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SOLARIS_Analysis/armory/AutoFit.C

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add Cleopatra from digios
2023-04-03 16:03:48 -04:00
/***************************************************
* This is a root macro for Auto fitting
*
* Created by Tsz Leung (Ryan) TANG, around 2019.
* updated 01-04-2022
*
* contact goluckyryan@gmail.com
***************************************************/
#ifndef AutoFit_C
#define AutoFit_C
#include <TF1.h>
#include <TGraph.h>
#include <TColor.h>
#include <TSpectrum.h>
#include <TMath.h>
#include <TRandom.h>
#include <TMarker.h>
#include <vector>
//Global fit paramaters
vector<double> BestFitMean;
vector<double> BestFitCount;
vector<double> BestFitSigma;
TString recentFitMethod;
void ShowFitMethod(){
printf("----------------------- Method of Fitting ---------------\n");
printf("---------------------------------------------------------\n");
printf(" fitAuto() - estimate BG, find peak, and fit n-Gauss \n");
printf(" fitGaussPol() - fit 1 Gauss + pol-n BG\n");
printf(" fit2GaussP1() - fit 2 Gauss + pol-1 BG \n");
printf(" fitGF3Pol() - fit GF3 + pol-n BG \n");
//printf(" fitNGF3() - fit n-GF3, estimated BG \n");
printf(" fitNGauss() - fit n-Gauss, estimated BG, need input\n");
printf(" fitNGaussSub() - fit estimated BG with Pol, subtract, fit n-Guass\n");
printf(" fitNGaussPol() - fit n-Gauss + pol-n BG \n");
printf(" fitNGaussPolSub() - subtract Pol-n BG, fit n-Gauss \n");
printf("\n");
printf("------- Mouse click Fit : \n");
printf(" clickFitNGaussPol() - fit n-Gauss + pol-n BG \n");
printf(" clickFitNGaussPolSub() - Fit Pol-n BG, subtract, fit n-Gauss\n");
printf("\n");
printf("------- Utility : \n");
printf(" SaveFitPara() - Save the inital/Best fit parameters.\n");
printf(" ShowFitMerhod() - Show this menual.\n");
printf("---------------------------------------------------------\n");
}
void AutoFit(){
ShowFitMethod();
}
std::vector<std::string> SplitStrAF(std::string tempLine, std::string splitter, int shift = 0){
std::vector<std::string> output;
size_t pos;
do{
pos = tempLine.find(splitter); // fine splitter
if( pos == 0 ){ //check if it is splitter again
tempLine = tempLine.substr(pos+1);
continue;
}
std::string secStr;
if( pos == std::string::npos ){
secStr = tempLine;
}else{
secStr = tempLine.substr(0, pos+shift);
tempLine = tempLine.substr(pos+shift);
}
//check if secStr is begin with space
if( secStr.substr(0, 1) == " "){
secStr = secStr.substr(1);
}
output.push_back(secStr);
//printf(" |%s---\n", secStr.c_str());
}while(pos != std::string::npos );
return output;
}
TColor RGBWheel(double ang){
ang = ang * TMath::DegToRad();
double r = max(0., (1+2*cos(ang))/3.);
double g = max(0., (1 - cos(ang) + sqrt(3)* sin(ang))/3.);
double b = max(0., (1 - cos(ang) - sqrt(3)* sin(ang))/3.);
TColor col(r,g,b);
return col;
}
int nPeaks = 16;
Double_t nGauss(Double_t *x, Double_t *par) {
Double_t result = 0;
for (Int_t p=0;p<nPeaks;p++) {
Double_t norm = par[3*p+0];
Double_t mean = par[3*p+1];
Double_t sigma = par[3*p+2];
result += norm * TMath::Gaus(x[0],mean,sigma, 1); // normalized Gaussian
}
return result;
}
int nPols = 1;
Double_t nPolFunc(Double_t *x, Double_t *par) {
Double_t result = 0;
for( int p = 0; p < nPols+1; p++){
result += par[p]*TMath::Power(x[0], p);
}
return result;
}
Double_t nGaussPol(Double_t *x, Double_t *par) {
Double_t result = 0;
for (Int_t p=0;p<nPeaks;p++) {
Double_t norm = par[3*p+0];
Double_t mean = par[3*p+1];
Double_t sigma = par[3*p+2];
result += norm * TMath::Gaus(x[0],mean,sigma, 1); // normalized Gaussian
}
for( int p = 0; p < nPols+1; p++){
result += par[3*nPeaks + p]*TMath::Power(x[0], p);
}
return result;
}
Double_t nGF3(Double_t *x, Double_t *par){
/** this is the fitting function for gamma peak from gf3, RadWare **/
Double_t result = 0;
for( Int_t p = 0; p < nPeaks; p++){
Double_t norm = par[6*p+0];
Double_t mean = par[6*p+1];
Double_t sigma = par[6*p+2];
Double_t ratio = par[6*p+3];
Double_t beta = par[6*p+4]; // skewness
Double_t step = par[6*p+5];
result += norm * (1-ratio)* TMath::Gaus(x[0], mean, sigma, 1) ;
result += norm * ratio * exp( sigma * sigma/2/beta/beta) / (2* beta )* exp((x[0]-mean)/beta) * TMath::Erfc( (x[0]-mean)/(sigma * sqrt(2)) + sigma/beta/sqrt(2)) ;
result += norm * step * TMath::Erfc( (x[0]-mean)/(sigma * sqrt(2)) );
}
return result;
}
Double_t nGF3Pol(Double_t *x, Double_t *par){
/** this is the fitting function for gamma peak from gf3, RadWare **/
Double_t result = 0;
for( Int_t p = 0; p < nPeaks; p++){
Double_t norm = par[6*p+0];
Double_t mean = par[6*p+1];
Double_t sigma = par[6*p+2];
Double_t ratio = par[6*p+3];
Double_t beta = par[6*p+4]; // skewness
Double_t step = par[6*p+5];
result += norm * (1.0-ratio)* TMath::Gaus(x[0], mean, sigma, 1) ;
result += norm * ratio * exp( sigma * sigma/2/beta/beta) / (2* beta )* exp((x[0]-mean)/beta) * TMath::Erfc( (x[0]-mean)/(sigma * sqrt(2)) + sigma/beta/sqrt(2)) ;
result += norm * step * TMath::Erfc( (x[0]-mean)/(sigma * sqrt(2)) );
}
for( int p = 0; p < nPols + 1; p++){
result += par[6*nPeaks + p]*TMath::Power(x[0], p);
}
return result;
}
void PrintPar(TF1 * fit, int numParPerPeak){
int totPar = fit->GetNpar();
int count = totPar/numParPerPeak;
printf("ID ");
for( int i = 0; i < numParPerPeak; i++){
printf("par%d ", i);
}
printf("\n");
for( int i = 0; i < count ; i++){
printf("%3d ", i);
for( int j = 0; j < numParPerPeak; j++){
int parID = numParPerPeak * i + j;
printf("%.3f(%.3f) ", fit->GetParameter(parID), fit->GetParError(parID));
}
printf("\n");
}
}
void GoodnessofFit(TH1F * hist, TF1 * fit){
int nBin = hist->GetNbinsX();
int effBin = 0;
double mean = 0;
double ysq = 0;
double SSR = 0;
double chisq = 0; //with estimated error be sqrt(y)
double Xsq = 0; // for Pearson's chi-sq test
for( int i = 1; i <= nBin; i++){
double e = hist->GetBinError(i);
if( e > 0 ) {
effBin ++;
double y = hist->GetBinContent(i);
double x = hist->GetBinCenter(i);
double ybar = fit->Eval(x);
ysq += y*y;
mean += y;
SSR += (y - ybar)*(y-ybar);
chisq += (y - ybar)*(y-ybar)/e/e;
if( ybar > e ) {
Xsq += (y - ybar)*(y-ybar)/ybar;
}else{
Xsq += (y - ybar)*(y-ybar)/e;
}
//printf(" %d | x : %f, y : %f, ybar : %f , X-sq : %f\n", i, x, y, ybar, Xsq);
}
}
mean = mean / nBin;
double SSTotal = ysq + mean*mean;
int npar = fit->GetNpar();
int ndf = effBin - npar;
printf("#################################################\n");
printf("## Goodness of Fit. ##\n");
printf("#################################################\n");
printf(" eff. Bin(%d) - numPar(%d) = ndf = %d \n", effBin, npar, ndf);
printf("============== Regression analysis\n");
printf("----------------- R-sq \n");
printf(" SSTotal = %f \n", SSTotal);
printf(" SSR = %f \n", SSR);
printf(" MSR = %f <-- is it similar to sample variance?\n", SSR/ndf);
double Rsq = 1 - SSR/SSTotal;
printf(" R-sq = %f \n", Rsq );
printf("----------------- Chi-sq \n");
printf(" Chi-sq = %f \n", chisq);
printf(" rd. Chi-sq = %f \n", chisq/ndf);
printf("ROOT Chi-Sq = %f , NDF = %d \n", fit->GetChisquare(), fit->GetNDF());
//================ chi-sq test
printf("============== Hypothesis testing\n");
printf(" Null Hypothesis : the fitting model is truth. \n");
printf(" * p-value = prob. that Null Hypo. is truth. \n");
printf(" * the Pearson's test in here only for background free data \n");
printf(" Pearson's X-sq = %.2f \n", Xsq);
double p = 1 - TMath::Prob(Xsq, ndf);
printf(" Pearson's p-value = %.2f %s 0.05 | %s\n", p, p < 0.05 ? "<": ">", p < 0.05 ? "reject" : "cannot reject");
double pchi = 1 - TMath::Prob(chisq, ndf);
printf(" Chi-sq p-value = %.2f %s 0.05 | %s\n", pchi, pchi < 0.05 ? "<": ">", pchi < 0.05 ? "reject" : "cannot reject");
double pRoot = 1- fit->GetProb();
printf("ROOT Chi-sq p-value = %.2f %s 0.05 | %s\n", pRoot, pRoot < 0.05 ? "<": ">", pRoot < 0.05 ? "reject" : "cannot reject");
printf("################################################\n");
}
vector<double> energy, height, sigma, lowE, highE ;
vector<int> energyFlag, sigmaFlag;
bool loadFitParameters(TString fitParaFile){
energy.clear(); energyFlag.clear();
sigma.clear(); sigmaFlag.clear();
lowE.clear(); highE.clear();
height.clear();
bool paraRead = false;
printf("====================================================================== \n");
printf("----- loading fit parameters from : %s", fitParaFile.Data());
ifstream file;
file.open(fitParaFile.Data());
if( !file){
printf("\ncannot read file : %s \n", fitParaFile.Data());
return 0;
}
while( file.good()) {
string tempLine;
getline(file, tempLine);
if( tempLine.substr(0, 1) == "#" ) continue;
if( tempLine.substr(0, 2) == "//" ) continue;
if( tempLine.size() < 5 ) continue;
///printf("%s\n", tempLine.c_str());
vector<string> temp = SplitStrAF(tempLine, " ");
if( temp.size() < 7 ) continue;
energy.push_back( atof(temp[0].c_str()));
lowE.push_back( atof(temp[1].c_str()));
highE.push_back( atof(temp[2].c_str()));
energyFlag.push_back(atoi(temp[3].c_str()));
sigma.push_back( atof(temp[4].c_str()));
sigmaFlag.push_back( atoi(temp[5].c_str()));
height.push_back( atof(temp[6].c_str()));
}
printf("... done.\n");
int n = energy.size();
TString limStr = "(fixed)";
printf("%2s| %34s | %10s \n", "ID", "Peak [MeV]", "Sigma [MeV]");
for( int j = 0; j < n; j ++){
if( energyFlag[j] == 0 ) limStr.Form("(limited, %6.3f - %6.3f)", lowE[j], highE[j]);
printf("%2d| %7.4f %-26s | %7.4f (%5s) \n", j, energy[j], limStr.Data(), sigma[j], sigmaFlag[j] == 1 ? "fixed" : "free");
}
paraRead = true;
printf("====================================================================== \n");
return paraRead;
}
TCanvas * NewCanvas(TString name, TString title, int divX, int divY, int padSizeX, int padSizeY){
TCanvas * output = NULL;
if( gROOT->FindObjectAny(name) == NULL ){
output = new TCanvas(name, title, divX * padSizeX, divY * padSizeY);
}else{
output = (TCanvas *) gROOT->FindObjectAny(name) ;
output->Clear();
}
output->Divide(divX, divY);
return output;
}
void ScaleAndDrawHist(TH1F * hist, double xMin, double xMax){
if ( xMin != xMax ) hist->GetXaxis()->SetRangeUser(xMin, xMax);
int maxBin = hist->GetMaximumBin();
double ymax = hist->GetBinContent(maxBin);
hist->GetYaxis()->SetRangeUser(0, 1.1 * ymax);
hist->Draw();
}
void PlotResidual(TH1F * hist, TF1 * fit){
TH1F * hRes = (TH1F*) hist->Clone();
hRes->GetListOfFunctions()->Clear();
hRes->SetTitle("Residual");
hRes->SetName("hRes");
hRes->SetYTitle("Hist - fit");
hRes->Sumw2(0);
hRes->Sumw2(1);
hRes->Add(fit, -1);
hRes->Draw();
}
//########################################
//### Fit a Gauss + Pol-n
//########################################
void fitGaussPol(TH1F * hist, double mean, double sigmaMax, int degPol, double xMin, double xMax, TString optStat = ""){
printf("=========================================================\n");
printf("================ fit 1-Gauss + Pol-%d BG ================\n", degPol);
printf(" * mean Range +- 5 sigmaMax \n");
printf(" * inital parameters of the polynomial is random/pow(10, i) \n");
printf("==========================================================\n");
recentFitMethod = "fitGaussPol";
gStyle->SetOptStat(optStat);
TCanvas * cFitGaussPol = NewCanvas("cFitGaussPol", Form("fit Gauss & Pol-%d | fitGaussPol", degPol), 1, 2, 800, 350);
cFitGaussPol->cd(1);
ScaleAndDrawHist(hist, xMin, xMax);
const int nPar = 3 + degPol + 1;
TString funcExp = "[0] * TMath::Gaus(x, [1], [2], 1)";
for( int i = 0; i < degPol+1 ; i++){
funcExp += Form(" + [%d]*TMath::Power(x,%d)", i+3 , i);
}
TF1 * fit = new TF1("fit", funcExp.Data(), xMin, xMax);
double * para = new double[nPar];
para[0] = 100 * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[1] = mean;
para[2] = sigmaMax/2.;
for( int i = 0 ; i < degPol+1; i++){
para[3 + i ] = gRandom->Rndm()/TMath::Power(10, i);
}
fit->SetLineWidth(2);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
fit->SetParLimits(0, 0, 1e9);
fit->SetParLimits(1, mean - 5*sigmaMax, mean + 5 * sigmaMax);
fit->SetParLimits(2, 0, sigmaMax);
hist->Fit("fit", "Rq");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
double bw = hist->GetBinWidth(1);
printf("histogram name : %s \n====== Gaussian:\ncount: %8.0f(%3.0f)\nmean : %8.4f(%8.4f)\nsigma: %8.4f(%8.4f) \n",
hist->GetName(),
paraA[0] / bw, paraE[0] /bw,
paraA[1], paraE[1],
paraA[2], paraE[2]);
printf("------- the polnomail BG:\n");
for(int i = 0 ; i < degPol+1; i++){
printf("%2d | %8.4f(%8.4f) \n", i, paraA[3+i], paraE[3+i]);
}
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.DrawLatex(0.12, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
text.DrawLatex(0.12, 0.75,Form("count: %4.0f(%3.0f)", paraA[0] / bw, paraE[0] /bw));
text.DrawLatex(0.12, 0.70,Form("E_{x}: %6.3f(%5.3f) MeV", paraA[1], paraE[1]));
text.DrawLatex(0.12, 0.65,Form("#sigma: %3.0f(%3.0f) keV", paraA[2] * 1000., paraE[2] * 1000.));
for( int i = 0; i < degPol + 1; i++){
text.DrawLatex(0.60, 0.85 - 0.05*i ,Form("%3s: %8.3f(%8.3f)", Form("p%d", i), paraA[3+i], paraE[3+i]));
}
TString expression = "[0] ";
for( int j = 1; j < degPol + 1; j++){
expression += Form(" + [%d]*TMath::Power(x, %d)", j, j);
}
TF1 * g0 = new TF1("g0", expression.Data(), xMin, xMax);
for( int j = 0; j < degPol + 1 ; j++){
g0->SetParameter(j, paraA[3+j]);
}
g0->SetLineColor(4);
g0->Draw("same");
TF1* f0 = new TF1("f0", "[0] * TMath::Gaus(x, [1], [2], 1)", xMin, xMax);
f0->SetParameter(0, paraA[0]);
f0->SetParameter(1, paraA[1]);
f0->SetParameter(2, paraA[2]);
f0->SetLineColor(2);
f0->SetNpx(1000);
f0->Draw("same");
// GoodnessofFit(hist, fit);
cFitGaussPol->cd(2);
PlotResidual(hist, fit);
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
BestFitCount.push_back(paraA[0]);
BestFitMean.push_back(paraA[1]);
BestFitSigma.push_back(paraA[2]);
}
//########################################
//#### fit 2 gauss + pol-1 // not updated
//########################################
vector<double> fit2GaussP1(TH1F * hist, double mean1, double sigma1,
double mean2, double sigma2,
double xMin, double xMax, TString optStat = "", bool newCanvas = false){
printf("=========================================================\n");
printf("================ fit 2-Gauss + Pol-1 BG ================\n" );
printf(" NOT updated. It works, but the code is old \n");
printf("==========================================================\n");
recentFitMethod = "fit2GaussP1";
vector<double> output;
output.clear();
gStyle->SetOptStat(optStat);
TCanvas * cFit2GaussP1 = NewCanvas("cFit2GaussP1", "fit Gauss & P1 | fit2GaussP1", 1, 1, 800, 350);
cFit2GaussP1->cd(1);
ScaleAndDrawHist(hist, xMin, xMax);
TF1 * fit = new TF1("fit", "[0] * TMath::Gaus(x, [1], [2], 1) + [3] * TMath::Gaus(x, [4], [5], 1) + [6] + [7]*x", xMin, xMax);
double * para = new double[8];
para[0] = 20 * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[1] = mean1;
para[2] = sigma1;
para[3] = 100 * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[4] = mean2;
para[5] = sigma2;
para[6] = 1;
para[7] = 0;
fit->SetLineWidth(2);
fit->SetLineColor(2);
fit->SetNpx(1000);
fit->SetParameters(para);
hist->Fit("fit", "Rq");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
double bw = hist->GetBinWidth(1);
printf("%7s ====== count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
hist->GetName(),
paraA[0] / bw, paraE[0] /bw,
paraA[1], paraE[1],
paraA[2], paraE[2]);
printf("%7s ====== count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
"",
paraA[3] / bw, paraE[3] /bw,
paraA[4], paraE[4],
paraA[5], paraE[5]);
output.push_back( paraA[0]/bw);
output.push_back( paraE[0]/bw);
output.push_back( paraA[1]);
output.push_back( paraE[1]);
output.push_back( paraA[2]);
output.push_back( paraE[2]);
output.push_back( paraA[3]/bw);
output.push_back( paraE[3]/bw);
output.push_back( paraA[4]);
output.push_back( paraE[4]);
output.push_back( paraA[5]);
output.push_back( paraE[5]);
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
text.DrawLatex(0.15, 0.75,Form("count: %4.0f(%3.0f), E_{x}: %6.3f(%5.3f) MeV, #sigma: %3.0f(%3.0f) keV ",
paraA[0] / bw, paraE[0] /bw,
paraA[1], paraE[1],
paraA[2] * 1000., paraE[2] * 1000.));
text.DrawLatex(0.15, 0.7, Form("count: %4.0f(%3.0f), E_{x}: %6.3f(%5.3f) MeV, #sigma: %3.0f(%3.0f) keV ",
paraA[3] / bw, paraE[3] /bw,
paraA[4], paraE[4],
paraA[5] * 1000., paraE[5] * 1000.));
text.DrawLatex(0.15, 0.6, Form("Line : %6.3f(%5.3f) + %6.3f(%5.3f)x ",
paraA[6], paraE[6],
paraA[7], paraE[7]));
GoodnessofFit(hist, fit);
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < 2; i++){
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
return output;
}
//########################################
//#### fit for gamma + pol-n BG
//########################################
void fitGF3Pol(TH1F * hist, double mean, double sigmaMax, double ratio, double beta, double step, int degPol, double xMin, double xMax, TString optStat = ""){
printf("=========================================================\n");
printf("================ fit GF1 + Pol-%d BG ================\n", degPol);
printf(" * mean Range = xMin, xMax \n");
printf(" * inital parameters of the polynomial is random/pow(10, i) \n");
printf("==========================================================\n");
recentFitMethod = "fitGF3Pol";
gStyle->SetOptStat(optStat);
gStyle->SetOptStat(optStat);
TCanvas * cFitGF3Pol = NewCanvas("cFitGF3Pol", Form("fit GF3 + pol-%d | fitGF3Pol", degPol), 1, 2, 800, 350);
cFitGF3Pol->cd(1);
ScaleAndDrawHist(hist, xMin, xMax);
nPeaks = 1;
nPols = degPol;
int nPar = 6*nPeaks + degPol + 1;
TF1 * fit = new TF1("fit", nGF3Pol, xMin, xMax, nPar);
fit->Print();
double * para = new double[nPar];
para[0] = hist->GetMaximum() *4;
para[1] = mean;
para[2] = sigmaMax/2.;
para[3] = ratio ;
para[4] = beta ;
para[5] = step ;
for( int i = 0 ; i < degPol + 1; i++){
para[6+i] = gRandom->Rndm()/TMath::Power(10, i);
}
fit->SetLineWidth(2);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
fit->SetParLimits(0, 0, 1e9);
fit->SetParLimits(1, xMin, xMax);
fit->SetParLimits(2, 0.00000001, sigmaMax);
fit->SetParLimits(3, 0, 0.5);
fit->SetParLimits(4, 1, 400);
fit->SetParLimits(5, 0, 0.5);
hist->Fit("fit", "Rq");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
double chisquare = fit->GetChisquare();
int ndf = fit->GetNDF();
double bw = hist->GetBinWidth(1);
printf("histogram : %s \n", hist->GetName());
printf("========= The Gaussian \n");
printf("count: %8.0f(%3.0f)\n", paraA[0] / bw, paraE[0] /bw);
printf("mean : %8.4f(%8.4f)\n", paraA[1], paraE[1]);
printf("sigma: %8.4f(%8.4f)\n", paraA[2], paraE[2]);
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
text.SetTextColor(1);
text.DrawLatex(0.12, 0.65, Form("count : %5.0f(%5.0f)", paraA[0]/bw, paraE[0]/bw));
text.DrawLatex(0.12, 0.60, Form(" mean : %5.3f(%5.3f) keV", paraA[1], paraE[1]));
text.DrawLatex(0.12, 0.55, Form("sigma : %5.3f(%5.3f) keV", paraA[2], paraE[2]));
text.DrawLatex(0.12, 0.50, Form(" FWHM : %5.3f(%5.3f) keV", paraA[2] *2.355, paraE[2]*2.355));
text.DrawLatex(0.12, 0.40, Form("#chi^2/ndf : %5.3f", chisquare/ndf));
//GoodnessofFit(hist, fit);
/// 0 1 2 3 4 5
string label[8] = {"Area", "mean", "sigma", "ratio", "beta", "step"};
printf("---------- The detail\n");
for(int i = 0 ; i < 6 ; i++){
printf("%d | %8s | %f (%f) \n", i, label[i].c_str(), paraA[i], paraE[i]);
text.DrawLatex(0.65, 0.85-0.05*i, Form("%6s: %5.3f(%5.3f)", label[i].c_str(), paraA[i], paraE[i]));
}
for(int i = 6 ; i < nPar; i++){
printf("%d | %8s | %f (%f) \n", i, Form("p%d", i-6), paraA[i], paraE[i]);
text.DrawLatex(0.65, 0.85-0.05*i, Form("%6s: %5.3f (%5.3f) \n", Form("p%d", i-6), paraA[i], paraE[i]));
}
/// norm * (1-ratio)* TMath::Gaus(x[0], mean, sigma, 1)
TF1 * g0 = new TF1("g0", "[0] * (1.0-[3]) * TMath::Gaus(x, [1], [2], 1)", xMin, xMax);
g0->SetParameter(0, paraA[0]);
g0->SetParameter(1, paraA[1]);
g0->SetParameter(2, paraA[2]);
g0->SetParameter(3, paraA[3]);
g0->SetNpx(1000);
g0->SetLineColor(kRed);
/// norm * ratio * exp( sigma * sigma/2/beta/beta)* exp((x[0]-mean)/beta) * TMath::Erfc( (x[0]-mean)/(sigma * sqrt(2)) + sigma/beta/sqrt(2)) ;
TF1 * g1 = new TF1("g1", "[0] * [3] * exp( [2] * [2]/2/[4]/[4]) / (2* [4])* exp((x-[1])/[4]) * TMath::Erfc( (x-[1])/([2] * sqrt(2)) + [2]/[4]/sqrt(2)) ", xMin, xMax);
g1->SetParameter(0, paraA[0]);
g1->SetParameter(1, paraA[1]);
g1->SetParameter(2, paraA[2]);
g1->SetParameter(3, paraA[3]);
g1->SetParameter(4, paraA[4]);
g1->SetNpx(1000);
g1->SetLineColor(kGreen +3);
/// norm * step * TMath::Erfc( (x[0]-mean)/(sigma * sqrt(2)) );
TF1 * g2 = new TF1("g2", "[0] * [3] * TMath::Erfc( (x-[1])/([2] * sqrt(2)) );", xMin, xMax);
g2->SetParameter(0, paraA[0]);
g2->SetParameter(1, paraA[1]);
g2->SetParameter(2, paraA[2]);
g2->SetParameter(3, paraA[5]);
g2->SetNpx(1000);
g2->SetLineColor(kViolet);
TString expression = "[0] ";
for( int j = 1; j < degPol + 1; j++){
expression += Form(" + [%d]*TMath::Power(x, %d)", j, j);
}
TF1 * g3 = new TF1("g3", expression.Data(), xMin, xMax);
for( int j = 0; j < degPol + 1 ; j++){
g3->SetParameter(j, paraA[6+j]);
}
g3->SetLineColor(kBlue);
g3->Draw("same");
g0->Draw("same");
g1->Draw("same");
g2->Draw("same");
g3->Draw("same");
cFitGF3Pol->cd(2);
PlotResidual(hist, fit);
}
//##############################################
//##### Auto Fit n-Gauss with estimated BG
//##############################################
vector<double> fitAuto(TH1F * hist, int bgEst = 10,
double peakThreshold = 0.05,
double sigmaMax = 0,
int peakDensity = 4,
TString optStat = ""){
printf("================================================================\n");
printf("========== Auto Fit n-Gauss with estimated BG ==================\n");
printf(" * bgEst = parameter of BG estimation, larger BG, more linear \n");
printf(" * peakThreshold = precentage of the highest peak that will count \n");
printf(" * sigmaMax = maximum sigma, if -1, program try to find the sigma \n");
printf(" * peakDensity = peak will closer when the number is larger ");
printf(" \n");
printf(" after peaks found, the i-th peaks will be limited by the mid-point\n");
printf(" by the (i-1)-th peak and the i-th peak, and the mid-point of the\n");
printf(" i-th peak and (i+1)-th peak \n");
printf(" i.e. [peak(i-1)+peak(i)]/2 < limit of peak(i) < [peak(i)+peak(i+1)]/2 \n");
printf("================================================================\n");
recentFitMethod = "fitAuto";
gStyle->SetOptStat(optStat);
TCanvas *cFitAuto = NewCanvas("cFitAuto","Auto Fitting | fitAuto", 1, 4, 800, 300);
cFitAuto->cd(1);
ScaleAndDrawHist(hist, 0, 0);
TH1F * specS = (TH1F*) hist->Clone();
double xMin = hist->GetXaxis()->GetXmin();
double xMax = hist->GetXaxis()->GetXmax();
int xBin = hist->GetXaxis()->GetNbins();
TString titleH;
titleH.Form("fitted spectrum (BG=%d); Ex [MeV]; Count / %4.0f keV", bgEst, (xMax-xMin)*1000./xBin );
specS->SetTitle(titleH);
specS->SetName("specS");
///specS->GetXaxis()->SetTitleSize(0.06);
///specS->GetYaxis()->SetTitleSize(0.06);
///specS->GetXaxis()->SetTitleOffset(0.7);
///specS->GetYaxis()->SetTitleOffset(0.6);
//=================== find peak and fit
gStyle->SetOptFit(0);
TSpectrum * peak = new TSpectrum(50);
nPeaks = peak->Search(hist, peakDensity, "", peakThreshold);
if( bgEst > 0 ) {
printf("============= estimating background...\n");
TH1 * h1 = peak->Background(hist, bgEst);
h1->Draw("same");
printf("============= substracting the linear background...\n");
specS->Sumw2();
specS->Add(h1, -1.);
}
cFitAuto->cd(2)->SetGrid();
cFitAuto->cd(2);
specS->Draw("hist");
//========== Fitting
printf("============= Fitting.....");
printf(" found %d peaks \n", nPeaks);
double * xpos = peak->GetPositionX();
double * ypos = peak->GetPositionY();
int * inX = new int[nPeaks];
TMath::Sort(nPeaks, xpos, inX, 0 );
vector<double> energy, height;
for( int j = 0; j < nPeaks; j++){
energy.push_back(xpos[inX[j]]);
height.push_back(ypos[inX[j]]);
}
for( int j = 0; j < nPeaks; j++){
printf(" energy : %f , %f \n", energy[j], height[j]);
}
if( sigmaMax == 0 ){
printf("------------- Estimate sigma for each peak \n");
sigma.clear();
int binMin = hist->FindBin(xMin);
int binMax = hist->FindBin(xMax);
for( int i = 0; i < nPeaks ; i++){
int b0 = hist->FindBin(energy[i]);
double sMin = (xMax-xMin)/5., sMax = (xMax-xMin)/5.;
//---- backward search, stop when
for( int b = b0-1 ; b > binMin ; b-- ){
double y = hist->GetBinContent(b);
double x = hist->GetBinCenter(b);
if( y < (height[i])/2. ) {
sMin = energy[i] - hist->GetBinCenter(b);
break;
}
}
//---- forward search, stop when
for( int b = b0+1 ; b < binMax ; b++ ){
double y = hist->GetBinContent(b);
double x = hist->GetBinCenter(b);
if( y < (height[i])/2. ) {
sMax = hist->GetBinCenter(b) - energy[i];
break;
}
}
double temp = TMath::Min(sMin, sMax);
/// When there are multiple peaks closely packed :
if( i > 0 && temp > 2.5 * sigma.back() ) temp = sigma.back();
sigma.push_back(temp);
printf("%2d | x : %8.2f | sigma(est) %f \n", i, energy[i], sigma[i]);
}
}else if( sigmaMax < 0 ){
printf("========== use user input sigma : %f (fixed)\n", abs(sigmaMax));
sigma.clear();
for( int i = 0; i < nPeaks ; i++) sigma.push_back(abs(sigmaMax));
}else if( sigmaMax > 0 ){
printf("========== use user input sigma : %f/2. \n", sigmaMax/2.);
sigma.clear();
for( int i = 0; i < nPeaks ; i++) sigma.push_back(sigmaMax/2.);
}
int numParPerPeak = 3;
const int n = numParPerPeak * nPeaks;
double * para = new double[n];
for(int i = 0; i < nPeaks ; i++){
para[numParPerPeak*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[numParPerPeak*i+1] = energy[i];
if( sigmaMax == 0 ){
para[numParPerPeak*i+2] = sigma[i];
}else if(sigmaMax < 0 ){
para[numParPerPeak*i+2] = abs(sigmaMax);
}else if(sigmaMax > 0 ){
para[numParPerPeak*i+2] = sigmaMax/2.;
}
}
TF1 * fit = new TF1("fit", nGauss, xMin, xMax, 3 * nPeaks );
fit->SetLineWidth(2);
fit->SetLineColor(2);
fit->SetNpx(1000);
fit->SetParameters(para);
if( nPeaks > 1 ){
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(numParPerPeak*i+0, 0, 1e+9);
double de1 = 1, de2 = 1;
if( i == 0 ){
de2 = (energy[i+1] - energy[i])/2.;
de1 = de2;
}else if( i < nPeaks -1 ){
de1 = (energy[i] - energy[i-1])/2.;
de2 = (energy[i+1] - energy[i])/2.;
}else{
de1 = (energy[i] - energy[i-1])/2.;
de2 = de1;
}
fit->SetParLimits(numParPerPeak*i+1, energy[i] - de1 , energy[i] + de2);
if( sigmaMax== 0 ) fit->SetParLimits(numParPerPeak*i+2, 0, 1.5*sigma[i]); // add 50% margin of sigma
if( sigmaMax < 0 ) fit->FixParameter(numParPerPeak*i+2, abs(sigmaMax));
if( sigmaMax > 0 ) fit->SetParLimits(numParPerPeak*i+2, 0, sigmaMax);
}
}else{
fit->SetParLimits(0, 0, 1e+9);
fit->SetParLimits(2, 0, sigmaMax);
}
specS->Fit("fit", "q");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
//======== calculate reduce chi-squared
//GoodnessofFit(specS, fit);
double bw = specS->GetBinWidth(1);
vector<double> exPos;
for(int i = 0; i < nPeaks ; i++){
exPos.push_back(paraA[numParPerPeak*i+1]);
printf("%2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[numParPerPeak*i] / bw, paraE[numParPerPeak*i] /bw,
paraA[numParPerPeak*i+1], paraE[numParPerPeak*i+1],
paraA[numParPerPeak*i+2], paraE[numParPerPeak*i+2]);
}
cFitAuto->Update();
//draw the indivual fit
fit->Draw("same");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[numParPerPeak*i]);
gFit[i]->SetParameter(1, paraA[numParPerPeak*i+1]);
gFit[i]->SetParameter(2, paraA[numParPerPeak*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
specS->Draw("hist same");
//======== print text on plot
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.SetTextSize(0.06);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
cFitAuto->cd(3);
PlotResidual(specS, fit);
cFitAuto->cd(4);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
return exPos;
}
//########################################
//###### NOT tested
//########################################
vector<double> fitNGF3(TH1 * hist, int bgEst = 10,
double peakThreshold = 0.1,
double sigmaMax = 20,
int peakDensity = 4,
TString optStat = "", bool newPlot = true){
TCanvas *cFitAuto = NULL;
if( newPlot ){
cFitAuto = new TCanvas("cFitAuto","Auto Fitting", 100, 100, 800,800);
cFitAuto->Divide(1,2);
gStyle->SetOptStat(optStat);
cFitAuto->cd(1);
hist->Draw();
}
recentFitMethod = "fitNGF3";
TH1F * specS = (TH1F*) hist->Clone();
double xMin = hist->GetXaxis()->GetXmin();
double xMax = hist->GetXaxis()->GetXmax();
int xBin = hist->GetXaxis()->GetNbins();
TString titleH;
titleH.Form("fitted spectrum (BG=%d); Ex [MeV]; Count / %4.0f keV", bgEst, (xMax-xMin)*1000./xBin );
specS->SetTitle(titleH);
specS->SetName("specS");
///specS->GetXaxis()->SetTitleSize(0.06);
///specS->GetYaxis()->SetTitleSize(0.06);
///specS->GetXaxis()->SetTitleOffset(0.7);
///specS->GetYaxis()->SetTitleOffset(0.6);
//=================== find peak and fit
gStyle->SetOptFit(0);
TSpectrum * peak = new TSpectrum(50);
nPeaks = peak->Search(hist, peakDensity, "", peakThreshold);
if( bgEst > 0 ) {
printf("============= estimating background...\n");
TH1 * h1 = peak->Background(hist, bgEst);
h1->Draw("same");
printf("============= substracting the linear background...\n");
specS->Sumw2();
specS->Add(h1, -1.);
}
if( newPlot ){
cFitAuto->cd(2)->SetGrid();
cFitAuto->cd(2);
}
specS->Draw("hist");
//========== Fitting
if( newPlot ){
printf("============= Fitting.....");
printf(" found %d peaks \n", nPeaks);
}
double * xpos = peak->GetPositionX();
double * ypos = peak->GetPositionY();
int * inX = new int[nPeaks];
TMath::Sort(nPeaks, xpos, inX, 0 );
vector<double> energy, height;
for( int j = 0; j < nPeaks; j++){
energy.push_back(xpos[inX[j]]);
height.push_back(ypos[inX[j]]);
}
if( newPlot ){
for( int j = 0; j < nPeaks; j++){
printf(" energy : %f , %f \n", energy[j], height[j]);
}
}
int numParPerPeak = 6;
const int n = numParPerPeak * nPeaks;
double * para = new double[n];
for(int i = 0; i < nPeaks ; i++){
para[numParPerPeak*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[numParPerPeak*i+1] = energy[i];
para[numParPerPeak*i+2] = sigmaMax;
para[numParPerPeak*i+3] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi()) * 0.1;
para[numParPerPeak*i+4] = sigmaMax;
para[numParPerPeak*i+5] = -4;
}
TF1 * fit = new TF1("fit", nGF3, xMin, xMax, numParPerPeak * nPeaks );
fit->SetLineWidth(2);
fit->SetLineColor(2);
fit->SetNpx(1000);
fit->SetParameters(para);
if( nPeaks > 1 ){
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(numParPerPeak*i+0, 0, 1e+9);
double de1 = 1, de2 = 1;
if( i == 0 ){
de2 = (energy[i+1] - energy[i])/2.;
de1 = de2;
}else if( i < nPeaks -1 ){
de1 = (energy[i] - energy[i-1])/2.;
de2 = (energy[i+1] - energy[i])/2.;
}else{
de1 = (energy[i] - energy[i-1])/2.;
de2 = de1;
}
fit->SetParLimits(numParPerPeak*i+1, energy[i] - de1 , energy[i] + de2);
fit->SetParLimits(numParPerPeak*i+2, 0, sigmaMax * 5);
fit->SetParLimits(numParPerPeak*i+3, 0, 1e+9);
fit->SetParLimits(numParPerPeak*i+4, 0, sigmaMax);
fit->SetParLimits(numParPerPeak*i+5, -10, -2);
}
}else{
fit->SetParLimits(0, 0, 1e+9);
fit->SetParLimits(2, 0, sigmaMax);
fit->SetParLimits(3, 0, 1e+9);
fit->SetParLimits(4, 0, sigmaMax);
fit->SetParLimits(5, -10, -2);
}
specS->Fit("fit", "q");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
//======== calculate reduce chi-squared
if( newPlot ) GoodnessofFit(specS, fit);
double bw = specS->GetBinWidth(1);
vector<double> exPos;
for(int i = 0; i < nPeaks ; i++){
exPos.push_back(paraA[numParPerPeak*i+1]);
double totCount = paraA[numParPerPeak*i] + paraA[numParPerPeak*i+3];
double totCountErr = sqrt(paraE[numParPerPeak*i]*paraE[numParPerPeak*i] + paraE[numParPerPeak*i+3]*paraE[numParPerPeak*i+3]);
printf("%2d , count: %8.0f(%3.0f)+%8.0f(%3.0f)=%8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f), skewneww: %4.1f(%4.1f) \n",
i,
paraA[numParPerPeak*i] / bw, paraE[numParPerPeak*i] /bw,
paraA[numParPerPeak*i+3] / bw, paraE[numParPerPeak*i+3] /bw,
totCount / bw, totCountErr /bw,
paraA[numParPerPeak*i+1], paraE[numParPerPeak*i+1],
paraA[numParPerPeak*i+2], paraE[numParPerPeak*i+2],
paraA[numParPerPeak*i+5], paraE[numParPerPeak*i+5]);
//PrintPar(fit, numParPerPeak);
}
if( newPlot ) cFitAuto->Update();
//draw the indivual fit
fit->Draw("same");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
TF1 ** kFit = new TF1 *[nn];
TF1 ** zFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1) + [3] * TMath::Gaus(x,[1],[4], 1) * ( 1 + TMath::Erf( [5]*(x-[1])/sqrt(2)/[4] ))", xMin, xMax);
gFit[i]->SetParameter(0, paraA[numParPerPeak*i]);
gFit[i]->SetParameter(1, paraA[numParPerPeak*i+1]);
gFit[i]->SetParameter(2, paraA[numParPerPeak*i+2]);
gFit[i]->SetParameter(3, paraA[numParPerPeak*i+3]);
gFit[i]->SetParameter(4, paraA[numParPerPeak*i+4]);
gFit[i]->SetParameter(5, paraA[numParPerPeak*i+5]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
kFit[i] = new TF1(Form("kFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1) * ( 1 + TMath::Erf( [3]*(x-[1])/sqrt(2)/[2] ))", xMin, xMax);
kFit[i]->SetParameter(0, paraA[numParPerPeak*i+3]);
kFit[i]->SetParameter(1, paraA[numParPerPeak*i+1]);
kFit[i]->SetParameter(2, paraA[numParPerPeak*i+4]);
kFit[i]->SetParameter(3, paraA[numParPerPeak*i+5]);
kFit[i]->SetLineColor(i+1);
kFit[i]->SetNpx(1000);
kFit[i]->SetLineWidth(1);
kFit[i]->Draw("same");
zFit[i] = new TF1(Form("zFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
zFit[i]->SetParameter(0, paraA[numParPerPeak*i]);
zFit[i]->SetParameter(1, paraA[numParPerPeak*i+1]);
zFit[i]->SetParameter(2, paraA[numParPerPeak*i+2]);
zFit[i]->SetLineColor(i+1);
zFit[i]->SetNpx(1000);
zFit[i]->SetLineWidth(1);
zFit[i]->Draw("same");
}
specS->Draw("hist same");
return exPos;
}
//########################################
//###### fir N Gauss with estimated BG
//########################################
void fitNGauss(TH1F * hist, int bgEst = 10, TString fitFile = "AutoFit_para.txt", TString optStat = ""){
printf("================================================================\n");
printf("================ fit N-Gauss with estimated BG ================\n");
printf(" * bgEst = larger of bgEst, more linear the estimated BG \n");
printf(" * need the file input \n");
printf(" \n");
printf(" 1) The histogram will be subtracted by the estimated BG. \n");
printf(" 2) n-Gauss will then be fitted the BG subtracted histogram \n");
printf("================================================================\n");
recentFitMethod = "fitNGauss";
bool isParaRead = loadFitParameters(fitFile);
if( !isParaRead ) {
printf("Please provide a valid input file\n");
return;
}
nPeaks = energy.size();
gStyle->SetOptStat(optStat);
TCanvas *cFitNGauss = NewCanvas("cFitNGauss","Fit n-Gauss | fitNGauss", 1,4, 800, 300);;
cFitNGauss->cd(1);
ScaleAndDrawHist(hist, 0, 0);
TH1F * specS = (TH1F*) hist->Clone();
double xMin = hist->GetXaxis()->GetXmin();
double xMax = hist->GetXaxis()->GetXmax();
int xBin = hist->GetXaxis()->GetNbins();
TString titleH;
titleH.Form("fitNGauss (BG = %2d); Ex [MeV]; Count / %4.0f keV", bgEst, (xMax-xMin)*1000./xBin );
specS->SetTitle(titleH);
specS->SetName("specS");
//=================== find peak and fi
gStyle->SetOptFit(0);
//cFitNGauss->cd(2)->SetGrid();
cFitNGauss->cd(2);
if( bgEst > 0 ) {
printf("============= estimating background...\n");
TSpectrum * peak = new TSpectrum(50);
TH1 * h1 = peak->Background(hist, bgEst);
cFitNGauss->cd(1);
h1->Draw("same");
cFitNGauss->cd(2);
printf("============= substracting the estimated background...\n");
specS->Sumw2();
specS->Add(h1, -1.);
}
specS->Draw("hist");
//========== Fitting
printf("============= Fitting %d-Gauss..... \n", nPeaks);
const int n = 3 * nPeaks;
double * para = new double[n];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = energy[i];
para[3*i+2] = sigma[i]/2.;
}
TF1 * fit = new TF1("fit", nGauss, xMin, xMax, 3* nPeaks );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//fixing parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( energyFlag[i] == 1 ) {
fit->FixParameter(3*i+1, energy[i]);
}else{
fit->SetParLimits(3*i+1, lowE[i], highE[i]);
}
if( sigmaFlag[i] == 1 ) {
fit->FixParameter(3*i+2, sigma[i]);
}else{
fit->SetParLimits(3*i+2, 0, sigma[i]);
}
}
specS->Fit("fit", "q");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
//======== calculate reduce chi-squared
//GoodnessofFit(specS, fit);
double bw = specS->GetBinWidth(1);
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
//draw the indivual fit
specS->Draw("hist");
fit->Draw("same");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
specS->Draw("hist same");
//specS->Draw("E same");
//======== print text on plot
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.06);
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
cFitNGauss->cd(3);
PlotResidual(specS, fit);
cFitNGauss->cd(4);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
cFitNGauss->Update();
}
//########################################
//#### not updated
//########################################
void fitNGaussSub(TH1F * hist, int bgEst = 10, int degPol = 1, TString fitFile = "AutoFit_para.txt", TString optStat = ""){
printf("==================================================================\n");
printf("======== fit N-Gauss with estimated BG (method-2) ================\n");
printf(" * bgEst = larger of bgEst, more linear the estimated BG \n");
printf(" * degPol = degree of polynomial \n");
printf(" * need the file input \n");
printf(" \n");
printf(" 1) A BG is estimated, and then the BG is fitted by a polynomial. \n");
printf(" 2) The histogram will be subtracted by the polynomial. \n");
printf(" 3) n-Gauss will then be fitted the subtracted histogram \n");
printf("================================================================\n");
recentFitMethod = "fitNGaussSub";
bool isParaRead = loadFitParameters(fitFile);
if( !isParaRead ) {
printf("Please provide a valid input file\n");
return;
}
nPeaks = energy.size();
nPols = degPol;
gStyle->SetOptStat(optStat);
TCanvas *cFitNGaussSub = NewCanvas("cFitNGaussSub","Fit n-Gauss, replace estimated BG with Pol-n | fitNGauss2", 1, 4, 800, 300 );
//if(! cFitNGaussSub->GetShowEventStatus() ) cFitNGaussSub->ToggleEventStatus();
cFitNGaussSub->cd(1);
ScaleAndDrawHist(hist, 0, 0);
TH1F * specS = (TH1F*) hist->Clone();
double xMin = hist->GetXaxis()->GetXmin();
double xMax = hist->GetXaxis()->GetXmax();
int xBin = hist->GetXaxis()->GetNbins();
TString titleH;
titleH.Form("fitNGauss2 (replace Est. BG with Pol-%d) (BG = %2d); Ex [MeV]; Count / %4.0f keV", degPol, bgEst, (xMax-xMin)*1000./xBin );
specS->SetTitle(titleH);
specS->SetName("specS");
printf("============= estimating background...\n");
TSpectrum * peak = new TSpectrum(50);
TH1 * h1 = peak->Background(hist, bgEst);
printf("============= fit the est-background with a polynomial function...\n");
TString polExp = "[0]";
for( int i = 1; i < degPol + 1; i++){
polExp += Form("+[%d]*TMath::Power(x,%d)", i, i );
}
TF1 * bg = new TF1("bg", polExp.Data(), xMin, xMax);
bg->SetParameter(0, 50);
bg->SetParameter(0, 0);
bg->SetLineColor(2);
bg->SetNpx(1000);
h1->Fit("bg", "R", "");
hist->Draw();
bg->Draw("same");
//======== print text on plot
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
const Double_t * paraAt = bg->GetParameters();
const Double_t * paraEt = bg->GetParErrors();
for( int i = 0; i < degPol + 1; i++){
text.DrawLatex(0.6, 0.85 - 0.05*i, Form("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraAt[i], paraEt[i]));
}
gStyle->SetOptFit(0);
// cFitNGaussSub->cd(2)->SetGrid();
cFitNGaussSub->cd(2);
printf("============= substracting the polynomial background...\n");
specS->Sumw2();
specS->Add(bg, -1.);
specS->Draw("hist");
//========== Fitting
printf("============= Fitting..... \n");
const int n = 3 * nPeaks;
double * para = new double[n];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = energy[i];
para[3*i+2] = sigma[i]/2.;
}
TF1 * fit = new TF1("fit", nGauss, xMin, xMax, 3* nPeaks );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//fixing parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( energyFlag[i] == 1 ) {
fit->FixParameter(3*i+1, energy[i]);
}else{
fit->SetParLimits(3*i+1, lowE[i], highE[i]);
}
if( sigmaFlag[i] == 1 ) {
fit->FixParameter(3*i+2, sigma[i]);
}else{
fit->SetParLimits(3*i+2, 0, sigma[i]);
}
}
specS->Fit("fit", "q");
const Double_t* paraE = fit->GetParErrors();
const Double_t* paraA = fit->GetParameters();
GoodnessofFit(specS, fit);
double bw = specS->GetBinWidth(1);
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
//draw the indivual fit
specS->Draw("hist");
fit->Draw("same");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
specS->Draw("hist same");
//specS->Draw("E same");
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.SetTextSize(0.06);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
cFitNGaussSub->cd(3);
PlotResidual(specS, fit);
cFitNGaussSub->cd(4);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
}
//########################################
//#### fit N-Gauss with pol-n BG
//########################################
void fitNGaussPol(TH1F * hist, int degPol, TString fitFile = "AutoFit_para.txt",double xMin = 0, double xMax = 0, TString optStat = ""){
printf("================================================================\n");
printf("================ fit N-Gauss with Pol-%1d BG ==================\n", degPol);
printf(" * degPol = degree of polynomial \n");
printf(" * need the file input \n");
printf(" * xMin, xMax = if left empty, full range will be used\n");
printf(" \n");
printf(" 1) The histogram will be fitted by n-Gauss + Pol \n");
printf("================================================================\n");
recentFitMethod = "fitNGaussPol";
bool isParaRead = loadFitParameters(fitFile);
if( !isParaRead ) {
printf("Please provide a valid input file\n");
return;
}
gStyle->SetOptStat(optStat);
nPeaks = energy.size();
nPols = degPol;
TCanvas * cFitNGaussPol = NewCanvas("cFitNGaussPol", Form("Fitting with n-Gauss + pol-%d | fitNGaussPol", degPol), 1, 3, 800, 300);
//if(! cFitNGaussPol->GetShowEventStatus() ) cFitNGaussPol->ToggleEventStatus();
cFitNGaussPol->cd(1);
ScaleAndDrawHist(hist, xMin, xMax);
if( xMin == xMax){
xMin = hist->GetXaxis()->GetXmin();
xMax = hist->GetXaxis()->GetXmax();
}
int xBin = hist->GetXaxis()->GetNbins();
printf("============= find the polynomial background ..... \n");
int nPar = 3 * nPeaks + nPols + 1;
double * para = new double[nPar];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = energy[i];
para[3*i+2] = sigma[i]/2.;
}
for(int i = 0; i < nPols + 1; i++){
//if( ggg == NULL ){
para[3*nPeaks+i] = gRandom->Rndm()/TMath::Power(10, 3*i);
//}else{
// para[3*nPeaks+i] = gPara[i];
//}
}
TF1 * fit = new TF1("fit", nGaussPol, xMin, xMax, nPar );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//fixing parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( energyFlag[i] == 1 ) {
fit->FixParameter(3*i+1, energy[i]);
}else{
fit->SetParLimits(3*i+1, lowE[i], highE[i]);
}
if( sigmaFlag[i] == 1 ) {
fit->FixParameter(3*i+2, sigma[i]);
}else{
fit->SetParLimits(3*i+2, 0, sigma[i]);
}
}
hist->Fit("fit", "Rq");
//=========== get the polynomial part
const Double_t* paraA = fit->GetParameters();
const Double_t* paraE = fit->GetParErrors();
TString polExp = "[0]";
for( int i = 1; i < degPol + 1; i++){
polExp += Form("+[%d]*TMath::Power(x,%d)", i, i );
}
TF1 * bg = new TF1("bg", polExp.Data(), xMin, xMax);
for( int i = 0; i < degPol + 1; i++){
bg->SetParameter(i, paraA[3*nPeaks+i]);
}
bg->SetNpx(1000);
for( int i = 0; i < degPol + 1; i++){
printf("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraA[3*nPeaks+i], paraE[3*nPeaks+i]);
}
printf("====================================\n");
cFitNGaussPol->cd(1);
bg->Draw("same");
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
text.SetTextColor(1);
for( int i = 0; i < degPol + 1; i++){
text.DrawLatex(0.6, 0.85 - 0.05*i, Form("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraA[3*nPeaks+i], paraE[3*nPeaks+i]));
}
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.SetTextSize(0.06);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
//GoodnessofFit(specS, fit);
double bw = hist->GetBinWidth(1);
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
cFitNGaussPol->Update();
cFitNGaussPol->cd(2);
PlotResidual(hist, fit);
cFitNGaussPol->cd(3);
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
}
//########################################
//#### fit N-Gauss with pol-n BG
//########################################
void fitNGaussPolSub(TH1F * hist, int degPol, TString fitFile = "AutoFit_para.txt",double xMin = 0, double xMax = 0, TString optStat = ""){
printf("================================================================\n");
printf("========= fit N-Gauss with Pol-%1d BG / 2nd method ============\n", degPol);
printf(" * degPol = degree of polynomial \n");
printf(" * need the file input \n");
printf(" * xMin, xMax = if left empty, full range will be used\n");
printf(" \n");
printf(" 1) The histogram will be fitted by n-Gauss + Pol -> to get the estimated BG \n");
printf(" 2) The histogram will be subtracted by the Pol BG. \n");
printf(" 3) n-Gauss will then be fitted the BG subtracted histogram \n");
printf("================================================================\n");
recentFitMethod = "fitNGaussPolSub";
bool isParaRead = loadFitParameters(fitFile);
if( !isParaRead ) {
printf("Please provide a valid input file\n");
return;
}
gStyle->SetOptStat(optStat);
nPeaks = energy.size();
nPols = degPol;
TCanvas * cFitNGaussPolSub = NewCanvas("cFitNGaussPolSub", Form("Fitting with n-Gauss + pol-%d (method-2) | fitGaussPol2", degPol), 1, 4, 800, 300);
//if(! cFitNGaussPol->GetShowEventStatus() ) cFitNGaussPol->ToggleEventStatus();
cFitNGaussPolSub->cd(1);
ScaleAndDrawHist(hist, xMin, xMax);
if( xMin == xMax){
xMin = hist->GetXaxis()->GetXmin();
xMax = hist->GetXaxis()->GetXmax();
}
int xBin = hist->GetXaxis()->GetNbins();
printf("============= find the polynomial background ..... \n");
int nPar = 3 * nPeaks + nPols + 1;
double * para = new double[nPar];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = energy[i];
para[3*i+2] = sigma[i]/2.;
}
for(int i = 0; i < nPols + 1; i++){
para[3*nPeaks+i] = gRandom->Rndm()/TMath::Power(10, 3*i);
}
TF1 * fit = new TF1("fit", nGaussPol, xMin, xMax, nPar );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//fixing parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( energyFlag[i] == 1 ) {
fit->FixParameter(3*i+1, energy[i]);
}else{
fit->SetParLimits(3*i+1, lowE[i], highE[i]);
}
if( sigmaFlag[i] == 1 ) {
fit->FixParameter(3*i+2, sigma[i]);
}else{
fit->SetParLimits(3*i+2, 0, sigma[i]);
}
}
hist->Fit("fit", "nq");
//=========== get the polynomial part and substract
const Double_t* paraAt = fit->GetParameters();
const Double_t* paraEt = fit->GetParErrors();
TString polExp = "[0]";
for( int i = 1; i < degPol + 1; i++){
polExp += Form("+[%d]*TMath::Power(x,%d)", i, i );
}
TF1 * bg = new TF1("bg", polExp.Data(), xMin, xMax);
for( int i = 0; i < degPol + 1; i++){
bg->SetParameter(i, paraAt[3*nPeaks+i]);
}
bg->SetNpx(1000);
for( int i = 0; i < degPol + 1; i++){
printf("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraAt[3*nPeaks+i], paraEt[3*nPeaks+i]);
}
printf("====================================\n");
cFitNGaussPolSub->cd(1);
bg->Draw("same");
TH1F * specS = (TH1F*) hist->Clone();
TString titleH;
titleH.Form("pol-%d BG Subtracted spectrum (fitNGaussPol-2); Ex [MeV]; Count / %4.0f keV", degPol, (xMax-xMin)*1000./xBin );
specS->SetTitle(titleH);
specS->SetName("specS");
//=================== find peak and fit
gStyle->SetOptFit(0);
///cFitNGaussPol->cd(2)->SetGrid();
cFitNGaussPolSub->cd(2);
printf("============= substracting the polynomial background...\n");
specS->Sumw2();
specS->Add(bg, -1.);
specS->Draw("hist");
//======= fit again
printf("============= fitting the subtracted spectrum.... \n");
nPar = 3* nPeaks;
TF1 * fita = new TF1("fita", nGauss, xMin, xMax, nPar );
fita->SetLineWidth(3);
fita->SetLineColor(1);
fita->SetNpx(1000);
fita->SetParameters(para);
//fixing parameters
for( int i = 0; i < nPeaks; i++){
fita->SetParLimits(3*i , 0, 1e9);
if( energyFlag[i] == 1 ) {
fita->FixParameter(3*i+1, energy[i]);
}else{
fita->SetParLimits(3*i+1, lowE[i], highE[i]);
}
if( sigmaFlag[i] == 1 ) {
fita->FixParameter(3*i+2, sigma[i]);
}else{
fita->SetParLimits(3*i+2, 0, sigma[i]);
}
}
specS->Fit("fita", "q");
const Double_t* paraE = fita->GetParErrors();
const Double_t* paraA = fita->GetParameters();
//GoodnessofFit(specS, fit);
double bw = specS->GetBinWidth(1);
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
//draw the indivual fit
specS->Draw("hist");
fita->Draw("same");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
specS->Draw("hist same");
//specS->Draw("E same");
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
text.SetTextColor(1);
for( int i = 0; i < degPol + 1; i++){
text.DrawLatex(0.6, 0.85 - 0.05*i, Form("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraA[3*nPeaks+i], paraE[3*nPeaks+i]));
}
double chi2 = fita->GetChisquare();
int ndf = fita->GetNDF();
text.SetTextSize(0.06);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
cFitNGaussPolSub->cd(3);
PlotResidual(specS, fita);
cFitNGaussPolSub->cd(4);
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
}
//#################################################
//#### fit N-Gauss with pol-n BG using mouse click
//#################################################
int nClick = 0;
bool peakFlag = 1;
vector<double> xPeakList;
vector<double> yPeakList;
vector<double> xBGList;
vector<double> yBGList;
TH1F * tempHist;
int markerStyle = 23;
int markerColor = 2;
int markerBGColor = 4;
void Clicked() {
int event = gPad->GetEvent();
if (event != 11) return;
TObject *select = gPad->GetSelected();
if (!select) return;
TH1F *h = (TH1F*)select;
int px = gPad->GetEventX();
double xd = gPad->AbsPixeltoX(px);
float x = gPad->PadtoX(xd);
if( peakFlag ) {
xPeakList.push_back(x);
}else{
xBGList.push_back(x);
}
int b = tempHist->FindBin(x);
double y = tempHist->GetBinContent(b);
if( peakFlag ){
yPeakList.push_back(y);
}else{
yBGList.push_back(y);
}
// add marker in the histogram
TMarker * mark = new TMarker(x,y, markerStyle);
mark->SetMarkerColor(markerColor);
tempHist->GetListOfFunctions()->Add(mark);
printf("%2d | x : %8.2f , y : %.0f \n", nClick, x, y);
nClick ++;
}
void SaveFitPara(bool isBestFit = true, TString fileName = "AutoFit_para.txt"){
if( xPeakList.size() == 0 && BestFitMean.size() == 0 ){
printf(" no fit paramaters availible. \n");
return;
}
if( recentFitMethod == "fitGF3Pol" || recentFitMethod == "fitGF3" ){
printf(" Not support for GF3 fitting. \n");
return;
}
printf("Save to : %s \n", fileName.Data());
FILE * file_out;
file_out = fopen (fileName.Data(), "w+");
fprintf(file_out, "# for n-Gauss fit, can use \"#\", or \"//\" to comment out whole line\n");
fprintf(file_out, "# peak low high fixed? sigma_Max fixed? hight\n");
if ( xPeakList.size() == 0 || isBestFit ){
for( int i = 0 ; i < BestFitMean.size() ; i++){
fprintf(file_out, "%.3f %.3f %.3f 0 %.3f 0 %.0f\n",
BestFitMean[i],
BestFitMean[i] - 5*BestFitSigma[i],
BestFitMean[i] + 5*BestFitSigma[i],
BestFitSigma[i],
BestFitCount[i]);
}
}else{
for( int i = 0 ; i < xPeakList.size() ; i++){
fprintf(file_out, "%.3f %.3f %.3f 0 %.3f 0 %.0f\n",
xPeakList[i],
xPeakList[i] - 5*sigma[i],
xPeakList[i] + 5*sigma[i],
sigma[i],
yPeakList[i]);
}
}
fclose(file_out);
}
void clickFitNGaussPol(TH1F * hist, int degPol, double sigmaMax = 0, double meanRange = 0){
printf("=========================================================\n");
printf("======== fit n-Gauss + Pol-%d BG using mouse click =====\n", degPol );
printf("==========================================================\n");
recentFitMethod = "clickFitNGaussPol";
gStyle->SetOptStat("");
gStyle->SetOptFit(0);
TCanvas * cClickFitNGaussPol = NULL;
if( gROOT->FindObjectAny("cClickFitGaussPol") == NULL ){
cClickFitNGaussPol = new TCanvas("cClickFitNGaussPol", Form("fit Gauss & Pol-%d by mouse click | clickFitGaussPol", degPol), 1400, 1200);
}else{
delete gROOT->FindObjectAny("cClickFitNGaussPol") ;
cClickFitNGaussPol = new TCanvas("cClickFitNGaussPol", Form("fit Gauss & Pol-%d by mouse click | clickFitGaussPol", degPol), 1400, 1200);
}
cClickFitNGaussPol->Divide(1, 4);
for(int i = 1; i <= 4 ; i++){
cClickFitNGaussPol->cd(i)->SetGrid(0,0);
}
if(! cClickFitNGaussPol->GetShowEventStatus() ) cClickFitNGaussPol->ToggleEventStatus();
cClickFitNGaussPol->cd(1);
hist->GetListOfFunctions()->Clear();
ScaleAndDrawHist(hist, 0, 0);
TH1F* hspec = (TH1F*) hist->Clone();
hspec->Sumw2();
cClickFitNGaussPol->Update();
cClickFitNGaussPol->Draw();
TLatex helpMsg;
helpMsg.SetNDC();
helpMsg.SetTextFont(82);
helpMsg.SetTextSize(0.06);
helpMsg.SetTextColor(kRed);
helpMsg.DrawLatex(0.15, 0.8, "Click for peaks: (Double-click / x / Ctrl to end) ");
printf("--------- Click the histogram for peaks: (Double-click / x / Ctrl to end) \n");
nClick = 0;
xPeakList.clear();
yPeakList.clear();
markerColor = 2;
markerStyle = 23;
peakFlag = 1;
cClickFitNGaussPol->cd(1)->SetCrosshair(1);
cClickFitNGaussPol->cd(1)->AddExec("ex", "Clicked()");
tempHist = hist;
TObject * obj ;
do{
obj = gPad->WaitPrimitive();
if( obj == NULL ) break;
}while( obj != NULL);
if( degPol >= 0 ){
printf("--------- Click the histogram for Background: (Double-click / x / Ctrl to end) \n");
printf(" * when no input, program will estimate \n");
cClickFitNGaussPol->cd(1)->Clear();
hist->Draw();
helpMsg.SetTextColor(markerBGColor);
helpMsg.DrawLatex(0.15, 0.8, "Click for BG: (Double-click / x / Ctrl to end) ");
helpMsg.DrawLatex(0.15, 0.75, "* when no input, program will estimate");
cClickFitNGaussPol->Update();
cClickFitNGaussPol->Draw();
nClick = 0;
xBGList.clear();
yBGList.clear();
markerColor = markerBGColor;
markerStyle = 33;
peakFlag = 0;
cClickFitNGaussPol->cd(1)->AddExec("ex", "Clicked()");
do{
obj = gPad->WaitPrimitive();
if( obj == NULL ) break;
}while( obj != NULL);
}
cClickFitNGaussPol->cd(1)->DeleteExec("ex");
cClickFitNGaussPol->cd(1)->SetCrosshair(0);
cClickFitNGaussPol->cd(1)->Clear();
hist->Draw();
tempHist = NULL;
cClickFitNGaussPol->Update();
cClickFitNGaussPol->Draw();
nPols = degPol;
double xMin = hspec->GetXaxis()->GetXmin();
double xMax = hspec->GetXaxis()->GetXmax();
TString polExp = "[0]";
for( int i = 1; i < degPol + 1; i++) polExp += Form("+[%d]*TMath::Power(x,%d)", i, i );
TF1 *bg = new TF1("bg", polExp.Data(), xMin, xMax);
bg->SetNpx(1000);
bg->SetLineColor(markerBGColor);
bg->SetLineWidth(1);
if( xBGList.size() > 0 ) {
printf("---------------- fit the BG with Pol-%d \n", nPols);
TGraph * gBG = new TGraph((int) xBGList.size(), &xBGList[0], &yBGList[0]);
for( int i = 0; i < degPol + 1; i++) bg->SetParameter(i, gRandom->Rndm()/TMath::Power(10, 3*i));
gBG->Fit("bg", "Rq");
bg->Draw("same");
//printf("--------------- Subtracting the BG \n");
//hspec->Add(bg, -1);
}else{
for( int i = 0; i < nPols+1; i++) bg->SetParameter(i, 0.);
}
nPeaks = (int) xPeakList.size();
if( sigmaMax == 0 ){
printf("------------- Estimate sigma for each peak \n");
sigma.clear();
int binMin = hist->FindBin(xMin);
int binMax = hist->FindBin(xMax);
for( int i = 0; i < nPeaks ; i++){
int b0 = hist->FindBin(xPeakList[i]);
double estBG = bg->Eval(xPeakList[i]);
double sMin = (xMax-xMin)/5., sMax = (xMax-xMin)/5.;
//---- backward search, stop when
for( int b = b0-1 ; b > binMin ; b-- ){
double y = hist->GetBinContent(b);
double x = hist->GetBinCenter(b);
if( y - (bg->Eval(x)) < (yPeakList[i]-estBG)/2. ) {
sMin = xPeakList[i] - hist->GetBinCenter(b);
break;
}
}
//---- forward search, stop when
for( int b = b0+1 ; b < binMax ; b++ ){
double y = hist->GetBinContent(b);
double x = hist->GetBinCenter(b);
if( y - (bg->Eval(x)) < (yPeakList[i]-estBG)/2. ) {
sMax = hist->GetBinCenter(b) - xPeakList[i];
break;
}
}
double temp = TMath::Min(sMin, sMax);
/// When there are multiple peaks closely packed :
if( i > 0 && temp > 2.5 * sigma.back() ) temp = sigma.back();
sigma.push_back(temp);
printf("%2d | x : %8.2f | sigma(est) %f \n", i, xPeakList[i], sigma[i]);
}
//---- use the mean of the sigma
double sigma0 = 0;
for( int i = 0; i < nPeaks ; i++) sigma0 += sigma[i];
sigma0 = sigma0/(nPeaks+1);
for( int i = 0; i < nPeaks ; i++) sigma[i] = sigma0;
printf("========== use the mean sigma : %f \n", sigma0);
}else if( sigmaMax < 0 ){
printf("========== use user input sigma : %f (fixed)\n", abs(sigmaMax));
sigma.clear();
for( int i = 0; i < nPeaks ; i++) sigma.push_back(abs(sigmaMax));
}else if( sigmaMax > 0 ){
printf("========== use user input sigma : %f/2. \n", sigmaMax/2.);
sigma.clear();
for( int i = 0; i < nPeaks ; i++) sigma.push_back(sigmaMax/2.);
}
printf("-------------- Fit the spectrum with %d-Gauss + Pol-%d\n", nPeaks, nPols );
cClickFitNGaussPol->cd(2);
hspec->Draw("hist");
int nPar = 3 * nPeaks + nPols + 1;
double * para = new double[nPar];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = yPeakList[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = xPeakList[i];
if( sigmaMax == 0){
para[3*i+2] = sigma[i];
}else if(sigmaMax < 0 ){
para[3*i+2] = abs(sigmaMax);
}else if(sigmaMax > 0 ){
para[3*i+2] = sigmaMax/2.;
}
}
for(int i = 0; i < nPols+1 ; i++){
para[3*nPeaks+i] = bg->GetParameter(i);
}
TF1 * fit = new TF1("fit", nGaussPol, xMin, xMax, nPar );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//limit parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( meanRange <= 0 ) {
double dE1, dE2;
if( i == 0 ){
dE2 = xPeakList[i+1] - xPeakList[i];
dE1 = dE2;
}else if ( i == nPeaks-1 ){
dE1 = xPeakList[i] - xPeakList[i-1];
dE2 = dE1;
}else{
dE1 = xPeakList[i] - xPeakList[i-1];
dE2 = xPeakList[i+1] - xPeakList[i];
}
fit->SetParLimits(3*i+1, xPeakList[i] - dE1 , xPeakList[i] + dE2 );
}else{
fit->SetParLimits(3*i+1, xPeakList[i] - meanRange/2. , xPeakList[i] + meanRange/2. );
}
if( sigmaMax== 0 ) fit->SetParLimits(3*i+2, 0, 1.5*sigma[i]); // add 50% margin of sigma
if( sigmaMax < 0 ) fit->FixParameter(3*i+2, abs(sigmaMax));
if( sigmaMax > 0 ) fit->SetParLimits(3*i+2, 0, sigmaMax);
}
hspec->Fit("fit", "Rq");
fit->Draw("same");
//=========== get the polynomial part
const Double_t* paraA = fit->GetParameters();
const Double_t* paraE = fit->GetParErrors();
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
double bw = hspec->GetBinWidth(1);
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.06);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
for( int i = 0; i < degPol + 1; i++){
text.DrawLatex(0.6, 0.85 - 0.05*i, Form("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraA[3*nPeaks+i], paraE[3*nPeaks+i]));
}
TF1 ** gFit = new TF1 *[nPeaks];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
if( degPol >= 0 ){
TF1 *bg2 = new TF1("bg", polExp.Data(), xMin, xMax);
bg2->SetNpx(1000);
bg2->SetLineColor(markerBGColor);
bg2->SetLineWidth(1);
for( int i = 0; i < nPols + 1; i++){
bg2->SetParameter(i, paraA[3*nPeaks+i]);
}
bg2->Draw("same");
}
cClickFitNGaussPol->cd(3);
PlotResidual(hspec, fit);
cClickFitNGaussPol->cd(4);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
}
void clickFitNGaussPolSub(TH1F * hist, int degPol, double sigmaMax = 0, double meanRange = 0){
printf("=========================================================\n");
printf("= fit n-Gauss + Pol-%d BG using mouse click (method-2) =\n", degPol );
printf("==========================================================\n");
recentFitMethod = "clickFitNGaussPolSub";
gStyle->SetOptStat("");
gStyle->SetOptFit(0);
TCanvas * cClickFitNGaussPolsub = NULL;
if( gROOT->FindObjectAny("cClickFitGaussPolsub") == NULL ){
cClickFitNGaussPolsub = new TCanvas("cClickFitNGaussPol", Form("fit Gauss & Pol-%d by mouse click (sub) | clickFitGaussPolsub", degPol), 1400, 1200);
}else{
delete gROOT->FindObjectAny("cClickFitNGaussPolsub") ;
cClickFitNGaussPolsub = new TCanvas("cClickFitNGaussPolsub", Form("fit Gauss & Pol-%d by mouse click (sub) | clickFitGaussPolsub", degPol), 1400, 1200);
}
cClickFitNGaussPolsub->Divide(1, 4);
for(int i = 1; i <= 4 ; i++){
cClickFitNGaussPolsub->cd(i)->SetGrid(0,0);
}
if(! cClickFitNGaussPolsub->GetShowEventStatus() ) cClickFitNGaussPolsub->ToggleEventStatus();
cClickFitNGaussPolsub->cd(1);
hist->GetListOfFunctions()->Clear();
ScaleAndDrawHist(hist, 0, 0);
TH1F* hspec = (TH1F*) hist->Clone();
hspec->Sumw2();
cClickFitNGaussPolsub->Update();
cClickFitNGaussPolsub->Draw();
TLatex helpMsg;
helpMsg.SetNDC();
helpMsg.SetTextFont(82);
helpMsg.SetTextSize(0.06);
helpMsg.SetTextColor(kRed);
helpMsg.DrawLatex(0.15, 0.8, "Click for peaks: (Double-click / x / Ctrl to end) ");
printf("--------- Click the histogram for peaks: (Double-click / x / Ctrl to end) \n");
nClick = 0;
xPeakList.clear();
yPeakList.clear();
markerColor = 2;
markerStyle = 23;
peakFlag = 1;
cClickFitNGaussPolsub->cd(1)->SetCrosshair(1);
cClickFitNGaussPolsub->cd(1)->AddExec("ex", "Clicked()");
tempHist = hist;
TObject * obj ;
do{
obj = gPad->WaitPrimitive();
if( obj == NULL ) break;
}while( obj != NULL);
if( degPol >= 0 ){
printf("--------- Click the histogram for Background: (Double-click / x / Ctrl to end) \n");
cClickFitNGaussPolsub->cd(1)->Clear();
hist->Draw();
helpMsg.SetTextColor(markerBGColor);
helpMsg.DrawLatex(0.15, 0.8, "Click for BG: (Double-click / x / Ctrl to end) ");
cClickFitNGaussPolsub->Update();
cClickFitNGaussPolsub->Draw();
nClick = 0;
xBGList.clear();
yBGList.clear();
markerColor = markerBGColor;
markerStyle = 33;
peakFlag = 0;
do{
obj = gPad->WaitPrimitive();
if( obj == NULL ) break;
}while( obj != NULL);
}
cClickFitNGaussPolsub->cd(1)->DeleteExec("ex");
cClickFitNGaussPolsub->cd(1)->SetCrosshair(0);
cClickFitNGaussPolsub->cd(1)->Clear();
hist->Draw();
tempHist = NULL;
if( xBGList.size() == 0 ) helpMsg.DrawLatex(0.15, 0.75, " No BG defined ! fitting could be problematics. ");
cClickFitNGaussPolsub->Update();
cClickFitNGaussPolsub->Draw();
nPols = degPol;
double xMin = hspec->GetXaxis()->GetXmin();
double xMax = hspec->GetXaxis()->GetXmax();
TString polExp = "[0]";
for( int i = 1; i < degPol + 1; i++) polExp += Form("+[%d]*TMath::Power(x,%d)", i, i );
TF1 *bg = new TF1("bg", polExp.Data(), xMin, xMax);
bg->SetNpx(1000);
bg->SetLineColor(markerBGColor);
bg->SetLineWidth(1);
if( xBGList.size() > 0 ) {
printf("---------------- fit the BG with Pol-%d \n", nPols);
TGraph * gBG = new TGraph((int) xBGList.size(), &xBGList[0], &yBGList[0]);
for( int i = 0; i < degPol + 1; i++) bg->SetParameter(i, gRandom->Rndm()/TMath::Power(10, 3*i));
gBG->Fit("bg", "Rq");
bg->Draw("same");
printf("--------------- Subtracting the BG \n");
hspec->Add(bg, -1);
}else{
for( int i = 0; i < nPols+1; i++) bg->SetParameter(i, 0.);
}
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.06);
double chi2BG = bg->GetChisquare();
int ndfBG = bg->GetNDF();
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2BG/ndfBG));
//=========== get the polynomial BG
const Double_t* paraAt = bg->GetParameters();
const Double_t* paraEt = bg->GetParErrors();
for( int i = 0; i < degPol + 1; i++){
text.DrawLatex(0.6, 0.85 - 0.05*i, Form("%4s : %8.2f(%8.2f)\n", Form("p%d", i), paraAt[i], paraEt[i]));
}
nPeaks = (int) xPeakList.size();
if( sigmaMax == 0 ){
printf("------------- Estimate sigma for each peak \n");
sigma.clear();
int binMin = hist->FindBin(xMin);
int binMax = hist->FindBin(xMax);
for( int i = 0; i < nPeaks ; i++){
int b0 = hist->FindBin(xPeakList[i]);
double estBG = bg->Eval(xPeakList[i]);
double sMin = (xMax-xMin)/5., sMax = (xMax-xMin)/5.;
//---- backward search, stop when
for( int b = b0-1 ; b > binMin ; b-- ){
double y = hist->GetBinContent(b);
double x = hist->GetBinCenter(b);
if( y - (bg->Eval(x)) < (yPeakList[i]-estBG)/2. ) {
sMin = xPeakList[i] - hist->GetBinCenter(b);
break;
}
}
//---- forward search, stop when
for( int b = b0+1 ; b < binMax ; b++ ){
double y = hist->GetBinContent(b);
double x = hist->GetBinCenter(b);
if( y - (bg->Eval(x)) < (yPeakList[i]-estBG)/2. ) {
sMax = hist->GetBinCenter(b) - xPeakList[i];
break;
}
}
double temp = TMath::Min(sMin, sMax);
/// When there are multiple peaks closely packed :
if( i > 0 && temp > 2.5 * sigma.back() ) temp = sigma.back();
sigma.push_back(temp);
printf("%2d | x : %8.2f | sigma(est) %f \n", i, xPeakList[i], sigma[i]);
}
}else if( sigmaMax < 0 ){
printf("========== use user input sigma : %f (fixed)\n", abs(sigmaMax));
sigma.clear();
for( int i = 0; i < nPeaks ; i++) sigma.push_back(abs(sigmaMax));
}else if( sigmaMax > 0 ){
printf("========== use user input sigma : %f/2. \n", sigmaMax/2.);
sigma.clear();
for( int i = 0; i < nPeaks ; i++) sigma.push_back(sigmaMax/2.);
}
printf("-------------- Fit the subtracted spectrum with %d-Gauss\n", nPeaks );
cClickFitNGaussPolsub->cd(2);
hspec->Draw("hist");
int nPar = 3 * nPeaks;
double * para = new double[nPar];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = yPeakList[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = xPeakList[i];
if( sigmaMax == 0){
para[3*i+2] = sigma[i];
}else if(sigmaMax < 0 ){
para[3*i+2] = abs(sigmaMax);
}else if(sigmaMax > 0 ){
para[3*i+2] = sigmaMax/2.;
}
}
TF1 * fit = new TF1("fit", nGauss, xMin, xMax, nPar );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//limit parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( meanRange <= 0 ) {
double dE1, dE2;
if( i == 0 ){
dE2 = xPeakList[i+1] - xPeakList[i];
dE1 = dE2;
}else if ( i == nPeaks-1 ){
dE1 = xPeakList[i] - xPeakList[i-1];
dE2 = dE1;
}else{
dE1 = xPeakList[i] - xPeakList[i-1];
dE2 = xPeakList[i+1] - xPeakList[i];
}
fit->SetParLimits(3*i+1, xPeakList[i] - dE1 , xPeakList[i] + dE2 );
}else{
fit->SetParLimits(3*i+1, xPeakList[i] - meanRange/2. , xPeakList[i] + meanRange/2. );
}
if( sigmaMax== 0 ) fit->SetParLimits(3*i+2, 0, 1.5*sigma[i]); // add 50% margin of sigma
if( sigmaMax < 0 ) fit->FixParameter(3*i+2, abs(sigmaMax));
if( sigmaMax > 0 ) fit->SetParLimits(3*i+2, 0, sigmaMax);
}
hspec->Fit("fit", "Rq");
fit->Draw("same");
//=========== get the fit parameters
const Double_t* paraA = fit->GetParameters();
const Double_t* paraE = fit->GetParErrors();
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
double bw = hspec->GetBinWidth(1);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
TF1 ** gFit = new TF1 *[nPeaks];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
cClickFitNGaussPolsub->cd(3);
PlotResidual(hspec, fit);
cClickFitNGaussPolsub->cd(4);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
BestFitCount.clear();
BestFitMean.clear();
BestFitSigma.clear();
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
BestFitCount.push_back(paraA[3*i]/ bw);
BestFitMean.push_back(paraA[3*i+1]);
BestFitSigma.push_back(paraA[3*i+2]);
}
}
//########################################
//#### fit N-Gauss with pol-1 BG at fixed for < 0
//########################################
void fitSpecial(TH1F * hist, TString fitFile = "AutoFit_para.txt"){
printf("================================================================\n");
printf("================ Special fit for h074_82Kr =====================\n");
printf(" * need the file input \n");
printf("================================================================\n");
bool isParaRead = loadFitParameters(fitFile);
if( !isParaRead ) {
printf("Please provide a valid input file\n");
return;
}
gStyle->SetOptStat("");
gStyle->SetOptFit(0);
nPeaks = energy.size();
nPols = 1;
TCanvas * cFitSpecial = NewCanvas("cFitSpecial", "Fitting for h074_82Kr", 1, 3, 800, 300);
//if(! cFitSpecial->GetShowEventStatus() ) cFitSpecial->ToggleEventStatus();
cFitSpecial->cd(1);
ScaleAndDrawHist(hist, 0, 0);
double xMin = hist->GetXaxis()->GetXmin();
double xMax = hist->GetXaxis()->GetXmax();
int xBin = hist->GetXaxis()->GetNbins();
printf("============= find the pol-1 background ..... \n");
TF1 * fitpol1 = new TF1("fitpol1", "pol1", xMin, -0.3);
fitpol1->SetParameter(0, gRandom->Rndm());
fitpol1->SetParameter(1, gRandom->Rndm());
hist->Fit("fitpol1", "Rq");
double x0 = fitpol1->GetParameter(0);
double x1 = fitpol1->GetParameter(1);
int nPar = 3 * nPeaks + nPols + 1;
double * para = new double[nPar];
for(int i = 0; i < nPeaks ; i++){
para[3*i+0] = height[i] * 0.05 * TMath::Sqrt(TMath::TwoPi());
para[3*i+1] = energy[i];
para[3*i+2] = sigma[i]/2.;
}
para[3*nPeaks+0] = x0;
para[3*nPeaks+1] = x1;
TF1 * fit = new TF1("fit", nGaussPol, xMin, xMax, nPar );
fit->SetLineWidth(3);
fit->SetLineColor(1);
fit->SetNpx(1000);
fit->SetParameters(para);
//fixing parameters
for( int i = 0; i < nPeaks; i++){
fit->SetParLimits(3*i , 0, 1e9);
if( energyFlag[i] == 1 ) {
fit->FixParameter(3*i+1, energy[i]);
}else{
fit->SetParLimits(3*i+1, lowE[i], highE[i]);
}
if( sigmaFlag[i] == 1 ) {
fit->FixParameter(3*i+2, sigma[i]);
}else{
fit->SetParLimits(3*i+2, 0, sigma[i]);
}
}
fit->FixParameter(3*nPeaks + 0, x0);
fit->FixParameter(3*nPeaks + 1, x1);
hist->Fit("fit", "Rq");
//=========== get the polynomial part
const Double_t* paraA = fit->GetParameters();
const Double_t* paraE = fit->GetParErrors();
TString polExp = "[0]";
for( int i = 1; i < nPols + 1; i++){
polExp += Form("+[%d]*TMath::Power(x,%d)", i, i );
}
TF1 * bg = new TF1("bg", polExp.Data(), xMin, xMax);
for( int i = 0; i < nPols + 1; i++){
bg->SetParameter(i, paraA[3*nPeaks+i]);
}
bg->SetNpx(1000);
for( int i = 0; i < nPols + 1; i++){
printf("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraA[3*nPeaks+i], paraE[3*nPeaks+i]);
}
printf("====================================\n");
cFitSpecial->cd(1);
bg->Draw("same");
TLatex text;
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.04);
text.SetTextColor(1);
for( int i = 0; i < nPols + 1; i++){
text.DrawLatex(0.6, 0.85 - 0.05*i, Form("%4s : %8.4e(%8.4e)\n", Form("p%d", i), paraA[3*nPeaks+i], paraE[3*nPeaks+i]));
}
double chi2 = fit->GetChisquare();
int ndf = fit->GetNDF();
text.SetTextSize(0.06);
text.DrawLatex(0.15, 0.8, Form("#bar{#chi^{2}} : %5.3f", chi2/ndf));
//GoodnessofFit(specS, fit);
double bw = hist->GetBinWidth(1);
for(int i = 0; i < nPeaks ; i++){
printf(" %2d , count: %8.0f(%3.0f), mean: %8.4f(%8.4f), sigma: %8.4f(%8.4f) \n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]);
}
printf("\n");
const int nn = nPeaks;
TF1 ** gFit = new TF1 *[nn];
for( int i = 0; i < nPeaks; i++){
gFit[i] = new TF1(Form("gFit%d", i), "[0] * TMath::Gaus(x,[1],[2], 1)", xMin, xMax);
gFit[i]->SetParameter(0, paraA[3*i]);
gFit[i]->SetParameter(1, paraA[3*i+1]);
gFit[i]->SetParameter(2, paraA[3*i+2]);
gFit[i]->SetLineColor(i+1);
gFit[i]->SetNpx(1000);
gFit[i]->SetLineWidth(1);
gFit[i]->Draw("same");
}
cFitSpecial->Update();
cFitSpecial->cd(2);
PlotResidual(hist, fit);
cFitSpecial->cd(3);
text.SetNDC();
text.SetTextFont(82);
text.SetTextSize(0.05);
text.SetTextColor(2);
text.DrawLatex(0.1, 0.9, Form(" %13s, %18s, %18s", "count", "mean", "sigma"));
for( int i = 0; i < nPeaks; i++){
text.DrawLatex(0.1, 0.8-0.05*i, Form(" %2d, %8.0f(%3.0f), %8.4f(%8.4f), %8.4f(%8.4f)\n",
i,
paraA[3*i] / bw, paraE[3*i] /bw,
paraA[3*i+1], paraE[3*i+1],
paraA[3*i+2], paraE[3*i+2]));
}
}
#endif
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