doc/uses/EPSCs-and-IPSCs/variance method/2016-12-18 01 curve fit.py
def phasicTonic(self,m1=None,m2=None,chunkMs=50,
                    quietPercentile=10,histResolution=1):
        """ 
        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 (marker positions)
        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]
        Y=self.sweepYfilteredHisto()[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
        
        # create histogram for all data in the sweep
        nChunks=int(len(Y)/chunkPoints)
        hist,bins=np.histogram(Y,bins=histBins,range=(-padding,padding))

        # create histogram for just the sweeps with the lowest variance
        chunks=np.reshape(Y[:nChunks*chunkPoints],(nChunks,chunkPoints))
        #variances=np.var(chunks,axis=1)
        variances=np.ptp(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()
        blHist,blBins=np.histogram(blData,bins=histBins,range=(-padding,padding))
        blHist=blHist/max(blHist)*max(hist)
        
        # determine the phasic current by subtracting-out the baseline
        diff=hist-blHist
        return diff/abf.pointsPerSec
def phasicTonic(self,m1=None,m2=None,chunkMs=50,
                    quietPercentile=10,histResolution=1):
        """ 
        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 (marker positions)
        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]
        Y=self.sweepYfilteredHisto()[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
        
        # create histogram for all data in the sweep
        nChunks=int(len(Y)/chunkPoints)
        hist,bins=np.histogram(Y,bins=histBins,range=(-padding,padding))

        # create histogram for just the sweeps with the lowest variance
        chunks=np.reshape(Y[:nChunks*chunkPoints],(nChunks,chunkPoints))
        #variances=np.var(chunks,axis=1)
        variances=np.ptp(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()
        blHist,blBins=np.histogram(blData,bins=histBins,range=(-padding,padding))
        blHist=blHist/max(blHist)*max(hist)
        
        # determine the phasic current by subtracting-out the baseline
        diff=hist-blHist
        return diff/abf.pointsPerSec
def phasicTonic ( self , m1 = None , m2 = None , chunkMs = 50 , quietPercentile = 10 , histResolution = 1 ) : # prepare sectioning values to be used later (marker positions) 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] Y = self . sweepYfilteredHisto ( ) [ 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 # create histogram for all data in the sweep nChunks = int ( len ( Y ) / chunkPoints ) hist , bins = np . histogram ( Y , bins = histBins , range = ( - padding , padding ) ) # create histogram for just the sweeps with the lowest variance chunks = np . reshape ( Y [ : nChunks * chunkPoints ] , ( nChunks , chunkPoints ) ) #variances=np.var(chunks,axis=1) variances = np . ptp ( 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 ( ) blHist , blBins = np . histogram ( blData , bins = histBins , range = ( - padding , padding ) ) blHist = blHist / max ( blHist ) * max ( hist ) # determine the phasic current by subtracting-out the baseline diff = hist - blHist return diff / 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 .