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利用Jupyter完成RooFit

时间:2021-4-24    作者:JourinTown    分类: 计算机技术


我們先把一些必要的包引用進來,事實上第二步不用那麼複雜,這裡不過是把常用的包引用進來了,你之後總還是可以用 ROOT.xxx來使用對應的函數

import ROOT
from ROOT import RooRealVar, RooFormulaVar, RooVoigtian, RooChebychev, RooArgList, \
                 RooArgSet, RooAddPdf, RooDataSet, RooCategory, RooSimultaneous, \
                 RooBreitWigner, RooCBShape, RooFFTConvPdf, RooGaussian

然後我們就可以Fit了,這裡是一個案例

tree = ROOT.TChain("mphi")
tree.Add(args.path)
x_min = 0.98
x_max = 1.06
n_bins = 100

mphi=RooRealVar("mphi","",x_min,x_max)

mean1=RooRealVar("mean1", "",          1.020, 1, 1.03)
sigma1=RooRealVar("sigma1", "",        1.94489e-03, 1.5e-03,0.01)
mean2=RooRealVar("mean2", "",          1.019, 1, 1.020)
sigma2=RooRealVar("sigma2", "",        1.94489e-03, 1.5e-03,3.0)
gaus1=RooGaussian("gaus1", "gaus1", mphi, mean1, sigma1)
gaus2=RooGaussian("gaus2", "gaus2", mphi, mean2, sigma2)

c2=RooRealVar("c2","coeffiicient #2",-0.1,-30000,30000) 
bkg=RooChebychev("bkg","background p.d.f",mphi,RooArgList(c2))

Nsig1=RooRealVar("Nsig1", "", 1000, 0, 20000)
Nsig2=RooRealVar("Nsig2", "", 1000, 0, 20000)
Nbg=RooRealVar("Nbg", "", 200,0,2000)

model=RooAddPdf("model", "model", RooArgList(gaus1, gaus2, bkg), RooArgList(Nsig1,Nsig2, Nbg))

dataset_data = RooDataSet("dataset_data", "data",tree, mphi)
fit_res_all = model.fitTo(dataset_data, ROOT.RooFit.Extended(ROOT.kTRUE))

canvas = ROOT.TCanvas()
frame = mphi.frame(ROOT.RooFit.Bins(n_bins),ROOT.RooFit.Title("unbin maximum likehood Fit"))
dataset_data.plotOn(frame, ROOT.RooFit.Binning(n_bins), ROOT.RooFit.LineColor(1), ROOT.RooFit.Name("data"));
model.plotOn(frame, ROOT.RooFit.LineColor(9), ROOT.RooFit.Name("model"));
model.plotOn(frame, ROOT.RooFit.Components("gaus1"), ROOT.RooFit.LineStyle(2),ROOT.RooFit.LineColor(2));
model.plotOn(frame, ROOT.RooFit.Components("gaus2"), ROOT.RooFit.LineStyle(2),ROOT.RooFit.LineColor(3));
model.plotOn(frame, ROOT.RooFit.Components("bkg"), ROOT.RooFit.LineStyle(2),ROOT.RooFit.LineColor(1));
frame.GetXaxis().SetTitle("M_{#phi}(GeV/c^{2})");
frame.GetYaxis().SetTitle("Event");
frame.GetXaxis().CenterTitle()
frame.GetYaxis().CenterTitle()
frame.Draw()
lt=ROOT.TLatex()
lt.SetNDC()
lt.SetTextAngle(0)
lt.SetTextSize(0.04)
NSig1text="Nsig1="+str(int(Nsig1.getVal()))+" #pm "+str(int(Nsig1.getError()))
NSig2text="Nsig2="+str(int(Nsig2.getVal()))+" #pm "+str(int(Nsig2.getError()))
Nbkgtext="Nbkg="+str(int(Nbg.getVal()))+" #pm "+str(int(Nbg.getError()))
lt.DrawLatex(0.6, 0.70,  "#phi #rightarrow K^{+} K^{-}")
lt.DrawLatex(0.6, 0.65,  NSig1text)
lt.DrawLatex(0.6, 0.60,  NSig2text)
lt.DrawLatex(0.6, 0.55,  Nbkgtext)
canvas.Draw()
canvas.Print(args.o)



你也來試試吧!

具體也可以查看這一處的案例https://cc.benhaotang.cn/share/b9146c1b60c7c3ce9cd51b06d426785e1843d556/Fit.ipynb?viewer=share

标签: ROOT 拟合

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评论:
JourinTown 2021-04-24 20:23
Fitting $\phi \to K^+ K^-$