# Randomly select NoOfWaveforms from all the waveforms.
Selection = random.sample(AllWaveforms, np.shape(AllWaveforms)[0])
if (not RandomSample):
## Or... find the NoOfWaveforms that have the greatest sum (Largest Signals)
SumAllWaveforms = PSD.GetFullIntegral(AllWaveforms)
## Get NoOfWaveforms indices of the AllWaveforms
N_Max = SumAllWaveforms.argsort()[-NoOfWaveforms:]
Selection = AllWaveforms[N_Max]
# Convert to Numpy Array
Waveforms = np.array(Selection)
#print "Length of Waveforms : ", len(Waveforms)
Waveforms = PSD.BaselineSubtraction(Waveforms, BaselineSamples)
# Get peak value of each waveform
PeakList = PSD.GetSignalPeak(Waveforms)
# Normalize each signal.
for wf, val in enumerate(Waveforms):
Waveforms[wf] = val / PeakList[wf]
del PeakList
# Get trigger sample (the sample at which the waveform crosses threshold)
TriggerList = PSD.GetTriggerPosition(Waveforms, Threshold)
#print TriggerList
TimeAlignedWaveforms, Status = TimeAlign(Waveforms, TriggerList)
# Normalize each timealigned averaged signal.
if (Normalise):
Peak = float('{0:0.8f}'.format(max(TimeAlignedWaveforms[:])))
TimeAlignedWaveforms /= Peak
