class Regression04Threading(MyTestCase):
hasThread = gROOT.IsBatch() and 5 or 6 # can't test if no display ...
noThread = 5
def test1SpecialCasegROOT(self):
"""Test the special role that gROOT plays vis-a-vis threading"""
cmd = sys.executable + " -c 'import sys, ROOT; ROOT.gROOT; %s "\
"sys.exit( 5 + int(\"thread\" in ROOT.__dict__) )'"
if self.hasThread == self.noThread:
cmd += " - -b"
stat, out = commands.getstatusoutput(cmd % "")
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(cmd % "ROOT.gROOT.SetBatch( 1 );")
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(cmd % "ROOT.gROOT.SetBatch( 0 );")
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(
cmd % "ROOT.gROOT.ProcessLine( \"cout << 42 << endl;\" ); ")
self.assertEqual(WEXITSTATUS(stat), self.hasThread)
stat, out = commands.getstatusoutput(cmd % "ROOT.gDebug;")
self.assertEqual(WEXITSTATUS(stat), self.hasThread)
def test2ImportStyles(self):
"""Test different import styles vis-a-vis threading"""
cmd = sys.executable + " -c 'import sys; %s ;"\
"import ROOT; sys.exit( 5 + int(\"thread\" in ROOT.__dict__) )'"
if self.hasThread == self.noThread:
cmd += " - -b"
# Do not test 'from ROOT import *' on Python 3.x, since it's not supported
if sys.hexversion < 0x300000:
stat, out = commands.getstatusoutput(cmd % "from ROOT import *")
self.assertEqual(WEXITSTATUS(stat), self.hasThread)
stat, out = commands.getstatusoutput(cmd % "from ROOT import gROOT")
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(cmd % "from ROOT import gDebug")
self.assertEqual(WEXITSTATUS(stat), self.hasThread)
def test3SettingOfBatchMode(self):
"""Test various ways of preventing GUI thread startup"""
cmd = sys.executable + " -c '%s import ROOT, sys; sys.exit( 5+int(\"thread\" in ROOT.__dict__ ) )'"
if self.hasThread == self.noThread:
cmd += " - -b"
stat, out = commands.getstatusoutput(
cmd % 'import ROOT; ROOT.PyConfig.StartGuiThread = 0;')
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(
cmd %
'from ROOT import PyConfig; PyConfig.StartGuiThread = 0; from ROOT import gDebug;'
)
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(
cmd %
'from ROOT import PyConfig; PyConfig.StartGuiThread = 1; from ROOT import gDebug;'
)
self.assertEqual(WEXITSTATUS(stat), self.hasThread)
# Do not test 'from ROOT import *' on Python 3.x, since it's not supported
if sys.hexversion < 0x300000:
stat, out = commands.getstatusoutput((cmd %
'from ROOT import *;') +
' - -b')
self.assertEqual(WEXITSTATUS(stat), self.noThread)
stat, out = commands.getstatusoutput(
cmd %
'from ROOT import gROOT; gROOT.SetBatch( 1 ); from ROOT import *;'
)
self.assertEqual(WEXITSTATUS(stat), self.noThread)
if not gROOT.IsBatch(): # can't test if no display ...
stat, out = commands.getstatusoutput(
cmd %
'from ROOT import gROOT; gROOT.SetBatch( 0 ); from ROOT import *;'
)
self.assertEqual(WEXITSTATUS(stat), self.hasThread)
def pullsVertical(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
b_pulls = TH1F("b_pulls", ";;Pulls", nbins, 0.-off, nbins-off)
s_pulls = TH1F("s_pulls", ";;Pulls", nbins, 0.+off, nbins+off) #
for i, s in enumerate(content):
l = s.split()
b_pulls.GetXaxis().SetBinLabel(i+1, l[0])
s_pulls.GetXaxis().SetBinLabel(i+1, l[0])
b_pulls.SetBinContent(i+1, float(l[1]))
b_pulls.SetBinError(i+1, float(l[2]))
s_pulls.SetBinContent(i+1, float(l[3]))
s_pulls.SetBinError(i+1, float(l[4]))
b_pulls.SetFillStyle(3005)
b_pulls.SetFillColor(923)
b_pulls.SetLineColor(923)
b_pulls.SetLineWidth(1)
b_pulls.SetMarkerStyle(20)
b_pulls.SetMarkerSize(1.25)
s_pulls.SetLineColor(602)
s_pulls.SetMarkerColor(602)
s_pulls.SetMarkerStyle(24) #24
s_pulls.SetLineWidth(1)
b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5)
# Graphs
h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins)
B_pulls = TGraphAsymmErrors(nbins)
S_pulls = TGraphAsymmErrors(nbins)
boxes = []
canvas = TCanvas("canvas", "Pulls", 600, 150+nbins*10)#nbins*20)
canvas.cd()
canvas.SetGrid(0, 1)
canvas.GetPad(0).SetTopMargin(0.01)
canvas.GetPad(0).SetRightMargin(0.01)
canvas.GetPad(0).SetBottomMargin(0.05)
canvas.GetPad(0).SetLeftMargin(0.25)#(0.25)#(0.065)
canvas.GetPad(0).SetTicks(1, 1)
for i, s in enumerate(content):
l = s.split()
if "1034h" in l[0]: l[0]="CMS_PDF_13TeV"
h_pulls.GetYaxis().SetBinLabel(i+1, l[0].replace('CMS2016_', ''))#C
#y1 = gStyle.GetPadBottomMargin()
#y2 = 1. - gStyle.GetPadTopMargin()
#h = (y2 - y1) / float(nbins)
#y1 = y1 + float(i) * h
#y2 = y1 + h
#box = TPaveText(0, y1, 1, y2, 'NDC')
#box.SetFillColor(0)
#box.SetTextSize(0.02)
#box.SetBorderSize(0)
#box.SetTextAlign(12)
#box.SetMargin(0.005)
#if i % 2 == 0:
# box.SetFillColor(18)
#box.Draw()
#boxes.append(box)
B_pulls.SetPoint(i+1,float(l[1]),float(i+1)-0.3)#C
B_pulls.SetPointError(i+1,float(l[2]),float(l[2]),0.,0.)#C
for i, s in enumerate(content):
l = s.split()
S_pulls.SetPoint(i+1,float(l[3]),float(i+1)-0.7)#C
S_pulls.SetPointError(i+1,float(l[4]),float(l[4]),0.,0.)#C
h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta")
h_pulls.GetXaxis().SetLabelOffset(-0.01)
h_pulls.GetXaxis().SetTitleOffset(.6)
h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0)
B_pulls.SetFillColor(1)
B_pulls.SetLineColor(1)
B_pulls.SetLineStyle(1)
B_pulls.SetLineWidth(2)
B_pulls.SetMarkerColor(1)
B_pulls.SetMarkerStyle(20)
B_pulls.SetMarkerSize(1)#(0.75)
S_pulls.SetFillColor(629)
S_pulls.SetLineColor(629)
S_pulls.SetMarkerColor(629)
S_pulls.SetLineWidth(2)
S_pulls.SetMarkerStyle(20)
S_pulls.SetMarkerSize(1)
box1 = TBox(-1., 0., 1., nbins)
box1.SetFillStyle(3001)
#box1.SetFillStyle(0)
box1.SetFillColor(417)
box1.SetLineWidth(2)
box1.SetLineStyle(2)
box1.SetLineColor(417)
box2 = TBox(-2., 0., 2., nbins)
box2.SetFillStyle(3001)
#box2.SetFillStyle(0)
box2.SetFillColor(800)
box2.SetLineWidth(2)
box2.SetLineStyle(2)
box2.SetLineColor(800)
leg = TLegend(0.1, -0.05, 0.7, 0.08)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetNColumns(2)
leg.AddEntry(B_pulls, "B-only fit", "lp")
leg.AddEntry(S_pulls, "S+B fit", "lp")
if text: leg.AddEntry(0, text, "")
h_pulls.Draw("")
box2.Draw()
box1.Draw()
B_pulls.Draw("P6SAME")
S_pulls.Draw("P6SAME")
leg.Draw()
# drawCMS(35867, "Preliminary")
# drawAnalysis("VH")
# drawRegion(outName)
canvas.Print(outName+".png")
canvas.Print(outName+".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def systematics(sys, isShape=False, antiCorr=False):
treeRead = True
file = {}
hist = {}
tree = {}
histUp = {}
histDown = {}
up = {}
down = {}
gUp = TGraph()
gDown = TGraph()
gUp.SetLineWidth(3)
gDown.SetLineWidth(3)
gUp.SetLineColor(632)
gDown.SetLineColor(602)
BTagAK4deepup = False
BTagAK4deepdown = False
BTagAK4deep = False
BTagAK8deepup = False
BTagAK8deepdown = False
BTagAK8deep = False
# g.SetMarkerStyle(20)
# g.SetMarkerColor(418)
# g.SetMarkerSize(1.25)
var = sys
cut = 'nnbbSR'
if var == 'BTagAK4Weight_deep_up':
var = 'X_mass'
BTagAK4deepup = True
elif var == 'BTagAK4Weight_deep_down':
var = 'X_mass'
BTagAK4deepdown = True
elif var == 'BTagAK4Weight_deep':
var = 'X_mass'
BTagAK4deep = True
elif var == 'BTagAK8Weight_deep_up':
var = 'X_mass'
BTagAK8deepup = True
elif var == 'BTagAK8Weight_deep_down':
var = 'X_mass'
BTagAK8deepdown = True
elif var == 'BTagAK8Weight_deep':
var = 'X_mass'
BTagAK8deep = True
upAvg, downAvg, upMin, downMin, upMax, downMax = 0., 0., 2., 2., 0., 0.
for k in sorted(selection.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(k, selection[k])
for i, s in enumerate(sign):
if '_MZ' in s: m = int((s.split('_MZ')[1]).split('_MA')[0])
elif '_M' in s: m = int(s.split('_M')[1])
else: m = 0
if treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
tree[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins'] > 0:
min_value = variable[var]['min']
max_value = variable[var]['max']
title = variable[var]['title']
if 'isZtoNN' in cut:
if var == 'MET':
min_value = 200
max_value = 2000
elif var == 'DPhi':
title = "#Delta #varphi (AK8 jet-#slash{E}_{T})"
elif var == 'VH_deltaR':
title = "#Delta R (#slash{E}_{T}, AK8 jet)"
hist[s] = TH1F(
s, ";" + title + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], min_value, max_value)
else:
hist[s] = TH1F(s, ";" + variable[var]['title'],
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
cutstring = "(eventWeightLumi)" + ("*(" + cut + ")")
if var == 'LeptonWeightUp':
cutstring = "(eventWeightLumi * LeptonWeightUp/LeptonWeight)" + (
"*(" + cut + ")")
elif var == 'LeptonWeightDown':
cutstring = "(eventWeightLumi * LeptonWeightDown/LeptonWeight)" + (
"*(" + cut + ")")
elif var == 'TriggerWeightUp':
cutstring = "(eventWeightLumi * TriggerWeightUp/TriggerWeight)" + (
"*(" + cut + ")")
elif var == 'TriggerWeightDown':
cutstring = "(eventWeightLumi * TriggerWeightDown/TriggerWeight)" + (
"*(" + cut + ")")
#division by BTagAk4Weight_deep is because the weighted samples are used
elif BTagAK4deepup:
cutstring = "(eventWeightLumi * BTagAK4Weight_deep_up/BTagAK4Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK4deepdown:
cutstring = "(eventWeightLumi * BTagAK4Weight_deep_down/BTagAK4Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK8deep:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep)" + (
"*(" + cut + ")")
elif BTagAK8deepup:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep_up)" + (
"*(" + cut + ")")
elif BTagAK8deepdown:
cutstring = "(eventWeightLumi * BTagAK8Weight_deep_down)" + (
"*(" + cut + ")")
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
"""
for j, ss in enumerate(sample[s]['files']):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "READ")
tmp = file[ss].Get("Sys/"+sys)
if tmp == None: continue
if not s in hist.keys(): hist[s] = tmp
else:
if antiCorr:
for x in range(1, hist[s].GetNbinsX()+1): hist[s].SetBinContent(x, hist[s].GetBinContent(x)+tmp.GetBinContent(tmp.GetNbinsX()+1-x))
else: hist[s].Add( tmp )
if isShape:
tmp = file[ss].Get("Sys/"+sys+"_up")
print "Sys/"+sys+"_up"
if tmp == None: continue
if not s in histUp.keys(): histUp[s] = tmp
else: histUp[s].Add( tmp )
#
tmp = file[ss].Get("Sys/"+sys+"_down")
if tmp == None: continue
if not s in histDown.keys(): histDown[s] = tmp
else: histDown[s].Add( tmp )
if 'accept' in sys:
norm = None
for j, ss in enumerate(sample[s]['files']):
tmp = file[ss].Get("Sys/PDF_scale")
if tmp == None: continue
if norm == None: norm = tmp
else: norm.Add( tmp )
hist[s].Divide(norm)
"""
if (isShape):
shape = TF1("shape", "gaus", 0, 5000)
shapeUp = TF1("shapeUp", "gaus", 0, 5000)
shapeDown = TF1("shapeDown", "gaus", 0, 5000)
hist[s].Fit(shape, "Q0", "")
histUp[s].Fit(shapeUp, "Q0", "")
histDown[s].Fit(shapeDown, "Q0", "")
if 'scale' in sys or 'unc' in sys:
up[s] = histUp[s].GetMean() / hist[s].GetMean()
down[s] = histDown[s].GetMean() / hist[s].GetMean()
# up[s] = shapeUp.GetParameter(1)/shape.GetParameter(1)
# down[s] = shapeDown.GetParameter(1)/shape.GetParameter(1)
elif 'res' in sys:
up[s] = histUp[s].GetRMS() / hist[s].GetRMS()
down[s] = histDown[s].GetRMS() / hist[s].GetRMS()
# up[s] = shapeUp.GetParameter(2)/shape.GetParameter(2)
# down[s] = shapeDown.GetParameter(2)/shape.GetParameter(2)
else:
up[s] = hist[s].GetBinContent(
hist[s].FindBin(+1)) / hist[s].GetBinContent(
hist[s].FindBin(0))
down[s] = hist[s].GetBinContent(
hist[s].FindBin(-1)) / hist[s].GetBinContent(
hist[s].FindBin(0))
gUp.SetPoint(i, m, up[s])
gDown.SetPoint(i, m, down[s])
#if mass < 1000: continue
upAvg += up[s]
downAvg += down[s]
if abs(up[s]) > upMax: upMax = abs(up[s])
if abs(up[s]) < upMin: upMin = abs(up[s])
if abs(down[s]) > downMax: downMax = abs(down[s])
if abs(down[s]) < downMin: downMin = abs(down[s])
upAvg /= len(sign)
downAvg /= len(sign)
print " ---", sys, "--- | up: %.3f, down: %.3f, average: %.3f" % (
upAvg, downAvg, abs(upAvg - 1 + 1. - downAvg) /
2.), "|", "^{%.1f-%.1f}_{%.1f-%.1f}" % (100. * (1. - upMin), 100. *
(1. - upMax), 100. *
(downMin - 1.), 100. *
(downMax - 1.))
c1 = TCanvas("c1", "Signals", 800, 600)
c1.cd()
c1.GetPad(0).SetTicky(2)
gUp.Draw("AL")
gDown.Draw("SAME, L")
gUp.GetYaxis().SetRangeUser(0.65, 1.35)
gUp.GetXaxis().SetTitle("m_{X} (GeV)")
gUp.GetYaxis().SetTitle("Uncertainty")
leg = TLegend(0.5, 0.90 - 0.20, 0.9, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader(sys.replace('_', ' '))
leg.AddEntry(gUp, "+1 s. d. (%.1f%%)" % (100. * (upAvg - 1.)), "l")
leg.AddEntry(gDown, " -1 s. d. (%.1f%%)" % (100. * (1. - downAvg)), "l")
leg.Draw()
drawCMS(-1, "Simulation", False)
c1.Update()
filename = sys
if sys.startswith('W_mass') or sys.startswith('Z_mass'):
filename += "_" + sign[0][:3]
# c1.Print("plots/Systematics/"+filename+".png")
# c1.Print("plots/Systematics/"+filename+".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'doubleB' in sys or 'subjet' in sys or 'mass' in sys:
print sys + " = {",
for m in [
800, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800,
2000, 2500, 3000, 3500, 4000, 4500
]:
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
if 'extr' in sys or 'tagging' in sys:
print sys + " = {",
for m in [800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500]:
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
if 'QCD_scale' in sys or 'PDF_scale' in sys:
print sys + " = {",
for m in range(800, 4500 + 1, 10):
print "%d : [%.3f, %.3f], " % (m, gUp.Eval(m), gDown.Eval(m)),
print "}"
def cutvariation_fitter(self, min_cv_cut, max_cv_cut):
# Test if we are in AliPhysics env
#self.test_aliphysics()
tmp_is_root_batch = gROOT.IsBatch()
gROOT.SetBatch(True)
from ROOT import AliHFInvMassFitter, AliVertexingHFUtils
# Enable ROOT batch mode and reset in the end
self.loadstyle()
lfile = TFile.Open(self.n_filemass_cutvar, "READ")
ntrials = 2 * self.p_ncutvar + 1
icvmax = 1
mass_fitter = []
ifit = 0
for icv in range(ntrials):
fileout_name = self.make_file_path(self.d_results_cv, self.yields_filename, "root", \
None, [self.typean, str(icv)])
fileout = TFile(fileout_name, "RECREATE")
yieldshistos = [TH1F("hyields%d" % (imult), "", \
self.p_nptfinbins, array("d", self.ptranges)) for imult in range(len(self.lvar2_binmin))]
if self.p_nptfinbins < 9:
nx = 4
ny = 2
canvy = 533
elif self.p_nptfinbins < 13:
nx = 4
ny = 3
canvy = 800
else:
nx = 5
ny = 4
canvy = 1200
canvas_data = [TCanvas("canvas_cutvar%d_%d" % (icv, imult), "Data", 1000, canvy) \
for imult in range(len(self.lvar2_binmin))]
for imult in range(len(self.lvar2_binmin)):
canvas_data[imult].Divide(nx, ny)
for imult in range(len(self.lvar2_binmin)):
mean_for_data, sigma_for_data = self.load_central_meansigma(imult)
for ipt in range(self.p_nptfinbins):
bin_id = self.bin_matching[ipt]
suffix = "%s%d_%d_%d_%s%.2f_%.2f" % \
(self.v_var_binning, self.lpt_finbinmin[ipt],
self.lpt_finbinmax[ipt], icv,
self.v_var2_binning, self.lvar2_binmin[imult],
self.lvar2_binmax[imult])
stepsmin = (self.lpt_probcutfin[bin_id] - min_cv_cut[ipt]) / self.p_ncutvar
stepsmax = (max_cv_cut[ipt] - self.lpt_probcutfin[bin_id]) / self.p_ncutvar
ntrials = 2 * self.p_ncutvar + 1
selml_cvval = 0
if icv < self.p_ncutvar:
selml_cvval = min_cv_cut[ipt] + icv * stepsmin
elif icv == self.p_ncutvar:
selml_cvval = self.lpt_probcutfin[bin_id]
else:
selml_cvval = self.lpt_probcutfin[bin_id] + icvmax * stepsmax
histname = "hmass"
if self.apply_weights is True:
histname = "h_invmass_weight"
self.logger.info("*********** I AM USING WEIGHTED HISTOGRAMS")
h_invmass = lfile.Get(histname + suffix)
h_invmass_rebin_ = AliVertexingHFUtils.RebinHisto(h_invmass, self.p_rebin[ipt], -1)
h_invmass_rebin = TH1F()
h_invmass_rebin_.Copy(h_invmass_rebin)
h_invmass_rebin.SetTitle("%.1f < #it{p}_{T} < %.1f (prob > %.4f)" \
% (self.lpt_finbinmin[ipt], self.lpt_finbinmax[ipt], \
selml_cvval))
h_invmass_rebin.GetXaxis().SetTitle("#it{M}_{inv} (GeV/#it{c}^{2})")
h_invmass_rebin.GetYaxis().SetTitle("Entries/(%.0f MeV/#it{c}^{2})" \
% (h_invmass_rebin.GetBinWidth(1) * 1000))
h_invmass_rebin.GetYaxis().SetTitleOffset(1.1)
mass_fitter.append(AliHFInvMassFitter(h_invmass_rebin, self.p_massmin[ipt],
self.p_massmax[ipt],
self.bkg_fmap[self.p_bkgfunc[ipt]],
self.sig_fmap[self.p_sgnfunc[ipt]]))
if self.p_dolike:
mass_fitter[ifit].SetUseLikelihoodFit()
mass_fitter[ifit].SetInitialGaussianMean(self.p_masspeak)
mass_fitter[ifit].SetInitialGaussianSigma(self.p_sigmaarray[ipt])
if self.p_fixedmean:
mass_fitter[ifit].SetFixGaussianMean(mean_for_data[ipt])
if self.p_fixedsigma:
mass_fitter[ifit].SetFixGaussianSigma(sigma_for_data[ipt])
mass_fitter[ifit].SetNSigma4SideBands(self.p_exclude_nsigma_sideband)
mass_fitter[ifit].SetCheckSignalCountsAfterFirstFit(False)
#Reflections to be included
if self.p_includesecpeak[ipt]:
mass_fitter[ifit].IncludeSecondGausPeak(self.p_masssecpeak,
self.p_fix_masssecpeak,
self.p_widthsecpeak,
self.p_fix_widthsecpeak)
success = mass_fitter[ifit].MassFitter(False)
canvas_data[imult].cd(ipt+1)
if success != 1:
mass_fitter[ifit].GetHistoClone().Draw()
self.logger.error("Fit failed for suffix %s", suffix)
ifit = ifit + 1
continue
mass_fitter[ifit].DrawHere(gPad, self.p_nsigma_signal)
# Write fitters to file
fit_root_dir = fileout.mkdir(suffix)
fit_root_dir.WriteObject(mass_fitter[ifit], "fittercutvar")
# In case of success == 2, no signal was found, in case of 0, fit failed
rawYield = mass_fitter[ifit].GetRawYield()
rawYieldErr = mass_fitter[ifit].GetRawYieldError()
yieldshistos[imult].SetBinContent(ipt + 1, rawYield)
yieldshistos[imult].SetBinError(ipt + 1, rawYieldErr)
ifit = ifit + 1
suffix2 = "cutvar%d_%s%.2f_%.2f" % \
(icv, self.v_var2_binning, self.lvar2_binmin[imult], \
self.lvar2_binmax[imult])
canvas_data[imult].SaveAs(self.make_file_path(self.d_results_cv,
"canvas_FinalData",
"eps", None, suffix2))
fileout.cd()
yieldshistos[imult].Write()
fileout.Close()
if icv > self.p_ncutvar:
icvmax = icvmax + 1
del mass_fitter[:]
# Reset to former mode
gROOT.SetBatch(tmp_is_root_batch)
def plotMM():
gStyle.SetOptFit(1111)
NMM = 4
hmms = [[], []]
hmm2s = [[], []]
#f = TF1("gaus","gaus",)
for idt in range(DTYPS):
hmms[idt].append(f[idt].top.hmmppippimVw.ProjectionY('hmmppippim_%s' %
DTYPS_NAME[idt]))
hmms[idt].append(f[idt].top.hmmppipVw.ProjectionY('hmmppip_%s' %
DTYPS_NAME[idt]))
hmms[idt].append(f[idt].top.hmmppimVw.ProjectionY('hmmppim_%s' %
DTYPS_NAME[idt]))
hmms[idt].append(f[idt].top.hmmpippimVw.ProjectionY('hmmpippim_%s' %
DTYPS_NAME[idt]))
hmm2s[idt].append(f[idt].top.hmm2ppippimVw.ProjectionY(
'hmm2ppippim_%s' % DTYPS_NAME[idt]))
hmm2s[idt].append(f[idt].top.hmm2ppipVw.ProjectionY('hmm2ppip_%s' %
DTYPS_NAME[idt]))
hmm2s[idt].append(f[idt].top.hmm2ppimVw.ProjectionY('hmm2ppim_%s' %
DTYPS_NAME[idt]))
hmm2s[idt].append(f[idt].top.hmm2pippimVw.ProjectionY('hmm2pippim_%s' %
DTYPS_NAME[idt]))
# hmms[idt].append(f[idt].Get("/top/hmmppippimVw").ProjectionY('hmmppippim_%s'%DTYPS_NAME[idt]))
# hmms[idt].append(f[idt].Get("/top/hmmppimVw").ProjectionY('hmmppim_%s'%DTYPS_NAME[idt]))
# hmms[idt].append(f[idt].Get("/top/hmmppipVw").ProjectionY('hmmppip_%s'%DTYPS_NAME[idt]))
# hmms[idt].append(f[idt].Get("/top/hmmpippimVw").ProjectionY('hmmpippim_%s'%DTYPS_NAME[idt]))
cmm = TCanvas("mm", "mm")
cmm.Divide(2, 2)
fsimc = []
for imm in range(NMM):
pad = cmm.cd(imm + 1)
# hmms[EXP][imm].SetLineColor(gROOT.ProcessLine("kBlue"))
# hmms[EXP][imm].DrawNormalized("",10000)
hmms[SIM][imm].SetLineColor(gROOT.ProcessLine("kRed"))
hmms[SIM][imm].SetMarkerColor(gROOT.ProcessLine("kRed"))
hsim = hmms[SIM][imm].DrawNormalized("", 10000)
hmms[EXP][imm].SetLineColor(gROOT.ProcessLine("kBlue"))
hmms[EXP][imm].SetMarkerColor(gROOT.ProcessLine("kBlue"))
hexp = hmms[EXP][imm].DrawNormalized("sames", 10000)
if imm == 1 or imm == 2:
hsim.Fit("gaus", "", "", 0.1, 0.17)
hexp.Fit("gaus", "", "", 0.1, 0.17)
elif imm == 3:
hsim.Fit("gaus", "", "", 0.9, 1.0)
hexp.Fit("gaus", "", "", 0.9, 0.96)
if imm != 0:
fsim = hsim.GetFunction("gaus")
fsim.SetLineColor(gROOT.ProcessLine("kBlack"))
fexp = hexp.GetFunction("gaus")
fexp.SetLineColor(gROOT.ProcessLine("kBlack"))
pad.Update()
fsimc.append(TF1(fsim))
fsimc[imm - 1].SetLineColor(gROOT.ProcessLine("kGreen"))
fsimc[imm - 1].SetRange(hmms[SIM][imm].GetXaxis().GetXmin(),
hmms[SIM][imm].GetXaxis().GetXmax())
fsimc[imm - 1].Draw("same")
#pad.Update()
cmm2 = TCanvas("mm2", "mm2")
cmm2.Divide(2, 2)
for imm in range(NMM):
pad = cmm2.cd(imm + 1)
# hmms[EXP][imm].SetLineColor(gROOT.ProcessLine("kBlue"))
# hmms[EXP][imm].DrawNormalized("",10000)
hmm2s[SIM][imm].SetLineColor(gROOT.ProcessLine("kRed"))
hmm2s[SIM][imm].SetMarkerColor(gROOT.ProcessLine("kRed"))
hsim = hmm2s[SIM][imm].DrawNormalized("", 10000)
hmm2s[EXP][imm].SetLineColor(gROOT.ProcessLine("kBlue"))
hmm2s[EXP][imm].SetMarkerColor(gROOT.ProcessLine("kBlue"))
hexp = hmm2s[EXP][imm].DrawNormalized("sames", 10000)
if imm == 1 or imm == 2:
hsim.Fit("gaus", "", "", 0.1, 0.17)
hexp.Fit("gaus", "", "", 0.1, 0.17)
elif imm == 3:
hsim.Fit("gaus", "", "", 0.9, 1.0)
hexp.Fit("gaus", "", "", 0.9, 0.96)
if imm != 0:
hsim.GetFunction("gaus").SetLineColor(gROOT.ProcessLine("kBlack"))
hexp.GetFunction("gaus").SetLineColor(gROOT.ProcessLine("kBlack"))
pad.Update()
if not gROOT.IsBatch():
plt.show()
# wait for you to close the ROOT canvas before exiting
wait(True)
graphs_rate_L4[j].GetYaxis().SetLimits(0,y_max_L4)
else:
graphs_rate_L4[j].SetMaximum(max(y_max_L4, graphs_rate_L4[j].GetHistogram().GetMaximum())*(1.01))
graphs_rate_L4[j].Draw("PL")
graphs_rate_L4[j].SetMaximum(max(y_max_L4, graphs_rate_L4[j].GetHistogram().GetMaximum())*(1.01))
graphs_rate_L4[j].GetYaxis().SetLimits(0,y_max_L4)
leg_L4.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
latex.SetTextSize(0.04)
latex.SetTextFont(62)
latex.DrawLatex(0.30, 0.96, "GIF++")
etichetta = TLatex()
etichetta.SetNDC()
etichetta.SetTextSize(0.04)
etichetta.SetTextColor(4)
etichetta.SetTextFont(102)
etichetta.DrawLatex(0.45, 0.93, "SL1_L4 HV scan rate")
#etichetta.DrawLatex(0.45, 0.8, "#splitline{SL1_L4 threshold scan_rate}{Runs: "+str(run_interval)+"}")
can_HV_scan_rate_SL1_L4.Update()
can_HV_scan_rate_SL1_L4.Print(outpath + "Compare_HV_scan_rate_SL1_L4.png")
can_HV_scan_rate_SL1_L4.Print(outpath + "Compare_HV_scan_rate_SL1_L4.pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
exit()
def plot(var, cut, year, norm=False, nm1=False):
### Preliminary Operations ###
treeRead = not cut in ["nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em", "emqq", "qqqq"] # Read from tree
channel = cut
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel
showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5
stype = "HVT model B"
if len(sign)>0 and 'AZh' in sign[0]: stype = "2HDM"
elif len(sign)>0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV"
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if BTAGGING=='semimedium':
if k in cut:
if ADDSELECTION:
cut = cut.replace(k, aliasSM[k]+SELECTIONS[options.selection])
else:
cut = cut.replace(k, aliasSM[k])
else:
if k in cut:
if ADDSELECTION:
cut = cut.replace(k, alias[k].format(WP=working_points[BTAGGING])+SELECTIONS[options.selection])
else:
cut = cut.replace(k, alias[k].format(WP=working_points[BTAGGING]))
# Determine Primary Dataset
pd = sample['data_obs']['files']
print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
if var == 'jj_deltaEta_widejet':
if "jj_deltaEta_widejet<1.1 && " in cut:
print
print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution"
print
cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "")
else:
print
print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly"
print
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(data+back+sign):
if treeRead: # Project from tree
tree[s] = TChain("tree")
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
if year=="run2" or year in ss:
if RECAL and 'data' in s:
tree[s].Add(NTUPLEDIR + "Recal/" + ss + "_Recal.root")
else:
tree[s].Add(NTUPLEDIR + ss + ".root")
if variable[var]['nbins']>0: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events / ( "+str((variable[var]['max']-variable[var]['min'])/variable[var]['nbins'])+unit+" );"+('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max'])
else: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events"+('log' if variable[var]['log'] else ''), len(variable[var]['bins'])-1, array('f', variable[var]['bins']))
hist[s].Sumw2()
cutstring = "(eventWeightLumi)" + ("*("+cut+")" if len(cut)>0 else "")
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree()==None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
else: # Histogram written to file
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s or ('data' in s and ss in pd):
file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
if file[ss].IsZombie():
print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
continue
tmphist = file[ss].Get(cut+"/"+var)
if tmphist==None: continue
if not s in hist.keys(): hist[s] = tmphist
else: hist[s].Add(tmphist)
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
if channel.endswith('TR') and channel.replace('TR', '') in topSF:
hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0])
hist['ST'].Scale(topSF[channel.replace('TR', '')][0])
hist['BkgSum'] = hist['data_obs'].Clone("BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back): hist['BkgSum'].Add(hist[s])
if options.norm:
for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(hist[data[0]].Integral()/hist['BkgSum'].Integral())
# Create data and Bkg sum histograms
if options.blind or 'SR' in channel:
hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
for i, s in enumerate(data+back+sign+['BkgSum']): addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign): hist[s].SetLineWidth(3)
for i, s in enumerate(sign): sample[s]['plot'] = True#sample[s]['plot'] and s.startswith(channel[:2])
if isAH:
for i, s in enumerate(back):
hist[s].SetFillStyle(3005)
hist[s].SetLineWidth(2)
#for i, s in enumerate(sign):
# hist[s].SetFillStyle(0)
if not var=="Events":
sfnorm = hist[data[0]].Integral()/hist['BkgSum'].Integral()
print "Applying SF:", sfnorm
for i, s in enumerate(back+['BkgSum']): hist[s].Scale(sfnorm)
if BLIND and var.endswith("Mass"):
for i, s in enumerate(data+back+['BkgSum']):
first, last = hist[s].FindBin(65), hist[s].FindBin(135)
for j in range(first, last): hist[s].SetBinContent(j, -1.e-4)
if BLIND and var.endswith("Tau21"):
for i, s in enumerate(data):
first, last = hist[s].FindBin(0), hist[s].FindBin(0.6)
for j in range(first, last): hist[s].SetBinContent(j, -1.e-4)
if SYNC and var == "jj_mass_widejet" and year in ["2016", "2017", "2018"]:
#iFile = TFile("sync/JetHT_run" + year + "_red_cert_scan.root", "READ")
#hist['sync'] = iFile.Get("Mjj")
if year == '2016':
iFile = TFile("sync/2016/2016_07Aug2017_1246_1p1.root", "READ")
hist['sync'] = iFile.Get("h_mjj_data")
elif year == '2017':
iFile = TFile("sync/2017/histos_Run2017BCDEF_17Nov2017_JEC2017_mjj1530_cemf_lt_0p8_deltaETA_lt_1p1.root", "READ")
hist['sync'] = iFile.Get("h_mjj_data")
elif year == '2018':
iFile = TFile("sync/2018/Double_sideband_inputs_18v10_preliminary_v2.root", "READ")
hist['sync'] = iFile.Get("h_mjj")
# hist['sync'] = tmp.Rebin(len(dijet_bins)-1, "sync", array('d', dijet_bins))
# hist['sync'] = tmp.Rebin(100, "sync")
hist['sync'].SetMarkerStyle(31)
hist['sync'].SetMarkerSize(1.25)
hist['sync'].SetMarkerColor(2)
print "Imported and drawing sync file"
# Create stack
if variable[var]['nbins']>0:
bkg = THStack("Bkg", ";"+hist['BkgSum'].GetXaxis().GetTitle()+";Events / ( "+str((variable[var]['max']-variable[var]['min'])/variable[var]['nbins'])+unit+" )")
else:
bkg = THStack("Bkg", ";"+hist['BkgSum'].GetXaxis().GetTitle()+";Events; " )
for i, s in enumerate(back): bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum']+back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9-leg.GetNRows()*0.05)
# --- Display ---
c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
outfile = TFile("Tyler_hist_"+channel+"_"+str(year)+".root", "RECREATE")
hist['data_obs'].Write()
outfile.Close()
if 'sync' in hist: hist['sync'].Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
# if sample[s]['plot']:
hist[s].Scale(smagn)
hist[s].Draw("SAME, HIST") # signals Normalized, hist[s].Integral()*sample[s]['weight']
textS = drawText(0.80, 0.9-leg.GetNRows()*0.05 - 0.02, stype+" (x%d)" % smagn, True)
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.GetYaxis().SetTitleOffset(0.9)
#bkg.GetYaxis().SetTitleOffset(2.)
bkg.SetMaximum((5. if log else 1.25)*max(bkg.GetMaximum(), hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin())+hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(max(min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin()), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
#bkg.GetYaxis().SetMoreLogLabels(True)
bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
#if log: bkg.SetMinimum(1)
leg.Draw()
#drawCMS(LUMI[year], "Preliminary")
#drawCMS(LUMI[year], "Work in Progress", suppressCMS=True)
drawCMS(LUMI[year], "", suppressCMS=True)
drawRegion('XVH'+channel, True)
drawAnalysis(channel)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
if SYNC:
err.GetYaxis().SetTitle("Nano/Mini")
else:
err.GetYaxis().SetTitle("Data / MC")
err.GetYaxis().SetTitleOffset(0.9)
err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
for i in range(1, err.GetNbinsX()+1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(i, hist['BkgSum'].GetBinError(i)/hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX()+1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(i, res.GetBinContent(i)/hist['BkgSum'].GetBinContent(i))
res.SetBinError(i, res.GetBinError(i)/hist['BkgSum'].GetBinContent(i))
if 'sync' in hist:
res.SetMarkerColor(1)
res.SetMarkerStyle(20)
res.Reset()
for i in range(0, res.GetNbinsX()+1):
x = hist['data_obs'].GetXaxis().GetBinCenter(i)
if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0:
res.SetBinContent(i, hist['data_obs'].GetBinContent(hist['data_obs'].FindBin(x))/hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
res.SetBinError(i, hist['data_obs'].GetBinError(hist['data_obs'].FindBin(x))/hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
if SYNC: err.GetYaxis().SetRangeUser(0.9,1.1)
c1.Update()
if gROOT.IsBatch():
if channel=="": channel="nocut"
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/"+channel): os.makedirs("plots/"+channel)
suffix = ''
if "b" in channel or 'mu' in channel: suffix+="_"+BTAGGING
if ADDSELECTION: suffix+="_"+options.selection
#c1.Print("plots/"+channel+"/"+varname+"_"+year+suffix+".png")
#c1.Print("plots/"+channel+"/"+varname+"_"+year+suffix+".pdf")
# Print table
printTable(hist, sign)
# if True:
# sFile = TFile("sync/data_2016.root", "RECREATE")
# sFile.cd()
# hist['data_obs'].
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def main():
try:
# retrive command line options
shortopts = "m:i:n:d:k:l:t:o:vh?"
longopts = [
"methods=", "inputfile=", "nTrees=", "maxDepth=", "mass=",
"varListKey=", "inputtrees=", "outputfile=", "verbose", "help",
"usage"
]
opts, args = getopt.getopt(sys.argv[1:], shortopts, longopts)
except getopt.GetoptError:
# print help information and exit:
print "ERROR: unknown options in argument %s" % sys.argv[1:]
usage()
sys.exit(1)
infname = DEFAULT_INFNAME
treeNameSig = DEFAULT_TREESIG
treeNameBkg = DEFAULT_TREEBKG
outfname = DEFAULT_OUTFNAME
methods = DEFAULT_METHODS
nTrees = DEFAULT_NTREES
mDepth = DEFAULT_MDEPTH
varListKey = DEFAULT_VARLISTKEY
verbose = True
for o, a in opts:
if o in ("-?", "-h", "--help", "--usage"):
usage()
sys.exit(0)
elif o in ("-m", "--methods"):
methods = a
elif o in ("-d", "--maxDepth"):
mDepth = a
elif o in ("-l", "--varListKey"):
varListKey = a
elif o in ("-i", "--inputfile"):
infname = a
elif o in ("-n", "--nTrees"):
nTrees = a
elif o in ("-o", "--outputfile"):
outfname = a
elif o in ("-t", "--inputtrees"):
a.strip()
trees = a.rsplit(' ')
trees.sort()
trees.reverse()
if len(trees) - trees.count('') != 2:
print "ERROR: need to give two trees (each one for signal and background)"
print trees
sys.exit(1)
treeNameSig = trees[0]
treeNameBkg = trees[1]
elif o in ("-v", "--verbose"):
verbose = True
varList = varsList.varList[varListKey]
nVars = str(len(varList)) + 'vars'
Note = methods + '_' + varListKey + '_' + nVars + '_mDepth' + mDepth
outfname = "dataset/weights/TMVA_" + Note + ".root"
# Print methods
mlist = methods.replace(' ', ',').split(',')
print "=== TMVAClassification: use method(s)..."
for m in mlist:
if m.strip() != '':
print "=== - <%s>" % m.strip()
# Import ROOT classes
from ROOT import gSystem, gROOT, gApplication, TFile, TTree, TCut
# check ROOT version, give alarm if 5.18
if gROOT.GetVersionCode() >= 332288 and gROOT.GetVersionCode() < 332544:
print "*** You are running ROOT version 5.18, which has problems in PyROOT such that TMVA"
print "*** does not run properly (function calls with enums in the argument are ignored)."
print "*** Solution: either use CINT or a C++ compiled version (see TMVA/macros or TMVA/examples),"
print "*** or use another ROOT version (e.g., ROOT 5.19)."
sys.exit(1)
# Import TMVA classes from ROOT
from ROOT import TMVA
# Output file
outputFile = TFile(outfname, 'RECREATE')
# Create instance of TMVA factory (see TMVA/macros/TMVAClassification.C for more factory options)
# All TMVA output can be suppressed by removing the "!" (not) in
# front of the "Silent" argument in the option string
factory = TMVA.Factory(
"TMVAClassification", outputFile,
"!V:!Silent:Color:DrawProgressBar:Transformations=I;:AnalysisType=Classification"
)
loader = TMVA.DataLoader("dataset")
# Set verbosity
# factory.SetVerbose( verbose )
# If you wish to modify default settings
# (please check "src/Config.h" to see all available global options)
# gConfig().GetVariablePlotting()).fTimesRMS = 8.0
(TMVA.gConfig().GetIONames()).fWeightFileDir = "weights/" + Note
# Define the input variables that shall be used for the classifier training
# note that you may also use variable expressions, such as: "3*var1/var2*abs(var3)"
# [all types of expressions that can also be parsed by TTree::Draw( "expression" )]
for iVar in varList:
if iVar[0] == 'NJets_JetSubCalc':
loader.AddVariable(iVar[0], iVar[1], iVar[2], 'I')
else:
loader.AddVariable(iVar[0], iVar[1], iVar[2], 'F')
# You can add so-called "Spectator variables", which are not used in the MVA training,
# but will appear in the final "TestTree" produced by TMVA. This TestTree will contain the
# input variables, the response values of all trained MVAs, and the spectator variables
inputDir = varsList.inputDir
infname = "TTTT_TuneCP5_13TeV-amcatnlo-pythia8_hadd.root"
iFileSig = TFile.Open(inputDir + infname)
sigChain = iFileSig.Get("ljmet")
loader.AddSignalTree(sigChain)
bkg_list = []
bkg_trees_list = []
hist_list = []
weightsList = []
bkgList = varsList.bkg
for i in range(len(bkgList)):
bkg_list.append(TFile.Open(inputDir + bkgList[i]))
print inputDir + bkgList[i]
bkg_trees_list.append(bkg_list[i].Get("ljmet"))
bkg_trees_list[i].GetEntry(0)
if bkg_trees_list[i].GetEntries() == 0:
continue
loader.AddBackgroundTree(bkg_trees_list[i], 1)
signalWeight = 1 #0.0159/sigChain.GetEntries() #xs (pb)
# ====== register trees ====================================================
# To give different trees for training and testing, do as follows:
# loader.AddSignalTree( signalTrainingTree, signalTrainWeight, "Training" )
# loader.AddSignalTree( signalTestTree, signalTestWeight, "Test" )
# Use the following code instead of the above two or four lines to add signal and background
# training and test events "by hand"
# NOTE that in this case one should not give expressions (such as "var1+var2") in the input
# variable definition, but simply compute the expression before adding the event
#
# # --- begin ----------------------------------------------------------
#
# ... *** please lookup code in TMVA/macros/TMVAClassification.C ***
#
# # --- end ------------------------------------------------------------
#
# ====== end of register trees ==============================================
# Set individual event weights (the variables must exist in the original TTree)
# for signal : loader.SetSignalWeightExpression ("weight1*weight2");
# for background: loader.SetBackgroundWeightExpression("weight1*weight2");
loader.SetSignalWeightExpression(weightStrS)
loader.SetBackgroundWeightExpression(weightStrB)
# Apply additional cuts on the signal and background sample.
# example for cut: mycut = TCut( "abs(var1)<0.5 && abs(var2-0.5)<1" )
mycutSig = TCut(cutStrS)
mycutBkg = TCut(cutStrB)
# Here, the relevant variables are copied over in new, slim trees that are
# used for TMVA training and testing
# "SplitMode=Random" means that the input events are randomly shuffled before
# splitting them into training and test samples
loader.PrepareTrainingAndTestTree(
mycutSig, mycutBkg,
"nTrain_Signal=0:nTrain_Background=0:SplitMode=Random:NormMode=NumEvents:!V"
)
# --------------------------------------------------------------------------------------------------
# ---- Book MVA methods
#
# please lookup the various method configuration options in the corresponding cxx files, eg:
# src/MethoCuts.cxx, etc, or here: http://tmva.sourceforge.net/optionRef.html
# it is possible to preset ranges in the option string in which the cut optimisation should be done:
# "...:CutRangeMin[2]=-1:CutRangeMax[2]=1"...", where [2] is the third input variable
# Cut optimisation
# bdtSetting for "BDT"
bdtSetting = '!H:!V:NTrees=%s:MaxDepth=%s' % (nTrees, mDepth)
bdtSetting += ':MinNodeSize=2.5%:BoostType=AdaBoost:AdaBoostBeta=0.5:UseBaggedBoost:BaggedSampleFraction=0.5:SeparationType=GiniIndex:nCuts=20'
bdtSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTMitFisher"
bdtFSetting = '!H:!V:NTrees=%s' % nTrees
bdtFSetting += ':MinNodeSize=2.5%:UseFisherCuts:MaxDepth=3:BoostType=AdaBoost:AdaBoostBeta=0.5:SeparationType=GiniIndex:nCuts=20'
bdtFSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTG"
bdtGSetting = '!H:!V:NTrees=%s:MaxDepth=%s' % (nTrees, mDepth)
bdtGSetting += ':MinNodeSize=2.5%:BoostType=Grad:Shrinkage=0.10:UseBaggedBoost:BaggedSampleFraction=0.5:nCuts=20'
bdtGSetting += ':Pray' #Pray takes into account the effect of negative bins in BDTG
#bdtGSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTB"
bdtBSetting = '!H:!V:NTrees=%s' % nTrees
bdtBSetting += ':MinNodeSize=2.5%:BoostType=Bagging:SeparationType=GiniIndex:nCuts=20'
bdtBSetting += ':IgnoreNegWeightsInTraining=True'
# bdtSetting for "BDTD"
bdtDSetting = '!H:!V:NTrees=%s' % nTrees
bdtDSetting += ':MinNodeSize=2.5%:MaxDepth=3:BoostType=AdaBoost:SeparationType=GiniIndex:nCuts=20:VarTransform=Decorrelate'
bdtDSetting += ':IgnoreNegWeightsInTraining=True'
#Note also that explicitly setting *nEventsMin* so far OVERWRITES the option recomeded ^[[0m
#BOOKING AN ALGORITHM
# if methods=="BDT": factory.BookMethod( TMVA.Types.kBDT, "BDT",bdtSetting)
if methods == "BDT":
factory.BookMethod(loader, TMVA.Types.kBDT, "BDT", bdtSetting)
if methods == "BDTG":
factory.BookMethod(TMVA.Types.kBDT, "BDTG", bdtGSetting)
if methods == "BDTMitFisher":
factory.BookMethod(TMVA.Types.kBDT, "BDTMitFisher", bdtFSetting)
if methods == "BDTB":
factory.BookMethod(TMVA.Types.kBDT, "BDTB", bdtBSetting)
if methods == "BDTD":
factory.BookMethod(TMVA.Types.kBDT, "BDTD", bdtDSetting)
# --------------------------------------------------------------------------------------------------
# ---- Now you can tell the loader to train, test, and evaluate the MVAs.
# Train MVAs
print "train all method"
factory.TrainAllMethods()
print "test all method"
# Test MVAs
factory.TestAllMethods()
# Evaluate MVAs
factory.EvaluateAllMethods()
# Save the output.
outputFile.Close()
# save plots:
os.chdir('dataset/weights/' + Note)
if not gROOT.IsBatch(): TMVA.TMVAGui(outfname)
print "DONE"
#!/usr/bin/env python3
import os, sys, re
from ROOT import gROOT
from rootUtil3 import useAtlasStyle, waitRootCmd, savehistory, mkupHistSimple, get_default_fig_dir
funlist = []
sDir = get_default_fig_dir()
sTag = 'test_'
sDirectly = False
if gROOT.IsBatch(): sDirectly = True
def test():
return "In test"
waitRootCmd()
funlist.append(test)
if __name__ == '__main__':
savehistory('.')
useAtlasStyle()
for fun in funlist:
print(fun())
def plot(var, cut, norm=False):
PD = getPrimaryDataset(selection[cut])
if cut in selection: plotdir = cut
PROC = data + back if not BLIND else back
print col.OKGREEN + "Primary Dataset : " + col.ENDC, PD
print col.OKGREEN + "CUT : " + col.ENDC, selection[cut]
print col.OKGREEN + "WEIGHT : " + col.ENDC, weight[cut]
if not sign:
Histlist = ProjectDraw(var, cut, LUMI, PROC, PD, NTUPLEDIR)
else:
Histlist = ProjectDraw(var, cut, LUMI, PROC + sign, PD, NTUPLEDIR)
if len(back) > 0:
#If data_obs present, dummy BkgSum == first background process
Histlist['BkgSum'] = Histlist['data_obs'].Clone(
"BkgSum") if 'data_obs' in Histlist else Histlist[back[0]].Clone(
"BkgSum")
Histlist['BkgSum'].Reset("MICES")
Histlist['BkgSum'].SetFillStyle(3003)
Histlist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
Histlist['BkgSum'].Add(Histlist[s].GetPtr())
if len(back) == 0 and len(data) == 0:
for i, s in enumerate(sign):
#Normalize signal to 1
#Histlist[s].Scale(1./Histlist[s].Integral())
Histlist[s].SetLineWidth(2)
Histlist[s].SetFillStyle(0)
if norm:
sfnorm = hist['data_obs'].Integral() / hist['BkgSum'].Integral()
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(sfnorm)
if len(data + back) > 0:
out = draw(
Histlist, data if not BLIND else [], back, sign, SIGNAL, RATIO,
POISSON, True if options.logy else (filter(
lambda x: x.name() == var.split('[')[0], variable)[0]).log())
out[0].cd(1)
drawCMS(LUMI, "Preliminary")
drawRegion(cut)
printTable(Histlist, sign)
else:
out = drawSignal(
Histlist, sign, True if options.logy else
(filter(lambda x: x.name() == var, variable)[0]).log())
out[0].cd(1)
drawCMS(LUMI, "Simulation")
drawRegion(cut)
out[0].Update()
if gROOT.IsBatch():
#pathname = PLOTDIR+"/Signal/"+cut if options.signal else PLOTDIR+"/"+plotdir
pathname = PLOTDIR + "/" + plotdir
if not os.path.exists(pathname): os.system('mkdir -p %s' % pathname)
out[0].Print(pathname + "/" + var.replace('.', '_') + ".png")
out[0].Print(pathname + "/" + var.replace('.', '_') + ".pdf")
else:
out[0].Draw()
#if options.all:
print col.WARNING + "PURGE OBJECTS IN MEMORY" + col.ENDC
for process in Histlist:
Histlist[process].Delete()
def limit2HDM():
global signals
signals = range(800, 2000 + 1, 50)
multF = HTOBB
THEORY = ['T1', 'T2']
mass, val = fillValues("./combine/AZh/AZh_M%d.txt")
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt")
Obs0sB = TGraph()
Exp0sB = TGraph()
Exp1sB = TGraphAsymmErrors()
Exp2sB = TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
Obs0sB.SetPoint(n, m, valB[m][0] * multF)
Exp0sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][2] * multF,
valB[m][4] * multF - valB[m][3] * multF)
Exp2sB.SetPoint(n, m, valB[m][3] * multF)
Exp2sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][1] * multF,
valB[m][5] * multF - valB[m][3] * multF)
col = 629
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp0s.SetLineColor(1)
# Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1
# Exp1s.SetLineColorAlpha(col, 0.4)
# Exp2s.SetFillColorAlpha(col, 0.2) #kOrange
# Exp2s.SetLineColorAlpha(col, 0.2)
Exp1s.SetFillColor(417)
Exp1s.SetLineColor(417)
Exp2s.SetFillColor(800)
Exp2s.SetLineColor(800)
colB = 922
Exp2sB.SetLineWidth(2)
Obs0sB.SetLineStyle(9)
Obs0sB.SetLineWidth(3)
Obs0sB.SetMarkerStyle(0)
Obs0sB.SetLineColor(colB)
Exp0sB.SetLineStyle(8)
Exp0sB.SetLineWidth(3)
Exp0sB.SetLineColor(colB)
Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1
Exp1sB.SetLineColorAlpha(colB, 0.4)
Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange
Exp2sB.SetLineColorAlpha(colB, 0.2)
Exp2s.GetXaxis().SetTitle("m_{A} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)"
)
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Theory = {}
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# for m in sorted(THDM[t]['ggA'].keys()):
# if m < mass[0] or m > mass[-1]: continue
# Xs, Xs_Up, Xs_Down = 0., 0., 0.
# Xs = THDM[t]['ggA'][m]
# Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2))
# Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2))
# n = Theory[t].GetN()
# Theory[t].SetPoint(n, m, Xs)
# Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs))
# Theory[t].SetLineColor(theoryLineColor[t])
# Theory[t].SetFillColor(theoryFillColor[t])
# Theory[t].SetFillStyle(theoryFillStyle[t])
# Theory[t].SetLineWidth(2)
# #Theory[t].SetLineStyle(7)
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
# Exp2sB.Draw("SAME, 3")
# Exp1sB.Draw("SAME, 3")
Exp0sB.Draw("SAME, L")
if not options.blind:
Obs0s.Draw("SAME, L")
Obs0sB.Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
#setHistStyle(Exp2s)
# Exp2s.GetXaxis().SetTitleSize(0.045)
# Exp2s.GetYaxis().SetTitleSize(0.04)
# Exp2s.GetXaxis().SetLabelSize(0.04)
# Exp2s.GetYaxis().SetLabelSize(0.04)
# Exp2s.GetXaxis().SetTitleOffset(1)
# Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis('AZh')
drawRegion('AZHsl', True)
#drawCMS(LUMI, "") #Preliminary
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
leg = TLegend(0.6, 0.90, 0.99, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh",
"") #"95% CL upper limits"
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
leg.AddEntry(None, "", "")
leg.AddEntry(None, "bbA #rightarrow Zh", "")
leg.AddEntry(Obs0sB, "Observed", "l")
leg.AddEntry(Exp0sB, "Expected", "l")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045)
leg.Draw()
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.040)
# latex.SetTextFont(42)
# latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1")
# legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4)
legB = TLegend(0.15, 0.27, 0.68, 0.27)
legB.SetBorderSize(0)
legB.SetFillStyle(0) #1001
legB.SetFillColor(0)
for t in THEORY:
legB.AddEntry(Theory[t], theoryLabel[t], "fl")
legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "")
legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045)
legB.Draw()
c1.GetPad(0).RedrawAxis()
leg.Draw()
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsLimit/Exclusion/THDM.png")
c1.Print("plotsLimit/Exclusion/THDM.pdf")
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['bstar']
if INCLUDEACC: THEORY.append('SSM')
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if INCLUDEACC: suffix += "_acc"
if SY:
filename = "./combine/limits/bstar/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/bstar/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
if SY:
filename = filename.replace(
BTAGGING + "/combined_run2/",
BTAGGING + "/single_category/combined_run2/" + CATEGORY + "_")
else:
filename = filename.replace(
BTAGGING + "/",
BTAGGING + "/single_category/" + CATEGORY + "_")
suffix += "_" + CATEGORY
if ISMC: filename = filename.replace(".txt", "_MC.txt")
mass, val = fillValues(filename)
#print "mass =",mass
#print "val =", val
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
if INCLUDEACC:
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF * acc_factor)
Exp0s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp1s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp1s.SetPointError(n, 0., 0.,
(val[m][3] - val[m][2]) * multF * acc_factor,
(val[m][4] - val[m][3]) * multF * acc_factor)
Exp2s.SetPoint(n, m, val[m][3] * multF * acc_factor)
Exp2s.SetPointError(n, 0., 0.,
(val[m][3] - val[m][1]) * multF * acc_factor,
(val[m][5] - val[m][3]) * multF * acc_factor)
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
if 'bstar' == t:
x = []
y = []
with open('bstar_deta1p1_lhc13TeV2.txt') as fin:
for line in fin.readlines():
x.append(float(line.split()[0]))
y.append(float(line.split()[1]) * 1000)
for ind in range(len(x)):
Theory[t].SetPoint(ind + 1, x[ind], y[ind])
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
continue
Xs_dict = HVT[t]['Z']['XS'] if t != 'SSM' else SSM['Z']
for m in sorted(Xs_dict.keys()):
if INCLUDEACC and t != 'SSM':
acc_factor = ACCEPTANCE[m]
else:
acc_factor = 1.
if m < SIGNALS[0] or m > SIGNALS[-1]: continue
#if m < mass[0] or m > mass[-1]: continue
#if t!= 'SSM' and m>4500: continue ## I don't have the higher mass xs
if m > 4500: continue
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if t != 'SSM':
XsZ = 1000. * HVT[t]['Z']['XS'][m] * SSM["BrZ"][
m] #assuming the same BR as the SSM Z' one
XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1.,
HVT[t]['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT[t]['Z']['QCD'][m][0],
1. - HVT[t]['Z']['PDF'][m][0]))
else:
XsZ = 1000. * SSM['Z'][m] * SSM["BrZ"][m]
XsZ_Up = XsZ * (1. +
math.hypot(HVT['A1']['Z']['QCD'][m][0] - 1.,
HVT['A1']['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT['A1']['Z']['QCD'][m][0],
1. - HVT['A1']['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ * acc_factor)
Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down) * acc_factor,
(XsZ_Up - XsZ) * acc_factor)
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(" + particleP + ") #bf{#it{#Beta}}(" + particleP +
" #rightarrow " + particle +
"){} (fb)".format(" #times #Alpha" if INCLUDEACC else ""))
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
if INCLUDEACC:
Exp2s.GetYaxis().SetRangeUser(0.05, 5.e3)
else:
Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
#Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#drawAnalysis(channel)
drawAnalysis("")
#drawRegion(channel, True)
drawRegion("", True)
#drawCMS(LUMI, "Simulation Preliminary") #Preliminary
if CATEGORY == "":
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "", suppressCMS=True)
else:
#drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True)
drawCMS(LUMI, CAT_LABELS[CATEGORY], suppressCMS=True)
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
#leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
leg2.Draw()
if not options.blind: Obs0s.Draw("SAME, L")
c1.GetPad(0).Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".png")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".C")
c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix +
".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m,
print ""
return ##FIXME
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".png")
c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".root")
# c2.Print("plotsLimit/Significance/"+YEAR+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [
1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303,
0.000000001973
]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci) - 1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2,
pVal.GetXaxis().GetXmax(), ci[i] / 2)
text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2,
"%d #sigma" % i)
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".png")
c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".root")
# c3.Print("plotsLimit/pValue/"+YEAR+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
#drawCMS(LUMI, "Preliminary")
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel[1:3])
c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".png")
c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'ah' in channel:
outFile = TFile("bands.root", "RECREATE")
outFile.cd()
pVal.Write("graph")
Best.Write("best")
outFile.Close()
def plot(var, cut, year, norm=False, nm1=False):
from root_numpy import root2array, fill_hist, array2root
import numpy.lib.recfunctions as rfn
### Preliminary Operations ###
treeRead = not cut in [
"nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em",
"emqq", "qqqq"
] # Read from tree
channel = cut
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel
showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5
stype = "HVT model B"
if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM"
elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV"
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if BTAGGING == 'semimedium':
if k in cut:
cut = cut.replace(k, aliasSM[k])
else:
if k in cut:
cut = cut.replace(
k, alias[k].format(WP=working_points[BTAGGING]))
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " cut :", cut
if var == 'jj_deltaEta_widejet':
if "jj_deltaEta_widejet<1.1 && " in cut:
print
print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution"
print
cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "")
else:
print
print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly"
print
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(back + sign):
if True: #FIXME
if variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " );" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events" +
('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s:
if year == "run2" or year in ss:
arr = root2array(
NTUPLEDIR + ss + ".root",
branches=[
var, "jpt_1", "jpt_2", "eventWeightLumi",
"TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)==5",
"TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)!=5",
"TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)==5",
"TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)!=5"
],
selection=cut if len(cut) > 0 else "")
print "imported " + NTUPLEDIR + ss + ".root"
arr.dtype.names = [
var, "jpt_1", "jpt_2", "eventWeightLumi", "bb",
"bq", "qb", "qq"
]
MANtag_eff1 = np.array(map(MANtag_eff, arr["jpt_1"]))
MANtag_eff2 = np.array(map(MANtag_eff, arr["jpt_2"]))
MANtag_mis1 = np.array(map(MANtag_mis, arr["jpt_1"]))
MANtag_mis2 = np.array(map(MANtag_mis, arr["jpt_2"]))
MANtag_weight = np.multiply(
arr["eventWeightLumi"],
np.multiply(arr['bb'],
np.multiply(MANtag_eff1, MANtag_eff2))
+ np.multiply(
arr['bq'], np.multiply(MANtag_eff1,
MANtag_mis2)) +
np.multiply(arr['qb'],
np.multiply(MANtag_mis1,
MANtag_eff2)) +
np.multiply(arr['qq'],
np.multiply(MANtag_mis1, MANtag_mis2)))
fill_hist(hist[s], arr[var], weights=MANtag_weight)
deepCSV_eff1 = np.array(map(deepCSV_eff, arr["jpt_1"]))
deepCSV_eff2 = np.array(map(deepCSV_eff, arr["jpt_2"]))
deepCSV_mis1 = np.array(map(deepCSV_mis, arr["jpt_1"]))
deepCSV_mis2 = np.array(map(deepCSV_mis, arr["jpt_2"]))
deepCSV_weight = np.multiply(
arr["eventWeightLumi"],
np.multiply(
arr['bb'],
np.multiply(deepCSV_eff1, deepCSV_eff2)) +
np.multiply(
arr['bq'],
np.multiply(deepCSV_eff1, deepCSV_mis2)) +
np.multiply(
arr['qb'],
np.multiply(deepCSV_mis1, deepCSV_eff2)) +
np.multiply(
arr['qq'],
np.multiply(deepCSV_mis1, deepCSV_mis2)))
if var == "jj_mass_widejet" and options.save and not "data" in ss:
arr = rfn.append_fields(arr,
"MANtag_weight",
MANtag_weight,
usemask=False)
arr = rfn.append_fields(arr,
"deepCSV_weight",
deepCSV_weight,
usemask=False)
array2root(arr,
NTUPLEDIR + "MANtag/" + ss + "_" +
BTAGGING + ".root",
treename="tree",
mode='recreate')
print "saved as", NTUPLEDIR + "MANtag/" + ss + "_" + BTAGGING + ".root"
arr = None
#tree[s] = TChain("tree")
#for j, ss in enumerate(sample[s]['files']):
# if not 'data' in s or ('data' in s and ss in pd):
# if year=="run2" or year in ss:
# tree[s].Add(NTUPLEDIR + ss + ".root")
#if variable[var]['nbins']>0: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events / ( "+str((variable[var]['max']-variable[var]['min'])/variable[var]['nbins'])+unit+" );"+('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max'])
#else: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events"+('log' if variable[var]['log'] else ''), len(variable[var]['bins'])-1, array('f', variable[var]['bins']))
#hist[s].Sumw2()
#cutstring = "(eventWeightLumi)" + ("*("+cut+")" if len(cut)>0 else "")
#tree[s].Project(s, var, cutstring)
#if not tree[s].GetTree()==None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
#else: # Histogram written to file
# for j, ss in enumerate(sample[s]['files']):
# if not 'data' in s or ('data' in s and ss in pd):
# file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
# if file[ss].IsZombie():
# print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
# continue
# tmphist = file[ss].Get(cut+"/"+var)
# if tmphist==None: continue
# if not s in hist.keys(): hist[s] = tmphist
# else: hist[s].Add(tmphist)
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
if channel.endswith('TR') and channel.replace('TR', '') in topSF:
hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0])
hist['ST'].Scale(topSF[channel.replace('TR', '')][0])
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
#if options.norm:
# for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(hist[data[0]].Integral()/hist['BkgSum'].Integral())
# Create data and Bkg sum histograms
if options.blind or 'SR' in channel:
hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
for i, s in enumerate(back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
if isAH:
for i, s in enumerate(back):
hist[s].SetFillStyle(3005)
hist[s].SetLineWidth(2)
#for i, s in enumerate(sign):
# hist[s].SetFillStyle(0)
if not var == "Events":
sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral()
print "Applying SF:", sfnorm
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(sfnorm)
if BLIND and var.endswith("Mass"):
for i, s in enumerate(data + back + ['BkgSum']):
first, last = hist[s].FindBin(65), hist[s].FindBin(135)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
if BLIND and var.endswith("Tau21"):
for i, s in enumerate(data):
first, last = hist[s].FindBin(0), hist[s].FindBin(0.6)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
# Create stack
if variable[var]['nbins'] > 0:
bkg = THStack(
"Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " )")
else:
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
if 'sync' in hist: hist['sync'].Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
# if sample[s]['plot']:
hist[s].Scale(smagn)
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02,
stype + " (x%d)" % smagn, True)
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.GetYaxis().SetTitleOffset(0.9)
#bkg.GetYaxis().SetTitleOffset(2.)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
#bkg.GetYaxis().SetMoreLogLabels(True)
bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
#if log: bkg.SetMinimum(1)
leg.Draw()
#drawCMS(LUMI[year], "Preliminary")
drawCMS(LUMI[year], "Work in Progress", suppressCMS=True)
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / MC")
err.GetYaxis().SetTitleOffset(0.9)
err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if 'sync' in hist:
res.SetMarkerColor(2)
res.SetMarkerStyle(31)
res.Reset()
for i in range(0, res.GetNbinsX() + 1):
x = hist['data_obs'].GetXaxis().GetBinCenter(i)
if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0:
res.SetBinContent(
i, hist['data_obs'].GetBinContent(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
res.SetBinError(
i, hist['data_obs'].GetBinError(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if gROOT.IsBatch():
if channel == "": channel = "nocut"
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
suffix = ''
if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING
c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year +
suffix + ".png")
c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year +
suffix + ".pdf")
# Print table
printTable(hist, sign)
# if True:
# sFile = TFile("sync/data_2016.root", "RECREATE")
# sFile.cd()
# hist['data_obs'].
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")