def fitTwoGaussians(x,y):
background = PolynomialModel(2)
pars = background.make_params()
peak1 = GaussianModel(prefix='p1_')
pars.update( peak1.make_params())
peak2 = GaussianModel(prefix='p2_')
pars.update( peak2.make_params())
# Guess some parameters from data to help the fitting
span = max(x)-min(x)
c1Guess = (y[-1]-y[0])/(x[-1]-x[0])
c0Guess = y[0]-c1Guess*x[0]
bgGuess = background.func(x=x,c0=c0Guess,c1=c1Guess,c2=0.)
signalGuess=min(y-bgGuess)
sigmaGuess = span/30.
amplitudeGuess = signalGuess*(sigmaGuess*np.sqrt(2.0*np.pi))
# Fit variables initialization
# pars.add('splitting',0.0001,max=span)
pars['c2'].set(0.,min=-0.000001,max=0.001)
pars['c1'].set(c1Guess)
pars['c0'].set(c0Guess)
pars['p1_center'].set(min(x)+span*0.35,min=min(x),max=max(x))
pars['p2_center'].set(min(x)+span*0.55,min=min(x),max=max(x))
# pars['p2_center'].set(min(x)+span*0.65,expr='p1_center+splitting')
pars['p1_amplitude'].set(amplitudeGuess,max=amplitudeGuess/10000.)
pars['p2_amplitude'].set(amplitudeGuess,max=amplitudeGuess/10000.)
pars['p1_sigma'].set(sigmaGuess, min=sigmaGuess/100.,max=sigmaGuess*10000.)
pars['p2_sigma'].set(sigmaGuess, min=sigmaGuess/100.,max=sigmaGuess*10000.)
#Add some useful parameters to evaluate
pars.add('p1_signal', expr='p1_amplitude/(p1_sigma*sqrt(2.0*pi))')
pars.add('p2_signal', expr='p2_amplitude/(p2_sigma*sqrt(2.0*pi))')
pars.add('p1_contrast', expr='-p1_amplitude/(p1_sigma*sqrt(2.0*pi)*(c0+c1*p1_center+c2*p1_center**2))')
pars.add('p2_contrast', expr='-p2_amplitude/(p2_sigma*sqrt(2.0*pi)*(c0+c1*p2_center+c2*p2_center**2))')
pars.add('splitting',pars['p2_center']-pars['p1_center'],expr='p2_center-p1_center',min=0.00001)
model = peak1 + peak2 + background
init = model.eval(pars, x=x)
out = model.fit(y, pars, x=x)
# print out.fit_report()
return init,out
