def compavg(clients):
signal.signal(signal.SIGINT, signal.SIG_DFL)
y = np.zeros(shape=(6,8)) # observation matrix
m = 8
n = 8
dg = DataGenerator(n=n, m=m);
se = StateEstimator(dg.EstimationMatrix)
while True:
global globcount
totals = [0]*8
averages = [0]*8 # [avg1, avg2, avg3,..., avg8]
for c in clients:
line = c.getLine().strip().split(",")
if len(line) != 8:
line = [0.0, time.time(), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
for i in range(len(line)):
if i == 1:
continue
if i > 1:
#print line[i]
totals[i-1] += float(line[i])
else:
totals[0] += float(line[0])
averages = map(lambda x: x / len(clients), totals)
#with comp_state_est_cv:
with cv:
y[globcount,:]=averages
globcount += 1
if globcount == 6:
times = y[:,1].reshape(-1, 1)
count = 0
timeno = 0
observation = sp.zeros((48, 3))
for t, row in zip(times, y):
for id, val in enumerate(row):
if timeno == 1:
timeno += 1
continue
observation[count, :] = sp.array([id, t[0] , val ] ).reshape(1, -1)
count += 1
timeno += 1
xhat = sp.zeros( (6, 8) )
T = 6
stepsize = 1
for t in range( 0, T + 1 - stepsize, stepsize ):
newstate,t0 = se.estimate( observation[t*m:(t+stepsize)*m,:] )
xhat[t:(t+stepsize),:] = se.interpolate(newstate,t0,times[t:(t+stepsize),:])
global state
state = xhat.reshape(-1).tolist()
globcount=0
cv.notifyAll()
print "interrupted"
