def customInit(self, initVars):
self.n_excluded = 0
self.n_kept = 0
self.n_skipped = 0
self.sample = initVars['sample']
self.config = initVars['config']
if self.sample.identifier in self.applyToSamples:
self.excludedEvents = {}
excludedSampleTree = SampleTree([self.excludeTreeFileName],
config=self.config)
excludedSampleTree.enableBranches(['run', 'event'])
print "INFO: loading list of events to filter"
n_events = 0
for ev in excludedSampleTree:
if ev.run not in self.excludedEvents:
self.excludedEvents[ev.run] = {}
if ev.event not in self.excludedEvents[ev.run]:
self.excludedEvents[ev.run][ev.event] = 0
self.excludedEvents[ev.run][ev.event] += 1
if self.excludedEvents[ev.run][ev.event] == 1:
n_events += 1
intrinsicDuplicates = sum(
[[[event, run, count]
for event, count in self.excludedEvents[run].items()
if count > 1] for run in self.excludedEvents.keys()], [])
print "INFO: done => ", n_events, "distinct events will be filtered out of", self.applyToSamples
if len(intrinsicDuplicates) > 0:
print "INFO: the event list provided contains", len(
intrinsicDuplicates), "duplicates itself!"
else:
print "INFO: event number filter disdable for this sample"
def customInit(self, initVars):
self.n_excluded = 0
self.n_kept = 0
self.n_skipped = 0
self.sample = initVars['sample']
self.config = initVars['config']
if self.sample.identifier in self.applyToSamples:
self.excludedEvents = {}
excludedSampleTree = SampleTree([self.excludeTreeFileName], config=self.config)
excludedSampleTree.enableBranches(['run','event'])
print "INFO: loading list of events to filter"
n_events = 0
for ev in excludedSampleTree:
if ev.run not in self.excludedEvents:
self.excludedEvents[ev.run] = {}
if ev.event not in self.excludedEvents[ev.run]:
self.excludedEvents[ev.run][ev.event] = 0
self.excludedEvents[ev.run][ev.event] += 1
if self.excludedEvents[ev.run][ev.event]==1:
n_events += 1
intrinsicDuplicates = sum([[[event,run,count] for event,count in self.excludedEvents[run].items() if count > 1] for run in self.excludedEvents.keys()], [])
print "INFO: done => ", n_events, "distinct events will be filtered out of", self.applyToSamples
if len(intrinsicDuplicates) > 0:
print "INFO: the event list provided contains",len(intrinsicDuplicates),"duplicates itself!"
else:
print "INFO: event number filter disdable for this sample"
dataSamples = eval(config.get('dc:' + region, 'data'))
for dataSample in dataSamples:
sampleTree = SampleTree({
'name': dataSample,
'folder': inputFolder
},
config=config)
outputFileName = logFolder + '/' + region + '_' + dataSample + '_' + opts.run + '_' + opts.event + '.txt'
print("save event list to:", outputFileName)
treePlayer = sampleTree.tree.GetPlayer()
treePlayer.SetScanRedirect(True)
treePlayer.SetScanFileName(outputFileName)
branchList = BranchList(["run", "event"])
regionCut = config.get(
'Cuts',
config.get('dc:' + region, 'cut') if config.has_option(
'dc:' + region, 'cut') else region)
branchList.addCut(regionCut)
expressions = ":".join(branchList.getListOfBranches()) if len(
opts.expressions) < 1 else opts.expressions
branchList.addCut(expressions)
sampleTree.enableBranches(branchList.getListOfBranches())
sampleTree.tree.Scan(expressions,
"run==" + opts.run + "&&event==" + opts.event,
"colsize=16")
sampleTree = SampleTree({
'sample': sample,
'folder': directory
},
config=config)
#raw_input()
# since we load all trees, we can compute the factor to scale cross section to luminosity directly (otherwise write it to ntuples
# first and then use it as branch, or compute it with full set of trees before)
scaleXStoLumi = sampleTree.getScale(sample)
# enable only used branches!
# this will speed up processing a lot
sampleTree.enableBranches(
BranchList([
signalRegionSelection, weightExpression_DeepCSV,
weightExpression_DeepJet, taggerExpression_DeepCSV,
taggerExpression_DeepJet
]).getListOfBranches() + ['Jet*'])
# this will create the TTreeformula objects
sampleTree.addFormula(signalRegionSelection)
sampleTree.addFormula(signalRegionSelection_roc)
sampleTree.addFormula(weightExpression_DeepCSV)
sampleTree.addFormula(weightExpression_DeepJet)
sampleTree.addFormula(weightExpression_DeepCSV_nosf)
sampleTree.addFormula(weightExpression_DeepJet_nosf)
sampleTree.addFormula(taggerExpression_DeepCSV)
sampleTree.addFormula(taggerExpression_DeepJet)
isSignal = 1 if sample.name in signalNames else 0
from __future__ import print_function
import ROOT
ROOT.gROOT.SetBatch(True)
from myutils.XbbConfig import XbbConfigReader, XbbConfigTools
from myutils.sampleTree import SampleTree as SampleTree
from myutils.BranchList import BranchList
config = XbbConfigTools(XbbConfigReader.read("Zvv2017"))
sampleTree = SampleTree(
{
'name': 'MET',
'folder': config.get('Directories', 'dcSamples')
},
config=config)
variables = ["H_pt", "MET_Pt", "H_pt/MET_Pt"]
# enable only explicitly used branches
sampleTree.enableBranches(BranchList(variables).getListOfBranches())
# create TTReeFormula's
for variable in variables:
sampleTree.addFormula(variable)
# loop over events
for event in sampleTree:
print(
sampleTree.tree.GetReadEntry(),
", ".join([x + "=%1.4f" % sampleTree.evaluate(x) for x in variables]))
if sampleTree.tree.GetReadEntry() > 98:
break