def fn(input_name, isData, full_path):
a = analysis('graph', ak=True, ak8=True, puppiJets=True)
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2016)
skimmer = root.pa.CTAnalyzer(a)
return utils.run_Analyzer(skimmer, isData, a.outpath)
def fn(input_name, isData, full_path):
a = analysis('graph', ak=True, ak8=True, puppiJets=True)
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2016)
a.processType = utils.classify_sample(full_path, isData)
skimmer = root.pa.JGAnalyzer(a)
return utils.run_PandaAnalyzer(skimmer, isData, a.outpath)
def fn(input_name, isData, full_path):
a = analysis('jes', varyJES=True, rerunJES=True)
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2016)
a.processType = utils.classify_sample(full_path, isData)
skimmer = root.pa.PandaAnalyzer(a)
return utils.run_PandaAnalyzer(skimmer, isData, a.outpath)
def fn(input_name, isData, full_path):
logger.info(sname + '.fn', 'Starting to process ' + input_name)
# now we instantiate and configure the analyzer
a = analysis("l1", verbose=False)
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2016)
skimmer = root.pa.L1Analyzer(a)
return utils.run_Analyzer(skimmer, isData, a.outpath)
def fn(input_name, isData, full_path):
a = analysis('substructure',
recalcECF=False,
reclusterFJ=True,
ak=False)
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2016)
a.processType = utils.classify_sample(full_path, isData)
skimmer = root.pa.HRAnalyzer(a)
return utils.run_HRAnalyzer(skimmer, isData, a.outpath)
def fn(input_name, isData, full_path):
logger.info(sname + '.fn', 'Starting to process ' + input_name)
# now we instantiate and configure the analyzer
a = analysis('substructure')
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2016)
a.processType = utils.classify_sample(full_path, isData)
skimmer = root.pa.HRAnalyzer(a)
return utils.run_HRAnalyzer(skimmer, isData, a.outpath)
output = 'testskim.root'
if len(argv) > 2:
debug_level = int(argv[2])
if len(argv) > 3:
output = argv[3]
argv = []
import ROOT as root
from PandaCore.Utils.load import *
from PandaAnalysis.Flat.analysis import *
import PandaAnalysis.T3.job_utilities as utils
Load('PandaAnalysisFlat')
a = analysis('jetgraph', ak=True, ak8=True, puppiJets=True)
a.processType = root.pa.kTop
a.inpath = torun
a.outpath = 'testskim.root'
a.datapath = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
a.isData = False
utils.set_year(a, 2016)
#utils.set_year(a, 2017)
skimmer = root.pa.JGAnalyzer(a, debug_level)
#skimmer.firstEvent=0
skimmer.lastEvent = 100
if a.isData:
debug_level = 0
torun = argv[1]
output = 'testl1.root'
if len(argv) > 2:
debug_level = int(argv[2])
if len(argv) > 3:
output = argv[3]
argv = []
import ROOT as root
from PandaCore.Utils.load import *
from PandaAnalysis.Flat.analysis import *
import PandaAnalysis.T3.job_utilities as utils
Load('PandaAnalysisFlat')
a = analysis("l1")
a.inpath = torun
a.outpath = output
a.datapath = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
skimmer = root.pa.L1Analyzer(a, debug_level)
#skimmer.firstEvent=0
#skimmer.lastEvent=10
skimmer.Run()
skimmer.Terminate()
output = 'testskim.root'
if len(argv) > 2:
debug_level = int(argv[2])
if len(argv) > 3:
output = argv[3]
argv = []
import ROOT as root
from PandaCore.Utils.load import *
from PandaAnalysis.Flat.analysis import *
import PandaAnalysis.T3.job_utilities as utils
Load('PandaAnalysisFlat')
a = analysis('ct', ak=True, ak8=True, puppiJets=True)
a.processType = root.pa.kTop
a.inpath = torun
a.outpath = 'testskim.root'
a.datapath = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
a.isData = False
utils.set_year(a, 2016)
#utils.set_year(a, 2017)
skimmer = root.pa.CTAnalyzer(a, debug_level)
#skimmer.firstEvent=0
skimmer.lastEvent = 100
self.ba.presel = 'clf_ECFN_2_4_20>0'
for v in tagcfg.variables:
self.ba.AddVariable(v[0],v[2].replace('fj1','clf_'))
for v in tagcfg.formulae:
self.ba.AddFormula(v[0],v[2].replace('fj1','clf_'))
for s in tagcfg.spectators:
self.ba.AddSpectator(s[0])
self.ba.BookMVA('clf_top_ecf_bdt',data_dir+'/trainings/top_ecfbdt_v8_BDT.weights.xml')
def __call__(self, fname='output.root', tname='events'):
# now run the BDT
self.ba.treename = tname
self.ba.RunFile(fname)
add_bdt = BDTAdder() #backwards compatability
a = analysis("substructure")
a.inpath = torun
a.outpath = 'testhr.root'
a.datapath = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
a.processType = root.pa.kTop
a.isData = False
utils.set_year(a, 2016)
skimmer = root.pa.HRAnalyzer(a, debug_level)
# skimmer.firstEvent=0
skimmer.lastEvent=10
skimmer.Run()
skimmer.Terminate()
