def test_find_lowest_cost_points(self, map_data: MapData) -> None:
cr = 7
safe_query_radius = 14
expected_max_distance = 2 * safe_query_radius
influence_grid = map_data.get_air_vs_ground_grid()
cost_point = (50, 130)
influence_grid = map_data.add_cost(position=cost_point, radius=cr, grid=influence_grid)
safe_points = map_data.find_lowest_cost_points(from_pos=cost_point, radius=safe_query_radius,
grid=influence_grid)
assert (
safe_points[0][0],
np.integer), f"safe_points[0][0] = {safe_points[0][0]}, type {type(safe_points[0][0])}"
assert isinstance(safe_points[0][1],
np.integer), f"safe_points[0][1] = {safe_points[0][1]}, type {type(safe_points[0][1])}"
cost = influence_grid[safe_points[0]]
for p in safe_points:
assert (influence_grid[
p] == cost), f"grid type = air_vs_ground_grid, p = {p}, " \
f"influence_grid[p] = {influence_grid[p]}, expected cost = {cost}"
assert (map_data.distance(cost_point, p) < expected_max_distance)
influence_grid = map_data.get_clean_air_grid()
cost_point = (50, 130)
influence_grid = map_data.add_cost(position=cost_point, radius=cr, grid=influence_grid)
safe_points = map_data.find_lowest_cost_points(from_pos=cost_point, radius=safe_query_radius,
grid=influence_grid)
cost = influence_grid[safe_points[0]]
for p in safe_points:
assert (influence_grid[
p] == cost), f"grid type = clean_air_grid, p = {p}, " \
f"influence_grid[p] = {influence_grid[p]}, expected cost = {cost}"
assert (map_data.distance(cost_point, p) < expected_max_distance)
influence_grid = map_data.get_pyastar_grid()
cost_point = (50, 130)
influence_grid = map_data.add_cost(position=cost_point, radius=cr, grid=influence_grid)
safe_points = map_data.find_lowest_cost_points(from_pos=cost_point, radius=safe_query_radius,
grid=influence_grid)
cost = influence_grid[safe_points[0]]
for p in safe_points:
assert (influence_grid[
p] == cost), f"grid type = pyastar_grid, p = {p}, " \
f"influence_grid[p] = {influence_grid[p]}, expected cost = {cost}"
assert (map_data.distance(cost_point, p) < expected_max_distance)
influence_grid = map_data.get_climber_grid()
cost_point = (50, 130)
influence_grid = map_data.add_cost(position=cost_point, radius=cr, grid=influence_grid)
safe_points = map_data.find_lowest_cost_points(from_pos=cost_point, radius=safe_query_radius,
grid=influence_grid)
cost = influence_grid[safe_points[0]]
for p in safe_points:
assert (influence_grid[
p] == cost), f"grid type = climber_grid, p = {p}, " \
f"influence_grid[p] = {influence_grid[p]}, expected cost = {cost}"
assert (map_data.distance(cost_point, p) < expected_max_distance)
def test_air_vs_ground(self, map_data: MapData) -> None:
default_weight = 99
grid = map_data.get_air_vs_ground_grid(default_weight=default_weight)
ramps = map_data.map_ramps
path_array = map_data.path_arr.T
for ramp in ramps:
for point in ramp.points:
if path_array[point.x][point.y] == 1:
assert (grid[point.x][point.y] == default_weight)
map_files = get_map_file_list()
for mf in map_files:
if 'death' in mf.lower():
# if 'abys' in mf.lower():
with lzma.open(mf, "rb") as f:
raw_game_data, raw_game_info, raw_observation = pickle.load(f)
bot = import_bot_instance(raw_game_data, raw_game_info,
raw_observation)
map_data = MapData(bot, loglevel="DEBUG")
base = map_data.bot.townhalls[0]
reg_start = map_data.where_all(base.position_tuple)[0]
reg_end = map_data.where_all(
map_data.bot.enemy_start_locations[0].position)[0]
p0 = Point2(reg_start.center)
p1 = Point2(reg_end.center)
influence_grid = map_data.get_air_vs_ground_grid(default_weight=50)
influence_grid = map_data.get_pyastar_grid()
# p = (50, 130)
# influence_grid = map_data.add_cost(grid=influence_grid, position=p, radius=10, initial_default_weights=50)
map_data.plot_influenced_path(start=p0,
goal=p1,
weight_array=influence_grid,
allow_diagonal=False)
map_data.show()
# import matplotlib.pyplot as plt
# import numpy as np
#
# print(np.unique(map_data.path_arr))
# plt.imshow(map_data.path_arr, origin="lower")
# plt.show()
# plt.imshow(map_data.placement_arr, origin="lower")