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
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)
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
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
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 ) )
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
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')
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 ) )
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)
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))
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
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
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
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
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
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)
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
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))
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 )
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
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
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
# 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])
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,
}
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",
#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'
# ---------------
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
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,
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')()
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",
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",
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
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()
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
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
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"])
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)
#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
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]
