def plot_vtx_z():
#primary vertex position along z from data and MC
vbin = 4.
vmax = 120.
mmin = 1.5
mmax = 5
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hVtx = ut.prepare_TH1D("hVtx", vbin, -vmax, vmax)
hVtxMC = ut.prepare_TH1D("hVtxMC", vbin/2., -vmax, vmax)
tree.Draw("jVtxZ >> hVtx", strsel)
mctree.Draw("jVtxZ >> hVtxMC", strsel)
ut.norm_to_data(hVtxMC, hVtx, rt.kBlue, -40, 40)
hVtx.SetYTitle("Counts / {0:.0f} cm".format(vbin));
hVtx.SetXTitle("#it{z} of primary vertex (cm)");
hVtx.SetTitleOffset(1.5, "Y")
hVtx.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.02)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.11)
cut_lo = ut.cut_line(-30, 0.8, hVtx)
cut_hi = ut.cut_line(30, 0.8, hVtx)
leg = ut.prepare_leg(0.16, 0.82, 0.26, 0.12, 0.025)
#leg.SetMargin(0.15)
#leg.SetBorderSize(1)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hVtx, "Data")
#leg.AddEntry(hVtxMC, "MC, coherent J/#it{#psi}", "l")
leg.AddEntry(hVtxMC, "MC, #it{#gamma}#it{#gamma} #rightarrow e^{+}e^{-}", "l")
leg.AddEntry(cut_lo, "Cut at #pm30 cm", "l")
hVtx.Draw()
hVtxMC.Draw("same")
leg.Draw("same")
cut_lo.Draw("same")
cut_hi.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_east():
zmin = 0
zbin = 10
zmax = 400
can = ut.box_canvas()
hZdc = ut.prepare_TH1D("hZdc", zbin, zmin, zmax)
tree.Draw("jZDCUnAttEast >> hZdc")
ut.set_margin_lbtr(gPad, 0.1, 0.08, 0.01, 0.04)
ut.put_yx_tit(hZdc, "ZDC East", "ADC")
#middle point from 1n and 2n positions, dl-202011.03
mid_1n2n = (164.996 + 75.671) / 2
print("mid_1n2n:", mid_1n2n)
lin = ut.cut_line(mid_1n2n, 0.7, hZdc)
gF = make_FastZDC("jZDCUnAttEast", hZdc)
hZdc.Draw()
gF.Draw("same")
hZdc.Draw("e1same")
lin.Draw("same")
gPad.SetGrid()
#gPad.SetLogy()
ut.invert_col(gPad)
can.SaveAs("01fig.pdf")
def plot_dphi_bemc():
#tracks opening angle at BEMC
phibin = 0.01
phimin = 2.4
phimax = 3.1
mmin = 1.5
mmax = 5
#mmin = 2.8
#mmax = 3.2
ptmax = 0.17
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f} && jRecPt<{2:.3f}".format(
mmin, mmax, ptmax)
can = ut.box_canvas()
hDphi = ut.prepare_TH1D("hDphi", phibin, phimin, phimax)
hDphiMC = ut.prepare_TH1D("hDphiMC", phibin, phimin, phimax)
ut.put_yx_tit(hDphi, "Events / {0:.2f}".format(phibin),
"Tracks #Delta#phi at BEMC")
ut.put_yx_tit(hDphiMC, "Events / {0:.2f}".format(phibin),
"Tracks #Delta#phi at BEMC")
ut.set_margin_lbtr(gPad, 0.1, 0.08, 0.014, 0.01)
tree.Draw("jDeltaPhiBemc >> hDphi", strsel)
mctree.Draw("jDeltaPhiBemc >> hDphiMC", strsel)
ut.norm_to_data(hDphiMC, hDphi, rt.kBlue)
hDphiMC.Draw()
hDphi.Draw("e1same")
#hDphi.Draw()
hDphiMC.Draw("same")
lin = ut.cut_line(2.618, 0.5, hDphi)
lin.Draw("same")
leg = ut.prepare_leg(0.14, 0.71, 0.14, 0.21, 0.03)
ut.add_leg_pt_mass(leg, ptmax, mmin, mmax)
leg.AddEntry(hDphi, "Data")
leg.AddEntry(hDphiMC, "MC", "l")
leg.AddEntry(lin, "Cut at 2.618", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hDphi, 0.14, 0.9, 0.01, 0.1)
uoleg.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def fit_pt_incoh():
#fit to incoherent MC pT
ptbin = 0.015
#ptbin = math.sqrt(0.005)
ptmin = 0.
ptmax = 1.4
mmin = 2.8
mmax = 3.2
fitran = [0.4, 1.]
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPtIncoh = ut.prepare_TH1D("hPtIncoh", ptbin, ptmin, ptmax)
ut.put_yx_tit(hPtIncoh, "Events / ({0:.3f}".format(ptbin) + " GeV)",
"#it{p}_{T} (GeV)")
tree_incoh.Draw("jRecPt >> hPtIncoh", strsel)
print "Input events:", hPtIncoh.GetEntries()
print "Histogram integral:", hPtIncoh.Integral()
print "Histogram integral (w):", hPtIncoh.Integral("width")
#hPtIncoh.Sumw2()
#hPtIncoh.Scale(1./hPtIncoh.Integral("width"))
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
func_incoh = TF1("func_incoh", "2*[0]*x*exp(-[1]*x*x)", 0., 10.)
func_incoh.SetParName(0, "A")
func_incoh.SetParName(1, "b")
func_incoh.SetNpx(1000)
func_incoh.SetLineColor(rt.kRed)
func_incoh.SetParameters(3000., 5.)
r1 = (hPtIncoh.Fit(func_incoh, "RS", "", fitran[0], fitran[1])).Get()
print "Fit integral:", func_incoh.Integral(0., 10.)
hPtIncoh.Draw()
func_incoh.Draw("same")
#normalize fit function to number of events
pdf_incoh = TF1("pdf_incoh", "2*[0]*x*exp(-[1]*x*x)", 0., 10.)
pdf_incoh.SetParName(0, "A")
pdf_incoh.SetParName(1, "b")
# tree_incoh.Draw("jRecPt >> hPtIncoh", strsel)
#strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
nevt = tree_incoh.Draw(
"", strsel +
" && jRecPt>{0:.3f} && jRecPt<{1:.3f}".format(fitran[0], fitran[1]))
k_norm = nevt / func_incoh.Integral(fitran[0], fitran[1])
pdf_incoh.SetParameter(0, k_norm * func_incoh.GetParameter(0))
pdf_incoh.SetParameter(1, func_incoh.GetParameter(1))
#verify the normalization:
print "Function integral after norm:", pdf_incoh.Integral(0., 10.)
#create pdf for pT^2 and verify normalization
pdf_pt2 = TF1("pdf_pt2", "[0]*exp(-[1]*x)", 0., 10.)
pdf_pt2.SetParameter(0, pdf_incoh.GetParameter(0))
pdf_pt2.SetParameter(1, pdf_incoh.GetParameter(1))
print "PDF for pT^2 integral:", pdf_pt2.Integral(0., 10.)
leg = ut.prepare_leg(0.67, 0.84, 0.14, 0.12, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPtIncoh, "Incoherent MC")
leg.AddEntry(func_incoh, "2#it{A}*#it{p}_{T}exp(-#it{b}*#it{p}_{T}^{2})",
"l")
leg.Draw("same")
desc = pdesc(hPtIncoh, 0.72, 0.84, 0.057)
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", r1.Chi2() / r1.Ndf(), -1, rt.kRed)
desc.prec = 2
desc.itemRes("#it{A}", r1, 0, rt.kRed)
desc.itemD("#it{A}", pdf_incoh.GetParameter(0), -1, rt.kRed)
desc.prec = 3
desc.itemRes("#it{b}", r1, 1, rt.kRed)
desc.draw()
l0 = ut.cut_line(fitran[0], 0.9, hPtIncoh)
l1 = ut.cut_line(fitran[1], 0.9, hPtIncoh)
l0.Draw()
l1.Draw()
uoleg = ut.make_uo_leg(hPtIncoh, 0.14, 0.9, 0.01, 0.1)
uoleg.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def pdf_logPt2_prelim():
#PDF fit to log_10(pT^2) for preliminary figure
#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
#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", "Dielectron log_{10}( #it{p}_{T}^{2} ) ((GeV/c)^{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)
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin, ptmin, ptmax)
hPtCoh.SetLineWidth(2)
#fill in binned data
tree_in.Draw(draw + " >> hPt", strsel)
tree_coh.Draw(draw + " >> hPtCoh", 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())
#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))
print "a =", a
#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 + 1)
print "Int GG:", hPtGG.Integral()
#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
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, -5., -2.2) # norm for coh
hSum.Add(hPtCoh)
#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, 0.1, 0.01, 0.01)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
frame.SetTitle("")
frame.SetYTitle("J/#psi candidates / ({0:.3f}".format(ptbin) +
" (GeV/c)^{2})")
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.6)
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()
leg = ut.prepare_leg(0.61, 0.77, 0.16, 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, 2)
leg.AddEntry(hx, "Data", "p")
leg.AddEntry(hSum, "Sum", "l")
leg.AddEntry(hPtCoh, "Coherent J/#psi", "l")
leg.AddEntry(ln, "Incoherent parametrization", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-}", "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.Draw("same")
l0 = ut.cut_line(fitran[0], 0.9, frame)
l1 = ut.cut_line(fitran[1], 0.9, frame)
#l0.Draw()
#l1.Draw()
pleg = ut.prepare_leg(0.12, 0.75, 0.14, 0.22, 0.03)
pleg.AddEntry(None, "#bf{|#kern[0.3]{#it{y}}| < 1}", "")
ut.add_leg_mass(pleg, mmin, mmax)
pleg.AddEntry(None, "STAR Preliminary", "")
pleg.AddEntry(None, "AuAu@200 GeV", "")
pleg.AddEntry(None, "UPC sample", "")
pleg.Draw("same")
desc = pdesc(frame, 0.14, 0.9, 0.057)
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")
frame.Draw("same")
#put gamma-gamma and coherent J/psi
hPtGG.Draw("same")
hPtCoh.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def fit_logPt2_incoh():
#fit to incoherent log_10(pT^2)
ptbin = 0.12
ptmin = -5.
ptmax = 1.
mmin = 2.8
mmax = 3.2
fitran = [-1., -0.1]
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPtIncoh = ut.prepare_TH1D("hPtIncoh", ptbin / 3., ptmin, ptmax)
ut.put_yx_tit(hPtIncoh, "Events / ({0:.3f}".format(ptbin) + " GeV^{2})",
"log_{10}( #it{p}_{T}^{2} ) (GeV^{2})")
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.01)
draw = "TMath::Log10(jRecPt*jRecPt)"
tree_incoh.Draw(draw + " >> hPtIncoh", strsel)
#hPtIncoh.Sumw2()
#hPtIncoh.Scale(1./hPtIncoh.Integral("width"))
func_incoh_logPt2 = TF1("func_incoh_logPt2",
"[0]*log(10.)*pow(10.,x)*exp(-[1]*pow(10.,x))",
-10., 10.)
func_incoh_logPt2.SetParName(0, "A")
func_incoh_logPt2.SetParName(1, "b")
func_incoh_logPt2.SetNpx(1000)
func_incoh_logPt2.SetLineColor(rt.kRed)
func_incoh_logPt2.SetParameters(3000., 5.)
r1 = (hPtIncoh.Fit(func_incoh_logPt2, "RS", "", fitran[0],
fitran[1])).Get()
#create pdf normalized to number of events
pdf_logPt2 = TF1("pdf_logPt2",
"[0]*log(10.)*pow(10.,x)*exp(-[1]*pow(10.,x))", -10., 10.)
nevt = tree_incoh.Draw(
"", strsel + " && " + draw + ">{0:.3f}".format(fitran[0]) + " && " +
draw + "<{1:.3f}".format(fitran[0], fitran[1]))
k_norm = nevt / func_incoh_logPt2.Integral(fitran[0], fitran[1])
pdf_logPt2.SetParameter(0, k_norm * func_incoh_logPt2.GetParameter(0))
pdf_logPt2.SetParameter(1, func_incoh_logPt2.GetParameter(1))
#verify the normalization:
print "PDF integral", pdf_logPt2.Integral(-10., 10.)
hPtIncoh.Draw()
func_incoh_logPt2.Draw("same")
leg = ut.prepare_leg(0.18, 0.78, 0.14, 0.15, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPtIncoh, "Incoherent MC")
leg.AddEntry(
func_incoh_logPt2,
"ln(10)*#it{A}*10^{log_{10}#it{p}_{T}^{2}}exp(-#it{b}10^{log_{10}#it{p}_{T}^{2}})",
"l")
leg.Draw("same")
desc = pdesc(hPtIncoh, 0.18, 0.78, 0.057)
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", r1.Chi2() / r1.Ndf(), -1, rt.kRed)
desc.itemRes("#it{A}", r1, 0, rt.kRed)
desc.itemD("#it{A}", pdf_logPt2.GetParameter(0), -1, rt.kRed)
desc.itemRes("#it{b}", r1, 1, rt.kRed)
desc.draw()
uoleg = ut.make_uo_leg(hPtIncoh, 0.14, 0.9, 0.01, 0.1)
uoleg.Draw("same")
l0 = ut.cut_line(fitran[0], 0.9, hPtIncoh)
l1 = ut.cut_line(fitran[1], 0.9, hPtIncoh)
l0.Draw()
l1.Draw()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
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")
desc.itemR("#sigma", sig, ccb)
if alphafix > 0.:
desc.itemD("#alpha", alpha.getVal(), -1, ccb)
else:
desc.itemR("#alpha", alpha, ccb)
if nfix > 0.:
desc.itemD("#it{n}", n.getVal(), -1, ccb)
else:
desc.itemR("#it{n}", n, ccb)
desc.itemR("#lambda", lam, cbkg)
desc.itemR("#it{c}_{1}", c1, cbkg)
desc.itemR("#it{c}_{2}", c2, cbkg)
desc.draw()
#integration range
lin_lo = ut.cut_line(intran[0], 0.33, frame)
lin_hi = ut.cut_line(intran[1], 0.33, frame)
lin_lo.Draw("same")
lin_hi.Draw("same")
#integration result
desc2 = pdesc(frame, 0.8, 0.6, 0.045)
desc2.prec = 0
#igg_desc = "#int_{#it{m} = %.1f}^{#it{m} = %.1f}#it{f}_{#gamma#gamma}" % (intran[0], intran[1])
#igg_desc = "#it{n}_{#gamma#gamma,#it{J}/#psi} = #int_{%.1f}^{%.1f}#color[2]{#it{f}_{#gamma#gamma}}" % (intran[0], intran[1])
#igg_desc = "#it{n}_{#gamma#gamma,lo} = #int_{%.1f}^{%.1f}#color[2]{#it{f}_{#gamma#gamma}}" % (intran[0], intran[1])
igg_desc = "#it{n}_{#gamma#gamma,hi} = #int_{%.1f}^{%.1f}#color[2]{#it{f}_{#gamma#gamma}}" % (intran[0], intran[1])
#desc2.itemD(igg_desc, intBkg.getVal(), intBkg.getError(), cbkg)
desc2.itemD(igg_desc, intBkg.getVal(), intBkg.getError())
desc2.draw()
def z_proj():
#z projection for primary photons
#plot range, upper and lower, mm
zmax = 300
zbin = 0.5
inp = TFile.Open("ew.root")
tree = inp.Get("prim_tree")
can = ut.box_canvas()
hZ = ut.prepare_TH1D("hZ", zbin, -zmax, zmax)
tree.Draw("z >> hZ")
ut.line_h1(hZ)
#lines at 250 mrad projected in z for a given aperture tmax in mrad, example: z (mm) = 18644*tan(0.002)/tan(0.25) = 144.9
z0 = 18644. # mm, exit window position in z
#lines for 2 mrad aperture
tmax = 2. # mrad, apperture by maximal theta for photons
zlim = z0 * tan(tmax * 1e-3) / tan(0.25)
zline = [
ut.cut_line(zlim, 0.75, hZ, True),
ut.cut_line(-zlim, 0.75, hZ, True)
]
zline[0].SetLineColor(rt.kRed)
zline[1].SetLineColor(rt.kRed)
zline[0].Draw("same")
zline[1].Draw("same")
#fraction of events at 2 mrad aperture in z, cross check to the xy projection
nz = float(tree.Draw("", "TMath::Abs(z)<" + str(zlim)))
nev = tree.GetEntries()
print(nz, "{0:.2f}".format(100. * nz / nev))
#lines at 1 mrad aperture
tmax = 1. # mrad, apperture by maximal theta for photons
zlim = z0 * tan(tmax * 1e-3) / tan(0.25)
zline2 = [
ut.cut_line(zlim, 0.75, hZ, True),
ut.cut_line(-zlim, 0.75, hZ, True)
]
zline2[0].SetLineColor(rt.kOrange)
zline2[1].SetLineColor(rt.kOrange)
zline2[0].Draw("same")
zline2[1].Draw("same")
#fraction of events at 1 mrad aperture in z, cross check to the xy projection
nz = float(tree.Draw("", "TMath::Abs(z)<" + str(zlim)))
print(nz, "{0:.2f}".format(100. * nz / nev))
ut.put_yx_tit(hZ, "Counts", "#it{z} (mm)")
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.02, 0.03)
gPad.SetGrid()
gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")