doc/v632/rf510__wsnamedsets_8py.html

import ROOT


def fillWorkspace(w):

    # Create model

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


    # Declare observable x

    x = ROOT.RooRealVar("x", "x", 0, 10)


    # Create two Gaussian PDFs g1(x,mean1,sigma) anf g2(x,mean2,sigma) and

    # their parameters

    mean = ROOT.RooRealVar("mean", "mean of gaussians", 5, 0, 10)

    sigma1 = ROOT.RooRealVar("sigma1", "width of gaussians", 0.5)

    sigma2 = ROOT.RooRealVar("sigma2", "width of gaussians", 1)


    sig1 = ROOT.RooGaussian("sig1", "Signal component 1", x, mean, sigma1)

    sig2 = ROOT.RooGaussian("sig2", "Signal component 2", x, mean, sigma2)


    # Build Chebychev polynomial p.d.f.

    a0 = ROOT.RooRealVar("a0", "a0", 0.5, 0.0, 1.0)

    a1 = ROOT.RooRealVar("a1", "a1", -0.2, 0.0, 1.0)

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


    # Sum the signal components into a composite signal p.d.f.

    sig1frac = ROOT.RooRealVar("sig1frac", "fraction of component 1 in signal", 0.8, 0.0, 1.0)

    sig = ROOT.RooAddPdf("sig", "Signal", [sig1, sig2], [sig1frac])


    # Sum the composite signal and background

    bkgfrac = ROOT.RooRealVar("bkgfrac", "fraction of background", 0.5, 0.0, 1.0)

    model = ROOT.RooAddPdf("model", "g1+g2+a", [bkg, sig], [bkgfrac])


    # Import model into p.d.f.

    w.Import(model)


    # Encode definition of parameters in workspace

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


    # Define named sets "parameters" and "observables", list which variables should be considered

    # parameters and observables by the users convention

    #

    # Variables appearing in sets _must_ live in the workspace already, the autoImport flag

    # of defineSet must be set to import them on the fly. Named sets contain only references

    # to the original variables, the value of observables in named sets already

    # reflect their 'current' value

    params = model.getParameters({x})

    w.defineSet("parameters", params)

    w.defineSet("observables", {x})


    # Encode reference value for parameters in workspace

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


    # Define a parameter 'snapshot' in the p.d.f.

    # Unlike a named set, parameter snapshot stores an independent set of values for

    # a given set of variables in the workspace. The values can be stored and reloaded

    # into the workspace variable objects using the loadSnapshot() and saveSnapshot()

    # methods. A snapshot saves the value of each variable, errors that are stored

    # with it as well as the 'Constant' flag that is used in fits to determine if a

    # parameter is kept fixed or not.


    # Do a dummy fit to a (supposedly) reference dataset here and store the results

    # of that fit into a snapshot

    refData = model.generate({x}, 10000)

    model.fitTo(refData, PrintLevel=-1)


    # The kTRUE flag imports the values of the objects in (*params) into the workspace

    # If not set, present values of the workspace parameters objects are stored

    w.saveSnapshot("reference_fit", params, True)


    # Make another fit with the signal componentforced to zero

    # and save those parameters too


    bkgfrac.setVal(1)

    bkgfrac.setConstant(True)

    bkgfrac.removeError()

    model.fitTo(refData, PrintLevel=-1)


    w.saveSnapshot("reference_fit_bkgonly", params, True)


# Create model and dataset

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


w = ROOT.RooWorkspace("w")

fillWorkspace(w)


# Exploit convention encoded in named set "parameters" and "observables"

# to use workspace contents w/o need for introspected

model = w["model"]


# Generate data from p.d.f. in given observables

data = model.generate(w.set("observables"), 1000)


# Fit model to data

model.fitTo(data, PrintLevel=-1)


# Plot fitted model and data on frame of first (only) observable

frame = (w.set("observables").first()).frame()

data.plotOn(frame)

model.plotOn(frame)


# Overlay plot with model with reference parameters as stored in snapshots

w.loadSnapshot("reference_fit")

model.plotOn(frame, LineColor="r")

w.loadSnapshot("reference_fit_bkgonly")

model.plotOn(frame, LineColor="r", LineStyle="--")


# Draw the frame on the canvas

c = ROOT.TCanvas("rf510_wsnamedsets", "rf503_wsnamedsets", 600, 600)

ROOT.gPad.SetLeftMargin(0.15)

frame.GetYaxis().SetTitleOffset(1.4)

frame.Draw()


c.SaveAs("rf510_wsnamedsets.png")


# Print workspace contents

w.Print()


# Workspace will remain in memory after macro finishes

ROOT.gDirectory.Add(w)

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