def save_to_ontology(event):
"""Saving an event into the graph"""
graph.set((home['clock'],
qol['hasValue'],
Literal(event['date'])))
if 'signal' in event:
# if we deal with a sensor event
sparql_event = graph.query(
event_query,
initBindings={'sensorLabel': Literal(event['sensor']),
'stateLabel': Literal(event['signal'])})
for row in sparql_event:
graph.set((row.sensor, qol['lastUpdate'], Literal(event['date'])))
graph.set((row.sensor, qol['hasLastUpdate'], Literal(True)))
graph.set((row.sensor, qol['hasCurrentState'], row.state))
class Sensor(object):
def __init__(self, sensor, room):
self.room = room
self.sensor = sensor
self.on_states = []
self.room.add_sensor(self)
# INIT
house = graph.value(predicate=RDF.type, object=qol["House"])
room_query = graph.query(
"""SELECT DISTINCT ?room
WHERE {
?room rdf:type ?class .
?class rdfs:subClassOf* qol:Room .
?room qol:partOf ?house .
}""",
initBindings={"house": house},
initNs={"qol": qol},
)
rooms = {row.room: Room(row.room) for row in room_query}
# We don't care of door sensors here
sensor_query = graph.query(
"SELECT DISTINCT ?sensor ?room WHERE { ?sensor qol:deployedIn ?room . }", initNs={"qol": qol}
)
sensors = {row.sensor: Sensor(row.sensor, rooms[row.room]) for row in sensor_query}
def estimate_motion():
"""Saves the motion level of the patient into the ontology"""
