import os
import signal
import json

from model.anneal_model import SimAnneal
from model.my_model import MyModel

def log(msg):
    print('[*] {msg}'.format(msg=msg))

def load_data(file):
    coords = []
    with open(file, "r") as infile:
        line = infile.readline()
        # Skip instance header
        while "NODE_COORD_SECTION" not in line:
            line = infile.readline()

        for line in infile.readlines():
            line = line.replace("\n", "")
            if line and 'EOF' not in line:
                line = [float(x) for x in line.lstrip().split(" ", 1)[1].split(" ") if x]
                coords.append(line)
    return coords

def timeout_handler(signum, frame):
    print("Timeout")
    raise Exception("Timeout")
 
def TSP(tsp_file, model):

    best_solution = []
    fitness_list = []

    coords = load_data(tsp_file)

    # Set timeout
    signal.signal(signal.SIGALRM, timeout_handler)
    signal.alarm(60)

    # Try your algorithm
    try:
        model.init(coords)
        best_solution, fitness_list = model.fit(max_it=100000)
    except Exception as exc: 
        if(str(exc) == "Timeout"):
            log("Timeout -3")
            with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
                outfile.write("-3")
        else:
            print(exc)
            log("Unkown -4")
            with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
                outfile.write("-4")

    # Collect results
    if(len(fitness_list) > 0):
        log("Best: " + str(fitness_list[-1]))

    if (len(best_solution) == 0):
        log("No Answer -1")
        with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
            outfile.write("-1")
    elif (len(best_solution) != len(coords)):
        log("Invalid -2")
        with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
            outfile.write("-2")
    else:
        log("Writing the best solution to file" )
        with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
            outfile.write(str(model.fitness(best_solution)))
            outfile.write("\n")
            outfile.write(", ".join(str(item) for item in best_solution))

        # Write to JSON
        data = {}

        data['nodes'] = []
        for i in range(len(coords)):
            data['nodes'].append({
                'title': str(i),
                'id': i,
                'x': int(coords[i][0]),
                'y': int(coords[i][1])
            })

        data['edges'] = []
        for i in range(len(best_solution)):
            if i == len(best_solution)-1:
                data['edges'].append({
                    'source': best_solution[i],
                    'target': best_solution[0]
                })
            else:
                data['edges'].append({
                    'source': best_solution[i],
                    'target': best_solution[i+1]
                })

        with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.json', 'w') as outfile:
            json.dump(data, outfile)


if __name__ == "__main__":

    for root, _, files in os.walk('./data'):
        if(files):
            for f in files:
                # Get input file name
                tsp_file = str(root) + '/' + str(f)
                log(tsp_file)

                # Your Model
                # model = MyModel()

                # Simulated Annealing
                model = SimAnneal()

                TSP(tsp_file, model)

                print()