def __init__(self,
actor,
target_speed=None,
plan=None,
blackboard_queue_name=None,
avoid_collision=False,
name="FollowWaypoints"):
"""
Set up actor and local planner
"""
super(WaypointFollower, self).__init__(name)
self._actor_list = []
self._actor_list.append(actor)
self._target_speed = target_speed
self._local_planner_list = []
self._plan = plan
self._blackboard_queue_name = blackboard_queue_name
if blackboard_queue_name is not None:
self._queue = Blackboard().get(blackboard_queue_name)
self._args_lateral_dict = {
'K_P': 1.0,
'K_D': 0.01,
'K_I': 0.0,
'dt': 0.05
}
self._avoid_collision = avoid_collision
def update(self):
'''
mild pain: ps <= 5
moderate: ps 6,7
severe: ps >= 8
'''
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs[
'hypoxemia'].score / 1000.
if blackboard.Signs['pain severity'].binary:
s = blackboard.Signs['pain severity'].score / 1000.
ps = int(blackboard.Signs['pain severity'].value)
if ps <= 5:
blackboard.feedback['nitronox'] += self.posi * s
blackboard.feedback['acetaminophen'] += self.posi * s
blackboard.feedback['ibuprofen'] += self.posi * s
elif ps == 6 or ps == 7:
blackboard.feedback['toradol'] += self.posi * s
else:
blackboard.feedback['normal saline'] += self.posi * s
blackboard.feedback['fentanyl'] += self.posi * s
# blackboard.feedback['morphine sulfate'] += self.posi * s
if blackboard.Signs['nausea'].binary:
blackboard.feedback['ondansetron'] += self.posi * blackboard.Signs[
'nausea'].score / 1000.
elif blackboard.Signs['vomiting'].binary:
blackboard.feedback['ondansetron'] += self.posi * blackboard.Signs[
'vomiting'].score / 1000.
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000.
# have to do?
# blackboard.feedback['capnography'] += self.posi
# blackboard.feedback['bronchodilator'] += self.posi
# blackboard.feedback['metered dose inhaler'] += self.posi
# if respiratory distress
if blackboard.Signs['shortness of breath'].binary and (blackboard.Signs['hypoxemia'].binary or blackboard.Signs['rhonchi'].binary):
s = blackboard.Signs['shortness of breath'].score / 1000. *\
((blackboard.Signs['hypoxemia'].binary * blackboard.Signs['hypoxemia'].score +\
blackboard.Signs['rhonchi'].binary * blackboard.Signs['rhonchi'].score) / \
(blackboard.Signs['hypoxemia'].binary +blackboard.Signs['rhonchi'].binary) / 1000.)
blackboard.feedback['bag valve mask ventilation'] += self.posi * s
# bvm ventilation have been done
if blackboard.Inters['bag valve mask ventilation'].binary:
blackboard.feedback['endotracheal tube'] += self.posi * s * blackboard.Inters['bag valve mask ventilation'].score / 1000.
if not blackboard.Signs['hypertension'].binary:
blackboard.feedback['albuterol'] += self.posi * s * blackboard.Signs['hypertension'].score / 1000.
blackboard.feedback['ipratropium'] += self.posi * s * blackboard.Signs['hypertension'].score / 1000.
if blackboard.Signs['wheezing'].binary:
blackboard.feedback['dexamethasone'] += self.posi * s * blackboard.Signs['wheezing'].score / 1000.
if blackboard.Signs['hypoxemia'].binary and blackboard.Signs['tachypnea'].binary \
and blackboard.Vitals['bp'].binary and blackboard.Vitals['bp'].value.strip().split('/')[0].isdigit() and int(blackboard.Vitals['bp'].value.strip().split('/')[0]) > 90:
blackboard.feedback['cpap'] += self.posi * s * blackboard.Signs['tachypnea'].score / 1000.
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000.
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
if blackboard.candi[0] == self.key and blackboard.Vitals['gcs'].value == '15':
blackboard.feedback['aspirin'] += self.posi
# consider add dependency on iv
if blackboard.Signs['substance abuse history'].binary:
blackboard.feedback['midazolam'] += self.posi * blackboard.Signs['substance abuse history'].score / 1000.
# blackboard.feedback['diazepam'] += self.posi * blackboard.Signs['substance abuse history'].score / 1000.
elif blackboard.Signs['abuse of substance'].binary:
blackboard.feedback['midazolam'] += self.posi * blackboard.Signs['abuse of substance'].score / 1000.
# blackboard.feedback['diazepam'] += self.posi * blackboard.Signs['abuse of substance'].score / 1000.
if 'STEMI' in blackboard.Vitals['ekg'].value and blackboard.Vitals['pain'].binary \
and blackboard.Vitals['bp'].binary and int(blackboard.Vitals['bp'].value.strip().split('/')[0]) > 100:
blackboard.feedback['nitroglycerin'] += self.posi * blackboard.Vitals['pain'].score / 1000. * blackboard.Vitals['bp'].score / 1000.
if blackboard.Vitals['pain'].binary and blackboard.Inters['nitroglycerin'].binary:
# blackboard.feedback['morphine'] += self.posi * blackboard.Vitals['pain'].score / 1000. * blackboard.Inters['nitroglycerin'].score / 1000.
blackboard.feedback['fentanyl'] += self.posi * blackboard.Vitals['pain'].score / 1000. * blackboard.Inters['nitroglycerin'].score / 1000.
return py_trees.Status.SUCCESS
def setup(self, unused_timeout=15):
'''
create a ConceptExtractor and initialize the patient status
the list here is the complete list
'''
'''
self.ce1 = CE.ConceptExtractor("Concept_List_1.csv")
self.ce1.StatusInit()
self.ce2 = CE.ConceptExtractor("Concept_List_2.csv")
self.ce2.StatusInit()
'''
vcl = pd.read_csv(self.exlist)
blackboard = Blackboard()
self.sce = CE.CEWithoutMM(self.slist, self.intlist,\
AllinOne_SS = self.aio_ss, AllinOne_Int = self.aio_int,\
neg_res = self.neg_res, WDistance = self.WDistance, aio_only = self.aio_only)
self.sce.StatusInit()
for item in vcl:
self.sce.SpecificInit(item)
self.vce = CE.ConceptExtractor(self.vlist)
#self.vce.StatusInit()
#self.ice = CE.ConceptExtractor(self.intlist)
#self.ice.StatusInit()
if self.inC:
self.vce.ConceptWrapper(self.inC)
blackboard.Signs = self.sce.Status
blackboard.Vitals = self.vce.Status
blackboard.Inters = self.sce.Interventions
blackboard.ConcLog = []
return True
def update(self):
blackboard = Blackboard()
# mm confidence encoding
TV = []
for item in blackboard.Signs:
if blackboard.Signs[item].binary:
TV.append(blackboard.Signs[item].score / 1000.)
else:
TV.append(0.)
#TV = [blackboard.Status[item].score/1000. for item in blackboard.Status]
# one-hot encoding
#TV = [float(blackboard.Signs[item].binary) for item in blackboard.Signs]
blackboard.TV = TV
maxsim = 0
result = ''
blackboard.ranking = []
for key in self.PV:
sim = 1 - spatial.distance.cosine(TV, self.PV[key])
blackboard.ranking.append((key, sim))
if sim > maxsim:
maxsim = sim
result = key
blackboard.protocol = result
blackboard.candi, blackboard.pos = rank(blackboard.ranking)[0], rank(
blackboard.ranking)[1]
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs[
'hypoxemia'].score / 1000.
if blackboard.Vitals['BP'].binary and int(
blackboard.Vitals['BP'].value.strip().split('/')[0]) <= 90:
blackboard.feedback[
'normal saline'] += self.posi * blackboard.Vitals[
'BP'].score / 1000.
if blackboard.Signs['abuse of substance'].binary:
blackboard.feedback['narcan'] += self.posi * blackboard.Signs[
'abuse of substance'].score / 1000.
elif blackboard.Signs['pin point pupils'].binary:
blackboard.feedback['narcan'] += self.posi * blackboard.Signs[
'pin point pupils'].score / 1000.
elif blackboard.Signs['decreased mental status'].binary:
blackboard.feedback['narcan'] += self.posi * blackboard.Signs[
'decreased mental status'].score / 1000.
elif blackboard.Signs['hypotension'].binary:
blackboard.feedback['narcan'] += self.posi * blackboard.Signs[
'hypotension'].score / 1000.
elif blackboard.Signs['bradypnea'].binary:
blackboard.feedback['narcan'] += self.posi * blackboard.Signs[
'bradypnea'].score / 1000.
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
return py_trees.Status.SUCCESS
def setup(self, unused_timeout = 15):
'''
create a ConceptExtractor and initialize the patient status
the list here is the complete list
'''
'''
self.ce1 = CE.ConceptExtractor("Concept_List_1.csv")
self.ce1.StatusInit()
self.ce2 = CE.ConceptExtractor("Concept_List_2.csv")
self.ce2.StatusInit()
'''
vcl = pd.read_csv(self.exlist)
blackboard = Blackboard()
self.sce = CE.ConceptExtractor(self.slist)
self.sce.StatusInit()
for item in vcl:
self.sce.SpecificInit(item)
self.vce = CE.ConceptExtractor(self.vlist)
#self.vce.StatusInit()
self.ice = CE.ConceptExtractor(self.intlist)
self.ice.StatusInit()
if self.inC:
self.vce.ConceptWrapper(self.inC)
blackboard.Signs = self.sce.Status
blackboard.Vitals = self.vce.Status
blackboard.Inters = self.ice.Status
blackboard.ConcLog = []
return True
def update(self):
blackboard = Blackboard()
num = sum(blackboard.pos[:3])
for idx,item in enumerate(blackboard.candi):
if idx < 3:
blackboard.protocol_flag[item] = (True,blackboard.pos[idx]/num)
return py_trees.Status.SUCCESS
def setup(self, unused_timeout = 15):
blackboard = Blackboard()
PC = dict()
pro_df = pd.read_excel(self.protocols)
for line in pro_df.iterrows():
line_ss = [(i.strip().lower()[:-1],i.strip().lower()[-1]) for i in line[1]['Signs&Symptoms'].split(';')]
if not pd.isnull(line[1]['Possible signs&symptoms additions']):
line_ssr = [(i.strip().lower()[:-1],i.strip().lower()[-1]) for i in line[1]['Possible signs&symptoms additions'].split(';')]
name = line[1]['Protocol']
PC[name] = line_ss + line_ssr
self.PV = dict()
for item in PC:
vec = list()
su = 0.
for i in blackboard.Signs:
res = 0.
for j in PC[item]:
if i == j[0]:
res = 8.**int(j[1])
break;
su += res
vec.append(res)
for i in xrange(len(vec)):
vec[i] = vec[i] / su
self.PV[item] = vec
blackboard.PV = self.PV
return True
def __init__(self,
actor_type_list,
transform,
threshold,
blackboard_queue_name,
actor_limit=999,
name="ActorSource",
# ======== additional args ========
init_speed=0., # initial speed of spawned vehicle
randomize=False,
):
"""
Setup class members
"""
super(ActorSource, self).__init__(name)
self._world = CarlaDataProvider.get_world()
self._actor_types = actor_type_list
self._spawn_point = transform
self._threshold = threshold + 5. # todo fix this with accurate vehicle length
self._queue = Blackboard().get(blackboard_queue_name)
self._actor_limit = actor_limit
self._last_blocking_actor = None
# ========== additional attributes ==========
self.init_speed = init_speed
self.randomize = randomize
self.distance_gap = None
def __init__(self, debug=False, name="TrafficJamChecker"):
super(TrafficJamChecker, self).__init__(name)
self.debug = debug
self.blackboard = Blackboard()
self.world = CarlaDataProvider.get_world()
self.map = CarlaDataProvider.get_map()
self.list_intersection_waypoints = []
# remove initial collisions during setup
list_actors = list(CarlaActorPool.get_actors())
for _, actor in list_actors:
if actor.attributes['role_name'] == 'autopilot':
if detect_lane_obstacle(actor, margin=0.2):
CarlaActorPool.remove_actor_by_id(actor.id)
# prepare a table to check for stalled vehicles during the execution of the scenario
self.table_blocked_actors = {}
current_game_time = GameTime.get_time()
for actor_id, actor in CarlaActorPool.get_actors():
if actor.attributes['role_name'] == 'autopilot':
actor.set_autopilot(True)
self.table_blocked_actors[actor_id] = {
'location': actor.get_location(),
'time': current_game_time
}
self.logger.debug("%s.__init__()" % (self.__class__.__name__))
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['combative'].binary:
s = blackboard.Signs['combative'].score / 1000.
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000. * s
blackboard.feedback['physical restraint'] += self.posi * s
if blackboard.Signs['agitation'].binary:
blackboard.feedback['midazolam'] += self.posi * blackboard.Signs['agitation'].score / 1000. * s
# blackboard.feedback['diazepam'] += self.posi * blackboard.Signs['agitation'].score / 1000. * s
# blackboard.feedback['geodon'] += self.posi * blackboard.Signs['agitation'].score / 1000. * s
blackboard.feedback['transport'] += self.posi * s
elif blackboard.Signs['violent'].binary:
s = blackboard.Signs['violent'].score / 1000.
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000. * s
blackboard.feedback['physical restraint'] += self.posi * s
if not blackboard.Signs['hypotension'].binary and blackboard.Signs['agitation'].binary:
blackboard.feedback['midazolam'] += self.posi * blackboard.Signs['agitation'].score / 1000. *\
blackboard.Signs['hypotension'].score / 1000. * s
# blackboard.feedback['diazepam'] += self.posi * blackboard.Signs['agitation'].score / 1000. * s
# blackboard.feedback['geodon'] += self.posi * blackboard.Signs['agitation'].score / 1000. * s
blackboard.feedback['transport'] += self.posi * s
else:
# blackboard.feedback['encourage patient to relax'] += self.posi
blackboard.feedback['transport'] += self.posi
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
# posi = Normalized confidence score (dervied from cosine similarity) for protocol indicated by key
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS #might be better to return FAILURE?
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000.
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
if blackboard.candi[0] == self.key and blackboard.Vitals['GCS'].value == '15':
blackboard.feedback['aspirin'] += self.posi
# consider add dependency on iv
if blackboard.Signs['substance abuse history'].binary:
blackboard.feedback['midazolam'] += self.posi * blackboard.Signs['substance abuse history'].score / 1000.
# blackboard.feedback['diazepam'] += self.posi * blackboard.Signs['substance abuse history'].score / 1000.
elif blackboard.Signs['abuse of substance'].binary:
blackboard.feedback['midazolam'] += self.posi * blackboard.Signs['abuse of substance'].score / 1000.
# blackboard.feedback['diazepam'] += self.posi * blackboard.Signs['abuse of substance'].score / 1000.
if 'STEMI' in blackboard.Vitals['EKG'].value and blackboard.Vitals['Pain'].binary \
and blackboard.Vitals['BP'].binary and int(blackboard.Vitals['BP'].value.strip().split('/')[0]) > 100:
blackboard.feedback['nitroglycerin'] += self.posi * blackboard.Vitals['Pain'].score / 1000. * blackboard.Vitals['BP'].score / 1000.
if blackboard.Vitals['Pain'].binary and blackboard.Inters['nitroglycerin'].binary:
# blackboard.feedback['morphine'] += self.posi * blackboard.Vitals['Pain'].score / 1000. * blackboard.Inters['nitroglycerin'].score / 1000.
blackboard.feedback['fentanyl'] += self.posi * blackboard.Vitals['Pain'].score / 1000. * blackboard.Inters['nitroglycerin'].score / 1000.
return py_trees.Status.SUCCESS
def terminate(self, new_status):
if new_status == Status.SUCCESS:
bb = Blackboard()
bb.set(
"biome_type",
analyze_type(bb.get("biome_type"), self.color_original,
self.color_left, self.color_right))
bb.set("biome_check", False)
def setup(self, unused_timeout = 15):
blackboard = Blackboard()
blackboard.protocol_flag = dict()
blackboard.feedback = dict()
for i in blackboard.PV:
blackboard.protocol_flag[i] = (False,0.)
for i in blackboard.Inters:
blackboard.feedback[i] = 0.
return True
def __init__(self, ego_vehicle, debug=False, name="TrafficLightManipulator"):
super(TrafficLightManipulator, self).__init__(name)
self.ego_vehicle = ego_vehicle
self.debug = debug
self.blackboard = Blackboard()
self.target_traffic_light = None
self.annotations = None
self.reset_annotations = None
self.intervention = False
self.logger.debug("%s.__init__()" % (self.__class__.__name__))
def setup(self, unused_timeout = 15):
level = 'I/P'
blackboard = Blackboard()
#blackboard.action = []
blackboard.level = level
blackboard.tick_num = 0
blackboard.protocol = "Universal Patient Care"
self.text = ''
self.sent_text = []
return True
def setup(self, unused_timeout=15):
#level = raw_input("Please type in your certification(EMT,A,I/P): \n")
level = 'I/P'
blackboard = Blackboard()
#blackboard.action = []
blackboard.level = level
blackboard.tick_num = 0
blackboard.protocol = "Universal Patient Care"
self.text = ''
self.sent_text = []
return True
def update(self):
bb = Blackboard()
if bb.get("color_floor"):
return Status.SUCCESS
color = self.robot.get_color()
result = bb.set("color_floor", color)
if result:
return Status.RUNNING
else:
return Status.FAILURE
def __init__(self):
super(BTAgent, self)
global BLACKBOARD
py_trees.logging.level = py_trees.logging.Level.DEBUG
BLACKBOARD = Blackboard()
self.tree = BehaviourTree(self.create_tree())
_thread.start_new_thread(self.tree.tick_tock,
(50, CONTINUOUS_TICK_TOCK, None, None))
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000.
blackboard.feedback['normal saline'] += self.posi
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
return py_trees.Status.SUCCESS
def main():
"""
Entry point for the demo script.
"""
command_line_argument_parser().parse_args()
print(description())
py_trees.logging.level = py_trees.logging.Level.DEBUG
tree = create_tree()
tree.setup(timeout=15)
robot = Pose2D(250,250);
ball = Pose2D(int(random.random()*500),
int(random.random()*500))
bin = Pose2D(int(random.random()*500),
int(random.random()*500))
blackboard = Blackboard()
blackboard.set("robot", robot)
blackboard.set("ball", ball)
blackboard.set("bin", bin)
try:
while tree.status != py_trees.common.Status.SUCCESS:
print("tick")
tree.tick_once()
img = np.full((500,500,3), 0, dtype=np.uint8)
cv2.circle(img, (robot.x,robot.y), 10, (0,0,255), thickness=-1)
cv2.line(img, (robot.x, robot.y),
(robot.x + int(50*math.cos(robot.th+1.0)),
robot.y + int(50*math.sin(robot.th+1.0))),
(0,0,255), 1, cv2.LINE_AA)
cv2.line(img, (robot.x, robot.y),
(robot.x + int(50*math.cos(robot.th-1.0)),
robot.y + int(50*math.sin(robot.th-1.0))),
(0,0,255), 1, cv2.LINE_AA)
cv2.circle(img, (ball.x,ball.y), 10, (0,255,0), thickness=-1)
cv2.circle(img, (bin.x,bin.y), 10, (255,0,0), thickness=-1)
cv2.imshow('image', img)
cv2.waitKey(100)
if random.random() < 0.05:
ball.x = int(random.random()*500)
ball.y = int(random.random()*500)
print("\n")
except KeyboardInterrupt:
print("")
pass
def __init__(self, actor_type_list, transform, threshold, blackboard_queue_name,
actor_limit=7, name="ActorSource"):
"""
Setup class members
"""
super(ActorSource, self).__init__(name)
self._world = CarlaDataProvider.get_world()
self._actor_types = actor_type_list
self._spawn_point = transform
self._threshold = threshold
self._queue = Blackboard().get(blackboard_queue_name)
self._actor_limit = actor_limit
self._last_blocking_actor = None
def update(self):
blackboard = Blackboard()
blackboard.protocol_flag = dict()
blackboard.feedback = dict()
for i in blackboard.PV:
blackboard.protocol_flag[i] = (False,0.)
for i in blackboard.Inters:
blackboard.feedback[i] = 0.
num = sum(blackboard.pos[:3])
for idx,item in enumerate(blackboard.candi):
if idx < 3:
blackboard.protocol_flag[item] = (True,blackboard.pos[idx]/num)
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000.
blackboard.feedback['normal saline'] += self.posi
if not blackboard.Signs['hypotension'].binary and blackboard.Signs['seizure'].binary and blackboard.Vitals['glucose'].value > 60:
s = blackboard.Signs['seizure'].score / 1000. * blackboard.Vitals['glucose'].score / 1000. * blackboard.Signs['hypotension'].score / 1000.
blackboard.feedback['midazolam'] += self.posi * s
# blackboard.feedback['diazepam'] += self.posi * s
blackboard.feedback['cardiac monitor'] += self.posi
# recovery position?
blackboard.feedback['transport'] += self.posi
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
self.posi = blackboard.protocol_flag[self.key][1]
if self.posi == 0:
return py_trees.Status.SUCCESS
if blackboard.Signs['hypoxemia'].binary:
blackboard.feedback['oxygen'] += self.posi * blackboard.Signs['hypoxemia'].score / 1000.
blackboard.feedback['cardiac monitor'] += self.posi
blackboard.feedback['transport'] += self.posi
if blackboard.Signs['hypoglycemia'].binary:
s = blackboard.Signs['hypoglycemia'].score / 1000.
if blackboard.Signs['loss of consciousness'].binary:
blackboard.feedback['normal saline'] += self.posi * blackboard.Signs['loss of consciousness'].score / 1000. * s
blackboard.feedback['dextrose'] += self.posi * blackboard.Signs['loss of consciousness'].score / 1000. * s
else:
blackboard.feedback['oral glucose'] += self.posi * s
return py_trees.Status.SUCCESS
def update(self):
blackboard = Blackboard()
self.sce.ConceptExtract(blackboard.text)
blackboard.concepts = self.sce.concepts
blackboard.confi = self.sce.scores
self.sce.FirstExtract(blackboard.text, blackboard.tick_num)
self.Vital2Symptom()
blackboard.Signs = self.sce.Status
#self.vce.concepts = blackboard.concepts
#self.vce.scores = self.sce.scores
#self.vce.FirstExtract(blackboard.text, blackboard.tick_num)
blackboard.Vitals = self.vce.Status
#self.sce.DisplayStatus()
self.ice.concepts = blackboard.concepts
self.ice.scores = self.sce.scores
self.ice.FirstExtract(blackboard.text, blackboard.tick_num)
blackboard.Inters = self.ice.Status
self.ice.DisplayStatus()
blackboard.ConcLog += self.sce.Log + self.vce.Log
return py_trees.Status.SUCCESS
def update(self):
bb = Blackboard()
if bb.get("biome_check") and self.status != Status.RUNNING:
self.color_original = self.robot.get_color()
return Status.RUNNING
elif self.status == Status.RUNNING:
if not self.robot.is_running():
if self.turning:
if not self.color_left:
self.color_left = self.robot.get_color()
self.robot.turn_for(30, 0, 2)
if not self.color_right:
self.color_right = self.robot.get_color()
self.robot.turn_for(0, 30, 1)
else:
self.turning = False
return Status.SUCCESS
elif not self.left_done:
self.robot.turn_for(0, 30, 1)
self.turning = True
return Status.RUNNING
else:
return Status.INVALID
def setup(self, unused_timeout=15):
'''
create a ConceptExtractor and initialize the patient status
the list here is the complete list
'''
'''
self.ce1 = CE.ConceptExtractor("Concept_List_1.csv")
self.ce1.StatusInit()
self.ce2 = CE.ConceptExtractor("Concept_List_2.csv")
self.ce2.StatusInit()
'''
vcl = pd.read_csv(self.exlist)
blackboard = Blackboard()
# Signs and Symptoms Concept Extractor
self.sce = CE.ConceptExtractor(self.slist)
self.sce.StatusInit()
for item in vcl:
self.sce.SpecificInit(item)
# Vital Signs Concept Extractor
self.vce = CE.ConceptExtractor(self.vlist)
#self.vce.StatusInit()
# Interventions Concept Extractor
self.ice = CE.ConceptExtractor(self.intlist)
self.ice.StatusInit()
self.vce.StatusInit()
if self.inC:
self.vce.ConceptWrapper(self.inC)
# The list of vital signs we support (from RAA data)
pool = ['Pulse', 'Resp', 'BP', 'GCS', 'Glucose', 'SPO2', 'Pain', 'EKG']
for item in pool:
self.vce.SpecificInit(item)
blackboard.Signs = self.sce.Status
blackboard.Vitals = self.vce.Status
blackboard.Inters = self.ice.Status
blackboard.ConcLog = []
return True