def getEfficiencyForPtCut(listMatchedPairs, ptCut):
global efficiencyCounter
efficiencyCounter += 1
effObj = TEfficiency("efficiency" + str(efficiencyCounter),
"Efficiency for p_{T,cut} = " + str(ptCut) + " GeV",
251, -0.5, 250.5)
ROOT.SetOwnership(effObj, False)
truePtValues = range(0, 200)
countPassed = [0] * 200
countTotal = [0] * 200
yVal = [0] * 200
xErr = [0.5] * 200
yErr = [0] * 200
for event in listMatchedPairs:
for pair in event:
effObj.Fill(pair[0].pt >= ptCut, pair[1].pt)
countTotal[int(pair[1].pt)] += 1
if (pair[0].pt >= ptCut):
countPassed[int(pair[1].pt)] += 1
for i, val in enumerate(countPassed):
if (countTotal[i] > 0):
yVal[i] = float(countPassed[i] / countTotal[i])
yErr[i] = float(math.sqrt(yVal[i] * (1 - yVal[i]) / countTotal[i]))
#print countPassed
#print countTotal
c = TCanvas("can", "vas", 1200, 1200)
#effObj.Draw()
return c, effObj
def trackmatch():
garanainit.init()
#ROOT.gROOT.SetBatch(True) # run in batch mode (don't display plots)
tm = ROOT.TreeManager("./test.root") #structuredtree.root")
# truth matching utility
bt = ROOT.Backtracker(tm)
print("constructed treemanager")
#tree accessors
#gen = tm.GetGenTree()
g4 = tm.GetG4Tree()
rec = tm.GetRecoTree()
nmatch = 0 # number of reco tracks matched to a G4Particle
ntrack = 0 # number of reco tracks
nprimary = 0 # true number of primaries
nprimary_match = 0 #number of primaries matched to at least one track
hg4_nmatch = TH1F('hg4_nmatch',
'G4Particle -> Track matching;N^{0} matches', 10, 0, 10)
hfrac = TH1F("hfrac",
"particle contributing most energy;fraction of total", 50, 0,
1.1)
hncont = TH1F("hncont", "particles contributing energy;N^{0} particles",
10, 0, 10)
hncontfrac = TH2F(
'hncontfrac',
'particles contributing energy;N^{0} particles;max fraction of total',
10, 0, 25, 0, 1.1)
eff_g4match = TEfficiency(
'eff_g4match',
'G4Particle -> Track matching; initial energy [GeV]; efficiency', 10,
0, 5)
# loop over events
for ientry in range(rec.NEntries()):
if ientry % 100 == 0: print('processing event ' + str(ientry))
tm.GetEntry(ientry)
bt.FillMaps() #load associations for this entry
#nprimary += g4.NPrimary()
for ig4 in range(g4.NSim()):
if not g4.IsPrimary(ig4): continue # only consider primaries
if not abs(g4.PDG(ig4)) == 2212: continue # muons only
nprimary += 1
if bt.G4ParticleToTracks(ig4).size() > 0:
nprimary_match += 1 # matched to something?
hg4_nmatch.Fill(bt.G4ParticleToTracks(ig4).size())
eff_g4match.Fill(
bt.G4ParticleToTracks(ig4).size() > 0,
g4.SimMomEnter(ig4, 0).E())
ntrack += rec.NTrack()
for itrack in range(rec.NTrack()):
imatch = bt.TrackToG4Particle(itrack)
if imatch < g4.NSim(): nmatch += 1
#imatches = bt.TrackToG4Particles(itrack)
hncont.Fill(rec.TrackNTrueTrack(itrack)) #len(imatches))
for ip in range(rec.TrackNTrueTrack(itrack)):
hfrac.Fill(rec.TrackMaxDepositFrac(itrack))
hncontfrac.Fill(rec.TrackNTrueTrack(itrack),
rec.TrackMaxDepositFrac(itrack))
if nprimary != 0:
print('number of primaries found: ', nprimary)
print('number of matched primaries: ', nprimary_match)
print('g4particle -> track matching efficiency: ',
1.0 * nprimary_match / nprimary)
else:
print('no G4particles found!')
if ntrack != 0:
print('track -> g4particle matching efficiency: ',
1.0 * nmatch / ntrack)
else:
print('no tracks found!')
print('hncont size is ', hncont.Integral())
print('hfrac size is ', hfrac.Integral())
cg4_nmatch = TCanvas()
hg4_nmatch.Draw()
cg4_nmatch.SaveAs('g4_nmatch.png')
cncont = TCanvas()
hncont.Draw()
cncont.SaveAs('ncont.png')
cfrac = TCanvas()
hfrac.Draw()
cfrac.SaveAs('frac.png')
cncontfrac = TCanvas()
hncontfrac.Draw()
cncontfrac.SaveAs('ncont_v_frac.png')
ceff_g4match = TCanvas()
eff_g4match.Draw()
ceff_g4match.SaveAs('g4match_eff.png')
# ask for user input as a way to keep the program from ending so we can
# see the histogram.
# python doesn't like empty input strings so I abuse exception handling
try:
null = input("press <Enter> to close canvas and exit program.")
except:
null = 'null'
def makePlots(f_ntuple, f_results, triggerpath):
t = f_ntuple.Get("tree")
print "Tree extracted..."
h_csv_inc_4Calo = TH1F("h_csv_inc_4Calo", "h_csv_inc_4Calo", 50, 0, 1)
h_csv_inc_4PF = TH1F("h_csv_inc_4PF", "h_csv_inc_4PF", 50, 0, 1)
h_csv_afcalo = TH1F("h_csv_afcalo", "h_csv_afcalo", 50, 0, 1)
h_csv_afpf = TH1F("h_csv_afpf", "h_csv_afpf", 50, 0, 1)
h_csv_inc_log_4Calo = TH1F("h_csv_inc_log_4Calo", "h_csv_inc_log_4Calo",
50, 0, 7)
h_csv_inc_log_4PF = TH1F("h_csv_inc_log_4PF", "h_csv_inc_log_4PF", 50, 0,
7)
h_csv_afcalo_log = TH1F("h_csv_afcalo_log", "h_csv_afcalo_log", 50, 0, 7)
h_csv_afpf_log = TH1F("h_csv_afpf_log", "h_csv_afpf_log", 50, 0, 7)
h_csv_inc_calo = TH1F("h_csv_inc_calo", "h_csv_inc_calo", 50, 0, 1)
h_csv_inc_pf = TH1F("h_csv_inc_pf", "h_csv_inc_pf", 50, 0, 1)
h_csv_calo_pf = TH1F("h_csv_calo_pf", "h_csv_calo_pf", 50, 0, 1)
h_csv_inc_calo_log = TH1F("h_csv_inc_calo_log", "h_csv_inc_calo_log", 50,
0, 7)
h_csv_inc_pf_log = TH1F("h_csv_inc_pf_log", "h_csv_inc_pf_log", 50, 0, 7)
h_csv_calo_pf_log = TH1F("h_csv_calo_pf_log", "h_csv_calog_pf_log", 50, 0,
7)
eff_csv_inc_calo = TEfficiency("eff_csv_inc_calo", "eff_csv_inc_calo", 50,
0, 1)
eff_csv_inc_pf = TEfficiency("eff_csv_inc_pf", "eff_csv_inc_pf", 50, 0, 1)
eff_csv_calo_pf = TEfficiency("eff_csv_calo_pf", "eff_csv_calo_pf", 50, 0,
1)
eff_csv_inc_calo_log = TEfficiency("eff_csv_inc_calo_log",
"eff_csv_inc_calo_log", 50, 0, 7)
eff_csv_inc_pf_log = TEfficiency("eff_csv_inc_pf_log",
"eff_csv_inc_pf_log", 50, 0, 7)
eff_csv_calo_pf_log = TEfficiency("eff_csv_calo_pf_log",
"eff_csv_calog_pf_log", 50, 0, 7)
i = 0
for event in t:
print
print "New event --------"
print
if not SelectTrigger(event, triggerpath, True): continue
print "CALO JET SECTION : # calo jet = ", event.caloJet_num
for i in range(0, event.caloJet_num - 1):
if event.caloJet_offmatch[i] >= 0:
print "calo jet[", i, "]"
print "event.caloJet_offmatch[i]: ", event.caloJet_offmatch[
i], " event.offJet_num: ", event.offJet_num
print "calo pt: ", event.caloJet_pt[
i], " offline pt: ", event.offJet_pt[
event.caloJet_offmatch[i]]
print "calo eta: ", event.caloJet_eta[
i], " offline eta: ", event.offJet_eta[
event.caloJet_offmatch[i]]
print "calo phi: ", event.caloJet_phi[
i], " offline phi: ", event.offJet_phi[
event.caloJet_offmatch[i]]
print "offline csv: ", event.offJet_csv[
event.caloJet_offmatch[i]]
if event.offJet_pt[event.caloJet_offmatch[i]] < 30: continue
passed = False
h_csv_inc_4Calo.Fill(
event.offJet_csv[event.caloJet_offmatch[i]])
if event.offJet_csv[event.caloJet_offmatch[i]] < 1.:
h_csv_inc_log_4Calo.Fill(-math.log(
1 - event.offJet_csv[event.caloJet_offmatch[i]]))
if SelectTrigger(event, triggerpath, False, "Calo", i):
passed = True
print "passes calo filter", event.offJet_csv[
event.caloJet_offmatch[i]]
h_csv_afcalo.Fill(
event.offJet_csv[event.caloJet_offmatch[i]])
h_csv_inc_calo.Fill(
event.offJet_csv[event.caloJet_offmatch[i]])
if event.offJet_csv[event.caloJet_offmatch[i]] != 1.:
h_csv_afcalo_log.Fill(-math.log(
1 - event.offJet_csv[event.caloJet_offmatch[i]]))
h_csv_inc_calo_log.Fill(-math.log(
1 - event.offJet_csv[event.caloJet_offmatch[i]]))
eff_csv_inc_calo.Fill(
passed, event.offJet_csv[event.caloJet_offmatch[i]])
print "PF JET SECTION : # pf jet = ", event.pfJet_num
for i in range(0, event.pfJet_num - 1):
if event.pfJet_offmatch[i] >= 0:
print "pf jet[", i, "]"
print "event.pfJet_offmatch[i]: ", event.pfJet_offmatch[
i], " event.offJet_num: ", event.offJet_num
print "pf pt: ", event.pfJet_pt[
i], " offline pt: ", event.offJet_pt[
event.pfJet_offmatch[i]]
print "pf eta: ", event.pfJet_eta[
i], " offline eta: ", event.offJet_eta[
event.pfJet_offmatch[i]]
print "pf phi: ", event.pfJet_phi[
i], " offline phi: ", event.offJet_phi[
event.pfJet_offmatch[i]]
print "offline csv: ", event.offJet_csv[
event.pfJet_offmatch[i]]
if event.offJet_pt[event.pfJet_offmatch[i]] < 30: continue
passed = False
h_csv_inc_4PF.Fill(event.offJet_csv[event.pfJet_offmatch[i]])
if event.offJet_csv[event.pfJet_offmatch[i]] < 1.:
h_csv_inc_log_4PF.Fill(-math.log(
1 - event.offJet_csv[event.pfJet_offmatch[i]]))
if SelectTrigger(event, triggerpath, False, "PF", i):
passed = True
print "passes pf filter", event.offJet_csv[
event.pfJet_offmatch[i]]
h_csv_afpf.Fill(event.offJet_csv[event.pfJet_offmatch[i]])
h_csv_inc_pf.Fill(
event.offJet_csv[event.pfJet_offmatch[i]])
h_csv_calo_pf.Fill(
event.offJet_csv[event.pfJet_offmatch[i]])
if event.offJet_csv[event.pfJet_offmatch[i]] < 1.:
h_csv_afpf_log.Fill(-math.log(
1 - event.offJet_csv[event.pfJet_offmatch[i]]))
h_csv_inc_pf_log.Fill(-math.log(
1 - event.offJet_csv[event.pfJet_offmatch[i]]))
h_csv_calo_pf_log.Fill(-math.log(
1 - event.offJet_csv[event.pfJet_offmatch[i]]))
eff_csv_inc_pf.Fill(passed,
event.offJet_csv[event.pfJet_offmatch[i]])
if event.offJet_csv[event.pfJet_offmatch[i]] < 1.:
eff_csv_inc_pf_log.Fill(
passed,
-math.log(1 -
event.offJet_csv[event.pfJet_offmatch[i]]))
f_results.Write()
def makePlots(f_ntuple, f_results):
t = f_ntuple.Get("tree")
print "Tree extracted..."
h_csv_inc_4PF = TH1F("h_csv_inc_4PF", "h_csv_inc_4PF", 50, 0, 1)
h_csv_inc_log_4PF = TH1F("h_csv_inc_log_4PF", "h_csv_inc_log_4PF", 50, 0,
7)
h_csv_inc_pf = TH1F("h_csv_inc_pf", "h_csv_inc_pf", 50, 0, 1)
h_csv_inc_pf_log = TH1F("h_csv_inc_pf_log", "h_csv_inc_pf_log", 50, 0, 7)
eff_csv_inc_pf = TEfficiency("eff_csv_inc_pf", "eff_csv_inc_pf", 50, 0, 1)
eff_csv_inc_pf_log = TEfficiency("eff_csv_inc_pf_log",
"eff_csv_inc_pf_log", 50, 0, 7)
i = 0
for event in t:
print
print "New event --------"
print
if not event.HLT_PFHT430_SixPFJet40: continue
print "PF JET SECTION : # pf jet = ", event.pfJet_num
for i in range(0, event.pfJet_num - 1):
if event.pfJet_offmatch[i] >= 0:
print "pf jet[", i, "]"
print "event.pfJet_offmatch[i]: ", event.pfJet_offmatch[
i], " event.offJet_num: ", event.offJet_num
print "pf pt: ", event.pfJet_pt[
i], " offline pt: ", event.offJet_pt[
event.pfJet_offmatch[i]]
print "pf eta: ", event.pfJet_eta[
i], " offline eta: ", event.offJet_eta[
event.pfJet_offmatch[i]]
print "pf phi: ", event.pfJet_phi[
i], " offline phi: ", event.offJet_phi[
event.pfJet_offmatch[i]]
print "offline csv: ", event.offJet_csv[
event.pfJet_offmatch[i]]
if event.offJet_pt[event.pfJet_offmatch[i]] < 30: continue
passed = False
h_csv_inc_4PF.Fill(event.offJet_csv[event.pfJet_offmatch[i]])
if event.offJet_csv[event.pfJet_offmatch[i]] < 1.:
h_csv_inc_log_4PF.Fill(-math.log(
1 - event.offJet_csv[event.pfJet_offmatch[i]]))
if event.pfJet_hltBTagPFCSVp080Single[
i]: # from HLT_PFHT430_SixPFJet40_PFBTagCSV_1p5
passed = True
print "passes pf filter", event.offJet_csv[
event.pfJet_offmatch[i]]
h_csv_inc_pf.Fill(
event.offJet_csv[event.pfJet_offmatch[i]])
if event.offJet_csv[event.pfJet_offmatch[i]] < 1.:
h_csv_inc_pf_log.Fill(-math.log(
1 - event.offJet_csv[event.pfJet_offmatch[i]]))
eff_csv_inc_pf.Fill(passed,
event.offJet_csv[event.pfJet_offmatch[i]])
if event.offJet_csv[event.pfJet_offmatch[i]] < 1.:
eff_csv_inc_pf_log.Fill(
passed,
-math.log(1 -
event.offJet_csv[event.pfJet_offmatch[i]]))
f_results.Write()
def RunAnalysis(input_name, output_name=""):
# Check output name, set by default if not passed to the function
if not output_name:
output_name = input_name.split("_")[0] + "_analysed.root"
print("INPUT:", input_name, "\nOUTPUT:", output_name)
# Configuring thresholds
(thr_start, thr_end, thr_step) = (0.3, 8, 0.1)
thr_range = np.arange(thr_start, thr_end + thr_step, thr_step)
n_thr = len(thr_range)
# Get hit dictionary
hit_dict = GetHitDict(input_name)
# Open root file to write the results into
write_file = TFile("data/" + output_name, "recreate")
write_file.cd()
# Initialize efficiency and cluster size objects
eff = TEfficiency("Efficiency", "Efficiency;Threshold [fC];Efficiency",
n_thr, thr_start, thr_end)
clus_graph = TGraphErrors(n_thr)
clus_graph.SetNameTitle("Average_cluster_size", "Average_cluster_size")
clus_graph.GetXaxis().SetTitle("Threshold [fC]")
clus_graph.GetYaxis().SetTitle("Average cluster size")
# Perform threshold scanning
for thr in thr_range:
thrE = thr * 6242.2
print("Threshold scanning:",
round(thr / thr_end * 100, 1),
"%",
end="\r")
# Scan a threshold and obtain a list of clusters
cluster_list = ScanThreshold(hit_dict, thrE)
# Analyze cluster list and fill TEfficiency object
for cluster in cluster_list:
eff.Fill(bool(cluster), thr)
# Replace zeros with NaN to obtain proper average later
cluster_list = [
np.nan if cluster == 0 else cluster for cluster in cluster_list
]
try:
# Calculate average cluster size and add points to clus_graph
cluster_size = np.nanmean(cluster_list)
N = np.count_nonzero(~np.isnan(cluster_list))
err = np.nanstd(cluster_list) / sqrt(N - 1)
clus_graph.SetPoint(int(np.where(thr_range == thr)[0][0]), thr,
cluster_size)
clus_graph.SetPointError(int(np.where(thr_range == thr)[0][0]),
ex=0,
ey=err)
except RuntimeWarning:
pass
print("\nDone.")
# Efficiency fit
fit_form = "0.5*[0]*TMath::Erfc((x-[1])/(TMath::Sqrt(2)*[2])*(1-0.6*TMath::TanH([3]*(x-[1])/TMath::Sqrt(2)*[2])))"
fit_func = TF1("Efficiency_fit", fit_form, 0, 8)
# Set initial parameter values and limits
fit_func.SetParameters(1, 4, 1, 1, 0.5)
fit_func.SetParLimits(1, 0, 5)
fit_func.SetParLimits(2, 0, 2)
fit_func.SetParLimits(3, 0, 2)
fit_func.SetParLimits(4, 0.5, 0.7)
# Perform fit
eff.Fit(fit_func, "R")
# Write outputs to file
eff.Write()
clus_graph.Write()
# Collect info
source = "allpix"
angle = input_name.split("-")[0]
descr = input_name.split("_")[0].strip("0deg-")
n_events = str(int(eff.GetTotalHistogram().GetEntries() / n_thr))
title = source + "," + angle + "," + descr + "(" + str(n_events) + "ev)"
vt50 = str(fit_func.GetParameter(1))
vt50_err = str(fit_func.GetParError(1))
thr_range = str(thr_start) + ":" + str(thr_end) + ":" + str(thr_step)
# Write info to Info directory
info_dir = write_file.mkdir("Info")
info_dir.cd()
info_dir.WriteObject(TString(source), "source")
info_dir.WriteObject(TString(angle), "angle")
info_dir.WriteObject(TString(descr), "descr")
info_dir.WriteObject(TString(n_events), "n_events")
info_dir.WriteObject(TString(title), "title")
info_dir.WriteObject(TString(vt50), "vt50")
info_dir.WriteObject(TString(vt50), "vt50_err")
info_dir.WriteObject(TString(thr_range), "thr_range")
write_file.Close()
