astar_undirected(self):
GG=self.XG.to_undirected()
GG['y']['v']['weight'] = 2
assert nx.astar_path(GG,'s','v')==['s', 'x', 'u', 'v']
assert nx.astar_path_length(GG,'s','v')==8
def getStaticDuration(self, startNodeId, nextNodeId):
return float(
nx.astar_path_length(graphService.G, startNodeId, nextNodeId))
def test_astar_MultiGraph():
graph = nx.MultiGraph()
e=[('a','b',0.3),('b','c',0.9),('a','c',0.5),('c','d',1.2),('c','e',1.6),('b','e',0.7),('d','e', 1.3)]
graph.add_weighted_edges_from(e)
assert nx.astar_path_length(graph,'a','e') == 1
lon1 = math.radians(graph.node[a]["lon"])
lat2 = math.radians(graph.node[b]["lat"])
lon2 = math.radians(graph.node[b]["lon"])
return gis.distance(lat1, lon1, lat2, lon2)
print("Remove short edges")
items, edges_removed, violates_triangle_inequality = 0, 0, 0
for node in graph.nodes_iter():
if graph.degree(node) == 2:
weight = 0
neighbours = dict(graph[node])
for n in neighbours:
weight += graph[node][n]["weight"]
if weight <= distance_threshold:
a, b = list(neighbours)
dist = astar_path_length(graph, a, b, heuristic=heuristic)
if dist < weight:
violates_triangle_inequality += 1
else:
# Only look at first type
type = graph[node][a]["type"]
graph.remove_edge(node, a)
graph.remove_edge(node, b)
graph.add_edge(a, b, weight=weight, type=type)
edges_removed += 2
items += 1
print("{0} items processed".format(items), end="\r")
print("{0} items processed".format(items))
print("Deleted {0} edges".format(edges_removed))
print("Kept {0} edges to preserve triangle inequality".format(violates_triangle_inequality))
def test_astar_multigraph(self):
G = nx.MultiDiGraph(self.XG)
G.add_weighted_edges_from((u, v, 1000) for (u, v) in list(G.edges()))
assert nx.astar_path(G, "s", "v") == ["s", "x", "u", "v"]
assert nx.astar_path_length(G, "s", "v") == 9
# Move Right (w = 1)
if j + 1 < n and mat[i][j + 1] != 1:
G.add_edge(str((i * n) + j), str((i * n) + (j + 1)), weight=1)
# Set the start timestamp
start_time = time.time()
# A* algorithm with the specified heuristic
# Part A: (0,0) --- (23,24)
print('------------ Part A ------------')
path = nx.astar_path(G, str(0 * n + 0), str(23 * n + 24), heuristic)
for cell in path:
print('(' + str(int(cell) // n) + ',' + str(int(cell) % m) + ')')
print('Minimum Cost:',
nx.astar_path_length(G, str(0), str(23 * n + 24), heuristic))
# Set the end timestamp
end_time = time.time()
# Measure execution time
print('Execution Time:', (end_time - start_time) * 1000, 'Milliseconds')
# Set the start timestamp
start_time = time.time()
# Part B: (17,1) --- (17,29)
print('------------ Part B ------------')
path = nx.astar_path(G, str(17 * n + 1), str(17 * n + 29), heuristic)
for cell in path:
print('(' + str(int(cell) // n) + ',' + str(int(cell) % m) + ')')
def astar(g, s, t, weight='weight'):
path = nx.astar_path(g, s, t, heuristic, weight=weight)
dist = nx.astar_path_length(g, s, t, heuristic, weight=weight)
return dist, path
z = 0
for x in V:
if Square[x[1], z] == Inf:
Square[x[1], z] = x[0]
else:
z = z + 1
Square[x[1], z] = x[0]
# print(Square[0])
Square = Square.reshape(10, 10, 10)
# print(Square[0])
for i in range(len(V)):
add_vertex((V[i][0]))
for i in range(len(E)):
add_edge(E[i][0], E[i][1], E[i][2])
for i in range(vertices_no):
for j in range(vertices_no):
if graph[i][j] == 0:
graph[i][j] = Inf
start = datetime.now()
g = nx.Graph()
for edge in range(len(E)):
g.add_edge(E[edge, 0], E[edge, 1], weight=E[edge, 2])
path = nx.astar_path(g, 0, 99, heuristic)
pl = nx.astar_path_length(g, 0, 99)
print(path)
print(pl)
#print(E[0])
print(datetime.now() - start)
def test_astar_w1(self):
G=nx.DiGraph()
G.add_edges_from([('s','u'), ('s','x'), ('u','v'), ('u','x'), ('v','y'), ('x','u'), ('x','v'), ('x','y'), ('y','s'), ('y','v')])
assert nx.astar_path(G,'s','v')==['s', 'u', 'v']
assert nx.astar_path_length(G,'s','v')== 2
def test_astar_undirected(self):
GG=self.XG.to_undirected()
assert nx.astar_path(GG,'s','v')==['s', 'x', 'u', 'v']
assert nx.astar_path_length(GG,'s','v')==8
def test_astar_directed(self):
assert nx.astar_path(self.XG, "s", "v") == ["s", "x", "u", "v"]
assert nx.astar_path_length(self.XG, "s", "v") == 9
def test_astar_undirected2(self):
XG3 = nx.Graph()
edges = [(0, 1, 2), (1, 2, 12), (2, 3, 1), (3, 4, 5), (4, 5, 1), (5, 0, 10)]
XG3.add_weighted_edges_from(edges)
assert nx.astar_path(XG3, 0, 3) == [0, 1, 2, 3]
assert nx.astar_path_length(XG3, 0, 3) == 15
pairs = []
for line in lines:
pairs.append([int(x) for x in line.split(" ")])
print(pairs)
pairs.pop(0)
# Create graph
G = nx.DiGraph()
# Unique nodes
flat_list = [val for sublist in pairs for val in sublist]
uniq_node = list(set(flat_list))
for node in range(1, max(uniq_node)):
G.add_node(node)
# Place edges
for pair in pairs:
G.add_edge(pair[0], pair[1])
# Compute shortest paths
results = []
for node in range(1, max(uniq_node) + 1):
try:
shortest = nx.astar_path_length(G, 1, node)
except nx.exception.NetworkXNoPath:
shortest = -1
results.append(shortest)
print(" ".join([str(x) for x in results]))
for eg in gp.edges(data=True):
if eg[0] in cost_dict:
if eg[1] in cost_dict[eg[0]]:
eg[2]['negative_weight'] = -1 * eg[2]['weight']
idcs = np.random.choice(len(gp.nodes()), int(5 * 5 * 0.2), replace=False)
# print idcs
# スタート・ゴール・障害物を設定する
st, gl, obs = (0, 0), (30, 60), [list(gp.nodes())[i] for i in idcs[2:]]
# st, gl, obs = (0,0), (np.random.randint(g_dim[0]),np.random.randint(g_dim[1])), [list(gp.nodes())[i] for i in idcs[2:]]
obs = [list(gp.nodes())[i] for i in idcs[2:]]
path = nx.astar_path(gp, st, gl, cost)
length = nx.astar_path_length(gp, st, gl, cost)
path2 = nx.johnson(gp, weight='negative_weight')
print('johnson: {0}'.format(path2[st][gl]))
for p in path2[st][gl]:
if gp.node[p].get('color', '') == 'black':
print('Invalid path')
continue
gp.node[p]['color'] = 'orange'
previous_nd = st
longest_length = 0
for nd in path2[st][gl][:]:
if not (previous_nd == st and nd == st):
# longest_length += cost_dict[previous_nd][nd]
def test_astar_directed(self):
assert nx.astar_path(self.XG,'s','v')==['s', 'x', 'u', 'v']
assert nx.astar_path_length(self.XG,'s','v')==9
# ------ A * search ------
import networkx as nx
import matplotlib.pyplot as plt
def dist(a, b):
(x1, y1) = a
(x2, y2) = b
return ((x1 - x2)**2 + (y1 - y2)**2)**0.5
G = nx.grid_graph(dim=[3, 3]) # nodes are two-tuples (x,y)
nx.set_edge_attributes(G, {e: e[1][0] * 2 for e in G.edges()}, "cost")
path = nx.astar_path(G, (0, 0), (2, 2), heuristic=dist, weight="cost")
length = nx.astar_path_length(G, (0, 0), (2, 2), heuristic=dist, weight="cost")
print('Path:')
print(path)
print("Path length: ")
print(length)
pos = nx.spring_layout(G)
nx.draw(G, pos, with_labels=True, node_color="#f86e00")
edge_labels = nx.get_edge_attributes(G, "cost")
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
plt.show()
Lugoj=(165, 379), Mehadia=(168, 339), Neamt=(406, 537),
Oradea=(131, 571), Pitesti=(320, 368), Rimnicu=(233, 410),
Sibiu=(207, 457), Timisoara=(94, 410), Urziceni=(456, 350),
Vaslui=(509, 444), Zerind=(108, 531))
g = nx.Graph()
node_labels = {}
for city, _ in romania_plot_positions.items():
g.add_node(city, position=romania_plot_positions[city])
node_labels[city]=city
for city, neighborhood in romania_neighbours.items():
for neighbour, distance in neighborhood.items():
g.add_edge(city, neighbour, weight=distance)
node_label_positions = {k:[v[0],v[1]] for k,v in romania_plot_positions.items()}
plt.figure(figsize=(18,13))
nx.draw(g, pos = node_label_positions, node_color='white')
drawn_labels = nx.draw_networkx_labels(g, pos=node_label_positions, labels=node_labels)
def distance(a, b):
(x1, y1) = g.nodes[a]['position']
(x2, y2) = g.nodes[b]['position']
return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
print(nx.greedy_path(g,'Arad','Bucharest',heuristic= distance))
print(nx.astar_path(g,'Arad','Bucharest',heuristic= distance))
nx.astar_path_length(g,'Arad','Bucharest',heuristic= distance, weight='weight')
#plt.show()
def test_astar_directed(self):
assert nx.astar_path(self.XG, 's', 'v') == ['s', 'x', 'u', 'v']
assert nx.astar_path_length(self.XG, 's', 'v') == 9
def transmission_probability(traj):
v_list = [[]]
for row1, row2 in pairwise(traj.values):
d = haversine(row1[0], row1[1], row2[0], row2[1])
row_v_list = []
for idx1, nd1 in enumerate(row1[-2]):
temp_list = []
for idx2, nd2 in enumerate(row2[-2]):
try: # nd1 and nd2 are not connected
if pd.notnull(nd1) and pd.notnull(nd2):
temp_list.append(d / nx.astar_path_length(
roadnetwork, nd1, nd2, weight='distance'))
elif pd.notnull(nd1):
nd2_back_node = edge_dict[row2[-3][idx2]][0]
nd2_back_node_cor = vertice_dict[nd2_back_node]
temp_list.append(d / (nx.astar_path_length(
roadnetwork, nd1, nd2_back_node,
weight='distance') + np.sqrt(
np.abs(
haversine(row2[0], row2[1],
nd2_back_node_cor[0],
nd2_back_node_cor[1])**2 -
row2[-4][idx2]**2))))
elif pd.notnull(nd2):
nd1_forward_node = edge_dict[row1[-3][idx1]][1]
nd1_forward_node_cor = vertice_dict[nd1_forward_node]
temp_list.append(
d /
(nx.astar_path_length(roadnetwork,
nd1_forward_node,
nd2,
weight='distance') +
np.sqrt(
np.abs(
haversine(row1[0], row1[1],
nd1_forward_node_cor[0],
nd1_forward_node_cor[1])**2 -
row1[-4][idx1]**2))))
else:
nd1_forward_node = edge_dict[row1[-3][idx1]][1]
nd1_forward_node_cor = vertice_dict[nd1_forward_node]
nd2_back_node = edge_dict[row2[-3][idx2]][0]
nd2_back_node_cor = vertice_dict[nd2_back_node]
temp_list.append(
d /
(nx.astar_path_length(roadnetwork,
nd1_forward_node,
nd2_back_node,
weight='distance') +
np.sqrt(
np.abs(
haversine(row1[0], row1[1],
nd1_forward_node_cor[0],
nd1_forward_node_cor[1])**2 -
row1[-4][idx1]**2)) +
np.sqrt(
np.abs(
haversine(row2[0], row2[1],
nd2_back_node_cor[0],
nd2_back_node_cor[1])**2 -
row2[-4][idx2]**2))))
except:
temp_list.append(0)
row_v_list.append(temp_list)
v_list.append(row_v_list)
return v_list
def manhattan(self, startNode, endNode):
source = startNode.node.nodeId
target = endNode.node.nodeId
if nx.has_path(graphService.G, source, target) == False:
return 3600
return float(nx.astar_path_length(graphService.G, source, target))
def test_astar_directed(self):
assert_equal(nx.astar_path(self.XG, 's', 'v'), ['s', 'x', 'u', 'v'])
assert_equal(nx.astar_path_length(self.XG, 's', 'v'), 9)
def main(argv):
filename = None
gritter_range = None
heuristic = None
show_graph = False
show_data = False
timed_run = False
try:
opts, args = getopt.getopt(argv, "i:r:h:dvt")
if not opts:
usage()
sys.exit()
except getopt.GetoptError:
usage()
sys.exit()
for opt, arg in opts:
if opt == "-i":
filename = arg
elif opt == "-r":
gritter_range = float(arg)
elif opt == "-h":
heuristic = arg
elif opt == "-v":
show_graph = True
elif opt == "-d":
show_data = True
elif opt == "-t":
timed_run = True
graph = nx.read_weighted_edgelist(filename, delimiter=',', nodetype=int)
nx.set_node_attributes(
graph,
dict(zip(list(graph.nodes), [nx.astar_path_length(graph, 0, node) for node in graph.nodes])),
"distance")
search = None
if heuristic == "vns":
search = VNS(graph, gritter_range)
elif heuristic == "tabu":
search = Tabu(graph, gritter_range)
start = timer()
result = search.run()
end = timer()
if show_data:
print("\nfound solution: ", result.nodes)
print("\ncost: ", result.function())
if timed_run:
print("\nelapsed:", end - start)
if show_graph:
node_list = result.nodes
cycles = []
current_cycle = []
for i in range(1, len(node_list)):
current_cycle.append((node_list[i - 1], node_list[i]))
if node_list[i] == 0:
cycles.append(current_cycle)
current_cycle = []
unvisited = list(graph.edges)
for index, cycle in enumerate(cycles):
nx.draw_networkx_nodes(graph, pos=nx.circular_layout(graph))
nx.draw_networkx_nodes(graph, pos=nx.circular_layout(graph), nodelist=[0], node_color='b')
nx.draw_networkx_edge_labels(graph, pos=nx.circular_layout(graph), edge_labels=nx.get_edge_attributes(graph, "weight"))
unvisited = [edge for edge in unvisited if tuple(sorted(edge)) not in cycle and tuple(reversed(sorted(edge))) not in cycle]
nx.draw_networkx_edges(graph, pos=nx.circular_layout(graph))
nx.draw_networkx_edges(graph, pos=nx.circular_layout(graph), edgelist=unvisited, width=5)
nx.draw_networkx_edges(graph, pos=nx.circular_layout(graph), edgelist=cycle, width=5, edge_color='y')
full_screen()
plt.show()
def test_astar_undirected(self):
GG = self.XG.to_undirected()
assert nx.astar_path(GG, 's', 'v') == ['s', 'x', 'u', 'v']
assert nx.astar_path_length(GG, 's', 'v') == 8
import csv
import networkx as nx
G = nx.Graph()
with open('in.txt') as input_file:
for row in csv.reader(input_file, delimiter=")"):
G.add_nodes_from(row)
G.add_edges_from([row])
print(nx.astar_path_length(G, source='YOU', target='SAN') - 2)
def astar(start, terminal):
cost = 0
path = nx.astar_path(G, start, terminal)
cost = nx.astar_path_length(G, start, terminal)
def construct_path(self) -> None:
"""Constructs the flight path using the A* Algorithm.
This is done in this order:
1. Construct path of just waypoints
2. Check to see if it is possible to do offaxis and drop while flying path
3. If not generate most optimal after
"""
off_check = self.off_axis == None
drop_check = self.drop == None
path = []
# Generate waypoints
for i in range(len(self.waypoints) - 1):
seg = []
seg.extend(
nx.astar_path(self.graph, *self.waypoints[i].point.coords,
*self.waypoints[i + 1].point.coords, heuristic))
print(i, seg, self.graph.has_edge(seg[0], seg[len(seg) - 1]))
if i == 0:
path.extend(seg)
else:
path.extend(seg[1:])
# Check path to see if passes through offaxis/drop
ring = LineString(path)
if self.off_axis != None:
off_point = ring.interpolate(ring.project(self.off_axis))
off_dis = off_point.distance(self.off_axis)
if off_point.z / off_dis > 2.74:
off_check = True
self.off_axis_optimal = off_point
if self.drop != None:
drop_point = ring.interpolate(ring.project(self.drop))
drop_dis = drop_point.distance(self.drop)
if drop_dis < 15:
drop_check = True
self.drop = drop_point
seg = []
# Generate remaining POIs
if not off_check and not drop_check:
off_drop_dis = nx.astar_path_length(self.graph,
path[len(path) - 1],
*self.off_axis_optimal.coords,
heuristic)
drop_off_dis = nx.astar_path_length(self.graph,
path[len(path) - 1],
*self.drop.coords, heuristic)
if off_drop_dis < drop_off_dis:
seg.extend(
nx.astar_path(self.graph, path[len(path) - 1],
*self.off_axis_optimal.coords,
heuristic)[1:])
seg.extend(
nx.astar_path(self.graph, *self.off_axis_optimal.coords,
*self.drop.coords, heuristic)[1:])
else:
seg.extend(
nx.astar_path(self.graph, path[len(path) - 1],
*self.drop.coords, heuristic)[1:])
seg.extend(
nx.astar_path(self.graph, *self.drop.coords,
*self.off_axis_optimal.coords,
heuristic)[1:])
elif not off_check:
seg.extend(
nx.astar_path(self.graph, path[len(path) - 1],
*self.off_axis_optimal.coords, heuristic)[1:])
elif not drop_check:
seg.extend(
nx.astar_path(self.graph, path[len(path) - 1],
*self.drop.coords, heuristic)[1:])
path.extend(seg)
self.path = path
