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_clean_air_grid_smoothing(self, map_data: MapData) -> None:
default_weight = 2
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)
grid = map_data.get_clean_air_grid(default_weight=default_weight)
cost_points = [(87, 76), (108, 64), (97, 53)]
cost_points = list(map(Point2, cost_points))
for cost_point in cost_points:
grid = map_data.add_cost(position=cost_point, radius=7, grid=grid)
path = map_data.pathfind(start=p0, goal=p1, grid=grid, smoothing=True)
assert (len(path) < 50)