def test_regions(self, map_data: MapData) -> None:
for region in map_data.regions.values():
for p in region.points:
assert (region in map_data.where_all(p)), f"expected {region}, got {map_data.where_all(p)}, point {p}"
assert (region == map_data.where(region.center))
# coverage
region.plot(testing=True)
def test_region_connectivity(self, map_data: MapData) -> None:
base = map_data.bot.townhalls[0]
region = map_data.where_all(base.position_tuple)[0]
destination = map_data.where_all(map_data.bot.enemy_start_locations[0].position)[0]
all_possible_paths = map_data.region_connectivity_all_paths(start_region=region,
goal_region=destination)
for p in all_possible_paths:
assert (destination in p), f"destination = {destination}"
bad_request = map_data.region_connectivity_all_paths(start_region=region,
goal_region=destination,
not_through=[destination])
assert (bad_request == [])
def test_sensitivity(self, map_data: MapData) -> None:
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 = reg_start.center
p1 = reg_end.center
arr = map_data.get_pyastar_grid()
path_pure = map_data.pathfind(p0, p1, grid=arr)
path_sensitive_5 = map_data.pathfind(p0, p1, grid=arr, sensitivity=5)
path_sensitive_1 = map_data.pathfind(p0, p1, grid=arr, sensitivity=1)
assert (len(path_sensitive_5) < len(path_pure))
assert (p in path_pure for p in path_sensitive_5)
assert (path_sensitive_1 == path_pure)
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)
def mock_map_data(map_file: str) -> "MapData":
from MapAnalyzer.MapData import MapData
with lzma.open(f"{map_file}", "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)
return MapData(bot=bot)
def test_vision_blockers(self, map_data: MapData) -> None:
all_chokes = map_data.map_chokes
for vb in map_data.map_vision_blockers:
assert (vb in all_chokes)
for p in vb.points:
assert (vb in map_data.where_all(p)), \
logger.error(f"<Map : {map_data}, Choke : {vb},"
f" where_all : {map_data.where_all(vb.center)} point : {vb.center}>")
def test_chokes(self, map_data: MapData) -> None:
for choke in map_data.map_chokes:
for p in choke.points:
assert (choke in map_data.where_all(p)), \
logger.error(f"<Map : {map_data}, Choke : {choke},"
f" where : {map_data.where(choke.center)} point : {choke.center}>")
assert (choke.side_a in choke.points), f"Choke {choke}, side a {choke.side_a} is not in choke points"
assert (choke.side_b in choke.points), f"Choke {choke}, side b {choke.side_b} is not in choke points"
def test_pathing_influence(self, map_data: MapData, caplog: LogCaptureFixture) -> None:
logger.info(map_data)
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 = reg_start.center
p1 = reg_end.center
pts = []
r = 10
for i in range(50):
pts.append(get_random_point(0, 200, 0, 200))
arr = map_data.get_pyastar_grid()
for p in pts:
arr = map_data.add_cost(p, r, arr)
path = map_data.pathfind(p0, p1, grid=arr)
assert (path is not None)
def test_handle_illegal_values(self, map_data: MapData) -> None:
base = map_data.bot.townhalls[0]
reg_start = map_data.where_all(base.position_tuple)[0]
assert (isinstance(reg_start,
Region)), f"reg_start = {reg_start}, base = {base}, position_tuple = {base.position_tuple}"
reg_end = map_data.where_all(map_data.bot.enemy_start_locations[0].position)[0]
p0 = reg_start.center
p1 = reg_end.center
pts = []
r = 10
for i in range(50):
pts.append(get_random_point(-500, -250, -500, -250))
arr = map_data.get_pyastar_grid()
for p in pts:
arr = map_data.add_cost(p, r, arr)
path = map_data.pathfind(p0, p1, grid=arr)
assert (path is not None), f"path = {path}"
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)
for map_file in map_files:
li.append(os.path.join(map_files_folder, map_file))
return li
map_files = get_map_file_list()
for mf in map_files:
if 'abys' in mf.lower():
# if 1==1:
# mf = random.choice(map_files)
# 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]
#
# for choke in map_data.map_chokes:
# x, y = zip(*choke.corner_walloff)
# plt.scatter(x, y)
#
# 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)
# p0 = Point2((104, 153))
# p1 = Point2((107, 140))
# # influence_grid = map_data.get_clean_air_grid(default_weight=10)
# influence_grid = map_data.get_pyastar_grid()
# cost_point = (50, 130)
def test_chokes(self, map_data: MapData) -> None:
for choke in map_data.map_chokes:
for p in choke.points:
assert (choke in map_data.where_all(p)), \
logger.error(f"<Map : {map_data}, Choke : {choke},"
f" where : {map_data.where(choke.center)} point : {choke.center}>")
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)
li.append(os.path.join(map_files_folder, map_file))
return li
map_files = get_map_file_list()
map_file = ""
for mf in map_files:
if 'goldenwall' in mf.lower():
map_file = mf
break
with lzma.open(map_file, "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")
map_data.plot_map()
map_data.show()
# map_data.save('fname')
# TEST ILLEGAL VALUES ISOLATED
base = map_data.bot.townhalls[0]
reg_start = map_data.where(base.position_tuple)
reg_end = map_data.where(map_data.bot.enemy_start_locations[0].position)
p0 = reg_start.center
p1 = reg_end.center
pts = []
r = 10
for i in range(50):
pts.append(get_random_point(-500, -250, -500, -250))
map_files = os.listdir(map_files_folder)
li = []
for map_file in map_files:
li.append(os.path.join(map_files_folder, map_file))
return li
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()
