def __init__(self,name,admin_in,admin_out,sensor_spec, sensors_dir,sensor_in,store,swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor. __init__(self,name=name,admin_in=admin_in, admin_out=admin_out,sensor_spec=sensor_spec, sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in +'/stores/' + store + '/swarms/' + swarm +'/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json')as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference({"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(name=self.name, lstnr=AbstractSensorListener(self.name,topic = '/topic/' +self.sensor_in,config=self.swarm_config_ng,model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +self.sensor_in, id=2, ack='auto')
self.values = []
self.self_announcement()
class AbstractSensor(Sensor, threading.Thread):
def __init__(self,name,admin_in,admin_out,sensor_spec, sensors_dir,sensor_in,store,swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor. __init__(self,name=name,admin_in=admin_in, admin_out=admin_out,sensor_spec=sensor_spec, sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in +'/stores/' + store + '/swarms/' + swarm +'/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json')as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference({"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(name=self.name, lstnr=AbstractSensorListener(self.name,topic = '/topic/' +self.sensor_in,config=self.swarm_config_ng,model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +self.sensor_in, id=2, ack='auto')
self.values = []
self.self_announcement()
def run(self):
while True:
self.announcement_check()
values = self.abstract_listener.check_input()
self.send_payload(values)
self.check_recording(values)
time.sleep(0.5)
def self_announcement(self):
stores = []
if not os.path.exists(self.sensor_data_dir):
os.makedirs(self.sensor_data_dir)
store_dirs = os.listdir(self.sensor_data_dir)
for store_dir_name in store_dirs:
store = {}
store['name']= store_dir_name
swarms_dir = self.sensor_data_dir + '/' + store_dir_name + '/swarms/'
if os.path.exists(swarms_dir):
swarm_dir_names = os.listdir(self.sensor_data_dir + '/' + store_dir_name + '/swarms/' )
print store_dir_name
swarms = []
for swarm_dir_name in swarm_dir_names:
if os.path.exists(self.sensor_data_dir + '/' + store_dir_name + '/swarms/'+swarm_dir_name + '/model_save/'):
print "jajajajajaaaaaaaaaa..........................................."
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
swarms.append(swarm_dir_name)
print '\t%s' % swarm_dir_name
store['swarms']= swarms
stores.append(store)
announce = {'message': {'type': "sensor_announcement",
'sensor': {'name': self.name, 'sensor_items': self.swarm_config_ng.get_column_names(self.swarm_config_ng.get_field_names()),
'stores': store_dirs,
'store_ng': stores
}
}
}
self.connection.send(body=json.dumps(announce), destination=self.admin_in)
class AbstractSensor(Sensor, threading.Thread):
def __init__(self, name, admin_in, admin_out, sensor_spec, sensors_dir,
sensor_in, store, swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor.__init__(self,
name=name,
admin_in=admin_in,
admin_out=admin_out,
sensor_spec=sensor_spec,
sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in + '/stores/' + store + '/swarms/' + swarm + '/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json') as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(
getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference(
{"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(
name=self.name,
lstnr=AbstractSensorListener(self.name,
topic='/topic/' + self.sensor_in,
config=self.swarm_config_ng,
model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(
name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +
self.sensor_in,
id=2,
ack='auto')
self.values = []
self.self_announcement()
def run(self):
while True:
self.announcement_check()
values = self.abstract_listener.check_input()
self.send_payload(values)
self.check_recording(values)
time.sleep(0.5)
def self_announcement(self):
stores = []
if not os.path.exists(self.sensor_data_dir):
os.makedirs(self.sensor_data_dir)
store_dirs = os.listdir(self.sensor_data_dir)
for store_dir_name in store_dirs:
store = {}
store['name'] = store_dir_name
swarms_dir = self.sensor_data_dir + '/' + store_dir_name + '/swarms/'
if os.path.exists(swarms_dir):
swarm_dir_names = os.listdir(self.sensor_data_dir + '/' +
store_dir_name + '/swarms/')
print store_dir_name
swarms = []
for swarm_dir_name in swarm_dir_names:
if os.path.exists(self.sensor_data_dir + '/' +
store_dir_name + '/swarms/' +
swarm_dir_name + '/model_save/'):
print "jajajajajaaaaaaaaaa..........................................."
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
swarms.append(swarm_dir_name)
print '\t%s' % swarm_dir_name
store['swarms'] = swarms
stores.append(store)
announce = {
'message': {
'type': "sensor_announcement",
'sensor': {
'name':
self.name,
'sensor_items':
self.swarm_config_ng.get_column_names(
self.swarm_config_ng.get_field_names()),
'stores':
store_dirs,
'store_ng':
stores
}
}
}
self.connection.send(body=json.dumps(announce),
destination=self.admin_in)
def __init__(self, name, admin_in, admin_out, sensor_spec, sensors_dir,
sensor_in, store, swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor.__init__(self,
name=name,
admin_in=admin_in,
admin_out=admin_out,
sensor_spec=sensor_spec,
sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in + '/stores/' + store + '/swarms/' + swarm + '/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json') as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(
getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference(
{"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(
name=self.name,
lstnr=AbstractSensorListener(self.name,
topic='/topic/' + self.sensor_in,
config=self.swarm_config_ng,
model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(
name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +
self.sensor_in,
id=2,
ack='auto')
self.values = []
self.self_announcement()
def __init__(self, config :dict) -> None:
super().__init__()
self.__azure_config = AzureConfig(config)
self.__swarm_config = SwarmConfig(config)