hist['sig_'+branchname].SetFillColor(4); hist['sig_'+branchname].SetLineColor(4); hist['sig_'+branchname].SetMarkerColor(4);
hist['bkg_'+branchname].SetFillColor(2); hist['bkg_'+branchname].SetLineColor(2); hist['bkg_'+branchname].SetMarkerColor(2);
leg1.Clear()
leg1.AddEntry(hist["sig_" + branchname],"W jets","l"); leg1.AddEntry(hist["bkg_" + branchname],"QCD jets","l");
hist['sig_' + branchname].Rebin(10)
hist['bkg_' + branchname].Rebin(10)
if (hist['sig_'+branchname].GetMaximum() > hist['bkg_'+branchname].GetMaximum()):
fn.drawHists(hist['sig_' + branchname], hist['bkg_' + branchname])
else:
fn.drawHists(hist['bkg_' + branchname], hist['sig_' + branchname])
leg1.Draw()
#if branchname.find('pt') != -1:
# fn.pTReweight(hist['sig_'+branchname], hist['bkg_'+branchname], Algorithm+fileid, varBinPt, xbins)
fn.addLatex(fn.getAlgorithmString(),fn.getAlgorithmSettings(),ptrange, fn.getE(), [nvtxlow, nvtx])
p = canv1.cd(index+1).Clone()
tc = TCanvas(branchname)
p.SetPad(0,0,1,1) # resize
p.Draw()
tc.SaveAs(varpath+branchname+".png")
canv2.cd()
if index==0:
roc[branchname].GetXaxis().SetTitle("Efficiency_{W jets}")
roc[branchname].GetYaxis().SetTitle("1 - Efficiency_{QCD jets}")
roc[branchname].Draw("al")
else:
roc[branchname].SetLineColor(index+2)
roc[branchname].Draw("same")
leg2.AddEntry(roc[branchname],branchname,"l");
L=CVend-CVini #initial dimensions ######################################### P1=load_data_range(CV_file,N,CVini,CVend) print P1.shape print L rn=(random.randint(0, L)) print rn print P1 print '#####################' #call delrn P2=delrn(P1,rn) print P2 #get the error E=getE(P1,P2) print E ############################### #### Monte Carlo EE=[] c=[] E1=1000000.0000 KT=0.01 L MC=100 #Monte Carlo iterations for i in range (MC): rn=(random.randint(0, L)) P2=delrn(P1,rn) E2=getE(P1,P2) E2=E2*(10**6)
print L
OI=[]
dd=40
for i in range(dd,L):
print i
EF=[]
deletedCVs=[]
for i in range(L-1):
if i==0:
Plast=P1
#get maximal
for j in range (i,L):
Ptmp=delrn(Plast,j-i)
Etmp=getE(P1,Ptmp)
if j==i:
maxE=Etmp
max_index=i
else:
if Etmp>maxE:
maxE=Etmp
max_index=j
EE=[]
minE=maxE
for j in range (i,L):
PP=delrn(Plast,j-i)
E=getE(P1,PP)
if E<minE:
def writeResponsePlots(weightedxAOD, Algorithm, plotconfig, trees, cutstring, fileid, ptreweight = True, varpath = "", mass_min = "0.0", mass_max = "1200000.0", scaleLumi = 1, applyMassWindow = True):
'''
Run through the Algorithm for a given mass range. Returns the bkg rej at 50% signal eff.
Keyword args:
weightedxAOD -- bool indicating if the xaod is already weighted.
Algorithm -- Name of the algorithm. Set in main, comes from config file.
plotconfig -- Contains the settings for the plot like bin number and hist limits. The key of the dict is the variable name.
trees -- contains the actual data.
cutstring -- basic selection cuts to be applied.
fileid -- File identifier that gets used in the output file names
ptreweight -- Reweight signal according to pT
varpath -- Output directory
mass_min -- Mass window minimum
mass_max -- Mass window maximum
scaleLumi -- luminosity scale factor
applyMassWindow -- Whether or not to apply the 68% mass window cut when plotting.
Writes out the response distributions for signal and background and then returns the dictionary of histograms.
'''
#global weightedxAOD
hist = {}
variables = []
# remove all of the leading underscores
for x, v in enumerate(plotconfig.keys()):
stub = plotconfig[v][STUB]
if not plotconfig[v][1] == True: # if it is not a jet variable
continue
if stub.startswith('_'):
stub = stub[1:]
variables.append(stub)
# create all of the histograms
for typename in ['sig','bkg']:
histnamestub = typename + '_jet_' + Algorithm + '_response'
for br in plotconfig.keys(): # variable names
if plotconfig[br][1] == True: # if it is a jet variable
histname = histnamestub+plotconfig[br][STUB]
else:
continue
hist_title = br
hist[histname] = TH1D(histname, hist_title, 100, 0, 4)#plotconfig[br][MINX], plotconfig[br][MAXX])
hist[histname].SetYTitle("Normalised Entries")
hist[histname].SetXTitle("response " + br)
# set the cutstring
if applyMassWindow:
cutstring_mass = cutstring+ " * (jet_" +Algorithm + "_m <= " +mass_max+ ")" + " * (jet_" +Algorithm + "_m > " +mass_min+ ") "
else:
cutstring_mass = cutstring
# the colours for the background and signal
col_sig = 4
col_bkg = 2
# loop through all of the variables
for index, branchname in enumerate(variables):
col = 1
responseName = "jet_" +Algorithm+'_response_' + branchname
# loop through the background and signal input trees
for indexin, datatype in enumerate(trees):
histname = datatype + "_jet_" +Algorithm+'_response_' + branchname
varexp = 'response_'+branchname + '>>' + histname
# the weights are stored in the ntuple for the weighted xAODs.
if not weightedxAOD:
cutstringandweight = '*mc_event_weight*1./NEvents(mc_channel_number)'
else:
cutstringandweight = '*mc_event_weight*1./evt_nEvts'
# add the cross section and filter efficiency for the background
if datatype == 'bkg':
# according to Chris we don't apply the kfactor
if not weightedxAOD:
cutstringandweight += '*filter_eff*xs'#*k_factor'
else:
cutstringandweight += '*evt_filtereff*evt_xsec'#*evt_kfactor'
hist[histname].SetMarkerStyle(21)
col = col_bkg
# filter efficiency for signal
elif datatype == 'sig':
# according to Chris we do not apply any of these to signal
# so reset the cutstring
cutstringandweight = ''
#if not weightedxAOD:
# cutstringandweight += '*filter_eff'
#else:
# cutstringandweight += '*evt_filtereff'
# apply pt reweighting to the signal
if ptreweight:
cutstringandweight +='*SignalPtWeight3(jet_CamKt12Truth_pt)'
col = col_sig
hist[histname].Sumw2();
#apply the selection.
trees[datatype].Draw(varexp,cutstring_mass+cutstringandweight)
# scale the histogram
if hist[histname].Integral() > 0.0:
if scaleLumi != 1:
hist[histname].Scale(scaleLumi);
else:
hist[histname].Scale(1.0/hist[histname].Integral());
# set the style for the histogram.
hist[histname].SetLineStyle(1); hist[histname].SetFillStyle(0); hist[histname].SetMarkerSize(1);
hist[histname].SetFillColor(col); hist[histname].SetLineColor(col); hist[histname].SetMarkerColor(col);
# need to clear leg1
# create a tlegend to go on the plot
leg1 = TLegend(0.8,0.55,0.9,0.65);leg1.SetFillColor(kWhite)
leg1.AddEntry(hist["sig_" + responseName],"W jets","l"); leg1.AddEntry(hist["bkg_" + responseName],"QCD jets","l");
# plot the maximum histogram
canv1 = TCanvas("tempCanvas")
if (hist['sig_'+responseName].GetMaximum() > hist['bkg_'+responseName].GetMaximum()):
fn.drawHists(hist['sig_' + responseName], hist['bkg_' + responseName])
else:
fn.drawHists(hist['bkg_' + responseName], hist['sig_' + responseName])
leg1.Draw()
# add correctly formatted text to the plot for the ATLAS collab text, energy, etc.
fn.addLatex(fn.getAlgorithmString(),fn.getAlgorithmSettings(),fn.getPtRange(), fn.getE(), [fn.getNvtxLow(), fn.getNvtx()])
# save individual plots
if applyMassWindow:
canv1.SaveAs(varpath+responseName+".png")
else:
canv1.SaveAs(varpath+responseName+"_noMW.png")
del canv1
return hist
def analyse(Algorithm, plotbranches, plotreverselookup, plotconfig, trees, cutstring, hist, leg1, leg2, fileid, records, ptreweight = True, varpath = "", savePlots = True, mass_min = "0.0", mass_max = "1200000.0", scaleLumi = 1, nTracks='999'):
'''
Run through the Algorithm for a given mass range. Returns the bkg rej at 50% signal eff.
Keyword args:
Algorithm -- Name of the algorithm. Set in main, comes from config file.
plotbranches -- the variables to be plotted. Set in main.
plotreverselookup -- Lookup for an algorithm from a variable stub
plotconfig --- Dictionary of the plotting arguments for different variables.
trees -- contains the actual data.
cutstring -- basic selection cuts to be applied.
hist -- Histograms that will be filled/ saved.
leg1 -- TLegend for histograms
leg2 -- TLegend for ROC curves
fileid -- File identifier that gets used in the output file names
ptreweight -- Reweight signal according to pT
varpath -- output folder to save single variable plots to
saveplots -- whether or not to write out plots to file
mass_min -- Mass window minimum
mass_max -- Mass window maximum
scaleLumi -- luminosity scale factor
nTracks -- cut on NTracks to apply. Default is 999 which is essentially no cut.
Returns:
Background rejection at 50% signal efficiency using the ROC curve and variable used to achieve maximum rejection.
'''
# open log file
#logfile = open(records,'w')
cutflow = open(records.replace('.out','.cutflow'),'w')
# canvas for histogram plots
canv1 = TCanvas("canv1")
canv1.Divide(5,5)
# canvas for rejection ROC curves
canv2 = TCanvas("canv2", "ROC curves showing 1-background rejection vs signal efficiency")
# canvas for power roc curves
power_canv = TCanvas('powercurves',"ROC curves showing background rejection power vs signal efficiency")
power_canv.SetLogy()
power_legend = TLegend(0.6,0.5,0.9,0.9); power_legend.SetFillColor(kWhite);
tempCanv = TCanvas("temp")
# reset hists
for h in hist.keys():
hist[h].Reset()
global weightedxAOD
global singleSidedROC
global totalrejection
# dict containing all of the ROC curves
roc={}
roc_nomw = {}
bkgRejROC= {}
bkgPowerROC = {}
# bool that is set to false if no ROC curves are drawn - this will happen if any
# hist added to the roc is empty
writeROC = False
# dictionary containing the 50% signal eff bkg rejection power
roc_rejection_scores = {}
# dictionary holding all of the histograms without a mass cut
hist_nomw = {}
saveNoMassWindowPlots = savePlots
hist_mass_nomw = {}
# record the integral of pt for mw and nomw
mw_int_pt = {}
full_int_pt = {}
# maximum rejection
maxrej = 0
maxrejvar = ''
#set up the cutstring/ selection to cut on the correct jet masses
cutstring_mass = cutstring+ " * (jet_" +Algorithm + "_m <= " +mass_max+ ")" + " * (jet_" +Algorithm + "_m > " +mass_min+ ") "
# loop through the indices and branchnames
for index, branchname in enumerate(plotbranches):
# add ROC dictionary entry
roc[branchname] = TGraph()#Errors()
roc[branchname].SetTitle(branchname)
roc[branchname].SetName('roc_'+branchname)
roc_nomw[branchname] = TGraph()#Errors()
roc_nomw[branchname].SetTitle('No mass window ' + branchname)
roc_nomw[branchname].SetName('roc_nomw_'+branchname)
# add bkg rej power dictionary entry
bkgRejROC[branchname] = TGraph()#Errors()
bkgRejROC[branchname].SetTitle(branchname)
bkgRejROC[branchname].SetName('bkgrej_roc_'+branchname)
bkgPowerROC[branchname] = TGraph()#Errors()
bkgPowerROC[branchname].SetTitle(branchname)
bkgPowerROC[branchname].SetName('bkgpower_roc_'+branchname)
# new canvas
canv1.cd(index+1)
# keep the integral when not applying mass window cuts, this
# allows us to calculate the efficiency of the mass window cut
signal_eff = 1.0
bkg_eff = 1.0
# setting the maximum of the plot - normally multiply by 1.2, but for some, like tauwta21 must be higher
max_multiplier = 1.2
if branchname.lower().find('tauwta2tauwta1') != -1:
max_multiplier = 1.25
# loop through the datatypes: signal and background
for indexin, datatype in enumerate(trees):
canv1.cd(index+1)
histname = datatype + "_" + branchname
print "plotting " + datatype + branchname
#logfile.write("plotting " + datatype + branchname+"\n")
minxaxis = hist[histname].GetXaxis().GetXmin()
maxxaxis = hist[histname].GetXaxis().GetXmax()
# add the mc_weight and weighted number of events to the selection string
# also make sure that the variable being plotted is within the bounds specified
# in the config file (the limits on the histogram)
if not weightedxAOD:
cutstringandweight = '*mc_event_weight*1./NEvents(mc_channel_number)'
else:
cutstringandweight = '*mc_event_weight*1./evt_nEvts'
# add the cross section and filter efficiency for the background
if datatype == 'bkg':
# no longer apply the k factor
if not weightedxAOD:
cutstringandweight += '*filter_eff*xs'#*k_factor'
else:
cutstringandweight += '*evt_filtereff*evt_xsec'#*evt_kfactor'
hist[histname].SetMarkerStyle(21)
# filter efficiency for signal
elif datatype == 'sig':
# we only apply pt rw now, so reset the cutstring
cutstringandweight=''
# apply pt reweighting to the signal
if ptreweight:
cutstringandweight +='*SignalPtWeight3(jet_CamKt12Truth_pt)'
hist[histname].Sumw2();
# set up the tree.Draw() variable expression for the histogram
varexp = branchname + '>>' + histname
# apply the selection to the tree and store the output in the histogram
if branchname.find('_m')==-1:
trees[datatype].Draw(varexp,cutstring_mass+cutstringandweight)#+'*(nTracks<'+nTracks+')')
else:
trees[datatype].Draw(varexp,cutstring+cutstringandweight)#+'*(nTracks<'+nTracks+')')
# if the histogram is not empty then normalise it
#mw_int = hist[histname].Integral()
if hist[histname].Integral() > 0.0:
if scaleLumi != 1:
hist[histname].Scale(scaleLumi);
else:
hist[histname].Scale(1.0/hist[histname].Integral());
# set up the axes titles and colours/ styles
hist[histname].SetLineStyle(1); hist[histname].SetFillStyle(0); hist[histname].SetMarkerSize(1);
if (branchname.find('jet_')!=-1):
hist[histname].SetXTitle(plotreverselookup[branchname.replace("jet_"+Algorithm,"")])
else:
hist[histname].SetXTitle(plotreverselookup[branchname])
hist[histname].SetYTitle("Normalised Entries")
#now get the same plot for no mass window cut to get the eff
hist_full_massonly = hist[histname].Clone()
hist_full_massonly.Reset()
hist_full_massonly.SetName(histname+'_massonly')
# need to store the variable in this histogram
varexpfull = branchname + ' >>' + histname+'_massonly'
# no nTracks cut here!!!!
if branchname.find('_m')==-1:
trees[datatype].Draw(varexpfull,cutstring_mass+cutstringandweight)
else:
trees[datatype].Draw(varexpfull,cutstring+cutstringandweight)
# get the integral and normalise
mw_int = hist_full_massonly.Integral()
hist_full_massonly.Reset()
hist_full_massonly.SetName(histname+'_full_massonly')
# need to store the variable in this histogram
varexpfull = branchname + ' >>' + histname+'_full_massonly'
# no nTracks cut here!!!!
trees[datatype].Draw(varexpfull,cutstring+cutstringandweight+"*(jet_" +Algorithm + "_m < 1200*1000)" + " * (jet_" +Algorithm + "_m > 0)")
# get the integral and normalise
full_int = hist_full_massonly.Integral()
if histname.find('_pt') != -1:
mw_int_pt[datatype] = mw_int
full_int_pt[datatype] = full_int
#now get the same plot for no mass window cut to get the eff
hist_full = hist[histname].Clone()
hist_full.Reset()
hist_full.SetName(histname+'_full')
# need to store the variable in this histogram
varexpfull = branchname + ' >>' + histname+'_full'
trees[datatype].Draw(varexpfull,cutstring+cutstringandweight+"*(jet_" +Algorithm + "_m < 1200*1000)" + " * (jet_" +Algorithm + "_m > 0)")#+'*(nTracks<'+nTracks+')')
histInt = hist_full.Integral()
# now scale
if histInt != 0.0:
if scaleLumi!=1:
hist_full.Scale(scaleLumi)
else:
hist_full.Scale(1./histInt)
# change the x title
hist_full.SetXTitle(hist[histname].GetXaxis().GetTitle()+' (no mass window)')
#save this histogram to the no mass window histo dictionary
hist_nomw[histname+'_full'] = hist_full.Clone()
if False:#datatype == 'sig':
if full_int !=0:
signal_eff = float(mw_int/full_int)
else:
signal_eff = 0.0
if False:#else:
if full_int != 0:
bkg_eff = float(mw_int/full_int)
else:
bkg_eff = 0.0
#Make ROC Curves before rebinning, but only if neither of the samples are zero
if (hist["sig_" +branchname].Integral() != 0 and hist["bkg_" +branchname].Integral() != 0):
if singleSidedROC == 'M':
# check where the median is so we can correctly choose L or R cut.
sig_median = fn.median(hist['sig_'+branchname])
bkg_median = fn.median(hist['bkg_'+branchname])
if sig_median > bkg_median:
side = 'R'
else:
side = 'L'
roc[branchname] = fn.RocCurve_SingleSided(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside=side)
#roc[branchname],hsigreg50,hcutval50,hsigreg25,hcutval25 = fn.RocCurve_SingleSided_WithUncer(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside=side)
bkgRejROC[branchname] = roc[branchname]
bkgPowerROC[branchname] = fn.RocCurve_SingleSided(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside=side,rejection=False)
elif singleSidedROC == 'L' or singleSidedROC == 'R':
roc[branchname] = fn.RocCurve_SingleSided(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside=singleSidedROC)
#roc[branchname],hsigreg50,hcutval50,hsigreg25,hcutval25 = fn.RocCurve_SingleSided_WithUncer(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside=singleSidedROC)
bkgRejROC[branchname] = roc[branchname]
bkgPowerROC[branchname] = fn.RocCurve_SingleSided(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside=singleSidedROC,rejection=False)
else:
MakeROCBen(1, hist["sig_" +branchname], hist["bkg_" +branchname], roc[branchname], bkgRejROC[branchname], signal_eff, bkg_eff)
bkgPowerROC[branchname] = fn.RocCurve_SingleSided(hist["sig_" +branchname], hist["bkg_" +branchname], signal_eff, bkg_eff, cutside='R',rejection=False)
writeROC = True
canv1.cd(index+1)
pX = Double(0.5)
pY = Double(0.0)
# find the corresponding bkg rejection for the 50% signal efficiency point from bkg rejection power ROC curve
# However, if we want the background rejection power for the mass variable we do not want to take 50% as we already have made
# a cut on the mass to get it to 68%.
# calculate error on this as well -> deltaX = X*(deltaY/Y)
eval_roc = 1.0
err_up = 1
err_do = 1
if not branchname.endswith("_m"):
#eval_roc = roc[branchname].Eval(0.5)
eval_roc = fn.GetBGRej50(roc[branchname])
# get the bin for this point so that we can find the associated error in roc error tgraphs
#rocBin = fn.findYValue(roc[branchname],Double(0.5), pY, 0.01, True, True)
bin_num = roc[branchname].GetXaxis().FindBin(0.5)#roc_errUp[branchname].Eval(0.5)
eval_rocup = eval_roc+roc[branchname].GetErrorX(bin_num)
eval_rocdo = eval_roc-roc[branchname].GetErrorX(bin_num)
else:
#eval_roc = roc[branchname].Eval(0.68)
eval_roc = fn.GetBGRej(roc[branchname], 0.68)
#rocBin = fn.findYValue(roc[branchname],Double(0.68), pY, 0.01, True, True)
bin_num = roc[branchname].GetXaxis().FindBin(0.68)#roc_errUp[branchname].Eval(0.5)
eval_rocup = eval_roc+roc[branchname].GetErrorX(bin_num)
eval_rocdo = eval_roc-roc[branchname].GetErrorX(bin_num)
if eval_roc != 1:
bkgrej = 1/(1-eval_roc)
else:
bkgrej = -1
roc_rejection_scores[branchname] = bkgrej
if (eval_rocup != 1):
bkgrej_errUp = abs(bkgrej-1/(1-eval_rocup))
else:
bkgrej_errUp = -1
if (eval_rocdo!= 1):
bkgrej_errDo = abs(bkgrej-1/(1-eval_rocdo))
else:
bkgrej_errDo = -1
# store a record of all background rejection values
# want to store only the variable name, not the algorithm name, so string manipulation. here it is stored as sig_jet_ALGO_variable.
groups = branchname.split('_')
j = '_'.join(groups[:2]), '_'.join(groups[2:])
if not j[1] == 'pt':
totalrejection.append([j[1], float(bkgrej), float(bkgrej_errUp), float(bkgrej_errDo)])
if bkgrej > maxrej:
maxrej = bkgrej
maxrejvar = branchname
# once the background rejection power has been calculated using the 200 bins the histograms can be rebinned.
hist['sig_'+branchname].SetFillColor(4); hist['sig_'+branchname].SetLineColor(4); hist['sig_'+branchname].SetMarkerColor(4); hist['sig_'+branchname].Rebin(4);
hist['bkg_'+branchname].SetFillColor(2); hist['bkg_'+branchname].SetLineColor(2); hist['bkg_'+branchname].SetMarkerColor(2); hist['bkg_'+branchname].Rebin(4);
if saveNoMassWindowPlots:
if singleSidedROC == 'M':
# check where the median is so we can correctly choose L or R cut.
sig_median = fn.median(hist_nomw['sig_'+branchname+'_full'])
bkg_median = fn.median(hist_nomw['bkg_'+branchname+'_full'])
if sig_median > bkg_median:
side = 'R'
else:
side = 'L'
#roc_nomw[branchname],v1,v2,v3,v4 = fn.RocCurve_SingleSided_WithUncer(hist_nomw["sig_" +branchname+'_full'], hist_nomw["bkg_" +branchname+'_full'], 1,1, cutside=side)
roc_nomw[branchname] = fn.RocCurve_SingleSided(hist_nomw["sig_" +branchname+'_full'], hist_nomw["bkg_" +branchname+'_full'], 1,1, cutside=side, rejection=True, debug_flag=False)
elif singleSidedROC == 'L' or singleSidedROC == 'R':
#roc_nomw[branchname],v1,v2,v3,v4 = fn.RocCurve_SingleSided_WithUncer(hist_nomw["sig_" +branchname+'_full'], hist_nomw["bkg_" +branchname+'_full'], 1,1, cutside=singleSidedROC)
roc_nomw[branchname] = fn.RocCurve_SingleSided(hist_nomw["sig_" +branchname+'_full'], hist_nomw["bkg_" +branchname+'_full'], 1,1, cutside=singleSidedROC, rejection=True, debug_flag=False)
else:
MakeROCBen(1, hist_nomw["sig_" +branchname+'_full'], hist_nomw["bkg_" +branchname+'_full'], roc_nomw[branchname], TGraphErrors(), signal_eff, bkg_eff)
canv1.cd(index+1)
#roc_nomw[branchname].GetXaxis().SetTitle("Efficiency_{W jets}")
#roc_nomw[branchname].GetYaxis().SetTitle("1 - Efficiency_{QCD jets}")
hist_nomw['sig_'+branchname+'_full'].SetFillColor(4); hist_nomw['sig_'+branchname+'_full'].SetLineColor(4); hist_nomw['sig_'+branchname+'_full'].SetMarkerColor(4);
hist_nomw['bkg_'+branchname+'_full'].SetFillColor(2); hist_nomw['bkg_'+branchname+'_full'].SetLineColor(2); hist_nomw['bkg_'+branchname+'_full'].SetMarkerColor(2);
# resize the mass plots to be in a better range
if branchname.endswith('_m'):
hist['sig_'+branchname].SetAxisRange(0.0,300.0*1000.0)
hist['bkg_'+branchname].SetAxisRange(0.0,300.0*1000.0)
hist['sig_'+branchname].GetXaxis().SetLimits(0.0,300.0)
hist['bkg_'+branchname].GetXaxis().SetLimits(0.0,300.0)
else:
hist_nomw['sig_'+branchname + '_full'].Rebin(4);
hist_nomw['bkg_'+branchname + '_full'].Rebin(4);
leg1.Clear()
# add legend entries for bkg and signal histograms
leg1.AddEntry(hist["sig_" + branchname],"W jets","l"); leg1.AddEntry(hist["bkg_" + branchname],"QCD jets","l");
y_high = max(hist['sig_'+branchname].GetMaximum(), hist['bkg_'+branchname].GetMaximum())
hist['bkg_'+branchname].SetMaximum(y_high*max_multiplier);hist['sig_'+branchname].SetMaximum(y_high*max_multiplier)
# plot the maximum histogram
#if (hist['sig_'+branchname].GetMaximum() > hist['bkg_'+branchname].GetMaximum()):
fn.drawHists(hist['sig_' + branchname], hist['bkg_' + branchname])
# else:
# fn.drawHists(hist['bkg_' + branchname], hist['sig_' + branchname])
# change the coordinates to LHS of the canvas if we are plotting ThrustMaj or YFilt.
offset_x = False
if branchname.lower().find('thrustmaj')!=-1 or branchname.lower().find('yfilt')!=-1:
offset_x = True
leg1.SetX1NDC(0.25)
leg1.SetX2NDC(0.35)
else:
leg1.SetX1NDC(0.8)
leg1.SetX2NDC(0.9)
leg1.Draw("same")
# add correctly formatted text to the plot for the ATLAS collab text, energy, etc.
fn.addLatex(fn.getAlgorithmString(),fn.getAlgorithmSettings(),fn.getPtRange(), fn.getE(), [fn.getNvtxLow(), fn.getNvtx()], offset_x)
# save individual plots
if savePlots:
p = canv1.cd(index+1).Clone()
tempCanv.cd()
p.SetPad(0,0,1,1) # resize
p.Draw()
tempCanv.SaveAs(varpath+branchname+".pdf")
#tempCanv.SaveAs(varpath+branchname+".eps")
del p
# now save "no mass window" plots
if saveNoMassWindowPlots:
# resize the mass plots to be in a better range
if branchname.find('_m') != -1:
hist_nomw['sig_'+branchname+'_full'].SetAxisRange(0.0,300.0*1000.0)
hist_nomw['sig_'+branchname+'_full'].GetXaxis().SetRangeUser(0.0,1000.0*300.0)
hist_nomw['sig_'+branchname+'_full'].GetXaxis().SetLimits(0.0,300.0)
hist_nomw['bkg_'+branchname+'_full'].SetAxisRange(0.0,300.0*1000.0)
hist_nomw['bkg_'+branchname+'_full'].GetXaxis().SetRangeUser(0.0,1000.0*300.0)
hist_nomw['bkg_'+branchname+'_full'].GetXaxis().SetLimits(0.0,300.0)
tempCanv2 = TCanvas("tempnomw"+branchname)
tempCanv2.cd()
y_high = max(hist_nomw['sig_'+branchname+'_full'].GetMaximum(), hist_nomw['bkg_'+branchname+'_full'].GetMaximum())
y_low = 0.0 #min(hist_nomw['sig_'+branchname+'_full'].GetMinimum(), hist_nomw['bkg_'+branchname+'_full'].GetMaximum())
hist_nomw['bkg_'+branchname+'_full'].SetMaximum(y_high*max_multiplier);hist_nomw['sig_'+branchname+'_full'].SetMaximum(y_high*max_multiplier)
#if (hist_nomw['sig_'+branchname+'_full'].GetMaximum() > hist_nomw['bkg_'+branchname+'_full'].GetMaximum()):
fn.drawHists(hist_nomw['sig_' + branchname+'_full'], hist_nomw['bkg_' + branchname+'_full'])
#else:
# fn.drawHists(hist_nomw['bkg_' + branchname+'_full'], hist_nomw['sig_' + branchname+'_full'])
offset_x = False
if branchname.lower().find('thrustmaj')!=-1 or branchname.lower().find('yfilt')!=-1:
offset_x = True
leg1.SetX1NDC(0.25)
leg1.SetX2NDC(0.35)
else:
leg1.SetX1NDC(0.8)
leg1.SetX2NDC(0.9)
leg1.Draw("same")
# if we are doing the mass plt we want to indicate the mass window
if branchname.find('_m') == -1:
mrange = []
else:
mrange = [float(mass_min), float(mass_max)]
# if it is the mass variable then draw lines indicating the mass window
line_min = TLine(float(mass_min)/1000.0, y_low, float(mass_min)/1000.0, y_high); line_min.SetLineColor(kBlack);
line_max = TLine(float(mass_max)/1000.0, y_low, float(mass_max)/1000.0, y_high); line_max.SetLineColor(kBlack);
line_min.Draw('same')
line_max.Draw('same')
# add the latex parts to the plot -> ATLAS, tev range, etc.
fn.addLatex(fn.getAlgorithmString(),fn.getAlgorithmSettings(),fn.getPtRange(), fn.getE(), [fn.getNvtxLow(), fn.getNvtx()], offset_x, massrange = mrange)
tempCanv2.SaveAs(varpath+branchname+"_noMW.pdf")
#tempCanv2.SaveAs(varpath+branchname+"_noMW.eps")
del tempCanv2
canv1.cd(index+1)
min_roc = 1.0
for b in roc.keys():
if b.find('_m') == -1 and b.find('_pt') == -1:
n = roc[b].GetN()
y = roc[b].GetY()
locmin = TMath.LocMin(n,y)
minval = y[locmin]
min_roc = min(min_roc, minval)
for branchidx, branchname in enumerate(roc.keys()):
# plot the ROC curves
canv2.cd()
roc[branchname].GetXaxis().SetTitle("Efficiency_{W jets}")
roc[branchname].GetYaxis().SetTitle("1 - Efficiency_{QCD jets}")
roc[branchname].SetMinimum(min_roc*0.98)
bkgPowerROC[branchname].GetXaxis().SetTitle("Efficiency_{W jets}")
bkgPowerROC[branchname].GetYaxis().SetTitle("1/Efficiency_{QCD jets}")
# only plot the roc and power curves if this is not a mass
if branchname.find('_m') == -1 and branchname.find('_pt') == -1:
roc[branchname].SetLineStyle(branchidx%10)
if branchidx==0 and roc[branchname].Integral() != 0:
roc[branchname].Draw("al")
elif roc[branchname].Integral() != 0:
roc[branchname].SetLineColor(colours[branchidx])
roc[branchname].Draw("same")
# legend for the roc curve
leg2.AddEntry(roc[branchname],branchname,"l");
leg2.Draw("same")
# plot the power curves
power_canv.cd()
bkgPowerROC[branchname].SetLineStyle(branchidx%10)
if branchidx==0 and bkgPowerROC[branchname].Integral() != 0:
bkgPowerROC[branchname].Draw("al")
elif bkgPowerROC[branchname].Integral() != 0:
bkgPowerROC[branchname].SetLineColor(colours[branchidx])
bkgPowerROC[branchname].Draw("same")
power_legend.AddEntry(bkgPowerROC[branchname],branchname,'l')
power_legend.Draw('same')
# write out canv1 and roc curves on one page/ png each
if savePlots:
#write out the plots after cuts
writePlots(Algorithm, fileid, canv1, canv2, writeROC, roc, power_canv)
writePlotsToROOT(Algorithm, fileid, hist, roc, bkgRejROC, roc_nomw, roc_rejection_scores, recreate=True, power_curves = bkgPowerROC)
#responseHists = resp.writeResponsePlots(weightedxAOD, Algorithm, plotconfig, trees, cutstring, fileid, ptreweight, varpath, mass_min, mass_max, scaleLumi)
#responseHists = resp.writeResponsePlots(weightedxAOD, Algorithm, plotconfig, trees, cutstring, fileid, ptreweight, varpath, mass_min, mass_max, scaleLumi, applyMassWindow=False)
#writePlotsToROOT(Algorithm, fileid, hist, recreate=False)
# write out event counts for mass window cuts (not weighted)
cutflow.write('Signal:\njet selection no mass window: '+'pt' +' '+ str(hist_nomw['sig_jet_'+Algorithm+'_pt_full'].GetEntries())+'\n')
if 'sig' in full_int_pt.keys():
cutflow.write('jet selection no mass window: '+'pt weighted '+ str(full_int_pt['sig'])+'\n')
cutflow.write('mass window and jet selection: pt ' + str(hist['sig_jet_'+Algorithm+'_pt'].GetEntries())+'\n')
if 'sig' in mw_int_pt.keys():
cutflow.write('mass window and jet selection: pt weighted ' + str(mw_int_pt['sig'])+'\n')
cutflow.write('Background:\njet selection no mass window: pt '+ str(hist_nomw['bkg_jet_'+Algorithm+'_pt_full'].GetEntries())+'\n')
if 'bkg' in full_int_pt.keys():
cutflow.write('jet selection no mass window: pt weighted '+ str(full_int_pt['bkg'])+'\n')
cutflow.write('mass window and jet selection: pt ' + str(hist['bkg_jet_'+Algorithm+'_pt'].GetEntries())+'\n')
if 'bkg' in mw_int_pt.keys():
cutflow.write('mass window and jet selection: pt weighted ' + str(mw_int_pt['bkg'])+'\n')
# close logfile
cutflow.close()
# return the variable with the maximum background rejection
return maxrej, maxrejvar