cmsrel CMSSW_8_1_0
cd CMSSW_8_1_0/src
cmsenv
git clone [email protected]:StealthStop/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
scram b -j8
Add CombineTools from CombineHarvester to your work area: (this is needed for impact plots, and possible crab submission)
cd $CMSSW_BASE/src
cmsenv
mkdir CombineHarvester
cd CombineHarvester
git init
git remote add origin [email protected]:StealthStop/CombineHarvester.git
git config core.sparsecheckout true; echo CombineTools/ >> .git/info/sparse-checkout
git pull origin master
cd $CMSSW_BASE/src
scram b -j8
Now copy input root files from eos (only needed for local, condor jobs copy the inputs from eos)
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
cmsenv
cp -r /eos/uscms/store/user/lpcsusyhad/StealthStop/FitInputs_FullRun2/Keras_2016_v1.2 .
cp -r /eos/uscms/store/user/lpcsusyhad/StealthStop/FitInputs_FullRun2/Keras_2017_v1.2 .
cp -r /eos/uscms/store/user/lpcsusyhad/StealthStop/FitInputs_FullRun2/Keras_2018pre_v1.2 .
cp -r /eos/uscms/store/user/lpcsusyhad/StealthStop/FitInputs_FullRun2/Keras_2018post_v1.2 .
To combine the datacards for 2016 and 2017:
combineCards.py Y16=Card2016.txt Y17=Card2017.txt > Card2016_2017.txt
combineCards.py Y18pre=Card2018pre.txt Y18post=Card2018post.txt > Card2018pre_2018post.txt
combineCards.py Y16=Card2016.txt Y17=Card2017.txt Y18pre=Card2018pre.txt > Card2016_2017_2018pre.txt
combineCards.py Y16=Card2016.txt Y17=Card2017.txt Y18pre=Card2018pre.txt Y18post=Card2018post.txt > Card2016_2017_2018pre_2018post.txt
combineCards.py Y16=Card2016.txt Y17=Card2017.txt Y18pre=Card2018pre.txt Y18post=Card2018post.txt > CardCombo.txt
To make a RooFit workspace that contains our PDF definitions and input histograms:
For 2016, 2017, 2018pre/post RPV 350:
root -l -q 'make_MVA_8bin_ws.C("2016", "Keras_2016_v1.2", "RPV","350","pseudodata","")'
root -l -q 'make_MVA_8bin_ws.C("2017", "Keras_2017_v1.2", "RPV","350","pseudodataS_0.3xRPV_350","")'
root -l -q 'make_MVA_8bin_ws.C("2018pre", "Keras_2018pre_v1.2", "RPV","350","pseudodataS_0.3xRPV_350","")'
root -l -q 'make_MVA_8bin_ws.C("2018post","Keras_2018post_v1.2","RPV","350","pseudodataS_0.3xRPV_350","")'
Can substitute other input directories, models, and mass points, The last arguement can be: pseudodata, pseudodataS, or data The workspace goes into a file called MVA__ws.root
Convert the card file (and the PDFs and input histograms references therein) into a workspace:
For 2016 RPV 350:
text2workspace.py Card2016.txt -o ws_2016_RPV_350.root -m 350 --keyword-value MODEL=RPV
For 2016 + 2017 + 2018pre + 2018post RPV 350:
text2workspace.py CardCombo.txt -o ws_Combo_RPV_350.root -m 350 --keyword-value MODEL=RPV
Can substitute other models. The above command produces a file called ws_.root and will be fed to combine in the following commands.
Calculate quick asymptotic limits:
For 2016 RPV 350:
combine -M AsymptoticLimits ws_2016_RPV_350.root -m 350 --keyword-value MODEL=RPV --verbose 2 -n 2016 > log_2016RPV350_Asymp.txt
For 2016 + 2017 + 2018pre + 2018post RPV 350:
combine -M AsymptoticLimits ws_Combo_RPV_350.root -m 350 --keyword-value MODEL=RPV --verbose 2 -n Combo > log_ComboRPV350_Asymp.txt
Outputs files with names such as: higgsCombine2017.AsymptoticLimits.mH550.MODELRPV.root which contains the expected (and observed) limits, and a log file with a name such as log_2017RPV650_Asymp.txt
To make a limit plot:
Collect the above asymptotic limit root files for all mass points into a results directory, such as fit_results_v5_Jan17_2019
root -l -q 'makePlots.C+("Jan17_2019","fit_results_v5_Jan17_2019","2017","RPV")'
(the first arguement above is just intended to be today's date, or other tag)
Calculate the significance using the asimov dataset and expected signal strength of 1. Significance is printed to screen and is available in the file higgsCombineTest.Significance.mH550.MODELRPV.root
combine -M Significance ws_2017_RPV_550.root -t -1 --expectSignal=1 -m 550 --keyword-value MODEL=RPV -n 2017RPV550_SignifExp
Calculate the observed significance:
combine -M ProfileLikelihood ws_2017_RPV_550.root --significance -m 550 --keyword-value MODEL=RPV -n 2017RPV550_SignifObs
Run the full fitDiagnostics:
combine -M FitDiagnostics ws_2017_RPV_550.root --plots --saveShapes --saveNormalizations -m 550 --keyword-value MODEL=RPV -n 2017RPV550 > log_2017RPV550.txt
The above command produces a root file called fitDiagnostics2017RPV550.root that contains RooPlots and RooFitResults. It also produces a file called higgsCombine2017RPV550.FitDiagnostics.mH550.MODELRPV.root that has the signal strength. It also produces a log file called log_2017RPV550.txt (yields and best fit signal strength are at the end of this log file).
Produce formatted plots of fit results for each MVA bin:
root -q -l fit_report_ESM.C("fitDiagnostics2017RPV550.root")
or do the same but without yellow fit uncertainty:
root -q -l fit_report_ESM.C("fitDiagnostics2017RPV550.root",false)
Print the parameters resulting from the fit:
python test/diffNuisances.py --all --abs fitDiagnostics2017RPV550.root
Study the impacts: (you need CombineTools from CombineHarvester for this step to work, see instructions above)
Impacts using Azimov, on data, with expected signal 0
root -l -q 'make_MVA_8bin_ws.C("2016","Keras_V1.2.6_v1_DataQCDShape","RPV","350","data")'
text2workspace.py Card2016.txt -o ws_2016_RPV_350.root -m 350 --keyword-value MODEL=RPV
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 --doInitialFit --robustFit 1 --rMin -10 -t -1 --expectSignal 0
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 --doFits --parallel 4 --rMin -10 -t -1 --expectSignal 0
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 -o impacts.json
../../CombineHarvester/CombineTools/scripts/plotImpacts.py -i impacts.json -o impacts_2016RPV350_ExpSig0
Impacts using Azimov, on data, with expected signal 1
root -l -q 'make_MVA_8bin_ws.C("2016","Keras_V1.2.6_v1_DataQCDShape","RPV","350","data")'
text2workspace.py Card2016.txt -o ws_2016_RPV_350.root -m 350 --keyword-value MODEL=RPV
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 --doInitialFit --robustFit 1 -t -1 --expectSignal 1
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 --doFits --parallel 4 -t -1 --expectSignal 1
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 -o impacts.json
../../CombineHarvester/CombineTools/scripts/plotImpacts.py -i impacts.json -o impacts_2016RPV350_ExpSig1
Impacts on data (add the --rMin -10 option if best fit is close to zero)
root -l -q 'make_MVA_8bin_ws.C("2016","Keras_V1.2.6_v1_DataQCDShape","RPV","350","data")'
text2workspace.py Card2016.txt -o ws_2016_RPV_350.root -m 350 --keyword-value MODEL=RPV
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 --doInitialFit --robustFit 1
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 --doFits --parallel 4
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350.root -m 350 -o impacts.json
../../CombineHarvester/CombineTools/scripts/plotImpacts.py -i impacts.json -o impacts_RPV350_data
Impacts on pseudodata without signal:
root -l -q 'make_MVA_8bin_ws.C("2016","Keras_V1.2.6_v1_DataQCDShape","RPV","350","pseudodata")'
text2workspace.py Card2016.txt -o ws_2016_RPV_350_pseudo.root -m 350 --keyword-value MODEL=RPV
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350_pseudo.root -m 350 --doInitialFit --robustFit 1 --rMin -10
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350_pseudo.root -m 350 --doFits --parallel 4 --rMin -10
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350_pseudo.root -m 350 -o impacts.json
../../CombineHarvester/CombineTools/scripts/plotImpacts.py -i impacts.json -o impacts_RPV350_pseudodata
Impacts on pseudodata with signal:
root -l -q 'make_MVA_8bin_ws.C("2016","Keras_V1.2.6_v1_DataQCDShape","RPV","350","pseudodataS")'
text2workspace.py Card2016.txt -o ws_2016_RPV_350_pseudoS.root -m 350 --keyword-value MODEL=RPV
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350_pseudoS.root -m 350 --doInitialFit --robustFit 1
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350_pseudoS.root -m 350 --doFits --parallel 4
../../CombineHarvester/CombineTools/scripts/combineTool.py -M Impacts -d ws_2016_RPV_350_pseudoS.root -m 350 -o impacts.json
../../CombineHarvester/CombineTools/scripts/plotImpacts.py -i impacts.json -o impacts_RPV350_pseudodataS
Background-only fits to tt background, before and after systematic variations:
root -l
.x make_BkgOnly_ws.C("_btgUp");
combine -M FitDiagnostics RPV_550_BkgOnlyCard.txt --plots --saveShapes --saveNormalizations -n _btgUp
root -l fitDiagnostics_btgUp.root
shapes_fit_b->cd();
D1->cd();
TT->Draw();
etc.
Running all of the fits using condor
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit/condor
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t data --output Fit_Data_2016
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t pseudodata --output Fit_pseudoData_2016
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t pseudodataS --output Fit_pseudoDataS_2016
python condorSubmit.py --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y 2017 -t data --output Fit_Data_2017
python condorSubmit.py --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y 2017 -t pseudodata --output Fit_pseudoData_2017
python condorSubmit.py --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y 2017 -t pseudodataS --output Fit_pseudoDataS_2017
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y Combo -t data --output Fit_Data_Combo
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y Combo -t pseudodata --output Fit_pseudoData_Combo
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y Combo -t pseudodataS --output Fit_pseudoDataS_Combo
Making limit plots from condor output
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
root -l -q 'makePlots.C("<date>_data2016","condor/Fit_Data_2016/output-files","2016","RPV")'
root -l -q 'makePlots.C("<date>_data2016","condor/Fit_Data_2016/output-files","2016","SYY")'
root -l -q 'makePlots.C("<date>_data2016","condor/Fit_Data_2016/output-files","2016","SHH")'
root -l -q 'makePlots.C("<date>_pseudodata2016","condor/Fit_pseudoData_2016/output-files","2016","RPV")'
root -l -q 'makePlots.C("<date>_pseudodata2016","condor/Fit_pseudoData_2016/output-files","2016","SYY")'
root -l -q 'makePlots.C("<date>_pseudodata2016","condor/Fit_pseudoData_2016/output-files","2016","SHH")'
root -l -q 'makePlots.C("<date>_data2017","condor/Fit_Data_2017/output-files","2017","RPV")'
root -l -q 'makePlots.C("<date>_data2017","condor/Fit_Data_2017/output-files","2017","SYY")'
root -l -q 'makePlots.C("<date>_data2017","condor/Fit_Data_2017/output-files","2017","SHH")'
root -l -q 'makePlots.C("<date>_pseudodata2017","condor/Fit_pseudoData_2017/output-files","2017","RPV")'
root -l -q 'makePlots.C("<date>_pseudodata2017","condor/Fit_pseudoData_2017/output-files","2017","SYY")'
root -l -q 'makePlots.C("<date>_pseudodata2017","condor/Fit_pseudoData_2017/output-files","2017","SHH")'
Making profile scan from condor output
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
python make_profile_plot.py --model RPV --year 2016 --data 0 --basedir condor --masses 350 450 550 650 750 850
python make_profile_plot.py --model SYY --year 2016 --data 0 --basedir condor --masses 350 450 550 650 750 850
python make_profile_plot.py --model SHH --year 2016 --data 0 --basedir condor --masses 350 450 550 650 750 850
python make_profile_plot.py --model RPV --year 2016 --data 1 --basedir condor --masses 350 450 550 650 750 850
python make_profile_plot.py --model SYY --year 2016 --data 1 --basedir condor --masses 350 450 550 650 750 850
python make_profile_plot.py --model SHH --year 2016 --data 1 --basedir condor --masses 350 450 550 650 750 850
python make_profile_plot.py --model RPV --year 2017 --data 0 --basedir condor --masses 300 350 400 450 500 550 600 650 700 750 800 850 900
python make_profile_plot.py --model SYY --year 2017 --data 0 --basedir condor --masses 300 350 400 450 500 550 600 650 700 750 800 850 900
python make_profile_plot.py --model SHH --year 2017 --data 0 --basedir condor --masses 300 350 400 450 500 550 600 650 700 750 800 850 900
python make_profile_plot.py --model RPV --year 2017 --data 1 --basedir condor --masses 300 350 400 450 500 550 600 650 700 750 800 850 900
python make_profile_plot.py --model SYY --year 2017 --data 1 --basedir condor --masses 300 350 400 450 500 550 600 650 700 750 800 850 900
python make_profile_plot.py --model SHH --year 2017 --data 1 --basedir condor --masses 300 350 400 450 500 550 600 650 700 750 800 850 900
Making fit results plots from condor output
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
python make_fit_report_plot.py
Similar to running the normal set of fits but add extra flags
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit/condor
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t data --output Fit_Data_2016 --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t pseudodata --output Fit_pseudoData_2016 --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t pseudodataS --output Fit_pseudoDataS_2016 --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y 2017 -t data --output Fit_Data_2017 --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y 2017 -t pseudodata --output Fit_pseudoData_2017 --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y 2017 -t pseudodataS --output Fit_pseudoDataS_2017 --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y Combo -t data --output Fit_Data_Combo --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y Combo -t pseudodata --output Fit_pseudoData_Combo --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
python condorSubmit.py --inPut_2016 Keras_V1.2.8_Approval_StatErrPlusFullDev_12JetFix --inPut_2017 Keras_V3.0.4_Approval_StatErrPlusFullDev_12JetFix -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y Combo -t pseudodataS --output Fit_pseudoDataS_Combo --toy --rMin 0.15 --rMax 0.3 --rStep 0.01 --jPerR 5 -T 500
Now hadd output of all of these toy jobs
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit/condor
python hadder.py -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t data -p Fit_Data_2016
python hadder.py -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t data -p Fit_pseudoData_2016
python hadder.py -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2016 -t data -p Fit_pseudoDatas_2016
python hadder.py -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2017 -t data -p Fit_Data_2016
python hadder.py -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2017 -t data -p Fit_pseudoData_2016
python hadder.py -d RPV,SYY,SHH -m 350,450,550,650,750,850 -y 2017 -t data -p Fit_pseudoDatas_2016
python hadder.py -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y Combo -t data -p Fit_Data_2016
python hadder.py -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y Combo -t data -p Fit_pseudoData_2016
python hadder.py -d RPV,SYY,SHH -m 300,350,400,450,500,550,600,650,700,750,800,850,900 -y Combo -t data -p Fit_pseudoDatas_2016
Now can make limit plots with toy output
cd $CMSSW_BASE/src/HiggsAnalysis/CombinedLimit
root -l -q 'makePlots.C("<date>_data2016","condor/Fit_Data_2016/output-files","2016","RPV","HybridNew")'
root -l -q 'makePlots.C("<date>_data2016","condor/Fit_Data_2016/output-files","2016","SYY","HybridNew")'
root -l -q 'makePlots.C("<date>_data2016","condor/Fit_Data_2016/output-files","2016","SHH","HybridNew")'
root -l -q 'makePlots.C("<date>_pseudodata2016","condor/Fit_pseudoData_2016/output-files","2016","RPV","HybridNew")'
root -l -q 'makePlots.C("<date>_pseudodata2016","condor/Fit_pseudoData_2016/output-files","2016","SYY","HybridNew")'
root -l -q 'makePlots.C("<date>_pseudodata2016","condor/Fit_pseudoData_2016/output-files","2016","SHH","HybridNew")'
root -l -q 'makePlots.C("<date>_data2017","condor/Fit_Data_2017/output-files","2017","RPV","HybridNew")'
root -l -q 'makePlots.C("<date>_data2017","condor/Fit_Data_2017/output-files","2017","SYY","HybridNew")'
root -l -q 'makePlots.C("<date>_data2017","condor/Fit_Data_2017/output-files","2017","SHH","HybridNew")'
root -l -q 'makePlots.C("<date>_pseudodata2017","condor/Fit_pseudoData_2017/output-files","2017","RPV","HybridNew")'
root -l -q 'makePlots.C("<date>_pseudodata2017","condor/Fit_pseudoData_2017/output-files","2017","SYY","HybridNew")'
root -l -q 'makePlots.C("<date>_pseudodata2017","condor/Fit_pseudoData_2017/output-files","2017","SHH","HybridNew")'
The first script run is draw_hist_and_pull_plot.C and the output of this script is run over by makeFitPlots.py. A couple of example calls are given below.
root -q -l -b 'draw_fit_hist_and_pull.c("condor/FullRun2_Unblinded_Jun15/Fit_Data_Combo/output-files/", "RPV_450_Combo/fitDiagnosticsComboRPV450.root", "RPV 450", "Y16_", "RPV450Combo16s", 1, 1, 1, "RPV_850_Combo/fitDiagnosticsComboRPV850.root", "RPV 850")'
python makeFitPlots.py --plotbkg --mass1 450 --model1 RPV
This would extract the Combo signal+background fit for 2016 as well as the RPV 450 signal shape and RPV 850 signal shape. All of these histograms get put into a ROOT file called KELVIN_RPV450Combo16s.root. Then we call makeFitPlots.py and ask for it to plot the background and pass the information about the signal used in the fit. Since we are running on a signal+background fit (RPV 450), the signal fit component is plotted rather than the raw shape.
root -q -l -b 'draw_fit_hist_and_pull.c("condor/FullRun2_Unblinded_Jun15/Fit_Data_Combo/output-files/", "RPV_450_Combo/fitDiagnosticsComboRPV450.root", "RPV 450", "Y16_", "RPV450Combo16b", 0, 1, 1, "RPV_850_Combo/fitDiagnosticsComboRPV850.root", "RPV 850")'
python makeFitPlots.py --twosigfit --bkgonlyfit --mass1 450 --model1 RPV --mass2 850 --model2 RPV
This would extract the background-only fit as well as RPV 450 signal shape and RPV 850 signal shape. All these histograms get put into a ROOT file called KELVIN_RPV450Combo16b.root. Then we call makeFitPlots.py and ask it to show both signal shapes (thus we pass information about both signals---model and mass) and explicitly say we want the background-only fit.