class TProfile: public TH1D

```
Profile histograms are used to display the mean
value of Y and its error for each bin in X. The displayed error is by default the
standard error on the mean (i.e. the standard deviation divided by the sqrt(n) )
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)                     mean of Y,
s(J)  =  sqrt(E(J)/L(J)- h(J)**2)      standard deviation of Y  (e.g. RMS)
e(J)  =  s(J)/sqrt(L(J))               standard error on the mean

The displayed bin content for bin J of a TProfile is always h(J). The corresponding bin error is by default
e(J). In case the option "s" is used (in the constructor or by calling TProfile::BuildOptions)
the displayed error is  s(J)

In the special case where s(J) is zero (eg, case of 1 entry only in one bin)
the bin error e(J) is computed from the average of the s(J) for all bins if
the static function TProfile::Approximate has been called.
This simple/crude approximation was suggested in order to keep the bin
during a fit operation. But note that this approximation is not the default behaviour.

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();
}

```
/* */
```
```

Function Members (Methods)

public:
protected:
 Bool_t TArray::BoundsOk(const char* where, Int_t at) const virtual Int_t BufferFill(Double_t, Double_t) virtual Int_t BufferFill(Double_t x, Double_t y, Double_t w) static bool TH1::CheckAxisLimits(const TAxis* a1, const TAxis* a2) static bool TH1::CheckBinLabels(const TAxis* a1, const TAxis* a2) static bool TH1::CheckBinLimits(const TAxis* a1, const TAxis* a2) static bool TH1::CheckConsistency(const TH1* h1, const TH1* h2) static bool TH1::CheckConsistentSubAxes(const TAxis* a1, Int_t firstBin1, Int_t lastBin1, const TAxis* a2, Int_t firstBin2 = 0, Int_t lastBin2 = 0) static bool TH1::CheckEqualAxes(const TAxis* a1, const TAxis* a2) virtual void TObject::DoError(int level, const char* location, const char* fmt, va_list va) const virtual void TH1::DoFillN(Int_t ntimes, const Double_t* x, const Double_t* w, Int_t stride = 1) virtual Double_t TH1::DoIntegral(Int_t ix1, Int_t ix2, Int_t iy1, Int_t iy2, Int_t iz1, Int_t iz2, Double_t& err, Option_t* opt, Bool_t doerr = kFALSE) const Int_t Fill(const Double_t* v) virtual Bool_t TH1::FindNewAxisLimits(const TAxis* axis, const Double_t point, Double_t& newMin, Double_t& newMax) virtual Double_t GetBinErrorSqUnchecked(Int_t bin) const void TObject::MakeZombie() Bool_t TArray::OutOfBoundsError(const char* where, Int_t i) const static Bool_t TH1::RecomputeAxisLimits(TAxis& destAxis, const TAxis& anAxis) virtual Double_t RetrieveBinContent(Int_t bin) const static Bool_t TH1::SameLimitsAndNBins(const TAxis& axis1, const TAxis& axis2) virtual void TH1::SavePrimitiveHelp(ostream& out, const char* hname, Option_t* option = "") void SetBins(const Int_t* nbins, const Double_t* range) virtual void TH1D::UpdateBinContent(Int_t bin, Double_t content)
private:
 virtual Int_t Fill(Double_t) virtual void FillN(Int_t, const Double_t*, const Double_t*, Int_t) Double_t* GetB() Double_t* GetB2() Double_t* GetW() Double_t* GetW2() virtual void SetBins(Int_t, const Double_t*, Int_t, const Double_t*) virtual void SetBins(Int_t, Double_t, Double_t, Int_t, Double_t, Double_t) virtual void SetBins(Int_t, const Double_t*, Int_t, const Double_t*, Int_t, const Double_t*) virtual void SetBins(Int_t, Double_t, Double_t, Int_t, Double_t, Double_t, Int_t, Double_t, Double_t)

Data Members

public:
 Double_t* TArrayD::fArray [fN] Array of fN doubles Int_t TArray::fN Number of array elements static TH1::(anonymous) TH1::kAllAxes static TObject::(anonymous) TObject::kBitMask static TObject::EStatusBits TObject::kCanDelete static TH1::(anonymous) TH1::kCanRebin static TObject::EStatusBits TObject::kCannotPick static TObject::EStatusBits TObject::kHasUUID static TObject::EStatusBits TObject::kInvalidObject static TH1::(anonymous) TH1::kIsAverage static TH1::(anonymous) TH1::kIsNotW static TObject::(anonymous) TObject::kIsOnHeap static TObject::EStatusBits TObject::kIsReferenced static TH1::(anonymous) TH1::kIsZoomed static TH1::(anonymous) TH1::kLogX static TObject::EStatusBits TObject::kMustCleanup static TH1::(anonymous) TH1::kNoAxis static TObject::EStatusBits TObject::kNoContextMenu static TH1::(anonymous) TH1::kNoStats static TH1::(anonymous) TH1::kNoTitle static TH1::EBinErrorOpt TH1::kNormal static TObject::(anonymous) TObject::kNotDeleted static TH1::(anonymous) TH1::kNstat static TObject::EStatusBits TObject::kObjInCanvas static TObject::(anonymous) TObject::kOverwrite static TH1::EBinErrorOpt TH1::kPoisson static TH1::EBinErrorOpt TH1::kPoisson2 static TObject::(anonymous) TObject::kSingleKey static TH1::(anonymous) TH1::kUserContour static TObject::(anonymous) TObject::kWriteDelete static TH1::(anonymous) TH1::kXaxis static TH1::(anonymous) TH1::kYaxis static TH1::(anonymous) TH1::kZaxis static TObject::(anonymous) TObject::kZombie
protected:
 Short_t TH1::fBarOffset (1000*offset) for bar charts or legos Short_t TH1::fBarWidth (1000*width) for bar charts or legos TArrayD fBinEntries number of entries per bin TH1::EBinErrorOpt TH1::fBinStatErrOpt option for bin statistical errors TArrayD fBinSumw2 Array of sum of squares of weights per bin Double_t* TH1::fBuffer [fBufferSize] entry buffer Int_t TH1::fBufferSize fBuffer size TArrayD TH1::fContour Array to display contour levels Int_t TH1::fDimension !Histogram dimension (1, 2 or 3 dim) TDirectory* TH1::fDirectory !Pointer to directory holding this histogram Double_t TH1::fEntries Number of entries EErrorType fErrorMode Option to compute errors Color_t TAttFill::fFillColor fill area color Style_t TAttFill::fFillStyle fill area style TList* TH1::fFunctions ->Pointer to list of functions (fits and user) Double_t* TH1::fIntegral !Integral of bins used by GetRandom Color_t TAttLine::fLineColor line color Style_t TAttLine::fLineStyle line style Width_t TAttLine::fLineWidth line width Color_t TAttMarker::fMarkerColor Marker color index Size_t TAttMarker::fMarkerSize Marker size Style_t TAttMarker::fMarkerStyle Marker style Double_t TH1::fMaximum Maximum value for plotting Double_t TH1::fMinimum Minimum value for plotting TString TNamed::fName object identifier Int_t TH1::fNcells number of bins(1D), cells (2D) +U/Overflows Double_t TH1::fNormFactor Normalization factor TString TH1::fOption histogram options TVirtualHistPainter* TH1::fPainter !pointer to histogram painter Bool_t fScaling !True when TProfile::Scale is called TArrayD TH1::fSumw2 Array of sum of squares of weights TString TNamed::fTitle object title Double_t TH1::fTsumw Total Sum of weights Double_t TH1::fTsumw2 Total Sum of squares of weights Double_t TH1::fTsumwx Total Sum of weight*X Double_t TH1::fTsumwx2 Total Sum of weight*X*X Double_t fTsumwy Total Sum of weight*Y Double_t fTsumwy2 Total Sum of weight*Y*Y TAxis TH1::fXaxis X axis descriptor TAxis TH1::fYaxis Y axis descriptor Double_t fYmax Upper limit in Y (if set) Double_t fYmin Lower limit in Y (if set) TAxis TH1::fZaxis Z axis descriptor static Bool_t TH1::fgAddDirectory !flag to add histograms to the directory static Bool_t fgApproximate bin error approximation option static Int_t TH1::fgBufferSize !default buffer size for automatic histograms static Bool_t TH1::fgDefaultSumw2 !flag to call TH1::Sumw2 automatically at histogram creation time static Bool_t TH1::fgStatOverflows !flag to use under/overflows in statistics

Function documentation

TProfile()
```Default constructor for Profile histograms*-*-*-
*-*        ==========================================
```

```Default destructor for Profile histograms*-*-*-
*-*        =========================================
```
TProfile(const char* name, const char* title, Int_t nbinsx, Double_t xlow, Double_t xup, Option_t* option = "")
```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

```
TProfile(const char* name, const char* title, Int_t nbinsx, const Float_t* xbins, Option_t* option = "")
```-*Constructor for Profile histograms with variable bin size
*-*        =========================================================

See TProfile::BuildOptions for more explanations on errors

```
TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Option_t* option = "")
```-*Constructor for Profile histograms with variable bin size
*-*        =========================================================

See TProfile::BuildOptions for more explanations on errors

```
TProfile(const char* name, const char* title, Int_t nbinsx, const Double_t* xbins, Double_t ylow, Double_t yup, Option_t* option = "")
```-*Constructor for Profile histograms with variable bin size
*-*        =========================================================

See TProfile::BuildOptions for more explanations on errors

```
TProfile(const char* name, const char* title, Int_t nbinsx, Double_t xlow, Double_t xup, Double_t ylow, Double_t yup, Option_t* option = "")
```Constructor for Profile histograms with range in y
*-*        ==================================================
The first five parameters are similar to TH1D::TH1D.
Only the values of Y between ylow and yup will be considered at filling time.
ylow and yup will also be the maximum and minimum values
on the y scale when drawing the profile.

See TProfile::BuildOptions for more explanations on errors

```
void BuildOptions(Double_t ymin, Double_t ymax, Option_t* option)
```Set Profile histogram structure and options*-*-
*-*          ===========================================
ymin:  minimum value allowed for y
ymax:  maximum value allowed for y
if (ymin = ymax = 0) there are no limits on the allowed y values (ymin = -inf, ymax = +inf)

option:  this is the option for the computation of the y error of the profile ( TProfile::GetBinError )
possible values for the options are:

' '  (Default) the bin errors are the standard error on the mean of Y  =  S(Y)/SQRT(N)
where S(Y) is the standard deviation (RMS) of the Y data in the bin
and N is the number of bin entries (from TProfile::GetBinEntries(ibin) )
(i.e the errors are the standard error on the bin content of the profile)

's'            Errors are the standard deviation of Y, S(Y)

'i'            Errors are S(Y)/SQRT(N) (standard error on the mean as in the default)
The only difference is only when the standard deviation in Y is zero.
In this  case the error a standard deviation = 1/SQRT(12) is assumed and the error is
1./SQRT(12*N).
This approximation assumes that the Y values are integer (e.g. ADC counts)
and have an implicit uncertainty of y +/- 0.5. With the assumption that the probability that y
takes any value between y-0.5 and y+0.5 is uniform, its standard error is 1/SQRT(12)

'g'            Errors are 1./SQRT(W) where W is the sum of the weights for the bin J
W is obtained as from TProfile::GetBinEntries(ibin)
This errors corresponds to the standard deviation of weighted mean where each
measurement Y is uncorrelated and has an error sigma, which is expressed in the
weight used to fill the Profile:  w = 1/sigma^2
The resulting  error in TProfile is then 1./SQRT( Sum(1./sigma^2) )
```
TProfile(const TProfile& profile)
``` Copy constructor.
```
Bool_t Add(TF1* h1, Double_t c1 = 1, Option_t* option = "")
``` Performs the operation: this = this + c1*f1
```
Bool_t Add(const TH1* h1, Double_t c1 = 1)
``` Performs the operation: this = this + c1*h1
```
Bool_t Add(const TH1* h1, const TH1* h2, Double_t c1 = 1, Double_t c2 = 1)
```-*-*Replace contents of this profile by the addition of h1 and h2
*-*      =============================================================

this = c1*h1 + c2*h2

c1 and c2 are considered as weights applied to the two summed profiles.
The operation acts therefore like merging the two profiles with a weight c1 and c2

```
void Approximate(Bool_t approx = kTRUE)
```     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 = 0)
``` 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
```

``` 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& hnew) const
```-*-*-*Copy a Profile histogram to a new profile histogram
*-*            ===================================================
```
Bool_t Divide(TF1* h1, Double_t c1 = 1)
``` Performs the operation: this = this/(c1*f1)
This function is not implemented for the TProfile
```
Bool_t Divide(const TH1* h1)
```Divide this profile by h1*-*-
*-*                  =========================

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

The function return kFALSE if the divide operation failed
```
Bool_t Divide(const TH1* h1, const TH1* h2, Double_t c1 = 1, Double_t c2 = 1, Option_t* option = "")
```-*-*Replace contents of this profile by the division of h1 by h2
*-*      ============================================================

this = c1*h1/(c2*h2)

The function return kFALSE if the divide operation failed
```

```-*-*-*Fill a Profile histogram (no weights)
*-*                  =====================================
```
Int_t Fill(const char* namex, Double_t y)
``` Fill a Profile histogram (no weights)

```

```-*-*-*Fill a Profile histogram with weights
*-*                  =====================================
```
Int_t Fill(const char* namex, Double_t y, Double_t w)
``` Fill a Profile histogram with weights

```
void FillN(Int_t ntimes, const Double_t* x, const Double_t* y, const Double_t* w, Int_t stride = 1)
```-*-*-*Fill a Profile histogram with weights
*-*                  =====================================
```
Double_t GetBinContent(Int_t bin) const
```Return bin content of a Profile histogram*-*-*-
*-*          =========================================
```
Double_t GetBinEntries(Int_t bin) const
```Return bin entries of a Profile histogram*-*-*-
*-*          =========================================
```
Double_t GetBinEffectiveEntries(Int_t bin) const
```            Return bin effective entries for a weighted filled Profile histogram.
In case of an unweighted profile, it is equivalent to the number of entries per bin
The effective entries is defined as the square of the sum of the weights divided by the
sum of the weights square.
TProfile::Sumw2() must be called before filling the profile with weights.
Only by calling this method the  sum of the square of the weights per bin is stored.

*-*          =========================================
```
Double_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
see for instance: http://root.cern.ch/root/roottalk/roottalk02/2916.html
```
Option_t * GetErrorOption() const
```-*Return option to compute profile errors
*-*                =======================================
```
void GetStats(Double_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

If no axis-subrange is specified (via TAxis::SetRange), the array stats
is simply a copy of the statistics quantities computed at filling time.
If a sub-range is specified, the function recomputes these quantities
from the bin contents in the current axis range.
```
void LabelsDeflate(Option_t* axis = "X")
``` Reduce the number of bins for this axis to the number of bins having a label.
```
void LabelsInflate(Option_t* axis = "X")
``` Double the number of bins for axis.
Refill histogram
This function is called by TAxis::FindBin(const char *label)
```
void LabelsOption(Option_t* option = "h", Option_t* axis = "X")
```  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* list)
```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.
```
Bool_t Multiply(TF1* h1, Double_t c1 = 1)
``` Performs the operation: this = this*c1*f1

The function return kFALSE if the Multiply operation failed
```
Bool_t Multiply(const TH1* h1)
```Multiply this profile by h1*-*-
*-*                  =============================

this = this*h1

```
Bool_t Multiply(const TH1* h1, const TH1* h2, Double_t c1 = 1, Double_t c2 = 1, Option_t* option = "")
```-*-*-*Replace contents of this profile by multiplication of h1 by h2
*-*      ================================================================

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

```
TH1D * ProjectionX(const char* name = "_px", Option_t* option = "e") 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 of the projected histogram are computed and set
to be equal to the errors of the profile.
Option "E" is defined as the default one in the header file.
if option "" is specified the histogram errors are simply the sqrt of its content
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)
if option "C=E" the bin contents of the projection are set to the
bin errors of the profile
if option "W" is specified the bin content of the projected histogram  is set to the
product of the bin content of the profile and the entries.
With this option the returned histogram will be equivalent to the one obtained by
filling directly a TH1D using the 2-nd value as a weight.
This makes sense only for profile filled with weights =1. If not, the error of the
projected histogram obtained with this option will not be correct.
```
void PutStats(Double_t* stats)
``` Replace current statistics with the values in array stats
```
TH1 * Rebin(Int_t ngroup = 2, const char* newname = "", const Double_t* xbins = 0)
```Rebin this profile grouping ngroup bins together*-*-*-*-
*-*      ================================================
-case 1  xbins=0
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.

-case 2  xbins!=0
a new profile is created (you should specify newname).
The parameter ngroup is the number of variable size bins in the created profile
The array xbins must contain ngroup+1 elements that represent the low-edge
of the bins.
The data of the old bins are added to the new bin which contains the bin center
of the old bins. It is possible that information from the old binning are attached
to the under-/overflow bins of the new binning.

examples: if hp is an existing TProfile with 100 bins
Double_t xbins[25] = {...} array of low-edges (xbins[25] is the upper edge of last bin
hp->Rebin(24,"hpnew",xbins);  //creates a new variable bin size profile hpnew
```
void ExtendAxis(Double_t x, TAxis* axis)
``` 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 extended histogram.
Takes into account errors (Sumw2) if any.
The axis must be extendable before invoking this function.
Ex: h->GetXaxis()->SetCanExtend(kTRUE)
```
void Reset(Option_t* option = "")
```-*Reset contents of a Profile histogram
*-*                =====================================
```
void SavePrimitive(ostream& out, Option_t* option = "")
``` Save primitive as a C++ statement(s) on output stream out
```
void Scale(Double_t c1 = 1, Option_t* option = "")
``` *-*-*-*-*Multiply this profile by a constant c1*-*-*-*-*-*-*-*-*
*-*      ======================================

this = c1*this

This function uses the services of TProfile::Add

```
void SetBinEntries(Int_t bin, Double_t w)
```Set the number of entries in bin*-*-*-
*-*              ================================
```
void SetBins(Int_t nbins, Double_t xmin, Double_t xmax)
```Redefine  x axis parameters*-*-*-
*-*              ===========================
```
void SetBins(Int_t nx, const Double_t* xbins)
```Redefine  x axis parameters*-*-*-
*-*              ===========================
```
void SetBinsLength(Int_t n = -1)
``` Set total number of bins including under/overflow
Reallocate bin contents array
```
void SetBuffer(Int_t buffersize, Option_t* option = "")
``` set the buffer size in units of 8 bytes (double)
```
void SetErrorOption(Option_t* option = "")
```-*Set option to compute profile errors
*-*                =====================================

The computation of the bin errors is based on the parameter option:
option:
' '  (Default) The bin errors are the standard error on the mean of the bin profiled values (Y),
i.e. the standard error of the bin contents.
Note that if TProfile::Approximate()  is called, an approximation is used when
the spread in Y is 0 and the number of bin entries  is > 0

's'            The bin errors are the standard deviations of the Y bin values
Note that if TProfile::Approximate()  is called, an approximation is used when
the spread in Y is 0 and the number of bin entries is > 0

'i'            Errors are as in default case (standard errors of the bin contents)
The only difference is for the case when the spread in Y is zero.
In this case for N > 0 the error is  1./SQRT(12.*N)

'g'            Errors are 1./SQRT(W)  for W not equal to 0 and 0 for W = 0.
W is the sum in the bin of the weights of the profile.
This option is for combining measurements y +/- dy,
and  the profile is filled with values y and weights w = 1/dy**2

See TProfile::BuildOptions for a detailed explanation of all options
```
void Streamer(TBuffer& )
``` Stream an object of class TProfile.
```
void Sumw2(Bool_t flag = kTRUE)
``` Create/delete structure to store sum of squares of weights per bin  *-*-*-*-*-*-*-*
This is needed to compute  the correct statistical quantities
of a profile filled with weights

This function is automatically called when the histogram is created
if the static function TH1::SetDefaultSumw2 has been called before.
If flag is false the structure is deleted
```

`{return -2;}`
void SetBins(const Int_t* nbins, const Double_t* range)
``` helper methods for the Merge unification in TProfileHelper
```
`{ SetBins(nbins[0], range[0], range[1]); }`
Int_t Fill(const Double_t* v)
`{ return Fill(v[0], v[1], v[2]); }`
Double_t RetrieveBinContent(Int_t bin) const
```virtual void     UpdateBinContent(Int_t bin, Double_t content);
```
`{ return (fBinEntries.fArray[bin] > 0) ? fArray[bin]/fBinEntries.fArray[bin] : 0; }`
Double_t GetBinErrorSqUnchecked(Int_t bin) const
`{ Double_t err = GetBinError(bin); return err*err; }`

`{ MayNotUse("Fill(Double_t)"); return -1;}`
void FillN(Int_t , const Double_t* , const Double_t* , Int_t )
`{ MayNotUse("FillN(Int_t, Double_t*, Double_t*, Int_t)"); }`
Double_t * GetB()
`{return &fBinEntries.fArray[0];}`

`{return (fBinSumw2.fN ? &fBinSumw2.fArray[0] : 0 ); }`
Double_t * GetW()
`{return &fArray[0];}`

`{return &fSumw2.fArray[0];}`

`{ MayNotUse("SetBins(Int_t, Double_t, Double_t, Int_t, Double_t, Double_t"); }`
void SetBins(Int_t , const Double_t* , Int_t , const Double_t* )
`{ MayNotUse("SetBins(Int_t, const Double_t*, Int_t, const Double_t*"); }`

`{ MayNotUse("SetBins(Int_t, Double_t, Double_t, Int_t, Double_t, Double_t, Int_t, Double_t, Double_t"); }`
void SetBins(Int_t , const Double_t* , Int_t , const Double_t* , Int_t , const Double_t* )
`{ MayNotUse("SetBins(Int_t, const Double_t*, Int_t, const Double_t*, Int_t, const Double_t*"); }`
Double_t GetBinContent(Int_t bin) const
Double_t GetBinContent(Int_t bin, Int_t ) const
`{return GetBinContent(bin);}`
Double_t GetBinError(Int_t bin) const
Double_t GetBinError(Int_t bin, Int_t ) const
`{return GetBinError(bin);}`

`{return &fBinSumw2;}`
const TArrayD * GetBinSumw2() const
`{return &fBinSumw2;}`
Double_t GetYmin() const
`{return fYmin;}`
Double_t GetYmax() const
`{return fYmax;}`