doc/v636/rf501__simultaneouspdf_8py.html

import ROOT


# Create model for physics sample

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


# Create observables

x = ROOT.RooRealVar("x", "x", -8, 8)


# Construct signal pdf

mean = ROOT.RooRealVar("mean", "mean", 0, -8, 8)

sigma = ROOT.RooRealVar("sigma", "sigma", 0.3, 0.1, 10)

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


# Construct background pdf

a0 = ROOT.RooRealVar("a0", "a0", -0.1, -1, 1)

a1 = ROOT.RooRealVar("a1", "a1", 0.004, -1, 1)

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


# Construct composite pdf

f = ROOT.RooRealVar("f", "f", 0.2, 0.0, 1.0)

model = ROOT.RooAddPdf("model", "model", [gx, px], [f])


# Create model for control sample

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


# Construct signal pdf.

# NOTE that sigma is shared with the signal sample model

mean_ctl = ROOT.RooRealVar("mean_ctl", "mean_ctl", -3, -8, 8)

gx_ctl = ROOT.RooGaussian("gx_ctl", "gx_ctl", x, mean_ctl, sigma)


# Construct the background pdf

a0_ctl = ROOT.RooRealVar("a0_ctl", "a0_ctl", -0.1, -1, 1)

a1_ctl = ROOT.RooRealVar("a1_ctl", "a1_ctl", 0.5, -0.1, 1)

px_ctl = ROOT.RooChebychev("px_ctl", "px_ctl", x, [a0_ctl, a1_ctl])


# Construct the composite model

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

model_ctl = ROOT.RooAddPdf("model_ctl", "model_ctl", [gx_ctl, px_ctl], [f_ctl])


# Generate events for both samples

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


# Generate 1000 events in x and y from model

data = model.generate({x}, 1000)

data_ctl = model_ctl.generate({x}, 2000)


# Create index category and join samples

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


# Define category to distinguish physics and control samples events

sample = ROOT.RooCategory("sample", "sample")

sample.defineType("physics")

sample.defineType("control")


# Construct combined dataset in (x,sample)

combData = ROOT.RooDataSet(

    "combData",

    "combined data",

    {x},

    Index=sample,

    Import={"physics": data, "control": data_ctl},

)


# Construct a simultaneous pdf in (x, sample)

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


# Construct a simultaneous pdf using category sample as index: associate model

# with the physics state and model_ctl with the control state

simPdf = ROOT.RooSimultaneous("simPdf", "simultaneous pdf", {"physics": model, "control": model_ctl}, sample)


# Perform a simultaneous fit

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


# Perform simultaneous fit of model to data and model_ctl to data_ctl

fitResult = simPdf.fitTo(combData, PrintLevel=-1, Save=True)

fitResult.Print()


# Plot model slices on data slices

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


# Make a frame for the physics sample

frame1 = x.frame(Title="Physics sample")


# Plot all data tagged as physics sample

combData.plotOn(frame1, Cut="sample==sample::physics")


# Plot "physics" slice of simultaneous pdf.

# NB: You *must* project the sample index category with data using ProjWData as

# a RooSimultaneous makes no prediction on the shape in the index category and

# can thus not be integrated. In other words: Since the PDF doesn't know the

# number of events in the different category states, it doesn't know how much

# of each component it has to project out. This info is read from the data.

simPdf.plotOn(frame1, Slice=(sample, "physics"), ProjWData=(sample, combData))

simPdf.plotOn(frame1, Slice=(sample, "physics"), Components="px", ProjWData=(sample, combData), LineStyle="--")


# The same plot for the control sample slice. We do this with a different

# approach this time, for illustration purposes. Here, we are slicing the

# dataset and then use the data slice for the projection, because then the

# RooFit::Slice() becomes unnecessary. This approach is more general,

# because you can plot sums of slices by using logical or in the Cut()

# command.

frame2 = x.frame(Title="Control sample")

slicedData = combData.reduce(Cut="sample==sample::control")

slicedData.plotOn(frame2)

simPdf.plotOn(frame2, ProjWData=(sample, slicedData))

simPdf.plotOn(frame2, Components="px_ctl", ProjWData=(sample, slicedData), LineStyle="--")


# The same plot for all the phase space. Here, we can just use the original

# combined dataset.

frame3 = x.frame(Title="Both samples")

combData.plotOn(frame3)

simPdf.plotOn(frame3, ProjWData=(sample, combData))

simPdf.plotOn(frame3, Components="px,px_ctl", ProjWData=(sample, combData), LineStyle="--")


c = ROOT.TCanvas("rf501_simultaneouspdf", "rf501_simultaneouspdf", 1200, 400)

c.Divide(3)


def draw(i, frame):

    c.cd(i)

    ROOT.gPad.SetLeftMargin(0.15)

    frame.GetYaxis().SetTitleOffset(1.4)

    frame.Draw()


draw(1, frame1)

draw(2, frame2)

draw(3, frame3)


c.SaveAs("rf501_simultaneouspdf.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

ROOT::Detail::TRangeCast
Definition TCollection.h:311