sampling scope time zero

scopefit.py

import numpy as np
import pylab as plt
from scipy.optimize import curve_fit
 
import gpib_instrument
scope = gpib_instrument.Gpib_Instrument(pad=3)
scope.ask("OUT TRA1")
def ask(s):
    scope.write(s)
    r = ""
    while '\x00' not in r:
        r+=scope.read()
    return r[:r.find('\x00')-1]
 
def getxinfo():
    r = ask("WFMP?")
    r2=r.split(",",10000)
    d={c.split(":")[0]:c.split(":")[1] for c in r2}
    xincr = float(d["XINCR"])
    ymult = float(d["YMULT"])
    return xincr, ymult
 
def askcurv():
    d = ask("CURV?")
    d2=d.split(",",1000000)
    curveid = d2.pop(0)
    print(curveid)
    d3 = [int(q) for q in d2]
    d3 = d3[:-1]
    xincr,ymult = getxinfo()
    return np.arange(len(d3))*xincr, np.array(d3)*ymult
 
def model(x,baseline, amp, t0, tau1, tau2):
    # model the turn on as an exponential
    t=x-t0
    # following http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1485918
    return baseline+amp*(np.exp(t/tau1)+tau2/tau1*(np.exp(t/tau1)-np.exp(t/tau2)))*np.greater(t,0)
 
def model_simple(x, baseline, amp, t0, tau):
    t = x-t0
    return baseline*np.less_equal(x,0)+(amp+(baseline-amp)*np.exp(-t/tau))*np.greater(t,0)

def dofit(x,y, guesses=[1,10, 30, 40,50]):
    p_guess = np.array(guesses, dtype="float64")
    firstnotuse = plt.find(y>np.amax(y)*0.5)[0]
    useinds = np.arange(firstnotuse)
    xfit = x[useinds]
    yfit = y[useinds]
 
    p_fit, p_covar =curve_fit(model, xfit, yfit, p0=p_guess)
    p_names = ["baseline", "amplitude", "t0", "tau1", "tau2"]
    if np.shape(p_covar)==():
        p_covar = np.eye(len(p_fit))*np.inf
    for i in xrange(len(p_fit)):
        print("%s %g +/- %g"%(p_names[i], p_fit[i], np.sqrt(p_covar[i,i])))
    plt.figure()
    plt.plot(x,y,label="trace")
    plt.plot(x[useinds], y[useinds],"o",label="used for fit")
    plt.plot(x[useinds], model(x[useinds],p_fit[0],p_fit[1],p_fit[2],p_fit[3],p_fit[4]),"s",label="fit")
    plt.plot([p_fit[2],p_fit[2]], plt.ylim())
    plt.xlim(x[0],x[-1])
    plt.legend()
    plt.show()

plt.ion()
data = np.loadtxt("apdrise")
x=data[0,:]
y=data[1,:]
y-=y[0]
plt.plot(x,y)
firstnotuse = plt.find(y>np.amax(y)*0.5)[0]
plt.plot(x[:firstnotuse],model(x[:firstnotuse], 0, 0.0002, 0.4e-9,50e-12,100e-12))
plt.plot(x[:firstnotuse],model_simple(x[:firstnotuse], 0, 0.0075, 0.4e-9,50e-12))

dofit(x,y,[0, 0.0002, 0.4e-9,50e-12,100e-12])

tau_rc = 35e-12
ymodel=model(x, 0,0.000217, 396e-12, 59.5e-12, 144e-12 )
yft = np.fft.rfft(ymodel)
f = np.fft.rfftfreq(len(y), x[1]-x[0])
cf = np.sqrt(1/(1+(f*tau_rc)**2))
ysm = np.fft.irfft(cf*yft,len(y))