def prepareDYScaleFactors(filename, plotfile, inputdir, options,
dytag="DY+Jets",
unmergedfile="MC8TeV_TTJets_MSDecays_172v5.root"):
"""
Prepares a dictionary of (key, process title) -> scale factor,
to be used in the runPlotter script to scale DY by a data-derived
scale factor.
A list of keys to be scaled is generated from a plotfile (for merged
histograms), and from one file of an input directory (for unmerged ones).
"""
## First calculate the actual scalefactors
DYSFs = extractFactors(filename, options=options)
## Get a list of all keys that are to be scaled
allkeys = []
## Need the list of processes
tagsToFilter = ['_ee', '_mm', '_mumu']
## From merged plots:
allkeys += getAllPlotsFrom(openTFile(plotfile),
tagsToFilter=tagsToFilter,
filterByProcsFromJSON=options.json)
## From unmerged plots:
allkeys += getAllPlotsFrom(openTFile(os.path.join(inputdir,
unmergedfile)),
tagsToFilter=tagsToFilter)
## Prepare dictionary of (key,tag) -> scale factor
scaleFactors = {}
if options.verbose > 0:
print 'Will scale the following histograms for %s:' % dytag
for key in allkeys:
if '_ee' in key:
scaleFactors[(key, dytag)] = DYSFs['ee']
if options.verbose > 0:
print ' %-25s: %5.3f' % (key, DYSFs['ee'])
else:
scaleFactors[(key, dytag)] = DYSFs['mm']
if options.verbose > 0:
print ' %-25s: %5.3f' % (key, DYSFs['mm'])
return scaleFactors
def extractFactors(inputFile, options):
try:
cachefile = open('.svldyscalefactors.pck','r')
scaleFactors = pickle.load(cachefile)
cachefile.close()
print ('>>> Read Drell-Yan scale factors from cache '
'(.svldyscalefactors.pck)')
return scaleFactors
except IOError:
pass
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "Please provide a valid input file"
allkeys = getAllPlotsFrom(tfile, tagsToFilter=HISTSTOPROCESS)
scaleFactors = {}
for histname in HISTSTOPROCESS:
tot_data = 0
tot_background = 0
tot_signal = 0
for key in allkeys:
if not key.endswith(histname): continue
integral = tfile.Get(key).Integral()
if 'Data8TeV' in key:
tot_data += integral
elif SIGNAL in key:
tot_signal += integral
else:
tot_background += integral
channame = histname.rsplit('_',1)[1]
if options.verbose>0:
print 30*'-'
print histname
print ("Total data: %6.1f, total bg: %6.1f, total signal: %6.1f "
% (tot_data, tot_background, tot_signal))
# tot_background + SF * tot_signal = tot_data
SF = (tot_data-tot_background)/tot_signal
if options.verbose>0:
print " -> scale factor: %5.3f" % SF
scaleFactors[channame] = SF
if options.verbose>0: print 30*'-'
cachefile = open(".svldyscalefactors.pck", 'w')
pickle.dump(scaleFactors, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
return scaleFactors
def main(args, opt):
try:
cachefile = open(".eventyields.pck", 'r')
yields = pickle.load(cachefile)
cachefile.close()
return printYields(yields)
except EOFError:
print "Error loading pickle file, please delete it and try again"
return -1
except IOError:
pass
try:
tfile = openTFile(args[0])
except ReferenceError:
print "Please provide a valid input file"
return -1
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gROOT.SetBatch(1)
allkeys = getAllPlotsFrom(tfile, tagsToFilter=[opt.dist])
channels = ['e', 'ee', 'em', 'm', 'mm']
print 30 * '-'
yields = {}
for chan in channels:
for key in allkeys:
histname = "%s_%s" % (opt.dist, chan)
if not key.endswith(histname): continue
hist = tfile.Get(key)
err = ROOT.Double(0.0)
firstBin = opt.firstBin
lastBin = hist.GetXaxis().GetNbins(
) if opt.lastBin < opt.firstBin else opt.lastBin
integral = hist.IntegralAndError(firstBin, lastBin, err)
err = float(err)
proc = key.rsplit('/', 1)[1]
proc = proc.replace('_%s' % histname, '')
yields[(chan, proc)] = (integral, err)
cachefile = open(".eventyields.pck", 'w')
pickle.dump(yields, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
return printYields(yields)
def main(args, opt):
try:
cachefile = open(".eventyields.pck", 'r')
yields = pickle.load(cachefile)
cachefile.close()
return printYields(yields)
except EOFError:
print "Error loading pickle file, please delete it and try again"
return -1
except IOError:
pass
try:
tfile = openTFile(args[0])
except ReferenceError:
print "Please provide a valid input file"
return -1
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gROOT.SetBatch(1)
allkeys = getAllPlotsFrom(tfile, tagsToFilter=[opt.dist])
channels = ['e', 'ee', 'em', 'm', 'mm']
print 30*'-'
yields = {}
for chan in channels:
for key in allkeys:
histname = "%s_%s"%(opt.dist,chan)
if not key.endswith(histname): continue
hist = tfile.Get(key)
err = ROOT.Double(0.0)
firstBin=opt.firstBin
lastBin=hist.GetXaxis().GetNbins() if opt.lastBin<opt.firstBin else opt.lastBin
integral = hist.IntegralAndError(firstBin,lastBin, err)
err = float(err)
proc = key.rsplit('/',1)[1]
proc = proc.replace('_%s'%histname, '')
yields[(chan,proc)] = (integral, err)
cachefile = open(".eventyields.pck", 'w')
pickle.dump(yields, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
return printYields(yields)
def extractLJNTrkWeights(inputFile=None, verbose=0):
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "[extractLJNTrkWeights] Please provide a valid input file"
allkeys = getAllPlotsFrom(tfile, tagsToFilter=HISTSTOPROCESS)
ofile = ROOT.TFile.Open('ljntkweights.root','recreate')
ofile.cd()
for histname in HISTSTOPROCESS:
tot_data, tot_mc = None, None
for key in allkeys:
if not key.endswith(histname): continue
hist = tfile.Get(key)
integral = hist.Integral()
if 'Data8TeV' in key:
if not tot_data:
tot_data = hist.Clone("SVNtrk_data")
tot_data.SetDirectory(0)
else:
print "WARNING: Found two data histograms"
tot_data = hist.Clone("SVNtrk_data")
tot_data.SetDirectory(0)
if 'MC8TeV_TTJets_MSDecays_172v5' in key:
if not tot_mc:
tot_mc = hist.Clone("SVNtrk_mc")
tot_mc.SetDirectory(0)
else:
tot_mc.Add(hist)
## Normalize them
tot_data.Scale(1.0/tot_data.Integral())
tot_mc.Scale(1.0/tot_mc.Integral())
ratio = tot_data.Clone("%s_weights"%histname)
ratio.Divide(tot_mc)
ofile.cd()
ratio.Write(ratio.GetName())
ofile.Write()
ofile.Close()
print ">>> Wrote light jet ntrk weights to ljntkweights.root"
def extractLJNTrkWeights(inputFile=None, verbose=0):
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "[extractLJNTrkWeights] Please provide a valid input file"
allkeys = getAllPlotsFrom(tfile, tagsToFilter=HISTSTOPROCESS)
ofile = ROOT.TFile.Open('ljntkweights.root', 'recreate')
ofile.cd()
for histname in HISTSTOPROCESS:
tot_data, tot_mc = None, None
for key in allkeys:
if not key.endswith(histname): continue
hist = tfile.Get(key)
integral = hist.Integral()
if 'Data8TeV' in key:
if not tot_data:
tot_data = hist.Clone("SVNtrk_data")
tot_data.SetDirectory(0)
else:
print "WARNING: Found two data histograms"
tot_data = hist.Clone("SVNtrk_data")
tot_data.SetDirectory(0)
if 'MC8TeV_TTJets_MSDecays_172v5' in key:
if not tot_mc:
tot_mc = hist.Clone("SVNtrk_mc")
tot_mc.SetDirectory(0)
else:
tot_mc.Add(hist)
## Normalize them
tot_data.Scale(1.0 / tot_data.Integral())
tot_mc.Scale(1.0 / tot_mc.Integral())
ratio = tot_data.Clone("%s_weights" % histname)
ratio.Divide(tot_mc)
ofile.cd()
ratio.Write(ratio.GetName())
ofile.Write()
ofile.Close()
print ">>> Wrote light jet ntrk weights to ljntkweights.root"
def compareNtrk(inputFile=None, verbose=0):
xsecweights=readXSecWeights()
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "Please provide a valid input file"
for reg in ['regA','regB','regC']:
for ch in ['e','m','mm','em','ee']:
histToProcess='SVNtrk_%s_optmrank' % ch
allkeys = getAllPlotsFrom(tfile, tagsToFilter=[histToProcess])
data=None
mc=None
for key in allkeys:
if not (reg in key) : continue
weight=1.0
for xsecKey in xsecweights:
procName=xsecKey.replace('MC8TeV','')+'_'
if procName in key : weight =xsecweights[xsecKey]*LUMI
if '2012' in key:
try:
data.Add( tfile.Get(key), weight )
except:
data=tfile.Get(key).Clone('data')
data.Scale(weight)
else:
try:
mc.Add( tfile.Get(key), weight )
except:
mc=tfile.Get(key).Clone('mc')
mc.Scale(weight)
varPlot = Plot('%s_%s_ntrks'%(ch,reg),False)
varPlot.add(mc, 'expected', ROOT.kGray, False)
varPlot.add(data, 'data', 1, True)
varPlot.show('~/www/tmp')
varPlot.reset()
def compareNtrk(inputFile=None, verbose=0):
xsecweights = readXSecWeights()
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "Please provide a valid input file"
for reg in ['regA', 'regB', 'regC']:
for ch in ['e', 'm', 'mm', 'em', 'ee']:
histToProcess = 'SVNtrk_%s_optmrank' % ch
allkeys = getAllPlotsFrom(tfile, tagsToFilter=[histToProcess])
data = None
mc = None
for key in allkeys:
if not (reg in key): continue
weight = 1.0
for xsecKey in xsecweights:
procName = xsecKey.replace('MC8TeV', '') + '_'
if procName in key: weight = xsecweights[xsecKey] * LUMI
if '2012' in key:
try:
data.Add(tfile.Get(key), weight)
except:
data = tfile.Get(key).Clone('data')
data.Scale(weight)
else:
try:
mc.Add(tfile.Get(key), weight)
except:
mc = tfile.Get(key).Clone('mc')
mc.Scale(weight)
varPlot = Plot('%s_%s_ntrks' % (ch, reg), False)
varPlot.add(mc, 'expected', ROOT.kGray, False)
varPlot.add(data, 'data', 1, True)
varPlot.show('~/www/tmp')
varPlot.reset()
def main():
global INPUTS
global TAGS
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i', '--input' , dest='input',
help='input directory',
default=None, type='string')
# parser.add_option('-t', '--title', dest='InputTitles',
# help='titles',
# default=None, type='string')
parser.add_option('-o', '--outDir' , dest='outDir',
help='destination for output plots',
default='charmplots', type='string')
parser.add_option('-d', '--dist' , dest='Dist',
help='distribution to compare',
default='ptfrac', type='string')
parser.add_option('--showMean' , dest='showMean',
help='show mean instead of pull',
default=False, action='store_true')
parser.add_option('-m', '--maxY', dest='MaxY',
help='max y',
default=0.75, type=float)
parser.add_option('-b', '--base', dest='BaseName',
help='base name for root files' ,
default='UnfoldedDistributions', type='string')
parser.add_option('--tag', dest='Tag', help='Add a tag label',
default='', type='string')
parser.add_option('-z', dest='z', help='use Z control region inputs',
default=False, action='store_true')
(opt, args) = parser.parse_args()
if opt.z:
print 'Using Z control region inputs'
INPUTS = [
('data' , 'Data' , 'Data8TeV_DoubleLepton_merged_filt23' , ROOT.kBlack),
('z2srblep' , 'Z2*LEP r_{b}' , 'MC8TeV_DY_merged_filt23_bfrag' , ROOT.kBlack),
('z2srblepup' , 'Z2*LEP r_{b}+' , 'MC8TeV_DY_merged_filt23_bfragup' , ROOT.kMagenta),
('z2srblepdn' , 'Z2*LEP r_{b}-' , 'MC8TeV_DY_merged_filt23_bfragdn' , ROOT.kMagenta+2),
('z2s' , 'Z2*' , 'MC8TeV_DY_merged_filt23' , ROOT.kRed+1),
('z2spete' , 'Z2* Peterson' , 'MC8TeV_DY_merged_filt23_bfragpete' , ROOT.kAzure+7),
('z2slund' , 'Z2* Lund' , 'MC8TeV_DY_merged_filt23_bfraglund' , ROOT.kBlue-7),
#('p11frag' , 'P11' , 'MC8TeV_DY_merged_filt23_bfragp11' , ROOT.kRed-9),
]
TAGS = {
'D0': 'Z/#gamma^{*}(ll) #mu D^{0} (K^{-}#pi^{+}) + X',
'Dpm': 'Z/#gamma^{*}(ll) D^{#pm}(K^{-}#pi^{+}#pi^{+}) + X',
'JPsi': 'Z/#gamma^{*}(ll) J/#Psi(#mu^{+}#mu^{-}) + X',
'Dsm': 'Z/#gamma^{*}(ll) D*^{#pm}(D^{0}(K^{-}#pi^{+})#pi^{+}) X',
}
#global ROOT configuration
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetPadTopMargin(0.05)
ROOT.gStyle.SetPadBottomMargin(0.12)
ROOT.gStyle.SetPadLeftMargin(0.15)
ROOT.gStyle.SetPadRightMargin(0.05)
print "Will store plots in", opt.outDir
#get graphs from files
allGr={}
for tag,title,dirname,color in INPUTS:
inputdir = os.path.join(opt.input,dirname)
if 'diff' in opt.BaseName:
inputdir = os.path.join(inputdir, 'diff')
files = glob.glob("%s/%s_*.root"%(inputdir,opt.BaseName))
if not files:
print "No files found:", "%s/%s_*.root"%(inputdir,opt.BaseName)
continue
assert(len(files) == 1)
inF=openTFile(files[0])
checkKeyInFile('%s'%opt.Dist, inF, doraise=True)
allGr[tag] = inF.Get('%s'%opt.Dist)
inF.Close()
#format the new plot
allGr[tag].SetName('gr%s'%tag)
allGr[tag].SetTitle(title)
allGr[tag].GetYaxis().SetTitleFont(43)
allGr[tag].GetYaxis().SetLabelFont(43)
allGr[tag].GetYaxis().SetTitleSize(24)
allGr[tag].GetXaxis().SetTitleSize(24)
allGr[tag].GetXaxis().SetTitleFont(43)
allGr[tag].GetXaxis().SetLabelFont(43)
allGr[tag].GetXaxis().SetLabelSize(18)
allGr[tag].GetYaxis().SetLabelSize(18)
allGr[tag].GetXaxis().SetTitleOffset(0.9)
allGr[tag].GetYaxis().SetTitleOffset(1.1)
axistitles = AXISTITLES.get(opt.Dist,(opt.Dist,'Normalized'))
allGr[tag].GetXaxis().SetTitle(axistitles[0])
allGr[tag].GetYaxis().SetTitle(axistitles[1])
if tag == 'data':
allGr[tag].SetFillStyle(0)
allGr[tag].SetFillColor(0)
allGr[tag].SetLineColor(ROOT.kBlack)
allGr[tag].SetLineWidth(2)
allGr[tag].SetMarkerColor(ROOT.kBlack)
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(0.8)
else:
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(0.8)
allGr[tag].SetMarkerColor(color)
allGr[tag].SetLineColor(color)
allGr[tag].SetFillStyle(3002)
allGr[tag].SetFillColor(color)
# compute all the pulls or means:
compGrs={}
itagCtr=0
for tag,_,_,_ in INPUTS:
graph=allGr[tag]
if opt.showMean:
mu0, avg, avgErr, mu2 = computeFirstMoments(graph)
rms = ROOT.TMath.Sqrt(mu2-avg*avg)
if tag=='data' : continue
compGrs[tag]=graph.Clone('compgr%s'%tag)
compGrs[tag].Set(0)
compGrs[tag].SetPoint(0,avg,len(compGrs))
compGrs[tag].SetPointError(0,avgErr,avgErr,0.5,0.5)
itagCtr+=1
else:
compGrs[tag]=computePullFromGraphs(graph, allGr['data'])
##########################################
# PLOTTING
canvas=ROOT.TCanvas('c','c',500,500)
canvas.cd()
try:
allGr['z2srblep'].Draw('A3')
allGr['z2srblep'].GetYaxis().SetRangeUser(0,opt.MaxY)
allGr['data'].Draw('p')
except KeyError:
print "WARNING: No data or Z2*LEP rb to plot"
pass
CMS_lumi(canvas,2,10)
if opt.Tag:
tl = ROOT.TLatex()
tl.SetNDC()
tl.SetTextFont(43)
tl.SetTextSize(18)
tl.SetTextAlign(32)
tl.DrawLatex(0.93, 0.92, TAGS[opt.Tag])
insetPad = ROOT.TPad('inset','inset',0.55,0.55,0.95,0.85)
if 'chfrac' in opt.Dist : insetPad = ROOT.TPad('inset','inset',0.15,0.55,0.55,0.85)
insetPad.SetTopMargin(0.05)
insetPad.SetLeftMargin(0.1)
insetPad.SetRightMargin(0.4)
insetPad.SetBottomMargin(0.2)
insetPad.SetFillStyle(0)
insetPad.Draw()
insetPad.cd()
insetPad.Clear()
frame,dataRef=ROOT.TGraph(),None
if opt.showMean:
mu0, avg, avgErr, mu2 = computeFirstMoments(allGr['data'])
rms = ROOT.TMath.Sqrt(mu2-avg*avg)
frame.SetTitle('frame')
frame.SetMarkerStyle(1)
frame.SetLineColor(1)
frame.SetPoint(0,avg-rms,-0.5)
frame.SetPoint(1,avg-rms,len(INPUTS)+0.5)
frame.SetPoint(2,avg+rms,len(INPUTS)+0.5)
frame.SetPoint(3,avg+rms,-0.5)
frame.SetPoint(4,avg-rms,-0.5)
dataRef=ROOT.TGraph()
dataRef.SetTitle('dataref')
dataRef.SetPoint(0,avg-avgErr,-0.5)
dataRef.SetPoint(1,avg-avgErr,len(INPUTS)+0.5)
dataRef.SetPoint(2,avg+avgErr,len(INPUTS)+0.5)
dataRef.SetPoint(3,avg+avgErr,-0.5)
dataRef.SetPoint(4,avg-avgErr,-0.5)
else:
frame.SetTitle('frame')
frame.SetMarkerStyle(1)
frame.SetLineColor(1)
frame.SetPoint(0,allGr['data'].GetHistogram().GetXaxis().GetXmin(),-5.1)
frame.SetPoint(1,allGr['data'].GetHistogram().GetXaxis().GetXmin(),5.1)
frame.SetPoint(2,allGr['data'].GetHistogram().GetXaxis().GetXmax(),5.1)
frame.SetPoint(3,allGr['data'].GetHistogram().GetXaxis().GetXmax(),-5.1)
frame.SetPoint(4,allGr['data'].GetHistogram().GetXaxis().GetXmin(),-5.1)
frame.Draw('ap')
frameXtitle,frameYtitle='','Pull'
frame.GetYaxis().SetNdivisions(5)
frame.GetXaxis().SetNdivisions(5)
if opt.showMean :
frameXtitle,frameYtitle='Average',''
frame.GetYaxis().SetNdivisions(0)
frame.GetYaxis().SetTitle(frameYtitle)
frame.GetXaxis().SetTitle(frameXtitle)
frame.GetXaxis().SetTitleSize(0.08)
frame.GetXaxis().SetLabelSize(0.08)
frame.GetYaxis().SetTitleOffset(0.5)
frame.GetXaxis().SetTitleOffset(1.0)
frame.GetYaxis().SetTitleSize(0.08)
frame.GetYaxis().SetLabelSize(0.08)
#inset legend
inleg=ROOT.TLegend(0.62,0.3,0.95,0.9)
inleg.SetBorderSize(0)
inleg.SetFillColor(0)
inleg.SetTextFont(42)
inleg.SetTextSize(0.07)
if dataRef :
dataRef.Draw('l')
inleg.AddEntry(dataRef,'data','l')
for tag,_,_,_ in INPUTS:
if tag=='data' : continue
if opt.showMean:
compGrs[tag].Draw('p')
inleg.AddEntry(compGrs[tag],compGrs[tag].GetTitle(),'p')
else:
compGrs[tag].Draw('3')
inleg.AddEntry(compGrs[tag],compGrs[tag].GetTitle(),'l')
inleg.Draw()
postfix='_mean' if opt.showMean else ''
os.system('mkdir -p %s' % opt.outDir)
for ext in ['.pdf', '.png', '.C']:
#for ext in ['.pdf', '.png']:
canvas.SaveAs(os.path.join(opt.outDir,'%s%s%s'%(opt.Dist,postfix,ext)))
insetPad.Delete()
def main():
global INPUTS
global TAGS
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i',
'--input',
dest='input',
help='input directory',
default=None,
type='string')
# parser.add_option('-t', '--title', dest='InputTitles',
# help='titles',
# default=None, type='string')
parser.add_option('-o',
'--outDir',
dest='outDir',
help='destination for output plots',
default='charmplots',
type='string')
parser.add_option('-d',
'--dist',
dest='Dist',
help='distribution to compare',
default='ptfrac',
type='string')
parser.add_option('-m',
'--maxY',
dest='MaxY',
help='max y',
default=0.75,
type=float)
parser.add_option('-b',
'--base',
dest='BaseName',
help='base name for root files',
default='UnfoldedDistributions',
type='string')
parser.add_option('--tag',
dest='Tag',
help='Add a tag label',
default='',
type='string')
parser.add_option('-z',
dest='z',
help='use Z control region inputs',
default=False,
action='store_true')
(opt, args) = parser.parse_args()
if opt.z:
print 'Using Z control region inputs'
INPUTS = [
('data', 'Data', 'Data8TeV_merged_filt23', ROOT.kBlack),
('z2srblep', 'Z2*LEP r_{b}', 'MC8TeV_DY_merged_filt23_bfrag',
ROOT.kBlack),
('z2srblepup', 'Z2*LEP r_{b}+', 'MC8TeV_DY_merged_filt23_bfragup',
ROOT.kMagenta),
('z2srblepdn', 'Z2*LEP r_{b}-', 'MC8TeV_DY_merged_filt23_bfragdn',
ROOT.kMagenta + 2),
('z2s', 'Z2*', 'MC8TeV_DY_merged_filt23', ROOT.kRed + 1),
('z2spete', 'Z2* Peterson', 'MC8TeV_DY_merged_filt23_bfragpete',
ROOT.kAzure + 7),
('z2slund', 'Z2* Lund', 'MC8TeV_DY_merged_filt23_bfraglund',
ROOT.kBlue - 7),
#('p11frag' , 'P11' , 'MC8TeV_DY_merged_filt23_bfragp11' , ROOT.kRed-9),
]
TAGS = {
'D0': 'Z/#gamma^{*}(ll) #mu D^{0} (K^{-}#pi^{+}) + X',
'Dpm': 'Z/#gamma^{*}(ll) D^{#pm}(K^{-}#pi^{+}#pi^{+}) + X',
'JPsi': 'Z/#gamma^{*}(ll) J/#Psi(#mu^{+}#mu^{-}) + X',
'Dsm': 'Z/#gamma^{*}(ll) D*^{#pm}(D^{0}(K^{-}#pi^{+})#pi^{+}) X',
}
#fragmentation variations to compare
fragToUse = [('Z2*LEP r_{b}', ['z2srblep', 'z2srblepup',
'z2srblepdn'], ROOT.kBlack),
('Z2*', ['z2s', 'z2s', 'z2s'], ROOT.kRed + 1),
('Z2* peterson', ['z2spete', 'z2spete',
'z2spete'], ROOT.kViolet + 2),
('Z2* Lund', ['z2slund', 'z2slund',
'z2slund'], ROOT.kAzure + 7)]
if not opt.z:
fragToUse.append(('Herwig AUET6', ['powherw', 'powherw',
'powherw'], ROOT.kMagenta + 2))
nominalFragWgt = fragToUse[0][0]
#global ROOT configuration
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
print "Will store plots in", opt.outDir
#get graphs from files
allGr = {}
for tag, title, dirname, color in INPUTS:
inputdir = os.path.join(opt.input, dirname)
if 'diff' in opt.BaseName:
inputdir = os.path.join(inputdir, 'diff')
files = glob.glob("%s/%s_*.root" % (inputdir, opt.BaseName))
if not files:
print "No files found:", "%s/%s_*.root" % (inputdir, opt.BaseName)
continue
assert (len(files) == 1)
inF = openTFile(files[0])
checkKeyInFile('%s' % opt.Dist, inF, doraise=True)
allGr[tag] = inF.Get('%s' % opt.Dist)
inF.Close()
#format the new plot
allGr[tag].SetName('gr%s' % tag)
allGr[tag].SetTitle(title)
if tag == 'data':
allGr[tag].SetFillStyle(0)
allGr[tag].SetFillColor(0)
allGr[tag].SetLineColor(ROOT.kBlack)
allGr[tag].SetLineWidth(2)
allGr[tag].SetMarkerColor(ROOT.kBlack)
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(0.8)
else:
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(0.8)
allGr[tag].SetMarkerColor(color)
allGr[tag].SetLineColor(color)
allGr[tag].SetFillStyle(3002)
allGr[tag].SetFillColor(color)
# compute all the pulls or means:
compGrs = {}
for wtitle, iwList, wcolor in fragToUse:
compGrs[wtitle] = [0, 0, 0, 0]
for itag in xrange(0, len(iwList)):
tag = iwList[itag]
mu0, avg, avgErr, mu2 = computeFirstMoments(allGr[tag])
rms = ROOT.TMath.Sqrt(mu2 - avg * avg)
if itag == 0:
compGrs[wtitle][0] = avg
compGrs[wtitle][1] = avgErr
else:
compGrs[wtitle][1 + itag] = avg - compGrs[wtitle][0]
print compGrs
##########################################
# PLOTTING
canvas = ROOT.TCanvas('c', 'c', 500, 500)
canvas.SetRightMargin(0)
canvas.SetLeftMargin(0.)
canvas.SetTopMargin(0)
canvas.SetBottomMargin(0.)
canvas.cd()
p1 = ROOT.TPad('p1', 'p1', 0.0, 0.85, 1.0, 0.0)
p1.SetLeftMargin(0.12)
p1.SetBottomMargin(0.12)
p1.SetRightMargin(0.05)
p1.SetTopMargin(0.01)
p1.Draw()
canvas.cd()
p2 = ROOT.TPad('p2', 'p2', 0.0, 0.85, 1.0, 1.0)
p2.SetLeftMargin(0.12)
p2.SetBottomMargin(0.01)
p2.SetRightMargin(0.05)
p2.SetTopMargin(0.05)
p2.Draw()
p1.cd()
p1.Clear()
try:
allGr['z2srblep'].Draw('A3')
allGr['z2srblep'].GetYaxis().SetRangeUser(0, opt.MaxY)
allGr['z2srblep'].GetYaxis().SetTitleOffset(1.25)
axistitles = AXISTITLES.get(opt.Dist, (opt.Dist, 'Normalized'))
allGr['z2srblep'].GetXaxis().SetTitle(axistitles[0])
allGr['z2srblep'].GetYaxis().SetTitle(axistitles[1])
allGr['z2srblep'].GetXaxis().SetTitleOffset(1.0)
allGr['z2srblep'].GetYaxis().SetTitleOffset(1.2)
allGr['z2srblep'].GetYaxis().SetTitleSize(0.04)
allGr['z2srblep'].GetXaxis().SetTitleSize(0.04)
allGr['z2srblep'].GetYaxis().SetLabelSize(0.035)
allGr['z2srblep'].GetXaxis().SetLabelSize(0.035)
allGr['z2srblep'].GetXaxis().SetNdivisions(10)
allGr['z2srblep'].GetYaxis().SetNdivisions(5)
allGr['data'].Draw('p')
except KeyError:
print "WARNING: No data or Z2*LEP rb to plot"
pass
label = ROOT.TLatex()
label.SetNDC()
label.SetTextFont(42)
label.SetTextSize(0.05)
label.SetTextAlign(32)
label.DrawLatex(0.25, 0.92, '#bf{CMS}')
if opt.Tag:
label.DrawLatex(0.92, 0.86, '#scale[0.8]{#it{%s}}' % TAGS[opt.Tag])
label.DrawLatex(0.92, 0.92, '#scale[0.8]{19.7 fb^{-1} (8 TeV)}')
p2.cd()
p2.Clear()
frame, dataRef = ROOT.TGraph(), None
mu0, avg, avgErr, mu2 = computeFirstMoments(allGr['data'])
rms = ROOT.TMath.Sqrt(mu2 - avg * avg)
frame.SetTitle('frame')
frame.SetMarkerStyle(1)
frame.SetLineColor(1)
frame.SetPoint(0, 0, avg - 0.5 * rms)
frame.SetPoint(1, len(fragToUse), avg - 0.5 * rms)
frame.SetPoint(2, len(fragToUse), avg + 0.5 * rms)
frame.SetPoint(3, 0, avg + 0.5 * rms)
frame.SetPoint(4, 0, avg - 0.5 * rms)
dataRef = ROOT.TGraph()
dataRef.SetTitle('dataref')
dataRef.SetFillStyle(3001)
dataRef.SetFillColor(ROOT.kGray)
dataRef.SetLineColor(ROOT.kGray)
dataRef.SetLineWidth(2)
dataRef.SetPoint(0, 0, avg - avgErr)
dataRef.SetPoint(1, len(fragToUse) + 1.0, avg - avgErr)
dataRef.SetPoint(2, len(fragToUse) + 1.0, avg + avgErr)
dataRef.SetPoint(3, 0, avg + avgErr)
dataRef.SetPoint(4, 0, avg - avgErr)
print avg, avgErr
frame.Draw('ap')
frame.GetXaxis().SetNdivisions(0)
frame.GetYaxis().SetNdivisions(5)
frameXtitle, frameYtitle = '', 'Average'
frame.GetXaxis().SetNdivisions(0)
frame.GetYaxis().SetTitle(frameYtitle)
frame.GetXaxis().SetTitle(frameXtitle)
frame.GetXaxis().SetTitleSize(0.0)
frame.GetXaxis().SetLabelSize(0.0)
frame.GetYaxis().SetTitleOffset(0.25)
frame.GetXaxis().SetTitleOffset(1.8)
frame.GetYaxis().SetTitleSize(0.2)
frame.GetYaxis().SetLabelSize(0.2)
#inset legend
fraglabel = ROOT.TLatex()
fraglabel.SetNDC()
fraglabel.SetTextFont(42)
fraglabel.SetTextSize(0.15)
#fraglabel.SetTextAlign(12)
if dataRef:
dataRef.Draw('lf')
grCtr = 0
statGrs, totalGrs = {}, {}
for wtitle, iwList, wcolor in fragToUse:
tag = iwList[0]
totalGrs[wtitle] = ROOT.TGraphAsymmErrors()
totalGrs[wtitle].SetMarkerColor(wcolor)
totalGrs[wtitle].SetLineColor(wcolor)
totalGrs[wtitle].SetLineWidth(2)
totalGrs[wtitle].SetMarkerStyle(20 + grCtr)
totalGrs[wtitle].SetPoint(0, grCtr + 1, compGrs[wtitle][0])
diff1 = ROOT.TMath.Min(compGrs[wtitle][2], compGrs[wtitle][2])
diff2 = ROOT.TMath.Max(compGrs[wtitle][2], compGrs[wtitle][3])
statunc = compGrs[wtitle][1]
if diff1 * diff2 < 0:
totalGrs[wtitle].SetPointError(
0, 0, 0, ROOT.TMath.Sqrt(statunc**2 + diff1**2),
ROOT.TMath.Sqrt(statunc**2 + diff2**2))
else:
maxDiff = ROOT.TMath.Max(ROOT.TMath.Abs(diff1),
ROOT.TMath.Abs(diff2))
totalGrs[wtitle].SetPointError(
0, 0, 0, ROOT.TMath.Sqrt(statunc**2 + maxDiff**2),
ROOT.TMath.Sqrt(statunc**2 + maxDiff**2))
statGrs[wtitle] = ROOT.TGraphAsymmErrors()
statGrs[wtitle].SetMarkerColor(wcolor)
statGrs[wtitle].SetLineColor(wcolor)
statGrs[wtitle].SetLineWidth(1)
statGrs[wtitle].SetMarkerStyle(20 + grCtr)
statGrs[wtitle].SetPoint(0, grCtr + 1, compGrs[wtitle][0])
statGrs[wtitle].SetPointError(0, 0, 0, statunc, statunc)
if wtitle == nominalFragWgt:
totalGrs[wtitle].SetMarkerSize(0.5)
statGrs[wtitle].SetMarkerSize(0.5)
totalGrs[wtitle].Draw('p')
statGrs[wtitle].Draw('p')
else:
statGrs[wtitle].Draw('pX')
xlabel = 0.09 + 0.68 * float(grCtr + 1) / float(len(fragToUse))
fraglabel.DrawLatex(xlabel, 0.8, '#it{%s}' % wtitle)
grCtr += 1
os.system('mkdir -p %s' % opt.outDir)
for ext in ['.pdf', '.png', '.C']:
canvas.SaveAs(os.path.join(opt.outDir, '%s%s' % (opt.Dist, ext)))
p1.Delete()
p2.Delete()
def main():
global INPUTS
global TAGS
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i', '--input' , dest='input',
help='input directory',
default=None, type='string')
# parser.add_option('-t', '--title', dest='InputTitles',
# help='titles',
# default=None, type='string')
parser.add_option('-o', '--outDir' , dest='outDir',
help='destination for output plots',
default='charmplots', type='string')
parser.add_option('-d', '--dist' , dest='Dist',
help='distribution to compare',
default='ptfrac', type='string')
parser.add_option('--pullrange' , dest='pullrange',
help='range of pull to plot',
default='-2,2', type='string')
parser.add_option('-m', '--maxY', dest='MaxY',
help='max y',
default=0.75, type=float)
parser.add_option('-b', '--base', dest='BaseName',
help='base name for root files' ,
default='UnfoldedDistributions', type='string')
parser.add_option('--tag', dest='Tag', help='Add a tag label',
default='', type='string')
parser.add_option('-z', dest='z', help='use Z control region inputs',
default=False, action='store_true')
(opt, args) = parser.parse_args()
if opt.z:
print 'Using Z control region inputs'
INPUTS = [
('data' , 'Data' , 'Data8TeV_DoubleLepton_merged_filt23' , ROOT.kBlack),
('z2s' , 'Madgraph+Pythia+Z2*' , 'MC8TeV_DY_merged_filt23' , ROOT.kBlue+3),
('z2srblep' , 'MG+PY+Z2*rbLEP' , 'MC8TeV_DY_merged_filt23_bfrag' , ROOT.kMagenta),
('z2srblepup' , 'MG+PY+Z2*rbLEP hard' , 'MC8TeV_DY_merged_filt23_bfragup' , ROOT.kMagenta+2),
('z2srblepdn' , 'MG+PY+Z2*rbLEP soft' , 'MC8TeV_DY_merged_filt23_bfragdn' , ROOT.kMagenta-9),
('p11frag' , 'MG+PY+P11 (frag)' , 'MC8TeV_DY_merged_filt23_bfragp11' , ROOT.kRed-9),
('z2spete' , 'MG+PY+Z2* Peterson' , 'MC8TeV_DY_merged_filt23_bfragpete' , ROOT.kAzure+7),
('z2slund' , 'MG+PY+Z2* Lund' , 'MC8TeV_DY_merged_filt23_bfraglund' , ROOT.kBlue-7)
]
TAGS = {
'D0': 'Z/#gamma^{*}(ll) #mu D^{0} (K^{-}#pi^{+}) + X',
'Dpm': 'Z/#gamma^{*}(ll) D^{#pm}(K^{-}#pi^{+}#pi^{+}) + X',
'JPsi': 'Z/#gamma^{*}(ll) J/#Psi(#mu^{+}#mu^{-}) + X',
'Dsm': 'Z/#gamma^{*}(ll) D*^{#pm}(D^{0}(K^{-}#pi^{+})#pi^{+}) X',
}
#global ROOT configuration
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetPadTopMargin(0.05)
ROOT.gStyle.SetPadBottomMargin(0.1)
ROOT.gStyle.SetPadLeftMargin(0.15)
ROOT.gStyle.SetPadRightMargin(0.05)
print "Will store plots in", opt.outDir
#get graphs from files
allGr={}
for tag,title,dirname,color in INPUTS:
inputdir = os.path.join(opt.input,dirname)
if 'diff' in opt.BaseName:
inputdir = os.path.join(inputdir, 'diff')
files = glob.glob("%s/%s_*.root"%(inputdir,opt.BaseName))
if not files:
print "No files found:", "%s/%s_*.root"%(inputdir,opt.BaseName)
continue
assert(len(files) == 1)
inF=openTFile(files[0])
checkKeyInFile('%s'%opt.Dist, inF, doraise=True)
allGr[tag] = inF.Get('%s'%opt.Dist)
inF.Close()
#format the new plot
allGr[tag].SetName('gr%s'%tag)
allGr[tag].SetTitle(title)
allGr[tag].GetYaxis().SetTitleFont(43)
allGr[tag].GetYaxis().SetLabelFont(43)
allGr[tag].GetYaxis().SetTitleSize(24)
allGr[tag].GetXaxis().SetTitleSize(24)
allGr[tag].GetXaxis().SetTitleFont(43)
allGr[tag].GetXaxis().SetLabelFont(43)
allGr[tag].GetXaxis().SetLabelSize(18)
allGr[tag].GetYaxis().SetLabelSize(18)
allGr[tag].GetXaxis().SetTitleOffset(0.9)
allGr[tag].GetYaxis().SetTitleOffset(2.5)
axistitles = AXISTITLES.get(opt.Dist,(opt.Dist,'Normalized'))
allGr[tag].GetXaxis().SetTitle(axistitles[0])
allGr[tag].GetYaxis().SetTitle(axistitles[1])
if tag == 'data':
allGr[tag].SetFillStyle(0)
allGr[tag].SetFillColor(0)
allGr[tag].SetLineColor(ROOT.kBlack)
allGr[tag].SetLineWidth(2)
allGr[tag].SetMarkerColor(ROOT.kBlack)
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(1.5)
else:
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(1)
allGr[tag].SetMarkerColor(color)
allGr[tag].SetLineColor(color)
allGr[tag].SetFillStyle(3002)
allGr[tag].SetFillColor(color)
# compute all the pulls:
pullGr = {}
for tag, graph in allGr.iteritems():
pullGr[tag] = computePullFromGraphs(graph, allGr['data'])
##########################################
# PLOTTING
canvas=ROOT.TCanvas('c','c',800,1200)
canvas.SetRightMargin(0)
canvas.SetLeftMargin(0)
canvas.SetTopMargin(0)
canvas.SetBottomMargin(0)
allLegs=[]
pads=[]
if opt.z:
pads.append( ROOT.TPad('pad0', 'pad0', 0.0, 0.40, 1.0, 1.00) )
pads.append( ROOT.TPad('pad1', 'pad1', 0.0, 0.40, 1.0, 0.00) )
else:
pads.append( ROOT.TPad('pad0', 'pad0', 0.0, 0.60, 1.0, 1.00) )
# pads.append( ROOT.TPad('pad1', 'pad1', 0.0, 0.60, 1.0, 0.42) )
# pads.append( ROOT.TPad('pad2', 'pad2', 0.0, 0.42, 1.0, 0.24) )
# pads.append( ROOT.TPad('pad3', 'pad3', 0.0, 0.24, 1.0, 0.00) )
for i in xrange(0,len(pads)):
pads[i].SetTopMargin(0.0)
pads[i].SetBottomMargin(0.0)
pads[0].SetTopMargin(0.1)
pads[-1].SetBottomMargin(0.25)
for p in pads: p.Draw()
canvas.cd()
pads[0].cd()
pads[0].Clear()
drawngraphs = []
try:
allGr['z2s'].Draw('A3')
allGr['p11'].Draw('3')
allGr['powpyth'].Draw('3')
allGr['powherw'].Draw('3')
allGr['data'].Draw('p')
# allGr['z2s'].GetYaxis().SetRangeUser(0,opt.MaxY)
# allGr['data'].GetYaxis().SetRangeUser(0,opt.MaxY)
drawngraphs.append(allGr['data'])
drawngraphs.append(allGr['z2s'])
for g in drawngraphs:
g.GetYaxis().SetRangeUser(0,opt.MaxY)
except KeyError:
print "WARNING: No data or Z2* to plot"
pass
# Build a legend
leg = makeLegend('main',len(drawngraphs),y2=0.85)
for gr in drawngraphs:
if 'data' in gr.GetName(): leg.AddEntry(gr, gr.GetTitle(), 'p')
else: leg.AddEntry(gr, gr.GetTitle(), 'f')
leg.Draw()
drawngraphs = []
CMS_lumi(pads[0],2,10)
#tag
if opt.Tag:
tl = ROOT.TLatex()
tl.SetNDC()
tl.SetTextFont(43)
tl.SetTextSize(24)
tl.DrawLatex(0.16, 0.92, TAGS[opt.Tag])
## Axis ranges
xaxisrange = (allGr['data'].GetHistogram().GetXaxis().GetXmin(),
allGr['data'].GetHistogram().GetXaxis().GetXmax())
pullrange = map(float, opt.pullrange.split(','))
haxispulls = ROOT.TH2F("haxispulls", "haxispulls",
1, xaxisrange[0], xaxisrange[1],
1, pullrange[0], pullrange[1])
haxispulls.GetXaxis().SetLabelFont(43)
haxispulls.GetXaxis().SetLabelSize(18)
haxispulls.GetYaxis().SetLabelFont(43)
haxispulls.GetYaxis().SetLabelSize(18)
haxispulls.GetXaxis().SetTitleFont(43)
haxispulls.GetXaxis().SetTitleSize(24)
haxispulls.GetXaxis().SetTitleOffset(4.0)
haxispulls.GetYaxis().SetTitleFont(43)
haxispulls.GetYaxis().SetTitleSize(24)
haxispulls.GetYaxis().SetTitleOffset(2.5)
haxispulls.GetYaxis().SetTitle("Pull")
axistitles = AXISTITLES.get(opt.Dist,(opt.Dist,'Pull'))
haxispulls.GetXaxis().SetTitle(axistitles[0])
## Draw the pull pads
line = ROOT.TLine()
line.SetLineColor(ROOT.kGray+2)
line.SetLineWidth(2)
line.SetLineStyle(2)
def drawPulls(pulls,drawOpt='L3'):
pad.cd()
drawngraphs = []
for pull in pulls:
pullGr[pull].Draw(drawOpt)
drawngraphs.append(allGr[pull])
leg = makeLegend('leg',len(drawngraphs))
for gr in drawngraphs: leg.AddEntry(gr, gr.GetTitle(), 'f')
return leg
for n,pad in enumerate(pads[1:]):
pad.cd()
pad.Clear()
# pad.SetGridy(True)
haxispulls.Draw("axis")
if opt.z or n==2:
leg2 = drawPulls(['z2srblep', 'z2srblepdn', 'z2srblepup',
'z2spete', 'z2slund'], drawOpt='LX')
leg2.Draw()
elif not opt.z:
if n==0:
leg0 = drawPulls(['z2s','p11','p11frag'])
leg0.Draw()
if n==1:
leg1 = drawPulls(['powpyth','powherw'])
leg1.Draw()
line.DrawLine(xaxisrange[0], 0., xaxisrange[1], 0.,)
os.system('mkdir -p %s' % opt.outDir)
# for ext in ['.pdf', '.png', '.C']:
for ext in ['.pdf', '.png']:
canvas.SaveAs(os.path.join(opt.outDir,'%s%s'%(opt.Dist,ext)))
for pad in pads:
pad.Delete()
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 extractNTrkWeights(inputFile=None, verbose=0):
try:
cachefile = open('.svntrkweights.pck', 'r')
ntkWeights = pickle.load(cachefile)
cachefile.close()
print('>>> Read SV ntrk weights from cache ' '(.svntrkweights.pck)')
return ntkWeights
except IOError:
pass
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "[extractNTrkWeights] Please provide a valid input file"
allkeys = getAllPlotsFrom(tfile, tagsToFilter=HISTSTOPROCESS)
ntkWeights = {}
for histname in HISTSTOPROCESS:
tot_data, tot_mc = None, None
for key in allkeys:
if not key.endswith(histname): continue
hist = tfile.Get(key)
integral = hist.Integral()
if 'Data8TeV' in key:
if not tot_data:
tot_data = hist.Clone("SVNtrk_data")
else:
print "WARNING: Found two data histograms"
tot_data = hist.Clone("SVNtrk_data")
else:
if not tot_mc:
tot_mc = hist.Clone("SVNtrk_mc")
else:
tot_mc.Add(hist)
ratio = tot_data.Clone("ratio")
ratio.Divide(tot_mc)
key = 'inclusive'
if len(histname.split('_')) > 2:
key = histname.rsplit('_', 1)[1]
if not key in ntkWeights:
ntkWeights[key] = {}
if verbose > 0:
print '---------------------------'
print histname
for x in xrange(1, ratio.GetNbinsX() + 1):
if verbose > 0:
print("Ntrk=%1d: Ndata: %6d / Nmc: %8.1f , weight = %5.3f" %
(x + 1, tot_data.GetBinContent(x),
tot_mc.GetBinContent(x), ratio.GetBinContent(x)))
ntkWeights[key][x + 1] = ratio.GetBinContent(x)
cachefile = open(".svntrkweights.pck", 'w')
pickle.dump(ntkWeights, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
print ">>> Dumped SV ntrk weights to .svntrkweights.pck"
return ntkWeights
def extractNTrkWeights(inputFile=None, verbose=0):
try:
cachefile = open('.svntrkweights.pck','r')
ntkWeights = pickle.load(cachefile)
cachefile.close()
print ('>>> Read SV ntrk weights from cache '
'(.svntrkweights.pck)')
return ntkWeights
except IOError:
pass
try:
tfile = openTFile(inputFile)
except ReferenceError:
print "[extractNTrkWeights] Please provide a valid input file"
allkeys = getAllPlotsFrom(tfile, tagsToFilter=HISTSTOPROCESS)
ntkWeights = {}
for histname in HISTSTOPROCESS:
tot_data, tot_mc = None, None
for key in allkeys:
if not key.endswith(histname): continue
hist = tfile.Get(key)
integral = hist.Integral()
if 'Data8TeV' in key:
if not tot_data:
tot_data = hist.Clone("SVNtrk_data")
else:
print "WARNING: Found two data histograms"
tot_data = hist.Clone("SVNtrk_data")
else:
if not tot_mc:
tot_mc = hist.Clone("SVNtrk_mc")
else:
tot_mc.Add(hist)
ratio = tot_data.Clone("ratio")
ratio.Divide(tot_mc)
key = 'inclusive'
if len(histname.split('_'))>2:
key = histname.rsplit('_',1)[1]
if not key in ntkWeights:
ntkWeights[key] = {}
if verbose>0:
print '---------------------------'
print histname
for x in xrange(1,ratio.GetNbinsX()+1):
if verbose>0:
print ("Ntrk=%1d: Ndata: %6d / Nmc: %8.1f , weight = %5.3f" %
(x+1,
tot_data.GetBinContent(x),
tot_mc.GetBinContent(x),
ratio.GetBinContent(x)))
ntkWeights[key][x+1] = ratio.GetBinContent(x)
cachefile = open(".svntrkweights.pck", 'w')
pickle.dump(ntkWeights, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
print ">>> Dumped SV ntrk weights to .svntrkweights.pck"
return ntkWeights
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():
global INPUTS
global TAGS
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-i', '--input' , dest='input',
help='input directory',
default=None, type='string')
# parser.add_option('-t', '--title', dest='InputTitles',
# help='titles',
# default=None, type='string')
parser.add_option('-o', '--outDir' , dest='outDir',
help='destination for output plots',
default='charmplots', type='string')
parser.add_option('-d', '--dist' , dest='Dist',
help='distribution to compare',
default='ptfrac', type='string')
parser.add_option('-m', '--maxY', dest='MaxY',
help='max y',
default=0.75, type=float)
parser.add_option('-b', '--base', dest='BaseName',
help='base name for root files' ,
default='UnfoldedDistributions', type='string')
parser.add_option('--tag', dest='Tag', help='Add a tag label',
default='', type='string')
parser.add_option('-z', dest='z', help='use Z control region inputs',
default=False, action='store_true')
(opt, args) = parser.parse_args()
if opt.z:
print 'Using Z control region inputs'
INPUTS = [
('data' , 'Data' , 'Data8TeV_merged_filt23' , ROOT.kBlack),
('z2srblep' , 'Z2*LEP r_{b}' , 'MC8TeV_DY_merged_filt23_bfrag' , ROOT.kBlack),
('z2srblepup' , 'Z2*LEP r_{b}+' , 'MC8TeV_DY_merged_filt23_bfragup' , ROOT.kMagenta),
('z2srblepdn' , 'Z2*LEP r_{b}-' , 'MC8TeV_DY_merged_filt23_bfragdn' , ROOT.kMagenta+2),
('z2s' , 'Z2*' , 'MC8TeV_DY_merged_filt23' , ROOT.kRed+1),
('z2spete' , 'Z2* Peterson' , 'MC8TeV_DY_merged_filt23_bfragpete' , ROOT.kAzure+7),
('z2slund' , 'Z2* Lund' , 'MC8TeV_DY_merged_filt23_bfraglund' , ROOT.kBlue-7),
#('p11frag' , 'P11' , 'MC8TeV_DY_merged_filt23_bfragp11' , ROOT.kRed-9),
]
TAGS = {
'D0': 'Z/#gamma^{*}(ll) #mu D^{0} (K^{-}#pi^{+}) + X',
'Dpm': 'Z/#gamma^{*}(ll) D^{#pm}(K^{-}#pi^{+}#pi^{+}) + X',
'JPsi': 'Z/#gamma^{*}(ll) J/#Psi(#mu^{+}#mu^{-}) + X',
'Dsm': 'Z/#gamma^{*}(ll) D*^{#pm}(D^{0}(K^{-}#pi^{+})#pi^{+}) X',
}
#fragmentation variations to compare
fragToUse=[('Z2*LEP r_{b}', ['z2srblep','z2srblepup','z2srblepdn'], ROOT.kBlack),
('Z2*', ['z2s', 'z2s', 'z2s'], ROOT.kRed+1),
('Z2* peterson', ['z2spete', 'z2spete', 'z2spete'], ROOT.kViolet+2),
('Z2* Lund', ['z2slund', 'z2slund', 'z2slund'], ROOT.kAzure+7)
]
if not opt.z:
fragToUse.append( ('Herwig AUET6', ['powherw', 'powherw', 'powherw'], ROOT.kMagenta+2) )
nominalFragWgt=fragToUse[0][0]
#global ROOT configuration
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
print "Will store plots in", opt.outDir
#get graphs from files
allGr={}
for tag,title,dirname,color in INPUTS:
inputdir = os.path.join(opt.input,dirname)
if 'diff' in opt.BaseName:
inputdir = os.path.join(inputdir, 'diff')
files = glob.glob("%s/%s_*.root"%(inputdir,opt.BaseName))
if not files:
print "No files found:", "%s/%s_*.root"%(inputdir,opt.BaseName)
continue
assert(len(files) == 1)
inF=openTFile(files[0])
checkKeyInFile('%s'%opt.Dist, inF, doraise=True)
allGr[tag] = inF.Get('%s'%opt.Dist)
inF.Close()
#format the new plot
allGr[tag].SetName('gr%s'%tag)
allGr[tag].SetTitle(title)
if tag == 'data':
allGr[tag].SetFillStyle(0)
allGr[tag].SetFillColor(0)
allGr[tag].SetLineColor(ROOT.kBlack)
allGr[tag].SetLineWidth(2)
allGr[tag].SetMarkerColor(ROOT.kBlack)
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(0.8)
else:
allGr[tag].SetMarkerStyle(20)
allGr[tag].SetMarkerSize(0.8)
allGr[tag].SetMarkerColor(color)
allGr[tag].SetLineColor(color)
allGr[tag].SetFillStyle(3002)
allGr[tag].SetFillColor(color)
# compute all the pulls or means:
compGrs={}
for wtitle,iwList,wcolor in fragToUse:
compGrs[wtitle]=[0,0,0,0]
for itag in xrange(0,len(iwList)):
tag=iwList[itag]
mu0, avg, avgErr, mu2 = computeFirstMoments(allGr[tag])
rms = ROOT.TMath.Sqrt(mu2-avg*avg)
if itag==0:
compGrs[wtitle][0]=avg
compGrs[wtitle][1]=avgErr
else:
compGrs[wtitle][1+itag]=avg-compGrs[wtitle][0]
print compGrs
##########################################
# PLOTTING
canvas=ROOT.TCanvas('c','c',500,500)
canvas.SetRightMargin(0)
canvas.SetLeftMargin(0.)
canvas.SetTopMargin(0)
canvas.SetBottomMargin(0.)
canvas.cd()
p1 = ROOT.TPad('p1','p1',0.0,0.85,1.0,0.0)
p1.SetLeftMargin(0.12)
p1.SetBottomMargin(0.12)
p1.SetRightMargin(0.05)
p1.SetTopMargin(0.01)
p1.Draw()
canvas.cd()
p2 = ROOT.TPad('p2','p2',0.0,0.85,1.0,1.0)
p2.SetLeftMargin(0.12)
p2.SetBottomMargin(0.01)
p2.SetRightMargin(0.05)
p2.SetTopMargin(0.05)
p2.Draw()
p1.cd()
p1.Clear()
try:
allGr['z2srblep'].Draw('A3')
allGr['z2srblep'].GetYaxis().SetRangeUser(0,opt.MaxY)
allGr['z2srblep'].GetYaxis().SetTitleOffset(1.25)
axistitles = AXISTITLES.get(opt.Dist,(opt.Dist,'Normalized'))
allGr['z2srblep'].GetXaxis().SetTitle(axistitles[0])
allGr['z2srblep'].GetYaxis().SetTitle(axistitles[1])
allGr['z2srblep'].GetXaxis().SetTitleOffset(1.0)
allGr['z2srblep'].GetYaxis().SetTitleOffset(1.2)
allGr['z2srblep'].GetYaxis().SetTitleSize(0.04)
allGr['z2srblep'].GetXaxis().SetTitleSize(0.04)
allGr['z2srblep'].GetYaxis().SetLabelSize(0.035)
allGr['z2srblep'].GetXaxis().SetLabelSize(0.035)
allGr['z2srblep'].GetXaxis().SetNdivisions(10)
allGr['z2srblep'].GetYaxis().SetNdivisions(5)
allGr['data'].Draw('p')
except KeyError:
print "WARNING: No data or Z2*LEP rb to plot"
pass
label=ROOT.TLatex()
label.SetNDC()
label.SetTextFont(42)
label.SetTextSize(0.05)
label.SetTextAlign(32)
label.DrawLatex(0.25,0.92,'#bf{CMS}')
if opt.Tag:
label.DrawLatex(0.92,0.86,'#scale[0.8]{#it{%s}}'%TAGS[opt.Tag])
label.DrawLatex(0.92,0.92,'#scale[0.8]{19.7 fb^{-1} (8 TeV)}')
p2.cd()
p2.Clear()
frame,dataRef=ROOT.TGraph(),None
mu0, avg, avgErr, mu2 = computeFirstMoments(allGr['data'])
rms = ROOT.TMath.Sqrt(mu2-avg*avg)
frame.SetTitle('frame')
frame.SetMarkerStyle(1)
frame.SetLineColor(1)
frame.SetPoint(0,0,avg-0.5*rms)
frame.SetPoint(1,len(fragToUse),avg-0.5*rms)
frame.SetPoint(2,len(fragToUse),avg+0.5*rms)
frame.SetPoint(3,0,avg+0.5*rms)
frame.SetPoint(4,0,avg-0.5*rms)
dataRef=ROOT.TGraph()
dataRef.SetTitle('dataref')
dataRef.SetFillStyle(3001)
dataRef.SetFillColor(ROOT.kGray)
dataRef.SetLineColor(ROOT.kGray)
dataRef.SetLineWidth(2)
dataRef.SetPoint(0,0,avg-avgErr)
dataRef.SetPoint(1,len(fragToUse)+1.0,avg-avgErr)
dataRef.SetPoint(2,len(fragToUse)+1.0,avg+avgErr)
dataRef.SetPoint(3,0,avg+avgErr)
dataRef.SetPoint(4,0,avg-avgErr)
print avg,avgErr
frame.Draw('ap')
frame.GetXaxis().SetNdivisions(0)
frame.GetYaxis().SetNdivisions(5)
frameXtitle,frameYtitle='','Average'
frame.GetXaxis().SetNdivisions(0)
frame.GetYaxis().SetTitle(frameYtitle)
frame.GetXaxis().SetTitle(frameXtitle)
frame.GetXaxis().SetTitleSize(0.0)
frame.GetXaxis().SetLabelSize(0.0)
frame.GetYaxis().SetTitleOffset(0.25)
frame.GetXaxis().SetTitleOffset(1.8)
frame.GetYaxis().SetTitleSize(0.2)
frame.GetYaxis().SetLabelSize(0.2)
#inset legend
fraglabel=ROOT.TLatex()
fraglabel.SetNDC()
fraglabel.SetTextFont(42)
fraglabel.SetTextSize(0.15)
#fraglabel.SetTextAlign(12)
if dataRef :
dataRef.Draw('lf')
grCtr=0
statGrs,totalGrs={},{}
for wtitle,iwList,wcolor in fragToUse:
tag=iwList[0]
totalGrs[wtitle]=ROOT.TGraphAsymmErrors()
totalGrs[wtitle].SetMarkerColor(wcolor)
totalGrs[wtitle].SetLineColor(wcolor)
totalGrs[wtitle].SetLineWidth(2)
totalGrs[wtitle].SetMarkerStyle(20+grCtr)
totalGrs[wtitle].SetPoint(0,grCtr+1,compGrs[wtitle][0])
diff1=ROOT.TMath.Min(compGrs[wtitle][2],compGrs[wtitle][2])
diff2=ROOT.TMath.Max(compGrs[wtitle][2],compGrs[wtitle][3])
statunc=compGrs[wtitle][1]
if diff1*diff2<0:
totalGrs[wtitle].SetPointError(0,0,0,
ROOT.TMath.Sqrt(statunc**2+diff1**2),
ROOT.TMath.Sqrt(statunc**2+diff2**2))
else:
maxDiff=ROOT.TMath.Max(ROOT.TMath.Abs(diff1),ROOT.TMath.Abs(diff2))
totalGrs[wtitle].SetPointError(0,0,0,
ROOT.TMath.Sqrt(statunc**2+maxDiff**2),
ROOT.TMath.Sqrt(statunc**2+maxDiff**2))
statGrs[wtitle]=ROOT.TGraphAsymmErrors()
statGrs[wtitle].SetMarkerColor(wcolor)
statGrs[wtitle].SetLineColor(wcolor)
statGrs[wtitle].SetLineWidth(1)
statGrs[wtitle].SetMarkerStyle(20+grCtr)
statGrs[wtitle].SetPoint(0,grCtr+1,compGrs[wtitle][0])
statGrs[wtitle].SetPointError(0,0,0,statunc,statunc)
if wtitle==nominalFragWgt :
totalGrs[wtitle].SetMarkerSize(0.5)
statGrs[wtitle].SetMarkerSize(0.5)
totalGrs[wtitle].Draw('p')
statGrs[wtitle].Draw('p')
else:
statGrs[wtitle].Draw('pX')
xlabel=0.09+0.68*float(grCtr+1)/float(len(fragToUse))
fraglabel.DrawLatex(xlabel,0.8,'#it{%s}'%wtitle)
grCtr+=1
os.system('mkdir -p %s' % opt.outDir)
for ext in ['.pdf', '.png', '.C']:
canvas.SaveAs(os.path.join(opt.outDir,'%s%s'%(opt.Dist,ext)))
p1.Delete()
p2.Delete()
def main():
#global ROOT configuration
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptFit(0)
ROOT.gSystem.Load("libUserCodeTopMassSecVtx")
ROOT.AutoLibraryLoader.enable()
ROOT.shushRooFit()
# see TError.h - gamma function prints lots of errors when scanning
ROOT.gROOT.ProcessLine("gErrorIgnoreLevel=kFatal")
ROOT.RooMsgService.instance().setSilentMode(True)
ROOT.gStyle.SetPadTopMargin(0.05)
ROOT.gStyle.SetPadBottomMargin(0.1)
ROOT.gStyle.SetPadLeftMargin(0.15)
ROOT.gStyle.SetPadRightMargin(0.05)
ROOT.gROOT.SetBatch(True)
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-c', '--cands', dest='CandTypes',
help='csv list of candidates',
default='421',
type='string')
parser.add_option('-i', '--input', dest='inputUrl' ,
help='csv list of files',
default=None,
type='string')
parser.add_option('-w', '--ws', dest='wsUrl',
help='ws url',
default=None,
type='string')
parser.add_option('-o', '--output', dest='output',
help='Where to put the output',
default=None,
type='string')
parser.add_option('--weight', dest='weight',
help='Apply a weight',
default=None,
type='string')
(opt, args) = parser.parse_args()
###########################################
# Differential xsec measurement: pT, eta
if opt.wsUrl:
if not opt.output:
opt.output = os.path.join(
os.path.dirname(opt.wsUrl).strip('.root'),'diff')
if not os.path.exists(opt.output):
os.system('mkdir -p %s' % opt.output)
print "Will store output in", opt.output
wsF = openTFile(opt.wsUrl)
if wsF == None:
print "ERROR: workspace file not found %s"%opt.wsUrl
return -1
ws = wsF.Get("w")
print "Read Workspace from %s"%opt.wsUrl
wsF.Close()
outUrl=os.path.join(opt.output,
os.path.basename(opt.wsUrl).replace('workspace','diff'))
outF = ROOT.TFile(outUrl,'RECREATE')
varRanges = { "pt" : [10,25,50,75],
"eta" : [0,0.9,1.5,2.5] }
for vname,ranges in varRanges.iteritems():
runDifferentialMeasurement(ws,vname,ranges,outF)
outF.Close()
###########################################
# Create workspace and run sPlot
else:
if not opt.output:
opt.output = os.path.join('unfolded',os.path.basename(
opt.inputUrl).strip('.root'))
else:
opt.output = os.path.join(opt.output,os.path.basename(
opt.inputUrl).strip('.root'))
if opt.weight:
wname = {
'BFragWeight[0]': 'bfrag',
'BFragWeight[1]': 'bfragup',
'BFragWeight[2]': 'bfragdn',
'BFragWeight[3]': 'bfragp11',
'BFragWeight[4]': 'bfragpete',
'BFragWeight[5]': 'bfraglund',
}.get(opt.weight, opt.weight.replace('[','').replace(']',''))
opt.output = '%s_%s'%(opt.output,wname)
if not opt.output.endswith('/'): opt.output += '/'
if not os.path.exists(opt.output):
os.system('mkdir -p %s' % opt.output)
print "Will store all output in", opt.output
#turn csvs to arrays
CandTypes = None
if len(opt.CandTypes)>0:
CandTypes=[int(val) for val in opt.CandTypes.split(',')]
postfixForOutputs=''
for c in CandTypes: postfixForOutputs +='_%d'%c
inputUrl = None
if not (opt.inputUrl is None):
if len(opt.inputUrl)>0:
inputUrl=opt.inputUrl.split(',')
# Create the workspace
wsUrl=generateWorkspace(CandTypes=CandTypes,
inputUrl=inputUrl,
postfixForOutputs=postfixForOutputs,
options=opt)
print "Retrieving workspace from ",wsUrl
wsF = ROOT.TFile.Open(wsUrl)
ws = wsF.Get("w")
wsF.Close()
#use the SPlot class to add SWeights to our data set
sData = ROOT.RooStats.SPlot("sData","An SPlot from mass",
ws.data('data'),ws.pdf('model'),
ROOT.RooArgList(ws.var('nsig'),ws.var('nbkg'))
)
getattr(ws,'import')(ws.data('data'), ROOT.RooFit.Rename("dataWithSWeights"))
data = ws.data("dataWithSWeights")
#the weighted data for signal and background species
sigData = ROOT.RooDataSet(data.GetName(),data.GetTitle(),data,data.get(),'','nsig_sw')
bkgData = ROOT.RooDataSet(data.GetName(),data.GetTitle(),data,data.get(),'','nbkg_sw')
#show the unfolded distributions and save them to a file
outFurl = os.path.join(opt.output,'UnfoldedDistributions%s.root'%postfixForOutputs)
outF = ROOT.TFile.Open(outFurl,'RECREATE')
varsToUnfold = [
['ptrel', 'p_{T,rel} [GeV]', 8],
['pfrac', 'p / p^{Jet} [GeV]', 8],
['ptfrac', 'p_{T} / p_{T}^{jet}', 8],
['pzfrac', 'p_{z} / p_{z}^{jet}', 8],
['ptchfrac', 'p_{T} / #Sigma_{ch} p_{T}', 8],
['pzchfrac', 'p_{z} / #Sigma_{ch} p_{z}', 8],
['dr', '#DeltaR to jet', 8]
]
for var,varTitle,nBins in varsToUnfold:
ws.var(var).SetTitle(varTitle)
ws.var(var).setBins(nBins)
showUnfolded(sigData=sigData,
bkgData=bkgData,
var=ws.var(var),
outD=opt.output,
outF=outF,
postfixForOutputs=postfixForOutputs)
outF.Close()
print 'Unfolded distributions can be found @ ',outFurl
def main():
#global ROOT configuration
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptFit(0)
ROOT.gSystem.Load("libUserCodeTopMassSecVtx")
ROOT.AutoLibraryLoader.enable()
ROOT.shushRooFit()
# see TError.h - gamma function prints lots of errors when scanning
ROOT.gROOT.ProcessLine("gErrorIgnoreLevel=kFatal")
ROOT.RooMsgService.instance().setSilentMode(True)
ROOT.gStyle.SetPadTopMargin(0.05)
ROOT.gStyle.SetPadBottomMargin(0.1)
ROOT.gStyle.SetPadLeftMargin(0.15)
ROOT.gStyle.SetPadRightMargin(0.05)
ROOT.gROOT.SetBatch(True)
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-c',
'--cands',
dest='CandTypes',
help='csv list of candidates',
default='421',
type='string')
parser.add_option('-i',
'--input',
dest='inputUrl',
help='csv list of files',
default=None,
type='string')
parser.add_option('-w',
'--ws',
dest='wsUrl',
help='ws url',
default=None,
type='string')
parser.add_option('-o',
'--output',
dest='output',
help='Where to put the output',
default=None,
type='string')
parser.add_option('--weight',
dest='weight',
help='Apply a weight',
default=None,
type='string')
(opt, args) = parser.parse_args()
###########################################
# Differential xsec measurement: pT, eta
if opt.wsUrl:
if not opt.output:
opt.output = os.path.join(
os.path.dirname(opt.wsUrl).strip('.root'), 'diff')
if not os.path.exists(opt.output):
os.system('mkdir -p %s' % opt.output)
print "Will store output in", opt.output
wsF = openTFile(opt.wsUrl)
if wsF == None:
print "ERROR: workspace file not found %s" % opt.wsUrl
return -1
ws = wsF.Get("w")
print "Read Workspace from %s" % opt.wsUrl
wsF.Close()
outUrl = os.path.join(
opt.output,
os.path.basename(opt.wsUrl).replace('workspace', 'diff'))
outF = ROOT.TFile(outUrl, 'RECREATE')
varRanges = {"pt": [10, 25, 50, 75], "eta": [0, 0.9, 1.5, 2.5]}
for vname, ranges in varRanges.iteritems():
runDifferentialMeasurement(ws, vname, ranges, outF)
outF.Close()
###########################################
# Create workspace and run sPlot
else:
if not opt.output:
opt.output = os.path.join(
'unfolded',
os.path.basename(opt.inputUrl).strip('.root'))
else:
opt.output = os.path.join(
opt.output,
os.path.basename(opt.inputUrl).strip('.root'))
if opt.weight:
wname = {
'BFragWeight[0]': 'bfrag',
'BFragWeight[1]': 'bfragup',
'BFragWeight[2]': 'bfragdn',
'BFragWeight[3]': 'bfragp11',
'BFragWeight[4]': 'bfragpete',
'BFragWeight[5]': 'bfraglund',
}.get(opt.weight,
opt.weight.replace('[', '').replace(']', ''))
opt.output = '%s_%s' % (opt.output, wname)
if not opt.output.endswith('/'): opt.output += '/'
if not os.path.exists(opt.output):
os.system('mkdir -p %s' % opt.output)
print "Will store all output in", opt.output
#turn csvs to arrays
CandTypes = None
if len(opt.CandTypes) > 0:
CandTypes = [int(val) for val in opt.CandTypes.split(',')]
postfixForOutputs = ''
for c in CandTypes:
postfixForOutputs += '_%d' % c
inputUrl = None
if not (opt.inputUrl is None):
if len(opt.inputUrl) > 0:
inputUrl = opt.inputUrl.split(',')
# Create the workspace
wsUrl = generateWorkspace(CandTypes=CandTypes,
inputUrl=inputUrl,
postfixForOutputs=postfixForOutputs,
options=opt)
print "Retrieving workspace from ", wsUrl
wsF = ROOT.TFile.Open(wsUrl)
ws = wsF.Get("w")
wsF.Close()
#use the SPlot class to add SWeights to our data set
sData = ROOT.RooStats.SPlot(
"sData", "An SPlot from mass", ws.data('data'), ws.pdf('model'),
ROOT.RooArgList(ws.var('nsig'), ws.var('nbkg')))
getattr(ws, 'import')(ws.data('data'),
ROOT.RooFit.Rename("dataWithSWeights"))
data = ws.data("dataWithSWeights")
#the weighted data for signal and background species
sigData = ROOT.RooDataSet(data.GetName(), data.GetTitle(), data,
data.get(), '', 'nsig_sw')
bkgData = ROOT.RooDataSet(data.GetName(), data.GetTitle(), data,
data.get(), '', 'nbkg_sw')
#show the unfolded distributions and save them to a file
outFurl = os.path.join(
opt.output, 'UnfoldedDistributions%s.root' % postfixForOutputs)
outF = ROOT.TFile.Open(outFurl, 'RECREATE')
varsToUnfold = [['ptrel', 'p_{T,rel} [GeV]', 8],
['pfrac', 'p / p^{Jet} [GeV]', 8],
['ptfrac', 'p_{T} / p_{T}^{jet}', 8],
['pzfrac', 'p_{z} / p_{z}^{jet}', 8],
['ptchfrac', 'p_{T} / #Sigma_{ch} p_{T}', 8],
['pzchfrac', 'p_{z} / #Sigma_{ch} p_{z}', 8],
['dr', '#DeltaR to jet', 8]]
for var, varTitle, nBins in varsToUnfold:
ws.var(var).SetTitle(varTitle)
ws.var(var).setBins(nBins)
showUnfolded(sigData=sigData,
bkgData=bkgData,
var=ws.var(var),
outD=opt.output,
outF=outF,
postfixForOutputs=postfixForOutputs)
outF.Close()
print 'Unfolded distributions can be found @ ', outFurl
