diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 8f4e8ce57..da41c43ae 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -87,7 +87,7 @@ jobs: run: | mkdir dir_for_topcoffea cd dir_for_topcoffea - git clone https://github.com/TopEFT/topcoffea.git --branch ch_update + git clone https://github.com/TopEFT/topcoffea.git cd topcoffea conda run -n coffea-env pip install -e . cd ../.. diff --git a/README.md b/README.md index 524318012..6700560eb 100644 --- a/README.md +++ b/README.md @@ -101,14 +101,14 @@ The [v0.5 tag](https://github.com/TopEFT/topcoffea/releases/tag/v0.5) was used t time source fullR2_run.sh ``` -2. Run the datacard maker to obtain the cards and templates (from the pickled histogram file produced in Step 1, be sure to use the version with the nonprompt estimation, i.e. the one with `_np` appended to the name you specified for the `OUT_NAME` in `fullR2_run.sh`). +2. Run the datacard maker to obtain the cards and templates from SM (from the pickled histogram file produced in Step 1, be sure to use the version with the nonprompt estimation, i.e. the one with `_np` appended to the name you specified for the `OUT_NAME` in `fullR2_run.sh`). This step would also produce scalings-preselect.json file which the later version is necessary for IM workspace making. Note that command option `--wc-scalings` is not mandatory but to enforce the ordering of wcs in scalings. Add command `-A` to include all EFT templates in datacards for previous AAC model. Add option `-C` to run on condor. ``` - time python make_cards.py /path/to/your/examplename_np.pkl.gz -C --do-nuisance --var-lst lj0pt ptz -d /scratch365/you/somedir --unblind --do-mc-stat + time python make_cards.py /path/to/your/examplename_np.pkl.gz --do-nuisance --var-lst lj0pt ptz -d /scratch365/you/somedir --unblind --do-mc-stat --wc-scalings cQQ1 cQei cQl3i cQlMi cQq11 cQq13 cQq81 cQq83 cQt1 cQt8 cbW cpQ3 cpQM cpt cptb ctG ctW ctZ ctei ctlSi ctlTi ctli ctp ctq1 ctq8 ctt1 ``` -3. Run the post-processing checks on the cards to look for any unexpected errors in the condor logs and to grab the right set of ptz and lj0pt templates and cards used in TOP-22-006. The script will copy the relevant cards/templates to a directory called `ptz-lj0pt_withSys` that it makes inside of the directory you pass that points to the cards and templates made in Step 2. This `ptz-lj0pt_withSys` is the directory that can be copied to wherever you plan to run the `combine` steps (e.g. PSI). +3. Run the post-processing checks on the cards to look for any unexpected errors, to grab the right set of ptz and lj0pt templates/cards used in TOP-22-006, and to get final version of scalings.json file. The script will copy the relevant cards/templates/ and create the json file to a directory called `ptz-lj0pt_withSys` that it makes inside of the directory you pass that points to the cards and templates made in Step 2. This `ptz-lj0pt_withSys` is the directory that can be copied to wherever you plan to run the `combine` steps (e.g. PSI). Can also run this on condor with `-c`. ``` - time python datacards_post_processing.py /scratch365/you/somedir -c -s + time python datacards_post_processing.py /scratch365/you/somedir -s ``` 4. Check the yields with `get_datacard_yields.py` script. This scrip will read the datacards in the directory produced in Step 3 and will dump the SM yields (summed over jet bins) to the screen (the text is formatted as a latex table). Use the `--unblind` option if you want to also see the data numbers. diff --git a/analysis/topeft_run2/analysis_processor.py b/analysis/topeft_run2/analysis_processor.py index 12b647fa4..117358aef 100644 --- a/analysis/topeft_run2/analysis_processor.py +++ b/analysis/topeft_run2/analysis_processor.py @@ -63,7 +63,7 @@ def __init__(self, samples, wc_names_lst=[], hist_lst=None, ecut_threshold=None, self._samples = samples self._wc_names_lst = wc_names_lst self._dtype = dtype - self._offZ_split = offZ_split + self.offZ_3l_split = offZ_split self.tau_h_analysis = tau_h_analysis self.fwd_analysis = fwd_analysis @@ -152,6 +152,15 @@ def process(self, events): xsec = self._samples[dataset]["xsec"] sow = self._samples[dataset]["nSumOfWeights"] + is_run3 = False + if year.startswith("202"): + is_run3 = True + is_run2 = not is_run3 + + run_era = None + if isData: + run_era = self._samples[dataset]["path"].split("/")[2].split("-")[0][-1] + # Get up down weights from input dict if (self._do_systematics and not isData): if histAxisName in get_te_param("lo_xsec_samples"): @@ -194,9 +203,10 @@ def process(self, events): sow_renormfactUp = -1 sow_renormfactDown = -1 - datasets = ["SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"] + datasets = ["Muon", "SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"] for d in datasets: - if d in dataset: dataset = dataset.split('_')[0] + if dataset.startswith(d): + dataset = dataset.split('_')[0] # Set the sampleType (used for MC matching requirement) sampleType = "prompt" @@ -215,13 +225,21 @@ def process(self, events): tau = events.Tau jets = events.Jet + + if is_run3: + leptonSelection = te_os.run3leptonselection() + jetsRho = events.Rho["fixedGridRhoFastjetAll"] + elif is_run2: + leptonSelection = te_os.run2leptonselection() + jetsRho = events.fixedGridRhoFastjetAll + # An array of lenght events that is just 1 for each event # Probably there's a better way to do this, but we use this method elsewhere so I guess why not.. events.nom = ak.ones_like(events.MET.pt) ele["idEmu"] = te_os.ttH_idEmu_cuts_E3(ele.hoe, ele.eta, ele.deltaEtaSC, ele.eInvMinusPInv, ele.sieie) - ele["conept"] = te_os.coneptElec(ele.pt, ele.mvaTTHUL, ele.jetRelIso) - mu["conept"] = te_os.coneptMuon(mu.pt, mu.mvaTTHUL, mu.jetRelIso, mu.mediumId) + ele["conept"] = leptonSelection.coneptElec(ele) + mu["conept"] = leptonSelection.coneptMuon(mu) ele["btagDeepFlavB"] = ak.fill_none(ele.matched_jet.btagDeepFlavB, -99) mu["btagDeepFlavB"] = ak.fill_none(mu.matched_jet.btagDeepFlavB, -99) if not isData: @@ -235,6 +253,10 @@ def process(self, events): golden_json_path = topcoffea_path("data/goldenJsons/Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt") elif year == "2018": golden_json_path = topcoffea_path("data/goldenJsons/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt") + elif year == "2022": + golden_json_path = topcoffea_path("data/goldenJsons/Cert_Collisions2022_355100_362760_Golden.txt") + elif year == "2023": + golden_json_path = topcoffea_path("data/goldenJsons/Cert_Collisions2023_366442_370790_Golden.txt") else: raise ValueError(f"Error: Unknown year \"{year}\".") lumi_mask = LumiMask(golden_json_path)(events.run,events.luminosityBlock) @@ -254,18 +276,18 @@ def process(self, events): ################### Electron selection #################### - ele["isPres"] = te_os.isPresElec(ele.pt, ele.eta, ele.dxy, ele.dz, ele.miniPFRelIso_all, ele.sip3d, getattr(ele,"mvaFall17V2noIso_WPL")) - ele["isLooseE"] = te_os.isLooseElec(ele.miniPFRelIso_all,ele.sip3d,ele.lostHits) - ele["isFO"] = te_os.isFOElec(ele.pt, ele.conept, ele.btagDeepFlavB, ele.idEmu, ele.convVeto, ele.lostHits, ele.mvaTTHUL, ele.jetRelIso, ele.mvaFall17V2noIso_WP90, year) - ele["isTightLep"] = te_os.tightSelElec(ele.isFO, ele.mvaTTHUL) + ele["isPres"] = leptonSelection.isPresElec(ele) + ele["isLooseE"] = leptonSelection.isLooseElec(ele) + ele["isFO"] = leptonSelection.isFOElec(ele, year) + ele["isTightLep"] = leptonSelection.tightSelElec(ele) ################### Muon selection #################### - mu["pt"] = ApplyRochesterCorrections(year, mu, isData) # Need to apply corrections before doing muon selection - mu["isPres"] = te_os.isPresMuon(mu.dxy, mu.dz, mu.sip3d, mu.eta, mu.pt, mu.miniPFRelIso_all) - mu["isLooseM"] = te_os.isLooseMuon(mu.miniPFRelIso_all,mu.sip3d,mu.looseId) - mu["isFO"] = te_os.isFOMuon(mu.pt, mu.conept, mu.btagDeepFlavB, mu.mvaTTHUL, mu.jetRelIso, year) - mu["isTightLep"]= te_os.tightSelMuon(mu.isFO, mu.mediumId, mu.mvaTTHUL) + mu["pt"] = ApplyRochesterCorrections(year, mu, isData) # Run3 ready + mu["isPres"] = leptonSelection.isPresMuon(mu) + mu["isLooseM"] = leptonSelection.isLooseMuon(mu) + mu["isFO"] = leptonSelection.isFOMuon(mu, year) + mu["isTightLep"]= leptonSelection.tightSelMuon(mu) ################### Loose selection #################### @@ -282,7 +304,7 @@ def process(self, events): e_fo = ele[ele.isPres & ele.isLooseE & ele.isFO] # Attach the lepton SFs to the electron and muons collections - AttachElectronSF(e_fo,year=year) + AttachElectronSF(e_fo,year=year, looseWP="none" if is_run3 else "wpLnoiso") #Run3 ready AttachMuonSF(m_fo,year=year) # Attach per lepton fake rates @@ -326,15 +348,22 @@ def process(self, events): ######### Systematics ########### # Define the lists of systematics we include + obj_jes_entries = [] + with open(topeft_path('modules/jerc_dict.json'), 'r') as f: + jerc_dict = json.load(f) + for junc in jerc_dict[year]['junc']: + junc = junc.replace("Regrouped_", "") + obj_jes_entries.append(f'JES_{junc}Up') + obj_jes_entries.append(f'JES_{junc}Down') obj_correction_syst_lst = [ - f'JER_{year}Up',f'JER_{year}Down', # Systs that affect the kinematics of objects - 'JES_FlavorQCDUp', 'JES_AbsoluteUp', 'JES_RelativeBalUp', 'JES_BBEC1Up', 'JES_RelativeSampleUp', 'JES_FlavorQCDDown', 'JES_AbsoluteDown', 'JES_RelativeBalDown', 'JES_BBEC1Down', 'JES_RelativeSampleDown' - ] + f'JER_{year}Up', f'JER_{year}Down' + ] + obj_jes_entries if self.tau_h_analysis: obj_correction_syst_lst.append("TESUp") obj_correction_syst_lst.append("TESDown") obj_correction_syst_lst.append("FESUp") obj_correction_syst_lst.append("FESDown") + wgt_correction_syst_lst = [ "lepSF_muonUp","lepSF_muonDown","lepSF_elecUp","lepSF_elecDown",f"btagSFbc_{year}Up",f"btagSFbc_{year}Down","btagSFbc_corrUp","btagSFbc_corrDown",f"btagSFlight_{year}Up",f"btagSFlight_{year}Down","btagSFlight_corrUp","btagSFlight_corrDown","PUUp","PUDown","PreFiringUp","PreFiringDown",f"triggerSF_{year}Up",f"triggerSF_{year}Down", # Exp systs "FSRUp","FSRDown","ISRUp","ISRDown","renormUp","renormDown","factUp","factDown", # Theory systs @@ -355,29 +384,35 @@ def process(self, events): # Note: Here we will to the weights object the SFs that do not depend on any of the forthcoming loops weights_obj_base = coffea.analysis_tools.Weights(len(events),storeIndividual=True) if not isData: - # If this is no an eft sample, get the genWeight - if eft_coeffs is None: genw = events["genWeight"] - else: genw= np.ones_like(events["event"]) + if eft_coeffs is None: + genw = events["genWeight"] + else: + genw= np.ones_like(events["event"]) # Normalize by (xsec/sow)*genw where genw is 1 for EFT samples # Note that for theory systs, will need to multiply by sow/sow_wgtUP to get (xsec/sow_wgtUp)*genw and same for Down lumi = 1000.0*get_tc_param(f"lumi_{year}") weights_obj_base.add("norm",(xsec/sow)*genw*lumi) + if is_run2: + l1prefiring_args = [events.L1PreFiringWeight.Nom, events.L1PreFiringWeight.Up, events.L1PreFiringWeight.Dn] + elif is_run3: + l1prefiring_args = [ak.ones_like(events.nom), ak.ones_like(events.nom), ak.ones_like(events.nom)] + # Attach PS weights (ISR/FSR) and scale weights (renormalization/factorization) and PDF weights - tc_cor.AttachPSWeights(events) - tc_cor.AttachScaleWeights(events) - #AttachPdfWeights(events) # TODO - # FSR/ISR weights + tc_cor.AttachPSWeights(events) #Run3 ready + tc_cor.AttachScaleWeights(events) #Run3 ready (with caveat on "nominal") + #AttachPdfWeights(events) #TODO + # FSR/ISR weights -- corrections come from AttachPSWeights weights_obj_base.add('ISR', events.nom, events.ISRUp*(sow/sow_ISRUp), events.ISRDown*(sow/sow_ISRDown)) weights_obj_base.add('FSR', events.nom, events.FSRUp*(sow/sow_FSRUp), events.FSRDown*(sow/sow_FSRDown)) - # renorm/fact scale + # renorm/fact scale -- corrections come from AttachScaleWeights weights_obj_base.add('renorm', events.nom, events.renormUp*(sow/sow_renormUp), events.renormDown*(sow/sow_renormDown)) weights_obj_base.add('fact', events.nom, events.factUp*(sow/sow_factUp), events.factDown*(sow/sow_factDown)) - # Prefiring and PU (note prefire weights only available in nanoAODv9) - weights_obj_base.add('PreFiring', events.L1PreFiringWeight.Nom, events.L1PreFiringWeight.Up, events.L1PreFiringWeight.Dn) - weights_obj_base.add('PU', tc_cor.GetPUSF((events.Pileup.nTrueInt), year), tc_cor.GetPUSF(events.Pileup.nTrueInt, year, 'up'), tc_cor.GetPUSF(events.Pileup.nTrueInt, year, 'down')) + # Prefiring and PU (note prefire weights only available in nanoAODv9 and for Run2) + weights_obj_base.add('PreFiring', *l1prefiring_args) #Run3 ready + weights_obj_base.add('PU', tc_cor.GetPUSF((events.Pileup.nTrueInt), year), tc_cor.GetPUSF(events.Pileup.nTrueInt, year, 'up'), tc_cor.GetPUSF(events.Pileup.nTrueInt, year, 'down')) #Run3 ready ######### The rest of the processor is inside this loop over systs that affect object kinematics ########### @@ -408,20 +443,22 @@ def process(self, events): # Selecting jets and cleaning them jetptname = "pt_nom" if hasattr(cleanedJets, "pt_nom") else "pt" + cleanedJets["pt_raw"] = (1 - cleanedJets.rawFactor)*cleanedJets.pt + cleanedJets["mass_raw"] = (1 - cleanedJets.rawFactor)*cleanedJets.mass + cleanedJets["rho"] = ak.broadcast_arrays(jetsRho, cleanedJets.pt)[0] + # Jet energy corrections if not isData: - cleanedJets["pt_raw"] = (1 - cleanedJets.rawFactor)*cleanedJets.pt - cleanedJets["mass_raw"] = (1 - cleanedJets.rawFactor)*cleanedJets.mass - cleanedJets["pt_gen"] =ak.values_astype(ak.fill_none(cleanedJets.matched_gen.pt, 0), np.float32) - cleanedJets["rho"] = ak.broadcast_arrays(events.fixedGridRhoFastjetAll, cleanedJets.pt)[0] - events_cache = events.caches[0] - cleanedJets = ApplyJetCorrections(year, corr_type='jets').build(cleanedJets, lazy_cache=events_cache) - cleanedJets=ApplyJetSystematics(year,cleanedJets,syst_var) - met=ApplyJetCorrections(year, corr_type='met').build(met_raw, cleanedJets, lazy_cache=events_cache) + cleanedJets["pt_gen"] = ak.values_astype(ak.fill_none(cleanedJets.matched_gen.pt, 0), np.float32) if self.tau_h_analysis: tau["pt"], tau["mass"] = ApplyTESSystematic(year, tau, isData, syst_var) tau["pt"], tau["mass"] = ApplyFESSystematic(year, tau, isData, syst_var) + events_cache = events.caches[0] + cleanedJets = ApplyJetCorrections(year, corr_type='jets', isData=isData, era=run_era).build(cleanedJets, lazy_cache=events_cache) #Run3 ready + cleanedJets = ApplyJetSystematics(year,cleanedJets,syst_var) + met = ApplyJetCorrections(year, corr_type='met', isData=isData, era=run_era).build(met_raw, cleanedJets, lazy_cache=events_cache) + cleanedJets["isGood"] = tc_os.is_tight_jet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, pt_cut=30., eta_cut=get_te_param("eta_j_cut"), id_cut=get_te_param("jet_id_cut")) cleanedJets["isFwd"] = te_os.isFwdJet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, jetPtCut=40.) goodJets = cleanedJets[cleanedJets.isGood] @@ -434,31 +471,15 @@ def process(self, events): j0 = goodJets[ak.argmax(goodJets.pt,axis=-1,keepdims=True)] # Loose DeepJet WP - if year == "2017": - btagwpl = get_tc_param("btag_wp_loose_UL17") - elif year == "2018": - btagwpl = get_tc_param("btag_wp_loose_UL18") - elif year=="2016": - btagwpl = get_tc_param("btag_wp_loose_UL16") - elif year=="2016APV": - btagwpl = get_tc_param("btag_wp_loose_UL16APV") - else: - raise ValueError(f"Error: Unknown year \"{year}\".") + loose_tag = "btag_wp_loose_" + year.replace("201", "UL1") + btagwpl = get_tc_param(loose_tag) isBtagJetsLoose = (goodJets.btagDeepFlavB > btagwpl) isNotBtagJetsLoose = np.invert(isBtagJetsLoose) nbtagsl = ak.num(goodJets[isBtagJetsLoose]) # Medium DeepJet WP - if year == "2017": - btagwpm = get_tc_param("btag_wp_medium_UL17") - elif year == "2018": - btagwpm = get_tc_param("btag_wp_medium_UL18") - elif year=="2016": - btagwpm = get_tc_param("btag_wp_medium_UL16") - elif year=="2016APV": - btagwpm = get_tc_param("btag_wp_medium_UL16APV") - else: - raise ValueError(f"Error: Unknown year \"{year}\".") + medium_tag = "btag_wp_medium_" + year.replace("201", "UL1") + btagwpm = get_tc_param(medium_tag) isBtagJetsMedium = (goodJets.btagDeepFlavB > btagwpm) isNotBtagJetsMedium = np.invert(isBtagJetsMedium) nbtagsm = ak.num(goodJets[isBtagJetsMedium]) @@ -571,7 +592,7 @@ def process(self, events): select_cat_dict = json.load(ch_json_test) # This dictionary keeps track of which selections go with which SR categories - if self._offZ_split: + if self.offZ_3l_split: import_sr_cat_dict = select_cat_dict["OFFZ_SPLIT_CH_LST_SR"] elif self.tau_h_analysis: import_sr_cat_dict = select_cat_dict["TAU_CH_LST_SR"] @@ -636,9 +657,11 @@ def process(self, events): # Get mask for events that have two sf os leps close to z peak sfosz_3l_OnZ_mask = tc_es.get_Z_peak_mask(l_fo_conept_sorted_padded[:,0:3],pt_window=10.0) - sfosz_3l_OffZ_mask = ~sfosz_3l_OnZ_mask - sfosz_3l_OffZ_low_mask = tc_es.get_off_Z_mask_low(l_fo_conept_sorted_padded[:,0:3],pt_window=0.0) - sfosz_3l_OffZ_any_mask = tc_es.get_any_sfos_pair(l_fo_conept_sorted_padded[:,0:3]) + if self.offZ_3l_split: + sfosz_3l_OffZ_low_mask = tc_es.get_off_Z_mask_low(l_fo_conept_sorted_padded[:,0:3],pt_window=0.0) + sfosz_3l_OffZ_any_mask = tc_es.get_any_sfos_pair(l_fo_conept_sorted_padded[:,0:3]) + else: + sfosz_3l_OffZ_mask = ~sfosz_3l_OnZ_mask sfosz_2l_mask = tc_es.get_Z_peak_mask(l_fo_conept_sorted_padded[:,0:2],pt_window=10.0) sfasz_2l_mask = tc_es.get_Z_peak_mask(l_fo_conept_sorted_padded[:,0:2],pt_window=30.0,flavor="as") # Any sign (do not enforce ss or os here) if self.tau_h_analysis: @@ -719,6 +742,9 @@ def process(self, events): preselections.add("3l_p", (events.is3l & pass_trg & charge3l_p)) preselections.add("3l_m", (events.is3l & pass_trg & charge3l_m)) preselections.add("3l_onZ", (sfosz_3l_OnZ_mask)) + preselections.add("2lss_fwd", (events.is2l & pass_trg & fwdjet_mask)) + preselections.add("2l_fwd_p", (chargel0_p & fwdjet_mask)) + preselections.add("2l_fwd_m", (chargel0_m & fwdjet_mask)) # 3l selection preselections.add("bmask_exactly0m", (bmask_exactly0med)) @@ -726,7 +752,7 @@ def process(self, events): preselections.add("bmask_exactly2m", (bmask_exactly2med)) preselections.add("bmask_atleast2m", (bmask_atleast2med)) - if self._offZ_split: + if self.offZ_3l_split: preselections.add("3l_offZ_low", (sfosz_3l_OffZ_mask & sfosz_3l_OffZ_any_mask & sfosz_3l_OffZ_low_mask)) preselections.add("3l_offZ_high", (sfosz_3l_OffZ_mask & sfosz_3l_OffZ_any_mask & ~sfosz_3l_OffZ_low_mask)) preselections.add("3l_offZ_none", (sfosz_3l_OffZ_mask & ~sfosz_3l_OffZ_any_mask)) @@ -824,7 +850,7 @@ def process(self, events): ptz = te_es.get_Z_pt(l_fo_conept_sorted_padded[:,0:3],10.0) if self.tau_h_analysis: ptz_wtau = (l0+tau0).pt - if self._offZ_split: + if self.offZ_3l_split: ptz = te_es.get_ll_pt(l_fo_conept_sorted_padded[:,0:3],10.0) # Leading (b+l) pair pt bjetsl = goodJets[isBtagJetsLoose][ak.argsort(goodJets[isBtagJetsLoose].pt, axis=-1, ascending=False)] @@ -1063,12 +1089,15 @@ def process(self, events): # Skip histos that are not defined (or not relevant) to given categories if ((("j0" in dense_axis_name) and ("lj0pt" not in dense_axis_name)) & (("CRZ" in ch_name) or ("CRflip" in ch_name))): continue if ((("j0" in dense_axis_name) and ("lj0pt" not in dense_axis_name)) & ("0j" in ch_name)): continue - if self._offZ_split: + if self.offZ_3l_split: if (("ptz" in dense_axis_name) & ("onZ" not in lep_chan) & ("offZ_high" not in lep_chan) & ("offZ_low" not in lep_chan)):continue elif self.tau_h_analysis: if (("ptz" in dense_axis_name) and ("onZ" not in lep_chan)): continue if (("ptz" in dense_axis_name) and ("2lss" not in lep_chan) and ("ptz_wtau" not in dense_axis_name)): continue if (("ptz_wtau" in dense_axis_name) and ("1tau" not in lep_chan)): continue + elif self.fwd_analysis: + if (("ptz" in dense_axis_name) & ("onZ" not in lep_chan)): continue + if (("lt" in dense_axis_name) and ("2lss" not in lep_chan)): continue else: if (("ptz" in dense_axis_name) & ("onZ" not in lep_chan)): continue if ((dense_axis_name in ["o0pt","b0pt","bl0pt"]) & ("CR" in ch_name)): continue diff --git a/analysis/topeft_run2/datacards_post_processing.py b/analysis/topeft_run2/datacards_post_processing.py index c07eda5cc..dd483c51f 100644 --- a/analysis/topeft_run2/datacards_post_processing.py +++ b/analysis/topeft_run2/datacards_post_processing.py @@ -48,6 +48,9 @@ def main(): ###### Print out general info ###### + with open(os.path.join(args.datacards_path,'scalings-preselect.json'), 'r') as file: + scalings_content = json.load(file) + # Count the number of text data cards and root templates n_text_cards = 0 n_root_templates = 0 @@ -93,8 +96,6 @@ def main(): for line in lines_from_condor_out_to_print: print(f"\t\t* In {line[0]}: {line[1]}") - - ####### Copy the TOP-22-006 relevant files to their own dir ###### @@ -134,12 +135,13 @@ def main(): channelname = lep_ch_name + "_" + jet + "j_ptz" elif args.tau_flag and ("1tau_onZ" in lep_ch_name): channelname = lep_ch_name + "_" + jet + "j_ptz_wtau" - elif args.fwd_flag and ("fwd" in lep_ch_name): + elif args.fwd_flag and ("2lss" in lep_ch_name): channelname = lep_ch_name + "_" + jet + "j_lt" else: channelname = lep_ch_name + "_" + jet + "j_lj0pt" CATSELECTED.append(channelname) + CATSELECTED = sorted(CATSELECTED) # Grab the ptz-lj0pt cards we want for TOP-22-006, copy into a dir n_txt = 0 n_root = 0 @@ -147,6 +149,7 @@ def main(): os.mkdir(ptzlj0pt_path) if args.set_up_top22006: print(f"\nCopying TOP-22-006 relevant files to {ptzlj0pt_path}...") + if args.set_up_offZdivision: print(f"\nCopying 3l-offZ-division relevant files to {ptzlj0pt_path}...") for fname in datacard_files: @@ -159,6 +162,17 @@ def main(): #also copy the selectedWCs.txt file shutil.copyfile(os.path.join(args.datacards_path,"selectedWCs.txt"),os.path.join(ptzlj0pt_path,"selectedWCs.txt")) + for item in scalings_content: + channel_name = item.get("channel") + if channel_name in CATSELECTED: + ch_index = CATSELECTED.index(channel_name) + 1 + item["channel"] = "ch" + str(ch_index) + else: + scalings_content = [d for d in scalings_content if d != item] + + with open(os.path.join(ptzlj0pt_path, 'scalings.json'), 'w') as file: + json.dump(scalings_content, file, indent=4) + # Check that we got the expected number and print what we learn print(f"\tNumber of text templates copied: {n_txt}") print(f"\tNumber of root templates copied: {n_txt}") @@ -167,5 +181,4 @@ def main(): print("Done.\n") - main() diff --git a/analysis/topeft_run2/make_cards.py b/analysis/topeft_run2/make_cards.py index 91015853b..6a91dc298 100644 --- a/analysis/topeft_run2/make_cards.py +++ b/analysis/topeft_run2/make_cards.py @@ -170,7 +170,9 @@ def main(): parser.add_argument("--condor","-C",action="store_true",help="Split up the channels into multiple condor jobs") parser.add_argument("--chunks","-n",default=1,help="The number of channels each condor job should process") parser.add_argument("--keep-negative-bins",action="store_true",help="Don't crop negative bins") + parser.add_argument("--use-AAC","-A",action="store_true",help="Include all EFT templates in datacards for AAC model") parser.add_argument("--wc-vals", default="",action="store", nargs="+", help="Specify the corresponding wc values to set for the wc list") + parser.add_argument("--wc-scalings", default=[],action="extend",nargs="+",help="Specify a list of wc ordering for scalings.json") args = parser.parse_args() pkl_file = args.pkl_file @@ -187,8 +189,10 @@ def main(): drop_syst = args.drop_syst unblind = args.unblind verbose = args.verbose + use_AAC = args.use_AAC wc_vals = args.wc_vals + wc_scalings = args.wc_scalings select_only = args.select_only use_selected = args.use_selected @@ -211,7 +215,9 @@ def main(): "unblind": unblind, "verbose": verbose, "year_lst": years, + "use_AAC": use_AAC, "wc_vals": wc_vals, + "wc_scalings": wc_scalings, } if out_dir != "." and not os.path.exists(out_dir): @@ -285,6 +291,12 @@ def main(): run_condor(dc,pkl_file,out_dir,dists,ch_lst,chunks) else: run_local(dc,dists,ch_lst,selected_wcs, not args.keep_negative_bins, wcs_dict) + + # make pre-selection scalings.json + print("Making scalings-preselect.json file...") + with open(os.path.join(out_dir,"scalings-preselect.json"),"w") as f: + json.dump(dc.scalings, f, indent=4) + dt = time.time() - tic print(f"Total Time: {dt:.2f} s") print("Finished!") diff --git a/analysis/topeft_run2/run_analysis.py b/analysis/topeft_run2/run_analysis.py index c1f4ac2ac..04a818a10 100644 --- a/analysis/topeft_run2/run_analysis.py +++ b/analysis/topeft_run2/run_analysis.py @@ -271,7 +271,7 @@ def LoadJsonToSampleName(jsonFile, prefix): ), 'extra_input_files': ["analysis_processor.py"], - 'retries': 5, + 'retries': 20, # use mid-range compression for chunks results. 9 is the default for work # queue in coffea. Valid values are 0 (minimum compression, less memory @@ -285,7 +285,7 @@ def LoadJsonToSampleName(jsonFile, prefix): # forever until a larger worker connects. 'resource_monitor': True, 'resources_mode': 'auto', - #'filepath': f'/project01/ndcms/{os.environ["USER"]}', + #'filepath': f'/tmp/{os.environ["USER"]}', ##Placeholder to comment out if you don't want to save wq-factory dirs in afs # this resource values may be omitted when using # resources_mode: 'auto', but they do make the initial portion diff --git a/input_samples/cfgs/2022_data_samples.cfg b/input_samples/cfgs/2022_data_samples.cfg new file mode 100644 index 000000000..5d92d46e9 --- /dev/null +++ b/input_samples/cfgs/2022_data_samples.cfg @@ -0,0 +1,17 @@ +#root://cms-xrd-global.cern.ch// +file:///cms/cephfs/data/ +#root://hactar01.crc.nd.edu// +../../input_samples/sample_jsons/data_samples/2022/EGamma_C-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/EGamma_D-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/EGamma_E-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/EGamma_F-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/EGamma_G-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/Muon_C-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/Muon_D-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/Muon_E-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/MuonEG_C-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/MuonEG_D-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/MuonEG_E-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/MuonEG_F-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/Muon_F-22Sep2023.json +../../input_samples/sample_jsons/data_samples/2022/Muon_G-22Sep2023.json diff --git a/input_samples/cfgs/2022_mc_background_samples.cfg b/input_samples/cfgs/2022_mc_background_samples.cfg new file mode 100644 index 000000000..e6e35aa0a --- /dev/null +++ b/input_samples/cfgs/2022_mc_background_samples.cfg @@ -0,0 +1,47 @@ +# Central background samples + +# Central 2022 background samples +#root://hactar01.crc.nd.edu// +file:///cms/cephfs/data/ +#../../input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-10to50-ext1.json +#../../input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-10to50.json +#../../input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-50-ext1.json +#../../input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-50.json +#../../input_samples/sample_jsons/background_samples/central_2022/ggToZZTo2e2mu.json +#../../input_samples/sample_jsons/background_samples/central_2022/ggToZZTo2e2tau.json +#../../input_samples/sample_jsons/background_samples/central_2022/ggToZZTo2mu2tau.json +#../../input_samples/sample_jsons/background_samples/central_2022/ggToZZTo4e.json +#../../input_samples/sample_jsons/background_samples/central_2022/ggToZZTo4mu.json +#../../input_samples/sample_jsons/background_samples/central_2022/ggToZZTo4tau.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_antitop_t-channel.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tbarW_Leptonic-ext1.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tbarW_Leptonic.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tbarW_Semileptonic-ext1.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tbarW_Semileptonic.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_top_s-channel.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_top_t-channel.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tW_Leptonic-ext1.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tW_Leptonic.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tW_Semileptonic-ext1.json +../../input_samples/sample_jsons/background_samples/central_2022/ST_tW_Semileptonic.json +../../input_samples/sample_jsons/background_samples/central_2022/TTG-1Jets_PTG-100to200.json +../../input_samples/sample_jsons/background_samples/central_2022/TTG-1Jets_PTG-10to100.json +../../input_samples/sample_jsons/background_samples/central_2022/TTG-1Jets_PTG-200.json +#../../input_samples/sample_jsons/background_samples/central_2022/TTto2L2Nu-2Jets.json +#../../input_samples/sample_jsons/background_samples/central_2022/TTto2L2Nu-3Jets.json +../../input_samples/sample_jsons/background_samples/central_2022/TTto2L2Nu-ext1.json +../../input_samples/sample_jsons/background_samples/central_2022/TTto2L2Nu.json +../../input_samples/sample_jsons/background_samples/central_2022/TTtoLNu2Q.json +../../input_samples/sample_jsons/background_samples/central_2022/TWZ_Tto2Q_WtoLNu_Zto2L.json +../../input_samples/sample_jsons/background_samples/central_2022/TWZ_TtoLNu_Wto2Q_Zto2L.json +../../input_samples/sample_jsons/background_samples/central_2022/TWZ_TtoLNu_WtoLNu_Zto2L.json +#../../input_samples/sample_jsons/background_samples/central_2022/WJetsToLNu.json +#../../input_samples/sample_jsons/background_samples/central_2022/WWTo2L2Nu-ext1.json +#../../input_samples/sample_jsons/background_samples/central_2022/WWTo2L2Nu.json +#../../input_samples/sample_jsons/background_samples/central_2022/WWW.json +#../../input_samples/sample_jsons/background_samples/central_2022/WWZ.json +#../../input_samples/sample_jsons/background_samples/central_2022/WZTo3LNu.json +#../../input_samples/sample_jsons/background_samples/central_2022/ZZTo4L-1Jet.json +#../../input_samples/sample_jsons/background_samples/central_2022/ZZTo4L-ext1.json +#../../input_samples/sample_jsons/background_samples/central_2022/ZZTo4L.json +#../../input_samples/sample_jsons/background_samples/central_2022/ZZZ.json diff --git a/input_samples/cfgs/2022_mc_signal_samples.cfg b/input_samples/cfgs/2022_mc_signal_samples.cfg new file mode 100644 index 000000000..c4db8a181 --- /dev/null +++ b/input_samples/cfgs/2022_mc_signal_samples.cfg @@ -0,0 +1,13 @@ +# Central background samples + +# Central 2022 background samples +#root://hactar01.crc.nd.edu// +file:///cms/cephfs/data/ +../../input_samples/sample_jsons/signal_samples/central_2022/2022_ttHnobb-1Jets.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_ttHnobb.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_TTLL_MLL-4to50.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_TTLL_MLL-50.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_TTLNu.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_TTTT.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_TZQB-4FS_OnshellZ.json +../../input_samples/sample_jsons/signal_samples/central_2022/2022_TZQB-Zto2L-4FS_MLL-30.json diff --git a/input_samples/cfgs/data_samples_NDSkim.cfg b/input_samples/cfgs/data_samples_NDSkim.cfg index 740c97680..d006ed158 100644 --- a/input_samples/cfgs/data_samples_NDSkim.cfg +++ b/input_samples/cfgs/data_samples_NDSkim.cfg @@ -1,6 +1,7 @@ # 2016 data # See: https://twiki.cern.ch/twiki/bin/view/CMS/PdmVDatasetsUL2016 root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/data_samples/2016/DoubleEG_B-ver1_HIPM_UL2016_NDSkim.json ../../input_samples/sample_jsons/data_samples/2016/DoubleEG_B-ver2_HIPM_UL2016_NDSkim.json ../../input_samples/sample_jsons/data_samples/2016/DoubleEG_C-HIPM_UL2016_NDSkim.json @@ -54,6 +55,7 @@ root://hactar01.crc.nd.edu/ # 2017 data # See: https://twiki.cern.ch/twiki/bin/view/CMS/PdmVDatasetsUL2017 root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/data_samples/2017/DoubleEG_B-UL2017_NDSkim.json ../../input_samples/sample_jsons/data_samples/2017/DoubleEG_C-UL2017_NDSkim.json ../../input_samples/sample_jsons/data_samples/2017/DoubleEG_D-UL2017_NDSkim.json @@ -88,6 +90,7 @@ root://hactar01.crc.nd.edu/ # 2018 data # See: https://twiki.cern.ch/twiki/bin/view/CMS/PdmVDatasetsUL2018 root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/data_samples/2018/DoubleMuon_A-UL2018_NDSkim.json ../../input_samples/sample_jsons/data_samples/2018/DoubleMuon_B-UL2018_NDSkim.json ../../input_samples/sample_jsons/data_samples/2018/DoubleMuon_C-UL2018_NDSkim.json @@ -106,4 +109,4 @@ root://hactar01.crc.nd.edu/ ../../input_samples/sample_jsons/data_samples/2018/SingleMuon_A-UL2018_NDSkim.json ../../input_samples/sample_jsons/data_samples/2018/SingleMuon_B-UL2018_NDSkim.json ../../input_samples/sample_jsons/data_samples/2018/SingleMuon_C-UL2018_NDSkim.json -../../input_samples/sample_jsons/data_samples/2018/SingleMuon_D-UL2018_NDSkim.json +../../input_samples/sample_jsons/data_samples/2018/SingleMuon_D-UL2018_NDSkim.json \ No newline at end of file diff --git a/input_samples/cfgs/mc_background_samples_NDSkim.cfg b/input_samples/cfgs/mc_background_samples_NDSkim.cfg index ee22d3ef8..c80cc255c 100644 --- a/input_samples/cfgs/mc_background_samples_NDSkim.cfg +++ b/input_samples/cfgs/mc_background_samples_NDSkim.cfg @@ -2,6 +2,7 @@ # Central UL16APV background samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/background_samples/central_UL/UL16APV_TWZToLL_thad_Wlept_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL16APV_TWZToLL_tlept_Whad_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL16APV_TWZToLL_tlept_Wlept_NDSkim.json @@ -28,6 +29,7 @@ root://hactar01.crc.nd.edu/ # Central UL16 background samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/background_samples/central_UL/UL16_TWZToLL_thad_Wlept_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL16_TWZToLL_tlept_Whad_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL16_TWZToLL_tlept_Wlept_NDSkim.json @@ -54,6 +56,7 @@ root://hactar01.crc.nd.edu/ # Central UL17 background samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/background_samples/central_UL/UL17_TWZToLL_thad_Wlept_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL17_TWZToLL_tlept_Whad_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL17_TWZToLL_tlept_Wlept_NDSkim.json @@ -80,6 +83,7 @@ root://hactar01.crc.nd.edu/ # Central UL18 background samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/background_samples/central_UL/UL18_TWZToLL_thad_Wlept_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL18_TWZToLL_tlept_Whad_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL18_TWZToLL_tlept_Wlept_NDSkim.json @@ -102,5 +106,4 @@ root://hactar01.crc.nd.edu/ ../../input_samples/sample_jsons/background_samples/central_UL/UL18_GluGluToContinToZZTo2mu2tau_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL18_GluGluToContinToZZTo4e_NDSkim.json ../../input_samples/sample_jsons/background_samples/central_UL/UL18_GluGluToContinToZZTo4mu_NDSkim.json -../../input_samples/sample_jsons/background_samples/central_UL/UL18_GluGluToContinToZZTo4tau_NDSkim.json - +../../input_samples/sample_jsons/background_samples/central_UL/UL18_GluGluToContinToZZTo4tau_NDSkim.json \ No newline at end of file diff --git a/input_samples/cfgs/mc_signal_samples_NDSkim.cfg b/input_samples/cfgs/mc_signal_samples_NDSkim.cfg index cbdf8d7d8..8e2eb047b 100644 --- a/input_samples/cfgs/mc_signal_samples_NDSkim.cfg +++ b/input_samples/cfgs/mc_signal_samples_NDSkim.cfg @@ -4,6 +4,7 @@ # Private UL17 samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/signal_samples/private_UL/UL17_tHq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL17_tllq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL17_ttHJet_b1_NDSkim.json @@ -13,6 +14,7 @@ root://hactar01.crc.nd.edu/ # Private UL18 samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/signal_samples/private_UL/UL18_tHq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL18_tllq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL18_ttHJet_b1_NDSkim.json @@ -22,6 +24,7 @@ root://hactar01.crc.nd.edu/ # Private UL16 samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/signal_samples/private_UL/UL16_tHq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL16_tllq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL16_ttHJet_b1_NDSkim.json @@ -31,10 +34,10 @@ root://hactar01.crc.nd.edu/ # Private UL16APV samples root://hactar01.crc.nd.edu/ +#file:///cms/cephfs/data/ ../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_tHq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_tllq_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_ttHJet_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_ttllJet_b1_NDSkim.json ../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_ttlnuJet_b1_NDSkim.json -../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_tttt_b1_NDSkim.json - +../../input_samples/sample_jsons/signal_samples/private_UL/UL16APV_tttt_b1_NDSkim.json \ No newline at end of file diff --git a/input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-10to50-ext1.json b/input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-10to50-ext1.json new file mode 100644 index 000000000..2e324b83f --- /dev/null +++ b/input_samples/sample_jsons/background_samples/central_2022/DYJetsToLL_MLL-10to50-ext1.json @@ -0,0 +1,98 @@ +{ + "xsec": 19982.5, + "year": "2022", + "treeName": "Events", + "histAxisName": "DYJetsToLL_MLL-10to50-ext1_central2022", + "options": "", + "WCnames": [], + "files": [ + "/store/mc/Run3Summer22NanoAODv12/DYto2L-2Jets_MLL-10to50_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5_ext1-v1/2550000/5955dfcd-689a-43b1-99e2-36e61dccf6a9.root", + "/store/mc/Run3Summer22NanoAODv12/DYto2L-2Jets_MLL-10to50_TuneCP5_13p6TeV_amcatnloFXFX-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5_ext1-v1/2550000/b43cd7b0-06e0-44cf-8de0-5b43b7ef6e3d.root", + 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"/store/mc/Run3Summer22NanoAODv12/TTHtoNon2B_M-125_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v4/2560000/851ff937-937a-49a7-9aa3-334c37ca9945.root", + "/store/mc/Run3Summer22NanoAODv12/TTHtoNon2B_M-125_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v4/2560000/950c57c4-6857-497b-a1c5-cd26c775972f.root", + "/store/mc/Run3Summer22NanoAODv12/TTHtoNon2B_M-125_TuneCP5_13p6TeV_powheg-pythia8/NANOAODSIM/130X_mcRun3_2022_realistic_v5-v4/2560000/f01dfe43-e0c2-4430-b3cc-0945e4f6052d.root" + ], + "nEvents": 3846525, + "nGenEvents": 3846525, + "nSumOfWeights": 2205776.540448999, + "isData": false, + "path": "/TTHtoNon2B_M-125_TuneCP5_13p6TeV_powheg-pythia8/Run3Summer22NanoAODv12-130X_mcRun3_2022_realistic_v5-v4/NANOAODSIM", + "nSumOfWeights_ISRUp": 2205660.241789456, + "nSumOfWeights_ISRDown": 2205782.5202612667, + "nSumOfWeights_FSRUp": 2207029.569285595, + "nSumOfWeights_FSRDown": 2205359.4906294383, + "nSumOfWeights_renormUp": 2062595.179586768, + "nSumOfWeights_renormDown": 2309888.887985143, + "nSumOfWeights_factUp": 2163536.3167036977, + "nSumOfWeights_factDown": 2263870.797902433, + "nSumOfWeights_renormfactUp": 2004738.708438368, + "nSumOfWeights_renormfactDown": 2338124.2623773674 +} \ No newline at end of file diff --git a/setup.py b/setup.py index aaab936fa..1322efff8 100644 --- a/setup.py +++ b/setup.py @@ -14,6 +14,7 @@ "data/triggerSF/*.pkl.gz", "data/btagSF/UL/*.pkl.gz", "data/btagSF/UL/*.csv", + "modules/*.json" ], } ) diff --git a/topeft/channels/ch_lst_test.json b/topeft/channels/ch_lst_test.json index 90e559c44..5dc92cf61 100644 --- a/topeft/channels/ch_lst_test.json +++ b/topeft/channels/ch_lst_test.json @@ -663,32 +663,28 @@ "2lss", "2l_p", "bmask_atleast1m2l", - "bmask_atleast3m", - "~fwdjet_mask" + "bmask_atleast3m" ], [ "2lss_4t_m", "2lss", "2l_m", "bmask_atleast1m2l", - "bmask_atleast3m", - "~fwdjet_mask" + "bmask_atleast3m" ], [ "2lss_fwd_p", "2lss_fwd", "2l_fwd_p", "bmask_atleast1m2l", - "bmask_atmost2m", - "fwdjet_mask" + "bmask_atmost2m" ], [ "2lss_fwd_m", "2lss_fwd", "2l_fwd_m", "bmask_atleast1m2l", - "bmask_atmost2m", - "fwdjet_mask" + "bmask_atmost2m" ] ], "lep_flav_lst": [ diff --git a/topeft/modules/corrections.py b/topeft/modules/corrections.py index c9441f938..30595a88b 100644 --- a/topeft/modules/corrections.py +++ b/topeft/modules/corrections.py @@ -1,4 +1,3 @@ - ''' This script is used to transform scale factors, which are tipically provided as 2D histograms within root files, into coffea format of corrections. @@ -13,7 +12,11 @@ import gzip import pickle import correctionlib -from coffea.jetmet_tools import JECStack, CorrectedJetsFactory, CorrectedMETFactory +import json +from coffea.jetmet_tools import CorrectedMETFactory +### workaround while waiting the correcion-lib integration will be provided in the coffea package +from topcoffea.modules.CorrectedJetsFactory import CorrectedJetsFactory +from topcoffea.modules.JECStack import JECStack from coffea.btag_tools.btagscalefactor import BTagScaleFactor from coffea.lookup_tools import txt_converters, rochester_lookup @@ -41,6 +44,252 @@ "2023BPix": "2022_Summer23BPix", } +egm_tag_map = { + "2016preVFP_UL": "2016preVFP", + "2016postVFP_UL": "2016postVFP", + "2017_UL": "2017", + "2018_UL": "2018", + "2022_Summer22": "2022Re-recoBCD", + "2022_Summer22EE": "2022Re-recoE+PromptFG", + "2022_Summer23": "2023PromptC", + "2022_Summer23BPix": "2023PromptD", +} + +egm_pt_bins = { + "Run2": OrderedDict([ + ("RecoBelow20", [10, 20]), + ("RecoAbove20", [20, 1000]) + ]), + "Run3": OrderedDict([ + ("RecoBelow20", [10, 20]), + ("Reco20to75", [20, 75]), + ("RecoAbove75", [75, 1000]) + ]), +} + +#Leftover for the old JERC implementation +jerc_tag_map = { + '2016': [ + '16_V7', + 'Summer20UL16_JRV3', + ], + '2016APV': [ + '16APV_V7', + 'Summer20UL16APV_JRV3', + ], + '2017': [ + '17_V5', + 'Summer19UL17_JRV2', + ], + '2018': [ + '18_V5', + 'Summer19UL18_JRV2', + ], + "2022": [], + "2022EE": [], + "2023": [], + "2023BPix": [], +} + +#JERC dictionary for various keys +jerc_dict = { + "2016": { + "jec_mc" : "Summer19UL16_V7_MC", + "jec_data": "Summer19UL16_RunFGH_V7_DATA", + "jec_levels": [ + "L1FastJet", + "L2Relative", + ], + "jer" : "Summer20UL16_JRV3_MC", + "junc" : [ + 'FlavorQCD', 'FlavorPureBottom', 'FlavorPureQuark', 'FlavorPureGluon', 'FlavorPureCharm', + 'Regrouped_BBEC1', 'Regrouped_Absolute', 'Regrouped_RelativeBal', 'RelativeSample' + ] + }, + "2016APV": { + "jec_mc": "Summer19UL16APV_V7_MC", + "jec_data": { + "B": "Summer19UL16APV_RunBCD_V7_DATA", + "C": "Summer19UL16APV_RunBCD_V7_DATA", + "D": "Summer19UL16APV_RunBCD_V7_DATA", + "E": "Summer19UL16APV_RunEF_V7_DATA", + "F": "Summer19UL16APV_RunEF_V7_DATA", + }, + "jec_levels": [ + "L1FastJet", + "L2Relative", + ], + "jer": "Summer20UL16APV_JRV3_MC", + "junc" : [ + 'FlavorQCD', 'FlavorPureBottom', 'FlavorPureQuark', 'FlavorPureGluon', 'FlavorPureCharm', + 'Regrouped_BBEC1', 'Regrouped_Absolute', 'Regrouped_RelativeBal', 'RelativeSample' + ] + }, + "2017": { + "jec_mc": "Summer19UL17_V5_MC", + "jec_data": { + "B": "Summer19UL17_RunB_V5_DATA", + "C": "Summer19UL17_RunC_V5_DATA", + "D": "Summer19UL17_RunD_V5_DATA", + "E": "Summer19UL17_RunE_V5_DATA", + "F": "Summer19UL17_RunF_V5_DATA", + }, + "jec_levels": [ + "L1FastJet", + "L2Relative", + ], + "jer": "Summer19UL17_JRV2_MC", + "junc" : [ + 'FlavorQCD', 'FlavorPureBottom', 'FlavorPureQuark', 'FlavorPureGluon', 'FlavorPureCharm', + 'Regrouped_BBEC1', 'Regrouped_Absolute', 'Regrouped_RelativeBal', 'RelativeSample' + ] + }, + "2018": { + "jec_mc": "Summer19UL18_V5_MC", + "jec_data": { + "A": "Summer19UL18_RunA_V5_DATA", + "B": "Summer19UL18_RunB_V5_DATA", + "C": "Summer19UL18_RunC_V5_DATA", + "D": "Summer19UL18_RunD_V5_DATA", + }, + "jec_levels": [ + "L1FastJet", + "L2Relative", + ], + "jer": "Summer19UL18_JRV2_MC", + "junc" : [ + 'FlavorQCD', 'FlavorPureBottom', 'FlavorPureQuark', 'FlavorPureGluon', 'FlavorPureCharm', + 'Regrouped_BBEC1', 'Regrouped_Absolute', 'Regrouped_RelativeBal', 'RelativeSample' + ] + + }, + "2022": { + "jec_mc" : "Summer22_22Sep2023_V2_MC", + "jec_data": "Summer22_22Sep2023_RunCD_V2_DATA", + "jec_levels": [ + "L1FastJet", + "L2Relative", + "L3Absolute", + "L2L3Residual", + ], + "jer" : "Summer22_22Sep2023_JRV1_MC", + "junc" : [ + "AbsoluteMPFBias","AbsoluteScale","FlavorQCD","Fragmentation","PileUpDataMC", + "PileUpPtBB","PileUpPtEC1","PileUpPtEC2","PileUpPtHF","PileUpPtRef", + "RelativeFSR","RelativeJERHF","RelativePtBB","RelativePtHF","RelativeBal", + "SinglePionECAL","SinglePionHCAL", + "AbsoluteStat","RelativeJEREC1","RelativeJEREC2","RelativePtEC1","RelativePtEC2", + "TimePtEta","RelativeSample","RelativeStatEC","RelativeStatFSR","RelativeStatHF", + "Total", + ] + }, + "2022EE": { + "jec_mc": "Summer22EE_22Sep2023_V2_MC", + "jec_data": { + "E": "Summer22EE_22Sep2023_RunE_V2_DATA", + "F": "Summer22EE_22Sep2023_RunF_V2_DATA", + "G": "Summer22EE_22Sep2023_RunG_V2_DATA", + }, + "jec_levels": [ + "L1FastJet", + "L2Relative", + "L3Absolute", + "L2L3Residual", + ], + "jer": "Summer22EE_22Sep2023_JRV1_MC", + "junc" : [ + "AbsoluteMPFBias","AbsoluteScale","FlavorQCD","Fragmentation","PileUpDataMC", + "PileUpPtBB","PileUpPtEC1","PileUpPtEC2","PileUpPtHF","PileUpPtRef", + "RelativeFSR","RelativeJERHF","RelativePtBB","RelativePtHF","RelativeBal", + "SinglePionECAL","SinglePionHCAL", + "AbsoluteStat","RelativeJEREC1","RelativeJEREC2","RelativePtEC1","RelativePtEC2", + "TimePtEta","RelativeSample","RelativeStatEC","RelativeStatFSR","RelativeStatHF", + "Total", + ] + }, + "2023": { + "jec_mc": "Summer23Prompt23_V1_MC", + "jec_data": { + "C1": "Summer23Prompt23_RunCv123_V1_DATA", + "C2": "Summer23Prompt23_RunCv123_V1_DATA", + "C3": "Summer23Prompt23_RunCv123_V1_DATA", + "C4": "Summer23Prompt23_RunCv4_V1_DATA", + }, + "jec_levels": [ + "L1FastJet", + "L2Relative", + "L3Absolute", + "L2L3Residual", + ], + "jer": "Summer23Prompt23_RunCv1234_JRV1_MC", + "junc" : [ + "AbsoluteMPFBias","AbsoluteScale","FlavorQCD","Fragmentation","PileUpDataMC", + "PileUpPtBB","PileUpPtEC1","PileUpPtEC2","PileUpPtHF","PileUpPtRef", + "RelativeFSR","RelativeJERHF","RelativePtBB","RelativePtHF","RelativeBal", + "SinglePionECAL","SinglePionHCAL", + "AbsoluteStat","RelativeJEREC1","RelativeJEREC2","RelativePtEC1","RelativePtEC2", + "TimePtEta","RelativeSample","RelativeStatEC","RelativeStatFSR","RelativeStatHF", + "Total", + ] + }, + "2023BPix": { + "jec_mc" : "Summer23BPixPrompt23_V1_MC", + "jec_data": "Summer23BPixPrompt23_RunD_V1_DATA", + "jec_levels": [ + "L1FastJet", + "L2Relative", + "L3Absolute", + "L2L3Residual", + ], + "jer" : "Summer23BPixPrompt23_RunD_JRV1_MC", + "junc" : [ + "AbsoluteMPFBias","AbsoluteScale","FlavorQCD","Fragmentation","PileUpDataMC", + "PileUpPtBB","PileUpPtEC1","PileUpPtEC2","PileUpPtHF","PileUpPtRef", + "RelativeFSR","RelativeJERHF","RelativePtBB","RelativePtHF","RelativeBal", + "SinglePionECAL","SinglePionHCAL", + "AbsoluteStat","RelativeJEREC1","RelativeJEREC2","RelativePtEC1","RelativePtEC2", + "TimePtEta","RelativeSample","RelativeStatEC","RelativeStatFSR","RelativeStatHF", + "Total", + ] + } +} + +with open(topeft_path('modules/jerc_dict.json'), 'r') as f: + jerc_dict = json.load(f) + +def get_jerc_keys(year, isdata, era=None): + # Jet Algorithm + if year.startswith("202"): + jet_algo = 'AK4PFPuppi' + else: + jet_algo = 'AK4PFchs' + + #jec levels + jec_levels = jerc_dict[year]['jec_levels'] + + # jerc keys and junc types + if not isdata: + jec_key = jerc_dict[year]['jec_mc'] + jer_key = jerc_dict[year]['jer'] + junc_types = jerc_dict[year]['junc'] + else: + if year in ['2016','2022','2023BPix']: + jec_key = jerc_dict[year]['jec_data'] + else: + jec_key = jerc_dict[year]['jec_data'][era] + jer_key = None + junc_types = None + + return jet_algo, jec_key, jec_levels, jer_key, junc_types + +def get_corr_inputs(objs, corr_obj, name_map): + """ + Helper function for getting values of input variables + given a dictionary and a correction object. + """ + input_values = [ak.flatten(objs[inp.name]) for inp in corr_obj.inputs if inp.name != "systematic"] + return input_values + # New UL Lepton SFs # Muon: reco ##clib ready extLepSF.add_weight_sets(["MuonRecoSF_2018 NUM_TrackerMuons_DEN_genTracks/abseta_pt_value %s" % topcoffea_path('data/leptonSF/muon/Efficiency_muon_generalTracks_Run2018_UL_trackerMuon.json')]) @@ -99,7 +348,7 @@ extLepSF.add_weight_sets(["ElecRecoSFAb_2016APV_er EGamma_SF2D_error %s" % topcoffea_path('data/leptonSF/elec/egammaEffi2016APV_ptAbove20_EGM2D.root')]) extLepSF.add_weight_sets(["ElecRecoSFBe_2016APV EGamma_SF2D %s" % topcoffea_path('data/leptonSF/elec/egammaEffi2016APV_ptBelow20_EGM2D.root')]) extLepSF.add_weight_sets(["ElecRecoSFBe_2016APV_er EGamma_SF2D_error %s" % topcoffea_path('data/leptonSF/elec/egammaEffi2016APV_ptBelow20_EGM2D.root')]) -# Elec: loose (Barbara) ##personal +# Elec: looseID (Barbara) ##personal extLepSF.add_weight_sets(["ElecLooseSF_2018 EGamma_SF2D %s" % topcoffea_path('data/leptonSF/elec/egammaEffi2018_recoToloose_EGM2D.root')]) extLepSF.add_weight_sets(["ElecLooseSF_2018_er EGamma_SF2D_error %s" % topcoffea_path('data/leptonSF/elec/egammaEffi2018_recoToloose_EGM2D.root')]) extLepSF.add_weight_sets(["ElecLooseSF_2017_er EGamma_SF2D_error %s" % topcoffea_path('data/leptonSF/elec/egammaEffi2017_recoToloose_EGM2D.root')]) @@ -294,44 +543,130 @@ for syst in ['','_up','_down','_be1','_be2','_pt1','_pt2']: extLepSF.add_weight_sets([("MuonFR_{year}{syst} FR_mva085_mu_data_comb_recorrected{syst} %s" % topcoffea_path(basepathFromTTH + 'fakerate/fr_{year}_recorrected.root')).format(year=year,syst=syst)]) extLepSF.add_weight_sets([("ElecFR_{year}{syst} FR_mva090_el_data_comb_NC_recorrected{syst} %s" % topcoffea_path(basepathFromTTH + 'fakerate/fr_{year}_recorrected.root')).format(year=year,syst=syst)]) - extLepSF.finalize() SFevaluator = extLepSF.make_evaluator() ffSysts=['','_up','_down','_be1','_be2','_pt1','_pt2'] -def ApplyTES(year, Taus, isData): +def ApplyTES(year, taus, isData, tagger, syst_name, vsJetWP): if isData: - return (Taus.pt, Taus.mass) + return (taus.pt, taus.mass) - pt = Taus.pt - dm = Taus.decayMode - gen = Taus.genPartFlav - eta = Taus.eta + pt = taus.pt + dm = taus.decayMode + gen = taus.genPartFlav + eta = taus.eta kinFlag = (pt>20) & (pt<205) & (gen==5) - dmFlag = ((Taus.decayMode==0) | (Taus.decayMode==1) | (Taus.decayMode==10) | (Taus.decayMode==11)) + dmFlag = ((taus.decayMode==0) | (taus.decayMode==1) | (taus.decayMode==10) | (taus.decayMode==11)) whereFlag = kinFlag & dmFlag #((pt>20) & (pt<205) & (gen==5) & (dm==0 | dm==1 | dm==10 | dm==11)) tes = np.where(whereFlag, SFevaluator['TauTES_{year}'.format(year=year)](dm,pt), 1) kinFlag = (pt>20) & (pt<205) & (gen>=1) & (gen<=4) - dmFlag = ((Taus.decayMode==0) | (Taus.decayMode==1)) + dmFlag = ((taus.decayMode==0) | (taus.decayMode==1)) whereFlag = kinFlag & dmFlag fes = np.where(whereFlag, SFevaluator['TauFES_{year}'.format(year=year)](eta,dm), 1) - return (Taus.pt*tes*fes, Taus.mass*tes*fes) -def ApplyTESSystematic(year, Taus, isData, syst_name): + if False: + ## Correction-lib implementation - MUST BE TESTED WHEN TAU IN THE MASTER BRANCH PROCESSOR + padded_taus = ak.pad_none(taus,1) + padded_taus = ak.with_name(padded_taus, "TauCandidate") + + pt = padded_taus.pt + dm = padded_taus.decayMode + wp = padded_taus.idDeepTau2017v2p1VSjet + eta = padded_taus.eta + gen = padded_taus.genPartFlav + mass= padded_taus.mass + + clib_year = clib_year_map[year] + json_path = topcoffea_path(f"data/POG/TAU/{clib_year}/tau.json.gz") + ceval = correctionlib.CorrectionSet.from_file(json_path) + + arg_tau = ["pt", "eta", "decayMode", "genPartFlav"] + pt_mask_flat = ak.flatten((pt>0) & (pt<1000)) + + deep_tau_cuts = [ + ("DeepTau2017v2p1VSjet", ak.flatten(padded_taus[f"is{vsJetWP}"]>0), ("pt", "decayMode", "genPartFlav", vsJetWP)), + ("DeepTau2017v2p1VSe", ak.flatten(padded_taus["iseTight"]>0), ("eta", "genPartFlav", "VVLoose")), + ("DeepTau2017v2p1VSmu", ak.flatten(padded_taus["ismTight"]>0), ("eta", "genPartFlav", "Loose")), + ] + + DT_sf_list = [] + DT_up_list = [] + DT_do_list = [] + + for deep_tau_cut in deep_tau_cuts: + discr = deep_tau_cut[0] + id_mask = deep_tau_cut[1] + arg_list = deep_tau_cut[2] + args = [] + for ttag in arg_list: + args.append( + ak.flatten(padded_taus[ttag]) + ) + tau_mask = id_mask & pt_mask_flat + + args_sf = args + ["sf"] + DT_sf_list.append( + ak.where( + ~tau_mask, + 1, + ceval[discr].evaluate(*args_sf) + ) + ) + args_up = args + ["up"] + DT_up_list.append( + ak.where( + ~tau_mask, + 1, + ceval[discr].evaluate(*args_up) + ) + ) + args_down = args + ["down"] + DT_do_list.append( + ak.where( + ~tau_mask, + 1, + ceval[discr].evaluate(*args_down) + ) + ) + + DT_sf_flat = None + DT_up_flat = None + DT_do_flat = None + for idr, DT_sf_discr in enumerate(DT_sf_list): + DT_sf_discr = ak.to_numpy(DT_sf_discr) + DT_up_discr = ak.to_numpy(DT_up_list[idr]) + DT_do_discr = ak.to_numpy(DT_do_list[idr]) + if idr == 0: + DT_sf_flat = DT_sf_discr + DT_up_flat = DT_up_discr + DT_do_flat = DT_do_discr + else: + DT_sf_flat *= DT_sf_discr + DT_up_flat *= DT_up_discr + DT_do_flat *= DT_do_discr + + DT_sf = ak.unflatten(DT_sf_flat, ak.num(pt)) + DT_up = ak.unflatten(DT_up_flat, ak.num(pt)) + DT_do = ak.unflatten(DT_do_flat, ak.num(pt)) + ## end of correction-lib implementation + + return (taus.pt*tes*fes, taus.mass*tes*fes) + +def ApplyTESSystematic(year, taus, isData, syst_name): if not syst_name.startswith('TES'): - return (Taus.pt) + return (taus.pt) if isData: - return (Taus.pt) + return (taus.pt) - pt = Taus.pt - dm = Taus.decayMode - gen = Taus.genPartFlav + pt = taus.pt + dm = taus.decayMode + gen = taus.genPartFlav kinFlag = (pt>20) & (pt<205) & (gen==5) - dmFlag = ((Taus.decayMode==0) | (Taus.decayMode==1) | (Taus.decayMode==10) | (Taus.decayMode==11)) + dmFlag = ((taus.decayMode==0) | (taus.decayMode==1) | (taus.decayMode==10) | (taus.decayMode==11)) whereFlag = kinFlag & dmFlag syst_lab = f'TauTES_{year}' @@ -341,21 +676,21 @@ def ApplyTESSystematic(year, Taus, isData, syst_name): syst_lab += '_down' tes_syst = np.where(whereFlag, SFevaluator['TauTES_{year}'.format(year=year)](dm,pt), 1) - return (Taus.pt*tes_syst, Taus.mass*tes_syst) + return (taus.pt*tes_syst, taus.mass*tes_syst) -def ApplyFESSystematic(year, Taus, isData, syst_name): +def ApplyFESSystematic(year, taus, isData, syst_name): if not syst_name.startswith('FES'): - return (Taus.pt) + return (taus.pt) if isData: - return (Taus.pt) + return (taus.pt) - pt = Taus.pt - eta = Taus.eta - dm = Taus.decayMode - gen = Taus.genPartFlav + pt = taus.pt + eta = taus.eta + dm = taus.decayMode + gen = taus.genPartFlav kinFlag = (pt>20) & (pt<205) & (gen==5) - dmFlag = ((Taus.decayMode==0) | (Taus.decayMode==1)) + dmFlag = ((taus.decayMode==0) | (taus.decayMode==1)) whereFlag = kinFlag & dmFlag syst_lab = f'TauFES_{year}' @@ -366,112 +701,42 @@ def ApplyFESSystematic(year, Taus, isData, syst_name): syst_lab += '_down' fes_syst = np.where(whereFlag, SFevaluator['TauFES_{year}'.format(year=year)](eta,dm), 1) - return (Taus.pt*fes_syst, Taus.mass*fes_syst) + return (taus.pt*fes_syst, taus.mass*fes_syst) -def AttachTauSF(events, Taus, year, vsJetWP="Loose"): - padded_Taus = ak.pad_none(Taus,1) - padded_Taus = ak.with_name(padded_Taus, "TauCandidate") - padded_Taus["sf_tau"] = 1.0 - padded_Taus["sf_tau_up"] = 1.0 - padded_Taus["sf_tau_down"] = 1.0 +def AttachTauSF(events, taus, year, vsJetWP="Loose"): + padded_taus = ak.pad_none(taus,1) + padded_taus = ak.with_name(padded_taus, "TauCandidate") + padded_taus["sf_tau"] = 1.0 + padded_taus["sf_tau_up"] = 1.0 + padded_taus["sf_tau_down"] = 1.0 clib_year = clib_year_map[year] json_path = topcoffea_path(f"data/POG/TAU/{clib_year}/tau.json.gz") ceval = correctionlib.CorrectionSet.from_file(json_path) - pt = padded_Taus.pt - dm = padded_Taus.decayMode - wp = padded_Taus.idDeepTau2017v2p1VSjet - eta = padded_Taus.eta - gen = padded_Taus.genPartFlav - mass= padded_Taus.mass - - ## Correction-lib implementation - MUST BE TESTED WHEN TAU IN THE MASTER BRANCH PROCESSOR - DeepTaus = [ - ("DeepTau2017v2p1VSjet", ak.flatten(padded_Taus[f"is{vsJetWP}"]>0), ("pt", "decayMode", "genPartFlav", vsJetWP)), - ("DeepTau2017v2p1VSe", ak.flatten(padded_Taus["iseTight"]>0), ("eta", "genPartFlav", "VVLoose")), - ("DeepTau2017v2p1VSmu", ak.flatten(padded_Taus["ismTight"]>0), ("eta", "genPartFlav", "Loose")), - ] - - DT_sf_list = [] - DT_up_list = [] - DT_do_list = [] - - pt_mask_flat = ak.flatten((pt>20) & (pt<205)) - - for DeepTau in DeepTaus: - discr = DeepTau[0] - id_mask = DeepTau[1] - arg_list = DeepTau[2] - args = [] - for ttag in arg_list: - args.append( - ak.flatten(padded_Taus[ttag]) - ) - tau_mask = id_mask & pt_mask_flat - - args_sf = args + ["sf"] - DT_sf_list.append( - ak.where( - ~tau_mask, - 1, - ceval[discr].evaluate(*args_sf) - ) - ) - args_up = args + ["up"] - DT_up_list.append( - ak.where( - ~tau_mask, - 1, - ceval[discr].evaluate(*args_up) - ) - ) - args_down = args + ["down"] - DT_do_list.append( - ak.where( - ~tau_mask, - 1, - ceval[discr].evaluate(*args_down) - ) - ) - - DT_sf_flat = None - DT_up_flat = None - DT_do_flat = None - for idr, DT_sf_discr in enumerate(DT_sf_list): - DT_sf_discr = ak.to_numpy(DT_sf_discr) - DT_up_discr = ak.to_numpy(DT_up_list[idr]) - DT_do_discr = ak.to_numpy(DT_do_list[idr]) - if idr == 0: - DT_sf_flat = DT_sf_discr - DT_up_flat = DT_up_discr - DT_do_flat = DT_do_discr - else: - DT_sf_flat *= DT_sf_discr - DT_up_flat *= DT_up_discr - DT_do_flat *= DT_do_discr - - DT_sf = ak.unflatten(DT_sf_flat, ak.num(pt)) - DT_up = ak.unflatten(DT_up_flat, ak.num(pt)) - DT_do = ak.unflatten(DT_do_flat, ak.num(pt)) - ## end of correction-lib implementation + pt = padded_taus.pt + dm = padded_taus.decayMode + wp = padded_taus.idDeepTau2017v2p1VSjet + eta = padded_taus.eta + gen = padded_taus.genPartFlav + mass= padded_taus.mass ## legacy - whereFlag = ((pt>20) & (pt<205) & (gen==5) & (padded_Taus[f"is{vsJetWP}"]>0)) + whereFlag = ((pt>20) & (pt<205) & (gen==5) & (padded_taus[f"is{vsJetWP}"]>0)) real_sf_loose = np.where(whereFlag, SFevaluator[f'TauSF_{year}_{vsJetWP}'](dm,pt), 1) real_sf_loose_up = np.where(whereFlag, SFevaluator[f'TauSF_{year}_{vsJetWP}_up'](dm,pt), 1) real_sf_loose_down = np.where(whereFlag, SFevaluator[f'TauSF_{year}_{vsJetWP}_down'](dm,pt), 1) - whereFlag = ((pt>20) & (pt<205) & ((gen==1)|(gen==3)) & (padded_Taus["iseTight"]>0)) + whereFlag = ((pt>20) & (pt<205) & ((gen==1)|(gen==3)) & (padded_taus["iseTight"]>0)) fake_elec_sf = np.where(whereFlag, SFevaluator[f'Tau_elecFakeSF_{year}'](np.abs(eta)), 1) fake_elec_sf_up = np.where(whereFlag, SFevaluator[f'Tau_elecFakeSF_{year}_up'](np.abs(eta)), 1) fake_elec_sf_down = np.where(whereFlag, SFevaluator[f'Tau_elecFakeSF_{year}_down'](np.abs(eta)), 1) - whereFlag = ((pt>20) & (pt<205) & ((gen==2)|(gen==4)) & (padded_Taus["ismTight"]>0)) + whereFlag = ((pt>20) & (pt<205) & ((gen==2)|(gen==4)) & (padded_taus["ismTight"]>0)) fake_muon_sf = np.where(whereFlag, SFevaluator[f'Tau_muonFakeSF_{year}'](np.abs(eta)), 1) fake_muon_sf_up = np.where(whereFlag, SFevaluator[f'Tau_muonFakeSF_{year}_up'](np.abs(eta)), 1) fake_muon_sf_down = np.where(whereFlag, SFevaluator[f'Tau_muonFakeSF_{year}_down'](np.abs(eta)), 1) - whereFlag = ((pt>20) & (pt<205) & (gen!=5) & (gen!=4) & (gen!=3) & (gen!=2) & (gen!=1) & (padded_Taus["isLoose"]>0)) + whereFlag = ((pt>20) & (pt<205) & (gen!=5) & (gen!=4) & (gen!=3) & (gen!=2) & (gen!=1) & (padded_taus["isLoose"]>0)) new_fake_sf = np.where(whereFlag, SFevaluator['TauFakeSF'](pt), 1) new_fake_sf_up = np.where(whereFlag, SFevaluator['TauFakeSF_up'](pt), 1) new_fake_sf_down = np.where(whereFlag, SFevaluator['TauFakeSF_down'](pt), 1) @@ -479,32 +744,117 @@ def AttachTauSF(events, Taus, year, vsJetWP="Loose"): real_sf = real_sf_loose real_sf_up = real_sf_loose_up real_sf_down = real_sf_loose_down - padded_Taus["sf_tau_real"] = real_sf - padded_Taus["sf_tau_real_up"] = real_sf_up - padded_Taus["sf_tau_real_down"] = real_sf_down - padded_Taus["sf_tau_fake"] = fake_elec_sf*fake_muon_sf*new_fake_sf - padded_Taus["sf_tau_fake_up"] = fake_elec_sf_up*fake_muon_sf_up*new_fake_sf_up - padded_Taus["sf_tau_fake_down"] = fake_elec_sf_down*fake_muon_sf_down*new_fake_sf_down - - events["sf_2l_taus_real"] = padded_Taus.sf_tau_real[:,0] - events["sf_2l_taus_real_hi"] = padded_Taus.sf_tau_real_up[:,0] - events["sf_2l_taus_real_lo"] = padded_Taus.sf_tau_real_down[:,0] - events["sf_2l_taus_fake"] = padded_Taus.sf_tau_fake[:,0] - events["sf_2l_taus_fake_hi"] = padded_Taus.sf_tau_fake_up[:,0] - events["sf_2l_taus_fake_lo"] = padded_Taus.sf_tau_fake_down[:,0] - - ## final step for correction-lib - DT_sf *= new_fake_sf - DT_up *= new_fake_sf_up - DT_do *= new_fake_sf_down + padded_taus["sf_tau_real"] = real_sf + padded_taus["sf_tau_real_up"] = real_sf_up + padded_taus["sf_tau_real_down"] = real_sf_down + padded_taus["sf_tau_fake"] = fake_elec_sf*fake_muon_sf*new_fake_sf + padded_taus["sf_tau_fake_up"] = fake_elec_sf_up*fake_muon_sf_up*new_fake_sf_up + padded_taus["sf_tau_fake_down"] = fake_elec_sf_down*fake_muon_sf_down*new_fake_sf_down + + events["sf_2l_taus_real"] = padded_taus.sf_tau_real[:,0] + events["sf_2l_taus_real_hi"] = padded_taus.sf_tau_real_up[:,0] + events["sf_2l_taus_real_lo"] = padded_taus.sf_tau_real_down[:,0] + events["sf_2l_taus_fake"] = padded_taus.sf_tau_fake[:,0] + events["sf_2l_taus_fake_hi"] = padded_taus.sf_tau_fake_up[:,0] + events["sf_2l_taus_fake_lo"] = padded_taus.sf_tau_fake_down[:,0] + + if False: + ## Correction-lib implementation - MUST BE TESTED WHEN TAU IN THE MASTER BRANCH PROCESSOR + padded_taus["sf_tau"] = 1.0 + padded_taus["sf_tau_up"] = 1.0 + padded_taus["sf_tau_down"] = 1.0 + + clib_year = clib_year_map[year] + json_path = topcoffea_path(f"data/POG/TAU/{clib_year}/tau.json.gz") + ceval = correctionlib.CorrectionSet.from_file(json_path) + + DT_sf_list = [] + DT_up_list = [] + DT_do_list = [] + + pt_mask_flat = ak.flatten((pt>20) & (pt<205)) + + deep_tau_cuts = [ + ("DeepTau2017v2p1VSjet", ak.flatten(padded_taus[f"is{vsJetWP}"]>0), ("pt", "decayMode", "genPartFlav", vsJetWP)), + ("DeepTau2017v2p1VSe", ak.flatten(padded_taus["iseTight"]>0), ("eta", "genPartFlav", "VVLoose")), + ("DeepTau2017v2p1VSmu", ak.flatten(padded_taus["ismTight"]>0), ("eta", "genPartFlav", "Loose")), + ] + + for deep_tau_cut in deep_tau_cuts: + discr = deep_tau_cut[0] + id_mask = deep_tau_cut[1] + arg_list = deep_tau_cut[2] + args = [] + for ttag in arg_list: + args.append( + ak.flatten(padded_taus[ttag]) + ) + tau_mask = id_mask & pt_mask_flat + + args_sf = args + ["sf"] + DT_sf_list.append( + ak.where( + ~tau_mask, + 1, + ceval[discr].evaluate(*args_sf) + ) + ) + args_up = args + ["up"] + DT_up_list.append( + ak.where( + ~tau_mask, + 1, + ceval[discr].evaluate(*args_up) + ) + ) + args_down = args + ["down"] + DT_do_list.append( + ak.where( + ~tau_mask, + 1, + ceval[discr].evaluate(*args_down) + ) + ) + + DT_sf_flat = None + DT_up_flat = None + DT_do_flat = None + for idr, DT_sf_discr in enumerate(DT_sf_list): + DT_sf_discr = ak.to_numpy(DT_sf_discr) + DT_up_discr = ak.to_numpy(DT_up_list[idr]) + DT_do_discr = ak.to_numpy(DT_do_list[idr]) + if idr == 0: + DT_sf_flat = DT_sf_discr + DT_up_flat = DT_up_discr + DT_do_flat = DT_do_discr + else: + DT_sf_flat *= DT_sf_discr + DT_up_flat *= DT_up_discr + DT_do_flat *= DT_do_discr + + DT_sf = ak.unflatten(DT_sf_flat, ak.num(pt)) + DT_up = ak.unflatten(DT_up_flat, ak.num(pt)) + DT_do = ak.unflatten(DT_do_flat, ak.num(pt)) + + DT_sf *= new_fake_sf + DT_up *= new_fake_sf_up + DT_do *= new_fake_sf_down + + events["sf_2l_taus"] = padded_taus.sf_tau[:,0] + events["sf_2l_taus_hi"] = padded_taus.sf_tau_up[:,0] + events["sf_2l_taus_lo"] = padded_taus.sf_tau_down[:,0] + ## end of correction-lib implementation def AttachPerLeptonFR(leps, flavor, year): # Get the flip rates lookup object + if year not in clib_year_map.keys(): + raise Exception(f"Error: Unknown year \"{year}\"\n"".") + if year == "2016APV": flip_year_name = "UL16APV" elif year == "2016": flip_year_name = "UL16" elif year == "2017": flip_year_name = "UL17" elif year == "2018": flip_year_name = "UL18" - else: raise Exception(f"Not a known year: {year}") + else: flip_year_name = "UL18" #TO READAPT when fakefactors are ready #raise Exception(f"Not a known year: {year}") with gzip.open(topeft_path(f"data/fliprates/flip_probs_topcoffea_{flip_year_name}.pkl.gz")) as fin: flip_hist = pickle.load(fin) flip_lookup = lookup_tools.dense_lookup.dense_lookup(flip_hist.values()[()],[flip_hist.axes["pt"].edges,flip_hist.axes["eta"].edges]) @@ -515,6 +865,8 @@ def AttachPerLeptonFR(leps, flavor, year): # For FR filepath naming conventions if '2016' in year: year = '2016APV_2016' + elif year.startswith("202"): + year = '2018' # Add the flip/fake info into the leps opject for syst in ffSysts: @@ -577,26 +929,43 @@ def AttachMuonSF(muons, year): Run2 strategy: - Use reco from TOP-22-006 - use loose from correction-lib + Run3 strategy + - use loose from correction-lib + - reco not available yet, but MUO don't bother about that ''' + is_run3 = False + if year.startswith("202"): + is_run3 = True + is_run2 = not is_run3 eta = np.abs(muons.eta) pt = muons.pt - if year not in ['2016','2016APV','2017','2018']: raise Exception(f"Error: Unknown year \"{year}\".") + if year not in clib_year_map.keys(): + raise Exception(f"Error: Unknown year \"{year}\"\n"".") + + ## Run2 pieces + new_sf = ak.ones_like(pt) + new_up = ak.ones_like(pt) + new_do = ak.ones_like(pt) + reco_sf = ak.ones_like(pt) + reco_up = ak.ones_like(pt) + reco_do = ak.ones_like(pt) + loose_sf = ak.ones_like(pt) + loose_up = ak.ones_like(pt) + loose_do = ak.ones_like(pt) + iso_sf = ak.ones_like(pt) + iso_up = ak.ones_like(pt) + iso_do = ak.ones_like(pt) + ## Run3 pieces + reco_loose_sf = ak.ones_like(pt) + reco_loose_up = ak.ones_like(pt) + reco_loose_do = ak.ones_like(pt) ## Run2: ## only loose_sf can be consistently used with correction-lib, for the other we use the TOP-22-006 original SFs ## Run3: ## reco_loose_sf will be used in place of reco*loose, both for nominal and systematics - ## these are the reco and loose TOP-22-006 SFs - reco_sf = np.where(pt < 20,SFevaluator['MuonRecoSF_{year}'.format(year=year)](eta,pt),1) # sf=1 when pt>20 becuase there is no reco SF available - reco_err = np.where(pt < 20,SFevaluator['MuonRecoSF_{year}_er'.format(year=year)](eta,pt),0) # sf error =0 when pt>20 becuase there is no reco SF available - loose_sf = SFevaluator['MuonLooseSF_{year}'.format(year=year)](eta,pt) - loose_err = np.sqrt( - SFevaluator['MuonLooseSF_{year}_stat'.format(year=year)](eta,pt) * SFevaluator['MuonLooseSF_{year}_stat'.format(year=year)](eta,pt) + - SFevaluator['MuonLooseSF_{year}_syst'.format(year=year)](eta,pt) * SFevaluator['MuonLooseSF_{year}_syst'.format(year=year)](eta,pt) - ) - clib_year = clib_year_map[year] json_path = topcoffea_path(f"data/POG/MUO/{clib_year}/muon_Z.json.gz") ceval = correctionlib.CorrectionSet.from_file(json_path) @@ -608,71 +977,93 @@ def AttachMuonSF(muons, year): pt_flat_reco = ak.where(~pt_mask_reco, 40., pt_flat) pt_flat_loose = ak.where(~pt_mask, 15., pt_flat) - # It won't be used neither for Run2 or Run3 - reco_sf_flat = ak.where( - ~pt_mask_reco, - 1, - ceval["NUM_TrackerMuons_DEN_genTracks"].evaluate(abseta_flat, pt_flat_reco, "nominal") - ) - reco_err_flat = ak.where( - ~pt_mask_reco, - 1, - np.sqrt( - ceval["NUM_TrackerMuons_DEN_genTracks"].evaluate(abseta_flat, pt_flat_reco, "syst") * ceval["NUM_TrackerMuons_DEN_genTracks"].evaluate(abseta_flat, pt_flat_reco, "syst") + ceval["NUM_TrackerMuons_DEN_genTracks"].evaluate(abseta_flat, pt_flat_reco, "stat") * ceval["NUM_TrackerMuons_DEN_genTracks"].evaluate(abseta_flat, pt_flat_reco, "stat") + if is_run2: + ## The only one to be actually got from clib for Run2< + loose_sf_flat = ak.where( + ~pt_mask, + 1, + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "nominal") ) - ) - - ## The only one to be actually got from clib for Run2 - loose_sf_flat = ak.where( - ~pt_mask, - 1, - ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "nominal") - ) - loose_err_flat = ak.where( - ~pt_mask, - 1, - np.sqrt( - ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "syst") * ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "syst") + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "stat") * ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "stat") + loose_err_flat = ak.where( + ~pt_mask, + 1, + np.sqrt( + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "syst") * ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "syst") + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "stat") * ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "stat") + ) ) - ) - - ## To keep for Run3 SFs - reco_loose_sf_flat = ak.where( - ~pt_mask, - 1, - ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "nominal") - ) - reco_loose_err_flat = ak.where( - ~pt_mask, - 1, - ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "syst") * ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "syst") + ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "stat") * ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "stat") - ) - - - reco_sf_clib = ak.unflatten(reco_sf_flat, ak.num(pt)) - reco_err_clib = ak.unflatten(reco_err_flat, ak.num(pt)) - loose_sf_clib = ak.unflatten(loose_sf_flat, ak.num(pt)) - loose_err_clib = ak.unflatten(loose_err_flat, ak.num(pt)) - reco_loose_sf_clib = ak.unflatten(reco_loose_sf_flat, ak.num(pt)) - reco_loose_err_clib = ak.unflatten(reco_loose_err_flat, ak.num(pt)) - - ## ad-hoc from TOP-22-006 for Run2 (not clib ready) - iso_sf = SFevaluator['MuonIsoSF_{year}'.format(year=year)](eta,pt) - iso_err = SFevaluator['MuonIsoSF_{year}_er'.format(year=year)](eta,pt) - new_sf = SFevaluator['MuonSF_{year}'.format(year=year)](eta,pt) - new_err = SFevaluator['MuonSF_{year}_er'.format(year=year)](eta,pt) - - ## For Run2, we can already use SFs and up/down variations from correction-lib for loose id selection - loose_sf = loose_sf_clib - loose_err = loose_err_clib - - #Run2 implementation - muons['sf_nom_2l_muon'] = new_sf * reco_sf * loose_sf * iso_sf - muons['sf_hi_2l_muon'] = (new_sf + new_err) * (reco_sf + reco_err) * (loose_sf + loose_err) * (iso_sf + iso_err) - muons['sf_lo_2l_muon'] = (new_sf - new_err) * (reco_sf - reco_err) * (loose_sf - loose_err) * (iso_sf - iso_err) - muons['sf_nom_3l_muon'] = new_sf * reco_sf * loose_sf - muons['sf_hi_3l_muon'] = (new_sf + new_err) * (reco_sf + reco_err) * (loose_sf + loose_err) * (iso_sf + iso_err) - muons['sf_lo_3l_muon'] = (new_sf - new_err) * (reco_sf - reco_err) * (loose_sf - loose_err) * (iso_sf - iso_err) + loose_sf = ak.unflatten(loose_sf_flat, ak.num(pt)) + loose_err = ak.unflatten(loose_err_flat, ak.num(pt)) + loose_up = loose_sf + loose_err + loose_do = loose_sf - loose_err + + ## these are the reco and loose TOP-22-006 SFs + reco_sf = np.where(pt < 20,SFevaluator['MuonRecoSF_{year}'.format(year=year)](eta,pt),1) # sf=1 when pt>20 becuase there is no reco SF available + reco_err = np.where(pt < 20,SFevaluator['MuonRecoSF_{year}_er'.format(year=year)](eta,pt),0) # sf error =0 when pt>20 becuase there is no reco SF available + reco_up = reco_sf + reco_err + reco_do = reco_sf - reco_err + + ## For Run2, reco and loose are multiplied + reco_loose_sf = reco_sf * loose_sf + reco_loose_up = reco_up * loose_up + reco_loose_do = reco_do * loose_do + + ## ad-hoc from TOP-22-006 for Run2 (not clib ready) + iso_sf = SFevaluator['MuonIsoSF_{year}'.format(year=year)](eta,pt) + iso_err = SFevaluator['MuonIsoSF_{year}_er'.format(year=year)](eta,pt) + iso_up = iso_sf + iso_err + iso_do = iso_sf - iso_err + + new_sf = SFevaluator['MuonSF_{year}'.format(year=year)](eta,pt) + new_err = SFevaluator['MuonSF_{year}_er'.format(year=year)](eta,pt) + new_up = new_sf + new_err + new_do = new_sf - new_err + + elif is_run3: + loose_sf_flat = ak.where( + ~pt_mask, + 1, + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "nominal") + ) + loose_err_flat = ak.where( + ~pt_mask, + 1, + np.sqrt( + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "syst") * ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "syst") + ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "stat") * ceval["NUM_LooseID_DEN_TrackerMuons"].evaluate(abseta_flat, pt_flat_loose, "stat") + ) + ) + loose_sf = ak.unflatten(loose_sf_flat, ak.num(pt)) + loose_err = ak.unflatten(loose_err_flat, ak.num(pt)) + loose_up = loose_sf + loose_err + loose_do = loose_sf - loose_err + + ##Not available yet for Run3 + #reco_loose_sf_flat = ak.where( + # ~pt_mask, + # 1, + # ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "nominal") + #) + #reco_loose_err_flat = ak.where( + # ~pt_mask, + # 1, + # ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "syst") * ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "syst") + ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "stat") * ceval["NUM_LooseID_DEN_genTracks"].evaluate(abseta_flat, pt_flat_loose, "stat") + #) + + #reco_sf = ak.unflatten(reco_sf_flat, ak.num(pt)) + #reco_err = ak.unflatten(reco_err_flat, ak.num(pt)) + #reco_up = reco_sf + reco_err + #reco_do = reco_sf - reco_err + #reco_loose_sf = ak.unflatten(reco_loose_sf_flat, ak.num(pt)) + #reco_loose_err = ak.unflatten(reco_loose_err_flat, ak.num(pt)) + #reco_loose_up = reco_loose_sf + reco_loose_err + #reco_loose_do = reco_loose_sf - reco_loose_err + + #Run2+Run3 implementation + muons['sf_nom_2l_muon'] = new_sf * reco_loose_sf * iso_sf + muons['sf_hi_2l_muon'] = new_up * reco_loose_up * iso_up + muons['sf_lo_2l_muon'] = new_do * reco_loose_do * iso_do + muons['sf_nom_3l_muon'] = new_sf * reco_loose_sf + muons['sf_hi_3l_muon'] = new_up * reco_loose_up * iso_up + muons['sf_lo_3l_muon'] = new_do * reco_loose_do * iso_do muons['sf_nom_2l_elec'] = ak.ones_like(new_sf) muons['sf_hi_2l_elec'] = ak.ones_like(new_sf) muons['sf_lo_2l_elec'] = ak.ones_like(new_sf) @@ -680,71 +1071,93 @@ def AttachMuonSF(muons, year): muons['sf_hi_3l_elec'] = ak.ones_like(new_sf) muons['sf_lo_3l_elec'] = ak.ones_like(new_sf) -def AttachElectronSF(electrons, year): +def AttachElectronSF(electrons, year, looseWP=None): ''' Description: Inserts 'sf_nom', 'sf_hi', and 'sf_lo' into the electrons array passed to this function. These values correspond to the nominal, up, and down electron scalefactor values respectively. ''' + + if looseWP is None: + raise ValueError('when calling AttachElectronSF, a looseWP value must be provided according to the ele ID isPres selection') + + is_run3 = False + if year.startswith("202"): + is_run3 = True + is_run2 = not is_run3 + dt_era = "Run3" if is_run3 else "Run2" + eta = electrons.eta pt = electrons.pt - - if year not in ['2016','2016APV','2017','2018']: + phi = electrons.phi + + #initializing the sf and up/down array to 1, so they are fixed to unit values if the a given SF is not available yet + ## Run2 and Run3 + reco_sf = ak.ones_like(pt) + reco_up = ak.ones_like(pt) + reco_do = ak.ones_like(pt) + loose_sf = ak.ones_like(pt) + loose_up = ak.ones_like(pt) + loose_do = ak.ones_like(pt) + ## Only Run2 for now + new_sf_2l = ak.ones_like(pt) + new_up_2l = ak.ones_like(pt) + new_do_2l = ak.ones_like(pt) + new_sf_3l = ak.ones_like(pt) + new_up_3l = ak.ones_like(pt) + new_do_3l = ak.ones_like(pt) + iso_sf = ak.ones_like(pt) + iso_up = ak.ones_like(pt) + iso_do = ak.ones_like(pt) + + if year not in clib_year_map.keys(): raise Exception(f"Error: Unknown year \"{year}\".") - new_sf_2l = SFevaluator['ElecSF_{year}_2lss'.format(year=year)](np.abs(eta),pt) - new_err_2l = SFevaluator['ElecSF_{year}_2lss_er'.format(year=year)](np.abs(eta),pt) - new_sf_3l = SFevaluator['ElecSF_{year}_3l'.format(year=year)](np.abs(eta),pt) - new_err_3l = SFevaluator['ElecSF_{year}_3l_er'.format(year=year)](np.abs(eta),pt) - loose_sf = SFevaluator['ElecLooseSF_{year}'.format(year=year)](np.abs(eta),pt) - loose_err = SFevaluator['ElecLooseSF_{year}_er'.format(year=year)](np.abs(eta),pt) - iso_sf = SFevaluator['ElecIsoSF_{year}'.format(year=year)](np.abs(eta),pt) - iso_err = SFevaluator['ElecIsoSF_{year}_er'.format(year=year)](np.abs(eta),pt) - - # Get the right sf json for the given year - if year.startswith("2016"): - clib_year = "2016preVFP" if year == "2016APV" else "2016postVFP" - else: - clib_year = year - clib_year = clib_year_map[year] json_path = topcoffea_path(f"data/POG/EGM/{clib_year}/electron.json.gz") ceval = correctionlib.CorrectionSet.from_file(json_path) eta_flat = ak.flatten(eta) pt_flat = ak.flatten(pt) + phi_flat = ak.flatten(phi) - pt_bins = OrderedDict([ - ("RecoBelow20", [10, 20]), - ("RecoAbove20", [20, 1000]) - ]) + pt_bins = egm_pt_bins[dt_era] reco_sf_perbin = [] reco_up_perbin = [] reco_do_perbin = [] + egm_year = egm_tag_map[clib_year] + egm_tag = "Electron-ID-SF" + if is_run2: + egm_tag = "UL-" + "Electron-ID-SF" + for bintag, bin_edges in pt_bins.items(): pt_mask = ak.flatten((pt >= bin_edges[0]) & (pt < bin_edges[1])) pt_bin_flat = ak.where(~pt_mask, bin_edges[1]-0.1, pt_flat) + egm_args = [bintag, eta_flat, pt_bin_flat] + if "2023" in year: + egm_args.append(phi_flat) + reco_sf_perbin.append( ak.where( ~pt_mask, 1, - ceval["UL-Electron-ID-SF"].evaluate(clib_year.replace("_UL", ""),"sf", bintag, eta_flat, pt_bin_flat) + ceval[egm_tag].evaluate(egm_year, "sf", *egm_args) ) ) reco_up_perbin.append( ak.where( ~pt_mask, 1, - ceval["UL-Electron-ID-SF"].evaluate(clib_year.replace("_UL", ""),"sfup", bintag, eta_flat, pt_bin_flat) + ceval[egm_tag].evaluate(egm_year, "sfup", *egm_args) ) ) reco_do_perbin.append( ak.where( ~pt_mask, 1, - ceval["UL-Electron-ID-SF"].evaluate(clib_year.replace("_UL", ""),"sfdown", bintag, eta_flat, pt_bin_flat) + ceval[egm_tag].evaluate(egm_year, "sfdown", *egm_args) ) ) @@ -769,12 +1182,32 @@ def AttachElectronSF(electrons, year): reco_up = ak.unflatten(reco_up_flat, ak.num(pt)) reco_do = ak.unflatten(reco_do_flat, ak.num(pt)) + if not is_run3: # run 3 dont need loose for the id because we dont have ID working point (to be checked!!) + loose_sf = SFevaluator['ElecLooseSF_{year}'.format(year=year)](np.abs(eta),pt) + loose_err = SFevaluator['ElecLooseSF_{year}_er'.format(year=year)](np.abs(eta),pt) + loose_up = loose_sf + loose_err + loose_do = loose_sf - loose_err + + new_sf_2l = SFevaluator['ElecSF_{year}_2lss'.format(year=year)](np.abs(eta),pt) + new_err_2l = SFevaluator['ElecSF_{year}_2lss_er'.format(year=year)](np.abs(eta),pt) + new_up_2l = new_sf_2l + new_err_2l + new_do_2l = new_sf_2l - new_err_2l + new_sf_3l = SFevaluator['ElecSF_{year}_3l'.format(year=year)](np.abs(eta),pt) + new_err_3l = SFevaluator['ElecSF_{year}_3l_er'.format(year=year)](np.abs(eta),pt) + new_up_3l = new_sf_3l + new_err_3l + new_do_3l = new_sf_3l - new_err_3l + + iso_sf = SFevaluator['ElecIsoSF_{year}'.format(year=year)](np.abs(eta),pt) + iso_err = SFevaluator['ElecIsoSF_{year}_er'.format(year=year)](np.abs(eta),pt) + iso_up = iso_sf + iso_err + iso_do = iso_sf - iso_err + electrons['sf_nom_2l_elec'] = reco_sf * new_sf_2l * loose_sf * iso_sf - electrons['sf_hi_2l_elec'] = (reco_up) * (new_sf_2l + new_err_2l) * (loose_sf + loose_err) * (iso_sf + iso_err) - electrons['sf_lo_2l_elec'] = (reco_do) * (new_sf_2l - new_err_2l) * (loose_sf - loose_err) * (iso_sf - iso_err) + electrons['sf_hi_2l_elec'] = (reco_up) * new_up_2l * loose_up * iso_up + electrons['sf_lo_2l_elec'] = (reco_do) * new_do_2l * loose_do * iso_do electrons['sf_nom_3l_elec'] = reco_sf * new_sf_3l * loose_sf - electrons['sf_hi_3l_elec'] = (reco_up) * (new_sf_3l + new_err_3l) * (loose_sf + loose_err) * (iso_sf + iso_err) - electrons['sf_lo_3l_elec'] = (reco_do) * (new_sf_3l - new_err_3l) * (loose_sf - loose_err) * (iso_sf - iso_err) + electrons['sf_hi_3l_elec'] = (reco_up) * new_up_3l * loose_up * iso_up + electrons['sf_lo_3l_elec'] = (reco_do) * new_do_3l * loose_do * iso_do electrons['sf_nom_2l_muon'] = ak.ones_like(reco_sf) electrons['sf_hi_2l_muon'] = ak.ones_like(reco_sf) electrons['sf_lo_2l_muon'] = ak.ones_like(reco_sf) @@ -788,7 +1221,7 @@ def AttachElectronSF(electrons, year): # MC efficiencies def GetMCeffFunc(year, wp='medium', flav='b'): - if year not in ['2016','2016APV','2017','2018']: + if year not in clib_year_map.keys(): raise Exception(f"Error: Unknown year \"{year}\".") pathToBtagMCeff = topeft_path('data/btagSF/UL/btagMCeff_%s.pkl.gz'%year) hists = {} @@ -821,12 +1254,14 @@ def GetMCeffFunc(year, wp='medium', flav='b'): values = hnum.values(flow=True)[1:,1:,1:] edges = [hnum.axes['pt'].edges, hnum.axes['abseta'].edges, hnum.axes['flav'].edges] fun = lambda pt, abseta, flav: getnum(pt,abseta,flav)/getden(pt,abseta,flav) + return fun def GetBtagEff(jets, year, wp='medium'): - if year not in ['2016','2016APV','2017','2018']: + if year not in clib_year_map.keys(): raise Exception(f"Error: Unknown year \"{year}\".") - return GetMCeffFunc(year,wp)(jets.pt, np.abs(jets.eta), jets.hadronFlavour) + result = GetMCeffFunc(year,wp)(jets.pt, np.abs(jets.eta), jets.hadronFlavour) if year[2] != "2" else ak.ones_like(jets.pt) + return result def GetBTagSF(jets, year, wp='MEDIUM', syst='central'): if year == '2016': SFevaluatorBtag = BTagScaleFactor(topeft_path("data/btagSF/UL/DeepJet_106XUL16postVFPSF_v2.csv"),wp) @@ -925,59 +1360,91 @@ def AttachPdfWeights(events): # JER: https://twiki.cern.ch/twiki/bin/viewauth/CMS/JetResolution # JES: https://twiki.cern.ch/twiki/bin/view/CMS/JECDataMC -def ApplyJetCorrections(year, corr_type): - if year == '2016': - jec_tag = '16_V7' - jer_tag = 'Summer20UL16_JRV3' - elif year == '2016APV': - jec_tag = '16APV_V7' - jer_tag = 'Summer20UL16APV_JRV3' - elif year == '2017': - jec_tag = '17_V5' - jer_tag = 'Summer19UL17_JRV2' - elif year == '2018': - jec_tag = '18_V5' - jer_tag = 'Summer19UL18_JRV2' - else: +def ApplyJetCorrections(year, corr_type, isData, era, useclib=True, savelevels=False): + usejecstack = not useclib + + if year not in clib_year_map.keys(): raise Exception(f"Error: Unknown year \"{year}\".") - extJEC = lookup_tools.extractor() - extJEC.add_weight_sets([ - "* * " + topcoffea_path('data/JER/%s_MC_SF_AK4PFchs.jersf.txt' % jer_tag), - "* * " + topcoffea_path('data/JER/%s_MC_PtResolution_AK4PFchs.jr.txt' % jer_tag), - "* * " + topcoffea_path('data/JEC/Summer19UL%s_MC_L1FastJet_AK4PFchs.txt' % jec_tag), - "* * " + topcoffea_path('data/JEC/Summer19UL%s_MC_L2Relative_AK4PFchs.txt' % jec_tag), - "* * " + topcoffea_path('data/JEC/Quad_Summer19UL%s_MC_UncertaintySources_AK4PFchs.junc.txt' % jec_tag) - ]) - jec_types = [ - 'FlavorQCD', 'FlavorPureBottom', 'FlavorPureQuark', 'FlavorPureGluon', 'FlavorPureCharm', - 'BBEC1', 'Absolute', 'RelativeBal', 'RelativeSample' - ] - jec_regroup = ["Quad_Summer19UL%s_MC_UncertaintySources_AK4PFchs_%s" % (jec_tag,jec_type) for jec_type in jec_types] - jec_names = [ - "%s_MC_SF_AK4PFchs" % jer_tag, - "%s_MC_PtResolution_AK4PFchs" % jer_tag, - "Summer19UL%s_MC_L1FastJet_AK4PFchs" % jec_tag, - "Summer19UL%s_MC_L2Relative_AK4PFchs" % jec_tag - ] - jec_names.extend(jec_regroup) - extJEC.finalize() - JECevaluator = extJEC.make_evaluator() - jec_inputs = {name: JECevaluator[name] for name in jec_names} - jec_stack = JECStack(jec_inputs) - name_map = jec_stack.blank_name_map - name_map['JetPt'] = 'pt' - name_map['JetMass'] = 'mass' - name_map['JetEta'] = 'eta' - name_map['JetPhi'] = 'phi' - name_map['JetA'] = 'area' - name_map['ptGenJet'] = 'pt_gen' - name_map['ptRaw'] = 'pt_raw' - name_map['massRaw'] = 'mass_raw' - name_map['Rho'] = 'rho' - name_map['METpt'] = 'pt' - name_map['METphi'] = 'phi' - name_map['UnClusteredEnergyDeltaX'] = 'MetUnclustEnUpDeltaX' - name_map['UnClusteredEnergyDeltaY'] = 'MetUnclustEnUpDeltaY' + + jec_year = clib_year_map[year] + if usejecstack: + jec_tag = jerc_tag_map[year][0] + jer_tag = jerc_tag_map[year][1] + jet_algo = "AK4PFchs" + extJEC = lookup_tools.extractor() + weight_sets = [] + if not isData: + weight_sets += [ + "* * " + topcoffea_path(f'data/JER/{jer_tag}_MC_SF_{jet_algo}.jersf.txt'), + "* * " + topcoffea_path(f'data/JER/{jer_tag}_MC_PtResolution_{jet_algo}.jr.txt'), + ] + weight_sets += [ + "* * " + topcoffea_path(f'data/JEC/Summer19UL{jec_tag}_MC_L1FastJet_{jet_algo}.txt'), + "* * " + topcoffea_path(f'data/JEC/Summer19UL{jec_tag}_MC_L2Relative_{jet_algo}.txt'), + ] + if not isData: + weight_sets += [ + "* * " + topcoffea_path(f'data/JEC/Quad_Summer19UL{jec_tag}_MC_UncertaintySources_{jet_algo}.junc.txt') + ] + extJEC.add_weight_sets(weight_sets) + jec_types = [ + 'FlavorQCD', 'FlavorPureBottom', 'FlavorPureQuark', 'FlavorPureGluon', 'FlavorPureCharm', + 'BBEC1', 'Absolute', 'RelativeBal', 'RelativeSample' + ] + jec_regroup = [f"Quad_Summer19UL%s_MC_UncertaintySources_{jet_algo}_%s" % (jec_tag,jec_type) for jec_type in jec_types] + jec_names = [] + if not isData: + jec_names += [ + f"{jer_tag}_MC_SF_{jet_algo}", + f"{jer_tag}_MC_PtResolution_{jet_algo}", + ] + jec_names += [ + f"Summer19UL{jec_tag}_MC_L1FastJet_{jet_algo}", + f"Summer19UL{jec_tag}_MC_L2Relative_{jet_algo}", + ] + if not isData: + jec_names.extend(jec_regroup) + + extJEC.finalize() + JECevaluator = extJEC.make_evaluator() + jec_inputs = {name: JECevaluator[name.replace("Regrouped_", "")] for name in jec_names} + jec_stack = JECStack(jec_inputs) + + elif useclib: + # Handle clib case + jet_algo, jec_tag, jec_levels, jer_tag, junc_types = get_jerc_keys(year, isData, era) + json_path = topcoffea_path(f"data/POG/JME/{jec_year}/jet_jerc.json.gz") + + # Create JECStack for clib scenario + jec_stack = JECStack( + jec_tag=jec_tag, + jec_levels=jec_levels, + jer_tag=jer_tag, + jet_algo=jet_algo, + junc_types=junc_types, + json_path=json_path, + use_clib=useclib, + savecorr=savelevels + ) + + # Name map for jet or MET corrections + name_map = { + 'JetPt': 'pt', + 'JetMass': 'mass', + 'JetEta': 'eta', + 'JetPhi': 'phi', + 'JetA': 'area', + 'ptGenJet': 'pt_gen', + 'ptRaw': 'pt_raw', + 'massRaw': 'mass_raw', + 'Rho': 'rho', + 'METpt': 'pt', + 'METphi': 'phi', + 'UnClusteredEnergyDeltaX': 'MetUnclustEnUpDeltaX', + 'UnClusteredEnergyDeltaY': 'MetUnclustEnUpDeltaY' + } + + # Return appropriate factory based on correction type if corr_type == 'met': return CorrectedMETFactory(name_map) return CorrectedJetsFactory(name_map, jec_stack) @@ -995,7 +1462,7 @@ def ApplyJetSystematics(year,cleanedJets,syst_var): return cleanedJets elif (syst_var in ['nominal','MuonESUp','MuonESDown', 'TESUp', 'TESDown', 'FESUp', 'FESDown']): return cleanedJets - elif ('JES_FlavorQCD' in syst_var in syst_var):# and (('Up' in syst_var and syst_var.replace('Up', '') in cleanedJets.fields) or ('Down' in syst_var and syst_var.replace('Down', '') in cleanedJets.fields))): + elif ('JES_FlavorQCD' in syst_var in syst_var): # Overwrite FlavorQCD with the proper jet flavor uncertainty bmask = np.array(ak.flatten(abs(cleanedJets.partonFlavour)==5)) cmask = abs(cleanedJets.partonFlavour)==4 @@ -1022,9 +1489,9 @@ def ApplyJetSystematics(year,cleanedJets,syst_var): cleanedJets['JES_FlavorQCD']['down']['pt'] = corrections return cleanedJets.JES_FlavorQCD.down # Save `2016APV` as `2016APV` but look up `2016` corrections (no separate APV corrections available) - elif ('Up' in syst_var and syst_var.replace('Up', '').replace('APV', '') in cleanedJets.fields): - return cleanedJets[syst_var.replace('Up', '').replace('APV', '')].up - elif ('Down' in syst_var and syst_var.replace('Down', '').replace('APV', '') in cleanedJets.fields): + elif ('Up' in syst_var and syst_var[:-2].replace('APV', '') in cleanedJets.fields): + return cleanedJets[syst_var.replace('Up', '').replace("Pile", "PileUp").replace('APV', '')].up + elif ('Down' in syst_var and syst_var[:-4].replace('APV', '') in cleanedJets.fields): return cleanedJets[syst_var.replace('Down', '').replace('APV', '')].down else: raise Exception(f"Error: Unknown variation \"{syst_var}\".") @@ -1034,40 +1501,45 @@ def ApplyJetSystematics(year,cleanedJets,syst_var): # https://gitlab.cern.ch/akhukhun/roccor # https://github.com/CoffeaTeam/coffea/blob/master/coffea/lookup_tools/rochester_lookup.py def ApplyRochesterCorrections(year, mu, is_data): - if year == '2016': - rochester_data = txt_converters.convert_rochester_file(topcoffea_path("data/MuonScale/RoccoR2016bUL.txt"), loaduncs=True) - elif year == '2016APV': - rochester_data = txt_converters.convert_rochester_file(topcoffea_path("data/MuonScale/RoccoR2016aUL.txt"), loaduncs=True) - elif year == '2017': - rochester_data = txt_converters.convert_rochester_file(topcoffea_path("data/MuonScale/RoccoR2017UL.txt"), loaduncs=True) - elif year == '2018': - rochester_data = txt_converters.convert_rochester_file(topcoffea_path("data/MuonScale/RoccoR2018UL.txt"), loaduncs=True) - rochester = rochester_lookup.rochester_lookup(rochester_data) - if not is_data: - hasgen = ~np.isnan(ak.fill_none(mu.matched_gen.pt, np.nan)) - mc_rand = np.random.rand(*ak.to_numpy(ak.flatten(mu.pt)).shape) - mc_rand = ak.unflatten(mc_rand, ak.num(mu.pt, axis=1)) - corrections = np.array(ak.flatten(ak.ones_like(mu.pt))) - mc_kspread = rochester.kSpreadMC( - mu.charge[hasgen],mu.pt[hasgen], - mu.eta[hasgen], - mu.phi[hasgen], - mu.matched_gen.pt[hasgen] - ) - mc_ksmear = rochester.kSmearMC( - mu.charge[~hasgen], - mu.pt[~hasgen], - mu.eta[~hasgen], - mu.phi[~hasgen], - mu.nTrackerLayers[~hasgen], - mc_rand[~hasgen] - ) - hasgen_flat = np.array(ak.flatten(hasgen)) - corrections[hasgen_flat] = np.array(ak.flatten(mc_kspread)) - corrections[~hasgen_flat] = np.array(ak.flatten(mc_ksmear)) - corrections = ak.unflatten(corrections, ak.num(mu.pt, axis=1)) + if year.startswith('201'): #Run2 scenario + rocco_tag = None + if year == '2016': + rocco_tag = "2016bUL" + elif year == '2016APV': + rocco_tag = "2016aUL" + elif year == '2017': + rocco_tag = "2017UL" + elif year == '2018': + rocco_tag = "2018UL" + rochester_data = txt_converters.convert_rochester_file(topcoffea_path(f"data/MuonScale/RoccoR{rocco_tag}.txt"), loaduncs=True) + rochester = rochester_lookup.rochester_lookup(rochester_data) + if not is_data: + hasgen = ~np.isnan(ak.fill_none(mu.matched_gen.pt, np.nan)) + mc_rand = np.random.rand(*ak.to_numpy(ak.flatten(mu.pt)).shape) + mc_rand = ak.unflatten(mc_rand, ak.num(mu.pt, axis=1)) + corrections = np.array(ak.flatten(ak.ones_like(mu.pt))) + mc_kspread = rochester.kSpreadMC( + mu.charge[hasgen],mu.pt[hasgen], + mu.eta[hasgen], + mu.phi[hasgen], + mu.matched_gen.pt[hasgen] + ) + mc_ksmear = rochester.kSmearMC( + mu.charge[~hasgen], + mu.pt[~hasgen], + mu.eta[~hasgen], + mu.phi[~hasgen], + mu.nTrackerLayers[~hasgen], + mc_rand[~hasgen] + ) + hasgen_flat = np.array(ak.flatten(hasgen)) + corrections[hasgen_flat] = np.array(ak.flatten(mc_kspread)) + corrections[~hasgen_flat] = np.array(ak.flatten(mc_ksmear)) + corrections = ak.unflatten(corrections, ak.num(mu.pt, axis=1)) + else: + corrections = rochester.kScaleDT(mu.charge, mu.pt, mu.eta, mu.phi) else: - corrections = rochester.kScaleDT(mu.charge, mu.pt, mu.eta, mu.phi) + corrections = ak.ones_like(mu.pt) return (mu.pt * corrections) ###### Trigger SFs @@ -1167,12 +1639,22 @@ def LoadTriggerSF(year, ch='2l', flav='em'): return [GetTrig, GetTrigDo, GetTrigUp] def GetTriggerSF(year, events, lep0, lep1): + is_run3 = False + if year.startswith("202"): + is_run3 = True + is_run2 = not is_run3 + ls = [] for syst in [0,1]: #2l - SF_ee = np.where((events.is2l & events.is_ee), LoadTriggerSF(year,ch='2l',flav='ee')[syst](lep0.pt,lep1.pt), 1.0) - SF_em = np.where((events.is2l & events.is_em), LoadTriggerSF(year,ch='2l',flav='em')[syst](lep0.pt,lep1.pt), 1.0) - SF_mm = np.where((events.is2l & events.is_mm), LoadTriggerSF(year,ch='2l',flav='mm')[syst](lep0.pt,lep1.pt), 1.0) + if is_run2: + SF_ee = np.where((events.is2l & events.is_ee), LoadTriggerSF(year,ch='2l',flav='ee')[syst](lep0.pt,lep1.pt), 1.0) + SF_em = np.where((events.is2l & events.is_em), LoadTriggerSF(year,ch='2l',flav='em')[syst](lep0.pt,lep1.pt), 1.0) + SF_mm = np.where((events.is2l & events.is_mm), LoadTriggerSF(year,ch='2l',flav='mm')[syst](lep0.pt,lep1.pt), 1.0) + elif is_run3: + SF_ee = ak.ones_like(events.is2l) + SF_em = ak.ones_like(events.is2l) + SF_mm = ak.ones_like(events.is2l) #3l ''' SF_eee=np.where((events.is3l & events.is_eee),LoadTriggerSF(year,ch='3l',flav='eee')[syst](lep0.pt,lep0.eta),1.0) diff --git a/topeft/modules/dataDrivenEstimation.py b/topeft/modules/dataDrivenEstimation.py index 0e17b443c..577e15d18 100644 --- a/topeft/modules/dataDrivenEstimation.py +++ b/topeft/modules/dataDrivenEstimation.py @@ -40,9 +40,13 @@ def DDFakes(self): pattern=re.compile(name_regex) for process in histo.axes['process']: - match = pattern.search(process) - sampleName=match.group('process') - year=match.group('year') + try: + match = pattern.search(process) + sampleName=match.group('process') + year=match.group('year') + except AttributeError as ae: + print(f"The following exception occured due to missing match {ae} for process {process}") + print("Moving to the next process") if not match: raise RuntimeError(f"Sample {process} does not match the naming convention.") if year not in ['16APV','16','17','18']: diff --git a/topeft/modules/datacard_tools.py b/topeft/modules/datacard_tools.py index d9f3fb5a0..8cb3d6fa9 100644 --- a/topeft/modules/datacard_tools.py +++ b/topeft/modules/datacard_tools.py @@ -298,6 +298,13 @@ def __init__(self,pkl_path,**kwargs): self.coeffs = kwargs.pop("wcs",[]) self.use_real_data = kwargs.pop("unblind",False) self.verbose = kwargs.pop("verbose",True) + self.use_AAC = kwargs.pop("use_AAC",False) + self.wc_scalings = kwargs.pop("wc_scalings",[]) + self.scalings = [] + + # get wc ranges from json + with open(topeft_path("params/wc_ranges.json"), "r") as wc_ranges_json: + self.wc_ranges = json.load(wc_ranges_json) if self.year_lst: for yr in self.year_lst: @@ -326,10 +333,6 @@ def __init__(self,pkl_path,**kwargs): # "TTTo2L2Nu", "TTToSemiLeptonic", # "data", ] - self.var_ignore = { - "3l": ["lt"], - "4l": ["lt"], - } if not self.use_real_data: # Since we're just going to generate Asimov data, this lets us drop the real data histograms @@ -769,6 +772,25 @@ def get_selected_wcs(self,km_dist,ch_lst=[]): print(f"WC Selection Time: {dt:.2f} s") return selected_wcs + def make_scalings_json(self,scalings_json,ch,km_dist,p,wc_names,scalings): + scalings = scalings.tolist() + scalings_json.append( + { + "channel": ch + "_" + str(km_dist), + "process": p + "_sm", # NOTE: needs to be in the datacard + "parameters": ["cSM[1]"] + [ + self.format_wc(wcname) for wcname in wc_names + ], + "scaling": + scalings[1:], # exclude underflow bin + } + ) + return scalings_json + + def format_wc(self,wcname): + lo, hi = self.wc_ranges[wcname] + return "%s[0,%.1f,%.1f]" % (wcname, lo, hi) + def analyze(self,km_dist,ch,selected_wcs, crop_negative_bins, wcs_dict): """ Handles the EFT decomposition and the actual writing of the ROOT and text datacard files.""" if not km_dist in self.hists: @@ -778,11 +800,6 @@ def analyze(self,km_dist,ch,selected_wcs, crop_negative_bins, wcs_dict): print(f"[ERROR] Unknown channel {ch}") return None - for lep_chan,var_ignore in self.var_ignore.items(): - if lep_chan in ch and km_dist in var_ignore: - print(f"Ignoring {km_dist} for {ch}") - return - print(f"Analyzing {km_dist} in {ch}") bin_str = f"bin_{ch}_{km_dist}" @@ -840,6 +857,8 @@ def analyze(self,km_dist,ch,selected_wcs, crop_negative_bins, wcs_dict): raise RuntimeError("filling obs data more than once!") for sp_key,arr in data_sm.items(): data_obs += arr + if not self.use_AAC: + decomposed_templates = {k: v for k, v in decomposed_templates.items() if k == 'sm'} for base,v in decomposed_templates.items(): proc_name = f"{p}_{base}" col_width = max(len(proc_name),col_width) @@ -941,6 +960,14 @@ def analyze(self,km_dist,ch,selected_wcs, crop_negative_bins, wcs_dict): if p == "tllq" or p == "tHq": # Handle the 'missing_parton' uncertainty pass + # obtain the scalings for scalings.json file + if p in self.SIGNALS: + if self.wc_scalings: + scalings = h[{'channel':ch,'process':p,'systematic':'nominal'}].make_scaling(self.wc_scalings) + self.scalings_json = self.make_scalings_json(self.scalings,ch,km_dist,p,self.wc_scalings,scalings) + else: + scalings = h[{'channel':ch,'process':p,'systematic':'nominal'}].make_scaling() + self.scalings_json = self.make_scalings_json(self.scalings,ch,km_dist,p,h.wc_names,scalings) f["data_obs"] = to_hist(data_obs,"data_obs") line_break = "##----------------------------------\n" @@ -1140,7 +1167,6 @@ def decompose(self,h,sumw2,wcs,vals={}): return r - if __name__ == '__main__': fpath = topeft_path("../analysis/topEFT/histos/may18_fullRun2_withSys_anatest08_np.pkl.gz") diff --git a/topeft/modules/event_selection.py b/topeft/modules/event_selection.py index 164c21c4f..7b3a2ef0c 100644 --- a/topeft/modules/event_selection.py +++ b/topeft/modules/event_selection.py @@ -1,6 +1,5 @@ ''' selection.py - This script contains several functions that implement the some event selection. The functinos defined here can be used to define a selection, signal/control region, etc. The functions are called with (jagged)arrays as imputs plus some custom paramenters and return a boolean mask. @@ -111,8 +110,34 @@ "Mu8_DiEle12_CaloIdL_TrackIdL_DZ", "DiMu9_Ele9_CaloIdL_TrackIdL_DZ", ] - } + }, + #NEW TRIGGERS - Should pull from https://docs.google.com/document/d/1zm9EkFExonAO2upU1V7_lw8uKjHknFrnxNQMq75HWnw/edit? + #Currently placeholders + "2022" : { + "Muon" : [ + "IsoMu24", + "IsoMu27", + "Mu17_TrkIsoVVL_Mu8_TrkIsoVVL_DZ_Mass3p8", + "TripleMu_12_10_5", + ], + "EGamma" : [ + "Ele32_WPTight_Gsf", + "Ele35_WPTight_Gsf", + "Ele23_Ele12_CaloIdL_TrackIdL_IsoVL", + "Ele23_Ele12_CaloIdL_TrackIdL_IsoVL_DZ", + "Ele16_Ele12_Ele8_CaloIdL_TrackIdL", + ], + "MuonEG" : [ + "Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL", + "Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_DZ", + "Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ", + "Mu8_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_DZ", + "Mu8_DiEle12_CaloIdL_TrackIdL", + "Mu8_DiEle12_CaloIdL_TrackIdL_DZ", + "DiMu9_Ele9_CaloIdL_TrackIdL_DZ", + ], + }, } @@ -141,6 +166,11 @@ "MuonEG" : dataset_dict_top22006["2018"]["DoubleMuon"] + dataset_dict_top22006["2018"]["EGamma"], "SingleMuon" : dataset_dict_top22006["2018"]["DoubleMuon"] + dataset_dict_top22006["2018"]["EGamma"] + dataset_dict_top22006["2018"]["MuonEG"], }, + "2022": { + "Muon" : [], + "EGamma" : dataset_dict_top22006["2022"]["Muon"], + "MuonEG" : dataset_dict_top22006["2022"]["Muon"] + dataset_dict_top22006["2022"]["EGamma"], + }, } @@ -205,7 +235,6 @@ def add1lMaskAndSFs(events, year, isData, sampleType): # 2l selection (we do not make the ss requirement here) def add2lMaskAndSFs(events, year, isData, sampleType): - # FOs and padded FOs FOs = events.l_fo_conept_sorted padded_FOs = ak.pad_none(FOs,2) diff --git a/topeft/modules/jerc_dict.json b/topeft/modules/jerc_dict.json new file mode 100644 index 000000000..8c1692568 --- /dev/null +++ b/topeft/modules/jerc_dict.json @@ -0,0 +1,84 @@ +{ + "2016": { + "jec_mc": "Summer19UL16_V7_MC", + "jec_data": "Summer19UL16_RunFGH_V7_DATA", + "jec_levels": ["L1FastJet", "L2Relative"], + "jer": "Summer20UL16_JRV3_MC", + "junc": ["FlavorQCD", "FlavorPureBottom", "FlavorPureQuark", "FlavorPureGluon", "FlavorPureCharm", "Regrouped_BBEC1", "Regrouped_Absolute", "Regrouped_RelativeBal", "RelativeSample"] + }, + "2016APV": { + "jec_mc": "Summer19UL16APV_V7_MC", + "jec_data": { + "B": "Summer19UL16APV_RunBCD_V7_DATA", + "C": "Summer19UL16APV_RunBCD_V7_DATA", + "D": "Summer19UL16APV_RunBCD_V7_DATA", + "E": "Summer19UL16APV_RunEF_V7_DATA", + "F": "Summer19UL16APV_RunEF_V7_DATA" + }, + "jec_levels": ["L1FastJet", "L2Relative"], + "jer": "Summer20UL16APV_JRV3_MC", + "junc": ["FlavorQCD", "FlavorPureBottom", "FlavorPureQuark", "FlavorPureGluon", "FlavorPureCharm", "Regrouped_BBEC1", "Regrouped_Absolute", "Regrouped_RelativeBal", "RelativeSample"] + }, + "2017": { + "jec_mc": "Summer19UL17_V5_MC", + "jec_data": { + "B": "Summer19UL17_RunB_V5_DATA", + "C": "Summer19UL17_RunC_V5_DATA", + "D": "Summer19UL17_RunD_V5_DATA", + "E": "Summer19UL17_RunE_V5_DATA", + "F": "Summer19UL17_RunF_V5_DATA" + }, + "jec_levels": ["L1FastJet", "L2Relative"], + "jer": "Summer19UL17_JRV2_MC", + "junc": ["FlavorQCD", "FlavorPureBottom", "FlavorPureQuark", "FlavorPureGluon", "FlavorPureCharm", "Regrouped_BBEC1", "Regrouped_Absolute", "Regrouped_RelativeBal", "RelativeSample"] + }, + "2018": { + "jec_mc": "Summer19UL18_V5_MC", + "jec_data": { + "A": "Summer19UL18_RunA_V5_DATA", + "B": "Summer19UL18_RunB_V5_DATA", + "C": "Summer19UL18_RunC_V5_DATA", + "D": "Summer19UL18_RunD_V5_DATA" + }, + "jec_levels": ["L1FastJet", "L2Relative"], + "jer": "Summer19UL18_JRV2_MC", + "junc": ["FlavorQCD", "FlavorPureBottom", "FlavorPureQuark", "FlavorPureGluon", "FlavorPureCharm", "Regrouped_BBEC1", "Regrouped_Absolute", "Regrouped_RelativeBal", "RelativeSample"] + }, + "2022": { + "jec_mc": "Summer22_22Sep2023_V2_MC", + "jec_data": "Summer22_22Sep2023_RunCD_V2_DATA", + "jec_levels": ["L1FastJet", "L2Relative", "L3Absolute", "L2L3Residual"], + "jer": "Summer22_22Sep2023_JRV1_MC", + "junc": ["AbsoluteMPFBias", "AbsoluteScale", "FlavorQCD", "Fragmentation", "PileUpDataMC", "PileUpPtBB", "PileUpPtEC1", "PileUpPtEC2", "PileUpPtHF", "PileUpPtRef", "RelativeFSR", "RelativeJERHF", "RelativePtBB", "RelativePtHF", "RelativeBal", "SinglePionECAL", "SinglePionHCAL", "AbsoluteStat", "RelativeJEREC1", "RelativeJEREC2", "RelativePtEC1", "RelativePtEC2", "TimePtEta", "RelativeSample", "RelativeStatEC", "RelativeStatFSR", "RelativeStatHF", "Total"] + }, + "2022EE": { + "jec_mc": "Summer22EE_22Sep2023_V2_MC", + "jec_data": { + "E": "Summer22EE_22Sep2023_RunE_V2_DATA", + "F": "Summer22EE_22Sep2023_RunF_V2_DATA", + "G": "Summer22EE_22Sep2023_RunG_V2_DATA" + }, + "jec_levels": ["L1FastJet", "L2Relative", "L3Absolute", "L2L3Residual"], + "jer": "Summer22EE_22Sep2023_JRV1_MC", + "junc": ["AbsoluteMPFBias", "AbsoluteScale", "FlavorQCD", "Fragmentation", "PileUpDataMC", "PileUpPtBB", "PileUpPtEC1", "PileUpPtEC2", "PileUpPtHF", "PileUpPtRef", "RelativeFSR", "RelativeJERHF", "RelativePtBB", "RelativePtHF", "RelativeBal", "SinglePionECAL", "SinglePionHCAL", "AbsoluteStat", "RelativeJEREC1", "RelativeJEREC2", "RelativePtEC1", "RelativePtEC2", "TimePtEta", "RelativeSample", "RelativeStatEC", "RelativeStatFSR", "RelativeStatHF", "Total"] + }, + "2023": { + "jec_mc": "Summer23Prompt23_V1_MC", + "jec_data": { + "C1": "Summer23Prompt23_RunCv123_V1_DATA", + "C2": "Summer23Prompt23_RunCv123_V1_DATA", + "C3": "Summer23Prompt23_RunCv123_V1_DATA", + "C4": "Summer23Prompt23_RunCv4_V1_DATA" + }, + "jec_levels": ["L1FastJet", "L2Relative", "L3Absolute", "L2L3Residual"], + "jer": "Summer23Prompt23_RunCv1234_JRV1_MC", + "junc": ["AbsoluteMPFBias", "AbsoluteScale", "FlavorQCD", "Fragmentation", "PileUpDataMC", "PileUpPtBB", "PileUpPtEC1", "PileUpPtEC2", "PileUpPtHF", "PileUpPtRef", "RelativeFSR", "RelativeJERHF", "RelativePtBB", "RelativePtHF", "RelativeBal", "SinglePionECAL", "SinglePionHCAL", "AbsoluteStat", "RelativeJEREC1", "RelativeJEREC2", "RelativePtEC1", "RelativePtEC2", "TimePtEta", "RelativeSample", "RelativeStatEC", "RelativeStatFSR", "RelativeStatHF", "Total"] + }, + "2023BPix": { + "jec_mc": "Summer23BPixPrompt23_V1_MC", + "jec_data": "Summer23BPixPrompt23_RunD_V1_DATA", + "jec_levels": ["L1FastJet", "L2Relative", "L3Absolute", "L2L3Residual"], + "jer": "Summer23BPixPrompt23_RunD_JRV1_MC", + "junc": ["AbsoluteMPFBias", "AbsoluteScale", "FlavorQCD", "Fragmentation", "PileUpDataMC", "PileUpPtBB", "PileUpPtEC1", "PileUpPtEC2", "PileUpPtHF", "PileUpPtRef", "RelativeFSR", "RelativeJERHF", "RelativePtBB", "RelativePtHF", "RelativeBal", "SinglePionECAL", "SinglePionHCAL", "AbsoluteStat", "RelativeJEREC1", "RelativeJEREC2", "RelativePtEC1", "RelativePtEC2", "TimePtEta", "RelativeSample", "RelativeStatEC", "RelativeStatFSR", "RelativeStatHF", "Total"] + } +} diff --git a/topeft/modules/object_selection.py b/topeft/modules/object_selection.py index daf783014..03294f324 100644 --- a/topeft/modules/object_selection.py +++ b/topeft/modules/object_selection.py @@ -64,89 +64,165 @@ def smoothBFlav(jetpt,ptmin,ptmax,year,scale_loose=1.0): elif (year == "2018"): wploose = get_tc_param("btag_wp_loose_UL18") wpmedium = get_tc_param("btag_wp_medium_UL18") + elif (year == "2022"): + wploose = get_tc_param("btag_wp_loose_2022") + wpmedium = get_tc_param("btag_wp_medium_2022") + elif (year == "2022EE"): + wploose = get_tc_param("btag_wp_loose_2022EE") + wpmedium = get_tc_param("btag_wp_medium_2022EE") + elif (year == "2023"): + wploose = get_tc_param("btag_wp_loose_2023") + wpmedium = get_tc_param("btag_wp_medium_2023") + elif (year == "2023BPix"): + wploose = get_tc_param("btag_wp_loose_2023BPix") + wpmedium = get_tc_param("btag_wp_medium_2023BPix") else: raise Exception(f"Error: Unknown year \"{year}\". Exiting...") x = np.minimum(np.maximum(0, jetpt - ptmin)/(ptmax-ptmin), 1.0) return x*wploose*scale_loose + (1-x)*wpmedium -def coneptElec(pt, mvaTTHUL, jetRelIso): - conePt = (0.90 * pt * (1 + jetRelIso)) - return ak.where((mvaTTHUL>get_tc_param("mva_TTH_e_cut")),pt,conePt) - -def coneptMuon(pt, mvaTTHUL, jetRelIso, mediumId): - conePt = (0.90 * pt * (1 + jetRelIso)) - return ak.where(((mvaTTHUL>get_tc_param("mva_TTH_m_cut"))&(mediumId>0)),pt,conePt) - -def isPresElec(pt, eta, dxy, dz, miniIso, sip3D, eleId): - pt_mask = (pt > get_te_param("pres_e_pt_cut")) - eta_mask = (abs(eta) < get_te_param("eta_e_cut")) - dxy_mask = (abs(dxy) < get_te_param("dxy_cut")) - dz_mask = (abs(dz) < get_te_param("dz_cut")) - iso_mask = (miniIso < get_te_param("iso_cut")) - sip3d_mask = (sip3D < get_te_param("sip3d_cut")) - return (pt_mask & eta_mask & dxy_mask & dz_mask & iso_mask & sip3d_mask & eleId) - -def isPresMuon(dxy, dz, sip3D, eta, pt, miniRelIso): - pt_mask = (pt > get_te_param("pres_m_pt_cut")) - eta_mask = (abs(eta) < get_te_param("eta_m_cut")) - dxy_mask = (abs(dxy) < get_te_param("dxy_cut")) - dz_mask = (abs(dz) < get_te_param("dz_cut")) - iso_mask = (miniRelIso < get_te_param("iso_cut")) - sip3d_mask = (sip3D < get_te_param("sip3d_cut")) - return (pt_mask & eta_mask & dxy_mask & dz_mask & iso_mask & sip3d_mask) - -def isLooseElec(miniPFRelIso_all,sip3d,lostHits): - return (miniPFRelIso_allget_te_param("fo_pt_cut")) - qualityReq = (ttH_idEmu_cuts_E3 & convVeto & (lostHits==0)) - mvaReq = ((mvaTTHUL>get_tc_param("mva_TTH_e_cut")) | ((mvaFall17V2noIso_WP90) & (jetBTagDeepFlavget_te_param("fo_pt_cut")) - mvaReq = ((mvaTTHUL>get_tc_param("mva_TTH_m_cut")) | ((jetBTagDeepFlav get_tc_param("mva_TTH_e_cut")) - -def tightSelMuon(clean_and_FO_selection_TTH, mediumId, mvaTTHUL): - return (clean_and_FO_selection_TTH) & (mediumId>0) & (mvaTTHUL > get_tc_param("mva_TTH_m_cut")) +class run2leptonselection: + + def __init__(self): + pass + + def coneptElec(self, ele): + conePt = (0.90 * ele.pt * (1 + ele.jetRelIso)) + return ak.where((ele.mvaTTHUL>get_te_param("mva_TTH_e_cut")),ele.pt,conePt) + + def coneptMuon(self, muo): + conePt = (0.90 * muo.pt * (1 + muo.jetRelIso)) + return ak.where(((muo.mvaTTHUL>get_te_param("mva_TTH_m_cut"))&(muo.mediumId>0)),muo.pt,conePt) + + def isPresElec(self, ele): + pt_mask = (ele.pt > get_te_param("pres_e_pt_cut")) + eta_mask = (abs(ele.eta) < get_te_param("eta_e_cut")) + dxy_mask = (abs(ele.dxy) < get_te_param("dxy_cut")) + dz_mask = (abs(ele.dz) < get_te_param("dz_cut")) + iso_mask = (ele.miniPFRelIso_all < get_te_param("iso_cut")) + sip3d_mask = (ele.sip3d < get_te_param("sip3d_cut")) + return (pt_mask & eta_mask & dxy_mask & dz_mask & iso_mask & sip3d_mask & ele.mvaFall17V2noIso_WPL) + + def isPresMuon(self, muon): + pt_mask = (muon.pt > get_te_param("pres_m_pt_cut")) + eta_mask = (abs(muon.eta) < get_te_param("eta_m_cut")) + dxy_mask = (abs(muon.dxy) < get_te_param("dxy_cut")) + dz_mask = (abs(muon.dz) < get_te_param("dz_cut")) + iso_mask = (muon.miniPFRelIso_all < get_te_param("iso_cut")) + sip3d_mask = (muon.sip3d < get_te_param("sip3d_cut")) + return (pt_mask & eta_mask & dxy_mask & dz_mask & iso_mask & sip3d_mask) + + def isLooseElec(self, ele): + return (ele.miniPFRelIso_allget_te_param("fo_pt_cut")) + qualityReq = (ele.idEmu & ele.convVeto & (ele.lostHits==0)) + mvaReq = ((ele.mvaTTHUL>get_te_param("mva_TTH_e_cut")) | ((ele.mvaFall17V2noIso_WP90) & (ele.jetBTagDeepFlavget_te_param("fo_pt_cut")) + mvaReq = ((muo.mvaTTHUL>get_te_param("mva_TTH_m_cut")) | ((muo.jetBTagDeepFlav get_te_param("mva_TTH_e_cut")) + + def tightSelMuon(self, muo): + return (muo.isFO) & (muo.mediumId>0) & (muo.mvaTTHUL > get_te_param("mva_TTH_m_cut")) + +class run3leptonselection: + + def __init__(self): + pass + + def coneptElec(self, ele): + conePt = (0.90 * ele.pt * (1 + ele.jetRelIso)) + return ak.where((ele.mvaTTH_Run3>get_te_param("mva_TTH_e_cut_run3")),ele.pt,conePt) + + def coneptMuon(self, muo): + conePt = (0.90 * muo.pt * (1 + muo.jetRelIso)) + return ak.where(((muo.mvaTTH_Run3>get_te_param("mva_TTH_m_cut_run3"))&(muo.mediumId>0)),muo.pt,conePt) + + def isPresElec(self, ele): + pt_mask = (ele.pt > get_te_param("pres_e_pt_cut")) + eta_mask = (abs(ele.eta) < get_te_param("eta_e_cut")) + dxy_mask = (abs(ele.dxy) < get_te_param("dxy_cut")) + dz_mask = (abs(ele.dz) < get_te_param("dz_cut")) + iso_mask = (ele.miniPFRelIso_all < get_te_param("iso_cut")) + sip3d_mask = (ele.sip3d < get_te_param("sip3d_cut")) + ecal_crack_mask = (((abs(ele.etaSC) < 1.4442) | (abs(ele.etaSC) > 1.566))) + return (pt_mask & eta_mask & dxy_mask & dz_mask & iso_mask & sip3d_mask & ecal_crack_mask) + + def isPresMuon(self, muon): + pt_mask = (muon.pt > get_te_param("pres_m_pt_cut")) + eta_mask = (abs(muon.eta) < get_te_param("eta_m_cut")) + dxy_mask = (abs(muon.dxy) < get_te_param("dxy_cut")) + dz_mask = (abs(muon.dz) < get_te_param("dz_cut")) + iso_mask = (muon.miniPFRelIso_all < get_te_param("iso_cut")) + sip3d_mask = (muon.sip3d < get_te_param("sip3d_cut")) + return (pt_mask & eta_mask & dxy_mask & dz_mask & iso_mask & sip3d_mask) + + def isLooseElec(self, ele): + return (ele.miniPFRelIso_allget_te_param("fo_pt_cut")) + qualityReq = (ele.idEmu & ele.convVeto & (ele.lostHits==0)) + mvaReq = ((ele.mvaTTH_Run3>get_te_param("mva_TTH_e_cut_run3")) | ((ele.mvaIso > get_te_param("fo_e_mvaiso_cut_run3")) & (ele.jetRelIso < get_te_param("fo_e_jetRelIso_cut")))) + + def isFOMuon(self, muo, year): + bTagCut=get_medium_btag_foryear(year) + btagReq = (muo.jetBTagDeepFlavget_te_param("fo_pt_cut")) + mvaReq = ((muo.mvaTTH_Run3>get_te_param("mva_TTH_m_cut_run3")) | ((muo.jetBTagDeepFlav get_te_param("mva_TTH_e_cut_run3")) + + def tightSelMuon(self, muo): + return (muo.isFO) & (muo.mediumId>0) & (muo.mvaTTH_Run3 > get_te_param("mva_TTH_m_cut_run3")) def isClean(obj_A, obj_B, drmin=0.4): objB_near, objB_DR = obj_A.nearest(obj_B, return_metric=True) diff --git a/topeft/modules/yield_tools.py b/topeft/modules/yield_tools.py index ed9f0decd..fc5e43997 100644 --- a/topeft/modules/yield_tools.py +++ b/topeft/modules/yield_tools.py @@ -350,6 +350,8 @@ def integrate_out_cats(self,h,cuts_dict): h_ret = h.copy() for axis_name,cat_lst in cuts_dict.items(): h_ret = h_ret.integrate(axis_name,cat_lst) + if isinstance(cat_lst, list): + h_ret[{axis_name: sum}] return h_ret diff --git a/topeft/params/params.json b/topeft/params/params.json index 4c27a230a..18819a357 100644 --- a/topeft/params/params.json +++ b/topeft/params/params.json @@ -8,11 +8,18 @@ "pres_e_pt_cut" : 7.0, "pres_m_pt_cut" : 5.0, + "mva_TTH_e_cut" : 0.90, + "mva_TTH_m_cut" : 0.85, + "mva_TTH_e_cut_run3" : 0.90, + "mva_TTH_m_cut_run3" : 0.64, + "iso_cut" : 0.4, "fo_m_jetRelIso_cut" : 0.5, "fo_e_jetRelIso_cut" : 1.0, + "fo_e_mvaiso_cut_run3" : 0.6, + "jet_id_cut" : 0, "dxy_cut" : 0.05, diff --git a/topeft/params/wc_ranges.json b/topeft/params/wc_ranges.json new file mode 100644 index 000000000..77a20f3f2 --- /dev/null +++ b/topeft/params/wc_ranges.json @@ -0,0 +1,28 @@ +{ + "cQQ1": [-6.0, 6.0], + "cQei": [-4.0, 4.0], + "cQl3i": [-5.5, 5.5], + "cQlMi": [-4.0, 4.0], + "cQq11": [-0.7, 0.7], + "cQq13": [-0.35, 0.35], + "cQq81": [-1.7, 1.5], + "cQq83": [-0.6, 0.6], + "cQt1": [-6.0, 6.0], + "cQt8": [-10.0, 10.0], + "cbW": [-3.0, 3.0], + "cpQ3": [-4.0, 4.0], + "cpQM": [-15.0, 20.0], + "cpt": [-15.0, 15.0], + "cptb": [-9.0, 9.0], + "ctG": [-0.8, 0.8], + "ctW": [-1.5, 1.5], + "ctZ": [-2.0, 2.0], + "ctei": [-4.0, 4.0], + "ctlSi": [-5.0, 5.0], + "ctlTi": [-0.9, 0.9], + "ctli": [-4.0, 4.0], + "ctp": [-15.0, 40.0], + "ctq1": [-0.6, 0.6], + "ctq8": [-1.4, 1.4], + "ctt1": [-2.6, 2.6] +}