def DoRooFit(histo, title):
can = makeCMSCanvas(str(random.random()),"Fit result ",900,700)
#Varible
if "ele" in title:
x1 = RooRealVar("x1","m_{e^{+}e^{-}}",80,100)
if "mu" in title:
x1 = RooRealVar("x1","m_{#mu^{+}#mu^{-}}",80,100)
#Define CB function
m = RooRealVar("mean_{CB}","mean of gaussian",60,120)
s = RooRealVar("#sigma_{CB}","width of gaussian",0,3)
a = RooRealVar("#alpha_{CB}","mean of gaussian",0,100)
n = RooRealVar("n_{CB}","width of gaussian",0,5)
CB = RooCBShape("CB","CB PDF",x1, m, s, a, n)
m.setConstant(kFALSE)
s.setConstant(kFALSE)
a.setConstant(kFALSE)
n.setConstant(kFALSE)
#Define Gaussian function
mean1 = RooRealVar("mean_{G}","mean of gaussian",-60,60)
sigma1 = RooRealVar("#sigma_{G}","width of gaussian",0,10)
gauss1 = RooGaussian("gauss1","gaussian PDF",x1,mean1,sigma1)
mean1.setConstant(kFALSE)
sigma1.setConstant(kFALSE)
#Starting values of the parameters
mean1.setVal(1.0)
sigma1.setVal(1.0)
m.setVal(90.0)
s.setVal(1.0)
a.setVal(10.0)
n.setVal(2.0)
# Construct CB (x) gauss
x1.setBins(10000, "cache")
CBxG = RooFFTConvPdf("CBxG", "CB (X) gauss", x1, CB, gauss1)
can.cd()
d = RooDataHist("d","d",RooArgList(x1),RooFit.Import(histo))
CBxG.fitTo(d, RooLinkedList())
# Plot PDF and toy data overlaid
xframe2 = x1.frame(RooFit.Name("xframe"),RooFit.Title("")) # RooPlot
d.plotOn(xframe2, RooLinkedList() )
CBxG.paramOn(xframe2, RooFit.Layout(0.65,0.99,0.9))
xframe2.getAttText().SetTextSize(0.03)
CBxG.plotOn(xframe2)
xframe2.Draw()
can.SaveAs("DataVsMC/FitResults/"+title+"_Roofit.pdf")
can.SaveAs("DataVsMC/FitResults/"+title+"_Roofit.png")
return;
def rooFit208():
print ">>> setup component pdfs..."
t = RooRealVar("t", "t", -10, 30)
ml = RooRealVar("ml", "mean landau", 5., -20, 20)
sl = RooRealVar("sl", "sigma landau", 1, 0.1, 10)
landau = RooLandau("lx", "lx", t, ml, sl)
mg = RooRealVar("mg", "mg", 0)
sg = RooRealVar("sg", "sg", 2, 0.1, 10)
gauss = RooGaussian("gauss", "gauss", t, mg, sg)
print ">>> construct convolution pdf..."
# Set #bins to be used for FFT sampling to 10000
t.setBins(10000, "cache")
# Construct landau (x) gauss
convolution = RooFFTConvPdf("lxg", "landau (X) gauss", t, landau, gauss)
print ">>> sample, fit and plot convoluted pdf..."
data = convolution.generate(RooArgSet(t), 10000) # RooDataSet
convolution.fitTo(data)
print "\n>>> plot everything..."
frame1 = t.frame(Title("landau #otimes gauss convolution")) # RooPlot
data.plotOn(frame1, Binning(50), Name("data"))
convolution.plotOn(frame1, Name("lxg"))
gauss.plotOn(frame1, LineStyle(kDashed), LineColor(kRed), Name("gauss"))
landau.plotOn(frame1, LineStyle(kDashed), LineColor(kGreen),
Name("landau"))
print "\n>>> draw pfds and fits on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 600)
legend = TLegend(0.6, 0.8, 0.8, 0.6)
legend.SetTextSize(0.032)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.5)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
legend.AddEntry("data", "data", 'LEP')
legend.AddEntry("lxg", "convolution", 'L')
legend.AddEntry("landau", "landau", 'L')
legend.AddEntry("gauss", "gauss", 'L')
legend.Draw()
canvas.SaveAs("rooFit208.png")
pdf = RooFFTConvPdf("pdf", 'convolution', x, p2, p1)
pdf.setBufferFraction(5.0)
#pdf.setBufferFraction(50.0)
# Plot PDF
canvas = TCanvas("c1","",1200,480);
canvas.Divide(3,1);
canvas.cd(1)
xframe = x.frame()
p1.plotOn( xframe )
xframe.Draw()
gPad.SetLogy()
#canvas.cd(2)
#xframe2 = x.frame()
#p2.plotOn( xframe2 )
#xframe2.Draw()
#gPad.SetLogy()
canvas.cd(3)
xframe3 = x.frame()
pdf.plotOn( xframe3 )
xframe3.Draw()
gPad.SetLogy()
print "Done"
raw_input("Press Enter to continue...")
TxDT1.setBufferFraction(0.5)
tFunc = w.factory("FormulaVar::tFunc('log(mass/91.2)', {mass})")
model = RooFFTConvPdf('model', 'model', tFunc, t, TxDT1, dt2Pdf)
w.Import(model)
model.setBufferFraction(0.25)
w.Print()
mPlot = mass.frame(Range(70,110))
tPlot = t.frame(Range(*trange))
mData.plotOn(mPlot)
tData.plotOn(tPlot)
mPdf.plotOn(mPlot)
tPdf.plotOn(tPlot)
model.plotOn(mPlot, LineColor(kRed))
TxDT1.plotOn(tPlot, LineColor(kRed))
dt1Pdf.plotOn(tPlot, LineStyle(kDashed))
mCanvas = TCanvas('mass')
mPlot.Draw()
tCanvas = TCanvas('t')
tPlot.Draw()
if __name__ == "__main__":
import user
model_Total_background_MC = RooAddPdf("model_Total_background_MC_xww","model_Total_background_MC_xww",RooArgList(old_workspace.pdf("WJets_xww_%s_%s"%(options.channel,options.category)), old_workspace.pdf("VV_xww_%s_%s"%(options.channel,options.category)),old_workspace.pdf("TTbar_xww_%s_%s"%(options.channel,options.category)),old_workspace.pdf("STop_xww_%s_%s"%(options.channel,options.category))),RooArgList(rrv_number_WJets,rrv_number_VV,rrv_number_TTbar,rrv_number_STop));
rrv_number_signal.setVal(rrv_number_signal.getVal()*6.25);
#### scale factor in order to scale MC to data in the final plot -> in order to avoid the normalization to data which is done by default in rooFit
scale_number_Total_background_MC = rrv_number_Total_background_MC.getVal()/old_workspace.data(datasetname+"_xww_"+options.channel+"_"+options.category).sumEntries();
scale_number_signal = rrv_number_signal.getVal()/old_workspace.data(datasetname+"_xww_"+options.channel+"_"+options.category).sumEntries();
model_Total_background_MC.plotOn(mplot,RooFit.Normalization(scale_number_Total_background_MC),RooFit.Name("total_MC"),RooFit.Components("WJets_xww_%s_%s,VV_xww_%s_%s,TTbar_xww_%s_%s,STop_xww_%s_%s"%(options.channel,options.category,options.channel,options.category,options.channel,options.category,options.channel,options.category)),RooFit.DrawOption("L"), RooFit.LineColor(kRed), RooFit.VLines(),RooFit.LineWidth(2));
model_signal_background_MC = RooAddPdf("model_signal_background_MC_xww","model_signal_background_MC_xww",RooArgList(model_pdf,model_Total_background_MC),RooArgList(rrv_number_signal,rrv_number_Total_background_MC));
model_signal_background_MC.plotOn(mplot,RooFit.Normalization(scale_number_Total_background_MC+scale_number_signal),RooFit.Name("total_SpB_MC"),RooFit.Components("BulkWW_xww_%s_%s,model_Total_background_MC_xww"%(options.channel,options.category)),RooFit.DrawOption("L"), RooFit.LineColor(kBlue), RooFit.VLines(),RooFit.LineWidth(2),RooFit.LineStyle(7));
model_pdf.plotOn(mplot,RooFit.Name("total_S_MC"),RooFit.Normalization(scale_number_signal),RooFit.DrawOption("L"), RooFit.LineColor(kGreen+2), RooFit.VLines(),RooFit.LineWidth(2),RooFit.LineStyle(kDashed));
os.system("mkdir -p ./plots_signal_width");
name = TString("check_%.3f_%.3f"%(mass[iMass],gammaVal));
name.ReplaceAll("0.","0_");
mplot.GetYaxis().SetRangeUser(1e-3,mplot.GetMaximum()*1.2);
draw_canvas(mplot,"plots_signal_width/",name,0,1);
new_workspace.writeToFile(new_file.GetName());
if ( (mass[iMass] == 1000 and gammaVal == 0.05) or (mass[iMass] == 1000 and gammaVal == 0.15) or (mass[iMass] == 1000 and gammaVal == 0.30) or
(mass[iMass] == 1500 and gammaVal == 0.05) or (mass[iMass] == 1500 and gammaVal == 0.15) or (mass[iMass] == 1500 and gammaVal == 0.30) or
(mass[iMass] == 2100 and gammaVal == 0.05) or (mass[iMass] == 2100 and gammaVal == 0.15) or (mass[iMass] == 2100 and gammaVal == 0.30) ):
signal_file_name = ("treeEDBR_BulkG_WW_inclusive_M%3d_W%d_xww.root"%(mass[iMass],int(mass[iMass]*gammaVal)));
print "#### making plot for ",signal_file_name;
def fitLandauGaus(hist, full = False):
## c1 = ROOT.TCanvas()
## c1.Divide(2)
## c1.cd(1)
## hist.Draw()
neg_landau = False
if hist.GetMean() < 0.:
neg_landau = True
if neg_landau:
hist = turnHisto(hist)
hist.Rebin(2)
hist.SetTitle('')
hist.SetName('hSignal')
## c1.cd(2)
## hist.Draw('hist')
## c1.SaveAs('foobar.pdf')
### #if neg_landau:
### # func = ROOT.TF1('my_landau','[0] * TMath::Landau(-x,[1],[2])', hist.GetXaxis().GetXmin(), hist.GetXaxis().GetXmax())
### # func.SetParameters(1, hist.GetMean(), hist.GetRMS() )
### #else:
### func = ROOT.TF1('my_landau','[0] * TMath::Landau(x,[1],[2])', hist.GetXaxis().GetXmin(), hist.GetXaxis().GetXmax())
### func.SetParameters(1, hist.GetMean(), hist.GetRMS() )
### hist.Fit('my_landau','q')
### fit_res = []
### fit_res.append(func.GetParameter(0) if not neg_landau else func.GetParameter(0))
### fit_res.append(func.GetParameter(1) if not neg_landau else -1.*func.GetParameter(1))
### fit_res.append(func.GetParameter(2) if not neg_landau else func.GetParameter(2))
### return hist, fit_res
## ROOFIT VERSION
xmin = hist.GetXaxis().GetXmin()
xmax = hist.GetXaxis().GetXmax()
mean = hist.GetMean()
mp = hist.GetXaxis().GetBinCenter(hist.GetMaximumBin())
rms = hist.GetRMS()
flandau = ROOT.TF1('flandau','landau',mp-20,mp+40)
flandau.SetLineWidth(1)
flandau.SetLineColor(ROOT.kBlue)
hist2 = hist.Clone(hist.GetName()+'_2')
hist2.Scale(1./hist2.GetBinContent(hist2.GetMaximumBin()))
hist2.Fit(flandau,'Q','',mp-20,mp+40)
flandau2 = flandau.Clone('flandau2')
flandau2.SetRange(0,500)
flandau2.SetLineStyle(2)
for i in range(flandau.GetNpar()):
flandau2.SetParLimits(i,flandau.GetParameter(i),flandau.GetParameter(i))
hist2.Fit(flandau2,'Q+')#,'same',mp-20,mp+40)
for i in range(flandau.GetNpar()):
hist2.GetFunction('flandau2').SetParameter(i,flandau.GetParameter(i))
for i in range(flandau.GetNpar()):
print flandau.GetParameter(i),flandau2.GetParameter(i)
x = RooRealVar('x', 'signal / adc', 0,500)
x.setRange("signal",mp - 40, mp+90)
x.setRange("draw",0,500)
ral = RooArgList(x)
dh = RooDataHist('dh', 'dh', ral, RooFit.Import(hist))
if full:
ml = RooRealVar('ml', 'mean landau' , mp, mp-20., mp+30)
sl = RooRealVar('sl', 'sigma landau', 10, 1., 25.)
else:
ml = RooRealVar('ml', 'mean landau' , mean, mean-40., mean)
sl = RooRealVar('sl', 'sigma landau', 10., 6., 14.)
landau = RooLandau ('lx', 'lx', x, ml, sl)
mean = 0
if full:
mg = RooRealVar ('mg', 'mean gaus' , 0,0,0)
sg = RooRealVar ('sg', 'sigma gaus', flandau.GetParameter(2), 0.1, 30.)
else:
mg = RooRealVar ('mg', 'mean gaus' , 0,0,0) #mean, mean-30., mean+30.)
sg = RooRealVar ('sg', 'sigma gaus', 2., 0.1, 20.)
gaus = RooGaussian('gx', 'gx', x, mg, sg)
x.setBins(1000,'cache')
## Construct landau (x) gauss
lxg = RooFFTConvPdf('lxg','landau (x) gaus', x, landau, gaus)
lxg.fitTo(dh,RooFit.Range("signal"))
#,RooFit.Normalization(ROOT.RooAbsReal.NumEvent,1))
a = lxg.getParameters(dh)
print 'fit par0 %+6.1f'%flandau.GetParameter(0)
print 'fit par1 %+6.1f'%flandau.GetParameter(1)
print 'fit par2 %+6.1f'%flandau.GetParameter(2)
print 'mp %+6.1f'%mp
print 'rms %+6.1f'%rms
print 'lxg.getParameters(dh).getRealValue(\'ml\'): %+6.1f'% a.getRealValue('ml')
print 'lxg.getParameters(dh).getRealValue(\'sl\'): %+6.1f'% a.getRealValue('sl')
print 'lxg.getParameters(dh).getRealValue(\'sg\'): %+6.1f'% a.getRealValue('sg')
frame = x.frame(RooFit.Title('landau (x) gauss convolution'),RooFit.Range("draw"))
#,RooFit.Normalization(ROOT.RooAbsReal.NumEvent,1))
dh.plotOn(frame,RooFit.Range("draw"))
#,RooFit.Normalization(1./dh.numEntries(),ROOT.RooAbsReal.Raw))
lxg.plotOn(frame,RooFit.LineColor(ROOT.kRed),RooFit.Range("draw"))
#,RooFit.Normalization(1,ROOT.RooAbsReal.Raw))
#lxg.plotOn(frame,RooFit.LineColor(ROOT.kBlue),RooFit.Range("signal"),RooFit.Components('lx,gx'))
# c = ROOT.TCanvas('lg_convolution','landau (x) gaus', 600, 600)
# c.Divide(2)
# c.cd(1)
# hist.Draw()
# c.cd(2)
# ROOT.gPad.SetLeftMargin(0.15)
# frame.GetYaxis().SetTitleOffset(1.4)
# frame.Draw()
# c.SaveAs('histograms/outputhisto'+hist.GetName().split('pz')[1]+'.pdf')
return dh, copy.deepcopy(a), copy.deepcopy(frame),copy.deepcopy(hist2)
