def ProjectDraw(var, cut, Lumi, samplelist, pd):
plt.cfg.register(samplelist)
groupList = plt.cfg.getGroupPlot()
histList = OrderedDict()
VAR = var
# remove extra space
CUT = ' '.join(filter(None, selection[cut].split(" ")))
# group tag
for igroup in groupList:
CUT_ = CUT
if igroup == 'BkgSum': continue
#if igroup in [ 'Fake', 'DATA' ] : CUT_ = CUT.replace("isbVeto && ","")
print col.CYAN + "drawLambda::GroupTag : ", igroup + col.ENDC
histList[igroup] = {}
##sample tag
for isample in groupList[igroup]['samples']:
print("isample : ", isample)
histList[igroup][isample] = []
if 'weights' in samples[isample].keys():
for jsample in samples[isample]['weights'].keys():
WEIGHTS = '(%s)*(%s)' % (
expressAliases(samples[isample]['weight']),
samples[isample]['weights'][jsample])
#WEIGHTS = '(%s)*(%s)' %( samples[isample]['weight'] , samples[isample]['weights'][jsample] )
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(
float(Lumi) / 1000.), WEIGHTS)
filelist = [
x for x in samples[isample]['name']
if os.path.basename(x).split('_', 1)[-1].replace(
'.root', '').split('__part')[0] == jsample
]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
histList[igroup][isample].append(
makeHisto(df, VAR, CUT_, WEIGHTS, jsample))
else:
WEIGHTS = expressAliases(samples[isample]['weight'])
#WEIGHTS = samples[isample]['weight']
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(float(Lumi) / 1000.), WEIGHTS)
filelist = samples[isample]['name']
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
histList[igroup][isample].append(
makeHisto(df, VAR, CUT_, WEIGHTS, isample))
pass
# apply action
return applyAction(histList, groupList)
def plot_signal( var, cut, norm=False ):
colors = [ 616+4 , 800 , 416+1 , 800+7 , 860+10 , 600 , 616 , 921 , 922 ]
if cut in selection: plotdir = cut
pathname = PLOTDIR+"/signals/"+plotdir
if not os.path.exists(pathname): os.system('mkdir -p %s'%pathname)
histlist={}
plt.cfg.register(sign) #sign is grouptag
samples = plt.cfg.getModule('samples')
CUT = ' '.join(filter( None , selection[cut].split(" ") ))
signlist = plt.cfg.getGroupPlot()['Higgs']['samples']
for i,isample in enumerate(signlist):
WEIGHTS = "%s*(%s)" %( str(float(LUMI)/1000.) , expressAliases(samples[isample]['weight']) )
filelist = [ x for x in samples[isample]['name'] ]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files); gROOT.cd()
histlist[isample] = makeHisto( df , var , CUT , WEIGHTS , isample )
histlist[isample] = histlist[isample].GetPtr()
histlist[isample].Sumw2()
histlist[isample].SetLineWidth(2)
histlist[isample].SetLineColor(colors[i])
#######################################################################################################
# add total histograms
histlist['Higgs']=None
for i, ihist in enumerate(histlist):
if i==0:
histlist['Higgs'] = histlist[ihist].Clone('totsignal')
histlist['Higgs'].Sumw2()
histlist['Higgs'].Add(histlist[ihist])
leg = TLegend( 0.7 if not options.Statebox else 0.4 , 0.9-0.035*len(signlist), 0.9, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(1001) #1001
leg.SetFillColor(0)
for s in signlist : leg.AddEntry(histlist[s], '%s [%.1f]' %( s , histlist[s].Integral() ) , "l")
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd().SetLogy() if options.logy else c1.cd()
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
hmax = 0.
for i, s in enumerate(signlist):
if histlist[s].GetMaximum() > hmax: hmax = histlist[s].GetMaximum()
if not options.logy:
histlist[signlist[0]].SetMaximum(hmax*1.2)
histlist[signlist[0]].SetMinimum(0.)
else:
histlist[signlist[0]].SetMaximum(hmax*4)
c1.cd()
for i, s in enumerate(signlist):
histlist[s].Draw("HIST" if i==0 else "SAMES, HIST")
if options.Statebox:
c1.GetPad(0).Update()
if i==1:
lof = histlist[s].GetListOfFunctions()
statbox = lof.FindObject('stats')
statbox.SetX1NDC(0.779026); statbox.SetX2NDC(0.979401)
statbox.SetY1NDC(0.593168); statbox.SetY2NDC(0.754658)
leg.Draw()
drawCMS(LUMI, "Simulation")
printTable_signal( histlist , pathname , '%s_%s.txt' %(plotdir,var) , signlist , histplease=True )
### fitting mass shape?
########################
if gROOT.IsBatch():
c1.Print(pathname+"/"+var.replace('.', '_')+".png")
#c1.Print(pathname+"/"+var.replace('.', '_')+".pdf")
#print col.WARNING+"PURGE OBJECTS IN MEMORY"+col.ENDC
#for process in histlist:
# histlist[process].Delete()
pass
if 'weights' in samples[isample].keys():
for jsample in samples[isample]['weights'].keys():
WEIGHTS = '(%s)*(%s)' % (
expressAliases(samples[isample]['weight']),
samples[isample]['weights'][jsample])
if itag not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(
float(LUMI) / 1000.), WEIGHTS)
filelist = [
x for x in samples[isample]['name']
if os.path.basename(x).split('_', 1)[-1].replace(
'.root', '').split('__part')[0] == jsample
]
files = makeVectorList(filelist)
#print " --> Caching in subsamples : ", jsample
DF[itag][isample][jsample] = ROOT.RDataFrame(
"Events", files).Define("weight", WEIGHTS) #.Cache();
else:
isptr = True
WEIGHTS = expressAliases(samples[isample]['weight'])
if itag not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(float(LUMI) / 1000.), WEIGHTS)
filelist = samples[isample]['name']
files = makeVectorList(filelist)
#print " --> Caching in samples : ", isample
DF[itag][isample] = ROOT.RDataFrame("Events", files).Define(
"weight", WEIGHTS) #.Cache();
###########################################################
df_result = OrderedDict()
if fake:
DF_fake = OrderedDict()
def ProjectDraw(var, cut, Lumi, samplelist, pd):
plt.cfg.register(samplelist)
groupList = plt.cfg.getGroupPlot()
histList = {}
VAR = var
# remove extra space
CUT = ' '.join(filter(None, selection[cut].split(" ")))
# group tag
for igroup in groupList:
CUT_ = CUT
if igroup == 'BkgSum': continue
#if igroup in [ 'Fake', 'DATA' ] : CUT_ = CUT.replace("isbVeto && ","")
print col.OKGREEN + "drawLambda::GroupTag : ", igroup + col.ENDC
#histList[igroup]={} ;
hists = {}
##sample tag
for isample in groupList[igroup]['samples']:
print("isample : ", isample)
hists[isample] = {}
isptr = False
## check if weights exist
if 'weights' in samples[isample].keys():
## sub-samples tag
subhists = {}
for jsample in samples[isample]['weights'].keys():
subhists[jsample] = {}
WEIGHTS = '(%s)*(%s)' % (
expressAliases(samples[isample]['weight']),
samples[isample]['weights'][jsample])
#WEIGHTS = '(%s)*(%s)' %( samples[isample]['weight'] , samples[isample]['weights'][jsample] )
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(
float(Lumi) / 1000.), WEIGHTS)
filelist = [
x for x in samples[isample]['name']
if os.path.basename(x).split('_', 1)[-1].replace(
'.root', '').split('__part')[0] == jsample
]
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
subhists[jsample] = makeHisto(df, VAR, CUT_, WEIGHTS,
jsample)
# Wg = (Wg*w1,Wg*w2)
hists[isample] = sumHistoPtr(subhists, isample, True)
else:
## all sub-samples share common weights
###print col.OKGREEN+ "drawLambda::Sub-Samples : " , samples[isample]['weights'].keys() + col.ENDC
isptr = True
WEIGHTS = expressAliases(samples[isample]['weight'])
#WEIGHTS = samples[isample]['weight']
if igroup not in ['Fake', 'DATA']:
WEIGHTS = "%s*(%s)" % (str(float(Lumi) / 1000.), WEIGHTS)
filelist = samples[isample]['name']
files = makeVectorList(filelist)
df = ROOT.RDataFrame("Events", files)
gROOT.cd()
hists[isample] = makeHisto(df, VAR, CUT_, WEIGHTS, isample)
# Vg = sum(Wg,Zq)
histList[igroup] = sumHistoPtr(hists, igroup, isptr)
print col.YELLOW + "group tag processed : ", histList.keys(), col.ENDC
histList[igroup].Sumw2()
histList[igroup].SetFillColor(groupList[igroup]['fillcolor'])
histList[igroup].SetFillStyle(groupList[igroup]['fillstyle'])
histList[igroup].SetLineColor(groupList[igroup]['linecolor'])
histList[igroup].SetLineStyle(groupList[igroup]['linestyle'])
return histList