def make_py():
#Pythia6 total cross section
sigma_py = 54.700142803416348 # micro barn
infile = "../data/lmon_py_18x275_Q2all_beff2_5Mevt.root"
lqbin = 0.2
lqmin = -11
lqmax = 5
inp = TFile.Open(infile)
tree = inp.Get("DetectorTree")
#can = ut.box_canvas()
hQ2 = ut.prepare_TH1D("hQ2", lqbin, lqmin, lqmax)
tree.Draw("TMath::Log10(true_Q2) >> hQ2")
ut.norm_to_integral(hQ2, sigma_py)
gPy = ut.h1_to_graph(hQ2)
gPy.SetLineColor(rt.kRed)
gPy.SetLineWidth(3)
gPy.SetLineStyle(rt.kDashed)
return gPy
#print hQ2.Integral("width")
hQ2.Draw()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def make_qr_5x41():
#quasi-real total cross section for 5x41 beams
sigma_qr = 65.0648514567 # micro barn
infile = "../../lgen/data/lgen_5x41_qr_Qe_5Mevt.root"
lqbin = 0.2
lqmin = -11
lqmax = 5
inp = TFile.Open(infile)
tree = inp.Get("ltree")
hQ2 = ut.prepare_TH1D("hQ2", lqbin, lqmin, lqmax)
tree.Draw("TMath::Log10(true_Q2) >> hQ2")
ut.norm_to_integral(hQ2, sigma_qr)
gQr = ut.h1_to_graph(hQ2)
gQr.SetLineColor(rt.kBlue)
gQr.SetLineWidth(3)
return gQr
def make_qr():
#quasi-real total cross section
#sigma_qr = 53.839868617 # micro barn
#infile = "../data/lmon_18x275_qr_Qd_beff2_5Mevt.root"
sigma_qr = 55.1027755249 # micro barn
infile = "../../lgen/data/lgen_18x275_qr_Qe_beff2_5Mevt.root"
lqbin = 0.2
lqmin = -11
lqmax = 5
inp = TFile.Open(infile)
#tree = inp.Get("DetectorTree")
tree = inp.Get("ltree")
#can = ut.box_canvas()
hQ2 = ut.prepare_TH1D("hQ2", lqbin, lqmin, lqmax)
tree.Draw("TMath::Log10(true_Q2) >> hQ2")
ut.norm_to_integral(hQ2, sigma_qr)
gQr = ut.h1_to_graph(hQ2)
gQr.SetLineColor(rt.kRed)
gQr.SetLineWidth(3)
return gQr
hQ2.Draw()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def make_sigma_2(inp, plot, xbin, xmin, xmax, sigma, tnam="ltree"):
#cross section in two binning intervals from the tree
infile = TFile.Open(inp)
tree = infile.Get(tnam)
#point to start longer bins
xmid = 1
bin2 = xbin * 10. # 4.
hx = ut.prepare_TH1D_vec("hx",
ut.get_bins_vec_2pt(xbin, bin2, xmin, xmax, xmid))
tree.Draw(plot + " >> hx")
#normalize to the width of each bin, necessary for variable binning
for ibin in range(hx.GetNbinsX() + 1):
hx.SetBinContent(ibin, hx.GetBinContent(ibin) / hx.GetBinWidth(ibin))
ut.norm_to_integral(hx, sigma)
gx = ut.h1_to_graph(hx)
gx.SetLineWidth(3)
return gx
def make_qr_10x100():
#quasi-real total cross section for 10x100 beams
sigma_qr = 56.3121075698 # micro barn
infile = "../../lgen/data/lgen_10x100_qr_Qe_5Mevt.root"
lqbin = 0.2
lqmin = -11
lqmax = 5
inp = TFile.Open(infile)
tree = inp.Get("ltree")
hQ2 = ut.prepare_TH1D("hQ2", lqbin, lqmin, lqmax)
tree.Draw("TMath::Log10(true_Q2) >> hQ2")
ut.norm_to_integral(hQ2, sigma_qr)
gQr = ut.h1_to_graph(hQ2)
gQr.SetLineColor(rt.kYellow + 1)
gQr.SetLineWidth(3)
return gQr
def get_en(infile, ebin=0.5):
emax = 100.
inp_lmon = TFile.Open(infile)
tree = inp_lmon.Get("bunch")
hE = ut.prepare_TH1D("hE", ebin, 0, emax)
#tree.Draw("bun_up_en+bun_down_en >> hE", "(bun_up_en>1)&&(bun_down_en>1)", "", 1000)
tree.Draw("bun_up_en+bun_down_en >> hE", "(bun_up_en>1)&&(bun_down_en>1)")
ut.norm_to_integral(hE, 1.)
return ut.h1_to_arrays(hE)
def lq_rec():
#spectrum of reconstructed log_10(Q^2)
#infile = "/home/jaroslav/sim/lmon/data/taggers/tag1a/Q2rec_s1.root"
#infile = "/home/jaroslav/sim/lmon/data/taggers/tag1a/Q2rec_s2.root"
#infile = "/home/jaroslav/sim/lmon/data/taggers/tag1ax1/Q2rec_s1.root"
#infile = "/home/jaroslav/sim/lmon/data/taggers/tag1ax1/Q2rec_s2.root"
#infile = "/home/jaroslav/sim/lmon/data/luminosity/lm2ax2/Q2rec_s1.root"
infile = "/home/jaroslav/sim/lmon/data/luminosity/lm2ax2/Q2rec_s2.root"
#range along x in log_10(Q^2) (GeV^2)
xmin = -10
xmax = -1
xbin = 0.1
inp = TFile.Open(infile)
tree = inp.Get("Q2rec")
hx = ut.prepare_TH1D("hx", xbin, xmin, xmax)
tree.Draw("rec_lq >> hx")
ut.norm_to_integral(hx, 1)
xp, yp = ut.h1_to_arrays(hx)
#plot
plt.style.use("dark_background")
col = "lime"
#col = "black"
fig = plt.figure()
fig.set_size_inches(5, 5)
ax = fig.add_subplot(1, 1, 1)
set_axes_color(ax, col)
set_grid(plt, col)
plt.plot(xp, yp, "-", color="blue", lw=1)
ax.set_xlabel("lQ2 (GeV2)")
ax.set_ylabel("Normalized counts")
ax.set_yscale("log")
#plt.xticks(ax.get_xticks()[1:-1], ["$10^{"+"{0:.0f}".format(i+6)+"}$" for i in ax.get_xticks()[1:-1]])
fig.savefig("01fig.pdf", bbox_inches="tight")
plt.close()
def loat_flat_pt2():
bgen = TFile.Open(
"/home/jaroslav/sim/bgen_tx/bgen_pTsq0p14_eta1p2_6Mevt.root")
bgen_tree = bgen.Get("bgen_tree")
hFlat = ut.prepare_TH1D("hFlat", 0.002, 0., 0.12)
bgen_tree.Draw("pT2rec >> hFlat")
ut.norm_to_integral(hFlat, 0.025) # norm to Starlight
gFlat = ut.h1_to_graph(hFlat)
gFlat.SetLineColor(rt.kViolet)
gFlat.SetLineWidth(3)
gFlat.SetLineStyle(rt.kDashed)
return gFlat
def make_sigma(inp, plot, xbin, xmin, xmax, sigma, tnam="ltree"):
#cross section plot from the tree
infile = TFile.Open(inp)
tree = infile.Get(tnam)
hx = ut.prepare_TH1D("hx", xbin, xmin, xmax)
tree.Draw(plot + " >> hx")
ut.norm_to_integral(hx, sigma)
#gx = ut.h1_to_graph(hx)
gx = ut.h1_to_graph_nz(hx)
gx.SetLineWidth(3)
return gx
def make_sigma(inp, sigma):
lqbin = 0.2
lqmin = -11
lqmax = 5
infile = TFile.Open(inp)
tree = infile.Get("ltree")
hx = ut.prepare_TH1D("hx", lqbin, lqmin, lqmax)
tree.Draw("TMath::Log10(true_Q2) >> hx")
ut.norm_to_integral(hx, sigma)
gx = ut.h1_to_graph(hx)
#gx.SetLineColor(rt.kYellow+1)
gx.SetLineWidth(3)
return gx
def load_sartre():
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", 0.002, 0., 0.12)
sartre_tree.Draw("-tval >> hSartre", "rapidity>-1 && rapidity<1")
ut.norm_to_integral(hSartre, 0.025) # now same as Starlight
gSartre = TGraphErrors(hSartre.GetNbinsX())
for ibin in xrange(1, hSartre.GetNbinsX() + 1):
gSartre.SetPoint(ibin - 1, hSartre.GetBinCenter(ibin),
hSartre.GetBinContent(ibin))
gSartre.SetLineColor(rt.kYellow + 1)
gSartre.SetLineWidth(3)
#gSartre.SetLineStyle(rt.kDashDotted)
return gSartre
def load_starlight(dy):
slight = TFile.Open(
"/home/jaroslav/sim/starlight_tx/slight_AuAu_200GeV_Jpsi_coh_intmax0p34_6Mevt.root"
)
slight_tree = slight.Get("slight_tree")
#hSlight = ut.prepare_TH1D("hSlight", 0.002, 0., 0.12)
hSlight = ut.prepare_TH1D_vec(
"hSlight", ut.get_bins_vec_2pt(0.0002, 0.002, 0, 0.12, 0.004))
nall = float(slight_tree.GetEntries())
ny = float(
slight_tree.Draw("pT*pT >> hSlight", "rapidity>-1 && rapidity<1"))
#normalize to the width of each bin, necessary for variable binning
for ibin in xrange(hSlight.GetNbinsX() + 1):
hSlight.SetBinContent(
ibin,
hSlight.GetBinContent(ibin) / hSlight.GetBinWidth(ibin))
sigma_sl_tot = 67.958 # total Starlight cross section, ub
sigma_sl = (ny / nall) * sigma_sl_tot / 1000. # ub to mb
sigma_sl = sigma_sl / dy # rapidity interval
print "sigma_sl:", sigma_sl
#normalize to Starlight total cross section
ut.norm_to_integral(hSlight, sigma_sl)
#convert to graph
gSlight = ut.h1_to_graph(hSlight)
gSlight.SetLineColor(rt.kBlue)
gSlight.SetLineWidth(3)
gSlight.SetLineStyle(9) # kDashDotted
return gSlight
def xres():
#position resolution
#mm
xmin = -20
xmax = 20
xbin = 2
infile = "bpc.root"
inp = TFile.Open(infile)
tree = inp.Get("bpc")
hx = ut.prepare_TH1D("hx", xbin, xmin, xmax)
tree.Draw("xrec >> hx")
ut.norm_to_integral(hx, 1)
xp, yp = ut.h1_to_arrays(hx)
#Gaussian fit
cen = []
val = []
for i in range(1, hx.GetNbinsX()+1):
cen.append( hx.GetBinCenter(i) )
val.append( hx.GetBinContent(i) )
pars, cov = curve_fit(lambda x, mu, sig : norm.pdf(x, loc=mu, scale=sig), cen, val)
#print(cen, val)
#fit function
x = np.linspace(cen[0], cen[-1], 300)
y = norm.pdf(x, pars[0], pars[1])
#plot
plt.style.use("dark_background")
col = "lime"
#col = "black"
fig = plt.figure()
fig.set_size_inches(5, 5)
ax = fig.add_subplot(1, 1, 1)
set_axes_color(ax, col)
set_grid(plt, col)
plt.plot(xp, yp, "-", color="blue", lw=1)
plt.plot(x, y, "-", color="red", lw=1)
ax.set_xlabel("x (mm)")
ax.set_ylabel("counts")
#ax.set_yscale("log")
leg = legend()
leg.add_entry(leg_lin("red"), "Gaussian fit")
leg.add_entry(leg_txt(), "mean: {0:.3f}".format(pars[0]))
leg.add_entry(leg_txt(), "sigma: {0:.3f}".format(pars[1]))
leg.draw(plt, col)
fig.savefig("01fig.pdf", bbox_inches = "tight")
plt.close()
def lq_compare():
#input, color, rate in MHz by taggers/hit_rate.py
#Tagger 1
inp_s1 = [\
{"in": "/home/jaroslav/sim/lmon/data/luminosity/lm2ax2/Q2rec_s1.root", "col": "red", "rate": 19.1405, "lab": "Bremsstrahlung"}, \
{"in": "/home/jaroslav/sim/lmon/data/taggers/tag1a/Q2rec_s1.root", "col": "blue", "rate": 0.002905, "lab": "Quasi-real"}, \
{"in": "/home/jaroslav/sim/lmon/data/taggers/tag1ax1/Q2rec_s1.root", "col": "orange", "rate": 0.004053, "lab": "Pythia6"} \
]
#Tagger 2
inp_s2 = [\
{"in": "/home/jaroslav/sim/lmon/data/luminosity/lm2ax2/Q2rec_s2.root", "col": "red", "rate": 22.0011, "lab": "Bremsstrahlung"}, \
{"in": "/home/jaroslav/sim/lmon/data/taggers/tag1a/Q2rec_s2.root", "col": "blue", "rate": 0.005642, "lab": "Quasi-real"}, \
{"in": "/home/jaroslav/sim/lmon/data/taggers/tag1ax1/Q2rec_s2.root", "col": "orange", "rate": 0.008656, "lab": "Pythia6"} \
]
#tnam = "Tagger 1"
#inp = inp_s1
tnam = "Tagger 2"
inp = inp_s2
#range along x in log_10(Q^2) (GeV^2)
xmin = -10
xmax = -1
xbin = 0.1
#plot
#plt.style.use("dark_background")
#col = "lime"
col = "black"
fig = plt.figure()
fig.set_size_inches(5, 5)
ax = fig.add_subplot(1, 1, 1)
set_axes_color(ax, col)
set_grid(plt, col)
leg = legend()
leg.add_entry(leg_txt(), tnam)
#inputs loop
for i in inp:
infile = TFile.Open(i["in"])
tree = infile.Get("Q2rec")
hx = ut.prepare_TH1D("hx_" + i["col"], xbin, xmin, xmax)
tree.Draw("rec_lq >> hx_" + i["col"])
ut.norm_to_integral(hx, i["rate"])
xp, yp = ut.h1_to_arrays(hx)
leg.add_entry(leg_lin(i["col"]), i["lab"])
plt.plot(xp, yp, "-", color=i["col"], lw=1)
ax.set_xlabel("Reconstructed log$_{10}(Q^2)$ (GeV$^2$)")
ax.set_ylabel("Counts normalized to event rate in MHz")
ax.set_yscale("log")
#plt.xticks(ax.get_xticks()[1:-1], ["$10^{"+"{0:.0f}".format(i+6)+"}$" for i in ax.get_xticks()[1:-1]])
leg.draw(plt, col)
fig.savefig("01fig.pdf", bbox_inches="tight")
plt.close()
