print("Total events in output histogram N_ele: %.2f" %
output['N_ele'].sum('dataset').sum('multiplicity').values(
overflow='all')[()])
my_hists = {}
#my_hists['N_ele'] = scale_and_merge(output['N_ele'], samples, fileset, nano_mapping)
my_hists['N_ele'] = scale_and_merge(output['N_ele'], meta, fileset,
nano_mapping)
print("Total scaled events in merged histogram N_ele: %.2f" %
my_hists['N_ele'].sum('dataset').sum('multiplicity').values(
overflow='all')[()])
# Now make a nice plot of the electron multiplicity.
# You can have a look at all the "magic" (and hard coded monstrosities) that happens in makePlot
# in plots/helpers.py
makePlot(
my_hists,
'N_ele',
'multiplicity',
data=[],
bins=N_bins_red,
log=True,
normalize=False,
axis_label=r'$N_{electron}$',
new_colors=my_colors,
new_labels=my_labels,
#order=[nano_mapping['DY'][0], nano_mapping['TTZ'][0]],
save=os.path.expandvars(cfg['meta']['plots']) +
'/nano_analysis/N_ele_test.png')
my_colors = {
'topW_v3': '#FF595E',
'topW_EFT_cp8': '#000000',
'topW_EFT_mix': '#0F7173',
'TTZ': '#FFCA3A',
'TTW': '#8AC926',
'TTH': '#34623F',
'diboson': '#525B76',
'ttbar': '#1982C4',
}
makePlot(output, 'node', 'multiplicity',
data=['DoubleMuon', 'MuonEG', 'EGamma'],
bins=N_bins_red, log=False, normalize=False, axis_label=r'node',
new_colors=my_colors, new_labels=my_labels,
order=['diboson', 'TTW', 'TTH', 'TTZ', 'ttbar'],
signals=['topW_v3', 'topW_EFT_cp8', 'topW_EFT_mix'],
save=os.path.expandvars('$TWHOME/dump/ML_node'),
)
makePlot(output, 'node0_score', 'score',
data=[],
bins=score_bins, log=False, normalize=False, axis_label=r'score',
new_colors=my_colors, new_labels=my_labels,
order=['diboson', 'TTW', 'TTH', 'TTZ', 'ttbar'],
signals=['topW_v3', 'topW_EFT_cp8', 'topW_EFT_mix'],
omit=['DoubleMuon', 'MuonEG', 'EGamma'],
save=os.path.expandvars('$TWHOME/dump/ML_node0_score'),
)
'TTW': '#8AC926',
'TTH': '#34623F',
'diboson': '#525B76',
'ttbar': '#1982C4',
'DY': '#6A4C93',
'WW': '#34623F',
'WZ': '#525B76',
}
makePlot(
output,
'lead_lep',
'pt',
data=['DoubleMuon', 'MuonEG', 'EGamma'],
bins=pt_bins,
log=True,
normalize=True,
axis_label=r'$p_{T}\ lead \ lep\ (GeV)$',
new_colors=my_colors,
new_labels=my_labels,
order=['topW_v3', 'diboson', 'TTW', 'TTXnoW', 'DY', 'ttbar'],
save=os.path.expandvars(plot_dir + '/OS_fwd_v1/lead_lep_pt'),
)
makePlot(
output,
'lead_lep',
'eta',
data=['DoubleMuon', 'MuonEG', 'EGamma'],
bins=eta_bins,
log=True,
normalize=True,
'rare': 'rare',
}
my_colors = {
'topW_v2': '#FF595E',
'TTZ': '#FFCA3A',
'TTW': '#8AC926',
'TTH': '#34623F',
'rare': '#525B76',
'ttbar': '#1982C4',
}
makePlot(output, 'score_topW_pos', 'score',
log=False, normalize=False, axis_label=r'$top-W\ score$',
new_colors=my_colors, new_labels=my_labels,
save=plot_dir+'/score_topW_pos',
order=['rare', 'TTH', 'ttbar', 'TTZ', 'TTW', 'topW_v2'],
lumi=137,
)
makePlot(output, 'score_topW_pos', 'score',
log=False, normalize=False, shape=True, axis_label=r'$top-W\ score$',
new_colors=my_colors, new_labels=my_labels,
order=['rare', 'TTH', 'TTZ', 'TTW', 'topW_v2'],
omit=['ttbar', 'rare'],
save=plot_dir+'/score_topW_pos_shape', ymax=0.5,
)
makePlot(output, 'score_topW_neg', 'score',
log=False, normalize=False, axis_label=r'$top-W\ score$',
new_colors=my_colors, new_labels=my_labels,
'cf_est_data', 'topW_v3'
]
signals = []
omit = [
x for x in all_processes
if (x not in signals and x not in order and x not in data)
]
makePlot(
output,
'MET',
'pt',
data=data,
bins=pt_bins_coarse,
log=False,
normalize=True,
axis_label=r'$p_{T}^{miss}$',
new_colors=my_colors,
new_labels=my_labels,
order=order,
signals=signals,
omit=omit,
save=os.path.expandvars(plot_dir + sub_dir + 'MET_pt'),
)
makePlot(
output,
'fwd_jet',
'pt',
data=data,
bins=pt_bins_coarse,
log=False,
'TTZ': '#FFCA3A',
'TTXnoW': '#FFCA3A',
'TTW': '#8AC926',
'TTH': '#34623F',
'diboson': '#525B76',
'ttbar': '#1982C4',
'DY': '#6A4C93',
'WW': '#34623F',
'WZ': '#525B76',
}
TFnormalize = True
version_dir = '/2018_Nb_weights/'
makePlot(
output,
'N_b',
'multiplicity',
data=['DoubleMuon', 'MuonEG', 'EGamma'],
bins=N_bins_red,
log=False,
normalize=TFnormalize,
axis_label=r'$N_{b-tag}$',
new_colors=my_colors,
new_labels=my_labels,
order=['topW_v3', 'diboson', 'TTW', 'TTXnoW', 'DY', 'ttbar'],
upHists=['centralUp', 'upCentral'],
downHists=['centralDown', 'downCentral'],
shape=False,
save=os.path.expandvars(plot_dir + version_dir + 'N_b'),
)
'rare': 'rare',
}
my_colors = {
'topW_v2': '#FF595E',
'TTZ': '#FFCA3A',
'TTW': '#8AC926',
'TTH': '#34623F',
'rare': '#525B76',
'ttbar': '#1982C4',
}
makePlot(
output,
'sm_opt',
'e',
log=False,
normalize=False,
axis_label=r'$top-W\ score$',
new_colors=my_colors,
new_labels=my_labels,
save=plot_dir + '/' + variable,
order=['rare', 'TTH', 'ttbar', 'TTZ', 'TTW'],
signals=['topW_v2'],
lumi=137,
binwnorm=True,
ymax=30,
)
print(results)