[RF] Don't use batch mode in rf611_weightedfits.C tutorial

This is done so we don't have to adopt this tutorial to the new batch
mode yet.

tutorials/roofit/rf611_weightedfits.C

@@ -65,6 +65,8 @@
 #include "RooDataSet.h"
 #include "RooPolynomial.h"
 
+#include "rbc.h"
+
 using namespace RooFit;
 
 
@@ -107,7 +109,7 @@ int rf611_weightedfits(int acceptancemodel=2) {
   TH1D* hc1pull3 = new TH1D("hc1pull3", "Asymptotically correct approach [Asymptotic(true)];Pull (c_{1}^{fit}-c_{1}^{gen})/#sigma(c_{1});", 20, -5.0, 5.0);
 
   //number of pseudoexperiments (toys) and number of events per pseudoexperiment
-  constexpr unsigned int ntoys = 500;
+  constexpr unsigned int ntoys = 20;
   constexpr unsigned int nstats = 5000;
   //parameters used in the generation
   constexpr double c0gen = 0.0;
@@ -123,6 +125,8 @@ int rf611_weightedfits(int acceptancemodel=2) {
   // M a i n   l o o p :   r u n   p s e u d o e x p e r i m e n t s
   //----------------------------------------------------------------
   for (unsigned int i=0; i<ntoys; i++) {
+    std::cout << i << std::endl;
+
     //S e t u p   p a r a m e t e r s   a n d   P D F
     //-----------------------------------------------
     //angle theta and the weight to account for the acceptance effect
@@ -165,18 +169,21 @@ int rf611_weightedfits(int acceptancemodel=2) {
 
     //F i t   t o y   u s i n g   t h e   t h r e e   d i f f e r e n t   a p p r o a c h e s   t o   u n c e r t a i n t y   d e t e r m i n a t i o n
     //-------------------------------------------------------------------------------------------------------------------------------------------------
+
+    auto batchMode = rbc::Off;
+
     //this uses the inverse weighted Hessian matrix
-    RooFitResult* result = pol.fitTo(data, Save(true), SumW2Error(false), PrintLevel(-1), BatchMode(true));
+    RooFitResult* result = pol.fitTo(data, Save(true), SumW2Error(false), PrintLevel(-1), BatchMode(batchMode));
     hc0pull1->Fill((c0.getVal()-c0gen)/c0.getError());
     hc1pull1->Fill((c1.getVal()-c1gen)/c1.getError());
 
     //this uses the correction with the Hesse matrix with squared weights
-    result = pol.fitTo(data, Save(true), SumW2Error(true), PrintLevel(-1), BatchMode(true));
+    result = pol.fitTo(data, Save(true), SumW2Error(true), PrintLevel(-1), BatchMode(batchMode));
     hc0pull2->Fill((c0.getVal()-c0gen)/c0.getError());
     hc1pull2->Fill((c1.getVal()-c1gen)/c1.getError());
 
     //this uses the asymptotically correct approach
-    result = pol.fitTo(data, Save(true), AsymptoticError(true), PrintLevel(-1), BatchMode(true));
+    result = pol.fitTo(data, Save(true), AsymptoticError(true), PrintLevel(-1), BatchMode(batchMode));
     hc0pull3->Fill((c0.getVal()-c0gen)/c0.getError());
     hc1pull3->Fill((c1.getVal()-c1gen)/c1.getError());
   }