#!/usr/bin/env python
import math
import pygame
import random
import time

from car import Car
from genetics import genetic_selection, genetic_reproduction
from maps import map1
from params import CELL_COLOR, screen



#https://medium.com/intel-student-ambassadors/demystifying-genetic-algorithms-to-enhance-neural-networks-cde902384b6e
clock = pygame.time.Clock()


map_lines = map1



all_cars = pygame.sprite.Group()

for x in range(100):
    car = Car()
    all_cars.add(car)


def run_round(all_cars):
    running_cars = True
    while running_cars :
        running_cars = False
        screen.fill(CELL_COLOR)
        all_cars.draw(screen)
        for c in all_cars :
            c.show_features()
            if c.run :
                running_cars = True
                c.probe_lines_proximity(map_lines)
                c.probe_brain()
                c.update()

        for line in map_lines :
            pygame.draw.line(screen, (255,255,255), line[0], line[1])

        pygame.display.flip()
        clock.tick(48)

    # for c in all_cars :
    #     print(f"Car {id(c)} Fitness : {c.brain.fitness})")
    
    print('Collecting brains')
    brains = [c.brain for c in all_cars]
    print(f"Max fitness = {max([b.fitness for b in brains])}" )
    print(f"Avg fitness = {sum([b.fitness for b in brains])/len(brains)}" )
    print('selecting')
    parents_pool = genetic_selection(brains)
    # import ipdb; ipdb.set_trace()
    print("breeding")
    new_brains = genetic_reproduction(parents_pool)
    print(f'building {len(new_brains)} cars with new brains')
    all_cars.empty()
    for b in new_brains :
        all_cars.add(Car(brain=b))
    print("Waiting 5 secs before new run")
    for x in range(10) :
        time.sleep(0.5)
        pygame.display.flip()


while True :
    run_round(all_cars)
    pygame.display.flip()
    clock.tick(24)