def drawFromDC(): config = BetterConfigParser() config.read(opts.config) print "opts.config:", opts.config dataname = '' if 'Zmm' in opts.bin: dataname = 'Zmm' elif 'Zee' in opts.bin: dataname = 'Zee' elif 'Wmunu' in opts.bin: dataname = 'Wmn' elif 'Wenu' in opts.bin: dataname = 'Wen' elif 'Znunu' in opts.bin: dataname = 'Znn' elif 'Wtn' in opts.bin: dataname = 'Wtn' print 'Variable printing' print opts.var if (opts.var == ''): var = 'BDT' if dataname == 'Zmm' or dataname == 'Zee': var = 'BDT_Zll' elif dataname == 'Wmn' or dataname == 'Wen': var = 'BDT_Wln' elif dataname == 'Znn': if 'HighPt' in opts.bin: var = 'BDT_ZnnHighPt' elif 'LowPt' in opts.bin: var = 'BDT_ZnnLowPt' elif 'LowCSV' in opts.bin: var = 'BDT_ZnnLowCSV' else: var = 'BDT_Znn' if dataname == '' or var == 'BDT': raise RuntimeError, "Did not recognise mode or var from %s" % opts.bin else: var = opts.var region = 'BDT' ws_var = config.get('plotDef:%s' % var, 'relPath') ws_var = ROOT.RooRealVar(ws_var, ws_var, -1., 1.) blind = eval(config.get('Plot:%s' % region, 'blind')) Stack = StackMaker(config, var, region, True) if 'LowPt' in opts.bin or 'ch1_Wenu' == opts.bin or 'ch2_Wmunu' == opts.bin: print 'Niklas %s' % opts.bin Stack.addFlag2 = 'Low p_{T}(V)' elif 'MedPt' in opts.bin or 'ch1_Wenu2' == opts.bin or 'ch2_Wmunu2' == opts.bin: Stack.addFlag2 = 'Intermediate p_{T}(V)' elif 'HighPt' in opts.bin or 'ch1_Wenu3' == opts.bin or 'ch2_Wmunu3' == opts.bin: Stack.addFlag2 = 'High p_{T}(V)' else: Stack.addFlag2 = '' preFit = False addName = 'PostFit_%s' % (opts.fit) if not opts.mlfit: addName = 'PreFit' preFit = True Stack.options['pdfName'] = '%s_%s_%s.pdf' % (var, opts.bin, addName) log = eval(config.get('Plot:%s' % region, 'log')) setup = config.get('Plot_general', 'setup').split(',') if dataname == 'Zmm' or dataname == 'Zee': try: setup.remove('W1b') setup.remove('W2b') setup.remove('Wlight') setup.remove('WH') except: print '@INFO: Wb / Wligh / WH not present in the datacard' # if not dataname == 'Znn' and 'QCD' in setup: # setup.remove('QCD') Stack.setup = setup Dict = eval(config.get('LimitGeneral', 'Dict')) lumi = eval(config.get('Plot_general', 'lumi')) options = copy(opts) options.dataname = "data_obs" options.mass = 0 options.format = "%8.3f +/- %6.3f" options.channel = opts.bin options.excludeSyst = [] options.norm = False options.stat = False options.bin = True # fake that is a binary output, so that we parse shape lines options.out = "tmp.root" options.fileName = args[0] options.cexpr = False options.fixpars = False options.libs = [] options.verbose = 0 options.poisson = 0 options.nuisancesToExclude = [] options.noJMax = None theBinning = ROOT.RooFit.Binning(Stack.nBins, Stack.xMin, Stack.xMax) file = open(opts.dc, "r") os.chdir(os.path.dirname(opts.dc)) DC = parseCard(file, options) if not DC.hasShapes: DC.hasShapes = True MB = ShapeBuilder(DC, options) theShapes = {} theSyst = {} nuiVar = {} if opts.mlfit: nuiVar = readBestFit(opts.mlfit) if not opts.bin in DC.bins: raise RuntimeError, "Cannot open find %s in bins %s of %s" % ( opts.bin, DC.bins, opts.dc) for b in DC.bins: if options.channel != None and (options.channel != b): continue exps = {} expNui = {} shapeNui = {} reducedShapeNui = {} for (p, e) in DC.exp[b].items( ): # so that we get only self.DC.processes contributing to this bin exps[p] = [e, []] expNui[p] = [e, []] for (lsyst, nofloat, pdf, pdfargs, errline) in DC.systs: if pdf in ('param', 'flatParam'): continue # begin skip systematics skipme = False for xs in options.excludeSyst: if re.search(xs, lsyst): skipme = True if skipme: continue # end skip systematics counter = 0 for p in DC.exp[b].keys( ): # so that we get only self.DC.processes contributing to this bin if errline[b][p] == 0: continue # if p == 'QCD' and not 'QCD' in setup: continue if pdf == 'gmN': exps[p][1].append(1 / sqrt(pdfargs[0] + 1)) elif pdf == 'gmM': exps[p][1].append(errline[b][p]) elif type(errline[b][p]) == list: kmax = max(errline[b][p][0], errline[b][p][1], 1.0 / errline[b][p][0], 1.0 / errline[b][p][1]) exps[p][1].append(kmax - 1.) elif pdf == 'lnN': lnNVar = max(errline[b][p], 1.0 / errline[b][p]) - 1. if not nuiVar.has_key('%s_%s' % (opts.fit, lsyst)): nui = 0. else: nui = nuiVar['%s_%s' % (opts.fit, lsyst)][0] lnNVar = lnNVar * nuiVar['%s_%s' % (opts.fit, lsyst)][1] exps[p][1].append(lnNVar) expNui[p][1].append(abs(1 - errline[b][p]) * nui) elif ("shape" in pdf): #print 'shape %s %s: %s'%(pdf,p,lsyst) s0 = MB.getShape(b, p) sUp = MB.getShape(b, p, lsyst + "Up") sDown = MB.getShape(b, p, lsyst + "Down") if (s0.InheritsFrom("RooDataHist")): s0 = ROOT.RooAbsData.createHistogram( s0, p, ws_var, theBinning) s0.SetName(p) sUp = ROOT.RooAbsData.createHistogram( sUp, p + lsyst + 'Up', ws_var, theBinning) sUp.SetName(p + lsyst + 'Up') sDown = ROOT.RooAbsData.createHistogram( sDown, p + lsyst + 'Down', ws_var, theBinning) sDown.SetName(p + lsyst + 'Down') theShapes[p] = s0.Clone() theShapes[p + lsyst + 'Up'] = sUp.Clone() theShapes[p + lsyst + 'Down'] = sDown.Clone() if not nuiVar.has_key('%s_%s' % (opts.fit, lsyst)): nui = 0. reducedNui = 1. else: nui = nuiVar['%s_%s' % (opts.fit, lsyst)][0] reducedNui = nuiVar['%s_%s' % (opts.fit, lsyst)][1] shapeNui[p + lsyst] = nui reducedShapeNui[lsyst] = reducedNui if not 'CMS_vhbb_stat' in lsyst: if counter == 0: theSyst[lsyst] = s0.Clone() theSyst[lsyst + 'Up'] = sUp.Clone() theSyst[lsyst + 'Down'] = sDown.Clone() else: theSyst[lsyst].Add(s0) theSyst[lsyst + 'Up'].Add(sUp.Clone()) theSyst[lsyst + 'Down'].Add(sDown.Clone()) counter += 1 procs = DC.exp[b].keys() procs.sort() print "Original procs:", procs # if not 'QCD' in setup and 'QCD' in procs: # procs.remove('QCD') if not 'W2b' in setup and 'WjHF' in procs: procs.remove('WjHF') if not 'Wlight' in setup and 'WjLF' in procs: procs.remove('WjLF') fmt = ("%%-%ds " % max([len(p) for p in procs])) + " " + options.format #Compute norm uncertainty and best fit theNormUncert = {} theBestFit = {} for p in procs: relunc = sqrt(sum([x * x for x in exps[p][1]])) print fmt % (p, exps[p][0], exps[p][0] * relunc) theNormUncert[p] = relunc absBestFit = sum([x for x in expNui[p][1]]) theBestFit[p] = 1. + absBestFit histos = [] typs = [] setup2 = copy(setup) shapesUp = [[] for _ in range(0, len(setup2))] shapesDown = [[] for _ in range(0, len(setup2))] sigCount = 0 signalList = ['ZH', 'WH'] #signalList = ['VVb'] for p in procs: b = opts.bin for s in setup: if not Dict[s] == p: continue if s in signalList: if sigCount == 0: Overlay = copy(theShapes[Dict[s]]) else: Overlay.Add(theShapes[Dict[s]]) sigCount += 1 else: histos.append(theShapes[Dict[s]]) typs.append(s) for (lsyst, nofloat, pdf, pdfargs, errline) in DC.systs: if errline[b][p] == 0: continue if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst: print 'syst %s' % lsyst shapesUp[setup2.index(s)].append(theShapes[Dict[s] + lsyst + 'Up']) shapesDown[setup2.index(s)].append( theShapes[Dict[s] + lsyst + 'Down']) #------------- #Compute absolute uncertainty from shapes counter = 0 for (lsyst, nofloat, pdf, pdfargs, errline) in DC.systs: sumErr = 0 for p in procs: sumErr += errline[b][p] if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst and not sumErr == 0: theSystUp = theSyst[lsyst + 'Up'].Clone() theSystUp.Add(theSyst[lsyst].Clone(), -1.) theSystUp.Multiply(theSystUp) theSystDown = theSyst[lsyst + 'Down'].Clone() theSystDown.Add(theSyst[lsyst].Clone(), -1.) theSystDown.Multiply(theSystDown) theSystUp.Scale(reducedShapeNui[lsyst]) theSystDown.Scale(reducedShapeNui[lsyst]) if counter == 0: theAbsSystUp = theSystUp.Clone() theAbsSystDown = theSystDown.Clone() else: theAbsSystUp.Add(theSystUp.Clone()) theAbsSystDown.Add(theSystDown.Clone()) counter += 1 #------------- #Best fit for shapes if not preFit: histos, Overlay, typs = getBestFitShapes(procs, theShapes, shapeNui, theBestFit, DC, setup, opts, Dict) counter = 0 errUp = [] total = [] errDown = [] nBins = histos[0].GetNbinsX() print 'total bins %s' % nBins Error = ROOT.TGraphAsymmErrors(histos[0]) theTotalMC = histos[0].Clone() for h in range(1, len(histos)): theTotalMC.Add(histos[h]) total = [[]] * nBins errUp = [[]] * nBins errDown = [[]] * nBins for bin in range(1, nBins + 1): binError = theTotalMC.GetBinError(bin) if math.isnan(binError): binError = 0. total[bin - 1] = theTotalMC.GetBinContent(bin) #Stat uncertainty of the MC outline errUp[bin - 1] = [binError] errDown[bin - 1] = [binError] #Relative norm uncertainty of the individual MC for h in range(0, len(histos)): print "h:", h print "bin:", bin print "histos:", histos print "theNormUncert:", theNormUncert print "histos[h]:", histos[h] errUp[bin - 1].append(histos[h].GetBinContent(bin) * theNormUncert[histos[h].GetName()]) errDown[bin - 1].append(histos[h].GetBinContent(bin) * theNormUncert[histos[h].GetName()]) #Shape uncertainty of the MC for bin in range(1, nBins + 1): #print sqrt(theSystUp.GetBinContent(bin)) errUp[bin - 1].append(sqrt(theAbsSystUp.GetBinContent(bin))) errDown[bin - 1].append(sqrt(theAbsSystDown.GetBinContent(bin))) #Add all in quadrature totErrUp = [sqrt(sum([x**2 for x in bin])) for bin in errUp] totErrDown = [sqrt(sum([x**2 for x in bin])) for bin in errDown] #Make TGraph with errors for bin in range(1, nBins + 1): if not total[bin - 1] == 0: point = histos[0].GetXaxis().GetBinCenter(bin) Error.SetPoint(bin - 1, point, 1) Error.SetPointEYlow(bin - 1, totErrDown[bin - 1] / total[bin - 1]) print 'down %s' % (totErrDown[bin - 1] / total[bin - 1]) Error.SetPointEYhigh(bin - 1, totErrUp[bin - 1] / total[bin - 1]) print 'up %s' % (totErrUp[bin - 1] / total[bin - 1]) #----------------------- #Read data data0 = MB.getShape(opts.bin, 'data_obs') if (data0.InheritsFrom("RooDataHist")): data0 = ROOT.RooAbsData.createHistogram(data0, 'data_obs', ws_var, theBinning) data0.SetName('data_obs') datas = [data0] datatyps = [None] datanames = [dataname] print "blind:", blind print "'BDT' in var:", 'BDT' in var if blind and 'BDT' in var: print "I'm blinding..." for bin in range(datas[0].GetNbinsX() / 2, datas[0].GetNbinsX() + 1): datas[0].SetBinContent(bin, 0) histos.append(copy(Overlay)) if 'ZH' in signalList and 'WH' in signalList: typs.append('VH') if 'ZH' in Stack.setup: Stack.setup.remove('ZH') if 'WH' in Stack.setup: Stack.setup.remove('WH') Stack.setup.insert(0, 'VH') elif 'ZH' in signalList: typs.append('ZH') elif 'WH' in signalList: typs.append('WH') elif 'VVb' in signalList: typs.append('VVb') print Stack.setup Stack.histos = histos Stack.typs = typs Stack.datas = datas Stack.datatyps = datatyps Stack.datanames = datanames Stack.overlay = [Overlay] Stack.AddErrors = Error if dataname == 'Wtn': lumi = 18300. Stack.lumi = lumi Stack.doPlot() print 'i am done!\n'
def drawFromDC(): config = BetterConfigParser() config.read(opts.config) region = opts.region print "\nopts.config:",opts.config print "opts:", opts print "var:", opts.var print "bin:", opts.bin #Should Read this from the parser datanames = config.get('dc:%s'%opts.bin,'data').split(' ') print 'dataname is', datanames region = opts.bin var = opts.var ws_var = config.get('plotDef:%s'%var,'relPath') nbin = int(config.get('plotDef:%s'%var,'nBins')) xmin = float(config.get('plotDef:%s'%var,'min')) xmax = float(config.get('plotDef:%s'%var,'max')) blind = eval(opts.blind) postfit = eval(opts.postfit) print 'config:', config print 'var: ', var print 'region: ', region print 'blind: ', blind print 'postfit: ', postfit Group_dc = eval(config.get('Plot_general','Group_dc')) Stack=StackMaker(config,var,region,True) # check for pre or post fit options preFit = False addName = 'PostFit_%s' %(opts.fit) if not opts.mlfit: addName = 'PreFit' preFit = True print '\n-----> Fit Type(opts.fit) : ', opts.fit print ' (opts.mlfit): ', opts.mlfit print ' preFit : ', preFit Stack.options['pdfName'] = '%s_%s_%s.pdf' %(var,opts.bin,addName) #log = eval(config.get('Plot:%s'%region,'log')) if 'Zee' in opts.bin or 'Zuu' in opts.bin: #VH setup = ['ggZHbb', 'qqZHbb','Zbb','Zb','Z_udscg','TT','VV2b','VVlight','ST'] #VV #setup = ['VV2b','ggZHbb','qqZHbb','Z_udscg','Zb','Zbb','TT','VVlight','ST'] channel = 'ZllHbb' if 'Zee' in opts.bin: lep_channel = 'Zee' elif 'Zuu' in opts.bin: lep_channel = 'Zuu' #region_dic = {'BDT':'SIG','CRZlight':'Zlf','CRZb':'Zhf','CRttbar':'TT'} region_dic = { 'BDT':'BDT', 'CRZlight':'CRZlight', 'CRZb':'CRZb', 'CRttbar':'CRttbar', 'ZeeMass_lowpt':'ZeeMass_lowpt', 'ZeeMass_highpt':'ZeeMass_highpt', 'ZuuMass_lowpt':'ZuuMass_lowpt', 'ZuuMass_highpt':'ZuuMass_highpt', 'ZeeMassVV_lowpt':'ZeeMassVV_lowpt', 'ZeeMassVV_highpt':'ZeeMassVV_highpt', 'ZuuMassVV_lowpt':'ZuuMassVV_lowpt', 'ZuuMassVV_highpt':'ZuuMassVV_highpt', 'ZuuMass_Vptbin0':'ZuuMass_Vptbin0', 'ZuuMass_Vptbin1':'ZuuMass_Vptbin1', 'ZuuMass_Vptbin2':'ZuuMass_Vptbin2', 'ZeeMass_Vptbin0':'ZeeMass_Vptbin0', 'ZeeMass_Vptbin1':'ZeeMass_Vptbin1', 'ZeeMass_Vptbin2':'ZeeMass_Vptbin2', } print 'opts.bin is', opts.bin region_name = [region_dic[key] for key in region_dic if (key in opts.bin)] region_name = region_name[0] print 'region_name is', region_name pt_region_dic = {'lowpt':'lowpt','highpt':'highpt','bin0':'bin0','bin1':'bin1','bin2':'bin2'} pt_region_name = [pt_region_dic[key] for key in pt_region_dic if (key in opts.bin)] pt_region_name = pt_region_name[0] else: print '@ERROR: This is not a Zll region. Aborting' sys.exit() Stack.setup = setup Dict = eval(config.get('LimitGeneral','Dict')) lumi = eval(config.get('General','lumi')) Stack.nBins = nbin Stack.xMin = xmin Stack.xMax = xmax print '/n----> The Binning:' print 'nBins:', Stack.nBins print 'xMin:', Stack.xMin print 'xMax:', Stack.xMax theBinning = ROOT.RooFit.Binning(Stack.nBins,Stack.xMin,Stack.xMax) ################# #We are now skiping a large part of the orginal code, as everything that remains to be done is to read the postfit plot from the mlfit.root #How should the scale sys (lnN be handled) ? # histos = [] typs = [] shapes = {} shapesUp = [[] for _ in range(0,len(setup))] shapesDown = [[] for _ in range(0,len(setup))] #signalList = ['ggZHbb', 'qqZHbb'] #signalList = [] sigCount = 0 #Overlay ={} Overlay = [] prefit_overlay = [] postfit_from_wc = False if opts.mlfit.split('/')[-1] != 'mlfit.root': postfit_from_wc = True dirname = '' #### #Open the mlfit.root and retrieve the mc print 'opts.mlfit is', opts.mlfit file = ROOT.TFile.Open(opts.mlfit) #if file == None: raise RuntimeError, "Cannot open file %s" % theFile #print '\n\n-----> Fit File: ',file print '=====================' print 'POSTFIT IS', postfit print '=====================' if postfit: if not postfit_from_wc: if not ROOT.gDirectory.cd('shapes_fit_s'): print '@ERROR: didn\'t find the shapes_fit_s directory. Aborting' sys.exit() else: pass else: if not ROOT.gDirectory.cd('shapes_prefit'): print '@ERROR: didn\'t find the shapes_prefit directory. Aborting' sys.exit() folder_found = False for dir in ROOT.gDirectory.GetListOfKeys(): dirinfo = dir.GetName().split('_') print 'dir name is', dir.GetName().split('_') ##if not (dirinfo[0] == channel and dirinfo[2] == lep_channel and dirinfo[3] == region_name and dirinfo[4] == pt_region_name): print 'dirinfo is', dirinfo print 'lep_channel is', lep_channel print 'region_name is', region_name print 'pt_region_name is', pt_region_name if not (dirinfo[0] == lep_channel and dirinfo[1] == region_name and dirinfo[2] == pt_region_name): #for VV if not (dirinfo[2] == region_name.split('_')[0] and dirinfo[3] == pt_region_name): if not (len(dirinfo) > 3 and dirinfo[3] == region_name.split('_')[0] and dirinfo[4] == 'Vpt'+pt_region_name): continue if postfit_from_wc and 'prefit' in dirinfo: continue folder_found = True dirname = dir.GetName() #signal, use prefit for s in setup: if ('ZHbb' in s and postfit) and not postfit_from_wc: print 'ERROR' sys.exit() ROOT.gDirectory.cd('/shapes_prefit') ROOT.gDirectory.cd(dirname) found = False for subdir in ROOT.gDirectory.GetListOfKeys(): #print 'subdir name is', subdir.GetName() if subdir.GetName() == Dict[s]: found = True hist = rebinHist(gDirectory.Get(subdir.GetName()).Clone(), nbin, xmin, xmax) histos.append(hist) typs.append(s) #print 's is', s #print 'signalList is', signalList #if s in signalList: # hist.SetTitle(s) # Overlay.append(hist) # print 'the Histogram title is', hist.GetTitle() else: #SF_ZJets = [0.95188, 0.94404, 1.0463] #SF_TTbar = 1.0373 #;Vpt high #SF_ZJets = [1.1235, 0.91368, 1.2435] #SF_TTbar = 1.0601 #Start be getting the SF print 'Gonna apply SF' scale = 1 #if 'low' in opts.dc: # if 'TT' in s: scale = 1.01 # if 'Z_udscg' in s: scale = 0.96 # if 'Zb' in s: scale = 0.99 # if 'Zbb' in s : scale = 1.04 #elif 'high' in opts.dc: # if 'TT' in s: scale = 1.01 # if 'Z_udscg' in s: scale = 1.03 # if 'Zb' in s: scale = 0.96 # if 'Zbb' in s: scale = 1.23 #else: # pass # #ROOT.gDirectory.cd('/shapes_fit_s') #if not postfit_from_wc: # ROOT.gDirectory.cd('/shapes_prefit') # print 'ERROR2' # sys.exit() #else: # scale = 1 ROOT.gDirectory.cd(dirname) found = False for subdir in ROOT.gDirectory.GetListOfKeys(): print 'subdir name is', subdir.GetName() #print 'Dict is ', Dict if subdir.GetName() == Dict[s] or (postfit_from_wc and subdir.GetName() == s): found = True hist = rebinHist(gDirectory.Get(subdir.GetName()).Clone(), nbin, xmin, xmax, scale) histos.append(hist) typs.append(s) print 's is', s #print 'signalList is', signalList #if s in signalList: # hist.SetTitle(s) # Overlay.append(hist) # print 'the Histogram title is', hist.GetTitle() #take prefit distr. for signal if not found: print '@ERROR: didn\'t find the postfit histogram. Aborting' sys.exit() if not postfit_from_wc: ROOT.gDirectory.cd('/shapes_prefit/'+dirname) print 'ERROR3' sys.exit() if not postfit_from_wc: total = rebinHist(gDirectory.Get('total').Clone(), nbin, xmin, xmax) total.SetTitle('prefit') prefit_overlay.append(total) break if not folder_found: print '@ERROR: Folder was not found.' print 'lep_channel', lep_channel print 'region_name', region_name print 'pt_region_name', pt_region_name sys.exit() #retrieve the data options = copy(opts) options.dataname = "data_obs" options.mass = 0 options.format = "%8.3f +/- %6.3f" options.channel = opts.bin options.excludeSyst = [] options.norm = False options.stat = False options.bin = True # fake that is a binary output, so that we parse shape lines options.out = "tmp.root" options.fileName = args[0] options.cexpr = False options.fixpars = False options.libs = [] options.verbose = 0 options.poisson = 0 options.nuisancesToExclude = [] options.noJMax = None #This needs to be done from the "dc" root file print 'file is ',opts.dc dc_file= open(opts.dc, "r") os.chdir(os.path.dirname(opts.dc)) DC = parseCard(dc_file, options) if not DC.hasShapes: DC.hasShapes = True MB = ShapeBuilder(DC, options) data0 = MB.getShape(opts.bin,'data_obs') if (data0.InheritsFrom("RooDataHist")): data0 = ROOT.RooAbsData.createHistogram(data0,'data_obs',ws_var,theBinning) data0.SetName('data_obs') datas=[data0] if blind and 'BDT' in var: for bin in range(datas[0].GetNbinsX()-3,datas[0].GetNbinsX()+1): datas[0].SetBinContent(bin,0) if blind and 'Mass' in var: for bin in range(datas[0].GetNbinsX()-13,datas[0].GetNbinsX()-7): datas[0].SetBinContent(bin,0) datatyps = [None] #print '\nshapes!!!', shapes print '\nOVERLAY!!!', Overlay #Add all the histos and overlay to the stackmaker such that they can be ploted #print 'before Stack, histos are', histos #sys.exit() Stack.histos = histos Stack.typs = typs Stack.overlay = Overlay Stack.prefit_overlay = prefit_overlay Stack.datas = datas Stack.datatyps = datatyps Stack.datanames= datanames Stack.AddErrors= True Stack.lumi = lumi if 'BDT' in var: Stack.forceLog = True Stack.doPlot() print 'i am done!\n'
def drawFromDC(): config = BetterConfigParser() config.read(opts.config) region = opts.region print "\nopts.config:",opts.config print "opts:", opts print "var:", opts.var print "bin:", opts.bin dataname = 'Zll' if 'Zmm' in opts.bin: dataname = 'Zmm' elif 'Zee' in opts.bin: dataname = 'Zee' elif 'Wmunu' in opts.bin: dataname = 'Wmn' elif 'Wenu' in opts.bin: dataname = 'Wen' elif 'Znunu' in opts.bin: dataname = 'Znn' elif 'Wtn' in opts.bin: dataname = 'Wtn' if (opts.var == ''): var = 'BDT' if dataname == 'Zmm' or dataname == 'Zee': var = 'BDT_Zll' elif dataname == 'Wmn' or dataname == 'Wen': var = 'BDT_Wln' elif dataname == 'Znn': if 'HighPt' in opts.bin: var = 'BDT_ZnnHighPt' if 'LowPt' in opts.bin: var = 'BDT_ZnnLowPt' if 'LowCSV' in opts.bin: var = 'BDT_ZnnLowCSV' if dataname == '' or var == 'BDT': raise RuntimeError, 'Did not recognise mode or var from '+opts.bin else: var = opts.var if 'BDT' in var: region = 'BDT' else: region = opts.bin ws_var = config.get('plotDef:%s'%var,'relPath') if region == 'BDT': ws_var = ROOT.RooRealVar(ws_var,ws_var,-1.,1.) else: ws_var = ROOT.RooRealVar(ws_var,ws_var, 0, 1.) blind = eval(config.get('Plot:%s'%region,'blind')) print 'config:', config print 'var: ', var print 'region: ', region Stack=StackMaker(config,var,region,True) if 'LowPt' in opts.bin or 'ch1_Wenu' == opts.bin or 'ch2_Wmunu' == opts.bin: Stack.addFlag2 = 'Low p_{T}(V)' elif 'MedPt' in opts.bin or 'ch1_Wenu2' == opts.bin or 'ch2_Wmunu2' == opts.bin: Stack.addFlag2 = 'Intermediate p_{T}(V)' elif 'HighPt' in opts.bin or 'ch1_Wenu3' == opts.bin or 'ch2_Wmunu3' == opts.bin: Stack.addFlag2 = 'High p_{T}(V)' # check for pre or post fit options preFit = False addName = 'PostFit_%s' %(opts.fit) if not opts.mlfit: addName = 'PreFit' preFit = True print '\n-----> Fit Type(opts.fit) : ', opts.fit print ' (opts.mlfit): ', opts.mlfit print ' preFit : ', preFit Stack.options['pdfName'] = '%s_%s_%s.pdf' %(var,opts.bin,addName) log = eval(config.get('Plot:%s'%region,'log')) setup = config.get('Plot_general','setup').split(',') if dataname == 'Zmm' or dataname == 'Zee': try: setup.remove('W1b') setup.remove('W2b') setup.remove('Wlight') setup.remove('WH') except: print '@INFO: Wb / Wligh / WH not present in the datacard' if not dataname == 'Znn' and 'QCD' in setup: setup.remove('QCD') Stack.setup = setup Dict = eval(config.get('LimitGeneral','Dict')) lumi = eval(config.get('Plot_general','lumi')) options = copy(opts) options.dataname = "data_obs" options.mass = 0 options.format = "%8.3f +/- %6.3f" options.channel = opts.bin options.excludeSyst = [] options.norm = False options.stat = False options.bin = True # fake that is a binary output, so that we parse shape lines options.out = "tmp.root" options.fileName = args[0] options.cexpr = False options.fixpars = False options.libs = [] options.verbose = 0 options.poisson = 0 options.nuisancesToExclude = [] options.noJMax = None theBinning = ROOT.RooFit.Binning(Stack.nBins,Stack.xMin,Stack.xMax) file = open(opts.dc, "r") os.chdir(os.path.dirname(opts.dc)) print '\nDC Path:', os.path.dirname(opts.dc) DC = parseCard(file, options) if not DC.hasShapes: DC.hasShapes = True MB = ShapeBuilder(DC, options) theShapes = {} theSyst = {} nuiVar = {} print '\n\n ------> Mlfit File: ', opts.mlfit if opts.mlfit: nuiVar = readBestFit(opts.mlfit) if not opts.bin in DC.bins: raise RuntimeError, "Cannot open find %s in bins %s of %s" % (opts.bin,DC.bins,opts.dc) print '\n-----> Looping over bins in datacard...' for b in DC.bins: print ' bin: ', b if options.channel != None and (options.channel != b): continue exps = {} expNui = {} shapeNui = {} reducedShapeNui = {} for (p,e) in DC.exp[b].items(): # so that we get only self.DC.processes contributing to this bin exps[p] = [ e, [] ] expNui[p] = [ e, [] ] print '\n-----> Datacard systematics: ', DC.systs for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs: print '\n-----> Looping over systematics in datacard: ', (lsyst,nofloat,pdf,pdfargs,errline) if pdf in ('param', 'flatParam'): continue # begin skip systematics skipme = False for xs in options.excludeSyst: if re.search(xs, lsyst): skipme = True if skipme: print '\n-----> skipping systematics...' continue # end skip systematics counter = 0 print '\n\t-----> Looping over keys in datacard: ', DC.exp[b].keys() for p in DC.exp[b].keys(): # so that we get only self.DC.processes contributing to this bin print '\n\t-----> Looping over process in this bin: ', p if errline[b][p] == 0: continue if p == 'QCD' and not 'QCD' in setup: continue if pdf == 'gmN': exps[p][1].append(1/sqrt(pdfargs[0]+1)); elif pdf == 'gmM': exps[p][1].append(errline[b][p]); elif type(errline[b][p]) == list: kmax = max(errline[b][p][0], errline[b][p][1], 1.0/errline[b][p][0], 1.0/errline[b][p][1]); exps[p][1].append(kmax-1.); elif pdf == 'lnN': lnNVar = max(errline[b][p], 1.0/errline[b][p])-1. if not nuiVar.has_key('%s_%s'%(opts.fit,lsyst)): nui = 0. else: nui= nuiVar['%s_%s'%(opts.fit,lsyst)][0] lnNVar = lnNVar*nuiVar['%s_%s'%(opts.fit,lsyst)][1] exps[p][1].append(lnNVar) expNui[p][1].append(abs(1-errline[b][p])*nui); elif 'shape' in pdf: print '\n\t-----> Filling the Shapes for this process...' #print 'shape %s %s: %s'%(pdf,p,lsyst) s0 = MB.getShape(b,p) sUp = MB.getShape(b,p,lsyst+"Up") sDown = MB.getShape(b,p,lsyst+"Down") if (s0.InheritsFrom("RooDataHist")): s0 = ROOT.RooAbsData.createHistogram(s0,p,ws_var,theBinning) s0.SetName(p) sUp = ROOT.RooAbsData.createHistogram(sUp,p+lsyst+'Up',ws_var,theBinning) sUp.SetName(p+lsyst+'Up') sDown = ROOT.RooAbsData.createHistogram(sDown,p+lsyst+'Down',ws_var,theBinning) sDown.SetName(p+lsyst+'Down') theShapes[p] = s0.Clone() theShapes[p+lsyst+'Up'] = sUp.Clone() theShapes[p+lsyst+'Down'] = sDown.Clone() if not nuiVar.has_key('%s_%s'%(opts.fit,lsyst)): nui = 0. reducedNui = 1. else: nui= nuiVar['%s_%s'%(opts.fit,lsyst)][0] reducedNui= nuiVar['%s_%s'%(opts.fit,lsyst)][1] shapeNui[p+lsyst] = nui reducedShapeNui[lsyst] = reducedNui if not 'CMS_vhbb_stat' in lsyst: if counter == 0: theSyst[lsyst] = s0.Clone() theSyst[lsyst+'Up'] = sUp.Clone() theSyst[lsyst+'Down'] = sDown.Clone() else: theSyst[lsyst].Add(s0) theSyst[lsyst+'Up'].Add(sUp.Clone()) theSyst[lsyst+'Down'].Add(sDown.Clone()) counter += 1 procs = DC.exp[b].keys(); procs.sort() if not 'QCD' in setup and 'QCD' in procs: procs.remove('QCD') if not 'W2b' in setup and 'WjHF' in procs: procs.remove('WjHF') if not 'Wlight' in setup and 'WjLF' in procs: procs.remove('WjLF') fmt = ("%%-%ds " % max([len(p) for p in procs]))+" "+options.format; #Compute norm uncertainty and best fit theNormUncert = {} theBestFit = {} print '\n-----> Computing norm uncertaint and best fit...' for p in procs: relunc = sqrt(sum([x*x for x in exps[p][1]])) print fmt % (p, exps[p][0], exps[p][0]*relunc) theNormUncert[p] = relunc absBestFit = sum([x for x in expNui[p][1]]) theBestFit[p] = 1.+absBestFit histos = [] typs = [] setup2=copy(setup) shapesUp = [[] for _ in range(0,len(setup2))] shapesDown = [[] for _ in range(0,len(setup2))] sigCount = 0 signalList = ['ZH','WH'] # for shape analysis? for p in procs: b = opts.bin print 'process: ', p print 'setup:',setup print 'Dict:', Dict print 'theShapes:', theShapes for s in setup: print '-----> Fillings the shapes for: ', s if not Dict[s] == p: continue if s in signalList: if sigCount ==0: Overlay=copy(theShapes[Dict[s]]) else: Overlay.Add(theShapes[Dict[s]]) sigCount += 1 else: histos.append(theShapes[Dict[s]]) typs.append(s) for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs: if errline[b][p] == 0: continue if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst: print 'syst %s'%lsyst shapesUp[setup2.index(s)].append(theShapes[Dict[s]+lsyst+'Up']) shapesDown[setup2.index(s)].append(theShapes[Dict[s]+lsyst+'Down']) #------------- #Compute absolute uncertainty from shapes counter = 0 for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs: sumErr = 0 for p in procs: sumErr += errline[b][p] print '---> PDF:',pdf, lsyst if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst and not sumErr == 0: theSystUp = theSyst[lsyst+'Up'].Clone() theSystUp.Add(theSyst[lsyst].Clone(),-1.) theSystUp.Multiply(theSystUp) theSystDown = theSyst[lsyst+'Down'].Clone() theSystDown.Add(theSyst[lsyst].Clone(),-1.) theSystDown.Multiply(theSystDown) theSystUp.Scale(reducedShapeNui[lsyst]) theSystDown.Scale(reducedShapeNui[lsyst]) if counter == 0: theAbsSystUp = theSystUp.Clone() theAbsSystDown = theSystDown.Clone() else: theAbsSystUp.Add(theSystUp.Clone()) theAbsSystDown.Add(theSystDown.Clone()) counter +=1 #------------- #Best fit for shapes if not preFit: # Set the preFit as an overlay print '\n Making prefit overlay...' print procs i = 0 for hist in theShapes: if hist not in procs: continue print 'Process:', hist print 'Shape:', theShapes[hist] print 'i:', i if i == 0: prefit_overlay=copy(theShapes[hist]) #prefit_overlay=theShapes[hist] print 'First Integral:', theShapes[hist].Integral() i+=1 else: #prefit_overlay.Add(theShapes[hist], 1.0) prefit_overlay.Add(theShapes[hist]) print 'Integral:', theShapes[hist].Integral() print 'prefit_overlay:', prefit_overlay print 'Integral:', prefit_overlay.Integral() print '\n-----> Getting best fit shapes(for postFit)...' histos, Overlay, typs = getBestFitShapes(procs,theShapes,shapeNui,theBestFit,DC,setup,opts,Dict) counter = 0 errUp=[] total=[] errDown=[] nBins = histos[0].GetNbinsX() #print histos # temp hack to get histo names right #names = ['ggZH','DY2B', 'DY1B', 'DYlight', 'TT', 'VV'] #for name,i in enumerate(histos): # i.SetName(names[name]) #Overlay.SetName('ZH') # end hack print '\n total bins %s'%nBins print '\n histos: ',histos print '\n theNormUncert: ',theNormUncert print '\n Overlay: ', Overlay Error = ROOT.TGraphAsymmErrors(histos[0]) theTotalMC = histos[0].Clone() for h in range(1,len(histos)): theTotalMC.Add(histos[h]) total = [[]]*nBins errUp = [[]]*nBins errDown = [[]]*nBins print '\n\n\t\t -----> The Histos: ', histos for bin in range(1,nBins+1): binError = theTotalMC.GetBinError(bin) if math.isnan(binError): binError = 0. total[bin-1]=theTotalMC.GetBinContent(bin) #Stat uncertainty of the MC outline errUp[bin-1] = [binError] errDown[bin-1] = [binError] # Temp hack to fix theNormUncert naming temp_theNormUncert = {} for i,hist in enumerate(histos): for x in theNormUncert: #print '\nx: ', x if x in histos[i].GetName(): temp_theNormUncert[histos[i].GetName()] = theNormUncert[x] #print temp_theNormUncert #Relative norm uncertainty of the individual MC for h in range(0,len(histos)): #errUp[bin-1].append(histos[h].GetBinContent(bin)*theNormUncert[histos[h].GetName()]) #errDown[bin-1].append(histos[h].GetBinContent(bin)*theNormUncert[histos[h].GetName()]) errUp[bin-1].append(histos[h].GetBinContent(bin)*temp_theNormUncert[histos[h].GetName()]) errDown[bin-1].append(histos[h].GetBinContent(bin)*temp_theNormUncert[histos[h].GetName()]) #Shape uncertainty of the MC for bin in range(1,nBins+1): #print sqrt(theSystUp.GetBinContent(bin)) errUp[bin-1].append(sqrt(theAbsSystUp.GetBinContent(bin))) errDown[bin-1].append(sqrt(theAbsSystDown.GetBinContent(bin))) #Add all in quadrature totErrUp=[sqrt(sum([x**2 for x in bin])) for bin in errUp] totErrDown=[sqrt(sum([x**2 for x in bin])) for bin in errDown] #Make TGraph with errors for bin in range(1,nBins+1): if not total[bin-1] == 0: point=histos[0].GetXaxis().GetBinCenter(bin) Error.SetPoint(bin-1,point,1) Error.SetPointEYlow(bin-1,totErrDown[bin-1]/total[bin-1]) #print 'down %s'%(totErrDown[bin-1]/total[bin-1]) Error.SetPointEYhigh(bin-1,totErrUp[bin-1]/total[bin-1]) #print 'up %s'%(totErrUp[bin-1]/total[bin-1]) #----------------------- #Read data data0 = MB.getShape(opts.bin,'data_obs') if (data0.InheritsFrom("RooDataHist")): data0 = ROOT.RooAbsData.createHistogram(data0,'data_obs',ws_var,theBinning) data0.SetName('data_obs') datas=[data0] datatyps = [None] datanames=[dataname] print '\nDATA HIST:', data0 print 'Data name:', dataname if blind and 'BDT' in var: for bin in range(10,datas[0].GetNbinsX()+1): datas[0].SetBinContent(bin,0) #for bin in range(0,datas[0].GetNbinsX()+1): # print 'Data in bin x:', datas[0].GetBinContent(bin) histos.append(copy(Overlay)) if 'ZH' in signalList and 'WH' in signalList: typs.append('ZH') if 'ZH' in Stack.setup: Stack.setup.remove('ZH') if 'WH' in Stack.setup: Stack.setup.remove('WH') Stack.setup.insert(0,'ZH') elif 'ZH' in signalList: typs.append('ZH') elif 'WH' in signalList: typs.append('WH') elif 'VVb' in signalList: typs.append('VVb') print '\n-----> Stack.setup(double check)...' print 'Histos:', histos print 'typs:', typs Stack.histos = histos Stack.typs = typs Stack.datas = datas Stack.datatyps = datatyps Stack.datanames= datanames Stack.prefit_overlay = [prefit_overlay] if region == 'BDT': Stack.overlay = [Overlay] print '\n\n\t\t Overlay: ',Stack.overlay Stack.AddErrors=Error if dataname == 'Wtn': lumi = 18300. Stack.lumi = lumi Stack.doPlot() print 'i am done!\n'
def drawFromDC(): config = BetterConfigParser() config.read(opts.config) region = opts.region print "\nopts.config:",opts.config print "opts:", opts print "var:", opts.var print "bin:", opts.bin #dataname = 'Zll' #dataname = 'SingleElectron_Run2016B_PromptReco' dataname = ['SingleElectron_Run2016B_PromptReco','SingleElectron_Run2016C_PromptReco','SingleElectron_Run2016D_PromptReco','SingleElectron_Run2016F_PromptReco','SingleElectron_Run2016G_PromptReco'] #if 'Zuu' in opts.bin: dataname = 'Zuu' #elif 'Zee' in opts.bin: dataname = 'Zee' #elif 'Wmunu' in opts.bin: dataname = 'Wmn' #elif 'Wenu' in opts.bin: dataname = 'Wen' #elif 'Znunu' in opts.bin: dataname = 'Znn' #elif 'Wtn' in opts.bin: dataname = 'Wtn' #if (opts.var == ''): # var = 'BDT' # if dataname == 'Zmm' or dataname == 'Zee': var = 'BDT_Zll' # elif dataname == 'Wmn' or dataname == 'Wen': var = 'BDT_Wln' # elif dataname == 'Znn': # if 'HighPt' in opts.bin: var = 'BDT_ZnnHighPt' # if 'LowPt' in opts.bin: var = 'BDT_ZnnLowPt' # if 'LowCSV' in opts.bin: var = 'BDT_ZnnLowCSV' # if dataname == '' or var == 'BDT': raise RuntimeError, 'Did not recognise mode or var from '+opts.bin #else: # var = opts.var if opts.var == 'BDT': if 'LowPt' in opts.bin: var = 'gg_plus_ZH125_low_Zpt' if 'MedPt' in opts.bin: var = 'gg_plus_ZH125_med_Zpt' if 'HighPt' in opts.bin: var = 'gg_plus_ZH125_high_Zpt' if 'VV' in opts.bin: var = 'VV_bdt' #if 'BDT' in var: # region = 'BDT' #else: region = opts.bin var = opts.var ws_var = config.get('plotDef:%s'%var,'relPath') if 'gg_plus' in var: ws_var = ROOT.RooRealVar(ws_var,ws_var,-1.,1.) else: ws_var = ROOT.RooRealVar(ws_var,ws_var, 0, 1) #blind = eval(config.get('Plot:%s'%region,'blind')) #blind = True #blind = False blind = eval(opts.blind) print 'config:', config print 'var: ', var print 'region: ', region Stack=StackMaker(config,var,region,True) if 'LowPt' in opts.bin or 'ch1_Wenu' == opts.bin or 'ch2_Wmunu' == opts.bin: Stack.addFlag2 = 'Low p_{T}(V)' elif 'MedPt' in opts.bin or 'ch1_Wenu2' == opts.bin or 'ch2_Wmunu2' == opts.bin: Stack.addFlag2 = 'Intermediate p_{T}(V)' elif 'HighPt' in opts.bin or 'ch1_Wenu3' == opts.bin or 'ch2_Wmunu3' == opts.bin: Stack.addFlag2 = 'High p_{T}(V)' # check for pre or post fit options preFit = False addName = 'PostFit_%s' %(opts.fit) if not opts.mlfit: addName = 'PreFit' preFit = True print '\n-----> Fit Type(opts.fit) : ', opts.fit print ' (opts.mlfit): ', opts.mlfit print ' preFit : ', preFit Stack.options['pdfName'] = '%s_%s_%s.pdf' %(var,opts.bin,addName) log = eval(config.get('Plot:%s'%region,'log')) setup = ['Z_udscg','Zb','Zbb','TT','VVlight','ST','VV2b','qqZHbb','ggZHbb'] if dataname == 'Zmm' or dataname == 'Zee': try: setup.remove('W1b') setup.remove('W2b') setup.remove('Wlight') setup.remove('WH') except: print '@INFO: Wb / Wligh / WH not present in the datacard' if not dataname == 'Znn' and 'QCD' in setup: setup.remove('QCD') Stack.setup = setup Dict = eval(config.get('LimitGeneral','Dict')) #Dict = eval(config.get('Plot_general','Group')) lumi = eval(config.get('Plot_general','lumi')) options = copy(opts) options.dataname = "data_obs" options.mass = 0 options.format = "%8.3f +/- %6.3f" options.channel = opts.bin options.excludeSyst = [] options.norm = False options.stat = False options.bin = True # fake that is a binary output, so that we parse shape lines options.out = "tmp.root" options.fileName = args[0] options.cexpr = False options.fixpars = False options.libs = [] options.verbose = 0 options.poisson = 0 options.nuisancesToExclude = [] options.noJMax = None theBinning = ROOT.RooFit.Binning(Stack.nBins,Stack.xMin,Stack.xMax) file = open(opts.dc, "r") os.chdir(os.path.dirname(opts.dc)) print '\nDC Path:', os.path.dirname(opts.dc) DC = parseCard(file, options) if not DC.hasShapes: DC.hasShapes = True MB = ShapeBuilder(DC, options) theShapes = {} theSyst = {} nuiVar = {} print '\n\n ------> Mlfit File: ', opts.mlfit if opts.mlfit: nuiVar = readBestFit(opts.mlfit) if not opts.bin in DC.bins: raise RuntimeError, "Cannot open find %s in bins %s of %s" % (opts.bin,DC.bins,opts.dc) print '\n-----> Looping over bins in datacard...' for b in DC.bins: print ' bin: ', b if options.channel != None and (options.channel != b): continue exps = {} expNui = {} shapeNui = {} reducedShapeNui = {} for (p,e) in DC.exp[b].items(): # so that we get only self.DC.processes contributing to this bin exps[p] = [ e, [] ] expNui[p] = [ e, [] ] print '\n-----> Datacard systematics: ', DC.systs for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs: print '\n-----> Looping over systematics in datacard: ', (lsyst,nofloat,pdf,pdfargs,errline) if pdf in ('param', 'flatParam'): continue # begin skip systematics skipme = False for xs in options.excludeSyst: if re.search(xs, lsyst): skipme = True if skipme: print '\n-----> skipping systematics...' continue # end skip systematics counter = 0 print '\n\t-----> Looping over keys in datacard: ', DC.exp[b].keys() for p in DC.exp[b].keys(): # so that we get only self.DC.processes contributing to this bin print '\n\t-----> Looping over process in this bin: ', p if errline[b][p] == 0: continue if p == 'QCD' and not 'QCD' in setup: continue if pdf == 'gmN': exps[p][1].append(1/sqrt(pdfargs[0]+1)); elif pdf == 'gmM': exps[p][1].append(errline[b][p]); elif type(errline[b][p]) == list: kmax = max(errline[b][p][0], errline[b][p][1], 1.0/errline[b][p][0], 1.0/errline[b][p][1]); exps[p][1].append(kmax-1.); elif pdf == 'lnN': lnNVar = max(errline[b][p], 1.0/errline[b][p])-1. if not nuiVar.has_key('%s_%s'%(opts.fit,lsyst)): nui = 0. else: nui= nuiVar['%s_%s'%(opts.fit,lsyst)][0] lnNVar = lnNVar*nuiVar['%s_%s'%(opts.fit,lsyst)][1] exps[p][1].append(lnNVar) expNui[p][1].append(abs(1-errline[b][p])*nui); elif 'shape' in pdf: print '\n\t-----> Filling the Shapes for this process...' #print 'shape %s %s: %s'%(pdf,p,lsyst) s0 = MB.getShape(b,p) sUp = MB.getShape(b,p,lsyst+"Up") sDown = MB.getShape(b,p,lsyst+"Down") if (s0.InheritsFrom("RooDataHist")): s0 = ROOT.RooAbsData.createHistogram(s0,p,ws_var,theBinning) s0.SetName(p) sUp = ROOT.RooAbsData.createHistogram(sUp,p+lsyst+'Up',ws_var,theBinning) sUp.SetName(p+lsyst+'Up') sDown = ROOT.RooAbsData.createHistogram(sDown,p+lsyst+'Down',ws_var,theBinning) sDown.SetName(p+lsyst+'Down') theShapes[p] = s0.Clone() theShapes[p+lsyst+'Up'] = sUp.Clone() theShapes[p+lsyst+'Down'] = sDown.Clone() if not nuiVar.has_key('%s_%s'%(opts.fit,lsyst)): nui = 0. reducedNui = 1. else: nui= nuiVar['%s_%s'%(opts.fit,lsyst)][0] reducedNui= nuiVar['%s_%s'%(opts.fit,lsyst)][1] shapeNui[p+lsyst] = nui reducedShapeNui[lsyst] = reducedNui if not 'CMS_vhbb_stat' in lsyst: if counter == 0: theSyst[lsyst] = s0.Clone() theSyst[lsyst+'Up'] = sUp.Clone() theSyst[lsyst+'Down'] = sDown.Clone() else: theSyst[lsyst].Add(s0) theSyst[lsyst+'Up'].Add(sUp.Clone()) theSyst[lsyst+'Down'].Add(sDown.Clone()) counter += 1 procs = DC.exp[b].keys(); procs.sort() if not 'QCD' in setup and 'QCD' in procs: procs.remove('QCD') if not 'W2b' in setup and 'WjHF' in procs: procs.remove('WjHF') if not 'Wlight' in setup and 'WjLF' in procs: procs.remove('WjLF') fmt = ("%%-%ds " % max([len(p) for p in procs]))+" "+options.format; #Compute norm uncertainty and best fit theNormUncert = {} theBestFit = {} print '\n-----> Computing norm uncertaint and best fit...' for p in procs: relunc = sqrt(sum([x*x for x in exps[p][1]])) print fmt % (p, exps[p][0], exps[p][0]*relunc) theNormUncert[p] = relunc absBestFit = sum([x for x in expNui[p][1]]) theBestFit[p] = 1.+absBestFit histos = [] typs = [] setup2=copy(setup) shapesUp = [[] for _ in range(0,len(setup2))] shapesDown = [[] for _ in range(0,len(setup2))] sigCount = 0 #signalList = ['Zbb','WH'] #signalList = ['Zbb'] signalList = ['ZH'] #signalList = ['ggZHbb','qqZHbb'] Overlay ={} # for shape analysis? for p in procs: b = opts.bin print 'process: ', p print 'setup:',setup print 'Dict:', Dict #print 'theShapes:', theShapes for s in setup: print '-----> Fillings the shapes for: ', s #print Dict[s], p if Dict[s] != p: print 'not equal', p print 'not equal', Dict[s] continue if s in signalList: if sigCount ==0: Overlay=copy(theShapes[Dict[s]]) else: Overlay.Add(theShapes[Dict[s]]) sigCount += 1 else: histos.append(theShapes[Dict[s]]) typs.append(s) for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs: if errline[b][p] == 0: continue if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst: print 'syst %s'%lsyst shapesUp[setup2.index(s)].append(theShapes[Dict[s]+lsyst+'Up']) shapesDown[setup2.index(s)].append(theShapes[Dict[s]+lsyst+'Down']) #------------- #Compute absolute uncertainty from shapes counter = 0 for (lsyst,nofloat,pdf,pdfargs,errline) in DC.systs: sumErr = 0 for p in procs: sumErr += errline[b][p] print '---> PDF:',pdf, lsyst if ("shape" in pdf) and not 'CMS_vhbb_stat' in lsyst and not sumErr == 0: theSystUp = theSyst[lsyst+'Up'].Clone() theSystUp.Add(theSyst[lsyst].Clone(),-1.) theSystUp.Multiply(theSystUp) theSystDown = theSyst[lsyst+'Down'].Clone() theSystDown.Add(theSyst[lsyst].Clone(),-1.) theSystDown.Multiply(theSystDown) theSystUp.Scale(reducedShapeNui[lsyst]) theSystDown.Scale(reducedShapeNui[lsyst]) if counter == 0: theAbsSystUp = theSystUp.Clone() theAbsSystDown = theSystDown.Clone() else: theAbsSystUp.Add(theSystUp.Clone()) theAbsSystDown.Add(theSystDown.Clone()) counter +=1 #------------- #Best fit for shapes if not preFit: # Set the preFit as an overlay print '\n Making prefit overlay...' print procs i = 0 for hist in theShapes: if hist not in procs: continue print 'Process:', hist print 'Shape:', theShapes[hist] print 'i:', i if i == 0: prefit_overlay=copy(theShapes[hist]) #prefit_overlay=theShapes[hist] print 'First Integral:', theShapes[hist].Integral() i+=1 else: #prefit_overlay.Add(theShapes[hist], 1.0) prefit_overlay.Add(theShapes[hist]) print 'Integral:', theShapes[hist].Integral() print 'prefit_overlay:', prefit_overlay print 'Integral:', prefit_overlay.Integral() print '\n-----> Getting best fit shapes(for postFit)...' histos, Overlay, typs = getBestFitShapes(procs,theShapes,shapeNui,theBestFit,DC,setup,opts,Dict) counter = 0 errUp=[] total=[] errDown=[] nBins = histos[0].GetNbinsX() #print histos # temp hack to get histo names right #names = ['ggZH','DY2B', 'DY1B', 'DYlight', 'TT', 'VV'] #for name,i in enumerate(histos): # i.SetName(names[name]) #Overlay.SetName('ZH') # end hack print '\n total bins %s'%nBins print '\n histos: ',histos print '\n theNormUncert: ',theNormUncert print '\n Overlay: ', Overlay Error = ROOT.TGraphAsymmErrors(histos[0]) theTotalMC = histos[0].Clone() for h in range(1,len(histos)): theTotalMC.Add(histos[h]) total = [[]]*nBins errUp = [[]]*nBins errDown = [[]]*nBins print '\n\n\t\t -----> The Histos: ', histos for bin in range(1,nBins+1): binError = theTotalMC.GetBinError(bin) if math.isnan(binError): binError = 0. total[bin-1]=theTotalMC.GetBinContent(bin) #Stat uncertainty of the MC outline errUp[bin-1] = [binError] errDown[bin-1] = [binError] # Temp hack to fix theNormUncert naming temp_theNormUncert = {} for i,hist in enumerate(histos): for x in theNormUncert: #print '\nx: ', x if x in histos[i].GetName(): temp_theNormUncert[histos[i].GetName()] = theNormUncert[x] #print temp_theNormUncert #Relative norm uncertainty of the individual MC for h in range(0,len(histos)): #errUp[bin-1].append(histos[h].GetBinContent(bin)*theNormUncert[histos[h].GetName()]) #errDown[bin-1].append(histos[h].GetBinContent(bin)*theNormUncert[histos[h].GetName()]) errUp[bin-1].append(histos[h].GetBinContent(bin)*temp_theNormUncert[histos[h].GetName()]) errDown[bin-1].append(histos[h].GetBinContent(bin)*temp_theNormUncert[histos[h].GetName()]) #Shape uncertainty of the MC for bin in range(1,nBins+1): #print sqrt(theSystUp.GetBinContent(bin)) errUp[bin-1].append(sqrt(theAbsSystUp.GetBinContent(bin))) errDown[bin-1].append(sqrt(theAbsSystDown.GetBinContent(bin))) #Add all in quadrature totErrUp=[sqrt(sum([x**2 for x in bin])) for bin in errUp] totErrDown=[sqrt(sum([x**2 for x in bin])) for bin in errDown] #Make TGraph with errors for bin in range(1,nBins+1): if not total[bin-1] == 0: point=histos[0].GetXaxis().GetBinCenter(bin) Error.SetPoint(bin-1,point,1) Error.SetPointEYlow(bin-1,totErrDown[bin-1]/total[bin-1]) #print 'down %s'%(totErrDown[bin-1]/total[bin-1]) Error.SetPointEYhigh(bin-1,totErrUp[bin-1]/total[bin-1]) #print 'up %s'%(totErrUp[bin-1]/total[bin-1]) #----------------------- #Read data data0 = MB.getShape(opts.bin,'data_obs') if (data0.InheritsFrom("RooDataHist")): data0 = ROOT.RooAbsData.createHistogram(data0,'data_obs',ws_var,theBinning) data0.SetName('data_obs') datas=[data0] datatyps = [None] #datanames=[dataname] datanames= dataname print '\nDATA HIST:', data0 print 'Data name:', dataname if blind: for bin in range(12,datas[0].GetNbinsX()+1): datas[0].SetBinContent(bin,0) #for bin in range(0,datas[0].GetNbinsX()+1): # print 'Data in bin x:', datas[0].GetBinContent(bin) if 'VV' in opts.bin: signalList = ['VVb',' VVlight'] print 'Signal List:', signalList histos.append(copy(Overlay)) if 'Zbb' in signalList and 'WH' in signalList: typs.append('Zbb') if 'Zbb' in Stack.setup: Stack.setup.remove('Zbb') if 'WH' in Stack.setup: Stack.setup.remove('WH') Stack.setup.insert(0,'Zbb') elif 'Zbb' in signalList: typs.append('Zbb') elif 'WH' in signalList: typs.append('WH') elif 'ZH' in signalList: typs.append('ZH') if 'VVb' in signalList: typs.append('VVb') #typs.append('VVlight') print '\n-----> Stack.setup(double check)...' print 'Histos:', histos print 'typs:', typs Stack.histos = histos Stack.typs = typs Stack.datas = datas Stack.datatyps = datatyps Stack.datanames= datanames #Stack.prefit_overlay = [prefit_overlay] if '13TeV' in region: Stack.overlay = [Overlay] print '\n\n\t\t Overlay: ',Stack.overlay Stack.AddErrors=Error if dataname == 'Wtn': lumi = 18300. Stack.lumi = lumi Stack.doPlot() print 'i am done!\n'