def plotBothChannels_talkPlot(self, dataOne, dataTwo, updatePlots, timeAxis): numSamples = 19960 times = timeAxis[0:numSamples]*5e9 #plots look better if the traces are to scale. try normalizing them normalizedOne = self.normalizeTrace(dataOne[0:numSamples]) normalizedTwo = self.normalizeTrace(dataTwo[0:numSamples]) if updatePlots: #find where hits are on the two traces rawData, hitIndicesOne, hitLimitsHighOne, convPeakMaxOne = CFD(normalizedOne) rawData, hitIndicesTwo, hitLimitsHighTwo, convPeakMaxTwo = CFD(normalizedTwo) #clear previous lines linesOld = self.axisTraces.lines for i in range(len(linesOld)): lineToDel = linesOld.pop(0) del lineToDel #clear previous scatter plots scatterOld = self.axisTraces.collections for i in range(len(scatterOld)): scatterToDel = scatterOld.pop(0) del scatterToDel #plot normalized traces self.axisTraces.plot(times, normalizedTwo, color="green") self.axisTraces.plot(times, normalizedOne, color="blue") plt.legend(["Short ToF", "Long ToF"], fontsize=20) #set limits self.axisTraces.set_xlim(times[self.xLimits]) self.axisTraces.set_ylim([-0.12, 1.1]) #update calls to draw the plot self.figHandleTraces.canvas.draw()
def executeSingleTraceAnalysis(dataIn):
#if each trace is being cycled through, it is probably nice to plot out each trace in the user output
if (flagEachTraceStop):
#call CFD to help make the per trace plots
rawData, hitIndices, hitLimitsHigh, convPeakMax = CFD(dataIn)
#make the per trace raw data plot
if (len(hitIndices) > 0):
makePlotTraceWithHits(dataIn,
np.concatenate((hitIndices, hitLimitsHigh),
axis=0),
xLimLow=hitIndices[0] - 50,
xLimHigh=hitIndices[0] + 1200)
# makePlotTraceWithHits(dataIn, hitIndices, colorIn="green")
else:
# makePlotTraceWithHits(dataIn, hitIndices, colorIn="green")
pass
numHits = len(hitIndices)
#if there's one hit, this trace may be useful to look at in fft domain
if (numHits == 1):
#calculate the FFT and only look at half of it (say, just positive frequencies)
fourierFull = scipy.fft(dataIn)
fourierMagnitude = np.absolute(fourierFull)
fourierHalf = cutFourierScaleInHalf(fourierMagnitude)
# plotSimple(fourierHalf)
#call a bunch of functions to try and calculate/profile the single hit. These functions should return meaningful characteristics of the single hit, and produce a quantity that can be used to compile a data set's statistics
#create a normalized wavefunction
normalizedData = normalizeTrace(dataIn)
def returnWaveformHitsAndPSD(dataIn):
#binary .trc files provide the raw waveform as the dataIn
dataTrace, hitIndices = CFD(dataIn)
#call calculatePSD to get the power spectral density of the FFT of the current trace
PSDOfTrace = calculatePSD(dataIn)
return dataIn, hitIndices, PSDOfTrace
def addToHistograms(self, dataOne, dataTwo, updatePlots): #normalize the raw traces normalizedOne = self.normalizeTrace(dataOne) normalizedTwo = self.normalizeTrace(dataTwo) #process new trace and add hits for channel 1 rawData, hitIndices, hitLimitsHigh, convPeakMax = CFD(normalizedOne) self.histogramOne = addHitsToHistogram(hitIndices, self.histogramOne) #do the same for channel 2 rawData, hitIndices, hitLimitsHigh, convPeakMax = CFD(normalizedTwo) self.histogramTwo = addHitsToHistogram(hitIndices, self.histogramTwo) if updatePlots: #plot the current histogram for channel 1 #clear previous lines in time histogram linesOld = self.axisHistogram.lines for i in range(len(linesOld)): lineToDel = linesOld.pop(0) del lineToDel #clear previous lines in energy histogram linesOld = self.axisEnergyHistogram.lines for i in range(len(linesOld)): lineToDel = linesOld.pop(0) del lineToDel #plot the current/new histograms self.axisHistogram.plot(calculateBinnedHistogramTrace(self.histogramOne, binWidth)) self.axisHistogram.plot(calculateBinnedHistogramTrace(self.histogramTwo, binWidth)) #set limits self.axisHistogram.set_xlim(self.xLimits) #calculate new energy histogram values self.energyHistogramOne = np.matmul(self.overlapMatrix, self.histogramOne) self.energyHistogramTwo = np.matmul(self.overlapMatrix, self.histogramTwo) #plot the new energy histogram values on the appropriate axis self.axisEnergyHistogram.plot(self.energyVector, self.energyHistogramOne) # self.axisEnergyHistogram.plot(self.energyHistogramTwo) #update calls to draw the plots self.figHandleHistograms.canvas.draw() self.figHandleEnergyHistogram.canvas.draw() pass
def plotBothChannels(self, dataOne, dataTwo, updatePlots): #plots look better if the traces are to scale. try normalizing them normalizedOne = dataOne#self.normalizeTrace(dataOne) normalizedTwo = dataTwo#self.normalizeTrace(dataTwo) if updatePlots: #find where hits are on the two traces rawData, hitIndicesOne, hitLimitsHighOne, convPeakMaxOne = CFD(normalizedOne) rawData, hitIndicesTwo, hitLimitsHighTwo, convPeakMaxTwo = CFD(normalizedTwo) #clear previous lines linesOld = self.axisTraces.lines for i in range(len(linesOld)): lineToDel = linesOld.pop(0) del lineToDel #clear previous scatter plots scatterOld = self.axisTraces.collections for i in range(len(scatterOld)): scatterToDel = scatterOld.pop(0) del scatterToDel #plot normalized traces self.axisTraces.plot(normalizedOne, color="blue") self.axisTraces.plot(normalizedTwo, color="green") #plot scatter markers to denote hits for i in range(len(hitIndicesOne)): collectionToPlot = [hitIndicesOne[i], hitLimitsHighOne[i]] self.axisTraces.scatter(collectionToPlot, normalizedOne[collectionToPlot], color="red") #repeat for hits for trace two for i in range(len(hitIndicesTwo)): collectionToPlot = [hitIndicesTwo[i], hitLimitsHighTwo[i]] # self.axisTraces.scatter(collectionToPlot, normalizedTwo[collectionToPlot], color="red") #set limits #self.axisTraces.set_xlim(self.xLimits) #update calls to draw the plot self.figHandleTraces.canvas.draw()
#convert binary segment into a numpy array of appropriate data type
traceNow = np.frombuffer(segmentNow, dtype=dt)
if (len(traceNow) == 0):
moreToRead = False
print(
"The end of file was reached, with 0 bytes in the final read")
elif (len(traceNow) < traceDataSize / savedDataTypeSizeInBytes):
print(
"A trace was read in with a byte size that is not equal to the size prescribed in the header file."
)
else:
#a normal, complete trace has been extracted. This section may be used to process traces as desired by user.
#perform processing similar to that done on the o-scope
rawData, hitIndices, hitLimitsHigh, convPeakMax = CFD(traceNow)
normedTrace = normalizeTrace(rawData)
# plt.figure()
# plt.plot(traceNow)
# if (hitIndices.size > 0):
# plt.scatter(hitIndices, traceNow[hitIndices], s=150)
# plt.draw()
# plt.pause(0.001)
totalNumberHits = totalNumberHits + len(hitIndices)
#if this is the first trace, set up some variables for the first time
if (tracesProcessed == 0):
histogramCollected = initializeHistogram(rawData)
def returnWaveformAndHits(dataIn):
#binary .trc files provide the raw waveform as the dataIn
dataTrace, hitIndices = CFD(dataIn)
return dataIn, hitIndices