doc/v632/rf801__mcstudy_8py.html

import ROOT


# Create model

# -----------------------


# Declare observable x

x = ROOT.RooRealVar("x", "x", 0, 10)

x.setBins(40)


# Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and

# their parameters

mean = ROOT.RooRealVar("mean", "mean of gaussians", 5, 0, 10)

sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)

sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)


sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)

sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)


# Build Chebychev polynomial pdf

a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)

a1 = ROOT.RooRealVar("a1", "a1", -0.2, -1, 1.0)

bkg = ROOT.RooChebychev("bkg", "Background", x, [a0, a1])


# Sum the signal components into a composite signal pdf

sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)

sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])


# Sum the composite signal and background

nbkg = ROOT.RooRealVar("nbkg", "number of background events, ", 150, 0, 1000)

nsig = ROOT.RooRealVar("nsig", "number of signal events", 150, 0, 1000)

model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [nbkg, nsig])


# Create manager

# ---------------------------


# Instantiate ROOT.RooMCStudy manager on model with x as observable and given choice of fit options

#

# The Silence() option kills all messages below the PROGRESS level, only a single message

# per sample executed, any error message that occur during fitting

#

# The Extended() option has two effects:

#    1) The extended ML term is included in the likelihood and

#    2) A poisson fluctuation is introduced on the number of generated events

#

# The FitOptions() given here are passed to the fitting stage of each toy experiment.

# If Save() is specified, fit result of each experiment is saved by the manager

#

# A Binned() option is added in self example to bin the data between generation and fitting

# to speed up the study at the expemse of some precision


mcstudy = ROOT.RooMCStudy(

    model,

    {x},

    Binned=True,

    Silence=True,

    Extended=True,

    FitOptions=dict(Save=True, PrintEvalErrors=0),

)


# Generate and fit events

# ---------------------------------------------


# Generate and fit 1000 samples of Poisson(nExpected) events

mcstudy.generateAndFit(1000)


# Explore results of study

# ------------------------------------------------


# Make plots of the distributions of mean, error on mean and the pull of

# mean

frame1 = mcstudy.plotParam(mean, Bins=40)

frame2 = mcstudy.plotError(mean, Bins=40)

frame3 = mcstudy.plotPull(mean, Bins=40, FitGauss=True)


# Plot distribution of minimized likelihood

frame4 = mcstudy.plotNLL(Bins=40)


# Make some histograms from the parameter dataset

hh_cor_a0_s1f = mcstudy.fitParDataSet().createHistogram("hh", a1, YVar=sig1frac)

hh_cor_a0_a1 = mcstudy.fitParDataSet().createHistogram("hh", a0, YVar=a1)


# Access some of the saved fit results from individual toys

corrHist000 = mcstudy.fitResult(0).correlationHist("c000")

corrHist127 = mcstudy.fitResult(127).correlationHist("c127")

corrHist953 = mcstudy.fitResult(953).correlationHist("c953")


# Draw all plots on a canvas

ROOT.gStyle.SetPalette(1)

ROOT.gStyle.SetOptStat(0)

c = ROOT.TCanvas("rf801_mcstudy", "rf801_mcstudy", 900, 900)

c.Divide(3, 3)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

frame1.GetYaxis().SetTitleOffset(1.4)

frame1.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.15)

frame2.GetYaxis().SetTitleOffset(1.4)

frame2.Draw()

c.cd(3)

ROOT.gPad.SetLeftMargin(0.15)

frame3.GetYaxis().SetTitleOffset(1.4)

frame3.Draw()

c.cd(4)

ROOT.gPad.SetLeftMargin(0.15)

frame4.GetYaxis().SetTitleOffset(1.4)

frame4.Draw()

c.cd(5)

ROOT.gPad.SetLeftMargin(0.15)

hh_cor_a0_s1f.GetYaxis().SetTitleOffset(1.4)

hh_cor_a0_s1f.Draw("box")

c.cd(6)

ROOT.gPad.SetLeftMargin(0.15)

hh_cor_a0_a1.GetYaxis().SetTitleOffset(1.4)

hh_cor_a0_a1.Draw("box")

c.cd(7)

ROOT.gPad.SetLeftMargin(0.15)

corrHist000.GetYaxis().SetTitleOffset(1.4)

corrHist000.Draw("colz")

c.cd(8)

ROOT.gPad.SetLeftMargin(0.15)

corrHist127.GetYaxis().SetTitleOffset(1.4)

corrHist127.Draw("colz")

c.cd(9)

ROOT.gPad.SetLeftMargin(0.15)

corrHist953.GetYaxis().SetTitleOffset(1.4)

corrHist953.Draw("colz")


c.SaveAs("rf801_mcstudy.png")


# Make ROOT.RooMCStudy object available on command line after

# macro finishes

ROOT.gDirectory.Add(mcstudy)

TRangeDynCast
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Definition TCollection.h:358