import pygame from pygame.math import Vector2 import math as m class Tractor(object): def __init__(self, game): self.game = game size = self.game.screen.get_size() self.pos = Vector2(22, 22) self.oy = True self.oz = False # A* self.g_score = [] self.f_score = [] self.h_score = [] self.came_from = [] self.neighbours() self.score() #ruszanie się self.road = self.algo(0, 24) def tick(self): # input if pygame.key.get_pressed()[pygame.K_SPACE]: # obecne pole pole = self.pos.y // 144 * 5 + self.pos.x // 144 if len(self.road) == 0: sys.exit(0) if self.road[0] == self.pole + 1: self.pos.x = self.pos.x + 144 elif self.road[0] == self.pole - 1: self.pos.x = self.pos.x - 144 elif self.road[0] == self.pole + 5: self.pos.y = self.pos.y + 144 elif self.road[0] == self.pole - 5: self.pos.y = self.pos.y - 144 self.road.pop(0) def draw(self): # drawing rect = pygame.Rect(self.pos.x, self.pos.y, 100, 100) pygame.draw.rect(self.game.screen, (255, 255, 0), rect) # Sąsiedztwo poszczególnych pól def neighbours(self): self.neighbours = list(range(25)) self.neighbours[0] = [1, 5] self.neighbours[4] = [3, 9] self.neighbours[20] = [15, 21] self.neighbours[24] = [19, 23] for x in range(1, 4): self.neighbours[x] = [x - 1, x + 5, x + 1] for x in range(5, 16, 5): self.neighbours[x] = [x - 5, x + 1, x + 5] for x in range(9, 20, 5): self.neighbours[x] = [x - 5, x - 1, x + 5] for x in range(21, 24): self.neighbours[x] = [x - 1, x - 5, x + 1] for x in [6, 7, 8, 11, 12, 13, 16, 17, 18]: self.neighbours[x] = [x - 5, x - 1, x + 1, x + 5] # Tworzenie list dla g, h, f oraz came_from def score(self): for x in range(25): self.g_score.append(0) self.f_score.append(0) self.h_score.append(0) self.came_from.append(0) # Obliczanie h (założenie - odległość pomiędzy sąsiednimi polami wynosi 2, tak jak koszt wjazdu na puste pole) # s to pole na którym jesteśmy # f to pole końcowe def hscore(self, s, f): if f >= s: a_h = (f - s) // 5 else: a_h = (s - f) // 5 if f % 5 >= s % 5: b_h = f % 5 - s % 5 else: b_h = s % 5 - f % 5 + 1 return 2 * m.sqrt(a_h ** 2 + b_h ** 2) # A* def algo(self, start, koniec): # definiowanie setów closed_set = [] open_set = [start] while open_set: # Szukanie pola w open_set z najniższym f temp1 = max(self.f_score) + 1 x = 0 for i in range(len(open_set)): if self.f_score[open_set[i]] <= temp1: x = open_set[i] temp1 = self.f_score[open_set[i]] if x == koniec: return self.reconstruct_path(self.came_from, koniec) open_set.remove(x) closed_set.append(x) for y in self.neighbours[x]: if y in closed_set: continue tentative_g_score = self.g_score[x] + self.game.fields[y][3] if y not in open_set: open_set.append(y) tentative_is_better = True elif tentative_g_score < self.g_score[y]: tentative_is_better = True if tentative_is_better == True: self.came_from[y] = x self.g_score[y] = tentative_g_score self.f_score[y] = self.g_score[y] + self.hscore(y, koniec) print("failure") def reconstruct_path(self, came_from, current): total_path = [current] while came_from[current] != 0: current = came_from[current] total_path.insert(0, current) return total_path