From af6cde6a050ced66e9e908d9d9d9ae7376331748 Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 19 Dec 2023 14:48:36 -0500 Subject: [PATCH 01/48] First updates for coffea2023 --- analysis/wwz/wwz4l.py | 23 ++++++++++++++++++----- ewkcoffea/modules/corrections.py | 8 ++++---- ewkcoffea/modules/selection_wwz.py | 13 +++++++------ 3 files changed, 29 insertions(+), 15 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 6e6b7428..d565877a 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -26,6 +26,8 @@ get_tc_param = GetParam(topcoffea_path("params/params.json")) get_ec_param = GetParam(ewkcoffea_path("params/params.json")) +import dask_awkward as dak + class AnalysisProcessor(processor.ProcessorABC): @@ -135,10 +137,19 @@ def columns(self): return self._columns # Main function: run on a given dataset - def process(self, events): + #def process(self, events): + def process(self, dataset, fpaths): # Dataset parameters - dataset = events.metadata["dataset"] + #dataset = events.metadata["dataset"] + + from coffea.nanoevents import NanoEventsFactory + from coffea.nanoevents import NanoAODSchema + events = NanoEventsFactory.from_root( + {fpath: "/Events" for fpath in fpaths}, + schemaclass=NanoAODSchema, + metadata={"dataset": dataset}, + ).events() isData = self._samples[dataset]["isData"] histAxisName = self._samples[dataset]["histAxisName"] @@ -253,12 +264,14 @@ def process(self, events): #''' # Do the object selection for the WWZ eleectrons ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True) - ele["topmva"] = os_ec.get_topmva_score_ele(events, year) + #ele["topmva"] = os_ec.get_topmva_score_ele(events, year) + ele["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask) # Do the object selection for the WWZ muons mu_presl_mask = os_ec.is_presel_wwz_mu(mu) - mu["topmva"] = os_ec.get_topmva_score_mu(events, year) + #mu["topmva"] = os_ec.get_topmva_score_mu(events, year) + mu["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask) # Get tight leptons for WWZ selection @@ -305,7 +318,7 @@ def process(self, events): # We only calculate these values if not isData # Note: add() will generally modify up/down weights, so if these are needed for any reason after this point, we should instead pass copies to add() # 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) + weights_obj_base = coffea.analysis_tools.Weights(None,storeIndividual=True) if not isData: genw = events["genWeight"] diff --git a/ewkcoffea/modules/corrections.py b/ewkcoffea/modules/corrections.py index afce9fb8..50ef9402 100644 --- a/ewkcoffea/modules/corrections.py +++ b/ewkcoffea/modules/corrections.py @@ -66,8 +66,8 @@ def AttachMuonSF(muons, year): eta = np.abs(muons.eta) pt = muons.pt if year not in ['2016','2016APV','2017','2018']: raise Exception(f"Error: Unknown year \"{year}\".") - 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 TODO: Remove all reco SFs, they are folded into Kirill's tight SFs - 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 TODO: Remove all reco SFs, they are folded into Kirill's tight SFs + reco_sf = ak.where(pt < 20,SFevaluator['MuonRecoSF_{year}'.format(year=year)](eta,pt),1) # sf=1 when pt>20 becuase there is no reco SF available TODO: Remove all reco SFs, they are folded into Kirill's tight SFs + reco_err = ak.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 TODO: Remove all reco SFs, they are folded into Kirill's tight SFs tight_sf = SFevaluator[f'MuonTightSF_{year}'](eta,pt) #tight_err = SFevaluator[f'MuonTightSF_{year}_er'](eta,pt) @@ -91,12 +91,12 @@ def AttachElectronSF(electrons, year): if year not in ['2016','2016APV','2017','2018']: raise Exception(f"Error: Unknown year \"{year}\".") - reco_sf = np.where( + reco_sf = ak.where( pt < 20, SFevaluator['ElecRecoSFBe_{year}'.format(year=year)](eta,pt), SFevaluator['ElecRecoSFAb_{year}'.format(year=year)](eta,pt) ) - reco_err = np.where( + reco_err = ak.where( pt < 20, SFevaluator['ElecRecoSFBe_{year}_er'.format(year=year)](eta,pt), SFevaluator['ElecRecoSFAb_{year}_er'.format(year=year)](eta,pt) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index faeb4709..b5d2a9b6 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -150,7 +150,7 @@ def trg_matching(events,year): if year == "2016APV": year = "2016" # Initialize return array to be True array with same shape as events - ret_arr = ak.zeros_like(np.array(events.event), dtype=bool) + ret_arr = ak.zeros_like(events.event, dtype=bool) # Get the leptons, sort and pad el = events.l_wwz_t[abs(events.l_wwz_t.pdgId)==11] @@ -176,7 +176,7 @@ def trg_matching(events,year): # Build the return mask # The return mask started from an array of False # The way an event becomes True is if it passes a trigger AND passes the offline pt cuts associated with that trg - false_arr = ak.zeros_like(np.array(events.event), dtype=bool) # False array with same shape as events + false_arr = ak.zeros_like(events.event, dtype=bool) # False array with same shape as events ret_arr = ret_arr | ak.where(trg_passes,offline_cut,false_arr) return ret_arr @@ -324,13 +324,14 @@ def attach_wwz_preselection_mask(events,lep_collection): def get_mt2(w_lep0,w_lep1,met): # Construct misspart vector, as implimented in c++: https://github.com/sgnoohc/mt2example/blob/main/main.cc#L7 (but pass 0 not pi/2 for met eta) - nevents = len(np.zeros_like(met)) + misspart_mass = ak.zeros_like(met.pt) # Just 0 + misspart_eta = ak.zeros_like(met.pt) # Just 0 misspart = ak.zip( { "pt": met.pt, - "eta": 0, + "eta": misspart_eta, "phi": met.phi, - "mass": np.full(nevents, 0), + "mass": misspart_mass, }, with_name="PtEtaPhiMLorentzVector", behavior=vector.behavior, @@ -353,7 +354,7 @@ def get_mt2(w_lep0,w_lep1,met): mass_l0, w_lep0_boosted.px, w_lep0_boosted.py, mass_l1, w_lep1_boosted.px, w_lep1_boosted.py, misspart_boosted.px, misspart_boosted.py, - np.zeros_like(met.pt), np.zeros_like(met.pt), + ak.zeros_like(met.pt), ak.zeros_like(met.pt), ) return mt2_var From 37c42eb623d1c5d6120383c5fe47b73a0afd8b40 Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 19 Dec 2023 18:34:10 -0500 Subject: [PATCH 02/48] More coffea 2023 --- analysis/wwz/run_wwz4l.py | 28 ++++++++++++++++++++++- analysis/wwz/wwz4l.py | 47 ++++++++++++++++++++++----------------- 2 files changed, 54 insertions(+), 21 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index fc242cc2..a7d9434e 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -6,8 +6,10 @@ import cloudpickle import gzip import os +import dask from coffea import processor from coffea.nanoevents import NanoAODSchema +from coffea.nanoevents import NanoEventsFactory import topcoffea.modules.remote_environment as remote_environment @@ -306,6 +308,29 @@ def LoadJsonToSampleName(jsonFile, prefix): # Run the processor and get the output tstart = time.time() + ### coffea2023 ### + + # Create dict of events objects + print("Number of datasets:",len(flist)) + events_dict = {} + for name, fpaths in flist.items(): + events_dict[name] = NanoEventsFactory.from_root( + {fpath: "/Events" for fpath in fpaths}, + schemaclass=NanoAODSchema, + metadata={"dataset": name}, + ).events() + + # Get the histograms + histos_to_compute = {} + for json_name in flist.keys(): + print(f"Getting histos for {json_name}") + histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) # Passing events object + #histos_to_compute[json_name] = processor_instance.process(json_name,flist[json_name]) # Passing root file list + + print("Compute histos") + output = dask.compute(histos_to_compute) + + ''' if executor == "futures": exec_instance = processor.FuturesExecutor(workers=nworkers) runner = processor.Runner(exec_instance, schema=NanoAODSchema, chunksize=chunksize, maxchunks=nchunks) @@ -315,8 +340,9 @@ def LoadJsonToSampleName(jsonFile, prefix): elif executor == "work_queue": executor = processor.WorkQueueExecutor(**executor_args) runner = processor.Runner(executor, schema=NanoAODSchema, chunksize=chunksize, maxchunks=nchunks, skipbadfiles=False, xrootdtimeout=180) + ''' - output = runner(flist, treename, processor_instance) + #output = runner(flist, treename, processor_instance) dt = time.time() - tstart diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index d565877a..718c0428 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -27,6 +27,7 @@ get_ec_param = GetParam(ewkcoffea_path("params/params.json")) import dask_awkward as dak +import hist.dask as hda @@ -137,19 +138,20 @@ def columns(self): return self._columns # Main function: run on a given dataset - #def process(self, events): - def process(self, dataset, fpaths): + def process(self, events): + #def process(self, dataset, fpaths): # Dataset parameters - #dataset = events.metadata["dataset"] + dataset = events.metadata["dataset"] - from coffea.nanoevents import NanoEventsFactory - from coffea.nanoevents import NanoAODSchema - events = NanoEventsFactory.from_root( - {fpath: "/Events" for fpath in fpaths}, - schemaclass=NanoAODSchema, - metadata={"dataset": dataset}, - ).events() + # If we pass the root files instead of events object + #from coffea.nanoevents import NanoEventsFactory + #from coffea.nanoevents import NanoAODSchema + #events = NanoEventsFactory.from_root( + # {fpath: "/Events" for fpath in fpaths}, + # schemaclass=NanoAODSchema, + # metadata={"dataset": dataset}, + #).events() isData = self._samples[dataset]["isData"] histAxisName = self._samples[dataset]["histAxisName"] @@ -569,9 +571,9 @@ def process(self, dataset, fpaths): # lb pairs (i.e. always one lep, one bjet) bjets = goodJets[isBtagJetsLoose] - lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets}) - mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) - mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) + #lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets}) + #mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) + #mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) # Get BDT values bdt_vars = [ @@ -593,10 +595,14 @@ def process(self, dataset, fpaths): ak.fill_none(w_lep1.pt,-9999), ] - bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) - bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) - bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) - bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) + #bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) + #bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) + #bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) + #bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) + bdt_of_wwz_raw = 0 # TODO: Update xgb for coffea23 + bdt_sf_wwz_raw = 0 # TODO: Update xgb for coffea23 + bdt_of_zh_raw = 0 # TODO: Update xgb for coffea23 + bdt_sf_zh_raw = 0 # TODO: Update xgb for coffea23 # Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0 bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0 @@ -665,8 +671,8 @@ def process(self, dataset, fpaths): "mll_min_afos" : mll_min_afos, "mll_min_sfos" : mll_min_sfos, - "mlb_min" : mlb_min, - "mlb_max" : mlb_max, + #"mlb_min" : mlb_min, + #"mlb_max" : mlb_max, "bdt_of_wwz_raw": bdt_of_wwz_raw, "bdt_sf_wwz_raw": bdt_sf_wwz_raw, @@ -742,9 +748,10 @@ def process(self, dataset, fpaths): for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): #print("\ndense_axis_name,vals",dense_axis_name) #print("dense_axis_name,vals",dense_axis_vals) + if "njets" not in dense_axis_name: continue # Create the hist for this dense axis variable - hout[dense_axis_name] = hist.Hist( + hout[dense_axis_name] = hda.Hist( hist.axis.StrCategory([], growth=True, name="process", label="process"), hist.axis.StrCategory([], growth=True, name="category", label="category"), self._dense_axes_dict[dense_axis_name], From ca7de0522a7f1965d169418715d172da4a7748bf Mon Sep 17 00:00:00 2001 From: Kelci Date: Wed, 3 Jan 2024 10:12:00 -0500 Subject: [PATCH 03/48] Do not need columns thing here --- analysis/wwz/wwz4l.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 718c0428..7bc9d858 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -133,10 +133,6 @@ def __init__(self, samples, wc_names_lst=[], hist_lst=None, ecut_threshold=None, self._skip_control_regions = skip_control_regions # Whether to skip the CR categories - @property - def columns(self): - return self._columns - # Main function: run on a given dataset def process(self, events): #def process(self, dataset, fpaths): From 747630dadddab8bbeaa0f51d48088c41d7b856c3 Mon Sep 17 00:00:00 2001 From: Kelci Date: Wed, 3 Jan 2024 13:31:52 -0500 Subject: [PATCH 04/48] Pass dict of samples to processor --- analysis/wwz/run_wwz4l.py | 10 +- analysis/wwz/wwz4l.py | 1313 +++++++++++++++++++------------------ 2 files changed, 662 insertions(+), 661 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index a7d9434e..8ec3e278 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -320,13 +320,9 @@ def LoadJsonToSampleName(jsonFile, prefix): metadata={"dataset": name}, ).events() - # Get the histograms - histos_to_compute = {} - for json_name in flist.keys(): - print(f"Getting histos for {json_name}") - histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) # Passing events object - #histos_to_compute[json_name] = processor_instance.process(json_name,flist[json_name]) # Passing root file list - + # Get and compute the histograms + print("Get histos") + histos_to_compute = processor_instance.process(events_dict) print("Compute histos") output = dask.compute(histos_to_compute) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 7bc9d858..e04151d5 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -134,668 +134,673 @@ def __init__(self, samples, wc_names_lst=[], hist_lst=None, ecut_threshold=None, # Main function: run on a given dataset - def process(self, events): - #def process(self, dataset, fpaths): - - # Dataset parameters - dataset = events.metadata["dataset"] - - # If we pass the root files instead of events object - #from coffea.nanoevents import NanoEventsFactory - #from coffea.nanoevents import NanoAODSchema - #events = NanoEventsFactory.from_root( - # {fpath: "/Events" for fpath in fpaths}, - # schemaclass=NanoAODSchema, - # metadata={"dataset": dataset}, - #).events() - - isData = self._samples[dataset]["isData"] - histAxisName = self._samples[dataset]["histAxisName"] - year = self._samples[dataset]["year"] - xsec = self._samples[dataset]["xsec"] - sow = self._samples[dataset]["nSumOfWeights"] - - # Get up down weights from input dict - if (self._do_systematics and not isData): - # Otherwise we have an NLO xsec, so for these systs we will have e.g. xsec_NLO*(N_pass_up/N_gen_up) - # Thus these systs should only affect acceptance and effeciency and shape - # The uncty on xsec comes from NLO and is applied as a rate uncty in the text datacard - sow_ISRUp = self._samples[dataset]["nSumOfWeights_ISRUp" ] - sow_ISRDown = self._samples[dataset]["nSumOfWeights_ISRDown" ] - sow_FSRUp = self._samples[dataset]["nSumOfWeights_FSRUp" ] - sow_FSRDown = self._samples[dataset]["nSumOfWeights_FSRDown" ] - sow_renormUp = self._samples[dataset]["nSumOfWeights_renormUp" ] - sow_renormDown = self._samples[dataset]["nSumOfWeights_renormDown" ] - sow_factUp = self._samples[dataset]["nSumOfWeights_factUp" ] - sow_factDown = self._samples[dataset]["nSumOfWeights_factDown" ] - sow_renormfactUp = self._samples[dataset]["nSumOfWeights_renormfactUp" ] - sow_renormfactDown = self._samples[dataset]["nSumOfWeights_renormfactDown" ] - else: - sow_ISRUp = -1 - sow_ISRDown = -1 - sow_FSRUp = -1 - sow_FSRDown = -1 - sow_renormUp = -1 - sow_renormDown = -1 - sow_factUp = -1 - sow_factDown = -1 - sow_renormfactUp = -1 - sow_renormfactDown = -1 - - datasets = ["SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"] - for d in datasets: - if d in dataset: dataset = dataset.split('_')[0] - - # Initialize objects - #met = events.MET - met = events.PuppiMET - ele = events.Electron - mu = events.Muon - tau = events.Tau - jets = events.Jet - - # 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(met.pt) - - # Get the lumi mask for data - if year == "2016" or year == "2016APV": - golden_json_path = topcoffea_path("data/goldenJsons/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt") - elif year == "2017": - 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") - else: - raise ValueError(f"Error: Unknown year \"{year}\".") - lumi_mask = LumiMask(golden_json_path)(events.run,events.luminosityBlock) - - ######################################################### - ### START top22006 lep obj sel ### - #import topeft.modules.object_selection as te_os - #from topeft.modules.corrections import AttachMuonSF, AttachElectronSF - - #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["btagDeepFlavB"] = ak.fill_none(ele.matched_jet.btagDeepFlavB, -99) - #mu["btagDeepFlavB"] = ak.fill_none(mu.matched_jet.btagDeepFlavB, -99) - #if not isData: - # ele["gen_pdgId"] = ak.fill_none(ele.matched_gen.pdgId, 0) - # mu["gen_pdgId"] = ak.fill_none(mu.matched_gen.pdgId, 0) - - #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) - - #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) - - #m_loose = mu[mu.isPres & mu.isLooseM] - #e_loose = ele[ele.isPres & ele.isLooseE] - #l_loose = ak.with_name(ak.concatenate([e_loose, m_loose], axis=1), 'PtEtaPhiMCandidate') - - ## Build FO collection - #m_fo = mu[mu.isPres & mu.isLooseM & mu.isFO] - #e_fo = ele[ele.isPres & ele.isLooseE & ele.isFO] - - ## Attach the lepton SFs to the electron and muons collections - #AttachElectronSF(e_fo,year=year) - #AttachMuonSF(m_fo,year=year) - - ## Attach per lepton fake rates - #m_fo['convVeto'] = ak.ones_like(m_fo.charge) - #m_fo['lostHits'] = ak.zeros_like(m_fo.charge) - #l_fo = ak.with_name(ak.concatenate([e_fo, m_fo], axis=1), 'PtEtaPhiMCandidate') - #l_fo_conept_sorted = l_fo[ak.argsort(l_fo.conept, axis=-1,ascending=False)] - - #l_wwz_t = l_fo_conept_sorted[l_fo_conept_sorted.isTightLep] - #l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt - - ### END top22006 obj sel ### - ######################################################## - - ################### Lepton selection #################### - - #''' - # Do the object selection for the WWZ eleectrons - ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True) - #ele["topmva"] = os_ec.get_topmva_score_ele(events, year) - ele["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 - ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask) - - # Do the object selection for the WWZ muons - mu_presl_mask = os_ec.is_presel_wwz_mu(mu) - #mu["topmva"] = os_ec.get_topmva_score_mu(events, year) - mu["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 - mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask) - - # Get tight leptons for WWZ selection - ele_wwz_t = ele[ele.is_tight_lep_for_wwz] - mu_wwz_t = mu[mu.is_tight_lep_for_wwz] - - # Attach the lepton SFs to the electron and muons collections - cor_ec.AttachElectronSF(ele_wwz_t,year=year) - cor_ec.AttachMuonSF(mu_wwz_t,year=year) - - l_wwz_t = ak.with_name(ak.concatenate([ele_wwz_t,mu_wwz_t],axis=1),'PtEtaPhiMCandidate') - l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt - - #''' - - # For WWZ: Compute pair invariant masses - llpairs_wwz = ak.combinations(l_wwz_t, 2, fields=["l0","l1"]) - os_pairs_mask = (llpairs_wwz.l0.pdgId*llpairs_wwz.l1.pdgId < 0) # Maks for opposite-sign pairs - sfos_pairs_mask = (llpairs_wwz.l0.pdgId == -llpairs_wwz.l1.pdgId) # Mask for same-flavor-opposite-sign pairs - ll_absdphi_pairs = abs(llpairs_wwz.l0.delta_phi(llpairs_wwz.l1)) - ll_mass_pairs = (llpairs_wwz.l0+llpairs_wwz.l1).mass # The mll for each ll pair - absdphi_min_afas = ak.min(ll_absdphi_pairs,axis=-1) - absdphi_min_afos = ak.min(ll_absdphi_pairs[os_pairs_mask],axis=-1) - absdphi_min_sfos = ak.min(ll_absdphi_pairs[sfos_pairs_mask],axis=-1) - mll_min_afas = ak.min(ll_mass_pairs,axis=-1) - mll_min_afos = ak.min(ll_mass_pairs[os_pairs_mask],axis=-1) - mll_min_sfos = ak.min(ll_mass_pairs[sfos_pairs_mask],axis=-1) - events["min_mll_afos"] = mll_min_afos # Attach this one to the event info since we need it for selection - - # For WWZ - l_wwz_t_padded = ak.pad_none(l_wwz_t, 4) - l0 = l_wwz_t_padded[:,0] - l1 = l_wwz_t_padded[:,1] - l2 = l_wwz_t_padded[:,2] - l3 = l_wwz_t_padded[:,3] - - nleps = ak.num(l_wwz_t) - - - ######### Systematics ########### - - - # These weights can go outside of the outside sys loop since they do not depend on pt of mu or jets - # We only calculate these values if not isData - # Note: add() will generally modify up/down weights, so if these are needed for any reason after this point, we should instead pass copies to add() - # 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(None,storeIndividual=True) - if not isData: - genw = events["genWeight"] - - # If it's an EFT sample, just take SM piece - sm_wgt = 1.0 - eft_coeffs = ak.to_numpy(events["EFTfitCoefficients"]) if hasattr(events, "EFTfitCoefficients") else None - if eft_coeffs is not None: - sm_wgt = eft_coeffs[:,0] - - # 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*sm_wgt) - - - # We do not have systematics yet - syst_var_list = ['nominal'] - - # Loop over the list of systematic variations we've constructed - for syst_var in syst_var_list: - # Make a copy of the base weights object, so that each time through the loop we do not double count systs - # In this loop over systs that impact kinematics, we will add to the weights objects the SFs that depend on the object kinematics - weights_obj_base_for_kinematic_syst = copy.deepcopy(weights_obj_base) - - - #################### Jets #################### - - # Jet cleaning, before any jet selection - ##vetos_tocleanjets = ak.with_name( ak.concatenate([tau, l_fo], axis=1), "PtEtaPhiMCandidate") - #vetos_tocleanjets = ak.with_name( l_wwz_t, "PtEtaPhiMCandidate") - #tmp = ak.cartesian([ak.local_index(jets.pt), vetos_tocleanjets.jetIdx], nested=True) - #cleanedJets = jets[~ak.any(tmp.slot0 == tmp.slot1, axis=-1)] # this line should go before *any selection*, otherwise lep.jetIdx is not aligned with the jet index - - # Clean with dr for now - cleanedJets = os_ec.get_cleaned_collection(l_wwz_t,jets) - - # Selecting jets and cleaning them - # NOTE: The jet id cut is commented for now in objects.py for the sync - jetptname = "pt_nom" if hasattr(cleanedJets, "pt_nom") else "pt" - cleanedJets["is_good"] = os_tc.is_tight_jet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, pt_cut=20., eta_cut=get_ec_param("wwz_eta_j_cut"), id_cut=get_ec_param("wwz_jet_id_cut")) - goodJets = cleanedJets[cleanedJets.is_good] - - # Count jets - njets = ak.num(goodJets) - ht = ak.sum(goodJets.pt,axis=-1) - j0 = goodJets[ak.argmax(goodJets.pt,axis=-1,keepdims=True)] - - - # Loose DeepJet WP - btagger = "btag" # For deep flavor WPs - #btagger = "btagcsv" # For deep CSV WPs - if year == "2017": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL17") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL17") + def process(self, events_dict): + + # Loop over samples and fill histos + hout = {} + for events in events_dict.values(): + + # Dataset parameters + dataset = events.metadata["dataset"] + + # If we pass the root files instead of events object + #from coffea.nanoevents import NanoEventsFactory + #from coffea.nanoevents import NanoAODSchema + #events = NanoEventsFactory.from_root( + # {fpath: "/Events" for fpath in fpaths}, + # schemaclass=NanoAODSchema, + # metadata={"dataset": dataset}, + #).events() + + isData = self._samples[dataset]["isData"] + histAxisName = self._samples[dataset]["histAxisName"] + year = self._samples[dataset]["year"] + xsec = self._samples[dataset]["xsec"] + sow = self._samples[dataset]["nSumOfWeights"] + + # Get up down weights from input dict + if (self._do_systematics and not isData): + # Otherwise we have an NLO xsec, so for these systs we will have e.g. xsec_NLO*(N_pass_up/N_gen_up) + # Thus these systs should only affect acceptance and effeciency and shape + # The uncty on xsec comes from NLO and is applied as a rate uncty in the text datacard + sow_ISRUp = self._samples[dataset]["nSumOfWeights_ISRUp" ] + sow_ISRDown = self._samples[dataset]["nSumOfWeights_ISRDown" ] + sow_FSRUp = self._samples[dataset]["nSumOfWeights_FSRUp" ] + sow_FSRDown = self._samples[dataset]["nSumOfWeights_FSRDown" ] + sow_renormUp = self._samples[dataset]["nSumOfWeights_renormUp" ] + sow_renormDown = self._samples[dataset]["nSumOfWeights_renormDown" ] + sow_factUp = self._samples[dataset]["nSumOfWeights_factUp" ] + sow_factDown = self._samples[dataset]["nSumOfWeights_factDown" ] + sow_renormfactUp = self._samples[dataset]["nSumOfWeights_renormfactUp" ] + sow_renormfactDown = self._samples[dataset]["nSumOfWeights_renormfactDown" ] + else: + sow_ISRUp = -1 + sow_ISRDown = -1 + sow_FSRUp = -1 + sow_FSRDown = -1 + sow_renormUp = -1 + sow_renormDown = -1 + sow_factUp = -1 + sow_factDown = -1 + sow_renormfactUp = -1 + sow_renormfactDown = -1 + + datasets = ["SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"] + for d in datasets: + if d in dataset: dataset = dataset.split('_')[0] + + # Initialize objects + #met = events.MET + met = events.PuppiMET + ele = events.Electron + mu = events.Muon + tau = events.Tau + jets = events.Jet + + # 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(met.pt) + + # Get the lumi mask for data + if year == "2016" or year == "2016APV": + golden_json_path = topcoffea_path("data/goldenJsons/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt") + elif year == "2017": + golden_json_path = topcoffea_path("data/goldenJsons/Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt") elif year == "2018": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL18") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL18") - elif year=="2016": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16") - elif year=="2016APV": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16APV") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16APV") + golden_json_path = topcoffea_path("data/goldenJsons/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt") else: raise ValueError(f"Error: Unknown year \"{year}\".") + lumi_mask = LumiMask(golden_json_path)(events.run,events.luminosityBlock) + + ######################################################### + ### START top22006 lep obj sel ### + #import topeft.modules.object_selection as te_os + #from topeft.modules.corrections import AttachMuonSF, AttachElectronSF + + #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["btagDeepFlavB"] = ak.fill_none(ele.matched_jet.btagDeepFlavB, -99) + #mu["btagDeepFlavB"] = ak.fill_none(mu.matched_jet.btagDeepFlavB, -99) + #if not isData: + # ele["gen_pdgId"] = ak.fill_none(ele.matched_gen.pdgId, 0) + # mu["gen_pdgId"] = ak.fill_none(mu.matched_gen.pdgId, 0) + + #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) + + #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) + + #m_loose = mu[mu.isPres & mu.isLooseM] + #e_loose = ele[ele.isPres & ele.isLooseE] + #l_loose = ak.with_name(ak.concatenate([e_loose, m_loose], axis=1), 'PtEtaPhiMCandidate') + + ## Build FO collection + #m_fo = mu[mu.isPres & mu.isLooseM & mu.isFO] + #e_fo = ele[ele.isPres & ele.isLooseE & ele.isFO] + + ## Attach the lepton SFs to the electron and muons collections + #AttachElectronSF(e_fo,year=year) + #AttachMuonSF(m_fo,year=year) + + ## Attach per lepton fake rates + #m_fo['convVeto'] = ak.ones_like(m_fo.charge) + #m_fo['lostHits'] = ak.zeros_like(m_fo.charge) + #l_fo = ak.with_name(ak.concatenate([e_fo, m_fo], axis=1), 'PtEtaPhiMCandidate') + #l_fo_conept_sorted = l_fo[ak.argsort(l_fo.conept, axis=-1,ascending=False)] + + #l_wwz_t = l_fo_conept_sorted[l_fo_conept_sorted.isTightLep] + #l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt + + ### END top22006 obj sel ### + ######################################################## + + ################### Lepton selection #################### + + #''' + # Do the object selection for the WWZ eleectrons + ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True) + #ele["topmva"] = os_ec.get_topmva_score_ele(events, year) + ele["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 + ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask) + + # Do the object selection for the WWZ muons + mu_presl_mask = os_ec.is_presel_wwz_mu(mu) + #mu["topmva"] = os_ec.get_topmva_score_mu(events, year) + mu["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 + mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask) + + # Get tight leptons for WWZ selection + ele_wwz_t = ele[ele.is_tight_lep_for_wwz] + mu_wwz_t = mu[mu.is_tight_lep_for_wwz] + + # Attach the lepton SFs to the electron and muons collections + cor_ec.AttachElectronSF(ele_wwz_t,year=year) + cor_ec.AttachMuonSF(mu_wwz_t,year=year) + + l_wwz_t = ak.with_name(ak.concatenate([ele_wwz_t,mu_wwz_t],axis=1),'PtEtaPhiMCandidate') + l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt + + #''' + + # For WWZ: Compute pair invariant masses + llpairs_wwz = ak.combinations(l_wwz_t, 2, fields=["l0","l1"]) + os_pairs_mask = (llpairs_wwz.l0.pdgId*llpairs_wwz.l1.pdgId < 0) # Maks for opposite-sign pairs + sfos_pairs_mask = (llpairs_wwz.l0.pdgId == -llpairs_wwz.l1.pdgId) # Mask for same-flavor-opposite-sign pairs + ll_absdphi_pairs = abs(llpairs_wwz.l0.delta_phi(llpairs_wwz.l1)) + ll_mass_pairs = (llpairs_wwz.l0+llpairs_wwz.l1).mass # The mll for each ll pair + absdphi_min_afas = ak.min(ll_absdphi_pairs,axis=-1) + absdphi_min_afos = ak.min(ll_absdphi_pairs[os_pairs_mask],axis=-1) + absdphi_min_sfos = ak.min(ll_absdphi_pairs[sfos_pairs_mask],axis=-1) + mll_min_afas = ak.min(ll_mass_pairs,axis=-1) + mll_min_afos = ak.min(ll_mass_pairs[os_pairs_mask],axis=-1) + mll_min_sfos = ak.min(ll_mass_pairs[sfos_pairs_mask],axis=-1) + events["min_mll_afos"] = mll_min_afos # Attach this one to the event info since we need it for selection + + # For WWZ + l_wwz_t_padded = ak.pad_none(l_wwz_t, 4) + l0 = l_wwz_t_padded[:,0] + l1 = l_wwz_t_padded[:,1] + l2 = l_wwz_t_padded[:,2] + l3 = l_wwz_t_padded[:,3] + + nleps = ak.num(l_wwz_t) + + + ######### Systematics ########### + + + # These weights can go outside of the outside sys loop since they do not depend on pt of mu or jets + # We only calculate these values if not isData + # Note: add() will generally modify up/down weights, so if these are needed for any reason after this point, we should instead pass copies to add() + # 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(None,storeIndividual=True) + if not isData: + genw = events["genWeight"] + + # If it's an EFT sample, just take SM piece + sm_wgt = 1.0 + eft_coeffs = ak.to_numpy(events["EFTfitCoefficients"]) if hasattr(events, "EFTfitCoefficients") else None + if eft_coeffs is not None: + sm_wgt = eft_coeffs[:,0] + + # 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*sm_wgt) + + + # We do not have systematics yet + syst_var_list = ['nominal'] + + # Loop over the list of systematic variations we've constructed + for syst_var in syst_var_list: + # Make a copy of the base weights object, so that each time through the loop we do not double count systs + # In this loop over systs that impact kinematics, we will add to the weights objects the SFs that depend on the object kinematics + weights_obj_base_for_kinematic_syst = copy.deepcopy(weights_obj_base) + + + #################### Jets #################### + + # Jet cleaning, before any jet selection + ##vetos_tocleanjets = ak.with_name( ak.concatenate([tau, l_fo], axis=1), "PtEtaPhiMCandidate") + #vetos_tocleanjets = ak.with_name( l_wwz_t, "PtEtaPhiMCandidate") + #tmp = ak.cartesian([ak.local_index(jets.pt), vetos_tocleanjets.jetIdx], nested=True) + #cleanedJets = jets[~ak.any(tmp.slot0 == tmp.slot1, axis=-1)] # this line should go before *any selection*, otherwise lep.jetIdx is not aligned with the jet index + + # Clean with dr for now + cleanedJets = os_ec.get_cleaned_collection(l_wwz_t,jets) + + # Selecting jets and cleaning them + # NOTE: The jet id cut is commented for now in objects.py for the sync + jetptname = "pt_nom" if hasattr(cleanedJets, "pt_nom") else "pt" + cleanedJets["is_good"] = os_tc.is_tight_jet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, pt_cut=20., eta_cut=get_ec_param("wwz_eta_j_cut"), id_cut=get_ec_param("wwz_jet_id_cut")) + goodJets = cleanedJets[cleanedJets.is_good] + + # Count jets + njets = ak.num(goodJets) + ht = ak.sum(goodJets.pt,axis=-1) + j0 = goodJets[ak.argmax(goodJets.pt,axis=-1,keepdims=True)] + + + # Loose DeepJet WP + btagger = "btag" # For deep flavor WPs + #btagger = "btagcsv" # For deep CSV WPs + if year == "2017": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL17") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL17") + elif year == "2018": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL18") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL18") + elif year=="2016": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16") + elif year=="2016APV": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16APV") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16APV") + else: + raise ValueError(f"Error: Unknown year \"{year}\".") + + if btagger == "btag": + isBtagJetsLoose = (goodJets.btagDeepFlavB > btagwpl) + isBtagJetsMedium = (goodJets.btagDeepFlavB > btagwpm) + if btagger == "btagcsv": + isBtagJetsLoose = (goodJets.btagDeepB > btagwpl) + isBtagJetsMedium = (goodJets.btagDeepB > btagwpm) + + isNotBtagJetsLoose = np.invert(isBtagJetsLoose) + nbtagsl = ak.num(goodJets[isBtagJetsLoose]) - if btagger == "btag": - isBtagJetsLoose = (goodJets.btagDeepFlavB > btagwpl) - isBtagJetsMedium = (goodJets.btagDeepFlavB > btagwpm) - if btagger == "btagcsv": - isBtagJetsLoose = (goodJets.btagDeepB > btagwpl) - isBtagJetsMedium = (goodJets.btagDeepB > btagwpm) - - isNotBtagJetsLoose = np.invert(isBtagJetsLoose) - nbtagsl = ak.num(goodJets[isBtagJetsLoose]) - - isNotBtagJetsMedium = np.invert(isBtagJetsMedium) - nbtagsm = ak.num(goodJets[isBtagJetsMedium]) - - #################### Add variables into event object so that they persist #################### - - # Put njets and l_fo_conept_sorted into events - events["njets"] = njets - events["l_wwz_t"] = l_wwz_t - - es_ec.add4lmask_wwz(events, year, isData, histAxisName) + isNotBtagJetsMedium = np.invert(isBtagJetsMedium) + nbtagsm = ak.num(goodJets[isBtagJetsMedium]) - ######### Apply SFs ######### + #################### Add variables into event object so that they persist #################### - if not isData: - weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) - weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) + # Put njets and l_fo_conept_sorted into events + events["njets"] = njets + events["l_wwz_t"] = l_wwz_t - ### OLD implimentation from TOP-22-006 - ## Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods - #bJetSF = cor_ec.GetBTagSF(goodJets, year, 'LOOSE') - ###bJetEff = cor_ec.GetBtagEff(goodJets, year, 'loose') # Need for now - #bJetEff_data = bJetEff*bJetSF - #pMC = ak.prod(bJetEff[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff[isNotBtagJetsLoose]), axis=-1) - #pMC = ak.where(pMC==0,1,pMC) # removeing zeroes from denominator... - #pData = ak.prod(bJetEff_data[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff_data[isNotBtagJetsLoose]), axis=-1) - #weights_obj_base_for_kinematic_syst.add("btagSF", pData/pMC) + es_ec.add4lmask_wwz(events, year, isData, histAxisName) - ### Evaluate btag weights ### - jets_light = goodJets[goodJets.hadronFlavour==0] - jets_bc = goodJets[goodJets.hadronFlavour>0] - - # Workaround to use UL16APV SFs for UL16 for light jets - year_light = year - if year == "2016": year_light = "2016APV" - btag_sf_light = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl","central") - btag_sf_bc = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb","central") - - #btag_eff_light = bJetEff[goodJets.hadronFlavour==0] # OLD eff, Will replace with our new eff - #btag_eff_bc = bJetEff[goodJets.hadronFlavour>0] # OLD eff, Will replace with our new eff - btag_eff_light = cor_ec.btag_eff_eval(jets_light,"L",year) - btag_eff_bc = cor_ec.btag_eff_eval(jets_bc,"L",year) - - wgt_light = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light, jets_light.btagDeepFlavB>btagwpl) - wgt_bc = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc, jets_bc.btagDeepFlavB>btagwpl) - - weights_obj_base_for_kinematic_syst.add("btagSF", wgt_light*wgt_bc) - - - ######### Masks we need for the selection ########## - - # Pass trigger mask - pass_trg = es_tc.trg_pass_no_overlap(events,isData,dataset,str(year),dataset_dict=es_ec.dataset_dict,exclude_dict=es_ec.exclude_dict) - pass_trg = (pass_trg & es_ec.trg_matching(events,year)) - - # b jet masks - bmask_atleast1med_atleast2loose = ((nbtagsm>=1)&(nbtagsl>=2)) # Used for 2lss and 4l - bmask_exactly0loose = (nbtagsl==0) # Used for 4l WWZ SR - bmask_exactly0med = (nbtagsm==0) # Used for 3l CR and 2los Z CR - bmask_exactly1med = (nbtagsm==1) # Used for 3l SR and 2lss CR - bmask_exactly2med = (nbtagsm==2) # Used for CRtt - bmask_atleast2med = (nbtagsm>=2) # Used for 3l SR - bmask_atmost2med = (nbtagsm< 3) # Used to make 2lss mutually exclusive from tttt enriched - bmask_atleast3med = (nbtagsm>=3) # Used for tttt enriched - bmask_atleast1med = (nbtagsm>=1) - bmask_atleast1loose = (nbtagsl>=1) - bmask_atleast2loose = (nbtagsl>=2) - - - ######### WWZ event selection stuff ######### - - # Get some preliminary things we'll need - es_ec.attach_wwz_preselection_mask(events,l_wwz_t_padded[:,0:4]) # Attach preselection sf and of flags to the events - leps_from_z_candidate_ptordered, leps_not_z_candidate_ptordered = es_ec.get_wwz_candidates(l_wwz_t_padded[:,0:4]) # Get ahold of the leptons from the Z and from the W - - w_lep0 = leps_not_z_candidate_ptordered[:,0] - w_lep1 = leps_not_z_candidate_ptordered[:,1] - mll_wl0_wl1 = (w_lep0 + w_lep1).mass - - # Make masks for the SF regions - w_candidates_mll_far_from_z = ak.fill_none(abs(mll_wl0_wl1 - get_ec_param("zmass")) > 10.0,False) # Will enforce this for SF in the PackedSelection - ptl4 = (l0+l1+l2+l3).pt - sf_A = ak.fill_none(met.pt >= 120.0,False) # This should never be None, but just keep syntax same as other categories - sf_B = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 70.0),False) - sf_C = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 40.0) & (ptl4 < 70.0),False) - - # Make masks for the OF regions - of_1 = ak.fill_none((mll_wl0_wl1 >= 0.0) & (mll_wl0_wl1 < 40.0),False) - of_2 = ak.fill_none((mll_wl0_wl1 >= 40.0) & (mll_wl0_wl1 < 60.0),False) - of_3 = ak.fill_none((mll_wl0_wl1 >= 60.0) & (mll_wl0_wl1 < 100.0),False) - of_4 = ak.fill_none((mll_wl0_wl1 >= 100.0),False) - - # Mask for mt2 cut - mt2_val = es_ec.get_mt2(w_lep0,w_lep1,met) - mt2_mask = ak.fill_none(mt2_val>25.0,False) - - - - ######### Store boolean masks with PackedSelection ########## - - - selections = PackedSelection(dtype='uint64') - - # Lumi mask (for data) - selections.add("is_good_lumi",lumi_mask) - - zeroj = (njets==0) - - # For WWZ selection - selections.add("sr_4l_sf_A", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_A)) - selections.add("sr_4l_sf_B", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_B)) - selections.add("sr_4l_sf_C", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_C)) - selections.add("sr_4l_of_1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_1 & mt2_mask)) - selections.add("sr_4l_of_2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_2 & mt2_mask)) - selections.add("sr_4l_of_3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_3 & mt2_mask)) - selections.add("sr_4l_of_4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_4)) - - selections.add("all_events", (events.is4lWWZ | (~events.is4lWWZ))) # All events.. this logic is a bit roundabout to just get an array of True - selections.add("4l_presel", (events.is4lWWZ)) # This matches the VVV looper selection (object selection and event selection) - - selections.add("sr_4l_sf_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 65.0))) - selections.add("sr_4l_of_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of)) - - # CRs - ww_ee = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 11)) - ww_mm = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 13)) - ww_em = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 13)) - ww_me = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 11)) - selections.add("cr_4l_btag_of", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_of)) - selections.add("cr_4l_btag_sf", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf)) - selections.add("cr_4l_btag_sf_offZ", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z)) - selections.add("cr_4l_btag_sf_offZ_met80", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 80.0))) - selections.add("cr_4l_sf", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & (~w_candidates_mll_far_from_z))) - - cat_dict = { - "lep_chan_lst" : [ - "sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", - "sr_4l_sf_presel", "sr_4l_of_presel", - "all_events","4l_presel", - "cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80", - ], - } - - ######### Get variables ######### - - l0pt = l0.pt - j0pt = ak.flatten(j0.pt) # Flatten to go from [[j0pt],[j0pt],...] -> [j0pt,j0pt,...] - mll_01 = (l0+l1).mass - mllll = (l0+l1+l2+l3).mass - scalarptsum_lep = l0.pt + l1.pt + l2.pt + l3.pt - scalarptsum_lepmet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt - scalarptsum_lepmetjet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + ak.sum(goodJets.pt,axis=-1) - - # Get lep from Z - z_lep0 = leps_from_z_candidate_ptordered[:,0] - z_lep1 = leps_from_z_candidate_ptordered[:,1] - - mll_zl0_zl1 = (z_lep0 + z_lep1).mass - - pt_zl0_zl1 = (z_lep0 + z_lep1).pt - pt_wl0_wl1 = (w_lep0 + w_lep1).pt - - dr_zl0_zl1 = z_lep0.delta_r(z_lep1) - dr_wl0_wl1 = w_lep0.delta_r(w_lep1) - dr_wleps_zleps = (w_lep0 + w_lep1).delta_r(z_lep0 + z_lep1) - - absdphi_zl0_zl1 = abs(z_lep0.delta_phi(z_lep1)) - absdphi_wl0_wl1 = abs(w_lep0.delta_phi(w_lep1)) - absdphi_z_ww = abs((z_lep0 + z_lep1).delta_phi(w_lep0 + w_lep1 + met)) - dphi_4l_met = (z_lep0 + z_lep1 + w_lep0 + w_lep1).delta_phi(met) - dphi_wleps_met = (w_lep0 + w_lep1).delta_phi(met) - dphi_zleps_met = (z_lep0 + z_lep1).delta_phi(met) - - # lb pairs (i.e. always one lep, one bjet) - bjets = goodJets[isBtagJetsLoose] - #lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets}) - #mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) - #mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) - - # Get BDT values - bdt_vars = [ - ak.fill_none(mll_wl0_wl1,-9999), - ak.fill_none(dphi_4l_met,-9999), - ak.fill_none(dphi_zleps_met,-9999), - ak.fill_none(dphi_wleps_met,-9999), - ak.fill_none(dr_wl0_wl1,-9999), - ak.fill_none(dr_zl0_zl1,-9999), - ak.fill_none(dr_wleps_zleps,-9999), - ak.fill_none(met.pt,-9999), - ak.fill_none(mt2_val,-9999), - ak.fill_none(ptl4,-9999), - ak.fill_none(scalarptsum_lepmet,-9999), - ak.fill_none(scalarptsum_lepmetjet,-9999), - ak.fill_none(z_lep0.pt,-9999), - ak.fill_none(z_lep1.pt,-9999), - ak.fill_none(w_lep0.pt,-9999), - ak.fill_none(w_lep1.pt,-9999), - ] - - #bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) - #bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) - #bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) - #bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) - bdt_of_wwz_raw = 0 # TODO: Update xgb for coffea23 - bdt_sf_wwz_raw = 0 # TODO: Update xgb for coffea23 - bdt_of_zh_raw = 0 # TODO: Update xgb for coffea23 - bdt_sf_zh_raw = 0 # TODO: Update xgb for coffea23 - # Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html - bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0 - bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0 - bdt_of_zh = (2.0*((1.0+math.e**(-2*bdt_of_zh_raw))**(-1))) - 1.0 - bdt_sf_zh = (2.0*((1.0+math.e**(-2*bdt_sf_zh_raw))**(-1))) - 1.0 - - - ######### Fill histos ######### - - hout = {} - - dense_variables_dict = { - "mt2" : mt2_val, - "met" : met.pt, - "metphi" : met.phi, - "ptl4" : ptl4, - "scalarptsum_lep" : scalarptsum_lep, - "scalarptsum_lepmet" : scalarptsum_lepmet, - "scalarptsum_lepmetjet" : scalarptsum_lepmetjet, - "mll_01" : mll_01, - "mllll" : mllll, - "l0pt" : l0pt, - "j0pt" : j0pt, - - "z_lep0_pt" : z_lep0.pt, - "z_lep1_pt" : z_lep1.pt, - "w_lep0_pt" : w_lep0.pt, - "w_lep1_pt" : w_lep1.pt, - "z_lep0_eta" : z_lep0.eta, - "z_lep1_eta" : z_lep1.eta, - "w_lep0_eta" : w_lep0.eta, - "w_lep1_eta" : w_lep1.eta, - "z_lep0_phi" : z_lep0.phi, - "z_lep1_phi" : z_lep1.phi, - "w_lep0_phi" : w_lep0.phi, - "w_lep1_phi" : w_lep1.phi, - - "mll_wl0_wl1" : mll_wl0_wl1, - "mll_zl0_zl1" : mll_zl0_zl1, - - "pt_zl0_zl1" : pt_zl0_zl1, - "pt_wl0_wl1" : pt_wl0_wl1, - "dr_zl0_zl1" : dr_zl0_zl1, - "dr_wl0_wl1" : dr_wl0_wl1, - "dr_wleps_zleps" : dr_wleps_zleps, - "absdphi_zl0_zl1" : absdphi_zl0_zl1, - "absdphi_wl0_wl1" : absdphi_wl0_wl1, - "absdphi_z_ww" : absdphi_z_ww, - "dphi_4l_met" : dphi_4l_met, - "dphi_zleps_met" : dphi_zleps_met, - "dphi_wleps_met" : dphi_wleps_met, - - "nleps" : nleps, - "njets" : njets, - "nbtagsl" : nbtagsl, - "nbtagsm" : nbtagsm, - - "nleps_counts" : nleps, - "njets_counts" : njets, - "nbtagsl_counts" : nbtagsl, - - "absdphi_min_afas" : absdphi_min_afas, - "absdphi_min_afos" : absdphi_min_afos, - "absdphi_min_sfos" : absdphi_min_sfos, - "mll_min_afas" : mll_min_afas, - "mll_min_afos" : mll_min_afos, - "mll_min_sfos" : mll_min_sfos, - - #"mlb_min" : mlb_min, - #"mlb_max" : mlb_max, - - "bdt_of_wwz_raw": bdt_of_wwz_raw, - "bdt_sf_wwz_raw": bdt_sf_wwz_raw, - "bdt_of_zh_raw" : bdt_of_zh_raw, - "bdt_sf_zh_raw" : bdt_sf_zh_raw, - "bdt_of_wwz": bdt_of_wwz, - "bdt_sf_wwz": bdt_sf_wwz, - "bdt_of_zh" : bdt_of_zh, - "bdt_sf_zh" : bdt_sf_zh, - } - - # List the hists that are only defined for some categories - analysis_cats = ["sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4"] - exclude_var_dict = { - "mt2" : ["all_events"], - "ptl4" : ["all_events"], - "j0pt" : ["all_events", "4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, - "l0pt" : ["all_events"], - "mll_01" : ["all_events"], - "mllll" : ["all_events"], - "scalarptsum_lep" : ["all_events"], - "scalarptsum_lepmet" : ["all_events"], - "scalarptsum_lepmetjet" : ["all_events"], - "w_lep0_pt" : ["all_events"], - "w_lep1_pt" : ["all_events"], - "z_lep0_pt" : ["all_events"], - "z_lep1_pt" : ["all_events"], - "w_lep0_eta" : ["all_events"], - "w_lep1_eta" : ["all_events"], - "z_lep0_eta" : ["all_events"], - "z_lep1_eta" : ["all_events"], - "w_lep0_phi" : ["all_events"], - "w_lep1_phi" : ["all_events"], - "z_lep0_phi" : ["all_events"], - "z_lep1_phi" : ["all_events"], - "mll_wl0_wl1" : ["all_events"], - "mll_zl0_zl1" : ["all_events"], - - "pt_zl0_zl1" : ["all_events"], - "pt_wl0_wl1" : ["all_events"], - "dr_zl0_zl1" : ["all_events"], - "dr_wl0_wl1" : ["all_events"], - "dr_wleps_zleps" : ["all_events"], - "absdphi_zl0_zl1" : ["all_events"], - "absdphi_wl0_wl1" : ["all_events"], - "absdphi_z_ww" : ["all_events"], - "dphi_4l_met" : ["all_events"], - "dphi_zleps_met" : ["all_events"], - "dphi_wleps_met" : ["all_events"], - - "absdphi_min_afas" : ["all_events"], - "absdphi_min_afos" : ["all_events"], - "absdphi_min_sfos" : ["all_events"], - "mll_min_afas" : ["all_events"], - "mll_min_afos" : ["all_events"], - "mll_min_sfos" : ["all_events"], - - "mlb_min" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, - "mlb_max" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, - - "bdt_of_wwz_raw": ["all_events"], - "bdt_sf_wwz_raw": ["all_events"], - "bdt_of_zh_raw" : ["all_events"], - "bdt_sf_zh_raw" : ["all_events"], - "bdt_of_wwz": ["all_events"], - "bdt_sf_wwz": ["all_events"], - "bdt_of_zh" : ["all_events"], - "bdt_sf_zh" : ["all_events"], - } - - - # Loop over the hists we want to fill - for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): - #print("\ndense_axis_name,vals",dense_axis_name) - #print("dense_axis_name,vals",dense_axis_vals) - if "njets" not in dense_axis_name: continue - - # Create the hist for this dense axis variable - hout[dense_axis_name] = hda.Hist( - hist.axis.StrCategory([], growth=True, name="process", label="process"), - hist.axis.StrCategory([], growth=True, name="category", label="category"), - self._dense_axes_dict[dense_axis_name], - storage="weight", # Keeps track of sumw2 - name="Counts", - ) - - # Decide if we are filling this hist with weight or raw event counts - if dense_axis_name.endswith("_counts"): weights = events.nom - #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) - else: weights = weights_obj_base_for_kinematic_syst.weight(None) - - # Loop over categories - for sr_cat in cat_dict["lep_chan_lst"]: - - # Skip filling if this variable is not relevant for this selection - if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue - - # Make the cuts mask - cuts_lst = [sr_cat] - if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data - all_cuts_mask = selections.all(*cuts_lst) - - #run = events.run[all_cuts_mask] - #luminosityBlock = events.luminosityBlock[all_cuts_mask] - #event = events.event[all_cuts_mask] - #w = weights[all_cuts_mask] - #if dense_axis_name == "njets": - # print("STARTPRINT") - # for i,j in enumerate(w): - # out_str = f"PRINTTAG {i} {dense_axis_name} {year} {sr_cat} {event[i]} {run[i]} {luminosityBlock[i]} {w[i]}" - # print(out_str,file=sys.stderr,flush=True) - # print("ENDPRINT") - # print("ENDPRINT") - #print("\ndense_axis_name",dense_axis_name) - #print("sr_cat",sr_cat) - #print("dense_axis_vals[all_cuts_mask]",dense_axis_vals[all_cuts_mask]) - #print("this") - - # Fill the histos - axes_fill_info_dict = { - dense_axis_name : dense_axis_vals[all_cuts_mask], - "weight" : weights[all_cuts_mask], - "process" : histAxisName, - "category" : sr_cat, - #"systematic" : "nominal", - } - hout[dense_axis_name].fill(**axes_fill_info_dict) + ######### Apply SFs ######### + + if not isData: + weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) + weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) + + ### OLD implimentation from TOP-22-006 + ## Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods + #bJetSF = cor_ec.GetBTagSF(goodJets, year, 'LOOSE') + ###bJetEff = cor_ec.GetBtagEff(goodJets, year, 'loose') # Need for now + #bJetEff_data = bJetEff*bJetSF + #pMC = ak.prod(bJetEff[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff[isNotBtagJetsLoose]), axis=-1) + #pMC = ak.where(pMC==0,1,pMC) # removeing zeroes from denominator... + #pData = ak.prod(bJetEff_data[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff_data[isNotBtagJetsLoose]), axis=-1) + #weights_obj_base_for_kinematic_syst.add("btagSF", pData/pMC) + + ### Evaluate btag weights ### + jets_light = goodJets[goodJets.hadronFlavour==0] + jets_bc = goodJets[goodJets.hadronFlavour>0] + + # Workaround to use UL16APV SFs for UL16 for light jets + year_light = year + if year == "2016": year_light = "2016APV" + btag_sf_light = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl","central") + btag_sf_bc = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb","central") + + #btag_eff_light = bJetEff[goodJets.hadronFlavour==0] # OLD eff, Will replace with our new eff + #btag_eff_bc = bJetEff[goodJets.hadronFlavour>0] # OLD eff, Will replace with our new eff + btag_eff_light = cor_ec.btag_eff_eval(jets_light,"L",year) + btag_eff_bc = cor_ec.btag_eff_eval(jets_bc,"L",year) + + wgt_light = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light, jets_light.btagDeepFlavB>btagwpl) + wgt_bc = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc, jets_bc.btagDeepFlavB>btagwpl) + + weights_obj_base_for_kinematic_syst.add("btagSF", wgt_light*wgt_bc) + + + ######### Masks we need for the selection ########## + + # Pass trigger mask + pass_trg = es_tc.trg_pass_no_overlap(events,isData,dataset,str(year),dataset_dict=es_ec.dataset_dict,exclude_dict=es_ec.exclude_dict) + pass_trg = (pass_trg & es_ec.trg_matching(events,year)) + + # b jet masks + bmask_atleast1med_atleast2loose = ((nbtagsm>=1)&(nbtagsl>=2)) # Used for 2lss and 4l + bmask_exactly0loose = (nbtagsl==0) # Used for 4l WWZ SR + bmask_exactly0med = (nbtagsm==0) # Used for 3l CR and 2los Z CR + bmask_exactly1med = (nbtagsm==1) # Used for 3l SR and 2lss CR + bmask_exactly2med = (nbtagsm==2) # Used for CRtt + bmask_atleast2med = (nbtagsm>=2) # Used for 3l SR + bmask_atmost2med = (nbtagsm< 3) # Used to make 2lss mutually exclusive from tttt enriched + bmask_atleast3med = (nbtagsm>=3) # Used for tttt enriched + bmask_atleast1med = (nbtagsm>=1) + bmask_atleast1loose = (nbtagsl>=1) + bmask_atleast2loose = (nbtagsl>=2) + + + ######### WWZ event selection stuff ######### + + # Get some preliminary things we'll need + es_ec.attach_wwz_preselection_mask(events,l_wwz_t_padded[:,0:4]) # Attach preselection sf and of flags to the events + leps_from_z_candidate_ptordered, leps_not_z_candidate_ptordered = es_ec.get_wwz_candidates(l_wwz_t_padded[:,0:4]) # Get ahold of the leptons from the Z and from the W + + w_lep0 = leps_not_z_candidate_ptordered[:,0] + w_lep1 = leps_not_z_candidate_ptordered[:,1] + mll_wl0_wl1 = (w_lep0 + w_lep1).mass + + # Make masks for the SF regions + w_candidates_mll_far_from_z = ak.fill_none(abs(mll_wl0_wl1 - get_ec_param("zmass")) > 10.0,False) # Will enforce this for SF in the PackedSelection + ptl4 = (l0+l1+l2+l3).pt + sf_A = ak.fill_none(met.pt >= 120.0,False) # This should never be None, but just keep syntax same as other categories + sf_B = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 70.0),False) + sf_C = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 40.0) & (ptl4 < 70.0),False) + + # Make masks for the OF regions + of_1 = ak.fill_none((mll_wl0_wl1 >= 0.0) & (mll_wl0_wl1 < 40.0),False) + of_2 = ak.fill_none((mll_wl0_wl1 >= 40.0) & (mll_wl0_wl1 < 60.0),False) + of_3 = ak.fill_none((mll_wl0_wl1 >= 60.0) & (mll_wl0_wl1 < 100.0),False) + of_4 = ak.fill_none((mll_wl0_wl1 >= 100.0),False) + + # Mask for mt2 cut + mt2_val = es_ec.get_mt2(w_lep0,w_lep1,met) + mt2_mask = ak.fill_none(mt2_val>25.0,False) + + + + ######### Store boolean masks with PackedSelection ########## + + + selections = PackedSelection(dtype='uint64') + + # Lumi mask (for data) + selections.add("is_good_lumi",lumi_mask) + + zeroj = (njets==0) + + # For WWZ selection + selections.add("sr_4l_sf_A", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_A)) + selections.add("sr_4l_sf_B", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_B)) + selections.add("sr_4l_sf_C", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_C)) + selections.add("sr_4l_of_1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_1 & mt2_mask)) + selections.add("sr_4l_of_2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_2 & mt2_mask)) + selections.add("sr_4l_of_3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_3 & mt2_mask)) + selections.add("sr_4l_of_4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_4)) + + selections.add("all_events", (events.is4lWWZ | (~events.is4lWWZ))) # All events.. this logic is a bit roundabout to just get an array of True + selections.add("all_events_trg", (bmask_atleast1loose & (events.is4lWWZ | (~events.is4lWWZ)))) + selections.add("4l_presel", (events.is4lWWZ)) # This matches the VVV looper selection (object selection and event selection) + + selections.add("sr_4l_sf_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 65.0))) + selections.add("sr_4l_of_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of)) + + # CRs + ww_ee = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 11)) + ww_mm = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 13)) + ww_em = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 13)) + ww_me = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 11)) + selections.add("cr_4l_btag_of", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_of)) + selections.add("cr_4l_btag_sf", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf)) + selections.add("cr_4l_btag_sf_offZ", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z)) + selections.add("cr_4l_btag_sf_offZ_met80", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 80.0))) + selections.add("cr_4l_sf", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & (~w_candidates_mll_far_from_z))) + + cat_dict = { + "lep_chan_lst" : [ + #"all_events_trg", + "all_events","all_events_trg", + #"sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", + #"sr_4l_sf_presel", "sr_4l_of_presel", + #"all_events","4l_presel", + #"cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80", + ], + } + + ######### Get variables ######### + + l0pt = l0.pt + j0pt = ak.flatten(j0.pt) # Flatten to go from [[j0pt],[j0pt],...] -> [j0pt,j0pt,...] + mll_01 = (l0+l1).mass + mllll = (l0+l1+l2+l3).mass + scalarptsum_lep = l0.pt + l1.pt + l2.pt + l3.pt + scalarptsum_lepmet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + scalarptsum_lepmetjet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + ak.sum(goodJets.pt,axis=-1) + + # Get lep from Z + z_lep0 = leps_from_z_candidate_ptordered[:,0] + z_lep1 = leps_from_z_candidate_ptordered[:,1] + + mll_zl0_zl1 = (z_lep0 + z_lep1).mass + + pt_zl0_zl1 = (z_lep0 + z_lep1).pt + pt_wl0_wl1 = (w_lep0 + w_lep1).pt + + dr_zl0_zl1 = z_lep0.delta_r(z_lep1) + dr_wl0_wl1 = w_lep0.delta_r(w_lep1) + dr_wleps_zleps = (w_lep0 + w_lep1).delta_r(z_lep0 + z_lep1) + + absdphi_zl0_zl1 = abs(z_lep0.delta_phi(z_lep1)) + absdphi_wl0_wl1 = abs(w_lep0.delta_phi(w_lep1)) + absdphi_z_ww = abs((z_lep0 + z_lep1).delta_phi(w_lep0 + w_lep1 + met)) + dphi_4l_met = (z_lep0 + z_lep1 + w_lep0 + w_lep1).delta_phi(met) + dphi_wleps_met = (w_lep0 + w_lep1).delta_phi(met) + dphi_zleps_met = (z_lep0 + z_lep1).delta_phi(met) + + # lb pairs (i.e. always one lep, one bjet) + bjets = goodJets[isBtagJetsLoose] + #lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets}) + #mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) + #mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) + + # Get BDT values + bdt_vars = [ + ak.fill_none(mll_wl0_wl1,-9999), + ak.fill_none(dphi_4l_met,-9999), + ak.fill_none(dphi_zleps_met,-9999), + ak.fill_none(dphi_wleps_met,-9999), + ak.fill_none(dr_wl0_wl1,-9999), + ak.fill_none(dr_zl0_zl1,-9999), + ak.fill_none(dr_wleps_zleps,-9999), + ak.fill_none(met.pt,-9999), + ak.fill_none(mt2_val,-9999), + ak.fill_none(ptl4,-9999), + ak.fill_none(scalarptsum_lepmet,-9999), + ak.fill_none(scalarptsum_lepmetjet,-9999), + ak.fill_none(z_lep0.pt,-9999), + ak.fill_none(z_lep1.pt,-9999), + ak.fill_none(w_lep0.pt,-9999), + ak.fill_none(w_lep1.pt,-9999), + ] + + #bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) + #bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) + #bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) + #bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) + bdt_of_wwz_raw = 0 # TODO: Update xgb for coffea23 + bdt_sf_wwz_raw = 0 # TODO: Update xgb for coffea23 + bdt_of_zh_raw = 0 # TODO: Update xgb for coffea23 + bdt_sf_zh_raw = 0 # TODO: Update xgb for coffea23 + # Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html + bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0 + bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0 + bdt_of_zh = (2.0*((1.0+math.e**(-2*bdt_of_zh_raw))**(-1))) - 1.0 + bdt_sf_zh = (2.0*((1.0+math.e**(-2*bdt_sf_zh_raw))**(-1))) - 1.0 + + + ######### Fill histos ######### + + dense_variables_dict = { + "mt2" : mt2_val, + "met" : met.pt, + "metphi" : met.phi, + "ptl4" : ptl4, + "scalarptsum_lep" : scalarptsum_lep, + "scalarptsum_lepmet" : scalarptsum_lepmet, + "scalarptsum_lepmetjet" : scalarptsum_lepmetjet, + "mll_01" : mll_01, + "mllll" : mllll, + "l0pt" : l0pt, + "j0pt" : j0pt, + + "z_lep0_pt" : z_lep0.pt, + "z_lep1_pt" : z_lep1.pt, + "w_lep0_pt" : w_lep0.pt, + "w_lep1_pt" : w_lep1.pt, + "z_lep0_eta" : z_lep0.eta, + "z_lep1_eta" : z_lep1.eta, + "w_lep0_eta" : w_lep0.eta, + "w_lep1_eta" : w_lep1.eta, + "z_lep0_phi" : z_lep0.phi, + "z_lep1_phi" : z_lep1.phi, + "w_lep0_phi" : w_lep0.phi, + "w_lep1_phi" : w_lep1.phi, + + "mll_wl0_wl1" : mll_wl0_wl1, + "mll_zl0_zl1" : mll_zl0_zl1, + + "pt_zl0_zl1" : pt_zl0_zl1, + "pt_wl0_wl1" : pt_wl0_wl1, + "dr_zl0_zl1" : dr_zl0_zl1, + "dr_wl0_wl1" : dr_wl0_wl1, + "dr_wleps_zleps" : dr_wleps_zleps, + "absdphi_zl0_zl1" : absdphi_zl0_zl1, + "absdphi_wl0_wl1" : absdphi_wl0_wl1, + "absdphi_z_ww" : absdphi_z_ww, + "dphi_4l_met" : dphi_4l_met, + "dphi_zleps_met" : dphi_zleps_met, + "dphi_wleps_met" : dphi_wleps_met, + + "nleps" : nleps, + "njets" : njets, + "nbtagsl" : nbtagsl, + "nbtagsm" : nbtagsm, + + "nleps_counts" : nleps, + "njets_counts" : njets, + "nbtagsl_counts" : nbtagsl, + + "absdphi_min_afas" : absdphi_min_afas, + "absdphi_min_afos" : absdphi_min_afos, + "absdphi_min_sfos" : absdphi_min_sfos, + "mll_min_afas" : mll_min_afas, + "mll_min_afos" : mll_min_afos, + "mll_min_sfos" : mll_min_sfos, + + #"mlb_min" : mlb_min, + #"mlb_max" : mlb_max, + + "bdt_of_wwz_raw": bdt_of_wwz_raw, + "bdt_sf_wwz_raw": bdt_sf_wwz_raw, + "bdt_of_zh_raw" : bdt_of_zh_raw, + "bdt_sf_zh_raw" : bdt_sf_zh_raw, + "bdt_of_wwz": bdt_of_wwz, + "bdt_sf_wwz": bdt_sf_wwz, + "bdt_of_zh" : bdt_of_zh, + "bdt_sf_zh" : bdt_sf_zh, + } + + # List the hists that are only defined for some categories + analysis_cats = ["sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4"] + exclude_var_dict = { + "mt2" : ["all_events"], + "ptl4" : ["all_events"], + "j0pt" : ["all_events", "4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, + "l0pt" : ["all_events"], + "mll_01" : ["all_events"], + "mllll" : ["all_events"], + "scalarptsum_lep" : ["all_events"], + "scalarptsum_lepmet" : ["all_events"], + "scalarptsum_lepmetjet" : ["all_events"], + "w_lep0_pt" : ["all_events"], + "w_lep1_pt" : ["all_events"], + "z_lep0_pt" : ["all_events"], + "z_lep1_pt" : ["all_events"], + "w_lep0_eta" : ["all_events"], + "w_lep1_eta" : ["all_events"], + "z_lep0_eta" : ["all_events"], + "z_lep1_eta" : ["all_events"], + "w_lep0_phi" : ["all_events"], + "w_lep1_phi" : ["all_events"], + "z_lep0_phi" : ["all_events"], + "z_lep1_phi" : ["all_events"], + "mll_wl0_wl1" : ["all_events"], + "mll_zl0_zl1" : ["all_events"], + + "pt_zl0_zl1" : ["all_events"], + "pt_wl0_wl1" : ["all_events"], + "dr_zl0_zl1" : ["all_events"], + "dr_wl0_wl1" : ["all_events"], + "dr_wleps_zleps" : ["all_events"], + "absdphi_zl0_zl1" : ["all_events"], + "absdphi_wl0_wl1" : ["all_events"], + "absdphi_z_ww" : ["all_events"], + "dphi_4l_met" : ["all_events"], + "dphi_zleps_met" : ["all_events"], + "dphi_wleps_met" : ["all_events"], + + "absdphi_min_afas" : ["all_events"], + "absdphi_min_afos" : ["all_events"], + "absdphi_min_sfos" : ["all_events"], + "mll_min_afas" : ["all_events"], + "mll_min_afos" : ["all_events"], + "mll_min_sfos" : ["all_events"], + + "mlb_min" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, + "mlb_max" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, + + "bdt_of_wwz_raw": ["all_events"], + "bdt_sf_wwz_raw": ["all_events"], + "bdt_of_zh_raw" : ["all_events"], + "bdt_sf_zh_raw" : ["all_events"], + "bdt_of_wwz": ["all_events"], + "bdt_sf_wwz": ["all_events"], + "bdt_of_zh" : ["all_events"], + "bdt_sf_zh" : ["all_events"], + } + + + # Loop over the hists we want to fill + for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): + #print("\ndense_axis_name,vals",dense_axis_name) + #print("dense_axis_name,vals",dense_axis_vals) + if ("njets" not in dense_axis_name) and ("met" != dense_axis_name): continue + + # Create the hist for this dense axis variable + if dense_axis_name not in hout: + hout[dense_axis_name] = hda.Hist( + hist.axis.StrCategory([], growth=True, name="process", label="process"), + hist.axis.StrCategory([], growth=True, name="category", label="category"), + self._dense_axes_dict[dense_axis_name], + storage="weight", # Keeps track of sumw2 + name="Counts", + ) + + # Decide if we are filling this hist with weight or raw event counts + if dense_axis_name.endswith("_counts"): weights = events.nom + #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) + else: weights = weights_obj_base_for_kinematic_syst.weight(None) + + # Loop over categories + for sr_cat in cat_dict["lep_chan_lst"]: + + # Skip filling if this variable is not relevant for this selection + if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue + + # Make the cuts mask + cuts_lst = [sr_cat] + if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data + all_cuts_mask = selections.all(*cuts_lst) + + #run = events.run[all_cuts_mask] + #luminosityBlock = events.luminosityBlock[all_cuts_mask] + #event = events.event[all_cuts_mask] + #w = weights[all_cuts_mask] + #if dense_axis_name == "njets": + # print("STARTPRINT") + # for i,j in enumerate(w): + # out_str = f"PRINTTAG {i} {dense_axis_name} {year} {sr_cat} {event[i]} {run[i]} {luminosityBlock[i]} {w[i]}" + # print(out_str,file=sys.stderr,flush=True) + # print("ENDPRINT") + # print("ENDPRINT") + #print("\ndense_axis_name",dense_axis_name) + #print("sr_cat",sr_cat) + #print("dense_axis_vals[all_cuts_mask]",dense_axis_vals[all_cuts_mask]) + #print("this") + + # Fill the histos + axes_fill_info_dict = { + dense_axis_name : dense_axis_vals[all_cuts_mask], + "weight" : weights[all_cuts_mask], + "process" : histAxisName, + "category" : sr_cat, + #"systematic" : "nominal", + } + hout[dense_axis_name].fill(**axes_fill_info_dict) return hout From ce8d019f4383a14f68f6b4c16569c89c1a0f604e Mon Sep 17 00:00:00 2001 From: Kelci Date: Wed, 3 Jan 2024 15:45:23 -0500 Subject: [PATCH 05/48] Do not loop over datasets in processor --- analysis/wwz/wwz4l.py | 1315 +++++++++++++++++++++-------------------- 1 file changed, 658 insertions(+), 657 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index e04151d5..fddf267c 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -134,673 +134,674 @@ def __init__(self, samples, wc_names_lst=[], hist_lst=None, ecut_threshold=None, # Main function: run on a given dataset - def process(self, events_dict): + def process(self, events): # Loop over samples and fill histos - hout = {} - for events in events_dict.values(): - - # Dataset parameters - dataset = events.metadata["dataset"] - - # If we pass the root files instead of events object - #from coffea.nanoevents import NanoEventsFactory - #from coffea.nanoevents import NanoAODSchema - #events = NanoEventsFactory.from_root( - # {fpath: "/Events" for fpath in fpaths}, - # schemaclass=NanoAODSchema, - # metadata={"dataset": dataset}, - #).events() - - isData = self._samples[dataset]["isData"] - histAxisName = self._samples[dataset]["histAxisName"] - year = self._samples[dataset]["year"] - xsec = self._samples[dataset]["xsec"] - sow = self._samples[dataset]["nSumOfWeights"] - - # Get up down weights from input dict - if (self._do_systematics and not isData): - # Otherwise we have an NLO xsec, so for these systs we will have e.g. xsec_NLO*(N_pass_up/N_gen_up) - # Thus these systs should only affect acceptance and effeciency and shape - # The uncty on xsec comes from NLO and is applied as a rate uncty in the text datacard - sow_ISRUp = self._samples[dataset]["nSumOfWeights_ISRUp" ] - sow_ISRDown = self._samples[dataset]["nSumOfWeights_ISRDown" ] - sow_FSRUp = self._samples[dataset]["nSumOfWeights_FSRUp" ] - sow_FSRDown = self._samples[dataset]["nSumOfWeights_FSRDown" ] - sow_renormUp = self._samples[dataset]["nSumOfWeights_renormUp" ] - sow_renormDown = self._samples[dataset]["nSumOfWeights_renormDown" ] - sow_factUp = self._samples[dataset]["nSumOfWeights_factUp" ] - sow_factDown = self._samples[dataset]["nSumOfWeights_factDown" ] - sow_renormfactUp = self._samples[dataset]["nSumOfWeights_renormfactUp" ] - sow_renormfactDown = self._samples[dataset]["nSumOfWeights_renormfactDown" ] - else: - sow_ISRUp = -1 - sow_ISRDown = -1 - sow_FSRUp = -1 - sow_FSRDown = -1 - sow_renormUp = -1 - sow_renormDown = -1 - sow_factUp = -1 - sow_factDown = -1 - sow_renormfactUp = -1 - sow_renormfactDown = -1 - - datasets = ["SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"] - for d in datasets: - if d in dataset: dataset = dataset.split('_')[0] - - # Initialize objects - #met = events.MET - met = events.PuppiMET - ele = events.Electron - mu = events.Muon - tau = events.Tau - jets = events.Jet - - # 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(met.pt) - - # Get the lumi mask for data - if year == "2016" or year == "2016APV": - golden_json_path = topcoffea_path("data/goldenJsons/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt") - elif year == "2017": - golden_json_path = topcoffea_path("data/goldenJsons/Cert_294927-306462_13TeV_UL2017_Collisions17_GoldenJSON.txt") + #hout = {} + #for events in events_dict.values(): + + # Dataset parameters + dataset = events.metadata["dataset"] + + # If we pass the root files instead of events object + #from coffea.nanoevents import NanoEventsFactory + #from coffea.nanoevents import NanoAODSchema + #events = NanoEventsFactory.from_root( + # {fpath: "/Events" for fpath in fpaths}, + # schemaclass=NanoAODSchema, + # metadata={"dataset": dataset}, + #).events() + + isData = self._samples[dataset]["isData"] + histAxisName = self._samples[dataset]["histAxisName"] + year = self._samples[dataset]["year"] + xsec = self._samples[dataset]["xsec"] + sow = self._samples[dataset]["nSumOfWeights"] + + # Get up down weights from input dict + if (self._do_systematics and not isData): + # Otherwise we have an NLO xsec, so for these systs we will have e.g. xsec_NLO*(N_pass_up/N_gen_up) + # Thus these systs should only affect acceptance and effeciency and shape + # The uncty on xsec comes from NLO and is applied as a rate uncty in the text datacard + sow_ISRUp = self._samples[dataset]["nSumOfWeights_ISRUp" ] + sow_ISRDown = self._samples[dataset]["nSumOfWeights_ISRDown" ] + sow_FSRUp = self._samples[dataset]["nSumOfWeights_FSRUp" ] + sow_FSRDown = self._samples[dataset]["nSumOfWeights_FSRDown" ] + sow_renormUp = self._samples[dataset]["nSumOfWeights_renormUp" ] + sow_renormDown = self._samples[dataset]["nSumOfWeights_renormDown" ] + sow_factUp = self._samples[dataset]["nSumOfWeights_factUp" ] + sow_factDown = self._samples[dataset]["nSumOfWeights_factDown" ] + sow_renormfactUp = self._samples[dataset]["nSumOfWeights_renormfactUp" ] + sow_renormfactDown = self._samples[dataset]["nSumOfWeights_renormfactDown" ] + else: + sow_ISRUp = -1 + sow_ISRDown = -1 + sow_FSRUp = -1 + sow_FSRDown = -1 + sow_renormUp = -1 + sow_renormDown = -1 + sow_factUp = -1 + sow_factDown = -1 + sow_renormfactUp = -1 + sow_renormfactDown = -1 + + datasets = ["SingleMuon", "SingleElectron", "EGamma", "MuonEG", "DoubleMuon", "DoubleElectron", "DoubleEG"] + for d in datasets: + if d in dataset: dataset = dataset.split('_')[0] + + # Initialize objects + #met = events.MET + met = events.PuppiMET + ele = events.Electron + mu = events.Muon + tau = events.Tau + jets = events.Jet + + # 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(met.pt) + + # Get the lumi mask for data + if year == "2016" or year == "2016APV": + golden_json_path = topcoffea_path("data/goldenJsons/Cert_271036-284044_13TeV_Legacy2016_Collisions16_JSON.txt") + elif year == "2017": + 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") + else: + raise ValueError(f"Error: Unknown year \"{year}\".") + lumi_mask = LumiMask(golden_json_path)(events.run,events.luminosityBlock) + + ######################################################### + ### START top22006 lep obj sel ### + #import topeft.modules.object_selection as te_os + #from topeft.modules.corrections import AttachMuonSF, AttachElectronSF + + #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["btagDeepFlavB"] = ak.fill_none(ele.matched_jet.btagDeepFlavB, -99) + #mu["btagDeepFlavB"] = ak.fill_none(mu.matched_jet.btagDeepFlavB, -99) + #if not isData: + # ele["gen_pdgId"] = ak.fill_none(ele.matched_gen.pdgId, 0) + # mu["gen_pdgId"] = ak.fill_none(mu.matched_gen.pdgId, 0) + + #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) + + #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) + + #m_loose = mu[mu.isPres & mu.isLooseM] + #e_loose = ele[ele.isPres & ele.isLooseE] + #l_loose = ak.with_name(ak.concatenate([e_loose, m_loose], axis=1), 'PtEtaPhiMCandidate') + + ## Build FO collection + #m_fo = mu[mu.isPres & mu.isLooseM & mu.isFO] + #e_fo = ele[ele.isPres & ele.isLooseE & ele.isFO] + + ## Attach the lepton SFs to the electron and muons collections + #AttachElectronSF(e_fo,year=year) + #AttachMuonSF(m_fo,year=year) + + ## Attach per lepton fake rates + #m_fo['convVeto'] = ak.ones_like(m_fo.charge) + #m_fo['lostHits'] = ak.zeros_like(m_fo.charge) + #l_fo = ak.with_name(ak.concatenate([e_fo, m_fo], axis=1), 'PtEtaPhiMCandidate') + #l_fo_conept_sorted = l_fo[ak.argsort(l_fo.conept, axis=-1,ascending=False)] + + #l_wwz_t = l_fo_conept_sorted[l_fo_conept_sorted.isTightLep] + #l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt + + ### END top22006 obj sel ### + ######################################################## + + ################### Lepton selection #################### + + #''' + # Do the object selection for the WWZ eleectrons + ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True) + #ele["topmva"] = os_ec.get_topmva_score_ele(events, year) + ele["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 + ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask) + + # Do the object selection for the WWZ muons + mu_presl_mask = os_ec.is_presel_wwz_mu(mu) + #mu["topmva"] = os_ec.get_topmva_score_mu(events, year) + mu["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 + mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask) + + # Get tight leptons for WWZ selection + ele_wwz_t = ele[ele.is_tight_lep_for_wwz] + mu_wwz_t = mu[mu.is_tight_lep_for_wwz] + + # Attach the lepton SFs to the electron and muons collections + cor_ec.AttachElectronSF(ele_wwz_t,year=year) + cor_ec.AttachMuonSF(mu_wwz_t,year=year) + + l_wwz_t = ak.with_name(ak.concatenate([ele_wwz_t,mu_wwz_t],axis=1),'PtEtaPhiMCandidate') + l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt + + #''' + + # For WWZ: Compute pair invariant masses + llpairs_wwz = ak.combinations(l_wwz_t, 2, fields=["l0","l1"]) + os_pairs_mask = (llpairs_wwz.l0.pdgId*llpairs_wwz.l1.pdgId < 0) # Maks for opposite-sign pairs + sfos_pairs_mask = (llpairs_wwz.l0.pdgId == -llpairs_wwz.l1.pdgId) # Mask for same-flavor-opposite-sign pairs + ll_absdphi_pairs = abs(llpairs_wwz.l0.delta_phi(llpairs_wwz.l1)) + ll_mass_pairs = (llpairs_wwz.l0+llpairs_wwz.l1).mass # The mll for each ll pair + absdphi_min_afas = ak.min(ll_absdphi_pairs,axis=-1) + absdphi_min_afos = ak.min(ll_absdphi_pairs[os_pairs_mask],axis=-1) + absdphi_min_sfos = ak.min(ll_absdphi_pairs[sfos_pairs_mask],axis=-1) + mll_min_afas = ak.min(ll_mass_pairs,axis=-1) + mll_min_afos = ak.min(ll_mass_pairs[os_pairs_mask],axis=-1) + mll_min_sfos = ak.min(ll_mass_pairs[sfos_pairs_mask],axis=-1) + events["min_mll_afos"] = mll_min_afos # Attach this one to the event info since we need it for selection + + # For WWZ + l_wwz_t_padded = ak.pad_none(l_wwz_t, 4) + l0 = l_wwz_t_padded[:,0] + l1 = l_wwz_t_padded[:,1] + l2 = l_wwz_t_padded[:,2] + l3 = l_wwz_t_padded[:,3] + + nleps = ak.num(l_wwz_t) + + + ######### Systematics ########### + + + # These weights can go outside of the outside sys loop since they do not depend on pt of mu or jets + # We only calculate these values if not isData + # Note: add() will generally modify up/down weights, so if these are needed for any reason after this point, we should instead pass copies to add() + # 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(None,storeIndividual=True) + if not isData: + genw = events["genWeight"] + + # If it's an EFT sample, just take SM piece + sm_wgt = 1.0 + eft_coeffs = ak.to_numpy(events["EFTfitCoefficients"]) if hasattr(events, "EFTfitCoefficients") else None + if eft_coeffs is not None: + sm_wgt = eft_coeffs[:,0] + + # 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*sm_wgt) + + + # We do not have systematics yet + syst_var_list = ['nominal'] + + # Loop over the list of systematic variations we've constructed + for syst_var in syst_var_list: + # Make a copy of the base weights object, so that each time through the loop we do not double count systs + # In this loop over systs that impact kinematics, we will add to the weights objects the SFs that depend on the object kinematics + weights_obj_base_for_kinematic_syst = copy.deepcopy(weights_obj_base) + + + #################### Jets #################### + + # Jet cleaning, before any jet selection + ##vetos_tocleanjets = ak.with_name( ak.concatenate([tau, l_fo], axis=1), "PtEtaPhiMCandidate") + #vetos_tocleanjets = ak.with_name( l_wwz_t, "PtEtaPhiMCandidate") + #tmp = ak.cartesian([ak.local_index(jets.pt), vetos_tocleanjets.jetIdx], nested=True) + #cleanedJets = jets[~ak.any(tmp.slot0 == tmp.slot1, axis=-1)] # this line should go before *any selection*, otherwise lep.jetIdx is not aligned with the jet index + + # Clean with dr for now + cleanedJets = os_ec.get_cleaned_collection(l_wwz_t,jets) + + # Selecting jets and cleaning them + # NOTE: The jet id cut is commented for now in objects.py for the sync + jetptname = "pt_nom" if hasattr(cleanedJets, "pt_nom") else "pt" + cleanedJets["is_good"] = os_tc.is_tight_jet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, pt_cut=20., eta_cut=get_ec_param("wwz_eta_j_cut"), id_cut=get_ec_param("wwz_jet_id_cut")) + goodJets = cleanedJets[cleanedJets.is_good] + + # Count jets + njets = ak.num(goodJets) + ht = ak.sum(goodJets.pt,axis=-1) + j0 = goodJets[ak.argmax(goodJets.pt,axis=-1,keepdims=True)] + + + # Loose DeepJet WP + btagger = "btag" # For deep flavor WPs + #btagger = "btagcsv" # For deep CSV WPs + if year == "2017": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL17") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL17") elif year == "2018": - golden_json_path = topcoffea_path("data/goldenJsons/Cert_314472-325175_13TeV_Legacy2018_Collisions18_JSON.txt") + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL18") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL18") + elif year=="2016": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16") + elif year=="2016APV": + btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16APV") + btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16APV") else: raise ValueError(f"Error: Unknown year \"{year}\".") - lumi_mask = LumiMask(golden_json_path)(events.run,events.luminosityBlock) - - ######################################################### - ### START top22006 lep obj sel ### - #import topeft.modules.object_selection as te_os - #from topeft.modules.corrections import AttachMuonSF, AttachElectronSF - - #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["btagDeepFlavB"] = ak.fill_none(ele.matched_jet.btagDeepFlavB, -99) - #mu["btagDeepFlavB"] = ak.fill_none(mu.matched_jet.btagDeepFlavB, -99) - #if not isData: - # ele["gen_pdgId"] = ak.fill_none(ele.matched_gen.pdgId, 0) - # mu["gen_pdgId"] = ak.fill_none(mu.matched_gen.pdgId, 0) - - #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) - - #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) - - #m_loose = mu[mu.isPres & mu.isLooseM] - #e_loose = ele[ele.isPres & ele.isLooseE] - #l_loose = ak.with_name(ak.concatenate([e_loose, m_loose], axis=1), 'PtEtaPhiMCandidate') - - ## Build FO collection - #m_fo = mu[mu.isPres & mu.isLooseM & mu.isFO] - #e_fo = ele[ele.isPres & ele.isLooseE & ele.isFO] - - ## Attach the lepton SFs to the electron and muons collections - #AttachElectronSF(e_fo,year=year) - #AttachMuonSF(m_fo,year=year) - - ## Attach per lepton fake rates - #m_fo['convVeto'] = ak.ones_like(m_fo.charge) - #m_fo['lostHits'] = ak.zeros_like(m_fo.charge) - #l_fo = ak.with_name(ak.concatenate([e_fo, m_fo], axis=1), 'PtEtaPhiMCandidate') - #l_fo_conept_sorted = l_fo[ak.argsort(l_fo.conept, axis=-1,ascending=False)] - - #l_wwz_t = l_fo_conept_sorted[l_fo_conept_sorted.isTightLep] - #l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt - - ### END top22006 obj sel ### - ######################################################## - - ################### Lepton selection #################### - - #''' - # Do the object selection for the WWZ eleectrons - ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True) - #ele["topmva"] = os_ec.get_topmva_score_ele(events, year) - ele["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 - ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask) - - # Do the object selection for the WWZ muons - mu_presl_mask = os_ec.is_presel_wwz_mu(mu) - #mu["topmva"] = os_ec.get_topmva_score_mu(events, year) - mu["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 - mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask) - - # Get tight leptons for WWZ selection - ele_wwz_t = ele[ele.is_tight_lep_for_wwz] - mu_wwz_t = mu[mu.is_tight_lep_for_wwz] - - # Attach the lepton SFs to the electron and muons collections - cor_ec.AttachElectronSF(ele_wwz_t,year=year) - cor_ec.AttachMuonSF(mu_wwz_t,year=year) - - l_wwz_t = ak.with_name(ak.concatenate([ele_wwz_t,mu_wwz_t],axis=1),'PtEtaPhiMCandidate') - l_wwz_t = l_wwz_t[ak.argsort(l_wwz_t.pt, axis=-1,ascending=False)] # Sort by pt - - #''' - - # For WWZ: Compute pair invariant masses - llpairs_wwz = ak.combinations(l_wwz_t, 2, fields=["l0","l1"]) - os_pairs_mask = (llpairs_wwz.l0.pdgId*llpairs_wwz.l1.pdgId < 0) # Maks for opposite-sign pairs - sfos_pairs_mask = (llpairs_wwz.l0.pdgId == -llpairs_wwz.l1.pdgId) # Mask for same-flavor-opposite-sign pairs - ll_absdphi_pairs = abs(llpairs_wwz.l0.delta_phi(llpairs_wwz.l1)) - ll_mass_pairs = (llpairs_wwz.l0+llpairs_wwz.l1).mass # The mll for each ll pair - absdphi_min_afas = ak.min(ll_absdphi_pairs,axis=-1) - absdphi_min_afos = ak.min(ll_absdphi_pairs[os_pairs_mask],axis=-1) - absdphi_min_sfos = ak.min(ll_absdphi_pairs[sfos_pairs_mask],axis=-1) - mll_min_afas = ak.min(ll_mass_pairs,axis=-1) - mll_min_afos = ak.min(ll_mass_pairs[os_pairs_mask],axis=-1) - mll_min_sfos = ak.min(ll_mass_pairs[sfos_pairs_mask],axis=-1) - events["min_mll_afos"] = mll_min_afos # Attach this one to the event info since we need it for selection - - # For WWZ - l_wwz_t_padded = ak.pad_none(l_wwz_t, 4) - l0 = l_wwz_t_padded[:,0] - l1 = l_wwz_t_padded[:,1] - l2 = l_wwz_t_padded[:,2] - l3 = l_wwz_t_padded[:,3] - - nleps = ak.num(l_wwz_t) - - - ######### Systematics ########### - - - # These weights can go outside of the outside sys loop since they do not depend on pt of mu or jets - # We only calculate these values if not isData - # Note: add() will generally modify up/down weights, so if these are needed for any reason after this point, we should instead pass copies to add() - # 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(None,storeIndividual=True) - if not isData: - genw = events["genWeight"] - - # If it's an EFT sample, just take SM piece - sm_wgt = 1.0 - eft_coeffs = ak.to_numpy(events["EFTfitCoefficients"]) if hasattr(events, "EFTfitCoefficients") else None - if eft_coeffs is not None: - sm_wgt = eft_coeffs[:,0] - - # 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*sm_wgt) - - - # We do not have systematics yet - syst_var_list = ['nominal'] - - # Loop over the list of systematic variations we've constructed - for syst_var in syst_var_list: - # Make a copy of the base weights object, so that each time through the loop we do not double count systs - # In this loop over systs that impact kinematics, we will add to the weights objects the SFs that depend on the object kinematics - weights_obj_base_for_kinematic_syst = copy.deepcopy(weights_obj_base) - - - #################### Jets #################### - - # Jet cleaning, before any jet selection - ##vetos_tocleanjets = ak.with_name( ak.concatenate([tau, l_fo], axis=1), "PtEtaPhiMCandidate") - #vetos_tocleanjets = ak.with_name( l_wwz_t, "PtEtaPhiMCandidate") - #tmp = ak.cartesian([ak.local_index(jets.pt), vetos_tocleanjets.jetIdx], nested=True) - #cleanedJets = jets[~ak.any(tmp.slot0 == tmp.slot1, axis=-1)] # this line should go before *any selection*, otherwise lep.jetIdx is not aligned with the jet index - - # Clean with dr for now - cleanedJets = os_ec.get_cleaned_collection(l_wwz_t,jets) - - # Selecting jets and cleaning them - # NOTE: The jet id cut is commented for now in objects.py for the sync - jetptname = "pt_nom" if hasattr(cleanedJets, "pt_nom") else "pt" - cleanedJets["is_good"] = os_tc.is_tight_jet(getattr(cleanedJets, jetptname), cleanedJets.eta, cleanedJets.jetId, pt_cut=20., eta_cut=get_ec_param("wwz_eta_j_cut"), id_cut=get_ec_param("wwz_jet_id_cut")) - goodJets = cleanedJets[cleanedJets.is_good] - - # Count jets - njets = ak.num(goodJets) - ht = ak.sum(goodJets.pt,axis=-1) - j0 = goodJets[ak.argmax(goodJets.pt,axis=-1,keepdims=True)] - - - # Loose DeepJet WP - btagger = "btag" # For deep flavor WPs - #btagger = "btagcsv" # For deep CSV WPs - if year == "2017": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL17") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL17") - elif year == "2018": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL18") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL18") - elif year=="2016": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16") - elif year=="2016APV": - btagwpl = get_tc_param(f"{btagger}_wp_loose_UL16APV") - btagwpm = get_tc_param(f"{btagger}_wp_medium_UL16APV") - else: - raise ValueError(f"Error: Unknown year \"{year}\".") - - if btagger == "btag": - isBtagJetsLoose = (goodJets.btagDeepFlavB > btagwpl) - isBtagJetsMedium = (goodJets.btagDeepFlavB > btagwpm) - if btagger == "btagcsv": - isBtagJetsLoose = (goodJets.btagDeepB > btagwpl) - isBtagJetsMedium = (goodJets.btagDeepB > btagwpm) - - isNotBtagJetsLoose = np.invert(isBtagJetsLoose) - nbtagsl = ak.num(goodJets[isBtagJetsLoose]) - isNotBtagJetsMedium = np.invert(isBtagJetsMedium) - nbtagsm = ak.num(goodJets[isBtagJetsMedium]) + if btagger == "btag": + isBtagJetsLoose = (goodJets.btagDeepFlavB > btagwpl) + isBtagJetsMedium = (goodJets.btagDeepFlavB > btagwpm) + if btagger == "btagcsv": + isBtagJetsLoose = (goodJets.btagDeepB > btagwpl) + isBtagJetsMedium = (goodJets.btagDeepB > btagwpm) + + isNotBtagJetsLoose = np.invert(isBtagJetsLoose) + nbtagsl = ak.num(goodJets[isBtagJetsLoose]) - #################### Add variables into event object so that they persist #################### + isNotBtagJetsMedium = np.invert(isBtagJetsMedium) + nbtagsm = ak.num(goodJets[isBtagJetsMedium]) - # Put njets and l_fo_conept_sorted into events - events["njets"] = njets - events["l_wwz_t"] = l_wwz_t + #################### Add variables into event object so that they persist #################### + + # Put njets and l_fo_conept_sorted into events + events["njets"] = njets + events["l_wwz_t"] = l_wwz_t + + es_ec.add4lmask_wwz(events, year, isData, histAxisName) + + ######### Apply SFs ######### + + if not isData: + ##weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) + ##weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) - es_ec.add4lmask_wwz(events, year, isData, histAxisName) + ### OLD implimentation from TOP-22-006 + ## Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods + #bJetSF = cor_ec.GetBTagSF(goodJets, year, 'LOOSE') + ###bJetEff = cor_ec.GetBtagEff(goodJets, year, 'loose') # Need for now + #bJetEff_data = bJetEff*bJetSF + #pMC = ak.prod(bJetEff[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff[isNotBtagJetsLoose]), axis=-1) + #pMC = ak.where(pMC==0,1,pMC) # removeing zeroes from denominator... + #pData = ak.prod(bJetEff_data[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff_data[isNotBtagJetsLoose]), axis=-1) + #weights_obj_base_for_kinematic_syst.add("btagSF", pData/pMC) - ######### Apply SFs ######### - - if not isData: - weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) - weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) - - ### OLD implimentation from TOP-22-006 - ## Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods - #bJetSF = cor_ec.GetBTagSF(goodJets, year, 'LOOSE') - ###bJetEff = cor_ec.GetBtagEff(goodJets, year, 'loose') # Need for now - #bJetEff_data = bJetEff*bJetSF - #pMC = ak.prod(bJetEff[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff[isNotBtagJetsLoose]), axis=-1) - #pMC = ak.where(pMC==0,1,pMC) # removeing zeroes from denominator... - #pData = ak.prod(bJetEff_data[isBtagJetsLoose], axis=-1) * ak.prod((1-bJetEff_data[isNotBtagJetsLoose]), axis=-1) - #weights_obj_base_for_kinematic_syst.add("btagSF", pData/pMC) - - ### Evaluate btag weights ### - jets_light = goodJets[goodJets.hadronFlavour==0] - jets_bc = goodJets[goodJets.hadronFlavour>0] - - # Workaround to use UL16APV SFs for UL16 for light jets - year_light = year - if year == "2016": year_light = "2016APV" - btag_sf_light = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl","central") - btag_sf_bc = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb","central") - - #btag_eff_light = bJetEff[goodJets.hadronFlavour==0] # OLD eff, Will replace with our new eff - #btag_eff_bc = bJetEff[goodJets.hadronFlavour>0] # OLD eff, Will replace with our new eff - btag_eff_light = cor_ec.btag_eff_eval(jets_light,"L",year) - btag_eff_bc = cor_ec.btag_eff_eval(jets_bc,"L",year) - - wgt_light = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light, jets_light.btagDeepFlavB>btagwpl) - wgt_bc = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc, jets_bc.btagDeepFlavB>btagwpl) - - weights_obj_base_for_kinematic_syst.add("btagSF", wgt_light*wgt_bc) - - - ######### Masks we need for the selection ########## - - # Pass trigger mask - pass_trg = es_tc.trg_pass_no_overlap(events,isData,dataset,str(year),dataset_dict=es_ec.dataset_dict,exclude_dict=es_ec.exclude_dict) - pass_trg = (pass_trg & es_ec.trg_matching(events,year)) - - # b jet masks - bmask_atleast1med_atleast2loose = ((nbtagsm>=1)&(nbtagsl>=2)) # Used for 2lss and 4l - bmask_exactly0loose = (nbtagsl==0) # Used for 4l WWZ SR - bmask_exactly0med = (nbtagsm==0) # Used for 3l CR and 2los Z CR - bmask_exactly1med = (nbtagsm==1) # Used for 3l SR and 2lss CR - bmask_exactly2med = (nbtagsm==2) # Used for CRtt - bmask_atleast2med = (nbtagsm>=2) # Used for 3l SR - bmask_atmost2med = (nbtagsm< 3) # Used to make 2lss mutually exclusive from tttt enriched - bmask_atleast3med = (nbtagsm>=3) # Used for tttt enriched - bmask_atleast1med = (nbtagsm>=1) - bmask_atleast1loose = (nbtagsl>=1) - bmask_atleast2loose = (nbtagsl>=2) - - - ######### WWZ event selection stuff ######### - - # Get some preliminary things we'll need - es_ec.attach_wwz_preselection_mask(events,l_wwz_t_padded[:,0:4]) # Attach preselection sf and of flags to the events - leps_from_z_candidate_ptordered, leps_not_z_candidate_ptordered = es_ec.get_wwz_candidates(l_wwz_t_padded[:,0:4]) # Get ahold of the leptons from the Z and from the W - - w_lep0 = leps_not_z_candidate_ptordered[:,0] - w_lep1 = leps_not_z_candidate_ptordered[:,1] - mll_wl0_wl1 = (w_lep0 + w_lep1).mass - - # Make masks for the SF regions - w_candidates_mll_far_from_z = ak.fill_none(abs(mll_wl0_wl1 - get_ec_param("zmass")) > 10.0,False) # Will enforce this for SF in the PackedSelection - ptl4 = (l0+l1+l2+l3).pt - sf_A = ak.fill_none(met.pt >= 120.0,False) # This should never be None, but just keep syntax same as other categories - sf_B = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 70.0),False) - sf_C = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 40.0) & (ptl4 < 70.0),False) - - # Make masks for the OF regions - of_1 = ak.fill_none((mll_wl0_wl1 >= 0.0) & (mll_wl0_wl1 < 40.0),False) - of_2 = ak.fill_none((mll_wl0_wl1 >= 40.0) & (mll_wl0_wl1 < 60.0),False) - of_3 = ak.fill_none((mll_wl0_wl1 >= 60.0) & (mll_wl0_wl1 < 100.0),False) - of_4 = ak.fill_none((mll_wl0_wl1 >= 100.0),False) - - # Mask for mt2 cut - mt2_val = es_ec.get_mt2(w_lep0,w_lep1,met) - mt2_mask = ak.fill_none(mt2_val>25.0,False) - - - - ######### Store boolean masks with PackedSelection ########## - - - selections = PackedSelection(dtype='uint64') - - # Lumi mask (for data) - selections.add("is_good_lumi",lumi_mask) - - zeroj = (njets==0) - - # For WWZ selection - selections.add("sr_4l_sf_A", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_A)) - selections.add("sr_4l_sf_B", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_B)) - selections.add("sr_4l_sf_C", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_C)) - selections.add("sr_4l_of_1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_1 & mt2_mask)) - selections.add("sr_4l_of_2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_2 & mt2_mask)) - selections.add("sr_4l_of_3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_3 & mt2_mask)) - selections.add("sr_4l_of_4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_4)) - - selections.add("all_events", (events.is4lWWZ | (~events.is4lWWZ))) # All events.. this logic is a bit roundabout to just get an array of True - selections.add("all_events_trg", (bmask_atleast1loose & (events.is4lWWZ | (~events.is4lWWZ)))) - selections.add("4l_presel", (events.is4lWWZ)) # This matches the VVV looper selection (object selection and event selection) - - selections.add("sr_4l_sf_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 65.0))) - selections.add("sr_4l_of_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of)) - - # CRs - ww_ee = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 11)) - ww_mm = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 13)) - ww_em = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 13)) - ww_me = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 11)) - selections.add("cr_4l_btag_of", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_of)) - selections.add("cr_4l_btag_sf", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf)) - selections.add("cr_4l_btag_sf_offZ", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z)) - selections.add("cr_4l_btag_sf_offZ_met80", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 80.0))) - selections.add("cr_4l_sf", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & (~w_candidates_mll_far_from_z))) - - cat_dict = { - "lep_chan_lst" : [ - #"all_events_trg", - "all_events","all_events_trg", - #"sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", - #"sr_4l_sf_presel", "sr_4l_of_presel", - #"all_events","4l_presel", - #"cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80", - ], - } - - ######### Get variables ######### - - l0pt = l0.pt - j0pt = ak.flatten(j0.pt) # Flatten to go from [[j0pt],[j0pt],...] -> [j0pt,j0pt,...] - mll_01 = (l0+l1).mass - mllll = (l0+l1+l2+l3).mass - scalarptsum_lep = l0.pt + l1.pt + l2.pt + l3.pt - scalarptsum_lepmet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt - scalarptsum_lepmetjet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + ak.sum(goodJets.pt,axis=-1) - - # Get lep from Z - z_lep0 = leps_from_z_candidate_ptordered[:,0] - z_lep1 = leps_from_z_candidate_ptordered[:,1] - - mll_zl0_zl1 = (z_lep0 + z_lep1).mass - - pt_zl0_zl1 = (z_lep0 + z_lep1).pt - pt_wl0_wl1 = (w_lep0 + w_lep1).pt - - dr_zl0_zl1 = z_lep0.delta_r(z_lep1) - dr_wl0_wl1 = w_lep0.delta_r(w_lep1) - dr_wleps_zleps = (w_lep0 + w_lep1).delta_r(z_lep0 + z_lep1) - - absdphi_zl0_zl1 = abs(z_lep0.delta_phi(z_lep1)) - absdphi_wl0_wl1 = abs(w_lep0.delta_phi(w_lep1)) - absdphi_z_ww = abs((z_lep0 + z_lep1).delta_phi(w_lep0 + w_lep1 + met)) - dphi_4l_met = (z_lep0 + z_lep1 + w_lep0 + w_lep1).delta_phi(met) - dphi_wleps_met = (w_lep0 + w_lep1).delta_phi(met) - dphi_zleps_met = (z_lep0 + z_lep1).delta_phi(met) - - # lb pairs (i.e. always one lep, one bjet) - bjets = goodJets[isBtagJetsLoose] - #lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets}) - #mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) - #mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) - - # Get BDT values - bdt_vars = [ - ak.fill_none(mll_wl0_wl1,-9999), - ak.fill_none(dphi_4l_met,-9999), - ak.fill_none(dphi_zleps_met,-9999), - ak.fill_none(dphi_wleps_met,-9999), - ak.fill_none(dr_wl0_wl1,-9999), - ak.fill_none(dr_zl0_zl1,-9999), - ak.fill_none(dr_wleps_zleps,-9999), - ak.fill_none(met.pt,-9999), - ak.fill_none(mt2_val,-9999), - ak.fill_none(ptl4,-9999), - ak.fill_none(scalarptsum_lepmet,-9999), - ak.fill_none(scalarptsum_lepmetjet,-9999), - ak.fill_none(z_lep0.pt,-9999), - ak.fill_none(z_lep1.pt,-9999), - ak.fill_none(w_lep0.pt,-9999), - ak.fill_none(w_lep1.pt,-9999), - ] - - #bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) - #bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) - #bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) - #bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) - bdt_of_wwz_raw = 0 # TODO: Update xgb for coffea23 - bdt_sf_wwz_raw = 0 # TODO: Update xgb for coffea23 - bdt_of_zh_raw = 0 # TODO: Update xgb for coffea23 - bdt_sf_zh_raw = 0 # TODO: Update xgb for coffea23 - # Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html - bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0 - bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0 - bdt_of_zh = (2.0*((1.0+math.e**(-2*bdt_of_zh_raw))**(-1))) - 1.0 - bdt_sf_zh = (2.0*((1.0+math.e**(-2*bdt_sf_zh_raw))**(-1))) - 1.0 - - - ######### Fill histos ######### - - dense_variables_dict = { - "mt2" : mt2_val, - "met" : met.pt, - "metphi" : met.phi, - "ptl4" : ptl4, - "scalarptsum_lep" : scalarptsum_lep, - "scalarptsum_lepmet" : scalarptsum_lepmet, - "scalarptsum_lepmetjet" : scalarptsum_lepmetjet, - "mll_01" : mll_01, - "mllll" : mllll, - "l0pt" : l0pt, - "j0pt" : j0pt, - - "z_lep0_pt" : z_lep0.pt, - "z_lep1_pt" : z_lep1.pt, - "w_lep0_pt" : w_lep0.pt, - "w_lep1_pt" : w_lep1.pt, - "z_lep0_eta" : z_lep0.eta, - "z_lep1_eta" : z_lep1.eta, - "w_lep0_eta" : w_lep0.eta, - "w_lep1_eta" : w_lep1.eta, - "z_lep0_phi" : z_lep0.phi, - "z_lep1_phi" : z_lep1.phi, - "w_lep0_phi" : w_lep0.phi, - "w_lep1_phi" : w_lep1.phi, - - "mll_wl0_wl1" : mll_wl0_wl1, - "mll_zl0_zl1" : mll_zl0_zl1, - - "pt_zl0_zl1" : pt_zl0_zl1, - "pt_wl0_wl1" : pt_wl0_wl1, - "dr_zl0_zl1" : dr_zl0_zl1, - "dr_wl0_wl1" : dr_wl0_wl1, - "dr_wleps_zleps" : dr_wleps_zleps, - "absdphi_zl0_zl1" : absdphi_zl0_zl1, - "absdphi_wl0_wl1" : absdphi_wl0_wl1, - "absdphi_z_ww" : absdphi_z_ww, - "dphi_4l_met" : dphi_4l_met, - "dphi_zleps_met" : dphi_zleps_met, - "dphi_wleps_met" : dphi_wleps_met, - - "nleps" : nleps, - "njets" : njets, - "nbtagsl" : nbtagsl, - "nbtagsm" : nbtagsm, - - "nleps_counts" : nleps, - "njets_counts" : njets, - "nbtagsl_counts" : nbtagsl, - - "absdphi_min_afas" : absdphi_min_afas, - "absdphi_min_afos" : absdphi_min_afos, - "absdphi_min_sfos" : absdphi_min_sfos, - "mll_min_afas" : mll_min_afas, - "mll_min_afos" : mll_min_afos, - "mll_min_sfos" : mll_min_sfos, - - #"mlb_min" : mlb_min, - #"mlb_max" : mlb_max, - - "bdt_of_wwz_raw": bdt_of_wwz_raw, - "bdt_sf_wwz_raw": bdt_sf_wwz_raw, - "bdt_of_zh_raw" : bdt_of_zh_raw, - "bdt_sf_zh_raw" : bdt_sf_zh_raw, - "bdt_of_wwz": bdt_of_wwz, - "bdt_sf_wwz": bdt_sf_wwz, - "bdt_of_zh" : bdt_of_zh, - "bdt_sf_zh" : bdt_sf_zh, - } - - # List the hists that are only defined for some categories - analysis_cats = ["sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4"] - exclude_var_dict = { - "mt2" : ["all_events"], - "ptl4" : ["all_events"], - "j0pt" : ["all_events", "4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, - "l0pt" : ["all_events"], - "mll_01" : ["all_events"], - "mllll" : ["all_events"], - "scalarptsum_lep" : ["all_events"], - "scalarptsum_lepmet" : ["all_events"], - "scalarptsum_lepmetjet" : ["all_events"], - "w_lep0_pt" : ["all_events"], - "w_lep1_pt" : ["all_events"], - "z_lep0_pt" : ["all_events"], - "z_lep1_pt" : ["all_events"], - "w_lep0_eta" : ["all_events"], - "w_lep1_eta" : ["all_events"], - "z_lep0_eta" : ["all_events"], - "z_lep1_eta" : ["all_events"], - "w_lep0_phi" : ["all_events"], - "w_lep1_phi" : ["all_events"], - "z_lep0_phi" : ["all_events"], - "z_lep1_phi" : ["all_events"], - "mll_wl0_wl1" : ["all_events"], - "mll_zl0_zl1" : ["all_events"], - - "pt_zl0_zl1" : ["all_events"], - "pt_wl0_wl1" : ["all_events"], - "dr_zl0_zl1" : ["all_events"], - "dr_wl0_wl1" : ["all_events"], - "dr_wleps_zleps" : ["all_events"], - "absdphi_zl0_zl1" : ["all_events"], - "absdphi_wl0_wl1" : ["all_events"], - "absdphi_z_ww" : ["all_events"], - "dphi_4l_met" : ["all_events"], - "dphi_zleps_met" : ["all_events"], - "dphi_wleps_met" : ["all_events"], - - "absdphi_min_afas" : ["all_events"], - "absdphi_min_afos" : ["all_events"], - "absdphi_min_sfos" : ["all_events"], - "mll_min_afas" : ["all_events"], - "mll_min_afos" : ["all_events"], - "mll_min_sfos" : ["all_events"], - - "mlb_min" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, - "mlb_max" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, - - "bdt_of_wwz_raw": ["all_events"], - "bdt_sf_wwz_raw": ["all_events"], - "bdt_of_zh_raw" : ["all_events"], - "bdt_sf_zh_raw" : ["all_events"], - "bdt_of_wwz": ["all_events"], - "bdt_sf_wwz": ["all_events"], - "bdt_of_zh" : ["all_events"], - "bdt_sf_zh" : ["all_events"], - } - - - # Loop over the hists we want to fill - for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): - #print("\ndense_axis_name,vals",dense_axis_name) - #print("dense_axis_name,vals",dense_axis_vals) - if ("njets" not in dense_axis_name) and ("met" != dense_axis_name): continue - - # Create the hist for this dense axis variable - if dense_axis_name not in hout: - hout[dense_axis_name] = hda.Hist( - hist.axis.StrCategory([], growth=True, name="process", label="process"), - hist.axis.StrCategory([], growth=True, name="category", label="category"), - self._dense_axes_dict[dense_axis_name], - storage="weight", # Keeps track of sumw2 - name="Counts", - ) - - # Decide if we are filling this hist with weight or raw event counts - if dense_axis_name.endswith("_counts"): weights = events.nom - #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) - else: weights = weights_obj_base_for_kinematic_syst.weight(None) - - # Loop over categories - for sr_cat in cat_dict["lep_chan_lst"]: - - # Skip filling if this variable is not relevant for this selection - if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue - - # Make the cuts mask - cuts_lst = [sr_cat] - if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data - all_cuts_mask = selections.all(*cuts_lst) - - #run = events.run[all_cuts_mask] - #luminosityBlock = events.luminosityBlock[all_cuts_mask] - #event = events.event[all_cuts_mask] - #w = weights[all_cuts_mask] - #if dense_axis_name == "njets": - # print("STARTPRINT") - # for i,j in enumerate(w): - # out_str = f"PRINTTAG {i} {dense_axis_name} {year} {sr_cat} {event[i]} {run[i]} {luminosityBlock[i]} {w[i]}" - # print(out_str,file=sys.stderr,flush=True) - # print("ENDPRINT") - # print("ENDPRINT") - #print("\ndense_axis_name",dense_axis_name) - #print("sr_cat",sr_cat) - #print("dense_axis_vals[all_cuts_mask]",dense_axis_vals[all_cuts_mask]) - #print("this") - - # Fill the histos - axes_fill_info_dict = { - dense_axis_name : dense_axis_vals[all_cuts_mask], - "weight" : weights[all_cuts_mask], - "process" : histAxisName, - "category" : sr_cat, - #"systematic" : "nominal", - } - hout[dense_axis_name].fill(**axes_fill_info_dict) + ### Evaluate btag weights ### + jets_light = goodJets[goodJets.hadronFlavour==0] + jets_bc = goodJets[goodJets.hadronFlavour>0] + + # Workaround to use UL16APV SFs for UL16 for light jets + year_light = year + if year == "2016": year_light = "2016APV" + btag_sf_light = cor_tc.btag_sf_eval(jets_light, "L",year_light,"deepJet_incl","central") + btag_sf_bc = cor_tc.btag_sf_eval(jets_bc, "L",year, "deepJet_comb","central") + + #btag_eff_light = bJetEff[goodJets.hadronFlavour==0] # OLD eff, Will replace with our new eff + #btag_eff_bc = bJetEff[goodJets.hadronFlavour>0] # OLD eff, Will replace with our new eff + btag_eff_light = cor_ec.btag_eff_eval(jets_light,"L",year) + btag_eff_bc = cor_ec.btag_eff_eval(jets_bc,"L",year) + + wgt_light = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light, jets_light.btagDeepFlavB>btagwpl) + wgt_bc = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc, jets_bc.btagDeepFlavB>btagwpl) + + ##weights_obj_base_for_kinematic_syst.add("btagSF", wgt_light*wgt_bc) + + + ######### Masks we need for the selection ########## + + # Pass trigger mask + pass_trg = es_tc.trg_pass_no_overlap(events,isData,dataset,str(year),dataset_dict=es_ec.dataset_dict,exclude_dict=es_ec.exclude_dict) + pass_trg = (pass_trg & es_ec.trg_matching(events,year)) + + # b jet masks + bmask_atleast1med_atleast2loose = ((nbtagsm>=1)&(nbtagsl>=2)) # Used for 2lss and 4l + bmask_exactly0loose = (nbtagsl==0) # Used for 4l WWZ SR + bmask_exactly0med = (nbtagsm==0) # Used for 3l CR and 2los Z CR + bmask_exactly1med = (nbtagsm==1) # Used for 3l SR and 2lss CR + bmask_exactly2med = (nbtagsm==2) # Used for CRtt + bmask_atleast2med = (nbtagsm>=2) # Used for 3l SR + bmask_atmost2med = (nbtagsm< 3) # Used to make 2lss mutually exclusive from tttt enriched + bmask_atleast3med = (nbtagsm>=3) # Used for tttt enriched + bmask_atleast1med = (nbtagsm>=1) + bmask_atleast1loose = (nbtagsl>=1) + bmask_atleast2loose = (nbtagsl>=2) + + + ######### WWZ event selection stuff ######### + + # Get some preliminary things we'll need + es_ec.attach_wwz_preselection_mask(events,l_wwz_t_padded[:,0:4]) # Attach preselection sf and of flags to the events + leps_from_z_candidate_ptordered, leps_not_z_candidate_ptordered = es_ec.get_wwz_candidates(l_wwz_t_padded[:,0:4]) # Get ahold of the leptons from the Z and from the W + + w_lep0 = leps_not_z_candidate_ptordered[:,0] + w_lep1 = leps_not_z_candidate_ptordered[:,1] + mll_wl0_wl1 = (w_lep0 + w_lep1).mass + + # Make masks for the SF regions + w_candidates_mll_far_from_z = ak.fill_none(abs(mll_wl0_wl1 - get_ec_param("zmass")) > 10.0,False) # Will enforce this for SF in the PackedSelection + ptl4 = (l0+l1+l2+l3).pt + sf_A = ak.fill_none(met.pt >= 120.0,False) # This should never be None, but just keep syntax same as other categories + sf_B = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 70.0),False) + sf_C = ak.fill_none((met.pt >= 65.0) & (met.pt < 120.0) & (ptl4 >= 40.0) & (ptl4 < 70.0),False) + + # Make masks for the OF regions + of_1 = ak.fill_none((mll_wl0_wl1 >= 0.0) & (mll_wl0_wl1 < 40.0),False) + of_2 = ak.fill_none((mll_wl0_wl1 >= 40.0) & (mll_wl0_wl1 < 60.0),False) + of_3 = ak.fill_none((mll_wl0_wl1 >= 60.0) & (mll_wl0_wl1 < 100.0),False) + of_4 = ak.fill_none((mll_wl0_wl1 >= 100.0),False) + + # Mask for mt2 cut + mt2_val = es_ec.get_mt2(w_lep0,w_lep1,met) + mt2_mask = ak.fill_none(mt2_val>25.0,False) + + + + ######### Store boolean masks with PackedSelection ########## + + + selections = PackedSelection(dtype='uint64') + + # Lumi mask (for data) + selections.add("is_good_lumi",lumi_mask) + + zeroj = (njets==0) + + # For WWZ selection + selections.add("sr_4l_sf_A", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_A)) + selections.add("sr_4l_sf_B", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_B)) + selections.add("sr_4l_sf_C", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & sf_C)) + selections.add("sr_4l_of_1", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_1 & mt2_mask)) + selections.add("sr_4l_of_2", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_2 & mt2_mask)) + selections.add("sr_4l_of_3", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_3 & mt2_mask)) + selections.add("sr_4l_of_4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_4)) + + selections.add("all_events", (events.is4lWWZ | (~events.is4lWWZ))) # All events.. this logic is a bit roundabout to just get an array of True + selections.add("all_events_trg", (bmask_atleast1loose & (events.is4lWWZ | (~events.is4lWWZ)))) + selections.add("4l_presel", (events.is4lWWZ)) # This matches the VVV looper selection (object selection and event selection) + + selections.add("sr_4l_sf_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 65.0))) + selections.add("sr_4l_of_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of)) + + # CRs + ww_ee = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 11)) + ww_mm = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 13)) + ww_em = ((abs(w_lep0.pdgId) == 11) & (abs(w_lep1.pdgId) == 13)) + ww_me = ((abs(w_lep0.pdgId) == 13) & (abs(w_lep1.pdgId) == 11)) + selections.add("cr_4l_btag_of", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_of)) + selections.add("cr_4l_btag_sf", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf)) + selections.add("cr_4l_btag_sf_offZ", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z)) + selections.add("cr_4l_btag_sf_offZ_met80", (pass_trg & events.is4lWWZ & bmask_atleast1loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 80.0))) + selections.add("cr_4l_sf", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & (~w_candidates_mll_far_from_z))) + + cat_dict = { + "lep_chan_lst" : [ + "all_events", + #"all_events","all_events_trg", + #"sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", + #"sr_4l_sf_presel", "sr_4l_of_presel", + #"all_events","4l_presel", + #"cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80", + ], + } + + ######### Get variables ######### + + l0pt = l0.pt + j0pt = ak.flatten(j0.pt) # Flatten to go from [[j0pt],[j0pt],...] -> [j0pt,j0pt,...] + mll_01 = (l0+l1).mass + mllll = (l0+l1+l2+l3).mass + scalarptsum_lep = l0.pt + l1.pt + l2.pt + l3.pt + scalarptsum_lepmet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + scalarptsum_lepmetjet = l0.pt + l1.pt + l2.pt + l3.pt + met.pt + ak.sum(goodJets.pt,axis=-1) + + # Get lep from Z + z_lep0 = leps_from_z_candidate_ptordered[:,0] + z_lep1 = leps_from_z_candidate_ptordered[:,1] + + mll_zl0_zl1 = (z_lep0 + z_lep1).mass + + pt_zl0_zl1 = (z_lep0 + z_lep1).pt + pt_wl0_wl1 = (w_lep0 + w_lep1).pt + + dr_zl0_zl1 = z_lep0.delta_r(z_lep1) + dr_wl0_wl1 = w_lep0.delta_r(w_lep1) + dr_wleps_zleps = (w_lep0 + w_lep1).delta_r(z_lep0 + z_lep1) + + absdphi_zl0_zl1 = abs(z_lep0.delta_phi(z_lep1)) + absdphi_wl0_wl1 = abs(w_lep0.delta_phi(w_lep1)) + absdphi_z_ww = abs((z_lep0 + z_lep1).delta_phi(w_lep0 + w_lep1 + met)) + dphi_4l_met = (z_lep0 + z_lep1 + w_lep0 + w_lep1).delta_phi(met) + dphi_wleps_met = (w_lep0 + w_lep1).delta_phi(met) + dphi_zleps_met = (z_lep0 + z_lep1).delta_phi(met) + + # lb pairs (i.e. always one lep, one bjet) + bjets = goodJets[isBtagJetsLoose] + #lb_pairs = ak.cartesian({"l":l_wwz_t,"j":bjets}) + #mlb_min = ak.min((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) + #mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) + + # Get BDT values + bdt_vars = [ + ak.fill_none(mll_wl0_wl1,-9999), + ak.fill_none(dphi_4l_met,-9999), + ak.fill_none(dphi_zleps_met,-9999), + ak.fill_none(dphi_wleps_met,-9999), + ak.fill_none(dr_wl0_wl1,-9999), + ak.fill_none(dr_zl0_zl1,-9999), + ak.fill_none(dr_wleps_zleps,-9999), + ak.fill_none(met.pt,-9999), + ak.fill_none(mt2_val,-9999), + ak.fill_none(ptl4,-9999), + ak.fill_none(scalarptsum_lepmet,-9999), + ak.fill_none(scalarptsum_lepmetjet,-9999), + ak.fill_none(z_lep0.pt,-9999), + ak.fill_none(z_lep1.pt,-9999), + ak.fill_none(w_lep0.pt,-9999), + ak.fill_none(w_lep1.pt,-9999), + ] + + #bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) + #bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) + #bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) + #bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) + bdt_of_wwz_raw = 0 # TODO: Update xgb for coffea23 + bdt_sf_wwz_raw = 0 # TODO: Update xgb for coffea23 + bdt_of_zh_raw = 0 # TODO: Update xgb for coffea23 + bdt_sf_zh_raw = 0 # TODO: Update xgb for coffea23 + # Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html + bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0 + bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0 + bdt_of_zh = (2.0*((1.0+math.e**(-2*bdt_of_zh_raw))**(-1))) - 1.0 + bdt_sf_zh = (2.0*((1.0+math.e**(-2*bdt_sf_zh_raw))**(-1))) - 1.0 + + + ######### Fill histos ######### + + dense_variables_dict = { + "mt2" : mt2_val, + "met" : met.pt, + "metphi" : met.phi, + "ptl4" : ptl4, + "scalarptsum_lep" : scalarptsum_lep, + "scalarptsum_lepmet" : scalarptsum_lepmet, + "scalarptsum_lepmetjet" : scalarptsum_lepmetjet, + "mll_01" : mll_01, + "mllll" : mllll, + "l0pt" : l0pt, + "j0pt" : j0pt, + + "z_lep0_pt" : z_lep0.pt, + "z_lep1_pt" : z_lep1.pt, + "w_lep0_pt" : w_lep0.pt, + "w_lep1_pt" : w_lep1.pt, + "z_lep0_eta" : z_lep0.eta, + "z_lep1_eta" : z_lep1.eta, + "w_lep0_eta" : w_lep0.eta, + "w_lep1_eta" : w_lep1.eta, + "z_lep0_phi" : z_lep0.phi, + "z_lep1_phi" : z_lep1.phi, + "w_lep0_phi" : w_lep0.phi, + "w_lep1_phi" : w_lep1.phi, + + "mll_wl0_wl1" : mll_wl0_wl1, + "mll_zl0_zl1" : mll_zl0_zl1, + + "pt_zl0_zl1" : pt_zl0_zl1, + "pt_wl0_wl1" : pt_wl0_wl1, + "dr_zl0_zl1" : dr_zl0_zl1, + "dr_wl0_wl1" : dr_wl0_wl1, + "dr_wleps_zleps" : dr_wleps_zleps, + "absdphi_zl0_zl1" : absdphi_zl0_zl1, + "absdphi_wl0_wl1" : absdphi_wl0_wl1, + "absdphi_z_ww" : absdphi_z_ww, + "dphi_4l_met" : dphi_4l_met, + "dphi_zleps_met" : dphi_zleps_met, + "dphi_wleps_met" : dphi_wleps_met, + + "nleps" : nleps, + "njets" : njets, + "nbtagsl" : nbtagsl, + "nbtagsm" : nbtagsm, + + "nleps_counts" : nleps, + "njets_counts" : njets, + "nbtagsl_counts" : nbtagsl, + + "absdphi_min_afas" : absdphi_min_afas, + "absdphi_min_afos" : absdphi_min_afos, + "absdphi_min_sfos" : absdphi_min_sfos, + "mll_min_afas" : mll_min_afas, + "mll_min_afos" : mll_min_afos, + "mll_min_sfos" : mll_min_sfos, + + #"mlb_min" : mlb_min, + #"mlb_max" : mlb_max, + + "bdt_of_wwz_raw": bdt_of_wwz_raw, + "bdt_sf_wwz_raw": bdt_sf_wwz_raw, + "bdt_of_zh_raw" : bdt_of_zh_raw, + "bdt_sf_zh_raw" : bdt_sf_zh_raw, + "bdt_of_wwz": bdt_of_wwz, + "bdt_sf_wwz": bdt_sf_wwz, + "bdt_of_zh" : bdt_of_zh, + "bdt_sf_zh" : bdt_sf_zh, + } + + # List the hists that are only defined for some categories + analysis_cats = ["sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4"] + exclude_var_dict = { + "mt2" : ["all_events"], + "ptl4" : ["all_events"], + "j0pt" : ["all_events", "4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, + "l0pt" : ["all_events"], + "mll_01" : ["all_events"], + "mllll" : ["all_events"], + "scalarptsum_lep" : ["all_events"], + "scalarptsum_lepmet" : ["all_events"], + "scalarptsum_lepmetjet" : ["all_events"], + "w_lep0_pt" : ["all_events"], + "w_lep1_pt" : ["all_events"], + "z_lep0_pt" : ["all_events"], + "z_lep1_pt" : ["all_events"], + "w_lep0_eta" : ["all_events"], + "w_lep1_eta" : ["all_events"], + "z_lep0_eta" : ["all_events"], + "z_lep1_eta" : ["all_events"], + "w_lep0_phi" : ["all_events"], + "w_lep1_phi" : ["all_events"], + "z_lep0_phi" : ["all_events"], + "z_lep1_phi" : ["all_events"], + "mll_wl0_wl1" : ["all_events"], + "mll_zl0_zl1" : ["all_events"], + + "pt_zl0_zl1" : ["all_events"], + "pt_wl0_wl1" : ["all_events"], + "dr_zl0_zl1" : ["all_events"], + "dr_wl0_wl1" : ["all_events"], + "dr_wleps_zleps" : ["all_events"], + "absdphi_zl0_zl1" : ["all_events"], + "absdphi_wl0_wl1" : ["all_events"], + "absdphi_z_ww" : ["all_events"], + "dphi_4l_met" : ["all_events"], + "dphi_zleps_met" : ["all_events"], + "dphi_wleps_met" : ["all_events"], + + "absdphi_min_afas" : ["all_events"], + "absdphi_min_afos" : ["all_events"], + "absdphi_min_sfos" : ["all_events"], + "mll_min_afas" : ["all_events"], + "mll_min_afos" : ["all_events"], + "mll_min_sfos" : ["all_events"], + + "mlb_min" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, + "mlb_max" : ["all_events","4l_presel", "sr_4l_sf_presel", "sr_4l_of_presel", "cr_4l_sf"] + analysis_cats, + + "bdt_of_wwz_raw": ["all_events"], + "bdt_sf_wwz_raw": ["all_events"], + "bdt_of_zh_raw" : ["all_events"], + "bdt_sf_zh_raw" : ["all_events"], + "bdt_of_wwz": ["all_events"], + "bdt_sf_wwz": ["all_events"], + "bdt_of_zh" : ["all_events"], + "bdt_sf_zh" : ["all_events"], + } + + + # Loop over the hists we want to fill + hout = {} + for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): + #print("\ndense_axis_name,vals",dense_axis_name) + #print("dense_axis_name,vals",dense_axis_vals) + if ("njets" not in dense_axis_name) and ("met" != dense_axis_name): continue + + # Create the hist for this dense axis variable + #if dense_axis_name not in hout: # IF Looping over all datasets !!! + hout[dense_axis_name] = hda.Hist( + hist.axis.StrCategory([], growth=True, name="process", label="process"), + hist.axis.StrCategory([], growth=True, name="category", label="category"), + self._dense_axes_dict[dense_axis_name], + storage="weight", # Keeps track of sumw2 + name="Counts", + ) + + # Decide if we are filling this hist with weight or raw event counts + if dense_axis_name.endswith("_counts"): weights = events.nom + #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) + else: weights = weights_obj_base_for_kinematic_syst.weight(None) + + # Loop over categories + for sr_cat in cat_dict["lep_chan_lst"]: + + # Skip filling if this variable is not relevant for this selection + if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue + + # Make the cuts mask + cuts_lst = [sr_cat] + if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data + all_cuts_mask = selections.all(*cuts_lst) + + #run = events.run[all_cuts_mask] + #luminosityBlock = events.luminosityBlock[all_cuts_mask] + #event = events.event[all_cuts_mask] + #w = weights[all_cuts_mask] + #if dense_axis_name == "njets": + # print("STARTPRINT") + # for i,j in enumerate(w): + # out_str = f"PRINTTAG {i} {dense_axis_name} {year} {sr_cat} {event[i]} {run[i]} {luminosityBlock[i]} {w[i]}" + # print(out_str,file=sys.stderr,flush=True) + # print("ENDPRINT") + # print("ENDPRINT") + #print("\ndense_axis_name",dense_axis_name) + #print("sr_cat",sr_cat) + #print("dense_axis_vals[all_cuts_mask]",dense_axis_vals[all_cuts_mask]) + #print("this") + + # Fill the histos + axes_fill_info_dict = { + dense_axis_name : dense_axis_vals[all_cuts_mask], + "weight" : weights[all_cuts_mask], + "process" : histAxisName, + "category" : sr_cat, + #"systematic" : "nominal", + } + hout[dense_axis_name].fill(**axes_fill_info_dict) return hout From de47ff1d73d26941480645900ca52de90051a01c Mon Sep 17 00:00:00 2001 From: Kelci Date: Wed, 3 Jan 2024 17:16:30 -0500 Subject: [PATCH 06/48] We pass the info for each json one at a time again --- analysis/wwz/run_wwz4l.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 8ec3e278..b6300a9a 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -10,6 +10,7 @@ from coffea import processor from coffea.nanoevents import NanoAODSchema from coffea.nanoevents import NanoEventsFactory +from coffea.dataset_tools import apply_processor import topcoffea.modules.remote_environment as remote_environment @@ -321,10 +322,14 @@ def LoadJsonToSampleName(jsonFile, prefix): ).events() # Get and compute the histograms - print("Get histos") - histos_to_compute = processor_instance.process(events_dict) + histos_to_compute = {} + for json_name in flist.keys(): + print(f"Getting histos for {json_name}") + histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) # Passing events object + #histos_to_compute[json_name] = processor_instance.process(json_name,flist[json_name]) # Passing root file list + print("Compute histos") - output = dask.compute(histos_to_compute) + output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple ''' if executor == "futures": From e20d047d9c3dd774a6fe803f5577b1a66a8d90d7 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 13:12:46 -0500 Subject: [PATCH 07/48] Update ele id xgb for coffea 2023 --- ewkcoffea/modules/objects_wwz.py | 91 +++++++++++++++++++++++--------- 1 file changed, 67 insertions(+), 24 deletions(-) diff --git a/ewkcoffea/modules/objects_wwz.py b/ewkcoffea/modules/objects_wwz.py index c63a7099..4177447d 100644 --- a/ewkcoffea/modules/objects_wwz.py +++ b/ewkcoffea/modules/objects_wwz.py @@ -1,7 +1,10 @@ import numpy as np import awkward as ak +import dask_awkward as dak import xgboost as xgb +from coffea.ml_tools.xgboost_wrapper import xgboost_wrapper + from topcoffea.modules.paths import topcoffea_path from ewkcoffea.modules.paths import ewkcoffea_path @@ -58,38 +61,78 @@ def get_topmva_score_ele(events, year): else: raise Exception(f"Error: Unknown year \"{year}\". Exiting...") model_fpath = topcoffea_path(f"data/topmva/lepid_weights/el_TOP{ulbase}_XGB.weights.bin") - # Get the input data + # Put some stuff into ele object ele["btagDeepFlavB"] = ak.fill_none(ele.matched_jet.btagDeepFlavB, 0) ele["jetPtRatio"] = 1./(ele.jetRelIso+1.) ele["miniPFRelIso_diff_all_chg"] = ele.miniPFRelIso_all - ele.miniPFRelIso_chg - # The order here comes from https://github.com/cmstas/VVVNanoLooper/blob/8a194165cdbbbee3bcf69f932d837e95a0a265e6/src/ElectronIDHelper.cc#L110-L122 - in_vals = np.array([ - ak.flatten(ele.pt), - ak.flatten(ele.eta), # Kirill confirms that signed eta was used in the training - ak.flatten(ele.jetNDauCharged), - ak.flatten(ele.miniPFRelIso_chg), - ak.flatten(ele.miniPFRelIso_diff_all_chg), - ak.flatten(ele.jetPtRelv2), - ak.flatten(ele.jetPtRatio), - ak.flatten(ele.pfRelIso03_all), - ak.flatten(ele.btagDeepFlavB), - ak.flatten(ele.sip3d), - ak.flatten(np.log(abs(ele.dxy))), - ak.flatten(np.log(abs(ele.dz))), - ak.flatten(ele.mvaFall17V2noIso), - ]) - in_vals = np.transpose(in_vals) # To go from e.g. [ [pt1,pt1] , [eta1,eta2] ] -> [ [pt1,eta1] , [pt2,eta2] ] - in_vals = xgb.DMatrix(in_vals) # The format xgb expects - # Load model and evaluate - xgb.set_config(verbosity = 0) - bst = xgb.Booster() - bst.load_model(model_fpath) - score = bst.predict(in_vals).reshape(-1) + # List order comes from https://github.com/cmstas/VVVNanoLooper/blob/8a194165cdbbbee3bcf69f932d837e95a0a265e6/src/ElectronIDHelper.cc#L110-L122 + feature_list = [ + "pt", + "eta", + "jetNDauCharged", + "miniPFRelIso_chg", + "miniPFRelIso_diff_all_chg", + "jetPtRelv2", + "jetPtRatio", + "pfRelIso03_all", + "ak4jet:btagDeepFlavB", + "sip3d", + "log_abs_dxy", + "log_abs_dz", + "mvaFall17V2noIso", + ] + + # Flatten, and store in a dict for easy access + in_vals_flat_dict = { + "pt" : ak.flatten(ele.pt), + "eta" : ak.flatten(ele.eta), # Kirill confirms that signed eta was used in the training + "jetNDauCharged" : ak.flatten(ele.jetNDauCharged), + "miniPFRelIso_chg" : ak.flatten(ele.miniPFRelIso_chg), + "miniPFRelIso_diff_all_chg" : ak.flatten(ele.miniPFRelIso_diff_all_chg), + "jetPtRelv2" : ak.flatten(ele.jetPtRelv2), + "jetPtRatio" : ak.flatten(ele.jetPtRatio), + "pfRelIso03_all" : ak.flatten(ele.pfRelIso03_all), + "ak4jet:btagDeepFlavB" : ak.flatten(ele.btagDeepFlavB), + "sip3d" : ak.flatten(ele.sip3d), + "log_abs_dxy" : ak.flatten(np.log(abs(ele.dxy))), + "log_abs_dz" : ak.flatten(np.log(abs(ele.dz))), + "mvaFall17V2noIso" : ak.flatten(ele.mvaFall17V2noIso), + } + + # From https://github.com/CoffeaTeam/coffea/blob/master/tests/test_ml_tools.py#L169-L174 + class xgboost_test(xgboost_wrapper): + def prepare_awkward(self, events): + ak = self.get_awkward_lib(events) + ret = ak.concatenate( + [events[name][:, np.newaxis] for name in feature_list], axis=1 + ) + return [], dict(data=ret) + + # Reshape the input array + # E.g. we want to go from something like this: + # { + # "a" : ak.Array([[1.1 ,2.1] ,[3.2]]), + # "b" : ak.Array([[-1.1,-2.1],[-3.2]]), + # } + # To something that looks like this: + # input_arr = ak.Array([ + # {"a": 1.1, "b": -1.1}, + # {"a": 2.1, "b": -2.1}, + # {"a": 3.1, "b": -3.1}, + # ]) + input_arr = ak.zip( + {feat_name: in_vals_flat_dict[feat_name] for feat_name in in_vals_flat_dict.keys()} + ) + + # Get the score + xgb_wrap = xgboost_test(model_fpath) + score = xgb_wrap(input_arr) # Restore the shape (i.e. unflatten) counts = ak.num(ele.pt) score = ak.unflatten(score,counts) + return score From a0e09379f88bfb255e4bacefb4dd7c4bb4541f88 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 13:47:26 -0500 Subject: [PATCH 08/48] Update mu xgb stuff too --- ewkcoffea/modules/objects_wwz.py | 91 +++++++++++++++++++++++--------- 1 file changed, 66 insertions(+), 25 deletions(-) diff --git a/ewkcoffea/modules/objects_wwz.py b/ewkcoffea/modules/objects_wwz.py index 4177447d..4327b804 100644 --- a/ewkcoffea/modules/objects_wwz.py +++ b/ewkcoffea/modules/objects_wwz.py @@ -149,36 +149,77 @@ def get_topmva_score_mu(events, year): else: raise Exception(f"Error: Unknown year \"{year}\". Exiting...") model_fpath = topcoffea_path(f"data/topmva/lepid_weights/mu_TOP{ulbase}_XGB.weights.bin") - # Get the input data + # Put some stuff into mu object mu["btagDeepFlavB"] = ak.fill_none(mu.matched_jet.btagDeepFlavB, 0) - mu["jetPtRatio"] = 1./(mu.jetRelIso+1.) mu["miniPFRelIso_diff_all_chg"] = mu.miniPFRelIso_all - mu.miniPFRelIso_chg - in_vals = np.array([ - ak.flatten(mu.pt), - ak.flatten(mu.eta), # Kirill confirms that signed eta was used in the training - ak.flatten(mu.jetNDauCharged), - ak.flatten(mu.miniPFRelIso_chg), - ak.flatten(mu.miniPFRelIso_diff_all_chg), - ak.flatten(mu.jetPtRelv2), - ak.flatten(mu.jetPtRatio), - ak.flatten(mu.pfRelIso03_all), - ak.flatten(mu.btagDeepFlavB), - ak.flatten(mu.sip3d), - ak.flatten(np.log(abs(mu.dxy))), - ak.flatten(np.log(abs(mu.dz))), - ak.flatten(mu.segmentComp), - ]) - in_vals = np.transpose(in_vals) - in_vals = xgb.DMatrix(in_vals) - - # Load model and evaluate - xgb.set_config(verbosity = 0) - bst = xgb.Booster() - bst.load_model(model_fpath) - score = bst.predict(in_vals).reshape(-1) + + # Order comes from https://github.com/cmstas/VVVNanoLooper/blob/8a194165cdbbbee3bcf69f932d837e95a0a265e6/src/MuonIDHelper.cc#L102-L116 + feature_list = [ + "pt", + "eta", + "jetNDauCharged", + "miniPFRelIso_chg", + "miniPFRelIso_diff_all_chg", + "jetPtRelv2", + "jetPtRatio", + "pfRelIso03_all", + "ak4jet:btagDeepFlavB", + "sip3d", + "log_abs_dxy", + "log_abs_dz", + "segmentComp", + ] + + # Flatten, and store in a dict for easy access + in_vals_flat_dict = { + "pt" : ak.flatten(mu.pt), + "eta" : ak.flatten(mu.eta), # Kirill confirms that signed eta was used in the training + "jetNDauCharged" : ak.flatten(mu.jetNDauCharged), + "miniPFRelIso_chg" : ak.flatten(mu.miniPFRelIso_chg), + "miniPFRelIso_diff_all_chg": ak.flatten(mu.miniPFRelIso_diff_all_chg), + "jetPtRelv2" : ak.flatten(mu.jetPtRelv2), + "jetPtRatio" : ak.flatten(mu.jetPtRatio), + "pfRelIso03_all" : ak.flatten(mu.pfRelIso03_all), + "ak4jet:btagDeepFlavB" : ak.flatten(mu.btagDeepFlavB), + "sip3d" : ak.flatten(mu.sip3d), + "log_abs_dxy" : ak.flatten(np.log(abs(mu.dxy))), + "log_abs_dz" : ak.flatten(np.log(abs(mu.dz))), + "segmentComp" : ak.flatten(mu.segmentComp), + } + + # From https://github.com/CoffeaTeam/coffea/blob/master/tests/test_ml_tools.py#L169-L174 + class xgboost_test(xgboost_wrapper): + def prepare_awkward(self, events): + ak = self.get_awkward_lib(events) + ret = ak.concatenate( + [events[name][:, np.newaxis] for name in feature_list], axis=1 + ) + return [], dict(data=ret) + + # Reshape the input array + # E.g. we want to go from something like this: + # { + # "a" : ak.Array([[1.1 ,2.1] ,[3.2]]), + # "b" : ak.Array([[-1.1,-2.1],[-3.2]]), + # } + # To something that looks like this: + # input_arr = ak.Array([ + # {"a": 1.1, "b": -1.1}, + # {"a": 2.1, "b": -2.1}, + # {"a": 3.1, "b": -3.1}, + # ]) + input_arr = ak.zip( + {feat_name: in_vals_flat_dict[feat_name] for feat_name in in_vals_flat_dict.keys()} + ) + + # Get the score + xgb_wrap = xgboost_test(model_fpath) + score = xgb_wrap(input_arr) # Restore the shape (i.e. unflatten) counts = ak.num(mu.pt) score = ak.unflatten(score,counts) + return score + From 4b643d2b37e5d630ffc83f27147c4a1937af1878 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 15:15:41 -0500 Subject: [PATCH 09/48] Reorganize xgb to factor out common part --- ewkcoffea/modules/objects_wwz.py | 99 +++++++++++++------------------- 1 file changed, 40 insertions(+), 59 deletions(-) diff --git a/ewkcoffea/modules/objects_wwz.py b/ewkcoffea/modules/objects_wwz.py index 4327b804..88b6f61e 100644 --- a/ewkcoffea/modules/objects_wwz.py +++ b/ewkcoffea/modules/objects_wwz.py @@ -17,6 +17,42 @@ def get_cleaned_collection(obj_collection_a,obj_collection_b,drcut=0.4): mask = ak.fill_none(dr>drcut,True) return obj_collection_b[mask] + +# Wrapper around evaluation of lep ID bdt, note returns flat +def xgb_eval_lep_id_wrapper(feature_list,in_vals_flat_dict,model_fpath): + + # From https://github.com/CoffeaTeam/coffea/blob/master/tests/test_ml_tools.py#L169-L174 + class xgboost_test(xgboost_wrapper): + def prepare_awkward(self, events): + ak = self.get_awkward_lib(events) + ret = ak.concatenate( + [events[name][:, np.newaxis] for name in feature_list], axis=1 + ) + return [], dict(data=ret) + + # Reshape the input array + # E.g. we want to go from something like this: + # { + # "a" : ak.Array([[1.1 ,2.1] ,[3.2]]), + # "b" : ak.Array([[-1.1,-2.1],[-3.2]]), + # } + # To something that looks like this: + # input_arr = ak.Array([ + # {"a": 1.1, "b": -1.1}, + # {"a": 2.1, "b": -2.1}, + # {"a": 3.1, "b": -3.1}, + # ]) + input_arr = ak.zip( + {feat_name: in_vals_flat_dict[feat_name] for feat_name in in_vals_flat_dict.keys()} + ) + + # Get the score + xgb_wrap = xgboost_test(model_fpath) + score = xgb_wrap(input_arr) + + return score + + ######### WWZ 4l analysis object selection ######### # WWZ preselection for electrons @@ -67,7 +103,7 @@ def get_topmva_score_ele(events, year): ele["miniPFRelIso_diff_all_chg"] = ele.miniPFRelIso_all - ele.miniPFRelIso_chg # List order comes from https://github.com/cmstas/VVVNanoLooper/blob/8a194165cdbbbee3bcf69f932d837e95a0a265e6/src/ElectronIDHelper.cc#L110-L122 - feature_list = [ + feature_lst = [ "pt", "eta", "jetNDauCharged", @@ -100,34 +136,7 @@ def get_topmva_score_ele(events, year): "mvaFall17V2noIso" : ak.flatten(ele.mvaFall17V2noIso), } - # From https://github.com/CoffeaTeam/coffea/blob/master/tests/test_ml_tools.py#L169-L174 - class xgboost_test(xgboost_wrapper): - def prepare_awkward(self, events): - ak = self.get_awkward_lib(events) - ret = ak.concatenate( - [events[name][:, np.newaxis] for name in feature_list], axis=1 - ) - return [], dict(data=ret) - - # Reshape the input array - # E.g. we want to go from something like this: - # { - # "a" : ak.Array([[1.1 ,2.1] ,[3.2]]), - # "b" : ak.Array([[-1.1,-2.1],[-3.2]]), - # } - # To something that looks like this: - # input_arr = ak.Array([ - # {"a": 1.1, "b": -1.1}, - # {"a": 2.1, "b": -2.1}, - # {"a": 3.1, "b": -3.1}, - # ]) - input_arr = ak.zip( - {feat_name: in_vals_flat_dict[feat_name] for feat_name in in_vals_flat_dict.keys()} - ) - - # Get the score - xgb_wrap = xgboost_test(model_fpath) - score = xgb_wrap(input_arr) + score = xgb_eval_lep_id_wrapper(feature_lst,in_vals_flat_dict,model_fpath) # Restore the shape (i.e. unflatten) counts = ak.num(ele.pt) @@ -155,7 +164,7 @@ def get_topmva_score_mu(events, year): mu["miniPFRelIso_diff_all_chg"] = mu.miniPFRelIso_all - mu.miniPFRelIso_chg # Order comes from https://github.com/cmstas/VVVNanoLooper/blob/8a194165cdbbbee3bcf69f932d837e95a0a265e6/src/MuonIDHelper.cc#L102-L116 - feature_list = [ + feature_lst = [ "pt", "eta", "jetNDauCharged", @@ -188,38 +197,10 @@ def get_topmva_score_mu(events, year): "segmentComp" : ak.flatten(mu.segmentComp), } - # From https://github.com/CoffeaTeam/coffea/blob/master/tests/test_ml_tools.py#L169-L174 - class xgboost_test(xgboost_wrapper): - def prepare_awkward(self, events): - ak = self.get_awkward_lib(events) - ret = ak.concatenate( - [events[name][:, np.newaxis] for name in feature_list], axis=1 - ) - return [], dict(data=ret) - - # Reshape the input array - # E.g. we want to go from something like this: - # { - # "a" : ak.Array([[1.1 ,2.1] ,[3.2]]), - # "b" : ak.Array([[-1.1,-2.1],[-3.2]]), - # } - # To something that looks like this: - # input_arr = ak.Array([ - # {"a": 1.1, "b": -1.1}, - # {"a": 2.1, "b": -2.1}, - # {"a": 3.1, "b": -3.1}, - # ]) - input_arr = ak.zip( - {feat_name: in_vals_flat_dict[feat_name] for feat_name in in_vals_flat_dict.keys()} - ) - - # Get the score - xgb_wrap = xgboost_test(model_fpath) - score = xgb_wrap(input_arr) + score = xgb_eval_lep_id_wrapper(feature_lst,in_vals_flat_dict,model_fpath) # Restore the shape (i.e. unflatten) counts = ak.num(mu.pt) score = ak.unflatten(score,counts) return score - From f1875f724a05964e509bbfc562f1cdd0b3670570 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 15:40:07 -0500 Subject: [PATCH 10/48] Uncomment lep ID and SFs --- analysis/wwz/wwz4l.py | 39 +++++++++++++++++---------------------- 1 file changed, 17 insertions(+), 22 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index fddf267c..4ddd4072 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -265,14 +265,12 @@ def process(self, events): #''' # Do the object selection for the WWZ eleectrons ele_presl_mask = os_ec.is_presel_wwz_ele(ele,tight=True) - #ele["topmva"] = os_ec.get_topmva_score_ele(events, year) - ele["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 + ele["topmva"] = os_ec.get_topmva_score_ele(events, year) ele["is_tight_lep_for_wwz"] = ((ele.topmva > get_tc_param("topmva_wp_t_e")) & ele_presl_mask) # Do the object selection for the WWZ muons mu_presl_mask = os_ec.is_presel_wwz_mu(mu) - #mu["topmva"] = os_ec.get_topmva_score_mu(events, year) - mu["topmva"] = 0 # TODO: Update xgb stuff for coffea2023 + mu["topmva"] = os_ec.get_topmva_score_mu(events, year) mu["is_tight_lep_for_wwz"] = ((mu.topmva > get_tc_param("topmva_wp_t_m")) & mu_presl_mask) # Get tight leptons for WWZ selection @@ -410,8 +408,8 @@ def process(self, events): ######### Apply SFs ######### if not isData: - ##weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) - ##weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) + weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) + weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) ### OLD implimentation from TOP-22-006 ## Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods @@ -441,7 +439,7 @@ def process(self, events): wgt_light = cor_tc.get_method1a_wgt_singlewp(btag_eff_light,btag_sf_light, jets_light.btagDeepFlavB>btagwpl) wgt_bc = cor_tc.get_method1a_wgt_singlewp(btag_eff_bc, btag_sf_bc, jets_bc.btagDeepFlavB>btagwpl) - ##weights_obj_base_for_kinematic_syst.add("btagSF", wgt_light*wgt_bc) + weights_obj_base_for_kinematic_syst.add("btagSF", wgt_light*wgt_bc) ######### Masks we need for the selection ########## @@ -513,7 +511,6 @@ def process(self, events): selections.add("sr_4l_of_4", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_of & of_4)) selections.add("all_events", (events.is4lWWZ | (~events.is4lWWZ))) # All events.. this logic is a bit roundabout to just get an array of True - selections.add("all_events_trg", (bmask_atleast1loose & (events.is4lWWZ | (~events.is4lWWZ)))) selections.add("4l_presel", (events.is4lWWZ)) # This matches the VVV looper selection (object selection and event selection) selections.add("sr_4l_sf_presel", (pass_trg & events.is4lWWZ & bmask_exactly0loose & events.wwz_presel_sf & w_candidates_mll_far_from_z & (met.pt > 65.0))) @@ -533,11 +530,10 @@ def process(self, events): cat_dict = { "lep_chan_lst" : [ "all_events", - #"all_events","all_events_trg", - #"sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", - #"sr_4l_sf_presel", "sr_4l_of_presel", - #"all_events","4l_presel", - #"cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80", + "sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", + "sr_4l_sf_presel", "sr_4l_of_presel", + "all_events","4l_presel", + "cr_4l_btag_of","cr_4l_sf", "cr_4l_btag_sf", "cr_4l_btag_sf_offZ", "cr_4l_btag_sf_offZ_met80", ], } @@ -674,14 +670,14 @@ def process(self, events): #"mlb_min" : mlb_min, #"mlb_max" : mlb_max, - "bdt_of_wwz_raw": bdt_of_wwz_raw, - "bdt_sf_wwz_raw": bdt_sf_wwz_raw, - "bdt_of_zh_raw" : bdt_of_zh_raw, - "bdt_sf_zh_raw" : bdt_sf_zh_raw, - "bdt_of_wwz": bdt_of_wwz, - "bdt_sf_wwz": bdt_sf_wwz, - "bdt_of_zh" : bdt_of_zh, - "bdt_sf_zh" : bdt_sf_zh, + #"bdt_of_wwz_raw": bdt_of_wwz_raw, + #"bdt_sf_wwz_raw": bdt_sf_wwz_raw, + #"bdt_of_zh_raw" : bdt_of_zh_raw, + #"bdt_sf_zh_raw" : bdt_sf_zh_raw, + #"bdt_of_wwz": bdt_of_wwz, + #"bdt_sf_wwz": bdt_sf_wwz, + #"bdt_of_zh" : bdt_of_zh, + #"bdt_sf_zh" : bdt_sf_zh, } # List the hists that are only defined for some categories @@ -749,7 +745,6 @@ def process(self, events): for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): #print("\ndense_axis_name,vals",dense_axis_name) #print("dense_axis_name,vals",dense_axis_vals) - if ("njets" not in dense_axis_name) and ("met" != dense_axis_name): continue # Create the hist for this dense axis variable #if dense_axis_name not in hout: # IF Looping over all datasets !!! From 40c50879a6ed497b33936d360dd7417c6aae530e Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 15:46:04 -0500 Subject: [PATCH 11/48] Make flake8 happy --- analysis/wwz/run_wwz4l.py | 16 ---------------- ewkcoffea/modules/objects_wwz.py | 28 +++++++++++++--------------- 2 files changed, 13 insertions(+), 31 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index b6300a9a..7d102898 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -7,10 +7,8 @@ import gzip import os import dask -from coffea import processor from coffea.nanoevents import NanoAODSchema from coffea.nanoevents import NanoEventsFactory -from coffea.dataset_tools import apply_processor import topcoffea.modules.remote_environment as remote_environment @@ -331,20 +329,6 @@ def LoadJsonToSampleName(jsonFile, prefix): print("Compute histos") output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple - ''' - if executor == "futures": - exec_instance = processor.FuturesExecutor(workers=nworkers) - runner = processor.Runner(exec_instance, schema=NanoAODSchema, chunksize=chunksize, maxchunks=nchunks) - elif executor == "iterative": - exec_instance = processor.IterativeExecutor() - runner = processor.Runner(exec_instance, schema=NanoAODSchema, chunksize=chunksize, maxchunks=nchunks) - elif executor == "work_queue": - executor = processor.WorkQueueExecutor(**executor_args) - runner = processor.Runner(executor, schema=NanoAODSchema, chunksize=chunksize, maxchunks=nchunks, skipbadfiles=False, xrootdtimeout=180) - ''' - - #output = runner(flist, treename, processor_instance) - dt = time.time() - tstart if executor == "work_queue": diff --git a/ewkcoffea/modules/objects_wwz.py b/ewkcoffea/modules/objects_wwz.py index 88b6f61e..162cd789 100644 --- a/ewkcoffea/modules/objects_wwz.py +++ b/ewkcoffea/modules/objects_wwz.py @@ -1,7 +1,5 @@ import numpy as np import awkward as ak -import dask_awkward as dak -import xgboost as xgb from coffea.ml_tools.xgboost_wrapper import xgboost_wrapper @@ -121,19 +119,19 @@ def get_topmva_score_ele(events, year): # Flatten, and store in a dict for easy access in_vals_flat_dict = { - "pt" : ak.flatten(ele.pt), - "eta" : ak.flatten(ele.eta), # Kirill confirms that signed eta was used in the training - "jetNDauCharged" : ak.flatten(ele.jetNDauCharged), - "miniPFRelIso_chg" : ak.flatten(ele.miniPFRelIso_chg), - "miniPFRelIso_diff_all_chg" : ak.flatten(ele.miniPFRelIso_diff_all_chg), - "jetPtRelv2" : ak.flatten(ele.jetPtRelv2), - "jetPtRatio" : ak.flatten(ele.jetPtRatio), - "pfRelIso03_all" : ak.flatten(ele.pfRelIso03_all), - "ak4jet:btagDeepFlavB" : ak.flatten(ele.btagDeepFlavB), - "sip3d" : ak.flatten(ele.sip3d), - "log_abs_dxy" : ak.flatten(np.log(abs(ele.dxy))), - "log_abs_dz" : ak.flatten(np.log(abs(ele.dz))), - "mvaFall17V2noIso" : ak.flatten(ele.mvaFall17V2noIso), + "pt" : ak.flatten(ele.pt), + "eta" : ak.flatten(ele.eta), # Kirill confirms that signed eta was used in the training + "jetNDauCharged" : ak.flatten(ele.jetNDauCharged), + "miniPFRelIso_chg" : ak.flatten(ele.miniPFRelIso_chg), + "miniPFRelIso_diff_all_chg" : ak.flatten(ele.miniPFRelIso_diff_all_chg), + "jetPtRelv2" : ak.flatten(ele.jetPtRelv2), + "jetPtRatio" : ak.flatten(ele.jetPtRatio), + "pfRelIso03_all" : ak.flatten(ele.pfRelIso03_all), + "ak4jet:btagDeepFlavB" : ak.flatten(ele.btagDeepFlavB), + "sip3d" : ak.flatten(ele.sip3d), + "log_abs_dxy" : ak.flatten(np.log(abs(ele.dxy))), + "log_abs_dz" : ak.flatten(np.log(abs(ele.dz))), + "mvaFall17V2noIso" : ak.flatten(ele.mvaFall17V2noIso), } score = xgb_eval_lep_id_wrapper(feature_lst,in_vals_flat_dict,model_fpath) From 70d45f28858b16191c7e3d761c7af736719c762a Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 15:47:19 -0500 Subject: [PATCH 12/48] Need dask in conda env? --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index c6cdd46d..fa8d022b 100644 --- a/environment.yml +++ b/environment.yml @@ -11,3 +11,4 @@ dependencies: - xrootd - git - pyyaml + - dask From 3b894de7c363aced681f319bf936f6b6a2362ee2 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 15:48:55 -0500 Subject: [PATCH 13/48] Dont need dak in processor --- analysis/wwz/wwz4l.py | 1 - 1 file changed, 1 deletion(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 4ddd4072..77c236de 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -26,7 +26,6 @@ get_tc_param = GetParam(topcoffea_path("params/params.json")) get_ec_param = GetParam(ewkcoffea_path("params/params.json")) -import dask_awkward as dak import hist.dask as hda From f1fedb829dbd2ce43d831c8c915f6251a2505a8a Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 15:51:17 -0500 Subject: [PATCH 14/48] Ahh dont pin coffea version --- environment.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index fa8d022b..eb226013 100644 --- a/environment.yml +++ b/environment.yml @@ -4,11 +4,10 @@ channels: dependencies: - conda-forge::python=3.9 - numpy=1.23.5 - - coffea==0.7.22 + - coffea - ndcctools - conda-pack - dill - xrootd - git - pyyaml - - dask From 2573205b5f4b15b9f5d58e66ecdb6b15b8fc805e Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 16:00:02 -0500 Subject: [PATCH 15/48] For now need coffea2023 branch of topcoffea --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index da1573ba..709b8b82 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 + git clone -b coffea2023 https://github.com/TopEFT/topcoffea.git cd topcoffea conda run -n coffea-env pip install -e . cd ../.. From 9a7f7d3701714b7909eb42bf042d63aac3f49e54 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 16:18:41 -0500 Subject: [PATCH 16/48] Clean up --- analysis/wwz/get_wwz_counts.py | 18 ++++++++++++++++-- analysis/wwz/wwz4l.py | 2 -- tests/test_yields_wwz.py | 1 - 3 files changed, 16 insertions(+), 5 deletions(-) diff --git a/analysis/wwz/get_wwz_counts.py b/analysis/wwz/get_wwz_counts.py index e5f74b64..41d401a6 100644 --- a/analysis/wwz/get_wwz_counts.py +++ b/analysis/wwz/get_wwz_counts.py @@ -47,18 +47,32 @@ def get_counts(histos_dict): return out_dict +# Restructure hists so samples are in category axis +def restructure_hist(in_dict): + + dense_axes = list(list(in_dict.values())[0].keys()) + restructured_hist = {} + for dense_axis in dense_axes: + for dataset_name in in_dict.keys(): + if dense_axis not in restructured_hist: + restructured_hist[dense_axis] = in_dict[dataset_name][dense_axis] + else: + restructured_hist[dense_axis] += in_dict[dataset_name][dense_axis] + return restructured_hist + + def main(): # Set up the command line parser parser = argparse.ArgumentParser() - parser.add_argument("-f", "--pkl-file-path", default="histos/plotsTopEFT.pkl.gz", help = "The path to the pkl file") + parser.add_argument("pkl_file_path", help = "The path to the pkl file") parser.add_argument("-o", "--output-path", default=".", help = "The path the output files should be saved to") parser.add_argument("-n", "--output-name", default="counts_wwz_sync", help = "A name for the output directory") args = parser.parse_args() # Get the counts from the input hiso histo_dict = pickle.load(gzip.open(args.pkl_file_path)) - + histo_dict = restructure_hist(histo_dict) counts_dict = get_counts(histo_dict) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 77c236de..8cccce1b 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -528,7 +528,6 @@ def process(self, events): cat_dict = { "lep_chan_lst" : [ - "all_events", "sr_4l_sf_A","sr_4l_sf_B","sr_4l_sf_C","sr_4l_of_1","sr_4l_of_2","sr_4l_of_3","sr_4l_of_4", "sr_4l_sf_presel", "sr_4l_of_presel", "all_events","4l_presel", @@ -746,7 +745,6 @@ def process(self, events): #print("dense_axis_name,vals",dense_axis_vals) # Create the hist for this dense axis variable - #if dense_axis_name not in hout: # IF Looping over all datasets !!! hout[dense_axis_name] = hda.Hist( hist.axis.StrCategory([], growth=True, name="process", label="process"), hist.axis.StrCategory([], growth=True, name="category", label="category"), diff --git a/tests/test_yields_wwz.py b/tests/test_yields_wwz.py index 910c5725..65fa3fcf 100644 --- a/tests/test_yields_wwz.py +++ b/tests/test_yields_wwz.py @@ -7,7 +7,6 @@ def test_make_yields_after_processor_wwz(): args = [ "python", "analysis/wwz/get_wwz_counts.py", - "-f", "analysis/wwz/histos/output_check_yields.pkl.gz", "-n", "analysis/wwz/output_check_yields" From 2cfacfb034289956c39e599a9edf49034993c416 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 17:00:39 -0500 Subject: [PATCH 17/48] Enable sr bdt evaluation --- analysis/wwz/wwz4l.py | 68 +++++++++++++++--------------- ewkcoffea/modules/objects_wwz.py | 12 +++--- ewkcoffea/modules/selection_wwz.py | 15 ------- 3 files changed, 40 insertions(+), 55 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 8cccce1b..a0100a1c 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -572,33 +572,31 @@ def process(self, events): #mlb_max = ak.max((lb_pairs["l"] + lb_pairs["j"]).mass,axis=-1) # Get BDT values - bdt_vars = [ - ak.fill_none(mll_wl0_wl1,-9999), - ak.fill_none(dphi_4l_met,-9999), - ak.fill_none(dphi_zleps_met,-9999), - ak.fill_none(dphi_wleps_met,-9999), - ak.fill_none(dr_wl0_wl1,-9999), - ak.fill_none(dr_zl0_zl1,-9999), - ak.fill_none(dr_wleps_zleps,-9999), - ak.fill_none(met.pt,-9999), - ak.fill_none(mt2_val,-9999), - ak.fill_none(ptl4,-9999), - ak.fill_none(scalarptsum_lepmet,-9999), - ak.fill_none(scalarptsum_lepmetjet,-9999), - ak.fill_none(z_lep0.pt,-9999), - ak.fill_none(z_lep1.pt,-9999), - ak.fill_none(w_lep0.pt,-9999), - ak.fill_none(w_lep1.pt,-9999), - ] - - #bdt_of_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) - #bdt_sf_wwz_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) - #bdt_of_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) - #bdt_sf_zh_raw = es_ec.eval_sig_bdt(events,bdt_vars,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) - bdt_of_wwz_raw = 0 # TODO: Update xgb for coffea23 - bdt_sf_wwz_raw = 0 # TODO: Update xgb for coffea23 - bdt_of_zh_raw = 0 # TODO: Update xgb for coffea23 - bdt_sf_zh_raw = 0 # TODO: Update xgb for coffea23 + bdt_feat_lst = [ "m_ll", "dPhi_4Lep_MET", "dPhi_Zcand_MET", "dPhi_WW_MET", "dR_Wcands", "dR_Zcands", "dR_WW_Z", "MET", "MT2", "Pt4l", "STLepHad", "STLep", "leading_Zcand_pt", "subleading_Zcand_pt", "leading_Wcand_pt", "subleading_Wcand_pt"] + bdt_var_dict = { + "m_ll" : ak.fill_none(mll_wl0_wl1,-9999), + "dPhi_4Lep_MET" : ak.fill_none(dphi_4l_met,-9999), + "dPhi_Zcand_MET" : ak.fill_none(dphi_zleps_met,-9999), + "dPhi_WW_MET" : ak.fill_none(dphi_wleps_met,-9999), + "dR_Wcands" : ak.fill_none(dr_wl0_wl1,-9999), + "dR_Zcands" : ak.fill_none(dr_zl0_zl1,-9999), + "dR_WW_Z" : ak.fill_none(dr_wleps_zleps,-9999), + "MET" : ak.fill_none(met.pt,-9999), + "MT2" : ak.fill_none(mt2_val,-9999), + "Pt4l" : ak.fill_none(ptl4,-9999), + "STLepHad" : ak.fill_none(scalarptsum_lepmet,-9999), + "STLep" : ak.fill_none(scalarptsum_lepmetjet,-9999), + "leading_Zcand_pt" : ak.fill_none(z_lep0.pt,-9999), + "subleading_Zcand_pt": ak.fill_none(z_lep1.pt,-9999), + "leading_Wcand_pt" : ak.fill_none(w_lep0.pt,-9999), + "subleading_Wcand_pt": ak.fill_none(w_lep1.pt,-9999), + } + + bdt_of_wwz_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/of_WWZ.json")) + bdt_sf_wwz_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/sf_WWZ.json")) + bdt_of_zh_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/of_ZH.json")) + bdt_sf_zh_raw = os_ec.xgb_eval_wrapper(bdt_feat_lst,bdt_var_dict,ewkcoffea_path("data/wwz_zh_bdt/sf_ZH.json")) + # Match TMVA's scaling https://root.cern.ch/doc/v606/MethodBDT_8cxx_source.html bdt_of_wwz = (2.0*((1.0+math.e**(-2*bdt_of_wwz_raw))**(-1))) - 1.0 bdt_sf_wwz = (2.0*((1.0+math.e**(-2*bdt_sf_wwz_raw))**(-1))) - 1.0 @@ -668,14 +666,14 @@ def process(self, events): #"mlb_min" : mlb_min, #"mlb_max" : mlb_max, - #"bdt_of_wwz_raw": bdt_of_wwz_raw, - #"bdt_sf_wwz_raw": bdt_sf_wwz_raw, - #"bdt_of_zh_raw" : bdt_of_zh_raw, - #"bdt_sf_zh_raw" : bdt_sf_zh_raw, - #"bdt_of_wwz": bdt_of_wwz, - #"bdt_sf_wwz": bdt_sf_wwz, - #"bdt_of_zh" : bdt_of_zh, - #"bdt_sf_zh" : bdt_sf_zh, + "bdt_of_wwz_raw": bdt_of_wwz_raw, + "bdt_sf_wwz_raw": bdt_sf_wwz_raw, + "bdt_of_zh_raw" : bdt_of_zh_raw, + "bdt_sf_zh_raw" : bdt_sf_zh_raw, + "bdt_of_wwz": bdt_of_wwz, + "bdt_sf_wwz": bdt_sf_wwz, + "bdt_of_zh" : bdt_of_zh, + "bdt_sf_zh" : bdt_sf_zh, } # List the hists that are only defined for some categories diff --git a/ewkcoffea/modules/objects_wwz.py b/ewkcoffea/modules/objects_wwz.py index 162cd789..2b965460 100644 --- a/ewkcoffea/modules/objects_wwz.py +++ b/ewkcoffea/modules/objects_wwz.py @@ -17,7 +17,8 @@ def get_cleaned_collection(obj_collection_a,obj_collection_b,drcut=0.4): # Wrapper around evaluation of lep ID bdt, note returns flat -def xgb_eval_lep_id_wrapper(feature_list,in_vals_flat_dict,model_fpath): +# Note this could potentially go somewhere more general like topcoffea modules +def xgb_eval_wrapper(feature_list,in_vals_flat_dict,model_fpath): # From https://github.com/CoffeaTeam/coffea/blob/master/tests/test_ml_tools.py#L169-L174 class xgboost_test(xgboost_wrapper): @@ -29,10 +30,11 @@ def prepare_awkward(self, events): return [], dict(data=ret) # Reshape the input array + # Note that if the inputs are object level like pt, then should have already flattned before passing to this function # E.g. we want to go from something like this: # { - # "a" : ak.Array([[1.1 ,2.1] ,[3.2]]), - # "b" : ak.Array([[-1.1,-2.1],[-3.2]]), + # "a" : ak.Array([1.1, 2.1, 3.2]]), # E.g. could have been [[1.1,2.1] ,[3.2]] prior to flattening + # "b" : ak.Array([-1.1,-2.1,-3.2]]), # E.g. could have been [[-1.1,-2.1],[-3.2]] prior to flattening # } # To something that looks like this: # input_arr = ak.Array([ @@ -134,7 +136,7 @@ def get_topmva_score_ele(events, year): "mvaFall17V2noIso" : ak.flatten(ele.mvaFall17V2noIso), } - score = xgb_eval_lep_id_wrapper(feature_lst,in_vals_flat_dict,model_fpath) + score = xgb_eval_wrapper(feature_lst,in_vals_flat_dict,model_fpath) # Restore the shape (i.e. unflatten) counts = ak.num(ele.pt) @@ -195,7 +197,7 @@ def get_topmva_score_mu(events, year): "segmentComp" : ak.flatten(mu.segmentComp), } - score = xgb_eval_lep_id_wrapper(feature_lst,in_vals_flat_dict,model_fpath) + score = xgb_eval_wrapper(feature_lst,in_vals_flat_dict,model_fpath) # Restore the shape (i.e. unflatten) counts = ak.num(mu.pt) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index b5d2a9b6..7885c86d 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -358,18 +358,3 @@ def get_mt2(w_lep0,w_lep1,met): ) return mt2_var - - -# Evaluate the BDTs from Keegan -def eval_sig_bdt(events,in_vals,model_fpath): - - in_vals = np.array(in_vals) - in_vals = np.transpose(in_vals) - in_vals = xgb.DMatrix(in_vals) # The format xgb expects - - # Load model and evaluate - xgb.set_config(verbosity = 0) - bst = xgb.Booster() - bst.load_model(model_fpath) - score = bst.predict(in_vals) - return score From 6d28fd117fa72d0239668f911023aad788dd486f Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 17:01:13 -0500 Subject: [PATCH 18/48] Dont need xgb import --- ewkcoffea/modules/selection_wwz.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index 7885c86d..293b89d2 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -1,6 +1,5 @@ import numpy as np import awkward as ak -import xgboost as xgb from mt2 import mt2 from coffea.nanoevents.methods import vector From 5f6bdb2bc177de4e8d3218f2c8b72df146d5a2ac Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 17:09:06 -0500 Subject: [PATCH 19/48] Dont need np here --- ewkcoffea/modules/selection_wwz.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index 293b89d2..c8e01a77 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -1,4 +1,3 @@ -import numpy as np import awkward as ak from mt2 import mt2 From f94e217d48c3b08421e77366e7f676871ff0e7f0 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 17:58:08 -0500 Subject: [PATCH 20/48] clean up run script --- analysis/wwz/run_wwz4l.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 7d102898..5743ab69 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -166,11 +166,11 @@ def LoadJsonToSampleName(jsonFile, prefix): else: LoadJsonToSampleName(l, prefix) - flist = {} + fdict = {} nevts_total = 0 for sname in samplesdict.keys(): redirector = samplesdict[sname]['redirector'] - flist[sname] = [(redirector+f) for f in samplesdict[sname]['files']] + fdict[sname] = [(redirector+f) for f in samplesdict[sname]['files']] samplesdict[sname]['year'] = samplesdict[sname]['year'] samplesdict[sname]['xsec'] = float(samplesdict[sname]['xsec']) samplesdict[sname]['nEvents'] = int(samplesdict[sname]['nEvents']) @@ -310,9 +310,9 @@ def LoadJsonToSampleName(jsonFile, prefix): ### coffea2023 ### # Create dict of events objects - print("Number of datasets:",len(flist)) + print("Number of datasets:",len(fdict)) events_dict = {} - for name, fpaths in flist.items(): + for name, fpaths in fdict.items(): events_dict[name] = NanoEventsFactory.from_root( {fpath: "/Events" for fpath in fpaths}, schemaclass=NanoAODSchema, @@ -321,10 +321,9 @@ def LoadJsonToSampleName(jsonFile, prefix): # Get and compute the histograms histos_to_compute = {} - for json_name in flist.keys(): + for json_name in fdict.keys(): print(f"Getting histos for {json_name}") - histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) # Passing events object - #histos_to_compute[json_name] = processor_instance.process(json_name,flist[json_name]) # Passing root file list + histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) print("Compute histos") output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple From e752129481211e64ee0c5634e668cdaa5b2d5fad Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 4 Jan 2024 18:56:22 -0500 Subject: [PATCH 21/48] In progress updates for scaling up --- analysis/wwz/run_wwz4l.py | 61 ++++++++++++++++++++++++++++++++------- 1 file changed, 51 insertions(+), 10 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 5743ab69..022e2545 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -10,6 +10,10 @@ from coffea.nanoevents import NanoAODSchema from coffea.nanoevents import NanoEventsFactory +from coffea.dataset_tools import preprocess +from coffea.dataset_tools import apply_to_fileset +from distributed import Client + import topcoffea.modules.remote_environment as remote_environment import wwz4l @@ -307,8 +311,52 @@ def LoadJsonToSampleName(jsonFile, prefix): # Run the processor and get the output tstart = time.time() + ####################################3 ### coffea2023 ### + # Trying this + + fileset = {} + for name, fpaths in fdict.items(): + fileset[name] = {} + fileset[name]["files"] = {} + for fpath in fpaths: + fileset[name]["files"][fpath] = {"object_path": "Events"} + fileset[name]["metadata"] = {"dataset": name} + print(fileset) + + with Client() as _: + print("Number of datasets:",len(fdict)) + dataset_runnable, dataset_updated = preprocess( + fileset, + maybe_step_size=50_000, + align_clusters=False, + files_per_batch=1, + skip_bad_files=True, + #calculate_form=True, + ) + + print('\n\nHere after preprocess!!!') + + # "runnable" means the dataset containing only files that were successfully opened, this either copies or creates metadata entries depending on if you defined some already + # moreover, you could save these two dicts to a file and just read them in later, only ever running preprocess once every time you update your analysis's datasets. + outputs, reports = apply_to_fileset( + processor_instance, + fileset, + uproot_options={"allow_read_errors_with_report": True} + ) + print('\n\nHere after apply_to_fileset') + + coutputs, creports = dask.compute(outputs, reports) # , scheduler=taskvine + + print('\n\nHere after compute!!!') + + exit() + + ####################################3 + + # Works + ''' # Create dict of events objects print("Number of datasets:",len(fdict)) events_dict = {} @@ -319,6 +367,7 @@ def LoadJsonToSampleName(jsonFile, prefix): metadata={"dataset": name}, ).events() + # Get and compute the histograms histos_to_compute = {} for json_name in fdict.keys(): @@ -328,17 +377,9 @@ def LoadJsonToSampleName(jsonFile, prefix): print("Compute histos") output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple - dt = time.time() - tstart - - if executor == "work_queue": - print('Processed {} events in {} seconds ({:.2f} evts/sec).'.format(nevts_total,dt,nevts_total/dt)) - - #nbins = sum(sum(arr.size for arr in h._sumw.values()) for h in output.values() if isinstance(h, hist.Hist)) - #nfilled = sum(sum(np.sum(arr > 0) for arr in h._sumw.values()) for h in output.values() if isinstance(h, hist.Hist)) - #print("Filled %.0f bins, nonzero bins: %1.1f %%" % (nbins, 100*nfilled/nbins,)) + ''' - if executor == "futures": - print("Processing time: %1.2f s with %i workers (%.2f s cpu overall)" % (dt, nworkers, dt*nworkers, )) + dt = time.time() - tstart # Save the output if not os.path.isdir(outpath): os.system("mkdir -p %s"%outpath) From d0568505395b9d5d05e6135365a18ad7ccd582eb Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 5 Jan 2024 10:40:58 -0500 Subject: [PATCH 22/48] Updates for distributed client --- analysis/wwz/run_wwz4l.py | 103 ++++++++++++++++++++------------------ analysis/wwz/wwz4l.py | 6 +-- 2 files changed, 57 insertions(+), 52 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 022e2545..85a31acf 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -7,12 +7,14 @@ import gzip import os import dask +import dask_awkward as dak +from distributed import Client + from coffea.nanoevents import NanoAODSchema from coffea.nanoevents import NanoEventsFactory from coffea.dataset_tools import preprocess from coffea.dataset_tools import apply_to_fileset -from distributed import Client import topcoffea.modules.remote_environment as remote_environment @@ -314,8 +316,7 @@ def LoadJsonToSampleName(jsonFile, prefix): ####################################3 ### coffea2023 ### - # Trying this - + # Get fileset fileset = {} for name, fpaths in fdict.items(): fileset[name] = {} @@ -324,60 +325,64 @@ def LoadJsonToSampleName(jsonFile, prefix): fileset[name]["files"][fpath] = {"object_path": "Events"} fileset[name]["metadata"] = {"dataset": name} print(fileset) + print("Number of datasets:",len(fdict)) - with Client() as _: - print("Number of datasets:",len(fdict)) - dataset_runnable, dataset_updated = preprocess( - fileset, - maybe_step_size=50_000, - align_clusters=False, - files_per_batch=1, - skip_bad_files=True, - #calculate_form=True, - ) - - print('\n\nHere after preprocess!!!') - - # "runnable" means the dataset containing only files that were successfully opened, this either copies or creates metadata entries depending on if you defined some already - # moreover, you could save these two dicts to a file and just read them in later, only ever running preprocess once every time you update your analysis's datasets. - outputs, reports = apply_to_fileset( - processor_instance, - fileset, - uproot_options={"allow_read_errors_with_report": True} - ) - print('\n\nHere after apply_to_fileset') - - coutputs, creports = dask.compute(outputs, reports) # , scheduler=taskvine - - print('\n\nHere after compute!!!') - exit() + #### Try with distributed Client #### + distributed_client = 0 # Tmp + if distributed_client: + with Client() as _: - ####################################3 + # Run preprocess + print("\nRunning preprocess...") + dataset_runnable, dataset_updated = preprocess( + fileset, + maybe_step_size=50_000, + align_clusters=False, + files_per_batch=1, + skip_bad_files=True, + #calculate_form=True, + ) - # Works - ''' - # Create dict of events objects - print("Number of datasets:",len(fdict)) - events_dict = {} - for name, fpaths in fdict.items(): - events_dict[name] = NanoEventsFactory.from_root( - {fpath: "/Events" for fpath in fpaths}, - schemaclass=NanoAODSchema, - metadata={"dataset": name}, - ).events() + # Run apply_to_fileset + print("\nRunning apply_to_fileset...") + histos_to_compute, reports = apply_to_fileset( + processor_instance, + fileset, + uproot_options={"allow_read_errors_with_report": True} + ) + print("\nRunning compute...") + output, report = dask.compute(histos_to_compute, reports) # , scheduler=taskvine - # Get and compute the histograms - histos_to_compute = {} - for json_name in fdict.keys(): - print(f"Getting histos for {json_name}") - histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) + print("\nColumns used...") + used_columns = dak.necessary_columns(histos_to_compute) + print(used_columns) - print("Compute histos") - output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple - ''' + #### Local example #### + local = 1 # Tmp + if local: + + # Create dict of events objects + print("Number of datasets:",len(fdict)) + events_dict = {} + for name, fpaths in fdict.items(): + events_dict[name] = NanoEventsFactory.from_root( + {fpath: "/Events" for fpath in fpaths}, + schemaclass=NanoAODSchema, + metadata={"dataset": name}, + ).events() + + # Get and compute the histograms + histos_to_compute = {} + for json_name in fdict.keys(): + print(f"Getting histos for {json_name}") + histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) + + print("Compute histos") + output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple + dt = time.time() - tstart diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index a0100a1c..d3c76ec3 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -339,7 +339,7 @@ def process(self, events): for syst_var in syst_var_list: # Make a copy of the base weights object, so that each time through the loop we do not double count systs # In this loop over systs that impact kinematics, we will add to the weights objects the SFs that depend on the object kinematics - weights_obj_base_for_kinematic_syst = copy.deepcopy(weights_obj_base) + weights_obj_base_for_kinematic_syst = copy.copy(weights_obj_base) # TODO do we need copy here? #################### Jets #################### @@ -407,8 +407,8 @@ def process(self, events): ######### Apply SFs ######### if not isData: - weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) - weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) + weights_obj_base_for_kinematic_syst.add("lepSF_muon", events.sf_4l_muon, copy.copy(events.sf_4l_hi_muon), copy.copy(events.sf_4l_lo_muon)) # TODO do we still need to copy in dask version + weights_obj_base_for_kinematic_syst.add("lepSF_elec", events.sf_4l_elec, copy.copy(events.sf_4l_hi_elec), copy.copy(events.sf_4l_lo_elec)) ### OLD implimentation from TOP-22-006 ## Btag SF following 1a) in https://twiki.cern.ch/twiki/bin/viewauth/CMS/BTagSFMethods From 0952ffca4d4253ab9ba5d0a853428cec602da5b0 Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 5 Jan 2024 13:25:22 -0500 Subject: [PATCH 23/48] Clean up run script a bit --- analysis/wwz/run_wwz4l.py | 84 +++++++++++++++------------------------ 1 file changed, 31 insertions(+), 53 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 85a31acf..b41e1841 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -329,59 +329,37 @@ def LoadJsonToSampleName(jsonFile, prefix): #### Try with distributed Client #### - distributed_client = 0 # Tmp - if distributed_client: - with Client() as _: - - # Run preprocess - print("\nRunning preprocess...") - dataset_runnable, dataset_updated = preprocess( - fileset, - maybe_step_size=50_000, - align_clusters=False, - files_per_batch=1, - skip_bad_files=True, - #calculate_form=True, - ) - - # Run apply_to_fileset - print("\nRunning apply_to_fileset...") - histos_to_compute, reports = apply_to_fileset( - processor_instance, - fileset, - uproot_options={"allow_read_errors_with_report": True} - ) - - print("\nRunning compute...") - output, report = dask.compute(histos_to_compute, reports) # , scheduler=taskvine - - print("\nColumns used...") - used_columns = dak.necessary_columns(histos_to_compute) - print(used_columns) - - - #### Local example #### - local = 1 # Tmp - if local: - - # Create dict of events objects - print("Number of datasets:",len(fdict)) - events_dict = {} - for name, fpaths in fdict.items(): - events_dict[name] = NanoEventsFactory.from_root( - {fpath: "/Events" for fpath in fpaths}, - schemaclass=NanoAODSchema, - metadata={"dataset": name}, - ).events() - - # Get and compute the histograms - histos_to_compute = {} - for json_name in fdict.keys(): - print(f"Getting histos for {json_name}") - histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) - - print("Compute histos") - output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple + + #with Client() as _: # distributed Client scheduler + with dask.config.set({"scheduler": "sync"}): # Single thread + + # Run preprocess + print("\nRunning preprocess...") + dataset_runnable, dataset_updated = preprocess( + fileset, + maybe_step_size=50_000, + align_clusters=False, + files_per_batch=1, + #skip_bad_files=True, + #calculate_form=True, + ) + + # Run apply_to_fileset + print("\nRunning apply_to_fileset...") + histos_to_compute, reports = apply_to_fileset( + processor_instance, + fileset, + uproot_options={"allow_read_errors_with_report": True} + ) + + # Check columns to be read + print("\nColumns to be read...") + columns_read = dak.necessary_columns(histos_to_compute) + print(columns_read) + + # Compute + print("\nRunning compute...") + output, report = dask.compute(histos_to_compute, reports) # , scheduler=taskvine dt = time.time() - tstart From d8765cd3d8a8fd28f7582970d0f7eadc4db55e00 Mon Sep 17 00:00:00 2001 From: Kelci Date: Sun, 7 Jan 2024 16:19:31 -0500 Subject: [PATCH 24/48] Tmp add run script and json for reproducing error --- analysis/wwz/run_wrapper.sh | 8 ++++++++ .../UL17_WWZJetsTo4L2Nu_forCI_extra.json | 15 +++++++++++++++ 2 files changed, 23 insertions(+) create mode 100644 analysis/wwz/run_wrapper.sh create mode 100644 input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI_extra.json diff --git a/analysis/wwz/run_wrapper.sh b/analysis/wwz/run_wrapper.sh new file mode 100644 index 00000000..24dccb99 --- /dev/null +++ b/analysis/wwz/run_wrapper.sh @@ -0,0 +1,8 @@ +# Get the file the CI uses, and move it to the directory the JSON expects +printf "\nDownloading root file...\n" +wget -nc http://uaf-10.t2.ucsd.edu/~kmohrman/for_ci/for_wwz/WWZJetsTo4L2Nu_4F_TuneCP5_13TeV-amcatnlo-pythia8_RunIISummer20UL17NanoAODv9-106X_mc2017_realistic_v9-v2_NANOAODSIM_3LepTau_4Lep/output_1.root + +# Run the processor via the run_wwz4l.py script +# Note the -x executor argument does not do anything anymore, though should keep it in for now since without it the code tries to default to wq so tries to start packaging up env + +time python run_wwz4l.py ../../input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI.json,../../input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI_extra.json -x iterative diff --git a/input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI_extra.json b/input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI_extra.json new file mode 100644 index 00000000..b30a0cd2 --- /dev/null +++ b/input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI_extra.json @@ -0,0 +1,15 @@ +{ + "xsec": 0.002067, + "year": "2017", + "treeName": "Events", + "histAxisName": "UL17_WWZJetsTo4L2Nu_extra", + "options": "", + "WCnames": [], + "files": [ + "output_1.root" + ], + "nEvents": 54868, + "nGenEvents": 109760, + "nSumOfWeights": 227.15914198199997, + "isData": false +} From 192056ada2aad36b56c50d881f3a8ce4df03f39b Mon Sep 17 00:00:00 2001 From: Kelci Date: Sun, 7 Jan 2024 16:27:25 -0500 Subject: [PATCH 25/48] Tmp make distributed client default in run script --- analysis/wwz/run_wwz4l.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index b41e1841..433ec342 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -330,8 +330,8 @@ def LoadJsonToSampleName(jsonFile, prefix): #### Try with distributed Client #### - #with Client() as _: # distributed Client scheduler - with dask.config.set({"scheduler": "sync"}): # Single thread + #with dask.config.set({"scheduler": "sync"}): # Single thread + with Client() as _: # distributed Client scheduler # Run preprocess print("\nRunning preprocess...") From 75ccf8b566ae07c260d975b904a0f7d4d4805a71 Mon Sep 17 00:00:00 2001 From: Kelci Date: Mon, 8 Jan 2024 18:46:06 -0500 Subject: [PATCH 26/48] Updates to run script --- analysis/wwz/run_wwz4l.py | 49 ++++++++++++++++++++++++++++++++++----- 1 file changed, 43 insertions(+), 6 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 433ec342..50a05e11 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -18,6 +18,8 @@ import topcoffea.modules.remote_environment as remote_environment +from ndcctools.taskvine import DaskVine + import wwz4l LST_OF_KNOWN_EXECUTORS = ["futures","work_queue","iterative"] @@ -331,7 +333,8 @@ def LoadJsonToSampleName(jsonFile, prefix): #### Try with distributed Client #### #with dask.config.set({"scheduler": "sync"}): # Single thread - with Client() as _: # distributed Client scheduler + #with Client() as _: # distributed Client scheduler + with Client() as client: # Run preprocess print("\nRunning preprocess...") @@ -348,18 +351,52 @@ def LoadJsonToSampleName(jsonFile, prefix): print("\nRunning apply_to_fileset...") histos_to_compute, reports = apply_to_fileset( processor_instance, - fileset, + dataset_runnable, uproot_options={"allow_read_errors_with_report": True} ) # Check columns to be read - print("\nColumns to be read...") - columns_read = dak.necessary_columns(histos_to_compute) + print("\nRunning necessary_columns...") + columns_read = dak.necessary_columns(histos_to_compute[list(histos_to_compute.keys())[0]]) print(columns_read) # Compute print("\nRunning compute...") - output, report = dask.compute(histos_to_compute, reports) # , scheduler=taskvine + output_futures, report_futures = {}, {} + for key in histos_to_compute: + output_futures[key], report_futures[key] = client.compute((histos_to_compute[key], reports[key],)) # , scheduler=taskvine + + coutputs, creports = client.gather((output_futures, report_futures,)) + + + + ### Task vine testing ### + do_tv = 0 + if do_tv: + + fdict = {"UL17_WWZJetsTo4L2Nu_forCI": ["/home/k.mohrman/coffea_dir/migrate_to_coffea2023_repo/ewkcoffea/analysis/wwz/output_1.root"]} + + # Create dict of events objects + print("Number of datasets:",len(fdict)) + events_dict = {} + for name, fpaths in fdict.items(): + events_dict[name] = NanoEventsFactory.from_root( + {fpath: "/Events" for fpath in fpaths}, + schemaclass=NanoAODSchema, + metadata={"dataset": name}, + ).events() + + # Get and compute the histograms + histos_to_compute = {} + for json_name in fdict.keys(): + print(f"Getting histos for {json_name}") + histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) + + m = DaskVine([9123,9128], name=f"coffea-vine-{os.environ['USER']}") + + print("Compute histos") + #output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple + coutputs = dask.compute(histos_to_compute, scheduler=m.get, resources={"cores": 1}, resources_mode=None, lazy_transfers=True) dt = time.time() - tstart @@ -369,5 +406,5 @@ def LoadJsonToSampleName(jsonFile, prefix): out_pkl_file = os.path.join(outpath,outname+".pkl.gz") print(f"\nSaving output in {out_pkl_file}...") with gzip.open(out_pkl_file, "wb") as fout: - cloudpickle.dump(output, fout) + cloudpickle.dump(coutputs, fout) print("Done!") From 4b70f10ec468b41e5e3b6c44754a8eb9b73dd4c0 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 11 Jan 2024 09:38:01 -0500 Subject: [PATCH 27/48] Workaround from Lindsey to avoid overtouching --- ewkcoffea/modules/selection_wwz.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index c8e01a77..94b8525e 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -235,7 +235,7 @@ def add4lmask_wwz(events, year, isData, sample_name): def get_z_candidate_mask(lep_collection): # Attach the local index to the lepton objects - lep_collection['idx'] = ak.local_index(lep_collection, axis=1) + lep_local_idx = ak.local_index(lep_collection, axis=1) # Make all pairs of leptons ll_pairs = ak.combinations(lep_collection, 2, fields=["l0","l1"]) @@ -251,8 +251,8 @@ def get_z_candidate_mask(lep_collection): sfos_pair_closest_to_z_idx = ak.argmin(dist_from_z_sfos_pairs,axis=-1,keepdims=True) # Construct a mask (of the shape of the original lep array) corresponding to the leps that are part of the Z candidate - mask = (lep_collection.idx == ak.flatten(ll_pairs_idx.l0[sfos_pair_closest_to_z_idx])) - mask = (mask | (lep_collection.idx == ak.flatten(ll_pairs_idx.l1[sfos_pair_closest_to_z_idx]))) + flat_pair_idxs = ak.flatten(ll_pairs_idx[sfos_pair_closest_to_z_idx]) + mask = ((lep_local_idx == flat_pair_idxs.l0) | (lep_local_idx == flat_pair_idxs.l1)) mask = ak.fill_none(mask, False) return mask From b5411a06d6ba34b8fe59e7c59c3f4979b4624440 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 11 Jan 2024 10:34:20 -0500 Subject: [PATCH 28/48] No longer need workaround for None arrays, see ak issue 2768 --- ewkcoffea/modules/selection_wwz.py | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index 94b8525e..edb3bc04 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -268,13 +268,9 @@ def get_wwz_candidates(lep_collection): leps_from_z_candidate = lep_collection[z_candidate_mask] leps_not_z_candidate = lep_collection[~z_candidate_mask] - if ak.any(leps_from_z_candidate.pt) & ak.any(leps_not_z_candidate.pt): - # Temp untill the ak argsort on None issue is resolved - leps_from_z_candidate_ptordered = leps_from_z_candidate[ak.argsort(leps_from_z_candidate.pt, axis=-1,ascending=False)] - leps_not_z_candidate_ptordered = leps_not_z_candidate[ak.argsort(leps_not_z_candidate.pt, axis=-1,ascending=False)] # This fails wehn the leps_not_z_candidate.pt is None, if/when we need this to be pt ordered will need to figure out how we want to work around this - return [leps_from_z_candidate_ptordered,leps_not_z_candidate_ptordered] - else: - return [leps_from_z_candidate,leps_not_z_candidate] + leps_from_z_candidate_ptordered = leps_from_z_candidate[ak.argsort(leps_from_z_candidate.pt, axis=-1,ascending=False)] + leps_not_z_candidate_ptordered = leps_not_z_candidate[ak.argsort(leps_not_z_candidate.pt, axis=-1,ascending=False)] + return [leps_from_z_candidate_ptordered,leps_not_z_candidate_ptordered] From 362a472aa4bc991c7c593062c7a633c3dd8971af Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 11 Jan 2024 10:35:04 -0500 Subject: [PATCH 29/48] UAF 10 not working so switch to 8 --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 709b8b82..87b6e690 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -105,7 +105,7 @@ jobs: - name: Download root files run: | - wget --no-verbose http://uaf-10.t2.ucsd.edu/~kmohrman/for_ci/for_wwz/WWZJetsTo4L2Nu_4F_TuneCP5_13TeV-amcatnlo-pythia8_RunIISummer20UL17NanoAODv9-106X_mc2017_realistic_v9-v2_NANOAODSIM_3LepTau_4Lep/output_1.root + wget --no-verbose http://uaf-8.t2.ucsd.edu/~kmohrman/for_ci/for_wwz/WWZJetsTo4L2Nu_4F_TuneCP5_13TeV-amcatnlo-pythia8_RunIISummer20UL17NanoAODv9-106X_mc2017_realistic_v9-v2_NANOAODSIM_3LepTau_4Lep/output_1.root - name: Pytest setup run: | From 3be1cd9f1a696765d5ed49c5bc60d06be65bf9db Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 11 Jan 2024 12:10:08 -0500 Subject: [PATCH 30/48] Temp un-fix overtouching workaround --- ewkcoffea/modules/selection_wwz.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index edb3bc04..d66724e8 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -235,7 +235,7 @@ def add4lmask_wwz(events, year, isData, sample_name): def get_z_candidate_mask(lep_collection): # Attach the local index to the lepton objects - lep_local_idx = ak.local_index(lep_collection, axis=1) + lep_collection['idx'] = ak.local_index(lep_collection, axis=1) # Make all pairs of leptons ll_pairs = ak.combinations(lep_collection, 2, fields=["l0","l1"]) @@ -251,8 +251,8 @@ def get_z_candidate_mask(lep_collection): sfos_pair_closest_to_z_idx = ak.argmin(dist_from_z_sfos_pairs,axis=-1,keepdims=True) # Construct a mask (of the shape of the original lep array) corresponding to the leps that are part of the Z candidate - flat_pair_idxs = ak.flatten(ll_pairs_idx[sfos_pair_closest_to_z_idx]) - mask = ((lep_local_idx == flat_pair_idxs.l0) | (lep_local_idx == flat_pair_idxs.l1)) + mask = (lep_collection.idx == ak.flatten(ll_pairs_idx.l0[sfos_pair_closest_to_z_idx])) + mask = (mask | (lep_collection.idx == ak.flatten(ll_pairs_idx.l1[sfos_pair_closest_to_z_idx]))) mask = ak.fill_none(mask, False) return mask From a4a1856c8e3c7c40681aaa350ee2c3b448fdabe8 Mon Sep 17 00:00:00 2001 From: Kelci Date: Thu, 11 Jan 2024 23:27:00 -0500 Subject: [PATCH 31/48] Use events.nom for data for now, as weights object seems now to be None for data --- analysis/wwz/wwz4l.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index d3c76ec3..c7b394a1 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -737,7 +737,8 @@ def process(self, events): # Loop over the hists we want to fill - hout = {} + hout = {} # This is what we'll eventually return + masks_cache = {} # So we don't need to build the same mask multiple times for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): #print("\ndense_axis_name,vals",dense_axis_name) #print("dense_axis_name,vals",dense_axis_vals) @@ -752,7 +753,7 @@ def process(self, events): ) # Decide if we are filling this hist with weight or raw event counts - if dense_axis_name.endswith("_counts"): weights = events.nom + if isData or dense_axis_name.endswith("_counts"): weights = events.nom #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) else: weights = weights_obj_base_for_kinematic_syst.weight(None) @@ -765,6 +766,13 @@ def process(self, events): # Make the cuts mask cuts_lst = [sr_cat] if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data + + # Do not recalculate the mask if we've already computed it + #if tuple(cuts_lst) in masks_cache: + # all_cuts_mask = masks_cache[tuple(cuts_lst)] + #else: + # masks_cache[tuple(cuts_lst)] = selections.all(*cuts_lst) + # all_cuts_mask = masks_cache[tuple(cuts_lst)] all_cuts_mask = selections.all(*cuts_lst) #run = events.run[all_cuts_mask] From 1086d263dbb6e907a91a2cc75111c525ffacd9f3 Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 12 Jan 2024 00:39:57 -0500 Subject: [PATCH 32/48] Remove this root file, it seems to be empty and causes error: files' argument's steps must be an iterable of integer offsets or start-stop pairs --- .../data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json b/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json index 53510137..a51cda25 100644 --- a/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json +++ b/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json @@ -43,7 +43,6 @@ "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_1.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_45.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_38.root", - "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_15.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_32.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_4.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_40.root", @@ -68,4 +67,4 @@ "nSumOfWeights": 52987, "isData": true, "path": "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep" -} \ No newline at end of file +} From bdb9bd5802fca2265f409e5775b2e5ce84a1f1c6 Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 12 Jan 2024 00:42:22 -0500 Subject: [PATCH 33/48] Add fsspec-xrootd to env file --- environment.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index eb226013..4017a201 100644 --- a/environment.yml +++ b/environment.yml @@ -3,7 +3,7 @@ channels: - conda-forge dependencies: - conda-forge::python=3.9 - - numpy=1.23.5 + - numpy - coffea - ndcctools - conda-pack @@ -11,3 +11,4 @@ dependencies: - xrootd - git - pyyaml + - fsspec-xrootd From 27da35d59ac587b049e638b293b3df77c987743a Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 26 Jan 2024 11:13:25 -0500 Subject: [PATCH 34/48] Do not need to skip empty file anymore --- analysis/wwz/run_wwz4l.py | 2 ++ .../data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json | 1 + 2 files changed, 3 insertions(+) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 50a05e11..54bbecb5 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -15,6 +15,7 @@ from coffea.dataset_tools import preprocess from coffea.dataset_tools import apply_to_fileset +from coffea.dataset_tools import filter_files import topcoffea.modules.remote_environment as remote_environment @@ -346,6 +347,7 @@ def LoadJsonToSampleName(jsonFile, prefix): #skip_bad_files=True, #calculate_form=True, ) + dataset_runnable = filter_files(dataset_runnable) # Run apply_to_fileset print("\nRunning apply_to_fileset...") diff --git a/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json b/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json index a51cda25..8f67c3e3 100644 --- a/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json +++ b/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json @@ -43,6 +43,7 @@ "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_1.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_45.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_38.root", + "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_15.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_32.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_4.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/DoubleEG_Run2016D-HIPM_UL2016_MiniAODv2_NanoAODv9-v2_NANOAOD_3LepTau_4Lep/output_40.root", From b54550da5681e6642901b8c8e01452d136cb1337 Mon Sep 17 00:00:00 2001 From: Kelci Date: Sun, 28 Jan 2024 15:42:25 -0500 Subject: [PATCH 35/48] Deepcopy causes crash, probably dont even need copy.copy here --- analysis/wwz/wwz4l.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index a92990de..05ca31c9 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -313,8 +313,8 @@ def process(self, events): weights_obj_base.add('PU', cor_tc.GetPUSF((events.Pileup.nTrueInt), year), cor_tc.GetPUSF(events.Pileup.nTrueInt, year, 'up'), cor_tc.GetPUSF(events.Pileup.nTrueInt, year, 'down')) # Lepton SFs and systs - weights_obj_base.add("lepSF_muon", events.sf_4l_muon, copy.deepcopy(events.sf_4l_hi_muon), copy.deepcopy(events.sf_4l_lo_muon)) - weights_obj_base.add("lepSF_elec", events.sf_4l_elec, copy.deepcopy(events.sf_4l_hi_elec), copy.deepcopy(events.sf_4l_lo_elec)) + weights_obj_base.add("lepSF_muon", events.sf_4l_muon, copy.copy(events.sf_4l_hi_muon), copy.copy(events.sf_4l_lo_muon)) + weights_obj_base.add("lepSF_elec", events.sf_4l_elec, copy.copy(events.sf_4l_hi_elec), copy.copy(events.sf_4l_lo_elec)) # Set up the list of systematics that are handled via event weight variations From bcbfcdb97108549b5bf4059571d3bb64e085630a Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 30 Jan 2024 14:30:03 -0500 Subject: [PATCH 36/48] Fix weights for data --- analysis/wwz/wwz4l.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 05ca31c9..193549b9 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -841,7 +841,7 @@ def process(self, events): if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue # If this is a counts hist, forget the weights and just fill with unit weights - if dense_axis_name.endswith("_counts"): weight = events.nom + if isData or dense_axis_name.endswith("_counts"): weight = events.nom #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) # For testing #else: weights = weights_obj_base_for_kinematic_syst.weight(None) # For testing @@ -851,11 +851,11 @@ def process(self, events): all_cuts_mask = selections.all(*cuts_lst) # Do not recalculate the mask if we've already computed it - #if tuple(cuts_lst) in masks_cache: - # all_cuts_mask = masks_cache[tuple(cuts_lst)] - #else: - # masks_cache[tuple(cuts_lst)] = selections.all(*cuts_lst) - # all_cuts_mask = masks_cache[tuple(cuts_lst)] + if tuple(cuts_lst) in masks_cache: + all_cuts_mask = masks_cache[tuple(cuts_lst)] + else: + masks_cache[tuple(cuts_lst)] = selections.all(*cuts_lst) + all_cuts_mask = masks_cache[tuple(cuts_lst)] #run = events.run[all_cuts_mask] #luminosityBlock = events.luminosityBlock[all_cuts_mask] From 26a0bb725058c3a94ecdf6a4f27471b0e299f871 Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 6 Feb 2024 08:44:41 -0500 Subject: [PATCH 37/48] Try out parallelize_with_dask, temporary add exit after build task graph --- analysis/wwz/run_wwz4l.py | 64 ++++++++++++++++++++++++++++++--------- 1 file changed, 49 insertions(+), 15 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index dd70865d..66b48bdf 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -315,41 +315,48 @@ def LoadJsonToSampleName(jsonFile, prefix): #### Try with distributed Client #### + t_beforepreprocess = time.time() #with dask.config.set({"scheduler": "sync"}): # Single thread #with Client() as _: # distributed Client scheduler - with Client() as client: + #with Client() as client: + with Client(n_workers=8, threads_per_worker=1) as client: # Run preprocess print("\nRunning preprocess...") dataset_runnable, dataset_updated = preprocess( fileset, - maybe_step_size=50_000, + step_size=50_000, align_clusters=False, files_per_batch=1, - #skip_bad_files=True, - #calculate_form=True, + save_form=True, ) dataset_runnable = filter_files(dataset_runnable) + t_beforeapplytofileset = time.time() # Run apply_to_fileset print("\nRunning apply_to_fileset...") histos_to_compute, reports = apply_to_fileset( processor_instance, dataset_runnable, - uproot_options={"allow_read_errors_with_report": True} + uproot_options={"allow_read_errors_with_report": True}, + parallelize_with_dask=True, ) + print("DONE with apply to fileset") + exit() + # Check columns to be read - print("\nRunning necessary_columns...") - columns_read = dak.necessary_columns(histos_to_compute[list(histos_to_compute.keys())[0]]) - print(columns_read) + #print("\nRunning necessary_columns...") + #columns_read = dak.necessary_columns(histos_to_compute[list(histos_to_compute.keys())[0]]) + #print(columns_read) + t_beforecompute = time.time() # Compute print("\nRunning compute...") output_futures, report_futures = {}, {} for key in histos_to_compute: - output_futures[key], report_futures[key] = client.compute((histos_to_compute[key], reports[key],)) # , scheduler=taskvine + output_futures[key], report_futures[key] = client.compute((histos_to_compute[key], reports[key],)) coutputs, creports = client.gather((output_futures, report_futures,)) @@ -359,7 +366,7 @@ def LoadJsonToSampleName(jsonFile, prefix): do_tv = 0 if do_tv: - fdict = {"UL17_WWZJetsTo4L2Nu_forCI": ["/home/k.mohrman/coffea_dir/migrate_to_coffea2023_repo/ewkcoffea/analysis/wwz/output_1.root"]} + #fdict = {"UL17_WWZJetsTo4L2Nu_forCI": ["/home/k.mohrman/coffea_dir/migrate_to_coffea2023_repo/ewkcoffea/analysis/wwz/output_1.root"]} # Create dict of events objects print("Number of datasets:",len(fdict)) @@ -371,6 +378,7 @@ def LoadJsonToSampleName(jsonFile, prefix): metadata={"dataset": name}, ).events() + t_beforeapplytofileset = time.time() # Get and compute the histograms histos_to_compute = {} for json_name in fdict.keys(): @@ -379,13 +387,39 @@ def LoadJsonToSampleName(jsonFile, prefix): m = DaskVine([9123,9128], name=f"coffea-vine-{os.environ['USER']}") - print("Compute histos") - #output = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple - coutputs = dask.compute(histos_to_compute, scheduler=m.get, resources={"cores": 1}, resources_mode=None, lazy_transfers=True) - - + t_beforecompute = time.time() + #coutputs = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple + #coutputs = dask.compute(histos_to_compute, scheduler=m.get, resources={"cores": 1}, resources_mode=None, lazy_transfers=True) + #with Client() as _: + #coutputs = dask.compute(histos_to_compute) + + #proxy = m.declare_file(f"/tmp/x509up_u{os.getuid()}", cache=True) + #coutputs = dask.compute( + # histos_to_compute, + # scheduler=m.get, + # resources={"cores": 1}, + # resources_mode=None, + # lazy_transfers=True, + # extra_files={proxy: "proxy.pem"}, + # #env_vars={"X509_USER_PROXY": "proxy.pem"}, + #) + + + t_end = time.time() dt = time.time() - tstart + time_for_preprocess = t_beforeapplytofileset - t_beforepreprocess + time_for_applytofset = t_beforecompute - t_beforeapplytofileset + time_for_compute = t_end - t_beforecompute + time_total = time_for_preprocess + time_for_applytofset + time_for_compute + print("") + print("time_pre",t_beforepreprocess-tstart) + print("time_for_preprocess",time_for_preprocess) + print("time_for_applytofset",time_for_applytofset) + print("time_for_compute",time_for_compute) + print("time_total",time_total) + print("dt",dt) + # Save the output if not os.path.isdir(outpath): os.system("mkdir -p %s"%outpath) out_pkl_file = os.path.join(outpath,outname+".pkl.gz") From acbfc5764318b9e74120392ef4cd611be66a563d Mon Sep 17 00:00:00 2001 From: Kelci Date: Sat, 16 Mar 2024 16:42:40 -0400 Subject: [PATCH 38/48] Clean up run script --- analysis/wwz/run_wwz4l.py | 67 +++++++++++++++++++++------------------ 1 file changed, 37 insertions(+), 30 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 66b48bdf..32e71184 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -297,7 +297,7 @@ def LoadJsonToSampleName(jsonFile, prefix): } # Run the processor and get the output - tstart = time.time() + t_start = time.time() ####################################3 ### coffea2023 ### @@ -315,53 +315,57 @@ def LoadJsonToSampleName(jsonFile, prefix): #### Try with distributed Client #### - t_beforepreprocess = time.time() + t_before_with_Client_as_client = time.time() #with dask.config.set({"scheduler": "sync"}): # Single thread - #with Client() as _: # distributed Client scheduler - #with Client() as client: - with Client(n_workers=8, threads_per_worker=1) as client: + #with Client(n_workers=8, threads_per_worker=1) as client: + with Client() as client: # Run preprocess print("\nRunning preprocess...") + t_before_preprocess = time.time() dataset_runnable, dataset_updated = preprocess( fileset, step_size=50_000, align_clusters=False, files_per_batch=1, - save_form=True, + save_form=False, ) dataset_runnable = filter_files(dataset_runnable) - t_beforeapplytofileset = time.time() + + # Dump to a json + #with gzip.open("dataset_runnable_test.json.gz", "wt") as fout: + # json.dump(dataset_runnable, fout) + #exit() + # Or load a json + #with gzip.open("dataset_runnable_test.json.gz", "r") as f: + # dataset_runnable = json.load(f) + + + t_before_applytofileset = time.time() # Run apply_to_fileset print("\nRunning apply_to_fileset...") histos_to_compute, reports = apply_to_fileset( processor_instance, dataset_runnable, uproot_options={"allow_read_errors_with_report": True}, - parallelize_with_dask=True, + #parallelize_with_dask=True, ) - print("DONE with apply to fileset") - exit() - # Check columns to be read #print("\nRunning necessary_columns...") #columns_read = dak.necessary_columns(histos_to_compute[list(histos_to_compute.keys())[0]]) #print(columns_read) - t_beforecompute = time.time() # Compute + t_before_compute = time.time() print("\nRunning compute...") - output_futures, report_futures = {}, {} - for key in histos_to_compute: - output_futures[key], report_futures[key] = client.compute((histos_to_compute[key], reports[key],)) - - coutputs, creports = client.gather((output_futures, report_futures,)) + coutputs, creports = dask.compute(histos_to_compute, reports) + ######################### ### Task vine testing ### do_tv = 0 if do_tv: @@ -378,6 +382,9 @@ def LoadJsonToSampleName(jsonFile, prefix): metadata={"dataset": name}, ).events() + print("done export to json") + exit() + t_beforeapplytofileset = time.time() # Get and compute the histograms histos_to_compute = {} @@ -403,22 +410,22 @@ def LoadJsonToSampleName(jsonFile, prefix): # extra_files={proxy: "proxy.pem"}, # #env_vars={"X509_USER_PROXY": "proxy.pem"}, #) + ######################### + # Print timing info t_end = time.time() - dt = time.time() - tstart - - time_for_preprocess = t_beforeapplytofileset - t_beforepreprocess - time_for_applytofset = t_beforecompute - t_beforeapplytofileset - time_for_compute = t_end - t_beforecompute - time_total = time_for_preprocess + time_for_applytofset + time_for_compute - print("") - print("time_pre",t_beforepreprocess-tstart) - print("time_for_preprocess",time_for_preprocess) - print("time_for_applytofset",time_for_applytofset) - print("time_for_compute",time_for_compute) - print("time_total",time_total) - print("dt",dt) + dt = t_end - t_start + time_for_with_Client_as_client = t_before_preprocess - t_before_with_Client_as_client + time_for_preprocess = t_before_applytofileset - t_before_preprocess + time_for_applytofset = t_before_compute - t_before_applytofileset + time_for_compute = t_end - t_before_compute + print("\nTiming info:") + print(f"\tTime for with Client() as client: {round(time_for_with_Client_as_client,3)}s , ({round(time_for_with_Client_as_client/60,3)}m)") + print(f"\tTime for preprocess : {round(time_for_preprocess,3)}s , ({round(time_for_preprocess/60,3)}m)") + print(f"\tTime for apply to fileset : {round(time_for_applytofset,3)}s , ({round(time_for_applytofset/60,3)}m)") + print(f"\tTime for compute : {round(time_for_compute,3)}s , ({round(time_for_compute/60,3)}m)") + #print(f"\tSanity check, these should equal: {round(dt,3)} , {round(time_for_with_Client_as_client+time_for_preprocess+time_for_applytofset+time_for_compute,3)}") # Save the output if not os.path.isdir(outpath): os.system("mkdir -p %s"%outpath) From fc033000cd94974025d44f706b7700d6b03d9981 Mon Sep 17 00:00:00 2001 From: Kelci Date: Sat, 16 Mar 2024 16:55:49 -0400 Subject: [PATCH 39/48] Remove some old WQ stuff --- analysis/wwz/run_wwz4l.py | 94 +-------------------------------------- 1 file changed, 1 insertion(+), 93 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 32e71184..a31f435b 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -7,7 +7,6 @@ import gzip import os import dask -import dask_awkward as dak from distributed import Client from coffea.nanoevents import NanoAODSchema @@ -200,101 +199,9 @@ def LoadJsonToSampleName(jsonFile, prefix): print('pretending...') exit() - # Extract the list of all WCs, as long as we haven't already specified one. - if len(wc_lst) == 0: - for k in samplesdict.keys(): - for wc in samplesdict[k]['WCnames']: - if wc not in wc_lst: - wc_lst.append(wc) - - if len(wc_lst) > 0: - # Yes, why not have the output be in correct English? - if len(wc_lst) == 1: - wc_print = wc_lst[0] - elif len(wc_lst) == 2: - wc_print = wc_lst[0] + ' and ' + wc_lst[1] - else: - wc_print = ', '.join(wc_lst[:-1]) + ', and ' + wc_lst[-1] - print('Wilson Coefficients: {}.'.format(wc_print)) - else: - print('No Wilson coefficients specified') processor_instance = wwz4l.AnalysisProcessor(samplesdict,wc_lst,hist_lst,ecut_threshold,do_errors,do_systs,split_lep_flavor,skip_sr,skip_cr) - if executor == "work_queue": - executor_args = { - 'master_name': '{}-workqueue-coffea'.format(os.environ['USER']), - - # find a port to run work queue in this range: - 'port': port, - - 'debug_log': 'debug.log', - 'transactions_log': 'tr.log', - 'stats_log': 'stats.log', - 'tasks_accum_log': 'tasks.log', - - 'environment_file': remote_environment.get_environment( - extra_pip=["mt2","xgboost"], - extra_pip_local = {"ewkcoffea": ["ewkcoffea", "setup.py"]}, - ), - 'extra_input_files': ["wwz4l.py"], - - 'retries': 5, - - # use mid-range compression for chunks results. 9 is the default for work - # queue in coffea. Valid values are 0 (minimum compression, less memory - # usage) to 16 (maximum compression, more memory usage). - 'compression': 9, - - # automatically find an adequate resource allocation for tasks. - # tasks are first tried using the maximum resources seen of previously ran - # tasks. on resource exhaustion, they are retried with the maximum resource - # values, if specified below. if a maximum is not specified, the task waits - # forever until a larger worker connects. - 'resource_monitor': True, - 'resources_mode': 'auto', - - # this resource values may be omitted when using - # resources_mode: 'auto', but they do make the initial portion - # of a workflow run a little bit faster. - # Rather than using whole workers in the exploratory mode of - # resources_mode: auto, tasks are forever limited to a maximum - # of 8GB of mem and disk. - # - # NOTE: The very first tasks in the exploratory - # mode will use the values specified here, so workers need to be at least - # this large. If left unspecified, tasks will use whole workers in the - # exploratory mode. - # 'cores': 1, - # 'disk': 8000, #MB - # 'memory': 10000, #MB - - # control the size of accumulation tasks. Results are - # accumulated in groups of size chunks_per_accum, keeping at - # most chunks_per_accum at the same time in memory per task. - 'chunks_per_accum': 25, - 'chunks_accum_in_mem': 2, - - # terminate workers on which tasks have been running longer than average. - # This is useful for temporary conditions on worker nodes where a task will - # be finish faster is ran in another worker. - # the time limit is computed by multipliying the average runtime of tasks - # by the value of 'fast_terminate_workers'. Since some tasks can be - # legitimately slow, no task can trigger the termination of workers twice. - # - # warning: small values (e.g. close to 1) may cause the workflow to misbehave, - # as most tasks will be terminated. - # - # Less than 1 disables it. - 'fast_terminate_workers': 0, - - # print messages when tasks are submitted, finished, etc., - # together with their resource allocation and usage. If a task - # fails, its standard output is also printed, so we can turn - # off print_stdout for all tasks. - 'verbose': True, - 'print_stdout': False, - } # Run the processor and get the output t_start = time.time() @@ -354,6 +261,7 @@ def LoadJsonToSampleName(jsonFile, prefix): ) # Check columns to be read + #import dask_awkward as dak #print("\nRunning necessary_columns...") #columns_read = dak.necessary_columns(histos_to_compute[list(histos_to_compute.keys())[0]]) #print(columns_read) From 8920081b3d4aac9563d977b246e75c5dc10a9d65 Mon Sep 17 00:00:00 2001 From: Kelci Date: Sun, 17 Mar 2024 13:49:48 -0400 Subject: [PATCH 40/48] Using cache for masks --- analysis/wwz/wwz4l.py | 33 ++++++++++++++++++++------------- 1 file changed, 20 insertions(+), 13 deletions(-) diff --git a/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index 193549b9..8e1d34db 100644 --- a/analysis/wwz/wwz4l.py +++ b/analysis/wwz/wwz4l.py @@ -805,7 +805,8 @@ def process(self, events): # Loop over the hists we want to fill hout = {} # This is what we'll eventually return - masks_cache = {} # So we don't need to build the same mask multiple times + masked_val_cache = {} # So we don't need to build the same mask multiple times + masked_weights_cache = {} # So we don't need to build the same mask multiple times for dense_axis_name, dense_axis_vals in dense_variables_dict.items(): if dense_axis_name not in self._hist_lst: print(f"Skipping \"{dense_axis_name}\", it is not in the list of hists to include.") @@ -840,22 +841,28 @@ def process(self, events): # Skip filling if this variable is not relevant for this selection if (dense_axis_name in exclude_var_dict) and (sr_cat in exclude_var_dict[dense_axis_name]): continue - # If this is a counts hist, forget the weights and just fill with unit weights - if isData or dense_axis_name.endswith("_counts"): weight = events.nom - #else: weights = weights_obj_base_for_kinematic_syst.partial_weight(include=["norm"]) # For testing - #else: weights = weights_obj_base_for_kinematic_syst.weight(None) # For testing - # Make the cuts mask cuts_lst = [sr_cat] if isData: cuts_lst.append("is_good_lumi") # Apply golden json requirements if this is data all_cuts_mask = selections.all(*cuts_lst) - # Do not recalculate the mask if we've already computed it - if tuple(cuts_lst) in masks_cache: - all_cuts_mask = masks_cache[tuple(cuts_lst)] + # Used cached masked dense axis variable vals if we've already computed it + var_key = tuple(cuts_lst + [dense_axis_name]) + if var_key not in masked_val_cache: + masked_val_cache[var_key] = dense_axis_vals[all_cuts_mask] + masked_vals = masked_val_cache[var_key] + + # Used cached masked weights if we've already computed it + if isData or dense_axis_name.endswith("_counts"): + # Special case for the *_counts histograms, these get filled with unit weights (like data) + # So need a different weights_key for this special case + weight = events.nom + weights_key = tuple(cuts_lst + [wgt_fluct, "weights_counts"]) else: - masks_cache[tuple(cuts_lst)] = selections.all(*cuts_lst) - all_cuts_mask = masks_cache[tuple(cuts_lst)] + weights_key = tuple(cuts_lst + [wgt_fluct, "weights_norm"]) + if weights_key not in masked_weights_cache: + masked_weights_cache[weights_key] = weight[all_cuts_mask] + masked_weight = masked_weights_cache[weights_key] #run = events.run[all_cuts_mask] #luminosityBlock = events.luminosityBlock[all_cuts_mask] @@ -874,8 +881,8 @@ def process(self, events): # Fill the histos axes_fill_info_dict = { - dense_axis_name : dense_axis_vals[all_cuts_mask], - "weight" : weight[all_cuts_mask], + dense_axis_name : masked_vals, + "weight" : masked_weight, "process" : histAxisName, "category" : sr_cat, "systematic" : wgt_fluct, From 43ae6b02a5974aae59c0f680b3108a9c922215e6 Mon Sep 17 00:00:00 2001 From: Kelci Date: Sun, 17 Mar 2024 15:50:50 -0400 Subject: [PATCH 41/48] Remove unused import to make flake8 happy --- analysis/wwz/run_wwz4l.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index a31f435b..37ffa972 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -16,8 +16,6 @@ from coffea.dataset_tools import apply_to_fileset from coffea.dataset_tools import filter_files -import topcoffea.modules.remote_environment as remote_environment - from ndcctools.taskvine import DaskVine import wwz4l From 6d541cc9c11a361ee93d644eb74f66d89a1e9de2 Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 26 Mar 2024 14:42:59 -0400 Subject: [PATCH 42/48] Adding jsons for hpg sample --- .../UL16APV_DYJetsToLL_M_10to50_MLM.json | 45 +++ .../UL16APV_DYJetsToLL_M_50_MLM.json | 92 +++++ .../bkg_samples/UL16APV_SSWW.json | 32 ++ .../UL16APV_ST_antitop_t-channel.json | 72 ++++ .../bkg_samples/UL16APV_ST_top_s-channel.json | 38 ++ .../bkg_samples/UL16APV_ST_top_t-channel.json | 75 ++++ .../bkg_samples/UL16APV_TTTo2L2Nu.json | 59 +++ .../bkg_samples/UL16APV_TTWJetsToLNu.json | 52 +++ .../bkg_samples/UL16APV_TTWJetsToQQ.json | 33 ++ .../bkg_samples/UL16APV_TTZToLLNuNu_M_10.json | 41 ++ .../bkg_samples/UL16APV_TTZToLL_M_1to10.json | 32 ++ .../bkg_samples/UL16APV_TTZToQQ.json | 41 ++ .../bkg_samples/UL16APV_VHnobb.json | 47 +++ .../bkg_samples/UL16APV_WJetsToLNu.json | 85 ++++ .../bkg_samples/UL16APV_WWTo2L2Nu.json | 38 ++ .../bkg_samples/UL16APV_WWW.json | 35 ++ .../bkg_samples/UL16APV_WZTo3LNu.json | 89 +++++ .../bkg_samples/UL16APV_WZZ.json | 34 ++ .../bkg_samples/UL16APV_ZZTo4L.json | 70 ++++ .../bkg_samples/UL16APV_ZZZ.json | 35 ++ .../bkg_samples/UL16APV_ggToZZTo2e2mu.json | 31 ++ .../bkg_samples/UL16APV_ggToZZTo2e2tau.json | 28 ++ .../bkg_samples/UL16APV_ggToZZTo2mu2tau.json | 24 ++ .../bkg_samples/UL16APV_ggToZZTo4e.json | 25 ++ .../bkg_samples/UL16APV_ggToZZTo4mu.json | 37 ++ .../bkg_samples/UL16APV_ggToZZTo4tau.json | 32 ++ .../bkg_samples/UL16APV_tW_noFullHad.json | 30 ++ .../bkg_samples/UL16APV_tWll.json | 26 ++ .../bkg_samples/UL16APV_tZq.json | 51 +++ .../bkg_samples/UL16APV_tbarW_noFullHad.json | 38 ++ .../bkg_samples/UL16APV_ttHnobb.json | 54 +++ .../UL16_DYJetsToLL_M_10to50_MLM.json | 60 +++ .../bkg_samples/UL16_DYJetsToLL_M_50_MLM.json | 87 ++++ .../bkg_samples/UL16_SSWW.json | 49 +++ .../UL16_ST_antitop_t-channel.json | 61 +++ .../bkg_samples/UL16_ST_top_s-channel.json | 45 +++ .../bkg_samples/UL16_ST_top_t-channel.json | 112 ++++++ .../bkg_samples/UL16_TTTo2L2Nu.json | 75 ++++ .../bkg_samples/UL16_TTWJetsToLNu.json | 38 ++ .../bkg_samples/UL16_TTWJetsToQQ.json | 34 ++ .../bkg_samples/UL16_TTZToLLNuNu_M_10.json | 68 ++++ .../bkg_samples/UL16_TTZToLL_M_1to10.json | 34 ++ .../bkg_samples/UL16_TTZToQQ.json | 46 +++ .../bkg_samples/UL16_VHnobb.json | 40 ++ .../bkg_samples/UL16_WJetsToLNu.json | 54 +++ .../bkg_samples/UL16_WWTo2L2Nu.json | 33 ++ .../bkg_samples/UL16_WWW.json | 40 ++ .../bkg_samples/UL16_WZTo3LNu.json | 57 +++ .../bkg_samples/UL16_WZZ.json | 44 +++ .../bkg_samples/UL16_ZZTo4L.json | 125 ++++++ .../bkg_samples/UL16_ZZZ.json | 30 ++ .../bkg_samples/UL16_ggToZZTo2e2mu.json | 28 ++ .../bkg_samples/UL16_ggToZZTo2e2tau.json | 35 ++ .../bkg_samples/UL16_ggToZZTo2mu2tau.json | 40 ++ .../bkg_samples/UL16_ggToZZTo4e.json | 22 ++ .../bkg_samples/UL16_ggToZZTo4mu.json | 18 + .../bkg_samples/UL16_ggToZZTo4tau.json | 38 ++ .../bkg_samples/UL16_tW_noFullHad.json | 37 ++ .../bkg_samples/UL16_tWll.json | 27 ++ .../bkg_samples/UL16_tZq.json | 78 ++++ .../bkg_samples/UL16_tbarW_noFullHad.json | 36 ++ .../bkg_samples/UL16_ttHnobb.json | 48 +++ .../UL17_DYJetsToLL_M_10to50_MLM.json | 103 +++++ .../bkg_samples/UL17_DYJetsToLL_M_50_MLM.json | 101 +++++ .../bkg_samples/UL17_SSWW.json | 28 ++ .../UL17_ST_antitop_t-channel.json | 86 ++++ .../bkg_samples/UL17_ST_top_s-channel.json | 42 ++ .../bkg_samples/UL17_ST_top_t-channel.json | 223 +++++++++++ .../bkg_samples/UL17_TTTo2L2Nu.json | 125 ++++++ .../bkg_samples/UL17_TTWJetsToLNu.json | 35 ++ .../bkg_samples/UL17_TTWJetsToQQ.json | 36 ++ 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b/input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL18_qqToZHToZTo2L.json new file mode 100644 index 00000000..4a2e9a21 --- /dev/null +++ b/input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL18_qqToZHToZTo2L.json @@ -0,0 +1,44 @@ +{ + "xsec": 0.0018639, + "year": "2018", + "treeName": "Events", + "histAxisName": "UL18_qqToZHToZTo2L", + "options": "", + "WCnames": [], + "files": [ + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep/output_8.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep/output_6.root", + 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"/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep/output_3.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep/output_9.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep/output_14.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep/output_2.root" + ], + "nEvents": 4026892, + "nGenEvents": 9771771, + "nSumOfWeights": 804944.0220015, + "isData": false, + "path": "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL18NanoAODv9-106X_upgrade2018_realistic_v16_L1v1-v2_NANOAODSIM_3LepTau_4Lep", + "nSumOfLheWeights": [ + 828820.9044735682, + 821990.0018258932, + 812886.1872470793, + 780981.1794060903, + 804941.2642200004, + 823061.5763448331, + 746563.0557096662, + 779247.023990807, + 805125.1505450414 + ] +} \ No newline at end of file From 7f4cac450694ff9e9c2666b759a625e17719d70d Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 26 Mar 2024 15:48:34 -0400 Subject: [PATCH 43/48] Adding option to run with TaskVine --- analysis/wwz/run_wwz4l.py | 78 ++++++++++++++------------------------- 1 file changed, 27 insertions(+), 51 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 37ffa972..93adb1d2 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -20,13 +20,13 @@ import wwz4l -LST_OF_KNOWN_EXECUTORS = ["futures","work_queue","iterative"] +LST_OF_KNOWN_EXECUTORS = ["local","task_vine"] if __name__ == '__main__': parser = argparse.ArgumentParser(description='You can customize your run') parser.add_argument('jsonFiles' , nargs='?', default='', help = 'Json file(s) containing files and metadata') - parser.add_argument('--executor','-x' , default='work_queue', help = 'Which executor to use', choices=LST_OF_KNOWN_EXECUTORS) + parser.add_argument('--executor','-x' , default='dask', help = 'Which executor to use', choices=LST_OF_KNOWN_EXECUTORS) parser.add_argument('--prefix', '-r' , nargs='?', default='', help = 'Prefix or redirector to look for the files') parser.add_argument('--test','-t' , action='store_true' , help = 'To perform a test, run over a few events in a couple of chunks') parser.add_argument('--pretend' , action='store_true', help = 'Read json files but, not execute the analysis') @@ -204,9 +204,12 @@ def LoadJsonToSampleName(jsonFile, prefix): # Run the processor and get the output t_start = time.time() + ####################################3 ### coffea2023 ### + #fdict = {"UL17_WWZJetsTo4L2Nu_forCI": ["/home/k.mohrman/coffea_dir/migrate_to_coffea2023_repo/ewkcoffea/analysis/wwz/output_1.root"]} + # Get fileset fileset = {} for name, fpaths in fdict.items(): @@ -218,11 +221,9 @@ def LoadJsonToSampleName(jsonFile, prefix): print(fileset) print("Number of datasets:",len(fdict)) - - #### Try with distributed Client #### + #### Run preprocess, build task graphs, compute #### t_before_with_Client_as_client = time.time() - #with dask.config.set({"scheduler": "sync"}): # Single thread #with Client(n_workers=8, threads_per_worker=1) as client: with Client() as client: @@ -244,7 +245,7 @@ def LoadJsonToSampleName(jsonFile, prefix): # json.dump(dataset_runnable, fout) #exit() # Or load a json - #with gzip.open("dataset_runnable_test.json.gz", "r") as f: + #with gzip.open("dataset_runnable_test_mar16_full.json.gz", "r") as f: # dataset_runnable = json.load(f) @@ -258,6 +259,10 @@ def LoadJsonToSampleName(jsonFile, prefix): #parallelize_with_dask=True, ) + # Does not work + #with gzip.open("histos_to_compute_full_mar25.json.gz", "wb") as fout: + # cloudpickle.dump(histos_to_compute, fout) + # Check columns to be read #import dask_awkward as dak #print("\nRunning necessary_columns...") @@ -267,56 +272,27 @@ def LoadJsonToSampleName(jsonFile, prefix): # Compute t_before_compute = time.time() print("\nRunning compute...") - coutputs, creports = dask.compute(histos_to_compute, reports) + if executor == "task_vine": + print("Running with Task vine") + m = DaskVine([9123,9128], name=f"coffea-vine-{os.environ['USER']}") + proxy = m.declare_file(f"/tmp/x509up_u{os.getuid()}", cache=True) + coutputs, reports = dask.compute( + histos_to_compute, + reports, + scheduler=m.get, + resources={"cores": 1}, + resources_mode=None, + lazy_transfers=True, + extra_files={proxy: "proxy.pem"}, + env_vars={"X509_USER_PROXY": "proxy.pem"}, + ) + else: + coutputs, creports = dask.compute(histos_to_compute, reports) - ######################### - ### Task vine testing ### - do_tv = 0 - if do_tv: - - #fdict = {"UL17_WWZJetsTo4L2Nu_forCI": ["/home/k.mohrman/coffea_dir/migrate_to_coffea2023_repo/ewkcoffea/analysis/wwz/output_1.root"]} - - # Create dict of events objects - print("Number of datasets:",len(fdict)) - events_dict = {} - for name, fpaths in fdict.items(): - events_dict[name] = NanoEventsFactory.from_root( - {fpath: "/Events" for fpath in fpaths}, - schemaclass=NanoAODSchema, - metadata={"dataset": name}, - ).events() - print("done export to json") - exit() - t_beforeapplytofileset = time.time() - # Get and compute the histograms - histos_to_compute = {} - for json_name in fdict.keys(): - print(f"Getting histos for {json_name}") - histos_to_compute[json_name] = processor_instance.process(events_dict[json_name]) - - m = DaskVine([9123,9128], name=f"coffea-vine-{os.environ['USER']}") - - t_beforecompute = time.time() - #coutputs = dask.compute(histos_to_compute)[0] # Output of dask.compute is a tuple - #coutputs = dask.compute(histos_to_compute, scheduler=m.get, resources={"cores": 1}, resources_mode=None, lazy_transfers=True) - #with Client() as _: - #coutputs = dask.compute(histos_to_compute) - - #proxy = m.declare_file(f"/tmp/x509up_u{os.getuid()}", cache=True) - #coutputs = dask.compute( - # histos_to_compute, - # scheduler=m.get, - # resources={"cores": 1}, - # resources_mode=None, - # lazy_transfers=True, - # extra_files={proxy: "proxy.pem"}, - # #env_vars={"X509_USER_PROXY": "proxy.pem"}, - #) - ######################### # Print timing info From 0ed383026e5e7547a4006b789fa8810a724e32e4 Mon Sep 17 00:00:00 2001 From: Kelci Date: Tue, 26 Mar 2024 16:00:33 -0400 Subject: [PATCH 44/48] Fix CI --- analysis/wwz/run_wwz4l.py | 3 --- tests/test_futures_wwz.py | 2 +- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 93adb1d2..42009f00 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -9,9 +9,6 @@ import dask from distributed import Client -from coffea.nanoevents import NanoAODSchema -from coffea.nanoevents import NanoEventsFactory - from coffea.dataset_tools import preprocess from coffea.dataset_tools import apply_to_fileset from coffea.dataset_tools import filter_files diff --git a/tests/test_futures_wwz.py b/tests/test_futures_wwz.py index 0ce8f2d4..ae5cb7af 100644 --- a/tests/test_futures_wwz.py +++ b/tests/test_futures_wwz.py @@ -7,7 +7,7 @@ def test_topcoffea(): "python", "analysis/wwz/run_wwz4l.py", "-x", - "futures", + "local", "input_samples/sample_jsons/test_samples/UL17_WWZJetsTo4L2Nu_forCI.json", "-o", "output_check_yields", From 817f592088471224ed12cee30d4b586faefec8f9 Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 5 Apr 2024 15:13:26 -0400 Subject: [PATCH 45/48] Fix some missing and duplicates, and add cfgs for the local jsons --- .../cfgs/wwz_analysis/data_samples_hpg.cfg | 63 +++++++ .../wwz_analysis/mc_sig_bkg_samples_hpg.cfg | 155 ++++++++++++++++++ .../UL16/.DoubleEG_Run2016F-UL2016.json.swp | Bin 12288 -> 0 bytes .../UL16/DoubleEG_Run2016F-UL2016.json | 6 +- .../sig_samples/UL16_qqToZHToZTo2L.json | 7 +- 5 files changed, 226 insertions(+), 5 deletions(-) create mode 100644 input_samples/cfgs/wwz_analysis/data_samples_hpg.cfg create mode 100644 input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples_hpg.cfg delete mode 100644 input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/.DoubleEG_Run2016F-UL2016.json.swp diff --git a/input_samples/cfgs/wwz_analysis/data_samples_hpg.cfg b/input_samples/cfgs/wwz_analysis/data_samples_hpg.cfg new file mode 100644 index 00000000..9bb055b9 --- /dev/null +++ b/input_samples/cfgs/wwz_analysis/data_samples_hpg.cfg @@ -0,0 +1,63 @@ +# Data skims + +# UL16 +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016F-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016G-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016H-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleMuon_Run2016F-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleMuon_Run2016G-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleMuon_Run2016H-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/MuonEG_Run2016F-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/MuonEG_Run2016G-UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/MuonEG_Run2016H-UL2016.json + +# UL16APV +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleEG_Run2016B-ver1_HIPM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleEG_Run2016B-ver2_HIPM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleEG_Run2016C-HIPM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleEG_Run2016E-HIPM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleEG_Run2016F-HIPM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleMuon_Run2016B-ver1_HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleMuon_Run2016B-ver2_HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleMuon_Run2016C-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleMuon_Run2016D-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleMuon_Run2016E-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/DoubleMuon_Run2016F-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/MuonEG_Run2016B-ver1_HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/MuonEG_Run2016B-ver2_HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/MuonEG_Run2016C-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/MuonEG_Run2016D-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/MuonEG_Run2016E-HIPM_UL2016.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16APV/MuonEG_Run2016F-HIPM_UL2016.json + +# UL17 +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleEG_Run2017B-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleEG_Run2017C-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleEG_Run2017D-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleEG_Run2017E-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleEG_Run2017F-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleMuon_Run2017B-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleMuon_Run2017C-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleMuon_Run2017D-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleMuon_Run2017E-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/DoubleMuon_Run2017F-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/MuonEG_Run2017B-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/MuonEG_Run2017C-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/MuonEG_Run2017D-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/MuonEG_Run2017E-UL2017.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL17/MuonEG_Run2017F-UL2017.json + +# UL18 +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/DoubleMuon_Run2018A-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/DoubleMuon_Run2018B-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/DoubleMuon_Run2018C-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/DoubleMuon_Run2018D-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/EGamma_Run2018A-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/EGamma_Run2018B-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/EGamma_Run2018C-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/EGamma_Run2018D-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/MuonEG_Run2018A-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/MuonEG_Run2018B-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/MuonEG_Run2018C-UL2018.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL18/MuonEG_Run2018D-UL2018.json diff --git a/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples_hpg.cfg b/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples_hpg.cfg new file mode 100644 index 00000000..18fb014c --- /dev/null +++ b/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples_hpg.cfg @@ -0,0 +1,155 @@ +### Sig samples ### + +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16APV_qqToZHToZTo2L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16APV_GluGluZH.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16APV_WWZJetsTo4L2Nu.json + +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_qqToZHToZTo2L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_GluGluZH.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_WWZJetsTo4L2Nu.json + +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL17_qqToZHToZTo2L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL17_GluGluZH.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL17_WWZJetsTo4L2Nu.json + +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL18_qqToZHToZTo2L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL18_GluGluZH.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL18_WWZJetsTo4L2Nu.json + +#### Bkg samples ### + +# Backgrounds UL16APV +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_TTZToLL_M_1to10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_TTZToLLNuNu_M_10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_TTZToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ZZTo4L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ggToZZTo2e2mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ggToZZTo2e2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ggToZZTo2mu2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ggToZZTo4e.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ggToZZTo4mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ggToZZTo4tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_WWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_WZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ZZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_SSWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ST_antitop_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ST_top_s-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ST_top_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_tbarW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_ttHnobb.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_TTTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_TTWJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_TTWJetsToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_tW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_tZq.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_VHnobb.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_WJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_WWTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_WZTo3LNu.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_DYJetsToLL_M_10to50_MLM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_50_MLM.json + +# Backgrounds UL16 +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_TTZToLL_M_1to10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_TTZToLLNuNu_M_10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_TTZToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ZZTo4L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ggToZZTo2e2mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ggToZZTo2e2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ggToZZTo2mu2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ggToZZTo4e.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ggToZZTo4mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ggToZZTo4tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_WWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_WZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ZZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_SSWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ST_antitop_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ST_top_s-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ST_top_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_tbarW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_ttHnobb.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_TTTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_TTWJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_TTWJetsToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_tW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_tZq.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_VHnobb.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_WJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_WWTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_WZTo3LNu.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_DYJetsToLL_M_10to50_MLM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16_DYJetsToLL_M_50_MLM.json + +# Backgrounds UL17 +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_TTZToLL_M_1to10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_TTZToLLNuNu_M_10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_TTZToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ZZTo4L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ggToZZTo2e2mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ggToZZTo2e2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ggToZZTo2mu2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ggToZZTo4e.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ggToZZTo4mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ggToZZTo4tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_WWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_WZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ZZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_SSWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ST_antitop_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ST_top_s-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ST_top_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_tbarW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_ttHnobb.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_TTTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_TTWJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_TTWJetsToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_tW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_tZq.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_VHnobb.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_WJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_WWTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_WZTo3LNu.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_DYJetsToLL_M_10to50_MLM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL17_DYJetsToLL_M_50_MLM.json + +# Backgrounds UL18 +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_TTZToLL_M_1to10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_TTZToLLNuNu_M_10.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_TTZToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ZZTo4L.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ggToZZTo2e2mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ggToZZTo2e2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ggToZZTo2mu2tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ggToZZTo4e.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ggToZZTo4mu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ggToZZTo4tau.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_WWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_WZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ZZZ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_SSWW.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ST_antitop_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ST_top_s-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ST_top_t-channel.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_tbarW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_ttHnobb.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_TTTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_TTWJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_TTWJetsToQQ.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_tW_noFullHad.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_tZq.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_VHnobb.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_WJetsToLNu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_WWTo2L2Nu.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_WZTo3LNu.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_DYJetsToLL_M_10to50_MLM.json +../../input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL18_DYJetsToLL_M_50_MLM.json + +# Using the TOP-22-006 skims for the tWZ samples (which are at UF) +# Put this at end, so prefix does not get used for any other samples +/cmsuf/data +../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_tWZ4l.json +../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_tWZ4l.json +../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_tWZ4l.json +../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_tWZ4l.json diff --git a/input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/.DoubleEG_Run2016F-UL2016.json.swp b/input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/.DoubleEG_Run2016F-UL2016.json.swp deleted file mode 100644 index 6e1a1491c173401884c5a54491f6ace4fe154b7c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 12288 zcmeI2v2GJV5QaCj5Q2af$sBEbduKa|L87p52uO~N2qpzWtF^s{7yI^3w|lk&f=IMH z03G51=y(81Xh29*G`s;l6=K$RMi$@>fl+3qU*nsZUC(^FEi6yRyM2APP<1^5>l7ht zPri!wwU5;|FU}DyI$Rscm8)%Z;p%!@UWpg=mYW9sxVtd#@!Ww_j0fE)2n456#i?PM zhw)Honpt*VM@rN2_yOaJ4-d4|Ob=wJ*~p_kZupP%<1-#X&)O!0!RP}AOR$R1dsp{KmtgB z5EHMELjZ9shjBKyD?i!%5e*VR0!RP}AOR$R1dsp{Kmter2_OL^@E;Q3JwiIi33(5) z|NqZ^|9@K}p52+oaZKVcg zZfUt8;#)jr6|f6uXxXk*vev4PO03CMWItUjj(pavHCyn&<_~eQ@0Yp`Bf3z5Nm8+% z`P5z-wt{^j@AZv_&_>1cXQ3UTwnsw!;{T2FZnkII=3j4*MpJIAKhfDP?266eo}{;tntWo$OBRUr{{F9Qk|gM6r=D`EGBolV6X%)TTC(x(T_~ pZztZFoq?Yvm_&$KFuyYS)&s#4yT#7J&$1_4biwDl-a2_mz5}-Toss|m diff --git a/input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016F-UL2016.json b/input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016F-UL2016.json index 0aa47484..9974fd1c 100644 --- a/input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016F-UL2016.json +++ b/input_samples/sample_jsons/wwz_analysis_skims_hpg/data_samples/UL16/DoubleEG_Run2016F-UL2016.json @@ -6,7 +6,11 @@ "options": "", "WCnames": [], "files": [ - "/blue/avery/p.chang/3LepTau_4Lep/DoubleEG_Run2016F-UL2016_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_5.root" + "/blue/avery/p.chang/3LepTau_4Lep/DoubleEG_Run2016F-UL2016_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_5.root", + "/blue/avery/p.chang/3LepTau_4Lep/DoubleEG_Run2016F-UL2016_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_1.root", + "/blue/avery/p.chang/3LepTau_4Lep/DoubleEG_Run2016F-UL2016_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_4.root", + "/blue/avery/p.chang/3LepTau_4Lep/DoubleEG_Run2016F-UL2016_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_3.root", + "/blue/avery/p.chang/3LepTau_4Lep/DoubleEG_Run2016F-UL2016_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_2.root" ], "nEvents": 5401, "nGenEvents": 5401, diff --git a/input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_qqToZHToZTo2L.json b/input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_qqToZHToZTo2L.json index a7251d7a..3345d58d 100644 --- a/input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_qqToZHToZTo2L.json +++ b/input_samples/sample_jsons/wwz_analysis_skims_hpg/sig_samples/UL16_qqToZHToZTo2L.json @@ -6,8 +6,7 @@ "options": "", "WCnames": [], "files": [ - "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_28.root", -"/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_8.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_8.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_23.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_6.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_11.root", @@ -38,7 +37,7 @@ "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_25.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_30.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_10.root", - "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_53.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_53.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_54.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_12.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_1.root", @@ -54,7 +53,7 @@ "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_17.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_3.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_31.root", - "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_47.root", + "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_47.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_37.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_9.root", "/blue/avery/p.chang/3LepTau_4Lep/HZJ_HToWWTo2L2Nu_ZTo2L_M-125_TuneCP5_13TeV-powheg-jhugen727-pythia8_RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v2_NANOAODSIM_3LepTau_4Lep/output_29.root", From d3853cb9be689465adecafa87e6bc69ff1c488ca Mon Sep 17 00:00:00 2001 From: Kelci Date: Fri, 21 Jun 2024 13:22:24 -0400 Subject: [PATCH 46/48] Update chunk size and just comment out the weird edge case problem files for now --- analysis/wwz/run_wwz4l.py | 11 +++++++++-- input_samples/cfgs/wwz_analysis/data_samples.cfg | 4 ++-- .../data_samples/UL18/EGamma_Run2018A-UL2018.json | 3 +-- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 42009f00..cea7e358 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -229,7 +229,7 @@ def LoadJsonToSampleName(jsonFile, prefix): t_before_preprocess = time.time() dataset_runnable, dataset_updated = preprocess( fileset, - step_size=50_000, + step_size=100_000, align_clusters=False, files_per_batch=1, save_form=False, @@ -272,7 +272,11 @@ def LoadJsonToSampleName(jsonFile, prefix): if executor == "task_vine": print("Running with Task vine") - m = DaskVine([9123,9128], name=f"coffea-vine-{os.environ['USER']}") + m = DaskVine( + [9123,9128], + name=f"coffea-vine-{os.environ['USER']}", + #run_info_path="/blue/p.chang/k.mohrman/vine-run-info", + ) proxy = m.declare_file(f"/tmp/x509up_u{os.getuid()}", cache=True) coutputs, reports = dask.compute( histos_to_compute, @@ -281,6 +285,9 @@ def LoadJsonToSampleName(jsonFile, prefix): resources={"cores": 1}, resources_mode=None, lazy_transfers=True, + #submit_per_cycle=200, + #max_pending=1500, + #task_mode='function-calls', extra_files={proxy: "proxy.pem"}, env_vars={"X509_USER_PROXY": "proxy.pem"}, ) diff --git a/input_samples/cfgs/wwz_analysis/data_samples.cfg b/input_samples/cfgs/wwz_analysis/data_samples.cfg index f8d319a2..8f0320b1 100644 --- a/input_samples/cfgs/wwz_analysis/data_samples.cfg +++ b/input_samples/cfgs/wwz_analysis/data_samples.cfg @@ -17,7 +17,7 @@ root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016C-HIPM.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016D-HIPM.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016E-HIPM.json -../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016F-HIPM.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleEG_Run2016F-HIPM.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleMuon_Run2016B-ver1_HIPM_UL2016.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleMuon_Run2016B-ver2_HIPM_UL2016.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/DoubleMuon_Run2016C-HIPM_UL2016.json @@ -29,7 +29,7 @@ root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/MuonEG_Run2016C-HIPM_UL2016.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/MuonEG_Run2016D-HIPM_UL2016.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/MuonEG_Run2016E-HIPM_UL2016.json -../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/MuonEG_Run2016F-HIPM_UL2016.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL16APV/MuonEG_Run2016F-HIPM_UL2016.json root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL17/DoubleEG_Run2017B-UL2017.json diff --git a/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL18/EGamma_Run2018A-UL2018.json b/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL18/EGamma_Run2018A-UL2018.json index c23c9233..0fc115c3 100644 --- a/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL18/EGamma_Run2018A-UL2018.json +++ b/input_samples/sample_jsons/wwz_analysis_skims_v0/data_samples/UL18/EGamma_Run2018A-UL2018.json @@ -132,7 +132,6 @@ "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_74.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_221.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_132.root", - "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_27.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_112.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_25.root", "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep/output_174.root", @@ -238,4 +237,4 @@ "nSumOfWeights": 216279, "isData": true, "path": "/store/user/kdownham/skimOutput/3LepTau_4Lep/EGamma_Run2018A-UL2018_MiniAODv2_NanoAODv9-v1_NANOAOD_3LepTau_4Lep" -} \ No newline at end of file +} From f91672ea2bbcef11d3e819992dbbd2fd474b2c8c Mon Sep 17 00:00:00 2001 From: kmohrman <36145025+kmohrman@users.noreply.github.com> Date: Fri, 21 Jun 2024 13:24:56 -0400 Subject: [PATCH 47/48] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 02a6cc97..ff0d0fd2 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ This analysis repository contains scripts and tools for performing analyses asso First, clone the repository and `cd` into the toplevel directory. ``` -git clone https://github.com/kmohrman/ewkcoffea.git +git clone -b coffea2023 https://github.com/kmohrman/ewkcoffea.git cd ewkcoffea ``` Next, create a `conda` environment and activate it. @@ -25,7 +25,7 @@ pip install mt2 The `topcoffea` package upon which this analysis also depends is not yet available on `PyPI`, so we need to clone the `topcoffea` repo and install it ourselves. ``` cd /your/favorite/directory -git clone https://github.com/TopEFT/topcoffea.git +git clone -b coffea2023 https://github.com/TopEFT/topcoffea.git cd topcoffea pip install -e . ``` From 63abb064b01ac8ef3a5ed0c46860885b04a6d45c Mon Sep 17 00:00:00 2001 From: Kelci Date: Sat, 22 Jun 2024 18:18:36 -0400 Subject: [PATCH 48/48] Comment out samples for consistency, add run script --- analysis/wwz/runit.sh | 7 +++++++ input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples.cfg | 8 ++++---- 2 files changed, 11 insertions(+), 4 deletions(-) create mode 100644 analysis/wwz/runit.sh diff --git a/analysis/wwz/runit.sh b/analysis/wwz/runit.sh new file mode 100644 index 00000000..f347c9bf --- /dev/null +++ b/analysis/wwz/runit.sh @@ -0,0 +1,7 @@ +# Example run commands + +# Process the copy of the files at UF (not xrd accessible) +#time python run_wwz4l.py ../../input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples_hpg.cfg,../../input_samples/cfgs/wwz_analysis/data_samples_hpg.cfg --hist-list njets -x task_vine + +# Process the copy of the files at UCSD (xrd accessible) +time python run_wwz4l.py ../../input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples.cfg,../../input_samples/cfgs/wwz_analysis/data_samples.cfg --hist-list njets -x task_vine diff --git a/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples.cfg b/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples.cfg index 1885ab2c..aa71bfc7 100644 --- a/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples.cfg +++ b/input_samples/cfgs/wwz_analysis/mc_sig_bkg_samples.cfg @@ -33,7 +33,7 @@ root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_ggToZZTo4e.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_ggToZZTo4mu.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_ggToZZTo4tau.json -../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_tWll.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_tWll.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_WWW.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_WZZ.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16APV_ZZZ.json @@ -66,7 +66,7 @@ root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_ggToZZTo4e.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_ggToZZTo4mu.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_ggToZZTo4tau.json -../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_tWll.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_tWll.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_WWW.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_WZZ.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL16_ZZZ.json @@ -99,7 +99,7 @@ root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_ggToZZTo4e.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_ggToZZTo4mu.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_ggToZZTo4tau.json -../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_tWll.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_tWll.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_WWW.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_WZZ.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL17_ZZZ.json @@ -132,7 +132,7 @@ root://redirector.t2.ucsd.edu:1095/ ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_ggToZZTo4e.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_ggToZZTo4mu.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_ggToZZTo4tau.json -../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_tWll.json +#../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_tWll.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_WWW.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_WZZ.json ../../input_samples/sample_jsons/wwz_analysis_skims_v0/bkg_samples/UL18_ZZZ.json