diff --git a/run/bbyy_analysis/Combine/card_maker_allCats.py b/run/bbyy_analysis/Combine/card_maker_allCats.py new file mode 100644 index 0000000000..11aab10743 --- /dev/null +++ b/run/bbyy_analysis/Combine/card_maker_allCats.py @@ -0,0 +1,139 @@ +#!/usr/bin/env python +# Author: C.Caputo (UCLouvain) + +import CombineHarvester.CombineTools.ch as ch + +import ROOT as R +import glob +import os + +import argparse +parser = argparse.ArgumentParser() +parser.add_argument("--reg", type=str, required=True, choices=['sb', 'c'], help="region in mbb" ) +parser.add_argument("--tag", type=str, required=True, help="Datacard name tag" ) +parser.add_argument("--input", type=str, required=True, help="Root files input path" ) +parser.add_argument("--scen", type=str, required=True, choices=['I', 'II', 'III'],help="Scenario (I, II, III)") +args = parser.parse_args() + +cb = ch.CombineHarvester() + +reg = args.reg +tag = args.tag +input = args.input +scen = args.scen + +####DEF: syst for different scenarios## +systScenI = {"lumi" : 0.005, + "btag" : 0.005, + "sigxs": 0.005, + "phid" : 0.005} +systScenII = {"lumi" : 0.01, + "btag" : 0.01, + "sigxs": 0.01, + "phid" : 0.01} +systScenIII = {"lumi" : 0.02, + "btag" : 0.02, + "sigxs": 0.02, + "phid" : 0.02} + +syst_dict = {"I" : systScenI, + "II" : systScenII, + "III": systScenIII + } + +auxiliaries = os.environ['CMSSW_BASE'] + '/src/HiggsAnalysis/CombinedLimit/HH_FCChh_newCard_2023' +aux_shapes = auxiliaries +'/{inputFolder}'.format(inputFolder=input) + +print auxiliaries +print aux_shapes + + +chns = ['great350_high_purity', 'great350_medium_purity', 'small350_high_purity', 'small350_medium_purity'] + +print(chns) + + +bkg_procs = { + 'great350_high_purity' : ['mgg_mgp8_pp_tth01j_5f_haa', 'mgg_mgp8_pp_h012j_5f_haa', 'mgg_mgp8_pp_jjaa_5f','mgg_mgp8_pp_jja_5f', 'mgg_mgp8_pp_vh012j_5f_haa', 'mgg_mgp8_pp_vbf_h01j_5f_haa'], + 'great350_medium_purity' : ['mgg_mgp8_pp_tth01j_5f_haa', 'mgg_mgp8_pp_h012j_5f_haa', 'mgg_mgp8_pp_jjaa_5f', 'mgg_mgp8_pp_jja_5f','mgg_mgp8_pp_vh012j_5f_haa', 'mgg_mgp8_pp_vbf_h01j_5f_haa'], + 'small350_high_purity' : ['mgg_mgp8_pp_tth01j_5f_haa', 'mgg_mgp8_pp_h012j_5f_haa', 'mgg_mgp8_pp_jjaa_5f','mgg_mgp8_pp_jja_5f', 'mgg_mgp8_pp_vh012j_5f_haa', 'mgg_mgp8_pp_vbf_h01j_5f_haa'], + 'small350_medium_purity' : ['mgg_mgp8_pp_tth01j_5f_haa', 'mgg_mgp8_pp_h012j_5f_haa', 'mgg_mgp8_pp_jjaa_5f', 'mgg_mgp8_pp_jja_5f','mgg_mgp8_pp_vh012j_5f_haa', 'mgg_mgp8_pp_vbf_h01j_5f_haa'] +} + +sig_procs = ['mgg_pwp8_pp_hh_lambda100_5f_hhbbaa', 'mgg_pwp8_pp_hh_lambda240_5f_hhbbaa', 'mgg_pwp8_pp_hh_lambda300_5f_hhbbaa'] + + + +cats = { + 'great350_medium_purity' : [ + (0, 'great350_medium_purity'), + ], + 'great350_high_purity' : [ + (0, 'great350_high_purity'), + ], + 'small350_medium_purity' : [ + (0, 'small350_medium_purity'), + ], + 'small350_high_purity' : [ + (0, 'small350_high_purity'), + ] +} + +print(cats) +print '>> Creating processes and observations...' + + +for chn in chns: + cb.AddObservations( ['*'], ['HHbbgg'], ['100TeV'], [chn], cats[chn] ) + cb.AddProcesses( ['*'], ['HHbbgg'], ['100TeV'], [chn], bkg_procs[chn], cats[chn], False ) + cb.AddProcesses( [''], ['HHbbgg'], ['100TeV'], [chn], sig_procs, cats[chn], True )#mod 125 + + + +print '>> Adding systematic uncertainties...' +signal = cb.cp().signals().process_set() + +MC_Backgrouds = ['mgg_mgp8_pp_tth01j_5f_haa', 'mgg_mgp8_pp_h012j_5f_haa', 'mgg_mgp8_pp_jjaa_5f', 'mgg_mgp8_pp_jja_5f','mgg_mgp8_pp_vh012j_5f_haa', 'mgg_mgp8_pp_vbf_h01j_5f_haa'] + +cb.cp().process(signal+MC_Backgrouds).AddSyst(cb, "lumi", "lnN", ch.SystMap()([1.+syst_dict[scen]["lumi"],1.-syst_dict[scen]["lumi"]])) +cb.cp().process(signal+MC_Backgrouds).AddSyst(cb, "phid", "lnN", ch.SystMap()([1.+syst_dict[scen]["phid"],1.-syst_dict[scen]["phid"]])) +cb.cp().process(signal+MC_Backgrouds).AddSyst(cb, "btag", "lnN", ch.SystMap()([1.+syst_dict[scen]["btag"],1-syst_dict[scen]["btag"]])) +cb.cp().process(signal).AddSyst(cb, "sigxs", "lnN", ch.SystMap()([1.+syst_dict[scen]["sigxs"],1-syst_dict[scen]["sigxs"]])) + + + +print '>> Extracting histograms from input root files...' +for chn in chns: + file = aux_shapes + "/" + chn + "_"+reg+".root" + cb.cp().channel([chn]).era(['100TeV']).backgrounds().ExtractShapes( + file, '$PROCESS', '$PROCESS_$SYSTEMATIC') + cb.cp().channel([chn]).era(['100TeV']).signals().ExtractShapes( + file, '$PROCESS$MASS', '$PROCESS$MASS_$SYSTEMATIC') + +print '>> Setting standardised bin names...' +ch.SetStandardBinNames(cb) + +writer = ch.CardWriter('LIMITS/$TAG/$ANALYSIS_$CHANNEL_$BINID_$ERA.txt', + 'LIMITS/$TAG/$ANALYSIS_$CHANNEL.input.root') + +print(writer) + +writer.SetVerbosity(2) +writer.WriteCards('{}/{}/{}'.format(tag,scen,reg), cb) ## the first argument is the $TAG +# for chn in chns: writer.WriteCards(chn,cb.cp().channel([chn])) +#HHbbtautau_tau_0_13TeV +#for chn in chns: +# with open("./LIMITS/"+tag+"/HHbbtautau_"+chn+"_0_14TeV.txt",'a') as f: +# f.write("* autoMCStats 0 1 1") + +print '>> Done!' +#for chn in chns: +##if() +# #with open("./LIMITS/"+tag+"/"+reg+"/"+str(chns[0])+"_"+str(chns[0])+"_0_13TeV.txt",'a') as f: +# print(chn) +# with open('LIMITS/'+tag+'/'+reg+'/HHbbgg_'+chn+'_0_100TeV.txt', 'a') as f: +# f.write("Signal_rateParam rateParam HHbbgg_"+chn+"_0_100TeV mgg_pwp8_pp_hh_lambda100_5f_hhbbaa 15.000\n") +# f.write("Signal_rateParam rateParam HHbbgg_"+chn+"_0_100TeV mgg_pwp8_pp_hh_lambda240_5f_hhbbaa 15.000\n") +# f.write("Signal_rateParam rateParam HHbbgg_"+chn+"_0_100TeV mgg_pwp8_pp_hh_lambda300_5f_hhbbaa 15.000\n") +# f.write(" ") +print '>> Done!' diff --git a/run/bbyy_analysis/Combine/run_cards_allCats.sh b/run/bbyy_analysis/Combine/run_cards_allCats.sh new file mode 100644 index 0000000000..4125fbbe33 --- /dev/null +++ b/run/bbyy_analysis/Combine/run_cards_allCats.sh @@ -0,0 +1,47 @@ +#!/bin/bash +#bbtautau or bbgg or bbbb or ALL +channel=$1 +#name of the dir to put limits +folder=$2 + +echo $channel +if [ $channel = "bbgg" ] || [ $channel = "ALL" ] +then + + SCENARIOS=('I' 'II' 'III') + + for scen in "${SCENARIOS[@]}" + do + python card_maker_allCats.py --input input_files --tag $folder --reg sb --scen "$scen" + python card_maker_allCats.py --input input_files --tag $folder --reg c --scen "$scen" + cd LIMITS/$folder/$scen + combineCards.py ./sb/HHbbgg_small350_medium_purity_0_100TeV.txt ./c/HHbbgg_small350_medium_purity_0_100TeV.txt > small350_medium_purity.txt + combineCards.py ./sb/HHbbgg_small350_high_purity_0_100TeV.txt ./c/HHbbgg_small350_high_purity_0_100TeV.txt > small350_high_purity.txt + combineCards.py small350_medium_purity.txt small350_high_purity.txt > small350_combine.txt + + combineCards.py ./sb/HHbbgg_great350_medium_purity_0_100TeV.txt ./c/HHbbgg_great350_medium_purity_0_100TeV.txt > great350_medium_purity.txt + combineCards.py ./sb/HHbbgg_great350_high_purity_0_100TeV.txt ./c/HHbbgg_great350_high_purity_0_100TeV.txt > great350_high_purity.txt + combineCards.py great350_medium_purity.txt great350_high_purity.txt > great350_combine.txt + + combineCards.py great350_combine.txt small350_combine.txt > combine_all.txt + text2workspace.py combine_all.txt -P HiggsAnalysis.CombinedLimit.HHModel:HHdefault --mass=125 + + combine -M MultiDimFit combine_all.root --verbose 1 --algo grid --points=200 -P kl --floatOtherPOIs=0 --setParameterRanges kl=0.8,1.5 --cl=0.68 --setParameters r=1,r_gghh=1,r_qqhh=1,CV=1,C2V=3,kt=1 --robustFit 1 -t -1 --fastScan -n fastScan + #plot1DScan.py higgsCombinefastScan.MultiDimFit.mH120.root --POI kl --output fastscan + + combine -M MultiDimFit combine_all.root --verbose 1 --algo grid --points=200 -P kl --floatOtherPOIs=0 --setParameterRanges kl=0.8,1.5 --cl=0.68 --setParameters r=1,r_gghh=1,r_qqhh=1,CV=1,C2V=3,kt=1 --robustFit 1 -t -1 + #plot1DScan.py higgsCombineTest.MultiDimFit.mH120.root --POI kl --output scan + cd - + done + cd LIMITS/$folder/ + plot1DScan.py ./I/higgsCombinefastScan.MultiDimFit.mH120.root --POI kl --others ./I/higgsCombineTest.MultiDimFit.mH120.root:scenI:4 ./II/higgsCombineTest.MultiDimFit.mH120.root:scenII:3 ./III/higgsCombineTest.MultiDimFit.mH120.root:scenIII:6 --main-label onlystat --logo FCC-hh --logo-sub "Work in progress" --y-max 15 + + + #combine -M AsymptoticLimits combine_all.root --run blind -m 125 > output_Asym_Lim.txt + #combine -M Significance combine_all.root -t -1 --expectSignal=1 > output_Sig.txt + #combineTool.py -M Impacts -d combine_all.root -m 125 -t -1 --doInitialFit --robustFit 1 --rMax 100 --expectSignal 10 + #combineTool.py -M Impacts -d combine_all.root -m 125 -t -1 --robustFit 1 --doFits --parallel 8 --rMax 100 --expectSignal 10 + #combineTool.py -M Impacts -d combine_all.root -m 125 -t -1 -o impacts.json --rMax 100 --expectSignal 10 + #plotImpacts.py -i impacts.json -o impacts_combined_10 + #rm higgsCombine* +fi diff --git a/run/bbyy_analysis/README.md b/run/bbyy_analysis/README.md index c4ce980c92..147ac92aef 100644 --- a/run/bbyy_analysis/README.md +++ b/run/bbyy_analysis/README.md @@ -15,7 +15,10 @@ Then run on batch by switching back `runBatch = True` (ideally this could be pas fccanalysis run analysis_bbyy_selections_v2.py ``` This step will produce root files needed for the following. - +To generate the json file with info on xs, total number of generated events, sumofweights etc. run +``` +python create_norm_dict.py -i +``` ## Process root files Process root files submittting jobs on condor with: ``` @@ -33,4 +36,19 @@ Finally, convert the root files in a Parquet: ``` python openTree.py ``` -At the end there will be only one parquet called `df_Sel_All.parquet` with all the samples. +At the end there will be only one parquet called `df_Sel_All_haa_Mbtag.parquet` with all the samples. + +## Notebooks: Run DNNs and define cuts and categorization +`full_3DNN.ipynb` performs ttH killer dnn training and other 2 "global" dnns training (one in Mx>350 GeV, th other in Mx<350 GeV). +It saves the dnn models to be retrieved later by `applyDNN_SelCat_full3DNN.ipynb`. +`applyDNN_SelCat_full3DNN.ipynb` applies the dnns to the whole dataset (trick to not make the notebook crush: run separatly on df_signal and df_bkg then concatenate the dfs). It finds the best cut for ttH killer and the best delimiters for high/low purity and central/sidebands regions. Finally it saves one dataframe for each category in a parquet file. + +## Create inputs for Combine +Run `create_histo_allCats.py` to open the dfs of the previous step, extract the m_gg, bin the distribution and save it in a root file. Here a+jj is added (scaled from the jj+aa) and 'data_obs' is append at the resulting root file to satisfy Combine requirements. + +## Create card and run Combine +Run: +``` +bash run_cards_allCats.sh bbgg +``` +It will produce cards, combine them, run the fit, plot the NLL scan for every scarios (I,II,III) diff --git a/run/bbyy_analysis/analysis_bbyy_selections_v2.py b/run/bbyy_analysis/analysis_bbyy_selections_v2.py index f01c79cdf7..0e2e18c97d 100644 --- a/run/bbyy_analysis/analysis_bbyy_selections_v2.py +++ b/run/bbyy_analysis/analysis_bbyy_selections_v2.py @@ -6,10 +6,11 @@ "pwp8_pp_hh_lambda240_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda240_5f_hhbbaa"}, "pwp8_pp_hh_lambda300_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda300_5f_hhbbaa"}, "pwp8_pp_hh_lambda000_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda000_5f_hhbbaa"}, - "mgp8_pp_h012j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_h012j_5f"}, - "mgp8_pp_vbf_h01j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vbf_h01j_5f"}, - "mgp8_pp_tth01j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_tth01j_5f"}, - "mgp8_pp_vh012j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vh012j_5f"}, + #"mgp8_pp_h012j_5f_haa":{#'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_h012j_5f_haa"}, +# "mgp8_pp_vbf_h01j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vbf_h01j_5f"}, +# "mgp8_pp_tth01j_5f_haa/events_000000143":{#'chunks':500, +#'output':"FCChh_EvtGen_mgp8_pp_tth01j_5f_haa"}, +# "mgp8_pp_vh012j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vh012j_5f"}, "mgp8_pp_jjaa_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_jjaa_5f"}, # "mgp8_pp_tt012j_5f": {'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_tt012j_5f"} @@ -34,7 +35,7 @@ inputDir = "/eos/experiment/fcc/hh/generation/DelphesEvents/fcc_v05_scenarioI/" #your directory with the input file #Optional: output directory, default is local dir -outputDir = "/eos/user/p/pmastrap/FCCFW/Analysis/FCCAnalyses/run/Delphescard_validation/FCCAnalysis_ntuples_forAnalysis/" +outputDir = "/eos/user/p/pmastrap/FCCFW/Analysis/FCCAnalyses/run/Delphescard_validation/FCCAnalysis_ntuples_forAnalysis_Mbtag/" #Optional: ncpus, default is 4 nCPUS = 8 @@ -73,10 +74,11 @@ def analysers(df, out_name): #b-tagged jets: .Alias("Jet3","Jet#3.index") - + #.Alias("Jet3","_Jet_particleIDs.index") #LOOSE WP : bit 1 = medium WP, bit 2 = tight WP #b tagged jets - .Define("bjets", "AnalysisFCChh::get_tagged_jets(Jet, Jet3, ParticleIDs, ParticleIDs_0, 0)") #bit 0 = loose WP, see: https://github.com/delphes/delphes/blob/master/cards/FCC/scenarios/FCChh_I.tcl + #.Define("bjets", "AnalysisFCChh::get_tagged_jets(Jet, Jet3, ParticleIDs, _ParticleIDs_parameters, 0)") + .Define("bjets", "AnalysisFCChh::get_tagged_jets(Jet, Jet3, ParticleIDs, ParticleIDs_0, 1)") #bit 0 = loose WP, see: https://github.com/delphes/delphes/blob/master/cards/FCC/scenarios/FCChh_I.tcl .Define("selpt_bjets", "FCCAnalyses::ReconstructedParticle::sel_pt(30.)(bjets)") .Define("sel_bjets_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_bjets)") .Define("sel_bjets", "AnalysisFCChh::SortParticleCollection(sel_bjets_unsort)") @@ -94,6 +96,7 @@ def analysers(df, out_name): #all isolated .Alias("Electron0", "Electron#0.index") + #.Alias("Electron0", "Electron_objIdx.index") .Define("ele", "FCCAnalyses::ReconstructedParticle::get(Electron0, ReconstructedParticles)") .Define("selpt_ele", "FCCAnalyses::ReconstructedParticle::sel_pt(10.)(ele)") .Define("sel_ele_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_ele)") @@ -112,6 +115,7 @@ def analysers(df, out_name): # all isolated .Alias("Muon0", "Muon#0.index") + #.Alias("Muon0", "Muon_objIdx.index") .Define("mu", "FCCAnalyses::ReconstructedParticle::get(Muon0, ReconstructedParticles)") .Define("selpt_mu", "FCCAnalyses::ReconstructedParticle::sel_pt(10.)(mu)") .Define("sel_mu_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_mu)") @@ -131,6 +135,7 @@ def analysers(df, out_name): #all, after isolation .Alias("Photon0", "Photon#0.index") + #.Alias("Photon0", "Photon_objIdx.index") .Define("gamma", "FCCAnalyses::ReconstructedParticle::get(Photon0, ReconstructedParticles)") .Define("selpt_gamma", "FCCAnalyses::ReconstructedParticle::sel_pt(30.)(gamma)") .Define("sel_gamma_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_gamma)") @@ -177,7 +182,8 @@ def analysers(df, out_name): def output(out_name): branchList = [ - # Jets: + "weight", + # Jets: "njets", "j1_e", "j1_pt", "j1_eta", "j1_phi", "j2_e", "j2_pt", "j2_eta", "j2_phi", # B-jets: @@ -201,5 +207,5 @@ def output(out_name): return branchList -# local test: fccanalysis run analysis_bbyy_selections_v2.py --nevents 100 - also switch runBatch to False +# local test: fccanalysis run analysis_noiso.py --nevents 100 - also switch runBatch to False diff --git a/run/bbyy_analysis/analysis_bbyy_selections_v2_NewRelease.py b/run/bbyy_analysis/analysis_bbyy_selections_v2_NewRelease.py new file mode 100644 index 0000000000..3755e07efe --- /dev/null +++ b/run/bbyy_analysis/analysis_bbyy_selections_v2_NewRelease.py @@ -0,0 +1,227 @@ +#NOTE: isovar branches temporarily commented out!! + + +processList = { + #"pwp8_pp_hh_lambda100_5f_hhbbaa":{'chunks':200, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda100_5f_hhbbaa"}, #put the name of your input file here (witho\ut .root), the output file will have the same name + #"pwp8_pp_hh_lambda240_5f_hhbbaa":{'chunks':200, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda240_5f_hhbbaa"}, + #"pwp8_pp_hh_lambda300_5f_hhbbaa":{'chunks':200, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda300_5f_hhbbaa"}, + #"pwp8_pp_hh_lambda000_5f_hhbbaa":{'chunks':200, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda000_5f_hhbbaa"}, + "mgp8_pp_h012j_5f_haa":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_h012j_5f_haa"}, + "mgp8_pp_vbf_h01j_5f_haa":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vbf_h01j_5f_haa"}, + "mgp8_pp_tth01j_5f_haa":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_tth01j_5f_haa"}, + "mgp8_pp_vh012j_5f_haa":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vh012j_5f_haa"}, + #"mgp8_pp_jjaa_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_jjaa_5f"}, + #"mgp8_pp_tt012j_5f": {'chunks':200, 'output':"FCChh_EvtGen_mgp8_pp_tt012j_5f"} + +} + + +#for batch submission +#processList = { +# "pwp8_pp_hh_lambda100_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda100_5f_hhbbaa"}, #put the name of your input file here (without .root), the output file will have the same name +# "pwp8_pp_hh_lambda240_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda240_5f_hhbbaa"}, +# "pwp8_pp_hh_lambda300_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda300_5f_hhbbaa"}, +# "pwp8_pp_hh_lambda000_5f_hhbbaa":{'chunks':500, 'output':"FCChh_EvtGen_pwp8_pp_hh_lambda000_5f_hhbbaa"}, + #"mgp8_pp_h012j_5f_haa":{#'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_h012j_5f_haa"}, +# "mgp8_pp_vbf_h01j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vbf_h01j_5f"}, +# "mgp8_pp_tth01j_5f_haa/events_000000143":{#'chunks':500, +#'output':"FCChh_EvtGen_mgp8_pp_tth01j_5f_haa"}, +# "mgp8_pp_vh012j_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_vh012j_5f"}, +# "mgp8_pp_jjaa_5f":{'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_jjaa_5f"}, +# "mgp8_pp_tt012j_5f": {'chunks':500, 'output':"FCChh_EvtGen_mgp8_pp_tt012j_5f"} + + #} + +#for local testing: +#processList= { +# "pwp8_pp_hh_lambda100_5f_hhbbaa":{'output':"FCChh_EvtGen_pwp8_pp_hh_lambda100_5f_hhbbaa"}, #put the name of your input file here (without .root), the output file will have the same name + #"pwp8_pp_hh_lambda240_5f_hhbbaa":{'output':"FCChh_EvtGen_pwp8_pp_hh_lambda240_5f_hhbbaa"}, + #"pwp8_pp_hh_lambda300_5f_hhbbaa":{'output':"FCChh_EvtGen_pwp8_pp_hh_lambda300_5f_hhbbaa"}, + #"pwp8_pp_hh_lambda000_5f_hhbbaa":{'output':"FCChh_EvtGen_pwp8_pp_hh_lambda000_5f_hhbbaa"}, + #"mgp8_pp_h012j_5f":{'output':"FCChh_EvtGen_mgp8_pp_h012j_5f"}, + #"mgp8_pp_vbf_h01j_5f":{'output':"FCChh_EvtGen_mgp8_pp_vbf_h01j_5f"}, + #"mgp8_pp_tth01j_5f":{'output':"FCChh_EvtGen_mgp8_pp_tth01j_5f"}, + #"mgp8_pp_vh012j_5f":{'output':"FCChh_EvtGen_mgp8_pp_vh012j_5f"}, + #"mgp8_pp_jjaa_5f":{'output':"FCChh_EvtGen_mgp8_pp_jjaa_5f"}, + #"mgp8_pp_tt012j_5f": {'output':"FCChh_EvtGen_mgp8_pp_tt012j_5f"} + +#} + +#Mandatory: input directory when not running over centrally produced edm4hep events. +inputDir = "/eos/experiment/fcc/hh/generation/DelphesEvents/fcc_v05_scenarioI/" #your directory with the input file + +#Optional: output directory, default is local dir +outputDir = "/eos/user/p/pmastrap/FCCFW/Analysis/FCCAnalyses/run/Delphescard_validation/FCCAnalysis_ntuples_forAnalysis_Mbtag/" + +#Optional: ncpus, default is 4 +nCPUS = 8 + +#Optional running on HTCondor, default is False +runBatch = True +#runBatch = True +#Mandatory: RDFanalysis class where the use defines the operations on the TTree +class RDFanalysis(): + + #__________________________________________________________ + #Mandatory: analysers funtion to define the analysers to process, please make sure you return the last dataframe, in this example it is df2 + def analysers(df, out_name): + import ROOT + + df2 = (df + .Define("weight", "EventHeader.weight") + ########################################### JETS ########################################### + + # jets after overlap removal is performed between jets and isolated electrons, muons and photons + + #selected jets above a pT threshold of 30 GeV, eta < 4, tight ID + .Define("selpt_jets", "FCCAnalyses::ReconstructedParticle::sel_pt(30.)(Jet)") + .Define("sel_jets_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_jets)") + .Define("sel_jets", "AnalysisFCChh::SortParticleCollection(sel_jets_unsort)") + .Define("njets", "FCCAnalyses::ReconstructedParticle::get_n(sel_jets)") + .Define("j1_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_jets)[0]") + .Define("j1_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_jets)[0]") + .Define("j1_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_jets)[0]") + .Define("j1_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_jets)[0]") + + .Define("j2_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_jets)[1]") + .Define("j2_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_jets)[1]") + .Define("j2_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_jets)[1]") + .Define("j2_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_jets)[1]") + + #b-tagged jets: + #.Alias("Jet3","Jet#3.index") + .Alias("Jet3","_Jet_particleIDs.index") + #LOOSE WP : bit 1 = medium WP, bit 2 = tight WP + #b tagged jets + .Define("bjets", "AnalysisFCChh::get_tagged_jets(Jet, Jet3, ParticleIDs, _ParticleIDs_parameters, 1)") + #.Define("bjets", "AnalysisFCChh::get_tagged_jets(Jet, Jet3, ParticleIDs, ParticleIDs_0, 0)") #bit 0 = loose WP, see: https://github.com/delphes/delphes/blob/master/cards/FCC/scenarios/FCChh_I.tcl + .Define("selpt_bjets", "FCCAnalyses::ReconstructedParticle::sel_pt(30.)(bjets)") + .Define("sel_bjets_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_bjets)") + .Define("sel_bjets", "AnalysisFCChh::SortParticleCollection(sel_bjets_unsort)") + .Define("nbjets", "FCCAnalyses::ReconstructedParticle::get_n(sel_bjets)") + .Define("b1_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_bjets)[0]") + .Define("b1_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_bjets)[0]") + .Define("b1_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_bjets)[0]") + .Define("b1_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_bjets)[0]") + .Define("b2_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_bjets)[1]") + .Define("b2_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_bjets)[1]") + .Define("b2_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_bjets)[1]") + .Define("b2_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_bjets)[1]") + + ########################################### ELECTRONS ########################################### + + #all isolated + #.Alias("Electron0", "Electron#0.index") + .Alias("Electron0", "Electron_objIdx.index") + .Define("ele", "FCCAnalyses::ReconstructedParticle::get(Electron0, ReconstructedParticles)") + .Define("selpt_ele", "FCCAnalyses::ReconstructedParticle::sel_pt(10.)(ele)") + .Define("sel_ele_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_ele)") + .Define("sel_ele", "AnalysisFCChh::SortParticleCollection(sel_ele_unsort)") + .Define("nele", "FCCAnalyses::ReconstructedParticle::get_n(sel_ele)") + .Define("e1_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_ele)[0]") + .Define("e1_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_ele)[0]") + .Define("e1_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_ele)[0]") + .Define("e1_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_ele)[0]") + .Define("e2_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_ele)[1]") + .Define("e2_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_ele)[1]") + .Define("e2_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_ele)[1]") + .Define("e2_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_ele)[1]") + + ########################################### MUONS ########################################### + + # all isolated + #.Alias("Muon0", "Muon#0.index") + .Alias("Muon0", "Muon_objIdx.index") + .Define("mu", "FCCAnalyses::ReconstructedParticle::get(Muon0, ReconstructedParticles)") + .Define("selpt_mu", "FCCAnalyses::ReconstructedParticle::sel_pt(10.)(mu)") + .Define("sel_mu_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_mu)") + .Define("sel_mu", "AnalysisFCChh::SortParticleCollection(sel_mu_unsort)") + .Define("nmu", "FCCAnalyses::ReconstructedParticle::get_n(sel_mu)") + .Define("m1_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_mu)[0]") + .Define("m1_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_mu)[0]") + .Define("m1_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_mu)[0]") + .Define("m1_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_mu)[0]") + .Define("m2_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_mu)[1]") + .Define("m2_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_mu)[1]") + .Define("m2_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_mu)[1]") + .Define("m2_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_mu)[1]") + + + ########################################### PHOTONS ########################################### + #all, after isolation + + #.Alias("Photon0", "Photon#0.index") + .Alias("Photon0", "Photon_objIdx.index") + .Define("gamma", "FCCAnalyses::ReconstructedParticle::get(Photon0, ReconstructedParticles)") + .Define("selpt_gamma", "FCCAnalyses::ReconstructedParticle::sel_pt(30.)(gamma)") + .Define("sel_gamma_unsort", "FCCAnalyses::ReconstructedParticle::sel_eta(4)(selpt_gamma)") + .Define("sel_gamma", "AnalysisFCChh::SortParticleCollection(sel_gamma_unsort)") + + .Define("ngamma", "FCCAnalyses::ReconstructedParticle::get_n(sel_gamma)") + .Define("g1_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_gamma)[0]") + .Define("g1_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_gamma)[0]") + .Define("g1_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_gamma)[0]") + .Define("g1_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_gamma)[0]") + .Define("g2_e", "FCCAnalyses::ReconstructedParticle::get_e(sel_gamma)[1]") + .Define("g2_pt", "FCCAnalyses::ReconstructedParticle::get_pt(sel_gamma)[1]") + .Define("g2_eta", "FCCAnalyses::ReconstructedParticle::get_eta(sel_gamma)[1]") + .Define("g2_phi", "FCCAnalyses::ReconstructedParticle::get_phi(sel_gamma)[1]") + + ########################################### EVENT WIDE KINEMATIC VARIABLES########################################### + + #H(bb) system - using the loose WP b-jets -> if the events has < 2 bjets these variables do not get filled! + .Define("bb_pairs_unmerged", "AnalysisFCChh::getPairs(sel_bjets)") #currently gets only leading pT pair, as a RecoParticlePair + #then merge the bb pair into one object and get its kinematic properties + .Define("bb_pairs", "AnalysisFCChh::merge_pairs(bb_pairs_unmerged)") #merge into one object to access inv masses etc + .Define("m_bb", "FCCAnalyses::ReconstructedParticle::get_mass(bb_pairs)") + + + #H(yy) if it exists, if there are no 2 selected photons, doesnt get filled + .Define("yy_pairs_unmerged", "AnalysisFCChh::getPairs(sel_gamma)") + .Define("yy_pairs", "AnalysisFCChh::merge_pairs(yy_pairs_unmerged)") + .Define("m_yy", "FCCAnalyses::ReconstructedParticle::get_mass(yy_pairs)") + + # Filter at least one candidate + .Filter("sel_bjets.size()>1") + .Filter("sel_gamma.size()>1") + .Filter("m_bb[0] < 200.") + .Filter("m_bb[0] > 80.") + .Filter("m_yy[0] < 180.") + .Filter("m_yy[0] > 100.") + ) + return df2 + + #__________________________________________________________ + #Mandatory: output function, please make sure you return the branchlist as a python list. + + #REMEMBER TO ADD ALL THE OUTPUT BRANCHES TO THIS LIST + + def output(out_name): + branchList = [ + "weight", + # Jets: + "njets", "j1_e", "j1_pt", "j1_eta", "j1_phi", + "j2_e", "j2_pt", "j2_eta", "j2_phi", + # B-jets: + "nbjets", "b1_e", "b1_pt", "b1_eta","b1_phi", + "b2_e", "b2_pt", "b2_eta","b2_phi", + # Electrons: + "nele", "e1_e", "e1_pt", "e1_eta", "e1_phi", + "e2_e", "e2_pt", "e2_eta", "e2_phi", + # Muons: + "nmu", "m1_e", "m1_pt", "m1_eta", "m1_phi", + "m2_e", "m2_pt", "m2_eta", "m2_phi", + # Photons: + "ngamma", "g1_e", "g1_pt", "g1_eta", "g1_phi", + "g2_e", "g2_pt", "g2_eta", "g2_phi", + # Hbb decay: + "m_bb", + #Hyy decay: + "m_yy" + ] + + return branchList + + +# local test: fccanalysis run analysis_noiso.py --nevents 100 - also switch runBatch to False + diff --git a/run/bbyy_analysis/applyDNN_SelCat_full3DNN.ipynb b/run/bbyy_analysis/applyDNN_SelCat_full3DNN.ipynb new file mode 100644 index 0000000000..0e91f4c244 --- /dev/null +++ b/run/bbyy_analysis/applyDNN_SelCat_full3DNN.ipynb @@ -0,0 +1,12383 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "eb5d6030", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-10 19:08:44.801436: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /cvmfs/sft.cern.ch/lcg/releases/MCGenerators/thepeg/2.2.3-6fa5c/x86_64-centos7-gcc11-opt/lib/ThePEG:/cvmfs/sft.cern.ch/lcg/releases/MCGenerators/herwig++/7.2.3-c1d8e/x86_64-centos7-gcc11-opt/lib/Herwig:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/jaxlib/mlir/_mlir_libs:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/torch/lib:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/onnxruntime/capi/:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow/contrib/tensor_forest:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow/python/framework:/cvmfs/sft.cern.ch/lcg/releases/java/11.0.14p1-8284a/x86_64-centos7-gcc11-opt/jre/lib/amd64:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib64:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib:/cvmfs/sft.cern.ch/lcg/releases/gcc/11.3.0-ad0f5/x86_64-centos7/lib:/cvmfs/sft.cern.ch/lcg/releases/gcc/11.3.0-ad0f5/x86_64-centos7/lib64:/cvmfs/sft.cern.ch/lcg/releases/binutils/2.37-355ed/x86_64-centos7/lib:/usr/local/lib/:/cvmfs/sft.cern.ch/lcg/releases/R/4.1.2-23baf/x86_64-centos7-gcc11-opt/lib64/R/library/readr/rcon\n", + "2023-08-10 19:08:44.801493: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "import pyarrow.parquet as pq\n", + "from sklearn.model_selection import train_test_split\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import matplotlib.mlab as mlab\n", + "import numpy as np\n", + "import scipy\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c421f498", + "metadata": {}, + "outputs": [], + "source": [ + "# skim = pq.read_table(\"/eos/user/a/atalierc/FCC_HH_13/FCCAnalyses/run/bbyy_analysis/df_Sel_All.parquet\")\n", + "# df_ = skim.to_pandas()\n", + "parquet_file = pq.ParquetFile('/eos/user/a/atalierc/FCC_HH_13/FCCAnalyses/run/bbyy_analysis/df_Sel_All_haaIncluded.parquet', memory_map=True)\n", + "df_ = parquet_file.read().to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dfc5858d", + "metadata": {}, + "outputs": [], + "source": [ + "df_[\"label\"] = 0\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda100_5f_hhbbaa\"), ['label']] = 1\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda000_5f_hhbbaa\"), ['label']] = 1\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda240_5f_hhbbaa\"), ['label']] = 1\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda300_5f_hhbbaa\"), ['label']] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3a10ac73", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-10 19:09:33.804645: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /cvmfs/sft.cern.ch/lcg/releases/MCGenerators/thepeg/2.2.3-6fa5c/x86_64-centos7-gcc11-opt/lib/ThePEG:/cvmfs/sft.cern.ch/lcg/releases/MCGenerators/herwig++/7.2.3-c1d8e/x86_64-centos7-gcc11-opt/lib/Herwig:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/jaxlib/mlir/_mlir_libs:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/torch/lib:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/onnxruntime/capi/:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow/contrib/tensor_forest:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow/python/framework:/cvmfs/sft.cern.ch/lcg/releases/java/11.0.14p1-8284a/x86_64-centos7-gcc11-opt/jre/lib/amd64:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib64:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib:/cvmfs/sft.cern.ch/lcg/releases/gcc/11.3.0-ad0f5/x86_64-centos7/lib:/cvmfs/sft.cern.ch/lcg/releases/gcc/11.3.0-ad0f5/x86_64-centos7/lib64:/cvmfs/sft.cern.ch/lcg/releases/binutils/2.37-355ed/x86_64-centos7/lib:/usr/local/lib/:/cvmfs/sft.cern.ch/lcg/releases/R/4.1.2-23baf/x86_64-centos7-gcc11-opt/lib64/R/library/readr/rcon\n", + "2023-08-10 19:09:33.804729: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2023-08-10 19:09:33.804793: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (0c17030de225): /proc/driver/nvidia/version does not exist\n", + "2023-08-10 19:09:33.806161: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "model_DNN = keras.models.load_model('model_ttH_killer_dnn')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bf75752a", + "metadata": {}, + "outputs": [], + "source": [ + "input_vars = [ \"njets\",\n", + " #\"tag_b1\",\n", + " #\"tag_b2\",\n", + " \"pTb1_o_m_bb\",\n", + " \"pTb2_o_m_bb\",\n", + " \"pTbb_o_m_HH\",\n", + " \"pTg1_o_m_gg\",\n", + " \"pTg2_o_m_gg\",\n", + " \"pTgg_o_m_HH\",\n", + " \"sum_pt\",\n", + " \"mindR_gb\",\n", + " \"otherdR_gb\",\n", + " \"GG_cosT_restHH\",\n", + " \"B1_cosT_restBB\",\n", + " \"G1_cosT_restGG\",\n", + " \"DeltaPhi_gg\",\n", + " \"DeltaEta_gg\",\n", + " \"DeltaPhi_bb\",\n", + " \"DeltaEta_bb\",\n", + " \"DeltaPhi_HH\",\n", + " \"DeltaEta_HH\"\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3ddc83f7", + "metadata": {}, + "outputs": [], + "source": [ + "df_signal = df_.loc[df_.label == 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "37680712", + "metadata": {}, + "outputs": [], + "source": [ + "df_bkg = df_.loc[df_.label == 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "13090f56", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2010/682329889.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_bkg['prediction_ttH'] = model_DNN.predict(df_bkg[input_vars])\n" + ] + } + ], + "source": [ + "df_bkg['prediction_ttH'] = model_DNN.predict(df_bkg[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "dc114efb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([57960.65234375, 48007.25390625, 34726.21875 , 26687.50390625,\n", + " 21662.84570312, 18235.59960938, 15538.81152344, 14232.84277344,\n", + " 11340.52832031, 8828.19921875, 7271.3671875 , 5991.22265625,\n", + " 5035.72607422, 4515.55224609, 4028.58081055, 3744.51416016,\n", + " 3279.67797852, 3231.71850586, 2873.86865234, 1789.25061035]),\n", + " array([0. , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n", + " 0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1. ],\n", + " dtype=float32),\n", + " [])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df_bkg.loc[df_bkg.process == \"mgp8_pp_tth01j_5f_haa\"].prediction_ttH, label='bkg', histtype=(\"step\"), bins=20, alpha=1.0, linewidth=2, range=(0,1), weights = df_bkg.loc[df_bkg.process == \"mgp8_pp_tth01j_5f_haa\"].weight)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5bc58563", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2010/671325918.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_signal['prediction_ttH'] = model_DNN.predict(df_signal[input_vars])\n" + ] + } + ], + "source": [ + "df_signal['prediction_ttH'] = model_DNN.predict(df_signal[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c965b6c2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df_bkg.prediction_ttH, label='bkg', histtype=(\"step\"), bins=20, alpha=1.0, linewidth=2, range=(0,1), weights = df_bkg.weight)\n", + "plt.hist(df_signal.prediction_ttH, label='signal', histtype=(\"step\"), bins=20, alpha=1.0, linewidth=2, range=(0,1), weights = df_signal.weight)\n", + "plt.yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9e3574ed", + "metadata": {}, + "outputs": [], + "source": [ + "EDGES = np.arange(0.,1.0,0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d3a3999e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(1, 1.01, '30 $ab^{-1}$ (100 TeV)')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "a = np.array(df_bkg.loc[(df_bkg.process.str.contains(\"jjaa\")),:]['prediction_ttH'])\n", + "w_a = np.array(df_bkg.loc[(df_bkg.process.str.contains(\"jjaa\")),:]['weight'])\n", + "b = np.array(df_bkg.loc[(df_bkg.process.str.contains(\"haa\")) ,:]['prediction_ttH'])\n", + "w_b = np.array(df_bkg.loc[(df_bkg.process.str.contains(\"haa\")),:]['weight'])\n", + "\n", + "c = np.array(df_bkg.loc[(df_bkg.process.str.contains(\"jjaa\")),:]['prediction_ttH'])\n", + "w_c = np.array(df_bkg.loc[(df_bkg.process.str.contains(\"jjaa\")),:]['weight'] * 0.09)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "plt.hist([b,c,a], weights = [w_b,w_c,w_a], label=('Single Higgs', 'g+jets', 'gg +jets'), histtype=(\"stepfilled\"),stacked = True, color = ('red', 'gold','yellow'), alpha = 0.7, bins=EDGES);\n", + "plt.hist(df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda100_5f_hhbbaa'),:]['prediction_ttH'], \n", + " weights = df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda100_5f_hhbbaa'),:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=1.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + "plt.hist(df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda240_5f_hhbbaa'),:]['prediction_ttH'], \n", + " weights = df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda240_5f_hhbbaa'),:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=2.4', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "plt.hist(df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda300_5f_hhbbaa'),:]['prediction_ttH'], \n", + " weights = df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda300_5f_hhbbaa'),:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=3.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + "plt.hist(df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda000_5f_hhbbaa'),:]['prediction_ttH'], \n", + " weights = df_signal.loc[(df_signal.process =='pwp8_pp_hh_lambda000_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=0.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + "plt.legend(loc = 'upper right', title = ' inclusive ')\n", + "#plt.xlim(100.,180.)\n", + "#plt.ylim(1e-10,1e6)\n", + "plt.ylabel('Events / bin')\n", + "plt.xlabel('$M_{\\gamma\\gamma} \\; [GeV]$')\n", + "plt.yscale('log')\n", + "plt.text(0.0, 1.01,'FCC', transform=ax.transAxes, fontweight = 'bold', fontsize=12)\n", + "plt.text(0.08, 1.01,'- hh', transform=ax.transAxes,fontstyle = 'italic', fontsize=12)\n", + "plt.text(1, 1.01,'30 $ab^{-1}$ (100 TeV)', transform=ax.transAxes,ha = 'right', fontsize=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e2594fcb", + "metadata": {}, + "outputs": [], + "source": [ + "model_DNN_small = keras.models.load_model('model_smaller350_dnn')\n", + "model_DNN_great = keras.models.load_model('model_greater350_dnn')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9d3048b3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2010/129342411.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_bkg['prediction_small'] = model_DNN_small.predict(df_bkg[input_vars])\n" + ] + } + ], + "source": [ + "df_bkg['prediction_small'] = model_DNN_small.predict(df_bkg[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "0b5e8677", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2010/3519867212.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_bkg['prediction_great'] = model_DNN_great.predict(df_bkg[input_vars])\n" + ] + } + ], + "source": [ + "df_bkg['prediction_great'] = model_DNN_great.predict(df_bkg[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "aef040dd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2010/3303235730.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_signal['prediction_small'] = model_DNN_small.predict(df_signal[input_vars])\n" + ] + } + ], + "source": [ + "df_signal['prediction_small'] = model_DNN_small.predict(df_signal[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "a81d9d00", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2010/4282784759.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_signal['prediction_great'] = model_DNN_great.predict(df_signal[input_vars])\n" + ] + } + ], + "source": [ + "df_signal['prediction_great'] = model_DNN_great.predict(df_signal[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "69417aeb", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.concat([df_bkg,df_signal])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b6a7e3e4", + "metadata": {}, + "outputs": [], + "source": [ + "df['Mx'] = df['hh_m'] - df['haa_m'] - df['hbb_m'] + 250" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2d10cd47", + "metadata": {}, + "outputs": [], + "source": [ + "df_small = df.loc[df.Mx < 350]\n", + "df_great = df.loc[df.Mx >= 350]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4c3d93f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weighta1_pta1_etaa1_phia1_ea2_pta2_etaa2_phia2_eb1_pt...DeltaPhi_bbDeltaEta_bbDeltaPhi_HHDeltaEta_HHprocesslabelprediction_ttHprediction_smallprediction_greatMx
50961403.68917773.308945-0.071720-2.64081273.49756660.4219060.5494171.46770869.77307975.394218...2.9972841.1218930.1439220.298008mgp8_pp_tth01j_5f_haa00.0375320.2980740.254483257.476868
50961473.689177119.7240450.1783503.111698121.63322434.1021390.210552-0.48695234.860847103.101257...1.2379840.530799-1.0165970.953612mgp8_pp_tth01j_5f_haa00.2413270.6621480.566909318.539307
50961503.68917789.993492-1.923856-2.556242314.67825364.941017-2.1387881.811256279.471771134.841797...2.1427880.844618-1.6722750.755966mgp8_pp_tth01j_5f_haa00.4122560.5499340.548669310.384277
50961523.68917783.9100191.866585-1.430180277.77700841.3287662.8900432.517393372.98501692.147408...2.3819511.6390942.9249430.452708mgp8_pp_tth01j_5f_haa00.1302750.1706440.208596276.831726
50961533.689177240.6679840.035126-1.659873240.81648372.851044-0.865594-1.516515101.890175161.386063...0.0192710.848365-0.6243870.163332mgp8_pp_tth01j_5f_haa00.5556240.9613820.811722305.947052
..................................................................
50961230.01270652.599461-0.7615710.78610968.60467547.660866-2.8598540.848214417.42004485.135048...2.0043170.390789-1.4198770.135109pwp8_pp_hh_lambda300_5f_hhbbaa10.5483520.8758670.686209276.537750
50961300.01270667.971375-2.0706061.083764273.77987741.291588-0.463329-0.67305045.80356281.991547...-2.3010280.1353512.9352600.021870pwp8_pp_hh_lambda300_5f_hhbbaa10.8740230.8172130.766209283.317444
50961360.01270669.8290630.658866-1.87392085.54191656.9216380.4067891.42859361.69654864.490623...-3.0638380.8322450.9882500.457453pwp8_pp_hh_lambda300_5f_hhbbaa10.5249320.7200470.573077250.928619
50961380.01270677.583496-0.473443-2.34524686.44223049.0903700.1920941.41908549.99887870.665230...-1.0507851.175069-2.4872140.283207pwp8_pp_hh_lambda300_5f_hhbbaa10.8431830.6053190.762542301.492706
50961390.012706173.1468960.240001-1.115681178.157562163.4241940.742368-1.650572210.563049171.175812...-2.5081800.2394270.0157150.341365pwp8_pp_hh_lambda300_5f_hhbbaa10.4217060.7422430.817596291.617920
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1016909 rows × 48 columns

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" + ], + "text/plain": [ + " weight a1_pt a1_eta a1_phi a1_e a2_pt \\\n", + "5096140 3.689177 73.308945 -0.071720 -2.640812 73.497566 60.421906 \n", + "5096147 3.689177 119.724045 0.178350 3.111698 121.633224 34.102139 \n", + "5096150 3.689177 89.993492 -1.923856 -2.556242 314.678253 64.941017 \n", + "5096152 3.689177 83.910019 1.866585 -1.430180 277.777008 41.328766 \n", + "5096153 3.689177 240.667984 0.035126 -1.659873 240.816483 72.851044 \n", + "... ... ... ... ... ... ... \n", + "5096123 0.012706 52.599461 -0.761571 0.786109 68.604675 47.660866 \n", + "5096130 0.012706 67.971375 -2.070606 1.083764 273.779877 41.291588 \n", + "5096136 0.012706 69.829063 0.658866 -1.873920 85.541916 56.921638 \n", + "5096138 0.012706 77.583496 -0.473443 -2.345246 86.442230 49.090370 \n", + "5096139 0.012706 173.146896 0.240001 -1.115681 178.157562 163.424194 \n", + "\n", + " a2_eta a2_phi a2_e b1_pt ... DeltaPhi_bb \\\n", + "5096140 0.549417 1.467708 69.773079 75.394218 ... 2.997284 \n", + "5096147 0.210552 -0.486952 34.860847 103.101257 ... 1.237984 \n", + "5096150 -2.138788 1.811256 279.471771 134.841797 ... 2.142788 \n", + "5096152 2.890043 2.517393 372.985016 92.147408 ... 2.381951 \n", + "5096153 -0.865594 -1.516515 101.890175 161.386063 ... 0.019271 \n", + "... ... ... ... ... ... ... \n", + "5096123 -2.859854 0.848214 417.420044 85.135048 ... 2.004317 \n", + "5096130 -0.463329 -0.673050 45.803562 81.991547 ... -2.301028 \n", + "5096136 0.406789 1.428593 61.696548 64.490623 ... -3.063838 \n", + "5096138 0.192094 1.419085 49.998878 70.665230 ... -1.050785 \n", + "5096139 0.742368 -1.650572 210.563049 171.175812 ... -2.508180 \n", + "\n", + " DeltaEta_bb DeltaPhi_HH DeltaEta_HH \\\n", + "5096140 1.121893 0.143922 0.298008 \n", + "5096147 0.530799 -1.016597 0.953612 \n", + "5096150 0.844618 -1.672275 0.755966 \n", + "5096152 1.639094 2.924943 0.452708 \n", + "5096153 0.848365 -0.624387 0.163332 \n", + "... ... ... ... \n", + "5096123 0.390789 -1.419877 0.135109 \n", + "5096130 0.135351 2.935260 0.021870 \n", + "5096136 0.832245 0.988250 0.457453 \n", + "5096138 1.175069 -2.487214 0.283207 \n", + "5096139 0.239427 0.015715 0.341365 \n", + "\n", + " process label prediction_ttH \\\n", + "5096140 mgp8_pp_tth01j_5f_haa 0 0.037532 \n", + "5096147 mgp8_pp_tth01j_5f_haa 0 0.241327 \n", + "5096150 mgp8_pp_tth01j_5f_haa 0 0.412256 \n", + "5096152 mgp8_pp_tth01j_5f_haa 0 0.130275 \n", + "5096153 mgp8_pp_tth01j_5f_haa 0 0.555624 \n", + "... ... ... ... \n", + "5096123 pwp8_pp_hh_lambda300_5f_hhbbaa 1 0.548352 \n", + "5096130 pwp8_pp_hh_lambda300_5f_hhbbaa 1 0.874023 \n", + "5096136 pwp8_pp_hh_lambda300_5f_hhbbaa 1 0.524932 \n", + "5096138 pwp8_pp_hh_lambda300_5f_hhbbaa 1 0.843183 \n", + "5096139 pwp8_pp_hh_lambda300_5f_hhbbaa 1 0.421706 \n", + "\n", + " prediction_small prediction_great Mx \n", + "5096140 0.298074 0.254483 257.476868 \n", + "5096147 0.662148 0.566909 318.539307 \n", + "5096150 0.549934 0.548669 310.384277 \n", + "5096152 0.170644 0.208596 276.831726 \n", + "5096153 0.961382 0.811722 305.947052 \n", + "... ... ... ... \n", + "5096123 0.875867 0.686209 276.537750 \n", + "5096130 0.817213 0.766209 283.317444 \n", + "5096136 0.720047 0.573077 250.928619 \n", + "5096138 0.605319 0.762542 301.492706 \n", + "5096139 0.742243 0.817596 291.617920 \n", + "\n", + "[1016909 rows x 48 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_small" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b9433850", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 348.21014404, 1338.13720703, 1957.67785645, 2322.05639648,\n", + " 2576.85766602, 2636.62353516, 2755.58520508, 2939.83935547,\n", + " 3157.36816406, 3368.38110352, 3369.9074707 , 3329.94042969,\n", + " 3461.25976562, 3750.36962891, 4209.72998047, 4822.20654297,\n", + " 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/IXAf8EbgRcBXktxdVSdHXNuk9J5f0xzom/GSA53Gk+S3gc8Cl1bV02OqbVS6jHkX8MVhmG8DLktyqqpuG0uF/ev6f/s7VfVD4IdJ7gIuBGY10LuM+VrghhosMB9L8i3gpcC/jafEses9v6Z5yWUzXnJg1TEn2QHcCrxthmdri6065qp6YVXNV9U88PfAn81wmEO3/9v/APx+ki1JfpXBFU6PjrnOPnUZ87cZ/EVCkl8HXgI8OtYqx6v3/JraGXpN5yUHRqrjmK8Hfg349HDGeqpm+Ep1HcfclC5jrqqjSb4EPAD8jME3hS27/W0WdPw5/yXw+SQPMliO+GBVzeZldYEkNwO7gW1JFoAPA1thdPnlqf+S1IhpXnKRJK2BgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIa8b9Z7D5r16goGgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df.loc[df.label==0].prediction, label='bkg', histtype=(\"step\"), bins=20, alpha=1.0, linewidth=2, range=(0.,1), weights = df.loc[df.label==0].weight)\n", + "plt.hist(df.loc[df.label==1].prediction, label='signal', histtype=(\"step\"), bins=20, alpha=1.0, linewidth=2, range=(0.,1), weights = df.loc[df.label==1].weight)\n", + "#plt.yscale(\"log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "34d355cd", + "metadata": {}, + "outputs": [], + "source": [ + "cut = 0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "38df8f93", + "metadata": {}, + "outputs": [], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "226a17a1", + "metadata": {}, + "outputs": [], + "source": [ + "def drawMgg(df,EDGES,label):\n", + " a = np.array(df.loc[(df.process.str.contains(\"jjaa\")) ,:]['haa_m'])\n", + " w_a = np.array(df.loc[(df.process.str.contains(\"jjaa\")) ,:]['weight'])\n", + " b = np.array(df.loc[(df.process.str.contains(\"haa\")) ,:]['haa_m'])\n", + " w_b = np.array(df.loc[(df.process.str.contains(\"haa\")) ,:]['weight'])\n", + "\n", + " c = np.array(df.loc[(df.process.str.contains(\"jjaa\")),:]['haa_m'])\n", + " w_c = np.array(df.loc[(df.process.str.contains(\"jjaa\")) ,:]['weight'] * 0.09)\n", + "\n", + " fig, ax = plt.subplots()\n", + "\n", + " plt.hist([b,c,a], weights = [w_b,w_c,w_a], label=('Single Higgs', 'g+jets', 'gg +jets'), histtype=(\"stepfilled\"),stacked = True, color = ('red', 'gold','yellow'), alpha = 0.7, bins=EDGES);\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda100_5f_hhbbaa') ,:]['haa_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda100_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=1.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda240_5f_hhbbaa') ,:]['haa_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda240_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=2.4', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda300_5f_hhbbaa') ,:]['haa_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda300_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=3.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda000_5f_hhbbaa') ,:]['haa_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda000_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=0.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + " plt.legend(loc = 'upper right', title = label)\n", + " plt.xlim(100.,180.)\n", + " #plt.ylim(1e-10,1e6)\n", + " plt.ylabel('Events / bin')\n", + " plt.xlabel('$M_{\\gamma\\gamma} \\; [GeV]$')\n", + " plt.yscale('log')\n", + " plt.text(0.0, 1.01,'FCC', transform=ax.transAxes, fontweight = 'bold', fontsize=12)\n", + " plt.text(0.08, 1.01,'- hh', transform=ax.transAxes,fontstyle = 'italic', fontsize=12)\n", + " plt.text(1, 1.01,'30 $ab^{-1}$ (100 TeV)', transform=ax.transAxes,ha = 'right', fontsize=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "700e7044", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df,EDGES,\"inclusive\")" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "14e23548", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_small,EDGES,\"Mx < 350 GeV\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "0afd0f1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great,EDGES,\"Mx > 350 GeV\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "af1b0cf4", + "metadata": {}, + "outputs": [], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "bc81519d", + "metadata": {}, + "outputs": [], + "source": [ + "def drawMbb(df,EDGES,label):\n", + "\n", + " a = np.array(df.loc[(df.process.str.contains(\"jjaa\")),:]['hbb_m'])\n", + " w_a = np.array(df.loc[(df.process.str.contains(\"jjaa\")) ,:]['weight'])\n", + " b = np.array(df.loc[(df.process.str.contains(\"haa\")) ,:]['hbb_m'])\n", + " w_b = np.array(df.loc[(df.process.str.contains(\"haa\")) ,:]['weight'])\n", + "\n", + " c = np.array(df.loc[(df.process.str.contains(\"jjaa\")) ,:]['hbb_m'])\n", + " w_c = np.array(df.loc[(df.process.str.contains(\"jjaa\")) ,:]['weight'] * 0.09)\n", + "\n", + " fig, ax = plt.subplots()\n", + "\n", + " plt.hist([b,c,a], weights = [w_b,w_c,w_a], label=('Single Higgs', 'g+jets', 'gg +jets'), histtype=(\"stepfilled\"),stacked = True, color = ('red', 'gold','yellow'), alpha = 0.7, bins=EDGES);\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda100_5f_hhbbaa') ,:]['hbb_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda100_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=1.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda240_5f_hhbbaa') ,:]['hbb_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda240_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=2.4', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda300_5f_hhbbaa'),:]['hbb_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda300_5f_hhbbaa') ,:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=3.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + " plt.hist(df.loc[(df.process =='pwp8_pp_hh_lambda000_5f_hhbbaa') ,:]['hbb_m'], \n", + " weights = df.loc[(df.process =='pwp8_pp_hh_lambda000_5f_hhbbaa'),:]['weight'],\n", + " label='HH \\u2192 \\u03B3\\u03B3bb kl=0.0', histtype=(\"step\"), bins =EDGES, linewidth=1.5);\n", + "\n", + " plt.legend(loc = 'upper right', title = label)\n", + " #plt.xlim(100.,180.)\n", + " #plt.ylim(1e-10,1e6)\n", + " plt.ylabel('Events / bin')\n", + " plt.xlabel('$M_{bb} \\; [GeV]$')\n", + " plt.yscale('log')\n", + " plt.text(0.0, 1.01,'FCC', transform=ax.transAxes, fontweight = 'bold', fontsize=12)\n", + " plt.text(0.08, 1.01,'- hh', transform=ax.transAxes,fontstyle = 'italic', fontsize=12)\n", + " plt.text(1, 1.01,'30 $ab^{-1}$ (100 TeV)', transform=ax.transAxes,ha = 'right', fontsize=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b678901b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df,EDGES,\"inclusive\")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "245a8dac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small,EDGES,\"Mx < 350 GeV\")" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "467c9c47", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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B4KqrkpkxY36+z2/dup3zznNyKG3btpPMzAN8/PHnALz55uO5Eu59//3PLF++mjPOcJ56lpl5gNNOOzVfnSecUI81a9Zz220P0bNnJ7p2PTN0HY5RURMUROQwYBRwBJCiqpMi3CRjTAgFy7PmNf9arVo1SE2dBgRfU/Cvs0uXM5g8+ekC66xRoxo//zyNOXO+4cUX32bq1E8ZP/4RT+0pq8I6fSQi40Vks4gsy7P9AhFZKSKrReRud/OFwHHAASAtnO0yxpS+M89sy/TpX5CensGePXuZOfNLwDkxV61ame+/TwXwPZSmJDp2bM233/7I6tV/AM6jMFetWgtA1aqV2b17L+BMM2VnK5de2o1Rowbz44+/lPjYsS7cI4WJwAvAGzkbRCQBeBHognPyXyQi04DGwAJVfVVE3gfmhbltxphS1K5dS3r3PpdWrXpTv34dkpKaU62a82jKceNGc+ON91O5ciU6dWpPtWpVi30cEeHII2syceIY+vYdQkaG89yDhx++nZNPbsjAgVfQvfsNHHvsUTzzzL3073+P79nQjzxyZ8k7GuPCnjpbRBoAM1S1ufv+NGCEqnZz39/jFl0PZKrqVBF5V1ULfrQSljrbRIKlzi6JPXv2UqVKZfbt28/ZZ1/N2LGjaNPmFN92gDFjXmXTpi08++z9Ra6/RYteTJv2Mg0b1gt106NQeFJnR2JN4TicAJAjDegAPAs8LyJnAV8F+7CIDAQGAhx/fOBnuhpjotPAgcNZvnw16ekZ9Ot3MW3anALAzJnzeeSRVzl4MIv69eswceKjRa67S5fraNHi5DgJCOETiaAQ6KnYqqr7gAGFfVhVxwJjwRkphLhtxpgweuedpwJu79OnJ336lOyh9p99NrFEnzeOSNynkAb4h/K6wMaiVGDPUzDGmPCIRFBYBJwkIg1FpAJwJTCtKBXY8xSMMSY8wn1J6mRgAdBYRNJEZICqHgRuBeYAK4Cpqlqk68BspGCMMeER1jUFVe0bZPssYFYJ6p0OTE9KSrqxuHUYY4zJz3IfGWOiWkrKUgYNGlVgmdTU5cyaNb90GlTGRU2ai6IQkWQguVGjRpFuijGxa32IU2fXK3nq7AYNOrNu3Re5tiUltSApqUWBn0tN/ZWUlKX06NGpxG2IdzEZFGz6yETM+sKLRK2DQKbf+1Bf0J1ZeJFRo1/k7cnTqFf3WGrXrkHbU5sz9I48V6LnqWf+lz/wxNPjmPHxWPbu3cdtQ0axdNlKDh7MYsT9t9H9grN54IFn2b8/nW++Xsw9d93EMUcfyeA7HwacO5y/mvc2VatWCVFHo0SF8FQbk0HBGBN7UhYv5YOP5vDTwk84ePAgbTpcTNtTmxepjv+OeZlzO3Vk/NhH2LFjF+3PuIzzzzudhx4YTMripbzw7IMAJF98Ey8++yBnnN6WPXv2kphYMRxdKpNiMijY9JExseebbxdzYbJf6uyeTurs/455mfc++BSAjRs307pdbwDOOK0NLz43Ilcdcz//lmkz/scTTzsP50nPyODPP/Pf5nTG6W24465HuPrK3lxyUVfq1q0cpl6VPTEZFGz6yJjYEyzP2n1338x9d98MQIOTO5O6KPhtS6rKB1Oep3HjE3Jt/2HRklzv7/7PTfTs3olZs7+k49mX8/msiTRpcmIJexAf7OojY0ypOPOMtkyf6Zc6+9Mvi1xHty5n8vxLb/oCzE+pywE3Hfaevb5yv//+Jy2aN2bY0IEktWnOryvXhKYTccCCgjGmVLRLaknvXufSKqk3l1xxK0ltD6XOLoyIkzJt+L3/5sCBg7Rsm0zzU3syfMQzAHQ+pwPLV6ymdbvevPveTJ55fiLNT+1Jq6RkKlVKpPsF54SrW2VOTE4f2ZqCMSFwTMkvIS2qoUMGMGL4ICd19nlXc+ft1+fav27VF/k+s3XbDmrWdFLaVKqUyKsv5b9noWbN6iz67kPf+z6Xlyy5XjyLyZGC5T4yJjYNvGU4rdv1pk2Hi7j04q60OfWUAstPmz6P+x54iptuuLKUWmhicqRgTMTcEukGlMBQ4I/INuGdUXlSZxfSnt4tz6P3jPM8lY073p6xU2QxOVIwxhgTHjEZFCxLqjHGhEdMBgVbUzDGmPCIyaBgjDEmPCwoGGNKzX9ffplTevSgZXIyrXv35oeffwbghnvvZfnq1cWqc11aGs17Fu0S1CqtW+d6P/HDD7l15EgAXpk8mTc++qhYbSkL7OojY+LVTSFOnf1qwfc9LPjpJ2Z88QU/fvwxFStU4J9t28g8cACA10ePDm1bSuBffQM+Gyxu2EjBGFMqNm3eTO0aNahYwcn5XLtmTeocfTQAna65hpSlSwHnW/x9Tz1Fq+RkOl5+OX//8w8Av//5Jx0vv5x2l1zCA88+m+/bPkBWVhb/efRR2l1yCS2Tk3l1ypQit3PEc8/xxDgn4d6iJUtomZzMaVdcwX8efdQ3Itm3fz9XDB5My+Rk+gweTIfLLiNl6VKysrK4btgwmvfsSYtevXh6woQiHz/SYnKkEJo7mpND1Rw/08NQpzFlQ9czz+ShF1/k5K5dOf/00+nTowfntG+fr9zeffvo2Lo1/73jDu567DFemzqV+2+5hcEPP8zgfv3o26sXr0yeHPAY4957j2pVq7Loww/JyMzkjCuvpOsZZ9CwXr1c5fanp9O6d2/f+207d9L73HPz1df/nnsYO2oUp7dpw92PP+7b/tI771DjiCNYMn06y1atovWFFwKQumIFG/7+m2UzZwKwY9euov+iIiwmg0L0ZkkNR6CJBAtuJvSqVK7M4o8+4uuUFL74/nv63H47Y4YO5bpLLslVrkL58vTq7KTVbnvKKXz27bcALEhN5eOXXgLgquRkhj76aL5jzP32W5asXMn7s2cDsHPPHn774498QaFSYiKp0w5lY5344Ye+kUqOHbt2sXvvXk5v08Z3zBnz5wPwTUoKg/v1A6D5ySfTsnFjAE6oV48169dz20MP0bNTJ7qeeWbRf1ERFpNBwYRbJIObBaSyLCEhgU4dOtCpQwdaNG7MpI8+yhcUypcv70uAl5CQwMGsLM/1qyrPDx9Ot7POKnFbg6X6huAPratRrRo/T5vGnG++4cW332bqp58y/pFHStyW0mRrCibKJHt8mVizcs0aflu3zvc+dcUK6tep4/nzHVu14oM5cwCYMmNGwDLdzjyTl995hwPuAvaqtWvZu29fsdpbo1o1qlauzPepqc4x3SkhgDPbtmXqp86DgZavXs3SVasA+GfbNrJVubRbN0YNHsyPv/xSrGNHko0UTIyywBBr9uzbx22jRrFj1y7KlStHo+OPZ+yo/BlPg3nmvvu4ZuhQnhw/np6dOlGtSv602zdccQXrNmygzcUXo6ocWbOmb8qpOMaNHs2N999P5UqV6NS+PdWqVgXglquuot+wYbRMTubUpk1p2bgx1apWZcPff9P/nnvIzs4G4JE77yz2sSNFChoiRbukpCRNSUkp5qftpGKKIYb/2awYOpSmRfhmHm327d9PpcRERIQpM2YweeZMPnn55bAec8/evVSp7DzKc8yrr7Jpyxaevf9+srKyOHDwIIkVK/L7n39yXr9+rJozhwrulVWl4iRvGfFWrFhB06ZNc20TkcWqmhSovI0UjDExYfGyZdz60EMoUL1q1VKZq585fz6PvPoqB7OyqF+nDhPdxe19+/fT+dprOXDgAAq8PGJE6QaEMLKgYIyJCWe1a8fP00v3QoQ+PXvSJ8Dd0lWrVCHlww8DfCL2xeRCs2VJNcaY8IjJoGBZUo0xJjxiMigYY4wJDwsKxhhjfGyh2Zg4NPL7zSzfmhHSOpvVqsiDHY8qsEyV1q3Z494MBofSS7zw4IOMeO45qlSuzNABA3z7G3TuTMoHH1C7Zs2QtrUggdoBMP+HH3hi3DhmjB1b4OeDtTlv3wvywptv8sykSfz+559s+f77oP2fNGkSDz/8MAD3338//dzUGyVhQcGYOLR8awbLt2XQrGbF0NS3LbQBJt6d0bYtvTp3ptP//V/QMtu2bWPkyJGkpKQgIrRt25bevXtTo0aNEh3bgoIxcapZzYq827Ne4QU96DNzfUjqKa71mzaxZds22pxySq7tqcuX0/eOO5j2yiuc1KABqkqTCy5gwiOPMODee/Nt/+KNNwD4ecUKzr32WtZv2sRdN9zAjX36ALBrzx4uvuUWVq5dy9nt2vHSiBEcdljgWfj96elc/O9/c2nXrr7Pe3Vqs2aFlpkzZw5dunShpjuK6NKlC7Nnz6ZvCZ8HYUHBGFNqCktZ/fSECbz1ySe+9xs3b/ZUb0ZmJv2GDWPimDG0bd7ct711s2Y8PGQIT02YwMsjRzL/hx9odPzxnN6mTcDtOc93WLJyJd+/9x579+3j1IsuomenTgAsXLKE5bNmUf+447hgwAA+nDuXyy64IF979uzbx5VDhnDtRRdx7cUX59q3e88ezrrqqoD9eOepp2jm8ZEAGzZsoJ5f9te6deuyYcMGT58tiAUFY0ypKSxl9ZD+/fOtKeT1vwULGOTOo/vbumMH3W+4gc3ff59re6/OnbnnySfJyspi3Pvv0//SSwvcDnDh+edTKTGRSomJdO7QgYVLllD9iCNo37IlJxx/PAB9e/Xim8WLAwaFC2++mbtuvJGr/QJgjqpVquT6HRRXoBRFOdllS8KCgjEmppx72mm+h9jk2LFrFz1uvJE7r78+X/mKFSrQoWVLPv78c75cuJDx7qM/g20HyHtqzTnZ5j3pBjsJn9G2LZ9+9RVXJSfnKxOqkULdunWZ7z7fASAtLY1O7oimJKLmklQR6SQiX4vIKyLSKdLtMcbEjj82bOCuG27g0m7dAu6/vHt3bn7wQS7p2jVXjqJg2z+ZN4/0jAy2bt/O/IULadeiBeBMH61dv57s7GzenTWLM9u2DXi8hwYNolb16twyYkS+fTkjhUAvrwEBoFu3bsydO5ft27ezfft25s6dS7cg/S+KsI4URGQ80AvYrKrN/bZfADwLJACvq+oYnOdW7AESgbRwtssY41wxFKoF4lBeyVQcrZo2pVWeTKD+up11FvszMuiXZ34/2Pb2LVvS88Yb+XPTJobfcgt1jj6aVevWcdqpp3L3E0+wdNUqzm7Xjou7dAl6zGfuu4/r77mHux57jMfuuqtI/XnujTd47LXX+Ouff2jZuzc9zj6b10ePJmXpUl6ZPJnXR4+mZs2aDB8+nHbt2gHwwAMP+BadSyKsqbNF5GycE/0bOUFBRBKAVUAXnJP/IqAv8KuqZovI0cBTqnp1YfVb6mxT6mL4n41/6uxI3acQScHuH4jEvRAhEYups1X1KxFpkGdze2C1qq5xGzcFuFBVl7v7twOR+8phTByI5pO3iaxILDQfB/iPWdOADiJyCdANqA68EOzDIjIQGAhwvHsVgDHGFGbdF18UaXu8ikRQCLRcr6r6IVBognJVHQuMBWf6KMRtM8aYuFbo1UcicomI/CYiO0Vkl4jsFpFdJThmGuB/G2VdYGNRKrDnKRhjTHh4uST1MaC3qlZT1SNUtaqqHlGCYy4CThKRhiJSAbgSKNKdHPY8BWOMCQ8vQeFvVV1RnMpFZDKwAGgsImkiMkBVDwK3AnOAFcBUVf2lOPUbY4wJLS9rCiki8i7wMeC7hs1dAyiQqgbMzKSqs4BZHtuYj4gkA8mNinCjhzHGT8qTsH1laOus0RiS7iywiKXOTvXUhqvvvJOUZcsoX64c7Vu25NWHHqJ8+fIBy+7atYumTZty8cUX88ILQa/R8czLSOEIYB/QFecq7WScG9IixqaPjCmh7Sth+6oQ1rcq9EEmjl2dnMyvs2ezdMYM9qen8/p77wUtO3z4cM4555yQHbvQkYKq9g/Z0UKkVEcKRbnhMzRZiI0pHTVOhi4Ff+v17LOBoamnmMpa6uwefjmM2rdsSdpffwUst3jxYv7++28uuOACin8jb25Bg4KI3KWqj4nI8zgpKHJR1UEhaUExqOp0YHpSUtKNxa4kHOnfvdZpwcPEKUudXbSEeAcOHODNTz7h2fvuy1c2OzubO++8kzfffJN58+Z5+j15UdBIIWdxOTThxxwS6oDkNchY0DIRZqmzi5Y6+5YRIzi7XTvOcvMb+XvppZfo0aNHrmcqhELQoOB+G0dVJwGIyBHOW90d0hYUgy005xHqIBPJh2hZQDKFiJfU2SOff54t27bx6qhRAcsuWLCAr7/+mpdeeok9e/aQmZlJlSpVGDNmTMDyXhW6piAiScAEoKrzVnYA16vq4hIduQRCMn1kopONZkwx5KTOvihI1tLLu3fnhvvuo2+vXvlSZwfa/sm8edzzr3+xd98+5i9cyJihQ1m1bp0vdXb9447j3VmzGBhkreChQYMY9dJL3DJiBC+PHJlrn5eRwutTpzLnm2+YN2lS0DWLt99+2/fzxIkTSUlJKXFAAG+XpI4HblHVrwFE5EycINGyxEc3JtwsyAS3fVXoFoi3r3IWriOkrKXO/teDD1K/Th1Ou+IKAC7p2pUHbr01V+rscCk0dbaIfKuqZxS2LRJKlDp7fQznQDaRc0ukG1B8/qmzI3WfQiRZ6uxDipU6W0TauD8uFJFXgck4VyH1AeZ7ak2Y2JqCMSUUxSdvE1kFTR89mef9g34/RzQ7aUjWFCL5je+lCB7bmDhlqbO9Kejqo/zXgpnS5TVwWZAxxoRIJJ6nYCI1Sgn1cS0YGVPmxGRQsDWFPMpKkCkKC0jGhEXQhHgicmxpNqQoLCGeMcaER0EjhfEiUgPnSqPZwDfusxCMiR22LhPQo2vH8evedSGts0nlBgxrOKDAMpY6O9VTGwbcey8pS5eiwMkNGjBxzBiqVK6cr9ykSZN42E35cf/999OvXz9P9RekoIXm7iKSCHQCLgaeEJE/cQLEbFX9s8RHNyZaxPD9B8Xx6951rNy7lsaVG4akvpV714akHuN4+t57OaJKFQDuGD2aF956i7tvuilXmW3btjFy5EhSUlIQEdq2bUvv3r2pUaNGiY5d4JqCqqbjBgEAEWkIdAdeEJFjVLV9iY5uTHHF2Uk8HBpXbsiE5oHz6hRV/2XDQ1JPcZW11Nk5AUFV2Z+RETDH0pw5c+jSpQs13RFJly5dmD17Nn37Bny2mWdFWmhW1bU4A+2X3OcrG2OMZ5Y623tCvP53382sL7+kWaNGPHn33fnKbtiwIVeG1Lp167JhwwZPv6+CFPvqI1XNLPHRi8muPjImNlnqbO+psyeMGUNWVha3jRrFu7Nm5WofOKOIvIJlbS2KmLwk1bKkGhO/4iV1NkBCQgJ9evTg8ddfzxcU6taty/z5833v09LS6OT3xLbi8vKMZh8RqSEilh3VGBNVclJnX9qtW8D9l3fvzs0PPsglXbvmS50daPsn8+aRnpHB1u3bmb9wIe1atADwpc7Ozs7m3VmzOLNt24DHe2jQIGpVr84tI0bk25czUgj0ataoEarK6j/+AJzRwPT//Y8mJ5yQr55u3boxd+5ctm/fzvbt25k7dy7dgvS/KLw8T2E+0NstmwpsEZEvVfWOEh/dGBMxK/euDdkCcSivZCqOspQ6W1XpN2wYu/bsQVVp1aSJ75kM/qmza9asyfDhw2nnPpXtgQce8C06l4SX1Nk/qeqpInIDUE9VHxSRJaoa8RFDiVJnJ1vqbBNf/FNnR+o+hUiy1NmHFCt1tn8Z9+7mK4D8T482xsScaD55m8jyEhRGAnNw7mheJCInAL+Ft1nGGBNaljrbGy9BYZP/VJGqrhGRp8LYJmOMMRHi5eqj5z1uKzUikiwiY3fu3BnJZhhjTJlT0OM4TwNOB44UEf8rjY4AEsLdsILYfQrGGBMeBU0fVQCquGWq+m3fBVwWzkYZY4yJjIKypH4JfCkiE1X1j1JskzEmzP567TUy1oQ2s2nFExpyzI0FD94tdXaqpzasXb+eK4cMYdvOnbRp1ow3H3881811OcKROtvLmkJFERkrInNF5H85rxIf2RgTMRlr1pK+NnRBIX3t2pAHmXg27IknGHLddfz22WfUqFaNce+/n69MTursH374gYULFzJy5Ei2b99e4mN7ufroPeAV4HUgq8RHNMZEhcSGDan/yOjCC3rwxz33hqSe4ipLqbNVlf8tWMA7Tz4JQL+LL2bE889zc558SZFMnX1QVV8u0VGMMQZLnQ2FJ8Q7qmZNqh9xBOXKOafnusccw4a//85XNpKps6eLyC3AR0BGzkZV3Vbioxtj4oqlzi48dfaWbflPrYGysUYydXbOysV//NsD5E/bZ4wxYVbWU2c3PfFEduzaxcGDBylXrhxpf/1FnaOOylc2YqmzVbVhgJcFBGNM1ChLqbNFhM4dO/L+7NkATProIy4877x89UQydfbhwB3A8ao6UEROAhqr6owSHz3/sSoDXwEPhqN+Y8wh6WvXhmyBOH3tWhIbWupsf8VNnQ3w6NChXDlkCPc/8wynNmvGgMsvB6Indfa7wGLgWlVtLiKVgAWq2rrQykXGA72Azara3G/7BcCzOHdGv66qY9ztDwF7gV+8BAVLnW2Md/6psyN1n0IkWersQ0qaOvtEVe0jIn0BVHW/eF/NmAi8ALzh15gE4EWgC5AGLBKRaUAdYDmQ6LFuY0wxRfPJ20SWl6CQ6Y4OFEBETsTvKqSCqOpXItIgz+b2wGpVXePWNwW4ECelRmWgGbBfRGaparanXhhjTCEsdbY3XoLCCGA2UE9E3gbOAK4rwTGPA9b7vU8DOqjqrQAich3wT7CAICIDgYEAx7uXhhljjAmNQoOCqs4VkcVAR5wrtQar6j8lOGagqSffwoaqTiykPWOBseCsKZSgHcbEF1VUNSTXspvYUNiacSCFXpLqzvd3Bear6owSBgRwRgb1/N7XBTYWpQJ7noIxRZf4999szcgo1onCxB5VZevWrSQmFm2Z1sv00ZNAH2CMiCwE3gVmqGp60ZsJwCLgJBFpCGwArgQC38kRhD1PwZiiq/vRR6QBW44+Gmy0EPsOHiy0SGJiInXr1i1StV6mj3JSaCcA5wI3AuNxHrZTIBGZDHQCaotIGs79B+NE5Fac5z4nAONV9ZcitdoYU2Tl9+6l4VtvRboZJlSmTw9LtV5GCrhXHyXjjBjaAJO8fE5VA6brU9VZwCyPbQzUnmQguVGjRsWtwhhjTABe1hTeBVbgjBJexLlv4bZwN6wgqjpdVQdWq1Ytks0wxpgyx8tIYQJwlapGzbMUbKRgjDHhEXSkICJ3AajqbOCSPPtC82SOYrKRgjHGhEdB00dX+v18T559+ROIG2OMiXkFBQUJ8nOg96XK7lMwxpjwKCgoaJCfA70vVTZ9ZIwx4VHQQnMrEdmFMyqo5P6M+94ymRpjTBkUNCioakJpNsQYY0zkFXqfQjSyNQVjjAkPT3c0RxvLfRRb/tqyhYwMT4/g8KxixYocc+SRIa3TGBOjIwUTWzIyMkjPyAxZfekZmSEPMsYYR0yOFEzkFeXbf3pGJokVK1C/iNkag/kjLS0k9Rhj8rOgYIol59t/YsUKhZZNrFiBihUrhvT46RmZnoJDKKeZwjENZkxxVRw9mmPuvTfk9cZkULDcR+Hj9cQX6m//ReE1wIRyygqKFgiNCaf0jExY8WtY6o7JoGALzeHj9cQXjm//Xnn95h+OaaZIBUJj/IVzCjUmg4IJLzvxGRO/LCjEiaJOCxlj4pNdkhonvF4WGslpIWNM5MXkSMEWmovHpoWMMYWJyaBgC82H2LSQMSaUYjIomENi4Wohr0YeczrLE2t7Ktss/R8e/Ou7MLfImPhjQaEMiPZpIa8n+x8q1wGgw96NBZZbnlgrJO0yxuRnQcGE3fLE2ixPrEWz9K0Fluuwd6OnEUCfBr1D2TxjjB8LClEq2tcKijLVkxMQ3l03LWTHX55Yq9DgcN0/U6mz628o5XQYxsQyCwpRKtrXCrx++wdolr6VZun/hOzYXuv6pfrxAFQvpLzXdBjrKlRj32HluctGKibCrvtnKodv3Uv9MNRtQSGKRWKtwOsIIBzf/r3yusDcp1FvFtGx0DZ6TRmw77Dy7D2svKeyxoTT3sPKQ2ZWWOq2oGBy8ToCCPW3/1hROftARAKhMf4+zT4AhGfaOCaDQizfvBaptYJYGAEYYyIvJtNcqOp0VR1YrVq1SDelyCKVbiJnBFCYeB0BGGMcMTlSiHWRuq/ARgDGmMJYUAiRWJkWMsaYgsTk9FE0smkhY0xZYCOFELJpIWNMrLORgjHGGB8bKUQpWysIDa/pMA7PPhCWu0ONiTUWFArgdfEYQr+AbDeRlZzX34vdpWzMIVETFESkKTAYqA3MU9WXI9wkz/mHIDw5iGytoGS8psP4NPsAx+zewh/pUmC5Yw4of1W1pHmmbAtrUBCR8UAvYLOqNvfbfgHwLJAAvK6qY1R1BfAvETkMeC2c7SqKaH9WgSm5nBN9zUJGFn9VPdItu7sUWmVMZIR7pDAReAF4I2eDiCQALwJdgDRgkYhMU9XlItIbuNv9TJlkawUFqPs7HL7HW9l9VSDtxJAcdnbjzgCFjspysqP2t9GbKcPCGhRU9SsRaZBnc3tgtaquARCRKcCFwHJVnQZME5GZwDvhatdfo0eT4SEzZjieVRCXawVeT/ZVdzr/3V1I+pJKHgOHMabIIrGmcByw3u99GtBBRDoBlwAVgVnBPiwiA4GBAMcff3yxGpCx4teIPqsg7tYKDt/jnMj3Vym43O5q3kYAJ/8curYZY3KJRFAItJqnqjofmF/Yh1V1LDAWICkpSYvbiFCvFcTdtFBRpnpyAsKqVqE7fqU93oJDCKeZjIkHkbh5LQ2o5/e+LlDwk9pjQNylm8j59u/F/irOyTlU9lUpfNQBTvu8Bi5jDBCZkcIi4CQRaQhsAK4EripKBdH6PIW4mxYK9bd/r7x+8w/xNFO/wz+hfrkNcHIZGOmZmFZ/exXSD4bwi5afcF+SOhnoBNQWkTTgQVUdJyK3AnNwLkkdr6q/FKVeVZ0OTE9KSrox1G2Oe16nhbysERTRo6228mt1b89LbrKjAsN+LnxkFkr1y22gQcJGnGUvYyInUTJB94el7nBffdQ3yPZZFLCYXJhoHSmUCV4XhYswJeT1ZJ9ypHP3eNKWgk+6K6t5CxzhsC6rDqessvtWTGSl6+9hqztq7mguitIaKXhdPIYYWEAu6ggghNNCv1bPZGW1TBrvLPhqr6QtFT2NAPqfs4mV1TLpf86mgg9cqTJN9mcxbFVRWxzYpCOzWFdRqVyjkOMaE2aXf6okkk2zMNQdk0GhtHi9pwBiYAE5xCOAokz15ASECV8e66l8YZrs8HbvyMpKCZ7r9JI4LyvxBdZXUJrt9VytMWGRcRigxb74skAxGRRKc/qoTC0eh3AE4PXbP0DjnRU8n8i98LqW0P+C1Z7KeQ3mh2k2J2RoyIKbMcU1K3t14Iv7QyAmg4ItNIeP1xFAqL/9R5LXxHn9mxwMc0uMibyYDAoltW7rXvYl1vblsgkm6tcJIORXC3kdAYT6278xJjrEZFAo6fTRvswsTzn0o36dAMJytVBZGQEAcFiW3flsTBHEZFAIxfRR5ewDcbVW8Girrfx6bCacWPCVM17XCWJCtseFZkuwZ4xPTAaFuGDTQiWXkej8d1UhI0pLsGeMT0wGhbi4ec2mhYwxERCTQSFurj6yaSFjTCmLyaBgDonLaaEiiMSdz8bEMgsKpS0MCedsWiiwcNz5bExZZ0GhtIVhrcAEFuo7n42JBzEZFGJ+odnWCowxUSoST14rMVWdrqoDq1Ur5AHvMSxnraAw8bpWYIwJj5gcKUQlWyswxpQBMTlSiEpen1lsawXGmChmI4VQsrUCY0yMs5FCKbO1AmNMNIvJkUKpXX3kdZ0AbK0g1nnJpnpYovcke8bEqJgcKZTa1Ude1wnA1gpiWXaCt5O913LGxLCYHCmUKo+PsLS1ghjmNZvqsYWkyzCmDIjJkUI0srUCY0xZEJcjhaMPbiQxYQ+cXMjUUBHWCcDWCowxsS8ug0Ki7idRCv9Wb+sE8cNLNlWb+jPxIC6DAkC6VoBVTQss43WdAOyEEcu8TufZ1J+JB3EbFLzw+qwCsBNGLPOaTdWYeBCTQaE0s6TaOoExJp7E5NVH8ZAl1RhjIiEmRwol9Ve5bNLLKY/bwqIxxuQSl0EhXZQMKbycrRMYY+JNXAYFgIrZ2FqBMcbkEZNrCsYYY8LDgoIxxhgfCwrGGGN8LCgYY4zxsaBgjDHGx4KCMcYYHwsKxhhjfCwoGGOM8RFVjXQbik1EtgB/FPPjtYF/QticSLK+RJ+y0g+wvkSrkvSlvqoeGWhHTAeFkhCRFFVNinQ7QsH6En3KSj/A+hKtwtUXmz4yxhjjY0HBGGOMTzwHhbGRbkAIWV+iT1npB1hfolVY+hK3awrGGGPyi+eRgjHGmDziJiiIyBAR+UVElonIZBFJFJGaIvKZiPzm/rdGpNsZiIiMF5HNIrLMb1vQtovIPSKyWkRWiki3yLQ6vyD9eFxEfhWRJSLykYhU99sXlf2AwH3x2zdURFREavtti7m+iMhtbnt/EZHH/LZHZV+C/PtqLSLfi0iqiKSISHu/fVHZDwARqSciX4jICvf3P9jdHv6/e1Ut8y/gOGAtUMl9PxW4DngMuNvddjfwaKTbGqT9ZwNtgGV+2wK2HWgG/AxUBBoCvwMJke5DAf3oCpRzf340FvoRrC/u9nrAHJz7Z2rHal+AzsDnQEX3/VHR3pcg/ZgLdHd/7gHMj/Z+uO07Fmjj/lwVWOW2Oex/93EzUsB5ylwlESkHHA5sBC4EJrn7JwEXRaZpBVPVr4BteTYHa/uFwBRVzVDVtcBqoD1RIFA/VHWuqh50334P1HV/jtp+QND/JwBPA3cB/ot1sdiXm4Exqprhltnsbo/avgTphwJHuD9Xw/m7hyjuB4CqblLVH92fdwMrcL7chv3vPi6CgqpuAJ4A/gQ2ATtVdS5wtKpucstsAo6KXCuLLFjbjwPW+5VLc7fFguuBT92fY64fItIb2KCqP+fZFXN9AU4GzhKRH0TkSxFp526Ptb7cDjwuIutxzgH3uNtjph8i0gA4FfiBUvi7j4ug4M67XYgzrKoDVBaRayLbqrCRANui/hIzEbkPOAi8nbMpQLGo7YeIHA7cBzwQaHeAbVHbF1c5oAbQEfgPMFVEhNjry83AEFWtBwwBxrnbY6IfIlIF+AC4XVV3FVQ0wLZi9ScuggJwPrBWVbeo6gHgQ+B04G8RORbA/e/mAuqINsHanoYzr52jLoeGzFFJRPoBvYCr1Z0gJfb6cSLOl46fRWQdTnt/FJFjiL2+gNPmD9WxEMjGybUTa33ph/P3DvAeh6ZUor4fIlIeJyC8rao5fQj73328BIU/gY4icrj7bec8nDm6aTj/aHD/+0mE2lccwdo+DbhSRCqKSEPgJGBhBNrniYhcAAwDeqvqPr9dMdUPVV2qqkepagNVbYDzR9pGVf8ixvri+hg4F0BETgYq4CRfi7W+bATOcX8+F/jN/Tmq++Gep8YBK1T1Kb9d4f+7j/Qqeymu5o8EfgWWAW/irNLXAubh/EOZB9SMdDuDtH0yzlrIAZyTzYCC2o4zjfE7sBL3yotoeAXpx2qcudBU9/VKtPcjWF/y7F+He/VRLPYFJwi85f69/AicG+19CdKPM4HFOFfm/AC0jfZ+uG07E2f6Z4nf30aP0vi7tzuajTHG+MTL9JExxhgPLCgYY4zxsaBgjDHGx4KCMcYYHwsKxhhjfCwoGGOM8bGgYIwxxseCgolbInKT+9yDc/y23epuO7+IdTUQkf0ikppnezcR+drN5b9URCb6P2chQD2NRGRpnm0VRWStiJziPhcgs6A6jCkJCwomnrXEuWO0KfiS2g0AtgBLC/hcML+rauucNyJyOU7++36qmgS0xrkTNbGAOtYA9UTE/29zIPClqv7i1h9VOXpM2WJBwcSzFjipEZq47wfhJE3LVtW/S1KxiFQGngeuUtU1AKqapar/VdU0t0xDEfnEHUUsFJHGqpqNk6urgVumEnAnMKIk7THGKwsKJp41xXkKXxMRqQb0Ab7DyfdTUj2An1X1l0A73QyYrwN3uKOIEThP0gInWWNOoPo3ME1V14WgTcYUyoKCiUsiUg/Y6n6LPwrnaWnP4zxcZolb5vUAn8u3LYhT8AsuIvKcOM8H/97ddJFb5gN3HeIxIN3dtwJo7ObS/zfw3yJ1zpgSsKBg4lVLDq0b7AYuwHm8YQtgqTtt00RERojIFHHk21ZA/fv936jqIGAoTvZOgFbAfara2n01V9Wb3X05I4XBOLn0SzSVZUxRWFAw8aoFh4LC48Ctqprlbl+C8/jD91V1BLAT5/m+gbYFMwe4RETqgC8/fhecNNTgpHjulrOgLCIt/ILMCpyHwVzvts2YUmNBwcSrFrjTO6o6Q1UXuNubActxTspL3G2Hq+qOINsCUtVU4H5gtoj8hJPLvxLOszwAxuP8/a1wp4+G6aE89ivd9o1V1Z0l6qUxRWTPUzAmABF5DdgKVAemqOr8QNv8yjcAZqhq81Jo2zogSVX/CfexTPyxoGBMCLgL19/hLF63DtMxKgELgCOBFqq6LRzHMfHNgoIxxhgfW1MwxhjjY0HBGGOMjwUFY4wxPhYUjDHG+FhQMMYY42NBwRhjjI8FBWOMMT4WFIwxxvj8P1H1NPUY2MyZAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_great,EDGES,\"Mx > 350 GeV\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f5b4589", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08b22c2d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "b3349679", + "metadata": {}, + "outputs": [], + "source": [ + "#find best cuts and categories delimiter" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "da7b8010", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.4, 0.5, 0.6000000000000001]\n", + "[0.7000000000000001, 0.8, 0.9]\n", + "[0.2, 0.30000000000000004, 0.4, 0.5]\n" + ] + } + ], + "source": [ + "x_1 = [i*0.1 for i in range(4,7)]\n", + "x_2 = [i*0.1 for i in range(7,10)]\n", + "ttHcut = [i*0.1 for i in range(2,6)]\n", + "print(x_1)\n", + "print(x_2)\n", + "print(ttHcut)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "1ed2d838", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0.4, 0.7000000000000001), (0.4, 0.8), (0.4, 0.9), (0.5, 0.7000000000000001), (0.5, 0.8), (0.5, 0.9), (0.6000000000000001, 0.7000000000000001), (0.6000000000000001, 0.8), (0.6000000000000001, 0.9)]\n" + ] + } + ], + "source": [ + "mix = [(x, y) for x in x_1 for y in x_2 if x < y]\n", + "print(mix)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ec953bf9", + "metadata": {}, + "outputs": [], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "c7adfba8", + "metadata": {}, + "outputs": [], + "source": [ + "def findx1x2(df,ttHcut,x_1,x_2,varT= \"prediction_ttH\",var = \"prediction\"):\n", + " import math\n", + " max_sig = 0.\n", + " x_1_max = 0.\n", + " x_2_max = 0.\n", + " ttHcut_max = 0.\n", + " mix = [(x, y) for x in x_1 for y in x_2 if x < y]\n", + " print(mix)\n", + " #medium purity region: x_1x_2\n", + " for cut in ttHcut:\n", + " print(\"ttH cut: \", cut)\n", + " \n", + " for (low,up) in mix:\n", + " print(\"----------------\")\n", + " print(\"----------------\")\n", + " print((low,up))\n", + " bkg_medium = 0\n", + " bkg_high = 0\n", + " bkg_medium_unw = 0\n", + " bkg_high_unw = 0\n", + " signal_medium = 0\n", + " signal_high = 0\n", + " sig_medium = 0\n", + " sig_high = 0\n", + " exited = False\n", + " for item in df.loc[df.label==0,:].process.unique():\n", + " (n_m, bins_m, _) = plt.hist(df.loc[(df.process == item) & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low)]['haa_m'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (n_h, bins_h, _) = plt.hist(df.loc[(df.process == item) & (df[varT] >= cut) & (df[var] > up)]['haa_m'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " if \"haa\"in item:\n", + " bkg_medium_unw += n_m\n", + " bkg_high_unw += n_h\n", + "\n", + " print(item)\n", + " print(n_m)\n", + " print(n_h)\n", + "\n", + " if \"jjaa\" in item:\n", + " if any([x < 10. for x in n_m ]) or any([x < 10. for x in n_h ]):\n", + " exited = True\n", + " break\n", + "\n", + " (n_m, bins_m, _) = plt.hist(df.loc[(df.process == item) & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low)]['haa_m'], \n", + " weights = df.loc[(df.process == item) & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low) ]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (n_h, bins_h, _) = plt.hist(df.loc[(df.process == item) & (df[varT] >= cut) & (df[var] > up)]['haa_m'], \n", + " weights = df.loc[(df.process == item) & (df[varT] >= cut) & (df[var] > up)]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + "\n", + " #print(\"n_m\",n_m)\n", + " #print(\"n_h\",n_h)\n", + " bkg_medium +=n_m\n", + " bkg_high +=n_h\n", + " print(\"bkg med : \", bkg_medium)\n", + " print(\"bkg high : \", bkg_high)\n", + "\n", + " ##add a+jj\n", + " (n_m, bins_m, _) = plt.hist(df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low)]['haa_m'], \n", + " weights = df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low) ]['weight']*0.09,\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (n_h, bins_h, _) = plt.hist(df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & (df[varT] >= cut) & (df[var] > up)]['haa_m'], \n", + " weights = df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & (df[varT] >= cut) & (df[var] > up)]['weight']*0.09,\n", + " histtype=(\"step\"), bins=EDGES)\n", + " bkg_medium +=n_m\n", + " bkg_high +=n_h\n", + " print(\"add a+jj\")\n", + " print(\"bkg med : \", bkg_medium)\n", + " print(\"bkg high : \", bkg_high)\n", + "\n", + " (signal_medium, bins_m, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low)]['haa_m'], \n", + " histtype=(\"step\"), bins=EDGES)\n", + " (signal_high, bins_h, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[varT] >= cut) & (df[var] > up) ]['haa_m'], \n", + " histtype=(\"step\"), bins=EDGES)\n", + "\n", + " if any([x < 10. for x in signal_medium ]) or any([x < 10. for x in signal_high ]) or any([x < 10. for x in bkg_high_unw ]) or any([x < 10. for x in bkg_medium_unw ]):\n", + " exited = True\n", + " continue\n", + "\n", + " (signal_medium, bins_m, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low)]['haa_m'], \n", + " weights = df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[varT] >= cut) & (df[var] <= up) & (df[var] >low) ]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (signal_high, bins_h, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[varT] >= cut) & (df[var] > up) ]['haa_m'], \n", + " weights = df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[varT] >= cut) & (df[var] > up) ]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + "\n", + " print(\"sig med : \",signal_medium)\n", + " print(\"sig high : \",signal_high)\n", + "\n", + " if not exited:\n", + "\n", + " print(\"sono dentro\")\n", + " try:\n", + " for i in range(0,len(bins_m)-1):\n", + " print(signal_medium[i])\n", + " print(bkg_medium[i])\n", + " sig_medium += math.sqrt(abs(2*((signal_medium[i]+bkg_medium[i])*math.log(1+signal_medium[i]/bkg_medium[i])-signal_medium[i])))\n", + " print(\"sono dentro\")\n", + " sig_high += math.sqrt(abs(2*((signal_high[i]+bkg_high[i])*math.log(1+signal_high[i]/bkg_high[i])-signal_high[i])))\n", + "\n", + " print(\":::::::\")\n", + " print(\"Exact significance\")\n", + " print(\"sig_med\",sig_medium)\n", + " print(\"sig_high\",sig_high)\n", + " print(\"Naive\")\n", + " print(\"sig_med\",np.sum(signal_medium)/math.sqrt(np.sum(bkg_medium)))\n", + " print(\"sig_high\",np.sum(signal_high)/math.sqrt(np.sum(bkg_high)))\n", + " #sig_medium = signal_medium/math.sqrt(bkg_medium)\n", + " #sig_high = signal_high/math.sqrt(bkg_high)\n", + " sig_tot = math.sqrt(sig_medium**2+sig_high**2)\n", + " if sig_tot > max_sig: #and sum(signal_medium) > 1.0 and sum(signal_high) > 1.0: \n", + " max_sig = sig_tot\n", + " x_1_max = low\n", + " x_2_max = up\n", + " ttHcut_max = cut\n", + " except:\n", + " print(\"error \"+str(ValueError))\n", + " print(\"ttH cut: \",ttHcut_max)\n", + " print(\"x_1: \",x_1_max)\n", + " print(\"x_2: \",x_2_max)\n", + " print(\"Significance: \",max_sig)\n", + " return ttHcut_max, x_1_max, x_2_max, max_sig" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "52b52f72", + "metadata": {}, + "outputs": [], + "source": [ + "def findm1m2(df,x_1,x_2,var = \"hbb_m\"):\n", + " import math\n", + " max_sig = 0.\n", + " x_1_max = 0.\n", + " x_2_max = 0.\n", + " mix = [(x, y) for x in x_1 for y in x_2 if x < y]\n", + " print(mix)\n", + " #medium purity region: x_1x_2\n", + " for (low,up) in mix:\n", + " print(\"----------------\")\n", + " print(\"----------------\")\n", + " print((low,up))\n", + " bkg_medium = 0\n", + " bkg_high = 0\n", + " bkg_medium_unw = 0\n", + " bkg_high_unw = 0\n", + " signal_medium = 0\n", + " signal_high = 0\n", + " sig_medium = 0\n", + " sig_high = 0\n", + " exited = False\n", + " for item in df.loc[df.label==0,:].process.unique():\n", + " (n_m, bins_m, _) = plt.hist(df.loc[(df.process == item) & ((df[var] > up) | (df[var] <=low))]['haa_m'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (n_h, bins_h, _) = plt.hist(df.loc[(df.process == item) & (df[var] <= up) & (df[var] > low)]['haa_m'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " if \"haa\"in item:\n", + " bkg_medium_unw += n_m\n", + " bkg_high_unw += n_h\n", + " \n", + " print(item)\n", + " print(n_m)\n", + " print(n_h)\n", + " \n", + " if \"jjaa\" in item:\n", + " if any([x < 10. for x in n_m ]) or any([x < 10. for x in n_h ]):\n", + " exited = True\n", + " break\n", + "\n", + " (n_m, bins_m, _) = plt.hist(df.loc[(df.process == item) & ((df[var] > up) | (df[var] <=low))]['haa_m'], \n", + " weights = df.loc[(df.process == item) & ((df[var] > up) | (df[var] <=low)) ]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (n_h, bins_h, _) = plt.hist(df.loc[(df.process == item) & (df[var] <= up) & (df[var] > low)]['haa_m'], \n", + " weights = df.loc[(df.process == item) & (df[var] <= up) & (df[var] > low)]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " \n", + " #print(\"n_m\",n_m)\n", + " #print(\"n_h\",n_h)\n", + " bkg_medium +=n_m\n", + " bkg_high +=n_h\n", + " print(\"bkg med : \", bkg_medium)\n", + " print(\"bkg high : \", bkg_high)\n", + " \n", + " ##add a+jj\n", + " (n_m, bins_m, _) = plt.hist(df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & ((df[var] > up) | (df[var] <=low))]['haa_m'], \n", + " weights = df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & ((df[var] > up) | (df[var] <=low)) ]['weight']*0.09,\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (n_h, bins_h, _) = plt.hist(df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & (df[var] <= up) & (df[var] > low)]['haa_m'], \n", + " weights = df.loc[(df.process == \"mgp8_pp_jjaa_5f\") & (df[var] <= up) & (df[var] > low)]['weight']*0.09,\n", + " histtype=(\"step\"), bins=EDGES)\n", + " bkg_medium +=n_m\n", + " bkg_high +=n_h\n", + " print(\"add a+jj\")\n", + " print(\"bkg med : \", bkg_medium)\n", + " print(\"bkg high : \", bkg_high)\n", + " \n", + " (signal_medium, bins_m, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\")& ((df[var] > up) | (df[var] <=low))]['haa_m'], \n", + " histtype=(\"step\"), bins=EDGES)\n", + " (signal_high, bins_h, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[var] <= up) & (df[var] > low) ]['haa_m'], \n", + " histtype=(\"step\"), bins=EDGES)\n", + " \n", + " if any([x < 10. for x in signal_medium ]) or any([x < 10. for x in signal_high ]) or any([x < 10. for x in bkg_high_unw ]) or any([x < 10. for x in bkg_medium_unw ]):\n", + " exited = True\n", + " continue\n", + " \n", + " (signal_medium, bins_m, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & ((df[var] > up) | (df[var] <=low))]['haa_m'], \n", + " weights = df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & ((df[var] > up) | (df[var] <=low)) ]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " (signal_high, bins_h, _) = plt.hist(df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[var] <= up) & (df[var] > low) ]['haa_m'], \n", + " weights = df.loc[(df.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\") & (df[var] <= up) & (df[var] > low)]['weight'],\n", + " histtype=(\"step\"), bins=EDGES)\n", + " \n", + " print(\"sig med : \",signal_medium)\n", + " print(\"sig high : \",signal_high)\n", + " \n", + " if not exited:\n", + " \n", + " print(\"sono dentro\")\n", + " try:\n", + " for i in range(0,len(bins_m)-1):\n", + " print(signal_medium[i])\n", + " print(bkg_medium[i])\n", + " sig_medium += math.sqrt(abs(2*((signal_medium[i]+bkg_medium[i])*math.log(1+signal_medium[i]/bkg_medium[i])-signal_medium[i])))\n", + " print(\"sono dentro\")\n", + " sig_high += math.sqrt(abs(2*((signal_high[i]+bkg_high[i])*math.log(1+signal_high[i]/bkg_high[i])-signal_high[i])))\n", + " \n", + " print(\":::::::\")\n", + " print(\"Exact significance\")\n", + " print(\"sig_med\",sig_medium)\n", + " print(\"sig_high\",sig_high)\n", + " print(\"Naive\")\n", + " print(\"sig_med\",np.sum(signal_medium)/math.sqrt(np.sum(bkg_medium)))\n", + " print(\"sig_high\",np.sum(signal_high)/math.sqrt(np.sum(bkg_high)))\n", + " #sig_medium = signal_medium/math.sqrt(bkg_medium)\n", + " #sig_high = signal_high/math.sqrt(bkg_high)\n", + " sig_tot = math.sqrt(sig_medium**2+sig_high**2)\n", + " if sig_tot > max_sig: #and sum(signal_medium) > 1.0 and sum(signal_high) > 1.0: \n", + " max_sig = sig_tot\n", + " x_1_max = low\n", + " x_2_max = up\n", + " except:\n", + " print(\"error \"+str(ValueError))\n", + " \n", + " print(\"x_1: \",x_1_max)\n", + " print(\"x_2: \",x_2_max)\n", + " print(\"Significance: \",max_sig)\n", + " return x_1_max, x_2_max, max_sig" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "03462807", + "metadata": {}, + "outputs": [], + "source": [ + "###smaller than 350 GeV" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "eb6750cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0.4, 0.7000000000000001), (0.4, 0.8), (0.4, 0.9), (0.5, 0.7000000000000001), (0.5, 0.8), (0.5, 0.9), (0.6000000000000001, 0.7000000000000001), (0.6000000000000001, 0.8), (0.6000000000000001, 0.9)]\n", + "ttH cut: 0.2\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 13. 51. 172. 382. 710. 1046. 1135. 758. 446. 172. 84. 31.]\n", + "[ 30. 82. 191. 373. 648. 852. 811. 653. 381. 183. 72. 48.]\n", + "bkg med : [ 47.95932007 188.14808655 634.53515625 1409.27636719 2619.28076172\n", + " 3858.74072266 4187.52929688 2796.60546875 1645.49609375 634.5859375\n", + " 309.9140625 114.37304688]\n", + "bkg high : [ 110.675354 302.51257324 704.63061523 1376.07495117 2390.5480957\n", + " 3142.99804688 2992.13867188 2409.21289062 1405.68164062 675.16992188\n", + " 265.640625 177.09375 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 7. 8. 34. 65. 83. 74. 68. 37. 11. 5. 0.]\n", + "[ 3. 2. 8. 13. 27. 37. 55. 20. 15. 4. 7. 2.]\n", + "bkg med : [ 47.95932007 1241.2794342 1838.11401367 6524.48339844\n", + " 12398.35302734 16345.86376953 15320.73242188 13027.15234375\n", + " 7212.1171875 2289.52734375 1062.16015625 114.37304688]\n", + "bkg high : [ 562.01739502 603.40722656 1908.20959473 3331.8894043\n", + " 6452.62451172 8709.54736328 11266.73828125 5418.15820312\n", + " 3662.390625 1276.95898438 1318.77148438 477.98828125]\n", + "mgp8_pp_jjaa_5f\n", + "[1348. 1359. 1351. 1329. 1272. 1270. 1315. 1282. 1324. 1313. 1277. 1248.]\n", + "[439. 517. 401. 459. 443. 441. 459. 421. 436. 428. 437. 484.]\n", + "bkg med : [43773.70932007 45323.8419342 45661.17651367 49633.92089844\n", + " 53658.85302734 57541.48876953 57976.04492188 54612.02734375\n", + " 50159.3671875 44859.77734375 42405.03515625 40518.37304688]\n", + "bkg high : [14788.36114502 17357.43847656 14903.13146973 18206.3581543\n", + " 20808.59326172 23000.70361328 26141.20703125 19061.18945312\n", + " 17791.515625 15146.83398438 15480.30273438 16162.61328125]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 3. 15. 53. 155. 288. 454. 445. 279. 156. 59. 19. 11.]\n", + "[ 4. 15. 44. 79. 136. 150. 164. 151. 79. 36. 13. 8.]\n", + "bkg med : [43783.80688858 45374.32974243 45839.56695557 50155.62683105\n", + " 54628.21630859 59069.62744141 59473.84960938 55551.06396484\n", + " 50684.41992188 45058.35498047 42468.98388672 40555.39599609]\n", + "bkg high : [14801.82456875 17407.92630768 15051.22917175 18472.2600708\n", + " 21266.34817505 23505.58032227 26693.20556641 19569.44873047\n", + " 18057.42700195 15268.00878906 15524.06030273 16189.54101562]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 5. 18. 28. 52. 80. 90. 67. 22. 16. 3. 2.]\n", + "[ 0. 4. 4. 15. 23. 41. 30. 22. 16. 11. 0. 1.]\n", + "bkg med : [43857.80726242 45497.66371155 46283.56921387 50846.29663086\n", + " 55910.8873291 61042.97241211 61693.87890625 57203.75195312\n", + " 51227.09375 45453.02685547 42542.98486328 40604.72998047]\n", + "bkg high : [14801.82456875 17506.59346771 15149.89634705 18842.26184082\n", + " 21833.68453979 24516.91711426 27433.20800781 20112.1171875\n", + " 18452.0949707 15539.34301758 15524.06030273 16214.20776367]\n", + "add a+jj\n", + "bkg med : [47791.22913742 49463.1832428 50221.47155762 54719.08569336\n", + " 59617.5748291 64743.83178711 65525.87109375 60939.58007812\n", + " 55085.3125 49279.19091797 46264.24267578 44241.47998047]\n", + "bkg high : [16081.95542812 19014.17354584 16319.21861267 20180.7130127\n", + " 23125.47946167 25802.88000488 28771.65917969 21339.75976562\n", + " 19723.4777832 16787.41333008 16799.21264648 17626.50463867]\n", + "sig med : [ 1.0534687 7.28525925 19.96248055 49.82070541 98.73516846\n", + " 139.8177948 138.62979126 97.64828491 51.85461426 21.74145508\n", + " 6.32043457 1.65991211]\n", + "sig high : [ 3.67117929 13.60729027 38.56386185 86.48080444 152.08843994\n", + " 211.85906982 214.21624756 157.5647583 87.35290527 42.10577393\n", + " 16.08966064 4.63745117]\n", + "sono dentro\n", + "1.0534687042236328\n", + "47791.229137420654\n", + "sono dentro\n", + "7.285259246826172\n", + "49463.18324279785\n", + "sono dentro\n", + "19.962480545043945\n", + "50221.47155761719\n", + "sono dentro\n", + "49.82070541381836\n", + "54719.085693359375\n", + "sono dentro\n", + "98.73516845703125\n", + "59617.57482910156\n", + "sono dentro\n", + "139.8177947998047\n", + "64743.831787109375\n", + "sono dentro\n", + "138.62979125976562\n", + "65525.87109375\n", + "sono dentro\n", + "97.64828491210938\n", + "60939.580078125\n", + "sono dentro\n", + "51.8546142578125\n", + "55085.3125\n", + "sono dentro\n", + "21.741455078125\n", + "49279.19091796875\n", + "sono dentro\n", + "6.3204345703125\n", + "46264.24267578125\n", + "sono dentro\n", + "1.659912109375\n", + "44241.47998046875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.5860973727373775\n", + "sig_high 6.797884756319708\n", + "Naive\n", + "sig_med 0.7883161096477634\n", + "sig_high 2.092038702670834\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 25. 81. 256. 555. 1048. 1513. 1535. 1082. 614. 248. 113. 45.]\n", + "[ 18. 52. 107. 200. 310. 385. 411. 329. 213. 107. 43. 34.]\n", + "bkg med : [ 92.22946167 298.82342529 944.42895508 2047.51098633 3866.07519531\n", + " 5582.03173828 5663.31054688 3991.98828125 2265.32421875 914.984375\n", + " 416.90820312 166.02539062]\n", + "bkg high : [ 66.4052124 191.83726501 394.74038696 737.83679199 1143.65478516\n", + " 1420.34545898 1516.16943359 1213.66943359 785.74951172 394.71923828\n", + " 158.62548828 125.42773438]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 7. 13. 40. 85. 108. 108. 82. 46. 12. 8. 2.]\n", + "[ 1. 2. 3. 7. 7. 12. 21. 6. 6. 3. 4. 0.]\n", + "bkg med : [ 393.12414551 1351.95489502 2900.24389648 8065.40258789\n", + " 16654.09277344 21830.41064453 21911.82617188 16328.7890625\n", + " 9185.80859375 2720.328125 1620.47070312 466.91601562]\n", + "bkg high : [ 216.85255432 492.73197937 846.08236694 1790.96838379 2196.78564453\n", + " 3225.71289062 4675.56201172 2116.35351562 1688.43310547 846.06103516\n", + " 760.41455078 125.42773438]\n", + "mgp8_pp_jjaa_5f\n", + "[1651. 1733. 1638. 1655. 1586. 1581. 1648. 1588. 1640. 1607. 1576. 1596.]\n", + "[136. 143. 114. 133. 129. 130. 126. 115. 120. 134. 138. 136.]\n", + "bkg med : [53947.43664551 57566.14239502 56032.86889648 61749.46508789\n", + " 68087.09277344 73015.28564453 75265.82617188 67740.2890625\n", + " 62280.80859375 54746.953125 52643.47070312 52137.41601562]\n", + "bkg high : [4625.14161682 5127.94291687 4541.28549194 6102.03869629 6378.19970703\n", + " 7439.54101562 8759.73388672 5843.97070312 5578.12060547 5189.54541016\n", + " 5233.55517578 4533.74023438]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 4. 24. 75. 203. 377. 545. 552. 370. 197. 80. 24. 16.]\n", + "[ 3. 6. 22. 31. 47. 59. 57. 60. 38. 15. 8. 3.]\n", + "bkg med : [53960.90007019 57646.92286682 56285.30783081 62432.73156738\n", + " 69356.02661133 74849.73217773 77123.70507812 68985.60644531\n", + " 62943.85595703 55016.21240234 52724.25976562 52191.27539062]\n", + "bkg high : [4635.23918438 5148.13805199 4615.33427429 6206.38031006 6536.39460754\n", + " 7638.12588501 8951.58703613 6045.92138672 5706.02270508 5240.03308105\n", + " 5260.48193359 4543.83776855]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 8. 21. 39. 67. 111. 109. 83. 32. 23. 3. 3.]\n", + "[ 0. 1. 1. 4. 8. 10. 11. 6. 6. 4. 0. 0.]\n", + "bkg med : [54034.90044403 57844.25718689 56803.31057739 63394.73522949\n", + " 71008.69885254 77587.76171875 79812.40673828 71032.96679688\n", + " 63733.19970703 55583.55322266 52798.26074219 52265.27636719]\n", + "bkg high : [4635.23918438 5172.804842 4640.0010643 6305.04747772 6733.72891235\n", + " 7884.79376221 9222.92193604 6193.92224121 5854.02325439 5338.70007324\n", + " 5260.48193359 4543.83776855]\n", + "add a+jj\n", + "bkg med : [58846.01763153 62894.32749939 61576.54495239 68217.50866699\n", + " 75630.40197754 82194.89453125 84614.78173828 75660.49804688\n", + " 68512.26220703 60266.45166016 57390.82324219 56916.12011719]\n", + "bkg high : [5031.95012188 5589.93472481 4972.53817368 6693.00743866 7110.02090454\n", + " 8264.00274658 9590.46246338 6529.37634277 6204.06231689 5729.57702637\n", + " 5663.02685547 4940.54870605]\n", + "sig med : [ 2.52194023 12.33020973 35.96111298 87.9053421 164.85015869\n", + " 234.49047852 233.14398193 165.67266846 90.24468994 38.24035645\n", + " 13.3034668 3.39013672]\n", + "sig high : [ 2.20270729 8.56234074 22.56506348 48.39634323 85.97338104\n", + " 117.18635559 119.70202637 89.54031372 48.97149658 25.67944336\n", + " 9.1295166 2.90484619]\n", + "sono dentro\n", + "2.521940231323242\n", + "58846.01763153076\n", + "sono dentro\n", + "12.330209732055664\n", + "62894.32749938965\n", + "sono dentro\n", + "35.96111297607422\n", + "61576.54495239258\n", + "sono dentro\n", + "87.90534210205078\n", + "68217.50866699219\n", + "sono dentro\n", + "164.85015869140625\n", + "75630.40197753906\n", + "sono dentro\n", + "234.490478515625\n", + "82194.89453125\n", + "sono dentro\n", + "233.14398193359375\n", + "84614.78173828125\n", + "sono dentro\n", + "165.67266845703125\n", + "75660.498046875\n", + "sono dentro\n", + "90.24468994140625\n", + "68512.26220703125\n", + "sono dentro\n", + "38.2403564453125\n", + "60266.45166015625\n", + "sono dentro\n", + "13.303466796875\n", + "57390.8232421875\n", + "sono dentro\n", + "3.39013671875\n", + "56916.1201171875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.9310968453506665\n", + "sig_high 6.807663284788599\n", + "Naive\n", + "sig_med 1.2002687802705885\n", + "sig_high 2.102432284611476\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_tth01j_5f_haa\n", + "[ 38. 116. 329. 701. 1295. 1820. 1853. 1326. 764. 334. 140. 67.]\n", + "[ 5. 17. 34. 54. 63. 78. 93. 85. 63. 21. 16. 12.]\n", + "bkg med : [ 140.18876648 427.94314575 1213.74438477 2586.10864258 4777.35302734\n", + " 6714.8046875 6836.55664062 4892.21484375 2818.7421875 1232.27734375\n", + " 516.5234375 247.19335938]\n", + "bkg high : [ 18.44589233 62.71603394 125.43206787 199.21562195 232.41687012\n", + " 287.75354004 343.09552002 313.58276367 232.42016602 77.47338867\n", + " 59.02734375 44.27050781]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 3. 9. 16. 46. 91. 120. 127. 87. 51. 14. 12. 2.]\n", + "[0. 0. 0. 1. 1. 0. 2. 1. 1. 1. 0. 0.]\n", + "bkg med : [ 591.5308075 1781.96939087 3620.90112305 9506.68334961\n", + " 18468.05419922 24768.609375 25943.60742188 17981.05078125\n", + " 10491.453125 3338.51171875 2321.8671875 548.08398438]\n", + "bkg high : [ 18.44589233 62.71603394 125.43206787 349.66296387 382.86421204\n", + " 287.75354004 643.99020386 464.03009033 382.86749268 227.92071533\n", + " 59.02734375 44.27050781]\n", + "mgp8_pp_jjaa_5f\n", + "[1777. 1863. 1742. 1778. 1702. 1704. 1770. 1695. 1745. 1729. 1695. 1719.]\n", + "[10. 13. 10. 10. 13. 7. 4. 8. 15. 12. 19. 13.]\n", + "add a+jj\n", + "bkg med : [ 5770.1714325 7211.22720337 8697.50268555 14688.24584961\n", + " 23428.10107422 29734.5 31101.80273438 22920.71484375\n", + " 15576.78125 8377.23828125 7261.53125 5557.67773438]\n", + "bkg high : [ 47.61581421 100.63693237 154.60198975 378.83288574 420.78511047\n", + " 308.17248535 655.65817261 487.36602783 426.62237549 262.92462158\n", + " 114.45019531 82.19140625]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 5. 40. 118. 249. 475. 720. 772. 518. 306. 117. 51. 19.]\n", + "[ 30. 82. 191. 373. 648. 852. 811. 653. 381. 183. 72. 48.]\n", + "bkg med : [ 18.44589233 147.56713867 435.32202148 918.60662842 1752.37426758\n", + " 2656.10693359 2847.96240234 1911.13671875 1128.97265625 431.66601562\n", + " 188.16210938 70.09960938]\n", + "bkg high : [ 110.675354 302.51257324 704.63061523 1376.07495117 2390.5480957\n", + " 3142.99804688 2992.13867188 2409.21289062 1405.68164062 675.16992188\n", + " 265.640625 177.09375 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 4. 6. 24. 38. 59. 44. 46. 27. 7. 2. 0.]\n", + "[ 3. 2. 8. 13. 27. 37. 55. 20. 15. 4. 7. 2.]\n", + "bkg med : [ 18.44589233 749.35650635 1338.00616455 4529.3414917\n", + " 7469.37084961 11532.49560547 9467.64208984 8831.71875\n", + " 5191.1015625 1484.81054688 489.06054688 70.09960938]\n", + "bkg high : [ 562.01739502 603.40722656 1908.20959473 3331.8894043\n", + " 6452.62451172 8709.54736328 11266.73828125 5418.15820312\n", + " 3662.390625 1276.95898438 1318.77148438 477.98828125]\n", + "mgp8_pp_jjaa_5f\n", + "[806. 817. 788. 809. 776. 764. 811. 771. 833. 783. 772. 748.]\n", + "[439. 517. 401. 459. 443. 441. 459. 421. 436. 428. 437. 484.]\n", + "bkg med : [26137.88339233 27225.26275635 26874.13116455 30745.9977417\n", + " 32616.62084961 36306.05810547 35774.45458984 33841.03125\n", + " 32211.5390625 26883.37304688 25530.81054688 24333.34960938]\n", + "bkg high : [14788.36114502 17357.43847656 14903.13146973 18206.3581543\n", + " 20808.59326172 23000.70361328 26141.20703125 19061.18945312\n", + " 17791.515625 15146.83398438 15480.30273438 16162.61328125]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 3. 10. 33. 98. 183. 272. 289. 174. 95. 31. 9. 7.]\n", + "[ 4. 15. 44. 79. 136. 150. 164. 151. 79. 36. 13. 8.]\n", + "bkg med : [26147.98095894 27258.92131424 26985.20439911 31075.85089111\n", + " 33232.57043457 37221.56787109 36747.21875 34426.70947266\n", + " 32531.3059082 26987.71801758 25561.10424805 24356.91137695]\n", + "bkg high : [14801.82456875 17407.92630768 15051.22917175 18472.2600708\n", + " 21266.34817505 23505.58032227 26693.20556641 19569.44873047\n", + " 18057.42700195 15268.00878906 15524.06030273 16189.54101562]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 3. 11. 21. 29. 49. 55. 41. 16. 10. 2. 2.]\n", + "[ 0. 4. 4. 15. 23. 41. 30. 22. 16. 11. 0. 1.]\n", + "bkg med : [26221.98132515 27332.92169571 27256.53905487 31593.85375977\n", + " 33947.90637207 38430.23864746 38103.89086914 35438.05615234\n", + " 32925.9777832 27234.38793945 25610.43823242 24406.24536133]\n", + "bkg high : [14801.82456875 17506.59346771 15149.89634705 18842.26184082\n", + " 21833.68453979 24516.91711426 27433.20800781 20112.1171875\n", + " 18452.0949707 15539.34301758 15524.06030273 16214.20776367]\n", + "add a+jj\n", + "bkg med : [28573.86413765 29716.90216446 29555.89842987 33954.49047852\n", + " 36212.25012207 40659.56677246 40470.36352539 37687.81005859\n", + " 35356.64575195 29519.1574707 27863.11010742 26588.88598633]\n", + "bkg high : [16081.95542812 19014.17354584 16319.21861267 20180.7130127\n", + " 23125.47946167 25802.88000488 28771.65917969 21339.75976562\n", + " 19723.4777832 16787.41333008 16799.21264648 17626.50463867]\n", + "sig med : [ 0.73423576 5.52342272 15.33360291 37.02005768 71.76031494\n", + " 103.26454163 102.83164978 72.17498779 38.53094482 16.1552124\n", + " 4.85205078 1.18109131]\n", + "sig high : [ 3.67117929 13.60729027 38.56386185 86.48080444 152.08843994\n", + " 211.85906982 214.21624756 157.5647583 87.35290527 42.10577393\n", + " 16.08966064 4.63745117]\n", + "sono dentro\n", + "0.7342357635498047\n", + "28573.864137649536\n", + "sono dentro\n", + "5.523422718048096\n", + "29716.90216445923\n", + "sono dentro\n", + "15.333602905273438\n", + "29555.898429870605\n", + "sono dentro\n", + "37.020057678222656\n", + "33954.490478515625\n", + "sono dentro\n", + "71.76031494140625\n", + "36212.25012207031\n", + "sono dentro\n", + "103.26454162597656\n", + "40659.56677246094\n", + "sono dentro\n", + "102.83164978027344\n", + "40470.363525390625\n", + "sono dentro\n", + "72.17498779296875\n", + "37687.81005859375\n", + "sono dentro\n", + "38.53094482421875\n", + "35356.645751953125\n", + "sono dentro\n", + "16.15521240234375\n", + "29519.157470703125\n", + "sono dentro\n", + "4.85205078125\n", + "27863.110107421875\n", + "sono dentro\n", + "1.18109130859375\n", + "26588.885986328125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.433126672827121\n", + "sig_high 6.797884756319708\n", + "Naive\n", + "sig_med 0.7457157172137745\n", + "sig_high 2.092038702670834\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 17. 70. 202. 422. 813. 1187. 1172. 842. 474. 193. 80. 33.]\n", + "[ 18. 52. 107. 200. 310. 385. 411. 329. 213. 107. 43. 34.]\n", + "bkg med : [ 62.71603394 258.24249268 745.21081543 1556.84619141 2999.22094727\n", + " 4379.05029297 4324.0390625 3106.51953125 1748.80078125 712.06445312\n", + " 295.15625 121.75195312]\n", + "bkg high : [ 66.4052124 191.83726501 394.74038696 737.83679199 1143.65478516\n", + " 1420.34545898 1516.16943359 1213.66943359 785.74951172 394.71923828\n", + " 158.62548828 125.42773438]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 4. 11. 30. 58. 84. 78. 60. 36. 8. 5. 2.]\n", + "[ 1. 2. 3. 7. 7. 12. 21. 6. 6. 3. 4. 0.]\n", + "bkg med : [ 363.61071777 860.03179932 2400.13146973 6070.26416016\n", + " 11725.16235352 17016.62060547 16059.0234375 12133.47265625\n", + " 7164.97265625 1915.65820312 1047.40234375 422.65039062]\n", + "bkg high : [ 216.85255432 492.73197937 846.08236694 1790.96838379 2196.78564453\n", + " 3225.71289062 4675.56201172 2116.35351562 1688.43310547 846.06103516\n", + " 760.41455078 125.42773438]\n", + "mgp8_pp_jjaa_5f\n", + "[1109. 1191. 1075. 1135. 1090. 1075. 1144. 1077. 1149. 1077. 1071. 1096.]\n", + "[136. 143. 114. 133. 129. 130. 126. 115. 120. 134. 138. 136.]\n", + "bkg med : [36336.79821777 39493.09429932 37270.44396973 42886.82666016\n", + " 47082.03735352 51886.93310547 53167.5234375 47068.66015625\n", + " 44435.66015625 36850.84570312 35787.96484375 35974.15039062]\n", + "bkg high : [4625.14161682 5127.94291687 4541.28549194 6102.03869629 6378.19970703\n", + " 7439.54101562 8759.73388672 5843.97070312 5578.12060547 5189.54541016\n", + " 5233.55517578 4533.74023438]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 4. 19. 55. 146. 272. 363. 396. 265. 136. 52. 14. 12.]\n", + "[ 3. 6. 22. 31. 47. 59. 57. 60. 38. 15. 8. 3.]\n", + "bkg med : [36350.26164055 39557.0455246 37455.56607056 43378.24002075\n", + " 47997.54711914 53108.76623535 54500.43505859 47960.57666016\n", + " 44893.3984375 37025.86328125 35835.08496094 36014.5390625 ]\n", + "bkg high : [4635.23918438 5148.13805199 4615.33427429 6206.38031006 6536.39460754\n", + " 7638.12588501 8951.58703613 6045.92138672 5706.02270508 5240.03308105\n", + " 5260.48193359 4543.83776855]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 6. 14. 32. 44. 80. 74. 57. 26. 17. 2. 3.]\n", + "[ 0. 1. 1. 4. 8. 10. 11. 6. 6. 4. 0. 0.]\n", + "bkg med : [36424.26200676 39705.04628754 37800.90106201 44167.57711792\n", + " 49082.88415527 55082.10900879 56325.79248047 49366.59521484\n", + " 45534.73974609 37445.20214844 35884.41894531 36088.54003906]\n", + "bkg high : [4635.23918438 5172.804842 4640.0010643 6305.04747772 6733.72891235\n", + " 7884.79376221 9222.92193604 6193.92224121 5854.02325439 5338.70007324\n", + " 5260.48193359 4543.83776855]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "add a+jj\n", + "bkg med : [39660.28935051 43180.34706879 40937.71746826 47479.47164917\n", + " 52263.47009277 58215.42541504 59659.47998047 52505.04052734\n", + " 48882.99755859 40583.64746094 39005.37988281 39282.35253906]\n", + "bkg high : [5031.95012188 5589.93472481 4972.53817368 6693.00743866 7110.02090454\n", + " 8264.00274658 9590.46246338 6529.37634277 6204.06231689 5729.57702637\n", + " 5663.02685547 4940.54870605]\n", + "sig med : [ 2.20270729 10.56837273 31.33181763 75.10569763 137.87480164\n", + " 197.93716431 197.34576416 140.19940186 76.91235352 32.58154297\n", + " 11.80957031 2.90484619]\n", + "sig high : [ 2.20270729 8.56234074 22.56506348 48.39634323 85.97338104\n", + " 117.18635559 119.70202637 89.54031372 48.97149658 25.67944336\n", + " 9.1295166 2.90484619]\n", + "sono dentro\n", + "2.202707290649414\n", + "39660.28935050964\n", + "sono dentro\n", + "10.56837272644043\n", + "43180.34706878662\n", + "sono dentro\n", + "31.331817626953125\n", + "40937.71746826172\n", + "sono dentro\n", + "75.10569763183594\n", + "47479.47164916992\n", + "sono dentro\n", + "137.8748016357422\n", + "52263.47009277344\n", + "sono dentro\n", + "197.93716430664062\n", + "58215.42541503906\n", + "sono dentro\n", + "197.34576416015625\n", + "59659.47998046875\n", + "sono dentro\n", + "140.19940185546875\n", + "52505.04052734375\n", + "sono dentro\n", + "76.912353515625\n", + "48882.99755859375\n", + "sono dentro\n", + "32.58154296875\n", + "40583.6474609375\n", + "sono dentro\n", + "11.8095703125\n", + "39005.3798828125\n", + "sono dentro\n", + "2.90484619140625\n", + "39282.3525390625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.98710663217367\n", + "sig_high 6.807663284788599\n", + "Naive\n", + "sig_med 1.2232838808288775\n", + "sig_high 2.102432284611476\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 30. 105. 275. 568. 1060. 1494. 1490. 1086. 624. 279. 107. 55.]\n", + "[ 5. 17. 34. 54. 63. 78. 93. 85. 63. 21. 16. 12.]\n", + "bkg med : [ 110.675354 387.36303711 1014.52545166 2095.47070312 3910.32836914\n", + " 5511.96679688 5497.28515625 4006.74609375 2302.21875 1029.35742188\n", + " 394.77148438 202.91992188]\n", + "bkg high : [ 18.44589233 62.71603394 125.43206787 199.21562195 232.41687012\n", + " 287.75354004 343.09552002 313.58276367 232.42016602 77.47338867\n", + " 59.02734375 44.27050781]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 3. 6. 14. 36. 64. 96. 97. 65. 41. 10. 9. 2.]\n", + "[0. 0. 0. 1. 1. 0. 2. 1. 1. 1. 0. 0.]\n", + "bkg med : [ 562.01739502 1290.04711914 3120.78765869 7511.57324219\n", + " 13538.95336914 19954.90820312 20090.859375 13785.9453125\n", + " 8470.63671875 2533.84960938 1748.79492188 503.81054688]\n", + "bkg high : [ 18.44589233 62.71603394 125.43206787 349.66296387 382.86421204\n", + " 287.75354004 643.99020386 464.03009033 382.86749268 227.92071533\n", + " 59.02734375 44.27050781]\n", + "mgp8_pp_jjaa_5f\n", + "[1235. 1321. 1179. 1258. 1206. 1198. 1266. 1184. 1254. 1199. 1190. 1219.]\n", + "[10. 13. 10. 10. 13. 7. 4. 8. 15. 12. 19. 13.]\n", + "add a+jj\n", + "bkg med : [ 4165.70880127 5144.68383789 6561.07281494 11178.26464844\n", + " 17053.31274414 23445.95507812 23780.0625 17236.1953125\n", + " 12124.87109375 6027.81054688 5216.52929688 4056.05273438]\n", + "bkg high : [ 47.61581421 100.63693237 154.60198975 378.83288574 420.78511047\n", + " 308.17248535 655.65817261 487.36602783 426.62237549 262.92462158\n", + " 114.45019531 82.19140625]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 3. 22. 63. 109. 276. 368. 423. 283. 163. 71. 25. 9.]\n", + "[ 30. 82. 191. 373. 648. 852. 811. 653. 381. 183. 72. 48.]\n", + "bkg med : [ 11.0675354 81.16192627 232.41822815 402.11843872 1018.21728516\n", + " 1357.62890625 1560.49584961 1043.97705078 601.30126953 261.91650391\n", + " 92.22412109 33.20068359]\n", + "bkg high : [ 110.675354 302.51257324 704.63061523 1376.07495117 2390.5480957\n", + " 3142.99804688 2992.13867188 2409.21289062 1405.68164062 675.16992188\n", + " 265.640625 177.09375 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 0. 4. 16. 22. 28. 21. 23. 14. 0. 2. 0.]\n", + "[ 3. 2. 8. 13. 27. 37. 55. 20. 15. 4. 7. 2.]\n", + "bkg med : [ 11.0675354 81.16192627 834.20759583 2809.27566528 4328.05737305\n", + " 5570.15283203 4719.88842773 4504.26416016 2707.56298828 261.91650391\n", + " 393.11865234 33.20068359]\n", + "bkg high : [ 562.01739502 603.40722656 1908.20959473 3331.8894043\n", + " 6452.62451172 8709.54736328 11266.73828125 5418.15820312\n", + " 3662.390625 1276.95898438 1318.77148438 477.98828125]\n", + "mgp8_pp_jjaa_5f\n", + "[372. 412. 389. 391. 364. 380. 380. 370. 399. 368. 370. 360.]\n", + "[439. 517. 401. 459. 443. 441. 459. 421. 436. 428. 437. 484.]\n", + "bkg med : [12066.1925354 13432.53692627 13440.23884583 15480.11941528\n", + " 16123.93237305 17884.52783203 17034.27905273 16494.57666016\n", + " 15637.65673828 12187.41650391 12383.43115234 11699.45068359]\n", + "bkg high : [14788.36114502 17357.43847656 14903.13146973 18206.3581543\n", + " 20808.59326172 23000.70361328 26141.20703125 19061.18945312\n", + " 17791.515625 15146.83398438 15480.30273438 16162.61328125]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 3. 8. 20. 55. 98. 136. 142. 85. 45. 16. 4. 2.]\n", + "[ 4. 15. 44. 79. 136. 150. 164. 151. 79. 36. 13. 8.]\n", + "bkg med : [12076.29010391 13459.46377373 13507.5559082 15665.2414856\n", + " 16453.78518677 18342.28271484 17512.22900391 16780.67346191\n", + " 15789.11975098 12241.27050781 12396.89501953 11706.18261719]\n", + "bkg high : [14801.82456875 17407.92630768 15051.22917175 18472.2600708\n", + " 21266.34817505 23505.58032227 26693.20556641 19569.44873047\n", + " 18057.42700195 15268.00878906 15524.06030273 16189.54101562]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 1. 4. 7. 16. 26. 28. 20. 6. 6. 0. 2.]\n", + "[ 0. 4. 4. 15. 23. 41. 30. 22. 16. 11. 0. 1.]\n", + "bkg med : [12100.95689392 13484.13056374 13606.22307587 15837.90901184\n", + " 16848.45388794 18983.61895752 18202.89794922 17274.00854492\n", + " 15937.12023926 12389.27099609 12396.89501953 11755.51611328]\n", + "bkg high : [14801.82456875 17506.59346771 15149.89634705 18842.26184082\n", + " 21833.68453979 24516.91711426 27433.20800781 20112.1171875\n", + " 18452.0949707 15539.34301758 15524.06030273 16214.20776367]\n", + "add a+jj\n", + "bkg med : [13185.71470642 14685.52900124 14740.55315399 16978.07112122\n", + " 17909.88357544 20091.70489502 19310.98388672 18352.93432617\n", + " 17100.61047363 13462.36474609 13475.82080078 12805.28173828]\n", + "bkg high : [16081.95542812 19014.17354584 16319.21861267 20180.7130127\n", + " 23125.47946167 25802.88000488 28771.65917969 21339.75976562\n", + " 19723.4777832 16787.41333008 16799.21264648 17626.50463867]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 15. 52. 147. 282. 614. 835. 823. 607. 331. 147. 54. 23.]\n", + "[ 18. 52. 107. 200. 310. 385. 411. 329. 213. 107. 43. 34.]\n", + "bkg med : [ 55.337677 191.83726501 542.30639648 1040.35424805 2265.16967773\n", + " 3080.28564453 3036.29443359 2239.49804688 1221.20898438 542.34960938\n", + " 199.23046875 84.85742188]\n", + "bkg high : [ 66.4052124 191.83726501 394.74038696 737.83679199 1143.65478516\n", + " 1420.34545898 1516.16943359 1213.66943359 785.74951172 394.71923828\n", + " 158.62548828 125.42773438]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 0. 9. 22. 42. 53. 55. 37. 23. 1. 5. 2.]\n", + "[ 1. 2. 3. 7. 7. 12. 21. 6. 6. 3. 4. 0.]\n", + "bkg med : [ 356.23236084 191.83726501 1896.3326416 4350.19458008\n", + " 8583.95532227 11053.99072266 11310.89404297 7806.05273438\n", + " 4681.54101562 692.79882812 951.4765625 385.75585938]\n", + "bkg high : [ 216.85255432 492.73197937 846.08236694 1790.96838379 2196.78564453\n", + " 3225.71289062 4675.56201172 2116.35351562 1688.43310547 846.06103516\n", + " 760.41455078 125.42773438]\n", + "mgp8_pp_jjaa_5f\n", + "[675. 786. 676. 717. 678. 691. 713. 676. 715. 662. 669. 708.]\n", + "[136. 143. 114. 133. 129. 130. 126. 115. 120. 134. 138. 136.]\n", + "bkg med : [22230.45111084 25663.14976501 23802.9576416 27585.47583008\n", + " 30555.39282227 33446.70947266 34416.55029297 29712.67773438\n", + " 27872.38476562 22166.42382812 22652.1640625 23351.50585938]\n", + "bkg high : [4625.14161682 5127.94291687 4541.28549194 6102.03869629 6378.19970703\n", + " 7439.54101562 8759.73388672 5843.97070312 5578.12060547 5189.54541016\n", + " 5233.55517578 4533.74023438]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 4. 17. 42. 103. 187. 227. 249. 176. 86. 37. 9. 7.]\n", + "[ 3. 6. 22. 31. 47. 59. 57. 60. 38. 15. 8. 3.]\n", + "bkg med : [22243.91453552 25720.36929321 23944.32365417 27932.15802002\n", + " 31184.80578613 34210.75622559 35254.6730957 30305.08789062\n", + " 28161.85791016 22290.96459961 22682.45776367 23375.06762695]\n", + "bkg high : [4635.23918438 5148.13805199 4615.33427429 6206.38031006 6536.39460754\n", + " 7638.12588501 8951.58703613 6045.92138672 5706.02270508 5240.03308105\n", + " 5260.48193359 4543.83776855]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 4. 7. 18. 31. 57. 47. 36. 16. 13. 0. 3.]\n", + "[ 0. 1. 1. 4. 8. 10. 11. 6. 6. 4. 0. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [22268.58132553 25819.03645325 24116.99119568 28376.16030884\n", + " 31949.47570801 35616.76098633 36414.01025391 31193.09912109\n", + " 28556.52978516 22611.63549805 22682.45776367 23449.06860352]\n", + "bkg high : [4635.23918438 5172.804842 4640.0010643 6305.04747772 6733.72891235\n", + " 7884.79376221 9222.92193604 6193.92224121 5854.02325439 5338.70007324\n", + " 5260.48193359 4543.83776855]\n", + "add a+jj\n", + "bkg med : [24236.89187241 28112.44465637 26089.53807068 30468.34390259\n", + " 33927.85852051 37633.07739258 38494.52197266 33165.64599609\n", + " 30642.87744141 24543.33081055 24634.57885742 25514.99047852]\n", + "bkg high : [5031.95012188 5589.93472481 4972.53817368 6693.00743866 7110.02090454\n", + " 8264.00274658 9590.46246338 6529.37634277 6204.06231689 5729.57702637\n", + " 5663.02685547 4940.54870605]\n", + "sig med : [ 1.91539776 8.30181885 25.41506004 58.72939682 108.73171997\n", + " 154.36450195 156.13525391 111.15039062 61.26904297 25.81311035\n", + " 9.95812988 2.36218262]\n", + "sig high : [ 2.20270729 8.56234074 22.56506348 48.39634323 85.97338104\n", + " 117.18635559 119.70202637 89.54031372 48.97149658 25.67944336\n", + " 9.1295166 2.90484619]\n", + "sono dentro\n", + "1.9153977632522583\n", + "24236.891872406006\n", + "sono dentro\n", + "8.30181884765625\n", + "28112.44465637207\n", + "sono dentro\n", + "25.41506004333496\n", + "26089.53807067871\n", + "sono dentro\n", + "58.72939682006836\n", + "30468.34390258789\n", + "sono dentro\n", + "108.73171997070312\n", + "33927.85852050781\n", + "sono dentro\n", + "154.364501953125\n", + "37633.077392578125\n", + "sono dentro\n", + "156.13525390625\n", + "38494.52197265625\n", + "sono dentro\n", + "111.150390625\n", + "33165.64599609375\n", + "sono dentro\n", + "61.26904296875\n", + "30642.87744140625\n", + "sono dentro\n", + "25.8131103515625\n", + "24543.330810546875\n", + "sono dentro\n", + "9.9581298828125\n", + "24634.578857421875\n", + "sono dentro\n", + "2.3621826171875\n", + "25514.990478515625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.938769406744035\n", + "sig_high 6.807663284788599\n", + "Naive\n", + "sig_med 1.2111834376143915\n", + "sig_high 2.102432284611476\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 28. 87. 220. 428. 861. 1142. 1141. 851. 481. 233. 81. 45.]\n", + "[ 5. 17. 34. 54. 63. 78. 93. 85. 63. 21. 16. 12.]\n", + "bkg med : [ 103.29699707 320.95843506 811.61767578 1578.98144531 3176.27709961\n", + " 4213.07666016 4209.66601562 3139.72460938 1774.62695312 859.64257812\n", + " 298.84570312 166.02539062]\n", + "bkg high : [ 18.44589233 62.71603394 125.43206787 199.21562195 232.41687012\n", + " 287.75354004 343.09552002 313.58276367 232.42016602 77.47338867\n", + " 59.02734375 44.27050781]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 3. 2. 12. 28. 48. 65. 74. 42. 28. 3. 9. 2.]\n", + "[0. 0. 0. 1. 1. 0. 2. 1. 1. 1. 0. 0.]\n", + "bkg med : [ 554.63903809 621.85308838 2616.98571777 5791.50488281\n", + " 10397.74633789 13992.14892578 15342.79296875 9458.59179688\n", + " 5987.20507812 1310.99023438 1652.88867188 466.92382812]\n", + "bkg high : [ 18.44589233 62.71603394 125.43206787 349.66296387 382.86421204\n", + " 287.75354004 643.99020386 464.03009033 382.86749268 227.92071533\n", + " 59.02734375 44.27050781]\n", + "mgp8_pp_jjaa_5f\n", + "[801. 916. 780. 840. 794. 814. 835. 783. 820. 784. 788. 831.]\n", + "[10. 13. 10. 10. 13. 7. 4. 8. 15. 12. 19. 13.]\n", + "add a+jj\n", + "bkg med : [ 2891.93200684 3294.71246338 4893.00134277 8242.59863281\n", + " 12714.61352539 16367.37548828 17779.296875 11743.36132812\n", + " 8379.93945312 3598.67773438 3952.24804688 2891.75585938]\n", + "bkg high : [ 47.61581421 100.63693237 154.60198975 378.83288574 420.78511047\n", + " 308.17248535 655.65817261 487.36602783 426.62237549 262.92462158\n", + " 114.45019531 82.19140625]\n", + "ttH cut: 0.30000000000000004\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 7. 27. 100. 227. 458. 650. 731. 466. 287. 106. 53. 16.]\n", + "[ 22. 57. 135. 276. 460. 596. 588. 496. 284. 146. 48. 39.]\n", + "bkg med : [ 25.82424927 99.6078186 368.91760254 837.44244385 1689.65771484\n", + " 2397.89526367 2696.63330078 1719.28076172 1058.87304688 391.08203125\n", + " 195.54101562 59.03125 ]\n", + "bkg high : [ 81.16192627 210.28315735 498.03625488 1018.21893311 1697.03613281\n", + " 2198.65771484 2169.11132812 1829.93505859 1047.8046875 538.66015625\n", + " 177.09375 143.88867188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 4. 7. 33. 58. 72. 68. 58. 34. 10. 4. 0.]\n", + "[ 2. 2. 6. 13. 26. 33. 49. 19. 14. 4. 6. 2.]\n", + "bkg med : [ 25.82424927 701.39718628 1422.04919434 5802.20269775\n", + " 10415.59912109 13230.09838867 12927.09619141 10445.33544922\n", + " 6174.14648438 1895.57421875 797.33789062 59.03125 ]\n", + "bkg high : [ 382.05661011 511.17784119 1400.72039795 2974.03375244 5608.6652832\n", + " 7163.41796875 9541.02734375 4688.43310547 3154.06640625 1140.44921875\n", + " 1079.77734375 444.78320312]\n", + "mgp8_pp_jjaa_5f\n", + "[1225. 1237. 1229. 1220. 1157. 1171. 1200. 1171. 1208. 1188. 1163. 1138.]\n", + "[411. 489. 380. 440. 409. 413. 426. 397. 410. 405. 415. 450.]\n", + "bkg med : [39761.76174927 40826.58468628 41287.73669434 45375.95269775\n", + " 47945.78662109 51214.41088867 51852.09619141 48429.64794922\n", + " 45358.64648438 40431.32421875 38522.15039062 36972.90625 ]\n", + "bkg high : [13701.02536011 16357.83409119 13715.09539795 17232.79937744\n", + " 18862.8215332 20547.19921875 23346.08984375 17553.71435547\n", + " 16440.62890625 14264.98046875 14528.37109375 15027.59570312]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 2. 13. 46. 138. 261. 413. 404. 248. 136. 51. 18. 10.]\n", + "[ 4. 14. 34. 67. 129. 138. 155. 137. 70. 34. 10. 7.]\n", + "bkg med : [39768.4934597 40870.34079742 41442.56613159 45840.43939209\n", + " 48824.27209473 52604.53820801 53211.9296875 49264.34716797\n", + " 45816.38476562 40602.97607422 38582.73339844 37006.56347656]\n", + "bkg high : [13714.48878384 16404.95606995 13829.53449249 17458.31132507\n", + " 19297.01553345 21011.68579102 23867.79577637 18014.84387207\n", + " 16676.24658203 14379.42333984 14562.03076172 15051.1574707 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 5. 16. 24. 47. 71. 86. 59. 22. 15. 1. 2.]\n", + "[ 0. 4. 3. 15. 21. 37. 30. 19. 16. 10. 0. 1.]\n", + "bkg med : [39842.49382591 40993.67476654 41837.23474121 46432.44238281\n", + " 49983.609375 54355.87731934 55333.29101562 50719.69970703\n", + " 46359.05810547 40972.98095703 38607.40039062 37055.89746094]\n", + "bkg high : [13714.48878384 16503.62322998 13903.53487396 17828.31309509\n", + " 19815.01846313 21924.35546875 24607.79833984 18483.51208496\n", + " 17070.91455078 14626.09082031 14562.03076172 15075.82421875]\n", + "add a+jj\n", + "bkg med : [43417.00554466 44603.20211029 45423.41833496 49991.46582031\n", + " 53355.1796875 57768.24450684 58830.16601562 54132.06689453\n", + " 49879.24560547 44434.88720703 41996.45507812 40372.10058594]\n", + "bkg high : [14912.97120571 17929.55487061 15011.62081146 19111.35997009\n", + " 21007.66885376 23128.66992188 25850.02099609 19641.17028809\n", + " 18266.48095703 15807.07714844 15772.17724609 16388.42773438]\n", + "sig med : [ 0.92577553 6.4552536 17.91938972 45.83071899 92.19128418\n", + " 129.88426208 128.28726196 90.49832153 47.92645264 19.63464355\n", + " 5.74584961 1.53222656]\n", + "sig high : [ 3.41579294 13.12737274 37.15525055 83.57623291 147.37890625\n", + " 206.33666992 207.43154907 152.51824951 84.57574463 40.60546875\n", + " 15.70397949 4.37322998]\n", + "sono dentro\n", + "0.9257755279541016\n", + "43417.005544662476\n", + "sono dentro\n", + "6.455253601074219\n", + "44603.20211029053\n", + "sono dentro\n", + "17.919389724731445\n", + "45423.41833496094\n", + "sono dentro\n", + "45.830718994140625\n", + "49991.4658203125\n", + "sono dentro\n", + "92.1912841796875\n", + "53355.1796875\n", + "sono dentro\n", + "129.88426208496094\n", + "57768.24450683594\n", + "sono dentro\n", + "128.28726196289062\n", + "58830.166015625\n", + "sono dentro\n", + "90.49832153320312\n", + "54132.06689453125\n", + "sono dentro\n", + "47.92645263671875\n", + "49879.24560546875\n", + "sono dentro\n", + "19.6346435546875\n", + "44434.88720703125\n", + "sono dentro\n", + "5.745849609375\n", + "41996.455078125\n", + "sono dentro\n", + "1.5322265625\n", + "40372.1005859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.524163811438772\n", + "sig_high 6.886243811702114\n", + "Naive\n", + "sig_med 0.7677703201202893\n", + "sig_high 2.110385327263212\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 16. 44. 163. 347. 681. 976. 1020. 694. 407. 168. 70. 26.]\n", + "[ 13. 40. 72. 156. 237. 270. 299. 268. 164. 84. 31. 29.]\n", + "bkg med : [ 59.02685547 162.32383728 601.33300781 1280.15332031 2512.3125\n", + " 3600.44140625 3763.2421875 2560.48046875 1501.60742188 619.828125\n", + " 258.26171875 95.92578125]\n", + "bkg high : [ 47.95932007 147.56713867 265.62084961 575.50811768 874.3425293\n", + " 996.08642578 1103.07348633 988.64526367 604.99023438 309.87304688\n", + " 114.35791016 106.97998047]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 4. 12. 39. 78. 95. 97. 71. 42. 11. 6. 2.]\n", + "[ 1. 2. 1. 7. 6. 10. 20. 6. 6. 3. 4. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 209.47419739 764.11320496 2406.7010498 7147.59765625\n", + " 14247.19921875 17892.95507812 18356.81640625 13242.375\n", + " 7820.46289062 2274.7265625 1160.93359375 396.81640625]\n", + "bkg high : [ 198.40666199 448.46185303 416.06817627 1628.63970947 1777.02636719\n", + " 2500.55908203 4112.01904297 1891.32885742 1507.67431641 761.21484375\n", + " 716.14697266 106.97998047]\n", + "mgp8_pp_jjaa_5f\n", + "[1510. 1592. 1496. 1534. 1444. 1463. 1506. 1456. 1506. 1465. 1450. 1466.]\n", + "[126. 134. 113. 126. 122. 121. 120. 112. 112. 128. 128. 122.]\n", + "bkg med : [49190.09919739 52404.61320496 50933.2010498 56906.72265625\n", + " 61086.94921875 65349.01757812 67177.50390625 60380.375\n", + " 56577.21289062 49704.1015625 48104.68359375 47858.56640625]\n", + "bkg high : [4282.09416199 4791.94622803 4078.85723877 5712.81158447 5731.54199219\n", + " 6422.66064453 8001.70654297 5521.70385742 5138.04931641 4910.21484375\n", + " 4865.14697266 4061.49560547]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 3. 21. 63. 179. 346. 496. 504. 332. 172. 70. 23. 14.]\n", + "[ 3. 6. 17. 26. 44. 55. 55. 53. 34. 15. 5. 3.]\n", + "bkg med : [49200.19676399 52475.29612732 51145.24989319 57509.2088623\n", + " 62251.53308105 67018.53710938 68873.85229492 61497.79492188\n", + " 57156.1171875 49939.70214844 48182.09521484 47905.68652344]\n", + "bkg high : [4292.19172955 4812.14136314 4136.07675934 5800.32386017 5879.63951111\n", + " 6607.78213501 8186.82800293 5700.09362793 5252.48803711 4960.70251465\n", + " 4881.97619629 4071.59313965]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 8. 18. 35. 61. 99. 105. 73. 32. 21. 1. 3.]\n", + "[ 0. 1. 1. 4. 7. 9. 11. 5. 6. 4. 0. 0.]\n", + "bkg med : [49274.1971302 52672.63046265 51589.25218201 58372.54577637\n", + " 63756.20483398 69460.55932617 71463.88647461 63298.48486328\n", + " 57945.4609375 50457.70898438 48206.76220703 47979.6875 ]\n", + "bkg high : [4292.19172955 4836.80815315 4160.74354935 5898.99102783 6052.30703735\n", + " 6829.78317261 8458.16290283 5823.42767334 5400.48876953 5059.36950684\n", + " 4881.97619629 4071.59313965]\n", + "add a+jj\n", + "bkg med : [53675.1580677 57311.81796265 55948.68968201 62842.71765137\n", + " 67964.11108398 73723.83276367 75852.46459961 67541.35986328\n", + " 62334.0390625 54726.81054688 52432.15283203 52251.703125 ]\n", + "bkg high : [4659.73274517 5227.68510628 4490.36366653 6266.53204346 6408.18008423\n", + " 7182.73922729 8808.20196533 6150.13031006 5727.19189453 5432.74450684\n", + " 5255.35119629 4427.46618652]\n", + "sig med : [ 2.26655388 11.37144089 32.92428589 82.16002655 155.25823975\n", + " 221.23703003 218.97088623 155.55114746 84.74371338 35.23101807\n", + " 12.54376221 3.1282959 ]\n", + "sig high : [ 2.07501411 8.2111845 22.15000153 47.24729156 84.31189728\n", + " 114.98376465 116.74789429 87.46542358 47.75845337 25.00909424\n", + " 8.90606689 2.77716064]\n", + "sono dentro\n", + "2.2665538787841797\n", + "53675.15806770325\n", + "sono dentro\n", + "11.371440887451172\n", + "57311.817962646484\n", + "sono dentro\n", + "32.924285888671875\n", + "55948.689682006836\n", + "sono dentro\n", + "82.16002655029297\n", + "62842.71765136719\n", + "sono dentro\n", + "155.25823974609375\n", + "67964.11108398438\n", + "sono dentro\n", + "221.23703002929688\n", + "73723.83276367188\n", + "sono dentro\n", + "218.97088623046875\n", + "75852.46459960938\n", + "sono dentro\n", + "155.5511474609375\n", + "67541.35986328125\n", + "sono dentro\n", + "84.74371337890625\n", + "62334.0390625\n", + "sono dentro\n", + "35.23101806640625\n", + "54726.810546875\n", + "sono dentro\n", + "12.54376220703125\n", + "52432.15283203125\n", + "sono dentro\n", + "3.1282958984375\n", + "52251.703125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.885232133490504\n", + "sig_high 6.962060686707405\n", + "Naive\n", + "sig_med 1.1830798378707041\n", + "sig_high 2.144933437270401\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 26. 71. 213. 459. 870. 1192. 1255. 895. 524. 237. 89. 46.]\n", + "[ 3. 13. 22. 44. 48. 54. 64. 67. 47. 15. 12. 9.]\n", + "bkg med : [ 95.91864014 261.93164062 785.79248047 1693.34692383 3209.47705078\n", + " 4397.55517578 4630.26367188 3302.06054688 1933.2734375 874.40039062\n", + " 328.36132812 169.71484375]\n", + "bkg high : [ 11.06753349 47.95932007 81.16192627 162.32383728 177.08056641\n", + " 199.21405029 236.10546875 247.17565918 173.39282227 55.33813477\n", + " 44.27050781 33.20288086]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 13. 45. 83. 105. 115. 76. 47. 13. 10. 2.]\n", + "[0. 0. 0. 1. 1. 0. 2. 1. 1. 1. 0. 0.]\n", + "bkg med : [ 396.81332397 1164.61572266 2741.60754395 8463.47485352\n", + " 15696.60009766 20194.59033203 21931.92382812 14736.16601562\n", + " 9004.203125 2830.18945312 1832.81445312 470.60546875]\n", + "bkg high : [ 11.06753349 47.95932007 81.16192627 312.7711792 327.52790833\n", + " 199.21405029 537.00015259 397.62298584 323.84014893 205.78546143\n", + " 44.27050781 33.20288086]\n", + "mgp8_pp_jjaa_5f\n", + "[1627. 1715. 1599. 1651. 1554. 1577. 1622. 1560. 1606. 1581. 1560. 1576.]\n", + "[ 9. 11. 10. 9. 12. 7. 4. 8. 12. 12. 18. 12.]\n", + "add a+jj\n", + "bkg med : [ 5137.99301147 6162.23291016 7401.19348145 13274.59204102\n", + " 20225.05322266 24790.06689453 26658.53320312 19282.10351562\n", + " 13684.1875 7437.32226562 6378.75195312 5063.16796875]\n", + "bkg high : [ 37.32018852 80.04614258 110.33184814 339.02410889 362.53181458\n", + " 219.63299561 548.66812134 420.95892334 358.84405518 240.78936768\n", + " 96.77636719 68.20678711]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 3. 23. 71. 154. 320. 468. 509. 335. 209. 70. 33. 11.]\n", + "[ 22. 57. 135. 276. 460. 596. 588. 496. 284. 146. 48. 39.]\n", + "bkg med : [ 11.06752968 84.85110474 261.93164062 568.12994385 1180.54553223\n", + " 1726.54980469 1877.69946289 1235.80322266 770.99365234 258.22753906\n", + " 121.73583984 40.57861328]\n", + "bkg high : [ 81.16192627 210.28315735 498.03625488 1018.21893311 1697.03613281\n", + " 2198.65771484 2169.11132812 1829.93505859 1047.8046875 538.66015625\n", + " 177.09375 143.88867188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 1. 6. 23. 35. 51. 40. 42. 25. 7. 2. 0.]\n", + "[ 2. 2. 6. 13. 26. 33. 49. 19. 14. 4. 6. 2.]\n", + "bkg med : [ 11.06752968 235.29844666 1164.61566162 4028.41802979 6446.20031738\n", + " 9399.36035156 7895.59008789 7554.58837891 4532.18701172 1311.37207031\n", + " 422.63427734 40.57861328]\n", + "bkg high : [ 382.05661011 511.17784119 1400.72039795 2974.03375244 5608.6652832\n", + " 7163.41796875 9541.02734375 4688.43310547 3154.06640625 1140.44921875\n", + " 1079.77734375 444.78320312]\n", + "mgp8_pp_jjaa_5f\n", + "[742. 749. 735. 744. 711. 704. 739. 720. 768. 721. 708. 691.]\n", + "[411. 489. 380. 440. 409. 413. 426. 397. 410. 405. 415. 450.]\n", + "bkg med : [24056.50502968 24507.57969666 24983.20941162 28138.66802979\n", + " 29487.04406738 32213.36035156 31843.80883789 30905.77587891\n", + " 29444.18701172 24698.80957031 23388.38427734 22454.89111328]\n", + "bkg high : [13701.02536011 16357.83409119 13715.09539795 17232.79937744\n", + " 18862.8215332 20547.19921875 23346.08984375 17553.71435547\n", + " 16440.62890625 14264.98046875 14528.37109375 15027.59570312]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 2. 9. 28. 91. 165. 244. 263. 153. 86. 26. 8. 7.]\n", + "[ 4. 14. 34. 67. 129. 138. 155. 137. 70. 34. 10. 7.]\n", + "bkg med : [24063.23674202 24537.87240028 25077.45333862 28444.96038818\n", + " 30042.40844727 33034.62646484 32729.05053711 31420.76879883\n", + " 29733.66015625 24786.32470703 23415.31201172 22478.45288086]\n", + "bkg high : [13714.48878384 16404.95606995 13829.53449249 17458.31132507\n", + " 19297.01553345 21011.68579102 23867.79577637 18014.84387207\n", + " 16676.24658203 14379.42333984 14562.03076172 15051.1574707 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 3. 10. 17. 27. 42. 53. 35. 16. 10. 1. 2.]\n", + "[ 0. 4. 3. 15. 21. 37. 30. 19. 16. 10. 0. 1.]\n", + "bkg med : [24137.23710823 24611.87278175 25324.12120056 28864.29598999\n", + " 30708.41119385 34070.63000488 34036.38818359 32284.1105957\n", + " 30128.33203125 25032.99462891 23439.97900391 22527.78686523]\n", + "bkg high : [13714.48878384 16503.62322998 13903.53487396 17828.31309509\n", + " 19815.01846313 21924.35546875 24607.79833984 18483.51208496\n", + " 17070.91455078 14626.09082031 14562.03076172 15075.82421875]\n", + "add a+jj\n", + "bkg med : [26301.77421761 26797.4313755 27468.82823181 31035.26473999\n", + " 32783.0869751 36124.88000488 36192.76708984 34385.0480957\n", + " 32369.33203125 27136.85009766 25505.90087891 24544.10327148]\n", + "bkg high : [14912.97120571 17929.55487061 15011.62081146 19111.35997009\n", + " 21007.66885376 23128.66992188 25850.02099609 19641.17028809\n", + " 18266.48095703 15807.07714844 15772.17724609 16388.42773438]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 12. 40. 134. 274. 543. 794. 798. 563. 329. 132. 50. 21.]\n", + "[ 13. 40. 72. 156. 237. 270. 299. 268. 164. 84. 31. 29.]\n", + "bkg med : [ 44.2701416 147.56713867 494.34814453 1010.83831787 2003.24047852\n", + " 2929.06176758 2943.98144531 2077.16210938 1213.83007812 487.0078125\n", + " 184.47265625 77.47851562]\n", + "bkg high : [ 47.95932007 147.56713867 265.62084961 575.50811768 874.3425293\n", + " 996.08642578 1103.07348633 988.64526367 604.99023438 309.87304688\n", + " 114.35791016 106.97998047]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 1. 11. 29. 55. 74. 69. 55. 33. 8. 4. 2.]\n", + "[ 1. 2. 1. 7. 6. 10. 20. 6. 6. 3. 4. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 194.71748352 298.01448059 2149.26916504 5373.809021\n", + " 10277.84008789 14062.15942383 13324.88769531 10351.86914062\n", + " 6178.65429688 1690.6015625 786.26953125 378.37695312]\n", + "bkg high : [ 198.40666199 448.46185303 416.06817627 1628.63970947 1777.02636719\n", + " 2500.55908203 4112.01904297 1891.32885742 1507.67431641 761.21484375\n", + " 716.14697266 106.97998047]\n", + "mgp8_pp_jjaa_5f\n", + "[1027. 1104. 1002. 1058. 998. 996. 1045. 1005. 1066. 998. 995. 1019.]\n", + "[126. 134. 113. 126. 122. 121. 120. 112. 112. 128. 128. 122.]\n", + "bkg med : [33484.56123352 36109.01448059 34651.64416504 39692.684021\n", + " 42650.46508789 46369.90942383 47222.07519531 42951.55664062\n", + " 40757.02929688 34063.2265625 33061.58203125 33432.18945312]\n", + "bkg high : [4282.09416199 4791.94622803 4078.85723877 5712.81158447 5731.54199219\n", + " 6422.66064453 8001.70654297 5521.70385742 5138.04931641 4910.21484375\n", + " 4865.14697266 4061.49560547]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 3. 17. 45. 132. 250. 327. 363. 237. 122. 45. 13. 11.]\n", + "[ 3. 6. 17. 26. 44. 55. 55. 53. 34. 15. 5. 3.]\n", + "bkg med : [33494.65880013 36166.23400497 34803.10777283 40136.97564697\n", + " 43491.92626953 47470.56176758 48443.92114258 43749.25048828\n", + " 41167.64746094 34214.68408203 33105.33642578 33469.21240234]\n", + "bkg high : [4292.19172955 4812.14136314 4136.07675934 5800.32386017 5879.63951111\n", + " 6607.78213501 8186.82800293 5700.09362793 5252.48803711 4960.70251465\n", + " 4881.97619629 4071.59313965]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 6. 12. 28. 41. 70. 72. 49. 26. 16. 1. 3.]\n", + "[ 0. 1. 1. 4. 7. 9. 11. 5. 6. 4. 0. 0.]\n", + "bkg med : [33568.65916634 36314.23476791 35099.10914612 40827.64587402\n", + " 44503.26306152 49197.23413086 50219.94287109 44957.93310547\n", + " 41808.98925781 34609.35595703 33130.00341797 33543.21337891]\n", + "bkg high : [4292.19172955 4836.80815315 4160.74354935 5898.99102783 6052.30703735\n", + " 6829.78317261 8458.16290283 5823.42767334 5400.48876953 5059.36950684\n", + " 4881.97619629 4071.59313965]\n", + "add a+jj\n", + "bkg med : [36565.41307259 39535.67226791 38022.91383362 43914.85681152\n", + " 47415.39587402 52103.53100586 53268.47412109 47886.56591797\n", + " 44915.37988281 37517.59033203 36029.49560547 36512.64306641]\n", + "bkg high : [4659.73274517 5227.68510628 4490.36366653 6266.53204346 6408.18008423\n", + " 7182.73922729 8808.20196533 6150.13031006 5727.19189453 5432.74450684\n", + " 5255.35119629 4427.46618652]\n", + "sig med : [ 1.97924435 9.8011446 28.90158463 70.66903687 130.42163086\n", + " 188.38668823 186.97134399 132.3762207 72.95227051 30.25128174\n", + " 11.29882812 2.71331787]\n", + "sig high : [ 2.07501411 8.2111845 22.15000153 47.24729156 84.31189728\n", + " 114.98376465 116.74789429 87.46542358 47.75845337 25.00909424\n", + " 8.90606689 2.77716064]\n", + "sono dentro\n", + "1.979244351387024\n", + "36565.41307258606\n", + "sono dentro\n", + "9.80114459991455\n", + "39535.67226791382\n", + "sono dentro\n", + "28.90158462524414\n", + "38022.913833618164\n", + "sono dentro\n", + "70.66903686523438\n", + "43914.85681152344\n", + "sono dentro\n", + "130.421630859375\n", + "47415.39587402344\n", + "sono dentro\n", + "188.38668823242188\n", + "52103.531005859375\n", + "sono dentro\n", + "186.97134399414062\n", + "53268.47412109375\n", + "sono dentro\n", + "132.376220703125\n", + "47886.56591796875\n", + "sono dentro\n", + "72.9522705078125\n", + "44915.3798828125\n", + "sono dentro\n", + "30.25128173828125\n", + "37517.59033203125\n", + "sono dentro\n", + "11.298828125\n", + "36029.49560546875\n", + "sono dentro\n", + "2.71331787109375\n", + "36512.64306640625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.9567549174611862\n", + "sig_high 6.962060686707405\n", + "Naive\n", + "sig_med 1.2092899283817629\n", + "sig_high 2.144933437270401\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 22. 67. 184. 386. 732. 1010. 1033. 764. 446. 201. 69. 41.]\n", + "[ 3. 13. 22. 44. 48. 54. 64. 67. 47. 15. 12. 9.]\n", + "bkg med : [ 81.16192627 247.17494202 678.80493164 1424.03466797 2700.42871094\n", + " 3725.94970703 3811.20507812 2818.7421875 1645.49609375 741.58007812\n", + " 254.57226562 151.26757812]\n", + "bkg high : [ 11.06753349 47.95932007 81.16192627 162.32383728 177.08056641\n", + " 199.21405029 236.10546875 247.17565918 173.39282227 55.33813477\n", + " 44.27050781 33.20288086]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 3. 12. 35. 60. 84. 87. 60. 38. 10. 8. 2.]\n", + "[0. 0. 0. 1. 1. 0. 2. 1. 1. 1. 0. 0.]\n", + "bkg med : [ 382.05661011 698.51695251 2484.17297363 6689.68896484\n", + " 11727.26464844 16363.52001953 16900.24609375 11845.6953125\n", + " 7362.56640625 2246.07226562 1458.16601562 452.16601562]\n", + "bkg high : [ 11.06753349 47.95932007 81.16192627 312.7711792 327.52790833\n", + " 199.21405029 537.00015259 397.62298584 323.84014893 205.78546143\n", + " 44.27050781 33.20288086]\n", + "mgp8_pp_jjaa_5f\n", + "[1144. 1227. 1105. 1175. 1108. 1110. 1161. 1109. 1166. 1114. 1105. 1129.]\n", + "[ 9. 11. 10. 9. 12. 7. 4. 8. 12. 12. 18. 12.]\n", + "add a+jj\n", + "bkg med : [ 3720.21286011 4278.86460876 5708.52844238 10118.30224609\n", + " 14958.59277344 19598.12939453 20283.47265625 15077.390625\n", + " 10760.36328125 5492.33789062 4678.20507812 3742.14257812]\n", + "bkg high : [ 37.32018852 80.04614258 110.33184814 339.02410889 362.53181458\n", + " 219.63299561 548.66812134 420.95892334 358.84405518 240.78936768\n", + " 96.77636719 68.20678711]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 2. 14. 39. 71. 188. 243. 282. 191. 111. 45. 14. 3.]\n", + "[ 22. 57. 135. 276. 460. 596. 588. 496. 284. 146. 48. 39.]\n", + "bkg med : [ 7.37835312 51.64849472 143.87796021 261.93164062 693.56262207\n", + " 896.4777832 1040.35693359 704.63891602 409.49365234 166.00341797\n", + " 51.64550781 11.06689453]\n", + "bkg high : [ 81.16192627 210.28315735 498.03625488 1018.21893311 1697.03613281\n", + " 2198.65771484 2169.11132812 1829.93505859 1047.8046875 538.66015625\n", + " 177.09375 143.88867188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 0. 4. 15. 20. 23. 18. 22. 13. 0. 2. 0.]\n", + "[ 2. 2. 6. 13. 26. 33. 49. 19. 14. 4. 6. 2.]\n", + "bkg med : [ 7.37835312 51.64849472 745.66732788 2518.64160156 3702.50817871\n", + " 4356.76538086 3748.40771484 4014.47875977 2365.30810547 166.00341797\n", + " 352.54003906 11.06689453]\n", + "bkg high : [ 382.05661011 511.17784119 1400.72039795 2974.03375244 5608.6652832\n", + " 7163.41796875 9541.02734375 4688.43310547 3154.06640625 1140.44921875\n", + " 1079.77734375 444.78320312]\n", + "mgp8_pp_jjaa_5f\n", + "[345. 378. 358. 360. 336. 349. 346. 350. 369. 341. 340. 332.]\n", + "[411. 489. 380. 440. 409. 413. 426. 397. 410. 405. 415. 450.]\n", + "bkg med : [11187.53460312 12301.21099472 12347.10482788 14184.89160156\n", + " 14591.00817871 15666.54663086 14960.97021484 15356.68188477\n", + " 14323.21435547 11216.53466797 11370.66503906 10769.94189453]\n", + "bkg high : [13701.02536011 16357.83409119 13715.09539795 17232.79937744\n", + " 18862.8215332 20547.19921875 23346.08984375 17553.71435547\n", + " 16440.62890625 14264.98046875 14528.37109375 15027.59570312]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 2. 7. 18. 52. 91. 126. 131. 73. 39. 11. 3. 2.]\n", + "[ 4. 14. 34. 67. 129. 138. 155. 137. 70. 34. 10. 7.]\n", + "bkg med : [11194.26631546 12324.77198601 12407.69019318 14359.91616821\n", + " 14897.30007935 16090.64306641 15401.89587402 15602.3885498\n", + " 14454.4822998 11253.55895996 11380.76257324 10776.67358398]\n", + "bkg high : [13714.48878384 16404.95606995 13829.53449249 17458.31132507\n", + " 19297.01553345 21011.68579102 23867.79577637 18014.84387207\n", + " 16676.24658203 14379.42333984 14562.03076172 15051.1574707 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 1. 3. 7. 15. 20. 27. 16. 6. 6. 0. 2.]\n", + "[ 0. 4. 3. 15. 21. 37. 30. 19. 16. 10. 0. 1.]\n", + "bkg med : [11218.93310547 12349.43877602 12481.69056702 14532.58370972\n", + " 15267.30194092 16583.97894287 16067.89807129 15997.05664062\n", + " 14602.48278809 11401.55944824 11380.76257324 10826.00708008]\n", + "bkg high : [13714.48878384 16503.62322998 13903.53487396 17828.31309509\n", + " 19815.01846313 21924.35546875 24607.79833984 18483.51208496\n", + " 17070.91455078 14626.09082031 14562.03076172 15075.82421875]\n", + "add a+jj\n", + "bkg med : [12225.28857422 13451.69268227 13525.62416077 15582.34933472\n", + " 16247.08319092 17601.668396 17076.83947754 17017.66210938\n", + " 15678.49255371 12395.92077637 12372.20788574 11794.12426758]\n", + "bkg high : [14912.97120571 17929.55487061 15011.62081146 19111.35997009\n", + " 21007.66885376 23128.66992188 25850.02099609 19641.17028809\n", + " 18266.48095703 15807.07714844 15772.17724609 16388.42773438]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 11. 31. 102. 191. 411. 569. 571. 419. 231. 107. 31. 13.]\n", + "[ 13. 40. 72. 156. 237. 270. 299. 268. 164. 84. 31. 29.]\n", + "bkg med : [ 40.58096313 114.36453247 376.29571533 704.63140869 1516.26489258\n", + " 2099.10742188 2106.39892578 1545.72216797 852.26367188 394.77148438\n", + " 114.37304688 47.96289062]\n", + "bkg high : [ 47.95932007 147.56713867 265.62084961 575.50811768 874.3425293\n", + " 996.08642578 1103.07348633 988.64526367 604.99023438 309.87304688\n", + " 114.35791016 106.97998047]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 0. 9. 21. 40. 46. 47. 35. 21. 1. 4. 2.]\n", + "[ 1. 2. 1. 7. 6. 10. 20. 6. 6. 3. 4. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 191.02830505 114.36453247 1730.32183838 3864.0244751 7534.15600586\n", + " 9019.68164062 9177.42041016 6811.37646484 4011.66210938 545.22070312\n", + " 716.16992188 348.86132812]\n", + "bkg high : [ 198.40666199 448.46185303 416.06817627 1628.63970947 1777.02636719\n", + " 2500.55908203 4112.01904297 1891.32885742 1507.67431641 761.21484375\n", + " 716.14697266 106.97998047]\n", + "mgp8_pp_jjaa_5f\n", + "[630. 733. 625. 674. 623. 641. 652. 635. 667. 618. 627. 660.]\n", + "[126. 134. 113. 126. 122. 121. 120. 112. 112. 128. 128. 122.]\n", + "bkg med : [20606.96580505 23868.14578247 21984.22808838 25705.8369751\n", + " 27723.24975586 29792.08789062 30306.29541016 27389.34521484\n", + " 25626.63085938 20575.25195312 21054.48242188 21757.61132812]\n", + "bkg high : [4282.09416199 4791.94622803 4078.85723877 5712.81158447 5731.54199219\n", + " 6422.66064453 8001.70654297 5521.70385742 5138.04931641 4910.21484375\n", + " 4865.14697266 4061.49560547]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 3. 15. 35. 93. 176. 209. 231. 157. 75. 30. 8. 6.]\n", + "[ 3. 6. 17. 26. 44. 55. 55. 53. 34. 15. 5. 3.]\n", + "bkg med : [20617.06337357 23918.63361359 22102.03305817 26018.86083984\n", + " 28315.63842773 30495.54943848 31083.8248291 27917.80200195\n", + " 25879.07836914 20676.23095703 21081.41015625 21777.80712891]\n", + "bkg high : [4292.19172955 4812.14136314 4136.07675934 5800.32386017 5879.63951111\n", + " 6607.78213501 8186.82800293 5700.09362793 5252.48803711 4960.70251465\n", + " 4881.97619629 4071.59313965]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 4. 5. 18. 29. 48. 46. 30. 16. 12. 0. 3.]\n", + "[ 0. 1. 1. 4. 7. 9. 11. 5. 6. 4. 0. 0.]\n", + "bkg med : [20641.73016357 24017.30077362 22225.36702728 26462.86309814\n", + " 29030.97497559 31679.5534668 32218.49523926 28657.80786133\n", + " 26273.75024414 20972.23486328 21081.41015625 21851.80810547]\n", + "bkg high : [4292.19172955 4836.80815315 4160.74354935 5898.99102783 6052.30703735\n", + " 6829.78317261 8458.16290283 5823.42767334 5400.48876953 5059.36950684\n", + " 4881.97619629 4071.59313965]\n", + "add a+jj\n", + "bkg med : [22478.82000732 26154.74022675 24048.90413666 28429.57403564\n", + " 30848.86950684 33549.97143555 34121.01086426 30510.71801758\n", + " 28220.03540039 22775.53955078 22910.9765625 23777.66748047]\n", + "bkg high : [4659.73274517 5227.68510628 4490.36366653 6266.53204346 6408.18008423\n", + " 7182.73922729 8808.20196533 6150.13031006 5727.19189453 5432.74450684\n", + " 5255.35119629 4427.46618652]\n", + "sig med : [ 1.723858 7.82190037 23.65528107 55.53756332 103.25765991\n", + " 147.68692017 148.4102478 105.68887329 58.39611816 24.05743408\n", + " 9.47930908 2.20257568]\n", + "sig high : [ 2.07501411 8.2111845 22.15000153 47.24729156 84.31189728\n", + " 114.98376465 116.74789429 87.46542358 47.75845337 25.00909424\n", + " 8.90606689 2.77716064]\n", + "sono dentro\n", + "1.7238579988479614\n", + "22478.82000732422\n", + "sono dentro\n", + "7.821900367736816\n", + "26154.740226745605\n", + "sono dentro\n", + "23.65528106689453\n", + "24048.904136657715\n", + "sono dentro\n", + "55.53756332397461\n", + "28429.57403564453\n", + "sono dentro\n", + "103.25765991210938\n", + "30848.869506835938\n", + "sono dentro\n", + "147.68692016601562\n", + "33549.971435546875\n", + "sono dentro\n", + "148.41024780273438\n", + "34121.01086425781\n", + "sono dentro\n", + "105.68887329101562\n", + "30510.718017578125\n", + "sono dentro\n", + "58.3961181640625\n", + "28220.035400390625\n", + "sono dentro\n", + "24.05743408203125\n", + "22775.53955078125\n", + "sono dentro\n", + "9.47930908203125\n", + "22910.9765625\n", + "sono dentro\n", + "2.20257568359375\n", + "23777.66748046875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.9262935297622623\n", + "sig_high 6.962060686707405\n", + "Naive\n", + "sig_med 1.2014736885465878\n", + "sig_high 2.144933437270401\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 21. 58. 152. 303. 600. 785. 806. 620. 348. 176. 50. 33.]\n", + "[ 3. 13. 22. 44. 48. 54. 64. 67. 47. 15. 12. 9.]\n", + "bkg med : [ 77.4727478 213.97233582 560.75183105 1117.82836914 2213.51586914\n", + " 2895.83740234 2973.55908203 2287.4609375 1283.9296875 649.34375\n", + " 184.47265625 121.75195312]\n", + "bkg high : [ 11.06753349 47.95932007 81.16192627 162.32383728 177.08056641\n", + " 199.21405029 236.10546875 247.17565918 173.39282227 55.33813477\n", + " 44.27050781 33.20288086]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 2. 10. 27. 45. 56. 65. 40. 26. 3. 8. 2.]\n", + "[0. 0. 0. 1. 1. 0. 2. 1. 1. 1. 0. 0.]\n", + "bkg med : [ 378.36743164 514.86701965 2065.22546387 5179.90478516\n", + " 8983.64331055 11320.88427734 12752.63134766 8305.41015625\n", + " 5195.609375 1100.69140625 1388.06640625 422.65039062]\n", + "bkg high : [ 11.06753349 47.95932007 81.16192627 312.7711792 327.52790833\n", + " 199.21405029 537.00015259 397.62298584 323.84014893 205.78546143\n", + " 44.27050781 33.20288086]\n", + "mgp8_pp_jjaa_5f\n", + "[747. 856. 728. 791. 733. 755. 768. 739. 767. 734. 737. 770.]\n", + "[ 9. 11. 10. 9. 12. 7. 4. 8. 12. 12. 18. 12.]\n", + "add a+jj\n", + "bkg med : [ 2558.09008789 3012.64826965 4189.50671387 7488.01806641\n", + " 11122.5144043 13523.95068359 14993.63134766 10461.7890625\n", + " 7433.69140625 3242.48046875 3538.609375 2669.48632812]\n", + "bkg high : [ 37.32018852 80.04614258 110.33184814 339.02410889 362.53181458\n", + " 219.63299561 548.66812134 420.95892334 358.84405518 240.78936768\n", + " 96.77636719 68.20678711]\n", + "ttH cut: 0.4\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 2. 16. 53. 145. 280. 387. 446. 297. 160. 64. 34. 9.]\n", + "[ 10. 36. 100. 180. 323. 402. 386. 326. 178. 92. 28. 24.]\n", + "bkg med : [ 7.37835693 59.02685547 195.52644348 534.92828369 1032.9753418\n", + " 1427.72387695 1645.33178711 1095.62255859 590.234375 236.09375\n", + " 125.42480469 33.20068359]\n", + "bkg high : [ 36.89178467 132.8104248 368.91723633 664.050354 1191.61450195\n", + " 1483.06201172 1423.9519043 1202.60253906 656.63574219 339.38476562\n", + " 103.29101562 88.53515625]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 4. 5. 26. 54. 63. 57. 51. 28. 9. 3. 0.]\n", + "[ 2. 2. 5. 12. 24. 31. 44. 17. 12. 3. 6. 1.]\n", + "bkg med : [7.37835693e+00 6.60816223e+02 9.47763199e+02 4.44655792e+03\n", + " 9.15712817e+03 1.09059016e+04 1.02208259e+04 8.76851709e+03\n", + " 4.80281250e+03 1.59013672e+03 5.76772461e+02 3.32006836e+01]\n", + "bkg high : [ 337.78646851 433.70510864 1121.1539917 2469.41802979 4802.34912109\n", + " 6146.92773438 8043.6315918 3760.20605469 2462.00292969 790.7265625\n", + " 1005.97460938 238.98242188]\n", + "mgp8_pp_jjaa_5f\n", + "[1082. 1080. 1080. 1060. 1026. 1030. 1040. 1025. 1062. 1032. 1035. 1004.]\n", + "[371. 445. 346. 403. 378. 379. 399. 366. 373. 374. 376. 409.]\n", + "bkg med : [35088.25335693 35693.31622314 35980.26319885 38830.30792236\n", + " 42438.00317383 44316.52661133 43955.82592773 42016.95458984\n", + " 39251.4375 35065.63671875 34149.58496094 32600.45068359]\n", + "bkg high : [12360.50521851 14854.48635864 12333.7164917 15529.13677979\n", + " 17051.91162109 18428.91210938 20973.7253418 15620.89355469\n", + " 14549.53417969 12910.6640625 13190.72460938 13493.13867188]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 11. 39. 116. 216. 348. 350. 217. 114. 44. 15. 6.]\n", + "[ 2. 11. 28. 61. 113. 128. 136. 125. 64. 30. 10. 6.]\n", + "bkg med : [35091.6192131 35730.34063721 36111.53157043 39220.74627686\n", + " 43165.02563477 45487.85595703 45133.91430664 42747.34863281\n", + " 39635.12988281 35213.72851562 34200.07080078 32620.64501953]\n", + "bkg high : [12367.23692989 14891.51077271 12427.96039581 15734.45385742\n", + " 17432.25210571 18859.74023438 21431.48022461 16041.62414551\n", + " 14764.95556641 13011.64306641 13224.38427734 13513.33447266]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 5. 15. 17. 43. 63. 72. 52. 21. 9. 1. 2.]\n", + "[ 0. 3. 3. 14. 16. 35. 28. 18. 16. 10. 0. 1.]\n", + "bkg med : [35165.61957932 35853.67460632 36481.53337097 39640.08203125\n", + " 44225.69604492 47041.86108398 46909.93286133 44030.03222656\n", + " 40153.13671875 35435.73144531 34224.73779297 32669.97900391]\n", + "bkg high : [12367.23692989 14965.51113892 12501.96077728 16079.78881836\n", + " 17826.92102051 19723.07678223 22122.14929199 16485.62561035\n", + " 15159.62353516 13258.31054688 13224.38427734 13538.0012207 ]\n", + "add a+jj\n", + "bkg med : [38322.86176682 39005.08085632 39632.93962097 42733.12890625\n", + " 47219.53198242 50047.36889648 49942.34692383 47016.94628906\n", + " 43247.87109375 38443.04394531 37240.79248047 35595.69775391]\n", + "bkg high : [13449.07872677 16263.13809204 13510.90218353 17254.94311523\n", + " 18929.17492676 20828.2467041 23285.63952637 17552.8873291\n", + " 16247.29736328 14348.90039062 14320.80615234 14730.65161133]\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 6. 25. 98. 228. 432. 604. 625. 451. 235. 101. 44. 15.]\n", + "[ 6. 27. 55. 97. 171. 185. 207. 172. 103. 55. 18. 18.]\n", + "bkg med : [ 22.1350708 92.22946167 361.53936768 841.13146973 1593.73828125\n", + " 2228.2097168 2305.60302734 1663.85107422 867.02148438 372.63476562\n", + " 162.3359375 55.34179688]\n", + "bkg high : [ 22.13506699 99.6078186 202.90480042 357.84872437 630.8494873\n", + " 682.50366211 763.66625977 634.54394531 379.98852539 202.90649414\n", + " 66.40576172 66.40332031]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 4. 9. 32. 72. 85. 84. 63. 35. 10. 5. 1.]\n", + "[ 1. 2. 1. 6. 6. 9. 17. 5. 5. 2. 4. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 172.58241272 694.01882935 1715.56561279 5655.44396973\n", + " 12425.94140625 15016.22729492 14943.31005859 11142.15185547\n", + " 6132.74414062 1877.12695312 914.58203125 205.79101562]\n", + "bkg high : [ 172.58240891 400.50253296 353.35212708 1260.53286743 1533.53344727\n", + " 2036.52905273 3321.27001953 1386.78027344 1132.22485352 503.80102539\n", + " 668.1953125 66.40332031]\n", + "mgp8_pp_jjaa_5f\n", + "[1341. 1401. 1324. 1349. 1293. 1296. 1328. 1288. 1334. 1289. 1299. 1300.]\n", + "[112. 124. 102. 114. 111. 113. 111. 103. 101. 117. 112. 113.]\n", + "bkg med : [43671.26991272 46138.95632935 44662.81561279 49413.63146973\n", + " 54367.62890625 57055.22729492 58020.31005859 52921.65185547\n", + " 49404.36914062 43648.62695312 42969.70703125 42293.29101562]\n", + "bkg high : [3802.51990891 4419.36190796 3659.37556458 4955.73599243 5131.49438477\n", + " 5699.31811523 6919.23095703 4725.42871094 4406.04516602 4296.24633789\n", + " 4298.5703125 3729.19238281]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 18. 53. 153. 289. 425. 434. 293. 145. 60. 20. 9.]\n", + "[ 2. 4. 14. 24. 40. 51. 52. 49. 33. 14. 5. 3.]\n", + "bkg med : [43674.63576889 46199.54172516 44841.20605469 49928.60577393\n", + " 55340.35803223 58485.7520752 59481.10058594 53907.80859375\n", + " 49892.39892578 43850.5703125 43037.02148438 42323.58251953]\n", + "bkg high : [3809.25162029 4432.82533169 3706.49754333 5036.51648712 5266.12867737\n", + " 5870.97619629 7094.25491333 4890.35510254 4517.11804199 4343.36816406\n", + " 4315.39953613 3739.28991699]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 7. 17. 28. 55. 89. 89. 66. 31. 15. 1. 3.]\n", + "[ 0. 1. 1. 3. 4. 9. 11. 4. 6. 4. 0. 0.]\n", + "bkg med : [43748.6361351 46372.20928192 45260.54150391 50619.27557373\n", + " 56697.02929688 60681.10070801 61676.46289062 55535.82958984\n", + " 50657.07568359 44220.57519531 43061.68847656 42397.58349609]\n", + "bkg high : [3809.25162029 4457.4921217 3731.16433334 5110.51686096 5364.79585266\n", + " 6092.97723389 7365.58972168 4989.02233887 4665.11889648 4442.03533936\n", + " 4315.39953613 3739.28991699]\n", + "add a+jj\n", + "bkg med : [47661.63222885 50460.19756317 49118.76025391 54550.34588623\n", + " 60464.91210938 64457.72570801 65546.33789062 59289.14208984\n", + " 54544.43505859 47976.80175781 46847.05566406 46185.86474609]\n", + "bkg high : [4135.95474529 4819.19915295 4028.69753647 5443.05397034 5688.58198547\n", + " 6422.59735107 7689.37585449 5289.47253418 4959.73510742 4783.32293701\n", + " 4642.10266113 4068.91003418]\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 11. 42. 135. 302. 571. 762. 787. 580. 307. 144. 54. 26.]\n", + "[ 1. 10. 18. 23. 32. 27. 45. 43. 31. 12. 8. 7.]\n", + "bkg med : [ 40.58096313 154.94548035 498.03707886 1114.13671875 2106.53833008\n", + " 2810.99902344 2903.41357422 2139.8828125 1132.66210938 531.28125\n", + " 199.23046875 95.92578125]\n", + "bkg high : [ 3.68917656 36.89177704 66.4052124 84.85110474 118.05369568\n", + " 99.6078186 166.01242065 158.63336182 114.36358643 44.26977539\n", + " 29.51318359 25.82403564]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 10. 37. 77. 94. 99. 68. 39. 11. 9. 1.]\n", + "[0. 0. 0. 1. 1. 0. 2. 0. 1. 1. 0. 0.]\n", + "bkg med : [ 341.47564697 1057.62956238 2002.51034546 6680.68652344\n", + " 13690.9777832 16953.05957031 17797.88623047 12370.4296875\n", + " 7000.18164062 2186.1953125 1553.23828125 246.37109375]\n", + "bkg high : [ 3.68917656 36.89177704 66.4052124 235.29844666 268.5010376\n", + " 99.6078186 466.90710449 158.63336182 264.81091309 194.71710205\n", + " 29.51318359 25.82403564]\n", + "mgp8_pp_jjaa_5f\n", + "[1445. 1515. 1417. 1455. 1393. 1405. 1435. 1385. 1424. 1396. 1398. 1402.]\n", + "[ 8. 10. 9. 8. 11. 4. 4. 6. 11. 10. 13. 11.]\n", + "add a+jj\n", + "bkg med : [ 4556.70611572 5472.43424988 6131.73690796 10920.64746094\n", + " 17750.2668457 21047.31738281 21979.56591797 16406.40625\n", + " 11149.80664062 6254.2265625 5627.09765625 4331.88671875]\n", + "bkg high : [ 27.02486992 66.06139374 92.65786743 258.63420105 300.58795166\n", + " 111.27578735 478.57507324 176.13531494 296.89782715 223.88702393\n", + " 67.43408203 57.91094971]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 1. 14. 41. 100. 193. 279. 314. 216. 103. 47. 21. 6.]\n", + "[ 10. 36. 100. 180. 323. 402. 386. 326. 178. 92. 28. 24.]\n", + "bkg med : [ 3.68917656 51.64849091 151.25631714 368.91723633 712.01013184\n", + " 1029.28930664 1158.41162109 796.85668945 379.96337891 173.38134766\n", + " 77.46826172 22.13378906]\n", + "bkg high : [ 36.89178467 132.8104248 368.91723633 664.050354 1191.61450195\n", + " 1483.06201172 1423.9519043 1202.60253906 656.63574219 339.38476562\n", + " 103.29101562 88.53515625]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 1. 4. 19. 32. 46. 34. 36. 21. 6. 1. 0.]\n", + "[ 2. 2. 5. 12. 24. 31. 44. 17. 12. 3. 6. 1.]\n", + "bkg med : [3.68917656e+00 2.02095833e+02 7.53045685e+02 3.22741626e+03\n", + " 5.52632361e+03 7.94986353e+03 6.27361865e+03 6.21295825e+03\n", + " 3.53935596e+03 1.07606494e+03 2.27915527e+02 2.21337891e+01]\n", + "bkg high : [ 337.78646851 433.70510864 1121.1539917 2469.41802979 4802.34912109\n", + " 6146.92773438 8043.6315918 3760.20605469 2462.00292969 790.7265625\n", + " 1005.97460938 238.98242188]\n", + "mgp8_pp_jjaa_5f\n", + "[667. 645. 650. 635. 631. 618. 623. 631. 682. 632. 629. 613.]\n", + "[371. 445. 346. 403. 378. 379. 399. 366. 373. 374. 376. 409.]\n", + "bkg med : [21618.65792656 21104.12708282 21817.10818481 23805.38500977\n", + " 25974.6673584 27976.92602539 26462.71240234 26661.30200195\n", + " 25640.41845703 21556.81494141 20626.29052734 19906.32128906]\n", + "bkg high : [12360.50521851 14854.48635864 12333.7164917 15529.13677979\n", + " 17051.91162109 18428.91210938 20973.7253418 15620.89355469\n", + " 14549.53417969 12910.6640625 13190.72460938 13493.13867188]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 8. 22. 78. 137. 209. 229. 130. 73. 25. 7. 5.]\n", + "[ 2. 11. 28. 61. 113. 128. 136. 125. 64. 30. 10. 6.]\n", + "bkg med : [21622.02378178 21131.05393028 21891.15696716 24067.92150879\n", + " 26435.78811646 28680.38757324 27233.50036621 27098.87768555\n", + " 25886.1340332 21640.96411133 20649.85229492 19923.15112305]\n", + "bkg high : [12367.23692989 14891.51077271 12427.96039581 15734.45385742\n", + " 17432.25210571 18859.74023438 21431.48022461 16041.62414551\n", + " 14764.95556641 13011.64306641 13224.38427734 13513.33447266]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 3. 10. 12. 23. 39. 44. 31. 15. 7. 1. 2.]\n", + "[ 0. 3. 3. 14. 16. 35. 28. 18. 16. 10. 0. 1.]\n", + "bkg med : [21696.02414799 21205.05431175 22137.8248291 24363.92306519\n", + " 27003.12417603 29642.39086914 28318.83728027 27863.546875\n", + " 26256.13842773 21813.63305664 20674.51928711 19972.48510742]\n", + "bkg high : [12367.23692989 14965.51113892 12501.96077728 16079.78881836\n", + " 17826.92102051 19723.07678223 22122.14929199 16485.62561035\n", + " 15159.62353516 13258.31054688 13224.38427734 13538.0012207 ]\n", + "add a+jj\n", + "bkg med : [23641.00656986 23085.88438988 24033.88928223 26216.83322144\n", + " 28844.36245728 31445.69555664 30136.73181152 29704.78515625\n", + " 28246.19311523 23657.78930664 22509.92163086 21761.19995117]\n", + "bkg high : [13449.07872677 16263.13809204 13510.90218353 17254.94311523\n", + " 18929.17492676 20828.2467041 23285.63952637 17552.8873291\n", + " 16247.29736328 14348.90039062 14320.80615234 14730.65161133]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 5. 23. 86. 183. 345. 496. 493. 370. 178. 84. 31. 12.]\n", + "[ 6. 27. 55. 97. 171. 185. 207. 172. 103. 55. 18. 18.]\n", + "bkg med : [ 18.44589233 84.85110474 317.26934814 675.11633301 1272.77709961\n", + " 1829.83862305 1818.65966797 1364.91699219 656.63574219 309.89794922\n", + " 114.37304688 44.2734375 ]\n", + "bkg high : [ 22.13506699 99.6078186 202.90480042 357.84872437 630.8494873\n", + " 682.50366211 763.66625977 634.54394531 379.98852539 202.90649414\n", + " 66.40576172 66.40332031]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 1. 8. 25. 50. 68. 61. 48. 28. 7. 3. 1.]\n", + "[ 1. 2. 1. 6. 6. 9. 17. 5. 5. 2. 4. 0.]\n", + "bkg med : [ 168.89323425 235.29844666 1520.84820557 4436.29846191\n", + " 8795.14086914 12060.25268555 10995.94287109 8586.47363281\n", + " 4869.21386719 1363.04248047 565.72070312 194.72265625]\n", + "bkg high : [ 172.58240891 400.50253296 353.35212708 1260.53286743 1533.53344727\n", + " 2036.52905273 3321.27001953 1386.78027344 1132.22485352 503.80102539\n", + " 668.1953125 66.40332031]\n", + "mgp8_pp_jjaa_5f\n", + "[926. 966. 894. 924. 898. 884. 911. 894. 954. 889. 893. 909.]\n", + "[112. 124. 102. 114. 111. 113. 111. 103. 101. 117. 112. 113.]\n", + "bkg med : [30177.08073425 31539.73594666 30501.84820557 34408.54846191\n", + " 37924.01586914 40735.00268555 40546.50537109 37585.59863281\n", + " 35814.58886719 30199.97998047 29532.40820312 29680.41015625]\n", + "bkg high : [3802.51990891 4419.36190796 3659.37556458 4955.73599243 5131.49438477\n", + " 5699.31811523 6919.23095703 4725.42871094 4406.04516602 4296.24633789\n", + " 4298.5703125 3729.19238281]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 15. 36. 115. 210. 286. 313. 206. 104. 41. 12. 8.]\n", + "[ 2. 4. 14. 24. 40. 51. 52. 49. 33. 14. 5. 3.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [30180.44658947 31590.2237854 30623.01901245 34795.62097168\n", + " 38630.84326172 41697.64123535 41600.05297852 38278.98779297\n", + " 36164.63183594 30337.97460938 29572.796875 29707.3359375 ]\n", + "bkg high : [3809.25162029 4432.82533169 3706.49754333 5036.51648712 5266.12867737\n", + " 5870.97619629 7094.25491333 4890.35510254 4517.11804199 4343.36816406\n", + " 4315.39953613 3739.28991699]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 5. 12. 23. 35. 65. 61. 45. 25. 13. 1. 3.]\n", + "[ 0. 1. 1. 3. 4. 9. 11. 4. 6. 4. 0. 0.]\n", + "bkg med : [30254.44695568 31713.55775452 30919.02041626 35362.95751953\n", + " 39494.17944336 43300.97998047 43104.73364258 39389.00244141\n", + " 36781.30664062 30658.64550781 29597.46386719 29781.33691406]\n", + "bkg high : [3809.25162029 4457.4921217 3731.16433334 5110.51686096 5364.79585266\n", + " 6092.97723389 7365.58972168 4989.02233887 4665.11889648 4442.03533936\n", + " 4315.39953613 3739.28991699]\n", + "add a+jj\n", + "bkg med : [32956.48601818 34532.31556702 33527.68447876 38059.16064453\n", + " 42114.51538086 45880.46435547 45763.00317383 41997.66650391\n", + " 39565.04882812 33251.23535156 32199.72167969 32430.21972656]\n", + "bkg high : [4135.95474529 4819.19915295 4028.69753647 5443.05397034 5688.58198547\n", + " 6422.59735107 7689.37585449 5289.47253418 4959.73510742 4783.32293701\n", + " 4642.10266113 4068.91003418]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 10. 40. 123. 257. 484. 654. 655. 499. 250. 127. 41. 23.]\n", + "[ 1. 10. 18. 23. 32. 27. 45. 43. 31. 12. 8. 7.]\n", + "bkg med : [ 36.89178467 147.56713867 453.76751709 948.12109375 1785.57714844\n", + " 2412.62792969 2416.27880859 1841.03710938 922.36328125 468.56054688\n", + " 151.26757812 84.85742188]\n", + "bkg high : [ 3.68917656 36.89177704 66.4052124 84.85110474 118.05369568\n", + " 99.6078186 166.01242065 158.63336182 114.36358643 44.26977539\n", + " 29.51318359 25.82403564]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 3. 9. 30. 55. 77. 76. 53. 32. 8. 7. 1.]\n", + "[0. 0. 0. 1. 1. 0. 2. 0. 1. 1. 0. 0.]\n", + "bkg med : [ 337.78646851 598.90914917 1807.79376221 5461.5390625\n", + " 10060.17675781 13997.06738281 13850.33935547 9814.84570312\n", + " 5736.73828125 1672.15429688 1204.41210938 235.30664062]\n", + "bkg high : [ 3.68917656 36.89177704 66.4052124 235.29844666 268.5010376\n", + " 99.6078186 466.90710449 158.63336182 264.81091309 194.71710205\n", + " 29.51318359 25.82403564]\n", + "mgp8_pp_jjaa_5f\n", + "[1030. 1080. 987. 1030. 998. 993. 1018. 991. 1044. 996. 992. 1011.]\n", + "[ 8. 10. 9. 8. 11. 4. 4. 6. 11. 10. 13. 11.]\n", + "add a+jj\n", + "bkg med : [ 3343.29428101 3750.31539917 4687.82891846 8467.046875\n", + " 12972.30957031 16894.61035156 16820.83154297 12702.81054688\n", + " 8779.01953125 4574.56054688 4095.16210938 3181.42382812]\n", + "bkg high : [ 27.02486992 66.06139374 92.65786743 258.63420105 300.58795166\n", + " 111.27578735 478.57507324 176.13531494 296.89782715 223.88702393\n", + " 67.43408203 57.91094971]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 1. 7. 24. 53. 120. 136. 173. 128. 58. 27. 7. 2.]\n", + "[ 10. 36. 100. 180. 323. 402. 386. 326. 178. 92. 28. 24.]\n", + "bkg med : [ 3.68917656 25.82424164 88.5402832 195.52644348 442.6993103\n", + " 501.72821045 638.2331543 472.21875 213.97412109 99.60864258\n", + " 25.82446289 7.37841797]\n", + "bkg high : [ 36.89178467 132.8104248 368.91723633 664.050354 1191.61450195\n", + " 1483.06201172 1423.9519043 1202.60253906 656.63574219 339.38476562\n", + " 103.29101562 88.53515625]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 0. 3. 12. 17. 22. 14. 18. 10. 0. 1. 0.]\n", + "[ 2. 2. 5. 12. 24. 31. 44. 17. 12. 3. 6. 1.]\n", + "bkg med : [3.68917656e+00 2.58242416e+01 5.39882324e+02 2.00089461e+03\n", + " 3.00030307e+03 3.81156805e+03 2.74449536e+03 3.18026953e+03\n", + " 1.71844678e+03 9.96086426e+01 1.76271729e+02 7.37841797e+00]\n", + "bkg high : [ 337.78646851 433.70510864 1121.1539917 2469.41802979 4802.34912109\n", + " 6146.92773438 8043.6315918 3760.20605469 2462.00292969 790.7265625\n", + " 1005.97460938 238.98242188]\n", + "mgp8_pp_jjaa_5f\n", + "[304. 334. 315. 310. 297. 310. 289. 310. 327. 294. 299. 298.]\n", + "[371. 445. 346. 403. 378. 379. 399. 366. 373. 374. 376. 409.]\n", + "bkg med : [ 9855.18917656 10849.51174164 10747.85107422 12046.83210754\n", + " 12624.95932007 13857.5055542 12109.90161133 13226.20703125\n", + " 12315.29052734 9627.04614258 9865.74047852 9664.45654297]\n", + "bkg high : [12360.50521851 14854.48635864 12333.7164917 15529.13677979\n", + " 17051.91162109 18428.91210938 20973.7253418 15620.89355469\n", + " 14549.53417969 12910.6640625 13190.72460938 13493.13867188]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 6. 13. 44. 72. 106. 116. 65. 36. 11. 3. 1.]\n", + "[ 2. 11. 28. 61. 113. 128. 136. 125. 64. 30. 10. 6.]\n", + "bkg med : [ 9858.55503178 10869.70687866 10791.60719299 12194.92982483\n", + " 12867.30030823 14214.28512573 12500.33959961 13444.98693848\n", + " 12436.4609375 9664.07043457 9875.8380127 9667.8223877 ]\n", + "bkg high : [12367.23692989 14891.51077271 12427.96039581 15734.45385742\n", + " 17432.25210571 18859.74023438 21431.48022461 16041.62414551\n", + " 14764.95556641 13011.64306641 13224.38427734 13513.33447266]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 1. 3. 6. 13. 18. 21. 13. 6. 5. 0. 2.]\n", + "[ 0. 3. 3. 14. 16. 35. 28. 18. 16. 10. 0. 1.]\n", + "bkg med : [ 9883.22182178 10894.37366867 10865.60756683 12342.93058777\n", + " 13187.96852112 14658.28768921 13018.34130859 13765.65466309\n", + " 12584.46142578 9787.40429688 9875.8380127 9717.15588379]\n", + "bkg high : [12367.23692989 14965.51113892 12501.96077728 16079.78881836\n", + " 17826.92102051 19723.07678223 22122.14929199 16485.62561035\n", + " 15159.62353516 13258.31054688 13224.38427734 13538.0012207 ]\n", + "add a+jj\n", + "bkg med : [10769.98744678 11868.6490593 11784.4601059 13246.89738464\n", + " 14054.02516174 15562.25253296 13861.06982422 14669.61950684\n", + " 13537.99853516 10644.71289062 10747.72668457 10586.12854004]\n", + "bkg high : [13449.07872677 16263.13809204 13510.90218353 17254.94311523\n", + " 18929.17492676 20828.2467041 23285.63952637 17552.8873291\n", + " 16247.29736328 14348.90039062 14320.80615234 14730.65161133]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 5. 16. 69. 136. 272. 353. 352. 282. 133. 64. 17. 8.]\n", + "[ 6. 27. 55. 97. 171. 185. 207. 172. 103. 55. 18. 18.]\n", + "bkg med : [ 18.44589233 59.02685547 254.55329895 501.7253418 1003.46228027\n", + " 1302.29077148 1298.57666016 1040.28808594 490.63232422 236.09375\n", + " 62.71240234 29.51171875]\n", + "bkg high : [ 22.13506699 99.6078186 202.90480042 357.84872437 630.8494873\n", + " 682.50366211 763.66625977 634.54394531 379.98852539 202.90649414\n", + " 66.40576172 66.40332031]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 0. 7. 18. 35. 44. 41. 30. 17. 1. 3. 1.]\n", + "[ 1. 2. 1. 6. 6. 9. 17. 5. 5. 2. 4. 0.]\n", + "bkg med : [ 168.89323425 59.02685547 1307.68464661 3209.77661133 6269.11755371\n", + " 7921.97045898 7466.91455078 5553.70605469 3048.23583984 386.54101562\n", + " 514.05419922 179.95898438]\n", + "bkg high : [ 172.58240891 400.50253296 353.35212708 1260.53286743 1533.53344727\n", + " 2036.52905273 3321.27001953 1386.78027344 1132.22485352 503.80102539\n", + " 668.1953125 66.40332031]\n", + "mgp8_pp_jjaa_5f\n", + "[563. 655. 559. 599. 564. 576. 577. 573. 599. 551. 563. 594.]\n", + "[112. 124. 102. 114. 111. 113. 111. 103. 101. 117. 112. 113.]\n", + "bkg med : [18413.61198425 21285.12060547 19422.77839661 22621.12036133\n", + " 24546.24255371 26587.97045898 26165.32080078 24122.48730469\n", + " 22459.57958984 18242.38476562 18758.77294922 19429.27148438]\n", + "bkg high : [3802.51990891 4419.36190796 3659.37556458 4955.73599243 5131.49438477\n", + " 5699.31811523 6919.23095703 4725.42871094 4406.04516602 4296.24633789\n", + " 4298.5703125 3729.19238281]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 13. 27. 81. 145. 183. 200. 141. 67. 27. 8. 4.]\n", + "[ 2. 4. 14. 24. 40. 51. 52. 49. 33. 14. 5. 3.]\n", + "bkg med : [18416.97783947 21328.87673187 19513.65644836 22893.75430298\n", + " 25034.29006958 27203.92004395 26838.49511719 24597.08862305\n", + " 22685.09936523 18333.26586914 18785.70068359 19442.73535156]\n", + "bkg high : [3809.25162029 4432.82533169 3706.49754333 5036.51648712 5266.12867737\n", + " 5870.97619629 7094.25491333 4890.35510254 4517.11804199 4343.36816406\n", + " 4315.39953613 3739.28991699]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 3. 5. 17. 25. 44. 38. 27. 16. 11. 0. 3.]\n", + "[ 0. 1. 1. 3. 4. 9. 11. 4. 6. 4. 0. 0.]\n", + "bkg med : [18441.64462948 21402.87709808 19636.99041748 23313.08972168\n", + " 25650.95974731 28289.25708008 27775.83154297 25263.09082031\n", + " 23079.76953125 18604.6027832 18785.70068359 19516.73632812]\n", + "bkg high : [3809.25162029 4457.4921217 3731.16433334 5110.51686096 5364.79585266\n", + " 6092.97723389 7365.58972168 4989.02233887 4665.11889648 4442.03533936\n", + " 4315.39953613 3739.28991699]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "add a+jj\n", + "bkg med : [20083.36142635 23312.86733246 21267.04315186 25059.78308105\n", + " 27296.49490356 29970.00708008 29459.49951172 26935.08691406\n", + " 24827.6328125 20212.40356445 20428.51708984 21250.00976562]\n", + "bkg high : [4135.95474529 4819.19915295 4028.69753647 5443.05397034 5688.58198547\n", + " 6422.59735107 7689.37585449 5289.47253418 4959.73510742 4783.32293701\n", + " 4642.10266113 4068.91003418]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 10. 33. 106. 210. 411. 511. 514. 411. 205. 107. 27. 19.]\n", + "[ 1. 10. 18. 23. 32. 27. 45. 43. 31. 12. 8. 7.]\n", + "bkg med : [ 36.89178467 121.7428894 391.05224609 774.72680664 1516.26489258\n", + " 1885.14111328 1896.12792969 1516.16455078 756.33642578 394.77148438\n", + " 99.61523438 70.09960938]\n", + "bkg high : [ 3.68917656 36.89177704 66.4052124 84.85110474 118.05369568\n", + " 99.6078186 166.01242065 158.63336182 114.36358643 44.26977539\n", + " 29.51318359 25.82403564]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 2. 8. 23. 40. 53. 56. 35. 21. 2. 7. 1.]\n", + "[0. 0. 0. 1. 1. 0. 2. 0. 1. 1. 0. 0.]\n", + "bkg med : [ 337.78646851 422.63757324 1594.63110352 4235.0144043\n", + " 7534.15600586 9858.84619141 10321.17480469 6781.82275391\n", + " 3915.77001953 695.66992188 1152.75976562 220.54882812]\n", + "bkg high : [ 3.68917656 36.89177704 66.4052124 235.29844666 268.5010376\n", + " 99.6078186 466.90710449 158.63336182 264.81091309 194.71710205\n", + " 29.51318359 25.82403564]\n", + "mgp8_pp_jjaa_5f\n", + "[667. 769. 652. 705. 664. 685. 684. 670. 689. 658. 662. 696.]\n", + "[ 8. 10. 9. 8. 11. 4. 4. 6. 11. 10. 13. 11.]\n", + "add a+jj\n", + "bkg med : [ 2282.76889038 2666.12585449 3497.14672852 6292.18237305\n", + " 9471.68725586 11857.65478516 12317.06542969 8736.86181641\n", + " 5926.25048828 2615.69335938 3084.45507812 2251.45507812]\n", + "bkg high : [ 27.02486992 66.06139374 92.65786743 258.63420105 300.58795166\n", + " 111.27578735 478.57507324 176.13531494 296.89782715 223.88702393\n", + " 67.43408203 57.91094971]\n", + "ttH cut: 0.5\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 2. 15. 34. 91. 176. 222. 280. 198. 92. 36. 19. 5.]\n", + "[ 5. 27. 61. 122. 229. 294. 258. 229. 111. 61. 14. 11.]\n", + "bkg med : [ 7.37835312 55.337677 125.43206787 335.71496582 649.29290771\n", + " 819.00439453 1032.97851562 730.46337891 339.40576172 132.80273438\n", + " 70.09033203 18.44482422]\n", + "bkg high : [ 18.44589233 99.6078186 225.03987122 450.07720947 844.82556152\n", + " 1084.62744141 951.81591797 844.79882812 409.47509766 225.02685547\n", + " 51.64550781 40.57861328]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 3. 5. 21. 45. 54. 50. 41. 26. 7. 3. 0.]\n", + "[ 2. 1. 5. 12. 24. 25. 41. 13. 12. 3. 5. 1.]\n", + "bkg med : [7.37835312e+00 5.06679718e+02 8.77668762e+02 3.49510852e+03\n", + " 7.41942035e+03 8.94315674e+03 8.55534180e+03 6.89880518e+03\n", + " 4.25108545e+03 1.18594727e+03 5.21437988e+02 1.84448242e+01]\n", + "bkg high : [ 319.34057617 250.05517578 977.27656555 2255.44500732 4455.56018066\n", + " 4845.80957031 7120.15380859 2800.61328125 2214.84228516 676.36865234\n", + " 803.88183594 191.02587891]\n", + "mgp8_pp_jjaa_5f\n", + "[947. 943. 948. 952. 903. 895. 904. 907. 923. 898. 893. 863.]\n", + "[333. 393. 311. 364. 341. 355. 358. 336. 343. 327. 333. 381.]\n", + "bkg med : [30696.09710312 31065.77346802 31613.38751221 34375.60852051\n", + " 36710.48284912 37974.71923828 37878.84179688 36319.61767578\n", + " 34190.89794922 30314.82226562 29488.12548828 28012.00732422]\n", + "bkg high : [11110.62182617 12985.71142578 11055.62031555 14051.32000732\n", + " 15506.09143066 16350.02832031 18721.59130859 13689.11328125\n", + " 13330.20166016 11273.21240234 11595.16308594 12537.80712891]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 9. 33. 97. 195. 305. 300. 190. 103. 37. 13. 5.]\n", + "[ 1. 9. 22. 56. 100. 117. 119. 111. 56. 27. 8. 3.]\n", + "bkg med : [30699.46295834 31096.06617165 31724.46070862 34702.09594727\n", + " 37366.8225708 39001.30603027 38888.63183594 36959.15136719\n", + " 34537.58789062 30439.35400391 29531.87988281 28028.8359375 ]\n", + "bkg high : [11113.98768187 13016.0041275 11129.6690979 14239.80795288\n", + " 15842.67593384 16743.83215332 19122.12683105 14062.7220459\n", + " 13518.68896484 11364.09143066 11622.09082031 12547.9050293 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 5. 12. 15. 35. 55. 63. 43. 18. 7. 1. 1.]\n", + "[ 0. 3. 3. 11. 14. 33. 22. 15. 16. 10. 0. 1.]\n", + "bkg med : [30773.46332455 31219.40014076 32020.46208191 35072.0980835\n", + " 38230.15911865 40357.97729492 40442.64233398 38019.83203125\n", + " 34981.59375 30612.02294922 29556.546875 28053.50292969]\n", + "bkg high : [11113.98768187 13090.00449371 11203.66947937 14511.14260864\n", + " 16188.01113892 17557.83551025 19664.79541016 14432.7232666\n", + " 13913.35693359 11610.75891113 11622.09082031 12572.57177734]\n", + "add a+jj\n", + "bkg med : [33536.7797308 33971.04467201 34786.69645691 37850.0043335\n", + " 40865.0848999 42969.55932617 43080.48608398 40666.4296875\n", + " 37674.87890625 33230.16748047 32158.8046875 30568.33886719]\n", + "bkg high : [12085.3460803 14236.01230621 12110.55033875 15572.57229614\n", + " 17182.37246704 18593.02105713 20708.72900391 15412.5045166\n", + " 14913.55029297 12564.29602051 12593.12402344 13683.57373047]\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 3. 22. 65. 146. 281. 379. 392. 303. 142. 59. 23. 9.]\n", + "[ 4. 20. 30. 67. 124. 137. 146. 124. 61. 38. 10. 7.]\n", + "bkg med : [ 11.0675354 81.16192627 239.79658508 538.61682129 1036.66577148\n", + " 1398.21020508 1446.12402344 1117.75634766 523.83300781 217.64892578\n", + " 84.84619141 33.20068359]\n", + "bkg high : [ 14.75670624 73.78356934 110.675354 247.17494202 457.45477295\n", + " 505.41992188 538.62451172 457.46191406 225.04174805 140.18994141\n", + " 36.89208984 25.82446289]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 3. 9. 27. 63. 72. 76. 50. 33. 8. 5. 1.]\n", + "[ 1. 1. 1. 6. 6. 7. 15. 4. 5. 2. 3. 0.]\n", + "bkg med : [ 161.51487732 532.50396729 1593.82295227 4600.6932373\n", + " 10514.84350586 12230.41333008 12880.18261719 8640.21728516\n", + " 5488.65722656 1421.24267578 837.09228516 183.64990234]\n", + "bkg high : [ 165.20404816 224.23091125 261.12271118 1149.85902405 1360.13885498\n", + " 1558.55078125 2795.33374023 1059.25097656 977.27807617 441.08447266\n", + " 488.23388672 25.82446289]\n", + "mgp8_pp_jjaa_5f\n", + "[1180. 1227. 1164. 1212. 1143. 1146. 1160. 1151. 1172. 1125. 1126. 1140.]\n", + "[100. 109. 95. 104. 101. 104. 102. 92. 94. 100. 100. 104.]\n", + "bkg med : [38437.76487732 40333.31646729 39351.07295227 43914.9432373\n", + " 47590.90600586 49403.78833008 50507.68261719 45975.77978516\n", + " 43505.40722656 37913.43017578 37361.71728516 37162.39990234]\n", + "bkg high : [3406.21967316 3756.9379425 3340.08755493 4520.51527405 4633.72088623\n", + " 4929.61328125 6101.56811523 4041.34472656 4024.19995117 3682.49072266\n", + " 3729.64013672 3396.88696289]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 14. 45. 130. 257. 373. 375. 260. 132. 53. 17. 6.]\n", + "[ 1. 4. 10. 23. 38. 49. 44. 41. 27. 11. 4. 2.]\n", + "bkg med : [38441.13073254 40380.43845367 39502.53648376 44352.5032959\n", + " 48455.92810059 50659.27453613 51769.92016602 46850.87231445\n", + " 43949.68261719 38091.81347656 37418.93457031 37182.59423828]\n", + "bkg high : [3409.58552885 3770.40136528 3373.74611664 4597.92989349 4761.62355804\n", + " 5094.53968811 6249.66531372 4179.34436035 4115.07775879 3719.51501465\n", + " 3743.10351562 3403.61865234]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 7. 14. 23. 45. 80. 75. 55. 28. 13. 1. 2.]\n", + "[ 0. 1. 1. 3. 4. 8. 10. 3. 6. 4. 0. 0.]\n", + "bkg med : [38515.13109875 40553.10601044 39847.87150574 44919.83953857\n", + " 49565.93188477 52632.61584473 53619.94458008 48207.55688477\n", + " 44640.35839844 38412.48388672 37443.6015625 37231.92822266]\n", + "bkg high : [3409.58552885 3795.06815529 3398.41290665 4671.93026733 4860.29073334\n", + " 5291.87394714 6496.33328247 4253.3447876 4263.07861328 3818.18225098\n", + " 3743.10351562 3403.61865234]\n", + "add a+jj\n", + "bkg med : [41958.33422375 44133.45366669 43244.38713074 48456.41766357\n", + " 52897.33032227 55972.13146973 57000.25708008 51561.64282227\n", + " 48055.63964844 41690.80419922 40724.8359375 40553.95947266]\n", + "bkg high : [3701.2847476 4113.02030373 3675.52716446 4975.29745483 5154.90694427\n", + " 5595.24113464 6793.8664856 4521.70806885 4537.27587891 4109.88146973\n", + " 4034.80273438 3706.98583984]\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 6. 35. 87. 198. 382. 493. 506. 402. 185. 90. 29. 14.]\n", + "[ 1. 7. 8. 15. 23. 23. 32. 25. 18. 7. 4. 2.]\n", + "bkg med : [ 22.1350708 129.12124634 320.95843506 730.45507812 1409.27783203\n", + " 1818.75463867 1866.61621094 1482.96386719 682.50048828 332.05078125\n", + " 106.99414062 51.65234375]\n", + "bkg high : [ 3.68917656 25.82423782 29.51342773 55.337677 84.85110474\n", + " 84.85108948 118.05371094 92.22946167 66.40478516 25.82403564\n", + " 14.7565918 7.3782959 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 4. 10. 32. 68. 79. 89. 54. 37. 9. 8. 1.]\n", + "[0. 0. 0. 1. 1. 0. 2. 0. 1. 1. 0. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 323.02975464 730.91055298 1825.43182373 5544.76757812\n", + " 11639.69189453 13704.08862305 15256.55371094 9607.22167969\n", + " 6249.12158203 1686.09375 1310.58789062 202.1015625 ]\n", + "bkg high : [ 3.68917656 25.82423782 29.51342773 205.78501892 235.29844666\n", + " 84.85108948 418.94839478 92.22946167 216.85211182 176.2713623\n", + " 14.7565918 7.3782959 ]\n", + 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18.44589233 99.6078186 225.03987122 450.07720947 844.82556152\n", + " 1084.62744141 951.81591797 844.79882812 409.47509766 225.02685547\n", + " 51.64550781 40.57861328]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 1. 4. 14. 25. 39. 29. 29. 20. 4. 1. 0.]\n", + "[ 2. 1. 5. 12. 24. 25. 41. 13. 12. 3. 5. 1.]\n", + "bkg med : [3.68917656e+00 1.98406654e+02 6.90329651e+02 2.34605928e+03\n", + " 4.22232639e+03 6.46140369e+03 5.10450171e+03 4.90897363e+03\n", + " 3.22291943e+03 7.01397705e+02 1.98406982e+02 1.47568359e+01]\n", + "bkg high : [ 319.34057617 250.05517578 977.27656555 2255.44500732 4455.56018066\n", + " 4845.80957031 7120.15380859 2800.61328125 2214.84228516 676.36865234\n", + " 803.88183594 191.02587891]\n", + "mgp8_pp_jjaa_5f\n", + "[594. 578. 576. 569. 551. 533. 539. 562. 586. 545. 551. 533.]\n", + "[333. 393. 311. 364. 341. 355. 358. 336. 343. 327. 333. 381.]\n", + "bkg med : [19253.00167656 18929.21915436 19356.32965088 20785.2155304\n", + " 22078.1701355 23733.93493652 22571.47045898 23121.28613281\n", + " 22212.98193359 18362.80395508 18054.25073242 17287.28808594]\n", + "bkg high : [11110.62182617 12985.71142578 11055.62031555 14051.32000732\n", + " 15506.09143066 16350.02832031 18721.59130859 13689.11328125\n", + " 13330.20166016 11273.21240234 11595.16308594 12537.80712891]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 8. 17. 69. 123. 187. 197. 115. 67. 20. 6. 4.]\n", + "[ 1. 9. 22. 56. 100. 117. 119. 111. 56. 27. 8. 3.]\n", + "bkg med : [19256.36753225 18956.14599991 19413.54917526 21017.45948792\n", + " 22492.16906738 24363.34790039 23234.54187012 23508.3717041\n", + " 22438.50170898 18430.12329102 18074.4465332 17300.75195312]\n", + "bkg high : [11113.98768187 13016.0041275 11129.6690979 14239.80795288\n", + " 15842.67593384 16743.83215332 19122.12683105 14062.7220459\n", + " 13518.68896484 11364.09143066 11622.09082031 12547.9050293 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 3. 8. 10. 20. 33. 40. 26. 14. 5. 1. 1.]\n", + "[ 0. 3. 3. 11. 14. 33. 22. 15. 16. 10. 0. 1.]\n", + "bkg med : [19330.36789846 19030.14638138 19610.88348007 21264.12736511\n", + " 22985.50512695 25177.35058594 24221.21191406 24149.70715332\n", + " 22783.83618164 18553.45703125 18099.11328125 17325.41870117]\n", + "bkg high : [11113.98768187 13090.00449371 11203.66947937 14511.14260864\n", + " 16188.01113892 17557.83551025 19664.79541016 14432.7232666\n", + " 13913.35693359 11610.75891113 11622.09082031 12572.57177734]\n", + "add a+jj\n", + "bkg med : [21062.48117971 20715.60341263 21290.50848007 22923.34025574\n", + " 24592.49536133 26732.62792969 25793.99707031 25789.60559082\n", + " 24493.76586914 20143.75 19706.9140625 18880.69604492]\n", + "bkg high : [12085.3460803 14236.01230621 12110.55033875 15572.57229614\n", + " 17182.37246704 18593.02105713 20708.72900391 15412.5045166\n", + " 14913.55029297 12564.29602051 12593.12402344 13683.57373047]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 2. 20. 55. 120. 230. 318. 313. 253. 108. 50. 17. 8.]\n", + "[ 4. 20. 30. 67. 124. 137. 146. 124. 61. 38. 10. 7.]\n", + "bkg med : [ 7.37835503 73.78356934 202.90480042 442.69943237 848.51361084\n", + " 1173.16845703 1154.72241211 933.3190918 398.40820312 184.44824219\n", + " 62.71240234 29.51171875]\n", + "bkg high : [ 14.75670624 73.78356934 110.675354 247.17494202 457.45477295\n", + " 505.41992188 538.62451172 457.46191406 225.04174805 140.18994141\n", + " 36.89208984 25.82446289]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 1. 8. 20. 43. 57. 55. 38. 27. 5. 3. 1.]\n", + "[ 1. 1. 1. 6. 6. 7. 15. 4. 5. 2. 3. 0.]\n", + "bkg med : [ 157.82569695 224.23091125 1406.48365784 3451.64523315 7317.746521\n", + " 9748.66259766 9429.32202148 6650.3269043 4460.53710938 936.69433594\n", + " 514.06005859 179.9609375 ]\n", + "bkg high : [ 165.20404816 224.23091125 261.12271118 1149.85902405 1360.13885498\n", + " 1558.55078125 2795.33374023 1059.25097656 977.27807617 441.08447266\n", + " 488.23388672 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3373.74611664 4597.92989349 4761.62355804\n", + " 5094.53968811 6249.66531372 4179.34436035 4115.07775879 3719.51501465\n", + " 3743.10351562 3403.61865234]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 5. 10. 18. 30. 58. 52. 38. 24. 11. 1. 2.]\n", + "[ 0. 1. 1. 3. 4. 8. 10. 3. 6. 4. 0. 0.]\n", + "bkg med : [27035.16066885 28325.50850677 27416.51126099 31103.74560547\n", + " 34318.27435303 37468.62451172 37415.34790039 34355.00146484\n", + " 32460.99023438 26370.95605469 26003.38671875 26520.49975586]\n", + "bkg high : [3409.58552885 3795.06815529 3398.41290665 4671.93026733 4860.29073334\n", + " 5291.87394714 6496.33328247 4253.3447876 4263.07861328 3818.18225098\n", + " 3743.10351562 3403.61865234]\n", + "add a+jj\n", + "bkg med : [29448.3208251 30840.79756927 29727.54251099 33522.74169922\n", + " 36626.38763428 39756.31201172 39735.13305664 36706.88427734\n", + " 34897.49414062 28623.62792969 28291.07421875 28884.05444336]\n", + "bkg high : [3701.2847476 4113.02030373 3675.52716446 4975.29745483 5154.90694427\n", + " 5595.24113464 6793.8664856 4521.70806885 4537.27587891 4109.88146973\n", + " 4034.80273438 3706.98583984]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 5. 33. 77. 172. 331. 432. 427. 352. 151. 81. 23. 13.]\n", + "[ 1. 7. 8. 15. 23. 23. 32. 25. 18. 7. 4. 2.]\n", + "bkg med : [ 18.44589233 121.7428894 284.06671143 634.53509521 1221.12817383\n", + " 1593.73828125 1575.20019531 1298.515625 557.03369141 298.80615234\n", + " 84.84619141 47.95654297]\n", + "bkg high : [ 3.68917656 25.82423782 29.51342773 55.337677 84.85110474\n", + " 84.85108948 118.05371094 92.22946167 66.40478516 25.82403564\n", + " 14.7565918 7.3782959 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 2. 9. 25. 48. 64. 68. 42. 31. 6. 6. 1.]\n", + "[0. 0. 0. 1. 1. 0. 2. 0. 1. 1. 0. 0.]\n", + "bkg med : [ 319.34057617 422.63757324 1638.09307861 4395.71697998\n", + " 8442.59741211 11222.36328125 11805.61621094 7617.3828125\n", + " 5220.95947266 1201.50146484 987.54150391 198.40576172]\n", + "bkg high : [ 3.68917656 25.82423782 29.51342773 205.78501892 235.29844666\n", + " 84.85108948 418.94839478 92.22946167 216.85211182 176.2713623\n", + " 14.7565918 7.3782959 ]\n", + "mgp8_pp_jjaa_5f\n", + "[920. 963. 878. 926. 882. 884. 893. 893. 920. 864. 875. 903.]\n", + "[ 7. 8. 9. 7. 10. 4. 4. 5. 9. 8. 9. 11.]\n", + "add a+jj\n", + "bkg med : [ 3003.87182617 3232.64147949 4200.06964111 7097.75604248\n", + " 11016.24584961 13801.84765625 14411.36230469 10223.12890625\n", + " 7905.49072266 3721.78271484 3537.34619141 2829.80419922]\n", + "bkg high : [ 24.10790825 49.15993118 55.76608276 226.20375061 264.46806335\n", + " 96.51893616 430.61624146 106.81427002 243.10488892 199.6072998\n", + " 41.00952148 39.46520996]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_tth01j_5f_haa\n", + "[ 1. 6. 15. 34. 74. 70. 117. 86. 30. 15. 4. 2.]\n", + "[ 5. 27. 61. 122. 229. 294. 258. 229. 111. 61. 14. 11.]\n", + "bkg med : [ 3.68917656 22.13506317 55.337677 125.43206787 272.99920654\n", + " 258.24053955 431.63293457 317.27197266 110.67626953 55.33813477\n", + " 14.75683594 7.37841797]\n", + "bkg high : [ 18.44589233 99.6078186 225.03987122 450.07720947 844.82556152\n", + " 1084.62744141 951.81591797 844.79882812 409.47509766 225.02685547\n", + " 51.64550781 40.57861328]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 0. 0. 3. 9. 13. 20. 10. 14. 10. 0. 1. 0.]\n", + "[ 2. 1. 5. 12. 24. 25. 41. 13. 12. 3. 5. 1.]\n", + "bkg med : [ 3.68917656 22.13506317 506.67971802 1479.45831299 2228.81378174\n", + " 3267.18609619 1936.1060791 2423.53369141 1615.14892578 55.33813477\n", + " 165.20410156 7.37841797]\n", + "bkg high : [ 319.34057617 250.05517578 977.27656555 2255.44500732 4455.56018066\n", + " 4845.80957031 7120.15380859 2800.61328125 2214.84228516 676.36865234\n", + " 803.88183594 191.02587891]\n", + 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14239.80795288\n", + " 15842.67593384 16743.83215332 19122.12683105 14062.7220459\n", + " 13518.68896484 11364.09143066 11622.09082031 12547.9050293 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 1. 2. 5. 10. 15. 20. 12. 6. 3. 0. 1.]\n", + "[ 0. 3. 3. 11. 14. 33. 22. 15. 16. 10. 0. 1.]\n", + "bkg med : [ 8943.44057226 10015.7157402 9631.01560974 10875.98902893\n", + " 11026.03388977 12609.41220093 10947.97802734 11920.32531738\n", + " 11200.49060059 8390.81848145 8924.98913574 8525.84851074]\n", + "bkg high : [11113.98768187 13090.00449371 11203.66947937 14511.14260864\n", + " 16188.01113892 17557.83551025 19664.79541016 14432.7232666\n", + " 13913.35693359 11610.75891113 11622.09082031 12572.57177734]\n", + "add a+jj\n", + "bkg med : [ 9745.61342382 10911.23234177 10444.85643005 11698.58082581\n", + " 11775.70088196 13388.04501343 11685.72998047 12730.97766113\n", + " 12040.30310059 9131.4864502 9712.31335449 9289.84460449]\n", + "bkg high : [12085.3460803 14236.01230621 12110.55033875 15572.57229614\n", + " 17182.37246704 18593.02105713 20708.72900391 15412.5045166\n", + " 14913.55029297 12564.29602051 12593.12402344 13683.57373047]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 2. 13. 46. 89. 179. 227. 229. 191. 80. 38. 8. 6.]\n", + "[ 4. 20. 30. 67. 124. 137. 146. 124. 61. 38. 10. 7.]\n", + "bkg med : [ 7.37835503 47.95932007 169.70220947 328.33630371 660.36108398\n", + " 837.45043945 844.82885742 704.63891602 295.13671875 140.18994141\n", + " 29.51367188 22.1340332 ]\n", + "bkg high : [ 14.75670624 73.78356934 110.675354 247.17494202 457.45477295\n", + " 505.41992188 538.62451172 457.46191406 225.04174805 140.18994141\n", + " 36.89208984 25.82446289]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 1. 0. 7. 15. 31. 38. 36. 23. 17. 1. 3. 1.]\n", + "[ 1. 1. 1. 6. 6. 7. 15. 4. 5. 2. 3. 0.]\n", + "bkg med : [ 157.82569695 47.95932007 1222.83355713 2585.04577637 5324.22631836\n", + " 6554.44702148 6260.93041992 4164.92602539 2852.74023438 290.63720703\n", + " 480.85546875 172.58129883]\n", + "bkg high : [ 165.20404816 224.23091125 261.12271118 1149.85902405 1360.13885498\n", + " 1558.55078125 2795.33374023 1059.25097656 977.27807617 441.08447266\n", + " 488.23388672 25.82446289]\n", + "mgp8_pp_jjaa_5f\n", + "[508. 591. 495. 542. 497. 518. 509. 522. 537. 481. 503. 539.]\n", + "[100. 109. 95. 104. 101. 104. 102. 92. 94. 100. 100. 104.]\n", + "bkg med : [16620.21632195 19200.05307007 17263.92730713 20149.23327637\n", + " 21430.13256836 23340.88452148 22755.71166992 21080.98852539\n", + " 20254.89648438 15878.04345703 16781.19921875 17639.55004883]\n", + "bkg high : [3406.21967316 3756.9379425 3340.08755493 4520.51527405 4633.72088623\n", + " 4929.61328125 6101.56811523 4041.34472656 4024.19995117 3682.49072266\n", + " 3729.64013672 3396.88696289]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 1. 11. 22. 73. 128. 163. 170. 127. 60. 25. 7. 2.]\n", + "[ 1. 4. 10. 23. 38. 49. 44. 41. 27. 11. 4. 2.]\n", + "bkg med : [16623.58217764 19237.07748413 17337.97608185 20394.94055176\n", + " 21860.96072388 23889.51721191 23327.90527344 21508.46154785\n", + " 20456.85449219 15962.19262695 16804.76098633 17646.28198242]\n", + "bkg high : [3409.58552885 3770.40136528 3373.74611664 4597.92989349 4761.62355804\n", + " 5094.53968811 6249.66531372 4179.34436035 4115.07775879 3719.51501465\n", + " 3743.10351562 3403.61865234]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 1. 3. 4. 13. 20. 40. 32. 24. 16. 9. 0. 2.]\n", + "[ 0. 1. 1. 3. 4. 8. 10. 3. 6. 4. 0. 0.]\n", + "bkg med : [16648.24896765 19311.07785034 17436.64325714 20715.60870361\n", + " 22354.29690552 24876.18719482 24117.24133301 22100.46350098\n", + " 20851.52246094 16184.19335938 16804.76098633 17695.61547852]\n", + "bkg high : [3409.58552885 3795.06815529 3398.41290665 4671.93026733 4860.29073334\n", + " 5291.87394714 6496.33328247 4253.3447876 4263.07861328 3818.18225098\n", + " 3743.10351562 3403.61865234]\n", + "add a+jj\n", + "bkg med : [18129.58490515 21034.44308472 18880.07099152 22296.08917236\n", + " 23803.55667114 26386.68328857 25602.07727051 23623.64318848\n", + " 22418.47167969 17587.73632812 18272.49926758 19268.40063477]\n", + "bkg high : [3701.2847476 4113.02030373 3675.52716446 4975.29745483 5154.90694427\n", + " 5595.24113464 6793.8664856 4521.70806885 4537.27587891 4109.88146973\n", + " 4034.80273438 3706.98583984]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 5. 26. 68. 141. 280. 341. 343. 290. 123. 69. 14. 11.]\n", + "[ 1. 7. 8. 15. 23. 23. 32. 25. 18. 7. 4. 2.]\n", + "bkg med : [ 18.44589233 95.91864014 250.86412048 520.17077637 1032.97680664\n", + " 1258.02026367 1265.37329102 1069.79980469 453.74267578 254.53857422\n", + " 51.64550781 40.57861328]\n", + "bkg high : [ 3.68917656 25.82423782 29.51342773 55.337677 84.85110474\n", + " 84.85108948 118.05371094 92.22946167 66.40478516 25.82403564\n", + " 14.7565918 7.3782959 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 1. 8. 20. 36. 45. 49. 27. 21. 2. 6. 1.]\n", + "[0. 0. 0. 1. 1. 0. 2. 0. 1. 1. 0. 0.]\n", + "bkg med : [ 319.34057617 246.36599731 1454.44297791 3529.11657715 6449.07885742\n", + " 8028.1472168 8637.28930664 5131.87597656 3613.13525391 555.43310547\n", + " 954.32910156 191.02783203]\n", + "bkg high : [ 3.68917656 25.82423782 29.51342773 205.78501892 235.29844666\n", + " 84.85108948 418.94839478 92.22946167 216.85211182 176.2713623\n", + " 14.7565918 7.3782959 ]\n", + "mgp8_pp_jjaa_5f\n", + "[601. 692. 581. 639. 588. 618. 607. 609. 622. 573. 594. 632.]\n", + "[ 7. 8. 9. 7. 10. 4. 4. 5. 9. 8. 9. 11.]\n", + "add a+jj\n", + "bkg med : [ 2071.8659668 2264.24880981 3148.64805603 5393.60290527\n", + " 8164.84448242 9831.4519043 10408.49633789 6908.91894531\n", + " 5428.11181641 2227.42919922 2687.60253906 2035.18408203]\n", + "bkg high : [ 24.10790825 49.15993118 55.76608276 226.20375061 264.46806335\n", + " 96.51893616 430.61624146 106.81427002 243.10488892 199.6072998\n", + " 41.00952148 39.46520996]\n", + "ttH cut: 0.30000000000000004\n", + "x_1: 0.5\n", + "x_2: 0.8\n", + "Significance: 8.007883520771895\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [122, 122.5, 123, 123.5, 124,124.5, 125, 125.5, 126, 126.5, 127, 127.5, 128]\n", + "small_ttHc, small_x1, small_x2, small_max_sig = findx1x2(df_small,ttHcut,x_1,x_2,varT= \"prediction_ttH\",var = \"prediction_small\")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "e6b0084a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0.4, 0.7000000000000001), (0.4, 0.8), (0.4, 0.9), (0.5, 0.7000000000000001), (0.5, 0.8), (0.5, 0.9), (0.6000000000000001, 0.7000000000000001), (0.6000000000000001, 0.8), (0.6000000000000001, 0.9)]\n", + "ttH cut: 0.2\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 149. 281. 468. 866. 1105. 1405. 1406. 1156. 790. 472. 293. 172.]\n", + "[ 180. 357. 492. 801. 990. 1266. 1206. 1019. 762. 487. 344. 181.]\n", + "bkg med : [ 549.69116211 1036.66772461 1726.5378418 3194.64355469 4076.75\n", + " 5183.68164062 5187.37109375 4265.0078125 2914.66796875 1741.421875\n", + " 1081.00976562 634.5859375 ]\n", + "bkg high : [ 664.05761719 1317.04760742 1815.02050781 2954.86083984 3652.54785156\n", + " 4670.84765625 4449.48046875 3759.55273438 2811.36328125 1796.76367188\n", + " 1269.171875 667.79101562]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 17. 30. 38. 71. 110. 125. 128. 99. 64. 32. 14. 13.]\n", + "[10. 24. 28. 61. 69. 86. 68. 76. 42. 30. 17. 13.]\n", + "bkg med : [ 3107.29492188 5550.08618164 7443.53393555 13876.39941406\n", + " 20626.0859375 23989.83398438 24444.46484375 19159.09375\n", + " 12543.16796875 6555.671875 3187.24414062 2590.375 ]\n", + "bkg high : [ 2168.53051758 4927.7824707 6027.54394531 12132.14404297\n", + " 14033.40917969 17609.47851562 14680.02734375 15193.65429688\n", + " 9130.06640625 6310.12304688 3826.7421875 2623.58007812]\n", + "mgp8_pp_jjaa_5f\n", + "[575. 643. 619. 609. 627. 632. 558. 637. 633. 559. 592. 587.]\n", + "[276. 282. 297. 277. 262. 273. 266. 314. 299. 255. 264. 318.]\n", + "bkg med : [21740.88867188 26387.30493164 27503.00268555 33611.80566406\n", + " 40944.8046875 44470.58398438 42527.15234375 39801.875\n", + " 33056.32421875 24670.765625 22371.74414062 21612.84375 ]\n", + "bkg high : [11112.65551758 14066.3449707 15652.20019531 21108.67529297\n", + " 22523.84667969 26456.38476562 23300.08984375 25369.21679688\n", + " 18819.53515625 14573.71679688 12381.9921875 12928.76757812]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 27. 48. 70. 122. 195. 237. 240. 213. 139. 75. 49. 24.]\n", + "[ 49. 80. 126. 218. 280. 320. 342. 267. 210. 124. 71. 52.]\n", + "bkg med : [21831.76677704 26548.86587524 27738.61184692 34022.4387207\n", + " 41601.14440918 45268.30322266 43334.984375 40518.82592773\n", + " 33524.16943359 24923.19482422 22536.66455078 21693.62109375]\n", + "bkg high : [11277.58216858 14335.61257935 16076.29663086 21842.42944336\n", + " 23466.30822754 27533.49414062 24451.18725586 26267.86474609\n", + " 19526.33691406 14991.06640625 12620.95849609 13103.78515625]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 28. 43. 68. 95. 113. 126. 95. 79. 42. 23. 14.]\n", + "[ 8. 12. 16. 34. 40. 53. 54. 42. 26. 26. 13. 9.]\n", + "bkg med : [21930.43390656 27239.5362854 28799.28213501 35699.77758789\n", + " 43944.50524902 48055.67285156 46443.02539062 42862.19018555\n", + " 35472.86181641 25959.17041016 23103.98291016 22038.9453125 ]\n", + "bkg high : [11474.91639709 14631.61425781 16470.96520996 22681.09887695\n", + " 24452.97827148 28840.83374023 25783.20483398 27303.87841797\n", + " 20167.67871094 15632.40820312 12941.62890625 13325.78808594]\n", + "add a+jj\n", + "bkg med : [23607.14289093 29114.53433228 30604.29580688 37475.63110352\n", + " 45773.6361084 49899.82910156 48071.25195312 44720.9362793\n", + " 37319.93603516 27590.31494141 24831.42041016 23751.79296875]\n", + "bkg high : [12280.00624084 15454.20605469 17337.31188965 23489.10571289\n", + " 25217.23022461 29637.01049805 26558.86499023 28219.50732422\n", + " 21039.56738281 16375.9921875 13711.45703125 14253.08105469]\n", + "sig med : [ 59.98265076 125.73073578 242.01513672 426.92654419 669.59619141\n", + " 866.68334961 865.01806641 688.91308594 432.68847656 243.71826172\n", + " 127.91308594 63.77294922]\n", + "sig high : [ 322.28613281 582.25097656 942.83178711 1436.78466797 1959.29492188\n", + " 2294.36328125 2289.39257812 1951.36914062 1427.36914062 940.82128906\n", + " 575.07421875 325.0078125 ]\n", + "sono dentro\n", + "59.98265075683594\n", + "23607.142890930176\n", + "sono dentro\n", + "125.7307357788086\n", + "29114.53433227539\n", + "sono dentro\n", + "242.01513671875\n", + "30604.295806884766\n", + "sono dentro\n", + "426.9265441894531\n", + "37475.631103515625\n", + "sono dentro\n", + "669.59619140625\n", + "45773.63610839844\n", + "sono dentro\n", + "866.683349609375\n", + "49899.8291015625\n", + "sono dentro\n", + "865.01806640625\n", + "48071.251953125\n", + "sono dentro\n", + "688.9130859375\n", + "44720.936279296875\n", + "sono dentro\n", + "432.6884765625\n", + "37319.93603515625\n", + "sono dentro\n", + "243.71826171875\n", + "27590.31494140625\n", + "sono dentro\n", + "127.9130859375\n", + "24831.42041015625\n", + "sono dentro\n", + "63.77294921875\n", + "23751.79296875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 23.80833340934158\n", + "sig_high 99.21724137184164\n", + "Naive\n", + "sig_med 7.4022679982825075\n", + "sig_high 30.488116716136403\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 230. 479. 735. 1327. 1689. 2197. 2173. 1796. 1208. 759. 474. 245.]\n", + "[ 99. 159. 225. 340. 406. 474. 439. 379. 344. 200. 163. 108.]\n", + "bkg med : [ 848.51806641 1767.13110352 2711.39038086 4895.71826172 6231.48632812\n", + " 8105.72851562 8016.5078125 6622.75 4454.5 2798.8125\n", + " 1747.875 903.4375 ]\n", + "bkg high : [ 365.22906494 586.58422852 830.07202148 1254.33105469 1497.73266602\n", + " 1748.56933594 1619.53125 1398.30273438 1269.171875 737.890625\n", + " 601.38085938 398.4609375 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 20. 42. 53. 106. 153. 171. 168. 140. 86. 45. 21. 16.]\n", + "[ 7. 12. 13. 26. 26. 40. 28. 35. 20. 17. 10. 10.]\n", + "bkg med : [ 3857.46337891 8085.91674805 10685.09545898 20843.19873047\n", + " 29250.21679688 33831.99414062 33291.3203125 27685.1953125\n", + " 17393.46875 9569.203125 4907.390625 3310.6875 ]\n", + "bkg high : [1418.35992432 2391.95166016 2785.88647461 5165.96044922 5409.36157227\n", + " 7766.45996094 5832.0546875 6663.95703125 4278.12304688 3295.52734375\n", + " 2105.87304688 1902.953125 ]\n", + "mgp8_pp_jjaa_5f\n", + "[757. 822. 804. 779. 809. 814. 738. 865. 824. 721. 783. 803.]\n", + "[ 94. 103. 112. 107. 80. 91. 86. 86. 108. 93. 73. 102.]\n", + "bkg med : [28388.99462891 34723.85424805 36739.72045898 46087.66748047\n", + " 55466.87304688 60210.68164062 57226.9765625 55743.6328125\n", + " 44121.96875 32956.640625 30305.953125 29358. ]\n", + "bkg high : [ 4464.91461182 5730.19775391 6415.82397461 8633.84716797\n", + " 8002.17407227 10715.78417969 8619.5 9451.56640625\n", + " 7778.84179688 6310.03515625 4472.09960938 5209.1875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 46. 85. 123. 223. 337. 397. 418. 363. 253. 133. 83. 42.]\n", + "[ 30. 43. 73. 117. 138. 160. 164. 117. 96. 66. 37. 34.]\n", + "bkg med : [28543.82397461 35009.95108032 37153.71936035 46838.25085449\n", + " 56601.19335938 61546.96069336 58633.84863281 56965.39013672\n", + " 44973.49658203 33404.28173828 30585.34326172 29499.38085938]\n", + "bkg high : [ 4565.89041138 5874.92919922 6661.53067017 9027.65100098\n", + " 8466.66064453 11254.31933594 9171.51757812 9845.38452148\n", + " 8101.97460938 6532.18896484 4596.64038086 5323.63037109]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 10. 34. 54. 92. 114. 151. 163. 122. 97. 57. 29. 16.]\n", + "[ 2. 6. 5. 10. 21. 15. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [28790.49188232 35848.62155151 38485.72387695 49107.60095215\n", + " 59413.23046875 65271.67602539 62654.56835938 59974.67431641\n", + " 47366.10009766 34810.24462891 31300.65771484 29894.03710938]\n", + "bkg high : [ 4615.22399902 6022.92990112 6784.86456299 9274.31906128\n", + " 8984.66308594 11624.32055664 9590.85229492 10215.38586426\n", + " 8299.30859375 6803.52319336 4769.30761719 5496.29760742]\n", + "add a+jj\n", + "bkg med : [30999.39422607 38247.19186401 40831.77075195 51380.6986084\n", + " 61773.8671875 67646.90258789 64808.02929688 62498.71728516\n", + " 49770.50634766 36914.10009766 33585.42724609 32237.16601562]\n", + "bkg high : [ 4889.42102051 6323.38009644 7111.56768799 9586.43722534\n", + " 9218.02246094 11889.7668457 9841.71362305 10466.24719238\n", + " 8614.34375 7074.8034668 4982.24804688 5793.83081055]\n", + "sig med : [ 139.78817749 290.34353638 538.84094238 927.52941895 1403.26330566\n", + " 1765.63574219 1764.35302734 1420.7578125 938.70507812 547.75\n", + " 295.1171875 147.01660156]\n", + "sig high : [ 242.46871948 417.65081787 646.00775146 936.18127441 1225.62670898\n", + " 1395.40722656 1390.10400391 1219.29003906 921.10693359 636.63867188\n", + " 407.77636719 241.71386719]\n", + "sono dentro\n", + "139.78817749023438\n", + "30999.39422607422\n", + "sono dentro\n", + "290.3435363769531\n", + "38247.19186401367\n", + "sono dentro\n", + "538.8409423828125\n", + "40831.770751953125\n", + "sono dentro\n", + "927.5294189453125\n", + "51380.69860839844\n", + "sono dentro\n", + "1403.2633056640625\n", + "61773.8671875\n", + "sono dentro\n", + "1765.6357421875\n", + "67646.90258789062\n", + "sono dentro\n", + "1764.35302734375\n", + "64808.029296875\n", + "sono dentro\n", + "1420.7578125\n", + "62498.71728515625\n", + "sono dentro\n", + "938.705078125\n", + "49770.50634765625\n", + "sono dentro\n", + "547.75\n", + "36914.10009765625\n", + "sono dentro\n", + "295.1171875\n", + "33585.42724609375\n", + "sono dentro\n", + "147.0166015625\n", + "32237.166015625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 43.42663873388795\n", + "sig_high 102.11865323573053\n", + "Naive\n", + "sig_med 13.47435080298666\n", + "sig_high 31.27591264566505\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_tth01j_5f_haa\n", + "[ 311. 607. 922. 1622. 2043. 2608. 2565. 2144. 1509. 933. 616. 339.]\n", + "[18. 31. 38. 45. 52. 63. 47. 31. 43. 26. 21. 14.]\n", + "bkg med : [1147.34399414 2239.26074219 3401.31152344 5984.29296875 7537.55273438\n", + " 9622.09375 9458.84960938 7906. 5564.4375 3440.4375\n", + " 2271.5 1250.0625 ]\n", + "bkg high : [ 66.4052124 114.36453247 140.18814087 166.01165771 191.83569336\n", + " 232.41894531 173.39282227 114.36547852 158.63598633 95.91943359\n", + " 77.47338867 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 27. 51. 63. 128. 173. 207. 194. 173. 104. 61. 29. 25.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 5209.4206543 9912.07128906 12879.48925781 25241.72460938\n", + " 33565.08398438 40764.2734375 38645.33398438 33934.390625\n", + " 21211.5625 12618.078125 6634.640625 5011.390625 ]\n", + "bkg high : [ 66.4052124 565.70651245 591.53030396 767.80108643 1094.51928711\n", + " 834.20800781 474.28735352 415.26025391 459.53051758 246.36669922\n", + " 378.36791992 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[845. 920. 912. 875. 885. 899. 822. 950. 923. 808. 853. 901.]\n", + "[ 6. 5. 4. 11. 4. 6. 2. 1. 9. 6. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 7675.10424805 12596.60253906 15540.67675781 27794.94726562\n", + " 36147.48632812 43387.52734375 41043.90429688 36706.4609375\n", + " 23904.84765625 14975.796875 9123.66796875 7637.62109375]\n", + "bkg high : [ 83.90707397 580.29135132 603.19815063 799.88766479 1106.18713379\n", + " 851.70977783 480.12127686 418.17721558 485.78317261 263.86846924\n", + " 387.11880493 213.76403809]\n", + "sig med : [ 312.26617432 599.76367188 1042.42810059 1686.67871094 2426.78955078\n", + " 2946.57666016 2938.51171875 2444.45996094 1691.24609375 1046.76367188\n", + " 599.7578125 318.19335938]\n", + "sig high : [ 69.99417114 108.22885132 142.42175293 177.03173828 202.10095215\n", + " 214.46630859 215.44335938 196.76208496 168.42102051 137.51477051\n", + " 103.12194824 70.51513672]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 113. 219. 370. 673. 845. 1075. 1092. 900. 608. 371. 232. 136.]\n", + "[ 180. 357. 492. 801. 990. 1266. 1206. 1019. 762. 487. 344. 181.]\n", + "bkg med : [ 416.87481689 807.93676758 1365.00732422 2482.72851562 3117.26367188\n", + " 3966.16210938 4028.8828125 3320.5078125 2243.1875 1368.78710938\n", + " 855.953125 501.765625 ]\n", + "bkg high : [ 664.05761719 1317.04760742 1815.02050781 2954.86083984 3652.54785156\n", + " 4670.84765625 4449.48046875 3759.55273438 2811.36328125 1796.76367188\n", + " 1269.171875 667.79101562]\n", + "mgp8_pp_h012j_5f_haa\n", + "[11. 18. 27. 50. 70. 85. 86. 70. 44. 25. 11. 10.]\n", + "[10. 24. 28. 61. 69. 86. 68. 76. 42. 30. 17. 13.]\n", + "bkg med : [ 2071.79498291 3515.98754883 5427.08398438 10005.09179688\n", + " 13648.57226562 16754.30078125 16967.515625 13851.953125\n", + " 8862.78515625 5129.91992188 2510.8515625 2006.21875 ]\n", + "bkg high : [ 2168.53051758 4927.7824707 6027.54394531 12132.14404297\n", + " 14033.40917969 17609.47851562 14680.02734375 15193.65429688\n", + " 9130.06640625 6310.12304688 3826.7421875 2623.58007812]\n", + "mgp8_pp_jjaa_5f\n", + "[364. 411. 379. 387. 387. 411. 367. 409. 387. 377. 391. 373.]\n", + "[276. 282. 297. 277. 262. 273. 266. 314. 299. 255. 264. 318.]\n", + "bkg med : [13867.66998291 16834.95629883 17709.05273438 22546.31054688\n", + " 26189.79101562 30073.26953125 28860.625 27106.109375\n", + " 21404.00390625 17347.07617188 15181.6953125 14093.75 ]\n", + "bkg high : [11112.65551758 14066.3449707 15652.20019531 21108.67529297\n", + " 22523.84667969 26456.38476562 23300.08984375 25369.21679688\n", + " 18819.53515625 14573.71679688 12381.9921875 12928.76757812]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 20. 39. 60. 94. 150. 179. 191. 150. 116. 56. 39. 22.]\n", + "[ 49. 80. 126. 218. 280. 320. 342. 267. 210. 124. 71. 52.]\n", + "bkg med : [13934.98704529 16966.224823 17911.00344849 22862.69998169\n", + " 26694.66772461 30675.7557373 29503.51855469 27611.00439453\n", + " 21794.45605469 17535.5703125 15312.96801758 14167.80126953]\n", + "bkg high : [11277.58216858 14335.61257935 16076.29663086 21842.42944336\n", + " 23466.30822754 27533.49414062 24451.18725586 26267.86474609\n", + " 19526.33691406 14991.06640625 12620.95849609 13103.78515625]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 20. 29. 44. 59. 67. 78. 56. 54. 28. 14. 12.]\n", + "[ 8. 12. 16. 34. 40. 53. 54. 42. 26. 26. 13. 9.]\n", + "bkg med : [14008.98742676 17459.56069946 18626.33987427 23948.03701782\n", + " 28150.00585938 32328.44299316 31427.54394531 28992.35546875\n", + " 23126.47363281 18226.24609375 15658.3059082 14463.80517578]\n", + "bkg high : [11474.91639709 14631.61425781 16470.96520996 22681.09887695\n", + " 24452.97827148 28840.83374023 25783.20483398 27303.87841797\n", + " 20167.67871094 15632.40820312 12941.62890625 13325.78808594]\n", + "add a+jj\n", + "bkg med : [15070.46398926 18658.04312134 19731.50979614 25076.5350647\n", + " 29278.50390625 33526.92541504 32497.72167969 30185.00585938\n", + " 24254.97167969 19325.58398438 16798.46801758 15551.47900391]\n", + "bkg high : [12280.00624084 15454.20605469 17337.31188965 23489.10571289\n", + " 25217.23022461 29637.01049805 26558.86499023 28219.50732422\n", + " 21039.56738281 16375.9921875 13711.45703125 14253.08105469]\n", + "sig med : [ 49.53231812 102.97764587 197.31277466 339.45257568 528.22460938\n", + " 676.42736816 675.75524902 543.39477539 343.31689453 196.97509766\n", + " 105.36230469 51.33325195]\n", + "sig high : [ 322.28613281 582.25097656 942.83178711 1436.78466797 1959.29492188\n", + " 2294.36328125 2289.39257812 1951.36914062 1427.36914062 940.82128906\n", + " 575.07421875 325.0078125 ]\n", + "sono dentro\n", + "49.532318115234375\n", + "15070.463989257812\n", + "sono dentro\n", + "102.97764587402344\n", + "18658.04312133789\n", + "sono dentro\n", + "197.31277465820312\n", + "19731.509796142578\n", + "sono dentro\n", + "339.45257568359375\n", + "25076.535064697266\n", + "sono dentro\n", + "528.224609375\n", + "29278.50390625\n", + "sono dentro\n", + "676.4273681640625\n", + "33526.92541503906\n", + "sono dentro\n", + "675.7552490234375\n", + "32497.7216796875\n", + "sono dentro\n", + "543.394775390625\n", + "30185.005859375\n", + "sono dentro\n", + "343.31689453125\n", + "24254.9716796875\n", + "sono dentro\n", + "196.97509765625\n", + "19325.583984375\n", + "sono dentro\n", + "105.3623046875\n", + "16798.468017578125\n", + "sono dentro\n", + "51.333251953125\n", + "15551.47900390625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 23.14863423453008\n", + "sig_high 99.21724137184164\n", + "Naive\n", + "sig_med 7.200921743039146\n", + "sig_high 30.488116716136403\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 194. 417. 637. 1134. 1429. 1867. 1859. 1540. 1026. 658. 413. 209.]\n", + "[ 99. 159. 225. 340. 406. 474. 439. 379. 344. 200. 163. 108.]\n", + "bkg med : [ 715.70654297 1538.40014648 2349.91503906 4183.51953125 5272.22851562\n", + " 6888.20898438 6858.69335938 5680.75585938 3783.375 2426.375\n", + " 1522.9375 770.6875 ]\n", + "bkg high : [ 365.22906494 586.58422852 830.07202148 1254.33105469 1497.73266602\n", + " 1748.56933594 1619.53125 1398.30273438 1269.171875 737.890625\n", + " 601.38085938 398.4609375 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 14. 30. 42. 85. 113. 131. 126. 111. 66. 38. 18. 13.]\n", + "[ 7. 12. 13. 26. 26. 40. 28. 35. 20. 17. 10. 10.]\n", + "bkg med : [ 2821.96826172 6051.81860352 8668.70019531 16971.53710938\n", + " 22272.95703125 26597.02539062 25814.80273438 22380.18554688\n", + " 13712.765625 8143.296875 4230.953125 2726.4765625 ]\n", + "bkg high : [1418.35992432 2391.95166016 2785.88647461 5165.96044922 5409.36157227\n", + " 7766.45996094 5832.0546875 6663.95703125 4278.12304688 3295.52734375\n", + " 2105.87304688 1902.953125 ]\n", + "mgp8_pp_jjaa_5f\n", + "[546. 590. 564. 557. 569. 593. 547. 637. 578. 539. 582. 589.]\n", + "[ 94. 103. 112. 107. 80. 91. 86. 86. 108. 93. 73. 102.]\n", + "bkg med : [20515.78076172 25171.50610352 26945.82519531 35021.81835938\n", + " 40712.11328125 45813.93164062 43541.02148438 43022.96679688\n", + " 32443.578125 25610.265625 23091.390625 21813.7578125 ]\n", + "bkg high : [ 4464.91461182 5730.19775391 6415.82397461 8633.84716797\n", + " 8002.17407227 10715.78417969 8619.5 9451.56640625\n", + " 7778.84179688 6310.03515625 4472.09960938 5209.1875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 39. 76. 113. 195. 292. 339. 369. 300. 230. 114. 73. 40.]\n", + "[ 30. 43. 73. 117. 138. 160. 164. 117. 96. 66. 37. 34.]\n", + "bkg med : [20647.04928589 25427.31031799 27326.16567993 35678.15808105\n", + " 41694.95751953 46954.99438477 44783.00366211 44032.68359375\n", + " 33217.69433594 25993.95800781 23337.08837891 21948.38671875]\n", + "bkg high : [ 4565.89041138 5874.92919922 6661.53067017 9027.65100098\n", + " 8466.66064453 11254.31933594 9171.51757812 9845.38452148\n", + " 8101.97460938 6532.18896484 4596.64038086 5323.63037109]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 9. 26. 40. 68. 78. 105. 115. 83. 72. 43. 20. 14.]\n", + "[ 2. 6. 5. 10. 21. 15. 17. 15. 8. 11. 7. 7.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [20869.05029297 26068.64704895 28312.83572388 37355.49694824\n", + " 43618.97998047 49545.02807617 47619.70776367 46080.04394531\n", + " 34993.71777344 27054.63867188 23830.42138672 22293.7109375 ]\n", + "bkg high : [ 4615.22399902 6022.92990112 6784.86456299 9274.31906128\n", + " 8984.66308594 11624.32055664 9590.85229492 10215.38586426\n", + " 8299.30859375 6803.52319336 4769.30761719 5496.29760742]\n", + "add a+jj\n", + "bkg med : [22461.19482422 27789.0962677 29957.46853638 38979.71765137\n", + " 45278.19287109 51275.15112305 49215.83666992 47938.79003906\n", + " 36680.30371094 28627.42382812 25528.67919922 24012.39453125]\n", + "bkg high : [ 4889.42102051 6323.38009644 7111.56768799 9586.43722534\n", + " 9218.02246094 11889.7668457 9841.71362305 10466.24719238\n", + " 8614.34375 7074.8034668 4982.24804688 5793.83081055]\n", + "sig med : [ 129.33813477 267.59033203 494.13275146 840.05535889 1261.89208984\n", + " 1575.37988281 1575.04638672 1275.53710938 849.57617188 501.14746094\n", + " 272.64453125 134.61523438]\n", + "sig high : [ 242.46871948 417.65081787 646.00775146 936.18127441 1225.62670898\n", + " 1395.40722656 1390.10400391 1219.29003906 921.10693359 636.63867188\n", + " 407.77636719 241.71386719]\n", + "sono dentro\n", + "129.338134765625\n", + "22461.19482421875\n", + "sono dentro\n", + "267.59033203125\n", + "27789.096267700195\n", + "sono dentro\n", + "494.13275146484375\n", + "29957.468536376953\n", + "sono dentro\n", + "840.0553588867188\n", + "38979.71765136719\n", + "sono dentro\n", + "1261.89208984375\n", + "45278.19287109375\n", + "sono dentro\n", + "1575.3798828125\n", + "51275.151123046875\n", + "sono dentro\n", + "1575.04638671875\n", + "49215.836669921875\n", + "sono dentro\n", + "1275.537109375\n", + "47938.7900390625\n", + "sono dentro\n", + "849.576171875\n", + "36680.3037109375\n", + "sono dentro\n", + "501.1474609375\n", + "28627.423828125\n", + "sono dentro\n", + "272.64453125\n", + "25528.67919921875\n", + "sono dentro\n", + "134.615234375\n", + "24012.39453125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 45.18286126221119\n", + "sig_high 102.11865323573053\n", + "Naive\n", + "sig_med 14.031578601375411\n", + "sig_high 31.27591264566505\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 275. 545. 824. 1429. 1783. 2278. 2251. 1888. 1327. 832. 555. 303.]\n", + "[18. 31. 38. 45. 52. 63. 47. 31. 43. 26. 21. 14.]\n", + "bkg med : [1014.5324707 2010.57299805 3039.70703125 5272.18017578 6578.29492188\n", + " 8404.57421875 8303.04101562 6962. 4893.3125 3068.\n", + " 2046.5625 1117.3125 ]\n", + "bkg high : [ 66.4052124 114.36453247 140.18814087 166.01165771 191.83569336\n", + " 232.41894531 173.39282227 114.36547852 158.63598633 95.91943359\n", + " 77.47338867 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 21. 39. 52. 107. 133. 167. 152. 144. 84. 54. 26. 22.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 4173.92553711 7878.01635742 10862.96484375 21370.11572266\n", + " 26588.04101562 33529.12109375 31170.72851562 28626.125\n", + " 17531.2265625 11192.46875 5958.34375 4427.28125 ]\n", + "bkg high : [ 66.4052124 565.70651245 591.53030396 767.80108643 1094.51928711\n", + " 834.20800781 474.28735352 415.26025391 459.53051758 246.36669922\n", + " 378.36791992 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[634. 688. 672. 653. 645. 678. 631. 722. 677. 626. 652. 687.]\n", + "[ 6. 5. 4. 11. 4. 6. 2. 1. 9. 6. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 6022.67944336 9884.23510742 12823.23828125 23275.54931641\n", + " 28470.13085938 35507.50390625 33011.96679688 30732.8984375\n", + " 19506.69140625 13019.1171875 7860.859375 6431.92578125]\n", + "bkg high : [ 83.90707397 580.29135132 603.19815063 799.88766479 1106.18713379\n", + " 851.70977783 480.12127686 418.17721558 485.78317261 263.86846924\n", + " 387.11880493 213.76403809]\n", + "sig med : [ 301.81588745 577.01184082 997.71875 1599.20507812 2285.41748047\n", + " 2756.32128906 2749.41113281 2298.81152344 1601.87304688 1000.04785156\n", + " 577.26953125 305.7890625 ]\n", + "sig high : [ 69.99417114 108.22885132 142.42175293 177.03173828 202.10095215\n", + " 214.46630859 215.44335938 196.76208496 168.42102051 137.51477051\n", + " 103.12194824 70.51513672]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 65. 136. 223. 401. 507. 611. 629. 520. 357. 222. 143. 70.]\n", + "[ 180. 357. 492. 801. 990. 1266. 1206. 1019. 762. 487. 344. 181.]\n", + "bkg med : [ 239.79562378 501.72595215 822.69360352 1479.37280273 1870.34887695\n", + " 2253.95751953 2320.58203125 1918.515625 1317.13476562 819.05859375\n", + " 527.59179688 258.26171875]\n", + "bkg high : [ 664.05761719 1317.04760742 1815.02050781 2954.86083984 3652.54785156\n", + " 4670.84765625 4449.48046875 3759.55273438 2811.36328125 1796.76367188\n", + " 1269.171875 667.79101562]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 7. 8. 17. 30. 39. 33. 44. 36. 20. 12. 7. 5.]\n", + "[10. 24. 28. 61. 69. 86. 68. 76. 42. 30. 17. 13.]\n", + "bkg med : [1292.92660522 1705.30407715 3380.29736328 5992.79125977 7737.79223633\n", + " 7218.71728516 8940.26171875 7334.63476562 4326.11914062 2624.44921875\n", + " 1580.73632812 1010.5078125 ]\n", + "bkg high : [ 2168.53051758 4927.7824707 6027.54394531 12132.14404297\n", + " 14033.40917969 17609.47851562 14680.02734375 15193.65429688\n", + " 9130.06640625 6310.12304688 3826.7421875 2623.58007812]\n", + "mgp8_pp_jjaa_5f\n", + "[188. 233. 190. 193. 204. 206. 168. 198. 175. 191. 226. 199.]\n", + "[276. 282. 297. 277. 262. 273. 266. 314. 299. 255. 264. 318.]\n", + "bkg med : [ 7386.77035522 9257.78063965 9538.96923828 12248.70532227\n", + " 14350.26098633 13895.99853516 14384.51171875 13751.07226562\n", + " 9997.21289062 8814.04296875 8904.54882812 7459.3515625 ]\n", + "bkg high : [11112.65551758 14066.3449707 15652.20019531 21108.67529297\n", + " 22523.84667969 26456.38476562 23300.08984375 25369.21679688\n", + " 18819.53515625 14573.71679688 12381.9921875 12928.76757812]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 9. 24. 34. 53. 89. 106. 124. 83. 71. 32. 22. 14.]\n", + "[ 49. 80. 126. 218. 280. 320. 342. 267. 210. 124. 71. 52.]\n", + "bkg med : [ 7417.06305695 9338.56113434 9653.40846252 12427.09509277\n", + " 14649.82119751 14252.77807617 14801.87646484 14030.43737793\n", + " 10236.18786621 8921.75341797 8978.60009766 7506.47509766]\n", + "bkg high : [11277.58216858 14335.61257935 16076.29663086 21842.42944336\n", + " 23466.30822754 27533.49414062 24451.18725586 26267.86474609\n", + " 19526.33691406 14991.06640625 12620.95849609 13103.78515625]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 2. 13. 13. 21. 29. 34. 31. 28. 30. 9. 9. 6.]\n", + "[ 8. 12. 16. 34. 40. 53. 54. 42. 26. 26. 13. 9.]\n", + "bkg med : [ 7466.39664459 9659.22934723 9974.07688904 12945.09747314\n", + " 15365.15689087 15091.44763184 15566.5456543 14721.10803223\n", + " 10976.19763184 9143.75634766 9200.60302734 7654.47705078]\n", + "bkg high : [11474.91639709 14631.61425781 16470.96520996 22681.09887695\n", + " 24452.97827148 28840.83374023 25783.20483398 27303.87841797\n", + " 20167.67871094 15632.40820312 12941.62890625 13325.78808594]\n", + "add a+jj\n", + "bkg med : [ 8014.79117584 10338.88852692 10528.30540466 13508.07696533\n", + " 15960.22329712 15692.34802246 16056.6003418 15298.67248535\n", + " 11486.67126465 9700.90185547 9859.84326172 8234.95849609]\n", + "bkg high : [12280.00624084 15454.20605469 17337.31188965 23489.10571289\n", + " 25217.23022461 29637.01049805 26558.86499023 28219.50732422\n", + " 21039.56738281 16375.9921875 13711.45703125 14253.08105469]\n", + "sig med : [ 33.59233475 68.59494019 128.98936462 218.38482666 336.15966797\n", + " 426.25268555 428.37475586 351.21337891 224.11962891 128.23022461\n", + " 70.41040039 34.28588867]\n", + "sig high : [ 322.28613281 582.25097656 942.83178711 1436.78466797 1959.29492188\n", + " 2294.36328125 2289.39257812 1951.36914062 1427.36914062 940.82128906\n", + " 575.07421875 325.0078125 ]\n", + "sono dentro\n", + "33.59233474731445\n", + "8014.791175842285\n", + "sono dentro\n", + "68.59494018554688\n", + "10338.888526916504\n", + "sono dentro\n", + "128.98936462402344\n", + "10528.305404663086\n", + "sono dentro\n", + "218.38482666015625\n", + "13508.076965332031\n", + "sono dentro\n", + "336.15966796875\n", + "15960.22329711914\n", + "sono dentro\n", + "426.252685546875\n", + "15692.348022460938\n", + "sono dentro\n", + "428.374755859375\n", + "16056.600341796875\n", + "sono dentro\n", + "351.21337890625\n", + "15298.672485351562\n", + "sono dentro\n", + "224.11962890625\n", + "11486.671264648438\n", + "sono dentro\n", + "128.230224609375\n", + "9700.90185546875\n", + "sono dentro\n", + "70.410400390625\n", + "9859.84326171875\n", + "sono dentro\n", + "34.285888671875\n", + "8234.95849609375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 20.880229484725778\n", + "sig_high 99.21724137184164\n", + "Naive\n", + "sig_med 6.43746097590238\n", + "sig_high 30.488116716136403\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 146. 334. 490. 862. 1091. 1403. 1396. 1160. 775. 509. 324. 143.]\n", + "[ 99. 159. 225. 340. 406. 474. 439. 379. 344. 200. 163. 108.]\n", + "bkg med : [ 538.62231445 1232.19580078 1807.68774414 3179.88769531 4025.12109375\n", + " 5176.30273438 5150.4765625 4279.765625 2859.32617188 1877.93164062\n", + " 1195.3828125 527.59179688]\n", + "bkg high : [ 365.22906494 586.58422852 830.07202148 1254.33105469 1497.73266602\n", + " 1748.56933594 1619.53125 1398.30273438 1269.171875 737.890625\n", + " 601.38085938 398.4609375 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_h012j_5f_haa\n", + "[10. 20. 32. 65. 82. 79. 84. 77. 42. 25. 14. 8.]\n", + "[ 7. 12. 13. 26. 26. 40. 28. 35. 20. 17. 10. 10.]\n", + "bkg med : [ 2043.09521484 4241.14111328 6622.00073242 12958.95996094\n", + " 16361.82617188 17061.79101562 17788.2109375 15864.21484375\n", + " 9178.02929688 5639.06445312 3301.6171875 1731.15429688]\n", + "bkg high : [1418.35992432 2391.95166016 2785.88647461 5165.96044922 5409.36157227\n", + " 7766.45996094 5832.0546875 6663.95703125 4278.12304688 3295.52734375\n", + " 2105.87304688 1902.953125 ]\n", + "mgp8_pp_jjaa_5f\n", + "[370. 412. 375. 363. 386. 388. 348. 426. 366. 353. 417. 415.]\n", + "[ 94. 103. 112. 107. 80. 91. 86. 86. 108. 93. 73. 102.]\n", + "bkg med : [14033.40771484 17592.51611328 18774.34448242 24722.42871094\n", + " 28870.63867188 29635.41601562 29065.6015625 29669.27734375\n", + " 21038.71679688 17078.47070312 16815.0234375 15179.74804688]\n", + "bkg high : [ 4464.91461182 5730.19775391 6415.82397461 8633.84716797\n", + " 8002.17407227 10715.78417969 8619.5 9451.56640625\n", + " 7778.84179688 6310.03515625 4472.09960938 5209.1875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 28. 61. 87. 154. 231. 266. 302. 233. 185. 90. 56. 32.]\n", + "[ 30. 43. 73. 117. 138. 160. 164. 117. 96. 66. 37. 34.]\n", + "bkg med : [14127.65174103 17797.832901 19067.17300415 25240.76879883\n", + " 29648.14880371 30530.76135254 30082.12353516 30453.50415039\n", + " 21661.37548828 17381.38574219 17003.50390625 15287.45117188]\n", + "bkg high : [ 4565.89041138 5874.92919922 6661.53067017 9027.65100098\n", + " 8466.66064453 11254.31933594 9171.51757812 9845.38452148\n", + " 8101.97460938 6532.18896484 4596.64038086 5323.63037109]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 19. 24. 45. 48. 72. 68. 55. 48. 24. 15. 8.]\n", + "[ 2. 6. 5. 10. 21. 15. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [14324.98601532 18266.50212097 19659.17538452 26350.77258301\n", + " 30832.15270996 32306.78112793 31759.47900391 31810.18823242\n", + " 22845.39111328 17973.39355469 17373.50878906 15484.78710938]\n", + "bkg high : [ 4615.22399902 6022.92990112 6784.86456299 9274.31906128\n", + " 8984.66308594 11624.32055664 9590.85229492 10215.38586426\n", + " 8299.30859375 6803.52319336 4769.30761719 5496.29760742]\n", + "add a+jj\n", + "bkg med : [15403.97429657 19467.90055847 20752.6812439 27409.28625488\n", + " 31957.73474121 33438.19519043 32774.25244141 33052.41088867\n", + " 23912.65283203 19002.74707031 18589.48730469 16694.93359375]\n", + "bkg high : [ 4889.42102051 6323.38009644 7111.56768799 9586.43722534\n", + " 9218.02246094 11889.7668457 9841.71362305 10466.24719238\n", + " 8614.34375 7074.8034668 4982.24804688 5793.83081055]\n", + "sig med : [ 113.39827728 233.20654297 425.81134033 718.98852539 1069.8293457\n", + " 1325.20654297 1327.734375 1083.46777344 730.40722656 432.41552734\n", + " 237.69726562 117.56884766]\n", + "sig high : [ 242.46871948 417.65081787 646.00775146 936.18127441 1225.62670898\n", + " 1395.40722656 1390.10400391 1219.29003906 921.10693359 636.63867188\n", + " 407.77636719 241.71386719]\n", + "sono dentro\n", + "113.39827728271484\n", + "15403.974296569824\n", + "sono dentro\n", + "233.20654296875\n", + "19467.90055847168\n", + "sono dentro\n", + "425.81134033203125\n", + "20752.681243896484\n", + "sono dentro\n", + "718.988525390625\n", + "27409.286254882812\n", + "sono dentro\n", + "1069.829345703125\n", + "31957.734741210938\n", + "sono dentro\n", + "1325.20654296875\n", + "33438.19519042969\n", + "sono dentro\n", + "1327.734375\n", + "32774.25244140625\n", + "sono dentro\n", + "1083.4677734375\n", + "33052.410888671875\n", + "sono dentro\n", + "730.4072265625\n", + "23912.65283203125\n", + "sono dentro\n", + "432.41552734375\n", + "19002.7470703125\n", + "sono dentro\n", + "237.697265625\n", + "18589.4873046875\n", + "sono dentro\n", + "117.56884765625\n", + "16694.93359375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 46.68802540438783\n", + "sig_high 102.11865323573053\n", + "Naive\n", + "sig_med 14.45237354435118\n", + "sig_high 31.27591264566505\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 227. 462. 677. 1157. 1445. 1814. 1788. 1508. 1076. 683. 466. 237.]\n", + "[18. 31. 38. 45. 52. 63. 47. 31. 43. 26. 21. 14.]\n", + "bkg med : [ 837.45043945 1704.41455078 2497.43383789 4268.47607422 5331.25976562\n", + " 6692.66796875 6596.7421875 5562.52539062 3967.75 2518.5625\n", + " 1718.375 873.9375 ]\n", + "bkg high : [ 66.4052124 114.36453247 140.18814087 166.01165771 191.83569336\n", + " 232.41894531 173.39282227 114.36547852 158.63598633 95.91943359\n", + " 77.47338867 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 17. 29. 42. 87. 102. 115. 110. 110. 60. 41. 22. 17.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 3395.05395508 6067.38574219 8816.21899414 17357.38818359\n", + " 20677.06054688 23994.328125 23145.7734375 22111.50976562\n", + " 12994.46875 8686.8203125 5028.171875 3431.5078125 ]\n", + "bkg high : [ 66.4052124 565.70651245 591.53030396 767.80108643 1094.51928711\n", + " 834.20800781 474.28735352 415.26025391 459.53051758 246.36669922\n", + " 378.36791992 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[458. 510. 483. 459. 462. 473. 432. 511. 465. 440. 487. 513.]\n", + "[ 6. 5. 4. 11. 4. 6. 2. 1. 9. 6. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 4730.58911133 7554.55371094 10224.65454102 18695.83935547\n", + " 22024.25976562 25373.60351562 24405.4921875 23601.59375\n", + " 14350.41601562 9970.50195312 6449.22265625 4928.42578125]\n", + "bkg high : [ 83.90707397 580.29135132 603.19815063 799.88766479 1106.18713379\n", + " 851.70977783 480.12127686 418.17721558 485.78317261 263.86846924\n", + " 387.11880493 213.76403809]\n", + "sig med : [ 285.8762207 542.62750244 929.39672852 1478.13696289 2093.35302734\n", + " 2506.14648438 2502.21484375 2106.578125 1482.91699219 931.41210938\n", + " 542.33203125 288.74609375]\n", + "sig high : [ 69.99417114 108.22885132 142.42175293 177.03173828 202.10095215\n", + " 214.46630859 215.44335938 196.76208496 168.42102051 137.51477051\n", + " 103.12194824 70.51513672]\n", + "ttH cut: 0.30000000000000004\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[105. 200. 304. 601. 748. 982. 962. 783. 549. 328. 198. 124.]\n", + "[ 164. 318. 442. 714. 870. 1096. 1032. 903. 679. 413. 302. 162.]\n", + "bkg med : [ 387.36053467 737.84136963 1121.51953125 2217.14990234 2759.34570312\n", + " 3622.99511719 3549.25390625 2888.84179688 2025.50976562 1210.140625\n", + " 730.51171875 457.4921875 ]\n", + "bkg high : [ 605.03027344 1173.16845703 1630.59667969 2633.92089844 3209.69775391\n", + " 4043.640625 3807.515625 3331.57617188 2505.13867188 1523.74414062\n", + " 1114.21484375 597.69140625]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 16. 26. 33. 68. 101. 116. 120. 92. 57. 29. 11. 11.]\n", + "[10. 23. 28. 60. 67. 82. 68. 76. 41. 29. 16. 13.]\n", + "bkg med : [ 2794.51702881 4649.47076416 6086.27929688 12447.56396484\n", + " 17954.60742188 21075.10449219 21602.91796875 16729.81054688\n", + " 10600.89257812 5573.0546875 2385.41015625 2112.390625 ]\n", + "bkg high : [ 2109.50317383 4633.45605469 5843.12011719 11660.75683594\n", + " 13289.66455078 16380.46484375 14038.0625 14765.71289062\n", + " 8673.39648438 5886.65820312 3521.33984375 2553.48046875]\n", + "mgp8_pp_jjaa_5f\n", + "[535. 595. 568. 564. 575. 565. 505. 592. 588. 522. 546. 548.]\n", + "[265. 276. 292. 273. 257. 270. 261. 304. 290. 250. 256. 312.]\n", + "bkg med : [20131.87640381 23931.18951416 24493.02929688 30724.68896484\n", + " 36588.20117188 39384.63574219 37968.07421875 35914.31054688\n", + " 29655.76757812 22489.1171875 20079.22265625 19871.015625 ]\n", + "bkg high : [10697.38598633 13577.58105469 15305.74511719 20507.66308594\n", + " 21618.07080078 25130.15234375 22496.09375 24617.21289062\n", + " 18071.20898438 13988.22070312 11817.33984375 12664.23046875]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 23. 43. 62. 108. 166. 211. 221. 182. 127. 69. 47. 24.]\n", + "[ 45. 80. 124. 217. 278. 318. 340. 262. 205. 124. 70. 52.]\n", + "bkg med : [20209.29103088 24075.92138672 24701.71166992 31088.20022583\n", + " 37146.93139648 40094.83178711 38711.95288086 36526.91650391\n", + " 30083.24536133 22721.36547852 20237.41162109 19951.79296875]\n", + "bkg high : [10848.8493042 13846.84866333 15723.10986328 21238.0513916\n", + " 22553.79919434 26200.52978516 23640.46313477 25499.03222656\n", + " 18761.18212891 14405.5703125 12052.94042969 12839.24804688]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 27. 41. 66. 83. 112. 114. 90. 78. 40. 23. 13.]\n", + "[ 8. 12. 16. 34. 40. 52. 54. 42. 26. 26. 12. 9.]\n", + "bkg med : [20307.95817566 24741.92498779 25713.04846191 32716.20571899\n", + " 39194.28686523 42857.53442383 41523.98999023 38746.94580078\n", + " 32007.27075195 23708.04516602 20804.72998047 20272.45117188]\n", + "bkg high : [11046.18353271 14142.8503418 16117.77844238 22076.7208252\n", + " 23540.46923828 27483.20239258 24972.48071289 26535.04589844\n", + " 19402.52392578 15046.91210938 12348.94384766 13061.25097656]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "add a+jj\n", + "bkg med : [21868.02653503 26476.95428467 27369.34533691 34360.83853149\n", + " 40870.99584961 44505.15356445 42997.56420898 40474.38330078\n", + " 33723.03637695 25231.22485352 22397.94091797 21871.49804688]\n", + "bkg high : [11819.1864624 14947.94018555 16969.54016113 22873.05969238\n", + " 24290.13623047 28270.76098633 25733.56079102 27421.51464844\n", + " 20248.16845703 15775.91601562 13095.44384766 13971.04785156]\n", + "sig med : [ 55.49106979 116.97606659 225.49694824 399.2081604 629.95025635\n", + " 812.85595703 814.49267578 647.23925781 405.40917969 228.14746094\n", + " 119.46923828 58.27636719]\n", + "sig high : [ 320.6602478 578.44519043 936.3927002 1427.40991211 1945.70239258\n", + " 2276.3515625 2271.52197266 1937.27441406 1417.43457031 933.71875\n", + " 571.14648438 322.93457031]\n", + "sono dentro\n", + "55.49106979370117\n", + "21868.02653503418\n", + "sono dentro\n", + "116.97606658935547\n", + "26476.95428466797\n", + "sono dentro\n", + "225.4969482421875\n", + "27369.345336914062\n", + "sono dentro\n", + "399.2081604003906\n", + "34360.83853149414\n", + "sono dentro\n", + "629.9502563476562\n", + "40870.995849609375\n", + "sono dentro\n", + "812.85595703125\n", + "44505.153564453125\n", + "sono dentro\n", + "814.49267578125\n", + "42997.564208984375\n", + "sono dentro\n", + "647.2392578125\n", + "40474.38330078125\n", + "sono dentro\n", + "405.4091796875\n", + "33723.036376953125\n", + "sono dentro\n", + "228.1474609375\n", + "25231.224853515625\n", + "sono dentro\n", + "119.46923828125\n", + "22397.94091796875\n", + "sono dentro\n", + "58.2763671875\n", + "21871.498046875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 23.506723219555237\n", + "sig_high 100.20088882351772\n", + "Naive\n", + "sig_med 7.300478799743629\n", + "sig_high 30.789531094376162\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 176. 372. 537. 1001. 1244. 1654. 1595. 1344. 911. 567. 354. 183.]\n", + "[ 93. 146. 209. 314. 374. 424. 399. 342. 317. 174. 146. 103.]\n", + "bkg med : [ 649.30059814 1372.38574219 1981.04443359 3692.72167969 4589.6796875\n", + " 6102.35546875 5884.67773438 4958.625 3360.6484375 2090.8125\n", + " 1305.375 674.8125 ]\n", + "bkg high : [ 343.09265137 538.62451172 771.04467773 1158.41162109 1379.70532227\n", + " 1564.12109375 1471.89697266 1261.77001953 1169.55664062 641.96484375\n", + " 538.66015625 380.01367188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 19. 37. 48. 102. 142. 158. 160. 133. 78. 42. 18. 14.]\n", + "[ 7. 12. 13. 26. 26. 40. 28. 35. 20. 16. 9. 10.]\n", + "bkg med : [ 3507.79864502 6938.93505859 9202.51318359 19038.37597656\n", + " 25953.46875 29872.94921875 29955.92773438 24967.8515625\n", + " 15095.640625 8409.84375 4013.53125 2781.15625 ]\n", + "bkg high : [1396.22351074 2343.99194336 2726.85913086 5070.04101562 5291.33422852\n", + " 7582.01171875 5684.42041016 6527.42431641 4178.5078125 3049.15234375\n", + " 1892.703125 1884.50585938]\n", + "mgp8_pp_jjaa_5f\n", + "[707. 768. 749. 731. 753. 745. 680. 810. 773. 680. 731. 759.]\n", + "[ 93. 103. 111. 106. 79. 90. 86. 86. 105. 92. 71. 101.]\n", + "bkg med : [26419.01739502 31826.93505859 33474.79443359 42727.34472656\n", + " 50355.375 54015.60546875 51992.17773438 51225.8515625\n", + " 40169.828125 30467.34375 27725.34375 27401.21875 ]\n", + "bkg high : [ 4410.36804199 5682.23803711 6324.38647461 8505.51757812\n", + " 7851.73657227 10498.92578125 8471.72900391 9315.03369141\n", + " 7581.984375 6031.24609375 4194.1015625 5158.32617188]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 40. 80. 113. 208. 307. 370. 397. 328. 236. 127. 80. 42.]\n", + "[ 28. 43. 73. 117. 137. 159. 164. 116. 96. 66. 37. 34.]\n", + "bkg med : [26553.65177917 32096.20266724 33855.1348877 43427.44042969\n", + " 51388.7109375 55261.01318359 53328.38452148 52329.80859375\n", + " 40964.13867188 30894.79052734 27994.6015625 27542.57910156]\n", + "bkg high : [ 4504.61211395 5826.9695282 6570.09317017 8899.32141113\n", + " 8312.8572998 11034.09509277 9023.74597168 9705.48583984\n", + " 7905.1171875 6253.39990234 4318.64233398 5272.76904297]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 10. 33. 52. 90. 102. 149. 151. 117. 96. 55. 28. 15.]\n", + "[ 2. 6. 5. 10. 21. 15. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [26800.31965637 32910.20645142 35137.8059082 45647.45556641\n", + " 53904.74414062 58936.39453125 57053.1003418 55215.79394531\n", + " 43332.07617188 32251.42138672 28685.25 27912.56933594]\n", + "bkg high : [ 4553.9457016 5974.9702301 6693.42706299 9145.98947144\n", + " 8830.85974121 11404.09631348 9443.08068848 10075.48718262\n", + " 8102.45117188 6524.73413086 4491.30957031 5445.4362793 ]\n", + "add a+jj\n", + "bkg med : [28861.94270325 35151.18887329 37323.36450195 47780.49072266\n", + " 56101.97460938 61110.28125 59037.3190918 57579.34863281\n", + " 45587.66601562 34235.64013672 30818.28515625 30127.30761719]\n", + "bkg high : [ 4825.2257309 6275.42042542 7017.2131958 9455.19064331\n", + " 9061.30212402 11666.62561035 9693.9420166 10326.34851074\n", + " 8408.73535156 6793.09741211 4698.41601562 5740.05249023]\n", + "sig med : [ 134.40301514 279.06491089 517.43914795 893.27954102 1353.45532227\n", + " 1698.33764648 1700.08935547 1369.34619141 904.45507812 527.19824219\n", + " 284.29589844 140.15332031]\n", + "sig high : [ 241.73675537 416.36712646 644.45288086 933.34033203 1222.19775391\n", + " 1390.86914062 1386.05957031 1215.32617188 918.20068359 634.546875\n", + " 406.24414062 241.01367188]\n", + "sono dentro\n", + "134.40301513671875\n", + "28861.94270324707\n", + "sono dentro\n", + "279.0649108886719\n", + "35151.188873291016\n", + "sono dentro\n", + "517.4391479492188\n", + "37323.364501953125\n", + "sono dentro\n", + "893.279541015625\n", + "47780.49072265625\n", + "sono dentro\n", + "1353.455322265625\n", + "56101.974609375\n", + "sono dentro\n", + "1698.337646484375\n", + "61110.28125\n", + "sono dentro\n", + "1700.08935546875\n", + "59037.319091796875\n", + "sono dentro\n", + "1369.34619140625\n", + "57579.3486328125\n", + "sono dentro\n", + "904.455078125\n", + "45587.666015625\n", + "sono dentro\n", + "527.1982421875\n", + "34235.64013671875\n", + "sono dentro\n", + "284.2958984375\n", + "30818.28515625\n", + "sono dentro\n", + "140.1533203125\n", + "30127.3076171875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 43.68931029548097\n", + "sig_high 102.79889141469707\n", + "Naive\n", + "sig_med 13.543967400169908\n", + "sig_high 31.482423955115\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 252. 488. 710. 1275. 1569. 2019. 1952. 1656. 1187. 718. 481. 272.]\n", + "[17. 30. 36. 40. 49. 59. 42. 30. 41. 23. 19. 14.]\n", + "bkg med : [ 929.68066406 1800.32128906 2619.16503906 4703.88232422 5788.75195312\n", + " 7449.00585938 7201.8125 6106.87890625 4377.0625 2647.625\n", + " 1773.6875 1003. ]\n", + "bkg high : [ 62.71601868 110.675354 132.81008911 147.56591797 180.76824951\n", + " 217.66101074 154.94677734 110.67626953 151.25756836 84.85180664\n", + " 70.0949707 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 26. 46. 58. 124. 162. 194. 186. 166. 96. 57. 25. 23.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 4841.31005859 8720.89550781 11345.10644531 23359.48779297\n", + " 30161.45898438 36635.39648438 35184.640625 31081.79296875\n", + " 18820.5625 11223.453125 5535.015625 4463.421875 ]\n", + "bkg high : [ 62.71601868 562.01733398 584.1522522 749.35534668 1083.45184326\n", + " 819.45007324 455.84130859 411.57104492 452.15209961 235.29907227\n", + " 370.98950195 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[794. 866. 856. 826. 828. 829. 764. 895. 869. 766. 799. 856.]\n", + "[ 6. 5. 4. 11. 4. 6. 2. 1. 9. 6. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 7158.17724609 11247.85644531 13842.88769531 25769.72998047\n", + " 32577.53710938 39054.39257812 37413.96875 33693.375\n", + " 21356.27734375 13458.6171875 7866.47265625 6961.203125 ]\n", + "bkg high : [ 80.21788025 576.60217285 595.82009888 781.44192505 1095.11968994\n", + " 836.95184326 461.67523193 414.48800659 478.40475464 252.80084229\n", + " 379.74038696 213.76403809]\n", + "sig med : [ 306.24484253 587.20129395 1019.62768555 1649.8425293 2373.61694336\n", + " 2875.09570312 2870.49902344 2389.296875 1654.46875 1024.14941406\n", + " 587.56445312 310.66601562]\n", + "sig high : [ 69.89842224 108.22880554 142.26217651 176.77639771 202.03704834\n", + " 214.11456299 215.15612793 196.5065918 168.03808594 137.48242188\n", + " 102.9621582 70.4831543 ]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 80. 165. 249. 481. 590. 757. 783. 634. 430. 269. 164. 105.]\n", + "[ 164. 318. 442. 714. 870. 1096. 1032. 903. 679. 413. 302. 162.]\n", + "bkg med : [ 295.13244629 608.71472168 918.61303711 1774.50952148 2176.49316406\n", + " 2792.65380859 2888.84179688 2339.11328125 1586.46484375 992.46289062\n", + " 605.0703125 387.39257812]\n", + "bkg high : [ 605.03027344 1173.16845703 1630.59667969 2633.92089844 3209.69775391\n", + " 4043.640625 3807.515625 3331.57617188 2505.13867188 1523.74414062\n", + " 1114.21484375 597.69140625]\n", + "mgp8_pp_h012j_5f_haa\n", + "[11. 17. 22. 49. 67. 79. 82. 67. 39. 23. 9. 8.]\n", + "[10. 23. 28. 60. 67. 82. 68. 76. 41. 29. 16. 13.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 1950.05236816 3166.31848145 4228.45336914 9146.42553711\n", + " 12256.45996094 14678.07177734 15225.67773438 12419.2109375\n", + " 7453.9375 4452.70507812 1959.078125 1590.95507812]\n", + "bkg high : [ 2109.50317383 4633.45605469 5843.12011719 11660.75683594\n", + " 13289.66455078 16380.46484375 14038.0625 14765.71289062\n", + " 8673.39648438 5886.65820312 3521.33984375 2553.48046875]\n", + "mgp8_pp_jjaa_5f\n", + "[342. 381. 356. 368. 357. 372. 332. 381. 361. 360. 367. 354.]\n", + "[265. 276. 292. 273. 257. 270. 261. 304. 290. 250. 256. 312.]\n", + "bkg med : [13032.98986816 15513.09973145 15765.07836914 21071.92553711\n", + " 23825.49121094 26733.19677734 25984.55273438 24765.9921875\n", + " 19152.609375 16118.95507812 13852.171875 13062.76757812]\n", + "bkg high : [10697.38598633 13577.58105469 15305.74511719 20507.66308594\n", + " 21618.07080078 25130.15234375 22496.09375 24617.21289062\n", + " 18071.20898438 13988.22070312 11817.33984375 12664.23046875]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 17. 35. 55. 87. 133. 163. 181. 129. 108. 52. 37. 22.]\n", + "[ 45. 80. 124. 217. 278. 318. 340. 262. 205. 124. 70. 52.]\n", + "bkg med : [13090.2093811 15630.90476227 15950.20001221 21364.75405884\n", + " 24273.14855957 27281.82946777 26593.77990723 25200.2019043\n", + " 19516.13378906 16293.98535156 13976.71264648 13136.81884766]\n", + "bkg high : [10848.8493042 13846.84866333 15723.10986328 21238.0513916\n", + " 22553.79919434 26200.52978516 23640.46313477 25499.03222656\n", + " 18761.18212891 14405.5703125 12052.94042969 12839.24804688]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 19. 28. 44. 53. 66. 70. 53. 53. 28. 14. 11.]\n", + "[ 8. 12. 16. 34. 40. 52. 54. 42. 26. 26. 12. 9.]\n", + "bkg med : [13164.20976257 16099.57379913 16640.86981201 22450.09109497\n", + " 25580.48620605 28909.84753418 28320.46936035 26507.55200195\n", + " 20823.484375 16984.66113281 14322.05053711 13408.15576172]\n", + "bkg high : [11046.18353271 14142.8503418 16117.77844238 22076.7208252\n", + " 23540.46923828 27483.20239258 24972.48071289 26535.04589844\n", + " 19402.52392578 15046.91210938 12348.94384766 13061.25097656]\n", + "add a+jj\n", + "bkg med : [14161.8210907 17210.68610382 17678.97137451 23523.18484497\n", + " 26621.50378418 29994.60534668 29288.58654785 27618.55395508\n", + " 21876.16601562 18034.42675781 15392.22827148 14440.42529297]\n", + "bkg high : [11819.1864624 14947.94018555 16969.54016113 22873.05969238\n", + " 24290.13623047 28270.76098633 25733.56079102 27421.51464844\n", + " 20248.16845703 15775.91601562 13095.44384766 13971.04785156]\n", + "sig med : [ 46.65217972 96.56112671 185.67651367 320.50701904 499.97613525\n", + " 637.72631836 640.12573242 513.4128418 324.32104492 185.83984375\n", + " 99.20068359 47.57006836]\n", + "sig high : [ 320.6602478 578.44519043 936.3927002 1427.40991211 1945.70239258\n", + " 2276.3515625 2271.52197266 1937.27441406 1417.43457031 933.71875\n", + " 571.14648438 322.93457031]\n", + "sono dentro\n", + "46.65217971801758\n", + "14161.821090698242\n", + "sono dentro\n", + "96.56112670898438\n", + "17210.6861038208\n", + "sono dentro\n", + "185.676513671875\n", + "17678.97137451172\n", + "sono dentro\n", + "320.50701904296875\n", + "23523.184844970703\n", + "sono dentro\n", + "499.97613525390625\n", + "26621.503784179688\n", + "sono dentro\n", + "637.726318359375\n", + "29994.605346679688\n", + "sono dentro\n", + "640.125732421875\n", + "29288.586547851562\n", + "sono dentro\n", + "513.412841796875\n", + "27618.553955078125\n", + "sono dentro\n", + "324.321044921875\n", + "21876.166015625\n", + "sono dentro\n", + "185.83984375\n", + "18034.4267578125\n", + "sono dentro\n", + "99.20068359375\n", + "15392.228271484375\n", + "sono dentro\n", + "47.570068359375\n", + "14440.42529296875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 22.900250462790527\n", + "sig_high 100.20088882351772\n", + "Naive\n", + "sig_med 7.112527954570674\n", + "sig_high 30.789531094376162\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 151. 337. 482. 881. 1086. 1429. 1416. 1195. 792. 508. 320. 164.]\n", + "[ 93. 146. 209. 314. 374. 424. 399. 342. 317. 174. 146. 103.]\n", + "bkg med : [ 557.06756592 1243.26342773 1778.17724609 3249.97802734 4006.67675781\n", + " 5272.22851562 5224.265625 4408.89648438 2922.046875 1874.2421875\n", + " 1180.625 604.859375 ]\n", + "bkg high : [ 343.09265137 538.62451172 771.04467773 1158.41162109 1379.70532227\n", + " 1564.12109375 1471.89697266 1261.77001953 1169.55664062 641.96484375\n", + " 538.66015625 380.01367188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 14. 28. 37. 83. 108. 121. 122. 108. 60. 36. 16. 11.]\n", + "[ 7. 12. 13. 26. 26. 40. 28. 35. 20. 16. 9. 10.]\n", + "bkg med : [ 2663.32952881 5455.78735352 7344.72607422 15737.10107422\n", + " 20255.13378906 23476.58398438 23578.671875 20656.99023438\n", + " 11948.765625 7290.2734375 3587.75 2259.7578125 ]\n", + "bkg high : [1396.22351074 2343.99194336 2726.85913086 5070.04101562 5291.33422852\n", + " 7582.01171875 5684.42041016 6527.42431641 4178.5078125 3049.15234375\n", + " 1892.703125 1884.50585938]\n", + "mgp8_pp_jjaa_5f\n", + "[514. 554. 537. 535. 535. 552. 507. 599. 546. 518. 552. 565.]\n", + "[ 93. 103. 111. 106. 79. 90. 86. 86. 105. 92. 71. 101.]\n", + "bkg med : [19320.15765381 23408.84985352 24746.88232422 33074.44482422\n", + " 37592.47753906 41364.83398438 40008.640625 40068.33398438\n", + " 29642.578125 24076.7109375 21476. 20569.2890625 ]\n", + "bkg high : [ 4410.36804199 5682.23803711 6324.38647461 8505.51757812\n", + " 7851.73657227 10498.92578125 8471.72900391 9315.03369141\n", + " 7581.984375 6031.24609375 4194.1015625 5158.32617188]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 34. 72. 106. 187. 274. 322. 357. 275. 217. 110. 70. 40.]\n", + "[ 28. 43. 73. 117. 137. 159. 164. 116. 96. 66. 37. 34.]\n", + "bkg med : [19434.5968399 23651.19084167 25103.66189575 33703.85778809\n", + " 38514.73083496 42448.67529297 41210.24975586 40993.90771484\n", + " 30372.93994141 24446.94042969 21711.60058594 20703.91796875]\n", + "bkg high : [ 4504.61211395 5826.9695282 6570.09317017 8899.32141113\n", + " 8312.8572998 11034.09509277 9023.74597168 9705.48583984\n", + " 7905.1171875 6253.39990234 4318.64233398 5272.76904297]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 9. 25. 39. 68. 72. 103. 107. 80. 71. 43. 19. 13.]\n", + "[ 2. 6. 5. 10. 21. 15. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [19656.59784698 24267.86088562 26065.66519165 35381.19665527\n", + " 40290.75061035 44989.37548828 43849.61743164 42967.26708984\n", + " 32124.29638672 25507.62109375 22180.2734375 21024.58886719]\n", + "bkg high : [ 4553.9457016 5974.9702301 6693.42706299 9145.98947144\n", + " 8830.85974121 11404.09631348 9443.08068848 10075.48718262\n", + " 8102.45117188 6524.73413086 4491.30957031 5445.4362793 ]\n", + "add a+jj\n", + "bkg med : [21155.42987823 25883.33354187 27631.56558228 36941.26501465\n", + " 41850.81896973 46599.01611328 45328.64282227 44715.13037109\n", + " 33717.50732422 27019.12890625 23790.9921875 22673.24121094]\n", + "bkg high : [ 4825.2257309 6275.42042542 7017.2131958 9455.19064331\n", + " 9061.30212402 11666.62561035 9693.9420166 10326.34851074\n", + " 8408.73535156 6793.09741211 4698.41601562 5740.05249023]\n", + "sig med : [ 125.56369019 258.65344238 477.61456299 814.57366943 1223.48168945\n", + " 1523.20849609 1525.58349609 1235.13818359 823.33740234 484.97021484\n", + " 264.07128906 129.46972656]\n", + "sig high : [ 241.73675537 416.36712646 644.45288086 933.34033203 1222.19775391\n", + " 1390.86914062 1386.05957031 1215.32617188 918.20068359 634.546875\n", + " 406.24414062 241.01367188]\n", + "sono dentro\n", + "125.56369018554688\n", + "21155.429878234863\n", + "sono dentro\n", + "258.6534423828125\n", + "25883.333541870117\n", + "sono dentro\n", + "477.61456298828125\n", + "27631.56558227539\n", + "sono dentro\n", + "814.5736694335938\n", + "36941.26501464844\n", + "sono dentro\n", + "1223.481689453125\n", + "41850.81896972656\n", + "sono dentro\n", + "1523.20849609375\n", + "46599.01611328125\n", + "sono dentro\n", + "1525.58349609375\n", + "45328.642822265625\n", + "sono dentro\n", + "1235.13818359375\n", + "44715.13037109375\n", + "sono dentro\n", + "823.33740234375\n", + "33717.50732421875\n", + "sono dentro\n", + "484.97021484375\n", + "27019.12890625\n", + "sono dentro\n", + "264.0712890625\n", + "23790.9921875\n", + "sono dentro\n", + "129.4697265625\n", + "22673.2412109375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 45.440955337075565\n", + "sig_high 102.79889141469707\n", + "Naive\n", + "sig_med 14.097021977875178\n", + "sig_high 31.482423955115\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 227. 453. 655. 1155. 1411. 1794. 1773. 1507. 1068. 659. 447. 253.]\n", + "[17. 30. 36. 40. 49. 59. 42. 30. 41. 23. 19. 14.]\n", + "bkg med : [ 837.45043945 1671.21166992 2416.28515625 4261.06933594 5205.81835938\n", + " 6618.87890625 6541.40039062 5559.08789062 3938.25 2430.0625\n", + " 1648.3125 932.9375 ]\n", + "bkg high : [ 62.71601868 110.675354 132.81008911 147.56591797 180.76824951\n", + " 217.66101074 154.94677734 110.67626953 151.25756836 84.85180664\n", + " 70.0949707 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 21. 37. 47. 105. 128. 157. 148. 141. 78. 51. 23. 20.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 3996.84350586 7237.76049805 9487.30664062 20058.08496094\n", + " 24463.31835938 30239.04296875 28807.30664062 26771.87695312\n", + " 15673.171875 10103.171875 5108.734375 3942. ]\n", + "bkg high : [ 62.71601868 562.01733398 584.1522522 749.35534668 1083.45184326\n", + " 819.45007324 455.84130859 411.57104492 452.15209961 235.29907227\n", + " 370.98950195 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[601. 652. 644. 630. 610. 636. 591. 684. 642. 604. 620. 662.]\n", + "[ 6. 5. 4. 11. 4. 6. 2. 1. 9. 6. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 5749.36889648 9139.00268555 11365.22070312 21895.93261719\n", + " 26243.27929688 32094.87109375 30531.82617188 28767.76757812\n", + " 17546.5078125 11865.625 6917.875 5873.6953125 ]\n", + "bkg high : [ 80.21788025 576.60217285 595.82009888 781.44192505 1095.11968994\n", + " 836.95184326 461.67523193 414.48800659 478.40475464 252.80084229\n", + " 379.74038696 213.76403809]\n", + "sig med : [ 297.40524292 566.79016113 979.80664062 1571.13696289 2243.64208984\n", + " 2699.96289062 2696.18847656 2254.97265625 1573.32617188 981.8046875\n", + " 567.2734375 299.94335938]\n", + "sig high : [ 69.89842224 108.22880554 142.26217651 176.77639771 202.03704834\n", + " 214.11456299 215.15612793 196.5065918 168.03808594 137.48242188\n", + " 102.9621582 70.4831543 ]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 50. 108. 164. 308. 373. 453. 480. 387. 270. 168. 106. 58.]\n", + "[ 164. 318. 442. 714. 870. 1096. 1032. 903. 679. 413. 302. 162.]\n", + "bkg med : [ 184.45892334 398.42837524 605.02764893 1136.27636719 1376.07495117\n", + " 1671.10864258 1770.703125 1427.71630859 996.15234375 619.828125\n", + " 391.08203125 213.98828125]\n", + "bkg high : [ 605.03027344 1173.16845703 1630.59667969 2633.92089844 3209.69775391\n", + " 4043.640625 3807.515625 3331.57617188 2505.13867188 1523.74414062\n", + " 1114.21484375 597.69140625]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 7. 8. 16. 30. 38. 28. 42. 34. 16. 12. 6. 4.]\n", + "[10. 23. 28. 60. 67. 82. 68. 76. 41. 29. 16. 13.]\n", + "bkg med : [1237.59014893 1602.00650024 3012.18414307 5649.69482422 7093.07104492\n", + " 5883.63208008 8089.48828125 6542.92333984 3403.33203125 2425.21875\n", + " 1293.77734375 815.78515625]\n", + "bkg high : [ 2109.50317383 4633.45605469 5843.12011719 11660.75683594\n", + " 13289.66455078 16380.46484375 14038.0625 14765.71289062\n", + " 8673.39648438 5886.65820312 3521.33984375 2553.48046875]\n", + "mgp8_pp_jjaa_5f\n", + "[183. 217. 180. 186. 190. 193. 160. 192. 164. 188. 215. 193.]\n", + "[265. 276. 292. 273. 257. 270. 261. 304. 290. 250. 256. 312.]\n", + "bkg med : [ 7169.36358643 8635.85806274 8846.71539307 11678.71044922\n", + " 13251.74291992 12139.54614258 13275.73828125 12765.38427734\n", + " 8717.95703125 8517.59375 8261.12109375 7070.19140625]\n", + "bkg high : [10697.38598633 13577.58105469 15305.74511719 20507.66308594\n", + " 21618.07080078 25130.15234375 22496.09375 24617.21289062\n", + " 18071.20898438 13988.22070312 11817.33984375 12664.23046875]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 8. 21. 31. 50. 82. 101. 117. 71. 66. 32. 21. 14.]\n", + "[ 45. 80. 124. 217. 278. 318. 340. 262. 205. 124. 70. 52.]\n", + "bkg med : [ 7196.29043579 8706.54096985 8951.05705261 11847.00279236\n", + " 13527.74221802 12479.49645996 13669.54211426 13004.35925293\n", + " 8940.1027832 8625.30078125 8331.80407715 7117.31494141]\n", + "bkg high : [10848.8493042 13846.84866333 15723.10986328 21238.0513916\n", + " 22553.79919434 26200.52978516 23640.46313477 25499.03222656\n", + " 18761.18212891 14405.5703125 12052.94042969 12839.24804688]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 2. 12. 12. 21. 27. 34. 28. 27. 30. 9. 9. 5.]\n", + "[ 8. 12. 16. 34. 40. 52. 54. 42. 26. 26. 12. 9.]\n", + "bkg med : [ 7245.62402344 9002.54240417 9247.05863953 12365.0052948\n", + " 14193.74441528 13318.16601562 14360.21105957 13670.3614502\n", + " 9680.11230469 8847.30371094 8553.80700684 7240.64990234]\n", + "bkg high : [11046.18353271 14142.8503418 16117.77844238 22076.7208252\n", + " 23540.46923828 27483.20239258 24972.48071289 26535.04589844\n", + " 19402.52392578 15046.91210938 12348.94384766 13061.25097656]\n", + "add a+jj\n", + "bkg med : [ 7779.43310547 9635.52970886 9772.11723328 12907.56584167\n", + " 14747.97293091 13881.14550781 14826.92980957 14230.4239502\n", + " 10158.49902344 9395.69824219 9180.96032715 7803.62939453]\n", + "bkg high : [11819.1864624 14947.94018555 16969.54016113 22873.05969238\n", + " 24290.13623047 28270.76098633 25733.56079102 27421.51464844\n", + " 20248.16845703 15775.91601562 13095.44384766 13971.04785156]\n", + "sig med : [ 31.96422195 65.17889404 123.07885742 208.27587891 322.33776855\n", + " 405.56396484 409.60693359 335.74890137 214.09143066 122.77050781\n", + " 67.53710938 32.71875 ]\n", + "sig high : [ 320.6602478 578.44519043 936.3927002 1427.40991211 1945.70239258\n", + " 2276.3515625 2271.52197266 1937.27441406 1417.43457031 933.71875\n", + " 571.14648438 322.93457031]\n", + "sono dentro\n", + "31.964221954345703\n", + "7779.43310546875\n", + "sono dentro\n", + "65.17889404296875\n", + "9635.529708862305\n", + "sono dentro\n", + "123.078857421875\n", + "9772.117233276367\n", + "sono dentro\n", + "208.27587890625\n", + "12907.565841674805\n", + "sono dentro\n", + "322.3377685546875\n", + "14747.972930908203\n", + "sono dentro\n", + "405.56396484375\n", + "13881.1455078125\n", + "sono dentro\n", + "409.60693359375\n", + "14826.929809570312\n", + "sono dentro\n", + "335.7489013671875\n", + "14230.423950195312\n", + "sono dentro\n", + "214.0914306640625\n", + "10158.4990234375\n", + "sono dentro\n", + "122.7705078125\n", + "9395.6982421875\n", + "sono dentro\n", + "67.537109375\n", + "9180.960327148438\n", + "sono dentro\n", + "32.71875\n", + "7803.62939453125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 20.773444916805364\n", + "sig_high 100.20088882351772\n", + "Naive\n", + "sig_med 6.381701593978729\n", + "sig_high 30.789531094376162\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 121. 280. 397. 708. 869. 1125. 1113. 948. 632. 407. 262. 117.]\n", + "[ 93. 146. 209. 314. 374. 424. 399. 342. 317. 174. 146. 103.]\n", + "bkg med : [ 446.38867188 1032.97851562 1464.6159668 2611.81811523 3205.87597656\n", + " 4150.63476562 4106.36132812 3497.6015625 2331.734375 1501.60742188\n", + " 966.63671875 431.66601562]\n", + "bkg high : [ 343.09265137 538.62451172 771.04467773 1158.41162109 1379.70532227\n", + " 1564.12109375 1471.89697266 1261.77001953 1169.55664062 641.96484375\n", + " 538.66015625 380.01367188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[10. 19. 31. 64. 79. 70. 82. 75. 37. 25. 13. 7.]\n", + "[ 7. 12. 13. 26. 26. 40. 28. 35. 20. 16. 9. 10.]\n", + "bkg med : [ 1950.86157227 3891.4765625 6128.48168945 12240.44311523\n", + " 15091.22167969 14682.08007812 16443.19726562 14781.23828125\n", + " 7898.2109375 5262.74023438 2922.42578125 1484.78320312]\n", + "bkg high : [1396.22351074 2343.99194336 2726.85913086 5070.04101562 5291.33422852\n", + " 7582.01171875 5684.42041016 6527.42431641 4178.5078125 3049.15234375\n", + " 1892.703125 1884.50585938]\n", + "mgp8_pp_jjaa_5f\n", + "[355. 390. 361. 353. 368. 373. 335. 410. 349. 346. 400. 404.]\n", + "[ 93. 103. 111. 106. 79. 90. 86. 86. 105. 92. 71. 101.]\n", + "bkg med : [13455.08032227 16529.9140625 17827.13793945 23679.84936523\n", + " 27016.72167969 26769.61132812 27299.29101562 28067.81640625\n", + " 19207.9921875 16475.30273438 15884.92578125 14576.90820312]\n", + "bkg high : [ 4410.36804199 5682.23803711 6324.38647461 8505.51757812\n", + " 7851.73657227 10498.92578125 8471.72900391 9315.03369141\n", + " 7581.984375 6031.24609375 4194.1015625 5158.32617188]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 25. 58. 82. 150. 223. 260. 293. 217. 175. 90. 54. 32.]\n", + "[ 28. 43. 73. 117. 137. 159. 164. 116. 96. 66. 37. 34.]\n", + "bkg med : [13539.22674561 16725.13340759 18103.13723755 24184.72607422\n", + " 27767.30505371 27644.75769043 28285.51928711 28798.20166016\n", + " 19796.99365234 16778.21777344 16066.67480469 14684.61132812]\n", + "bkg high : [ 4504.61211395 5826.9695282 6570.09317017 8899.32141113\n", + " 8312.8572998 11034.09509277 9023.74597168 9705.48583984\n", + " 7905.1171875 6253.39990234 4318.64233398 5272.76904297]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 18. 23. 45. 46. 71. 65. 54. 48. 24. 14. 7.]\n", + "[ 2. 6. 5. 10. 21. 15. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [13736.5610199 17169.13581848 18670.47293091 25294.7298584\n", + " 28901.97546387 29396.10949707 29888.8737793 30130.21875\n", + " 20981.00927734 17370.22558594 16412.01269531 14857.28027344]\n", + "bkg high : [ 4553.9457016 5974.9702301 6693.42706299 9145.98947144\n", + " 8830.85974121 11404.09631348 9443.08068848 10075.48718262\n", + " 8102.45117188 6524.73413086 4491.30957031 5445.4362793 ]\n", + "add a+jj\n", + "bkg med : [14772.03172302 18306.38191223 19723.15457153 26324.08337402\n", + " 29975.06921387 30483.7833252 30865.73901367 31325.78515625\n", + " 21998.69873047 18379.16699219 17578.41894531 16035.35058594]\n", + "bkg high : [ 4825.2257309 6275.42042542 7017.2131958 9455.19064331\n", + " 9061.30212402 11666.62561035 9693.9420166 10326.34851074\n", + " 8408.73535156 6793.09741211 4698.41601562 5740.05249023]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sig med : [ 110.8762207 227.26907349 415.01574707 702.34552002 1045.84338379\n", + " 1291.0456543 1295.0871582 1057.86523438 713.34326172 421.93945312\n", + " 232.42822266 114.62792969]\n", + "sig high : [ 241.73675537 416.36712646 644.45288086 933.34033203 1222.19775391\n", + " 1390.86914062 1386.05957031 1215.32617188 918.20068359 634.546875\n", + " 406.24414062 241.01367188]\n", + "sono dentro\n", + "110.876220703125\n", + "14772.031723022461\n", + "sono dentro\n", + "227.26907348632812\n", + "18306.381912231445\n", + "sono dentro\n", + "415.0157470703125\n", + "19723.154571533203\n", + "sono dentro\n", + "702.3455200195312\n", + "26324.083374023438\n", + "sono dentro\n", + "1045.8433837890625\n", + "29975.069213867188\n", + "sono dentro\n", + "1291.045654296875\n", + "30483.783325195312\n", + "sono dentro\n", + "1295.087158203125\n", + "30865.739013671875\n", + "sono dentro\n", + "1057.865234375\n", + "31325.78515625\n", + "sono dentro\n", + "713.34326171875\n", + "21998.69873046875\n", + "sono dentro\n", + "421.939453125\n", + "18379.1669921875\n", + "sono dentro\n", + "232.42822265625\n", + "17578.4189453125\n", + "sono dentro\n", + "114.6279296875\n", + "16035.3505859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 46.99376330420512\n", + "sig_high 102.79889141469707\n", + "Naive\n", + "sig_med 14.525168722212394\n", + "sig_high 31.482423955115\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 197. 396. 570. 982. 1194. 1490. 1470. 1260. 908. 558. 389. 206.]\n", + "[17. 30. 36. 40. 49. 59. 42. 30. 41. 23. 19. 14.]\n", + "bkg med : [ 726.77416992 1460.92675781 2102.75488281 3622.68896484 4405.20703125\n", + " 5497.28515625 5423.49609375 4648.7109375 3350.0234375 2057.78320312\n", + " 1434.4375 759.625 ]\n", + "bkg high : [ 62.71601868 110.675354 132.81008911 147.56591797 180.76824951\n", + " 217.66101074 154.94677734 110.67626953 151.25756836 84.85180664\n", + " 70.0949707 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 17. 28. 41. 86. 99. 106. 108. 108. 55. 40. 20. 16.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 3284.37768555 5673.45068359 8271.09277344 16561.15380859\n", + " 19299.6484375 21444.90234375 21671.70703125 20896.8046875\n", + " 11624.515625 8075.59570312 4443.34375 3166.75 ]\n", + "bkg high : [ 62.71601868 562.01733398 584.1522522 749.35534668 1083.45184326\n", + " 819.45007324 455.84130859 411.57104492 452.15209961 235.29907227\n", + " 370.98950195 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[442. 488. 468. 448. 443. 457. 419. 495. 445. 432. 468. 501.]\n", + "[ 6. 5. 4. 11. 4. 6. 2. 1. 9. 6. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 4573.2565918 7096.46630859 9635.78808594 17867.52880859\n", + " 20591.44335938 22777.52148438 22893.51757812 22340.23242188\n", + " 12922.14257812 9335.31445312 5808.77929688 4628.65234375]\n", + "bkg high : [ 80.21788025 576.60217285 595.82009888 781.44192505 1095.11968994\n", + " 836.95184326 461.67523193 414.48800659 478.40475464 252.80084229\n", + " 379.74038696 213.76403809]\n", + "sig med : [ 282.71762085 535.40698242 917.2064209 1458.90905762 2066.00439453\n", + " 2467.79980469 2465.86279297 2077.18457031 1463.10546875 918.82714844\n", + " 535.67773438 285.13378906]\n", + "sig high : [ 69.89842224 108.22880554 142.26217651 176.77639771 202.03704834\n", + " 214.11456299 215.15612793 196.5065918 168.03808594 137.48242188\n", + " 102.9621582 70.4831543 ]\n", + "ttH cut: 0.4\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 67. 114. 199. 382. 474. 631. 597. 490. 344. 185. 122. 64.]\n", + "[135. 258. 367. 592. 706. 879. 847. 752. 569. 336. 241. 124.]\n", + "bkg med : [ 247.1739502 420.56341553 734.15258789 1409.27783203 1748.62866211\n", + " 2327.73681641 2202.48193359 1807.83203125 1269.171875 682.54882812\n", + " 450.11328125 236.125 ]\n", + "bkg high : [ 498.04229736 951.81591797 1353.93969727 2183.88867188 2604.45751953\n", + " 3243.02929688 3124.96679688 2774.46875 2099.29882812 1239.65625\n", + " 889.15820312 457.4921875 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 16. 22. 27. 62. 82. 102. 110. 85. 52. 23. 8. 10.]\n", + "[10. 22. 27. 59. 64. 80. 67. 74. 41. 25. 15. 13.]\n", + "bkg med : [ 2654.33068848 3730.40325928 4796.22924805 10737.00830078\n", + " 14085.31225586 17673.55712891 18751.89599609 14595.6953125\n", + " 9092.328125 4142.79101562 1653.67578125 1740.578125 ]\n", + "bkg high : [ 2002.51519775 4261.65625 5416.01586914 11060.27734375\n", + " 12233.08251953 15278.94335938 13205.06445312 13907.7109375\n", + " 8267.59570312 5000.7890625 3145.83789062 2413.28125 ]\n", + "mgp8_pp_jjaa_5f\n", + "[475. 526. 506. 496. 501. 489. 441. 522. 508. 467. 471. 464.]\n", + "[254. 266. 280. 247. 252. 261. 254. 285. 282. 243. 244. 296.]\n", + "bkg med : [18047.29943848 20776.09075928 21193.80737305 26810.50830078\n", + " 30320.84350586 33520.21337891 33043.05224609 31511.7578125\n", + " 25554.703125 19276.50976562 16917.01953125 16777.078125 ]\n", + "bkg high : [10235.25738525 12881.71875 14489.76586914 19064.62109375\n", + " 20399.45751953 23736.97460938 21436.25195312 23143.4921875\n", + " 17406.15820312 12875.5078125 11052.96289062 12005.53125 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 17. 35. 49. 91. 140. 187. 183. 157. 105. 56. 41. 22.]\n", + "[ 45. 79. 122. 209. 270. 309. 337. 256. 199. 115. 70. 49.]\n", + "bkg med : [18104.51893997 20893.89581299 21358.73390198 27116.80020142\n", + " 30792.06176758 34149.62634277 33659.01525879 32040.21459961\n", + " 25908.12963867 19465.00390625 17055.02416992 16851.12939453]\n", + "bkg high : [10386.72070312 13147.62051392 14900.39892578 19768.0826416\n", + " 21308.25683594 24777.05834961 22570.53100586 24005.1171875\n", + " 18075.93701172 13262.56591797 11288.56347656 12170.45166016]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 25. 37. 61. 80. 105. 109. 84. 74. 35. 21. 13.]\n", + "[ 8. 11. 15. 34. 39. 50. 53. 42. 26. 25. 12. 9.]\n", + "bkg med : [18178.51930618 21510.56573486 22271.40388489 28621.47195435\n", + " 32765.41333008 36739.66052246 36347.71691895 34112.24194336\n", + " 27733.48706055 20328.34863281 17573.03100586 17171.80029297]\n", + "bkg high : [10584.05493164 13418.9553833 15270.40081787 20606.7520752\n", + " 22270.26013184 26010.39624023 23877.8815918 25041.13085938\n", + " 18717.27880859 13879.24072266 11584.56738281 12392.45410156]\n", + "add a+jj\n", + "bkg med : [19563.62672806 23044.38995361 23746.90779114 30067.81570435\n", + " 34226.3371582 38165.59216309 37633.67980957 35634.40209961\n", + " 29215.1472168 21691.04003906 18947.39428711 18525.73779297]\n", + "bkg high : [11324.97094727 14194.87530518 16087.15863037 21327.24914551\n", + " 23005.34216309 26771.73120117 24618.75756836 25872.1953125\n", + " 19539.59521484 14587.83251953 12296.07519531 13255.59472656]\n", + "sig med : [ 48.51002884 104.03074646 202.45184326 358.74423218 569.9385376\n", + " 736.91223145 738.70776367 583.51879883 362.38574219 204.00683594\n", + " 105.74951172 50.20068359]\n", + "sig high : [ 316.29397583 570.6630249 924.07983398 1405.68408203 1915.12402344\n", + " 2237.54638672 2230.87890625 1905.28027344 1393.64453125 918.65527344\n", + " 562.66015625 318.0078125 ]\n", + "sono dentro\n", + "48.51002883911133\n", + "19563.62672805786\n", + "sono dentro\n", + "104.03074645996094\n", + "23044.38995361328\n", + "sono dentro\n", + "202.45184326171875\n", + "23746.907791137695\n", + "sono dentro\n", + "358.7442321777344\n", + "30067.815704345703\n", + "sono dentro\n", + "569.9385375976562\n", + "34226.337158203125\n", + "sono dentro\n", + "736.9122314453125\n", + "38165.59216308594\n", + "sono dentro\n", + "738.707763671875\n", + "37633.67980957031\n", + "sono dentro\n", + "583.518798828125\n", + "35634.402099609375\n", + "sono dentro\n", + "362.3857421875\n", + "29215.147216796875\n", + "sono dentro\n", + "204.0068359375\n", + "21691.0400390625\n", + "sono dentro\n", + "105.74951171875\n", + "18947.394287109375\n", + "sono dentro\n", + "50.20068359375\n", + "18525.73779296875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 22.753679831752834\n", + "sig_high 101.29050639724007\n", + "Naive\n", + "sig_med 7.0715808067435235\n", + "sig_high 31.13412500658404\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 125. 242. 381. 705. 875. 1146. 1095. 941. 633. 373. 236. 108.]\n", + "[ 77. 130. 185. 269. 305. 364. 349. 301. 280. 148. 127. 80.]\n", + "bkg med : [ 461.14575195 892.78857422 1405.58862305 2600.76245117 3227.98876953\n", + " 4228.11328125 4039.95117188 3471.77539062 2335.42382812 1376.16601562\n", + " 870.7109375 398.4609375 ]\n", + "bkg high : [ 284.06439209 479.5958252 682.50366211 992.3972168 1125.20043945\n", + " 1342.78320312 1287.44873047 1110.37841797 1033.01611328 546.0390625\n", + " 468.56054688 295.15625 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 19. 33. 41. 96. 120. 143. 149. 124. 73. 34. 14. 13.]\n", + "[ 7. 11. 13. 25. 26. 39. 28. 35. 20. 14. 9. 10.]\n", + "bkg med : [ 3319.64404297 5857.54882812 7573.92651367 17043.69995117\n", + " 21281.88330078 25742.203125 26456.30273438 22126.99414062\n", + " 13317.93164062 6491.30664062 2976.9453125 2354.3203125 ]\n", + "bkg high : [1337.19525146 2134.51599121 2638.31811523 4753.5793457 5036.8293457\n", + " 7210.2265625 5499.97216797 6376.03271484 4041.96142578 2652.328125\n", + " 1822.60351562 1799.6484375 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_jjaa_5f\n", + "[640. 693. 680. 641. 675. 661. 609. 726. 687. 620. 646. 659.]\n", + "[ 89. 99. 106. 102. 78. 89. 86. 81. 103. 90. 69. 101.]\n", + "bkg med : [24059.64404297 28315.08007812 29610.17651367 37816.10620117\n", + " 43156.10205078 47162.734375 46191.70898438 45653.93164062\n", + " 35581.02539062 26583.18164062 23928.6015625 23730.6328125 ]\n", + "bkg high : [ 4221.69915771 5343.12145996 6073.79467773 8059.4152832\n", + " 7564.8215332 10094.73046875 8287.24560547 9001.40771484\n", + " 7380.60986328 5569.59375 4059.17382812 5073.46875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 34. 71. 98. 185. 273. 340. 357. 298. 211. 107. 74. 38.]\n", + "[ 28. 43. 73. 115. 137. 156. 163. 115. 93. 64. 37. 33.]\n", + "bkg med : [24174.08325195 28554.05517578 29940.02932739 38438.78747559\n", + " 44074.98840332 48307.16308594 47393.31591797 46656.91699219\n", + " 36291.19287109 26943.31396484 24177.66503906 23858.53027344]\n", + "bkg high : [ 4315.94322968 5487.85295105 6319.50137329 8446.48742676\n", + " 8025.94226074 10619.80224609 8835.89599609 9388.49389648\n", + " 7693.64477539 5785.015625 4183.71459961 5184.5456543 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 9. 31. 47. 85. 99. 141. 145. 111. 92. 49. 26. 15.]\n", + "[ 2. 5. 5. 10. 20. 14. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [24396.08432007 29318.7255249 31099.36660767 40535.46569824\n", + " 46517.02062988 51785.20849609 50970.02978516 49394.93554688\n", + " 38560.46630859 28151.94873047 24818.98144531 24228.52050781]\n", + "bkg high : [ 4365.27681732 5611.18687439 6442.83526611 8693.15545654\n", + " 8519.27801514 10965.13671875 9255.23071289 9758.49523926\n", + " 7890.97875977 6056.34985352 4356.38183594 5357.21289062]\n", + "add a+jj\n", + "bkg med : [26262.33432007 31339.52435303 33082.70059204 42405.88366699\n", + " 48486.64953613 53713.98583984 52747.07275391 51513.38085938\n", + " 40565.11083984 29961.08935547 26703.98925781 26151.46191406]\n", + "bkg high : [ 4624.88887787 5899.96910095 6752.03643799 8990.68865967\n", + " 8746.80340576 11224.74902344 9506.09204102 9994.77160645\n", + " 8191.42895508 6318.87915039 4557.65429688 5651.82910156]\n", + "sig med : [ 124.22927856 260.96820068 485.66665649 837.22314453 1270.78674316\n", + " 1593.99267578 1593.875 1282.63769531 845.04492188 492.99804688\n", + " 265.28320312 129.13671875]\n", + "sig high : [ 240.56307983 413.73712158 640.86260986 927.20532227 1214.27587891\n", + " 1380.46582031 1375.73730469 1206.31982422 911.22119141 629.671875\n", + " 403.11816406 239.06640625]\n", + "sono dentro\n", + "124.22927856445312\n", + "26262.33432006836\n", + "sono dentro\n", + "260.96820068359375\n", + "31339.524353027344\n", + "sono dentro\n", + "485.6666564941406\n", + "33082.700592041016\n", + "sono dentro\n", + "837.22314453125\n", + "42405.88366699219\n", + "sono dentro\n", + "1270.7867431640625\n", + "48486.64953613281\n", + "sono dentro\n", + "1593.99267578125\n", + "53713.98583984375\n", + "sono dentro\n", + "1593.875\n", + "52747.07275390625\n", + "sono dentro\n", + "1282.6376953125\n", + "51513.380859375\n", + "sono dentro\n", + "845.044921875\n", + "40565.11083984375\n", + "sono dentro\n", + "492.998046875\n", + "29961.08935546875\n", + "sono dentro\n", + "265.283203125\n", + "26703.9892578125\n", + "sono dentro\n", + "129.13671875\n", + "26151.4619140625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 43.51811845636009\n", + "sig_high 103.95780501416994\n", + "Naive\n", + "sig_med 13.494931130012985\n", + "sig_high 31.859537239562968\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 186. 344. 531. 937. 1138. 1460. 1406. 1218. 875. 499. 345. 174.]\n", + "[16. 28. 35. 37. 42. 50. 38. 24. 38. 22. 18. 14.]\n", + "bkg med : [ 686.19287109 1269.08789062 1958.90576172 3456.60351562 4198.59765625\n", + " 5386.6015625 5187.37109375 4493.75390625 3228.27148438 1840.98828125\n", + " 1272.1875 641.625 ]\n", + "bkg high : [ 59.02684021 103.29699707 129.12124634 136.49862671 154.94421387\n", + " 184.45739746 140.18908691 88.54101562 140.18994141 81.16259766\n", + " 66.40576172 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 26. 41. 51. 117. 140. 178. 175. 157. 91. 47. 21. 22.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 4597.82226562 7437.42578125 9631.71630859 21059.02929688\n", + " 25261.48828125 32165.984375 31515.30078125 28114.01171875\n", + " 16919.50585938 8912.28515625 4431.703125 3951.59375 ]\n", + "bkg high : [ 59.02684021 554.63897705 580.46340942 738.28805542 1057.62780762\n", + " 786.24645996 441.08361816 389.43579102 441.08447266 231.60986328\n", + " 367.30029297 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[723. 787. 783. 732. 749. 745. 693. 806. 781. 705. 712. 756.]\n", + "[ 6. 5. 3. 11. 4. 5. 2. 1. 9. 5. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 6706.47265625 9733.8671875 11916.48583984 23194.98242188\n", + " 27447.046875 34339.87109375 33537.453125 30465.89453125\n", + " 19198.43945312 10969.453125 6509.296875 6157.578125 ]\n", + "bkg high : [ 76.52870178 569.22381592 589.21429443 770.37463379 1069.2956543\n", + " 800.83126831 446.9175415 392.35275269 467.33712769 246.19467163\n", + " 376.05117798 213.76403809]\n", + "sig med : [ 295.02462769 566.63635254 984.29821777 1588.03686523 2283.52612305\n", + " 2760.90136719 2754.72363281 2293.9609375 1588.26757812 985.35253906\n", + " 565.50390625 297.81835938]\n", + "sig high : [ 69.77079773 108.06924438 142.23022461 176.39187622 201.53619385\n", + " 213.55743408 214.48352051 195.96398926 167.62304688 137.06335449\n", + " 102.80297852 70.32348633]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 51. 94. 169. 307. 382. 502. 494. 408. 272. 157. 106. 57.]\n", + "[135. 258. 367. 592. 706. 879. 847. 752. 569. 336. 241. 124.]\n", + "bkg med : [ 188.14810181 346.78024292 623.47296143 1132.5871582 1409.27783203\n", + " 1851.86767578 1822.34863281 1505.22607422 1003.53125 579.24414062\n", + " 391.08203125 210.29882812]\n", + "bkg high : [ 498.04229736 951.81591797 1353.93969727 2183.88867188 2604.45751953\n", + " 3243.02929688 3124.96679688 2774.46875 2099.29882812 1239.65625\n", + " 889.15820312 457.4921875 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[11. 14. 19. 46. 59. 69. 76. 61. 35. 18. 6. 7.]\n", + "[10. 22. 27. 59. 64. 80. 67. 74. 41. 25. 15. 13.]\n", + "bkg med : [ 1843.06838989 2453.04220581 3481.9710083 8053.16186523\n", + " 10285.66650391 12232.75048828 13256.48925781 10682.62841797\n", + " 6269.25390625 3287.33007812 1293.77734375 1263.44335938]\n", + "bkg high : [ 2002.51519775 4261.65625 5416.01586914 11060.27734375\n", + " 12233.08251953 15278.94335938 13205.06445312 13907.7109375\n", + " 8267.59570312 5000.7890625 3145.83789062 2413.28125 ]\n", + "mgp8_pp_jjaa_5f\n", + "[302. 339. 312. 333. 308. 321. 297. 342. 322. 325. 321. 298.]\n", + "[254. 266. 280. 247. 252. 261. 254. 285. 282. 243. 244. 296.]\n", + "bkg med : [11629.75588989 13438.76095581 13592.7210083 18844.44311523\n", + " 20266.79150391 22635.15673828 22881.14550781 21765.56591797\n", + " 16704.06640625 13819.36132812 11696.19921875 10920.50585938]\n", + "bkg high : [10235.25738525 12881.71875 14489.76586914 19064.62109375\n", + " 20399.45751953 23736.97460938 21436.25195312 23143.4921875\n", + " 17406.15820312 12875.5078125 11052.96289062 12005.53125 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 12. 29. 43. 78. 112. 146. 149. 112. 91. 43. 35. 20.]\n", + "[ 45. 79. 122. 209. 270. 309. 337. 256. 199. 115. 70. 49.]\n", + "bkg med : [11670.14614487 13536.37078094 13737.45269775 19106.97903442\n", + " 20643.76611328 23126.57006836 23382.65637207 22142.55090332\n", + " 17010.36938477 13964.09790039 11814.00805664 10987.82519531]\n", + "bkg high : [10386.72070312 13147.62051392 14900.39892578 19768.0826416\n", + " 21308.25683594 24777.05834961 22570.53100586 24005.1171875\n", + " 18075.93701172 13262.56591797 11288.56347656 12170.45166016]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 2. 17. 26. 40. 53. 66. 67. 52. 49. 26. 13. 11.]\n", + "[ 8. 11. 15. 34. 39. 50. 53. 42. 26. 25. 12. 9.]\n", + "bkg med : [11719.47973251 13955.70616913 14378.78918457 20093.64907837\n", + " 21951.10375977 24754.5859375 25035.34484863 23425.23449707\n", + " 18219.05151367 14605.43969727 12134.67895508 11259.16210938]\n", + "bkg high : [10584.05493164 13418.9553833 15270.40081787 20606.7520752\n", + " 22270.26013184 26010.39624023 23877.8815918 25041.13085938\n", + " 18717.27880859 13879.24072266 11584.56738281 12392.45410156]\n", + "add a+jj\n", + "bkg med : [12600.41137314 14944.56652069 15288.89074707 21064.73208618\n", + " 22849.23657227 25690.62695312 25901.40148926 24422.51184082\n", + " 19158.00854492 15553.14477539 13070.7199707 12128.13476562]\n", + "bkg high : [11324.97094727 14194.87530518 16087.15863037 21327.24914551\n", + " 23005.34216309 26771.73120117 24618.75756836 25872.1953125\n", + " 19539.59521484 14587.83251953 12296.07519531 13255.59472656]\n", + "sig med : [ 41.16922379 87.00551605 168.30587769 290.02053833 455.12097168\n", + " 581.35144043 582.94116211 465.8972168 292.94213867 167.79614258\n", + " 88.93530273 41.22119141]\n", + "sig high : [ 316.29397583 570.6630249 924.07983398 1405.68408203 1915.12402344\n", + " 2237.54638672 2230.87890625 1905.28027344 1393.64453125 918.65527344\n", + " 562.66015625 318.0078125 ]\n", + "sono dentro\n", + "41.16922378540039\n", + "12600.411373138428\n", + "sono dentro\n", + "87.0055160522461\n", + "14944.566520690918\n", + "sono dentro\n", + "168.30587768554688\n", + "15288.890747070312\n", + "sono dentro\n", + "290.0205383300781\n", + "21064.73208618164\n", + "sono dentro\n", + "455.1209716796875\n", + "22849.236572265625\n", + "sono dentro\n", + "581.3514404296875\n", + "25690.626953125\n", + "sono dentro\n", + "582.941162109375\n", + "25901.401489257812\n", + "sono dentro\n", + "465.897216796875\n", + "24422.511840820312\n", + "sono dentro\n", + "292.942138671875\n", + "19158.008544921875\n", + "sono dentro\n", + "167.796142578125\n", + "15553.144775390625\n", + "sono dentro\n", + "88.935302734375\n", + "13070.719970703125\n", + "sono dentro\n", + "41.22119140625\n", + "12128.134765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 22.23012784382162\n", + "sig_high 101.29050639724007\n", + "Naive\n", + "sig_med 6.914246471237364\n", + "sig_high 31.13412500658404\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_tth01j_5f_haa\n", + "[ 109. 222. 351. 630. 783. 1017. 992. 859. 561. 345. 220. 101.]\n", + "[ 77. 130. 185. 269. 305. 364. 349. 301. 280. 148. 127. 80.]\n", + "bkg med : [ 402.11712646 819.00396729 1294.91235352 2324.11303711 2888.4765625\n", + " 3752.17382812 3659.9375 3169.24023438 2069.78320312 1272.86132812\n", + " 811.6796875 372.63476562]\n", + "bkg high : [ 284.06439209 479.5958252 682.50366211 992.3972168 1125.20043945\n", + " 1342.78320312 1287.44873047 1110.37841797 1033.01611328 546.0390625\n", + " 468.56054688 295.15625 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 14. 25. 33. 80. 97. 110. 115. 100. 56. 29. 12. 10.]\n", + "[ 7. 11. 13. 25. 26. 39. 28. 35. 20. 14. 9. 10.]\n", + "bkg med : [ 2508.37908936 4580.18609619 6259.67211914 14359.89428711\n", + " 17481.96289062 20301.58789062 20961.37109375 18213.77148438\n", + " 10494.72070312 5635.77539062 2617.0234375 1877.08789062]\n", + "bkg high : [1337.19525146 2134.51599121 2638.31811523 4753.5793457 5036.8293457\n", + " 7210.2265625 5499.97216797 6376.03271484 4041.96142578 2652.328125\n", + " 1822.60351562 1799.6484375 ]\n", + "mgp8_pp_jjaa_5f\n", + "[467. 506. 486. 478. 482. 493. 465. 546. 501. 478. 496. 493.]\n", + "[ 89. 99. 106. 102. 78. 89. 86. 81. 103. 90. 69. 101.]\n", + "bkg med : [17642.09783936 20977.76422119 22009.10961914 29850.08178711\n", + " 33101.77539062 36277.86914062 36030.27734375 35907.58398438\n", + " 26730.25195312 21125.96289062 18690.5234375 17853.36914062]\n", + "bkg high : [ 4221.69915771 5343.12145996 6073.79467773 8059.4152832\n", + " 7564.8215332 10094.73046875 8287.24560547 9001.40771484\n", + " 7380.60986328 5569.59375 4059.17382812 5073.46875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 29. 65. 92. 172. 245. 299. 323. 253. 197. 94. 68. 36.]\n", + "[ 28. 43. 73. 115. 137. 156. 163. 115. 93. 64. 37. 33.]\n", + "bkg med : [17739.7077179 21196.54438782 22318.7673645 30429.00708008\n", + " 33926.41052246 37284.29321289 37117.47485352 36759.11181641\n", + " 27393.29931641 21442.34082031 18919.39257812 17974.53515625]\n", + "bkg high : [ 4315.94322968 5487.85295105 6319.50137329 8446.48742676\n", + " 8025.94226074 10619.80224609 8835.89599609 9388.49389648\n", + " 7693.64477539 5785.015625 4183.71459961 5184.5456543 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 23. 36. 64. 72. 102. 103. 79. 67. 40. 18. 13.]\n", + "[ 2. 5. 5. 10. 20. 14. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [17937.04194641 21763.88093567 23206.77029419 32007.67907715\n", + " 35702.42785645 39800.32641602 39658.17456055 38707.80419922\n", + " 29045.98779297 22429.02050781 19363.3984375 18295.20605469]\n", + "bkg high : [ 4365.27681732 5611.18687439 6442.83526611 8693.15545654\n", + " 8519.27801514 10965.13671875 9255.23071289 9758.49523926\n", + " 7890.97875977 6056.34985352 4356.38183594 5357.21289062]\n", + "add a+jj\n", + "bkg med : [19298.82124329 23239.38484192 24623.95388794 33401.5345459\n", + " 37107.9473877 41237.92211914 41014.12182617 40299.94873047\n", + " 30507.52294922 23823.80957031 20810.7109375 19733.76464844]\n", + "bkg high : [ 4624.88887787 5899.96910095 6752.03643799 8990.68865967\n", + " 8746.80340576 11224.74902344 9506.09204102 9994.77160645\n", + " 8191.42895508 6318.87915039 4557.65429688 5651.82910156]\n", + "sig med : [ 116.88839722 243.94230652 451.5269165 768.49914551 1155.96862793\n", + " 1438.43188477 1438.07861328 1164.79345703 775.35742188 456.6484375\n", + " 248.39355469 120.12011719]\n", + "sig high : [ 240.56307983 413.73712158 640.86260986 927.20532227 1214.27587891\n", + " 1380.46582031 1375.73730469 1206.31982422 911.22119141 629.671875\n", + " 403.11816406 239.06640625]\n", + "sono dentro\n", + "116.88839721679688\n", + "19298.821243286133\n", + "sono dentro\n", + "243.9423065185547\n", + "23239.384841918945\n", + "sono dentro\n", + "451.52691650390625\n", + "24623.953887939453\n", + "sono dentro\n", + "768.4991455078125\n", + "33401.53454589844\n", + "sono dentro\n", + "1155.9686279296875\n", + "37107.94738769531\n", + "sono dentro\n", + "1438.431884765625\n", + "41237.922119140625\n", + "sono dentro\n", + "1438.07861328125\n", + "41014.121826171875\n", + "sono dentro\n", + "1164.79345703125\n", + "40299.94873046875\n", + "sono dentro\n", + "775.357421875\n", + "30507.52294921875\n", + "sono dentro\n", + "456.6484375\n", + "23823.8095703125\n", + "sono dentro\n", + "248.3935546875\n", + "20810.7109375\n", + "sono dentro\n", + "120.1201171875\n", + "19733.7646484375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 45.28555984676077\n", + "sig_high 103.95780501416994\n", + "Naive\n", + "sig_med 14.060442726167766\n", + "sig_high 31.859537239562968\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 170. 324. 501. 862. 1046. 1331. 1303. 1136. 803. 471. 329. 167.]\n", + "[16. 28. 35. 37. 42. 50. 38. 24. 38. 22. 18. 14.]\n", + "bkg med : [ 627.16552734 1195.30371094 1848.25244141 3179.88769531 3859.12890625\n", + " 4910.66210938 4807.35742188 4191.21875 2962.63085938 1737.73242188\n", + " 1213.83007812 616.13867188]\n", + "bkg high : [ 59.02684021 103.29699707 129.12124634 136.49862671 154.94421387\n", + " 184.45739746 140.18908691 88.54101562 140.18994141 81.16259766\n", + " 66.40576172 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 21. 33. 43. 101. 117. 145. 141. 133. 74. 42. 19. 19.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 3786.55810547 6160.06396484 8317.48486328 18375.08105469\n", + " 21461.6875 26725.59179688 26020.14648438 24200.4453125\n", + " 14095.58398438 8056.49023438 4072.43945312 3474.74804688]\n", + "bkg high : [ 59.02684021 554.63897705 580.46340942 738.28805542 1057.62780762\n", + " 786.24645996 441.08361816 389.43579102 441.08447266 231.60986328\n", + " 367.30029297 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[550. 600. 589. 569. 556. 577. 549. 626. 595. 563. 562. 590.]\n", + "[ 6. 5. 3. 11. 4. 5. 2. 1. 9. 5. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 5390.36669922 7909.67333984 10035.01806641 20034.29394531\n", + " 23083.00390625 28409.25976562 27622.11132812 26027.09375\n", + " 15831.77539062 9699.30664062 5712.33789062 5196.34960938]\n", + "bkg high : [ 76.52870178 569.22381592 589.21429443 770.37463379 1069.2956543\n", + " 800.83126831 446.9175415 392.35275269 467.33712769 246.19467163\n", + " 376.05117798 213.76403809]\n", + "sig med : [ 287.6842041 549.60870361 950.15625 1519.31518555 2168.70947266\n", + " 2605.33935547 2599.02832031 2176.11816406 1518.81347656 949.14257812\n", + " 548.6953125 288.84179688]\n", + "sig high : [ 69.77079773 108.06924438 142.23022461 176.39187622 201.53619385\n", + " 213.55743408 214.48352051 195.96398926 167.62304688 137.06335449\n", + " 102.80297852 70.32348633]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 33. 66. 114. 205. 251. 307. 305. 265. 176. 101. 71. 36.]\n", + "[135. 258. 367. 592. 706. 879. 847. 752. 569. 336. 241. 124.]\n", + "bkg med : [ 121.74287415 243.48547363 420.56286621 756.2869873 925.99145508\n", + " 1132.5871582 1125.15478516 977.57568359 649.2578125 372.58544922\n", + " 261.91650391 132.80273438]\n", + "bkg high : [ 498.04229736 951.81591797 1353.93969727 2183.88867188 2604.45751953\n", + " 3243.02929688 3124.96679688 2774.46875 2099.29882812 1239.65625\n", + " 889.15820312 457.4921875 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 7. 6. 14. 30. 36. 25. 40. 31. 15. 10. 6. 3.]\n", + "[10. 22. 27. 59. 64. 80. 67. 74. 41. 25. 15. 13.]\n", + "bkg med : [1174.87446594 1146.16906738 2526.8248291 5269.70544434 6342.09301758\n", + " 4893.76879883 7143.04541016 5641.44091797 2905.96679688 1877.06591797\n", + " 1164.61181641 584.15039062]\n", + "bkg high : [ 2002.51519775 4261.65625 5416.01586914 11060.27734375\n", + " 12233.08251953 15278.94335938 13205.06445312 13907.7109375\n", + " 8267.59570312 5000.7890625 3145.83789062 2413.28125 ]\n", + "mgp8_pp_jjaa_5f\n", + "[167. 196. 161. 172. 174. 170. 141. 176. 150. 167. 192. 167.]\n", + "[254. 266. 280. 247. 252. 261. 254. 285. 282. 243. 244. 296.]\n", + "bkg med : [ 6588.02290344 7499.32531738 7745.4888916 10844.92419434\n", + " 11982.13989258 10404.15942383 11713.42822266 11346.31591797\n", + " 7768.07617188 7289.54248047 7386.61181641 5995.99414062]\n", + "bkg high : [10235.25738525 12881.71875 14489.76586914 19064.62109375\n", + " 20399.45751953 23736.97460938 21436.25195312 23143.4921875\n", + " 17406.15820312 12875.5078125 11052.96289062 12005.53125 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 4. 17. 26. 47. 70. 96. 96. 63. 57. 26. 20. 13.]\n", + "[ 45. 79. 122. 209. 270. 309. 337. 256. 199. 115. 70. 49.]\n", + "bkg med : [ 6601.48632622 7556.54483032 7833.00118256 11003.11921692\n", + " 12217.74905396 10727.28051758 12036.54931641 11558.36413574\n", + " 7959.92932129 7377.05444336 7453.92871094 6039.75012207]\n", + "bkg high : [10386.72070312 13147.62051392 14900.39892578 19768.0826416\n", + " 21308.25683594 24777.05834961 22570.53100586 24005.1171875\n", + " 18075.93701172 13262.56591797 11288.56347656 12170.45166016]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 2. 10. 10. 18. 27. 34. 27. 26. 28. 8. 8. 5.]\n", + "[ 8. 11. 15. 34. 39. 50. 53. 42. 26. 25. 12. 9.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 6650.81991386 7803.21270752 8079.66909027 11447.12171936\n", + " 12883.75125122 11565.95007324 12702.55151367 12199.69958496\n", + " 8650.60266113 7574.39038086 7651.26464844 6163.08508301]\n", + "bkg high : [10584.05493164 13418.9553833 15270.40081787 20606.7520752\n", + " 22270.26013184 26010.39624023 23877.8815918 25041.13085938\n", + " 18717.27880859 13879.24072266 11584.56738281 12392.45410156]\n", + "add a+jj\n", + "bkg med : [ 7137.95760918 8374.94268799 8549.30483246 11948.84437561\n", + " 13391.30789185 12061.83874512 13113.84741211 12713.09020996\n", + " 9088.15148926 8061.52807617 8211.32714844 6650.22277832]\n", + "bkg high : [11324.97094727 14194.87530518 16087.15863037 21327.24914551\n", + " 23005.34216309 26771.73120117 24618.75756836 25872.1953125\n", + " 19539.59521484 14587.83251953 12296.07519531 13255.59472656]\n", + "sig med : [ 29.15625191 59.5172348 113.49055481 191.42248535 296.3687439\n", + " 374.59338379 377.12731934 307.83105469 195.26586914 112.93237305\n", + " 61.45483398 29.13964844]\n", + "sig high : [ 316.29397583 570.6630249 924.07983398 1405.68408203 1915.12402344\n", + " 2237.54638672 2230.87890625 1905.28027344 1393.64453125 918.65527344\n", + " 562.66015625 318.0078125 ]\n", + "sono dentro\n", + "29.156251907348633\n", + "7137.957609176636\n", + "sono dentro\n", + "59.517234802246094\n", + "8374.942687988281\n", + "sono dentro\n", + "113.49055480957031\n", + "8549.304832458496\n", + "sono dentro\n", + "191.4224853515625\n", + "11948.844375610352\n", + "sono dentro\n", + "296.3687438964844\n", + "13391.307891845703\n", + "sono dentro\n", + "374.5933837890625\n", + "12061.838745117188\n", + "sono dentro\n", + "377.1273193359375\n", + "13113.847412109375\n", + "sono dentro\n", + "307.8310546875\n", + "12713.090209960938\n", + "sono dentro\n", + "195.265869140625\n", + "9088.151489257812\n", + "sono dentro\n", + "112.932373046875\n", + "8061.528076171875\n", + "sono dentro\n", + "61.454833984375\n", + "8211.3271484375\n", + "sono dentro\n", + "29.1396484375\n", + "6650.2227783203125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 20.23793671278329\n", + "sig_high 101.29050639724007\n", + "Naive\n", + "sig_med 6.219713142822365\n", + "sig_high 31.13412500658404\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 91. 194. 296. 528. 652. 822. 803. 716. 465. 289. 185. 80.]\n", + "[ 77. 130. 185. 269. 305. 364. 349. 301. 280. 148. 127. 80.]\n", + "bkg med : [ 335.71261597 715.70336914 1092.00585938 1947.86987305 2405.20507812\n", + " 3032.57128906 2962.63085938 2641.6484375 1715.59570312 1066.25195312\n", + " 682.54882812 295.15625 ]\n", + "bkg high : [ 284.06439209 479.5958252 682.50366211 992.3972168 1125.20043945\n", + " 1342.78320312 1287.44873047 1110.37841797 1033.01611328 546.0390625\n", + " 468.56054688 295.15625 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[10. 17. 28. 64. 74. 66. 79. 70. 36. 21. 12. 6.]\n", + "[ 7. 11. 13. 25. 26. 39. 28. 35. 20. 14. 9. 10.]\n", + "bkg med : [ 1840.18527222 3273.30712891 5304.52978516 11576.49487305\n", + " 13538.30273438 12962.20605469 14848.11914062 13173.09375\n", + " 7131.66992188 4225.60351562 2487.89257812 1197.828125 ]\n", + "bkg high : [1337.19525146 2134.51599121 2638.31811523 4753.5793457 5036.8293457\n", + " 7210.2265625 5499.97216797 6376.03271484 4041.96142578 2652.328125\n", + " 1822.60351562 1799.6484375 ]\n", + "mgp8_pp_jjaa_5f\n", + "[332. 363. 335. 317. 348. 342. 309. 380. 329. 320. 367. 362.]\n", + "[ 89. 99. 106. 102. 78. 89. 86. 81. 103. 90. 69. 101.]\n", + "bkg med : [12599.06027222 15036.77587891 16160.62353516 21849.27612305\n", + " 24815.67773438 24045.14355469 24861.65039062 25487.46875\n", + " 17793.32617188 14595.61914062 14380.98632812 12928.890625 ]\n", + "bkg high : [ 4221.69915771 5343.12145996 6073.79467773 8059.4152832\n", + " 7564.8215332 10094.73046875 8287.24560547 9001.40771484\n", + " 7380.60986328 5569.59375 4059.17382812 5073.46875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 21. 53. 75. 141. 203. 249. 270. 204. 163. 77. 53. 29.]\n", + "[ 28. 43. 73. 115. 137. 156. 163. 115. 93. 64. 37. 33.]\n", + "bkg med : [12669.74324799 15215.16607666 16413.06192017 22323.86022949\n", + " 25498.94421387 24883.25866699 25770.46142578 26174.11914062\n", + " 18341.93896484 14854.77978516 14559.36962891 13026.49658203]\n", + "bkg high : [ 4315.94322968 5487.85295105 6319.50137329 8446.48742676\n", + " 8025.94226074 10619.80224609 8835.89599609 9388.49389648\n", + " 7693.64477539 5785.015625 4183.71459961 5184.5456543 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 16. 20. 42. 46. 70. 63. 53. 46. 22. 13. 7.]\n", + "[ 2. 5. 5. 10. 20. 14. 17. 15. 8. 11. 7. 7.]\n", + "bkg med : [12867.07752228 15609.83486938 16906.39749146 23359.86376953\n", + " 26633.61462402 26609.94152832 27324.48193359 27481.46972656\n", + " 19476.62011719 15397.45361328 14880.04052734 13199.16552734]\n", + "bkg high : [ 4365.27681732 5611.18687439 6442.83526611 8693.15545654\n", + " 8519.27801514 10965.13671875 9255.23071289 9758.49523926\n", + " 7890.97875977 6056.34985352 4356.38183594 5357.21289062]\n", + "add a+jj\n", + "bkg med : [13835.51892853 16668.65908813 17883.26272583 24284.24072266\n", + " 27648.38806152 27607.21887207 28225.53076172 28589.55566406\n", + " 20435.98925781 16330.57861328 15950.21826172 14254.76318359]\n", + "bkg high : [ 4624.88887787 5899.96910095 6752.03643799 8990.68865967\n", + " 8746.80340576 11224.74902344 9506.09204102 9994.77160645\n", + " 8191.42895508 6318.87915039 4557.65429688 5651.82910156]\n", + "sig med : [ 104.87536621 216.45581055 396.70492554 669.90075684 997.21691895\n", + " 1231.67358398 1232.28613281 1006.74609375 677.70166016 401.90283203\n", + " 220.97802734 108.0703125 ]\n", + "sig high : [ 240.56307983 413.73712158 640.86260986 927.20532227 1214.27587891\n", + " 1380.46582031 1375.73730469 1206.31982422 911.22119141 629.671875\n", + " 403.11816406 239.06640625]\n", + "sono dentro\n", + "104.8753662109375\n", + "13835.518928527832\n", + "sono dentro\n", + "216.455810546875\n", + "16668.659088134766\n", + "sono dentro\n", + "396.7049255371094\n", + "17883.262725830078\n", + "sono dentro\n", + "669.9007568359375\n", + "24284.24072265625\n", + "sono dentro\n", + "997.2169189453125\n", + "27648.388061523438\n", + "sono dentro\n", + "1231.673583984375\n", + "27607.218872070312\n", + "sono dentro\n", + "1232.2861328125\n", + "28225.53076171875\n", + "sono dentro\n", + "1006.74609375\n", + "28589.5556640625\n", + "sono dentro\n", + "677.70166015625\n", + "20435.9892578125\n", + "sono dentro\n", + "401.90283203125\n", + "16330.57861328125\n", + "sono dentro\n", + "220.97802734375\n", + "15950.21826171875\n", + "sono dentro\n", + "108.0703125\n", + "14254.76318359375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 46.817670413206024\n", + "sig_high 103.95780501416994\n", + "Naive\n", + "sig_med 14.479476171399531\n", + "sig_high 31.859537239562968\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 152. 296. 446. 760. 915. 1136. 1114. 993. 707. 415. 294. 146.]\n", + "[16. 28. 35. 37. 42. 50. 38. 24. 38. 22. 18. 14.]\n", + "bkg med : [ 560.75854492 1092.00585938 1645.37719727 2803.61328125 3375.69824219\n", + " 4191.21875 4110.05078125 3663.62695312 2608.44335938 1531.12304688\n", + " 1084.69921875 538.66015625]\n", + "bkg high : [ 59.02684021 103.29699707 129.12124634 136.49862671 154.94421387\n", + " 184.45739746 140.18908691 88.54101562 140.18994141 81.16259766\n", + " 66.40576172 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 17. 25. 38. 85. 94. 101. 105. 103. 54. 34. 19. 15.]\n", + "[0. 3. 3. 4. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 3118.36230469 4853.18798828 7362.37329102 15591.63085938\n", + " 17517.87402344 19386.58984375 19907.00390625 19159.49414062\n", + " 10732.49023438 6646.26367188 3943.16015625 2795.33984375]\n", + "bkg high : [ 59.02684021 554.63897705 580.46340942 738.28805542 1057.62780762\n", + " 786.24645996 441.08361816 389.43579102 441.08447266 231.60986328\n", + " 367.30029297 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[415. 457. 438. 408. 422. 426. 393. 460. 423. 405. 433. 459.]\n", + "[ 6. 5. 3. 11. 4. 5. 2. 1. 9. 5. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 4328.50878906 6185.80712891 8639.58813477 16781.36523438\n", + " 18748.43261719 20628.8125 21052.99804688 20500.86132812\n", + " 11965.96484375 7827.25 5205.79492188 4134.03125 ]\n", + "bkg high : [ 76.52870178 569.22381592 589.21429443 770.37463379 1069.2956543\n", + " 800.83126831 446.9175415 392.35275269 467.33712769 246.19467163\n", + " 376.05117798 213.76403809]\n", + "sig med : [ 275.67095947 522.12194824 895.33837891 1420.71411133 2009.95678711\n", + " 2398.58154297 2393.40917969 2017.95214844 1421.14550781 894.28125\n", + " 521.20996094 276.75878906]\n", + "sig high : [ 69.77079773 108.06924438 142.23022461 176.39187622 201.53619385\n", + " 213.55743408 214.48352051 195.96398926 167.62304688 137.06335449\n", + " 102.80297852 70.32348633]\n", + "ttH cut: 0.5\n", + "----------------\n", + "----------------\n", + "(0.4, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 37. 72. 128. 237. 285. 372. 370. 319. 212. 115. 72. 41.]\n", + "[102. 203. 296. 458. 534. 662. 636. 569. 430. 266. 195. 102.]\n", + "bkg med : [ 136.49958801 265.62008667 472.21185303 874.3425293 1051.42456055\n", + " 1372.3671875 1364.91699219 1176.77978516 782.06054688 424.23095703\n", + " 265.63134766 151.26757812]\n", + "bkg high : [ 376.29492188 748.90942383 1092.00585938 1689.62011719 1969.90722656\n", + " 2442.21142578 2346.4921875 2099.29882812 1586.46484375 981.39453125\n", + " 719.44335938 376.32421875]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_h012j_5f_haa\n", + "[ 14. 19. 24. 52. 70. 89. 102. 79. 41. 20. 7. 9.]\n", + "[ 9. 21. 24. 57. 58. 76. 62. 68. 41. 25. 13. 13.]\n", + "bkg med : [ 2242.76179504 3124.11837769 4082.94671631 8697.6003418\n", + " 11582.7331543 14762.29101562 16710.73730469 13062.18212891\n", + " 6950.31835938 3433.13720703 1318.74853516 1505.27539062]\n", + "bkg high : [ 1730.3203125 3908.30224609 4702.74072266 10265.11425781\n", + " 10695.84863281 13876.29345703 11674.34375 12329.84570312\n", + " 7754.8828125 4742.53515625 2675.23242188 2332.11328125]\n", + "mgp8_pp_jjaa_5f\n", + "[406. 451. 431. 412. 443. 404. 377. 455. 416. 386. 401. 378.]\n", + "[236. 251. 258. 221. 228. 244. 240. 265. 259. 226. 227. 282.]\n", + "bkg med : [15399.69929504 17739.33712769 18050.04046631 22048.9753418\n", + " 25938.7175293 27854.41601562 28927.89355469 27807.02587891\n", + " 20431.31835938 15941.94970703 14313.65478516 13754.83789062]\n", + "bkg high : [ 9380.0390625 12044.01318359 13063.55322266 17426.89550781\n", + " 18084.47363281 21783.41845703 19451.84375 20917.50195312\n", + " 16148.1015625 12066.34765625 10031.45117188 11470.67578125]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 14. 32. 42. 69. 105. 151. 150. 123. 86. 41. 33. 15.]\n", + "[ 44. 76. 118. 195. 259. 292. 323. 249. 185. 109. 67. 49.]\n", + "bkg med : [15446.8212471 17847.04456329 18191.40620422 22281.21865845\n", + " 26292.13122559 28362.65856934 29432.77026367 28221.03601074\n", + " 20720.79150391 16079.9543457 14424.73168945 13805.32739258]\n", + "bkg high : [ 9528.13659668 12299.81741333 13460.72290039 18083.23522949\n", + " 18956.24414062 22766.28076172 20539.01586914 21755.56689453\n", + " 16770.76025391 12433.21142578 10256.95458984 11635.59619141]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 3. 19. 34. 57. 72. 90. 99. 79. 71. 34. 19. 9.]\n", + "[ 8. 11. 14. 34. 37. 49. 50. 41. 24. 24. 11. 8.]\n", + "bkg med : [15520.82162857 18315.71360016 19030.07649231 23687.22341919\n", + " 28068.14294434 30582.68786621 31874.80200195 30169.72839355\n", + " 22472.14794922 16918.63208008 14893.40454102 14027.33032227]\n", + "bkg high : [ 9725.47084045 12571.15222168 13806.0581665 18921.90466309\n", + " 19868.91394043 23974.95141602 21772.36450195 22766.91357422\n", + " 17362.76806641 13025.21923828 10528.29150391 11832.93212891]\n", + "add a+jj\n", + "bkg med : [16704.72397232 19630.83664703 20286.87922668 24888.62185669\n", + " 29359.93786621 31760.75817871 32974.13989258 31496.51550293\n", + " 23685.21044922 18044.21411133 16062.72680664 15129.58422852]\n", + "bkg high : [10413.8809967 13303.31726074 14558.64215088 19566.55993652\n", + " 20533.98815918 24686.69750977 22472.44262695 23539.91650391\n", + " 18118.10400391 13684.23876953 11190.22705078 12655.24853516]\n", + "sig med : [ 40.63893127 88.81848907 176.33691406 317.44137573 506.33734131\n", + " 656.01306152 661.59436035 518.5456543 320.51879883 177.57128906\n", + " 92.38354492 41.66210938]\n", + "sig high : [ 306.41033936 554.50787354 898.64819336 1364.05004883 1861.52929688\n", + " 2170.44970703 2165.18505859 1850.50683594 1354.45117188 893.26855469\n", + " 547.13574219 309.03125 ]\n", + "sono dentro\n", + "40.63893127441406\n", + "16704.723972320557\n", + "sono dentro\n", + "88.81848907470703\n", + "19630.83664703369\n", + "sono dentro\n", + "176.3369140625\n", + "20286.87922668457\n", + "sono dentro\n", + "317.4413757324219\n", + "24888.621856689453\n", + "sono dentro\n", + "506.33734130859375\n", + "29359.937866210938\n", + "sono dentro\n", + "656.0130615234375\n", + "31760.758178710938\n", + "sono dentro\n", + "661.5943603515625\n", + "32974.139892578125\n", + "sono dentro\n", + "518.545654296875\n", + "31496.515502929688\n", + "sono dentro\n", + "320.518798828125\n", + "23685.21044921875\n", + "sono dentro\n", + "177.5712890625\n", + "18044.214111328125\n", + "sono dentro\n", + "92.383544921875\n", + "16062.726806640625\n", + "sono dentro\n", + "41.662109375\n", + "15129.584228515625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 21.816643642089574\n", + "sig_high 102.68882431252997\n", + "Naive\n", + "sig_med 6.799026641526537\n", + "sig_high 31.549887482202493\n", + "----------------\n", + "----------------\n", + "(0.4, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 76. 169. 270. 473. 569. 736. 724. 653. 410. 258. 156. 74.]\n", + "[ 63. 106. 154. 222. 250. 298. 282. 235. 232. 123. 111. 69.]\n", + "bkg med : [ 280.37597656 623.47106934 996.08642578 1744.99584961 2099.02270508\n", + " 2715.16943359 2671.1640625 2409.21289062 1512.67578125 951.87890625\n", + " 575.5546875 273.01953125]\n", + "bkg high : [ 232.4163208 391.05194092 568.13818359 819.00439453 922.30224609\n", + " 1099.35302734 1040.28808594 866.90673828 855.83984375 453.74267578\n", + " 409.47509766 254.57226562]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 17. 30. 35. 85. 104. 127. 138. 113. 62. 31. 13. 12.]\n", + "[ 6. 10. 13. 24. 24. 38. 26. 34. 20. 14. 7. 10.]\n", + "bkg med : [ 2837.97973633 5136.88952637 6261.74072266 14533.01342773\n", + " 17745.67504883 21822.22021484 23432.703125 19409.53320312\n", + " 10840.28515625 5615.68359375 2531.34375 2078.36328125]\n", + "bkg high : [1135.10015869 1895.52459717 2523.95288086 4429.73925781 4533.03662109\n", + " 6816.34912109 4951.91699219 5982.11376953 3864.78515625 2560.01025391\n", + " 1462.61962891 1759.06445312]\n", + "mgp8_pp_jjaa_5f\n", + "[555. 607. 588. 540. 597. 562. 536. 643. 579. 530. 562. 561.]\n", + "[ 87. 95. 101. 93. 74. 86. 81. 77. 96. 82. 66. 99.]\n", + "bkg med : [20823.44848633 24807.48327637 25316.61572266 32032.38842773\n", + " 37092.20629883 40034.53271484 40802.453125 40246.75195312\n", + " 29603.50390625 22790.99609375 20743.65625 20258.26953125]\n", + "bkg high : [3954.78375244 4974.48944092 5797.37866211 7443.88378906 6931.38818359\n", + " 9603.62255859 7577.13964844 8477.69580078 6976.33984375 5217.96337891\n", + " 3601.94775391 4968.05664062]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 30. 67. 89. 157. 231. 292. 314. 258. 181. 87. 63. 31.]\n", + "[ 28. 41. 71. 107. 133. 151. 159. 114. 90. 63. 37. 33.]\n", + "bkg med : [20924.42422485 25032.99511719 25616.17593384 32560.8260498\n", + " 37869.71643066 41017.39465332 41859.36669922 41115.10961914\n", + " 30212.69970703 23083.81396484 20955.69677734 20362.60693359]\n", + "bkg high : [ 4049.02780914 5112.48927307 6036.35366821 7804.0291748\n", + " 7379.04553223 10111.8651123 8112.32336426 8861.41601562\n", + " 7279.27685547 5430.01928711 3726.48852539 5079.13354492]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 9. 25. 43. 81. 90. 125. 133. 105. 88. 47. 23. 10.]\n", + "[ 2. 5. 5. 10. 19. 14. 16. 15. 7. 11. 7. 7.]\n", + "bkg med : [21146.42526245 25649.66516113 26676.84622192 34558.8326416\n", + " 40089.74572754 44100.76818848 45140.07666016 43705.14379883\n", + " 32383.35205078 24243.11669922 21523.01513672 20609.26708984]\n", + "bkg high : [ 4098.36139679 5235.82319641 6159.68756104 8050.69720459\n", + " 7847.71453857 10457.19958496 8506.99133301 9231.4173584\n", + " 7451.9440918 5701.35351562 3899.15576172 5251.80078125]\n", + "add a+jj\n", + "bkg med : [22764.81393433 27419.68664551 28391.46340942 36133.4810791\n", + " 41830.60705566 45740.51037598 46704.10791016 45581.39770508\n", + " 34072.85595703 25789.64013672 23162.91357422 22246.24755859]\n", + "bkg high : [ 4352.1397171 5512.93721008 6454.30377197 8321.97747803\n", + " 8063.57196045 10708.06091309 8743.2677002 9456.02575684\n", + " 7731.9753418 5940.546875 4091.67724609 5540.58300781]\n", + "sig med : [ 111.0509491 237.05119324 443.09817505 768.86328125 1171.40087891\n", + " 1467.2277832 1469.78857422 1180.49511719 778.02490234 450.77783203\n", + " 242.66308594 115.35058594]\n", + "sig high : [ 235.98733521 406.28265381 631.89178467 912.62524414 1196.46484375\n", + " 1359.23388672 1357.00585938 1188.54150391 896.94433594 620.06152344\n", + " 396.85644531 235.34472656]\n", + "sono dentro\n", + "111.05094909667969\n", + "22764.813934326172\n", + "sono dentro\n", + "237.0511932373047\n", + "27419.686645507812\n", + "sono dentro\n", + "443.0981750488281\n", + "28391.463409423828\n", + "sono dentro\n", + "768.86328125\n", + "36133.48107910156\n", + "sono dentro\n", + "1171.40087890625\n", + "41830.60705566406\n", + "sono dentro\n", + "1467.227783203125\n", + "45740.51037597656\n", + "sono dentro\n", + "1469.78857421875\n", + "46704.10791015625\n", + "sono dentro\n", + "1180.4951171875\n", + "45581.397705078125\n", + "sono dentro\n", + "778.02490234375\n", + "34072.85595703125\n", + "sono dentro\n", + "450.77783203125\n", + "25789.64013671875\n", + "sono dentro\n", + "242.6630859375\n", + "23162.91357421875\n", + "sono dentro\n", + "115.3505859375\n", + "22246.24755859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 42.977364540017895\n", + "sig_high 105.71679344222584\n", + "Naive\n", + "sig_med 13.34086524033841\n", + "sig_high 32.385269370448846\n", + "----------------\n", + "----------------\n", + "(0.4, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[125. 252. 395. 662. 781. 995. 975. 868. 609. 362. 253. 130.]\n", + "[14. 23. 29. 33. 38. 39. 31. 20. 33. 19. 14. 13.]\n", + "bkg med : [ 461.14727783 929.68066406 1457.23754883 2442.125 2881.18310547\n", + " 3671.00585938 3597.21679688 3202.4453125 2246.87695312 1335.58203125\n", + " 933.43164062 479.62890625]\n", + "bkg high : [ 51.64849854 84.85108948 106.98617554 121.7427063 140.18762207\n", + " 143.87677002 114.36358643 73.78295898 121.74365234 70.0949707\n", + " 51.64892578 47.9597168 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 37. 45. 106. 122. 161. 162. 145. 80. 44. 18. 21.]\n", + "[0. 3. 3. 3. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 3921.43463135 6496.22998047 8227.36450195 18389.56835938\n", + " 21235.98779297 27893.01367188 27969.35742188 25017.015625\n", + " 14282.75195312 7955.51953125 3641.58789062 3639.14453125]\n", + "bkg high : [ 51.64849854 536.19306946 558.32833862 573.08486938 1042.87121582\n", + " 745.66583252 415.25811768 374.67749023 422.63842773 220.54223633\n", + " 352.54345703 198.40698242]\n", + "mgp8_pp_jjaa_5f\n", + "[636. 697. 686. 624. 667. 643. 615. 719. 666. 607. 625. 656.]\n", + "[6. 5. 3. 9. 4. 5. 2. 1. 9. 5. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 5776.02056885 8528.69287109 10228.24926758 20210.38085938\n", + " 23182.27294922 29769.26757812 29763.90820312 27115.03515625\n", + " 16226.11914062 9726.7265625 5465.31835938 5553.33203125]\n", + "bkg high : [ 69.15036011 550.7779541 567.07922363 599.33752441 1054.5390625\n", + " 760.25064087 421.09204102 377.5944519 448.89108276 235.12704468\n", + " 361.29434204 210.0748291 ]\n", + "sig med : [ 277.78277588 536.1942749 933.70800781 1506.21179199 2167.62597656\n", + " 2614.24755859 2613.34570312 2175.59375 1508.06835938 935.03027344\n", + " 537.21289062 280.79492188]\n", + "sig high : [ 69.25883484 107.13945007 141.27639771 175.27624512 200.2409668\n", + " 212.21661377 213.02612305 194.55944824 166.75939941 135.69311523\n", + " 102.29199219 69.87658691]\n", + "----------------\n", + "----------------\n", + "(0.5, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 30. 61. 115. 202. 239. 294. 316. 270. 177. 96. 63. 37.]\n", + "[102. 203. 296. 458. 534. 662. 636. 569. 430. 266. 195. 102.]\n", + "bkg med : [ 110.67533875 225.03988647 424.25219727 745.21844482 881.72094727\n", + " 1084.62744141 1165.74389648 996.02050781 652.94677734 354.140625\n", + " 232.40478516 136.49169922]\n", + "bkg high : [ 376.29492188 748.90942383 1092.00585938 1689.62011719 1969.90722656\n", + " 2442.21142578 2346.4921875 2099.29882812 1586.46484375 981.39453125\n", + " 719.44335938 376.32421875]\n", + "mgp8_pp_h012j_5f_haa\n", + "[10. 13. 17. 40. 50. 61. 71. 59. 28. 16. 5. 6.]\n", + "[ 9. 21. 24. 57. 58. 76. 62. 68. 41. 25. 13. 13.]\n", + "bkg med : [ 1615.14860535 2180.85458374 2981.85571289 6763.10955811\n", + " 8404.08422852 10261.91064453 11847.60327148 9872.52441406\n", + " 4865.52490234 2761.328125 984.65087891 1039.18701172]\n", + "bkg high : [ 1730.3203125 3908.30224609 4702.74072266 10265.11425781\n", + " 10695.84863281 13876.29345703 11674.34375 12329.84570312\n", + " 7754.8828125 4742.53515625 2675.23242188 2332.11328125]\n", + "mgp8_pp_jjaa_5f\n", + "[259. 296. 258. 282. 274. 262. 254. 295. 265. 266. 277. 242.]\n", + "[236. 251. 258. 221. 228. 244. 240. 265. 259. 226. 227. 282.]\n", + "bkg med : [10008.75016785 11773.10458374 11342.66821289 15901.67205811\n", + " 17283.39672852 18752.34814453 20078.79077148 19432.36816406\n", + " 13453.18115234 11381.390625 9961.18212891 8881.49951172]\n", + "bkg high : [ 9380.0390625 12044.01318359 13063.55322266 17426.89550781\n", + " 18084.47363281 21783.41845703 19451.84375 20917.50195312\n", + " 16148.1015625 12066.34765625 10031.45117188 11470.67578125]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 9. 26. 36. 60. 90. 124. 124. 90. 75. 35. 30. 14.]\n", + "[ 44. 76. 118. 195. 259. 292. 323. 249. 185. 109. 67. 49.]\n", + "bkg med : [10039.04286575 11860.61680603 11463.83908081 16103.6227417\n", + " 17586.32278442 19169.71289062 20496.15551758 19735.29418945\n", + " 13705.62414551 11499.19946289 10062.16113281 8928.62304688]\n", + "bkg high : [ 9528.13659668 12299.81741333 13460.72290039 18083.23522949\n", + " 18956.24414062 22766.28076172 20539.01586914 21755.56689453\n", + " 16770.76025391 12433.21142578 10256.95458984 11635.59619141]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 2. 13. 23. 38. 48. 60. 62. 49. 49. 25. 13. 8.]\n", + "[ 8. 11. 14. 34. 37. 49. 50. 41. 24. 24. 11. 8.]\n", + "bkg med : [10088.3764534 12181.28501892 12031.17562866 17040.95916748\n", + " 18770.32681274 20649.72290039 22025.5090332 20943.97680664\n", + " 14914.30627441 12115.87426758 10382.83203125 9125.95898438]\n", + "bkg high : [ 9725.47084045 12571.15222168 13806.0581665 18921.90466309\n", + " 19868.91394043 23974.95141602 21772.36450195 22766.91357422\n", + " 17362.76806641 13025.21923828 10528.29150391 11832.93212891]\n", + "add a+jj\n", + "bkg med : [10843.87742996 13044.71470642 12783.75961304 17863.55096436\n", + " 19569.58267212 21413.96704102 22766.17700195 21804.20141602\n", + " 15687.05041504 12891.53442383 11190.56835938 9831.63476562]\n", + "bkg high : [10413.8809967 13303.31726074 14558.64215088 19566.55993652\n", + " 20533.98815918 24686.69750977 22472.44262695 23539.91650391\n", + " 18118.10400391 13684.23876953 11190.22705078 12655.24853516]\n", + "sig med : [ 34.94377518 74.63909912 147.80657959 257.11187744 405.22070312\n", + " 519.28808594 522.28686523 414.36865234 259.79101562 146.94604492\n", + " 77.88110352 34.59130859]\n", + "sig high : [ 306.41033936 554.50787354 898.64819336 1364.05004883 1861.52929688\n", + " 2170.44970703 2165.18505859 1850.50683594 1354.45117188 893.26855469\n", + " 547.13574219 309.03125 ]\n", + "sono dentro\n", + "34.94377517700195\n", + "10843.877429962158\n", + "sono dentro\n", + "74.63909912109375\n", + "13044.714706420898\n", + "sono dentro\n", + "147.80657958984375\n", + "12783.75961303711\n", + "sono dentro\n", + "257.11187744140625\n", + "17863.55096435547\n", + "sono dentro\n", + "405.220703125\n", + "19569.58267211914\n", + "sono dentro\n", + "519.2880859375\n", + "21413.967041015625\n", + "sono dentro\n", + "522.286865234375\n", + "22766.177001953125\n", + "sono dentro\n", + "414.36865234375\n", + "21804.201416015625\n", + "sono dentro\n", + "259.791015625\n", + "15687.050415039062\n", + "sono dentro\n", + "146.946044921875\n", + "12891.534423828125\n", + "sono dentro\n", + "77.881103515625\n", + "11190.568359375\n", + "sono dentro\n", + "34.59130859375\n", + "9831.634765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 21.323414313683298\n", + "sig_high 102.68882431252997\n", + "Naive\n", + "sig_med 6.646712678826825\n", + "sig_high 31.549887482202493\n", + "----------------\n", + "----------------\n", + "(0.5, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 69. 158. 257. 438. 523. 658. 670. 604. 375. 239. 147. 70.]\n", + "[ 63. 106. 154. 222. 250. 298. 282. 235. 232. 123. 111. 69.]\n", + "bkg med : [ 254.55233765 582.8885498 948.12670898 1615.87353516 1929.34960938\n", + " 2427.33886719 2471.92578125 2228.4296875 1383.54492188 881.77929688\n", + " 542.34960938 258.26171875]\n", + "bkg high : [ 232.4163208 391.05194092 568.13818359 819.00439453 922.30224609\n", + " 1099.35302734 1040.28808594 866.90673828 855.83984375 453.74267578\n", + " 409.47509766 254.57226562]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 13. 24. 28. 73. 84. 99. 107. 93. 49. 27. 11. 9.]\n", + "[ 6. 10. 13. 24. 24. 38. 26. 34. 20. 14. 7. 10.]\n", + "bkg med : [ 2210.36703491 4193.6229248 5160.65063477 12598.52392578\n", + " 14566.95898438 17321.81152344 18569.96484375 16219.84375\n", + " 8755.36523438 4943.80273438 2197.24804688 1612.26953125]\n", + "bkg high : [1135.10015869 1895.52459717 2523.95288086 4429.73925781 4533.03662109\n", + " 6816.34912109 4951.91699219 5982.11376953 3864.78515625 2560.01025391\n", + " 1462.61962891 1759.06445312]\n", + "mgp8_pp_jjaa_5f\n", + "[408. 452. 415. 410. 428. 420. 413. 483. 428. 410. 438. 425.]\n", + "[ 87. 95. 101. 93. 74. 86. 81. 77. 96. 82. 66. 99.]\n", + "bkg med : [15432.11703491 18841.2479248 18609.24438477 25885.08642578\n", + " 28436.84960938 30932.43652344 31953.74609375 31872.0625\n", + " 22625.24023438 18230.36523438 16391.18554688 15384.92578125]\n", + "bkg high : [3954.78375244 4974.48944092 5797.37866211 7443.88378906 6931.38818359\n", + " 9603.62255859 7577.13964844 8477.69580078 6976.33984375 5217.96337891\n", + " 3601.94775391 4968.05664062]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 25. 61. 83. 148. 216. 265. 288. 225. 170. 81. 60. 30.]\n", + "[ 28. 41. 71. 107. 133. 151. 159. 114. 90. 63. 37. 33.]\n", + "bkg med : [15516.26345825 19046.56480408 18888.60952759 26383.23144531\n", + " 29163.87207031 31824.41210938 32923.14453125 32629.37475586\n", + " 23197.41308594 18502.98876953 16593.12890625 15485.89746094]\n", + "bkg high : [ 4049.02780914 5112.48927307 6036.35366821 7804.0291748\n", + " 7379.04553223 10111.8651123 8112.32336426 8861.41601562\n", + " 7279.27685547 5430.01928711 3726.48852539 5079.13354492]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 19. 32. 62. 66. 95. 96. 75. 66. 38. 17. 9.]\n", + "[ 2. 5. 5. 10. 19. 14. 16. 15. 7. 11. 7. 7.]\n", + "bkg med : [15713.5977478 19515.23399353 19677.94595337 27912.56994629\n", + " 30791.8840332 34167.77636719 35291.17529297 34479.39916992\n", + " 24825.43457031 19440.33447266 17012.46777344 15707.90039062]\n", + "bkg high : [ 4098.36139679 5235.82319641 6159.68756104 8050.69720459\n", + " 7847.71453857 10457.19958496 8506.99133301 9231.4173584\n", + " 7451.9440918 5701.35351562 3899.15576172 5251.80078125]\n", + "add a+jj\n", + "bkg med : [16903.3321228 20833.27305603 20888.09243774 29108.13635254\n", + " 32039.9387207 35392.50292969 36495.48974609 35887.8347168\n", + " 26073.48925781 20635.90087891 18289.68261719 16947.58398438]\n", + "bkg high : [ 4352.1397171 5512.93721008 6454.30377197 8321.97747803\n", + " 8063.57196045 10708.06091309 8743.2677002 9456.02575684\n", + " 7731.9753418 5940.546875 4091.67724609 5540.58300781]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sig med : [ 105.35603333 222.87089539 414.56463623 708.53686523 1070.28479004\n", + " 1330.50292969 1330.57421875 1076.68554688 717.34716797 420.18359375\n", + " 228.18017578 108.28808594]\n", + "sig high : [ 235.98733521 406.28265381 631.89178467 912.62524414 1196.46484375\n", + " 1359.23388672 1357.00585938 1188.54150391 896.94433594 620.06152344\n", + " 396.85644531 235.34472656]\n", + "sono dentro\n", + "105.35603332519531\n", + "16903.332122802734\n", + "sono dentro\n", + "222.8708953857422\n", + "20833.273056030273\n", + "sono dentro\n", + "414.56463623046875\n", + "20888.09243774414\n", + "sono dentro\n", + "708.536865234375\n", + "29108.136352539062\n", + "sono dentro\n", + "1070.2847900390625\n", + "32039.938720703125\n", + "sono dentro\n", + "1330.5029296875\n", + "35392.5029296875\n", + "sono dentro\n", + "1330.57421875\n", + "36495.48974609375\n", + "sono dentro\n", + "1076.685546875\n", + "35887.834716796875\n", + "sono dentro\n", + "717.34716796875\n", + "26073.4892578125\n", + "sono dentro\n", + "420.18359375\n", + "20635.90087890625\n", + "sono dentro\n", + "228.18017578125\n", + "18289.6826171875\n", + "sono dentro\n", + "108.2880859375\n", + "16947.583984375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 44.7522332637758\n", + "sig_high 105.71679344222584\n", + "Naive\n", + "sig_med 13.90087275317881\n", + "sig_high 32.385269370448846\n", + "----------------\n", + "----------------\n", + "(0.5, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[118. 241. 382. 627. 735. 917. 921. 819. 574. 343. 244. 126.]\n", + "[14. 23. 29. 33. 38. 39. 31. 20. 33. 19. 14. 13.]\n", + "bkg med : [ 435.32202148 889.09936523 1409.27783203 2313.02197266 2711.4296875\n", + " 3383.22851562 3397.98632812 3021.66210938 2117.74609375 1265.48242188\n", + " 900.2265625 464.87109375]\n", + "bkg high : [ 51.64849854 84.85108948 106.98617554 121.7427063 140.18762207\n", + " 143.87677002 114.36358643 73.78295898 121.74365234 70.0949707\n", + " 51.64892578 47.9597168 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 19. 31. 38. 94. 102. 133. 131. 125. 67. 40. 16. 18.]\n", + "[0. 3. 3. 3. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 3293.8203125 5552.96508789 7126.27392578 16455.06494141\n", + " 18057.22460938 23392.96289062 23106.32226562 21827.32617188\n", + " 12197.58203125 7283.29492188 3307.3515625 3172.88671875]\n", + "bkg high : [ 51.64849854 536.19306946 558.32833862 573.08486938 1042.87121582\n", + " 745.66583252 415.25811768 374.67749023 422.63842773 220.54223633\n", + " 352.54345703 198.40698242]\n", + "mgp8_pp_jjaa_5f\n", + "[489. 542. 513. 494. 498. 501. 492. 559. 515. 487. 501. 520.]\n", + "[6. 5. 3. 9. 4. 5. 2. 1. 9. 5. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 4719.75195312 7133.44555664 8622.18994141 17895.57666016\n", + " 19509.40039062 24853.88671875 24541.00195312 23458.0703125\n", + " 13700.3359375 8704.34570312 4769.25390625 4690.23046875]\n", + "bkg high : [ 69.15036011 550.7779541 567.07922363 599.33752441 1054.5390625\n", + " 760.25064087 421.09204102 377.5944519 448.89108276 235.12704468\n", + " 361.29434204 210.0748291 ]\n", + "sig med : [ 272.08703613 522.01641846 905.17773438 1445.88549805 2066.51000977\n", + " 2477.52001953 2474.27148438 2071.30957031 1447.13085938 904.28417969\n", + " 522.63671875 273.6875 ]\n", + "sig high : [ 69.25883484 107.13945007 141.27639771 175.27624512 200.2409668\n", + " 212.21661377 213.02612305 194.55944824 166.75939941 135.69311523\n", + " 102.29199219 69.87658691]\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.7000000000000001)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 19. 46. 79. 134. 164. 183. 200. 179. 115. 60. 45. 25.]\n", + "[102. 203. 296. 458. 534. 662. 636. 569. 430. 266. 195. 102.]\n", + "bkg med : [ 70.09439087 169.70219421 291.44406128 494.34765625 605.03027344\n", + " 675.12524414 737.84179688 660.3684082 424.24975586 221.33789062\n", + " 166.00341797 92.22412109]\n", + "bkg high : [ 376.29492188 748.90942383 1092.00585938 1689.62011719 1969.90722656\n", + " 2442.21142578 2346.4921875 2099.29882812 1586.46484375 981.39453125\n", + " 719.44335938 376.32421875]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 6. 6. 13. 27. 33. 22. 38. 30. 14. 8. 5. 3.]\n", + "[ 9. 21. 24. 57. 58. 76. 62. 68. 41. 25. 13. 13.]\n", + "bkg med : [ 972.77865601 1072.38591003 2247.25875854 4556.42431641 5569.79003906\n", + " 3984.96508789 6454.83789062 5173.78637695 2530.51147461 1424.91601562\n", + " 918.23974609 543.56591797]\n", + "bkg high : [ 1730.3203125 3908.30224609 4702.74072266 10265.11425781\n", + " 10695.84863281 13876.29345703 11674.34375 12329.84570312\n", + " 7754.8828125 4742.53515625 2675.23242188 2332.11328125]\n", + "mgp8_pp_jjaa_5f\n", + "[151. 174. 137. 145. 157. 140. 123. 155. 124. 136. 169. 139.]\n", + "[236. 251. 258. 221. 228. 244. 240. 265. 259. 226. 227. 282.]\n", + "bkg med : [ 5867.30209351 6712.43278503 6687.98532104 9256.46337891\n", + " 10658.79785156 8522.93383789 10441.76757812 10197.96606445\n", + " 6549.85522461 5833.22851562 6396.21630859 5049.12060547]\n", + "bkg high : [ 9380.0390625 12044.01318359 13063.55322266 17426.89550781\n", + " 18084.47363281 21783.41845703 19451.84375 20917.50195312\n", + " 16148.1015625 12066.34765625 10031.45117188 11470.67578125]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 4. 15. 21. 37. 58. 84. 83. 54. 49. 21. 18. 9.]\n", + "[ 44. 76. 118. 195. 259. 292. 323. 249. 185. 109. 67. 49.]\n", + "bkg med : [ 5880.76551628 6762.92059326 6758.66829681 9381.00006104\n", + " 10854.0168457 8805.66482544 10721.13269043 10379.72167969\n", + " 6714.78161621 5903.91125488 6456.80151367 5079.41320801]\n", + "bkg high : [ 9528.13659668 12299.81741333 13460.72290039 18083.23522949\n", + " 18956.24414062 22766.28076172 20539.01586914 21755.56689453\n", + " 16770.76025391 12433.21142578 10256.95458984 11635.59619141]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 2. 7. 10. 18. 25. 32. 25. 25. 28. 8. 8. 3.]\n", + "[ 8. 11. 14. 34. 37. 49. 50. 41. 24. 24. 11. 8.]\n", + "bkg med : [ 5930.0990963 6935.58815002 7005.33608246 9825.00262451\n", + " 11470.68572998 9595.00088501 11337.8013916 10996.39038086\n", + " 7405.45227051 6101.24719238 6654.13745117 5153.41418457]\n", + "bkg high : [ 9725.47084045 12571.15222168 13806.0581665 18921.90466309\n", + " 19868.91394043 23974.95141602 21772.36450195 22766.91357422\n", + " 17362.76806641 13025.21923828 10528.29150391 11832.93212891]\n", + "add a+jj\n", + "bkg med : [ 6370.56491661 7443.14479065 7404.96401215 10247.9664917\n", + " 11928.65301514 10003.37979126 11696.59143066 11448.52416992\n", + " 7767.15930176 6497.95812988 7147.10913086 5558.87609863]\n", + "bkg high : [10413.8809967 13303.31726074 14558.64215088 19566.55993652\n", + " 20533.98815918 24686.69750977 22472.44262695 23539.91650391\n", + " 18118.10400391 13684.23876953 11190.22705078 12655.24853516]\n", + "sig med : [ 24.97414589 51.87449646 100.90035248 171.32081604 265.61932373\n", + " 336.26037598 339.50823975 276.6887207 175.4375 100.49145508\n", + " 54.82495117 24.38049316]\n", + "sig high : [ 306.41033936 554.50787354 898.64819336 1364.05004883 1861.52929688\n", + " 2170.44970703 2165.18505859 1850.50683594 1354.45117188 893.26855469\n", + " 547.13574219 309.03125 ]\n", + "sono dentro\n", + "24.974145889282227\n", + "6370.564916610718\n", + "sono dentro\n", + "51.87449645996094\n", + "7443.144790649414\n", + "sono dentro\n", + "100.90035247802734\n", + "7404.964012145996\n", + "sono dentro\n", + "171.32081604003906\n", + "10247.966491699219\n", + "sono dentro\n", + "265.61932373046875\n", + "11928.653015136719\n", + "sono dentro\n", + "336.2603759765625\n", + "10003.379791259766\n", + "sono dentro\n", + "339.50823974609375\n", + "11696.591430664062\n", + "sono dentro\n", + "276.688720703125\n", + "11448.524169921875\n", + "sono dentro\n", + "175.4375\n", + "7767.1593017578125\n", + "sono dentro\n", + "100.491455078125\n", + "6497.9581298828125\n", + "sono dentro\n", + "54.824951171875\n", + "7147.109130859375\n", + "sono dentro\n", + "24.3804931640625\n", + "5558.8760986328125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 19.438286010473973\n", + "sig_high 102.68882431252997\n", + "Naive\n", + "sig_med 5.974690719794809\n", + "sig_high 31.549887482202493\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.8)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 58. 143. 221. 370. 448. 547. 554. 513. 313. 203. 129. 58.]\n", + "[ 63. 106. 154. 222. 250. 298. 282. 235. 232. 123. 111. 69.]\n", + "bkg med : [ 213.97210693 527.5489502 815.31518555 1365.00732422 1652.7121582\n", + " 2017.86376953 2043.78857422 1892.68945312 1154.79882812 748.95898438\n", + " 475.93945312 213.98828125]\n", + "bkg high : [ 232.4163208 391.05194092 568.13818359 819.00439453 922.30224609\n", + " 1099.35302734 1040.28808594 866.90673828 855.83984375 453.74267578\n", + " 409.47509766 254.57226562]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 9. 17. 24. 60. 67. 60. 74. 64. 35. 19. 11. 6.]\n", + "[ 6. 10. 13. 24. 24. 38. 26. 34. 20. 14. 7. 10.]\n", + "bkg med : [ 1567.99749756 3085.15270996 4426.05004883 10391.84326172\n", + " 11732.67895508 11044.76416016 13177.03076172 11521.43945312\n", + " 6420.52148438 3607.47070312 2130.83789062 1116.66015625]\n", + "bkg high : [1135.10015869 1895.52459717 2523.95288086 4429.73925781 4533.03662109\n", + " 6816.34912109 4951.91699219 5982.11376953 3864.78515625 2560.01025391\n", + " 1462.61962891 1759.06445312]\n", + "mgp8_pp_jjaa_5f\n", + "[300. 330. 294. 273. 311. 298. 282. 343. 287. 280. 330. 322.]\n", + "[ 87. 95. 101. 93. 74. 86. 81. 77. 96. 82. 66. 99.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [11289.87249756 13779.21520996 13953.48754883 19238.74951172\n", + " 21811.02270508 20701.82666016 22315.59326172 22636.78320312\n", + " 15721.11523438 12681.22070312 12824.90039062 11551.48828125]\n", + "bkg high : [3954.78375244 4974.48944092 5797.37866211 7443.88378906 6931.38818359\n", + " 9603.62255859 7577.13964844 8477.69580078 6976.33984375 5217.96337891\n", + " 3601.94775391 4968.05664062]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 20. 50. 68. 125. 184. 225. 247. 189. 144. 67. 48. 25.]\n", + "[ 28. 41. 71. 107. 133. 151. 159. 114. 90. 63. 37. 33.]\n", + "bkg med : [11357.18959808 13947.50791931 14182.36502075 19659.48010254\n", + " 22430.33813477 21459.15270996 23146.98706055 23272.95092773\n", + " 16205.79931641 12906.72412109 12986.45507812 11635.63134766]\n", + "bkg high : [ 4049.02780914 5112.48927307 6036.35366821 7804.0291748\n", + " 7379.04553223 10111.8651123 8112.32336426 8861.41601562\n", + " 7279.27685547 5430.01928711 3726.48852539 5079.13354492]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 13. 19. 42. 43. 67. 59. 51. 45. 21. 12. 4.]\n", + "[ 2. 5. 5. 10. 19. 14. 16. 15. 7. 11. 7. 7.]\n", + "bkg med : [11554.52391815 14268.17619324 14651.03421021 20695.48364258\n", + " 23491.00830078 23111.83215332 24602.33959961 24530.9675293\n", + " 17315.81347656 13424.73095703 13282.45898438 11734.29931641]\n", + "bkg high : [ 4098.36139679 5235.82319641 6159.68756104 8050.69720459\n", + " 7847.71453857 10457.19958496 8506.99133301 9231.4173584\n", + " 7451.9440918 5701.35351562 3899.15576172 5251.80078125]\n", + "add a+jj\n", + "bkg med : [12429.6215744 15230.78361511 15508.62991333 21491.78051758\n", + " 24397.88916016 23980.80480957 25424.65600586 25531.16088867\n", + " 18152.70996094 14241.21533203 14244.74414062 12673.25634766]\n", + "bkg high : [ 4352.1397171 5512.93721008 6454.30377197 8321.97747803\n", + " 8063.57196045 10708.06091309 8743.2677002 9456.02575684\n", + " 7731.9753418 5940.546875 4091.67724609 5540.58300781]\n", + "sig med : [ 95.38618469 200.10731506 367.66110229 622.74230957 930.68347168\n", + " 1147.47509766 1147.70507812 938.64111328 632.94335938 373.69677734\n", + " 205.10449219 98.06884766]\n", + "sig high : [ 235.98733521 406.28265381 631.89178467 912.62524414 1196.46484375\n", + " 1359.23388672 1357.00585938 1188.54150391 896.94433594 620.06152344\n", + " 396.85644531 235.34472656]\n", + "sono dentro\n", + "95.38618469238281\n", + "12429.621574401855\n", + "sono dentro\n", + "200.10731506347656\n", + "15230.783615112305\n", + "sono dentro\n", + "367.6611022949219\n", + "15508.629913330078\n", + "sono dentro\n", + "622.7423095703125\n", + "21491.780517578125\n", + "sono dentro\n", + "930.6834716796875\n", + "24397.88916015625\n", + "sono dentro\n", + "1147.47509765625\n", + "23980.804809570312\n", + "sono dentro\n", + "1147.705078125\n", + "25424.656005859375\n", + "sono dentro\n", + "938.64111328125\n", + "25531.160888671875\n", + "sono dentro\n", + "632.943359375\n", + "18152.7099609375\n", + "sono dentro\n", + "373.69677734375\n", + "14241.21533203125\n", + "sono dentro\n", + "205.1044921875\n", + "14244.744140625\n", + "sono dentro\n", + "98.06884765625\n", + "12673.25634765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 46.270596123082875\n", + "sig_high 105.71679344222584\n", + "Naive\n", + "sig_med 14.305699693289927\n", + "sig_high 32.385269370448846\n", + "----------------\n", + "----------------\n", + "(0.6000000000000001, 0.9)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[107. 226. 346. 559. 660. 806. 805. 728. 512. 307. 226. 114.]\n", + "[14. 23. 29. 33. 38. 39. 31. 20. 33. 19. 14. 13.]\n", + "bkg med : [ 394.7399292 833.76123047 1276.46630859 2062.19067383 2434.71679688\n", + " 2973.63330078 2970.00976562 2685.921875 1889. 1132.66210938\n", + " 833.81640625 420.59765625]\n", + "bkg high : [ 51.64849854 84.85108948 106.98617554 121.7427063 140.18762207\n", + " 143.87677002 114.36358643 73.78295898 121.74365234 70.0949707\n", + " 51.64892578 47.9597168 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[15. 24. 34. 81. 85. 94. 98. 96. 53. 32. 16. 15.]\n", + "[0. 3. 3. 3. 6. 4. 2. 2. 2. 1. 2. 1.]\n", + "bkg med : [ 2651.44915771 4444.49609375 6391.67333984 14248.41918945\n", + " 15222.81640625 17115.85986328 17713.97460938 17128.671875\n", + " 9862.6015625 5946.91210938 3240.94140625 2677.27734375]\n", + "bkg high : [ 51.64849854 536.19306946 558.32833862 573.08486938 1042.87121582\n", + " 745.66583252 415.25811768 374.67749023 422.63842773 220.54223633\n", + " 352.54345703 198.40698242]\n", + "mgp8_pp_jjaa_5f\n", + "[381. 420. 392. 357. 381. 379. 361. 419. 374. 357. 393. 417.]\n", + "[6. 5. 3. 9. 4. 5. 2. 1. 9. 5. 3. 4.]\n", + "add a+jj\n", + "bkg med : [ 3762.45111084 5669.22265625 7534.75146484 15289.43676758\n", + " 16333.81835938 18221.02978516 18766.65625 18350.48242188\n", + " 10953.19140625 6987.9296875 4386.93554688 3893.25585938]\n", + "bkg high : [ 69.15036011 550.7779541 567.07922363 599.33752441 1054.5390625\n", + " 760.25064087 421.09204102 377.5944519 448.89108276 235.12704468\n", + " 361.29434204 210.0748291 ]\n", + "sig med : [ 262.11804199 499.25012207 858.27130127 1360.09130859 1926.90820312\n", + " 2294.49511719 2291.54931641 1933.43847656 1362.98632812 857.953125\n", + " 499.65332031 263.51464844]\n", + "sig high : [ 69.25883484 107.13945007 141.27639771 175.27624512 200.2409668\n", + " 212.21661377 213.02612305 194.55944824 166.75939941 135.69311523\n", + " 102.29199219 69.87658691]\n", + "ttH cut: 0.5\n", + "x_1: 0.6000000000000001\n", + "x_2: 0.8\n", + "Significance: 115.39934350459579\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [122, 122.5, 123, 123.5, 124,124.5, 125, 125.5, 126, 126.5, 127, 127.5, 128]\n", + "great_ttHc, great_x1, great_x2, great_max_sig = findx1x2(df_great,ttHcut,x_1,x_2,varT= \"prediction_ttH\",var = \"prediction_great\")" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "41630351", + "metadata": {}, + "outputs": [], + "source": [ + "#4 categories\n", + "#df_small_med (mx<350, medium purity)\n", + "#df_small_high (mx<350, high purity)\n", + "#df_great_med (mx>350, medium purity)\n", + "#df_great_high (mx>350, high purity)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "b1e4d0e9", + "metadata": {}, + "outputs": [], + "source": [ + "df_small_med = df_small.loc[(df_small.prediction_small >= small_x1) & (df_small.prediction_small <= small_x2) & (df_small.prediction_ttH >= small_ttHc)]\n", + "df_small_high = df_small.loc[(df_small.prediction_small > small_x2) & (df_small.prediction_ttH >= small_ttHc)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "edeb3d74", + "metadata": {}, + "outputs": [], + "source": [ + "df_great_med = df_great.loc[(df_great.prediction_great >= great_x1) & (df_great.prediction_great <= great_x2) & (df_great.prediction_ttH >= great_ttHc)]\n", + "df_great_high = df_great.loc[(df_great.prediction_great > great_x2) & (df_great.prediction_ttH >= great_ttHc)]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "51e1ccb4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_great_med,EDGES,\"Mx > 350 GeV, medium purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "fa566a91", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_great_high,EDGES,\"Mx > 350 GeV, high purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "609f0472", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small_high,EDGES,\"Mx < 350 GeV, high purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "c4b0148d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small_med,EDGES,\"Mx < 350 GeV, medium purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "f6cb8031", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_high,EDGES,\"Mx > 350 GeV, high purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "af872d47", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LK2TsGz68P716WRPW1apVYv/+Qxn74uMPU6XKZZn6qlWrBv/8c4izZxMoVaokQ4b0Y8iQfjRo0JPU1FSMMQwadBOTJo3KEkebNk05dOgYmzZt57ff/mDOnDeyjTtdWFgxAEJCQjJ+Tt++ePEixhjGjr2He+65LdPz3nhjFu5cnVqkSChpaVbSTExMcvnaRYsWzUikISHCxYupTvtzTLYiQpEiRTKderR/nRIlInIO0uY//+nDZ58tZM6cJcyYMcnt5+VVUI44dI4jUPV28lDZufvuWxg//n4aNqyTqX3fvgO89toM/vjjG7799mfWrMm6NP2GDX/SqtWtDBs2jrp1r2TjxgVMn/5iplNQ6Y4dO5nxTffChUR++OE36ta9EiBjngHg66+/p0GDqwGIienEnDlLSEpKZu/e/ezaFUeLFtGZ+i1ePIKhQ2/hgQeezfjwS01NJTnZ+tvs1Ok6vvxyGUePWisknzx5in37rG/mIkL//t0ZNGgMPXpcnzFHYj3vLg4cOOzBb/KSrl3bMWPGlyQkWKvHHjhwmKNHT9C+fXO+/voHLlxI5OzZBBYt+snp86OiqrJ+/VYAvvpqea5isDd3rnWV2KpVsURGliIyshRRUVXZsMG6fW3Dhj/ZuzfnlSRKlSrB2bPnMrUNHnwzb7zxEQDXXHN1nmN1V1COOPSqqtxw50NcRwP5rVq1Sjz88KBMbcYYhg59ildfHUOVKpczffqLDB78JOvWfZXpwzUiIoyZMydRr17ONdsOHTrKoEFjSE1NIy0tjf79u9OrV0cARo9+mY0b/0JEiIqqyvvvPwtYH0T9+/egfv3uFClShHfeeSbLFVUAL7zwKE8//QYNGvSkVKkSRESEM2hQX6pUuYxixYrx/POPcOONQ0hLMxQtavWTfu5+4MBevPLKh0yefGlEkpaWxu7d/1CuXBmPf58AN97Ylu3b/6Z1a2vkVbJkcT799FWaNLmGAQN60KhRH2rUqEK7ds2cPv+ZZx5g6NBxvPjie06TsKfKli3NddcN4MyZBGbMeBGAfv268vHH39CoUQzNmzekdu2oHPspX74sbdo0oUGDnnTv3p5XXhnD5ZdXoF69q3K8qMDbAmYFwNxo1qyMiY1t5+8wCpC8Jo78Sk7BNZLZvn0U9epV8XcYQWPr1p3MmPElr7+e80RyoOvQ4U5efXUMzZo19En/589foGHDXmzY8A2RkaWcHrN9+0Hq1Xs1U5vI4vXGGOeZ0w16qkopFVAaNKhdIJKGr/3ww6/UrduNBx/8j8uk4St6qkoppXxkxYpPfdZ3585t+OeflT7rPztBmThUoAiuU0ZKKe8IylNVSiml/EdHHCqf6X0dSgW7oEwcmavjqoC232G7utOjlFJBJCgTh06OByjHJAHgWAFaBxheck/Oh3jkfS/357lhw57iscfupn591/elvPHGLEaMGEDx4u7fXa28LygTh/KXHCbDc7VMgJ66KmxmzZpPXFx8pgq6AB9++GKOz33jjY+4884YTRx+ppPjSim3PPfcO9St25UuXQYzcOCjGWXB163bTHR0b1q37s8TT7xEgwbuFSt01KHDncTGWhVrly9fRevW/WnS5CZuvfUhEhLO8dZbH3Pw4FE6dryLjh3/Q2pqKoMHj6FBg540bNiL//1vptfeq8qeJg6lVI5iY7fw1VfL+OOPBcyfP4XY2K0Z+4YMGct7701k9ep5hIbm/SPl+PGTPP/8VH74YRYbNnxDs2YNeP31mTz00F1UqXIZP/30MT/99AkbN27nwIEjbN26hC1bFjNkiPOqwMr79FSVyoY/7tPQU1eBaNWq9fTp04mIiHAAeve26lydOnWGs2fPcd11TQC4/fbeLF68IsvzT5z4l06drJpcJ0+eJjk5hW++sRYh+uSTVzIVefz9901s27abNm2s6rbJySm0bt04S59XXlmdPXv28+CDz9KzZwduvLGt996wypYmDmUnEG/oC8SYCh9XNe3crXVXvnxZNm5cCLie47Dvs0uXNsye/b9s+yxbNpJNmxaybNkq3nnnM+bN+zZfS4sXZnqqSimVo7Ztm7Jo0U8kJiaRkHCOJUusUhdly0ZSqlQJfv99I0DGwkx50apVI379dQO7d+8DrEJ+O3day7zalxY/fvwkaWmGfv268txzD2eUKVe+F5QjDr2PQ6n8vXy2efNoYmJu4NprY6hRowrNmjUgMtJa5nX69BcZPvy/lCgRQYcOLfJUcE9EqFixHLNmTWbgwEdJSrIKmT7//CPUrl2TESP60737MCpXvow33niKIUPGZiyINGnS43l/o8otWlZd5Z7jfRvuXI5bCKcsCkpZ9YSEc5QsWYLz5y/Qvv0dTJv2HE2aXJPRDjB58vscOnSMN9/8r8f9N2zYi4UL36VmTb1L1Jt8UVY9KEccKkDk6r4NFaxGjHiabdt2k5iYxKBBfWnS5BoAlixZwaRJ73PxYio1alRh1qyXPO67S5fBNGxYW5NGkNDEoZRyy+efv+60fcCAngwYkLt7N9J9//2sPD1f5S+dHFdKKeURTRxKKaU8oqeqVP7SarlKBT0dcSillPJIQI04RKQE8DPwjDFmsb/jUT6gZda9Y7+Xy6pX9819IbGxW/j44294662nXR6zceM2Dh48So8eHXwSg/I+n444RGSGiBwVka0O7d1EZIeI7BaRJ+12jQHm+TImpZRvREV1zNLWrFnDbJMGwMaNf7F06UpfhaV8wNenqmYB3ewbRCQUeAfoDtQHBopIfRHpDGwDjvg4JqVULrgqq56dFSvW0KvXCADOnTvP3XePpXnzm2ncuA8LFvxAcnIy48e/ydy5S2nUKIa5c5ewcuVaGjWKoVGjGBo37sPZswm+fmvKQz49VWWM+VlEohyaWwC7jTF7AERkDtAHKAmUwEomF0RkqTEmzZfxKaXcY19W/eLFizRp0pemTRt41McLL7zLDTe0YsaMSZw6dYYWLW6hc+frePbZh4mN3cKUKc8A0Lv3PbzzzjO0adOUhIRzhIeH+eItqTzwxxxHVTJfWxMPtDTGPAAgIoOB466ShoiMAEYAXHGFrgKmVH5wVVb9hRfe5YsvvgXg4MGjNGoUA0CbNk14550JmfpYvvxXFi78v4yRSmJiEv/8czDLa7Vp04THHpvEHXfEcPPNN1KtWglfvS2VS/5IHOKkLaNgljFmVnZPNsZMA6aBVavKq5EppZxyVdNu3Lj7GDfuPsCa40gvne6qj6++eps6da7M1L5mzeZM208+eQ89e3Zg6dKVtGp1Kz/8MIu6da/K4ztQ3uSPy3HjyXz1fjUg69eObIhIbxGZdvp0ilcDU0o556qsuie6dm3L229/kpGE/vhjG5C5VDrA33//Q8OGdRgzZgTNmjXgr7/2eOdNKK/xx4hjHXC1iNQEDgC3Abd70oExZhGwqFmzMsN9EJ9Sgc9Hl8+6kl1Z9ZyIWCcZnn76fh555AWio3tjjCEqqiqLF0+jY8eWTJ78Po0axTB27D2sWrWen35aQ2hoCPXr16J79+t9+dZULvg0cYjIbKADUEFE4rHuz5guIg8Ay4BQYIYxxqMVWHQ9DqXy36hRQ5kw4aGMsuqPP353pv1xcT9lec6JE6coVy4SgIiIcN5//7ksx5QrV4Z16+ZnbOe1YKLyPV9fVTXQRftSYGke+tURhz84lgtRhYqrsuquLFz4I+PGva7LuRZAAXXnuLt0xKFU/nNVVt2VmJhOxMR08lE0yp+CslaVMWaRMWZEZGRRf4eilFKFTlAmDqWUUv4T3KeqahTX8+7uCtTy5VpmXamgE5SJI2NyPFonx5UTgfZl4iKQ7O8gVKF1Ea//TQRl4lB+4lgSXflPXy+XVf865/tCXpj8Lp/PWURoaCghIcL77zxHyxbXMuzep3js4bupX6+Wxy8bFxdPr773sPWPJW4/p2S5RiSc3JixPevj+cSu38KUN5/hvWmzKV48nLvu7OtxLMp9QZk4Mp2qUkr53Orf/2Dx0p/YsOYbwsKKcfz4SZKTrcoNH773op+ju+TeEU7vAFBeFpST4xlXVZXWq6qUyg+HDh+lQvmyhIUVA6BChXJUqXI5AB263Ens+i2ANRoYN/51rm3Wm1btbuXIkeOAVUakVbtbaX7dzYyf+CYlyzXK8hqpqak88eRLNL/uZqKb9ub9D+Z4HOeE597i1detIorrYjcT3bQ3rdv354knX6JBY+vGwvPnL9D/9oeJbtqbAXc8TMu2txC7fgupqakMHjaGBo170rBJL/735kyPX7+wCMrEoZTKXzd2bsv++EPUvuZGRj44gZU/r3V63Llz52nVohGbYhfRvm1zPphhrcv28OPP8/ADg1j323yqVL7M6XOnz/yCyMhSrPttPut++4oPZsxj796sJ+cvXEikUfOYjMf4Z9902t+Q4WN5b8pEVv88j9DQSx91U9/7nLJlSrN5/SKefup+1m+wClds3LSdAweOsPWPJWzZsJghg/p59DsqTDRxKK84fOwY++Ljc3wcPnbM36GqXChZsgTrf/+aaVOfo2LFsgy48xFmfTw/y3HFihWlV0+r5HrTJtcQty8egNVrNnJrP2tNt9tv6+30NZb/8Csff/oNjZrH0LLtLZw4cYpdu/dlOS4iIpyN6xZmPJ4d/3CWY06dOsPZhHNc17pJltdc9Vsst/W3Rh8NrqlNdMM6AFxZszp79u7nwUee5btlP1O6tHu1uAojneMoLNy5qiIPl8ImJSWRmJRMuO1UhjOJSU4uLdI1yINGaGgoHa5vSYfrW9KwQR0++uRrBt91c6ZjihYtmlHUMDQ0lIsXU93u3xjD2/97mq43tstzrK7KwFv7nLeXLRvJptiFLPt+Fe+89xnzvvqWGdO0XIozQTni0DmOwBQeVowa1aq5fGSXVDyyP4eH8rodO/awa1dcxvbGTdupcUUVt5/fqsW1fPX1MgDmzFvs9JiuXdry7rTPSUmxJt137tzLuXPncxVv2bKRlCpZgt/XbLS95qWrttpe15R5X1qLT23bvpstW3cCcPz4SdLSDP36duW5CQ+z4Q+Paq8WKkE54lD5RD+EA5cbl896U8K58zz46HOcOnWGIkWKUOuqK5g2NWulW1feeHUcdw4ZxWtvzKBn9w5OS7IPu7s/cfsO0KRlX4wxVKxYjm++mJrrmKe//yLD7/svJUpE0KF9CyIjSwEw8t7bGTR0DNFNe9O4UT2iG9YhsnQpDhw8wpDhY0lLsxYfnfTc47l+7YJOshvSBbpm0WVM7JK8D2uVjeOpKsfEkc19HPvirXPZNapVy9Mxbp2qCrKEtv3UKOrVcf/beUF0/vwFIiLCERHmzFvM7LlLWPDVuz59zYSEc5QsaS07O/mV9zl06Bhvvv5fUlNTSUm5SHh4GH///Q+dug9i59ZlFCvmpRFxgNm+4yD1yryaqU2uWLzeGNMst33qiEMFFi1BUiCt37CVBx55FmOgTJlSzHjf93MHS75dwaSX3+fixVRqXFGFWR++BFhJrOONd5GSkoIx8O5bEwps0vCVoEwcOjmuVHBp17Y5m2Lz98qHAbf2ZMCtWReFKlWqJLGrs14Rptynk+NKKaU8EpQjDlWIBNl8hlKFQVCOOJRSSvmPjjiUCjITlx5l2+Ekr/ZZv1IYz/RwXgpEKUc64lBeEVcskm3hFRgQFePysS28AnHFIv0datDbdjjJq4nD3f4cCxPO+ng+Dzw8EchcXDBdVO2OHD9+0mtxusNZHAArVq6h100jcny+q5idFWV0ZcrUT6hVrzMSVjvb9//RJ/O5un4Xrq7fhY8+Ca7Jeh1xqEvyMJ9wPqQo50Kyv1ghp/3KffUrhTH3bu9cqzxghk4keVOb65rSq0dHOtz4H5fHnDx5ionPTyF29XxEhKat+hLTqxNlywbHF6ugHHGISG8RmXb6TIq/Q1F2SqSlMDduoctHibQUKp09pkUPVSb79x9yWt5j46Zt1IvullHqxBhDnQZd+W31BqftBw8eAWDTlu3c0PUurq7fhQ+mz83o78zZBPreOpL613bn3vvHZ9wh7syFC4l06zU00/Pd1bhRfaKisrnJFVj2/Sq6dGpDuXJlKFs2ki6d2vDd8l88fi1/CcoRhy4d63+Hjx0jKenS6Y1KKYbDpSpm/xzb/nKJ1hoNToseqoCVXs483cl/TxPT84aM7f+9PZNPZy/I2D548Khb/SYlJTNo6BhmfTiZpk0aZLQ3urY+z098lNffnMm7UyayYuUaal11Bde1buK0PX19kM1bdvD7L19w7tx5Gre8iZ7dOwCwdt1mtm1cSo0aVenWayjzv1nOLTd3yxJPQsJ5brvzUe6686YsKwmePZtAuxtud/o+Pv/4dbdXQTxw4AjVq1fO2K5WrRIHDhxx67mBICgTh/I/x2q4h0tVtCWGsy6f810dq9z23LiFwKUSJCo4pJczT5e+ZGu6Rx8cwqjHhmZsR9XumKWP//tpNQ899nyW9hMnT9E9ZhhH43/P1N6rR0fG/vc1UlNTmT7ry4w1Mly1A/Tp3ZmIiHAiIsLpeH1L1q7bTJkypWnRPJorr7wCgIEDerHq1/VOE0efW+5j9OPDuWNgTJZ9pUqVzPQ7yC1npZ5sRYWDgiYO5VoOa4ynV8MFGB1l/ZENicv7H5UquG7o2DrL+uKnTp2hR5/hPP7I3VmODwsrRssW0Xyz8AdW/rKWGdNezLYdsn4Ap5d5F4cdjtvp2rRuyrfLfub223pnOcZbI45q1SqxYuWajO34+MN0uL6lW88NBJo4lApC2w4neW1Se9vhJOpXCvNKX7mx758DjH5sGDf16eJ0/603d2fYfeMY2L9XpppSrtoXLPqRsaPv5dy586z4eS2Tnx/Fzl1xrF23mb1791OjRlXmfrGUEcMGOH29Z595iOdenMrIByfw7pSJmfZ5a8TRtUtbnnr6df799zRgLWI16fngqcYblJPjShVm9SuFefWD3tv9eera6HoukwZA1xvbceFCEoP+09et9hbNounZZzit2vXn6bEjM+Y+WrdqzJP/fZUGjXtSM6oafbN5zTdeG0diUhKjx77s8ft5a8rHVLuyHfHxh4luFsOwe58CIHb9loyfy5Urw9NPjaT5df1ofl0/xo+7n3Llynj8Wv6iZdWVax6UUR9gO1U1N5tTVY7HOC2znvvlFwKWllXPu6jaHYn97SsqVCjnVru6RMuqq4LPMVkVwESiVLDTxKGUCnhxO3/yqF35VsDMcYhIPRF5T0S+FJH7/B2PUkop53JMHCJys4jsEpHTInJGRM6KyBl3OheRGSJyVES2OrR3E5EdIrJbRJ4EMMZsN8bcC/QHcn3uTSmllG+5M+J4GYgxxkQaY0obY0oZY0q72f8sINMdNiISCrwDdAfqAwNFpL5tXwywCvjRzf6VnzgWNdwWXt6t520LL69FD5UKcu7McRwxxmzPTefGmJ9FJMqhuQWw2xizB0BE5gB9gG3GmIXAQhFZAnzurE8RGQGMALiiakRuwlJe4FjUsH7iCerbSom44rhfix7m0vevwZEd3u3z8jrQJXjuI1D+5c6II1ZE5orIQNtpq5tF5OY8vGZVMtdhjQeqikgHEXlLRN4Hlrp6sjFmmjGmmTGmWcVyusC8PzkWNXzm8G/ZHv/M4d+yFD1UuXBkBxzZ6cX+drqViLSsunvuGPQ4dRp0pUHjntw9YiwpKa7/Pz9zJoGqNdtm/B6DhTsjjtLAeeBGuzYD5LaAvLP7/I0xZgWwwq0ORHoDvWvVKJ7LEJQKcpfXhjuneaevT3P+QFXuu+O23nw6y7pv4va7HuPDGV9w3z3Oy5Q8PeENrm/XIj/D84ocRxzGmCFOHlmLyrgvHrBfSKAacNCTDowxi4wxIyJL66kOpYJdQSur3qN7B0QEEaFFs2jiDxx2etz6DVs5cvQ4N3Zu6/Fr+JvLEYeIjDbGvCwib2ONMDIxxjyUy9dcB1wtIjWBA8BtgPN07Do2HXEUFs7uXtebAv1Cy6p7VuQwJSWFTz5fwJuvjctybFpaGo+PmcwnM17hx59Wu/V7CiTZnapKnxCPzW3nIjIb6ABUEJF44BljzHQReQBYBoQCM4wxWb9uZEPX41Aq/2lZdc+KHI58aALt2zanXdvmWfZNfe8zenS9PtOaHMHEZeKwfThjjPkIQERKW5vG9YILWfsY6KJ9KdlMgOdERxxKBafCUlZ94vNvc+zYSd6f95zTY1ev2cgvv8YyddrnJCScIzk5hZIlizP5hSecHh9ocpwcF5FmwEyglLUpp4C7jTHrfRybSzriCEDV/obiCTkfd74kxF/l+3gKuiM7vTepfWSnNdnuJwWtrPqHM+ax7PtV/PjdR4SEOJ9G/uyj1zJ+Th+5BUvSAPcux50BjDTGRBljagD3YyUSpS4pngAROSSOiAT3kovK3uV1vPtBf3ltq08/KWhl1e994BmOHDlO6/b9adQ8hmdfmAJkLqse7HIsqy4ivxpj2uTUlp/sTlUN3/XLDTker3Ipm7Lq3x5PBKB7hXCrofYm69+d17p+ksMxWfpwV5BNjmtZ9bzTsuq554uy6i5HHCLSRESaAGtF5H3bDXrXi8hU3Lzfwlf0clyllPKf7OY4XnPYfsbu5+Bd/UnlyuFjx0hKSsrYrpRiOFyqYp77rXT2GPsSL01AhoWFUali3vtVBYuWVQ8s2V1VlfVaugChV1Xlv6SkJBKTkgkPsyYhD5eqaEscbl9kl0V64ilnq2GVmJSc5ziVUr4XlAs56VVV/hEeVixjmdfRtmVgh9iWgX2pcjh/RYRC5UOuO4goQd0LqYyxlVn6ro713cRxKVmlVGALysShAs9fEaHsiAilzgXXx+yICM2/gJRSPpNdyZHKxphsvj4qlVmdC6nMXOn6Ttgh3XbnYzQF10vrp/PXqTiv9lm3TBRjmg7N+UClyP4+jhki8ruITLZdURUwoxMR6S0i006f0bLcqvD561QcO/7d67X+dvy7161EpGXV3TP0nqe4tllvopv25pbbHiQh4ZzT4z76ZD5X1+/C1fW78NEnuS027h/ZTY53F5FwrFpTfYFXReQf4DvgO2PMP/kTotPYdI5DFWp1ytZkZifn5Sw8NeTHp73Sj7L875WnKF26JACPPfEiU979lCefuCfTMSdPnmLi81OIXT0fEaFpq77E9OpE2bLBsSJmtneOG2MSjTHfGWMett0s8jhWspkiImvzJUIVkAYVX8D40lOtm/pqb4KQVPeeGJKa8ZzxpacyqPiCnJ+jCrSCVlY9PWkYY7hwIclpTaxl36+iS6c2lCtXhrJlI+nSqQ3fLf/F49fyF49OPxlj9mLdtztVRHT5vWCSzV3gGTy4I7tGkQNEhR4EwqyGtFDrkR2H/dbzwVqSRQU6LavufpHDIcOfZOl3K6lfrxavvfxklmMPHDiSqTJutWqVOHDgiFu/r0CQ63kLY4xedF/QZJNc4opFcj6kaMZluKNCPiAutQrX7LR96Gd3GW66JFtpkZ3WH1dcdDznQ4oywNbn4OPzKJ6WQo1cvwHlS1pW3f2y6jM/mExqaioPPvIcc79Ymik+sEYjjlwU6w1IATPh7Qm9ATD/nQ8pyrmQSyVeiqel5HnNcMfn2/evCqbCUlYdIDQ0lAG39uCV1z/MkjiqVavEipVrMrbj4w/T4fqWTvsNRB4lDhEpC1Q3xmz2UTxu0clx/yiRlpJxsx61T+S5v6jkM8ClGwC/zWMiKkx2/LvXa5PaO/7dS52yNb3SV24UpLLqxhj+/vsfatWqgTGGRUv+j7p1rsxyXNcubXnq6df599/TACz/4VcmPf+4619SgHFnPY4VQIzt2I3AMRFZaYx5zLehqUDmeKf4jshk6pzOedprR2QyQ663ndZyuJNcuadumSiv9lenbE2v9+mJa6PrcW10PZf7c1tW/Z/9hzLKqu/cFZdRVn3L1p20b9s8x7Lqd48Yy+ixL/PypNFuvxdjDIOGjeHMmQSMMVwbXZd337aST+z6Lbz3wWw+fO9FypUrw9NPjaT5ddZIZPy4+ylXrozbr+Nv7pRV/8MY01hEhmGNNp4Rkc3GmOj8CdG1ZtFlTOySdv4OIzi4MzmeDccS6EO67bbuFD95qSR63VPFGLOpvMs+Xrr2BH+VuTQ1tqNconXT4He1nL6GS1pWvdDRsuq554uy6u6cqioiIpWB/kDWVddVoZXTneKOHJOK3kmuVHByJ3FMBJYBq4wx60TkSmCXb8NSKg/yOLryulHAPn8HEdzivv8JzmE93GhXdk4CXl540J3Eccj+tJQxZo+IvO7dMJRSSgULd9Ycf9vNtnyjtaqUUsp/squO2xq4DqgoIvZXUJUG/FofWy/HLeQC7VSUUoVMdqeqigElbceUsms/A9ziy6CUUq4d/uADkvZ4rzouQNiVNak0XL+HKfe4PFVljFlpjJkItDLGTLR7vG6M0clxpfwkac9eEvd6L3Ek7t3rViIq2ahRpu1Z8+fzwERbWfW33uLV6Q5l1Tt25PjJfC6r7iQOgBVr1tBrhBtl1V3E7Pjes7N3/35a3nILV3fpwoCHHyY52Xl1po/mz+fqLl24uksXPppfQMqq2wkTkWlAlP3xxpgbXD5DKeVT4TVrUmPSizkf6IZ9Y718yU0hN+bVV3l08GBu69WLe8ePZ/qXX3Lf7ZnLlJw8dYqJU6YQO99WVr1vX2I6daJsZHCUVXcncXwBvAd8CLhZOzuf/IOe71YqyO0/dIhjJ0/S5JprMrVv3LaNgY89xsL33uPqqCiMMdTt1o2ZkyYx9KmnsrT/9PHHAGzavp0b7rqL/YcOMXrYMIYPsEqLnElIoO/IkezYu5f2zZszdcIEQkKcn3S5kJhI3/vvp9+NN2Y83x3GGP5v9Wo+f+01AAb17cuEt9/OkjiWrVpFlzZtKFemDABd2rThu19+YWCvXm6/lj+5kzguGmPe9XkkSqmAdiExkUYxdmXVT58m5ga7suozZ/LpAruy6kfdLKuenMygMWOYNXkyTRvYlVWvX5/nH32U12fO5N2JE1mxZg21rriC65o0cdpe5XJbWfUdO/j9iy84d/48jW+6iZ4dOgCwdvNmti1dSo2qVek2dCjzly/nlm5OyqqfP89tjz7KXTfdxF19HcqqJyTQ7nYXRQ5ff53LypWjTOnSFClifbRWq1SJA0eylks/cOQI1SvblVV3cVygcidxLBKRkcDXQFJ6ozEmf09eKqX8KiI8nI0L7cqqz59P7Ba7supDhjBqqF1Z9Y5OyqqvXs1Dzzspq37qFN2HDePo7w5l1Tt2ZOxrtvLpX37JkH79sm0H6NO5MxHh4USEh9OxZUvWbt5MmdKlaREdzZVX2Mqq9+rFqvXrnSaOPvfdx+jhw7kjxklZ9ZIlM/0OHB1zMj/irAqv07LqLnsNPO4kjkG2f5+wazNA1pKPSimVjRtat2brEoey6mfO0GP4cB6/20lZ9WLFaBkdzTc//MDKtWuZ8eKL2bZD1g9gj8uqN23Ktz//zO29nZRVz2HEUe+qqzh15gwXL16kSJEixB8+TJXLLstybLVKlVixxq6s+uHDdGhZgMqqG2Pyrd6yiNwE9AQuA94xxizPr9dW2bs85DjhkgS1E6yGkPCcV/xzR/pSskCNf0uSaMLQFQFzlrh3r9cmtRP37iW8ph/Lqh84wOhhw7ipi4uy6t27M2zcOAb2ciir7qJ9wY8/Mvbeezl3/jwr1q5l8qhR7IyLY+3mzezdv58aVasyd+lSRriYu3j2oYd4bupURk6YwLsTHcqq5zDiAOjYqhVffvcdt/XqxUdff02fTp2yHNO1bVueev11/j1tK6v+669Mejx4yqrneOe4iBQXkf/arqxCRK4WEbdncERkhogcFZGtDu3dRGSHiOwWkScBjDHfGGOGA4MB92eklM+FSxLhYndZoTtLxebEoY9wSSb8TCL74uMzHoePHcvbaxRAYVfW9OoHfXjNmoRd6b/EcW29ei6TBkDXdu24kJTEIIf5BlftLaKj6Tl8OK369+fpkSMz5j5aN27Mk6++SoOePalZrRp9s3nNN8aNIzEpidEvv+zx+3lp1ChenzmTWp07c+LUKYbeeisAsVu2MOwpK9mXK1OGp0eOpHm/fjTv14/x99+fMVEeDNwpqz4XWA/cZYxpICIRwGpjTCO3XkCkPZAAfGyMaWBrCwV2Al2AeGAdMNAYs822/zXgM2PMhuz6blamjIltp2XV88O2f/8GoH7ZqwAy1tTwpDquI8c+tpm9cCqNEuesdcwTk5IJDytGjWrBPQLZPmoU9apoWfW8iOrYkdivvqJCuXJutatLth88SL1XHcqqL/Z9WfWrjDEDRGQggDHmgrg6OeiEMeZnEYlyaG4B7DbG7AEQkTlAHxHZDkwGvnWVNERkBDAC4IqICHfDUB46fOwYSUlJlxouGoj07fSdudZaOrbGZitR7IuP9+nrKaVyx53EkWwbZRgAEbkKu6urcqkqsN9uOx5oCTwIdAYiRaSWMeY9xycaY6YB08AaceQxDuVCUlJSxjd+wEoaZdypiamU98X99JNH7cq33EkcE4DvgOoi8hnQBmsOIi+cfXU1xpi3gLdyfLJIb6B3reLF8xiGyo79aaLRnfYRF2Yocc6zpWJzYr+U7LkSF4lKEjw/qxzgjMEY4/IqHqV8xRgDOUxH5EaOXyFtVzbdjJUsZgPNjDEr8vi68UB1u+1qwEF3n2yMWWSMGRFZtGgew1Duigsz7Au7tF3ndDHqnspb4qh7qlim5LMvzHqdgib8yBFOJCU5vXZfKV8xxnAiKYlwH9xYmOOIQ0QWYiWMhcYYb62ztQ64WkRqAgeA2wDnF0c7j0lHHH5QIylvk+GOHJeS7d8jzmt9B5JqX39NPHDs8stBRx0qvxhD+JEjVPv6a6937c6pqtewLo2dLCJrgbnAYmNMojsvICKzgQ5ABRGJB54xxkwXkQewlqQNBWYYY/50N+iM9TjK6HocKvAVPXeOmp9+6u8wlPIad24AXAmstF1CewMwHJiBtaBTjowxA120LwWWuh+qUkqpQODOiAPbVVW9sUYeTYCPfBmUG/HoqaoCKk1CGBBl1QgafHwexdNSqOHnmJRSmblz5/hcYDvWaOMdrPs6HvR1YNnRyfGCKRRDiEnL2D4XUpTzIfrfWKlA486IYyZwuzEmYNbi0BGH78UVi+R8SFFG2779p8mUTB/qvhCeZv0vNjPOqgX0bVqKT19PKZU7LkccIjIawBjzHdbluPb7vLP0WC7piMP3zocU5Zzdt/0Qk0YoejmpUir7U1W32f081mFf1iL2qsApkZbC3LiFzI1bSIm0ixkjAqVU4ZZd4hAXPzvbVkopVUhklziMi5+dbecrEektItNOp+g5cKWUym/ZJY5rReSMiJwFom0/p283zKf4nNI5DqWU8h+XV1UZY7ywvJtSSqmCJijrZOupKqWU8p+gTBx6qkoppfwnKBOHUkop/3GrVpVS+SYkFWpvAqDGvyVJNGFYy7UopQKFJg7l1OUhxwmXJKidYDWEhEOaj6+XcOg/XJJ9+3pKqVzRxKGcCpekzB/caaG+TxxJ4da/O2sBkGj+9u3rKaVyJSgThxY5zB+JphjsrGdtVD7k32CUUgEjKCfH9aoqpZTyn6AccaiCa0dkMkOut0Y3t35rCE8T6vs5JqVUZpo4lFOHi0JiiOEV24f4jshk6pwu5tPXrHsqc/9JIeDnsmhKKSc0cSinEkOM7YPbUud0sSwf7N42ZlP5TNtL03b79PWUUrmjiUO5FJYGM1dW9msMlY7BvtD4TG1hYWFUqljRTxEppTRxqIB1uKIAhvInL7UlJum9HUr5W1AmDr0ct3D47nrrvpF5Sy/dOb4vPt7V4UqpfKKX4yqllPJIUCYOpZRS/hOUp6qU9x0+doykpKSM7UqpcFjnn5VSTuiIQwGQlJSUaeL5cMX0yWmllMpMRxwqQ3hYMWpUsyain+gRB8DdS/0YEJAmIQyIisnYHnx8HsXTUqjhx5iUKuw0cSgA4opFcj6kKKNtH9JpMoUQk+bXmEIx4BDDuRC9IEIpf9PEoQA4H1I004dyiEmzPrj9KDwtFYCZcQsz2r5N03XmlfK3gEkcInIlMA6INMbc4u94CqMSaSnMtX1ID6l70c/RKKUClU8nx0VkhogcFZGtDu3dRGSHiOwWkScBjDF7jDFDfRmPUkqpvPP1VVWzgG72DSISCrwDdAfqAwNFRCtnK6VUkPBp4jDG/AycdGhuAey2jTCSgTlAH1/GoZRSynv8cR9HVWC/3XY8UFVEyovIe0BjERnr6skiMkJEYkUk9liyFrxTSqn85o/JcWd3lRljzAng3pyebIyZBkwDaFamjK7yU9CFpELtTRmbNf4tSaIJA6q5fo5Syqf8kTjigep229WAg550oNVxC4m00CxN4aKjTKX8zR+JYx1wtYjUBA4AtwG3e9KBMWYRsKhZmTLDfRBfoXR5yHHCJQlqJ1gNIeFOP7jzVVK49e/OWhlNieZvPwWjlErn08QhIrOBDkAFEYkHnjHGTBeRB4BlQCgwwxjzp4f96ojDy8IlKfO3+bRQ/ycOrLXOh9jWPQe49VtDeJqgl+Ep5T8+TRzGmIEu2pcCua6CpCMOzzhWvnUmOdUQV7Eoz1UuA8COYsnUOe3bNcZz4myNc2sddJ3aUsqfAubOcU/oiMMz6ZVvw8NcJ4KDFWFXpUvXLdQ5XczpB3d+GrOpfJa2pWm7/RCJUspeUCYOHXF4zr7yrTPp1XDnLa2cTxHlXqVjsC/U9RKyYWFhVKqoi4ko5StBmThU4WWtEWIo73hbqY39miJKKd8IysShp6oKr++utybs5y11PnraF+96JKKU8o6gTBx6qsozjmttOBMI628opYJDUCYO5RnHtTacCYT1N9zluCqgPV0hUCnfC8rEoaeqPGe/1oYzwbL+hrNVAe3pCoFK+V5QJg49VVV4OVsV0J6uEKiU7/mjOq5SSqkgFpQjDlXIOVTMtafVc5XyvaBMHDrHUYjlUD9Lq+cq5XtBmTh0jqMQc1Ix155Wz1XK94IycaRLTk7WG77cUDk1iYMVDUO6uT5mR0QIdS4Ex30cjhVz7Wn1XKV8L6gTR5oJjvsO/O1gRcPuStkfU+dCGnXPBv61EjkVXtTquUr5XlAnjhCRbAv3KculAoZRfo3DG5xVzLWn1XOV8r3A/4rphIj0FpFpF3XEoZRS+S4oE4cxZpExZkQRkZwPVkop5VVBfapKKWdyWq9DKZU3mjhUgZLTeh1KFXbeWLNGE4cqUHJar0Opws4btzAE5RyHUkop/9HEoZRSyiNBmTj0clyllPKfoEwcejmuUkr5T1AmDqWUUv6jiUMppZRH9HJcVeCkSQgDomL8HYZSAWnw8Xl57kMThypQQjFggqM8vFL+cC6kaJ770MShCpTwtFQAZsYt9HMkSgWmb9NS8tyHznEopZTySFCPOC6EFNFz2W5IkymE6OkbpZSXBEziEJESwFQgGVhhjPksp+cUkxTGl57q89iC3UskkkLez2sqpRT4OHGIyAygF3DUGNPArr0b8CYQCnxojJkM3Ax8aYxZJCJzgRwTRwiGaxJP+Cb4AiQ0tQShhWjAsSMi1OWa5EoVdjFLL+a5D1+POGYBU4CP0xtEJBR4B+gCxAPrRGQhUA3YYjss1Z3OE0UYUvkyb8ZbIO0olkyd09mv1V1Q1L3g1v86ShVaaaF5nxz3aeIwxvwsIlEOzS2A3caYPQAiMgfog5VEqgEbyWbSXkRGACMArioV7v2gC6A6p4tR91ThSBxjDiVaP+ys5d9AlApQX6fuz3Mf/pjjqArYRx4PtATeAqaISE9gkasnG2OmAdMAri4dYWaurOzDUJVSSjnyR+JwVpnQGGPOAUPc6kCkN9C7lo44lDMRCVB7k7+jUCoghW0skec+/HEfRzxQ3W67GnDQkw7Sq+N6NSpVMJwvCRdK+jsKpQJWCHlfjsIfI451wNUiUhM4ANwG3O5JBzriUC7FX+XvCJQKaGnsznMfPh1xiMhsYDVQR0TiRWSoMeYi8ACwDNgOzDPG/OlJvzriUEop//H1VVUDXbQvBZbmtl8dcSillP8EZa0qHXEopZT/BGXiUEop5T9BmThEpLeITPN3HEopVRgFZeLQU1VKKeU/QZk4lFJK+Y8Yk/ebQfJb+lVVwH8Ajy7l9ZMKwHF/B+EGjdN7giFG0Di9LVjirGOMKZXbJwdl4kgnIrHGmGb+jiMnGqd3BUOcwRAjaJzeVlji1FNVSimlPKKJQymllEeCPXEEyyW5Gqd3BUOcwRAjaJzeVijiDOo5DqWUUvkv2EccSiml8pkmDqWUUh4J6MQhIjNE5KiIbLVrKyci34vILtu/Ze32jRWR3SKyQ0S6+jHGW0XkTxFJE5FmDsfne4zZxPmKiPwlIptF5GsRKROgcT5ni3GjiCwXkSqBGKfdvlEiYkSkQiDGKSITROSA7fe5UUR6BGKctvYHbbH8KSIvB2KcIjLX7ncZJyIb/RmnixgbicjvthhjRaRFnmI0xgTsA2gPNAG22rW9DDxp+/lJ4CXbz/WBTUAYUBP4Gwj1U4z1gDrACqCZXbtfYswmzhuBIrafX/L37zKbOEvb/fwQ8F4gxmlrr4611sw+oEIgxglMAEY5OTbQ4uwI/ACE2bYvC8Q4Hfa/Boz3Z5wufpfLge62n3sAK/ISY0CPOIwxPwMnHZr7AB/Zfv4IuMmufY4xJskYsxfYDbTAx5zFaIzZbozZ4eRwv8Roi8lZnMuNtbAWwO9Yy/gGYpxn7DZLQMbalwEVp83/gNF2MUJgxulMoMV5HzDZGJNkO+ZogMYJgIgI0B+Y7c84XcRogNK2nyO5tFx3rmIM6MThwuXGmEMAtn8vs7VXBfbbHRdvawskgRzj3cC3tp8DLk4ReUFE9gN3AONtzQEVp4jEAAeMMZscdgVUnDYP2E7/zbA73RtocdYG2onIGhFZKSLNbe2BFme6dsARY8wu23YgxfkI8Irtb+hVYKytPVcxBmPicEWctAXatcYBGaOIjAMuAp+lNzk5zK9xGmPGGWOqY8X4gK05YOIUkeLAOC4ltUy7nbT58/f5LnAV0Ag4hHV6BQIvziJAWaAV8AQwz/atPtDiTDeQS6MNCKw47wMetf0NPQpMt7XnKsZgTBxHRKQygO3f9OFrPNb55XTVuDQcCxQBF6OIDAJ6AXcY20lPAjBOO58D/Ww/B1KcV2GdI94kInG2WDaISCUCK06MMUeMManGmDTgAy6dmgioOLHimW8sa4E0rCKCgRYnIlIEuBmYa9ccSHEOAubbfv6CPP43D8bEsRDrl4Dt3wV27beJSJiI1ASuBtb6Ib7sBFSMItINGAPEGGPO2+0KtDivttuMAf6y/RwwcRpjthhjLjPGRBljorD+IJsYYw4HUpyQ8YUrXV8g/eqbgIoT+Aa4AUBEagPFsCrPBlqcAJ2Bv4wx8XZtgRTnQeB62883AOmn03IXY35ciZCHqwNmYw2lU7D+EIcC5YEfbW/8R6Cc3fHjsK4K2IHtCgI/xdjX9nMScARY5s8Ys4lzN9b5zY22x3sBGudXWB9um4FFQNVAjNNhfxy2q6oCLU7gE2CL7fe5EKgcoHEWAz61/bffANwQiHHa2mcB9zo5PlA+k9oC67GuoFoDNM1LjFpyRCmllEeC8VSVUkopP9LEoZRSyiOaOJRSSnlEE4dSSimPaOJQSinlEU0cSimlPKKJQymllEc0cahCTUTusa2dcb1d2wO2ts4e9BMlIhfs12KwtXcVkV9sayBsEZFZ9ut0OOmnlohscWgLE5G9InKNbT2F5Oz6UMrXNHGowi4a6w7qepBRrHAocAzr7mpP/G2MaZS+ISK3Yq0fM8gY0wyrqOAuIDybPvYA1UXE/m9zBLDSGPOnrf9AqRumCilNHKqwa4hVoqGubfshrCJwacaYI7ntVERKAG8Dtxtj9gAYq7DgC8ZWz0hEaorIAttoZK2I1DFW4cF/gCjbMRHA41iLLykVEDRxqMKuHjAPqCsikcAA4DcuFf7LrR7AJmPMn852ikhR4EPgMdtoZALWipYA27mUyO4HFhpj4vIYj1JeU8TfASjlLyJSHThhjNkjIpdhrdz3NtYCQpvz2P012CUfEXkLqyppgjGmFdbKldcAX1lLTFAE+MV2+Hagjoj8jJU4WuUxFqW8SkccqjCL5tI8xlmgG9ZyxA2BLSLSUUSeBRCRiSJyn8N2vWz6vmC/YYx5CBiFVa0U4FpgnDGmke3RwBhzn21f+ojjYeCzvJwyU8oXNHGowqwhlxLHK8ADxphUW/tmY8xPQFURaQAkGWPeddjenk3fy4CbRaQKZKxH3QWrPDhYZa+7pk+Ci0hD2zFgJY4WWMv5vuKl96qU1+ipKlWYNcRa6wNjzGK79vrANrvtR7GW3nS1nYUxZqOI/Bf4TkRSsdZGiMVaCwNgBtAR2C4iF4Ctxpg7bft22GIbZ4w5nZs3ppQvaeJQhZYx5g4X7ZfZbR4CfjLGJLvYzq7/z7i0jrvjvgvALS72JaF/myqA6akqpbJXCViRzXa6VCDS8QZAbxKRCFv/RbHW31bKL3QFQKWUUh7REYdSSimPaOJQSinlEU0cSimlPKKJQymllEc0cSillPKIJg6llFIe0cShlFLKI5o4lFJKeeT/AXQOPk0HJ3fvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_med,EDGES,\"Mx > 350 GeV, medium purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "9ac36e1b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_small_high,EDGES,\"Mx < 350 GeV, high purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "242a9424", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_med,EDGES,\"Mx > 350 GeV, medium purity\")" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "5bd1ee3f", + "metadata": {}, + "outputs": [], + "source": [ + "m_1 = np.arange(start = 112, stop = 122, step = 2)\n", + "m_2 = np.arange(start = 126, stop = 136, step = 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "188c2a78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([112, 114, 116, 118])" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m_1" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "818da8f6", + "metadata": {}, + "outputs": [], + "source": [ + "EDGES = [122, 123, 124, 125, 126, 127, 128]" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "ecabf0fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(112, 126), (112, 128), (112, 130), (112, 132), (114, 126), (114, 128), (114, 130), (114, 132), (116, 126), (116, 128), (116, 130), (116, 132), (118, 126), (118, 128), (118, 130), (118, 132)]\n", + "----------------\n", + "----------------\n", + "(112, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 41. 317. 1040. 1047. 372. 62.]\n", + "[ 11. 91. 297. 314. 89. 9.]\n", + "bkg med : [ 151.25631714 1169.46801758 3836.70532227 3862.45898438 1372.4765625\n", + " 228.74609375]\n", + "bkg high : [ 40.58095551 335.71520996 1095.68554688 1158.41162109 328.33959961\n", + " 33.20288086]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 30. 98. 103. 34. 3.]\n", + "[ 0. 10. 31. 21. 7. 3.]\n", + "bkg med : [ 452.15100098 5682.88745117 18580.53735352 19358.55859375\n", + " 6487.75 680.09375 ]\n", + "bkg high : [ 40.58095551 1840.1887207 5759.55126953 4317.8046875 1381.47045898\n", + " 484.54467773]\n", + "mgp8_pp_jjaa_5f\n", + "[1546. 1470. 1431. 1458. 1492. 1445.]\n", + "[585. 590. 563. 592. 572. 569.]\n", + "bkg med : [50552.21350098 53320.07495117 64953.88110352 66631.15234375\n", + " 54884.5 47552.28125 ]\n", + "bkg high : [18998.23720551 20959.8762207 24004.27001953 23502.3046875\n", + " 19917.84545898 18923.70092773]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 17. 142. 478. 508. 146. 22.]\n", + "[ 3. 35. 99. 92. 21. 2.]\n", + "bkg med : [50609.43304443 53798.02563477 66562.7590332 68341.06347656\n", + " 55375.91162109 47626.32714844]\n", + "bkg high : [19008.33477306 21077.6811142 24337.48919678 23811.96243286\n", + " 19988.52819824 18930.43261719]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 35. 79. 91. 32. 4.]\n", + "[ 1. 5. 32. 30. 10. 0.]\n", + "bkg med : [50806.76737976 54661.36352539 68511.43225098 70585.7487793\n", + " 56165.25537109 47724.99511719]\n", + "bkg high : [19033.00156307 21201.01506805 25126.82672119 24551.96511841\n", + " 20235.19567871 18930.43261719]\n", + "add a+jj\n", + "bkg med : [55317.58183289 58950.77758789 72687.04553223 74840.1472168\n", + " 60518.86474609 51941.45996094]\n", + "bkg high : [20739.44199276 22922.04045868 26768.60699463 26278.24636841\n", + " 21903.15661621 20589.64550781]\n", + "sig med : [ 8.74698353 71.47502899 223.3460083 220.06109619 73.23956299\n", + " 10.08581543]\n", + "sig high : [ 3.03340578 28.09747124 95.46614838 99.2864151 29.963974 3.92633057]\n", + "sono dentro\n", + "8.746983528137207\n", + "55317.58183288574\n", + "sono dentro\n", + "71.47502899169922\n", + "58950.777587890625\n", + "sono dentro\n", + "223.34600830078125\n", + "72687.04553222656\n", + "sono dentro\n", + "220.06109619140625\n", + "74840.14721679688\n", + "sono dentro\n", + "73.23956298828125\n", + "60518.86474609375\n", + "sono dentro\n", + "10.0858154296875\n", + "51941.4599609375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.3054259951244087\n", + "sig_high 1.631630674631331\n", + "Naive\n", + "sig_med 0.9921373925715313\n", + "sig_high 0.6962638748323704\n", + "----------------\n", + "----------------\n", + "(112, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 41. 297. 992. 995. 347. 60.]\n", + "[ 11. 111. 345. 366. 114. 11.]\n", + "bkg med : [ 151.25631714 1095.68383789 3659.63989258 3670.57421875 1280.24023438\n", + " 221.3671875 ]\n", + "bkg high : [ 40.58095551 409.4987793 1272.76831055 1350.25048828 420.56982422\n", + " 40.58129883]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 30. 93. 99. 34. 3.]\n", + "[ 0. 10. 36. 25. 7. 3.]\n", + "bkg med : [ 452.15100098 5609.10327148 17651.2355957 18564.8671875\n", + " 6395.51367188 672.71484375]\n", + "bkg high : [ 40.58095551 1913.97229004 6688.87036133 5111.43261719 1473.70068359\n", + " 491.9230957 ]\n", + "mgp8_pp_jjaa_5f\n", + "[1486. 1407. 1375. 1388. 1429. 1381.]\n", + "[645. 653. 619. 662. 635. 633.]\n", + "bkg med : [48607.83850098 51204.69702148 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[22862.20238018 25240.84341431 29773.47076416 29740.66082764\n", + " 24224.05554199 22857.66455078]\n", + "sig med : [ 8.30005741 68.50637054 213.29077148 210.65640259 70.11129761\n", + " 9.76660156]\n", + "sig high : [ 3.4803319 31.06651688 105.52101135 108.69110107 33.0922699\n", + " 4.24554443]\n", + "sono dentro\n", + "8.300057411193848\n", + "53193.796058654785\n", + "sono dentro\n", + "68.5063705444336\n", + "56631.37255859375\n", + "sono dentro\n", + "213.290771484375\n", + "69682.02465820312\n", + "sono dentro\n", + "210.65640258789062\n", + "71354.27905273438\n", + "sono dentro\n", + "70.11129760742188\n", + "58195.873291015625\n", + "sono dentro\n", + "9.7666015625\n", + "49671.3310546875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.254011178512871\n", + "sig_high 1.700224379015682\n", + "Naive\n", + "sig_med 0.9694324354539827\n", + "sig_high 0.7273933960717878\n", + "----------------\n", + "----------------\n", + "(112, 130)\n", + 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+ "[ 16. 133. 463. 493. 138. 22.]\n", + "[ 4. 44. 114. 107. 29. 2.]\n", + "bkg med : [47073.78594208 49243.74133301 61914.7121582 63006.7878418\n", + " 50773.16357422 43556.88183594]\n", + "bkg high : [22543.98187923 25631.96488953 28985.56158447 29121.86828613\n", + " 24567.9276123 22995.97070312]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 7. 33. 72. 81. 32. 4.]\n", + "[ 2. 7. 39. 40. 10. 0.]\n", + "bkg med : [47246.45348358 50057.7456665 63690.71826172 65004.80078125\n", + " 51562.50732422 43655.54980469]\n", + "bkg high : [22593.31545925 25804.63243103 29947.56646729 30108.53833008\n", + " 24814.59509277 22995.97070312]\n", + "add a+jj\n", + "bkg med : [51437.40270233 53961.987854 67562.86279297 68874.02734375\n", + " 55560.12451172 47507.26855469]\n", + "bkg high : [24617.33792019 27909.99571228 31892.54888916 32219.73364258\n", + " 26838.30993652 25019.68554688]\n", + "sig med : [ 7.98082447 65.79450989 205.69346619 202.99423218 67.23834229\n", + " 9.47930908]\n", + "sig high : [ 3.79956484 33.7787323 113.11793518 116.35327148 35.96517944\n", + " 4.53283691]\n", + "sono dentro\n", + "7.9808244705200195\n", + "51437.40270233154\n", + "sono dentro\n", + "65.79450988769531\n", + "53961.987854003906\n", + "sono dentro\n", + "205.69346618652344\n", + "67562.86279296875\n", + "sono dentro\n", + "202.99423217773438\n", + "68874.02734375\n", + "sono dentro\n", + "67.23834228515625\n", + "55560.12451171875\n", + "sono dentro\n", + "9.47930908203125\n", + "47507.2685546875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.2111108664663575\n", + "sig_high 1.7553716800414445\n", + "Naive\n", + "sig_med 0.9521453502731093\n", + "sig_high 0.7492303384111877\n", + "----------------\n", + "----------------\n", + "(112, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 40. 280. 917. 909. 316. 53.]\n", + "[ 12. 128. 420. 452. 145. 18.]\n", + "bkg med : [ 147.56713867 1032.96728516 3382.97192383 3353.26904297 1165.83398438\n", + " 195.54101562]\n", + "bkg high 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27014.66545105 31098.59020996 30654.9498291\n", + " 25730.63464355 24605.68444824]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 7. 31. 71. 80. 32. 4.]\n", + "[ 2. 9. 40. 41. 10. 0.]\n", + "bkg med : [45752.07680511 48625.71112061 61553.03381348 63447.04370117\n", + " 50382.78637695 42044.29931641]\n", + "bkg high : [24087.69213772 27236.6665802 32085.26171875 31666.28662109\n", + " 25977.30212402 24605.68444824]\n", + "add a+jj\n", + "bkg med : [49808.25453949 52410.19158936 65276.36193848 67219.97729492\n", + " 54295.7824707 45753.03759766]\n", + "bkg high : [26245.54370022 29461.58650208 34178.9609375 33873.71044922\n", + " 28085.5814209 26772.28405762]\n", + "sig med : [ 7.75736141 64.13461304 199.02189636 197.63092041 65.3230896\n", + " 9.38354492]\n", + "sig high : [ 4.0230279 35.43886185 119.78929138 121.71659851 37.88046265\n", + " 4.62860107]\n", + "sono dentro\n", + "7.75736141204834\n", + "49808.254539489746\n", + "sono dentro\n", + "64.13461303710938\n", + "52410.19158935547\n", + "sono dentro\n", + "199.0218963623047\n", + "65276.36193847656\n", + "sono dentro\n", + "197.63092041015625\n", + "67219.97729492188\n", + "sono dentro\n", + "65.32308959960938\n", + "54295.782470703125\n", + "sono dentro\n", + "9.383544921875\n", + "45753.03759765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.1794671340424654\n", + "sig_high 1.7940314192986038\n", + "Naive\n", + "sig_med 0.9389268455799201\n", + "sig_high 0.7653868387863304\n", + "----------------\n", + "----------------\n", + "(114, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 43. 329. 1084. 1093. 390. 63.]\n", + "[ 9. 79. 253. 268. 71. 8.]\n", + "bkg med : [ 158.63467407 1213.73864746 3999.01635742 4032.20214844 1438.88671875\n", + " 232.43554688]\n", + "bkg high : [ 33.20259857 291.44506836 933.35998535 988.70800781 261.93383789\n", + " 29.51367188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 32. 101. 104. 37. 4.]\n", + "[ 0. 8. 28. 20. 4. 2.]\n", + "bkg med : [ 459.52935791 6028.0526123 19194.19018555 19678.76074219\n", + " 7005.5078125 834.23242188]\n", + "bkg high : [ 33.20259857 1495.02380371 5145.88439941 3997.65380859 863.72290039\n", + " 330.40820312]\n", + "mgp8_pp_jjaa_5f\n", + "[1643. 1572. 1523. 1549. 1597. 1546.]\n", + "[488. 488. 471. 501. 467. 468.]\n", + "bkg med : [53702.99810791 56970.6776123 68559.47143555 69924.44824219\n", + " 58808.1953125 50982.60742188]\n", + "bkg high : [15850.53072357 17309.27380371 20409.22814941 20233.18505859\n", + " 15997.44165039 15496.53320312]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 17. 148. 490. 525. 147. 22.]\n", + "[ 3. 29. 87. 75. 20. 2.]\n", + "bkg med : [53760.21765137 57468.82336426 70208.74169922 71691.58081055\n", + " 59302.96411133 51056.65332031]\n", + "bkg high : [15860.62829113 17406.88357544 20702.05725098 20485.6234436\n", + " 16064.75854492 15503.26489258]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 37. 83. 102. 35. 4.]\n", + "[ 1. 3. 28. 19. 7. 0.]\n", + "bkg med : [53957.55198669 58381.49475098 72256.08190918 74207.60449219\n", + " 60166.30883789 51155.32128906]\n", + "bkg high : [15885.29508114 17480.88394165 21392.72747803 20954.29226685\n", + " 16237.42578125 15503.26489258]\n", + "add a+jj\n", + "bkg med : [58751.77464294 62968.54162598 76700.14831543 78727.53808594\n", + " 64826.30493164 55666.50097656]\n", + "bkg high : [17308.78726864 18904.37612915 22766.63079834 22415.70535278\n", + " 17599.22460938 16867.96020508]\n", + "sig med : [ 9.19390965 76.20433807 236.78788757 235.67642212 78.44274902\n", + " 10.53271484]\n", + "sig high : [ 2.58647919 23.36785316 82.02458191 83.6711731 24.76077271 3.47943115]\n", + "sono dentro\n", + "9.193909645080566\n", + "58751.774642944336\n", + "sono dentro\n", + "76.20433807373047\n", + "62968.54162597656\n", + "sono dentro\n", + "236.7878875732422\n", + "76700.14831542969\n", + "sono dentro\n", + "235.67642211914062\n", + "78727.5380859375\n", + "sono dentro\n", + "78.4427490234375\n", + "64826.304931640625\n", + "sono dentro\n", + "10.53271484375\n", + "55666.5009765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.3882992754421712\n", + "sig_high 1.5047732690256133\n", + "Naive\n", + "sig_med 1.0257701709794869\n", + "sig_high 0.6460025931572841\n", + "----------------\n", + "----------------\n", + "(114, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 43. 309. 1036. 1041. 365. 61.]\n", + "[ 9. 99. 301. 320. 96. 10.]\n", + "bkg med : [ 158.63467407 1139.95446777 3821.95092773 3840.31738281 1346.65039062\n", + " 225.05664062]\n", + "bkg high : [ 33.20259857 365.2286377 1110.44262695 1180.546875 354.1640625\n", + " 36.89208984]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 32. 96. 100. 37. 4.]\n", + "[ 0. 8. 33. 24. 4. 2.]\n", + "bkg med : [ 459.52935791 5954.26843262 18264.88842773 18885.06933594\n", + " 6913.27148438 826.85351562]\n", + "bkg high : [ 33.20259857 1568.80737305 6075.20336914 4791.28173828 955.953125\n", + " 337.78662109]\n", + "mgp8_pp_jjaa_5f\n", + "[1583. 1509. 1467. 1479. 1534. 1482.]\n", + "[548. 551. 527. 571. 530. 532.]\n", + "bkg med : [51758.62310791 54855.29968262 65804.85717773 66849.63183594\n", + " 56672.39648438 48899.22851562]\n", + "bkg high : [17791.82759857 19424.65112305 23153.29711914 23295.25048828\n", + " 18131.265625 17577.91162109]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 16. 142. 483. 518. 146. 22.]\n", + "[ 4. 35. 94. 82. 21. 2.]\n", + "bkg med : [51812.47679901 55333.25036621 67430.56481934 68593.20263672\n", + " 57163.8046875 48973.27441406]\n", + "bkg high : [17805.2910223 19542.45601654 23469.68707275 23571.24978638\n", + " 18201.94836426 17584.64331055]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 37. 80. 95. 35. 4.]\n", + "[ 1. 3. 31. 26. 7. 0.]\n", + "bkg med : [52009.81113434 56245.92175293 69403.90478516 70936.55664062\n", + " 58027.14941406 49071.94238281]\n", + "bkg high : [17829.95781231 19616.45638275 24234.35772705 24212.58566284\n", + " 18374.61560059 17584.64331055]\n", + "add a+jj\n", + "bkg med : [56628.95566559 60649.13659668 73684.56494141 75252.23242188\n", + " 62503.31347656 53396.37207031]\n", + "bkg high : [19428.46953106 21223.71907806 25771.4631958 25877.63058472\n", + " 19920.10388184 19135.96362305]\n", + "sig med : [ 8.74698353 73.23566437 226.7326355 226.27166748 75.31445312\n", + " 10.21350098]\n", + "sig high : [ 3.03340578 26.33671951 92.07962036 93.07585907 27.8890686 3.79864502]\n", + "sono dentro\n", + "8.746983528137207\n", + "56628.95566558838\n", + "sono dentro\n", + "73.23566436767578\n", + "60649.13659667969\n", + "sono dentro\n", + "226.73263549804688\n", + "73684.56494140625\n", + "sono dentro\n", + "226.27166748046875\n", + "75252.232421875\n", + "sono dentro\n", + "75.314453125\n", + "62503.3134765625\n", + "sono dentro\n", + "10.2135009765625\n", + "53396.3720703125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.3387314219637996\n", + "sig_high 1.5790064550654133\n", + "Naive\n", + "sig_med 1.0038190103629248\n", + "sig_high 0.6793355661323452\n", + "----------------\n", + "----------------\n", + "(114, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 43. 303. 996. 990. 350. 59.]\n", + "[ 9. 105. 341. 371. 111. 12.]\n", + "bkg med : [ 158.63467407 1117.81921387 3674.39379883 3652.1328125 1291.30859375\n", + " 217.67773438]\n", + "bkg high : [ 33.20259857 387.3637085 1258.01123047 1368.6965332 409.50219727\n", + " 44.27050781]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 31. 95. 100. 34. 4.]\n", + "[ 0. 9. 34. 24. 7. 2.]\n", + "bkg med : [ 459.52935791 5781.68591309 17966.8840332 18696.88085938\n", + " 6406.58203125 819.47460938]\n", + "bkg high : [ 33.20259857 1741.38983154 6373.21923828 4979.43139648 1462.63305664\n", + " 345.16503906]\n", + "mgp8_pp_jjaa_5f\n", + "[1534. 1440. 1419. 1417. 1475. 1421.]\n", + "[597. 620. 575. 633. 589. 593.]\n", + "bkg med : [50170.71685791 52446.68591309 63951.3527832 64631.69335938\n", + " 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27890.17657471 28356.68728638\n", + " 22534.35839844 21298.01586914]\n", + "sig med : [ 8.42775059 70.52380371 219.13537598 218.60949707 72.44152832\n", + " 9.9262085 ]\n", + "sig high : [ 3.35263872 29.0487957 99.67668915 100.73802185 30.76199341\n", + " 4.0859375 ]\n", + "sono dentro\n", + "8.427750587463379\n", + "54872.65215301514\n", + "sono dentro\n", + "70.5238037109375\n", + "57979.752014160156\n", + "sono dentro\n", + "219.1353759765625\n", + "71565.40295410156\n", + "sono dentro\n", + "218.6094970703125\n", + "72752.44946289062\n", + "sono dentro\n", + "72.4415283203125\n", + "59887.095947265625\n", + "sono dentro\n", + "9.92620849609375\n", + "51232.3095703125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.297423666211785\n", + "sig_high 1.63817056779312\n", + "Naive\n", + "sig_med 0.9871396055506995\n", + "sig_high 0.7025422903794063\n", + "----------------\n", + "----------------\n", + "(114, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 42. 292. 961. 955. 334. 54.]\n", + "[ 10. 116. 376. 406. 127. 17.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 154.94549561 1077.23779297 3545.28295898 3522.97900391 1232.27734375\n", + " 199.23046875]\n", + "bkg high : [ 36.89177704 427.94470215 1387.13378906 1497.81884766 468.52954102\n", + " 62.71655273]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 31. 93. 98. 33. 4.]\n", + "[ 0. 9. 36. 26. 8. 2.]\n", + "bkg med : [ 455.84017944 5741.10449219 17536.87866211 18266.82470703\n", + " 6197.1015625 801.02734375]\n", + "bkg high : [ 36.89177704 1781.9708252 6803.23632812 5409.44824219 1672.10766602\n", + " 363.61108398]\n", + "mgp8_pp_jjaa_5f\n", + "[1488. 1399. 1368. 1384. 1446. 1372.]\n", + "[643. 661. 626. 666. 618. 642.]\n", + "bkg med : [48676.34017944 51077.44824219 61868.62866211 63117.07470703\n", + " 53100.8203125 45305.27734375]\n", + "bkg high : [20874.11052704 23202.5020752 27089.54882812 26992.01074219\n", + " 21699.18579102 21168.42358398]\n", 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9.83044434]\n", + "sig high : [ 3.57610178 30.70892143 106.34803009 106.10134888 32.67729187\n", + " 4.18170166]\n", + "sono dentro\n", + "8.2042875289917\n", + "53243.37899017334\n", + "sono dentro\n", + "68.86390686035156\n", + "56428.080627441406\n", + "sono dentro\n", + "212.46380615234375\n", + "69278.90161132812\n", + "sono dentro\n", + "213.24618530273438\n", + "71083.21044921875\n", + "sono dentro\n", + "70.52627563476562\n", + "58637.943359375\n", + "sono dentro\n", + "9.8304443359375\n", + "49478.07861328125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.266999365082064\n", + "sig_high 1.679495865949795\n", + "Naive\n", + "sig_med 0.9743989498080879\n", + "sig_high 0.7196871879649313\n", + "----------------\n", + "----------------\n", + "(116, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 44. 341. 1121. 1144. 402. 64.]\n", + "[ 8. 67. 216. 217. 59. 7.]\n", + "bkg med : [ 162.32385254 1258.00915527 4135.50488281 4220.38867188 1483.16015625\n", + " 236.125 ]\n", + "bkg high : [ 29.5134182 247.17494202 796.85888672 800.55834961 217.66333008\n", + " 25.82446289]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 33. 105. 105. 39. 4.]\n", + "[ 0. 7. 24. 19. 2. 2.]\n", + "bkg med : [ 463.21853638 6222.77038574 19932.46777344 20017.40625\n", + " 7350.6796875 837.921875 ]\n", + "bkg high : [ 29.5134182 1300.30628967 4407.59423828 3659.05639648 518.55786133\n", + " 326.71899414]\n", + "mgp8_pp_jjaa_5f\n", + "[1746. 1673. 1613. 1634. 1684. 1648.]\n", + "[385. 387. 381. 416. 380. 366.]\n", + "bkg med : [57044.53103638 60438.42663574 72243.34277344 73020.28125\n", + " 61975.4296875 54294.921875 ]\n", + "bkg high : [12508.9274807 13844.54847717 16756.38330078 17140.05639648\n", + " 12832.93286133 12187.40649414]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 17. 155. 507. 538. 150. 24.]\n", + "[ 3. 22. 70. 62. 17. 0.]\n", + "bkg med : [57101.75057983 60960.13330078 73949.83532715 74831.16992188\n", + " 62480.28808594 54375.69921875]\n", + "bkg high : [12519.02504826 13918.59728241 16991.99310303 17348.73880005\n", + " 12890.15222168 12187.40649414]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 38. 87. 106. 39. 4.]\n", + "[ 1. 2. 24. 15. 3. 0.]\n", + "bkg med : [57299.08491516 61897.47143555 76095.84265137 77445.86279297\n", + " 63442.30078125 54474.3671875 ]\n", + "bkg high : [12543.69183826 13967.93085861 17583.99609375 17718.74093628\n", + " 12964.15246582 12187.40649414]\n", + "add a+jj\n", + "bkg med : [62393.85835266 66779.2331543 80802.52624512 82213.82373047\n", + " 68356.16015625 59283.1796875 ]\n", + "bkg high : [13666.73383045 15096.80683517 18695.37011719 18932.20968628\n", + " 14072.60949707 13255.02563477]\n", + "----------------\n", + "----------------\n", + "(116, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 44. 321. 1073. 1092. 377. 62.]\n", + "[ 8. 87. 264. 269. 84. 9.]\n", + "bkg med : [ 162.32385254 1184.22497559 3958.43945312 4028.50390625 1390.92382812\n", + " 228.74609375]\n", + "bkg high : [ 29.5134182 320.95849609 973.94152832 992.3972168 309.89355469\n", + " 33.20288086]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 33. 100. 101. 39. 4.]\n", + "[ 0. 7. 29. 23. 2. 2.]\n", + "bkg med : [ 463.21853638 6148.98620605 19003.16601562 19223.71484375\n", + " 7258.44335938 830.54296875]\n", + "bkg high : [ 29.5134182 1374.08984375 5336.91320801 4452.68481445 610.78808594\n", + " 334.09741211]\n", + "mgp8_pp_jjaa_5f\n", + "[1686. 1610. 1557. 1564. 1621. 1584.]\n", + "[445. 450. 437. 486. 443. 430.]\n", + "bkg med : [55100.15603638 58323.04870605 69478.22851562 69955.96484375\n", + " 59839.63085938 52211.54296875]\n", + "bkg high : [14453.7712307 15957.59765625 19498.44445801 20202.12231445\n", + " 14966.75683594 14268.78491211]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 16. 149. 500. 531. 149. 24.]\n", + "[ 4. 28. 77. 69. 18. 0.]\n", + "bkg med : [55154.00972748 58824.56030273 71161.15844727 71743.29321289\n", + " 60341.12719727 52292.3203125 ]\n", + "bkg high : [14467.23465443 16051.84156799 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"239.98060607910156\n", + "77776.44262695312\n", + "sono dentro\n", + "239.7425537109375\n", + "78749.01953125\n", + "sono dentro\n", + "79.6568603515625\n", + "66033.16723632812\n", + "sono dentro\n", + "10.915771484375\n", + "57013.05078125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.4099134862002\n", + "sig_high 1.4674311828671571\n", + "Naive\n", + "sig_med 1.0323165574922895\n", + "sig_high 0.6364560215287712\n", + "----------------\n", + "----------------\n", + "(116, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 44. 315. 1033. 1041. 362. 60.]\n", + "[ 8. 93. 304. 320. 99. 11.]\n", + "bkg med : [ 162.32385254 1162.08972168 3810.88232422 3840.31933594 1335.58203125\n", + " 221.3671875 ]\n", + "bkg high : [ 29.5134182 343.09356689 1121.51000977 1180.546875 365.23168945\n", + " 40.58129883]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 32. 99. 101. 36. 4.]\n", + "[ 0. 8. 30. 23. 5. 2.]\n", + "bkg med : [ 463.21853638 5976.40368652 18705.16162109 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"10.62847900390625\n", + "54848.98828125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.369809670757714\n", + "sig_high 1.5306261609994716\n", + "Naive\n", + "sig_med 1.0161034135271119\n", + "sig_high 0.6608669159461918\n", + "----------------\n", + "----------------\n", + "(116, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 43. 304. 998. 1006. 346. 55.]\n", + "[ 9. 104. 339. 355. 115. 16.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 158.63467407 1121.50842285 3681.77148438 3711.16552734 1276.55078125\n", + " 202.91992188]\n", + "bkg high : [ 33.20259857 383.67456055 1250.63269043 1309.66918945 424.2590332\n", + " 59.02734375]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 32. 97. 99. 35. 4.]\n", + "[ 0. 8. 32. 25. 6. 2.]\n", + "bkg med : [ 459.52935791 5935.8223877 18275.15625 18605.47021484\n", + " 6542.2734375 804.71679688]\n", + "bkg high : [ 33.20259857 1587.2532959 6064.94616699 5070.85131836 1326.94262695\n", + " 359.921875 ]\n", + "mgp8_pp_jjaa_5f\n", + "[1591. 1500. 1458. 1469. 1533. 1474.]\n", + "[540. 560. 536. 581. 531. 540.]\n", + "bkg med : [52017.87310791 54545.1973877 65523.46875 66241.03271484\n", + " 56268.9609375 48617.59179688]\n", + "bkg high : [17532.57759857 19734.7532959 23434.69616699 23898.88256836\n", + " 18534.66137695 17859.296875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 16. 142. 483. 513. 138. 23.]\n", + "[ 4. 35. 94. 87. 29. 1.]\n", + "bkg med : [52071.72679901 55023.14807129 67149.1763916 67967.77368164\n", + " 56733.44458008 48695.00341797]\n", + "bkg high : [17546.0410223 19852.55818939 23751.08612061 24191.71109009\n", + " 18632.27087402 17862.66271973]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 7. 34. 79. 95. 39. 4.]\n", + "[ 2. 6. 32. 26. 3. 0.]\n", + "bkg med : [52244.39434052 55861.81921387 69097.84973145 70311.12646484\n", + " 57695.45727539 48793.67138672]\n", + "bkg high : [17595.37460232 20000.55895233 24540.42364502 24833.04666138\n", + " 18706.27111816 17862.66271973]\n", + "add a+jj\n", + "bkg med : [56886.70293427 60238.77233887 73352.24816895 74597.62255859\n", + " 62168.70336914 53094.75732422]\n", + "bkg high : [19170.55038357 21634.07457733 26103.63751221 26527.2517395\n", + " 20254.67541504 19437.31115723]\n", + "sig med : [ 8.81083012 72.37522125 225.71173096 226.71704102 74.86865234\n", + " 10.53271484]\n", + "sig high : [ 2.96955919 27.19720459 93.10049438 92.63052368 28.33486938 3.47943115]\n", + "sono dentro\n", + "8.810830116271973\n", + "56886.70293426514\n", + "sono dentro\n", + "72.3752212524414\n", + "60238.77233886719\n", + "sono dentro\n", + "225.71173095703125\n", + "73352.24816894531\n", + "sono dentro\n", + "226.717041015625\n", + "74597.62255859375\n", + "sono dentro\n", + "74.86865234375\n", + "62168.703369140625\n", + "sono dentro\n", + "10.53271484375\n", + "53094.75732421875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.340310628913458\n", + "sig_high 1.5746171202569421\n", + "Naive\n", + "sig_med 1.0037295001132884\n", + "sig_high 0.6789117118258012\n", + "----------------\n", + "----------------\n", + "(118, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 44. 349. 1165. 1184. 416. 65.]\n", + "[ 8. 59. 172. 177. 45. 6.]\n", + "bkg med : [ 162.32385254 1287.52282715 4297.81738281 4367.9921875 1534.8125\n", + " 239.81445312]\n", + "bkg high : [ 29.5134182 217.66151428 634.53552246 652.987854 166.0144043\n", + " 22.13525391]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 33. 108. 107. 40. 5.]\n", + "[ 0. 7. 21. 17. 1. 1.]\n", + "bkg med : [ 463.21853638 6252.28405762 20546.12207031 20465.9140625\n", + " 7552.78125 992.06054688]\n", + "bkg high : [ 29.5134182 1270.79286194 3793.92907715 3210.59136963 316.46166992\n", + " 172.58251953]\n", + "mgp8_pp_jjaa_5f\n", + "[1830. 1762. 1684. 1718. 1753. 1731.]\n", + "[301. 298. 310. 332. 311. 283.]\n", + "bkg med : [59766.65603638 63366.97155762 75170.87207031 76193.5390625\n", + " 64415.71875 57141.37304688]\n", + "bkg high : [ 9786.1462307 10930.18348694 13842.28845215 13972.06011963\n", + " 10397.23510742 9344.53564453]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 17. 158. 525. 550. 154. 24.]\n", + "[ 3. 19. 52. 50. 13. 0.]\n", + "bkg med : [59823.87557983 63898.77575684 76937.95239258 78044.8112793\n", + " 64934.04003906 57222.15039062]\n", + "bkg high : [ 9796.24379826 10994.13473511 14017.31304932 14140.35237122\n", + " 10440.99108887 9344.53564453]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 38. 92. 110. 39. 4.]\n", + "[ 1. 2. 19. 11. 3. 0.]\n", + "bkg med : [60021.20991516 64836.1138916 79207.29321289 80758.17333984\n", + " 65896.05273438 57320.81835938]\n", + "bkg high : [ 9820.91058826 11043.46831131 14485.98199463 14411.68727112\n", + " 10514.99151611 9344.53564453]\n", + "add a+jj\n", + "bkg med : [65361.09272766 69977.5748291 84121.15258789 85771.24365234\n", + " 71011.25195312 62367.62695312]\n", + "bkg high : [10698.9252367 11912.7314949 15390.24957275 15380.12867737\n", + " 11422.17608643 10170.04443359]\n", + "----------------\n", + "----------------\n", + "(118, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 44. 329. 1117. 1132. 391. 63.]\n", + "[ 8. 79. 220. 229. 70. 8.]\n", + "bkg med : [ 162.32385254 1213.73864746 4120.75195312 4176.10742188 1442.57617188\n", + " 232.43554688]\n", + "bkg high : [ 29.5134182 291.44506836 811.61608887 844.82885742 258.24462891\n", + " 29.51367188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 33. 103. 103. 40. 5.]\n", + "[ 0. 7. 26. 21. 1. 1.]\n", + "bkg med : [ 463.21853638 6178.49987793 19616.8203125 19672.22265625\n", + " 7460.54492188 984.68164062]\n", + "bkg high : [ 29.5134182 1344.57641602 4723.24597168 4004.22143555 408.69238281\n", + " 179.9609375 ]\n", + "mgp8_pp_jjaa_5f\n", + "[1770. 1699. 1628. 1648. 1690. 1667.]\n", + "[361. 361. 366. 402. 374. 347.]\n", + "bkg med : [57822.28103638 61236.71862793 72420.6328125 73129.22265625\n", + " 62279.91992188 55057.99414062]\n", + "bkg high : [11730.9899807 13046.05297852 16586.79284668 17032.27612305\n", + " 12528.62988281 11424.9296875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 16. 152. 518. 543. 153. 24.]\n", + "[ 4. 25. 59. 57. 14. 0.]\n", + "bkg med : [57876.13472748 61748.32775879 74164.1505127 74956.93823242\n", + " 62794.87548828 55138.77148438]\n", + "bkg high : [11744.45340443 13130.19933319 16785.37832642 17224.12927246\n", + " 12575.7517395 11424.9296875 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 8. 38. 89. 103. 39. 4.]\n", + "[ 1. 2. 22. 18. 3. 0.]\n", + "bkg med : [58073.46906281 62685.66589355 76359.49133301 77497.63061523\n", + " 63756.88818359 55237.43945312]\n", + "bkg high : [11769.12019444 13179.53290939 17328.04769897 17668.1317749\n", + " 12649.75198364 11424.9296875 ]\n", + "add a+jj\n", + "bkg med : [63238.27375031 67643.2947998 81109.94445801 82306.44311523\n", + " 68688.25537109 60100.90039062]\n", + "bkg high : [12822.15437412 14232.56708908 18395.6668396 18840.76263428\n", + " 13740.70706177 12437.12597656]\n", + "----------------\n", + "----------------\n", + "(118, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 44. 323. 1077. 1081. 376. 61.]\n", + "[ 8. 85. 260. 280. 85. 10.]\n", + "bkg med : [ 162.32385254 1191.60339355 3973.19482422 3987.92285156 1387.234375\n", + " 225.05664062]\n", + "bkg high : [ 29.5134182 313.58013916 959.18457031 1032.97851562 313.58276367\n", + " 36.89208984]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 32. 102. 103. 37. 5.]\n", + "[ 0. 8. 27. 21. 4. 1.]\n", + "bkg med : [ 463.21853638 6005.9173584 19318.81591797 19484.03417969\n", + " 6953.85546875 977.30273438]\n", + "bkg high : [ 29.5134182 1517.15887451 5021.26171875 4192.37158203 915.37182617\n", + " 187.33935547]\n", + "mgp8_pp_jjaa_5f\n", + "[1721. 1630. 1580. 1586. 1631. 1606.]\n", + "[410. 430. 414. 464. 433. 408.]\n", + "bkg med : [56234.37478638 58828.1048584 70548.50341797 70929.90917969\n", + " 59859.41796875 53071.92773438]\n", + "bkg high : [13319.2790432 15454.52606201 18437.44921875 19228.87158203\n", + " 14947.27807617 13409.08935547]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 16. 149. 510. 535. 146. 24.]\n", + "[ 4. 28. 67. 65. 21. 0.]\n", + "bkg med : [56288.22847748 59329.61645508 72265.09301758 72730.70141602\n", + " 60350.8137207 53152.70507812]\n", + "bkg high : [13332.74246693 15548.76997375 18662.96142578 19447.65151978\n", + " 15017.96081543 13409.08935547]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 7. 36. 85. 100. 39. 4.]\n", + "[ 2. 4. 26. 21. 3. 0.]\n", + "bkg med : [56460.89601898 60217.62115479 74361.7668457 75197.39111328\n", + " 61312.82641602 53251.37304688]\n", + "bkg high : [13382.07604694 15647.43714142 19304.2980957 19965.65383911\n", + " 15091.96105957 13409.08935547]\n", + "add a+jj\n", + "bkg med : [61482.72023773 64973.91021729 78972.1574707 79825.28955078\n", + " 66072.03344727 57937.63085938]\n", + "bkg high : [14578.04284382 16901.74378204 20511.93286133 21319.13821411\n", + " 16355.01867676 14598.99560547]\n", + "sig med : [ 9.57698917 77.64221954 246.72183228 246.15765381 80.51873779\n", + " 11.01153564]\n", + "sig high : [ 2.2033999 21.92986107 72.09075165 73.18987274 22.68478394 3.00061035]\n", + "sono dentro\n", + "9.57698917388916\n", + "61482.72023773193\n", + "sono dentro\n", + "77.64221954345703\n", + "64973.910217285156\n", + "sono dentro\n", + "246.72183227539062\n", + "78972.15747070312\n", + "sono dentro\n", + "246.15765380859375\n", + "79825.28955078125\n", + "sono dentro\n", + "80.51873779296875\n", + "66072.03344726562\n", + "sono dentro\n", + "11.01153564453125\n", + "57937.630859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.450389717592377\n", + "sig_high 1.3931088450987608\n", + "Naive\n", + "sig_med 1.049851285338353\n", + "sig_high 0.6042082608001935\n", + "----------------\n", + "----------------\n", + "(118, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 43. 312. 1042. 1046. 360. 56.]\n", + "[ 9. 96. 295. 315. 101. 15.]\n", + "bkg med : [ 158.63467407 1151.02209473 3844.08398438 3858.76904297 1328.203125\n", + " 206.609375 ]\n", + "bkg high : [ 33.20259857 354.16113281 1088.30725098 1162.10083008 372.61010742\n", + " 55.33813477]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 32. 100. 101. 36. 5.]\n", + "[ 0. 8. 29. 23. 5. 1.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 459.52935791 5965.33605957 18888.81054688 19053.97802734\n", + " 6744.375 958.85546875]\n", + "bkg high : [ 33.20259857 1557.73986816 5451.27893066 4622.38842773 1124.84643555\n", + " 205.78540039]\n", + "mgp8_pp_jjaa_5f\n", + "[1675. 1589. 1529. 1553. 1602. 1557.]\n", + "[456. 471. 465. 497. 462. 457.]\n", + "bkg med : [54739.99810791 57458.86730957 68450.74804688 69429.41552734\n", + " 58709.25 51464.04296875]\n", + "bkg high : [14813.65572357 16821.08361816 20520.18518066 20728.29467773\n", + " 16096.53393555 15015.44165039]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 16. 145. 501. 525. 142. 23.]\n", + "[ 4. 32. 76. 75. 25. 1.]\n", + "bkg med : [54793.85179901 57946.91552734 70137.04345703 71196.5480957\n", + " 59187.18847656 51541.45458984]\n", + "bkg high : [14827.1191473 16928.79093933 20775.98995972 20980.73306274\n", + " 16180.68005371 15018.80749512]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 7. 34. 84. 99. 39. 4.]\n", + "[ 2. 6. 27. 22. 3. 0.]\n", + "bkg med : [54966.51934052 58785.58666992 72209.05053711 73638.57006836\n", + " 60149.20117188 51640.12255859]\n", + "bkg high : [14876.45272732 17076.79170227 21441.99343872 21523.40194702\n", + " 16254.68029785 15018.80749512]\n", + "add a+jj\n", + "bkg med : [59854.11699677 63422.23901367 76670.62475586 78170.17553711\n", + " 64823.78710938 56183.39990234]\n", + "bkg high : [16206.60116482 18450.69502258 22798.39480591 22973.14706421\n", + " 17601.97229004 16351.42663574]\n", + "sig med : [ 9.35352612 75.98231506 240.05026245 240.79437256 78.60345459\n", + " 10.91577148]\n", + "sig high : [ 2.42686296 23.58987236 78.76220703 78.55319977 24.60006714 3.09637451]\n", + "sono dentro\n", + "9.35352611541748\n", + "59854.11699676514\n", + "sono dentro\n", + "75.98231506347656\n", + "63422.239013671875\n", + "sono dentro\n", + "240.05026245117188\n", + "76670.62475585938\n", + "sono dentro\n", + "240.79437255859375\n", + "78170.17553710938\n", + "sono dentro\n", + "78.60345458984375\n", + "64823.787109375\n", + "sono dentro\n", + "10.915771484375\n", + "56183.39990234375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 2.4218846118930966\n", + "sig_high 1.441589891624504\n", + "Naive\n", + "sig_med 1.037888925513443\n", + "sig_high 0.6239675313922564\n", + "x_1: 112\n", + "x_2: 126\n", + "Significance: 2.8243950993784956\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m_1 = np.arange(start = 112, stop = 120, step = 2)\n", + "m_2 = np.arange(start = 126, stop = 134, step = 2)\n", + "m1_small_med,m2_small_med,max_sig_small_med = findm1m2(df_small_med,m_1,m_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "97e3059b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(112, 126), (112, 128), (112, 130), (112, 132), (114, 126), (114, 128), (114, 130), (114, 132), (116, 126), (116, 128), (116, 130), (116, 132), (118, 126), (118, 128), (118, 130), (118, 132)]\n", + "----------------\n", + "----------------\n", + "(112, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 31. 162. 336. 354. 157. 47.]\n", + "[ 22. 66. 171. 213. 91. 13.]\n", + "bkg med : [ 114.36453247 597.6449585 1239.56958008 1305.97998047 579.20581055\n", + " 173.39282227]\n", + "bkg high : [ 81.16191864 243.48576355 630.84576416 785.80059814 335.71801758\n", + " 47.9597168 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 7. 15. 4. 2.]\n", + "[ 1. 2. 9. 11. 5. 2.]\n", + "bkg med : [ 415.25921631 1500.32904053 2292.7010498 3562.68920898 1180.99487305\n", + " 474.28735352]\n", + "bkg high : [ 231.60926056 544.38047791 1984.87200928 2440.72064209 1087.95458984\n", + " 348.85424805]\n", + "mgp8_pp_jjaa_5f\n", + "[158. 144. 159. 140. 137. 140.]\n", + "[102. 95. 84. 92. 103. 110.]\n", + "bkg med : [5536.06390381 6167.39154053 7445.91589355 8100.11108398 5621.1862793\n", + " 5011.70922852]\n", + "bkg high : [3537.44519806 3623.34532166 4707.32513428 5422.45501709 4426.20068359\n", + " 3913.97143555]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 34. 82. 84. 38. 7.]\n", + "[ 2. 9. 17. 24. 11. 1.]\n", + "bkg med : [5559.624897 6281.83058929 7721.91567993 8382.84207153 5749.08837891\n", + " 5035.2701416 ]\n", + "bkg high : [3544.17690945 3653.63802338 4764.54465485 5503.23557281 4463.22514343\n", + " 3917.33729553]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 13. 13. 8. 0.]\n", + "[1. 2. 3. 3. 2. 0.]\n", + "bkg med : [5559.624897 6355.83095551 8042.58392334 8703.51043701 5946.42285156\n", + " 5035.2701416 ]\n", + "bkg high : [3568.84369946 3702.97159958 4838.54503632 5577.23595428 4512.55873108\n", + " 3917.33729553]\n", + "add a+jj\n", + "bkg med : [6020.50966263 6775.87783051 8506.38543701 9111.88934326 6346.05078125\n", + " 5443.64904785]\n", + "bkg high : [3866.37690258 3980.08585739 5083.57238007 5845.59923553 4813.00892639\n", + " 4238.20643616]\n", + "sig med : [ 6.16119623 42.48608017 114.69077301 116.20832825 41.6574707\n", + " 7.6930542 ]\n", + "sig high : [ 4.12500238 26.91133881 84.6105423 88.00497437 31.1100769 3.99017334]\n", + "sono dentro\n", + "6.161196231842041\n", + "6020.509662628174\n", + "sono dentro\n", + "42.486080169677734\n", + "6775.877830505371\n", + "sono dentro\n", + "114.6907730102539\n", + "8506.385437011719\n", + "sono dentro\n", + "116.20832824707031\n", + "9111.889343261719\n", + "sono dentro\n", + "41.657470703125\n", + "6346.05078125\n", + "sono dentro\n", + "7.69305419921875\n", + "5443.6490478515625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.6771654116142805\n", + "sig_high 3.3332492915225105\n", + "Naive\n", + "sig_med 1.6009623297872067\n", + "sig_high 1.43124899094057\n", + "----------------\n", + "----------------\n", + "(112, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 30. 151. 315. 337. 149. 46.]\n", + "[ 23. 77. 192. 230. 99. 14.]\n", + "bkg med : [ 110.675354 557.06439209 1162.09545898 1243.26342773 549.69213867\n", + " 169.70361328]\n", + "bkg high : [ 84.85109711 284.06671143 708.31860352 848.51806641 365.23168945\n", + " 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 7. 14. 4. 2.]\n", + "[ 1. 2. 9. 12. 5. 2.]\n", + "bkg med : [ 411.57003784 1459.74847412 2215.22692871 3349.52539062 1151.48120117\n", + " 470.59814453]\n", + "bkg high : [ 235.29843903 584.96142578 2062.34484863 2653.88537598 1117.46826172\n", + " 352.54345703]\n", + "mgp8_pp_jjaa_5f\n", + "[152. 134. 151. 125. 130. 129.]\n", + "[108. 105. 92. 107. 110. 121.]\n", + "bkg med : [5337.91378784 5802.70941162 7109.16052246 7400.79492188 5364.80151367\n", + " 4651.50830078]\n", + "bkg high : [3735.59531403 3988.02783203 5044.07922363 6121.77209473 4682.58544922\n", + " 4274.17236328]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 6. 32. 76. 79. 37. 7.]\n", + "[ 3. 11. 23. 29. 12. 1.]\n", + "bkg med : [5358.10892487 5910.41673279 7364.96528625 7666.69668579 5489.33776855\n", + " 4675.06921387]\n", + "bkg high : [3745.69288063 4025.05224609 5121.49385071 6219.38201904 4722.97575378\n", + " 4277.53820801]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 13. 13. 8. 0.]\n", + "[1. 2. 3. 3. 2. 0.]\n", + "bkg med : [5358.10892487 5984.417099 7685.63352966 7987.36505127 5686.67224121\n", + " 4675.06921387]\n", + "bkg high : [3770.35967064 4074.3858223 5195.49423218 6293.38240051 4772.30934143\n", + " 4277.53820801]\n", + "add a+jj\n", + "bkg med : [5801.49173737 6375.29405212 8126.09910583 8351.98907471 6065.88122559\n", + " 5051.36120605]\n", + "bkg high : [4085.39482689 4380.67000198 5463.85751343 6605.50056458 5093.17848206\n", + " 4630.49401855]\n", + "sig med : [ 5.96965647 40.85788345 110.0304718 110.71784973 39.86987305\n", + " 7.46960449]\n", + "sig high : [ 4.31654215 28.5395031 89.27088165 93.49545288 32.89767456 4.21362305]\n", + "sono dentro\n", + "5.969656467437744\n", + "5801.491737365723\n", + "sono dentro\n", + "40.85788345336914\n", + "6375.294052124023\n", + "sono dentro\n", + "110.03047180175781\n", + "8126.099105834961\n", + "sono dentro\n", + "110.71784973144531\n", + "8351.989074707031\n", + "sono dentro\n", + "39.869873046875\n", + "6065.8812255859375\n", + "sono dentro\n", + "7.4696044921875\n", + "5051.3612060546875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.6326493642426825\n", + "sig_high 3.3727461246463077\n", + "Naive\n", + "sig_med 1.5790812036109882\n", + "sig_high 1.4528980679395613\n", + "----------------\n", + "----------------\n", + "(112, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 30. 144. 294. 321. 145. 43.]\n", + "[ 23. 84. 213. 246. 103. 17.]\n", + "bkg med : [ 110.675354 531.24041748 1084.62158203 1184.23608398 534.93530273\n", + " 158.63598633]\n", + "bkg high : [ 84.85109711 309.89099121 785.79223633 907.54541016 379.98852539\n", + " 62.71655273]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 7. 14. 4. 2.]\n", + "[ 1. 2. 9. 12. 5. 2.]\n", + "bkg med : [ 411.57003784 1433.92449951 2137.75305176 3290.49804688 1136.72436523\n", + " 459.53051758]\n", + "bkg high : [ 235.29843903 610.78570557 2139.81848145 2712.91271973 1132.22509766\n", + " 363.61108398]\n", + "mgp8_pp_jjaa_5f\n", + "[146. 126. 146. 118. 125. 124.]\n", + "[114. 113. 97. 114. 115. 126.]\n", + "bkg med : [5143.45285034 5517.60418701 6869.63586426 7114.89648438 5187.99389648\n", + " 4478.38989258]\n", + "bkg high : [3930.05625153 4273.13336182 5283.6036377 6407.67053223 4859.39306641\n", + " 4447.29077148]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 6. 32. 75. 79. 36. 7.]\n", + "[ 3. 11. 24. 29. 13. 1.]\n", + "bkg med : [5163.64798737 5625.31150818 7122.07478333 7380.79824829 5309.16430664\n", + " 4501.95080566]\n", + "bkg high : [3940.15381813 4310.15777588 5364.3841095 6505.28046417 4903.14920044\n", + " 4450.65661621]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 12. 13. 8. 0.]\n", + "[1. 2. 4. 3. 2. 0.]\n", + "bkg med : [5163.64798737 5699.31187439 7418.07624817 7701.46658325 5506.4987793\n", + " 4501.95080566]\n", + "bkg high : [3964.82060814 4359.49135208 5463.05128479 6579.28084564 4952.48277283\n", + " 4450.65661621]\n", + "add a+jj\n", + "bkg med : [5589.52884674 6066.85289001 7843.9568634 8045.67166138 5871.12280273\n", + " 4863.65783691]\n", + "bkg high : [4297.35771751 4689.11146927 5745.99952698 6911.81795502 5287.93663025\n", + " 4818.19763184]\n", + "sig med : [ 5.87388659 39.70856857 106.77464294 107.49302673 38.59301758\n", + " 7.24615479]\n", + "sig high : [ 4.41231203 29.68881989 92.52670288 96.72027588 34.17453003 4.43707275]\n", + "sono dentro\n", + "5.873886585235596\n", + "5589.528846740723\n", + "sono dentro\n", + "39.70856857299805\n", + "6066.852890014648\n", + "sono dentro\n", + "106.77464294433594\n", + "7843.95686340332\n", + "sono dentro\n", + "107.49302673339844\n", + "8045.671661376953\n", + "sono dentro\n", + "38.593017578125\n", + "5871.122802734375\n", + "sono dentro\n", + "7.24615478515625\n", + "4863.6578369140625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.593411837879703\n", + "sig_high 3.4118231947094144\n", + "Naive\n", + "sig_med 1.5623911939544528\n", + "sig_high 1.4701436183341823\n", + "----------------\n", + "----------------\n", + "(112, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 30. 140. 274. 307. 136. 42.]\n", + "[ 23. 88. 233. 260. 112. 18.]\n", + "bkg med : [ 110.675354 516.48382568 1010.8371582 1132.5871582 501.73242188\n", + " 154.94677734]\n", + "bkg high : [ 84.85109711 324.64770508 859.57653809 959.19433594 413.19140625\n", + " 66.40576172]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 7. 13. 4. 2.]\n", + "[ 1. 2. 9. 13. 5. 2.]\n", + "bkg med : [ 411.57003784 1419.16790771 2063.96862793 3088.40185547 1103.52148438\n", + " 455.84130859]\n", + "bkg high : [ 235.29843903 625.54241943 2213.6027832 2915.00891113 1165.42797852\n", + " 367.30029297]\n", + "mgp8_pp_jjaa_5f\n", + "[143. 123. 144. 115. 125. 118.]\n", + "[117. 116. 99. 117. 115. 132.]\n", + "bkg med : [5046.22238159 5405.61712646 6731.03112793 6815.56982422 5154.79101562\n", + " 4280.23974609]\n", + "bkg high : [4027.28672028 4385.12054443 5422.20825195 6706.99719238 4892.59594727\n", + " 4645.44091797]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 6. 32. 75. 78. 36. 6.]\n", + "[ 3. 11. 24. 30. 13. 2.]\n", + "bkg med : [5066.41751862 5513.32444763 6983.470047 7078.10574341 5275.96142578\n", + " 4300.43481445]\n", + "bkg high : [4037.38428688 4422.1449585 5502.98872375 6807.97298431 4936.35206604\n", + " 4652.17260742]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 11. 13. 8. 0.]\n", + "[1. 2. 5. 3. 2. 0.]\n", + "bkg med : [5066.41751862 5587.32481384 7254.80473328 7398.77404785 5473.29589844\n", + " 4300.43481445]\n", + "bkg high : [4062.05107689 4471.4785347 5626.32269287 6881.97336578 4985.68562317\n", + " 4652.17260742]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "add a+jj\n", + "bkg med : [5483.54740143 5946.11485291 7674.85160828 7734.22790527 5837.91992188\n", + " 4644.63989258]\n", + "bkg high : [4403.33916283 4809.84962845 5915.10491943 7223.26145172 5321.13948059\n", + " 5037.21557617]\n", + "sig med : [ 5.7781167 39.32545853 105.0509491 105.86503601 38.050354\n", + " 7.05462646]\n", + "sig high : [ 4.50808191 30.07192993 94.25039673 98.3482666 34.7171936 4.62860107]\n", + "sono dentro\n", + "5.778116703033447\n", + "5483.547401428223\n", + "sono dentro\n", + "39.32545852661133\n", + "5946.114852905273\n", + "sono dentro\n", + "105.05094909667969\n", + "7674.851608276367\n", + "sono dentro\n", + "105.86503601074219\n", + "7734.2279052734375\n", + "sono dentro\n", + "38.05035400390625\n", + "5837.919921875\n", + "sono dentro\n", + "7.05462646484375\n", + "4644.639892578125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.5858349809035825\n", + "sig_high 3.4185007175364945\n", + "Naive\n", + "sig_med 1.5587187515566252\n", + "sig_high 1.4736603957268994\n", + "----------------\n", + "----------------\n", + "(114, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 35. 176. 365. 387. 175. 51.]\n", + "[ 18. 52. 142. 180. 73. 9.]\n", + "bkg med : [ 129.12124634 649.29290771 1346.55786133 1427.72387695 645.60253906\n", + " 188.13720703]\n", + "bkg high : [ 66.40520477 191.83726501 523.86102295 664.05383301 269.31225586\n", + " 33.20288086]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 15. 4. 3.]\n", + "[ 1. 2. 7. 11. 5. 1.]\n", + "bkg med : [ 430.01593018 1551.97698975 2700.5838623 3684.43310547 1247.39160156\n", + " 639.47900391]\n", + "bkg high : [ 216.85254669 492.73197937 1576.99249268 2318.97412109 1021.54858398\n", + " 183.65014648]\n", + "mgp8_pp_jjaa_5f\n", + "[184. 164. 171. 166. 151. 161.]\n", + "[76. 75. 72. 66. 89. 89.]\n", + "bkg med : [6393.48468018 6867.24261475 8242.72058105 9064.51904297 6141.53222656\n", + " 5858.14306641]\n", + "bkg high : [2680.02442169 2923.49369812 3910.52374268 4458.04443359 3906.05249023\n", + " 3068.15405273]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 8. 36. 86. 94. 40. 7.]\n", + "[ 1. 7. 13. 14. 9. 1.]\n", + "bkg med : [6420.41152573 6988.41339111 8532.18371582 9380.90847778 6276.16601562\n", + " 5881.70397949]\n", + "bkg high : [2683.39027739 2947.05468941 3954.27986145 4505.16636658 3936.34519958\n", + " 3071.51991272]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 13. 13. 8. 0.]\n", + "[1. 2. 3. 3. 2. 0.]\n", + "bkg med : [6420.41152573 7062.41375732 8852.85195923 9701.57684326 6473.50048828\n", + " 5881.70397949]\n", + "bkg high : [2708.05706739 2996.38826561 4028.28024292 4579.16674805 3985.67878723\n", + " 3071.51991272]\n", + "add a+jj\n", + "bkg med : [ 6957.13784409 7540.80047607 9351.65762329 10185.79754639\n", + " 6913.96630859 6351.33972168]\n", + "bkg high : [2929.74847364 3215.16267967 4238.30368042 4771.68823242 4245.29109192\n", + " 3331.13221741]\n", + "sig med : [ 7.15081835 47.75379944 129.30065918 131.62322998 46.44567871\n", + " 8.71453857]\n", + "sig high : [ 3.13538027 21.64399529 70.0002594 72.59007263 26.3218689 2.96868896]\n", + "sono dentro\n", + "7.150818347930908\n", + "6957.137844085693\n", + "sono dentro\n", + "47.75379943847656\n", + "7540.800476074219\n", + "sono dentro\n", + "129.3006591796875\n", + "9351.657623291016\n", + "sono dentro\n", + "131.62322998046875\n", + "10185.797546386719\n", + "sono dentro\n", + "46.4456787109375\n", + "6913.96630859375\n", + "sono dentro\n", + "8.71453857421875\n", + "6351.3397216796875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.9377245473004168\n", + "sig_high 3.014695448692989\n", + "Naive\n", + "sig_med 1.7057953579216776\n", + "sig_high 1.3043803035212431\n", + "----------------\n", + "----------------\n", + "(114, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 34. 165. 344. 370. 167. 50.]\n", + "[ 19. 63. 163. 197. 81. 10.]\n", + "bkg med : [ 125.43206787 608.71234131 1269.08361816 1365.00732422 616.09790039\n", + " 184.45361328]\n", + "bkg high : [ 70.09438324 232.41822815 601.33276367 726.77246094 298.82592773\n", + " 36.89208984]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 14. 4. 3.]\n", + "[ 1. 2. 7. 12. 5. 1.]\n", + "bkg med : [ 426.32675171 1511.39642334 2623.10961914 3471.26928711 1217.88696289\n", + " 635.79541016]\n", + "bkg high : [ 220.54172516 533.3129425 1654.4642334 2532.14001465 1051.06225586\n", + " 187.33959961]\n", + "mgp8_pp_jjaa_5f\n", + "[178. 154. 163. 151. 144. 150.]\n", + "[ 82. 85. 80. 81. 96. 100.]\n", + "bkg med : [6195.33456421 6502.56048584 7905.96508789 8365.20288086 5884.94946289\n", + " 5497.69775391]\n", + "bkg high : [2878.17453766 3288.17622375 4247.2767334 5157.3626709 4162.43725586\n", + " 3428.35522461]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 34. 80. 89. 39. 7.]\n", + "[ 2. 9. 19. 19. 10. 1.]\n", + "bkg med : [6218.8955574 6616.99953461 8175.23318481 8664.76309204 6016.21740723\n", + " 5521.25866699]\n", + "bkg high : [2884.90624905 3318.46892548 4311.22795868 5221.31394196 4196.09585571\n", + " 3431.72108459]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 13. 13. 8. 0.]\n", + "[1. 2. 3. 3. 2. 0.]\n", + "bkg med : [6218.8955574 6690.99990082 8495.90142822 8985.43145752 6213.55187988\n", + " 5521.25866699]\n", + "bkg high : [2909.57303905 3367.80250168 4385.22834015 5295.31432343 4245.42944336\n", + " 3431.72108459]\n", + "add a+jj\n", + "bkg med : [6738.12016678 7140.21645355 8971.37115479 9425.89727783 6633.59875488\n", + " 5958.80749512]\n", + "bkg high : [3148.76639843 3615.74683762 4618.58771515 5531.59069061 4525.46069336\n", + " 3723.42030334]\n", + "sig med : [ 6.95927858 46.12560272 124.6403656 126.13275146 44.65808105\n", + " 8.49108887]\n", + "sig high : [ 3.32692003 23.27208328 74.66067505 78.08055115 28.10946655 3.19213867]\n", + "sono dentro\n", + "6.959278583526611\n", + "6738.120166778564\n", + "sono dentro\n", + "46.12560272216797\n", + "7140.216453552246\n", + "sono dentro\n", + "124.64036560058594\n", + "8971.371154785156\n", + "sono dentro\n", + "126.13275146484375\n", + "9425.897277832031\n", + "sono dentro\n", + "44.6580810546875\n", + "6633.5987548828125\n", + "sono dentro\n", + "8.4910888671875\n", + "5958.8074951171875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.8968932492808253\n", + "sig_high 3.0586377919140353\n", + "Naive\n", + "sig_med 1.6854214858415033\n", + "sig_high 1.3278789259697081\n", + "----------------\n", + "----------------\n", + "(114, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 34. 158. 323. 354. 163. 47.]\n", + "[ 19. 70. 184. 213. 85. 13.]\n", + "bkg med : [ 125.43206787 582.8883667 1191.60974121 1305.97998047 601.34106445\n", + " 173.39282227]\n", + "bkg high : [ 70.09438324 258.24249268 678.8046875 785.80151367 313.58276367\n", + " 47.9597168 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 14. 4. 3.]\n", + "[ 1. 2. 7. 12. 5. 1.]\n", + "bkg med : [ 426.32675171 1485.57244873 2545.63574219 3412.24194336 1203.13012695\n", + " 624.73461914]\n", + "bkg high : [ 220.54172516 559.13720703 1731.93615723 2591.16906738 1065.8190918\n", + " 198.40722656]\n", + "mgp8_pp_jjaa_5f\n", + "[172. 146. 158. 144. 139. 145.]\n", + "[ 88. 93. 85. 88. 101. 105.]\n", + "bkg med : [6000.87362671 6217.45526123 7666.44042969 8079.30444336 5708.1418457\n", + " 5324.36352539]\n", + "bkg high : [3072.63547516 3573.28173828 4486.79943848 5443.26281738 4339.24487305\n", + " 3601.47363281]\n", + 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81.3053894 29.38632202 3.41558838]\n", + "sono dentro\n", + "6.863508701324463\n", + "6526.157276153564\n", + "sono dentro\n", + "44.97628402709961\n", + "6831.775291442871\n", + "sono dentro\n", + "121.38453674316406\n", + "8689.228912353516\n", + "sono dentro\n", + "122.90792846679688\n", + "9119.579864501953\n", + "sono dentro\n", + "43.3812255859375\n", + "6438.84033203125\n", + "sono dentro\n", + "8.26763916015625\n", + "5770.8883056640625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.860664102873847\n", + "sig_high 3.102109287840636\n", + "Naive\n", + "sig_med 1.6698555417110221\n", + "sig_high 1.3467075602425018\n", + "----------------\n", + "----------------\n", + "(114, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 34. 154. 303. 340. 154. 46.]\n", + "[ 19. 74. 204. 227. 94. 14.]\n", + "bkg med : [ 125.43206787 568.1317749 1117.82531738 1254.33105469 568.13818359\n", + " 169.70361328]\n", + "bkg high : [ 70.09438324 272.99920654 752.58898926 837.45043945 346.78564453\n", + " 51.64892578]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 13. 4. 3.]\n", + "[ 1. 2. 7. 13. 5. 1.]\n", + "bkg med : [ 426.32675171 1470.81585693 2471.85131836 3210.14575195 1169.92724609\n", + " 621.04541016]\n", + "bkg high : [ 220.54172516 573.8939209 1805.72045898 2793.26525879 1099.0222168\n", + " 202.09619141]\n", + "mgp8_pp_jjaa_5f\n", + "[169. 143. 156. 141. 139. 139.]\n", + "[ 91. 96. 87. 91. 101. 111.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [5903.64315796 6105.46820068 7527.83569336 7779.9777832 5674.93896484\n", + " 5126.05712891]\n", + "bkg high : [3169.86594391 3685.2689209 4625.40405273 5742.58947754 4372.44799805\n", + " 3799.62353516]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 34. 79. 88. 38. 6.]\n", + "[ 2. 9. 20. 20. 11. 2.]\n", + "bkg med : [5927.20415115 6219.90724945 7793.73794556 8076.17214966 5802.84106445\n", + " 5146.25219727]\n", + "bkg high : [3176.5976553 3715.56162262 4692.72113037 5809.90661621 4409.47245789\n", + " 3806.35525513]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 11. 13. 8. 0.]\n", + "[1. 2. 5. 3. 2. 0.]\n", + "bkg med : [5927.20415115 6293.90761566 8065.07263184 8396.8404541 6000.17553711\n", + " 5146.25219727]\n", + "bkg high : [3201.2644453 3764.89519882 4816.05509949 5883.90699768 4458.80601501\n", + " 3806.35525513]\n", + "add a+jj\n", + "bkg med : [6420.17583084 6711.03725433 8520.12341309 8808.13635254 6405.63745117\n", + " 5551.71411133]\n", + "bkg high : [3466.71073437 4044.92644882 5069.8334198 6149.35328674 4753.42222595\n", + " 4130.14138794]\n", + "sig med : [ 6.76773882 44.59317398 119.6608429 121.27993774 42.83856201\n", + " 8.07611084]\n", + "sig high : [ 3.5184598 24.8044014 79.64030457 82.93338013 29.9289856 3.6071167 ]\n", + "sono dentro\n", + "6.7677388191223145\n", + "6420.175830841064\n", + "sono dentro\n", + "44.59317398071289\n", + "6711.037254333496\n", + "sono dentro\n", + "119.66084289550781\n", + "8520.123413085938\n", + "sono dentro\n", + "121.27993774414062\n", + "8808.136352539062\n", + "sono dentro\n", + "42.83856201171875\n", + "6405.637451171875\n", + "sono dentro\n", + "8.07611083984375\n", + "5551.714111328125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 3.8538630237940104\n", + "sig_high 3.1099325575941204\n", + "Naive\n", + "sig_med 1.6664754237752701\n", + "sig_high 1.3505738185904286\n", + "----------------\n", + "----------------\n", + "(116, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 41. 189. 393. 426. 187. 53.]\n", + "[ 12. 39. 114. 141. 61. 7.]\n", + "bkg med : [ 151.25631714 697.25164795 1449.85693359 1571.60302734 689.84887695\n", + " 195.51513672]\n", + "bkg high : [ 44.27013016 143.87796021 420.56549072 520.17175293 225.04174805\n", + " 25.82446289]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 17. 6. 3.]\n", + "[1. 2. 7. 9. 3. 1.]\n", + "bkg med : [ 452.15100098 1599.93572998 2803.88293457 4129.20678711 1592.5324707\n", + " 646.85693359]\n", + "bkg high : [ 194.71747208 444.77267456 1473.69696045 1874.19750977 676.38354492\n", + " 176.27172852]\n", + "mgp8_pp_jjaa_5f\n", + "[200. 176. 189. 179. 177. 181.]\n", + "[60. 63. 54. 53. 63. 69.]\n", + "bkg med : [6934.18225098 7304.12322998 8929.40246582 9931.10913086 7329.8215332\n", + " 6513.80224609]\n", + "bkg high : [2139.33563614 2486.61251831 3223.84539795 3591.93579102 2718.22338867\n", + " 2412.57250977]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 8. 39. 90. 97. 40. 8.]\n", + "[ 1. 4. 9. 11. 9. 0.]\n", + "bkg med : [ 6961.10909653 7435.3915863 9232.32891846 10257.59609985\n", + " 7464.45532227 6540.72900391]\n", + "bkg high : [2142.70149183 2500.07594109 3254.13810158 3628.960186 2748.51605988\n", + " 2412.57250977]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 13. 13. 8. 0.]\n", + "[1. 2. 3. 3. 2. 0.]\n", + "bkg med : [ 6961.10909653 7509.39195251 9552.99716187 10578.26446533\n", + " 7661.78979492 6540.72900391]\n", + "bkg high : [2167.36828184 2549.40951729 3328.13848305 3702.96056747 2797.84964752\n", + " 2412.57250977]\n", + "add a+jj\n", + "bkg med : [ 7544.50728989 8022.78257751 10104.3086853 11100.40606689\n", + " 8178.09741211 7068.70458984]\n", + "bkg high : [2342.38781309 2733.1800251 3485.65606117 3857.56115341 2981.62015533\n", + " 2613.8449707 ]\n", + "----------------\n", + "----------------\n", + "(116, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 40. 178. 372. 409. 179. 52.]\n", + "[ 13. 50. 135. 158. 69. 8.]\n", + "bkg med : [ 147.56713867 656.67108154 1372.38269043 1508.88647461 660.34960938\n", + " 191.82617188]\n", + "bkg high : [ 47.95931244 184.45892334 498.03717041 582.89038086 254.55541992\n", + " 29.51367188]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 16. 6. 3.]\n", + "[ 1. 2. 7. 10. 3. 1.]\n", + "bkg med : [ 448.46182251 1559.35516357 2726.40869141 3916.04296875 1563.03320312\n", + " 643.16796875]\n", + "bkg high : [ 198.40665436 485.3536377 1551.16864014 2087.36340332 705.8972168\n", + " 179.9609375 ]\n", + "mgp8_pp_jjaa_5f\n", + "[194. 166. 181. 164. 170. 170.]\n", + "[66. 73. 62. 68. 70. 80.]\n", + "bkg med : [6736.03213501 6939.44110107 8592.64697266 9231.40625 7073.42382812\n", + " 6153.55859375]\n", + "bkg high : [2337.47696686 2851.29504395 3560.59832764 4291.25402832 2974.6081543\n", + " 2772.7734375 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 37. 84. 92. 39. 8.]\n", + "[ 2. 6. 15. 16. 10. 0.]\n", + "bkg med : [6759.5931282 7063.9777298 8875.37841797 9541.06399536 7204.69177246\n", + " 6180.48535156]\n", + "bkg high : [2344.20867825 2871.49018097 3611.08615112 4345.10766602 3008.26675415\n", + " 2772.7734375 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 13. 13. 8. 0.]\n", + "[1. 2. 3. 3. 2. 0.]\n", + "bkg med : [6759.5931282 7137.97809601 9196.04666138 9861.73236084 7402.02624512\n", + " 6180.48535156]\n", + "bkg high : [2368.87546825 2920.82375717 3685.08653259 4419.10804749 3057.6003418\n", + " 2772.7734375 ]\n", + "add a+jj\n", + "bkg med : [ 7325.48936844 7622.19879913 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8353.12231445 8945.41015625 6896.52270508\n", + " 5980.42138672]\n", + "bkg high : [2531.93790436 3136.40052795 3800.12097168 4577.15423584 3151.41577148\n", + " 2945.8918457 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 37. 83. 92. 38. 8.]\n", + "[ 2. 6. 16. 16. 11. 0.]\n", + "bkg med : [6565.1321907 6778.87244415 8632.48791504 9255.06790161 7024.42480469\n", + " 6007.34814453]\n", + "bkg high : [2538.66961575 3156.59566498 3853.97464752 4631.00788116 3188.44023132\n", + " 2945.8918457 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 3. 12. 13. 8. 0.]\n", + "[1. 2. 4. 3. 2. 0.]\n", + "bkg med : [6565.1321907 6852.87281036 8928.48937988 9575.73623657 7221.75927734\n", + " 6007.34814453]\n", + "bkg high : [2563.33640575 3205.92924118 3952.64182281 4705.00826263 3237.77380371\n", + " 2945.8918457 ]\n", + "add a+jj\n", + "bkg med : [ 7113.52647781 7313.75757599 9441.88000488 10033.70401001\n", + " 7703.06298828 6488.65185547]\n", + "bkg high : [2773.35984325 3442.20560837 4148.08029938 4923.7826767 3456.54821777\n", + " 3193.83618164]\n", + "sig med : [ 7.6684885 49.50972366 134.98318481 138.20498657 48.36096191\n", + " 9.00183105]\n", + "sig high : [ 2.61771011 19.88821411 64.31759644 66.00830841 24.40658569 2.68139648]\n", + "sono dentro\n", + "7.668488502502441\n", + "7113.526477813721\n", + "sono dentro\n", + "49.50972366333008\n", + "7313.7575759887695\n", + "sono dentro\n", + "134.98318481445312\n", + "9441.880004882812\n", + "sono dentro\n", + "138.20498657226562\n", + "10033.704010009766\n", + "sono dentro\n", + "48.3609619140625\n", + "7703.06298828125\n", + "sono dentro\n", + "9.0018310546875\n", + "6488.65185546875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 4.093782712717016\n", + "sig_high 2.7851156618752166\n", + "Naive\n", + "sig_med 1.767992430929837\n", + "sig_high 1.2147371774662084\n", + "----------------\n", + "----------------\n", + "(116, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 40. 167. 331. 379. 166. 48.]\n", + "[ 13. 61. 176. 188. 82. 12.]\n", + "bkg med : [ 147.56713867 616.0904541 1221.12438965 1398.21020508 612.40869141\n", + " 177.07495117]\n", + "bkg high : [ 47.95931244 225.03987122 649.29187012 693.56988525 302.51513672\n", + " 44.27050781]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 9. 15. 6. 3.]\n", + "[ 1. 2. 7. 11. 3. 1.]\n", + "bkg med : [ 448.46182251 1518.77453613 2575.15039062 3654.91943359 1515.09228516\n", + " 628.41674805]\n", + "bkg high : [ 198.40665436 525.93458557 1702.42333984 2348.49017334 753.85693359\n", + " 194.71777344]\n", + "mgp8_pp_jjaa_5f\n", + "[185. 155. 174. 154. 165. 159.]\n", + "[75. 84. 69. 78. 75. 91.]\n", + "bkg med : [6444.34072876 6542.34875488 8214.51757812 8646.08349609 6863.14697266\n", + " 5782.25268555]\n", + "bkg high : [2629.16837311 3248.38771057 3938.72412109 4876.48236084 3184.61865234\n", + " 3144.04199219]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 37. 83. 91. 38. 7.]\n", + "[ 2. 6. 16. 17. 11. 1.]\n", + "bkg med : [6467.90172195 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"sono dentro\n", + "7.572718620300293\n", + "7007.545032501221\n", + "sono dentro\n", + "49.12661361694336\n", + "7193.0195388793945\n", + "sono dentro\n", + "133.25949096679688\n", + "9272.774505615234\n", + "sono dentro\n", + "136.57699584960938\n", + "9722.260498046875\n", + "sono dentro\n", + "47.81829833984375\n", + "7669.687255859375\n", + "sono dentro\n", + "8.810302734375\n", + "6269.6153564453125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 4.08820967757512\n", + "sig_high 2.7962049362977672\n", + "Naive\n", + "sig_med 1.7648746677641247\n", + "sig_high 1.219180156005483\n", + "----------------\n", + "----------------\n", + "(118, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 43. 197. 416. 460. 199. 54.]\n", + "[ 10. 31. 91. 107. 49. 6.]\n", + "bkg med : [ 158.63467407 726.76477051 1534.70922852 1697.03222656 734.10400391\n", + " 199.20410156]\n", + "bkg high : [ 36.89177322 114.36453247 335.71520996 394.73898315 180.76904297\n", + " 22.13525391]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 12. 20. 7. 3.]\n", + "[1. 2. 4. 6. 2. 1.]\n", + "bkg med : [ 459.52935791 1629.44885254 3340.07702637 4705.9777832 1787.23486328\n", + " 650.54589844]\n", + "bkg high : [ 187.33911514 415.25924683 937.5045166 1297.42330933 481.66357422\n", + " 172.58251953]\n", + "mgp8_pp_jjaa_5f\n", + "[215. 192. 205. 195. 191. 200.]\n", + "[45. 47. 38. 37. 49. 50.]\n", + "bkg med : [ 7427.71295166 7852.19885254 9984.67077637 11026.7199707\n", + " 7978.32080078 7133.35839844]\n", + "bkg high : [1645.84009171 1938.58248901 2169.12756348 2496.6166687 2069.76123047\n", + " 1793.09033203]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 8. 39. 93. 99. 44. 8.]\n", + "[1. 4. 6. 9. 5. 0.]\n", + "bkg med : [ 7454.63979721 7983.46720886 10297.69476318 11359.93862915\n", + " 8126.41796875 7160.28515625]\n", + "bkg high : [1649.2059474 1952.04591179 2189.32269859 2526.90937042 2086.59049225\n", + " 1793.09033203]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 4. 14. 14. 8. 0.]\n", + "[1. 1. 2. 2. 2. 0.]\n", + "bkg med : [ 7454.63979721 8082.1343689 10643.02978516 11705.27386475\n", + " 8323.75244141 7160.28515625]\n", + "bkg high : [1673.87273741 1976.7127018 2238.65627861 2576.24295807 2135.9240799\n", + " 1793.09033203]\n", + "add a+jj\n", + "bkg med : [ 8081.79311752 8642.1968689 11241.01318359 12274.08734131\n", + " 8880.89794922 7743.68359375]\n", + "bkg high : [1805.13738585 2113.81133461 2349.50198174 2684.17166901 2278.85669708\n", + " 1938.93994141]\n", + "----------------\n", + "----------------\n", + "(118, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 42. 186. 395. 443. 191. 53.]\n", + "[ 11. 42. 112. 124. 57. 7.]\n", + "bkg med : [ 154.94549561 686.1842041 1457.23510742 1634.31958008 704.60083008\n", + " 195.51513672]\n", + "bkg high : [ 40.58095551 154.94549561 413.18707275 457.45526123 210.28491211\n", + " 25.82446289]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 12. 19. 7. 3.]\n", + "[1. 2. 4. 7. 2. 1.]\n", + "bkg med : [ 455.84017944 1588.86828613 3262.60290527 4492.81787109 1757.73168945\n", + " 646.85693359]\n", + "bkg high : [ 191.02829742 455.84020996 1014.97637939 1510.58685303 511.17944336\n", + " 176.27172852]\n", + "mgp8_pp_jjaa_5f\n", + "[209. 182. 197. 180. 184. 189.]\n", + "[51. 57. 46. 52. 56. 61.]\n", + "bkg med : [ 7229.56283569 7487.51672363 9647.58728027 10327.34912109\n", + " 7721.91918945 6773.11474609]\n", + "bkg high : [1843.99607086 2303.25524902 2505.84356689 3195.91497803 2326.14819336\n", + " 2153.29125977]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 37. 87. 94. 43. 8.]\n", + "[ 2. 6. 12. 14. 6. 0.]\n", + "bkg med : [ 7253.12382889 7612.05335236 9940.41625977 10643.73855591\n", + " 7866.6505127 6800.04150391]\n", + "bkg high : [1850.72778225 2323.45038605 2546.23383331 3243.03691101 2346.34331512\n", + " 2153.29125977]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 4. 14. 14. 8. 0.]\n", + "[1. 1. 2. 2. 2. 0.]\n", + "bkg med : [ 7253.12382889 7710.72051239 10285.75128174 10989.0737915\n", + " 8063.98498535 6800.04150391]\n", + "bkg high : [1875.39457226 2348.11717606 2595.56741333 3292.37049866 2395.67690277\n", + " 2153.29125977]\n", + "add a+jj\n", + "bkg med : [ 7862.77519608 8241.61309052 10860.39874268 11514.13238525\n", + " 8600.71154785 7351.35302734]\n", + "bkg high : [2024.16117382 2514.38573074 2729.74905396 3444.05409241 2559.02846527\n", + " 2331.2277832 ]\n", + "----------------\n", + "----------------\n", + "(118, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 42. 179. 374. 427. 187. 50.]\n", + "[ 11. 49. 133. 140. 61. 10.]\n", + "bkg med : [ 154.94549561 660.36016846 1379.76123047 1575.29223633 689.85595703\n", + " 184.44824219]\n", + "bkg high : [ 40.58095551 180.76974487 490.65893555 516.484375 225.04174805\n", + " 36.89208984]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 12. 19. 7. 3.]\n", + "[1. 2. 4. 7. 2. 1.]\n", + "bkg med : [ 455.84017944 1563.04425049 3185.12902832 4433.79052734 1742.98681641\n", + " 635.79003906]\n", + "bkg high : [ 191.02829742 481.66445923 1092.44824219 1569.6159668 525.9362793\n", + " 187.33935547]\n", + "mgp8_pp_jjaa_5f\n", + "[203. 174. 192. 173. 179. 184.]\n", + "[57. 65. 51. 59. 61. 66.]\n", + "bkg med : [ 7035.10189819 7202.41143799 9407.90637207 10041.42333984\n", + " 7545.10400391 6599.97753906]\n", + "bkg high : [2038.46286774 2588.3343811 2745.36621094 3481.81518555 2502.95581055\n", + " 2326.40966797]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 37. 86. 94. 42. 8.]\n", + "[ 2. 6. 13. 14. 7. 0.]\n", + "bkg med : [ 7058.66289139 7326.94806671 9697.36950684 10357.81277466\n", + " 7686.46948242 6626.90429688]\n", + "bkg high : [2045.19457912 2608.52951813 2789.12232971 3528.93711853 2526.51679993\n", + " 2326.40966797]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 4. 13. 14. 8. 0.]\n", + "[1. 1. 3. 2. 2. 0.]\n", + "bkg med : [ 7058.66289139 7425.61522675 10018.03771973 10703.14797974\n", + " 7883.80395508 6626.90429688]\n", + "bkg high : [2069.86136913 2633.19630814 2863.12270355 3578.27070618 2575.85038757\n", + " 2326.40966797]\n", + "add a+jj\n", + "bkg med : [ 7650.81230545 7933.17186737 10578.10021973 11207.78762817\n", + " 8405.94555664 7163.63085938]\n", + "bkg high : [2236.12992382 2822.80080032 3011.88930511 3750.37324524 2753.78691101\n", + " 2518.93115234]\n", + "sig med : [ 8.43464756 53.91545105 149.05987549 152.69729614 54.61755371\n", + " 9.51257324]\n", + "sig high : [ 1.85155118 15.48279953 50.24057007 51.51600647 18.1499939 2.1706543 ]\n", + "sono dentro\n", + "8.434647560119629\n", + "7650.812305450439\n", + "sono dentro\n", + "53.91545104980469\n", + "7933.1718673706055\n", + "sono dentro\n", + "149.05987548828125\n", + "10578.100219726562\n", + "sono dentro\n", + "152.69729614257812\n", + "11207.787628173828\n", + "sono dentro\n", + "54.6175537109375\n", + "8405.945556640625\n", + "sono dentro\n", + "9.5125732421875\n", + "7163.630859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 4.293501024626241\n", + "sig_high 2.4712498481959257\n", + "Naive\n", + "sig_med 1.8612078889451602\n", + "sig_high 1.0662968138111903\n", + "----------------\n", + "----------------\n", + "(118, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[ 42. 175. 354. 413. 178. 49.]\n", + "[ 11. 53. 153. 154. 70. 11.]\n", + "bkg med : [ 154.94549561 645.60357666 1305.97680664 1523.64331055 656.66455078\n", + " 180.75927734]\n", + "bkg high : [ 40.58095551 195.52645874 564.44177246 568.13476562 258.24462891\n", + " 40.58129883]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 2. 6. 12. 18. 7. 3.]\n", + "[1. 2. 4. 8. 2. 1.]\n", + "bkg med : [ 455.84017944 1548.28765869 3111.34460449 4231.69433594 1709.79541016\n", + " 632.10107422]\n", + "bkg high : [ 191.02829742 496.4211731 1166.2310791 1771.71362305 559.13916016\n", + " 191.02856445]\n", + "mgp8_pp_jjaa_5f\n", + "[200. 171. 190. 170. 179. 178.]\n", + "[60. 68. 53. 62. 61. 72.]\n", + "bkg med : [6937.87142944 7090.42437744 9269.27429199 9741.94433594 7511.91259766\n", + " 6401.80419922]\n", + "bkg high : [2135.66892242 2700.3117981 2883.96936035 3781.14331055 2536.15869141\n", + " 2524.55981445]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 7. 37. 86. 93. 42. 7.]\n", + "[ 2. 6. 13. 15. 7. 1.]\n", + "bkg med : [ 6961.43242264 7214.96100616 9558.73742676 10054.96792603\n", + " 7653.27807617 6425.3651123 ]\n", + "bkg high : [2142.40063381 2720.50693512 2927.72547913 3831.63109589 2559.71968842\n", + " 2527.92567444]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 0. 4. 12. 14. 8. 0.]\n", + "[1. 1. 4. 2. 2. 0.]\n", + "bkg med : [ 6961.43242264 7313.6281662 9854.7388916 10400.30310059\n", + " 7850.61254883 6425.3651123 ]\n", + "bkg high : [2167.06742382 2745.17372513 3026.39264679 3880.96468353 2609.05327606\n", + " 2527.92567444]\n", + "add a+jj\n", + "bkg med : [ 7544.83086014 7812.43383026 10408.96740723 10896.19177246\n", + " 8372.75415039 6944.58972168]\n", + "bkg high : [2342.08695507 2943.52919388 3180.99323273 4061.81819916 2786.9897995\n", + " 2737.94911194]\n", + "sig med : [ 8.33887768 53.532341 147.33618164 151.06930542 54.07489014\n", + " 9.32104492]\n", + "sig high : [ 1.94732106 15.86587906 51.96429443 53.14399719 18.69265747 2.36218262]\n", + "sono dentro\n", + "8.33887767791748\n", + "7544.830860137939\n", + "sono dentro\n", + "53.53234100341797\n", + "7812.4338302612305\n", + "sono dentro\n", + "147.336181640625\n", + "10408.967407226562\n", + "sono dentro\n", + "151.06930541992188\n", + "10896.191772460938\n", + "sono dentro\n", + "54.07489013671875\n", + "8372.754150390625\n", + "sono dentro\n", + "9.321044921875\n", + "6944.5897216796875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 4.287753686866445\n", + "sig_high 2.482139610231651\n", + "Naive\n", + "sig_med 1.8582889843339174\n", + "sig_high 1.0715489258341033\n", + "x_1: 112\n", + "x_2: 126\n", + "Significance: 4.963073272057117\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m_1 = np.arange(start = 112, stop = 120, step = 2)\n", + "m_2 = np.arange(start = 126, stop = 134, step = 2)\n", + "m1_small_high,m2_small_high,max_sig_small_high = findm1m2(df_small_high,m_1,m_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "f5e6da2b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(112, 126), (112, 128), (112, 130), (112, 132), (114, 126), (114, 128), (114, 130), (114, 132), (116, 126), (116, 128), (116, 130), (116, 132), (118, 126), (118, 128), (118, 130), (118, 132)]\n", + "----------------\n", + "----------------\n", + "(112, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[171. 488. 825. 874. 408. 145.]\n", + "[ 30. 103. 170. 193. 108. 42.]\n", + "bkg med : [ 630.84667969 1800.33117676 3043.49145508 3224.25146484 1505.296875\n", + " 534.97070312]\n", + "bkg high : [110.675354 379.98468018 627.1585083 712.01733398 398.43457031\n", + " 154.94677734]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 24. 64. 111. 107. 51. 16.]\n", + "[ 2. 20. 16. 31. 3. 1.]\n", + "bkg med : [ 4241.58154297 11428.95617676 19743.13793945 19322.30224609\n", + " 9178.20703125 2942.15820312]\n", + "bkg high : [ 411.57000732 3388.93096924 3034.31500244 5375.88305664 849.77636719\n", + " 305.39404297]\n", + "mgp8_pp_jjaa_5f\n", + "[538. 476. 476. 542. 472. 554.]\n", + "[ 92. 91. 133. 83. 95. 98.]\n", + "bkg med : [21678.30029297 26854.33117676 35168.51293945 36886.48974609\n", + " 24473.95703125 20895.22070312]\n", + "bkg high : [3393.30438232 6338.25518799 7344.86578369 8065.92602539 3928.74121094\n", + " 3481.58935547]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 175. 372. 405. 187. 65.]\n", + "[ 6. 18. 37. 31. 24. 8.]\n", + "bkg med : [21893.71505737 27443.35400391 36420.61047363 38249.70629883\n", + " 25103.39013672 21113.99267578]\n", + "bkg high : [3413.49951935 6398.84056854 7469.40250397 8170.26742554 4009.52148438\n", + " 3508.51611328]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 19. 48. 101. 87. 53. 16.]\n", + "[ 2. 13. 9. 23. 13. 0.]\n", + "bkg med : [22362.38400269 28627.35913086 38911.95324707 40395.73461914\n", + " 26410.74072266 21508.66455078]\n", + "bkg high : [3462.83309555 6719.50881195 7691.40363312 8737.60366821 4330.18920898\n", + " 3508.51611328]\n", + "add a+jj\n", + "bkg med : [23931.72579956 30015.84741211 40300.44152832 41976.56665039\n", + " 27787.10009766 23124.13720703]\n", + "bkg high : [3731.1963768 6984.95510101 8079.36359406 8979.71401978 4607.3034668\n", + " 3794.38134766]\n", + "sig med : [ 200.68966675 666.99133301 1382.13146973 1385.84814453 679.46533203\n", + " 206.34863281]\n", + "sig high : [ 94.80419922 323.41174316 696.02728271 700.39697266 327.15820312\n", + " 96.82373047]\n", + "sono dentro\n", + "200.68966674804688\n", + "23931.725799560547\n", + "sono dentro\n", + "666.9913330078125\n", + "30015.847412109375\n", + "sono dentro\n", + "1382.1314697265625\n", + "40300.44152832031\n", + "sono dentro\n", + "1385.84814453125\n", + "41976.566650390625\n", + "sono dentro\n", + "679.46533203125\n", + "27787.10009765625\n", + "sono dentro\n", + "206.3486328125\n", + "23124.13720703125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 24.119301000492307\n", + "sig_high 26.64980059122687\n", + "Naive\n", + "sig_med 10.452053708067972\n", + "sig_high 11.769690287033333\n", + "----------------\n", + "----------------\n", + "(112, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[171. 470. 806. 852. 400. 141.]\n", + "[ 30. 121. 189. 215. 116. 46.]\n", + "bkg med : [ 630.84674072 1733.92529297 2973.40600586 3143.06445312 1475.78125\n", + " 520.21289062]\n", + "bkg high : [110.675354 446.38928223 697.25469971 793.17993164 427.94824219\n", + " 169.70361328]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 22. 64. 110. 105. 50. 16.]\n", + "[ 4. 20. 17. 33. 4. 1.]\n", + "bkg med : [ 3940.68707275 11362.55029297 19522.60522461 18940.2109375\n", + " 8998.2421875 2927.40039062]\n", + "bkg high : [ 712.46466064 3455.33532715 3254.85845947 5757.94018555 1029.73730469\n", + " 320.15087891]\n", + "mgp8_pp_jjaa_5f\n", + "[527. 464. 465. 528. 466. 547.]\n", + "[103. 103. 144. 97. 101. 105.]\n", + "bkg med : [21021.51519775 26399.05029297 34591.51147461 36050.7109375\n", + " 24099.5546875 20653.61914062]\n", + "bkg high : [4050.71075439 6793.5814209 7921.92095947 8901.7253418 4303.16308594\n", + " 3723.21728516]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 173. 369. 404. 186. 64.]\n", + "[ 6. 20. 40. 32. 25. 9.]\n", + "bkg med : [21236.92996216 26981.34143066 35833.51074219 37410.56152344\n", + " 24725.6237793 20869.02539062]\n", + "bkg high : [4070.90589142 6860.89850616 8056.55528259 9009.43249512 4387.3092041\n", + " 3753.5098877 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[19. 46. 95. 86. 51. 16.]\n", + "[ 2. 15. 15. 24. 15. 0.]\n", + "bkg med : [21705.59890747 28116.01306152 38176.85205078 39531.921875\n", + " 25983.64038086 21263.69726562]\n", + "bkg high : [4120.23947144 7230.90027618 8426.55735779 9601.43493652 4757.3104248\n", + " 3753.5098877 ]\n", + "add a+jj\n", + "bkg med : [23242.85379028 29469.49743652 39533.25341797 41072.03515625\n", + " 27342.50366211 22858.7578125 ]\n", + "bkg high : [4420.68966675 7531.3504715 8846.60423279 9884.38317871 5051.92663574\n", + " 4059.79382324]\n", + "sig med : [ 191.39627075 636.84277344 1319.43664551 1320.36376953 649.98730469\n", + " 196.98535156]\n", + "sig high : [104.09893036 353.55886841 758.72192383 765.55371094 356.57958984\n", + " 106.16845703]\n", + "sono dentro\n", + "191.39627075195312\n", + "23242.853790283203\n", + "sono dentro\n", + "636.8427734375\n", + "29469.497436523438\n", + "sono dentro\n", + "1319.4366455078125\n", + "39533.25341796875\n", + "sono dentro\n", + "1320.36376953125\n", + "41072.03515625\n", + "sono dentro\n", + "649.9873046875\n", + "27342.503662109375\n", + "sono dentro\n", + "196.9853515625\n", + "22858.7578125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 23.24691391703038\n", + "sig_high 27.780066027377682\n", + "Naive\n", + "sig_med 10.07260073043231\n", + "sig_high 12.254889596280071\n", + "----------------\n", + "----------------\n", + "(112, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[168. 457. 782. 838. 395. 139.]\n", + "[ 33. 134. 213. 229. 121. 48.]\n", + "bkg med : [ 619.77929688 1685.96533203 2884.87475586 3091.39257812 1457.33398438\n", + " 512.83398438]\n", + "bkg high : [121.7428894 494.3480835 785.79669189 844.82885742 446.39428711\n", + " 177.08203125]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 22. 64. 108. 103. 50. 14.]\n", + "[ 4. 20. 19. 35. 4. 3.]\n", + "bkg med : [ 3929.61962891 11314.59033203 19133.17944336 18587.63671875\n", + " 8979.79492188 2619.12304688]\n", + "bkg high : [ 723.53219604 3503.29412842 3644.29498291 6110.48364258 1048.18334961\n", + " 628.42382812]\n", + "mgp8_pp_jjaa_5f\n", + "[518. 453. 448. 519. 450. 536.]\n", + "[112. 114. 161. 106. 117. 116.]\n", + "bkg med : [20719.38525391 25994.62158203 33651.17944336 35406.48046875\n", + " 23562.60742188 19988.87304688]\n", + "bkg high : [4353.46969604 7198.05194092 8862.33013916 9545.96020508 4840.17163086\n", + " 4388.00195312]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 169. 367. 401. 184. 63.]\n", + "[ 6. 24. 42. 35. 27. 10.]\n", + "bkg med : [20934.80001831 26563.44934082 34886.44628906 36756.2331543\n", + " 24181.9453125 20200.91552734]\n", + "bkg high : [4373.66483307 7278.83243561 9003.69621277 9663.76480103 4931.04943848\n", + " 4421.66040039]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[18. 42. 92. 84. 49. 16.]\n", + "[ 3. 19. 18. 26. 17. 0.]\n", + "bkg med : [21378.80215454 27599.45397949 37155.78710938 38828.25756836\n", + " 25390.62792969 20595.58740234]\n", + "bkg high : [ 4447.66520691 7747.50141144 9447.69877625 10305.10025024\n", + " 5350.38427734 4421.66040039]\n", + "add a+jj\n", + "bkg med : [22889.80410767 28920.85144043 38462.59960938 40342.17651367\n", + " 26702.89550781 22158.57177734]\n", + "bkg high : [ 4774.36833191 8080.03852081 9917.33451843 10614.30142212\n", + " 5691.67211914 4760.03149414]\n", + "sig med : [ 185.68113708 617.2064209 1274.91455078 1274.24707031 627.60180664\n", + " 189.72875977]\n", + "sig high : [109.81496429 373.19433594 803.24414062 811.65563965 378.70849609\n", + " 113.45898438]\n", + "sono dentro\n", + "185.68113708496094\n", + "22889.804107666016\n", + "sono dentro\n", + "617.2064208984375\n", + "28920.851440429688\n", + "sono dentro\n", + "1274.91455078125\n", + "38462.599609375\n", + "sono dentro\n", + "1274.2470703125\n", + "40342.176513671875\n", + "sono dentro\n", + "627.601806640625\n", + "26702.8955078125\n", + "sono dentro\n", + "189.728759765625\n", + "22158.57177734375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 22.717153829174663\n", + "sig_high 28.048592272746426\n", + "Naive\n", + "sig_med 9.841633177706862\n", + "sig_high 12.37053526647347\n", + "----------------\n", + "----------------\n", + "(112, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[159. 443. 764. 826. 383. 135.]\n", + "[ 42. 148. 231. 241. 133. 52.]\n", + "bkg med : [ 586.57702637 1634.3157959 2818.47949219 3047.09814453 1413.06054688\n", + " 498.07617188]\n", + "bkg high : [154.94548035 545.99584961 852.20397949 889.09936523 490.66479492\n", + " 191.83886719]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 22. 63. 108. 97. 50. 14.]\n", + "[ 4. 21. 19. 41. 4. 3.]\n", + "bkg med : [ 3896.4173584 11112.49353027 19066.78417969 17640.64501953\n", + " 8935.52148438 2604.36523438]\n", + "bkg high : [ 756.73478699 3705.3894043 3710.70202637 7057.43774414 1092.45385742\n", + " 643.18066406]\n", + "mgp8_pp_jjaa_5f\n", + "[510. 444. 436. 506. 441. 527.]\n", + "[120. 123. 173. 119. 126. 125.]\n", + "bkg med : [20427.4095459 25500.86853027 33195.90917969 34038.20751953\n", + " 23226.67773438 19682.45898438]\n", + "bkg high : [ 4645.95353699 7691.83862305 9317.65905762 10914.24633789\n", + " 5176.13354492 4694.45019531]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 169. 362. 399. 183. 61.]\n", + "[ 6. 24. 47. 37. 28. 12.]\n", + "bkg med : [20642.8243103 26069.69628906 34414.34606934 35381.22827148\n", + " 23842.6496582 19887.7722168 ]\n", + "bkg high : [ 4666.1486721 7772.61911011 9475.85437012 11038.78259277\n", + " 5270.37719727 4734.84036255]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[17. 40. 90. 82. 49. 16.]\n", + "[ 4. 21. 20. 28. 17. 0.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [21062.15966797 27056.36755371 36634.35339355 37403.91748047\n", + " 25051.33227539 20282.4440918 ]\n", + "bkg high : [ 4764.81583977 8290.62173462 9969.19024658 11729.45153809\n", + " 5689.71203613 4734.84036255]\n", + "add a+jj\n", + "bkg med : [22549.82568359 28351.51208496 37906.1619873 38879.91552734\n", + " 26337.45629883 21819.18432617]\n", + "bkg high : [ 5114.85490227 8649.41177368 10473.82989502 12076.57336426\n", + " 6057.25305176 5099.46438599]\n", + "sig med : [ 180.43598938 601.8604126 1241.90063477 1240.77783203 610.68554688\n", + " 184.29052734]\n", + "sig high : [115.06015778 388.54040527 836.25787354 845.12036133 395.68505859\n", + " 118.84936523]\n", + "sono dentro\n", + "180.4359893798828\n", + "22549.82568359375\n", + "sono dentro\n", + "601.8604125976562\n", + "28351.512084960938\n", + "sono dentro\n", + "1241.900634765625\n", + "37906.16198730469\n", + "sono dentro\n", + "1240.77783203125\n", + "38879.91552734375\n", + "sono dentro\n", + "610.685546875\n", + "26337.456298828125\n", + "sono dentro\n", + "184.29052734375\n", + "21819.184326171875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 22.360240427657637\n", + "sig_high 28.1071225215874\n", + "Naive\n", + "sig_med 9.681818564524534\n", + "sig_high 12.389948372697347\n", + "----------------\n", + "----------------\n", + "(114, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[174. 498. 856. 903. 430. 149.]\n", + "[ 27. 93. 139. 164. 86. 38.]\n", + "bkg med : [ 641.9140625 1837.22363281 3157.84545898 3331.26855469 1586.46484375\n", + " 549.72851562]\n", + "bkg high : [ 99.6078186 343.09338379 512.79205322 605.03027344 317.27197266\n", + " 140.18994141]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 24. 69. 114. 110. 51. 16.]\n", + "[ 2. 15. 13. 28. 3. 1.]\n", + "bkg med : [ 4252.64892578 12218.08496094 20308.83374023 19880.68261719\n", + " 9259.375 2956.91601562]\n", + "bkg high : [ 400.50247192 2599.80334473 2468.60675049 4817.55419922 768.61376953\n", + " 290.63720703]\n", + "mgp8_pp_jjaa_5f\n", + "[553. 493. 494. 554. 484. 566.]\n", + "[ 77. 74. 115. 71. 83. 86.]\n", + "bkg med : [22174.63330078 28194.36621094 36317.52124023 37833.74511719\n", + " 24944. 21298.85351562]\n", + "bkg high : [2896.08450317 4998.15490723 6195.77471924 7118.67529297 3458.65673828\n", + " 3077.91064453]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 178. 377. 411. 189. 66.]\n", + "[ 5. 15. 32. 25. 22. 7.]\n", + "bkg med : [22393.41389465 28793.48657227 37586.44921875 39217.1574707\n", + " 25580.16064453 21520.99121094]\n", + "bkg high : [2912.91378212 5048.64273834 6303.48210144 7202.82177734 3532.70532227\n", + " 3101.47155762]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 19. 50. 103. 89. 57. 16.]\n", + "[ 2. 11. 7. 21. 9. 0.]\n", + "bkg med : [22862.08287048 30026.82507324 40127.12695312 41412.51977539\n", + " 26986.17919922 21915.66259766]\n", + "bkg high : [2962.24736214 5319.97742462 6476.14955139 7720.82470703 3754.70611572\n", + " 3101.47155762]\n", + "add a+jj\n", + "bkg med : [24475.17955017 31464.90222168 41568.12109375 43028.19165039\n", + " 28397.53076172 23566.12744141]\n", + "bkg high : [3186.85576057 5535.8348465 6811.60365295 7927.93115234 3996.81646729\n", + " 3352.33288574]\n", + "sig med : [ 214.3699646 715.40142822 1483.93457031 1490.13549805 725.9855957\n", + " 220.55517578]\n", + "sig high : [ 81.12345886 275.00692749 594.22503662 596.19763184 280.65576172\n", + " 82.62023926]\n", + "sono dentro\n", + "214.36996459960938\n", + "24475.1795501709\n", + "sono dentro\n", + "715.4014282226562\n", + "31464.902221679688\n", + "sono dentro\n", + "1483.9345703125\n", + "41568.12109375\n", + "sono dentro\n", + "1490.135498046875\n", + "43028.191650390625\n", + "sono dentro\n", + "725.985595703125\n", + "28397.53076171875\n", + "sono dentro\n", + "220.55517578125\n", + "23566.12744140625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 25.489189373380377\n", + "sig_high 24.620540611848014\n", + "Naive\n", + "sig_med 11.055045591886193\n", + "sig_high 10.880252096275068\n", + "----------------\n", + "----------------\n", + "(114, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[174. 480. 837. 881. 422. 145.]\n", + "[ 27. 111. 158. 186. 94. 42.]\n", + "bkg med : [ 641.91412354 1770.81774902 3087.76000977 3250.08154297 1556.94921875\n", + " 534.97070312]\n", + "bkg high : [ 99.6078186 409.49798584 582.88824463 686.19287109 346.78564453\n", + " 154.94677734]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 22. 69. 113. 108. 50. 16.]\n", + "[ 4. 15. 14. 30. 4. 1.]\n", + "bkg med : [ 3951.75445557 12151.67907715 20088.30102539 19498.59130859\n", + " 9079.41015625 2942.15820312]\n", + "bkg high : [ 701.39712524 2666.20770264 2689.15020752 5199.61132812 948.57470703\n", + " 305.39404297]\n", + "mgp8_pp_jjaa_5f\n", + "[542. 481. 483. 540. 478. 559.]\n", + "[ 88. 86. 126. 85. 89. 93.]\n", + "bkg med : [21517.84820557 27739.08532715 35740.51977539 36997.96630859\n", + " 24569.59765625 21057.25195312]\n", + "bkg high : [3553.49087524 5453.48114014 6772.82989502 7954.47460938 3833.07861328\n", + " 3319.53857422]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 176. 374. 410. 188. 65.]\n", + "[ 5. 17. 35. 26. 23. 8.]\n", + "bkg med : [21736.6288147 28331.47399902 36999.3494873 38378.01269531\n", + " 25202.39428711 21276.02392578]\n", + "bkg high : [3570.3201561 5510.70067596 6890.63488007 8041.98684692 3910.49304199\n", + " 3346.46533203]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[19. 48. 97. 88. 55. 16.]\n", + "[ 2. 13. 13. 22. 11. 0.]\n", + "bkg med : [22205.29776001 29515.47912598 39392.02429199 40548.70800781\n", + " 26559.07885742 21670.69580078]\n", + "bkg high : [3619.6537323 5831.36891937 7211.30324554 8584.65609741 4181.82727051\n", + " 3346.46533203]\n", + "add a+jj\n", + "bkg med : [23786.30752563 30918.55236816 40800.93151855 42123.66113281\n", + " 27952.93432617 23300.74853516]\n", + "bkg high : [3876.3490448 6082.2302475 7578.84426117 8832.60043335 4441.4395752\n", + " 3617.74560547]\n", + "sig med : [ 205.07588196 685.24951172 1421.24023438 1424.97241211 696.56347656\n", + " 211.21044922]\n", + "sig high : [ 90.4175415 305.15472412 656.91967773 661.3614502 310.07592773\n", + " 91.96655273]\n", + "sono dentro\n", + "205.0758819580078\n", + "23786.307525634766\n", + "sono dentro\n", + "685.24951171875\n", + "30918.552368164062\n", + "sono dentro\n", + "1421.240234375\n", + "40800.93151855469\n", + "sono dentro\n", + 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[ 712.46466064 2714.16650391 3078.58673096 5552.15478516 967.02075195\n", + " 613.66699219]\n", + "mgp8_pp_jjaa_5f\n", + "[533. 470. 466. 531. 462. 548.]\n", + "[ 97. 97. 143. 94. 105. 104.]\n", + "bkg med : [21215.71826172 27334.65673828 34800.18774414 36353.73583984\n", + " 24032.65039062 20392.50585938]\n", + "bkg high : [3856.24981689 5857.95166016 7713.23907471 8598.70947266 4370.0871582\n", + " 3984.32324219]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 172. 372. 407. 186. 64.]\n", + "[ 5. 21. 37. 29. 25. 9.]\n", + "bkg med : [21434.49887085 27913.58203125 36052.28503418 37723.68432617\n", + " 24658.71777344 20607.91210938]\n", + "bkg high : [3873.07909775 5928.63460541 7837.77581024 8696.31915283 4454.23327637\n", + " 4014.61584473]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[18. 44. 94. 86. 53. 16.]\n", + "[ 3. 17. 16. 24. 13. 0.]\n", + "bkg med : [21878.50100708 28998.92016602 38370.95935059 39845.04394531\n", + " 25966.06835938 21002.58398438]\n", + "bkg high : [3947.07946396 6347.96993256 8232.44475555 9288.32141113 4774.90100098\n", + " 4014.61584473]\n", + "add a+jj\n", + "bkg med : [23433.25784302 30369.90649414 39730.27770996 41393.86328125\n", + " 27313.26757812 22600.56054688]\n", + "bkg high : [4230.02770615 6630.91817474 8649.57463837 9562.51867676 5081.18518066\n", + " 4317.98303223]\n", + "sig med : [ 199.3605957 665.61242676 1376.71655273 1378.73950195 674.40820312\n", + " 203.95947266]\n", + "sig high : [ 96.1335144 324.79034424 701.44189453 707.46228027 332.20605469\n", + " 99.20874023]\n", + "sono dentro\n", + "199.360595703125\n", + "23433.257843017578\n", + "sono dentro\n", + "665.6124267578125\n", + "30369.906494140625\n", + "sono dentro\n", + "1376.716552734375\n", + "39730.27770996094\n", + "sono dentro\n", + "1378.739501953125\n", + "41393.86328125\n", + "sono dentro\n", + "674.408203125\n", + "27313.267578125\n", + "sono dentro\n", + "203.95947265625\n", + "22600.560546875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 24.13198390523878\n", + "sig_high 26.13578374889194\n", + "Naive\n", + "sig_med 10.463983870062167\n", + "sig_high 11.52852245410519\n", + "----------------\n", + "----------------\n", + "(114, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[162. 453. 795. 855. 405. 139.]\n", + "[ 39. 138. 200. 212. 111. 48.]\n", + "bkg med : [ 597.64440918 1671.20825195 2932.83349609 3154.11523438 1494.22851562\n", + " 512.83398438]\n", + "bkg high : [143.87796021 509.10455322 737.83752441 782.11230469 409.50219727\n", + " 177.08203125]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 22. 68. 111. 100. 50. 14.]\n", + "[ 4. 16. 16. 38. 4. 3.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 3907.48474121 11901.62231445 19632.47998047 18199.02539062\n", + " 9016.68945312 2619.12304688]\n", + "bkg high : [ 745.66726685 2916.26153564 3144.99401855 6499.10888672 1011.29125977\n", + " 628.42382812]\n", + "mgp8_pp_jjaa_5f\n", + "[525. 461. 454. 518. 453. 539.]\n", + "[105. 106. 155. 107. 114. 113.]\n", + "bkg med : [20923.74255371 26840.90356445 34344.91748047 34985.46289062\n", + " 23696.72070312 20086.09179688]\n", + "bkg high : [4148.7336731 6351.73809814 8168.5682373 9966.99560547 4706.04907227\n", + " 4290.77148438]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 172. 367. 405. 185. 62.]\n", + "[ 5. 21. 42. 31. 26. 11.]\n", + "bkg med : [21142.52316284 27419.82885742 35580.18481445 36348.67944336\n", + " 24319.42431641 20294.76660156]\n", + "bkg high : [ 4165.56295395 6422.42103577 8309.9342804 10071.33688354\n", + " 4793.56103516 4327.79577637]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[17. 42. 92. 84. 53. 16.]\n", + "[ 4. 19. 18. 26. 13. 0.]\n", + "bkg med : [21561.85848999 28455.83349609 37849.52563477 38420.70385742\n", + " 25626.77490234 20689.43847656]\n", + "bkg high : [ 4264.23011398 6891.0900116 8753.93684387 10712.67233276\n", + " 5114.22875977 4327.79577637]\n", + "add a+jj\n", + "bkg med : [23093.27938843 29800.56689453 39173.84008789 39931.70288086\n", + " 26947.72998047 22261.17089844]\n", + "bkg high : [ 4570.51429367 7200.29118347 9206.07063293 11024.79049683\n", + " 5446.76586914 4657.41564941]\n", + "sig med : [ 194.11616516 650.26568604 1343.70288086 1345.1015625 657.56494141\n", + " 198.60888672]\n", + "sig high : [101.3788147 340.13623047 734.45556641 740.92590332 349.01855469\n", + " 104.54931641]\n", + "sono dentro\n", + "194.1161651611328\n", + "23093.279388427734\n", + "sono dentro\n", + "650.2656860351562\n", + "29800.56689453125\n", + "sono dentro\n", + "1343.702880859375\n", + "39173.840087890625\n", + "sono dentro\n", + "1345.1015625\n", + "39931.702880859375\n", + "sono dentro\n", + "657.56494140625\n", + "26947.72998046875\n", + "sono dentro\n", + "198.60888671875\n", + "22261.1708984375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 23.792314733430384\n", + "sig_high 26.21353156794472\n", + "Naive\n", + "sig_med 10.311270559751758\n", + "sig_high 11.552134189447571\n", + "----------------\n", + "----------------\n", + "(116, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[182. 518. 881. 932. 443. 156.]\n", + "[ 19. 73. 114. 135. 73. 31.]\n", + "bkg med : [ 671.42700195 1911.00878906 3250.06103516 3438.29199219 1634.42773438\n", + " 575.5546875 ]\n", + "bkg high : [ 70.09439087 269.30999756 420.5637207 498.04003906 269.31225586\n", + " 114.36547852]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 25. 70. 117. 116. 51. 16.]\n", + "[ 1. 14. 10. 22. 3. 1.]\n", + "bkg med : [ 4432.60913086 12442.31738281 20852.40087891 20890.40136719\n", + " 9307.33789062 2982.7421875 ]\n", + "bkg high : [ 220.54171753 2375.57269287 1925.03662109 3807.87988281 720.65405273\n", + " 264.81274414]\n", + "mgp8_pp_jjaa_5f\n", + "[568. 506. 522. 569. 499. 586.]\n", + "[62. 61. 87. 56. 68. 66.]\n", + "bkg med : [22839.78881836 28839.87988281 37768.46337891 39329.55761719\n", + " 25478.05664062 21972.8046875 ]\n", + "bkg high : [2230.0007019 4352.59222412 4744.72021484 5622.84863281 2924.54467773\n", + " 2403.88305664]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 183. 385. 416. 191. 68.]\n", + "[ 4. 10. 24. 20. 20. 5.]\n", + "bkg med : [23061.93525696 29455.82946777 39064.31982422 40729.79980469\n", + " 26120.9440918 22201.67382812]\n", + "bkg high : [2243.46412468 4386.25078583 4825.50068665 5690.16583252 2991.86184692\n", + " 2420.71228027]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 19. 50. 106. 93. 57. 16.]\n", + "[ 2. 11. 4. 17. 9. 0.]\n", + "bkg med : [23530.60423279 30689.16796875 41678.99853516 43023.83007812\n", + " 27526.96264648 22596.34521484]\n", + "bkg high : [2292.7977047 4657.58547211 4924.1678009 6109.50146484 3213.86306763\n", + " 2420.71228027]\n", + "add a+jj\n", + "bkg med : [25187.45579529 32165.16601562 43201.66845703 44683.07519531\n", + " 28982.05444336 24305.13037109]\n", + "bkg high : [2473.65122032 4835.52199554 5177.94612122 6272.85302734 3412.21853638\n", + " 2613.23376465]\n", + "sig med : [ 228.47541809 762.01300049 1590.12548828 1592.23388672 775.14892578\n", + " 235.16845703]\n", + "sig high : [ 67.01812744 228.39064026 488.03366089 494.11560059 231.4942627\n", + " 68.00219727]\n", + "sono dentro\n", + "228.4754180908203\n", + "25187.455795288086\n", + "sono dentro\n", + "762.0130004882812\n", + "32165.166015625\n", + "sono dentro\n", + "1590.12548828125\n", + "43201.66845703125\n", + "sono dentro\n", + "1592.23388671875\n", + "44683.0751953125\n", + "sono dentro\n", + "775.14892578125\n", + "28982.054443359375\n", + "sono dentro\n", + "235.16845703125\n", + "24305.13037109375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 26.801330938196863\n", + "sig_high 22.68348981542723\n", + "Naive\n", + "sig_med 11.632898496740252\n", + "sig_high 10.017250097541554\n", + "----------------\n", + "----------------\n", + "(116, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[182. 500. 862. 910. 435. 152.]\n", + "[ 19. 91. 133. 157. 81. 35.]\n", + "bkg med : [ 671.42706299 1844.6027832 3179.97558594 3357.10498047 1604.91210938\n", + " 560.796875 ]\n", + "bkg high : [ 70.09439087 335.71520996 490.65692139 579.20501709 298.82592773\n", + " 129.12231445]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 70. 116. 114. 50. 16.]\n", + "[ 3. 14. 11. 24. 4. 1.]\n", + "bkg med : [ 4131.71466064 12375.91137695 20631.86230469 20508.31591797\n", + " 9127.37304688 2967.984375 ]\n", + "bkg high : [ 521.43637085 2441.97766113 2145.5770874 4189.93988037 900.61499023\n", + " 279.56958008]\n", + "mgp8_pp_jjaa_5f\n", + "[557. 494. 511. 555. 493. 579.]\n", + "[73. 73. 98. 70. 74. 73.]\n", + "bkg med : [22183.00372314 28384.59887695 37191.45605469 38493.78466797\n", + " 25103.65429688 21731.203125 ]\n", + "bkg high : [2887.3777771 4807.91906738 5321.7723999 6458.65081787 3298.96655273\n", + " 2645.51098633]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 181. 382. 415. 190. 67.]\n", + "[ 4. 12. 27. 21. 21. 6.]\n", + "bkg med : [22405.150177 28993.81677246 38477.2142334 39890.66088867\n", + " 25743.17773438 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dentro\n", + "731.8652954101562\n", + "31618.816040039062\n", + "sono dentro\n", + "1527.4306640625\n", + "42434.47302246094\n", + "sono dentro\n", + "1527.069580078125\n", + "43778.550537109375\n", + "sono dentro\n", + "745.726318359375\n", + "28537.4580078125\n", + "sono dentro\n", + "225.82470703125\n", + "24039.7509765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 25.97556600444797\n", + "sig_high 23.993223979600984\n", + "Naive\n", + "sig_med 11.273577304677564\n", + "sig_high 10.580286764645018\n", + "----------------\n", + "----------------\n", + "(116, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[179. 487. 838. 896. 430. 150.]\n", + "[ 22. 104. 157. 171. 86. 37.]\n", + "bkg med : [ 660.35961914 1796.64294434 3091.44433594 3305.43310547 1586.46484375\n", + " 553.41796875]\n", + "bkg high : [ 81.16192627 383.67431641 579.19787598 630.85473633 317.27197266\n", + " 136.50073242]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 70. 114. 112. 50. 14.]\n", + "[ 3. 14. 13. 26. 4. 3.]\n", + "bkg med : [ 4120.6472168 12327.95153809 20242.43261719 20155.74560547\n", + " 9108.92578125 2659.70703125]\n", + "bkg high : [ 532.50390625 2489.93676758 2535.01257324 4542.48413086 919.06103516\n", + " 587.8425293 ]\n", + "mgp8_pp_jjaa_5f\n", + "[548. 483. 494. 546. 477. 568.]\n", + "[ 82. 84. 115. 79. 90. 84.]\n", + "bkg med : [21880.8737793 27980.17028809 36251.12011719 37849.55810547\n", + " 24566.70703125 21066.45703125]\n", + "bkg high : [3190.13671875 5212.38989258 6262.18054199 7102.88647461 3835.97509766\n", + " 3310.2956543 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 177. 380. 412. 188. 66.]\n", + "[ 4. 16. 29. 24. 23. 7.]\n", + "bkg med : [22103.02023315 28575.92480469 37530.14587402 39236.33642578\n", + " 25199.5012207 21288.59472656]\n", + "bkg high : [3203.60014248 5266.24357605 6359.79035187 7183.66711426 3913.38955688\n", + " 3333.85656738]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[18. 44. 97. 90. 53. 16.]\n", + "[ 3. 17. 13. 20. 13. 0.]\n", + "bkg med : [22547.02236938 29661.26293945 39922.82067871 41456.36474609\n", + " 26506.85180664 21683.26660156]\n", + "bkg high : [3277.60050869 5685.5789032 6680.45886993 7677.00256348 4234.05728149\n", + " 3333.85656738]\n", + "add a+jj\n", + "bkg med : [24145.53408813 31070.17016602 41363.81481934 43048.75732422\n", + " 27897.79125977 23339.56347656]\n", + "bkg high : [3516.79386806 5930.60624695 7015.9129715 7907.44494629 4496.58657837\n", + " 3578.88391113]\n", + "sig med : [ 213.46565247 712.22802734 1482.90966797 1480.96459961 723.59960938\n", + " 218.58056641]\n", + "sig high : [ 82.02771759 278.17990112 595.25024414 605.37084961 283.04199219\n", + " 84.59313965]\n", + "sono dentro\n", + "213.4656524658203\n", + "24145.534088134766\n", + "sono dentro\n", + "712.22802734375\n", + "31070.170166015625\n", + "sono dentro\n", + "1482.90966796875\n", + "41363.81481933594\n", + "sono dentro\n", + "1480.964599609375\n", + "43048.75732421875\n", + "sono dentro\n", + "723.599609375\n", + "27897.791259765625\n", + "sono dentro\n", + "218.58056640625\n", + "23339.5634765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 25.485123682702195\n", + "sig_high 24.281697382320033\n", + "Naive\n", + "sig_med 11.059625476746207\n", + "sig_high 10.706053014689862\n", + "----------------\n", + "----------------\n", + "(116, 132)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_tth01j_5f_haa\n", + "[170. 473. 820. 884. 418. 146.]\n", + "[ 31. 118. 175. 183. 98. 41.]\n", + "bkg med : [ 627.15734863 1744.9934082 3025.04907227 3261.13867188 1542.19140625\n", + " 538.66015625]\n", + "bkg high : [114.36453247 435.32202148 645.60516357 675.12524414 361.54248047\n", + " 151.25756836]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 69. 114. 106. 50. 14.]\n", + "[ 3. 15. 13. 32. 4. 3.]\n", + "bkg med : [ 4087.44494629 12125.85473633 20176.03735352 19208.75390625\n", + " 9064.65234375 2644.94921875]\n", + "bkg high : [ 565.70651245 2692.03173828 2601.41986084 5489.43823242 963.33154297\n", + " 602.59936523]\n", + "mgp8_pp_jjaa_5f\n", + "[540. 474. 482. 533. 468. 559.]\n", + "[ 90. 93. 127. 92. 99. 93.]\n", + "bkg med : [21588.89807129 27486.41723633 35795.84985352 36481.28515625\n", + " 24230.77734375 20760.04296875]\n", + "bkg high : [3482.62057495 5706.17626953 6717.50970459 8471.17260742 4171.93701172\n", + " 3616.74389648]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 177. 375. 410. 187. 64.]\n", + "[ 4. 16. 34. 26. 24. 9.]\n", + "bkg med : [21811.04452515 28082.17175293 37058.0456543 37861.33154297\n", + " 24860.20776367 20975.44921875]\n", + "bkg high : [3496.08399963 5760.029953 6831.94882202 8558.68487549 4252.71728516\n", + " 3647.03649902]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[17. 42. 95. 88. 53. 16.]\n", + "[ 4. 19. 15. 22. 13. 0.]\n", + "bkg med : [22230.37985229 29118.1763916 39401.38696289 40032.0246582\n", + " 26167.55834961 21370.12109375]\n", + "bkg high : [3594.75115967 6228.69892883 7201.95095825 9101.35351562 4573.38500977\n", + " 3647.03649902]\n", + "add a+jj\n", + "bkg med : [23805.55563354 30500.83068848 40807.37719727 41586.59692383\n", + " 27532.25366211 23000.17382812]\n", + "bkg high : [3857.28045654 6499.97920227 7572.40896606 9369.71679688 4862.16723633\n", + " 3918.31677246]\n", + "sig med : [ 208.22080994 696.87896729 1449.8984375 1447.49902344 706.78540039\n", + " 213.24023438]\n", + "sig high : [ 87.27252197 293.52630615 628.26391602 638.83825684 299.85327148\n", + " 89.93334961]\n", + "sono dentro\n", + "208.22080993652344\n", + "23805.555633544922\n", + "sono dentro\n", + "696.8789672851562\n", + "30500.830688476562\n", + "sono dentro\n", + "1449.8984375\n", + "40807.377197265625\n", + "sono dentro\n", + "1447.4990234375\n", + "41586.596923828125\n", + "sono dentro\n", + "706.785400390625\n", + "27532.253662109375\n", + "sono dentro\n", + "213.240234375\n", + "23000.173828125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 25.161262612629415\n", + "sig_high 24.35229646731808\n", + "Naive\n", + "sig_med 10.913979105608803\n", + "sig_high 10.727663164395578\n", + "----------------\n", + "----------------\n", + "(118, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[191. 533. 899. 957. 466. 162.]\n", + "[ 10. 58. 96. 110. 50. 25.]\n", + "bkg med : [ 704.62927246 1966.34765625 3316.45556641 3530.55078125 1719.28515625\n", + " 597.69140625]\n", + "bkg high : [ 36.89178467 213.97233582 354.15991211 405.80700684 184.46044922\n", + " 92.23022461]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 25. 73. 118. 119. 53. 16.]\n", + "[ 1. 11. 9. 19. 1. 1.]\n", + "bkg med : [ 4465.81140137 12948.99804688 21069.25244141 21434.0078125\n", + " 9693.09375 3004.87890625]\n", + "bkg high : [ 187.33914185 1868.89323425 1708.18530273 3264.30529785 334.90771484\n", + " 242.67749023]\n", + "mgp8_pp_jjaa_5f\n", + "[582. 521. 548. 577. 515. 600.]\n", + "[48. 46. 61. 48. 52. 52.]\n", + "bkg med : [23326.24890137 29832.65429688 38827.87744141 40132.4140625\n", + " 26382.3125 22448.62890625]\n", + "bkg high : [1743.07351685 3359.80534363 3685.23999023 4819.99279785 2020.23583984\n", + " 1928.00561523]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 185. 387. 421. 195. 70.]\n", + "[ 4. 8. 22. 15. 16. 3.]\n", + "bkg med : [23548.39532471 30455.33557129 40130.46618652 41549.48608398\n", + " 27038.66040039 22684.22949219]\n", + "bkg high : [1756.53693962 3386.73219109 3759.28877258 4870.48065948 2074.08959961\n", + " 1938.10319519]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 19. 53. 107. 97. 59. 16.]\n", + "[ 2. 8. 3. 13. 7. 0.]\n", + "bkg med : [24017.06430054 31762.67431641 42769.81237793 43942.18432617\n", + " 28494.01245117 23078.90136719]\n", + "bkg high : [1805.87051964 3584.06654167 3833.28912354 5191.14893341 2246.75726318\n", + " 1938.10319519]\n", + "add a+jj\n", + "bkg med : [25714.75375366 33282.42724609 44368.20495605 45624.7253418\n", + " 29995.76049805 24828.51074219]\n", + "bkg high : [1945.88614464 3718.2481823 4011.22564697 5331.16455841 2398.44085693\n", + " 2089.78678894]\n", + "sig med : [ 242.33863831 810.83343506 1693.54541016 1698.44799805 824.98388672\n", + " 248.96875 ]\n", + "sig high : [ 53.1569519 179.5677948 384.6131897 387.43023682 181.55895996\n", + " 54.17785645]\n", + "sono dentro\n", + "242.33863830566406\n", + "25714.75375366211\n", + "sono dentro\n", + "810.8334350585938\n", + "33282.42724609375\n", + "sono dentro\n", + "1693.54541015625\n", + "44368.20495605469\n", + "sono dentro\n", + "1698.447998046875\n", + "45624.725341796875\n", + "sono dentro\n", + "824.98388671875\n", + "29995.760498046875\n", + "sono dentro\n", + "248.96875\n", + "24828.5107421875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 28.147435535485478\n", + "sig_high 20.186359611331696\n", + "Naive\n", + "sig_med 12.225095910566843\n", + "sig_high 8.8846363368647\n", + "----------------\n", + "----------------\n", + "(118, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[191. 515. 880. 935. 458. 158.]\n", + "[ 10. 76. 115. 132. 58. 29.]\n", + "bkg med : [ 704.62927246 1899.94177246 3246.37011719 3449.36376953 1689.76953125\n", + " 582.93359375]\n", + "bkg high : [ 36.89178467 280.37756348 424.25311279 486.97198486 213.97412109\n", + " 106.98706055]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 73. 117. 117. 52. 16.]\n", + "[ 3. 11. 10. 21. 2. 1.]\n", + "bkg med : [ 4164.91687012 12882.59216309 20848.71386719 21051.92236328\n", + " 9513.12890625 2990.12109375]\n", + "bkg high : [ 488.23379517 1935.29833984 1928.72576904 3646.36480713 514.86865234\n", + " 257.43432617]\n", + "mgp8_pp_jjaa_5f\n", + "[571. 509. 537. 563. 509. 593.]\n", + "[59. 58. 72. 62. 58. 59.]\n", + "bkg med : [22669.11218262 29377.37341309 38250.87011719 39296.64111328\n", + " 26007.91015625 22207.02734375]\n", + "bkg high : [2400.48770142 3815.08740234 4262.25701904 5655.79449463 2394.65771484\n", + " 2169.63354492]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 183. 384. 420. 194. 69.]\n", + "[ 4. 10. 25. 16. 17. 4.]\n", + "bkg med : [22891.25862122 29993.32299805 39543.3605957 40710.34716797\n", + " 26660.89404297 22439.26220703]\n", + "bkg high : [2413.95112419 3848.74596405 4346.40335083 5709.64825439 2451.87733459\n", + " 2183.09698486]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 19. 51. 101. 96. 57. 16.]\n", + "[ 2. 10. 9. 14. 9. 0.]\n", + "bkg med : [23359.92756653 31251.32836914 42034.7043457 43078.37841797\n", + " 28066.91259766 22833.93359375]\n", + "bkg high : [2463.28470039 4095.41387177 4568.40438843 6054.98358154 2673.87843323\n", + " 2183.09698486]\n", + "add a+jj\n", + "bkg med : [25025.53010559 32736.07739258 43601.11547852 44720.09521484\n", + " 29551.16455078 24563.13085938]\n", + "bkg high : [2635.38723946 4264.59941864 4778.42782593 6235.83709717 2843.0639801\n", + " 2355.19952393]\n", + "sig med : [ 233.04382324 780.68640137 1630.85046387 1633.28540039 795.56347656\n", + " 239.62011719]\n", + "sig high : [ 62.45031738 209.71627808 447.30810547 452.90411377 211.04760742\n", + " 63.5411377 ]\n", + "sono dentro\n", + "233.0438232421875\n", + "25025.53010559082\n", + "sono dentro\n", + "780.6864013671875\n", + "32736.077392578125\n", + "sono dentro\n", + "1630.8504638671875\n", + "43601.115478515625\n", + "sono dentro\n", + "1633.285400390625\n", + "44720.09521484375\n", + "sono dentro\n", + "795.5634765625\n", + "29551.16455078125\n", + "sono dentro\n", + "239.6201171875\n", + "24563.130859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 27.34198714872708\n", + "sig_high 21.653022867415576\n", + "Naive\n", + "sig_med 11.874490140433583\n", + "sig_high 9.517769672560817\n", + "----------------\n", + "----------------\n", + "(118, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[188. 502. 856. 921. 453. 156.]\n", + "[ 13. 89. 139. 146. 63. 31.]\n", + "bkg med : [ 693.56188965 1851.98193359 3157.83886719 3397.69189453 1671.32226562\n", + " 575.5546875 ]\n", + "bkg high : [ 47.95932007 328.33685303 512.79217529 538.62335205 232.42016602\n", + " 114.36547852]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 73. 115. 115. 52. 14.]\n", + "[ 3. 11. 12. 23. 2. 3.]\n", + "bkg med : [ 4153.8494873 12834.63232422 20459.28417969 20699.35205078\n", + " 9494.68164062 2681.84375 ]\n", + "bkg high : [ 499.30133057 1983.25762939 2318.15960693 3998.91046143 533.31469727\n", + " 565.70776367]\n", + "mgp8_pp_jjaa_5f\n", + "[562. 498. 520. 554. 493. 582.]\n", + "[68. 69. 89. 71. 74. 70.]\n", + "bkg med : [22366.9822998 28972.94482422 37310.53417969 38652.41455078\n", + " 25470.96289062 21542.28125 ]\n", + "bkg high : [2703.19195557 4219.55841064 5202.66351318 6300.03155518 2931.66625977\n", + " 2834.41870117]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 179. 382. 417. 192. 68.]\n", + "[ 4. 14. 27. 19. 19. 5.]\n", + "bkg med : [22589.1287384 29575.43103027 38596.29223633 40056.02270508\n", + " 26117.2175293 21771.15039062]\n", + "bkg high : [2716.65537834 4266.68039703 5293.5415802 6363.9828949 2995.61749268\n", + " 2851.2479248 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[18. 47. 98. 94. 55. 16.]\n", + "[ 3. 14. 12. 16. 11. 0.]\n", + "bkg med : [23033.13087463 30734.76940918 41013.63354492 42374.7199707\n", + " 27473.90209961 22165.82226562]\n", + "bkg high : [2790.65574455 4612.01541901 5589.54319763 6758.65159607 3266.95172119\n", + " 2851.2479248 ]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "add a+jj\n", + "bkg med : [24672.48048401 32187.43151855 42530.46948242 43990.28930664\n", + " 28911.49780273 23862.94335938]\n", + "bkg high : [2989.0112133 4813.28787994 5849.15550232 6965.75804138 3482.80914307\n", + " 3055.43737793]\n", + "sig med : [ 227.32789612 761.05090332 1586.32861328 1587.18359375 773.43408203\n", + " 232.37792969]\n", + "sig high : [ 68.1654892 229.35310364 491.83123779 499.1696167 233.20812988\n", + " 70.79382324]\n", + "sono dentro\n", + "227.32789611816406\n", + "24672.48048400879\n", + "sono dentro\n", + "761.0509033203125\n", + "32187.431518554688\n", + "sono dentro\n", + "1586.32861328125\n", + "42530.469482421875\n", + "sono dentro\n", + "1587.18359375\n", + "43990.289306640625\n", + "sono dentro\n", + "773.43408203125\n", + "28911.497802734375\n", + "sono dentro\n", + "232.3779296875\n", + "23862.943359375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 26.868581164404592\n", + "sig_high 21.962044787952976\n", + "Naive\n", + "sig_med 11.668035236364114\n", + "sig_high 9.663995118563564\n", + "----------------\n", + "----------------\n", + "(118, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[179. 488. 838. 909. 441. 152.]\n", + "[ 22. 103. 157. 158. 75. 35.]\n", + "bkg med : [ 660.35961914 1800.33227539 3091.44360352 3353.39746094 1627.04882812\n", + " 560.796875 ]\n", + "bkg high : [ 81.16192627 379.98522949 579.19769287 582.89501953 276.69067383\n", + " 129.12231445]\n", + "mgp8_pp_h012j_5f_haa\n", + "[ 23. 72. 115. 109. 52. 14.]\n", + "[ 3. 12. 12. 29. 2. 3.]\n", + "bkg med : [ 4120.6472168 12632.53540039 20392.88696289 19752.36230469\n", + " 9450.40820312 2667.0859375 ]\n", + "bkg high : [ 532.50393677 2185.35327148 2384.56512451 4945.86621094 577.58520508\n", + " 580.46411133]\n", + "mgp8_pp_jjaa_5f\n", + "[554. 489. 508. 541. 484. 573.]\n", + "[ 76. 78. 101. 84. 83. 79.]\n", + "bkg med : [22075.0065918 28479.19165039 36855.26196289 37284.14355469\n", + " 25135.03320312 21235.8671875 ]\n", + "bkg high : [2995.67581177 4713.34545898 5657.99090576 7668.31933594 3267.62817383\n", + " 3140.86645508]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 179. 377. 415. 191. 66.]\n", + "[ 4. 14. 32. 21. 20. 7.]\n", + "bkg med : [22297.15304565 29081.67785645 38124.19006348 38681.01977539\n", + " 25777.92407227 21458.00488281]\n", + "bkg high : [3009.1392355 4760.46743774 5765.69828033 7739.00239563 3334.94512939\n", + " 3164.42736816]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[17. 45. 96. 92. 55. 16.]\n", + "[ 4. 16. 14. 18. 11. 0.]\n", + "bkg med : [22716.4883728 30191.68273926 40492.19787598 40950.38183594\n", + " 27134.60864258 21852.67675781]\n", + "bkg high : [3107.80639553 5155.13598633 6111.03360748 8183.00428772 3606.27935791\n", + " 3164.42736816]\n", + "add a+jj\n", + "bkg med : [24332.50204468 31618.09191895 41974.02990723 42528.13085938\n", + " 28545.96020508 23523.55371094]\n", + "bkg high : [3329.49780178 5382.66137695 6405.64981842 8428.03163147 3848.38970947\n", + " 3394.86975098]\n", + "sig med : [ 222.08299255 745.70446777 1553.31433105 1553.71679688 756.62060547\n", + " 227.03759766]\n", + "sig high : [ 73.4103241 244.70085144 524.84814453 532.63085938 250.01928711\n", + " 76.13574219]\n", + "sono dentro\n", + "222.08299255371094\n", + "24332.502044677734\n", + "sono dentro\n", + "745.7044677734375\n", + "31618.091918945312\n", + "sono dentro\n", + "1553.3143310546875\n", + "41974.02990722656\n", + "sono dentro\n", + "1553.716796875\n", + "42528.130859375\n", + "sono dentro\n", + "756.62060546875\n", + "28545.960205078125\n", + "sono dentro\n", + "227.03759765625\n", + "23523.5537109375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 26.56015381316175\n", + "sig_high 22.081083283010916\n", + "Naive\n", + "sig_med 11.528671816603735\n", + "sig_high 9.698309483574217\n", + "x_1: 112\n", + "x_2: 128\n", + "Significance: 36.22362592495793\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m_1 = np.arange(start = 112, stop = 120, step = 2)\n", + "m_2 = np.arange(start = 126, stop = 134, step = 2)\n", + "m1_great_med,m2_great_med,max_sig_great_med = findm1m2(df_great_med,m_1,m_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "033d70ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(112, 126), (112, 128), (112, 130), (112, 132), (114, 126), (114, 128), (114, 130), (114, 132), (116, 126), (116, 128), (116, 130), (116, 132), (118, 126), (118, 128), (118, 130), (118, 132)]\n", + "----------------\n", + "----------------\n", + "(112, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[123. 288. 411. 404. 270. 133.]\n", + "[ 46. 88. 137. 113. 85. 47.]\n", + "bkg med : [ 453.76626587 1062.48913574 1516.26489258 1490.38671875 996.02050781\n", + " 490.63232422]\n", + "bkg high : [169.70219421 324.64660645 505.41540527 416.88061523 313.58276367\n", + " 173.39282227]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 28. 49. 47. 27. 13.]\n", + "[ 2. 9. 13. 13. 7. 4.]\n", + "bkg med : [2560.02871704 5275.01257324 8888.18139648 8561.40820312 5058.09667969\n", + " 2446.44677734]\n", + "bkg high : [ 470.59684753 1678.67272949 2461.23010254 2372.69506836 1366.71362305\n", + " 775.18188477]\n", + "mgp8_pp_jjaa_5f\n", + "[146. 156. 128. 131. 138. 135.]\n", + "[36. 38. 32. 27. 40. 30.]\n", + "bkg med : [ 7291.91152954 10330.99694824 13036.68139648 12807.13867188\n", + " 9530.69824219 6821.81787109]\n", + "bkg high : [1637.39176941 2910.29577637 3498.38635254 3247.7956543 2663.15893555\n", + " 1747.51586914]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 164. 263. 256. 144. 66.]\n", + "[ 5. 14. 21. 17. 9. 4.]\n", + "bkg med : [ 7507.32620239 10882.9954834 13921.89855957 13668.81616211\n", + " 10015.39746094 7043.97167969]\n", + "bkg high : [1654.22104836 2957.41775513 3569.06926727 3305.01527405 2693.45167542\n", + " 1760.97930908]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 25. 20. 12. 10.]\n", + "[ 3. 5. 8. 11. 6. 4.]\n", + "bkg med : [ 7605.99337769 11129.6632843 14538.56860352 14162.15112305\n", + " 10311.3984375 7290.63928223]\n", + "bkg high : [1728.22141457 3080.75172424 3766.40352631 3576.35005188 2841.45252991\n", + " 1859.64654541]\n", + "add a+jj\n", + "bkg med : [ 8031.87423706 11584.71406555 14911.94335938 14544.27709961\n", + " 10713.94335938 7684.43322754]\n", + "bkg high : [1833.23313332 3191.59742737 3859.74727631 3655.10877991 2958.13221741\n", + " 1947.15631104]\n", + "sig med : [ 372.59326172 882.77539062 1452.83300781 1449.20678711 865.30761719\n", + " 363.75 ]\n", + "sig high : [ 269.67590332 661.74633789 1102.86816406 1096.34008789 651.69824219\n", + " 268.45068359]\n", + "sono dentro\n", + "372.59326171875\n", + "8031.874237060547\n", + "sono dentro\n", + "882.775390625\n", + "11584.714065551758\n", + "sono dentro\n", + "1452.8330078125\n", + "14911.943359375\n", + "sono dentro\n", + "1449.206787109375\n", + "14544.277099609375\n", + "sono dentro\n", + "865.3076171875\n", + "10713.943359375\n", + "sono dentro\n", + "363.75\n", + "7684.4332275390625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 48.131519419829125\n", + "sig_high 69.34124458319359\n", + "Naive\n", + "sig_med 20.736944153070958\n", + "sig_high 30.669268472265845\n", + "----------------\n", + "----------------\n", + "(112, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[120. 278. 398. 385. 255. 127.]\n", + "[ 49. 98. 150. 132. 100. 53.]\n", + "bkg med : [ 442.69897461 1025.59655762 1468.30517578 1420.30395508 940.68603516\n", + " 468.49853516]\n", + "bkg high : [180.76972961 361.5378418 553.3762207 486.97558594 368.92089844\n", + " 195.52807617]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 26. 48. 44. 27. 13.]\n", + "[ 2. 11. 14. 16. 7. 4.]\n", + "bkg med : [2548.96142578 4937.22570801 8689.77441406 8039.98364258 5002.76220703\n", + " 2424.31298828]\n", + "bkg high : [ 481.66438293 2016.45849609 2659.63818359 2894.13183594 1422.05224609\n", + " 797.31713867]\n", + "mgp8_pp_jjaa_5f\n", + "[141. 149. 124. 124. 135. 129.]\n", + "[41. 45. 36. 34. 43. 36.]\n", + "bkg med : [ 7118.79345703 9766.33898926 12708.63378906 12058.84301758\n", + " 9378.13330078 6605.22314453]\n", + "bkg high : [1810.52082825 3474.95947266 3826.43896484 3996.11035156 2815.73095703\n", + " 1964.11108398]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 162. 262. 256. 142. 66.]\n", + "[ 5. 16. 22. 17. 11. 4.]\n", + "bkg med : [ 7334.20812988 10311.60583496 13590.48510742 12920.5201416\n", + " 9856.10058594 6827.37695312]\n", + "bkg high : [1827.35010719 3528.81315613 3900.48775482 4053.32997131 2852.75541687\n", + " 1977.57452393]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 9. 23. 19. 11. 10.]\n", + "[ 3. 6. 10. 12. 7. 4.]\n", + "bkg med : [ 7432.87530518 10533.6068573 14157.82165527 13389.18835449\n", + " 10127.43481445 7074.04443359]\n", + "bkg high : [1901.3504734 3676.81391907 4147.15555573 4349.33164978 3025.42308044\n", + " 2076.24169922]\n", + "add a+jj\n", + "bkg med : [ 7844.17120361 10968.23869324 14519.52844238 13750.89538574\n", + " 10521.22875977 7450.33642578]\n", + "bkg high : [2020.94715309 3808.07850647 4252.16727448 4448.50938416 3150.85374451\n", + " 2181.25341797]\n", + "sig med : [ 338.10595703 799.46392822 1320.0871582 1316.11157227 786.50439453\n", + " 329.52539062]\n", + "sig high : [ 304.16680908 745.05084229 1235.61230469 1229.41064453 730.11083984\n", + " 302.58984375]\n", + "sono dentro\n", + "338.10595703125\n", + "7844.171203613281\n", + "sono dentro\n", + "799.4639282226562\n", + "10968.238693237305\n", + "sono dentro\n", + "1320.087158203125\n", + "14519.528442382812\n", + "sono dentro\n", + "1316.111572265625\n", + "13750.895385742188\n", + "sono dentro\n", + "786.50439453125\n", + "10521.228759765625\n", + "sono dentro\n", + "329.525390625\n", + "7450.33642578125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 44.545837583372126\n", + "sig_high 72.99542153266624\n", + "Naive\n", + "sig_med 19.171346646938527\n", + "sig_high 32.26338571374698\n", + "----------------\n", + "----------------\n", + "(112, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[112. 272. 376. 373. 247. 119.]\n", + "[ 57. 104. 172. 144. 108. 61.]\n", + "bkg med : [ 413.18591309 1003.46057129 1387.14257812 1376.04614258 911.17431641\n", + " 438.98681641]\n", + "bkg high : [210.28315735 383.67236328 634.53991699 531.24609375 398.43457031\n", + " 225.04174805]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 26. 46. 44. 25. 13.]\n", + "[ 2. 11. 16. 16. 9. 4.]\n", + "bkg med : [2519.44836426 4915.08972168 8307.71728516 7995.72583008 4672.35595703\n", + " 2394.80126953]\n", + "bkg high : [ 511.17781067 2038.59301758 3041.69641113 2938.40234375 1752.46044922\n", + " 826.83081055]\n", + "mgp8_pp_jjaa_5f\n", + "[135. 144. 121. 122. 130. 123.]\n", + "[47. 50. 39. 36. 48. 42.]\n", + "bkg med : [ 6894.81945801 9582.15222168 12229.34619141 11949.76489258\n", + " 8885.67626953 6381.25048828]\n", + "bkg high : [2034.50105286 3659.1496582 4305.73059082 4105.203125 3308.17626953\n", + " 2188.05737305]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 63. 161. 258. 255. 141. 66.]\n", + "[ 6. 17. 26. 18. 12. 4.]\n", + "bkg med : [ 7106.86828613 10124.05322266 13097.73413086 12808.07531738\n", + " 9360.27758789 6603.40429688]\n", + "bkg high : [2054.69618797 3716.36919403 4393.242836 4165.78860474 3348.56654358\n", + " 2201.52075195]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 9. 23. 18. 11. 10.]\n", + "[ 3. 6. 10. 13. 7. 4.]\n", + "bkg med : [ 7205.53546143 10346.054245 13665.07067871 13252.07678223\n", + " 9631.61181641 6850.07177734]\n", + "bkg high : [2128.69655418 3864.36995697 4639.9106369 4486.45709229 3521.23420715\n", + " 2300.18786621]\n", + "add a+jj\n", + "bkg med : [ 7599.32940674 10766.10112 14018.0267334 13607.94958496\n", + " 10010.82080078 7208.86181641]\n", + "bkg high : [2265.795187 4010.21950531 4753.67333221 4591.46881104 3661.24983215\n", + " 2422.70153809]\n", + "sig med : [ 312.9498291 742.97570801 1225.87597656 1224.2644043 731.28417969\n", + " 304.75292969]\n", + "sig high : [ 329.32290649 801.5390625 1329.82348633 1321.26196289 785.32641602\n", + " 327.38183594]\n", + "sono dentro\n", + "312.9498291015625\n", + "7599.329406738281\n", + "sono dentro\n", + "742.9757080078125\n", + "10766.101119995117\n", + "sono dentro\n", + "1225.8759765625\n", + "14018.026733398438\n", + "sono dentro\n", + "1224.264404296875\n", + "13607.949584960938\n", + "sono dentro\n", + "731.2841796875\n", + "10010.82080078125\n", + "sono dentro\n", + "304.7529296875\n", + "7208.86181640625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 41.98454894530706\n", + "sig_high 75.2291698570599\n", + "Naive\n", + "sig_med 18.06593083402774\n", + "sig_high 33.22317759926814\n", + "----------------\n", + "----------------\n", + "(112, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[111. 265. 363. 359. 234. 116.]\n", + "[ 58. 111. 185. 158. 121. 64.]\n", + "bkg med : [ 409.49682617 977.63592529 1339.18286133 1324.40625 863.21777344\n", + " 427.91992188]\n", + "bkg high : [213.97233582 409.49630737 682.50024414 582.89501953 446.39428711\n", + " 236.109375 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[12. 26. 46. 43. 24. 12.]\n", + "[ 4. 11. 16. 17. 10. 5.]\n", + "bkg med : [2214.86474609 4889.26507568 8259.75756836 7793.63867188 4473.95214844\n", + " 2233.28710938]\n", + "bkg high : [ 815.76176453 2064.41671753 3089.65673828 3140.49853516 1950.86743164\n", + " 988.34570312]\n", + "mgp8_pp_jjaa_5f\n", + "[133. 137. 120. 119. 129. 123.]\n", + "[49. 57. 40. 39. 49. 42.]\n", + "bkg med : [ 6525.41552734 9329.45648193 12148.97631836 11650.44726562\n", + " 8654.86230469 6219.73632812]\n", + "bkg high : [2403.90727234 3911.85128784 4386.10205078 4404.52490234 3538.96508789\n", + " 2349.57226562]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 62. 160. 253. 251. 141. 65.]\n", + "[ 7. 18. 31. 22. 12. 5.]\n", + "bkg med : [ 6734.09851074 9867.99163818 13000.53503418 12495.29296875\n", + " 9129.46362305 6438.52416992]\n", + "bkg high : [2427.46826363 3972.4366684 4490.44361115 4478.5737915 3579.35522461\n", + " 2366.40148926]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 9. 23. 18. 10. 10.]\n", + "[ 3. 6. 10. 13. 8. 4.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [ 6832.76568604 10089.99267578 13567.87158203 12939.29449463\n", + " 9376.13110352 6685.19165039]\n", + "bkg high : [2501.46863747 4120.43743134 4737.11139679 4799.24230957 3776.68969727\n", + " 2465.06848145]\n", + "add a+jj\n", + "bkg med : [ 7220.72564697 10489.62060547 13917.91064453 13286.4163208\n", + " 9752.4230957 7043.98168945]\n", + "bkg high : [2644.40119362 4286.70598602 4853.79108429 4913.00500488 3919.62231445\n", + " 2587.58215332]\n", + "sig med : [ 295.67132568 702.68249512 1163.48632812 1159.52124023 691.3840332\n", + " 288.06640625]\n", + "sig high : [ 346.59869385 841.83435059 1392.21350098 1386.02246094 825.62255859\n", + " 344.13720703]\n", + "sono dentro\n", + "295.67132568359375\n", + "7220.725646972656\n", + "sono dentro\n", + "702.6824951171875\n", + "10489.62060546875\n", + "sono dentro\n", + "1163.486328125\n", + "13917.91064453125\n", + "sono dentro\n", + "1159.521240234375\n", + "13286.416320800781\n", + "sono dentro\n", + "691.384033203125\n", + "9752.423095703125\n", + "sono dentro\n", + "288.06640625\n", + "7043.981689453125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 40.220060920191514\n", + "sig_high 76.51815412444593\n", + "Naive\n", + "sig_med 17.312862306948425\n", + "sig_high 33.71859542771027\n", + "----------------\n", + "----------------\n", + "(114, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[133. 301. 434. 418. 282. 136.]\n", + "[ 36. 75. 114. 99. 73. 44.]\n", + "bkg med : [ 490.65765381 1110.44970703 1601.11669922 1542.02026367 1040.28808594\n", + " 501.69921875]\n", + "bkg high : [132.81040955 276.68807983 420.56286621 365.23083496 269.31225586\n", + " 162.32519531]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 29. 50. 50. 27. 14.]\n", + "[ 2. 8. 12. 10. 7. 3.]\n", + "bkg med : [2596.92010498 5473.42041016 9123.48046875 9064.38354492 5102.36425781\n", + " 2607.9609375 ]\n", + "bkg high : [ 433.70506287 1480.26693726 2225.93005371 1869.70373535 1322.44311523\n", + " 613.66699219]\n", + "mgp8_pp_jjaa_5f\n", + "[152. 160. 133. 136. 145. 141.]\n", + "[30. 34. 27. 22. 33. 24.]\n", + "bkg med : [ 7523.26385498 10659.04541016 13434.03125 13472.16479492\n", + " 9801.83691406 7177.79296875]\n", + "bkg high : [1406.0243988 2582.23861694 3101.03063965 2582.74865723 2392.01049805\n", + " 1391.53417969]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 165. 270. 258. 145. 67.]\n", + "[ 4. 13. 14. 15. 8. 3.]\n", + "bkg med : [ 7742.04437256 11214.40979004 14342.80932617 14340.57531738\n", + " 10289.90209961 7403.31274414]\n", + "bkg high : [1419.48782158 2625.99474335 3148.15257645 2633.2365036 2418.93737793\n", + " 1401.63175964]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 27. 22. 13. 11.]\n", + "[3. 5. 6. 9. 5. 3.]\n", + "bkg med : [ 7840.71154785 11461.07757568 15008.81280518 14883.24377441\n", + " 10610.56982422 7674.64709473]\n", + "bkg high : [1493.48818779 2749.32871246 3296.15329361 2855.23757172 2542.27142334\n", + " 1475.63218689]\n", + "add a+jj\n", + "bkg med : [ 8284.09436035 11927.79632568 15396.77252197 15279.95471191\n", + " 11033.53369141 8085.94299316]\n", + "bkg high : [1580.99795341 2848.50644684 3374.91208267 2919.41139984 2638.53210449\n", + " 1545.63999939]\n", + "sig med : [ 404.22134399 960.16351318 1584.88183594 1580.18408203 947.18212891\n", + " 395.3125 ]\n", + "sig high : [238.05152893 584.35107422 970.81750488 965.33154297 569.43066406\n", + " 236.72338867]\n", + "sono dentro\n", + "404.2213439941406\n", + "8284.094360351562\n", + "sono dentro\n", + "960.1635131835938\n", + "11927.796325683594\n", + "sono dentro\n", + "1584.8818359375\n", + "15396.772521972656\n", + "sono dentro\n", + "1580.18408203125\n", + "15279.954711914062\n", + "sono dentro\n", + "947.18212890625\n", + "11033.53369140625\n", + "sono dentro\n", + "395.3125\n", + "8085.9429931640625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 51.471399297335694\n", + "sig_high 66.0338049117587\n", + "Naive\n", + "sig_med 22.192584394672014\n", + "sig_high 29.19537037907239\n", + "----------------\n", + "----------------\n", + "(114, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[130. 291. 421. 399. 267. 130.]\n", + "[ 39. 85. 127. 118. 88. 50.]\n", + "bkg med : [ 479.59036255 1073.55712891 1553.15698242 1471.9375 984.95361328\n", + " 479.56542969]\n", + "bkg high : [143.87794495 313.57931519 468.52282715 435.32666016 324.65039062\n", + " 184.46044922]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 27. 49. 47. 27. 14.]\n", + "[ 2. 10. 13. 13. 7. 3.]\n", + "bkg med : [2585.85281372 5135.63354492 8925.07348633 8542.95898438 5047.02978516\n", + " 2585.82714844]\n", + "bkg high : [ 444.77259827 1818.05270386 2424.33752441 2391.14111328 1377.78125\n", + " 635.80224609]\n", + "mgp8_pp_jjaa_5f\n", + "[147. 153. 129. 129. 142. 135.]\n", + "[35. 41. 31. 29. 36. 30.]\n", + "bkg med : [ 7350.14578247 10094.38745117 13105.98364258 12723.86914062\n", + " 9649.27197266 6961.19824219]\n", + "bkg high : [1579.15834045 3146.90914917 3429.0826416 3331.06396484 2544.58203125\n", + " 1608.13623047]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 163. 269. 258. 143. 67.]\n", + "[ 4. 15. 15. 15. 10. 3.]\n", + "bkg med : [ 7568.92630005 10643.0201416 14011.39587402 13592.27929688\n", + " 10130.60522461 7186.71801758]\n", + "bkg high : [1592.62176323 3197.39698792 3479.57042694 3381.55183411 2578.2406311\n", + " 1618.23381042]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 9. 25. 21. 12. 11.]\n", + "[ 3. 6. 8. 10. 6. 3.]\n", + "bkg med : [ 7667.59347534 10865.02114868 14628.06591797 14110.28100586\n", + " 10426.60620117 7458.05236816]\n", + "bkg high : [1666.62212944 3345.39775085 3676.90468597 3628.21980286 2726.2414856\n", + " 1692.23423767]\n", + "add a+jj\n", + "bkg med : [ 8096.3913269 11311.32095337 15004.35766602 14486.57299805\n", + " 10840.8190918 7851.84631348]\n", + "bkg high : [1768.716856 3464.99443054 3767.33138275 3712.81257629 2831.25320435\n", + " 1779.7440033 ]\n", + "sig med : [ 369.73083496 876.86022949 1452.13659668 1447.10766602 868.77148438\n", + " 361.17382812]\n", + "sig high : [ 272.53839111 667.66162109 1103.56445312 1098.44091797 648.23339844\n", + " 271.02661133]\n", + "sono dentro\n", + "369.7308349609375\n", + "8096.391326904297\n", + "sono dentro\n", + "876.8602294921875\n", + "11311.32095336914\n", + "sono dentro\n", + "1452.1365966796875\n", + "15004.357666015625\n", + "sono dentro\n", + "1447.107666015625\n", + "14486.572998046875\n", + "sono dentro\n", + "868.771484375\n", + "10840.819091796875\n", + "sono dentro\n", + "361.173828125\n", + "7851.8463134765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 48.00311484071599\n", + "sig_high 69.783455683387\n", + "Naive\n", + "sig_med 20.67740872190859\n", + "sig_high 30.85659389159877\n", + "----------------\n", + "----------------\n", + "(114, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[122. 285. 399. 387. 259. 122.]\n", + "[ 47. 91. 149. 130. 96. 58.]\n", + "bkg med : [ 450.07730103 1051.42114258 1471.99438477 1427.6796875 955.44189453\n", + " 450.05371094]\n", + "bkg high : [173.39137268 335.71383667 549.68652344 479.59716797 354.1640625\n", + " 213.97412109]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 27. 47. 47. 25. 14.]\n", + "[ 2. 10. 15. 13. 9. 3.]\n", + "bkg med : [2556.3397522 5113.49755859 8543.01635742 8498.70117188 4716.62353516\n", + " 2556.31542969]\n", + "bkg high : [ 474.286026 1840.18722534 2806.39575195 2435.41162109 1708.18945312\n", + " 665.31640625]\n", + "mgp8_pp_jjaa_5f\n", + "[141. 148. 126. 127. 137. 129.]\n", + "[41. 46. 34. 31. 41. 36.]\n", + "bkg med : [ 7126.17178345 9910.20068359 12626.69604492 12614.79101562\n", + " 9156.81494141 6737.22558594]\n", + "bkg high : [1803.14247131 3331.09933472 3908.37426758 3440.15673828 3037.04589844\n", + " 1832.1171875 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 162. 265. 257. 142. 67.]\n", + "[ 5. 16. 19. 16. 11. 3.]\n", + "bkg med : [ 7341.5864563 10455.4675293 13518.64489746 13479.83447266\n", + " 9634.78222656 6962.74536133]\n", + "bkg high : [1819.97175026 3384.95301819 3972.3254776 3494.01049805 3074.07035828\n", + " 1842.21476746]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 9. 25. 20. 12. 11.]\n", + "[ 3. 6. 8. 11. 6. 3.]\n", + "bkg med : [ 7440.25363159 10677.46853638 14135.31494141 13973.16943359\n", + " 9930.78320312 7234.07971191]\n", + "bkg high : [1893.97211647 3532.95378113 4169.65973663 3765.34527588 3222.07121277\n", + " 1916.2151947 ]\n", + "add a+jj\n", + "bkg med : [ 7851.54953003 11109.18338013 14502.85571289 14343.62744141\n", + " 10330.41113281 7610.3717041 ]\n", + "bkg high : [2013.56879616 3667.13536072 4268.83747101 3855.77203369 3341.66789246\n", + " 2021.22691345]\n", + "sig med : [ 344.57330322 820.3682251 1357.92480469 1355.23657227 813.35449219\n", + " 336.46435547]\n", + "sig high : [ 297.69824219 724.14746094 1197.7746582 1190.28540039 703.45141602\n", + " 295.71533203]\n", + "sono dentro\n", + "344.57330322265625\n", + "7851.549530029297\n", + "sono dentro\n", + "820.3682250976562\n", + "11109.183380126953\n", + "sono dentro\n", + "1357.9248046875\n", + "14502.855712890625\n", + "sono dentro\n", + "1355.236572265625\n", + "14343.62744140625\n", + "sono dentro\n", + "813.3544921875\n", + "10330.4111328125\n", + "sono dentro\n", + "336.46435546875\n", + "7610.3717041015625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 45.53154062238828\n", + "sig_high 72.12918077345611\n", + "Naive\n", + "sig_med 19.60862950120964\n", + "sig_high 31.84611647089156\n", + "----------------\n", + "----------------\n", + "(114, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[121. 278. 386. 373. 246. 119.]\n", + "[ 48. 98. 162. 144. 109. 61.]\n", + "bkg med : [ 446.38821411 1025.59643555 1424.03466797 1376.03979492 907.48535156\n", + " 438.98681641]\n", + "bkg high : [177.08055115 361.53778076 597.64685059 531.24609375 402.1237793\n", + " 225.04174805]\n", + "mgp8_pp_h012j_5f_haa\n", + "[12. 27. 47. 46. 24. 13.]\n", + "[ 4. 10. 15. 14. 10. 4.]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bkg med : [2251.75613403 5087.67285156 8495.05664062 8296.61401367 4518.21972656\n", + " 2394.80126953]\n", + "bkg high : [ 778.86991882 1866.01104736 2854.3560791 2637.5078125 1906.59692383\n", + " 826.83081055]\n", + "mgp8_pp_jjaa_5f\n", + "[139. 141. 125. 124. 136. 129.]\n", + "[43. 53. 35. 34. 42. 36.]\n", + "bkg med : [ 6756.76785278 9657.50488281 12546.32617188 12315.47338867\n", + " 8926.00097656 6575.71142578]\n", + "bkg high : [2172.54862976 3583.80108643 3988.74572754 3739.48632812 3267.86450195\n", + " 1993.59838867]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 63. 161. 260. 253. 142. 66.]\n", + "[ 6. 17. 24. 20. 11. 4.]\n", + "bkg med : [ 6968.81668091 10199.40588379 13421.44580078 13167.05212402\n", + " 9403.96826172 6797.86523438]\n", + "bkg high : [2192.74376488 3641.02062225 4069.52625275 3806.80352783 3304.88893127\n", + " 2007.06176758]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 9. 25. 20. 11. 11.]\n", + "[ 3. 6. 8. 11. 7. 3.]\n", + "bkg med : [ 7067.4838562 10421.40690613 14038.11584473 13660.38708496\n", + " 9675.30249023 7069.19958496]\n", + "bkg high : [2266.74413109 3789.02138519 4266.86049652 4078.13833618 3477.55659485\n", + " 2081.06219482]\n", + "add a+jj\n", + "bkg med : [ 7472.94577026 10832.70280457 14402.73986816 14022.09387207\n", + " 10072.01342773 7445.49157715]\n", + "bkg high : [2392.17479515 3943.6219101 4368.95522308 4177.31607056 3600.07026672\n", + " 2186.07391357]\n", + "sig med : [ 327.29742432 780.07275391 1295.53515625 1290.49462891 773.25830078\n", + " 319.62890625]\n", + "sig high : [ 314.97558594 764.44189453 1260.1640625 1255.03027344 743.35253906\n", + " 312.40673828]\n", + "sono dentro\n", + "327.29742431640625\n", + "7472.945770263672\n", + "sono dentro\n", + "780.07275390625\n", + "10832.70280456543\n", + "sono dentro\n", + "1295.53515625\n", + "14402.739868164062\n", + "sono dentro\n", + "1290.49462890625\n", + "14022.093872070312\n", + "sono dentro\n", + "773.25830078125\n", + "10072.013427734375\n", + "sono dentro\n", + "319.62890625\n", + "7445.4915771484375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 43.83013757923218\n", + "sig_high 73.43557090934706\n", + "Naive\n", + "sig_med 18.88291283986733\n", + "sig_high 32.34715985434733\n", + "----------------\n", + "----------------\n", + "(116, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[139. 317. 453. 433. 294. 146.]\n", + "[30. 59. 95. 84. 61. 34.]\n", + "bkg med : [ 512.79241943 1169.47753906 1671.21166992 1597.34375 1084.55566406\n", + " 538.58886719]\n", + "bkg high : [110.675354 217.66151428 350.46954346 309.88995361 225.04174805\n", + " 125.43310547]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 30. 58. 52. 29. 14.]\n", + "[2. 7. 4. 8. 5. 3.]\n", + "bkg med : [ 2619.05462646 5682.89550781 10397.15307617 9420.6015625\n", + " 5447.52636719 2644.85058594]\n", + "bkg high : [ 411.57000732 1270.79310608 952.25872803 1513.46807861 977.27832031\n", + " 576.77490234]\n", + "mgp8_pp_jjaa_5f\n", + "[155. 165. 138. 141. 153. 142.]\n", + "[27. 29. 22. 17. 25. 23.]\n", + "bkg med : [ 7642.62884521 11030.57128906 14869.75463867 13990.43359375\n", + " 10406.28027344 7247.09277344]\n", + "bkg high : [1286.65740967 2210.70179749 1665.29290771 2064.45294189 1787.55664062\n", + " 1322.23095703]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 168. 272. 261. 146. 68.]\n", + "[ 4. 10. 12. 12. 7. 2.]\n", + "bkg med : [ 7861.40936279 11596.03320312 15785.2644043 14868.94262695\n", + " 10897.71142578 7475.97851562]\n", + "bkg high : [1300.12083244 2244.36035919 1705.68315125 2104.84317017 1811.11766052\n", + " 1328.962677 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 11. 29. 23. 14. 11.]\n", + "[3. 4. 4. 8. 4. 3.]\n", + "bkg med : [ 7960.07653809 11867.36779785 16500.60131836 15436.27783203\n", + " 11243.04602051 7747.31274414]\n", + "bkg high : [1374.12119865 2343.02753448 1804.35032654 2302.17739868 1909.78483582\n", + " 1402.96310425]\n", + "add a+jj\n", + "bkg med : [ 8412.21032715 12348.67126465 16903.14624023 15847.57373047\n", + " 11689.3458252 8161.52563477]\n", + "bkg high : [1452.87971306 2427.62030792 1868.52415466 2351.76626587 1982.7096405\n", + " 1470.05392456]\n", + "sig med : [ 438.25939941 1043.97363281 1729.9597168 1724.32958984 1034.53564453\n", + " 431.08642578]\n", + "sig high : [204.01112366 500.54302979 825.73962402 821.19458008 482.4453125\n", + " 201.10546875]\n", + "sono dentro\n", + "438.2593994140625\n", + "8412.210327148438\n", + "sono dentro\n", + "1043.9736328125\n", + "12348.671264648438\n", + "sono dentro\n", + "1729.959716796875\n", + "16903.146240234375\n", + "sono dentro\n", + "1724.32958984375\n", + "15847.57373046875\n", + "sono dentro\n", + "1034.53564453125\n", + "11689.345825195312\n", + "sono dentro\n", + "431.08642578125\n", + "8161.525634765625\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 54.71553305465912\n", + "sig_high 64.61114231552756\n", + "Naive\n", + "sig_med 23.63678933996812\n", + "sig_high 28.236216070692716\n", + "----------------\n", + "----------------\n", + "(116, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[136. 307. 440. 414. 279. 140.]\n", + "[ 33. 69. 108. 103. 76. 40.]\n", + "bkg med : [ 501.72512817 1132.58496094 1623.25195312 1527.26098633 1029.22119141\n", + " 516.45507812]\n", + "bkg high : [121.74287415 254.55325317 398.42797852 379.98681641 280.37988281\n", + " 147.56835938]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 28. 57. 49. 29. 14.]\n", + "[ 2. 9. 5. 11. 5. 3.]\n", + "bkg med : [ 2607.98733521 5345.10839844 10198.74609375 8899.17700195\n", + " 5392.19189453 2622.71679688]\n", + "bkg high : [ 422.63752747 1608.57949829 1150.66430664 2034.90698242 1032.61621094\n", + " 598.91015625]\n", + "mgp8_pp_jjaa_5f\n", + "[150. 158. 134. 134. 150. 136.]\n", + "[32. 36. 26. 24. 28. 29.]\n", + "bkg med : [ 7469.51077271 10465.91308594 14541.70703125 13242.13793945\n", + " 10253.71533203 7030.49804688]\n", + "bkg high : [1459.77815247 2775.3710022 1993.35375977 2812.77416992 1940.12792969\n", + " 1538.83300781]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 166. 271. 261. 144. 68.]\n", + "[ 4. 12. 13. 12. 9. 2.]\n", + "bkg med : [ 7688.29129028 11024.64331055 15453.85095215 14120.64660645\n", + " 10738.41455078 7259.38378906]\n", + "bkg high : [1473.24157524 2815.76127625 2037.10984802 2853.16442108 1970.42066956\n", + " 1545.56472778]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 27. 22. 13. 11.]\n", + "[3. 5. 6. 9. 5. 3.]\n", + "bkg med : [ 7786.95846558 11271.31109619 16119.85443115 14663.31506348\n", + " 11059.08227539 7530.71813965]\n", + "bkg high : [1547.24194145 2939.09524536 2185.11056519 3075.1654892 2093.75471497\n", + " 1619.56515503]\n", + "add a+jj\n", + "bkg med : [ 8224.5072937 11732.19586182 16510.73114014 15054.1920166\n", + " 11496.63110352 7927.42907715]\n", + "bkg high : [1640.58569145 3044.10696411 2260.95236206 3145.1733017 2175.43049622\n", + " 1704.15786743]\n", + "sig med : [ 403.77368164 960.66619873 1597.21374512 1591.25634766 955.75634766\n", + " 396.78662109]\n", + "sig high : [238.49919128 583.84851074 958.48553467 954.26367188 560.85693359\n", + " 235.2487793 ]\n", + "sono dentro\n", + "403.773681640625\n", + "8224.507293701172\n", + "sono dentro\n", + "960.6661987304688\n", + "11732.195861816406\n", + "sono dentro\n", + "1597.2137451171875\n", + "16510.73114013672\n", + "sono dentro\n", + "1591.25634765625\n", + "15054.192016601562\n", + "sono dentro\n", + "955.75634765625\n", + "11496.631103515625\n", + "sono dentro\n", + "396.78662109375\n", + "7927.4290771484375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 51.370763755620736\n", + "sig_high 68.34016419554247\n", + "Naive\n", + "sig_med 22.171251858135093\n", + "sig_high 29.875701656238288\n", + "----------------\n", + "----------------\n", + "(116, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[128. 301. 418. 402. 271. 132.]\n", + "[ 41. 75. 130. 115. 84. 48.]\n", + "bkg med : [ 472.21206665 1110.44897461 1542.08935547 1483.00317383 999.70947266\n", + " 486.94335938]\n", + "bkg high : [151.25630188 276.68777466 479.58996582 424.2590332 309.89355469\n", + " 177.08203125]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 28. 55. 49. 27. 14.]\n", + "[ 2. 9. 7. 11. 7. 3.]\n", + "bkg med : [2578.47427368 5322.97241211 9816.68896484 8854.91918945 5061.78564453\n", + " 2593.20507812]\n", + "bkg high : [ 452.1509552 1630.71401978 1532.7208252 2079.17919922 1363.02441406\n", + " 628.42382812]\n", + "mgp8_pp_jjaa_5f\n", + "[144. 153. 131. 132. 145. 130.]\n", + "[38. 41. 29. 26. 33. 35.]\n", + "bkg med : [ 7245.53677368 10281.72631836 14062.41943359 13133.05981445\n", + " 9761.25830078 6806.52539062]\n", + "bkg high : [1683.76228333 2959.57046509 2472.64367676 2921.86865234 2432.59179688\n", + " 1762.81347656]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 64. 165. 267. 260. 143. 68.]\n", + "[ 5. 13. 17. 13. 10. 2.]\n", + "bkg med : [ 7460.95144653 10837.09069824 14961.09997559 14008.20178223\n", + " 10242.59155273 7035.41113281]\n", + "bkg high : [1700.59156227 3003.32659149 2529.86316681 2965.62480927 2466.25039673\n", + " 1769.54519653]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 27. 21. 13. 11.]\n", + "[ 3. 5. 6. 10. 5. 3.]\n", + "bkg med : [ 7559.61862183 11083.75848389 15627.10345459 14526.20349121\n", + " 10563.25927734 7306.7454834 ]\n", + "bkg high : [1774.59192848 3126.66056061 2677.86388397 3212.29268646 2589.58444214\n", + " 1843.54562378]\n", + "add a+jj\n", + "bkg med : [ 7979.66549683 11530.05828857 16009.22918701 14911.24645996\n", + " 10986.22314453 7685.95446777]\n", + "bkg high : [1885.43763161 3246.2572403 2762.45665741 3288.1344223 2685.84518433\n", + " 1945.64035034]\n", + "sig med : [ 378.61480713 904.18066406 1503.00292969 1499.40283203 900.54394531\n", + " 372.09814453]\n", + "sig high : [ 263.65496826 640.33691406 1052.6973877 1046.14624023 616.46411133\n", + " 260.10351562]\n", + "sono dentro\n", + "378.61480712890625\n", + "7979.665496826172\n", + "sono dentro\n", + "904.1806640625\n", + "11530.058288574219\n", + "sono dentro\n", + "1503.0029296875\n", + "16009.229187011719\n", + "sono dentro\n", + "1499.40283203125\n", + "14911.246459960938\n", + "sono dentro\n", + "900.5439453125\n", + "10986.22314453125\n", + "sono dentro\n", + "372.09814453125\n", + "7685.9544677734375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 48.98966248197931\n", + "sig_high 70.3960146814894\n", + "Naive\n", + "sig_med 21.14266878366287\n", + "sig_high 30.849432784701214\n", + "----------------\n", + "----------------\n", + "(116, 132)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mgp8_pp_tth01j_5f_haa\n", + "[127. 294. 405. 388. 258. 129.]\n", + "[ 42. 82. 143. 129. 97. 51.]\n", + "bkg med : [ 468.52297974 1084.62438965 1494.12963867 1431.36328125 951.75292969\n", + " 475.87646484]\n", + "bkg high : [154.94548035 302.51171875 527.55029297 475.90795898 357.85327148\n", + " 188.1496582 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[12. 28. 55. 48. 26. 13.]\n", + "[ 4. 9. 7. 12. 8. 4.]\n", + "bkg med : [2273.89077759 5297.14782715 9768.72924805 8652.83203125 4863.38183594\n", + " 2431.69091797]\n", + "bkg high : [ 756.73478699 1656.5378418 1580.68115234 2281.27539062 1561.43139648\n", + " 789.9387207 ]\n", + "mgp8_pp_jjaa_5f\n", + "[142. 146. 130. 129. 144. 130.]\n", + "[40. 48. 30. 29. 34. 35.]\n", + "bkg med : [ 6876.13296509 10029.03063965 13982.04956055 12833.7421875\n", + " 9530.44433594 6645.01123047]\n", + "bkg high : [2053.18009949 3212.2722168 2553.01513672 3221.19824219 2663.40991211\n", + " 1924.32836914]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 63. 164. 262. 256. 143. 67.]\n", + "[ 6. 14. 22. 17. 10. 3.]\n", + "bkg med : [ 7088.18179321 10581.0291748 14863.90087891 13695.41943359\n", + " 10011.77758789 6870.53100586]\n", + "bkg high : [2073.3752346 3259.39419556 2627.06391907 3278.41786194 2697.06851196\n", + " 1934.4259491 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 27. 21. 12. 11.]\n", + "[ 3. 5. 6. 10. 6. 3.]\n", + "bkg med : [ 7186.84896851 10827.69697571 15529.90441895 14213.42114258\n", + " 10307.77856445 7141.86535645]\n", + "bkg high : [2147.37560081 3382.72816467 2775.06462097 3525.08576965 2845.06936646\n", + " 2008.42637634]\n", + "add a+jj\n", + "bkg med : [ 7601.06185913 11253.57783508 15909.11315918 14589.71313477\n", + " 10727.82543945 7521.07434082]\n", + "bkg high : [2264.05528831 3522.74378967 2862.57432556 3609.67854309 2944.24710083\n", + " 2110.52110291]\n", + "sig med : [ 361.33648682 863.88476562 1440.61560059 1434.66210938 860.63916016\n", + " 355.41015625]\n", + "sig high : [ 280.93273926 680.63623047 1115.08654785 1110.88598633 656.36621094\n", + " 276.7890625 ]\n", + "sono dentro\n", + "361.33648681640625\n", + "7601.061859130859\n", + "sono dentro\n", + "863.884765625\n", + "11253.577835083008\n", + "sono dentro\n", + "1440.6156005859375\n", + "15909.113159179688\n", + "sono dentro\n", + "1434.662109375\n", + "14589.713134765625\n", + "sono dentro\n", + "860.63916015625\n", + "10727.825439453125\n", + "sono dentro\n", + "355.41015625\n", + "7521.0743408203125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 47.36891221910735\n", + "sig_high 71.81331566035988\n", + "Naive\n", + "sig_med 20.447904810621207\n", + "sig_high 31.31657129094886\n", + "----------------\n", + "----------------\n", + "(118, 126)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[147. 332. 473. 451. 303. 149.]\n", + "[22. 44. 75. 66. 52. 31.]\n", + "bkg med : [ 542.30529785 1224.81689453 1744.99584961 1663.73193359 1117.75634766\n", + " 549.65576172]\n", + "bkg high : [ 81.16192627 162.32383728 276.68762207 243.48376465 191.83599854\n", + " 114.36547852]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 30. 59. 54. 31. 15.]\n", + "[2. 7. 3. 6. 3. 2.]\n", + "bkg med : [ 2648.56750488 5738.23486328 10621.38452148 9787.88427734\n", + " 5781.62158203 2806.36474609]\n", + "bkg high : [ 382.05661011 1215.45542908 728.02966309 1146.1673584 643.17779541\n", + " 415.26000977]\n", + "mgp8_pp_jjaa_5f\n", + "[159. 171. 142. 143. 158. 145.]\n", + "[23. 23. 18. 15. 20. 20.]\n", + "bkg med : [ 7801.78234863 11280.37158203 15223.62670898 14422.53662109\n", + " 10902.42626953 7505.96240234]\n", + "bkg high : [1127.50143433 1960.9002533 1311.42126465 1632.32702637 1291.39068604\n", + " 1063.47680664]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 67. 171. 274. 266. 148. 69.]\n", + "[ 2. 7. 10. 7. 5. 1.]\n", + "bkg med : [ 8027.29455566 11855.93103027 16145.86816406 15317.87634277\n", + " 11400.58935547 7738.21411133]\n", + "bkg high : [1134.23314524 1984.46124458 1345.07982254 1655.88799286 1308.21994781\n", + " 1066.842659 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 11. 30. 25. 14. 11.]\n", + "[3. 4. 3. 6. 4. 3.]\n", + "bkg med : [ 8125.96173096 12127.265625 16885.87182617 15934.54504395\n", + " 11745.9239502 8009.54833984]\n", + "bkg high : [1208.23351145 2083.12841988 1419.08020401 1803.8886795 1406.88706207\n", + " 1140.84305573]\n", + "add a+jj\n", + "bkg med : [ 8589.76348877 12626.07104492 17300.0847168 16351.67492676\n", + " 12206.80871582 8432.51220703]\n", + "bkg high : [1275.32362986 2150.21893501 1471.58606339 1847.64356232 1465.22690582\n", + " 1199.18289948]\n", + "sig med : [ 473.32702637 1135.11401367 1886.2097168 1875.79492188 1123.22949219\n", + " 467.05712891]\n", + "sig high : [168.94415283 409.40179443 669.48986816 669.71191406 393.63745117\n", + " 165.13476562]\n", + "sono dentro\n", + "473.3270263671875\n", + "8589.763488769531\n", + "sono dentro\n", + "1135.114013671875\n", + "12626.071044921875\n", + "sono dentro\n", + "1886.209716796875\n", + "17300.084716796875\n", + "sono dentro\n", + "1875.794921875\n", + "16351.674926757812\n", + "sono dentro\n", + "1123.2294921875\n", + "12206.808715820312\n", + "sono dentro\n", + "467.05712890625\n", + "8432.51220703125\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 58.56605858720299\n", + "sig_high 58.8224780661998\n", + "Naive\n", + "sig_med 25.331538549964005\n", + "sig_high 25.528824713673874\n", + "----------------\n", + "----------------\n", + "(118, 128)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[144. 322. 460. 432. 288. 143.]\n", + "[25. 54. 88. 85. 67. 37.]\n", + "bkg med : [ 531.23797607 1187.92431641 1697.03613281 1593.64916992 1062.421875\n", + " 527.52197266]\n", + "bkg high : [ 92.22946167 199.21562195 324.64599609 313.57775879 247.17700195\n", + " 136.50073242]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 28. 58. 51. 31. 15.]\n", + "[2. 9. 4. 9. 3. 2.]\n", + "bkg med : [ 2637.50018311 5400.44775391 10422.97753906 9266.4597168\n", + " 5726.28710938 2784.23095703]\n", + "bkg high : [ 393.12414551 1553.24198914 926.43505859 1667.60314941 698.51904297\n", + " 437.39526367]\n", + "mgp8_pp_jjaa_5f\n", + "[154. 164. 138. 136. 155. 139.]\n", + "[28. 30. 22. 22. 23. 26.]\n", + "bkg med : [ 7628.66424561 10715.71337891 14895.57910156 13674.2409668\n", + " 10749.86132812 7289.24267578]\n", + "bkg high : [1300.62219238 2525.56132507 1639.47167969 2380.64807129 1443.97509766\n", + " 1280.0847168 ]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 67. 169. 273. 266. 146. 69.]\n", + "[ 2. 9. 11. 7. 7. 1.]\n", + "bkg med : [ 7854.17645264 11284.5411377 15814.45471191 14569.58032227\n", + " 11241.29248047 7521.49438477]\n", + "bkg high : [1307.35390329 2555.8540287 1676.49608231 2404.20903778 1467.53606415\n", + " 1283.45056915]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 28. 24. 13. 11.]\n", + "[3. 5. 5. 7. 5. 3.]\n", + "bkg med : [ 7952.84362793 11531.20892334 16505.12493896 15161.58227539\n", + " 11561.96032715 7792.82861328]\n", + "bkg high : [1381.3542695 2679.18799782 1799.8300209 2576.87648773 1590.87007904\n", + " 1357.4509964 ]\n", + "add a+jj\n", + "bkg med : [ 8402.0604248 12009.59539795 16907.66986084 15558.29321289\n", + " 12014.09411621 8198.29052734]\n", + "bkg high : [1463.03005075 2766.69776344 1864.00384903 2641.05031586 1657.96089935\n", + " 1433.29279327]\n", + "sig med : [ 438.83648682 1051.80566406 1753.46447754 1742.71826172 1044.81884766\n", + " 432.91259766]\n", + "sig high : [203.43328857 492.71185303 802.23577881 802.82580566 472.1887207\n", + " 199.28149414]\n", + "sono dentro\n", + "438.83648681640625\n", + "8402.060424804688\n", + "sono dentro\n", + "1051.8056640625\n", + "12009.595397949219\n", + "sono dentro\n", + "1753.4644775390625\n", + "16907.669860839844\n", + "sono dentro\n", + "1742.71826171875\n", + "15558.293212890625\n", + "sono dentro\n", + "1044.81884765625\n", + "12014.094116210938\n", + "sono dentro\n", + "432.91259765625\n", + "8198.29052734375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 55.332007000015054\n", + "sig_high 62.920270131928376\n", + "Naive\n", + "sig_med 23.911660339818145\n", + "sig_high 27.335569230755276\n", + "----------------\n", + "----------------\n", + "(118, 130)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[136. 316. 438. 420. 280. 135.]\n", + "[ 33. 60. 110. 97. 75. 45.]\n", + "bkg med : [ 501.72491455 1165.78833008 1615.87353516 1549.39135742 1032.91015625\n", + " 498.01025391]\n", + "bkg high : [121.7428894 221.35069275 405.80670166 357.85070801 276.69067383\n", + " 166.0144043 ]\n", + "mgp8_pp_h012j_5f_haa\n", + "[14. 28. 56. 51. 29. 15.]\n", + "[2. 9. 6. 9. 5. 2.]\n", + "bkg med : [ 2607.98712158 5378.31176758 10040.92041016 9222.2019043\n", + " 5395.88085938 2754.71923828]\n", + "bkg high : [ 422.63757324 1575.37705994 1308.49029541 1711.87634277 1028.92700195\n", + " 466.90893555]\n", + "mgp8_pp_jjaa_5f\n", + "[148. 159. 135. 134. 150. 133.]\n", + "[34. 35. 25. 24. 28. 32.]\n", + "bkg med : [ 7404.69024658 10531.52661133 14416.29150391 13565.1628418\n", + " 10257.40429688 7065.27001953]\n", + "bkg high : [1524.5994873 2709.75987244 2118.76861572 2489.74353027 1936.4387207\n", + " 1504.06518555]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 66. 168. 269. 265. 145. 69.]\n", + "[ 3. 10. 15. 8. 8. 1.]\n", + "bkg med : [ 7626.83660889 11096.98852539 15321.70373535 14457.13549805\n", + " 10745.46948242 7297.52172852]\n", + "bkg high : [1534.69705391 2743.41843414 2169.25642014 2516.67034912 1963.36558533\n", + " 1507.43104553]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 28. 23. 13. 11.]\n", + "[3. 5. 5. 8. 5. 3.]\n", + "bkg med : [ 7725.50378418 11343.65631104 16012.3739624 15024.47070312\n", + " 11066.1373291 7568.85595703]\n", + "bkg high : [1608.69742012 2866.75240326 2292.59035873 2714.00457764 2086.69963074\n", + " 1581.43147278]\n", + "add a+jj\n", + "bkg med : [ 8157.21862793 11807.45806885 16406.16766357 15415.34765625\n", + " 11503.68615723 7956.81591797]\n", + "bkg high : [1707.8751545 2968.84712982 2365.51516342 2784.01239014 2168.37541199\n", + " 1674.77516174]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sig med : [ 413.68060303 995.31732178 1659.25268555 1650.84765625 989.20605469\n", + " 408.06152344]\n", + "sig high : [228.59234619 549.1973877 896.44677734 894.6809082 527.40380859\n", + " 223.97436523]\n", + "sono dentro\n", + "413.68060302734375\n", + "8157.2186279296875\n", + "sono dentro\n", + "995.3173217773438\n", + "11807.458068847656\n", + "sono dentro\n", + "1659.252685546875\n", + "16406.16766357422\n", + "sono dentro\n", + "1650.84765625\n", + "15415.34765625\n", + "sono dentro\n", + "989.2060546875\n", + "11503.686157226562\n", + "sono dentro\n", + "408.0615234375\n", + "7956.81591796875\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 53.022841875605074\n", + "sig_high 65.04187595802613\n", + "Naive\n", + "sig_med 22.914537483130008\n", + "sig_high 28.398932558599068\n", + "----------------\n", + "----------------\n", + "(118, 132)\n", + "mgp8_pp_tth01j_5f_haa\n", + "[135. 309. 425. 406. 267. 132.]\n", + "[ 34. 67. 123. 111. 88. 48.]\n", + "bkg med : [ 498.03582764 1139.96362305 1567.91381836 1497.75146484 984.95361328\n", + " 486.94335938]\n", + "bkg high : [125.43206787 247.17494202 453.76531982 409.5010376 324.65039062\n", + " 177.08203125]\n", + "mgp8_pp_h012j_5f_haa\n", + "[12. 28. 56. 50. 28. 14.]\n", + "[ 4. 9. 6. 10. 6. 3.]\n", + "bkg med : [2303.40350342 5352.48706055 9992.96069336 9020.11474609 5197.47705078\n", + " 2593.20507812]\n", + "bkg high : [ 727.22137451 1601.20106506 1356.44891357 1913.97393799 1227.33398438\n", + " 628.42382812]\n", + "mgp8_pp_jjaa_5f\n", + "[146. 152. 134. 131. 149. 133.]\n", + "[36. 42. 26. 27. 29. 32.]\n", + "bkg med : [ 7035.28631592 10278.83081055 14335.92163086 13265.84521484\n", + " 10026.59033203 6903.75585938]\n", + "bkg high : [1894.01043701 2962.46864319 2199.1383667 2789.07452393 2167.25683594\n", + " 1665.58007812]\n", + "mgp8_pp_vh012j_5f_haa\n", + "[ 65. 167. 264. 261. 145. 68.]\n", + "[ 4. 11. 20. 12. 8. 2.]\n", + "bkg med : [ 7254.0668335 10840.92687988 15224.50463867 14144.35314941\n", + " 10514.65551758 7132.64160156]\n", + "bkg high : [1907.47385979 2999.49305725 2266.45542908 2829.46482086 2194.18371582\n", + " 1672.3117981 ]\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "[ 4. 10. 28. 23. 12. 11.]\n", + "[3. 5. 5. 8. 6. 3.]\n", + "bkg med : [ 7352.73400879 11087.59468079 15915.17492676 14711.68835449\n", + " 10810.65649414 7403.97595215]\n", + "bkg high : [1981.474226 3122.82702637 2389.78935242 3026.7990799 2342.18457031\n", + " 1746.31222534]\n", + "add a+jj\n", + "bkg med : [ 7778.61486816 11530.97749329 16306.05163574 15093.81433105\n", + " 11245.28833008 7791.93591309]\n", + "bkg high : [2086.48594475 3245.34069824 2465.63114929 3105.55780792 2426.77734375\n", + " 1839.65597534]\n", + "sig med : [ 396.40429688 955.02191162 1596.86254883 1586.10473633 949.30810547\n", + " 391.37255859]\n", + "sig high : [245.86849976 589.49267578 958.83666992 959.41955566 567.30834961\n", + " 240.68017578]\n", + "sono dentro\n", + "396.404296875\n", + "7778.6148681640625\n", + "sono dentro\n", + "955.0219116210938\n", + "11530.977493286133\n", + "sono dentro\n", + "1596.862548828125\n", + "16306.051635742188\n", + "sono dentro\n", + "1586.104736328125\n", + "15093.814331054688\n", + "sono dentro\n", + "949.30810546875\n", + "11245.288330078125\n", + "sono dentro\n", + "391.37255859375\n", + "7791.9359130859375\n", + "sono dentro\n", + ":::::::\n", + "Exact significance\n", + "sig_med 51.46229081640535\n", + "sig_high 66.59300561135424\n", + "Naive\n", + "sig_med 22.245981667667554\n", + "sig_high 28.917514590558696\n", + "x_1: 112\n", + "x_2: 132\n", + "Significance: 86.4446713860165\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "m_1 = np.arange(start = 112, stop = 120, step = 2)\n", + "m_2 = np.arange(start = 126, stop = 134, step = 2)\n", + "m1_great_high,m2_great_high,max_sig_great_high = findm1m2(df_great_high,m_1,m_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "a8c1c7de", + "metadata": {}, + "outputs": [], + "source": [ + "#8 categories \n", + "#df_small_med_c\n", + "#df_small_med_sb\n", + "#df_small_high_c\n", + "#df_small_high_sb\n", + "#df_great_med_c\n", + "#df_great_med_sb\n", + "#df_great_high_c\n", + "#df_great_high_sb" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "e10b1015", + "metadata": {}, + "outputs": [], + "source": [ + "df_small_med_c = df_small_med.loc[(df_small_med.hbb_m > m1_small_med) & (df_small_med.hbb_m <= m2_small_med)]\n", + "df_small_med_sb = df_small_med.loc[(df_small_med.hbb_m <= m1_small_med) | (df_small_med.hbb_m > m2_small_med)]" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "b0208033", + "metadata": {}, + "outputs": [], + "source": [ + "df_great_med_c = df_great_med.loc[(df_great_med.hbb_m > m1_great_med) & (df_great_med.hbb_m <= m2_great_med)]\n", + "df_great_med_sb = df_great_med.loc[(df_great_med.hbb_m <= m1_great_med) | (df_great_med.hbb_m > m2_great_med)]" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "608a519b", + "metadata": {}, + "outputs": [], + "source": [ + "df_small_high_c = df_small_high.loc[(df_small_high.hbb_m > m1_small_high) & (df_small_high.hbb_m <= m2_small_high)]\n", + "df_small_high_sb = df_small_high.loc[(df_small_high.hbb_m <= m1_small_high) | (df_small_high.hbb_m > m2_small_high)]" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "b5c4878f", + "metadata": {}, + "outputs": [], + "source": [ + "df_great_high_c = df_great_high.loc[(df_great_high.hbb_m > m1_great_high) & (df_great_high.hbb_m <= m2_great_high)]\n", + "df_great_high_sb = df_great_high.loc[(df_great_high.hbb_m <= m1_great_high) | (df_great_high.hbb_m > m2_great_high)]" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "77ebd0d3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small_med_c,EDGES, \"Mx < 350, medium purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "5e8186ae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small_med_sb,EDGES, \"Mx < 350, medium purity, sb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "4875caed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small_high_c,EDGES, \"Mx < 350, high purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "957cd8bd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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cgwcPMnDgQFsP5eWXX7Ydq1ChAq1bt+bMmTO23kN+BfXOa3FxcUb3UwD/DR8t9dP7quy2b99OgwYN/N0Mrzlz5gyDBg1i0aJFeXp9eno6ISEhFC9enHXr1vHII4+QkJDg8esffPBBunfvzl133ZWn93cnMzOT2NhYFi1aRJ06dQBYtmwZe/bscbvWIrcc/dsQkc3GGIf372pPQSkVcMqWLZvngACwf/9+7rnnHjIzMylZsiTvv/++F1uXP4mJiXTv3p3evXvbAgLgs0WFuaVBQSlV6NSpU4dff/01z6+fN2+e9xqTTcOGDdmzZ4/Pzp9fQTnRrJRSyjc0KCillLLR4aOApusPlFIFK6B6CiJSWkQ2i0hgzLgopVQR49OgICJzRORvEdmWrbyriOwUkd0iMtru0NPAp75sk1KqcLBPTe3MlClTSE1NLaAWFQ6+Hj6aB0wFPrxcICIhwDSgE5AMbBKRJUA1IBFwvX5eKZUH3h6KLLg1KvPmzSMpKYkJEyZkKbdPTe3MlClTuP/++ylVqpSPWlf4+LSnYIz5ETiRrbg5sNsYs8cYcwFYAPQCOgAtgXuBISISUENbSqnceeGFF6hfvz6dOnWif//+vPbaawBs2rSJ6OhoWrVqxb/+9a88b3pjv8nMqlWraNWqFbGxsdx9992kpKTw9ttvc+jQITp06ECHDh1cpqBWV/hjork6cMDueTLQwhjzGICIPAgcM8Y4TOwuIkOBoQDXXXedb1uqlMqT+Ph4Fi9ezK+//kpGRgaxsbE0bdoUgIEDBzJz5kxat27N6NGj3ZzJvWPHjvHiiy/y7bffUrp0aV555RXeeOMNxo0bxxtvvMEPP/xA5cqV2bx5sy0FNeCVjKKFkT+CgqM0frZcG8aYea5ebIyZCcwES5oLr7ZMKeUVa9eupVevXoSHhwPQo4dl+OrUqVOcPXuW1q1bA3DvvffaNrCxd/z4cW655RbAssvbhQsX+PLLLwH46KOPsiT3W79+PYmJibRp0waACxcu0KpVqxzntE9Bffvtt9O5c2fvXXAh4o+gkAxca/e8BuA4b68TmjpbqcDmLKeap7nWKlWqZMtV5GxOwf6cnTp1Yv78+S7P6SoFtbrCH+P2m4A6IlJLREoC/YAluTmBps5WKrC1bduWpUuXkpaWRkpKim1L0AoVKlCmTBnWr18PwIIFC/L9Xi1btuTnn39m9+7dAKSmprJr1y4AypQpw9mzZwHXKajVFT7tKYjIfKA9UFlEkoHxxpjZIvIYsBIIAeYYY373ZTsCjy5KU4Vbs2bN6NmzJ40bN6ZmzZrExcXZcv3Pnj2bIUOGULp0adq3b5+vPQBEhCpVqjBv3jz69+9Peno6AC+++CJ169Zl6NChdOvWjWuuuYYpU6Y4TUGtrgjK1Nl2w0dD/vjjD383Jw8KS1DQ1NmBIhBTZ6ekpBAREUFqairt2rVj5syZxMbG2srBsn/x4cOHs2xk76lGjRqxZMkSatWq5e2mFyq5TZ0dlLd96vCRUoFv6NChxMTEEBsbS58+fWwb0nz99dfExMQQFRXFTz/9xLPPPpvrc3fq1IlGjRppQPAB7Sn4hfYUlHcFYk9BBQbtKSillMqzoAwKSimlfCMoU2frOgXlOU+H6nQoTCkI0qBgjFkKLI2Lixvi77Yofyks8zJKBRYdPlJKBZT4+HiGDx/usk5CQgLLly8voBYVLUHZU1BK5dIBL/esrvXOcFtkZCRJSUlZyuLi4oiLc3hjjE1CQgLx8fHcdtttXmmHuiIoewoi0kNEZp4+fdrfTVFKueAsfbYrq1evpnt3y+aL586d46GHHqJZs2Y0adKEr776igsXLjBu3DgWLlxITEwMCxcuZM2aNcTExBATE0OTJk1sqS1U7gVlT0HnFJQKfK7SZ3vqpZdeomPHjsyZM4dTp07RvHlzbr31Vp5//nni4+OZOnUqYMnCOm3aNNq0aUNKSgphYbpXV14FZU9BKRX47NNnlylTxpY++6WXXrJ9qz906JDt50cffTTHOVatWsXkyZOJiYmhffv2pKWlsX///hz12rRpw5NPPsnbb7/NqVOnKF48KL/vBgT9zXmV3hGj1GXOsiWMHTuWsWPHApY5hcspsp2dY/HixdSrVy9L+YYNG7I8Hz16NLfffjvLly+nZcuWfPvtt9SvXz9/F1BEBWVPQecU8uiAhw+lvMBZ+uzc6NKlC++8844twPz6669A1pTYAH/++SeNGjXi6aefJi4ujh07dnjnIoqgoAwKmuZCqcBnnz77zjvvzJI+2x0RywaNzz33HBcvXiQ6OpqoqCiee+45ADp06EBiYqJtonnKlClERUXRuHFjwsPD6datm8+uq7DT4aPCQL/dK3e8dAtpbo0cOZIJEybY0mc/9dRTWY5nvx0VLFtxVqxYEYDw8HDee++9HHUqVqzIpk2bbM/79u3r3YYXYRoUVE6eBplr3VdRRdvQoUNJTEwkLS2NAQMG2NJnO7NkyRLGjh2r22T6kQYFpZTP/Oc//8lV/Z49e9KzZ08ftUZ5QoOCUoAmzlPKIiiDQsFmSdXbTJVSRYfefaSUUsomKIOCUkop39CgoJTyiZdeeokbb7yR6OhoYmJibKuQBw8eTGJiYp7OmZSURFRUVK5eExERkeX5vHnzeOyxxwCYMWMGH374YZ7aUlgF5ZyCUiqXenh5bmyp6wn3devWsWzZMn755RdCQ0M5duwYFy5cAGDWrFnebUs+PPzww/5uQsDRnkIg83ZaimEePpTKp8OHD1O5cmVCQ0MBqFy5MtWqVQOgffv2xMfHA5Zv8WPHjqVx48a0bNmSv/76C7CkrWjZsiXNmjVj3LhxOb7tA1y6dIl//etfNGvWjOjoaIeL3NyZMGGCLZ33pk2biI6OplWrVvzrX/+y9UhSU1O55557iI6Opm/fvrRo0YL4+HguXbrEgw8+SFRUFI0aNeLNN9/M/S8qAGlPoTDQD3IVYDp37szzzz9P3bp1ufXWW+nbty8333xzjnrnzp2jZcuWvPTSS4waNYr333+fZ599lieeeIInnniC/v37M2PGDIfvMXv2bMqVK8emTZtIT0+nTZs2dO7cmVq1amWpd/78eWJiYmzPT5w44XAtxMCBA5k5cyatW7dm9OjRtvLp06dToUIFtmzZwrZt22znSkhI4ODBg2zbtg2AU6dO5fK3FJiCsqegCfGUCmwRERFs3ryZmTNnUqVKFfr27cu8efNy1CtZsqRtQ52mTZva0l6sW7eOu+++G4B7773X4XusWrWKDz/8kJiYGFq0aMHx48f5448/ctQLDw8nISHB9nj++edz1Dl16hRnz56ldevWOd5z7dq19OvXD4CoqCiio6MBuP7669mzZw+PP/44K1asoGzZsh7+dgJbUAYFvSVVqcAXEhJC+/btmThxIlOnTmXx4sU56pQoUcKW/C4kJISMjAyPz2+M4Z133rF92O/du5fOnTvnqa3O0ny7OlahQgV+++032rdvz7Rp0xg8eHCe3jvQBGVQCHqawloVcjt37szyrT0hIYGaNWt6/PqWLVvagsiCBQsc1unSpQvvvvsuFy9eBGDXrl2cO3cuT+2tUKECZcqUYf369Tnes23btnz66acAJCYmsnXrVgCOHTtGZmYmffr04YUXXuCXX37J03sHGp1TUEp5XUpKCo8//rhtF7TatWszc+ZMj18/ZcoU7r//fl5//XVuv/12hym3Bw8eTFJSErGxsRhjqFKlCl9++WWe2zx79myGDBlC6dKlad++ve09hw0bxoABA4iOjqZJkyZER0dTrlw5Dh48yMCBA8nMzATg5ZdfzvN7BxJx1W0KdHFxcebyXQy+44M0F97uBXh7onm6h/X8lI7Zwl/pRwIz99H27dtp0KCBv5vhNampqYSHhyMiLFiwgPnz5/PVV1/59D1TUlJsdzlNnjyZw4cP89Zbb3Hp0iUuXrxIWFgYf/75J7fccgu7du2iZMmSPm2Ptzj6tyEim40xcY7qa09BKRVwNm/ezGOPPYYxhvLlyxdIKu2vv/6al19+mYyMDGrWrGmbGE9NTaVDhw5cvHgRYwzvvvtu0ASEvNCegltFsKfgKTcLmHxLExXa2759JA0aVPN3M1SBquNRrdz2FHSiWSmllI0GBaWUUjZFeE4hCIYfdKWyUqqABUxQEJEGwBNAZeA7Y8y7fm6S7+iHvVIqQPk0KIjIHKA78LcxJsquvCvwFhACzDLGTDbGbAceFpFiwPu+bJdSRcnEpX+TeCjdq+dsWC2U8T2uclknIiKGlJQE2/N58z4nPn4rU6eOZ8KEt4mIKM3IkYNsxyMjOxAfv5jKlSt6ta2uOGoHwOrVG3jttdksW+Z6bYWzNme/dlemTv2IKVM+4M8/93P06Hqn1//BB5/z4ouW78rPPvsIAwY87dH5c8vXcwrzgK72BSISAkwDugENgf4i0tB6rCewFvjOx+1SqshIPJRO4mHvBYXEw+leDzJFWZs2Tfn223nUrFndaZ0TJ04xceJUNmxYxMaNnzFx4lROnjzpk/b4tKdgjPlRRCKzFTcHdhtj9gCIyAKgF5BojFkCLBGRr4H/+LJtqojw9Pbfa33aCr9reE0oC//pnYvs+55/c7AcOHCYo0dPEBt7Y5byhIRE+vd/kiVLZlCnTiTGGOrX78rcuS8zaNAzOcp/+MGyuc5vv22nY8cHOHDgMKNGDWbIkL4AnDmTQu/ew9i5cy/t2jVj+vQJFCvm+Hv0+fNp9O79KH36dLa93lNNmjR0W2flyrV06tSGihXLA9CpUxtWrFhB//79c/VenvDHnEJ1sv6pJgMtRKQ9cCcQCix39mIRGQoMBbjuuut81kilVP6cP59GTMyVFNUnTpymZ8+OtudvvjmXjz++skr50KG/PTpvevoFBgx4mnnzJtO06ZVd2GJiGvLiiyN44425vPvuRFav3kDt2tfRunWsw/Jq1a4GYMuWnaxfv4hz51Jp0uQObr+9PQAbN24hMXE5NWtWp2vXQXz++SruuqtrjvakpKTSr98IHnjgDh54oHeWY2fPpnDTTY6zvP7nP2/QsGFtj6754MG/uPbaa2zPa9SoysGDBz16bW75IyiIgzJjjFkNrHb3YmPMTGAmWBavebVlSimvCQ8PIyFhie355TmFy0aMGJhjTiG7779fx/DhL+YoP378FN26Debvv9dnKe/evQNjxrzOpUuXmD37MwYO7OOyHKBXr1sJDw8jPDyMDh1asHHjFsqXL0vz5tFcf73li2f//t1Zu3azw6DQq9cjjBo1hPvuy7lHQ5kyEVl+B3nlaJHx5eyy3uaPoJBM1s56DeBQbk4gIj2AHrVrexZl80WzlSrlNx07tmLbtq+zlJ06dYbbbhvCU089lKN+aGhJWrSI5ssvv2XNmo3MmTPJZTlA9s/Wyx+22T90nX0It2nTlG+++ZF77+2Ro463ego1alRl9eoNtufJyUdo376FR6/NLX8sXtsE1BGRWiJSEugH5CqU6n4KStOPF1379h1k1KjB9OnTxeHxu+/uxiOPjOfOOztnyVHkrPyrr74jLS2d48dPsnr1Rpo1awRYho/27j1AZmYmCxcup23bpg7f7/nnh1OpUnmGDZuQ49jlnoKjh6cBAaBLl7asWvUzJ0+e5uTJ06xa9TNduji+/vzy9S2p84H2QGURSQbGG2Nmi8hjwEost6TOMcb8nsvzFlxPQalCIPFwutcmiBMPp9PwmlCvnCsvGjduQOPGzjPCdulyE+fPpzNgQG+Pyps3j+b224ewf/9hnntuGNWqXc2uXUm0atWE0aNfY+vWXbRr14zevTs5fc8pU8by0ENjGDXqVV59dVSurufttz/k1Vff58iRY0RH9+S229oxa9Yk4uO3MmPGfGbNmkTFiuV57rlhNGtmGfYaN+5RKlb0za27RTghnocrmn3xbbOwLF7zZ0K8A15ekR7kdx+5Sojnr3UK/uRs/YA/1kL4jm8S4gXMimallG8E8oe3CjxBGRQKdPgoN9/qPd2cRinlU0lJP+SqXF0RlFlSdaJZKaV8w21PQUTuBF4BrsKyxkCwrCso6+O2BZ/CMleglCqyPBk+ehXoYU1YFxD07iPlMU8DdWBuvaxUgfNk+OivQAoIoMNHSinlK570FOJFZCHwJWC7r80Y87mvGqWU8qJvXocjO717zqr1oNtTLqto6uwEj9pw331PER+/jRIlitO8eTTvvfc8JUqUcFj3zJkUGjToSu/enZg69WOPzp9bnvQUygKpQGcsN/f3wLJHgt+ISA8RmXn69Gl/NkOp4HBkJxzZ5cXz7fJ+kCnC7ruvBzt2rGDr1mWcP5/GrFmLnNZ97rkp3Hxzc5+2x21PwRgz0KctyANjzFJgaVxc3BB/t0WpoFC1Lgx0/a3XY3OHeuc8eVTYUmffdlt728/Nm0eTnHzEYb3Nm7fx11/H6Nq1XZbEgt7mNCiIyChjzKsi8g6QY9mzMWa4z1qllAp6mjo7dwnxLl68yEcffcVbb43NUTczM5OnnprMRx/9m+++W+fR7ymvXPUULk8u5zWPhFK+4+3bf3UzHq/T1Nm5S509bNgE2rVrxk03NctxbPr0T7jttpuz7KngK06DgnWIBmPMBwAiUtby1Jz1eavc0FtSlSoaikrq7IkT3+Ho0RO8994LDuuuW5fATz/FM336f0hJOceFCxeJiBjN5MmTHdbPD7cTzSISJyJbgS3ANhH5TUQc55AtIHpLqlJFV2FLnT1r1qesXLmW+fPfdDpn8cknr7N//xqSkn7gtddG88ADd/gkIIBnt6TOAYYZY34CEJG2wFwg2ictUkp535Fd3psgPrLLMnHtJ4UtdfbDD4+nZs1qtGp1DwB33tmZceMey5I6uyC5TZ0tIj8bY9q4K/OHAkmd7eUMzYWKP1Nn9/Dy/xhPkxkG6JyCq9TZ/lqn4E+aOvsKr6XOFpFY648bReQ9YD6Wu5D64sFeygFPd+VSRUUAf3irwONq+Oj1bM/H2/0cvDvzKKUKPU2dnXeu7j7KeX9YgNC7j3znyNGjpKd7tktX6KRJVH3mGR+3SClVkHQ/BZVFeno6aekX3NZLS79A+vYdBdAipVRBCsqd15RvhYWWpGaNGi7r7EtOLqDWKKUKktOegoj4fumcUkqpgOKqpzBHRCpgudNoBbDWGJNRIK0qCEVslzRP5wrS0i8QFlrSbb0iK1DvWssAnIz6vbJ5NjtOJXn17eqXj+TppoNc1omoGEPKiQTb83kffk785q1MfWs8E154m4jSpRn5pF2ai7odiP9fAafOdtAOgNVrNvDam7NZ9qWb1NlO2pz92l0Z9M9niN+8FWOgbp1I5s2aTERE6Rz1Pvjoc158+V0Anh3zCAMGPe3R+XPLaU/BGNMNaI8lKPQG1ovI5yIyVESu80lrlM94OlcQFlqS0NDQAmiRKig7TiWx8+Rer51v58m9Xg8yRdmb/36G3+KXsmXzUq679hqmvptzn4QTJ04x8cWpbFi7iI0/f8bEF6dy8uRJn7TH5ZyCMSYNSy9hBYCI1AK6AVNFpKoxxreJvZVXeTJXoAqnehVqMfcWx3l1cmvgd8955Tx5deDAYY4eO0Fsk2yps39LpP8/nmTJYrsU2Y26Mvf9lxn0z2dylP+wypo6e+t2Onaxps5+ajBDBllTZ59Noffdw9i5ay/t2jZj+jtuUmff/Sh9ene2vd5TZctGAGCM4fz5dIc5llb+dy2dbmlDxYrlAeh0SxtWrFhB//79c/VensjVRLMxZi+WtZ/TRUTHGJRSTp0/n0ZMM7vU2SdP0/N2u9TZ78zl4/l5TJ096GnmzZpM01i71NmNG/LixBG88dZc3p06kdVrNlD7huto3SrWYbktdfbWnaz/yZo6u8Ud3N6tPQAbN20hMcGaOrv7ID7/chV33ekkdfb9I3jg/jt44H4HqbM7OkmI9+EbNGxgua1+4JDRLF+xhoYNavP6q6Nz1D148K8sGVJr1KjKwYMHPfp95Vae7z4yxrgfi/ARXadwhc4VeImnc0yepsNQltTZm+xSZ1vnFC4b8fjAHHMK2X3/wzqGP+kgdfaJU3TrOZi/k7Olzr6tA2OetabInvcZAwf0cVkO0KuHXersm1uwcZM1dXYzu9TZfbuz9ufNDoNCr7seYdRTQ7ivv5PU2Zvcp86e+/5kLl26xOP/9wILFy3P0j6w9CKyc5a1Nb90nUKQ07kCVZh17NCKbb9+neWx9of51IqswbvvTMxRPzS0JC2aR/Plkm9Z89NGenbv6LIcvJA6u1VTvln5o8MP7rNnU4hp1tPhI3H77ix1Q0JC6Hv3bSz+YmWO89SoUZUDBw7bnicnH6FaNSe5rvIpV0FBRCqIiGZHDTCX5wrcPapWqeLvpiqVb/v2H2TUk4Pp09tJ6uw7u/HI4+O5s1e21NlOyr9aapc6+8eNNIuzps7eZJc6e9Fy2rZxkjp7/HAqVSzPsMcn5Dh2uafg6NGwQW2MMezevQ+w9AaWfv099etdn+M8XTq1ZdW3P3Py5GlOnjzNqm9/pksXx9efX26Hj0RkNdDTWjcBOCoia4wxT/qkRUopr9t5cq/XJoh3ntxLvQq1vHKuvGgc3YDG0S5SZ3e2psj+R2+PypvHRXN7ryHsP3CY58ZYU2f/kUSrlk0Y/exrbN22i3Ztm9G7l4vU2a+P5aGhYxg15lVefdnz1NnGGAYMfpozZ1IwxtA4ur6tBxS/eSsz3p/PrBmTqFixPM89M4xmrS3DSuPGPkrFir65ddeT1Nm/GmOaiMhg4FpjzHgR2WKM8XuPIV+ps72detlPLq8sLui7ivYlJ0NUI2p+9GGBvq+Nv/7/BeicwvZTI2lQz/Fwgr/WKfiTs/UD/lgL4TMlCzh1tn0d6+rme4CcO0orpQJaIH94q8DjSVCYCKzEsqJ5k4hcD/zh22YpvatIqbxL2uUkdbaTcnWFJ0HhsP1QkTFmj4i84cM2Ka7cVeTuA1/vKlJKeZMnQeEdINaDMuVlugI5AAVqzqyRwD5/N0IVKM+mFHLN1XacrYDWQBURsb/TqCwQ4ovGiMgdwO3AVcA0Y8wqX7yPUkopx1ytUygJRGAJHGXsHmeAuzx9AxGZIyJ/i8i2bOVdRWSniOwWkdEAxpgvjTFDgAex7AWtlFKqALnajnMNsEZE5hlj8tMxnQdMBWz3LopICDAN6AQkA5tEZIkxJtFa5VnrcaVUPh15/33S93gvSypA6PW1qDpkiMs6ETExpCQk2J7P+/xz4rduZer48Ux425qyepBdmosOHYhfvJjKPrr/3hFH7QBYvWEDr82ezbKZblJnO2lz9mt3Ze+BA/QbMYITp08T27AhH/3731kW1132weef8+K71tTZjzzCgKd9kzrbkzmFUBGZCUTa1zfGdHT6CjvGmB9FJDJbcXNgtzFmD4CILAB6ich2YDLwjTHmF0/Or7zrlcbH2VHefdqMfssuUOrsfmoWQJtU/qTv2Uva3r2E1fLOgrO0vd4NMEXd06+9xogHH6Rf9+48PG4csz/7jEfuzZpE78SpU0ycOpX4zz9HRGjauzc9hw6lQoUKXm+PJ0FhETADmAVc8tL7VifrdiXJQAvgceBWoJyI1DbGzMj+QhEZCgwFuO463dbB23aUv8DOcheod9r1XU+pxQ1cTC2gVqn8CqtVi5ovT/LKufaNecYr58mrA4cPc/TECWJvzJY6OzGR/k8+yZIZM6gTaU2R3bUrc19+mUHPPJOj/IcPramzt2+n4wMPcODwYUYNHsyQvtbU2Skp9B42jJ1799KuWTOmT3CROjstjd6PPkqfzp1tr/eEMYbv163jP6+/DsCA3r2Z8M47OYLCyrVr6dSmDRXLlwegUxv/ps7OMMa86+X3dZRZyhhj3gbedvVCY8xMYCZYVjR7uV0KqHe6JHPXuN6NdWXGHtA7YZUL59PSiOlplzr79Gl6drRLnT13Lh9/ZZc6+28PU2dfuMCAp59m3uTJNI2yS53dsCEvjhjBG3Pn8u7EiazesIHa111H69hYh+XVrramzt65k/WLFnEuNZUmd9zB7e3bA7BxyxYSly+nZvXqdB00iM9XreKurg5SZ6em0m/ECB644w4e6J0tdXZKCjfd6yR19htvcFXFipQvW5bixS0fxTWqVuXgX3/lqHvwr7+49hq71NlV/Zs6e6mIDAO+AGyrqYwxJ/LxvsnAtXbPawCHPH1xMKfO1kVpqqgIDwsjYYld6mzrnMJlIwYOzDGnkN3369Yx/EUHqbNPnaLb4MH8vT5b6uwOHRjzujVF9mefMbBPH5flAL1uvZXwsDDCw8Lo0KIFG7dsoXzZsjSPjuZ662hE/+7dWbt5s8Og0OuRRxg1ZAj39XSQOjsiIsvvILujJ3J+jDrKxlqQqbM9CQoDrP/9l12ZAXKm8vPcJqCOdSe3g0A/wHE4dcAYsxRYGhcX53qmKwDpojSlPNexVSu2ff11lrJTZ85w25AhPPXQQznqh5YsSYvoaL789lvWbNzInEmTXJZDzmGLXKfObtqUb378kXt79MhRx11PocENN3DqzBkyMjIoXrw4yUeOUO2qq3LUrVG1Kqs3bLA9Tz5yhPYtWjg8b365DQrGmHzNTonIfCx7PVcWkWRgvDFmtog8hiV9Rggwxxjzey7OGbQ9BfDPojRPJ5A9mU9Qyp/2HTzIqMGDuaOT46yld3frxuCxY+nfvXvW1NlOyr/67jvGPPww51JTWb1xI5NHjmRXUhIbt2xh74ED1KxenYXLlzPUyVzB88OH88L06QybMIF3J2bd48FdTwGgQ8uWfLZiBf26d+eDL76g1y235KjTpW1bnnnjDU6ePg3Aqp9/5uXpvsnO6Enq7FLAk8B1xpihIlIHqGeMWebJGxhjHM6EGGOWA8tz01i71wZtT8FfPJ1ADj1fhT2Z1egb2c5lvX6/T6NYRqY3m6h8KG3vXq9NEHvzTqa8aNygAY0buEidfdNNnE9PZ0C28X1n5c2jo7l9yBD2Hz7Mc8OGUe3qq9mVlESrJk0Y/dprbN21i3bNmtHbSRACmDJ2LA+NGcOoV1/l1VGep84GeGXkSPqNGMGzU6bQpGFDBt19NwDxW7cyY/58Zk2aRMXy5Xlu2DCaWYe9xj3q39TZC4HNwAPGmCgRCQfWGWNifNKiXAjG1Nn+SnXdoVMqqcVKELmzj8t6G0pb0i+3OOd6iufuxOmEFCtG7/9u9lobc6WQpD73lu0jR9LAyU5c/lqn4E/O1g/4Yy2Ez9TxX+rsG4wxfUWkP4Ax5rz4aobDQ8E+fOQPqcVKkFqshNt6Lc4domHaMcYf+Z/Lel+4+TKhAkcgf3irwONJULhg7R0YABG5Abu7kPxBh4/yplTmRRYmud9EXKlgl/SDk9TZTsrVFZ4EhQnACuBaEfkEaIMlN5FSSqlCxpO7j1aJyGagJZa7t54wxhzzectc0OGjK3q3CuVQ2RS39VJLHadUaqUCaJHyC2Mwxvjs3nUVnNzNGTviyd1HS4D5wBJjzLk8tMvrdPjoikNlUzz6wC+VWolqZyLw88ifVxyZNIl064S9t4SGhlK1ShWvnrMghf31F8crVaJSaKgGBgVYAsLx48cJCwvL1es8GT56HUsa68kishFYCCwzxqTlvpnKF0qlVmLDSveTyIUhIACkb9/h1RXfaenu128EuhpffEEycPTqq0GDQtGQkeG2SlhYGDVyeaejJ8NHl1NohwAdgSHAHCyb7SjlF95cALjPy70Ofyhx7hy1Pv7Y381QBWnpUp+c1tUmOzbWu4/6AA8DzYAPfNIaD4lIDxGZedq6uk8ppZR3eDKnsBBLWusVWDa+WW2M8etS1kCcU/B2oruJVVuTGFbZbb3UYksolXnRozYqpZQ7nswpzAXuNcZ4ay+FQsnbie4SwyqTGFaJhmnHXdYrlXnRGhQ8mVNQSinXnAYFERlljHnVGLNCRO7GstnO5WOTjDH+3WkjAHk70V3DtONuF5sNrHn57uBSXntfpVTR5aqn0A941frzGOyCAtAV8FtQKArrFI5c+yNppY7afeg7pllNlVLe5CooiJOfHT0vUIE4p+BtaaWOkhZ+FNJc/6rrnS5J/VNeDAo1/oRS7hfDhSaUxpgQ772vUioguAoKxsnPjp4rHwg7X4W5awp4rqBUCoSnwPkIl9WKYchEU2crVdi4CgqNReQMll5BuPVnrM9zt0ROBZfzEbCrscsqmewuoMYopQqS06BgdGxAKaWKHI8WrwUaXbymlFK+4ck6hYATzBPNfluU5uEEsifzCUqpwisoewrB7PKiNHeuLErzkssTyO6cj4BUDQpKFVVB2VMIdn5blObBBLJSqmjToKCCzv6z+0ktd4EJNx92W7f+qZI8/ZtuLqSUpzQouOBpkjvwPNGdyr/Ui6mkFne/VCa+SjrxVdLZUd71fgn9ll2gVEYxanqrgUoFMQ0KLnia5A48T3Sn6Su8o1SGMHfNNS7rvNL4uNuAAFgDjC7EUwo0KLjl7SR3Xk9foXcVOeXpsNHKjD0+bolSwSMog0KwJ8TzavoKD9NS6F1FSilPBGVQCOZ1Cj6hdxUppbxE1ykopZSy0aCglFLKJiiHjwKR7qmslCoMtKfgJX5LX6GUUl6kPQUv8mr6Cr3V1Km0jEwyi5Wgb2RPr5yv3+/TuO7YBfadTnZbNzQ0lKpVqnjlfZUKRBoUApXeaupUZqbhknhvR9g/r7LsGVX5pOt6aenuF8IpFew0KAQyvdXUqRBj3PbKPNWiS3kANqx0vXZkX7L7noRSwU6Dgpdo+gqlVGEQMBPNInK9iMwWkc/83Za8sKWvcMPj9BVKKeUHPu0piMgcoDvwtzEmyq68K/AWEALMMsZMNsbsAQYFa1AAL6evUEopP/B1T2Ee0NW+QERCgGlAN6Ah0F9EGvq4HUoppTzg06BgjPkROJGtuDmw2xizxxhzAVgA9PJlO5RSSnnGH3MK1YEDds+TgeoiUklEZgBNRGSMsxeLyFARiReR+KNH3Y/hK6WU8pw/7j5ydIO5McYcBx5292JjzExgJkBcXJz77bcCjS5Ky7eSJh2RS1D3N/eVUyMg+QbfN0qpQsIfQSEZuNbueQ3gUG5OENT7KeiitHwTMimGB98Hypy2PNwE4Zoh5Ug3oYDrndyUKgr8ERQ2AXVEpBZwEOgH3JubEwT9fgq6KC3fMhH3v0MPe2VhoiuVlbrM17ekzgfaA5VFJBkYb4yZLSKPASux3JI6xxjzey7Pm6+ewpFJk0j3YHWqp/szqwDl4bBRWu0k37ZDqSDi06BgjOnvpHw5sDwf581XTyF9+w6PPvDDQksSGhqal7dQQWZfKAy8+bDLOv2WXaBURjFqFlCblPKHoExz4Y05hbDQktSsUcNlHU/3SADdJyGYRaYLeDBHkVrcAJk+b49S/hSUQaGg5hQu75HQMO2427pX9knQFc3BZsDREABu3OJ6onllxp6CaI5SfhWUQaEgebJHAuRinwSllApgQRkUCuqWVE8zn4JmP1VKFQ5BGRQKavjIlvk0zf2GLvXSMqnPGaj7t+uKuihNKRXAgjIoFCSPM5/W/c3ygY8uSlNKBS8NCt6ki9KUUkEuYDbZyQ0R6SEiM0+fPu3vpiilVKESlD2FoE9zoQJOarES9I3s6bJOv9+neZZzSakgFpRBQSlvKu3hosNLIp6scVMqqGlQUEVe5IUzAG7Xo3xhNCKowk/nFJRSStkEZVAwxiw1xgwtV66cv5uilFKFSpEcPtp/dj+p5S4wwU1WzLRwQ9j5KgXUKhUMrjt2gX0n3addV8rXQidNouozz3j9vEUyKJw4n0JGCdxmQM1ML0FYanngVEE0SwW4/VUsaUwqn/RzQ1SRl5Z+Abbv8Mm5i2RQyMw0mMySRO7s47LegFJf0bDYr1D3jPuTavqKQu8/N1UEYMNKzYSr/GufB5uE5VWRDAoAIca4z35ad4tnqStA01copQqFoAwKBZUlFdDUFUVFeIolf5ULNUPKkW5CAdf7LigVzPTuI6VSIzwa+guTC4RKegE0SCn/CcqeglJelXyDR9XSaif5th1KBQANCkrlwr5QGOjmVmalfK3fsguUOrufmj44twYFpTwUmS5o8iMVCFKLG7iY6pNza1BQykMDjoYAcOMWnWhW/rUyYw+E+ubcQTnRrJRSyjc0KCillLIJyqCgWVKVUso3gjIo6DoFpZTyjaAMCkoppXyjSN59VNKkI3LJbVoDTXKnlCpqimRPQcj0bAN2TXKnlCpiimRPASAT0UR3SimVTZHsKSillHJMg4JSSikbDQpKKaVsAmZOQURKA9OBC8BqY8wnfm6SUkoVOT7tKYjIHBH5W0S2ZSvvKiI7RWS3iIy2Ft8JfGaMGQL09GW7lFJKOebr4aN5QFf7AhEJAaYB3YCGQH8RaQjUAA5Yq13ycbuUUko54NPhI2PMjyISma24ObDbGLMHQEQWAL2AZCyBIQGd61ABKjLkENQ97u9mqKIuoTRkpPnk1P748K3OlR4BWIJBdeBzoI+IvAssdfZiERkqIvEiEn/06FHftlQpO/syqpN0qZq/m6EUYCDTNwMq/phoFgdlxhhzDhjo7sXGmJnATIC4uDjdBksVmA9Se5EYVomGF7SnoPxrAG8gxjcff/4ICsnAtXbPawCHcnMCEekB9Khdu7Y326WUSw3Tjvm7CUpZic82hvVHUNgE1BGRWsBBoB9wb25OYIxZCiyNi4sb4oP2KeXQ+CP/83cTlAJgOQbHgy755+tbUucD64B6IpIsIoOMMRnAY8BKYDvwqTHm91yeVzfZUUopH/D13Uf9nZQvB5bn47zaU1BKKR/QWz+VUkrZBGVQ0OEjpZTyjaAMCrpHs1JK+UZQBgWllFK+EZRBQYePlFLKN4IyKOjwkVJK+YYYHy2VLggichTYl8eXVwYKyxJVvZbAU1iuA/RaAlV+rqWmMaaKowNBHRTyQ0TijTFx/m6HN+i1BJ7Cch2g1xKofHUtQTl8pJRSyjc0KCillLIpykFhpr8b4EV6LYGnsFwH6LUEKp9cS5GdU1BKKZVTUe4pKKWUyqbIBAURGSEiv4vINhGZLyJhIlJRRP4rIn9Y/1vB3+10RETmiMjfIrLNrsxp20VkjIjsFpGdItLFP63Oycl1/FtEdojIFhH5QkTK2x0LyOsAx9did2ykiBgRqWxXFnTXIiKPW9v7u4i8alcekNfi5N9XjIisF5EE6za+ze2OBeR1AIjItSLyg4hst/7+n7CW+/7v3hhT6B9Y9oDeC4Rbn38KPAi8Coy2lo0GXvF3W520vx0QC2yzK3PYdqAh8BsQCtQC/gRC/H0NLq6jM1Dc+vMrwXAdzq7FWn4tlr1C9gGVg/VagA7At0Co9flVgX4tTq5jFdDN+vNtwOpAvw5r+64BYq0/lwF2Wdvs87/7ItNTwLJ3RLiIFAdKYdkCtBfwgfX4B8Ad/mmaa8aYH4ET2Yqdtb0XsMAYk26M2QvsBpoTABxdhzFmlbFsvASwHsv2rBDA1wFO/58AvAmMgiy7JQbjtTwCTDbGpFvr/G0tD9hrcXIdBihr/bkcV7b+DdjrADDGHDbG/GL9+SyWDcmqUwB/90UiKBhjDgKvAfuBw8BpY8wq4GpjzGFrncPAVf5rZa45a3t14IBdvWRrWTB4CPjG+nPQXYeI9AQOGmN+y3Yo6K4FqAvcJCIbRGSNiDSzlgfbtfwf8G8ROYDlM2CMtTxorkNEIoEmwAYK4O++SAQF67hbLyzdqmpAaRG537+t8hlHG7cG/C1mIjIWyAA+uVzkoFrAXoeIlALGAuMcHXZQFrDXYlUcqAC0BP4FfCoiQvBdyyPACGPMtcAIYLa1PCiuQ0QigMXA/xljzriq6qAsT9dTJIICcCuw1xhz1BhzEfgcaA38JSLXAFj/+7eLcwQaZ21PxjKufVkNrnSZA5KIDAC6A/cZ6wApwXcdN2D50vGbiCRhae8vIlKV4LsWsLT5c2OxEcjEkmsn2K5lAJa/d4BFXBlSCfjrEJESWALCJ8aYy9fg87/7ohIU9gMtRaSU9dvOLVjG6JZg+UeD9b9f+al9eeGs7UuAfiISKiK1gDrARj+0zyMi0hV4GuhpjEm1OxRU12GM2WqMucoYE2mMicTyRxprjDlCkF2L1ZdARwARqQuUxJJ8Ldiu5RBws/XnjsAf1p8D+jqsn1Ozge3GmDfsDvn+797fs+wFOJs/EdgBbAM+wjJLXwn4Dss/lO+Aiv5up5O2z8cyF3IRy4fNIFdtxzKM8SewE+udF4HwcHIdu7GMhSZYHzMC/TqcXUu240lY7z4KxmvBEgQ+tv69/AJ0DPRrcXIdbYHNWO7M2QA0DfTrsLatLZbhny12fxu3FcTfva5oVkopZVNUho+UUkp5QIOCUkopGw0KSimlbDQoKKWUstGgoJRSykaDglJKKRsNCkoppWw0KKgiS0T+ad334Ga7ssesZbfm8lyRInJeRBKylXcRkZ+sufy3isg8+30WHJyntohszVYWKiJ7ReRG674AF1ydQ6n80KCgirJoLCtGG4Atqd0g4Ciw1cXrnPnTGBNz+YmI3I0l//0AY0wcEINlJWqYi3PsAa4VEfu/zaHAGmPM79bzB1SOHlW4aFBQRVkjLKkR6lufD8eSNC3TGPNXfk4sIqWBd4B7jTF7AIwxl4wxLxljkq11aonIV9ZexEYRqWeMycSSqyvSWicceAqYkJ/2KOUpDQqqKGuAZRe++iJSDugL/A9Lvp/8ug34zRjzu6OD1gyYs4Anrb2ICVh20gJLssbLgepRYIkxJskLbVLKLQ0KqkgSkWuB49Zv8Vdh2S3tHSyby2yx1pnl4HU5ypy4EbvgIiJvi2V/8PXWojusdRZb5yFeBdKsx7YD9ay59B8FXsrVxSmVDxoUVFEVzZV5g7NAVyzbGzYCtlqHbeqLyAQRWSAWOcpcnP+8/RNjzHBgJJbsnQCNgbHGmBjrI8oY84j12OWewhNYcunnayhLqdzQoKCKqkZcCQr/Bh4zxlyylm/Bsv3hZ8aYCcBpLPv7OipzZiVwp4hUA1t+/E5Y0lCDJcVzl8sTyiLSyC7IbMeyGcxD1rYpVWA0KKiiqhHW4R1jzDJjzDpreUMgEcuH8hZrWSljzCknZQ4ZYxKAZ4EVIvIrllz+4Vj28gCYg+Xvb7t1+OhpcyWP/U5r+2YaY07n6yqVyiXdT0EpB0TkfeA4UB5YYIxZ7ajMrn4ksMwYE1UAbUsC4owxx3z9Xqro0aCglBdYJ67/h2XyOsZH7xEOrAOqAI2MMSd88T6qaNOgoJRSykbnFJRSStloUFBKKWWjQUEppZSNBgWllFI2GhSUUkrZaFBQSillo0FBKaWUjQYFpZRSNv8PzoHrhv5aJocAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_small_high_sb,EDGES, \"Mx < 350, high purity, sb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "id": "1ecdb208", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7dm2mTp3KzTffTG5uLpdddhlffvml43Z4E/CV10SkPvCpMSbWfn0r0M0YM9x+/SegtTHm/uIeOykpyeh6CsFUnPsPnF7u0ctH/vTDDz84+uAOF8ePH2fYsGElHlQ9ffo0ERERVKhQgRUrVnDPPfeQmprq+P1Dhgyhd+/e/OEPfyjR+YuSm5tLYmIiH374IY0aNQLg008/ZceOHUXea+GNp98BEVlrjPE4ABSMnkIGcKXb6xjA94VLpZQCLr300guaZbNr1y5uu+02cnNzqVSpEu+++64fa3dh0tLS6N27N/3793cFBCBgNxV6E4ygsAZoJCINgD3AQKxxBKWUCqhGjRqxfv36Er9/1qxZ/qtMAc2bN2fHjh0BO75TAQ0KIjIH6ATUEpEM4CljzHQRuQ9YAkQAM4wx3weyHioU+DvVhV5mUioQAhoUjDGDvGz/HPjc0z6llFLBE5bTPkWkj4hMPXbsWLCropRSZUpYBgVjzCJjzMiqVasGuypKKVWmhGVQUEopd+7pqL2ZPHkyp06dKqUahS9Nna1UuePvQf/SG8yfNWsW6enpjB8/Pt9293TU3kyePJk//vGPXHzxxQGqXdmgPQWlVMA988wzNG3alK5duzJo0CBeeuklANasWUN8fDzt2rXj0UcfLfFCN+4L7CxdupR27dqRmJjIrbfeSmZmJq+99hp79+6lc+fOdO7cmXPnzjFkyBBiY2OJi4vjlVde8Vtbw50GBaVUQKWkpLBgwQLWr1/PRx99lG91tKFDh/L222+zYsWKEi2gU9DBgwd59tln+eqrr1i3bh1JSUn84x//YNSoUdStW5dvv/2Wb7/9ltTUVPbs2cPmzZvZtGkTQ4cOveBzlxVheflIRPoAfRo2bBjsqiilirB8+XL69etHVFQUAH36WJevjh49yokTJ7juuusAuP32212L1rg7dOiQawGZw4cPk5OTw8cffwzAe++9ly+h38qVK0lLS6N9+/YA5OTk0K5du0LHvOqqq9ixYwf3338/vXr14qabbvJfg8NcWAYFY8wiYFFSUtKIYNdFKeWbt/xqTvOu1axZ05WfyNuYgvsxu3btypw5c3wes3r16mzYsIElS5bwxhtvMH/+fGbMmOGoPmWdXj5SSgVUhw4dWLRoEdnZ2WRmZrqWAa1evTpVqlRh5cqVAMydO/eCz9W2bVv+7//+j59++gmAU6dOsW3bNgCqVKnCiRMnAOsyU25uLrfccgvPPPMM69atu+BzlxVh2VNQSoWPa6+9lr59+9KiRQvq1atHUlISefcYTZ8+nREjRnDJJZfQqVMnLuTeIxGhdu3azJo1i0GDBnH69GkAnn32WRo3bszIkSPp0aMHderUYfLkyQwdOtS1MM7EiRMvvKFlRMBTZweSps4ONn9PbQyE8p37KFRSZ2dmZhIdHc2pU6fo2LEjU6dOJTEx0bUdrDWL9+3bl2/xeqfi4uJYuHCha2lKdV44pM5WqhRp4rxQMHLkSNLS0sjOzmbw4MGuRWg+++wzJk6cyNmzZ6lXr16JspB27dqVuLg4DQh+okFBKRVwH3zwgcftAwYMYMCAARd0bH+sNqbOC8uBZk2Ip5RSgRGWQUET4imlVGCEZVBQSikVGBoUlFJKuWhQUEqFrJSUFEaNGuWzTGpqKp9/rgs5+ovOPlKqvNnt5/tLrvTPdN769euTnp6eb1tSUhJJSR6n07ukpqaSkpJCz549/VKP8k57CkqpUuEtfbYvycnJ9O7dG4CTJ09y1113ce2119KyZUs++eQTcnJyGDduHPPmzSMhIYF58+axbNkyEhISSEhIoGXLlq7UFsoZ7SkopQLOPX322bNnSUxMpFWrVsU6xnPPPUeXLl2YMWMGR48epXXr1vzud7/j6aefJiUlhSlTpgBWFtY33niD9u3bk5mZSWRkZCCaVGaFZU9B71NQKry4p8+uUqWKK332c8895/pWv3fvXtfzv/zlL4WOsXTpUiZNmkRCQgKdOnUiOzubXbt2FSrXvn17HnroIV577TWOHj1KhQr63bc4wvKnpamzlQov3nKsjRkzhjFjxgDWmEJeimxvx1iwYAFNmjTJt33VqlX5Xj/++OP06tWLzz//nLZt2/LVV1/RtGnTC2tAORKWPQWlVHjxlj67OLp168brr7/uCjDr168H8qfEBti+fTtxcXE89thjJCUlsWXLFv80opzQoKCUCjj39Nk333xzvvTZRRERAMaOHcuZM2eIj48nNjaWsWPHAtC5c2fS0tJcA82TJ08mNjaWFi1aEBUVRY8ePQLWrrIoLC8fKaUugJ+mkBbXI488wvjx413psx9++OF8+wtORwVrKc4aNWoAEBUVxTvvvFOoTI0aNVizZo3r9YUm2CvvNCgopUqFt/TZ3ixcuJAxY8boMpmlTIOCUqpUeEuf7U3fvn3p27dvgGqjvNExBaWUUi4aFJRSSrmEZVDQm9eUUiowwnJMQW9eCxG7i1H2yoDVQinlR2HZU1BKhZfnnnuOa665hvj4eBISElx3IQ8fPpy0tLQSHTM9PZ3Y2NhivSc6Ojrf61mzZnHfffcB8Pbbb/PPf/6zRHUpS8Kyp6CUugB9/Jw6e5Hv+x5WrFjBp59+yrp166hcuTIHDx4kJycHgGnTpvm3Lhfg7rvvDnYVQoL2FJRSAbVv3z5q1apF5cqVAahVqxZ169YFoFOnTqSkpADWt/gxY8bQokUL2rZtyy+//AJYaSvatm3Ltddey7hx4wp92wc4d+4cjz76KNdeey3x8fEeb3Iryvjx413pvNesWUN8fDzt2rXj0UcfdfVITp06xW233UZ8fDwDBgygTZs2pKSkcO7cOYYMGUJsbCxxcXG88sorxf9BhQgNCkqpgLrpppvYvXs3jRs35t5772XZsmUey508eZK2bduyYcMGOnbsyLvvvgvAAw88wAMPPMCaNWtcwaSg6dOnU7VqVdasWcOaNWt499132blzZ6FyWVlZrkysCQkJjBs3zuPxhg4dyttvv82KFSuIiIhwbX/zzTepXr06GzduZOzYsaxduxawFvrZs2cPmzdvZtOmTQwdOrRYP6NQokFBKRVQ0dHRrF27lqlTp1K7dm0GDBjArFmzCpWrVKmSa0GdVq1audJerFixgltvvRWA22+/3eM5li5dyj//+U8SEhJo06YNhw4d4scffyxULioqitTUVNfj6aefLlTm6NGjnDhxguuuu67QOZcvX87AgQMBiI2NJT4+HoCrrrqKHTt2cP/997N48WIuvfRShz+d0KNjCkqpgIuIiKBTp0506tSJuLg4Zs+ezZAhQ/KVqVixoiv5XUREBGfPnnV8fGMMr7/+Ot26dbvgunpL8+1rX/Xq1dmwYQNLlizhjTfeYP78+WGbnkODgiq5e4tRNjg52FQI2Lp1KxdddBGNGjUCrEst9erVc/z+tm3bsmDBAgYMGMDcuXM9lunWrRtvvfUWXbp0oWLFimzbto0rrriCSy65pNj1rV69OlWqVGHlypW0bds23zk7dOjA/PnzXZlZN23aBMDBgwepVKkSt9xyC1dffXWhgBdONCgopQIqMzOT+++/37UKWsOGDZk6darj90+ePJk//vGPvPzyy/Tq1ctjyu3hw4eTnp5OYmIixhhq167Nxx9/XOI6T58+nREjRnDJJZfQqVMn1znvvfdeBg8eTHx8PC1btiQ+Pp6qVauyZ88ehg4dSm5uLgATJ04s8bmDTXx1lUJdUlKSyZu5oIKgOFMbQ76nEPIVLJEffviBZs2aBbsaF+TUqVNERUUhIsydO5c5c+bwySefBPScmZmZrllOkyZNYt++fbz66qucO3eOM2fOEBkZyfbt27nxxhvZtm0blSpVCmh9LoSn3wERWWuMSfJUXnsKSqmQtnbtWu677z6MMVSrVq1UrtV/9tlnTJw4kbNnz1KvXj3XwPipU6fo3LkzZ86cwRjDW2+9FdIBoSTCMiiISB+gT8OGDYNdFaVUgF1//fVs2LChVM85YMAAj4v1VKlShbJ+dSIsp6QaYxYZY0Y6Xc5PKaWUM2EZFJRSSgVGWF4+UmWY08yrmnVVqYDQnoJSSikX7SkoVY5MWPQ9aXuP+/WYzeteylN9rvFZJjo6mszMTNfrWbNmkZKSwpQpUxg/fjzR0dE88sgjrv3169cnJSWFWrVq+bWuvniqB0BycjIvvfQSn376qc/3e6tzwbb7MmXKFCZPnsz27ds5cOCA1/bPnj2bZ599FoAnn3ySwYMHOzq+ExoUlCpH0vYeJ23fcZrX8U9unrR9/g0w5V379u3p3bs3nTp18lrm8OHDTJgwgZSUFESEVq1a0bdvX6pXr+6XOmhQUKqcaV7nUub9uZ1fjjXgnRV+OU5J7d69mwMHDpCYmJhve2pqKoMGDWLhwoU0atQIYwxNmzZl5syZDBs2rND2b7/9FoANGzbQpUsXdu/ezejRoxkxwlrc8fjx4/Tv35+tW7fSsWNH3nzzTS66yPPV96ysLPr3788tt9zier9TLVu2LLLMkiVL6Nq1KzVq1ACga9euLF68mEGDBhXrXN5oUFClozhLd6oyJy9ldZ7Dhw/Tt29f1+tXXnmFf/3rX67Xe/fudXTc06dPM3jwYGbNmkWrVq1c2xMSEnj22Wf5xz/+wVtvvUVycjINGzbkuuuu87g9LyX3xo0bWblyJSdPnqRly5b06tULgNWrV5OWlka9evXo3r07H330EX/4wx8K1SczM5OBAwdy5513cuedd+bbd+LECa6//nqP7fjggw9o3ry5ozbv2bOHK688P9MiJiaGPXv2OHqvExoUVHjSWUphJS9ldZ68MYU8Dz74YKExhYK++eYbRo0aVWj7oUOH6NGjB7/++mu+7b179+aJJ57g3LlzTJ8+3bXGgbftAP369SMqKoqoqCg6d+7M6tWrqVatGq1bt+aqq64CYNCgQSxfvtxjUOjXrx+jR4/mjjvuKLSvSpUq+X4GJeUpNVFedll/0KCglAoLXbp0YfPmzfm2HT16lJ49e/Lwww8XKl+5cmXatGnDxx9/zLJly1zpMbxth8IfrnmvvW0vqH379nzxxRfcfvvthcr4q6cQExNDcnKy63VGRobPMYji0impSqmw9fPPPzN69GhuueUWj/tvvfVW7rnnHm6++eZ8OYq8bf/kk0/Izs7m0KFDJCcnc+211wLW5aOdO3eSm5vLvHnz6NChg8fzPf3009SsWZN77y2cVz6vp+Dp4TQggJUmfOnSpRw5coQjR46wdOlSv6wjkUd7Ciq0OF2j4c2A1qJMS9t33G8DxP6cyVQSLVq0oEWLFl73d+vWjaysrEJTNr1tb926Nb169WLXrl2MHTuWunXrsm3bNtq1a8fjjz/Opk2b6NixI/379/d6zsmTJ3PXXXcxevRoXnzxxWK157XXXuPFF19k//79xMfH07NnT6ZNm0ZKSgpvv/0206ZNo0aNGowdO9YVsMaNG+cadPYHTZ2tSq44qbOdfoj7Oyg4HlMoH6mzg3WfQjB5u38gGPdCBEO5SJ2tWVKVKplQ/vBWoSEsxxQ0S6pSyqn09HSPvQFv28u7sAwKSimlAqPIy0cicjPwAnAZIPbDGGOCN7qkwo/TsQKlVFA5GVN4EehjjPkh0JVRyjGnQaZsjh8rFTBOLh/9ogFBKaXKByc9hRQRmQd8DJzO22iM+ShQlVJKBcgXj8P+Tf495uVx0GOSzyKaOttZ6uw77riDlJQUKlasSOvWrXnnnXeoWLGix7LHjx+nWbNm9O/fnylTpjg6vhNOegqXAqeAm4A+9qO332qglCo9+zf5Nyj4+3jl3B133MGWLVvYtGkTWVlZTJs2zWvZsWPHcsMNN/i9DkX2FIwxQ4sqo5QKI5fHwdDP/HOsmb38c5wSKmups3v27Ol63rp1azIyMjyWW7t2Lb/88gvdu3fH3zfweg0KIjLaGPOiiLwOFLrt2RhTOF2hUkp5oKmzi5cQ78yZM7z33nu8+uqrhcrm5uby8MMP89577/H11187+jkVh6+eQt7gsuaRUEpdEE2dXbzU2ffeey8dO3b0GETefPNNevbsmW9NBX/yGhSMMYvsf2cDiMil1ktzIiA1USoQdN2FMqO8pM6eMGECBw4c4J133vFYdsWKFXz33Xe8+eabZGZmkpOTQ3R0NJMm+R7sd6rIgWYRSRKRTcBGYLOIbBCRVkW9TymlAq2spc6eNm0aS5YsYc6cOV7HLN5//3127dpFeno6L730EnfeeaffAgI4m5I6A7jXGPMdgIh0AGYC8X6rhVKq9Ozf5L8B4v2brIHrIClrqbPvvvtu6tWrR7t21hraN998M+PGjcuXOjvQikydLSL/Z4xpX9S2YNDU2UFWnNTZweI4xXbZvPW5UNrkIN2nEEyaOttPqbNFJG+O12oReQeYgzULaQCQ7JfaKuVm/4EDnD59uuiCWNeFL69dO8A1KoNC+MNbhQZfl49eLvD6Kbfn4bsyjwpZp0+fJvt0DpGVK/ksl306p5RqpMqC9PT0Ym0v73zNPupcmhVRCiCyciXqxcT4LPOzlxt6lFIXTtdTUEop5aJBQSmllIuvgeY6xph9pVkZVTY5HUB2Mp6glAosXwPNM0SkOtZMo8XAcmPM2VKpVRFEpA/Qp2HDhsGuinLA6QByZOVKVK5c2b8n18V48nlh9QtsObzFr8dsWqMpj7V+zGcZTZ3tLHX2sGHDSElJwRhD48aNmTVrFtHR0YXKzZ49m2effRaAJ598stD9FhfC10BzDxGJBDoB/YGXRGQXVoBYbIzZ5bdaFJOdgmNRUlJS8VIQqqBxMoCsAm/L4S1sPbyVJjWa+OV4Ww9v9ctxlOWVV17h0kutlY4feughpkyZwuOPP56vzOHDh5kwYQIpKSmICK1ataJv375Ur17dL3XweUezMSYbOwgAiEgDoAcwRUQuN8a09kstlCqm7NM5jmYh6f0MhTWp0YSZ3Wf65VhDFwc3s35ZS52dFxCMMWRlZXnMsbRkyRK6du1KjRo1AOjatSuLFy9m0KBBxTqXN07SXLgYY3Zi3SP6pojoxV8VFE4vMen9DKFDU2c7T4g3dOhQPv/8c5o3b87LLxe8XQz27NmTL0NqTEwMe/bscfTzcqJYQcGdMUb/4lRQOP3mr/czhA5Nne08dfbMmTM5d+4c999/P/PmzctXP7B6EQV5y9paEjolVSkVFvJSZ7s/li9fToMGDXjrrbcKlS+YIjuvZ+JtO/gvdbanD+4TJ06QkJDg8ZGWlpavbEREBAMGDGDBggWFjhMTE8Pu3edzwmdkZLh6Ov5QrKAgItVFRLOjKqVCQllKnW2M4aeffgKs3sCiRYto2rRpoeN069aNpUuXcuTIEY4cOcLSpUvp1q2b8x9aEYq8fCQiyUBfu2wqcEBElhljHvJbLVTY2f/885x2eHlG7z8ILVsPb/XbALE/ZzKVRFlKnW2MYfDgwRw/fhxjDC1atHD1gNxTZ9eoUYOxY8e6Ata4ceNcg87+4CR19npjTEsRGQ5caYx5SkQ2GmOC3mPQ1NnB8/Of7iR77VrHH/bBmAWUN6bgaCrsorJ5o0LBtMnBuk8hmDR1tp9SZ7uXEZE6wG3AmAuvoior9N6D8BPKH94qNDgJChOAJVh3NK8RkauAHwNbLaWU8g9NnV08ToLCPvdLRcaYHSLyjwDWSSmlVJA4mX30usNtSimlwpyvLKntgOuA2iLiPtPoUiAi0BVTSilV+nxdPqoERNtlqrhtPw4UvpVPKaVU2POVJXUZsExEZhljfi7FOimlAmT/889z+gf/Tkmt3Kwpl//tbz7LaOpsZ6mzd+7cycCBAzl8+DCJiYm89957+W6uyxPI1NlOxhQqi8hUEVkqIt/kPfxWA6VUqTn9wxayt/gvKGRv2eL3IFOePfbYYzz44IP8+OOPVK9enenTpxcqk5c6e9WqVaxevZoJEyZw5MgRv9XByeyjD4G3gWnAOb+dWSkVFJFNm1LvvX/65Vg//+nOogsFUFlKnW2M4ZtvvuGDDz4AYPDgwYwfP5577rknX7lQSJ191hhTONuUUko5pKmzi06dfdlll1GtWjUqVLA+lr2lxA6F1NmLRORe4D+Aa6FdY8xhv9VCKWDC5deRFunsGnLz7IM8tf9/Aa6R8hdNnV106uwDBw4U2uYpG2ugU2c7CQp5IxiPum0zwFV+q4VSQFpkLdIia9I8+1AR5WqWUo1UKMlLne3u6NGj9OzZk4cffrhQ+YIpsmfMmOFzO/gvdfbtt99eqExRPYVmzZpx9OhRzp49S4UKFbymxI6JiSE5Odn1OiMjg06dOnk8bkkUGRSMMQ38djalitA8+xDz0hf6LDOgfl+f+1X5kZc6+/e//73H/bfeeivDhw9n0KBBhVJne9r+ySef8MQTT3Dy5EmSk5OZNGkS27Ztc6XOrlevHvPmzWPkyJEez/f000/zzDPPcO+99xZa48HJIjudO3fm3//+NwMHDmT27Nn069evUJlu3brxt7/9zTW4vHTpUiZOnOjzuMXhJHX2xcBDwG+NMSNFpBHQxBjje36WUiokZW/Z4rcB4uwtW4j0kPO/tJSl1NkAL7zwAgMHDuTJJ5+kZcuWDBs2DAi91NnzgLXAncaYWBGJAlYYYxL8VosS0tTZwfPzn+6EzZv8miU1rwfgtKdQVDlNnV04bXKw7lMIJk2d7f/U2VcbYwaIyCAAY0yW+HNUQylVakL5w1uFBidBIcfuHRgAEbkat1lIShXF6awiJ4PMShWXps4uHidBYTywGLhSRN4H2gNDAlgnVcY4nVXUPPsQzbMPOjxmzSIHnIccnM/FuWeo57imSikns4+WishaoC0gwAPGGGd/uUrZnMwqcn4sZ79+Jy+q6JfzhTtjjF/nsavwUdSYsSdOZh8tBOYAC40xJ0tQL6X8yulNa1/knglwTUJfZGQkhw4dombNmhoYyhljDIcOHSIyMrJY73Ny+ehlYAAwSURWA/OAT40x2cWvplKqNMXExJCRkeHxbllV9kVGRhJTzBmCTi4f5aXQjgC6ACOAGViL7SilQljFihVp0EDvP1XOOekpYM8+6oPVY0gEZgeyUir0pR86yanIWox2cHexzipSKnw4GVOYB7TBmoH0BpBsjMn1d0VEpBnwAFAL+Fozs4a2UznnHA/kFmdWkVIquJz0FGYCtxtjir2WgojMAHoDvxpjYt22dwdexVrreZoxZpIx5gfgbhG5CHi3uOdSpe+S3DN+m1GklAoNXldeE5HRAMaYxcDNBfY97/D4s4DuBd4bgdXj6AE0BwaJSHN7X19gOfC1w+MrpZTyI189hYFAXjanJ7BWYMvTHSjyfnljzH9FpH6Bza2Bn4wxOwBEZC7QD0gzxiwEForIZ8AHjlqglA+XnzjAz9lFT8Ws/PzzmgJCKXwHBfHy3NPr4rgC2O32OgNoIyKdsHoklYHPvVZKZCQwEuC3v/3tBVRDlXX7q9QGoEYR4xnZp3NA1xlWCvAdFIyX555eF4engGKMMclAclFvNsZMBaaClSX1AuqhyrjFTToDzrOpKqV8B4UWInIc60M8yn6O/bp4t8jllwFc6fY6BnC2IKtSSqmA8hoUjDERATrnGqCRiDQA9mCNXdweoHMppZQqBq+zj/xBROYAK4AmIpIhIsOMMWeB+4AlwA/AfGPM94Gsh1JKKWcc3dFcUsaYQV62f46PweSiiEgfoE/Dhg1LegillFIeBLSnECjGmEXGmJFVq1YNdlWUUqpMCcugoJRSKjA0KCillHLRoKCUUspFg4JSSimXsAwKItJHRKYeO3Ys2FVRSqkyJSyDgs4+UkqpwAjLoKCUUiowNCgopZRy0aCglFLKRYOCUkopFw0KSimlXMIyKOiUVKWUCoywDAo6JVUppQIjLIOCUkqpwNCgoJRSyiWgi+woVSwx2+HiTGdlT0VDxtVFFkuLrMmA+n19lhlycD4XHzpJPWdnVqpM06CgQsfFmRCVCVnRvstFOQsczbMPOip38qKKkHPOUVmlyjoNCiq0ZEXDtha+yzTe4OhQT+3/n6NyX+SeASo5KqtUWadjCkoppVzCMijofQpKKRUYYRkU9D4FpZQKDB1TUIHndFaRk0Fm97JOxhYczlJSSlk0KKjAczqrKCva+hAvipMy4HiWklLqPA0KqnQ4mVXklNNv/g5nKSmlzgvLMQWllFKBoUFBKaWUiwYFpZRSLjqmoErkN2f3EhmRCY39PKtIKRVU2lNQJRJpsoiUHGeFnc4qUkoFXVj2FESkD9CnYcOGwa5KuZZtKsG2Zn473gstDrGlmrNA0/RoJR7bUNNv51ZKWcKyp6B3NJdNW6rlsLVq0UFha9Ucx8FDKVU8YdlTUGVXk2OVmLmsjs8yQ2/YV0q1Uar8CcueglJKqcDQnoIKS1ur5hTdY4i6hKZZ53hsW+nUSamyQIOCCjinA8hbq+bQ5FjRi900PepsQZytURGOyimlztOgoAIubwC5qA/8JscqOfrAdzrraGj3nxyVU0qdp0FBlQonA8hKqeDTgWallFIuGhSUUkq56OUjVSL7K+SSXcHwdwf3DDgdQFZKBV9Y9hREpI+ITD127Fiwq1JuZYvhtMPfHqcDyEqp4AvLnoIxZhGwKCkpaUSw61KeVc5FB4+VKmPCsqeglFIqMDQoKKWUctGgoJRSykWDglJKKRcNCkoppVw0KCillHLRoKCUUspFg4JSSikXDQpKKaVcNCgopZRyCcs0F0o5dtE5aLzBZ5F6R6LJPhtdShVSKrRpUFBlV66z5TgjJQdMVoAro1R40KCgyq7Tkda/2xr6LJZttpdCZZQKD2E5pqCps5VSKjDCMigYYxYZY0ZWrVo12FVRSqkyJSyDglJKqcDQoKCUUspFg4JSSikXDQpKKaVcNCgopZRy0aCglFLKRYOCUkopF72jWZVpW6vmMPSGfT7L3PqFIZJcmpdSnZQKZRoUVJnV9GglR+VOXwQYE9jKKBUmNCioMuuxDTUdlfs89yeQAFdGqTChYwpKKaVcNCgopZRy0aCglFLKRYOCUkopFw0KSimlXDQoKKWUctGgoJRSykWDglJKKRcxYXwnp4gcAH4u4dtrAQf9WJ1g0raEnrLSDtC2hKoLaUs9Y0xtTzvCOihcCBFJMcYkBbse/qBtCT1lpR2gbQlVgWqLXj5SSinlokFBKaWUS3kOClODXQE/0raEnrLSDtC2hKqAtKXcjikopZQqrDz3FJRSShVQboKCiDwoIt+LyGYRmSMikSJSQ0S+FJEf7X+rB7uenojIDBH5VUQ2u23zWncReUJEfhKRrSLSLTi1LsxLO/4uIltEZKOI/EdEqrntC8l2gOe2uO17RESMiNRy2xZ2bRGR++36fi8iL7ptD8m2ePn9ShCRlSKSKiIpItLabV9ItgNARK4UkW9F5Af75/+AvT3wf/fGmDL/AK4AdgJR9uv5wBDgReBxe9vjwAvBrquX+ncEEoHNbts81h1oDmwAKgMNgO1ARLDb4KMdNwEV7OcvhEM7vLXF3n4lsATr/pla4doWoDPwFVDZfn1ZqLfFSzuWAj3s5z2B5FBvh12/OkCi/bwKsM2uc8D/7stNTwFrlbkoEakAXAzsBfoBs+39s4HfB6dqvhlj/gscLrDZW937AXONMaeNMTuBn4DWhABP7TDGLDXGnLVfrgRi7Och2w7w+n8C8AowGnAfrAvHttwDTDLGnLbL/GpvD9m2eGmHAS61n1fF+ruHEG4HgDFmnzFmnf38BPAD1pfbgP/dl4ugYIzZA7wE7AL2AceMMUuB3xhj9tll9gGXBa+Wxeat7lcAu93KZdjbwsFdwBf287Brh4j0BfYYYzYU2BV2bQEaA9eLyCoRWSYi19rbw60tfwX+LiK7sT4DnrC3h007RKQ+0BJYRSn83ZeLoGBfd+uH1a2qC1wiIn8Mbq0CxtNqwyE/xUxExgBngffzNnkoFrLtEJGLgTHAOE+7PWwL2bbYKgDVgbbAo8B8ERHCry33AA8aY64EHgSm29vDoh0iEg0sAP5qjDnuq6iHbSVqT7kICsDvgJ3GmAPGmDPAR8B1wC8iUgfA/vdXH8cINd7qnoF1XTtPDOe7zCFJRAYDvYE7jH2BlPBrx9VYXzo2iEg6Vn3XicjlhF9bwKrzR8ayGsjFyrUTbm0ZjPX3DvAh5y+phHw7RKQiVkB43xiT14aA/92Xl6CwC2grIhfb33ZuxLpGtxDrlwb730+CVL+S8Fb3hcBAEaksIg2ARsDqINTPERHpDjwG9DXGnHLbFVbtMMZsMsZcZoypb4ypj/VHmmiM2U+YtcX2MdAFQEQaA5Wwkq+FW1v2AjfYz7sAP9rPQ7od9ufUdOAHY8w/3HYF/u8+2KPspTiaPwHYAmwG3sMapa8JfI31i/I1UCPY9fRS9zlYYyFnsD5shvmqO9ZljO3AVuyZF6Hw8NKOn7Cuhabaj7dDvR3e2lJgfzr27KNwbAtWEPiX/feyDugS6m3x0o4OwFqsmTmrgFah3g67bh2wLv9sdPvb6Fkaf/d6R7NSSimX8nL5SCmllAMaFJRSSrloUFBKKeWiQUEppZSLBgWllFIuGhSUUkq5aFBQSinlokFBlVsi8md73YMb3LbdZ2/7XTGPVV9EskQktcD2biLynZ3Lf5OIzHJfZ8HDcRqKyKYC2yqLyE4RucZeFyDH1zGUuhAaFFR5Fo91x2gzcCW1GwYcADb5eJ83240xCXkvRORWrPz3g40xSUAC1p2okT6OsQO4UkTc/zZHAsuMMd/bxw+pHD2qbNGgoMqzOKzUCE3t16OwkqblGmN+uZADi8glwOvA7caYHQDGmHPGmOeMMRl2mQYi8ondi1gtIk2MMblYubrq22WigIeB8RdSH6Wc0qCgyrNmWKvwNRWRqsAA4H9Y+X4uVE9ggzHme0877QyY04CH7F7EeKyVtMBK1pgXqP4CLDTGpPuhTkoVSYOCKpdE5ErgkP0t/jKs1dJex1pcZqNdZpqH9xXa5sU1uAUXEXlNrPXBV9qbfm+XWWCPQ7wIZNv7fgCa2Ln0/wI8V6zGKXUBNCio8iqe8+MGJ4DuWMsbxgGb7Ms2TUVkvIjMFUuhbT6On+X+whgzCngEK3snQAtgjDEmwX7EGmPusffl9RQewMqlf0GXspQqDg0KqryK43xQ+DtwnzHmnL19I9byh/82xowHjmGt7+tpmzdLgJtFpC648uN3xUpDDVaK5255A8oiEucWZH7AWgzmLrtuSpUaDQqqvIrDvrxjjPnUGLPC3t4cSMP6UN5ob7vYGHPUyzaPjDGpwJPAYhFZj5XLPwprLQ+AGVh/fz/Yl48eM+fz2G+16zfVGHPsglqpVDHpegpKeSAi7wKHgGrAXGNMsqdtbuXrA58aY2JLoW7pQJIx5mCgz6XKHw0KSvmBPXD9P6zB64QAnSMKWAHUBuKMMYcDcR5VvmlQUEop5aJjCkoppVw0KCillHLRoKCUUspFg4JSSikXDQpKKaVcNCgopZRy0aCglFLKRYOCUkopl/8HuilScf+qmDUAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = np.arange(start = 80, stop = 138, step = 4)\n", + "EDGES = np.append(EDGES,200)\n", + "drawMbb(df_great_high_c,EDGES, \"Mx > 350, high purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "7f9d353c", + "metadata": {}, + "outputs": [], + "source": [ + "####################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "f1bf73b0", + "metadata": {}, + "outputs": [], + "source": [ + "##input to combine: these histograms with this binning" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "b5b272fc", + "metadata": {}, + "outputs": [], + "source": [ + "####################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "5e9cfccb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_high_c,EDGES,\"Mx > 350, high purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "b4203696", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_high_sb,EDGES,\"Mx > 350, high purity, sb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "407bf161", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_med_c,EDGES,\"Mx > 350, medium purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "f5f70dfe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180]\n", + "drawMgg(df_great_med_sb,EDGES,\"Mx > 350, medium purity, sb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "7087d4b9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100., 122, 123, 124, 125, 126, 127, 128, 180]\n", + "drawMgg(df_small_high_c,EDGES,\"Mx < 350, high purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "7a64210a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100., 122, 123, 124, 125, 126, 127, 128, 180]\n", + "drawMgg(df_small_high_sb,EDGES,\"Mx < 350, high purity, sb\")" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "72af6213", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100., 122, 123, 124, 125, 126, 127, 128, 180]\n", + "drawMgg(df_small_med_c,EDGES,\"Mx < 350, medium purity, c\")" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "46156534", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "EDGES = [100., 122, 123, 124, 125, 126, 127, 128, 180]\n", + "drawMgg(df_small_med_sb,EDGES,\"Mx < 350, medium purity, sb\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "220662e0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "a2c97d9b", + "metadata": {}, + "outputs": [], + "source": [ + "####################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "702c7b8b", + "metadata": {}, + "outputs": [], + "source": [ + "##### SAVE PARQUET (INPUT TO COMBINE) ##########" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "3428bbfc", + "metadata": {}, + "outputs": [], + "source": [ + "####################################################" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "cf107118", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Welcome to JupyROOT 6.28/00\n" + ] + } + ], + "source": [ + "import ROOT" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "84dd0b2d", + "metadata": {}, + "outputs": [], + "source": [ + "df_dict={\"great350_high_purity_sb\": df_great_high_sb,\n", + " \"great350_high_purity_c\" : df_great_high_c,\n", + " \"great350_medium_purity_sb\": df_great_med_sb,\n", + " \"great350_medium_purity_c\" : df_great_med_c,\n", + " \n", + " \"small350_high_purity_sb\": df_small_high_sb,\n", + " \"small350_high_purity_c\" : df_small_high_c,\n", + " \"small350_medium_purity_sb\": df_small_med_sb,\n", + " \"small350_medium_purity_c\" : df_small_med_c,\n", + "}\n", + "edges_dict ={\n", + " \"great350_high_purity_sb\": [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180],\n", + " \"great350_high_purity_c\" : [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180],\n", + " \"great350_medium_purity_sb\": [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180],\n", + " \"great350_medium_purity_c\" : [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180],\n", + " \n", + " \"small350_high_purity_sb\": [100., 122, 123, 124, 125, 126, 127, 128, 180],\n", + " \"small350_high_purity_c\": [100., 122, 123, 124, 125, 126, 127, 128, 180],\n", + " \"small350_medium_purity_sb\": [100., 122, 123, 124, 125, 126, 127, 128, 180],\n", + " \"small350_medium_purity_c\": [100., 122, 123, 124, 125, 126, 127, 128, 180],\n", + " \n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "3b1732f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved\n", + "saved\n", + "saved\n", + "saved\n", + "saved\n", + "saved\n", + "saved\n", + "saved\n" + ] + } + ], + "source": [ + "#!mkdir InputParquets\n", + "for kdf in df_dict:\n", + " df_dict[kdf].to_parquet(\"./InputParquets/\"+kdf+\".parquet\")\n", + " print(\"saved\")" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "b10d8871", + "metadata": {}, + "outputs": [], + "source": [ + "#try to run with LCG101\n", + "#from root_numpy import fill_hist\n", + "#for kdf in df_dict:\n", + "# out_file = ROOT.TFile(kdf+\".root\",\"RECREATE\")\n", + "# for i in df_dict[kdf].process.unique():\n", + "# histo = ROOT.TH1D(\"mgg_\"+str(i) ,\"mgg_\"+str(i), len(np.array(edges_dict[kdf]))-1,np.array(edges_dict[kdf]))\n", + "# histo.Sumw2()\n", + "# fill_hist(histo, (df_dict[kdf].loc[df_dict[kdf].process == str(i),:]['haa_m']).to_numpy(),\n", + "# weights=(df_dict[kdf].loc[df_dict[kdf].process == str(i),:]['weight']).to_numpy())\n", + "# histo.Write()\n", + "# for ibin in range(1,histo.GetNbinsX()+1):\n", + "# if histo.GetBinContent(ibin) < 0:\n", + "# print(\"sono entrato\")\n", + "# print(\"process \", str(i))\n", + "# histo.SetBinContent(ibin,1e-3)\n", + "\n", + "# data_obs = ROOT.TH1D( \"data_obs\" ,\"data_obs\",1,0.,1.)\n", + "# data_obs.Fill(1)\n", + "# data_obs.Write()\n", + "# out_file.Close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b7fe917", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run/bbyy_analysis/create_histo_allCats.py b/run/bbyy_analysis/create_histo_allCats.py new file mode 100644 index 0000000000..a90a2952b7 --- /dev/null +++ b/run/bbyy_analysis/create_histo_allCats.py @@ -0,0 +1,57 @@ +from root_numpy import fill_hist +import pyarrow.parquet as pq +import pandas as pd +import ROOT +import numpy as np + +df_dict=["great350_high_purity_sb", "great350_high_purity_c","great350_medium_purity_sb","great350_medium_purity_c","small350_high_purity_sb","small350_high_purity_c" , "small350_medium_purity_sb", "small350_medium_purity_c" ] + +edges_dict ={ + "great350_high_purity_sb": [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180], + "great350_high_purity_c" : [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180], + "great350_medium_purity_sb": [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180], + "great350_medium_purity_c" : [100.,118, 120, 121, 122, 123, 124, 125, 126, 127, 128,129, 130, 132, 180], + + "small350_high_purity_sb": [100., 122, 123, 124, 125, 126, 127, 128, 180], + "small350_high_purity_c": [100., 122, 123, 124, 125, 126, 127, 128, 180], + "small350_medium_purity_sb": [100., 122, 123, 124, 125, 126, 127, 128, 180], + "small350_medium_purity_c": [100., 122, 123, 124, 125, 126, 127, 128, 180], + +} + + +for kdf in df_dict: + skim_pq = pq.read_table("./InputParquets/"+kdf+".parquet") + df = skim_pq.to_pandas() + out_file = ROOT.TFile("/afs/cern.ch/user/p/pmastrap/Combine/CMSSW_10_2_13/src/HiggsAnalysis/CombinedLimit/HH_FCChh_newCard_2023/input_files/"+kdf+".root","RECREATE") + for i in df.process.unique(): + histo = ROOT.TH1D("mgg_"+str(i) ,"mgg_"+str(i), len(np.array(edges_dict[kdf]))-1,np.array(edges_dict[kdf])) + histo.Sumw2() + fill_hist(histo, (df.loc[df.process == str(i),:]['haa_m']).to_numpy(), + weights=(df.loc[df.process == str(i),:]['weight']).to_numpy()) + histo.Write() + + #addition of jja (scaling jjaa) + if "jjaa" in i: + name = i.replace("jjaa","jja") + histo_jja = ROOT.TH1D("mgg_"+str(name) ,"mgg_"+str(name), len(np.array(edges_dict[kdf]))-1,np.array(edges_dict[kdf])) + histo_jja.Sumw2() + fill_hist(histo_jja, (df.loc[df.process == str(i),:]['haa_m']).to_numpy(), + weights=(df.loc[df.process == str(i),:]['weight']*0.09).to_numpy()) + histo_jja.Write() + for ibin in range(1,histo_jja.GetNbinsX()+1): + if histo_jja.GetBinContent(ibin) < 0: + print("sono entrato") + print("process ", str(i)) + histo_jja.SetBinContent(ibin,1e-3) + + for ibin in range(1,histo.GetNbinsX()+1): + if histo.GetBinContent(ibin) < 0: + print("sono entrato") + print("process ", str(i)) + histo.SetBinContent(ibin,1e-3) + + data_obs = ROOT.TH1D( "data_obs" ,"data_obs",1,0.,1.) + data_obs.Fill(1) + data_obs.Write() + out_file.Close() diff --git a/run/bbyy_analysis/full_3DNN.ipynb b/run/bbyy_analysis/full_3DNN.ipynb new file mode 100644 index 0000000000..4e4b39df17 --- /dev/null +++ b/run/bbyy_analysis/full_3DNN.ipynb @@ -0,0 +1,2737 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "fd63b6e9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-10 18:29:16.286655: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /cvmfs/sft.cern.ch/lcg/releases/MCGenerators/thepeg/2.2.3-6fa5c/x86_64-centos7-gcc11-opt/lib/ThePEG:/cvmfs/sft.cern.ch/lcg/releases/MCGenerators/herwig++/7.2.3-c1d8e/x86_64-centos7-gcc11-opt/lib/Herwig:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/jaxlib/mlir/_mlir_libs:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/torch/lib:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/onnxruntime/capi/:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow/contrib/tensor_forest:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib/python3.9/site-packages/tensorflow/python/framework:/cvmfs/sft.cern.ch/lcg/releases/java/11.0.14p1-8284a/x86_64-centos7-gcc11-opt/jre/lib/amd64:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib64:/cvmfs/sft.cern.ch/lcg/views/LCG_103swan/x86_64-centos7-gcc11-opt/lib:/cvmfs/sft.cern.ch/lcg/releases/gcc/11.3.0-ad0f5/x86_64-centos7/lib:/cvmfs/sft.cern.ch/lcg/releases/gcc/11.3.0-ad0f5/x86_64-centos7/lib64:/cvmfs/sft.cern.ch/lcg/releases/binutils/2.37-355ed/x86_64-centos7/lib:/usr/local/lib/:/cvmfs/sft.cern.ch/lcg/releases/R/4.1.2-23baf/x86_64-centos7-gcc11-opt/lib64/R/library/readr/rcon\n", + "2023-08-10 18:29:16.286711: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], + "source": [ + "import pyarrow.parquet as pq\n", + "from sklearn.model_selection import train_test_split\n", + "import tensorflow as tf\n", + "from tensorflow import keras\n", + "from tensorflow.keras import layers\n", + "import matplotlib.pyplot as plt\n", + "from scipy.stats import norm\n", + "import matplotlib.mlab as mlab\n", + "import numpy as np\n", + "import scipy" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ea88ba1c", + "metadata": {}, + "outputs": [], + "source": [ + "PLOT_FOLDER = \"./Plots_bbgg/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "00a9cac9", + "metadata": {}, + "outputs": [], + "source": [ + "skim = pq.read_table(\"/eos/user/a/atalierc/FCC_HH_13/FCCAnalyses/run/bbyy_analysis/df_Sel_All_haaIncluded.parquet\")#df_Sel_All.parquet\")\n", + "df_ = skim.to_pandas()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3c4b4726", + "metadata": {}, + "outputs": [], + "source": [ + "df_[\"label\"] = 0\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda100_5f_hhbbaa\"), ['label']] = 1\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda000_5f_hhbbaa\"), ['label']] = 1\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda240_5f_hhbbaa\"), ['label']] = 1\n", + "df_.loc[df_.process.str.contains(\"pwp8_pp_hh_lambda300_5f_hhbbaa\"), ['label']] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b24c8b18", + "metadata": {}, + "outputs": [], + "source": [ + "df_['Mx'] = df_['hh_m'] - df_['haa_m'] - df_['hbb_m'] + 250" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e40ab961", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "51\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='pwp8_pp_hh_lambda100_5f_hhbbaa') ].Mx, label='kl = 1', histtype=(\"step\"), bins=50,density=True, alpha=1.0, linewidth=2, range=(200,700));\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='pwp8_pp_hh_lambda000_5f_hhbbaa') ].Mx, label='kl = 0', histtype=(\"step\"), bins=50,density=True, alpha=1.0, linewidth=2, range=(200,700));\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='pwp8_pp_hh_lambda240_5f_hhbbaa')].Mx, label='kl = 2.4', histtype=(\"step\"), bins=50,density=True, alpha=1.0, linewidth=2, range=(200,700));\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='pwp8_pp_hh_lambda300_5f_hhbbaa') ].Mx, label='kl = 3', histtype=(\"step\"), bins=50,density=True, alpha=1.0, linewidth=2, range=(200,700));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='mgp8_pp_tth01j_5f_haa')].Mx, label='ttH', histtype=(\"step\"), bins=50,density=True, range=(200,700));\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='mgp8_pp_vh012j_5f_haa')].Mx, label='VH', histtype=(\"step\"), bins=50,density=True, range=(200,700));\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='mgp8_pp_h012j_5f_haa')].Mx, label='SM Higgs', histtype=(\"step\"), bins=50,density=True, range=(200,700));\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process=='mgp8_pp_vbf_h01j_5f_haa')].Mx, label='VBF Higgs', histtype=(\"step\"), bins=50,density=True, range=(200,700));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_.loc[(df_.process==\"mgp8_pp_jjaa_5f\")].Mx, label='other bkg', histtype=(\"step\"), bins=50,density=True, range=(200,700));\n", + "\n", + "\n", + "plt.legend(loc = 'upper right')\n", + "plt.xlabel('Mx (GeV)')\n", + "plt.savefig(PLOT_FOLDER+'h_Mx.pdf')\n", + "#plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "45252314", + "metadata": {}, + "outputs": [], + "source": [ + "var = { \"njets\" : [-0.5, 10.5, 11, \"nJets\"],\n", + " #\"tag_b1\",\n", + " #\"tag_b2\",\n", + " \"pTb1_o_m_bb\" : [0, 6, 30, r\"$p_{T b1}$ \\ $mass_{bb}$ (GeV)\"],\n", + " \"pTb2_o_m_bb\" : [0, 4, 30, r\"$p_{T b2}$ \\ $mass_{bb}$ (GeV)\"],\n", + " \"pTbb_o_m_HH\" : [0, 3, 30, r\"$p_{T bb}$ \\ $mass_{HH}$ (GeV)\"],\n", + " \"pTg1_o_m_gg\" : [0, 6, 30, r\"$p_{T g1}$ \\ $mass_{gg}$ (GeV)\"],\n", + " \"pTg2_o_m_gg\" : [0, 4, 30, r\"$p_{T g2}$ \\ $mass_{gg}$ (GeV)\"],\n", + " \"pTgg_o_m_HH\" : [0, 3, 30, r\"$p_{T gg}$ \\ $mass_{HH}$ (GeV)\"],\n", + " \"nLep\" : [-0.5, 10.5, 11, \"nLep\"],\n", + " \"pT_l1\" : [0, 300, 30, r\"$pT_{l1}$\"],\n", + " \"pT_l2\" : [0, 300, 30, r\"$pT_{l1}$\"],\n", + " \"sum_pt\" : [0, 500, 30, r\"sum $p_T$ (GeV)\"],\n", + " \"mindR_gb\" : [0, 5, 20, r\"min $\\Delta$ $R_{gb}$\"],\n", + " \"otherdR_gb\" : [0, 5, 20, r\"other $\\Delta$ $R_{gb}$\"],\n", + " \"GG_cosT_restHH\" : [0, 1, 20, r\"cosT rest frame HH\"],\n", + " \"B1_cosT_restBB\" : [0, 1, 20, r\"cosT rest frame bb\"],\n", + " \"G1_cosT_restGG\": [0, 1, 20, r\"cosT rest frame gg\"],\n", + " \"DeltaPhi_gg\" : [0, 4, 20, r\"$\\Delta \\phi (gg)$\"],\n", + " \"DeltaEta_gg\": [0, 4, 20, r\"$\\Delta \\eta (gg)$\"],\n", + " \"DeltaPhi_bb\": [0, 4, 20, r\"$\\Delta \\phi (bb)$\"],\n", + " \"DeltaEta_bb\": [0, 4, 20, r\"$\\Delta \\eta (bb)$\"],\n", + " \"DeltaPhi_HH\": [0, 4, 20, r\"$\\Delta \\phi (HH)$\"],\n", + " \"DeltaEta_HH\": [0, 4, 20, r\"$\\Delta \\eta (HH)$\"]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bb833063", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa',\n", + " 'pwp8_pp_hh_lambda240_5f_hhbbaa', 'pwp8_pp_hh_lambda300_5f_hhbbaa',\n", + " 'mgp8_pp_tth01j_5f_haa', 'mgp8_pp_h012j_5f_haa', 'mgp8_pp_jjaa_5f',\n", + " 'mgp8_pp_vh012j_5f_haa', 'mgp8_pp_vbf_h01j_5f_haa'], dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_.process.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e773d508", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "50491.312\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "26060.646\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "11185.368\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "11880.53\n", + "---\n", + "mgp8_pp_tth01j_5f_haa\n", + "298982.06\n", + "---\n", + "mgp8_pp_h012j_5f_haa\n", + "926454.9\n", + "---\n", + "mgp8_pp_jjaa_5f\n", + "11593152.0\n", + "---\n", + "mgp8_pp_vh012j_5f_haa\n", + "41548.125\n", + "---\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "160531.47\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_.process.unique():\n", + " print(proc)\n", + " print(df_.loc[df_.process==proc,\"weight\"].sum())\n", + " print(\"---\")\n", + "#df_.duplicated().unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e389862c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "1296776\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "1113154\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "1415969\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "1270241\n", + "---\n", + "mgp8_pp_tth01j_5f_haa\n", + "81047\n", + "---\n", + "mgp8_pp_h012j_5f_haa\n", + "6158\n", + "---\n", + "mgp8_pp_jjaa_5f\n", + "357694\n", + "---\n", + "mgp8_pp_vh012j_5f_haa\n", + "12344\n", + "---\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "6514\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_.process.unique():\n", + " print(proc)\n", + " print(len(df_.loc[df_.process==proc,\"weight\"]))\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "cfd44098", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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Ul5dzwgknhJqeN5Oohy4ih2jM0wwTueSSS1i/fj39+/cHoE2bNjz11FNs2bKFn//85zRr1oysrCwefjjxbRdOOOEEBgwYwJ49e5g5c+Yh684991ymTJnCsGHDeOONN+qcnjdTZWSgD/rzIMr31pr7K6GOx+em/LxaEUmdgoKCWjd+Hj9+/MEbOlc79dRTGTRo0GG3tXDhwoTLJ02adPDxoEGDDm4nOzs74fS8mSojA718bznvXPNOqLYKcxGpy6uvvlpret5MlpGBLiKSComm581k+lJURCJ5xkemO5JjokAXOcZlZ2eza9cuhXoT4u7s2rWL7Ozser1OQy4ix7j8/HzKysrYuXNnukuRONnZ2eTn59frNQp0kWNcVlYWXbt2TXcZkgIachERiQgFuohIRCjQRUQiQoEuIhIRSQPdzDqZ2QIzW29m68xsfII2JWa228xKg5/aU6CJiEiDCnOWSxVwm7uvNrMcYJWZveHu79Zot9jdh6e+RBERCSNpD93dd7j76uDxZ8B64JSGLkxEROqnXmPoZlYA9AaWJVjd38zeNrN5ZpZw/k0zG2dmK81spS5iEBFJrdCBbmZtgFnAze6+p8bq1UAXd+8FPADMTrQNd5/u7sXuXty+ffsjLFlERBIJFehmlkUszJ929xdrrnf3Pe5eGTyeC2SZWW5KKxURkcMKc5aLATOA9e7++zradAjaYWZ9g+3uSmWhIiJyeGHOchkI/BB4x8xKg2V3Ap0B3H0aMAq4wcyqgH3AaNfUbSIijSppoLv7EsCStJkKTE1VUSIiUn+6UlREJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCIiY29Bt2/tutRvtPyt1G9TRKSRqIcuIhIRCnQRkYjI2CGXhrBv0/tJWmQ3Sh0iIkdCgR7o2LwtfT+YdNg2Od3hqwPtgGGNUZKISL0o0AMv59+StE3317LJ6T6hEaoREak/jaGLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiEh6YZGZdQKeBDoAXwHT3f0PNdoY8AdgKPA5MMbdV6e+3CYizKyMeb0bvg4RkThhrhStAm5z99VmlgOsMrM33P3duDZDgG7BzznAw8G/IiLSSJIOubj7juretrt/BqwHTqnRbCTwpMcsBdqZWceUVysiInWq1xi6mRUAvYFlNVadAmyLe15G7dDHzMaZ2UozW7lz5856lioiIocTOtDNrA0wC7jZ3ffUXJ3gJV5rgft0dy929+L27dvXr1IRETmsUIFuZlnEwvxpd38xQZMyoFPc83yg/OjLExGRsJIGenAGywxgvbv/vo5mrwBXW0w/YLe770hhnSIikkSYs1wGAj8E3jGz0mDZnUBnAHefBswldsriFmKnLV6b8kpFROSwkga6uy8h8Rh5fBsHfpqqokREpP50paiISERE/xZ0n2xI2qT7a9noBtAikunUQxcRiYjo99DrYf3g/Unb9P2gEQoRETkC6qGLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiFCgi4hEhAJdRCQiFOgiIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRSQPdzGaa2cdmtraO9SVmttvMSoOfu1JfpoiIJBPmBhePA1OBJw/TZrG7D09JRSIickSS9tDdfRHwn41Qi4iIHIVUjaH3N7O3zWyemZ1RVyMzG2dmK81s5c6dO1P01iIiAqm5p+hqoIu7V5rZUGA20C1RQ3efDkwHKC4u9hS8d1rs2/R+0jYt83o3QiUiIl876h66u+9x98rg8Vwgy8xyj7oyERGpl6MOdDPrYGYWPO4bbHPX0W5XRETqJ+mQi5k9C5QAuWZWBtwNZAG4+zRgFHCDmVUB+4DR7p6xwykiIpkqaaC7+1VJ1k8ldlqjiIikka4UFRGJCAW6iEhEKNBFRCIiFeehSyLlb6W7AhE5xqiHLiISEQp0EZGIUKCLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiFCgi4hEhAJdRCQiFOgiIhGhQBcRiQgFuohIRCQNdDObaWYfm9naOtabmd1vZlvMbI2Z9Ul9mSIikkyYG1w8Tuwm0E/WsX4I0C34OQd4OPg3kjo2b0vfDyYlbZf3US6vf/v3DV+QiEggaaC7+yIzKzhMk5HAk+7uwFIza2dmHd19R6qKbEpezr8lVLswoS8ikkqpGEM/BdgW97wsWFaLmY0zs5VmtnLnzp0peGsREamWikC3BMs8UUN3n+7uxe5e3L59+xS8tYiIVEtFoJcBneKe5wPlKdiuiIjUQyoC/RXg6uBsl37A7qiOn4uINGVJvxQ1s2eBEiDXzMqAu4EsAHefBswFhgJbgM+BaxuqWBERqVuYs1yuSrLegZ+mrCIRETkiulJURCQiFOgiIhGhQBcRiQgFuohIRISZy6Vp+mRDuisQEWlS1EMXEYmIzO2hh9D9tex0lyAi0mgiHejptm/T+0la6A+OiKTOMRHo6wfvT3cJIiINTmPoIiIRcUz00Ju88reSt8nr3fB1iEhGUw9dRCQiFOgiIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRESoQDezwWa20cy2mNmEBOtLzGy3mZUGP3elvlQRETmcpFeKmllz4EHgYqAMWGFmr7j7uzWaLnb34Q1Qo4iIhBCmh94X2OLu77v7AeA5YGTDliUiIvUVJtBPAbbFPS8LltXU38zeNrN5ZnZGSqoTEZHQwkzOZQmWeY3nq4Eu7l5pZkOB2UC3WhsyGweMA+jcuXP9Ko2wgvvLQ7QqZ+vkYQ1ei4hkrjA99DKgU9zzfOCQBHL3Pe5eGTyeC2SZWW7NDbn7dHcvdvfi9u3bH0XZIiJSU5ge+gqgm5l1BbYDo4HvxTcwsw7AR+7uZtaX2B+KXakuNmrWD95Py9O/mbRduB68iBzrkga6u1eZ2c+A14HmwEx3X2dm1wfrpwGjgBvMrArYB4x295rDMiIi0oBC3eAiGEaZW2PZtLjHU4GpqS1NRETqQ3csaiAdm7el7weTkrbL+yiX17/9+4YvSEQiT4HeQF7OvyVUuzChLyIShgI9k+jeoyJyGJqcS0QkIhToIiIRoUAXEYkIBbqISERk3JeiBRNeJac7dH8tO92liIg0Keqhi4hERMb10KutH7w/3SWkzL5N7ydpoU8jIpKceugiIhGRsT10qUOYi4+q6SIkkUhRD11EJCIU6CIiEaEhlzQLMytjTnf46kA74P7GKElEMpQCPc3CzMrY/bVscrpPaIRqRCSTKdAzSNhb0W39t7xwGwz7Baq+PBXJCBpDFxGJCPXQM8D6wfvp+0Hyi6k0HYLIsU2BniHS+uWphmZEMkKoQDezwcAfgObAo+4+ucZ6C9YPBT4Hxrj76hTXekyrz5enYcfaoR7j7WEo+EXSKmmgm1lz4EHgYqAMWGFmr7j7u3HNhgDdgp9zgIeDf6WRfXWgXegzYr460I6C+8O1VfCLNH1heuh9gS3u/j6AmT0HjATiA30k8KS7O7DUzNqZWUd335HyiqVOsTH2m0O17f5aNq1PnRw6/Hu+ceR11fTVgXbsfS/M+4b/pCGSSbZOHtYg2w0T6KcA2+Kel1G7952ozSnAIYFuZuOAccHTSjPbWK9qv5bbilGfHOFrm5pc4Bjcl+ENWkgKROW4RGU/IEL7Yr85qn3pUteKMIFuCZb5EbTB3acD00O85+ELMlvp7sVHu52mQPvSNEVlX6KyH6B9CSPMeehlQKe45/nU/iwcpo2IiDSgMIG+AuhmZl3N7HhgNPBKjTavAFdbTD9gt8bPRUQaV9IhF3evMrOfAa8TO21xpruvM7Prg/XTgLnETlncQuy0xWsbrmQgBcM2TYj2pWmKyr5EZT9A+5KUxU5MERGRTKe5XEREIkKBLiISEU060M1ssJltNLMtZlbrSpTgS9j7g/VrzKxPOuoMI8S+lJjZbjMrDX7uSkedyZjZTDP72MzW1rE+k45Jsn3JlGPSycwWmNl6M1tnZuMTtMmI4xJyXzLluGSb2XIzezvYl3sStEntcXH3JvlD7AvY94BvAscDbwM9arQZCswjdh58P2BZuus+in0pAeaku9YQ+3Ie0AdYW8f6jDgmIfclU45JR6BP8DgH2JTB/6+E2ZdMOS4GtAkeZwHLgH4NeVyacg/94JQD7n4AqJ5yIN7BKQfcfSnQzsw6NnahIYTZl4zg7ouA/zxMk0w5JmH2JSO4+w4PJsNz98+A9cSu1I6XEccl5L5khOB3XRk8zQp+ap6FktLj0pQDva7pBOrbpikIW2f/4OPZPDM7o3FKS7lMOSZhZdQxMbMCoDex3mC8jDsuh9kXyJDjYmbNzawU+Bh4w90b9Lg05fnQUzblQBMQps7VQBd3rzSzocBsYrNXZppMOSZhZNQxMbM2wCzgZnffU3N1gpc02eOSZF8y5ri4+5dAkZm1A14ys0J3j//OJqXHpSn30KM05UDSOt19T/XHM3efC2SZWW7jlZgymXJMksqkY2JmWcQC8Gl3fzFBk4w5Lsn2JZOOSzV3rwAWAoNrrErpcWnKgR6lKQeS7ouZdTAzCx73JXZsdjV6pUcvU45JUplyTIIaZwDr3f33dTTLiOMSZl8y6Li0D3rmmFlL4CJgQ41mKT0uTXbIxZvmlANHJOS+jAJuMLMqYB8w2oOvwZsSM3uW2FkGuWZWBtxN7MuejDomEGpfMuKYAAOBHwLvBOO1AHcCnSHjjkuYfcmU49IReMJiNwlqBrzg7nMaMsN06b+ISEQ05SEXERGpBwW6iEhEKNBFRCJCgS4iEhEKdBGRiFCgi4hEhAJdRCQiFOiSUcxsdXBpeMYxs5Zm9vfgQhPM7L+Y2TNm9r6ZrTKzf5rZ5Um2sdDMBtVYdrOZPWRmx5vZIjNrshcMSsNSoEumeZPY1YSZaCzwort/GVy6PhtY5O7fdPeziE0JkZ9kG88G7eKNBp4Npmb+G3BlasuWTKFAlwZhZs+Z2fNmtszMPjCzYSna9DxqT3BU/Z7/x8ymmtmS4D3PNbMnzWyTmc2IazfKzJYG068uMbP2wfJrgp7yGjNbXNeyo/B94OXg8YXAgeDybwDc/QN3fyCuzh9Y7I43pWb2SNCz/zMw3MxaBG0KgDxgSfCy2cH7yDFIgS4NpRfwvrufQyxg7k7RdhcAF9SxrmfwnucCTxCb5OkOoBD4b9UhCCxw937u3gt4A/jvZpYTtO3v7mcClyZadqRFB5OyfdPdtwaLziA2DWxd7bsT62kPdPci4Evg++6+C1jO13/URgPPx81lshY4+0jrlMymQJeUC2aWywWq76H4LnCCmV1rZtPM7D+Cf39iZo8meH2tZdXc/XOgwszyarwmG2gH/O9g0T5gRnAHnAPEJj46EKwbE/R83wZuBPYTC8yWwL+bWXEw3WmiZZhZdS+7+lNB88MtD+QCFXXtl5k9GHxiWBEs+g5wFrAimKTqO8RuYQiHDruMDp5X/36+BA4Ef4zkGKNAl4ZQCGx29/3B8z7A2+7+GLGZ8/7m7tcDTwLfMrNJwRCNBX8MDlmWYPuvU3vY5Qxgtbt/FTzvRXCnGzPLB8rd3c3samK3BLww6KFvBNYFfygKgX8A083sxkTLzKwTED+9abNgTDzh8rjn+4DsuOfrgt8LAO7+U2Kh3T5YZMAT7l4U/PxXd58UrJsNfMdiNxRuWX3LtjgtiP2RkmOMAl0aQi+gs8Xuet6aWE/9fwXrzuLroYbewJ+DoNoNtK1jWU2JxtF7Erv5drUzgTVx9ayJa/dmcLebK4ABxKZq7ebue939OWAOkJ1oWVB/j+ATxhN8fTOCupYD4O6fAs2DTxIA/zd4jxvimrWKe/w3YJSZnQxgZieaWZdgW5XEbpYwk7jeedDuJGCnu3+R4PcmEadAl4bQC3iaWOisAB52938E684CVgWP+/J10LYKhjQSLTuEu68HTq8xpNETKIWDwy8tgxCFQ8P9CeDfgi84Tyc25r4X+B9mttHMVgNdgYfqWHYWcFvwCeOFuH2pa3m8+cC5wT44cBlwfjAEtTyo7Y5g/bvA/wTmm9kaYmP98TcPfpbY7/m5Gu9xAbE5tuUYpPnQJeXMbBHwY3ffmGDds8BYd99nZn8kdqeZdsBz7r4w0bI63mMa8Ke4PxSNwsxeAq509wNmdg+xTxPv1LW8xmt7A7e6+w8bsL4XgYmJfvcSfQp0STkz2w50ihvPloCZjSU2Nv5l0sb13/bxxO7e82Sqty2ZQYEuIhIRGkMXEYkIBbqISEQo0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCL+P+Xe9XROAxZNAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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yySdkZma2aj8NfYi0U71796ayspLq6upUl5JWPqz9EOv6VWz7fllNp+pD938zMzPp3bt3q46roBZppzIyMujXr1+qy0g733ngO6w5JbZ7ry9/82esn7Q+zhVp6ENEJPQU1CIiIRc4qM2ss5mtM7MliSxIREQO1poe9bXApkQVIiIiTQsU1GbWGxgP3JvYckREpKGgPep/A/4noM/SERFJshaD2swuBD529/IWtptmZmVmVqb7NkVE4idIj3oUcLGZbQMeBc4xs4cbbuTu89292N2Le/XqFecyRUQ6rhaD2t1nuXtvdy8Avgv8l7t/P+GViYgIoPuoRURCr1WPkLv7MmBZQioREZEmqUctIhJyCmoRkZBTUIuIhJyCWkQk5BTUIiIhp6AWEQk5BbWISMgpqEVEQk5BLSIScgpqEZGQU1CLiIScglpEJOQU1CIiIaegFhEJOQW1iEjIKahFREJOQS0iEnIKahGRkFNQi4iEnIJaRCTkFNQiIiGnoBYRCTkFtYhIyCmoRURCTkEtIhJyCmoRkZBTUIuIhFyLQW1mmWa2xsxeN7ONZnZLMgoTEZGILgG2+W/gHHevNbMMYIWZPevuqxJcm4iIECCo3d2B2ujLjOiXJ7IoERH5WqAxajPrbGYVwMfAUndf3cQ208yszMzKqqur41ymiEjHFSio3f0rdy8CegPDzaywiW3mu3uxuxf36tUrzmWKiHRcrbrrw91rgGXA+YkoRkREGgty10cvMzssutwdOBfYnOC6REQkKshdH0cDD5hZZyLB/ri7L0lsWSIisl+Quz7eAIYmoRYREWmCnkwUEQk5BbWISMgpqEVEQk5BLSIScgpqEZGQU1CLiIScglpEJOQU1CIiIaegFhEJOQW1iEjIKahFREJOQS0iEnIKahGRkFNQi4iEnIJaRCTkFNQiIiGnoBYRCTkFtYhIyCmoRURCLsiH24qIpJWxT4ylaldVTPse3TUvztW0nYJaRNqdql1VrJ+0PqZ9d2/YGOdq2k5DHyIiIaegFhEJOQW1iEjIKahFREJOQS0iEnIKahGRkGsxqM2sj5mVmtkmM9toZtcmozAREYkIch91HXC9u681sxyg3MyWuvubCa5NRCRmYbwfOlYt9qjdfYe7r40ufwFsAo5JdGEiIhLRqjFqMysAhgKrm1g3zczKzKysuro6TuWJiEjgoDazbGAx8DN3/7zhenef7+7F7l7cq1eveNYoItKhBQpqM8sgEtKL3P3PiS1JREQOFOSuDwMWAJvc/f8kviQRETlQkB71KOCfgXPMrCL6dUGC6xIRkagWb89z9xWAJaEWERFpgp5MFBEJOQW1iEjIKahFREJOQS0iEnIKahGRkFNQi4iEnIJaRCTkFNQiIiGnoBYRCbkgHxwgIpJ0Y58YS9Wuqpj2PbprXpyrSS0FtYiEUtWuKtZPWh/Tvu3p011AQx8iIqGnoBYRCTkFtYhIyGmMWkRCq72NNcdKPWoRkZBTUIuIhJyCWkQk5BTUIiIhp6AWEQm5UN31UTDz6VSXkDLb5oxPdQkiElKhCuqOrC2/pBTyInG0c3OqK2gklEHdkYInHn9FKOQljNoyqRK0v4mV2iKUQd2RtCUoUx3ybaVfEu1bWyZVgjY+7BLCXnFbKKjTWKpDXkSSQ0HdQaWyN7v/l0SqflmoJ5886hXHR4tBbWYLgQuBj929MPEliUi7obCNiyA96vuBecCDiS1FOopU9Wjj0ZNPy9541brY980fGr86JGYtPvDi7suBvyehFhERaULcxqjNbBowDeDYY4+N12FF4iYeF1/b1Bv/aX7M+6bK2MfOpmrPzpj2Pbpzbpyr6bjiFtTuPh+YD1BcXOzxOq5IXLVlGKCNCu6M/Z5iSE3QV+3ZyZq+s5N+XjlY+7rrI1X/E7Z1HC9VY4gpDK1005aQbGtAi4QzqNMtQFJZb7r9t+qA2toT3h/0bQn8dBx2ka+1eDHRzP4deBU4ycwqzWxq4ssSEZH9WuxRu/sVyShERJoWj2GXWHvjOf1jPrXEUTiHPkTkILvfejfGPTPJOm4OnbrWxLT3vi8Pi/G8Ek8KapEkiD1o22bT+XsY/l5NTHdu9H8uM/L9nbadX9pOn/AiIhJy6lGLpIEJlf+XHV99FtO+sT540pbecH1vPPo9VuqRRyioRdLAjq8+65APnrQl6NtTyCuopUNJ1VhxW3rEkH6PY7c1JNvaE29vFNSSEqkKzFTpqD3iWIVh2CUWibqdUUGd5lIVeN1P/FZKzivSEbWroE7X0ErH3mU61txWqbigJ62XqrHpRPbgQxnU6RYC6VavxEbDF5IqoQxqkUNp64W5WKlXLKmioJaUaOswgnq20pEoqCUm8bjdTGErEoyCugNTr1YkPYQuqLOOm8Pw92pi2vfozrn8R+/rYto3VeOeqaSwFUkPoQvqTl1jm+kLImE7/L3Y9lVoiUhYhS6o2yLW3rSISJhpmlMRkZBTUIuIhJyCWkQk5BTUIiIhp6AWEQk5BbWISMgpqEVEQk5BLSIScgpqEZGQU1CLiIRcoKA2s/PNbIuZvW1mMxNdlIiIfK3FoDazzsAfgHHAAOAKMxuQ6MJERCQiSI96OPC2u7/r7l8CjwITEluWiIjsF2T2vGOA7Qe8rgROa7iRmU0DpkVf1prZlhhryuvBxJ0x7puu8gC1uX3raO2FDtpmm2yxtrlvcyuCBLU18Z43esN9PjC/FUU1fTKzMncvbutx0ona3P51tPaC2hxPQYY+KoE+B7zuDVTFuxAREWlakKB+DTjBzPqZWVfgu8B/JrYsERHZr8WhD3evM7N/AZ4HOgML3X1jAmtq8/BJGlKb27+O1l5Qm+PG3BsNN4uISIjoyUQRkZBTUIuIhFxKgrqlR9It4s7o+jfMbFgq6oynAG2+MtrWN8xspZkNSUWd8RR06gEzO9XMvjKzicmsLxGCtNnMSsyswsw2mtlfk11jvAX4t51rZk+Z2evRNl+dijrjxcwWmtnHZrahmfXxzy93T+oXkQuS7wDfAroCrwMDGmxzAfAskXu4RwCrk11nCto8Ejg8ujyuI7T5gO3+C3gGmJjqupPwcz4MeBM4Nvr6yFTXnYQ23wT8NrrcC/g70DXVtbehzaOBYcCGZtbHPb9S0aMO8kj6BOBBj1gFHGZmRye70Dhqsc3uvtLdP42+XEXkfvV0FnTqgWuAxcDHySwuQYK0+XvAn939fQB3T/d2B2mzAzlmZkA2kaCuS26Z8ePuy4m0oTlxz69UBHVTj6QfE8M26aS17ZlK5DdyOmuxzWZ2DHApcHcS60qkID/nE4HDzWyZmZWb2VVJqy4xgrR5HtCfyINy64Fr3X1fcspLibjnV5BHyOMtyCPpgR5bTyOB22NmZxMJ6jMSWlHiBWnzvwE3uvtXkc5W2gvS5i7AKcA/Ad2BV81slbu/lejiEiRIm8cCFcA5wHHAUjN72d0/T3BtqRL3/EpFUAd5JL29PbYeqD1mNhi4Fxjn7p8kqbZECdLmYuDRaEjnAReYWZ27P5mUCuMv6L/tne6+C9hlZsuBIUC6BnWQNl8NzPHIAO7bZvY34GRgTXJKTLq451cqhj6CPJL+n8BV0aunI4DP3H1HsguNoxbbbGbHAn8G/jmNe1cHarHN7t7P3QvcvQB4ApiexiENwf5t/wdwppl1MbMeRGai3JTkOuMpSJvfJ/IXBGb2TeAk4N2kVplccc+vpPeovZlH0s3sx9H1dxO5A+AC4G3gH0R+I6etgG2+GegJ3BXtYdZ5Gs88FrDN7UqQNrv7JjN7DngD2Afc6+5N3uaVDgL+nG8F7jez9USGBW5097Sd/tTM/h0oAfLMrBL4JZABicsvPUIuIhJyejJRRCTkFNQiIiGnoBYRCTkFtYhIyCmoRURCTkEtSWVmY6Mzx1WYWW101rUKM3swzue56RDrvm1mm8ysNJ7nDMrMChrOvGZms81sRnT5/oYzCZpZbTJrlHBRUEtSufvz7l7k7kVAGXBl9HWzc16YWecYTtVsUBN5RH+6u5/d4DypeFJXpEUKamk1M7sqOs/u62b2UPS9vmb2UvT9l6JPWu7vvW6Ibru8FecoMbNSM3sEWG9mnc3sdjN7LXqOH0W3O9rMlkd75RvM7EwzmwN0j763qMFxbyYyj8rd0eNNNrP/Z2ZPAS+YWXa0/rVmtt7MJkT3KzCzzWZ2b/Q8i8zsXDN7xcy2mtnw6HZZFpmv+DUzW7d/f5E2SfXcrvpKry9gILAFyIu+PiL6/SlgUnR5CvBkdHk9cEx0+bAGx1oGFDdznhJgF9Av+noa8L+jy92I9Mb7AdcD/yv6fmcgJ7pce4g21J8XmExkbob97egCfCO6nEfk6TIDCohMzTmISAenHFgYXTfhgPb+K/D9/e0lModHVoPzFwC7iUxUtP/rQ2BGdP39wN8arG+2Pfpq/1/6U09a6xzgCY8+Auzu++flPR34H9Hlh4DfRZdfIfL48ONE5jJpjTXu/rfo8hhg8AFjt7nACUTmmlhoZhlEwrKilecAWHpAOwz4VzMbTeQR72OAb0bX/c3d1wOY2UbgJXf36KPRBQfUefH+8WYgEziWxvN5vOOR4R+ix5vdYP0N7v7EAes1Rt2BKailtYxgUzY6gLv/2MxOA8YDFWZW5MFnBtzV4LzXuPvzjQqKhOp44CEzu93dW3th8sDzXEnkU0hOcfe9ZraNSNgC/PcB2+074PU+vv5/yYDL3H1LK2sQaZbGqKW1XgK+Y2Y9AczsiOj7K4nMnAaRsFsRXX+cu69295uBnRw8/WNrPA/8JNpzxsxOjI4H9wU+dvc/AQuIfEQSwN7927ZSbvR4ey0yN3jfGOq8xqIza5nZ0BhqEDmIetTSKh6ZGe024K9m9hWwjsg470+JDEHcAFTz9Yxht5vZCUR6mi8R+Uy9WNxLZHhhbTQEq4FLiIxl32Bme4FaYP/dI/OBN8xsrbtf2YrzLAKeMrMyImPDm1tZ561EPhDhjWid24ALW3kMkYN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\n", 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\n", 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\n", 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079+frVu3smPHDvr06cMbb7zBQw89xOrVq6vmdYk1duxYAC6//HI+//xzysvLAfjd737HsmXLWLhwIW3atKG8vDzQ9LzNSZAhlwHATnffBWBm84ERwOaoNjcCL7v7RwDu/kmyCxWR8MrKyqp67O5MmTKFO+64o1q79evXU1hYyJQpUxg8eDCPPPJItTZmFnf5/PPPp7i4mNLSUnr06BF4et7mJMiQSxdgT9RyaWRdtHOAU81spZmtN7Nx8Q5kZhPMrMjMivbv359YxSISakOGDGHu3LkcOnQIgL179/LJJ59QVlZGu3bt+MEPfsADDzzAhg0b4u7/0ksvAZU3zejQoUNVT75fv348/fTTXHfddZSVlXHqqadWTc8L1Dg9b3MSpIducdbFvrW1BC4C/gVoC6w1s7fcffsJO7nPBmYD5Ofnh+/tsREl+1IykZo05WWG8QwePJgtW7bw7W9/G4D27dvz/PPPs3PnTh588EFatGhBq1at+M1vfhN3/1NPPZVBgwZVfSga7bLLLmP69Olcc801vP766zVOz9tcBQn0UqBr1HIuUBanzQF3PwwcNrNVwIXAdqRKgy553NeJV/rMSG5BImkgLy+v2pUl99xzT9UNnY8766yzGDJkSK3HWrlyZdz1U6dOrXo8ZMiQquNkZmbGnZ63uQoS6OuAnmbWA9gLjKFyzDzaK8AMM2sJtAYuAf5PMgsNA13yKJJeli5dWm163uaszkB39wozmwQsBzKAue6+ycwmRrbPcvctZvYq8B7wNTDH3ZvvxZwiclKINz1vcxboi0XuXggUxqybFbP8S+CXyStNRJqKu1e7OkRSK5GrcPRN0ebkQKJ3Kc9MahkSLpmZmRw8eJCOHTsq1NOEu3Pw4EEyM+v3b1eBLnKSy83NpbS0FF1KnF4yMzPJzc2t1z4KdJGTXKtWrejRo0eqy5Ak0JRmIiIhoUAXEQkJDbk0Ew35UlJ2L/j62CnANcksSUTSjAK9mWjIl5J6vZpJdq+4sx6LSIhoyEVEJCQU6CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiERKNDNbKiZbTOznWZW7TvkZlZgZp+ZWXHk55HklyoiIrWpcy4XM8sAZgJXAaXAOjNb7O6bY5qudvfhjVCjJEne5KUJ77t7mib2Ekl3QSbnGgDsdPddAGY2HxgBxAa6pLGvj52S8ARdmqlRpHkIEuhdgD1Ry6XAJXHafdvM3gXKgAfcfVNsAzObAEwA6NatW/2rlYRsGXqUtgWrE9o3b/JSzdQo0kwEGUOPd9fY2NtRbwC6u/uFwJPAongHcvfZ7p7v7vmdO3euV6EiIlK7ID30UqBr1HIulb3wKu7+edTjQjN7ysw6ufuB5JQpDXWkpNofTCISMkF66OuAnmbWw8xaA2OAxdENzOwMM7PI4wGR4x5MdrEiIlKzOnvo7l5hZpOA5UAGMNfdN5nZxMj2WcAo4E4zqwCOAGPcPXZYRkREGlGgW9C5eyFQGLNuVtTjGcCM5JYmIiL1oW+KioiEhAJdRCQkAg25SAgc2JrgjplJLUNEGo966CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhK5Dlzp9fewU+jzXJ6F9c7JyWD5qeZIrEpF4FOhSp8MfTE74FnSJvhGISP1pyEVEJCQU6CIiIaFAFxEJCQW6iEhIKNBFREIiUKCb2VAz22ZmO81sci3tLjazr8xsVPJKFBGRIOoMdDPLAGYCw4DewFgz611DuyeovPeoiIg0sSA99AHATnff5e7HgPnAiDjt7gYWAp8ksT4REQkoSKB3AfZELZdG1lUxsy7ASGAWtTCzCWZWZGZF+/fvr2+tIiJSiyCBbnHWeczyvwMPuftXtR3I3We7e76753fu3DlgiSIiEkSQr/6XAl2jlnOBspg2+cB8MwPoBFxtZhXuvigZRYqISN2CBPo6oKeZ9QD2AmOAG6MbuHuP44/NbB6wRGEuItK06gx0d68ws0lUXr2SAcx1901mNjGyvdZxcxERaRqBZlt090KgMGZd3CB39/ENL0tEROpL3xQVEQkJBbqISEjoBhcSyJGSTakuQUTqoECXYA5sTXUFIlIHDbmIiISEAl1EJCQU6CIiIaExdAmk16uZCe2XddYp9HmuT0L75mTlsHyUZmMWCUqBLo3q8AeT2T3tmoT2TfSNQORkpUCXWm0ZejThfRPt1YtIYjSGLiISEgp0EZGQUKCLiISEAl1EJCQU6CIiIaFAFxEJCQW6iEhIBAp0MxtqZtvMbKeZTY6zfYSZvWdmxWZWZGaXJb9UERGpTZ1fLDKzDGAmcBVQCqwzs8Xuvjmq2R+Bxe7uZnYBsAA4tzEKFhGR+IL00AcAO919l7sfA+YDI6IbuPshd/fIYhbgiIhIkwoS6F2APVHLpZF1JzCzkWa2FVgK3BrvQGY2ITIkU7R///5E6hURkRoECXSLs65aD9zdf+/u5wLXA4/FO5C7z3b3fHfP79y5c70KFRGR2gUJ9FKga9RyLlBWU2N3XwWcZWadGlibiIjUQ5BAXwf0NLMeZtYaGAMsjm5gZmebmUUe9wdaAweTXayIiNSszqtc3L3CzCYBy4EMYK67bzKziZHts4AbgHFm9iVwBBgd9SGpiIg0gUDzobt7IVAYs25W1OMngCeSW5qIiNSHvikqIhISCnQRkZDQLegkbeVk5egG0yL1oECXtNWQQNYNpuVkpECXxle2MbH9cvoltw6RkFOgS6M7sn1XQvu1VaCL1Is+FBURCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhIQCXUQkJBToIiIhoUAXEQkJBbqISEgo0EVEQiLQXC5mNhT4FZW3oJvj7tNitt8EPBRZPATc6e7vJrNQab56vZqZ2I6vLmX3tGuSW4xIiNXZQzezDGAmMAzoDYw1s94xzf4CXOHuFwCPAbOTXaiIiNQuSA99ALDT3XcBmNl8YASw+XgDd38zqv1bQG4yi5TmacvQownvm3CvXuQkFmQMvQuwJ2q5NLKuJrcByxpSlIiI1F+QHrrFWedxG5pdSWWgX1bD9gnABIBu3boFLFFERIII0kMvBbpGLecCZbGNzOwCYA4wwt0PxjuQu89293x3z+/cuXMi9YqISA2CBPo6oKeZ9TCz1sAYYHF0AzPrBrwM/Ku7b09+mSIiUpc6h1zcvcLMJgHLqbxsca67bzKziZHts4BHgI7AU2YGUOHu+Y1XtkjtcrJyGnSj6JysnAbdpFokFQJdh+7uhUBhzLpZUY9/CPwwuaWJwJGSTQnt19AwbsibgUiq6JuiIiIhEaiHLpIyB7YmuON5SS1DpDlQD11EJCQU6CIiIaFAFxEJCQW6iEhIKNBFREJCgS4iEhIKdBGRkFCgi4iEhAJdRCQkFOgiIiGhQBcRCQkFuohISCjQRURCQoEuIhISmj5X0lqvVzMT2/HVpeyedk1yixFJcwp0Ca1E73Yk0lwFCnQzGwr8isp7is5x92kx288FngX6A//T3acnu1A5uWwZejThfat69QnfHKNh9yTV/UglVeoMdDPLAGYCVwGlwDozW+zum6Oa/Q34EXB9YxQp0tQaEsi6H6mkSpAPRQcAO919l7sfA+YDI6IbuPsn7r4O+LIRahQRkQCCBHoXYE/UcmlkXb2Z2QQzKzKzov379ydyCBERqUGQQLc46zyRJ3P32e6e7+75nTt3TuQQIiJSgyCBXgp0jVrOBcoapxwREUlUkEBfB/Q0sx5m1hoYAyxu3LJERKS+6rzKxd0rzGwSsJzKyxbnuvsmM5sY2T7LzM4AioBvAV+b2Y+B3u7+eeOVLiIi0QJdh+7uhUBhzLpZUY//SuVQjIiIpIjmchERCQkFuohISCjQRURCQpNzicRxZOXCVJcgUm8KdAmthKfejWjIBGEiqaAhFxGRkFAPXUKnoT3rhvbsNfWupIoCXSTJNPWupIqGXEREQkKBLiISEgp0EZGQ0Bi6SJLpGnZJFQW6SA0acrWLrmGXVNCQi4hISKiHLhKjIb3rhl7DLtIQCnSRNKIvJUlDKNBFGkHiPfUfJfwXwoAPpyb4nBIWgcbQzWyomW0zs51mNjnOdjOzX0e2v2dm/ZNfqoiI1KbOHrqZZQAzgauAUmCdmS12981RzYYBPSM/lwC/ifxX5KSSjPH3RHv32b0gb/LShJ9/97RrEt5X0kOQIZcBwE533wVgZvOBEUB0oI8A/sPdHXjLzE4xszPdfV/SKxaRuL4+dgrZvar9AR1437zEdpUENNabZ5BA7wLsiVoupXrvO16bLsAJgW5mE4AJkcVDZratXtV+o1M7Rh1IcN/G1AlIx7ogfWtTXfXTiHUNb8jOJ+H5Spw90aC6ute0IUigW5x1nkAb3H02MDvAc9ZekFmRu+c39DjJlq51QfrWprrqR3XVz8lWV5APRUuBrlHLuUBZAm1ERKQRBQn0dUBPM+thZq2BMcDimDaLgXGRq10GAp9p/FxEpGnVOeTi7hVmNglYDmQAc919k5lNjGyfBRQCVwM7gb8DtzReyUAShm0aSbrWBelbm+qqH9VVPydVXVZ5YYqIiDR3mpxLRCQkFOgiIiGR1oGerlMOBKirwMw+M7PiyM8jTVTXXDP7xMxKatieqvNVV11Nfr7MrKuZrTCzLWa2yczuidOmyc9XwLpScb4yzewdM3s3Utejcdqk4nwFqSsl/x4jz51hZhvNbEmcbck/X+6elj9UfgD7AfBPQGvgXaB3TJurgWVUXgc/EHg7TeoqAJak4JxdDvQHSmrY3uTnK2BdTX6+gDOB/pHH2cD2NPn9ClJXKs6XAe0jj1sBbwMD0+B8BakrJf8eI899H/BivOdvjPOVzj30qikH3P0YcHzKgWhVUw64+1vAKWZ2ZhrUlRLuvgr4Wy1NUnG+gtTV5Nx9n7tviDz+AthC5bebozX5+QpYV5OLnINDkcVWkZ/YKypScb6C1JUSZpYLXAPMqaFJ0s9XOgd6TdMJ1LdNKuoC+Hbkz8BlZnZeI9cUVCrOV1ApO19mlgf0o7J3Fy2l56uWuiAF5ysyfFAMfAK87u5pcb4C1AWp+f36d+B/AF/XsD3p5yudAz1pUw4kWZDn3AB0d/cLgSeBRY1cU1CpOF9BpOx8mVl7YCHwY3f/PHZznF2a5HzVUVdKzpe7f+Xufan8JvgAMzs/pklKzleAupr8fJnZcOATd19fW7M46xp0vtI50NN1yoE6n9PdPz/+Z6C7FwKtzKxTI9cVRFpO0ZCq82VmragMzRfc/eU4TVJyvuqqK9W/X+5eDqwEhsZsSunvV011peh8XQpcZ2a7qRyW/Y6ZPR/TJunnK50DPV2nHKizLjM7w8ws8ngAlef5YCPXFURaTtGQivMVeb5ngC3u/r9raNbk5ytIXSk6X53N7JTI47bAd4GtMc1Scb7qrCsV58vdp7h7rrvnUZkR/8/dfxDTLOnnK21vQefpOeVA0LpGAXeaWQVwBBjjkY+1G5OZ/SeVn+h3MrNS4H9R+SFRys5XwLpScb4uBf4VeD8y/grwMNAtqq5UnK8gdaXifJ0JPGeVN7xpASxw9yWp/vcYsK6U/HuMp7HPl776LyISEuk85CIiIvWgQBcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhIQCXULLzEaamZvZuQHbtzWzP0W+pFLf52ptZqvMLG2/rCfhp0CXMBsLFFH51esgbgVedvev6vtEkamU/wiMru++IsmiQJdQisxWeAVwG5XBfnz9Q2Y228wWmdlfzOyBqN1uAl6Jatsr0ut+z8weNLOdta2ncha/mxr7tYnURIEuYXU98Ia7vwcctm9u79UHaAPcAAwGxkHlkAnwT+6+O7LcEngBuMfdL6DyDlUlNa2PHLsEuLjxX5pIfBrvk7AaC8yOPF4QWd4AXACMdPevzOwrvrmTUiegPGr//wa86+4bI8ubqbyBQk3riRzzmJllR+42JNKk1EOX0DGzjlTeKvDVyKqXgNGRecY7ufsHkfUXAO9HHh8BMqMOcwFQHLV8fmS5pvXHtQGONvAliCREgS5hNAoodPd/ALj7X4C/Unkj3i1R7fpSeZNv3P1TIMPMjof6QeAcADPrC/wg0ram9cffSPa7+5eN9spEaqEhFwmjscAFkbvFHNcR+A9OvP1YX2Bp1PJrwGXAG8DvgKVmtg5YC+x2911mFnd9ZP8rqZzjWiQlFOgSOu5eELDp+XzzgSbADOA+KgP9qLtfAmBmDwK/j7SpaT3AjcCUxCsXaRgNuchJKXLbsmPufuT4usgHnSsiXyy618w2Re4alAc8FmkWd33kKplF7r6tqV6DSCzdsUhEJCTUQxcRCQkFuohISCjQRURCQoEuIhISCnQRkZBQoIuIhIQCXUQkJP4/AcnxhxHENhwAAAAASUVORK5CYII=\n", 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\n", 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\n", 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Xwz35IUOGcO2113LRRRexfPlyiouLDy/PO2LEiAaX521NlNBF5AgtOc0wkVGjRrFp0yZOP/10AAoKCnj88cfZtm0bt912G+3atSM/P58//OEPCY//1re+xciRI/nss89YsGDBEWVnnnkms2fP5oILLuDll19ucHne1sqy9bWjtLTUKyoqmn3cobsmq37a9BrLYdHrgcgT/7SWi2TCpk2bEj7hpy2ora2loKAAiCzPu3v3bu6///4sR/Ufif5tzGytu5cmqq8euoi0WS+88EK95XlbMyV0EWmzEi3P25pp2qKIhHLGR2uXzL+JErpIG9exY0f27dunpJ5D3J19+/bRsWPHZh0XaMjFzEYD9wN5wHx3nxVXXgY8C/w9uusZd/9lsyIRkawoKSmhurqaPXv2ZDsUidGxY0dKSkqadUyTCd3M8oAHgXOBamCNmT3n7hvjqr7m7hc2691FJOvy8/Pp3bt3tsOQNAgy5DIc2Obu2939C2ARMC6zYYmISHMFSejdgR0x29XRffFON7N3zGyZmSW8M8HMpppZhZlV6OudiEh6BUnolmBf/NWTt4Ge7j4I+D2wJNGJ3H2eu5e6e2m3bt2aFaiIiDQuyEXRaqBHzHYJsCu2grt/FvO63MweMrMid9+bnjAFUn+2qO40FQm3ID30NUAfM+ttZu2BicBzsRXM7DiLLmlmZsOj592X7mBFRKRhTfbQ3b3OzK4HlhOZtrjA3TeY2bRo+VxgPHCtmdUBB4CJnuFJrQfe3570sZ1O+nYaI8m8qp8WQ/GQpI9PtWcvIq1DoHno7l4OlMftmxvzeg4wJ72hiYhIc+hOURGRkFBCFxEJCSV0EZGQ0PK5rYUeXyciTVAPXUQkJJTQRURCok0OubSlOewi0naohy4iEhJK6CIiIaGELiISEq1yDL3Ld2Yx/MOapI8/Pq8rz5bclL6ARERyQKtM6O3a17C658ykjx/+YfLHiojkqlaZ0FN1fF7XpJN68cdFLP/ub9MbkIhIGrTJhJ7KcMvwD2cmPe1RUx5FJJN0UVREJCSU0EVEQqJNDrmkQuPvIpKrlNCbKdXxdxGRTNGQi4hISKiH3oak8rDoqlkXpDESEckE9dBFREIiUA/dzEYD9wN5wHx3n9VAvWHAm8AEd//vtEUZItmYw1710+Kkj+31wK6kjxWRltVkD93M8oAHgTFAP2CSmfVroN69wPJ0BykiIk0L0kMfDmxz9+0AZrYIGAdsjKt3A7AYGJbWCENEUx5FJJOCJPTuwI6Y7WrgtNgKZtYduAT4Ho0kdDObCkwFOOGEE5oba6unKY8ikklBLopagn0et/074HZ3/6qxE7n7PHcvdffSbt26BQxRRESCCNJDrwZ6xGyXAPFXykqBRWYGUAScb2Z17r4kHUGKiEjTgiT0NUAfM+sN7AQmApfFVnD33odem9lCYKmSefrp4dYi0pgmE7q715nZ9URmr+QBC9x9g5lNi5bPzXCMIiISQKB56O5eDpTH7UuYyN19SuphiYhIc+lOURGRkNBaLq1EKnPYQfPYRdoCJfRWIpU57KB57CJtgYZcRERCQgldRCQklNBFREJCCV1EJCSU0EVEQkKzXNqI4o5FDHh5crOPK+wLX3/xTUCPoBPJdUrobUSyc9B7PbCLwr7TYde65N64eEhyx4lIsymhSyDJP4pulx4wLdJClNDbiORXauyY1jhEJHOU0KVRm0YfZPiHyT1oWg+YFmlZmuUiIhISSugiIiGhhC4iEhIaQ5dAkruoqguqIi1JCV2alOxa7LopSaRlKaFLk5Jdi73vix0jNyWJSIvQGLqISEgESuhmNtrMtpjZNjOr1+Uys3Fm9q6ZVZpZhZmdmf5QRUSkMU0OuZhZHvAgcC5QDawxs+fcfWNMtf8BnnN3N7OBwNPAyZkIWEREEgvSQx8ObHP37e7+BbAIGBdbwd1r3d2jm10AR0REWlSQhN4d2BGzXR3ddwQzu8TMNgMvAFcnOpGZTY0OyVTs2bMnmXhFRKQBQRK6JdhXrwfu7n9x95OBi4G7E53I3ee5e6m7l3br1q1ZgYqISOOCTFusBnrEbJcADa665O4rzew7Zlbk7ntTDVBCQGupi7SIIAl9DdDHzHoDO4GJwGWxFczsROCD6EXRoUB7YF+6g5XW5+svvpnUk5IAirsUs3z88jRHJBJeTSZ0d68zs+uB5UAesMDdN5jZtGj5XOBSYLKZfQkcACbEXCSVNmz/B9OTWnoXSPoPgUhbFehOUXcvB8rj9s2NeX0vcG96QxMRkebQnaIiIiGhtVwk45J9clFh3zQHIhJy6qGLiISEeuiSMZtGH0z62L4vdozMkHlsQFLHa4aMtEVK6JKz9n8wnapZya2lnuwfApHWTEMuIiIhoYQuIhISGnKRnHZg/YZshyDSaqiHLiISEkroIiIhoYQuIhISSugiIiGhi6ISSsVdilOai64bk6Q1UkKX3LZ3c1KHpZqMdWOStEYachERCQkldBGRkNCQi4SSbkiStkg9dBGRkFAPXXJa3xc7JnlkFZuu6JX0+6YyS0YzZCRblNBFEkglIWuGjGRLoIRuZqOB+4E8YL67z4orvxy4PbpZC1zr7u+kM1BpW1J9OIZIW9TkGLqZ5QEPAmOAfsAkM+sXV+3vwNnuPhC4G5iX7kBFRKRxQXrow4Ft7r4dwMwWAeOAjYcquPvrMfXfBErSGaRIS9MsGWmNgsxy6Q7siNmuju5ryDXAskQFZjbVzCrMrGLPnj3BoxQRkSYF6aFbgn2esKLZOUQS+pmJyt19HtHhmNLS0oTnEEmbJJcNAKDo5KQP1QwZyZYgCb0a6BGzXQLsiq9kZgOB+cAYd9+XnvBEWh/NkJFsCTLksgboY2a9zaw9MBF4LraCmZ0APAP80N3fT3+YIiLSlCZ76O5eZ2bXA8uJTFtc4O4bzGxatHwucBdwDPCQmQHUuXtp5sIWEZF4geahu3s5UB63b27M6x8BP0pvaCIi0hy6U1QkzTTlUbJFi3OJiISEeugiOURTHiUVSugSWqmt6ZLaao3J0pRHSYUSukgO0fi7pEIJXUInlZUaQas1Suuli6IiIiGhHrpIQ5JdCyaFdWBSoQuqooQuEhJLTv5t0scOXzsxjZFItmjIRUQkJJTQRURCQkMuIsLx7YtSmseuMfjcoIQuIjw7YE5Kx2sMPjdoyEVEJCTUQxdpQPI3GGVn2YBsSmXIRsM16aOELpIJrWwOe6pSGbLRcE36KKGLxEll6QAtG5CcZNew6dT/lDRH0ropoYtIVh3fvijpXnrxZg3XxFJCF5Gs0nBN+miWi4hISATqoZvZaOB+IA+Y7+6z4spPBh4FhgL/x91npztQEZFENP7+H00mdDPLAx4EzgWqgTVm9py7b4yp9k/gp8DFmQhSRCQRjb8fKUgPfTiwzd23A5jZImAccDihu/snwCdmdkFGohQRSUDj70cKktC7AztitquB0zITjkg4pHRTUrLTJlvpHPZsSWX9+EPH51oPP0hCtwT7PJk3M7OpwFSAE044IZlTiIikRSrrx0Nu9vCDJPRqoEfMdgmwK5k3c/d5wDyA0tLSpP4oiOQy3ZTUduTiE6KCJPQ1QB8z6w3sBCYCl6U9EhGRViSVhJzKUE9jmkzo7l5nZtcDy4lMW1zg7hvMbFq0fK6ZHQdUAN8AvjaznwH93P2zjEQtIiL1BJqH7u7lQHncvrkxr/9BZChGRKRNSHb+eybpTlERkZDQWi4iOUbrsEuylNBFwqSNrcMuR1JCF8kRmvIoqdIYuohISCihi4iEhIZcREIkK2vIgMbgc4R66CIiIaEeukgI6IKqgHroIiKhoR66iACp9tS1jnsuUA9dRCQk1EMXaeNSmt2CxuBziRK6iKSF1qDJPiV0Eck+rUGTFkroIpKSdEyZ1EO100MXRUVEQkI9dBHJmlbbu4ec7OEroYtIm5Xy3Pscu5irhC4irVIuLHfQ9/GqpI4r7JuWt69HCV1E2pywzr0PlNDNbDRwP5AHzHf3WXHlFi0/H/g3MMXd305zrCIiOSEXvh0k0uQsFzPLAx4ExgD9gElm1i+u2higT/RnKvCHNMcpIiJNCDJtcTiwzd23u/sXwCJgXFydccCfPOJN4JtmdnyaYxURkUYEGXLpDuyI2a4GTgtQpzuwO7aSmU0l0oMHqDWzLc2K9j+KOjN+b5LHZlIRkItxQe7GpriaR3E1T87GZVMs2bh6NlQQJKFbgn2eRB3cfR4wL8B7Nh6QWYW7l6Z6nnTL1bggd2NTXM2juJqnrcUVZMilGugRs10C7EqijoiIZFCQhL4G6GNmvc2sPTAReC6uznPAZIsYAfzL3XfHn0hERDKnySEXd68zs+uB5USmLS5w9w1mNi1aPhcoJzJlcRuRaYtXZS5kIA3DNhmSq3FB7samuJpHcTVPm4rL3OsNdYuISCuk1RZFREJCCV1EJCRyOqGb2Wgz22Jm28xseoJyM7MHouXvmtnQHImrzMz+ZWaV0Z+7WiiuBWb2iZmtb6A8W+3VVFwt3l5m1sPMVpjZJjPbYGY3JqjT4u0VMK5stFdHM1ttZu9E4/pFgjrZaK8gcWXl/2P0vfPMbJ2ZLU1Qlv72cvec/CFyAfYD4NtAe+AdoF9cnfOBZUTmwY8A3sqRuMqApVlos7OAocD6BspbvL0CxtXi7QUcDwyNvi4E3s+R368gcWWjvQwoiL7OB94CRuRAewWJKyv/H6PvfTPwZKL3z0R75XIPPVeXHAgSV1a4+0rgn41UycoSDQHianHuvtujC8i5++fAJiJ3N8dq8fYKGFeLi7ZBbXQzP/oTP6MiG+0VJK6sMLMS4AJgfgNV0t5euZzQG1pOoLl1shEXwOnRr4HLzOyUDMcUVDbaK6istZeZ9QKGEOndxcpqezUSF2ShvaLDB5XAJ8DL7p4T7RUgLsjO79fvgP8NfN1AedrbK5cTetqWHEizIO/5NtDT3QcBvweWZDimoLLRXkFkrb3MrABYDPzM3T+LL05wSIu0VxNxZaW93P0rdx9M5E7w4WbWP65KVtorQFwt3l5mdiHwibuvbaxagn0ptVcuJ/RcXXKgyfd0988OfQ1093Ig38yKMhxXEDm5REO22svM8okkzSfc/ZkEVbLSXk3Fle3fL3evAV4FRscVZfX3q6G4stReZwAXmVkVkWHZ75nZ43F10t5euZzQc3XJgSbjMrPjzMyir4cTaed9GY4riJxcoiEb7RV9v0eATe7+2waqtXh7BYkrS+3Vzcy+GX3dCfg+sDmuWjbaq8m4stFe7j7D3UvcvReRHPF/3f2KuGppb6+cfQSd5+aSA0HjGg9ca2Z1wAFgokcva2eSmf0XkSv6RWZWDfycyEWirLVXwLiy0V5nAD8E3ouOvwLcAZwQE1c22itIXNlor+OBxyzywJt2wNPuvjTb/x8DxpWV/4+JZLq9dOu/iEhI5PKQi4iINIMSuohISCihi4iEhBK6iEhIKKGLiISEErqISEgooYuIhIQSuoSSmV1iZm5mJwes38nM/hq9QQUz+4mZPRRXZ4OZndxEWXszW2lmOXvTnoSXErqE1SSggsht10FcDTzj7l9FtwcC6w4VmllHIndrbm2sLLqk8v8AE1L9ACLNpYQuoRNdqfBs4Boiif3Q/tvNbJ6ZLTGzv5vZrTGHXQ48G7M9gMgqfbHb70cTfmNlEFnN7/J0fR6RoPS1UMLoYuAVd3/XzPab2dDoQyMGAF8BlxJ54tRiYHZ0kbVvu3tVzDlOAZ4xs0NrYxQASwOUAawHhqX/Y4k0TgldwmgSMC/6+uno9ttEhkoucfevzOwr/vMUpSKg5tDBZtYD2OPuJ8fsmwNsb6zs0Hb0/F+YWWH0qUMiLUJDLhIqZnYMkccEvhjd9RQwIbrGeJG7fxDdPxB4L/r6ANAx5jQDgQ1xp+4Xrd9YWawOwMEkP4ZIUpTQJWzGA+Xu/v8A3P3vwD+IPIR3U0y9wUQe8I27fwrkRS9uQmRoZmPceU8B3m2iDDj8R2WPu3+Zhs8jEpgSuoTNJGCsmVUd+gH6An8iJukSk9CjXgLOjL4+Immb2dFElpr+uImyQ84hsta1SIvSeujSJpnZNmCAux+Ibg8Bbnb3H6bh3M8AM9x9S6rnEmkO9dClzYk+suyLQ8kcwN3XASsO3ViUwrnbA0uUzCUb1EMXEQkJ9dBFREJCCV1EJCSU0EVEQkIJXUQkJJTQRURCQgldRCQklNBFRELi/wNvIPsqv5zkaAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for key in var:\n", + " #print(key)\n", + "\n", + " plt.hist(df_.loc[df_.process==\"pwp8_pp_hh_lambda100_5f_hhbbaa\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"Signal kl=1.0\", alpha=1.0, linewidth=2)\n", + " #plt.hist(df_.loc[df_.process==\"pwp8_pp_hh_lambda240_5f_hhbbaa\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"Signal kl=2.4\" )\n", + " #plt.hist(df_.loc[df_.process==\"pwp8_pp_hh_lambda300_5f_hhbbaa\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"Signal kl=3.0\" )\n", + " #plt.hist(df_.loc[df_.process==\"pwp8_pp_hh_lambda000_5f_hhbbaa\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"Signal kl=0\" )\n", + " plt.hist(df_.loc[df_.process==\"mgp8_pp_tth01j_5f_haa\",:][key], histtype=(\"stepfilled\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"ttH\", alpha=0.2 )\n", + " plt.hist(df_.loc[df_.process==\"mgp8_pp_jjaa_5f\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"ggJets\" )\n", + " plt.hist(df_.loc[(df_.label==0) & (df_.process!='mgp8_pp_tth01j_5f_haa') & (df_.process != \"mgp8_pp_jjaa_5f\"),:][key], histtype=(\"stepfilled\"),range=(var[key][0],var[key][1]), bins=var[key][2],alpha=0.2, density = True, label = \"res bkg\" )\n", + "\n", + " #plt.hist(df.loc[df.process==\"ZZTo4L\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"bkg\" ) \n", + " #plt.hist(df.loc[df.process==\"DY\",:][key], histtype=(\"step\"),range=(var[key][0],var[key][1]), bins=var[key][2], density = True, label = \"bkg\" ) \n", + " plt.legend(loc='best')\n", + " plt.xlabel(var[key][3])\n", + "\n", + " plt.savefig(PLOT_FOLDER+'h_'+key+'.pdf')\n", + " print(\"Saved\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0cc525bf", + "metadata": {}, + "outputs": [], + "source": [ + "input_vars = [ \"njets\",\n", + " #\"tag_b1\",\n", + " #\"tag_b2\",\n", + " \"pTb1_o_m_bb\",\n", + " \"pTb2_o_m_bb\",\n", + " \"pTbb_o_m_HH\",\n", + " \"pTg1_o_m_gg\",\n", + " \"pTg2_o_m_gg\",\n", + " \"pTgg_o_m_HH\",\n", + " \"sum_pt\",\n", + " \"mindR_gb\",\n", + " \"otherdR_gb\",\n", + " \"GG_cosT_restHH\",\n", + " \"B1_cosT_restBB\",\n", + " \"G1_cosT_restGG\",\n", + " \"DeltaPhi_gg\",\n", + " \"DeltaEta_gg\",\n", + " \"DeltaPhi_bb\",\n", + " \"DeltaEta_bb\",\n", + " \"DeltaPhi_HH\",\n", + " \"DeltaEta_HH\"\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "82cdeacd", + "metadata": {}, + "outputs": [], + "source": [ + "def simpleDNN(input_vars,X_train, X_test, y_train, y_test):\n", + " inputs = keras.Input(shape=(len(input_vars),), name=\"particles\")\n", + " x = layers.Dense(132, activation=\"relu\", name=\"dense_1\")(inputs)\n", + " x = layers.Dropout(0.2)(x)\n", + " x = layers.Dense(64, activation=\"relu\", name=\"dense_2\")(x)\n", + " x = layers.Dense(64, activation=\"relu\", name=\"dense_3\")(x)\n", + " x = layers.Dropout(0.1)(x)\n", + " outputs = layers.Dense(1, activation=\"sigmoid\", name=\"predictions\")(x)\n", + " model = keras.Model(inputs=inputs, outputs=outputs)\n", + "\n", + " model.compile(\n", + " #optimizer=keras.optimizers.RMSprop(), # Optimizer\n", + " optimizer=keras.optimizers.Adam(),\n", + " # Loss function to minimize\n", + " loss=keras.losses.BinaryCrossentropy(),\n", + " # List of metrics to monitor\n", + " metrics=[keras.metrics.BinaryAccuracy()],\n", + " )\n", + " \n", + " earlystop = tf.keras.callbacks.EarlyStopping(monitor='binary_accuracy', min_delta=0.001, patience=10, verbose=1, mode='auto')\n", + " history = model.fit(\n", + " X_train,\n", + " y_train,\n", + " batch_size=1028,\n", + " epochs=500,\n", + " callbacks = [earlystop],\n", + " # We pass some validation for\n", + " # monitoring validation loss and metrics\n", + " # at the end of each epoch\n", + " validation_data=(X_test, y_test),\n", + " )\n", + " return model, history" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d11c5d40", + "metadata": {}, + "outputs": [], + "source": [ + "df_only_bkg = df_.loc[df_.label==0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "424d5ac9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99e0d205", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "46a46a42", + "metadata": {}, + "outputs": [], + "source": [ + "####ttH killer" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "26e63b46", + "metadata": {}, + "outputs": [], + "source": [ + "df_ttHKiller = df_.loc[(df_.label ==1) | (df_.process == \"mgp8_pp_tth01j_5f_haa\") ]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8974df80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa',\n", + " 'pwp8_pp_hh_lambda240_5f_hhbbaa', 'pwp8_pp_hh_lambda300_5f_hhbbaa',\n", + " 'mgp8_pp_tth01j_5f_haa'], dtype=object)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_ttHKiller.process.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "1c0e66af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "1296776\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "1113154\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "1415969\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "1270241\n", + "---\n", + "mgp8_pp_tth01j_5f_haa\n", + "81047\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_ttHKiller.process.unique():\n", + " print(proc)\n", + " print(len(df_ttHKiller.loc[df_ttHKiller.process==proc,\"weight\"]))\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8d7fc59f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "81047\n", + "5096140\n" + ] + } + ], + "source": [ + "print(len(df_ttHKiller.loc[df_ttHKiller.label==0,\"weight\"]))\n", + "print(len(df_ttHKiller.loc[df_ttHKiller.label==1,\"weight\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e02634a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5177187, 45)\n" + ] + } + ], + "source": [ + "#reduce dataset\n", + "print(df_ttHKiller.shape)\n", + "drop_indices = np.random.choice(df_ttHKiller.loc[df_ttHKiller.label == 1].index, 5000000, replace=False)\n", + "df_subset = df_ttHKiller.drop(drop_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c9f4623e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(177187, 45)\n", + "96140\n" + ] + } + ], + "source": [ + "print(df_subset.shape)\n", + "print(len(df_subset.loc[df_subset.label==1,\"weight\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "2bce89f4", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(df_subset[input_vars], df_subset.label, \n", + " test_size=0.50, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4d4e6189", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/500\n", + "87/87 [==============================] - 2s 11ms/step - loss: 1.3323 - binary_accuracy: 0.5194 - val_loss: 0.6748 - val_binary_accuracy: 0.5797\n", + "Epoch 2/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.7197 - binary_accuracy: 0.5300 - val_loss: 0.6872 - val_binary_accuracy: 0.5416\n", + "Epoch 3/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.6870 - binary_accuracy: 0.5549 - val_loss: 0.6799 - val_binary_accuracy: 0.5671\n", + "Epoch 4/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.6749 - binary_accuracy: 0.5951 - val_loss: 0.6379 - val_binary_accuracy: 0.6875\n", + "Epoch 5/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.6166 - binary_accuracy: 0.6744 - val_loss: 0.4988 - val_binary_accuracy: 0.7953\n", + "Epoch 6/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.5228 - binary_accuracy: 0.7577 - val_loss: 0.4393 - val_binary_accuracy: 0.8101\n", + "Epoch 7/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.4801 - binary_accuracy: 0.7835 - val_loss: 0.4299 - val_binary_accuracy: 0.8104\n", + "Epoch 8/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4621 - binary_accuracy: 0.7941 - val_loss: 0.4227 - val_binary_accuracy: 0.8139\n", + "Epoch 9/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4502 - binary_accuracy: 0.8011 - val_loss: 0.4167 - val_binary_accuracy: 0.8128\n", + "Epoch 10/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4411 - binary_accuracy: 0.8034 - val_loss: 0.4140 - val_binary_accuracy: 0.8158\n", + "Epoch 11/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4331 - binary_accuracy: 0.8075 - val_loss: 0.4106 - val_binary_accuracy: 0.8163\n", + "Epoch 12/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4289 - binary_accuracy: 0.8106 - val_loss: 0.4091 - val_binary_accuracy: 0.8191\n", + "Epoch 13/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4255 - binary_accuracy: 0.8120 - val_loss: 0.4070 - val_binary_accuracy: 0.8202\n", + "Epoch 14/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.4222 - binary_accuracy: 0.8129 - val_loss: 0.4035 - val_binary_accuracy: 0.8209\n", + "Epoch 15/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4175 - binary_accuracy: 0.8159 - val_loss: 0.4010 - val_binary_accuracy: 0.8209\n", + "Epoch 16/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4167 - binary_accuracy: 0.8150 - val_loss: 0.4045 - val_binary_accuracy: 0.8235\n", + "Epoch 17/500\n", + "87/87 [==============================] - 1s 7ms/step - loss: 0.4138 - binary_accuracy: 0.8180 - val_loss: 0.3986 - val_binary_accuracy: 0.8236\n", + "Epoch 18/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.4103 - binary_accuracy: 0.8182 - val_loss: 0.4008 - val_binary_accuracy: 0.8215\n", + "Epoch 19/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.4096 - binary_accuracy: 0.8187 - val_loss: 0.4149 - val_binary_accuracy: 0.8054\n", + "Epoch 20/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.4082 - binary_accuracy: 0.8198 - val_loss: 0.3965 - val_binary_accuracy: 0.8261\n", + "Epoch 21/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.4053 - binary_accuracy: 0.8207 - val_loss: 0.3929 - val_binary_accuracy: 0.8238\n", + "Epoch 22/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.4032 - binary_accuracy: 0.8218 - val_loss: 0.3900 - val_binary_accuracy: 0.8253\n", + "Epoch 23/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3991 - binary_accuracy: 0.8242 - val_loss: 0.3893 - val_binary_accuracy: 0.8257\n", + "Epoch 24/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.3987 - binary_accuracy: 0.8245 - val_loss: 0.3867 - val_binary_accuracy: 0.8286\n", + "Epoch 25/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3969 - binary_accuracy: 0.8256 - val_loss: 0.3964 - val_binary_accuracy: 0.8211\n", + "Epoch 26/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3961 - binary_accuracy: 0.8250 - val_loss: 0.3822 - val_binary_accuracy: 0.8302\n", + "Epoch 27/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3932 - binary_accuracy: 0.8264 - val_loss: 0.3882 - val_binary_accuracy: 0.8242\n", + "Epoch 28/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3908 - binary_accuracy: 0.8267 - val_loss: 0.3797 - val_binary_accuracy: 0.8318\n", + "Epoch 29/500\n", + "87/87 [==============================] - 1s 10ms/step - loss: 0.3904 - binary_accuracy: 0.8272 - val_loss: 0.3796 - val_binary_accuracy: 0.8319\n", + "Epoch 30/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3896 - binary_accuracy: 0.8291 - val_loss: 0.3781 - val_binary_accuracy: 0.8329\n", + "Epoch 31/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3906 - binary_accuracy: 0.8278 - val_loss: 0.3749 - val_binary_accuracy: 0.8322\n", + "Epoch 32/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3862 - binary_accuracy: 0.8308 - val_loss: 0.3761 - val_binary_accuracy: 0.8310\n", + "Epoch 33/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3855 - binary_accuracy: 0.8296 - val_loss: 0.3722 - val_binary_accuracy: 0.8346\n", + "Epoch 34/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3841 - binary_accuracy: 0.8301 - val_loss: 0.3758 - val_binary_accuracy: 0.8334\n", + "Epoch 35/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3824 - binary_accuracy: 0.8304 - val_loss: 0.3709 - val_binary_accuracy: 0.8344\n", + "Epoch 36/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3815 - binary_accuracy: 0.8320 - val_loss: 0.3695 - val_binary_accuracy: 0.8350\n", + "Epoch 37/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.3822 - binary_accuracy: 0.8305 - val_loss: 0.3699 - val_binary_accuracy: 0.8354\n", + "Epoch 38/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3816 - binary_accuracy: 0.8317 - val_loss: 0.3700 - val_binary_accuracy: 0.8359\n", + "Epoch 39/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3792 - binary_accuracy: 0.8330 - val_loss: 0.3686 - val_binary_accuracy: 0.8370\n", + "Epoch 40/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3789 - binary_accuracy: 0.8320 - val_loss: 0.3669 - val_binary_accuracy: 0.8366\n", + "Epoch 41/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3782 - binary_accuracy: 0.8324 - val_loss: 0.3717 - val_binary_accuracy: 0.8367\n", + "Epoch 42/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3794 - binary_accuracy: 0.8325 - val_loss: 0.3672 - val_binary_accuracy: 0.8375\n", + "Epoch 43/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3791 - binary_accuracy: 0.8323 - val_loss: 0.3637 - val_binary_accuracy: 0.8385\n", + "Epoch 44/500\n", + "87/87 [==============================] - 1s 10ms/step - loss: 0.3788 - binary_accuracy: 0.8329 - val_loss: 0.3647 - val_binary_accuracy: 0.8374\n", + "Epoch 45/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3756 - binary_accuracy: 0.8341 - val_loss: 0.3663 - val_binary_accuracy: 0.8369\n", + "Epoch 46/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3767 - binary_accuracy: 0.8338 - val_loss: 0.3663 - val_binary_accuracy: 0.8366\n", + "Epoch 47/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3740 - binary_accuracy: 0.8342 - val_loss: 0.3631 - val_binary_accuracy: 0.8383\n", + "Epoch 48/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3742 - binary_accuracy: 0.8339 - val_loss: 0.3647 - val_binary_accuracy: 0.8373\n", + "Epoch 49/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3754 - binary_accuracy: 0.8341 - val_loss: 0.3682 - val_binary_accuracy: 0.8368\n", + "Epoch 50/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3756 - binary_accuracy: 0.8338 - val_loss: 0.3627 - val_binary_accuracy: 0.8391\n", + "Epoch 51/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.3729 - binary_accuracy: 0.8352 - val_loss: 0.3600 - val_binary_accuracy: 0.8410\n", + "Epoch 52/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3730 - binary_accuracy: 0.8350 - val_loss: 0.3597 - val_binary_accuracy: 0.8397\n", + "Epoch 53/500\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "87/87 [==============================] - 1s 9ms/step - loss: 0.3700 - binary_accuracy: 0.8354 - val_loss: 0.3602 - val_binary_accuracy: 0.8411\n", + "Epoch 54/500\n", + "87/87 [==============================] - 1s 10ms/step - loss: 0.3704 - binary_accuracy: 0.8355 - val_loss: 0.3626 - val_binary_accuracy: 0.8398\n", + "Epoch 55/500\n", + "87/87 [==============================] - 1s 8ms/step - loss: 0.3690 - binary_accuracy: 0.8370 - val_loss: 0.3587 - val_binary_accuracy: 0.8402\n", + "Epoch 56/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3692 - binary_accuracy: 0.8368 - val_loss: 0.3594 - val_binary_accuracy: 0.8403\n", + "Epoch 57/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3688 - binary_accuracy: 0.8365 - val_loss: 0.3601 - val_binary_accuracy: 0.8405\n", + "Epoch 58/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3698 - binary_accuracy: 0.8353 - val_loss: 0.3560 - val_binary_accuracy: 0.8414\n", + "Epoch 59/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3690 - binary_accuracy: 0.8350 - val_loss: 0.3576 - val_binary_accuracy: 0.8418\n", + "Epoch 60/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3663 - binary_accuracy: 0.8371 - val_loss: 0.3628 - val_binary_accuracy: 0.8386\n", + "Epoch 61/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3694 - binary_accuracy: 0.8355 - val_loss: 0.3600 - val_binary_accuracy: 0.8400\n", + "Epoch 62/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3666 - binary_accuracy: 0.8369 - val_loss: 0.3582 - val_binary_accuracy: 0.8424\n", + "Epoch 63/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3678 - binary_accuracy: 0.8369 - val_loss: 0.3579 - val_binary_accuracy: 0.8416\n", + "Epoch 64/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3645 - binary_accuracy: 0.8380 - val_loss: 0.3629 - val_binary_accuracy: 0.8388\n", + "Epoch 65/500\n", + "87/87 [==============================] - 1s 9ms/step - loss: 0.3665 - binary_accuracy: 0.8375 - val_loss: 0.3567 - val_binary_accuracy: 0.8418\n", + "Epoch 65: early stopping\n" + ] + } + ], + "source": [ + "model, history = simpleDNN(input_vars, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b84c375d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(history.history['loss'],color='m',label='Training loss')\n", + "plt.plot(history.history['val_loss'],color='b',label='Validation loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss function')\n", + "plt.legend(loc='upper right')\n", + "plt.savefig(PLOT_FOLDER+'ttH_killer_training_validation_loss.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "d2071aa3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.92682576]\n", + " [0.48126626]\n", + " [0.25246033]\n", + " ...\n", + " [0.6396885 ]\n", + " [0.8864881 ]\n", + " [0.9225954 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "prediction = model.predict(X_test)\n", + "from sklearn.metrics import roc_curve\n", + "#y_pred_keras = keras_model.predict(X_test).ravel()\n", + "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, prediction)\n", + "\n", + "from sklearn.metrics import auc\n", + "auc_keras = auc(fpr_keras, tpr_keras)\n", + "\n", + "print(prediction)\n", + "\n", + "plt.figure(1)\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n", + "#plt.plot(fpr_rf, tpr_rf, label='XGBoost (area = {:.3f})'.format(auc_rf))\n", + "plt.xlabel('False positive rate')\n", + "plt.ylabel('True positive rate')\n", + "plt.title('ROC curve')\n", + "plt.legend(loc='best')\n", + "plt.savefig(PLOT_FOLDER+'ttH_killer_ROC_dnn.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "6c0fa568", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-10 18:37:33.397783: W tensorflow/python/util/util.cc:368] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: ./model_ttH_killer_dnn/assets\n", + "Saved model to disk\n" + ] + } + ], + "source": [ + "model.save(\"./model_ttH_killer_dnn\")\n", + "print(\"Saved model to disk\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d69a5d2e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(prediction[y_test==0], label='ttH', histtype=(\"step\"), bins=50,density=True);\n", + "(n_s, bins_s, _) = plt.hist(prediction[y_test==1], label='HH \\u2192 \\u03B3 \\u03B3 bb', histtype=(\"step\"), bins =50,density=True);\n", + "plt.legend(loc = 'best')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'ttH_killer_score_dnn.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b25c15da", + "metadata": {}, + "outputs": [], + "source": [ + "df_subset['prediction'] = model.predict(df_subset[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "15e990f8", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_17516/1536561302.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_only_bkg['prediction'] = model.predict(df_only_bkg[input_vars])\n" + ] + } + ], + "source": [ + "df_only_bkg['prediction'] = model.predict(df_only_bkg[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "a3f71c08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda100_5f_hhbbaa'].prediction, label='kl = 1', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda000_5f_hhbbaa'].prediction, label='kl = 0', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda240_5f_hhbbaa'].prediction, label='kl = 2.4', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda300_5f_hhbbaa'].prediction, label='kl = 3', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[df_only_bkg.process=='mgp8_pp_tth01j_5f_haa'].prediction, label='ttH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[df_only_bkg.process=='mgp8_pp_vh012j_5f_haa'].prediction, label='VH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[df_only_bkg.process=='mgp8_pp_h012j_5f_haa'].prediction, label='SM Higgs', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[df_only_bkg.process=='mgp8_pp_vbf_h01j_5f_haa'].prediction, label='VBF Higgs', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[df_only_bkg.process == 'mgp8_pp_jjaa_5f'].prediction, label='other bkg', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "\n", + "\n", + "plt.legend(loc = 'upper center')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'ttH_killer_dnn_score_log_allBkg.pdf')\n", + "plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ed804db5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda100_5f_hhbbaa'].prediction, label='kl = 1', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda000_5f_hhbbaa'].prediction, label='kl = 0', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda240_5f_hhbbaa'].prediction, label='kl = 2.4', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda300_5f_hhbbaa'].prediction, label='kl = 3', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[df_only_bkg.process=='mgp8_pp_tth01j_5f_haa'].prediction, label='ttH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "plt.legend(loc = 'upper center')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'ttH_killer_dnn_score_log.pdf')\n", + "plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9389ac05", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e78299e8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aeae156a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0e207a8d", + "metadata": {}, + "outputs": [], + "source": [ + "###dnn Mx > 350" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "d8327ebe", + "metadata": {}, + "outputs": [], + "source": [ + "df_small = df_.loc[(df_.process!=\"mgp8_pp_tth01j_5f_haa\") & (df_.Mx >=350) ]" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "27ae1b69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa',\n", + " 'pwp8_pp_hh_lambda240_5f_hhbbaa', 'pwp8_pp_hh_lambda300_5f_hhbbaa',\n", + " 'mgp8_pp_h012j_5f_haa', 'mgp8_pp_jjaa_5f', 'mgp8_pp_vh012j_5f_haa',\n", + " 'mgp8_pp_vbf_h01j_5f_haa'], dtype=object)" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_small.process.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "dbfdd874", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "187290\n", + "4309703\n" + ] + } + ], + "source": [ + "print(len(df_small.loc[df_small.label==0,\"weight\"]))\n", + "print(len(df_small.loc[df_small.label==1,\"weight\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "2b19499a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "1107831\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "1037610\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "1265274\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "898988\n", + "---\n", + "mgp8_pp_h012j_5f_haa\n", + "4701\n", + "---\n", + "mgp8_pp_jjaa_5f\n", + "170556\n", + "---\n", + "mgp8_pp_vh012j_5f_haa\n", + "6918\n", + "---\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "5115\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_small.process.unique():\n", + " print(proc)\n", + " print(len(df_small.loc[df_small.process==proc,\"weight\"]))\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "a9abe063", + "metadata": {}, + "outputs": [], + "source": [ + "drop_indices = np.random.choice(df_small.loc[df_small.label == 1].index, 4280000, replace=False)\n", + "df_sub_small = df_small.drop(drop_indices)\n", + "drop_indices = np.random.choice(df_sub_small.loc[df_sub_small.process == \"mgp8_pp_jjaa_5f\"].index, 165000, replace=False)\n", + "#df_sub_small = df_small.drop(drop_indices)\n", + "df_sub_small = df_sub_small.drop(drop_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "967f5f99", + "metadata": {}, + "outputs": [], + "source": [ + "#drop_indices = np.random.choice(df_small.loc[df_small.process == \"pwp8_pp_hh_lambda000_5f_hhbbaa\"].index, 188000, replace=False)\n", + "#df_sub_small = df_small.drop(drop_indices)\n", + "#drop_indices = np.random.choice(df_small.loc[df_small.process == \"pwp8_pp_hh_lambda100_5f_hhbbaa\"].index, 74500, replace=False)\n", + "#df_sub_small = df_sub_small.drop(drop_indices)\n", + "#drop_indices = np.random.choice(df_small.loc[df_small.process == \"pwp8_pp_hh_lambda240_5f_hhbbaa\"].index, 149600, replace=False)\n", + "#df_sub_small = df_sub_small.drop(drop_indices)\n", + "#drop_indices = np.random.choice(df_small.loc[df_small.process == \"pwp8_pp_hh_lambda300_5f_hhbbaa\"].index, 370200, replace=False)\n", + "#df_sub_small = df_sub_small.drop(drop_indices)\n", + "#drop_indices = np.random.choice(df_small.loc[df_small.process == \"mgp8_pp_jjaa_5f\"].index, 182000, replace=False)\n", + "#df_sub_small = df_sub_small.drop(drop_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "951c8905", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "7528\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "7214\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "8743\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "6218\n", + "---\n", + "mgp8_pp_h012j_5f_haa\n", + "4701\n", + "---\n", + "mgp8_pp_jjaa_5f\n", + "5556\n", + "---\n", + "mgp8_pp_vh012j_5f_haa\n", + "6918\n", + "---\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "5115\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_sub_small.process.unique():\n", + " print(proc)\n", + " print(len(df_sub_small.loc[df_sub_small.process==proc,\"weight\"]))\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "679924a2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "22290\n", + "29703\n" + ] + } + ], + "source": [ + "print(len(df_sub_small.loc[df_sub_small.label==0,\"weight\"]))\n", + "print(len(df_sub_small.loc[df_sub_small.label==1,\"weight\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "9db13e14", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(df_sub_small[input_vars], df_sub_small.label, \n", + " test_size=0.30, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "b033c6e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/500\n", + "36/36 [==============================] - 1s 16ms/step - loss: 1.8326 - binary_accuracy: 0.5311 - val_loss: 0.6477 - val_binary_accuracy: 0.6386\n", + "Epoch 2/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.8809 - binary_accuracy: 0.5508 - val_loss: 0.6585 - val_binary_accuracy: 0.7133\n", + "Epoch 3/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.7542 - binary_accuracy: 0.5798 - val_loss: 0.6218 - val_binary_accuracy: 0.7384\n", + "Epoch 4/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.6874 - binary_accuracy: 0.6268 - val_loss: 0.5855 - val_binary_accuracy: 0.7403\n", + "Epoch 5/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.6258 - binary_accuracy: 0.6811 - val_loss: 0.5611 - val_binary_accuracy: 0.7389\n", + "Epoch 6/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5976 - binary_accuracy: 0.7023 - val_loss: 0.5425 - val_binary_accuracy: 0.7429\n", + "Epoch 7/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5785 - binary_accuracy: 0.7231 - val_loss: 0.5424 - val_binary_accuracy: 0.7434\n", + "Epoch 8/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.5683 - binary_accuracy: 0.7286 - val_loss: 0.5346 - val_binary_accuracy: 0.7505\n", + "Epoch 9/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.5594 - binary_accuracy: 0.7356 - val_loss: 0.5333 - val_binary_accuracy: 0.7514\n", + "Epoch 10/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.5553 - binary_accuracy: 0.7385 - val_loss: 0.5318 - val_binary_accuracy: 0.7540\n", + "Epoch 11/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.5487 - binary_accuracy: 0.7438 - val_loss: 0.5287 - val_binary_accuracy: 0.7551\n", + "Epoch 12/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.5447 - binary_accuracy: 0.7479 - val_loss: 0.5262 - val_binary_accuracy: 0.7602\n", + "Epoch 13/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.5409 - binary_accuracy: 0.7511 - val_loss: 0.5200 - val_binary_accuracy: 0.7593\n", + "Epoch 14/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.5378 - binary_accuracy: 0.7533 - val_loss: 0.5215 - val_binary_accuracy: 0.7627\n", + "Epoch 15/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.5329 - binary_accuracy: 0.7550 - val_loss: 0.5146 - val_binary_accuracy: 0.7652\n", + "Epoch 16/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5290 - binary_accuracy: 0.7594 - val_loss: 0.5102 - val_binary_accuracy: 0.7678\n", + "Epoch 17/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5239 - binary_accuracy: 0.7629 - val_loss: 0.5062 - val_binary_accuracy: 0.7734\n", + "Epoch 18/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5185 - binary_accuracy: 0.7638 - val_loss: 0.4996 - val_binary_accuracy: 0.7733\n", + "Epoch 19/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5168 - binary_accuracy: 0.7655 - val_loss: 0.4954 - val_binary_accuracy: 0.7725\n", + "Epoch 20/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5105 - binary_accuracy: 0.7663 - val_loss: 0.4913 - val_binary_accuracy: 0.7798\n", + "Epoch 21/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.5047 - binary_accuracy: 0.7714 - val_loss: 0.4854 - val_binary_accuracy: 0.7823\n", + "Epoch 22/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.5031 - binary_accuracy: 0.7705 - val_loss: 0.4863 - val_binary_accuracy: 0.7837\n", + "Epoch 23/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4976 - binary_accuracy: 0.7735 - val_loss: 0.4786 - val_binary_accuracy: 0.7800\n", + "Epoch 24/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4950 - binary_accuracy: 0.7784 - val_loss: 0.4865 - val_binary_accuracy: 0.7835\n", + "Epoch 25/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4944 - binary_accuracy: 0.7762 - val_loss: 0.4781 - val_binary_accuracy: 0.7854\n", + "Epoch 26/500\n", + "36/36 [==============================] - 0s 10ms/step - loss: 0.4921 - binary_accuracy: 0.7790 - val_loss: 0.4741 - val_binary_accuracy: 0.7841\n", + "Epoch 27/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4883 - binary_accuracy: 0.7786 - val_loss: 0.4745 - val_binary_accuracy: 0.7861\n", + "Epoch 28/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4865 - binary_accuracy: 0.7793 - val_loss: 0.4722 - val_binary_accuracy: 0.7847\n", + "Epoch 29/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4871 - binary_accuracy: 0.7808 - val_loss: 0.4718 - val_binary_accuracy: 0.7854\n", + "Epoch 30/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4835 - binary_accuracy: 0.7822 - val_loss: 0.4704 - val_binary_accuracy: 0.7873\n", + "Epoch 31/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4854 - binary_accuracy: 0.7819 - val_loss: 0.4762 - val_binary_accuracy: 0.7864\n", + "Epoch 32/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4827 - binary_accuracy: 0.7812 - val_loss: 0.4689 - val_binary_accuracy: 0.7873\n", + "Epoch 33/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4826 - binary_accuracy: 0.7829 - val_loss: 0.4690 - val_binary_accuracy: 0.7885\n", + "Epoch 34/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4806 - binary_accuracy: 0.7849 - val_loss: 0.4684 - val_binary_accuracy: 0.7882\n", + "Epoch 35/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4793 - binary_accuracy: 0.7841 - val_loss: 0.4676 - val_binary_accuracy: 0.7888\n", + "Epoch 36/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4864 - binary_accuracy: 0.7812 - val_loss: 0.4739 - val_binary_accuracy: 0.7878\n", + "Epoch 37/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4800 - binary_accuracy: 0.7849 - val_loss: 0.4720 - val_binary_accuracy: 0.7855\n", + "Epoch 38/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4766 - binary_accuracy: 0.7854 - val_loss: 0.4688 - val_binary_accuracy: 0.7891\n", + "Epoch 39/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4785 - binary_accuracy: 0.7850 - val_loss: 0.4662 - val_binary_accuracy: 0.7893\n", + "Epoch 40/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4773 - binary_accuracy: 0.7844 - val_loss: 0.4656 - val_binary_accuracy: 0.7910\n", + "Epoch 41/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4759 - binary_accuracy: 0.7859 - val_loss: 0.4650 - val_binary_accuracy: 0.7912\n", + "Epoch 42/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4769 - binary_accuracy: 0.7856 - val_loss: 0.4651 - val_binary_accuracy: 0.7902\n", + "Epoch 43/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4729 - binary_accuracy: 0.7876 - val_loss: 0.4727 - val_binary_accuracy: 0.7867\n", + "Epoch 44/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4757 - binary_accuracy: 0.7855 - val_loss: 0.4700 - val_binary_accuracy: 0.7867\n", + "Epoch 45/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4756 - binary_accuracy: 0.7860 - val_loss: 0.4639 - val_binary_accuracy: 0.7891\n", + "Epoch 46/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4731 - binary_accuracy: 0.7872 - val_loss: 0.4647 - val_binary_accuracy: 0.7913\n", + "Epoch 47/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4736 - binary_accuracy: 0.7874 - val_loss: 0.4627 - val_binary_accuracy: 0.7914\n", + "Epoch 48/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4707 - binary_accuracy: 0.7880 - val_loss: 0.4625 - val_binary_accuracy: 0.7901\n", + "Epoch 49/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4713 - binary_accuracy: 0.7874 - val_loss: 0.4665 - val_binary_accuracy: 0.7900\n", + "Epoch 50/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4725 - binary_accuracy: 0.7864 - val_loss: 0.4619 - val_binary_accuracy: 0.7909\n", + "Epoch 51/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4704 - binary_accuracy: 0.7879 - val_loss: 0.4638 - val_binary_accuracy: 0.7916\n", + "Epoch 52/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4682 - binary_accuracy: 0.7893 - val_loss: 0.4602 - val_binary_accuracy: 0.7939\n", + "Epoch 53/500\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "36/36 [==============================] - 0s 7ms/step - loss: 0.4689 - binary_accuracy: 0.7893 - val_loss: 0.4634 - val_binary_accuracy: 0.7915\n", + "Epoch 54/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4689 - binary_accuracy: 0.7906 - val_loss: 0.4663 - val_binary_accuracy: 0.7919\n", + "Epoch 55/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4685 - binary_accuracy: 0.7888 - val_loss: 0.4591 - val_binary_accuracy: 0.7925\n", + "Epoch 56/500\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4666 - binary_accuracy: 0.7901 - val_loss: 0.4581 - val_binary_accuracy: 0.7927\n", + "Epoch 57/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4665 - binary_accuracy: 0.7896 - val_loss: 0.4590 - val_binary_accuracy: 0.7923\n", + "Epoch 58/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4651 - binary_accuracy: 0.7908 - val_loss: 0.4584 - val_binary_accuracy: 0.7931\n", + "Epoch 59/500\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4625 - binary_accuracy: 0.7918 - val_loss: 0.4563 - val_binary_accuracy: 0.7924\n", + "Epoch 60/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4645 - binary_accuracy: 0.7921 - val_loss: 0.4582 - val_binary_accuracy: 0.7952\n", + "Epoch 61/500\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4652 - binary_accuracy: 0.7920 - val_loss: 0.4558 - val_binary_accuracy: 0.7937\n", + "Epoch 62/500\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4608 - binary_accuracy: 0.7930 - val_loss: 0.4556 - val_binary_accuracy: 0.7939\n", + "Epoch 63/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4631 - binary_accuracy: 0.7916 - val_loss: 0.4614 - val_binary_accuracy: 0.7914\n", + "Epoch 64/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4624 - binary_accuracy: 0.7926 - val_loss: 0.4554 - val_binary_accuracy: 0.7949\n", + "Epoch 65/500\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4594 - binary_accuracy: 0.7946 - val_loss: 0.4562 - val_binary_accuracy: 0.7941\n", + "Epoch 66/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4585 - binary_accuracy: 0.7953 - val_loss: 0.4545 - val_binary_accuracy: 0.7945\n", + "Epoch 67/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4589 - binary_accuracy: 0.7933 - val_loss: 0.4523 - val_binary_accuracy: 0.7948\n", + "Epoch 68/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4584 - binary_accuracy: 0.7941 - val_loss: 0.4508 - val_binary_accuracy: 0.7961\n", + "Epoch 69/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4586 - binary_accuracy: 0.7925 - val_loss: 0.4508 - val_binary_accuracy: 0.7959\n", + "Epoch 70/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4554 - binary_accuracy: 0.7945 - val_loss: 0.4507 - val_binary_accuracy: 0.7949\n", + "Epoch 71/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4571 - binary_accuracy: 0.7952 - val_loss: 0.4484 - val_binary_accuracy: 0.7967\n", + "Epoch 72/500\n", + "36/36 [==============================] - 0s 7ms/step - loss: 0.4566 - binary_accuracy: 0.7943 - val_loss: 0.4489 - val_binary_accuracy: 0.7963\n", + "Epoch 73/500\n", + "36/36 [==============================] - 0s 9ms/step - loss: 0.4539 - binary_accuracy: 0.7949 - val_loss: 0.4473 - val_binary_accuracy: 0.7978\n", + "Epoch 74/500\n", + "36/36 [==============================] - 0s 10ms/step - loss: 0.4551 - binary_accuracy: 0.7953 - val_loss: 0.4488 - val_binary_accuracy: 0.7959\n", + "Epoch 75/500\n", + "36/36 [==============================] - 0s 8ms/step - loss: 0.4557 - binary_accuracy: 0.7946 - val_loss: 0.4459 - val_binary_accuracy: 0.7991\n", + "Epoch 75: early stopping\n" + ] + } + ], + "source": [ + "model, history = simpleDNN(input_vars, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "934fe2e0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(history.history['loss'],color='m',label='Training loss')\n", + "plt.plot(history.history['val_loss'],color='b',label='Validation loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss function')\n", + "plt.legend(loc='upper right')\n", + "plt.savefig(PLOT_FOLDER+'greater350_dnn_training_validation_loss.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "69efc6cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.5696238 ]\n", + " [0.1480191 ]\n", + " [0.7893981 ]\n", + " ...\n", + " [0.26288617]\n", + " [0.8448404 ]\n", + " [0.7467581 ]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "prediction = model.predict(X_test)\n", + "from sklearn.metrics import roc_curve\n", + "#y_pred_keras = keras_model.predict(X_test).ravel()\n", + "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, prediction)\n", + "\n", + "from sklearn.metrics import auc\n", + "auc_keras = auc(fpr_keras, tpr_keras)\n", + "\n", + "print(prediction)\n", + "\n", + "plt.figure(1)\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n", + "#plt.plot(fpr_rf, tpr_rf, label='XGBoost (area = {:.3f})'.format(auc_rf))\n", + "plt.xlabel('False positive rate')\n", + "plt.ylabel('True positive rate')\n", + "plt.title('ROC curve')\n", + "plt.legend(loc='best')\n", + "plt.savefig(PLOT_FOLDER+'greater350_dnn_ROC_dnn.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "f2ab8583", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(prediction[y_test==0], label='other bkg', histtype=(\"step\"), bins=20,density=True);\n", + "(n_s, bins_s, _) = plt.hist(prediction[y_test==1], label='HH \\u2192 \\u03B3 \\u03B3 bb', histtype=(\"step\"), bins =20,density=True);\n", + "plt.legend(loc = 'best')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'greater350_score_dnn.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "id": "3062c6c3", + "metadata": {}, + "outputs": [], + "source": [ + "df_sub_small['prediction_smaller350'] = model.predict(df_sub_small[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "4017cdc6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_17516/428889027.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_only_bkg['prediction_smaller350'] = model.predict(df_only_bkg[input_vars])\n" + ] + } + ], + "source": [ + "df_only_bkg['prediction_smaller350'] = model.predict(df_only_bkg[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "5727f6f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda100_5f_hhbbaa'].prediction_smaller350, label='kl = 1', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda000_5f_hhbbaa'].prediction_smaller350, label='kl = 0', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda240_5f_hhbbaa'].prediction_smaller350, label='kl = 2.4', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda300_5f_hhbbaa'].prediction_smaller350, label='kl = 3', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process!='mgp8_pp_tth01j_5f_haa') & (df_only_bkg.Mx >=350)].prediction_smaller350, label='ttH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "plt.legend(loc = 'center left')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'greater350_dnn_score_log.pdf')\n", + "#plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "id": "109378a5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda100_5f_hhbbaa'].prediction_smaller350, label='kl = 1', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda000_5f_hhbbaa'].prediction, label='kl = 0', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda240_5f_hhbbaa'].prediction, label='kl = 2.4', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda300_5f_hhbbaa'].prediction, label='kl = 3', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_tth01j_5f_haa')& (df_only_bkg.Mx >=350)].prediction_smaller350, label='ttH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_vh012j_5f_haa')& (df_only_bkg.Mx >=350)].prediction_smaller350, label='VH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_h012j_5f_haa')& (df_only_bkg.Mx >=350)].prediction_smaller350, label='SM Higgs', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_vbf_h01j_5f_haa') & (df_only_bkg.Mx >=350)].prediction_smaller350, label='VBF Higgs', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process == 'mgp8_pp_jjaa_5f') & (df_only_bkg.Mx >=350)].prediction_smaller350, label='other bkg', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "\n", + "\n", + "plt.legend(loc = 'upper center')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'greater350_dnn_score_log_allBkg.pdf')\n", + "#plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "id": "2933719c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: ./model_greater350_dnn/assets\n", + "Saved model to disk\n" + ] + } + ], + "source": [ + "model.save(\"./model_greater350_dnn\")\n", + "print(\"Saved model to disk\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1db453bb", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20d6f3e9", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dbcd5694", + "metadata": {}, + "outputs": [], + "source": [ + "###smaller than 350" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1ca0fb9", + "metadata": {}, + "outputs": [], + "source": [ + "def simpleWeightedDNN(input_vars,X_train, X_test, y_train, y_test):\n", + " inputs = keras.Input(shape=(len(input_vars)-1,), name=\"particles\")\n", + " x = layers.Dense(132, activation=\"relu\", name=\"dense_1\")(inputs)\n", + " x = layers.Dropout(0.2)(x)\n", + " x = layers.Dense(64, activation=\"relu\", name=\"dense_2\")(x)\n", + " x = layers.Dense(64, activation=\"relu\", name=\"dense_3\")(x)\n", + " x = layers.Dropout(0.1)(x)\n", + " outputs = layers.Dense(1, activation=\"sigmoid\", name=\"predictions\")(x)\n", + " model = keras.Model(inputs=inputs, outputs=outputs)\n", + "\n", + " model.compile(\n", + " #optimizer=keras.optimizers.RMSprop(), # Optimizer\n", + " optimizer=keras.optimizers.Adam(),\n", + " # Loss function to minimize\n", + " loss=keras.losses.BinaryCrossentropy(),\n", + " # List of metrics to monitor\n", + " metrics=[keras.metrics.BinaryAccuracy()],\n", + " )\n", + " \n", + " earlystop = tf.keras.callbacks.EarlyStopping(monitor='binary_accuracy', min_delta=0.001, patience=10, verbose=1, mode='auto')\n", + " history = model.fit(\n", + " X_train[input_vars[:-1]],\n", + " y_train,\n", + " sample_weight=X_train[input_vars[-1]],\n", + " batch_size=1028,\n", + " epochs=100,\n", + " callbacks = [earlystop],\n", + " # We pass some validation for\n", + " # monitoring validation loss and metrics\n", + " # at the end of each epoch\n", + " validation_data=(X_test[input_vars[:-1]], y_test),\n", + " )\n", + " return model, history" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "9e610634", + "metadata": {}, + "outputs": [], + "source": [ + "df_small = df_.loc[(df_.process!=\"mgp8_pp_tth01j_5f_haa\") & (df_.Mx <350) ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d0712bd3", + "metadata": {}, + "outputs": [], + "source": [ + "#df_small[\"sample_weight\"] = 1\n", + "#df_small.loc[df_small.process==\"mgp8_pp_jjaa_5f\",\"sample_weight\"] =0.1\n", + "#df_small.loc[df_small.process==\"mgp8_pp_vh012j_5f_haa\",\"sample_weight\"] =100\n", + "#df_small.loc[df_small.process==\"mgp8_pp_vbf_h01j_5f_haa\",\"sample_weight\"] =100" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "d60fdedf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "195420\n", + "786437\n" + ] + } + ], + "source": [ + "print(len(df_small.loc[df_small.label==0,\"weight\"]))\n", + "print(len(df_small.loc[df_small.label==1,\"weight\"])) " + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "9fa4768b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "188945\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "75544\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "150695\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "371253\n", + "---\n", + "mgp8_pp_h012j_5f_haa\n", + "1457\n", + "---\n", + "mgp8_pp_jjaa_5f\n", + "187138\n", + "---\n", + "mgp8_pp_vh012j_5f_haa\n", + "5426\n", + "---\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "1399\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_small.process.unique():\n", + " print(proc)\n", + " print(len(df_small.loc[df_small.process==proc,\"weight\"]))\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "d390499c", + "metadata": {}, + "outputs": [], + "source": [ + "drop_indices = np.random.choice(df_small.loc[df_small.label == 1].index, 600000, replace=False)\n", + "df_sub_small = df_small.drop(drop_indices)\n", + "drop_indices = np.random.choice(df_sub_small.loc[df_sub_small.process == \"mgp8_pp_jjaa_5f\"].index, 10000, replace=False)\n", + "#df_sub_small = df_small.drop(drop_indices)\n", + "df_sub_small = df_sub_small.drop(drop_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "8769ee16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pwp8_pp_hh_lambda000_5f_hhbbaa\n", + "44854\n", + "---\n", + "pwp8_pp_hh_lambda100_5f_hhbbaa\n", + "17893\n", + "---\n", + "pwp8_pp_hh_lambda240_5f_hhbbaa\n", + "35727\n", + "---\n", + "pwp8_pp_hh_lambda300_5f_hhbbaa\n", + "87963\n", + "---\n", + "mgp8_pp_h012j_5f_haa\n", + "1457\n", + "---\n", + "mgp8_pp_jjaa_5f\n", + "177138\n", + "---\n", + "mgp8_pp_vh012j_5f_haa\n", + "5426\n", + "---\n", + "mgp8_pp_vbf_h01j_5f_haa\n", + "1399\n", + "---\n" + ] + } + ], + "source": [ + "for proc in df_sub_small.process.unique():\n", + " print(proc)\n", + " print(len(df_sub_small.loc[df_sub_small.process==proc,\"weight\"]))\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "0ad0c149", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "185420\n", + "186437\n" + ] + } + ], + "source": [ + "print(len(df_sub_small.loc[df_sub_small.label==0,\"weight\"]))\n", + "print(len(df_sub_small.loc[df_sub_small.label==1,\"weight\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "f0446918", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['njets',\n", + " 'pTb1_o_m_bb',\n", + " 'pTb2_o_m_bb',\n", + " 'pTbb_o_m_HH',\n", + " 'pTg1_o_m_gg',\n", + " 'pTg2_o_m_gg',\n", + " 'pTgg_o_m_HH',\n", + " 'sum_pt',\n", + " 'mindR_gb',\n", + " 'otherdR_gb',\n", + " 'GG_cosT_restHH',\n", + " 'B1_cosT_restBB',\n", + " 'G1_cosT_restGG',\n", + " 'DeltaPhi_gg',\n", + " 'DeltaEta_gg',\n", + " 'DeltaPhi_bb',\n", + " 'DeltaEta_bb',\n", + " 'DeltaPhi_HH',\n", + " 'DeltaEta_HH']" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_vars" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "a2609a48", + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(df_sub_small[input_vars], df_sub_small.label, \n", + " test_size=0.30, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "id": "2fa7f65f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'DeltaEta_HH'" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_vars[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "id": "25255a20", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/500\n", + "254/254 [==============================] - 3s 7ms/step - loss: 0.8104 - binary_accuracy: 0.5089 - val_loss: 0.6910 - val_binary_accuracy: 0.5721\n", + "Epoch 2/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.6811 - binary_accuracy: 0.5636 - val_loss: 0.6436 - val_binary_accuracy: 0.6380\n", + "Epoch 3/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.6334 - binary_accuracy: 0.6407 - val_loss: 0.5984 - val_binary_accuracy: 0.6762\n", + "Epoch 4/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.6086 - binary_accuracy: 0.6697 - val_loss: 0.5878 - val_binary_accuracy: 0.6871\n", + "Epoch 5/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5970 - binary_accuracy: 0.6822 - val_loss: 0.5844 - val_binary_accuracy: 0.6912\n", + "Epoch 6/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5943 - binary_accuracy: 0.6844 - val_loss: 0.5809 - val_binary_accuracy: 0.6931\n", + "Epoch 7/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5904 - binary_accuracy: 0.6882 - val_loss: 0.5817 - val_binary_accuracy: 0.6928\n", + "Epoch 8/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5873 - binary_accuracy: 0.6910 - val_loss: 0.5778 - val_binary_accuracy: 0.6973\n", + "Epoch 9/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5855 - binary_accuracy: 0.6934 - val_loss: 0.5771 - val_binary_accuracy: 0.6986\n", + "Epoch 10/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5834 - binary_accuracy: 0.6952 - val_loss: 0.5753 - val_binary_accuracy: 0.6998\n", + "Epoch 11/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5828 - binary_accuracy: 0.6970 - val_loss: 0.5738 - val_binary_accuracy: 0.7007\n", + "Epoch 12/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5811 - binary_accuracy: 0.6990 - val_loss: 0.5758 - val_binary_accuracy: 0.6993\n", + "Epoch 13/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5795 - binary_accuracy: 0.7002 - val_loss: 0.5759 - val_binary_accuracy: 0.7017\n", + "Epoch 14/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5782 - binary_accuracy: 0.7018 - val_loss: 0.5715 - val_binary_accuracy: 0.7061\n", + "Epoch 15/500\n", + "254/254 [==============================] - 1s 5ms/step - loss: 0.5763 - binary_accuracy: 0.7029 - val_loss: 0.5688 - val_binary_accuracy: 0.7074\n", + "Epoch 16/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5746 - binary_accuracy: 0.7048 - val_loss: 0.5684 - val_binary_accuracy: 0.7089\n", + "Epoch 17/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5730 - binary_accuracy: 0.7062 - val_loss: 0.5688 - val_binary_accuracy: 0.7083\n", + "Epoch 18/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5690 - binary_accuracy: 0.7093 - val_loss: 0.5648 - val_binary_accuracy: 0.7128\n", + "Epoch 19/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5653 - binary_accuracy: 0.7116 - val_loss: 0.5592 - val_binary_accuracy: 0.7151\n", + "Epoch 20/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5624 - binary_accuracy: 0.7135 - val_loss: 0.5568 - val_binary_accuracy: 0.7175\n", + "Epoch 21/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5600 - binary_accuracy: 0.7144 - val_loss: 0.5540 - val_binary_accuracy: 0.7193\n", + "Epoch 22/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5588 - binary_accuracy: 0.7152 - val_loss: 0.5561 - val_binary_accuracy: 0.7173\n", + "Epoch 23/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5573 - binary_accuracy: 0.7166 - val_loss: 0.5518 - val_binary_accuracy: 0.7201\n", + "Epoch 24/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5573 - binary_accuracy: 0.7159 - val_loss: 0.5507 - val_binary_accuracy: 0.7210\n", + "Epoch 25/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5562 - binary_accuracy: 0.7166 - val_loss: 0.5530 - val_binary_accuracy: 0.7205\n", + "Epoch 26/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5559 - binary_accuracy: 0.7174 - val_loss: 0.5495 - val_binary_accuracy: 0.7215\n", + "Epoch 27/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5546 - binary_accuracy: 0.7173 - val_loss: 0.5504 - val_binary_accuracy: 0.7202\n", + "Epoch 28/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5557 - binary_accuracy: 0.7169 - val_loss: 0.5497 - val_binary_accuracy: 0.7188\n", + "Epoch 29/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5539 - binary_accuracy: 0.7185 - val_loss: 0.5523 - val_binary_accuracy: 0.7214\n", + "Epoch 30/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5531 - binary_accuracy: 0.7196 - val_loss: 0.5499 - val_binary_accuracy: 0.7189\n", + "Epoch 31/500\n", + "254/254 [==============================] - 1s 6ms/step - loss: 0.5533 - binary_accuracy: 0.7185 - val_loss: 0.5536 - val_binary_accuracy: 0.7185\n", + "Epoch 32/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5532 - binary_accuracy: 0.7190 - val_loss: 0.5474 - val_binary_accuracy: 0.7228\n", + "Epoch 33/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5524 - binary_accuracy: 0.7192 - val_loss: 0.5480 - val_binary_accuracy: 0.7220\n", + "Epoch 34/500\n", + "254/254 [==============================] - 2s 6ms/step - loss: 0.5519 - binary_accuracy: 0.7195 - val_loss: 0.5478 - val_binary_accuracy: 0.7223\n", + "Epoch 35/500\n", + "254/254 [==============================] - 2s 7ms/step - loss: 0.5514 - binary_accuracy: 0.7200 - val_loss: 0.5524 - val_binary_accuracy: 0.7168\n", + "Epoch 36/500\n", + "254/254 [==============================] - 2s 7ms/step - loss: 0.5515 - binary_accuracy: 0.7205 - val_loss: 0.5505 - val_binary_accuracy: 0.7192\n", + "Epoch 37/500\n", + "254/254 [==============================] - 2s 7ms/step - loss: 0.5509 - binary_accuracy: 0.7202 - val_loss: 0.5478 - val_binary_accuracy: 0.7219\n", + "Epoch 38/500\n", + "254/254 [==============================] - 2s 7ms/step - loss: 0.5515 - binary_accuracy: 0.7196 - val_loss: 0.5480 - val_binary_accuracy: 0.7219\n", + "Epoch 39/500\n", + "254/254 [==============================] - 2s 7ms/step - loss: 0.5510 - binary_accuracy: 0.7193 - val_loss: 0.5479 - val_binary_accuracy: 0.7221\n", + "Epoch 40/500\n", + "254/254 [==============================] - 2s 8ms/step - loss: 0.5507 - binary_accuracy: 0.7206 - val_loss: 0.5460 - val_binary_accuracy: 0.7238\n", + "Epoch 40: early stopping\n" + ] + } + ], + "source": [ + "model, history = simpleDNN(input_vars, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "id": "5bb30656", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.plot(history.history['loss'],color='m',label='Training loss')\n", + "plt.plot(history.history['val_loss'],color='b',label='Validation loss')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss function')\n", + "plt.legend(loc='upper right')\n", + "plt.savefig(PLOT_FOLDER+'smaller350_dnn_training_validation_loss.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "id": "6c0a308d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.639566 ]\n", + " [0.8543965 ]\n", + " [0.14706305]\n", + " ...\n", + " [0.24662694]\n", + " [0.42271772]\n", + " [0.15350777]]\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "prediction = model.predict(X_test)\n", + "from sklearn.metrics import roc_curve\n", + "#y_pred_keras = keras_model.predict(X_test).ravel()\n", + "fpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, prediction)\n", + "\n", + "from sklearn.metrics import auc\n", + "auc_keras = auc(fpr_keras, tpr_keras)\n", + "\n", + "print(prediction)\n", + "\n", + "plt.figure(1)\n", + "plt.plot([0, 1], [0, 1], 'k--')\n", + "plt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n", + "#plt.plot(fpr_rf, tpr_rf, label='XGBoost (area = {:.3f})'.format(auc_rf))\n", + "plt.xlabel('False positive rate')\n", + "plt.ylabel('True positive rate')\n", + "plt.title('ROC curve')\n", + "plt.legend(loc='best')\n", + "plt.savefig(PLOT_FOLDER+'smaller350_dnn_ROC_dnn.pdf')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "id": "6ad3af65", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(prediction[y_test==0], label='other bkg', histtype=(\"step\"), bins=20,density=True);\n", + "(n_s, bins_s, _) = plt.hist(prediction[y_test==1], label='HH \\u2192 \\u03B3 \\u03B3 bb', histtype=(\"step\"), bins =20,density=True);\n", + "plt.legend(loc = 'best')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'smaller350_score_dnn.pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "id": "58dbdbe1", + "metadata": {}, + "outputs": [], + "source": [ + "df_sub_small['prediction_smaller350'] = model.predict(df_sub_small[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "348ab60e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_17516/428889027.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df_only_bkg['prediction_smaller350'] = model.predict(df_only_bkg[input_vars])\n" + ] + } + ], + "source": [ + "df_only_bkg['prediction_smaller350'] = model.predict(df_only_bkg[input_vars])" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "2d2298c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda100_5f_hhbbaa'].prediction_smaller350, label='kl = 1', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda000_5f_hhbbaa'].prediction_smaller350, label='kl = 0', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda240_5f_hhbbaa'].prediction_smaller350, label='kl = 2.4', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda300_5f_hhbbaa'].prediction_smaller350, label='kl = 3', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process!='mgp8_pp_tth01j_5f_haa') & (df_only_bkg.Mx < 350)].prediction_smaller350, label='ttH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "plt.legend(loc = 'center left')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'smaller350_dnn_score_log.pdf')\n", + "#plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "98b97897", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "21\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(n_b, bins_b, _) = plt.hist(df_sub_small.loc[df_sub_small.process=='pwp8_pp_hh_lambda100_5f_hhbbaa'].prediction_smaller350, label='kl = 1', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda000_5f_hhbbaa'].prediction, label='kl = 0', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda240_5f_hhbbaa'].prediction, label='kl = 2.4', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "#(n_b, bins_b, _) = plt.hist(df_subset.loc[df_subset.process=='pwp8_pp_hh_lambda300_5f_hhbbaa'].prediction, label='kl = 3', histtype=(\"step\"), bins=20,density=True, alpha=1.0, linewidth=2, range=(0,1));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_tth01j_5f_haa')& (df_only_bkg.Mx < 350)].prediction_smaller350, label='ttH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_vh012j_5f_haa')& (df_only_bkg.Mx < 350)].prediction_smaller350, label='VH', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_h012j_5f_haa')& (df_only_bkg.Mx < 350)].prediction_smaller350, label='SM Higgs', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process=='mgp8_pp_vbf_h01j_5f_haa')& (df_only_bkg.Mx < 350)].prediction_smaller350, label='VBF Higgs', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "\n", + "(n_b, bins_b, _) = plt.hist(df_only_bkg.loc[(df_only_bkg.process == 'mgp8_pp_jjaa_5f')& (df_only_bkg.Mx < 350)].prediction_smaller350, label='other bkg', histtype=(\"step\"), bins=20,density=True, range=(0,1));\n", + "\n", + "\n", + "plt.legend(loc = 'upper center')\n", + "plt.xlabel('DNN score')\n", + "plt.savefig(PLOT_FOLDER+'smaller350_dnn_score_log_allBkg.pdf')\n", + "#plt.yscale('log')\n", + "print(len(bins_b))" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "id": "9fe76c4f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Assets written to: ./model_smaller350_dnn/assets\n", + "Saved model to disk\n" + ] + } + ], + "source": [ + "model.save(\"./model_smaller350_dnn\")\n", + "print(\"Saved model to disk\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7a9e6ef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/run/bbyy_analysis/mergeTree.py b/run/bbyy_analysis/mergeTree.py index 2e29ac6f73..7d34cc9687 100644 --- a/run/bbyy_analysis/mergeTree.py +++ b/run/bbyy_analysis/mergeTree.py @@ -3,7 +3,7 @@ #Careful, it work only in a cmsenv # # -file_name = ['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa', 'pwp8_pp_hh_lambda240_5f_hhbbaa','pwp8_pp_hh_lambda300_5f_hhbbaa', 'mgp8_pp_tth01j_5f','mgp8_pp_h012j_5f','mgp8_pp_jjaa_5f', 'mgp8_pp_vh012j_5f','mgp8_pp_vbf_h01j_5f']#, 'mgp8_pp_tt012j_5f'] +file_name = ['mgp8_pp_h012j_5f_haa', 'mgp8_pp_vbf_h01j_5f_haa', 'mgp8_pp_tth01j_5f_haa', 'mgp8_pp_vh012j_5f_haa','pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa', 'pwp8_pp_hh_lambda240_5f_hhbbaa','pwp8_pp_hh_lambda300_5f_hhbbaa','mgp8_pp_jjaa_5f'] import os for name in file_name: diff --git a/run/bbyy_analysis/openTree.py b/run/bbyy_analysis/openTree.py index 56ed525a33..62b8db9383 100644 --- a/run/bbyy_analysis/openTree.py +++ b/run/bbyy_analysis/openTree.py @@ -4,7 +4,7 @@ import numpy as np import pyarrow -file_name = ['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa', 'pwp8_pp_hh_lambda240_5f_hhbbaa','pwp8_pp_hh_lambda300_5f_hhbbaa', 'mgp8_pp_tth01j_5f','mgp8_pp_h012j_5f','mgp8_pp_jjaa_5f', 'mgp8_pp_vh012j_5f','mgp8_pp_vbf_h01j_5f']#, 'mgp8_pp_tt012j_5f'] +file_name = ['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa', 'pwp8_pp_hh_lambda240_5f_hhbbaa','pwp8_pp_hh_lambda300_5f_hhbbaa', 'mgp8_pp_tth01j_5f_haa','mgp8_pp_h012j_5f_haa','mgp8_pp_jjaa_5f', 'mgp8_pp_vh012j_5f_haa','mgp8_pp_vbf_h01j_5f_haa']#, 'mgp8_pp_tt012j_5f'] file_ = uproot.open("./"+file_name[0]+".root") tree = file_["events"] @@ -34,5 +34,5 @@ print(df.shape) print(df.columns) -df.to_parquet('df_Sel_All.parquet') +df.to_parquet('df_Sel_All_haa_Mbtag.parquet') diff --git a/run/bbyy_analysis/processTrees.py b/run/bbyy_analysis/processTrees.py index ad2b163a0f..b616e56013 100644 --- a/run/bbyy_analysis/processTrees.py +++ b/run/bbyy_analysis/processTrees.py @@ -4,6 +4,7 @@ import sys import uproot import os +import json # Import the library import argparse @@ -19,14 +20,15 @@ print("Process ID: ",ID) #print("Number of submitted jobs: ", JN) + +file_name = ['mgp8_pp_h012j_5f_haa', 'mgp8_pp_vh012j_5f_haa', 'mgp8_pp_vbf_h01j_5f_haa', 'mgp8_pp_tth01j_5f_haa','pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa', 'pwp8_pp_hh_lambda240_5f_hhbbaa','pwp8_pp_hh_lambda300_5f_hhbbaa', 'mgp8_pp_jjaa_5f']#,'mgp8_pp_tt012j_5f'] -file_name = ['pwp8_pp_hh_lambda000_5f_hhbbaa', 'pwp8_pp_hh_lambda100_5f_hhbbaa', 'pwp8_pp_hh_lambda240_5f_hhbbaa','pwp8_pp_hh_lambda300_5f_hhbbaa', 'mgp8_pp_tth01j_5f','mgp8_pp_h012j_5f','mgp8_pp_jjaa_5f', 'mgp8_pp_vh012j_5f','mgp8_pp_vbf_h01j_5f']#,'mgp8_pp_tt012j_5f'] - +lumi = 30e+06 for name in file_name: - nameINPUT = "./FCCAnalysis_ntuples_forAnalysis/"+name+"/chunk"+str(ID)+".root" - nameOUTPUT = "./FCCAnalysis_ntuples_forAnalysis/"+name+"/processed"+str(ID)+".root" + nameINPUT = "./FCCAnalysis_ntuples_forAnalysis_Mbtag/"+name+"/chunk"+str(ID)+".root" + nameOUTPUT = "./FCCAnalysis_ntuples_forAnalysis_Mbtag/"+name+"/processed"+str(ID)+".root" #input file if not os.path.isfile(nameINPUT): print("File do not exist") @@ -35,7 +37,7 @@ fin = ROOT.TFile.Open(nameINPUT) fout = ROOT.TFile.Open(nameOUTPUT, "RECREATE") - #weight = array('f', [0]) + weight = array('f', [0]) njets = array('i', [0]) pTb1_o_m_bb = array('f', [0]) pTb2_o_m_bb = array('f', [0]) @@ -80,7 +82,18 @@ #def of skimmed tree with needed branches tree = ROOT.TTree( "events", "events" ) - #tree.Branch( "weight",weight, "weight/F" ) + ##open json file for the weights + + json_file = open('FCC_bbyy_Ntuples_for_analysis/FCChh_procDict_v05_scenarioI.json', 'r') + all_weights = json.loads(json_file.read()) + xsec_weight = all_weights[name]['crossSection'] + k_factor_weight = all_weights[name]['kfactor'] + sumofweights = all_weights[name]['sumOfWeights'] + matching_eff = all_weights[name]['matchingEfficiency'] + + weight_xs = xsec_weight*lumi*k_factor_weight*matching_eff/sumofweights + + tree.Branch( "weight",weight, "weight/F" ) tree.Branch( "a1_pt",a1_pt, "a1_pt/F" ) tree.Branch( "a1_eta",a1_eta, "a1_eta/F" ) @@ -144,13 +157,18 @@ #if "events" not in fup.keys()[0]: print("No tree in file") continue + #elif "events" not in fup.keys()[0]: + # print("No events in file") + # continue fup.close() - print("Number of events : ",(fin.events).GetEntries()) - + try: + print("Number of events : ",(fin.events).GetEntries()) + except: + continue iter=0 for event in fin.events: - + #print(event) #iter_ +=1 #if iter_ % 1000 == 0 : # print("Iteration number ",iter_) @@ -161,8 +179,10 @@ ################ #print(event.njets) - - #weight[0] = event.weight + #print('event.weight ', event.weight[0]) + #print('event.weight ', float(event.weight[0])) + #print('weight_xs ', weight_xs) + weight[0] = event.weight[0]*weight_xs a1_pt[0] = event.g1_pt a1_e[0] = event.g1_e diff --git a/run/bbyy_analysis/run_the_skim.sh b/run/bbyy_analysis/run_the_skim.sh index b48363381d..57a7105b90 100755 --- a/run/bbyy_analysis/run_the_skim.sh +++ b/run/bbyy_analysis/run_the_skim.sh @@ -8,15 +8,3 @@ LAUNCH_FOLDER="/eos/user/p/pmastrap/FCCFW/Analysis/FCCAnalyses/run/Delphescard_v cd ${LAUNCH_FOLDER} source /cvmfs/sft.cern.ch/lcg/views/LCG_102/x86_64-centos7-gcc11-opt/setup.sh python processTrees.py --ID ${chunkID} - - -file_name=( pwp8_pp_hh_lambda000_5f_hhbbaa pwp8_pp_hh_lambda020_5f_hhbbaa pwp8_pp_hh_lambda040_5f_hhbbaa pwp8_pp_hh_lambda060_5f_hhbbaa pwp8_pp_hh_lambda080_5f_hhbbaa pwp8_pp_hh_lambda090_5f_hhbbaa pwp8_pp_hh_lambda094_5f_hhbbaa pwp8_pp_hh_lambda096_5f_hhbbaa pwp8_pp_hh_lambda097_5f_hhbbaa pwp8_pp_hh_lambda098_5f_hhbbaa pwp8_pp_hh_lambda099_5f_hhbba pwp8_pp_hh_lambda101_5f_hhbbaa pwp8_pp_hh_lambda102_5f_hhbbaa pwp8_pp_hh_lambda103_5f_hhbbaa pwp8_pp_hh_lambda104_5f_hhbbaa pwp8_pp_hh_lambda106_5f_hhbbaa pwp8_pp_hh_lambda108_5f_hhbbaa pwp8_pp_hh_lambda110_5f_hhbbaa pwp8_pp_hh_lambda120_5f_hhbbaa pwp8_pp_hh_lambda130_5f_hhbbaa pwp8_pp_hh_lambda140_5f_hhbbaa pwp8_pp_hh_lambda150_5f_hhbbaa pwp8_pp_hh_lambda155_5f_hhbbaa pwp8_pp_hh_lambda170_5f_hhbbaa pwp8_pp_hh_lambda190_5f_hhbbaa pwp8_pp_hh_lambda200_5f_hhbbaa pwp8_pp_hh_lambda220_5f_hhbbaa pwp8_pp_hh_lambda240_5f_hhbbaa pwp8_pp_hh_lambda260_5f_hhbbaa pwp8_pp_hh_lambda280_5f_hhbbaa pwp8_pp_hh_lambda300_5f_hhbbaa pwp8_pp_hh_lambda100_5f_hhbbaa mgp8_pp_tth01j_5f_haa mgp8_pp_h012j_5f_haa mgp8_pp_jjja_5f mgp8_pp_jjaa_5f mgp8_pp_vh012j_5f_haa mgp8_pp_vbf_h01j_5f_haa ) - -STORAGE_FOLDER="/eos/user/p/pmastrap/HHSnowExt/CondorSubmit/SkimmedSplitted" - -for i in "${file_name[@]}" -do - cp -r $i_skim_Sel_$ProcId.root ${STORAGE_FOLDER} - rm -r $i_skim_sel_$ProcId.root -done -