def phasicTonic(self,m1=None,m2=None,chunkMs=50,quietPercentile=10,
                    histResolution=.5,plotToo=False,rmsExpected=5):
        """ 
        chunkMs should be ~50 ms or greater.
        bin sizes must be equal to or multiples of the data resolution.
        transients smaller than the expected RMS will be silenced.
        """
        # prepare sectioning values to be used later
        m1=0 if m1 is None else m1*self.pointsPerSec
        m2=len(abf.sweepY) if m2 is None else m2*self.pointsPerSec
        m1,m2=int(m1),int(m2)
        
        # prepare histogram values to be used later
        padding=200 # pA or mV of maximum expected deviation
        chunkPoints=int(chunkMs*self.pointsPerMs)
        histBins=int((padding*2)/histResolution)
        
        # center the data at 0 using peak histogram, not the mean
        Y=self.sweepY[m1:m2]
        hist,bins=np.histogram(Y,bins=2*padding)
        Yoffset=bins[np.where(hist==max(hist))[0][0]]
        Y=Y-Yoffset # we don't have to, but PDF math is easier
        
        # calculate all histogram
        nChunks=int(len(Y)/chunkPoints)
        hist,bins=np.histogram(Y,bins=histBins,range=(-padding,padding))
        hist=hist/len(Y) # count as a fraction of total
        Xs=bins[1:]

        # get baseline data from chunks with smallest variance
        chunks=np.reshape(Y[:nChunks*chunkPoints],(nChunks,chunkPoints))
        variances=np.var(chunks,axis=1)
        percentiles=np.empty(len(variances))
        for i,variance in enumerate(variances):
            percentiles[i]=sorted(variances).index(variance)/len(variances)*100
        blData=chunks[np.where(percentiles<=quietPercentile)[0]].flatten()
        
        # generate the standard curve and pull it to the histogram height
        sigma=np.sqrt(np.var(blData))
        center=np.average(blData)+histResolution/2
        blCurve=mlab.normpdf(Xs,center,sigma)
        blCurve=blCurve*max(hist)/max(blCurve)
                
        # determine the phasic current by subtracting-out the baseline
        diff=hist-blCurve
        
        # manually zero-out data which we expect to be within the RMS range
        ignrCenter=len(Xs)/2
        ignrPad=rmsExpected/histResolution
        ignr1,ignt2=int(ignrCenter-ignrPad),int(ignrCenter+ignrPad)
        diff[ignr1:ignt2]=0
            
        return diff/len(Y)*abf.pointsPerSec
def phasicTonic(self,m1=None,m2=None,chunkMs=50,quietPercentile=10,
                    histResolution=.5,plotToo=False,rmsExpected=5):
        """ 
        chunkMs should be ~50 ms or greater.
        bin sizes must be equal to or multiples of the data resolution.
        transients smaller than the expected RMS will be silenced.
        """
        # prepare sectioning values to be used later
        m1=0 if m1 is None else m1*self.pointsPerSec
        m2=len(abf.sweepY) if m2 is None else m2*self.pointsPerSec
        m1,m2=int(m1),int(m2)
        
        # prepare histogram values to be used later
        padding=200 # pA or mV of maximum expected deviation
        chunkPoints=int(chunkMs*self.pointsPerMs)
        histBins=int((padding*2)/histResolution)
        
        # center the data at 0 using peak histogram, not the mean
        Y=self.sweepY[m1:m2]
        hist,bins=np.histogram(Y,bins=2*padding)
        Yoffset=bins[np.where(hist==max(hist))[0][0]]
        Y=Y-Yoffset # we don't have to, but PDF math is easier
        
        # calculate all histogram
        nChunks=int(len(Y)/chunkPoints)
        hist,bins=np.histogram(Y,bins=histBins,range=(-padding,padding))
        hist=hist/len(Y) # count as a fraction of total
        Xs=bins[1:]

        # get baseline data from chunks with smallest variance
        chunks=np.reshape(Y[:nChunks*chunkPoints],(nChunks,chunkPoints))
        variances=np.var(chunks,axis=1)
        percentiles=np.empty(len(variances))
        for i,variance in enumerate(variances):
            percentiles[i]=sorted(variances).index(variance)/len(variances)*100
        blData=chunks[np.where(percentiles<=quietPercentile)[0]].flatten()
        
        # generate the standard curve and pull it to the histogram height
        sigma=np.sqrt(np.var(blData))
        center=np.average(blData)+histResolution/2
        blCurve=mlab.normpdf(Xs,center,sigma)
        blCurve=blCurve*max(hist)/max(blCurve)
                
        # determine the phasic current by subtracting-out the baseline
        diff=hist-blCurve
        
        # manually zero-out data which we expect to be within the RMS range
        ignrCenter=len(Xs)/2
        ignrPad=rmsExpected/histResolution
        ignr1,ignt2=int(ignrCenter-ignrPad),int(ignrCenter+ignrPad)
        diff[ignr1:ignt2]=0
            
        return diff/len(Y)*abf.pointsPerSec
def phasicTonic ( self , m1 = None , m2 = None , chunkMs = 50 , quietPercentile = 10 , histResolution = .5 , plotToo = False , rmsExpected = 5 ) : # prepare sectioning values to be used later m1 = 0 if m1 is None else m1 * self . pointsPerSec m2 = len ( abf . sweepY ) if m2 is None else m2 * self . pointsPerSec m1 , m2 = int ( m1 ) , int ( m2 ) # prepare histogram values to be used later padding = 200 # pA or mV of maximum expected deviation chunkPoints = int ( chunkMs * self . pointsPerMs ) histBins = int ( ( padding * 2 ) / histResolution ) # center the data at 0 using peak histogram, not the mean Y = self . sweepY [ m1 : m2 ] hist , bins = np . histogram ( Y , bins = 2 * padding ) Yoffset = bins [ np . where ( hist == max ( hist ) ) [ 0 ] [ 0 ] ] Y = Y - Yoffset # we don't have to, but PDF math is easier # calculate all histogram nChunks = int ( len ( Y ) / chunkPoints ) hist , bins = np . histogram ( Y , bins = histBins , range = ( - padding , padding ) ) hist = hist / len ( Y ) # count as a fraction of total Xs = bins [ 1 : ] # get baseline data from chunks with smallest variance chunks = np . reshape ( Y [ : nChunks * chunkPoints ] , ( nChunks , chunkPoints ) ) variances = np . var ( chunks , axis = 1 ) percentiles = np . empty ( len ( variances ) ) for i , variance in enumerate ( variances ) : percentiles [ i ] = sorted ( variances ) . index ( variance ) / len ( variances ) * 100 blData = chunks [ np . where ( percentiles <= quietPercentile ) [ 0 ] ] . flatten ( ) # generate the standard curve and pull it to the histogram height sigma = np . sqrt ( np . var ( blData ) ) center = np . average ( blData ) + histResolution / 2 blCurve = mlab . normpdf ( Xs , center , sigma ) blCurve = blCurve * max ( hist ) / max ( blCurve ) # determine the phasic current by subtracting-out the baseline diff = hist - blCurve # manually zero-out data which we expect to be within the RMS range ignrCenter = len ( Xs ) / 2 ignrPad = rmsExpected / histResolution ignr1 , ignt2 = int ( ignrCenter - ignrPad ) , int ( ignrCenter + ignrPad ) diff [ ignr1 : ignt2 ] = 0 return diff / len ( Y ) * abf . pointsPerSec
chunkMs should be ~50 ms or greater.
        bin sizes must be equal to or multiples of the data resolution.
        transients smaller than the expected RMS will be silenced.
chunkMs should be ~50 ms or greater . bin sizes must be equal to or multiples of the data resolution . transients smaller than the expected RMS will be silenced .