def draw_branch(name, hist_stats, cut=""):
h_D = TH1D("D" + name, "#mu from D", *hist_stats)
h_B = TH1D("B" + name, "#mu from B", *hist_stats)
h_D.SetLineColor(2)
h_D.SetLineWidth(2)
h_B.SetLineWidth(2)
trees = {"B": "B_tree", "D": "D_tree"}
for tree in trees:
trees[tree] = os.path.join(dir_name, trees[tree])
trees[tree] = root_file.Get(trees[tree])
trees[tree].Project(tree + name, name, cut)
can = TCanvas("can", "can")
can.cd()
h_D.Draw()
h_B.Draw("SAME")
can.SetFillColor(10)
can.SetLogy()
legend = can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
h_D.GetXaxis().SetTitle(x_axis_title)
h_D.GetYaxis().SetTitle(y_axis_title)
h_D.GetXaxis().SetDecimals()
h_D.GetYaxis().SetDecimals()
h_D.GetXaxis().SetTitleSize(size)
h_D.GetYaxis().SetTitleSize(size)
h_D.GetYaxis().SetTitleOffset(1.2)
h_D.GetXaxis().SetLabelSize(size)
h_D.GetYaxis().SetLabelSize(size)
h_D.SetTitle(name)
h_D.SetStats(0)
can.SaveAs("BD" + name + ".gif")
def plot_calibrations(group_data, voltage, cal_pars_list, stacked=False):
group_name = str(group_data[0].run_number)
run = group_data[2]
name = str(run.run_number)
hists_and_lines = []
for cal_pars in cal_pars_list:
pars = parameters.gain_pars[cal_pars]
run.calibrate_energies(voltage, pars)
hist = make_hist(name, run.calibrated_energies, 0, 3500)
K_line = TLine(1460.820, 0, 1460.820, 1000000)
K_line.SetLineColor(632)
Tl_line = TLine(2614.511, 0, 2614.511, 1000000)
Tl_line.SetLineColor(632)
hists_and_lines.append([hist, K_line, Tl_line])
if stacked:
print('Plotting stacked calibrated histograms.')
run_canvas = TCanvas()
run_canvas.SetLogy()
colors = [808, 397, 436, 424, 628, 852, 800, 863, 403, 797]
color_index = 0
hs = THStack('hs', 'Group %s Histograms' % group_name)
for entry in hists_and_lines:
title = str(run.run_number)
entry[0].SetLineColor(colors[color_index])
color_index += 1
hs.Add(entry[0])
hs.Draw('nostack')
K_line = TLine(1460.820, 0, 1460.820, 1000000)
K_line.SetLineColor(632)
K_line.Draw()
Tl_line = TLine(2614.511, 0, 2614.511, 1000000)
Tl_line.SetLineColor(632)
Tl_line.Draw()
run_canvas.BuildLegend()
path = 'output/group_' + str(group_data[0].run_number) + '/'
run_canvas.Print((path + str(group_data[0].run_number) +
'_stacked_calibrated_histogram.pdf['))
run_canvas.Print((path + str(group_data[0].run_number) +
'_stacked_calibrated_histogram.pdf'))
run_canvas.Print((path + str(group_data[0].run_number) +
'_stacked_calibrated_histogram.pdf]'))
else:
print('Plotting calibrated energy histogram.')
run_canvas = TCanvas("run_canvas", "run canvas")
run_canvas.Divide(3, int(len(cal_pars_list) / 2))
canvas_index = 1
for entry in hists_and_lines:
pad = run_canvas.cd(canvas_index)
pad.SetLogy()
entry[0].Draw()
entry[1].Draw()
entry[2].Draw()
pad.Update()
canvas_index += 1
path = 'output/group_' + group_name + '/'
run_canvas.Print((path + name + '_calibrated_histograms.pdf['))
run_canvas.Print((path + name + '_calibrated_histograms.pdf'))
run_canvas.Print((path + name + '_calibrated_histograms.pdf]'))
def plotunc(typ, name, htruth, hcen, hdown, hup, lgh=''):
global cana
cana = TCanvas('cana')
htruth.Draw()
MIN = 1000000.
MAX = 0.
for b in range(1, htruth.GetNbinsX() + 1):
bmin = min(htruth.GetBinContent(b), hcen.GetBinContent(b),
hdown.GetBinContent(b), hup.GetBinContent(b))
bmax = max(htruth.GetBinContent(b), hcen.GetBinContent(b),
hdown.GetBinContent(b), hup.GetBinContent(b))
MIN = min(bmin, MIN)
MAX = max(bmax, MAX)
htruth.GetYaxis().SetRangeUser(MIN * .8, MAX * 1.2)
htruth.SetLineColor(ROOT.kGreen - 2)
htruth.SetLineWidth(6)
hcen.SetMarkerStyle(20)
hcen.SetMarkerSize(2)
hcen.SetLineColor(ROOT.kBlack)
hcen.Draw('same')
hup.Draw('samehist')
hdown.Draw('samehist')
lg = cana.BuildLegend(0.7, 0.7, 0.9, 0.94)
lg.SetHeader(lgh)
lg.SetBorderSize(0)
lg.SetFillStyle(0)
cana.SaveAs('PLOTS/' + typ + '_unfolded_' + name + '.pdf')
cana.SaveAs('PLOTS/' + typ + '_unfolded_' + name + '.png')
def DrawNonResHistogram(h_, opt_, fn_, log_, N_, plotLabels_):
c_tmp = TCanvas('c_tmp', 'c_tmp', 1300, 800)
if (log_):
gPad.SetLogy()
# set bin labels
# frame = c_tmp.DrawFrame(1.4,0.001, 4.1, 10)
# frame.SetDirectory(0)
# frame.GetXaxis().SetLimits(-0.5,N_-0.5)
# frame.GetXaxis().CenterLabels(True)
# for i in range(N_):
# frame.GetXaxis().SetBinLabel(int(1000.*(2*i+1)/(2*N_)),plotLabels_[i])
# if(N_ > 3): frame.GetXaxis().SetLabelSize(0.07)
# else: frame.GetXaxis().SetLabelSize(0.12)
# frame.LabelsOption("h","X")
# frame.Draw('axis')
# print"frame:",frame
h_.Draw(opt_)
gPad.Update()
mg_hist = h_.GetHistogram()
# mg_hist.Rebin(N_)
# print"nbins:",mg_hist.GetNbinsX()
N_xbins = mg_hist.GetNbinsX()
# N_rebin = int(N_xbins) / 4
N_rebin = int(N_xbins) / 100
# N_rebin = 25
mg_hist.Rebin(N_rebin)
# mg_hist.SetBarOffset(100)
# want 4 total
# rebin means divide total by this. 4 = NXbins / Rebin -> Rebin = NXbins / 4
h_.Draw(opt_)
# h_.GetXaxis().CenterLabels(True)
# h_.SetBarOffset(-0.5)
if (N_ > 3): h_.GetXaxis().SetLabelSize(0.05)
else: h_.GetXaxis().SetLabelSize(0.12)
for i in range(N_):
h_.GetXaxis().LabelsOption("h")
# h_.GetXaxis().LabelsOption("d")
# bin = h_.GetXaxis().FindBin(i+1)
bin = h_.GetXaxis().FindBin(i)
#39 62 85
# print"bin:",bin
# h_.GetXaxis().SetBinLabel(bin,plotLabels_[i])
h_.GetXaxis().SetBinLabel(bin, plotLabels_[i])
# print"frame:",frame
# frame.Draw('axissame')
# frame.Draw("sameaxis")
# c_tmp.BuildLegend()
# c_tmp.BuildLegend(0.65,0.52,0.95,0.82)
# c_tmp.BuildLegend(0.75,0.62,0.95,0.84)
# c_tmp.BuildLegend(0.75,0.42,0.95,0.65)
c_tmp.BuildLegend(0.75, 0.57, 0.95, 0.8)
# c_tmp.BuildLegend(0.83, 0.83, 0.99, 0.99)
# c_tmp.BuildLegend(0.5, 0.3, 0.8, 0.8)
# c_tmp.BuildLegend(0.6, 0.1, 0.9, 0.6)
c_tmp.SaveAs(fn_)
class HowManyGraph:
def __init__(self, trees, cutting_on, start, end, step, other_cuts, title,
x_title, y_title):
self.names = [name for name in trees]
files = [open(name, 'w') for name in self.names]
for cut, n1, n2 in HowManyEvents(trees, cutting_on, start, end, step,
other_cuts, title, x_title, y_title):
for n, file in zip((n1, n2), files):
string = str(cut) + '\t\t' + str(n) + '\n'
file.write(string)
for file in files:
file.close()
self.can = TCanvas("graph", "graph")
self.title = title
self.x_title = x_title
self.y_title = y_title
self.gr_0 = TGraph(self.names[0])
self.gr_1 = TGraph(self.names[1])
self.set_graph_style()
def set_graph_style(self):
self.can.SetFillColor(10)
self.gr_0.Draw("AP")
self.gr_1.Draw("P")
legend = self.can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
legend.Paint()
self.gr_0.SetMarkerStyle(21)
self.gr_1.SetMarkerStyle(23)
self.gr_1.SetMarkerColor(3)
self.gr_0.SetMarkerColor(9)
self.set_graph_title(*self.names)
size = 0.04
self.gr_0.SetTitle(self.title)
gStyle.SetTitleFillColor(0)
self.gr_0.GetXaxis().SetTitle(self.x_title)
self.gr_0.GetYaxis().SetTitle(self.y_title)
self.gr_0.GetXaxis().SetDecimals()
self.gr_0.GetYaxis().SetDecimals()
self.gr_0.GetXaxis().SetTitleSize(size)
self.gr_0.GetYaxis().SetTitleSize(size)
self.gr_0.GetYaxis().SetTitleOffset(1.2)
self.gr_0.GetXaxis().SetLabelSize(size)
self.gr_0.GetYaxis().SetLabelSize(size)
def set_graph_title(self, title_0, title_1):
self.gr_0.SetTitle(title_0)
self.gr_1.SetTitle(title_1)
def SetLogy(self):
self.can.SetLogy()
def save_canvas(self, file_name):
self.can.SaveAs(file_name)
def plot(Polarization):
Rootfiles = []
for pol in Polarization:
#print pol
for plotstring in Plots:
print plotstring
Rootfiles.append(
TFile(Outpath + "TwoPhotonThrust_" + pol + "._" + plotstring +
".root"))
for plotstring in Plots:
print plotstring
canvas1 = TCanvas("c1", "c1")
firsthist = True
for i, rootfile in enumerate(Rootfiles):
#print i
exec('plot = rootfile.' + ptype + '_' + plotstring)
if i + 1 >= 5: plot.SetLineColor(i + 2)
else: plot.SetLineColor(i + 1)
plot.SetLineWidth(2)
plot.SetLineStyle(1)
#if ptype == 'S': plot.SetLineStyle(1)
#else: plot.SetLineStyle(2)
plot.SetTitle(Pol[Polarization[i]])
plot.Draw("same")
#plot.SetLogy()
if firsthist:
plot.SetStats(False)
plot.SetXTitle("Thrust")
plot.GetXaxis().CenterTitle()
plot.SetYTitle("Events")
plot.GetYaxis().CenterTitle()
plot.GetXaxis().SetRangeUser(-3, 4)
plot.GetYaxis().SetRangeUser(0, 10000)
firsthist = False
#STOP rootfiles loop
gStyle.SetOptTitle(0)
#turn off default title
title = TPaveText(0.25, 0.9, 0.75, 1.0, "brNDC")
title.AddText(Plotnames[plotstring] + ' Thrust Value')
title.Draw("same")
canvas1.SetGrid(1, 0)
canvas1.BuildLegend()
canvas1.SaveAs(Outpath + ptype + '_' + plotstring + '_' + 'aalowpt' +
".png")
canvas1.SaveAs(Outpath + ptype + '_' + plotstring + '_' + 'aalowpt' +
".ps")
def study_cal_eff():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/data_nn50.root", 'read')
t_data = f_data.data
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_noise=lumical",
"../trees_5gev_e/lucas_geocuts_noise_cal.root")
t_mc.AddFriend("lumical_eff=lumical",
"../trees_5gev_e/lucas_geocuts_noise_cal_eff.root")
c = TCanvas()
hs = THStack("hs", "title")
t_data.Draw("energy*(layer > 1)>>h_data50(200, 0, 50)",
"energy*(layer > 1) > 0")
h_data50 = gROOT.FindObject("h_data50")
h_data50.SetTitle("Data")
h_data50.SetLineWidth(2)
h_data50.SetLineColor(1)
hs.Add(h_data50, "histo")
t_mc.Draw("cal_energy_new/0.0885>>h_mc(200, 0, 50)",
"cal_energy_new/0.0885 > 0", "", 100000)
h_mc = gROOT.FindObject("h_mc")
h_mc.SetTitle("MC smearing and cal eff")
h_mc.SetLineWidth(2)
h_mc.SetLineColor(3)
h_mc.Scale(t_data.GetEntries() / 100000) #t_mc.GetEntries())
hs.Add(h_mc, "histo")
t_mc.Draw("cal_energy_smeared7/0.0885>>h_mc7(200, 0, 50)",
"cal_energy_smeared7/0.0885 > 0", "", 100000)
h_mc7 = gROOT.FindObject("h_mc7")
h_mc7.SetTitle("MC smearing=0.7*#sigma")
h_mc7.SetLineWidth(2)
h_mc7.SetLineColor(2)
h_mc7.Scale(t_data.GetEntries() / 100000) #t_mc.GetEntries())
hs.Add(h_mc7, "histo")
print(h_mc7.GetEntries())
hs.Draw("nostack")
hs.SetTitle(
"Deposited energy in the pads of calorimeter;E, [MIP]; N_{hits}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
def Draw_Histogram(h_,opt_,fn_,log_):
c_tmp = TCanvas('c_tmp','c_tmp',1300,800)
h_.Draw("apl")
if(log_): gPad.SetLogy()
h_.Draw(opt_)
# c_tmp.BuildLegend(0.75,0.62,0.95,0.84)
# c_tmp.BuildLegend(0.75,0.42,0.95,0.65)
c_tmp.BuildLegend(0.75,0.57,0.95,0.8)
# c_tmp.BuildLegend(0.83, 0.83, 0.99, 0.99)
# c_tmp.BuildLegend(0.5, 0.3, 0.8, 0.8)
# c_tmp.BuildLegend(0.6, 0.1, 0.9, 0.6)
c_tmp.SaveAs(fn_)
return
def study_trigger():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/.root", 'read')
t_data = f_data.data
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_tr_clusters=lumical", "../trees_5gev_e/.root")
c = TCanvas()
t_data.Draw("tr1_n_clusters>>h_data(6, 0, 6)")
h_data = gROOT.FindObject("h_data")
h_data.SetTitle("Data")
h_data.SetLineWidth(2)
h_data.SetLineColor(1)
t_mc.Draw("tr1_n_hits>>h_mc(6, 0, 6)")
h_mc = gROOT.FindObject("h_mc")
h_mc.SetTitle("MC")
h_mc.SetLineWidth(2)
h_mc.SetLineColor(2)
h_mc.Scale(t_data.GetEntries() / t_mc.GetEntries())
t_mc.Draw("tr1_n_clusters>>h_mc2(6, 0, 6)", "n_triggers == 3")
h_mc2 = gROOT.FindObject("h_mc2")
h_mc2.SetTitle("MC w/ trigger")
h_mc2.SetLineWidth(2)
h_mc2.SetLineColor(4)
h_mc2.Scale(t_data.GetEntries() / t_mc.GetEntries())
hs = THStack("hs", "title")
hs.Add(h_data, "histo")
hs.Add(h_mc, "histo")
hs.Add(h_mc2, "histo")
hs.Draw("nostack")
hs.SetTitle(
"Number of clusters in the 1st tracker;N_{particles}; N_{events}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
def Draw_Histogram(h_, opt_, fn_, log_):
c_tmp = TCanvas('c_tmp', 'c_tmp', 1300, 800)
h_.Draw("apl")
if (log_):
#h_.SetMinimum(0.0001)
print "histogram minimum:", h_.GetMinimum()
h_.SetMinimum(1.)
gPad.SetLogy()
h_.SetMinimum(1.)
print "histogram minumum after:", h_GetMinimum()
h_.Draw(opt_)
# c_tmp.BuildLegend(0.75,0.62,0.95,0.84)
# c_tmp.BuildLegend(0.75,0.42,0.95,0.65)
c_tmp.BuildLegend(0.75, 0.57, 0.95, 0.8)
# c_tmp.BuildLegend(0.83, 0.83, 0.99, 0.99)
# c_tmp.BuildLegend(0.5, 0.3, 0.8, 0.8)
# c_tmp.BuildLegend(0.6, 0.1, 0.9, 0.6)
c_tmp.SaveAs(fn_)
return
#leg = TLegend()
canvas = TCanvas()
ymax = max(h1.GetMaximum(), h2.GetMaximum())
h1.SetMaximum(ymax * 1.2)
h2.SetLineColor(kRed)
gStyle.SetOptStat(0)
#leg.AddEntry(h1, args.branch1+"("+args.cut1+")", "l")
#leg.AddEntry(h2, args.branch2+"("+args.cut2+")", "l")
h1.Draw(args.drawoption1)
option2 = "same" if args.drawoption2 == "" else "same " + args.drawoption2
h2.Draw(option2)
#leg.Draw("same")
canvas.BuildLegend()
if args.title is None:
t = args.input.split("/")[-1]
t = t.replace(".root", "")
t = t.replace("mvaVars_", "")
t = t.replace("ntuple_", "")
h1.SetTitle(t)
else:
h1.SetTitle(args.title)
canvas.SaveAs(args.output)
for histo in histos_chib2.values():
histo.SetLineColor(ROOT.kRed)
histo.SetLineWidth(2)
histo.SetMarkerColor(ROOT.kRed)
histo.SetMarkerStyle(21)
canvas=TCanvas("canvas","canvas")
canvas.Print(outputFile_name+".ps[") #apre file .ps
for key in histos_chib1.keys():
hs = THStack('hs', titles[key])
hs.Add(histos_chib1[key])
hs.Add(histos_chib2[key])
hs.Draw('nostack')
leg = canvas.BuildLegend(.65,.93,.95,.7)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.Draw("SAME")
canvas.Update()
canvas.Print(outputFile_name+'.ps')
canvas.Print(key+'.png')
<<<<<<< HEAD
h2D_chib1.Draw('colz')
canvas.Update()
canvas.Print(outputFile_name+'.ps')
h2D_chib2.Draw('colz')
canvas.Update()
canvas.Print(outputFile_name+'.ps')
canvas.Print(outputFile_name+".ps]") #chiude file .ps
=======
def makePlots():
inputFile = TFile('CutsPlots.root','READ')
histos_data = dict()
histos_MC = dict()
keys = ['Y1S_nsigma', 'probFit1S', 'photon_pt', 'photon_eta', 'dimuon_pt', 'dimuon_rapidity', 'dz', 'invm1S']
for key in keys:
histos_data[key] = inputFile.Get(key+'_data')
histos_MC[key] = inputFile.Get(key+'_MC')
titles = dict(
Y1S_nsigma = 'Y1S_nsigma;Number of theoric standad deviation from #Upsilon(1S);',
probFit1S = 'probFit1S;fit probability based on #chi^{2} over number dof;',
photon_pt = 'photon p_{T};p_{T} [GeV];',
photon_eta = 'photon_eta;#eta;',
dimuon_pt = '#Upsilon p_{T};p_{T} [GeV];',
dimuon_rapidity = '#Upsilon rapidity;y;',
dz = '|dz|;|dz| [cm];',
invm1S = 'Q-Value;m_{#mu#mu#gamma} - m_{#mu#mu} + m_{#Upsilon}^{PDG};'
)
logY = dict(
Y1S_nsigma = 0,
probFit1S = 1,
photon_pt = 0,
photon_eta = 0,
dimuon_pt = 0,
dimuon_rapidity = 0,
dz = 0,
invm1S = 0
)
for histo in histos_data.values() + histos_MC.values():
if(histo.Integral() != 0):
histo.Scale(1./histo.Integral())
for histo in histos_data.values():
histo.SetLineColor(ROOT.kGreen+2)
histo.SetLineWidth(2)
# histo.SetMarkerColor(ROOT.kGreen+2)
# histo.SetMarkerStyle(21)
for histo in histos_MC.values():
histo.SetLineColor(ROOT.kBlue)
histo.SetLineWidth(2)
# histo.SetMarkerColor(ROOT.kBlue)
# histo.SetMarkerStyle(20)
canvas=TCanvas("canvas","canvas")
canvas.Print(outputFile_name+"[") #apre file .ps
for key in keys:
canvas.SetLogy(logY[key])
hs = THStack('hs', titles[key])
hs.Add(histos_data[key])
hs.Add(histos_MC[key])
hs.Draw('nostack')
leg=canvas.BuildLegend(.8,.95,.92,.8);
leg.SetFillColor(0);
leg.Draw("SAME");
canvas.Update()
canvas.Print(outputFile_name)
canvas.Print('CutsPlots/'+key+'.png')
canvas.Print(outputFile_name+"]") #chiude file .ps
inputFile.Close()
h1.SetLineColor(ROOT.kBlue)
h2.SetLineColor(ROOT.kRed)
h1.SetTitle(file1Name.replace(".root",""))
h2.SetTitle(file2Name.replace(".root",""))
#h1.Draw()
#h2.Draw("same")
hs.Draw()
T = TText()
T.SetTextFont(42)
T.SetTextAlign(21)
T.DrawTextNDC(.5,.95,var)
c1.BuildLegend(0.66, 0.87, 0.93, 0.77, "")
c1.SetTitle(var)
#c.cd()
#Tl = ROOT.TLatex(0.5,0.9,var)
#Tl.SetTextSize(0.04)
#Tl.SetTextAlign(13)
#Tl.Draw()
pdfName=var+"_"+file1Name.replace(".root","")+"_vs_"+file2Name.replace(".root","")
c1.SaveAs("plots/"+pdfName+".pdf")
#/lstore/cms/cbeiraod/Stop4Body/puWeights
#/lstore/cms/dbastos/Stop4Body/nTuples_v2018-04-03/
#puWeights_stop675.json.root
#python plotStep0.py -f /lstore/cms/dbastos/Stop4Body/nTuples_v2018-04-03/puWeights_stop675.json.root -k /lstore/cms/cbeiraod/Stop4Body/puWeights/puWeights_stop675.json.root -p sample_T2DegStop_675_595_nTrueInt
#python plotStep0.py -y -f puWeights_Wjets.json.root -p process_WJets_puWeight
gr2.GetYaxis().SetNdivisions(505)
gr2.GetYaxis().SetLabelOffset(1.4)
gr2.GetXaxis().SetTitleOffset(4.0)
gr2.SetLineColor(3)
gr2.SetLineWidth(5)
gr2.SetMarkerStyle(21)
gr3 = TGraph(n, MH3name, PS_M2_list)
gr3.SetTitle("Medium2 (FJetCSV > 0.89)")
gr3.GetYaxis().SetNdivisions(505)
gr3.GetYaxis().SetLabelOffset(1.4)
gr3.GetXaxis().SetTitleOffset(4.0)
gr3.SetLineColor(4)
gr3.SetLineWidth(5)
gr3.SetMarkerStyle(22)
mg = TMultiGraph("mg", "")
mg.SetTitle("QCD Background; MH3 (GeV); Punzi Significance")
mg.GetYaxis().SetNdivisions(505)
mg.GetYaxis().SetLabelOffset(1.4)
mg.GetXaxis().SetTitleOffset(4.0)
mg.Add(gr1, "ALP")
mg.Add(gr2, "ALP")
mg.Add(gr3, "ALP")
mg.Draw("ALP")
c1.BuildLegend()
c1.cd()
c1.Update()
c1.SaveAs("PS_test.pdf")
histCumul1_fast.Write()
histCumul2_fast.Write()
histCumul0_fast_hit.SetDrawOption("COL")
histCumul1_fast_hit.SetDrawOption("COL")
histCumul2_fast_hit.SetDrawOption("COL")
histCumul0_fast_hit.Write()
histCumul1_fast_hit.Write()
histCumul2_fast_hit.Write()
c1 = TCanvas('c1','Time Histograms',1400,1400)
#c1.SetLogy()
c1.SetGrid()
hist1D0.Draw('hist')
hist1D1.Draw('SameHist')
hist1D2.Draw('SameHist')
c1.BuildLegend()
c1.SaveAs(sys.argv[2]+'timeHist_all_asics_0x%i.jpg'%(j))
c2 = TCanvas('c2','Number of Hits VS BL',1400,1400)
c2.SetGrid()
mg = TMultiGraph()
mg.SetTitle('numberOfHits_all_asics_trim70x%i;BL;hits'%(j))
mg.Add(g0)
mg.Add(g1)
mg.Add(g2)
mg.Draw("AL*")
gPad.Modified()
#mg.GetXaxis().SetLimits(0,40)
#mg.SetMinimum(0.)
#mg.SetMaximum(11.)
leg = TLegend(.5,.1,.9,.2)
leg.SetFillColor(0)
def makePlots():
# gROOT.SetStyle('Plain') # white background
# gStyle.SetOptStat(222211)
ROOT.gROOT.ProcessLine('.L ../tdrstyle.C')
ROOT.gROOT.Reset()
ROOT.gROOT.ProcessLine('setTDRStyle()') #Set CMS TDR style
inputFile = TFile(output_name + '.root', 'READ')
histos_SR1 = dict()
histos_SR2 = dict()
histos_LSB = dict()
histos_RSB = dict()
histos_MC = dict()
keys = [
'photon_pt', 'photon_eta', 'dimuon_pt', 'dimuon_rapidity', 'chib_pt'
]
for key in keys:
histos_SR1[key] = inputFile.Get(key + '_SR1')
histos_SR2[key] = inputFile.Get(key + '_SR2')
histos_LSB[key] = inputFile.Get(key + '_LSB')
histos_RSB[key] = inputFile.Get(key + '_RSB')
histos_MC[key] = inputFile.Get(key + '_MC')
titles = dict(
photon_pt='photon E_{T};E_{T}(#gamma) [GeV];Entries / 100 MeV',
photon_eta='photon_eta;#eta(#gamma);Entries / 0.05',
dimuon_pt='#Upsilon p_{T};p_{T}(#Upsilon) [GeV];Entries / GeV',
dimuon_rapidity='#Upsilon rapidity(#Upsilon);y(#Upsilon);Entries / 0.1',
chib_pt='#chi_{b} p_{T};p_{T}(#chi_{b}) [GeV];Entries / GeV',
)
titles_chib = dict(
photon_pt='photon E_{T};E_{T}(#gamma) [GeV];A.U.',
photon_eta='photon_eta;#eta(#gamma);A.U.',
dimuon_pt='#Upsilon p_{T};p_{T}(#Upsilon) [GeV];A.U.',
dimuon_rapidity='#Upsilon rapidity(#Upsilon);y(#Upsilon);A.U.',
chib_pt='#chi_{b} p_{T};p_{T}(#chi_{b}) [GeV];A.U.',
)
logY = dict(
photon_pt=0,
photon_eta=0,
dimuon_pt=0,
dimuon_rapidity=0,
chib_pt=0,
)
maxY = dict(
photon_pt=750,
photon_eta=350,
dimuon_pt=800,
dimuon_rapidity=400,
chib_pt=800,
)
integralData1 = histos_SR1[keys[0]].Integral()
integralData2 = histos_SR2[keys[0]].Integral()
integralData = integralData1 + integralData2
for histo in histos_LSB.values() + histos_RSB.values() + histos_MC.values(
):
if (histo.Integral() != 0):
histo.Scale(1. / histo.Integral())
histo.Rebin(3)
for histo in histos_SR1.values() + histos_SR2.values():
histo.Rebin(3)
fBG = 0.48 #0.28
fLSB = 0.5
histos_BG = dict()
for key in keys:
histos_BG[key] = histos_RSB[key].Clone()
histos_BG[key].SetNameTitle(key + '_BG', 'bgd from side-bands')
histos_BG[key].Scale(1 - fLSB)
histos_BG[key].Add(histos_LSB[key], fLSB)
histos_Signal1 = dict()
histos_Signal2 = dict()
histos_Signal = dict()
histos_SR = dict()
for key in keys:
histos_Signal1[key] = histos_SR1[key].Clone()
histos_Signal1[key].SetNameTitle(key + '_signal1', 'Signal1')
histos_Signal1[key].Add(histos_BG[key], -1 * integralData1 * fBG)
histos_Signal2[key] = histos_SR2[key].Clone()
histos_Signal2[key].SetNameTitle(key + '_signal2', 'Signal2')
histos_Signal2[key].Add(histos_BG[key], -1 * integralData2 * fBG)
histos_Signal[key] = histos_Signal1[key].Clone()
histos_Signal[key].Add(histos_Signal2[key])
histos_Signal[key].SetNameTitle(key + '_signal', 'data bkg-subtracted')
histos_SR[key] = histos_SR1[key].Clone()
histos_SR[key].Add(histos_SR2[key])
histos_SR[key].SetNameTitle(key + '_SR', 'data')
for histo in histos_Signal.values() + histos_BG.values():
if (histo.Integral() != 0):
histo.Scale(1. / histo.Integral())
# Normalize SR to their original value, MC to be comparable with signal background-subtracted and background such that Signal+background = SR
for histo in histos_MC.values():
histo.Scale(integralData * (1 - fBG))
for histo in histos_BG.values():
histo.Scale(integralData * fBG)
for histo in histos_LSB.values():
histo.Scale(integralData * fBG * fLSB)
for histo in histos_RSB.values():
histo.Scale(integralData * fBG * (1 - fLSB))
chi2Prob = dict()
kolmogorovProb = dict()
h_ratio = dict()
for key in histos_Signal.keys():
h_ratio[key] = histos_SR[key].Clone()
histo_sum_MC_BG = histos_MC[key].Clone()
histo_sum_MC_BG.Add(histos_BG[key])
h_ratio[key].Divide(histo_sum_MC_BG)
h_ratio[key].SetNameTitle(key + '_ratio', titles[key])
h_ratio[key].SetMarkerStyle(20)
h_ratio[key].SetMarkerSize(1)
h_ratio[key].GetYaxis().SetLabelSize(0.18)
h_ratio[key].GetYaxis().SetTitle('DATA/MC')
h_ratio[key].GetYaxis().SetTitleSize(0.22)
h_ratio[key].GetYaxis().SetTitleOffset(0.2)
h_ratio[key].GetYaxis().SetNdivisions(005)
h_ratio[key].GetXaxis().SetLabelSize(0.16)
h_ratio[key].GetXaxis().SetTitleSize(0.16)
h_ratio[key].SetMaximum(1.5)
h_ratio[key].SetMinimum(0.667)
chi2Prob[key] = histos_SR[key].Chi2Test(
histo_sum_MC_BG) #, 'UW')#, 'NORM')
kolmogorovProb[key] = histos_SR[key].KolmogorovTest(histo_sum_MC_BG)
for histo in histos_SR.values():
histo.SetLineColor(ROOT.kBlack)
histo.SetLineWidth(2)
histo.SetMarkerColor(ROOT.kBlack)
histo.SetMarkerStyle(20)
for histo in histos_SR1.values() + histos_Signal1.values():
histo.SetLineColor(ROOT.kBlue)
histo.SetLineWidth(2)
histo.SetMarkerColor(ROOT.kBlue)
histo.SetMarkerStyle(22)
for histo in histos_SR2.values() + histos_Signal2.values():
histo.SetLineColor(ROOT.kRed)
histo.SetLineWidth(2)
histo.SetMarkerColor(ROOT.kRed)
histo.SetMarkerStyle(23)
for histo in histos_Signal.values():
histo.SetLineColor(ROOT.kBlack)
histo.SetLineWidth(2)
# histo.SetMarkerColor(ROOT.kBlack)
# histo.SetMarkerStyle(21)
for histo in histos_BG.values():
histo.SetLineColor(ROOT.kRed)
histo.SetLineWidth(2)
histo.SetFillColor(ROOT.kRed)
# histo.SetMarkerColor(ROOT.kRed)
# histo.SetMarkerStyle(22)
# for histo in histos_RSB.values():
# histo.SetLineColor(ROOT.kViolet)
# histo.SetLineWidth(2)
for histo in histos_MC.values():
histo.SetLineColor(ROOT.kBlue)
histo.SetLineWidth(2)
histo.SetFillColor(ROOT.kBlue)
# histo.SetMarkerColor(ROOT.kBlue)
# histo.SetMarkerStyle(20)
outputFile = TFile(output_name + '_plots.root', 'RECREATE')
for histo in histos_Signal.values() + histos_MC.values(
) + histos_BG.values() + histos_SR.values():
histo.Write()
outputFile.Close()
canvas = TCanvas("canvas", "canvas")
canvas.Print(outputFile_name + "[") #apre file .ps
for key in keys:
pad1 = TPad("pad1", "The pad 80% of the height", 0.0, 0.2, 1.0, 1.0)
pad1.cd()
pad1.SetLogy(logY[key])
hs = THStack('hs', titles[key])
#hs.Add(histos_SR[key])
#hs.Add(histos_Signal[key])
hs.Add(histos_BG[key])
hs.Add(histos_MC[key])
#hs.Add(histos_LSB[key])
#hs.Add(histos_RSB[key])
#maxHisto = max(hs.GetMaximum(), histos_SR[key].GetMaximum())
hs.SetMaximum(maxY[key]) #maxHisto)
hs.Draw('HIST')
histos_SR[key].Draw('same')
leg = pad1.BuildLegend(.65, .93, .95, .7)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.Draw("SAME")
pad2 = TPad("pad2", "The pad 20% of the height", 0.0, 0.01, 1.0, 0.2)
pad2.cd()
h_ratio[key].Draw()
xmin = h_ratio[key].GetBinLowEdge(1)
xmax = h_ratio[key].GetBinLowEdge(
h_ratio[key].GetNbinsX()) + h_ratio[key].GetBinWidth(
h_ratio[key].GetNbinsX())
line0 = TLine(xmin, 1, xmax, 1)
line0.SetLineColor(ROOT.kBlue)
line0.SetLineWidth(2)
line0.Draw('same')
canvas.cd()
pad1.Draw()
pad2.Draw()
canvas.Update()
canvas.Print(outputFile_name)
canvas.Print('plots/' + key + '_' + mc_ptSpectrum + '.png')
canvas.Print('plots/' + key + '_' + mc_ptSpectrum + '.pdf')
canvas.Print('plots/' + key + '_' + mc_ptSpectrum + '.root')
# Make comparisison between chib1 and chib2
histos_SR1[key].Scale(1. / histos_SR1[key].Integral())
histos_SR2[key].Scale(1. / histos_SR2[key].Integral())
histos_Signal1[key].Scale(1. / histos_Signal1[key].Integral())
histos_Signal2[key].Scale(1. / histos_Signal2[key].Integral())
# histos_SR1[key].Rebin(3)
# histos_SR2[key].Rebin(3)
# histos_Signal1[key].Rebin(3)
# histos_Signal2[key].Rebin(3)
hsc = THStack('hsc', titles_chib[key])
hsc.Add(histos_Signal1[key])
hsc.Add(histos_Signal2[key])
#hsc.SetMaximum(maxY[key])
hsc.Draw('nostack')
legc = canvas.BuildLegend(.65, .93, .95, .7)
legc.SetFillStyle(0)
legc.SetBorderSize(0)
legc.Draw("SAME")
print 'kolmogorov prob SR ' + key + ' ' + str(
histos_SR1[key].KolmogorovTest(histos_SR2[key]))
print 'kolmogorov prob Signal ' + key + ' ' + str(
histos_Signal1[key].KolmogorovTest(histos_Signal2[key]))
cfr = 2 if key not in ['dimuon_pt', 'chib_pt'] else 5
kolmogorovProb_chi = round(
histos_Signal1[key].KolmogorovTest(histos_Signal2[key]), cfr)
pvtxt = TPaveText(.2, .0, 0.5, 0.1, "NDC")
pvtxt.SetFillStyle(0)
pvtxt.SetBorderSize(0)
pvtxt.SetTextSize(0.04)
pvtxt.AddText('Kolmogorov prob = ' + str(kolmogorovProb_chi))
pvtxt.Draw()
canvas.Update()
canvas.Print(outputFile_name)
canvas.Print('plots/' + key + '_' + mc_ptSpectrum + '_comparison' +
'.png')
canvas.Print('plots/' + key + '_' + mc_ptSpectrum + '_comparison' +
'.pdf')
canvas.Print('plots/' + key + '_' + mc_ptSpectrum + '_comparison' +
'.root')
canvas.Clear()
canvas.Print(outputFile_name + "]") #chiude file .ps
for key in keys:
print '\n'
print key + ' Kolmogorov probability: ' + str(chi2Prob[key])
print key + ' chiSqure probability: ' + str(kolmogorovProb[key])
#print 'Kolmogorov probability with sidebands: '+str(histos_MC['dimuon_pt'].KolmogorovTest(histos_LSB['dimuon_pt'])) #
inputFile.Close()
fname.append('LG1_SE5')
fname.append('LG1_SE5_2')
fname.append('LG1_SE5_3')
fname.append('LG1_SE5_4')
fname.append('LG1_SE5_NM')
fname.append('PIN1_SE5')
fname.append('PIN1_SE5_NM')
mg = TMultiGraph("mg", "")
for iFile in fname:
g = TGraph(iFile + ".csv", "%lf,%*lf,%lf")
g.SetMarkerStyle(8)
g.SetMarkerSize(0.8)
mg.Add(g)
print g, iFile
c = TCanvas("c", "canvas", 800, 600)
c.cd()
gPad.SetLogy()
mg.SetTitle(name + ";Bias Voltage [V];Measured Current [A]"
) # set the graph title, x-axis title and y-axis title
mg.Draw("APL PMC PLC") # draw all the graphs using automatic coloring
mg.GetHistogram().GetYaxis().SetRangeUser(1E-10, 0.001)
# y-axis range
#mg.GetHistogram().GetXaxis().SetRangeUser(0,150); # x-axis range
c.BuildLegend(0.4, 0.8 - 0.05 * len(fname), 0.6, 0.8, "",
"PL") # generate the legend and define its position and style
c.Print(name + ".pdf")
h_fracdifference.SetFillColor(3)
h_score.SetFillColor(4)
h_maximum.SetFillColor(5)
h_percentage.SetFillColor(6)
h_average.SetFillColor(7)
h_day.SetFillColor(8)
h_players.SetFillColor(9)
c_forgov = TCanvas("forgov", "Canvas for Gov. <3", 1000, 1000)
h_fracdifference.GetXaxis().SetTitle("This is the x axis")
h_fracdifference.GetYaxis().SetTitle("This is the y axis")
h_fracdifference.Draw()
text = TPaveText(0.1, 0.7, 0.3, 0.9)
text.AddText("There is no z axis")
text.Draw()
c_forgov.BuildLegend()
c_forgov.Print("canvasforgov.png")
s_file = TFile("SporcleAnalysis.root", "RECREATE")
h_difference.Write("Difference")
h_fracdifference.Write("FractionalDifference")
h_score.Write("Score")
h_maximum.Write("Maximum")
h_percentage.Write("Percentage")
h_average.Write("Average")
h_day.Write("Day")
h_players.Write("Players")
s_file.Close()
def study_smearing():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/data_nn50.root", 'read')
t_data = f_data.data
t_data.AddFriend("data10=data", "../trees_5gev_e/data_nn10.root")
t_data.AddFriend("data20=data", "../trees_5gev_e/data_nn20.root")
t_data.AddFriend("data90=data", "../trees_5gev_e/data_nn90.root")
t_data.AddFriend("data99=data", "../trees_5gev_e/data_nn99.root")
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_noise=lumical",
"../trees_5gev_e/lucas_geocuts_noise_test.root")
c = TCanvas()
hs = THStack("hs", "title")
t_data.Draw("Sum$(data10.energy*(data10.layer == 0))>>h_data10(200, 0, 3)",
"Sum$(data10.energy*(data10.layer == 0)) > 0")
h_data10 = gROOT.FindObject("h_data10")
h_data10.SetTitle("Data nn>0.1")
h_data10.SetLineWidth(2)
h_data10.SetLineColor(3)
hs.Add(h_data10, "histo")
t_data.Draw("Sum$(data20.energy*(data20.layer == 0))>>h_data20(200, 0, 3)",
"Sum$(data20.energy*(data20.layer == 0)) > 0")
h_data20 = gROOT.FindObject("h_data20")
h_data20.SetTitle("Data nn>0.2")
h_data20.SetLineWidth(2)
h_data20.SetLineColor(4)
hs.Add(h_data20, "histo")
t_data.Draw("Sum$(energy*(layer == 0))>>h_data50(200, 0, 3)",
"Sum$(energy*(layer == 0)) > 0")
h_data50 = gROOT.FindObject("h_data50")
h_data50.SetTitle("Data nn>0.5")
h_data50.SetLineWidth(2)
h_data50.SetLineColor(1)
hs.Add(h_data50, "histo")
t_data.Draw("Sum$(data90.energy*(data90.layer == 0))>>h_data90(200, 0, 3)",
"Sum$(data90.energy*(data90.layer == 0)) > 0")
h_data90 = gROOT.FindObject("h_data90")
h_data90.SetTitle("Data nn>0.90")
h_data90.SetLineWidth(2)
h_data90.SetLineColor(5)
hs.Add(h_data90, "histo")
t_data.Draw("Sum$(data99.energy*(data99.layer == 0))>>h_data99(200, 0, 3)",
"Sum$(data99.energy*(data99.layer == 0)) > 0")
h_data99 = gROOT.FindObject("h_data99")
h_data99.SetTitle("Data nn>0.99")
h_data99.SetLineWidth(2)
h_data99.SetLineColor(6)
hs.Add(h_data99, "histo")
# t_mc.Draw("Sum$(tr1_energy/0.0885)>>h_mc(200, 0, 3)", "Sum$(tr1_energy/0.0885) > 0")
# h_mc = gROOT.FindObject("h_mc")
# h_mc.SetTitle("MC no smearing")
# h_mc.SetLineWidth(2)
# h_mc.SetLineColor(2)
# h_mc.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc, "histo")
# t_mc.Draw("Sum$(tr1_energy_smeared5/0.0885)>>h_mc5(200, 0, 3)", "Sum$(tr1_energy_smeared5/0.0885) > 0")
# h_mc5 = gROOT.FindObject("h_mc5")
# h_mc5.SetLineWidth(2)
# h_mc5.SetLineColor(3)
# h_mc5.SetMarkerStyle(0)
# h_mc5.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc5, "histo")
# t_mc.Draw("Sum$(tr1_energy_smeared6/0.0885)>>h_mc6(200, 0, 3)", "Sum$(tr1_energy_smeared6/0.0885) > 0")
# h_mc6 = gROOT.FindObject("h_mc6")
# h_mc6.SetTitle("MC smearing #sigma=0.6*stdNoise")
# h_mc6.SetLineWidth(2)
# h_mc6.SetLineColor(2)
# h_mc6.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc6, "histo")
t_mc.Draw("Sum$(tr1_energy_smeared7/0.0885)>>h_mc7(200, 0, 3)",
"Sum$(tr1_energy_smeared7/0.0885) > 0")
h_mc7 = gROOT.FindObject("h_mc7")
h_mc7.SetTitle("MC smearing #sigma=0.7*stdNoise")
h_mc7.SetLineWidth(2)
h_mc7.SetLineColor(2)
h_mc7.Scale(t_data.GetEntries() / t_mc.GetEntries())
hs.Add(h_mc7, "histo")
# t_mc.Draw("Sum$((tr1_energy_smeared10)/0.0885)>>h_mc10(200, 0, 3)", "Sum$((tr1_energy_smeared10)/0.0885) > 0")
# h_mc10 = gROOT.FindObject("h_mc10")
# h_mc10.SetTitle("MC smearing #sigma=stdNoise")
# h_mc10.SetLineWidth(2)
# h_mc10.SetLineColor(18)
# h_mc10.Scale(t_data.GetEntries()/t_mc.GetEntries())
# hs.Add(h_mc10, "histo")
hs.Draw("nostack")
hs.SetTitle("Deposited energy in the 1st tracker;E, [MIP]; N_{events}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
dir_name = "PatAnalyzerSkeleton"
"""open tree"""
tree = "ass_muons"
tree = os.path.join(dir_name, tree)
tree = root_file.Get(tree)
"""Transverse momentum"""
hist_stats = 100, 0, 50
B_pt_hist = TH1D("B_pt", "B", *hist_stats)
D_pt_hist = TH1D("D_pt", "D", *hist_stats)
B_pt_hist.SetStats(0)
D_pt_hist.SetLineColor(2)
tree.Project("B_pt", "pt", "abs(mother_pdgid) == 521")
tree.Project("D_pt", "pt", "abs(mother_pdgid) == 411")
[
set_histogram(x, """p_{t} #[]{GeV/c}""", """entries / 0.5 #[]{GeV/c}""")
for x in [B_pt_hist, D_pt_hist]
]
title = "p_{t} distribution"
pt_can = TCanvas("p_{t} distribution", title)
pt_can.cd()
pt_can.SetLogy()
B_pt_hist.Draw()
D_pt_hist.Draw("SAME")
legend = pt_can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
B_pt_hist.SetTitle(title)
raw_input()
class PainterScientist:
def __init__(self, sci1, sci2, title, x_title, y_title):
self.sci1, self.sci2 = sci1, sci2
self.can = TCanvas("graph", "graph")
self.title = title
self.x_title = x_title
self.y_title = y_title
self.build_graphs()
def build_graphs(self):
scientists = [self.sci1, self.sci2]
names = ['tmp_global', 'tmp_inner']
for name, scientist in zip(names, scientists):
with open(name, 'w') as file:
for step, n_sigma, err in scientist:
list = [step, n_sigma, 0, err]
list = [str(x) for x in list]
output_line = '\t'.join(list)
output_line += '\n'
file.write(output_line)
unique_name = "can" + str(random.randint(0, 1000))
self.can = TCanvas(unique_name, "graph")
self.gr_global = TGraphErrors(names[0])
self.gr_inner = TGraphErrors(names[1])
titles = ("global track", "inner track")
self.set_graph_title(*titles)
self.set_graph_style()
def SetLogy(self):
self.can.SetLogy()
def set_graph_style(self):
self.can.SetFillColor(10)
self.gr_global.Draw("AP")
self.gr_inner.Draw("P")
legend = self.can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
legend.Paint()
self.gr_global.SetMarkerStyle(20)
self.gr_inner.SetMarkerStyle(22)
self.gr_inner.SetMarkerColor(2)
self.gr_global.SetMarkerColor(4)
size = 0.04
self.gr_global.SetTitle(self.title)
gStyle.SetTitleFillColor(0)
self.gr_global.GetXaxis().SetTitle(self.x_title)
self.gr_global.GetYaxis().SetTitle(self.y_title)
self.gr_global.GetXaxis().SetDecimals()
self.gr_global.GetYaxis().SetDecimals()
self.gr_global.GetXaxis().SetTitleSize(size)
self.gr_global.GetYaxis().SetTitleSize(size)
self.gr_global.GetYaxis().SetTitleOffset(1.2)
self.gr_global.GetXaxis().SetLabelSize(size)
self.gr_global.GetYaxis().SetLabelSize(size)
def set_graph_title(self, title_global, title_inner):
self.gr_global.SetTitle(title_global)
self.gr_inner.SetTitle(title_inner)
def save_canvas(self, file_name):
self.can.SaveAs(file_name)
gr2.GetYaxis().SetLabelOffset(1.4)
gr2.GetXaxis().SetTitleOffset(4.0)
gr2.SetLineColor(2)
gr2.SetLineWidth(5)
gr2.SetMarkerStyle(21)
gr2.SetMarkerColor(2)
mg = TMultiGraph("mg", "")
mg.SetTitle("Punzi Significance; MH3 (GeV); Punzi Significance")
mg.GetYaxis().SetNdivisions(505)
mg.GetYaxis().SetLabelOffset(1.4)
mg.GetXaxis().SetTitleOffset(4.0)
mg.Add(gr1, "ALP")
mg.Add(gr2, "ALP")
mg.Draw("ALP")
c1.BuildLegend()
c1.cd()
c1.Update()
c1.SaveAs("vetos_PS.pdf")
#Draw Signal Efficiency
c2 = TCanvas("c2", "c2", 900, 700) #width-height
c2.SetLeftMargin(0.15)
gPad.Modified()
gr3 = TGraph(n, MH3name, effL)
gr3.SetTitle("Loose")
gr3.GetYaxis().SetNdivisions(505)
gr3.GetYaxis().SetLabelOffset(1.4)
tree.Project("B_d_lin_inner", "d_lin_inner", "abs(mother_pdgid) == 521")
tree.Project("D_d_lin_inner", "d_lin_inner", "abs(mother_pdgid) == 411")
[set_histogram(x,
"""d_{lin} #[]{cm}""",
"""entries / 0.005 #[]{cm}""") for x in [B_d_lin_inner_hist, D_d_lin_inner_hist]]
title = "d_{lin} distribution, inner track"
d_lin_inner_can = TCanvas("distribution", title)
d_lin_inner_can.cd()
d_lin_inner_can.SetLogy()
set_canvas(d_lin_inner_can)
B_d_lin_inner_hist.Draw()
D_d_lin_inner_hist.Draw("SAME")
legend = d_lin_inner_can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
B_d_lin_inner_hist.SetTitle(title)
d_lin_inner_can.SaveAs("d_lin_inner.eps")
"""inner circ"""
B_d_circ_inner_hist = TH1D("B_d_circ_inner", "B", *hist_stats)
D_d_circ_inner_hist = TH1D("D_d_circ_inner", "D", *hist_stats)
B_d_circ_inner_hist.SetStats(0)
D_d_circ_inner_hist.SetLineColor(2)
tree.Project("B_d_circ_inner", "d_circ_inner", "abs(mother_pdgid) == 521")
tree.Project("D_d_circ_inner", "d_circ_inner", "abs(mother_pdgid) == 411")
[set_histogram(x,
"""d_{circ} #[]{cm}""",
print(glob_lin_circ_hist.GetBinContent(0))
print(glob_lin_circ_hist.GetBinContent(glob_lin_circ_hist.GetNbinsX() + 1))
title = "distribution of d_{lin} - d_{circ}"
lin_circ_can = TCanvas("lin_circ_can", title)
lin_circ_can.cd()
set_canvas(lin_circ_can)
lin_circ_can.SetLogy()
set_histogram(in_lin_circ_hist, """d_{lin} - d_{circ} #[]{cm}""",
"""entries / 1e-10 #[]{cm}""")
set_histogram(glob_lin_circ_hist, "", "")
in_lin_circ_hist.Draw()
in_lin_circ_hist.SetStats(0)
glob_lin_circ_hist.SetLineColor(2)
glob_lin_circ_hist.Draw("SAME")
legend = lin_circ_can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
in_lin_circ_hist.SetTitle(title)
in_mean = in_lin_circ_hist.GetMean()
glob_mean = glob_lin_circ_hist.GetMean()
glob_RMS = glob_lin_circ_hist.GetRMS()
in_RMS = in_lin_circ_hist.GetRMS()
print("global mean, RMS", glob_mean, glob_RMS)
print("inner mean, RMS", in_mean, in_RMS)
#lin_circ_can.SaveAs("lin_circ2.eps")
#global_diff_can = TCanvas("glob_diff_can", "glob_diff_can")
#x_axis = "fabs(d_lin_global - d_circ_global)"
#y_axis = "sqrt(ref_x_global*ref_x_global + ref_y_global*ref_y_global)"
#global_diff_hist = TH2D("glob_diff", "glob_diff", 2000, 0, 1e-4, 1000, 0,
# 500)
canvas.Print(outputFile_name+"[") #apre file .ps
for key in keys:
h_chib1[key].SetLineColor(ROOT.kBlue)
h_chib1[key].SetMarkerColor(ROOT.kBlue)
h_chib1[key].SetMarkerStyle(22)
h_chib2[key].SetLineColor(ROOT.kRed)
h_chib2[key].SetMarkerColor(ROOT.kRed)
h_chib2[key].SetMarkerStyle(23)
hs = THStack('hs', key+axisLabels[key])
hs.Add(h_chib1[key])
hs.Add(h_chib2[key])
hs.SetMinimum(estremiGrafico[key][0])
hs.SetMaximum(estremiGrafico[key][-1])
hs.Draw('nostack')
leg=canvas.BuildLegend(.8,.95,.92,.8)
line1 = TLine(ptBins[0],getattr(Chib1_params_all, key),ptBins[-1],getattr(Chib1_params_all, key))
line2 = TLine(ptBins[0],getattr(Chib2_params_all, key),ptBins[-1],getattr(Chib2_params_all, key))
line1.SetLineColor(ROOT.kBlue)
line2.SetLineColor(ROOT.kRed)
line1.SetLineStyle(2)
line2.SetLineStyle(2)
print line1.GetTitle()
line1.Draw('same')
line2.Draw('same')
leg.SetFillColor(0)
leg.Draw("SAME")
canvas.Update()
canvas.Print(outputFile_name)
canvas.Print('plots/'+key+'.pdf')
canvas.Print('plots/'+key+'.root')
c = TCanvas()
c.SetLogy()
h1.SetXTitle("P_{T}(#phi)")
h1.SetYTitle("#events")
h1.Scale(1 / h1.Integral())
h1.SetXTitle("P_{T}")
h1.SetYTitle("#events")
h1.SetLineColor(2)
h1.Draw()
h2.Scale(1 / h2.Integral())
h2.SetLineColor(3)
h2.Draw("same")
h3.Scale(1 / h3.Integral())
h3.SetLineColor(4)
h3.Draw("same")
h4.Scale(1 / h4.Integral())
h4.SetLineColor(6)
h4.Draw("same")
h5.Scale(1 / h5.Integral())
h5.SetLineColor(1)
h5.Draw("same")
c.BuildLegend(0.2, 0.7, 0.48, 0.9, "M_{#chi}=1")
c.SaveAs("pT(phi).png")
class KolmogorovResults:
"""compare d_global with d_inner"""
def __init__(self, trees, cutting_on, start, end, step, hist_stats,
other_cuts, title, x_title, y_title):
self.kd_global = KolmogorovDistribution(trees, "d_lin_global",
cutting_on, start, end, step,
hist_stats, other_cuts)
self.kd_inner = KolmogorovDistribution(trees, "d_lin_inner",
cutting_on, start, end, step,
hist_stats, other_cuts)
self.can = TCanvas("graph", "graph")
self.title = title
self.x_title = x_title
self.y_title = y_title
self.build_graphs()
def build_graphs(self):
kds = [self.kd_global, self.kd_inner]
names = ['tmp_global', 'tmp_inner']
for name, kd in zip(names, kds):
with open(name, 'w') as file:
for step, n_sigma in kd:
output_line = '\t'.join([str(x) for x in [step, n_sigma]])
output_line += '\n'
file.write(output_line)
unique_name = "can" + str(random.randint(0, 1000))
self.can = TCanvas(unique_name, "graph")
self.gr_global = TGraph(names[0])
self.gr_inner = TGraph(names[1])
titles = ("global track", "inner track")
self.set_graph_title(*titles)
self.set_graph_style()
def SetLogy(self):
self.can.SetLogy()
def set_graph_style(self):
self.can.SetFillColor(10)
self.gr_global.Draw("AP")
self.gr_inner.Draw("P")
legend = self.can.BuildLegend(0.7, 0.8, 0.9, 0.9)
legend.SetFillColor(10)
legend.Paint()
self.gr_global.SetMarkerStyle(20)
self.gr_inner.SetMarkerStyle(22)
self.gr_inner.SetMarkerColor(2)
self.gr_global.SetMarkerColor(4)
size = 0.04
self.gr_global.SetTitle(self.title)
gStyle.SetTitleFillColor(0)
self.gr_global.GetXaxis().SetTitle(self.x_title)
self.gr_global.GetYaxis().SetTitle(self.y_title)
self.gr_global.GetXaxis().SetDecimals()
self.gr_global.GetYaxis().SetDecimals()
self.gr_global.GetXaxis().SetTitleSize(size)
self.gr_global.GetYaxis().SetTitleSize(size)
self.gr_global.GetYaxis().SetTitleOffset(1.2)
self.gr_global.GetXaxis().SetLabelSize(size)
self.gr_global.GetYaxis().SetLabelSize(size)
def set_graph_title(self, title_global, title_inner):
self.gr_global.SetTitle(title_global)
self.gr_inner.SetTitle(title_inner)
def save_canvas(self, file_name):
self.can.SaveAs(file_name)
f1 = TFile.Open('basic_kin_m300.root')
h1 = f1.Get(s + '_nmssm')
h1.SetXTitle("p_{T}(b) [GeV]")
h1.SetYTitle("Events")
h1.SetTitle('NMSSM')
h1.SetLineColor(600)
h1.Draw('hist')
c.Update()
h2 = f1.Get(s + '_radion')
h2.SetXTitle("p_{T}(b) [GeV]")
h2.SetYTitle("Event")
h2.SetTitle('NMSSM')
h2.SetLineColor(418)
h2.Draw('hist&same')
c.Update()
h3 = f1.Get(s + '_graviton')
h3.SetXTitle("p_{T}(b) [GeV]")
h3.SetYTitle("Events")
h3.SetTitle('graviton')
h3.SetLineColor(625)
h3.Draw('hist&same')
c.Update()
l = c.BuildLegend(0.75, 0.4, 0.9, 0.5)
c.Update()
f = TFile.Open('plots.root', 'UPDATE')
c.Write()
def plot_hist(group_data, stacked=False, fitted=False):
group_name = str(group_data[0].run_number)
group_size = len(group_data)
if stacked:
print('Plotting stacked histogram.')
run_canvas = TCanvas()
run_canvas.SetLogy()
gStyle.SetPalette(87)
if group_name == '2946':
TColor.InvertPalette()
hs = THStack('hs', 'Group %s Histograms' % group_name)
for run in group_data:
tab = ' '
title = str(run.run_number) + tab + 'Average temperature: ' + str(
round(run.temperature_average, 2))
hist = make_hist(title, run.energies, 0, 270000)
hs.Add(hist)
hs.Draw('nostack PLC')
run_canvas.BuildLegend()
path = 'output/group_' + str(group_data[0].run_number) + '/'
run_canvas.Print(
(path + str(group_data[0].run_number) + '_stacked_histogram.pdf['))
run_canvas.Print(
(path + str(group_data[0].run_number) + '_stacked_histogram.pdf'))
run_canvas.Print(
(path + str(group_data[0].run_number) + '_stacked_histogram.pdf]'))
else:
hists_and_fits = []
hist_range = [20000, 60000, 150000, 350000, 550000]
for i in range(len(group_data)):
run = group_data[i]
hist = make_hist(str(run.run_number), run.energies, 0,
hist_range[i])
if fitted:
K_key = 'K_' + str(run.run_number)
K_pars = parameters.fit_pars[K_key]
K_fit = find_peak_fit('K', K_pars[0], K_pars[1], K_pars[2],
K_pars[3], K_pars[4], K_pars[5],
K_pars[6])
Tl_key = 'Tl_' + str(run.run_number)
Tl_pars = parameters.fit_pars[Tl_key]
Tl_fit = find_peak_fit('Tl', Tl_pars[0], Tl_pars[1],
Tl_pars[2], Tl_pars[3], Tl_pars[4],
Tl_pars[5], Tl_pars[6])
hists_and_fits.append([hist, K_fit, Tl_fit])
write_run_output(group_name, run, K_fit, Tl_fit)
else:
hists_and_fits.append([hist])
run_canvas = TCanvas("run_canvas", "run canvas")
run_canvas.Divide(3, int(group_size / 2))
file_name = ''
canvas_index = 1
for entry in hists_and_fits:
pad = run_canvas.cd(canvas_index)
pad.SetLogy()
run = entry[0]
if len(entry) == 3:
print('Plotting fitted histogram.')
file_name = '_fitted_histograms.pdf'
fit1 = entry[1]
fit2 = entry[2]
run.Fit(fit1, 'LR')
run.Fit(fit2, 'LR+')
else:
print('Plotting histogram.')
file_name = '_histograms.pdf'
run.Draw()
canvas_index += 1
path = 'output/group_' + group_name + '/'
run_canvas.Print((path + group_name + file_name + '['))
run_canvas.Print((path + group_name + file_name))
run_canvas.Print((path + group_name + file_name + ']'))
