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()