def plan_diff(self, lambda_1, lambda_2, length):
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(3, lambda_1, lambda_2)
self.planner.update_edge_weight()
path = self.planner.difference_pathing(start_node, length)
self.plot_graph_route_over_cost(path)
def plan_random(self, branch_per_expans, lambda_1, lambda_2, length):
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.update_edge_weight()
end, graph, dict = self.planner.random_paths_unique_random_queue(start_node, length, 300)
path_converted = self.planner.path_converter(dict, graph, 0, end)
self.plot_graph_route_over_cost(path_converted)
def plan_dijkstras_obj_sum_test(self, branch_per_expans, lambda_1, lambda_2, length, paths_to_consider):
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(branch_per_expans, lambda_1, lambda_2)
self.planner.update_edge_weight()
sorted_end_list, graph, dict = self.planner.dijkstras_obj_sum_test(start_node, length, paths_to_consider)
path_converted = self.planner.path_converter(dict, graph, 0, sorted_end_list[0])
self.plot_graph_route_over_cost(path_converted)
def plan_brute_force(self, lambda_1, lambda_2, length):
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(4, lambda_1, lambda_2)
self.planner.update_edge_weight()
sorted_end_list, graph, dict = self.planner.path_brute_force(start_node, length)
path_converted = self.planner.path_converter(dict, graph, 0, sorted_end_list)
self.plot_graph_route_over_cost(path_converted)
def grid_weighting_nodes(self, meters, n):
self.planner = randomplanner.planner(self.kalman.env)
coords = self.env.grid_weight_nodes(self.north, self.south, self.east, self.west, meters, n)
nc = ox.get_node_colors_by_attr(self.env.graph, 'grid_weight', cmap='plasma', num_bins=20)
fig, ax = ox.plot_graph(self.env.G, fig_height=6, node_color=nc, node_zorder=2,
edge_color='#dddddd', use_geom=True, show=False, close=False)
for lat, long in coords:
print("(" + str(lat) + ", " + str(long) +")")
ax.scatter(long, lat, c='red', alpha=0.2, s=2)
ax.set_title("Weighted nodes from Grid")
plt.show()
def plan_difference(self, branch_per_expans, lambda_1, lambda_2, length, paths_to_consider,
from_data=False):
if from_data:
start_node = ox.get_nearest_node(self.graph, self.kalman.last_loc)
else:
print(self.env.origin)
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(branch_per_expans, lambda_1, lambda_2)
self.planner.update_edge_weight()
path = self.planner.difference_pathing(start_node, length)
self.plot_graph_route_over_cost(path)
def plan_random_cost_per_node(self, branch_per_expans, lambda_1, lambda_2, length, paths_to_consider, from_data=False):
if from_data:
start_node = ox.get_nearest_node(self.unproj, self.kalman.last_loc)
else:
print(self.env.origin)
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(branch_per_expans, lambda_1, lambda_2)
self.planner.update_edge_weight()
sorted_end_list, graph, dict = self.planner.random_paths_random_queue(start_node, length, paths_to_consider)
path_converted = self.planner.path_converter(dict, graph, 0, sorted_end_list)
self.plot_graph_route_over_cost(path_converted)
def timer_test2(self):
times = []
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
print("running")
for x in range(200):
self.kalman.wipe_initilize()
self.random_kalman(200, 20000, 50000)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(2, 0.1, 0.4)
self.planner.update_edge_weight()
start = time.time()
sorted_end_list, graph, dict, z = self.planner.dij_timer(start_node, 21000, 200, 0.5)
path_converted = self.planner.path_converter(dict, graph, 0, sorted_end_list[0])
times.append(time.time() - start)
def plan_dijkstras_edge_length(self, branch_per_expans, lambda_1, lambda_2, length, paths_to_consider, from_data=False, filename=None):
if from_data:
start_node = ox.get_nearest_node(self.unproj, self.kalman.last_loc)
else:
print(self.env.origin)
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(branch_per_expans, lambda_1, lambda_2)
self.planner.update_edge_weight()
start = time.time()
sorted_end_list, graph, dict = self.planner.dijkstras_edge_lengths(start_node, length, paths_to_consider)
print(time.time() - start)
path_converted = self.planner.path_converter(dict, graph, 0, sorted_end_list[0])
self.plot_graph_route_over_cost(path_converted, filename=filename)
def plan_dijkstras1(self, branch_per_expans, lambda_1, lambda_2, length, paths_to_consider, from_data=False, filename=None):
if from_data:
start_node = ox.get_nearest_node(self.unproj, self.kalman.last_loc)
else:
print(self.env.origin)
start_node = ox.get_nearest_node(self.unproj, self.env.origin)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(branch_per_expans, lambda_1, lambda_2)
self.planner.update_edge_weight()
start = time.time()
route = self.planner.dijkstras1(start_node, length, paths_to_consider)
print("time was " + str(time.time() - start))
# nc = ox.get_node_colors_by_attr(self.kalman.env.graph, 'pol', cmap='plasma', num_bins=20)
# ns = [50 if node in self.updated_nodes else 8 for node in self.kalman.env.graph.nodes()]
# fig, ax = ox.plot_graph(self.kalman.env.graph, fig_height=6, node_color=nc, node_size=ns, node_zorder=2, edge_color='#dddddd', use_geom=True)
self.plot_graph_route_over_cost(route, filename=filename)
def grid_weighting_nodes_dist(self, meters, n, dist):
self.planner = randomplanner.planner(self.kalman.env)
coords = self.planner.grid_weight_nodes_max_dist(self.north, self.south, self.east, self.west, meters, n, dist)
nc = ox.get_node_colors_by_attr(self.planner.graph, 'grid_weight', cmap='plasma', num_bins=20)
fig, ax = ox.plot_graph(self.planner.env.G, fig_height=6, node_color=nc, node_zorder=2,
edge_color='#dddddd', use_geom=True, show=False, close=False)
for lat, long in coords:
print("(" + str(lat) + ", " + str(long) +")")
ax.scatter(long, lat, c='red', alpha=0.3, s=3)
ax.set_title("Weighted nodes from Grid")
for node in self.planner.graph.nodes():
lat = self.planner.env.G.node[node]['lat']
long = self.planner.env.G.node[node]['long']
data = self.planner.graph.node[node]['grid_weight']
print(str(long) + ", " + str(lat) + ", " + str(data))
ax.annotate(long, lat, str(data))
plt.show()
def timer_test(self):
times = []
#start_node = ox.get_nearest_node(self.unproj, self.env.origin)
start_node = self.randomStartNode()
self.kalman.wipe_initilize()
self.random_kalman(200, 20000, 50000)
self.planner = randomplanner.planner(self.kalman.env)
self.planner.set_config(2, 0.1, 0.4)
self.planner.update_edge_weight()
print("running")
with open('testingdeleteme.csv', 'w', newline='') as csvfile:
spamwriter = csv.writer(csvfile, dialect='excel')
spamwriter.writerow(['time 2 expanc', '2 expanc objective', '2 count', 'time 3 expanc', '3 expanc objective', '3 count', 'time 4 expanc', '4 expanc objective', '4 count', 'time 2 expanc dij', '2 expanc objective', '2 dij count', 'time 3 expanc dij', '3 expanc objective', '3 dij count', 'time 4 expanc dij', '4 expanc objective', '4 dij count'])
distanceForRoute = 21000
for x in range(5):
start_node = self.randomStartNode()
self.kalman.wipe_initilize()
self.random_kalman(200, 20000, 50000)
# self.planner.set_config(2, 0.1, 0.4)
# start2 = time.time()
# y, objective2, count2 = self.planner.random_paths_unique_random_queue(start_node, distanceForRoute, 200, loopcounting=True)
# end2 = time.time()
# self.planner.set_config(3, 0.1, 0.4)
# start3 = time.time()
#
# y, objective3, count3 = self.planner.random_paths_unique_random_queue(start_node, distanceForRoute, 200, loopcounting=True)
# end3 = time.time()
# self.planner.set_config(4, 0.1, 0.4)
# start4 = time.time()
# y, objective4, count4 = self.planner.random_paths_unique_random_queue(start_node, distanceForRoute, 200, loopcounting=True)
# end4 = time.time()
start2= ""
start3=""
start4=""
end2=""
end3=""
end4=""
objective2=""
objective3=""
objective4=""
count2=""
count3=""
count4=""
#self.kalman.set_random_loc()
self.planner.set_config(2, 0.1, 0.4)
start5 = time.time()
y, objective5, count5 = self.planner.dijkstras1(start_node, distanceForRoute, 200, loopcounting=True)
end5 = time.time()
self.planner.set_config(3, 0.1, 0.4)
start6 = time.time()
y, objective6, count6 = self.planner.dijkstras1(start_node, distanceForRoute, 200, loopcounting=True)
end6 = time.time()
self.planner.set_config(4, 0.1, 0.4)
start7 = time.time()
y, objective7, count7 = self.planner.dijkstras1(start_node, distanceForRoute, 200, loopcounting=True)
end7 = time.time()
#spamwriter.writerow([end2-start2, objective2, count2, end3-start3, objective3, count3, end4-start4, objective4, count4, end5-start5, objective5, count5, end6-start6, objective6, count6, end7-start7, objective7, count7])
spamwriter.writerow([end2, objective2, count2, end3, objective3, count3, end4, objective4, count4, end5-start5, objective5, count5, end6-start6, objective6, count6, end7-start7, objective7, count7])
print(x)
print(x)
print(x)
print(times)