def _openchanstats(self, curr):
"""
Estimate the mean, standard deviation and slope of
the channel's open state current using self.blockSizeSec of data.
Args:
curr: numpy array to use for calculating statistics
Returns:
meanOpenCurr mean open channel current
sdOpenCurr standard deviation of open channel current
slopeOpenCurr slope of the open channel current (measure of drift)
in pA/s
Errors:
None
"""
n=len(curr)
#curr=self.trajDataObj.previewdata(nPoints)
t=1./self.FsHz
tstamp=np.arange(0, n*t, t, dtype=np.float64)[:n]
#print "nPoints=", n, "len(tstamp)=", len(tstamp), "type(curr)", type(curr)
# Calculate the mean and standard deviation of the open state
mu, sig=OpenCurrentDist(curr, 0.5, self.minBaseline, self.maxBaseline)
# Fit the data to a straight line to calculate the slope
slope, intercept, r_value, p_value, std_err=scipy.stats.linregress(tstamp, curr)
# self.logger.debug(_d("mu={0}, sigma={1}, slope={2}", mu, sig, slope ))
# Return stats
return [ mu, sig, slope ]
def _openchanstats(self, curr):
"""
Estimate the mean, standard deviation and slope of
the channel's open state current using self.blockSizeSec of data.
Args:
curr: numpy array to use for calculating statistics
Returns:
meanOpenCurr mean open channel current
sdOpenCurr standard deviation of open channel current
slopeOpenCurr slope of the open channel current (measure of drift)
in pA/s
Errors:
None
"""
n = len(curr)
#curr=self.trajDataObj.previewdata(nPoints)
t = 1. / self.trajDataObj.FsHz
tstamp = np.arange(0, n * t, t, dtype=np.float64)[:n]
#print "nPoints=", n, "len(tstamp)=", len(tstamp), "type(curr)", type(curr)
# Calculate the mean and standard deviation of the open state
mu, sig = OpenCurrentDist(curr, 0.5)
# Fit the data to a straight line to calculate the slope
# ft[0]: slope
# ft[1]: y-intercept (mean current)
ft = np.polyfit(tstamp, curr, 1)
# Return stats
return [mu, sig, ft[0]]
def _openChanStats(self, ydat):
try:
mu, sigma = OpenCurrentDist(ydat, 0.5)
except:
mu = 0
sigma = 0
self.returnMessageJSON['currMeanAuto'] = round(mu, 3)
self.returnMessageJSON['currSigmaAuto'] = round(sigma, 3)
def _calculateThreshold(self, dat):
if float(self.datadict["meanOpenCurr"]) == -1 or float(
self.datadict["sdOpenCurr"]) == -1:
try:
self.mu, self.sd = OpenCurrentDist(dat, 0.5)
if self.thrCurrPicoA == 0.:
self.thr = float(self.datadict["eventThreshold"])
# if eventThresholdpA is not set, the program was initialized with
# baseline auto. Store the threshold current in pA in self.thrCurrPicoA.
# The settings window will then pick it up on the first controls update.
self.thrCurrPicoA = self.mu - self.sd * self.thr
else:
# If self.thrCurrPicoA is set, then calculate a new threshold in SD
# so as to keep self.thrCurrPicoA constant.
self.thr = (self.mu - self.thrCurrPicoA) / self.sd
except AttributeError:
self.mu, self.sd = OpenCurrentDist(dat, 0.5)
self.thr = (self.mu - self.thrCurrPicoA) / self.sd
else:
self.mu = abs(float(self.datadict["meanOpenCurr"]))
self.sd = float(self.datadict["sdOpenCurr"])
self.thr = (self.mu - self.thrCurrPicoA) / self.sd
def currentStats(self, curr):
return OpenCurrentDist(curr, 0.5)