def analyzeSweep(abf,sweep,m1=None,m2=None,plotToo=False):
    """
    m1 and m2, if given, are in seconds.
    returns [# EPSCs, # IPSCs]
    """
    abf.setsweep(sweep)
    if m1 is None: m1=0
    else: m1=m1*abf.pointsPerSec
    if m2 is None: m2=-1
    else: m2=m2*abf.pointsPerSec

    # obtain X and Y
    Yorig=abf.sweepY[int(m1):int(m2)]
    X=np.arange(len(Yorig))/abf.pointsPerSec

    Ylpf=linear_gaussian(Yorig,sigmaSize=abf.pointsPerMs*300,forwardOnly=False)
    Yflat=Yorig-Ylpf

    EPSCs,IPSCs=[],[]

    if plotToo:
        plt.figure(figsize=(15,6))
        ax1=plt.subplot(211)
        plt.title("%s sweep %d"%(abf.ID,sweep))
        plt.grid()
        plt.plot(X,Yorig,alpha=.5)
        plt.plot(X,Ylpf,'k',alpha=.5,lw=2)
        plt.margins(0,.2)

        plt.subplot(212,sharex=ax1)
        plt.title("gaussian baseline subtraction")
        plt.grid()
        plt.plot(X,Yflat,alpha=.5)
        plt.axhline(0,color='k',lw=2,alpha=.5)

        plt.tight_layout()
        plt.show()

    # TEST GAUSS
    hist, bin_edges = np.histogram(Yflat, density=True, bins=200)
    peakPa=bin_edges[np.where(hist==max(hist))[0][0]+1]

    if plotToo:
        plt.figure()
        plt.grid()
        plt.plot(bin_edges[1:],hist,alpha=.5)
        plt.axvline(0,color='k')
        plt.axvline(peakPa,color='r',ls='--',lw=2,alpha=.5)
        plt.semilogy()
        plt.title("sweep data distribution")
        plt.ylabel("power")
        plt.xlabel("pA deviation")
        plt.show()

    return peakPa
def analyzeSweep(abf,sweep,m1=None,m2=None,plotToo=False):
    """
    m1 and m2, if given, are in seconds.
    returns [# EPSCs, # IPSCs]
    """
    abf.setsweep(sweep)
    if m1 is None: m1=0
    else: m1=m1*abf.pointsPerSec
    if m2 is None: m2=-1
    else: m2=m2*abf.pointsPerSec

    # obtain X and Y
    Yorig=abf.sweepY[int(m1):int(m2)]
    X=np.arange(len(Yorig))/abf.pointsPerSec

    Ylpf=linear_gaussian(Yorig,sigmaSize=abf.pointsPerMs*300,forwardOnly=False)
    Yflat=Yorig-Ylpf

    EPSCs,IPSCs=[],[]

    if plotToo:
        plt.figure(figsize=(15,6))
        ax1=plt.subplot(211)
        plt.title("%s sweep %d"%(abf.ID,sweep))
        plt.grid()
        plt.plot(X,Yorig,alpha=.5)
        plt.plot(X,Ylpf,'k',alpha=.5,lw=2)
        plt.margins(0,.2)

        plt.subplot(212,sharex=ax1)
        plt.title("gaussian baseline subtraction")
        plt.grid()
        plt.plot(X,Yflat,alpha=.5)
        plt.axhline(0,color='k',lw=2,alpha=.5)

        plt.tight_layout()
        plt.show()

    # TEST GAUSS
    hist, bin_edges = np.histogram(Yflat, density=True, bins=200)
    peakPa=bin_edges[np.where(hist==max(hist))[0][0]+1]

    if plotToo:
        plt.figure()
        plt.grid()
        plt.plot(bin_edges[1:],hist,alpha=.5)
        plt.axvline(0,color='k')
        plt.axvline(peakPa,color='r',ls='--',lw=2,alpha=.5)
        plt.semilogy()
        plt.title("sweep data distribution")
        plt.ylabel("power")
        plt.xlabel("pA deviation")
        plt.show()

    return peakPa
def analyzeSweep ( abf , sweep , m1 = None , m2 = None , plotToo = False ) : abf . setsweep ( sweep ) if m1 is None : m1 = 0 else : m1 = m1 * abf . pointsPerSec if m2 is None : m2 = - 1 else : m2 = m2 * abf . pointsPerSec # obtain X and Y Yorig = abf . sweepY [ int ( m1 ) : int ( m2 ) ] X = np . arange ( len ( Yorig ) ) / abf . pointsPerSec Ylpf = linear_gaussian ( Yorig , sigmaSize = abf . pointsPerMs * 300 , forwardOnly = False ) Yflat = Yorig - Ylpf EPSCs , IPSCs = [ ] , [ ] if plotToo : plt . figure ( figsize = ( 15 , 6 ) ) ax1 = plt . subplot ( 211 ) plt . title ( "%s sweep %d" % ( abf . ID , sweep ) ) plt . grid ( ) plt . plot ( X , Yorig , alpha = .5 ) plt . plot ( X , Ylpf , 'k' , alpha = .5 , lw = 2 ) plt . margins ( 0 , .2 ) plt . subplot ( 212 , sharex = ax1 ) plt . title ( "gaussian baseline subtraction" ) plt . grid ( ) plt . plot ( X , Yflat , alpha = .5 ) plt . axhline ( 0 , color = 'k' , lw = 2 , alpha = .5 ) plt . tight_layout ( ) plt . show ( ) # TEST GAUSS hist , bin_edges = np . histogram ( Yflat , density = True , bins = 200 ) peakPa = bin_edges [ np . where ( hist == max ( hist ) ) [ 0 ] [ 0 ] + 1 ] if plotToo : plt . figure ( ) plt . grid ( ) plt . plot ( bin_edges [ 1 : ] , hist , alpha = .5 ) plt . axvline ( 0 , color = 'k' ) plt . axvline ( peakPa , color = 'r' , ls = '--' , lw = 2 , alpha = .5 ) plt . semilogy ( ) plt . title ( "sweep data distribution" ) plt . ylabel ( "power" ) plt . xlabel ( "pA deviation" ) plt . show ( ) return peakPa
m1 and m2, if given, are in seconds.
    returns [# EPSCs, # IPSCs]
m1 and m2 if given are in seconds . returns [ # EPSCs # IPSCs ]