def main():
global results_queue
global event_loop
global pinger
args = parse_args()
log_format = '%(asctime)s %(levelname)s:%(module)s:%(funcName)s# ' \
+ '%(message)s'
if args.foreground:
logging.basicConfig(format=log_format, level=args.log_level)
else:
log_filename = env.get_env_string('PROBER_LOG_FILE')
logging.basicConfig(filename=log_filename,
format=log_format,
level=args.log_level)
setup_signal_handler()
results_queue = TQueue()
unconfirmed_list = []
hosts = []
logging.info("Starting ping thread")
pinger = Pinger(hosts, output=results_queue)
pinger.start()
logging.info("Starting event loop")
event_loop = asyncio.get_event_loop()
main_task = maintain_collector_connection(results_queue, unconfirmed_list)
try:
event_loop.run_until_complete(main_task)
except KeyboardInterrupt:
logging.debug("Caught KeyboardInterrupt in main()")
shutdown()
def main():
p = Pinger(timeout=5)
p.loop()
while True:
try:
sleep(1)
except KeyboardInterrupt:
break
p.stop()
def monitor():
ping = Pinger()
ping.thread_count = 4
while True:
ping.hosts = pingips()
nodes=ping.start()
for ip in nodes['dead']:
updatedead(ip)
time.sleep(1)
os.system("clear")
select()
def __init__(self):
self.blah = "blah"
self.ping = Pinger()
self.network = [
"10.35.0", "10.35.180", "10.35.155", "10.36.0", "10.36.1",
"10.255.0", "10.255.1", "10.255.2", "10.255.3", "172.18.1",
"172.18.3", "172.18.10"
]
# self.network = ["10.35.180"] # testing network
self.today = datetime.datetime.now()
self.dayNow = self.today.strftime('%Y-%m-%d')
self.parentDir = os.path.dirname(os.path.realpath(__file__))
self.logDir_main = os.path.join(self.parentDir, "logs")
self.logDir_responses = os.path.join(self.parentDir, "logs",
"ping_responses")
self.htmlOutDir = os.path.join("/" + "var", "www", "html",
"pings" + "/")
def updateScilentVm(self):
from pinger import Pinger
alivestatus = self.engine.getModel('AliveStatus')
ne_server = self.engine.getModel('NEServer')
dtnow = datetime.now()
interval = dtnow - timedelta(minutes=self.interval)
ping = Pinger()
hosts = []
for alive_rs in self.db_session.query(alivestatus).filter(
alivestatus.updated_at < interval):
ne_rs = self.db_session.query(ne_server).filter(
ne_server.uuid == alive_rs.uuid).one()
if ping.ping(ne_rs.nic2):
self.db_session.query(alivestatus).filter_by(uuid=uuid).update(
{'updated_at': datetime.now()})
self.db_session.commit()
help='Ping the specified host until stopped.')
# Number of pings
parser.add_argument('-n',
dest='count',
type=int,
help='Number of echo requests to send.')
# Interval between pings (for Continous is True or Count > 1)
parser.add_argument(
'-i',
dest='interval',
type=int,
default=defaults.interval,
help='Wait interval seconds between sending each packet.')
args = parser.parse_args()
# If multiple pings, user Pinger()
if args.continuous or args.count:
sys.exit(
Pinger(args.hostname, args.size, args.timeout, args.continuous,
args.count, args.interval))
# Otherwise, just use single ping
else:
p = Ping(args.hostname, args.size, args.timeout)
delay = p.do()
print p
print delay
def reportAlive(self):
aliveinfo = AliveInfo()
ping = Pinger()
hosts = []
hosts.append(self.api_conf['public_router'])
hosts.append(self.api_conf['public_gateway'])
ex_hosts = []
ex_hosts.append(self.api_conf['aws'])
ex_hosts.append(self.api_conf['kt'])
ex_hosts.append(self.api_conf['legacy'])
ping.hosts = hosts
status = ping.start()
status2 = ping.start()
if self.api_conf['public_router'] in status['alive'] or self.api_conf[
'public_router'] in status2['alive']:
aliveinfo.normal_public_fx = True
aliveinfo.normal_public_fx_latency = status['alive'][
self.api_conf['public_router']]
else:
aliveinfo.normal_public_fx = False
aliveinfo.normal_public_fx_latency = status['dead'][
self.api_conf['public_router']]
if self.api_conf['public_gateway'] in status['alive'] or self.api_conf[
'public_gateway'] in status2['alive']:
aliveinfo.normal_public_fl = True
aliveinfo.normal_public_fl_latency = status['alive'][
self.api_conf['public_gateway']]
else:
ping.hosts = ex_hosts
ex_status = ping.start()
if len(ex_status['alive']) > 0:
aliveinfo.normal_public_fl = True
if ex_status['alive'].has_key(self.api_conf['aws']):
aliveinfo.normal_public_fl_latency = ex_status['alive'][
self.api_conf['aws']]
elif ex_status['alive'].has_key(self.api_conf['kt']):
aliveinfo.normal_public_fl_latency = ex_status['alive'][
self.api_conf['kt']]
elif ex_status['alive'].has_key(self.api_conf['legacy']):
aliveinfo.normal_public_fl_latency = ex_status['alive'][
self.api_conf['legacy']]
else:
aliveinfo.normal_public_fl_latency = -1
else:
aliveinfo.normal_public_fl = False
aliveinfo.normal_public_fl_latency = ex_status['dead'][
self.api_conf['aws']]
aliveinfo.wmi_private_fx = None
aliveinfo.wmi_private_fl = None
aliveinfo.wmi_public_fx = None
aliveinfo.wmi_public_fl = None
#influx_container = "tc_npath_monitor,host=%s normal_public_fx=%d,normal_public_fl=%d,normal_public_fx_latency=%.4f,normal_public_fl_latency=%.4f" %(self.data['name'], aliveinfo.normal_public_fx,
#aliveinfo.normal_public_fl,
#aliveinfo.normal_public_fx_latency,
#aliveinfo.normal_public_fl_latency)
metric = "normal_npath"
json_body = [{
"measurement": metric,
"tags": {
"host": self.data['name'],
},
"fields": {
"normal_public_fx":
aliveinfo.normal_public_fx,
"normal_public_fl":
aliveinfo.normal_public_fl,
"normal_public_fx_latency":
round(aliveinfo.normal_public_fx_latency, 3),
"normal_public_fl_latency":
round(aliveinfo.normal_public_fl_latency, 3)
}
}]
self.influxdb_client.write_points(json_body)
#print influx_container
#headers = { "Content-Type" : "text/plain"}
#headers = {}
#postData(DB_URL,influx_container,headers)
ret = self.tcrpc.reportAlive(self.data['uuid'], aliveinfo)
return ret
from flask import Flask, render_template, json
from pinger import Pinger
from configparser import ConfigParser
import _thread
import time
app = Flask(__name__,
static_url_path='',
static_folder='../static',
template_folder='../templates')
config = ConfigParser()
pinger = Pinger(config)
def update_values():
while True:
pinger.ping_all()
time.sleep(config.get_check_interval())
@app.route('/')
def index():
return render_template('index.html',
title=config.get_name(),
description=config.get_description(),
icon=config.get_icon())
@app.route("/json")
def get_data():
response = app.response_class(response=json.dumps(pinger.get_values()),
def ping(timeout=500, packet_size=55, *args, **kwargs):
hosts = ('192.168.5.5', '192.168.5.18', '8.8.8.8', 'ucla.edu')
p = Pinger(hosts, timeout, packet_size, *args, **kwargs)
p.run()
from pinger import Pinger import db_module pinger = Pinger(db=db_module) pinger.start_ping()
# Define the number of discrete depths between the surface and seabed.
num_actions = 6
# Define the environment with the number of discrete depths for the detectable
# object.
env = Environment(num_actions)
# Define the LRI automata with the same number of actions. This number does
# not correspond to the number of receivers on the array. It is merely the
# representation of the array's ability to detect the object at that depth.
lri = LRI(num_actions) # The learning automata.
# The most probable depth that the object exists at, as calculated by the
# learner.
bestdepth = np.zeros(num_actions)
# The penalty probabilities for the learner.
E = [0.1, 0.2, 0.4, 0.2, 0.01, 0.09]
det_obj = Pinger(E) # Create the detectable object.
# Run 5 individual experiments experiments.
for k in range(5):
# Generate an ensemble of 100000 experiments
for j in range(100000):
# reset the action probabilities.
lri.reset_actions()
# Run a single experiment. Terminate if it reaches 10000 iterations.
for i in range(10000):
# Define m as the next action predicting the depth of the object.
m = lri.next_action()
# Defin req as the next detectable object depth.
req = det_obj.request()
# reward if m = req.
resp = env.response(m, req)
class MainClass:
def __init__(self):
self.blah = "blah"
self.ping = Pinger()
self.network = [
"10.35.0", "10.35.180", "10.35.155", "10.36.0", "10.36.1",
"10.255.0", "10.255.1", "10.255.2", "10.255.3", "172.18.1",
"172.18.3", "172.18.10"
]
# self.network = ["10.35.180"] # testing network
self.today = datetime.datetime.now()
self.dayNow = self.today.strftime('%Y-%m-%d')
self.parentDir = os.path.dirname(os.path.realpath(__file__))
self.logDir_main = os.path.join(self.parentDir, "logs")
self.logDir_responses = os.path.join(self.parentDir, "logs",
"ping_responses")
self.htmlOutDir = os.path.join("/" + "var", "www", "html",
"pings" + "/")
def pingNetwork(self):
self.setupEnv()
# pause = raw_input("ctrl-C to stop, enter to continue")
for network in self.network:
networkFile = (os.path.join(
self.logDir_responses,
network + "-" + self.dayNow + "-" + "responses.txt"))
with open(networkFile, "a+") as outfile:
with open(os.path.join(self.logDir_main, network),
"a+") as netFileOut:
netSummary = []
count = 0
for _ in range(1, 254): #testing
result = self.ping.pingIt(network, _)
if result == True:
count += 1
outfile.write(
str(network) + "." + str(_) + "," + "True" +
"\n")
else:
netSummary.append(result)
# for item in netSummary:
outfile.write(result + "," + "False" + "\n")
print "Pings from network %s.x: %s" % (network, count)
netFileOut.write(str(count) + "," + self.dayNow + "\n")
self.makeWebPage(networkFile, network)
def setupEnv(self):
if not os.path.exists(self.logDir_main):
print "Creating log directory"
os.mkdir(self.logDir_main)
if not os.path.exists(self.logDir_responses):
print "Creating responses directory"
os.mkdir(self.logDir_responses)
def makeWebPage(self, networkFile, network):
with open(networkFile, "r") as inFile:
with open(os.path.join(self.htmlOutDir + network + ".html"),
"w") as outFile:
outFile.write("<!DOCTYPE html>")
outFile.write("<html>")
outFile.write("<head>")
outFile.write(" <link rel=stylesheet href=style.css>")
outFile.write("</head>")
outFile.write("<body>")
outFile.write("<h2>" + "Pings from " + network + " on " +
self.dayNow + "</h2>")
outFile.write("<table border=1>" + "\n")
for line in inFile:
splitLine = line.split(",")
pingResponse = splitLine[1].strip()
if pingResponse == "True":
responseColor = "#00FF00"
else:
responseColor = "#FF0000"
outFile.write("<tr>" + "<td bgcolor=" + responseColor +
">" + "{:>15}".format(splitLine[0]) +
"</td>" + "<td>" +
"{:>25}".format(splitLine[1]) + "</td>" +
" " + "<td>" +
"{:>40}".format("placeholder2" +
"</td></tr>" + "\n"))
outFile.write("</table>")
outFile.write("</body>")
outFile.write("</html>")
# object.
env = Environment(num_actions)
# Define the LRI automata with the same number of actions. This number does
# not correspond to the number of receivers on the array. It is merely the
# representation of the array's ability to detect the object at that depth.
lrp = LRP(num_actions) # The learning automata.
# The most probable depth that the object exists at, as calculated by the
# learner.
bestdepth = np.zeros(num_actions)
# Define the Markovian Switching Environment that will feed probabilities to
# the Pinger object.
Es = [[0.1, 0.2, 0.4, 0.2, 0.01, 0.09], [0, 0, 0.8, 0.1, 0, 0.1],
[0, 0, 0, 1, 0, 0], [0.1, 0.1, 0.6, 0.05, 0.01, 0.04]]
mse = MSE(Es)
det_obj = Pinger(mse.env_now()) # Create the detectable object.
# Run 5 individual experiments experiments.
for k in range(len(Es)):
# Generate an ensemble of n experiments
for j in range(n):
# reset the action probabilities.
lrp.reset_actions()
# Run a single experiment. Terminate if it reaches 10000 iterations.
while (True):
# Define m as the next action predicting the depth of the object.
m = lrp.next_action()
# Defin req as the next detectable object depth.
req = det_obj.request()
# reward if m = req.
resp = env.response(m, req)
def service():
app = Pinger()
app.run()
# Define the Markovian Switching Environment that will feed probabilities to
# the Pinger object.
Es = [[0.48796, 0.024438, 0.067891, 0.41971, 0.00],
[0.021431, 0.071479, 0.40562, 0.50147, 0.00],
[0.018288, 0.083153, 0.50582, 0.39274, 0.00],
[0.48455, 0.015527, 0.18197, 0.31795, 0.00],
[0.01675, 0.58845, 0.11313, 0.28167, 0.00]]
En = [[0.021431, 0.071479, 0.40562, 0.50147, 0.00],
[0.48796, 0.024438, 0.067891, 0.41971, 0.00],
[0.018288, 0.083153, 0.50582, 0.39274, 0.00],
[0.01675, 0.58845, 0.11313, 0.28167, 0.00],
[0.48455, 0.015527, 0.18197, 0.31795, 0.00]]
mse = MSE(Es)
mse1 = MSE(En)
det_obj = Pinger(mse.env_now()) # Create the detectable object.
first_uav = Pinger(mse1.env_now())
# set up transmission vectors
for i in range(num_actions):
transmission.append(turtle.Turtle())
transmission1.append(turtle.Turtle())
# Run 5 individual experiments experiments.
for k in range(len(mse.envs)):
# Generate an ensemble of n experiments
source.goto(-300, depths[k])
receiver.clear()
receiver1.clear()
for i in range(num_actions):
transmission[i].clear()
'''Elevator test.'''
from discretized_lri import DLRI as DLRI
from environment import Environment
from pinger import Pinger
import numpy as np
import helpers as h
import math
num_actions = 6
env = Environment(num_actions)
dlri = DLRI(num_actions)
bestdepth = np.zeros(num_actions)
E = [0.1, 0.2, 0.4, 0.2, 0.01, 0.09]
det_obj = Pinger(E)
for k in range(5):
for j in range(1000):
# Caught me again...
dlri.p = np.array(h.make_dp(num_actions))
m = math.floor(num_actions / 2)
while (True):
req = det_obj.request()
resp = env.response(m, req)
if (not resp):
dlri.do_reward(m)
else:
dlri.do_penalty()
m = dlri.next_action()
if (max(dlri.p) == (num_actions * num_actions)):
# The best depth counting from 0 (seasurface).
bestdepth[np.argmax(dlri.p)] += 1
if __name__ == '__main__':
monitor_port = 5000
app_port = 5001
if len(sys.argv) == 2:
app_port = sys.argv[1]
interval = 15
log.info('PORT {} ARGS {}'.format(app_port, len(sys.argv)))
log.info(sys.argv)
pinger = Pinger(
app_name='reporting',
app_host='127.0.0.1',
app_port=app_port,
monitor_host='127.0.0.1',
monitor_port=monitor_port,
interval=interval
)
rt = RepeatedTimer(interval=interval, function=pinger.ping)
try:
# app = Flask(__name__)
# app.run(host='127.0.0.1', port=5001)
app = connexion.App(__name__, specification_dir='./openapi')
app.add_api('swagger.yml')
# NOTE: debug=True causes the restart
app.run(host='127.0.0.1', port=app_port, debug=False)
finally:
pinger.shutdown()
rt.stop()
from lrp import Linear_Reward_Penalty as LRP
from environment import Environment
from pinger import Pinger
import tune_lrp as tune
import numpy as np
test_lrp = LRP(5)
penaly_probs = [0.3, 0.1, 0.1, 0.1, 0.4]
penalizer = Pinger(np.array(penaly_probs))
env = Environment(5)
a = tune.find_optimal_a(test_lrp, env, penalizer)
print("The value for a after tuning is " + str(test_lrp.a))
b = tune.find_optimal_b(test_lrp, env, penalizer)
print("The value for b after tuning is " + str(test_lrp.b))
test_lrp.a = a
test_lrp.b = b
n = 10000
bestdepth = np.zeros(5)
for j in range(n):
# reset the action probabilities.
test_lrp.reset_actions()
# Run a single experiment. Terminate if it reaches 10000 iterations.
while (True):
# Define m as the next action predicting the depth of the object.
m = test_lrp.next_action()
# Define req as the next detectable object depth.
req = penalizer.request()
# reward if m = req.
resp = env.response(m, req)
if (not resp):
test_lrp.do_reward(m)
