From 500049b7ec6bea294f0d6ff873f34fb70391a5a0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Renata=20Kopecn=C3=A1?= <kopecna@physi.uni-heidelberg.de>
Date: Mon, 24 Jan 2022 11:18:25 +0100
Subject: [PATCH] Update 'Selection code'

---
 Selection-code.md | 56 +++++++++++++++++++++++------------------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/Selection-code.md b/Selection-code.md
index e3bc954..8b4972e 100644
--- a/Selection-code.md
+++ b/Selection-code.md
@@ -3,46 +3,46 @@ The selection code is a set of C++ scripts. First, the running of the code is in
 # Running the code
 When re-running everything, do the following
 
-First, compile and run the preselection
+First, compile and run the preselection. It is defined in [[BDTSelection.cpp|BDTSelection]] .
 ```
- .L BDTSelection.cpp+
- runAllSignalData(1); runAllSignalData(2);
- runAllSignalMC(1); runAllSignalMC(2);
- runAllRefMC(1);  runAllRefMC(2);
- runAllPHSPMC(1); runAllPHSPMC(2);
+.L BDTSelection.cpp+
+runAllSignalData(1); runAllSignalData(2);
+runAllSignalMC(1); runAllSignalMC(2);
+runAllRefMC(1);  runAllRefMC(2);
+runAllPHSPMC(1); runAllPHSPMC(2);
 ```
 
 Then,run a python script performing the Kstar MacGyver DTF
 ```
- lb-conda default python Rescale_pi0momentum.py
+lb-conda default python Rescale_pi0momentum.py
 ```
 
 Next step is to compile and perform the MC Truth-Matching
 ```
- .L MCtruthmatching.cpp+
- TruthMatchAllAll(1); TruthMatchAllAll(2);
+.L MCtruthmatching.cpp+
+TruthMatchAllAll(1); TruthMatchAllAll(2);
 ```
 
 Then, we need to add the XMuMu mass variable and apply the KplusMuMu veto
  ```
 .L CodeForTests/AddVariable.cpp+
- addAllXMuMuMass(true,true,1); addAllXMuMuMass(false,true,1); applyAllVetoKplusMuMuMass(1);
- addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMuMuMass(2);
+addAllXMuMuMass(true,true,1); addAllXMuMuMass(false,true,1); applyAllVetoKplusMuMuMass(1);
+addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMuMuMass(2);
 ```
 
 We have all the preselection finished. Now we will need to fit the reconstructed B mass peak. For the instructions how to compile the code and make RooFit use double-sided Crystal Ball or ExpGauss, see [B mass model section](https://git.physi.uni-heidelberg.de/kopecna/EWP-BplusToKstMuMu-AngAna/wiki/B-mass-model).
 
 Now the peaking background is removed, we can proceed to reweighting
 ```
- .L nTrackWeights.cpp+
- WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
- WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
+.L nTrackWeights.cpp+
+WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
+WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
 ```
 
 Check the MVA variables are agreeing after weighting them with sWeights
 ```
- .L CodeForTests/compareVariables.cc+
- compareAll(1); compareAll(2);
+.L CodeForTests/compareVariables.cc+
+compareAll(1); compareAll(2);
 ```
 
 Reweighted Data and Monte Carlo can be used for the MVA training
@@ -54,7 +54,7 @@ Reweighted Data and Monte Carlo can be used for the MVA training
 Apply the MVA to all the MC and Data 
  ```
 .L TMVAClassApp.cpp+
- TMVAClassAppAll(1); TMVAClassAppAll(2);
+TMVAClassAppAll(1); TMVAClassAppAll(2);
 ```
 
 Remove all multiple candidates
@@ -65,8 +65,8 @@ python RemoveMultipleCandidates.py -all
 We have to rerun the weights and therefore also the MVA: the shape of the B mass peak is fixed to the one after MVA. 
 ```
 .L nTrackWeights.cpp+
- WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
- WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
+WeightAll(true,1,true); ReweightReferenceMC(true,1,true); ReweightPHSPMC(true,1,true);
+WeightAll(true,2,true); ReweightReferenceMC(true,2,true); ReweightPHSPMC(true,2,true);
 ```
 
 Check the variables again
@@ -94,14 +94,14 @@ python RemoveMultipleCandidates.py -all
 
 Add variables to the MC samples **TODO**
 ```
- .L CodeForTests/AddVariable.cpp+
- addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2);
+.L CodeForTests/AddVariable.cpp+
+addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2);
 ```
 
 Get the eficiencies needed for the estimation  of the best MVA response cut
 ```
- .L Efficiency.cpp+
- runAllEff();
+.L Efficiency.cpp+
+runAllEff();
 ```
 
 Scan the significance in the MVA cut. Don't mind the 2012 and 2016 tags, they are just dummies
@@ -121,11 +121,11 @@ python ReorganizeTGraph.py
 Use the MVA scan to plot the signal yields, apply the MVA cut and compare the yields to the CMS results.
  ```
 .L SignalStudy.cpp+
- plotYieldInQ2(true); plotYieldInQ2(false);
- ApplyCutPerYearAll(1);  ApplyCutPerYearAll(2);
- printYileds(false); printYileds(true)
- yieldComparison(1,getTMVAcut(1));
- yieldComparison(2,getTMVAcut(2));
+plotYieldInQ2(true); plotYieldInQ2(false);
+ApplyCutPerYearAll(1);  ApplyCutPerYearAll(2);
+printYileds(false); printYileds(true)
+yieldComparison(1,getTMVAcut(1));
+yieldComparison(2,getTMVAcut(2));
 ```