diff --git a/MFVNeutralino/test/.gitignore b/MFVNeutralino/test/.gitignore index f94ebdfdb..1180bbcf0 100644 --- a/MFVNeutralino/test/.gitignore +++ b/MFVNeutralino/test/.gitignore @@ -1,4 +1,7 @@ *.root +*.pkl +*.png +*.pdf out.* crab crab.cfg @@ -6,3 +9,18 @@ plots events_to_debug temp *.haddlog +ForLimits/Datacards/ +ForLimits/LimitsInput/ +ForLimits/CombineOutput/ +ForLimits/CombineCondor/ +ForLimits/LimitPlots/ +ForLimits/BinningStudy/datacards/ +ForLimits/BinningStudy/datacards_systs/ +ForLimits/BinningStudy/combine_output/ +ForLimits/BinningStudy/combine_output_systs/ +ForLimits/BinningStudy/condor_hybridnew/ +ForLimits/BinningStudy/root_output/ +ForLimits/BinningStudy/__pycache__/ +ForLimits/BinningStudy/*.pyc +ForLimits/BinningStudy/*.log +ForLimits/BinningStudy/*.err diff --git a/MFVNeutralino/test/ForLimits/NuisTabStore_7p4p1/pickle_ggHToSSTodddd.pkl b/MFVNeutralino/test/ForLimits/NuisTabStore_7p4p1/pickle_ggHToSSTodddd.pkl new file mode 100644 index 000000000..4d1686421 --- 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+Rp13 +(I3 +S'<' +p14 +NNNI-1 +I-1 +I0 +tp15 +bI00 +S'\x00\x00\x00\x00\x00\x00.@\x00\x00\x00\x00\x00\x00D@\x00\x00\x00\x00\x00\x80K@' +p16 +tp17 +bsS'dtype' +p18 +c__builtin__ +float +p19 +sS'x_unit' +p20 +S'mm' +p21 +sS'arr_len' +p22 +I0 +sS'years' +p23 +c__builtin__ +set +p24 +((lp25 +S'2017-8' +p26 +aS'20161-2' +p27 +atp28 +Rp29 +sS'y_unit' +p30 +S'GeV' +p31 +sg27 +g3 +(g4 +(I0 +tp32 +g6 +tp33 +Rp34 +(I1 +(I4 +I3 +tp35 +g13 +I00 +S'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?h"lxz\xa5\xe4?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?9EGr\xf9\x0f\xe1?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?9EGr\xf9\x0f\xe1?' +p36 +tp37 +bsS'x_vals' +p38 +g3 +(g4 +(I0 +tp39 +g6 +tp40 +Rp41 +(I1 +(I4 +tp42 +g13 +I00 +S'\x9a\x99\x99\x99\x99\x99\xb9?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00Y@' +p43 +tp44 +bsg26 +g3 +(g4 +(I0 +tp45 +g6 +tp46 +Rp47 +(I1 +(I4 +I3 +tp48 +g13 +I00 +S'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x18&S\x05\xa3\x92\xda?C\x1c\xeb\xe26\x1a\xd0?\x00\x00\x00\x00\x00\x00\xf0?(\xa0\x89\xb0\xe1\xe9\xd5?d;\xdfO\x8d\x97\xce?\x00\x00\x00\x00\x00\x00\xf0?(\xa0\x89\xb0\xe1\xe9\xd5?d;\xdfO\x8d\x97\xce?' +p49 +tp50 +bsS'proc' +p51 +S'ggHToSSTodddd' +p52 +s. \ No newline at end of file diff --git a/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_neu.pkl b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_neu.pkl new file mode 100644 index 000000000..41d57579c --- /dev/null +++ b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_neu.pkl @@ -0,0 +1,125 @@ +(dp0 +S'perc' +p1 +I01 +sS'y_vals' +p2 +cnumpy.core.multiarray +_reconstruct +p3 +(cnumpy +ndarray +p4 +(I0 +tp5 +S'b' +p6 +tp7 +Rp8 +(I1 +(I3 +tp9 +cnumpy +dtype +p10 +(S'f8' +p11 +I0 +I1 +tp12 +Rp13 +(I3 +S'<' +p14 +NNNI-1 +I-1 +I0 +tp15 +bI00 +S'\x00\x00\x00\x00\x00\x00i@\x00\x00\x00\x00\x00\x00y@\x00\x00\x00\x00\x00\x00\x89@' +p16 +tp17 +bsS'dtype' +p18 +c__builtin__ +float +p19 +sS'x_unit' +p20 +S'mm' +p21 +sS'arr_len' +p22 +I0 +sS'years' +p23 +c__builtin__ +set +p24 +((lp25 +S'2017-8' +p26 +aS'20161-2' +p27 +atp28 +Rp29 +sS'y_unit' +p30 +S'GeV' +p31 +sg27 +g3 +(g4 +(I0 +tp32 +g6 +tp33 +Rp34 +(I1 +(I5 +I3 +tp35 +g13 +I00 +S'\xeeZB>\xe8\xd9\xdc?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x18\x95\xd4\th"\xcc?\xe8\xfb\xa9\xf1\xd2M\xd2?\xed\r\xbe0\x99*\xd8?(\xa0\x89\xb0\xe1\xe9\xc5?\xb8\x1e\x85\xebQ\xb8\xce?\x9a\x99\x99\x99\x99\x99\xc9?\x08\xce\x19Q\xda\x1b\xbc?)\\\x8f\xc2\xf5(\xbc?)\\\x8f\xc2\xf5(\xbc?\xfee\xf7\xe4a\xa1\xc6?\n\xd7\xa3p=\n\xc7?\n\xd7\xa3p=\n\xc7?' +p36 +tp37 +bsS'x_vals' +p38 +g3 +(g4 +(I0 +tp39 +g6 +tp40 +Rp41 +(I1 +(I5 +tp42 +g13 +I00 +S'\x9a\x99\x99\x99\x99\x99\xb9?333333\xd3?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00>@' +p43 +tp44 +bsg26 +g3 +(g4 +(I0 +tp45 +g6 +tp46 +Rp47 +(I1 +(I5 +I3 +tp48 +g13 +I00 +S"\xa1g\xb3\xeas\xb5\xe1?\xc3d\xaa`TR\xe7?\x01M\x84\rO\xaf\xec?\xaf%\xe4\x83\x9e\xcd\xd2?'S\x05\xa3\x92:\xd9?%\xe4\x83\x9e\xcd\xaa\xdf?\xfa\xa0g\xb3\xeas\xc5?\xb8\x1e\x85\xebQ\xb8\xce?\x9a\x99\x99\x99\x99\x99\xc9?\xc2\x17&S\x05\xa3\xb2?{\x14\xaeG\xe1z\xb4?\xb8\x1e\x85\xebQ\xb8\xae?\xf8\xc2d\xaa`T\xb2?{\x14\xaeG\xe1z\xb4?\xb8\x1e\x85\xebQ\xb8\xae?" +p49 +tp50 +bsS'proc' +p51 +S'mfv_neu' +p52 +s. \ No newline at end of file diff --git a/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_stopbbarbbar.pkl b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_stopbbarbbar.pkl new file mode 100644 index 000000000..df7e4b991 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_stopbbarbbar.pkl @@ -0,0 +1,125 @@ +(dp0 +S'perc' +p1 +I01 +sS'y_vals' +p2 +cnumpy.core.multiarray +_reconstruct +p3 +(cnumpy +ndarray +p4 +(I0 +tp5 +S'b' +p6 +tp7 +Rp8 +(I1 +(I3 +tp9 +cnumpy +dtype +p10 +(S'f8' +p11 +I0 +I1 +tp12 +Rp13 +(I3 +S'<' +p14 +NNNI-1 +I-1 +I0 +tp15 +bI00 +S'\x00\x00\x00\x00\x00\x00i@\x00\x00\x00\x00\x00\x00y@\x00\x00\x00\x00\x00\x00\x89@' +p16 +tp17 +bsS'dtype' +p18 +c__builtin__ +float +p19 +sS'x_unit' +p20 +S'mm' +p21 +sS'arr_len' +p22 +I0 +sS'years' +p23 +c__builtin__ +set +p24 +((lp25 +S'2017-8' +p26 +aS'20161-2' +p27 +atp28 +Rp29 +sS'y_unit' +p30 +S'GeV' +p31 +sg27 +g3 +(g4 +(I0 +tp32 +g6 +tp33 +Rp34 +(I1 +(I5 +I3 +tp35 +g13 +I00 +S'\xc1\xa8\xa4N@\x13\xe5?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\xd6\xc5m4\x80\xb7\xe0?\xc0[ A\xf1c\xcc?@' +p43 +tp44 +bsg26 +g3 +(g4 +(I0 +tp45 +g6 +tp46 +Rp47 +(I1 +(I5 +I3 +tp48 +g13 +I00 +S'\x88\xf4\xdb\xd7\x81s\xee?L7\x89A`\xe5\xec?\nF%u\x02\x9a\xe8?.\xff!\xfd\xf6u\xd8?\x9f\xab\xad\xd8_v\xcf?\xaf%\xe4\x83\x9e\xcd\xd2?\xf7\x06_\x98L\x15\xd4?\x05\xc5\x8f1w-\xc1?)\\\x8f\xc2\xf5(\xac?5^\xbaI\x0c\x02\xd3?\xf6\x97\xdd\x93\x87\x85\xba?\xe8j+\xf6\x97\xdd\xa3?\xc5\xb1.n\xa3\x01\xd4?\xe0\x9c\x11\xa5\xbd\xc1\xb7?\xdc\xb5\x84|\xd0\xb3\xa9?' +p49 +tp50 +bsS'proc' +p51 +S'mfv_stopbbarbbar' +p52 +s. \ No newline at end of file diff --git a/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_stopdbardbar.pkl b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_stopdbardbar.pkl new file mode 100644 index 000000000..c90f43658 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkMvr/pickle_mfv_stopdbardbar.pkl @@ -0,0 +1,125 @@ +(dp0 +S'perc' +p1 +I01 +sS'y_vals' +p2 +cnumpy.core.multiarray +_reconstruct +p3 +(cnumpy +ndarray +p4 +(I0 +tp5 +S'b' +p6 +tp7 +Rp8 +(I1 +(I3 +tp9 +cnumpy +dtype +p10 +(S'f8' +p11 +I0 +I1 +tp12 +Rp13 +(I3 +S'<' +p14 +NNNI-1 +I-1 +I0 +tp15 +bI00 +S'\x00\x00\x00\x00\x00\x00i@\x00\x00\x00\x00\x00\x00y@\x00\x00\x00\x00\x00\x00\x89@' +p16 +tp17 +bsS'dtype' +p18 +c__builtin__ +float +p19 +sS'x_unit' +p20 +S'mm' +p21 +sS'arr_len' +p22 +I0 +sS'years' +p23 +c__builtin__ +set +p24 +((lp25 +S'2017-8' +p26 +aS'20161-2' +p27 +atp28 +Rp29 +sS'y_unit' +p30 +S'GeV' +p31 +sg27 +g3 +(g4 +(I0 +tp32 +g6 +tp33 +Rp34 +(I1 +(I5 +I3 +tp35 +g13 +I00 +S'3\xc4\xb1.n\xa3\xd1?_)\xcb\x10\xc7\xba\xe8?\x80H\xbf}\x1d8\xeb?1\x08\xac\x1cZd\xbb?\xa3#\xb9\xfc\x87\xf4\xd3?D\x8bl\xe7\xfb\xa9\xd9?\xfd\x87\xf4\xdb\xd7\x81\xb3?\x18&S\x05\xa3\x92\xba?\xb8\x1e\x85\xebQ\xb8\xbe?n\xa3\x01\xbc\x05\x12\xb4?\xdc\xb5\x84|\xd0\xb3\xa9?\xb5\xa6y\xc7):\xa2?\x81sF\x94\xf6\x06\xbf?>\x9bU\x9f\xab\xad\xa8?X\xa85\xcd;N\xa1?' +p36 +tp37 +bsS'x_vals' +p38 +g3 +(g4 +(I0 +tp39 +g6 +tp40 +Rp41 +(I1 +(I5 +tp42 +g13 +I00 +S'\x9a\x99\x99\x99\x99\x99\xb9?333333\xd3?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00$@\x00\x00\x00\x00\x00\x00>@' +p43 +tp44 +bsg26 +g3 +(g4 +(I0 +tp45 +g6 +tp46 +Rp47 +(I1 +(I5 +I3 +tp48 +g13 +I00 +S'\xa2\xb47\xf8\xc2d\xd2?j\xdeq\x8a\x8e\xe4\xe2?\x17\xd9\xce\xf7S\xe3\xe1?1*\xa9\x13\xd0D\xb8?\x13a\xc3\xd3+e\xc9?\x9f\xcd\xaa\xcf\xd5V\xcc?\xb3{\xf2\xb0Pk\xaa?1*\xa9\x13\xd0D\xa8?\x0e\xbe0\x99*\x18\xa5?333333\xb3?\xcd]K\xc8\x07=\x9b?\x83\xe2\xc7\x98\xbb\x96\x90?\x80\xb7@\x82\xe2\xc7\xb8?\x9f<,\xd4\x9a\xe6\x9d?\xc2\x17&S\x05\xa3\x92?' +p49 +tp50 +bsS'proc' +p51 +S'mfv_stopdbardbar' +p52 +s. \ No newline at end of file diff --git a/MFVNeutralino/test/ForLimits/NuisTabStore_TrkRec/ct_pickle_VH.pkl b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkRec/ct_pickle_VH.pkl new file mode 100644 index 000000000..c0d82064d --- /dev/null +++ b/MFVNeutralino/test/ForLimits/NuisTabStore_TrkRec/ct_pickle_VH.pkl @@ -0,0 +1,167 @@ +(dp0 +S'perc' +p1 +I00 +sS'y_vals' +p2 +cnumpy.core.multiarray +_reconstruct +p3 +(cnumpy +ndarray +p4 +(I0 +tp5 +S'b' +p6 +tp7 +Rp8 +(I1 +(I3 +tp9 +cnumpy +dtype +p10 +(S'f8' +p11 +I0 +I1 +tp12 +Rp13 +(I3 +S'<' +p14 +NNNI-1 +I-1 +I0 +tp15 +bI00 +S'\x00\x00\x00\x00\x00\x00.@\x00\x00\x00\x00\x00\x00D@\x00\x00\x00\x00\x00\x80K@' +p16 +tp17 +bsS'dtype' +p18 +c__builtin__ +float +p19 +sS'20162' +p20 +g3 +(g4 +(I0 +tp21 +g6 +tp22 +Rp23 +(I1 +(I6 +I3 +I3 +tp24 +g13 +I00 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+p63 +tp64 +bsS'proc' +p65 +S'VH' +p66 +s. \ No newline at end of file diff --git a/MFVNeutralino/test/ForLimits/ReadMe.txt b/MFVNeutralino/test/ForLimits/ReadMe.txt new file mode 100644 index 000000000..4264ebb59 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/ReadMe.txt @@ -0,0 +1,75 @@ +MFV Displaced Vertex -- Limit Setting +====================================== + +Two environments needed: + - CMSSW_10_6_48 (el7/apptainer) for datacard making + - CMSSW_14_1_0_pre4 (el9, native) for combine + + +FIRST-TIME COMBINE SETUP (only once, on an el9 node) +----------------------------------------------------- +Build CMSSW + CombinedLimit: + + cmsrel CMSSW_14_1_0_pre4 + cd CMSSW_14_1_0_pre4/src && cmsenv + git clone https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit + scram b -j8 + +Then build the tarball that gets shipped to Condor workers: + + # from ForLimits/, with CMSSW_14_1_0_pre4 cmsenv active: + bash make_combine_tarball.sh + +This writes combine_env.tar.gz one level above CMSSW_BASE. +The path is hardcoded in submitCombine.py as COMBINE_TARBALL -- update it there if needed. + + +MAKING DATACARDS (el7 apptainer, CMSSW_10_6_48) +------------------------------------------------ +Run makeLimitsInputROOT.py for each year/channel combo. +Outputs go to LimitsInput_4bin/ (ROOT histograms) and Datacards_4bin/ (combine .txt cards). + + python makeLimitsInputROOT.py --year 2018 --channel lep + python makeLimitsInputROOT.py --year all --channel lep + bash run_limits_bjet_allyears.sh # convenience wrapper for bjet all years + +Config is in limits_config.yaml -- edit paths, bins, or year/channel defaults there. +The 4-bin setup is [0, 0.1, 0.4, 2.0, 4.0] cm and is the default. + + +RUNNING COMBINE (el9, CMSSW_14_1_0_pre4) +----------------------------------------- +Always run asymptotic first -- HybridNew uses the asymptotic result to set rMax. + + python submitCombine.py --tag 4bin --method asymptotic + python submitCombine.py --tag 4bin --method hybridnew + +Useful flags: + --dry-run write job files but don't submit + --skip-existing skip hypotheses that already have output + --subset VH,mfv_neu only submit these processes + --sig-id VH_tau1mm_M15 only submit this one hypothesis + --limit 5 cap at N jobs (good for testing) + +The --tag 4bin flag routes everything through Datacards_4bin/, CombineOutput_4bin/, CombineCondor_4bin/. +ttH signals are skipped -- combineCards fails for those due to a negative signal rate corner case. + + +PLOTTING +-------- +Run inside CMSSW_14_1_0_pre4 (has scipy + matplotlib). + + python3 plotLimits.py --combine-out CombineOutput_4bin --out-dir LimitPlots_4bin + +Add --comparison-dir LimitPlots_4bin_Comparison to also make HybridNew vs Asymptotic overlays. +Add --subset VH,mfv_neu to only plot specific processes. + + +NUISANCE TABLES +--------------- +Pickle files under NuisTabStore_*/ are precomputed and read at datacard-making time. +To regenerate them (usually not needed): + + python turn_7p4p1_to_2darr.py # displaced trigger uncertainties + python turn_TrkMvr_to_2darr.py # TrackMover vertex reco uncertainties + python turn_TrkRec_to_2darr.py # track reco efficiency (VH only) diff --git a/MFVNeutralino/test/ForLimits/cleanup_failed_limits.sh b/MFVNeutralino/test/ForLimits/cleanup_failed_limits.sh new file mode 100755 index 000000000..a22f96f8d --- /dev/null +++ b/MFVNeutralino/test/ForLimits/cleanup_failed_limits.sh @@ -0,0 +1,31 @@ +#!/bin/bash +# Delete output files for jobs that hit "Cannot set higher limit" +# so they can be resubmitted with --skip-existing. +CONDOR=/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/CombineCondor +OUT=/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/CombineOutput + +n_del=0; n_keep=0; n_running=0 +for d in $CONDOR/*/; do + sig=$(basename $d) + log="$d/job.out" + outf="$OUT/$sig/higgsCombine${sig}.HybridNew.mH120.1234.root" + [ -f "$log" ] || continue + if grep -q "=== Done:" "$log" 2>/dev/null; then + if grep -q "Limit: r <" "$log" 2>/dev/null; then + n_keep=$((n_keep+1)) + else + # "Cannot set higher limit" — delete output so it gets resubmitted + if [ -f "$outf" ]; then + echo "DEL $sig" + rm -f "$outf" + n_del=$((n_del+1)) + fi + fi + else + n_running=$((n_running+1)) + fi +done +echo "" +echo "Kept (good limit): $n_keep" +echo "Deleted (bad/no limit): $n_del" +echo "Still running: $n_running" diff --git a/MFVNeutralino/test/ForLimits/getNuisanceFromSig.py b/MFVNeutralino/test/ForLimits/getNuisanceFromSig.py new file mode 100644 index 000000000..6cbc76ad5 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/getNuisanceFromSig.py @@ -0,0 +1,175 @@ +from __future__ import print_function +import numpy as np + +import script_configs as config +import helper_PyStorage_objects as sth +import nuisance_configs_and_functions as nsfc + + +# --------------------------------------------------------------------------- +# Nuisance tag sets +# --------------------------------------------------------------------------- +nuis_allsigs = set([ + "mc_stat", # MC Gamma-N + "reco_effi", # Reconstruction efficiency + "vtx_reco_TM", # TrackMover + "pileup", # Pileup + "int_lumi", +]) + +# lep_effi is NOT included here -- it is added conditionally per signal below. +nuis_lep = set() + +nuis_bjet = set([ + "disp_kine", # Displacement trigger kinematic filters + "bjet_filt", # B-Jet filters + "disp_filt", # Displaced jet track filters + "disp_tres", # Track resolution, displaced filters + "bjet_inef", # Inefficiencies in offline b jet selection + "btag_scfa", # B-tag scale factors + "JES", # Jet Energy Scale + "JER", # Jet Energy Resolution + "calo_inef", +]) + +# Replacements merge groups of nuisances into a single combined uncertainty +nuis_replacements = { + "all": {}, + "bjet": { + frozenset(["trig_JESR_btag"]): set([ + "disp_kine", + "bjet_filt", + "disp_filt", + "disp_tres", + "bjet_inef", + "btag_scfa", + "JES", + "JER", + ]), + }, + "lep": {}, +} + +# disabled until CRs unblinded +nuis_bkg = set() +nuis_bkg_replacements = {} + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def replace_all_ones(new_nuis): + """Drop a nuisance list if every entry is all 1.0s (no effect on the limit).""" + for nuis in new_nuis: + if not np.prod(nuis.nuis_val == np.ones_like(nuis.nuis_val)): + return new_nuis + return [] + + +def get_nuis_fromname(nuis_name, siginfo, nuis_ls, debug_mode=False): + """Dispatch to the appropriate nuisance-building function and append to nuis_ls.""" + new_nuis = [] + + if nuis_name == "mc_stat": new_nuis = nsfc.get_mc_stat("mc_stat", siginfo, debug_mode=debug_mode) + elif nuis_name == "reco_effi": new_nuis = nsfc.get_reco_effi("tk_reco_eff", siginfo, debug_mode=debug_mode) + elif nuis_name == "vtx_reco_TM": new_nuis = nsfc.get_vtx_reco_TM("vtx_reco_TM", siginfo, debug_mode=debug_mode) + elif nuis_name == "pileup": new_nuis = nsfc.get_pileup("CMS_pileup", siginfo, debug_mode=debug_mode) + elif nuis_name == "int_lumi": new_nuis = nsfc.get_int_lumi("lumi", siginfo, debug_mode=debug_mode) + elif nuis_name == "lep_effi": new_nuis = nsfc.get_lep_effi("CMS_eff_", siginfo, debug_mode=debug_mode) + elif nuis_name == "trig_JESR_btag": new_nuis = nsfc.get_trig_JESR_btag("disp_trig_uncerts", siginfo, debug_mode=debug_mode) + elif nuis_name == "calo_inef": new_nuis = nsfc.get_calo_inef("calo_ineff", siginfo, debug_mode=debug_mode) + elif nuis_name == "qcd_scale_ren_ggH": new_nuis = nsfc.get_qcd_scale_ren_ggH("QCDscale_ren_ggH", siginfo, debug_mode=debug_mode) + elif nuis_name == "qcd_scale_fac_VH": new_nuis = nsfc.get_qcd_scale_fac_VH("QCDscale_fac_VH", siginfo, debug_mode=debug_mode) + else: + print("Error: nuisance name not implemented for", nuis_name) + + new_nuis = replace_all_ones(new_nuis) + nuis_ls += new_nuis + + +def get_bkg_nuis_fromname(nuis_name, nuis_bkg_ls, debug_mode=False): + new_nuis = [] + + if nuis_name == "bkg_jet_ang": new_nuis = nsfc.get_bkg_jet_ang("dphiVV", debug_mode=debug_mode) + elif nuis_name == "bkg_vtx_arbi": new_nuis = nsfc.get_bkg_vtx_arbi("vtx_pair_eff_NtkSeeds", debug_mode=debug_mode) + elif nuis_name == "bkg_vtx_refi": new_nuis = nsfc.get_bkg_vtx_refi("vtx_pair_eff_MC", debug_mode=debug_mode) + elif nuis_name == "pileup": new_nuis = nsfc.get_bkg_pileup("CMS_pileup", debug_mode=debug_mode) + elif nuis_name == "sig_cont": new_nuis = nsfc.get_bkg_sig_cont(nuis_name, debug_mode=debug_mode) + elif nuis_name == "bkg_norm": new_nuis = nsfc.get_bkg_bkg_norm("bkg_norm", debug_mode=debug_mode) + elif nuis_name == "n2v_unc": new_nuis = nsfc.get_bkg_n2v_unc("num_vtx_pair_unc", debug_mode=debug_mode) + else: + raise Exception("Error: background nuisance name not implemented for " + nuis_name) + + new_nuis = replace_all_ones(new_nuis) + nuis_bkg_ls += new_nuis + + +# --------------------------------------------------------------------------- +# Main public interface +# --------------------------------------------------------------------------- + +def get_nuis_fromsig(siginfo, nuis_ls, debug_mode=False): + """Build the nuisance list for one signal hypothesis. + + lep_effi is only added for processes that have a lepton in the hard scatter + (VH, ttH). Pure SUSY signals fired by the lepton trigger do not receive it. + """ + trig_type = siginfo.trig_type + + nuis_set = nuis_allsigs.copy() + if trig_type == "lep": + nuis_set.update(nuis_lep) + # Only VH and ttH carry a lepton reco efficiency uncertainty + if siginfo.proc in config.lep_reco_effi_sigs: + nuis_set.add("lep_effi") + elif trig_type == "bjet": + nuis_set.update(nuis_bjet) + else: + print("FAIL: please make sure the input SignalROOTInfo object has a trig_type identified") + return + + for repl in nuis_replacements["all"]: + nuis_set.update(repl) + nuis_set = nuis_set.difference(nuis_replacements["all"][repl]) + for repl in nuis_replacements[trig_type]: + nuis_set.update(repl) + nuis_set = nuis_set.difference(nuis_replacements[trig_type][repl]) + + if debug_mode: + print("\n" + siginfo.return_nuis_key()) + print("Nuisances identified:", nuis_set, "\n") + + for nuis in sorted(nuis_set): + get_nuis_fromname(nuis, siginfo, nuis_ls, debug_mode=debug_mode) + + # Process-specific theory systematics (year- and bin-correlated, no CMS_EXO24035_ prefix) + if siginfo.proc == "ggHToSSTodddd": + get_nuis_fromname("qcd_scale_ren_ggH", siginfo, nuis_ls, debug_mode=debug_mode) + if siginfo.proc == "VH": + get_nuis_fromname("qcd_scale_fac_VH", siginfo, nuis_ls, debug_mode=debug_mode) + + if debug_mode: + print("Nuisances that produced a SIG Nuisance object:") + for nuis in nuis_ls: + nuis.print_diagnostics() + + +def get_nuis_frombkg(nuis_bkg_ls, debug_mode=False): + """Build the nuisance list for the background estimate.""" + nuis_set = nuis_bkg.copy() + + for repl in nuis_bkg_replacements: + nuis_set.update(repl) + nuis_set = nuis_set.difference(nuis_bkg_replacements[repl]) + + if debug_mode: + print("Nuisances identified:", nuis_set, "\n") + + for nuis in sorted(nuis_set): + get_bkg_nuis_fromname(nuis, nuis_bkg_ls, debug_mode=debug_mode) + + if debug_mode: + print("Nuisances that produced a BKG Nuisance object:") + for nuis in nuis_bkg_ls: + nuis.print_diagnostics() diff --git a/MFVNeutralino/test/ForLimits/helper_PyStorage_objects.py b/MFVNeutralino/test/ForLimits/helper_PyStorage_objects.py new file mode 100644 index 000000000..45c050a88 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/helper_PyStorage_objects.py @@ -0,0 +1,551 @@ +import ROOT +import numpy as np +import os +import pickle + +import JMTucker.Tools.Samples as Samples +from JMTucker.Tools.Sample import MCSample, nevents_from_file +from JMTucker.Tools.ROOTTools import lerp, bilerp + +import helper_ROOT_functions as ROOThelper +import script_configs as config +import sig_and_bkg_configs as sb_conf + +""" +DEFINITION: SignalROOTInfo +-Store filename, extract information from filename + +DEFINITION: NuisanceInfo +-Store information to write a nuisance parameter + +DEFINITION: NuisanceTable +""" + +nbins = config.datacard["nbins"] +prt_dt = sb_conf.printout_flags["PyStorage"] + + +class SignalROOTInfo(object): + """ + Based on a ROOT MiniTree filename, extract information like process name, lifetime. + -INPUTS- + full_fn: full filename + root_exists: Boolean. If True, it'll make an accompanying ROOT.TFile() and MCSample() + """ + + def __init__(self, full_fn, root_exists=False, nbins=3): + self.full_fn = full_fn + bn = os.path.basename(full_fn) + if bn.startswith("minitree"): + # File lives inside condor_/ -- use the directory name for parsing + dirn = os.path.basename(os.path.dirname(full_fn)) + self.fn = dirn.replace("condor_", "", 1) + ".root" + else: + self.fn = bn + + success = self.get_processtag() + if success: + self.get_type() + self.get_lifetime() + self.get_mass() + self.get_year() + self.nbins = nbins + + self.root_exists = root_exists + if root_exists: + self.sample_file = ROOT.TFile.Open(self.full_fn) + self.nevents = nevents_from_file(self.sample_file) + try: + self.mcsample = eval("Samples." + self.return_nuis_key()) + Samples._set_signal_stuff(self.mcsample) + except Exception: + raise ValueError("No Samples.py entry") + + def get_processtag(self): + """ + Get the process tag, e.g. mfv_stopdbardbar + """ + name_start = self.fn.split("tau")[0] + success = True + if name_start == self.fn: + print("Unable to extract signal process tag. Is the name of the form __tau__ ?") + print("Error reported for", self.full_fn) + success = False + self.proc = name_start[:-1] + return success + + def get_type(self): + if (self.proc in config.sig["bjet_sigs"]) and (self.proc in config.sig["lep_sigs"]): + if prt_dt["sig_type_conflict"]: + print("Note: this signal can be bjet or lep. Setting to match script_config.") + self.trig_type = config.sig["type"] + elif self.proc in config.sig["bjet_sigs"]: + self.trig_type = "bjet" + elif self.proc in config.sig["lep_sigs"]: + self.trig_type = "lep" + else: + print("Signal " + self.fn + " not found in either bjet or lep lists.") + self.trig_type = "none" + + def get_lifetime(self): + self.lifetime = self.fn.split("tau")[1].split("_")[0] + + def get_mass(self): + self.mass = self.fn.split("tau")[1].split("_")[1][1:] + + def get_year(self): + self.year = self.return_nuis_key().split("_")[-1] + + def return_nuis_key(self): + return self.fn.replace(".root", "").replace(".ROOT", "") + + def return_lifetime_in_unit(self, unit=None): + """Returns original string if unit=None, else converts into whatever unit is inputted""" + if unit is None: + return self.lifetime + return ROOThelper.convert_units(to_unit=unit, from_expr=self.lifetime) + + def return_mass_as_int(self): + return int(self.mass) + + def return_name2details(self, lifetime_unit="mm"): + """This function is constructed to resemble name2details of the old code""" + return [self.proc, self.return_lifetime_in_unit(unit=lifetime_unit), self.mass, self.year] + + def print_diagnostics(self, lifetime_unit="mm"): + print("Full filename:", self.full_fn) + print("Trigger type:", self.trig_type) + print("Process, lifetime, mass, year", self.return_name2details(lifetime_unit=lifetime_unit)) + + def get_sumw(self): + if self.root_exists: + return self.mcsample.sumw(self.sample_file) + raise NotImplementedError() + + def get_ngen(self): + if self.root_exists: + return self.nevents + raise NotImplementedError() + + def get_xsec(self): + if self.root_exists: + return self.mcsample.xsec + raise NotImplementedError() + + +class SigRInf_Grp(object): + """ + Make a cluster of SignalROOTInfo objects, that behave like the first object of the group but with + + This code defaults most things to the first item of the list. So it will fail if full_fn_ls entries don't have matching lifetime/mass/year etc. + + -INPUTS- + full_fn: ls-like, full filenames + root_exists: Boolean. Should it search for the ROOT and MCSample()? + """ + + def __init__(self, full_fn_ls, root_exists=False, nbins=3, overwrite_proc=None): + assert len(full_fn_ls) > 0 + self.sig_ls = [SignalROOTInfo(fn, root_exists=root_exists, nbins=nbins) + for fn in full_fn_ls] + + self.proc = overwrite_proc if overwrite_proc is not None else self.sig_ls[0].proc + + for s in self.sig_ls: + assert s.trig_type == self.trig_type + + for item in ("fn", "full_fn"): + old_val = getattr(self.sig_ls[0], item) + setattr(self, item, old_val.replace(self.sig_ls[0].proc, self.proc)) + + def return_nuis_key(self): + """Defining explicitly because it's not forwarded by getattribute""" + return self.sig_ls[0].return_nuis_key().replace(self.sig_ls[0].proc, self.proc) + + def return_name2details(self, lifetime_unit="mm"): + ls = self.sig_ls[0].return_name2details(lifetime_unit=lifetime_unit) + ls[0] = ls[0].replace(self.sig_ls[0].proc, self.proc) + return ls + + def print_diagnostics(self, lifetime_unit="mm"): + if len(self.sig_ls) > 1: + print("Overriding process name:", self.proc) + for s in self.sig_ls: + s.print_diagnostics(lifetime_unit=lifetime_unit) + + def get_sumw(self): + return sum(s.get_sumw() for s in self.sig_ls) + + def get_ngen(self): + return sum(s.get_ngen() for s in self.sig_ls) + + def get_xsec(self): + return sum(s.get_xsec() for s in self.sig_ls) + + def __getattr__(self, name): + """Delegate unknown attributes to the first list item.""" + result = getattr(self.sig_ls[0], name) + if isinstance(result, str): + result = result.replace(self.sig_ls[0].proc, self.proc) + return result + + +class NuisanceInfo(object): + """ + Object to store information to make a nuisance parameter. This object was defined assuming log-normal parameters, but it can be adjusted to non-log-normal parameters. + + The convention is to store e.g. 1.01 (NOT 0.01) if some fluctuation is ~1%. + + -INPUTS- + nuis_name: string. + nuis_val: int or arr-like. Note this is meaningless if type is shape, but provide something anyway otherwise it'll crash. + make_updn: Boolean. Will this make a shape uncertainty? + sep_yrs: Boolean. If true, the nuis_name will be tagged with year number, e.g. lepSF -> lepSF8 (prevents ROOT chaining unrelated nuisances together) + -Optional Inputs- + corr: Boolean. Will this nuisance produce one line on the datacard, or more than one? If non-correlated, it will produce nuisb1, nuisb2 (or nuis7b1) etc + nuis_type: string. If make_updn is True, it MUST be "shape". + nbins: int + ana_spec: Boolean. Is this analysis-specific and gets tagged with CMS+CADI? + add_era_tags: if False, neither a year- nor Run2-tag will be added + extra_info: list + + -Other Things Stored- + """ + + def __init__(self, nuis_name, nuis_val, make_updn, sep_yrs, corr, + nuis_type="lnN", nbins=3, ana_spec=False, add_era_tags=True, extra_info=None): + """It will always expand one entry into an array of size nbins. If you don't want this behavior, give it e.g. [1, 1.05, 1] so some bins don't fluctuate.""" + if extra_info is None: + extra_info = [] + + try: + test = float(nuis_val[0]) + self.nuis_val = np.array(nuis_val, dtype=float) + except Exception: + try: + self.nuis_val = float(nuis_val) * np.ones(nbins) + except Exception: + raise Exception("Unable to parse nuis_val") + + # Pad with 1.0 or truncate to match nbins. + if len(self.nuis_val) != nbins: + if len(self.nuis_val) < nbins: + self.nuis_val = np.concatenate( + [self.nuis_val, np.ones(nbins - len(self.nuis_val))]) + else: + self.nuis_val = self.nuis_val[:nbins] + + if np.any(self.nuis_val < 0): + raise Exception("Nuisance must be >=0. Also remember 1 (not 0) is the central value") + + if (make_updn is True and nuis_type != "shape") or (make_updn is not True and nuis_type == "shape"): + raise Exception("If make_updn is True, nuis_type must be shape (and vice versa)") + if nuis_type == "shape": + if corr is False: + raise Exception("Shape uncertainties should be initiated as bin-correlated") + corr = True + if nuis_type == "GammaN" and corr is True: + print("Warning: GammaN pipeline assumes corr=False. Please double-check the input") + + self.nuis_name = nuis_name + self.make_updn = bool(make_updn) + self.sep_yrs = sep_yrs + self.corr = corr + self.nuis_type = nuis_type + self.nbins = int(nbins) + self.add_anaID = ana_spec + self.add_era_tags = add_era_tags + self.extra_info = extra_info + + if corr is False: + if len(self.nuis_val) != nbins: + raise Exception("Length of array must equal nbins") + + def print_diagnostics(self): + msg = ("Nuisance name %s Is shape? %s Year-Sep %s Correlation %s" + " Contents %s Type %s Bins %s" % ( + self.nuis_name, self.make_updn, self.sep_yrs, self.corr, + self.nuis_val, self.nuis_type, self.nbins)) + if self.extra_info: + msg += " Notes: %s" % self.extra_info + print(msg) + + +class NuisanceTable(object): + """ + Make and query a table of nuisances. + + -STORAGE- + Nuisance grid: Indexed by year (string). The values are ALWAYS interpreted as fractions (not percent). + """ + + def __init__(self, proc="", x_vals=None, x_unit=None, y_vals=None, y_unit=None, + as_percent=None, + years=None, + nbin_len=False, dtype=float, pickle_loc=None, + make_pickle_fn=True, trig_for_pickle=None): + """ + -INPUTS- + as_percent: Boolean. If True, input 10 -> store 0.1. If False, stores exactly the input. The value stored is always interpreted as a FRACTION. + years: array-like, must be strings + nbin_len: Boolean. If False, assumes Nuisances are integers. Else it assumes len-nbins arrays. Writes arr_len. + dtype: how the np.array should represent the data + pickle_loc: if not None, it will un-pickle the specified dictionary, and construct itself. + make_pickle_fn: if True, it will add pickle_loc + "_" + proc + ".pkl" + trig_for_pickle: addresses the issue that aliases are needed for some processes, and these are trig-dependent + -Good practice: if you don't think it needs aliases, don't feed it one, so it catches errors + """ + if years is None: + years = set(["20161", "20162", "2017", "2018"]) + + if pickle_loc is None: + if any(v is None for v in [proc, x_vals, x_unit, y_vals, y_unit, as_percent]): + raise Exception("Missing inputs") + self.arr_len = nbins if nbin_len else 0 + self.nuis_dict = {} + try: + self.nuis_dict.update({ + "proc": str(proc), + "x_vals": np.sort(np.array(x_vals, dtype=float)), + "y_vals": np.sort(np.array(y_vals, dtype=float)), + "x_unit": str(x_unit), + "y_unit": str(y_unit), + "perc": as_percent, + "years": years, + "arr_len": self.arr_len, + "dtype": dtype, + }) + except Exception: + raise Exception("Error: input cannot be converted into arrays/strings") + self.proc = str(proc) + self.perc = as_percent + + for y in years: + if self.arr_len == 0: + arr = np.empty((len(self.nuis_dict["x_vals"]), len(self.nuis_dict["y_vals"])), dtype=dtype) + else: + arr = np.empty((len(self.nuis_dict["x_vals"]), len(self.nuis_dict["y_vals"]), self.arr_len), dtype=dtype) + arr.fill(np.nan) + self.nuis_dict[y] = arr + else: + pickle_loc_tosearch = pickle_loc + try: + if make_pickle_fn: + pickle_loc_tosearch += "_" + proc + ".pkl" + with open(pickle_loc_tosearch, "rb") as fh: + use_dict = pickle.load(fh) + except Exception: + could_find = False + for alias in config.sig["aliases"][trig_for_pickle][proc]: + try: + pickle_loc_tosearch = pickle_loc + if make_pickle_fn: + pickle_loc_tosearch += "_" + alias + ".pkl" + with open(pickle_loc_tosearch, "rb") as fh: + use_dict = pickle.load(fh) + could_find = True + print("Warning: redirected pickle name to " + alias) + break + except Exception: + pass + if not could_find: + raise Exception("Did not find any valid pickle to read, for " + proc) + + self.nuis_dict = use_dict + self.proc = self.nuis_dict["proc"] + self.perc = self.nuis_dict["perc"] + self.arr_len = self.nuis_dict["arr_len"] + + def add_entry(self, proc, x_val, y_val, year, val, x_unit=None, y_unit=None, debug_mode=False): + if proc != self.proc: + return + if year not in self.nuis_dict["years"]: + raise Exception("Queried year %s not in entries" % year) + + tau = x_val if x_unit is None else ROOThelper.convert_units(to_unit=self.nuis_dict["x_unit"], from_num=x_val, from_unit=x_unit) + mass = y_val if y_unit is None else ROOThelper.convert_units(to_unit=self.nuis_dict["y_unit"], from_num=y_val, from_unit=y_unit) + + x_ind = np.where(self.nuis_dict["x_vals"] == tau)[0][0] + y_ind = np.where(self.nuis_dict["y_vals"] == float(mass))[0][0] + + to_fill = val * 0.01 if self.perc else val + if np.prod(np.isfinite(self.nuis_dict[year][x_ind, y_ind])): + raise Exception("Overwriting non-nan value") + self.nuis_dict[year][x_ind, y_ind] = to_fill + if debug_mode: + print("Filled", x_ind, ",", y_ind, "of", year) + + def add_entry_from_fn(self, file_info, val, debug_mode=False): + try: + new_sig = SignalROOTInfo(file_info) + except Exception: + new_sig = file_info + proc, tau, mass, yr = new_sig.return_name2details(lifetime_unit=self.nuis_dict["x_unit"]) + self.add_entry(proc=proc, x_val=tau, y_val=mass, year=yr, val=val, debug_mode=debug_mode) + + def add_dictionary(self, in_dict, debug_mode=False): + for k in in_dict.keys(): + self.add_entry_from_fn(k, in_dict[k], debug_mode=debug_mode) + + def add_array(self, proc, x_vals, y_vals, year, val_arr, x_unit=None, y_unit=None, debug_mode=False): + if len(x_vals) != len(self.nuis_dict["x_vals"]) or len(y_vals) != len(self.nuis_dict["y_vals"]): + raise Exception("Input array dimensions incompatible.") + val_arr = np.array(val_arr) + if val_arr.shape != self.nuis_dict[year].shape: + raise Exception("Bad array dimensions, or wrong year.") + for i in xrange(len(x_vals)): + for j in xrange(len(y_vals)): + self.add_entry(proc, x_vals[i], y_vals[j], year, val_arr[i, j], + x_unit=x_unit, y_unit=y_unit, debug_mode=debug_mode) + + def get_point(self, year, x_val, y_val, x_unit=None, y_unit=None, use_log=False, debug_mode=False): + """Interpolate the table at (x_val, y_val). Returns None if outside grid.""" + if year not in self.nuis_dict["years"]: + raise Exception("%s not in list of years." % year) + + if x_unit is None: + x_unit = self.nuis_dict["x_unit"] + if y_unit is None: + y_unit = self.nuis_dict["y_unit"] + x_val, y_val = float(x_val), float(y_val) + + xy_vals = [x_val, y_val] + xy_units = [x_unit, y_unit] + dict_unit_k = ["x_unit", "y_unit"] + dict_vals_k = ["x_vals", "y_vals"] + s_ind = [] + vsu_ls = [] + + for i in xrange(len(xy_vals)): + su = self.nuis_dict[dict_unit_k[i]] + ua = self.nuis_dict[dict_vals_k[i]] + vsu = ROOThelper.convert_units(su, from_num=xy_vals[i], from_unit=xy_units[i]) + vsu_ls.append(vsu) + + if vsu in ua: + s_ind.append([np.where(ua == vsu)[0][0]]) + else: + if vsu < ua[0] or vsu > ua[-1]: + print("Warning: requested", vsu, su, "is outside the storage grid.") + print("Extrapolation from 2D array not implemented. Ending function.") + return None + low_ele = np.where(ua < vsu)[0][-1] + upp_ele = np.where(ua > vsu)[0][0] + s_ind.append([low_ele, upp_ele]) + + if debug_mode: + print("Searching coordinates:", s_ind, "corresponding to", vsu_ls, "(in same units as grid)") + + interp_q = 0.0 + + if len(s_ind[0]) == 1 and len(s_ind[1]) == 1: + interp_q = self.nuis_dict[year][s_ind[0][0], s_ind[1][0]] + if debug_mode: + print("Extracted", interp_q) + + elif len(s_ind[0]) == 1 and len(s_ind[1]) != 1: + if use_log: + y, y0, y1 = np.log([vsu_ls[1], self.nuis_dict["y_vals"][s_ind[1][0]], self.nuis_dict["y_vals"][s_ind[1][1]]]) + else: + y, y0, y1 = vsu_ls[1], self.nuis_dict["y_vals"][s_ind[1][0]], self.nuis_dict["y_vals"][s_ind[1][1]] + q0 = self.nuis_dict[year][s_ind[0][0], s_ind[1][0]] + q1 = self.nuis_dict[year][s_ind[0][0], s_ind[1][1]] + interp_q = lerp(y, y0, y1, q0, q1) + if debug_mode: + print("Interpolated y-axis", y0, y1, "with nuis", q0, q1, "to get y=", y, "as", interp_q) + + elif len(s_ind[0]) != 1 and len(s_ind[1]) == 1: + if use_log: + x, x0, x1 = np.log([vsu_ls[0], self.nuis_dict["x_vals"][s_ind[0][0]], self.nuis_dict["x_vals"][s_ind[0][1]]]) + else: + x, x0, x1 = vsu_ls[0], self.nuis_dict["x_vals"][s_ind[0][0]], self.nuis_dict["x_vals"][s_ind[0][1]] + q0 = self.nuis_dict[year][s_ind[0][0], s_ind[1][0]] + q1 = self.nuis_dict[year][s_ind[0][1], s_ind[1][0]] + interp_q = lerp(x, x0, x1, q0, q1) + if debug_mode: + print("Interpolated x-axis", x0, x1, "with nuis", q0, q1, "to get x=", x, "as", interp_q) + + elif len(s_ind[0]) != 1 and len(s_ind[1]) != 1: + if use_log: + x, x1, x2 = np.log([vsu_ls[0], self.nuis_dict["x_vals"][s_ind[0][0]], self.nuis_dict["x_vals"][s_ind[0][1]]]) + y, y1, y2 = np.log([vsu_ls[1], self.nuis_dict["y_vals"][s_ind[1][0]], self.nuis_dict["y_vals"][s_ind[1][1]]]) + else: + x, x1, x2 = vsu_ls[0], self.nuis_dict["x_vals"][s_ind[0][0]], self.nuis_dict["x_vals"][s_ind[0][1]] + y, y1, y2 = vsu_ls[1], self.nuis_dict["y_vals"][s_ind[1][0]], self.nuis_dict["y_vals"][s_ind[1][1]] + q11 = self.nuis_dict[year][s_ind[0][0], s_ind[1][0]] + q12 = self.nuis_dict[year][s_ind[0][0], s_ind[1][1]] + q21 = self.nuis_dict[year][s_ind[0][1], s_ind[1][0]] + q22 = self.nuis_dict[year][s_ind[0][1], s_ind[1][1]] + points = [(x1, y1, q11), (x1, y2, q12), (x2, y1, q21), (x2, y2, q22)] + interp_q = bilerp(x, y, points) + if debug_mode: + print("Interpolated nuis", interp_q, "from nuis", [q11, q12, q21, q22], + "at x's", [x1, x2], "and y's", [y1, y2]) + else: + raise Exception("Error: coordinate list is not 1x1-2x2") + + if self.arr_len == 0: + if not np.isfinite(interp_q): + print("Warning: unable to interpolate, likely missing grid value. Exiting code.") + return None + return interp_q + + def get_point_from_fn(self, file_info, overrides=None, use_log=False, debug_mode=False): + try: + new_sig = SignalROOTInfo(file_info) + except Exception: + new_sig = file_info + proc, tau, mass, yr = new_sig.return_name2details(lifetime_unit=self.nuis_dict["x_unit"]) + if proc != self.proc: + return None + if overrides is not None: + for k in overrides: + if k == "yr": + yr = overrides[k] + return self.get_point(yr, tau, mass, use_log=use_log, debug_mode=debug_mode) + + def print_diagnostics(self): + print(self.nuis_dict) + + def save_pickle(self, to_fn_prefix, tag_proc=True, to_fn_suffix=".pkl", debug_mode=False): + out_fn = to_fn_prefix + if tag_proc: + out_fn += "_" + self.proc + out_fn += to_fn_suffix + with open(out_fn, "wb") as fh: + pickle.dump(self.nuis_dict, fh) + if debug_mode: + print("Saved pickle:", out_fn) + + def pretty_print_diagnostics(self): + str_out = str(self.nuis_dict).replace(", '", """,\n '""") + temp_yr_dict = {"years": self.nuis_dict["years"]} + str_yr_replace = str(temp_yr_dict)[1:-1] + str_out = str_out.replace( + str_yr_replace.replace(", '", """,\n '"""), + str_yr_replace) + return str_out + + +def collect_xyvals_from_namearr(p, fns, x_unit, y_unit, debug_mode=False, **kwargs): + """Scan filenames and return sorted lists of unique x (lifetime) and y (mass) values.""" + x_vals = [] + y_vals = [] + + for k in fns: + new_sig = SignalROOTInfo(k, **kwargs) + proc, tau, mass, _ = new_sig.return_name2details(lifetime_unit=x_unit) + if proc != p: + continue + for val, ls in [(tau, x_vals), (mass, y_vals)]: + if val not in ls: + ls.append(val) + + x_vals.sort() + y_vals.sort() + + if debug_mode: + print("Identified x-s:", x_vals, "in", x_unit) + print("Identified y-s:", y_vals) + + return x_vals, y_vals diff --git a/MFVNeutralino/test/ForLimits/helper_ROOT_functions.py b/MFVNeutralino/test/ForLimits/helper_ROOT_functions.py new file mode 100644 index 000000000..2fc8d0ab3 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/helper_ROOT_functions.py @@ -0,0 +1,56 @@ +from __future__ import division +from __future__ import absolute_import + +import ROOT + +import sig_and_bkg_configs as sb_conf + + +def search_dict_w_startkey(dt, p): + """Look up p in dt; fall back to prefix-matched entry in dt['index_start'], then dt['others'].""" + if p in dt.keys(): + return dt[p] + else: + for st in dt["index_start"].keys(): + str_loc = p.find(st) + if str_loc == 0: + return dt["index_start"][st] + return dt["others"] + + +def mult_hist_w_array(h, arr): + """Multiply each bin content and error of h by the corresponding element of arr.""" + if (h.GetNbinsX() != len(arr)): + raise Exception("Bad multiplicative array dimensions for ROOT histogram") + for i in range(h.GetNbinsX()): + h.SetBinContent(i+1, h.GetBinContent(i+1) * arr[i]) + h.SetBinError(i+1, h.GetBinError(i+1) * arr[i]) + return + + +def convert_units(to_unit, from_num=None, from_unit=None, from_expr=None): + """Convert a displacement value between mm and um. + + from_expr: if given, interprets the last 2 chars as the unit (filename convention). + """ + if "eV" in to_unit and "eV" in from_unit: + return from_num + + if from_expr is not None: + from_num = float(from_expr[:-2]) + from_unit = from_expr[-2:] + + num_asfloat = float(from_num) + if from_unit == "mm": + num_in_mm = num_asfloat + elif from_unit == "um": + num_in_mm = num_asfloat * 1e-3 + else: + raise Exception("Unit input %s not recognized" % from_unit) + + if to_unit == "mm": + return num_in_mm + elif to_unit == "um": + return num_in_mm * 1e3 + else: + raise Exception("Unit requested %s not recognized" % to_unit) diff --git a/MFVNeutralino/test/ForLimits/hepdata_ins1861146.json b/MFVNeutralino/test/ForLimits/hepdata_ins1861146.json new file mode 100644 index 000000000..67f81bd80 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/hepdata_ins1861146.json @@ -0,0 +1,1632 @@ +{ + "mfv_neu": { + "ctau_mm": [ + 0.15, + 0.25, + 0.35, + 0.45, + 0.55, + 0.65, + 0.75, + 0.85, + 0.95, + 1.5, + 2.5, + 3.5, + 5.5, + 8.5, + 11.5, + 14.5, + 17.5, + 20.5, + 23.5, + 26.5, + 29.5, + 32.5, + 35.5, + 38.5, + 41.5, + 44.5, + 47.5, + 50.5, + 53.5, + 56.5, + 59.5, + 62.5, + 65.5, + 68.5, + 71.5, + 74.5, + 77.5, + 80.5, + 83.5, + 86.5, + 89.5, + 92.5, + 95.5, + 98.5 + ], + "mass_gev": [ + 350, + 450, + 550, + 700, + 900, + 1100, + 1300, + 1500, + 1700, + 1900, + 2100, + 2300, + 2500, + 2700, + 2900 + ], + "obs": [ + [ + 0.0023, + 0.0005, + 0.0004, + 0.0011, + 0.0033, + 0.0108, + 0.0363, + 0.107, + 0.3024, + 0.7908, + 2.5189, + 6.5765, + 17.6484, + 49.4568, + 146.2079 + ], + [ + 0.0004, + 0.0001, + 0.0001, + 0.0002, + 0.0006, + 0.0023, + 0.0069, + 0.022, + 0.0654, + 0.1909, + 0.5395, + 1.5219, + 4.0772, + 11.4791, + 30.7851 + ], + [ + 0.0003, + 0.0001, + 0.0, + 0.0001, + 0.0003, + 0.0011, + 0.0037, + 0.0116, + 0.0347, + 0.0964, + 0.2823, + 0.7905, + 2.1214, + 6.0115, + 16.8267 + ], + [ + 0.0002, + 0.0, + 0.0, + 0.0001, + 0.0002, + 0.0007, + 0.0025, + 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137.36, + 392.1037, + 1090.25 + ], + [ + 0.0053, + 0.0061, + 0.0044, + 0.0073, + 0.0156, + 0.0522, + 0.1754, + 0.5739, + 1.8188, + 5.7376, + 17.3625, + 49.9247, + 147.86, + 407.8674, + 1163.8333 + ], + [ + 0.0082, + 0.0056, + 0.0053, + 0.0074, + 0.0158, + 0.0544, + 0.188, + 0.6208, + 1.9318, + 6.0298, + 17.5338, + 51.4349, + 151.69, + 442.7954, + 1214.6667 + ], + [ + 0.0046, + 0.0067, + 0.005, + 0.0083, + 0.0166, + 0.0573, + 0.195, + 0.6425, + 2.0649, + 6.1376, + 18.5396, + 55.4486, + 155.71, + 449.9424, + 1230.25 + ], + [ + 0.0127, + 0.0073, + 0.0054, + 0.0088, + 0.0176, + 0.0597, + 0.1996, + 0.6666, + 2.152, + 6.5752, + 19.5023, + 55.5171, + 162.56, + 467.8386, + 1302.9167 + ], + [ + 0.0089, + 0.0074, + 0.0056, + 0.0093, + 0.0186, + 0.0641, + 0.2016, + 0.6783, + 2.1117, + 6.9519, + 20.366, + 59.476, + 171.25, + 490.0, + 1341.3333 + ] + ] + } +} \ No newline at end of file diff --git a/MFVNeutralino/test/ForLimits/limits_config.yaml b/MFVNeutralino/test/ForLimits/limits_config.yaml new file mode 100644 index 000000000..bd7e1c54c --- /dev/null +++ b/MFVNeutralino/test/ForLimits/limits_config.yaml @@ -0,0 +1,74 @@ +# ============================================================ +# Limit framework configuration +# Edit paths and runtime settings here. Physics configuration +# (signal groupings, nuisance definitions) lives in script_configs.py. +# ============================================================ + +# Runtime: override with --year and --channel CLI flags +year: "2018" # one of: 20161, 20162, 2017, 2018 +channel: "lep" # "lep" or "bjet" +debug: true # verbose output while running + +# ---- Binning ------------------------------------------------- +# Units: cm. Four bins: [0, 0.1, 0.4, 2.0, 4.0] cm +bins: [0., 0.1, 0.4, 2.0, 4.0] +nbins: 4 + +# ---- Input signal MiniTree paths ---------------------------- +# Folder should contain files named like: {process}_tau{ct}_{year}.root +signal: + lep: + folder: "/uscms/home/gdecastr/nobackup/crabdirs/MiniTree_tag001Lepm_VH/" + file_key: "condor_*tau*/minitree_0.root" + bjet: + folder: "/uscms/home/gdecastr/nobackup/crabdirs/MiniTree_tag001BvetoLHTm_bjet/" + file_key: "condor_*tau*/minitree_0.root" + +# ---- Input background paths --------------------------------- +# Local copies of the MC Run 2 background templates (copied from alecduqu's One2Two). +# Per-year files (2v_from_jets_{year}_5track_default_*.root) are also present in +# BackgroundTemplates/{lep,bjet}/ for reference; only the run2-combined files are +# currently used (no {year} placeholder in filename → .format(year) is a no-op). +# TODO: replace with data-driven per-year estimates when available after unblinding. +background: + lep: + folder: "/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/BackgroundTemplates/lep/" + filename: "2v_from_jets_run2_5track_default_ULV30Lepm.root" + bjet: + folder: "/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/BackgroundTemplates/bjet/" + filename: "2v_from_jets_run2_5track_default_ULV30BvetoLHTm.root" + +# ---- Output paths ------------------------------------------- +# root_output: the intermediate ROOT file holding histograms +# datacard_output: the final .txt combine datacards +root_output: + lep: + folder: "/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/LimitsInput_4bin/lep/" + bjet: + folder: "/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/LimitsInput_4bin/bjet/" + filename: "limitsinput" + +datacard_output: + lep: + folder: "/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/Datacards_4bin/lep/" + bjet: + folder: "/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src/JMTucker/MFVNeutralino/test/ForLimits/Datacards_4bin/bjet/" + prefix: "Datacard_" + suffix: ".txt" + +# ---- Nuisance table paths (relative to ForLimits/ directory) ---- +nuisance_tables: + vtx_reco_TM: "NuisTabStore_TrkMvr/pickle" + disp_trig_uncerts: "NuisTabStore_7p4p1/pickle" + tk_reco_eff: + base: "NuisTabStore_TrkRec/" + up_prefix: "up_pickle" + dn_prefix: "dn_pickle" + fac_scale_VH_csv: "/uscms/home/gdecastr/nobackup/crabdirs/TheorySystematics/fac_scale_shape_bins.csv" + +# ---- Observed events (set to 0 for blind analysis) ----------- +observations: + "20161": [0, 0, 0, 0] + "20162": [0, 0, 0, 0] + "2017": [0, 0, 0, 0] + "2018": [0, 0, 0, 0] diff --git a/MFVNeutralino/test/ForLimits/makeDatacard.py b/MFVNeutralino/test/ForLimits/makeDatacard.py new file mode 100644 index 000000000..b668989f1 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/makeDatacard.py @@ -0,0 +1,423 @@ +from __future__ import print_function +import ROOT +import numpy as np + +import script_configs as config +import nuisance_configs as ns_conf +import helper_PyStorage_objects as sth + +""" +Naming conventions (Note: apparently if I put template as an input, the function refuses to change template, idk why) + return_XX : it will give something back, append to template + add_XX: overwrite template with the return value + replace_XX: like add + write_XX: overwrite template (apparently this can't be done) + +The only stuff that are hard-coded are: +-Sig and bkg are called 0 and 1 +-Sig and bkg are written in this order +""" + +# Hard codes +n_proc = 2 + +# Module-level globals -- updated per year via _init_for_year() +year = config.datacard["year"] +year_id = config.datacard["year_key"].index(year) +nbins = config.datacard["nbins"] +bins = np.array(config.datacard["bins"]) +sig_type = config.sig["type"] + +year_to_tag = config.datacard["year_to_tag"] +year_tag = year_to_tag[year] + + +def _init_for_year(yr): + global year, year_id, nbins, bins, sig_type, year_to_tag, year_tag + year = yr + year_id = config.datacard["year_key"].index(yr) + nbins = config.datacard["nbins"] + bins = np.array(config.datacard["bins"]) + sig_type = config.sig["type"] + year_to_tag = config.datacard["year_to_tag"] + year_tag = year_to_tag[yr] + + +# --------------------------------------------------------------------------- +# Short helpers +# --------------------------------------------------------------------------- + +def pad(some_str, n, align_left=True): + """Pad any given string to n characters (does not truncate); always adds one space.""" + if len(some_str) >= n - 1: + return (some_str + " ") if align_left else (" " + some_str) + elif align_left: + return some_str + (n - len(some_str)) * " " + else: + return (n - len(some_str)) * " " + some_str + + +# Name helpers reference year_tag as a global -- picked up at call time +sig_nm = lambda sid: " " + "sig" + year_tag + sid +bkg_nm = lambda: " " + "bkg" + year_tag +b_nm = lambda bn: " " + "b" + year_tag + bn + + +def return_sep(): + return " " + + +def make_nuis_dcnm(nuis, siggrp, force_no_CADItag=False): + """Return the datacard name(s) for a nuisance. + + Correlated -> single string; un-correlated -> list of per-bin strings. + """ + nuis_rootname = nuis.nuis_name + if not force_no_CADItag: + if nuis.add_anaID: + nuis_rootname = ns_conf.nuis_names["CMS-CADI-tag"] + nuis_rootname + if nuis.add_era_tags: + nuis_rootname = nuis_rootname + "_" + if nuis.sep_yrs: + nuis_rootname += year_tag + else: + nuis_rootname += ns_conf.nuis_names["Run2-key"] + + if nuis.corr is True: + return nuis_rootname + elif nuis.corr is False: + return [nuis_rootname + "b" + str(i + 1) for i in range(nbins)] + else: + raise Exception("Unable to interpret nuisance for name->datacard conversion:", nuis.nuis_name) + + +def return_newline(): + return """ +________ +""" + + +def turn_info_to_line(ns_name, ns_type, strls, write_sig): + """Build one datacard line string.""" + if len(strls) != nbins: + raise Exception("Bad line input.") + new_line = "\n" + nuis_seg = "" + dash = pad("-", 7, False) + + for i in range(nbins): + nuis_seg += dash if strls[i] is None else pad(str(strls[i]), 7, False) + + new_line += pad(ns_name, 30) + pad(ns_type, 5) + dash_tag = "DASH%d" % nbins + if write_sig is True: + new_line += nuis_seg + return_sep() + dash_tag + elif write_sig is False: + new_line += dash_tag + return_sep() + nuis_seg + else: + raise Exception("Specify whether to write signal or bkg") + return new_line + + +def turn_info_to_nlines(ns_names, ns_type, strls, write_sig): + """Build per-bin datacard lines (un-correlated nuisances).""" + if len(strls) != nbins or len(ns_names) != nbins: + raise Exception("Bad line input.") + new_lines = "" + for i in range(nbins): + in_strls = [None if j != i else strls[i] for j in range(nbins)] + new_lines += turn_info_to_line(ns_names[i], ns_type, in_strls, write_sig) + return new_lines + + +def return_no_dashes(template): + new_template = template + for d in range(nbins + 1): + new_template = new_template.replace("DASH%i" % d, d * pad("-", 7, False)) + return new_template + + +# --------------------------------------------------------------------------- +# Long functions +# --------------------------------------------------------------------------- + +def add_ijkmax(template, nuis_ls, nuis_bkg_ls, siggrp): + new_template = template + "\nimax " + str(nbins) + "\njmax 1\nkmax " + + k_ct = 0 + for nuis in nuis_ls + nuis_bkg_ls: + if nuis.nuis_type == "special": + if nuis.extra_info[0] == "updn_pair": + if nuis.extra_info[1] == "dn": + continue + if nuis.corr is True: + k_ct += 1 + elif nuis.corr is False: + k_ct += nbins + else: + print("Warning: unable to count k due to bad nuisance object:", nuis.nuis_name) + + new_template += str(k_ct) + return new_template + + +def add_observations(template, f, siggrp, sig_id): + new_template = template + pad("bin", 12) + for i in range(nbins): + new_template += b_nm(str(i)) + + new_template += "\n" + pad("observation", 12) + h_obs = ROOT.TH1D(f.Get("h_observed_%s" % year)) + for i in range(nbins): + new_template += pad(str(int(h_obs.GetBinContent(i + 1))), 7, False) + h_obs.Delete() + return new_template + + +def add_central_vals(template, f, sig_norm_ls, siggrp, sig_id): + """Build the bin/process/rate block; fills sig_norm_ls in place for Gamma-N.""" + new_template = template + pad("bin", 12) + for j in range(n_proc): + for i in range(nbins): + new_template += b_nm(str(i)) + if j != n_proc - 1: + new_template += return_sep() + + new_template += "\n" + pad("process", 12) + nbins * sig_nm(sig_id) + return_sep() + nbins * bkg_nm() + new_template += "\n" + pad("process", 12) + nbins * pad("0", 7, False) + return_sep() + nbins * pad("1", 7, False) + + h_sig = f.Get(sig_nm(sig_id).replace(" ", "")) + h_bkg = f.Get(bkg_nm().replace(" ", "")) + to_write = [h_sig, h_bkg] + + new_template += "\n" + pad("rate", 12) + for j in range(n_proc): + for i in range(nbins): + new_template += pad(str(to_write[j].GetBinContent(i + 1)), 3, False) + if j == 0: + sig_norm_ls.append(to_write[j].GetBinContent(i + 1)) + if j != n_proc - 1: + new_template += return_sep() + + h_sig.Delete() + h_bkg.Delete() + return new_template, sig_norm_ls + + +def return_lnN_corr(f, ns_ls, siggrp, write_sig): + new_lines = "" + for nuis in ns_ls: + try: + if nuis.nuis_type != "lnN": + continue + if nuis.corr is not True: + continue + except Exception: + raise Exception("Failure: Nuisance object not complete") + dc_name = make_nuis_dcnm(nuis, siggrp) + new_lines += turn_info_to_line(dc_name, "lnN", nuis.nuis_val, write_sig) + new_lines += "\n" + return new_lines + + +def return_lnN_uncorr(f, ns_ls, siggrp, write_sig): + new_lines = "" + for nuis in ns_ls: + try: + if nuis.nuis_type != "lnN": + continue + if nuis.corr is not False: + continue + except Exception: + raise Exception("Failure: Nuisance object not complete") + dc_names = make_nuis_dcnm(nuis, siggrp) + new_lines += turn_info_to_nlines(dc_names, "lnN", nuis.nuis_val, write_sig) + new_lines += "\n" + return new_lines + + +def return_shape_lines(f, ns_ls, siggrp, sig_id, write_sig=True): + """Write shape systematics as lnN lines using Up/Down histogram ratios.""" + new_lines = "" + for nuis in ns_ls: + if nuis.nuis_type != "shape": + continue + + dc_name = make_nuis_dcnm(nuis, siggrp) + process_htag = (sig_nm(sig_id) if write_sig else bkg_nm()).replace(" ", "") + + hname_up = process_htag + "_" + dc_name + "Up" + hname_dn = process_htag + "_" + dc_name + "Down" + + h_central = f.Get(process_htag) + h_shape_up = f.Get(hname_up) + h_shape_dn = f.Get(hname_dn) + + strls = [] + for i in range(nbins): + try: + strls.append( + str(h_shape_dn.GetBinContent(i + 1) / h_central.GetBinContent(i + 1)) + + "/" + + str(h_shape_up.GetBinContent(i + 1) / h_central.GetBinContent(i + 1)) + ) + except Exception: + strls.append("1.0/1.0") + print("Warning: division by 0 in up/down variations. Filling 1.0 for", + siggrp.fn, "bin", i) + + new_lines += turn_info_to_line(dc_name, "lnN", strls, write_sig) + + new_lines += "\n" + return new_lines + + +def return_special_lines(f, ns_ls, sig_norm_ls, siggrp, sig_id, write_sig=True): + """Handle GammaN, updn_pair, and anti-lnN nuisance types.""" + new_lines = "" + + for nuis in ns_ls: + try: + if nuis.nuis_type in ("lnN", "shape"): + continue + except Exception: + raise Exception("Failure: Nuisance object not complete") + + if nuis.nuis_type == "GammaN": + if not write_sig: + raise Exception("GammaN not implemented for background") + + dc_names = make_nuis_dcnm(nuis, siggrp) + sig_cts_hname = "h_sig%s_ngen_perbin_%s" % (sig_id, year) + h_sig_cts = f.Get(sig_cts_hname) + + # Precompute per-event weight fallback (AN formula): + # w = Lumi x XSection / Generated_MC_Events_Pre-Cuts + # Used when a bin has zero unweighted events. + _h_ngen = f.Get("h_sig%s_ngen_total_%s" % (sig_id, year)) + _h_sigyield = f.Get("h_sig%s_sigyield_total_%s" % (sig_id, year)) + _ngen_total = _h_ngen.GetBinContent(1) if _h_ngen else 0.0 + _sigyield_total = _h_sigyield.GetBinContent(1) if _h_sigyield else 0.0 + _w_precut = (_sigyield_total / _ngen_total) if _ngen_total > 0 else 0.0 + + for i in range(nbins): + strls = [] + for j in range(nbins): + if j != i: + strls.append(None) + else: + count = h_sig_cts.GetBinContent(j + 1) + if count > 0: + kappa = sig_norm_ls[j] / count + else: + # Fall back to pre-cut weight estimate per AN: + # w = Lumi x XSection / Generated_MC_Events_Pre-Cuts + kappa = _w_precut + strls.append(kappa) + new_lines += turn_info_to_line( + dc_names[i], + "gmN " + pad(str(int(h_sig_cts.GetBinContent(i + 1))), 5), + strls, write_sig + ) + continue + + elif nuis.nuis_type == "special": + if nuis.extra_info[0] == "updn_pair": + if nuis.extra_info[1] == "up": + pair_found = False + for n2 in ns_ls: + if (n2.nuis_name == nuis.nuis_name and + n2.nuis_type == nuis.nuis_type and + n2.extra_info[1] == "dn"): + npair = n2 + pair_found = True + break + if not pair_found: + raise Exception("updn_pair UP has no corresponding DOWN") + + strls = [str(n2.nuis_val[i]) + "/" + str(nuis.nuis_val[i]) + for i in range(nbins)] + if nuis.corr is True: + new_lines += turn_info_to_line(make_nuis_dcnm(nuis, siggrp), "lnN", strls, write_sig) + else: + new_lines += turn_info_to_nlines(make_nuis_dcnm(nuis, siggrp), "lnN", strls, write_sig) + elif nuis.extra_info[1] != "dn": + raise Exception("Bad extra_info field, use up or dn") + + if nuis.extra_info[0] == "anti-lnN": + if nuis.corr is not True: + raise Exception("Anti-correlated lnN does not support un-correlated bins") + dc_name = make_nuis_dcnm(nuis, siggrp) + strls = [str(nuis.nuis_val[i]) + "/" + str(1 / nuis.nuis_val[i]) + for i in range(nbins)] + new_lines += turn_info_to_line(dc_name, "lnN", strls, write_sig) + + else: + print("Warning: nuisance", nuis.nuis_name, "has not been interpreted as a datacard line") + if write_sig: + print(" Generated when evaluating signal") + else: + print(" Generated when evaluating background") + continue + + new_lines += "\n" + return new_lines + + +# --------------------------------------------------------------------------- +# Main entry point +# --------------------------------------------------------------------------- + +def make_datacard(f, nuis_ls, nuis_bkg_ls, siggrp, sig_id, debug_mode=False): + """Write a combine .txt datacard to disk for one signal hypothesis.""" + sig_name = "sig" + year_tag + sig_id + if debug_mode: + print("\nSearching for signal named", sig_name) + + template = ( + "# sample id in ROOT file " + sig_id + "\n" + "# filename = " + siggrp.fn + "\n" + "# total sig rate = FIXME, not implemented" + ) + + template = add_ijkmax(template=template, nuis_ls=nuis_ls, nuis_bkg_ls=nuis_bkg_ls, siggrp=siggrp) + template += return_newline() + + template = add_observations(template=template, f=f, siggrp=siggrp, sig_id=sig_id) + template += return_newline() + + sig_norm_ls = [] + template, sig_norm_ls = add_central_vals(template=template, f=f, sig_norm_ls=sig_norm_ls, + siggrp=siggrp, sig_id=sig_id) + template += return_newline() + + # Signal nuisances + template += return_lnN_corr(f=f, ns_ls=nuis_ls, siggrp=siggrp, write_sig=True) + template += return_lnN_uncorr(f=f, ns_ls=nuis_ls, siggrp=siggrp, write_sig=True) + template += return_shape_lines(f=f, ns_ls=nuis_ls, siggrp=siggrp, sig_id=sig_id, write_sig=True) + template += return_special_lines(f=f, ns_ls=nuis_ls, sig_norm_ls=sig_norm_ls, + siggrp=siggrp, sig_id=sig_id, write_sig=True) + + # Background nuisances + template += return_lnN_corr(f=f, ns_ls=nuis_bkg_ls, siggrp=siggrp, write_sig=False) + template += return_lnN_uncorr(f=f, ns_ls=nuis_bkg_ls, siggrp=siggrp, write_sig=False) + template += return_special_lines(f=f, ns_ls=nuis_bkg_ls, sig_norm_ls=sig_norm_ls, + siggrp=siggrp, sig_id=sig_id, write_sig=False) + + template = return_no_dashes(template) + + if debug_mode: + print("Printing DATACARD:\n") + print(template) + + dc_dict = config.datacard_out_loc + out_dc_fn = (dc_dict[sig_type]["out_folder"] + dc_dict["out_fn_prefix"] + + siggrp.trig_type + "_" + siggrp.return_nuis_key()) + if config.debug_settings["scale_bkg_fake"]: + out_dc_fn += "_fakebkg" + out_dc_fn += dc_dict["out_fn_suffix"] + + if debug_mode: + print("Writing datacard to:", out_dc_fn) + with open(out_dc_fn, "w") as datacard: + datacard.write(template) diff --git a/MFVNeutralino/test/ForLimits/makeLimitsInputROOT.py b/MFVNeutralino/test/ForLimits/makeLimitsInputROOT.py new file mode 100644 index 000000000..21a6ca4d3 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/makeLimitsInputROOT.py @@ -0,0 +1,395 @@ +from __future__ import print_function +import argparse +import os +import sys +from glob import glob + +import ROOT +import numpy as np + +from JMTucker.Tools.ROOTTools import to_TH1D, move_overflow_into_last_bin + +import script_configs as config +import sig_and_bkg_configs as sb_conf +import getNuisanceFromSig as getns +import makeDatacard as mkdat +import nuisance_configs_and_functions as nsfc +import helper_PyStorage_objects as sth +import helper_ROOT_functions as ROOThelper +from makeDatacard import make_nuis_dcnm as mk_dcnm + + +# --------------------------------------------------------------------------- +# CLI arguments -- override limits_config.yaml at runtime +# --------------------------------------------------------------------------- +def _parse_args(): + p = argparse.ArgumentParser(description="Build limits input ROOT file and datacards") + p.add_argument("--year", default=None, help="Year to process: 20161/20162/2017/2018/all") + p.add_argument("--channel", default=None, choices=["lep", "bjet"], help="Trigger channel") + p.add_argument("--debug", action="store_true", default=None, help="Verbose output") + p.add_argument("--no-debug", dest="debug", action="store_false") + return p.parse_args() + + +def _apply_args(args): + """Push CLI overrides back into config dicts so the rest of the code is unchanged.""" + if args.year and args.year != "all": + config.datacard["year"] = args.year + if args.channel: + config.sig["type"] = args.channel + if args.debug is not None: + config.debug_settings["enabled"] = args.debug + + +# --------------------------------------------------------------------------- +# Module-level globals (set per-year run inside run_one_year) +# --------------------------------------------------------------------------- +debug = None +sig_type = None +year = None +year_id = None +year_tag = None +nbins = None +bins = None +print_mapping = True + + +def _init_globals(yr): + global debug, sig_type, year, year_id, year_tag, nbins, bins + debug = config.debug_settings["enabled"] + sig_type = config.sig["type"] + year = yr + year_id = config.datacard["year_key"].index(yr) + year_tag = config.datacard["year_to_tag"][yr] + nbins = config.datacard["nbins"] + bins = np.array(config.datacard["bins"]) + mkdat._init_for_year(yr) + nsfc._init_for_year(yr) + + +# --------------------------------------------------------------------------- +# Config sanity check +# --------------------------------------------------------------------------- +def check_config(yr): + if yr not in config.datacard["year_key"]: + raise ValueError("year %s not in year_key" % yr) + if len(config.datacard["bins"]) != config.datacard["nbins"] + 1: + raise ValueError("nbins must equal len(bins)-1") + if config.sig["type"] not in ("lep", "bjet"): + raise ValueError("channel must be 'lep' or 'bjet'") + if len(config.obs[yr]) != config.datacard["nbins"]: + raise ValueError("len(observations[year]) must equal nbins") + if config.debug_settings["scale_bkg_fake"]: + print("WARNING: scale_bkg_fake is True -- background will be artificially scaled") + + +# --------------------------------------------------------------------------- +# Background +# --------------------------------------------------------------------------- +def make_bkg(f): + bkg_fn = os.path.join( + config.bkg[sig_type]["folder"], + config.bkg[sig_type]["fn"].format(year) + ) + if debug: + print("Opening bkg file:", bkg_fn) + bkg_f = ROOT.TFile.Open(bkg_fn) + + new_bkg_hs = [] + + h_int_lumi = ROOT.TH1D("h_int_lumi_%s" % year, "", 1, 0, 1) + h_int_lumi.SetBinContent(1, sb_conf.template_norms["lumi"][sig_type][year_id]) + if debug: + print("Lumi:", sb_conf.template_norms["lumi"][sig_type][year_id]) + new_bkg_hs.append(h_int_lumi) + + h_observed = ROOT.TH1D("h_observed_%s" % year, "", nbins, bins) + for i, v in enumerate(config.obs[year]): + h_observed.SetBinContent(i+1, v) + if debug: + print(" observed bin %d = %d" % (i, v)) + new_bkg_hs.append(h_observed) + + # Full Run 2 MC background -- shape normalized to per-year n2v from sig_and_bkg_configs.py + h_bkg_sumdbv = to_TH1D(bkg_f.Get("h_c1v_sumdbv_w_errorbars"), "h_bkg_sumdbv_%s" % year) + scale_num = sb_conf.template_norms["n2v"][sig_type][year_id] + scale_den = h_bkg_sumdbv.Integral() + h_bkg_sumdbv.Scale(scale_num / scale_den) + if debug: + print("Bkg Sum_dBV rescaled %.3g -> %.3g" % (scale_den, scale_num)) + + if config.debug_settings["scale_bkg_fake"]: + h_bkg_sumdbv.Scale(config.debug_settings["bkg_fake_sf"]) + print("WARNING: scaled bkg by fake factor", config.debug_settings["bkg_fake_sf"]) + + h_bkg_sumdbv_rebin = h_bkg_sumdbv.Rebin(nbins, "bkg" + year_tag, bins) + move_overflow_into_last_bin(h_bkg_sumdbv_rebin) + new_bkg_hs.append(h_bkg_sumdbv_rebin) + + f.cd() + for h in new_bkg_hs: + h.SetTitle("") + h.Write() + if debug: + print(" wrote:", h.GetName()) + + print("BACKGROUND processing complete") + + +# --------------------------------------------------------------------------- +# Signal +# --------------------------------------------------------------------------- +def make_sigs(f, sig_nums, sig_scales): + sig_fn_syntax = config.sig[sig_type]["folder"] + config.sig[sig_type]["file_key"] + candidate_files = sorted(glob(sig_fn_syntax)) + sig_id = 0 + + for cand in candidate_files: + cand_id = os.path.basename(cand) + if cand_id.startswith("minitree"): + cand_id = os.path.basename(os.path.dirname(cand)) + if cand_id.find(year) == -1: + continue + generated_siggrp = False + try: + siginfo = sth.SignalROOTInfo(cand, root_exists=True, nbins=nbins) + in_cluster = False + for sc, members in config.sig["sig_grps"].items(): + if siginfo.proc in members: + in_cluster = True + if siginfo.proc == members[0]: + sig_str_ls = [cand.replace(siginfo.proc, p) for p in members] + print("Found cluster", sc, ":", sig_str_ls) + siggrp = sth.SigRInf_Grp(sig_str_ls, root_exists=True, nbins=nbins, overwrite_proc=sc) + generated_siggrp = True + break + if not in_cluster: + siggrp = sth.SigRInf_Grp([cand], root_exists=True, nbins=nbins, overwrite_proc=None) + generated_siggrp = True + except ValueError as e: + if str(e) == "No Samples.py entry": + print("Signal not in Samples.py, skipping:", os.path.basename(cand)) + else: + raise + if not generated_siggrp: + continue + if siggrp.trig_type != sig_type: + continue + if debug: + print("Queued signal #%d: %s (%s)" % (sig_id, os.path.basename(cand), siggrp.trig_type)) + sig_nums[siggrp] = str(sig_id) + sig_id += 1 + siggrp.print_diagnostics() + + if print_mapping: + print("\nSignal mapping:") + for k, v in sig_nums.items(): + print(" %s : %s" % (k.fn, v)) + + n = lambda sid, x: "h_sig%s_%s_%s" % (sid, x, year) + + for siggrp, sig_id in sig_nums.items(): + if debug: + print("\nProcessing cluster:", siggrp.fn) + + sumw = 0.0 + ngen = 0 + sum_sigyield = 0.0 + scales = [] + + ROOT.TH1.AddDirectory(1) + h_sumdbv_tot = ROOT.TH1D(n(sig_id, "sumdbv"), "", 800, 0, 8) + h_sumdbv_nw_tot = ROOT.TH1D(n(sig_id, "sumdbv") + "_nw", "", 800, 0, 8) + + for sig in siggrp.sig_ls: + if debug: + print(" sub-sig:", sig.fn) + t = ROOT.TChain("mfvMiniTree/t") + t.Add(sig.full_fn) + + this_sumw = sig.get_sumw() + this_ngen = sig.get_ngen() + this_xsec = sig.get_xsec() + this_sigyield = this_xsec * sb_conf.template_norms["lumi"][sig.trig_type][year_id] + this_scale = this_sigyield / this_sumw + scales.append(this_scale) + if debug: + print(" xsec=%.3g sumw=%.3g yield=%.3g scale=%.3g" % ( + this_xsec, this_sumw, this_sigyield, this_scale)) + + ROOT.TH1.AddDirectory(1) + h_child = ROOT.TH1D(n(sig_id, "child_sumdbv"), "", 800, 0, 8) + h_child_nw = ROOT.TH1D(n(sig_id, "child_sumdbv") + "_nw", "", 800, 0, 8) + + t.Draw("sumdbv>>%s" % n(sig_id, "child_sumdbv"), "weight*(nvtx>=2)") + t.Draw("sumdbv>>%s" % (n(sig_id, "child_sumdbv") + "_nw"), "1.0*(nvtx>=2)") + + ROOT.TH1.AddDirectory(0) + h_child.SetDirectory(0) + h_child_nw.SetDirectory(0) + h_child.Scale(this_scale) + + h_sumdbv_tot.Add(h_child) + h_sumdbv_nw_tot.Add(h_child_nw) + + sumw += this_sumw + ngen += this_ngen + sum_sigyield += this_sigyield + t.Reset() + h_child = ROOT.TH1D() + h_child_nw = ROOT.TH1D() + + sig_scales[siggrp] = scales + + h_sig = h_sumdbv_tot.Rebin(nbins, "sig" + year_tag + sig_id, bins) + move_overflow_into_last_bin(h_sig) + + h_sig_nw = h_sumdbv_nw_tot.Rebin(nbins, n(sig_id, "ngen_perbin"), bins) + move_overflow_into_last_bin(h_sig_nw) + + h_sumw_hist = ROOT.TH1D(n(sig_id, "sumw"), "", 1, 0, 1) + h_ngen_hist = ROOT.TH1D(n(sig_id, "ngen_total"), "", 1, 0, 1) + h_sigyield_hist = ROOT.TH1D(n(sig_id, "sigyield_total"), "", 1, 0, 1) + h_sumw_hist.SetBinContent(1, sumw) + h_ngen_hist.SetBinContent(1, ngen) + h_sigyield_hist.SetBinContent(1, sum_sigyield) + + f.cd() + for h in [h_sig, h_sig_nw, h_sumw_hist, h_ngen_hist, h_sigyield_hist]: + h.SetTitle(siggrp.proc + "_" + year) + h.Write() + if debug: + print(" wrote:", h.GetName()) + + print("SIGNAL processing complete") + + +# --------------------------------------------------------------------------- +# Shape systematics (up/down histograms) +# --------------------------------------------------------------------------- +def write_sig_updown(f, nuis_ls, siggrp, sig_scales, sig_id): + scales = sig_scales[siggrp] + if debug: + print("\nShape systematics for:", siggrp.fn, "scales:", scales) + + for nuis in nuis_ls: + if not nuis.make_updn: + continue + + nname = mk_dcnm(nuis, siggrp) + nname_dict = mk_dcnm(nuis, siggrp, force_no_CADItag=True) + + hname_up = "sig" + year_tag + sig_id + "_" + nname + "Up" + hname_dn = "sig" + year_tag + sig_id + "_" + nname + "Down" + + ROOT.TH1.AddDirectory(1) + h_up_tot = ROOT.TH1D(hname_up + "_orig", "", 800, 0, 8) + h_dn_tot = ROOT.TH1D(hname_dn + "_orig", "", 800, 0, 8) + + for i, sig in enumerate(siggrp.sig_ls): + t = ROOT.TChain("mfvMiniTree/t") + t.Add(sig.full_fn) + + ROOT.TH1.AddDirectory(1) + h_up = ROOT.TH1D(hname_up + "_child_orig", "", 800, 0, 8) + h_dn = ROOT.TH1D(hname_dn + "_child_orig", "", 800, 0, 8) + + wt_up, wt_dn = sb_conf.updn_wt_dict[nname_dict] + t.Draw("sumdbv>>%s" % (hname_up + "_child_orig"), + "weight*(nvtx>=2)".replace("weight", wt_up)) + t.Draw("sumdbv>>%s" % (hname_dn + "_child_orig"), + "weight*(nvtx>=2)".replace("weight", wt_dn)) + + h_up.Scale(scales[i]) + h_dn.Scale(scales[i]) + h_up_tot.Add(h_up) + h_dn_tot.Add(h_dn) + + t.Reset() + h_up = ROOT.TH1D() + h_dn = ROOT.TH1D() + + ROOT.TH1.AddDirectory(0) + h_sig_up = h_up_tot.Rebin(nbins, hname_up, bins) + h_sig_dn = h_dn_tot.Rebin(nbins, hname_dn, bins) + + for h in [h_sig_up, h_sig_dn]: + move_overflow_into_last_bin(h) + h.SetDirectory(0) + + f.cd() + for h in [h_sig_up, h_sig_dn]: + h.SetTitle(siggrp.proc + "_" + year) + h.Write() + if debug: + print(" wrote:", h.GetName()) + + h_up_tot = ROOT.TH1D() + h_dn_tot = ROOT.TH1D() + h_sig_up = ROOT.TH1D() + h_sig_dn = ROOT.TH1D() + + +# --------------------------------------------------------------------------- +# Main pipeline for one year +# --------------------------------------------------------------------------- +def run_one_year(yr): + _init_globals(yr) + check_config(yr) + + if debug: + print("\n=== Year: %s Channel: %s ===" % (yr, sig_type)) + + out_fn = config.output[sig_type]["out_folder"] + config.output["out_fn"] + out_fn += "_%s_%s" % (sig_type, yr) + if config.debug_settings["scale_bkg_fake"]: + out_fn += "_fakebkg" + out_fn += ".root" + + if debug: + print("Output ROOT file:", out_fn) + + ROOT.TH1.AddDirectory(0) + f = ROOT.TFile(out_fn, "recreate") + + if debug: + print("\n--- Background ---") + make_bkg(f) + + sig_nums = {} + sig_scales = {} + + if debug: + print("\n--- Signal ---") + make_sigs(f, sig_nums, sig_scales) + + if debug: + print("\n--- Background nuisances ---") + nuis_bkg_ls = [] + getns.get_nuis_frombkg(nuis_bkg_ls, debug_mode=debug) + + if debug: + print("\n--- Signal nuisances + datacards ---") + for siggrp, s_id in sig_nums.items(): + nuis_ls = [] + getns.get_nuis_fromsig(siggrp, nuis_ls, debug_mode=debug) + write_sig_updown(f=f, nuis_ls=nuis_ls, siggrp=siggrp, + sig_scales=sig_scales, sig_id=s_id) + mkdat.make_datacard(f=f, nuis_ls=nuis_ls, nuis_bkg_ls=nuis_bkg_ls, + siggrp=siggrp, sig_id=s_id, debug_mode=debug) + + f.Close() + print("Done: %s year=%s" % (sig_type, yr)) + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- +if __name__ == "__main__": + args = _parse_args() + _apply_args(args) + + years_to_run = config.datacard["year_key"] if (args.year == "all") else [config.datacard["year"]] + + for yr in years_to_run: + run_one_year(yr) diff --git a/MFVNeutralino/test/ForLimits/make_combine_tarball.sh b/MFVNeutralino/test/ForLimits/make_combine_tarball.sh new file mode 100644 index 000000000..cca8d815b --- /dev/null +++ b/MFVNeutralino/test/ForLimits/make_combine_tarball.sh @@ -0,0 +1,50 @@ +#!/bin/bash +# Build combine_env.tar.gz for shipping to Condor worker nodes. +# +# Run this once after building CMSSW_14_1_0_pre4 + HiggsAnalysis/CombinedLimit. +# The tarball is placed at $CMSSW_BASE/../combine_env.tar.gz (one level above +# the CMSSW installation), which is where submitCombine.py expects it by default. +# +# Prerequisites: source CMSSW_14_1_0_pre4 cmsenv before running this script. +# source /cvmfs/cms.cern.ch/cmsset_default.sh +# cd /src && cmsenv && cd - +# bash make_combine_tarball.sh +set -e + +if [ -z "$CMSSW_BASE" ]; then + echo "ERROR: CMSSW_BASE not set -- source cmsenv first" + exit 1 +fi + +ARCH=$(ls "$CMSSW_BASE/bin") +OUT="$(dirname "$CMSSW_BASE")/combine_env.tar.gz" +TMPDIR=$(mktemp -d) +trap "rm -rf $TMPDIR" EXIT + +mkdir -p "$TMPDIR/combine_env/bin" +mkdir -p "$TMPDIR/combine_env/lib" +mkdir -p "$TMPDIR/combine_env/python/HiggsAnalysis/CombinedLimit" +mkdir -p "$TMPDIR/combine_env/src/HiggsAnalysis/CombinedLimit/python" + +# Binaries +cp "$CMSSW_BASE/bin/$ARCH/combine" "$TMPDIR/combine_env/bin/" +cp "$CMSSW_BASE/bin/$ARCH/text2workspace.py" "$TMPDIR/combine_env/bin/" + +# Library + ROOT dictionary files +cp "$CMSSW_BASE/lib/$ARCH/libHiggsAnalysisCombinedLimit.so" "$TMPDIR/combine_env/lib/" +cp "$CMSSW_BASE/lib/$ARCH/HiggsAnalysisCombinedLimit_xr_rdict.pcm" "$TMPDIR/combine_env/lib/" +cp "$CMSSW_BASE/lib/$ARCH/HiggsAnalysisCombinedLimit_xr.rootmap" "$TMPDIR/combine_env/lib/" + +# Python modules (CMSSW src/ layout required by CombinedLimit/__init__.py) +cp "$CMSSW_BASE/src/HiggsAnalysis/CombinedLimit/python/"*.py \ + "$TMPDIR/combine_env/src/HiggsAnalysis/CombinedLimit/python/" + +# Python namespace stubs (used at import time to locate the src/ tree) +# The CombinedLimit __init__.py is installed to $CMSSW_BASE/python/ by scram b, +# not in src/ -- it contains the CMSSW path-resolution logic. +cp "$CMSSW_BASE/python/HiggsAnalysis/CombinedLimit/__init__.py" \ + "$TMPDIR/combine_env/python/HiggsAnalysis/CombinedLimit/__init__.py" +echo "# Namespace package" > "$TMPDIR/combine_env/python/HiggsAnalysis/__init__.py" + +tar -czf "$OUT" -C "$TMPDIR" combine_env +echo "Written: $OUT ($(du -sh "$OUT" | cut -f1))" diff --git a/MFVNeutralino/test/ForLimits/nuisance_configs.py b/MFVNeutralino/test/ForLimits/nuisance_configs.py new file mode 100644 index 000000000..e07d96807 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/nuisance_configs.py @@ -0,0 +1,36 @@ +""" +A place to store the messy dictionaries required to make nuisance_configs_and_functions.py work. + +nuis_names: nuisance naming + +pickle_prefixes: where to find nuisance table storage +year_remaps: some filenames are provided with a different convention to our 20161-2018 conventions +""" +import script_configs as config + +_ntpaths = config.nuisance_table_paths + +nuis_names = { + "CMS-CADI-tag": "CMS_EXO24035_", + "Run2-key": "13TeV", +} + +pickle_prefixes = { + "vtx_reco_TM": _ntpaths["vtx_reco_TM"], + "disp_trig_uncerts": _ntpaths["disp_trig_uncerts"], +} + +pickle_triple_prefixes = { + "tk_reco_eff": { + "base": _ntpaths["tk_reco_eff"]["base"], + "up": _ntpaths["tk_reco_eff"]["up_prefix"], + "dn": _ntpaths["tk_reco_eff"]["dn_prefix"], + }, +} + +year_remaps = { + "vtx_reco_TM": {"20161": "20161-2", "20162": "20161-2", "2017": "2017-8", "2018": "2017-8"}, + "disp_trig_uncerts": {"20161": "2016", "20162": "2016APV", "2017": "2017", "2018": "2018"}, +} + +fac_scale_VH_csv_path = _ntpaths["fac_scale_VH_csv"] diff --git a/MFVNeutralino/test/ForLimits/nuisance_configs_and_functions.py b/MFVNeutralino/test/ForLimits/nuisance_configs_and_functions.py new file mode 100644 index 000000000..eff42190e --- /dev/null +++ b/MFVNeutralino/test/ForLimits/nuisance_configs_and_functions.py @@ -0,0 +1,331 @@ +from __future__ import print_function +import csv as _csv +import numpy as np + +import helper_PyStorage_objects as sth +import nuisance_configs as ns_conf +import sig_and_bkg_configs as sb_conf +import script_configs as config + + +""" +This file is called by getNuisanceFromSig.py + +-INPUTS- +nuis_name: string. It becomes the nuisance name, and indexes dictionaries. + +Required inputs for NuisanceInfo: +nuis_name: string +nuis_val: float or array-like, values (meaningless for shape uncertainties) +make_updn: Boolean, is this a shape uncertainty? +sep_yrs: Boolean, should this nuisance be combined across the different years or not? +corr: Boolean, are the N bins correlated? +""" + +# Module-level globals -- updated per year via _init_for_year() +year = config.datacard["year"] +year_id = config.datacard["year_key"].index(year) +sig_type = config.sig["type"] +nbins = config.datacard["nbins"] + + +def _init_for_year(yr): + global year, year_id, sig_type, nbins + year = yr + year_id = config.datacard["year_key"].index(yr) + sig_type = config.sig["type"] + nbins = config.datacard["nbins"] + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + +def interp_pickle_triple(nn): + """Given nuisance name (e.g. 'tk_reco_eff'), return (up_path, dn_path).""" + loc_dict = ns_conf.pickle_triple_prefixes[nn] + base = loc_dict["base"] + return base + loc_dict["up"], base + loc_dict["dn"] + + +def make_anticorr_bkg(val, dp=4, in_is_b1=True): + """Anti-correlate bin 1 vs. bins 2+3 for a background nuisance. + + val: 1+x (e.g. 1.1 for 10%). in_is_b1: whether val is the bin-1 direction. + """ + out_arr = val * np.ones(nbins) if in_is_b1 else (1.0 / val) * np.ones(nbins) + out_arr[1:] = 1.0 / out_arr[1:] + if dp is not None: + out_arr = np.round(out_arr, decimals=int(dp)) + return out_arr + + +# --------------------------------------------------------------------------- +# Signal nuisances +# --------------------------------------------------------------------------- + +def get_mc_stat(nuis_name, siginfo, debug_mode=False): + nuis = sth.NuisanceInfo(nuis_name, 1.2, make_updn=False, sep_yrs=True, corr=False, + nuis_type="GammaN", nbins=siginfo.nbins, ana_spec=True) + return [nuis] + + +def get_reco_effi(nuis_name, siginfo, debug_mode=False): + """Track reconstruction efficiency uncertainty. + + Bjet channel: flat 5% working placeholder per AN Sec. 6.2.1 (Table 39/40). + Lep channel: per-bin asymmetric values from dedicated VH scale factor tables. + """ + if siginfo.trig_type == "bjet": + # AN working placeholder: flat 5% symmetric lnN for all bjet signals + if debug_mode: + print("tk_reco_eff: using flat 5% placeholder for bjet signal") + return [sth.NuisanceInfo(nuis_name, [1.05] * siginfo.nbins, make_updn=False, + sep_yrs=False, corr=True, nbins=siginfo.nbins, ana_spec=True)] + + # Lep channel: per-bin asymmetric values from VH scale factor tables + pickle_locs = interp_pickle_triple(nuis_name) + + up_ntab = sth.NuisanceTable(proc="VH", pickle_loc=pickle_locs[0]) + up_arr = up_ntab.get_point_from_fn(siginfo.fn.replace(siginfo.proc, "VH")) + if up_arr is None: + up_arr = [1.0] * siginfo.nbins + + dn_ntab = sth.NuisanceTable(proc="VH", pickle_loc=pickle_locs[1]) + dn_arr = dn_ntab.get_point_from_fn(siginfo.fn.replace(siginfo.proc, "VH")) + if dn_arr is None: + dn_arr = [1.0] * siginfo.nbins + + if debug_mode: + print("Identified trk-reco fractional uncertainties:", up_arr, dn_arr) + + up_nuis = sth.NuisanceInfo(nuis_name, up_arr, make_updn=False, sep_yrs=False, corr=True, + nuis_type="special", nbins=siginfo.nbins, ana_spec=True, + extra_info=["updn_pair", "up"]) + dn_nuis = sth.NuisanceInfo(nuis_name, dn_arr, make_updn=False, sep_yrs=False, corr=True, + nuis_type="special", nbins=siginfo.nbins, ana_spec=True, + extra_info=["updn_pair", "dn"]) + return [up_nuis, dn_nuis] + + +def get_vtx_reco_TM(nuis_name, siginfo, debug_mode=False): + ntab = sth.NuisanceTable(proc=siginfo.proc, pickle_loc=ns_conf.pickle_prefixes[nuis_name], + trig_for_pickle=siginfo.trig_type) + frac_unc = ntab.get_point_from_fn(siginfo, overrides={"yr": ns_conf.year_remaps[nuis_name][siginfo.year]}) + + if debug_mode: + print("Identified TM fractional uncertainty:", frac_unc) + if frac_unc is None: + print("Warning: vtx_reco_TM value not found. Writing fake value.") + frac_unc = 1.0 + frac_unc = np.round(frac_unc, 7) + + return [sth.NuisanceInfo(nuis_name, 1 + frac_unc, make_updn=False, sep_yrs=False, corr=True, + nbins=siginfo.nbins, ana_spec=True)] + + +def get_pileup(nuis_name, siginfo, debug_mode=False): + if siginfo.trig_type == "lep": + nuis = sth.NuisanceInfo(nuis_name, [1.03, 1.04, 1.06, 1.06], make_updn=False, + sep_yrs=False, corr=True, nbins=siginfo.nbins) + elif siginfo.trig_type == "bjet": + nuis = sth.NuisanceInfo(nuis_name, 1.03, make_updn=False, + sep_yrs=False, corr=True, nbins=siginfo.nbins) + else: + raise Exception("Could not add pileup") + return [nuis] + + +def get_int_lumi(nuis_name, siginfo, debug_mode=False): + lumi_components = sb_conf.lumi_lit_corrs + year_tosearch = "2016" if year in ("20161", "20162") else year + + out_ls = [] + for comp_name in sorted(lumi_components): + val = lumi_components[comp_name][year_tosearch] + if val is None: + continue + nuis = sth.NuisanceInfo(comp_name, val, make_updn=False, sep_yrs=False, + corr=True, nbins=siginfo.nbins, add_era_tags=False) + out_ls.append(nuis) + return out_ls + + +def get_lep_effi(nuis_name, siginfo, debug_mode=False): + """Lepton reconstruction efficiency (electron ID only; muon not yet implemented).""" + nuis_e_id = sth.NuisanceInfo(nuis_name + "e_id", 1 + 0.01, make_updn=False, + sep_yrs=False, corr=True, nbins=siginfo.nbins, ana_spec=False) + return [nuis_e_id] + + +def get_trig_JESR_btag(nuis_name, siginfo, debug_mode=False): + ntab = sth.NuisanceTable(proc=siginfo.proc, pickle_loc=ns_conf.pickle_prefixes[nuis_name], + trig_for_pickle=siginfo.trig_type) + frac_unc = ntab.get_point_from_fn(siginfo, overrides={"yr": ns_conf.year_remaps[nuis_name][siginfo.year]}) + + if debug_mode: + print("Identified b-tag fractional uncertainty:", frac_unc) + if frac_unc is None: + print("Warning: trig_JESR_btag value not found. Writing arbitrary value.") + return [sth.NuisanceInfo(nuis_name + "_fake", 1 + 0.1, make_updn=False, + sep_yrs=True, corr=True, nbins=siginfo.nbins, ana_spec=True)] + return [sth.NuisanceInfo(nuis_name, 1 + frac_unc, make_updn=False, + sep_yrs=True, corr=True, nbins=siginfo.nbins, ana_spec=True)] + + +def get_calo_inef(nuis_name, siginfo, debug_mode=False): + if ("2016" in siginfo.year and siginfo.return_mass_as_int() <= 300 + and siginfo.return_lifetime_in_unit(unit="mm") >= 10): + frac_unc = 0.05 + else: + frac_unc = 0.01 + return [sth.NuisanceInfo(nuis_name, 1 + frac_unc, make_updn=False, + sep_yrs=True, corr=True, nbins=siginfo.nbins, ana_spec=True)] + + +def get_qcd_scale_ren_ggH(nuis_name, siginfo, debug_mode=False): + """QCD renormalization scale theory uncertainty for ggH (2%, year- and bin-correlated).""" + return [sth.NuisanceInfo(nuis_name, 1.02, make_updn=False, sep_yrs=False, corr=True, + nbins=siginfo.nbins, ana_spec=False, add_era_tags=False)] + + +def _load_fac_scale_VH_table(csv_path): + """Parse fac_scale_shape_bins.csv into yield-weighted VH kappas. + + Returns {(mass_gev, ctau_um, year): ([kup_b0..b3], [kdn_b0..b3])}. + ggZH -> 1.0 (negligible fac-scale dep.). + Low-stats bins (flags_bin{i}='low_bin') -> kappa=1.0, weight=0. + """ + NBINS_CSV = 4 + NBINS_ALL = 4 + + raw = {} + with open(csv_path) as f: + for r in _csv.DictReader(f): + key = (int(r['mass_gev']), int(r['ctau_um']), r['year']) + entry = {} + for b in range(NBINS_CSV): + if r['flags_bin%d' % b] == 'ok': + entry[b] = (float(r['kappa_up_bin%d' % b]), + float(r['kappa_dn_bin%d' % b]), + int(r['n_bin%d' % b])) + else: + entry[b] = (1.0, 1.0, 0) + raw.setdefault(key, {})[r['stype']] = entry + + table = {} + for key, stypes_d in raw.items(): + kup, kdn = [], [] + for b in range(NBINS_ALL): + if b >= NBINS_CSV: + kup.append(1.0) + kdn.append(1.0) + continue + total_n = sum_ku = sum_kd = 0.0 + for st in ('ZH', 'WplusH', 'WminusH'): + ku, kd, n = stypes_d.get(st, {}).get(b, (1.0, 1.0, 0)) + sum_ku += ku * n + sum_kd += kd * n + total_n += n + if total_n > 0: + kup.append(sum_ku / total_n) + kdn.append(sum_kd / total_n) + else: + kup.append(1.0) + kdn.append(1.0) + table[key] = (kup, kdn) + return table + + +_fac_scale_VH_table = None + + +def _get_fac_scale_VH_table(): + global _fac_scale_VH_table + if _fac_scale_VH_table is None: + _fac_scale_VH_table = _load_fac_scale_VH_table(ns_conf.fac_scale_VH_csv_path) + return _fac_scale_VH_table + + +def get_qcd_scale_fac_VH(nuis_name, siginfo, debug_mode=False): + """Factorization scale shape uncertainty for VH (ZH/WH+/WH-), year-correlated. + + Per-bin kappas from CSV; bin 3 and ggZH use kappa=1.0; low-stats bins use kappa=1.0. + Missing signal points fall back to kappa=1.0 (no systematic applied). + """ + table = _get_fac_scale_VH_table() + mass = siginfo.return_mass_as_int() + ctau = int(siginfo.return_lifetime_in_unit(unit="um")) + result = table.get((mass, ctau, year)) + + if result is None: + if debug_mode: + print("QCDscale_fac_VH: no entry for mass=%d ctau=%d year=%s; using 1.0" % ( + mass, ctau, year)) + kup = [1.0] * siginfo.nbins + kdn = [1.0] * siginfo.nbins + else: + kup = list(result[0])[:siginfo.nbins] + kdn = list(result[1])[:siginfo.nbins] + while len(kup) < siginfo.nbins: + kup.append(1.0) + kdn.append(1.0) + + if debug_mode: + print("QCDscale_fac_VH: mass=%d ctau=%d year=%s kup=%s kdn=%s" % ( + mass, ctau, year, kup, kdn)) + + up_nuis = sth.NuisanceInfo(nuis_name, kup, make_updn=False, sep_yrs=False, corr=True, + nuis_type="special", nbins=siginfo.nbins, + ana_spec=False, add_era_tags=False, + extra_info=["updn_pair", "up"]) + dn_nuis = sth.NuisanceInfo(nuis_name, kdn, make_updn=False, sep_yrs=False, corr=True, + nuis_type="special", nbins=siginfo.nbins, + ana_spec=False, add_era_tags=False, + extra_info=["updn_pair", "dn"]) + return [up_nuis, dn_nuis] + + +# --------------------------------------------------------------------------- +# Background nuisances +# --------------------------------------------------------------------------- + +def get_bkg_jet_ang(nuis_name, debug_mode=False): + return [sth.NuisanceInfo(nuis_name, make_anticorr_bkg(1.06), make_updn=False, + sep_yrs=False, corr=True, nbins=nbins, ana_spec=True)] + + +def get_bkg_vtx_arbi(nuis_name, debug_mode=False): + return [sth.NuisanceInfo(nuis_name, make_anticorr_bkg(1.37), make_updn=False, + sep_yrs=False, corr=True, nbins=nbins, ana_spec=True)] + + +def get_bkg_vtx_refi(nuis_name, debug_mode=False): + return [sth.NuisanceInfo(nuis_name, make_anticorr_bkg(1.1), make_updn=False, + sep_yrs=False, corr=True, nbins=nbins, ana_spec=True)] + + +def get_bkg_pileup(nuis_name, debug_mode=False): + return [sth.NuisanceInfo(nuis_name, 1.0001, make_updn=False, + sep_yrs=False, corr=True, nbins=nbins)] + + +def get_bkg_sig_cont(nuis_name, debug_mode=False): + up_nuis = sth.NuisanceInfo(nuis_name, 1.05, make_updn=False, sep_yrs=False, corr=True, + nuis_type="special", nbins=nbins, ana_spec=True, + extra_info=["updn_pair", "up"]) + dn_nuis = sth.NuisanceInfo(nuis_name, 1.00, make_updn=False, sep_yrs=False, corr=True, + nuis_type="special", nbins=nbins, ana_spec=True, + extra_info=["updn_pair", "dn"]) + return [up_nuis, dn_nuis] + + +def get_bkg_bkg_norm(nuis_name, debug_mode=False): + return [sth.NuisanceInfo(nuis_name, 1.15, make_updn=False, + sep_yrs=False, corr=True, nbins=nbins, ana_spec=True)] + + +def get_bkg_n2v_unc(nuis_name, debug_mode=False): + frac_unc = sb_conf.n2v_uncs[sig_type][year_id] / sb_conf.template_norms["n2v"][sig_type][year_id] + return [sth.NuisanceInfo(nuis_name, 1 + frac_unc, make_updn=False, + sep_yrs=True, corr=True, nbins=nbins, ana_spec=True)] diff --git a/MFVNeutralino/test/ForLimits/plotLimits.py b/MFVNeutralino/test/ForLimits/plotLimits.py new file mode 100644 index 000000000..c224277fe --- /dev/null +++ b/MFVNeutralino/test/ForLimits/plotLimits.py @@ -0,0 +1,1157 @@ +#!/usr/bin/env python3 +""" +Plot 95% CL upper limits on sigma x B^2 [fb] (SUSY) or BR(H->SS) (Higgs). + +Reads CombineOutput//higgsCombine.HybridNew.mH120.root +(falls back to AsymptoticLimits if HybridNew output is absent) +for all available hypotheses and produces per-process plots. + +Output (in LimitPlots/): + _1D.pdf -- ctau on x, one curve per mass + _1D_vsmass.pdf -- mass on x, all ctau values overlaid + _1D_vsmass__vs_.pdf -- mass on x, one plot per adjacent ctau pair + _2D.pdf -- ctau vs mass 2D color map + r=1 exclusion contour + _1D_hepcomp_MvsM.pdf -- ctau on x, Low-HT vs High-HT (HepData) per mass pair + _1D_hepcomp_vsmass_vs.pdf -- mass on x, Low-HT vs High-HT per ctau pair + +HepData reference (ins1861146, old high-HT displaced vertex analysis): + Only used for comparison 1D plots (never shown on 2D plots). + Load from hepdata_ins1861146.json if present. + Template: {"mfv_neu": {"ctau_mm": [c1,...], "mass_gev": [m1,...], + "obs": [[r_c1m1, r_c1m2,...], [r_c2m1,...],...] }} + where obs[i][j] = observed limit at ctau_mm[i], mass_gev[j]. + +Usage + python3 plotLimits.py [--subset VH,mfv_neu] [--out-dir LimitPlots] +""" +import os +import sys +import json +import argparse +import numpy as np + +import ROOT +ROOT.gROOT.SetBatch(True) +ROOT.gStyle.SetOptStat(0) + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import matplotlib.colors as mcolors + +try: + import mplhep as hep + hep.style.use("CMS") + _HAS_MPLHEP = True +except ImportError: + _HAS_MPLHEP = False + +plt.rcParams.update({ + "axes.labelsize": 13, + "xtick.labelsize": 11, + "ytick.labelsize": 11, +}) + +try: + from scipy.interpolate import RectBivariateSpline + _HAS_SCIPY = True +except ImportError: + _HAS_SCIPY = False + print("Warning: scipy not available; 2D plots will show grid points only") + +HERE = os.path.dirname(os.path.abspath(__file__)) +COMBINE_OUT = os.path.join(HERE, "CombineOutput") +HEPDATA_JSON = os.path.join(HERE, "hepdata_ins1861146.json") +PLOT_DIR = os.path.join(HERE, "LimitPlots") +_ONE2TWO = os.path.join(HERE, "..", "One2Two") + +# Theory XS CSV files (SUSY XS WG, NNLO_approx+NNLL) used to convert HepData +# from r = σ×B²/σ_theory (how it is stored) to σ×B² in fb (our r units, σ_ref=1fb). +_HEPDATA_THEORY_CSV = { + "mfv_neu": os.path.join(_ONE2TWO, "gluglu.csv"), + "mfv_stopdbardbar": os.path.join(_ONE2TWO, "stopstop.csv"), + "mfv_stopbbarbbar": os.path.join(_ONE2TWO, "stopstop.csv"), +} + +# Extra theory curves drawn on top (per process). For mfv_neu: EWK Higgsino N2N1 +# (aNNLO-NNLL, 13 TeV) -- both final-state particles are neutral, matching the gluino LLP. +_EXTRA_THEORY_CSV = { + "mfv_neu": os.path.join(_ONE2TWO, "higgsino_N2N1.csv"), +} + + +PROC_LABELS = { + "VH": r"WH + ZH (incl. gg), H$\to$SS$\to$dddd", + "ggZHToSSTobbbb": r"ggZH, H$\to$SS$\to$bbbb", + "ggHToSSTodddd": r"ggH, H$\to$SS$\to$dddd", + "ttHToLLPs_bbbb": r"ttH, H$\to$SS$\to$bbbb", + "ttHToLLPs_dddd": r"ttH, H$\to$SS$\to$dddd", + "mfv_neu": r"RPV SUSY, $\tilde{g}\to tbs$", + "mfv_stopdbardbar": r"RPV SUSY, $\tilde{t}\to\bar{d}\bar{d}$", + "mfv_stopbbarbbar": r"RPV SUSY, $\tilde{t}\to\bar{b}\bar{b}$", +} + +_SUSY_PROCS = {"mfv_neu", "mfv_stopdbardbar", "mfv_stopbbarbbar"} + +# (vmin, vref, vmax) in fb for the 2D color scale; vref is the white pivot on RdBu_r. +# Unspecified processes fall back to geometric-mean auto-scaling. +_2D_VSCALE = { + "mfv_neu": (0.1, 10.0, 1e5), + "mfv_stopdbardbar": (0.1, 10.0, 1e5), + "mfv_stopbbarbbar": (0.1, 10.0, 1e5), + # Higgs: pivot at SM benchmark BR=1%; range covers 0.1%-100% (well beyond any limit) + "VH": (1e-3, 0.01, 1.0), + "ggZHToSSTobbbb": (1e-3, 0.01, 1.0), + "ggHToSSTodddd": (1e-3, 0.01, 1.0), + "ttHToLLPs_bbbb": (1e-3, 0.01, 1.0), + "ttHToLLPs_dddd": (1e-3, 0.01, 1.0), +} + +# BR(H→SS) benchmark used as datacard normalization and as reference line on plots. +_BR_HSS = 0.01 + + +def _sig_scale_fb(proc, mass=None): + """Scale factor to convert Combine r to physical units. + + SUSY: σ_ref = 1 fb → r = σ×B² [fb] → return 1.0. + H→SS: σ_ref = σ_SM_H × BR(H→SS=1%) [× filter_eff, which cancels]. + r = σ(H)×BR(H→SS) / (σ_SM × 0.01), so BR(H→SS) = r × 0.01. + σ_SM cancels regardless of production mode → return _BR_HSS for all Higgs. + """ + if proc in _SUSY_PROCS: + return 1.0 + return _BR_HSS + + +_COLORS = ["#e41a1c", "#377eb8", "#4daf4a", "#984ea3", "#ff7f00", "#a65628"] + +_RUN2_LUMI = r"137.9 fb$^{-1}$ (13 TeV)" + +# Per-process normalization footnote shown in the plot annotation box. +_PROC_NORM_NOTES = {} + + +# --------------------------------------------------------------------------- +# Parsing / I/O +# --------------------------------------------------------------------------- + +def ctau_to_mm(s): + """'300um' -> 0.3, '1mm' -> 1.0, '10mm' -> 10.0""" + if s.endswith("um"): + return float(s[:-2]) * 1e-3 + if s.endswith("mm"): + return float(s[:-2]) + if s.endswith("cm"): + return float(s[:-2]) * 10.0 + return float(s) + + +def parse_sig_id(sig_id): + """'VH_tau1mm_M15' -> ('VH', '1mm', '15')""" + parts = sig_id.split("_tau") + if len(parts) != 2: + return None, None, None + proc = parts[0] + sub = parts[1].split("_", 1) + if len(sub) < 2: + return None, None, None + ctau = sub[0] + mass = sub[1].lstrip("M").lstrip("0") or "0" + return proc, ctau, mass + + +def read_limits(sig_id): + """Return {key: r_value} or None. Keys: obs, exp, dn1, up1, dn2, up2.""" + method_found = None + fn = None + for method in ("HybridNew", "AsymptoticLimits"): + # HybridNew jobs run with -s 1234, which appends the seed to the filename. + for suffix in (".mH120.1234.root", ".mH120.root"): + candidate = os.path.join(COMBINE_OUT, sig_id, + "higgsCombine%s.%s%s" % (sig_id, method, suffix)) + if os.path.exists(candidate): + fn = candidate + method_found = method + break + if fn: + break + if fn is None: + return None + try: + f = ROOT.TFile.Open(fn) + if not f or f.IsZombie(): + return None + t = f.Get("limit") + if not t: + f.Close() + return None + quant_map = { + -1.0: "obs", + 0.025: "dn2", + 0.16: "dn1", + 0.5: "exp", + 0.84: "up1", + 0.975: "up2", + } + result = {} + for _ in t: + q = round(float(t.quantileExpected), 3) + for qref, key in quant_map.items(): + if abs(q - qref) < 0.01: + result[key] = float(t.limit) + f.Close() + # -t -1 (Asimov) puts the result in quantileExpected=-1 ("obs" slot) -- remap to "exp". + if method_found == "HybridNew" and "obs" in result and "exp" not in result: + result["exp"] = result.pop("obs") + return result if "exp" in result else None + except Exception as exc: + print("Could not read %s: %s" % (fn, exc)) + return None + + +def collect_all(): + """Return {proc -> {mass_str -> {ctau_mm_float -> {obs/exp/...}}}}""" + data = {} + if not os.path.isdir(COMBINE_OUT): + return data + for sig_id in sorted(os.listdir(COMBINE_OUT)): + if not os.path.isdir(os.path.join(COMBINE_OUT, sig_id)): + continue + proc, ctau_str, mass = parse_sig_id(sig_id) + if proc is None: + continue + ctau_mm = ctau_to_mm(ctau_str) + if ctau_mm <= 0: + continue # skip prompt (ctau=0) signals -- not meaningful for dv limits + lims = read_limits(sig_id) + if lims is None: + continue + data.setdefault(proc, {}).setdefault(mass, {})[ctau_mm] = lims + return data + + +def _load_theory_xsec_csv(csv_path): + """Return (mass_arr, xsec_fb_arr) from a SUSY XS WG CSV (cols: mass_GeV, xs_pb, unc_pct).""" + rows = [eval(l.strip()) for l in open(csv_path) if l.strip()] + masses = np.array([r[0] for r in rows], dtype=float) + xsec_fb = np.array([r[1] for r in rows], dtype=float) * 1000.0 # pb -> fb + return masses, xsec_fb + + +def _interp_theory_xsec_fb(mass_gev, masses, xsec_fb): + """Log-log interpolation/extrapolation of theory XS in fb at mass_gev.""" + lm = np.log(masses) + lxs = np.log(xsec_fb) + return float(np.exp(np.interp(np.log(float(mass_gev)), lm, lxs))) + + +def _hepdata_r_to_sigxb2_fb(hd_proc, csv_path): + """Convert HepData obs from r=σ×B²/σ_theory to σ×B² in fb using the theory XS CSV.""" + masses_csv, xsec_fb_csv = _load_theory_xsec_csv(csv_path) + hd_masses = hd_proc["mass_gev"] + obs_raw = np.array(hd_proc["obs"]) # shape: (n_ctau, n_mass) + xs_col = np.array([_interp_theory_xsec_fb(m, masses_csv, xsec_fb_csv) + for m in hd_masses]) + obs_fb = obs_raw * xs_col[np.newaxis, :] # broadcast over ctau axis + return { + "ctau_mm": hd_proc["ctau_mm"], + "mass_gev": hd_proc["mass_gev"], + "obs": obs_fb.tolist(), + } + + +def load_hepdata(): + """Load HepData JSON and convert SUSY proc obs from r to σ×B² in fb.""" + if not os.path.exists(HEPDATA_JSON): + return {} + with open(HEPDATA_JSON) as fh: + raw = json.load(fh) + out = {} + for proc, hd_proc in raw.items(): + csv_path = _HEPDATA_THEORY_CSV.get(proc) + if csv_path and os.path.exists(csv_path): + out[proc] = _hepdata_r_to_sigxb2_fb(hd_proc, csv_path) + elif csv_path: + print("Warning: theory XS CSV not found for %s; HepData comparison skipped" % proc) + else: + out[proc] = hd_proc + return out + + +# --------------------------------------------------------------------------- +# Shared helpers +# --------------------------------------------------------------------------- + +def _format_ctau(ctau_mm): + """Format a ctau float for labels and filenames, e.g. 0.3 -> '0.3mm'.""" + if ctau_mm < 1.0: + s = "%.3g" % ctau_mm + else: + s = "%.4g" % ctau_mm + return s + "mm" + + +def _pair_list(items): + """Non-overlapping adjacent pairs from a sorted list: (0,1), (2,3), ...""" + return list(zip(items[0::2], items[1::2])) + + +def _invert_mass_data(mass_data): + """Return {ctau_mm -> {mass_str -> limits}} from the standard mass-keyed dict.""" + ctau_data = {} + for mass, cdict in mass_data.items(): + for ctau, lims in cdict.items(): + ctau_data.setdefault(ctau, {})[mass] = lims + return ctau_data + + +def _sorted_masses(mass_data): + return sorted(mass_data.keys(), key=lambda m: int(m) if m.isdigit() else 0) + + +def _hepdata_slice_at_mass(hd, mass_gev): + """Interpolate HepData in mass; return (ctau_arr, r_arr) or (None, None).""" + hd_ctaus = np.array(hd["ctau_mm"]) + hd_masses = np.array(hd["mass_gev"]) + hd_obs = np.array(hd["obs"]) # shape: (n_ctau, n_mass) + if mass_gev < hd_masses[0] or mass_gev > hd_masses[-1]: + return None, None + r = np.array([np.interp(mass_gev, hd_masses, hd_obs[i, :]) + for i in range(len(hd_ctaus))]) + return hd_ctaus, r + + +def _hepdata_slice_at_ctau(hd, ctau_mm): + """Interpolate HepData in log(ctau); return (mass_arr, r_arr) or (None, None). + + ctau grids are log-spaced, so interpolation must be done in log space to avoid + large biases between grid points (e.g. between 1 mm and 10 mm). + """ + hd_ctaus = np.array(hd["ctau_mm"]) + hd_masses = np.array(hd["mass_gev"]) + hd_obs = np.array(hd["obs"]) # shape: (n_ctau, n_mass) + if ctau_mm < hd_ctaus[0] or ctau_mm > hd_ctaus[-1]: + return None, None + log_ctaus = np.log(hd_ctaus) + r = np.array([np.interp(np.log(ctau_mm), log_ctaus, hd_obs[:, j]) + for j in range(len(hd_masses))]) + return hd_masses, r + + +def _draw_ref_curve_vsmass(ax, proc): + """Draw theory/benchmark reference curves vs mass. Returns True if anything drawn.""" + if proc in _SUSY_PROCS: + csv_path = _HEPDATA_THEORY_CSV.get(proc) + if not csv_path or not os.path.exists(csv_path): + return False + masses_csv, xsec_fb_csv = _load_theory_xsec_csv(csv_path) + label = r"$\tilde{g}\tilde{g}$ NLO+NNLL" if proc == "mfv_neu" else r"Theory $\sigma\mathcal{B}^{2}$" + ax.plot(masses_csv, xsec_fb_csv, color="black", lw=1.5, ls="--", + label=label, zorder=3) + extra_path = _EXTRA_THEORY_CSV.get(proc) + if extra_path and os.path.exists(extra_path): + em, exs = _load_theory_xsec_csv(extra_path) + ax.plot(em, exs, color="dimgray", lw=1.5, ls=":", + label=r"$\tilde{\chi}^{0}_{1}\tilde{\chi}^{0}_{2}$ aNNLO-NNLL", zorder=3) + return True + # Higgs: reference line at BR benchmark + if proc not in _SUSY_PROCS: + ax.axhline(_BR_HSS, color="black", lw=1.5, ls="--", + label=r"$\mathcal{B}(H{\to}SS) = 1\%$", zorder=3) + return True + return False + + +def _draw_ref_lines_ctau(ax, proc, masses_sorted, color_list): + """Draw theory/benchmark reference lines on ctau plots. Returns True if anything drawn.""" + if proc in _SUSY_PROCS: + return False # theory curve shown on vsmass plots; per-mass lines clutter ctau plots + # Higgs: single benchmark line at BR = 1% + ax.axhline(_BR_HSS, color="black", lw=1.5, ls="--", + label=r"$\mathcal{B}(H{\to}SS) = 1\%$", zorder=3) + return True + + +def _annotate_proc(ax, proc): + _proc_label = PROC_LABELS.get(proc, proc) + _norm = _PROC_NORM_NOTES.get(proc, "") + _annot = _proc_label + ("\n" + _norm if _norm else "") + ax.text(0.0, -0.13, _annot, + transform=ax.transAxes, fontsize=10, ha="left", va="top", clip_on=False) + + +def _ylabel(proc): + if proc in _SUSY_PROCS: + return r"95% CL upper limit on $\sigma\mathcal{B}^{2}$ [fb]" + return r"95% CL upper limit on $\mathcal{B}(H{\to}SS)$" + + +def _mass_xlabel(proc): + if proc == "mfv_neu": + return r"Neutralino mass [GeV]" + return "Mass [GeV]" + + +def _cms_label(ax): + if _HAS_MPLHEP: + hep.cms.label("Preliminary", data=False, ax=ax, fontsize=12, rlabel=_RUN2_LUMI) + + +def _band(lims_list, key, fallback_key=None): + """Return list of limit values for `key` if ALL entries have it, else None.""" + vals = [l.get(key) if fallback_key is None else l.get(key, l.get(fallback_key)) + for l in lims_list] + return vals if all(v is not None for v in vals) else None + + +def _save(fig, out_fn): + plt.tight_layout() + fig.savefig(out_fn, bbox_inches="tight") + plt.close(fig) + print(" -> %s" % out_fn) + + +# --------------------------------------------------------------------------- +# 1D: ctau on x-axis, one curve per mass (original plot) +# --------------------------------------------------------------------------- + +def plot_1d(proc, mass_data, out_dir, hepdata): + fig, ax = plt.subplots(figsize=(8, 6)) + + masses = _sorted_masses(mass_data) + + for i, mass in enumerate(masses): + scale = _sig_scale_fb(proc, mass) + cdict = mass_data[mass] + ctaus = sorted(cdict.keys()) + if not ctaus: + continue + lims_c = [cdict[c] for c in ctaus] + exp = [l["exp"] * scale for l in lims_c] + _raw1 = _band(lims_c, "dn1"); dn1 = [v * scale for v in _raw1] if _raw1 else None + _raw2 = _band(lims_c, "up1"); up1 = [v * scale for v in _raw2] if _raw2 else None + _raw3 = _band(lims_c, "dn2", "dn1"); dn2 = [v * scale for v in _raw3] if _raw3 else None + _raw4 = _band(lims_c, "up2", "up1"); up2 = [v * scale for v in _raw4] if _raw4 else None + + col = _COLORS[i % len(_COLORS)] + if dn2 and up2: + ax.fill_between(ctaus, dn2, up2, alpha=0.15, color=col, edgecolor="none") + if dn1 and up1: + ax.fill_between(ctaus, dn1, up1, alpha=0.35, color=col, edgecolor="none") + ax.plot(ctaus, exp, color=col, lw=2, ls="--", + label="m = %s GeV (exp)" % mass) + + _obs_raw = _band(lims_c, "obs") + if _obs_raw: + obs = [v * scale for v in _obs_raw] + ax.plot(ctaus, obs, color=col, lw=2, ls="-", + label="m = %s GeV (obs)" % mass) + + if not _draw_ref_lines_ctau(ax, proc, masses, _COLORS): + pass + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel(r"$c\tau$ [mm]") + ax.set_ylabel(_ylabel(proc)) + ax.legend(fontsize=9, ncol=2) + ax.grid(True, which="both", ls=":", alpha=0.4) + _cms_label(ax) + _annotate_proc(ax, proc) + _save(fig, os.path.join(out_dir, "%s_1D.pdf" % proc)) + + +# --------------------------------------------------------------------------- +# 1D: mass on x-axis, ctau as overlaid lines — all ctau in one plot +# --------------------------------------------------------------------------- + +def plot_1d_vs_mass_all(proc, mass_data, out_dir): + ctau_data = _invert_mass_data(mass_data) + ctaus_sorted = sorted(ctau_data.keys()) + + fig, ax = plt.subplots(figsize=(8, 6)) + + for i, ctau in enumerate(ctaus_sorted): + mdict = ctau_data[ctau] + masses = _sorted_masses(mdict) + mass_vals = [int(m) for m in masses] + scales = [_sig_scale_fb(proc, m) for m in masses] + + lims_m = [mdict[m] for m in masses] + exp = [l["exp"] * s for l, s in zip(lims_m, scales)] + _r1 = _band(lims_m, "dn1"); dn1 = [v * s for v, s in zip(_r1, scales)] if _r1 else None + _r2 = _band(lims_m, "up1"); up1 = [v * s for v, s in zip(_r2, scales)] if _r2 else None + _r3 = _band(lims_m, "dn2", "dn1"); dn2 = [v * s for v, s in zip(_r3, scales)] if _r3 else None + _r4 = _band(lims_m, "up2", "up1"); up2 = [v * s for v, s in zip(_r4, scales)] if _r4 else None + + col = _COLORS[i % len(_COLORS)] + lbl = _format_ctau(ctau) + if dn2 and up2: + ax.fill_between(mass_vals, dn2, up2, alpha=0.15, color=col, edgecolor="none") + if dn1 and up1: + ax.fill_between(mass_vals, dn1, up1, alpha=0.35, color=col, edgecolor="none") + ax.plot(mass_vals, exp, color=col, lw=2, ls="--", + label=r"$c\tau$ = %s (exp)" % lbl) + + _obs_raw = _band(lims_m, "obs") + if _obs_raw: + obs = [v * s for v, s in zip(_obs_raw, scales)] + ax.plot(mass_vals, obs, color=col, lw=2, ls="-", + label=r"$c\tau$ = %s (obs)" % lbl) + + if not _draw_ref_curve_vsmass(ax, proc): + pass + ax.set_yscale("log") + ax.set_xlabel(_mass_xlabel(proc)) + ax.set_ylabel(_ylabel(proc)) + ax.legend(fontsize=9, ncol=2) + ax.grid(True, which="both", ls=":", alpha=0.4) + _cms_label(ax) + _annotate_proc(ax, proc) + _save(fig, os.path.join(out_dir, "%s_1D_vsmass.pdf" % proc)) + + +# --------------------------------------------------------------------------- +# 1D: mass on x-axis, one plot per adjacent ctau pair (+ optional HepData) +# --------------------------------------------------------------------------- + +def _draw_lowht_pair(ax, proc, ctau_data, c1, c2): + """Draw Low-HT exp+bands+obs for two ctau values, scaled to σ×B² [fb].""" + for i, ctau in enumerate([c1, c2]): + mdict = ctau_data[ctau] + masses = _sorted_masses(mdict) + mass_vals = [int(m) for m in masses] + scales = [_sig_scale_fb(proc, m) for m in masses] + lims_m = [mdict[m] for m in masses] + exp = [l["exp"] * s for l, s in zip(lims_m, scales)] + _r1 = _band(lims_m, "dn1"); dn1 = [v * s for v, s in zip(_r1, scales)] if _r1 else None + _r2 = _band(lims_m, "up1"); up1 = [v * s for v, s in zip(_r2, scales)] if _r2 else None + _r3 = _band(lims_m, "dn2", "dn1"); dn2 = [v * s for v, s in zip(_r3, scales)] if _r3 else None + _r4 = _band(lims_m, "up2", "up1"); up2 = [v * s for v, s in zip(_r4, scales)] if _r4 else None + col = _COLORS[i] + lbl = _format_ctau(ctau) + if dn2 and up2: + ax.fill_between(mass_vals, dn2, up2, alpha=0.15, color=col, edgecolor="none") + if dn1 and up1: + ax.fill_between(mass_vals, dn1, up1, alpha=0.35, color=col, edgecolor="none") + ax.plot(mass_vals, exp, color=col, lw=2, ls="--", + label=r"$c\tau$ = %s Low-HT exp." % lbl) + _obs_raw = _band(lims_m, "obs") + if _obs_raw: + obs = [v * s for v, s in zip(_obs_raw, scales)] + ax.plot(mass_vals, obs, color=col, lw=2, ls="-", + label=r"$c\tau$ = %s Low-HT obs." % lbl) + + +def _draw_hepdata_pair(ax, hd, c1, c2): + """Overlay High-HT (HepData EXO-19-013) obs lines for two ctau values.""" + for i, ctau in enumerate([c1, c2]): + hd_masses_sl, hd_r_sl = _hepdata_slice_at_ctau(hd, ctau) + if hd_masses_sl is None: + continue + # Mask out zero/non-positive entries (JSON stores 0 for very strong exclusions) + hd_masses_sl = np.array(hd_masses_sl) + hd_r_sl = np.array(hd_r_sl) + keep = hd_r_sl > 0 + if not np.any(keep): + continue + col = _COLORS[i + 2] + lbl = _format_ctau(ctau) + ax.plot(hd_masses_sl[keep], hd_r_sl[keep], color=col, lw=2, ls="-", + label=r"$c\tau$ = %s High-HT obs. (EXO-19-013)" % lbl) + + +def plot_1d_vs_mass_pairs(proc, mass_data, out_dir, hepdata=None): + ctau_data = _invert_mass_data(mass_data) + ctaus_sorted = sorted(ctau_data.keys()) + pairs = _pair_list(ctaus_sorted) + hd = hepdata.get(proc) if hepdata else None + + for c1, c2 in pairs: + fig, ax = plt.subplots(figsize=(8, 6)) + _draw_lowht_pair(ax, proc, ctau_data, c1, c2) + if hd: + _draw_hepdata_pair(ax, hd, c1, c2) + if not _draw_ref_curve_vsmass(ax, proc): + pass + ax.set_yscale("log") + ax.set_xlabel(_mass_xlabel(proc)) + ax.set_ylabel(_ylabel(proc)) + ax.legend(fontsize=9) + ax.grid(True, which="both", ls=":", alpha=0.4) + _cms_label(ax) + _annotate_proc(ax, proc) + tag = "%s_vs_%s" % (_format_ctau(c1), _format_ctau(c2)) + _save(fig, os.path.join(out_dir, "%s_1D_vsmass_%s.pdf" % (proc, tag))) + + +# --------------------------------------------------------------------------- +# 1D: mass on x-axis, two user-specified ctau values +# --------------------------------------------------------------------------- + +def plot_1d_vs_mass_ctau_pair(proc, mass_data, out_dir, c1_mm, c2_mm, hepdata=None): + """One plot with exactly two ctau values (specified in mm) overlaid.""" + ctau_data = _invert_mass_data(mass_data) + available = sorted(ctau_data.keys()) + def _nearest(target): + return min(available, key=lambda c: abs(c - target)) + c1 = _nearest(c1_mm) + c2 = _nearest(c2_mm) + if c1 == c2: + print(" Skipping specific pair for %s: c1=c2=%s" % (proc, c1)) + return + hd = hepdata.get(proc) if hepdata else None + + fig, ax = plt.subplots(figsize=(8, 6)) + _draw_lowht_pair(ax, proc, ctau_data, c1, c2) + if hd: + _draw_hepdata_pair(ax, hd, c1, c2) + if not _draw_ref_curve_vsmass(ax, proc): + pass + ax.set_yscale("log") + ax.set_xlabel(_mass_xlabel(proc)) + ax.set_ylabel(_ylabel(proc)) + ax.legend(fontsize=9) + ax.grid(True, which="both", ls=":", alpha=0.4) + _cms_label(ax) + _annotate_proc(ax, proc) + tag = "%s_vs_%s" % (_format_ctau(c1), _format_ctau(c2)) + _save(fig, os.path.join(out_dir, "%s_1D_vsmass_%s.pdf" % (proc, tag))) + + +# --------------------------------------------------------------------------- +# 2D: ctau vs mass color map + r=1 contour (no HepData overlay) +# --------------------------------------------------------------------------- + +def _excl_thresholds_2d(proc, masses, mass_vals, theory_csv=None): + """Per-mass σ×B² [fb] threshold where r=1 is the physical exclusion boundary. + SUSY: σ_theory_NLO from CSV. H→SS: σ_ref_fb (_sig_scale_fb), so r=1 = SM boundary. + theory_csv overrides the SUSY default (for EWK variant plots). + """ + if proc in _SUSY_PROCS: + csv_path = theory_csv if theory_csv is not None else _HEPDATA_THEORY_CSV.get(proc) + if csv_path and os.path.exists(csv_path): + masses_csv, xsec_fb_csv = _load_theory_xsec_csv(csv_path) + return np.array([_interp_theory_xsec_fb(m, masses_csv, xsec_fb_csv) + for m in mass_vals]) + return np.array([_sig_scale_fb(proc, m) for m in masses]) + + +def _interp_grid(log_ctaus, mass_vals, grid, fine_lct, fine_mass): + if not _HAS_SCIPY: + return None + g = grid.copy() + if np.all(np.isnan(g)): + return None + # Interpolate interior NaN holes along ctau per mass column before capping edges + x_all = np.arange(g.shape[0]) + for j in range(g.shape[1]): + col = g[:, j] + valid = ~np.isnan(col) + if valid.sum() >= 2 and not valid.all(): + g[:, j] = np.interp(x_all, x_all[valid], col[valid]) + # fill remaining edge NaNs with a large cap so contour at r=1 can still be drawn + cap = max(200.0, float(np.nanmax(g)) * 2.0) + g[np.isnan(g) | (g <= 0)] = cap + sp = RectBivariateSpline(log_ctaus, mass_vals, np.log10(g), kx=1, ky=1) + return 10.0 ** sp(fine_lct, fine_mass) + + +def plot_2d(proc, mass_data, out_dir, hepdata, theory_csv=None, fname_suffix=""): + masses = _sorted_masses(mass_data) + + # Rectangular grid with NaN for missing cells (capped to "not excluded" in _interp_grid). + # min_n: drop masses with too few valid ctau points (SUSY needs 3+, Higgs 2+). + ctau_counts = {m: len(mass_data[m]) for m in masses} + max_n = max(ctau_counts.values()) + min_n = max(3, max_n - 1) if proc in _SUSY_PROCS else 2 + masses = [m for m in masses if ctau_counts[m] >= min_n] + ctaus_all = sorted(set.union(*[set(mass_data[m].keys()) for m in masses])) + + if len(masses) < 2 or len(ctaus_all) < 2: + print(" Skipping 2D for %s: need at least 2x2 grid" % proc) + return + + mass_vals = np.array([int(m) for m in masses], dtype=float) + ctau_vals = np.array(ctaus_all, dtype=float) + + grid_exp = np.full((len(ctau_vals), len(mass_vals)), np.nan) + grid_obs = np.full((len(ctau_vals), len(mass_vals)), np.nan) + + for j, mass in enumerate(masses): + for i, ctau in enumerate(ctau_vals): + if ctau in mass_data[mass]: + ent = mass_data[mass][ctau] + grid_exp[i, j] = ent["exp"] + if "obs" in ent: + grid_obs[i, j] = ent["obs"] + + log_ctaus = np.log10(ctau_vals) + # Extend 0.7 decades left of the first data point so the contour closes + log_ctau_lo = log_ctaus[0] - 0.7 + fine_lct = np.linspace(log_ctau_lo, log_ctaus[-1], 200) + fine_mass = np.linspace(mass_vals[0], mass_vals[-1], 200) + fine_ctau = 10.0 ** fine_lct + + fine_exp = _interp_grid(log_ctaus, mass_vals, grid_exp, fine_lct, fine_mass) + fine_obs = _interp_grid(log_ctaus, mass_vals, grid_obs, fine_lct, fine_mass) + + # Per-mass scale factors: σ_ref [fb] used in the datacard for each mass column. + # For SUSY scale=1, so the raw Combine r grid is already in σ×B² [fb]. + scales = np.array([_sig_scale_fb(proc, m) for m in masses]) + scale_fine = np.interp(fine_mass, mass_vals, scales) + + # Scaled grids for the colour map (σ×B² [fb]). + grid_exp_s = grid_exp * scales[np.newaxis, :] + grid_obs_s = grid_obs * scales[np.newaxis, :] + fine_exp_s = fine_exp * scale_fine[np.newaxis, :] if fine_exp is not None else None + fine_obs_s = fine_obs * scale_fine[np.newaxis, :] if fine_obs is not None else None + + # Exclusion threshold per mass: σ×B² [fb] value at which r=1 boundary is physical. + # For SUSY this is σ_theory_NLO(mass); for H→SS it equals σ_ref_fb (= scales). + thresholds = _excl_thresholds_2d(proc, masses, mass_vals, theory_csv=theory_csv) + thresh_fine = np.interp(fine_mass, mass_vals, thresholds) + + # Ratio grids: σ×B²_limit / threshold. Contour at 1.0 = genuine exclusion boundary. + ratio_exp = grid_exp_s / thresholds[np.newaxis, :] + ratio_obs = grid_obs_s / thresholds[np.newaxis, :] + fine_ratio_exp = _interp_grid(log_ctaus, mass_vals, ratio_exp, fine_lct, fine_mass) + fine_ratio_obs = _interp_grid(log_ctaus, mass_vals, ratio_obs, fine_lct, fine_mass) + + # Colormap: SUSY uses ratio = σ×B²_limit / σ_theory (pivot=1 = exclusion boundary). + # Higgs uses absolute BR limit (pivot at benchmark BR=1%). + if proc in _SUSY_PROCS: + cmap_data_exp = fine_ratio_exp + cmap_data_obs = fine_ratio_obs + vmin, vref, vmax = 0.01, 1.0, 100.0 + cbar_label = r"$\sigma\mathcal{B}^{2}\ /\ \sigma_\mathrm{theory}$" + else: + cmap_data_exp = fine_exp_s + cmap_data_obs = fine_obs_s + vmin, vref, vmax = _2D_VSCALE.get(proc, (None, None, None)) + if vref is None: + vref = float(np.exp(np.mean(np.log(thresholds[thresholds > 0])))) + vmin, vmax = vref * 0.05, vref * 200.0 + cbar_label = _ylabel(proc) + + fig, ax = plt.subplots(figsize=(9, 6)) + + n_half = 30 + levels = np.concatenate([ + np.logspace(np.log10(vmin), np.log10(vref), n_half + 1)[:-1], + np.logspace(np.log10(vref), np.log10(vmax), n_half + 1), + ]) + norm = mcolors.LogNorm(vmin=vmin, vmax=vmax) + cmap = plt.get_cmap("RdBu_r") # blue=excluded (ratio<1), red=not excluded + + _all_nice = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, + 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, + 10000, 20000, 50000, 100000] + _nice_ticks = [t for t in _all_nice if vmin <= t <= vmax] + + if cmap_data_exp is not None: + cf = ax.contourf(fine_ctau, fine_mass, cmap_data_exp.T, + levels=levels, norm=norm, cmap=cmap, extend="both") + cbar = plt.colorbar(cf, ax=ax, pad=0.02) + cbar.set_label(cbar_label, fontsize=11) + cbar.set_ticks(_nice_ticks) + cbar.set_ticklabels(["%g" % t for t in _nice_ticks]) + cbar.ax.axhline(y=vref, color="black", lw=1.0, ls="--") + + # Exclusion contours on the ratio grid (= 1.0 where limit = theory/SM benchmark) + ax.contour(fine_ctau, fine_mass, fine_ratio_exp.T, levels=[1.0], + colors=["black"], linewidths=[2.5], linestyles=["dashed"]) + ax.plot([], [], color="black", lw=2.5, ls="--", label="Exp. excl.") + + if fine_ratio_obs is not None: + ax.contour(fine_ctau, fine_mass, fine_ratio_obs.T, levels=[1.0], + colors=["black"], linewidths=[2.5], linestyles=["solid"]) + ax.plot([], [], color="black", lw=2.5, ls="-", label="Obs. excl.") + else: + grid_cmap = ratio_exp if proc in _SUSY_PROCS else grid_exp_s + xs, ys, cs = [], [], [] + for j, mass in enumerate(masses): + for i, ctau in enumerate(ctau_vals): + if not np.isnan(grid_cmap[i, j]): + xs.append(ctau) + ys.append(mass_vals[j]) + cs.append(np.clip(grid_cmap[i, j], vmin, vmax)) + if xs: + sc = ax.scatter(xs, ys, c=cs, s=300, zorder=5, + norm=norm, cmap=cmap, + edgecolors="black", linewidths=0.5) + cbar = plt.colorbar(sc, ax=ax, pad=0.02) + cbar.set_label(cbar_label, fontsize=11) + cbar.set_ticks(_nice_ticks) + cbar.set_ticklabels(["%g" % t for t in _nice_ticks]) + cbar.ax.axhline(y=vref, color="black", lw=1.0, ls="--") + + for j, mass in enumerate(masses): + for i, ctau in enumerate(ctau_vals): + ax.scatter(ctau, mass_vals[j], color="black", s=20, zorder=6) + + y_pad = max(3.0, (mass_vals[-1] - mass_vals[0]) * 0.08) + ax.set_ylim(mass_vals[0] - y_pad, mass_vals[-1] + y_pad) + ax.set_xscale("log") + ax.set_xlim(10.0 ** log_ctau_lo, 10.0 ** (log_ctaus[-1] + 0.25)) + ax.set_xlabel(r"$c\tau$ [mm]") + ax.set_ylabel("Mass [GeV]") + ax.legend(fontsize=11, loc="upper right", framealpha=0.92, edgecolor="0.7") + ax.grid(True, which="both", ls=":", alpha=0.3) + _cms_label(ax) + _annotate_proc(ax, proc) + _save(fig, os.path.join(out_dir, "%s_2D%s.pdf" % (proc, fname_suffix))) + + + + +# --------------------------------------------------------------------------- +# Method comparison: collect both HybridNew and AsymptoticLimits per signal +# --------------------------------------------------------------------------- + +_HN_COLOR = "#2166ac" # blue — HybridNew +_AS_COLOR = "#d6604d" # red — AsymptoticLimits + + +def read_limits_method(sig_id, method): + """Read limits for a specific method without priority fallback.""" + for suffix in (".mH120.1234.root", ".mH120.root"): + fn = os.path.join(COMBINE_OUT, sig_id, + "higgsCombine%s.%s%s" % (sig_id, method, suffix)) + if os.path.exists(fn): + break + else: + return None + try: + f = ROOT.TFile.Open(fn) + if not f or f.IsZombie(): + return None + t = f.Get("limit") + if not t: + f.Close() + return None + quant_map = {-1.0: "obs", 0.025: "dn2", 0.16: "dn1", + 0.5: "exp", 0.84: "up1", 0.975: "up2"} + result = {} + for _ in t: + q = round(float(t.quantileExpected), 3) + for qref, key in quant_map.items(): + if abs(q - qref) < 0.01: + result[key] = float(t.limit) + f.Close() + if method == "HybridNew" and "obs" in result and "exp" not in result: + result["exp"] = result.pop("obs") + return result if "exp" in result else None + except Exception as exc: + print("Could not read %s %s: %s" % (sig_id, method, exc)) + return None + + +def collect_all_methods(): + """Return {proc -> {mass_str -> {ctau_mm -> {method: lims}}}}""" + data = {} + if not os.path.isdir(COMBINE_OUT): + return data + for sig_id in sorted(os.listdir(COMBINE_OUT)): + if not os.path.isdir(os.path.join(COMBINE_OUT, sig_id)): + continue + proc, ctau_str, mass = parse_sig_id(sig_id) + if proc is None: + continue + ctau_mm = ctau_to_mm(ctau_str) + if ctau_mm <= 0: + continue + methods = {} + for method in ("HybridNew", "AsymptoticLimits"): + lims = read_limits_method(sig_id, method) + if lims is not None: + methods[method] = lims + if not methods: + continue + data.setdefault(proc, {}).setdefault(mass, {})[ctau_mm] = methods + return data + + +# --------------------------------------------------------------------------- +# Comparison 1D: one plot per ctau, HybridNew median vs Asymptotic median+bands +# --------------------------------------------------------------------------- + +def plot_comparison_1d_per_ctau(proc, mass_data_m, out_dir, hepdata=None): + """Per-ctau 1D vs mass: HybridNew median (blue) + Asymptotic median+bands (red).""" + # Invert to {ctau -> {mass -> {method -> lims}}} + ctau_data = {} + for mass, cdict in mass_data_m.items(): + for ctau, mdict in cdict.items(): + ctau_data.setdefault(ctau, {})[mass] = mdict + + hd = hepdata.get(proc) if hepdata else None + + for ctau in sorted(ctau_data.keys()): + mdict = ctau_data[ctau] + masses = _sorted_masses(mdict) + if not masses: + continue + mass_vals = [int(m) for m in masses] + scales = [_sig_scale_fb(proc, m) for m in masses] + + fig, ax = plt.subplots(figsize=(8, 6)) + drew = False + + # AsymptoticLimits: median + ±1σ/2σ bands + as_idx = [i for i, m in enumerate(masses) if "AsymptoticLimits" in mdict[m]] + if as_idx: + as_mv = [mass_vals[i] for i in as_idx] + as_sc = [scales[i] for i in as_idx] + as_lims = [mdict[masses[i]]["AsymptoticLimits"] for i in as_idx] + as_exp = [l["exp"] * s for l, s in zip(as_lims, as_sc)] + as_dn1 = [l["dn1"] * s for l, s in zip(as_lims, as_sc)] if all("dn1" in l for l in as_lims) else None + as_up1 = [l["up1"] * s for l, s in zip(as_lims, as_sc)] if all("up1" in l for l in as_lims) else None + as_dn2 = [l.get("dn2", l["dn1"]) * s for l, s in zip(as_lims, as_sc)] if as_dn1 else None + as_up2 = [l.get("up2", l["up1"]) * s for l, s in zip(as_lims, as_sc)] if as_up1 else None + if as_dn2 and as_up2: + ax.fill_between(as_mv, as_dn2, as_up2, alpha=0.15, color=_AS_COLOR, edgecolor="none") + if as_dn1 and as_up1: + ax.fill_between(as_mv, as_dn1, as_up1, alpha=0.35, color=_AS_COLOR, edgecolor="none") + ax.plot(as_mv, as_exp, color=_AS_COLOR, lw=2, ls="--", label="Asymptotic exp.", zorder=4) + drew = True + + # HybridNew: median only (solid, marker at missing points) + hn_idx = [i for i, m in enumerate(masses) if "HybridNew" in mdict[m]] + if hn_idx: + hn_mv = [mass_vals[i] for i in hn_idx] + hn_sc = [scales[i] for i in hn_idx] + hn_lims = [mdict[masses[i]]["HybridNew"] for i in hn_idx] + hn_exp = [l["exp"] * s for l, s in zip(hn_lims, hn_sc)] + ax.plot(hn_mv, hn_exp, color=_HN_COLOR, lw=2.5, ls="-", + marker="o", ms=5, label="HybridNew exp.", zorder=5) + drew = True + + # HepData reference (EXO-19-013), where available for this ctau + if hd is not None: + hd_masses_sl, hd_r_sl = _hepdata_slice_at_ctau(hd, ctau) + if hd_masses_sl is not None: + hd_masses_sl = np.array(hd_masses_sl) + hd_r_sl = np.array(hd_r_sl) + keep = hd_r_sl > 0 + if np.any(keep): + ax.plot(hd_masses_sl[keep], hd_r_sl[keep], color="black", lw=1.5, ls="-", + label="High-HT obs. (EXO-19-013)", zorder=3) + drew = True + + if not drew: + plt.close(fig) + continue + + _draw_ref_curve_vsmass(ax, proc) + ax.set_yscale("log") + ax.set_xlabel(_mass_xlabel(proc)) + ax.set_ylabel(_ylabel(proc)) + ax.set_title(r"$c\tau = %s$" % _format_ctau(ctau), fontsize=12) + ax.legend(fontsize=10) + ax.grid(True, which="both", ls=":", alpha=0.4) + _cms_label(ax) + _annotate_proc(ax, proc) + ctau_tag = _format_ctau(ctau).replace(".", "p") + _save(fig, os.path.join(out_dir, "%s_compare_%s.pdf" % (proc, ctau_tag))) + + +# --------------------------------------------------------------------------- +# Comparison 2D: color map + HybridNew contour + Asymptotic contour +# --------------------------------------------------------------------------- + +def plot_comparison_2d(proc, mass_data_m, out_dir, hepdata, theory_csv=None, fname_suffix=""): + """2D comparison: HybridNew color map with both exclusion contours overlaid.""" + masses = _sorted_masses(mass_data_m) + ctau_counts = {m: len(mass_data_m[m]) for m in masses} + max_n = max(ctau_counts.values()) + min_n = max(3, max_n - 1) if proc in _SUSY_PROCS else 2 + masses = [m for m in masses if ctau_counts[m] >= min_n] + ctaus_all = sorted(set.union(*[set(mass_data_m[m].keys()) for m in masses])) + + if len(masses) < 2 or len(ctaus_all) < 2: + print(" Skipping comparison 2D for %s: need at least 2x2 grid" % proc) + return + + mass_vals = np.array([int(m) for m in masses], dtype=float) + ctau_vals = np.array(ctaus_all, dtype=float) + scales = np.array([_sig_scale_fb(proc, m) for m in masses]) + thresholds = _excl_thresholds_2d(proc, masses, mass_vals, theory_csv=theory_csv) + + grid_hn = np.full((len(ctau_vals), len(mass_vals)), np.nan) + grid_as = np.full((len(ctau_vals), len(mass_vals)), np.nan) + + for j, mass in enumerate(masses): + for i, ctau in enumerate(ctau_vals): + if ctau not in mass_data_m[mass]: + continue + entry = mass_data_m[mass][ctau] + if "HybridNew" in entry: + grid_hn[i, j] = entry["HybridNew"]["exp"] + if "AsymptoticLimits" in entry: + grid_as[i, j] = entry["AsymptoticLimits"]["exp"] + + grid_hn_s = grid_hn * scales[np.newaxis, :] + grid_as_s = grid_as * scales[np.newaxis, :] + + # Color map: HybridNew where available, fill Asymptotic for missing cells + grid_cmap = np.where(np.isnan(grid_hn_s), grid_as_s, grid_hn_s) + + if proc in _SUSY_PROCS: + ratio_cmap = grid_cmap / thresholds[np.newaxis, :] + ratio_hn = grid_hn_s / thresholds[np.newaxis, :] + ratio_as = grid_as_s / thresholds[np.newaxis, :] + vmin, vref, vmax = 0.01, 1.0, 100.0 + cbar_label = r"$\sigma\mathcal{B}^{2}\ /\ \sigma_\mathrm{theory}$ [HybridNew]" + else: + ratio_cmap = grid_cmap + ratio_hn = grid_hn_s / thresholds[np.newaxis, :] + ratio_as = grid_as_s / thresholds[np.newaxis, :] + vmin, vref, vmax = _2D_VSCALE.get(proc, (None, None, None)) + if vref is None: + vref = float(np.exp(np.mean(np.log(thresholds[thresholds > 0])))) + vmin, vmax = vref * 0.05, vref * 200.0 + cbar_label = _ylabel(proc) + " [HybridNew]" + + log_ctaus = np.log10(ctau_vals) + log_ctau_lo = log_ctaus[0] - 0.7 + fine_lct = np.linspace(log_ctau_lo, log_ctaus[-1], 200) + fine_mass = np.linspace(mass_vals[0], mass_vals[-1], 200) + fine_ctau = 10.0 ** fine_lct + + fine_cmap = _interp_grid(log_ctaus, mass_vals, ratio_cmap, fine_lct, fine_mass) + fine_ratio_hn = _interp_grid(log_ctaus, mass_vals, ratio_hn, fine_lct, fine_mass) + fine_ratio_as = _interp_grid(log_ctaus, mass_vals, ratio_as, fine_lct, fine_mass) + + n_half = 30 + levels = np.concatenate([ + np.logspace(np.log10(vmin), np.log10(vref), n_half + 1)[:-1], + np.logspace(np.log10(vref), np.log10(vmax), n_half + 1), + ]) + norm = mcolors.LogNorm(vmin=vmin, vmax=vmax) + cmap = plt.get_cmap("RdBu_r") + _all_nice = [0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5, + 1, 2, 5, 10, 20, 50, 100, 200, 500, 1000, 2000, 5000, + 10000, 20000, 50000, 100000] + _nice_ticks = [t for t in _all_nice if vmin <= t <= vmax] + + fig, ax = plt.subplots(figsize=(9, 6)) + + if fine_cmap is not None: + cf = ax.contourf(fine_ctau, fine_mass, fine_cmap.T, + levels=levels, norm=norm, cmap=cmap, extend="both") + cbar = plt.colorbar(cf, ax=ax, pad=0.02) + cbar.set_label(cbar_label, fontsize=11) + cbar.set_ticks(_nice_ticks) + cbar.set_ticklabels(["%g" % t for t in _nice_ticks]) + cbar.ax.axhline(y=vref, color="black", lw=1.0, ls="--") + + if fine_ratio_hn is not None: + ax.contour(fine_ctau, fine_mass, fine_ratio_hn.T, levels=[1.0], + colors=[_HN_COLOR], linewidths=[2.5], linestyles=["solid"]) + ax.plot([], [], color=_HN_COLOR, lw=2.5, ls="-", label="HybridNew exp.") + + if fine_ratio_as is not None: + ax.contour(fine_ctau, fine_mass, fine_ratio_as.T, levels=[1.0], + colors=[_AS_COLOR], linewidths=[2.0], linestyles=["dashed"]) + ax.plot([], [], color=_AS_COLOR, lw=2.0, ls="--", label="Asymptotic exp.") + + for j, mass in enumerate(masses): + for i, ctau in enumerate(ctau_vals): + ax.scatter(ctau, mass_vals[j], color="black", s=20, zorder=6) + + y_pad = max(3.0, (mass_vals[-1] - mass_vals[0]) * 0.08) + ax.set_ylim(mass_vals[0] - y_pad, mass_vals[-1] + y_pad) + ax.set_xscale("log") + ax.set_xlim(10.0 ** log_ctau_lo, 10.0 ** (log_ctaus[-1] + 0.25)) + ax.set_xlabel(r"$c\tau$ [mm]") + ax.set_ylabel("Mass [GeV]") + ax.legend(fontsize=11, loc="upper right", framealpha=0.92, edgecolor="0.7") + ax.grid(True, which="both", ls=":", alpha=0.3) + _cms_label(ax) + _annotate_proc(ax, proc) + _save(fig, os.path.join(out_dir, "%s_2D_compare%s.pdf" % (proc, fname_suffix))) + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + +def main(): + global COMBINE_OUT + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--out-dir", default=PLOT_DIR) + ap.add_argument("--combine-out", default=COMBINE_OUT) + ap.add_argument("--subset", default=None, + help="Comma-separated process names to plot") + ap.add_argument("--comparison-dir", default=None, + help="If set, write HybridNew vs Asymptotic comparison plots here") + args = ap.parse_args() + + COMBINE_OUT = args.combine_out + + if not os.path.isdir(COMBINE_OUT): + print("CombineOutput not found: %s" % COMBINE_OUT) + sys.exit(1) + + os.makedirs(args.out_dir, exist_ok=True) + + subset = set(args.subset.split(",")) if args.subset else None + hepdata = load_hepdata() + if hepdata: + print("Loaded HepData reference for: %s" % ", ".join(sorted(hepdata))) + else: + print("No HepData reference found at %s (comparison plots skipped)" % HEPDATA_JSON) + + data = collect_all() + if not data: + print("No limit results found in %s" % COMBINE_OUT) + sys.exit(0) + + # ggZH is always grouped into VH; never plot it standalone + _skip_procs = {"ggZHToSSTodddd", "ggZHToSSTobbbb"} + + for proc in sorted(data): + if proc in _skip_procs: + continue + if subset and proc not in subset: + continue + n_masses = len(data[proc]) + n_pts = sum(len(v) for v in data[proc].values()) + print("\n%s: %d masses, %d total hypotheses" % (proc, n_masses, n_pts)) + + plot_1d(proc, data[proc], args.out_dir, hepdata) + plot_1d_vs_mass_all(proc, data[proc], args.out_dir) + plot_1d_vs_mass_pairs(proc, data[proc], args.out_dir, hepdata) + if proc in ("ggHToSSTodddd", "VH"): + plot_1d_vs_mass_ctau_pair(proc, data[proc], args.out_dir, 1.0, 10.0) + plot_2d(proc, data[proc], args.out_dir, hepdata) + if proc == "mfv_neu" and "mfv_neu" in _EXTRA_THEORY_CSV: + plot_2d(proc, data[proc], args.out_dir, hepdata, + theory_csv=_EXTRA_THEORY_CSV["mfv_neu"], + fname_suffix="_ewk") + + print("\nDone. Plots saved to %s" % args.out_dir) + + if args.comparison_dir: + os.makedirs(args.comparison_dir, exist_ok=True) + print("\n--- Generating HybridNew vs Asymptotic comparison plots ---") + data_m = collect_all_methods() + for proc in sorted(data_m): + if proc in _skip_procs: + continue + if subset and proc not in subset: + continue + n_pts = sum(len(v) for v in data_m[proc].values()) + print("\n%s: %d hypotheses" % (proc, n_pts)) + plot_comparison_1d_per_ctau(proc, data_m[proc], args.comparison_dir, hepdata) + plot_comparison_2d(proc, data_m[proc], args.comparison_dir, hepdata) + if proc == "mfv_neu" and "mfv_neu" in _EXTRA_THEORY_CSV: + plot_comparison_2d(proc, data_m[proc], args.comparison_dir, hepdata, + theory_csv=_EXTRA_THEORY_CSV["mfv_neu"], + fname_suffix="_ewk") + print("\nComparison plots saved to %s" % args.comparison_dir) + + +if __name__ == "__main__": + main() diff --git a/MFVNeutralino/test/ForLimits/run_limits_bjet_allyears.sh b/MFVNeutralino/test/ForLimits/run_limits_bjet_allyears.sh new file mode 100755 index 000000000..42d9774c4 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/run_limits_bjet_allyears.sh @@ -0,0 +1,26 @@ +#!/bin/bash +# Run makeLimitsInputROOT.py for bjet channel, all 4 years. +# Run inside el7 apptainer with CMSSW_10_6_48 sourced. +# Usage: +# apptainer exec ... bash run_limits_bjet_allyears.sh +# or interactively inside the apptainer: +# bash run_limits_bjet_allyears.sh + +set -e + +CMSSW_SRC=/uscms/home/gdecastr/nobackup/work/DVCode/mfv_10648/src +FORLIMITS=${CMSSW_SRC}/JMTucker/MFVNeutralino/test/ForLimits + +echo "=== Setting up CMSSW ===" +source /cvmfs/cms.cern.ch/cmsset_default.sh +cd ${CMSSW_SRC} +eval $(scramv1 runtime -sh) +echo "CMSSW_BASE=${CMSSW_BASE}" + +echo "" +echo "=== Running makeLimitsInputROOT.py (bjet, all years) ===" +cd ${FORLIMITS} +python makeLimitsInputROOT.py --year all --channel bjet + +echo "" +echo "=== Done! ===" diff --git a/MFVNeutralino/test/ForLimits/script_configs.py b/MFVNeutralino/test/ForLimits/script_configs.py new file mode 100644 index 000000000..6a9c69065 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/script_configs.py @@ -0,0 +1,147 @@ +# Runtime paths and settings come from limits_config.yaml. +# Physics configuration (signal groupings, nuisance lists) lives here. +import os +import yaml + +# -------------------------------------------------------------------------- +# Load YAML config +# -------------------------------------------------------------------------- +_here = os.path.dirname(os.path.abspath(__file__)) +_yaml_path = os.path.join(_here, "limits_config.yaml") + +with open(_yaml_path) as _f: + _cfg = yaml.safe_load(_f) + +def _abs(rel): + """Resolve a path relative to the ForLimits directory.""" + return os.path.join(_here, rel) + +# -------------------------------------------------------------------------- +# Datacard settings +# -------------------------------------------------------------------------- +_year_to_tag = { + "20161": "2016preAPV", + "20162": "2016postAPV", + "2017": "2017", + "2018": "2018", +} + +datacard = { + "year": _cfg["year"], + "year_key": ["20161", "20162", "2017", "2018"], + "year_to_tag": _year_to_tag, + "bins": _cfg["bins"], + "nbins": _cfg["nbins"], +} + +# -------------------------------------------------------------------------- +# Signal configuration +# -------------------------------------------------------------------------- +# Lepton trigger signals (mfv_neu excluded -- no hard-scatter lepton). +_lep_sigs = [ + "WminusHToSSTodddd", "WplusHToSSTodddd", "ZHToSSTodddd", + "ggZHToSSTobbbb", "ggZHToSSTodddd", + "ttHToLLPs_bbbb", "ttHToLLPs_dddd", +] + +# Signals that fire the displaced (b-jet) trigger +_bjet_sigs = [ + "ggHToSSTodddd", "mfv_neu", + "mfv_stopbbarbbar", "mfv_stopdbardbar", + "ttHToLLPs_bbbb", "ttHToLLPs_dddd", +] + +# Processes with a hard-scatter lepton -- get lep_effi nuisance (VH, ttH; not SUSY). +# "VH" is the combined signal group (ZH+WH++WH-+ggZH); individual sub-process names +# are also listed for any cards generated outside the combined group path. +lep_reco_effi_sigs = frozenset([ + "VH", + "WminusHToSSTodddd", "WplusHToSSTodddd", "ZHToSSTodddd", + "ggZHToSSTobbbb", "ggZHToSSTodddd", + "ttHToLLPs_bbbb", "ttHToLLPs_dddd", +]) + +sig = { + "type": _cfg["channel"], + "lep": _cfg["signal"]["lep"], + "bjet": _cfg["signal"]["bjet"], + "lep_sigs": _lep_sigs, + "bjet_sigs": _bjet_sigs, + # VH = ZH + WH+ + WH- summed (same lifetime/mass grid, different xsec) + "sig_grps": { + "VH": ["ZHToSSTodddd", "WminusHToSSTodddd", "WplusHToSSTodddd", "ggZHToSSTodddd"], + }, + # Nuisance aliases: use when a dedicated table doesn't exist for a process + "aliases": { + "bjet": { + "ttHToLLPs_bbbb": {"ggHToSSTodddd"}, + "ttHToLLPs_dddd": {"ggHToSSTodddd"}, + }, + "lep": { + "ttHToLLPs_bbbb": {"VH"}, + "ttHToLLPs_dddd": {"VH"}, + "ggZHToSSTobbbb": {"VH"}, + "ggZHToSSTodddd": {"VH"}, + "WminusHToSSTodddd": {"VH"}, + "WplusHToSSTodddd": {"VH"}, + "ZHToSSTodddd": {"VH"}, + }, + }, +} + +# -------------------------------------------------------------------------- +# Background configuration +# -------------------------------------------------------------------------- +bkg = { + "lep": _cfg["background"]["lep"], + "bjet": _cfg["background"]["bjet"], +} +# Rename YAML key "filename" -> "fn" to preserve the existing interface +bkg["lep"]["fn"] = bkg["lep"].pop("filename", bkg["lep"].get("fn", "")) +bkg["bjet"]["fn"] = bkg["bjet"].pop("filename", bkg["bjet"].get("fn", "")) + +# -------------------------------------------------------------------------- +# Output paths +# root_output : intermediate ROOT file with signal/bkg histograms +# datacard_out_loc : final combine .txt datacard files +# -------------------------------------------------------------------------- +output = { + "lep": {"out_folder": _cfg["root_output"]["lep"]["folder"]}, + "bjet": {"out_folder": _cfg["root_output"]["bjet"]["folder"]}, + "out_fn": _cfg["root_output"]["filename"], +} + +datacard_out_loc = { + "lep": {"out_folder": _cfg["datacard_output"]["lep"]["folder"]}, + "bjet": {"out_folder": _cfg["datacard_output"]["bjet"]["folder"]}, + "out_fn_prefix": _cfg["datacard_output"]["prefix"], + "out_fn_suffix": _cfg["datacard_output"]["suffix"], +} + +# -------------------------------------------------------------------------- +# Observed events (kept at 0 for blind analysis) +# -------------------------------------------------------------------------- +obs = {k: list(v) for k, v in _cfg["observations"].items()} + +# -------------------------------------------------------------------------- +# Nuisance table paths (absolute, resolved relative to ForLimits/) +# -------------------------------------------------------------------------- +nuisance_table_paths = { + "vtx_reco_TM": _abs(_cfg["nuisance_tables"]["vtx_reco_TM"]), + "disp_trig_uncerts": _abs(_cfg["nuisance_tables"]["disp_trig_uncerts"]), + "tk_reco_eff": { + "base": _abs(_cfg["nuisance_tables"]["tk_reco_eff"]["base"]), + "up_prefix": _cfg["nuisance_tables"]["tk_reco_eff"]["up_prefix"], + "dn_prefix": _cfg["nuisance_tables"]["tk_reco_eff"]["dn_prefix"], + }, + "fac_scale_VH_csv": _cfg["nuisance_tables"]["fac_scale_VH_csv"], # absolute path +} + +# -------------------------------------------------------------------------- +# Debug settings +# -------------------------------------------------------------------------- +debug_settings = { + "enabled": _cfg.get("debug", True), + "scale_bkg_fake": False, # set True only for explicit testing; never in production + "bkg_fake_sf": 100, +} diff --git a/MFVNeutralino/test/ForLimits/sig_and_bkg_configs.py b/MFVNeutralino/test/ForLimits/sig_and_bkg_configs.py new file mode 100644 index 000000000..9b7a1f1d9 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/sig_and_bkg_configs.py @@ -0,0 +1,62 @@ +import JMTucker.MFVNeutralino.AnalysisConstants as ac + + +template_norms = { + "n1v": { + "lep": [52.19, 77.35, 442.23, 694.77], + "bjet": [1004.1395, 434.13558, 187.79967, 276.98182], + }, + "n2v": { + "lep": [0.001, 0.012, 0.002, 0.034], # 0.001 (20161) is a placeholder -- update when lepton bkg estimate for 20161 is available + "bjet": [0.258, 0.062, 0.078, 0.122], + }, + "lumi": { + "lep": [ + ac.scaled_int_lumi_20161, + ac.scaled_int_lumi_20162, + ac.scaled_int_lumi_2017, + ac.scaled_int_lumi_2018], + "bjet": [ + ac.scaled_int_lumi_20161, + ac.scaled_int_lumi_20162, + ac.scaled_int_lumi_2017, + ac.scaled_int_lumi_2018], + }, + "old_lumis": [19664., 16978., 40610., 59683.], # Derived from AnalysisConstants.h. I feel like if these != new lumis, we need corrections. +} + + +# n2v_uncs disabled until real uncertainties measured after unblinding. +# To re-enable: fill values below, uncomment, add "n2v_unc" to nuis_bkg. +# +# n2v_uncs = { +# "lep": [0.0001, 0.0001, 0.0001, 0.0001], +# "bjet": [0.0001, 0.0001, 0.0001, 0.0001], +# } +n2v_uncs = None # sentinel; code must not use this until real values are filled + + +# CMS Run 2 luminosity uncertainty decomposition (CMS-LUM-17-003/4, CMS-LUM-18-002). +# Totals: 2016 1.2%, 2017 2.3%, 2018 2.5%. +lumi_lit_corrs = { + "lumi_13TeV_correlated": {"2016": 1.006, "2017": 1.009, "2018": 1.020 }, + "lumi_13TeV_1718": {"2016": None, "2017": 1.006, "2018": 1.002 }, + "lumi_2016": {"2016": 1.010, "2017": None, "2018": None }, + "lumi_2017": {"2016": None, "2017": 1.020, "2018": None }, + "lumi_2018": {"2016": None, "2017": None, "2018": 1.015 }, +} + + +updn_wt_dict = { + "fake_fact_CMS_eff_lep": ["weight*fac_weight_up", "weight*fac_weight_dn"], + "fake_fact_CMS_pileup": ["weight*fac_weight_up", "weight*fac_weight_dn"], +} + + +printout_flags = { # Convention: True for printing statements, False for silence + "PyStorage": { + "sig_type_conflict": False, + }, +} + + diff --git a/MFVNeutralino/test/ForLimits/submitCombine.py b/MFVNeutralino/test/ForLimits/submitCombine.py new file mode 100644 index 000000000..6b1141914 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/submitCombine.py @@ -0,0 +1,354 @@ +#!/usr/bin/env python +""" +Discover all signal datacards under ForLimits/Datacards/, group them by +(proc, ctau, mass), and submit one Condor job per hypothesis that runs: + 1. combine -M AsymptoticLimits -- 95% CL expected/observed limits + 2. combine -M FitDiagnostics -- best-fit signal strength + s+b shapes + +Cards are merged locally by combineCards.py before submission (requires the +CMSSW_14_1_0_pre4 environment to be active when running this script). +The combine binary and libHiggsAnalysisCombinedLimit.so are shipped via +transfer_input_files so worker nodes do not need /uscms/home or /uscms_data. + +Prerequisites + - makeLimitsInputROOT.py must have been run for all years + channels so that + Datacards/{lep,bjet}/Datacard_*.txt files exist. + - Run this script with CMSSW_14_1_0_pre4 + Combine sourced (cmsenv). + combineCards.py and the combine binary must be in PATH. + To install from scratch (on LPC EL9 node, outside apptainer): + cmsrel CMSSW_14_1_0_pre4 + cd CMSSW_14_1_0_pre4/src && cmsenv + git clone https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit + scram b -j8 + +Usage + python submitCombine.py # all signals + python submitCombine.py --subset VH,mfv_neu # selected processes + python submitCombine.py --dry-run # list jobs without submitting + python submitCombine.py --skip-existing # skip already-completed jobs +""" +import os +import sys +import glob +import stat +import argparse +import subprocess + +HERE = os.path.dirname(os.path.abspath(__file__)) +DATACARD_DIR = os.path.join(HERE, "Datacards") +COMBINE_OUT = os.path.join(HERE, "CombineOutput") +CONDOR_DIR = os.path.join(HERE, "CombineCondor") + +def _apply_tag(tag): + """Redirect all I/O directories to tagged variants (e.g. tag='4bin').""" + global DATACARD_DIR, COMBINE_OUT, CONDOR_DIR + DATACARD_DIR = os.path.join(HERE, "Datacards_%s" % tag) + COMBINE_OUT = os.path.join(HERE, "CombineOutput_%s" % tag) + CONDOR_DIR = os.path.join(HERE, "CombineCondor_%s" % tag) + +COMBINE_TARBALL = os.environ.get( + "COMBINE_TARBALL", + "/uscms_data/d3/gdecastr/work/combine_env.tar.gz", +) +# CMSSW_14_1_0 (final release) is in CVMFS and has the same ABI as pre4. +# Worker nodes only bind /cvmfs, so we set up the environment from there. +CVMFS_CMSSW14 = "/cvmfs/cms.cern.ch/el9_amd64_gcc12/cms/cmssw/CMSSW_14_1_0/src" + +YEARS = ("20161", "20162", "2017", "2018") +CHANNELS = ("lep", "bjet") + +# --------------------------------------------------------------------------- +# Per-job shell scripts +# --------------------------------------------------------------------------- + +# AsymptoticLimits: analytic CLs, fast (~5s), produces bands + median. +# Output: higgsCombine{sig}.AsymptoticLimits.mH120.root + combine.log +_JOB_SH_ASYMPTOTIC = """\ +#!/bin/bash +set -e +source /cvmfs/cms.cern.ch/cmsset_default.sh +cd {cvmfs_cmssw14} +eval $(scramv1 runtime -sh) +cd /srv +tar xf combine_env.tar.gz +export PATH=/srv/combine_env/bin:$PATH +export LD_LIBRARY_PATH=/srv/combine_env/lib:$LD_LIBRARY_PATH + +SIG={sig_id} +echo "=== AsymptoticLimits: $SIG ===" +combine -M AsymptoticLimits workspace_$SIG.root \\ + --name $SIG --run blind -v 0 \\ + 2>&1 | tee combine.log +echo "=== Done: $SIG ===" +""" + +# HybridNew: single Asimov (-t -1) run; blinded analysis so data = background. +_JOB_SH_HYBRIDNEW = """\ +#!/bin/bash +set -e +source /cvmfs/cms.cern.ch/cmsset_default.sh +cd {cvmfs_cmssw14} +eval $(scramv1 runtime -sh) +cd /srv +tar xf combine_env.tar.gz +export PATH=/srv/combine_env/bin:$PATH +export LD_LIBRARY_PATH=/srv/combine_env/lib:$LD_LIBRARY_PATH + +SIG={sig_id} +echo "=== HybridNew Asimov expected: $SIG ===" +combine -M HybridNew --frequentist --testStat LHC \\ + -T 20000 --fork 2 -t -1 -s 1234 \\ + --rMax {rmax} \\ + --name $SIG \\ + workspace_$SIG.root -v 0 +echo "=== Done: $SIG ===" +""" + +# --------------------------------------------------------------------------- +# Condor JDL templates (one per method) +# --------------------------------------------------------------------------- + +_JDL_ASYMPTOTIC = """\ +universe = vanilla +executable = {job_sh} +initialdir = {work_dir} +output = {log_pfx}.out +error = {log_pfx}.err +log = {log_pfx}.log +request_cpus = 1 +request_memory = 2000MB ++DesiredOS = "EL9" +should_transfer_files = YES +when_to_transfer_output = ON_EXIT +transfer_input_files = {combine_tarball},{workspace} +transfer_output_files = higgsCombine{sig_id}.AsymptoticLimits.mH120.root,combine.log +queue 1 +""" + +_JDL_HYBRIDNEW = """\ +universe = vanilla +executable = {job_sh} +initialdir = {work_dir} +output = {log_pfx}.out +error = {log_pfx}.err +log = {log_pfx}.log +request_cpus = 2 +request_memory = 4000MB ++DesiredOS = "EL9" +should_transfer_files = YES +when_to_transfer_output = ON_EXIT +transfer_input_files = {combine_tarball},{workspace} +transfer_output_files = higgsCombine{sig_id}.HybridNew.mH120.1234.root +queue 1 +""" + +# Keep _JOB_SH / _JDL as aliases for backward compatibility +_JOB_SH = _JOB_SH_HYBRIDNEW +_JDL = _JDL_HYBRIDNEW + + +def _read_asymptotic_exp(sig_id): + """Return median expected limit (quantile=0.5) from AsymptoticLimits ROOT file, or None.""" + root_fn = os.path.join(COMBINE_OUT, sig_id, + "higgsCombine%s.AsymptoticLimits.mH120.root" % sig_id) + if not os.path.exists(root_fn): + return None + try: + import ROOT + ROOT.PyConfig.IgnoreCommandLineOptions = True + ROOT.gROOT.SetBatch(True) + f = ROOT.TFile.Open(root_fn) + if not f or f.IsZombie(): + return None + t = f.Get("limit") + if not t: + f.Close() + return None + for _ in t: + if abs(float(t.quantileExpected) - 0.5) < 0.01: + val = float(t.limit) + f.Close() + return val + f.Close() + except Exception: + pass + return None + + +def _makedirs(path): + if not os.path.exists(path): + os.makedirs(path) + + +def find_hypotheses(): + """Return {sig_base: {ch_year: card_path}} for every available datacard. + + sig_base = "_tau_M" (no year suffix). + ch_year = "lep_2018", "bjet_20161", etc. + """ + hyps = {} + for ch in CHANNELS: + ch_dir = os.path.join(DATACARD_DIR, ch) + if not os.path.isdir(ch_dir): + continue + prefix = "Datacard_%s_" % ch + for fn in sorted(glob.glob(os.path.join(ch_dir, prefix + "*.txt"))): + bn = os.path.basename(fn).replace(".txt", "") + rest = bn[len(prefix):] # "VH_tau1mm_M15_2018" + parts = rest.rsplit("_", 1) + if len(parts) != 2: + continue + sig_base, year = parts + if year not in YEARS: + continue + hyps.setdefault(sig_base, {}) + hyps[sig_base]["%s_%s" % (ch, year)] = fn + return hyps + + +def _write_job(sig_id, cards, dry_run, method="hybridnew"): + work_dir = os.path.join(COMBINE_OUT, sig_id) + condor_dir = os.path.join(CONDOR_DIR, sig_id) + _makedirs(work_dir) + _makedirs(condor_dir) + + # Pre-merge cards and build workspace locally (submit node has NFS access). + card_args = " ".join("%s=%s" % (k, v) for k, v in sorted(cards.items())) + combined_card = os.path.join(condor_dir, "combined_%s.txt" % sig_id) + workspace = os.path.join(condor_dir, "workspace_%s.root" % sig_id) + if not dry_run: + if not os.path.exists(workspace): + import shutil + for tool in ("combineCards.py", "text2workspace.py"): + if not shutil.which(tool): + print("ERROR: %s not in PATH; workspace missing for %s -- skipping" % (tool, sig_id)) + return False + ret = subprocess.call("combineCards.py %s > %s" % (card_args, combined_card), shell=True) + if ret != 0: + print("WARNING: combineCards.py failed for %s -- skipping" % sig_id) + return False + ret = subprocess.call( + "text2workspace.py %s -m 125 -o %s" % (combined_card, workspace), shell=True) + if ret != 0: + print("WARNING: text2workspace.py failed for %s -- skipping" % sig_id) + return False + + job_sh = os.path.join(condor_dir, "run.sh") + log_pfx = os.path.join(condor_dir, "job") + jdl_fn = os.path.join(condor_dir, "submit.jdl") + + if method == "asymptotic": + with open(job_sh, "w") as fh: + fh.write(_JOB_SH_ASYMPTOTIC.format( + cvmfs_cmssw14 = CVMFS_CMSSW14, + sig_id = sig_id, + )) + os.chmod(job_sh, stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH) + with open(jdl_fn, "w") as fh: + fh.write(_JDL_ASYMPTOTIC.format( + job_sh = job_sh, + work_dir = work_dir, + log_pfx = log_pfx, + combine_tarball = COMBINE_TARBALL, + workspace = workspace, + sig_id = sig_id, + )) + else: + asymp_exp = _read_asymptotic_exp(sig_id) + if asymp_exp and asymp_exp > 0: + rmax = 10.0 * asymp_exp # no floor: at r=10*expected, CLs~0 and fork 2 is stable + else: + rmax = 20.0 # fallback for signals with no asymptotic log + with open(job_sh, "w") as fh: + fh.write(_JOB_SH_HYBRIDNEW.format( + cvmfs_cmssw14 = CVMFS_CMSSW14, + sig_id = sig_id, + rmax = rmax, + )) + os.chmod(job_sh, stat.S_IRWXU | stat.S_IRGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IXOTH) + with open(jdl_fn, "w") as fh: + fh.write(_JDL_HYBRIDNEW.format( + job_sh = job_sh, + work_dir = work_dir, + log_pfx = log_pfx, + combine_tarball = COMBINE_TARBALL, + workspace = workspace, + sig_id = sig_id, + )) + + if not dry_run: + ret = subprocess.call("condor_submit " + jdl_fn, shell=True) + if ret != 0: + print("WARNING: condor_submit returned %d for %s" % (ret, sig_id)) + return False + else: + print(" [dry-run] %s (%d cards: %s)" % (sig_id, len(cards), ", ".join(sorted(cards)))) + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--subset", default=None, + help="Comma-separated process names to include, e.g. VH,mfv_neu") + ap.add_argument("--dry-run", action="store_true", + help="Write job files but do not call condor_submit") + ap.add_argument("--skip-existing", action="store_true", + help="Skip hypotheses that already have output for the chosen method") + ap.add_argument("--limit", type=int, default=None, + help="Submit at most N jobs (for testing)") + ap.add_argument("--sig-id", default=None, + help="Submit only this exact hypothesis (e.g. mfv_neu_tau001000um_M0400)") + ap.add_argument("--tag", default=None, + help="Redirect I/O to tagged directories, e.g. --tag 4bin uses Datacards_4bin/, CombineOutput_4bin/, CombineCondor_4bin/") + ap.add_argument("--method", default="hybridnew", choices=["hybridnew", "asymptotic"], + help="Which combine method to run: hybridnew (default) or asymptotic") + args = ap.parse_args() + + if args.tag: + _apply_tag(args.tag) + + if not args.dry_run: + for path in (COMBINE_TARBALL, CVMFS_CMSSW14): + if not os.path.exists(path): + print("ERROR: required path not found: %s" % path) + sys.exit(1) + + subset = set(args.subset.split(",")) if args.subset else None + hyps = find_hypotheses() + + if not hyps: + print("No datacards found under %s" % DATACARD_DIR) + sys.exit(1) + + n = 0 + n_skip = 0 + for sig_id in sorted(hyps): + proc = sig_id.split("_tau")[0] + if args.sig_id and sig_id != args.sig_id: + continue + if subset and proc not in subset: + continue + if args.skip_existing: + if args.method == "asymptotic": + out_fn = os.path.join(COMBINE_OUT, sig_id, + "higgsCombine%s.AsymptoticLimits.mH120.root" % sig_id) + else: + out_fn = os.path.join(COMBINE_OUT, sig_id, + "higgsCombine%s.HybridNew.mH120.1234.root" % sig_id) + if os.path.exists(out_fn): + n_skip += 1 + continue + if _write_job(sig_id, hyps[sig_id], args.dry_run, method=args.method) is not False: + n += 1 + if args.limit and n >= args.limit: + break + + if n_skip: + print("Skipped %d already-completed hypotheses (--skip-existing)" % n_skip) + + status = "queued (dry-run, job files written)" if args.dry_run else "submitted to Condor" + print("\n%d hypotheses %s" % (n, status)) + + +if __name__ == "__main__": + main() diff --git a/MFVNeutralino/test/ForLimits/turn_7p4p1_to_2darr.py b/MFVNeutralino/test/ForLimits/turn_7p4p1_to_2darr.py new file mode 100644 index 000000000..419dc6118 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/turn_7p4p1_to_2darr.py @@ -0,0 +1,78 @@ +from __future__ import absolute_import +import numpy as np + +import script_configs as config # everything hard-coded goes into config +import helper_PyStorage_objects as sth +import uncerts_trigger as trig_unc # Python dictionaries +import uncerts_trigger_patch as trig_unc_patch # Extrapolation pack +import nuisance_configs as ns_conf + + +""" +-Make nuisance tables for 4 processes (based on total_uncerts of trigger_uncerts, which is the 7.4.1 summary.) +-Save information as pickles (check pickles can be re-read) +""" + + +proc_nm = set([ + "mfv_neu", + "mfv_stopbbarbbar", + "mfv_stopdbardbar", + "ggHToSSTodddd",]) +is_percent = False +x_unit = "mm" +y_unit = "GeV" + +# output_str = """""" # Defunct method: write output to .txt +# save_loc = "7p4p1_trigger_unc.txt" # For manual +pickle_prefix = ns_conf.pickle_prefixes["disp_trig_uncerts"] + + + +for p in proc_nm: + + x_vals = [] + y_vals = [] + + x_vals, y_vals = sth.collect_xyvals_from_namearr(p, trig_unc.total_uncerts.keys(), x_unit, y_unit, debug_mode=True, nbins=3) + + + ntab = sth.NuisanceTable(p, x_vals, x_unit, y_vals, y_unit, as_percent=is_percent, years=set(["2016", "2016APV", "2017", "2018"])) + ntab.add_dictionary(trig_unc.total_uncerts, debug_mode=False) + ntab.add_dictionary(trig_unc_patch.total_uncerts_patch, debug_mode=False) + + + # Mess around with object + ntab.get_point("2017", x_val=1.01, y_val=401, x_unit="mm", use_log=False, debug_mode=True) + + point_from_fn = ntab.get_point_from_fn("mfv_neu_tau010000um_M0400_2018", debug_mode=True) + print "Searching mfv_neu_tau010000um_M0400_2018 returned", point_from_fn + + + if False: + print "\nSummary of", p + print ntab.pretty_print_diagnostics() + print "\n\n" + + ntab.save_pickle(pickle_prefix) + + # output_str += str(ntab.proc) + " = " + ntab.pretty_print_diagnostics() + "\n\n\n" + + +# Check if pickling is right +for p in proc_nm: + ntab = sth.NuisanceTable(proc=p, pickle_loc=pickle_prefix) + print "Successfully reconstructed:", ntab.proc + + if True: + print "\nSummary of", p + print ntab.pretty_print_diagnostics() + print "\n\n" + + + +#out_file = open(save_loc, "w") +#out_file.write(output_str) +#out_file.close() + + diff --git a/MFVNeutralino/test/ForLimits/turn_TrkMvr_to_2darr.py b/MFVNeutralino/test/ForLimits/turn_TrkMvr_to_2darr.py new file mode 100644 index 000000000..c167f1f23 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/turn_TrkMvr_to_2darr.py @@ -0,0 +1,49 @@ +from __future__ import absolute_import +import numpy as np + +import script_configs as config +import helper_PyStorage_objects as sth +import uncerts_trkmvr as TM_unc +import nuisance_configs as ns_conf + + + +proc_nm = set([ + "VH", + "mfv_neu", + "mfv_stopbbarbbar", + "mfv_stopdbardbar", + "ggHToSSTodddd",]) +is_percent = True + +pickle_prefix = ns_conf.pickle_prefixes["vtx_reco_TM"] + + + +for p in proc_nm: + + arr_1612 = np.array(TM_unc.TM_Tables[p]["20161-2"].T) + arr_178 = np.array(TM_unc.TM_Tables[p]["2017-8"].T) + + x_vals = TM_unc.TM_Tables[p]["x_vals"] + y_vals = TM_unc.TM_Tables[p]["y_vals"] + + x_unit = TM_unc.TM_Tables[p]["x_unit"] + y_unit = TM_unc.TM_Tables[p]["y_unit"] + + + ntab = sth.NuisanceTable(p, x_vals, x_unit, y_vals, y_unit, as_percent=is_percent, years=set(["20161-2", "2017-8"])) + + ntab.add_array(p, x_vals, y_vals, "20161-2", arr_1612, x_unit=x_unit, y_unit=y_unit, debug_mode=False) + ntab.add_array(p, x_vals, y_vals, "2017-8", arr_178, x_unit=x_unit, y_unit=y_unit, debug_mode=False) + + + ntab.save_pickle(pickle_prefix) + + + if True: + print "\nSummary of", p + print ntab.pretty_print_diagnostics() + print "\n\n" + + diff --git a/MFVNeutralino/test/ForLimits/turn_TrkRec_to_2darr.py b/MFVNeutralino/test/ForLimits/turn_TrkRec_to_2darr.py new file mode 100644 index 000000000..34325180b --- /dev/null +++ b/MFVNeutralino/test/ForLimits/turn_TrkRec_to_2darr.py @@ -0,0 +1,92 @@ +from __future__ import absolute_import +import numpy as np + +import script_configs as config # everything hard-coded goes into config +import helper_PyStorage_objects as sth +import uncerts_trkrec as tkrc_unc # Python dictionaries +import nuisance_configs as ns_conf + + +""" +-Make nuisance tables, for central/up/down +-Save information as pickles (check pickles can be re-read) +""" + + +proc_nm = set([ + "VH",]) +is_percent = False +x_unit = "mm" +y_unit = "GeV" + +prefix_dict = ns_conf.pickle_triple_prefixes["tk_reco_eff"] +pickle_prefix_base = prefix_dict["base"] + +pickle_prefix_ct = pickle_prefix_base + prefix_dict["central"] +pickle_prefix_up = pickle_prefix_base + prefix_dict["up"] +pickle_prefix_dn = pickle_prefix_base + prefix_dict["dn"] + + + +for p in proc_nm: + + x_vals = [] + y_vals = [] + + x_vals, y_vals = sth.collect_xyvals_from_namearr(p, tkrc_unc.trkdisp_central.keys(), x_unit, y_unit, debug_mode=True) + + + ct_ntab = sth.NuisanceTable(p, x_vals, x_unit, y_vals, y_unit, as_percent=is_percent, years=set(["20161", "20162", "2017", "2018"]), nbin_len=True) + ct_ntab.add_dictionary(tkrc_unc.trkdisp_central, debug_mode=False) + + up_ntab = sth.NuisanceTable(p, x_vals, x_unit, y_vals, y_unit, as_percent=is_percent, years=set(["20161", "20162", "2017", "2018"]), nbin_len=True) + up_ntab.add_dictionary(tkrc_unc.trkdisp_up, debug_mode=False) + + dn_ntab = sth.NuisanceTable(p, x_vals, x_unit, y_vals, y_unit, as_percent=is_percent, years=set(["20161", "20162", "2017", "2018"]), nbin_len=True) + dn_ntab.add_dictionary(tkrc_unc.trkdisp_dn, debug_mode=False) + + + # Mess around with object + ct_ntab.get_point("2017", x_val=1, y_val=40, x_unit="mm", use_log=False, debug_mode=True) + + point_from_fn = ct_ntab.get_point_from_fn("VH_tau1mm_M040_2018", debug_mode=True) + print "Searching VH_tau1mm_M040_2018 returned", point_from_fn + + point_from_fn = up_ntab.get_point_from_fn("VH_tau1mm_M040_2018", debug_mode=True) + print "Searching VH_tau1mm_M040_2018 returned", point_from_fn + + point_from_fn = dn_ntab.get_point_from_fn("VH_tau1mm_M040_2018", debug_mode=True) + print "Searching VH_tau1mm_M040_2018 returned", point_from_fn + + + if True: + print "\nSummary of", p + print ct_ntab.pretty_print_diagnostics() + print up_ntab.pretty_print_diagnostics() + print dn_ntab.pretty_print_diagnostics() + print "\n\n" + + ct_ntab.save_pickle(pickle_prefix_ct) + up_ntab.save_pickle(pickle_prefix_up) + dn_ntab.save_pickle(pickle_prefix_dn) + + # output_str += str(ntab.proc) + " = " + ntab.pretty_print_diagnostics() + "\n\n\n" + + +# Check if pickling is right +for p in proc_nm: + ct_ntab = sth.NuisanceTable(proc=p, pickle_loc=pickle_prefix_ct) + print "Successfully reconstructed:", ct_ntab.proc + + if False: + print "\nSummary of", p + print ct_ntab.pretty_print_diagnostics() + print "\n\n" + + + +#out_file = open(save_loc, "w") +#out_file.write(output_str) +#out_file.close() + + diff --git a/MFVNeutralino/test/ForLimits/uncerts_trigger.py b/MFVNeutralino/test/ForLimits/uncerts_trigger.py new file mode 100644 index 000000000..4712e05d0 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/uncerts_trigger.py @@ -0,0 +1,5037 @@ +tracking_hlt_uncerts = { +"ggHToSSTodddd_tau1mm_M55_2018": 0.0030, +"ggHToSSTodddd_tau10mm_M55_2018": 0.0017, +"ggHToSSTodddd_tau100mm_M55_2018": 0.0000, +"ggHToSSTodddd_tau1mm_M55_2017": 0.0182, +"ggHToSSTodddd_tau10mm_M55_2017": 0.0185, +"ggHToSSTodddd_tau100mm_M55_2017": 0.0000, +"ggHToSSTodddd_tau1mm_M55_2016": 0.0078, +"ggHToSSTodddd_tau10mm_M55_2016": 0.0030, +"ggHToSSTodddd_tau1mm_M55_2016APV": 0.1429, +"ggHToSSTodddd_tau10mm_M55_2016APV": 0.2141, +"ggHToSSTodddd_tau100mm_M55_2016APV": 0.7763, +"mfv_neu_tau000100um_M0200_2018": 0.0000, +"mfv_neu_tau000300um_M0200_2018": 0.0009, +"mfv_neu_tau010000um_M0200_2018": 0.0011, +"mfv_neu_tau030000um_M0200_2018": 0.0042, +"mfv_neu_tau000100um_M0300_2018": 0.0005, +"mfv_neu_tau000300um_M0300_2018": 0.0003, +"mfv_neu_tau001000um_M0300_2018": 0.0018, +"mfv_neu_tau010000um_M0300_2018": 0.0019, +"mfv_neu_tau030000um_M0300_2018": 0.0013, +"mfv_neu_tau000100um_M0400_2018": 0.0000, +"mfv_neu_tau000300um_M0400_2018": 0.0002, +"mfv_neu_tau001000um_M0400_2018": 0.0058, +"mfv_neu_tau010000um_M0400_2018": 0.0004, +"mfv_neu_tau030000um_M0400_2018": 0.0020, +"mfv_neu_tau000100um_M0600_2018": 0.0001, +"mfv_neu_tau001000um_M0600_2018": 0.0086, +"mfv_neu_tau010000um_M0600_2018": 0.0005, +"mfv_neu_tau030000um_M0600_2018": 0.0011, +"mfv_neu_tau000100um_M0800_2018": 0.0001, +"mfv_neu_tau000300um_M0800_2018": 0.0001, +"mfv_neu_tau001000um_M0800_2018": 0.0083, +"mfv_neu_tau010000um_M0800_2018": 0.0004, +"mfv_neu_tau030000um_M0800_2018": 0.0007, +"mfv_neu_tau000100um_M1200_2018": 0.0001, 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+"mfv_stopbbarbbar_tau000100um_M1600_2016": 0.0351, +"mfv_stopbbarbbar_tau000300um_M1600_2016": 0.0241, +"mfv_stopbbarbbar_tau001000um_M1600_2016": 0.0237, +"mfv_stopbbarbbar_tau010000um_M1600_2016": 0.0093, +"mfv_stopbbarbbar_tau030000um_M1600_2016": 0.0073, +"mfv_stopbbarbbar_tau000300um_M3000_2016": 0.0772, +"mfv_stopbbarbbar_tau001000um_M3000_2016": 0.0430, +"mfv_stopbbarbbar_tau010000um_M3000_2016": 0.0153, +"mfv_stopbbarbbar_tau030000um_M3000_2016": 0.0110, +"mfv_neu_tau000100um_M0200_2016APV": 0.0402, +"mfv_neu_tau000300um_M0200_2016APV": 0.0446, +"mfv_neu_tau001000um_M0200_2016APV": 0.0954, +"mfv_neu_tau010000um_M0200_2016APV": 0.1729, +"mfv_neu_tau030000um_M0200_2016APV": 0.1745, +"mfv_neu_tau000100um_M0300_2016APV": 0.0375, +"mfv_neu_tau000300um_M0300_2016APV": 0.0440, +"mfv_neu_tau001000um_M0300_2016APV": 0.1003, +"mfv_neu_tau010000um_M0300_2016APV": 0.1362, +"mfv_neu_tau030000um_M0300_2016APV": 0.1551, +"mfv_neu_tau000100um_M0400_2016APV": 0.0203, +"mfv_neu_tau000300um_M0400_2016APV": 0.0298, +"mfv_neu_tau001000um_M0400_2016APV": 0.0585, +"mfv_neu_tau010000um_M0400_2016APV": 0.1022, +"mfv_neu_tau030000um_M0400_2016APV": 0.0991, +"mfv_neu_tau000100um_M0600_2016APV": 0.0213, +"mfv_neu_tau000300um_M0600_2016APV": 0.0222, +"mfv_neu_tau001000um_M0600_2016APV": 0.0382, +"mfv_neu_tau010000um_M0600_2016APV": 0.0684, +"mfv_neu_tau030000um_M0600_2016APV": 0.0537, +"mfv_neu_tau000100um_M0800_2016APV": 0.0240, +"mfv_neu_tau000300um_M0800_2016APV": 0.0216, +"mfv_neu_tau001000um_M0800_2016APV": 0.0297, +"mfv_neu_tau010000um_M0800_2016APV": 0.0597, +"mfv_neu_tau030000um_M0800_2016APV": 0.0456, +"mfv_neu_tau000100um_M1200_2016APV": 0.0214, +"mfv_neu_tau000300um_M1200_2016APV": 0.0211, +"mfv_neu_tau001000um_M1200_2016APV": 0.0268, +"mfv_neu_tau010000um_M1200_2016APV": 0.0467, +"mfv_neu_tau000100um_M1600_2016APV": 0.0200, +"mfv_neu_tau000300um_M1600_2016APV": 0.0214, +"mfv_neu_tau001000um_M1600_2016APV": 0.0262, +"mfv_neu_tau010000um_M1600_2016APV": 0.0476, +"mfv_neu_tau030000um_M1600_2016APV": 0.0355, +"mfv_neu_tau001000um_M3000_2016APV": 0.0280, +"mfv_neu_tau010000um_M3000_2016APV": 0.0703, +"mfv_neu_tau030000um_M3000_2016APV": 0.0394, +"mfv_stopdbardbar_tau000100um_M0200_2016APV": 0.0200, +"mfv_stopdbardbar_tau000300um_M0200_2016APV": 0.0336, +"mfv_stopdbardbar_tau001000um_M0200_2016APV": 0.0757, +"mfv_stopdbardbar_tau010000um_M0200_2016APV": 0.1448, +"mfv_stopdbardbar_tau030000um_M0200_2016APV": 0.1591, +"mfv_stopdbardbar_tau000100um_M0300_2016APV": 0.0202, +"mfv_stopdbardbar_tau000300um_M0300_2016APV": 0.0281, +"mfv_stopdbardbar_tau001000um_M0300_2016APV": 0.0686, +"mfv_stopdbardbar_tau010000um_M0300_2016APV": 0.1328, +"mfv_stopdbardbar_tau030000um_M0300_2016APV": 0.1300, +"mfv_stopdbardbar_tau000100um_M0400_2016APV": 0.0216, +"mfv_stopdbardbar_tau000300um_M0400_2016APV": 0.0259, +"mfv_stopdbardbar_tau010000um_M0400_2016APV": 0.1226, +"mfv_stopdbardbar_tau030000um_M0400_2016APV": 0.1114, +"mfv_stopdbardbar_tau000100um_M0600_2016APV": 0.0226, +"mfv_stopdbardbar_tau000300um_M0600_2016APV": 0.0240, +"mfv_stopdbardbar_tau001000um_M0600_2016APV": 0.0515, +"mfv_stopdbardbar_tau010000um_M0600_2016APV": 0.1152, +"mfv_stopdbardbar_tau030000um_M0600_2016APV": 0.0985, +"mfv_stopdbardbar_tau000100um_M0800_2016APV": 0.0218, +"mfv_stopdbardbar_tau000300um_M0800_2016APV": 0.0227, +"mfv_stopdbardbar_tau001000um_M0800_2016APV": 0.0436, +"mfv_stopdbardbar_tau010000um_M0800_2016APV": 0.1019, +"mfv_stopdbardbar_tau030000um_M0800_2016APV": 0.0813, +"mfv_stopdbardbar_tau000100um_M1200_2016APV": 0.0311, +"mfv_stopdbardbar_tau000300um_M1200_2016APV": 0.0224, +"mfv_stopdbardbar_tau001000um_M1200_2016APV": 0.0382, +"mfv_stopdbardbar_tau010000um_M1200_2016APV": 0.0946, +"mfv_stopdbardbar_tau030000um_M1200_2016APV": 0.0852, +"mfv_stopdbardbar_tau000100um_M1600_2016APV": 0.0763, +"mfv_stopdbardbar_tau000300um_M1600_2016APV": 0.0380, +"mfv_stopdbardbar_tau001000um_M1600_2016APV": 0.0415, +"mfv_stopdbardbar_tau010000um_M1600_2016APV": 0.1119, +"mfv_stopdbardbar_tau030000um_M1600_2016APV": 0.0846, +"mfv_stopdbardbar_tau000300um_M3000_2016APV": 0.1121, +"mfv_stopdbardbar_tau001000um_M3000_2016APV": 0.0750, +"mfv_stopdbardbar_tau010000um_M3000_2016APV": 0.1507, +"mfv_stopdbardbar_tau030000um_M3000_2016APV": 0.0938, +"mfv_stopbbarbbar_tau000100um_M0200_2016APV": 0.0200, +"mfv_stopbbarbbar_tau000300um_M0200_2016APV": 0.0327, +"mfv_stopbbarbbar_tau001000um_M0200_2016APV": 0.0743, +"mfv_stopbbarbbar_tau010000um_M0200_2016APV": 0.1953, +"mfv_stopbbarbbar_tau030000um_M0200_2016APV": 0.1950, +"mfv_stopbbarbbar_tau000100um_M0300_2016APV": 0.0212, +"mfv_stopbbarbbar_tau000300um_M0300_2016APV": 0.0272, +"mfv_stopbbarbbar_tau001000um_M0300_2016APV": 0.0654, +"mfv_stopbbarbbar_tau010000um_M0300_2016APV": 0.1396, +"mfv_stopbbarbbar_tau030000um_M0300_2016APV": 0.1486, +"mfv_stopbbarbbar_tau000100um_M0400_2016APV": 0.0204, +"mfv_stopbbarbbar_tau000300um_M0400_2016APV": 0.0226, +"mfv_stopbbarbbar_tau001000um_M0400_2016APV": 0.0508, +"mfv_stopbbarbbar_tau010000um_M0400_2016APV": 0.1285, +"mfv_stopbbarbbar_tau030000um_M0400_2016APV": 0.1193, +"mfv_stopbbarbbar_tau000100um_M0600_2016APV": 0.0204, +"mfv_stopbbarbbar_tau000300um_M0600_2016APV": 0.0223, +"mfv_stopbbarbbar_tau001000um_M0600_2016APV": 0.0425, +"mfv_stopbbarbbar_tau010000um_M0600_2016APV": 0.1035, +"mfv_stopbbarbbar_tau030000um_M0600_2016APV": 0.0975, +"mfv_stopbbarbbar_tau000100um_M0800_2016APV": 0.0210, +"mfv_stopbbarbbar_tau000300um_M0800_2016APV": 0.0212, +"mfv_stopbbarbbar_tau001000um_M0800_2016APV": 0.0373, +"mfv_stopbbarbbar_tau010000um_M0800_2016APV": 0.0990, +"mfv_stopbbarbbar_tau030000um_M0800_2016APV": 0.0866, +"mfv_stopbbarbbar_tau000100um_M1200_2016APV": 0.0220, +"mfv_stopbbarbbar_tau000300um_M1200_2016APV": 0.0210, +"mfv_stopbbarbbar_tau001000um_M1200_2016APV": 0.0360, +"mfv_stopbbarbbar_tau010000um_M1200_2016APV": 0.0916, +"mfv_stopbbarbbar_tau030000um_M1200_2016APV": 0.0743, +"mfv_stopbbarbbar_tau000100um_M1600_2016APV": 0.0483, +"mfv_stopbbarbbar_tau000300um_M1600_2016APV": 0.0260, +"mfv_stopbbarbbar_tau001000um_M1600_2016APV": 0.0322, +"mfv_stopbbarbbar_tau010000um_M1600_2016APV": 0.0978, +"mfv_stopbbarbbar_tau030000um_M1600_2016APV": 0.0791, +"mfv_stopbbarbbar_tau001000um_M3000_2016APV": 0.0646, +"mfv_stopbbarbbar_tau010000um_M3000_2016APV": 0.1258, +"mfv_stopbbarbbar_tau030000um_M3000_2016APV": 0.0954, +} diff --git a/MFVNeutralino/test/ForLimits/uncerts_trigger_patch.py b/MFVNeutralino/test/ForLimits/uncerts_trigger_patch.py new file mode 100644 index 000000000..2a570079f --- /dev/null +++ b/MFVNeutralino/test/ForLimits/uncerts_trigger_patch.py @@ -0,0 +1,55 @@ +total_uncerts_patch = { + + # 2016 + "mfv_stopbbarbbar_tau000100um_M0200_2016": 0.0243, + "mfv_stopbbarbbar_tau000300um_M1200_2016": 0.0241, + "mfv_stopbbarbbar_tau000100um_M3000_2016": 0.1124, + # 2016APV + "mfv_stopbbarbbar_tau000100um_M3000_2016APV": 0.1200, + "mfv_stopbbarbbar_tau000300um_M3000_2016APV": 0.0646, + # 2017 + "mfv_stopbbarbbar_tau000100um_M1600_2017": 0.0215, + "mfv_stopbbarbbar_tau000100um_M3000_2017": 0.0407, + "mfv_stopbbarbbar_tau000300um_M3000_2017": 0.0390, + # 2018 + "mfv_stopbbarbbar_tau000100um_M3000_2018": 0.0894, + "mfv_stopbbarbbar_tau000300um_M3000_2018": 0.0409, + + + # 2016 + "mfv_stopdbardbar_tau000100um_M3000_2016": 0.2311, + "mfv_stopdbardbar_tau010000um_M3000_2016": 0.0617, + # 2016APV + "mfv_stopdbardbar_tau001000um_M0400_2016APV": 0.1226, + "mfv_stopdbardbar_tau000100um_M3000_2016APV": 0.2251, + # 2017 + "mfv_stopdbardbar_tau001000um_M0600_2017": 0.0345, + "mfv_stopdbardbar_tau000100um_M1600_2017": 0.0266, + "mfv_stopdbardbar_tau000300um_M1600_2017": 0.0248, + "mfv_stopdbardbar_tau000100um_M3000_2017": 0.0520, + "mfv_stopdbardbar_tau000300um_M3000_2017": 0.0485, + # 2018 + "mfv_stopdbardbar_tau030000um_M0400_2018": 0.0497, + "mfv_stopdbardbar_tau000100um_M3000_2018": 0.0912, + "mfv_stopdbardbar_tau000300um_M3000_2018": 0.0538, + + + # 2016 + "mfv_neu_tau000100um_M3000_2016": 0.0294, + # 2016APV + "mfv_neu_tau030000um_M1200_2016APV": 0.0467, + "mfv_neu_tau000100um_M3000_2016APV": 0.0280, + "mfv_neu_tau000300um_M3000_2016APV": 0.0280, + # 2017 + "mfv_neu_tau001000um_M0200_2017": 0.0519, + "mfv_neu_tau000300um_M0400_2017": 0.0336, + "mfv_neu_tau000100um_M0800_2017": 0.0220, + "mfv_neu_tau030000um_M1200_2017": 0.0068, + "mfv_neu_tau000100um_M3000_2017": 0.0221, + "mfv_neu_tau000300um_M3000_2017": 0.0217, + # 2018 + "mfv_neu_tau001000um_M0200_2018": 0.0556, + "mfv_neu_tau000300um_M0600_2018": 0.0248, + "mfv_neu_tau000100um_M3000_2018": 0.0490, + "mfv_neu_tau010000um_M3000_2018": 0.0207, +} diff --git a/MFVNeutralino/test/ForLimits/uncerts_trkmvr.py b/MFVNeutralino/test/ForLimits/uncerts_trkmvr.py new file mode 100644 index 000000000..e023897f8 --- /dev/null +++ b/MFVNeutralino/test/ForLimits/uncerts_trkmvr.py @@ -0,0 +1,120 @@ +from __future__ import absolute_import +import numpy as np + +""" +Need transposing +""" + + +#from __future__ import absolute_import +TM_Tables = { + + "VH" : { #FIXME, what's the name + + "20161-2" : np.array([[100, 100, 100, 100, 100, 100], + [100, 60.34, 29.14, 23.44, 19.38, 22.58], + [100, 40.24, 25.34, 14.88, 17.24, 17.68]]), + + "2017-8" : np.array([[100, 100, 79.78, 66.28, 42.22, 43.84], + [100, 29.08, 22.38, 21.7, 23.72, 27.82], + [100, 35.14, 22.64, 19.94, 21.34, 22.18]]), + + "x_vals" : [0.1, 0.3, 1., 3., 10., 30.], + "x_unit" : "mm", + + "y_vals" : [15, 40, 55], + "y_unit" : "GeV", + + }, + + + + + + "ggHToSSTodddd" : { + + "20161-2" : np.array([[100, 100, 100, 100], + [100, 100, 100, 100], + [100, 64.52, 53.32, 53.32]]), + + "2017-8" : np.array([[100, 100, 100, 100], + [100, 41.52, 34.24, 34.24], + [100, 25.16, 23.9, 23.9]]), + + "x_vals" : [0.1, 1., 10., 100.], + "x_unit" : "mm", + + "y_vals" : [15, 40, 55], + "y_unit" : "GeV", + + }, + + + + + + "mfv_stopdbardbar" : { + + "20161-2" : np.array([[27.56, 10.7, 7.62, 7.84, 12.12], + [77.28, 31.18, 10.38, 5.02, 4.82], + [85.06, 40.1, 12, 3.56, 3.38]]), + + "2017-8" : np.array([[28.74, 9.48, 5.16, 7.5, 9.68], + [59.04, 19.84, 4.74, 2.66, 2.92], + [55.9, 22.14, 4.12, 1.62, 1.82]]), + + "x_vals" : [0.1, 0.3, 1., 10., 30.], + "x_unit" : "mm", + + "y_vals" : [200, 400, 800], + "y_unit" : "GeV", + + }, + + + + + + "mfv_stopbbarbbar" : { + + "20161-2" : np.array([[65.86, 52.24, 43.4, 53.18, 39.26], + [100, 22.18, 23.62, 32.76, 35.82], + [100, 31.24, 13.74, 17.58, 16.84]]), + + "2017-8" : np.array([[95.16, 38.22, 31.38, 29.7, 31.26], + [90.3, 24.58, 13.42, 10.36, 9.28], + [76.88, 29.38, 5.5, 3.88, 5.02]]), + + "x_vals" : [0.1, 0.3, 1., 10., 30.], + "x_unit" : "mm", + + "y_vals" : [200, 400, 800], + "y_unit" : "GeV", + + }, + + + + + + "mfv_neu" : { + + "20161-2" : np.array([[45.08, 21.98, 17.12, 10.98, 17.68], + [100, 28.6, 24.0, 11.0, 18.0], + [100, 37.76, 20.0, 11.0, 18.0]]), + + "2017-8" : np.array([[55.34, 29.38, 16.76, 7.28, 7.16], + [72.88, 39.42, 24, 8, 8], + [89.64, 49.48, 20, 6, 6]]), + + "x_vals" : [0.1, 0.3, 1., 10., 30.], + "x_unit" : "mm", + + "y_vals" : [200, 400, 800], + "y_unit" : "GeV", + + }, + +} + + diff --git a/MFVNeutralino/test/ForLimits/uncerts_trkrec.py b/MFVNeutralino/test/ForLimits/uncerts_trkrec.py new file mode 100644 index 000000000..bae78651f --- /dev/null +++ b/MFVNeutralino/test/ForLimits/uncerts_trkrec.py @@ -0,0 +1,224 @@ +trkdisp_central = { + 'VH_tau100um_M15_20161': [1, 1, 1], + 'VH_tau100um_M40_20161': [1, 1, 1], + 'VH_tau100um_M55_20161': [1, 1, 1], + 'VH_tau300um_M15_20161': [0.9872, 1, 1], + 'VH_tau300um_M40_20161': [0.9872, 1, 1], + 'VH_tau300um_M55_20161': [0.9872, 1, 1], + 'VH_tau1mm_M15_20161': [0.925396, 0.89459, 0.743117], + 'VH_tau1mm_M40_20161': [0.925396, 0.89459, 0.743117], + 'VH_tau1mm_M55_20161': [0.925396, 0.89459, 0.743117], + 'VH_tau3mm_M15_20161': [0.902718, 0.795838, 0.714358], + 'VH_tau3mm_M40_20161': [0.902718, 0.795838, 0.714358], + 'VH_tau3mm_M55_20161': [0.902718, 0.795838, 0.714358], + 'VH_tau10mm_M15_20161': [0.895352, 0.739882, 0.684449], + 'VH_tau10mm_M40_20161': [0.895352, 0.739882, 0.684449], + 'VH_tau10mm_M55_20161': [0.895352, 0.739882, 0.684449], + 'VH_tau30mm_M15_20161': [0.889738, 0.721038, 0.670345], + 'VH_tau30mm_M40_20161': [0.889738, 0.721038, 0.670345], + 'VH_tau30mm_M55_20161': [0.889738, 0.721038, 0.670345], + 'VH_tau100um_M15_20162': [1, 1, 1], + 'VH_tau100um_M40_20162': [1, 1, 1], + 'VH_tau100um_M55_20162': [1, 1, 1], + 'VH_tau300um_M15_20162': [0.949081, 1, 1], + 'VH_tau300um_M40_20162': [0.949081, 1, 1], + 'VH_tau300um_M55_20162': [0.949081, 1, 1], + 'VH_tau1mm_M15_20162': [0.924969, 0.844629, 0.937082], + 'VH_tau1mm_M40_20162': [0.924969, 0.844629, 0.937082], + 'VH_tau1mm_M55_20162': [0.924969, 0.844629, 0.937082], + 'VH_tau3mm_M15_20162': [0.897707, 0.773077, 0.782656], + 'VH_tau3mm_M40_20162': [0.897707, 0.773077, 0.782656], + 'VH_tau3mm_M55_20162': [0.897707, 0.773077, 0.782656], + 'VH_tau10mm_M15_20162': [0.828945, 0.739645, 0.690249], + 'VH_tau10mm_M40_20162': [0.828945, 0.739645, 0.690249], + 'VH_tau10mm_M55_20162': [0.828945, 0.739645, 0.690249], + 'VH_tau30mm_M15_20162': [0.779882, 0.699189, 0.684918], + 'VH_tau30mm_M40_20162': [0.779882, 0.699189, 0.684918], + 'VH_tau30mm_M55_20162': [0.779882, 0.699189, 0.684918], + 'VH_tau100um_M15_2017': [0.895221, 1, 1], + 'VH_tau100um_M40_2017': [0.895221, 1, 1], + 'VH_tau100um_M55_2017': [0.895221, 1, 1], + 'VH_tau300um_M15_2017': [0.951566, 1, 1], + 'VH_tau300um_M40_2017': [0.951566, 1, 1], + 'VH_tau300um_M55_2017': [0.951566, 1, 1], + 'VH_tau1mm_M15_2017': [0.936599, 0.910362, 0.882833], + 'VH_tau1mm_M40_2017': [0.936599, 0.910362, 0.882833], + 'VH_tau1mm_M55_2017': [0.936599, 0.910362, 0.882833], + 'VH_tau3mm_M15_2017': [0.918775, 0.830554, 0.802288], + 'VH_tau3mm_M40_2017': [0.918775, 0.830554, 0.802288], + 'VH_tau3mm_M55_2017': [0.918775, 0.830554, 0.802288], + 'VH_tau10mm_M15_2017': [0.89829, 0.786591, 0.730466], + 'VH_tau10mm_M40_2017': [0.89829, 0.786591, 0.730466], + 'VH_tau10mm_M55_2017': [0.89829, 0.786591, 0.730466], + 'VH_tau30mm_M15_2017': [0.852676, 0.76553, 0.750921], + 'VH_tau30mm_M40_2017': [0.852676, 0.76553, 0.750921], + 'VH_tau30mm_M55_2017': [0.852676, 0.76553, 0.750921], + 'VH_tau100um_M15_2018': [0.988414, 1, 1], + 'VH_tau100um_M40_2018': [0.988414, 1, 1], + 'VH_tau100um_M55_2018': [0.988414, 1, 1], + 'VH_tau300um_M15_2018': [0.952073, 0.999818, 1], + 'VH_tau300um_M40_2018': [0.952073, 0.999818, 1], + 'VH_tau300um_M55_2018': [0.952073, 0.999818, 1], + 'VH_tau1mm_M15_2018': [0.941214, 0.887036, 0.663632], + 'VH_tau1mm_M40_2018': [0.941214, 0.887036, 0.663632], + 'VH_tau1mm_M55_2018': [0.941214, 0.887036, 0.663632], + 'VH_tau3mm_M15_2018': [0.907038, 0.847061, 0.809965], + 'VH_tau3mm_M40_2018': [0.907038, 0.847061, 0.809965], + 'VH_tau3mm_M55_2018': [0.907038, 0.847061, 0.809965], + 'VH_tau10mm_M15_2018': [0.880467, 0.803971, 0.745638], + 'VH_tau10mm_M40_2018': [0.880467, 0.803971, 0.745638], + 'VH_tau10mm_M55_2018': [0.880467, 0.803971, 0.745638], + 'VH_tau30mm_M15_2018': [0.860119, 0.818202, 0.721382], + 'VH_tau30mm_M40_2018': [0.860119, 0.818202, 0.721382], + 'VH_tau30mm_M55_2018': [0.860119, 0.818202, 0.721382], +} + +trkdisp_up = { + 'VH_tau100um_M15_20161': [1, 1, 1], + 'VH_tau100um_M40_20161': [1, 1, 1], + 'VH_tau100um_M55_20161': [1, 1, 1], + 'VH_tau300um_M15_20161': [1, 1, 1], + 'VH_tau300um_M40_20161': [1, 1, 1], + 'VH_tau300um_M55_20161': [1, 1, 1], + 'VH_tau1mm_M15_20161': [0.994297, 0.976545, 1], + 'VH_tau1mm_M40_20161': [0.994297, 0.976545, 1], + 'VH_tau1mm_M55_20161': [0.994297, 0.976545, 1], + 'VH_tau3mm_M15_20161': [0.976509, 0.953819, 0.943271], + 'VH_tau3mm_M40_20161': [0.976509, 0.953819, 0.943271], + 'VH_tau3mm_M55_20161': [0.976509, 0.953819, 0.943271], + 'VH_tau10mm_M15_20161': [0.966141, 0.934601, 0.952049], + 'VH_tau10mm_M40_20161': [0.966141, 0.934601, 0.952049], + 'VH_tau10mm_M55_20161': [0.966141, 0.934601, 0.952049], + 'VH_tau30mm_M15_20161': [0.935294, 0.962106, 0.952075], + 'VH_tau30mm_M40_20161': [0.935294, 0.962106, 0.952075], + 'VH_tau30mm_M55_20161': [0.935294, 0.962106, 0.952075], + 'VH_tau100um_M15_20162': [1, 1, 1], + 'VH_tau100um_M40_20162': [1, 1, 1], + 'VH_tau100um_M55_20162': [1, 1, 1], + 'VH_tau300um_M15_20162': [1, 1, 1], + 'VH_tau300um_M40_20162': [1, 1, 1], + 'VH_tau300um_M55_20162': [1, 1, 1], + 'VH_tau1mm_M15_20162': [0.980562, 0.962113, 1], + 'VH_tau1mm_M40_20162': [0.980562, 0.962113, 1], + 'VH_tau1mm_M55_20162': [0.980562, 0.962113, 1], + 'VH_tau3mm_M15_20162': [0.985976, 0.955266, 0.979416], + 'VH_tau3mm_M40_20162': [0.985976, 0.955266, 0.979416], + 'VH_tau3mm_M55_20162': [0.985976, 0.955266, 0.979416], + 'VH_tau10mm_M15_20162': [0.985602, 0.975556, 0.946397], + 'VH_tau10mm_M40_20162': [0.985602, 0.975556, 0.946397], + 'VH_tau10mm_M55_20162': [0.985602, 0.975556, 0.946397], + 'VH_tau30mm_M15_20162': [0.982434, 0.950015, 0.981138], + 'VH_tau30mm_M40_20162': [0.982434, 0.950015, 0.981138], + 'VH_tau30mm_M55_20162': [0.982434, 0.950015, 0.981138], + 'VH_tau100um_M15_2017': [1.00037, 1, 1], + 'VH_tau100um_M40_2017': [1.00037, 1, 1], + 'VH_tau100um_M55_2017': [1.00037, 1, 1], + 'VH_tau300um_M15_2017': [1.00002, 1, 1], + 'VH_tau300um_M40_2017': [1.00002, 1, 1], + 'VH_tau300um_M55_2017': [1.00002, 1, 1], + 'VH_tau1mm_M15_2017': [0.998376, 0.998634, 0.986045], + 'VH_tau1mm_M40_2017': [0.998376, 0.998634, 0.986045], + 'VH_tau1mm_M55_2017': [0.998376, 0.998634, 0.986045], + 'VH_tau3mm_M15_2017': [0.993497, 0.972243, 0.975201], + 'VH_tau3mm_M40_2017': [0.993497, 0.972243, 0.975201], + 'VH_tau3mm_M55_2017': [0.993497, 0.972243, 0.975201], + 'VH_tau10mm_M15_2017': [0.987749, 0.981613, 0.973105], + 'VH_tau10mm_M40_2017': [0.987749, 0.981613, 0.973105], + 'VH_tau10mm_M55_2017': [0.987749, 0.981613, 0.973105], + 'VH_tau30mm_M15_2017': [0.978787, 0.953755, 0.95511], + 'VH_tau30mm_M40_2017': [0.978787, 0.953755, 0.95511], + 'VH_tau30mm_M55_2017': [0.978787, 0.953755, 0.95511], + 'VH_tau100um_M15_2018': [1, 1, 1], + 'VH_tau100um_M40_2018': [1, 1, 1], + 'VH_tau100um_M55_2018': [1, 1, 1], + 'VH_tau300um_M15_2018': [0.997504, 1, 1], + 'VH_tau300um_M40_2018': [0.997504, 1, 1], + 'VH_tau300um_M55_2018': [0.997504, 1, 1], + 'VH_tau1mm_M15_2018': [0.996056, 0.993887, 0.931636], + 'VH_tau1mm_M40_2018': [0.996056, 0.993887, 0.931636], + 'VH_tau1mm_M55_2018': [0.996056, 0.993887, 0.931636], + 'VH_tau3mm_M15_2018': [0.992802, 0.979138, 0.972268], + 'VH_tau3mm_M40_2018': [0.992802, 0.979138, 0.972268], + 'VH_tau3mm_M55_2018': [0.992802, 0.979138, 0.972268], + 'VH_tau10mm_M15_2018': [0.98697, 0.976131, 0.971271], + 'VH_tau10mm_M40_2018': [0.98697, 0.976131, 0.971271], + 'VH_tau10mm_M55_2018': [0.98697, 0.976131, 0.971271], + 'VH_tau30mm_M15_2018': [0.991683, 0.963786, 0.947353], + 'VH_tau30mm_M40_2018': [0.991683, 0.963786, 0.947353], + 'VH_tau30mm_M55_2018': [0.991683, 0.963786, 0.947353], +} + +trkdisp_dn = { + 'VH_tau100um_M15_20161': [1, 1, 1], + 'VH_tau100um_M40_20161': [1, 1, 1], + 'VH_tau100um_M55_20161': [1, 1, 1], + 'VH_tau300um_M15_20161': [1, 1, 1], + 'VH_tau300um_M40_20161': [1, 1, 1], + 'VH_tau300um_M55_20161': [1, 1, 1], + 'VH_tau1mm_M15_20161': [1.0135, 1.00603, 1.06455], + 'VH_tau1mm_M40_20161': [1.0135, 1.00603, 1.06455], + 'VH_tau1mm_M55_20161': [1.0135, 1.00603, 1.06455], + 'VH_tau3mm_M15_20161': [1.00909, 1.02264, 1.07135], + 'VH_tau3mm_M40_20161': [1.00909, 1.02264, 1.07135], + 'VH_tau3mm_M55_20161': [1.00909, 1.02264, 1.07135], + 'VH_tau10mm_M15_20161': [1.01828, 1.04599, 1.0434], + 'VH_tau10mm_M40_20161': [1.01828, 1.04599, 1.0434], + 'VH_tau10mm_M55_20161': [1.01828, 1.04599, 1.0434], + 'VH_tau30mm_M15_20161': [1, 1.06634, 1.09033], + 'VH_tau30mm_M40_20161': [1, 1.06634, 1.09033], + 'VH_tau30mm_M55_20161': [1, 1.06634, 1.09033], + 'VH_tau100um_M15_20162': [1, 1, 1], + 'VH_tau100um_M40_20162': [1, 1, 1], + 'VH_tau100um_M55_20162': [1, 1, 1], + 'VH_tau300um_M15_20162': [1, 1, 1], + 'VH_tau300um_M40_20162': [1, 1, 1], + 'VH_tau300um_M55_20162': [1, 1, 1], + 'VH_tau1mm_M15_20162': [1.00164, 1.02913, 1], + 'VH_tau1mm_M40_20162': [1.00164, 1.02913, 1], + 'VH_tau1mm_M55_20162': [1.00164, 1.02913, 1], + 'VH_tau3mm_M15_20162': [1.01293, 1.03407, 1.06277], + 'VH_tau3mm_M40_20162': [1.01293, 1.03407, 1.06277], + 'VH_tau3mm_M55_20162': [1.01293, 1.03407, 1.06277], + 'VH_tau10mm_M15_20162': [1.02724, 1.05553, 1.04562], + 'VH_tau10mm_M40_20162': [1.02724, 1.05553, 1.04562], + 'VH_tau10mm_M55_20162': [1.02724, 1.05553, 1.04562], + 'VH_tau30mm_M15_20162': [1.11323, 1.08077, 1.10501], + 'VH_tau30mm_M40_20162': [1.11323, 1.08077, 1.10501], + 'VH_tau30mm_M55_20162': [1.11323, 1.08077, 1.10501], + 'VH_tau100um_M15_2017': [1, 1, 1], + 'VH_tau100um_M40_2017': [1, 1, 1], + 'VH_tau100um_M55_2017': [1, 1, 1], + 'VH_tau300um_M15_2017': [0.999904, 1, 1], + 'VH_tau300um_M40_2017': [0.999904, 1, 1], + 'VH_tau300um_M55_2017': [0.999904, 1, 1], + 'VH_tau1mm_M15_2017': [1.00431, 1.01411, 1.00011], + 'VH_tau1mm_M40_2017': [1.00431, 1.01411, 1.00011], + 'VH_tau1mm_M55_2017': [1.00431, 1.01411, 1.00011], + 'VH_tau3mm_M15_2017': [1.00322, 1.0183, 1.01686], + 'VH_tau3mm_M40_2017': [1.00322, 1.0183, 1.01686], + 'VH_tau3mm_M55_2017': [1.00322, 1.0183, 1.01686], + 'VH_tau10mm_M15_2017': [1.0105, 1.01902, 1.02312], + 'VH_tau10mm_M40_2017': [1.0105, 1.01902, 1.02312], + 'VH_tau10mm_M55_2017': [1.0105, 1.01902, 1.02312], + 'VH_tau30mm_M15_2017': [1.01534, 1.02821, 1.02704], + 'VH_tau30mm_M40_2017': [1.01534, 1.02821, 1.02704], + 'VH_tau30mm_M55_2017': [1.01534, 1.02821, 1.02704], + 'VH_tau100um_M15_2018': [1.0002, 1, 1], + 'VH_tau100um_M40_2018': [1.0002, 1, 1], + 'VH_tau100um_M55_2018': [1.0002, 1, 1], + 'VH_tau300um_M15_2018': [0.999356, 1.00261, 1], + 'VH_tau300um_M40_2018': [0.999356, 1.00261, 1], + 'VH_tau300um_M55_2018': [0.999356, 1.00261, 1], + 'VH_tau1mm_M15_2018': [1.00103, 1.00259, 1.01406], + 'VH_tau1mm_M40_2018': [1.00103, 1.00259, 1.01406], + 'VH_tau1mm_M55_2018': 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-2020,6.26063E-07,12.5356 -2025,6.09739E-07,12.5434 -2030,5.93416E-07,12.5516 -2035,5.77753E-07,12.5595 -2040,5.62531E-07,12.5674 -2045,5.47309E-07,12.5756 -2050,5.33114E-07,12.5831 -2055,5.18918E-07,12.591 -2060,5.05106E-07,12.5988 -2065,4.91868E-07,12.6063 -2070,4.7863E-07,12.6143 -2075,4.66106E-07,12.6217 -2080,4.53761E-07,12.6293 -2085,4.41583E-07,12.637 -2090,4.30071E-07,12.6442 -2095,4.18558E-07,12.6518 -2100,4.07512E-07,12.6592 -2105,3.96776E-07,12.6664 -2110,3.8604E-07,12.6741 -2115,3.76028E-07,12.681 -2120,3.66016E-07,12.6883 -2125,3.56275E-07,12.6956 -2130,3.46938E-07,12.7025 -2135,3.37601E-07,12.7099 -2140,3.28769E-07,12.7167 -2145,3.20062E-07,12.7237 -2150,3.11472E-07,12.7309 -2155,3.03353E-07,12.7376 -2160,2.95233E-07,12.7446 -2165,2.87442E-07,12.7514 -2170,2.7987E-07,12.7581 -2175,2.72298E-07,12.7652 -2180,2.65237E-07,12.7716 -2185,2.58175E-07,12.7783 -2190,2.51304E-07,12.785 -2195,2.44719E-07,12.7915 -2200,2.38134E-07,12.7983 -2205,2.31904E-07,12.8046 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+1250,3.919e-05,23 +1300,2.986e-05,25.6 +1350,2.284e-05,28.4 +1400,1.755e-05,31.6 +1450,1.354e-05,35.1 +1500,1.048e-05,39 +1550,8.138e-06,43.4 +1600,6.342e-06,48.1 +1650,4.958e-06,53.3 +1700,3.888e-06,59 +1750,3.058e-06,65.2 +1800,2.412e-06,71.9 +1850,1.908e-06,79.1 +1900,1.513e-06,86.9 diff --git a/Tools/python/SampleFiles.py b/Tools/python/SampleFiles.py index 106695b9e..6a7534555 100644 --- a/Tools/python/SampleFiles.py +++ b/Tools/python/SampleFiles.py @@ -1140,6 +1140,146 @@ def who(name, ds): 'ttHToLLPs_dddd_tau10mm_M55_2018': _fromnum0("/store/user/joeyr/ttHToLLPs_dddd_tau10mm_M55_2018/Ntuple_tagTestFixTrigThresholdsBvetoLHTm_2018/260409_112745", 30), }) +_add_ds("ntuple_tag001bvetolhtm_highM", { + # High-mass signal ntuples (M=1200,1600,3000 GeV). Tag: Ntuple_tag001BvetoLHTm_highM. + # Submitted 2026-05-08. M3000 at long lifetimes hit 3-day wall time; those entries are omitted. + 'mfv_neu_tau000100um_M1200_20161': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1200_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20161/260508_182554", 14), + 'mfv_neu_tau000100um_M1200_20162': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1200_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20162/260508_182247", 14), + 'mfv_neu_tau000100um_M1200_2017': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1200_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2017/260508_181936", 27), + 'mfv_neu_tau000100um_M1200_2018': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1200_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2018/260508_181617", 27), + 'mfv_neu_tau000100um_M1600_20161': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1600_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20161/260508_182559", 14), + 'mfv_neu_tau000100um_M1600_20162': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1600_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20162/260508_182252", 14), + 'mfv_neu_tau000100um_M1600_2017': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1600_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2017/260508_181941", 27), + 'mfv_neu_tau000100um_M1600_2018': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-1600_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2018/260508_181622", 27), + 'mfv_neu_tau000100um_M3000_20161': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-3000_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20161/260508_182604", 14), + 'mfv_neu_tau000100um_M3000_20162': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-3000_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20162/260508_182257", 14), + 'mfv_neu_tau000100um_M3000_2017': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-3000_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2017/260508_181946", 27), + 'mfv_neu_tau000100um_M3000_2018': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/GluinoGluinoToNeutralinoNeutralinoTo2T2B2S_M-3000_CTau-100um_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2018/260508_181627", 27), + 'mfv_neu_tau000300um_M1200_20161': 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_fromnum0("/store/group/lpcdisplacedvertices/gdecastr/StopStopbarTo2Dbar2D_M-1600_CTau-30mm_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20161/260508_182618", 2), + 'mfv_stopdbardbar_tau030000um_M1600_20162': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/StopStopbarTo2Dbar2D_M-1600_CTau-30mm_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_20162/260508_182311", 2), + 'mfv_stopdbardbar_tau030000um_M1600_2017': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/StopStopbarTo2Dbar2D_M-1600_CTau-30mm_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2017/260508_182000", 4), + 'mfv_stopdbardbar_tau030000um_M1600_2018': _fromnum0("/store/group/lpcdisplacedvertices/gdecastr/StopStopbarTo2Dbar2D_M-1600_CTau-30mm_TuneCP5_13TeV-pythia8/Ntuple_tag001BvetoLHTm_highM_2018/260508_181641", 4), +}) _add_ds("NtupleLepton_SF_corrections_trkineffLepm", { @@ -4329,6 +4469,7 @@ def who(name, ds): }) + ################################################################################ if __name__ == '__main__': diff --git a/Tools/python/Samples.py b/Tools/python/Samples.py index 5b7102e23..745054da2 100644 --- a/Tools/python/Samples.py +++ b/Tools/python/Samples.py @@ -82,7 +82,7 @@ def _set_signal_stuff(sample): sample.mass = _mass(sample) sample.latex = _latex(sample) #sample.xsec = 1e-3 - br_h_llps = 1.0 + br_h_llps = 0.01 if (sample.name.startswith('WplusH')): sample.xsec = 3*(9.426e-02)*br_h_llps# Higgs 125, once for each lepton flavor - https://twiki.cern.ch/twiki/bin/view/LHCPhysics/CERNYellowReportPageAt13TeV#WHlH_l_e_or_Process elif (sample.name.startswith('WminusH')): @@ -621,7 +621,7 @@ def _set_signal_stuff(sample): #MCSample('ggZHToSSTodddd_tau1000mm_M55_20161', '/ggZH_HToSSTodddd_ZToLL_MH-125_MS-55_ctauS-1000_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODAPVv2-106X_mcRun2_asymptotic_preVFP_v11-v2/MINIAODSIM', 24999), ] -all_bjet_signal_samples_20161 = mfv_signal_samples_20161 + mfv_stopdbardbar_samples_20161 + mfv_stopbbarbbar_samples_20161 + ggHToSSTodddd_samples_20161 + ttHToLLPs_bbbb_samples_20161 + ttHToLLPs_dddd_samples_20161 +all_bjet_signal_samples_20161 = mfv_signal_samples_20161 + mfv_stopdbardbar_samples_20161 + mfv_stopbbarbbar_samples_20161 + ggHToSSTodddd_samples_20161 + ttHToLLPs_bbbb_samples_20161 + ttHToLLPs_dddd_samples_20161 + mfv_signal_highM_samples_20161 + mfv_stopdbardbar_highM_samples_20161 + mfv_stopbbarbbar_highM_samples_20161 all_lep_signal_samples_20161 = ZHToSSTodddd_samples_20161 + WplusHToSSTodddd_samples_20161 + WminusHToSSTodddd_samples_20161 + ttHToLLPs_bbbb_samples_20161 + ttHToLLPs_dddd_samples_20161 + ggZHToSSTobbbb_samples_20161 + ggZHToSSTodddd_samples_20161 all_signal_samples_20161 = list(set(all_bjet_signal_samples_20161 + all_lep_signal_samples_20161)) # the list and set are needed to get the unique entries, to avoid double counting later on @@ -1139,7 +1139,7 @@ def _set_signal_stuff(sample): #MCSample('ggZHToSSTodddd_tau1000mm_M55_20162', '/ggZH_HToSSTodddd_ZToLL_MH-125_MS-55_ctauS-1000_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM', 25000), ] -all_bjet_signal_samples_20162 = mfv_signal_samples_20162 + mfv_stopdbardbar_samples_20162 + mfv_stopbbarbbar_samples_20162 + ggHToSSTodddd_samples_20162 + ttHToLLPs_bbbb_samples_20162 + ttHToLLPs_dddd_samples_20162 +all_bjet_signal_samples_20162 = mfv_signal_samples_20162 + mfv_stopdbardbar_samples_20162 + mfv_stopbbarbbar_samples_20162 + ggHToSSTodddd_samples_20162 + ttHToLLPs_bbbb_samples_20162 + ttHToLLPs_dddd_samples_20162 + mfv_signal_highM_samples_20162 + mfv_stopdbardbar_highM_samples_20162 + mfv_stopbbarbbar_highM_samples_20162 all_lep_signal_samples_20162 = ZHToSSTodddd_samples_20162 + WplusHToSSTodddd_samples_20162 + WminusHToSSTodddd_samples_20162 + ttHToLLPs_bbbb_samples_20162 + ttHToLLPs_dddd_samples_20162 + ggZHToSSTobbbb_samples_20162 + ggZHToSSTodddd_samples_20162 all_signal_samples_20162 = list(set(all_bjet_signal_samples_20162 + all_lep_signal_samples_20162)) # the list and set are needed to get the unique entries, to avoid double counting later on @@ -1763,7 +1763,7 @@ def _set_signal_stuff(sample): #MCSample('ggZHToSSTodddd_tau1000mm_M55_2017', '/ggZH_HToSSTodddd_ZToLL_MH-125_MS-55_ctauS-1000_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL17MiniAODv2-106X_mc2017_realistic_v9-v2/MINIAODSIM', 49999), ] -all_bjet_signal_samples_2017 = mfv_signal_samples_2017 + mfv_stopdbardbar_samples_2017 + mfv_stopbbarbbar_samples_2017 + ggHToSSTodddd_samples_2017 + ttHToLLPs_bbbb_samples_2017 + ttHToLLPs_dddd_samples_2017 +all_bjet_signal_samples_2017 = mfv_signal_samples_2017 + mfv_stopdbardbar_samples_2017 + mfv_stopbbarbbar_samples_2017 + ggHToSSTodddd_samples_2017 + ttHToLLPs_bbbb_samples_2017 + ttHToLLPs_dddd_samples_2017 + mfv_signal_highM_samples_2017 + mfv_stopdbardbar_highM_samples_2017 + mfv_stopbbarbbar_highM_samples_2017 all_lep_signal_samples_2017 = ZHToSSTodddd_samples_2017 + WplusHToSSTodddd_samples_2017 + WminusHToSSTodddd_samples_2017 + ttHToLLPs_bbbb_samples_2017 + ttHToLLPs_dddd_samples_2017 + ggZHToSSTobbbb_samples_2017 + ggZHToSSTodddd_samples_2017 all_signal_samples_2017 = list(set(all_bjet_signal_samples_2017 + all_lep_signal_samples_2017)) # the list and set are needed to get the unique entries, to avoid double counting later on @@ -2323,7 +2323,7 @@ def _set_signal_stuff(sample): #MCSample('ggZHToSSTodddd_tau1000mm_M55_2018', '/ggZH_HToSSTodddd_ZToLL_MH-125_MS-55_ctauS-1000_TuneCP5_13TeV-powheg-pythia8/RunIISummer20UL18MiniAODv2-106X_upgrade2018_realistic_v16_L1v1-v2/MINIAODSIM', 50000), ] -all_bjet_signal_samples_2018 = mfv_signal_samples_2018 + mfv_stopdbardbar_samples_2018 + mfv_stopbbarbbar_samples_2018 + ggHToSSTodddd_samples_2018 + ttHToLLPs_bbbb_samples_2018 + ttHToLLPs_dddd_samples_2018 +all_bjet_signal_samples_2018 = mfv_signal_samples_2018 + mfv_stopdbardbar_samples_2018 + mfv_stopbbarbbar_samples_2018 + ggHToSSTodddd_samples_2018 + ttHToLLPs_bbbb_samples_2018 + ttHToLLPs_dddd_samples_2018 + mfv_signal_highM_samples_2018 + mfv_stopdbardbar_highM_samples_2018 + mfv_stopbbarbbar_highM_samples_2018 all_lep_signal_samples_2018 = ZHToSSTodddd_samples_2018 + WplusHToSSTodddd_samples_2018 + WminusHToSSTodddd_samples_2018 + ttHToLLPs_bbbb_samples_2018 + ttHToLLPs_dddd_samples_2018 + ggZHToSSTobbbb_samples_2018 + ggZHToSSTodddd_samples_2018 all_signal_samples_2018 = list(set(all_bjet_signal_samples_2018 + all_lep_signal_samples_2018)) # the list and set are needed to get the unique entries, to avoid double counting later on @@ -2758,6 +2758,11 @@ def _set_signal_stuff(sample): sample.add_dataset('miniaod', sample.dataset, sample.nevents_orig) for sample in mfv_signal_samples_2018 + mfv_stopdbardbar_samples_2018 + mfv_stopbbarbbar_samples_2018: sample.add_dataset('miniaod', sample.dataset, sample.nevents_orig) +for sample in (mfv_signal_highM_samples_20161 + mfv_stopdbardbar_highM_samples_20161 + mfv_stopbbarbbar_highM_samples_20161 + + mfv_signal_highM_samples_20162 + mfv_stopdbardbar_highM_samples_20162 + mfv_stopbbarbbar_highM_samples_20162 + + mfv_signal_highM_samples_2017 + mfv_stopdbardbar_highM_samples_2017 + mfv_stopbbarbbar_highM_samples_2017 + + mfv_signal_highM_samples_2018 + mfv_stopdbardbar_highM_samples_2018 + mfv_stopbbarbbar_highM_samples_2018): + sample.add_dataset('miniaod', sample.dataset, sample.nevents_orig) for sample in mfv_stoplb_samples_20161 : sample.add_dataset('miniaod', sample.dataset, sample.nevents_orig) for sample in mfv_stopld_samples_20161 : @@ -2813,6 +2818,20 @@ def _set_signal_stuff(sample): for x in qcdht0100_20161, qcdht0200_20161, qcdht0300_20161, qcdht0500_20161, qcdht0700_20161, qcdht1000_20161, qcdht1500_20161, qcdht2000_20161, ttbar_20161, mfv_neu_tau000100um_M0200_20161, mfv_neu_tau000300um_M0200_20161, mfv_neu_tau001000um_M0200_20161, mfv_neu_tau010000um_M0200_20161, mfv_neu_tau030000um_M0200_20161, mfv_neu_tau000100um_M0300_20161, mfv_neu_tau000300um_M0300_20161, mfv_neu_tau001000um_M0300_20161, mfv_neu_tau010000um_M0300_20161, mfv_neu_tau030000um_M0300_20161, mfv_neu_tau000100um_M0400_20161, mfv_neu_tau000300um_M0400_20161, mfv_neu_tau001000um_M0400_20161, mfv_neu_tau010000um_M0400_20161, mfv_neu_tau030000um_M0400_20161, mfv_neu_tau000100um_M0600_20161, mfv_neu_tau000300um_M0600_20161, mfv_neu_tau001000um_M0600_20161, mfv_neu_tau010000um_M0600_20161, mfv_neu_tau030000um_M0600_20161, mfv_neu_tau000100um_M0800_20161, mfv_neu_tau000300um_M0800_20161, mfv_neu_tau001000um_M0800_20161, mfv_neu_tau010000um_M0800_20161, mfv_neu_tau030000um_M0800_20161, mfv_stopdbardbar_tau000100um_M0200_20161, mfv_stopdbardbar_tau000300um_M0200_20161, mfv_stopdbardbar_tau001000um_M0200_20161, mfv_stopdbardbar_tau010000um_M0200_20161, mfv_stopdbardbar_tau030000um_M0200_20161, mfv_stopdbardbar_tau000100um_M0300_20161, mfv_stopdbardbar_tau000300um_M0300_20161, mfv_stopdbardbar_tau001000um_M0300_20161, mfv_stopdbardbar_tau010000um_M0300_20161, mfv_stopdbardbar_tau030000um_M0300_20161, mfv_stopdbardbar_tau000100um_M0400_20161, mfv_stopdbardbar_tau000300um_M0400_20161, mfv_stopdbardbar_tau001000um_M0400_20161, mfv_stopdbardbar_tau010000um_M0400_20161, mfv_stopdbardbar_tau030000um_M0400_20161, mfv_stopdbardbar_tau000100um_M0600_20161, mfv_stopdbardbar_tau000300um_M0600_20161, mfv_stopdbardbar_tau001000um_M0600_20161, mfv_stopdbardbar_tau010000um_M0600_20161, mfv_stopdbardbar_tau030000um_M0600_20161, mfv_stopdbardbar_tau000100um_M0800_20161, mfv_stopdbardbar_tau000300um_M0800_20161, mfv_stopdbardbar_tau001000um_M0800_20161, mfv_stopdbardbar_tau010000um_M0800_20161, mfv_stopdbardbar_tau030000um_M0800_20161, mfv_stopbbarbbar_tau000100um_M0200_20161, mfv_stopbbarbbar_tau000300um_M0200_20161, mfv_stopbbarbbar_tau001000um_M0200_20161, mfv_stopbbarbbar_tau010000um_M0200_20161, mfv_stopbbarbbar_tau030000um_M0200_20161, mfv_stopbbarbbar_tau000100um_M0300_20161, mfv_stopbbarbbar_tau000300um_M0300_20161, mfv_stopbbarbbar_tau001000um_M0300_20161, mfv_stopbbarbbar_tau010000um_M0300_20161, mfv_stopbbarbbar_tau030000um_M0300_20161, mfv_stopbbarbbar_tau000100um_M0400_20161, mfv_stopbbarbbar_tau000300um_M0400_20161, mfv_stopbbarbbar_tau001000um_M0400_20161, mfv_stopbbarbbar_tau010000um_M0400_20161, mfv_stopbbarbbar_tau030000um_M0400_20161, mfv_stopbbarbbar_tau000100um_M0600_20161, mfv_stopbbarbbar_tau000300um_M0600_20161, mfv_stopbbarbbar_tau001000um_M0600_20161, mfv_stopbbarbbar_tau010000um_M0600_20161, mfv_stopbbarbbar_tau030000um_M0600_20161, mfv_stopbbarbbar_tau000100um_M0800_20161, mfv_stopbbarbbar_tau000300um_M0800_20161, mfv_stopbbarbbar_tau001000um_M0800_20161, mfv_stopbbarbbar_tau010000um_M0800_20161, mfv_stopbbarbbar_tau030000um_M0800_20161, ggHToSSTodddd_tau100um_M15_20161, ggHToSSTodddd_tau1mm_M15_20161, ggHToSSTodddd_tau10mm_M15_20161, ggHToSSTodddd_tau100mm_M15_20161, ggHToSSTodddd_tau100um_M40_20161, ggHToSSTodddd_tau1mm_M40_20161, ggHToSSTodddd_tau10mm_M40_20161, ggHToSSTodddd_tau100mm_M40_20161, ggHToSSTodddd_tau100um_M55_20161, ggHToSSTodddd_tau1mm_M55_20161, ggHToSSTodddd_tau10mm_M55_20161, ggHToSSTodddd_tau100mm_M55_20161, qcdht0100_20162, qcdht0200_20162, qcdht0300_20162, qcdht0500_20162, qcdht0700_20162, qcdht1000_20162, qcdht1500_20162, qcdht2000_20162, ttbar_20162, mfv_neu_tau000100um_M0200_20162, mfv_neu_tau000300um_M0200_20162, mfv_neu_tau001000um_M0200_20162, mfv_neu_tau010000um_M0200_20162, mfv_neu_tau030000um_M0200_20162, mfv_neu_tau000100um_M0300_20162, mfv_neu_tau000300um_M0300_20162, mfv_neu_tau001000um_M0300_20162, mfv_neu_tau010000um_M0300_20162, mfv_neu_tau030000um_M0300_20162, mfv_neu_tau000100um_M0400_20162, mfv_neu_tau000300um_M0400_20162, mfv_neu_tau001000um_M0400_20162, mfv_neu_tau010000um_M0400_20162, mfv_neu_tau030000um_M0400_20162, mfv_neu_tau000100um_M0600_20162, mfv_neu_tau000300um_M0600_20162, mfv_neu_tau001000um_M0600_20162, mfv_neu_tau010000um_M0600_20162, mfv_neu_tau030000um_M0600_20162, mfv_neu_tau000100um_M0800_20162, mfv_neu_tau000300um_M0800_20162, mfv_neu_tau001000um_M0800_20162, mfv_neu_tau010000um_M0800_20162, mfv_neu_tau030000um_M0800_20162, mfv_stopdbardbar_tau000100um_M0200_20162, mfv_stopdbardbar_tau000300um_M0200_20162, mfv_stopdbardbar_tau001000um_M0200_20162, mfv_stopdbardbar_tau010000um_M0200_20162, mfv_stopdbardbar_tau030000um_M0200_20162, mfv_stopdbardbar_tau000100um_M0300_20162, mfv_stopdbardbar_tau000300um_M0300_20162, mfv_stopdbardbar_tau001000um_M0300_20162, mfv_stopdbardbar_tau010000um_M0300_20162, mfv_stopdbardbar_tau030000um_M0300_20162, mfv_stopdbardbar_tau000100um_M0400_20162, mfv_stopdbardbar_tau000300um_M0400_20162, mfv_stopdbardbar_tau001000um_M0400_20162, mfv_stopdbardbar_tau010000um_M0400_20162, mfv_stopdbardbar_tau030000um_M0400_20162, mfv_stopdbardbar_tau000100um_M0600_20162, mfv_stopdbardbar_tau000300um_M0600_20162, mfv_stopdbardbar_tau001000um_M0600_20162, mfv_stopdbardbar_tau010000um_M0600_20162, mfv_stopdbardbar_tau030000um_M0600_20162, mfv_stopdbardbar_tau000100um_M0800_20162, mfv_stopdbardbar_tau000300um_M0800_20162, mfv_stopdbardbar_tau001000um_M0800_20162, mfv_stopdbardbar_tau010000um_M0800_20162, mfv_stopdbardbar_tau030000um_M0800_20162, mfv_stopbbarbbar_tau000100um_M0200_20162, mfv_stopbbarbbar_tau000300um_M0200_20162, mfv_stopbbarbbar_tau001000um_M0200_20162, mfv_stopbbarbbar_tau010000um_M0200_20162, mfv_stopbbarbbar_tau030000um_M0200_20162, mfv_stopbbarbbar_tau000100um_M0300_20162, mfv_stopbbarbbar_tau000300um_M0300_20162, mfv_stopbbarbbar_tau001000um_M0300_20162, mfv_stopbbarbbar_tau010000um_M0300_20162, mfv_stopbbarbbar_tau030000um_M0300_20162, mfv_stopbbarbbar_tau000100um_M0400_20162, mfv_stopbbarbbar_tau000300um_M0400_20162, mfv_stopbbarbbar_tau001000um_M0400_20162, mfv_stopbbarbbar_tau010000um_M0400_20162, mfv_stopbbarbbar_tau030000um_M0400_20162, mfv_stopbbarbbar_tau000100um_M0600_20162, mfv_stopbbarbbar_tau000300um_M0600_20162, mfv_stopbbarbbar_tau001000um_M0600_20162, mfv_stopbbarbbar_tau010000um_M0600_20162, mfv_stopbbarbbar_tau030000um_M0600_20162, mfv_stopbbarbbar_tau000100um_M0800_20162, mfv_stopbbarbbar_tau000300um_M0800_20162, mfv_stopbbarbbar_tau001000um_M0800_20162, mfv_stopbbarbbar_tau010000um_M0800_20162, mfv_stopbbarbbar_tau030000um_M0800_20162, ggHToSSTodddd_tau100um_M15_20162, ggHToSSTodddd_tau1mm_M15_20162, ggHToSSTodddd_tau10mm_M15_20162, ggHToSSTodddd_tau100mm_M15_20162, ggHToSSTodddd_tau100um_M40_20162, ggHToSSTodddd_tau1mm_M40_20162, ggHToSSTodddd_tau10mm_M40_20162, ggHToSSTodddd_tau100mm_M40_20162, ggHToSSTodddd_tau100um_M55_20162, ggHToSSTodddd_tau1mm_M55_20162, ggHToSSTodddd_tau10mm_M55_20162, ggHToSSTodddd_tau100mm_M55_20162, qcdht0200_2017, qcdht0300_2017, qcdht0500_2017, qcdht0700_2017, qcdht1000_2017, qcdht1500_2017, qcdht2000_2017, ttbar_2017, mfv_neu_tau000100um_M0200_2017, mfv_neu_tau000300um_M0200_2017, mfv_neu_tau001000um_M0200_2017, mfv_neu_tau010000um_M0200_2017, mfv_neu_tau030000um_M0200_2017, mfv_neu_tau000100um_M0300_2017, mfv_neu_tau000300um_M0300_2017, mfv_neu_tau001000um_M0300_2017, mfv_neu_tau010000um_M0300_2017, mfv_neu_tau030000um_M0300_2017, mfv_neu_tau000100um_M0400_2017, mfv_neu_tau000300um_M0400_2017, mfv_neu_tau001000um_M0400_2017, mfv_neu_tau010000um_M0400_2017, mfv_neu_tau030000um_M0400_2017, mfv_neu_tau000100um_M0600_2017, mfv_neu_tau000300um_M0600_2017, mfv_neu_tau001000um_M0600_2017, mfv_neu_tau010000um_M0600_2017, mfv_neu_tau030000um_M0600_2017, mfv_neu_tau000100um_M0800_2017, mfv_neu_tau000300um_M0800_2017, mfv_neu_tau001000um_M0800_2017, mfv_neu_tau010000um_M0800_2017, mfv_neu_tau030000um_M0800_2017, mfv_stopdbardbar_tau000100um_M0200_2017, mfv_stopdbardbar_tau000300um_M0200_2017, mfv_stopdbardbar_tau001000um_M0200_2017, mfv_stopdbardbar_tau010000um_M0200_2017, mfv_stopdbardbar_tau030000um_M0200_2017, mfv_stopdbardbar_tau000100um_M0300_2017, mfv_stopdbardbar_tau000300um_M0300_2017, mfv_stopdbardbar_tau001000um_M0300_2017, mfv_stopdbardbar_tau010000um_M0300_2017, mfv_stopdbardbar_tau030000um_M0300_2017, mfv_stopdbardbar_tau000100um_M0400_2017, mfv_stopdbardbar_tau000300um_M0400_2017, mfv_stopdbardbar_tau001000um_M0400_2017, mfv_stopdbardbar_tau010000um_M0400_2017, mfv_stopdbardbar_tau030000um_M0400_2017, mfv_stopdbardbar_tau000100um_M0600_2017, mfv_stopdbardbar_tau000300um_M0600_2017, mfv_stopdbardbar_tau001000um_M0600_2017, mfv_stopdbardbar_tau010000um_M0600_2017, mfv_stopdbardbar_tau030000um_M0600_2017, mfv_stopdbardbar_tau000100um_M0800_2017, mfv_stopdbardbar_tau000300um_M0800_2017, mfv_stopdbardbar_tau001000um_M0800_2017, mfv_stopdbardbar_tau010000um_M0800_2017, mfv_stopdbardbar_tau030000um_M0800_2017, mfv_stopbbarbbar_tau000100um_M0200_2017, mfv_stopbbarbbar_tau000300um_M0200_2017, mfv_stopbbarbbar_tau001000um_M0200_2017, mfv_stopbbarbbar_tau010000um_M0200_2017, mfv_stopbbarbbar_tau030000um_M0200_2017, mfv_stopbbarbbar_tau000100um_M0300_2017, mfv_stopbbarbbar_tau000300um_M0300_2017, mfv_stopbbarbbar_tau001000um_M0300_2017, mfv_stopbbarbbar_tau010000um_M0300_2017, mfv_stopbbarbbar_tau030000um_M0300_2017, mfv_stopbbarbbar_tau000100um_M0400_2017, mfv_stopbbarbbar_tau000300um_M0400_2017, mfv_stopbbarbbar_tau001000um_M0400_2017, mfv_stopbbarbbar_tau010000um_M0400_2017, mfv_stopbbarbbar_tau030000um_M0400_2017, mfv_stopbbarbbar_tau000100um_M0600_2017, mfv_stopbbarbbar_tau000300um_M0600_2017, mfv_stopbbarbbar_tau001000um_M0600_2017, mfv_stopbbarbbar_tau010000um_M0600_2017, mfv_stopbbarbbar_tau030000um_M0600_2017, mfv_stopbbarbbar_tau000100um_M0800_2017, mfv_stopbbarbbar_tau000300um_M0800_2017, mfv_stopbbarbbar_tau001000um_M0800_2017, mfv_stopbbarbbar_tau010000um_M0800_2017, mfv_stopbbarbbar_tau030000um_M0800_2017, ggHToSSTodddd_tau100um_M15_2017, ggHToSSTodddd_tau1mm_M15_2017, ggHToSSTodddd_tau10mm_M15_2017, ggHToSSTodddd_tau100mm_M15_2017, ggHToSSTodddd_tau100um_M40_2017, ggHToSSTodddd_tau1mm_M40_2017, ggHToSSTodddd_tau10mm_M40_2017, ggHToSSTodddd_tau100mm_M40_2017, ggHToSSTodddd_tau100um_M55_2017, ggHToSSTodddd_tau1mm_M55_2017, ggHToSSTodddd_tau10mm_M55_2017, ggHToSSTodddd_tau100mm_M55_2017, qcdht0200_2018, qcdht0300_2018, qcdht0500_2018, qcdht0700_2018, qcdht1000_2018, qcdht1500_2018, qcdht2000_2018, ttbar_2018, mfv_neu_tau000100um_M0200_2018, mfv_neu_tau000300um_M0200_2018, mfv_neu_tau001000um_M0200_2018, mfv_neu_tau010000um_M0200_2018, mfv_neu_tau030000um_M0200_2018, mfv_neu_tau000100um_M0300_2018, mfv_neu_tau000300um_M0300_2018, mfv_neu_tau001000um_M0300_2018, mfv_neu_tau010000um_M0300_2018, mfv_neu_tau030000um_M0300_2018, mfv_neu_tau000100um_M0400_2018, mfv_neu_tau000300um_M0400_2018, mfv_neu_tau001000um_M0400_2018, mfv_neu_tau010000um_M0400_2018, mfv_neu_tau030000um_M0400_2018, mfv_neu_tau000100um_M0600_2018, mfv_neu_tau001000um_M0600_2018, mfv_neu_tau000300um_M0600_2018, mfv_neu_tau010000um_M0600_2018, mfv_neu_tau030000um_M0600_2018, mfv_neu_tau000100um_M0800_2018, mfv_neu_tau000300um_M0800_2018, mfv_neu_tau001000um_M0800_2018, mfv_neu_tau010000um_M0800_2018, mfv_neu_tau030000um_M0800_2018, mfv_stopdbardbar_tau000100um_M0200_2018, mfv_stopdbardbar_tau000300um_M0200_2018, mfv_stopdbardbar_tau001000um_M0200_2018, mfv_stopdbardbar_tau010000um_M0200_2018, mfv_stopdbardbar_tau030000um_M0200_2018, mfv_stopdbardbar_tau000100um_M0300_2018, mfv_stopdbardbar_tau000300um_M0300_2018, mfv_stopdbardbar_tau001000um_M0300_2018, mfv_stopdbardbar_tau010000um_M0300_2018, mfv_stopdbardbar_tau030000um_M0300_2018, mfv_stopdbardbar_tau000100um_M0400_2018, mfv_stopdbardbar_tau000300um_M0400_2018, mfv_stopdbardbar_tau001000um_M0400_2018, mfv_stopdbardbar_tau010000um_M0400_2018, mfv_stopdbardbar_tau030000um_M0400_2018, mfv_stopdbardbar_tau000100um_M0600_2018, mfv_stopdbardbar_tau000300um_M0600_2018, mfv_stopdbardbar_tau001000um_M0600_2018, mfv_stopdbardbar_tau010000um_M0600_2018, mfv_stopdbardbar_tau030000um_M0600_2018, mfv_stopdbardbar_tau000100um_M0800_2018, mfv_stopdbardbar_tau000300um_M0800_2018, mfv_stopdbardbar_tau001000um_M0800_2018, mfv_stopdbardbar_tau010000um_M0800_2018, mfv_stopdbardbar_tau030000um_M0800_2018, mfv_stopbbarbbar_tau000100um_M0200_2018, mfv_stopbbarbbar_tau000300um_M0200_2018, mfv_stopbbarbbar_tau001000um_M0200_2018, mfv_stopbbarbbar_tau010000um_M0200_2018, mfv_stopbbarbbar_tau030000um_M0200_2018, mfv_stopbbarbbar_tau000100um_M0300_2018, mfv_stopbbarbbar_tau000300um_M0300_2018, mfv_stopbbarbbar_tau001000um_M0300_2018, mfv_stopbbarbbar_tau010000um_M0300_2018, mfv_stopbbarbbar_tau030000um_M0300_2018, mfv_stopbbarbbar_tau000100um_M0400_2018, mfv_stopbbarbbar_tau000300um_M0400_2018, mfv_stopbbarbbar_tau001000um_M0400_2018, mfv_stopbbarbbar_tau010000um_M0400_2018, mfv_stopbbarbbar_tau030000um_M0400_2018, mfv_stopbbarbbar_tau000100um_M0600_2018, mfv_stopbbarbbar_tau000300um_M0600_2018, mfv_stopbbarbbar_tau001000um_M0600_2018, mfv_stopbbarbbar_tau010000um_M0600_2018, mfv_stopbbarbbar_tau030000um_M0600_2018, mfv_stopbbarbbar_tau000100um_M0800_2018, mfv_stopbbarbbar_tau000300um_M0800_2018, mfv_stopbbarbbar_tau001000um_M0800_2018, mfv_stopbbarbbar_tau010000um_M0800_2018, mfv_stopbbarbbar_tau030000um_M0800_2018, ggHToSSTodddd_tau100um_M15_2018, ggHToSSTodddd_tau1mm_M15_2018, ggHToSSTodddd_tau10mm_M15_2018, ggHToSSTodddd_tau100mm_M15_2018, ggHToSSTodddd_tau100um_M40_2018, ggHToSSTodddd_tau1mm_M40_2018, ggHToSSTodddd_tau10mm_M40_2018, ggHToSSTodddd_tau100mm_M40_2018, ggHToSSTodddd_tau100um_M55_2018, ggHToSSTodddd_tau1mm_M55_2018, ggHToSSTodddd_tau10mm_M55_2018, ggHToSSTodddd_tau100mm_M55_2018, ttHToLLPs_bbbb_tau10mm_M55_20161, ttHToLLPs_dddd_tau10mm_M55_20161, ttHToLLPs_bbbb_tau10mm_M55_20162, ttHToLLPs_dddd_tau10mm_M55_20162, ttHToLLPs_bbbb_tau10mm_M55_2017, ttHToLLPs_dddd_tau10mm_M55_2017, ttHToLLPs_bbbb_tau10mm_M55_2018, ttHToLLPs_dddd_tau10mm_M55_2018: x.add_dataset("ntuple_tag001bvetolhtm") +for x in (mfv_signal_highM_samples_20161 + mfv_stopdbardbar_highM_samples_20161 + mfv_stopbbarbbar_highM_samples_20161 + + mfv_signal_highM_samples_20162 + mfv_stopdbardbar_highM_samples_20162 + mfv_stopbbarbbar_highM_samples_20162 + + mfv_signal_highM_samples_2017 + mfv_stopdbardbar_highM_samples_2017 + mfv_stopbbarbbar_highM_samples_2017 + + mfv_signal_highM_samples_2018 + mfv_stopdbardbar_highM_samples_2018 + mfv_stopbbarbbar_highM_samples_2018): + x.add_dataset("ntuple_tag001bvetolhtm") + +import JMTucker.Tools.SampleFiles as _SFmod +for x in (mfv_signal_highM_samples_20161 + mfv_stopdbardbar_highM_samples_20161 + mfv_stopbbarbbar_highM_samples_20161 + + mfv_signal_highM_samples_20162 + mfv_stopdbardbar_highM_samples_20162 + mfv_stopbbarbbar_highM_samples_20162 + + mfv_signal_highM_samples_2017 + mfv_stopdbardbar_highM_samples_2017 + mfv_stopbbarbbar_highM_samples_2017 + + mfv_signal_highM_samples_2018 + mfv_stopdbardbar_highM_samples_2018 + mfv_stopbbarbbar_highM_samples_2018): + if _SFmod.get(x.name, "ntuple_tag001bvetolhtm_highM") is not None: + x.add_dataset("ntuple_tag001bvetolhtm_highM") + #for x in all_signal_samples_20161 + all_signal_samples_20162 + all_signal_samples_2017 + all_signal_samples_2018: #for x in ggHToSSTodddd_tau1mm_M55_20161, mfv_neu_tau001000um_M0400_20161, mfv_stopdbardbar_tau001000um_M0200_20161, mfv_stopdbardbar_tau000300um_M0400_20161, ggHToSSTodddd_tau1mm_M55_20162, mfv_neu_tau001000um_M0400_20162, mfv_stopdbardbar_tau001000um_M0200_20162, mfv_stopdbardbar_tau000300um_M0400_20162, ggHToSSTodddd_tau1mm_M55_2017, mfv_neu_tau001000um_M0400_2017, mfv_stopdbardbar_tau001000um_M0200_2017, mfv_stopdbardbar_tau000300um_M0400_2017, ggHToSSTodddd_tau1mm_M55_2018, mfv_neu_tau001000um_M0400_2018, mfv_stopdbardbar_tau001000um_M0200_2018, mfv_stopdbardbar_tau000300um_M0400_2018