superAmps=superFilteredData[superPeakDict['peakIndices']]
print "amplitudes extracted"
#fig=plt.figure()
#plt.plot(template)
#plt.hist(amps,100,alpha=.7)
#plt.hist(superAmps,100,alpha=.7)
#plt.show()
##### Find expected energy resolution of different filters #####
if False:
isPlot=False
isVerbose=False
#make fake data
rawData, rawTime = mAD.makePoissonData(totalTime=2*131.072e-3,amplitudes='random',maxSignalToNoise=10,isVerbose=isVerbose)
rawData*=2
#make template
finalTemplate, time , noiseSpectrumDict, _ , _ = mT.makeTemplate(rawData,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)
#make matched filter
matchedFilter=mF.makeMatchedFilter(fittedTemplate, noiseSpectrumDict['noiseSpectrum'], nTaps=50, tempOffs=75)
superMatchedFilter=mF.makeSuperMatchedFilter(fittedTemplate, noiseSpectrumDict['noiseSpectrum'], fallFit, nTaps=50, tempOffs=75)
#make more fake data
rawData, rawTime = mAD.makePoissonData(totalTime=2*131.072e-3, rate =1./5e-3, amplitudes='constant',maxSignalToNoise=10,isVerbose=isVerbose)
#filter data
data=mT.hpFilter(rawData)
#convolve with filter
filteredData=np.convolve(data,matchedFilter,mode='same')
#Turn plotting on or off
isPlot=False
isPlotRes=True
isPlotPoisson=False
isPlotFit=False
#Starting template values
sampleRate=1e6
nPointsBefore=100.
riseTime=2e-6
fallTime=50e-6
tmax=nPointsBefore/sampleRate
t0=tmax+riseTime*np.log(riseTime/(riseTime+fallTime))
#get fake poissonian distributed pulse data
rawdata, rawtime = mAD.makePoissonData(totalTime=2*131.072e-3,isVerbose=True)
if isPlotPoisson:
fig1=plt.figure(0)
plt.plot(rawtime,rawdata)
plt.show()
#calculate templates
finalTemplate, time , _, templateList, _ = mT.makeTemplate(rawdata,nSigmaTrig=4.,numOffsCorrIters=2,isVerbose=True,isPlot=isPlot)
roughTemplate = templateList[0]
#make fitted template
fittedTemplate, startFit, riseFit, fallFit = mT.makeFittedTemplate(finalTemplate,time,riseGuess=3.e-6,fallGuess=55.e-6)
#calculate real template
realTemplate = mAD.makePulse(time,t0,riseTime,fallTime)
#Turn plotting on or off
isPlot=False
isPlotRes=True
isPlotPoisson=False
isPlotFit=False
#Starting template values
sampleRate=1e6
nPointsBefore=100.
riseTime=2e-6
fallTime=50e-6
tmax=nPointsBefore/sampleRate
t0=tmax+riseTime*np.log(riseTime/(riseTime+fallTime))
#get fake poissonian distributed pulse data
rawdata, rawtime = mAD.makePoissonData(totalTime=2*131.072e-3,isVerbose=True,maxSignalToNoise=2)
if isPlotPoisson:
fig1=plt.figure(0)
plt.plot(rawtime,rawdata)
plt.show()
#calculate templates
finalTemplate, time , _, templateList, _ = mT.makeTemplate(rawdata,nSigmaTrig=4.,numOffsCorrIters=2,isVerbose=True,isPlot=isPlot)
roughTemplate = templateList[0]
#make fitted template
fittedTemplate, startFit, riseFit, fallFit = mT.makeFittedTemplate(finalTemplate,time,riseGuess=3.e-6,fallGuess=55.e-6)
#calculate real template
realTemplate = mAD.makePulse(time,t0,riseTime,fallTime)
for peak in peakIndices:
firCoeffs += makeCausalWiener(template, rawdata[peak-nPointsBefore:peak+nPointsAfter], nTaps)
firCoeffs /= len(peakIndices)
return firCoeffs
if __name__=='__main__':
testMatched = False
testWiener = True
if testMatched:
#test makeMatchedFilter
template = np.zeros(800)
template[100:800] = 10.*np.exp(-np.arange(0,700)/30.)
data,time = mAD.makePoissonData(totalTime=1000.e-3, maxSignalToNoise=0.0)
noiseCov = noise.covFromData(data-np.mean(data), 100)['covMatrix']
plt.show()
if testWiener:
templateData, time = mAD.makePoissonData(totalTime=10*131.072e-3)
finalTemplate, time, _,_,_ = mkt.makeTemplate(templateData)
filterData, time = mAD.makePoissonData(totalTime=10*131.072e-3,maxSignalToNoise=10) #new data set for Wiener filter raw pulses
_,_,_,_,peakIndices = mkt.makeTemplate(filterData)
wienerFilt = makeAvgCausalWiener(finalTemplate, filterData, peakIndices)
plt.plot(wienerFilt)
plt.plot(finalTemplate)
#plt.plot(filterData[peakIndices[0]-100:peakIndices[0]+800]/5)
plt.show()
