def escape(life, targets):
_avoid_positions = []
_zones = [zones.get_zone_at_coords(life['pos'])]
if lfe.find_action(life, [{
'action': 'dijkstra_move',
'reason': 'escape'
}]):
if lfe.ticker(life, 'escape_refresh', 4):
lfe.stop(life)
else:
return False
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
_zone = zones.get_zone_at_coords(_target['last_seen_at'])
if not _zone in _zones:
_zones.append(_zone)
_avoid_positions.append(_target['last_seen_at'])
lfe.add_action(
life, {
'action': 'dijkstra_move',
'rolldown': False,
'zones': _zones,
'goals': _avoid_positions,
'reason': 'escape'
}, 100)
def find_target(life, target, distance=5, follow=False, call=True):
_target = brain.knows_alife_by_id(life, target)
_dist = numbers.distance(life['pos'], _target['last_seen_at'])
_can_see = sight.can_see_target(life, target)
if _can_see and _dist<=distance:
if follow:
return True
lfe.stop(life)
return True
if _target['escaped'] == 1:
search_for_target(life, target)
return False
if not _can_see and sight.can_see_position(life, _target['last_seen_at']) and _dist<distance:
if call:
if not _target['escaped']:
memory.create_question(life, target, 'GET_LOCATION')
speech.communicate(life, 'call', matches=[target])
_target['escaped'] = 1
return False
if not lfe.path_dest(life) == tuple(_target['last_seen_at'][:2]):
lfe.clear_actions(life)
lfe.add_action(life,
{'action': 'move','to': _target['last_seen_at'][:2]},
200)
return False
def escape(life, targets):
_avoid_positions = []
_zones = [zones.get_zone_at_coords(life['pos'])]
if lfe.find_action(life, [{'action': 'dijkstra_move', 'reason': 'escape'}]):
if lfe.ticker(life, 'escape_refresh', 4):
lfe.stop(life)
else:
return False
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
_zone = zones.get_zone_at_coords(_target['last_seen_at'])
if not _zone in _zones:
_zones.append(_zone)
_avoid_positions.append(_target['last_seen_at'])
lfe.add_action(life, {'action': 'dijkstra_move',
'rolldown': False,
'zones': _zones,
'goals': _avoid_positions,
'reason': 'escape'},
100)
def find_target(life, target, distance=5, follow=False, call=True):
_target = brain.knows_alife_by_id(life, target)
_dist = bad_numbers.distance(life['pos'], _target['last_seen_at'])
_can_see = sight.can_see_target(life, target)
if _can_see and _dist <= distance:
if follow:
return True
lfe.stop(life)
return True
if _target['escaped'] == 1:
search_for_target(life, target)
return False
if not _can_see and sight.can_see_position(
life, _target['last_seen_at']) and _dist < distance:
if call:
if not _target['escaped']:
memory.create_question(life, target, 'GET_LOCATION')
speech.communicate(life, 'call', matches=[target])
_target['escaped'] = 1
return False
if not lfe.path_dest(life) == tuple(_target['last_seen_at'][:2]):
lfe.walk_to(life, _target['last_seen_at'])
return False
def find_target(life, target, distance=5, follow=False, call=True):
_target = brain.knows_alife_by_id(life, target)
_dist = numbers.distance(life["pos"], _target["last_seen_at"])
_can_see = sight.can_see_target(life, target)
if _can_see and _dist <= distance:
if follow:
return True
lfe.stop(life)
return True
if _target["escaped"] == 1:
search_for_target(life, target)
return False
if not _can_see and sight.can_see_position(life, _target["last_seen_at"]) and _dist < distance:
if call:
if not _target["escaped"]:
memory.create_question(life, target, "GET_LOCATION")
speech.communicate(life, "call", matches=[target])
_target["escaped"] = 1
return False
if not lfe.path_dest(life) == tuple(_target["last_seen_at"][:2]):
lfe.walk_to(life, _target["last_seen_at"])
return False
def follow_alife(life):
if _find_alife(life,
jobs.get_job_detail(life['job'], 'target'),
distance=7):
lfe.stop(life)
return True
return False
def conditions(life, alife_seen, alife_not_seen, targets_seen, targets_not_seen, source_map): RETURN_VALUE = STATE_UNCHANGED if not lfe.execute_raw(life, 'state', 'surrender'): return False if not life['state'] == STATE: lfe.stop(life) lfe.say(life, '@n gives up.', action=True) RETURN_VALUE = STATE_CHANGE return RETURN_VALUE
def conditions(life, alife_seen, alife_not_seen, targets_seen, targets_not_seen, source_map): RETURN_VALUE = STATE_UNCHANGED if not lfe.execute_raw(life, 'state', 'surrender'): return False if not life['state'] == STATE: lfe.stop(life) lfe.say(life, '@n gives up.', action=True) RETURN_VALUE = STATE_CHANGE return RETURN_VALUE
def search_for_target(life, target_id):
#TODO: Variable size instead of hardcoded
_know = brain.knows_alife_by_id(life, target_id)
_size = 30
if brain.alife_has_flag(life, target_id, 'search_map'):
_search_map = brain.get_alife_flag(life, target_id, 'search_map')
else:
_search_map = maps.create_search_map(life, _know['last_seen_at'], _size)
brain.flag_alife(life, target_id, 'search_map', value=_search_map)
lfe.stop(life)
lfe.walk_to(life, _know['last_seen_at'][:2])
if life['path'] or lfe.find_action(life, matches=[{'action': 'move'}]):
return False
_lowest = {'score': -1, 'pos': None}
_x_top_left = numbers.clip(_know['last_seen_at'][0]-(_size/2), 0, MAP_SIZE[0])
_y_top_left = numbers.clip(_know['last_seen_at'][1]-(_size/2), 0, MAP_SIZE[1])
for x in range(0, _size):
_x = _x_top_left+x
if _x >= MAP_SIZE[0]-1:
continue
for y in range(0, _size):
_y = _y_top_left+y
if _y >= MAP_SIZE[1]-1:
continue
if not _search_map[y, x]:
continue
if sight.can_see_position(life, (_x, _y)):
_search_map[y, x] = 0
if _search_map[y, x]>0 and (not _lowest['pos'] or _search_map[y, x] <= _lowest['score']):
_lowest['score'] = _search_map[y, x]
_lowest['pos'] = (_x, _y, x, y)
if _lowest['pos']:
x, y, _x, _y = _lowest['pos']
if travel_to_position(life, (x, y, _know['last_seen_at'][2]), stop_on_sight=True):
_search_map[_y, _x] = 0
else:
_know['escaped'] = 2
def position_to_attack(life, target):
if lfe.find_action(life, [{'action': 'dijkstra_move', 'reason': 'positioning for attack'}]):
if not lfe.ticker(life, 'attack_position', 4):
return False
_target_positions, _zones = combat.get_target_positions_and_zones(life, [target])
_nearest_target_score = zones.dijkstra_map(life['pos'], _target_positions, _zones, return_score=True)
#TODO: Short or long-range weapon?
#if _nearest_target_score >= sight.get_vision(life)/2:
if not sight.can_see_position(life, brain.knows_alife_by_id(life, target)['last_seen_at'], block_check=True, strict=True) or sight.view_blocked_by_life(life, _target_positions[0], allow=[target]):
print life['name'], 'changing position for combat...', life['name'], LIFE[target]['name']
_avoid_positions = []
for life_id in life['seen']:
if life_id == target or life['id'] == life_id:
continue
if alife.judgement.can_trust(life, life_id):
_avoid_positions.append(lfe.path_dest(LIFE[life_id]))
else:
_avoid_positions.append(brain.knows_alife_by_id(life, life_id)['last_seen_at'])
_cover = _target_positions
_zones = []
for pos in _cover:
_zone = zones.get_zone_at_coords(pos)
if not _zone in _zones:
_zones.append(_zone)
if not lfe.find_action(life, [{'action': 'dijkstra_move', 'orig_goals': _cover[:], 'avoid_positions': _avoid_positions}]):
lfe.stop(life)
lfe.add_action(life, {'action': 'dijkstra_move',
'rolldown': True,
'goals': _cover[:],
'orig_goals': _cover[:],
'avoid_positions': _avoid_positions,
'reason': 'positioning for attack'},
999)
return False
else:
return False
elif life['path']:
lfe.stop(life)
return True
def travel_to_position(life, pos, stop_on_sight=False, force=False):
if not numbers.distance(life["pos"], pos):
return True
if stop_on_sight and sight.can_see_position(life, pos, get_path=True, ignore_z=True):
lfe.stop(life)
return True
_dest = lfe.path_dest(life)
if not force and _dest and tuple(_dest[:2]) == tuple(pos[:2]):
return False
lfe.walk_to(life, pos[:3])
return False
def travel_to_position(life, pos, stop_on_sight=False, force=False):
if not bad_numbers.distance(life['pos'], pos):
return True
if stop_on_sight and sight.can_see_position(
life, pos, get_path=True, ignore_z=True):
lfe.stop(life)
return True
_dest = lfe.path_dest(life)
if not force and _dest and tuple(_dest[:2]) == tuple(pos[:2]):
return False
lfe.walk_to(life, pos[:3])
return False
def escape(life, targets):
_avoid_positions = []
_zones = [zones.get_zone_at_coords(life["pos"])]
if lfe.find_action(life, [{"action": "dijkstra_move", "reason": "escape"}]):
if lfe.ticker(life, "escape_refresh", 4):
lfe.stop(life)
else:
return False
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
_zone = zones.get_zone_at_coords(_target["last_seen_at"])
if not _zone in _zones:
_zones.append(_zone)
_avoid_positions.append(_target["last_seen_at"])
lfe.add_action(
life,
{"action": "dijkstra_move", "rolldown": False, "zones": _zones, "goals": _avoid_positions, "reason": "escape"},
100,
)
def hide(life, targets):
_target_positions = []
_avoid_positions = []
_zones = [zones.get_zone_at_coords(life['pos'])]
if lfe.find_action(life, [{
'action': 'dijkstra_move',
'reason': 'escaping'
}]):
if not lfe.ticker(life, 'escaping', 6):
return False
#What can the targets see?
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
_zone = zones.get_zone_at_coords(_target['last_seen_at'])
if not _zone in _zones:
_zones.append(_zone)
fov.fov(_target['last_seen_at'],
sight.get_vision(_target['life']),
callback=lambda pos: _avoid_positions.append(pos))
#What can we see?
_can_see_positions = []
fov.fov(life['pos'],
sight.get_vision(life),
callback=lambda pos: _can_see_positions.append(pos))
#If there are no visible targets, we could be running away from a position we were attacked from
_cover_exposed_at = brain.get_flag(life, 'cover_exposed_at')
if _cover_exposed_at:
_avoid_exposed_cover_positions = set()
for pos in _cover_exposed_at[:]:
if tuple(pos[:2]) in _can_see_positions:
_cover_exposed_at.remove(pos)
continue
fov.fov(
pos,
int(round(sight.get_vision(life) * .25)),
callback=lambda pos: _avoid_exposed_cover_positions.add(pos))
for pos in _avoid_exposed_cover_positions:
if not pos in _avoid_positions:
_avoid_positions.append(pos)
else:
print 'Something went wrong'
return False
#Overlay the two, finding positions we can see but the target can't
for pos in _can_see_positions[:]:
if pos in _avoid_positions:
_can_see_positions.remove(pos)
continue
#Get rid of positions that are too close
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
if bad_numbers.distance(_target['last_seen_at'], pos) < 4:
_can_see_positions.remove(pos)
break
#Now scan for cover to prevent hiding in the open
for pos in _can_see_positions[:]:
if chunks.get_chunk(chunks.get_chunk_key_at(pos))['max_z'] == 2:
_can_see_positions.remove(pos)
if not _can_see_positions:
if life['pos'] in _cover_exposed_at:
_cover_exposed_at.remove(life['pos'])
return False
if lfe.find_action(life, [{
'action': 'dijkstra_move',
'goals': _can_see_positions[:]
}]):
return True
lfe.stop(life)
lfe.add_action(
life, {
'action': 'dijkstra_move',
'rolldown': True,
'zones': _zones,
'goals': _can_see_positions[:],
'reason': 'escaping'
}, 200)
def follow_alife(life): if _find_alife(life, jobs.get_job_detail(life['job'], 'target'), distance=7): lfe.stop(life) return True return False
def position_to_attack(life, target, engage_distance):
if lfe.find_action(life, [{
'action': 'dijkstra_move',
'reason': 'positioning for attack'
}]):
if not lfe.ticker(life, 'attack_position', 4):
return False
_target_positions, _zones = combat.get_target_positions_and_zones(
life, [target])
_can_see = alife.sight.can_see_position(life,
_target_positions[0],
get_path=True)
_distance = bad_numbers.distance(life['pos'], _target_positions[0])
if _can_see and len(_can_see) < engage_distance * .85:
if life['path']:
lfe.stop(life)
elif _distance < engage_distance * .9:
_avoid_positions = set()
_target_area = set()
for life_id in alife.judgement.get_trusted(life,
visible=False,
only_recent=True):
fov.fov(LIFE[life_id]['pos'],
int(round(sight.get_vision(life) * .25)),
callback=lambda pos: _avoid_positions.add(pos))
fov.fov(_target_positions[0],
int(round(sight.get_vision(life) * .15)),
callback=lambda pos: _target_area.add(pos))
_min_view_distance = int(round(sight.get_vision(life) * .25))
_max_view_distance = int(round(sight.get_vision(life) * .5))
_attack_positions = set(
zones.dijkstra_map(
life['pos'],
_target_positions,
_zones,
rolldown=True,
return_score_in_range=[_min_view_distance,
_max_view_distance]))
_attack_positions = _attack_positions - _target_area
if not _attack_positions:
return False
if not lfe.find_action(life, [{
'action': 'dijkstra_move',
'orig_goals': list(_attack_positions),
'avoid_positions': list(_avoid_positions)
}]):
lfe.stop(life)
lfe.add_action(
life, {
'action':
'dijkstra_move',
'rolldown':
True,
'goals': [
list(p)
for p in random.sample(_attack_positions,
len(_attack_positions) / 2)
],
'orig_goals':
list(_attack_positions),
'avoid_positions':
list(_avoid_positions),
'reason':
'positioning for attack'
}, 999)
return False
else:
_can_see_positions = set()
_target_area = set()
_avoid_positions = set()
fov.fov(life['pos'],
int(round(sight.get_vision(life) * .75)),
callback=lambda pos: _can_see_positions.add(pos))
fov.fov(_target_positions[0],
int(round(sight.get_vision(life) * .75)),
callback=lambda pos: _target_area.add(pos))
for life_id in alife.judgement.get_trusted(life,
visible=False,
only_recent=True):
_path_dest = lfe.path_dest(LIFE[life_id])
if not _path_dest:
continue
if len(_path_dest) == 2:
_path_dest = list(_path_dest[:])
_path_dest.append(LIFE[life_id]['pos'][2])
fov.fov(_path_dest,
5,
callback=lambda pos: _avoid_positions.add(pos))
_avoid_positions = list(_avoid_positions)
_sneak_positions = _can_see_positions - _target_area
_move_positions = zones.dijkstra_map(LIFE[target]['pos'],
list(_sneak_positions),
_zones,
rolldown=True)
if not _move_positions:
travel_to_position(life, list(_target_positions[0]))
return False
if not lfe.find_action(life, [{
'action': 'dijkstra_move',
'orig_goals': _move_positions,
'avoid_positions': _avoid_positions
}]):
lfe.stop(life)
lfe.add_action(
life, {
'action': 'dijkstra_move',
'rolldown': True,
'goals': [list(p) for p in _move_positions],
'orig_goals': _move_positions,
'avoid_positions': _avoid_positions,
'reason': 'positioning for attack'
}, 999)
return False
return True
def follow_alife(life):
if _find_alife(life, jobs.get_job_detail(life["job"], "target"), distance=7):
lfe.stop(life)
return True
return False
def hide(life, targets):
_target_positions = []
_avoid_positions = []
_zones = [zones.get_zone_at_coords(life["pos"])]
if lfe.find_action(life, [{"action": "dijkstra_move", "reason": "escaping"}]):
if not lfe.ticker(life, "escaping", 6):
return False
# What can the targets see?
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
_zone = zones.get_zone_at_coords(_target["last_seen_at"])
if not _zone in _zones:
_zones.append(_zone)
fov.fov(
_target["last_seen_at"],
sight.get_vision(_target["life"]),
callback=lambda pos: _avoid_positions.append(pos),
)
# What can we see?
_can_see_positions = []
fov.fov(life["pos"], sight.get_vision(life), callback=lambda pos: _can_see_positions.append(pos))
# If there are no visible targets, we could be running away from a position we were attacked from
_cover_exposed_at = brain.get_flag(life, "cover_exposed_at")
if _cover_exposed_at:
_avoid_exposed_cover_positions = set()
for pos in _cover_exposed_at[:]:
if tuple(pos[:2]) in _can_see_positions:
_cover_exposed_at.remove(pos)
continue
fov.fov(
pos,
int(round(sight.get_vision(life) * 0.25)),
callback=lambda pos: _avoid_exposed_cover_positions.add(pos),
)
for pos in _avoid_exposed_cover_positions:
if not pos in _avoid_positions:
_avoid_positions.append(pos)
else:
print "Something went wrong"
return False
# Overlay the two, finding positions we can see but the target can't
for pos in _can_see_positions[:]:
if pos in _avoid_positions:
_can_see_positions.remove(pos)
continue
# Get rid of positions that are too close
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
if numbers.distance(_target["last_seen_at"], pos) < 4:
_can_see_positions.remove(pos)
break
# Now scan for cover to prevent hiding in the open
for pos in _can_see_positions[:]:
if chunks.get_chunk(chunks.get_chunk_key_at(pos))["max_z"] == 2:
_can_see_positions.remove(pos)
if not _can_see_positions:
if life["pos"] in _cover_exposed_at:
_cover_exposed_at.remove(life["pos"])
return False
if lfe.find_action(life, [{"action": "dijkstra_move", "goals": _can_see_positions[:]}]):
return True
lfe.stop(life)
lfe.add_action(
life,
{
"action": "dijkstra_move",
"rolldown": True,
"zones": _zones,
"goals": _can_see_positions[:],
"reason": "escaping",
},
200,
)
def escape(life, targets):
_target_positions = []
_avoid_positions = []
_zones = [zones.get_zone_at_coords(life['pos'])]
if lfe.find_action(life, [{'action': 'dijkstra_move', 'reason': 'escaping'}]):
if not lfe.ticker(life, 'escaping', 4):
return False
#What can the targets see?
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
_zone = zones.get_zone_at_coords(_target['last_seen_at'])
if not _zone in _zones:
_zones.append(_zone)
fov.fov(_target['last_seen_at'], sight.get_vision(_target['life']), callback=lambda pos: _avoid_positions.append(pos))
#What can we see?
_can_see_positions = []
fov.fov(life['pos'], sight.get_vision(life), callback=lambda pos: _can_see_positions.append(pos))
#If there are no visible targets, we could be running away from a position we were attacked from
_cover_exposed_at = brain.get_flag(life, 'cover_exposed_at')
if _cover_exposed_at:
_avoid_exposed_cover_positions = set()
for pos in _cover_exposed_at[:]:
if tuple(pos[:2]) in _can_see_positions:
print 'ok!!!'*20
_cover_exposed_at.remove(pos)
continue
fov.fov(pos, int(round(sight.get_vision(life)*.25)), callback=lambda pos: _avoid_exposed_cover_positions.add(pos))
for pos in _avoid_exposed_cover_positions:
if not pos in _avoid_positions:
_avoid_positions.append(pos)
#Overlay the two, finding positions we can see but the target can't
for pos in _can_see_positions[:]:
if pos in _avoid_positions:
_can_see_positions.remove(pos)
continue
#Get rid of positions that are too close
for target_id in targets:
_target = brain.knows_alife_by_id(life, target_id)
#TODO: Unhardcode 15
if numbers.distance(_target['last_seen_at'], pos)<10:
_can_see_positions.remove(pos)
break
#Now scan for cover to prevent hiding in the open
for pos in _can_see_positions[:]:
if chunks.get_chunk(chunks.get_chunk_key_at(pos))['max_z'] == 2:
_can_see_positions.remove(pos)
#for target_id in targets:
#_target = brain.knows_alife_by_id(life, target_id)
#_target_positions.append(_target['last_seen_at'][:])
#_zone = zones.get_zone_at_coords(_target['last_seen_at'])
#if not _zone in _zones:
# _zones.append(_zone)
#for chunk_key in chunks.get_visible_chunks_from(_target['last_seen_at'], sight.get_vision(_target['life'])):
# if chunk_key in _visible_target_chunks:
# continue
# _visible_target_chunks.append(chunk_key)
#for friendly_id in life['seen']:
# _chunk_key = lfe.get_current_chunk_id(LIFE[friendly_id])
#
# if not _chunk_key in _visible_target_chunks:
# _visible_target_chunks.append(_chunk_key)
#if not _target_positions:
# return False
#TODO: #combat: For lower limit in return_score_in_range, use range of weapon
#_cover = zones.dijkstra_map(life['pos'],
# _avoid_positions,
# _zones,
# avoid_chunks=[],
# return_score_in_range=[1, 5]) # sight.get_vision(life)
#_cover = [(c[0], c[1], life['pos'][2]) for c in _cover]
#if not _cover:
# return False
#_zones = [zones.get_zone_at_coords(life['pos'])]
#for _pos in _cover:
# _zone = zones.get_zone_at_coords(_pos)
# if not _zone in _zones:
# _zones.append(_zone)
if not _can_see_positions:
return False
if lfe.find_action(life, [{'action': 'dijkstra_move', 'goals': _can_see_positions[:]}]):
return True
lfe.stop(life)
lfe.add_action(life, {'action': 'dijkstra_move',
'rolldown': True,
'zones': _zones,
'goals': _can_see_positions[:],
'reason': 'escaping'},
999)
def position_to_attack(life, target, engage_distance):
if lfe.find_action(life, [{"action": "dijkstra_move", "reason": "positioning for attack"}]):
if not lfe.ticker(life, "attack_position", 4):
return False
_target_positions, _zones = combat.get_target_positions_and_zones(life, [target])
_can_see = alife.sight.can_see_position(life, _target_positions[0], get_path=True)
_distance = numbers.distance(life["pos"], _target_positions[0])
if _can_see and len(_can_see) < engage_distance * 0.85:
if life["path"]:
lfe.stop(life)
elif _distance < engage_distance * 0.9:
_avoid_positions = set()
_target_area = set()
for life_id in alife.judgement.get_trusted(life, visible=False, only_recent=True):
fov.fov(
LIFE[life_id]["pos"],
int(round(sight.get_vision(life) * 0.25)),
callback=lambda pos: _avoid_positions.add(pos),
)
fov.fov(
_target_positions[0], int(round(sight.get_vision(life) * 0.15)), callback=lambda pos: _target_area.add(pos)
)
_min_view_distance = int(round(sight.get_vision(life) * 0.25))
_max_view_distance = int(round(sight.get_vision(life) * 0.5))
_attack_positions = set(
zones.dijkstra_map(
life["pos"],
_target_positions,
_zones,
rolldown=True,
return_score_in_range=[_min_view_distance, _max_view_distance],
)
)
_attack_positions = _attack_positions - _target_area
if not _attack_positions:
return False
if not lfe.find_action(
life,
[
{
"action": "dijkstra_move",
"orig_goals": list(_attack_positions),
"avoid_positions": list(_avoid_positions),
}
],
):
lfe.stop(life)
lfe.add_action(
life,
{
"action": "dijkstra_move",
"rolldown": True,
"goals": [list(p) for p in random.sample(_attack_positions, len(_attack_positions) / 2)],
"orig_goals": list(_attack_positions),
"avoid_positions": list(_avoid_positions),
"reason": "positioning for attack",
},
999,
)
return False
else:
_can_see_positions = set()
_target_area = set()
_avoid_positions = set()
fov.fov(
life["pos"], int(round(sight.get_vision(life) * 0.75)), callback=lambda pos: _can_see_positions.add(pos)
)
fov.fov(
_target_positions[0], int(round(sight.get_vision(life) * 0.75)), callback=lambda pos: _target_area.add(pos)
)
for life_id in alife.judgement.get_trusted(life, visible=False, only_recent=True):
_path_dest = lfe.path_dest(LIFE[life_id])
if not _path_dest:
continue
if len(_path_dest) == 2:
_path_dest = list(_path_dest[:])
_path_dest.append(LIFE[life_id]["pos"][2])
fov.fov(_path_dest, 5, callback=lambda pos: _avoid_positions.add(pos))
_avoid_positions = list(_avoid_positions)
_sneak_positions = _can_see_positions - _target_area
_move_positions = zones.dijkstra_map(LIFE[target]["pos"], list(_sneak_positions), _zones, rolldown=True)
if not _move_positions:
travel_to_position(life, list(_target_positions[0]))
return False
if not lfe.find_action(
life, [{"action": "dijkstra_move", "orig_goals": _move_positions, "avoid_positions": _avoid_positions}]
):
lfe.stop(life)
lfe.add_action(
life,
{
"action": "dijkstra_move",
"rolldown": True,
"goals": [list(p) for p in _move_positions],
"orig_goals": _move_positions,
"avoid_positions": _avoid_positions,
"reason": "positioning for attack",
},
999,
)
return False
return True
