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+import osmnx as ox
+import networkx as nx
+import parameters as params
+import yaml
+import itertools
+
+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
+from itertools import combinations
+
+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 eulerization(G):
+ """
+ Eulérise le graphe G (non orienté) en dupliquant des arêtes existantes.
+ Retourne un MultiGraph eulérien.
+ """
+
+ if not nx.is_connected(G):
+ return None
+
+ odd_nodes = [v for v in G.nodes if G.degree(v) % 2 == 1]
+
+ all_pairs = {}
+ for u in odd_nodes:
+ dist, path = nx.single_source_dijkstra(G, u, weight="length")
+ all_pairs[u] = (dist, path)
+
+ H = nx.Graph()
+ for u, v in itertools.combinations(odd_nodes, 2):
+ if v in all_pairs[u][0]:
+ length = all_pairs[u][0][v]
+ H.add_edge(u, v, length=length)
+ else:
+ return None
+
+ pairs = sorted(H.edges(data=True), key=lambda e: e[2]['length'])
+ matched = set()
+ min_matching = []
+ for u, v, d in pairs:
+ if u not in matched and v not in matched:
+ matched.update([u, v])
+ min_matching.append((u, v))
+
+ MG = nx.MultiGraph(G)
+ for u, v in min_matching:
+ path = all_pairs[u][1][v]
+ for i in range(len(path) - 1):
+ MG.add_edge(path[i], path[i+1])
+
+ return MG
+
+def find_circuit(G_undirected, debug_mode):
+ # debug_print(f"Avant eulérisation : {len(G_undirected.edges)} arêtes", debug_mode)
+ if nx.is_eulerian(G_undirected):
+ G_eulerian = G_undirected
+ else:
+ G_eulerian = eulerization(G_undirected)
+ # debug_print(f"Apres eulérisation : {len(G_eulerian.edges)} arêtes", debug_mode)
+ return list(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, G_eulerian = 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")
+
+ 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_v3(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-PostierChinoisV3.yml")
+
+ return paths, finalPath