def characterize(signal, start, stop):
'''
Characterize the peak in the signal bracketed by [start,stop).
Return characterization as a PeakedPulse.
'''
base = stop-start
peak = signal[start:stop]
sumq = sum(peak)
height = max(peak)
loc = peak.index(height)
h = histutil.sig2hist(peak)
fit = histutil.fit_gaus(h)
del(h)
return PeakedPulse(start, loc, base, height, sumq, *fit)
def set_channel(self, chn, sig):
minq = min(sig)
iminq = int(minq+0.5)
self.obj.qmin[chn] = iminq
self.obj.tmin[chn] = minbin = sig.index(minq)
maxq = max(sig)
imaxq = int(maxq+0.5)
self.obj.qmax[chn] = imaxq
self.obj.tmax[chn] = maxinb = sig.index(maxq)
low,high = 50,150
start = max(0, minbin - low)
stop = min(len(sig), minbin + high)
windowed = sig[:start] + sig[stop:]
iminq = int(min(windowed)+0.5)
pwr = ROOT.TH1F('pwr','power', imaxq-iminq+2,iminq-1,imaxq+1)
map(pwr.Fill, windowed)
self.obj.avg[chn] = pwr.GetMean()
self.obj.rms[chn] = pwr.GetRMS()
# guess-fit the pedestal
fit = histutil.fit_gaus(pwr)
const, mean, sigma = fit
self.obj.const[chn] = const
self.obj.mean[chn] = mean
self.obj.sigma[chn] = sigma
# simple, fixed window around peak
winlow, winhigh = 10,40
winstart = max(0, minbin - winlow)
winstop = min(len(sig), minbin + winhigh)
winsig = sig[winstart:winstop]
self.obj.qwin[chn] = mean * len(winsig) - sum(winsig)
#if not chn:
# print 'Fit:',mean, sigma, const
for c in sig[start:stop]:
if c > mean - 3*sigma:
continue
self.obj.n3[chn] += 1
self.obj.sum3[chn] += c
if c > mean - 4*sigma:
continue
self.obj.n4[chn] += 1
self.obj.sum4[chn] += c
if c > mean - 5*sigma:
continue
self.obj.n5[chn] += 1
self.obj.sum5[chn] += c
continue
# make a positive-going signal w.r.t. fit "pedestal"
pulse = [mean - s for s in sig]
ps = peaks.downhills(pulse, 0.0, 3*sigma)
npeaks = len(ps)
self.obj.qnpeaks[chn] = npeaks
if not npeaks:
return
# Characterize the dominant peak and transfer the returned
# PulsedPeak values to the tree. What a coincidence the two
# are named so similarly....
if npeaks == 1:
pp = peaks.characterize(pulse, *ps[0])
for field_name in pp._fields:
val = self.obj.__dict__['dp%s'%field_name]
val[chn] = pp.__dict__[field_name]
saved_peaks = []
for count, (l,r) in enumerate(ps):
peak = pulse[l:r]
totq = sum(peak)
if not count:
self.obj.qpeak[chn] = totq
self.obj.qwid[chn] = len(peak)
saved_peaks.append({'t':l, 'q':peak, 'c':chn})
maxq = max(pulse[l:r])
if maxq < 3*sigma:
continue # shouldn't happen
self.obj.qpeaks3[chn] += totq
if maxq < 4*sigma:
continue
self.obj.qpeaks4[chn] += totq
if maxq < 5*sigma:
continue
self.obj.qpeaks5[chn] += totq
continue
if self.save_peaks:
self.saved_peaks.append(saved_peaks)
return