def runFilter(filterCoeffs,data):
filteredData = scipy.signal.fftconvolve(data[np.newaxis,:],filterCoeffs[np.newaxis,:],mode='valid')
criticalFreq = 200. #Hz
sampleRate=1.e6
f=2*np.sin(np.pi*criticalFreq/sampleRate)
Q=.7
q=1./Q
hpSvf = IirFilter(sampleFreqHz=sampleRate,numCoeffs=np.array([1,-2,1]),denomCoeffs=np.array([1+f**2, f*q-2,1-f*q]))
filteredData = hpSvf.filterData(filteredData)
return filteredData
def hpFilter(rawData, criticalFreq=20, sampleRate = 1e6):
'''
High pass filters the raw phase timestream
INPUTS:
rawData - data to be filtered
criticalFreq - cutoff frequency of filter (in Hz)
sampleRate - sample rate of rawData
OUTPUTS:
data - filtered data
'''
f=2*np.sin(np.pi*criticalFreq/sampleRate)
Q=.7
q=1./Q
hpSvf = IirFilter(sampleFreqHz=sampleRate,numCoeffs=np.array([1,-2,1]),denomCoeffs=np.array([1+f**2, f*q-2,1-f*q]))
data = hpSvf.filterData(rawData)
return data
def hpFilter(rawData, criticalFreq=20, sampleRate=1e6):
"""
High pass filters the raw phase timestream
INPUTS:
rawData - data to be filtered
criticalFreq - cutoff frequency of filter (in Hz)
sampleRate - sample rate of rawData
OUTPUTS:
data - filtered data
"""
f = 2 * np.sin(np.pi * criticalFreq / sampleRate)
Q = 0.7
q = 1.0 / Q
hpSvf = IirFilter(
sampleFreqHz=sampleRate,
numCoeffs=np.array([1, -2, 1]),
denomCoeffs=np.array([1 + f ** 2, f * q - 2, 1 - f * q]),
)
data = hpSvf.filterData(rawData)
return data
data = scipy.signal.lfilter(filter,1,rawdata)
#quietData = np.correlate(filter,quietRawdata,mode='same')[::-1]
quietData = scipy.signal.lfilter(filter,1,quietRawdata)
print 'filtering done'
sys.stdout.flush()
else:
data = np.array(sample)
quietData = np.array(quietSample)
criticalFreq = 200 #Hz
hpSos = IirFilter(sampleFreqHz=sampleRate,criticalFreqHz=criticalFreq,btype='highpass')
f=2*np.sin(np.pi*criticalFreq/sampleRate)
Q=.7
q=1./Q
hpSvf = IirFilter(sampleFreqHz=sampleRate,numCoeffs=np.array([1,-2,1]),denomCoeffs=np.array([1+f**2, f*q-2,1-f*q]))
baselines = data - hpSvf.filterData(data)
print 'baselines done'
threshold = calcThreshold(quietData,Nsigma=thresholdSigma)
print 'threshold done'
sys.stdout.flush()
endIdx = 1000*thresholdLength
if bPlotPeaks:
ax=fig.add_subplot(NAxes,1,iAxes)
ax.plot(rawdata[0:endIdx],'.-',color='gray',label='raw phase')
ax.plot(data[0:endIdx],'k.-',label='optimal filtered phase')
ax.plot(baselines[0:endIdx],'b',label='lpf baseline')
ax.plot(baselines[0:endIdx]+threshold,'y--',label='threshold')
qdrPhaseValues = np.array(qdrValues,dtype=np.float32)*360./2**16*4/np.pi #convert from adc units to degrees
nPhaseValues=len(qdrValues)
print nPhaseValues,'us'
rawdata = np.array(qdrPhaseValues)* -np.pi/180. #in radians, and flip sign
#create a highpass filter, then apply it to the data to take out the low frequency baseline
sampleRate=1e6 # samples per second
criticalFreq = 20 #Hz
f=2*np.sin(np.pi*criticalFreq/sampleRate)
Q=.7
q=1./Q
hpSvf = IirFilter(sampleFreqHz=sampleRate,numCoeffs=np.array([1,-2,1]),denomCoeffs=np.array([1+f**2, f*q-2,1-f*q]))
data = hpSvf.filterData(rawdata)
#data = scipy.signal.lfilter(filter,1,rawdata)
trigDict = sigmaTrigger(data)
peakIndices = trigDict['peakIndices']
peaks = trigDict['peakHeights']
nPeaksDetected = len(peaks)
print len(peaks),'peaks detected'
print 1.*nPeaksDetected/secs, 'cps'
bPlotPeakHist = False
if bPlotPeakHist:
figHist,axHist = plt.subplots(1,1)
