def set_intvl(self, intvl):
paras = util.load_conf('conf')
if intvl >= 1:
MonitorManager._intvl = intvl
paras['intvl'] = intvl
util.update_conf('conf', paras)
self.restart()
def main():
cfg = load_conf()
if cfg["useSSL"]:
create_ssl_cert([cfg["backendIP"]])
if cfg["dbMode"] and cfg["sqlDatabase"] and cfg["sqlPassword"] and cfg[
"sqlServerIP"] and cfg["sqlUsername"]:
print("Using " + cfg["dbMode"] + " DB")
try:
conn = load_db_conn()[0]
if cfg["dbMode"] == "mssql":
init_mssql_db(conn)
elif cfg["dbMode"] == "mysql":
init_mysql_db(conn)
else:
print(
"Error! No valid db mode found. Please use mssql or mysql")
except Exception as e:
print(e)
else:
print("No db mode set.")
check_existing_token()
create_web_config()
server()
def server():
cfg = load_conf()
api_token = check_existing_token()
if cfg["useSSL"]:
http_server = WSGIServer(
(cfg["backendIP"], int(cfg["backendPort"])),
app,
certfile="ssl/cert.crt",
keyfile="ssl/key.pem",
)
print(
"Server started. Running on https://"
+ str(cfg["backendIP"])
+ ":"
+ str(cfg["backendPort"])
)
else:
http_server = WSGIServer((cfg["backendIP"], int(cfg["backendPort"])), app)
print(
"Server started. Running on http://"
+ str(cfg["backendIP"])
+ ":"
+ str(cfg["backendPort"])
)
print("API Token: " + api_token)
if cfg['parserIP']:
print("Parser IP set to: " + str(cfg["parserIP"]) + ":" + str(cfg["parserPort"]))
else:
print("No parser IP set.")
http_server.serve_forever()
def __init__(self,
pidfile='/tmp/test-monitor.pid',
stdin='/dev/stdin',
stdout='/dev/stdout',
stderr='/dev/stderr',
intvl=10,
logfile='/opt/monitor.log'):
super(MonitorManager, self).__init__(pidfile=pidfile, stdin=stdin, stdout=stdout, stderr=stderr)
paras = util.load_conf('conf')
MonitorManager._logfile = logfile
if not paras.has_key('intvl'):
MonitorManager._intvl = intvl
paras['intvl'] = intvl
else:
MonitorManager._intvl = int(paras['intvl'])
if paras.has_key('pollsters'):
tmp_list = eval(paras['pollsters'])
for poll in tmp_list:
p_name, cls = util.load_class(poll)
if p_name and cls:
MonitorManager._pollsters[p_name] = cls()
else:
MonitorManager._pollsters = OrderedDict()
util.update_conf('conf', paras)
def set_intvl(self, intvl):
paras = util.load_conf('conf')
if intvl >= 1:
MonitorManager._intvl = intvl
paras['intvl'] = intvl
util.update_conf('conf', paras)
self.restart()
def __init__(self,
pidfile='/tmp/test-monitor.pid',
stdin='/dev/stdin',
stdout='/dev/stdout',
stderr='/dev/stderr',
intvl=10,
logfile='/opt/monitor.log'):
super(MonitorManager, self).__init__(pidfile=pidfile,
stdin=stdin,
stdout=stdout,
stderr=stderr)
paras = util.load_conf('conf')
MonitorManager._logfile = logfile
if not paras.has_key('intvl'):
MonitorManager._intvl = intvl
paras['intvl'] = intvl
else:
MonitorManager._intvl = int(paras['intvl'])
if paras.has_key('pollsters'):
tmp_list = eval(paras['pollsters'])
for poll in tmp_list:
p_name, cls = util.load_class(poll)
if p_name and cls:
MonitorManager._pollsters[p_name] = cls()
else:
MonitorManager._pollsters = OrderedDict()
util.update_conf('conf', paras)
def set_pollsters(self, poll_list):
paras = util.load_conf('conf')
MonitorManager._pollsters = OrderedDict()
for poll in poll_list:
p_name, cls = util.load_class(poll)
if p_name and cls:
MonitorManager._pollsters[p_name] = cls()
if poll_list:
paras['pollsters']='%s' %poll_list
util.update_conf('conf', paras)
self.restart()
def set_pollsters(self, poll_list):
paras = util.load_conf('conf')
MonitorManager._pollsters = OrderedDict()
for poll in poll_list:
p_name, cls = util.load_class(poll)
if p_name and cls:
MonitorManager._pollsters[p_name] = cls()
if poll_list:
paras['pollsters'] = '%s' % poll_list
util.update_conf('conf', paras)
self.restart()
def before_request():
if request.endpoint in ('static', 'index'):
return
if not request.args:
return "No token provided! Add &token= to URL", 401
if api_token != request.args["token"]:
return "Unauthorized", 401
global cfg
cfg = load_conf()
if ((not cfg["dbMode"] or not cfg["sqlDatabase"] or not cfg["sqlPassword"]
or not cfg["sqlServerIP"] or not cfg["sqlUsername"])
and request.endpoint != 'updateConfig'
and request.endpoint != 'getBackendConfig'):
return "Settings incomplete!", 512
def optimise():
conf = load_conf('geojson/counties.geojson', 'Name')
#conf = load_conf('geojson/us.geojson', 'NAME')
#conf = load_conf('geojson/constituencies.geojson', 'pcon16nm')
regions = conf['regions']
neighbours = conf['neighbours']
rows = 13
cols = 16
#rows = 27
#cols = 45
grid = random_grid(regions, rows, cols)
adj_matrix = adjacency_matrix(neighbours, regions)
old_score = best_score = eval_candidate_mod(grid, adj_matrix)
# highest possible score.
max_score = len(neighbours)
#t = 0.05
#a = 0.9999999
# ['{:.9f}'.format(100*math.exp(-x/1)) for x in range(1, 9)]
# ['36.787944117', '13.533528324', '4.978706837', '1.831563889', '0.673794700', '0.247875218', '0.091188197', '0.033546263']
t = 0.6
# cooling schedule
a = 0.999999999
# ['{:.9f}'.format(100*math.exp(-x/0.3)) for x in range(1, 9)]
# ['3.567399335', '0.127263380', '0.004539993', '0.000161960', '0.000005778', '0.000000206', '0.000000007', '0.000000000']
min_temp = 0.3
# stash best result for final output + restarts.
best_grid = None
restarts = 0
restart_limit = 20
print_every = 100
best_s = ''
i = 0
last_cell = rows*cols - 1
#while 1:
for _ in range(0, 10000000):
# check if we should restart
# https://en.wikipedia.org/wiki/Simulated_annealing#Restarts
if best_score - old_score > restart_limit:
# we have deviated too far from a good solution..
grid = copy.deepcopy(best_grid)
old_score = best_score
restarts += 1
# and also restart the cooling schedule?
r1 = random_int(last_cell)
r2 = random_int(last_cell)
i1, j1 = r1 // cols, r1 % cols
i2, j2 = r2 // cols, r2 % cols
new_score = old_score
v1 = grid[i1][j1]
v2 = grid[i2][j2]
new_score -= eval_position(grid, adj_matrix, i1, j1)
new_score -= eval_position(grid, adj_matrix, i2, j2)
# dont swap empty positions...
if v1 is None and v1 is None:
continue
i = i+1
if t > min_temp: t = t*a
grid[i1][j1] = v2
grid[i2][j2] = v1
new_score += eval_position(grid, adj_matrix, i1, j1)
new_score += eval_position(grid, adj_matrix, i2, j2)
if acceptance_probability(old_score, new_score, t) >= random.random():
# accept the candidate move
old_score = new_score
if new_score > best_score:
# new maxima found
best_score = new_score
best_s = res_to_string(grid, regions, 'white', 'on_blue', 'on_grey')
best_grid = copy.deepcopy(grid)
else:
# exploring
if i % print_every == 0:
subprocess.call('clear', shell=True)
print(best_s)
print(res_to_string(grid, regions, 'cyan', 'on_grey', 'on_grey'))
print('temperature = {:.6f} state restarts = {}'.format(t, restarts))
graph_data = [('best result', best_score), ('current result', new_score)]
for g in ascii_graph.Pyasciigraph(force_max_value=max_score).graph(data=graph_data):
print(g)
else:
# reject the candidate move
grid[i1][j1] = v1
grid[i2][j2] = v2
import pickle
import os
import json
import math
import util
conf = util.load_conf()
data_folder_path = os.path.join(conf["input_folder"], '*.json')
print(data_folder_path)
filteredPlane = {}
# filter parameter
minDataCnt = 1
minTotalTime = util.min2ms(75) # 60min
minSequenceDuration = util.min2ms(75) # 30min
minSequenceCnt = 1
maxSequenceCnt = 1
maxSpeed = util.km2m(700) / util.hour2ms(1)
minSpeed = util.km2m(600) / util.hour2ms(1)
def filterPlanes(stat, filteredPlane):
longFlightCnt = 0
for key in stat:
# if stat[key]
# print(stat[key])
# break
if stat[key]['Count'] < minDataCnt: continue
if stat[key]['TotalTime'] < minTotalTime: continue
def first():
global cfg
global api_token
cfg = load_conf()
api_token = check_existing_token()
#!/usr/bin/env python
import logging, urllib
import datetime
from datetime import date, timedelta
import math
import mysql as Mysql
import domino_twilio as Twilio
import user as User
import team as Team
import email as Email
import notification as Notification
import util as Util
conf = Util.load_conf()
def frequent_alerts(since=7):
'''
Get the most frequent alerts
'''
alerts = Mysql.rawquery('''SELECT DISTINCT environment,colo,host,service, COUNT(*) AS count FROM alerts_history WHERE createDate BETWEEN DATE_SUB(CURDATE(),INTERVAL %s DAY) AND DATE_SUB(CURDATE(),INTERVAL -1 DAY) GROUP BY environment,colo,host,service ORDER BY count DESC;''' % (since))
for alert in alerts:
for key in alert:
if isinstance(alert[key], str):
if (alert[key].startswith("'") and alert[key].endswith("'")) or (alert[key].startswith('"') and alert[key].endswith('"')):
alert[key] = alert[key][1:-1]
return alerts
def round_date(t,unit):
'''
# use pulp - a python abstraction layer for linear programming modeling.
import multiprocessing
import math
from pulp import *
from util import load_conf
#conf = load_conf('geojson/countries.geojson', 'ctry16nm')
conf = load_conf('../geojson/regions.geojson', 'rgn16nm')
#conf = load_conf('geojson/counties.geojson', 'Name')
regions = len(conf['regions'])
# (decision variables). N_0_2 = region 0 and 2 are neighbours.
neighbours = {
k: LpVariable(cat=LpBinary, name='N_{}_{}'.format(*k))
for k in conf['neighbours']
}
# maximise... (maybe later want to minimise or penalise non-assignments..
model = LpProblem('tiles', LpMaximize)
model += lpSum(neighbours.values())
# subject to...
# all possibl8e assignments for regions to cells matrix.
rows = cols = 10
keys = itertools.product(range(0, rows), range(0, cols), range(0, regions))
cell_assignments = {
k: LpVariable(cat=LpBinary, name='ASS_{}_{}_{}'.format(*k))
for k in keys
}
#for _, ass in cell_assignments.items():
def train():
conf = load_conf()
no_replay = conf['noreplay']
solver = conf['solver']
n = conf['n']
eps = conf['eps']
savedir = conf['dirname']
makedir(savedir)
tmpdir = os.path.join(savedir, 'tmp')
makedir(tmpdir)
np.random.seed(conf['seed'])
logfile = os.path.join(savedir, 'log')
ave = 0
aves = []
ma = 0
global_ma = 0
channels = [10, 100, 500, n * (n - 1) // 2]
if 'channels' in conf:
channels = conf['channels']
channels.append(n * (n - 1) // 2)
bias = -np.log(1.0 / conf['p'] - 1)
net = MLP(channels, bias)
if conf['gpu'] != -1:
chainer.cuda.get_device_from_id(conf['gpu']).use()
net.to_gpu()
if conf['opt'] == 'SGD':
opt = chainer.optimizers.SGD(lr=conf['lr'])
elif conf['opt'] == 'Adam':
opt = chainer.optimizers.Adam(alpha=conf['lr'])
opt.setup(net)
stop = 0
pool_size = 10
start_training = 20
r_bests = []
edges_bests = []
z_bests = []
if no_replay:
pool_size = 1
start_training = 1e9
iteration = 0
from_restart = 0
start_time = time.time()
while True:
iteration += 1
from_restart += 1
z = net.z(1)
x = net(z)[0]
edges_li, edges, lp = gen_edges(n, x, net.xp)
r = calc_reward(n, edges, solver, tmpdir)
entropy = F.mean(x * F.log(x + 1e-6) + (1 - x) * F.log(1 - x + 1e-6))
if no_replay:
loss = -r * lp
net.cleargrads()
loss.backward()
opt.update()
if r > ma:
ma = r
stop = 0
else:
stop += 1
if r > global_ma:
global_ma = r
output_graph(os.path.join(savedir, 'output_{}.txt'.format(r)), n,
edges)
output_distribution(
os.path.join(savedir, 'distribution_{}.txt'.format(r)), n,
x.data)
chainer.serializers.save_npz(
os.path.join(savedir, 'snapshot_at_reward_{}'.format(r)), net)
elapsed = time.time() - start_time
ave = ave * (1 - conf['eps']) + r * conf['eps']
aves.append(ave)
with open(logfile, 'a') as f:
print(savedir,
iteration,
elapsed,
r,
len(edges),
entropy.data,
global_ma,
ma,
ave,
flush=True)
print(iteration,
elapsed,
r,
len(edges),
entropy.data,
global_ma,
ma,
ave,
flush=True,
file=f)
f = False
for es in edges_bests:
if (es == edges_li).all():
f = True
if not f:
r_bests.append(r)
edges_bests.append(edges_li)
z_bests.append(z)
while len(r_bests) > pool_size:
mi = 0
for j in range(len(r_bests)):
if r_bests[j] < r_bests[mi]:
mi = j
r_bests.pop(mi)
edges_bests.pop(mi)
z_bests.pop(mi)
if from_restart >= start_training:
ind = np.random.randint(len(r_bests))
x = net(z_bests[ind])[0]
lp = calc_lp(n, x, edges_bests[ind], net.xp)
loss = -r_bests[ind] * lp
net.cleargrads()
loss.backward()
opt.update()
if stop >= conf['restart']:
stop = 0
ma = 0
r_bests = []
edges_bests = []
z_bests = []
from_restart = 0
net = MLP(channels, bias)
if conf['gpu'] != -1:
chainer.cuda.get_device_from_id(conf['gpu']).use()
net.to_gpu()
if conf['opt'] == 'SGD':
opt = chainer.optimizers.SGD(lr=conf['lr'])
elif conf['opt'] == 'Adam':
opt = chainer.optimizers.Adam(alpha=conf['lr'])
opt.setup(net)
continue
if iteration % 100 == 0:
plt.clf()
plt.plot(range(len(aves)), aves)
plt.savefig(os.path.join(savedir, 'graph.png'))
if iteration % 1000 == 0:
plt.savefig(os.path.join(savedir,
'graph_{}.png'.format(iteration)))
plt.savefig(os.path.join(savedir,
'graph_{}.eps'.format(iteration)))
chainer.serializers.save_npz(
os.path.join(savedir, 'snapshot_{}'.format(iteration)), net)
chainer.serializers.save_npz(
os.path.join(savedir, 'opt_{}'.format(iteration)), opt)