def create_histogram(key, title):
if is2D:
histograms[key] = ROOT.TH2D(key, title,
len(bins_x) - 1, bins_x,
len(bins_y) - 1, bins_y)
for bin_idx in range(len(bins_x) - 1):
histograms[key].GetXaxis().SetBinLabel(
bin_idx + 1, '%d <= %s < %d' %
(bins_x[bin_idx], x_var, bins_x[bin_idx + 1]))
for bin_idx in range(len(bins_y) - 1):
histograms[key].GetYaxis().SetBinLabel(
bin_idx + 1, '%d <= %s < %d' %
(bins_y[bin_idx], y_var, bins_y[bin_idx + 1]))
histograms[key].SetXTitle(x_var)
histograms[key].SetYTitle(y_var)
else:
histograms[key] = ROOT.TH1D(key, title, len(bins_x) - 1, bins_x)
for bin_idx in range(len(bins_x) - 1):
histograms[key].GetXaxis().SetBinLabel(
bin_idx + 1, '%d <= %s < %d' %
(bins_x[bin_idx], x_var, bins_x[bin_idx + 1]))
histograms[key].SetXTitle(x_var)
def comp_weights_2_wo_inclusive(f, samples, samples_to_stitch, split_var_1,
split_var_2):
inclusive_samples = samples_to_stitch['inclusive']['samples']
inclusive_binning_1 = samples_to_stitch['inclusive'][split_var_1]
inclusive_binning_2 = samples_to_stitch['inclusive'][split_var_2]
split_dict_1 = samples_to_stitch['exclusive'][split_var_1]
split_dict_2 = samples_to_stitch['exclusive'][split_var_2]
split_binning_1 = [sample['value'] for sample in split_dict_1]
split_binning_2 = [sample['value'] for sample in split_dict_2]
complete_binning_1 = list(
sorted(
list(
map(
float,
set(inclusive_binning_1)
| set(list(
itertools.chain.from_iterable(split_binning_1)))))))
complete_binning_2 = list(
sorted(
list(
map(
float,
set(inclusive_binning_2)
| set(list(
itertools.chain.from_iterable(split_binning_2)))))))
inclusive_xs = -1
for sample_key, sample_entry in samples.items():
if sample_key == 'sum_events': continue
if sample_entry['process_name_specific'] == inclusive_samples[0]:
inclusive_xs = sample_entry['xsection']
assert (inclusive_xs > 0)
# sum the inclusive nof events
inclusive_nof_events = {}
for sample_key, sample_entry in samples.items():
if sample_key == 'sum_events': continue
if sample_entry['process_name_specific'] in inclusive_samples:
if not inclusive_nof_events:
inclusive_nof_events = copy_nof_events(sample_entry)
else:
nof_events_keys = set(nof_key
for nof_key in sample_entry['nof_events']
if is_valid_event_type(nof_key))
assert (nof_events_keys == set(inclusive_nof_events))
for nof_key, nof_arr in sample_entry['nof_events'].items():
if not is_valid_event_type(nof_key):
continue
assert (len(nof_arr) == len(inclusive_nof_events[nof_key]))
for idx, nof in enumerate(nof_arr):
assert (nof > 0)
inclusive_nof_events[nof_key][idx] += nof
# sum the binned nof events
for binned_sample in split_dict_1:
nof_events = {}
xs = -1
for sample_key, sample_entry in samples.items():
if sample_key == 'sum_events': continue
if sample_entry['process_name_specific'] in binned_sample[
'samples']:
if not nof_events:
nof_events = copy_nof_events(sample_entry)
inclusive_nof_events_type = set(
event_type
for event_type in inclusive_nof_events.keys()
if is_valid_event_type(event_type))
nof_events_type = set(event_type
for event_type in nof_events.keys()
if is_valid_event_type(event_type))
assert (inclusive_nof_events_type == nof_events_type)
else:
nof_events_keys = set(
nof_key for nof_key in sample_entry['nof_events']
if is_valid_event_type(nof_key))
assert (nof_events_keys == set(nof_events.keys()))
for nof_key, nof_arr in sample_entry['nof_events'].items():
if not is_valid_event_type(nof_key):
continue
assert (len(nof_arr) == len(nof_events[nof_key]))
for idx, nof in enumerate(nof_arr):
assert (nof > 0)
nof_events[nof_key][idx] += nof
if xs < 0:
xs = sample_entry['xsection']
assert (xs > 0)
binned_sample['xsection'] = xs
binned_sample['nof_events'] = nof_events
lumis = {}
for nof_key, nof_arr in binned_sample['nof_events'].items():
lumis[nof_key] = list(
map(lambda nof: nof / binned_sample['xsection'], nof_arr))
binned_sample['lumis'] = lumis
for binned_sample in split_dict_2:
nof_events = {}
xs = -1
for sample_key, sample_entry in samples.items():
if sample_key == 'sum_events': continue
if sample_entry['process_name_specific'] in binned_sample[
'samples']:
if not nof_events:
nof_events = copy_nof_events(sample_entry)
inclusive_nof_events_type = set(
event_type
for event_type in inclusive_nof_events.keys()
if is_valid_event_type(event_type))
nof_events_type = set(event_type
for event_type in nof_events.keys()
if is_valid_event_type(event_type))
assert (inclusive_nof_events_type == nof_events_type)
else:
nof_events_keys = set(
nof_key for nof_key in sample_entry['nof_events']
if is_valid_event_type(nof_key))
assert (nof_events_keys == set(nof_events.keys()))
for nof_key, nof_arr in sample_entry['nof_events'].items():
if not is_valid_event_type(nof_key):
continue
assert (len(nof_arr) == len(nof_events[nof_key]))
for idx, nof in enumerate(nof_arr):
assert (nof > 0)
nof_events[nof_key][idx] += nof
if xs < 0:
xs = sample_entry['xsection']
assert (xs > 0)
binned_sample['xsection'] = xs
binned_sample['nof_events'] = nof_events
lumis = {}
for nof_key, nof_arr in binned_sample['nof_events'].items():
lumis[nof_key] = list(
map(lambda nof: nof / binned_sample['xsection'], nof_arr))
binned_sample['lumis'] = lumis
# compute integrated luminosities for the inclusive sample
inclusive_lumis = {}
for nof_key, nof_arr in inclusive_nof_events.items():
inclusive_lumis[nof_key] = list(
map(lambda nof: nof / inclusive_xs, nof_arr))
# decide on the bin indices
idxs_split_sample_1 = []
idxs_split_sample_2 = []
for binned_sample in split_dict_1:
binned_idx = complete_binning_1.index(binned_sample['value'][0]) + 1
binned_sample['idx'] = binned_idx
idxs_split_sample_1.append(binned_idx)
for binned_sample in split_dict_2:
binned_idx = complete_binning_2.index(binned_sample['value'][0]) + 1
binned_sample['idx'] = binned_idx
idxs_split_sample_2.append(binned_idx)
for binned_sample_1 in split_dict_1:
for sample_name in binned_sample_1['samples']:
if sample_name not in [key.GetName() for key in f.GetListOfKeys()]:
histogram_dir_root = f.mkdir(sample_name)
else:
histogram_dir_root = f.Get(sample_name)
subdir_name = '%s_v_%s_wo_inclusive' % (split_var_1, split_var_2)
if subdir_name not in [
key.GetName()
for key in histogram_dir_root.GetListOfKeys()
]:
histogram_dir = histogram_dir_root.mkdir(subdir_name)
else:
histogram_dir = histogram_dir_root.Get(subdir_name)
histogram_dir.cd()
for nof_key, lumi_arr in inclusive_lumis.items():
if not is_valid_event_type(nof_key):
continue
for idx, lumi_incl in enumerate(lumi_arr):
histogram_name = '%s_%d' % (nof_key, idx)
binning_1 = array.array('f', complete_binning_1)
binning_2 = array.array('f', complete_binning_2)
histogram = ROOT.TH2D(histogram_name, histogram_name,
len(binning_1) - 1, binning_1,
len(binning_2) - 1, binning_2)
histogram.SetDirectory(histogram_dir)
histogram.SetXTitle(split_var_1)
histogram.SetYTitle(split_var_2)
lumi_split_1 = -1
for split_idx_1 in range(1, len(binning_1)):
if split_idx_1 == binned_sample_1['idx']:
lumi_split_1 = binned_sample_1['lumis'][nof_key][
idx]
for split_idx_2 in range(1, len(binning_2)):
lumi_split_2 = 0.
for binned_sample_2 in split_dict_2:
if split_idx_2 == binned_sample_2['idx']:
lumi_split_2 = binned_sample_2[
'lumis'][nof_key][idx]
break
if binning_1[split_idx_1] > inclusive_binning_1[-1] or \
binning_1[split_idx_1] < inclusive_binning_1[0] or \
binning_2[split_idx_2] > inclusive_binning_2[-1] or \
binning_2[split_idx_2] < inclusive_binning_2[0]:
if lumi_split_2 == 0.:
weight = 1.
else:
weight = lumi_split_1 / (lumi_split_1 +
lumi_split_2)
else:
weight = lumi_split_1 / (lumi_split_1 +
lumi_split_2)
assert (weight >= 0.)
histogram.SetBinContent(
split_idx_1, split_idx_2, weight)
else:
for split_idx_2 in range(1, len(binning_2)):
histogram.SetBinContent(
split_idx_1, split_idx_2, 0.)
histogram.GetXaxis().SetBinLabel(
split_idx_1, '%.0f <= %s < %.0f' %
(complete_binning_1[split_idx_1 - 1], split_var_1,
complete_binning_1[split_idx_1]))
for split_idx_2 in range(1, len(binning_2)):
histogram.GetYaxis().SetBinLabel(
split_idx_2, '%.0f <= %s < %.0f' %
(complete_binning_2[split_idx_2 - 1], split_var_2,
complete_binning_2[split_idx_2]))
histogram.Write()
for binned_sample_2 in split_dict_2:
for sample_name in binned_sample_2['samples']:
if sample_name not in [key.GetName() for key in f.GetListOfKeys()]:
histogram_dir_root = f.mkdir(sample_name)
else:
histogram_dir_root = f.Get(sample_name)
subdir_name = '%s_v_%s_wo_inclusive' % (split_var_1, split_var_2)
if subdir_name not in [
key.GetName()
for key in histogram_dir_root.GetListOfKeys()
]:
histogram_dir = histogram_dir_root.mkdir(subdir_name)
else:
histogram_dir = histogram_dir_root.Get(subdir_name)
histogram_dir.cd()
for nof_key, lumi_arr in inclusive_lumis.items():
if not is_valid_event_type(nof_key):
continue
for idx, lumi_incl in enumerate(lumi_arr):
histogram_name = '%s_%d' % (nof_key, idx)
binning_1 = array.array('f', complete_binning_1)
binning_2 = array.array('f', complete_binning_2)
histogram = ROOT.TH2D(histogram_name, histogram_name,
len(binning_1) - 1, binning_1,
len(binning_2) - 1, binning_2)
histogram.SetDirectory(histogram_dir)
histogram.SetXTitle(split_var_1)
histogram.SetYTitle(split_var_2)
lumi_split_2 = -1
for split_idx_2 in range(1, len(binning_2)):
if split_idx_2 == binned_sample_2['idx']:
lumi_split_2 = binned_sample_2['lumis'][nof_key][
idx]
for split_idx_1 in range(1, len(binning_1)):
lumi_split_1 = 0.
for binned_sample_1 in split_dict_1:
if split_idx_1 == binned_sample_1['idx']:
lumi_split_1 = binned_sample_1[
'lumis'][nof_key][idx]
break
if binning_1[split_idx_1] > inclusive_binning_1[-1] or \
binning_1[split_idx_1] < inclusive_binning_1[0] or \
binning_2[split_idx_2] > inclusive_binning_2[-1] or \
binning_2[split_idx_2] < inclusive_binning_2[0]:
if lumi_split_1 == 0.:
weight = 1.
else:
weight = lumi_split_2 / (lumi_split_1 +
lumi_split_2)
else:
weight = lumi_split_2 / (lumi_split_1 +
lumi_split_2)
histogram.SetBinContent(
split_idx_1, split_idx_2, weight)
else:
for split_idx_1 in range(1, len(binning_1)):
histogram.SetBinContent(
split_idx_1, split_idx_2, 0.)
histogram.GetYaxis().SetBinLabel(
split_idx_2, '%.0f <= %s < %.0f' %
(complete_binning_2[split_idx_2 - 1], split_var_2,
complete_binning_2[split_idx_2]))
for split_idx_1 in range(1, len(binning_1)):
histogram.GetXaxis().SetBinLabel(
split_idx_1, '%.0f <= %s < %.0f' %
(complete_binning_1[split_idx_1 - 1], split_var_1,
complete_binning_1[split_idx_1]))
histogram.Write()