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import osmnx as ox
import networkx as nx
import parameters as params
from src.helper.debug_printer import debug_print
from src.helper.display_graph import display_graph
from src.helper.duplicate_removal import remove_duplicates
from src.helper.export_import_yaml import save_paths_to_yaml
arrondissements = [
'Ahuntsic-Cartierville',
'Anjou',
'Côte-des-Neiges–Notre-Dame-de-Grâce',
'Lachine',
'LaSalle',
'Le Plateau-Mont-Royal',
'Le Sud-Ouest',
'L\'Île-Bizard–Sainte-Geneviève',
'Mercier–Hochelaga-Maisonneuve',
'Montréal-Nord',
'Outremont',
'Pierrefonds-Roxboro',
'Rivière-des-Prairies–Pointe-aux-Trembles',
'Rosemont–La Petite-Patrie',
'Saint-Laurent',
'Saint-Léonard',
'Verdun',
'Ville-Marie',
'Villeray–Saint-Michel–Parc-Extension'
] # first list, we changed its order manually to make a "smart path" for the drone
connection_order = [
'Rivière-des-Prairies–Pointe-aux-Trembles',
'Montréal-Nord',
'Saint-Léonard',
'Anjou',
'Mercier–Hochelaga-Maisonneuve',
'Rosemont–La Petite-Patrie',
'Villeray–Saint-Michel–Parc-Extension',
'Outremont',
'Le Sud-Ouest',
'Ville-Marie',
'L\'Île-Bizard–Sainte-Geneviève',
'Verdun',
'LaSalle',
'Côte-des-Neiges–Notre-Dame-de-Grâce',
'Le Plateau-Mont-Royal',
'Saint-Laurent',
'Ahuntsic-Cartierville',
'Pierrefonds-Roxboro',
'Lachine'
]
def find_circuit(G_undirected, debug_mode):
if nx.is_eulerian(G_undirected):
G_eulerian = G_undirected
else:
G_eulerian = nx.eulerize(G_undirected)
return nx.eulerian_circuit(G_eulerian), G_eulerian
def generate_graph(name, debug_mode=False):
"""
permet de charger un graphe
d'une localisation a partir du nom de cette derniere.
"""
G = ox.graph_from_place(name, network_type='drive')
G = remove_duplicates(G, False)
G = ox.project_graph(G)
return G
def total_length_of_circuit(G, circuit):
return sum(G[u][v][0]['length'] for u, v in circuit) / 1000
def total_length_of_arrondissement(G):
distance = 0
for u, v, data in G.edges(data=True):
distance += data["length"]
return distance / 1000
def process_graphs(name, debug_mode):
"""
Création des paths
"""
paths = {}
for i in arrondissements:
sub_name = i + ", Montréal, Québec, Canada"
debug_print(f"Génération : {sub_name}", debug_mode)
sub_graph = generate_graph(sub_name, debug_mode)
G_undirected = sub_graph.to_undirected()
circuit, _ = find_circuit(G_undirected, debug_mode)
c = [edge for edge in circuit]
# print(f"distance kilometre du quartier = {total_length_of_arrondissement(G_undirected):.2f}km")
# print(f"distance kilometre du drone = {total_length_of_circuit(G_eulerian, circuit):.2f}km")
# debug_print(f"Nombre de Route du Path : {len(c)}", debug_mode)
paths[i] = c
del sub_graph # to save as much memory as possible
del circuit
return paths
def connect_circuits(G, path_dict, arrondissement_order=connection_order):
"""
La fonction trouve le chemin le plus cours entre 2 arrondissements
"""
connections = []
for i in range(len(arrondissement_order) - 1):
from_name = arrondissement_order[i]
to_name = arrondissement_order[i + 1]
from_path = path_dict[from_name]
to_path = path_dict[to_name]
start_node = from_path[0][0]
end_node = from_path[-1][1]
next_start_node = to_path[0][0]
try:
path = nx.shortest_path(G, source=end_node, target=next_start_node, weight='length')
connections.append((from_name, to_name, path))
except nx.NetworkXNoPath:
print(f"Aucun chemin trouvé entre {from_name} et {to_name}")
connections.append((from_name, to_name, None))
return connections
def final_path(graph, paths, connection_order=connection_order):
full_path = []
connections = connect_circuits(graph, paths)
for i, arr_name in enumerate(connection_order):
full_path.extend(paths[arr_name])
if i < len(connection_order) - 1:
next_arr = connection_order[i+1]
for conn in connections:
if conn[0] == arr_name and conn[1] == next_arr:
node_path = conn[2]
for j in range(len(node_path) - 1):
full_path.append((node_path[j], node_path[j+1]))
break
return full_path
def distance_total(G, paths, connections):
G_undir = G.to_undirected()
total_distance = 0.0
used_edges = set()
for arrondissement, edges in paths.items():
for u, v in edges:
key = (min(u, v), max(u, v))
used_edges.add(key)
total_distance += G_undir[u][v][0]['length']
for from_arr, to_arr, node_path in connections:
for i in range(len(node_path) - 1):
u = node_path[i]
v = node_path[i+1]
key = (min(u, v), max(u, v))
used_edges.add(key)
total_distance += G_undir[u][v][0]['length']
unused_distance = 0.0
for u, v, data in G_undir.edges(data=True):
key = (min(u, v), max(u, v))
if key not in used_edges:
unused_distance += data['length']
total_distance /= 1000
unused_distance /= 1000
return total_distance, unused_distance
def distance_optimal(G):
"""
Calcule la somme des longueurs de toutes les routes dans les arrondissements
"""
covered_edges = set()
for i in arrondissements:
sub_name = i + ", Montréal, Québec, Canada"
sub_graph = generate_graph(sub_name)
for u, v, data in sub_graph.edges(data=True):
covered_edges.add((min(u, v), max(u, v)))
del sub_graph
G_undir = G.to_undirected()
total = 0.0
for u, v in covered_edges:
total += G_undir[u][v][0]['length']
return total / 1000
def postier_chinois_process_v1(G, debug_mode):
name = "Montréal, Québec, Canada"
# initially, we ran find circuit on the whole graph
# => processes all suburbs instead of the whole graph
paths = process_graphs(name, debug_mode)
# it was our closest attempt to get an optimal answer but way to long (ran for more than 12h and did not finish)
finalPath = final_path(G, paths)
# Save Path with YML
save_paths_to_yaml(paths, "paths-PostierChinoisV1.yml")
return paths, finalPath
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