Rf 5 1 0_Wsnamedsets¶

'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #510

Working with named parameter sets and parameter snapshots in workspaces

Author: Wouter Verkerke (C version)
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, January 17, 2022 at 10:06 AM.

In [ ]:
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¶

In [ ]:
w = ROOT.RooWorkspace("w")
fillWorkspace(w)

Exploit convention encoded in named set "parameters" and "observables" to use workspace contents w/o need for introspected

In [ ]:
model = w["model"]

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

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

Fit model to data

In [ ]:
model.fitTo(data)

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

In [ ]:
frame = (w.set("observables").first()).frame()
data.plotOn(frame)
model.plotOn(frame)

Overlay plot with model with reference parameters as stored in snapshots

In [ ]:
model.plotOn(frame, LineColor="r")
model.plotOn(frame, LineColor="r", LineStyle="--")

Draw the frame on the canvas

In [ ]:
c = ROOT.TCanvas("rf510_wsnamedsets", "rf503_wsnamedsets", 600, 600)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()

c.SaveAs("rf510_wsnamedsets.png")

Print workspace contents

In [ ]:
w.Print()

Workspace will remain in memory after macro finishes

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