Ubungen zu Moderne Methoden der Datenanalyse ¨ Some useful ROOT commands

Institut f¨ur Experimentelle Kernphysik (EKP) Prof. Dr. M. Feindt, Dr. T. Kuhr

M. R¨ohrken, B. Kronenbitter, Dr. A. Zupanc

21. October 2010

Ubungen zu Moderne Methoden der Datenanalyse ¨ Some useful ROOT commands

1D histograms:

Create histogram TH1F* h1 = new TH1F(“name”,”title”,nbin, x_min, x_max);

Fill histogram h1→ Fill(x, weight = 1);

Set entry of i-th bin h1→ SetBinContent(i,x);

Set error of i-th bin h1→ SetBinError(i,x);

Get entry of i-th bin h1→ GetBinContent(i,x);

Draw histogram h1→ Draw();

Reset histogram h1→ Reset();

Write histogram content

into an array Float t* content = h1→ GetArray();

Set maximum of y-axis h1→ SetMaximum(y_max);

Set minimum of y-axis h1→ SetMaximum(y_min);

2

2D histograms:

Create histogram TH2F* h2

= new TH2F(“name”,”title”,nbin_x, x_min, x_max,nbin_y, y_min, y_max);

Fill histogram h2→ Fill(x,y, weight = 1);

Draw histogram h2→ Draw();

Useful draw options h2→ Draw(“BOX”), h2 →Draw(“SAME”);

Profiles of scatter plots are sometimes more demonstrative Create profile TProfile* tp

= new TProfile(“name”,”title”,nbin_x, x_min, x_max,nbin_y, y_min, y_max);

Filling and drawing analog to TH2F

3

Canvas:

Create canvas TCanvas* c1 = new TCanvas(“c1”,”c1”,800,600);

Draw histogram h1 on canvas c1 →cd(); h1 → Draw(); c1 →Update();

Divide canvas c1 →Divide(3,2);

Draw h1 in pad c1 →cd(1); h1 → Draw(); c1 → Update();

Functions:

Create functions TF1* f1 = new TF1(“f1”,”sin(x)/x”,x_min, x_max);

Create parameterized TF1* f2

functions [0],[1],[2] = new TF1(“f2”,”[0]*sin([1]*x)/x + [2]”,x_min, x_max);

Set parameter Double t par[3];

f2 → SetParameter(par);

Get i-th parameter Double t pari = f2 → GetParameter(i);

Get error Double t erri = f2 → GetParError(i);

Fit histogram with function h1 →Fit(“f1”);

Fit within the definiton range of the fct. h1 →Fit(“f1”,”R”);

Fit within the range x₁,x₂ h1 →Fit(“f1”,””,””,x₁,x₂);

Random generators:

Preset generator is already initialized

with time-depended start value (seed = 0) Change preset generator delete gRandom;

(faster and higher periodicity) gRandom = new TRandom3(seed = 0);

predefined random distribution gaus(E=0, σ = 1), uniform(x₁, x₂),

landau(E = 0, σ = 1), poisson(E), binomial(n,p) Fill histogram with gaus distributed

random numbers h1 →FillRandom(“gaus”,n);

Fill histogram with random numbers

according to fct. f1 (TH1F) h1 →FillRandom(“f1”,n);

gStyle / gSystem:

Show fitparamter in statistic box gStyle → SetOptFit(11....1);

Additional paramter in statistic box gStyle → SetOptStat(11....1);

Include pause (t in ms) gSystem → Sleep(t);

4

Ntuple:

Create ntuple (x₁, x₂, ...x_n(n <15)) TNtuple* nt

= new TNtuple(“name”,”title”,x₁ :x₂ :...:x_n);

Fill ntuple nt → Fill(x₁, x₂, ...x_n);

Draw a variable of the ntuple nt → Draw(“x₁”);

Project a variable of the ntuple

into a histogram nt → Project(“h1”, “x₁”);

Draw a variable with cuts nt → Draw(“x₁”,”x₂ <3”);

Draw two variables nt → Draw(“x₁ :x₂”);

Tree:

As far as TTree is used in the exercises, they can be used analog to TNtuple (draw, project, ...)

File:

Create file TFile* file = new TFile(“file.root”);

Write histogram h1 into file file → cd();

h1→ Write();

Close file file → Close();

Read histogram from file TH1F* h1 = (TH1F*) file → Get(“histoname”);

Read tree from file TTree* t1 = (TTree*) file →Get(“treename”);