rf510_wsnamedsets.py

## \ingroup tutorial_roofit_main

## \notebook

## 'ORGANIZATION AND SIMULTANEOUS FITS' RooFit tutorial macro #510

##

## Working with named parameter sets and parameter snapshots in

## workspaces

##

## \macro_image

## \macro_code

## \macro_output

##

## \date February 2018

## \authors Clemens Lange, Wouter Verkerke (C version)

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)