import ROOT
import rootnotes #for plotting with iPython
c1 = rootnotes.default_canvas()

w = ROOT.RooWorkspace()
w.factory('Gaussian::g(x[-5,5],mu[-3,3],sigma[1])')
w.factory('Exponential::e(x,tau[-.5,-3,0])')
w.factory('SUM::model(s[50,0,100]*g,b[100,0,1000]*e)')
w.Print() #this isn't displaying in iPython

x = w.var('x')
pdf = w.pdf('model')
frame = x.frame()
data = pdf.generate(ROOT.RooArgSet(x))
data.plotOn(frame)
fitResult = pdf.fitTo(data,ROOT.RooFit.Save())
pdf.plotOn(frame)
frame.Draw()
c1

mu = w.var('mu')
print 'best fit value of mean for Gaussian is', mu.getVal(), '±', mu.getError()

s=w.var('s')
sframe=s.frame()
lnL = pdf.createNLL(data)
lnProfileL = lnL.createProfile(ROOT.RooArgSet(s))
lnProfileL.plotOn(sframe)
sframe.Draw()
c1

corrMatrixHist = fitResult.correlationHist()
corrMatrixHist.SetStats(False)
corrMatrixHist.Draw('colz text')
c1