def pdf_logPt2_incoh():
#PDF fit to log_10(pT^2)
#tree_in = tree_incoh
tree_in = tree
#ptbin = 0.04
ptbin = 0.12
ptmin = -5.
ptmax = 1.
mmin = 2.8
mmax = 3.2
#fitran = [-5., 1.]
fitran = [-0.9, 0.1]
binned = False
#gamma-gamma 131 evt for pT<0.18
#output log file
out = open("out.txt", "w")
ut.log_results(
out, "in " + infile + " in_coh " + infile_coh + " in_gg " + infile_gg)
loglist = [(x, eval(x)) for x in
["ptbin", "ptmin", "ptmax", "mmin", "mmax", "fitran", "binned"]]
strlog = ut.make_log_string(loglist)
ut.log_results(out, strlog + "\n")
#input data
pT = RooRealVar("jRecPt", "pT", 0, 10)
m = RooRealVar("jRecM", "mass", 0, 10)
dataIN = RooDataSet("data", "data", tree_in, RooArgSet(pT, m))
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
data = dataIN.reduce(strsel)
#x is RooRealVar for log(Pt2)
draw = "TMath::Log10(jRecPt*jRecPt)"
draw_func = RooFormulaVar("x", "log_{10}( #it{p}_{T}^{2} ) (GeV^{2})",
draw, RooArgList(pT))
x = data.addColumn(draw_func)
x.setRange("fitran", fitran[0], fitran[1])
#binned data
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPt = TH1D("hPt", "hPt", nbins, ptmin, ptmax)
tree_in.Draw(draw + " >> hPt", strsel)
dataH = RooDataHist("dataH", "dataH", RooArgList(x), hPt)
#range for plot
x.setMin(ptmin)
x.setMax(ptmax)
x.setRange("plotran", ptmin, ptmax)
#create the pdf
b = RooRealVar("b", "b", 5., 0., 10.)
pdf_func = "log(10.)*pow(10.,x)*exp(-b*pow(10.,x))"
pdf_logPt2 = RooGenericPdf("pdf_logPt2", pdf_func, RooArgList(x, b))
#make the fit
if binned == True:
r1 = pdf_logPt2.fitTo(dataH, rf.Range("fitran"), rf.Save())
else:
r1 = pdf_logPt2.fitTo(data, rf.Range("fitran"), rf.Save())
ut.log_results(out, ut.log_fit_result(r1))
#calculate norm to number of events
xset = RooArgSet(x)
ipdf = pdf_logPt2.createIntegral(xset, rf.NormSet(xset),
rf.Range("fitran"))
print "PDF integral:", ipdf.getVal()
if binned == True:
nevt = tree_incoh.Draw(
"", strsel + " && " + draw + ">{0:.3f}".format(fitran[0]) +
" && " + draw + "<{1:.3f}".format(fitran[0], fitran[1]))
else:
nevt = data.sumEntries("x", "fitran")
print "nevt:", nevt
pdf_logPt2.setNormRange("fitran")
print "PDF norm:", pdf_logPt2.getNorm(RooArgSet(x))
#a = nevt/ipdf.getVal()
a = nevt / pdf_logPt2.getNorm(RooArgSet(x))
ut.log_results(out, "log_10(pT^2) parametrization:")
ut.log_results(out, "A = {0:.2f}".format(a))
ut.log_results(out, ut.log_fit_parameters(r1, 0, 2))
print "a =", a
#Coherent contribution
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin, ptmin, ptmax)
hPtCoh.Sumw2()
#tree_coh.Draw(draw + " >> hPtCoh", strsel)
tree_coh.Draw("TMath::Log10(jGenPt*jGenPt) >> hPtCoh", strsel)
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, -5., -2.2) # norm for coh
#ut.norm_to_data(hPtCoh, hPt, rt.kBlue, -5, -2.1)
#ut.norm_to_num(hPtCoh, 405, rt.kBlue)
print "Coherent integral:", hPtCoh.Integral()
#TMath::Log10(jRecPt*jRecPt)
#Sartre generated coherent shape
sartre = TFile.Open(
"/home/jaroslav/sim/sartre_tx/sartre_AuAu_200GeV_Jpsi_coh_2p7Mevt.root"
)
sartre_tree = sartre.Get("sartre_tree")
hSartre = ut.prepare_TH1D("hSartre", ptbin, ptmin, ptmax)
sartre_tree.Draw("TMath::Log10(pT*pT) >> hSartre",
"rapidity>-1 && rapidity<1")
ut.norm_to_data(hSartre, hPt, rt.kViolet, -5, -2) # norm for Sartre
#gamma-gamma contribution
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
tree_gg.Draw(draw + " >> hPtGG", strsel)
#ut.norm_to_data(hPtGG, hPt, rt.kGreen, -5., -2.9)
ut.norm_to_num(hPtGG, 131., rt.kGreen)
print "Int GG:", hPtGG.Integral()
#psi' contribution
psiP = TFile.Open(basedir_mc + "/ana_slight14e4x1_s6_sel5z.root")
psiP_tree = psiP.Get("jRecTree")
hPtPsiP = ut.prepare_TH1D("hPtPsiP", ptbin, ptmin, ptmax)
psiP_tree.Draw(draw + " >> hPtPsiP", strsel)
ut.norm_to_num(hPtPsiP, 12, rt.kViolet)
#sum of all contributions
hSum = ut.prepare_TH1D("hSum", ptbin, ptmin, ptmax)
hSum.SetLineWidth(3)
#add ggel to the sum
hSum.Add(hPtGG)
#add incoherent contribution
func_logPt2 = TF1("pdf_logPt2",
"[0]*log(10.)*pow(10.,x)*exp(-[1]*pow(10.,x))", -10.,
10.)
func_logPt2.SetParameters(a, b.getVal())
hInc = ut.prepare_TH1D("hInc", ptbin, ptmin, ptmax)
ut.fill_h1_tf(hInc, func_logPt2)
hSum.Add(hInc)
#add coherent contribution
hSum.Add(hPtCoh)
#add psi(2S) contribution
#hSum.Add(hPtPsiP)
#set to draw as a lines
ut.line_h1(hSum, rt.kBlack)
#create canvas frame
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.01)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
frame.SetTitle("")
frame.SetMaximum(75)
frame.SetYTitle("Events / ({0:.3f}".format(ptbin) + " GeV^{2})")
print "Int data:", hPt.Integral()
#plot the data
if binned == True:
dataH.plotOn(frame, rf.Name("data"))
else:
data.plotOn(frame, rf.Name("data"))
pdf_logPt2.plotOn(frame, rf.Range("fitran"), rf.LineColor(rt.kRed),
rf.Name("pdf_logPt2"))
pdf_logPt2.plotOn(frame, rf.Range("plotran"), rf.LineColor(rt.kRed),
rf.Name("pdf_logPt2_full"), rf.LineStyle(rt.kDashed))
frame.Draw()
amin = TMath.Power(10, ptmin)
amax = TMath.Power(10, ptmax) - 1
print amin, amax
pt2func = TF1("f1", "TMath::Power(10, x)", amin,
amax) #TMath::Power(x, 10)
aPt2 = TGaxis(-5, 75, 1, 75, "f1", 510, "-")
ut.set_axis(aPt2)
aPt2.SetTitle("pt2")
#aPt2.Draw();
leg = ut.prepare_leg(0.57, 0.78, 0.14, 0.19, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
hx = ut.prepare_TH1D("hx", 1, 0, 1)
hx.Draw("same")
ln = ut.col_lin(rt.kRed)
leg.AddEntry(hx, "Data")
leg.AddEntry(hPtCoh, "Sartre MC", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-} MC", "l")
#leg.AddEntry(ln, "ln(10)*#it{A}*10^{log_{10}#it{p}_{T}^{2}}exp(-#it{b}10^{log_{10}#it{p}_{T}^{2}})", "l")
#leg.AddEntry(ln, "Incoherent fit", "l")
leg.Draw("same")
l0 = ut.cut_line(fitran[0], 0.9, frame)
l1 = ut.cut_line(fitran[1], 0.9, frame)
#l0.Draw()
#l1.Draw()
desc = pdesc(frame, 0.14, 0.8, 0.054)
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("pdf_logPt2", "data", 2), -1,
rt.kRed)
desc.itemD("#it{A}", a, -1, rt.kRed)
desc.itemR("#it{b}", b, rt.kRed)
desc.draw()
#put the sum
#hSum.Draw("same")
#gPad.SetLogy()
frame.Draw("same")
#put gamma-gamma
hPtGG.Draw("same")
#put coherent J/psi
hPtCoh.Draw("same")
#put Sartre generated coherent shape
#hSartre.Draw("same")
#put psi(2S) contribution
#hPtPsiP.Draw("same")
leg2 = ut.prepare_leg(0.14, 0.9, 0.14, 0.08, 0.03)
leg2.AddEntry(
ln,
"ln(10)*#it{A}*10^{log_{10}#it{p}_{T}^{2}}exp(-#it{b}10^{log_{10}#it{p}_{T}^{2}})",
"l")
#leg2.AddEntry(hPtCoh, "Sartre MC reconstructed", "l")
#leg2.AddEntry(hSartre, "Sartre MC generated", "l")
leg2.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
#-- end of config --
#get input
gROOT.SetBatch()
inp = TFile.Open(basedir + "/" + infile)
tree = inp.Get("jRecTree")
#output log file
out = open("out.txt", "w")
#log fit parameters
loglist1 = [(x, eval(x)) for x in ["infile", "mbin", "mmin", "mmax"]]
loglist2 = [
(x, eval(x))
for x in ["ymin", "ymax", "ptmax", "binned", "fitran[0]", "fitran[1]"]
]
strlog = ut.make_log_string(loglist1, loglist2)
ut.log_results(out, strlog + "\n")
#unbinned and binned input data
nbins, mmax = ut.get_nbins(mbin, mmin, mmax)
strsel = "jRecY>{0:.3f} && jRecY<{1:.3f} && jRecPt<{2:.3f}".format(
ymin, ymax, ptmax)
#unbinned data
m.setMin(mmin)
m.setMax(mmax)
m.setRange("fitran", fitran[0], fitran[1])
dataIN = RooDataSet("data", "data", tree, RooArgSet(m, y, pT))
data = dataIN.reduce(strsel)
#binned data
hMass = TH1D("hMass", "hMass", nbins, mmin, mmax)
tree.Draw("jRecM >> hMass", strsel)
def plot_rec_gen_track_pt():
#track pT resolution as ( pT_track_rec - pT_track_gen )/pT_track_gen
ptbin = 0.001
ptmin = -0.3
ptmax = 0.1
#generated dielectron pT selection to input data
ptlo = 0.2
pthi = 1
fitran = [-0.15, 0.018]
mmin = 2.8
mmax = 3.2
ccb = rt.kBlue
#output log file
out = open("out.txt", "w")
#log fit parameters
loglist1 = [(x, eval(x)) for x in ["infile_mc", "ptbin", "ptmin", "ptmax"]]
loglist2 = [(x, eval(x))
for x in ["ptlo", "pthi", "fitran", "mmin", "mmax"]]
strlog = ut.make_log_string(loglist1, loglist2)
ut.log_results(out, strlog + "\n")
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
strsel += " && jGenPt>{0:.3f}".format(ptlo)
strsel += " && jGenPt<{0:.3f}".format(pthi)
#strsel = ""
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPtTrackRel = ut.prepare_TH1D_n("hPtTrackRel", nbins, ptmin, ptmax)
ytit = "Events / ({0:.3f})".format(ptbin)
xtit = "(#it{p}_{T, rec}^{track} - #it{p}_{T, gen}^{track})/#it{p}_{T, gen}^{track}"
mctree.Draw("(jT0pT-jGenP0pT)/jGenP0pT >> hPtTrackRel",
strsel) # positive charge
mctree.Draw("(jT1pT-jGenP1pT)/jGenP1pT >>+hPtTrackRel",
strsel) # add negative charge
x = RooRealVar("x", "x", ptmin, ptmax)
x.setRange("fitran", fitran[0], fitran[1])
rfPtTrackRel = RooDataHist("rfPtTrackRel", "rfPtTrackRel", RooArgList(x),
hPtTrackRel)
#standard Crystal Ball
mean = RooRealVar("mean", "mean", -0.003, -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.01, 0., 0.9)
alpha = RooRealVar("alpha", "alpha", 1.2, 0., 10.)
n = RooRealVar("n", "n", 1.3, 0., 20.)
cbpdf = RooCBShape("cbpdf", "cbpdf", x, mean, sigma, alpha, n)
res = cbpdf.fitTo(rfPtTrackRel, rf.Range("fitran"), rf.Save())
#log fit results
ut.log_results(out, ut.log_fit_result(res))
#generate new distribution according to the fit
gROOT.LoadMacro("cb_gen.h")
#Crystal Ball generator, min, max, mean, sigma, alpha, n
#cbgen = rt.cb_gen(-0.18, 0.05, -0.00226, 0.00908, 1.40165, 1.114) # -0.18, 0.05 ptmin, ptmax
cbgen = rt.cb_gen(-0.5, 0.05, -0.00226, 0.00908, 0.2,
2.) # -0.18, 0.05 ptmin, ptmax
hRelGen = ut.prepare_TH1D_n("hRelGen", nbins, ptmin, ptmax)
ut.set_H1D_col(hRelGen, rt.kBlue)
#rt.cb_generate_n(cbgen, hRelGen, int(hPtTrackRel.GetEntries()))
rfRelGen = RooDataHist("rfRelGen", "rfRelGen", RooArgList(x), hRelGen)
#generate distribution with additional smearing applied
hRelSmear = ut.prepare_TH1D_n("hRelSmear", nbins, ptmin, ptmax)
ut.set_H1D_col(hRelSmear, rt.kOrange)
#tcopy = mctree.CopyTree(strsel)
#rt.cb_apply_smear(cbgen, mctree, hRelSmear)
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.05, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
ut.put_frame_yx_tit(frame, ytit, xtit)
rfPtTrackRel.plotOn(frame, rf.Name("data"))
#rfRelGen.plotOn(frame, rf.Name("data"))
cbpdf.plotOn(frame, rf.Precision(1e-6), rf.Name("cbpdf"),
rf.LineColor(ccb))
frame.Draw()
#hRelGen.Draw("e1same")
#hRelSmear.Draw("e1same")
desc = pdesc(frame, 0.2, 0.8, 0.057)
#x, y, sep
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("cbpdf", "data", 4), -1, ccb)
desc.prec = 5
desc.itemR("mean", mean, ccb)
desc.itemR("#sigma", sigma, ccb)
desc.itemR("#alpha", alpha, ccb)
desc.prec = 3
desc.itemR("#it{n}", n, ccb)
desc.draw()
leg = ut.prepare_leg(0.2, 0.82, 0.21, 0.12, 0.03) # x, y, dx, dy, tsiz
leg.SetMargin(0.05)
leg.AddEntry(0, "#bf{%.1f < #it{p}_{T}^{pair} < %.1f GeV}" % (ptlo, pthi),
"")
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_rec_gen_pt_relative():
# relative dielectron pT resolution as ( pT_rec - pT_gen )/pT_gen
ptbin = 0.01
ptmin = -1.2
ptmax = 4
#generated pT selection to input data
ptlo = 0.2
pthi = 1.
fitran = [-0.1, 3]
mmin = 2.8
mmax = 3.2
#output log file
out = open("out.txt", "w")
#log fit parameters
loglist1 = [(x, eval(x)) for x in ["infile_mc", "ptbin", "ptmin", "ptmax"]]
loglist2 = [(x, eval(x))
for x in ["ptlo", "pthi", "fitran", "mmin", "mmax"]]
strlog = ut.make_log_string(loglist1, loglist2)
ut.log_results(out, strlog + "\n")
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
strsel += " && jGenPt>{0:.3f}".format(ptlo)
strsel += " && jGenPt<{0:.3f}".format(pthi)
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPtRel = ut.prepare_TH1D("hPtRel", ptbin, ptmin, ptmax)
ytit = "Events / ({0:.3f})".format(ptbin)
xtit = "(#it{p}_{T, rec} - #it{p}_{T, gen})/#it{p}_{T, gen}"
mctree.Draw("(jRecPt-jGenPt)/jGenPt >> hPtRel", strsel)
x = RooRealVar("x", "x", ptmin, ptmax)
x.setRange("fitran", fitran[0], fitran[1])
rfPtRel = RooDataHist("rfPtRel", "rfPtRel", RooArgList(x), hPtRel)
#reversed Crystal Ball
mean = RooRealVar("mean", "mean", 0., -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.2, 0., 0.9)
alpha = RooRealVar("alpha", "alpha", -1.2, -10., 0.)
n = RooRealVar("n", "n", 1.3, 0., 20.)
cbpdf = RooCBShape("cbpdf", "cbpdf", x, mean, sigma, alpha, n)
res = cbpdf.fitTo(rfPtRel, rf.Range("fitran"), rf.Save())
#log fit results
ut.log_results(out, ut.log_fit_result(res))
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.05, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
ut.put_frame_yx_tit(frame, ytit, xtit)
rfPtRel.plotOn(frame, rf.Name("data"))
cbpdf.plotOn(frame, rf.Precision(1e-6), rf.Name("cbpdf"))
frame.Draw()
desc = pdesc(frame, 0.65, 0.8, 0.057)
#x, y, sep
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("cbpdf", "data", 4), -1,
rt.kBlue)
desc.prec = 5
desc.itemR("mean", mean, rt.kBlue)
desc.prec = 4
desc.itemR("#sigma", sigma, rt.kBlue)
desc.itemR("#alpha", alpha, rt.kBlue)
desc.prec = 3
desc.itemR("#it{n}", n, rt.kBlue)
desc.draw()
leg = ut.prepare_leg(0.6, 0.82, 0.21, 0.12, 0.03) # x, y, dx, dy, tsiz
leg.SetMargin(0.05)
leg.AddEntry(0, "#bf{%.1f < #it{p}_{T}^{pair} < %.1f GeV}" % (ptlo, pthi),
"")
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_rec_gen_track_phi():
#track azimuthal angle phi resolution as ( phi_track_rec - phi_track_gen )/phi_track_gen
phibin = 0.0001
phimin = -0.02
phimax = 0.02
#ptlo = 0.
#pthi = 0.9
fitran = [-0.01, 0.01]
mmin = 2.8
mmax = 3.2
cbw = rt.kBlue
#output log file
out = open("out.txt", "w")
#log fit parameters
loglist1 = [(x, eval(x))
for x in ["infile_mc", "phibin", "phimin", "phimax"]]
loglist2 = [(x, eval(x)) for x in ["fitran", "mmin", "mmax"]]
strlog = ut.make_log_string(loglist1, loglist2)
ut.log_results(out, strlog + "\n")
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
#strsel += " && jGenPt>{0:.3f}".format(ptlo)
#strsel += " && jGenPt<{0:.3f}".format(pthi)
nbins, phimax = ut.get_nbins(phibin, phimin, phimax)
hPhiRel = ut.prepare_TH1D_n("hPhiRel", nbins, phimin, phimax)
ytit = "Events / ({0:.4f})".format(phibin)
xtit = "(#phi_{rec} - #phi_{gen})/#phi_{gen}"
mctree.Draw("(jT0phi-jGenP0phi)/jGenP0phi >> hPhiRel",
strsel) # positive charge
mctree.Draw("(jT1phi-jGenP1phi)/jGenP1phi >>+hPhiRel",
strsel) # add negative charge
x = RooRealVar("x", "x", phimin, phimax)
x.setRange("fitran", fitran[0], fitran[1])
rfPhiRel = RooDataHist("rfPhiRel", "rfPhiRel", RooArgList(x), hPhiRel)
#Breit-Wigner pdf
mean = RooRealVar("mean", "mean", 0., -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.01, 0., 0.9)
bwpdf = RooBreitWigner("bwpdf", "bwpdf", x, mean, sigma)
res = bwpdf.fitTo(rfPhiRel, rf.Range("fitran"), rf.Save())
#log fit results
ut.log_results(out, ut.log_fit_result(res))
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.05, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
ut.put_frame_yx_tit(frame, ytit, xtit)
rfPhiRel.plotOn(frame, rf.Name("data"))
bwpdf.plotOn(frame, rf.Precision(1e-6), rf.Name("bwpdf"))
frame.Draw()
desc = pdesc(frame, 0.12, 0.93, 0.057)
#x, y, sep
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("bwpdf", "data", 2), -1, cbw)
desc.prec = 2
desc.fmt = "e"
desc.itemR("mean", mean, cbw)
desc.itemR("#sigma", sigma, cbw)
desc.draw()
leg = ut.make_uo_leg(hPhiRel, 0.5, 0.8, 0.2, 0.2)
#leg.Draw("same")
#print "Entries: ", hPhiRel.GetEntries()
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")