def setup_class(cls):
CompileCpp('examples/example.cc')
CompileCpp('test/test_weight.cc')
cls.a = analyzer('examples/GluGluToHToTauTau.root')
cls.a.Cut("test_cut1", "Jet_pt.size() > 0")
cls.a.Define('lead_vector', 'hardware::TLvector(Jet_pt[0],Jet_eta[0],Jet_phi[0],Jet_mass[0])')
cls.a.Define('sublead_vector','hardware::TLvector(Jet_pt[1],Jet_eta[1],Jet_phi[1],Jet_mass[1])')
cls.a.Define('invariantMass','hardware::InvariantMass({lead_vector,sublead_vector})')
def setup_class(cls):
CompileCpp('TIMBER/Framework/include/common.h'
) # Compile (via gInterpreter) commonly used c++ code
CompileCpp('examples/example.cc')
# CompileCpp('test/test_weight.cc')
cls.a = analyzer('examples/GluGluToHToTauTau.root')
cls.a.Cut("test_cut1", "Jet_pt.size() > 0")
cls.a.Define(
'lead_vector',
'hardware::TLvector(Jet_pt[0],Jet_eta[0],Jet_phi[0],Jet_mass[0])')
cls.a.Define(
'sublead_vector',
'hardware::TLvector(Jet_pt[1],Jet_eta[1],Jet_phi[1],Jet_mass[1])')
cls.a.Define('invariantMass',
'hardware::invariantMass({lead_vector,sublead_vector})')
def BuildCppCollection(self, collection, node, silent=True):
'''Build the collection as a struct in C++ so that it's accessible
to the RDataFrame loop.
@param collection (str): Collection name.
@param node (Node): Node on which to act.
@param silent (bool, optional): Whether output should be silenced. Defaults to True.
Raises:
RuntimeError: Collection already built.
Returns:
Node: Manipulated node with the collection struct now defined.
'''
newNode = node
attributes = []
for aname in self.GetCollectionAttributes(collection):
attributes.append(
'%s %s' %
(self._collectionDict[collection][aname]['type'], aname))
if collection + 's' not in self._builtCollections:
self._builtCollections.append(collection + 's')
CompileCpp(StructDef(collection, attributes))
newNode = newNode.Define(collection + 's',
StructObj(collection, attributes),
silent=silent)
else:
raise RuntimeError('Collections `%s` already built.' %
(collection + 's'))
return newNode
def test_Groups():
a = analyzer('examples/GluGluToHToTauTau.root')
CompileCpp('TIMBER/Framework/include/common.h'
) # Compile (via gInterpreter) commonly used c++ code
CompileCpp('examples/example.cc')
test_vg = VarGroup('test_vg')
test_vg.Add(
'lead_vector',
'analyzer::TLvector(Jet_pt[0],Jet_eta[0],Jet_phi[0],Jet_mass[0])')
test_vg.Add(
'sublead_vector',
'analyzer::TLvector(Jet_pt[1],Jet_eta[1],Jet_phi[1],Jet_mass[1])')
test_vg.Add('invariantMass',
'analyzer::invariantMass(lead_vector,sublead_vector)')
test_cg = CutGroup('test_cg')
test_cg.Add("test_cut1", "Jet_pt.size() > 0")
a.Apply([test_vg, test_cg])
rep = a.DataFrame.Report()
rep.Print()
def multicore(infiles=[],doStudies=False,topcut=''):
CompileCpp('THmodules.cc')
if infiles == []: files = GetAllFiles()
else: files = infiles
teff = {
"16": Correction("TriggerEff16",'TIMBER/Framework/include/EffLoader.h',['THtrigger2D_16.root','Pretag'], corrtype='weight'),
"17": Correction("TriggerEff17",'TIMBER/Framework/include/EffLoader.h',['THtrigger2D_17.root','Pretag'], corrtype='weight'),
"18": Correction("TriggerEff18",'TIMBER/Framework/include/EffLoader.h',['THtrigger2D_18.root','Pretag'], corrtype='weight')
}
pool = multiprocessing.Pool(processes=4 if doStudies else 6,maxtasksperchild=1)
nthreads = 4 if doStudies else 2
process_args = []
for f in files:
setname, era = GetProcYearFromTxt(f)
if doStudies:
if 'Data' not in setname:
process_args.append(Namespace(threads=nthreads,setname=setname,era=era,variation='None',trigEff=teff[era],topcut=topcut))
else:
if 'Data' not in setname and 'QCD' not in setname:
process_args.append(Namespace(threads=nthreads,setname=setname,era=era,variation='None',trigEff=teff[era],topcut=topcut))
if not doStudies:
for jme in ['JES','JER','JMS','JMR']:
for v in ['up','down']:
process_args.append(Namespace(threads=nthreads,setname=setname,era=era,variation='%s_%s'%(jme,v),trigEff=teff[era],topcut=topcut))
else:
process_args.append(Namespace(threads=nthreads,setname=setname,era=era,variation='None',trigEff=teff[era],topcut=topcut))
start = time.time()
if doStudies:
pool.map(THstudies,process_args)
else:
for a in process_args:
print ('PROCESSING: %s %s %s'%(a.setname, a.era, a.variation))
THselection(a)
# pool.map(THselection,process_args)
print ('Total multicore time: %s'%(time.time()-start))
'Whether to run the selection. If False, will attempt to recycle previous run histograms.'
)
(options, args) = parser.parse_args()
###########################################
# Establish some global variables for use #
###########################################
plotdir = 'plots/' # this is where we'll save your plots
if not os.path.exists(plotdir):
os.makedirs(plotdir)
rootfile_path = 'root://cmsxrootd.fnal.gov///store/user/cmsdas/2021/long_exercises/BstarTW/rootfiles'
config = OpenJSON('bstar_config.json')
cuts = config['CUTS'][options.year]
CompileCpp("TIMBER/Framework/include/common.h")
CompileCpp(
'bstar.cc') # has the c++ functions we need when looping of the RDataFrame
# Sets we want to process and some nice naming for our plots
signal_names = ['signalLH%s' % (mass)
for mass in [2000]] #range(1400,4200,600)]
bkg_names = [
'singletop_tW', 'singletop_tWB', 'ttbar', 'QCDHT700', 'QCDHT1000',
'QCDHT1500', 'QCDHT2000'
]
##########MY ASSIGNED BACKGROUNDS##############
#bkg_names = ['QCDHT1000','QCDHT1500']
names = {
eff.SetName(name)
eff.Write()
out.Close()
if __name__ == '__main__':
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--recycle',
dest='recycle',
action='store_true',
default=False,
help='Recycle existing files and just plot.')
args = parser.parse_args()
start = time.time()
CompileCpp('THmodules.cc')
if not args.recycle:
for y in [16, 17, 18]:
MakeEfficiency(y)
files = {
16: ROOT.TFile.Open('THtrigger2D_16.root'),
17: ROOT.TFile.Open('THtrigger2D_17.root'),
18: ROOT.TFile.Open('THtrigger2D_18.root')
}
hists = {
hname.GetName(): [files[y].Get(hname.GetName()) for y in [16, 17, 18]]
for hname in files[16].GetListOfKeys() if '_hist' in hname.GetName()
}
colors = [ROOT.kBlack, ROOT.kGreen + 1, ROOT.kOrange - 3]
'''Plot the Missing ET for events that have an opposite-sign muon
pair mass in the range 60-120 GeV (double loop over single collection, math)'''
import time
start = time.time()
#---------------------
from TIMBER.Analyzer import analyzer
from TIMBER.Tools.Common import CompileCpp
CompileCpp('benchmark/ex.cc')
a = analyzer('examples/GluGluToHToTauTau_full.root')
a.Cut('twoMuons','nMuon>1')
a.Define('invMassMuMu','InvMassOppMuMu(Muon_pt, Muon_eta, Muon_phi, Muon_mass, Muon_charge)')
a.Cut('invMassMuMu_cut','Any(invMassMuMu > 60 && invMassMuMu < 120)')
h = a.DataFrame.Histo1D('MET_pt')
h.Draw('hist e')
#---------------------
print ('%s secs'%(time.time() - start))
'Whether to run the selection. If False, will attempt to recycle previous run histograms.'
)
(options, args) = parser.parse_args()
###########################################
# Establish some global variables for use #
###########################################
plotdir = 'plots/' # this is where we'll save your plots
if not os.path.exists(plotdir):
os.makedirs(plotdir)
rootfile_path = 'root://cmsxrootd.fnal.gov///store/user/cmsdas/2021/long_exercises/BstarTW/rootfiles'
config = 'bstar_config.json' # holds luminosities and cross sections
# common c++ functions that we will need when looping of the RDataFrame
CompileCpp("TIMBER/Framework/include/common.h")
CompileCpp('bstar.cc')
# define sample sets that we want to process, label them and define colors
# here we are only going to work with a single signal dataset
#signal_names = ['signalLH2000']
#names = {'signalLH2000': "b*_{LH} 2000 (GeV)"}
#colors = {'signalLH2000': ROOT.kBlue}
#####################COPY PASTE FROM SELECTION.PY#######################
# Sets we want to process and some nice naming for our plots
signal_names = ['signalLH%s' % (mass) for mass in range(1400, 4200, 600)]
bkg_names = [
'singletop_tW', 'singletop_tWB', 'ttbar', 'QCDHT700', 'QCDHT1000',
'QCDHT1500', 'QCDHT2000'
]
def setup_class(cls):
CompileCpp('TIMBER/Framework/include/common.h')
cls.a = analyzer('examples/GluGluToHToTauTau.root')
'Whether to run the selection. If False, will attempt to recycle previous run histograms.'
)
(options, args) = parser.parse_args()
###########################################
# Establish some global variables for use #
###########################################
cernbox = '/eos/home-%s/%s/Documents/plots' % (
os.environ['USER'][0], os.environ['USER']
) # this is where we'll save your plots (accessible via web on cernbox.cern.ch)
if not os.path.exists(cernbox):
os.makedirs(cernbox)
rootfile_path = '/eos/user/c/cmsdas/long-exercises/bstarToTW/rootfiles/'
config = openJSON('bstar_config.json')
cuts = config['CUTS'][options.year]
CompileCpp(
'bstar.cc') # has the c++ functions we need when looping of the RDataFrame
# Sets we want to process and some nice naming for our plots
signal_names = ['signalLH%s' % (mass)
for mass in [2000]] #range(1400,4200,600)]
bkg_names = [
'singletop_tW', 'singletop_tWB', 'ttbar', 'QCDHT700', 'QCDHT1000',
'QCDHT1500', 'QCDHT2000'
]
names = {
"singletop_tW": "single top (tW)",
"singletop_tWB": "single top (tW)",
"ttbar": "t#bar{t}",
"QCDHT700": "QCD",
"QCDHT1000": "QCD",
"QCDHT1500": "QCD",