def drawDsPlot(ds,catNum = 0):
from ROOT import RooAbsData,TCanvas
etaHist = RooAbsData.createHistogram(ds,'eta',100);
theCanvas = TCanvas('dsCanvas','Eta histogram (category' + str(catNum) + ')' )
etaHist.Draw("hist PFC");
theCanvas.SaveAs("etaCat"+str(catNum)+"Hist"+TIME_NOW+".pdf");
#s = raw_input("Press Enter to continue...");
theCanvas.Close();
def saveEta(g, numBins = 100):
from ROOT import TFile, RooAbsData
import os
if not os.path.isdir('etaHist'):
os.makedirs('etaHist');
os.chdir('etaHist');
etaHist = RooAbsData.createHistogram(g,'eta',100);
f = TFile('etaHist003_%04d.root' % SEED, 'recreate')
etaHist.Write('etaHist')
f.Close()
del f
def _convert_rooabspdf_to_tf1(x: RooRealVar,
roofit_func: RooAbsPdf,
roofit_data: RooAbsData = None,
*args,
**kwargs) -> TF1:
'''
Convert a RooAbsPdf into a TF1.
Parameters
----------
x : RooRealVar
RooFit RooRealVar observable
roofit_func : RooAbsPdf
RooFit pdf
roofit_data : RooAbsData
RooFit dataset
Returns
-------
TF1
ROOT TF1
'''
assert (isinstance(x, RooRealVar))
assert (isinstance(roofit_func, RooAbsPdf))
# Dirty trick to get correct normalization.
# Plot data and model on frame then get the model back from the frame.
frame = x.frame()
if roofit_data:
assert (isinstance(roofit_data, RooAbsData))
roofit_data.plotOn(frame)
roofit_func.plotOn(frame, RooFit.Name('tmp_pdf'))
if 'component' in kwargs.keys():
roofit_func.plotOn(frame, RooFit.Components(kwargs['component']),
RooFit.Name('tmp_pdf_component'))
return frame.findObject('tmp_pdf_component')
return frame.findObject('tmp_pdf')
def _convert_rooabsdata_to_th1(x: RooRealVar, roofit_obj: RooAbsData) -> TH1:
'''
Convert a RooDataHist into a TH1.
Parameters
----------
x : RooRealVar
ROOT RooRealVar observable
roofit_obj : RooAbsData
RooFit dataset
Returns
-------
TH1
ROOT histogram
'''
assert (isinstance(x, RooRealVar) and isinstance(roofit_obj, RooAbsData))
return roofit_obj.createHistogram(x.GetName(), x.getBinning().numBins())
def createCategoryHistogram(ds,x,numCat,categoryList = None):
from ROOT import RooAbsData, RooDataSet, RooPrintable, RooThresholdCategory, THStack, TF1
from ROOT.Math import GaussIntegrator
import ROOT
if categoryList==None:
categoryList = []
for i in range(numCat):
categoryList += ["Cat"+str(i+1)]
etaHist = RooAbsData.createHistogram(ds,'eta',100);
histSum = etaHist.Integral();
#print "HISTSUM",histSum, etaHist.Integral(0,50)+etaHist.Integral(51,100),etaHist.Integral(0,50),etaHist.Integral(51,100),"\n";
limList = [-1];
for i in range(1,numCat):
start = limList[i-1]+1;
targetVal = histSum/numCat;
left = start;
right = etaHist.GetNbinsX();
#print "\n\n\n",etaHist.Integral(start,start+1),"\n\n\n\n";
while abs(left-right)>1:
if etaHist.Integral(start,int((left+right)/2))>targetVal:
right = (left + right) / 2;
else:
left = (left + right) / 2;
#print start, left, right, etaHist.Integral(start,int((left+right)/2)),targetVal,",",
if abs(etaHist.Integral(start,left)-targetVal) > abs(etaHist.Integral(start,right)-targetVal):
limList += [right];
else:
limList += [left];
limList[0] = 0;
print limList
xRegions = RooThresholdCategory("tageffRegion", "region of tageff", x, categoryList[numCat-1]);
for i in range(0,numCat-1):
xRegions.addThreshold(etaHist.GetXaxis().GetBinCenter(limList[i+1]),categoryList[i]);
xRegions.writeToStream(ROOT.cout,False);
xRegions.writeToStream(ROOT.cout,True);
'''
#etaSet = RooDataSet('etaSet','etaSet',ds,RooArgSet(ds.get().find['eta']));
#etaSet.Print();
#etaHist.SetAxisRange(0.1,0.2);
#etaHist.SetAxisRange(0.1,0.3);
ROOT.gStyle.SetPalette(ROOT.kOcean);
#create stack to contain the chopped TH1Ds
etaHistStack = THStack("etaHistStack", "Stack of TH1Ds");
limList += [etaHist.GetNbinsX()];
histCutterFunc = TF1("histCutterFunc","((x>=[0])?((x<[1])?1:0):0)*x",0.0,1.0);
for i in range(len(limList)-1):
etaHistClone = etaHist.Clone();
#etaHistClone.SetBins(limList[i+1]-limList[i],etaHist.GetBinContent(limList[i]),etaHist.GetBinContent(limList[i+1]));
histCutterFunc.SetParameter(0,etaHist.GetXaxis().GetBinCenter(limList[i]));
histCutterFunc.SetParameter(1,etaHist.GetXaxis().GetBinCenter(limList[i+1]));
etaHistClone.Multiply(histCutterFunc);
etaHistClone.SetFillColor(38+i);
#etaHist.DrawCopy("hist PFC");
etaHistStack.Add(etaHistClone);
etaHistStack.Draw("hist PFC");
s = raw_input("Press Enter to continue...");'''
return xRegions#,x
def main():
output = argv[1]
rap = argv[2]
flavour = argv[3]
trackType = argv[4]
funct = argv[5]
fit_min = int(argv[6])
fit_max = int(argv[7])
rebinFactor = int(argv[8])
i = int(argv[9])
if funct == "CB":
DOCRYSTALBALL = True
DOCRUIJFF = False
DODOUBLECB = False
elif funct == "cruijff":
DOCRUIJFF = True
DOCRYSTALBALL = False
DODOUBLECB = False
elif funct == "doubleCB":
DODOUBLECB = True
DOCRYSTALBALL = False
DOCRUIJFF = False
print("+++++++++++++++++++++++++++++++++++++++++")
print("Fitting histogram for %d < pt_{l} <%d" % (ptbins[i], ptbins[i + 1]))
print("+++++++++++++++++++++++++++++++++++++++++\n")
wsFile = TFile("tmpWorkspace.root")
ws = wsFile.Get("tempWS")
# fit with a gaussian
if DOCRYSTALBALL:
funct = TF1("crystal", "crystalball", fit_min, fit_max)
funct.SetLineColor(kRed)
if ws.data("hist").sum(False) < 1500:
nDOF = (fit_max - fit_min) * 2 / (rebinFactor * 4) - 3
else:
nDOF = (fit_max - fit_min) * 2 / rebinFactor - 3
ws.factory(
"RooCBShape::cb(mass, mean[0.0], sigma[2,0,10], alphaL[3,-25,25], nL[5,-25,25])"
)
ws.factory("BreitWigner::bw(mass,meanZ[91.187], width[2.495])")
bw = ws.pdf("bw")
cb = ws.pdf("cb")
ws.var("mass").setBins(2000, "cache")
ws.var("mass").setMin("cache", 0)
ws.var("mass").setMax("cache", 1000)
## need to be adjusted to be higher than limit setting
sigpdf = RooFFTConvPdf("sig", "sig", ws.var("mass"), bw, cb)
getattr(ws, 'import')(sigpdf, RooCmdArg())
fitResult = ws.pdf("sig").fitTo(ws.data("hist"), RooFit.Save(),
RooFit.SumW2Error(kFALSE),
RooFit.Minos(kFALSE))
elif DOCRUIJFF:
gSystem.Load("./RooCruijff_cxx.so")
ws.factory(
"RooCruijff::cb(mass, mean[0.0], sigma[2,0,20], sigma, alphaL[1,0,25], alphaR[1,0,25])"
)
if ws.data("hist").sum(False) < 1500:
nDOF = (fit_max - fit_min) * 2 / (6) - 3
elif ws.data("hist").sum(False) < 2500:
nDOF = (fit_max - fit_min) * 2 / (4) - 3
else:
nDOF = (fit_max - fit_min) * 2 / rebinFactor - 3
ws.factory("BreitWigner::bw(mass,meanZ[91.187], width[2.495])")
bw = ws.pdf("bw")
cb = ws.pdf("cb")
ws.var("mass").setBins(2000, "cache")
ws.var("mass").setMin("cache", 0)
ws.var("mass").setMax("cache", 1000)
## need to be adjusted to be higher than limit setting
sigpdf = RooFFTConvPdf("sig", "sig", ws.var("mass"), bw, cb)
getattr(ws, 'import')(sigpdf, RooCmdArg())
fitResult = ws.pdf("sig").fitTo(ws.data("hist"), RooFit.Save(),
RooFit.SumW2Error(kFALSE),
RooFit.Minos(kFALSE))
elif DODOUBLECB:
gSystem.Load("./RooDCBShape_cxx.so")
ws.factory(
"RooDCBShape::cb(mass, mean[0.0,-1.5,1.5], sigma[2,0,20], alphaL[2,0,25] , alphaR[2,0,25], nL[1.5,0,25], nR[1.5,0,25])"
)
if i == 0:
ws.var("nL").setVal(1)
ws.var("nR").setVal(1)
ws.factory("BreitWigner::bw(mass,meanZ[91.187], width[2.495])")
bw = ws.pdf("bw")
cb = ws.pdf("cb")
ws.var("mass").setBins(2000, "cache")
ws.var("mass").setMin("cache", 0)
ws.var("mass").setMax("cache", 1000)
## need to be adjusted to be higher than limit setting
sigpdf = RooFFTConvPdf("sig", "sig", ws.var("mass"), bw, cb)
getattr(ws, 'import')(sigpdf, RooCmdArg())
fitResult = ws.pdf("sig").fitTo(ws.data("hist"), RooFit.Save(),
RooFit.SumW2Error(kFALSE),
RooFit.Minos(kFALSE))
chi2 = RooChi2Var("bla", "blubb", ws.pdf("sig"),
ws.data("hist")).getVal()
if ws.data("hist").sum(False) < 1500:
nDOF = (fit_max - fit_min) * 2 / (6) - 5
elif ws.data("hist").sum(False) < 2500:
nDOF = (fit_max - fit_min) * 2 / (4) - 5
else:
nDOF = (fit_max - fit_min) * 2 / rebinFactor - 5
chi2 = RooChi2Var("bla", "blubb", ws.pdf("sig"), ws.data("hist")).getVal()
nDOFforWS = RooRealVar('nDOF', 'nDOF', nDOF)
getattr(ws, 'import')(nDOFforWS, RooCmdArg())
chi2forWS = RooRealVar('chi2', 'chi2', chi2)
getattr(ws, 'import')(chi2forWS, RooCmdArg())
mean = RooRealVar('Mean', 'Mean', ws.var("meanZ").getVal())
getattr(ws, 'import')(mean, RooCmdArg())
meane = RooRealVar('Meane', 'Meane', ws.var("meanZ").getError())
getattr(ws, 'import')(meane, RooCmdArg())
sig = RooRealVar('Sig', 'Sig', ws.var("sigma").getVal())
getattr(ws, 'import')(sig, RooCmdArg())
sige = RooRealVar('Sige', 'Sige', ws.var("sigma").getError())
getattr(ws, 'import')(sige, RooCmdArg())
c1 = TCanvas("c1", "c1", 700, 700)
c1.cd()
plotPad = TPad("plotPad", "plotPad", 0, 0, 1, 1)
style = setTDRStyle()
gStyle.SetOptStat(0)
gStyle.SetTitleXOffset(1.45)
gStyle.SetPadLeftMargin(0.2)
gStyle.SetTitleYOffset(2)
plotPad.UseCurrentStyle()
plotPad.Draw()
plotPad.cd()
if DODOUBLECB or DOCRYSTALBALL or DOCRUIJFF:
ws.var("mass").setBins(30)
frame = ws.var('mass').frame(
RooFit.Title('Invariant mass of dimuon pairs'))
frame.GetXaxis().SetTitle('m_{#mu#mu} [GeV]')
frame.GetYaxis().SetTitle("Events / 2 GeV")
RooAbsData.plotOn(ws.data('hist'), frame, RooFit.Name("hist"))
ws.pdf('sig').plotOn(frame, RooFit.Name("sig"))
frame.Draw()
#chi2 = frame.chiSquare("sig","hist",nDOF)
else:
h.GetXaxis().SetTitle("m_{ll} [GeV]")
h.SetLineColor(kBlack)
h.GetXaxis().SetRangeUser(fit_min, fit_max)
h.SetMarkerStyle(20)
h.SetMarkerSize(0.7)
h.Draw("E")
if DOCRYSTALBALL or DOCRUIJFF or DODOUBLECB:
funct.Draw("SAME")
else:
gaus.Draw("SAME")
latex = TLatex()
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(0.04)
latex.SetNDC(True)
latexCMS = TLatex()
latexCMS.SetTextFont(61)
latexCMS.SetTextSize(0.055)
latexCMS.SetNDC(True)
latexCMSExtra = TLatex()
latexCMSExtra.SetTextFont(52)
latexCMSExtra.SetTextSize(0.03)
latexCMSExtra.SetNDC(True)
latex.DrawLatex(0.95, 0.96, "(13 TeV)")
cmsExtra = "Preliminary"
latexCMS.DrawLatex(0.78, 0.88, "CMS")
yLabelPos = 0.84
latexCMSExtra.DrawLatex(0.78, yLabelPos, "%s" % (cmsExtra))
latexFit1 = TLatex()
latexFit1.SetTextFont(42)
latexFit1.SetTextSize(0.035)
latexFit1.SetNDC(True)
latexFit1.DrawLatex(0.25, 0.84,
"%d GeV < p_{T} < %d GeV" % (ptbins[i], ptbins[i + 1]))
latexFit = TLatex()
latexFit.SetTextFont(42)
latexFit.SetTextSize(0.030)
latexFit.SetNDC(True)
latexFit.DrawLatex(
0.25, 0.74, "%s = %5.3g #pm %5.3g GeV" %
("mean bias", ws.var("mean").getVal(), ws.var("mean").getError()))
if funct == "CB":
latexFit.DrawLatex(
0.25, 0.7, "%s = %5.3g #pm %5.3g GeV" %
("#sigma", ws.var("sigma").getVal(), ws.var("sigma").getError()))
latexFit.DrawLatex(
0.25, 0.66, "%s = %5.3g #pm %5.3g" %
("alphaL", ws.var("alphaL").getVal(), ws.var("alphaL").getError()))
latexFit.DrawLatex(
0.25, 0.62, "%s = %5.3g #pm %5.3g" %
("nL", ws.var("nL").getVal(), ws.var("nL").getError()))
if funct == "cruijff":
latexFit.DrawLatex(
0.25, 0.7, "%s = %5.3g #pm %5.3g GeV" %
("#sigma", ws.var("sigma").getVal(), ws.var("sigma").getError()))
latexFit.DrawLatex(
0.25, 0.66, "%s = %5.3g #pm %5.3g" %
("alphaL", ws.var("alphaL").getVal(), ws.var("alphaL").getError()))
latexFit.DrawLatex(
0.25, 0.62, "%s = %5.3g #pm %5.3g" %
("alphaR", ws.var("alphaR").getVal(), ws.var("alphaR").getError()))
if funct == "doubleCB":
latexFit.DrawLatex(
0.25, 0.7, "%s = %5.3g #pm %5.3g GeV" %
("#sigma", ws.var("sigma").getVal(), ws.var("sigma").getError()))
latexFit.DrawLatex(
0.25, 0.66, "%s = %5.3g #pm %5.3g" %
("alphaL", ws.var("alphaL").getVal(), ws.var("alphaL").getError()))
latexFit.DrawLatex(
0.25, 0.62, "%s = %5.3g #pm %5.3g" %
("alphaR", ws.var("alphaR").getVal(), ws.var("alphaR").getError()))
latexFit.DrawLatex(
0.25, 0.58, "%s = %5.3g #pm %5.3g" %
("nL", ws.var("nL").getVal(), ws.var("nL").getError()))
latexFit.DrawLatex(
0.25, 0.54, "%s = %5.3g #pm %5.3g" %
("nR", ws.var("nR").getVal(), ws.var("nR").getError()))
latexFit.DrawLatex(
0.25, 0.5,
"#chi^{2}/ndf = %5.1f / %2.0f = %4.2f" % (chi2, nDOF, chi2 / nDOF))
saveas = "/MassRes_%s_%s_Pt%d_%d_%s" % (trackType, flavour, ptbins[i],
ptbins[i + 1], rap)
c1.SaveAs(output + saveas + ".root")
c1.SaveAs(output + saveas + ".C")
c1.SaveAs(output + saveas + ".png")
c1.SaveAs(output + saveas + ".pdf")
print("DONE Fitting...")
ws.writeToFile("tmpWorkspaceReturn.root")
def mbc_dline_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa,
fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2,
title1, title2, epsfile, txtfile, ymin=0.5,
cuts=None, err_type='SYMM', test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous,
RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData,
RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta,
TPaveText, RooDLineShape)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam','Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav','D0 flavor')
dflav.defineType('dflav',1)
dflav.defineType('dbarflav',-1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' %cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres","mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE","sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1","sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2","scalep2",2.00,1.500,5.500)
scalep3 = RooRealVar("scalep3","scalep3",5.00,3.00,10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1,scalep2)
sigmap2 = RooFormulaVar("sigmap2","sigma2","sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1,scalep2,scalep3)
sigmap3 = RooFormulaVar("sigmap3","sigma3","sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md","md", setmd,1.863,1.875)
f2 = RooRealVar("f2","f2", fa)
f3 = RooRealVar("f3","f3", fb)
al23 = RooArgList(f2,f3)
f1 = RooFormulaVar("f1","f1","1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1","DLineShape1_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn1_2 = RooDLineShape("DLineshape1_2","DLineShape1_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn1_3 = RooDLineShape("DLineshape1_3","DLineShape1_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
fcn2_1 = RooDLineShape("DLineshape2_1","DLineShape2_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn2_2 = RooDLineShape("DLineshape2_2","DLineShape2_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn2_3 = RooDLineShape("DLineshape2_3","DLineShape2_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
alf1_123 = RooArgList(fcn1_1,fcn1_2,fcn1_3)
af12 = RooArgList(f1,f2)
sigpdf = RooAddPdf("signal1_3","signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1,fcn2_2,fcn2_3)
sigbarpdf = RooAddPdf("signal2_3","signal2_3", alf2_123, af12)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2))
bkgbarpdf = RooChebychev('bkgbarpdf', 'Background',
mbc, RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigbarpdf, bkgbarpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' %epsfile)
sys.stdout.write('Will save txtfile as: %s \n' %txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas','mbc', 400, 400);
xframe=mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, md, sigmap1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_gau_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa,
fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2,
title1, title2, epsfile, txtfile, ymin=0.5,
cuts=None, err_type='SYMM', test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous,
RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData,
RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta,
TPaveText)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam','Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav','D0 flavor')
dflav.defineType('dflav',1)
dflav.defineType('dbarflav',-1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' %cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
#sigma = RooRealVar('sigma', 'D width', 0.0001, 0.005, 'GeV')
sigma = RooRealVar('sigma', 'D width', 0.00468, 'GeV')
mbc_dp = RooRealVar('mbc_dp', 'D+ Mass', 1.86962, 'GeV')
sigpdf = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma)
#arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.88, 1.89, 'GeV')
#arg_slope = RooRealVar('arg_slope', 'Argus slope', -10, -100, -1)
#bkgpdf = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background',
mbc, RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' %epsfile)
sys.stdout.write('Will save txtfile as: %s \n' %txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas','mbc', 400, 400);
xframe=mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
from ROOT import TLine, TLegend, TPad
from ROOT import TFile, TH1, TH1F, TCanvas, TNtuple, TTreeReader, TTreeReaderValue
from ROOT import RooDataSet, RooFormulaVar, RooLinkedList, RooBernstein
from ROOT import RooStats, gPad, RooAbsData, RooAbsReal, RooBinning
from ROOT import kGreen, kRed, kBlack, kBlue, kDashed, kDotted, kMagenta
from ROOT import RooRealVar, RooAbsPdf, RooChebychev, RooExponential, RooGaussian, RooAbsPdf
from ROOT import RooPlot, RooAddPdf, RooDataHist, RooArgSet, RooArgList
from ROOT import RooDataSet, RooFormulaVar, RooLinkedList, RooBernstein, RooVoigtian
from ROOT import RooFit, gStyle, kFullCircle, kTRUE, kFALSE
from ROOT.RooFit import Layout, Components, Title, Name, Normalization, Layout, PrintLevel
from ROOT.RooFit import Format, Label, Parameters, Range, LineColor, LineStyle, SelectVars
from ROOT.RooAbsReal import Relative
RooAbsData.setDefaultStorageType(RooAbsData.Tree)
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true')
parser.add_argument('--input', type=str, default="2mu2k_tree.root")
parser.add_argument('--binned', action='store_true')
parser.add_argument('--nonprompt', action='store_true')
parser.add_argument('--prompt', action='store_true')
parser.add_argument('--nofit', action='store_false')
parser.add_argument('--nofitkk', action='store_false')
parser.add_argument('--nofitb0', action='store_false')
parser.add_argument('--nofitmm', action='store_false')
parser.add_argument('--noplot', action='store_false')
parser.add_argument('--numcpu', type=int, default=4)
parser.add_argument('--nsignal', type=float, default=100000.)
parser.add_argument('--ptcuts', type=float, default=None)
def studyVqqResolution(rootFile):
#get all from file
histos = {}
inF = TFile.Open(rootFile)
keys = inF.GetListOfKeys()
for k in keys:
obj = inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()] = obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin = ['', '30to40', '40to50', '50to75', '75to100', '100toInf']
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
c.SetCanvasSize(1000, 500)
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
c.cd(1)
histos['deta' + k + 'barrel'].SetLineWidth(2)
histos['deta' + k + 'barrel'].SetTitle('barrel')
histos['deta' + k + 'barrel'].Draw('hist')
histos['deta' + k + 'endcap'].SetLineWidth(2)
histos['deta' + k + 'endcap'].SetLineStyle(7)
histos['deta' + k + 'endcap'].SetTitle('endcap')
histos['deta' + k + 'endcap'].Draw('histsame')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta' + k + 'barrel'], 'barrel', 'f')
leg.AddEntry(histos['deta' + k + 'endcap'], 'endcap', 'f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi' + k + 'barrel'].SetLineWidth(2)
histos['dphi' + k + 'barrel'].SetTitle('barrel')
histos['dphi' + k + 'barrel'].Draw('hist')
histos['dphi' + k + 'endcap'].SetLineWidth(2)
histos['dphi' + k + 'endcap'].SetLineStyle(7)
histos['dphi' + k + 'endcap'].SetTitle('endcap')
histos['dphi' + k + 'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png' % k)
labels = []
responseVars = ['dpt', 'den', 'dphi', 'deta', 'dr']
for r in responseVars:
barrelResponse = TGraphErrors()
barrelResponse.SetName(r + 'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse = barrelResponse.Clone(r + 'barrelcoreresponse')
endcapResponse = TGraphErrors()
endcapResponse.SetName(r + 'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse = endcapResponse.Clone(r + 'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
for i in [1, 2]:
c.cd(i)
reg = 'barrel'
if i == 2: reg = 'endcap'
h = histos[r + k + reg]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame,
RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
if r == 'dpt' or r == 'den':
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb,
ncb)
else:
mean1.setRange(0, 0.5)
mean2 = RooRealVar("mean2", "mean", 0, 0, 1)
sigma2 = RooRealVar("sigma2", "sigma", 0.1, 0.01, 1.0)
gauss2 = RooGaussian("g2", "g", x, mean2, sigma2)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
if data.sumEntries() < 100:
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
model_cdf = model.createCdf(RooArgSet(x))
cl = 0.90
ul = 0.5 * (1.0 + cl)
closestToCL = 1.0
closestToUL = -1
closestToMedianCL = 1.0
closestToMedian = -1
for ibin in xrange(1, h.GetXaxis().GetNbins() * 10):
xval = h.GetXaxis().GetXmin() + (
ibin - 1) * h.GetXaxis().GetBinWidth(ibin) / 10.
x.setVal(xval)
cdfValToCL = math.fabs(model_cdf.getVal() - ul)
if cdfValToCL < closestToCL:
closestToCL = cdfValToCL
closestToUL = xval
cdfValToCL = math.fabs(model_cdf.getVal() - 0.5)
if cdfValToCL < closestToMedianCL:
closestToMedianCL = cdfValToCL
closestToMedian = xval
RooAbsPdf.plotOn(model, frame)
frame.Draw()
if i == 1: drawHeader()
labels.append(TPaveText(0.6, 0.92, 0.9, 0.98, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg = k.replace('to', '-')
kinReg = kinReg.replace('Inf', '#infty')
labels[ilab].AddText('[' + reg + '] ' + kinReg)
labels[ilab].Draw()
resolutionVal = math.fabs(closestToUL - closestToMedian)
responseGr = barrelResponse
responseCoreGr = barrelCoreResponse
coreResolutionVal = sigma1.getVal()
coreResolutionErr = sigma1.getError()
if frac.getVal() < 0.7 and (sigma2.getVal() < sigma1.getVal()):
coreResolutionVal = sigma2.getVal()
coreResolutionErr = sigma2.getError()
if i == 2:
responseGr = endcapResponse
responseCoreGr = endcapCoreResponse
if k != '':
nrespPts = responseGr.GetN()
kinAvg = 150
kinWidth = 50
if k == '30to40':
kinAvg = 35
kinWidth = 5
if k == '40to50':
kinAvg = 45
kinWidth = 5
if k == '50to75':
kinAvg = 62.5
kinWidth = 12.5
elif k == '75to100':
kinAvg = 87.5
kinWidth = 12.5
responseGr.SetPoint(nrespPts, kinAvg, resolutionVal)
responseCoreGr.SetPoint(nrespPts, kinAvg,
coreResolutionVal)
responseCoreGr.SetPointError(nrespPts, kinWidth,
coreResolutionErr)
labels.append(TPaveText(0.15, 0.7, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' %
(1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r + 'res_' + k + '.png')
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp = TCanvas('cresp', 'cresp', 500, 500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L." % cl)
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse, 'barrel', 'fp')
leg.AddEntry(endcapResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'res_evol.png')
frameCore = frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0, 0.2)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse, 'barrel', 'fp')
leg.AddEntry(endcapCoreResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'rescore_evol.png')
bosons = ['h', 'z', 'w']
kin = ['', '50', '100']
region = ['', 'bb', 'eb', 'ee']
for k in kin:
for r in region:
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['mjj' + k + r].Rebin()
histos['mjj' + k + r].Draw()
ic = 1
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj' + k + r], 'inclusive', 'f')
for b in bosons:
if histos[b + 'mjj' + k + r].Integral() <= 0: continue
ic = ic + 1
histos[b + 'mjj' + k + r].Rebin()
histos[b + 'mjj' + k + r].SetLineColor(ic)
histos[b + 'mjj' + k + r].SetLineWidth(2)
histos[b + 'mjj' + k + r].SetMarkerColor(ic)
histos[b + 'mjj' + k + r].SetMarkerStyle(1)
histos[b + 'mjj' + k + r].SetFillStyle(3000 + ic)
histos[b + 'mjj' + k + r].SetFillColor(ic)
histos[b + 'mjj' + k + r].Draw('histsame')
leg.AddEntry(histos[b + 'mjj' + k + r], b, "f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append(TPaveText(0.65, 0.8, 0.9, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj' + k + r + '.png')
massResolutionGrs = []
for r in region:
massResolution = TGraphErrors()
massResolution.SetName(r + 'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20 + len(massResolutionGrs))
massResolution.SetMarkerColor(1 + len(massResolutionGrs))
massResolution.SetLineColor(1 + len(massResolutionGrs))
massResolution.SetFillColor(1 + len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
h = histos['dmjj' + k + r]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame, RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb, ncb)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
RooAbsPdf.plotOn(model, frame)
frame.Draw()
labels.append(TPaveText(0.6, 0.65, 0.85, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' % (1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append(TPaveText(0.15, 0.8, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj' + k + r + '.png')
massResolution.SetTitle(regionTitle)
ip = massResolution.GetN()
x = 40
xerr = 10
if k == '50':
x = 75
xerr = 25
elif k == '100':
x = 150
xerr = 50
y = sigma1.getVal()
yerr = sigma1.getError()
if frac.getVal() < 0.8:
if sigma2.getVal() < sigma1.getVal():
y = sigma2.getVal()
ey = sigma2.getError()
massResolution.SetPoint(ip, x, y)
massResolution.SetPointError(ip, xerr, yerr)
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol = TCanvas('cdmevol', 'cdmevol', 500, 500)
cdmevol.cd()
frame.Draw('ap')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs:
dmGr.Draw('pl')
leg.AddEntry(dmGr, dmGr.GetTitle(), 'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
#uncVZ.setRange("fitRange",mean-2*sigma,50)
#result = gaussexp_pdf.fitTo(dataInRange,RooFit.Range("fitRange"),RooFit.PrintLevel(-1),RooFit.Save())
#gaussexp_params.printLatex()
gaussexp_pdf.paramOn(frame)
#gaussexp_pdf.plotOn(frame,RooFit.Range("fitRange"),RooFit.NormRange("fitRange"))
#gaussexp_pdf.plotOn(frame,RooFit.Range("fitRange"),RooFit.NormRange("fitRange"),RooFit.LineColor(result.status()+1))
frame.SetAxisRange(-50,50)
frame.SetMinimum(0.5)
frame.Draw()
c.Print(remainder[0]+".pdf","Title:slice_"+str(i)+"_roofit")
#h1d = dataInRange.createHistogram("data_in_range",w.var("uncVZ"),"Binning(100,-50,50)")
#h1d = dataInRange.createHistogram("data_in_range",w.var("uncVZ"))
h1d = RooAbsData.createHistogram(dataInRange,"data_in_range",w.var("uncVZ"),RooFit.Binning(100,-50,50))
print h1d.GetEntries()
#events.Draw("uncVZ>>hnew(100,-50,50)",cuts+"&&uncM>{0}&&uncM<{1}".format(lowedge,highedge),"goff")
#h1d = gDirectory.Get("hnew")
h1d.Sumw2()
h1d.Scale(1/scale_factor)
#h1d=totalH.ProjectionY("slice_{}".format(i),i,i+binning-1)
#h1d=h1d.Rebin(2,"slice")
integrals.append(h1d.Integral())
h1d.SetTitle(name)
print name
if (h1d.GetEntries()>100):
fit=h1d.Fit("gaus","QS")
peak=fit.Get().Parameter(0)
mean=fit.Get().Parameter(1)
def createCategoryHistogram(ds, x, numCat, categoryList=None):
from ROOT import RooAbsData, RooDataSet, RooPrintable, RooThresholdCategory, THStack, TF1
from ROOT.Math import GaussIntegrator
import ROOT
if categoryList == None:
categoryList = []
for i in range(numCat):
categoryList += ["Cat" + str(i + 1)]
etaHist = RooAbsData.createHistogram(ds, 'eta', 100)
histSum = etaHist.Integral()
#print "HISTSUM",histSum, etaHist.Integral(0,50)+etaHist.Integral(51,100),etaHist.Integral(0,50),etaHist.Integral(51,100),"\n";
limList = [-1]
for i in range(1, numCat):
start = limList[i - 1] + 1
targetVal = histSum / numCat
left = start
right = etaHist.GetNbinsX()
#print "\n\n\n",etaHist.Integral(start,start+1),"\n\n\n\n";
while abs(left - right) > 1:
if etaHist.Integral(start, int((left + right) / 2)) > targetVal:
right = (left + right) / 2
else:
left = (left + right) / 2
#print start, left, right, etaHist.Integral(start,int((left+right)/2)),targetVal,",",
if abs(etaHist.Integral(start, left) -
targetVal) > abs(etaHist.Integral(start, right) - targetVal):
limList += [right]
else:
limList += [left]
limList[0] = 0
print limList
xRegions = RooThresholdCategory("tageffRegion", "region of tageff", x,
categoryList[numCat - 1])
for i in range(0, numCat - 1):
xRegions.addThreshold(etaHist.GetXaxis().GetBinCenter(limList[i + 1]),
categoryList[i])
xRegions.writeToStream(ROOT.cout, False)
xRegions.writeToStream(ROOT.cout, True)
#etaSet = RooDataSet('etaSet','etaSet',ds,RooArgSet(ds.get().find['eta']));
#etaSet.Print();
#etaHist.SetAxisRange(0.1,0.2);
#etaHist.SetAxisRange(0.1,0.3);
from ROOT import TCanvas
histCanvas = TCanvas()
ROOT.gStyle.SetPalette(ROOT.kOcean)
#create stack to contain the chopped TH1Ds
#etaHistStack = THStack("etaHistStack", "Distribution of #eta;#eta;number of pseudo-experiments");
limList += [etaHist.GetNbinsX()]
histCutterFunc = TF1("histCutterFunc", "((x>=[0])?((x<[1])?1:0):0)", 0.0,
1.0)
etaMax = etaHist.GetMaximum()
print etaMax
cloneList = []
etaHist.GetYaxis().SetRangeUser(0, etaMax * 1.05)
etaHist.SetLineColor(ROOT.kWhite)
etaHist.Draw("histSame")
etaHist.SetTitle(
'Distribution of #eta (%s %s)' %
((originSuffix if originSuffix == 'MC' else 'Data'), taggerType))
etaHist.GetXaxis().SetTitle('#eta')
for i in range(len(limList) - 1):
cloneList = [etaHist.Clone()]
#etaHistClone.SetBins(limList[i+1]-limList[i],etaHist.GetBinContent(limList[i]),etaHist.GetBinContent(limList[i+1]));
histCutterFunc.SetParameter(
0,
etaHist.GetXaxis().GetBinCenter(limList[i]))
histCutterFunc.SetParameter(
1,
etaHist.GetXaxis().GetBinCenter(limList[i + 1]))
cloneList[-1].Multiply(histCutterFunc)
cloneList[-1].SetFillColor(38 + i)
cloneList[-1].SetLineColor(ROOT.kBlack)
if i == 0:
cloneList[-1].GetYaxis().SetRangeUser(0, etaMax * 1.05)
cloneList[-1].Draw("histSAME")
#etaHistStack.Add(etaHistClone);
#etaHistStack.Draw();
histCanvas.Update()
histCanvas.SaveAs('catHist-%s-%s.pdf' % (originSuffix, taggerType))
s = raw_input("Press Enter to continue...")
sys.exit(0)
return xRegions #,x
interf[-1].SetLineColor(kBlue+2)
interf[-1].SetLineStyle(kDashed)
interf[-1].Draw('same')
qqHPdf = fitter_mWW.ws.pdf('qqH')
qqHHisto = qqHPdf.createHistogram("HWW%snujj_qqH" % mode,
fitter_mWW.ws.var(pars_mWW.var[0]))
qqHHisto.Scale(fitter_mWW.ws.var('n_qqH').getVal()/qqHHisto.Integral())
qqHHisto.SetName("HWW%snujj_qqH" % mode)
qqHHisto.SetLineColor(kRed+2)
qqHHisto.Draw('same')
dataHisto = RooAbsData.createHistogram(fitter_mWW.ws.data('data_obs'),
'HWW%snujj_data_obs' % mode,
fitter_mWW.ws.var(pars_mWW.var[0]))
dataHisto.SetMarkerStyle(20)
dataHisto.SetName('HWW%snujj_data_obs' % mode)
dataHisto.Draw('same')
c_bkg.Update()
fitter_mWW.ws.saveSnapshot('nullFitSnapshot', fitter_mWW.ws.allVars())
upperHist = None
if opts.doLimit:
(expectedLimit, toys) = \
limits.expectedPlcLimit(fitter_mWW.ws.var(pars_mWW.var[0]),
fitter_mWW.ws.var('r_signal'),
full_pdf, fitter_mWW.ws,
ntoys = opts.doLimit,
p0FitError = fitresult.ParError(0);
p0End = p0End.getValV()-etaAvg.getValV();
print 'P0FIT = ', p0Fit, '\nDSMEANVAL = ', dsMeanVal.getValV(), '\nP0END = ', p0End, '\n'
p0Dev.setVal((p0Fit - p0End) / p0FitError);
p0DevSet.add(RooArgSet(p0Dev))
#graphHolder.Add(mistagVsEtaGraph)
#currentColor += 1
#break;
currentTime = time.time();
theHistCanvas = TCanvas();
p1DevHist = RooAbsData.createHistogram(p1DevSet,"p1Dev",20);
p1DevHist.Fit('gaus');
p1DevHist.Draw();
os.chdir(os.environ['B2DXFITTERSROOT']+'/tutorial');
if(not os.path.exists('p1HistPlots')):
os.mkdir("p1HistPlots")
os.chdir('p1HistPlots');
theHistCanvas.SaveAs("p1Hist_%f.pdf" % currentTime);
raw_input("Press Enter to continue");
theHistCanvas.Close();
theHistCanvas = TCanvas();
p0DevHist = RooAbsData.createHistogram(p0DevSet,"p0Dev",20);
# background
psi_c = RealVar( 'psi_c', Unit = '1/MeV', Value = -0.0004, MinMax = (-0.1, -0.0000001))
bkg_m = Pdf(Name = 'bkg_m', Type = Exponential, Parameters = [m, psi_c])
background = Component('background', (bkg_m,), Yield= (200000,500,5000000) )
signal = Component('signal', (sig_m, sig_t.pdf()), Yield= (200000,500,500000) )
from P2VV.Utilities.DataHandling import readData
tree_name = 'DecayTree'
## cut = 'runNumber > 0 && sel == 1 && (hlt1_biased == 1 || hlt1_unbiased_dec == 1) && hlt2_biased == 1 && '
cut = 'runNumber > 0 && sel == 1 && hlt1_unbiased_dec == 1 && hlt2_unbiased == 1 && '
cut += ' && '.join(['%s < 4' % e for e in ['muplus_track_chi2ndof', 'muminus_track_chi2ndof', 'Kplus_track_chi2ndof', 'Kminus_track_chi2ndof']])
from ROOT import RooAbsData
storage = RooAbsData.getDefaultStorageType()
RooAbsData.setDefaultStorageType(RooAbsData.Tree)
data = readData(input_data[args[0]]['data'], tree_name, ntupleCuts = cut,
NTuple = True, observables = observables)
from ROOT import TFile
tmp_file = TFile.Open(os.path.join(prefix, 'p2vv/data/tmp.root'), 'recreate')
data.store().tree().SetDirectory(tmp_file)
data_cut = data.reduce("time > 0.3")
data_cut.store().tree().SetDirectory(tmp_file)
## Build PDF
mass_pdf = buildPdf(Components = (background, signal), Observables = (m, ), Name = 'mass_pdf')
mass_pdf.Print("t")
interf[-1].SetLineColor(kBlue+2)
interf[-1].SetLineStyle(kDashed)
interf[-1].Draw('same')
qqHPdf = fitter_mWW.ws.pdf('qqH')
qqHHisto = qqHPdf.createHistogram("HWW%snujj_qqH" % mode,
fitter_mWW.ws.var(pars_mWW.var[0]))
qqHHisto.Scale(fitter_mWW.ws.var('n_qqH').getVal()/qqHHisto.Integral())
qqHHisto.SetName("HWW%snujj_qqH" % mode)
qqHHisto.SetLineColor(kRed+2)
qqHHisto.Draw('same')
dataHisto = RooAbsData.createHistogram(fitter_mWW.ws.data('data_obs'),
'HWW%snujj_data_obs' % mode,
fitter_mWW.ws.var(pars_mWW.var[0]))
dataHisto.SetMarkerStyle(20)
dataHisto.SetName('HWW%snujj_data_obs' % mode)
dataHisto.Draw('same')
dataHisto.Print()
c_bkg.Update()
fitter_mWW.ws.saveSnapshot('nullFitSnapshot', fitter_mWW.ws.allVars())
fitter_mWW.ws.loadSnapshot('nullFitSnapshot')
if opts.obsLimit:
limit = limits.plcLimit(fitter_mWW.ws.var(pars_mWW.var[0]),
fitter_mWW.ws.var('r_signal'),
full_pdf, fitter_mWW.ws,
def mbc_gau_che(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev,
RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen,
kMagenta, TPaveText)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D0 flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4,
'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
#sigma = RooRealVar('sigma', 'D width', 0.0001, 0.005, 'GeV')
sigma = RooRealVar('sigma', 'D width', 0.00468, 'GeV')
mbc_dp = RooRealVar('mbc_dp', 'D+ Mass', 1.86962, 'GeV')
sigpdf = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma)
#arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.88, 1.89, 'GeV')
#arg_slope = RooRealVar('arg_slope', 'Argus slope', -10, -100, -1)
#bkgpdf = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 400, 400)
xframe = mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def studyVqqResolution(rootFile):
#get all from file
histos={}
inF=TFile.Open(rootFile)
keys=inF.GetListOfKeys()
for k in keys:
obj=inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()]=obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin=['','30to40','40to50','50to75','75to100','100toInf']
for k in kin:
c=TCanvas('c','c',600,600)
c.cd()
c.SetCanvasSize(1000,500)
c.SetWindowSize(1000,500)
c.Divide(2,1)
c.cd(1)
histos['deta'+k+'barrel'].SetLineWidth(2)
histos['deta'+k+'barrel'].SetTitle('barrel')
histos['deta'+k+'barrel'].Draw('hist')
histos['deta'+k+'endcap'].SetLineWidth(2)
histos['deta'+k+'endcap'].SetLineStyle(7)
histos['deta'+k+'endcap'].SetTitle('endcap')
histos['deta'+k+'endcap'].Draw('histsame')
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta'+k+'barrel'],'barrel','f')
leg.AddEntry(histos['deta'+k+'endcap'],'endcap','f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi'+k+'barrel'].SetLineWidth(2)
histos['dphi'+k+'barrel'].SetTitle('barrel')
histos['dphi'+k+'barrel'].Draw('hist')
histos['dphi'+k+'endcap'].SetLineWidth(2)
histos['dphi'+k+'endcap'].SetLineStyle(7)
histos['dphi'+k+'endcap'].SetTitle('endcap')
histos['dphi'+k+'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png'%k)
labels=[]
responseVars=['dpt','den','dphi','deta','dr']
for r in responseVars:
barrelResponse=TGraphErrors()
barrelResponse.SetName(r+'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse=barrelResponse.Clone(r+'barrelcoreresponse')
endcapResponse=TGraphErrors()
endcapResponse.SetName(r+'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse=endcapResponse.Clone(r+'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000,500)
c.Divide(2,1)
for i in [1,2] :
c.cd(i)
reg='barrel'
if i==2: reg='endcap'
h=histos[r+k+reg]
x=RooRealVar("x", h.GetXaxis().GetTitle(), h.GetXaxis().GetXmin(), h.GetXaxis().GetXmax())
data=RooDataHist("data", "dataset with x", RooArgList(x), h)
frame=x.frame()
RooAbsData.plotOn( data, frame, RooFit.DataError(RooAbsData.SumW2) )
mean1=RooRealVar("mean1","mean1",0,-0.5,0.5);
sigma1=RooRealVar("sigma1","sigma1",0.1,0.01,1.0);
gauss1=RooGaussian("g1","g",x,mean1,sigma1)
if r=='dpt' or r=='den' :
mean2=RooRealVar("mean2","mean2",0,-0.5,0.5);
sigma2=RooRealVar("sigma2","sigma2",0.1,0.01,1.0);
alphacb=RooRealVar("alphacb","alphacb",1,0.1,3);
ncb=RooRealVar("ncb","ncb",4,1,100)
gauss2 = RooCBShape("cb2","cb",x,mean2,sigma2,alphacb,ncb);
else:
mean1.setRange(0,0.5)
mean2=RooRealVar("mean2","mean",0,0,1);
sigma2=RooRealVar("sigma2","sigma",0.1,0.01,1.0);
gauss2=RooGaussian("g2","g",x,mean2,sigma2) ;
frac = RooRealVar("frac","fraction",0.9,0.0,1.0)
if data.sumEntries()<100 :
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum","g1+g2",RooArgList(gauss1,gauss2), RooArgList(frac))
status=model.fitTo(data,RooFit.Save()).status()
if status!=0 : continue
model_cdf=model.createCdf(RooArgSet(x)) ;
cl=0.90
ul=0.5*(1.0+cl)
closestToCL=1.0
closestToUL=-1
closestToMedianCL=1.0
closestToMedian=-1
for ibin in xrange(1,h.GetXaxis().GetNbins()*10):
xval=h.GetXaxis().GetXmin()+(ibin-1)*h.GetXaxis().GetBinWidth(ibin)/10.
x.setVal(xval)
cdfValToCL=math.fabs(model_cdf.getVal()-ul)
if cdfValToCL<closestToCL:
closestToCL=cdfValToCL
closestToUL=xval
cdfValToCL=math.fabs(model_cdf.getVal()-0.5)
if cdfValToCL<closestToMedianCL:
closestToMedianCL=cdfValToCL
closestToMedian=xval
RooAbsPdf.plotOn(model,frame)
frame.Draw()
if i==1: drawHeader()
labels.append( TPaveText(0.6,0.92,0.9,0.98,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(r+k+'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg=k.replace('to','-')
kinReg=kinReg.replace('Inf','#infty')
labels[ilab].AddText('['+reg+'] '+kinReg)
labels[ilab].Draw()
resolutionVal=math.fabs(closestToUL-closestToMedian)
responseGr=barrelResponse
responseCoreGr=barrelCoreResponse
coreResolutionVal=sigma1.getVal()
coreResolutionErr=sigma1.getError()
if frac.getVal()<0.7 and (sigma2.getVal()<sigma1.getVal()) :
coreResolutionVal=sigma2.getVal()
coreResolutionErr=sigma2.getError()
if i==2 :
responseGr=endcapResponse
responseCoreGr=endcapCoreResponse
if k!='' :
nrespPts=responseGr.GetN()
kinAvg=150
kinWidth=50
if k=='30to40' :
kinAvg=35
kinWidth=5
if k=='40to50' :
kinAvg=45
kinWidth=5
if k=='50to75' :
kinAvg=62.5
kinWidth=12.5
elif k=='75to100' :
kinAvg=87.5
kinWidth=12.5
responseGr.SetPoint(nrespPts,kinAvg,resolutionVal)
responseCoreGr.SetPoint(nrespPts,kinAvg,coreResolutionVal)
responseCoreGr.SetPointError(nrespPts,kinWidth,coreResolutionErr)
labels.append( TPaveText(0.15,0.7,0.4,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(r+k+'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)'%frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean1.getVal(),mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma1.getVal(),sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)'%(1-frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean2.getVal(),mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma2.getVal(),sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r+'res_'+k+'.png')
frame=TGraphErrors()
frame.SetPoint(0,0,0)
frame.SetPoint(1,200,0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp=TCanvas('cresp','cresp',500,500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L."%cl )
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse,'barrel','fp')
leg.AddEntry(endcapResponse,'endcap','fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r+'res_evol.png')
frameCore=frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0,0.2)
drawHeader()
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse,'barrel','fp')
leg.AddEntry(endcapCoreResponse,'endcap','fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r+'rescore_evol.png')
bosons=['h','z','w']
kin=['','50','100']
region=['','bb','eb','ee']
for k in kin:
for r in region:
c=TCanvas('c','c',600,600)
c.cd()
histos['mjj'+k+r].Rebin()
histos['mjj'+k+r].Draw()
ic=1
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj'+k+r],'inclusive','f')
for b in bosons:
if histos[b+'mjj'+k+r].Integral()<=0 : continue
ic=ic+1
histos[b+'mjj'+k+r].Rebin()
histos[b+'mjj'+k+r].SetLineColor(ic)
histos[b+'mjj'+k+r].SetLineWidth(2)
histos[b+'mjj'+k+r].SetMarkerColor(ic)
histos[b+'mjj'+k+r].SetMarkerStyle(1)
histos[b+'mjj'+k+r].SetFillStyle(3000+ic)
histos[b+'mjj'+k+r].SetFillColor(ic)
histos[b+'mjj'+k+r].Draw('histsame')
leg.AddEntry(histos[b+'mjj'+k+r],b,"f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append( TPaveText(0.65,0.8,0.9,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(k+r+'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle="inclusive"
if r == 'bb' : regionTitle='barrel-barrel'
if r == 'eb' : regionTitle='endcap-barrel'
if r == 'ee' : regionTitle='endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold=30
if k!='' : ptthreshold=float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV'%ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj'+k+r+'.png')
massResolutionGrs=[]
for r in region:
massResolution=TGraphErrors()
massResolution.SetName(r+'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20+len(massResolutionGrs))
massResolution.SetMarkerColor(1+len(massResolutionGrs))
massResolution.SetLineColor(1+len(massResolutionGrs))
massResolution.SetFillColor(1+len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c=TCanvas('c','c',600,600)
c.cd()
h=histos['dmjj'+k+r]
x=RooRealVar("x", h.GetXaxis().GetTitle(), h.GetXaxis().GetXmin(), h.GetXaxis().GetXmax())
data=RooDataHist("data", "dataset with x", RooArgList(x), h)
frame=x.frame()
RooAbsData.plotOn( data, frame, RooFit.DataError(RooAbsData.SumW2) )
mean1=RooRealVar("mean1","mean1",0,-0.5,0.5);
sigma1=RooRealVar("sigma1","sigma1",0.1,0.01,1.0);
gauss1=RooGaussian("g1","g",x,mean1,sigma1)
mean2=RooRealVar("mean2","mean2",0,-0.5,0.5);
sigma2=RooRealVar("sigma2","sigma2",0.1,0.01,1.0);
alphacb=RooRealVar("alphacb","alphacb",1,0.1,3);
ncb=RooRealVar("ncb","ncb",4,1,100)
gauss2 = RooCBShape("cb2","cb",x,mean2,sigma2,alphacb,ncb);
frac = RooRealVar("frac","fraction",0.9,0.0,1.0)
model = RooAddPdf("sum","g1+g2",RooArgList(gauss1,gauss2), RooArgList(frac))
status=model.fitTo(data,RooFit.Save()).status()
if status!=0 : continue
RooAbsPdf.plotOn(model,frame)
frame.Draw()
labels.append( TPaveText(0.6,0.65,0.85,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(r+k+'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)'%frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean1.getVal(),mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma1.getVal(),sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)'%(1-frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f'%(mean2.getVal(),mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f'%(sigma2.getVal(),sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append( TPaveText(0.15,0.8,0.4,0.9,'brNDC') )
ilab=len(labels)-1
labels[ilab].SetName(k+r+'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle="inclusive"
if r == 'bb' : regionTitle='barrel-barrel'
if r == 'eb' : regionTitle='endcap-barrel'
if r == 'ee' : regionTitle='endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold=30
if k!='' : ptthreshold=float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV'%ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj'+k+r+'.png')
massResolution.SetTitle(regionTitle)
ip=massResolution.GetN()
x=40
xerr=10
if k=='50' :
x=75
xerr=25
elif k=='100':
x=150
xerr=50
y=sigma1.getVal()
yerr=sigma1.getError()
if frac.getVal()<0.8:
if sigma2.getVal()<sigma1.getVal():
y=sigma2.getVal()
ey=sigma2.getError()
massResolution.SetPoint(ip,x,y)
massResolution.SetPointError(ip,xerr,yerr)
frame=TGraphErrors()
frame.SetPoint(0,0,0)
frame.SetPoint(1,200,0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol=TCanvas('cdmevol','cdmevol',500,500)
cdmevol.cd()
frame.Draw('ap')
leg=TLegend(0.6,0.92,0.9,0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs :
dmGr.Draw('pl')
leg.AddEntry(dmGr,dmGr.GetTitle(),'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c=TCanvas('c','c',600,600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
def mbc_dline_che(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev,
RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen,
kMagenta, TPaveText, RooDLineShape)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D0 flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres", "mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500)
scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1, scalep2)
sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1, scalep2, scalep3)
sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md", "md", setmd, 1.863, 1.875)
f2 = RooRealVar("f2", "f2", fa)
f3 = RooRealVar("f3", "f3", fb)
al23 = RooArgList(f2, f3)
f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3)
af12 = RooArgList(f1, f2)
sigpdf = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3)
sigbarpdf = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2))
bkgbarpdf = RooChebychev('bkgbarpdf', 'Background', mbc,
RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf',
RooArgList(sigbarpdf, bkgbarpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 400, 400)
xframe = mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, md, sigmap1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
