ClassImp(AliITSstatistics2)
//
-AliITSstatistics2::AliITSstatistics2() : TObject(){
-//
-// default constructor
-//
- fX = 0;
- fY = 0;
- fYx = 0;
- fW = 0;
- fN = 0;
- fOrder = 0;
+AliITSstatistics2::AliITSstatistics2() :
+TObject(), // Base Class
+fN(-1), // number of enetries -1 => Uninitilized
+fOrder(0), // maximum moment of distributions (^n)
+fX(0), //[fOrder] array of sums of x^n
+fYx(0), //[fOrder] array of sums of (xy)^n
+fY(0), //[fOrder] array of sums of y^n
+fW(0){ //[fOrder] array of sums of w^n (weights)
+ // default constructor
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // A default constructed AliITSstatistics class
+
return;
}
+//______________________________________________________________________
+AliITSstatistics2::AliITSstatistics2(Int_t order) :
+TObject(), // Base Class
+fN(0), // number of enetries -1 => Uninitilized
+fOrder(order), // maximum moment of distributions (^n)
+fX(new Double_t[order]), //[fOrder] array of sums of x^n
+fYx(new Double_t[order]), //[fOrder] array of sums of (xy)^n
+fY(new Double_t[order]), //[fOrder] array of sums of y^n
+fW(new Double_t[order]){ //[fOrder] array of sums of w^n (weights)
+ // constructor to maximum moment/order order
+ // Inputs:
+ // Int_t order The maximum moment of distributions {for example x^n}
+ Int_t i;
-
-AliITSstatistics2::AliITSstatistics2(Int_t order) : TObject(){
-//
-// constructor to maximum moment/order order
-//
- fOrder = order;
- fX = new Double_t[order];
- fY = new Double_t[order];
- fYx = new Double_t[order];
- fW = new Double_t[order];
- for(Int_t i=0;i<order;i++) {fX[i] = 0.0;fY[i] = 0.0;
- fYx[i] = 0.0; fW[i] = 0.0;}
+ for(i=0;i<order;i++) {
+ fX[i] = 0.0;
+ fY[i] = 0.0;
+ fYx[i] = 0.0;
+ fW[i] = 0.0;
+ } // end for i
fN = 0;
return;
}
-
+//______________________________________________________________________
AliITSstatistics2::~AliITSstatistics2(){
-//
-// destructor
-//
+ // destructor
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // none.
+
if(fX!=0) delete[] fX;
if(fY!=0) delete[] fY;
if(fYx!=0) delete[] fYx;
fN = 0;
fOrder = 0;
}
-
//_______________________________________________________________
AliITSstatistics2& AliITSstatistics2::operator=(AliITSstatistics2 &source){
-// operator =
-
- if(this==&source) return *this;
- if(source.fOrder!=0){
- this->fOrder = source.fOrder;
- this->fN = source.fN;
- this->fX = new Double_t[this->fOrder];
- this->fW = new Double_t[this->fOrder];
- for(Int_t i=0;i<source.fOrder;i++){
- this->fX[i] = source.fX[i];
- this->fW[i] = source.fW[i];
- } // end for i
- }else{
- this->fX = 0;
- this->fW = 0;
- this->fN = 0;
- this->fOrder = 0;
- }// end if source.fOrder!=0
- return *this;
+ // operator =
+ // Inputs:
+ // AliITSstaticstics2 &source The source of this copy.
+ // Outputs:
+ // none.
+ // Return:
+ // A copy of the source class
+
+ if(this==&source) return *this;
+ TObject::operator=(source);
+ Reset(source.GetOrder());
+ fN = source.GetN();
+ fOrder=source.GetOrder();
+ for(Int_t i=0;i<source.fOrder;i++){
+ this->fX[i] = source.fX[i];
+ this->fYx[i] = source.fYx[i];
+ this->fY[i] = source.fY[i];
+ this->fW[i] = source.fW[i];
+ } // end for i
+ return *this;
}
//_______________________________________________________________
-AliITSstatistics2::AliITSstatistics2(AliITSstatistics2 &source):
- TObject(source){
-// Copy constructor
-
- if(this==&source) return;
- if(source.fOrder!=0){
- this->fOrder = source.fOrder;
- this->fN = source.fN;
- this->fX = new Double_t[this->fOrder];
- this->fW = new Double_t[this->fOrder];
- for(Int_t i=0;i<source.fOrder;i++){
- this->fX[i] = source.fX[i];
- this->fW[i] = source.fW[i];
- } // end for i
- }else{
- this->fX = 0;
- this->fW = 0;
- this->fN = 0;
- this->fOrder = 0;
- }// end if source.fOrder!=0
+AliITSstatistics2::AliITSstatistics2(AliITSstatistics2 &source):
+TObject(source), // Base Class
+fN(source.GetN()), // number of enetries -1 => Uninitilized
+fOrder(source.GetOrder()),// maximum moment of distributions (^n)
+fX(new Double_t[source.GetOrder()]),//[fOrder] array of sums of x^n
+fYx(new Double_t[source.GetOrder()]),//[fOrder] array of sums of (xy)^n
+fY(new Double_t[source.GetOrder()]),//[fOrder] array of sums of y^n
+fW(new Double_t[source.GetOrder()]){//[fOrder] array of sums of w^n (weights)
+ // Copy constructor
+ // Inputs:
+ // AliITSstatistics2 & source the source of this copy
+ // Outputs:
+ // none.
+ // Return:
+ // A copy of the source.
+
+ if(this==&source) return;
+ for(Int_t i=0;i<source.fOrder;i++){
+ this->fX[i] = source.fX[i];
+ this->fYx[i] = source.fYx[i];
+ this->fY[i] = source.fY[i];
+ this->fW[i] = source.fW[i];
+ } // end for i
}
+//______________________________________________________________________
+void AliITSstatistics2::Reset(Int_t order){
+ // Reset/zero all statistics variables statistics
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // none.
+ Int_t i;
-void AliITSstatistics2::Reset(){
-//
-// Reset/zero statistics
-//
- for(Int_t i=0;i<fOrder;i++) {fX[i] = 0.0;fY[i] = 0.0;
- fYx[i] = 0.0; fW[i] = 0.0;}
+ for(i=0;i<fOrder;i++) {
+ fX[i] = 0.0;
+ fY[i] = 0.0;
+ fYx[i] = 0.0;
+ fW[i] = 0.0;
+ } // end for i
fN = 0;
+ if(order<0) return; // just zero
+ if(fX!=0) delete[] fX;
+ if(fY!=0) delete[] fY;
+ if(fYx!=0) delete[] fYx;
+ if(fW!=0) delete[] fW;
+ fX = 0;
+ fY = 0;
+ fYx = 0;
+ fW = 0;
+ fN = 0;
+ fOrder = 0;
+ if(order==0) return;
+ fOrder = order;
+ fX = new Double_t[fOrder];
+ fY = new Double_t[fOrder];
+ fYx = new Double_t[fOrder];
+ fW = new Double_t[fOrder];
return;
}
-
+//______________________________________________________________________
void AliITSstatistics2::AddValue(Double_t y,Double_t x,Double_t w=1.0){
-//
-// add next x,y pair to statistics
-//
+ // add next x,y pair to statistics
+ // Inputs:
+ // Double_t y y value of pair
+ // Double_t x x value of pair
+ // Double_t w weight of pair
+ // Outputs:
+ // none.
+ // Return:
+ // none.
Double_t xs=1.0,ys=1.0,yxs=1.0,ws=1.0;
Int_t i;
-
const Double_t kBig=1.0e+38;
if(y>kBig || x>kBig || w>kBig) return;
-
-
fN++;
for(i=0;i<fOrder;i++){
xs *= x;
fW[i] += ws;
} // end for i
}
-
-Double_t AliITSstatistics2::GetXNth(Int_t order){
-//
-// This give the unbiased estimator for the RMS.
-//
-
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetXNth(Int_t order)const{
+ // This give the unbiased estimator for the RMS.
+ // Inputs:
+ // Int_t order the order of x^n value to be returned
+ // Output:
+ // none.
+ // Return:
+ // The value sum{x^n}.
Double_t s;
if(fW[0]!=0.0&&order<=fOrder) s = fX[order-1]/fW[0];
else {
s = 0.0;
- printf("AliITSstatistics2: error in GetNth: fOrder=%d fN=%d fW[0]=%f\n",
- fOrder,fN,fW[0]);
+ Error("GetXNth","error fOrder=%d fN=%d fW[0]=%f\n",
+ fOrder,fN,fW[0]);
} // end else
return s;
}
-Double_t AliITSstatistics2::GetYNth(Int_t order){
-//
-// This give the unbiased estimator for the RMS.
-//
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetYNth(Int_t order)const{
+ // This give the unbiased estimator for the RMS.
+ // Inputs:
+ // Int_t order the order of y^n value to be returned
+ // Outputs:
+ // none.
+ // Return:
+ // The value sum{y^n}
Double_t s;
if(fW[0]!=0.0&&order<=fOrder) s = fY[order-1]/fW[0];
else {
s = 0.0;
- printf("AliITSstatistics2: error in GetNth: fOrder=%d fN=%d fW[0]=%f\n",
- fOrder,fN,fW[0]);
+ Error("GetYNth","fOrder=%d fN=%d fW[0]=%f\n",
+ fOrder,fN,fW[0]);
} // end else
return s;
}
-Double_t AliITSstatistics2::GetYXNth(Int_t order){
-// This give the unbiased estimator for the RMS.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetYXNth(Int_t order)const{
+ // This give the unbiased estimator for the RMS.
+ // Inputs:
+ // Int_t order the order of (xy)^n value to be returned
+ // Outputs:
+ // none.
+ // Return:
+ // The value sum{(xy)^n}
Double_t s;
if(fW[0]!=0.0&&order<=fOrder) s = fYx[order-1]/fW[0];
else {
s = 0.0;
- printf("AliITSstatistics2: error in GetNth: fOrder=%d fN=%d fW[0]=%f\n",
+ Error("GetYXNth","fOrder=%d fN=%d fW[0]=%f\n",
fOrder,fN,fW[0]);
} // end else
return s;
}
-Double_t AliITSstatistics2::GetRMSX(){
-// This give the unbiased estimator for the RMS.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetRMSX()const{
+ // This give the unbiased estimator for the RMS.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The rms value
Double_t x,x2,w,ww,s;
x = GetMeanX(); // first order
s = (x2-x*x)*w*w/(w*w-ww);
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetRMSY(){
-// This give the unbiased estimator for the RMS.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetRMSY()const{
+ // This give the unbiased estimator for the RMS.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The rms value
Double_t x,x2,w,ww,s;
x = GetMeanY(); // first order
s = (x2-x*x)*w*w/(w*w-ww);
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetRMSYX(){
-// This give the unbiased estimator for the RMS.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetRMSYX()const{
+ // This give the unbiased estimator for the RMS.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The rms value
Double_t x,x2,w,ww,s;
x = GetMeanYX(); // first order
s = (x2-x*x)*w*w/(w*w-ww);
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetErrorMeanY(){
-//This is the error in the mean or the square root of the variance of the mean.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetErrorMeanY()const{
+ //This is the error in the mean or the square root of the
+ // variance of the mean.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The error on the mean
Double_t rms,w,ww,s;
rms = GetRMSY();
s = rms*rms*ww/(w*w);
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetErrorMeanX(){
-//This is the error in the mean or the square root of the variance of the mean.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetErrorMeanX()const{
+ //This is the error in the mean or the square root of the
+ // variance of the mean.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The error on the mean
Double_t rms,w,ww,s;
rms = GetRMSX();
s = rms*rms*ww/(w*w);
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetErrorMeanYX(){
-//This is the error in the mean or the square root of the variance of the mean.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetErrorMeanYX()const{
+ //This is the error in the mean or the square root of the
+ // variance of the mean.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The error on the mean
Double_t rms,w,ww,s;
rms = GetRMSYX();
s = rms*rms*ww/(w*w);
return TMath::Sqrt(s);
}
-
-
-Double_t AliITSstatistics2::GetErrorRMSY(){
-//This is the error in the mean or the square root of the variance of the mean.
-// at this moment this routine is only defined for weights=1.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetErrorRMSY()const{
+ // This is the error in the mean or the square root of the variance
+ // of the mean. at this moment this routine is only defined for
+ // weights=1.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The error on the rms
Double_t x,x2,x3,x4,w,ww,m2,m4,n,s;
if(fW[0]!=(Double_t)fN||GetN()<4) return (-1.);
n = (Double_t) GetN();
x3 = GetYNth(3);
x4 = GetYNth(4);
-// This equation assumes that all of the weights are equal to 1.
+ // This equation assumes that all of the weights are equal to 1.
m4 = (n/(n-1.))*(x4-3.*x*x3+6.*x*x*x2-2.*x*x*x*x);
s = (m4-(n-3.)*m2*m2/(n-1.))/n;
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetErrorRMSX(){
-//This is the error in the mean or the square root of the variance of the mean.
-// at this moment this routine is only defined for weights=1.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetErrorRMSX()const{
+ // This is the error in the mean or the square root of the variance
+ // of the mean. at this moment this routine is only defined for
+ // weights=1.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The error on the rms
Double_t x,x2,x3,x4,w,ww,m2,m4,n,s;
if(fW[0]!=(Double_t)fN||GetN()<4) return (-1.);
s = (m4-(n-3.)*m2*m2/(n-1.))/n;
return TMath::Sqrt(s);
}
-Double_t AliITSstatistics2::GetErrorRMSYX(){
-//This is the error in the mean or the square root of the variance of the mean.
-// at this moment this routine is only defined for weights=1.
+//______________________________________________________________________
+Double_t AliITSstatistics2::GetErrorRMSYX()const{
+ // This is the error in the mean or the square root of the variance
+ // of the mean. at this moment this routine is only defined for
+ // weights=1.
+ // Inputs:
+ // none.
+ // Outputs:
+ // none.
+ // Return:
+ // The error on the rms
Double_t x,x2,x3,x4,w,ww,m2,m4,n,s;
if(fW[0]!=(Double_t)fN||GetN()<4) return (-1.);
n = (Double_t) GetN();
x3 = GetYXNth(3);
x4 = GetYXNth(4);
-// This equation assumes that all of the weights are equal to 1.
+ // This equation assumes that all of the weights are equal to 1.
m4 = (n/(n-1.))*(x4-3.*x*x3+6.*x*x*x2-2.*x*x*x*x);
s = (m4-(n-3.)*m2*m2/(n-1.))/n;
return TMath::Sqrt(s);
}
//_______________________________________________________________________
-Double_t AliITSstatistics2::FitToLine(Double_t &a,Double_t &b){
-// fit to y = a+bx returns Chi squared or -1.0 if an error
+Double_t AliITSstatistics2::FitToLine(Double_t &a,Double_t &b)const{
+ // fit to y = ax+b returns Chi squared or -1.0 if an error
+ // Inputs:
+ // none.
+ // Outputs:
+ // Double_t a The slope parameter
+ // Double_t b The intercept paramter
+ // Return:
+ // The Chi^2 of the fit
Double_t c,d,e,f,g,h;
a = b = 0.0;
- if(fOrder<2 || fN<3) return -1.0;
+ if(fOrder<2 || fN<3){
+ Error("FitToLine","Order=%d<2 or N=%d<3",fOrder,fN);
+ return -1.0;
+ } // end if
c = GetWN(1);
d = GetYN(1);
e = GetXN(1);
f = GetYXN(1);
g = GetXN(2);
h = c*g-e*e;
- a = d*g-f*e;
- b = c*f-d*e;
+ b = d*g-f*e;
+ a = c*f-d*e;
if(h!=0.0){
a = a/h;
b = b/h;
}else{
- printf("AliITSstatistics2: Error in FitToLine vertical line\n");
+ Error("FitToLine","vertical line: fOrder=%d fN=%d "
+ "GetWN(1)=%g X GetXN(2)=%g - GetXN(1)=%g^2 = 0",fOrder,fN,c,g,e);
return -1.0;
} // end if h
- h = GetYN(2)+a*a*c+b*b*g-2.0*a*d-2.0*b*f+2.0*a*b*e;
- h /= (Double_t)fN - 2.0;
- return h;
+ return GetChiSquared(a,b);
+}
+//_______________________________________________________________________
+Double_t AliITSstatistics2::GetChiSquared(Double_t a,Double_t b)const{
+ // returns Chi^2 value of data to line y=ax+b with given a,b
+ // Inputs:
+ // Double_t a The slope parameter
+ // Double_t b The intercept paramter
+ // Outputs::
+ // none.
+ // Return:
+ // The Chi^2 of the fit
+ Double_t c2;
+
+ c2 = GetYN(2)+b*b*GetWN(1)+
+ a*a*GetXN(2)-2.0*b*GetYN(1)-2.0*a*GetYXN(1)+2.0*b*a*GetXN(1);
+ c2 /= (Double_t)fN - 2.0;
+ return c2;
+}
+//______________________________________________________________________
+void AliITSstatistics2::PrintAscii(ostream *os)const{
+ // Print out class data values in Ascii Form to output stream
+ // Inputs:
+ // ostream *os Output stream where Ascii data is to be writen
+ // Outputs:
+ // none.
+ // Return:
+ // none.
+ Int_t i;
+#if defined __GNUC__
+#if __GNUC__ > 2
+ ios::fmtflags fmt;
+#else
+ Int_t fmt;
+#endif
+#else
+#if defined __ICC || defined __ECC || defined __xlC__
+ ios::fmtflags fmt;
+#else
+ Int_t fmt;
+#endif
+#endif
+
+ *os << fN <<" "<< fOrder;
+ fmt = os->setf(ios::scientific); // set scientific floating point output
+ for(i=0;i<fOrder;i++) *os <<" "<< fX[i];
+ for(i=0;i<fOrder;i++) *os <<" "<< fYx[i];
+ for(i=0;i<fOrder;i++) *os <<" "<< fY[i];
+ for(i=0;i<fOrder;i++) *os <<" "<< fW[i];
+ os->flags(fmt); // reset back to old Formating.
+ return;
+}
+//______________________________________________________________________
+void AliITSstatistics2::ReadAscii(istream *is){
+ // Read in class data values in Ascii Form to output stream
+ // Inputs:
+ // istream *is Input stream where Ascii data is to be read in from
+ // Outputs:
+ // none.
+ // Return:
+ // none.
+ Int_t i;
+
+ *is >> i >> fOrder;
+ Reset(fOrder);
+ fN = i;
+ for(i=0;i<fOrder;i++) *is >> fX[i];
+ for(i=0;i<fOrder;i++) *is >> fYx[i];
+ for(i=0;i<fOrder;i++) *is >> fY[i];
+ for(i=0;i<fOrder;i++) *is >> fW[i];
+}
+//______________________________________________________________________
+ostream &operator<<(ostream &os,const AliITSstatistics2 &s){
+ // Standard output streaming function
+ // Inputs:
+ // ostream &os output steam
+ // AliITSstatistics2 &s class to be streamed.
+ // Output:
+ // none.
+ // Return:
+ // ostream &os The stream pointer
+
+ s.PrintAscii(&os);
+ return os;
+}
+//______________________________________________________________________
+istream &operator>>(istream &is,AliITSstatistics2 &s){
+ // Standard inputput streaming function
+ // Inputs:
+ // istream &is input steam
+ // AliITSstatistics2 &s class to be streamed.
+ // Output:
+ // none.
+ // Return:
+ // ostream &os The stream pointer
+
+ s.ReadAscii(&is);
+ return is;
}
class AliITSstatistics2 : public TObject {
//
-
public:
- AliITSstatistics2();
- AliITSstatistics2(Int_t order);
- AliITSstatistics2(AliITSstatistics2 &source); // copy constructor
- AliITSstatistics2& operator=(AliITSstatistics2 &source); // operator=
- virtual ~AliITSstatistics2();
- void Reset();
+ AliITSstatistics2(); // default constructor
+ AliITSstatistics2(Int_t order); // Standard constructor
+ AliITSstatistics2(AliITSstatistics2 &source); // copy constructor
+ AliITSstatistics2& operator=(AliITSstatistics2 &source); // operator=
+ virtual ~AliITSstatistics2();// destructor
+ void Reset(Int_t order=-1); // Resets statictisc order=-1 do not change order.
void AddValue(Double_t y,Double_t x,Double_t w);
- Double_t GetXNth (Int_t order);
- Double_t GetYNth (Int_t order);
- Double_t GetYXNth(Int_t order);
- Double_t GetMeanY() {
- // return mean y
- return GetYNth(1);
- };
- Double_t GetMeanX() {
- // return mean x
- return GetXNth(1);
- };
- Double_t GetMeanYX() {
- // return mean Y*X
- return GetYXNth(1);
- };
- Int_t GetN(){
- // retrun the number of entries
- return fN;
- };
- Int_t GetOrder(){
- // return the maximum moment order
- return fOrder;
- };
- Double_t GetXN (Int_t order){
- // returns x^n
- return fX[order-1];
- };
- Double_t GetYN (Int_t order){
- // returns y^n
- return fY[order-1];
- };
- Double_t GetYXN(Int_t order){
- // returns (yx)^n
- return fYx[order-1];
- };
- Double_t GetWN (Int_t order){
- // returns w^n (weight)
- return fW[order-1];
- };
- Double_t GetRMSY();
- Double_t GetRMSX();
- Double_t GetRMSYX();
- Double_t GetErrorMeanY();
- Double_t GetErrorMeanX();
- Double_t GetErrorMeanYX();
- Double_t GetErrorRMSY();
- Double_t GetErrorRMSX();
- Double_t GetErrorRMSYX();
- Double_t FitToLine(Double_t &a,Double_t &b);
+ Double_t GetXNth (Int_t order)const;
+ Double_t GetYNth (Int_t order)const;
+ Double_t GetYXNth(Int_t order)const;
+ Double_t GetMeanY()const{// return mean y
+ return GetYNth(1);};
+ Double_t GetMeanX()const{// return mean x
+ return GetXNth(1);};
+ Double_t GetMeanYX()const{// return mean Y*X
+ return GetYXNth(1);};
+ Int_t GetN()const{// retrun the number of entries
+ return fN;};
+ Int_t GetOrder()const{// return the maximum moment order
+ return fOrder;};
+ Double_t GetXN (Int_t order)const{// returns x^n
+ return fX[order-1];};
+ Double_t GetYN (Int_t order)const{// returns y^n
+ return fY[order-1];};
+ Double_t GetYXN(Int_t order)const{// returns (yx)^n
+ return fYx[order-1];};
+ Double_t GetWN (Int_t order)const{// returns w^n (weight)
+ return fW[order-1];};
+ Double_t GetRMSY()const; // Returns Y rms value
+ Double_t GetRMSX()const; // Returns X rms value
+ Double_t GetRMSYX()const;// Return XY rms value
+ Double_t GetErrorMeanY()const; // returns error on y mean
+ Double_t GetErrorMeanX()const; // returns error on x mean
+ Double_t GetErrorMeanYX()const;// returns error on xy mean
+ Double_t GetErrorRMSY()const; // returns error on y rms
+ Double_t GetErrorRMSX()const; // returns error on x rms
+ Double_t GetErrorRMSYX()const; // returns error on xy rms
+ // returns Chi^2 value of data to line y=ax+b with given a,b
+ Double_t GetChiSquared(Double_t a,Double_t b)const;
+ // Fits data to a line of the form y=ax+b and returns values
+ // for a and b.
+ Double_t FitToLine(Double_t &a,Double_t &b)const;
+ // Print class in ascii form to stream
+ virtual void PrintAscii(ostream *os)const;
+ // Read in class in ascii form from stream
+ virtual void ReadAscii(istream *is);
private:
Int_t fN; // number of enetries
Double_t *fY; //[fOrder] array of sums of y^n
Double_t *fW; //[fOrder] array of sums of w^n (weights)
-
ClassDef(AliITSstatistics2,1) //compute usueful statistics in 2D
};
+// Input and output function for standard C++ input/output.
+ostream &operator<<(ostream &os,const AliITSstatistics2 &s);
+istream &operator>>(istream &is,AliITSstatistics2 &s);
#endif
git://git.uio.no / u / mrichter / AliRoot.git / commitdiff