library: libHist #include "TProfile.h"

# TProfile

class description - source file - inheritance tree (.pdf)

## class TProfile : public TH1D

| Inheritance Chart:

`TObject`

<-

`TNamed`

`TAttLine`

`TAttFill`

`TAttMarker`

<-

`TH1`

`TArray`

<-

`TArrayD`

<-

`TH1D`

<-

`TProfile`

        private:

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

        protected:

          virtual Int_t BufferFill(Axis_t, Stat_t)
          virtual Int_t BufferFill(Axis_t x, Axis_t y, Stat_t w)

        public:

                           TProfile()
                           TProfile(const char* name, const char* title, Int_t nbinsx, Axis_t xlow, Axis_t xup, Option_t* option = "")
                           TProfile(const char* name, const char* title, Int_t nbinsx, Axis_t xlow, Axis_t xup, Axis_t ylow, Axis_t yup, Option_t* option = "")
                           TProfile(const char* name, const char* title, Int_t nbinsx, const Float_t* xbins, Option_t* option = "")
                           TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Option_t* option = "")
                           TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Axis_t ylow, Axis_t yup, Option_t* option = "")
                           TProfile(const TProfile& profile)
                   virtual ~TProfile()
              virtual void Add(TF1* h1, Double_t c1 = 1, Option_t* option = "")
              virtual void Add(const TH1* h1, Double_t c1 = 1)
              virtual void Add(const TH1* h1, const TH1* h2, Double_t c1 = 1, Double_t c2 = 1)
               static void Approximate(Bool_t approx = kTRUE)
             virtual Int_t BufferEmpty(Int_t action = 0)
                      void BuildOptions(Double_t ymin, Double_t ymax, Option_t* option)
            static TClass* Class()
              virtual void Copy(TObject& hnew) const
              virtual void Divide(TF1* h1, Double_t c1 = 1)
              virtual void Divide(const TH1* h1)
              virtual void Divide(const TH1* h1, const TH1* h2, Double_t c1 = 1, Double_t c2 = 1, Option_t* option = "")
              virtual TH1* DrawCopy(Option_t* option = "") const
             virtual Int_t Fill(Axis_t x, Axis_t y)
             virtual Int_t Fill(const char* namex, Axis_t y)
             virtual Int_t Fill(Axis_t x, Axis_t y, Stat_t w)
             virtual Int_t Fill(const char* namex, Axis_t y, Stat_t w)
              virtual void FillN(Int_t ntimes, const Axis_t* x, const Axis_t* y, const Double_t* w, Int_t stride = 1)
            virtual Stat_t GetBinContent(Int_t bin) const
            virtual Stat_t GetBinContent(Int_t bin, Int_t) const
            virtual Stat_t GetBinContent(Int_t bin, Int_t, Int_t) const
            virtual Stat_t GetBinEntries(Int_t bin) const
            virtual Stat_t GetBinError(Int_t bin) const
            virtual Stat_t GetBinError(Int_t bin, Int_t) const
            virtual Stat_t GetBinError(Int_t bin, Int_t, Int_t) const
                 Option_t* GetErrorOption() const
              virtual void GetStats(Stat_t* stats) const
          virtual Double_t GetYmax() const
          virtual Double_t GetYmin() const
           virtual TClass* IsA() const
              virtual void LabelsDeflate(Option_t* axis = "X")
              virtual void LabelsInflate(Option_t* axis = "X")
              virtual void LabelsOption(Option_t* option = "h", Option_t* axis = "X")
          virtual Long64_t Merge(TCollection* list)
              virtual void Multiply(TF1* h1, Double_t c1 = 1)
              virtual void Multiply(const TH1* h1)
              virtual void Multiply(const TH1* h1, const TH1* h2, Double_t c1 = 1, Double_t c2 = 1, Option_t* option = "")
                 TProfile& operator=(const TProfile&)
                     TH1D* ProjectionX(const char* name = "_px", Option_t* option = "e") const
              virtual TH1* Rebin(Int_t ngroup = 2, const char* newname = "")
              virtual void RebinAxis(Axis_t x, const char* ax)
              virtual void Reset(Option_t* option = "")
              virtual void SavePrimitive(ofstream& out, Option_t* option)
              virtual void Scale(Double_t c1 = 1)
              virtual void SetBinEntries(Int_t bin, Stat_t w)
              virtual void SetBins(Int_t nbins, Axis_t xmin, Axis_t xmax)
              virtual void SetBins(Int_t nx, const Axis_t* xbins)
              virtual void SetBuffer(Int_t buffersize, Option_t* option = "")
              virtual void SetErrorOption(Option_t* option = "")
              virtual void ShowMembers(TMemberInspector& insp, char* parent)
              virtual void Streamer(TBuffer& b)
                      void StreamerNVirtual(TBuffer& b)

### Data Members

        protected:

                TArrayD fBinEntries    number of entries per bin
             EErrorType fErrorMode     Option to compute errors
               Double_t fYmin          Lower limit in Y (if set)
               Double_t fYmax          Upper limit in Y (if set)
                 Bool_t fScaling       !True when TProfile::Scale is called
                 Stat_t fTsumwy        Total Sum of weight*Y
                 Stat_t fTsumwy2       Total Sum of weight*Y*Y
          static Bool_t fgApproximate  bin error approximation option

## 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();
    }

/* */

TProfile() : TH1D()

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

~TProfile()

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

TProfile(const char *name,const char *title,Int_t nbins,Axis_t xlow,Axis_t xup,Option_t *option) : TH1D(name,title,nbins,xlow,xup)

    *-*-*-*-*-*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

     see also comments in the TH1 base class constructors

TProfile(const char *name,const char *title,Int_t nbins,const Float_t *xbins,Option_t *option) : TH1D(name,title,nbins,xbins)

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

            See TProfile::BuildOptions for more explanations on errors

     see also comments in the TH1 base class constructors

TProfile(const char *name,const char *title,Int_t nbins,const Double_t *xbins,Option_t *option) : TH1D(name,title,nbins,xbins)

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

            See TProfile::BuildOptions for more explanations on errors

     see also comments in the TH1 base class constructors

TProfile(const char *name,const char *title,Int_t nbins,const Double_t *xbins,Axis_t ylow,Axis_t yup,Option_t *option) : TH1D(name,title,nbins,xbins)

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

            See TProfile::BuildOptions for more explanations on errors

     see also comments in the TH1 base class constructors

TProfile(const char *name,const char *title,Int_t nbins,Axis_t xlow,Axis_t xup,Axis_t ylow,Axis_t yup,Option_t *option) : TH1D(name,title,nbins,xlow,xup)

    *-*-*-*-*-*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

     see also comments in the TH1 base class constructors

void BuildOptions(Double_t ymin, Double_t ymax, Option_t *option)

    *-*-*-*-*-*-*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.

TProfile(const TProfile &profile) : TH1D()

void Add(TF1 *, Double_t, Option_t * )

     Performs the operation: this = this + c1*f1

void Add(const TH1 *h1, Double_t c1)

     Performs the operation: this = this + c1*h1

void Add(const TH1 *h1, const TH1 *h2, Double_t c1, Double_t c2)

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

       this = c1*h1 + c2*h2

void Approximate(Bool_t approx)

         static function
     set the fgApproximate flag. When the flag is true, the function GetBinError
     will approximate the bin error with the average profile error on all bins
     in the following situation only
      - the number of bins in the profile is less than 1002
      - the bin number of entries is small ( <5)
      - the estimated bin error is extremely small compared to the bin content
      (see TProfile::GetBinError)

Int_t BufferEmpty(Int_t action)

     Fill histogram with all entries in the buffer.
     action = -1 histogram is reset and refilled from the buffer (called by THistPainter::Paint)
     action =  0 histogram is filled from the buffer
     action =  1 histogram is filled and buffer is deleted
                 The buffer is automatically deleted when the number of entries
                 in the buffer is greater than the number of entries in the histogram

Int_t BufferFill(Axis_t x, Axis_t y, Stat_t w)

     accumulate arguments in buffer. When buffer is full, empty the buffer
     fBuffer[0] = number of entries in buffer
     fBuffer[1] = w of first entry
     fBuffer[2] = x of first entry
     fBuffer[3] = y of first entry

void Copy(TObject &obj) const

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

void Divide(TF1 *, Double_t )

     Performs the operation: this = this/(c1*f1)

void Divide(const TH1 *h1)

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

       this = this/h1
     This function accepts to divide a TProfile by a histogram

void Divide(const TH1 *h1, const TH1 *h2, Double_t c1, Double_t c2, Option_t *option)

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

       this = c1*h1/(c2*h2)

TH1* DrawCopy(Option_t *option) const

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

Int_t Fill(Axis_t x, Axis_t y)

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

Int_t Fill(const char *namex, Axis_t y)

     Fill a Profile histogram (no weights)

Int_t Fill(Axis_t x, Axis_t y, Stat_t w)

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

Int_t Fill(const char *namex, Axis_t y, Stat_t w)

     Fill a Profile histogram with weights

void FillN(Int_t ntimes, const Axis_t *x, const Axis_t *y, const Stat_t *w, Int_t stride)

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

Stat_t GetBinContent(Int_t bin) const

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

Stat_t GetBinEntries(Int_t bin) const

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

Stat_t GetBinError(Int_t bin) const

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

     Computing errors: A moving field
     =================================
     The computation of errors for a TProfile has evolved with the versions
     of ROOT. The difficulty is in computing errors for bins with low statistics.
     - prior to version 3.00, we had no special treatment of low statistic bins.
       As a result, these bins had huge errors. The reason is that the
       expression eprim2 is very close to 0 (rounding problems) or 0.
     - in version 3.00 (18 Dec 2000), the algorithm is protected for values of
       eprim2 very small and the bin errors set to the average bin errors, following
       recommendations from a group of users.
     - in version 3.01 (19 Apr 2001), it is realized that the algorithm above
       should be applied only to low statistic bins.
     - in version 3.02 (26 Sep 2001), the same group of users recommend instead
       to take two times the average error on all bins for these low
       statistics bins giving a very small value for eprim2.
     - in version 3.04 (Nov 2002), the algorithm is modified/protected for the case
       when a TProfile is projected (ProjectionX). The previous algorithm
       generated a N^2 problem when projecting a TProfile with a large number of
       bins (eg 100000).
     - in version 3.05/06, a new static function TProfile::Approximate
       is introduced to enable or disable (default) the approximation.

     Ideas for improvements of this algorithm are welcome. No suggestions
     received since our call for advice to roottalk in Jul 2002.
     see for instance: http://root.cern.ch/root/roottalk/roottalk02/2916.html

Option_t* GetErrorOption() const

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

void GetStats(Stat_t *stats) const

     fill the array stats from the contents of this profile
     The array stats must be correctly dimensionned in the calling program.
     stats[0] = sumw
     stats[1] = sumw2
     stats[2] = sumwx
     stats[3] = sumwx2
     stats[4] = sumwy
     stats[5] = sumwy2

     The function recomputes the statistics quantities
     from the bin contents in the current axis range.

void LabelsDeflate(Option_t *)

     Reduce the number of bins for this axis to the number of bins having a label.

void LabelsInflate(Option_t *)

     Double the number of bins for axis.
     Refill histogram
     This function is called by TAxis::FindBin(const char *label)

void LabelsOption(Option_t *option, Option_t * /*ax*/)

      Set option(s) to draw axis with labels
      option = "a" sort by alphabetic order
             = ">" sort by decreasing values
             = "<" sort by increasing values
             = "h" draw labels horizonthal
             = "v" draw labels vertical
             = "u" draw labels up (end of label right adjusted)
             = "d" draw labels down (start of label left adjusted)

Long64_t Merge(TCollection *li)

    Merge all histograms in the collection in this histogram.
    This function computes the min/max for the x axis,
    compute a new number of bins, if necessary,
    add bin contents, errors and statistics.
    If overflows are present and limits are different the function will fail.
    The function returns the total number of entries in the result histogram
    if the merge is successfull, -1 otherwise.

    IMPORTANT remark. The axis x may have different number
    of bins and different limits, BUT the largest bin width must be
    a multiple of the smallest bin width and the upper limit must also
    be a multiple of the bin width.

void Multiply(TF1 *f1, Double_t c1)

     Performs the operation: this = this*c1*f1

void Multiply(const TH1 *)

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

       this = this*h1

void Multiply(const TH1 *, const TH1 *, Double_t, Double_t, Option_t *)

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

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

TH1D* ProjectionX(const char *name, Option_t *option) const

    *-*-*-*-*Project this profile into a 1-D histogram along X*-*-*-*-*-*-*
    *-*      =================================================

       The projection is always of the type TH1D.

       if option "E" is specified, the errors are computed. (default)
       if option "B" is specified, the content of bin of the returned histogram
          will be equal to the GetBinEntries(bin) of the profile,
          otherwise (default) it will be equal to GetBinContent(bin)

TH1* Rebin(Int_t ngroup, const char*newname)

    *-*-*-*-*Rebin this profile grouping ngroup bins together*-*-*-*-*-*-*-*-*
    *-*      ================================================
       if newname is not blank a new temporary profile hnew is created.
       else the current profile is modified (default)
       The parameter ngroup indicates how many bins of this have to me merged
       into one bin of hnew
       If the original profile has errors stored (via Sumw2), the resulting
       profile has new errors correctly calculated.

       examples: if hp is an existing TProfile histogram with 100 bins
         hp->Rebin();  //merges two bins in one in hp: previous contents of hp are lost
         hp->Rebin(5); //merges five bins in one in hp
         TProfile *hnew = hp->Rebin(5,"hnew"); // creates a new profile hnew
                                           //merging 5 bins of hp in one bin

       NOTE:  If ngroup is not an exact divider of the number of bins,
              the top limit of the rebinned profile is changed
              to the upper edge of the bin=newbins*ngroup and the corresponding
              bins are added to the overflow bin.
              Statistics will be recomputed from the new bin contents.

void RebinAxis(Axis_t x, const char* /* ax */)

     Profile histogram is resized along x axis such that x is in the axis range.
     The new axis limits are recomputed by doubling iteratively
     the current axis range until the specified value x is within the limits.
     The algorithm makes a copy of the histogram, then loops on all bins
     of the old histogram to fill the rebinned histogram.
     Takes into account errors (Sumw2) if any.
     The bit kCanRebin must be set before invoking this function.
      Ex:  h->SetBit(TH1::kCanRebin);

void Reset(Option_t *option)

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

void SavePrimitive(ofstream &out, Option_t *option)

     Save primitive as a C++ statement(s) on output stream out

void Scale(Double_t c1)

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

       this = c1*this

     This function uses the services of TProfile::Add

void SetBinEntries(Int_t bin, Stat_t w)

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

void SetBins(Int_t nx, Axis_t xmin, Axis_t xmax)

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

void SetBins(Int_t nx, const Axis_t *xbins)

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

void SetBuffer(Int_t buffersize, Option_t *)

     set the buffer size in units of 8 bytes (double)

void SetErrorOption(Option_t *option)

    *-*-*-*-*-*-*-*-*-*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

void Streamer(TBuffer &R__b)

     Stream an object of class TProfile.

### Inline Functions

                  Int_t BufferFill(Axis_t x, Axis_t y, Stat_t w)
                   void SetBins(Int_t, Axis_t, Axis_t, Int_t, Axis_t, Axis_t, Int_t, Axis_t, Axis_t)
              Double_t* GetB()
              Double_t* GetW()
              Double_t* GetW2()
                  Int_t Fill(const char* namex, Axis_t y, Stat_t w)
                   void FillN(Int_t ntimes, const Axis_t* x, const Axis_t* y, const Double_t* w, Int_t stride = 1)
                 Stat_t GetBinContent(Int_t bin, Int_t) const
                 Stat_t GetBinContent(Int_t bin, Int_t, Int_t) const
                 Stat_t GetBinError(Int_t bin, Int_t) const
                 Stat_t GetBinError(Int_t bin, Int_t, Int_t) const
               Double_t GetYmin() const
               Double_t GetYmax() const
                   void SetBins(Int_t nbins, Axis_t xmin, Axis_t xmax)
                   void SetBins(Int_t nx, const Axis_t* xbins)
                TClass* Class()
                TClass* IsA() const
                   void ShowMembers(TMemberInspector& insp, char* parent)
                   void StreamerNVirtual(TBuffer& b)
              TProfile& operator=(const TProfile&)

Author: Rene Brun 29/09/95
Last update: root/hist:$Name: $:$Id: TProfile.cxx,v 1.62 2005/06/13 16:32:40 brun Exp $
Copyright (C) 1995-2000, Rene Brun and Fons Rademakers. *