from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import random, time, math
from libs.clustering import split_tour_across_clusters
from libs.aco import AntColony, total_distance

def generate_cities(nb, max_coords=1000):
    return [random.sample(range(max_coords), 2) for _ in range(nb)]

nb_ville = 2000
max_coords = 1000
nb_truck = 1
max_time = 2
nb_ants = 10

max_time_per_cluster = max_time / nb_truck

start_time_generate = time.time()
cities = [[6734, 1453], [2233, 10], [5530, 1424], [401, 841], [3082, 1644], [7608, 4458], [7573, 3716], [7265, 1268], [6898, 1885], [1112, 2049], [5468, 2606], [5989, 2873], [4706, 2674], [4612, 2035], [6347, 2683], [6107, 669], [7611, 5184], [7462, 3590], [7732, 4723], [5900, 3561], [4483, 3369], [6101, 1110], [5199, 2182], [1633, 2809], [4307, 2322], [675, 1006], [7555, 4819], [7541, 3981], [3177, 756], [7352, 4506], [7545, 2801], [3245, 3305], [6426, 3173], [4608, 1198], [23, 2216], [7248, 3779], [7762, 4595], [7392, 2244], [3484, 2829], [6271, 2135], [4985, 140], [1916, 1569], [7280, 4899], [7509, 3239], [10, 2676], [6807, 2993], [5185, 3258], [3023, 1942]]
#cities[0] = [max_coords/2, max_coords/2]
stop_time_generate = time.time()

start_time_split = time.time()
clusters = split_tour_across_clusters(cities, nb_truck)
stop_time_split = time.time()

for cluster in clusters.values():
    print(len(cluster))
print("\n---- TIME ----")
print("generate cities time: ", stop_time_generate - start_time_generate)
print("split cities time: ", stop_time_split - start_time_split)

# create new figure for annealing paths
plt.figure()
colors = [
    '#1f77b4',  # Bleu moyen
    '#ff7f0e',  # Orange
    '#2ca02c',  # Vert
    '#d62728',  # Rouge
    '#9467bd',  # Violet
    '#8c564b',  # Marron
    '#e377c2',  # Rose
    '#7f7f7f',  # Gris
    '#bcbd22',  # Vert olive
    '#17becf',  # Turquoise
    '#1b9e77',  # Vert Teal
    '#d95f02',  # Orange foncé
    '#7570b3',  # Violet moyen
    '#e7298a',  # Fuchsia
    '#66a61e',  # Vert pomme
    '#e6ab02',  # Jaune or
    '#a6761d',  # Bronze
    '#666666',  # Gris foncé
    '#f781bf',  # Rose clair
    '#999999',  # Gris moyen
]

best_routes = []

for i, cluster_indices in enumerate(clusters.values()):
    # Sélection d'une couleur pour le cluster
    color = colors[i % len(colors)]
    
    # Récupération des coordonnées de la ville
    cluster_cities = [cities[index] for index in cluster_indices]

    # Appel de la fonction AntColony.run
    ant_colony = AntColony(cluster_cities, n_ants=nb_ants, max_time=max_time_per_cluster, alpha=1, beta=5)
    best_route = ant_colony.run()
    best_routes.append((best_route, color))

    print("Total distance for cluster", i, ": ", total_distance(best_route))

# calculate total distance for all clusters
full_total_distance = 0
for route, color in best_routes:
    full_total_distance += total_distance(route)

print("Total distance for all clusters: ", full_total_distance)

for i, (route, color) in enumerate(best_routes):
    x = [city[0] for city in route]
    y = [city[1] for city in route]
    x.append(x[0])
    y.append(y[0])
    plt.plot(x, y, color=color, marker='x', linestyle='-', label=f"Cluster {i}")
    # add title with nb_ville, nb_truck and max_time
    plt.title(f"nb_ville = {nb_ville}, nb_truck = {nb_truck}, max_time = {max_time}")   

plt.show()