def makeWienerNoiseSpectrum(data, peakIndices=[], numBefore=100, numAfter=700, noiseOffsetFromPeak=200, sampleRate=1e6, template=[],isVerbose=False,baselineSubtract=True):
nFftPoints = numBefore + numAfter
peakIndices=np.array(peakIndices).astype(int)
#If no peaks, choose random indices to make spectrum
if len(peakIndices)==0:
print 'warning: makeWienerNoiseSpectrum was not passed any peakIndices. Generating random indicies now'
peakIndices=np.array([0])
rate = len(data)/nFftPoints/10
while peakIndices[-1]<(len(data)-1):
prob=np.random.rand()
currentIndex=peakIndices[-1]
peakIndices=np.append(peakIndices,currentIndex+np.ceil(-np.log(prob)/rate).astype(int))
peakIndices=peakIndices[:-2]
if len(peakIndices)==0:
raise ValueError('makeWienerNoiseSpectrum: input data set is too short for the number of FFT points specified')
#Baseline subtract noise data
if(baselineSubtract):
noiseStream = np.array([])
for iPeak,peakIndex in enumerate(peakIndices):
if peakIndex > nFftPoints+noiseOffsetFromPeak and peakIndex < len(data)-numAfter:
noiseStream = np.append(noiseStream, data[peakIndex-nFftPoints-noiseOffsetFromPeak:peakIndex-noiseOffsetFromPeak])
data = data - np.mean(noiseStream)
#Calculate noise spectra for the defined area before each pulse
noiseSpectra = np.zeros((len(peakIndices), nFftPoints))
rejectInd=np.array([])
for iPeak,peakIndex in enumerate(peakIndices):
if peakIndex > nFftPoints+noiseOffsetFromPeak and peakIndex < len(data)-numAfter:
noiseData = data[peakIndex-nFftPoints-noiseOffsetFromPeak:peakIndex-noiseOffsetFromPeak]
noiseSpectra[iPeak] = np.abs(np.fft.fft(data[peakIndex-nFftPoints-noiseOffsetFromPeak:peakIndex-noiseOffsetFromPeak])/nFftPoints)**2
if len(template)!=0:
filteredData=np.correlate(noiseData,template,mode='same')
peakDict=tP.detectPulses(filteredData, nSigmaThreshold = 3., negDerivLenience = 1, bNegativePulses=False)
if len(peakDict['peakIndices'])!=0:
rejectInd=np.append(rejectInd,iPeak)
#Remove indicies with pulses by coorelating with a template if provided
if len(template)!=0:
noiseSpectra = np.delete(noiseSpectra, rejectInd, axis=0)
noiseFreqs = np.fft.fftfreq(nFftPoints,1./sampleRate)
noiseSpectrum = np.median(noiseSpectra,axis=0)
#noiseSpectrum[0] = 2.*noiseSpectrum[1] #look into this later 8/15/16
if isVerbose:
print len(noiseSpectra[:,0]),'traces used to make noise spectrum', len(rejectInd), 'cut for pulse contamination'
return {'noiseSpectrum':noiseSpectrum, 'noiseFreqs':noiseFreqs}
def makeNoiseSpectrum(data,
peakIndices=(),
window=800,
noiseOffsetFromPeak=200,
sampleRate=1e6,
filt=(),
isVerbose=False,
baselineSubtract=True):
'''
makes one sided noise power spectrum in units of V^2/Hz by averaging together Fourier transforms of noise
traces. The code screens out any potential pulse contamination with the provided peak indices and filter
INPUTS:
data - raw data to calculate noise from
peakIndices - list of known peak indices. Will choose randomly if not specified
window - data size to take individual transforms of for averaging
noiseOffsetFromPeak - takes window this many points to the left of peak
sampleRate - sample rate of data
filt - filter for detecting unspecified pulses in data
isVerbose - print extra info to terminal
baselineSubtract - subtract baseline
OUTPUTS:
dictionary containing frequencies, noise spectrum and indicies used to make the noise spectrum
'''
peakIndices = np.array(peakIndices).astype(int)
#If no peaks, choose random indices to make spectrum
if len(peakIndices) == 0:
peakIndices = np.array([0])
rate = len(data) / float(window) / 1000.
while peakIndices[-1] < (len(data) - 1):
prob = np.random.rand()
currentIndex = peakIndices[-1]
peakIndices = np.append(
peakIndices, currentIndex +
np.ceil(-np.log(prob) / rate * sampleRate).astype(int))
peakIndices = peakIndices[:-2]
if len(peakIndices) == 0:
raise ValueError(
'makeNoiseSpectrum: input data set is too short for the number of FFT points specified'
)
#Baseline subtract noise data
if (baselineSubtract):
noiseStream = np.array([])
for iPeak, peakIndex in enumerate(peakIndices):
if peakIndex > window + noiseOffsetFromPeak and peakIndex < len(
data) + noiseOffsetFromPeak:
noiseStream = np.append(
noiseStream,
data[peakIndex - window - noiseOffsetFromPeak:peakIndex -
noiseOffsetFromPeak])
data = data - np.mean(noiseStream)
#Calculate noise spectra for the defined area before each pulse
if len(peakIndices) > 2000:
peakIndices = peakIndices[:2000]
noiseSpectra = np.zeros(
(len(peakIndices), len(np.fft.rfftfreq(window))))
else:
noiseSpectra = np.zeros(
(len(peakIndices), len(np.fft.rfftfreq(window))))
rejectInd = np.array([])
goodInd = np.array([])
counter = 0
for iPeak, peakIndex in enumerate(peakIndices):
if peakIndex > window + noiseOffsetFromPeak and peakIndex < len(
data) + noiseOffsetFromPeak:
noiseData = data[peakIndex - window -
noiseOffsetFromPeak:peakIndex -
noiseOffsetFromPeak]
noiseSpectra[counter] = 4 * window / sampleRate * np.abs(
np.fft.rfft(
data[peakIndex - window - noiseOffsetFromPeak:peakIndex -
noiseOffsetFromPeak]))**2
if len(filt) != 0:
filteredData = np.convolve(noiseData, filt, mode='same')
peakDict = tP.detectPulses(filteredData,
nSigmaThreshold=2.,
negDerivLenience=1,
bNegativePulses=True)
if len(peakDict['peakIndices']) != 0:
rejectInd = np.append(rejectInd, int(counter - 1))
else:
goodInd = np.append(goodInd, int(peakIndex))
counter += 1
if counter == 500:
break
noiseSpectra = noiseSpectra[0:counter]
#Remove indicies with pulses by convolving with a filt if provided
if len(filt) != 0:
noiseSpectra = np.delete(noiseSpectra, rejectInd.astype(int), axis=0)
noiseFreqs = np.fft.rfftfreq(window, 1. / sampleRate)
if len(np.shape(noiseSpectra)) == 0:
raise ValueError(
'makeWienerNoiseSpectrum: not enough spectra to average')
if np.shape(noiseSpectra)[0] < 5:
raise ValueError(
'makeWienerNoiseSpectrum: not enough spectra to average')
noiseSpectrum = np.median(noiseSpectra, axis=0)
noiseSpectrum[0] = noiseSpectrum[1]
if not np.all(noiseSpectrum > 0):
raise ValueError('makeWienerNoiseSpectrum: not all noise data >0')
if isVerbose:
print len(noiseSpectra[:,
0]), 'traces used to make noise spectrum', len(
rejectInd), 'cut for pulse contamination'
return {'spectrum': noiseSpectrum, 'freqs': noiseFreqs, 'indices': goodInd}
#noiseSpectrumDict['noiseSpectrum'][np.abs(noiseSpectrumDict['noiseFreqs'])>210000]=5e-5
#make matched filter
matchedFilter=mF.makeMatchedFilter(template, noiseSpectrumDict['noiseSpectrum'], nTaps=50, tempOffs=75)
coef, _ = opt.curve_fit(lambda x, a, t0 : a*exp(-x/t0), time[len(time)*1/5:len(time)*4/5],template[len(time)*1/5:len(time)*4/5], [-1 , 30e-6])
fallFit=coef[1]
superMatchedFilter=mF.makeSuperMatchedFilter(template, noiseSpectrumDict['noiseSpectrum'], fallFit, nTaps=50, tempOffs=75)
print "filters made"
#convolve with filter
filteredData=np.convolve(rawData,matchedFilter,mode='same')
superFilteredData=np.convolve(rawData,superMatchedFilter,mode='same')
print "data filtered"
#find peak indices
peakDict=tP.detectPulses(filteredData, nSigmaThreshold = 3., negDerivLenience = 1, bNegativePulses=False)
superPeakDict=tP.detectPulses(superFilteredData, nSigmaThreshold = 3., negDerivLenience = 1, bNegativePulses=False)
print "peaks found"
#find peak amplitudes
amps=filteredData[peakDict['peakIndices']]
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 #####
