def DrawStackHistograms(histograms, outname, blind=True, verbose=False):
#hists_sorted = sorted(histograms, key=lambda h: h.Integral())
hs = THStack("h_stack", "")
h_obs = None
l = TLegend(0.77, 0.60, 0.90, 0.87)
for hist in histograms:
hname = hist.GetName()
if "data_obs" in hname:
if not blind:
h_obs = hist
SetHistogramStyle(h_obs, "Observed")
l.AddEntry(h_obs, "Observed", "p")
else:
SetHistogramStyle(hist)
label = GetLabelName(hist)
hs.Add(hist)
l.AddEntry(hist, label, "f")
h_est = hs.GetStack().Last().Clone()
if verbose:
est_err = Double(0.)
nbins_est = h_est.GetNbinsX()
est_yields = h_est.IntegralAndError(1, nbins_est, est_err)
print "predicted yields :", est_yields, "+/-", est_err
if not blind:
print "observed yields :", h_obs.Integral()
# draw
# SetCMSStyle() TODO
canvas = TCanvas()
gPad.SetLogy()
#ymax_hs = hs.GetMaximum()
#ymax = ymax_hs if h_obs is None else max(ymax_hs, h_obs.GetMaximum())
ymax = 10000
ymin = 0.1
hs.SetMaximum(ymax)
hs.SetMinimum(ymin)
hs.Draw("HIST")
l.Draw("same")
if not blind:
h_obs.Draw("same")
canvas.SaveAs(outname)
ntotalbkg = ntotalbkg + numevt
if bkgsamples[fname]["name"] == "QCD": numqcd = numevt
if hnames[1] == printHistName:
string = "%s : %s = %f \n" % (fname, bkgsamples[fname]["name"],
numevt)
fNevt.write(string)
print fname, " : ", bkgsamples[fname][
"name"], " = ", "{0:.5g}".format(
numevt), " scale : ", "{0:.3g}".format(scale)
#print fname, " : ", scale
## Add to Stack
hs.Add(h_tmp) #hh_tmp -> add h tmp sig, hs->other
k = k + 1
h_bkg = hs.GetStack().Last()
"""
#Sig Stack
hsHct = THStack()
ntotalHct = 0
m = 0
for fname in hctsamples.keys():
h_Hct = hctsamples[fname]["file"].Get(hctsamples[fname]["hname"][i])
nbins = h_Hct.GetNbinsX()
h_Hct.AddBinContent( nbins, h_Hct.GetBinContent( nbins+1 ) )
h_Hct.SetLineColor(hctsamples[fname]["col"])
## normalization
scale = datasamples[datasamples.keys()[mode]]["lumi"]/(hctsamples[fname]["total"]/hctsamples[fname]["xsection"])
def createPlots_(plot, compounddetectorname):
"""Cumulative material budget from simulation.
Internal function that will produce a cumulative profile of the
material budget inferred from the simulation starting from the
single detectors that compose the tracker. It will iterate over
all existing detectors contained in the DETECTORS
dictionary. The function will automatically skip non-existent
detectors.
"""
theDirname = "Figures"
hist_X0_detectors = OrderedDict()
if plot not in plots.keys():
print("Error: chosen plot name not known %s" % plot)
return
# We need to keep the file content alive for the lifetime of the
# full function....
subDetectorFiles = []
hist_X0_elements = OrderedDict()
prof_X0_elements = OrderedDict()
for subDetector, color in DETECTORS.iteritems():
subDetectorFilename = "matbdg_%s.root" % subDetector
if not checkFile_(subDetectorFilename):
print("Error opening file: %s" % subDetectorFilename)
continue
subDetectorFiles.append(TFile(subDetectorFilename))
subDetectorFile = subDetectorFiles[-1]
print("Opening file: %s" % subDetectorFilename)
prof_X0_XXX = subDetectorFile.Get("%d" % plots[plot].plotNumber)
hist_X0_detectors[subDetector] = prof_X0_XXX.ProjectionX()
# category profiles
for label, [num, color, leg] in hist_label_to_num.iteritems():
prof_X0_elements[label] = subDetectorFile.Get(
"%d" % (num + plots[plot].plotNumber))
hist_X0_elements[label] = assignOrAddIfExists_(
hist_X0_elements.setdefault(label, None),
prof_X0_elements[label])
cumulative_matbdg = TH1D(
"CumulativeSimulMatBdg", "CumulativeSimulMatBdg",
hist_X0_detectors["BeamPipe"].GetNbinsX(),
hist_X0_detectors["BeamPipe"].GetXaxis().GetXmin(),
hist_X0_detectors["BeamPipe"].GetXaxis().GetXmax())
cumulative_matbdg.SetDirectory(0)
# colors
for det, color in DETECTORS.iteritems():
setColorIfExists_(hist_X0_detectors, det, color)
for label, [num, color, leg] in hist_label_to_num.iteritems():
hist_X0_elements[label].SetFillColor(color)
# First Plot: BeamPipe + Tracker + ECAL + HCal + HGCal + MB + MGNT
# stack
stackTitle_SubDetectors = "Material Budget;%s;%s" % (plots[plot].abscissa,
plots[plot].ordinate)
stack_X0_SubDetectors = THStack("stack_X0", stackTitle_SubDetectors)
for det, histo in hist_X0_detectors.iteritems():
stack_X0_SubDetectors.Add(histo)
cumulative_matbdg.Add(histo, 1)
# canvas
can_SubDetectors = TCanvas("can_SubDetectors", "can_SubDetectors", 800,
800)
#can_SubDetectors.Range(0,0,25,25)
can_SubDetectors.SetFillColor(kWhite)
# Draw
stack_X0_SubDetectors.SetMinimum(plots[plot].ymin)
stack_X0_SubDetectors.SetMaximum(plots[plot].ymax)
stack_X0_SubDetectors.Draw("HIST")
#stack_X0_SubDetectors.GetXaxis().SetLimits(plots[plot].xmin, plots[plot].xmax)
# Legenda
theLegend_SubDetectors = TLegend(0.130, 0.7, 0.93,
0.90) #(0.180,0.8,0.98,0.90)
theLegend_SubDetectors.SetNColumns(2)
theLegend_SubDetectors.SetFillColor(0)
theLegend_SubDetectors.SetFillStyle(0)
theLegend_SubDetectors.SetBorderSize(0)
for det, histo in hist_X0_detectors.iteritems():
theLegend_SubDetectors.AddEntry(histo, det, "f")
theLegend_SubDetectors.Draw()
# text
text_SubDetectors = TPaveText(0.130, 0.627, 0.352, 0.687,
"NDC") #(0.180,0.727,0.402,0.787,"NDC")
text_SubDetectors.SetFillColor(0)
text_SubDetectors.SetBorderSize(0)
text_SubDetectors.AddText("CMS Simulation")
text_SubDetectors.SetTextAlign(11)
text_SubDetectors.Draw()
# Store
can_SubDetectors.Update()
if not checkFile_(theDirname):
os.mkdir(theDirname)
can_SubDetectors.SaveAs("%s/MaterialBdg_%s_%s.pdf" %
(theDirname, compounddetectorname, plot))
can_SubDetectors.SaveAs("%s/MaterialBdg_%s_%s.png" %
(theDirname, compounddetectorname, plot))
can_SubDetectors.SaveAs("%s/MaterialBdg_%s_%s.root" %
(theDirname, compounddetectorname, plot))
if plot == "x_vs_eta" or plot == "l_vs_eta":
canname = "MBCan_1D_%s_%s_total" % (compounddetectorname, plot)
can2 = TCanvas(canname, canname, 800, 800)
can2.Range(0, 0, 25, 25)
can2.SetFillColor(kWhite)
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
#title = TPaveLabel(.11,.95,.35,.99,"Total accumulated material budget","brndc")
stack_X0_SubDetectors.GetStack().Last().SetMarkerStyle(34)
stack_X0_SubDetectors.GetStack().Last().GetXaxis().SetRangeUser(
1.0, 3.5)
stack_X0_SubDetectors.GetStack().Last().Draw()
stack_X0_SubDetectors.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zplus.pdf" %
(theDirname, compounddetectorname, plot))
can2.SaveAs("%s/%s_%s_total_Zplus.png" %
(theDirname, compounddetectorname, plot))
stack_X0_SubDetectors.GetStack().Last().GetXaxis().SetRangeUser(
-3.5, -1.0)
stack_X0_SubDetectors.GetStack().Last().Draw()
stack_X0_SubDetectors.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zminus.pdf" %
(theDirname, compounddetectorname, plot))
can2.SaveAs("%s/%s_%s_total_Zminus.png" %
(theDirname, compounddetectorname, plot))
#Also print them to give them exact numbers
etavalues = []
matbudginX0 = []
matbudginIntLen = []
for binx in range(
0,
stack_X0_SubDetectors.GetStack().Last().GetXaxis().GetNbins()):
bincontent = stack_X0_SubDetectors.GetStack().Last().GetBinContent(
binx)
if bincontent == 0: continue
etavalues.append(
stack_X0_SubDetectors.GetStack().Last().GetBinCenter(binx))
if plot == "x_vs_eta":
matbudginX0.append(bincontent)
d1 = {'Eta': etavalues, 'MatBudInX0': matbudginX0}
df1 = pd.DataFrame(data=d1).round(2)
df1.to_csv(
r'/afs/cern.ch/work/a/apsallid/CMS/PFCalStudies/CMS-HGCAL/matbudV10fromVertexToBackofHGCal/CMSSW_11_0_X_2019-06-04-2300/src/Validation/Geometry/test/EtavsMatBudinXo.txt',
sep=' ',
index=False,
header=False)
#print df1
if plot == "l_vs_eta":
matbudginIntLen.append(bincontent)
d2 = {'Eta': etavalues, 'MatBudInIntLen': matbudginIntLen}
df2 = pd.DataFrame(data=d2).round(2)
df2.to_csv(
r'/afs/cern.ch/work/a/apsallid/CMS/PFCalStudies/CMS-HGCAL/matbudV10fromVertexToBackofHGCal/CMSSW_11_0_X_2019-06-04-2300/src/Validation/Geometry/test/EtavsMatBudInIntLen.txt',
sep=' ',
index=False,
header=False)
#print df2
return cumulative_matbdg
def createCompoundPlots(detector, plot):
"""Produce the requested plot for the specified detector.
Function that will plot the requested @plot for the specified
@detector. The specified detector could either be a real
detector or a compound one. The list of available plots are the
keys of plots dictionary (imported from plot_utils.
"""
theDirname = 'Images'
if not checkFile_(theDirname):
os.mkdir(theDirname)
goodToGo, theDetectorFilename = paramsGood_(detector, plot)
if not goodToGo:
return
theDetectorFile = TFile(theDetectorFilename)
#
# get TProfiles
prof_X0_elements = OrderedDict()
hist_X0_elements = OrderedDict()
for label, [num, color, leg] in hist_label_to_num.iteritems():
#print label, num, color, leg
prof_X0_elements[label] = theDetectorFile.Get(
"%d" % (num + plots[plot].plotNumber))
hist_X0_elements[label] = prof_X0_elements[label].ProjectionX()
hist_X0_elements[label].SetFillColor(color)
hist_X0_elements[label].SetLineColor(kBlack)
files = []
if detector in COMPOUNDS.keys():
for subDetector in COMPOUNDS[detector][1:]:
subDetectorFilename = "matbdg_%s.root" % subDetector
# open file
if not checkFile_(subDetectorFilename):
continue
subDetectorFile = TFile(subDetectorFilename)
files.append(subDetectorFile)
print("*** Open file... %s" % subDetectorFilename)
# subdetector profiles
for label, [num, color, leg] in hist_label_to_num.iteritems():
prof_X0_elements[label] = subDetectorFile.Get(
"%d" % (num + plots[plot].plotNumber))
hist_X0_elements[label].Add(
prof_X0_elements[label].ProjectionX(
"B_%s" % prof_X0_elements[label].GetName()), +1.000)
# stack
stackTitle = "Material Budget %s;%s;%s" % (detector, plots[plot].abscissa,
plots[plot].ordinate)
stack_X0 = THStack("stack_X0", stackTitle)
for label, [num, color, leg] in hist_label_to_num.iteritems():
stack_X0.Add(hist_X0_elements[label])
# canvas
canname = "MBCan_1D_%s_%s" % (detector, plot)
can = TCanvas(canname, canname, 800, 800)
can.Range(0, 0, 25, 25)
can.SetFillColor(kWhite)
gStyle.SetOptStat(0)
gStyle.SetOptTitle(1)
# Draw
stack_X0.Draw("HIST")
stack_X0.GetYaxis().SetTitleOffset(1.15)
# Legenda
theLegend = TLegend(0.40, 0.65, 0.60, 0.89)
if plot == "x_vs_phi" or plot == "l_vs_phi":
theLegend = TLegend(0.65, 0.30, 0.89, 0.70)
if plot == "x_vs_R" or plot == "l_vs_R":
theLegend = TLegend(0.75, 0.60, 0.95, 0.90)
for label, [num, color, leg] in hist_label_to_num.iteritems():
theLegend.AddEntry(hist_X0_elements[label], leg, "f")
theLegend.Draw()
# Store
can.Update()
can.SaveAs("%s/%s_%s.pdf" % (theDirname, detector, plot))
can.SaveAs("%s/%s_%s.png" % (theDirname, detector, plot))
#Let's also save the total accumulated budget vs eta since muon id relies
#on adequate calorimeter thickness
if plot == "x_vs_eta" or plot == "l_vs_eta":
canname = "MBCan_1D_%s_%s_total" % (detector, plot)
can2 = TCanvas(canname, canname, 800, 800)
can2.Range(0, 0, 25, 25)
can2.SetFillColor(kWhite)
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
#title = TPaveLabel(.11,.95,.35,.99,"Total accumulated material budget","brndc")
stack_X0.GetStack().Last().GetXaxis().SetRangeUser(0., 3.)
stack_X0.GetStack().Last().Draw()
stack_X0.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zplus.pdf" % (theDirname, detector, plot))
can2.SaveAs("%s/%s_%s_total_Zplus.png" % (theDirname, detector, plot))
stack_X0.GetStack().Last().GetXaxis().SetRangeUser(-3., 0.)
stack_X0.GetStack().Last().Draw()
stack_X0.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zminus.pdf" % (theDirname, detector, plot))
can2.SaveAs("%s/%s_%s_total_Zminus.png" % (theDirname, detector, plot))
rebinnedHist[ih].Write()
rebinnedData.Write()
# forget about plotting right now. Just make a template.
## purpose for plotting
rebinnedData.Scale(1., "width")
stack = THStack()
print rebinnedHist.keys()
for ih in rebinnedHist:
rebinnedHist[ih].Scale(1., "width")
stack.Add(rebinnedHist[ih])
if postfitPlots:
MC = stack.GetStack().Last().Clone("MC")
x = rebinnedData.Chi2Test(MC, "UW CHI2/NDF")
chi2Text = "#chi^{2}/NDF=%.2f" % x
canvasRatio = TCanvas('c1Ratio', 'c1Ratio', W, H)
canvasRatio.SetFillColor(0)
canvasRatio.SetBorderMode(0)
canvasRatio.SetFrameFillStyle(0)
canvasRatio.SetFrameBorderMode(0)
canvasRatio.SetLeftMargin(L / W)
canvasRatio.SetRightMargin(R / W)
canvasRatio.SetTopMargin(T / H)
canvasRatio.SetBottomMargin(B / H)
canvasRatio.SetTickx(0)
canvasRatio.SetTicky(0)
canvasRatio.Draw()
def createCompoundPlots(detector, plot, geometry):
"""Produce the requested plot for the specified detector.
Function that will plot the requested @plot for the specified
@detector. The specified detector could either be a real
detector or a compound one. The list of available plots are the
keys of plots dictionary (imported from plot_utils.
"""
setTDRStyle()
theDirname = 'Images'
if not checkFile_(theDirname):
os.mkdir(theDirname)
goodToGo, theDetectorFilename = paramsGood_(detector, plot, geometry)
if not goodToGo:
return
hist_X0_elements = OrderedDict()
# stack
stackTitle = "%s;%s;%s" % (detector,
plots[plot].abscissa,
plots[plot].ordinate)
stack_X0 = THStack("stack_X0", stackTitle);
theLegend = TLegend(0.50, 0.70, 0.70, 0.90);
def setRanges(h):
legendSpace = 1. + 0.3 # 30%
minY = h.GetYaxis().GetXmin()
maxY = h.GetBinContent(h.GetMaximumBin()) * legendSpace
h.GetYaxis().SetRangeUser(minY, maxY)
for label, [num, color, leg] in six.iteritems(hist_label_to_num):
# We don't want the sum to be added as part of the stack
if label is 'SUM':
continue
hist_X0_elements[label] = get1DHisto_(detector,
num + plots[plot].plotNumber,
geometry)
hist_X0_elements[label].SetFillColor(color)
hist_X0_elements[label].SetLineColor(kBlack)
stack_X0.Add(hist_X0_elements[label])
if hist_X0_elements[label].Integral() > 0.: theLegend.AddEntry(hist_X0_elements[label], leg, "f")
# canvas
canname = "MBCan_1D_%s_%s" % (detector, plot)
can = TCanvas(canname, canname, 800, 800)
can.Range(0,0,25,25)
gStyle.SetOptTitle(0)
# Draw
setRanges(stack_X0.GetStack().Last())
stack_X0.Draw("HIST");
stack_X0.GetXaxis().SetLabelSize(0.035)
stack_X0.GetYaxis().SetLabelSize(0.035)
theLegend.Draw();
cmsMark = TLatex()
cmsMark.SetNDC();
cmsMark.SetTextAngle(0);
cmsMark.SetTextColor(kBlack);
cmsMark.SetTextFont(61)
cmsMark.SetTextSize(5e-2)
cmsMark.SetTextAlign(11)
cmsMark.DrawLatex(0.16,0.86,"CMS")
simuMark = TLatex()
simuMark.SetNDC();
simuMark.SetTextAngle(0);
simuMark.SetTextColor(kBlack);
simuMark.SetTextSize(3e-2)
simuMark.SetTextAlign(11)
simuMark.DrawLatex(0.16,0.82,"#font[52]{Simulation Internal}")
# Store
can.Update();
can.SaveAs( "%s/%s_%s_%s.pdf"
% (theDirname, detector, plot, geometry))
can.SaveAs( "%s/%s_%s_%s.png"
% (theDirname, detector, plot, geometry))
bdt_data.Draw('P SAME')
bdt_sig.SetFillStyle(0)
bdt_sig.Draw('HIST SAME E')
if gen:
bdt_sig_gen.SetFillStyle(0)
bdt_sig_gen.Draw('HIST SAME E')
bdt_data.Draw("axis same")
l.Draw("same")
label.Draw("same")
pad2.cd()
r_bdt_data = bdt_data.Clone("r_bdt_data")
r_bdt_data.SetLineColor(1)
r_bdt_data.SetMarkerStyle(20)
r_bdt_data.SetMarkerSize(0.5)
r_bdt_data.SetTitle("")
r_bdt_data.Divide(hs_bdt_bkg.GetStack().Last())
y = r_bdt_data.GetYaxis()
y.SetTitle("Data/MC")
y.SetNdivisions(505)
y.SetTitleSize(0.11)
y.SetRangeUser(0.6, 1.4)
y.SetTitleOffset(0.35)
y.SetLabelSize(0.1)
x = r_bdt_data.GetXaxis()
x.SetTitleSize(0.11)
x.SetTitleOffset(1.0)
x.SetLabelSize(0.1)
r_bdt_data.Draw("EP")
c1.Print('histo_' + tmva_version + '_' + ch + '_bdt.pdf')
################################################
if bkgsamples[fname]["name"] == "QCD": numqcd = numevt
if hnames[1] == printHistName:
string = "%s : %s = %f \n" % (fname, bkgsamples[fname]["name"],
numevt)
fNevt.write(string)
print fname, " : ", bkgsamples[fname][
"name"], " = ", "{0:.5g}".format(
numevt) # " scale : " ,"{0:.1g}".format(scale)
#print fname, " : ", scale
## Add to Stack
hs.Add(h_tmp) #hh_tmp -> add h tmp sig, hs->other
k = k + 1
h_bkg = hs.GetStack().Last()
#Sig Stack
hsHct = THStack()
ntotalHct = 0
m = 0
for fname in hctsamples.keys():
h_Hct = hctsamples[fname]["file"].Get(hctsamples[fname]["hname"][i])
nbins = h_Hct.GetNbinsX()
h_Hct.AddBinContent(nbins, h_Hct.GetBinContent(nbins + 1))
h_Hct.SetLineColor(hctsamples[fname]["col"])
## normalization
scale = datasamples[datasamples.keys()[mode]]["lumi"] / (
def PlotDataMC(dataFiles_, mcFiles_, signalFiles_, dataDirec_, mcDirec_,
signalDirec_, drawPads_, Lumi_, SigScale_, ol_, log_, Tags_,
VarBatch_, CutsType_, verbose_, noQCD_):
print "Plotting Data / MC"
gROOT.ProcessLine(
"gErrorIgnoreLevel = kError"
) # kPrint, kInfo, kWarning, kError, kBreak, kSysError, kFatal
gStyle.SetOptStat(0)
gStyle.SetErrorX(0.0001)
HHWWggTags = []
##-- Get Tags
for t in Tags_:
HHWWggTags.append(t)
cuts, cutNames = GetCuts(CutsType_)
##-- if var batch is loose, need separate titles for variables since it will be sum of vars * bools
if (VarBatch_ == "Loose"):
finalStateVars, varNames = GetVars(
VarBatch_) # get vars from var batch
if (verbose_):
print "finalStateVars = ", finalStateVars
print "varNames = ", varNames
else:
finalStateVars = GetVars(VarBatch_) # get vars from var batch
if (verbose_):
#print"cuts:",cuts
print "cutNames:", cutNames
print "vars:", finalStateVars
##-- For each data file (can just be one)
for dF_ in dataFiles_:
cutBatchTag_pairs = []
dataNevents_list = []
MC_names = []
MC_Nevents_lists = []
MC_Nevents_noweight_lists = []
##-- For each category (Ex: HHWWggTag_0, HHWWggTag_1, HHWWggTag_2, combined)
for itag, HHWWggTag in enumerate(HHWWggTags):
if (verbose_):
print "tag:", HHWWggTag
dPath = "%s/%s" % (dataDirec_, dF_)
dFile = TFile.Open(dPath)
if (HHWWggTag == "combined"):
ch = TChain('tagsDumper/trees/Data_13TeV_HHWWggTag_0')
ch.Add("%s/tagsDumper/trees/Data_13TeV_HHWWggTag_0" % (dPath))
ch.Add("%s/tagsDumper/trees/Data_13TeV_HHWWggTag_1" % (dPath))
ch.Add("%s/tagsDumper/trees/Data_13TeV_HHWWggTag_2" % (dPath))
else:
ch = TChain('tagsDumper/trees/Data_13TeV_%s' % (HHWWggTag))
ch.Add(dPath)
BLIND_CUT = "(CMS_hgg_mass < 115 || CMS_hgg_mass > 135)"
MC_WEIGHT = "1*weight"
ZERO_CUT = "ZERO_CUT"
MC_CUT = "%s*(%s)*(%s)" % (MC_WEIGHT, BLIND_CUT, ZERO_CUT)
DATA_CUT = "%s*(%s)" % (BLIND_CUT, ZERO_CUT)
SIGNAL_CUT = "%s*(%s)" % (MC_WEIGHT, ZERO_CUT
) # no blind cut on signal
##-- For each cut
for ic, cut in enumerate(cuts):
#if(verbose_): print"Plotting with selection:",cut
cutName = cutNames[ic]
cutBatchTag = "%s_%s" % (cutName, HHWWggTag)
cutBatchTag_pairs.append(cutBatchTag)
dataNevents = -999
these_MC_Nevents = []
these_MC_Nevents_noweights = []
outputFolder = "%s/%s" % (ol_, cutName)
if (not os.path.exists(outputFolder)):
os.system('mkdir %s' % (outputFolder))
os.system('cp %s/index.php %s' % (ol_, outputFolder))
MC_CUT += "*(%s)" % (cut)
DATA_CUT += "*(%s)" % (cut)
SIGNAL_CUT += "*(%s)" % (cut)
##-- For each variable
for iv, v in enumerate(finalStateVars):
if (VarBatch_ == "Loose"): varTitle = varNames[iv]
else: varTitle = GetVarTitle(v)
if (verbose_): print "Plotting variable:", varTitle
MC_CUT = MC_CUT.replace(
"ZERO_CUT", "(%s != 0) && (%s != -999)" % (v, v))
DATA_CUT = DATA_CUT.replace(
"ZERO_CUT", "(%s != 0) && (%s != -999)" % (v, v))
SIGNAL_CUT = SIGNAL_CUT.replace(
"ZERO_CUT", "(%s != 0) && (%s != -999)" % (v, v))
MC_CUT_NOWEIGHT = MC_WEIGHT.replace(MC_WEIGHT, "(1)")
if (varTitle == "weight"):
MC_CUT = MC_CUT.replace(
MC_WEIGHT, "(1)"
) # if you want to plot the "weight" variable, you should not scale it by weight!
##-- Can add printing of cuts to debug
# if(verbose_):
# print"MC_CUT:",MC_CUT
# print"DATA_CUT:",DATA_CUT
##--
legend = TLegend(0.55, 0.65, 0.89, 0.89)
legend.SetTextSize(0.025)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
xbins, xmin, xmax = GetBins(varTitle)
##-- Fill histogram with data
Data_h_tmp = TH1F('Data_h_tmp', varTitle, xbins, xmin,
xmax)
Data_h_tmp.SetTitle("%s" % (varTitle))
Data_h_tmp.SetMarkerStyle(8)
exec('ch.Draw("%s >> Data_h_tmp","%s")' % (v, DATA_CUT))
##-- Only save number of events for first variable. Should be same for all because same cut is used
if (iv == 0):
dataNevents = Data_h_tmp.GetEntries()
dataNevents_list.append(dataNevents)
# print"Blinded Data numEvents:",Data_h_tmp.GetEntries()
DataHist = Data_h_tmp.Clone("DataHist")
DataHist.SetDirectory(0)
legend.AddEntry(DataHist, "Data", "P")
##-- Get histograms with MC Backgrounds
bkgStack = THStack("bkgStack", "bkgStack")
histos = []
histCategories = []
for i, mcF_ in enumerate(mcFiles_):
mcPath = "%s/%s" % (mcDirec_, mcF_)
mcFile = TFile.Open(mcPath)
treeName = GetMCTreeName(mcF_)
MC_Category = GetMCCategory(mcF_)
if (verbose_):
# print"Background File:",mcPath
print "Background:", MC_Category
##-- If noQCD set to true, skip QCD for Tag_0 and Tag_1
if (MC_Category == "QCD") and (
noQCD_) and (HHWWggTag == "HHWWggTag_0"
or HHWWggTag == "HHWWggTag_1"):
print "Skipping QCD"
these_MC_Nevents_noweights.append(
0) # Set yields to 0 for table
these_MC_Nevents.append(
0) # Set yields to 0 for table
if (itag == 0 and ic == 0 and iv == 0):
MCname = GetMCName(mcF_)
MC_names.append(
MCname
) # Skipping QCD, but still save name for yields because tag_2 may not be 0
continue
##-- Define TChain based on categories
if (HHWWggTag == "combined"):
mc_ch = TChain(
'tagsDumper/trees/%s_13TeV_HHWWggTag_0' %
(treeName))
mc_ch.Add(
"%s/tagsDumper/trees/%s_13TeV_HHWWggTag_0" %
(mcPath, treeName))
mc_ch.Add(
"%s/tagsDumper/trees/%s_13TeV_HHWWggTag_1" %
(mcPath, treeName))
mc_ch.Add(
"%s/tagsDumper/trees/%s_13TeV_HHWWggTag_2" %
(mcPath, treeName))
else:
mc_ch = TChain('tagsDumper/trees/%s_13TeV_%s' %
(treeName, HHWWggTag))
mc_ch.Add(mcPath)
xbins, xmin, xmax = GetBins(varTitle)
exec(
"MC_h_tmp_%s = TH1F('MC_h_tmp_%s',varTitle,xbins,xmin,xmax)"
% (i, i))
exec(
"MC_h_tmp_noweight_%s = TH1F('MC_h_tmp_noweight_%s',varTitle,xbins,xmin,xmax)"
% (i, i))
thisHist = eval("MC_h_tmp_%s" % (i))
mcColor = GetMCColor(MC_Category)
##-- If GJet or QCD sample, need to remove prompt-prompt events
if (MC_Category == "GJet" or MC_Category == "QCD"):
if (verbose_): print "Remove prompt-prompt"
removePromptPromptCut = "(!((Leading_Photon_genMatchType == 1) && (Subleading_Photon_genMatchType == 1)))" # selection: remove events where both photons are prompt
original_MC_CUT = "%s" % (MC_CUT)
this_MC_CUT = "%s*(%s)" % (original_MC_CUT,
removePromptPromptCut)
this_MC_CUT_NOWEIGHT = this_MC_CUT.replace(
MC_WEIGHT, "(1)")
eval("MC_h_tmp_%s.SetFillColor(eval(mcColor))" % (i))
eval("MC_h_tmp_%s.SetLineColor(eval(mcColor))" % (i))
if (MC_Category == "GJet" or MC_Category == "QCD"):
exec('mc_ch.Draw("%s >> MC_h_tmp_%s","%s")' %
(v, i, this_MC_CUT))
else:
exec('mc_ch.Draw("%s >> MC_h_tmp_%s","%s")' %
(v, i, MC_CUT))
eval("MC_h_tmp_%s.Scale(float(Lumi_))" % (i))
##-- MC reweighting for HHWWgg backgrounds (turned off for now)
# reWeightVals = ReWeightMC(mcF_)
# doReWeight, reWeightScale = reWeightVals[0], reWeightVals[1]
# print"doReWeight,reWeightScale:",doReWeight, reWeightScale
# if(doReWeight):
# if(verbose_):
# print"ReWeighting MC"
# print"With scale: ",reWeightScale
# eval("MC_h_tmp_%s.Scale(float(reWeightScale))"%(i))
##
##-- Only save for 1st variable. Should be same for all variables
if (iv == 0):
if (MC_Category == "GJet" or MC_Category == "QCD"):
exec(
'mc_ch.Draw("%s >> MC_h_tmp_noweight_%s","%s")'
% (v, i, this_MC_CUT_NOWEIGHT))
else:
exec(
'mc_ch.Draw("%s >> MC_h_tmp_noweight_%s","%s")'
% (v, i, MC_CUT_NOWEIGHT))
these_MC_Nevents_noweights.append(
eval("MC_h_tmp_noweight_%s.Integral()" % (i)))
these_MC_Nevents.append(
eval("MC_h_tmp_%s.Integral()" % (i)))
##-- Only need to get MC names once
if (itag == 0 and ic == 0 and iv == 0):
MCname = GetMCName(mcF_)
MC_names.append(
MCname) # get shorter MC name here
newHist = thisHist.Clone("newHist")
##-- Set title based on treeName
newHist.SetTitle(MC_Category)
newHist.GetXaxis().SetTitle(mcF_)
newHist.SetDirectory(0)
histos.append(newHist)
histCategories.append(MC_Category)
sig_histos = []
sig_histCategories = []
##-- Add Signal to plots
for i, sigF_ in enumerate(signalFiles_):
sigPath = "%s/%s" % (signalDirec_, sigF_)
sigFile = TFile.Open(sigPath)
treeName = GetMCTreeName(sigF_)
MC_Category = GetMCCategory(sigF_)
if (verbose_):
# print"Signal File:",sigPath
print "Signal:", MC_Category
if (HHWWggTag == "combined"):
mc_ch = TChain(
'tagsDumper/trees/%s_13TeV_HHWWggTag_0' %
(treeName))
mc_ch.Add(
"%s/tagsDumper/trees/%s_13TeV_HHWWggTag_0" %
(sigPath, treeName))
mc_ch.Add(
"%s/tagsDumper/trees/%s_13TeV_HHWWggTag_1" %
(sigPath, treeName))
mc_ch.Add(
"%s/tagsDumper/trees/%s_13TeV_HHWWggTag_2" %
(sigPath, treeName))
else:
mc_ch = TChain('tagsDumper/trees/%s_13TeV_%s' %
(treeName, HHWWggTag))
mc_ch.Add(sigPath)
xbins, xmin, xmax = GetBins(varTitle)
exec(
"MC_h_tmp_%s = TH1F('MC_h_tmp_%s',v,xbins,xmin,xmax)"
% (i, i))
thisHist = eval("MC_h_tmp_%s" % (i))
mcColor = GetMCColor(MC_Category)
##-- Style options for signal to distinguish from Data, Background
# eval("MC_h_tmp_%s.SetFillColor(eval(mcColor))"%(i))
# eval("MC_h_tmp_%s.SetFillStyle(3004)"%(i))
##--
eval("MC_h_tmp_%s.SetFillColorAlpha(eval(mcColor),0.1)"
% (i))
eval("MC_h_tmp_%s.SetLineColor(eval(mcColor))" % (i))
exec('mc_ch.Draw("%s >> MC_h_tmp_%s","%s")' %
(v, i, SIGNAL_CUT))
eval("MC_h_tmp_%s.Scale(float(Lumi_))" %
(i)) # should scale to luminosity by default
SigXS_Scale = GetXScale(
"HHWWgg_v2-6"
) # how to scale the XS which is by default in flashgg 1fb
if (verbose_): print "SigXS_Scale: ", SigXS_Scale
eval("MC_h_tmp_%s.Scale(float(SigXS_Scale))" %
(i)) # should scale to luminosity by default
newHist = thisHist.Clone("newHist")
##-- Set title based on treeName
newHist.SetTitle(MC_Category)
newHist.GetXaxis().SetTitle(sigF_)
newHist.SetLineStyle(1)
newHist.SetLineWidth(5)
newHist.SetDirectory(0)
sig_histos.append(newHist)
sig_histCategories.append(MC_Category)
MC_AddedtoLegend = {
"QCD": 0,
"SMhgg": 0,
"GJet": 0,
"DiPhoJets": 0,
"DiPhoJetsBox": 0,
# "W1JetsToLNu" : 0,
# "W2JetsToLNu" : 0,
# "W3JetsToLNu" : 0,
# "W4JetsToLNu" : 0,
"WJets": 0,
"WW": 0,
"tt": 0,
"DY": 0,
"WGGJets": 0,
"WGJJ": 0,
"ttW": 0
}
Signals_AddedtoLegend = {"HHWWgg_SM": 0}
##-- Order histograms by MC category
orderedHistos = OrderHistos(histos, histCategories)
sig_orderedHistos = OrderHistos(sig_histos,
sig_histCategories)
##-- Add backgrounds to background stack
for h in orderedHistos:
bkgStack.Add(h, 'hist')
bkgName = h.GetTitle()
added = MC_AddedtoLegend[bkgName]
if (added): continue
else:
legend.AddEntry(h, bkgName, "F")
MC_AddedtoLegend[bkgName] = 1
##-- By default draw background save background contributions. Later delete if not wanted
outName = "%s/BackgroundsTest_%s.png" % (outputFolder,
HHWWggTag)
bkgOutName = "%s/BackgroundsPADS_%s_%s.png" % (
outputFolder, varTitle, HHWWggTag)
SimpleDrawHisto(bkgStack, "PADS", bkgOutName, varTitle)
bkgOutName = bkgOutName.replace(".png", ".pdf")
SimpleDrawHisto(bkgStack, "PADS", bkgOutName, varTitle)
##-- Add text box with selection type
offset = 0
selText = TLatex(0.129, 0.85, cutName)
selText.SetNDC(1)
selText.SetTextSize(0.04)
CatText = TLatex(0.129, 0.8, HHWWggTag)
CatText.SetNDC(1)
CatText.SetTextSize(0.04)
stackSum = bkgStack.GetStack().Last(
) #->Draw(); # for computing ratio
stackSum.Sumw2()
stackSum.SetLineColor(kBlack)
stackSum.SetLineStyle(
7) # to distinguish from data uncertainty
DataHist.SetLineColor(kBlack)
DataHist.Sumw2()
xTitle = GetXaxisTitle(varTitle)
DataHist.GetXaxis().SetTitle(xTitle)
if (log_):
DataHist.SetMinimum(0.01)
stackSum.SetMinimum(0.01)
bkgStack.SetMinimum(0.01)
##-- Define ratio plot for computing Data / MC ratio
rp = TRatioPlot(DataHist, stackSum)
rp.SetH1DrawOpt("P")
rp.SetH2DrawOpt("hist")
# rp.SetGraphDrawOpt("PE2")
dMax = DataHist.GetMaximum()
bMax = stackSum.GetMaximum()
maxHeight = max(dMax, bMax)
##-- Create the entire picture: Combine Data, MC, Data / MC ratio and signal in one plot
for fileType in ["pdf"]:
gStyle.SetErrorX(0.0001)
# varTitle = GetVarTitle(v)
outName = "%s/DataMC_%s_%s.%s" % (
outputFolder, varTitle, HHWWggTag, fileType)
if (log_):
outName = "%s/DataMC_%s_%s_log.%s" % (
outputFolder, varTitle, HHWWggTag, fileType)
else:
outName = "%s/DataMC_%s_%s_nonLog.%s" % (
outputFolder, varTitle, HHWWggTag, fileType)
DataMCRatio_c = TCanvas("DataMCRatio_c",
"DataMCRatio_c", 600, 800)
rp.Draw("nogrid")
rp.GetLowYaxis().SetNdivisions(5)
DataMCRatio_c.Update()
ratioGraph = rp.GetCalculationOutputGraph()
ratioGraph.SetMarkerStyle(8)
ratioGraph.SetMarkerSize(0.5)
# rp.SetGraphDrawOpt("EP")
# rp.SetGraphDrawOpt("EPZ2")
# rp.GetLowerRefYaxis().SetTitle("Data / MC")
rp.GetUpperRefYaxis().SetTitle("Entries")
rp.GetLowerRefYaxis().SetTitle("Data / MC")
rp.GetLowerPad().Update()
if (log_):
rp.GetUpperRefYaxis().SetRangeUser(
0.1, maxHeight * 100.)
else:
rp.GetUpperRefYaxis().SetRangeUser(
0, maxHeight * 1.3)
UpperPad = rp.GetUpperPad()
UpperPad.cd()
bkgStack.Draw("same")
stackSum.Draw("sameE")
DataHist.Draw("samePE")
for sig_hist in sig_histos:
sigMax = sig_hist.GetMaximum()
if sigMax == 0: sigMax = 1
##-- No user input signal scale
if (SigScale_ == -999):
sigScale = (float(maxHeight) / 10.) / float(
sigMax
) # in order to scale signal to 10th of max of plot
sig_hist.Scale(sigScale)
##-- User input signal scale
else:
if (verbose_):
print "user sig scale:", SigScale_
sigScale = SigScale_
sig_hist.Scale(sigScale)
for sig_h in sig_orderedHistos:
# sigName = "%s X %d"%(sig_h.GetTitle(),sigScale)
sigName = sig_h.GetTitle()
added = Signals_AddedtoLegend[sigName]
if (added): continue
else:
legend.AddEntry(
sig_h, "%s * %.5g" %
(sig_h.GetTitle(), sigScale), "FL")
Signals_AddedtoLegend[sigName]
sig_hist.Draw("samehist")
legend.Draw("same")
selText.Draw("same")
CatText.Draw("same")
rp.GetLowerRefGraph().SetMinimum(0.5)
rp.GetLowerRefGraph().SetMaximum(1.5)
Npoints = rp.GetLowerRefGraph().GetN()
for ip in range(0, Npoints):
rp.GetLowerRefGraph().SetPointEXhigh(ip, 0)
rp.GetLowerRefGraph().SetPointEXlow(ip, 0)
if (log_):
UpperPad.SetLogy()
UpperPad.Update()
rp.GetLowerPad().cd()
lowerPad = rp.GetLowerPad()
rp.GetLowerRefYaxis().SetTitle("Data / MC")
lineAtOne = TLine(lowerPad.GetUxmin(), 1,
lowerPad.GetUxmax(), 1)
lineAtOne.SetLineStyle(3)
lineAtOne.Draw("same")
rp.GetLowerPad().Update()
DataMCRatio_c.Update()
DataMCRatio_c.SaveAs(outName)
outName = outName.replace(".pdf", ".png")
DataMCRatio_c.SaveAs(outName)
if (not drawPads_):
bkgOutName = "%s/BackgroundsPADS_%s_%s.png" % (
outputFolder, varTitle, HHWWggTag)
os.system('rm %s' % (bkgOutName))
bkgOutName = bkgOutName.replace(".png", ".pdf")
os.system('rm %s' % (bkgOutName))
MC_CUT = MC_CUT.replace(
"(%s != 0) && (%s != -999)" % (v, v), "ZERO_CUT")
DATA_CUT = DATA_CUT.replace(
"(%s != 0) && (%s != -999)" % (v, v), "ZERO_CUT")
MC_Nevents_lists.append(these_MC_Nevents)
MC_Nevents_noweight_lists.append(these_MC_Nevents_noweights)
##-- Produce table with number of events for each MC, total MC, and data
CreateYieldsTables(cutBatchTag_pairs, dataNevents_list, MC_names,
MC_Nevents_lists, MC_Nevents_noweight_lists, ol_)
hist1 = TH1D("hist1", "hist1", 10, 0, 100)
hist2 = TH1D("hist2", "hist2", 10, 0, 100)
hist1.Sumw2()
hist2.Sumw2()
stack = THStack("stack", "stack")
for i in xrange(1000):
weight1 = gRandom.Gaus(1, 0.1)
weight2 = gRandom.Gaus(1, 0.2)
hist1.Fill(gRandom.Gaus(55, 20), weight1)
hist2.Fill(gRandom.Gaus(50, 25), weight2)
stack.Add(hist1)
stack.Add(hist2)
hist_stack = stack.GetStack().Last()
print "\n>>> hist_stack = stack.GetStack().Last()"
print ">>> hist_stack = %s" % hist_stack
print ">>> type(hist_stack) = %s" % type(hist_stack)
print ">>> hist_stack.GetName() = %s" % hist_stack.GetName()
print ">>> hist_stack.GetTitle() = %s" % hist_stack.GetTitle()
print "\n>>> comparing integrals"
print ">>> stack = %s" % hist_stack.Integral()
print ">>> hist1 = %s" % hist1.Integral()
print ">>> hist2 = %s" % hist2.Integral()
print "\n>>> comparing entries"
print ">>> stack = %s" % hist_stack.GetEntries()
print ">>> hist1 = %s" % hist1.GetEntries()
print ">>> hist2 = %s" % hist2.GetEntries()
class Histogram:
def __init__(self, Samples, directory):
self.PropName = directory.GetName()
dircontents = directory.GetListOfKeys()
firsthisto = directory.Get(dircontents.At(0).GetName())
#firsthisto.Print("ALL")
self.ForLegend = {}
self.XSections = {}
self.AllSampleHistos = {}
for sample in Samples:
if sample.IsData:
self.DataSName = sample.HistoCat
# h_in_dir = directory.Get( "%s_%s" % ( self.PropName , sample.Name ) )
# if h_in_dir :
# h_in_dir.reset()
# setattr( self , sample.Name , h_in_dir )
# else:
gROOT.cd()
hnew = firsthisto.Clone("%s_%s" % (self.PropName, sample.Name))
hnew.Reset()
setattr(self, sample.Name, hnew)
hhh = getattr(self, sample.Name)
hhh.SetLineColor(1)
hhh.SetLineWidth(2)
if not sample.IsData:
hhh.SetFillColor(sample.Color)
hhh.SetFillStyle(1001)
else:
hhh.SetStats(0)
self.AllSampleHistos[sample.Name] = hhh
if (self.ForLegend.get(sample.HistoCat)):
self.ForLegend[sample.HistoCat].append(sample.Name)
else:
self.ForLegend[sample.HistoCat] = [sample.Name]
self.XSections[sample.Name] = sample.XSection
def AddFile(self, directory, catname=""):
##find all relevant histograms in the file and add them to your histos
for sample in self.AllSampleHistos:
h_name = ""
if catname == "":
h_name = "%s/%s_%s" % (self.PropName, self.PropName, sample)
else:
h_name = "%s/%s_%s_%s" % (self.PropName, catname,
self.PropName, sample)
h_in_dir = directory.Get(h_name)
if h_in_dir:
# print h_name
# print directory.GetPath()
# h_in_dir.Print("ALL")
self.AllSampleHistos[sample].Add(h_in_dir)
def Write(self, fout):
fout.mkdir(self.PropName).cd()
for sample in self.AllSampleHistos:
self.AllSampleHistos[sample].Write()
if hasattr(self, "Canvas"):
self.Canvas.Write()
for leg in self.FinalHistos:
self.FinalHistos[leg].Write()
self.Stack.GetStack().Last().Write("SumMC")
fout.cd()
@staticmethod
def AddLabels(histo, labels):
if labels:
for i in range(1, histo.GetNbinsX() + 1):
if not i > len(labels):
histo.GetXaxis().SetBinLabel(i, labels[i - 1])
def Draw(self, lumi, cft, labels=None, catname=""):
gStyle.SetOptTitle(0)
self.FinalHistos = {}
for sample in self.AllSampleHistos:
Histogram.AddLabels(self.AllSampleHistos[sample], labels)
self.AllSampleHistos[sample].SetBit(TH1.kNoTitle)
if sample in self.ForLegend[self.DataSName]:
continue
ntotal = getattr(cft, sample).GetBinContent(1)
if ntotal == 0:
print "Sample %s has no entries" % (sample)
continue
factor = lumi * self.XSections[sample] / ntotal
#print "%s factor : (%.2f*%.2f)/%.0f = %.3f" % (sample , lumi , self.XSections[sample] , ntotal , factor)
self.AllSampleHistos[sample].Scale(factor)
self.Stack = THStack("%s_stack" % (self.PropName), self.PropName)
#Histogram.AddLabels( self.Stack.GetHistogram() , labels )
for finalh in self.ForLegend:
for hname in self.ForLegend[finalh]:
#print "Adding %s to %s" % ( hname , finalh )
if self.FinalHistos.get(finalh):
self.FinalHistos[finalh].Add(self.AllSampleHistos[hname])
else:
self.FinalHistos[finalh] = self.AllSampleHistos[
hname].Clone("_%s_%s" % (self.PropName, finalh))
Histogram.AddLabels(self.FinalHistos[finalh], labels)
self.FinalHistos[finalh].SetBit(TH1.kNoTitle)
#self.FinalHistos[finalh].SetLineColor( 0 )
self.FinalHistos[finalh].SetTitle(finalh)
if finalh == self.DataSName:
self.DataHisto = self.FinalHistos[finalh]
else:
self.Stack.Add(self.FinalHistos[finalh])
self.Canvas = TCanvas("%s_%s_C" % (self.PropName, catname))
self.Pad1 = TPad("pad1", "pad1", 0, 0.25, 1, 1)
self.Pad1.SetBottomMargin(0.1)
self.Pad1.Draw()
self.Pad1.cd()
self.DataHisto.Draw("E")
#getattr( self , self.DataSName ).Draw("E")
self.Stack.Draw("HIST SAME")
self.DataHisto.Draw("E SAME P")
#getattr( self , self.DataSName ).Draw("E SAME")
self.Legend = TLegend(0.7, 0.6, 0.9, 0.9, "", "brNDC")
entry = self.Legend.AddEntry(self.DataHisto, "Data", "lp")
for finalh in reversed(self.ForLegend.keys()):
if finalh == self.DataSName:
continue
self.Legend.AddEntry(self.FinalHistos[finalh], finalh, "f")
self.Legend.Draw()
self.Canvas.cd()
self.Pad2 = TPad("pad2", "pad2", 0, 0, 1, 0.24)
self.Pad2.SetTopMargin(0.1)
self.Pad2.SetBottomMargin(0.1)
self.Pad2.Draw()
self.Pad2.cd()
self.Ratio = self.DataHisto.Clone("Ratio_%s" % (self.PropName))
self.Ratio.SetStats(0)
self.Ratio.Divide(self.Stack.GetStack().Last())
if labels:
Histogram.AddLabels(self.Ratio, ["" for s in labels])
self.Ratio.SetMarkerStyle(20)
self.Ratio.GetYaxis().SetRangeUser(0, 2)
self.Ratio.GetXaxis().SetLabelSize(0.)
self.Ratio.GetYaxis().SetTitle("Data / MC")
#self.Ratio.GetXaxis().SetTitle(xtitle)
self.Ratio.GetXaxis().SetTitleSize(0.2)
self.Ratio.GetXaxis().SetTitleOffset(0.25)
self.Ratio.GetYaxis().SetLabelSize(0.1)
self.Ratio.GetXaxis().SetTickLength(0.09)
self.Ratio.GetYaxis().SetTitleSize(0.18)
self.Ratio.GetYaxis().SetNdivisions(509)
self.Ratio.GetYaxis().SetTitleOffset(0.25)
self.Ratio.SetFillStyle(3001)
self.Ratio.Draw("ep")
self.LineOne = TLine(self.Ratio.GetXaxis().GetXmin(), 1.00,
self.Ratio.GetXaxis().GetXmax(), 1.00)
self.LineOne.SetLineWidth(2)
self.LineOne.SetLineStyle(7)
self.LineOne.Draw()
rawevt = h_tmp.GetEntries()
ntotalbkg = ntotalbkg + numevt
if hnames[1] == printHistName or 'DNN' in hnames[1]:
string_nevt += "%f \n" % (numevt)
string_fname += "%s : %s = %f \n" % (
fname, bkgsamples[fname]["name"], numevt)
print fname, " : ", bkgsamples[fname][
"name"], " = ", "{0:.5g}".format(
numevt), " scale : ", "{0:.3g}".format(scale)
if printscale: print scale
## Add to Stack
hs.Add(h_tmp) #hh_tmp -> add h tmp sig, hs->other
k = k + 1
h_bkg = hs.GetStack().Last()
#Sig Stack
#Add Hut
hsHut = THStack()
ntotalHut = 0
n = 0
for fname in hutsamples.keys():
h_Hut = hutsamples[fname]["file"].Get(
datasamples[datasamples.keys()[0]]["hname"][i])
nbins = h_Hut.GetNbinsX()
h_Hut.AddBinContent(nbins, h_Hut.GetBinContent(nbins + 1))
h_Hut.SetLineColor(hutsamples[fname]["col"])
## normalization
def drawHisto(histlist,histoD,xTitle,yTitle,outName,uncHists=""):
legend = TLegend(0.35, 0.72, 0.85, 0.92,"") #,"brNDC"
legend.SetFillStyle(0)
legend.SetTextSize(0.045)
legend.SetNColumns(2)
myStack = THStack("myStack","")
for hist in histlist:
myStack.Add(hist,"hist")
histoErr = histlist[0].Clone()
for iHist in range(1,len(histlist)):
histoErr.Add(histlist[iHist])
MCCount = myStack.GetStack().Last().Integral()
DataCount = histoD.Integral()
MCNormFactor = DataCount/MCCount
# myStack.Delete()
myStack = THStack("myStack","")
for hist in histlist:
hist.Scale(MCNormFactor)
myStack.Add(hist,"hist")
histoErr.Scale(MCNormFactor)
if uncHists!="":
MCMean = myStack.GetStack().Last().Clone()
MCErr = MCMean.Clone()
MCErr.Reset()
for ibin in range(1,MCMean.GetNbinsX()+1):
thisbinunc = 0
for ihist in uncHists:
thisbinunc += ihist.GetBinContent(ibin)**2 #sum assuming correlated
sfunc = thisbinunc**0.5*MCNormFactor
if args.nostat:
MCMean.SetBinError(ibin, sfunc)
MCErr.SetBinContent(ibin, sfunc)
else:
MCMean.SetBinError(ibin, math.sqrt(sfunc**2 + histoErr.GetBinError(ibin)**2))
MCErr.SetBinContent(ibin, math.sqrt(sfunc**2 + histoErr.GetBinError(ibin)**2))
c = TCanvas("main","main",1200,1200)
c.SetCanvasSize(1200,1200)
upperCanvas = TPad("up","up",0,0.24,1,1)
upperCanvas.SetBottomMargin(0.03)
upperCanvas.SetLeftMargin(0.12)
upperCanvas.SetTopMargin(0.06)
# upperCanvas.SetLogy(isLog)
upperCanvas.Draw()
upperCanvas.cd()
myStack.Draw()
myStack.GetYaxis().SetTitle(yTitle)
myStack.GetHistogram().GetZaxis().SetTitle()
myStack.GetYaxis().SetLabelSize(0.048)
myStack.GetYaxis().SetTitleSize(0.07)
myStack.GetYaxis().SetTitleOffset(0.78)
myStack.GetYaxis().SetTitleFont(42)
legend.Draw("same")
if uncHists!="":
MCMean.Draw("e2 same")
MCMean.SetFillColor(kGreen)
MCMean.SetLineColor(kGreen)
MCMean.SetMarkerSize(0)
MCMean.SetFillStyle(3013)
if not args.nostat:
histoErr.Draw("e2 same")
# histoErr.Sumw2()
histoErr.SetFillColor(kGray+3)
histoErr.SetLineColor(kGray+3)
histoErr.SetMarkerSize(0)
histoErr.SetFillStyle(3013)
histoMC = myStack.GetStack().Last()
histoBkg= myStack.GetStack().Before(histoMC)
print "Total MC:", histoMC.Integral(), ", Data:",histoD.Integral()
histoD.Draw("same p e")
legend.AddEntry(histoD,"Data","PE")
for hist in histlist:
legend.AddEntry(hist,hist.GetName().replace('uds','udsg').replace('b','bottom').replace('c','charm'),'f')
legend.AddEntry(histoErr,"MC Stat. Unc.",'f')
if uncHists!="": legend.AddEntry(MCMean,"SF Unc.",'f')
maxY = histoD.GetMaximum()
#if myStack.GetMinimum() >= 0:
myStack.SetMinimum(1e-3)
myStack.SetMaximum(maxY*1.35)
c.cd()
lowerCanvas = TPad("down","down",0,0,1,0.26)
lowerCanvas.Draw()
lowerCanvas.cd()
lowerCanvas.SetTicky(1)
lowerCanvas.SetLeftMargin(0.1)
lowerCanvas.SetRightMargin(0.1)
lowerCanvas.SetTopMargin(0.0)
lowerCanvas.SetBottomMargin(0.4)
lowerCanvas.SetLeftMargin(0.12)
lowerCanvas.SetFrameFillStyle(0)
lowerCanvas.SetFrameBorderMode(0)
lowerCanvas.SetGridy()
histoRatio = histoD.Clone()
histoRatio.Divide(histoMC)
for ib in range(1,histoRatio.GetNbinsX()+1):
if histoD.GetBinContent(ib) != 0:
histoRatio.SetBinError(ib, histoD.GetBinError(ib)/histoD.GetBinContent(ib)*histoRatio.GetBinContent(ib))
else:
histoRatio.SetBinError(ib,0.)
histoRatio.GetYaxis().SetTitle("Data/MC")
histoRatio.GetYaxis().SetTitleSize(0.15)
histoRatio.GetYaxis().SetTitleOffset(0.38)
histoRatio.GetYaxis().SetTitleFont(42)
histoRatio.GetYaxis().SetLabelSize(0.14)
histoRatio.GetYaxis().CenterTitle()
histoRatio.GetYaxis().SetLabelFont(42)
histoRatio.GetYaxis().SetNdivisions(5)
histoRatio.GetXaxis().SetTitle(xTitle)
histoRatio.GetXaxis().SetLabelSize(0.14)
histoRatio.GetXaxis().SetTitleSize(0.19)
histoRatio.GetXaxis().SetTitleOffset(0.88)
histoRatio.GetXaxis().SetTitleFont(42)
histoRatio.GetXaxis().SetTickLength(0.07)
histoRatio.GetXaxis().SetLabelFont(42)
histoRatio.SetTitle("")
histoRatio.Draw("P e")
histoRatio.SetMaximum(1.49)
histoRatio.SetMinimum(0.51)
if uncHists!="":
RatioErr = MCErr.Clone()
RatioErr.Divide(histoMC)
RatioErrVals = RatioErr.Clone()
for ibin in range(1,RatioErr.GetNbinsX()+1):
RatioErrVals.SetBinContent(ibin,1.)
RatioErrVals.SetBinError(ibin,RatioErr.GetBinContent(ibin))
RatioErrVals.Draw("e2 same")
RatioErrVals.SetFillColor(kGreen)
RatioErrVals.SetLineColor(kGreen)
RatioErrVals.SetMarkerSize(0)
RatioErrVals.SetFillStyle(3013)
if not args.nostat:
RatioStatUnc = histoMC.Clone()
for ibin in range(1,RatioStatUnc.GetNbinsX()+1):
RatioStatUnc.SetBinContent(ibin,1)
if histoMC.GetBinContent(ibin) != 0:
RatioStatUnc.SetBinError(ibin,histoMC.GetBinError(ibin)/histoMC.GetBinContent(ibin))
else:
RatioStatUnc.SetBinError(ibin,0)
RatioStatUnc.Draw("e2 same")
RatioStatUnc.SetFillColor(kGray+3)
RatioStatUnc.SetLineColor(kGray+3)
RatioStatUnc.SetMarkerSize(0)
RatioStatUnc.SetFillStyle(3013)
histoRatio.Draw("P e same") # To bring on top
hLine = TLine(-0.2,1,1,1)
hLine.SetLineColor(kRed)
hLine.Draw()
# if drawDataMCRatioLine:
# MCCount = histoMC.Integral()
# if MCCount > 0.:
# DataCount = histoD.Integral()
# MCNormFactor = DataCount/MCCount
# hLine2 = TLine(start,MCNormFactor,end,MCNormFactor)
# hLine2.SetLineColor(kBlue)
# hLine2.Draw()
# ----------------------------------------------------------
# ======================== LaTeX ==========================
texTL = TLatex()
texTL.SetTextSize(0.07)
texTL.SetTextAlign(13)
texTR = TLatex()
texTR.SetTextSize(0.05)
texTR.SetTextAlign(31)
# if dataset=="" or noRatio:
# texTL.DrawLatexNDC(0.13,0.87, "CMS #it{#bf{Preliminary}}")
# texTR.DrawLatexNDC(0.89,0.91, "#bf{"+str(lumi/1000.)+" fb^{-1} (13 TeV)}")
# else:
upperCanvas.cd()
texTL.DrawLatexNDC(0.16,0.92, "CMS") # #it{#bf{Preliminary}}")
if args.prelim:
texTL2 = TLatex()
texTL2.SetTextSize(0.05)
texTL2.SetTextAlign(13)
suff = "#it{#bf{Preliminary}}"
texTL2.DrawLatexNDC(0.16,0.85, suff)
texTR.DrawLatexNDC(0.89,0.95, "#bf{"+str(lumi/1000.)+" fb^{-1} (13 TeV)}")
# ----------------------------------------------------------
c.SaveAs(outName+".pdf")
c.SaveAs(outName+".png")
