import ROOT,random


sigma=20
mpv=3*sigma
scale=0.5
hrange=1000


hist=ROOT.TH1D('hist','hist',500,0,hrange)
hist2=ROOT.TH1D('hist2','hist2',500,0,hrange)
#hist2.SetLineColor(ROOT.kRed)


func=ROOT.TF1("func","TMath::Landau(x,"+str(mpv)+","+str(sigma)+")",0,hrange)
for i in range(1000):
    rand=func.GetRandom(0,hrange)
    hist.Fill(rand)
    hist2.Fill(rand*scale)
    
fitfunc=ROOT.TF1("fitfunc","TMath::Landau(x,[0],[1],0)*[2]")
fitfunc.SetParName(0,'MPV')
fitfunc.SetParName(1,'Sigma')
fitfunc.SetParName(2,'Const')

canv=ROOT.TCanvas('c','c',500,1000)
canv.Divide(1,2)
canv.cd(1)
hist.Draw()
fitfunc.SetParameter(0,mpv)
fitfunc.SetParameter(1,sigma)
fitfunc.SetParameter(2,hist.GetMaximum())
hist.Fit(fitfunc,"Q","",0,hrange)
print 'MPV unscaled:',fitfunc.GetParameter(0)
print 'Sigma unscaled:',fitfunc.GetParameter(1)
canv.cd(2)
hist2.Draw()
fitfunc.SetParameter(0,mpv*scale)
fitfunc.SetParameter(1,sigma*scale)
fitfunc.SetParameter(2,hist2.GetMaximum())
hist2.Fit(fitfunc,"Q","",0,hrange)
print 'MPV scaled:',fitfunc.GetParameter(0)
print 'Sigma scaled:',fitfunc.GetParameter(1)

canv.Update()
u=raw_input('done?')