from PandaCore.Utils.root import root
from PandaCore.Tools.Misc import *
from PandaCore.Utils.load import *
import PandaAnalysis.T3.job_utilities as utils
from PandaAnalysis.Flat.analysis import *
Load('PandaAnalyzer')
data_dir = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
def fn(input_name, isData, full_path):
a = wlnhbb(True)
a.inpath = input_name
a.outpath = utils.input_to_output(input_name)
a.datapath = data_dir
a.isData = isData
utils.set_year(a, 2017)
a.processType = utils.classify_sample(full_path, isData)
if a.processType in {root.pa.kTT, root.pa.kH}:
a.reclusterGen = True # only turn on if necessary
skimmer = root.pa.PandaAnalyzer(a)
skimmer.AddPresel(root.pa.VHbbSel())
skimmer.AddPresel(root.pa.TriggerSel())
return utils.run_PandaAnalyzer(skimmer, isData, a.outpath)
if __name__ == "__main__":
utils.wrapper(fn)
from PandaCore.Tools.Misc import *
from PandaCore.Utils.load import *
import PandaAnalysis.T3.job_utilities as utils
from PandaAnalysis.Flat.analysis import *
Load('PandaAnalyzer')
data_dir = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
def fn(input_name, isData, full_path):
# now we instantiate and configure the analyzer
a = monotop(True)
a.recalcECF = True
a.varyJESTotal = True
a.mcTriggers = 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)
skimmer.AddPresel(root.pa.FatJetSel())
return utils.run_PandaAnalyzer(skimmer, isData, a.outpath)
if __name__ == "__main__":
utils.wrapper(fn, post_fn=utils.BDTAdder())
return utils.run_PandaAnalyzer(skimmer, isData, a.outpath)
def post_fn():
sleep(60)
do('du -hs output.root 1>&2')
f = root.TFile.Open('output.root')
t = f.Get('events')
logger.debug('post_fn', 'Tree has %i entries' % (t.GetEntries()))
logger.info('post_fn', 'Creating numpy arrays')
arr = read_files(['output.root'], branches=None)
adj = arr['adj']
x = np.stack([
arr['node' + f]
for f in ['Pt', 'Eta', 'Phi', 'E', 'IsFinal', 'IsRoot']
],
axis=-1)
jets = np.stack([
arr['jet' + f]
for f in ['Tau32', 'Tau21', 'MSD', 'Pt', 'Eta', 'Phi', 'M', 'PdgId']
],
axis=-1)
logger.info('post_fn', 'Saving numpy arrays')
np.savez_compressed('output.npz', adj=adj, x=x, jets=jets)
do('mv -v output.npz output.root')
if __name__ == "__main__":
utils.wrapper(fn, post_fn=post_fn)
from PandaCore.Utils.root import root
from PandaCore.Tools.Misc import *
from PandaCore.Utils.load import *
import PandaAnalysis.T3.job_utilities as utils
from PandaAnalysis.Flat.analysis import *
Load('PandaAnalyzer')
data_dir = getenv('CMSSW_BASE') + '/src/PandaAnalysis/data/'
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)
if __name__ == "__main__":
utils.wrapper(
fn,
post_fn=lambda: utils.drop_branches(to_keep=[
'jotPt*', 'fjPt*', 'mcWeight', 'jotEta', 'npv', 'jotRawPt'
]))