def fill_histos_data(tree_name, mode="nue"):
f_data = ROOT.TFile("root_files_sys/%s_%s_file.root" %
(tree_name, variation))
t_data = f_data.Get("%s_tree" % tree_name)
ROOT.TMVA.Tools.Instance()
reader = ROOT.TMVA.Reader(":".join(["!V", "!Silent", "Color"]))
variables_dict = load_variables(t_data)
load_bdt(reader)
histograms = []
for i, n in enumerate(variables_dict.keys()):
if n != "reco_energy":
h = ROOT.TH1F("h_%s" % n, labels[n], binning[n][0], binning[n][1],
binning[n][2])
else:
h = ROOT.TH1F("h_%s" % n, labels[n], len(bins) - 1, bins)
histograms.append(h)
histo_dict = dict(zip(variables_dict.keys(), histograms))
h_bdts = {}
for bdt_name in bdt_types:
h_bdts[bdt_name] = ROOT.TH1F(
"h_bdt_%s_%s" % (bdt_name, tree_name),
"BDT %s response; N. Entries / 0.04" % bdt_name, 50, -1, 1)
passed_events = 0
entries = int(t_data.GetEntries() / 1)
# bdt_ntuple = ROOT.TNtuple(
# "bdt_ntuple", "bdt_ntuple", "single"+":".join(bdt_types) + ":category")
f_clone = ROOT.TFile("root_files_sys/filtered_%s.root" % tree_name,
"RECREATE")
clone = t_data.CloneTree(0)
for i_evt in tqdm(range(entries)):
t_data.GetEntry(i_evt)
bdt_values = {}
for bdt_name in bdt_types:
bdt_values[bdt_name] = reader.EvaluateMVA("BDT%s" % bdt_name)
numu = mode == "numu"
if pre_cuts(variables_dict, numu):
for bdt_name in bdt_types:
h_bdts[bdt_name].Fill(bdt_values[bdt_name],
t_data.event_weight)
if apply_cuts(bdt_values, variables_dict, BDT, MANUAL, mode):
passed_events += t_data.event_weight
all_vars = variables + spectators
clone.Fill()
for name, var in all_vars:
for v in var:
if v == -999:
break
histo_dict[name].Fill(v, t_data.event_weight)
clone.Write()
f_clone.Close()
f_bdt = ROOT.TFile("plots/h_bdt_%s.root" % tree_name, "RECREATE")
for h in h_bdts:
h_bdts[h].Write()
f_bdt.Close()
for h in histograms:
f = ROOT.TFile(
"%splots%s/%s_%s.root" % (detsyst, folder, h.GetName(), tree_name),
"RECREATE")
h.Write()
f.Close()
return passed_events
def fill_histos(chain, histo_dict, h_bdt_types, option="", mode="nue"):
ROOT.TMVA.Tools.Instance()
reader = ROOT.TMVA.Reader(":".join(["!V", "!Silent", "Color"]))
bdt_ntuple = ROOT.TNtuple("bdt_ntuple", "bdt_ntuple",
"single" + ":".join(bdt_types) + ":category")
variables_dict = load_variables(chain)
load_bdt(reader)
passed_events = 0
entries = int(chain.GetEntries() / 1)
f = open("selected_events/%s_passed.txt" % option, "w")
clone = chain.GetTree().CloneTree(0)
for i in tqdm(range(entries)):
chain.GetEntry(i)
category = int(chain.category)
# cuts_response = reader.EvaluateMVA("Cuts method", rectangular_cut)
# likelihood_response = reader.EvaluateMVA("Likelihood method")
bdts = {}
for bdt_name in bdt_types:
bdts[bdt_name] = reader.EvaluateMVA("BDT%s" % bdt_name)
numu = mode == "numu"
if pre_cuts(variables_dict, numu):
for bdt_name in bdt_types:
h_bdt_types[bdt_name][category].Fill(bdts[bdt_name],
chain.event_weight)
else:
continue
bdt_ntuple.Fill(array("f", list(bdts.values()) + [chain.category]))
if apply_cuts(bdts, variables_dict, BDT, MANUAL, mode):
if not chain.true_nu_is_fidvol and category != 0 and category != 6 and category != 1 and category != 7:
category = 5
clone.Fill()
print(int(chain.run),
int(chain.subrun),
int(chain.event),
int(category),
chain.reco_energy,
file=f)
if option == "nue":
if category == 2:
passed_events += chain.event_weight
else:
passed_events += chain.event_weight
all_vars = variables + spectators
var_dict = dict(all_vars)
for name, var in all_vars:
for i_v, v in enumerate(var):
if name == "interaction_type" and v == 10:
v = 4
if v == -999:
break
pdg_code = 99999
if "track" in name and name != "no_tracks":
pdg_code = int(var_dict["track_pdg"][i_v])
if option != "bnbext" and abs(pdg_code) == 2147483648:
pdg_code = int(var_dict["shower_pdg"][0])
elif "shower" in name:
pdg_code = int(var_dict["shower_pdg"][i_v])
if option != "bnbext" and abs(pdg_code) == 2147483648:
pdg_code = int(var_dict["track_pdg"][0])
if abs(pdg_code) in h_pdgs[name]:
if MANUAL and category == 2:
h_pdgs[name][abs(pdg_code)].Fill(
v, chain.event_weight * 3.3 / 2.9)
else:
h_pdgs[name][abs(pdg_code)].Fill(
v, chain.event_weight)
else:
h_pdgs[name][99999].Fill(v, chain.event_weight)
if MANUAL and category == 2:
histo_dict[name][category].Fill(
v, chain.event_weight * 3.3 / 2.9)
else:
histo_dict[name][category].Fill(v, chain.event_weight)
f.close()
f = ROOT.TFile("plots/bdt_ntuple_%s.root" % option, "RECREATE")
clone.Write()
bdt_ntuple.Write()
f.Close()
return passed_events
def fill_histos(chain, histo_dict, h_bdt_types, option="", mode="nue"):
ROOT.TMVA.Tools.Instance()
reader = ROOT.TMVA.Reader(":".join(["!V", "!Silent", "Color"]))
bdt_ntuple = ROOT.TNtuple("bdt_ntuple", "bdt_ntuple",
"single" + ":".join(bdt_types) + ":category")
variables_dict = load_variables(chain)
load_bdt(reader)
passed_events = 0
entries = int(chain.GetEntries() / 1)
for i in tqdm(range(entries)):
chain.GetEntry(i)
category = int(chain.category)
# cuts_response = reader.EvaluateMVA("Cuts method", rectangular_cut)
# likelihood_response = reader.EvaluateMVA("Likelihood method")
bdts = {}
for bdt_name in bdt_types:
bdts[bdt_name] = reader.EvaluateMVA("BDT%s" % bdt_name)
numu = mode == "numu"
if pre_cuts(variables_dict, numu):
for bdt_name in bdt_types:
h_bdt_types[bdt_name][category].Fill(bdts[bdt_name],
chain.event_weight)
else:
continue
if apply_cuts(bdts, variables_dict, BDT, MANUAL, mode):
if not chain.true_nu_is_fidvol and category != 0 and category != 6 and category != 1 and category != 7:
category = 5
if option == "nue":
if category == 2:
passed_events += chain.event_weight
else:
passed_events += chain.event_weight
all_vars = variables + spectators
var_dict = dict(all_vars)
for name, var in all_vars:
for i_v, v in enumerate(var):
if v == -999:
break
if "track" in name and name != "no_tracks":
pdg_code = int(var_dict["track_pdg"][i_v])
if option != "bnbext" and abs(pdg_code) == 2147483648:
pdg_code = int(var_dict["shower_pdg"][0])
elif "shower" in name:
pdg_code = int(var_dict["shower_pdg"][i_v])
if option != "bnbext" and abs(pdg_code) == 2147483648:
pdg_code = int(var_dict["track_pdg"][0])
else:
pdg_code = int(var_dict["shower_pdg"][0])
if option != "bnbext" and abs(pdg_code) == 2147483648:
pdg_code = 99999
if abs(pdg_code) in h_pdgs[name]:
h_pdgs[name][abs(pdg_code)].Fill(v, chain.event_weight)
else:
h_pdgs[name][99999].Fill(v, chain.event_weight)
histo_dict[name][category].Fill(v, chain.event_weight)
return passed_events
chain = ROOT.TChain("mc_tree")
chain.Add("root_files/sys_mc_file.root")
# chain.Add("root_files/dirt.root")
chain.Add("root_files/sys_nue_file.root/nue_tree")
total_entries = int(chain.GetEntries() / 1)
h_sys = {}
h_2d = {}
h_cv = {}
h_covariance = {}
h_frac = {}
h_corr = {}
vars = load_variables(chain)
for n, b in vars.items():
h_uni = []
for u in range(N_UNI):
if n == "reco_energy":
h_uni.append(
ROOT.TH1F("h_%s_%i" % (n, u), labels[n],
len(bins) - 1, bins))
else:
h_uni.append(
ROOT.TH1F("h_%s_%i" % (n, u), labels[n], binning[n][0],
binning[n][1], binning[n][2]))
h_sys[n] = h_uni