def AsimovFitTables(model, analysis, fit_functions, expected_signal, masses):
import ROOT
from ROOT import TFile, TTree, TLeaf
table_file = open("/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Fits/AsimovFits/table_{}_{}_{}.tex".format(model, analysis, expected_signal), 'w')
table_file.write("\\begin{table}\n")
table_file.write("\t\\centering\n")
table_file.write("\t\\begin{tabular}{|c|")
for i in xrange(len(fit_functions)):
table_file.write("c|")
table_file.write("}\n")
table_file.write("\t\t\\hline\n")
table_file.write("\t\tMass\t")
for fit_function in fit_functions:
table_file.write("\t&\t$\\mu_{" + fit_function + "}$")
table_file.write("\\\\\n\t\t\\hline\n")
for mass in masses:
table_file.write("\t\t{} GeV ".format(mass))
for fit_function in fit_functions:
tree_file = "/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Fits/AsimovFits/mlfit_{}_{}_{}_{}_correctTrigger_{}.root".format(analysis, model, mass, fit_function, expected_signal)
f = TFile(tree_file, "READ")
if not f.IsOpen():
print "[AsimovFitTables] WARNING : File {} was not found. Skipping.".format(tree_file)
t = f.Get("tree_fit_sb")
if not t:
print "[AsimovFitTables] ERROR : Didn't find tree_fit_sb in file {}. Printing contents of file.".format(tree_file)
f.Print()
sys.exit(1)
t.GetEntry(0)
mu = t.GetLeaf("mu").GetValue()
mu_err = t.GetLeaf("muErr").GetValue()
mu_sig = (mu / mu_err if mu_err > 0 else -1.)
#table_file.write("\t\t{} GeV & {} & ${:.2}$ & ${:.2}$ & ${:.2}$ \n".format(mass, t.GetLeaf("fit_status").GetValue(), mu, mu_err, mu_sig))
table_file.write("\t&\t ${:.2} \pm {:.2}$".format(mu, mu_err))
#" & ${:.2}$ & ${:.2}$ & ${:.2}$ \n".format(mass, t.GetLeaf("fit_status").GetValue(), mu, mu_err, mu_sig))
f.Close()
table_file.write("\t\\\\\n\t\t\\hline\n")
table_file.write("\t\\end{tabular}\n")
table_file.write("\\end{table}\n")
table_file.close()
def AsimovFitPlots(model, analysis, fit_functions, expected_signal, masses):
graph_mu = {}
graph_pull = {}
canvas_mu = TCanvas("c_asimov_fit_mu_{}_{}_{}".format(model, analysis, expected_signal), "c_asimov_fit_mu_{}_{}_{}".format(model, analysis, expected_signal), 800, 600)
legend_mu = TLegend(0.83, 0.7, 0.89, 0.88)
legend_mu.SetFillColor(0)
legend_mu.SetBorderSize(0)
frame_mu = TH1D("frame_mu", "frame_mu", 100, min(masses) - 50., max(masses) + 150.)
frame_mu.GetXaxis().SetTitle("Signal Mass [GeV]")
frame_mu.GetYaxis().SetTitle("#mu")
frame_mu.SetMinimum(expected_signal -1.)
frame_mu.SetMaximum(expected_signal + 1.)
frame_mu.Draw("axis")
canvas_pull = TCanvas("c_asimov_fit_pull_{}_{}_{}".format(model, analysis, expected_signal), "c_asimov_fit_pull_{}_{}_{}".format(model, analysis, expected_signal), 800, 600)
legend_pull = TLegend(0.83, 0.7, 0.89, 0.88)
legend_pull.SetFillColor(0)
legend_pull.SetBorderSize(0)
frame_pull = TH1D("frame_pull", "frame_pull", 100, min(masses) - 50., max(masses) + 150.)
frame_pull.GetXaxis().SetTitle("Signal Mass [GeV]")
frame_pull.GetYaxis().SetTitle("#mu/#sigma_{#mu}")
frame_pull.SetMinimum(-3.)
frame_pull.SetMaximum(3.)
frame_pull.Draw("axis")
style_counter = 0
for fit_function in fit_functions:
graph_mu[fit_function] = TGraphErrors(len(masses))
graph_pull[fit_function] = TGraph(len(masses))
for i in xrange(len(masses)):
tree_file = "/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Fits/AsimovFits/mlfit_{}_{}_{}_{}_correctTrigger_{}.root".format(analysis, model, masses[i], fit_function, expected_signal)
f = TFile(tree_file, "READ")
if not f.IsOpen():
print "[AsimovFitTables] WARNING : File {} was not found. Skipping.".format(tree_file)
t = f.Get("tree_fit_sb")
if not t:
print "[AsimovFitTables] ERROR : Didn't find tree_fit_sb in file {}. Printing contents of file.".format(tree_file)
f.Print()
sys.exit(1)
t.GetEntry(0)
mu = t.GetLeaf("mu").GetValue()
mu_err = t.GetLeaf("muErr").GetValue()
mu_sig = ((mu - expected_signal) / mu_err if mu_err > 0 else -3.)
graph_mu[fit_function].SetPoint(i, masses[i], mu)
graph_mu[fit_function].SetPointError(i, 0., mu_err)
graph_pull[fit_function].SetPoint(i, masses[i], mu_sig)
canvas_mu.cd()
graph_mu[fit_function].SetMarkerStyle(20 + style_counter)
graph_mu[fit_function].SetMarkerColor(seaborn.GetColorRoot("default", style_counter))
graph_mu[fit_function].Draw("p")
legend_mu.AddEntry(graph_mu[fit_function], fit_function, "p")
canvas_pull.cd()
graph_pull[fit_function].SetMarkerStyle(20 + style_counter)
graph_pull[fit_function].SetMarkerColor(seaborn.GetColorRoot("default", style_counter))
graph_pull[fit_function].Draw("p")
legend_pull.AddEntry(graph_pull[fit_function], fit_function, "p")
style_counter += 1
canvas_mu.cd()
line_expected = TLine(min(masses) - 50., expected_signal, max(masses) + 150., expected_signal)
line_expected.SetLineColor(kGray)
line_expected.SetLineStyle(2)
line_expected.Draw("same")
legend_mu.Draw()
canvas_mu.SaveAs("/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Fits/AsimovFits/{}.pdf".format(canvas_mu.GetName()))
canvas_pull.cd()
legend_pull.Draw()
line_zero = TLine(min(masses) - 50., 0., max(masses) + 150., 0.)
line_zero.SetLineColor(kGray)
line_zero.SetLineStyle(2)
line_zero.Draw("same")
canvas_pull.SaveAs("/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Fits/AsimovFits/{}.pdf".format(canvas_pull.GetName()))
pccF2p2[0] = PCCF2P2[b - 1] * sigma_pccF2P2
pccF3p2[0] = PCCF3P2[b - 1] * sigma_pccF3P2
hfet[0] = HFET[b - 1] * sigma_hfet
bcm[0] = BCM[b - 1] * sigma_bcm
plt[0] = PLT[b - 1] * sigma_plt
# plt_0[0] = PLT_0[b-1]*sigma_plt
# plt_1[0] = PLT_1[b-1]*sigma_plt
# plt_2[0] = PLT_2[b-1]*sigma_plt
# plt_3[0] = PLT_3[b-1]*sigma_plt
# plt_4[0] = PLT_4[b-1]*sigma_plt
# plt_5[0] = PLT_5[b-1]*sigma_plt
# plt_6[0] = PLT_6[b-1]*sigma_plt
# plt_7[0] = PLT_7[b-1]*sigma_plt
# plt_8[0] = PLT_8[b-1]*sigma_plt
# plt_9[0] = PLT_9[b-1]*sigma_plt
# plt_10[0] = PLT_10[b-1]*sigma_plt
# plt_11[0] = PLT_11[b-1]*sigma_plt
# plt_12[0] = PLT_12[b-1]*sigma_plt
# plt_13[0] = PLT_13[b-1]*sigma_plt
# plt_14[0] = PLT_14[b-1]*sigma_plt
# plt_15[0] = PLT_15[b-1]*sigma_plt
tree.Fill()
#print fill[0],run[0],ls[0],time[0],bx[0],hfoc[0],pcc[0]
#print fill[0],run[0],ls[0],':',idx_pccB1,pccB1[0],idx_pccF3p1,pccF3p1[0]
outFile.Write()
outFile.Print()
outFile.Close()
options, remainder = getopt.gnu_getopt(sys.argv[1:], 'hl')
print(options)
print(remainder)
# Parse the command line arguments
for opt, arg in options:
if opt == 'l':
isL0 = True
if opt == '-h':
print_usage()
sys.exit(0)
outfile = remainder[0]
print(outfile)
infile = TFile(remainder[1])
infile.Print()
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-no-killing-all.root")
#mcfile=TFile("OutputHistograms/MC/tritrig-beam_2500kBunches_pass4_v4_5_0_svt_efficiency_allow_missed_sensor.root")
mcfile = TFile(
"OutputHistograms/MC/tritrig-beam_2500kBunches_pass4_v4_5_0_svt_efficiency_allow_missed_sensor_5sigma_weighted_ratios_rereco.root"
)
#mcfile=TFile("OutputHistograms/MC/tritrig-beam_2500kBunches_pass4_v4_5_0_svt_efficiency_allow_missed_sensor_5sigma_weighted_ratios_scalekilling_2pt0.root")
#mcfile=TFile("OutputHistograms/MC/tritrig-beam_2500kBunches_pass4_v4_5_0_svt_efficiency_allow_missed_sensor_5sigma_weighted_ratios_scalekilling_First3pt0_Second2pt0.root")
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-cluster-killing-allow-missed-sensor-all.root")
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-cluster-killing-5-sigma-L1b-axial-all.root")
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-cluster-killing-5-sigma-L1-L6-all.root")
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-cluster-killing-5-sigma-L1-L6-all.root")
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-cluster-killing-5-sigma-L1t-axial-all.root")
#mcfile=TFile("SVTEfficiencyData/tritrig-WB_HPS-PhysicsRun2016-Pass2-svt-efficiency-cluster-killing-weighted-ratios-all.root")
