diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index da1573ba..87b6e690 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 ../.. @@ -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: | 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 . ``` diff --git a/analysis/wwz/get_wwz_counts.py b/analysis/wwz/get_wwz_counts.py index f2418461..ecc18da5 100644 --- a/analysis/wwz/get_wwz_counts.py +++ b/analysis/wwz/get_wwz_counts.py @@ -47,17 +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/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/analysis/wwz/run_wwz4l.py b/analysis/wwz/run_wwz4l.py index 927da21f..cea7e358 100644 --- a/analysis/wwz/run_wwz4l.py +++ b/analysis/wwz/run_wwz4l.py @@ -6,20 +6,24 @@ import cloudpickle import gzip import os -from coffea import processor -from coffea.nanoevents import NanoAODSchema +import dask +from distributed import Client -import topcoffea.modules.remote_environment as remote_environment +from coffea.dataset_tools import preprocess +from coffea.dataset_tools import apply_to_fileset +from coffea.dataset_tools import filter_files + +from ndcctools.taskvine import DaskVine 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') @@ -152,11 +156,11 @@ 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']) @@ -190,133 +194,129 @@ 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 - tstart = time.time() + 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(): + fileset[name] = {} + fileset[name]["files"] = {} + for fpath in fpaths: + fileset[name]["files"][fpath] = {"object_path": "Events"} + fileset[name]["metadata"] = {"dataset": name} + print(fileset) + print("Number of datasets:",len(fdict)) + + #### Run preprocess, build task graphs, compute #### + + t_before_with_Client_as_client = time.time() + #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=100_000, + align_clusters=False, + files_per_batch=1, + save_form=False, + ) + dataset_runnable = filter_files(dataset_runnable) + + + # 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_mar16_full.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, + ) + + # 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...") + #columns_read = dak.necessary_columns(histos_to_compute[list(histos_to_compute.keys())[0]]) + #print(columns_read) + + # Compute + t_before_compute = time.time() + print("\nRunning compute...") + + if executor == "task_vine": + print("Running with Task vine") + 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, + reports, + scheduler=m.get, + 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"}, + ) - 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) + else: + coutputs, creports = dask.compute(histos_to_compute, reports) - output = runner(flist, treename, processor_instance) - 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, )) + # Print timing info + t_end = time.time() + 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) 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!") 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/analysis/wwz/wwz4l.py b/analysis/wwz/wwz4l.py index fd467075..8e1d34db 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 hist.dask as hda + # Small helper function for creating the list of systematics # Append "Up" and "Down" to all base strings in a given syst list @@ -138,16 +140,25 @@ 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): + # 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"] @@ -270,7 +281,7 @@ 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"] @@ -302,8 +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 @@ -328,7 +339,7 @@ def process(self, events): for obj_corr_syst_var in obj_corr_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 #################### @@ -506,34 +517,36 @@ def process(self, events): # 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 = [ - 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_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")) - 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")) # 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 @@ -647,8 +660,6 @@ def process(self, events): ######### Fill histos ######### - hout = {} - dense_variables_dict = { "mt2" : mt2_val, "met" : met.pt, @@ -705,8 +716,8 @@ def process(self, events): "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, @@ -793,6 +804,9 @@ def process(self, events): # Loop over the hists we want to fill + hout = {} # This is what we'll eventually return + 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.") @@ -801,7 +815,7 @@ def process(self, events): #print("dense_axis_name,vals",dense_axis_vals) # 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"), hist.axis.StrCategory([], growth=True, name="systematic", label="systematic"), @@ -821,23 +835,35 @@ def process(self, events): # Otherwise get the weight from the Weights object weight = weights_obj_base_for_kinematic_syst.weight(wgt_fluct) - # 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 - # If this is a counts hist, forget the weights and just fill with unit weights - if 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) + # 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: + 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] #event = events.event[all_cuts_mask] @@ -855,8 +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, diff --git a/environment.yml b/environment.yml index c6cdd46d..4017a201 100644 --- a/environment.yml +++ b/environment.yml @@ -3,11 +3,12 @@ channels: - conda-forge dependencies: - conda-forge::python=3.9 - - numpy=1.23.5 - - coffea==0.7.22 + - numpy + - coffea - ndcctools - conda-pack - dill - xrootd - git - pyyaml + - fsspec-xrootd diff --git a/ewkcoffea/modules/objects_wwz.py b/ewkcoffea/modules/objects_wwz.py index c63a7099..2b965460 100644 --- a/ewkcoffea/modules/objects_wwz.py +++ b/ewkcoffea/modules/objects_wwz.py @@ -1,6 +1,7 @@ import numpy as np import awkward as ak -import xgboost as xgb + +from coffea.ml_tools.xgboost_wrapper import xgboost_wrapper from topcoffea.modules.paths import topcoffea_path @@ -14,6 +15,44 @@ 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 +# 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): + 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 + # 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]]), # 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([ + # {"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 @@ -58,38 +97,51 @@ 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_lst = [ + "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), + } + + score = xgb_eval_wrapper(feature_lst,in_vals_flat_dict,model_fpath) # Restore the shape (i.e. unflatten) counts = ak.num(ele.pt) score = ak.unflatten(score,counts) + return score @@ -106,36 +158,49 @@ 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_lst = [ + "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), + } + + score = xgb_eval_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 diff --git a/ewkcoffea/modules/selection_wwz.py b/ewkcoffea/modules/selection_wwz.py index 6089ac2d..0aebfced 100644 --- a/ewkcoffea/modules/selection_wwz.py +++ b/ewkcoffea/modules/selection_wwz.py @@ -1,6 +1,4 @@ -import numpy as np import awkward as ak -import xgboost as xgb from mt2 import mt2 from coffea.nanoevents.methods import vector @@ -150,7 +148,7 @@ 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 +174,7 @@ 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 @@ -270,13 +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] @@ -324,13 +318,14 @@ 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, @@ -353,22 +348,7 @@ 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 - - -# 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 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/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.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 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/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 +} diff --git a/input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_10to50_MLM.json b/input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_10to50_MLM.json new file mode 100644 index 00000000..4098dd53 --- /dev/null +++ b/input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_10to50_MLM.json @@ -0,0 +1,45 @@ +{ + "xsec": 18610.0, + "year": "2016APV", + "treeName": "Events", + "histAxisName": "UL16APV_DYJetsToLL_M_10to50_MLM", + "options": "", + "WCnames": [], + "files": [ + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_8.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_6.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_11.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_16.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_19.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_13.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_5.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_18.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_7.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_10.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_12.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_1.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_15.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_4.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_17.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_3.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_9.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_14.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_2.root" + ], + "nEvents": 1940, + "nGenEvents": 25799525, + "nSumOfWeights": 25799525.0, + "isData": false, + "path": "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-10to50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep", + "nSumOfLheWeights": [ + 17431163.963879712, + 25916306.27003188, + 35630706.78087667, + 17328114.16515236, + 25799525.0, + 35502226.53233897, + 17250638.474185154, + 25711708.380446654, + 35405582.30596607 + ] +} \ No newline at end of file diff --git a/input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_50_MLM.json b/input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_50_MLM.json new file mode 100644 index 00000000..85e2f570 --- /dev/null +++ b/input_samples/sample_jsons/wwz_analysis_skims_hpg/bkg_samples/UL16APV_DYJetsToLL_M_50_MLM.json @@ -0,0 +1,92 @@ +{ + "xsec": 6025.2, + "year": "2016APV", + "treeName": "Events", + "histAxisName": "UL16APV_DYJetsToLL_M_50_MLM", + "options": "", + "WCnames": [], + "files": [ + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_60.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_8.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_23.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_64.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_62.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_6.root", + "/blue/avery/p.chang/3LepTau_4Lep/DYJetsToLL_M-50_TuneCP5_13TeV-madgraphMLM-pythia8_RunIISummer20UL16NanoAODAPVv9-106X_mcRun2_asymptotic_preVFP_v11-v1_NANOAODSIM_3LepTau_4Lep/output_11.root", + 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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..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 @@ -68,4 +68,4 @@ "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 +} 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 +} 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", 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"