# Test and Train event cuts #TrainCut = '%s & EventForTraining==1' % TCut #EvalCut = '%s & EventForTraining==0' % TCut TrainCut= TCut +' & evt%2==0' EvalCut = TCut +' & evt%2!=0' cuts = [TrainCut,EvalCut] print '\n ------> with Train Cuts: ', TrainCut print ' Test Cuts : ', 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 = []
newtree.Fill()
print "Exit loop"
newtree.AutoSave()
print "Save"
output.Close()
print "Close"
targetStorage = (
pathOUT.replace("gsidcap://t3se01.psi.ch:22128/", "srm://t3se01.psi.ch:8443/srm/managerv2?SFN=")
+ "/"
+ job.prefix
+ job.identifier
+ ".root"
)
if TreeCache.get_slc_version() == "111SLC5": # NOT WORKING !!! ALWAYS USE SRM
command = "lcg-del -b -D srmv2 -l %s" % (targetStorage)
print (command)
subprocess.call([command], shell=True)
command = "lcg-cp -b -D srmv2 file:///%s %s" % (
tmpDir + "/" + job.prefix + job.identifier + ".root",
targetStorage,
)
print (command)
subprocess.call([command], shell=True)
else:
command = "srmrm %s" % (targetStorage)
print (command)
subprocess.call([command], shell=True)
command = "srmcp -2 -globus_tcp_port_range 20000,25000 file:///%s %s" % (
tmpDir + "/" + job.prefix + job.identifier + ".root",
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 = []
