diff --git a/Selection-code.md b/Selection-code.md
index 07821c3..a39c762 100644
--- a/Selection-code.md
+++ b/Selection-code.md
@@ -10,10 +10,10 @@ The selection code is a set of C++ scripts. First, the running of the code is in
 ## Setting up ROOT
 **TODO**
 
-In order to make pretty plots, dedicated code in [[Design.cpp|Design.cpp]] is always checked for compilation when starting ROOT. This is done by creating a `.rootrc` file in your home directory and telling ROOT to always compile the Design.cpp:
+In order to make pretty plots, dedicated code in [[Design.cpp|Design.cpp]] is always checked for compilation when starting ROOT. This is done by creating a `.rootalias.C` file in your home directory and telling ROOT to always compile the Design.cpp:
 ```
 {
-gROOT->ProcessLine(".L path/to/your/folder/Design.cpp+");
+gROOT->ProcessLine(".L path/to/your/folder/Code/Design.cpp+");
 }
 ```
 
@@ -55,18 +55,20 @@ There are 5 levels of verbosity: The level of verbosity is defined in [GlobalFun
 
 
 # Running the code
-The code consists of several C++ scripts that are compiled and executed in ROOT. Before every step, it is worth closing and opening ROOT, as it is possible ROOT will complain about redefinition of functions. 
+The code consists of several C++ scripts that are compiled and executed in ROOT. **Before every step, it is worth closing and opening ROOT**, as it is possible ROOT will complain about redefinition of functions. 
 
 We used ROOT 6.06.02. 
 
 
 First, compile and run the preselection. It is defined in [[BDTSelection.cpp|BDTSelection]]. This reads the files with **stripped** data and creates new tuples with **preselected** data.
 ```
+root
 .L BDTSelection.cpp+
 runAllSignalData(1); runAllSignalData(2);
 runAllSignalMC(1); runAllSignalMC(2);
 runAllRefMC(1);  runAllRefMC(2);
 runAllPHSPMC(1); runAllPHSPMC(2);
+.q
 ```
 
 Then, run a python script  [[Rescale_pi0momentum.py|Rescale-pi0-momentum]] performing the Kstar MacGyver DTF
@@ -76,42 +78,54 @@ lb-conda default python Scripts/Rescale_pi0momentum.py
 
 Next step is to compile and perform the MC Truth-Matching, saved in  [[MCtruthmatching.cpp|MCtruthmatching.cpp]]. The Truth-matching procedure is in detail described in [my thesis](http://www.physi.uni-heidelberg.de/Publications/thesis_Kopecna_final.pdf). 
 ```
+root
 .L MCtruthmatching.cpp+
 TruthMatchAllAll(1); TruthMatchAllAll(2);
+.q
 ```
 
 Then, we need to add the XMuMu mass variable and apply the KplusMuMu veto ([[AddVariable.cpp|AddVariable.cpp]])
  ```
+ root
 .L AddVariable.cpp+
 addAllXMuMuMass(true,true,1); addAllXMuMuMass(false,true,1); applyAllVetoKplusMuMuMass(1);
 addAllXMuMuMass(true,true,2); addAllXMuMuMass(false,true,2); applyAllVetoKplusMuMuMass(2);
+.q
 ```
 
 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|B-mass-model]].
 
 Now the peaking background is removed, we can proceed to reweighting via [[nTrackWeights.cpp|nTrackWeights.cpp]]. It takes the **preselected** tuples and create new **weighted** ones, with the tag BDT input.
 ```
+root
 .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);
+.q
 ```
 
 Check the MVA variables are agreeing after weighting them with sWeights [[CompareVariables.cpp|CompareVariables.cpp]]. (Yes, there is a [[dedicated tool|Comparison-tool]], but this was intially working, and working well, so it was kept for checking the variables used in the MVA training.) **TODO**
 ```
+root
 .L Scripts/compareVariables.cc+
 compareAll(1); compareAll(2);
+.q
 ```
 
 Reweighted Data and Monte Carlo can be used for the [[MVA.cpp|MVA-Class]]
  ```
+root
 .L MVA.cpp+
- RunMVA(1); RunMVA(2);
+RunMVA(1); RunMVA(2);
+.q
 ```
 
 Apply the MVA to all the MC and Data using [[TMVAClassApp.cpp|TMVA Class application]]. This also creates new tuples with the tag BDT output.
  ```
+root
 .L TMVAClassApp.cpp+
 TMVAClassAppAll(1); TMVAClassAppAll(2);
+.q
 ```
 
 Remove all multiple candidates, defined in [[RemoveMultipleCandidates.py|RemoveMultipleCandidates.py]]
@@ -121,20 +135,25 @@ 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. 
 ```
+root
 .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);
+.q
 ```
 
 Check the variables again
 ```
+root
 .L Scripts/compareVariables.cc+
 compareAll(1); compareAll(2);
+.q
 ```
 
 Run the MVA training, [[make nice plots|PlotTMVA.cpp]], apply the MVA and remove multiple candidates
 
 ```
+root
 .L MVA.cpp+
 RunMVA(1); RunMVA(2);
 
@@ -145,29 +164,36 @@ testFunction(1); testFunction(2)
 
 .L TMVAClassApp.cpp+
 TMVAClassAppAll(1); TMVAClassAppAll(2);
+.q
 
 python RemoveMultipleCandidates.py -all
 ```
 
 [[Add variables|AddVariable.cpp]] to the MC samples
 ```
+root
 .L AddVariable.cpp+
 addAllVariablesAllMCSamples(1); addAllVariablesAllMCSamples(2);
+.q
 ```
 
 Get the eficiencies needed for the estimation  of the best MVA response cut, defined in [[Efficiency.cpp|Efficiency.cpp]]
 ```
+root
 .L Efficiency.cpp+
 runAllEff();
+.q
 ```
 
 Scan the significance in the MVA cut using the code in [[BDTcutScanner.cpp|BDTcutScanner.cpp]]. Don't mind the 2012 and 2016 tags, they are just dummies
 ```
+root
 .L BDTcutScanner.cpp+
 ScanSignalAndBckgndEstimation("2012",1,0.01,false,false,false,true)
 ScanSignalAndBckgndEstimation("2016",2,0.01,false,false,false,true)
 getMaxBDTresponse("2012",1,true,true,0,false,false)
 getMaxBDTresponse("2016",2,true,false,0,false,false)
+.q
 ```
 
 Make a nice TGraph from the scan; when creating the scan, it can happen that eg an estimation at cut at  0.95 happens before a cut at 0.92. [[This script|ReorganizeTGraph.py]] just takes it and makes a pretty clean plot.
@@ -177,33 +203,41 @@ python ReorganizeTGraph.py
 
 Use the MVA scan to plot the signal yields, apply the MVA cut and compare the yields to the CMS results (see [[SignalStudy.cpp|Signal Study]]). It also creates the tuples used by the [[FCNC fitter|FCNC fitter]] tagged as BDT output selection.
  ```
+root
 .L SignalStudy.cpp+
 plotYieldInQ2(true); plotYieldInQ2(false);
 ApplyCutPerYearAll(1);  ApplyCutPerYearAll(2);
 printYileds(false); printYileds(true)
 yieldComparison(1,getTMVAcut(1));
 yieldComparison(2,getTMVAcut(2));
+.q
 ```
 
 
  ## Checking the inclusive sample
 ```
+root
 .L BDTSelection.cpp+
 runAllIncMC(1); runAllIncMC(2)
+.q
 ```
 
- lb-conda default python Rescale_pi0momentum.py (CAREFUL, NEEDS TO BE SET BY HAND)
+ lb-conda default python Rescale_pi0momentum.py (**CAREFUL, NEEDS TO BE SET BY HAND**)
 
 ```
+root
 .L MCtruthmatching.cpp+
 TruthMatchAllBkg(true,1,false,false,true); TruthMatchAllBkg(true,2,false,false,true);
+.q
 ```
 
-Then, we need to add the variables to the inclusive sample. This HAS TO BE SET BY HAND in [[AddVariable.cpp|AddVariable.cpp]] before compilation!
+Then, we need to add the variables to the inclusive sample. **This HAS TO BE SET BY HAND** in [[AddVariable.cpp|AddVariable.cpp]] before compilation!
 ```
+root
 .L AddVariable.cpp+
 addAllXMuMuMass(true,true,1,true,true,false); addAllXMuMuMass(false,true,1,true,true,false); applyAllVetoKplusMuMuMass(1,true,true,false);
 addAllXMuMuMass(true,true,2,true,true,false); addAllXMuMuMass(false,true,2,true,true,false); applyAllVetoKplusMuMuMass(2,true,true,false);
+.q
 ```
 Similarly, before applying the MacGyver DTF, the paths in [[Rescale_pi0momentum.py|Rescale-pi0-momentum]] have to be set by hand!
 ```
@@ -211,8 +245,10 @@ lb-conda default python Rescale_pi0momentum.py
 ```
 
 ```
+root
 .L TMVAClassApp.cpp+
 TMVAClassAppInc(1); TMVAClassAppInc(2);
+.q
 ```
 Also  the paths in [[RemoveMultipleCandidates.py|RemoveMultipleCandidates.py]] have to be set by hand!
 ```
@@ -220,8 +256,10 @@ python RemoveMultipleCandidates.py -all
 ```
 Lastly, make the truth-matching plots using [[InclusiveCheck.cpp||InclusiveCheck.cpp]].
 ```
+root
 .L Scripts/InclusiveCheck.cpp+
 plotTM(1,true); plotTM(2,true)
 plotTM(1,false); plotTM(2,false)
+.q
 ```