# define a base event selection which is applied for all Samples
# select only events with GEN weight > 0 because training with negative weights is weird
base = "(N_Jets >= 4 and N_BTagsM >= 3 and Evt_MET_Pt > 20. and Weight_GEN_nom > 0.)"
# single lepton selections
single_mu_sel = "(N_LooseElectrons == 0 and N_TightMuons == 1 and Muon_Pt > 29. and Triggered_HLT_IsoMu27_vX == 1)"
single_el_sel = "(N_LooseMuons == 0 and N_TightElectrons == 1 and (Triggered_HLT_Ele35_WPTight_Gsf_vX == 1 or Triggered_HLT_Ele28_eta2p1_WPTight_Gsf_HT150_vX == 1))"
base_selection = "("+base+" and ("+single_mu_sel+" or "+single_el_sel+"))"
ttH_selection = "(Evt_Odd == 1)"
ttbb_selection = "(Evt_Odd == 1)"
# define output classes
ttH_categories = root2pandas.EventCategories()
ttH_categories.addCategory("ttH", selection = None)
ttlf_categories = root2pandas.EventCategories()
ttlf_categories.addCategory("ttlf", selection = "(GenEvt_I_TTPlusBB == 0 and GenEvt_I_TTPlusCC == 0)")
ttlf_categories.addCategory("ttcc", selection = "(GenEvt_I_TTPlusBB == 0 and GenEvt_I_TTPlusCC == 1)")
ttbb_categories = root2pandas.EventCategories()
ttbb_categories.addCategory("ttbb", selection = "(GenEvt_I_TTPlusBB == 3 and GenEvt_I_TTPlusCC == 0)")
ttbb_categories.addCategory("tt2b", selection = "(GenEvt_I_TTPlusBB == 2 and GenEvt_I_TTPlusCC == 0)")
ttbb_categories.addCategory("ttb", selection = "(GenEvt_I_TTPlusBB == 1 and GenEvt_I_TTPlusCC == 0)")
ttbb_categories.addCategory("ttb_bb", selection = "(GenEvt_I_TTPlusBB == 3 and GenEvt_I_TTPlusCC == 0) or \
(GenEvt_I_TTPlusBB == 1 and GenEvt_I_TTPlusCC == 0)")
ttbb_categories.addCategory("tthf", selection = "(GenEvt_I_TTPlusBB == 3 and GenEvt_I_TTPlusCC == 0) or \
ttbar_selection = "(\
abs(Weight_scale_variation_muR_0p5_muF_0p5) <= 100 and \
abs(Weight_scale_variation_muR_0p5_muF_1p0) <= 100 and \
abs(Weight_scale_variation_muR_0p5_muF_2p0) <= 100 and \
abs(Weight_scale_variation_muR_1p0_muF_0p5) <= 100 and \
abs(Weight_scale_variation_muR_1p0_muF_1p0) <= 100 and \
abs(Weight_scale_variation_muR_1p0_muF_2p0) <= 100 and \
abs(Weight_scale_variation_muR_2p0_muF_0p5) <= 100 and \
abs(Weight_scale_variation_muR_2p0_muF_1p0) <= 100 and \
abs(Weight_scale_variation_muR_2p0_muF_2p0) <= 100 \
)"
ttH_selection = "(Evt_Odd == 1)"
# define output classes
ttH_categories = root2pandas.EventCategories()
ttH_categories.addCategory("ttHbb", selection=None)
ttbar_categories = root2pandas.EventCategories()
ttbar_categories.addCategory(
"ttbb", selection="(GenEvt_I_TTPlusBB == 3 and GenEvt_I_TTPlusCC == 0)")
ttbar_categories.addCategory(
"tt2b", selection="(GenEvt_I_TTPlusBB == 2 and GenEvt_I_TTPlusCC == 0)")
ttbar_categories.addCategory(
"ttb", selection="(GenEvt_I_TTPlusBB == 1 and GenEvt_I_TTPlusCC == 0)")
ttbar_categories.addCategory(
"ttlf", selection="(GenEvt_I_TTPlusBB == 0 and GenEvt_I_TTPlusCC == 0)")
ttbar_categories.addCategory(
"ttcc", selection="(GenEvt_I_TTPlusBB == 0 and GenEvt_I_TTPlusCC == 1)")
# initialize dataset class
# initialize dataset class
dataset = root2pandas.Dataset(
outputdir=outputdir,
naming=options.Name,
addMEM=options.MEM,
maxEntries=options.maxEntries,
tree=options.treeName,
varName_Run="runNumber",
varName_LumiBlock="lumiBlock",
varName_Event="eventNumber",
)
ntuplesPath = "/nfs/dust/cms/user/missirol/sandbox/ttHbb/output_190914_DeepJet/2016/exe1/selectionRoot_reco_liteTreeTTH/Nominal"
ttH_categories = root2pandas.EventCategories()
ttH_categories.addCategory("ttH", selection=None)
ttbar_categories = root2pandas.EventCategories()
ttbar_categories.addCategory("ttbar", selection=None)
ttbar_bb_categories = root2pandas.EventCategories()
ttbar_bb_categories.addCategory("ttbb", selection=None)
ttbar_b_categories = root2pandas.EventCategories()
ttbar_b_categories.addCategory("ttb", selection=None)
ttbar_2b_categories = root2pandas.EventCategories()
ttbar_2b_categories.addCategory("tt2b", selection=None)
ttbar_cc_categories = root2pandas.EventCategories()
if not os.path.isabs(options.outputDir):
outputdir = basedir + "/workdir/" + options.outputDir
elif os.path.exists(options.outputDir) or os.path.exists(
os.path.dirname(options.outputDir)):
outputdir = options.outputDir
else:
sys.exit("ERROR: Output Directory does not exist!")
# define a base event selection which is applied for all Samples
# select only events with GEN weight > 0 because training with negative weights is weird
base = "(N_Jets >= 4 and N_BTagsM >= 3 and ttbbReco_matchable > 0.)"
base_selection = "(" + base + ")"
# define output classes
sig_categories = root2pandas.EventCategories()
sig_categories.addCategory("sig", selection=None)
bkg_categories = root2pandas.EventCategories()
bkg_categories.addCategory("bkg", selection=None)
# initialize dataset class
dataset = root2pandas.Dataset(outputdir=outputdir,
naming=options.Name,
addMEM=options.MEM,
maxEntries=options.maxEntries,
ttbarReco=options.ttbarReco,
ncores=options.ncores)
# add base event selection
dataset.addBaseSelection(base_selection)
outputdir = outputdir,
naming = options.Name,
addMEM = options.MEM,
maxEntries = options.maxEntries,
tree = options.treeName,
varName_Run = "runNumber",
varName_LumiBlock = "lumiBlock",
varName_Event = "eventNumber",
)
# add base event selection
dataset.addBaseSelection(base_selection)
ntuplesPath = "/nfs/dust/cms/user/missirol/sandbox/ttHbb/output_190914_DeepJet/2017/exe1/selectionRoot_reco_liteTreeTTH/Nominal"
ttH_categories = root2pandas.EventCategories()
ttH_categories.addCategory("ttH", selection = None)
ttbar_categories = root2pandas.EventCategories()
ttbar_categories.addCategory("ttbar")
ttbar_bb = root2pandas.EventCategories()
ttbar_bb.addCategory("ttbb")
ttbar_b = root2pandas.EventCategories()
ttbar_b.addCategory("ttb")
ttbar_2b = root2pandas.EventCategories()
ttbar_2b.addCategory("tt2b")
ttbar_cc = root2pandas.EventCategories()
basedir = os.path.dirname(os.path.dirname(filedir))
sys.path.append(basedir)
import root2pandas
import variable_sets.ntuplesVariablesWithIndex as variable_set
# define a base event selection which is applied for all Samples
base_selection = "(\
(N_LooseMuons == 0 and N_TightElectrons == 1) \
or \
(N_LooseElectrons == 0 and N_TightMuons == 1) \
)"
categories = root2pandas.EventCategories()
categories.addCategory("ttbar", selection = None)
# initialize dataset class
dataset = root2pandas.Dataset(
outputdir = "/nfs/dust/cms/user/vdlinden/DNNInputFiles/ttbarMatcher/",
naming = "input",
addCNNmap = False,
addMEM = False,
maxEntries = 200000)
# add base event selection
dataset.addBaseSelection(base_selection)
# add samples to dataset
dataset.addSample(
single_el_sel_16 = "(N_LooseMuons == 0 and N_TightElectrons == 1 and Triggered_HLT_Ele27_WPTight_Gsf_vX == 1)"
trigger_sel_16 = "(" + single_mu_sel_16 + " or " + single_el_sel_16 + ")"
single_mu_sel_17 = "(N_LooseElectrons == 0 and N_TightMuons == 1 and Triggered_HLT_IsoMu27_vX == 1)"
single_el_sel_17 = "(N_LooseMuons == 0 and N_TightElectrons == 1 and (Triggered_HLT_Ele28_eta2p1_WPTight_Gsf_HT150_vX == 1 or ( Triggered_HLT_Ele32_WPTight_Gsf_L1DoubleEG_vX == 1 and Triggered_HLT_Ele32_WPTight_Gsf_2017SeedsX == 1 )))"
trigger_sel_17 = "(" + single_mu_sel_17 + " or " + single_el_sel_17 + ")"
single_mu_sel_18 = "(N_LooseElectrons == 0 and N_TightMuons == 1 and Triggered_HLT_IsoMu24_vX == 1)"
single_el_sel_18 = "(N_LooseMuons == 0 and N_TightElectrons == 1 and (Triggered_HLT_Ele28_eta2p1_WPTight_Gsf_HT150_vX == 1 or Triggered_HLT_Ele32_WPTight_Gsf_vX == 1))"
trigger_sel_18 = "(" + single_mu_sel_18 + " or " + single_el_sel_18 + ")"
# define output classes
STXS_Stage0 = "abs(GenHiggs_Eta) < 2.5"
# define output classes
ttH_categories = root2pandas.EventCategories()
ttH_categories.addCategory("ttH", selection=None)
ttH_categories.addCategory("ttHbb",
selection="(matchH_ft_RecoHiggs_matchable > 0.)")
ttH_categories.addCategory("ttHnonbb",
selection="(matchH_ft_RecoHiggs_matchable <= 0.)")
ttH_categories.addCategory("ttH_STXS_0",
selection=STXS_Stage0 +
" and GenHiggs_Pt >= 0 and GenHiggs_Pt < 60")
ttH_categories.addCategory("ttH_STXS_1",
selection=STXS_Stage0 +
" and GenHiggs_Pt >= 60 and GenHiggs_Pt < 120")
ttH_categories.addCategory("ttH_STXS_2",
selection=STXS_Stage0 +
" and GenHiggs_Pt >= 120 and GenHiggs_Pt < 200")
