file = TFile(output + ".root")
runPileup = None
ipoint = 0
for key in file.GetListOfKeys():
kname = key.GetName()
if (kname.find('pileup_') < 0): continue
runnb = int(kname.split('_')[1])
h = file.Get(kname)
avgpileup = h.GetMean()
rmspileup = h.GetRMS()
evol.SetPoint(ipoint, runnb, avgpileup)
evol.SetPointError(ipoint, 0, 0, rmspileup, rmspileup)
ipoint = ipoint + 1
if (runPileup is None):
runPileup = h.Clone('runPileup')
formatPlot(runPileup, 1, 1, 1, 20, 0, True, True, 1, 1, 1)
file.Close()
setStyle()
c = getNewCanvas("evolc", "evolc", False)
c.SetWindowSize(1200, 600)
c.Divide(2, 1)
c.cd(1)
evol.Draw('ap')
c.cd(2)
runPileup.DrawNormalized('hist')
c.SaveAs(output + "_summary.C")
c.SaveAs(output + "_summary.root")
print "Average pileup: " + str(runPileup.GetMean()) + ' +/- ' + str(
runPileup.GetMeanError())
ipoint=0
for key in file.GetListOfKeys() :
kname=key.GetName()
if(kname.find('pileup_')<0): continue
runnb=int(kname.split('_')[1])
h=file.Get(kname)
avgpileup=h.GetMean()
rmspileup=h.GetRMS()
evol.SetPoint(ipoint,runnb,avgpileup)
evol.SetPointError(ipoint,0,0,rmspileup,rmspileup)
ipoint=ipoint+1
if(runPileup is None) :
runPileup = h.Clone('runPileup')
formatPlot( runPileup, 1,1,1,20,0,True,True,1,1,1)
file.Close()
setStyle()
c = getNewCanvas("evolc","evolc",False)
c.SetWindowSize(1200,600)
c.Divide(2,1)
c.cd(1)
evol.Draw('ap')
c.cd(2)
runPileup.DrawNormalized('hist')
c.SaveAs(output + "_summary.C")
c.SaveAs(output + "_summary.root")
print "Average pileup: " + str(runPileup.GetMean()) + ' +/- ' + str(runPileup.GetMeanError())
ensembleHandler.getTree().Delete("all")
misMeasurement.saveMonitoringHistograms()
inF.Close()
#save ensemble info to file
fout = open('ensemble_info.html', 'w')
fout.write(ensembleInfo)
fout.close()
#display results
plotF = ROOT.TFile.Open("MisassignmentMeasurement.root")
cats=['all','emu','ll']
setStyle()
cnv = getNewCanvas('misc','misc',False)
cnv.SetWindowSize(1500,500)
cnv.Divide(3,1)
cnv2 =getNewCanvas('misresc','misresc',False)
cnv2.SetWindowSize(1500,500)
cnv2.Divide(3,1)
cnv3 =getNewCanvas('fractfitc','fracfitc',False)
cnv3.SetWindowSize(1500,500)
cnv3.Divide(3,1)
fracFitter=[]
icnv=0
for c in cats:
prefix=''
if(c!='all') : prefix=c+'_'
icnv = icnv+1
def showControlPlots(stackplots=None,spimposeplots=None,dataplots=None,plottitles=None,generalLabel='CMS preliminary',outputDir='data/plots', samplehtml='',forceNormalization=True) :
if(len(stackplots)==0 and len(spimposeplots)==0 and len(dataplots)==0) : return
#convert to lists
stackLists=[]
for p in stackplots:
for i in xrange(0,len(p)):
if(len(stackLists)<=i):
newlist=ROOT.TList()
stackLists.append(newlist)
if( p[i].IsA().InheritsFrom("TH1") ):
if(p[i].GetEntries()==0) : continue
stackLists[i].Add(p[i])
spimposeLists=[]
for p in spimposeplots:
for i in xrange(0,len(p)):
if(len(spimposeLists)<=i):
newlist=ROOT.TList()
spimposeLists.append(newlist)
if( p[i].IsA().InheritsFrom("TH1") ):
if(p[i].GetEntries()==0) : continue
spimposeLists[i].Add(p[i])
dataLists=[]
for p in dataplots:
for i in xrange(0,len(p)):
if(len(dataLists)<=i):
newlist=ROOT.TList()
dataLists.append(newlist)
if( p[i].IsA().InheritsFrom("TH1") ):
#print p[i].GetEntries()
if(p[i].GetEntries()==0) : continue
dataLists[i].Add(p[i])
#draw result
nplots=max([len(stackLists), len(dataLists), len(spimposeLists)])
if(nplots==0) : return
#output dir
import getpass
os.system('mkdir -p ' + outputDir)
plotsToDisplay={}
setStyle()
c = getNewCanvas("recolevelc","recolevelc",False)
c.SetWindowSize(800,800)
c.SetCanvasSize(800,800)
for iplot in xrange(0,nplots):
stack=ROOT.TList()
if(len(stackLists)>iplot): stack=stackLists[iplot]
spimpose=ROOT.TList()
if(len(spimposeLists)>iplot): spimpose=spimposeLists[iplot]
data=ROOT.TList()
if(len(dataLists)>iplot): data=dataLists[iplot]
pname=''
if(stack.At(0) != None) : pname=stack.At(0).GetName()
elif(spimpose.At(0) != None) : pname=spimpose.At(0).GetName()
elif(data.At(0) != None) : pname=data.At(0).GetName()
if(len(pname)<=0): continue
tagname = pname.split('_')[0]
#print tagname
if(tagname.find('all')==0) : chtag='All events'
elif(tagname.find('mumu')==0): chtag='#mu#mu events'
elif(tagname.find('emu')==0): chtag='e#mu events'
elif(tagname.find('ee')==0): chtag='ee events'
elif(tagname.find('etau')==0): chtag='e#tau events'
elif(tagname.find('mutau')==0): chtag='#mu#tau events'
else : chtag=tagname
#add the plot
try :
plotsToDisplay[chtag].append(pname)
except :
plotsToDisplay[chtag]=[]
plotsToDisplay[chtag].append(pname)
isamp=0
for iplot in stack : isamp = isamp+1
for iplot in spimpose : isamp = isamp+1
for iplot in data: isamp = isamp+1
plotLabel = generalLabel
c.Clear()
leg=showPlotsAndMCtoDataComparison(c,stack,spimpose,data,True,forceNormalization)#,forceNormalization)
formatForCmsPublic(c.cd(1),leg,plotLabel,isamp)
c.SaveAs(outputDir+'/'+pname+'.png')
c.SaveAs(outputDir+'/'+pname+'.C')
if(pname.find('cutflow')>=0 or pname.find('eventflow')>=0 or pname.find('evtflow')>=0) :
try :
savePlotAsTable(stack,spimpose,data,outputDir+'/'+pname+'.tex')
except :
continue
#build a summary for the plot browser
categories=[]
subcategories=[]
finalplots=[]
for tag in plotsToDisplay.items() :
tagch=''
if( tag[0]=='All events') : tagch='all'
elif( tag[0]=='ee events') : tagch='ee'
elif( tag[0]=='#mu#mu events') : tagch='mumu'
elif( tag[0]=='e#mu events') : tagch='emu'
elif( tag[0]=='e#tau events') : tagch='etau'
elif( tag[0]=='#mu#tau events') : tagch='mutau'
#else : tagch=tag[0]
categories.append(tagch)
for pname in tag[1] :
ptokens=pname.split('_')
if(len(ptokens)>1):
prefix=ptokens[0]
subcat=''
if(prefix.find('eq0jets')>=0) : subcat='eq0jets'
elif(prefix.find('geq1jets')>=0) : subcat='geq1jets'
elif(prefix.find('eq1jets')>=0) : subcat='eq1jets'
elif(prefix.find('geq2jets')>=0) : subcat='geq2jets'
elif(prefix.find('eq2jets')>=0) : subcat='eq2jets'
elif(prefix.find('geq3jets')>=0) : subcat='geq3jets'
elif(prefix.find('eq3jets')>=0) : subcat='eq3jets'
elif(prefix.find('eq0btags')>=0) : subcat='eq0btags'
elif(prefix.find('geq1btags')>=0) : subcat='geq1btags'
elif(prefix.find('eq1btags')>=0) : subcat='eq1btags'
elif(prefix.find('geq2btags')>=0) : subcat='geq2btags'
elif(prefix.find('eq2btags')>=0) : subcat='eq2btags'
elif(prefix.find('vbf')>=0) : subcat='vbf'
else :
subcat=prefix
subcat=subcat.replace(tagch,'')
subcategories.append(subcat)
finalplots.append( ptokens[1] )
#remove duplicates
categories=list(set(categories))
subcategories=list(set(subcategories))
finalplots=list(set(finalplots))
#write in json format
fileObj = open(outputDir+'/plotter.json','w')
fileObj.write(json.dumps( { 'categories':categories,
'subcategories':subcategories,
'plots':finalplots,
'ploturl':''},
sort_keys=True,
indent=4 ) )
fileObj.close()
ensembleInfo += '<tr><td></td></tr>'
ensembleHandler.getTree().Delete("all")
misMeasurement.saveMonitoringHistograms()
inF.Close()
#save ensemble info to file
fout = open('ensemble_info.html', 'w')
fout.write(ensembleInfo)
fout.close()
#display results
plotF = ROOT.TFile.Open("MisassignmentMeasurement.root")
cats = ['all', 'emu', 'll']
setStyle()
cnv = getNewCanvas('misc', 'misc', False)
cnv.SetWindowSize(1500, 500)
cnv.Divide(3, 1)
cnv2 = getNewCanvas('misresc', 'misresc', False)
cnv2.SetWindowSize(1500, 500)
cnv2.Divide(3, 1)
cnv3 = getNewCanvas('fractfitc', 'fracfitc', False)
cnv3.SetWindowSize(1500, 500)
cnv3.Divide(3, 1)
fracFitter = []
icnv = 0
for c in cats:
prefix = ''
if (c != 'all'): prefix = c + '_'
icnv = icnv + 1
fmtValue = toLatexRounded(fitfunc.GetParameter(0),fitfunc.GetParError(0))
fmtValue = fmtValue.replace("[","(")
fmtValue = fmtValue.replace("]",")")
print ' others: ' + fmtValue
except:
print ' others: n/a'
otherRescalingHistos.Add(otherRescaleFactor)
#display results
setStyle()
#show the inclusive output
ROOT.gStyle.SetOptFit(0)
ROOT.gStyle.SetOptStat(0)
c = getNewCanvas("kfactors","kfactors",False)
c.SetWindowSize(1000,500)
c.Divide(2,1)
spimpose=ROOT.TList()
stack=ROOT.TList()
p=c.cd(1)
leg=showSimplePlot(p,topRescalingHistos)
formatForCmsPublic(p,leg,'CMS simulation',3)
p=c.cd(2)
leg=showSimplePlot(p,otherRescalingHistos)
leg.Delete()
c.cd()
c.Modified()
c.Update()
raw_input('...')
def showControlPlots(stackplots=None,spimposeplots=None,dataplots=None,plottitles=None,generalLabel='CMS preliminary',outputDir='data/plots', samplehtml='',forceNormalization=True) :
if(len(stackplots)==0 and len(spimposeplots)==0 and len(dataplots)==0) : return
#convert to lists
stackLists=[]
for p in stackplots:
for i in xrange(0,len(p)):
if(len(stackLists)<=i):
newlist=ROOT.TList()
stackLists.append(newlist)
if( p[i].IsA().InheritsFrom("TH1") ):
if(p[i].GetEntries()==0) : continue
stackLists[i].Add(p[i])
spimposeLists=[]
for p in spimposeplots:
for i in xrange(0,len(p)):
if(len(spimposeLists)<=i):
newlist=ROOT.TList()
spimposeLists.append(newlist)
if( p[i].IsA().InheritsFrom("TH1") ):
if(p[i].GetEntries()==0) : continue
spimposeLists[i].Add(p[i])
dataLists=[]
for p in dataplots:
for i in xrange(0,len(p)):
if(len(dataLists)<=i):
newlist=ROOT.TList()
dataLists.append(newlist)
if( p[i].IsA().InheritsFrom("TH1") ):
#print p[i].GetEntries()
if(p[i].GetEntries()==0) : continue
dataLists[i].Add(p[i])
#draw result
nplots=max([len(stackLists), len(dataLists), len(spimposeLists)])
if(nplots==0) : return
#output dir
import getpass
os.system('mkdir -p ' + outputDir)
plotsToDisplay={}
setStyle()
c = getNewCanvas("recolevelc","recolevelc",False)
c.SetWindowSize(800,800)
c.SetCanvasSize(800,800)
for iplot in xrange(0,nplots):
stack=ROOT.TList()
if(len(stackLists)>iplot): stack=stackLists[iplot]
spimpose=ROOT.TList()
if(len(spimposeLists)>iplot): spimpose=spimposeLists[iplot]
data=ROOT.TList()
if(len(dataLists)>iplot): data=dataLists[iplot]
pname=''
if(stack.At(0) != None) : pname=stack.At(0).GetName()
elif(spimpose.At(0) != None) : pname=spimpose.At(0).GetName()
elif(data.At(0) != None) : pname=data.At(0).GetName()
if(len(pname)<=0): continue
tagname = pname.split('_')[0]
#print tagname
if(tagname.find('all')==0) : chtag='All events'
elif(tagname.find('mumu')==0): chtag='#mu#mu events'
elif(tagname.find('emu')==0): chtag='e#mu events'
elif(tagname.find('ee')==0): chtag='ee events'
elif(tagname.find('etau')==0): chtag='e#tau events'
elif(tagname.find('mutau')==0): chtag='#mu#tau events'
else : chtag=tagname
#add the plot
try :
plotsToDisplay[chtag].append(pname)
except :
plotsToDisplay[chtag]=[]
plotsToDisplay[chtag].append(pname)
isamp=0
for iplot in stack : isamp = isamp+1
for iplot in spimpose : isamp = isamp+1
for iplot in data: isamp = isamp+1
plotLabel = generalLabel
c.Clear()
leg=showPlotsAndMCtoDataComparison(c,stack,spimpose,data,True,forceNormalization)#,forceNormalization)
formatForCmsPublic(c.cd(1),leg,plotLabel,isamp)
c.SaveAs(outputDir+'/'+pname+'.png')
c.SaveAs(outputDir+'/'+pname+'.C')
if(pname.find('cutflow')>=0 or pname.find('eventflow')>=0 or pname.find('evtflow')>=0) :
try :
savePlotAsTable(stack,spimpose,data,outputDir+'/'+pname+'.tex')
except :
continue
#build a summary for the plot browser
categories=[]
subcategories=[]
finalplots=[]
for tag in plotsToDisplay.items() :
tagch=''
if( tag[0]=='All events') : tagch='all'
elif( tag[0]=='ee events') : tagch='ee'
elif( tag[0]=='#mu#mu events') : tagch='mumu'
elif( tag[0]=='e#mu events') : tagch='emu'
elif( tag[0]=='e#tau events') : tagch='etau'
elif( tag[0]=='#mu#tau events') : tagch='mutau'
#else : tagch=tag[0]
categories.append(tagch)
for pname in tag[1] :
ptokens=pname.split('_')
if(len(ptokens)>1):
prefix=ptokens[0]
subcat=''
if(prefix.find('eq0jets')>=0) : subcat='eq0jets'
elif(prefix.find('geq1jets')>=0) : subcat='geq1jets'
elif(prefix.find('eq1jets')>=0) : subcat='eq1jets'
elif(prefix.find('geq2jets')>=0) : subcat='geq2jets'
elif(prefix.find('eq2jets')>=0) : subcat='eq2jets'
elif(prefix.find('geq3jets')>=0) : subcat='geq3jets'
elif(prefix.find('eq3jets')>=0) : subcat='eq3jets'
elif(prefix.find('eq0btags')>=0) : subcat='eq0btags'
elif(prefix.find('geq1btags')>=0) : subcat='geq1btags'
elif(prefix.find('eq1btags')>=0) : subcat='eq1btags'
elif(prefix.find('geq2btags')>=0) : subcat='geq2btags'
elif(prefix.find('eq2btags')>=0) : subcat='eq2btags'
elif(prefix.find('vbf')>=0) : subcat='vbf'
else :
subcat=prefix
subcat=subcat.replace(tagch,'')
subcategories.append(subcat)
finalplots.append( ptokens[1] )
#remove duplicates
categories=list(set(categories))
subcategories=list(set(subcategories))
finalplots=list(set(finalplots))
#write in json format
fileObj = open(outputDir+'/plotter.json','w')
fileObj.write(json.dumps( { 'categories':categories,
'subcategories':subcategories,
'plots':finalplots,
'ploturl':''},
sort_keys=True,
indent=4 ) )
fileObj.close()
fmtValue = toLatexRounded(fitfunc.GetParameter(0),
fitfunc.GetParError(0))
fmtValue = fmtValue.replace("[", "(")
fmtValue = fmtValue.replace("]", ")")
print ' others: ' + fmtValue
except:
print ' others: n/a'
otherRescalingHistos.Add(otherRescaleFactor)
#display results
setStyle()
#show the inclusive output
ROOT.gStyle.SetOptFit(0)
ROOT.gStyle.SetOptStat(0)
c = getNewCanvas("kfactors", "kfactors", False)
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
spimpose = ROOT.TList()
stack = ROOT.TList()
p = c.cd(1)
leg = showSimplePlot(p, topRescalingHistos)
formatForCmsPublic(p, leg, 'CMS simulation', 3)
p = c.cd(2)
leg = showSimplePlot(p, otherRescalingHistos)
leg.Delete()
c.cd()
c.Modified()
c.Update()
raw_input('...')
