sample_sums[data.title] += data
samples = []
for sample in datacard.processes:
histo = shape_dir.Get(sample)
if sample in signals:
histo.Scale(sig_yields[cat_name][sample])
if sample not in sample_sums:
sample_sums[sample] = histo.Clone()
else:
sample_sums[sample] += histo
samples.append(histo)
samples.sort(key=ordering)
stack = plotter.create_stack(*samples, sort=False)
legend = LegendDefinition(position='NE')
plotter.overlay_and_compare(
[stack], data,
writeTo=cat_name,
legend_def = legend,
xtitle='discriminant',
ytitle='Events',
method='ratio'
)
samples = [j for i, j in sample_sums.iteritems() if i <> 'data_obs'
if i <> 'postfit S+B']
data = sample_sums['data_obs']
samples.sort(key=ordering)
plotter.overlay_and_compare(
sample_sums[data.title] = data.Clone()
sample_sums['postfit S+B'] = hsum.Clone()
else:
sample_sums[data.title] += data
sample_sums['postfit S+B'] += hsum
sample_names = [i.name for i in cat_dir.keys() if not i.name.startswith('total')]
samples = [fix_binning(cat_dir.Get(i), data) for i in sample_names]
for i, j in zip(sample_names, samples):
if i not in sample_sums:
sample_sums[i] = j.Clone()
else:
sample_sums[i] += j
samples.sort(key=ordering)
stack = plotter.create_stack(*samples, sort=False)
legend = LegendDefinition(position='NE')
plotter.overlay_and_compare(
[stack, hsum], data,
writeTo=cat_name,
legend_def = legend,
xtitle='discriminant',
ytitle='Events',
method='ratio'
)
samples = [j for i, j in sample_sums.iteritems() if i <> 'data_obs'
if i <> 'postfit S+B']
samples.sort(key=ordering)
data = sample_sums['data_obs']
hsum = sample_sums['postfit S+B']