def makeSVMassPlots(histodict):
ratplot = RatioPlot('SVMass')
ratplot.normalized = True
ratplot.ratiorange = (0.5,1.5)
ratplot.legpos = (0.65, 0.60)
ratplot.ratiotitle = 'Data/MC'
ratplot.extratext = '' #Work in Progress'
ratplot.tag = "Inclusive"
# ratplot.tag = "e#mu channel"
ratplot.tagpos = (0.80,0.55)
ratplot.add(histodict[('nominal','SVMass',3)], 'N_{trk}=3 t#bar{t} MC', includeInRatio=False)
ratplot.add(histodict[('data', 'SVMass',3)], 'N_{trk}=3 Data', includeInRatio=True)
ratplot.add(histodict[('nominal','SVMass',4)], 'N_{trk}=4 t#bar{t} MC', includeInRatio=False)
ratplot.add(histodict[('data', 'SVMass',4)], 'N_{trk}=4 Data', includeInRatio=True)
ratplot.add(histodict[('nominal','SVMass',5)], 'N_{trk}=5 t#bar{t} MC', includeInRatio=False)
ratplot.add(histodict[('data', 'SVMass',5)], 'N_{trk}=5 Data', includeInRatio=True)
ratplot.reference = [histodict[('nominal','SVMass',3)]]
ratplot.reference.append(histodict[('nominal','SVMass',4)])
ratplot.reference.append(histodict[('nominal','SVMass',5)])
ratplot.colors = [ROOT.kMagenta+1, ROOT.kMagenta+1,
ROOT.kViolet+2, ROOT.kViolet+2,
ROOT.kAzure+7, ROOT.kAzure+7]
ratplot.drawoptions = ['hist', 'PE', 'hist', 'PE', 'hist', 'PE']
ratplot.markerstyles = [24, 24, 25, 25, 26, 26]
ratplot.markersizes = len(ratplot.histos)*[1.4]
ratplot.show('svmassplot',opt.outDir)
ratplot.saveRatios('svmass_weights',opt.outDir,['SVMass_weight_3','SVMass_weight_4','SVMass_weight_5'])
def makeMassPlot(histos, outname, tag='', subtag=''):
ratplot = RatioPlot('ratioplot')
ratplot.normalized = False
ratplot.ratiotitle = "Ratio wrt 172.5 GeV"
ratplot.extratext = ''
ratplot.tag = tag
ratplot.subtag = subtag
ratplot.legpos = (0.65, 0.62)
ratplot.ratiorange = (0.7, 1.3)
ratplot.titlex = 'm_{svl} [GeV]'
ratplot.xlabelsize = 28
ratplot.xtitlesize = 34
ratplot.ylabelsize = 28
ratplot.ytitlesize = 34
ratplot.ytitleoffset = 1.8
ratplot.xtitleoffset = 3
ratplot.legendtextsize = 28
ratplot.legendrowsize = 0.08
ratplot.reference = [histos[172.5]]
ratplot.titley = "Combinations / %3.1f GeV" %histos['data'].GetXaxis().GetBinWidth(1)
####################################################
# Construct dummy data for now
# dummy = histos[172.5].Clone('dummy_data')
# dummy.Reset('ICE')
# dummy.FillRandom(histos[173.5], ROOT.gRandom.Poisson(histos[172.5].Integral()))
# ratplot.add(dummy,'Pseudo Data (@173.5 GeV)')
####################################################
####################################################
# REAL DATA:
ratplot.add(histos['data'], 'Data')
####################################################
# histos[MASSESTOPLOT[0]].SetLineStyle(2)
# histos[MASSESTOPLOT[-1]].SetLineStyle(3)
for mass in MASSESTOPLOT:
legentry = 'm_{t} = %5.1f GeV' % mass
try:
histo = histos[mass]
ratplot.add(histo, legentry, includeInRatio=(mass != 172.5))
except KeyError: pass
# ratplot.colors = [ROOT.kBlack, ROOT.kAzure-4, ROOT.kAzure+1, ROOT.kAzure-6]
# ratplot.colors = [ROOT.kBlack, ROOT.kAzure-2, ROOT.kGray, ROOT.kOrange+9]
ratplot.colors = [ROOT.kBlack, ROOT.kSpring-8, ROOT.kGray+1, ROOT.kOrange+9]
# ratplot.colors = [ROOT.kBlack, ROOT.kOrange-4, ROOT.kOrange-3, ROOT.kOrange+9]
ratplot.drawoptions = ['PE', 'hist', 'hist', 'hist']
ratplot.markerstyles = [20,1,1,1]
ratplot.markersizes = [1.5,1,1,1]
ratplot.ratioydivisions = 405
ratplot.show("massscan_paper_%s"%outname, opt.outDir)
def makeSVMassPlots(histodict):
ratplot = RatioPlot('SVMass')
ratplot.normalized = True
ratplot.ratiorange = (0.5, 1.5)
ratplot.legpos = (0.65, 0.60)
ratplot.ratiotitle = 'Data/MC'
ratplot.extratext = '' #Work in Progress'
ratplot.tag = "Inclusive"
# ratplot.tag = "e#mu channel"
ratplot.tagpos = (0.80, 0.55)
ratplot.add(histodict[('nominal', 'SVMass', 3)],
'N_{trk}=3 t#bar{t} MC',
includeInRatio=False)
ratplot.add(histodict[('data', 'SVMass', 3)],
'N_{trk}=3 Data',
includeInRatio=True)
ratplot.add(histodict[('nominal', 'SVMass', 4)],
'N_{trk}=4 t#bar{t} MC',
includeInRatio=False)
ratplot.add(histodict[('data', 'SVMass', 4)],
'N_{trk}=4 Data',
includeInRatio=True)
ratplot.add(histodict[('nominal', 'SVMass', 5)],
'N_{trk}=5 t#bar{t} MC',
includeInRatio=False)
ratplot.add(histodict[('data', 'SVMass', 5)],
'N_{trk}=5 Data',
includeInRatio=True)
ratplot.reference = [histodict[('nominal', 'SVMass', 3)]]
ratplot.reference.append(histodict[('nominal', 'SVMass', 4)])
ratplot.reference.append(histodict[('nominal', 'SVMass', 5)])
ratplot.colors = [
ROOT.kMagenta + 1, ROOT.kMagenta + 1, ROOT.kViolet + 2,
ROOT.kViolet + 2, ROOT.kAzure + 7, ROOT.kAzure + 7
]
ratplot.drawoptions = ['hist', 'PE', 'hist', 'PE', 'hist', 'PE']
ratplot.markerstyles = [24, 24, 25, 25, 26, 26]
ratplot.markersizes = len(ratplot.histos) * [1.4]
ratplot.show('svmassplot', opt.outDir)
ratplot.saveRatios(
'svmass_weights', opt.outDir,
['SVMass_weight_3', 'SVMass_weight_4', 'SVMass_weight_5'])
def main(args, options):
os.system('mkdir -p %s'%options.outDir)
try:
treefiles = {} # procname -> filename
for filename in os.listdir(args[0]):
if not os.path.splitext(filename)[1] == '.root': continue
procname = filename.split('_',1)[1][:-5]
treefiles[procname] = os.path.join(args[0],filename)
except OSError:
print "Not a valid input directory: %s" % args[0]
return -1
except IndexError:
print "Need to provide an input directory"
return -1
## Collect all the trees
svltrees = {} # proc -> tree
for proc in treefiles.keys():
tfile = ROOT.TFile.Open(treefiles[proc], 'READ')
if not filterUseless(treefiles[proc]): continue
svltrees[proc] = tfile.Get(TREENAME)
## Produce all the relevant histograms
if not options.cached:
masshistos = {} # (selection tag, process) -> histo
methistos = {} # (selection tag, process) -> histo
fittertkhistos = {} # (selection tag, process) -> [h_ntk1, h_ntk2, ...]
for tag,sel,_ in SELECTIONS:
for proc, tree in svltrees.iteritems():
if not filterUseless(treefiles[proc], sel): continue
htag = ("%s_%s"%(tag, proc)).replace('.','')
print ' ... processing %-30s %s htag=%s' % (proc, sel, htag)
masshistos[(tag, proc)] = getHistoFromTree(tree, sel=sel,
var='SVLMass',
hname="SVLMass_%s"%(htag),
titlex=MASSXAXISTITLE)
methistos[(tag, proc)] = getHistoFromTree(tree, sel=sel,
var='MET',
hname="MET_%s"%(htag),
xmin=0,xmax=200,
titlex="Missing E_{T} [GeV]")
fittertkhistos[(tag,proc)] = getNTrkHistos(tree, sel=sel,
tag=htag,
var='SVLMass',
titlex=MASSXAXISTITLE)
cachefile = open(".svlqcdmasshistos.pck", 'w')
pickle.dump(masshistos, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(methistos, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(fittertkhistos, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
else:
cachefile = open(".svlqcdmasshistos.pck", 'r')
masshistos = pickle.load(cachefile)
methistos = pickle.load(cachefile)
fittertkhistos = pickle.load(cachefile)
cachefile.close()
#########################################################
## Write out the histograms, make data/mc plots
outputFileName = os.path.join(options.outDir,'qcd_DataMCHists.root')
ofi = ROOT.TFile(outputFileName, 'recreate')
ofi.cd()
for hist in [h for h in masshistos.values() + methistos.values()]:
hist.Write(hist.GetName())
for hist in [h for hists in fittertkhistos.values() for h in hists]:
hist.Write(hist.GetName())
ofi.Write()
ofi.Close()
## Run the plotter to get scaled MET plots
## Can then use those to subtract non-QCD backgrounds from data template
## Overwrite some of the options
options.filter = 'SVLMass,MET' ## only run SVLMass and MET plots
# options.excludeProcesses = 'QCD'
options.outFile = 'scaled_met_inputs.root'
options.cutUnderOverFlow = True
os.system('rm %s' % os.path.join(options.outDir, options.outFile))
runPlotter(outputFileName, options)
#########################################################
## Build the actual templates from the single histograms
templates = {} # selection tag -> template histo
inputfile = openTFile(os.path.join(options.outDir, options.outFile))
for tag,sel,_ in SELECTIONS:
categories = ['MET', 'SVLMass']
for x,_ in NTRKBINS:
categories.append('SVLMass_tot_%d'%int(x))
for category in categories:
plotdirname = '%s_%s'%(category,tag)
plotdir = inputfile.Get(plotdirname)
h_bg = None
h_data = None
for tkey in plotdir.GetListOfKeys():
key = tkey.GetName()
if key.startswith('Graph_from'): continue
if key.startswith('MC8TeV_QCD'): continue
hist = inputfile.Get('%s/%s'%(plotdirname,key))
if key.startswith('MC8TeV'):
if not h_bg:
h_bg = hist.Clone("%s_BGs" % tag)
else:
h_bg.Add(hist)
if key.startswith('Data8TeV'):
h_data = hist.Clone("%s_Data" % tag)
## Determine a suitable output name
histname = '%s_template'%tag
if category == 'MET': histname = "%s_%s" % ('met',histname)
if 'tot' in category:
tkbin = int(category.split('_')[2])
histname = "%s_%d" % (histname, tkbin)
h_data_subtr = h_data.Clone(histname)
## Now subtract the combined MC from the data
h_data_subtr.Add(h_bg, -1)
dicttag = tag
if category == 'MET':
dicttag = 'met_%s'%tag
if '_tot_' in category:
dicttag = '%s_%d' % (tag, tkbin)
templates[dicttag] = h_data_subtr
ofi = ROOT.TFile(os.path.join(options.outDir,'qcd_templates.root'),
'recreate')
ofi.cd()
for hist in templates.values(): hist.Write(hist.GetName())
ofi.Write()
ofi.Close()
for key,hist in sorted(templates.iteritems()):
print key
#########################################################
## Make a plot comparing the templates
for tag,_,seltag in SELECTIONS:
if not tag.endswith('_qcd'): continue
templateplot = RatioPlot('qcdtemplates_%s'%tag)
for key,hist in sorted(templates.iteritems()):
if not tag in key: continue
if key.startswith('met'): continue
if key == tag:
templateplot.add(hist, 'Inclusive')
templateplot.reference = hist
else:
ntrkbin = int(key.rsplit('_',1)[1])
templateplot.add(hist, 'N_{track} = %d'%ntrkbin)
templateplot.tag = 'QCD templates'
templateplot.subtag = seltag
templateplot.tagpos = (0.90, 0.85)
templateplot.subtagpos = (0.90, 0.78)
templateplot.legpos = (0.75, 0.25)
templateplot.ratiotitle = 'Ratio wrt Inclusive'
templateplot.extratext = '' #Work in Progress'
templateplot.ratiorange = (0.2, 2.2)
templateplot.colors = [ROOT.kBlack, ROOT.kBlue-8, ROOT.kAzure-2,
ROOT.kCyan-3]
templateplot.show("qcdtemplates_%s"%tag, options.outDir)
templateplot.reset()
return 0
def main(args, options):
os.system('mkdir -p %s' % options.outDir)
try:
treefiles = {} # procname -> filename
for filename in os.listdir(args[0]):
if not os.path.splitext(filename)[1] == '.root': continue
procname = filename.split('_', 1)[1][:-5]
treefiles[procname] = os.path.join(args[0], filename)
except OSError:
print "Not a valid input directory: %s" % args[0]
return -1
except IndexError:
print "Need to provide an input directory"
return -1
## Collect all the trees
svltrees = {} # proc -> tree
for proc in treefiles.keys():
tfile = ROOT.TFile.Open(treefiles[proc], 'READ')
if not filterUseless(treefiles[proc]): continue
svltrees[proc] = tfile.Get(TREENAME)
## Produce all the relevant histograms
if not options.cached:
masshistos = {} # (selection tag, process) -> histo
methistos = {} # (selection tag, process) -> histo
fittertkhistos = {
} # (selection tag, process) -> [h_ntk1, h_ntk2, ...]
for tag, sel, _ in SELECTIONS:
for proc, tree in svltrees.iteritems():
if not filterUseless(treefiles[proc], sel): continue
htag = ("%s_%s" % (tag, proc)).replace('.', '')
print ' ... processing %-30s %s htag=%s' % (proc, sel, htag)
masshistos[(tag, proc)] = getHistoFromTree(
tree,
sel=sel,
var='SVLMass',
hname="SVLMass_%s" % (htag),
titlex=MASSXAXISTITLE)
methistos[(tag, proc)] = getHistoFromTree(
tree,
sel=sel,
var='MET',
hname="MET_%s" % (htag),
xmin=0,
xmax=200,
titlex="Missing E_{T} [GeV]")
fittertkhistos[(tag,
proc)] = getNTrkHistos(tree,
sel=sel,
tag=htag,
var='SVLMass',
titlex=MASSXAXISTITLE)
cachefile = open(".svlqcdmasshistos.pck", 'w')
pickle.dump(masshistos, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(methistos, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(fittertkhistos, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
else:
cachefile = open(".svlqcdmasshistos.pck", 'r')
masshistos = pickle.load(cachefile)
methistos = pickle.load(cachefile)
fittertkhistos = pickle.load(cachefile)
cachefile.close()
#########################################################
## Write out the histograms, make data/mc plots
outputFileName = os.path.join(options.outDir, 'qcd_DataMCHists.root')
ofi = ROOT.TFile(outputFileName, 'recreate')
ofi.cd()
for hist in [h for h in masshistos.values() + methistos.values()]:
hist.Write(hist.GetName())
for hist in [h for hists in fittertkhistos.values() for h in hists]:
hist.Write(hist.GetName())
ofi.Write()
ofi.Close()
## Run the plotter to get scaled MET plots
## Can then use those to subtract non-QCD backgrounds from data template
## Overwrite some of the options
options.filter = 'SVLMass,MET' ## only run SVLMass and MET plots
# options.excludeProcesses = 'QCD'
options.outFile = 'scaled_met_inputs.root'
options.cutUnderOverFlow = True
os.system('rm %s' % os.path.join(options.outDir, options.outFile))
runPlotter(outputFileName, options)
#########################################################
## Build the actual templates from the single histograms
templates = {} # selection tag -> template histo
inputfile = openTFile(os.path.join(options.outDir, options.outFile))
for tag, sel, _ in SELECTIONS:
categories = ['MET', 'SVLMass']
for x, _ in NTRKBINS:
categories.append('SVLMass_tot_%d' % int(x))
for category in categories:
plotdirname = '%s_%s' % (category, tag)
plotdir = inputfile.Get(plotdirname)
h_bg = None
h_data = None
for tkey in plotdir.GetListOfKeys():
key = tkey.GetName()
if key.startswith('Graph_from'): continue
if key.startswith('MC8TeV_QCD'): continue
hist = inputfile.Get('%s/%s' % (plotdirname, key))
if key.startswith('MC8TeV'):
if not h_bg:
h_bg = hist.Clone("%s_BGs" % tag)
else:
h_bg.Add(hist)
if key.startswith('Data8TeV'):
h_data = hist.Clone("%s_Data" % tag)
## Determine a suitable output name
histname = '%s_template' % tag
if category == 'MET': histname = "%s_%s" % ('met', histname)
if 'tot' in category:
tkbin = int(category.split('_')[2])
histname = "%s_%d" % (histname, tkbin)
h_data_subtr = h_data.Clone(histname)
## Now subtract the combined MC from the data
h_data_subtr.Add(h_bg, -1)
dicttag = tag
if category == 'MET':
dicttag = 'met_%s' % tag
if '_tot_' in category:
dicttag = '%s_%d' % (tag, tkbin)
templates[dicttag] = h_data_subtr
ofi = ROOT.TFile(os.path.join(options.outDir, 'qcd_templates.root'),
'recreate')
ofi.cd()
for hist in templates.values():
hist.Write(hist.GetName())
ofi.Write()
ofi.Close()
for key, hist in sorted(templates.iteritems()):
print key
#########################################################
## Make a plot comparing the templates
for tag, _, seltag in SELECTIONS:
if not tag.endswith('_qcd'): continue
templateplot = RatioPlot('qcdtemplates_%s' % tag)
for key, hist in sorted(templates.iteritems()):
if not tag in key: continue
if key.startswith('met'): continue
if key == tag:
templateplot.add(hist, 'Inclusive')
templateplot.reference = hist
else:
ntrkbin = int(key.rsplit('_', 1)[1])
templateplot.add(hist, 'N_{track} = %d' % ntrkbin)
templateplot.tag = 'QCD templates'
templateplot.subtag = seltag
templateplot.tagpos = (0.90, 0.85)
templateplot.subtagpos = (0.90, 0.78)
templateplot.legpos = (0.75, 0.25)
templateplot.ratiotitle = 'Ratio wrt Inclusive'
templateplot.extratext = '' #Work in Progress'
templateplot.ratiorange = (0.2, 2.2)
templateplot.colors = [
ROOT.kBlack, ROOT.kBlue - 8, ROOT.kAzure - 2, ROOT.kCyan - 3
]
templateplot.show("qcdtemplates_%s" % tag, options.outDir)
templateplot.reset()
return 0
def main(args, opt):
os.system('mkdir -p %s'%opt.outDir)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gROOT.SetBatch(1)
# Construct histograms
peInputFile = ROOT.TFile.Open(args[0], 'READ')
masshistos = {}
for mass in ALLMASSES+['data']:
masshist = None
masstag = mass
if type(mass) == float:
masstag = 'nominal_'+str(mass).replace('.','v')
for trk,_ in NTRKBINS:
ihist = peInputFile.Get('%s/SVLMass_inclusive_optmrank_%s_%d'%(masstag,masstag,trk))
try:
masshist.Add(ihist)
except AttributeError:
try:
masshist = ihist.Clone(masstag)
except ReferenceError:
print "Histogram not found: %s/SVLMass_inclusive_optmrank_%s_%d"%(masstag,masstag,trk)
continue
masshistos[mass] = masshist
ratplot = RatioPlot('ratioplot')
ratplot.normalized = False
ratplot.ratiotitle = "Ratio wrt 172.5 GeV"
ratplot.tag = 'All channels combined'
ratplot.extratext = 'Preliminary'
ratplot.rebin = 1
ratplot.legpos = (0.55, 0.15)
ratplot.ratiorange = (0.5, 1.5)
ratplot.reference = [masshistos[172.5]]
# Construct dummy data for now
dummy = masshistos[172.5].Clone('dummy_data')
dummy.Reset('ICE')
dummy.FillRandom(masshistos[173.5], ROOT.gRandom.Poisson(masshistos[172.5].Integral()))
ratplot.add(dummy,'Pseudo Data (@173.5 GeV)')
####################################################
# REAL DATA:
# ratplot.add(masshistos['data'], 'Data')
####################################################
for mass in MASSESTOPLOT:
legentry = 'MC (m_{top} = %5.1f GeV)' % mass
try:
histo = masshistos[mass]
ratplot.add(histo, legentry, includeInRatio=(mass != 172.5))
except KeyError: pass
ratplot.colors = [ROOT.kBlack, ROOT.kAzure-4, ROOT.kAzure+1, ROOT.kAzure-6]
# ratplot.colors = [ROOT.kBlack, ROOT.kOrange-4, ROOT.kOrange-3, ROOT.kOrange+9]
ratplot.drawoptions = ['PE', 'hist', 'hist', 'hist']
ratplot.markerstyles = [20,1,1,1]
ratplot.markersizes = [1.5,1,1,1]
ratplot.show("massscan_paper_optmrank", opt.outDir)
return 0
