[RF] Update rf604_constraints to follow global observables convention

The RooFit convention for constraint terms is to have the constrained
parameter also as the mean parameter of the constraint Gaussian, while a
so-called global observable (a constant) is put in place of the
pdf observable.

Author: guitargeek

tutorials/roofit/rf604_constraints.C

@@ -50,7 +50,7 @@ void rf604_constraints()
    // -----------------------------------------
 
    // Construct Gaussian constraint pdf on parameter f at 0.8 with resolution of 0.1
-   RooGaussian fconstraint("fconstraint", "fconstraint", f, RooConst(0.8), RooConst(0.2));
+   RooGaussian fconstraint("fconstraint", "fconstraint", RooConst(0.8), f, RooConst(0.2));
 
    // M E T H O D   1   -   A d d   i n t e r n a l   c o n s t r a i n t   t o   m o d e l
    // -------------------------------------------------------------------------------------
@@ -62,19 +62,19 @@ void rf604_constraints()
    RooProdPdf modelc("modelc", "model with constraint", RooArgSet(model, fconstraint));
 
    // Fit model (without use of constraint term)
-   RooFitResult *r1 = model.fitTo(*d, Save());
+   RooFitResult *r1 = model.fitTo(*d, Save(), PrintLevel(-1));
 
    // Fit modelc with constraint term on parameter f
-   RooFitResult *r2 = modelc.fitTo(*d, Constrain(f), Save());
+   RooFitResult *r2 = modelc.fitTo(*d, Constrain(f), Save(), PrintLevel(-1));
 
    // M E T H O D   2   -     S p e c i f y   e x t e r n a l   c o n s t r a i n t   w h e n   f i t t i n g
    // -------------------------------------------------------------------------------------------------------
 
    // Construct another Gaussian constraint pdf on parameter f at 0.2 with resolution of 0.1
-   RooGaussian fconstext("fconstext", "fconstext", f, RooConst(0.2), RooConst(0.1));
+   RooGaussian fconstext("fconstext", "fconstext", RooConst(0.2), f, RooConst(0.1));
 
    // Fit with external constraint
-   RooFitResult *r3 = model.fitTo(*d, ExternalConstraints(fconstext), Save());
+   RooFitResult *r3 = model.fitTo(*d, ExternalConstraints(fconstext), Save(), PrintLevel(-1));
 
    // Print the fit results
    cout << "fit result without constraint (data generated at f=0.5)" << endl;

tutorials/roofit/rf604_constraints.py

@@ -37,33 +37,32 @@
 
 # Construct Gaussian constraint pdf on parameter f at 0.8 with
 # resolution of 0.1
-fconstraint = ROOT.RooGaussian("fconstraint", "fconstraint", f, ROOT.RooFit.RooConst(0.8), ROOT.RooFit.RooConst(0.1))
+fconstraint = ROOT.RooGaussian("fconstraint", "fconstraint", ROOT.RooFit.RooConst(0.8), f, ROOT.RooFit.RooConst(0.1))
 
 # Method 1 - add internal constraint to model
 # -------------------------------------------------------------------------------------
 
-# Multiply constraint term with regular pdf using ROOT.RooProdPdf
-# Specify in fitTo() that internal constraints on parameter f should be
-# used
+# Multiply constraint term with regular pdf using ROOT.RooProdPdf Specify in
+# fitTo() that internal constraints on parameter f should be used
 
 # Multiply constraint with pdf
 modelc = ROOT.RooProdPdf("modelc", "model with constraint", [model, fconstraint])
 
 # Fit model (without use of constraint term)
-r1 = model.fitTo(d, Save=True)
+r1 = model.fitTo(d, Save=True, PrintLevel=-1)
 
 # Fit modelc with constraint term on parameter f
-r2 = modelc.fitTo(d, Constrain={f}, Save=True)
+r2 = modelc.fitTo(d, Constrain={f}, Save=True, PrintLevel=-1)
 
 # Method 2 - specify external constraint when fitting
 # ------------------------------------------------------------------------------------------
 
 # Construct another Gaussian constraint pdf on parameter f at 0.8 with
 # resolution of 0.1
-fconstext = ROOT.RooGaussian("fconstext", "fconstext", f, ROOT.RooFit.RooConst(0.2), ROOT.RooFit.RooConst(0.1))
+fconstext = ROOT.RooGaussian("fconstext", "fconstext", ROOT.RooFit.RooConst(0.2), f, ROOT.RooFit.RooConst(0.1))
 
 # Fit with external constraint
-r3 = model.fitTo(d, ExternalConstraints={fconstext}, Save=True)
+r3 = model.fitTo(d, ExternalConstraints={fconstext}, Save=True, PrintLevel=-1)
 
 # Print the fit results
 print("fit result without constraint (data generated at f=0.5)")