from time import time as t
import numpy as np
import matplotlib.pyplot as plt
from aco.ant_colony import ant_colony_opt

class TSPSolver:
    def __init__(self, problem_instance):
        self.duration = np.inf
        self.found_length = np.inf
        self.solved = False
        self.problem_instance = problem_instance
        self.solution = None

    def compute_solution(self, verbose=True, return_value=True):
        self.solved = False
        if verbose:
            print(f"### solving with 'C++ ant colony optimization' ####")
        start_time = t()
        self.solution = ant_colony_opt(self.problem_instance)
        if not self.check_if_solution_is_valid():
            print(f"Error the solution of 'C++ ant colony optimization'"
                  f" for problem {self.problem_instance.name} is not valid")
            if return_value:
                return False

        end_time = t()
        self.duration = np.around(end_time - start_time, 3)
        self.solved = True
        self.evaluate_solution()
        self._gap()
        if return_value:
            return self.solution

    def plot_solution(self):
        assert self.solved, "You can't plot the solution, you need to compute it first!"
        plt.figure(figsize=(8, 8))
        self._gap()
        plt.title(f"{self.problem_instance.name} solved with 'C++ ant colony optimization'"
                  f" solver, gap {self.gap}")
        ordered_points = self.problem_instance.points[self.solution]
        plt.plot(ordered_points[:, 1], ordered_points[:, 2], 'b-')
        plt.show()

    def check_if_solution_is_valid(self):
        rights_values = np.sum(
            [self.check_validation(i, self.solution[:-1]) for i in
             np.arange(self.problem_instance.nPoints)])
        return rights_values == self.problem_instance.nPoints

    def check_validation(self, node, solution):
        if np.sum(solution == node) == 1:
            return 1
        else:
            return 0

    def evaluate_solution(self, return_value=False):
        total_length = 0
        starting_node = self.solution[0]
        from_node = starting_node
        for node in self.solution[1:]:
            total_length += self.problem_instance.dist_matrix[from_node, node]
            from_node = node

        self.found_length = total_length
        if return_value:
            return total_length

    def pass_and_check_if_solution_is_valid(self, solution):
        rights_values = np.sum(
            [self.check_validation(i, solution[:-1]) for i in
             np.arange(self.problem_instance.nPoints)])
        return rights_values == self.problem_instance.nPoints

    def _gap(self):
        self.evaluate_solution(return_value=False)
        self.gap = np.round(
            ((self.found_length - self.problem_instance.best_sol) /
             self.problem_instance.best_sol) * 100, 2)