doc/v624/rf603__multicpu_8py.html

import ROOT


# Create 3D pdf and data

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


# Create observables

x = ROOT.RooRealVar("x", "x", -5, 5)

y = ROOT.RooRealVar("y", "y", -5, 5)

z = ROOT.RooRealVar("z", "z", -5, 5)


# Create signal pdf gauss(x)*gauss(y)*gauss(z)

gx = ROOT.RooGaussian(

    "gx", "gx", x, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(1))

gy = ROOT.RooGaussian(

    "gy", "gy", y, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(1))

gz = ROOT.RooGaussian(

    "gz", "gz", z, ROOT.RooFit.RooConst(0), ROOT.RooFit.RooConst(1))

sig = ROOT.RooProdPdf("sig", "sig", ROOT.RooArgList(gx, gy, gz))


# Create background pdf poly(x)*poly(y)*poly(z)

px = ROOT.RooPolynomial("px", "px", x, ROOT.RooArgList(

    ROOT.RooFit.RooConst(-0.1), ROOT.RooFit.RooConst(0.004)))

py = ROOT.RooPolynomial("py", "py", y, ROOT.RooArgList(

    ROOT.RooFit.RooConst(0.1), ROOT.RooFit.RooConst(-0.004)))

pz = ROOT.RooPolynomial("pz", "pz", z)

bkg = ROOT.RooProdPdf("bkg", "bkg", ROOT.RooArgList(px, py, pz))


# Create composite pdf sig+bkg

fsig = ROOT.RooRealVar("fsig", "signal fraction", 0.1, 0., 1.)

model = ROOT.RooAddPdf("model", "model", ROOT.RooArgList(

    sig, bkg), ROOT.RooArgList(fsig))


# Generate large dataset

data = model.generate(ROOT.RooArgSet(x, y, z), 200000)


# Parallel fitting

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


# In parallel mode the likelihood calculation is split in N pieces,

# that are calculated in parallel and added a posteriori before passing

# it back to MINUIT.


# Use four processes and time results both in wall time and CPU time

model.fitTo(data, ROOT.RooFit.NumCPU(4), ROOT.RooFit.Timer(ROOT.kTRUE))


# Parallel MC projections

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


# Construct signal, likelihood projection on (y,z) observables and

# likelihood ratio

sigyz = sig.createProjection(ROOT.RooArgSet(x))

totyz = model.createProjection(ROOT.RooArgSet(x))

llratio_func = ROOT.RooFormulaVar(

    "llratio", "log10(@0)-log10(@1)", ROOT.RooArgList(sigyz, totyz))


# Calculate likelihood ratio for each event, subset of events with high

# signal likelihood

data.addColumn(llratio_func)

dataSel = data.reduce(ROOT.RooFit.Cut("llratio>0.7"))


# Make plot frame and plot data

frame = x.frame(ROOT.RooFit.Title(

    "Projection on X with LLratio(y,z)>0.7"), ROOT.RooFit.Bins(40))

dataSel.plotOn(frame)


# Perform parallel projection using MC integration of pdf using given input dataSet.

# In self mode the data-weighted average of the pdf is calculated by splitting the

# input dataset in N equal pieces and calculating in parallel the weighted average

# one each subset. The N results of those calculations are then weighted into the

# final result


# Use four processes

model.plotOn(frame, ROOT.RooFit.ProjWData(dataSel), ROOT.RooFit.NumCPU(4))


c = ROOT.TCanvas("rf603_multicpu", "rf603_multicpu", 600, 600)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.6)

frame.Draw()


c.SaveAs("rf603_multicpu.png")