doc/v634/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, 0.0, 1.0)

gy = ROOT.RooGaussian("gy", "gy", y, 0.0, 1.0)

gz = ROOT.RooGaussian("gz", "gz", z, 0.0, 1.0)

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


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

px = ROOT.RooPolynomial("px", "px", x, [-0.1, 0.004])

py = ROOT.RooPolynomial("py", "py", y, [0.1, -0.004])

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

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


# Create composite pdf sig+bkg

fsig = ROOT.RooRealVar("fsig", "signal fraction", 0.1, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "model", [sig, bkg], [fsig])


# Generate large dataset

data = model.generate({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, NumCPU=4, Timer=True, PrintLevel=-1)


# Parallel MC projections

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


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

# likelihood ratio

sigyz = sig.createProjection({x})

totyz = model.createProjection({x})

llratio_func = ROOT.RooFormulaVar("llratio", "log10(@0)-log10(@1)", [sigyz, totyz])


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

# signal likelihood

data.addColumn(llratio_func)

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


# Make plot frame and plot data

frame = x.frame(Title="Projection on X with LLratio(y,z)>0.7", 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, ProjWData=dataSel, 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")

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