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from globals import *
from functions import *
from functions_plotting import *
def run_mapf_alg(alg, params, final_render: bool = True):
# set_seed(random_seed_bool=False, seed=5)
# set_seed(random_seed_bool=False, seed=123)
# set_seed(random_seed_bool=False, seed=2953)
set_seed(random_seed_bool=True)
# img_dir = '10_10_my_rand.map'
# img_dir = '15-15-two-rooms.map'
# img_dir = '15-15-four-rooms.map'
# img_dir = '15-15-six-rooms.map'
# img_dir = '15-15-eight-rooms.map'
# img_dir = '10_10_my_corridor.map'
# img_dir = 'empty-32-32.map'
# img_dir = 'random-32-32-10.map'
img_dir = 'random-32-32-20.map'
# img_dir = 'maze-32-32-4.map'
# img_dir = 'maze-32-32-2.map'
# img_dir = 'room-32-32-4.map'
# n_agents = 700
# n_agents = 600
# n_agents = 550
# n_agents = 500
# n_agents = 450
# n_agents = 400
# n_agents = 350
# n_agents = 300
# n_agents = 250
# n_agents = 200
# n_agents = 170
# n_agents = 150
# n_agents = 100
n_agents = 80
# n_agents = 70
# n_agents = 50
# n_agents = 40
# n_agents = 15
# n_agents = 10
# n_agents = 3
path_to_maps: str = '../maps'
path_to_heuristics: str = '../logs_for_heuristics'
path_to_sv_maps: str = '../logs_for_freedom_maps'
img_np, (height, width) = get_np_from_dot_map(f'{path_to_maps}/{img_dir}')
map_dim = (height, width)
nodes, nodes_dict = build_graph_from_np(img_np, show_map=False)
h_dict: Dict[str, np.ndarray] = exctract_h_dict(img_dir[:-4])
# blocked_sv_map: np.ndarray = get_blocked_sv_map(img_dir, folder_dir=path_to_sv_maps)
# sv_map: np.ndarray = get_sv_map(img_dir, folder_dir=path_to_sv_maps)
start_nodes: List[Node] = random.sample(nodes, n_agents)
goal_nodes: List[Node] = random.sample(nodes, n_agents)
# start_nodes: List[Node] = [nodes_dict['4_8'], nodes_dict['4_4'], nodes_dict['8_8']]
# goal_nodes: List[Node] = [nodes_dict['4_2'], nodes_dict['4_4'], nodes_dict['8_8']]
# start_nodes: List[Node] = [nodes_dict['4_8'], nodes_dict['4_4']]
# goal_nodes: List[Node] = [nodes_dict['4_2'], nodes_dict['4_4']]
# goal_nodes: List[Node] = [nodes_dict['4_1'], nodes_dict['4_0']]
# start_nodes: List[Node] = [nodes_dict['4_0'], nodes_dict['4_1'], nodes_dict['4_2']]
# goal_nodes: List[Node] = [nodes_dict['4_1'], nodes_dict['4_0'], nodes_dict['4_2']]
# start_nodes: List[Node] = [nodes_dict['4_0'], nodes_dict['4_1'], nodes_dict['4_2'], nodes_dict['4_3']]
# goal_nodes: List[Node] = [nodes_dict['4_1'], nodes_dict['4_0'], nodes_dict['4_2'], nodes_dict['4_3']]
params['img_np'] = img_np
params['to_render'] = False
# params['sv_map'] = sv_map
# params['blocked_sv_map'] = blocked_sv_map
paths_dict, info = alg(
start_nodes, goal_nodes, nodes, nodes_dict, h_dict, map_dim, params
)
# plot
if final_render and paths_dict is not None:
agents: List = info['agents']
plt.close()
fig, ax = plt.subplots(1, 2, figsize=(14, 7))
plot_rate = 0.001
# plot_rate = 0.5
# plot_rate = 1
max_path_len = max([len(a.path) for a in agents])
soc = sum([len(a.path) for a in agents])
print(f'\n{max_path_len=}')
print(f'{soc=}')
for i in range(max_path_len):
# update curr nodes
for a in agents:
a.update_curr_node(i)
# plot the iteration
i_agent = agents[0]
plot_info = {
'img_np': img_np,
'agents': agents,
'i_agent': i_agent,
'iteration': i,
}
plot_step_in_env(ax[0], plot_info)
plt.pause(plot_rate)
plt.show()
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