diff --git a/hist/hist/inc/TF1.h b/hist/hist/inc/TF1.h
index 964d731e2fe330f59f166eb1bb43559a9bae7a8e..bb7bde2385414c776720537472ef7c3bb31889d6 100644
--- a/hist/hist/inc/TF1.h
+++ b/hist/hist/inc/TF1.h
@@ -45,6 +45,12 @@ class TH1;
class TAxis;
class TMethodCall;
+namespace ROOT {
+ namespace Fit {
+ class FitResult;
+ }
+}
+
class TF1 : public TFormula, public TAttLine, public TAttFill, public TAttMarker {
protected:
@@ -255,6 +261,7 @@ public:
virtual void Save(Double_t xmin, Double_t xmax, Double_t ymin, Double_t ymax, Double_t zmin, Double_t zmax);
virtual void SavePrimitive(ostream &out, Option_t *option = "");
virtual void SetChisquare(Double_t chi2) {fChisquare = chi2;}
+ virtual void SetFitResult(const ROOT::Fit::FitResult & result, const Int_t * indpar = 0);
template <class PtrObj, typename MemFn>
void SetFunction( PtrObj& p, MemFn memFn );
template <typename Func>
diff --git a/hist/hist/src/TF1.cxx b/hist/hist/src/TF1.cxx
index c70a9f284133f4291df829a061cc0fb4c10633c7..4e17b8a1bde3d091b409624fbd114b05adff1a75 100644
--- a/hist/hist/src/TF1.cxx
+++ b/hist/hist/src/TF1.cxx
@@ -35,6 +35,7 @@
#include "Math/AdaptiveIntegratorMultiDim.h"
#include "Math/RichardsonDerivator.h"
#include "Math/Functor.h"
+#include "Fit/FitResult.h"
//#include <iostream>
@@ -2536,11 +2537,21 @@ Double_t TF1::IntegralError(Double_t a, Double_t b, const Double_t * params, con
{
// Return Error on Integral of a parameteric function between a and b
// due to the parameter uncertainties.
- // A pointer to a vector of parameter values and to the elements of the covariance matrix (covmat) can be optionally passed.
- // By default (i.e. when a zero pointer is passed) the current stored parameter values are used to estimate the integral error
- // together with the covariance matrix from the last fit (retrieved from the global fitter instance)
- // IMPORTANT NOTE: When no covariance matrix is passed and in the meantime a fit is done
- // using another function, the routine will signal an error and return zero.
+ // A pointer to a vector of parameter values and to the elements of the covariance matrix (covmat)
+ // can be optionally passed. By default (i.e. when a zero pointer is passed) the current stored
+ // parameter values are used to estimate the integral error together with the covariance matrix
+ // from the last fit (retrieved from the global fitter instance)
+ //
+ // IMPORTANT NOTE1: When no covariance matrix is passed and in the meantime a fit is done
+ // using another function, the routine will signal an error and it will return zero only
+ // when the number of fit parameter is different than the values stored in TF1 (TF1::GetNpar() ).
+ // In the case that npar is the same, an incorrect result is returned.
+ //
+ // IMPORTANT NOTE2: The user must pass a pointer to the elements of the full covariance matrix
+ // dimensioned with the right size (npar*npar), where npar is the total number of parameters (TF1::GetNpar()),
+ // including also the fixed parameters. When there are fixed parameters, the pointer returned from
+ // TVirtualFitter::GetCovarianceMatrix() cannot be used.
+ // One should use the TFitResult class, as shown in the example below.
//
// To get the matrix and values from an old fit do for example:
// TFitResultPtr r = histo->Fit(func, "S");
@@ -2563,10 +2574,23 @@ Double_t TF1::IntegralError(Int_t n, const Double_t * a, const Double_t * b, con
//
// A pointer to a vector of parameter values and to the elements of the covariance matrix (covmat) can be optionally passed.
// By default (i.e. when a zero pointer is passed) the current stored parameter values are used to estimate the integral error
- // together with the covariance matrix from the last fit (retrieved from the global fitter instance)
- // IMPORTANT NOTE: When no covariance matrix is passed and in the meantime a fit is done
- // using another function, the routine will signal an error and return zero.
- //
+ // together with the covariance matrix from the last fit (retrieved from the global fitter instance).
+ //
+ // IMPORTANT NOTE1: When no covariance matrix is passed and in the meantime a fit is done
+ // using another function, the routine will signal an error and it will return zero only
+ // when the number of fit parameter is different than the values stored in TF1 (TF1::GetNpar() ).
+ // In the case that npar is the same, an incorrect result is returned.
+ //
+ // IMPORTANT NOTE2: The user must pass a pointer to the elements of the full covariance matrix
+ // dimensioned with the right size (npar*npar), where npar is the total number of parameters (TF1::GetNpar()),
+ // including also the fixed parameters. When there are fixed parameters, the pointer returned from
+ // TVirtualFitter::GetCovarianceMatrix() cannot be used.
+ // One should use the TFitResult class, as shown in the example below.
+ //
+ // To get the matrix and values from an old fit do for example:
+ // TFitResultPtr r = histo->Fit(func, "S");
+ // ..... after performing other fits on the same function do
+ // func->IntegralError(x1,x2,r->GetParams(), r->GetCovarianceMatrix()->GetMatrixArray() );
return ROOT::TF1Helper::IntegralError(this,n,a,b,params,covmat,epsilon);
}
@@ -3022,6 +3046,45 @@ void TF1::SetCurrent(TF1 *f1)
fgCurrent = f1;
}
+//______________________________________________________________________________
+void TF1::SetFitResult(const ROOT::Fit::FitResult & result, const Int_t* indpar )
+{
+ // Set the result from the fit
+ // parameter values, errors, chi2, etc...
+ // Optionally a pointer to a vector (with size fNpar) of the parameter indices in the FitResult can be passed
+ // This is useful in the case of a combined fit with different functions, and the FitResult contains the global result
+ // By default it is assume that indpar = {0,1,2,....,fNpar-1}.
+
+ if (result.IsEmpty()) {
+ Warning("SetFitResult","Empty Fit result - nathing is set in TF1");
+ return;
+ }
+ if (indpar == 0 && fNpar != (int) result.NPar() ) {
+ Error("SetFitResult","Invalid Fit result passed - number of parameter is %d , different than TF1::GetNpar() = %d",fNpar,result.NPar());
+ return;
+ }
+ if (result.Chi2() > 0)
+ SetChisquare(result.Chi2() );
+ else
+ SetChisquare(result.MinFcnValue() );
+
+ SetNDF(result.Ndf() );
+ SetNumberFitPoints(result.Ndf() + result.NFreeParameters() );
+
+
+ for (Int_t i = 0; i < fNpar; ++i) {
+ Int_t ipar = (indpar != 0) ? indpar[i] : i;
+ if (ipar < 0) continue;
+ fParams[i] = result.Parameter(ipar);
+ // in case errors are not present do not set them
+ if (ipar < (int) result.Errors().size() )
+ fParErrors[i] = result.Error(ipar);
+ }
+ //invalidate cached integral since parameters have changed
+ Update();
+
+}
+
//______________________________________________________________________________
void TF1::SetMaximum(Double_t maximum)
diff --git a/hist/hist/src/TF1Helper.cxx b/hist/hist/src/TF1Helper.cxx
index eb4d3f4a9f5b62ff451f3b3d0f1a3ef8c3ed43b5..1f16613f37dc202b3e1fcf33d75de33cb33bb960 100644
--- a/hist/hist/src/TF1Helper.cxx
+++ b/hist/hist/src/TF1Helper.cxx
@@ -95,7 +95,7 @@ double IntegralError(TF1 * func, Int_t ndim, const double * a, const double * b,
TVectorD ig(npar);
for (int i=0; i < npar; ++i) {
- // check that parameter error is not zero - otehrwise skip
+ // check that parameter error is not zero - otherwise skip it
// should check the limits
double integral = 0;
if (covMatrix(i,i) > 0 ) {