def load_map_template(self, template_name):
# Open map template
with open(path.join(assets.map_folder, template_name), "rt") as f:
# Construct tiles from map file
for y, row in enumerate(f):
row = row.strip("\n")
# count map height
self.tile_height += 1
self.tile_width = 0
for x, tile in enumerate(row):
# count map width (lazy mf)
self.tile_width += 1
# fog is everywhere
self.fog_data[(x, y)] = tiles.Fog(self, (x, y))
new_tile = None
if tile == "T": # Forest
new_tile = tiles.Forest(self, (x, y))
new_tile.add_resource(entities.WildTree, 5)
elif tile == "V": # Water
new_tile = tiles.Water(self, (x, y))
self.unpassable_tiles.append((x, y))
elif tile == "G": # Bog
new_tile = tiles.Bog(self, (x, y))
elif tile == "B": # Mountain
new_tile = tiles.Mountain(self, (x, y))
self.unpassable_tiles.append((x, y))
elif tile == "M": # Ground
new_tile = tiles.Ground(self, (x, y))
self.tile_data[(x, y)] = new_tile
for i in range(0, 1000):
tile = self.get_random_background_tile()
while not tile.passable:
tile = self.get_random_background_tile()
tile.add_resource(entities.WildIronOre, 1)
self.width = self.tile_width * g_vars["Game"]["TileSize"]
self.height = self.tile_height * g_vars["Game"]["TileSize"]
camera.set_resolution(self.width, self.height)
self.weighted_graph = alg.WeightedGraph(self)
def astar_cost(self):
path, cost = alg.Astar(alg.WeightedGraph(self), self.custom_start,
self.custom_goal)
return cost
def update(self):
self.sprite_group_all.update()
# catch inputs
keystate = pg.key.get_pressed()
if keystate[pg.K_ESCAPE]:
pg.event.post(pg.event.Event(pg.QUIT))
if keystate[pg.K_TAB]:
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
if keystate[pg.K_KP0]:
self.neural.train()
if keystate[pg.K_KP1]:
self.neural.save(assets.neural_network_model_folder)
if keystate[pg.K_KP2]:
self.neural.load(assets.neural_network_model_folder)
if keystate[pg.K_q]: # BFS
self.pathqueue = None
self.path = alg.BFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_a]: # Visual BFS
self.path = alg.BFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_w]: # DFS
self.pathqueue = None
self.path = alg.DFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_s]: # Visual DFS
self.path = alg.DFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_e]: # Dijkstra
self.pathqueue = None
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_d]: # Visual Dijkstra
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_z]: # Dijkstra - only path
self.pathqueue = None
d_path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_x]: # Visual Dijkstra - only path
d_path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper(True)
if keystate[pg.K_r]: # Astar
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_f]: # Visual Astar
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_c]: # Astar - only path
self.pathqueue = None
d_path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_v]: # Visual Astar - only path
d_path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper(True)
if keystate[pg.K_t]: # Astar NN
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
if keystate[pg.K_g]: # Visual Astar NN
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
self.visual_helper()
if keystate[pg.K_p]: # test
num_tests=50
def bfs_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.BFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def dfs_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.DFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def dijkstra_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def astar_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def neural_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
bfs_time = timeit.timeit(bfs_test, number=num_tests) / num_tests
print("BFS: " + str(bfs_time))
dfs_time = timeit.timeit(dfs_test, number=num_tests) / num_tests
print("DFS: " + str(dfs_time))
# dijkstra_time = timeit.timeit(dijkstra_test, number=num_tests) / num_tests
# print("Dijkstra: " + str(dijkstra_time))
astar_time = timeit.timeit(astar_test, number=num_tests) / num_tests
print("Astar: " + str(astar_time))
neural_time = timeit.timeit(neural_test, number=num_tests) / num_tests
print("Neural: " + str(neural_time) + "\n\n")
def neural_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
def dijkstra_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)