Exemplo n.º 1
0
    def __toStd(self):
        self._stdDir = {}

        ##Translate string and list to std::string and std::vector
        for key, value in self.iteritems():
            if type(value) == str:
                self._stdDir[key] = string(value)

            if type(value) == int:
                self._stdDir[key] = ctypes.c_int(value)

            elif type(value) == list:
                if type(value[0]) == str:
                    self._stdDir[key] = vector("string")()

                elif type(value[0]) == float:   
                    self._stdDir[key] = vector("float")()

                elif type(value[0]) == int:   
                    self._stdDir[key] = vector("int")()
                
                for v in value:
                   self._stdDir[key].push_back(v) 

            else:
                pass
Exemplo n.º 2
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def GetSignalSignificanceFiveSigma(doSignificanceDir,fileList,modelName):
  if(len(fileList) == 0):
    return -666
  poiSigDict = {}
  for fileName in fileList:
    if modelName in fileName and fileEnd in fileName:
      if 'asymptotic' in fileName:
        if 'N' in fileName and not 'N' in modelName:
          continue
        if 'f5' in fileName and not 'f5' in modelName:
          continue
        medianSignificance = GetExpDiscSignif(doSignificanceDir+fileName)
        #print 'modelName=',modelName
        #print 'fileName=',fileName
        #print 'poi=',fileName[fileName.find(modelName)+len(modelName)+1:fileName.rfind("asymptotic")-1]
        poi = float(fileName[fileName.find(modelName)+len(modelName)+1:fileName.rfind("asymptotic")-1])
        if(medianSignificance > 0):
          poiSigDict[poi] = medianSignificance
  medianSignifs = vector("double")()
  poiValues = vector("double")()
  for poi,sig in sorted(poiSigDict.items()):
    poiValues.push_back(poi)
    medianSignifs.push_back(sig)
  if(len(medianSignifs) < 3):
    print 'ERROR: Not enough (nonzero) points to do interpolation!'
    return -666
  interp = Math.Interpolator(medianSignifs,poiValues)
  return interp.Eval(5)
Exemplo n.º 3
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def getLumisToRun(JSON):
    if JSON == "": return vector('edm::LuminosityBlockRange')()

    vlumis = vector('edm::LuminosityBlockRange')()
    myList = LumiList.LumiList(filename="/".join(
        ["utils/cert_data", JSONfile])).getCMSSWString().split(',')
    lumisToProcess = cms.untracked.VLuminosityBlockRange(myList)

    for BlockRange in lumisToProcess:
        Block = BlockRange.split('-')

        startRun = int(Block[0].split(':')[0])
        startLumiBlock = int(Block[0].split(':')[1])

        if len(Block) > 1:
            endRun = int(Block[1].split(':')[0])
            endLumiBlock = int(Block[1].split(':')[1])
        else:
            endRun = startRun
            endLumiBlock = endLumiBlock

        vlumis.push_back(
            edm.LuminosityBlockRange(
                edm.LuminosityBlockID(startRun, startLumiBlock),
                edm.LuminosityBlockID(endRun, endLumiBlock)))
    return vlumis
def GetPDFPairInfo(tree):
    xfx_pair_dict = {}
    for pdf_name in PDF_NAMES:
        print pdf_name
        xfx_pair_dict[pdf_name] = PDFPairStruct(vector('double')(), vector('double')())
        tree.SetBranchAddress('xfx_first_'+pdf_name, xfx_pair_dict[pdf_name][0])
        tree.SetBranchAddress('xfx_second_'+pdf_name, xfx_pair_dict[pdf_name][1])
    return xfx_pair_dict
Exemplo n.º 5
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    def toolTip(self):
        tip =  "Hits: \t\t " + str(self._params.N_Hits) + "\n"
        tip += "Start: \t\t (" + "{:.2f}".format(self._params.start_point.w) + ", "
        tip += "{:.2f}".format(self._params.start_point.t) + ")\n"
        tip += ("Shower Start \t (" +
                "{:.2f}".format(self._params.showering_point.w) + ", ")
        tip += "{:.2f}".format(self._params.showering_point.t) + ")\n"
        tip += "End: \t\t (" + "{:.2f}".format(self._params.end_point.w) + ", "
        tip += "{:.2f}".format(self._params.end_point.t) + ")\n"
        if self._params.principal_dir[0] != 0:
            slope = self._params.principal_dir[1]/self._params.principal_dir[0]
            tip += ("Slope: \t\t " + "{:.2f}".format(slope) + "\n")
        else:
            tip += "Slope: \t\t inf\n"
        if self._params.start_dir[0] != 0:
            tip += "Start Slope: \t " + \
                "{:.2f}".format(
                    self._params.start_dir[1]/self._params.start_dir[0]) + "\n"
        else:
            tip += "Start Slope: \t inf\n"
        tip += "Angle:  \t\t " + "{:.2f}".format(self._params.angle_2d) + "\n"
        tip += "\n"
        fannVec = vector(float)()
        self._params.GetFANNVector(fannVec)
        fannTitle = self._params.GetFANNVectorTitle()
        for title, value in zip(fannTitle,fannVec):
            tip += "{:.2f}: ".format(value) + "{title}\n".format(title=title)

        tip += "\nAdd more in python/datatypes/cluster.py:clusterParams:toolTip!"
        return tip
Exemplo n.º 6
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def copyObjectParameters( obj, copyObj ):
    ''' Copies the parameters of an LCObject to another LCObject'''
    parameters = obj.getParameters()
    copyParameters = copyObj.parameters()
    keyVec = vector('string')()
    for intKey in parameters.getIntKeys( keyVec ):
        intVec = vector('int')()
        copyParameters.setValues( intKey, parameters.getIntVals( intKey, intVec ) )
    keyVec.clear()
    for floatKey in parameters.getFloatKeys( keyVec ):
        floatVec = vector('float')()
        copyParameters.setValues( floatKey, parameters.getFloatVals( floatKey, floatVec ) )
    keyVec.clear()
    for stringKey in parameters.getStringKeys( keyVec ):
        stringVec = vector('string')()
        copyParameters.setValues( stringKey, parameters.getStringVals( stringKey, stringVec ) )
Exemplo n.º 7
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def get_collection_name(raw_data_words):
    '''Use Python bindings of C++ StringSerialiser to deserialise collection names'''

    from ROOT import string, vector, StringSerializer
    nw = raw_data_words[NameLengthOffset]
    name_raw = raw_data_words[NameOffset:NameOffset + nw]
    name_raw_vec = vector('unsigned int')()
    for w in name_raw:
        name_raw_vec.push_back(w)
    name_str_vec = vector(string)()
    ss = StringSerializer()
    ss.deserialize(name_raw_vec, name_str_vec)
    name_list = []
    for s in name_str_vec:
        name_list.append(s)
    return name_list
Exemplo n.º 8
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def UpdateFile(fil,tree_names,y_pred,var_name='score'):
    print (('FileName to be read: %s')%fil)
    tfile = TFile(fil,'update')
    trees=[]
    
    if len(tree_names) != len(y_pred):
        print('Number of trees and number of prediction must be equal')
        exit()
        
    for t in tree_names:
        print ('Tree will be updated: %s'%t)
        trees.append(tfile.Get(t))

    score = vector('float')()
    print ('\nUpdating File --------------------------')
    for t in trees:
        t.Branch(var_name,score)
    
    for i in range(len(trees)):
        for x in y_pred[i]:
            score.clear()
            if not np.isscalar(x):
                for e in x: score.push_back(e)
            else:
                score.push_back(x)
            trees[i].GetBranch(var_name).Fill()
        trees[i].Write()
    tfile.Close()
    del trees[:]
    del tfile
    print ('Closing File --------------------------\n')
Exemplo n.º 9
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def printParameters( parameters ):
    from ROOT import vector
    ''' Helper method to print the content of an LCParameters object '''
    keyVec = vector( 'string' )()
    for intKey in parameters.getIntKeys( keyVec ):
        intVec = vector( 'int' )()
        parameters.getIntVals( intKey, intVec )
        print ' parameter %s [int]: %s' % ( intKey, formatVecValues( intVec ) )
    keyVec.clear()
    for floatKey in parameters.getFloatKeys( keyVec ):
        floatVec = vector( 'float' )()
        parameters.getFloatVals( floatKey, floatVec )
        print ' parameter %s [float]: %s' % ( floatKey, formatVecValues( floatVec ) )
    keyVec.clear()
    for stringKey in parameters.getStringKeys( keyVec ):
        stringVec = vector( 'string' )()
        parameters.getStringVals( stringKey, stringVec )
        print ' parameter %s [string]: %s' % ( stringKey, formatVecValues( stringVec ) )
Exemplo n.º 10
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def GetTStringVectorSamples(path, samples):
    from ROOT import vector, TString, gSystem
    # Add the input data files
    v = vector(TString)()
    for s in samples:
        t = TString(path + s)
        v.push_back(t)
    return v
    v = GetTStringVector(samples)
Exemplo n.º 11
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def copyObjectParameters(obj, copyObj):
    ''' Copies the parameters of an LCObject to another LCObject'''
    parameters = obj.getParameters()
    copyParameters = copyObj.parameters()
    keyVec = vector('string')()
    for intKey in parameters.getIntKeys(keyVec):
        intVec = vector('int')()
        copyParameters.setValues(intKey, parameters.getIntVals(intKey, intVec))
    keyVec.clear()
    for floatKey in parameters.getFloatKeys(keyVec):
        floatVec = vector('float')()
        copyParameters.setValues(floatKey,
                                 parameters.getFloatVals(floatKey, floatVec))
    keyVec.clear()
    for stringKey in parameters.getStringKeys(keyVec):
        stringVec = vector('string')()
        copyParameters.setValues(
            stringKey, parameters.getStringVals(stringKey, stringVec))
Exemplo n.º 12
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    def _tags(self):
        """ Read available tags in the file.

        This method is used internally by the `tags()` method which turns the
        `std::vector<std::string>` into a python list of python strings.
        """
        from ROOT import vector
        tags = vector('string')()
        if not self.ttool.tags(tags):
            raise CouldNotLoadTagsException()
        return tags
Exemplo n.º 13
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def pu_weight( data_pu, mc_pu, return_std_vector = True ):
    print "sum(data_pu) - {}".format(sum(data_pu))
    print "sum(mc_pu) - {}".format(sum(mc_pu))
    weight = []
    for i in range(min(len(data_pu),len(mc_pu))):
        weight.append(data_pu[i]/mc_pu[i])
    if return_std_vector:
        v = vector('double')()    
        v += weight
        return v 
    else:
        return weight
Exemplo n.º 14
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    def __init__(self, ptBins, etaBins, taggers, out_dir = "../output/" ):
    
        # pointer to selector
        self.jetCounter = JetCounter()

        # pt bins
        vec_ptBins = vector('double')()
        vec_ptBins += ptBins
        self.jetCounter.setPtBins(vec_ptBins)
        # eta bins
        vec_etaBins = vector('double')()
        vec_etaBins += etaBins 
        self.jetCounter.setEtaBins(vec_etaBins)

        for k, v in taggers.items():
            workPoints = vector('double')()
            workPoints += v
            self.jetCounter.addTagger(k, workPoints) 

        self.out_dir = out_dir    

        return None
Exemplo n.º 15
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def get_bins_vec_2pt(bin1, bin2, xmin, xmax, xmid):

    #evaluate binning with bin1 width below xmid and bin2 above xmid
    bins = vector(rt.double)()
    bins.push_back(xmin)
    while True:
        if bins[bins.size() - 1] < xmid:
            increment = bin1
        else:
            increment = bin2
        bins.push_back(bins[bins.size() - 1] + increment)
        if bins[bins.size() - 1] > xmax: break

    return bins
Exemplo n.º 16
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    def __init__(self, filename, plane, config, save_dir=None, first_run=None):

        Converter.__init__(self, filename, config)
        self.SaveDir = save_dir
        self.CalibrationDir = join(self.SaveDir, 'calibrations')
        self.RunNumber = basename(filename).strip('.root').split('_')[-1]
        self.FirstRun = first_run if first_run is not None else self.RunNumber

        self.OldFile = read_root_file(filename)
        self.OldTree = self.OldFile.Get('Plane{}'.format(6 +
                                                         plane)).Get('Hits')
        self.EventTree = self.OldFile.Get('Event')
        self.NewFile = self.create_new_file(filename, save_dir)
        self.NewTree = self.OldTree.CloneTree(0)

        # New Branches
        self.ScalarBranches = OrderedDict([('NCluster', array([0], 'u2')),
                                           ('TimeStamp', array([0], 'f8'))])
        self.VectorBranches = OrderedDict([('VCal', vector('float')()),
                                           ('ClusterSize',
                                            vector('unsigned short')()),
                                           ('ClusterX', vector('float')()),
                                           ('ClusterY', vector('float')()),
                                           ('ClusterVcal', vector('float')())])

        # Charge Fits
        self.FitParameters = zeros((52, 80, 4))
        self.Fit = TF1('ErFit', '[3] * (TMath::Erf((x - [0]) / [1]) + [2])',
                       -500, 255 * 7)
        self.get_fits()

        # Vars
        self.Hits = []
        self.ClusteredHits = []
        self.Clusters = []

        self.ProgressBar = None
Exemplo n.º 17
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def getMedian(th1d):
	n = th1d.GetXaxis().GetNbins()
	xVect = vector('double')(n)
	print xVect
	xVect = np.array(xVect)
	print xVect
	th1d.GetXaxis().GetCenter( xVect )
	print xVect
	yVect = th1d.GetArray()
	print yVect
	yVect.SetSize(n)
	print yVect
	yVect = np.array(yVect)
	print yVect
	print np.median([xVect,yVect])
	print TMath.Median(n,xVect,yVect)
Exemplo n.º 18
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def CloneFile(path,fil,tree_names,y_pred,pref='',var_name='score',ntup_opt='recreate',same_path=False):
    print (('FileName to be read: %s')%fil)
    tfile = TFile(fil)
    trees=[]
    
    if len(tree_names) != len(y_pred):
        print('Number of trees and number of prediction must be equal')
        exit()
        
    for t in tree_names:
        print ('Tree will be cloned: %s'%t)
        trees.append(tfile.Get(t))

    score = vector('float')()
    print ('\nUpdating File --------------------------')
    if (ntup_opt=='recreate' and pref==''):
        print('Attention :: you are trying to create a new ntuple with same file name.')
        print('Please specify a prefix.')
        exit()
    fil_new = fil.replace('.root',pref+'.root')
    if not same_path:
        fil_new = path+fil[fil.rfind('/')+1:]
    print (('FileName to be recorded: %s')%fil_new)
    trees_new=[]
    tfile_new = TFile(fil_new,ntup_opt)
    for t in trees:
        trees_new.append(t.CloneTree())
        trees_new[-1].Branch(var_name,score)
    
    for i in range(len(trees_new)):
        for x in y_pred[i]:
            score.clear()
            if not np.isscalar(x):
                for e in x: score.push_back(e)
            else:
                score.push_back(x)
            trees_new[i].GetBranch(var_name).Fill()
        trees_new[i].Write()
    tfile_new.Write()
    tfile_new.Close()
    tfile.Close()
    del trees[:]
    del trees_new[:]
    del tfile
    del tfile_new
    print ('Closing File --------------------------\n')
    return fil_new
Exemplo n.º 19
0
def anajob(fileNames):
    from ROOT import vector
    ''' Method to loop over all events of the given slcio files and print
        information about all collections'''

    from pyLCIO import IOIMPL
    reader = IOIMPL.LCFactory.getInstance().createLCReader()

    # first loop over all files: open them and print the total number of runs and events
    fileNameVec = vector('string')()
    print('anajob: will open and read from files:')
    for fileName in fileNames:
        fileNameVec.push_back(fileName)
        reader.open(fileName)
        print('     %s     [ number of runs: %d, number of events: %d ]\n' %
              (fileName, reader.getNumberOfRuns(), reader.getNumberOfEvents()))
        reader.close()

    # second loop: print information on all run headers
    # this time we open all files in one stream by passing them as a std::vector<std::string>
    reader.open(fileNameVec)
    runHeader = reader.readNextRunHeader()
    while runHeader:
        dumpRunHeader(runHeader)
        runHeader = reader.readNextRunHeader()
    print('')
    reader.close()

    print(' will reopen and read from files:')
    for fileName in fileNames:
        print('     %s' % (fileName))

    # final loop: print information on all events
    reader.open(fileNameVec)
    nEvents = 0
    for event in reader:
        # use the c++ method or the one defined above for printing
        # from pyLCIO import UTIL
        # UTIL.LCTOOLS.dumpEvent( event )
        dumpEvent(event)
        nEvents += 1
    reader.close()

    print('  %s events read from files:' % (nEvents))
    for fileName in fileNames:
        print('     %s' % (fileName))
Exemplo n.º 20
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def anajob( fileNames ):
    from ROOT import vector
    ''' Method to loop over all events of the given slcio files and print
        information about all collections'''

    from pyLCIO import IOIMPL
    reader = IOIMPL.LCFactory.getInstance().createLCReader()
    
    # first loop over all files: open them and print the total number of runs and events
    fileNameVec = vector( 'string' )()
    print 'anajob: will open and read from files:'
    for fileName in fileNames:
        fileNameVec.push_back( fileName )
        reader.open( fileName )
        print '     %s     [ number of runs: %d, number of events: %d ]\n' % ( fileName, reader.getNumberOfRuns(), reader.getNumberOfEvents() )
        reader.close()
    
    # second loop: print information on all run headers
    # this time we open all files in one stream by passing them as a std::vector<std::string>
    reader.open( fileNameVec )
    runHeader = reader.readNextRunHeader()
    while runHeader:
        dumpRunHeader( runHeader )
        runHeader = reader.readNextRunHeader()
    print ''
    reader.close()
    
    print ' will reopen and read from files:'
    for fileName in fileNames:
        print '     %s' % ( fileName )
    
    # final loop: print information on all events
    reader.open( fileNameVec )
    nEvents = 0
    for event in reader:
        # use the c++ method or the one defined above for printing
        # from pyLCIO import UTIL
        # UTIL.LCTOOLS.dumpEvent( event )
        dumpEvent( event )
        nEvents += 1
    reader.close()
        
    print '  %s events read from files:' % ( nEvents )
    for fileName in fileNames:
        print '     %s' % ( fileName )
Exemplo n.º 21
0
def get_bins(tree, bnam, bmatch, prec, ons, delt):

    #load tracks momenta to lists for all and matched tracks

    tree.SetBranchStatus("*", 0)
    tree.SetBranchStatus(bnam[0], 1)
    tree.SetBranchStatus(bnam[1], 1)
    tree.SetBranchStatus(bmatch[0], 1)
    tree.SetBranchStatus(bmatch[1], 1)

    #C++ structure for tree entry
    gROOT.ProcessLine("struct Entry {Double_t p0, p1; Bool_t match0, match1;};")
    entry = rt.Entry()
    tree.SetBranchAddress(bnam[0], AddressOf(entry, "p0"))
    tree.SetBranchAddress(bnam[1], AddressOf(entry, "p1"))
    tree.SetBranchAddress(bmatch[0], AddressOf(entry, "match0"))
    tree.SetBranchAddress(bmatch[1], AddressOf(entry, "match1"))

    #momenta values for all and matched tracks
    valAll = rt.list(double)()
    valSel = rt.list(double)()

    for i in xrange(tree.GetEntriesFast()):
        tree.GetEntry(i)
        valAll.push_back(entry.p0)
        valAll.push_back(entry.p1)
        if entry.match0 == 1: valSel.push_back(entry.p0)
        if entry.match1 == 1: valSel.push_back(entry.p1)

    tree.ResetBranchAddresses()

    #bin edges
    bins = vector(rt.double)()

    t0 = time()

    #runs faster when the algorithm is in plain ROOT
    gROOT.LoadMacro("get_bins.C")
    rt.get_bins(bins, valAll, valSel, prec, ons, delt)

    t1 = time()
    print "Time to calculate the bins (sec):", t1-t0

    return bins
Exemplo n.º 22
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def get_bins_vec_3pt(bin1, bin2, bin3, xmin, xmax, xmid1, xmid2):

    #evaluate binning with bin1 below xmid1, bin2 between xmid1 and xmid2
    #and bin3 above xmid2

    bins = vector(rt.double)()
    bins.push_back(xmin)

    while True:
        xpos = bins[bins.size() - 1]

        increment = bin1
        if xpos > xmid1: increment = bin2
        if xpos > xmid2: increment = bin3

        bins.push_back(xpos + increment)
        if bins[bins.size() - 1] > xmax: break

    return bins
Exemplo n.º 23
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    def _drawPDF(self, pdfType, pdfW, var, rng, global_weight, weights, totCut,
                 sampleName, trees, columns, doFold, cutName, variableName,
                 sample, fixZeros):
        '''
    pdfType       : alphaS,...
    pdfW          :  pdfW
    var           :   the variable to plot
    rng           :   the variable to plot
    global_weight :   sample global_weight 
    weights       :   the wieghts 'root file' dependent
    totCut           :   the selection
    trees        :   the list of input files for this particular sample
    '''

        self._logger.info('Yields by process')
        print 'pdfW treeName', pdfW

        numTree = 0
        if pdfW is 'PDFWeights_AlphaS':
            bigNameUp = 'histo_' + sampleName + 'Up_' + cutName + '_' + variableName
            bigNameDo = 'histo_' + sampleName + 'Do_' + cutName + '_' + variableName
            hTotalAlphaUp = self._makeshape(bigNameUp, rng)
            hTotalAlphaDo = self._makeshape(bigNameDo, rng)
        elif pdfW is 'PDFWeights_Scale':
            bigNameAUp = 'histo_' + sampleName + 'AUp_' + cutName + '_' + variableName
            bigNameADo = 'histo_' + sampleName + 'ADo_' + cutName + '_' + variableName
            hTotalAUp = self._makeshape(bigNameAUp, rng)
            hTotalADo = self._makeshape(bigNameADo, rng)
            bigNameBUp = 'histo_' + sampleName + 'BUp_' + cutName + '_' + variableName
            bigNameBDo = 'histo_' + sampleName + 'BDo_' + cutName + '_' + variableName
            hTotalBUp = self._makeshape(bigNameBUp, rng)
            hTotalBDo = self._makeshape(bigNameBDo, rng)
            bigNameABUp = 'histo_' + sampleName + 'ABUp_' + cutName + '_' + variableName
            bigNameABDo = 'histo_' + sampleName + 'ABDo_' + cutName + '_' + variableName
            hTotalABUp = self._makeshape(bigNameABUp, rng)
            hTotalABDo = self._makeshape(bigNameABDo, rng)
        elif pdfW is 'PDFWeights_Error':
            hTotal_Err = [None] * 101
            for idx in xrange(101):
                bigName = 'histo_' + sampleName + str(
                    idx) + '_' + cutName + '_' + variableName
                hTotal_Err[idx] = self._makeshape(bigName, rng)
        else:
            print 'This pdfW', pdfW, 'is not ready, exiting...'
            exit()

        for tree in trees:
            #myBr = tree.GetBranch(pdfW)
            #myBr = tree.GetBranch(pdfW).GetListOfLeaves()
            print '        {0:<20} : {1:^9}'.format(sampleName,
                                                    tree.GetEntries())
            RDF = RDataFrame
            if ('ALL' in columns) or (len(columns) == 0):
                Dtree = RDF(tree)
            else:
                v_columns = vector('string')()
                for column in columns:
                    v_columns.push_back(column)
                Dtree = RDF(tree, v_columns)

            for key in self._definitions:
                #print key, 'crspdto', self._definitions[key]
                Dtree = Dtree.Define(key, self._definitions[key])

            totalWeight = global_weight
            ## if weights vector is not given, do not apply file dependent weights
            if len(weights) != 0:
                # if weight is not given for a given root file, '-', do not apply file dependent weight for that root file
                if weights[numTree] != '-':
                    totalWeight = "(" + totalWeight + ") * (" + weights[
                        numTree] + ")"
            ################################################
            # PDFWeights_AlphaS
            ################################################
            if pdfW is 'PDFWeights_AlphaS':
                print 'checking size of PDFWeights_AlphaS'
                size = 0
                for event in tree:
                    size = event.PDFWeights_AlphaS.size()
                    break
                if size == 2:
                    print 'PDFWeights_AlphaS size is 2, let evaluate'
                    totalWeightDo = "(" + totalWeight + ") * (PDFWeights_AlphaS[0])"
                    totalWeightUp = "(" + totalWeight + ") * (PDFWeights_AlphaS[1])"
                else:
                    print 'PDFWeights_AlphaS size is not 2, let us make Up and Down is the same to norminal'
                    totalWeightDo = totalWeight
                    totalWeightUp = totalWeight

                Dtree = Dtree.Define('totwDo', totalWeightDo) \
                                   .Define('totwUp', totalWeightUp)
                # New histogram
                shapeNameUp = 'histo_' + sampleName + 'Up' + str(numTree)
                shapeNameDo = 'histo_' + sampleName + 'Do' + str(numTree)
                # prepare a dummy to fill
                hclass, hargs, ndim = self._bins2hclass(rng)
                hModelUp = (
                    shapeNameUp,
                    shapeNameUp,
                ) + hargs
                hModelDo = (
                    shapeNameDo,
                    shapeNameDo,
                ) + hargs
                if ndim == 1:
                    shapeUp = Dtree.Filter(totCut).Histo1D(
                        hModelUp, var, 'totwUp')
                    shapeDo = Dtree.Filter(totCut).Histo1D(
                        hModelDo, var, 'totwDo')
                elif ndim == 2:
                    shapeUp = Dtree.Filter(totCut).Histo2D(
                        hModelUp, var, 'totwUp')
                    shapeDo = Dtree.Filter(totCut).Histo2D(
                        hModelDo, var, 'totwDo')

                nTriesUp = shapeUp.Integral()
                nTriesDo = shapeDo.Integral()
                print 'integral  Up and Do', nTriesUp, nTriesDo
                if nTriesUp == 0:
                    print 'Warning : entries is 0 for', hModelUp
                if nTriesDo == 0:
                    print 'Warning : entries is 0 for', hModelDo
                if math.isnan(nTriesUp):
                    print 'ERROR : entries is nan for', hModelUp
                if math.isnan(nTriesDo):
                    print 'ERROR : entries is nan for', hModelDo

                if (numTree == 0):
                    shapeUp.SetTitle(bigNameUp)
                    shapeUp.SetName(bigNameUp)
                    hTotalAlphaUp = shapeUp

                    shapeDo.SetTitle(bigNameDo)
                    shapeDo.SetName(bigNameDo)
                    hTotalAlphaDo = shapeDo
                else:
                    cloneUp = shapeUp.Clone()
                    cloneDo = shapeDo.Clone()
                    hTotalAlphaUp.Add(cloneUp)
                    hTotalAlphaDo.Add(cloneDo)
                    cloneUp.Delete()
                    cloneDo.Delete()
                shapeUp.Delete()
                shapeDo.Delete()

            #####################################################
            #  PDFWeights_Scale
            #####################################################
            if pdfW is 'PDFWeights_Scale':
                # Using Three nuisances for muR up/do, muF up/do, correlated up/do
                # as recommanded: https://indico.cern.ch/event/494682/contributions/1172505/attachments/1223578/1800218/mcaod-Feb15-2016.pdf

                print 'checking size of PDFWeights_Scale'
                size = 0
                for event in tree:
                    size = event.PDFWeights_Scale.size()
                    break
                #tree.GetEntry(0)
                #size = len( tree.PDFWeights_Scale )
                #for idx in xrange(size) :
                #  if idx == 5 or idx == 7:
                #    continue
                #  print idx, tree.PDFWeights_Scale[idx]
                #print 'scale size', size
                if size == 9:
                    print 'saving scale nuisance variation: Renormalization and Factorization order changing'
                    totalW_muAUp = "(" + totalWeight + ") * (PDFWeights_Scale[1])"
                    totalW_muADo = "(" + totalWeight + ") * (PDFWeights_Scale[2])"
                    totalW_muBUp = "(" + totalWeight + ") * (PDFWeights_Scale[3])"
                    totalW_muBDo = "(" + totalWeight + ") * (PDFWeights_Scale[6])"
                    totalW_muABUp = "(" + totalWeight + ") * (PDFWeights_Scale[4])"
                    totalW_muABDo = "(" + totalWeight + ") * (PDFWeights_Scale[8])"
                else:
                    totalW_muAUp = totalWeight
                    totalW_muADo = totalWeight
                    totalW_muBUp = totalWeight
                    totalW_muBDo = totalWeight
                    totalW_muABUp = totalWeight
                    totalW_muABDo = totalWeight

                Dtree = Dtree.Define('totalW_muAUp', totalW_muAUp) \
                                   .Define('totalW_muADo', totalW_muADo) \
                     .Define('totalW_muBUp', totalW_muBUp) \
                     .Define('totalW_muBDo', totalW_muBDo) \
                     .Define('totalW_muABUp', totalW_muABUp) \
                     .Define('totalW_muABDo', totalW_muABDo)
                # New histogram
                shapeNameAUp = 'histo_' + sampleName + 'AUp' + str(numTree)
                shapeNameADo = 'histo_' + sampleName + 'ADo' + str(numTree)
                shapeNameBUp = 'histo_' + sampleName + 'BUp' + str(numTree)
                shapeNameBDo = 'histo_' + sampleName + 'BDo' + str(numTree)
                shapeNameABUp = 'histo_' + sampleName + 'ABUp' + str(numTree)
                shapeNameABDo = 'histo_' + sampleName + 'ABDo' + str(numTree)
                # prepare a dummy to fill
                hclass, hargs, ndim = self._bins2hclass(rng)
                hModelAUp = (
                    shapeNameAUp,
                    shapeNameAUp,
                ) + hargs
                hModelADo = (
                    shapeNameADo,
                    shapeNameADo,
                ) + hargs
                hModelBUp = (
                    shapeNameBUp,
                    shapeNameBUp,
                ) + hargs
                hModelBDo = (
                    shapeNameBDo,
                    shapeNameBDo,
                ) + hargs
                hModelABUp = (
                    shapeNameABUp,
                    shapeNameABUp,
                ) + hargs
                hModelABDo = (
                    shapeNameABDo,
                    shapeNameABDo,
                ) + hargs
                if ndim == 1:
                    shapeAUp = Dtree.Filter(totCut).Histo1D(
                        hModelAUp, var, 'totalW_muAUp')
                    shapeADo = Dtree.Filter(totCut).Histo1D(
                        hModelADo, var, 'totalW_muADo')
                    shapeBUp = Dtree.Filter(totCut).Histo1D(
                        hModelBUp, var, 'totalW_muBUp')
                    shapeBDo = Dtree.Filter(totCut).Histo1D(
                        hModelBDo, var, 'totalW_muBDo')
                    shapeABUp = Dtree.Filter(totCut).Histo1D(
                        hModelABUp, var, 'totalW_muABUp')
                    shapeABDo = Dtree.Filter(totCut).Histo1D(
                        hModelABDo, var, 'totalW_muABDo')
                elif ndim == 2:
                    shapeAUp = Dtree.Filter(totCut).Histo2D(
                        hModelAUp, var, 'totalW_muAUp')
                    shapeADo = Dtree.Filter(totCut).Histo2D(
                        hModelADo, var, 'totalW_muADo')
                    shapeBUp = Dtree.Filter(totCut).Histo2D(
                        hModelBUp, var, 'totalW_muBUp')
                    shapeBDo = Dtree.Filter(totCut).Histo2D(
                        hModelBDo, var, 'totalW_muBDo')
                    shapeABUp = Dtree.Filter(totCut).Histo2D(
                        hModelABUp, var, 'totalW_muABUp')
                    shapeABDo = Dtree.Filter(totCut).Histo2D(
                        hModelABDo, var, 'totalW_muABDo')

                nTriesAUp = shapeAUp.Integral()
                nTriesADo = shapeADo.Integral()
                nTriesBUp = shapeBUp.Integral()
                nTriesBDo = shapeBDo.Integral()
                nTriesABUp = shapeABUp.Integral()
                nTriesABDo = shapeABDo.Integral()
                print 'integral  AUp and ADo', nTriesAUp, nTriesADo
                print 'integral  AUp and ADo', nTriesAUp, nTriesADo
                print 'integral  BUp and BDo', nTriesBUp, nTriesBDo
                print 'integral  BUp and BDo', nTriesBUp, nTriesBDo
                print 'integral  ABUp and ABDo', nTriesABUp, nTriesABDo
                print 'integral  ABUp and ABDo', nTriesABUp, nTriesABDo
                if nTriesAUp == 0:
                    print 'Warning : entries is 0 for', hModelAUp
                if nTriesADo == 0:
                    print 'Warning : entries is 0 for', hModelADo
                if nTriesBUp == 0:
                    print 'Warning : entries is 0 for', hModelBUp
                if nTriesBDo == 0:
                    print 'Warning : entries is 0 for', hModelBDo
                if nTriesABUp == 0:
                    print 'Warning : entries is 0 for', hModelABUp
                if nTriesABDo == 0:
                    print 'Warning : entries is 0 for', hModelABDo

                if math.isnan(nTriesAUp):
                    print 'ERROR : entries is nan for', hModelAUp
                if math.isnan(nTriesADo):
                    print 'ERROR : entries is nan for', hModelADo
                if math.isnan(nTriesBUp):
                    print 'ERROR : entries is nan for', hModelBUp
                if math.isnan(nTriesBDo):
                    print 'ERROR : entries is nan for', hModelBDo
                if math.isnan(nTriesABUp):
                    print 'ERROR : entries is nan for', hModelABUp
                if math.isnan(nTriesABDo):
                    print 'ERROR : entries is nan for', hModelABDo

                if (numTree == 0):
                    shapeAUp.SetTitle(bigNameAUp)
                    shapeAUp.SetName(bigNameAUp)
                    hTotalAUp = shapeAUp

                    shapeADo.SetTitle(bigNameADo)
                    shapeADo.SetName(bigNameADo)
                    hTotalADo = shapeADo

                    shapeBUp.SetTitle(bigNameBUp)
                    shapeBUp.SetName(bigNameBUp)
                    hTotalBUp = shapeBUp
                    shapeBDo.SetTitle(bigNameBDo)
                    shapeBDo.SetName(bigNameBDo)
                    hTotalBDo = shapeBDo

                    shapeABUp.SetTitle(bigNameABUp)
                    shapeABUp.SetName(bigNameABUp)
                    hTotalABUp = shapeABUp

                    shapeABDo.SetTitle(bigNameABDo)
                    shapeABDo.SetName(bigNameABDo)
                    hTotalABDo = shapeABDo
                else:
                    cloneAUp = shapeAUp.Clone()
                    cloneADo = shapeADo.Clone()
                    cloneBUp = shapeBUp.Clone()
                    cloneBDo = shapeBDo.Clone()
                    cloneABUp = shapeABUp.Clone()
                    cloneABDo = shapeABDo.Clone()
                    hTotalAUp.Add(cloneAUp)
                    hTotalADo.Add(cloneADo)
                    hTotalBUp.Add(cloneBUp)
                    hTotalBDo.Add(cloneBDo)
                    hTotalABUp.Add(cloneABUp)
                    hTotalABDo.Add(cloneABDo)
                    cloneAUp.Delete()
                    cloneADo.Delete()
                    cloneBUp.Delete()
                    cloneBDo.Delete()
                    cloneABUp.Delete()
                    cloneABDo.Delete()
                shapeAUp.Delete()
                shapeADo.Delete()
                shapeBUp.Delete()
                shapeBDo.Delete()
                shapeABUp.Delete()
                shapeABDo.Delete()

            #########################################
            # PDFWeights_Error
            #########################################
            # 0-100, 0=> nominal?
            if pdfW is 'PDFWeights_Error':
                totalW_pdfErr = []
                size = 0
                for event in tree:
                    size = event.PDFWeights_Error.size()
                    break
                #tree.GetEntry(0)
                #size = len( tree.PDFWeights_Error )
                print 'PDFWeights_Error size', size
                if size > 101:
                    print 'size of PDFWeights_Error is gt 101, exiting...'
                    exit()
                for idx in xrange(size):
                    #print idx, tree.PDFWeights_Error[idx]
                    totalW_pdfErr.append("(" + totalWeight +
                                         ") * (PDFWeights_Error[" + str(idx) +
                                         "])")
                for idx in xrange(101 - size):
                    totalW_pdfErr.append("1")
                for idx in xrange(101):
                    #  print idx, totalW_pdfErr[idx]
                    Dtree = Dtree.Define('totalW_pdfErr' + str(idx),
                                         totalW_pdfErr[idx])
                    # new histogram
                    shapeName = 'histo_' + sampleName + '_' + str(
                        idx) + '_' + str(numTree)
                    # prepare a dummy to fill
                    hclass, hargs, ndim = self._bins2hclass(rng)
                    hModel = (
                        shapeName,
                        shapeName,
                    ) + hargs
                    if ndim == 1:
                        shape = Dtree.Filter(totCut).Histo1D(
                            hModel, var, 'totalW_pdfErr' + str(idx))
                    elif ndim == 2:
                        shape = Dtree.Filter(totCut).Histo2D(
                            hModel, var, 'totalW_pdfErr' + str(idx))
                    else:
                        print 'this dim of hist not ready', ndim, 'exiting'
                        exit()
                    nTries = shape.Integral()
                    #print idx, 'integral  ', nTries
                    if nTries == 0:
                        print 'Warning : entries is 0 for', hModel
                    if math.isnan(nTries):
                        print 'ERROR : entries is nan for', hModel
                    if (numTree == 0):
                        bigName = 'histo_' + sampleName + str(
                            idx) + '_' + cutName + '_' + variableName
                        shape.SetTitle(bigName)
                        shape.SetName(bigName)
                        hTotal_Err[idx] = shape
                    else:
                        cloneH = shape.Clone()
                        hTotal_Err[idx].Add(cloneH)
                        cloneH.Delete()
                    shape.Delete()

            numTree += 1
            #Dtree.Delete()
            #RDF.Delete()

        # fold if needed
        if doFold == 1 or doFold == 3:
            if pdfW is 'PDFWeights_AlphaS':
                self._FoldOverflow(hTotalAlphaUp)
                self._FoldOverflow(hTotalAlphaDo)
            if pdfW is 'PDFWeights_Scale':
                self._FoldOverflow(hTotalAUp)
                self._FoldOverflow(hTotalADo)
                self._FoldOverflow(hTotalBUp)
                self._FoldOverflow(hTotalBDo)
                self._FoldOverflow(hTotalABUp)
                self._FoldOverflow(hTotalABDo)
            if pdfW is 'PDFWeights_Error':
                for idx in xrange(101):
                    self._FoldOverflow(hTotal_Err[idx])
        if doFold == 2 or doFold == 3:
            if pdfW is 'PDFWeights_AlphaS':
                self._FoldUnderflow(hTotalAlphaUp)
                self._FoldUnderflow(hTotalAlphaDo)
            if pdfW is 'PDFWeights_Scale':
                self._FoldUnderflow(hTotalAUp)
                self._FoldUnderflow(hTotalADo)
                self._FoldUnderflow(hTotalBUp)
                self._FoldUnderflow(hTotalBDo)
                self._FoldUnderflow(hTotalABUp)
                self._FoldUnderflow(hTotalABDo)
            if pdfW is 'PDFWeights_Error':
                for idx in xrange(101):
                    self._FoldUnderflow(hTotal_Err[idx])

        # go 1d
        if pdfW is 'PDFWeights_AlphaS':
            hTotalFinalAlphaUp = self._h2toh1(hTotalAlphaUp)
            hTotalFinalAlphaDo = self._h2toh1(hTotalAlphaDo)
            hTotalFinalAlphaUp.SetTitle('histo_' + sampleName + 'Up')
            hTotalFinalAlphaDo.SetTitle('histo_' + sampleName + 'Do')
            hTotalFinalAlphaUp.SetName('histo_' + sampleName + 'Up')
            hTotalFinalAlphaDo.SetName('histo_' + sampleName + 'Do')

        if pdfW is 'PDFWeights_Scale':
            hTotalFinalAUp = self._h2toh1(hTotalAUp)
            hTotalFinalADo = self._h2toh1(hTotalADo)
            hTotalFinalAUp.SetTitle('histo_' + sampleName + 'AUp')
            hTotalFinalADo.SetTitle('histo_' + sampleName + 'ADo')
            hTotalFinalAUp.SetName('histo_' + sampleName + 'AUp')
            hTotalFinalADo.SetName('histo_' + sampleName + 'ADo')

            hTotalFinalBUp = self._h2toh1(hTotalBUp)
            hTotalFinalBDo = self._h2toh1(hTotalBDo)
            hTotalFinalBUp.SetTitle('histo_' + sampleName + 'BUp')
            hTotalFinalBDo.SetTitle('histo_' + sampleName + 'BDo')
            hTotalFinalBUp.SetName('histo_' + sampleName + 'BUp')
            hTotalFinalBDo.SetName('histo_' + sampleName + 'BDo')

            hTotalFinalABUp = self._h2toh1(hTotalABUp)
            hTotalFinalABDo = self._h2toh1(hTotalABDo)
            hTotalFinalABUp.SetTitle('histo_' + sampleName + 'ABUp')
            hTotalFinalABDo.SetTitle('histo_' + sampleName + 'ABDo')
            hTotalFinalABUp.SetName('histo_' + sampleName + 'ABUp')
            hTotalFinalABDo.SetName('histo_' + sampleName + 'ABDo')

        if pdfW is 'PDFWeights_Error':
            hTotalFinalErr = [None] * 101
            for idx in xrange(101):
                hTotalFinalErr[idx] = self._h2toh1(hTotal_Err[idx])
                hTotalFinalErr[idx].SetTitle('histo_' + sampleName +
                                             str(idx).zfill(3))
                hTotalFinalErr[idx].SetName('histo_' + sampleName +
                                            str(idx).zfill(3))

        #fix negative (almost never happening)
        # don't do it here by default, because you may have interference that is actually negative!
        # do this only if triggered: use with caution!
        # This also checks that only in specific phase spaces this is activated, "cutName"
        #
        # To be used with caution -> do not use this option if you don't know what you are playing with
        #
        if fixZeros and 'suppressNegative' in sample.keys() and (
                cutName in sample['suppressNegative']
                or 'all' in sample['suppressNegative']):
            if pdfW is 'PDFWeights_AlphaS':
                self._fixNegativeBinAndError(hTotalFinalAlphaUp)
                self._fixNegativeBinAndError(hTotalFinalAlphaDo)
            if pdfW is 'PDFWeights_Scale':
                self._fixNegativeBinAndError(hTotalFinalAUp)
                self._fixNegativeBinAndError(hTotalFinalADo)
                self._fixNegativeBinAndError(hTotalFinalBUp)
                self._fixNegativeBinAndError(hTotalFinalBDo)
                self._fixNegativeBinAndError(hTotalFinalABUp)
                self._fixNegativeBinAndError(hTotalFinalABDo)
            if pdfW is 'PDFWeights_Error':
                for idx in xrange(101):
                    self._fixNegativeBinAndError(hTotalFinalErr[idx])

        histoList = []
        if pdfW is 'PDFWeights_AlphaS':
            histoList.append(hTotalFinalAlphaUp)
            histoList.append(hTotalFinalAlphaDo)
        if pdfW is 'PDFWeights_Scale':
            histoList.append(hTotalFinalAUp)
            histoList.append(hTotalFinalADo)
            histoList.append(hTotalFinalBUp)
            histoList.append(hTotalFinalBDo)
            histoList.append(hTotalFinalABUp)
            histoList.append(hTotalFinalABDo)

        if pdfW is 'PDFWeights_Error':
            return hTotalFinalErr
        else:
            return histoList
Exemplo n.º 24
0
# exe parameters
numEvents = -1  # -1 to process all (10000)
samList = {
    'signals'
}  # list of samples to be processed - append multiple lists , 'data', 'mainbkg'    , 'datall', 'mainbkg', 'minortt', 'dibosons', 'bosons','trigger'
trgList = 'def_2016'  # trigger paths - remove TriggerOperator to not apply trigger

iDir = '/lustre/cmswork/hh/alpha_ntuples/'
ntuplesVer = 'v0_20161004'  # equal to ntuple's folder
oDir = './output/v0_AccTrg_sig'  # output dir ('./test')
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)

trg_names = triggerlists[trgList]
trg_names_v = vector("string")()
if not trg_names: print "### WARNING: empty hlt_names ###"
for trg_name in trg_names:
    trg_names_v.push_back(trg_name)

# to parse variables to the anlyzer
config = {
    "jets_branch_name": "Jets",
    "hlt_names": trg_names,
    "n_gen_events": 0
}

snames = []
for s in samList:
    snames.extend(samlists[s])
Exemplo n.º 25
0
data_path = "{}/src/Analysis/alp_analysis/data/".format(os.environ["CMSSW_BASE"])
weights = {'PUWeight', 'GenWeight', 'BTagWeight'}  #weights to be applied
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)

if args.btag == 'cmva':  
    btagAlgo = "pfCombinedMVAV2BJetTags"
    btag_wp = wps['CMVAv2_moriond']
elif args.btag == 'csv': 
    btagAlgo  = "pfCombinedInclusiveSecondaryVertexV2BJetTags"
    btag_wp = wps['CSVv2_moriond']

# to convert weights 
weights_v = vector("string")()
for w in weights: weights_v.push_back(w)

# to parse variables to the anlyzer
config = {"eventInfo_branch_name" : "EventInfo",
          "jets_branch_name": "Jets",
          "dijets_branch_name": "DiJets",
          "genbfromhs_branch_name" : "GenBFromHs",
          "genhs_branch_name" : "GenHs",
          "n_gen_events":0,
          "xsec_br" : 0,
          "matcheff": 0,
          "kfactor" : 0,
          "isData" : False,
          "lumiFb" : intLumi_fb,
         }
Exemplo n.º 26
0
intLumi_fb = 12.6  #36.26 12.6

iDir = "/lustre/cmswork/hh/alpha_ntuples/" + args.iDir
oDir = '/lustre/cmswork/hh/alp_baseSelector/' + args.oDir
if args.jesUp: oDir += "_JESup"
elif args.jesDown: oDir += "_JESdown"

data_path = "{}/src/Analysis/alp_analysis/data/".format(
    os.environ["CMSSW_BASE"])

#weights to be applied
weights = {'PUWeight', 'GenWeight', 'BTagWeight'}
weights_nobTag = {'PUWeight', 'GenWeight'}

# to convert weights
weights_v = vector("string")()
for w in weights:
    weights_v.push_back(w)
w_nobTag_v = vector("string")()
for w in weights_nobTag:
    w_nobTag_v.push_back(w)
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)

if args.doMixed: config = {
        "jets_branch_name": "Jets",
}
else:
    config = {
        "eventInfo_branch_name": "EventInfo",
Exemplo n.º 27
0
#parser.add_argument("-s", "--samList", help="sample list", default="")
args = parser.parse_args()

# exe parameters

numEvents = args.numEvts
oname = "QCD500_tt_SM100k"
samList = ['qcd_500toInf_m', 'tt', 'SM'
           ]  #,'SM' 'qcd_200to500_m','tt' # debug - qcd never as last sample
times_sm = 100000.  #, args.hh_times_sm
intLumi_fb = 12.6
lumi_factor = args.lumi_factor
lumi = intLumi_fb * lumi_factor

nn_vars = ["thrustMayor", "thrustMinor", "sumPz", "invMass"]
nn_vars_v = vector("string")()
for v in nn_vars:
    nn_vars_v.push_back(v)

mult = args.mult
extra_cut = args.extra_cut
extra_cut_name = args.extra_cut_name

iDir = "/lustre/cmswork/hh/alp_baseSelector/"
ntuplesVer = args.ntuplesVer
oDir = args.oDir
data_path = "{}/src/Analysis/alp_analysis/data/".format(
    os.environ["CMSSW_BASE"])
weights = {}  #weights to be applied 'PUWeight', 'GenWeight', 'BTagWeight'
# ---------------
Exemplo n.º 28
0
def readEvents(filename, nevents):
    inp = open(filename)
    
    # skip header
    while 1:
        record = split(inp.readline())
        token = record[0]
        if token == '</init>': break
        
    events = []
    nn = 0
    metx = 0.0
    mety = 0.0


    ppx = vector('double')
    ppy = vector('double')
    ppz = vector('double')
    pE  = vector('double')
        
    jetpx = vector('double')
    jetpy = vector('double')
    jetpz = vector('double')
    jetE  = vector('double')

                
    while nn < nevents:
        record = split(inp.readline())
        token = record[0]
        if token == '<event>':
            nn += 1
            if nn % 1000 == 0: print nn
            partons = []
            nonpartons = []
            recobjs = []
            record  = split(inp.readline())
            token = record[0]
            nparticles = atoi(token)
            if nn < 5:
                print "Event: %d" % nn
                print "\tnumber of particles: %d" % nparticles

            # initialize missing ET sums
            metx = 0.0
            mety = 0.0
            continue
        elif token == '</event>':
            
            # missing ET
            energy = sqrt(metx*metx + mety*mety)
            particles = [(12, metx, mety, 0, energy)]
            
            # parton jets
            ppx.clear()
            ppy.clear()
            ppz.clear()
            pE.clear()
            for pid, px, py, pz, E in partons:                
                ppx.push_back(px)
                ppy.push_back(py)
                ppz.push_back(pz)
                pE.push_back(E)
            findJets(vpx, vpy, vpz, vE, jetpx, jetpy, jetpz, jetE)
            for i in xrange(jetpx.size()):
                particles.append( (21, jetpx[i], jetpy[i], jetpz[i], jetE[i]) )

            for p in nonpartons:
                particles.append(p)
            events.append((particles, recobjs))
            continue
        else:
            pid = atoi(token)
            ptype  = record[1]
            px, py, pz, energy, mass = map(atof, record[6:11])
            # truth-level code = 3
            # reco-level code  = 1
            if ptype == '3':
                # exclude beam particles
                if px == py == 0: continue

                # temporary hack: exclude t/W/Z/H
                ID = abs(pid)
                if not PARTONID.has_key(ID): continue
                
                # compute true missing ET
                if MISSINGETID.has_key(ID):
                    metx += px
                    mety += py
                elif (ID < 6)  or (ID == 2):
                    partons.append((pid, px, py, pz, energy))
                else:
                    nonpartons.append((pid, px, py, pz, energy))
            else:
                recobjs.append((pid, px, py, pz, energy))
            if nn < 5:
                if ptype == '3':
                    level = 'truth'
                else:
                    level = 'reco'
                rec = "%6s: %12s %10d\t%10.3f %10.3f %10.3f %10.3f" % \
                  (level, nic.particleName(pid), pid, px, py, pz, energy)
                print rec
    return events
Exemplo n.º 29
0
data_path = "{}/src/Analysis/alp_analysis/data/".format(
    os.environ["CMSSW_BASE"])
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)

if args.btag == 'cmva':
    btagAlgo = "pfCombinedMVAV2BJetTags"
    btag_wp = wps['CMVAv2_moriond']
elif args.btag == 'csv':
    btagAlgo = "pfCombinedInclusiveSecondaryVertexV2BJetTags"
    btag_wp = wps['CSVv2_moriond']

# variables to check nearest-neightbour
nn_vars = ["thrustMayor", "thrustMinor", "sumPz", "invMass"]
nn_vars_v = vector("string")()
for v in nn_vars:
    nn_vars_v.push_back(v)

# to parse variables to the anlyzer
config = {
    "eventInfo_branch_name": "EventInfo",
    "jets_branch_name": "Jets",
    "dijets_branch_name": "DiJets",
    #"dihiggs_branch_name": "DiHiggs",
    #"muons_branch_name" : "",
    #"electrons_branch_name" : "",
    #"met_branch_name" : "",
    "n_gen_events": 0,
    "xsec_br": 0,
    "matcheff": 0,
Exemplo n.º 30
0
 def init_vector_branches():
     dic = OrderedDict([('plane', vector('unsigned short')()),
                        ('col', vector('unsigned short')()),
                        ('row', vector('unsigned short')()),
                        ('adc', vector('short')()),
                        ('header', vector('unsigned int')()),
                        ('trailer', vector('unsigned int')()),
                        ('pkam', vector('unsigned short')()),
                        ('token_pass', vector('unsigned short')()),
                        ('reset_tbm', vector('unsigned short')()),
                        ('reset_roc', vector('unsigned short')()),
                        ('auto_reset', vector('unsigned short')()),
                        ('cal_trigger', vector('unsigned short')()),
                        ('trigger_count', vector('unsigned short')()),
                        ('trigger_phase', vector('unsigned short')()),
                        ('stack_count', vector('unsigned short')()),
                        ('invalid_address', vector('bool')()),
                        ('invalid_pulse_height', vector('bool')()),
                        ('buffer_corruption', vector('bool')()),
                        ('incomplete_data', vector('bool')()),
                        ('missing_roc_headers', vector('bool')()),
                        ('roc_readback', vector('bool')()),
                        ('no_data', vector('bool')()),
                        ('eventid_mismatch', vector('bool')())])
     return dic
 def setUp(self):
     gROOT.SetBatch(True)
     self.runOnMC = True
     self.tfile = TFile.Open(self.infile)
     self.ttree = self.tfile.Get("ntupler/tree")
     self.nevents = self.ttree.GetEntries()
     self.fvec1 = vector('float')()
     self.fvec2 = vector('float')()
     self.fvec3 = vector('float')()
     self.fvec4 = vector('float')()
     self.fvec5 = vector('float')()
     self.fvec6 = vector('float')()
     self.ivec1 = vector('int')()
     self.ivec2 = vector('int')()
     self.ivec3 = vector('int')()
     self.uvec1 = vector('unsigned int')()
     self.uvec2 = vector('unsigned int')()
     self.uvec3 = vector('unsigned int')()
     self.uvec4 = vector('unsigned int')()
     self.uvec5 = vector('unsigned int')()
     self.uvec6 = vector('unsigned int')()
     self.uvec7 = vector('unsigned int')()
     self.uvec8 = vector('unsigned int')()
     self.uvec9 = vector('unsigned int')()
     self.bvec1 = vector('bool')()
     self.bvec2 = vector('bool')()
     self.bvec3 = vector('bool')()
     self.bvec4 = vector('bool')()
Exemplo n.º 32
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intLumi_fb = 12.6

iDir       = "/lustre/cmswork/hh/alpha_ntuples/"
ntuplesVer = "v1_20161028"        
if not args.oDir: oDir = "/lustre/cmswork/hh/alp_baseSelector/data_def"
else: oDir = args.oDir

data_path = "{}/src/Analysis/alp_analysis/data/".format(os.environ["CMSSW_BASE"])
weights = {'EventWeight'}  #weights to be applied - EventWeight, PUWeight, GenWeight
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)

trg_names = triggerlists[trgList]
if not trg_names: print "### WARNING: empty hlt_names ###"
trg_names_v = vector("string")()
for t in trg_names: trg_names_v.push_back(t)

# to convert weights 
weights_v = vector("string")()
for w in weights: weights_v.push_back(w)


# to parse variables to the anlyzer
config = {"eventInfo_branch_name" : "EventInfo",
          "jets_branch_name": "Jets",
          #"muons_branch_name" : "",
          #"electrons_branch_name" : "",
          #"met_branch_name" : "",
          "genbfromhs_branch_name" : "GenBFromHs",
          "genhs_branch_name" : "GenHs",
Exemplo n.º 33
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oDir = args.oDir
data_path = "{}/src/Analysis/alp_analysis/data/".format(os.environ["CMSSW_BASE"])
btagAlgo  = "pfCombinedInclusiveSecondaryVertexV2BJetTags"
#btagAlgo  = "pfCombinedMVAV2BJetTags"
weights   = {'PUWeight', 'GenWeight', 'BTagWeight'}  #weights to be applied 
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)

trg_namesD = triggerlists[trgListD]
trg_namesN = triggerlists[trgListN]
trg_names  = trg_namesD + trg_namesN
print trg_namesD
print trg_namesN
if not trg_names: print "### WARNING: empty hlt_names ###"
trg_namesD_v = vector("string")()
for t in trg_namesD: trg_namesD_v.push_back(t)
trg_namesN_v = vector("string")()
for t in trg_namesN: trg_namesN_v.push_back(t)

# to convert weights 
weights_v = vector("string")()
for w in weights: weights_v.push_back(w)

# to parse variables to the anlyzer
config = {"eventInfo_branch_name" : "EventInfo",
          "jets_branch_name": "Jets",
          "muons_branch_name" : "Muons",
          "electrons_branch_name" : "Electrons",
          "met_branch_name" : "MET",
          "genbfromhs_branch_name" : "GenBFromHs",
Exemplo n.º 34
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def toVector(vtype, list):
    from ROOT import vector
    v = vector(vtype)()
    for o in list:
        v.push_back(o)
    return v
def main():
    """
    Main function (c++ style)
    """

    # The name of the application
    import os
    APP_NAME = os.path.basename(__file__).rstrip('.py')
    print APP_NAME
    # Set up a logger object
    import logging
    logger = logging.getLogger(APP_NAME)
    logger.setLevel(logging.INFO)
    hdlr = logging.StreamHandler(sys.stdout)
    frmt = logging.Formatter('%(name)-14s%(levelname)8s %(message)s')
    hdlr.setFormatter(frmt)
    logger.addHandler(hdlr)

    # Setup the environment
    import ROOT
    if ROOT.gROOT.Macro('$ROOTCOREDIR/scripts/load_packages.C'):
        logger.error("Couldn't load the RootCore packages")
        return 1

    if ROOT.xAOD.Init(APP_NAME).isFailure():
        logger.error('Failed to call xAOD::Init(...)')
        return 1


    from ROOT import vector

    #registry
    registry = ROOT.ToolsRegistry('ToolsRegistry')

    # level1 emulation tool
    tool_l1 = ROOT.TrigTauEmul.Level1EmulationTool("Level1EmulationTool")
    chains_to_test = vector('std::string')()
    chains_to_test.push_back("L1_TAU12")
    chains_to_test.push_back("L1_TAU12IL")
    chains_to_test.push_back("L1_TAU12IM")
    chains_to_test.push_back("L1_TAU12IT")
    chains_to_test.push_back("L1_TAU20")
    chains_to_test.push_back("L1_TAU20IL")

    if not tool_l1.setProperty('l1_chains', chains_to_test).isSuccess():
        logger.error('Failed to set the property')
        return 1

    sc = tool_l1.initialize()
    if sc.isFailure():
        logger.error('Failed to initialize the tool')
        return 1

    # TauTriggerEmulation import and setup
    tool = ROOT.TrigTauEmul.HltEmulationTool("TauTriggerEmulationTool")
    chains_to_test = vector('std::string')()
    chains_to_test.push_back("HLT_tau5_perf_ptonly_L1TAU8")
    chains_to_test.push_back("HLT_tau25_perf_ptonly")
    chains_to_test.push_back("HLT_tau25_loose1_ptonly")
    chains_to_test.push_back("HLT_tau25_medium1_ptonly")
    chains_to_test.push_back("HLT_tau25_tight1_ptonly")

    if not tool.setProperty('hlt_chains', chains_to_test).isSuccess():
        logger.error('Failed to set the property')
        return 1

    sc = tool.initialize()
    if sc.isFailure():
        logger.error('Failed to initialize the tool')
        return 1

    # Return gracefully
    return 0
Exemplo n.º 36
0
    exit(-1)

from ROOT import HMuTauhTreeFromNano, HTauhTauhTreeFromNano
fileNames = [
    "0E6F4B78-CC12-E811-B37D-FA163EA12C78.root",
    "50BE09DD-CC12-E811-869D-F04DA27542B9.root",
    "844BE355-CD12-E811-8871-FA163ED9B872.root",
    "DEBF5F61-CC12-E811-B47A-0CC47AA9943A.root",
    "5A038C2A-CC12-E811-B729-7845C4FC3B8D.root",
]

lumisToProcess = process.source.lumisToProcess
#import FWCore.ParameterSet.Config as cms
#lumisToProcess = cms.untracked.VLuminosityBlockRange( ("1:2047-1:2047", "1:2048-1:2048", "1:6145-1:6145", "1:4098-1:4098", "1:3-1:7", "1:6152-1:6152", "1:9-1:11", "1:273-1:273", "1:4109-1:4109", "1:4112-1:4112", "1:4115-1:4116") )
from ROOT import vector
vlumis = vector('string')()
for lumi in lumisToProcess:
    vlumis.push_back(lumi)

for name in fileNames:
    aFile = "file:///home/mbluj/work/data/NanoAOD/80X_with944/VBFHToTauTau_M125_13TeV_powheg_pythia8/RunIISummer16NanoAOD_PUMoriond17_05Feb2018_94X_mcRun2_asymptotic_v2-v1/" + name
    print "Adding file: ", aFile
    print "Making the MuTau tree"
    aROOTFile = TFile.Open(aFile)
    aTree = aROOTFile.Get("Events")
    print "TTree entries: ", aTree.GetEntries()
    HMuTauhTreeFromNano(aTree, doSvFit, applyRecoil, vlumis).Loop()
    print "Making the TauTau tree"
    aROOTFile = TFile.Open(aFile)
    aTree = aROOTFile.Get("Events")
    HTauhTauhTreeFromNano(aTree, doSvFit, applyRecoil, vlumis).Loop()
Exemplo n.º 37
0
try:
    gSystem.Load("libSusyFitter.so")
    from ROOT import *

except Exception, msg:
    print "Couldn't import HistFitter - please setup appropriate root version"
    sys.exit()

### file lists

fi1 = glob.glob('SoftLeptonMoriond2013_SRs1L_SM_GG1step_*/Fit_SRs1L_SM_GG1step_*_combined_BasicMeasurement_model.root')
fi2 = glob.glob('Sig_SM_SS1step_*_combined_BasicMeasurement_model.root')
#fi3 = glob.glob('Sig_SM_SS1step_*_combined_BasicMeasurement_model.root')

f1 = vector('TString')()
f2 = vector('TString')()
#f3 = vector('TString')()
for file in fi1: f1.push_back(TString(file))
for file in fi2: f2.push_back(TString(file))
#for file in fi3: f3.push_back(TString(file))

format1 = 'filename+SoftLeptonMoriond2013_SRs1L_SM_GG1step_%f_%f_%f+combined'
format2 = 'filename+Sig_SM_SS1step_%f_%f_%f+combined'
#format3 = 'filename+Sig_SM_SS1step_%f_%f_%f'

interpretation = 'm1:m2:m3'

outfile = 'combined+wsid' ## workspace id will be added to filename
outws_prefix = 'combined' ## prefix for workspace name
Exemplo n.º 38
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    def __init__(self, globalTag, jetFlavour, doResidualJECs=True, **kwargs):
        """Create a corrector object that reads the payloads from the text dumps of a global tag under
        CMGTools/RootTools/data/jec  (see the getJec.py there to make the dumps).
        It will apply the L1,L2,L3 and possibly the residual corrections to the jets.
        If configured to do so, it will also compute the type1 MET corrections."""

        globalTag = globalTag
        jetFlavour = jetFlavour
        doResidualJECs = doResidualJECs
        era = kwargs.get('era', "")
        path = kwargs.get('path', pathJEC)
        upToLevel = kwargs.get('upToLevel', 3)
        correctType1MET = kwargs.get('correctType1MET', False)
        correctSeparate = kwargs.get('correctSeparate', False)
        type1METParams = kwargs.get(
            'type1METParams', {
                'jetPtThreshold': 15.,
                'skipEMfractionThreshold': 0.9,
                'skipMuons': True
            })
        ###if era:
        ###  globalTag = re.sub(r"(201[678])(_V\d+)",r"\1%s\2"%era,globalTag)

        # BASE CORRECTIONS
        path = os.path.expandvars(
            path
        )  #"%s/src/CMGTools/RootTools/data/jec"%os.environ['CMSSW_BASE'];
        print("Loading JES corrections from '%s' with globalTag '%s'..." %
              (path, globalTag))
        filenameL1 = ensureFile("%s/%s_L1FastJet_%s.txt" %
                                (path, globalTag, jetFlavour))
        filenameL2 = ensureFile("%s/%s_L2Relative_%s.txt" %
                                (path, globalTag, jetFlavour))
        filenameL3 = ensureFile("%s/%s_L3Absolute_%s.txt" %
                                (path, globalTag, jetFlavour))
        self.L1JetPar = JetCorrectorParameters(filenameL1, "")
        self.L2JetPar = JetCorrectorParameters(filenameL2, "")
        self.L3JetPar = JetCorrectorParameters(filenameL3, "")
        self.vPar = vector(JetCorrectorParameters)()
        self.vPar.push_back(self.L1JetPar)
        if upToLevel >= 2: self.vPar.push_back(self.L2JetPar)
        if upToLevel >= 3: self.vPar.push_back(self.L3JetPar)

        # ADD RESIDUALS
        if doResidualJECs:
            filenameL2L3 = ensureFile("%s/%s_L2L3Residual_%s.txt" %
                                      (path, globalTag, jetFlavour))
            self.ResJetPar = JetCorrectorParameters(filenameL2L3)
            self.vPar.push_back(self.ResJetPar)

        # STEP 3: Construct a FactorizedJetCorrector object
        self.JetCorrector = FactorizedJetCorrector(self.vPar)
        if os.path.exists("%s/%s_Uncertainty_%s.txt" %
                          (path, globalTag, jetFlavour)):
            self.JetUncertainty = JetCorrectionUncertainty(
                "%s/%s_Uncertainty_%s.txt" % (path, globalTag, jetFlavour))
        elif os.path.exists("%s/Uncertainty_FAKE.txt" % path):
            self.JetUncertainty = JetCorrectionUncertainty(
                "%s/Uncertainty_FAKE.txt" % path)
        else:
            print 'Missing JEC uncertainty file "%s/%s_Uncertainty_%s.txt", so jet energy uncertainties will not be available' % (
                path, globalTag, jetFlavour)
            self.JetUncertainty = None
        self.separateJetCorrectors = {}
        if correctSeparate or correctType1MET:
            self.vParL1 = vector(JetCorrectorParameters)()
            self.vParL1.push_back(self.L1JetPar)
            self.separateJetCorrectors['L1'] = FactorizedJetCorrector(
                self.vParL1)
            if upToLevel >= 2 and correctSeparate:
                self.vParL2 = vector(JetCorrectorParameters)()
                for i in [self.L1JetPar, self.L2JetPar]:
                    self.vParL2.push_back(i)
                self.separateJetCorrectors['L1L2'] = FactorizedJetCorrector(
                    self.vParL2)
            if upToLevel >= 3 and correctSeparate:
                self.vParL3 = vector(JetCorrectorParameters)()
                for i in [self.L1JetPar, self.L2JetPar, self.L3JetPar]:
                    self.vParL3.push_back(i)
                self.separateJetCorrectors['L1L2L3'] = FactorizedJetCorrector(
                    self.vParL3)
            if doResidualJECs and correctSeparate:
                self.vParL3Res = vector(JetCorrectorParameters)()
                for i in [
                        self.L1JetPar, self.L2JetPar, self.L3JetPar,
                        self.ResJetPar
                ]:
                    self.vParL3Res.push_back(i)
                self.separateJetCorrectors[
                    'L1L2L3Res'] = FactorizedJetCorrector(self.vParL3Res)

        self.globalTag = globalTag
        self.jetFlavour = jetFlavour
        self.doResidualJECs = doResidualJECs
        self.path = path
        self.upToLevel = upToLevel
        self.correctType1MET = correctType1MET
        self.correctSeparate = correctSeparate
        self.type1METParams = type1METParams
Exemplo n.º 39
0
from di_higgs.hh2bbbb.samples_25ns import mc_samples

max_events = -100 
inEllipse = False 
freeJetTagged = True 
isMC = True

TH1.AddDirectory(False)

hlt_paths = ["HLT_BIT_HLT_QuadJet45_TripleBTagCSV0p67_v",
             "HLT_BIT_HLT_QuadJet45_DoubleBTagCSV0p67_v",
             "HLT_BIT_HLT_DoubleJet90_Double30_TripleBTagCSV0p67_v",
             "HLT_BIT_HLT_DoubleJet90_Double30_DoubleBTagCSV0p67_v",
             "HLT_HH4bAll"]

hlt_paths_v = vector("string")()
for hlt_path in hlt_paths: hlt_paths_v.push_back(hlt_path)

hlt_paths_or = hlt_paths[0:1] +  hlt_paths[2:3] 
hlt_paths_or_v = vector("string")()
for hlt_path in hlt_paths_or: hlt_paths_or_v.push_back(hlt_path)

mc_names = mc_samples.keys()
mc_names=[mc_name for mc_name in mc_names if 'HH' in mc_name]
for name in mc_names:
    isHH = False
    if "HH" in name: isHH = True
    selector = SkimSelector(ExtEvent(VHBBEvent))(0, hlt_paths_v, isHH,
                                   hlt_paths_or_v)
    tchain = TChain("tree")
    tchain.Add(mc_samples[name]["lustre_path"])
Exemplo n.º 40
0
    gSlight = load_starlight(dy)
    gMS = load_ms()
    gCCK = load_cck()
    #gSartre = load_sartre()

    #open the inputs
    inp = TFile.Open(basedir + "/" + infile)
    tree = inp.Get("jRecTree")
    inp_gg = TFile.Open(basedir_gg + "/" + infile_gg)
    tree_gg = inp_gg.Get("jRecTree")
    inp_coh = TFile.Open(basedir_coh + "/" + infile_coh)
    tree_coh_gen = inp_coh.Get("jGenTree")

    #evaluate binning
    print "bins:", ut.get_nbins(ptbin, ptmin, ptmax)
    bins = vector(rt.double)()
    #bins.push_back(ptmin)
    #while True:
    #    if bins[bins.size()-1] < ptmid:
    #        increment = ptbin
    #    else:
    #        increment = ptlon
    #    bins.push_back( bins[bins.size()-1] + increment )
    #    if bins[bins.size()-1] > ptmax: break

    bins = ut.get_bins_vec_2pt(ptbin, ptlon, ptmin, ptmax, ptmid)

    print "bins2:", bins.size() - 1

    #data and gamma-gamma histograms
    #hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
Exemplo n.º 41
0
#weights to be applied
weights = {'PUWeight', 'LeptonWeight', 'BTagWeight'}  #'lhe_weight_10',
weights_nobTag = {'PUWeight', 'LeptonWeight'}  #'lhe_weight_10'
# ---------------

if not os.path.exists(oDir): os.mkdir(oDir)
print oDir

trg_namesD = triggerlists[trgListD]
trg_namesN = triggerlists[trgListN]
trg_names = trg_namesD + trg_namesN
print trg_namesD
print trg_namesN
if not trg_names: print "### WARNING: empty hlt_names ###"
trg_namesD_v = vector("string")()
for t in trg_namesD:
    trg_namesD_v.push_back(t)
trg_namesN_v = vector("string")()
for t in trg_namesN:
    trg_namesN_v.push_back(t)

# to convert weights
weights_v = vector("string")()
for w in weights:
    weights_v.push_back(w)
w_nobTag_v = vector("string")()
for w in weights_nobTag:
    w_nobTag_v.push_back(w)

# to parse variables to the anlyzer
Exemplo n.º 42
0
        def matchAndSort(inputCollection, outputCollection):
          for cand in inputCollection:
            decaymatched = False
            vbfmatched=False
            if self.domcmatching:
              decaymatched = cand.leg2().leg1().getSelection('cuts_genParton') and cand.leg2().leg2().getSelection('cuts_genParton')
              if cand.vbfptr().isNonnull():
               if self.cfg_ana.matchvbfgen:  
                vbfjets = self.handles['genVBF'].product()
                if (len(vbfjets))>1:
                  phileg1 = cand.vbfptr().leg1().phi()
                  etaleg1 = cand.vbfptr().leg1().eta()
                  phileg2 = cand.vbfptr().leg2().phi()
                  etaleg2 = cand.vbfptr().leg2().eta()


                  phigen1 = vbfjets[0].phi() 
                  etagen1 = vbfjets[0].eta()
                  phigen2 = vbfjets[1].phi()
                  etagen2 = vbfjets[1].eta()
##                   vbfmatched = ((deltaR(phileg1, etaleg1, phigen1, etagen1)<1 or deltaR(phileg1, etaleg1, phigen2, etagen2)<1) or
##                                (deltaR(phileg2, etaleg2, phigen1, etagen1)<1 or deltaR(phileg2, etaleg2, phigen2, etagen2)<1))

                  vbfmatched = ((deltaR(phileg1, etaleg1, phigen1, etagen1)<0.5 or deltaR(phileg1, etaleg1, phigen2, etagen2)<0.5) or
                               (deltaR(phileg2, etaleg2, phigen1, etagen1)<0.5 or deltaR(phileg2, etaleg2, phigen2, etagen2)<0.5))
                  
            if ( cand.leg2().leg1().pt()>self.cfg_ana.jetptmin and
                 cand.leg2().leg2().pt()>self.cfg_ana.jetptmin ):
              if cand.vbfptr().isNonnull():
                if ( cand.vbfptr().leg1().pt()>self.cfg_ana.jetptmin and
                     cand.vbfptr().leg2().pt()>self.cfg_ana.jetptmin ):
                  varnames = vector("string") ()
                  varnames.push_back("ZJJMass")
                  varnames.push_back("J1Pt")
                  varnames.push_back("J2Pt")
                  varnames.push_back("ZJJdeltaEtaDecay")
                  varnames.push_back("HMMJJMass>0?abs(HMMJJDeltaPhiZ):abs(HEEJJDeltaPhiZ)")
                  varnames.push_back("HMMJJMass>0?abs(HMMJJSumAbsEtaJ1J2):abs(HEEJJSumAbsEtaJ1J2)")
                  varnames.push_back("HMMJJMass>0?HMMJJcosthetastar:HEEJJcosthetastar")
                  varnames.push_back("HMMJJMass>0?HMMJJhelphi:HEEJJhelphi")
                  varnames.push_back("HMMJJMass>0?HMMJJhelphiZl1:HEEJJhelphiZl1")
                  varnames.push_back("HMMJJMass>0?HMMJJhelphiZl2:HEEJJhelphiZl2")
                  varnames.push_back("HMMJJMass>0?HMMJJphistarZl1:HEEJJphistarZl1")
                  varnames.push_back("HMMJJMass>0?HMMJJphistarZl2:HEEJJphistarZl2")
                  varnames.push_back("HMMJJMass>0?HMMJJhelcosthetaZl1:HEEJJhelcosthetaZl1")
                  varnames.push_back("HMMJJMass>0?HMMJJhelcosthetaZl2:HEEJJhelcosthetaZl2")
                  vars = vector("double") ()
                  vars.push_back(cand.leg2().mass())
                  vars.push_back(cand.leg2().leg1().pt())
                  vars.push_back(cand.leg2().leg2().pt())
                  vars.push_back(abs(cand.leg2().leg1().eta() - cand.leg2().leg2().eta()))
                  vars.push_back(abs(deltaPhi(cand.leg1().eta(), cand.leg2().eta())))
                  vars.push_back(abs(cand.leg2().leg1().eta())+abs(cand.leg2().leg2().eta()))
                  vars.push_back(cand.costhetastar())
                  vars.push_back(cand.helphi())
                  vars.push_back(cand.helphiZl1())
                  vars.push_back(cand.helphiZl2())
                  vars.push_back(cand.phistarZl1())
                  vars.push_back(cand.phistarZl2())
                  vars.push_back(cand.helcosthetaZl1())
                  vars.push_back(cand.helcosthetaZl2())
                  if self.cfg_ana.computeClassifier:         
                    classifier = ReadBDT(varnames)
                    value = classifier.GetMvaValue(vars)  
                  else: 
                    value = -1.
                  outputCollection.append([cand, decaymatched, vbfmatched,value])
          outputCollection.sort(key=lambda a: a[0].vbfptr().mass(), reverse=True)       
          if self.cfg_ana.computeClassifier:
            outputCollection.sort(key=lambda a: a[3], reverse=True)  
          #print "initial size", len(outputCollection)  
          leadingmass = outputCollection[0][0].vbfptr().mass()  
          #print "maxvbfmass is ",leadingmass  
          outputCollection = [x for x in outputCollection if x[0].vbfptr().mass() >= leadingmass]