print 'after the selections, backgrounds are', backgrounds
print 'after the selections, signals are', signals
samples = []
samples = info.get_samples(signals + backgrounds)
print "XXXXXXXXXXXXXXXX"
print 'filelist is', filelist
#tc = TreeCache(cuts,samples,path,config, [])
#to be compatible with mergecaching
tc = TreeCache(
cuts,
samples,
path,
config,
filelist=filelist,
mergeplot=opts.mergeplot,
sample_to_merge=sample_to_cache_,
mergeCachingPart=mergeCachingPart,
plotMergeCached=opts.mergecachingplot,
remove_sys=remove_sys_
) # created cached tree i.e. create new skimmed trees using the list of cuts
#for mergesubcaching step, need to continue even if some root files are missing to perform the caching in parallel
if sample_to_cache_ or mergeCachingPart:
tc = TreeCache(cuts,
samples,
path,
config,
filelist=filelist,
mergeplot=opts.mergeplot,
sample_to_merge=None,
MVA_Vars['Nominal'] = MVA_Vars['Nominal'].split(' ')
#Infofile
info = ParseInfo(samplesinfo, path)
#Workdir
workdir = ROOT.gDirectory.GetPath()
TrainCut = '%s & EventForTraining==1' % TCut
EvalCut = '%s & EventForTraining==0' % TCut
cuts = [TrainCut, EvalCut]
samples = []
samples = info.get_samples(signals + backgrounds)
tc = TreeCache(cuts, samples, path, config)
output = ROOT.TFile.Open(fnameOutput, "RECREATE")
print '\n\t>>> READING EVENTS <<<\n'
signal_samples = info.get_samples(signals)
background_samples = info.get_samples(backgrounds)
#TRAIN trees
Tbackgrounds = []
TbScales = []
Tsignals = []
TsScales = []
#EVAL trees
Ebackgrounds = []
