def __blockadeDepthFit(self):
"""
"""
# Get first order estimates of the open channel and blocked state currents
# We will use these to guide the final fits.
pre = self.__currentEstimate(
self.eventData[:self.eStartEstimate - self.datPad],
min(
50,
int(
len(self.eventData[:self.eStartEstimate - self.datPad]) /
10.0)))
post = self.__currentEstimate(
self.eventData[self.eEndEstimate + self.datPad:],
min(
50,
int(
len(self.eventData[self.eEndEstimate + self.datPad:]) /
10.0)))
bcCurrEst = self.__meanBlockedCurrent(
self.eventData[self.eStartEstimate -
self.datPad:self.eEndEstimate + self.datPad], 2.0)
if self.mdProcessingStatus == 'normal':
ocCurrEst = util.flat2([pre, post])
self.__fitsumgauss(ocCurrEst + bcCurrEst, util.avg(ocCurrEst),
np.std(ocCurrEst), util.avg(bcCurrEst))
def _levelchange(self, dat, sMean, sSD, nSD, blksz):
for i in range(int(blksz / 2.0), len(dat) - int(blksz / 2.0)):
if abs(
util.avg(dat[i - int(blksz / 2.0):i + int(blksz / 2.0)]) -
sMean) > nSD * sSD:
return [i + 1, util.avg(dat[i:i + blksz])]
raise InvalidEvent()
def __blockadeDepthFit(self): """ """ # Get first order estimates of the open channel and blocked state currents # We will use these to guide the final fits. pre=self.__currentEstimate(self.eventData[:self.eStartEstimate-self.datPad], min(50, int(len(self.eventData[:self.eStartEstimate-self.datPad])/10.0)) ) post=self.__currentEstimate(self.eventData[self.eEndEstimate+self.datPad:], min(50, int(len(self.eventData[self.eEndEstimate+self.datPad:])/10.0)) ) bcCurrEst=self.__meanBlockedCurrent(self.eventData[self.eStartEstimate-self.datPad:self.eEndEstimate+self.datPad], 2.0) if self.mdProcessingStatus=='normal': ocCurrEst=util.flat2( [pre, post] ) self.__fitsumgauss( ocCurrEst+bcCurrEst, util.avg(ocCurrEst), np.std(ocCurrEst), util.avg(bcCurrEst) )
def __fitsumgauss(self, cleandat, oc, ocsd, bc):
"""
"""
np.seterr(invalid='raise', over='raise')
# keep track of the current polarity
sign=np.sign(util.avg(cleandat))
# use the absolute current value to simplify the calc.
# We put the sign back in the current when returning the results
evntabs=np.abs(cleandat)
# Histogram and fit
try:
# PDF of event data
ehist,bins=np.histogram(
evntabs,
bins=np.arange(np.min(evntabs)-self.histPad,np.max(evntabs)+self.histPad, self.histBinSz),
density=True
)
popt, pcov = scipy.optimize.curve_fit(
self.__sumgauss,
[ b+(0.5*self.histBinSz) for b in bins[:-1] ],
ehist,
p0=[np.max(ehist), np.abs(bc), np.abs(ocsd), np.max(ehist), np.abs(oc), np.abs(ocsd)],
maxfev=self.maxFitIters
)
except BaseException, err:
#print oc, ocsd, bc, len(cleandat)
# reject event if it can't be fit
self.rejectEvent('eBDFit')
return
def __FitEvent(self):
try:
dt = 1000. / self.Fs # time-step in ms.
# edat=np.asarray( np.abs(self.eventData), dtype='float64' )
edat = self.dataPolarity * np.asarray(self.eventData,
dtype='float64')
# control numpy error reporting
np.seterr(invalid='ignore', over='ignore', under='ignore')
ts = np.array([t * dt for t in range(0, len(edat))],
dtype='float64')
# estimate initial guess for events
initguess = self._characterizeevent(edat,
np.abs(util.avg(edat[:10])),
self.baseSD,
self.InitThreshold, 6.)
self.nStates = len(initguess) - 1
# setup fit params
params = Parameters()
for i in range(1, len(initguess)):
params.add('a' + str(i - 1),
value=initguess[i][0] - initguess[i - 1][0])
params.add('mu' + str(i - 1), value=initguess[i][1] * dt)
params.add('tau' + str(i - 1), value=dt * 7.5)
params.add('b', value=initguess[0][0])
optfit = Minimizer(self.__objfunc, params, fcn_args=(
ts,
edat,
))
optfit.prepare_fit()
optfit.leastsq(xtol=self.FitTol,
ftol=self.FitTol,
maxfev=self.FitIters)
if optfit.success:
self.__recordevent(optfit)
else:
#print optfit.message, optfit.lmdif_message
self.rejectEvent('eFitConvergence')
except KeyboardInterrupt:
self.rejectEvent('eFitUserStop')
raise
except InvalidEvent:
self.rejectEvent('eInvalidEvent')
except:
self.rejectEvent('eFitFailure')
raise
def __FitEvent(self):
try:
dt = 1000./self.Fs # time-step in ms.
# edat=np.asarray( np.abs(self.eventData), dtype='float64' )
edat=self.dataPolarity*np.asarray( self.eventData, dtype='float64' )
# control numpy error reporting
np.seterr(invalid='ignore', over='ignore', under='ignore')
ts = np.array([ t*dt for t in range(0,len(edat)) ], dtype='float64')
# estimate initial guess for events
initguess=self._characterizeevent(edat, np.abs(util.avg(edat[:10])), self.baseSD, self.InitThreshold, 6.)
self.nStates=len(initguess)-1
# setup fit params
params=Parameters()
for i in range(1, len(initguess)):
params.add('a'+str(i-1), value=initguess[i][0]-initguess[i-1][0])
params.add('mu'+str(i-1), value=initguess[i][1]*dt)
params.add('tau'+str(i-1), value=dt*7.5)
params.add('b', value=initguess[0][0])
optfit=Minimizer(self.__objfunc, params, fcn_args=(ts,edat,))
optfit.prepare_fit()
optfit.leastsq(xtol=self.FitTol,ftol=self.FitTol,maxfev=self.FitIters)
if optfit.success:
self.__recordevent(optfit)
else:
#print optfit.message, optfit.lmdif_message
self.rejectEvent('eFitConvergence')
except KeyboardInterrupt:
self.rejectEvent('eFitUserStop')
raise
except InvalidEvent:
self.rejectEvent('eInvalidEvent')
except:
self.rejectEvent('eFitFailure')
raise
def __fitsumgauss(self, cleandat, oc, ocsd, bc):
"""
"""
np.seterr(invalid='raise', over='raise')
# keep track of the current polarity
sign = np.sign(util.avg(cleandat))
# use the absolute current value to simplify the calc.
# We put the sign back in the current when returning the results
evntabs = np.abs(cleandat)
# Histogram and fit
try:
# PDF of event data
ehist, bins = np.histogram(evntabs,
bins=np.arange(
np.min(evntabs) - self.histPad,
np.max(evntabs) + self.histPad,
self.histBinSz),
density=True)
popt, pcov = scipy.optimize.curve_fit(
self.__sumgauss,
[b + (0.5 * self.histBinSz) for b in bins[:-1]],
ehist,
p0=[
np.max(ehist),
np.abs(bc),
np.abs(ocsd),
np.max(ehist),
np.abs(oc),
np.abs(ocsd)
],
maxfev=self.maxFitIters)
except BaseException, err:
#print oc, ocsd, bc, len(cleandat)
# reject event if it can't be fit
self.rejectEvent('eBDFit')
return
def __init__(self, dat):
self.data = dat
self.mean = util.avg(dat)
self.sd = util.sd(dat)
def __init__(self, dat): self.data=dat self.mean=util.avg(dat) self.sd=util.sd(dat)
def _levelchange(self, dat, sMean, sSD, nSD, blksz): for i in range(int(blksz/2.0), len(dat)-int(blksz/2.0)): if abs(util.avg(dat[i-int(blksz/2.0):i+int(blksz/2.0)])-sMean) > nSD * sSD: return [i+1, util.avg(dat[i:i+blksz])] raise InvalidEvent()
def _eventsegment(self):
"""
Cut up a trajectory into individual events. This algorithm uses
simple thresholding. By working with absolute values of currents,
we handle positive and negative potentials without switches. When the
current drops below 'thrCurr', mark the start of the event. When the current
returns to the baseline (obtained by averaging the open channel current
immediately preceeding the start of the event), mark the event end. Pad
the event by 'eventPad' points and hand off to the event processing algorithm.
"""
try:
while(1):
t=self.currData.popleft()
self.globalDataIndex+=1
# store the latest point in a fixed buffer
if not self.eventstart:
self.preeventdat.append(t)
# Mark the start of the event
if abs(t) < self.thrCurr:
#print "event",
self.eventstart=True
self.eventdat=[]
self.eventdat.append(t)
self.dataStart=self.globalDataIndex-len(self.preeventdat)-1
if self.eventstart:
mean=abs(util.avg(self.preeventdat))
while(abs(t)<mean):
t=self.currData.popleft()
self.eventdat.append(t)
self.globalDataIndex+=1
# end of event. Reset the flag
self.eventstart=False
# Check if there are enough data points to pad the event. If not pop more.
if len(self.currData) < self.eventPad:
self.currData.extend(list(self.trajDataObj.popdata(self.nPoints)))
# Cleanup event pad data before adding it to the event. We look for:
# 1. the start of a second event
# 2. Outliers
# The threshold for accepting a point is eventThreshold/2.0
eventpaddat = util.selectS(
[ self.currData[i] for i in range(self.eventPad) ],
self.eventThreshold/2.0,
self.meanOpenCurr,
self.sdOpenCurr
)
#print self.trajDataObj.FsHz, self.windowOpenCurrentMean, self.sdOpenCurr, self.slopeOpenCurr
if len(self.eventdat)>=self.minEventLength:
self.eventcount+=1
# print "i=", self.eventcount
#sys.stderr.write('event mean curr={0:0.2f}, len(preeventdat)={1}\n'.format(sum(self.eventdat)/len(self.eventdat),len(self.preeventdat)))
#print list(self.preeventdat) + self.eventdat + [ self.currData[i] for i in range(self.eventPad) ]
#print "ecount=", self.eventcount, self.eventProcHnd
# print "eventProcSettings", self.eventProcSettingsDict
self._processEvent(
self.eventProcHnd(
list(self.preeventdat) + self.eventdat + eventpaddat,
self.FsHz,
eventstart=len(self.preeventdat)+1, # event start point
eventend=len(self.preeventdat)+len(self.eventdat)+1, # event end point
baselinestats=[ self.meanOpenCurr, self.sdOpenCurr, self.slopeOpenCurr ],
algosettingsdict=self.eventProcSettingsDict.copy(),
savets=self.writeEventTS,
absdatidx=self.dataStart
)
)
self.preeventdat.clear()
except IndexError:
return