def main ():
N_bkg1 = 9000
N_signal = 1000
N_bkg1_obs = 10000
N_signal_obs = 2000
N_data = N_bkg1_obs + N_signal_obs
mu1, mu2, sigma1, sigma2 = 100, 140, 15, 5
x1 = mu1 + sigma1 * np.random.randn( N_bkg1 )
x2 = mu2 + sigma2 * np.random.randn( N_signal )
x1_obs = mu1 + sigma1 * np.random.randn( N_bkg1_obs )
x2_obs = mu2 + sigma2 * np.random.randn( N_signal_obs )
h1 = Hist( 100, 40, 200, title = 'Background' )
h2 = h1.Clone( title = 'Signal' )
h3 = h1.Clone( title = 'Data' )
h3.markersize = 1.2
# fill the histograms with our distributions
map( h1.Fill, x1 )
map( h2.Fill, x2 )
map( h3.Fill, x1_obs )
map( h3.Fill, x2_obs )
histograms_1 = {'signal': h2,
'bkg1': h1,
'data': h3}
histograms_2 = {'signal': h2,
'bkg1': h1,
'data': h3}
# roofit_histograms contains RooDataHist
# model = RooAddPdf
model1, roofit_histograms_1,fit_variable_1 = get_roofit_model( histograms_1, fit_boundaries = ( 40, 200 ), name = 'm1' )
model2, roofit_histograms_2, fit_variable_2 = get_roofit_model( histograms_2, fit_boundaries = ( 40, 200 ), name = 'm2' )
sample = RooCategory( 'sample', 'sample' )
sample.defineType( 'm1', 1 )
sample.defineType( 'm2', 2 )
combined_data = deepcopy( roofit_histograms_1['data'] )
combined_data.add( roofit_histograms_2['data'] )
# RooDataHist(const char* name, const char* title, const RooArgList& vars, RooCategory& indexCat, map<std::string,TH1*> histMap, Double_t initWgt = 1.0)
sim_pdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample )
sim_pdf.addPdf( model1, 'm1' )
sim_pdf.addPdf( model2, 'm2' )
variables = RooArgList()
variables.add(fit_variable_1)
variables.add(fit_variable_2)
# combined_data = RooDataHist('combined_data', 'combined_data',
# variables, RooFit.Index(sample),
# RooFit.Import('m1', roofit_histograms_1['data']),
# RooFit.Import('m2', roofit_histograms_2['data']))
fitResult = sim_pdf.fitTo( combined_data,
# RooFit.Minimizer( "Minuit2", "Migrad" ),
# RooFit.NumCPU( 1 ),
# RooFit.Extended(),
RooFit.Save(),
)
def fit(self):
sim_pdf = RooSimultaneous("simPdf", "simultaneous pdf", self.sample)
self.individual_results = {}
for name, model in self.models.iteritems():
fit_input = self.fit_data[name]
model.fitTo(fit_input.real_data_roofit_histogram())
self.individual_results[name] = fit_input.get_results()
sim_pdf.addPdf(model, name)
argument_list = RooLinkedList()
argument_list.Add(RooFit.Minimizer("Minuit2", "Migrad"))
argument_list.Add(RooFit.NumCPU(1))
argument_list.Add(RooFit.Extended())
argument_list.Add(RooFit.Save())
sim_pdf.fitTo(
self.comb_data,
# argument_list
)
# sim_pdf.fitTo( self.combined_data,
# RooFit.Minimizer( "Minuit2", "Migrad" ) )
# sim_pdf.fitTo( data = self.combined_data,
# arg1 = RooFit.Minimizer( "Minuit2", "Migrad" ),
# arg2 = RooFit.NumCPU( 1 ),
# arg3 = RooFit.Extended(),
# arg4 = RooFit.Save() )
# sim_pdf.fitTo( self.combined_data,
# argument_list )
# get fit results
results = {}
for variable, fit_input in self.fit_data.iteritems():
results[variable] = fit_input.get_results()
self.results = results
return results
def fit(self):
sim_pdf = RooSimultaneous("simPdf", "simultaneous pdf", self.sample)
self.individual_results = {}
for name, model in self.models.iteritems():
fit_input = self.fit_data[name]
model.fitTo(fit_input.real_data_roofit_histogram())
self.individual_results[name] = fit_input.get_results()
sim_pdf.addPdf(model, name)
argument_list = RooLinkedList()
argument_list.Add(RooFit.Minimizer("Minuit2", "Migrad"))
argument_list.Add(RooFit.NumCPU(1))
argument_list.Add(RooFit.Extended())
argument_list.Add(RooFit.Save())
sim_pdf.fitTo(self.comb_data,
# argument_list
)
# sim_pdf.fitTo( self.combined_data,
# RooFit.Minimizer( "Minuit2", "Migrad" ) )
# sim_pdf.fitTo( data = self.combined_data,
# arg1 = RooFit.Minimizer( "Minuit2", "Migrad" ),
# arg2 = RooFit.NumCPU( 1 ),
# arg3 = RooFit.Extended(),
# arg4 = RooFit.Save() )
# sim_pdf.fitTo( self.combined_data,
# argument_list )
# get fit results
results = {}
for variable, fit_input in self.fit_data.iteritems():
results[variable] = fit_input.get_results()
self.results = results
return results
def dump_simple_simul():
# idea: take two data sets, both low counts, but same lambda
# and fit said lambda simultanously with both sets
r_x = RooRealVar('x', 'x', 0, bins)
r_lam = RooRealVar('lam', 'lam', 0.5, 0.0, 1.5)
r_lam1 = RooRealVar('lam1', 'lam1', 0.5, 0.0, 1.5)
r_lam2 = RooRealVar('lam2', 'lam2', 0.5, 0.0, 1.5)
model1 = RooPoisson('pois1', 'pois1', r_x, r_lam1)
model2 = RooPoisson('pois2', 'pois2', r_x, r_lam2)
r_index = RooCategory('index', 'index')
r_index.defineType('1')
r_index.defineType('2')
simul_model = RooSimultaneous('model', 'model', r_index)
simul_model.addPdf(model1, '1')
simul_model.addPdf(model2, '2')
data1 = RooDataSet('', '', RooArgSet(r_x))
for val in unbinned_from_binned(xdata, ydata):
r_x.setVal(val)
data1.add(RooArgSet(r_x))
r_index.setLabel('1')
data1.addColumn(r_index)
data2 = RooDataSet('', '', RooArgSet(r_x))
for val in unbinned_from_binned(xdata, ydata2):
r_x.setVal(val)
data2.add(RooArgSet(r_x))
r_index.setLabel('2')
data2.addColumn(r_index)
data1.append(data2)
_result = simul_model.fitTo(data1)
print(r_lam.getVal(), '+-', r_lam.getError(), lam, np.abs(r_lam.getVal() - lam) / lam)
print(r_lam1.getVal(), '+-', r_lam1.getError(), lam, np.abs(r_lam1.getVal() - lam) / lam)
print(r_lam2.getVal(), '+-', r_lam2.getError(), lam, np.abs(r_lam2.getVal() - lam) / lam)
def mbc_single_3s(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa,
fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2,
title1, title2, epsfile, txtfile, ymin=0.5,
cuts=None, err_type='SYMM', test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous,
RooArgusBG, RooFormulaVar, RooDLineShape, RooAbsData,
RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta,
TPaveText)
set_root_style(stat=1, grid=0)
# // sp1 = sigma of signal
# // sp2 = ratio of sigmas betwwen sigma2 sigma 1
# // sp3 = ratio of sigmas betwwen sigma3 sigma 2
# // fa, fb, - fractions
# // xi_side - slope of argus
# // p_side - power of argus
# mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc
# mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav','D flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' %cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres","mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE","sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1","sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2","scalep2",2.00,1.500,5.500)
scalep3 = RooRealVar("scalep3","scalep3",5.00,3.00,10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1,scalep2)
sigmap2 = RooFormulaVar("sigmap2","sigma2","sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1,scalep2,scalep3)
sigmap3 = RooFormulaVar("sigmap3","sigma3","sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md","md", setmd,1.863,1.875)
f2 = RooRealVar("f2","f2", fa)
f3 = RooRealVar("f3","f3", fb)
al23 = RooArgList(f2,f3)
f1 = RooFormulaVar("f1","f1","1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1","DLineShape1_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn1_2 = RooDLineShape("DLineshape1_2","DLineShape1_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn1_3 = RooDLineShape("DLineshape1_3","DLineShape1_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
fcn2_1 = RooDLineShape("DLineshape2_1","DLineShape2_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn2_2 = RooDLineShape("DLineshape2_2","DLineShape2_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn2_3 = RooDLineShape("DLineshape2_3","DLineShape2_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
alf1_123 = RooArgList(fcn1_1,fcn1_2,fcn1_3)
af12 = RooArgList(f1,f2)
signal1_3 = RooAddPdf("signal1_3","signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1,fcn2_2,fcn2_3)
signal2_3 = RooAddPdf("signal2_3","signal2_3", alf2_123, af12)
p = RooRealVar("p","p", setp, 0.1, 1.5)
xi= RooRealVar("xi","xi",setxi,-100.0,-0.1)
Bkgd1 = RooArgusBG("argus1","argus1",mbc,ebeam,xi,p)
Bkgd2 = RooArgusBG("argus2","argus2",mbc,ebeam,xi,p)
shapes1 = RooArgList(signal1_3)
shapes1.add(signal1_3)
shapes1.add(Bkgd1)
shapes2 = RooArgList(signal2_3)
shapes2.add(signal2_3)
shapes2.add(Bkgd2)
N1 = RooRealVar("N1","N1",setN1,0.0,200000000.0)
N2 = RooRealVar("N2","N2",setN2,0.0,200000000.0)
Nbkgd1 = RooRealVar("Nbkgd1","Nbkgd1",setNbkgd1, 0.0, 200000000.0)
Nbkgd2 = RooRealVar("Nbkgd2","Nbkgd2",setNbkgd2, 0.0, 200000000.0)
yields1 = RooArgList(N1)
yields1.add(N1)
yields1.add(Nbkgd1)
yields2 = RooArgList(N2)
yields2.add(N2)
yields2.add(Nbkgd2)
totalPdf1 = RooAddPdf("totalPdf1","totalPdf1", shapes1,yields1)
totalPdf2 = RooAddPdf("totalPdf2","totalPdf2", shapes2,yields2)
totalPdf = RooSimultaneous("totalPdf","totalPdf",dflav)
totalPdf.addPdf(totalPdf1,"dflav")
totalPdf.addPdf(totalPdf2,"dbarflav")
# Check fitTo options at:
# http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo
#
# Available fit options:
# "m" = MIGRAD only, i.e. no MINOS
# "s" = estimate step size with HESSE before starting MIGRAD
# "h" = run HESSE after MIGRAD
# "e" = Perform extended MLL fit
# "0" = Run MIGRAD with strategy MINUIT 0
# (no correlation matrix calculation at end)
# Does not apply to HESSE or MINOS, if run afterwards.
# "q" = Switch off verbose mode
# "l" = Save log file with parameter values at each MINUIT step
# "v" = Show changed parameters at each MINUIT step
# "t" = Time fit
# "r" = Save fit output in RooFitResult object
# Available optimizer options
# "c" = Cache and precalculate components of PDF that exclusively
# depend on constant parameters
# "2" = Do NLL calculation in multi-processor mode on 2 processors
# "3" = Do NLL calculation in multi-processor mode on 3 processors
# "4" = Do NLL calculation in multi-processor mode on 4 processors
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' %epsfile)
sys.stdout.write('Will save txtfile as: %s \n' %txtfile)
return
if dataset.numEntries() == 0:
N1.setVal(0)
N2.setVal(0)
else:
# Start Fitting
fitres = totalPdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas','mbc', 1200, 400);
canvas.Divide(3,1)
canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1')
canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2')
canvas_1.SetLogy(1)
canvas_2.SetLogy(1)
LineColorRed = RooFit.LineColor(kRed)
LineColorBlue = RooFit.LineColor(kBlue)
LineWidth = RooFit.LineWidth(1) #0.6)
# Plot the D
canvas.cd(1)
mbcFrame=mbc.frame()
mbcFrame=mbc.frame(60)
dflav.setLabel('dflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq1 = mbcFrame.chiSquare()*mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg1 = RooArgSet(Bkgd1)
cp_bkg1 = RooFit.Components(as_bkg1)
totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth, ProjWData)
mbcFrame.SetTitle(title1)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot the D bar
canvas.cd(2)
mbcFrame=mbc.frame()
mbcFrame=mbc.frame(60)
dflav.setLabel('dbarflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq2 = mbcFrame.chiSquare()*mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg2 = RooArgSet(Bkgd2)
cp_bkg2 = RooFit.Components(as_bkg2)
totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth, ProjWData)
mbcFrame.SetTitle(title2)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot Statistics Box
canvas.cd(3)
mbcFrame = mbc.frame()
paramWin1 = totalPdf.paramOn(mbcFrame,dataset, "",2,"NELU",0.1,0.9,0.9)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTickLength(0)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTitle("")
mbcFrame.GetXaxis().CenterTitle()
mbcFrame.GetYaxis().SetLabelSize(0)
mbcFrame.GetYaxis().SetTitleSize(0.03)
mbcFrame.GetYaxis().SetTickLength(0)
paramWin1.getAttText().SetTextSize(0.06)
mbcFrame.Draw()
mbcFrame.SetTitle("Fit Parameters")
ATextBox = TPaveText(.1, .1, .8, .2,"BRNDC")
tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1,chisq2)
ATextBox.AddText(tempString)
ATextBox.SetFillColor(0)
ATextBox.SetBorderSize(1)
mbcFrame.addObject(ATextBox)
mbcFrame.Draw()
canvas.Print(epsfile)
rootfile = epsfile.replace('.eps', '.root')
canvas.Print(rootfile)
# Save fitting parameters
pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_dline_che(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev,
RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen,
kMagenta, TPaveText, RooDLineShape)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D0 flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres", "mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500)
scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1, scalep2)
sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1, scalep2, scalep3)
sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md", "md", setmd, 1.863, 1.875)
f2 = RooRealVar("f2", "f2", fa)
f3 = RooRealVar("f3", "f3", fb)
al23 = RooArgList(f2, f3)
f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3)
af12 = RooArgList(f1, f2)
sigpdf = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3)
sigbarpdf = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2))
bkgbarpdf = RooChebychev('bkgbarpdf', 'Background', mbc,
RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf',
RooArgList(sigbarpdf, bkgbarpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 400, 400)
xframe = mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, md, sigmap1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_gau_che(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar, RooChebychev,
RooAbsData, RooDataHist, TCanvas, kRed, kBlue, kGreen,
kMagenta, TPaveText)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D0 flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4,
'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
#sigma = RooRealVar('sigma', 'D width', 0.0001, 0.005, 'GeV')
sigma = RooRealVar('sigma', 'D width', 0.00468, 'GeV')
mbc_dp = RooRealVar('mbc_dp', 'D+ Mass', 1.86962, 'GeV')
sigpdf = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma)
#arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.88, 1.89, 'GeV')
#arg_slope = RooRealVar('arg_slope', 'Argus slope', -10, -100, -1)
#bkgpdf = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 400, 400)
xframe = mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def mbc_single_3s(evtfile,
mc,
setMres,
setGamma,
setR,
sp1,
sp2,
sp3,
fa,
fb,
setmd,
setp,
setxi,
setN1,
setN2,
setNbkgd1,
setNbkgd2,
title1,
title2,
epsfile,
txtfile,
ymin=0.5,
cuts=None,
err_type='SYMM',
test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf,
RooSimultaneous, RooArgusBG, RooFormulaVar,
RooDLineShape, RooAbsData, RooDataHist, TCanvas, kRed,
kBlue, kGreen, kMagenta, TPaveText)
set_root_style(stat=1, grid=0)
# // sp1 = sigma of signal
# // sp2 = ratio of sigmas betwwen sigma2 sigma 1
# // sp3 = ratio of sigmas betwwen sigma3 sigma 2
# // fa, fb, - fractions
# // xi_side - slope of argus
# // p_side - power of argus
# mc = 1 Monte Carlo Model: EvtGenModels/Class/EvtVPHOtoVISR.cc
# mc = 3 Data Model: with BES 2007 paper (BES2006 lineshape hepex/0612056)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam', 'Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav', 'D flavor')
dflav.defineType('dflav', 1)
dflav.defineType('dbarflav', -1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90,
'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4,
'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' % cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres", "mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE", "sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1", "sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2", "scalep2", 2.00, 1.500, 5.500)
scalep3 = RooRealVar("scalep3", "scalep3", 5.00, 3.00, 10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1, scalep2)
sigmap2 = RooFormulaVar("sigmap2", "sigma2", "sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1, scalep2, scalep3)
sigmap3 = RooFormulaVar("sigmap3", "sigma3", "sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md", "md", setmd, 1.863, 1.875)
f2 = RooRealVar("f2", "f2", fa)
f3 = RooRealVar("f3", "f3", fb)
al23 = RooArgList(f2, f3)
f1 = RooFormulaVar("f1", "f1", "1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1", "DLineShape1_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn1_2 = RooDLineShape("DLineshape1_2", "DLineShape1_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn1_3 = RooDLineShape("DLineshape1_3", "DLineShape1_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
fcn2_1 = RooDLineShape("DLineshape2_1", "DLineShape2_1", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap1, md, res)
fcn2_2 = RooDLineShape("DLineshape2_2", "DLineShape2_2", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap2, md, res)
fcn2_3 = RooDLineShape("DLineshape2_3", "DLineShape2_3", 4, mbc, ebeam,
mres, gamma, r, sigmaE, sigmap3, md, res)
alf1_123 = RooArgList(fcn1_1, fcn1_2, fcn1_3)
af12 = RooArgList(f1, f2)
signal1_3 = RooAddPdf("signal1_3", "signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1, fcn2_2, fcn2_3)
signal2_3 = RooAddPdf("signal2_3", "signal2_3", alf2_123, af12)
p = RooRealVar("p", "p", setp, 0.1, 1.5)
xi = RooRealVar("xi", "xi", setxi, -100.0, -0.1)
Bkgd1 = RooArgusBG("argus1", "argus1", mbc, ebeam, xi, p)
Bkgd2 = RooArgusBG("argus2", "argus2", mbc, ebeam, xi, p)
shapes1 = RooArgList(signal1_3)
shapes1.add(signal1_3)
shapes1.add(Bkgd1)
shapes2 = RooArgList(signal2_3)
shapes2.add(signal2_3)
shapes2.add(Bkgd2)
N1 = RooRealVar("N1", "N1", setN1, 0.0, 200000000.0)
N2 = RooRealVar("N2", "N2", setN2, 0.0, 200000000.0)
Nbkgd1 = RooRealVar("Nbkgd1", "Nbkgd1", setNbkgd1, 0.0, 200000000.0)
Nbkgd2 = RooRealVar("Nbkgd2", "Nbkgd2", setNbkgd2, 0.0, 200000000.0)
yields1 = RooArgList(N1)
yields1.add(N1)
yields1.add(Nbkgd1)
yields2 = RooArgList(N2)
yields2.add(N2)
yields2.add(Nbkgd2)
totalPdf1 = RooAddPdf("totalPdf1", "totalPdf1", shapes1, yields1)
totalPdf2 = RooAddPdf("totalPdf2", "totalPdf2", shapes2, yields2)
totalPdf = RooSimultaneous("totalPdf", "totalPdf", dflav)
totalPdf.addPdf(totalPdf1, "dflav")
totalPdf.addPdf(totalPdf2, "dbarflav")
# Check fitTo options at:
# http://root.cern.ch/root/html512/RooAbsPdf.html#RooAbsPdf:fitTo
#
# Available fit options:
# "m" = MIGRAD only, i.e. no MINOS
# "s" = estimate step size with HESSE before starting MIGRAD
# "h" = run HESSE after MIGRAD
# "e" = Perform extended MLL fit
# "0" = Run MIGRAD with strategy MINUIT 0
# (no correlation matrix calculation at end)
# Does not apply to HESSE or MINOS, if run afterwards.
# "q" = Switch off verbose mode
# "l" = Save log file with parameter values at each MINUIT step
# "v" = Show changed parameters at each MINUIT step
# "t" = Time fit
# "r" = Save fit output in RooFitResult object
# Available optimizer options
# "c" = Cache and precalculate components of PDF that exclusively
# depend on constant parameters
# "2" = Do NLL calculation in multi-processor mode on 2 processors
# "3" = Do NLL calculation in multi-processor mode on 3 processors
# "4" = Do NLL calculation in multi-processor mode on 4 processors
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' % epsfile)
sys.stdout.write('Will save txtfile as: %s \n' % txtfile)
return
if dataset.numEntries() == 0:
N1.setVal(0)
N2.setVal(0)
else:
# Start Fitting
fitres = totalPdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas', 'mbc', 1200, 400)
canvas.Divide(3, 1)
canvas_1 = canvas.GetListOfPrimitives().FindObject('canvas_1')
canvas_2 = canvas.GetListOfPrimitives().FindObject('canvas_2')
canvas_1.SetLogy(1)
canvas_2.SetLogy(1)
LineColorRed = RooFit.LineColor(kRed)
LineColorBlue = RooFit.LineColor(kBlue)
LineWidth = RooFit.LineWidth(1) #0.6)
# Plot the D
canvas.cd(1)
mbcFrame = mbc.frame()
mbcFrame = mbc.frame(60)
dflav.setLabel('dflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq1 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg1 = RooArgSet(Bkgd1)
cp_bkg1 = RooFit.Components(as_bkg1)
totalPdf.plotOn(mbcFrame, cp_bkg1, Slice, LineColorBlue, LineWidth,
ProjWData)
mbcFrame.SetTitle(title1)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot the D bar
canvas.cd(2)
mbcFrame = mbc.frame()
mbcFrame = mbc.frame(60)
dflav.setLabel('dbarflav')
ebas = RooArgSet(ebeam, dflav)
ebeamdata = RooDataHist("ebeamdata", "ebeamdata", ebas, dataset)
dataset.plotOn(mbcFrame, RooFit.Cut("dflav==dflav::dbarflav"))
mbcFrame.getAttMarker().SetMarkerSize(0.6)
mbcFrame.Draw()
Slice = RooFit.Slice(dflav)
ProjWData = RooFit.ProjWData(ebas, ebeamdata)
totalPdf.plotOn(mbcFrame, LineColorRed, LineWidth, Slice, ProjWData)
chisq2 = mbcFrame.chiSquare() * mbcFrame.GetNbinsX()
mbcFrame.Draw()
as_bkg2 = RooArgSet(Bkgd2)
cp_bkg2 = RooFit.Components(as_bkg2)
totalPdf.plotOn(mbcFrame, cp_bkg2, Slice, LineColorBlue, LineWidth,
ProjWData)
mbcFrame.SetTitle(title2)
mbcFrame.SetMinimum(ymin)
mbcFrame.Draw()
# Plot Statistics Box
canvas.cd(3)
mbcFrame = mbc.frame()
paramWin1 = totalPdf.paramOn(mbcFrame, dataset, "", 2, "NELU", 0.1, 0.9,
0.9)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTickLength(0)
mbcFrame.GetXaxis().SetLabelSize(0)
mbcFrame.GetXaxis().SetTitle("")
mbcFrame.GetXaxis().CenterTitle()
mbcFrame.GetYaxis().SetLabelSize(0)
mbcFrame.GetYaxis().SetTitleSize(0.03)
mbcFrame.GetYaxis().SetTickLength(0)
paramWin1.getAttText().SetTextSize(0.06)
mbcFrame.Draw()
mbcFrame.SetTitle("Fit Parameters")
ATextBox = TPaveText(.1, .1, .8, .2, "BRNDC")
tempString = "#chi^{2}_{1} = %.1f, #chi^{2}_{2} = %.1f" % (chisq1, chisq2)
ATextBox.AddText(tempString)
ATextBox.SetFillColor(0)
ATextBox.SetBorderSize(1)
mbcFrame.addObject(ATextBox)
mbcFrame.Draw()
canvas.Print(epsfile)
rootfile = epsfile.replace('.eps', '.root')
canvas.Print(rootfile)
# Save fitting parameters
pars = [N1, N2, Nbkgd1, Nbkgd2, md, p, sigmap1, xi]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
altscenario=scenario
name=str(altscenario)+category
name+="WvsZ"
sample.defineType(name)
datasets+=[RooFit.Import(name,datalist[name])]
combData=RooDataHist("combData","combined data",RooArgList(mass),RooFit.Index(sample),*datasets)
simPdf=RooSimultaneous("simPdf","simultaneous pdf",sample)
for category in ["anti-btag","btag"]:
if category=="btag":
altscenario=btagscenario
else:
altscenario=scenario
name=str(altscenario)+category
name+="WvsZ"
simPdf.addPdf(modellist[name],name)
simPdf.fitTo(combData)
simPdf.fitTo(combData)
simPdf.fitTo(combData)
#xframe=mass.frame(RooFit.Title("#Delta m="+str(int((meanWZ.getValV())*1000.)/1000.)+"#pm"+str(int(meanWZ.getError()*1000.)/1000.)+" GeV, #mu_{#sigma}="+str(int(musigv*1000.)/1000.)+"#pm"+str(int(musige*1000.)/1000.)+", #chi^{2}/N="+str(int((chi2.getVal()/(nbins-nfree))*10.)/10.)))
xframe=mass.frame(RooFit.Title(" #Delta m="+str(int((meanWZ.getValV())*1000.)/1000.)+"#pm"+str(int(meanWZ.getError()*1000.)/1000.)+" GeV, #chi^{2}/N="+str(int((chi2.getVal()/(nbins-nfree))*10.)/10.)))
for category in ["anti-btag","btag"]:
if category=="btag":
altscenario=btagscenario
color=kRed
else:
altscenario=scenario
color=kBlue
name=str(altscenario)+category
name+="WvsZ"
datalist[name].plotOn(xframe,RooFit.MarkerSize(1))
def rf501_simultaneouspdf():
# C r e a t e m o d e l f o r p h y s i c s s a m p l e
# -------------------------------------------------------------
# Create observables
x = RooRealVar( "x", "x", -8, 8 )
# Construct signal pdf
mean = RooRealVar( "mean", "mean", 0, -8, 8 )
sigma = RooRealVar( "sigma", "sigma", 0.3, 0.1, 10 )
gx = RooGaussian( "gx", "gx", x, mean, sigma )
# Construct background pdf
a0 = RooRealVar( "a0", "a0", -0.1, -1, 1 )
a1 = RooRealVar( "a1", "a1", 0.004, -1, 1 )
px = RooChebychev( "px", "px", x, RooArgList( a0, a1 ) )
# Construct composite pdf
f = RooRealVar( "f", "f", 0.2, 0., 1. )
model = RooAddPdf( "model", "model", RooArgList( gx, px ), RooArgList( f ) )
# C r e a t e m o d e l f o r c o n t r o l s a m p l e
# --------------------------------------------------------------
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", -3, -8, 8 )
gx_ctl = RooGaussian( "gx_ctl", "gx_ctl", x, mean_ctl, sigma )
# Construct the background pdf
a0_ctl = RooRealVar( "a0_ctl", "a0_ctl", -0.1, -1, 1 )
a1_ctl = RooRealVar( "a1_ctl", "a1_ctl", 0.5, -0.1, 1 )
px_ctl = RooChebychev( "px_ctl", "px_ctl", x, RooArgList( a0_ctl, a1_ctl ) )
# Construct the composite model
f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 1. )
model_ctl = RooAddPdf( "model_ctl", "model_ctl", RooArgList( gx_ctl, px_ctl ),
RooArgList( f_ctl ) )
# G e n e r a t e e v e n t s f o r b o t h s a m p l e s
# ---------------------------------------------------------------
# Generate 1000 events in x and y from model
data = model.generate( RooArgSet( x ), 100 )
data_ctl = model_ctl.generate( RooArgSet( x ), 2000 )
# C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s
# ---------------------------------------------------------------------------
# Define category to distinguish physics and control samples events
sample = RooCategory( "sample", "sample" )
sample.defineType( "physics" )
sample.defineType( "control" )
# Construct combined dataset in (x,sample)
combData = RooDataSet( "combData", "combined data", RooArgSet(x), RooFit.Index( sample ),
RooFit.Import( "physics", data ),
RooFit.Import( "control", data_ctl ) )
# C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e )
# -----------------------------------------------------------------------------------
# Construct a simultaneous pdf using category sample as index
simPdf = RooSimultaneous( "simPdf", "simultaneous pdf", sample )
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf( model, "physics" )
simPdf.addPdf( model_ctl, "control" )
# P e r f o r m a s i m u l t a n e o u s f i t
# ---------------------------------------------------
# Perform simultaneous fit of model to data and model_ctl to data_ctl
simPdf.fitTo( combData )
# P l o t m o d e l s l i c e s o n d a t a s l i c e s
# ----------------------------------------------------------------
# Make a frame for the physics sample
frame1 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Physics sample" ) )
# Plot all data tagged as physics sample
combData.plotOn( frame1, RooFit.Cut( "sample==sample::physics" ) )
# Plot "physics" slice of simultaneous pdf.
# NBL 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
simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
RooFit.ProjWData( RooArgSet(sample), combData ) )
simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
RooFit.Components( "px" ),
RooFit.ProjWData( RooArgSet(sample), combData ),
RooFit.LineStyle( kDashed ) )
# The same plot for the control sample slice
frame2 = x.frame( RooFit.Bins( 30 ), RooFit.Title( "Control sample" ) )
combData.plotOn( frame2, RooFit.Cut( "sample==sample::control" ) )
simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
RooFit.ProjWData( RooArgSet(sample), combData ) )
simPdf.plotOn( frame2, RooFit.Slice( sample, "control" ),
RooFit.Components( "px_ctl" ),
RooFit.ProjWData( RooArgSet(sample), combData ),
RooFit.LineStyle( kDashed ) )
c = TCanvas( "rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400 )
c.Divide( 2 )
c.cd( 1 )
gPad.SetLeftMargin( 0.15 )
frame1.GetYaxis().SetTitleOffset( 1.4 )
frame1.Draw()
c.cd( 2 )
gPad.SetLeftMargin( 0.15 )
frame2.GetYaxis().SetTitleOffset( 1.4 )
frame2.Draw()
raw_input()
bin = "1"
mean1 = RooRealVar("mean{}".format(bin), "mean{}".format(bin), 3090, 2900, 3300)
sigma1 = RooRealVar("sigma{}".format(bin), "sigma{}".format(bin), 10, 0, 30)
gaus1 = RooGaussian("gx{}".format(bin), "gx{}".format(bin), m, mean1, sigma1)
# define background pdf
slope1 = RooRealVar("slope{}".format(bin), "slope{}".format(bin), -0.005, -0.01, -0.0001)
exp1 = RooExponential("exp{}".format(bin), "exp{}".format(bin), m, slope1)
# define yields
nsig1 = RooRealVar("nsig{}".format(bin), "n. sig bin{}".format(bin), 1000, 0., 1000000)
nbkg1 = RooRealVar("nbkg{}".format(bin), "n. bkg bin{}".format(bin), 1000, 0, 2000000)
# sum pdfs
model1 = RooAddPdf("model{}".format(bin), "model{}".format(bin),
RooArgList(exp1, gaus1),
RooArgList(nbkg1, nsig1))
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
simPdf.addPdf(model0, "matched")
simPdf.addPdf(model1, "unmatched")
start = time.time()
result = simPdf.fitTo(combData, RooFit.Save(True), RooFit.NumCPU(12))
time_roofit = time.time() - start
if do_probfit:
print(f"time probfit: {time_probfit}")
print(f"time RooFit: {time_roofit}")
print(f"time zfit {'eager' if zfit_eager else 'graph'} compile: {time_zfit_compile}, time zfit min={time_zfit_min}")
def rf501_simultaneouspdf():
# C r e a t e m o d e l f o r p h y s i c s s a m p l e
# -------------------------------------------------------------
# Create observables
x = RooRealVar("x", "x", 40, 200)
nsig = RooRealVar("nsig", "#signal events", 200, 0.0, 10000)
nbkg = RooRealVar("nbkg", "#background events", 800, 0.0, 200000)
# Construct signal pdf
mean = RooRealVar("mean", "mean", mu4, 40, 200)
sigma = RooRealVar("sigma", "sigma", sigma4, 0.1, 20)
gx = RooGaussian("gx", "gx", x, mean, sigma)
# Construct background pdf
mean_bkg = RooRealVar("mean_bkg", "mean_bkg", mu3, 40, 200)
sigma_bkg = RooRealVar("sigma_bkg", "sigma_bkg", sigma3, 0.1, 20)
px = RooGaussian("px", "px", x, mean_bkg, sigma_bkg)
# Construct composite pdf
model = RooAddPdf("model", "model", RooArgList(gx, px), RooArgList(nsig, nbkg))
# C r e a t e m o d e l f o r c o n t r o l s a m p l e
# --------------------------------------------------------------
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
y = RooRealVar("y", "y", 40, 200)
mean_ctl = RooRealVar("mean_ctl", "mean_ctl", mu2, 40, 200)
sigma_ctl = RooRealVar("sigma", "sigma", sigma2, 0.1, 10)
gx_ctl = RooGaussian("gx_ctl", "gx_ctl", y, mean_ctl, sigma_ctl)
# Construct the background pdf
mean_bkg_ctl = RooRealVar("mean_bkg_ctl", "mean_bkg_ctl", mu1, 40, 200)
sigma_bkg_ctl = RooRealVar("sigma_bkg_ctl", "sigma_bkg_ctl", sigma1, 0.1, 20)
px_ctl = RooGaussian("px_ctl", "px_ctl", y, mean_bkg_ctl, sigma_bkg_ctl)
# Construct the composite model
# f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 20. )
model_ctl = RooAddPdf("model_ctl", "model_ctl", RooArgList(gx_ctl, px_ctl), RooArgList(nsig, nbkg))
# G e t e v e n t s f o r b o t h s a m p l e s
# ---------------------------------------------------------------
real_data, real_data_ctl = get_data()
real_data_hist = RooDataHist("real_data_hist", "real_data_hist", RooArgList(x), real_data)
real_data_ctl_hist = RooDataHist("real_data_ctl_hist", "real_data_ctl_hist", RooArgList(y), real_data_ctl)
input_hists = MapStrRootPtr()
input_hists.insert(StrHist("physics", real_data))
input_hists.insert(StrHist("control", real_data_ctl))
# C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s
# ---------------------------------------------------------------------------
# Define category to distinguish physics and control samples events
sample = RooCategory("sample", "sample")
sample.defineType("physics")
sample.defineType("control")
# Construct combined dataset in (x,sample)
combData = RooDataHist("combData", "combined data", RooArgList(x), sample, input_hists)
# C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e )
# -----------------------------------------------------------------------------------
# Construct a simultaneous pdf using category sample as index
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(model, "physics")
simPdf.addPdf(model_ctl, "control")
# P e r f o r m a s i m u l t a n e o u s f i t
# ---------------------------------------------------
model.fitTo(real_data_hist)
summary = "fit in signal region\n"
summary += "nsig: " + str(nsig.getValV()) + " +- " + str(nsig.getError()) + "\n"
summary += "nbkg: " + str(nbkg.getValV()) + " +- " + str(nbkg.getError()) + "\n"
model_ctl.fitTo(real_data_ctl_hist)
summary += "fit in control region\n"
summary += "nsig: " + str(nsig.getValV()) + " +- " + str(nsig.getError()) + "\n"
summary += "nbkg: " + str(nbkg.getValV()) + " +- " + str(nbkg.getError()) + "\n"
# Perform simultaneous fit of model to data and model_ctl to data_ctl
simPdf.fitTo(combData)
summary += "Combined fit\n"
summary += "nsig: " + str(nsig.getValV()) + " +- " + str(nsig.getError()) + "\n"
summary += "nbkg: " + str(nbkg.getValV()) + " +- " + str(nbkg.getError()) + "\n"
# P l o t m o d e l s l i c e s o n d a t a s l i c e s
# ----------------------------------------------------------------
# Make a frame for the physics sample
frame1 = x.frame(RooFit.Bins(30), RooFit.Title("Physics sample"))
# Plot all data tagged as physics sample
combData.plotOn(frame1, RooFit.Cut("sample==sample::physics"))
# Plot "physics" slice of simultaneous pdf.
# NBL 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
simPdf.plotOn(frame1, RooFit.Slice(sample, "physics"), RooFit.ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(
frame1,
RooFit.Slice(sample, "physics"),
RooFit.Components("px"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed),
)
# The same plot for the control sample slice
frame2 = y.frame(RooFit.Bins(30), RooFit.Title("Control sample"))
combData.plotOn(frame2, RooFit.Cut("sample==sample::control"))
simPdf.plotOn(frame2, RooFit.Slice(sample, "control"), RooFit.ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(
frame2,
RooFit.Slice(sample, "control"),
RooFit.Components("px_ctl"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed),
)
simPdf.plotOn(
frame2,
RooFit.Slice(sample, "control"),
RooFit.Components("gx_ctl"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed),
)
c = TCanvas("rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400)
c.Divide(2)
c.cd(1)
gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.Draw()
c.cd(2)
gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.4)
frame2.Draw()
print summary
raw_input()
def rooFit501():
print ">>> setup model for physics sample..."
x = RooRealVar("x", "x", -8, 8)
mean = RooRealVar("mean", "mean", 0, -8, 8)
sigma = RooRealVar("sigma", "sigma", 0.3, 0.1, 10)
gauss = RooGaussian("gx", "gx", x, mean, sigma)
a0 = RooRealVar("a0", "a0", -0.1, -1, 1)
a1 = RooRealVar("a1", "a1", 0.004, -1, 1)
px = RooChebychev("px", "px", x, RooArgList(a0, a1))
f = RooRealVar("f", "f", 0.2, 0., 1.)
model = RooAddPdf("model", "model", RooArgList(gauss, px), RooArgList(f))
print ">>> setup model for control sample..."
# NOTE: sigma is shared with the signal sample model
mean_ctrl = RooRealVar("mean_ctrl", "mean_ctrl", -3, -8, 8)
gauss_ctrl = RooGaussian("gauss_ctrl", "gauss_ctrl", x, mean_ctrl, sigma)
a0_ctrl = RooRealVar("a0_ctrl", "a0_ctrl", -0.1, -1, 1)
a1_ctrl = RooRealVar("a1_ctrl", "a1_ctrl", 0.5, -0.1, 1)
px_ctrl = RooChebychev("px_ctrl", "px_ctrl", x,
RooArgList(a0_ctrl, a1_ctrl))
f_ctrl = RooRealVar("f_ctrl", "f_ctrl", 0.5, 0., 1.)
model_ctrl = RooAddPdf("model_ctrl", "model_ctrl",
RooArgList(gauss_ctrl, px_ctrl), RooArgList(f_ctrl))
print ">>> generate events for both samples..."
data = model.generate(RooArgSet(x), 100) # RooDataSet
data_ctrl = model_ctrl.generate(RooArgSet(x), 2000) # RooDataSet
print ">>> create index category and join samples..."
# Define category to distinguish physics and control samples events
sample = RooCategory("sample", "sample")
sample.defineType("physics")
sample.defineType("control")
print ">>> construct combined dataset in (x,sample)..."
combData = RooDataSet("combData", "combined data", RooArgSet(x),
Index(sample), Import("physics", data),
Import("control", data_ctrl))
print ">>> construct a simultaneous pdf in (x,sample)..."
# Construct a simultaneous pdf using category sample as index
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
# Associate model with the physics state and model_ctrl with the control state
simPdf.addPdf(model, "physics")
simPdf.addPdf(model_ctrl, "control")
print ">>> perform a simultaneous fit..."
# Perform simultaneous fit of model to data and model_ctrl to data_ctrl
simPdf.fitTo(combData)
print "\n>>> plot model slices on data slices..."
frame1 = x.frame(Bins(30), Title("Physics sample")) # RooPlot
combData.plotOn(frame1, Cut("sample==sample::physics"))
# Plot "physics" slice of simultaneous pdf.
# NBL 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
simPdf.plotOn(frame1, Slice(sample, "physics"),
ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(frame1, Slice(sample, "physics"), Components("px"),
ProjWData(RooArgSet(sample), combData), LineStyle(kDashed))
print "\n>>> plot control sample slices..."
frame2 = x.frame(Bins(30), Title("Control sample")) # RooPlot
combData.plotOn(frame2, Cut("sample==sample::control"))
simPdf.plotOn(frame2, Slice(sample, "control"),
ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(frame2, Slice(sample, "control"), Components("px_ctrl"),
ProjWData(RooArgSet(sample), combData), LineStyle(kDashed))
print "\n>>> draw on canvas..."
canvas = TCanvas("canvas", "canvas", 100, 100, 1400, 600)
canvas.Divide(2)
canvas.cd(1)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame1.GetYaxis().SetLabelOffset(0.008)
frame1.GetYaxis().SetTitleOffset(1.6)
frame1.GetYaxis().SetTitleSize(0.045)
frame1.GetXaxis().SetTitleSize(0.045)
frame1.Draw()
canvas.cd(2)
gPad.SetLeftMargin(0.15)
gPad.SetRightMargin(0.02)
frame2.GetYaxis().SetLabelOffset(0.008)
frame2.GetYaxis().SetTitleOffset(1.6)
frame2.GetYaxis().SetTitleSize(0.045)
frame2.GetXaxis().SetTitleSize(0.045)
frame2.Draw()
canvas.SaveAs("rooFit501.png")
#Create PDF for simultaneous fit
totalPdf = RooSimultaneous("totalPdf", "totalPdf", myFitCat)
totalPdf.addPdf(sumpass, "pass")
totalPdf.addPdf(sumfail, "fail")
#Generate data and combine
#For combining binned data look rf_combine_binned_data.py
data_pass = gauss1.generate(RooArgSet(Mkk), 6000)
data_fail = gauss1.generate(RooArgSet(Mkk), 4000)
combData = RooDataSet("combData", "combined data", RooArgSet(Mkk),
RooFit.Index(myFitCat), RooFit.Import("pass", data_pass),
RooFit.Import("fail", data_fail))
#FIT
totalPdf.fitTo(combData)
#PLOT
sampleSet = RooArgSet(myFitCat)
frame1 = Mkk.frame(RooFit.Bins(50), RooFit.Title("Passed sample"))
# Plot all data tagged as passed sample
combData.plotOn(frame1, RooFit.Cut("myFitCat==myFitCat::pass"))
totalPdf.plotOn(frame1, RooFit.Slice(myFitCat, "pass"),
RooFit.ProjWData(sampleSet, combData),
RooFit.LineStyle(kDashed))
totalPdf.paramOn(frame1, RooFit.Layout(0.55, 0.9, 0.9),
RooFit.Format("NEU", RooFit.AutoPrecision(1)))
# The same plot for the failled sample slice
frame2 = Mkk.frame(RooFit.Bins(50), RooFit.Title("Failed sample"))
def mbc_gau_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa,
fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2,
title1, title2, epsfile, txtfile, ymin=0.5,
cuts=None, err_type='SYMM', test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous,
RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData,
RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta,
TPaveText)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam','Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav','D0 flavor')
dflav.defineType('dflav',1)
dflav.defineType('dbarflav',-1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
elif 'kpimass' in cuts:
kpimass = RooRealVar('kpimass', 'Kpi invariant mass', 0.6, 1.4, 'GeV')
ras = RooArgSet(mbc, ebeam, kpimass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' %cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
#sigma = RooRealVar('sigma', 'D width', 0.0001, 0.005, 'GeV')
sigma = RooRealVar('sigma', 'D width', 0.00468, 'GeV')
mbc_dp = RooRealVar('mbc_dp', 'D+ Mass', 1.86962, 'GeV')
sigpdf = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma)
#arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.88, 1.89, 'GeV')
#arg_slope = RooRealVar('arg_slope', 'Argus slope', -10, -100, -1)
#bkgpdf = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background',
mbc, RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' %epsfile)
sys.stdout.write('Will save txtfile as: %s \n' %txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas','mbc', 400, 400);
xframe=mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
def rf501_simultaneouspdf():
# C r e a t e m o d e l f o r p h y s i c s s a m p l e
# -------------------------------------------------------------
# Create observables
x = RooRealVar("x", "x", 40, 200)
nsig = RooRealVar("nsig", "#signal events", 200, 0., 10000)
nbkg = RooRealVar("nbkg", "#background events", 800, 0., 200000)
# Construct signal pdf
mean = RooRealVar("mean", "mean", mu4, 40, 200)
sigma = RooRealVar("sigma", "sigma", sigma4, 0.1, 20)
gx = RooGaussian("gx", "gx", x, mean, sigma)
# Construct background pdf
mean_bkg = RooRealVar("mean_bkg", "mean_bkg", mu3, 40, 200)
sigma_bkg = RooRealVar("sigma_bkg", "sigma_bkg", sigma3, 0.1, 20)
px = RooGaussian("px", "px", x, mean_bkg, sigma_bkg)
# Construct composite pdf
model = RooAddPdf("model", "model", RooArgList(gx, px),
RooArgList(nsig, nbkg))
# C r e a t e m o d e l f o r c o n t r o l s a m p l e
# --------------------------------------------------------------
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
y = RooRealVar("y", "y", 40, 200)
mean_ctl = RooRealVar("mean_ctl", "mean_ctl", mu2, 40, 200)
sigma_ctl = RooRealVar("sigma", "sigma", sigma2, 0.1, 10)
gx_ctl = RooGaussian("gx_ctl", "gx_ctl", y, mean_ctl, sigma_ctl)
# Construct the background pdf
mean_bkg_ctl = RooRealVar("mean_bkg_ctl", "mean_bkg_ctl", mu1, 40, 200)
sigma_bkg_ctl = RooRealVar("sigma_bkg_ctl", "sigma_bkg_ctl", sigma1, 0.1,
20)
px_ctl = RooGaussian("px_ctl", "px_ctl", y, mean_bkg_ctl, sigma_bkg_ctl)
# Construct the composite model
# f_ctl = RooRealVar( "f_ctl", "f_ctl", 0.5, 0., 20. )
model_ctl = RooAddPdf("model_ctl", "model_ctl", RooArgList(gx_ctl, px_ctl),
RooArgList(nsig, nbkg))
# G e t e v e n t s f o r b o t h s a m p l e s
# ---------------------------------------------------------------
real_data, real_data_ctl = get_data()
real_data_hist = RooDataHist('real_data_hist', 'real_data_hist',
RooArgList(x), real_data)
real_data_ctl_hist = RooDataHist('real_data_ctl_hist',
'real_data_ctl_hist', RooArgList(y),
real_data_ctl)
input_hists = MapStrRootPtr()
input_hists.insert(StrHist("physics", real_data))
input_hists.insert(StrHist("control", real_data_ctl))
# C r e a t e i n d e x c a t e g o r y a n d j o i n s a m p l e s
# ---------------------------------------------------------------------------
# Define category to distinguish physics and control samples events
sample = RooCategory("sample", "sample")
sample.defineType("physics")
sample.defineType("control")
# Construct combined dataset in (x,sample)
combData = RooDataHist("combData", "combined data", RooArgList(x), sample,
input_hists)
# C o n s t r u c t a s i m u l t a n e o u s p d f i n ( x , s a m p l e )
# -----------------------------------------------------------------------------------
# Construct a simultaneous pdf using category sample as index
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(model, "physics")
simPdf.addPdf(model_ctl, "control")
# P e r f o r m a s i m u l t a n e o u s f i t
# ---------------------------------------------------
model.fitTo(real_data_hist)
summary = 'fit in signal region\n'
summary += 'nsig: ' + str(nsig.getValV()) + ' +- ' + str(
nsig.getError()) + '\n'
summary += 'nbkg: ' + str(nbkg.getValV()) + ' +- ' + str(
nbkg.getError()) + '\n'
model_ctl.fitTo(real_data_ctl_hist)
summary += 'fit in control region\n'
summary += 'nsig: ' + str(nsig.getValV()) + ' +- ' + str(
nsig.getError()) + '\n'
summary += 'nbkg: ' + str(nbkg.getValV()) + ' +- ' + str(
nbkg.getError()) + '\n'
# Perform simultaneous fit of model to data and model_ctl to data_ctl
simPdf.fitTo(combData)
summary += 'Combined fit\n'
summary += 'nsig: ' + str(nsig.getValV()) + ' +- ' + str(
nsig.getError()) + '\n'
summary += 'nbkg: ' + str(nbkg.getValV()) + ' +- ' + str(
nbkg.getError()) + '\n'
# P l o t m o d e l s l i c e s o n d a t a s l i c e s
# ----------------------------------------------------------------
# Make a frame for the physics sample
frame1 = x.frame(RooFit.Bins(30), RooFit.Title("Physics sample"))
# Plot all data tagged as physics sample
combData.plotOn(frame1, RooFit.Cut("sample==sample::physics"))
# Plot "physics" slice of simultaneous pdf.
# NBL 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
simPdf.plotOn(frame1, RooFit.Slice(sample, "physics"),
RooFit.ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(frame1, RooFit.Slice(sample, "physics"),
RooFit.Components("px"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed))
# The same plot for the control sample slice
frame2 = y.frame(RooFit.Bins(30), RooFit.Title("Control sample"))
combData.plotOn(frame2, RooFit.Cut("sample==sample::control"))
simPdf.plotOn(frame2, RooFit.Slice(sample, "control"),
RooFit.ProjWData(RooArgSet(sample), combData))
simPdf.plotOn(frame2, RooFit.Slice(sample, "control"),
RooFit.Components("px_ctl"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed))
simPdf.plotOn(frame2, RooFit.Slice(sample, "control"),
RooFit.Components("gx_ctl"),
RooFit.ProjWData(RooArgSet(sample), combData),
RooFit.LineStyle(kDashed))
c = TCanvas("rf501_simultaneouspdf", "rf403_simultaneouspdf", 800, 400)
c.Divide(2)
c.cd(1)
gPad.SetLeftMargin(0.15)
frame1.GetYaxis().SetTitleOffset(1.4)
frame1.Draw()
c.cd(2)
gPad.SetLeftMargin(0.15)
frame2.GetYaxis().SetTitleOffset(1.4)
frame2.Draw()
print summary
raw_input()
def mbc_dline_che(evtfile, mc, setMres, setGamma, setR, sp1, sp2, sp3, fa,
fb, setmd, setp, setxi, setN1, setN2, setNbkgd1, setNbkgd2,
title1, title2, epsfile, txtfile, ymin=0.5,
cuts=None, err_type='SYMM', test=False):
from ROOT import (gROOT, RooRealVar, RooCategory, RooArgSet, RooDataSet,
RooFit, RooGaussian, RooArgList, RooAddPdf, RooSimultaneous,
RooArgusBG, RooFormulaVar, RooChebychev, RooAbsData,
RooDataHist, TCanvas, kRed, kBlue, kGreen, kMagenta,
TPaveText, RooDLineShape)
set_root_style(stat=1, grid=0)
mbc = RooRealVar('mbc', 'Beam constrained mass', 1.83, 1.89, 'GeV')
ebeam = RooRealVar('ebeam','Ebeam', 1.8815, 1.892, 'GeV')
dflav = RooCategory('dflav','D0 flavor')
dflav.defineType('dflav',1)
dflav.defineType('dbarflav',-1)
if cuts != None:
if 'kkmass' in cuts:
kkmass = RooRealVar('kkmass', 'KK invariant mass', 0.97, 1.90, 'GeV')
ras = RooArgSet(mbc, ebeam, kkmass, dflav)
dataset = RooDataSet.read(evtfile, ras)
else:
raise NameError(cuts)
sys.stdout.write('Using cuts: %s...' %cuts)
dataset = dataset.reduce(cuts)
sys.stdout.write(' selected %s events.\n' % dataset.numEntries())
else:
ras = RooArgSet(mbc, ebeam, dflav)
dataset = RooDataSet.read(evtfile, ras)
res = RooRealVar("datares", "datares", mc)
mres = RooRealVar("mres","mres", setMres)
gamma = RooRealVar('gamma', 'gamma', setGamma)
r = RooRealVar('r', 'r', setR)
sigmaE = RooRealVar("sigmaE","sigmaE", 0.0021)
sigmap1 = RooRealVar("sigmap1","sigmap1", sp1, 0.002, 0.040)
scalep2 = RooRealVar("scalep2","scalep2",2.00,1.500,5.500)
scalep3 = RooRealVar("scalep3","scalep3",5.00,3.00,10.000)
scalep2.setVal(sp2)
scalep2.setConstant(1)
scalep3.setVal(sp3)
scalep3.setConstant(1)
as12 = RooArgList(sigmap1,scalep2)
sigmap2 = RooFormulaVar("sigmap2","sigma2","sigmap1*scalep2", as12)
as123 = RooArgList(sigmap1,scalep2,scalep3)
sigmap3 = RooFormulaVar("sigmap3","sigma3","sigmap1*scalep2*scalep3",
as123)
md = RooRealVar("md","md", setmd,1.863,1.875)
f2 = RooRealVar("f2","f2", fa)
f3 = RooRealVar("f3","f3", fb)
al23 = RooArgList(f2,f3)
f1 = RooFormulaVar("f1","f1","1.0-f2-f3", al23)
# Construct signal shape
fcn1_1 = RooDLineShape("DLineshape1_1","DLineShape1_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn1_2 = RooDLineShape("DLineshape1_2","DLineShape1_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn1_3 = RooDLineShape("DLineshape1_3","DLineShape1_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
fcn2_1 = RooDLineShape("DLineshape2_1","DLineShape2_1",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap1,md,res)
fcn2_2 = RooDLineShape("DLineshape2_2","DLineShape2_2",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap2,md,res)
fcn2_3 = RooDLineShape("DLineshape2_3","DLineShape2_3",4,mbc,ebeam,
mres,gamma,r,sigmaE,sigmap3,md,res)
alf1_123 = RooArgList(fcn1_1,fcn1_2,fcn1_3)
af12 = RooArgList(f1,f2)
sigpdf = RooAddPdf("signal1_3","signal1_3", alf1_123, af12)
alf2_123 = RooArgList(fcn2_1,fcn2_2,fcn2_3)
sigbarpdf = RooAddPdf("signal2_3","signal2_3", alf2_123, af12)
con0 = RooRealVar('c0', 'constant', -1, 1)
con1 = RooRealVar('c1', 'linear', -10, 10)
con2 = RooRealVar('c2', 'quadratic', 1)
bkgpdf = RooChebychev('bkgpdf', 'Background', mbc, RooArgList(con1, con2))
bkgbarpdf = RooChebychev('bkgbarpdf', 'Background',
mbc, RooArgList(con1, con2))
yld = RooRealVar('yld', 'D yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 100, 0, 1000)
sumpdf = RooAddPdf('sumpdf', 'Sum pdf', RooArgList(sigpdf, bkgpdf),
RooArgList(yld, bkg))
yldbar = RooRealVar('yldbar', 'Dbar yield', 100, 0, 2000)
bkgbar = RooRealVar('bkgbar', 'Background', 100, 0, 1000)
sumpdfbar = RooAddPdf('sumpdfbar', 'Sum pdf', RooArgList(sigbarpdf, bkgbarpdf),
RooArgList(yldbar, bkgbar))
totalpdf = RooSimultaneous('rs', 'Simultaneous PDF', dflav)
totalpdf.addPdf(sumpdf, 'dflav')
totalpdf.addPdf(sumpdfbar, 'dbarflav')
MINUIT = 'ermh4'
if err_type == 'ASYM':
MINUIT = 'erh4'
if test:
sys.stdout.write('Will save epsfile as: %s \n' %epsfile)
sys.stdout.write('Will save txtfile as: %s \n' %txtfile)
return
if dataset.numEntries() == 0:
yld.setVal(0)
yldbar.setVal(0)
else:
# Start Fitting
fitres = totalpdf.fitTo(dataset, MINUIT)
fitres.Print('v')
# Save plots
canvas = TCanvas('canvas','mbc', 400, 400);
xframe=mbc.frame(50)
ProjWData = RooFit.ProjWData(dataset)
RooAbsData.plotOn(dataset, xframe)
totalpdf.plotOn(xframe, ProjWData)
totalpdf.paramOn(xframe)
xframe.Draw()
canvas.Print(epsfile)
# Save fitting parameters
pars = [bkg, bkgbar, con1, md, sigmap1, yld, yldbar]
save_fit_result(pars, txtfile, err_type=err_type, verbose=1)
for j in range(n_sig_pdf) :
shiftedMeans[i].append(RooFormulaVar("shifted_mean_{}_".format(j)+region, "mean_{}+shift_".format(j)+region, RooArgList(means[j], shifts[i])))
scaledWidths[i].append(RooFormulaVar("scaled_width_{}_".format(j)+region, "width_{}*scale_".format(j)+region, RooArgList(widths[j], scales[i])))
sig[i].append(RooGaussian("sig_{}_".format(j)+region, "sig_{}_".format(j)+region, deltam, shiftedMeans[i][j], scaledWidths[i][j]))
#Adding components to form total signal pdf
region_sig_pdfs.append(RooAddPdf("signal_pdf_"+region, "signal_pdf_"+region, RooArgList(*sig[i]), RooArgList(*fracs)))
bg_fracs.append(RooRealVar("bg_frac_"+region, "bg_frac_"+region, 0.107, 0, 1))
region_total_pdfs.append(RooAddPdf("total_pdf_"+region, "total_pdf_"+region, RooArgList(region_sig_pdfs[i], bg_pdf), RooArgList(bg_fracs[i])))
#Adding to simultaneous pdf
total_pdf.addPdf(region_total_pdfs[i], region)
#Simultaneously fit all mass regions - bg free and signal with shift/scale only
total_pdf.fitTo(datahist)
#Now, fix bg and fit signal only
bgvar1.setConstant(True)
bgvar2.setConstant(True)
bgvar3.setConstant(True)
deltam0.setConstant(True)
for i in range(n_sig_pdf) :
means[i].setConstant(False)
widths[i].setConstant(False)
if i < n_sig_pdf - 1 :
fracs[i].setConstant(False)
#Fixing scale and shift in middle D0 mass region to 1 and 0
#shifts[1].setVal(0.)
shifts[1].setConstant(True)
simPdf = RooSimultaneous("simPdf","simultaneous pdf",sample) ;
simPdf.addPdf(model1S,'b1')
simPdf.addPdf(model2S,'b2')
simPdf.addPdf(model3S,'b3')
fitregion = x.setRange('fitregion',10.35,10.8)
fitregion_1S = x.setRange('fitregion_1S',10.35,10.8)
fitregion_2S = x.setRange('fitregion_2S',10.35,10.8)
fitregion_3S = x.setRange('fitregion_3S',10.35,10.8)
#simPdf.fitTo(combData,RooFit.Save(), RooFit.Range('fitregion'),RooFit.SplitRange())
simPdf.fitTo(combData,RooFit.Save())
#plotting
frame1 = x.frame()
combData.plotOn(frame1,
RooFit.Cut("sample==sample::b1"),
RooFit.MarkerSize(0.7))
simPdf.plotOn(frame1, RooFit.Slice(sample,'b1'),
RooFit.ProjWData(RooArgSet(sample),combData),
RooFit.LineWidth(2))
c1 = TCanvas('c1','c1')
frame1.Draw()
frame2 = x.frame()
combData.plotOn(frame2,
# PDF.setParameterizeIntegral(RooArgSet(dt))
## Logging
print '=' * 5, ' Simultaneous fit with initial values from 2-step fit', '=' * 5
print 'Variables: ', varlist
for var in varlist:
var.Print('v')
print 'PDFs: ', pdflist
for pdf in pdflist:
pdf.Print('v')
dataset.Print('v')
## Fit
gSystem.ListLibraries()
fitresult = PDF.fitTo(dataset, RooFit.Optimize(0), RooFit.Strategy(2),
RooFit.Save(True), RooFit.NumCPU(1),
RooFit.SumW2Error(True), RooFit.Offset(True),
RooFit.Verbose(True))
fitresult.Print()
from helpers import FitStatus
print FitStatus(fitresult.status())
## Plot results
# # Use when debugging plots
# dataset = dataset.reduce(RooFit.EventRange(0, 100))
# reduce precision otherwise plotting doesn't work
time.setRange('fullrange', epsilon, 15.0)
RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-5)
RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-5)
dkcatset = RooArgSet(decaycat)
## Logging
print '=' * 5, ' Simultaneous fit with initial values from 2-step fit', '=' * 5
print 'Variables: ', varlist
for var in varlist:
var.Print('v')
print 'PDFs: ', pdflist
for pdf in pdflist:
pdf.Print('v')
dataset.Print('v')
## Fit
gSystem.ListLibraries()
fitresult = PDF.fitTo(dataset, RooFit.Optimize(0), RooFit.Strategy(2),
RooFit.Save(True), RooFit.NumCPU(1),
RooFit.SumW2Error(True), RooFit.Offset(True),
RooFit.Verbose(True))
fitresult.Print()
from helpers import FitStatus
print FitStatus(fitresult.status())
## Plot results
# # Use when debugging plots
# dataset = dataset.reduce(RooFit.EventRange(0, 100))
# reduce precision otherwise plotting doesn't work
time.setRange('fullrange', epsilon, 15.0)
RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-5)
RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-5)
map_data.insert(("passed", data2))
combined = RooDataHist("combined", "combined", RooArgList(x), myCat, map_data)
# 1. Construct a simultaneous pdf using category sample as index
# ---------------------------------------------------------------
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", myCat)
# Associate model with the physics state and model_ctl with the control
# state
simPdf.addPdf(model, "failed")
simPdf.addPdf(model_ctl, "passed")
# FIT
# ---------------------------------------------------------------
# 2. Fit model to data
simPdf.fitTo(combined)
# 3. Create frame
frame1 = x.frame(RooFit.Bins(30), RooFit.Title("Passed sample"))
frame2 = x.frame(RooFit.Bins(30), RooFit.Title("Failed sample"))
# 4. Put data on frame
combined.plotOn(frame1, RooFit.Cut("myCat==myCat::passed"))
combined.plotOn(frame2, RooFit.Cut("myCat==myCat::failed"))
sampleSet = RooArgSet(myCat)
# 5. Put model on frame
# put one component
simPdf.plotOn(frame1, RooFit.Slice(myCat,
"passed"), RooFit.Components("px_ctl"),
RooFit.ProjWData(sampleSet, combined), RooFit.LineStyle(kDashed),
