//
// Subset of matheamtical functions not included in the TMath
//
-
-///////////////////////////////////////////////////////////////////////////
+//
+/////////////////////////////////////////////////////////////////////////
#include "TMath.h"
#include "Riostream.h"
#include "TH1F.h"
#include "TCanvas.h"
#include "TLatex.h"
#include "TCut.h"
-
//
// includes neccessary for test functions
//
#include "TStatToolkit.h"
+
+using std::cout;
+using std::cerr;
+using std::endl;
+
ClassImp(TStatToolkit) // Class implementation to enable ROOT I/O
Double_t *data = new Double_t[nentries];
Int_t npoints=0;
for (Int_t ibin=1;ibin<nbins; ibin++){
- Float_t entriesI = his->GetBinContent(ibin);
- Float_t xcenter= his->GetBinCenter(ibin);
+ Double_t entriesI = his->GetBinContent(ibin);
+ //Double_t xcenter= his->GetBinCenter(ibin);
+ Double_t x0 = his->GetXaxis()->GetBinLowEdge(ibin);
+ Double_t w = his->GetXaxis()->GetBinWidth(ibin);
for (Int_t ic=0; ic<entriesI; ic++){
if (npoints<nentries){
- data[npoints]= xcenter;
+ data[npoints]= x0+w*Double_t((ic+0.5)/entriesI);
npoints++;
}
}
}
}
+
+void TStatToolkit::MedianFilter(TH1 * his1D, Int_t nmedian){
+ //
+ // Algorithm to filter histogram
+ // author: marian.ivanov@cern.ch
+ // Details of algorithm:
+ // http://en.wikipedia.org/w/index.php?title=Median_filter&oldid=582191524
+ // Input parameters:
+ // his1D - input histogam - to be modiefied by Medianfilter
+ // nmendian - number of bins in median filter
+ //
+ Int_t nbins = his1D->GetNbinsX();
+ TVectorD vectorH(nbins);
+ for (Int_t ibin=0; ibin<nbins; ibin++) vectorH[ibin]=his1D->GetBinContent(ibin+1);
+ for (Int_t ibin=0; ibin<nbins; ibin++) {
+ Int_t index0=ibin-nmedian;
+ Int_t index1=ibin+nmedian;
+ if (index0<0) {index1+=-index0; index0=0;}
+ if (index1>=nbins) {index0-=index1-nbins+1; index1=nbins-1;}
+ Double_t value= TMath::Median(index1-index0,&(vectorH.GetMatrixArray()[index0]));
+ his1D->SetBinContent(ibin+1, value);
+ }
+}
+
+Bool_t TStatToolkit::LTMHisto(TH1 *his1D, TVectorD ¶ms , Float_t fraction){
+ //
+ // LTM : Trimmed mean on histogram - Modified version for binned data
+ //
+ // Robust statistic to estimate properties of the distribution
+ // To handle binning error special treatment
+ // for definition of unbinned data see:
+ // http://en.wikipedia.org/w/index.php?title=Trimmed_estimator&oldid=582847999
+ //
+ // Function parameters:
+ // his1D - input histogram
+ // params - vector with parameters
+ // - 0 - area
+ // - 1 - mean
+ // - 2 - rms
+ // - 3 - error estimate of mean
+ // - 4 - error estimate of RMS
+ // - 5 - first accepted bin position
+ // - 6 - last accepted bin position
+ //
+ Int_t nbins = his1D->GetNbinsX();
+ Int_t nentries = (Int_t)his1D->GetEntries();
+ const Double_t kEpsilon=0.0000000001;
+
+ if (nentries<=0) return 0;
+ if (fraction>1) fraction=0;
+ if (fraction<0) return 0;
+ TVectorD vectorX(nbins);
+ TVectorD vectorMean(nbins);
+ TVectorD vectorRMS(nbins);
+ Double_t sumCont=0;
+ for (Int_t ibin0=1; ibin0<=nbins; ibin0++) sumCont+=his1D->GetBinContent(ibin0);
+ //
+ Double_t minRMS=his1D->GetRMS()*10000;
+ Int_t maxBin=0;
+ //
+ for (Int_t ibin0=1; ibin0<nbins; ibin0++){
+ Double_t sum0=0, sum1=0, sum2=0;
+ Int_t ibin1=ibin0;
+ for ( ibin1=ibin0; ibin1<nbins; ibin1++){
+ Double_t cont=his1D->GetBinContent(ibin1);
+ Double_t x= his1D->GetBinCenter(ibin1);
+ sum0+=cont;
+ sum1+=cont*x;
+ sum2+=cont*x*x;
+ if ( (ibin0!=ibin1) && sum0>=fraction*sumCont) break;
+ }
+ vectorX[ibin0]=his1D->GetBinCenter(ibin0);
+ if (sum0<fraction*sumCont) continue;
+ //
+ // substract fractions of bin0 and bin1 to keep sum0=fration*sumCont
+ //
+ Double_t diff = sum0-fraction*sumCont;
+ Double_t mean = sum1/sum0;
+ //
+ Double_t x0=his1D->GetBinCenter(ibin0);
+ Double_t x1=his1D->GetBinCenter(ibin1);
+ Double_t y0=his1D->GetBinContent(ibin0);
+ Double_t y1=his1D->GetBinContent(ibin1);
+ //
+ Double_t d = y0+y1-diff; //enties to keep
+ Double_t w0=0,w1=0;
+ if (y0<=kEpsilon&&y1>kEpsilon){
+ w1=d/y1;
+ }
+ if (y1<=kEpsilon&&y0>kEpsilon){
+ w0=d/y0;
+ }
+ if (y0>kEpsilon && y1>kEpsilon && x1>x0 ){
+ w0 = (d*(x1-mean))/((x1-x0)*y0);
+ w1 = (d-y0*w0)/y1;
+ //
+ if (w0>1) {w1+=(w0-1)*y0/y1; w0=1;}
+ if (w1>1) {w0+=(w1-1)*y1/y0; w1=1;}
+ }
+ if ( (x1>x0) &&TMath::Abs(y0*w0+y1*w1-d)>kEpsilon*sum0){
+ printf(" TStatToolkit::LTMHisto error\n");
+ }
+ sum0-=y0+y1;
+ sum1-=y0*x0;
+ sum1-=y1*x1;
+ sum2-=y0*x0*x0;
+ sum2-=y1*x1*x1;
+ //
+ Double_t xx0=his1D->GetXaxis()->GetBinUpEdge(ibin0)-0.5*w0*his1D->GetBinWidth(ibin0);
+ Double_t xx1=his1D->GetXaxis()->GetBinLowEdge(ibin1)+0.5*w1*his1D->GetBinWidth(ibin1);
+ sum0+=y0*w0+y1*w1;
+ sum1+=y0*w0*xx0;
+ sum1+=y1*w1*xx1;
+ sum2+=y0*w0*xx0*xx0;
+ sum2+=y1*w1*xx1*xx1;
+
+ //
+ // choose the bin with smallest rms
+ //
+ if (sum0>0){
+ vectorMean[ibin0]=sum1/sum0;
+ vectorRMS[ibin0]=TMath::Sqrt(TMath::Abs(sum2/sum0-vectorMean[ibin0]*vectorMean[ibin0]));
+ if (vectorRMS[ibin0]<minRMS){
+ minRMS=vectorRMS[ibin0];
+ params[0]=sum0;
+ params[1]=vectorMean[ibin0];
+ params[2]=vectorRMS[ibin0];
+ params[3]=vectorRMS[ibin0]/TMath::Sqrt(sumCont*fraction);
+ params[4]=0; // what is the formula for error of RMS???
+ params[5]=ibin0;
+ params[6]=ibin1;
+ params[7]=his1D->GetBinCenter(ibin0);
+ params[8]=his1D->GetBinCenter(ibin1);
+ maxBin=ibin0;
+ }
+ }else{
+ break;
+ }
+ }
+ return kTRUE;
+}
+
+
Double_t TStatToolkit::FitGaus(TH1* his, TVectorD *param, TMatrixD */*matrix*/, Float_t xmin, Float_t xmax, Bool_t verbose){
//
// Fit histogram with gaussian function
if (type==0) stat = projection->GetMean();
if (type==1) stat = projection->GetRMS();
if (type==2 || type==3){
- TVectorD vec(3);
+ TVectorD vec(10);
TStatToolkit::LTM((TH1F*)projection,&vec,0.7);
if (type==2) stat= vec[1];
if (type==3) stat= vec[0];
TVectorD vecYErr(nbinx);
//
TF1 f1("f1","gaus");
- TVectorD vecLTM(3);
+ TVectorD vecLTM(10);
- for (Int_t ix=0; ix<nbinx;ix++){
- Float_t xcenter = xaxis->GetBinCenter(ix);
+ for (Int_t jx=1; jx<=nbinx;jx++){
+ Int_t ix=jx-1;
+ Float_t xcenter = xaxis->GetBinCenter(jx);
snprintf(name,1000,"%s_%d",his->GetName(), ix);
- TH1 *projection = his->ProjectionY(name,TMath::Max(ix-deltaBin,1),TMath::Min(ix+deltaBin,nbinx));
+ TH1 *projection = his->ProjectionY(name,TMath::Max(jx-deltaBin,1),TMath::Min(jx+deltaBin,nbinx));
Double_t stat= 0;
Double_t err =0;
- TStatToolkit::LTM((TH1F*)projection,&vecLTM,fraction);
+ TStatToolkit::LTMHisto((TH1F*)projection,vecLTM,fraction);
//
if (returnType==0) {
stat = projection->GetMean();
err = projection->GetRMSError();
}
if (returnType==2 || returnType==3){
- if (returnType==2) stat= vecLTM[1];
- if (returnType==3) stat= vecLTM[2];
+ if (returnType==2) {stat= vecLTM[1]; err =projection->GetRMSError();}
+ if (returnType==3) {stat= vecLTM[2]; err =projection->GetRMSError();}
}
if (returnType==4|| returnType==5){
- projection->Fit(&f1,"QNR","QNR");
+ projection->Fit(&f1,"QN","QN", vecLTM[7], vecLTM[8]);
if (returnType==4) {
stat= f1.GetParameter(1);
err=f1.GetParError(1);
graph->SetMarkerStyle(mstyle);
graph->SetMarkerColor(mcolor);
graph->SetLineColor(mcolor);
+ graph->SetTitle(expr);
+ TString chstring(expr);
+ TObjArray *charray = chstring.Tokenize(":");
+ graph->GetXaxis()->SetTitle(charray->At(1)->GetName());
+ graph->GetYaxis()->SetTitle(charray->At(0)->GetName());
+ delete charray;
if (msize>0) graph->SetMarkerSize(msize);
for(Int_t i=0;i<graph->GetN();i++) graph->GetX()[i]+=offset;
return graph;
delete [] runNumber;
delete [] index;
delete [] newBins;
- //
+ //
+ graphNew->SetTitle(expr);
+ TString chstring(expr);
+ TObjArray *charray = chstring.Tokenize(":");
+ graphNew->GetXaxis()->SetTitle(charray->At(1)->GetName());
+ graphNew->GetYaxis()->SetTitle(charray->At(0)->GetName());
+ delete charray;
return graphNew;
}
*/
//
Int_t entries = tree->Draw(expr,cut,"goff");
- if (entries<=1){
+ if (entries<1){
printf("Expression or cut not valid:\t%s\t%s\n", expr, cut);
return 0;
}
}
-void TStatToolkit::DrawHistogram(TTree * tree, const char* drawCommand, const char* cuts, const char* histoname, const char* histotitle, Int_t nsigma, Float_t fraction )
+TH1* TStatToolkit::DrawHistogram(TTree * tree, const char* drawCommand, const char* cuts, const char* histoname, const char* histotitle, Int_t nsigma, Float_t fraction )
{
//
// Draw histogram from TTree with robust range
if(!tree) {
cerr<<" Tree pointer is NULL!"<<endl;
- return;
+ return 0;
}
// get entries
Int_t entries = tree->Draw(drawStr.Data(), cutStr.Data(), "goff");
if (entries == -1) {
cerr<<"TTree draw returns -1"<<endl;
- return;
+ return 0;
}
// get dimension
if(tree->GetV3()) dim = 3;
if(dim > 2){
cerr<<"TTree has more than 2 dimensions (not yet supported)"<<endl;
- return;
+ return 0;
}
// draw robust
hOut->GetYaxis()->SetTitle(tree->GetHistogram()->GetYaxis()->GetTitle());
hOut->Draw("colz");
}
-
+ return hOut;
}
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff