def create_roc_counter(tree,
netcuts,
min_value,
max_value,
reco_true=0,
et_function=et_identity):
'''
Create and fill ROC counter array holding number of events left after performing cuts on Et-derived value
:param tree: Tree holding events to be loaded
:param netcuts: Number of cuts to be performed on events
:param min_value: Minimum of Et-derived values
:param max_value: Maximum of Et-derived values
:param reco_true: Bit value, 0 for using the reconstructed Et and 1 for using the true Pt
:param et_function: Arbitrary to be applied to the Et of event
:return: Numpy array holding number events left after cuts
'''
entries = tree.entries
# Scale factor to be used in increment_roc_counter
scaler = float(max_value - min_value) / float(netcuts)
# This array will hold the number of signal events with Et-derived value greater than a given index
# (assuming scaler = 1)
roc_counter = np.zeros(netcuts)
for i in range(entries):
event = prepare_event(tree, i, 1, 1, 0)
# Use the reconstructed Et if reco_true set to 0, true Pt if set to 1
if reco_true == 0:
event_et = event.reco_et
else:
event_et = event.true_tau_pt
event_et = et_function(event_et)
increment_roc_counter(event_et, roc_counter, scaler, min_value)
if roc_counter[0] < entries:
print('Values exist below defined minimum')
if roc_counter[-1] > 0:
print('Values exist above defined maximum')
return roc_counter
def reco_et_tree_histogram(tree, bins, min_value, max_value):
'''
Create a histogram of the reconstructed Et values of the events in tree
:param tree: A tree containing events whose reconstructed Et will be loaded into the returned histogram
:param bins: Number of bins in the histogram
:param min_value: Minimum of values loaded into histogram
:param max_value: Maximum of values loaded into histogram
:return: A histogram populated with the reconstructed Et values of the events in tree
'''
tree_entries = tree.entries
histo = TH1F("histo", "Reconstructed Et", bins, min_value, max_value)
for i in range(tree_entries):
event = prepare_event(tree, i, 0, 1, 0)
histo.Fill(event.reco_et)
histo.GetXaxis().SetTitle("Reconstructed Et")
histo.GetYaxis().SetTitle("Entries")
return histo
set_po_tree_parameters(sig_t)
print 'New entries'
print new_t.entries
print 'Old entries'
print old_t.entries
new_had_histo = TH1F('histo', 'New Hadronic Energy', 100, 0, 20)
new_05_had_histo = TH1F('histo', 'New 0.5 GeV Hadronic Energy', 100, 0, 20)
old_had_histo = TH1F('histo', 'Old Hadronic Energy', 100, 0, 20)
zero_new = 0
zero_05_new = 0
zero_old = 0
for i in range(new_t.entries):
event = prepare_event(new_t, i)
new_had_histo.Fill(event.had_layer.reco_et)
if event.had_layer.reco_et == 0:
zero_new += 1
for i in range(new_05_t.entries):
event = prepare_event(new_05_t, i)
new_05_had_histo.Fill(event.had_layer.reco_et)
if event.had_layer.reco_et == 0:
zero_05_new += 1
for i in range(old_t.entries):
event = prepare_event(old_t, i)
old_had_histo.Fill(event.had_layer.reco_et)
if event.had_layer.reco_et == 0:
zero_old += 1
'New Background L2 Supercell Energy (Constant eta/phi)',
bins, min_bin, max_bin)
sig_cell_histo = TH1F('hist', 'Signal L2 Supercell Energy (Constant eta/phi)',
bins, min_bin, max_bin)
old_off_histo = TH1F('hist',
'Old Background L2 Supercell Energy (Seed eta - 2)', bins,
min_bin, max_bin)
new_off_histo = TH1F('hist',
'New Background L2 Supercell Energy (Seed eta - 2)', bins,
min_bin, max_bin)
sig_off_histo = TH1F('hist', 'Signal L2 Supercell Energy (Seed eta - 2)', bins,
min_bin, max_bin)
for i in range(t_old.entries):
event = prepare_event(t_old, i)
old_seed_histo.Fill(event.seed_et)
old_cell_histo.Fill(event.l2_layer.cell_et[3][1])
old_off_histo.Fill(event.l2_layer.cell_et[event.seed_eta -
2][event.seed_phi])
for i in range(t_new.entries):
event = prepare_event(t_new, i)
new_seed_histo.Fill(event.seed_et)
new_cell_histo.Fill(event.l2_layer.cell_et[3][1])
new_off_histo.Fill(event.l2_layer.cell_et[event.seed_eta -
2][event.seed_phi])
for i in range(t_sig.entries):
event = prepare_event(t_sig, i)
sig_seed_histo.Fill(event.seed_et)
file_path = '~/NewTauSamples/dataFiles/sig_ntuple.root'
f = ROOT.TFile(file_path)
t = Tree(f.Get('mytree'))
set_po_tree_parameters(t)
afs_name = '~/NewTauSamples/plots/sanityCheck.pdf'
eos_name = '/eos/user/n/nicholas/NewTauSamples/plots/sanityCheck.pdf'
plot_names = [afs_name, eos_name]
h0 = ROOT.TH1F('hist', 'New Signal True Tau Pt', 100, 0, 100)
h1 = ROOT.TH1F('hist', 'New Signal Seed Et', 100, 0, 20)
h2 = ROOT.TH1F('hist', 'New Signal Eta', 50, -5, 5)
for i in range(t.root_ttree.GetEntries()):
t.root_ttree.GetEntry(i)
event = prepare_event(t, i)
h0.Fill(t.root_ttree.TrueTauPt)
h1.Fill(event.l2_layer.cell_et[t.root_ttree.SeedEta][t.root_ttree.SeedPhi])
h2.Fill(t.root_ttree.TOBEta)
h0.Draw('hist')
multi_print(plot_names, c1, '(')
h1.Draw('hist')
multi_print(plot_names, c1)
h2.Draw('hist')
multi_print(plot_names, c1, ')')
tsig, fsig, tback, fback = get_signal_and_background_files(
'/eos/user/n/nicholas/SWAN_projects/NewTauSamples/dataFiles/ztt_Output_formatted.root',
'/eos/user/n/nicholas/SWAN_projects/NewTauSamples/dataFiles/output_MB80_formatted.root'
)
set_et_tree_parameters(tback)
audit_event = 0
sig_or_back = 0
if sig_or_back == 1:
tree = tsig
else:
tree = tback
event = prepare_event(tree, audit_event, 0, 0, 1)
print('L2 Layer Cells')
print(event.l2_layer.cell_et)
print('FCore Definition')
print(event.fcore_def)
print('FCore Core Et')
print(
layer_reco_et(event.l2_layer, event.fcore_def[0][0], event.fcore_def[0][1],
event.seed_eta, event.seed_phi))
print('FCore Isolation Et')
print(
layer_reco_et(event.l2_layer, event.fcore_def[1][0], event.fcore_def[1][1],
event.seed_eta, event.seed_phi))
print('FCore')
print(event.fcore)