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distributions.py
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import ROOT
import sys
import os.path
RDF = ROOT.ROOT.RDataFrame
# Load C++ headers
ROOT.gInterpreter.Declare('#include "common_definitions.h"')
ROOT.gInterpreter.Declare('#include "parameters_global.h"')
ROOT.gInterpreter.Declare('#include "common_algorithms.h"')
ROOT.gInterpreter.Declare('#include "parameters.h"')
ROOT.gInterpreter.Declare('#include "common.h"')
# FIXME: Do not manage to get it from ROOT
M_PI = 3.14159265358979323846264338328 # Pi
th_tag="no_MT"
if len(sys.argv) < 2:
print('Usage: python distributions.py input_filename')
sys.exit(1) # no input file specified
if len(sys.argv) == 3:
nthreads = int(sys.argv[2])
if nthreads != 0:
th_tag="threads_"+sys.argv[2]
ROOT.ROOT.EnableImplicitMT(nthreads)
# Read input files
fname = sys.argv[1]
# Get input (temporal options)
treename = "distilled"
selected_diagonal = "d45b_56t"
prefix = ""
outputDir = "."
input_file = prefix + fname # Created with distill.py
if not os.path.isfile(input_file):
print('File does not exists: %s' % input_file)
sys.exit(1)
# Line 112
# init diagonal settings
ROOT.Init("45b_56t");
# Line 117
# default parameters
detailsLevel = 0 # 0: no details, 1: some details, >= 2 all details
overrideCutSelection = False # whether the default cut selection should be overriden by the command-line selection
cutSelectionString = None
outputDir = "."
inputDir = "."
input_n_si = 4.0
time_group_divisor = 0
time_group_remainder = 0
event_group_divisor = 0
event_group_index = 0
evIdxStep = 1
maxTaggedEvents = 0 # 0 means no maximum
# Line 131
# parse command line arguments, starting from index 2
# Line 226 - Print parameters
print("* detailsLevel = %s" % detailsLevel)
print("* outputDir = %s" % outputDir)
print("* inputDir = %s" % inputDir)
print("* input n_si = %s" % input_n_si)
print("* time_group_divisor = %s" % time_group_divisor)
print("* time_group_remainder = %s" % time_group_remainder)
print("* event_group_divisor = %s" % event_group_divisor)
print("* event_group_index = %s" % event_group_index)
print("* evIdxStep = %s" % evIdxStep)
print("* maxTaggedEvents = %s" % maxTaggedEvents)
# Line 237
# select cuts
ROOT.anal.BuildCuts()
ROOT.anal.n_si = input_n_si
# Line 241 - Let's start assuming no overrideCutSelection
# TODO if (overrideCutSelection)
# Line 272
# print info
print("\n");
print("------------------------------ environment ------------------------------\n");
ROOT.env.Print();
print("\n");
print("------------------------------- analysis --------------------------------\n");
ROOT.anal.Print();
print("\n");
# Line 281
# alignment init
for i,_ in enumerate(ROOT.alignmentSources):
print("\n---------- alignment source %s ----------\n" % i);
ROOT.alignmentSources[i].Init();
print("\n\n");
# Line 289
# binnings
binnings = ROOT.vector('string')()
binnings.push_back("ub");
binnings.push_back("ob-1-10-0.2");
binnings.push_back("ob-1-30-0.2");
#########################################################
###### READ INPUT FILE, INITIALIZE RDATAFRAME ######
#########################################################
# Line 286 - 325
# equivalent to: get input
# init input data
# get input data
# Read all branches
rdf = RDF(treename, input_file)
# Line 327
# get time-dependent corrections
corrg_pileup = None
if ROOT.anal.use_pileup_efficiency_fits:
path = inputDir + "/pileup_fit_combined.root"
puF = ROOT.TFile.Open(path)
if not os.path.exists(puF):
print("ERROR: pile-up correction file `%s' cannot be opened.\n" % path);
if diagonal == "d45b_56t":
#corrg_pileup = (TGraph *) puF.Get("45b_56t/dgn");
corrg_pileup = puF.Get("45b_56t/dgn")
if diagonal == "d45t_56b":
#corrg_pileup = (TGraph *) puF.Get("45b_56t/dgn");
corrg_pileup = puF.Get("45t_56b/dgn")
# Line 358
# get th_y* dependent efficiency correction
f_3outof4_efficiency_L_F = None;
f_3outof4_efficiency_L_N = None;
f_3outof4_efficiency_R_N = None;
f_3outof4_efficiency_R_F = None;
if ROOT.anal.use_3outof4_efficiency_fits:
path = inputDir + "/eff3outof4_details_fit_old.root"
effFile = ROOT.TFile.Open(path)
if (os.path.exists(effFile)):
print("ERROR: 3-out-of-4 efficiency file `%s' cannot be opened.\n" % path);
diagonal = selected_diagonal;
f_3outof4_efficiency_L_F = effFile.Get(diagonal + "/L_F/fit");
f_3outof4_efficiency_L_N = effFile.Get(diagonal + "/L_N/fit");
f_3outof4_efficiency_R_N = effFile.Get(diagonal + "/R_N/fit");
f_3outof4_efficiency_R_F = effFile.Get(diagonal + "/R_F/fit");
print("\n>> using 3-out-of-4 fits: %s, %s, %s, %s\n" %
(f_3outof4_efficiency_L_F, f_3outof4_efficiency_L_N,
f_3outof4_efficiency_R_N, f_3outof4_efficiency_R_F))
# TODO Line 380 (not needed AFAIK)
# get unsmearing correction
# Line 394
# book metadata histograms
ROOT.timestamp_bins = ROOT.timestamp_max - ROOT.timestamp_min + 1.;
# Long TODO
# Lines 397 - 779
# THistograms, TGraphs and TProfiles declaration
# Create bh_t_* hists
# FIXME Define proper binnings
bh_t_Nev_before = dict()
bh_t_Nev_after_no_corr = dict()
bh_t_before = dict()
bh_t_after = dict()
bh_t_after_no_corr = dict()
bp_t_phi_corr = dict()
bp_t_full_corr = dict()
binning_setup = dict()
for b in binnings:
binning = ROOT.BuildBinningRDF(ROOT.anal, b)
binning_setup[b] = binning
#Line 780
# zero counters
n_ev_full = 0;
n_ev_cut = dict();
for ci in range(ROOT.anal.N_cuts):
n_ev_cut[ci] = 0
th_min = 1E100;
th_y_L_min = +1E100; th_y_R_min = +1E100
N_anal=0; N_anal_zeroBias=0;
N_zeroBias_el=0; N_zeroBias_el_RP_trig=0;
N_4outof4=0; N_el=0;
N_el_T2trig=0; N_4outof4_T2trig=0;
N_el_raw=0;
# TODO
# Line 795
# map<unsigned int, pair<unsigned int, unsigned int> > runTimestampBoundaries;
#########################################################
###### FILTER, BUILD HISTOGRAMS - START EVENT LOOP ######
#########################################################
# TODO
# Line 802
# Initial cuts
# remove troublesome runs
# unsigned int run = ev.run_num / 100000;
# unsigned int file = ev.run_num % 100000;
# NOT IMPLEMENTED: return always false
# if (SkipRun(run, file, true))
# continue;
# NOT IMPLEMENTED: Not revelevant for plots
# update timestamp run boundaries
# auto rtbit = runTimestampBoundaries.find(run);
# if (rtbit == runTimestampBoundaries.end())
# {
# runTimestampBoundaries.insert({run, {ev.timestamp, ev.timestamp}});
# } else {
# rtbit->second.first = min(rtbit->second.first, ev.timestamp);
# rtbit->second.second = max(rtbit->second.second, ev.timestamp);
# }
# Line 818
# check time - selected?
# if (anal.SkipTime(ev.timestamp))
# continue;
f1 = rdf.Filter("! SkipTime( timestamp )", 'check time - selected')
# Line 822
if time_group_divisor != 0:
f1 = f1.Filter("! SkipTimeInterval( timestamp, %s, %s )".format(time_group_divisor, time_group_remainder),
'time interval')
# Diagonal cut (L831)
f2 = f1.Filter("v_L_2_F && v_L_2_N && v_R_2_F && v_R_2_N", 'allDiagonalRPs')
# FIXME: N_4outof4_T2trig is not used to produce any plot
# Line 835
# if ((ev.trigger_bits & 64) != 0)
# N_4outof4_T2trig++;
# Line 838
model = ("h_timestamp_dgn", ";timestamp;rate (Hz)", int(ROOT.timestamp_bins), ROOT.timestamp_min-0.5, ROOT.timestamp_max+0.5)
h_timestamp_dgn = f2.Histo1D(model, "timestamp")
# Line 841
# NOT IMPLEMENTED
# select the elastic-trigger bunch(es) only
# if (SkipBunch(run, ev.bunch_num))
#
# SkipBunch will return False for whatever DS in use,
# since all of them define keepAllBunches = True within
# parameters.h
# Line 845
# zero bias event?
# Create named filter with number of zero bias events
# Not cut for this filter in original code
f_zerobias = f2.Filter("! ((trigger_bits & 512) != 0)", 'zero_bias_event')
xs = ["x_L_1_F", "x_L_2_N", "x_L_2_F", "x_R_1_F", "x_R_2_N", "x_R_2_F"]
ys = ["y_L_1_F", "y_L_2_N", "y_L_2_F", "y_R_1_F", "y_R_2_N", "y_R_2_F"]
# Apply fine alignment (L 852)
r2 = f2.Define("h_al", "ApplyFineAlignment( timestamp ,{}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {})".format(*(xs+ys) )) \
.Define("h_al_x_L_1_F", "h_al.L_1_F.x") \
.Define("h_al_x_L_2_N", "h_al.L_2_N.x") \
.Define("h_al_x_L_2_F", "h_al.L_2_F.x") \
.Define("h_al_y_L_1_F", "h_al.L_1_F.y") \
.Define("h_al_y_L_2_N", "h_al.L_2_N.y") \
.Define("h_al_y_L_2_F", "h_al.L_2_F.y") \
.Define("h_al_x_R_1_F", "h_al.R_1_F.x") \
.Define("h_al_x_R_2_N", "h_al.R_2_N.x") \
.Define("h_al_x_R_2_F", "h_al.R_2_F.x") \
.Define("h_al_y_R_1_F", "h_al.R_1_F.y") \
.Define("h_al_y_R_2_N", "h_al.R_2_N.y") \
.Define("h_al_y_R_2_F", "h_al.R_2_F.y") \
# fill pre-selection histograms (Line 860 - 866)
al_nosel_models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_y_L_1_F_vs_x_L_1_F_al_nosel", ";x^{L,1,F};y^{L,1,F}", 150, -15., 15., 300, -30., +30.),
("h_y_L_2_N_vs_x_L_2_N_al_nosel", ";x^{L,2,N};y^{L,2,N}", 150, -15., 15., 300, -30., +30.),
("h_y_L_2_F_vs_x_L_2_F_al_nosel", ";x^{L,2,F};y^{L,2,F}", 150, -15., 15., 300, -30., +30.),
("h_y_R_1_F_vs_x_R_1_F_al_nosel", ";x^{R,1,F};y^{R,1,F}", 150, -15., 15., 300, -30., +30.),
("h_y_R_2_N_vs_x_R_2_N_al_nosel", ";x^{R,2,N};y^{R,2,N}", 150, -15., 15., 300, -30., +30.),
("h_y_R_2_F_vs_x_R_2_F_al_nosel", ";x^{R,2,F};y^{R,2,F}", 150, -15., 15., 300, -30., +30.)
])
h_y_L_1_F_vs_x_L_1_F_al_nosel = r2.Histo2D(al_nosel_models[0], "h_al_x_L_1_F", "h_al_y_L_1_F")
h_y_L_2_N_vs_x_L_2_N_al_nosel = r2.Histo2D(al_nosel_models[1], "h_al_x_L_2_N", "h_al_y_L_2_N")
h_y_L_2_F_vs_x_L_2_F_al_nosel = r2.Histo2D(al_nosel_models[2], "h_al_x_L_2_F", "h_al_y_L_2_F")
h_y_R_1_F_vs_x_R_1_F_al_nosel = r2.Histo2D(al_nosel_models[3], "h_al_x_R_1_F", "h_al_y_R_1_F")
h_y_R_2_N_vs_x_R_2_N_al_nosel = r2.Histo2D(al_nosel_models[4], "h_al_x_R_2_N", "h_al_y_R_2_N")
h_y_R_2_F_vs_x_R_2_F_al_nosel = r2.Histo2D(al_nosel_models[5], "h_al_x_R_2_F", "h_al_y_R_2_F")
# TODO (not needed so far AFAIK)
#if (detailsLevel >= 2)
#{
# h_timestamp_B0->Fill(ev.timestamp);
# g_run_vs_timestamp->SetPoint(g_run_vs_timestamp->GetN(), ev.timestamp, ev.run_num);
# //g_ev_num_vs_timestamp->SetPoint(g_ev_num_vs_timestamp->GetN(), ev.timestamp, ev.event_num);
# //g_tr_num_vs_timestamp->SetPoint(g_tr_num_vs_timestamp->GetN(), ev.timestamp, ev.trigger_num);
#}
# run reconstruction (Line 876)
### kinematics struct
r3 = r2.Define("kinematics", 'DoReconstruction( h_al )')
r4 = r3.Define("k_th_x_R", "kinematics.th_x_R") \
.Define("k_th_y_R", "kinematics.th_y_R") \
.Define("k_th_x_L", "kinematics.th_x_L") \
.Define("k_th_y_L", "kinematics.th_y_L") \
.Define("k_th_x", "kinematics.th_x") \
.Define("k_th_y", "kinematics.th_y") \
.Define("minus_k_th_y", "- kinematics.th_y") \
.Define("k_vtx_x", "kinematics.vtx_x") \
.Define("k_vtx_x_L", "kinematics.vtx_x_L") \
.Define("k_vtx_x_R", "kinematics.vtx_x_R") \
.Define("k_vtx_y", "kinematics.vtx_y") \
.Define("k_vtx_y_L", "kinematics.vtx_y_L") \
.Define("k_vtx_y_R", "kinematics.vtx_y_R") \
.Define("k_th_y_L_F", "kinematics.th_y_L_F") \
.Define("k_th_y_L_N", "kinematics.th_y_L_N") \
.Define("k_th_y_R_F", "kinematics.th_y_R_F") \
.Define("k_th_y_R_N", "kinematics.th_y_R_N") \
.Define("k_th_x_diffLR", "kinematics.th_x_R - kinematics.th_x_L") \
.Define("k_th_y_diffLR", "kinematics.th_y_R - kinematics.th_y_L") \
.Define("k_th_x_diffLF", "kinematics.th_x_L - kinematics.th_x") \
.Define("k_th_x_diffRF", "kinematics.th_x_R - kinematics.th_x") \
.Define("k_th_y_L_diffNF", "kinematics.th_y_L_F - kinematics.th_y_L_N") \
.Define("k_th_y_R_diffNF", "kinematics.th_y_R_F - kinematics.th_y_R_N") \
.Define("k_vtx_x_diffLR", "kinematics.vtx_x_R - kinematics.vtx_x_L") \
.Define("k_vtx_y_diffLR", "kinematics.vtx_y_R - kinematics.vtx_y_L") \
.Define("k_t", "kinematics.t") \
.Define("k_th", "kinematics.th") \
.Define("k_phi", "kinematics.phi")
# Line 906
# cut evaluation
r5 = r4.Define("cutdata", "EvaluateCutsRDF( h_al, kinematics )")
# Line 919
# fill background distributions
# Not IMPLEMENTED, not used ANYWHERE
## TODO fill no-cut histograms (L957)
# for (unsigned int ci = 1; ci <= anal.N_cuts; ++ci)
# {
# //h2_cq_full[ci]->Fill(ccb[ci]*cqa[ci] - cca[ci]*cqb[ci], cca[ci]*cqa[ci] + ccb[ci]*cqb[ci] + ccc[ci]);
# h2_cq_full[ci]->Fill(cd.cqa[ci], cd.cqb[ci]);
# }
# Line 979
# Elastic cut
f4 = r5.Filter("cutdata.select", "elastic cut")
# TODO (L985)
# if (maxTaggedEvents > 0 && N_el > maxTaggedEvents)
# break;
# TODO (L988)
# g_selected_bunch_num_vs_timestamp->SetPoint(g_selected_bunch_num_vs_timestamp->GetN(), ev.timestamp, ev.bunch_num);
# TODO Line 1010
# if event_group_divisor > 0:
# int event_group = (N_el_raw-1) / event_group_divisor;
# if (event_group < event_group_index)
# continue;
# if (event_group > event_group_index)
# break;
# }
## TODO : Not used AFAIK
## use_3outof4_efficiency_fits
# Line 1039
# Moved to custom function in common_algorithms.h
# Line 1048
# Moved to custom function in common_algorithms.h
# Define normalization and norm_corr colums
r6 = r5.Define("norm_corr", "getNorm_corr( timestamp )" ) \
.Define("normalization", "getNormalization( norm_corr )")
# Line 1044 TODO (TProiles)
# p_norm_corr->Fill(ev.timestamp, norm_corr);
# p_3outof4_corr->Fill(k.th_y, inefficiency_3outof4);
# TODO Skipped
# Line 1050 - 1087
# // data for alignment
# // (SHOULD use hit positions WITHOUT alignment corrections, i.e. ev.h)
# signed int period = int((ev.timestamp - anal.alignment_t0) / anal.alignment_ts);
# Line 1089
# Fill raw histograms
model = ("h_timestamp_sel", ";timestamp;rate (Hz)", int(ROOT.timestamp_bins), ROOT.timestamp_min-0.5, ROOT.timestamp_max+0.5)
h_timestamp_sel = f4.Histo1D(model, "timestamp");
# Line 1110
# fill histograms
noal_sel_models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_y_L_1_F_vs_x_L_1_F_noal_sel", ";x^{L,1,F};y^{L,1,F}", 100, -3., +3., 300, -30., +30.),
("h_y_L_2_N_vs_x_L_2_N_noal_sel", ";x^{L,2,N};y^{L,2,N}", 100, -3., +3., 300, -30., +30.),
("h_y_L_2_F_vs_x_L_2_F_noal_sel", ";x^{L,2,F};y^{L,2,F}", 100, -3., +3., 300, -30., +30.),
("h_y_R_1_F_vs_x_R_1_F_noal_sel", ";x^{R,1,F};y^{R,1,F}", 100, -3., +3., 300, -30., +30.),
("h_y_R_2_N_vs_x_R_2_N_noal_sel", ";x^{R,2,N};y^{R,2,N}", 100, -3., +3., 300, -30., +30.),
("h_y_R_2_F_vs_x_R_2_F_noal_sel", ";x^{R,2,F};y^{R,2,F}", 100, -3., +3., 300, -30., +30.)
])
h_y_L_1_F_vs_x_L_1_F_noal_sel = f4.Histo2D(noal_sel_models[0], "x_L_1_F", "y_L_1_F")
h_y_L_2_N_vs_x_L_2_N_noal_sel = f4.Histo2D(noal_sel_models[1], "x_L_2_N", "y_L_2_N")
h_y_L_2_F_vs_x_L_2_F_noal_sel = f4.Histo2D(noal_sel_models[2], "x_L_2_F", "y_L_2_F")
h_y_R_1_F_vs_x_R_1_F_noal_sel = f4.Histo2D(noal_sel_models[3], "x_R_1_F", "y_R_1_F")
h_y_R_2_N_vs_x_R_2_N_noal_sel = f4.Histo2D(noal_sel_models[4], "x_R_2_N", "y_R_2_N")
h_y_R_2_F_vs_x_R_2_F_noal_sel = f4.Histo2D(noal_sel_models[5], "x_R_2_F", "y_R_2_F")
# Line 1117
al_sel_models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_y_L_1_F_vs_x_L_1_F_al_sel", ";x^{L,1,F};y^{L,1,F}", 100, -3., +3., 300, -30., +30.),
("h_y_L_2_N_vs_x_L_2_N_al_sel", ";x^{L,2,N};y^{L,2,N}", 100, -3., +3., 300, -30., +30.),
("h_y_L_2_F_vs_x_L_2_F_al_sel", ";x^{L,2,F};y^{L,2,F}", 100, -3., +3., 300, -30., +30.),
("h_y_R_1_F_vs_x_R_1_F_al_sel", ";x^{R,1,F};y^{R,1,F}", 100, -3., +3., 300, -30., +30.),
("h_y_R_2_N_vs_x_R_2_N_al_sel", ";x^{R,2,N};y^{R,2,N}", 100, -3., +3., 300, -30., +30.),
("h_y_R_2_F_vs_x_R_2_F_al_sel", ";x^{R,2,F};y^{R,2,F}", 100, -3., +3., 300, -30., +30.)
])
h_y_L_1_F_vs_x_L_1_F_al_sel = f4.Histo2D(al_sel_models[0], "h_al_x_L_1_F", "h_al_y_L_1_F")
h_y_L_2_N_vs_x_L_2_N_al_sel = f4.Histo2D(al_sel_models[1], "h_al_x_L_2_N", "h_al_y_L_2_N")
h_y_L_2_F_vs_x_L_2_F_al_sel = f4.Histo2D(al_sel_models[2], "h_al_x_L_2_F", "h_al_y_L_2_F")
h_y_R_1_F_vs_x_R_1_F_al_sel = f4.Histo2D(al_sel_models[3], "h_al_x_R_1_F", "h_al_y_R_1_F")
h_y_R_2_N_vs_x_R_2_N_al_sel = f4.Histo2D(al_sel_models[4], "h_al_x_R_2_N", "h_al_y_R_2_N")
h_y_R_2_F_vs_x_R_2_F_al_sel = f4.Histo2D(al_sel_models[5], "h_al_x_R_2_F", "h_al_y_R_2_F")
# Line 1157 (k.th_x_R - k.th_x_L)
# (k.th_y_R - k.th_y_L)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("th_x_diffLR", ";#theta_{x}^{R} - #theta_{x}^{L}", 1000, -500E-6, +500E-6),
("th_y_diffLR", ";#theta_{y}^{R} - #theta_{y}^{L}", 500, -50E-6, +50E-6)
])
th_x_diffLR = f4.Histo1D(models[0], "k_th_x_diffLR")
th_y_diffLR = f4.Histo1D(models[1], "k_th_y_diffLR")
# Line 1160 (k.th_x_L - k.th_x)
# (k.th_x_R - k.th_x)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("th_x_diffLF", ";#theta_{x}^{L} - #theta_{x}", 400, -200E-6, +200E-6),
("th_x_diffRF", ";#theta_{x}^{R} - #theta_{x}", 400, -200E-6, +200E-6)
])
th_x_diffLF = f4.Histo1D(models[0], "k_th_x_diffLF")
th_x_diffRF = f4.Histo1D(models[1], "k_th_x_diffRF")
# Line 1163 (k.th_x, k.th_x_R - k.th_x_L)
# (k.th_y, k.th_y_R - k.th_y_L)
# (k.vtx_x, k.th_x_R - k.th_x_L)
models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_th_x_diffLR_vs_th_x", ";#theta_{x};#theta_{x}^{R} - #theta_{x}^{L}", 100, -300E-6, +300E-6, 120, -120E-6, +120E-6),
("h_th_y_diffLR_vs_th_y", ";#theta_{y};#theta_{y}^{R} - #theta_{y}^{L}", 100, -500E-6, +500E-6, 120, -120E-6, +120E-6),
("h_th_x_diffLR_vs_vtx_x", ";vtx_{x};#theta_{x}^{R} - #theta_{x}^{L}", 100, -300E-3, +300E-3, 120, -120E-6, +120E-6)
])
h_th_x_diffLR_vs_th_x = f4.Histo2D(models[0], "k_th_x", "k_th_x_diffLR")
h_th_y_diffLR_vs_th_y = f4.Histo2D(models[1], "k_th_y", "k_th_y_diffLR")
h_th_x_diffLR_vs_vtx_x = f4.Histo2D(models[2], "k_vtx_x", "k_th_x_diffLR")
# TODO:
# Line 1168 TProfile
#p_th_x_diffLR_vs_th_x = f4.Histo1D("k_th_x", "k_th_x_diffLR")
#p_th_y_diffLR_vs_th_y = f4.Histo1D("k_th_y", "k_th_y_diffLR")
#p_th_y_L_diffNF_vs_th_y_L = f4.Histo1D("k_th_y_L", "k_th_y_L_diffNF")
#p_th_y_R_diffNF_vs_th_y_R = f4.Histo1D("k_th_y_R", "k_th_y_R_diffNF")
# TODO:
# Line 1172 TProfile
#p_th_x_diffLR_vs_vtx_x = f4.Histo1D("k_vtx_x", "k_th_x_diffRF")
# TODO:
# if (safe)
# {
# th_y_diffLR_safe->Fill(k.th_y_R - k.th_y_L);
# th_x_diffLR_safe->Fill(k.th_x_R - k.th_x_L);
# }
# TODO:
# Line 1184 TProfile
#p_th_x_vs_th_y = f4.Histo1D("k_th_y", "k_th_x")
#p_th_x_L_vs_th_y_L = f4.Histo1D("k_th_y_L", "k_th_x_L")
#p_th_x_R_vs_th_y_R = f4.Histo1D("k_th_y_R", "k_th_x_R")
# Line 1188 (k.th_x_L, k.th_y_L)
# (k.th_x_R, k.th_y_R)
# (k.th_x, k.th_y)
models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_th_y_L_vs_th_x_L", ";#theta_{x}^{L};#theta_{y}^{L}", 100, -115E-6, +11E-5, 100, 22E-6, +102E-6),
("h_th_y_R_vs_th_x_R", ";#theta_{x}^{R};#theta_{y}^{R}", 100, -125E-6, +12E-5, 100, 27E-6, +102E-6),
("h_th_y_vs_th_x", ";#theta_{x};#theta_{y}", 100, -300E-6, +300E-6, 100, -150E-6, +150E-6)
])
h_th_y_L_vs_th_x_L = f4.Histo2D(models[0], "k_th_x_L", "k_th_y_L")
h_th_y_R_vs_th_x_R = f4.Histo2D(models[1], "k_th_x_R", "k_th_y_R")
h_th_y_vs_th_x = f4.Histo2D(models[2], "k_th_x", "k_th_y")
# TODO: TGraph
# Line 1192
# if (detailsLevel >= 1)
# {
# g_th_y_L_vs_th_x_L->SetPoint(g_th_y_L_vs_th_x_L->GetN(), k.th_x_L, k.th_y_L);
# g_th_y_R_vs_th_x_R->SetPoint(g_th_y_R_vs_th_x_R->GetN(), k.th_x_R, k.th_y_R);
# g_th_y_vs_th_x->SetPoint(g_th_y_vs_th_x->GetN(), k.th_x, k.th_y);
# }
# Line 1199 (k.th_y_R, k.th_y_L)
model = ROOT.ROOT.RDF.TH2DModel("h_th_y_L_vs_th_y_R", ";#theta_{y}^{R};#theta_{y}^{L}",
300, -150E-6, +150E-6, 300, -150E-6, +150E-6)
h_th_y_L_vs_th_y_R = f4.Histo2D(model, "k_th_y_R", "k_th_y_L")
# TODO TGraph
# if (detailsLevel > 2)
# g_th_y_L_vs_th_y_R->SetPoint(g_th_y_L_vs_th_y_R->GetN(), k.th_y_R, k.th_y_L);
# Line 1203: (k.th_x)
# (k.th_y)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_th_x", ";#theta_{x}", 250, -500E-6, +500E-6),
("h_th_y", ";#theta_{y}", 250, -500E-6, +500E-6)
])
h_th_x = f4.Histo1D(models[0], "k_th_x")
h_th_y = f4.Histo1D(models[1], "k_th_y")
# Line 1205: (-k.th_y)
model = ("h_th_y_flipped", ";#theta_{y}", 250, -500E-6, +500E-6)
h_th_y_flipped = f4.Histo1D(model, "minus_k_th_y")
# Line 1207: (k.th_x_L)
# (k.th_x_R)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_th_x_L", ";#theta_{x}^{L}", 250, -500E-6, +500E-6),
("h_th_x_R", ";#theta_{x}^{R}", 250, -500E-6, +500E-6)
])
h_th_x_L = f4.Histo1D(models[0], "k_th_x_L")
h_th_x_R = f4.Histo1D(models[1], "k_th_x_R")
# Line 1210: (k.th_y_L)
# (k.th_y_R)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_th_y_L", ";#theta_{y}^{L}", 250, -500E-6, +500E-6),
("h_th_y_R", ";#theta_{y}^{R}", 250, -500E-6, +500E-6)
])
h_th_y_L = f4.Histo1D(models[0], "k_th_y_L")
h_th_y_R = f4.Histo1D(models[1], "k_th_y_R")
# Line 1213: (k.th_y_L_F)
# (k.th_y_L_N)
# (k.th_y_R_N)
# (k.th_y_R_F)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_th_y_L_F", ";#theta_{y}^{L_F}", 250, -500E-6, +500E-6),
("h_th_y_L_N", ";#theta_{y}^{L_N}", 250, -500E-6, +500E-6),
("h_th_y_R_N", ";#theta_{y}^{R_N}", 250, -500E-6, +500E-6),
("h_th_y_R_F", ";#theta_{y}^{R_F}", 250, -500E-6, +500E-6)
])
h_th_y_L_F = f4.Histo1D(models[0], "k_th_y_L_F")
h_th_y_L_N = f4.Histo1D(models[1], "k_th_y_L_N")
h_th_y_R_N = f4.Histo1D(models[2], "k_th_y_R_N")
h_th_y_R_F = f4.Histo1D(models[3], "k_th_y_R_F")
# fill vertex histograms
# Line 1220 (k.vtx_x)
# (k.vtx_x_L)
# (k.vtx_x_R)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_vtx_x", ";x^{*}", 100, -0.5, +0.5),
("h_vtx_x_L", ";x^{*,L}", 100, -0.5, +0.5),
("h_vtx_x_R", ";x^{*,R}", 100, -0.5, +0.5)
])
h_vtx_x = f4.Histo1D(models[0], "k_vtx_x")
h_vtx_x_L = f4.Histo1D(models[1], "k_vtx_x_L")
h_vtx_x_R = f4.Histo1D(models[2], "k_vtx_x_R")
# Line 1224 (k.vtx_y)
# (k.vtx_y_L)
# (k.vtx_y_R)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_vtx_y", ";y^{*}", 100, -0.5, +0.5),
("h_vtx_y_L", ";y^{*,L}", 100, -0.5, +0.5),
("h_vtx_y_R", ";y^{*,R}", 100, -0.5, +0.5)
])
h_vtx_y = f4.Histo1D(models[0], "k_vtx_y")
h_vtx_y_L = f4.Histo1D(models[1], "k_vtx_y_L")
h_vtx_y_R = f4.Histo1D(models[2], "k_vtx_y_R")
# Line 1228:
# (k.vtx_x_R, k.vtx_x_L)
# (k.vtx_y_R, k.vtx_y_L)
models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_vtx_x_L_vs_vtx_x_R", ";x^{*,R};x^{*,L}", 100, -0.5, +0.5, 100, -0.5, +0.5),
("h_vtx_y_L_vs_vtx_y_R", ";y^{*,R};y^{*,L}", 100, -0.5, +0.5, 100, -0.5, +0.5)
])
h_vtx_x_L_vs_vtx_x_R = f4.Histo2D(models[0], "k_vtx_x_R", "k_vtx_x_L")
h_vtx_y_L_vs_vtx_y_R = f4.Histo2D(models[1], "k_vtx_y_R", "k_vtx_y_L")
# Line 1231:
# (k.th_x_L, k.vtx_x_L)
# (k.th_x_R, k.vtx_x_R)
# (k.th_y_L, k.vtx_y_L)
# (k.th_y_R, k.vtx_y_R)
models = map(ROOT.ROOT.RDF.TH2DModel, [
("h_vtx_x_L_vs_th_x_L", ";#theta_{x}^{L};x^{*,L}", 100, -600E-6, +600E-6, 100, -0.5, +0.5),
("h_vtx_x_R_vs_th_x_R", ";#theta_{x}^{R};x^{*,R}", 100, -600E-6, +600E-6, 100, -0.5, +0.5),
("h_vtx_y_L_vs_th_y_L", ";#theta_{y}^{L};y^{*,L}", 100, -600E-6, +600E-6, 100, -0.5, +0.5),
("h_vtx_y_R_vs_th_y_R", ";#theta_{y}^{R};y^{*,R}", 100, -600E-6, +600E-6, 100, -0.5, +0.5)
])
h_vtx_x_L_vs_th_x_L = f4.Histo2D(models[0], "k_th_x_L", "k_vtx_x_L")
h_vtx_x_R_vs_th_x_R = f4.Histo2D(models[1], "k_th_x_R", "k_vtx_x_R")
h_vtx_y_L_vs_th_y_L = f4.Histo2D(models[2], "k_th_y_L", "k_vtx_y_L")
h_vtx_y_R_vs_th_y_R = f4.Histo2D(models[3], "k_th_y_R", "k_vtx_y_R")
# Line 1236:
# (k.vtx_x_R - k.vtx_x_L)
# (k.vtx_y_R - k.vtx_y_L)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_vtx_x_diffLR", ";x^{*,R} - x^{*,L}", 100, -0.5, +0.5),
("h_vtx_y_diffLR", ";y^{*,R} - y^{*,L}", 100, -0.5, +0.5)
])
h_vtx_x_diffLR = f4.Histo1D(models[0], "k_vtx_x_diffLR");
h_vtx_y_diffLR = f4.Histo1D(models[1], "k_vtx_y_diffLR");
# Line 1239:
# (k.th_x, k.vtx_x_R - k.vtx_x_L)
# (k.th_y, k.vtx_y_R - k.vtx_y_L)
models = map(ROOT.ROOT.RDF.TH1DModel, [
("h_vtx_x_diffLR", ";x^{*,R} - x^{*,L}", 100, -0.5, +0.5),
("h_vtx_y_diffLR", ";y^{*,R} - y^{*,L}", 100, -0.5, +0.5)
])
h_vtx_x_diffLR_vs_th_x = f4.Histo1D(models[0], "k_th_x", "k_vtx_x_diffLR");
h_vtx_y_diffLR_vs_th_y = f4.Histo1D(models[1], "k_th_y", "k_vtx_y_diffLR");
# TODO TProfile
#p_vtx_x_diffLR_vs_th_x = f4.Profile1D("k_th_x", "k_vtx_y_diffLR");
#p_vtx_y_diffLR_vs_th_y = f4.Profile1D("k_th_y", "k_vtx_y_diffLR");
# Line 1245:
# (k.vtx_x_R, k.vtx_x_R - k.vtx_x_L)
# (k.vtx_y_R, k.vtx_y_R - k.vtx_y_L)
models = map(ROOT.ROOT.RDF.TH2DModel,[
("h_vtx_x_diffLR_vs_vtx_x_R", ";x^{*,R};x^{*,R} - x^{*,L}", 100, -0.5, +0.5, 100, -0.5, +0.5),
("h_vtx_y_diffLR_vs_vtx_y_R", ";y^{*,R};y^{*,R} - y^{*,L}", 100, -0.5, +0.5, 100, -0.5, +0.5)
])
h_vtx_x_diffLR_vs_vtx_x_R = f4.Histo2D(models[0], "k_vtx_x_R", "k_vtx_y_diffLR");
h_vtx_y_diffLR_vs_vtx_y_R = f4.Histo2D(models[1], "k_vtx_y_R", "k_vtx_y_diffLR");
# TODO: safe
# if (safe)
# {
# h_vtx_x_safe->Fill(k.vtx_x);
# h_vtx_y_safe->Fill(k.vtx_y);
#
# h_vtx_x_diffLR_safe->Fill(k.vtx_x_R - k.vtx_x_L);
# h_vtx_y_diffLR_safe->Fill(k.vtx_y_R - k.vtx_y_L);
#
# h_vtx_x_diffLR_safe_corr->Fill((k.vtx_x_R - k.vtx_x_L) - 1080.*k.th_x);
# h_vtx_y_diffLR_safe_corr->Fill((k.vtx_y_R - k.vtx_y_L) + 156. *k.th_y);
# }
# TODO: min
# Line 1338
#th_y_L_min = min(th_y_L_min, k.th_y_L);
#th_y_R_min = min(th_y_R_min, k.th_y_R);
# TODO: from line 1358 to 1404 (end of event loop)
# Line 1401
# calculate acceptance divergence correction
r7 = f4.Define("correction", "CalculateAcceptanceCorrectionsRDF( kinematics )") \
.Define("corr", "correction.corr") \
.Define("div_corr", "correction.div_corr") \
.Define("one", "One()")
# Line 1406
for bi in binnings:
bis = binning_setup[bi]
model = ROOT.RDF.TH1DModel("h_t_Nev_before", ";|t|;events per bin", bis.N_bins, bis.bin_edges)
bh_t_Nev_before[bi] = r7.Histo1D(model, "k_t", "one");
model = ROOT.RDF.TH1DModel("h_t_before", ";|t|", bis.N_bins, bis.bin_edges)
bh_t_before[bi] = r7.Histo1D(model, "k_t", "one");
# Line 1412
model = ROOT.RDF.TH2DModel("h_th_y_vs_th_x_before", ";#theta_{x};#theta_{y}", 150, -300E-6, +300E-6, 150, -150E-6, +150E-6)
h_th_y_vs_th_x_before = r7.Histo2D(model, "k_th_x", "k_th_y", "one");
# Line 1414
# Filter skip
f5 = r7.Filter("! correction.skip", "acceptance correction")
# Line 1429
for bi in binnings:
bis = binning_setup[bi]
model = ROOT.RDF.TH1DModel("h_t_Nev_after_no_corr", ";|t|;events per bin", bis.N_bins, bis.bin_edges)
bh_t_Nev_after_no_corr[bi] = f5.Histo1D(model, "k_t", "one");
model = ROOT.RDF.TH1DModel("h_t_after_no_corr", ";|t|", bis.N_bins, bis.bin_edges)
bh_t_after_no_corr[bi] = f5.Histo1D(model, "k_t", "one");
model = ROOT.RDF.TH1DModel("h_t_after", ";|t|", bis.N_bins, bis.bin_edges)
bh_t_after[bi] = f5.Histo1D(model, "k_t", "corr");
# Line 1435
model = ROOT.RDF.TH2DModel("h_th_y_vs_th_x_after", ";#theta_{x};#theta_{y}", 150, -300E-6, +300E-6, 150, -150E-6, +150E-6);
h_th_y_vs_th_x_after = f5.Histo2D(model, "k_th_x", "k_th_y", "div_corr");
# Line 1435
model = ROOT.RDF.TH2DModel("h_th_vs_phi_after", ";#phi;#theta", 50, -M_PI, +M_PI, 50, 150E-6, 550E-6);
h_th_vs_phi_after = f5.Histo2D(model, "k_phi", "k_th", "div_corr");
# Line 1441
# apply normalization
model = ROOT.ROOT.RDF.TH1DModel("h_t_normalized", ";|t|",128, 0., 4.)
bh_t_normalized_ob_1_30_02 = f5.Define("corr_norm", "corr * normalization") \
.Histo1D(model, "k_t", "corr_norm")
# Line 1445
model = ROOT.RDF.TH2DModel("h_th_y_vs_th_x_normalized", ";#theta_{x};#theta_{y}", 150, -600E-6, +600E-6, 150, -600E-6, +600E-6);
h_th_y_vs_th_x_normalized = f5.Define("div_corr_norm", "correction.div_corr * normalization") \
.Histo2D(model, "k_th_x", "k_th_y", "div_corr_norm");
# normalize histograms
for bi in binnings:
bh_t_before[bi].Scale(1., "width");
bh_t_after_no_corr[bi].Scale(1., "width");
bh_t_after[bi].Scale(1., "width");
#bh_t_normalized[bi]->Scale(1., "width");
###############################
###### END OF EVENT LOOP ######
###############################
print("---------------------------- after event loop ---------------------------\n");
print(">> th_min = %s\n" % th_min);
print(">> th_y_L_min = %s\n" % th_y_L_min);
print(">> th_y_R_min = %s\n" % th_y_R_min);
print("\n");
print("N_anal = %s\n" % N_anal);
print("N_anal_zeroBias = %s\n" % N_anal_zeroBias);
print("N_zeroBias_el = %s\n" % N_zeroBias_el);
print("N_zeroBias_el_RP_trig = %s\n" % N_zeroBias_el_RP_trig);
print("N_4outof4 = %s\n" % N_4outof4);
print("N_el = %s\n" % N_el);
print("N_el_T2trig = %s\n" % N_el_T2trig);
print("N_4outof4_T2trig = %s\n" % N_4outof4_T2trig);
# TODO: From line 1454 on, mostly pure root
# Line 1492
# Normalize histograms
#bh_t_after_ob_1_30_02.Scale(1., "width");
# Line 1494
#bh_t_normalized_ob_1_30_02.Scale(1., "width")
# TODO Line 1497
#h_th_y_vs_th_x_normalized.Scale(1., "width")
# Line 1506
th_y_diffLR.Scale(1., "width");
th_x_diffLR.Scale(1., "width");
# TODO
#th_y_diffLR_safe.Scale(1., "width");
#th_x_diffLR_safe.Scale(1., "width");
# TODO
# for (unsigned int bi = 0; bi < binnings.size(); bi++)
# HideLowTBins(bh_t_normalized[bi], anal.t_min_fit);
# TODO
# fit histograms
# From 1515 - 1607 (TProfiles)
# TODO
# th_y_diffLR_safe->Fit("gaus");
# th_x_diffLR_safe->Fit("gaus");
# save histograms
c = ROOT.TCanvas();
outputname = "/distributions_{}_{}_{}.root".format("DS1", th_tag, selected_diagonal)
outF = ROOT.TFile.Open(outputDir+outputname, "recreate");
ROOT.gDirectory = outF.mkdir("metadata");
c = ROOT.TCanvas("rate cmp");
h_timestamp_dgn.Draw();
#h_timestamp_B0.SetLineColor(4);
#h_timestamp_B0.Draw("sames");
h_timestamp_sel.SetLineColor(2);
h_timestamp_sel.Draw("sames");
c.Write();
# Line 1700
hitDistDir = outF.mkdir("hit distributions");
ROOT.gDirectory = hitDistDir.mkdir("vertical, aligned, before selection");
h_y_L_2_F_vs_x_L_2_F_al_nosel.Write();
h_y_L_2_N_vs_x_L_2_N_al_nosel.Write();
h_y_L_1_F_vs_x_L_1_F_al_nosel.Write();
h_y_R_1_F_vs_x_R_1_F_al_nosel.Write();
h_y_R_2_N_vs_x_R_2_N_al_nosel.Write();
h_y_R_2_F_vs_x_R_2_F_al_nosel.Write();
ROOT.gDirectory = hitDistDir.mkdir("vertical, not aligned, after selection");
h_y_L_2_F_vs_x_L_2_F_noal_sel.Write();
h_y_L_2_N_vs_x_L_2_N_noal_sel.Write();
h_y_L_1_F_vs_x_L_1_F_noal_sel.Write();
h_y_R_1_F_vs_x_R_1_F_noal_sel.Write();
h_y_R_2_N_vs_x_R_2_N_noal_sel.Write();
h_y_R_2_F_vs_x_R_2_F_noal_sel.Write();
ROOT.gDirectory = hitDistDir.mkdir("vertical, aligned, after selection");
h_y_L_2_F_vs_x_L_2_F_al_sel.Write();
h_y_L_2_N_vs_x_L_2_N_al_sel.Write();
h_y_L_1_F_vs_x_L_1_F_al_sel.Write();
h_y_R_1_F_vs_x_R_1_F_al_sel.Write();
h_y_R_2_N_vs_x_R_2_N_al_sel.Write();
h_y_R_2_F_vs_x_R_2_F_al_sel.Write();
ROOT.gDirectory = outF.mkdir("selected - hits");
ROOT.gDirectory = outF.mkdir("selected - angles");
th_x_diffLR.Sumw2(); th_x_diffLR.Write()
th_y_diffLR.Sumw2(); th_y_diffLR.Write()
th_x_diffLF.Sumw2(); th_x_diffLF.Write()
th_x_diffRF.Sumw2(); th_x_diffRF.Write()
h_th_x_diffLR_vs_th_x.Write();
h_th_y_diffLR_vs_th_y.Write();
h_th_x_diffLR_vs_vtx_x.Write();
# TODO TProfile
# p_th_x_diffLR_vs_th_x->Write();
# p_th_y_diffLR_vs_th_y->Write();
# p_th_y_L_diffNF_vs_th_y_L->Write();
# p_th_y_R_diffNF_vs_th_y_R->Write();
# p_th_x_diffLR_vs_vtx_x->Write();
## TODO:
#th_x_sigmaLR_vs_th_x.Write();
#th_y_sigmaLR_vs_th_y.Write();
#th_x_diffLR_safe.Write();
#th_y_diffLR_safe.Write();
# TODO TProfile
# p_th_x_vs_th_y->Write();
# p_th_x_L_vs_th_y_L->Write();
# p_th_x_R_vs_th_y_R->Write();
h_th_y_L_vs_th_x_L.Write();
h_th_y_R_vs_th_x_R.Write();
h_th_y_vs_th_x.Write();
# TODO TProfile
# g_th_y_L_vs_th_x_L->Write();
# g_th_y_R_vs_th_x_R->Write();
# g_th_y_vs_th_x->Write();
c = ROOT.TCanvas();
c.SetLogz(1);
c.ToggleEventStatus();
c.SetCrosshair(1);
h_th_y_L_vs_th_y_R.Draw("colz");
#g_th_y_L_vs_th_y_R.Draw("p");
c.Write("canvas_th_y_L_vs_th_y_R");
h_th_x.SetLineColor(1); h_th_x.Write();
h_th_y.SetLineColor(1); h_th_y.Write();
h_th_y_flipped.SetLineColor(1); h_th_y_flipped.Write();
h_th_x_L.SetLineColor(2); h_th_x_L.Write();
h_th_x_R.SetLineColor(4); h_th_x_R.Write();
h_th_y_L.SetLineColor(2); h_th_y_L.Write();
h_th_y_R.SetLineColor(4); h_th_y_R.Write();
h_th_y_L_F.SetLineColor(2); h_th_y_L_F.Write();
h_th_y_L_N.SetLineColor(6); h_th_y_L_N.Write();
h_th_y_R_N.SetLineColor(4); h_th_y_R_N.Write();
h_th_y_R_F.SetLineColor(7); h_th_y_R_F.Write();
# TODO TGraph
# {
# double x[] = {0, 1, 2, 3};
# double y[] = {anal.th_y_lcut_L, anal.th_y_hcut_L, anal.th_y_lcut_R, anal.th_y_hcut_R};
# TGraph *g = new TGraph(4, x, y);
# g->SetName("g_th_y_cuts");
# g->Write();
# }
ROOT.gDirectory = outF.mkdir("selected - vertex");
h_vtx_x.SetLineColor(1); h_vtx_x.Write();
h_vtx_x_L.SetLineColor(2); h_vtx_x_L.Write();
h_vtx_x_R.SetLineColor(4); h_vtx_x_R.Write();
h_vtx_y.SetLineColor(1); h_vtx_y.Write();
h_vtx_y_L.SetLineColor(2); h_vtx_y_L.Write();
h_vtx_y_R.SetLineColor(4); h_vtx_y_R.Write();
# # TODO:
#h_vtx_x_safe.Write();
#h_vtx_y_safe.Write();
h_vtx_x_L_vs_vtx_x_R.Write();
h_vtx_y_L_vs_vtx_y_R.Write();
h_vtx_x_L_vs_th_x_L.Write();
h_vtx_x_R_vs_th_x_R.Write();
h_vtx_y_L_vs_th_y_L.Write();
h_vtx_y_R_vs_th_y_R.Write();
h_vtx_x_diffLR.Sumw2(); h_vtx_x_diffLR.Write();
h_vtx_y_diffLR.Sumw2(); h_vtx_y_diffLR.Write();
# TODO
#h_vtx_x_diffLR_safe.Write();
#h_vtx_y_diffLR_safe.Write();
# TODO
#h_vtx_x_diffLR_safe_corr.Write();
#h_vtx_y_diffLR_safe_corr.Write();
h_vtx_x_diffLR_vs_th_x.Write();
h_vtx_y_diffLR_vs_th_y.Write();
# TODO TProfile
# p_vtx_x_diffLR_vs_th_x.Write();
# p_vtx_y_diffLR_vs_th_y.Write();
h_vtx_x_diffLR_vs_vtx_x_R.Write();
h_vtx_y_diffLR_vs_vtx_y_R.Write();
ROOT.gDirectory = outF.mkdir("optics");
#h_x_L_F_vs_th_x_L.Write();
#h_x_R_F_vs_th_x_R.Write();
# TODO
#p_x_L_N_vs_th_x_L.Write();
#p_x_L_F_vs_th_x_L.Write();
#p_x_R_N_vs_th_x_R.Write();
#p_x_R_F_vs_th_x_R.Write();
# TODO TProfile
# p_th_x_R_vs_th_x_L.Write();
# p_th_y_R_vs_th_y_L.Write();
# p_th_y_LF_vs_th_y_LN.Write();
# p_th_y_RF_vs_th_y_RN.Write();
#h_thl_y_L_vs_y_LF.Write();
#h_thl_y_R_vs_y_RF.Write();
#h_thl_y_L_vs_thl_y_R.Write();
# TODO TProfile
# p_thl_y_L_vs_y_LF.Write();
# p_thl_y_R_vs_y_RF.Write();
# p_thl_y_L_vs_thl_y_R.Write();
# TODO
# ROOT.gDirectory = outF->mkdir("binning");
# for (unsigned int bi = 0; bi < binnings.size(); bi++)
# {
# TGraph *g = new TGraph();