TProfile

class TProfile : public TH1D

    private:

      virtual Int_t Fill(Axis_t)
      virtual Int_t Fill(Axis_t, Stat_t)
      virtual Int_t Fill(Axis_t, Axis_t, Axis_t, Stat_t)
       virtual void FillN(Int_t, Axis_t*, Double_t*, Int_t)
          Double_t* GetB()
          Double_t* GetW()
          Double_t* GetW2()
       virtual void SetBins(Int_t, Float_t, Float_t, Int_t, Float_t, Float_t)
       virtual void SetBins(Int_t, Float_t, Float_t, Int_t, Float_t, Float_t, Int_t, Float_t, Float_t)

    public:

             TProfile TProfile(const Text_t* name, const Text_t* title, Int_t nbinsx, Axis_t xlow, Axis_t xup, Option_t* option)
             TProfile TProfile(const Text_t* name, const Text_t* title, Int_t nbinsx, Axis_t xlow, Axis_t xup, Axis_t ylow, Axis_t yup, Option_t* option)
             TProfile TProfile()
             TProfile TProfile(const Text_t* name, const Text_t* title, Int_t nbinsx, Axis_t* xbins, Option_t* option)
             TProfile TProfile(const TProfile& profile)
         virtual void ~TProfile()
         virtual void Add(TH1* h1, Float_t c1 = 1)
         virtual void Add(TH1* h1, TH1* h2, Float_t c1 = 1, Float_t c2 = 1)
                 void BuildOptions(Float_t ymin, Float_t ymax, Option_t* option)
              TClass* Class()
         virtual void Copy(TObject& hnew)
         virtual void Divide(TH1* h1, TH1* h2, Float_t c1 = 1, Float_t c2 = 1, Option_t* option)
         virtual void Divide(TH1* h1)
         virtual TH1* DrawCopy(Option_t* option)
        virtual Int_t Fill(Axis_t x, Axis_t y)
        virtual Int_t Fill(Axis_t x, Axis_t y, Stat_t w)
         virtual void FillN(Int_t ntimes, Axis_t* x, Axis_t* y, Double_t* w, Int_t stride = 1)
       virtual Stat_t GetBinContent(Int_t bin)
       virtual Stat_t GetBinEntries(Int_t bin)
       virtual Stat_t GetBinError(Int_t bin)
            Option_t* GetErrorOption() const
      virtual Float_t GetYmax()
      virtual Float_t GetYmin()
      virtual TClass* IsA() const
         virtual void Multiply(TH1* h1, TH1* h2, Float_t c1 = 1, Float_t c2 = 1, Option_t* option)
         virtual void Multiply(TH1* h1)
         virtual void Reset(Option_t* option)
         virtual void Scale(Float_t c1 = 1)
         virtual void SetBinEntries(Int_t bin, Stat_t w)
         virtual void SetBins(Int_t nbins, Float_t xmin, Float_t xmax)
         virtual void SetErrorOption(Option_t* option)
         virtual void ShowMembers(TMemberInspector& insp, char* parent)
         virtual void Streamer(TBuffer& b)

  Data Members

    protected:

         TArrayD fBinEntries  number of entries per bin
      EErrorType fErrorMode   Option to compute errors
         Float_t fYmin        Lower limit in Y (if set)
         Float_t fYmax        Upper limit in Y (if set)

Class Description

  Profile histograms are used to display the mean
  value of Y and its RMS for each bin in X. Profile histograms are in many cases an
  elegant replacement of two-dimensional histograms : the inter-relation of two
  measured quantities X and Y can always be visualized by a two-dimensional
  histogram or scatter-plot; its representation on the line-printer is not particularly
  satisfactory, except for sparse data. If Y is an unknown (but single-valued)
  approximate function of X, this function is displayed by a profile histogram with
  much better precision than by a scatter-plot.

  The following formulae show the cumulated contents (capital letters) and the values
  displayed by the printing or plotting routines (small letters) of the elements for bin J.

                                                    2
      H(J)  =  sum Y                  E(J)  =  sum Y
      l(J)  =  sum l                  L(J)  =  sum l
      h(J)  =  H(J)/L(J)              s(J)  =  sqrt(E(J)/L(J)- h(J)**2)
      e(J)  =  s(J)/sqrt(L(J))

  In the special case where s(J) is zero (eg, case of 1 entry only in one bin)
  e(J) is computed from the average of the s(J) for all bins.
  This simple/crude approximation was suggested in order to keep the bin
  during a fit operation.

           Example of a profile histogram with its graphics output
{
  TCanvas *c1 = new TCanvas("c1","Profile histogram example",200,10,700,500);
  hprof  = new TProfile("hprof","Profile of pz versus px",100,-4,4,0,20);
  Float_t px, py, pz;
  for ( Int_t i=0; i<25000; i++) {
     gRandom->Rannor(px,py);
     pz = px*px + py*py;
     hprof->Fill(px,pz,1);
  }
  hprof->Draw();
}

/*

*/

*-*-*-*-*-*Default constructor for Profile histograms*-*-*-*-*-*-*-*-*
*-*        ==========================================

*-*-*-*-*-*Default destructor for Profile histograms*-*-*-*-*-*-*-*-*
*-*        =========================================

*-*-*-*-*-*Normal Constructor for Profile histograms*-*-*-*-*-*-*-*-*-*
*-*        ==========================================

  The first five parameters are similar to TH1D::TH1D.
  All values of y are accepted at filling time.
  To fill a profile histogram, one must use TProfile::Fill function.

  Note that when filling the profile histogram the function Fill
  checks if the variable y is betyween fYmin and fYmax.
  If a minimum or maximum value is set for the Y scale before filling,
  then all values below ymin or above ymax will be discarded.
  Setting the minimum or maximum value for the Y scale before filling
  has the same effect as calling the special TProfile constructor below
  where ymin and ymax are specified.

  H(J) is printed as the channel contents. The errors displayed are s(J) if CHOPT='S'
  (spread option), or e(J) if CHOPT=' ' (error on mean).

        See TProfile::BuildOptions for explanation of parameters

*-*-*-*-*-*Constructor for Profile histograms with variable bin size*-*-*-*-*
*-*        =========================================================

        See TProfile::BuildOptions for more explanations on errors

*-*-*-*-*-*Constructor for Profile histograms with range in y*-*-*-*-*-*
*-*        ==================================================
  The first five parameters are similar to TH1D::TH1D.
  Only the values of Y between YMIN and YMAX will be considered at filling time.
  ymin and ymax will also be the maximum and minimum values
  on the y scale when drawing the profile.

        See TProfile::BuildOptions for more explanations on errors

*-*-*-*-*-*-*Set Profile histogram structure and options*-*-*-*-*-*-*-*-*
*-*          ===========================================

    If a bin has N data points all with the same value Y (especially
    possible when dealing with integers), the spread in Y for that bin
    is zero, and the uncertainty assigned is also zero, and the bin is
    ignored in making subsequent fits. If SQRT(Y) was the correct error
    in the case above, then SQRT(Y)/SQRT(N) would be the correct error here.
    In fact, any bin with non-zero number of entries N but with zero spread
    should have an uncertainty SQRT(Y)/SQRT(N).

    Now, is SQRT(Y)/SQRT(N) really the correct uncertainty?
    that it is only in the case where the Y variable is some sort
    of counting statistics, following a Poisson distribution. This should
    probably be set as the default case. However, Y can be any variable
    from an original NTUPLE, not necessarily distributed "Poissonly".
    The computation of errors is based on the parameter option:
    option:
     ' '  (Default) Errors are Spread/SQRT(N) for Spread.ne.0. ,
                      "     "  SQRT(Y)/SQRT(N) for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0
     's'            Errors are Spread  for Spread.ne.0. ,
                      "     "  SQRT(Y)  for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0
     'i'            Errors are Spread/SQRT(N) for Spread.ne.0. ,
                      "     "  1./SQRT(12.*N) for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0

    The third case above corresponds to Integer Y values for which the
    uncertainty is +-0.5, with the assumption that the probability that Y
    takes any value between Y-0.5 and Y+0.5 is uniform (the same argument
    goes for Y uniformly distributed between Y and Y+1); this would be
    useful if Y is an ADC measurement, for example. Other, fancier options
    would be possible, at the cost of adding one more parameter to the PROFILE
    command. For example, if all Y variables are distributed according to some
    known Gaussian of standard deviation Sigma, then:
     'G'            Errors are Spread/SQRT(N) for Spread.ne.0. ,
                      "     "  Sigma/SQRT(N) for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0
    For example, this would be useful when all Y's are experimental quantities
    measured with the same instrument with precision Sigma.

 Performs the operation: this = this + c1*h1

*-*-*-*-*Replace contents of this profile by the addition of h1 and h2*-*-*
*-*      =============================================================

   this = c1*h1 + c2*h2

*-*-*-*-*-*-*-*Copy a Profile histogram to a new profile histogram*-*-*-*-*
*-*            ===================================================

*-*-*-*-*-*-*-*-*-*-*Divide this profile by h1*-*-*-*-*-*-*-*-*-*-*-*-*
*-*                  =========================

   this = this/h1

*-*-*-*-*Replace contents of this profile by the division of h1 by h2*-*-*
*-*      ============================================================

   this = c1*h1/(c2*h2)

*-*-*-*-*-*-*-*Draw a copy of this profile histogram*-*-*-*-*-*-*-*-*-*-*-*
*-*            =====================================

*-*-*-*-*-*-*-*-*-*-*Fill a Profile histogram (no weights)*-*-*-*-*-*-*-*
*-*                  =====================================

*-*-*-*-*-*-*-*-*-*-*Fill a Profile histogram with weights*-*-*-*-*-*-*-*
*-*                  =====================================

*-*-*-*-*-*-*-*-*-*-*Fill a Profile histogram with weights*-*-*-*-*-*-*-*
*-*                  =====================================

*-*-*-*-*-*-*Return bin content of a Profile histogram*-*-*-*-*-*-*-*-*-*
*-*          =========================================

*-*-*-*-*-*-*Return bin entries of a Profile histogram*-*-*-*-*-*-*-*-*-*
*-*          =========================================

*-*-*-*-*-*-*Return bin error of a Profile histogram*-*-*-*-*-*-*-*-*-*
*-*          =======================================

*-*-*-*-*-*-*-*-*-*Return option to compute profile errors*-*-*-*-*-*-*-*-*
*-*                =======================================

*-*-*-*-*-*-*-*-*-*-*Multiply this profile by h1*-*-*-*-*-*-*-*-*-*-*-*-*
*-*                  =============================

   this = this*h1

*-*-*-*-*Replace contents of this profile by multiplication of h1 by h2*-*
*-*      ================================================================

   this = (c1*h1)*(c2*h2)

*-*-*-*-*-*-*-*-*-*Reset contents of a Profile histogram*-*-*-*-*-*-*-*-*
*-*                =====================================

*-*-*-*-*Multiply this profile by a constant c1*-*-*-*-*-*-*-*-*
*-*      ======================================

   this = c1*this

 This function uses the services of TProfile::Add

*-*-*-*-*-*-*-*-*Set the number of entries in bin*-*-*-*-*-*-*-*-*-*-*-*
*-*              ================================

*-*-*-*-*-*-*-*-*Redefine  x axis parameters*-*-*-*-*-*-*-*-*-*-*-*
*-*              ===========================

*-*-*-*-*-*-*-*-*-*Set option to compute profile errors*-*-*-*-*-*-*-*-*
*-*                =====================================

    The computation of errors is based on the parameter option:
    option:
     ' '  (Default) Errors are Spread/SQRT(N) for Spread.ne.0. ,
                      "     "  SQRT(Y)/SQRT(N) for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0
     's'            Errors are Spread  for Spread.ne.0. ,
                      "     "  SQRT(Y)  for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0
     'i'            Errors are Spread/SQRT(N) for Spread.ne.0. ,
                      "     "  1./SQRT(12.*N) for Spread.eq.0,N.gt.0 ,
                      "     "  0.  for N.eq.0
   See TProfile::BuildOptions for explanation of all options

Inline Functions

              Int_t Fill(Axis_t, Axis_t, Axis_t, Stat_t)
               void SetBins(Int_t, Float_t, Float_t, Int_t, Float_t, Float_t, Int_t, Float_t, Float_t)
          Double_t* GetB()
          Double_t* GetW()
          Double_t* GetW2()
              Int_t Fill(Axis_t x, Axis_t y)
              Int_t Fill(Axis_t x, Axis_t y, Stat_t w)
               void FillN(Int_t ntimes, Axis_t* x, Axis_t* y, Double_t* w, Int_t stride = 1)
            Float_t GetYmin()
            Float_t GetYmax()
               void SetBins(Int_t nbins, Float_t xmin, Float_t xmax)
            TClass* Class()
            TClass* IsA() const
               void ShowMembers(TMemberInspector& insp, char* parent)
               void Streamer(TBuffer& b)