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')
