T = re.search('_(.*)C.bin', file)
print T.group(1)
Temperature.append(T.group(1))
print "Files to read: ", DataList
print "Temperatures: ", Temperature
# NormalizeTimeAlignedWaveforms = np.empty([len(DataList), NoOfWaveforms])
for F, file in enumerate(DataList):
print "Reading ", file
## Read in N waveforms from bin file.
## For each waveform with a peak above PeakThreshold,
## do a baseline correction and time alignment of the sample at which
## the signal crosses 'Threshold'.
## Normalise each signal
## Add signals together and divide by NoOfWaveforms to get the average signal.
FID, cols, rows = PSD.OpenFile(file)
#print cols, rows
## ReadBinary(..) returns a NxM matrix of waveforms. FID = fileID, NoOfWaveforms.
AllWaveforms = np.array(
PSD.ReadBinaryFile(FID, ReadWaveforms, cols, rows))
# Remove waveforms that show saturation. i.e. Ones in which the maximum value is 100mV
AllWaveforms = RemoveSaturation(AllWaveforms, SatLimit)
if (RandomSample):
# 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:]
