doc/v632/rf211__paramconv_8py.html

import ROOT


# Set up component pdfs

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


# Gaussian g(x ; mean,sigma)

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

mean = ROOT.RooRealVar("mean", "mean", -3, 3)

sigma = ROOT.RooRealVar("sigma", "sigma", 0.5, 0.1, 10)

modelx = ROOT.RooGaussian("gx", "gx", x, mean, sigma)


# Block function in mean

a = ROOT.RooRealVar("a", "a", 2, 1, 10)

model_mean = ROOT.RooGenericPdf("model_mean", "abs(mean)<a", [mean, a])


# Convolution in mean model = g(x,mean,sigma) (x) block(mean)

x.setBins(1000, "cache")

mean.setBins(50, "cache")

model = ROOT.RooFFTConvPdf("model", "model", mean, modelx, model_mean)


# Configure convolution to construct a 2-D cache in (x,mean)

# rather than a 1-d cache in mean that needs to be recalculated

# for each value of x

model.setCacheObservables({x})

model.setBufferFraction(1.0)


# Integrate model over projModel = Int model dmean

projModel = model.createProjection({mean})


# Generate 1000 toy events

d = projModel.generateBinned({x}, 1000)


# Fit p.d.f. to toy data

projModel.fitTo(d, Verbose=True, PrintLevel=-1)


# Plot data and fitted p.d.f.

frame = x.frame(Bins=25)

d.plotOn(frame)

projModel.plotOn(frame)


# Make 2d histogram of model(x;mean)

hh = model.createHistogram(

    "hh",

    x,

    Binning=50,

    YVar=dict(var=mean, Binning=50),

    ConditionalObservables={mean},

)

hh.SetTitle("histogram of model(x|mean)")

hh.SetLineColor(ROOT.kBlue)


# Draw frame on canvas

c = ROOT.TCanvas("rf211_paramconv", "rf211_paramconv", 800, 400)

c.Divide(2)

c.cd(1)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.4)

frame.Draw()

c.cd(2)

ROOT.gPad.SetLeftMargin(0.20)

hh.GetZaxis().SetTitleOffset(2.5)

hh.Draw("surf")


c.SaveAs("rf211_paramconv.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