doc/master/rf303__conditional_8py.html

import ROOT


def makeFakeDataXY():


    trnd = ROOT.TRandom3()


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

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

    coord = {x, y}


    d = ROOT.RooDataSet("d", "d", coord)


    for i in range(10000):

        tmpy = trnd.Gaus(0, 10)

        tmpx = trnd.Gaus(0.5 * tmpy, 1)

        if (abs(tmpy) < 10) and (abs(tmpx) < 10):

            x.setVal(tmpx)

            y.setVal(tmpy)

            d.add(coord)


    return d


# Set up composed model gauss(x, m(y), s)

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


# Create observables

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

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


# Create function f(y) = a0 + a1*y

a0 = ROOT.RooRealVar("a0", "a0", -0.5, -5, 5)

a1 = ROOT.RooRealVar("a1", "a1", -0.5, -1, 1)

fy = ROOT.RooPolyVar("fy", "fy", y, [a0, a1])


# Creat gauss(x,f(y),s)

sigma = ROOT.RooRealVar("sigma", "width of gaussian", 0.5, 0.1, 2.0)

model = ROOT.RooGaussian("model", "Gaussian with shifting mean", x, fy, sigma)


# Obtain fake external experimental dataset with values for x and y

expDataXY = makeFakeDataXY()


# Generate data from conditional p.d.f. model(x|y)

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


# Make subset of experimental data with only y values

expDataY = expDataXY.reduce({y})


# Generate 10000 events in x obtained from _conditional_ model(x|y) with y

# values taken from experimental data

data = model.generate({x}, ProtoData=expDataY)

data.Print()


# Fit conditional p.d.f model(x|y) to data

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


model.fitTo(expDataXY, ConditionalObservables={y}, PrintLevel=-1)


# Project conditional p.d.f on x and y dimensions

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


# Plot x distribution of data and projection of model x = 1/Ndata

# sum(data(y_i)) model(x;y_i)

xframe = x.frame()

expDataXY.plotOn(xframe)

model.plotOn(xframe, ProjWData=expDataY)


# Speed up (and approximate) projection by using binned clone of data for

# projection

binnedDataY = expDataY.binnedClone()

model.plotOn(xframe, ProjWData=binnedDataY, LineColor="c", LineStyle=":")


# Show effect of projection with too coarse binning

(expDataY.get().find("y")).setBins(5)

binnedDataY2 = expDataY.binnedClone()

model.plotOn(xframe, ProjWData=binnedDataY2, LineColor="r")


# Make canvas and draw ROOT.RooPlots

c = ROOT.TCanvas("rf303_conditional", "rf303_conditional", 600, 460)

ROOT.gPad.SetLeftMargin(0.15)

xframe.GetYaxis().SetTitleOffset(1.2)

xframe.Draw()


c.SaveAs("rf303_conditional.png")