def plot_pt2():
#pT^2 with coherent incoherent and gamma-gamma components
ptbin = 0.002
ptmin = 0.
ptmax = 0.2 # 0.3
mmin = 2.8
mmax = 3.2
#mmin = 1.5
#mmax = 2.6
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin / 3., ptmin, ptmax)
hPtIncoh = ut.prepare_TH1D("hPtIncoh", ptbin, ptmin, ptmax)
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
ut.put_yx_tit(hPt, "Events / ({0:.3f}".format(ptbin) + " GeV^{2})",
"#it{p}_{T}^{2} (GeV^{2})")
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
draw = "jRecPt*jRecPt"
tree.Draw(draw + " >> hPt", strsel)
tree_coh.Draw(draw + " >> hPtCoh", strsel)
tree_incoh.Draw(draw + " >> hPtIncoh", strsel)
tree_gg.Draw(draw + " >> hPtGG", strsel)
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, 0., 0.015)
ut.norm_to_data(hPtIncoh, hPt, rt.kRed, 0.05, 0.16) # 0.3
ut.norm_to_data(hPtGG, hPt, rt.kGreen, 0., 0.001)
hPt.Draw()
hPtCoh.Draw("same")
hPtIncoh.Draw("same")
hPtGG.Draw("same")
leg = ut.prepare_leg(0.67, 0.78, 0.14, 0.18, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data")
leg.AddEntry(hPtCoh, "Coherent MC", "l")
leg.AddEntry(hPtIncoh, "Incoherent MC", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-} MC", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hPt, 0.14, 0.9, 0.01, 0.1)
uoleg.Draw("same")
gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def fit_pt2_incoh():
#fit to incoherent MC pT^2
ptbin = 0.008
ptmin = 0.
ptmax = 1.
mmin = 2.8
mmax = 3.2
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^{2})",
"#it{p}_{T}^{2} (GeV^{2})")
tree_incoh.Draw("jRecPt*jRecPt >> hPtIncoh", strsel)
#hPtIncoh.Sumw2()
#hPtIncoh.Scale(1./hPtIncoh.Integral("width"))
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.01)
func_incoh_pt2 = TF1("func_incoh", "[0]*exp(-[1]*x)", 0., 10.)
func_incoh_pt2.SetParName(0, "A")
func_incoh_pt2.SetParName(1, "b")
func_incoh_pt2.SetNpx(1000)
func_incoh_pt2.SetLineColor(rt.kRed)
func_incoh_pt2.SetParameters(3000., 5.)
r1 = (hPtIncoh.Fit(func_incoh_pt2, "RS")).Get()
hPtIncoh.Draw()
func_incoh_pt2.Draw("same")
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_pt2, "#it{A}*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.itemRes("#it{A}", r1, 0, rt.kRed)
desc.itemRes("#it{b}", r1, 1, rt.kRed)
desc.draw()
#gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_jpsi_logPt2():
# log_10(pT^2)
ptbin = 0.12
ptmin = -5.
ptmax = 1.
mmin = 2.8
mmax = 3.2
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin / 3., ptmin, ptmax)
hPtIncoh = ut.prepare_TH1D("hPtIncoh", ptbin / 3., ptmin, ptmax)
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
ut.put_yx_tit(hPt, "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.Draw(draw + " >> hPt", strsel)
tree_coh.Draw(draw + " >> hPtCoh", strsel)
tree_incoh.Draw(draw + " >> hPtIncoh", strsel)
tree_gg.Draw(draw + " >> hPtGG", strsel)
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, -5., -1.8) # norm for coh
ut.norm_to_data(hPtIncoh, hPt, rt.kRed, -1.1, 1.) # for incoh
ut.norm_to_data(hPtGG, hPt, rt.kGreen, -5., -2.9) # for ggel
hPt.Draw()
hPtCoh.Draw("same")
hPtIncoh.Draw("same")
hPtGG.Draw("same")
leg = ut.prepare_leg(0.67, 0.79, 0.14, 0.17, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data")
leg.AddEntry(hPtCoh, "Coherent MC", "l")
leg.AddEntry(hPtIncoh, "Incoherent MC", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-} MC", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hPt, 0.14, 0.9, 0.01, 0.1)
uoleg.Draw("same")
#gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_pt_gg():
#pT of gamma-gamma below and above J/psi
ptbin = 0.02
#ptbin = 0.03
ptmin = 0.
ptmax = 1.1
mmin = 2.1
mmax = 2.6
#mmin = 3.4
#mmax = 5.
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
#ut.put_yx_tit(hPt, "Events / ({0:.3f}".format(ptbin)+" GeV)", "#it{p}_{T} (GeV})")
ytit = "#gamma#gamma#rightarrow e^{+}e^{-} candidates / " + "({0:.3f}".format(
ptbin) + " GeV)"
ut.put_yx_tit(hPt, ytit, "Dielectron #it{p}_{T} (GeV)", 1.5, 1.2)
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
draw = "jRecPt"
tree.Draw(draw + " >> hPt", strsel)
tree_gg.Draw(draw + " >> hPtGG", strsel)
ut.norm_to_data(hPtGG, hPt, rt.kGreen, 0., 0.3)
#ut.norm_to_data(hPtGG, hPt, rt.kGreen, 0., 0.18)
hPt.Draw()
hPtGG.Draw("same")
leg = ut.prepare_leg(0.67, 0.78, 0.14, 0.18, 0.03)
leg.AddEntry(None, "#bf{|#kern[0.3]{#it{y}}| < 1}", "")
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-}", "l")
leg.Draw("same")
pleg = ut.prepare_leg(0.33, 0.8, 0.01, 0.14, 0.035)
pleg.AddEntry(None, "STAR Preliminary", "")
pleg.AddEntry(None, "AuAu@200 GeV", "")
pleg.AddEntry(None, "UPC sample", "")
pleg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
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_zdc_vtx():
#ZDC vertex from selected events and from all triggered events
vbin = 0.1
vmin = -5.
vmax = 7.
mmin = 1.5
mmax = 5.
can = ut.box_canvas()
#selection string
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
#make the histograms
hZdcVtx = ut.prepare_TH1D("hZdcVtx", vbin, vmin, vmax)
hZdcVtxAll = ut.prepare_TH1D("hZdcVtxAll", vbin / 10., vmin, vmax)
#convert to meters for plot
tree.Draw("jZDCVtxZ/100. >> hZdcVtx", strsel)
treeAll = inp.Get("jAllTree")
treeAll.Draw("jZDCVtxZ/100. >> hZdcVtxAll")
ut.norm_to_data(hZdcVtxAll, hZdcVtx, rt.kRed)
hZdcVtx.SetYTitle("Events / {0:.0f} cm".format(vbin * 100.))
hZdcVtx.SetXTitle("ZDC vertex along #it{z} (meters)")
hZdcVtx.SetTitleOffset(1.5, "Y")
hZdcVtx.SetTitleOffset(1.1, "X")
gPad.SetTopMargin(0.01)
gPad.SetRightMargin(0.04)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.1)
leg = ut.prepare_leg(0.67, 0.8, 0.28, 0.14, 0.025)
leg.SetMargin(0.17)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hZdcVtx, "Selected events")
leg.AddEntry(hZdcVtxAll, "All UPC-JpsiB triggers", "l")
#gPad.SetLogy()
hZdcVtx.Draw()
hZdcVtxAll.Draw("same")
leg.Draw("same")
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_zdc_tpc_vtx():
#2D ZDC and TPC vertices
zbin = 2.
#zmin = -50.
#zmax = 100.
zmin = -100
zmax = 150
tbin = 2.
#tmin = -55.
#tmax = 55.
tmin = -110
tmax = 110
mmin = 1.5
mmax = 5.
can = ut.box_canvas()
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
hVtx = ut.prepare_TH2D("hVtx", tbin, tmin, tmax, zbin, zmin, zmax)
tree.Draw("jZDCVtxZ:jVtxZ >> hVtx", strsel)
hVtx.SetXTitle("TPC vertex along #it{z} / " + "{0:.0f} cm".format(zbin))
hVtx.SetYTitle("ZDC vertex along #it{z} / " + "{0:.0f} cm".format(zbin))
hVtx.SetTitleOffset(1.5, "Y")
hVtx.SetTitleOffset(1.1, "X")
gPad.SetTopMargin(0.02)
gPad.SetRightMargin(0.09)
gPad.SetBottomMargin(0.08)
gPad.SetLeftMargin(0.11)
leg = ut.prepare_leg(0.16, 0.82, 0.23, 0.05, 0.025)
leg.SetMargin(0.02)
leg.SetBorderSize(1)
ut.add_leg_mass(leg, mmin, mmax)
hVtx.Draw()
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_jpsi_pt2():
#J/psi pT^2
ptbin = 0.002
ptmin = 0.
ptmax = 0.15
mmin = 2.8
mmax = 3.2
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtMC = ut.prepare_TH1D("hPtMC", ptbin / 3., ptmin, ptmax)
ut.put_yx_tit(hPt, "Events / ({0:.3f}".format(ptbin) + " GeV^{2})",
"#it{p}_{T}^{2} (GeV^{2})")
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
draw = "jRecPt*jRecPt"
tree.Draw(draw + " >> hPt", strsel)
mctree.Draw(draw + " >> hPtMC", strsel)
ut.norm_to_data(hPtMC, hPt, rt.kBlue, 0., 0.015)
hPt.Draw()
hPtMC.Draw("same")
leg = ut.prepare_leg(0.67, 0.8, 0.14, 0.16, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data")
leg.AddEntry(hPtMC, "Coherent MC", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hPt, 0.14, 0.9, 0.01, 0.1)
uoleg.Draw("same")
gPad.SetLogy()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_jpsi_logPt2():
#J/psi log_10(pT^2)
ptbin = 0.12
ptmin = -5.
ptmax = 1.
mmin = 2.8
mmax = 3.2
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtMC = ut.prepare_TH1D("hPtMC", ptbin / 3., ptmin, ptmax)
ut.put_yx_tit(hPt, "Events / ({0:.3f}".format(ptbin) + " GeV^{2})",
"#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.Draw(draw + " >> hPt", strsel)
mctree.Draw(draw + " >> hPtMC", strsel)
ut.norm_to_data(hPtMC, hPt, rt.kBlue, -5., -1.8) # norm for coh
#ut.norm_to_data(hPtMC, hPt, rt.kBlue, -1.1, 1.) # for incoh
#ut.norm_to_data(hPtMC, hPt, rt.kBlue, -5., -2.4) # for ggel
hPt.Draw()
hPtMC.Draw("same")
leg = ut.prepare_leg(0.67, 0.8, 0.14, 0.16, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data")
leg.AddEntry(hPtMC, "Coherent MC", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hPt, 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")
def plot_pt2_real():
#pT^2 with realistic normalization for incoherent and gamma-gamma components
ptbin = 0.002
ptmin = 0.
ptmax = 0.2 # 0.3
mmin = 2.8
mmax = 3.2
ngg = 131 # number of gamma-gamma from mass fit
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin, ptmin, ptmax)
hPtIncoh = ut.prepare_TH1D("hPtIncoh", ptbin, ptmin, ptmax)
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
ut.put_yx_tit(hPt, "Events / ({0:.3f}".format(ptbin) + " GeV^{2})",
"#it{p}_{T}^{2} (GeV^{2})")
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
draw = "jRecPt*jRecPt"
tree.Draw(draw + " >> hPt", strsel)
tree_coh.Draw(draw + " >> hPtCoh", strsel)
tree_gg.Draw(draw + " >> hPtGG", strsel)
#incoherent functional shape
func_incoh_pt2 = TF1("func_incoh", "[0]*exp(-[1]*x)", 0., 10.)
func_incoh_pt2.SetParameters(873.04, 3.28)
#fill incoherent histogram from functional shape
ut.fill_h1_tf(hPtIncoh, func_incoh_pt2, rt.kRed)
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, 0., 0.015)
ut.norm_to_num(hPtGG, ngg, rt.kGreen)
hPt.Draw()
hPtCoh.Draw("same")
hPtIncoh.Draw("same")
hPtGG.Draw("same")
leg = ut.prepare_leg(0.6, 0.78, 0.14, 0.18, 0.03)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data")
leg.AddEntry(hPtCoh, "Coherent MC, Sartre", "l")
leg.AddEntry(hPtIncoh, "Incoherent parametrization", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-} MC", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hPt, 0.14, 0.9, 0.01, 0.1)
#uoleg.Draw("same")
gPad.SetLogy()
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_pt():
#pT with coherent incoherent and gamma-gamma components
ptbin = 0.02
ptmin = 0.
ptmax = 1.1
mmin = 2.8
mmax = 3.2
#mmin = 3.4
#mmax = 5.
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
hPt = ut.prepare_TH1D("hPt", ptbin, ptmin, ptmax)
hPtCoh = ut.prepare_TH1D("hPtCoh", ptbin, ptmin, ptmax)
hPtIncoh = ut.prepare_TH1D("hPtIncoh", ptbin, ptmin, ptmax)
hPtGG = ut.prepare_TH1D("hPtGG", ptbin, ptmin, ptmax)
#ut.put_yx_tit(hPt, "Events / ({0:.3f}".format(ptbin)+" GeV)", "#it{p}_{T} (GeV})")
ut.put_yx_tit(hPt,
"J/#psi candidates / ({0:.3f}".format(ptbin) + " GeV/c)",
"Dielectron #it{p}_{T} (GeV/c)", 1.5, 1.2)
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
draw = "jRecPt"
tree.Draw(draw + " >> hPt", strsel)
tree_coh.Draw(draw + " >> hPtCoh", strsel)
tree_incoh.Draw(draw + " >> hPtIncoh", strsel)
tree_gg.Draw(draw + " >> hPtGG", strsel)
ut.norm_to_data(hPtCoh, hPt, rt.kBlue, 0., 0.08)
ut.norm_to_data(hPtIncoh, hPt, rt.kRed, 0.28, 1.)
#ut.norm_to_data(hPtGG, hPt, rt.kGreen, 0., 0.03)
ut.norm_to_num(hPtGG, 131, rt.kGreen + 1)
#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
hSum.Add(hPtIncoh)
#add coherent contribution
hSum.Add(hPtCoh)
#set to draw as a lines
ut.line_h1(hSum, rt.kBlack)
hPt.Draw()
hSum.Draw("same")
hPtCoh.Draw("same")
hPtIncoh.Draw("same")
hPtGG.Draw("same")
leg = ut.prepare_leg(0.64, 0.65, 0.14, 0.3, 0.03)
leg.AddEntry(None, "#bf{|#kern[0.3]{#it{y}}| < 1}", "")
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hPt, "Data", "p")
leg.AddEntry(hSum, "Sum", "l")
leg.AddEntry(hPtCoh, "Coherent J/#psi", "l")
leg.AddEntry(hPtIncoh, "Incoherent J/#psi", "l")
leg.AddEntry(hPtGG, "#gamma#gamma#rightarrow e^{+}e^{-}", "l")
leg.Draw("same")
uoleg = ut.make_uo_leg(hPt, 0.14, 0.9, 0.01, 0.1)
#uoleg.Draw("same")
pleg = ut.prepare_leg(0.33, 0.8, 0.01, 0.14, 0.035)
pleg.AddEntry(None, "STAR Preliminary", "")
pleg.AddEntry(None, "AuAu@200 GeV", "")
pleg.AddEntry(None, "UPC sample", "")
pleg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def fit():
#fit to log_10(pT^2) with components and plot of plain pT^2
#range in log_10(pT^2)
ptbin = 0.12
ptmin = -5.
ptmax = 0.99 # 1.01
#range in pT^2
ptsq_bin = 0.03
ptsq_min = 1e-5
ptsq_max = 1
mmin = 2.8
mmax = 3.2
#range for incoherent fit
fitran = [-0.9, 0.1]
#number of gamma-gamma events
ngg = 131
#number of psi' events
npsiP = 20
#input data
pT = RooRealVar("jRecPt", "pT", 0, 10)
m = RooRealVar("jRecM", "mass", 0, 10)
data_all = RooDataSet("data", "data", tree, RooArgSet(pT, m))
#select for mass range
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
data = data_all.reduce(strsel)
#create log(pT^2) from pT
ptsq_draw = "jRecPt*jRecPt" # will be used for pT^2
logPtSq_draw = "TMath::Log10(" + ptsq_draw + ")"
logPtSq_form = RooFormulaVar("logPtSq", "logPtSq", logPtSq_draw,
RooArgList(pT))
logPtSq = data.addColumn(logPtSq_form)
logPtSq.setRange("fitran", fitran[0], fitran[1])
#bins and range for the plot
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
logPtSq.setMin(ptmin)
logPtSq.setMax(ptmax)
logPtSq.setRange("plotran", ptmin, ptmax)
#range for pT^2
ptsq_nbins, ptsq_max = ut.get_nbins(ptsq_bin, ptsq_min, ptsq_max)
#incoherent parametrization
bval = RooRealVar("bval", "bval", 3.3, 0, 10)
inc_form = "log(10.)*pow(10.,logPtSq)*exp(-bval*pow(10.,logPtSq))"
incpdf = RooGenericPdf("incpdf", inc_form, RooArgList(logPtSq, bval))
#make the incoherent fit
res = incpdf.fitTo(data, rf.Range("fitran"), rf.Save())
#get incoherent norm to the number of events
lset = RooArgSet(logPtSq)
iinc = incpdf.createIntegral(lset, rf.NormSet(lset), rf.Range("fitran"))
inc_nevt = data.sumEntries("logPtSq", "fitran")
incpdf.setNormRange("fitran")
aval = RooRealVar("aval", "aval", inc_nevt / incpdf.getNorm(lset))
#print "A =", aval.getVal()
#print "b =", bval.getVal()
#incoherent distribution from log_10(pT^2) function for the sum with gamma-gamma
hIncPdf = ut.prepare_TH1D_n("hGG", nbins, ptmin, ptmax)
func_incoh_logPt2 = TF1("func_incoh_logPt2",
"[0]*log(10.)*pow(10.,x)*exp(-[1]*pow(10.,x))",
-10., 10.)
func_incoh_logPt2.SetNpx(1000)
func_incoh_logPt2.SetLineColor(rt.kMagenta)
func_incoh_logPt2.SetParameters(
aval.getVal(),
bval.getVal()) # 4.9 from incoherent mc, 3.3 from data fit
ut.fill_h1_tf(hIncPdf, func_incoh_logPt2, rt.kMagenta)
#gamma-gamma contribution
hGG = ut.prepare_TH1D_n("hGG", nbins, ptmin, ptmax)
tree_gg.Draw(logPtSq_draw + " >> hGG", strsel)
ut.norm_to_num(hGG, ngg, rt.kGreen + 1)
#sum of incoherent distribution and gamma-gamma
hSumIncGG = ut.prepare_TH1D_n("hSumIncGG", nbins, ptmin, ptmax)
hSumIncGG.Add(hIncPdf)
hSumIncGG.Add(hGG)
ut.line_h1(hSumIncGG, rt.kMagenta)
#gamma-gamma in pT^2
hGG_ptsq = ut.prepare_TH1D_n("hGG_ptsq", ptsq_nbins, ptsq_min, ptsq_max)
tree_gg.Draw(ptsq_draw + " >> hGG_ptsq", strsel)
ut.norm_to_num(hGG_ptsq, ngg, rt.kGreen + 1)
#psi' contribution
psiP_file = TFile.Open(basedir_mc + "/ana_slight14e4x1_s6_sel5z.root")
psiP_tree = psiP_file.Get("jRecTree")
hPsiP = ut.prepare_TH1D_n("hPsiP", nbins, ptmin, ptmax)
psiP_tree.Draw(logPtSq_draw + " >> hPsiP", strsel)
ut.norm_to_num(hPsiP, npsiP, rt.kViolet)
#psi' in pT^2
hPsiP_ptsq = ut.prepare_TH1D_n("hPsiP_ptsq", ptsq_nbins, ptsq_min,
ptsq_max)
psiP_tree.Draw(ptsq_draw + " >> hPsiP_ptsq", strsel)
ut.norm_to_num(hPsiP_ptsq, npsiP, rt.kViolet)
#create canvas frame
gStyle.SetPadTickY(1)
can = ut.box_canvas(1086, 543) # square area is still 768^2
can.SetMargin(0, 0, 0, 0)
can.Divide(2, 1, 0, 0)
gStyle.SetLineWidth(1)
can.cd(1)
ut.set_margin_lbtr(gPad, 0.11, 0.1, 0.01, 0)
frame = logPtSq.frame(rf.Bins(nbins))
frame.SetTitle("")
frame.SetMaximum(80)
frame.SetYTitle("Events / ({0:.3f}".format(ptbin) + " GeV^{2})")
frame.SetXTitle("log_{10}( #it{p}_{T}^{2} ) (GeV^{2})")
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.6)
#plot the data
data.plotOn(frame, rf.Name("data"), rf.LineWidth(2))
#incoherent parametrization
incpdf.plotOn(frame, rf.Range("fitran"), rf.LineColor(rt.kRed),
rf.Name("incpdf"), rf.LineWidth(2))
incpdf.plotOn(frame, rf.Range("plotran"), rf.LineColor(rt.kRed),
rf.Name("incpdf_full"), rf.LineStyle(rt.kDashed),
rf.LineWidth(2))
frame.Draw()
#add gamma-gamma contribution
hGG.Draw("same")
#sum of incoherent distribution and gamma-gamma
#hSumIncGG.Draw("same")
#add psi'
#hPsiP.Draw("same")
#legend for log_10(pT^2)
leg = ut.prepare_leg(0.15, 0.77, 0.28, 0.19, 0.035)
hxl = ut.prepare_TH1D("hxl", 1, 0, 1)
hxl.Draw("same")
ilin = ut.col_lin(rt.kRed, 2)
ilin2 = ut.col_lin(rt.kRed, 2)
ilin2.SetLineStyle(rt.kDashed)
leg.AddEntry(ilin, "Incoherent parametrization, fit region", "l")
leg.AddEntry(ilin2, "Incoherent parametrization, extrapolation region",
"l")
leg.AddEntry(hGG, "#gamma#gamma#rightarrow e^{+}e^{-}", "l")
#leg.AddEntry(hxl, "Data", "lp")
leg.AddEntry(hxl, "Data, log_{10}( #it{p}_{T}^{2} )", "lp")
leg.Draw("same")
#----- plot pT^2 on the right -----
#pT^2 variable from pT
ptsq_form = RooFormulaVar("ptsq", "ptsq", ptsq_draw, RooArgList(pT))
ptsq = data.addColumn(ptsq_form)
#range for pT^2 plot
ptsq.setMin(ptsq_min)
ptsq.setMax(ptsq_max)
#make the pT^2 plot
can.cd(2)
gPad.SetLogy()
#gPad.SetLineWidth(3)
#gPad.SetFrameLineWidth(1)
ut.set_margin_lbtr(gPad, 0, 0.1, 0.01, 0.15)
ptsq_frame = ptsq.frame(rf.Bins(ptsq_nbins), rf.Title(""))
#print type(ptsq_frame), type(ptsq)
ptsq_frame.SetTitle("")
ptsq_frame.SetXTitle("#it{p}_{T}^{2} (GeV^{2})")
ptsq_frame.GetXaxis().SetTitleOffset(1.2)
data.plotOn(ptsq_frame, rf.Name("data"), rf.LineWidth(2))
ptsq_frame.SetMaximum(9e2)
ptsq_frame.SetMinimum(0.8) # 0.101
ptsq_frame.Draw()
#incoherent parametrization in pT^2 over the fit region, scaled to the plot
inc_ptsq = TF1("inc_ptsq", "[0]*exp(-[1]*x)", 10**fitran[0], 10**fitran[1])
inc_ptsq.SetParameters(aval.getVal() * ptsq_bin, bval.getVal())
#incoherent parametrization in the extrapolation region, below and above the fit region
inc_ptsq_ext1 = TF1("inc_ptsq_ext1", "[0]*exp(-[1]*x)", 0., 10**fitran[0])
inc_ptsq_ext2 = TF1("inc_ptsq_ext2", "[0]*exp(-[1]*x)", 10**fitran[1], 10)
inc_ptsq_ext1.SetParameters(aval.getVal() * ptsq_bin, bval.getVal())
inc_ptsq_ext1.SetLineStyle(rt.kDashed)
inc_ptsq_ext2.SetParameters(aval.getVal() * ptsq_bin, bval.getVal())
inc_ptsq_ext2.SetLineStyle(rt.kDashed)
inc_ptsq.Draw("same")
inc_ptsq_ext1.Draw("same")
inc_ptsq_ext2.Draw("same")
#add gamma-gamma in pT^2
hGG_ptsq.Draw("same")
#add psi' in pT^2
#hPsiP_ptsq.Draw("same")
#redraw the frame
#ptsq_frame.Draw("same")
ptsq_frame.GetXaxis().SetLimits(-9e-3, ptsq_frame.GetXaxis().GetXmax())
#vertical axis for pT^2 plot
xpos = ptsq_frame.GetXaxis().GetXmax()
ypos = ptsq_frame.GetMaximum()
ymin = ptsq_frame.GetMinimum()
ptsq_axis = TGaxis(xpos, 0, xpos, ypos, ymin, ypos, 510, "+GL")
ut.set_axis(ptsq_axis)
ptsq_axis.SetMoreLogLabels()
ptsq_axis.SetTitle("Events / ({0:.3f}".format(ptsq_bin) + " GeV^{2})")
ptsq_axis.SetTitleOffset(2.2)
ptsq_axis.Draw()
#legend for input data
#dleg = ut.prepare_leg(0.4, 0.77, 0.14, 0.18, 0.035)
dleg = ut.prepare_leg(0.4, 0.71, 0.16, 0.24, 0.035)
dleg.AddEntry(None, "#bf{|#kern[0.3]{#it{y}}| < 1}", "")
ut.add_leg_mass(dleg, mmin, mmax)
dleg.AddEntry(None, "AuAu@200 GeV", "")
dleg.AddEntry(None, "UPC sample", "")
dleg.AddEntry(hxl, "Data, #it{p}_{T}^{2}", "lp")
dleg.Draw("same")
#ut.invert_col_can(can)
can.SaveAs("01fig.pdf")
def fit_vtx_z():
#gaussian fit to vertex z-position
datamc = False #true - data, false - mc
if datamc:
vbin = 4.
else:
vbin = 2
vmax = 120.
mmin = 1.5
mmax = 5.
if datamc:
fit_lo = -30.
fit_hi = 35.
else:
fit_lo = -40.
fit_hi = 45.
fitcol = rt.kBlue
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
out = open("out.txt", "w")
can = ut.box_canvas()
hVtx = ut.prepare_TH1D("hVtx", vbin, -vmax, vmax)
if datamc:
tree.Draw("jVtxZ >> hVtx", strsel)
else:
mctree.Draw("jVtxZ >> hVtx", strsel)
f1 = TF1("f1", "gaus", fit_lo, fit_hi)
f1.SetNpx(1000)
f1.SetLineColor(fitcol)
#make the fit
r1 = (hVtx.Fit(f1, "RS")).Get()
out.write(ut.log_tfit_result(r1))
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)
leg = ut.prepare_leg(0.15, 0.82, 0.28, 0.12, 0.025)
leg.SetMargin(0.17)
ut.add_leg_mass(leg, mmin, mmax)
if datamc:
leg.AddEntry(hVtx, "Data")
else:
leg.AddEntry(hVtx, "Embedding MC #gamma#gamma")
leg.AddEntry(f1, "Gaussian fit", "l")
#fit parameters on the plot
desc = pdesc(hVtx, 0.14, 0.82, 0.057)
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", r1.Chi2()/r1.Ndf(), -1, fitcol)
desc.prec = 2
desc.itemRes("mean", r1, 1, fitcol)
desc.itemRes("#it{#sigma}", r1, 2, fitcol)
desc.itemRes("norm", r1, 0, fitcol)
hVtx.Draw()
leg.Draw("same")
desc.draw()
#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 plot_zdc_tpc_vtx_diff():
#difference between TPC and ZDC vertex
dbin = 2.5
dmin = -90
dmax = 130
#dmin = -1500
#dmax = 2000
mmin = 1.5
mmax = 5.
fitcol = rt.kBlue
out = open("out.txt", "w")
ut.log_results(out, "in " + infile)
strlog = "dbin " + str(dbin) + " dmin " + str(dmin) + " dmax " + str(dmax)
strlog += " mmin " + str(mmin) + " mmax " + str(mmax) + "\n"
ut.log_results(out, strlog)
can = ut.box_canvas()
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
hDVtx = ut.prepare_TH1D("hDVtx", dbin, dmin, dmax)
tree.Draw("jZDCVtxZ-jVtxZ >> hDVtx", strsel)
#fit function
f1 = TF1("f1", "gaus+[3]", -50, 105)
f1.SetNpx(1000)
f1.SetLineColor(fitcol)
f1.SetParameter(0, 77)
f1.SetParameter(1, 25)
f1.SetParameter(2, 13)
f1.SetParameter(3, 5)
f1.SetParName(0, "norm")
f1.SetParName(1, "mean")
f1.SetParName(2, "sigma")
f1.SetParName(3, "ofs")
#make the fit
r1 = (hDVtx.Fit(f1, "RS")).Get()
out.write(ut.log_tfit_result(r1))
#r1.Print()
#fraction of events within +/- 4 sigma
t1 = tree.CopyTree(strsel)
nall = t1.GetEntries()
lo = f1.GetParameter(1) - 4. * f1.GetParameter(2)
hi = f1.GetParameter(1) + 4. * f1.GetParameter(2)
nsel = t1.Draw(
"",
"(jZDCVtxZ-jVtxZ)>{0:.3f} && (jZDCVtxZ-jVtxZ)<{1:.3f}".format(lo, hi))
fraction = float(nsel) / float(nall)
err = fraction * ma.sqrt(float(nall - nsel) / (nall * nsel))
ut.log_results(out, "Fraction of events within +/- 4 sigma")
ut.log_results(out, "4sigma interval: " + str(lo) + " " + str(hi))
ut.log_results(out, "nall: " + str(nall))
ut.log_results(out, "nsel: " + str(nsel))
ut.log_results(out, "f_4s: {0:.3f} +/- {1:.3f}".format(fraction, err))
print("4sigma interval:", lo, hi)
print("nall:", nall)
print("nsel:", nsel)
print("f_4s: {0:.3f} +/- {1:.3f}".format(fraction, err))
hDVtx.SetYTitle("Events / {0:.1f} cm".format(dbin))
hDVtx.SetXTitle("Vertex #it{z}_{ZDC} - #it{z}_{TPC} (cm)")
hDVtx.SetTitleOffset(1.5, "Y")
hDVtx.SetTitleOffset(1.3, "X")
gPad.SetTopMargin(0.012)
gPad.SetRightMargin(0.04)
gPad.SetBottomMargin(0.1)
gPad.SetLeftMargin(0.1)
#fit parameters on the plot
desc = pdesc(hDVtx, 0.16, 0.84, 0.057)
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", r1.Chi2() / r1.Ndf(), -1, fitcol)
desc.prec = 2
desc.itemRes("norm", r1, 0, fitcol)
desc.itemRes("mean", r1, 1, fitcol)
desc.itemRes("#it{#sigma}", r1, 2, fitcol)
desc.itemRes("ofs", r1, 3, fitcol)
#cut lines at mean +/- 4sigma
#cut_lo = ut.cut_line(-20, 0.5, hDVtx)
#cut_hi = ut.cut_line(70, 0.5, hDVtx)
leg = ut.prepare_leg(0.14, 0.82, 0.28, 0.136, 0.025)
leg.SetMargin(0.17)
ut.add_leg_mass(leg, mmin, mmax)
leg.AddEntry(hDVtx, "Data")
leg.AddEntry(f1, "Gaussian + offset", "l")
#leg.AddEntry(cut_lo, "4#it{#sigma} at -20 and 70 cm", "l")
hDVtx.Draw()
leg.Draw("same")
desc.draw()
#cut_lo.Draw("same")
#cut_hi.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
