def writeFullUnc(pred_file):
''' Update the input root file, add a hist with total prediction and full uncertainty. '''
f = rt.TFile(pred_file, 'UPDATE')
h = TGraphAsymmErrors(f.Get(pred_total_name).Clone('bkgtotal_unc_sr'))
h_syst = TGraphAsymmErrors(
f.Get(pred_total_name).Clone('bkgtotal_syst_unc_sr'))
h_pieces = {}
h_syst_pieces = {}
for hname, sample in zip(graph_names, all_samples):
h_pieces[sample] = TGraphAsymmErrors(
f.Get(hname).Clone(sample + '_unc_sr'))
h_syst_pieces[sample + "_up"] = TH1F(sample + '_syst_up',
sample + '_syst_up', 183, 0, 183)
h_syst_pieces[sample + "_dn"] = TH1F(sample + '_syst_dn',
sample + '_syst_dn', 183, 0, 183)
print "%30s %10s %16s" % ('bin', 'total pred', 'total unc.')
for ibin in xrange(0, h.GetN()):
bin = binlist[ibin]
val = h.GetY()[ibin]
e_low, e_up = fullUnc[bin]
h.SetPointEYlow(ibin, e_low)
h.SetPointEYhigh(ibin, e_up)
print "%30s %10.4f +%8.4f -%8.4f" % (bin, val, e_up, e_low)
allVals[bin] = {'bkg': (val, e_low, e_up)}
# Test for only syst histograms
val = h_syst.GetY()[ibin]
e_low, e_up = systUnc[bin]
h_syst.SetPointEYlow(ibin, e_low)
h_syst.SetPointEYhigh(ibin, e_up)
for sample in all_samples:
val = yields[bin][sample]
e_low, e_up = fullUnc_pieces[sample][bin]
#print "%11s %30s %10.4f +%8.4f -%8.4f" % (sample, bin, val, e_up, e_low)
h_pieces[sample].SetPointEYlow(ibin, e_low)
h_pieces[sample].SetPointEYhigh(ibin, e_up)
allVals[bin][sample] = (val, e_low, e_up)
# Test for only syst histograms
e_low, e_up = systUnc_rel_pieces[sample][bin]
#if sample in test_samp and bin in test_bin and type in test_type and debug:
#if 'TTZ' in sample and e_up > 8:
# print("%11s %30s %10.4f +%8.4f -%8.4f" % (sample, bin, val, e_up, e_low))
h_syst_pieces[sample + "_up"].SetBinContent(ibin + 1, e_up)
h_syst_pieces[sample + "_dn"].SetBinContent(ibin + 1, e_low)
h_syst_pieces[sample + "_up"].SetBinError(ibin + 1, 0)
h_syst_pieces[sample + "_dn"].SetBinError(ibin + 1, 0)
h.Write('bkgtotal_unc_sr', rt.TObject.kOverwrite)
h.Write('bkgtotal_syst_unc_sr', rt.TObject.kOverwrite)
for sample in all_samples:
h_pieces[sample].Write(sample + '_unc_sr', rt.TObject.kOverwrite)
h_syst_pieces[sample + "_up"].Write(sample + '_syst_up',
rt.TObject.kOverwrite)
h_syst_pieces[sample + "_dn"].Write(sample + '_syst_dn',
rt.TObject.kOverwrite)
f.Close()
def TextFileToRootGraphs(self, limit_text_file,med_idx=0):#, limit_text_filename):
filename = limit_text_file #limit_text_filename
limit_root_file = limit_text_file.replace(".txt",".root")
f = open(filename,"r")
med=array('f')
mchi=array('f')
expm2=array('f')
expm1=array('f')
expmed=array('f')
expp1=array('f')
expp2=array('f')
obs=array('f')
errx=array('f')
for line in f:
if len(line.rsplit())<7: continue
med.append(float(line.rstrip().split()[1]))
mchi.append(float(line.rstrip().split()[0]))
expm2.append(float(line.rstrip().split()[4]) - float(line.rstrip().split()[2]) )
expm1.append(float(line.rstrip().split()[4]) - float(line.rstrip().split()[3]) )
expmed.append(float(line.rstrip().split()[4]))
expp1.append(float(line.rstrip().split()[5]) - float(line.rstrip().split()[4]) )
expp2.append(float(line.rstrip().split()[6]) - float(line.rstrip().split()[4]) )
obs.append(float(line.rstrip().split()[7]))
errx.append(0.0)
print ('expm2: ', expm2)
print ('expm1: ', expm1)
print ('expmed: ', expmed)
print ('expp1: ', expp1)
print ('expp2: ', expp2)
g_exp2 = TGraphAsymmErrors(int(len(med)), med, expmed, errx, errx, expm2, expp2 ) ; g_exp2.SetName("exp2")
g_exp1 = TGraphAsymmErrors(int(len(med)), med, expmed, errx, errx, expm1, expp1 ) ; g_exp1.SetName("exp1")
g_expmed = TGraphAsymmErrors(int(len(med)), med, expmed) ; g_expmed.SetName("expmed")
g_obs = TGraphAsymmErrors(int(len(med)), med, obs ) ; g_obs.SetName("obs")
f1 = TFile(limit_root_file,'RECREATE')
g_exp2.Write()
g_exp1.Write()
g_expmed.Write()
g_obs.Write()
f1.Write()
f1.Close()
return limit_root_file
def writeFullUnc(pred_file):
''' Update the input root file, add a hist with total prediction and full uncertainty. '''
f = rt.TFile(pred_file, 'UPDATE')
h = TGraphAsymmErrors(f.Get(pred_total_name).Clone('bkgtotal_unc_sr'))
h_pieces = {}
for hname, sample in zip(graph_names, all_samples):
h_pieces[sample] = TGraphAsymmErrors(
f.Get(hname).Clone(sample + '_unc_sr'))
print "%30s %10s %16s" % ('bin', 'total pred', 'total unc.')
for ibin in xrange(0, h.GetN()):
bin = binlist[ibin]
val = h.GetY()[ibin]
e_low, e_up = fullUnc[bin]
h.SetPointEYlow(ibin, e_low)
h.SetPointEYhigh(ibin, e_up)
print "%30s %10.2f +%8.2f -%8.2f" % (bin, val, e_up, e_low)
allVals[bin] = {'bkg': (val, e_low, e_up)}
for sample in all_samples:
val = yields[bin][sample]
e_low, e_up = fullUnc_pieces[sample][bin]
h_pieces[sample].SetPointEYlow(ibin, e_low)
h_pieces[sample].SetPointEYhigh(ibin, e_up)
allVals[bin][sample] = (val, e_low, e_up)
h.Write('bkgtotal_unc_sr', rt.TObject.kOverwrite)
for sample in all_samples:
h_pieces[sample].Write(sample + '_unc_sr', rt.TObject.kOverwrite)
f.Close()
def createAssymSFFile(filename, sftable, name):
"""Create histogram from table."""
print(">>> Creating '%s'..." % filename)
file = TFile(filename, 'RECREATE')
file.cd()
x = [float(i) + 0.5 for i in range(len(sftable.keys()))]
ex = [0.0] * len(sftable.keys())
x_names = sorted(sftable.keys())
y = [sftable[k]['val'] for k in x_names]
eyl = [sftable[k]['down'] for k in x_names]
eyh = [sftable[k]['up'] for k in x_names]
g = TGraphAsymmErrors(len(x), np.array(x), np.array(y), np.array(ex),
np.array(ex), np.array(eyl), np.array(eyh))
g.GetXaxis().SetNdivisions(len(x) * 2)
g.GetXaxis().ChangeLabel(0, -1, 0, -1, -1, -1, "")
for i in x:
print(1 + int(i), x_names[int(i)])
g.GetXaxis().ChangeLabel(2 + int(i) * 2, -1, 0)
g.GetXaxis().ChangeLabel(1 + int(i) * 2, -1, -1, -1, -1, -1,
x_names[int(i)])
g.Write(name)
# file.ls() ; g.Draw("A*") ; raw_input()
file.Close()
return file
def getEff(name, den_input, num_input, rebin=0, xtitle='', ytitle=''):
c = TCanvas(name + '_Canvas')
legend = TLegend(0.8, 0.1, 0.999, 0.6)
legend.SetFillColor(kWhite)
den = den_input.Clone()
num = num_input.Clone()
if rebin != 0:
den.Rebin(rebin)
num.Rebin(rebin)
error_bars = TGraphAsymmErrors()
error_bars.Divide(num, den, "cl=0.683 b(1,1) mode")
error_bars.SetLineWidth(3)
if xtitle == '':
error_bars.GetXaxis().SetTitle(num.GetXaxis().GetTitle())
else:
error_bars.GetXaxis().SetTitle(xtitle)
if ytitle == '':
error_bars.GetYaxis().SetTitle(num.GetYaxis().GetTitle())
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.GetXaxis().SetRangeUser(400, 2000)
error_bars.SetLineColor(kBlack)
error_bars.SetMaximum(1.01)
error_bars.SetMinimum(0.)
if ytitle == '':
error_bars.GetYaxis().SetTitle("Trigger rate")
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.SetTitle('')
error_bars.Draw('AP')
c.SaveAs('pdf/' + name + '.pdf')
c.Write(name + '_Canvas')
error_bars.Write(name)
def doeff(filename, histoname_den, histoname_num, histoname_out, outfile,rebin=1):
numerator=filename.Get(histoname_num)
denominator=filename.Get(histoname_den)
if not rebin==1:
numerator.Rebin(rebin)
denominator.Rebin(rebin)
n_num=numerator.Integral()
n_den=denominator.Integral()
error_bars=TGraphAsymmErrors()
error_bars.Divide(numerator,denominator,"cl=0.683 b(1,1) mode")
outfile.cd()
error_bars.Write(histoname_out)
return n_num/n_den
def PseudoTextToGraphTimeScan(textfilename):
print "parsing: ", textfilename
dirname = "timescanOutput/"
filename = textfilename
f = open(filename, "r").readlines()
firstLine = f.pop(0) #removes the first line
adc_ = array('f')
adc_1 = array('f')
adc_2 = array('f')
adc_3 = array('f')
adc_4 = array('f')
prob = array('f')
prob_e = array('f')
errx = array('f')
for line in f:
adc_.append(float(line.rstrip().split()[0]))
adc_1.append(float(line.rstrip().split()[0]) + 0.1)
adc_2.append(float(line.rstrip().split()[0]) + 0.2)
adc_3.append(float(line.rstrip().split()[0]) + 0.3)
adc_4.append(float(line.rstrip().split()[0]) + 0.4)
prob.append(float(line.rstrip().split()[1]))
prob_e.append(float(line.rstrip().split()[2]))
errx.append(0.0)
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, prob_e,
prob_e)
g_prob_.SetName("prob_")
f1 = TFile(filename.replace('.txt', '.root'), 'RECREATE')
g_prob_.Write()
f1.Close()
#adc_3 = [i+0.3 for i in adc_]
#adc_4 = [i+0.4 for i in adc_]
print adc_1
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, prob_e,
prob_e)
g_prob_.SetName("prob_")
g_prob_26_ = TGraphAsymmErrors(int(len(adc_1)), adc_1, prob_26, errx, errx,
prob_e_26, prob_e_26)
g_prob_26_.SetName("prob_26_")
g_prob_27_ = TGraphAsymmErrors(int(len(adc_)), adc_2, prob_27, errx, errx,
prob_e_27, prob_e_27)
g_prob_27_.SetName("prob_27_")
g_prob_28_ = TGraphAsymmErrors(int(len(adc_)), adc_3, prob_28, errx, errx,
prob_e_28, prob_e_28)
g_prob_28_.SetName("prob_28_")
g_prob_29_ = TGraphAsymmErrors(int(len(adc_)), adc_4, prob_29, errx, errx,
prob_e_29, prob_e_29)
g_prob_29_.SetName("prob_29_")
f1 = TFile(filename.replace('.txt', '.root'), 'RECREATE')
g_prob_.Write()
g_prob_26_.Write()
g_prob_27_.Write()
g_prob_28_.Write()
g_prob_29_.Write()
f1.Close()
dirs['Andrew'] + "2017/zToMuMu")
nEst = fakeTrackBkgdEstimate.printNest()
g0.SetPoint(i, minD0, nEst.centralValue())
g0.SetPointError(i, D / 2.0, D / 2.0,
min(nEst.maxUncertainty(), nEst.centralValue()),
nEst.maxUncertainty())
if i == 0:
nominal = nEst.centralValue()
else:
if nEst.centralValue() < nominal:
maxFluctuationDown = max(maxFluctuationDown,
nominal - nEst.centralValue())
else:
maxFluctuationUp = max(maxFluctuationUp,
nEst.centralValue() - nominal)
sys.stdout = stdout
print "[2017" + runPeriod + "] systematic uncertainty: - " + str(
maxFluctuationDown) + " + " + str(maxFluctuationUp) + " (- " + str(
(maxFluctuationDown / nominal) * 100.0) + " + " + str(
(maxFluctuationUp / nominal) * 100.0) + ")%"
sys.stdout = nullout
fout = TFile("fakeTrackEstimateVsD0.root", "update")
fout.cd()
g0.Write("est_ZtoMuMu_2017" + runPeriod)
fout.Close()
def makeplot_single(
h1_sig=None,
h1_bkg=None,
h1_data=None,
sig_legends_=None,
bkg_legends_=None,
sig_colors_=None,
bkg_colors_=None,
hist_name_=None,
sig_scale_=1.0,
dir_name_="plots",
output_name_=None,
extraoptions=None
):
if h1_sig == None or h1_bkg == None:
print("nothing to plot...")
return
os.system("mkdir -p "+dir_name_)
os.system("cp index.php "+dir_name_)
s_color = [632, 617, 839, 800, 1]
b_color = [920, 2007, 2005, 2003, 2001, 2011]
if sig_colors_:
s_color = sig_colors_
if bkg_colors_:
b_color = bkg_colors_
for idx in range(len(h1_sig)):
h1_sig[idx].SetLineWidth(3)
h1_sig[idx].SetLineColor(s_color[idx])
for idx in range(len(h1_bkg)):
h1_bkg[idx].SetLineWidth(2)
h1_bkg[idx].SetLineColor(b_color[idx])
h1_bkg[idx].SetFillColorAlpha(b_color[idx], 1)
if h1_data:
h1_data.SetBinErrorOption(1)
h1_data.SetLineColor(1)
h1_data.SetLineWidth(2)
h1_data.SetMarkerColor(1)
h1_data.SetMarkerStyle(20)
myC = r.TCanvas("myC","myC", 600, 600)
myC.SetTicky(1)
pad1 = r.TPad("pad1","pad1", 0.05, 0.33,0.95, 0.97)
pad1.SetBottomMargin(0.027)
pad1.SetRightMargin( rightMargin )
pad1.SetLeftMargin( leftMargin )
pad2 = r.TPad("pad2","pad2", 0.05, 0.04, 0.95, 0.31)
pad2.SetBottomMargin(0.4)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin( rightMargin )
pad2.SetLeftMargin( leftMargin )
pad2.Draw()
pad1.Draw()
pad1.cd()
for idx in range(len(h1_sig)):
print("before signal scaling",h1_sig[idx].Integral())
h1_sig[idx].Scale(sig_scale_)
print("after signal scaling",h1_sig[idx].Integral())
stack = r.THStack("stack", "stack")
nS = np.zeros(h1_bkg[0].GetNbinsX())
eS = np.zeros(h1_bkg[0].GetNbinsX())
#hist_all is used to make the data/mc ratio. remove signal for the moment due to signal is scaled right now
hist_all = h1_sig[0].Clone("hist_all")
hist_all.Scale(0.0)
hist_s = h1_sig[0].Clone("hist_s")
hist_b = h1_bkg[0].Clone("hist_b")
for idx in range(len(h1_bkg)):
stack.Add(h1_bkg[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_bkg[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_bkg[idx].GetBinError(ib+1)*h1_bkg[idx].GetBinError(ib+1))
hist_all.Add(h1_bkg[idx])
if idx > 0:
hist_b.Add(h1_bkg[idx])
for idx in range(len(h1_sig)):
print("ggH signal yield: ", hist_s.Integral())
if idx > 0:
hist_temp = h1_sig[idx].Clone(h1_sig[idx].GetName()+"_temp")
#hist_all.Add(hist_temp)
hist_s.Add(h1_sig[idx])
print("all signal yield: ", hist_s.Integral())
stack.SetTitle("")
maxY = 0.0
if "stack_signal" in extraoptions and extraoptions["stack_signal"]:
for idx in range(len(h1_sig)):
h1_sig[idx].SetFillColorAlpha(s_color[idx], 1)
stack.Add(h1_sig[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_sig[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_sig[idx].GetBinError(ib+1)*h1_sig[idx].GetBinError(ib+1))
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
#if "SR" in h.GetTitle():
stack.Draw("hist")
else:
stack.Draw("hist")
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
for idx in range(len(h1_sig)):
if h1_sig[idx].GetMaximum() > maxY:
maxY = h1_sig[idx].GetMaximum()
if "SR" in h1_bkg[0].GetTitle():
#h1_sig[idx].Draw("samehist")
hist_s.Draw("samehist")
##draw stack total unc on top of total histogram
box = r.TBox(0,0,1,1,)
box.SetFillStyle(3002)
box.SetLineWidth(0)
box.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
box.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]-eS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]+eS[idx])
if h1_data:
if h1_data.GetMaximum() > maxY:
maxY = h1_data.GetMaximum()+np.sqrt(h1_data.GetMaximum())
#if not "SR" in h1_data.GetTitle() or "fail" in h1_data.GetTitle():
if True:
#print("debug h1_data.GetName()",h1_data.GetName(), h1_data.GetTitle())
TGraph_data = TGraphAsymmErrors(h1_data)
for i in range(TGraph_data.GetN()):
#data point
var_x, var_y = Double(0.), Double(0.)
TGraph_data.GetPoint(i,var_x,var_y)
if np.fabs(var_y) < 1e-5:
TGraph_data.SetPoint(i,var_x,-1.0)
TGraph_data.SetPointEYlow(i,-1)
TGraph_data.SetPointEYhigh(i,-1)
#print("zero bins in the data TGraph: bin",i+1)
else:
TGraph_data.SetPoint(i,var_x,var_y)
err_low = var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y))
TGraph_data.SetPointEYlow(i, var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))
TGraph_data.SetPointEYhigh(i, (0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)
TGraph_data.SetMarkerColor(1)
TGraph_data.SetMarkerSize(1)
TGraph_data.SetMarkerStyle(20)
TGraph_data.Draw("same P")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetTitleOffset(1.05)
stack.GetYaxis().SetTitleSize(0.08)
stack.GetYaxis().SetLabelSize(0.06)
#stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetLabelSize(0.)
#stack.GetXaxis().SetLabelOffset(0.013)
#if "xaxis_range" in extraoptions:
# stack.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
leg = r.TLegend(0.2, 0.60, 0.9, 0.88)
leg.SetNColumns(3)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.SetTextSize(0.05)
for idx in range(len(h1_bkg)):
leg.AddEntry(h1_bkg[idx], bkg_legends_[idx], "F")
if "SR" in hist_s.GetTitle():
leg.AddEntry(hist_s, 'HH #times {:1.2}'.format(sig_scale_), "L")
leg.AddEntry(box, "Total unc", "F")
if h1_data:
leg.AddEntry(h1_data, "Data", "ep")
leg.Draw()
pad2.cd()
pad2.SetGridy(1)
ratio = None
ratio_Low = 0.0
ratio_High = 4
if h1_data:
ratio = TGraphAsymmErrors(h1_data)
for i in range(ratio.GetN()):
#bkg prediction
imc = Double(hist_all.GetBinContent(i+1))
#data point
var_x, var_y = Double(0.), Double(0.)
if not ("SR" in h1_data.GetTitle() and (i>5 and i<9)):
ratio.GetPoint(i,var_x,var_y)
if var_y == 0.:
ratio.SetPoint(i,var_x,-1.0)
ratio.SetPointEYlow(i,-1)
ratio.SetPointEYhigh(i,-1)
continue
ratio.SetPoint(i,var_x,var_y/imc)
err_low = (var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))/imc
err_high = ((0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)/imc
ratio.SetPointEYlow(i, err_low)
ratio.SetPointEYhigh(i, err_high)
ratio.SetMarkerColor(1)
ratio.SetMarkerSize(1)
ratio.SetMarkerStyle(20)
ratio.GetXaxis().SetTitle("j_{2} regressed mass [GeV]")
#myC.Update()
if "ratio_range" in extraoptions:
ratio_Low = extraoptions["ratio_range"][0]
ratio_High = extraoptions["ratio_range"][1]
ratio.GetYaxis().SetTitle("data/mc")
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High)
ratio.GetXaxis().SetRangeUser(50, 220)
ratio.SetTitle("")
ratio.Draw("same AP")
pad2.Update()
print(ratio.GetTitle(),ratio.GetName(),"debug")
else:
ratio = h1_sig[0].Clone("ratio")
ratio_High = 0.0
for ibin in range(1,ratio.GetNbinsX()+1):
s = hist_s.GetBinContent(ibin)
b = hist_b.GetBinContent(ibin)
L = 0.0
if b > 0.0:
L = s/math.sqrt(b)
if L > ratio_High:
ratio_High = L
ratio.SetBinContent(ibin, L)
if ratio_High > 1.0:
ratio_High = 1.0
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High*1.2)
ratio.GetYaxis().SetTitle("S/#sqrt{B}")
ratio.Draw("samehist")
ratio.SetLineColor(1)
ratio.SetLineWidth(2)
ratio.SetMarkerStyle(20)
ratio.SetMarkerColor(1)
ratio.SetFillColorAlpha(1, 0)
ratio.GetXaxis().SetTitleOffset(0.94)
ratio.GetXaxis().SetTitleSize(0.18)
ratio.GetXaxis().SetLabelSize(0.12)
ratio.GetXaxis().SetLabelOffset(0.013)
ratio.GetYaxis().SetTitleOffset(0.40)
ratio.GetYaxis().SetTitleSize(0.17)
ratio.GetYaxis().SetLabelSize(0.13)
ratio.GetYaxis().SetTickLength(0.01)
ratio.GetYaxis().SetNdivisions(505)
#if "xaxis_range" in extraoptions:
# ratio.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
#draw stack total unc on the ratio plot to present the background uncertainty
box_ratio = r.TBox(0,0,1,1,)
box_ratio.SetFillStyle(3002)
box_ratio.SetLineWidth(0)
box_ratio.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
if np.fabs(nS[idx])> 1e-06:
box_ratio.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]-eS[idx])/nS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]+eS[idx])/nS[idx])
else:
print("blinded Higgs peak region")
if "xaxis_label" in extraoptions and extraoptions["xaxis_label"] != None:
x_title = extraoptions["xaxis_label"]
ratio.GetXaxis().SetTitle(x_title)
ratio.GetYaxis().CenterTitle()
##########draw CMS preliminary
pad1.cd()
tex1 = r.TLatex(leftMargin, 0.91, "CMS")
tex1.SetNDC()
tex1.SetTextFont(61)
tex1.SetTextSize(0.070)
tex1.SetLineWidth(2)
tex1.Draw()
tex2 = r.TLatex(leftMargin+0.12,0.912,"Internal")
tex2.SetNDC()
tex2.SetTextFont(52)
tex2.SetTextSize(0.055)
tex2.SetLineWidth(2)
tex2.Draw()
lumi_value = 137
if "lumi_value" in extraoptions:
lumi_value = extraoptions["lumi_value"]
tex3 = r.TLatex(0.72,0.912,"%d"%lumi_value+" fb^{-1} (13 TeV)")
tex3.SetNDC()
tex3.SetTextFont(42)
tex3.SetTextSize(0.055)
tex3.SetLineWidth(2)
tex3.Draw()
outFile = dir_name_
if output_name_:
outFile = outFile + "/" +output_name_
else:
outFile = outFile + "/" + hist_name_
#print("maxY = "+str(maxY))
stack.SetMaximum(maxY*1.7)
#print everything into txt file
text_file = open(outFile+"_linY.txt", "w")
text_file.write("bin | x ")
for idx in range(len(h1_bkg)):
text_file.write(" | %21s"%bkg_legends_[idx])
text_file.write(" | %21s"%("total B"))
for idx in range(len(sig_legends_)):
text_file.write(" | %25s"%sig_legends_[idx])
if h1_data:
text_file.write(" | data | data/mc")
text_file.write("\n-------------")
for idx in range(24*(len(h1_bkg) + 1)+ 29*len(sig_legends_)):
text_file.write("-")
if h1_data:
text_file.write("-------")
text_file.write("\n")
for ibin in range(0,h1_sig[0].GetNbinsX()+1):
text_file.write("%3d"%ibin+" ")
text_file.write(" | %6.3f"%h1_data.GetBinCenter(ibin)+" ")
for idx in range(len(h1_bkg)):
text_file.write(" | %7.3f "%h1_bkg[idx].GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%h1_bkg[idx].GetBinError(ibin))
text_file.write(" | %7.3f "%hist_b.GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%hist_b.GetBinError(ibin))
for idx in range(len(sig_legends_)):
text_file.write(" | %9.3f "%h1_sig[idx].GetBinContent(ibin)+"$\\pm$"+ " %9.3f"%h1_sig[idx].GetBinError(ibin))
if h1_data:
text_file.write(" | %d"%h1_data.GetBinContent(ibin) + " | %7.3f "%h1_data.GetBinContent(ibin) +"$\\pm$"+ " %7.3f"%h1_data.GetBinError(ibin))
text_file.write("\n\n")
#print yield table for AN
text_file.write("print yield table for AN\n")
bkg_all = 0
bkg_all_errsq = 0
for idx in range(len(h1_bkg)):
bkg_tmp = h1_bkg[idx].GetBinContent(7)+h1_bkg[idx].GetBinContent(8)+h1_bkg[idx].GetBinContent(9)
bkg_errsq_tmp = h1_bkg[idx].GetBinError(7)*h1_bkg[idx].GetBinError(7)+h1_bkg[idx].GetBinError(8)*h1_bkg[idx].GetBinError(8)+h1_bkg[idx].GetBinError(9)*h1_bkg[idx].GetBinError(9)
bkg_all += bkg_tmp
bkg_all_errsq += bkg_errsq_tmp
text_file.write("%s"%(bkg_legends_[idx])+"& %7.2f"%(bkg_tmp)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_errsq_tmp)+"\n")
text_file.write("total background & %7.2f"%(bkg_all)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_all_errsq)+"\n")
text_file.write("\ggHH SM ($\kapl=1$) & %7.2f"%((h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9)))+"\n")
text_file.write("\VBFHH SM ($\kapl=1$) & %7.2f"%((h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9)))+"\n")
text_file.write("HH bin 8 value %s"%h1_sig[0].GetBinContent(8)+"\n")
text_file.write("HH bin 9 value %s"%h1_sig[0].GetBinContent(9)+"\n")
text_file.write("HH bin 7 value %s"%h1_sig[0].GetBinContent(7)+"\n")
text_file.write("HH bin 8 error %s"%h1_sig[0].GetBinError(8)+"\n")
text_file.write("HH bin 9 error %s"%h1_sig[0].GetBinError(9)+"\n")
text_file.write("HH bin 7 error %s"%h1_sig[0].GetBinError(7)+"\n")
text_file.write("total & %7.2f"%(bkg_all+(h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9)+h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.2f"%(np.sqrt((h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9))/(sig_scale_*sig_scale_)+(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9))/(sig_scale_*sig_scale_)+bkg_all_errsq))+"\n")
text_file.close()
os.system("cp "+outFile+"_linY.txt "+outFile+"_logY.txt")
pad1.RedrawAxis()
myC.SaveAs(outFile+"_linY.png")
myC.SaveAs(outFile+"_linY.pdf")
myC.SaveAs(outFile+"_linY.C")
pad1.cd()
stack.SetMaximum(maxY*100.0)
stack.SetMinimum(0.5)
pad1.SetLogy()
pad1.RedrawAxis()
myC.SaveAs(outFile+"_logY.png")
myC.SaveAs(outFile+"_logY.pdf")
myC.SaveAs(outFile+"_logY.C")
#save histogram and ratio to root file
outFile_root = r.TFile(outFile+".root", "recreate")
outFile_root.cd()
for idx in range(len(h1_bkg)):
h1_bkg[idx].Write()
for idx in range(len(sig_legends_)):
h1_sig[idx].Write()
if h1_data:
h1_data.Write()
ratio.Write()
#outFile_root.Write()
outFile_root.Close()
grRatio.SetLineColor(1)
grRatio.SetLineWidth(1)
grRatio.GetXaxis().SetLabelSize(0.04)
grRatio.GetXaxis().SetTitleSize(0.04)
grRatio.GetXaxis().SetTitleOffset(1.25)
grRatio.GetYaxis().SetLabelSize(0.04)
grRatio.GetYaxis().SetTitleSize(0.04)
grRatio.GetYaxis().SetTitleOffset(1.5)
# grRatio.GetYaxis().SetRangeUser(ylo, yhi)
grRatio.Draw("ALP")
fOutput = TFile('durp.root', 'recreate')
grRatio.Write('durp')
canvas.Write('canv')
fOutput.Close()
chain4 = TChain('disTrkSelectionSmearedJetsNLayers4TreeMaker/Tree')
chain5 = TChain('disTrkSelectionSmearedJetsNLayers5TreeMaker/Tree')
chain6 = TChain('disTrkSelectionSmearedJetsNLayers6plusTreeMaker/Tree')
chain4.Add('condor/2017/signalAcceptance_full_v8/AMSB_chargino_700GeV_100cm_94X/hist_*.root')
chain5.Add('condor/2017/signalAcceptance_full_v8/AMSB_chargino_700GeV_100cm_94X/hist_*.root')
chain6.Add('condor/2017/signalAcceptance_full_v8/AMSB_chargino_700GeV_100cm_94X/hist_*.root')
(n4, w4) = reweightMET(chain4, grRatio)
(n5, w5) = reweightMET(chain5, grRatio)
(n6, w6) = reweightMET(chain6, grRatio)
leg.AddEntry(hCrossSec, f'measured', 'p')
leg.AddEntry(hCrossSecExtrap, f'extrapolated', 'p')
leg.AddEntry(hFONLL['cent'], f'FONLL + PYTHIA8 decayer', 'f')
cCrossSec = TCanvas('cCrossSec', '', 500, 500)
cCrossSec.DrawFrame(
ptMinMeas,
hCrossSecExtrap.GetMinimum() / 5, ptLimsForExtrap[-1],
hCrossSecExtrap.GetMaximum() * 5,
';#it{p}_{T} (GeV/#it{c});d#sigma/d#it{p}_{T} #times BR (#mub GeV^{-1} #it{c})'
)
cCrossSec.SetLogy()
for var in ['cent', 'min', 'max']:
hFONLL[var].Scale(1.e-6)
hFONLL[var].Draw('hist same')
gCrossSecExtrap.Draw('2')
hCrossSecExtrap.Draw('same')
gCrossSec.Draw('2')
hCrossSec.Draw('same')
leg.Draw()
outFile = TFile(inCfg['outfilename'], 'recreate')
hCrossSecExtrap.Write()
gCrossSecExtrap.Write()
gCrossSecExtrapSyst.Write()
gCrossSecLumi.Write()
systErr.Write()
outFile.Close()
input('Press enter to exit')
def plotEfficiency(self, doFit=False):
if hasattr(self, "Denominator") and hasattr(self, "Numerator"):
if self._rebinFactor != 1:
self.Denominator["hist"].Rebin(self._rebinFactor)
self.Numerator["hist"].Rebin(self._rebinFactor)
for ibin in range(-1, self.Numerator["hist"].GetNbinsX() + 1):
if self.Numerator["hist"].GetBinContent(
ibin +
1) > self.Denominator["hist"].GetBinContent(ibin + 1):
print 'Fixing bad bin:', (ibin + 1)
self.Numerator["hist"].SetBinContent(
ibin + 1,
self.Denominator["hist"].GetBinContent(ibin + 1))
self.Numerator["hist"].SetBinError(
ibin + 1,
self.Denominator["hist"].GetBinError(ibin + 1))
efficiencyGraph = TGraphAsymmErrors(self.Numerator["hist"],
self.Denominator["hist"], "cp")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716,
"brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
pt_cmsPrelim.AddText("CMS Preliminary")
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText(self._luminosityLabel)
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.0349127)
pt_leg.SetTextAlign(12)
if self._leg == "METLeg":
legLabel = ""
for filt in self._metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + self._metHLTFilters[-1]
pt_leg.AddText(legLabel)
pt_leg.AddText(self._datasetLabel)
if self._leg == "TrackLeg":
pt_leg.AddText(self._path + "*")
pt_leg.AddText(self._datasetLabel)
legLabel = ""
for filt in self._metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + self._metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if self._leg == "METPath":
if self._path == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
pt_leg.AddText(self._path + "*")
pt_leg.AddText(self._datasetLabel)
lumiLabel = TPaveText(0.66416, 0.937339, 0.962406, 0.992894,
"brNDC")
lumiLabel.SetBorderSize(0)
lumiLabel.SetFillStyle(0)
lumiLabel.SetTextFont(42)
lumiLabel.SetTextSize(0.0387597)
lumiLabel.AddText(str(self._luminosityLabel))
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo,
xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
if self._leg == "METLeg" or self._leg == "METPath":
backgroundHist.GetXaxis().SetTitle(self._metLegAxisTitle)
elif self._leg == "TrackLeg":
backgroundHist.GetXaxis().SetTitle(self._trackLegAxisTitle)
SetStyle(backgroundHist)
SetStyle(efficiencyGraph)
self._canvas.cd()
backgroundHist.Draw()
efficiencyGraph.Draw("P")
pt_cmsPrelim.Draw("same")
pt_lumi.Draw("same")
pt_leg.Draw("same")
oneLine.Draw("same")
if doFit:
(fitFunc, fitText) = self.PlotFit(efficiencyGraph)
fitFunc.Draw("same")
fitText.Draw("same")
if not os.path.exists('plots_' + self.Denominator["sample"]):
os.mkdir('plots_' + self.Denominator["sample"])
if self._leg == "METPath":
self._canvas.SaveAs('plots_' + self.Denominator["sample"] +
'/' + self._path + "_Efficiency.pdf")
else:
self._canvas.SaveAs('plots_' + self.Denominator["sample"] +
'/' + self._path + "_" + self._leg +
".pdf")
self._fout.cd()
if self._tgraphSuffix is not None:
efficiencyGraph.Write(self._path + "_" + self._leg + "_" +
self._tgraphSuffix)
if doFit:
fitFunc.Write(self._path + "_" + self._leg + "_" +
self._tgraphSuffix + "_fitResult")
else:
efficiencyGraph.Write(self._path + "_" + self._leg)
if doFit:
fitFunc.Write(self._path + "_" + self._leg + "_fitResult")
else:
print "Denominator and Numerator must be defined for path ", self._path, ", leg ", self._leg
return 0.0
def RootifyCrossBR(self):
for processname in self.processnames:
outfilename_cross = self.crosssecfolder+'/Crosssections_%s%s.root' % (processname, self.tag)
cross_module = importlib.import_module('crosssections.ChiPsi.Crosssections_%s' % (processname))
crosssections = cross_module.crosssection
if self.submit:
outfile = TFile(outfilename_cross, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_cross))
print green('--> Now at sample %s' % (processname))
for lamb in self.lambdas:
if not lamb in crosssections: continue
print green(' --> Now at lambda: %s' % (get_lambdastring(lamb)))
xsecs_per_mref = crosssections[lamb]
graph2d = TGraph2D()
npoints2d=0
set_points ={}
all_combinations = get_all_combinations(preferred_configurations=preferred_configurations)
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = False
for mref in xsecs_per_mref:
xsecs = xsecs_per_mref[mref]
final_xsecs = []
mdeps = array('d')
sigmas = array('d')
tot_los = array('d')
tot_his = array('d')
mdeps_lo = array('d')
mdeps_hi = array('d')
for tuple in xsecs:
mdep, sigma, q_lo, q_hi, pdf = tuple
tot_lo = XsecTotErr(sigma, q_lo, pdf)
tot_hi = XsecTotErr(sigma, q_hi, pdf)
final_xsecs.append((mdep, sigma, tot_lo, tot_hi))
mdeps.append(mdep)
sigmas.append(sigma)
tot_los.append(tot_lo)
tot_his.append(tot_hi)
mdeps_lo.append(0.)
mdeps_hi.append(0.)
if 'LQLQ' in processname or 'LQTChannel' in processname:
graph2d.SetPoint(npoints2d, mdep, mref, sigma)
set_points[mdep][mref] = True
elif 'PsiPsi' in processname:
graph2d.SetPoint(npoints2d, mref, mdep, sigma)
set_points[mref][mdep] = True
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
npoints2d += 1
# make TGraph out of it
graph = TGraphAsymmErrors(len(mdeps), mdeps, sigmas, mdeps_lo, mdeps_hi, tot_los, tot_his)
xaxistitle = 'M_{LQ} [GeV]' if ('LQLQ' in processname or 'LQTChannel' in processname) else 'M_{#chi_{1}} [GeV]'
graph.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph.GetYaxis().SetTitle('#sigma [pb]')
graphname = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphname += '_MC1%i' % (mref)
elif 'PsiPsi' in processname:
graphname += '_MLQ%i' % (mref)
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
graphname += '_L%s' % (get_lambdastring(lamb))
graph.SetName(graphname)
# print 'graphname: %s' % (graphname)
graphtitle = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphtitle += ', M_{#chi_{1}} = %i GeV' % (mref)
elif 'PsiPsi' in processname:
graphtitle += ', M_{LQ} = %i GeV' % (mref)
graphtitle += ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.'))
# print 'graphtitle: %s' % (graphtitle)
graph.SetTitle(graphtitle)
if self.submit:
outfile.cd()
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
if not set_points[mlq][mch]:
graph2d.SetPoint(npoints2d, mlq, mch, 0.)
npoints2d += 1
graph2d.SetName(processname + '_L%s' % (get_lambdastring(lamb)))
graph2d.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph2d.GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
graph2d.GetZaxis().SetTitle('#sigma [pb]')
graph2d.SetTitle(processname + ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.')))
if self.submit:
graph2d.Write()
else:
print yellow(' --> Would have written 2d-graph to outfile')
if self.submit:
outfile.Close()
# also rootify BRs if we are looking at the LQLQ process without decays (just for fun, could also be any other LQLQ process)
if processname == 'LQLQ':
outfilename_br = self.crosssecfolder+'/Branchingratios_%s%s.root' % (processname, self.tag)
if self.submit:
outfile = TFile(outfilename_br, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_br))
br_module = importlib.import_module('crosssections.ChiPsi.Branchingratios_%s' % (processname))
allbrs = br_module.branchingratio
for lamb in allbrs.keys():
brs = allbrs[lamb]
brs2d = {}
npoints2d = {}
set_points ={}
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = {}
for decaymode in decaymode_dict.keys():
set_points[mlq][mch][decaymode] = False
decaymodes_present = []
for mlq in sorted(brs):
mchs_per_decaymode = {}
brs_per_decaymode = {}
for mch in sorted(brs[mlq]):
for decaymode in brs[mlq][mch]:
if not decaymode in decaymodes_present:
decaymodes_present.append(decaymode)
if not decaymode in mchs_per_decaymode.keys(): mchs_per_decaymode[decaymode] = array('d')
if not decaymode in brs_per_decaymode.keys(): brs_per_decaymode[decaymode] = array('d')
# if not decaymode in set_points[mlq][mch].keys():
# # print mlq, mch, decaymode
# set_points[mlq][mch][decaymode] = False
if not decaymode in brs2d.keys():
graphname2d = processname + ('_L%s_%i_%i' % (get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
# print graphname2d
npoints2d[decaymode] = 0
brs2d[decaymode] = TGraph2D()
brs2d[decaymode].SetName(graphname2d)
brs2d[decaymode].GetXaxis().SetTitle('M_{LQ} [GeV]')
brs2d[decaymode].GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
brs2d[decaymode].GetZaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
brs2d[decaymode].SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
mchs_per_decaymode[decaymode].append(mch)
brs_per_decaymode[decaymode].append(brs[mlq][mch][decaymode][0])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, brs[mlq][mch][decaymode][0])
set_points[mlq][mch][decaymode] = True
npoints2d[decaymode] += 1
for decaymode in mchs_per_decaymode.keys():
graph = TGraph(len(mchs_per_decaymode[decaymode]), mchs_per_decaymode[decaymode], brs_per_decaymode[decaymode])
graphname = processname + ('_MLQ%i_L%s_%i_%i' % (mlq, get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
graph.SetName(graphname)
graph.GetXaxis().SetTitle('M_{#chi_{1}} [GeV]')
graph.GetYaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
graph.SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
if self.submit:
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
for decaymode in set_points[mlq][mch]:
if not set_points[mlq][mch][decaymode] and decaymode in decaymodes_present:
# print decaymode
# print brs2d.keys()
# print npoints2d.keys()
# print 'Setting BR for MLQ=%i, MCH=%i, decay=(%i, %i) to 0' % (mlq, mch, decaymode[0], decaymode[1])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, 0)
npoints2d[decaymode] += 1
if self.submit:
for decaymode in brs2d:
brs2d[decaymode].Write()
else:
print yellow(' --> Would have written 2d-graphs to outfile')
outfile.Close()
def plotHistos(dac, histVals, qiCalib = None, outDir = "results", currentMode = ""):
adcGraphs = []
chargeGraphs = []
gStyle.SetMarkerColor(kBlue)
gStyle.SetLineColor(kBlack)
convFactor = 1.
if qiCalib is None:
# Use nominal qiCalib
print "Using nominal QI calibrations"
if currentMode == "low":
convFactor = 1.375
elif currentMode == "high":
convFactor = 7.7
offset = 0.
else:
print "Using custom QI calibrations"
#for i in range(len(histVals[dac[0]])):
for i in range(12):
adcGr = TGraphAsymmErrors()
adcGr.SetMarkerStyle(22)
#adcGr.SetMarkerSize(1.0)
adcGr.SetName("h%d_ADC" % i)
adcGr.SetTitle("h%d ADC vs %s" % (i, "Charge %s current mode" % currentMode if currentMode in ["low", "high"] else "DAC value"))
chGr = TGraphAsymmErrors()
chGr.SetMarkerStyle(22)
#chGr.SetMarkerSize(1.3)
chGr.SetName("h%d_LinADC" % i)
chGr.SetTitle("h%d Lin ADC vs %s" % (i, "Charge %s current mode" % currentMode if currentMode in ["low", "high"] else "DAC value"))
adcGraphs.append(adcGr)
chargeGraphs.append(chGr)
for i, dv in enumerate(dac):
for n in range(len(adcGraphs)):
mean = histVals[dv][n]["mean"]
err = histVals[dv][n]["rms"]
if qiCalib is not None:
QIchan = int(n/6)*8 + n%6 + 1
convFactor = qiCalib[QIchan]["slope"]
offset = qiCalib[QIchan]["offset"]
# Index of new point
np = adcGraphs[n].GetN()
# Set value and error
adcGraphs[n].SetPoint(np, dv*convFactor + offset, mean)
adcGraphs[n].SetPointError(np, 0., 0., err, err)
chMean, chErr = linADC(mean, err)
chargeGraphs[n].SetPoint(np, dv*convFactor + offset, chMean)
chargeGraphs[n].SetPointError(np, 0., 0., chErr, chErr)
if outDir[-1] == "/": outDir = outDir[:-1]
os.system("mkdir -p %s/adcH; mkdir -p %s/chargeH" % (outDir, outDir) )
c1 = TCanvas('c1','c1', 1200, 800)
c1.SetLeftMargin(0.15);
c1.SetRightMargin(0.25)
c1.SetBottomMargin(0.25);
pad1=TPad('p1','p1',0.,0.,1.0,1.0)
pad1.Draw()
outF = TFile.Open(outDir + "/histos.root", "RECREATE")
xAxisTitle = "Charge [fC]" if currentMode in ["low", "high"] else "DAC value"
for i,adcGr in enumerate(adcGraphs):
"""
adcGr.Fit('pol1', "Q") # Q: Quiet mode
f = adcGr.GetFunction("pol1")
p0 = f.GetParameter(0)
p0_err = f.GetParError(0)
p1 = f.GetParameter(1)
p1_err = f.GetParError(1)
fitline = "offset %.3f #pm %.2f slope %.3f #pm %.6f" % (p0, p0_err, p1, p1_err)
"""
adcGr.GetXaxis().SetTitle(xAxisTitle)
adcGr.GetYaxis().SetTitle("ADC")
adcGr.GetYaxis().SetTitleOffset(1.2)
pad1.cd()
pad1.SetLogx(True)
adcGr.Draw("AP")
adcGr.Write()
Quiet(c1.SaveAs)(outDir + "/adcH/adc_hist_%d.png" % i)
c1.SetLogy(True)
for i,chGr in enumerate(chargeGraphs):
chGr.Fit('pol1', "Q") # Q: Quiet mode
f = chGr.GetFunction("pol1")
p0 = f.GetParameter(0)
p0_err = f.GetParError(0)
p1 = f.GetParameter(1)
p1_err = f.GetParError(1)
fitline = "offset %.3f #pm %.2f slope %.3f #pm %.6f" % (p0, p0_err, p1, p1_err)
chGr.GetXaxis().SetTitle(xAxisTitle)
chGr.GetYaxis().SetTitle("Linearized ADC")
chGr.GetYaxis().SetTitleOffset(1.2)
pad1.cd()
pad1.SetLogx(True)
pad1.SetLogy(True)
chGr.Draw("AP")
chGr.Write()
txt=TLatex()
txt.SetNDC(True)
txt.SetTextFont(43)
txt.SetTextSize(20)
txt.SetTextAlign(12)
txt.DrawLatex(0.45,0.87, fitline)
Quiet(c1.SaveAs)(outDir + "/chargeH/charge_hist_%d.png" % i)
# for g in adcGraphs:
#
# g.Write()
# for g in chargeGraphs:
# g.Write()
#outF.Write()
outF.Close()
def limit(method, channel):
particle = channel[1:2]
particleP = particle + "'" if channel.startswith('X') else channel[0]
THEORY = ['A1', 'B3'] if channel.startswith('X') else []
suffix = ""
if method == "hvt": suffix = "_HVT"
if method == "cls": suffix = "_CLs"
if method == "monoH": suffix = "_monoH"
filename = "./combine/" + method + "/" + channel + "_M%d.txt"
mass, val = fillValues(filename)
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
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0])
Exp0s.SetPoint(n, m, val[m][3])
Exp1s.SetPoint(n, m, val[m][3])
Exp1s.SetPointError(n, 0., 0., val[m][3] - val[m][2],
val[m][4] - val[m][3])
Exp2s.SetPoint(n, m, val[m][3])
Exp2s.SetPointError(n, 0., 0., val[m][3] - val[m][1],
val[m][5] - val[m][3])
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()
for m in sorted(HVT[t]['Z']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
XsW, XsW_Up, XsW_Down, XsZ, XsZ_Up, XsZ_Down = 0., 0., 0., 0., 0., 0.
XsZ = 1000. * HVT[t]['Z']['XS'][m] * HVT[t]['Z']['BR'][m]
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]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsW + XsZ)
Theory[t].SetPointError(n, 0., 0.,
(XsW - XsW_Down) + (XsZ - XsZ_Down),
(XsW_Up - XsW) + (XsZ_Up - XsZ))
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 +
"h) (fb)")
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)
Exp2s.GetYaxis().SetRangeUser(0.01, 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))
drawAnalysis(channel)
drawRegion(channel, True)
drawCMS(LUMI, YEAR, "Preliminary") #Preliminary
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, 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()
"""
if True and channel.endswith('sl'):
mass, val = fillValues("./combine/alpha/X"+particle+"Hbonly_M%d.txt")
Exp, Obs = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val: continue
Exp.SetPoint(Exp.GetN(), m, val[m][3])
Obs.SetPoint(Obs.GetN(), m, val[m][0])
Exp.SetLineWidth(3)
Exp.SetLineColor(602) #602
Exp.SetLineStyle(5)
Obs.SetLineWidth(3)
Obs.SetLineColor(602)
Exp.Draw("SAME, L")
Obs.Draw("SAME, L")
mass15, val15 = fillValues("./combine/Vh_2015/X"+particle+"h_M%d.txt")
Exp15, Obs15 = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass15):
if not m in val: continue
Exp15.SetPoint(Exp15.GetN(), m, val15[m][3]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Obs15.SetPoint(Obs15.GetN(), m, val15[m][0]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Exp15.SetLineWidth(3)
Exp15.SetLineColor(856) #602
Exp15.SetLineStyle(6)
Obs15.SetLineWidth(3)
Obs15.SetLineColor(856)
Exp15.Draw("SAME, L")
#Obs15.Draw("SAME, L")
leg2.AddEntry(Exp, "1+2 b-tag", "l")
"""
if True and channel == 'AZh':
massLL, valLL = fillValues("./combine/AZh/AZhll_M%d.txt")
ExpLL, ObsLL = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massLL):
if not m in val: continue
ExpLL.SetPoint(ExpLL.GetN(), m, valLL[m][3] * multF)
ObsLL.SetPoint(ObsLL.GetN(), m, valLL[m][0] * multF)
ExpLL.SetLineWidth(3)
ExpLL.SetLineColor(833) #602
ExpLL.SetLineStyle(5)
ObsLL.SetLineWidth(3)
ObsLL.SetLineColor(833)
ExpLL.Draw("SAME, L")
#ObsLL.Draw("SAME, L")
massNN, valNN = fillValues("./combine/AZh/AZhnn_M%d.txt")
ExpNN, ObsNN = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massNN):
if not m in val: continue
ExpNN.SetPoint(ExpNN.GetN(), m, valNN[m][3] * multF)
ObsNN.SetPoint(ObsNN.GetN(), m, valNN[m][0] * multF)
ExpNN.SetLineWidth(3)
ExpNN.SetLineColor(855) #602
ExpNN.SetLineStyle(6)
ObsNN.SetLineWidth(3)
ObsNN.SetLineColor(855)
ExpNN.Draw("SAME, L")
#ObsNN.Draw("SAME, L")
leg2.AddEntry(ExpLL,
"Expected, A #rightarrow Zh #rightarrow llb#bar{b}", "l")
leg2.AddEntry(ExpNN,
"Expected, A #rightarrow Zh #rightarrow #nu#nub#bar{b}",
"l")
if method == 'combo':
massAH, valAH = fillValues("./combine/dijet/X" + particle +
"Hah_M%d.txt")
ExpAH = TGraphAsymmErrors()
for i, m in enumerate(massAH):
if not m in val: continue
ExpAH.SetPoint(ExpAH.GetN(), m, valAH[m][3] * multF)
ExpAH.SetLineWidth(2)
ExpAH.SetLineColor(602) #602
ExpAH.SetLineStyle(4)
ExpAH.Draw("SAME, L")
massSL, valSL = fillValues("./combine/alpha/X" + particle +
"Hsl_M%d.txt")
ExpSL = TGraphAsymmErrors()
for i, m in enumerate(massSL):
if not m in val: continue
ExpSL.SetPoint(ExpSL.GetN(), m, valSL[m][3] * multF)
ExpSL.SetLineWidth(3)
ExpSL.SetLineColor(860 - 9) #602
ExpSL.SetLineStyle(7)
ExpSL.Draw("SAME, L")
leg2.AddEntry(ExpAH, "B2G-17-002", "l")
leg2.AddEntry(ExpSL, "B2G-17-004", "l")
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("plotsLimit/Exclusion/" + channel + suffix + ".png")
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".C")
c1.Print("plotsLimit/Exclusion/" + channel + 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 ""
print "p1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] + Exp1s.GetErrorYhigh(i), ",",
print "],"
print "m1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] - Exp1s.GetErrorYlow(i), ",",
print "],"
print "[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i], ",",
print "]"
#if not 'ah' in channel and not 'sl' in channel: return
# ---------- 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, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c2.Print("plotsLimit/Significance/" + channel + suffix + ".png")
c2.Print("plotsLimit/Significance/" + channel + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".root")
# c2.Print("plotsLimit/Significance/"+channel+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, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c3.Print("plotsLimit/pValue/" + channel + suffix + ".png")
c3.Print("plotsLimit/pValue/" + channel + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".root")
# c3.Print("plotsLimit/pValue/"+channel+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, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".png")
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+channel+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(inputfilename=None,
histofilename="EfficiencyHistos.root",
basecut="nTelTracks == 1",
matchcut="hasHit == 0",
ucells=0,
vcells=0):
ubins = ucells
vbins = vcells
total_cut = TCut(basecut)
pass_cut = TCut(basecut) + TCut(matchcut)
if inputfilename == None:
return None
rawfile = gROOT.FindObject(inputfilename)
if rawfile:
rawfile.Close()
rawfile = TFile(inputfilename, 'READ')
histofile = gROOT.FindObject(histofilename)
if histofile:
histofile.Close()
histofile = TFile(
histofilename, 'RECREATE',
'Efficiency histos created from input file ' + inputfilename)
# Get access to tracks
tree = rawfile.Get("Track")
# Compute efficiency for v cells
h_track_v_total = TH1F("h_track_v_total", "", vbins, 0, vcells)
tree.Draw("cellV_fit >> +h_track_v_total", total_cut, "goff")
h_track_v_total.GetXaxis().SetTitle("cellV_fit [cellID]")
h_track_v_total.GetYaxis().SetTitle("tracks")
h_track_v_pass = TH1F("h_track_v_pass", "", vbins, 0, vcells)
tree.Draw("cellV_fit >> +h_track_v_pass", pass_cut, "goff")
h_track_v_pass.GetXaxis().SetTitle("cellV_fit [cellID]")
h_track_v_pass.GetYaxis().SetTitle("tracks")
g_efficiency_v = TGraphAsymmErrors()
g_efficiency_v.SetName("g_efficiency_v")
g_efficiency_v.Divide(h_track_v_pass, h_track_v_total)
g_efficiency_v.SetTitle("")
g_efficiency_v.GetXaxis().SetTitle("cellV_fit [cellID]")
g_efficiency_v.GetYaxis().SetTitle("efficiency")
g_efficiency_v.Write()
# Compute efficiency for u cells
h_track_u_total = TH1F("h_track_u_total", "", ubins, 0, ucells)
tree.Draw("cellU_fit >> +h_track_u_total", total_cut, "goff")
h_track_u_total.GetXaxis().SetTitle("cellU_fit [cellID]")
h_track_u_total.GetYaxis().SetTitle("tracks")
h_track_u_pass = TH1F("h_track_u_pass", "", ubins, 0, ucells)
tree.Draw("cellU_fit >> +h_track_u_pass", pass_cut, "goff")
h_track_u_pass.GetXaxis().SetTitle("cellU_fit [cellID]")
h_track_u_pass.GetYaxis().SetTitle("tracks")
g_efficiency_u = TGraphAsymmErrors()
g_efficiency_u.SetName("g_efficiency_u")
g_efficiency_u.Divide(h_track_u_pass, h_track_u_total)
g_efficiency_u.SetTitle("")
g_efficiency_u.GetXaxis().SetTitle("cellU_fit [cellID]")
g_efficiency_u.GetYaxis().SetTitle("efficiency")
g_efficiency_u.Write()
# Compute efficiency for u:v
h_track_total = TH2F("h_track_total", "", ubins, 0, ucells, vbins, 0,
vcells)
tree.Draw("cellV_fit:cellU_fit >> +h_track_total", total_cut, "goff")
h_track_total.GetXaxis().SetTitle("cellU_fit [cellID]")
h_track_total.GetYaxis().SetTitle("cellV_fit [cellID]")
h_track_total.GetZaxis().SetTitle("tracks")
h_track_pass = TH2F("h_track_pass", "", ubins, 0, ucells, vbins, 0, vcells)
tree.Draw("cellV_fit:cellU_fit >> +h_track_pass", pass_cut, "goff")
h_track_pass.GetXaxis().SetTitle("cellU_fit [cellID]")
h_track_pass.GetYaxis().SetTitle("cellV_fit [cellID]")
h_track_pass.GetZaxis().SetTitle("tracks")
g_efficiency = h_track_pass
g_efficiency.SetName("g_efficiency")
g_efficiency.Divide(h_track_pass, h_track_total)
g_efficiency.SetTitle("")
g_efficiency.GetXaxis().SetTitle("cellU_fit [cellID]")
g_efficiency.GetYaxis().SetTitle("cellV_fit [cellID]")
g_efficiency.GetZaxis().SetTitle("efficiency")
g_efficiency.SetStats(0)
g_efficiency.Write()
# write the tree into the output file and close the file
histofile.Write()
histofile.Close()
rawfile.Close()
AVGCORRYIELD, AVGSTATUNC, AVGFPROMPT, \
AVGFPROMPTLOW, AVGFPROMPTHIGH, AVGERROR = average_pkpi_pk0s(HISTO_PKPI, HISTO_PK0S,
GRFD_PKPI, GRFD_PK0S,
ERRSPKPI, ERRSPK0S,
MATCHPKPI, MATCHPK0S,
MATCHPKPIGR, MATCHPK0SGR)
HISTAVG = HISTO_PK0S.Clone("histoSigmaCorr_average")
GRFDAVG = TGraphAsymmErrors(PTBINS)
for ipt in range(PTBINS):
HISTAVG.SetBinContent(ipt + 1, AVGCORRYIELD[ipt])
HISTAVG.SetBinError(ipt + 1, AVGSTATUNC[ipt])
GRFDAVG.SetPoint(ipt + 1, GRFD_PK0S.GetX()[ipt + 1], AVGFPROMPT[ipt])
GRFDAVG.SetPointError(ipt+1, GRFD_PK0S.GetEXlow()[ipt+1], GRFD_PK0S.GetEXhigh()[ipt+1], \
AVGFPROMPTLOW[ipt], AVGFPROMPTHIGH[ipt])
AVGERROR.print()
print("\n\n Store above in", ERROR_OUT)
print("\n\n\nStoring ROOT objects in", FILE_OUT)
FOUT = TFile(FILE_OUT, "RECREATE")
FOUT.cd()
HISTAVG.Write("histoSigmaCorr_average")
HISTO_PKPI.Write("histoSigmaCorr_pKpi")
HISTO_PK0S.Write("histoSigmaCorr_pK0s")
GRFDAVG.Write("gFcCorrConservative_average")
GRFD_PKPI.Write("gFcCorrConservative_pKpi")
GRFD_PK0S.Write("gNbCorrConservative_pK0s")
FOUT.Close()
def limit():
method = ''
channel = "bb"
if INCLUDEACC:
particleP = "X"
else:
particleP = "Z'"
particle = 'b#bar{b}'
multF = ZPTOBB
THEORY = ['A1', 'B3']
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/" + BTAGGING + "/combined_run2/"+ YEAR + "_M%d.txt"
else:
filename = "./combine/limits/" + 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()
#print "m =", m, " --> fitted r / injected r = {:<7} / {:<7} = {:<5}".format(val[m][3], pull0[m], val[m][3]/pull0[m])
#print "m =", m, " --> central r = {:<5}".format(val[m][3])
print "m =", m, " --> 2.5 x central r = {:<5}".format(2.5*val[m][3])
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# 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, "Preliminary")
# #drawCMS(LUMI, "", suppressCMS=True)
#else:
# #drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True)
# drawCMS(LUMI, "Preliminary "+CAT_LABELS[CATEGORY])
# #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 plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Fake rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
# if g_data.GetN() != hist_data_tight.GetNbinsX():
# import pdb; pdb.set_trace()
g_data.GetYaxis().SetTitle('Fake rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i)/g_data_vals[i])**2 + (g.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((g_data.GetErrorYhigh(i)/g_data_vals[i])**2 + (g.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
maxy = 1.3 * max(g.GetMaximum(), g_data.GetMaximum(), 0.05)
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), 'MC', 'lep')
legend.AddEntry(g_data.GetName(), 'Observed', 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle('Obs/MC')
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
g_data.Write()
cv.Write()
f.Close()
def TextToGraphTimeScan(textfilename):
dirname="timescanOutput/"
#filenames=['timescanOutput/Rate_2017data_FrontTrain_cutval_2.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_4.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_6.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_8.txt','timescanOutput/Rate_2017data_FrontTrain_cutval_10.txt']
filename = textfilename
f = open(filename,"r").readlines()
firstLine = f.pop(0) #removes the first line
adc_ = array('f')
adc_1 = array('f')
adc_2 = array('f')
adc_3 = array('f')
adc_4 = array('f')
prob = array('f')
prob_e = array('f')
prob_26 = array('f')
prob_e_26 = array('f')
prob_27 = array('f')
prob_e_27 = array('f')
prob_28 = array('f')
prob_e_28 = array('f')
prob_29 = array('f')
prob_e_29 = array('f')
errx = array('f')
for line in f:
adc_.append( float( line.rstrip().split()[0]))
adc_1.append( float( line.rstrip().split()[0]) + 0.1)
adc_2.append( float( line.rstrip().split()[0]) + 0.2)
adc_3.append( float( line.rstrip().split()[0]) + 0.3)
adc_4.append( float( line.rstrip().split()[0]) + 0.4)
prob.append( float( line.rstrip().split()[1]) )
prob_e.append( float( line.rstrip().split()[2]) )
prob_26.append( float( line.rstrip().split()[3]) )
prob_e_26.append( float( line.rstrip().split()[4]) )
prob_27.append( float( line.rstrip().split()[5]) )
prob_e_27.append( float( line.rstrip().split()[6]) )
prob_28.append( float( line.rstrip().split()[7]) )
prob_e_28.append( float( line.rstrip().split()[8]) )
prob_29.append( float( line.rstrip().split()[9]) )
prob_e_29.append( float( line.rstrip().split()[10]) )
errx.append(0.0)
#adc_1 = [i+0.1 for i in adc_]
#adc_2 = [i+0.2 for i in adc_]
#adc_3 = [i+0.3 for i in adc_]
#adc_4 = [i+0.4 for i in adc_]
print adc_1
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, prob_e, prob_e ) ; g_prob_.SetName("prob_")
g_prob_26_ = TGraphAsymmErrors(int(len(adc_1)), adc_1, prob_26, errx, errx, prob_e_26, prob_e_26 ) ; g_prob_26_.SetName("prob_26_")
g_prob_27_ = TGraphAsymmErrors(int(len(adc_)), adc_2, prob_27, errx, errx, prob_e_27, prob_e_27 ) ; g_prob_27_.SetName("prob_27_")
g_prob_28_ = TGraphAsymmErrors(int(len(adc_)), adc_3, prob_28, errx, errx, prob_e_28, prob_e_28 ) ; g_prob_28_.SetName("prob_28_")
g_prob_29_ = TGraphAsymmErrors(int(len(adc_)), adc_4, prob_29, errx, errx, prob_e_29, prob_e_29 ) ; g_prob_29_.SetName("prob_29_")
f1 = TFile(filename.replace('.txt','.root'), 'RECREATE')
g_prob_.Write()
g_prob_26_.Write()
g_prob_27_.Write()
g_prob_28_.Write()
g_prob_29_.Write()
f1.Close()
adc_3.append(float(line.rstrip().split()[0]) + 0.3)
adc_4.append(float(line.rstrip().split()[0]) + 0.4)
prob.append(float(line.rstrip().split()[1]))
#prob_e.append( float( line.rstrip().split()[2]) )
prob_26.append(float(line.rstrip().split()[2]))
#prob_e_26.append( float( line.rstrip().split()[4]) )
errx.append(0.0)
#adc_1 = [i+0.1 for i in adc_]
#adc_2 = [i+0.2 for i in adc_]
#adc_3 = [i+0.3 for i in adc_]
#adc_4 = [i+0.4 for i in adc_]
print adc_1
g_prob_ = TGraphAsymmErrors(int(len(adc_)), adc_, prob, errx, errx, errx,
errx)
g_prob_.SetName("iso_")
g_prob_26_ = TGraphAsymmErrors(int(len(adc_1)), adc_1, prob_26, errx, errx,
errx, errx)
g_prob_26_.SetName("noniso_")
f1 = TFile(filename.replace('.txt', '.root'), 'RECREATE')
g_prob_.Write()
g_prob_26_.Write()
f1.Close()
def main():
"""
Main function
"""
parser = argparse.ArgumentParser(description="Arguments to pass")
parser.add_argument(
"configfile_name",
metavar="text",
default="config_Dplus_pp5TeV.yml",
help="input yaml config file",
)
parser.add_argument("--batch",
action="store_true",
default=False,
help="suppress video output")
args = parser.parse_args()
if args.batch:
gROOT.SetBatch(True)
# load info from config file
with open(args.configfile_name, "r") as yml_configfile:
cfg = yaml.safe_load(yml_configfile)
frac_method = cfg["fraction"]
histos, norm = load_inputs(cfg)
# consistency check of bins
ptlims = {}
for histo in ["rawyields", "acceffp", "acceffnp"]:
ptlims[histo] = get_hist_binlimits(histos[histo])
if (histo != "rawyields"
and not np.equal(ptlims[histo], ptlims["rawyields"]).all()):
print("\033[91mERROR: histo binning not consistent. Exit\033[0m")
sys.exit(10)
# compute cross section
axistit_cross = "d#sigma/d#it{p}_{T} (pb GeV^{-1} #it{c})"
axistit_cross_times_br = "d#sigma/d#it{p}_{T} #times BR (pb GeV^{-1} #it{c})"
axistit_pt = "#it{p}_{T} (GeV/#it{c})"
axistit_fprompt = "#if{f}_{prompt}"
gfraction = TGraphAsymmErrors(0)
gfraction.SetNameTitle("gfraction", f";{axistit_pt};{axistit_fprompt}")
hptspectrum = TH1F(
"hptspectrum",
f";{axistit_pt};{axistit_cross}",
len(ptlims["rawyields"]) - 1,
ptlims["rawyields"],
)
hptspectrum_wo_br = TH1F(
"hptspectrum_wo_br",
f";{axistit_pt};{axistit_cross_times_br}",
len(ptlims["rawyields"]) - 1,
ptlims["rawyields"],
)
for i_pt, (ptmin, ptmax) in enumerate(
zip(ptlims["rawyields"][:-1], ptlims["rawyields"][1:])):
pt_cent = (ptmax + ptmin) / 2
pt_delta = ptmax - ptmin
rawy = histos["rawyields"].GetBinContent(i_pt + 1)
rawy_unc = histos["rawyields"].GetBinError(i_pt + 1)
eff_times_acc_prompt = histos["acceffp"].GetBinContent(i_pt + 1)
eff_times_acc_nonprompt = histos["acceffnp"].GetBinContent(i_pt + 1)
ptmin_fonll = (
histos["FONLL"]["nonprompt"]["central"].GetXaxis().FindBin(ptmin *
1.0001))
ptmax_fonll = (
histos["FONLL"]["nonprompt"]["central"].GetXaxis().FindBin(ptmax *
0.9999))
crosssec_nonprompt_fonll = [
histos["FONLL"]["nonprompt"][pred].Integral(
ptmin_fonll, ptmax_fonll, "width") / (ptmax - ptmin)
for pred in histos["FONLL"]["nonprompt"]
]
# compute prompt fraction
if frac_method == "Nb":
frac = compute_fraction_nb( # BR already included in FONLL prediction
rawy,
eff_times_acc_prompt,
eff_times_acc_nonprompt,
crosssec_nonprompt_fonll,
pt_delta,
1.0,
1.0,
norm["events"],
norm["sigmaMB"],
)
elif frac_method == "fc":
crosssec_prompt_fonll = [
histos["FONLL"]["prompt"][pred].Integral(
ptmin_fonll, ptmax_fonll, "width") / (ptmax - ptmin)
for pred in histos["FONLL"]["prompt"]
]
frac = compute_fraction_fc(
eff_times_acc_prompt,
eff_times_acc_nonprompt,
crosssec_prompt_fonll,
crosssec_nonprompt_fonll,
)
# compute cross section times BR
crosssec, crosssec_unc = compute_crosssection(
rawy,
rawy_unc,
frac[0],
eff_times_acc_prompt,
ptmax - ptmin,
1.0,
norm["sigmaMB"],
norm["events"],
1.0,
frac_method,
)
hptspectrum.SetBinContent(i_pt + 1, crosssec / norm["BR"])
hptspectrum.SetBinError(i_pt + 1, crosssec_unc / norm["BR"])
hptspectrum_wo_br.SetBinContent(i_pt + 1, crosssec)
hptspectrum_wo_br.SetBinError(i_pt + 1, crosssec_unc)
gfraction.SetPoint(i_pt, pt_cent, frac[0])
gfraction.SetPointError(i_pt, pt_delta / 2, pt_delta / 2,
frac[0] - frac[1], frac[2] - frac[0])
# create plots
style_hist = StyleObject1D()
style_hist.markercolor = kAzure + 4
style_hist.markerstyle = kFullCircle
style_hist.markersize = 1
style_hist.linecolor = kAzure + 4
style_hist.linewidth = 2
style_hist.draw_options = "P"
fig_crosssec = ROOTFigure(1,
1,
column_margin=(0.14, 0.035),
row_margin=(0.1, 0.035),
size=(600, 800))
fig_crosssec.axes(label_size=0.025, title_size=0.030)
fig_crosssec.axes("x", title=axistit_pt, title_offset=1.5)
fig_crosssec.axes("y", title=axistit_cross_times_br, title_offset=1.8)
fig_crosssec.define_plot(0, 0, y_log=True)
fig_crosssec.add_object(hptspectrum_wo_br, style=style_hist)
fig_crosssec.create()
output_dir = cfg["output"]["directory"]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
fig_crosssec.save(
os.path.join(output_dir, f'{cfg["output"]["filename"]}.pdf'))
# save output file
output_file = TFile(
os.path.join(output_dir, f'{cfg["output"]["filename"]}.root'),
"recreate")
hptspectrum.Write()
hptspectrum_wo_br.Write()
gfraction.Write()
for hist in histos:
if isinstance(histos[hist], TH1):
histos[hist].Write()
else:
for flav in histos[hist]:
for pred in histos[hist][flav]:
histos[hist][flav][pred].Write()
output_file.Close()
def plotQIcalibration(results, outDir = "results", currentMode = ""):
gROOT.SetBatch(True)
calibGraphs = []
gStyle.SetMarkerColor(kBlue)
gStyle.SetLineColor(kBlack)
#for ch in xrange(1, len(results)+1): # Number of channels
for ch in sorted(results.keys()):
gr = TGraphAsymmErrors()
gr.SetMarkerStyle(22)
gr.SetMarkerSize(1.2)
gr.SetName("Channel_%d_Charge_vs_DAC" % ch)
gr.SetTitle("Channel %d Charge vs DAC value%s" % (ch, " %s current mode" % currentMode if currentMode in ["low", "high"] else ""))
res = results[ch]
for i, dacVal in enumerate(sorted(res.keys())):
mean = res[dacVal]["mean"] * 25.e6 # Convert to fC
err = res[dacVal]["std"] * 25.e6
# Index of new point
np = gr.GetN()
# Set value and error
gr.SetPoint(np, dacVal, mean)
gr.SetPointError(np, 0., 0., err, err)
calibGraphs.append((gr,ch))
if outDir[-1] == "/": outDir = outDir[:-1]
os.system("mkdir -p %s/calibGraphs" % outDir )
c1 = TCanvas('c1','c1', 1200, 800)
c1.SetLeftMargin(0.15);
c1.SetRightMargin(0.25)
c1.SetBottomMargin(0.25);
pad1=TPad('p1','p1',0.,0.,1.0,1.0)
pad1.Draw()
outF = TFile.Open(outDir + "/calibration.root", "RECREATE")
fitParams = {} # Converted to charge by * 25ns
for i,(gr,ch) in enumerate(calibGraphs):
gr.Fit('pol1', "Q") # Q: Quiet mode
f = gr.GetFunction("pol1")
p0 = f.GetParameter(0)
p0_err = f.GetParError(0)
p1 = f.GetParameter(1)
p1_err = f.GetParError(1)
fitline = "offset %g #pm %g slope %g #pm %g" % (p0, p0_err, p1, p1_err)
# Convert to fC
fitParams[ch] = {"slope":(p1), "offset":(p0)}
#fitParams[(i+1)] = {"slope":(p1), "offset":(p0)}
gr.GetXaxis().SetTitle("DAC value")
gr.GetYaxis().SetTitle("Charge [fC]")
if currentMode == "low":
gr.GetYaxis().SetTitleOffset(1.37)
pad1.cd()
gr.Draw("AP")
gr.Write()
txt=TLatex()
txt.SetNDC(True)
txt.SetTextFont(43)
txt.SetTextSize(20)
txt.SetTextAlign(12)
txt.DrawLatex(0.40,0.87, fitline)
Quiet(c1.SaveAs)(outDir + "/calibGraphs/channel_%d.png" % (i+1))
# for g in adcGraphs:
#
# g.Write()
# for g in chargeGraphs:
# g.Write()
#outF.Write()
outF.Close()
return fitParams
hSignifCatania.Write('hSignifCatania')
cSignif.Write()
cSignif.SaveAs('%s_Significance.pdf' % outFileName)
cBackground = TCanvas('cBackground', '', 500, 500)
cBackground.SetLogy()
hBackground.Draw()
hBackground.Write('hBackground')
cBackground.SaveAs('%s_Background.pdf' % outFileName)
cFprompt = TCanvas('cFprompt', '', 500, 500)
cFprompt.DrawFrame(cutVars['Pt']['min'][0], 0.,
cutVars['Pt']['max'][len(cutVars['Pt']['max']) - 1], 1.,
';#it{p}_{T} (GeV/#it{c});#it{f}_{prompt} (#it{f}_{c})')
gFprompt.Draw('p')
gFprompt.Write('gFprompt')
cFprompt.SaveAs('%s_Fprompt.pdf' % outFileName)
cEff = TCanvas('cEff', '', 500, 500)
cEff.DrawFrame(
cutVars['Pt']['min'][0], 1.e-4,
cutVars['Pt']['max'][len(cutVars['Pt']['max']) - 1], 1.,
';#it{p}_{T} (GeV/#it{c});(Acc #times #epsilon) #times 2#it{y}_{fid}')
cEff.SetLogy()
hAccEffPrompt.Draw('same')
hAccEffFD.Draw('same')
hAccEffPrompt.Write()
hAccEffFD.Write()
cEff.Write()
cEff.SaveAs('%s_Efficiency.pdf' % outFileName)
totalGr.GetXaxis().SetTitle("Higgs mass [GeV/c^{2}]")
totalGr.GetYaxis().SetTitle("Cross section [pb]")
glugluGr.SetName("gg")
glugluGr.SetTitle("gg")
glugluGr.SetMarkerStyle(21)
glugluGr.SetMarkerColor(2)
glugluGr.SetFillColor(0)
glugluGr.SetFillStyle(0)
glugluGr.Draw("e1p")
vbfGr.SetName("vbf")
vbfGr.SetTitle("VBF")
vbfGr.SetMarkerStyle(22)
vbfGr.SetMarkerColor(4)
vbfGr.SetFillColor(0)
vbfGr.SetFillStyle(0)
vbfGr.Draw("e1p")
leg = c.BuildLegend()
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
fout = ROOT.TFile.Open('thxsec.root', 'RECREATE')
fout.cd()
totalGr.Write()
glugluGr.Write()
vbfGr.Write()
fout.Close()
"2016_final_prompt/fakeTrackBackground_d0Sideband_new")
zToMuMuEstimate.addChannel(
"DisTrkInvertD0NHits4", "ZtoMuMuDisTrkSidebandD0CutNHits4",
"SingleMu_2016" + runPeriod, dirs['Andrew'] +
"2016_final_prompt/fakeTrackBackground_d0Sideband_new")
zToMuMuEstimate.addChannel(
"DisTrkInvertD0NHits5", "ZtoMuMuDisTrkSidebandD0CutNHits5",
"SingleMu_2016" + runPeriod, dirs['Andrew'] +
"2016_final_prompt/fakeTrackBackground_d0Sideband_new")
zToMuMuEstimate.addChannel(
"DisTrkInvertD0NHits6", "ZtoMuMuDisTrkSidebandD0CutNHits6",
"SingleMu_2016" + runPeriod, dirs['Andrew'] +
"2016_final_prompt/fakeTrackBackground_d0Sideband_new")
zToMuMuEstimate.addChannel(
"Basic", "BasicSelection", "MET_2016" + runPeriod,
dirs['Andrew'] + "2016_final_prompt/basicSelection_new")
zToMuMuEstimate.addChannel(
"ZtoLL", "ZtoMuMu", "SingleMu_2016" + runPeriod,
dirs['Andrew'] + "2016_final_prompt/zToMuMu_new")
nEst = zToMuMuEstimate.printNest()
g1.SetPoint(i, minD0, nEst[0])
g1.SetPointError(i, D / 2.0, D / 2.0, min(nEst[1], nEst[0]), nEst[1])
fout = TFile("fakeTrackEstimateVsD0.root", "update")
fout.cd()
g0.Write("est_BasicSelection_2016" + runPeriod)
g1.Write("est_ZtoMuMu_2016" + runPeriod)
fout.Close()
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, 0.8, "#splitline{SL1_L1 threshold scan}{Runs: "+str(run_interval)+"}")
can_scan_SL1_L1.Update()
can_scan_SL1_L1.Print(outpath + "ThresholdScan_SL1_L1_"+run_interval+".png")
can_scan_SL1_L1.Print(outpath + "ThresholdScan_SL1_L1_"+run_interval+".pdf")
graph.SetName("ThresholdScan_SL1_L1_"+run_interval)
new_file_SL1_L1 = TFile(outpath + "Graph_thresholdScan_SL1_L1_"+run_interval+ ".root",'RECREATE')
graph.Write()
graph.Set(0)
#SL1_L4
can_scan_SL1_L4 = TCanvas("can_scan_SL1_L4","can_scan_SL1_L4", 1000, 800)
can_scan_SL1_L4.SetGrid()
can_scan_SL1_L4.cd()
graph = TGraphAsymmErrors()
n=0
for a in sorted(scan_SL1_L4):
#print scan_SL1_L4[a]
graph.SetPoint(n,a,scan_SL1_L4[a])
n = n+1
graph.SetMarkerSize(1.)
graph.SetMarkerStyle(21)
graph.SetMarkerColor(862)
log += str(ival_) + " " + str(expm2_) + " " + str(
expm1_) + " " + str(expmed_) + " " + str(expp1_) + " " + str(
expp2_) + " " + str(obs_) + "\n"
# print ival, float(line.rstrip().split()[4]), xsec_, float(line.rstrip().split()[4])/xsec_
log += "\n"
g_exp2 = TGraphAsymmErrors(int(len(xvals)), xvals, expmed, errx, errx,
expm2, expp2)
g_exp2.SetName("exp2")
g_exp1 = TGraphAsymmErrors(int(len(xvals)), xvals, expmed, errx, errx,
expm1, expp1)
g_exp1.SetName("exp1")
g_expmed = TGraphAsymmErrors(int(len(xvals)), xvals, expmed)
g_expmed.SetName("expmed")
g_obs = TGraphAsymmErrors(int(len(xvals)), xvals, obs)
g_obs.SetName("obs")
f1 = TFile(scanname + '/' + scanname + '_MH4-' + str(M) + '.root',
'RECREATE')
g_exp2.Write()
g_exp1.Write()
g_expmed.Write()
g_obs.Write()
f1.Write()
f1.Close()
f.close()
logfile = open("alllimits_" + scanname + ".txt", "w")
logfile.write(log)
logfile.close()
etichetta.SetNDC()
etichetta.SetTextSize(0.04)
etichetta.SetTextColor(418)
etichetta.SetTextFont(102)
etichetta.DrawLatex(
0.2, 0.8, "#splitline{SL1_L1 HV scan}{Runs: " + str(run_interval) + "}")
can_HV_scan_SL1_L1.Update()
can_HV_scan_SL1_L1.Print(outpath + "HVScan_SL1_L1_" + str(run_interval) +
options.optionalstring + ".png")
can_HV_scan_SL1_L1.Print(outpath + "HVScan_SL1_L1_" + str(run_interval) +
options.optionalstring + ".pdf")
graph_HV_L1.SetName("HV_scan_SL1_L1_" + run_interval)
new_file_HV_scan_SL1_L1 = TFile(
outpath + "Graph_HV_scan_SL1_L1_" + run_interval + options.optionalstring +
".root", 'RECREATE')
graph_HV_L1.Write()
graph_HV_L1.Set(0)
print "Graph_HV_scan_SL1_L1_" + run_interval + options.optionalstring + ".root written in " + outpath
##Prepare the canvas to plot the HV scan for SL1_L4
can_HV_scan_SL1_L4 = TCanvas("can_HV_scan_SL1_L4", "can_HV_scan_SL1_L4", 1000,
800)
can_HV_scan_SL1_L4.SetGrid()
can_HV_scan_SL1_L4.cd()
##Prepare summary TGraph
graph_HV_L4 = TGraphAsymmErrors()
n = 0
for a in sorted(HV_scan_L4):
#for a in sorted(run_parameters):
##Fill the TGraph with threshold (x-axis) and rate (y-axis)
#######graph.SetPoint(n,int(run_parameters[a]['VTHR']),float(run_parameters[a]['RATE_SL4_L4']))
