def __init__(self, data_type='bkg', bothNeutral=True): dataset_axis = hist.Cat('dataset', 'dataset') sumpt_axis = hist.Bin('sumpt', '$\sum p_T$ [GeV]', 50, 0, 50) iso_axis = hist.Bin('iso', 'Isolation', np.arange(0, 1, 0.04)) channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'sumpt': hist.Hist('Counts', dataset_axis, sumpt_axis, channel_axis), 'pfiso': hist.Hist('Counts', dataset_axis, iso_axis, channel_axis), 'isodbeta': hist.Hist('Counts', dataset_axis, iso_axis, channel_axis), 'minpfiso': hist.Hist('Counts', dataset_axis, iso_axis, channel_axis), 'maxpfiso': hist.Hist('Counts', dataset_axis, iso_axis, channel_axis), 'lj0pfiso': hist.Hist('Counts', dataset_axis, iso_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z') self.data_type = data_type self.bothNeutral = bothNeutral
def __init__(self, region='SR', data_type='bkg'): self.region = region self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') pt_axis = hist.Bin('pt', '$p_T$ [GeV]', 100, 0, 200) invm_axis = hist.Bin('invm', 'mass [GeV]', 100, 0, 200) mass_axis = hist.Bin('mass', 'mass [GeV]', 100, 0, 200) channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'pt0': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), 'pt1': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), 'ptegm': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), # leading EGM-type for 2mu2e channel 'ptmu': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), # leading mu-type for 2mu2e channel 'invm': hist.Hist('Counts', dataset_axis, invm_axis, channel_axis), 'massmu': hist.Hist('Counts', dataset_axis, mass_axis, channel_axis), # mass of mu-type leptonjet }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, dphi_control=False, data_type='sig'): self.dphi_control = dphi_control self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') self._accumulator = processor.dict_accumulator({ 'all05': processor.column_accumulator(np.zeros(shape=(0, ))), 'nopu05': processor.column_accumulator(np.zeros(shape=(0, ))), 'dbeta': processor.column_accumulator(np.zeros(shape=(0, ))), 'all05w': processor.column_accumulator(np.zeros(shape=(0, ))), 'nopu05w': processor.column_accumulator(np.zeros(shape=(0, ))), 'dbetaw': processor.column_accumulator(np.zeros(shape=(0, ))), 'pt': processor.column_accumulator(np.zeros(shape=(0, ))), 'eta': processor.column_accumulator(np.zeros(shape=(0, ))), 'wgt': processor.column_accumulator(np.zeros(shape=(0, ))), 'ljtype': processor.column_accumulator(np.zeros(shape=(0, ))), 'channel': processor.column_accumulator(np.zeros(shape=(0, ))), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='data'): self.data_type = data_type self._accumulator = processor.dict_accumulator({ 'run_1': processor.column_accumulator(np.zeros(shape=(0, ))), 'lumi_1': processor.column_accumulator(np.zeros(shape=(0, ))), 'event_1': processor.column_accumulator(np.zeros(shape=(0, ))), 'run_2': processor.column_accumulator(np.zeros(shape=(0, ))), 'lumi_2': processor.column_accumulator(np.zeros(shape=(0, ))), 'event_2': processor.column_accumulator(np.zeros(shape=(0, ))), 'era_1': processor.column_accumulator(np.zeros(shape=(0, ))), 'era_2': processor.column_accumulator(np.zeros(shape=(0, ))), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='sig-2mu2e'): self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') count_axis = hist.Bin('cnt', 'Number of Jets', 10, 0, 10) self._accumulator = processor.dict_accumulator({ 'njets': hist.Hist('Counts', dataset_axis, count_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self): dataset_axis = hist.Cat('dataset', 'dataset') dphi_axis = hist.Bin('dphi', '$\Delta\phi$', 50, 0, np.pi) self._accumulator = processor.dict_accumulator({ 'dphi': hist.Hist('Counts', dataset_axis, dphi_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z') self.data_type = 'bkg'
def __init__(self, data_type='bkg'): self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') pt_axis = hist.Bin('pt', '$p_T$ [GeV]', 100, 0, 200) ljmass_axis = hist.Bin('ljmass', 'mass [GeV]', 100, 0, 20) pairmass_axis = hist.Bin('pairmass', 'mass [GeV]', 100, 0, 200) vxy_axis = hist.Bin('vxy', 'vxy [cm]', 100, 0, 20) error_axis = hist.Bin('error', '$\sigma_{lxy}$', 100, 0, 100) sig_axis = hist.Bin('sig', 'lxy/$\sigma_{lxy}$', 50, 0, 50) cos_axis = hist.Bin('cos', r'$cos(\theta)$', 100, -1, 1) qsum_axis = hist.Bin('qsum', '$\sum$q', 2, 0, 2) dphi_axis = hist.Bin('dphi', '$\Delta\phi$', 50, 0, np.pi) channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'lj0pt': hist.Hist('Counts/2GeV', dataset_axis, pt_axis, channel_axis), 'lj1pt': hist.Hist('Counts/2GeV', dataset_axis, pt_axis, channel_axis), 'muljmass': hist.Hist('Counts/0.2GeV', dataset_axis, ljmass_axis, channel_axis), 'muljvxy': hist.Hist('Counts/0.2cm', dataset_axis, vxy_axis, channel_axis), 'muljlxyerr': hist.Hist('Norm. Frequency/1', dataset_axis, error_axis, channel_axis), 'muljlxysig': hist.Hist('Norm. Frequency/1', dataset_axis, sig_axis, channel_axis), 'muljcostheta': hist.Hist('Norm. Frequency/0.02', dataset_axis, cos_axis, channel_axis), 'muljqsum': hist.Hist('Counts', dataset_axis, qsum_axis, channel_axis), 'ljpairmass': hist.Hist('Counts/2GeV', dataset_axis, pairmass_axis, channel_axis), 'ljpairdphi': hist.Hist('Counts/$\pi$/50', dataset_axis, dphi_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg'): self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') iso_axis = hist.Bin('iso', 'min pfIso', 50, 0, 0.5) bin_axis = hist.Bin('val', 'binary value', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'chan-4mu': hist.Hist('Counts', dataset_axis, iso_axis, bin_axis), 'chan-2mu2e': hist.Hist('Counts', dataset_axis, iso_axis, bin_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg', region='SR'): self.data_type = data_type self.region = region dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) count_axis = hist.Bin('cnt', 'count', 3, 0, 3) time_axis = hist.Bin('t', 'timing(ns)', 100, -50, 50) self._accumulator = processor.dict_accumulator({ 'ndsa': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), 'mutiming': hist.Hist('Counts', dataset_axis, time_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='sig-2mu2e', lj_type='neutral'): self.data_type = data_type self.lj_type = lj_type dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) dist_axis = hist.Bin('dist', 'two track min distance [cm]', 50, 0, 200) self._accumulator = processor.dict_accumulator({ 'mindist': hist.Hist('Counts', dataset_axis, dist_axis, channel_axis), 'maxdist': hist.Hist('Counts', dataset_axis, dist_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg', region='SR'): self.data_type = data_type self.region = region dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) count_axis = hist.Bin('cnt', 'event count', 10, 0, 10) dphi_axis = hist.Bin('dphi', '$\Delta\phi$', 20, 0, np.pi) self._accumulator = processor.dict_accumulator({ 'count': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg'): self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') dphi_axis = hist.Bin('dphi', '$\Delta\phi$', 8, 0, np.pi) categ_axis = hist.Bin('categ', 'categ', 4, 1, 5) self._accumulator = processor.dict_accumulator({ 'chan-4mu': hist.Hist('Counts', dataset_axis, dphi_axis, categ_axis), 'chan-2mu2e': hist.Hist('Counts', dataset_axis, dphi_axis, categ_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg', region='SR', enforceNeutral=True): self.data_type = data_type self.region = region self.enforceNeutral = enforceNeutral dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) count_axis = hist.Bin('cnt', 'event count', 5, 0, 5) self._accumulator = processor.dict_accumulator({ 'count': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg'): dataset_axis = hist.Cat('dataset', 'dataset') dphi_axis = hist.Bin('dphi', '$\Delta\phi$', 20, 0, np.pi) channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'dphi-neu': hist.Hist('Counts', dataset_axis, dphi_axis, channel_axis), 'dphi-cha': hist.Hist('Counts', dataset_axis, dphi_axis, channel_axis), 'dphi-0mucha': hist.Hist('Counts', dataset_axis, dphi_axis, channel_axis), 'dphi-1mucha': hist.Hist('Counts', dataset_axis, dphi_axis, channel_axis), 'dphi-01mucha': hist.Hist('Counts', dataset_axis, dphi_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z') self.data_type = data_type
def __init__(self, region='SR', data_type='sig-2mu2e'): self.region = region self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) bool_axis = hist.Bin('boolean', 'true/false', 2, 0, 2) vxy_axis = hist.Bin('vxy', 'vxy [cm]', 100, 0, 20) self._accumulator = processor.dict_accumulator({ 'vertexgood': hist.Hist('Frequency', dataset_axis, channel_axis, bool_axis), 'vxy': hist.Hist('Counts', dataset_axis, channel_axis, vxy_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, dphi_control=False, data_type='bkg'): self.dphi_control = dphi_control self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') pt_axis = hist.Bin('pt', '$p_T$ [GeV]', 60, 0, 300) njet_axis = hist.Bin('njet', 'multiplicity', 10, 0, 10) channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'pt': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis, njet_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg', region='SR'): self.data_type = data_type self.region = region dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) count_axis = hist.Bin('cnt', 'count', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'nloose': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), 'nmedium': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), 'ntight': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), 'nisoloose': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), 'nisomedium': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), 'nisotight': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, dphi_control=False, data_type='bkg'): self.dphi_control = dphi_control self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') lj0iso_axis = hist.Bin('lj0iso', 'iso value', 20, 0, 1) lj1iso_axis = hist.Bin('lj1iso', 'iso value', 20, 0, 1) channel_axis = hist.Cat('channel', 'channel') type_axis = hist.Cat('isotype', 'isotype') njet_axis = hist.Bin('njet', 'njet', 10, 0, 10) self._accumulator = processor.dict_accumulator({ 'ljpfiso': hist.Hist('Counts', dataset_axis, lj0iso_axis, lj1iso_axis, type_axis, channel_axis, njet_axis) }) ## NOT applied for now self.pucorrs = get_pu_weights_function() self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type, region='SR'): self.data_type = data_type self.region = region dataset_axis = hist.Cat('dataset', 'dataset') channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) pt_axis = hist.Bin('pt', '$p_T$ [GeV]', 60, 0, 300) fraction_axis = hist.Bin('frac', 'fraction', 50, 0, 1) count_axis = hist.Bin('cnt', 'multiplicity', 5, 0, 5) self._accumulator = processor.dict_accumulator({ 'pt': hist.Hist('Counts', dataset_axis, pt_axis, channel_axis), 'hadfrac': hist.Hist('Counts', dataset_axis, fraction_axis, channel_axis), 'njet': hist.Hist('Counts', dataset_axis, count_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')
def __init__(self, data_type='bkg'): self.data_type = data_type dataset_axis = hist.Cat('dataset', 'dataset') pt_axis = hist.Bin('pt', '$p_T$ [GeV]', 100, 0, 200) ljmass_axis = hist.Bin('ljmass', 'mass [GeV]', 100, 0, 20) pairmass_axis = hist.Bin('pairmass', 'mass [GeV]', 100, 0, 200) vxy_axis = hist.Bin('vxy', 'vxy [cm]', 100, 0, 20) qsum_axis = hist.Bin('qsum', '$\sum$q', 2, 0, 2) dphi_axis = hist.Bin('dphi', '$\Delta\phi$', 50, 0, np.pi) channel_axis = hist.Bin('channel', 'channel', 3, 0, 3) self._accumulator = processor.dict_accumulator({ 'lj0pt': hist.Hist('Counts/2GeV', dataset_axis, pt_axis, channel_axis), 'lj1pt': hist.Hist('Counts/2GeV', dataset_axis, pt_axis, channel_axis), 'muljmass': hist.Hist('Counts/0.2GeV', dataset_axis, ljmass_axis, channel_axis), 'muljvxy': hist.Hist('Counts/0.2cm', dataset_axis, vxy_axis, channel_axis), 'muljqsum': hist.Hist('Counts', dataset_axis, qsum_axis, channel_axis), 'ljpairmass': hist.Hist('Counts/2GeV', dataset_axis, pairmass_axis, channel_axis), 'ljpairdphi': hist.Hist('Counts/$\pi$/50', dataset_axis, dphi_axis, channel_axis), }) self.pucorrs = get_pu_weights_function() ## NOT applied for now self.nlo_w = get_nlo_weight_function('w') self.nlo_z = get_nlo_weight_function('z')