reco_data = readData(input_data[args[0]]['data'], tree_name,
NTuple = True, observables = observables[:-1],
ntupleCuts = cut, Rename = tree_name + '_' + year)
# We build a mass PDF always wrt the J/psi, since there is not enough
# signal to do it with. Let's hope this is OK...
reco_mass_pdf = buildPdf(Components = (psi_ll, background), Observables = (mpsi,), Name='reco_mass_pdf')
reco_mass_result = reco_mass_pdf.fitTo(reco_data, **fitOpts)
reco_mass_result.SetName('reco_mass_result')
reco_sData = SData(Pdf = reco_mass_pdf, Data = reco_data, Name = 'RecoMassSPlot')
del reco_sData
reco_sig_sdata = reco_sData.data(psi_ll.GetName())
reco_bkg_sdata = reco_sData.data('background')
from P2VV.Reweighing import reweigh
for target, source, n in [(sig_sdata_full, reco_sig_sdata, 'full_sig_sdata'),
(bkg_sdata_full, reco_bkg_sdata, 'full_bkg_sdata')]:
ds, weights = reweigh(target, 'nPV', source, 'nPV', binning = PV_bounds)
sdatas[n] = ds
else:
sdatas[sub_dir + '/sig_sdata'] = sig_sdata_full
sdatas[sub_dir + '/bkg_sdata'] = bkg_sdata_full
sig_sdata = sig_sdata_full
bkg_sdata = bkg_sdata_full
elif fit_mass:
if (signal_MC or prompt_MC) and options.reweigh:
reco_data = readData(input_data[args[0]]['data'], tree_name,
NTuple = True, observables = observables[:-1],
ntupleCuts = cut, Rename = tree_name + '_' + year)
reco_mass_pdf = buildPdf(Components = (psi_ll, background), Observables = (mpsi,), Name='reco_mass_pdf')
reco_mass_result = reco_mass_pdf.fitTo(reco_data, **fitOpts)
reco_mass_result.SetName('reco_mass_result')
def __init__(self, time, masses, t_diff = None, MassResult = None,
InputFile = "/bfys/raaij/p2vv/data/Bs2JpsiPhiPrescaled_2011.root",
Workspace = 'Bs2JpsiPhiPrescaled_2011_workspace', Data = 'data',
UseKeysPdf = False, Weights = 'B', Draw = False, Reweigh = {}):
assert(Weights in ShapeBuilder.__weights)
self.__weights = Weights
self.__time = time
self.__t_diff = t_diff
pos = Workspace.find('201')
self.__year = Workspace[pos : pos + 4]
self.__input_ws = None
self.__ws = RooObject().ws()
self.__shapes = {}
self.__diff__shapes = {}
self.__masses = masses
self._sig = Component('wpv_signal', [], Yield = (1000, 10, 5e5))
self._psi = Component('wpv_jpsi', [], Yield = (5000, 10, 5e5))
self._bkg = Component('wpv_bkg', [], Yield = (5000, 10, 5e5))
if 'B' in masses:
## m_sig_mean = RealVar('wpv_m_sig_mean', Unit = 'MeV', Value = 5365, MinMax = (5363, 5372))
## m_sig_sigma = RealVar('wpv_m_sig_sigma', Unit = 'MeV', Value = 10, MinMax = (1, 20))
## from ROOT import RooGaussian as Gaussian
## self._sig_mass = Pdf(Name = 'wpv_sig_m', Type = Gaussian, Parameters = (masses['B'], m_sig_mean, m_sig_sigma ))
self._sig_mass = BMassPdf(masses['B'], Name = 'wpv_sig_mass', ParNamePrefix = "wpv",
AvSigParameterisation = True)
self._bkg_mass = BBkgPdf(masses['B'], Name = 'wpv_bkg_mass', ParNamePrefix = "wpv",
m_bkg_exp = dict(Name = 'm_bkg_exp', Value = -0.0017,
MinMax = (-0.01, -0.00001)))
self._sig[masses['B']] = self._sig_mass.pdf()
self._psi[masses['B']] = self._bkg_mass.pdf()
self._bkg[masses['B']] = self._bkg_mass.pdf()
if 'jpsi' in masses:
self._sig_mpsi = PsiMassPdf(masses['jpsi'], Name = 'wpv_sig_mpsi', ParNamePrefix = "wpv")
self._bkg_mpsi = PsiBkgPdf(masses['jpsi'], Name = 'wpv_bkg_mpsi', ParNamePrefix = "wpv")
self._sig[masses['jpsi']] = self._sig_mpsi.pdf()
self._psi[masses['jpsi']] = self._sig_mpsi.pdf()
self._bkg[masses['jpsi']] = self._bkg_mpsi.pdf()
self.__components = {'jpsi' : dict(jpsi = self._psi, bkg = self._bkg),
'B' : dict(B = self._sig, bkg = self._bkg),
'both' : dict(B = self._sig, jpsi = self._psi, bkg = self._bkg)}
self.__pdf = buildPdf(self.__components[Weights].values(), Observables = masses.values(),
Name = 'wpv_mass_pdf')
if MassResult:
## Use the provided mass result to set all the parameter values, only float the yields
pdf_params = self.__pdf.getParameters(RooArgSet(*masses.values()))
for p in MassResult.floatParsFinal():
## ignore yields
if any((p.GetName().startswith(n) for n in ['N_', 'mpsi_c'])):
continue
## Find pdf parameter, add "wpv_" prefix
pdf_p = pdf_params.find('wpv_' + p.GetName())
if pdf_p:
pdf_p.setVal(p.getVal())
pdf_p.setError(p.getError())
pdf_p.setConstant(True)
self.__pdf.Print("t")
from ROOT import TFile
input_file = TFile.Open(InputFile)
if not input_file or not input_file.IsOpen():
raise OSError
if Workspace:
self.__input_ws = input_file.Get(Workspace)
if not self.__input_ws:
print 'Cannot find workspace %s in mixing file.' % Workspace
raise RuntimeError
self._data = self.__input_ws.data(Data)
if not self._data:
print 'Cannot find data in workspace %s.' % Workspace
raise RuntimeError
else:
self._data = input_file.Get(Data)
if not self._data.get().find(time.GetName()) and "refit" in time.GetName():
from ROOT import RooFormulaVar, RooArgList
def __add_alias(name, obs):
obs_name = obs.GetName()[:-6]
do = self._data.get().find(obs_name)
rf = RooFormulaVar(name, name, "@0", RooArgList(do))
a = self._data.addColumn(rf)
a.setMin(obs.getMin())
a.setMax(obs.getMax())
return a
## Add refit observables
time = __add_alias("time_refit", time)
self.__time = time
self.__time.Print()
if t_diff:
t_diff = __add_alias("time_diff_refit", t_diff)
self.__t_diff = t_diff
if t_diff:
self._data = self._data.reduce("{0} > {1} && {0} < {2} && {3} > {4} && {3} < {5}".format(time.GetName(), time.getMin(), time.getMax(), t_diff.GetName(), t_diff.getMin(), t_diff.getMax()))
else:
self._data = self._data.reduce("{0} > {1} && {0} < {2}".format(time.GetName(), time.getMin(), time.getMax()))
# self._data = self._data.reduce("mass > 5348 && mass < 5388")
fitOpts = dict(NumCPU = 4, Save = True, Minimizer = 'Minuit2', Optimize = 2)
self.__result = self.__pdf.fitTo(self._data, **fitOpts)
from P2VV.Utilities.SWeights import SData
for p in self.__pdf.Parameters(): p.setConstant(not p.getAttribute('Yield'))
splot = SData(Pdf = self.__pdf, Data = self._data, Name = 'MixingMassSplot')
self.__sdatas = {}
self.__reweigh_weights = {}
for key, c in self.__components[Weights].iteritems():
sdata = splot.data(c.GetName())
if 'Data' in Reweigh and key in Reweigh['Data']:
from array import array
from ROOT import RooBinning
source = Reweigh['Data'][key]
binning = Reweigh['Binning']
if type(binning) == array:
binning = RooBinning(len(binning) - 1, binning)
binning.SetName('reweigh')
Reweigh['DataVar'].setBinning(binning, 'reweigh')
source_obs = source.get().find('nPV')
cat_name = 'nPVs_' + key
source_cat = source.get().find(cat_name)
if source_cat:
# Remove previous weights to make sure we get it right
new_vars = source.get()
new_vars.remove(source_cat)
source = source.reduce(new_vars)
source_obs = source.get().find(source_obs.GetName())
source_obs.setBinning(binning, 'reweigh')
source_cat = BinningCategory(Name = cat_name, Observable = source_obs,
Binning = binning, Data = source, Fundamental = True)
from P2VV.Reweighing import reweigh
sdata, weights = reweigh(sdata, sdata.get().find('nPV'),
source, source_cat)
self.__reweigh_weights[key] = weights
sdata = self.__ws.put(sdata)
self.__sdatas[c] = sdata
rho_keys = dict((v, k) for k, v in self.__components[Weights].iteritems())
self.__shapes = {}
self.__diff_shapes = {}
for c, sdata in self.__sdatas.iteritems():
if UseKeysPdf:
rk = rho_keys[c]
rho = ShapeBuilder.__rho[rk] if rk in ShapeBuilder.__rho else 1.
time_shape = KeysPdf(Name = 'wpv_%s_pdf' % c.GetName(), Observable = time, Data = sdata, Rho = rho)
if t_diff:
diff_shape = KeysPdf(Name = 'wpv_%s_diff_pdf' % c.GetName(), Observable = t_diff, Data = sdata)
else:
time_shape = HistPdf(Name = 'wpv_%s_pdf' % c.GetName(), Observables = [time],
Data = sdata, Binning = {time : 35})
if t_diff:
diff_shape = HistPdf(Name = 'wpv_%s_diff_pdf' % c.GetName(), Observables = [t_diff],
Data = sdata, Binning = {time : 35})
self.__shapes[c] = time_shape
if t_diff:
self.__diff_shapes[c] = diff_shape
if Draw:
self.__draw()
st = RealVar('sigmat',Title = '#sigma(t)', Unit = 'ps', Observable = True, MinMax = (0.01, 0.07))
# add 20 bins for caching the normalization integral
for i in [ st ] : i.setBins( 20 , 'cache' )
# Categories needed for selecting events
unbiased = Category('triggerDecisionUnbiasedPrescaled', States = {'unbiased' : 1, 'not_unbiased' : 0}, Observable = True)
nPV = RealVar('nPV', Title = 'Number of PVs', Observable = True, MinMax = (0, 10))
zerr = RealVar('B_s0_bpv_zerr', Title = 'Best PV Z error', Unit = 'mm', Observable = True, MinMax = (0, 1))
observables = [t, m, mpsi, st, unbiased, nPV, zerr]
cut = 'sel == 1 && triggerDecisionUnbiasedPrescaled == 1 && '
cut += ' && '.join(['%s < 4' % e for e in ['muplus_track_chi2ndof', 'muminus_track_chi2ndof', 'Kplus_track_chi2ndof', 'Kminus_track_chi2ndof']])
cut += ' && sel_cleantail == 1'
from P2VV.GeneralUtils import readData
tree_name = 'DecayTree'
data = {}
for s in ['2011', 'MC11a_incl_Jpsi']:
ds = readData(input_data[s]['data'], tree_name, NTuple = True, observables = observables,
ntupleCuts = cut, Rename = tree_name + '_' + s)
ds.SetName(tree_name + '_' + s)
data[s] = ds
from array import array
PV_bounds = array('d', [-0.5 + i for i in range(12)])
from P2VV.Reweighing import reweigh
reweighed_data, weights = reweigh(data['MC11a_incl_Jpsi'], 'nPV',
data['2011'], nPV, binning = PV_bounds)
