def fit(model, hists, fitmethod, eps=1.0e-7):
"""Fit beam shapes to Beam Imaging data.
model: Beam shape model (derived from BeamShapeCore).
hists: List of four TH2F with BI data.
fitmethod: Function(pdf, data) that fits pdf to data.
eps: Value of convergence criteria.
"""
RooAbsReal.defaultIntegratorConfig().setEpsAbs(eps)
RooAbsReal.defaultIntegratorConfig().setEpsRel(eps)
modfuncs = model.model_functions()
datahist = [
RooDataHist('scan{0}Beam{1}RestDataHist'.format(c, i),
'scan{0}Beam{1}RestDataHist'.format(c, i),
RooArgList(model.xvar(), model.yvar()), hists[j])
for j, (i, c) in enumerate(ic)
]
sample = RooCategory('sample', 'sample')
for (i, c) in ic:
sample.defineType('{0}_ScanData_Beam{1}Rest'.format(c, i))
combdata = RooDataHist('combdata', 'combined data',
RooArgList(model.xvar(), model.yvar()),
RooFit.Index(sample),
RooFit.Import('X_ScanData_Beam1Rest', datahist[0]),
RooFit.Import('Y_ScanData_Beam1Rest', datahist[1]),
RooFit.Import('X_ScanData_Beam2Rest', datahist[2]),
RooFit.Import('Y_ScanData_Beam2Rest', datahist[3]))
simpdf = RooSimultaneous('simpdf', 'simultaneous pdf', sample)
for j, (i, c) in enumerate(ic):
simpdf.addPdf(modfuncs[j], '{0}_ScanData_Beam{1}Rest'.format(c, i))
result = fitmethod(simpdf, combdata)
return result, modfuncs, datahist
def main1():
m = RooRealVar('evt.m', 'evt.m', 1.92, 2.02)
mode = RooCategory('evt.mode', 'evt.mode')
mode.defineType('phipi', 0)
aset = RooArgSet('aset')
aset.add(m)
aset.add(mode)
tuplist = tupleList('genmc', 7)
dst, _, f = getTree('/'.join([tuplePath(), tuplist[-1]]))
# dst.Print()
# dst.Show(0)
# for evt in dst:
# print('Hello!')
# print(evt.evt.m)
# break
ds = RooDataSet('ds', 'ds', dst, aset)
print(ds.numEntries())
mean = RooRealVar('mean', 'mean', 1.96, 1.92, 2.0)
width = RooRealVar('width', 'width', 0.006, 0.001, 0.010)
pdf = RooGaussian('pdf', 'pdf', m, mean, width)
pdf.fitTo(ds, Verbose(), Timer(True))
makePlot(m, ds, pdf)
raw_input("Press Enter to continue...")
def buildPdf(ws, p):
mass = RooRealVar("mass", "mass", p.minMass, p.maxMass)
getattr(ws,'import')(mass)
# Construct signal pdf
mean = RooRealVar("mean", "mean", 90, 85, 95)
width = RooRealVar("width", "width", 2.4952, 1, 3)
width.setConstant(ROOT.kTRUE)
sigma = RooRealVar("sigma", "sigma", 1.2, 0.2, 10)
signalAll = RooVoigtian("signalAll", "signalAll", mass, mean, width, sigma)
turnOnAll = RooRealVar("turnOnAll","turnOnAll", 80., 40., 150.)
widthAll_bkg = RooRealVar("widthAll","widthAll", 2., 0., 50.)
decayAll_bkg = RooRealVar("decayAll","decayAll", 80., 20., 150.)
meanB = RooRealVar("meanB", "meanB", 90, 60, 130)
sigmaB = RooRealVar("sigmaB", "sigmaB", 10, 1, 20)
bkg_a1 = RooRealVar("bkg_a1", "bkg_a1", 0., -2., 2.)
bkg_a2 = RooRealVar("bkg_a2", "bkg_a2", 0., -2., 2.)
backgroundAll = RooGaussian("backgroundAll", "backgroundAll", mass, meanB, sigmaB)
# Construct composite pdf
sigAll = RooRealVar("sigAll", "sigAll", 2000, 0, 100000)
bkgAll = RooRealVar("bkgAll", "bkgAll", 100, 0, 10000)
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll, backgroundAll), RooArgList(sigAll, bkgAll))
if p.NoBkgd:
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll), RooArgList(sigAll))
# Define pdf for all probes
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
signalPass = RooVoigtian("signalPass","signalPass",mass,mean,width,sigma)
# Construct the background pdf
backgroundPass = RooGaussian("backgroundPass", "backgroundPass", mass, meanB, sigmaB)
# Construct the composite model
efficiency = RooRealVar("efficiency","efficiency",0.9,0.3,1.)
sigPass = RooFormulaVar("sigPass", "@0*@1", RooArgList(sigAll, efficiency))
bkgPass = RooRealVar("bkgPass", "bkgPass", 100, 0, 10000)
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass, backgroundPass), RooArgList(sigPass, bkgPass))
if p.NoBkgd:
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass), RooArgList(sigPass))
frac = RooRealVar("frac", "frac", 0.8, 0., 1.)
# Define combined pdf for simultaneous fit
# Define category to distinguish physics and control samples events
sample = RooCategory("sample","sample")
sample.defineType("all")
sample.defineType("pass")
simPdf = RooSimultaneous("simPdf","simultaneous pdf",sample)
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(modelAll,"all")
simPdf.addPdf(modelPass,"pass")
# ws.import(simPdf)
getattr(ws,'import')(simPdf)
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(),
)
class MLFit :
def __init__(self, plot_dire, condition):
self.plot_dire = plot_dire
self.condition = condition
self.mjj = RooRealVar("ZJJMass", "ZJJMass", 50., 7000.)
#self.costhetastar = RooRealVar("costhetastar", "costhetastar", -1., 1.)
self.weight = RooRealVar("weight", "weight", -100., 100.)
self.isSignal = RooCategory("isSignal", "isSignal")
self.isSignal.defineType("signal", 1);
self.isSignal.defineType("background", 0);
self.ras = RooArgSet(self.mjj, self.weight)
self.ds = RooDataSet("ds"+condition, "ds"+condition, self.ras, "weight")
#self.mu = RooRealVar("mu", "mu", 90., 80., 100.)
#self.widthL = RooRealVar("widthL", "widthL", 15., 2., 30.)
#self.widthR = RooRealVar("widthR", "widthR", 4., 2., 15.)
#self.sigmass = RooBifurGauss("bifurgauss", "bifurgauss", self.mjj, self.mu, self.widthL, self.widthR)
self.c0 = RooRealVar("c0", "c0", -100., 100.)
self.bkgmass = RooExponential("expo", "expo", self.mjj, self.c0)
#self.nsig = RooRealVar("nsig", "nsig", 100, 0, 200)
#self.nbkg = RooRealVar("nbkg", "nbkg", 100, 0, 200)
#self.components = RooArgList(self.sigmass, self.bkgmass)
#self.coefficients = RooArgList(self.nsig, self.nbkg)
self.modelmass = self.bkgmass #RooAddPdf("massmodel", "massmodel", self.components, self.coefficients)
def addToDataset(self, event, isSignal):
if not eval(self.condition):
return
self.mjj = event.ZJJMass
print self.mjj
#self.costhetastar = event.costhetastar
self.weight = event.weight
#self.isSignal = isSignal
self.ds.fill()
def fit(self):
print "nentries", self.ds.numEntries()
#for i in range(self.ds.numEntries()):
# argset = self.ds.get(i)
# argset.Dump()
fitresult = self.modelmass.fitTo(self.ds, RooFit.Save(True), RooFit.Extended(), RooFit.PrintLevel(3), RooFit.Strategy(2)) #, RooFit.SumW2Error(True))
def __init__(self, plot_dire, condition):
self.plot_dire = plot_dire
self.condition = condition
self.mjj = RooRealVar("ZJJMass", "ZJJMass", 50., 7000.)
#self.costhetastar = RooRealVar("costhetastar", "costhetastar", -1., 1.)
self.weight = RooRealVar("weight", "weight", -100., 100.)
self.isSignal = RooCategory("isSignal", "isSignal")
self.isSignal.defineType("signal", 1);
self.isSignal.defineType("background", 0);
self.ras = RooArgSet(self.mjj, self.weight)
self.ds = RooDataSet("ds"+condition, "ds"+condition, self.ras, "weight")
#self.mu = RooRealVar("mu", "mu", 90., 80., 100.)
#self.widthL = RooRealVar("widthL", "widthL", 15., 2., 30.)
#self.widthR = RooRealVar("widthR", "widthR", 4., 2., 15.)
#self.sigmass = RooBifurGauss("bifurgauss", "bifurgauss", self.mjj, self.mu, self.widthL, self.widthR)
self.c0 = RooRealVar("c0", "c0", -100., 100.)
self.bkgmass = RooExponential("expo", "expo", self.mjj, self.c0)
#self.nsig = RooRealVar("nsig", "nsig", 100, 0, 200)
#self.nbkg = RooRealVar("nbkg", "nbkg", 100, 0, 200)
#self.components = RooArgList(self.sigmass, self.bkgmass)
#self.coefficients = RooArgList(self.nsig, self.nbkg)
self.modelmass = self.bkgmass #RooAddPdf("massmodel", "massmodel", self.components, self.coefficients)
def build_model_ext(params, starting_pixels=None, starting_nights=None):
num_pixels, num_nights, nstat = params
# only need one x
x = RooRealVar('x', 'x', 0, 20)
starting_pixels = [0.005 for i in range(num_pixels)]
starting_nights = [0.3 for i in range(num_nights)]
# one dkct value for each pixel
dkcts = [RooRealVar('dkct_{}'.format(i), 'dkct_{}'.format(i),
starting_pixels[i], 0.000001, 10)
for i in range(num_pixels)]
# nsbg
nsbgs = [RooRealVar('nsbg_{}'.format(i), 'nsbg_{}'.format(i),
starting_nights[i], 0.000001, 10)
for i in range(num_nights)]
# model every spectrum as sum of dkcts and nsbg
# for said night and pixel
# need to create sum variables
sumvars = [[RooFormulaVar('n{}px{}'.format(n, px), 'n{}px{}'.format(n, px),
'@0+@1', RooArgList(dkcts[px], nsbgs[n]))
for px, _ in enumerate(dkcts)] for n, _ in enumerate(nsbgs)]
norm_models = [[RooPoisson('n_model_n{}_px{}'.format(n, px),
'n_model_n{}px{}'.format(n, px),
x, sumvars[n][px])
for px, _ in enumerate(dkcts)]
for n, _ in enumerate(nsbgs)]
norms = [RooRealVar('nm', 'nm', nstat[i]) for i in range(num_nights)]
models = [[RooExtendPdf('model_n{}_px{}'.format(n, px),
'model_n{}px{}'.format(n, px),
norm_models[n][px], norms[n])
for px, _ in enumerate(dkcts)] for n, _ in enumerate(nsbgs)]
# print('models shape:', np.array(models).shape)
# indices and simul model
index = RooCategory('index', 'index')
for night in range(num_nights):
for px in range(num_pixels):
index.defineType('n{}px{}'.format(night, px))
# simul_model
simul_model = RooSimultaneous('smodel', 'smodel', index)
for night in range(num_nights):
for px in range(num_pixels):
simul_model.addPdf(models[night][px], 'n{}px{}'.format(night, px))
return ModelContainer(x, dkcts, nsbgs, index, simul_model,
models, sumvars, norm_models, norms,
starting_pixels, starting_nights)
def __init__( self, fit_data ):
MapStrRootPtr = stl.map( stl.string, "TH1*" )
StrHist = stl.pair( stl.string, "TH1*" )
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory( 'sample', 'sample' )
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable( 'n_' + sample,
'number of ' + sample + " events",
fit_data_1.normalisation[sample],
N_min,
N_max,
"events" )
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model( variable, self.observables )
self.sample.defineType( variable )
self.sample.setLabel ( variable )
data = deepcopy( fit_input.real_data_histogram() )
input_hists.insert( StrHist( variable, data ) )
self.roofit_variables.append( fit_input.fit_variable )
self.comb_data = RooDataHist( "combData",
"combined data",
RooArgList( self.roofit_variables[0] ),
self.sample,
input_hists,
)
def __init__(self, pdf, component, fit_opts, correct_weights=True):
self.__comp = component
self.__pdf = pdf
self.__fit_opts = fit_opts
self.__result = None
self.__correct_weights = correct_weights
from ROOT import RooCategory
self.__status = RooCategory("sweight_status", "sweight fit status")
self.__status.defineType("success", 0)
self.__status.defineType("one", 1)
self.__status.defineType("two", 2)
self.__status.defineType("three", 3)
self.__status.defineType("other", 4)
self.__parameters = None
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)
model2S = RooAddPdf('model2S','model2S',RooArgList(signal2S_1,signal2S_2,background2S),RooArgList(nsig2S1,nsig2S2,nbck2S))
n_evts_1_3s = RooRealVar('N_{3P_{1}}','N_{3P_{1}}',50,10,1000)
n_evts_2_3s = RooFormulaVar('N_{3P_{2}}','@0*@1',RooArgList(n_evts_1_3s,ratio21_v3s))
n_bck3s = RooRealVar('nbkg3s','n_{bkg}',500,0,100000)
model3S = RooAddPdf('model3S', 'model3S', RooArgList(chib13s,chib23s,background3s),RooArgList(n_evts_1_3s,n_evts_2_3s,n_bck3s))
# start simultaneous fit
sample= RooCategory('sample','sample')
sample.defineType('b1')
sample.defineType('b2')
sample.defineType('b3')
combData= RooDataSet('combData','combined data',RooArgSet( x),
RooFit.Index(sample),
RooFit.Import('b1',dataset1S),
RooFit.Import('b2',dataset2S),
RooFit.Import('b3',dataset3S),
)
#model1S,model2S = definemodel(x)
simPdf = RooSimultaneous("simPdf","simultaneous pdf",sample) ;
class SWeightTransform(object):
def __init__(self, pdf, component, fit_opts, correct_weights=True):
self.__comp = component
self.__pdf = pdf
self.__fit_opts = fit_opts
self.__result = None
self.__correct_weights = correct_weights
from ROOT import RooCategory
self.__status = RooCategory("sweight_status", "sweight fit status")
self.__status.defineType("success", 0)
self.__status.defineType("one", 1)
self.__status.defineType("two", 2)
self.__status.defineType("three", 3)
self.__status.defineType("other", 4)
self.__parameters = None
def __call__(self, data):
pdf_pars = self.__pdf.getParameters(data.get())
if not self.__parameters:
self.__parameters = pdf_pars
self.__parameters = [p for p in pdf_pars] + [self.__status]
else:
for p in self.__parameters:
pdf_par = pdf_pars.find(p.GetName())
if not pdf_par:
continue
pdf_par.setVal(p.getVal())
pdf_par.setError(p.getError())
success = False
status = 4
for i in range(3):
self.__result = self.__pdf.fitTo(data, **self.__fit_opts)
status = self.__result.status()
if status == 0:
success = True
break
if status < 4:
self.__status.setIndex(self.__result.status())
else:
self.__status.setIndex(4)
if success:
from P2VV.Utilities.SWeights import SData
self.__sData = SData(Pdf=self.__pdf, Data=data, Name="MassSPlot")
data = self.__sData.data(self.__comp)
if self.__correct_weights:
from P2VV.Utilities.DataHandling import correctWeights
data = correctWeights(data, splitCatNames=None, ImportIntoWS=False)
return data
else:
return None
def gen_params(self, observables=None):
from ROOT import RooArgSet
if self.__parameters:
return self.__parameters
else:
if observables and not isinstance(observables, RooArgSet):
obs = RooArgSet()
for o in observables:
obs.add(o._target_() if hasattr(o, "_target_") else o)
observables = obs
params = self.__pdf.getParameters(observables)
self.__parameters = [p for p in params] + [self.__status]
return self.__parameters
def result_params(self):
if not self.__result:
return []
else:
return [p for p in self.__result.floatParsFinal()] + [self.__status]
def set_params(self, data_params):
from ROOT import RooCategory
for trans_param in self.result_params():
data_param = data_params.find(trans_param.GetName())
if isinstance(trans_param, RooCategory):
data_param.setIndex(trans_param.getIndex())
else:
data_param.setVal(trans_param.getVal())
# This sets a symmetric error, but since we don't run Minos, that's ok
data_param.setError(trans_param.getError())
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)
runPeriods = [ 2011, 2012 ]
from P2VV import RooFitDecorators
from ROOT import TFile
print 'reading data set "%s" from file "%s"' % ( dataSetName, filePathIn )
fileIn = TFile.Open(filePathIn)
dsIn = fileIn.Get(dataSetName)
obsSetIn = dsIn.get()
dsIn.Print()
runPeriodCat = obsSetIn.find('runPeriod')
for period in runPeriods :
assert runPeriodCat.isValidIndex(period)
if len(runPeriods) < runPeriodCat.numTypes() :
from ROOT import RooCategory
runPeriodCatOut = RooCategory( runPeriodCat.GetName(), runPeriodCat.GetTitle() )
for catType in runPeriodCat :
if catType.getVal() in runPeriods : runPeriodCatOut.defineType( catType.GetName(), catType.getVal() )
from ROOT import RooArgSet
obsSetOut = RooArgSet(obsSetIn)
obsSetOut.remove(runPeriodCat)
obsSetOut.add(runPeriodCatOut)
else :
obsSetOut = obsSetIn
cuts = '||'.join( 'runPeriod==%d' % period for period in runPeriods )
from ROOT import RooDataSet, RooFit
print 'creating new data set for run periods %s' % runPeriods
print 'applying cuts "%s"' % cuts
scale = 1
elif pidcut.find(' 10') > 0:
wt = 'wt3'
scale = 10
else:
print 'Unknown PID selection. Weights not applied.'
wtvar = RooRealVar(wt, 'weight', 0.0, 1.0)
# dataset = fill_dataset(RooArgSet(time, wtvar), ftree, wt, wtvar, cutstr)
dataset = get_dataset(RooArgSet(time, wtvar), ftree, cutstr, wt, scale)
name_title = '{}_{}'.format(dataset.GetName(), mode)
dataset.SetNameTitle(name_title, name_title)
print '%s is weighted: %s' % (dataset.GetName(), dataset.isWeighted())
dsetlist += [dataset]
decaycat = RooCategory('decaycat', 'Decay mode category')
decaycat.defineType('DsPi')
decaycat.defineType('DsK')
varlist += [decaycat]
for idx, mode in enumerate(['DsPi', 'DsK']):
decaycat.setLabel(mode)
dsetlist[idx].addColumn(decaycat)
dataset = RooDataSet('dataset', 'Combined dataset (DsK + DsPi)',
RooArgSet(time, decaycat), RooFit.Import(dsetlist[0]))
dataset.append(dsetlist[1])
for dset in dsetlist:
dset.Print()
model.plotOn(
xframe,
RooFit.Normalization(1.0, RooAbsReal.RelativeExpected))
model.plotOn(
xframe, RooFit.Components("bkg" + str(cut)),
RooFit.LineStyle(kDashed),
RooFit.Normalization(1.0, RooAbsReal.RelativeExpected))
model.plotOn(
xframe, RooFit.Components("sig"),
RooFit.LineStyle(kDotted),
RooFit.Normalization(1.0, RooAbsReal.RelativeExpected))
canvas = TCanvas("c2", "c2", 0, 0, 600, 600)
xframe.Draw()
canvas.SaveAs(prefix + "_" + plot[0] + str(cut) + "_fit" +
fit + ".pdf")
sample = RooCategory("sample", "sample")
datasets = []
for cut in cuts[1:-1]:
sample.defineType(str(cut))
datasets += [RooFit.Import(str(cut), datalist[cut])]
combData = RooDataHist("combData",
"combined data", RooArgList(mass),
RooFit.Index(sample), *datasets)
simPdf = RooSimultaneous("simPdf", "simultaneous pdf", sample)
for cut in cuts[1:-1]:
simPdf.addPdf(modellist[cut], str(cut))
#simPdf.fitTo(combData)
#simPdf.fitTo(combData)
#simPdf.fitTo(combData)
def buildPdf(ws, p):
mass = RooRealVar("mass", "mass", p.minMass, p.maxMass)
# ws.import(mass)
getattr(ws,'import')(mass)
# Construct signal pdf
mean = RooRealVar("mean", "mean", 60, 0, 130)
width = RooRealVar("width", "width", 10, 1, 40)
# alpha = RooRealVar("alpha", "#alpha", -0.1, -10.0, -0.1)
# npow = RooRealVar("npow", "n_{CB}", 2.3, 0.1, 10.)
# width.setConstant(ROOT.kTRUE)
sigma = RooRealVar("sigma", "sigma", 1.2, 0.2, 40)
# minusMass = RooFormulaVar("minusMass", "@0*-1", RooArgList(mass))
# signalAll = RooCBShape("signalAll", "signalAll", mass, mean, width, alpha, npow);
signalAll = RooVoigtian("signalAll", "signalAll", mass, mean, width, sigma)
# Construct background pdf
# a0_all = RooRealVar("a0_all","a0_all",-0.1,-1,1)
# a1_all = RooRealVar("a1_all","a1_all",0.004,-1,1)
# backgroundAll = RooChebychev("backgroundAll","backgroundAll",mass,RooArgList(a0_all,a1_all))
turnOnAll = RooRealVar("turnOnAll","turnOnAll", 80., 40., 150.)
widthAll_bkg = RooRealVar("widthAll","widthAll", 2., 0., 50.)
decayAll_bkg = RooRealVar("decayAll","decayAll", 80., 20., 150.)
meanB = RooRealVar("meanB", "meanB", 90, 60, 130)
sigmaB = RooRealVar("sigmaB", "sigmaB", 10, 1, 20)
bkg_a1 = RooRealVar("bkg_a1", "bkg_a1", 0., -2., 2.)
bkg_a2 = RooRealVar("bkg_a2", "bkg_a2", 0., -2., 2.)
#backgroundAll = RooChebychev("backgroundAll", "backgroundAll", mass, RooArgList(bkg_a1, bkg_a2))
#backgroundAll = RooGenericPdf("backgroundAll","backgroundAll", "exp(-@0/@3)*(TMath::Erf((@0-@1)/@2)+1)", RooArgList(mass, turnOnAll, widthAll_bkg, decayAll_bkg))
backgroundAll = RooGenericPdf("backgroundAll","backgroundAll","exp(-@0/@1)",RooArgList(mass, decayAll_bkg))
#backgroundAll = RooGaussian("backgroundAll", "backgroundAll", mass, meanB, sigmaB)
# Construct composite pdf
sigAll = RooRealVar("sigAll", "sigAll", 2000, 0, 10000000)
bkgAll = RooRealVar("bkgAll", "bkgAll", 100, 0, 1000000)
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll, backgroundAll), RooArgList(sigAll, bkgAll))
if p.NoBkgd:
modelAll = RooAddPdf("modelAll", "modelAll", RooArgList(signalAll), RooArgList(sigAll))
# Define pdf for all probes
# Construct signal pdf.
# NOTE that sigma is shared with the signal sample model
signalPass = RooVoigtian("signalPass","signalPass",mass,mean,width,sigma)
# signalPass = RooCBShape("signalPass", "signalPass", mass, mean, width, alpha, npow);
# Construct the background pdf
# a0_pass = RooRealVar("a0_pass","a0_pass",-0.1,-1,1)
# a1_pass = RooRealVar("a1_pass","a1_pass",0.5,-0.1,1)
# backgroundPass = RooChebychev("backgroundPass","backgroundPass",mass,RooArgList(a0_pass,a1_pass))
# turnOnPass = RooRealVar("turnOnPass","turnOnPass", 80., 50., 150.)
# widthPass_bkg = RooRealVar("widthPass","widthPass", 2., 0., 50.)
decayPass_bkg = RooRealVar("decayPass","decayPass", 80., 20., 150.)
#backgroundPass = RooChebychev("backgroundPass", "backgroundPass", mass, RooArgList(bkg_a1, bkg_a2))
#backgroundPass = RooGenericPdf("backgroundPass","backgroundPass", "exp(-@0/@3)*(TMath::Erf((@0-@1)/@2)+1)", RooArgList(mass, turnOnPass, widthPass_bkg, decayPass_bkg));
#backgroundPass = RooGenericPdf("backgroundPass","backgroundPass", "exp(-@0/@3)*(TMath::Erf((@0-@1)/@2)+1)", RooArgList(mass, turnOnAll, widthAll_bkg, decayAll_bkg));
backgroundPass = RooGenericPdf("backgroundPass","backgroundPass","exp(-@0/@1)",RooArgList(mass, decayPass_bkg))
#backgroundPass = RooGaussian("backgroundPass", "backgroundPass", mass, meanB, sigmaB)
# Construct the composite model
efficiency = RooRealVar("efficiency","efficiency",0.9,0.1,1.)
# Use it only analyzing the data prescaling value set to be 20
sigPass = RooFormulaVar("sigPass", "@0*@1*"+str(p.scaleFactor), RooArgList(sigAll, efficiency))
bkgPass = RooRealVar("bkgPass", "bkgPass", 100, 0, 1000000)
# bkgPass = RooFormulaVar("bkgPass", "@0*@1", RooArgList(bkgAll, efficiency))
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass, backgroundPass), RooArgList(sigPass, bkgPass))
if p.NoBkgd:
modelPass = RooAddPdf("modelPass", "modelPass", RooArgList(signalPass), RooArgList(sigPass))
frac = RooRealVar("frac", "frac", 0.8, 0., 1.)
# Define combined pdf for simultaneous fit
# Define category to distinguish physics and control samples events
sample = RooCategory("sample","sample")
sample.defineType("all")
sample.defineType("pass")
simPdf = RooSimultaneous("simPdf","simultaneous pdf",sample)
# Associate model with the physics state and model_ctl with the control state
simPdf.addPdf(modelAll,"all")
simPdf.addPdf(modelPass,"pass")
# ws.import(simPdf)
getattr(ws,'import')(simPdf)
else:
data.plotOn(xframe,RooFit.DataError(RooAbsData.SumW2),RooFit.MarkerSize(1))
model.plotOn(xframe,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
model.plotOn(xframe,RooFit.Components("bkg"+name),RooFit.LineStyle(kDashed),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
model.plotOn(xframe,RooFit.Components("sigW"),RooFit.LineStyle(kDotted),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
model.plotOn(xframe,RooFit.Components("sigZ"),RooFit.LineStyle(kDashDotted),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
canvas=TCanvas("c2","c2",0,0,600,600)
xframe.GetYaxis().SetTitle("Events")
xframe.Draw()
canvas.SaveAs(prefix+"_"+plot[0]+name+"_fit"+fit+".pdf")
canvas.SaveAs(prefix+"_"+plot[0]+name+"_fit"+fit+".root")
if Wonly: continue
if fit=="data":
sample=RooCategory("sample","sample")
datasets=[]
for category in ["anti-btag","btag"]:
if category=="btag":
altscenario=btagscenario
else:
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
def setupWorkspace(ws, options):
cfg = options.config #for convenience
fit_sections = cfg.sections()
fit_sections.remove(
'Global') #don't need to iterate over the global configuration
if not isinstance(ws, RooWorkspace):
print "You didn't pass a RooWorkspace!"
exit(1)
cpling_type = cfg.get('Global', 'couplingType')
par1 = cfg.get('Global', 'par1Name')
par1bound = [
-cfg.getfloat('Global', 'par1Max'),
cfg.getfloat('Global', 'par1Max')
]
par2 = cfg.get('Global', 'par2Name')
par2bound = [
-cfg.getfloat('Global', 'par2Max'),
cfg.getfloat('Global', 'par2Max')
]
#create the parameters in the workspace
ws.factory('%s_%s[0,%f,%f]' %
(par1, cpling_type, par1bound[0], par1bound[1]))
ws.factory('%s_%s[0,%f,%f]' %
(par2, cpling_type, par2bound[0], par2bound[1]))
# since the lumi error is correlated among all channels we only need one penalty term for it
lumi_err = exp(options.config.getfloat(
'Global', 'lumi_err')) # exp because we use log normal
ws.factory('luminosityError[%f]' % lumi_err)
ws.factory('RooLognormal::lumiErr(err_gl[1,0.0001,50],1,luminosityError)')
channel_cat = RooCategory('channels', 'channels')
#first pass: process the backgrounds, signal and data into
# simultaneous counting pdfs over the bins
for section in fit_sections:
#create the basic observable, this is used behind the scenes
#in the background and signal models
channel_cat.defineType(section)
channel_cat.setLabel(section)
print 'Building pdf for configuration section:', section
for it, bkg in getBackgroundsInCfg(section, cfg).iteritems():
ws.factory('backgroundError_%s_%s[%f]' %
(section, it, exp(bkg[1])))
ws.factory('selectionError_%s[%f]' %
(section, exp(cfg.getfloat(section, 'selection_err'))))
processFittingData(ws, cfg, section)
processSignalModel(ws, cfg, section)
processBackgroundModel(ws, cfg, section)
createPdfForChannel(ws, cfg, section)
ws.data('countingdata_%s' % section).addColumn(channel_cat)
getattr(ws, 'import')(channel_cat)
top = RooSimultaneous('TopLevelPdf', 'TopLevelPdf', ws.cat('channels'))
alldatavars = RooArgSet(ws.cat('channels'))
conditionals = RooArgSet()
#second pass: process counting pdfs into simultaneous pdf over channels
for section in fit_sections:
top.addPdf(ws.pdf('countingpdf_%s' % section), section)
alldatavars.add(ws.var('%s_%s' %
(cfg.get(section, 'obsVar'), section)))
conditionals.add(
ws.var('%s_%s' % (cfg.get(section, 'obsVar'), section)))
alldatavars.add(ws.var('n_observed_%s' % section))
getattr(ws, 'import')(top)
ws.defineSet('condObs', conditionals)
allcountingdata = RooDataSet('allcountingdata', 'allcountingdata',
alldatavars)
getattr(ws, 'import')(allcountingdata)
allcountingdata = ws.data('allcountingdata')
#third pass: make the final combined dataset
for section in fit_sections:
current = ws.data('countingdata_%s' % section)
print 'countingdata_%s has %d entries' % (section,
current.numEntries())
for i in range(current.numEntries()):
alldatavars = current.get(i)
allcountingdata.add(alldatavars)
# seed the Random number generator
rndm = TRandom3(SEED + 1)
RooRandom.randomGenerator().SetSeed(int(rndm.Uniform(4294967295)))
del rndm
# start building the fit
from B2DXFitters.WS import WS
ws = RooWorkspace('ws')
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType( 'B+', +1)
qt.defineType('Untagged', 0)
qt.defineType( 'B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar( 'Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar( 'Dm', 'Dm', 17.719)) # ps^-1
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
def rooFit108():
print ">>> setup model - a B decay with mixing..."
dt = RooRealVar("dt","dt",-20,20)
dm = RooRealVar("dm","dm",0.472)
tau = RooRealVar("tau","tau",1.547)
w = RooRealVar("w","mistag rate",0.1)
dw = RooRealVar("dw","delta mistag rate",0.)
# Build categories - possible values states
# https://root.cern/doc/v610/classRooCategory.html
mixState = RooCategory("mixState","B0/B0bar mixing state")
mixState.defineType("mixed",-1)
mixState.defineType("unmixed",1)
tagFlav = RooCategory("tagFlav","Flavour of the tagged B0")
tagFlav.defineType("B0",1)
tagFlav.defineType("B0bar",-1)
# Build a gaussian resolution model
dterr = RooRealVar("dterr","dterr",0.1,1.0)
bias1 = RooRealVar("bias1","bias1",0)
sigma1 = RooRealVar("sigma1","sigma1",0.1)
gm1 = RooGaussModel("gm1","gauss model 1",dt,bias1,sigma1)
# Construct Bdecay (x) gauss
# https://root.cern/doc/v610/classRooBMixDecay.html
bmix = RooBMixDecay("bmix","decay",dt,mixState,tagFlav,tau,dm,w,dw,gm1,RooBMixDecay.DoubleSided)
print ">>> sample data from data..."
data = bmix.generate(RooArgSet(dt,mixState,tagFlav),2000) # RooDataSet
print ">>> show dt distribution with custom binning..."
# Make plot of dt distribution of data in range (-15,15) with fine binning for dt>0
# and coarse binning for dt<0
tbins = RooBinning(-15,15) # Create binning object with range (-15,15)
tbins.addUniform(60,-15,0) # Add 60 bins with uniform spacing in range (-15,0)
tbins.addUniform(15,0,15) # Add 15 bins with uniform spacing in range (0,15)
dtframe = dt.frame(Range(-15,15),Title("dt distribution with custom binning")) # RooPlot
data.plotOn(dtframe,Binning(tbins))
bmix.plotOn(dtframe)
# NB: Note that bin density for each bin is adjusted to that of default frame
# binning as shown in Y axis label (100 bins --> Events/0.4*Xaxis-dim) so that
# all bins represent a consistent density distribution
print ">>> plot mixstate asymmetry with custom binning..."
# Make plot of dt distribution of data asymmetry in 'mixState' with variable binning
abins = RooBinning(-10,10) # Create binning object with range (-10,10)
abins.addBoundary(0) # Add boundaries at 0
abins.addBoundaryPair(1) # Add boundaries at (-1,1)
abins.addBoundaryPair(2) # Add boundaries at (-2,2)
abins.addBoundaryPair(3) # Add boundaries at (-3,3)
abins.addBoundaryPair(4) # Add boundaries at (-4,4)
abins.addBoundaryPair(6) # Add boundaries at (-6,6)
aframe = dt.frame(Range(-10,10),Title("MixState asymmetry distribution with custom binning")) # RooPlot
# Plot mixState asymmetry of data with specified customg binning
data.plotOn(aframe,Asymmetry(mixState),Binning(abins))
# Plot corresponding property of pdf
bmix.plotOn(aframe,Asymmetry(mixState))
# Adjust vertical range of plot to sensible values for an asymmetry
aframe.SetMinimum(-1.1)
aframe.SetMaximum( 1.1)
# NB: For asymmetry distributions no density corrects are needed (and are thus not applied)
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)
dtframe.GetYaxis().SetLabelOffset(0.008)
dtframe.GetYaxis().SetTitleOffset(1.6)
dtframe.GetYaxis().SetTitleSize(0.045)
dtframe.GetXaxis().SetTitleSize(0.045)
dtframe.Draw()
canvas.cd(2)
gPad.SetLeftMargin(0.15); gPad.SetRightMargin(0.02)
aframe.GetYaxis().SetLabelOffset(0.008)
aframe.GetYaxis().SetTitleOffset(1.6)
aframe.GetYaxis().SetTitleSize(0.045)
aframe.GetXaxis().SetTitleSize(0.045)
aframe.Draw()
canvas.SaveAs("rooFit108.png")
RooAbsRealLValue.__assign__(var, entry)
dataset.add(RooArgSet(var))
return dataset
m = RooRealVar("Jpsi_M", "mass", fit_range[0], fit_range[1])
data_m = RooDataSet("data_m", "data_m", RooArgSet(m))
data_u = RooDataSet("data_u", "data_u", RooArgSet(m))
data_m = load_set(tot_m, m, data_m)
data_u = load_set(tot_u, m, data_u)
data_m.Print("v")
data_u.Print("v")
sample = RooCategory("sample", "sample")
sample.defineType("matched")
sample.defineType("unmatched")
# define the combined set
combData = RooDataSet(
"combData",
"combined data",
RooArgSet(m),
RooFit.Index(sample),
RooFit.Import(
"matched",
data_m),
RooFit.Import(
"unmatched",
data_u))
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 buildDataAndCategories(ws, options, args):
#Get the input data
inputData = TChain(options.treeName, 'The input data')
for arg in args:
print 'Adding data from: ', arg
inputData.Add(arg)
foldname = ''
phirange = [0, 90]
if not options.folded:
foldname = ''
phirange = [-180, 180]
#variables necessary for j/psi mass,lifetime,polarization fit
jPsiMass = RooRealVar('JpsiMass', 'M [GeV]', 2.7, 3.5)
jPsiRap = RooRealVar('JpsiRap', '#nu', -2.3, 2.3)
jPsiPt = RooRealVar("JpsiPt", "pT [GeV]", 0, 40)
jPsicTau = RooRealVar('Jpsict', 'l_{J/#psi} [mm]', -1, 2.5)
jPsicTauError = RooRealVar('JpsictErr', 'Error on l_{J/#psi} [mm]', 0, 2)
jPsiVprob = RooRealVar('JpsiVprob', '', .01, 1)
jPsiHXcosth = None
jPsiHXphi = None
jPsicTau.setBins(10000, "cache")
if options.fitFrame is not None:
jPsiHXcosth = RooRealVar('costh_' + options.fitFrame + foldname,
'cos(#theta)_{' + options.fitFrame + '}', -1,
1)
jPsiHXphi = RooRealVar('phi_' + options.fitFrame + foldname,
'#phi_{' + options.fitFrame + '}', phirange[0],
phirange[1])
else:
jPsiHXcosth = RooRealVar('costh_CS' + foldname, 'cos(#theta)_{CS}', -1,
1)
jPsiHXphi = RooRealVar('phi_CS' + foldname, '#phi_{CS}', phirange[0],
phirange[1])
#vars needed for on the fly calc of polarization variables
jPsimuPosPx = RooRealVar('muPosPx', '+ Muon P_{x} [GeV]', 0)
jPsimuPosPy = RooRealVar('muPosPy', '+ Muon P_{y} [GeV]', 0)
jPsimuPosPz = RooRealVar('muPosPz', '+ Muon P_{z} [GeV]', 0)
jPsimuNegPx = RooRealVar('muNegPx', '- Muon P_{x} [GeV]', 0)
jPsimuNegPy = RooRealVar('muNegPy', '- Muon P_{y} [GeV]', 0)
jPsimuNegPz = RooRealVar('muNegPz', '- Muon P_{z} [GeV]', 0)
#create RooArgSet for eventual dataset creation
dataVars = RooArgSet(jPsiMass, jPsiRap, jPsiPt, jPsicTau, jPsicTauError,
jPsimuPosPx, jPsimuPosPy, jPsimuPosPz)
#add trigger requirement if specified
if options.triggerName:
trigger = RooRealVar(options.triggerName, 'Passes Trigger', 0.5, 1.5)
dataVars.add(trigger)
dataVars.add(jPsiVprob)
dataVars.add(jPsimuNegPx)
dataVars.add(jPsimuNegPy)
dataVars.add(jPsimuNegPz)
dataVars.add(jPsiHXcosth)
dataVars.add(jPsiHXphi)
redVars = RooArgSet(jPsiMass, jPsiRap, jPsiPt, jPsicTau, jPsicTauError)
redVars.add(jPsiHXcosth)
redVars.add(jPsiHXphi)
fitVars = redVars.Clone()
### HERE IS WHERE THE BIT FOR CALCULATING POLARIZATION VARS GOES
ctauStates = RooCategory('ctauRegion', 'Cut Region in lifetime')
ctauStates.defineType('prompt', 0)
ctauStates.defineType('nonPrompt', 1)
massStates = RooCategory('massRegion', 'Cut Region in mass')
massStates.defineType('signal', 1)
massStates.defineType('separation', 0)
massStates.defineType('leftMassSideBand', -2)
massStates.defineType('rightMassSideBand', -1)
states = RooCategory('mlRegion', 'Cut Region in mass')
states.defineType('nonPromptSignal', 2)
states.defineType('promptSignal', 1)
states.defineType('separation', 0)
states.defineType('leftMassSideBand', -2)
states.defineType('rightMassSideBand', -1)
#define corresponding ranges in roorealvars
#mass is a little tricky since the sidebands change definitions in each rap bin
#define the names here and change as we do the fits
#jPsiMass.setRange('NormalizationRangeFormlfit_promptSignal',2.7,3.5)
#jPsiMass.setRange('NormalizationRangeFormlfit_nonPromptSignal',2.7,3.5)
#jPsiMass.setRange('NormalizationRangeFormlfit_leftMassSideBand',2.7,3.1)
#jPsiMass.setRange('NormalizationRangeFormlfit_rightMassSideBand',3.1,3.5)
#want the prompt fit only done in prompt region
#non-prompt only in non-prompt region
#background over entire cTau range
#jPsicTau.setRange('NormalizationRangeFormlfit_promptSignal',-1,.1)
#jPsicTau.setRange('NormalizationRangeFormlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#redVars.add(ctauStates)
#redVars.add(massStates)
#redVars.add(states)
fitVars.add(ctauStates)
fitVars.add(massStates)
fitVars.add(states)
fullData = RooDataSet('fullData',
'The Full Data From the Input ROOT Trees', dataVars,
ROOT.RooFit.Import(inputData))
for rap_bin in range(1, len(jpsi.pTRange)):
yMin = jpsi.rapForPTRange[rap_bin - 1][0]
yMax = jpsi.rapForPTRange[rap_bin - 1][-1]
for pt_bin in range(len(jpsi.pTRange[rap_bin])):
ptMin = jpsi.pTRange[rap_bin][pt_bin][0]
ptMax = jpsi.pTRange[rap_bin][pt_bin][-1]
sigMaxMass = jpsi.polMassJpsi[
rap_bin] + jpsi.nSigMass * jpsi.sigmaMassJpsi[rap_bin]
sigMinMass = jpsi.polMassJpsi[
rap_bin] - jpsi.nSigMass * jpsi.sigmaMassJpsi[rap_bin]
sbHighMass = jpsi.polMassJpsi[
rap_bin] + jpsi.nSigBkgHigh * jpsi.sigmaMassJpsi[rap_bin]
sbLowMass = jpsi.polMassJpsi[
rap_bin] - jpsi.nSigBkgLow * jpsi.sigmaMassJpsi[rap_bin]
ctauNonPrompt = .1
massFun = RooFormulaVar(
'massRegion', 'Function that returns the mass state.',
'(' + jPsiMass.GetName() + ' < ' + str(sigMaxMass) + ' && ' +
jPsiMass.GetName() + ' > ' + str(sigMinMass) + ') - (' +
jPsiMass.GetName() + ' > ' + str(sbHighMass) + ')' + '-2*(' +
jPsiMass.GetName() + ' < ' + str(sbLowMass) + ')',
RooArgList(jPsiMass, jPsicTau))
ctauFun = RooFormulaVar(
'ctauRegion', 'Function that returns the ctau state.',
'(' + jPsicTau.GetName() + ' > ' + str(ctauNonPrompt) + ')',
RooArgList(jPsiMass, jPsicTau))
mlFun = RooFormulaVar(
'mlRegion',
'Function that returns the mass and lifetime state.',
'(' + jPsiMass.GetName() + ' < ' + str(sigMaxMass) + ' && ' +
jPsiMass.GetName() + ' > ' + str(sigMinMass) + ') + (' +
jPsiMass.GetName() + ' < ' + str(sigMaxMass) + ' && ' +
jPsiMass.GetName() + ' > ' + str(sigMinMass) + ' && ' +
jPsicTau.GetName() + ' > ' + str(ctauNonPrompt) + ') - (' +
jPsiMass.GetName() + ' > ' + str(sbHighMass) + ')' + '-2*(' +
jPsiMass.GetName() + ' < ' + str(sbLowMass) + ')',
RooArgList(jPsiMass, jPsicTau))
cutStringPt = '(' + jPsiPt.GetName() + ' > ' + str(
ptMin) + ' && ' + jPsiPt.GetName() + ' < ' + str(ptMax) + ')'
cutStringY = '( abs(' + jPsiRap.GetName() + ') > ' + str(
yMin) + ' && abs(' + jPsiRap.GetName() + ') < ' + str(
yMax) + ')'
#cutStringM1 = '('+jPsiMass.GetName()+' < '+str(sigMinMass)+' && '+jPsiMass.GetName()+' > '+str(sbLowMass)+')'
#cutStringM2 = '('+jPsiMass.GetName()+' < '+str(sbHighMass)+' && '+jPsiMass.GetName()+' > '+str(sigMaxMass)+')'
#cutStringMT = '!('+cutStringM1+' || '+cutStringM2+')'
cutString = cutStringPt + ' && ' + cutStringY #+' && '+cutStringMT
print cutString
#get the reduced dataset we'll do the fit on
binData = fullData.reduce(
ROOT.RooFit.SelectVars(redVars), ROOT.RooFit.Cut(cutString),
ROOT.RooFit.Name('data_rap' + str(rap_bin) + '_pt' +
str(pt_bin + 1)),
ROOT.RooFit.Title('Data For Fitting'))
binDataWithCategory = RooDataSet(
'data_rap' + str(rap_bin) + '_pt' + str(pt_bin + 1),
'Data For Fitting', fitVars)
#categorize
binData.addColumn(ctauStates)
binData.addColumn(massStates)
binData.addColumn(states)
for ev in range(binData.numEntries()):
args = binData.get(ev)
jPsiMass.setVal(args.find(jPsiMass.GetName()).getVal())
jPsiRap.setVal(args.find(jPsiRap.GetName()).getVal())
jPsiPt.setVal(args.find(jPsiPt.GetName()).getVal())
jPsicTau.setVal(args.find(jPsicTau.GetName()).getVal())
jPsicTauError.setVal(
args.find(jPsicTauError.GetName()).getVal())
jPsiHXcosth.setVal(args.find(jPsiHXcosth.GetName()).getVal())
jPsiHXphi.setVal(args.find(jPsiHXphi.GetName()).getVal())
massStates.setIndex(int(massFun.getVal()))
ctauStates.setIndex(int(ctauFun.getVal()))
states.setIndex(int(mlFun.getVal()))
binDataWithCategory.add(fitVars)
getattr(ws, 'import')(binDataWithCategory)
RooArgList(nSig, efficiency))
#Pass
componentspass = RooArgList(sigShapePdf) #,bkgShapePdf);
yieldspass = RooArgList(nSigpass) #, nBkgpass);
sumpass = RooAddPdf("sumpass", "fixed extended sum pdf", componentspass,
yieldspass)
#Fail
componentsfail = RooArgList(sigShapePdf) #,bkgShapePdf );
yieldsfail = RooArgList(nSigfail) #, nBkgfail );
sumfail = RooAddPdf("sumfail", "fixed extended sum pdf", componentsfail,
yieldsfail)
#Creates category
myFitCat = RooCategory("myFitCat", "Category: pass or fail")
myFitCat.defineType("fail", 1)
myFitCat.defineType("pass", 0)
#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))
class SimultaneousFit:
""" A fit that performs a simultaneous fit in more than one variable.
It expects the input of fit_data which is a dictionary of the form
{variable_name: FitData()}"""
def __init__(self, fit_data):
MapStrRootPtr = stl.map(stl.string, "TH1*")
StrHist = stl.pair(stl.string, "TH1*")
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory("sample", "sample")
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable(
"n_" + sample,
"number of " + sample + " events",
fit_data_1.normalisation[sample],
N_min,
N_max,
"events",
)
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model(variable, self.observables)
self.sample.defineType(variable)
self.sample.setLabel(variable)
data = deepcopy(fit_input.real_data_histogram())
input_hists.insert(StrHist(variable, data))
self.roofit_variables.append(fit_input.fit_variable)
self.comb_data = RooDataHist(
"combData", "combined data", RooArgList(self.roofit_variables[0]), self.sample, input_hists
)
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 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)
print "Getting deltam..."
#deltam = m_D* - m_D0
deltam = lo_dataset.get(0)["deltam"]
deltam.setMax(152)
dm_min = deltam.getMin()
dm_max = deltam.getMax()
print "deltam accessed. \nBinning dataset..."
##### Dividing dataset based on D0 mass #####
#Define allowed regionsi
regions = ["lo_mid", "mid", "hi_mid"]
n_regions= len(regions)
#Create index for RooFit to distinguish regions
regionIndex = RooCategory("regionIndex", "regionIndex")
for i in range(n_regions) :
regionIndex.defineType(regions[i])
lo_mass_cut = 1845
hi_mass_cut = 1885
lo_mid_dataset = mid_dataset_raw.reduce("D0_mass < "+str(lo_mass_cut))
mid_dataset = mid_dataset_raw.reduce("D0_mass >= "+str(lo_mass_cut)+" && D0_mass <= "+str(hi_mass_cut))
hi_mid_dataset = mid_dataset_raw.reduce("D0_mass > "+str(hi_mass_cut))
#Create new dataset separated by region
dataset = RooDataSet("dataset_"+mag, "dataset_"+mag, RooArgSet(deltam), Index(regionIndex), Import("lo_mid", lo_mid_dataset), Import("mid", mid_dataset), Import("hi_mid", hi_mid_dataset))
datahist = dataset.binnedClone()
##############################################
print "Dataset binned successfully!\n"
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_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 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")
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 main(options,args):
pt_bins = [[12, 15],
[15, 20]]
eta_bins = [[0.0,1.4442]] # try to split material dependence
# [1.560,2.5]]
sieie = RooRealVar(options.showerShapeName,'#sigma_{i #eta i #eta}',.1,0,.15)
sieie.setBins(200)
pt = RooRealVar(options.ptName,'Photon p_{T}',50,10,1000)
eta = RooRealVar(options.etaName,'Photon #eta',0.0,-3.0,3.0)
chIso = RooRealVar(options.isoName, 'phoCHIso', 100, 0, 20)
fakeMom = RooRealVar(options.momName, 'phohasFakemom', 0,1)
#containor for the root variables
vars = RooArgSet()
vars.add(sieie)
vars.add(pt)
vars.add(eta)
vars.add(chIso)
vars.add(fakeMom)
allSig = None
allBkg = None
if options.weight is not None:
weight = RooRealVar(options.weight,'',1)
vars.add(weight)
allSig = RooDataSet('allSig','All Signal Template Events',
vars,
RooFit.ImportFromFile(options.signalInput,
options.sigTreeName),
RooFit.WeightVar(options.weight)
)
if options.useFakeMCBkg:
fake = RooRealVar('Fake','',0.5,1.5)
#RooRealVar isSidebands("isSidebands","isSidebands",0,1);
#fake = RooRealVar(options.momName,"phohasFakemom",0,1)
vars.add(fake)
weight.setVal(1)
ctauStates = RooCategory('ctauRegion','Cut Region in lifetime')
ctauStates.defineType('phohasFakemom',0)
#ctauStates.defineType('nonPrompt',1)
allBkg = RooDataSet('allBkg','All Background Template Events',
RooArgSet(ctauStates,vars),
RooFit.ImportFromFile(options.backgroundInput,
options.bkgTreeName),
RooFit.WeightVar(options.weight)
)
else:
allSig = RooDataSet('allSig','All Signal Template Events',
vars,
RooFit.ImportFromFile(options.signalInput,
options.sigTreeName)
)
allBkg = RooDataSet('allBkg','All Background Template Events',
vars,
RooFit.ImportFromFile(options.backgroundInput,
options.bkgTreeName)
)
output = TFile.Open(options.outputFile,'RECREATE')
ws = RooWorkspace(options.outputFile[:options.outputFile.find('.root')],'Template Workspace')
# put in the raw datasets, no cuts or manipulation
getattr(ws,'import')(allSig,
RooFit.RenameVariable(options.showerShapeName,'Pho_SigmaIEtaIEta'),
RooFit.RenameVariable(options.ptName,'Pho_Pt'),
#RooFit.RenameVariable(options.isoName,'phoCHIso'),
RooFit.RenameVariable(options.etaName,'Pho_Eta'))
getattr(ws,'import')(allBkg,
RooFit.RenameVariable(options.showerShapeName,'Pho_SigmaIEtaIEta'),
RooFit.RenameVariable(options.ptName,'Pho_Pt'),
#RooFit.RenameVariable(options.isoName,'phoCHIso'),
RooFit.RenameVariable(options.etaName,'Pho_Eta'))
#loop through bins making all templates
for etabin in eta_bins:
for ptbin in pt_bins:
if 'abs(Pho_Eta) < 1.4442':
phoselsig = 'abs(Pho_Eta) > %f && abs(Pho_Eta) < %f && Pho_Pt > %f && Pho_Pt < %f'%(etabin[0],
etabin[1],
ptbin[0],
ptbin[1])
# if fake:
phoselbkg = 'phoCHIso < 2 && abs(Pho_Eta) > %f && abs(Pho_Eta) < %f && Pho_Pt > %f && Pho_Pt < %f'%(etabin[0],
etabin[1],
ptbin[0],
ptbin[1])
postfix = '_Eta_%.4f_%.4f_Pt_%.2f_%.2f'%(etabin[0],etabin[1],ptbin[0],ptbin[1])
binSig = ws.data('allSig').reduce(RooFit.Cut(phoselsig),
RooFit.Name('sigEta'+postfix),
RooFit.Title('Signal Template Events'))
binBkg = ws.data('allBkg').reduce(RooFit.Cut(phoselbkg),
RooFit.Name('bkgEta'+postfix),
RooFit.Title('Background Template Events'))
#save it all in the workspace
getattr(ws,'import')(binSig)
getattr(ws,'import')(binBkg)
smoothingPars = RooArgList(ws.var('Pho_SigmaIEtaIEta')) #ws.var('Pho_Pt'),
print 'Making binSigTemplate'+postfix+' with '+str(binSig.numEntries())+' events.'
bSigTempl = None
if( binSig.numEntries() <= 80000 ):
bSigTempl = RooNDKeysPdf('binSigTemplate'+postfix,'',smoothingPars,binSig,"am")
else:
bSigTempl = super(RooDataSet,binSig)
print 'Making binBkgTemplate'+postfix+' with '+str(binBkg.numEntries())+' events.'
bBkgTempl = None
if( binBkg.numEntries() <= 80000 ):
bBkgTempl = RooNDKeysPdf('binBkgTemplate'+postfix,'',smoothingPars,binBkg,"am")
else:
bBkgTempl = super(RooDataSet,binBkg)
#finely binned histograms so that we can store the results of the smoothing
#These will be linearly interpolated by RooHistPdf
bsTHist = bSigTempl.createHistogram('binSigSmoothedHist'+postfix,
ws.var('Pho_SigmaIEtaIEta'),
RooFit.Binning(options.nBins,0,.15) )
getattr(ws,'import')(bsTHist)
bbTHist = bBkgTempl.createHistogram('binBkgSmoothedHist'+postfix,
ws.var('Pho_SigmaIEtaIEta'),
RooFit.Binning(options.nBins,0,.15) )
getattr(ws,'import')(bbTHist)
#make RooDataHists for consuption by RooHistPdf Later
bsTDHist = RooDataHist('binSigSmoothedDataHist'+postfix,
'',
RooArgList(ws.var('Pho_SigmaIEtaIEta')),
ws.obj('binSigSmoothedHist'+postfix+'__Pho_SigmaIEtaIEta'))
getattr(ws,'import')(bsTDHist)
bbTDHist = RooDataHist('binBkgSmoothedDataHist'+postfix,
'',
RooArgList(ws.var('Pho_SigmaIEtaIEta')),
ws.obj('binBkgSmoothedHist'+postfix+'__Pho_SigmaIEtaIEta'))
getattr(ws,'import')(bbTDHist)
ws.Write()
output.Close()
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 dijet(category):
channel = 'bb'
stype = channel
isSB = True # relict from using Alberto's more complex script
isData = not ISMC
nTupleDir = NTUPLEDIR
samples = data if isData else back
pd = []
for sample_name in samples:
if YEAR == 'run2':
pd += sample[sample_name]['files']
else:
pd += [x for x in sample[sample_name]['files'] if YEAR in x]
print "datasets:", pd
if not os.path.exists(PLOTDIR): os.makedirs(PLOTDIR)
if BIAS: print "Running in BIAS mode"
order = 0
RSS = {}
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
weight = RooRealVar("MANtag_weight", "", -1.e9, 1.e9)
variables = RooArgSet(X_mass)
variables.add(RooArgSet(weight))
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(RooBinning(len(abins_narrow) - 1, abins_narrow))
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
baseCut = ""
print stype, "|", baseCut
print " - Reading from Tree"
treeBkg = TChain("tree")
for ss in pd:
if os.path.exists(nTupleDir + ss + "_" + BTAGGING + ".root"):
treeBkg.Add(nTupleDir + ss + "_" + BTAGGING + ".root")
else:
print "found no file for sample:", ss
setData = RooDataSet("setData", "Data (QCD+TTbar MC)", variables,
RooFit.Cut(baseCut), RooFit.WeightVar(weight),
RooFit.Import(treeBkg))
nevents = setData.sumEntries()
dataMin, dataMax = array('d', [0.]), array('d', [0.])
setData.getRange(X_mass, dataMin, dataMax)
xmin, xmax = dataMin[0], dataMax[0]
lastBin = X_mass.getMax()
if VARBINS:
for b in narrow_bins:
if b > xmax:
lastBin = b
break
print "Imported", (
"data" if isData else "MC"
), "RooDataSet with", nevents, "events between [%.1f, %.1f]" % (xmin, xmax)
#xmax = xmax+binsXmass.averageBinWidth() # start form next bin
# 1 parameter
print "fitting 1 parameter model"
p1_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p1_1", "p1", 7.0, 0.,
2000.)
modelBkg1 = RooGenericPdf("Bkg1", "Bkg. fit (2 par.)",
"1./pow(@0/13000, @1)", RooArgList(X_mass, p1_1))
normzBkg1 = RooRealVar(
modelBkg1.GetName() + "_norm", "Number of background events", nevents,
0., 5. * nevents) #range dependent of actual number of events!
modelExt1 = RooExtendPdf(modelBkg1.GetName() + "_ext",
modelBkg1.GetTitle(), modelBkg1, normzBkg1)
fitRes1 = modelExt1.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes1.Print()
RSS[1] = drawFit("Bkg1", category, X_mass, modelBkg1, setData, binsXmass,
[fitRes1], normzBkg1.getVal())
# 2 parameters
print "fitting 2 parameter model"
p2_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_1", "p1", 0., -100.,
1000.)
p2_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_2", "p2",
p1_1.getVal(), -100., 600.)
modelBkg2 = RooGenericPdf("Bkg2", "Bkg. fit (3 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2)",
RooArgList(X_mass, p2_1, p2_2))
normzBkg2 = RooRealVar(modelBkg2.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt2 = RooExtendPdf(modelBkg2.GetName() + "_ext",
modelBkg2.GetTitle(), modelBkg2, normzBkg2)
fitRes2 = modelExt2.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes2.Print()
RSS[2] = drawFit("Bkg2", category, X_mass, modelBkg2, setData, binsXmass,
[fitRes2], normzBkg2.getVal())
# 3 parameters
print "fitting 3 parameter model"
p3_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_1", "p1",
p2_1.getVal(), -2000., 2000.)
p3_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_2", "p2",
p2_2.getVal(), -400., 2000.)
p3_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_3", "p3", -2.5,
-500., 500.)
modelBkg3 = RooGenericPdf(
"Bkg3", "Bkg. fit (4 par.)",
"pow(1-@0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000))",
RooArgList(X_mass, p3_1, p3_2, p3_3))
normzBkg3 = RooRealVar(modelBkg3.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt3 = RooExtendPdf(modelBkg3.GetName() + "_ext",
modelBkg3.GetTitle(), modelBkg3, normzBkg3)
fitRes3 = modelExt3.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes3.Print()
RSS[3] = drawFit("Bkg3", category, X_mass, modelBkg3, setData, binsXmass,
[fitRes3], normzBkg3.getVal())
# 4 parameters
print "fitting 4 parameter model"
p4_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_1", "p1",
p3_1.getVal(), -2000., 2000.)
p4_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_2", "p2",
p3_2.getVal(), -2000., 2000.)
p4_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_3", "p3",
p3_3.getVal(), -50., 50.)
p4_4 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_4", "p4", 0.1, -50.,
50.)
modelBkg4 = RooGenericPdf(
"Bkg4", "Bkg. fit (5 par.)",
"pow(1 - @0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000)+@4*pow(log(@0/13000), 2))",
RooArgList(X_mass, p4_1, p4_2, p4_3, p4_4))
normzBkg4 = RooRealVar(modelBkg4.GetName() + "_norm",
"Number of background events", nevents, 0.,
5. * nevents)
modelExt4 = RooExtendPdf(modelBkg4.GetName() + "_ext",
modelBkg4.GetTitle(), modelBkg4, normzBkg4)
fitRes4 = modelExt4.fitTo(setData, RooFit.Extended(True), RooFit.Save(1),
RooFit.SumW2Error(not isData),
RooFit.Strategy(2), RooFit.Minimizer("Minuit2"),
RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes4.Print()
RSS[4] = drawFit("Bkg4", category, X_mass, modelBkg4, setData, binsXmass,
[fitRes4], normzBkg4.getVal())
# Normalization parameters are should be set constant, but shape ones should not
# if BIAS:
# p1_1.setConstant(True)
# p2_1.setConstant(True)
# p2_2.setConstant(True)
# p3_1.setConstant(True)
# p3_2.setConstant(True)
# p3_3.setConstant(True)
# p4_1.setConstant(True)
# p4_2.setConstant(True)
# p4_3.setConstant(True)
# p4_4.setConstant(True)
normzBkg1.setConstant(True)
normzBkg2.setConstant(True)
normzBkg3.setConstant(True)
normzBkg4.setConstant(True)
#*******************************************************#
# #
# Fisher #
# #
#*******************************************************#
# Fisher test
with open(PLOTDIR + "/Fisher_" + category + ".tex", 'w') as fout:
fout.write(r"\begin{tabular}{c|c|c|c|c}")
fout.write("\n")
fout.write(r"function & $\chi^2$ & RSS & ndof & F-test \\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
CL_high = False
for o1 in range(1, 5):
o2 = min(o1 + 1, 5)
fout.write("%d par & %.2f & %.2f & %d & " %
(o1 + 1, RSS[o1]["chi2"], RSS[o1]["rss"],
RSS[o1]["nbins"] - RSS[o1]["npar"]))
if o2 > len(RSS):
fout.write(r"\\")
fout.write("\n")
continue #order==0 and
CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1 + 1., o2 + 1.,
RSS[o1]["nbins"])
fout.write("CL=%.3f " % (CL))
if CL > 0.10: # The function with less parameters is enough
if not CL_high:
order = o1
#fout.write( "%d par are sufficient " % (o1+1))
CL_high = True
else:
#fout.write( "%d par are needed " % (o2+1))
if not CL_high:
order = o2
fout.write(r"\\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
fout.write(r"\end{tabular}")
print "saved F-test table as", PLOTDIR + "/Fisher_" + category + ".tex"
#print "-"*25
#print "function & $\\chi^2$ & RSS & ndof & F-test & result \\\\"
#print "\\multicolumn{6}{c}{", "Zprime_to_bb", "} \\\\"
#print "\\hline"
#CL_high = False
#for o1 in range(1, 5):
# o2 = min(o1 + 1, 5)
# print "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]),
# if o2 > len(RSS):
# print "\\\\"
# continue #order==0 and
# CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"])
# print "%d par vs %d par CL=%f & " % (o1+1, o2+1, CL),
# if CL > 0.10: # The function with less parameters is enough
# if not CL_high:
# order = o1
# print "%d par are sufficient" % (o1+1),
# CL_high=True
# else:
# print "%d par are needed" % (o2+1),
# if not CL_high:
# order = o2
# print "\\\\"
#print "\\hline"
#print "-"*25
#print "@ Order is", order, "("+category+")"
#order = min(3, order)
#order = 2
if order == 1:
modelBkg = modelBkg1 #.Clone("Bkg")
modelAlt = modelBkg2 #.Clone("BkgAlt")
normzBkg = normzBkg1 #.Clone("Bkg_norm")
fitRes = fitRes1
elif order == 2:
modelBkg = modelBkg2 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg2 #.Clone("Bkg_norm")
fitRes = fitRes2
elif order == 3:
modelBkg = modelBkg3 #.Clone("Bkg")
modelAlt = modelBkg4 #.Clone("BkgAlt")
normzBkg = normzBkg3 #.Clone("Bkg_norm")
fitRes = fitRes3
elif order == 4:
modelBkg = modelBkg4 #.Clone("Bkg")
modelAlt = modelBkg3 #.Clone("BkgAlt")
normzBkg = normzBkg4 #.Clone("Bkg_norm")
fitRes = fitRes4
else:
print "Functions with", order + 1, "or more parameters are needed to fit the background"
exit()
modelBkg.SetName("Bkg_" + YEAR + "_" + category)
modelAlt.SetName("Alt_" + YEAR + "_" + category)
normzBkg.SetName("Bkg_" + YEAR + "_" + category + "_norm")
print "-" * 25
# Generate pseudo data
setToys = RooDataSet()
setToys.SetName("data_toys")
setToys.SetTitle("Data (toys)")
if not isData:
print " - Generating", nevents, "events for toy data"
setToys = modelBkg.generate(RooArgSet(X_mass), nevents)
#setToys = modelAlt.generate(RooArgSet(X_mass), nevents)
print "toy data generated"
if VERBOSE: raw_input("Press Enter to continue...")
#*******************************************************#
# #
# Plot #
# #
#*******************************************************#
print "starting to plot"
c = TCanvas("c_" + category, category, 800, 800)
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
c.cd(1)
frame = X_mass.frame()
setPadStyle(frame, 1.25, True)
if VARBINS: frame.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
signal = getSignal(
category, stype,
2000) #replacing Alberto's getSignal by own dummy function
graphData = setData.plotOn(frame, RooFit.Binning(plot_binning),
RooFit.Scaling(False), RooFit.Invisible())
modelBkg.plotOn(frame, RooFit.VisualizeError(fitRes, 1, False),
RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("FL"),
RooFit.Name("1sigma"))
modelBkg.plotOn(frame, RooFit.LineColor(602), RooFit.FillColor(590),
RooFit.FillStyle(1001), RooFit.DrawOption("L"),
RooFit.Name(modelBkg.GetName()))
modelAlt.plotOn(frame, RooFit.LineStyle(7), RooFit.LineColor(613),
RooFit.FillColor(609), RooFit.FillStyle(1001),
RooFit.DrawOption("L"), RooFit.Name(modelAlt.GetName()))
if not isSB and signal[0] is not None: # FIXME remove /(2./3.)
signal[0].plotOn(
frame,
RooFit.Normalization(signal[1] * signal[2], RooAbsReal.NumEvent),
RooFit.LineStyle(3), RooFit.LineWidth(6), RooFit.LineColor(629),
RooFit.DrawOption("L"), RooFit.Name("Signal"))
graphData = setData.plotOn(
frame, RooFit.Binning(plot_binning), RooFit.Scaling(False),
RooFit.XErrorSize(0 if not VARBINS else 1),
RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2),
RooFit.DrawOption("PE0"), RooFit.Name(setData.GetName()))
fixData(graphData.getHist(), True, True, not isData)
pulls = frame.pullHist(setData.GetName(), modelBkg.GetName(), True)
chi = frame.chiSquare(setData.GetName(), modelBkg.GetName(), True)
#setToys.plotOn(frame, RooFit.DataError(RooAbsData.Poisson), RooFit.DrawOption("PE0"), RooFit.MarkerColor(2))
frame.GetYaxis().SetTitle("Events / ( 100 GeV )")
frame.GetYaxis().SetTitleOffset(1.05)
frame.Draw()
#print "frame drawn"
# Get Chi2
# chi2[1] = frame.chiSquare(modelBkg1.GetName(), setData.GetName())
# chi2[2] = frame.chiSquare(modelBkg2.GetName(), setData.GetName())
# chi2[3] = frame.chiSquare(modelBkg3.GetName(), setData.GetName())
# chi2[4] = frame.chiSquare(modelBkg4.GetName(), setData.GetName())
frame.SetMaximum(frame.GetMaximum() * 10)
frame.SetMinimum(max(frame.GetMinimum(), 1.e-1))
c.GetPad(1).SetLogy()
drawAnalysis(category)
drawRegion(category, True)
#drawCMS(LUMI, "Simulation Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
leg = TLegend(0.575, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.AddEntry(setData.GetName(),
setData.GetTitle() + " (%d events)" % nevents, "PEL")
leg.AddEntry(modelBkg.GetName(), modelBkg.GetTitle(),
"FL") #.SetTextColor(629)
leg.AddEntry(modelAlt.GetName(), modelAlt.GetTitle(), "L")
if not isSB and signal[0] is not None:
leg.AddEntry("Signal", signal[0].GetTitle(), "L")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextFont(42)
if not isSB:
latex.DrawLatex(leg.GetX1() * 1.16,
leg.GetY1() - 0.04, "HVT model B (g_{V}=3)")
# latex.DrawLatex(0.67, leg.GetY1()-0.045, "#sigma_{X} = 1.0 pb")
c.cd(2)
frame_res = X_mass.frame()
setPadStyle(frame_res, 1.25)
frame_res.addPlotable(pulls, "P")
setBotStyle(frame_res, RATIO, False)
if VARBINS: frame_res.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
frame_res.GetYaxis().SetRangeUser(-5, 5)
frame_res.GetYaxis().SetTitle("pulls(#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.3)
frame_res.Draw()
fixData(pulls, False, True, False)
drawChi2(RSS[order]["chi2"], RSS[order]["nbins"] - (order + 1), True)
line = drawLine(X_mass.getMin(), 0, lastBin, 0)
if VARBINS:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
else:
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf")
c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
if BIAS:
gSystem.Load("libHiggsAnalysisCombinedLimit.so")
from ROOT import RooMultiPdf
cat = RooCategory("pdf_index", "Index of Pdf which is active")
pdfs = RooArgList(modelBkg, modelAlt)
roomultipdf = RooMultiPdf("roomultipdf", "All Pdfs", cat, pdfs)
normulti = RooRealVar("roomultipdf_norm",
"Number of background events", nevents, 0., 1.e6)
normzBkg.setConstant(
False
) ## newly put here to ensure it's freely floating in the combine fit
# create workspace
w = RooWorkspace("Zprime_" + YEAR, "workspace")
# Dataset
if isData: getattr(w, "import")(setData, RooFit.Rename("data_obs"))
else: getattr(w, "import")(setToys, RooFit.Rename("data_obs"))
#getattr(w, "import")(setData, RooFit.Rename("data_obs"))
if BIAS:
getattr(w, "import")(cat, RooFit.Rename(cat.GetName()))
getattr(w, "import")(normulti, RooFit.Rename(normulti.GetName()))
getattr(w, "import")(roomultipdf, RooFit.Rename(roomultipdf.GetName()))
getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName()))
getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName()))
getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName()))
w.writeToFile(WORKDIR + "%s_%s.root" % (DATA_TYPE + "_" + YEAR, category),
True)
print "Workspace", WORKDIR + "%s_%s.root" % (
DATA_TYPE + "_" + YEAR, category), "saved successfully"
if VERBOSE: raw_input("Press Enter to continue...")
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
# start building the fit
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
# HACK (1/2): be careful about lower bound on eta, since mistagpdf below
# is zero below a certain value - generation in accept/reject would get
# stuck
eta = WS(
ws,
RooRealVar(
'eta', 'eta', 0.35, 0.0 if 'FIT' in config['Context'] else
(1. + 1e-5) * max(0.0, config['TrivialMistagParams']['omega0']),
0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qt, qf, time, eta, timeerr]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context']],
'FIT' in config['Context']) # float for fitting
if 'GEN' in config['Context']:
acc = accnorm # use normalised acceptance for generation
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
# build a (mock) mistag distribution
mistagpdfparams = {} # start with parameters of mock distribution
for sfx in ('omega0', 'omegaavg', 'f'):
mistagpdfparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagpdf_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['TrivialMistagParams'][sfx]))
# build mistag pdf itself
mistagpdf = WS(
ws,
MistagDistribution('Bs2DsPi_mistagpdf', 'Bs2DsPi_mistagpdf', eta,
mistagpdfparams['omega0'],
mistagpdfparams['omegaavg'], mistagpdfparams['f']))
# build mistag calibration
mistagcalibparams = {} # start with parameters of calibration
for sfx in ('p0', 'p1', 'etaavg'):
mistagcalibparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagcalib_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['MistagCalibParams'][sfx]))
for sfx in ('p0', 'p1'): # float calibration paramters
mistagcalibparams[sfx].setConstant(False)
mistagcalibparams[sfx].setError(0.1)
# build mistag pdf itself
omega = WS(
ws,
MistagCalibration('Bs2DsPi_mistagcalib', 'Bs2DsPi_mistagcalib', eta,
mistagcalibparams['p0'], mistagcalibparams['p1'],
mistagcalibparams['etaavg']))
# build mock decay time error distribution (~ timeerr^6 * exp(-timerr /
# (timerr_av / 7))
terrpdf_shape = WS(
ws,
RooConstVar('timeerr_ac', 'timeerr_ac',
config['DecayTimeResolutionAvg'] / 7.))
terrpdf_truth = WS(
ws, RooTruthModel('terrpdf_truth', 'terrpdf_truth', timeerr))
terrpdf_i0 = WS(
ws,
RooDecay('terrpdf_i0', 'terrpdf_i0', timeerr, terrpdf_shape,
terrpdf_truth, RooDecay.SingleSided))
terrpdf_i1 = WS(
ws,
RooPolynomial('terrpdf_i1', 'terrpdf_i1', timeerr,
RooArgList(zero, zero, zero, zero, zero, zero, one), 0))
terrpdf = WS(ws, RooProdPdf('terrpdf', 'terrpdf', terrpdf_i0, terrpdf_i1))
# build the time pdf
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[omega]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=terrpdf,
mistagpdf=[mistagpdf],
mistagobs=eta)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
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()
scale = 1
elif pidcut.find(' 10') > 0:
wt = 'wt3'
scale = 10
else:
print 'Unknown PID selection. Weights not applied.'
wtvar = RooRealVar(wt, 'weight', 0.0, 1.0)
# dataset = fill_dataset(RooArgSet(time, wtvar), ftree, wt, wtvar, cutstr)
dataset = get_dataset(RooArgSet(time, wtvar), ftree, cutstr, wt, scale)
name_title = '{}_{}'.format(dataset.GetName(), mode)
dataset.SetNameTitle(name_title, name_title)
print '%s is weighted: %s' % (dataset.GetName(), dataset.isWeighted())
dsetlist += [dataset]
decaycat = RooCategory('decaycat', 'Decay mode category')
decaycat.defineType('DsPi')
decaycat.defineType('DsK')
varlist += [decaycat]
for idx, mode in enumerate(['DsPi', 'DsK']):
decaycat.setLabel(mode)
dsetlist[idx].addColumn(decaycat)
dataset = RooDataSet('dataset', 'Combined dataset (DsK + DsPi)',
RooArgSet(time, decaycat),
RooFit.Import(dsetlist[0]))
dataset.append(dsetlist[1])
for dset in dsetlist:
def run(self, **kwargs):
from ROOT import RooArgSet
__check_req_kw__("Observables", kwargs)
__check_req_kw__("Pdf", kwargs)
observables = kwargs.pop("Observables")
obs_set = RooArgSet(*observables)
pdf = kwargs.pop("Pdf")
genPdf = kwargs.pop("GenPdf", pdf)
gen_obs_set = RooArgSet()
for o in list(observables) + list(genPdf.ConditionalObservables()):
gen_obs_set.add(o._target_())
gen_pdf_params = genPdf.getParameters(gen_obs_set).snapshot(True)
genPdf = genPdf.clone(genPdf.GetName() + "_toy_clone")
genPdf.recursiveRedirectServers(gen_pdf_params)
fit_obs_set = RooArgSet()
for o in list(observables) + list(pdf.ConditionalObservables()):
fit_obs_set.add(o._target_())
params = pdf.getParameters(fit_obs_set)
pdf_params = RooArgSet()
for p in params:
if p.isConstant():
continue
pdf_params.add(p)
## for param in pdf_params:
## if param.GetName() not in ['Gamma', 'dGamma']:
## param.setConstant()
self._gen_params = pdf_params.snapshot(True)
# Make another ArgSet to put the fit results in
result_params = RooArgSet(pdf_params, "result_params")
transform = self.transform()
if transform:
trans_params = transform.gen_params(gen_obs_set)
for p in trans_params:
result_params.add(p)
# Some extra numbers of interest
from ROOT import RooRealVar
NLL = RooRealVar("NLL", "-log(Likelihood)", 1.0)
ngen = RooRealVar("ngen", "number of generated events", self.options().nevents)
seed = RooRealVar("seed", "random seed", 0.0)
from ROOT import RooCategory
status = RooCategory("status", "fit status")
status.defineType("success", 0)
status.defineType("one", 1)
status.defineType("two", 2)
status.defineType("three", 3)
status.defineType("other", 4)
result_params.add(status)
result_params.add(NLL)
result_params.add(ngen)
result_params.add(seed)
# The dataset to store the results
from ROOT import RooDataSet
self._data = RooDataSet("result_data", "result_data", result_params)
data_params = self._data.get()
from ROOT import RooRandom
import struct, os
while self._data.numEntries() < self.options().ntoys:
# Get a good random seed, set it and store it
s = struct.unpack("I", os.urandom(4))[0]
RooRandom.randomGenerator().SetSeed(s)
seed.setVal(s)
# Reset pdf parameters to initial values. Note: this does not reset the estimated errors...
pdf_params.assignValueOnly(self.gen_params())
args = dict(NumEvents=self.options().nevents)
if "ProtoData" in kwargs:
args["ProtoData"] = kwargs.pop("ProtoData")
genPdf.getParameters(obs_set).assignValueOnly(gen_pdf_params)
data = genPdf.generate(obs_set, **args)
if transform:
data = transform(data)
if not data:
# Transform has failed
transform.set_params(data_params)
self._data.add(data_params)
continue
if data.isWeighted() and "SumW2Error" not in self.fit_opts():
self.fit_opts()["SumW2Error"] = False
j = 0
while j < 4:
fit_result = pdf.fitTo(data, NumCPU=self.options().ncpu, **(self.fit_opts()))
if fit_result.status() == 0:
fit_result.Print()
break
j += 1
if fit_result.status() != 0:
print "Fit result status = %s" % fit_result.status()
NLL.setVal(fit_result.minNll())
if fit_result.status() < 4:
status.setIndex(fit_result.status())
else:
status.setIndex(4)
for result_param in result_params:
data_param = data_params.find(result_param.GetName())
if isinstance(result_param, RooCategory):
data_param.setIndex(result_param.getIndex())
else:
data_param.setVal(result_param.getVal())
# This sets a symmetric error, but since we don't run Minos, that's ok
data_param.setError(result_param.getError())
if transform:
transform.set_params(data_params)
self._data.add(data_params)
return self.data()
def setupWorkspace(ws,options):
cfg = options.config #for convenience
fit_sections = cfg.sections()
fit_sections.remove('Global') #don't need to iterate over the global configuration
if not isinstance(ws,RooWorkspace):
print "You didn't pass a RooWorkspace!"
exit(1)
cpling_type = cfg.get('Global','couplingType')
par1 = cfg.get('Global','par1Name')
par1bound = [-cfg.getfloat('Global','par1Max'),
cfg.getfloat('Global','par1Max')]
par2 = cfg.get('Global','par2Name')
par2bound = [-cfg.getfloat('Global','par2Max'),
cfg.getfloat('Global','par2Max')]
#create the parameters in the workspace
ws.factory('%s_%s[0,%f,%f]'%(par1,cpling_type,par1bound[0],par1bound[1]))
ws.factory('%s_%s[0,%f,%f]'%(par2,cpling_type,par2bound[0],par2bound[1]))
# since the lumi error is correlated among all channels we only need one penalty term for it
lumi_err = exp(options.config.getfloat('Global','lumi_err')) # exp because we use log normal
ws.factory('luminosityError[%f]'%lumi_err)
ws.factory('RooLognormal::lumiErr(err_gl[1,0.0001,50],1,luminosityError)')
channel_cat = RooCategory('channels','channels')
#first pass: process the backgrounds, signal and data into
# simultaneous counting pdfs over the bins
for section in fit_sections:
#create the basic observable, this is used behind the scenes
#in the background and signal models
channel_cat.defineType(section)
channel_cat.setLabel(section)
print 'Building pdf for configuration section:',section
for it,bkg in getBackgroundsInCfg(section,cfg).iteritems():
ws.factory('backgroundError_%s_%s[%f]'%(section,it,exp(bkg[1])))
ws.factory('selectionError_%s[%f]'%(section,exp(cfg.getfloat(section,'selection_err'))))
processFittingData(ws,cfg,section)
processSignalModel(ws,cfg,section)
processBackgroundModel(ws,cfg,section)
createPdfForChannel(ws,cfg,section)
ws.data('countingdata_%s'%section).addColumn(channel_cat)
getattr(ws,'import')(channel_cat)
top = RooSimultaneous('TopLevelPdf',
'TopLevelPdf',
ws.cat('channels'))
alldatavars = RooArgSet(ws.cat('channels'))
conditionals = RooArgSet()
#second pass: process counting pdfs into simultaneous pdf over channels
for section in fit_sections:
top.addPdf(ws.pdf('countingpdf_%s'%section),section)
alldatavars.add(ws.var('%s_%s'%(cfg.get(section,'obsVar'),section)))
conditionals.add(ws.var('%s_%s'%(cfg.get(section,'obsVar'),section)))
alldatavars.add(ws.var('n_observed_%s'%section))
getattr(ws,'import')(top)
ws.defineSet('condObs',conditionals)
allcountingdata = RooDataSet('allcountingdata',
'allcountingdata',
alldatavars)
getattr(ws,'import')(allcountingdata)
allcountingdata = ws.data('allcountingdata')
#third pass: make the final combined dataset
for section in fit_sections:
current = ws.data('countingdata_%s'%section)
print 'countingdata_%s has %d entries'%(section,current.numEntries())
for i in range(current.numEntries()):
alldatavars = current.get(i)
allcountingdata.add(alldatavars)
def buildDataAndCategories(ws,options,args):
#Get the input data
inputData = TChain(options.treeName,'The input data')
for arg in args:
print 'Adding data from: ',arg
inputData.Add(arg)
foldname = ''
phirange = [0,90]
if not options.folded:
foldname=''
phirange = [-180,180]
#variables necessary for j/psi mass,lifetime,polarization fit
jPsiMass = RooRealVar('JpsiMass','M [GeV]',2.7,3.5)
jPsiRap = RooRealVar('JpsiRap','#nu',-2.3,2.3)
jPsiPt = RooRealVar("JpsiPt","pT [GeV]",0,40);
jPsicTau = RooRealVar('Jpsict','l_{J/#psi} [mm]',-1,2.5)
jPsicTauError = RooRealVar('JpsictErr','Error on l_{J/#psi} [mm]',0,2)
jPsiVprob = RooRealVar('JpsiVprob','',.01,1)
jPsiHXcosth = None
jPsiHXphi = None
jPsicTau.setBins(10000,"cache")
if options.fitFrame is not None:
jPsiHXcosth = RooRealVar('costh_'+options.fitFrame+foldname,'cos(#theta)_{'+options.fitFrame+'}',-1,1)
jPsiHXphi = RooRealVar('phi_'+options.fitFrame+foldname,'#phi_{'+options.fitFrame+'}',phirange[0],phirange[1])
else:
jPsiHXcosth = RooRealVar('costh_CS'+foldname,'cos(#theta)_{CS}',-1,1)
jPsiHXphi = RooRealVar('phi_CS'+foldname,'#phi_{CS}',phirange[0],phirange[1])
#vars needed for on the fly calc of polarization variables
jPsimuPosPx = RooRealVar('muPosPx','+ Muon P_{x} [GeV]',0)
jPsimuPosPy = RooRealVar('muPosPy','+ Muon P_{y} [GeV]',0)
jPsimuPosPz = RooRealVar('muPosPz','+ Muon P_{z} [GeV]',0)
jPsimuNegPx = RooRealVar('muNegPx','- Muon P_{x} [GeV]',0)
jPsimuNegPy = RooRealVar('muNegPy','- Muon P_{y} [GeV]',0)
jPsimuNegPz = RooRealVar('muNegPz','- Muon P_{z} [GeV]',0)
#create RooArgSet for eventual dataset creation
dataVars = RooArgSet(jPsiMass,jPsiRap,jPsiPt,
jPsicTau,jPsicTauError,
jPsimuPosPx,jPsimuPosPy,jPsimuPosPz)
#add trigger requirement if specified
if options.triggerName:
trigger = RooRealVar(options.triggerName,'Passes Trigger',0.5,1.5)
dataVars.add(trigger)
dataVars.add(jPsiVprob)
dataVars.add(jPsimuNegPx)
dataVars.add(jPsimuNegPy)
dataVars.add(jPsimuNegPz)
dataVars.add(jPsiHXcosth)
dataVars.add(jPsiHXphi)
redVars = RooArgSet(jPsiMass,jPsiRap,jPsiPt,
jPsicTau,jPsicTauError)
redVars.add(jPsiHXcosth)
redVars.add(jPsiHXphi)
fitVars = redVars.Clone()
### HERE IS WHERE THE BIT FOR CALCULATING POLARIZATION VARS GOES
ctauStates = RooCategory('ctauRegion','Cut Region in lifetime')
ctauStates.defineType('prompt',0)
ctauStates.defineType('nonPrompt',1)
massStates = RooCategory('massRegion','Cut Region in mass')
massStates.defineType('signal',1)
massStates.defineType('separation',0)
massStates.defineType('leftMassSideBand',-2)
massStates.defineType('rightMassSideBand',-1)
states = RooCategory('mlRegion','Cut Region in mass')
states.defineType('nonPromptSignal',2)
states.defineType('promptSignal',1)
states.defineType('separation',0)
states.defineType('leftMassSideBand',-2)
states.defineType('rightMassSideBand',-1)
#define corresponding ranges in roorealvars
#mass is a little tricky since the sidebands change definitions in each rap bin
#define the names here and change as we do the fits
#jPsiMass.setRange('NormalizationRangeFormlfit_promptSignal',2.7,3.5)
#jPsiMass.setRange('NormalizationRangeFormlfit_nonPromptSignal',2.7,3.5)
#jPsiMass.setRange('NormalizationRangeFormlfit_leftMassSideBand',2.7,3.1)
#jPsiMass.setRange('NormalizationRangeFormlfit_rightMassSideBand',3.1,3.5)
#want the prompt fit only done in prompt region
#non-prompt only in non-prompt region
#background over entire cTau range
#jPsicTau.setRange('NormalizationRangeFormlfit_promptSignal',-1,.1)
#jPsicTau.setRange('NormalizationRangeFormlfit_nonPromptSignal',.1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_leftMassSideBand',-1,2.5)
#jPsicTau.setRange('NormalizationRangeFormlfit_rightMassSideBand',-1,2.5)
#redVars.add(ctauStates)
#redVars.add(massStates)
#redVars.add(states)
fitVars.add(ctauStates)
fitVars.add(massStates)
fitVars.add(states)
fullData = RooDataSet('fullData','The Full Data From the Input ROOT Trees',
dataVars,
ROOT.RooFit.Import(inputData))
for rap_bin in range(1,len(jpsi.pTRange)):
yMin = jpsi.rapForPTRange[rap_bin-1][0]
yMax = jpsi.rapForPTRange[rap_bin-1][-1]
for pt_bin in range(len(jpsi.pTRange[rap_bin])):
ptMin = jpsi.pTRange[rap_bin][pt_bin][0]
ptMax = jpsi.pTRange[rap_bin][pt_bin][-1]
sigMaxMass = jpsi.polMassJpsi[rap_bin] + jpsi.nSigMass*jpsi.sigmaMassJpsi[rap_bin]
sigMinMass = jpsi.polMassJpsi[rap_bin] - jpsi.nSigMass*jpsi.sigmaMassJpsi[rap_bin]
sbHighMass = jpsi.polMassJpsi[rap_bin] + jpsi.nSigBkgHigh*jpsi.sigmaMassJpsi[rap_bin]
sbLowMass = jpsi.polMassJpsi[rap_bin] - jpsi.nSigBkgLow*jpsi.sigmaMassJpsi[rap_bin]
ctauNonPrompt = .1
massFun = RooFormulaVar('massRegion','Function that returns the mass state.',
'('+jPsiMass.GetName()+' < '+str(sigMaxMass)+' && '+jPsiMass.GetName()+' > '+str(sigMinMass)+
') - ('+jPsiMass.GetName()+' > '+str(sbHighMass)+')'+
'-2*('+jPsiMass.GetName()+' < '+str(sbLowMass)+')',
RooArgList(jPsiMass,jPsicTau))
ctauFun = RooFormulaVar('ctauRegion','Function that returns the ctau state.',
'('+jPsicTau.GetName()+' > '+str(ctauNonPrompt)+')',
RooArgList(jPsiMass,jPsicTau))
mlFun = RooFormulaVar('mlRegion','Function that returns the mass and lifetime state.',
'('+jPsiMass.GetName()+' < '+str(sigMaxMass)+' && '+jPsiMass.GetName()+' > '+str(sigMinMass)+
') + ('+jPsiMass.GetName()+' < '+str(sigMaxMass)+' && '+jPsiMass.GetName()+' > '+
str(sigMinMass)+' && '+jPsicTau.GetName()+' > '+str(ctauNonPrompt)+
') - ('+jPsiMass.GetName()+' > '+str(sbHighMass)+')'+
'-2*('+jPsiMass.GetName()+' < '+str(sbLowMass)+')',
RooArgList(jPsiMass,jPsicTau))
cutStringPt = '('+jPsiPt.GetName()+' > '+str(ptMin)+' && '+jPsiPt.GetName()+' < '+str(ptMax)+')'
cutStringY = '( abs('+jPsiRap.GetName()+') > '+str(yMin)+' && abs('+jPsiRap.GetName()+') < '+str(yMax)+')'
#cutStringM1 = '('+jPsiMass.GetName()+' < '+str(sigMinMass)+' && '+jPsiMass.GetName()+' > '+str(sbLowMass)+')'
#cutStringM2 = '('+jPsiMass.GetName()+' < '+str(sbHighMass)+' && '+jPsiMass.GetName()+' > '+str(sigMaxMass)+')'
#cutStringMT = '!('+cutStringM1+' || '+cutStringM2+')'
cutString = cutStringPt+' && '+cutStringY #+' && '+cutStringMT
print cutString
#get the reduced dataset we'll do the fit on
binData = fullData.reduce(ROOT.RooFit.SelectVars(redVars),
ROOT.RooFit.Cut(cutString),
ROOT.RooFit.Name('data_rap'+str(rap_bin)+'_pt'+str(pt_bin+1)),
ROOT.RooFit.Title('Data For Fitting'))
binDataWithCategory = RooDataSet('data_rap'+str(rap_bin)+'_pt'+str(pt_bin+1),
'Data For Fitting',
fitVars)
#categorize
binData.addColumn(ctauStates)
binData.addColumn(massStates)
binData.addColumn(states)
for ev in range(binData.numEntries()):
args = binData.get(ev)
jPsiMass.setVal(args.find(jPsiMass.GetName()).getVal())
jPsiRap.setVal(args.find(jPsiRap.GetName()).getVal())
jPsiPt.setVal(args.find(jPsiPt.GetName()).getVal())
jPsicTau.setVal(args.find(jPsicTau.GetName()).getVal())
jPsicTauError.setVal(args.find(jPsicTauError.GetName()).getVal())
jPsiHXcosth.setVal(args.find(jPsiHXcosth.GetName()).getVal())
jPsiHXphi.setVal(args.find(jPsiHXphi.GetName()).getVal())
massStates.setIndex(int(massFun.getVal()))
ctauStates.setIndex(int(ctauFun.getVal()))
states.setIndex(int(mlFun.getVal()))
binDataWithCategory.add(fitVars)
getattr(ws,'import')(binDataWithCategory)
numDataSets = 40
weightName = ''
import P2VV.RooFitWrappers
from ROOT import TFile, RooArgSet
protoFile = TFile.Open(protoFilePathIn)
nTupleFileIn = TFile.Open(nTupleFilePathIn)
protoData = protoFile.Get('JpsiKK_sigSWeight')
nTupleIn = nTupleFileIn.Get('DecayTree')
from ROOT import RooRealVar, RooCategory
obsSet = RooArgSet( protoData.get() )
if runPeriods :
rp = obsSet.find('runPeriod')
if rp : obsSet.remove(rp)
rp = RooCategory( 'runPeriod', 'runPeriod' )
for per in runPeriods : rp.defineType( 'p%d' % per, per )
obsSet.add(rp)
if KKMassBins :
KKCat = obsSet.find('KKMassCat')
if KKCat : obsSet.remove(KKCat)
KKCat = RooCategory( 'KKMassCat', 'KKMassCat' )
for ind in range( len(KKMassBins) - 1 ) : KKCat.defineType( 'bin%d' % ind, ind )
obsSet.add(KKCat)
from array import array
KKBinsArray = array( 'd', KKMassBins )
from ROOT import RooBinning
KKBinning = RooBinning( len(KKMassBins) - 1, KKBinsArray, 'KKMassBinning' )
obsSet.find('mdau2').setBinning( KKBinning, 'KKMassBinning' )
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
# start building the fit
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context']],
'FIT' in config['Context']) # float for fitting
if 'GEN' in config['Context']:
acc = accnorm # use normalised acceptance for generation
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
# build the time pdf
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[mistag]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
# start building the fit
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.3, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
# HACK (1/2): be careful about lower bound on eta, since mistagpdf below
# is zero below a certain value - generation in accept/reject would get
# stuck
eta = WS(ws, RooRealVar('eta', 'eta', 0.35, 0., 0.5))
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 1.0))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr',
0.039)) #CHANGE THIS LATER
# fit average mistag
# add mistagged
#ge rid of untagged events by putting restriction on qf or something when reduceing ds
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
if 'GEN' in config['Context']:
obs = [qf, qt, time, eta]
else:
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context'][0:3]],
'FIT' in config['Context']) # float for fitting
if 'GEN' in config['Context']:
acc = accnorm # use normalised acceptance for generation
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
if 'GEN' in config['Context']:
# build a (mock) mistag distribution
mistagpdfparams = {} # start with parameters of mock distribution
for sfx in ('omega0', 'omegaavg', 'f'):
mistagpdfparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagpdf_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['TrivialMistagParams'][sfx]))
# build mistag pdf itself
mistagpdf = WS(
ws,
MistagDistribution('Bs2DsPi_mistagpdf', 'Bs2DsPi_mistagpdf', eta,
mistagpdfparams['omega0'],
mistagpdfparams['omegaavg'],
mistagpdfparams['f']))
mistagcalibparams = {} # start with parameters of calibration
for sfx in ('p0', 'p1', 'etaavg'):
mistagcalibparams[sfx] = WS(
ws,
RooRealVar('Bs2DsPi_mistagcalib_%s' % sfx,
'Bs2DsPi_mistagpdf_%s' % sfx,
config['MistagCalibParams'][sfx]))
for sfx in ('p0', 'p1'): # float calibration paramters
mistagcalibparams[sfx].setConstant(False)
mistagcalibparams[sfx].setError(0.1)
# build mistag pdf itself
omega = WS(
ws,
MistagCalibration('Bs2DsPi_mistagcalib', 'Bs2DsPi_mistagcalib',
eta, mistagcalibparams['p0'],
mistagcalibparams['p1'],
mistagcalibparams['etaavg']))
# build the time pdf
if 'GEN' in config['Context']:
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[omega]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=None,
mistagpdf=[mistagpdf],
mistagobs=eta)
else:
adet = WS(ws, RooRealVar('adet', 'adet', 0., -.15, .15))
aprod = WS(ws, RooRealVar('aprod', 'aprod', 0., -.15, .15))
adet.setError(0.005)
aprod.setError(0.005)
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[eta]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=None,
aprod=aprod,
adet=adet)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
class SimultaneousFit():
''' A fit that performs a simultaneous fit in more than one variable.
It expects the input of fit_data which is a dictionary of the form
{variable_name: FitData()}'''
def __init__(self, fit_data):
MapStrRootPtr = stl.map(stl.string, "TH1*")
StrHist = stl.pair(stl.string, "TH1*")
self.fit_data = fit_data
self.models = {}
self.sample = RooCategory('sample', 'sample')
self.roofit_variables = []
input_hists = MapStrRootPtr()
# first create observables
# Since we are looking for normalisation in equivalent regions
# the number of events in each sample has to be identical
# Hence, pick one fit_data to create the set of observables
fit_data_1 = fit_data.itervalues().next()
samples = fit_data_1.samples
self.observables = {}
N_min = 0
N_max = fit_data_1.n_data() * 2
for sample in samples:
self.observables[sample] = Observable(
'n_' + sample, 'number of ' + sample + " events",
fit_data_1.normalisation[sample], N_min, N_max, "events")
# next create the models
for variable, fit_input in fit_data.iteritems():
self.models[variable] = fit_input.get_roofit_model(
variable, self.observables)
self.sample.defineType(variable)
self.sample.setLabel(variable)
data = deepcopy(fit_input.real_data_histogram())
input_hists.insert(StrHist(variable, data))
self.roofit_variables.append(fit_input.fit_variable)
self.comb_data = RooDataHist(
"combData",
"combined data",
RooArgList(self.roofit_variables[0]),
self.sample,
input_hists,
)
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 rf501_simultaneouspdf():
signal_1, bkg_1, signal_2, bkg_2 = get_templates()
# 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", 0, 200 )
x.setBins(n_bins)
nsig = RooRealVar( "nsig", "#signal events", N_signal_obs, 0., 2*N_data )
nbkg = RooRealVar( "nbkg", "#background events", N_bkg1_obs, 0., 2*N_data )
# 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 )
roofit_signal_1 = RooDataHist( 'signal_1', 'signal_1', RooArgList(x), signal_1 )
signal_1_pdf = RooHistPdf ( 'signal_1_pdf' , 'signal_1_pdf', RooArgSet(x), roofit_signal_1)
# 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 )
roofit_bkg_1 = RooDataHist( 'bkg_1', 'bkg_1', RooArgList(x), bkg_1 )
bkg_1_pdf = RooHistPdf ( 'bkg_1_pdf' , 'bkg_1_pdf', RooArgSet(x), roofit_bkg_1)
# Construct composite pdf
model = RooAddPdf( "model", "model", RooArgList( signal_1_pdf, bkg_1_pdf ), 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", 0, 200 )
y.setBins(n_bins)
mean_ctl = RooRealVar( "mean_ctl", "mean_ctl", mu2, 0, 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, 0, 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" )
#60093.048127 173.205689173 44.7112503776
# 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,
RooFit.Minimizer( "Minuit2", "Migrad" ),
RooFit.NumCPU( 1 ),
# RooFit.Extended(),
# RooFit.Save(),
)
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( "signal_1_pdf" ),
RooFit.ProjWData( RooArgSet( sample ), combData ),
RooFit.LineStyle( kDashed ),
)
simPdf.plotOn( frame1, RooFit.Slice( sample, "physics" ),
RooFit.Components( "bkg_1_pdf" ),
RooFit.ProjWData( RooArgSet( sample ), combData ),
RooFit.LineStyle( kDashed ),
RooFit.LineColor( kRed ) )
# 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 ) )
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
print real_data.Integral()
raw_input()
def fit_mass(data,
column,
x,
sig_pdf=None,
bkg_pdf=None,
n_sig=None,
n_bkg=None,
blind=False,
nll_profile=False,
second_storage=None,
log_plot=False,
pulls=True,
sPlot=False,
bkg_in_region=False,
importance=3,
plot_importance=3):
"""Fit a given pdf to a variable distribution
Parameter
---------
data : |hepds_type|
The data containing the variable to fit to
column : str
The name of the column to fit the pdf to
sig_pdf : RooFit pdf
The signal Probability Density Function. The variable to fit to has
to be named 'x'.
bkg_pdf : RooFit pdf
The background Probability Density Function. The variable to fit to has
to be named 'x'.
n_sig : None or numeric
The number of signals in the data. If it should be fitted, use None.
n_bkg : None or numeric
The number of background events in the data.
If it should be fitted, use None.
blind : boolean or tuple(numberic, numberic)
If False, the data is fitted. If a tuple is provided, the values are
used as the lower (the first value) and the upper (the second value)
limit of a blinding region, which will be omitted in plots.
Additionally, no true number of signal will be returned but only fake.
nll_profile : boolean
If True, a Negative Log-Likelihood Profile will be generated. Does not
work with blind fits.
second_storage : |hepds_type|
A second data-storage that will be concatenated with the first one.
importance : |importance_type|
|importance_docstring|
plot_importance : |plot_importance_type|
|plot_importance_docstring|
Return
------
tuple(numerical, numerical)
Return the number of signals and the number of backgrounds in the
signal-region. If a blind fit is performed, the signal will be a fake
number. If no number of background events is required, -999 will be
returned.
"""
if not (isinstance(column, str) or len(column) == 1):
raise ValueError("Fitting to several columns " + str(column) +
" not supported.")
if type(sig_pdf) == type(bkg_pdf) == None:
raise ValueError("sig_pdf and bkg_pdf are both None-> no fit possible")
if blind is not False:
lower_blind, upper_blind = blind
blind = True
n_bkg_below_sig = -999
# create data
data_name = data.name
data_array, _t1, _t2 = data.make_dataset(second_storage, columns=column)
del _t1, _t2
# double crystalball variables
min_x, max_x = min(data_array[column]), max(data_array[column])
# x = RooRealVar("x", "x variable", min_x, max_x)
# create data
data_array = np.array([i[0] for i in data_array.as_matrix()])
data_array.dtype = [('x', np.float64)]
tree1 = array2tree(data_array, "x")
data = RooDataSet("data", "Data", RooArgSet(x), RooFit.Import(tree1))
# # TODO: export somewhere? does not need to be defined inside...
# mean = RooRealVar("mean", "Mean of Double CB PDF", 5280, 5100, 5600)#, 5300, 5500)
# sigma = RooRealVar("sigma", "Sigma of Double CB PDF", 40, 0.001, 200)
# alpha_0 = RooRealVar("alpha_0", "alpha_0 of one side", 5.715)#, 0, 150)
# alpha_1 = RooRealVar("alpha_1", "alpha_1 of other side", -4.019)#, -200, 0.)
# lambda_0 = RooRealVar("lambda_0", "Exponent of one side", 3.42)#, 0, 150)
# lambda_1 = RooRealVar("lambda_1", "Exponent of other side", 3.7914)#, 0, 500)
#
# # TODO: export somewhere? pdf construction
# frac = RooRealVar("frac", "Fraction of crystal ball pdfs", 0.479, 0.01, 0.99)
#
# crystalball1 = RooCBShape("crystallball1", "First CrystalBall PDF", x,
# mean, sigma, alpha_0, lambda_0)
# crystalball2 = RooCBShape("crystallball2", "Second CrystalBall PDF", x,
# mean, sigma, alpha_1, lambda_1)
# doubleCB = RooAddPdf("doubleCB", "Double CrystalBall PDF",
# crystalball1, crystalball2, frac)
# n_sig = RooRealVar("n_sig", "Number of signals events", 10000, 0, 1000000)
# test input
if n_sig == n_bkg == 0:
raise ValueError("n_sig as well as n_bkg is 0...")
if n_bkg is None:
n_bkg = RooRealVar("n_bkg", "Number of background events", 10000, 0,
500000)
elif n_bkg >= 0:
n_bkg = RooRealVar("n_bkg", "Number of background events", int(n_bkg))
else:
raise ValueError("n_bkg is not >= 0 or None")
if n_sig is None:
n_sig = RooRealVar("n_sig", "Number of signal events", 1050, 0, 200000)
# START BLINDING
blind_cat = RooCategory("blind_cat", "blind state category")
blind_cat.defineType("unblind", 0)
blind_cat.defineType("blind", 1)
if blind:
blind_cat.setLabel("blind")
blind_n_sig = RooUnblindPrecision("blind_n_sig",
"blind number of signals",
"wasistdas", n_sig.getVal(),
10000, n_sig, blind_cat)
else:
# blind_cat.setLabel("unblind")
blind_n_sig = n_sig
print "n_sig value " + str(n_sig.getVal())
# raw_input("blind value " + str(blind_n_sig.getVal()))
# n_sig = blind_n_sig
# END BLINDING
elif n_sig >= 0:
n_sig = RooRealVar("n_sig", "Number of signal events", int(n_sig))
else:
raise ValueError("n_sig is not >= 0")
# if not blind:
# blind_n_sig = n_sig
# # create bkg-pdf
# lambda_exp = RooRealVar("lambda_exp", "lambda exp pdf bkg", -0.00025, -1., 1.)
# bkg_pdf = RooExponential("bkg_pdf", "Background PDF exp", x, lambda_exp)
if blind:
comb_pdf = RooAddPdf("comb_pdf", "Combined DoubleCB and bkg PDF",
RooArgList(sig_pdf, bkg_pdf),
RooArgList(blind_n_sig, n_bkg))
else:
comb_pdf = RooAddPdf("comb_pdf", "Combined DoubleCB and bkg PDF",
RooArgList(sig_pdf, bkg_pdf),
RooArgList(n_sig, n_bkg))
# create test dataset
# mean_gauss = RooRealVar("mean_gauss", "Mean of Gaussian", 5553, -10000, 10000)
# sigma_gauss = RooRealVar("sigma_gauss", "Width of Gaussian", 20, 0.0001, 300)
# gauss1 = RooGaussian("gauss1", "Gaussian test dist", x, mean_gauss, sigma_gauss)
# lambda_data = RooRealVar("lambda_data", "lambda exp data", -.002)
# exp_data = RooExponential("exp_data", "data example exp", x, lambda_data)
# frac_data = RooRealVar("frac_data", "Fraction PDF of data", 0.15)
#
# data_pdf = RooAddPdf("data_pdf", "Data PDF", gauss1, exp_data, frac_data)
# data = data_pdf.generate(RooArgSet(x), 30000)
# data.printValue()
# xframe = x.frame()
# data_pdf.plotOn(xframe)
# print "n_cpu:", meta_config.get_n_cpu()
# input("test")
# comb_pdf.fitTo(data, RooFit.Extended(ROOT.kTRUE), RooFit.NumCPU(meta_config.get_n_cpu()))
# HACK to get 8 cores in testing
c5 = TCanvas("c5", "RooFit pdf not fit vs " + data_name)
c5.cd()
x_frame1 = x.frame()
# data.plotOn(x_frame1)
# comb_pdf.pdfList()[1].plotOn(x_frame1)
if __name__ == "__main__":
n_cpu = 8
else:
n_cpu = meta_config.get_n_cpu()
print "n_cpu = ", n_cpu
# HACK
# n_cpu = 8
result_fit = comb_pdf.fitTo(data, RooFit.Minos(ROOT.kTRUE),
RooFit.Extended(ROOT.kTRUE),
RooFit.NumCPU(n_cpu))
# HACK end
if bkg_in_region:
x.setRange("signal", bkg_in_region[0], bkg_in_region[1])
bkg_pdf_fitted = comb_pdf.pdfList()[1]
int_argset = RooArgSet(x)
# int_argset = x
# int_argset.setRange("signal", bkg_in_region[0], bkg_in_region[1])
integral = bkg_pdf_fitted.createIntegral(int_argset,
RooFit.NormSet(int_argset),
RooFit.Range("signal"))
bkg_cdf = bkg_pdf_fitted.createCdf(int_argset, RooFit.Range("signal"))
bkg_cdf.plotOn(x_frame1)
# integral.plotOn(x_frame1)
n_bkg_below_sig = integral.getVal(int_argset) * n_bkg.getVal()
x_frame1.Draw()
if plot_importance >= 3:
c2 = TCanvas("c2", "RooFit pdf fit vs " + data_name)
c2.cd()
x_frame = x.frame()
# if log_plot:
# c2.SetLogy()
# x_frame.SetTitle("RooFit pdf vs " + data_name)
x_frame.SetTitle(data_name)
if pulls:
pad_data = ROOT.TPad("pad_data", "Pad with data and fit", 0, 0.33,
1, 1)
pad_pulls = ROOT.TPad("pad_pulls", "Pad with data and fit", 0, 0,
1, 0.33)
pad_data.SetBottomMargin(0.00001)
pad_data.SetBorderMode(0)
if log_plot:
pad_data.SetLogy()
pad_pulls.SetTopMargin(0.00001)
pad_pulls.SetBottomMargin(0.2)
pad_pulls.SetBorderMode(0)
pad_data.Draw()
pad_pulls.Draw()
pad_data.cd()
else:
if log_plot:
c2.SetLogy()
if blind:
# HACK
column = 'x'
# END HACK
x.setRange("lower", min_x, lower_blind)
x.setRange("upper", upper_blind, max_x)
range_str = "lower,upper"
lower_cut_str = str(
min_x) + "<=" + column + "&&" + column + "<=" + str(lower_blind)
upper_cut_str = str(
upper_blind) + "<=" + column + "&&" + column + "<=" + str(max_x)
sideband_cut_str = "(" + lower_cut_str + ")" + "||" + "(" + upper_cut_str + ")"
n_entries = data.reduce(
sideband_cut_str).numEntries() / data.numEntries()
# raw_input("n_entries: " + str(n_entries))
if plot_importance >= 3:
data.plotOn(x_frame, RooFit.CutRange(range_str),
RooFit.NormRange(range_str))
comb_pdf.plotOn(
x_frame, RooFit.Range(range_str),
RooFit.Normalization(n_entries, RooAbsReal.Relative),
RooFit.NormRange(range_str))
if pulls:
# pull_hist(pull_frame=x_frame, pad_data=pad_data, pad_pulls=pad_pulls)
x_frame_pullhist = x_frame.pullHist()
else:
if plot_importance >= 3:
data.plotOn(x_frame)
comb_pdf.plotOn(x_frame)
if pulls:
pad_pulls.cd()
x_frame_pullhist = x_frame.pullHist()
pad_data.cd()
comb_pdf.plotOn(x_frame,
RooFit.Components(sig_pdf.namePtr().GetName()),
RooFit.LineStyle(ROOT.kDashed))
comb_pdf.plotOn(x_frame,
RooFit.Components(bkg_pdf.namePtr().GetName()),
RooFit.LineStyle(ROOT.kDotted))
# comb_pdf.plotPull(n_sig)
if plot_importance >= 3:
x_frame.Draw()
if pulls:
pad_pulls.cd()
x_frame.SetTitleSize(0.05, 'Y')
x_frame.SetTitleOffset(0.7, 'Y')
x_frame.SetLabelSize(0.04, 'Y')
# c11 = TCanvas("c11", "RooFit\ pulls" + data_name)
# c11.cd()
# frame_tmp = x_frame
frame_tmp = x.frame()
# frame_tmp.SetTitle("significance")
frame_tmp.SetTitle("Roofit\ pulls\ " + data_name)
frame_tmp.addObject(x_frame_pullhist)
frame_tmp.SetMinimum(-5)
frame_tmp.SetMaximum(5)
# frame_tmp.GetYaxis().SetTitle("significance")
frame_tmp.GetYaxis().SetNdivisions(5)
frame_tmp.SetTitleSize(0.1, 'X')
frame_tmp.SetTitleOffset(1, 'X')
frame_tmp.SetLabelSize(0.1, 'X')
frame_tmp.SetTitleSize(0.1, 'Y')
frame_tmp.SetTitleOffset(0.5, 'Y')
frame_tmp.SetLabelSize(0.1, 'Y')
frame_tmp.Draw()
# raw_input("")
if not blind and nll_profile:
# nll_range = RooRealVar("nll_range", "Signal for nLL", n_sig.getVal(),
# -10, 2 * n_sig.getVal())
sframe = n_sig.frame(RooFit.Bins(20), RooFit.Range(1, 1000))
# HACK for best n_cpu
lnL = comb_pdf.createNLL(data, RooFit.NumCPU(8))
# HACK end
lnProfileL = lnL.createProfile(ROOT.RooArgSet(n_sig))
lnProfileL.plotOn(sframe, RooFit.ShiftToZero())
c4 = TCanvas("c4", "NLL Profile")
c4.cd()
# input("press ENTER to show plot")
sframe.Draw()
if plot_importance >= 3:
pass
params = comb_pdf.getVariables()
params.Print("v")
# print bkg_cdf.getVal()
if sPlot:
sPlotData = ROOT.RooStats.SPlot(
"sPlotData",
"sPlotData",
data, # variable fitted to, RooDataSet
comb_pdf, # fitted pdf
ROOT.RooArgList(
n_sig,
n_bkg,
# NSigB0s
))
sweights = np.array([
sPlotData.GetSWeight(i, 'n_sig') for i in range(data.numEntries())
])
return n_sig.getVal(), n_bkg_below_sig, sweights
if blind:
return blind_n_sig.getVal(), n_bkg_below_sig, comb_pdf
else:
return n_sig.getVal(), n_bkg_below_sig, comb_pdf
def add_category(self, name, values):
cat = RooCategory(name, name)
for val in pd.unique(values):
cat.defineType(str(val), int(val))
self.param_list.add(name)
setattr(self, name, cat)
# #
# Python script to test the taggingutils module #
# #
# Example usage: #
# ./test_taggingutils.py #
# #
# Author: Eduardo Rodrigues #
# Date : 09 / 06 / 2011 #
# #
# --------------------------------------------------------------------------- #
from ROOT import RooCategory, RooRealVar
from B2DXFitters.taggingutils import tagEfficiencyWeight
mixState = RooCategory( 'mixstate', 'B/Bbar -> D pi mixing state' )
mixState.defineType( "unmixed" , 1 )
mixState.defineType( "mixed" , -1 )
mixState.defineType( "untagged", 0 )
SigTagEff = 0.25
sigTagEff = RooRealVar( "sigTagEff", "Signal tagging efficiency",
SigTagEff, 0., 1. )
sigTagWeight = tagEfficiencyWeight( mixState, sigTagEff, 'Bd2DPi' )
sigTagWeight.Print( 'v' )
print '\ntafEff =', sigTagEff.getVal()
mixState.setIndex(0)
print 'mixState = %2d => tagWeight = %f' % \
dataNames = [ 'JpsiKK_sigSWeight' ] #[ 'JpsiKK', 'JpsiKK_sigSWeight', 'JpsiKK_cbkgSWeight' ]
removeObs = [ 'wMC', 'mdau1', 'tagCatP2VV' ] #, 'polarity', 'hlt2_prescale', 'nPVCat', 'BpTCat' ]
dataFilePathIn = 'P2VVDataSets20112012Reco14_I2Mass_6KKMassBins_2TagCats_20140309.root'
dataFilePathOut = 'P2VVDataSets2012Reco14_I2Mass_6KKMassBins_2TagCats_20140309.root'
import P2VV.RooFitWrappers
from ROOT import TObject, TFile, RooFit, RooDataSet, RooArgSet, RooCategory
dataFile = TFile.Open(dataFilePathIn)
newDataFile = TFile.Open( dataFilePathOut, 'RECREATE' )
newData = [ ]
print 'read datasets from file "%s"' % dataFile.GetName()
for dataName in dataNames :
print 'reading dataset "%s"' % dataName
data = dataFile.Get(dataName)
data.Print()
newArgSet = RooArgSet( data.get() )
for name in removeObs : newArgSet.remove( newArgSet.find(name) )
if runPeriod :
newArgSet.remove( newArgSet.find('runPeriod') )
rp = RooCategory( 'runPeriod', 'runPeriod' )
rp.defineType( 'p%d' % runPeriod, runPeriod )
newArgSet.add(rp)
newData.append( RooDataSet( dataName, dataName, newArgSet, RooFit.Import(data), RooFit.Cut(cut) ) )
newData[-1].Print()
newDataFile.Add( newData[-1] )
print 'write dataset to file "%s"' % newDataFile.GetName()
newDataFile.Write( dataFilePathOut, TObject.kOverwrite )
elif args[0] not in input_data:
print parser.print_usage()
sys.exit(-2)
from P2VV.Load import LHCbStyle
from P2VV.RooFitWrappers import *
from ROOT import RooRealVar
from ROOT import RooDataSet
from ROOT import RooArgSet
from ROOT import RooCategory
input_data = input_data[args[0]]
weight = RooRealVar('sWeights_ipatia', 'sWeights_ipatia', -1e3, 1e3)
momentum = RooRealVar('B_P', 'B_P', 0, 1e6, 'MeV')
runPeriod = RooCategory('runPeriod', 'runPeriod')
runPeriod.defineType('p2011', 2011)
runPeriod.defineType('p2012', 2012)
from ROOT import TFile
if args[0].startswith('MC'):
from ROOT import TFile
sig_file = TFile(input_data['sig_cache'])
sig_data = sig_file.Get(input_data['sig_dataset'])
prompt_file = TFile(input_data['prompt_cache'])
prompt_data = prompt_file.Get(input_data['prompt_dataset'])
else:
sig_file = TFile("/project/bfys/jleerdam/data/Bs2Jpsiphi/Reco14/fitNTuple_peakBkg_2011_2012_Reco14_TOS_HLT2B_20140415.root")
sig_tree = sig_file.Get("DecayTree")
period = 'p' + args[0][:4]
sig_data = RooDataSet('sig_data', 'sig_data', RooArgSet(momentum, weight, runPeriod),
metavar='FILE',
type=lambda x: is_valid_file(parser, x))
arguments = parser.parse_args()
from ROOT import TGraphErrors
from ROOT import TFile, TF2, gDirectory, TF1, TH1D, TCanvas, TH2D
from ROOT import kBlue, kGreen, kPink
from ROOT import RooRealVar, RooArgSet, RooDataSet, RooFit
from ROOT import RooGaussian, RooAddModel, RooArgList, RooFormulaVar
from ROOT import RooCategory
zMagnet = 'zTrueX'
input_file = TFile(arguments.input_file[0])
tree = input_file.Get("MatchResidualAlgo/match_zerr")
q = RooCategory('q', 'q')
q.defineType('minux', -1)
q.defineType('plus', 1)
q.defineType('neutral', 0)
res_x = RooRealVar('res_x', 'res_x', -1000, 1000)
res_y = RooRealVar('res_y', 'res_y', -1000, 1000)
pull_x = RooRealVar('pull_x', 'pull_x', -20, 20)
pull_y = RooRealVar('pull_y', 'pull_y', -20, 20)
p_match = RooRealVar('p_match', 'p_{m}', 3., 50.)
match_res_x = RooRealVar('match_res_x', 'match_res_x', -1000, 1000)
match_res_y = RooRealVar('match_res_y', 'match_res_y', -1000, 1000)
match_pull_x = RooRealVar('match_pull_x', 'match_pull_x', -10, 10.)
match_pull_x.setRange('narrow', -2, 2)
match_pull_x.setRange('wide', match_pull_x.getMin(), match_pull_x.getMax())
match_pull_y = RooRealVar('match_pull_y', 'match_pull_y', -10, 10.)
obs = RooArgSet(q, res_x, res_y, pull_x, pull_y, match_res_x, match_res_y,
def buildTimePdf(config, tupleDataSet, tupleDict):
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
ws = RooWorkspace('ws_%s' % config['Context'])
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
###USE FIT CONTEXT
"""
build time pdf, return pdf and associated data in dictionary
"""
# start by defining observables
time = WS(ws,
tupleDataSet.get().find('ct'))
#qt = WS(ws, tupleDataSet.get().find('ssDecision'));
'''
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
'''
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
qt.defineType('B+', +1)
qt.defineType('Untagged', 0)
qt.defineType('B-', -1)
# now other settings
Gamma = WS(ws, RooRealVar('Gamma', 'Gamma', 0.661)) # ps^-1
DGamma = WS(ws, RooRealVar('DGamma', 'DGamma', 0.106)) # ps^-1
Dm = WS(ws, RooRealVar('Dm', 'Dm', 17.719)) # ps^-1
# HACK (1/2): be careful about lower bound on eta, since mistagpdf below
# is zero below a certain value - generation in accept/reject would get
# stuck
if 'GEN' in config['Context'] or 'FIT' in config['Context']:
eta = WS(
ws,
RooRealVar(
'eta', 'eta', 0.35, 0.0 if 'FIT' in config['Context'] else
(1. + 1e-5) *
max(0.0, config['TrivialMistagParams']['omega0']), 0.5))
mistag = WS(ws, RooRealVar('mistag', 'mistag', 0.35, 0.0, 0.5))
tageff = WS(ws, RooRealVar('tageff', 'tageff', 0.60, 0.0, 1.0))
terrpdf = WS(ws,
tupleDataSet.get().find('cterr'))
timeerr = WS(ws, RooRealVar('timeerr', 'timeerr', 0.040, 0.001, 0.100))
#MISTAGPDF
# fit average mistag
# add mistagged
#ge rid of untagged events by putting restriction on qf or something when reduceing ds
# now build the PDF
from B2DXFitters.timepdfutils import buildBDecayTimePdf
from B2DXFitters.resmodelutils import getResolutionModel
from B2DXFitters.acceptanceutils import buildSplineAcceptance
obs = [qf, qt, time]
acc, accnorm = buildSplineAcceptance(
ws, time, 'Bs2DsPi_accpetance',
config['SplineAcceptance']['KnotPositions'],
config['SplineAcceptance']['KnotCoefficients'][config['Context'][0:3]],
'FIT' in config['Context']) # float for fitting
# get resolution model
resmodel, acc = getResolutionModel(ws, config, time, timeerr, acc)
mistagpdf = WS(
ws,
RooArgList(tupleDataSet.get().find('ssMistag'),
tupleDataSet.get().find('osMistag')))
#???
'''
if 'GEN' in config['Context']:
# build a (mock) mistag distribution
mistagpdfparams = {} # start with parameters of mock distribution
for sfx in ('omega0', 'omegaavg', 'f'):
mistagpdfparams[sfx] = WS(ws, RooRealVar(
'Bs2DsPi_mistagpdf_%s' % sfx, 'Bs2DsPi_mistagpdf_%s' % sfx,
config['TrivialMistagParams'][sfx]))
# build mistag pdf itself
mistagpdf = WS(ws, [tupleDataSet.reduce('ssMistag'), tupleDataSet.reduce('osMistag')]);
mistagcalibparams = {} # start with parameters of calibration
for sfx in ('p0', 'p1', 'etaavg'):
mistagcalibparams[sfx] = WS(ws, RooRealVar('Bs2DsPi_mistagcalib_%s' % sfx, 'Bs2DsPi_mistagpdf_%s' % sfx,config['MistagCalibParams'][sfx]));
for sfx in ('p0', 'p1'): # float calibration paramters
mistagcalibparams[sfx].setConstant(False)
mistagcalibparams[sfx].setError(0.1)
# build mistag pdf itself
omega = WS(ws, MistagCalibration(
'Bs2DsPi_mistagcalib', 'Bs2DsPi_mistagcalib',
eta, mistagcalibparams['p0'], mistagcalibparams['p1'],
mistagcalibparams['etaavg']))
# build the time pdf
if 'GEN' in config['Context']:
pdf = buildBDecayTimePdf(
config, 'Bs2DsPi', ws,
time, timeerr, qt, qf, [ [ omega ] ], [ tageff ],
Gamma, DGamma, Dm,
C = one, D = zero, Dbar = zero, S = zero, Sbar = zero,
timeresmodel = resmodel, acceptance = acc, timeerrpdf,
mistagpdf = [mistagpdf], mistagobs = eta)
else:
pdf = buildBDecayTimePdf(
config, 'Bs2DsPi', ws,
time, timeerr, qt, qf, [ [ eta ] ], [ tageff ],
Gamma, DGamma, Dm,
C = one, D = zero, Dbar = zero, S = zero, Sbar = zero,
timeresmodel = resmodel, acceptance = acc, timeerrpdf = None)
'''
pdf = buildBDecayTimePdf(config,
'Bs2DsPi',
ws,
time,
timeerr,
qt,
qf, [[eta]], [tageff],
Gamma,
DGamma,
Dm,
C=one,
D=zero,
Dbar=zero,
S=zero,
Sbar=zero,
timeresmodel=resmodel,
acceptance=acc,
timeerrpdf=terrpdf,
mistagpdf=[mistagpdf],
mistagobs=eta)
return { # return things
'ws': ws,
'pdf': pdf,
'obs': obs
}
musige=9999.
#if fit=="sb":
# musigv/=signalweight
# musige/=signalweight
#N(sig)="+str(int(nsigv))+"+-"+str(int(nsige))+",
xframe=mass.frame(RooFit.Title("#mu(sig)="+str(int(musigv*1000.)/1000.)+"+-"+str(int(musige*1000.)/1000.)+", #chi^{2}/DOF = "+str(int((chi2.getVal()/(nbins-nfree))*10.)/10.)))
if fit=="data":
data.plotOn(xframe)
else:
data.plotOn(xframe,RooFit.DataError(RooAbsData.SumW2))
model.plotOn(xframe,RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
model.plotOn(xframe,RooFit.Components("bkg"+str(cut)),RooFit.LineStyle(kDashed),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
model.plotOn(xframe,RooFit.Components("sig"),RooFit.LineStyle(kDotted),RooFit.Normalization(1.0,RooAbsReal.RelativeExpected))
canvas=TCanvas("c2","c2",0,0,600,600)
xframe.Draw()
canvas.SaveAs(prefix+"_"+plot[0]+str(cut)+"_fit"+fit+".pdf")
sample=RooCategory("sample","sample")
datasets=[]
for cut in cuts[1:-1]:
sample.defineType(str(cut))
datasets+=[RooFit.Import(str(cut),datalist[cut])]
combData=RooDataHist("combData","combined data",RooArgList(mass),RooFit.Index(sample),*datasets)
simPdf=RooSimultaneous("simPdf","simultaneous pdf",sample)
for cut in cuts[1:-1]:
simPdf.addPdf(modellist[cut],str(cut))
#simPdf.fitTo(combData)
#simPdf.fitTo(combData)
#simPdf.fitTo(combData)
