def getWeightsFromFile(fileW,category,BINS,PT,ETA):
"""
getWeightsFromFile( ROOT.TFile, 'category', FWCore.ParameterSet.Config.BINS, 'PT', 'ETA' ) ->
"""
mapBinVal = {}
weights = {}
k = 0
for i in xrange(len(BINS.__getattribute__(PT))-1):
pt = (BINS.__getattribute__(PT)[i],BINS.__getattribute__(PT)[i+1])
for j in xrange(len(BINS.__getattribute__(ETA))-1):
eta = (BINS.__getattribute__(ETA)[j],BINS.__getattribute__(ETA)[j+1])
weights[category+'_bin'+str(k)] = ( eta, pt, fileW.Get(category+'_bin'+str(k)) )
try:
weights[category+'_bin'+str(k)][2].GetXaxis().SetLimits( pt[0], pt[1] )
except AttributeError:
# Then we are using a binning different of this one which contains the weight_out file
message = """\033[1;31mError: The binning defined by the config python file is different than
the defined in the weights_out_*.root files. If your config file is correct,
run this script in another location (to avoid remove the weights_out_*.root
files). These are the bins used by the config python you have introduced
%s\033[1;m""" % str(BINS)
print message
raise AttributeError
k += 1
return weights
def getWeightsFromFile(fileW, category, BINS, PT, ETA):
"""
getWeightsFromFile( ROOT.TFile, 'category', FWCore.ParameterSet.Config.BINS, 'PT', 'ETA' ) ->
"""
mapBinVal = {}
weights = {}
k = 0
for i in xrange(len(BINS.__getattribute__(PT)) - 1):
pt = (BINS.__getattribute__(PT)[i], BINS.__getattribute__(PT)[i + 1])
for j in xrange(len(BINS.__getattribute__(ETA)) - 1):
eta = (BINS.__getattribute__(ETA)[j],
BINS.__getattribute__(ETA)[j + 1])
weights[category + '_bin' +
str(k)] = (eta, pt, fileW.Get(category + '_bin' + str(k)))
try:
weights[category + '_bin' + str(k)][2].GetXaxis().SetLimits(
pt[0], pt[1])
except AttributeError:
# Then we are using a binning different of this one which contains the weight_out file
message = """\033[1;31mError: The binning defined by the config python file is different than
the defined in the weights_out_*.root files. If your config file is correct,
run this script in another location (to avoid remove the weights_out_*.root
files). These are the bins used by the config python you have introduced
%s\033[1;m""" % str(BINS)
print message
raise AttributeError
k += 1
return weights
def makeWeights(_files,treeName,category,_outputFile, BINS, PT, ETA):
"""
makeWeights( _fileDict, 'treename', 'category', 'outputfile' ) ->
"""
ROOT.gROOT.SetBatch(1)
#treeName = 'histoMuFromTk/fitter_tree'
_trees = dict( [ ( name, _file.Get(treeName) ) for name,_file in _files.iteritems()] )
#Check if in both files are the tree
for _tree in _trees.itervalues():
if not _tree:
return None
histos = {}
weights = {}
#-- The ':' token in A:B read as 'B conditioned to A' (look this unregular order)
#-- The categories are datamembers which can be 1 or 0, a condition;
#-- if we want to weight the pt-distribution of all probes for the L1Mu3 trigger
#-- category, we must decided with respect which muonID category (Glb, TMLSAT, ...), then
#-- reduce to a subset which the muonID category == 1 and calculate the weight of the
#-- pt-distribution
#-- The category variable can be A:B:C:..., the last one is the only one which we don't
#-- want to reduce (see find category)
condCategory = ''
storeCategory = 'weight'
if category.find(':') != -1:
_catList = category.split(':')
#-- This for is to include the quality cuts and other possible categories
for i in xrange(len(_catList)-1):
condCategory += ' && '+_catList[i]+' == 1 '# BUG------> && '+triggerCat+' == 1'
storeCategory += '_'+_catList[i]
instName = lambda k,pt : PT+'>>h_'+category+name+str(k)+'(50,'+str(pt[0])+','+str(pt[1])+')'
cuts = lambda pt,eta: PT+' >= '+str(pt[0])+' && '+PT+' <'+str(pt[1])+\
' && '+ETA+' >= '+str(eta[0])+' && '+ETA+' < '+str(eta[1])+condCategory
#print cuts #--------------------------> PROVISONAL: PARECE QUE SE RECUPERAN LOS ESPECTROS DE LOS PASSING
#--------------------------> NO DE LOS ALL
k = 0
for i in xrange(len(BINS.__getattribute__(PT))-1):
pt = (BINS.__getattribute__(PT)[i],BINS.__getattribute__(PT)[i+1])
for j in xrange(len(BINS.__getattribute__(ETA))-1):
eta = (BINS.__getattribute__(ETA)[j],BINS.__getattribute__(ETA)[j+1])
for name,_t in _trees.iteritems():
N = _t.Draw( instName(k,pt),cuts(pt,eta) )
histos[name] = ROOT.gDirectory.Get('h_'+category+name+str(k))
print ' \033[1;34mDoing bin'+str(k)+' '+PT+'=('+str(pt[0])+','+str(pt[1])+') '+ETA+'=('+str(eta[0])+','+str(eta[1])+')\033[1;m'
swap = histos['numerator'].Clone(category+'_bin'+str(k))
dummy = swap.Divide(histos['denominator'])
weights[category+'_bin'+str(k)] =( (eta[0],eta[1]), (pt[0],pt[1]), ROOT.gDirectory.Get(category+'_bin'+str(k)) )
#Acura els limits
weights[category+'_bin'+str(k)][2].GetXaxis().SetLimits( pt[0], pt[1] )
#weights[category+'_bin'+str(k)][2].SetNormFactor(1)
k += 1
_out = ROOT.TFile(_outputFile,'RECREATE')
for name,(etaBins,ptBins,histo) in weights.iteritems():
histo.Write()
_out.Close()
return weights
def makeWeights(_files, treeName, category, _outputFile, BINS, PT, ETA):
"""
makeWeights( _fileDict, 'treename', 'category', 'outputfile' ) ->
"""
ROOT.gROOT.SetBatch(1)
#treeName = 'histoMuFromTk/fitter_tree'
_trees = dict([(name, _file.Get(treeName))
for name, _file in _files.iteritems()])
#Check if in both files are the tree
for _tree in _trees.itervalues():
if not _tree:
return None
histos = {}
weights = {}
#-- The ':' token in A:B read as 'B conditioned to A' (look this unregular order)
#-- The categories are datamembers which can be 1 or 0, a condition;
#-- if we want to weight the pt-distribution of all probes for the L1Mu3 trigger
#-- category, we must decided with respect which muonID category (Glb, TMLSAT, ...), then
#-- reduce to a subset which the muonID category == 1 and calculate the weight of the
#-- pt-distribution
#-- The category variable can be A:B:C:..., the last one is the only one which we don't
#-- want to reduce (see find category)
condCategory = ''
storeCategory = 'weight'
if category.find(':') != -1:
_catList = category.split(':')
#-- This for is to include the quality cuts and other possible categories
for i in xrange(len(_catList) - 1):
condCategory += ' && ' + _catList[
i] + ' == 1 ' # BUG------> && '+triggerCat+' == 1'
storeCategory += '_' + _catList[i]
instName = lambda k, pt: PT + '>>h_' + category + name + str(
k) + '(50,' + str(pt[0]) + ',' + str(pt[1]) + ')'
cuts = lambda pt,eta: PT+' >= '+str(pt[0])+' && '+PT+' <'+str(pt[1])+\
' && '+ETA+' >= '+str(eta[0])+' && '+ETA+' < '+str(eta[1])+condCategory
#print cuts #--------------------------> PROVISONAL: PARECE QUE SE RECUPERAN LOS ESPECTROS DE LOS PASSING
#--------------------------> NO DE LOS ALL
k = 0
for i in xrange(len(BINS.__getattribute__(PT)) - 1):
pt = (BINS.__getattribute__(PT)[i], BINS.__getattribute__(PT)[i + 1])
for j in xrange(len(BINS.__getattribute__(ETA)) - 1):
eta = (BINS.__getattribute__(ETA)[j],
BINS.__getattribute__(ETA)[j + 1])
for name, _t in _trees.iteritems():
N = _t.Draw(instName(k, pt), cuts(pt, eta))
histos[name] = ROOT.gDirectory.Get('h_' + category + name +
str(k))
print ' \033[1;34mDoing bin' + str(k) + ' ' + PT + '=(' + str(
pt[0]) + ',' + str(pt[1]) + ') ' + ETA + '=(' + str(
eta[0]) + ',' + str(eta[1]) + ')\033[1;m'
swap = histos['numerator'].Clone(category + '_bin' + str(k))
dummy = swap.Divide(histos['denominator'])
weights[category + '_bin' +
str(k)] = ((eta[0], eta[1]), (pt[0], pt[1]),
ROOT.gDirectory.Get(category + '_bin' + str(k)))
#Acura els limits
weights[category + '_bin' + str(k)][2].GetXaxis().SetLimits(
pt[0], pt[1])
#weights[category+'_bin'+str(k)][2].SetNormFactor(1)
k += 1
_out = ROOT.TFile(_outputFile, 'RECREATE')
for name, (etaBins, ptBins, histo) in weights.iteritems():
histo.Write()
_out.Close()
return weights
