import math
import os

import matplotlib.pyplot as plt
import numpy as np
from gen import (
    Chromosome,
    GARunConfig,
    genetic_algorithm,
    initialize_random_population,
    inversion_mutation_fn,
    partially_mapped_crossover_fn,
    plot_fitness_history,
    plot_tour,
    swap_mutation_fn,
)

best = [
    0,
    29,
    9,
    27,
    18,
    14,
    5,
    17,
    13,
    30,
    20,
    34,
    15,
    22,
    23,
    24,
    26,
    33,
    32,
    7,
    12,
    37,
    11,
    2,
    6,
    16,
    35,
    1,
    36,
    3,
    28,
    21,
    8,
    31,
    4,
    10,
    25,
    19,
]


cities = set()
with open("data.txt", "r") as file:
    for line in file:
        # x и y поменяны местами в визуализациях в методичке
        _, y, x = line.split()
        cities.add((float(x), float(y)))
cities = list(cities)


def euclidean_distance(city1, city2):
    return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)


def build_fitness_function(cities):
    def fitness_function(chromosome: Chromosome) -> float:
        return sum(
            euclidean_distance(cities[chromosome[i]], cities[chromosome[i + 1]])
            for i in range(len(chromosome) - 1)
        ) + euclidean_distance(cities[chromosome[0]], cities[chromosome[-1]])

    return fitness_function


fitness_function = build_fitness_function(cities)

# Сохраняем лучшую особь за всё время
fig = plt.figure(figsize=(7, 7))
fig.suptitle(
    f"Лучший возможный маршрут. " f"Длина: {fitness_function(best):.4f}",
    fontsize=14,
    y=0.95,
)

# Рисуем лучший маршрут в поколении
ax = fig.add_subplot(1, 1, 1)
plot_tour(cities, best, ax)
filename = f"best_possible.png"
path_png = os.path.join("", filename)
fig.savefig(path_png, dpi=150, bbox_inches="tight")
plt.close(fig)