def fit_exp(y,graphToo=False):
    """Exponential fit. Returns [multiplier, t, offset, time constant]"""
    x=np.arange(len(y))
    try:
        params, cv = scipy.optimize.curve_fit(algo_exp, x, y, p0=(1,1e-6,1))
    except:
        print(" !! curve fit failed (%.02f points)"%len(x))
        return np.nan,np.nan,np.nan,np.nan #can't fit so little data!
    m,t,b=params
    tau=1/t
    if graphToo:
        pylab.figure(figsize=(6,4))
        pylab.grid()
        pylab.title("FIT ASSESSMENT")
        pylab.plot(x,y,'o',mfc='none',ms=10)
        pylab.plot(x,algo_exp(x,m,t,b),'b-',lw=2)
        pylab.show()
    return m,t,b,tau
def fit_exp(y,graphToo=False):
    """Exponential fit. Returns [multiplier, t, offset, time constant]"""
    x=np.arange(len(y))
    try:
        params, cv = scipy.optimize.curve_fit(algo_exp, x, y, p0=(1,1e-6,1))
    except:
        print(" !! curve fit failed (%.02f points)"%len(x))
        return np.nan,np.nan,np.nan,np.nan #can't fit so little data!
    m,t,b=params
    tau=1/t
    if graphToo:
        pylab.figure(figsize=(6,4))
        pylab.grid()
        pylab.title("FIT ASSESSMENT")
        pylab.plot(x,y,'o',mfc='none',ms=10)
        pylab.plot(x,algo_exp(x,m,t,b),'b-',lw=2)
        pylab.show()
    return m,t,b,tau
def fit_exp ( y , graphToo = False ) : x = np . arange ( len ( y ) ) try : params , cv = scipy . optimize . curve_fit ( algo_exp , x , y , p0 = ( 1 , 1e-6 , 1 ) ) except : print ( " !! curve fit failed (%.02f points)" % len ( x ) ) return np . nan , np . nan , np . nan , np . nan #can't fit so little data! m , t , b = params tau = 1 / t if graphToo : pylab . figure ( figsize = ( 6 , 4 ) ) pylab . grid ( ) pylab . title ( "FIT ASSESSMENT" ) pylab . plot ( x , y , 'o' , mfc = 'none' , ms = 10 ) pylab . plot ( x , algo_exp ( x , m , t , b ) , 'b-' , lw = 2 ) pylab . show ( ) return m , t , b , tau
Exponential fit. Returns [multiplier, t, offset, time constant]
Exponential fit . Returns [ multiplier t offset time constant ]