ROOT-数据读取-直方图-Roofit拟合基本流程-（入门实用）
梁佐佐
发布于 2020-09-04 09:25:02
发布于 2020-09-04 09:25:02
代码可运行
运行总次数：0
代码可运行
笔者最近在测核素能谱，测出的能谱需要分析，比如计算某全能峰的分辨率。用到的数据处理分析工具是ROOT(cern)，整个能谱读取分析的流程可给各位看官当入门或干货材料使用。不过，ROOT大神就必看本文了，至少节约2分钟的时间，日后要是有新鲜的ROOT技巧会另作分享。
本文ROOT分析的实验背景和数据处理目的如下图1所示：
图1 实验背景和ROOT数据处理的目的
目的1：得到扣除本底后的能谱并绘制出图
基本过程为：1）*.Spe文件数据读取；2）定义一个ROOT文件用于存储能谱；3）数据读取到直方图，并作本底扣除；4）写入ROOT文件；5）画图。
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "RooBreitWigner.h"
#include "RooPolynomial.h"
#include "RooAddPdf.h"
#include "RooFitResult.h"
#include "RooRealIntegral.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TFile.h"
#include "TTree.h"
#include "TH2D.h"
#include "TH1D.h"
#include "THStack.h"
#include "TROOT.h"
#include "TLatex.h"
#include "TF2.h"
#include "TF1.h"
#include "iostream"
#include "fstream"
#include "RooCBShape.h"
#include "TMath.h"
#include "RooFit.h"
#include "RooMath.h"
using namespace RooFit;
using namespace std;
void templateread(){
const int varnum=2;
const char *newlabelname[varnum]={"CsI-B","CsI-Cs137"};
string filename[varnum]={"20190401-csi-bg-1725s.Spe","cs137-csi-1725s.Spe"};
double measuringtime[varnum]={1725,1725};
const int channelnum=24+2048;
string tempdata;
ifstream fin[varnum];
TH1F *spec[varnum];
int font=22;
TFile *outputFile=new
TFile("CsI-1cm1cm1cm-new.root","RECREATE");
for (int i1=0;i1<2;i1++)
{ fin[i1].open(filename[i1],ios_base::in);
spec[i1] = new TH1F(newlabelname[i1],newlabelname[i1],2048,0,2048);
for (int i=0;i<channelnum;i++)
{
fin[i1]>>tempdata;
if(i>24)
{
spec[i1]->SetBinContent(i-24,stod(tempdata));
}
}
spec[i1]->SetFillColor(kRed);
spec[i1]->SetYTitle("Counts");
spec[i1]->SetXTitle("Channel");
spec[i1]->SetLineColor(kRed);
spec[i1]->SetLabelSize(0.04,"X");
spec[i1]->SetLabelSize(0.04,"Y");
spec[i1]->SetLabelFont(font,"X");
spec[i1]->SetLabelFont(font,"Y");
spec[i1]->SetLabelOffset(0.01,"X");
spec[i1]->SetLabelOffset(0.01,"Y");
spec[i1]->SetTitleOffset(1.0,"X");
spec[i1]->SetTitleOffset(1.2,"Y");
spec[i1]->SetTitleOffset(1.2,"Z");
spec[i1]->SetTitleSize(0.05,"X");
spec[i1]->SetTitleSize(0.05,"Y");
spec[i1]->SetTitleSize(0.05,"Z");
spec[i1]->SetTitleFont(font,"X");
spec[i1]->SetTitleFont(font,"Y");
spec[i1]->SetTitleFont(font,"Z");
}
gStyle->SetOptStat("");
spec[1]->Add(spec[0],-(measuringtime[1]/measuringtime[0]));
for (int j=0;j<2;j++)
{
for (int i=0;i<2048;i++)
{
if(spec[j]->GetBinContent(i+1)<0) {spec[j]->SetBinContent(i+1,0);}
}
}
for (int i=0;i<20;i++)
{
spec[1]->SetBinContent(i+1,0);
}
outputFile->Write();
//spec[0]->Write();
//spec[1]->Write();
//outputFile->Close();
TCanvas*myc=new
TCanvas("myc","myc",650,450);
myc->SetLeftMargin(0.12);
myc->SetRightMargin(0.08);
myc->SetTopMargin(0.08);
myc->SetBottomMargin(0.12);
spec[1]->Draw();
//myc->Print(Form("CsI-NoBackground-sec1725.png"));
}
图2 绘制能谱
目的2：对能谱全能峰进行拟合
基本过程为：1）已经保存的ROOT文件中读取直方图；2）定义拟合函数的参数区间；3）选择感兴趣的几个函数用于全能峰拟合；4）绘制拟合结果。
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooGaussian.h"
#include "TCanvas.h"
#include "RooPlot.h"
#include "TAxis.h"
//#include <stdlib.h>
using namespace RooFit ;
using namespace std ;
void goodfit()
{
//TFile f1("20190501bgona22.root");
//TH1D* hist =(TH1D*)f1.Get("BGO-Na22");
TFile* inputFile = TFile::Open("20190501bgona22.root");
//TFile* inputFile = TFile::Open("CsI-1cm1cm1cm-new.root");
//TFile* inputFile = TFile::Open("GAGG-6mm6mm6mm-cs137-exp.root");
TH1F *spec[4];
//spec[0]=new TH1F();
spec[0]=(TH1F*)inputFile->Get("BGO-Na22");
//spec[0]=(TH1F*)inputFile->Get("Cs137-TC");
//spec[0]=(TH1F*)inputFile->Get("CsI-Cs137");
//spec[0]=(TH1F*)inputFile->Get("BGO-Cs137");
// S e t u p m o d e l
// ---------------------
//double myscale=1.0/h1->Integral();
//h1->Scale(myscale);//normalize the hist
int meanv=450;
int rangexmin=390;int rangexmax=580;
int xmin=rangexmin;int xmax=rangexmax;
RooRealVar x("x","x",xmin,xmax) ;
RooRealVar mean("mean","mean",meanv,rangexmin,rangexmax) ;
RooRealVar sigma("sigma","sigma",20,5.,500) ;
RooGaussian sig("sig","signal p.d.f.",x,mean,sigma) ;
RooRealVar argpar1("argpar1","argus shape parameter",20,0.,40.) ;
RooRealVar argpar2("argpar2","argus shape parameter",20,0.,40.) ;
RooArgusBG argus("argus","Argus PDF",x,argpar1,argpar2) ;
RooRealVar a0("a0","a0",-0.0001,-1.,0.1);
// RooRealVar a1("a1","", 0.5, -1, 100);
RooExponential bkg("bkg","background p.d.f.", x,a0);
RooRealVar c0("c0","coefficient #0", -1.0,-300.,10.) ;
RooRealVar c1("c1","coefficient #1", 0.1,-100.,1.) ;
RooRealVar c2("c2","coefficient #2",-0.1,-100.,2.) ;
RooChebychev compton("bkg","background p.d.f.",x,RooArgList(c0,c1,c2)) ;
RooRealVar mean_bkg("mean_bkg","mean",rangexmin/2+rangexmax/2,rangexmin,rangexmax);
RooRealVar sigma_bkg("sigma_bkg","sigma",30,10.,60.);
RooGaussian bkg_peak("bkg_peak","peaking bkg p.d.f.",x,mean_bkg,sigma_bkg) ;
RooRealVar fpeakbkg("fpeakbkg","peaking background fraction",0.5,0.4,1.) ;
RooAddPdf totalbkg("totalbkg","compton+bkg_peak",RooArgList(bkg_peak,compton),fpeakbkg);
RooRealVar fsig("fsig","signal fraction",0.9,0.5,1.) ;
//RooAddPdf
model("model","sig+(compton+bkg_peak)",RooArgList(sig,totalbkg),fsig);
RooAddPdf model("model","sig+bkg-e",RooArgList(sig,bkg),fsig) ;
//RooAddPdf
model("model","sig+compton",RooArgList(sig,compton),fsig) ;
//RooAddPdf
model("model","sig+compton",RooArgList(sig),fsig) ;
//RooAddPdf
model("model","sig+compton",RooArgList(sig,argus),fsig) ;
//RooPlot* frame = x.frame(Title("CsI Cs-137 662keV Photopeak Fitting;Channel"));
//RooPlot* frame = x.frame(Title("BGO Cs-137 662keV Photopeak Fitting;Channel"));
RooPlot* frame = x.frame(Title("BGO Na-22 511keV Photopeak Fitting;Channel"));
//RooPlot* frame = x.frame(Title("GAGG Ba-133 81keV Photopeak Fitting"));
RooAbsData* data = new RooDataHist("data","data",x,spec[0]);
data->plotOn(frame);
//data->plotOn(xframe,Binning(1000));
//model.fitTo(*data,Save(),Extended());
model.fitTo(*data,Save(),Extended(),Range(rangexmin,rangexmax));
//model.fitTo(*data,Save(),Extended(),Range(rangexmin,rangexmax));
model.plotOn(frame,LineColor(kGreen)) ;
//model.plotOn(frame,Components(argus),LineColor(kBlue),LineStyle(kDashed)) ;
model.plotOn(frame, Components(compton),LineColor(kBlue),LineStyle(kDashed)) ;
model.plotOn(frame, Components(sig),LineColor(kRed),LineStyle(kDashed)) ;
//model.plotOn(frame, Components(bkg_peak),LineColor(kViolet),LineStyle(kDashed)) ;
//model.plotOn(frame, Components(totalbkg),LineColor(kBlue),LineStyle(kDashed)) ;
//model.plotOn(frame, Components(bkg),LineColor(kBlue),LineStyle(kDashed)) ;
//model.paramOn(frame, Format("NEU"), Layout(0.15,0.50,0.9));
model.paramOn(frame,Parameters(RooArgSet(sigma,mean)), Format("NEU"), Layout(0.55,0.9,0.9));
//model.paramOn(frame,Parameters(RooArgSet(sigma,mean,c0,c1,c2)), Format("NEU"),Layout(0.55,0.9,0.9));
//model.paramOn(frame,Parameters(RooArgSet(sigma,mean,fsig,a0)), Format("NEU"), Layout(0.55,0.9,0.9));
TCanvas* c = new TCanvas("c2","Fitting test",1200,800);
frame->Draw() ;
//c->Print(Form("Na22-511keVfit0903.png"));
//c->Print(Form("Ba133-81keVfit0903.png"));
}
图3 全能峰拟合
到这里，两个目的均已达成，Roofit其实算是种很偷懒的拟合，未来的教程将探讨普适的Fit以及TSpectrum的机智用法。
图4 TSpectrum用作峰位的初始化值很方便，且很准确
喜欢的话，分享一下吧~^o^~
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原始发表：2019-05-13，如有侵权请联系 cloudcommunity@tencent.com 删除
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