def detect_signals():
vector, label = weeklydataset_sg_ndata(
"/media/4AC0AB31C0AB21E5/Documents and Settings/Claudio/Documenti/Thesis/Workloads/MSClaudio/ews/access_log-20110805.csv",
[],
)
x, target = aggregatebymins_sg_ndata(vector[1])
starttime = time.time()
y = array(target)
t = array(x)
thr = max(y) * 2 / 3
print thr
I = pylab.find(y > thr)
# print I
# pylab.plot(t,y, 'b',label='signal')
# pylab.plot(t[I], y[I],'ro',label='detections')
# pylab.plot([0, t[len(t)-1]], [thr,thr], 'g--')
J = pylab.find(diff(I) > 1)
argpeak = []
targetpeak = []
for K in split(I, J + 1):
ytag = y[K]
peak = pylab.find(ytag == max(ytag))
# pylab.plot(peak+K[0],ytag[peak],'sg',ms=7)
argpeak.append(peak + K[0])
targetpeak.append(ytag[peak])
eta = time.time() - starttime
print "time elapsed %f" % eta
return list(itertools.chain(*argpeak)), list(itertools.chain(*targetpeak))
def peaks():
vector, label = weeklydataset_sg_ndata(
"/media/4AC0AB31C0AB21E5/Documents and Settings/Claudio/Documenti/Thesis/Workloads/MSClaudio/ews/access_log-20110805.csv",
[],
)
x, target = aggregatebymins_sg_ndata(vector[1])
data = array(target)
t = array(x)
data = data.ravel()
length = len(data)
print length
step = 40
if length % step == 0:
data.shape = (length / step, step)
else:
data.resize((length / step, step))
max_data = maximum.reduce(data, 1)
min_data = minimum.reduce(data, 1)
pylab.plot(t, array(target), "b", label="signal")
return concatenate((max_data[:, newaxis], min_data[:, newaxis]), 1)
