def processData(self, data):
s = self.stateGroup.state()
events = functions.zeroCrossingEvents(data,
minLength=s['minLength'],
minPeak=s['minPeak'],
minSum=s['minSum'])
events = events[:s['eventLimit']]
return events
def process(self, data, display=True):
rp = self.ctrls['risePower'].value()
stimTime = self.ctrls['stimulusTime'].value()
times = data.xvals('Time')
dt = times[1] - times[0]
data1 = data.view(np.ndarray)
if stimTime > times[-1]:
raise Exception("stimulusTime is larger than length of input data.")
stimInd = np.argwhere(times>=stimTime)[0][0]
# 1. make sure offset is removed
offset = np.median(data1[:stimInd])
data2 = data1 - offset
# 2. check for zero-crossing events within 4ms after stimulus
cross = functions.zeroCrossingEvents(data2[stimInd:])
maxInd = 4e-3 / dt
minLength = self.ctrls['minDirectDuration'].value() / dt
gotEvent = None
for start, length, sum, peak in cross:
if start > maxInd:
break
if length < minLength:
continue
if gotEvent is None or length > gotEvent[1]:
gotEvent = (start+stimInd, length)
#if len(cross) == 0: ## must be a single large event
#gotEvent = (0, len(data2)-stimInd)
fitParams = dict(
directFitAmp1=0., directFitAmp2=0., directFitTime=0.,
directFitRise=0., directFitDecay1=0., directFitDecay2=0.,
directFitPeakTime=0., directFitPeak=0., directFitSubtracted=False,
directFitValid=False,
)
# 3. if there is no large event near stimulus, return original data
if gotEvent is None:
if display:
self.plotItem.clear()
return {'output': data, 'fitParams': fitParams, 'plot': self.plotItem}
#print "============"
#print stimInd
#print gotEvent
#print cross[:20]
# 4. guess amplitude, tau from detected event
evStart = gotEvent[0]
evEnd = evStart + gotEvent[1]
evRgn = data2[evStart:evEnd]
#pg.plot(evRgn)
mx = evRgn.max()
mn = evRgn.min()
ampGuess = mx if abs(mx) > abs(mn) else mn
tauGuess = gotEvent[1] * dt
if ampGuess < 0:
ampBound = [None, 0]
else:
ampBound = [0, None]
# 5. fit
guess = [ampGuess*2, ampGuess*2, stimTime, 1e-3, tauGuess*0.5, tauGuess*2]
bounds = [
ampBound, ampBound,
[stimTime, stimTime+4e-3],
[1e-4, 100e-3],
[1e-3, 1],
[5e-3, 10],
]
#print guess
#print bounds
endInd = evStart + gotEvent[1] * 10
fitYData = data2[stimInd:endInd]
fitXData = times[stimInd:endInd]
fit = functions.fitDoublePsp(x=fitXData, y=fitYData, guess=guess, bounds=bounds, risePower=rp)
# 6. subtract fit from original data (offset included), return
y = functions.doublePspFunc(fit, times, rp)
if display:
self.plotItem.setData(x=fitXData, y=y[stimInd:endInd], pen=self.ctrls['plotColor'].color())
## prepare list of fit params for output
(xMax, yMax) = functions.doublePspMax(fit)
xMax -= fit[2] ## really interested in time-to-peak, not the exact peak time.
fitParams = dict(
directFitAmp1=fit[0], directFitAmp2=fit[1], directFitTime=fit[2],
directFitRise=fit[3], directFitDecay1=fit[4], directFitDecay2=fit[5],
directFitPeakTime=xMax, directFitPeak=yMax, directFitSubtracted=None, directFitValid=True,
)
if self.ctrls['subtractDirect'].isChecked():
out = metaarray.MetaArray(data-y, info=data.infoCopy())
fitParams['directFitSubtracted'] = True
else:
out = data
fitParams['directFitSubtracted'] = False
return {'fitParams': fitParams, 'output': out, 'plot': self.plotItem}
def processEventFits(events, startEvent, stopEvent, opts):
## This function does all the processing work for EventFitter.
dt = opts['dt']
origTau = opts['tau']
multiFit = opts['multiFit']
waveform = opts['waveform']
tvals = opts['tvals']
nFields = len(events.dtype.fields)
dtype = [(n, events[n].dtype) for n in events.dtype.names]
output = np.empty(len(events), dtype=dtype + [
('fitAmplitude', float),
('fitTime', float),
('fitRiseTau', float),
('fitDecayTau', float),
('fitTimeToPeak', float),
('fitError', float),
('fitFractionalError', float),
('fitLengthOverDecay', float),
])
offset = 0 ## not all input events will produce output events; offset keeps track of the difference.
outputState = {
'guesses': [],
'eventData': [],
'indexes': [],
'xVals': [],
'yVals': []
}
for i in range(startEvent, stopEvent):
start = events[i]['time']
#sliceLen = 50e-3
sliceLen = dt*300. ## Ca2+ events are much longer than 50ms
if i+1 < len(events):
nextStart = events[i+1]['time']
sliceLen = min(sliceLen, nextStart-start)
guessLen = events[i]['len']*dt
tau = origTau
if tau is not None:
guessLen += tau*2.
#print i, guessLen, tau, events[i]['len']*dt
#sliceLen = 50e-3
sliceLen = guessLen
if i+1 < len(events): ## cut slice back if there is another event coming up
nextStart = events[i+1]['time']
sliceLen = min(sliceLen, nextStart-start)
## Figure out from where to pull waveform data that will be fitted
startIndex = np.argwhere(tvals>=start)[0][0]
stopIndex = startIndex + int(sliceLen/dt)
eventData = waveform[startIndex:stopIndex]
times = tvals[startIndex:stopIndex]
#print i, startIndex, stopIndex, dt
if len(times) < 4: ## PSP fit requires at least 4 points; skip this one
offset += 1
continue
## reconvolve this chunk of the signal if it was previously deconvolved
if tau is not None:
eventData = functions.expReconvolve(eventData, tau=tau, dt=dt)
#print i, len(eventData)
## Make guesses as to the shape of the event
mx = eventData.max()
mn = eventData.min()
if mx > -mn:
peakVal = mx
else:
peakVal = mn
guessAmp = peakVal * 2 ## fit converges more reliably if we start too large
guessRise = guessLen/4.
guessDecay = guessLen/2.
guessStart = times[0]
zc = functions.zeroCrossingEvents(eventData - (peakVal/3.))
## eliminate events going the wrong direction
if len(zc) > 0:
if guessAmp > 0:
zc = zc[zc['peak']>0]
else:
zc = zc[zc['peak']<0]
#print zc
## measure properties for the largest event within 10ms of start
zc = zc[zc['index'] < 10e-3/dt]
if len(zc) > 0:
if guessAmp > 0:
zcInd = np.argmax(zc['sum']) ## the largest event in this clip
else:
zcInd = np.argmin(zc['sum']) ## the largest event in this clip
zcEv = zc[zcInd]
#guessLen = dt*zc[zcInd]['len']
guessRise = .1e-3 #dt*zcEv['len'] * 0.2
guessDecay = dt*zcEv['len'] * 0.8
guessStart = times[0] + dt*zcEv['index'] - guessRise*3.
## cull down the data set if possible
cullLen = zcEv['index'] + zcEv['len']*3
if len(eventData) > cullLen:
eventData = eventData[:cullLen]
times = times[:cullLen]
## fitting to exponential rise * decay
## parameters are [amplitude, x-offset, rise tau, fall tau]
guess = [guessAmp, guessStart, guessRise, guessDecay]
#guess = [amp, times[0], guessLen/4., guessLen/2.] ## careful!
bounds = [
sorted((guessAmp * 0.1, guessAmp)),
sorted((guessStart-min(guessRise, 0.01), guessStart+guessRise*2)),
sorted((dt*0.5, guessDecay)),
sorted((dt*0.5, guessDecay * 50.))
]
yVals = eventData.view(np.ndarray)
fit = functions.fitPsp(times, yVals, guess=guess, bounds=bounds, multiFit=multiFit)
computed = functions.pspFunc(fit, times)
peakTime = functions.pspMaxTime(fit[2], fit[3])
diff = (yVals - computed)
err = (diff**2).sum()
fracError = diff.std() / computed.std()
lengthOverDecay = (times[-1] - fit[1]) / fit[3] # ratio of (length of data that was fit : decay constant)
output[i-offset] = tuple(events[i]) + tuple(fit) + (peakTime, err, fracError, lengthOverDecay)
#output['fitTime'] += output['time']
#print fit
#self.events.append(eventData)
outputState['guesses'].append(guess)
outputState['eventData'].append(eventData)
outputState['indexes'].append(i)
outputState['xVals'].append(times)
outputState['yVals'].append(computed)
if offset > 0:
output = output[:-offset]
outputState['output'] = output
return outputState
def processData(self, data):
s = self.stateGroup.state()
events = functions.zeroCrossingEvents(data, minLength=s['minLength'], minPeak=s['minPeak'], minSum=s['minSum'])
events = events[:s['eventLimit']]
return events
