def makeTemplate(rawData, numOffsCorrIters=1, decayTime=50, nSigmaTrig=4.0, isVerbose=False, isPlot=False, sigPass=1):
"""
Make a matched filter template using a raw phase timestream
INPUTS:
rawData - noisy phase timestream with photon pulses
numOffsCorrIters - number of pulse offset corrections to perform
decayTime - approximate decay time of pulses (units: ticks)
nSigmaTrig - threshold to detect pulse in terms of standard deviation of data
isVerbose - print information about the template fitting process
isPlot - plot information about Chi2 cut
sigPass - std of data left after Chi2 cut
OUTPUTS:
finalTemplate - template of pulse shape
time - use as x-axis when plotting template
noiseSpectDict - dictionary containing noise spectrum and corresponding frequencies
templateList - list of template itterations by correcting offsets
peakIndices - list of peak indicies from rawData used for template
"""
# hipass filter data to remove any baseline
data = hpFilter(rawData)
# trigger on pulses in data
peakDict = sigmaTrigger(data, nSigmaTrig=nSigmaTrig, decayTime=decayTime, isVerbose=isVerbose)
# remove pulses with additional triggers in the pulse window
peakIndices = cutPulsePileup(peakDict["peakMaxIndices"], decayTime=decayTime, isVerbose=isVerbose)
# remove pulses with a large chi squared value
peakIndices = cutChiSquared(
data, peakIndices, sigPass=sigPass, decayTime=decayTime, isVerbose=isVerbose, isPlot=isPlot
)
# Create rough template
roughTemplate, time = averagePulses(data, peakIndices, decayTime=decayTime)
# create noise spectrum from pre-pulse data for filter
noiseSpectDict = mNS.makeWienerNoiseSpectrum(data, peakIndices, template=roughTemplate, isVerbose=isVerbose)
# Correct for errors in peak offsets due to noise
templateList = [roughTemplate]
for i in range(numOffsCorrIters):
peakIndices = correctPeakOffs(data, peakIndices, noiseSpectDict, roughTemplate, "wiener")
# calculate a new corrected template
roughTemplate, time = averagePulses(data, peakIndices, isoffset=True, decayTime=decayTime)
templateList.append(roughTemplate)
finalTemplate = roughTemplate
return finalTemplate, time, noiseSpectDict, templateList, peakIndices
plt.show()
#####Test energy resolution with real data. Assumes constant photon energy#####
if False:
isPlot=False
isVerbose=False
#extract raw data
rawData = eRD.parseQDRPhaseSnap(os.path.join(os.getcwd(),'20140915/redLaser'),pixelNum=1,steps=30)
rawTemplateData = rawData[0:1000000]
rawTestData = rawData[1000000:2000000]
#make template
finalTemplate, time , noiseSpectrumDict, _ , tempPeakIndices = mT.makeTemplate(rawTemplateData,nSigmaTrig=4.,numOffsCorrIters=2,isVerbose=isVerbose,isPlot=isPlot)
#fit to arbitrary pulse shape
fittedTemplate, startFit, riseFit, fallFit = mT.makeFittedTemplate(finalTemplate,time,riseGuess=3.e-6,fallGuess=55.e-6)
noiseSpectrumDictCovMat = mNS.makeWienerNoiseSpectrum(rawTemplateData, tempPeakIndices, 8000, 1000)
#make matched filter
matchedFilter=mF.makeMatchedFilter(finalTemplate, noiseSpectrumDictCovMat['noiseSpectrum'], nTaps=50, tempOffs=75)
superMatchedFilter=mF.makeSuperMatchedFilter(finalTemplate, noiseSpectrumDictCovMat['noiseSpectrum'], fallFit, nTaps=50, tempOffs=75)
#plot templates
plt.plot(time,finalTemplate)
plt.plot(time,fittedTemplate)
plt.show()
#filter data
#data=mT.hpFilter(rawTestData)
data = rawTestData
#convolve with filter
filteredData=np.convolve(data,matchedFilter,mode='same')
superFilteredData=np.convolve(data,superMatchedFilter,mode='same')
