leg.SetTextFont(42)
leg.SetTextSize(0.05)
leg.Draw()
can2 = TCanvas('can_TrigEff', 'can_TrigEff', 900, 600)
eff = TEfficiency('efficiency', 'efficiency', hRef.GetNbinsX(), 0, 2000)
eff.SetPassedHistogram(hSig, 'f')
eff.SetTotalHistogram(hRef, 'f')
eff.SetLineColor(ROOT.kBlack)
eff.SetMarkerColor(ROOT.kBlack)
eff.SetMarkerStyle(20)
fit = TF1('fit', '(1-[2])+[2]*TMath::Erf((x-[0])/[1])', xmin, xmax)
fit.SetParameters(800, 100, 0.3)
fit.SetLineColor(ROOT.kBlue)
eff.Fit(fit, 'RQ')
line = TF1('line', '1', xmin, xmax)
line.GetXaxis().SetTitle('m_{jj} (GeV)')
line.GetYaxis().SetTitle('Trigger Efficiency')
line.SetLineColor(ROOT.kBlack)
line.SetLineStyle(2)
line.SetMinimum(0.3)
line.SetMaximum(1.1)
line.Draw()
eff.Draw('samePE1')
gPad.Update()
x0 = fit.GetX(0.99)
cut = TLine(x0, gPad.GetFrame().GetY1(), x0, gPad.GetFrame().GetY2())
cut.SetLineColor(ROOT.kRed)
#efffunc.SetParameter(0,1.0)
#efffunc.SetParameter(1,500)
massArr = array.array('d')
zeroArr = array.array('d')
xintArr = array.array('d')
yintArr = array.array('d')
xintErrArr = array.array('d')
yintErrArr = array.array('d')
for iy in range(1, cleanhist.GetNbinsY() + 1):
cleanslice = cleanhist.ProjectionX("cleanslice", iy, iy)
messyslice = messycleanhist.ProjectionX("messyslice", iy, iy)
sliceeff = TEfficiency(messyslice, cleanslice)
efffunc.SetParameters(1.0, 500)
efffunc.FixParameter(0, 1.0)
s = sliceeff.Fit(efffunc, "S")
sliceeff.Draw("AP")
#c.Print(outfilename+".pdf");
if s.Get() and s.Get().IsValid() and s.Get().CovMatrixStatus() == 3:
massArr.append(cleanhist.GetYaxis().GetBinCenter(iy))
zeroArr.append(0)
xintArr.append(s.Parameter(1))
yintArr.append(s.Parameter(0))
xintErrArr.append(s.ParError(1))
yintErrArr.append(s.ParError(0))
xintgraph = TGraphErrors(len(massArr), massArr, xintArr, zeroArr, xintErrArr)
xintgraph.SetTitle("X-intercept;mass [GeV];D1")
xintgraph.SetName("xintgraph")
xintgraph.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()
