def run(self, **kwargs):
from ROOT import RooArgSet
__check_req_kw__("Observables", kwargs)
__check_req_kw__("Pdf", kwargs)
__check_req_kw__("Sigmat", kwargs)
__check_req_kw__("Time", kwargs)
__check_req_kw__("SigmaGen", kwargs)
sigma_gen = kwargs.pop("SigmaGen")
observables = kwargs.pop("Observables")
obs_set = RooArgSet(*observables)
pdf = kwargs.pop("Pdf")
sigmat = kwargs.pop("Sigmat")
time = kwargs.pop("Time")
gen_obs_set = RooArgSet(*observables)
# Make another ArgSet to put the fit results in
result_params = RooArgSet("result_params")
from P2VV.RooFitWrappers import RealVar
da = RealVar("da", Observable=True, MinMax=(0.01, 1.1))
dft = RealVar("dft", Observable=True, MinMax=(0.01, 1.1))
result_params.add(da._target_())
result_params.add(dft._target_())
transform = self.transform()
if transform:
trans_params = transform.gen_params(gen_obs_set)
self._gen_params.extend(trans_params)
for p in trans_params:
result_params.add(p)
# Some extra numbers of interest
from ROOT import RooRealVar
seed = RooRealVar("seed", "random seed", 0.0)
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
# Reset pdf parameters to initial values. Note: this does not reset the estimated errors...
args = dict(NumEvents=self.options().nevents)
if "ProtoData" in kwargs:
args["ProtoData"] = kwargs.pop("ProtoData")
spec = pdf.prepareMultiGen(obs_set, **args)
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)
data = pdf.generate(spec)
if self.transform():
old_data = data
data = self.transform()(old_data)
if not data:
transform.set_params(data_params)
self._data.add(data_params)
continue
from P2VV import Dilution
d_ft = Dilution.dilution_ft(data, time, t_range=2, quiet=True)
d_a = Dilution.signal_dilution_dg(data, sigmat, *sigma_gen)
da.setVal(d_a[0])
da.setError(d_a[1] if d_a[1] != None else 0.0)
dft.setVal(d_ft[0])
dft.setError(d_ft[1] if d_ft[1] != None else 0.0)
if transform:
transform.set_params(data_params)
self._data.add(result_params)
return self.data()
# generate data and compute moments in loop
import sys
from ROOT import RooRandom
for it in range(nIters) :
if it % 100 == 0 :
print 'iteration %d' % it
sys.stdout.flush()
# generate data with (1 + x) / 2
RooRandom.randomGenerator().SetSeed( 100000 + it )
noWeightData.reset()
weightData.reset()
for evIt in range(nEvents) :
weight = RooRandom.uniform()
obs.setVal( 2. * weight - 1. )
if RooRandom.uniform() <= weight :
noWeightData.add(obsSet)
weightData.add( obsSet, weight )
# compute moments of functions
moms.compute( noWeightData, ResetFirst = True, Verbose = False )
momsW.compute( weightData, ResetFirst = True, Verbose = False )
# update covariance sums
momCoefs = moms.coefficients()
momCoefsW = momsW.coefficients()
for name1 in funcNames :
for name2 in funcNames :
covs[name1][name2] += ( momCoefs[name1][0] - funcMeans[name1] ) * ( momCoefs[name2][0] - funcMeans[name2] )
covsW[name1][name2] += ( momCoefsW[name1][0] - funcMeans[name1] ) * ( momCoefsW[name2][0] - funcMeans[name2] )
for key, comb in cutCombs.iteritems() :
print '--> creating dataset for "%s" selection' % key
DTT.Draw( '>>selList', ' && '.join( cuts[cutName] for cutName in comb ), 'entrylist' )
selList = gDirectory.Get('selList')
print '%d selected events out of %d (%.1f%%)'\
% ( selList.GetN(), DTT.GetEntries(), float( selList.GetN() ) / float( DTT.GetEntries() ) * 100. )
dataSets[key] = RooDataSet( 'data_%s' % key, 'data_%s' % key, massSet )
evIt = -1
evInd = selList.Next()
while evInd >= 0 :
evIt += 1
if evIt % 100000 == 0 :
print 'event %d' % evIt
sys.stdout.flush()
DTT.GetEntry(evInd)
mass.setVal(DTT.B_s0_M)
dataSets[key].add(massSet)
evInd = selList.Next()
print '%d events in dataset' % dataSets[key].numEntries()
DTTFile.Close()
from ROOT import TObject
dataFile = TFile.Open( dataFilePath, 'RECREATE' )
for dataSet in dataSets.itervalues() : dataFile.Append(dataSet)
dataFile.Write( dataFilePath, TObject.kOverwrite )
dataFile.Close()
else :
dataFile = TFile.Open(dataFilePath)
dataSets = dict( [ ( key, dataFile.Get( 'data_' + key ) ) for key in cutKeys ] )
dataFile.Close()
tagDilAlt = 1. - 2. * evSet.getRealValue('tagomega_ss_nn')
if tags[0] == tags[1] :
tagDil = ( tagDil + tagDilAlt ) / ( 1 + tagDil * tagDilAlt )
else :
tagDil = ( tagDil - tagDilAlt ) / ( 1 - tagDil * tagDilAlt )
if tagDil < 0. :
tag = -tag
tagDil = -tagDil
elif tagCat[1] == 1 :
tag = tags[1]
tagDil = 1. - 2. * evSet.getRealValue('tagomega_ss_nn')
if resDil * tagDil < dilutionCut : continue
# set signal weight
sigWeight.setVal( dataSet.weight() )
# set tagging observables
dilution.setVal( resDil * tagDil )
asymCat.setIndex(tag)
# calculate angular weight
if applyAngWeights :
ctk = evSet.getRealValue('helcosthetaK')
ctl = evSet.getRealValue('helcosthetaL')
phi = evSet.getRealValue('helphi')
if applyAngWeights == 'ang' :
angWeight.setVal( 2. - 5. * ( 1. - ctl**2 ) * sin(phi)**2 )
elif applyAngWeights == 'para_perp' :
angWeight.setVal( ( 9. - 15. * ctk**2 ) * ( 1. - ctl**2 ) * sin(2. * phi) )
#angWeight.setVal( ( 1. - ctl**2 ) * sin(2. * phi) )