def plot_hist_diff(histlist1, histlist2, labellist, log, overflow, filename,
options):
canv = TCanvas("c1", "c1", 800, 600)
canv.SetTopMargin(10)
canv.SetRightMargin(100)
if log: gPad.SetLogy()
histstack = THStack("stack", histlist1[0].GetTitle())
legend = TLegend(0.76, 0.88 - 0.08 * len(histlist1), 0.91, 0.88, '', 'NDC')
colorlist = [4, 8, 2, 6, 1]
if options: options += " NOSTACK"
else: options = "NOSTACK"
maximum = 0
for i in range(len(histlist1)):
entries = histlist1[i].GetEntries()
bad_entries = histlist2[i].GetEntries()
histlist1[i].SetLineColorAlpha(colorlist[i], 0.65)
histlist1[i].SetLineWidth(3)
if entries: histlist1[i].Scale(1. / entries)
if bad_entries: histlist2[i].Scale(1. / bad_entries)
histlist1[i].Add(histlist2[i], -1.)
nbins = histlist1[i].GetNbinsX()
legend.AddEntry(
histlist1[i], "#splitline{" + labellist[i] +
"}{#splitline{%d total jets}{%d bad jets}}" %
(entries, bad_entries), "l")
if overflow:
histlist1[i].SetBinContent(
nbins, histlist1[i].GetBinContent(nbins) +
histlist1[i].GetBinContent(nbins + 1)) #overflow
histlist1[i].SetBinContent(
1, histlist1[i].GetBinContent(0) +
histlist1[i].GetBinContent(1)) #underflow
if histlist1[i].GetMaximum() > maximum:
maximum = histlist1[i].GetMaximum()
histstack.Add(histlist1[i])
#if i == 0: histlist[i].Draw(options)
#else: histlist[i].Draw(same+options)
histstack.SetMaximum(maximum * 1.4)
histstack.Draw(options)
histstack.GetXaxis().SetTitle(histlist1[0].GetXaxis().GetTitle())
histstack.GetYaxis().SetTitle(histlist1[0].GetYaxis().GetTitle())
legend.SetTextSize(0.02)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw("SAME")
canv.SaveAs(filename)
if log: gPad.Clear()
canv.Clear()
del canv
def DrawStackHistograms(histograms, outname, blind=True, verbose=False):
#hists_sorted = sorted(histograms, key=lambda h: h.Integral())
hs = THStack("h_stack", "")
h_obs = None
l = TLegend(0.77, 0.60, 0.90, 0.87)
for hist in histograms:
hname = hist.GetName()
if "data_obs" in hname:
if not blind:
h_obs = hist
SetHistogramStyle(h_obs, "Observed")
l.AddEntry(h_obs, "Observed", "p")
else:
SetHistogramStyle(hist)
label = GetLabelName(hist)
hs.Add(hist)
l.AddEntry(hist, label, "f")
h_est = hs.GetStack().Last().Clone()
if verbose:
est_err = Double(0.)
nbins_est = h_est.GetNbinsX()
est_yields = h_est.IntegralAndError(1, nbins_est, est_err)
print "predicted yields :", est_yields, "+/-", est_err
if not blind:
print "observed yields :", h_obs.Integral()
# draw
# SetCMSStyle() TODO
canvas = TCanvas()
gPad.SetLogy()
#ymax_hs = hs.GetMaximum()
#ymax = ymax_hs if h_obs is None else max(ymax_hs, h_obs.GetMaximum())
ymax = 10000
ymin = 0.1
hs.SetMaximum(ymax)
hs.SetMinimum(ymin)
hs.Draw("HIST")
l.Draw("same")
if not blind:
h_obs.Draw("same")
canvas.SaveAs(outname)
def plot_bar(histlist, axislabels, labels, norm, log, filename, options):
canv = TCanvas("c1", "c1", 800, 600)
canv.SetTopMargin(10)
canv.SetRightMargin(100)
if log: gPad.SetLogy()
histstack = THStack("stack", histlist[0].GetTitle())
if labels:
legend = TLegend(0.76, 0.88 - 0.08 * len(histlist), 0.91, 0.88, '',
'NDC')
colorlist = [4, 8, 2, 6, 1]
if options: options += " NOSTACK"
else: options = "NOSTACK"
maximum = 0
for i in range(len(histlist)):
histlist[i].SetLineColorAlpha(colorlist[i], 0.65)
histlist[i].SetLineWidth(3)
entries = histlist[i].GetEntries()
if entries and norm: histlist[i].Scale(1. / entries)
if histlist[i].GetMaximum() > maximum:
maximum = histlist[i].GetMaximum()
if labels: legend.AddEntry(histlist[i], labels[i], "l")
histstack.Add(histlist[i])
histstack.SetMaximum(maximum * 1.4)
histstack.Draw(options)
histstack.GetXaxis().SetTitle(histlist[0].GetXaxis().GetTitle())
histstack.GetYaxis().SetTitle(histlist[0].GetYaxis().GetTitle())
histstack.GetXaxis().SetNdivisions(len(axislabels))
histstack.GetXaxis().CenterLabels(True)
for i, label in enumerate(axislabels):
histstack.GetXaxis().ChangeLabel(i + 1, -1, -1, -1, -1, -1, str(label))
if labels:
legend.SetTextSize(0.02)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw("SAME")
canv.SaveAs(filename)
if log: gPad.Clear()
canv.Clear()
del canv
def DeepCSV_pt_distribution(
year): ## everything below is jsut copy&past from above
from root_numpy import root2array, fill_hist, array2root
import numpy.lib.recfunctions as rfn
from aliases import alias_deepCSV, WP_deepCSV
### Preliminary Operations ###
treeRead = True
var = 'jpt_1'
channel = 'preselection'
cut = alias_deepCSV['preselection']
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False
showSignal = True
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_deepCSV.keys(), key=len, reverse=True):
if k in cut:
cut = cut.replace(
k, alias_deepCSV[k].format(WP=WP_deepCSV[BTAGGING][year]))
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " cut :", cut
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
N_signal_tot = 0.
N_signal_tag = 0.
### Create and fill MC histograms ###
for i, s in enumerate(back + sign):
if variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";jet p_{T};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,
";jet p_{T};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=["jpt_1", "eventWeightLumi"],
selection=cut + " && jdeepCSV_1>" +
str(WP_deepCSV[BTAGGING][year]))
if 'signal' in ss.lower():
N_signal_tag += len(arr['jpt_1'][arr['jpt_1'] > 3500])
print "imported " + NTUPLEDIR + ss + ".root"
fill_hist(hist[s],
arr["jpt_1"],
weights=arr["eventWeightLumi"])
arr = None
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_2", "eventWeightLumi"],
selection=cut + " && jdeepCSV_2>" +
str(WP_deepCSV[BTAGGING][year]))
print "imported " + NTUPLEDIR + ss + ".root"
if 'signal' in ss.lower():
N_signal_tag += len(arr['jpt_2'][arr['jpt_2'] > 3500])
fill_hist(hist[s],
arr["jpt_2"],
weights=arr["eventWeightLumi"])
arr = None
if 'signal' in ss.lower():
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_1", "eventWeightLumi"],
selection=cut)
N_signal_tot += len(arr['jpt_1'][arr['jpt_1'] > 3500])
arr = None
arr = root2array(NTUPLEDIR + ss + ".root",
branches=["jpt_2", "eventWeightLumi"],
selection=cut)
N_signal_tot += len(arr['jpt_2'][arr['jpt_2'] > 3500])
arr = None
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])
# 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], "", 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/deepCSV_plots/pt_" + year + suffix +
".png")
c1.Print("plots/MANtag_study/deepCSV_plots/pt_" + year + suffix +
".pdf")
# Print table
printTable(hist, sign)
print 'deepCSV efficiency:', N_signal_tag / N_signal_tot
def createPlots_(plot, geometry):
"""Cumulative material budget from simulation.
Internal function that will produce a cumulative profile of the
material budget inferred from the simulation starting from the
single detectors that compose the tracker. It will iterate over
all existing detectors contained in the DETECTORS
dictionary. The function will automatically skip non-existent
detectors.
"""
IBs = ["InnerServices", "Phase2PixelBarrel", "TIB", "TIDF", "TIDB"]
theDirname = "Figures"
if plot not in plots.keys():
print("Error: chosen plot name not known %s" % plot)
return
hist_X0_detectors = OrderedDict()
hist_X0_IB = None
hist_X0_elements = OrderedDict()
for subDetector,color in six.iteritems(DETECTORS):
h = get1DHisto_(subDetector,plots[plot].plotNumber,geometry)
if not h:
print('Warning: Skipping %s'%subDetector)
continue
hist_X0_detectors[subDetector] = h
# Merge together the "inner barrel detectors".
if subDetector in IBs:
hist_X0_IB = assignOrAddIfExists_(
hist_X0_IB,
hist_X0_detectors[subDetector]
)
# category profiles
for label, [num, color, leg] in six.iteritems(hist_label_to_num):
if label is 'SUM': continue
hist_label = get1DHisto_(subDetector, num + plots[plot].plotNumber, geometry)
hist_X0_elements[label] = assignOrAddIfExists_(
hist_X0_elements.setdefault(label,None),
hist_label,
)
hist_X0_elements[label].SetFillColor(color)
cumulative_matbdg = TH1D("CumulativeSimulMatBdg",
"CumulativeSimulMatBdg",
hist_X0_IB.GetNbinsX(),
hist_X0_IB.GetXaxis().GetXmin(),
hist_X0_IB.GetXaxis().GetXmax())
cumulative_matbdg.SetDirectory(0)
# colors
for det, color in six.iteritems(DETECTORS):
setColorIfExists_(hist_X0_detectors, det, color)
# First Plot: BeamPipe + Pixel + TIB/TID + TOB + TEC + Outside
# stack
stackTitle_SubDetectors = "Tracker Material Budget;%s;%s" % (
plots[plot].abscissa,plots[plot].ordinate)
stack_X0_SubDetectors = THStack("stack_X0",stackTitle_SubDetectors)
for det, histo in six.iteritems(hist_X0_detectors):
stack_X0_SubDetectors.Add(histo)
cumulative_matbdg.Add(histo, 1)
# canvas
can_SubDetectors = TCanvas("can_SubDetectors","can_SubDetectors",800,800)
can_SubDetectors.Range(0,0,25,25)
can_SubDetectors.SetFillColor(kWhite)
# Draw
stack_X0_SubDetectors.SetMinimum(plots[plot].ymin)
stack_X0_SubDetectors.SetMaximum(plots[plot].ymax)
stack_X0_SubDetectors.Draw("HIST")
stack_X0_SubDetectors.GetXaxis().SetLimits(plots[plot].xmin, plots[plot].xmax)
# Legenda
theLegend_SubDetectors = TLegend(0.180,0.8,0.98,0.92)
theLegend_SubDetectors.SetNColumns(3)
theLegend_SubDetectors.SetFillColor(0)
theLegend_SubDetectors.SetFillStyle(0)
theLegend_SubDetectors.SetBorderSize(0)
for det, histo in six.iteritems(hist_X0_detectors):
theLegend_SubDetectors.AddEntry(histo, det, "f")
theLegend_SubDetectors.Draw()
# text
text_SubDetectors = TPaveText(0.180,0.727,0.402,0.787,"NDC")
text_SubDetectors.SetFillColor(0)
text_SubDetectors.SetBorderSize(0)
text_SubDetectors.AddText("CMS Simulation")
text_SubDetectors.SetTextAlign(11)
text_SubDetectors.Draw()
# Store
can_SubDetectors.Update()
if not checkFile_(theDirname):
os.mkdir(theDirname)
can_SubDetectors.SaveAs("%s/Tracker_SubDetectors_%s.pdf" % (theDirname, plot))
can_SubDetectors.SaveAs("%s/Tracker_SubDetectors_%s.root" % (theDirname, plot))
# Second Plot: BeamPipe + SEN + ELE + CAB + COL + SUP + OTH/AIR +
# Outside stack
stackTitle_Materials = "Tracker Material Budget;%s;%s" % (plots[plot].abscissa,
plots[plot].ordinate)
stack_X0_Materials = THStack("stack_X0",stackTitle_Materials)
stack_X0_Materials.Add(hist_X0_detectors["BeamPipe"])
for label, [num, color, leg] in six.iteritems(hist_label_to_num):
if label is 'SUM':
continue
stack_X0_Materials.Add(hist_X0_elements[label])
# canvas
can_Materials = TCanvas("can_Materials","can_Materials",800,800)
can_Materials.Range(0,0,25,25)
can_Materials.SetFillColor(kWhite)
# Draw
stack_X0_Materials.SetMinimum(plots[plot].ymin)
stack_X0_Materials.SetMaximum(plots[plot].ymax)
stack_X0_Materials.Draw("HIST")
stack_X0_Materials.GetXaxis().SetLimits(plots[plot].xmin, plots[plot].xmax)
# Legenda
theLegend_Materials = TLegend(0.180,0.8,0.95,0.92)
theLegend_Materials.SetNColumns(3)
theLegend_Materials.SetFillColor(0)
theLegend_Materials.SetBorderSize(0)
theLegend_Materials.AddEntry(hist_X0_detectors["BeamPipe"], "Beam Pipe", "f")
for label, [num, color, leg] in six.iteritems(hist_label_to_num):
if label is 'SUM':
continue
theLegend_Materials.AddEntry(hist_X0_elements[label], leg, "f")
theLegend_Materials.Draw()
# text
text_Materials = TPaveText(0.180,0.727,0.402,0.787,"NDC")
text_Materials.SetFillColor(0)
text_Materials.SetBorderSize(0)
text_Materials.AddText("CMS Simulation")
text_Materials.SetTextAlign(11)
text_Materials.Draw()
# Store
can_Materials.Update()
can_Materials.SaveAs("%s/Tracker_Materials_%s.pdf" % (theDirname, plot))
can_Materials.SaveAs("%s/Tracker_Materials_%s.root" % (theDirname, plot))
return cumulative_matbdg
def SaveHisto1D(HIST,
suffix,
output,
snorm=1,
ratio=0,
poisson=True,
logy=False,
isVal=False):
# only data and mc
# SUFFIX = 2016_ss_lep1_pt
bkgsum = 'BkgSum_%s' % (suffix)
HIST[bkgsum] = HIST['DATA_%s' % (suffix)].Clone("BkgSum") if 'DATA_%s' % (
suffix) in HIST else HIST['MC_%s' % (suffix)].Clone("BkgSum")
HIST[bkgsum].Reset("MICES")
HIST[bkgsum].SetFillStyle(3003)
HIST[bkgsum].SetFillColor(1)
HIST[bkgsum].SetMarkerStyle(0)
HIST[bkgsum].Add(HIST['MC_%s' % (suffix)])
HIST['DATA_%s' % (suffix)].SetMarkerStyle(20)
HIST['DATA_%s' % (suffix)].SetMarkerSize(1.25)
HIST['DATA_%s' % (suffix)].SetFillColor(418)
HIST['DATA_%s' % (suffix)].SetFillStyle(1001)
HIST['DATA_%s' % (suffix)].SetLineColor(1)
HIST['DATA_%s' % (suffix)].SetLineStyle(1)
HIST['DATA_%s' % (suffix)].SetLineWidth(2)
HIST['MC_%s' % (suffix)].SetFillColor(418)
HIST['MC_%s' % (suffix)].SetFillStyle(1001)
HIST['MC_%s' % (suffix)].SetLineColor(418)
HIST['MC_%s' % (suffix)].SetLineStyle(1)
HIST['MC_%s' % (suffix)].SetLineWidth(2)
for i, s in enumerate(HIST):
addOverflow(HIST[s], False) # Add overflow
#Stack
bkg = THStack(
'bkg', ";" + HIST[bkgsum].GetXaxis().GetTitle() + ";" +
HIST[bkgsum].GetYaxis().GetTitle())
bkg.Add(HIST['MC_%s' % (suffix)]) # ADD ALL BKG
#Legend
n = len(HIST)
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetTextSize(0.03)
leg.AddEntry(HIST['DATA_%s' % (suffix)],
'Data [%.1f]' % (HIST['DATA_%s' % (suffix)].Integral()), "pl")
leg.AddEntry(HIST['MC_%s' % (suffix)],
'DY [%.1f]' % (HIST['MC_%s' % (suffix)].Integral()), "f")
#if isFake: leg.AddEntry(HIST['FAKE_%s' %(suffix)], 'Fake [%.1f]' %(HIST['FAKE_%s' %(suffix)].Integral()), "f")
leg.AddEntry(HIST[bkgsum], 'BkgSum [%.1f]' % (HIST[bkgsum].Integral()),
"f")
c1 = TCanvas("c1",
HIST.values()[-1].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
#Ratio pad
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)
if logy:
c1.GetPad(bool(ratio)).SetLogy()
#Draw
bkg.Draw("HIST") # stack
HIST[bkgsum].Draw("SAME, E2") # sum of bkg
HIST['DATA_%s' % (suffix)].Draw("SAME, PE") # data
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1) #1.075
bkg.SetMaximum((6.0 if logy else 1.5) * max(
bkg.GetMaximum(),
HIST['DATA_%s' %
(suffix)].GetBinContent(HIST['DATA_%s' %
(suffix)].GetMaximumBin()) +
HIST['DATA_%s' %
(suffix)].GetBinError(HIST['DATA_%s' %
(suffix)].GetMaximumBin())))
bkg.SetMinimum(
max(
min(HIST[bkgsum].GetBinContent(HIST[bkgsum].GetMinimumBin(
)), HIST['DATA_%s' %
(suffix)].GetMinimum()), 5.e-1) if logy else 0.)
#bkg.SetMinimum(1.0)
leg.Draw()
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 / Bkg")
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)
errLine.SetLineColor(1)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'DATA_%s' % (suffix) in HIST:
res = HIST['DATA_%s' % (suffix)].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))
setBotStyle(res)
res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(HIST['DATA_%s' % (suffix)], HIST[bkgsum])
drawKolmogorov(HIST['DATA_%s' % (suffix)], HIST[bkgsum])
#drawRelativeYield(HIST['DATA_%s' %(suffix)], HIST[bkgsum])
else:
res = None
c1.cd(1)
if '2016' in output:
drawCMS("35.87", "Object Study")
elif '2017' in output:
drawCMS("41.53", "Object Study")
elif '2018' in output:
drawCMS("59.74", "Object Study")
if 'os' in suffix:
drawRegion('Opposite Sign')
elif 'ss' in suffix:
drawRegion('Same Sign')
c1.Update()
c1.Print('%s/hstack_%s.png' % (output, suffix))
if args.single:
drawResoLin(histLin, legTitle, "#LT E_{pred}#GT/E_{true}",
"lin_stage_" + str(st), False, 0.95, 1.05, "pe")
else:
drawResoLin(histLin, legTitle, "#LT E_{pred}#GT/E_{true}",
"lin_stage_" + str(st), False, 0.95, 1.05, False)
tfile.Close()
r.gROOT.SetBatch(False)
can = TCanvas("can", "")
multi.Draw("nostack")
multi.GetXaxis().SetTitle("E [GeV]")
multi.GetYaxis().SetTitle("#sigma_{E_{pred}}/#LT E_{pred}#GT")
multi.GetXaxis().SetRangeUser(0, 102)
multi.SetMaximum(0.1)
multi.Draw("nostack")
leg.Draw()
legT.Draw()
can.Print("plots/allStages_reso.pdf")
canLin = TCanvas("canLin", "")
multiLin.Draw("nostack")
multiLin.GetXaxis().SetTitle("E [GeV]")
multiLin.GetYaxis().SetTitle("#LT E_{pred}#GT/E_{true}")
multiLin.GetXaxis().SetRangeUser(0, 102)
multiLin.SetMaximum(1.15)
multiLin.SetMinimum(0.95)
multiLin.Draw("nostack")
leg.Draw()
legT.Draw()
def draw(hist,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.7, 0.9 - 0.05 * n, 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'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
# --- 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)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
#if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
data_graph.Draw("SAME, PE")
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((2. if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
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)
#if log: bkg.SetMinimum(1)
leg.Draw()
drawCMS(lumi, "Preliminary")
drawRegion(channel)
drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
if len(sign) > 0:
if channel.startswith('X') and len(sign) > 0:
drawNorm(0.9 - 0.04 * (n + 1),
"#sigma(X) #times B(X #rightarrow Vh) = %.1f pb" % snorm)
#elif "SR" in channel: drawNorm(0.9-0.04*(n+1), "DM+bb/tt, scaled by %.0f" % snorm, "m_{#chi}=1 GeV, scalar mediator")
elif "SR" in channel:
drawNorm(0.9 - 0.04 * (n + 1), "DM+bb/tt, m_{#chi}=1 GeV",
"scalar mediator")
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 / Bkg")
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))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 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'])
# drawKolmogorov(hist['data_obs'], hist['BkgSum'])
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, err, errLine, res, data_graph, res_graph]
def Draw_1D_Comp(rootfile_name, var, min, max, WP_list, leg, isTail=False):
gStyle.SetErrorX(0.5)
## The TStack is created with min and max set
stack = THStack("stack", var.name)
stack.SetMinimum(min)
stack.SetMaximum(max)
for wp in WP_list:
## Loading the graph using its name and file location
graph_name = "{}_{}".format(var.name, wp.name)
if isTail:
graph_name += "_cumulative"
graph = GetGraphFromFile(rootfile_name, graph_name)
if graph == -1:
continue
## Setting the colors specific to the working point
graph.SetLineColor(wp.colour)
graph.SetMarkerColor(wp.colour)
graph.SetMarkerStyle((wp.marker))
leg.AddEntry(graph, wp.name, "p")
width = graph.GetBinWidth(0)
## We check if the legend requires values for statistics
if leg.GetNColumns() == 2:
## Calculate the MEAN and RMSE and add to the legend
mean = graph.GetMean()
stdev = graph.GetStdDev()
rms = np.sqrt(mean * mean + stdev * stdev)
## Adding the legend entry
# leg.AddEntry( 0, "{:5.2f}".format(mean), "")
leg.AddEntry(0, "{:5.2f}".format(rms), "")
## Adding the object to the stack
stack.Add(graph)
del graph
## Checking to see if any graphs were found for this variable
if stack.GetNhists() == 0:
print("\n\n\nNo graphs found for working point {}\n\n\n".format(
wp.name))
return -1
## Drawing the stack on the currrent canvas
stack.Draw("nostack EP")
leg.Draw()
## Setting axis labels
if isTail:
stack.GetXaxis().SetTitle(var.x_label + " threshold " + var.units)
stack.GetYaxis().SetTitle("#it{f}_{tail}")
else:
stack_y_label = "Events per {:.0f} GeV".format(
width) if width > 1 else "Events per GeV"
stack.GetXaxis().SetTitle(var.x_label + " " + var.units)
stack.GetYaxis().SetTitle(stack_y_label)
## Moving axis tick marks
stack.GetYaxis().SetMaxDigits(3)
stack.GetXaxis().SetLabelOffset(0.017)
return stack
def plot_hist(histlist,
labellist,
norm,
log,
overflow,
filename,
options,
scaling=[]):
canv = TCanvas("c1", "c1", 800, 600)
canv.SetTopMargin(10)
canv.SetRightMargin(100)
if log: gPad.SetLogy()
histstack = THStack("stack", histlist[0].GetTitle())
legend = TLegend(0.76, 0.88 - 0.08 * len(histlist), 0.91, 0.88, '', 'NDC')
colorlist = [4, 8, 2, 6, 1]
if len(scaling) > 1 and scaling[1] == 0: scaling = []
if options: options += " NOSTACK"
else: options = "NOSTACK"
maximum = 0
for i in range(len(histlist)):
entries = histlist[i].GetEntries()
mean = histlist[i].GetMean()
histlist[i].SetLineColorAlpha(colorlist[i], 0.65)
histlist[i].SetLineWidth(3)
nbins = histlist[i].GetNbinsX()
legend.AddEntry(
histlist[i], "#splitline{" + labellist[i] +
"}{#splitline{%d entries}{mean=%.2f}}" % (entries, mean), "l")
if overflow:
histlist[i].SetBinContent(
nbins, histlist[i].GetBinContent(nbins) +
histlist[i].GetBinContent(nbins + 1)) #overflow
histlist[i].SetBinContent(1, histlist[i].GetBinContent(0) +
histlist[i].GetBinContent(1)) #underflow
if entries and norm: histlist[i].Scale(1. / entries)
if histlist[i].GetMaximum() > maximum:
maximum = histlist[i].GetMaximum()
histstack.Add(histlist[i])
histstack.SetMaximum(maximum * 1.4)
histstack.Draw(options)
histstack.GetXaxis().SetTitle(histlist[0].GetXaxis().GetTitle())
histstack.GetYaxis().SetTitle(histlist[0].GetYaxis().GetTitle())
xlimit = [
histlist[0].GetBinLowEdge(1),
histlist[0].GetBinLowEdge(histlist[0].GetNbinsX()) +
histlist[0].GetBinWidth(histlist[0].GetNbinsX())
]
if len(scaling) != 0:
top_axis = TGaxis(xlimit[0], maximum * 1.4, xlimit[1], maximum * 1.4,
(xlimit[0] - scaling[0]) / scaling[1],
(xlimit[1] - scaling[0]) / scaling[1], 510, "-")
top_axis.SetTitle("normalized scale")
top_axis.SetTickSize(0)
top_axis.Draw("SAME")
legend.SetTextSize(0.02)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw("SAME")
canv.SaveAs(filename)
if log: gPad.Clear()
canv.Clear()
del canv
def plotFractionsOfBxId(self):
##BX right plotting pt
canvasBxRightPt = TCanvas("cBxRightPt","cBxRightPt",1200,1200)
canvasBxRightPt.cd().SetLeftMargin(0.15)
hBxRightPt = self.fileHandler.getHistogram('BxRightGen_Pt').Clone()
setPlotStyle()
hBxRightPt.Rebin(50)
hBxRightPt.GetXaxis().SetRangeUser(0,200)
hBxRightPt.GetYaxis().SetTitle("normalized Entries / 5 GeV")
hBxRightPt.GetXaxis().SetTitle("p_{T} Gen")
hBxRightPt.GetYaxis().SetTitleOffset(2)
hBxRightPt.SetTitle("Events with right BX ID vs. p_{T}")
hBxRightPt.SetStats(0)
hBxRightPt.SetLineWidth(2)
hBxRightPt.Scale(1/hBxRightPt.Integral())
hBxRightPt.Draw()
label = getLabelCmsPrivateSimulation()
label.Draw()
self.storeCanvas(canvasBxRightPt,"bxRightPt")
##BX wrong plotting pt
canvasBxWrongPt = TCanvas("cBxWrongPt","cBxWrongPt",1200,1200)
canvasBxWrongPt.cd().SetLeftMargin(0.15)
hBxWrongPt = self.fileHandler.getHistogram('BxWrongGen_Pt').Clone()
setPlotStyle()
hBxWrongPt.Rebin(50)
hBxWrongPt.GetXaxis().SetRangeUser(0,200)
hBxWrongPt.GetYaxis().SetTitle("normalized Entries / 5 GeV")
hBxWrongPt.GetXaxis().SetTitle("p_{T} Gen")
hBxWrongPt.GetYaxis().SetTitleOffset(2)
hBxWrongPt.SetTitle("Events with wrong BX ID vs. p_{T}")
hBxWrongPt.SetStats(0)
hBxWrongPt.SetLineWidth(2)
hBxWrongPt.Scale(1/hBxWrongPt.Integral())
hBxWrongPt.DrawCopy()
label = getLabelCmsPrivateSimulation()
label.Draw()
self.storeCanvas(canvasBxWrongPt,"bxWrongPt")
#Plot the histogram stack
canvasStack = TCanvas("cStacked","cStacked",1200,1200)
canvasStack.cd().SetLeftMargin(0.15)
hWrong = self.fileHandler.getHistogram('BxWrongGen_Pt')
hRight = self.fileHandler.getHistogram('BxRightGen_Pt')
hRightFraction = TH1D('hRightFraction','',100,0,500)
hWrongFraction = TH1D('hWrongFraction','',100,0,500)
#hWrong.Rebin(50)
#hRight.Rebin(50)
#Fill the histograms with the bin wise fractions
for i in range(0,hRight.GetNbinsX()):
nRight = hRight.GetBinContent(i+1)
nWrong = hWrong.GetBinContent(i+1)
if(nRight + nWrong == 0):
continue
hRightFraction.SetBinContent(i+1,nRight/(nRight+nWrong))
hWrongFraction.SetBinContent(i+1,nWrong/(nRight+nWrong))
#Create the stack
stack = THStack("hstack","Fractions of events for BX ID correct and wrong")
nRight = hRight.Integral()
nWrong = hWrong.Integral()
hRightFraction.SetLineColor(colorRwthDarkBlue)
hRightFraction.SetFillColor(colorRwthDarkBlue)
hRightFraction.SetFillStyle(3002)
hWrongFraction.SetLineColor(colorRwthMagenta)
hWrongFraction.SetFillColor(colorRwthMagenta)
hWrongFraction.SetFillStyle(3002)
stack.Add(hRightFraction)
stack.Add(hWrongFraction)
stack.Draw()
stack.GetXaxis().SetRangeUser(0.5,201)
stack.GetYaxis().SetTitle('rel. fraction / 5 GeV')
stack.GetYaxis().SetTitleOffset(2)
stack.GetXaxis().SetTitle('p_{T} Gen')
stack.SetMinimum(0.9)
stack.SetMaximum(1)
legend = TLegend(0.6,0.75,0.9,0.9)
legend.AddEntry(hRightFraction,"BX ID right","f")
legend.AddEntry(hWrongFraction,"BX ID wrong","f")
legend.Draw()
label = getLabelCmsPrivateSimulation()
label.Draw()
canvasStack.Update()
self.storeCanvas(canvasStack,"bxStacked")
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-N", "--multiplicity", dest="N", type="int", default=3)
parser.add_option("-x", "--exclusive", action="store_true",\
dest="isExclusive", default=False)
parser.add_option("-l", "--label", dest="label", type="string", default="")
(options, args) = parser.parse_args()
N = options.N
isExclusive = options.isExclusive
label_text = options.label
if isExclusive and not (N == 2 or N == 3):
parser.error("Exclusive plot only for N =2 or 3")
import configurations as config
from ROOT import TFile, TCanvas, THStack, TLegend, TPaveText, gStyle
from ModelParser import ModelKey
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadRightMargin(0.05)
suffix = ""
if not isExclusive:
suffix = "up"
sm_files = []
for model in config.sm_models:
f = TFile("%s/%s.root" % (config.sm_dir, model), "READ")
sm_files.append(f)
bh_weights = []
bh_files = []
from BHXsec import BHXsec
xsec = BHXsec()
for model in config.bh_showcase:
f = TFile("%s/%s.root" % (config.bh_dir, model), "READ")
bh_files.append(f)
h = f.Get("plotsNoCut/ST")
nEvents = h.GetEntries()
bh_weights.append(
xsec.get(model) / nEvents * config.integrated_luminosity)
c = TCanvas("ST_Mul%d%s" % (N, suffix), "ST_Mul%d%s" % (N, suffix), 500,
500)
hs = THStack()
infile = TFile(options.inputfile, "READ")
hBkg = infile.Get("Background_N%d%s" % (N, suffix))
gBkg = infile.Get("BackgroundGraph_N%d%s" % (N, suffix))
hData = infile.Get("Data_N%d%s" % (N, suffix))
hBkg = infile.Get("Background_N%d%s" % (N, suffix))
hBkg.SetMarkerSize(0)
hBkg_ = hBkg.Clone("BkgLine")
hBkg.SetFillColor(33)
hBkg.SetLineColor(33)
hBkg_.SetLineWidth(3)
hBkg_.SetLineColor(862)
hs.Add(hBkg, "e3")
legend = TLegend(0.3326613, 0.6419492, 0.9294355, 0.9216102)
legend.SetTextSize(0.02966102)
legend.SetTextFont(42)
legend.SetFillColor(0)
legend.SetLineColor(0)
if isExclusive:
legend.SetHeader("N = %d" % N)
else:
legend.SetHeader("N #geq %d" % N)
legend.AddEntry(hData, "Data", "p")
legend.AddEntry(hBkg_, "Background", "l")
legend.AddEntry(hBkg, "Uncertainty", "f")
legend_sm = TLegend(0.6471774, 0.7669492, 0.8508065, 0.8771186)
legend_sm.SetTextSize(0.02966102)
legend_sm.SetTextFont(42)
legend_sm.SetFillColor(0)
legend_sm.SetLineColor(0)
for i, f in enumerate(bh_files):
h = f.Get("plotsN%d%s/ST" % (N, suffix))
h.Rebin(config.rebin)
h.Scale(bh_weights[i])
# Add background
for ibin in range(h.GetNbinsX()):
h.SetBinContent(ibin+1,\
h.GetBinContent(ibin+1)\
+ hBkg.GetBinContent(ibin+1))
h.SetLineWidth(2)
h.SetLineStyle(i + 2)
hs.Add(h, "hist")
model = ModelKey(config.bh_showcase[i])
bh_legend = "M_{D} = %.1f TeV, M_{BH}^{ min} = %.1f TeV, n = %d" % (\
model.parameter["MD"],
model.parameter["M"],
model.parameter["n"])
legend.AddEntry(h, bh_legend, "l")
if isExclusive:
for i, f in enumerate(sm_files):
h = f.Get("plotsN%d%s/ST" % (N, suffix))
h.Rebin(config.rebin)
h.Scale(config.integrated_luminosity)
h.SetFillColor(config.sm_colors[i])
h.SetLineColor(config.sm_colors[i])
hs.Add(h, "hist")
legend_sm.AddEntry(h, config.sm_models[i], "f")
#hs.Add(hData, "e")
hs.Draw("nostack")
c.SetLogy(1)
hs.GetXaxis().SetTitle("S_{T} (GeV)")
hs.GetYaxis().SetTitle(hData.GetYaxis().GetTitle())
hs.GetYaxis().SetTitleOffset(1.2)
ibin = 0
if isExclusive:
hs.GetXaxis().SetRangeUser(config.fit_range[0], config.maxST)
ibin = hData.FindBin(config.fit_range[0])
else:
hs.GetXaxis().SetRangeUser(config.norm_range[0], config.maxST)
ibin = hData.FindBin(config.norm_range[0])
from Styles import formatUncertainty
formatUncertainty(gBkg)
gBkg.Draw("LX")
hData.Draw("esame")
hs.SetMinimum(5e-2)
if isExclusive:
hs.SetMaximum(hData.GetBinContent(ibin) * 20)
else:
#hs.SetMaximum(4e4)
hs.SetMaximum(hData.GetBinContent(ibin) * 20)
legend.Draw("plain")
if isExclusive:
legend_sm.Draw("plain")
if isExclusive:
cmslabel = TPaveText(0.5544355, 0.5127119, 0.8991935, 0.6292373,
"brNDC")
else:
cmslabel = TPaveText(0.1955645, 0.1631356, 0.5403226, 0.279661,
"brNDC")
cmslabel.AddText(config.cmsTitle)
cmslabel.AddText(config.cmsSubtitle)
cmslabel.SetFillColor(0)
cmslabel.Draw("plain")
label = TPaveText(0.6891129, 0.8644068, 0.9435484, 0.9258475, "brNDC")
label.SetFillColor(0)
label.SetTextSize(0.0529661)
label.AddText(label_text)
label.Draw("plain")
c.Update()
raw_input("Press Enter to continue...")
SipMCTTbar[i].SetFillColor(kAzure - 3)
SipMCTTbar[i].SetLineColor(kBlack)
hs.Add(SipMCTTbar[i])
#MC DY hist
SipMCDY[i].Scale(norm) # MC normalization
SipMCDY[i].SetFillColor(kAzure + 6)
SipMCDY[i].SetLineColor(kBlack)
hs.Add(SipMCDY[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.3 * max(hs.GetMaximum(), SipDATA[i].GetMaximum()))
SipDATA[i].SetMaximum(1.3 * max(hs.GetMaximum(), SipDATA[i].GetMaximum()))
hs.Draw("histo")
SipDATA[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle("SIP")
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
hs.GetYaxis().SetTitle("Events")
def read_file(arr, meas_type, mapsa_fitter_inst, path):
if not os.path.isfile(str(path + arr[0])):
print "Root file not found at", str(path + arr[0])
#sys.exit(1)
return
f = TFile(str(path + arr[0]), 'READ')
if (f.IsZombie()):
print "Error opening file"
return
else:
print "Reading File ", arr[0]
#f.ls()
tree = f.Get('tree')
f_GlobalData_Map = ROOT.TMap()
f_GlobalData_Map.Add(ROOT.TObjString("tree"), tree)
#tree.Print()
outfile = TString(arr[0])
outfile.ReplaceAll(".root", "")
outfile.ReplaceAll(" ", "")
# print outfile
if (meas_type == 0):
outfile = "pedestal"
if (not g.FindKey(str(outfile))):
g.mkdir(str(outfile))
g.cd(str(outfile))
else:
return
channels = 288
if (arr[2] == 'inv'):
channels = 96
mapsa_mat = [[1, 0, 0], [1, 0, 0]]
elif (arr[2] == 'norm'):
channels = 288
mapsa_mat = [[1, 1, 1], [1, 0, 1]]
#print "channels", channels
#print "mapsa_mat", mapsa_mat
c1 = TCanvas('c1', 'Pixel Monitor ', 700, 900)
c2 = TCanvas('c2', 'Pixel Monitor ', 500, 500)
c3 = TCanvas('c3', 'Pixel Monitor ', 1280, 720)
c4 = TCanvas('c4', 'Pixel Monitor ', 500, 500)
c5 = TCanvas('c5', 'Pixel Monitor ', 500, 500)
f_GlobalData_Map.Add(ROOT.TObjString("c1"), c1)
f_GlobalData_Map.Add(ROOT.TObjString("c2"), c2)
f_GlobalData_Map.Add(ROOT.TObjString("c3"), c3)
f_GlobalData_Map.Add(ROOT.TObjString("c4"), c4)
f_GlobalData_Map.Add(ROOT.TObjString("c5"), c5)
# c2.Divide(2,1)
#c2.cd(0)
c1.Divide(3, 2)
for i in range(1, 7):
c1.cd(i)
ROOT.gPad.SetGridx()
ROOT.gPad.SetGridy()
# channelcounts = TH2I('HitMap','Counts; Channel; DAC Value (1.456 mV)', 288, .5,288.5,256, .5, 256.5)
channelcounts = TH2I('HitMap', 'Counts; Channel; DAC Value (a.u.)', 288,
.5, 288.5, 256, .5, 256.5)
channelcounts_norm = TH2F('HitMap_norm',
'Occupancy ; Channel; DAC Value (a.u.)', 288, .5,
288.5, 256, .5, 256.5)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts_norm)
norm_2d = TH2F('Norm2D', 'Normalization; Column; Row', 48, .5, 48.5, 6, .5,
6.5)
mean_2d = TH2F('Mean2D', 'Mean; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
sigma_2d = TH2F('Sigma2D', 'Sigma; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
chisquare = TH2F('Chisquare2D', 'Chisquare; Column; Row', 48, .5, 48.5, 6,
.5, 6.5)
f_GlobalData_Map.Add(ROOT.TObjString("Norm2D"), norm_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Mean2D"), mean_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Sigma2D"), sigma_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Chisquare2D"), chisquare)
objarr2d = []
objarr2d.append(norm_2d)
objarr2d.append(mean_2d)
objarr2d.append(sigma_2d)
objarr2d.append(chisquare)
normgraph = TGraphErrors()
meangraph = TGraphErrors()
sigmagraph = TGraphErrors()
chisquaregraph = TGraphErrors()
mean_corrgraph = TGraphErrors()
f_GlobalData_Map.Add(ROOT.TObjString("normgraph "), normgraph)
f_GlobalData_Map.Add(ROOT.TObjString("meangraph "), meangraph)
f_GlobalData_Map.Add(ROOT.TObjString("sigmagraph "), sigmagraph)
f_GlobalData_Map.Add(ROOT.TObjString("chisquaregraph"), chisquaregraph)
f_GlobalData_Map.Add(ROOT.TObjString("mean_corrgraph"), mean_corrgraph)
meanhist = TH1F('meanhist', 'Mean DAC; DAC Value (a.u.); counts', 2560, 0,
255)
sigmahist = TH1F('sigmahist', 'Sigma DAC; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_std = TH1F('meanhist_std',
'Mean DAC Standard; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_std = TH1F('sigmahist_std',
'Sigma DAC Standard; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double = TH1F('meanhist_double',
'Mean DAC Double; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_double = TH1F('sigmahist_double',
'Sigma DAC Double; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double_neighbour = TH1F(
'meanhist_double_neighbour',
'Mean DAC Double Neighbour; DAC Value (a.u.); counts', 2560, 0, 255)
sigmahist_double_neighbour = TH1F(
'sigmahist_double_neighbour',
'Sigma DAC Double Neighbour; DAC Value (a.u.); counts', 100, 0, 10)
objarr = []
objarr.append(normgraph)
objarr.append(meangraph)
objarr.append(sigmagraph)
objarr.append(chisquaregraph)
objarr.append(mean_corrgraph)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist'), meanhist)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist'), sigmahist)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_std'), meanhist_std)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_std'), sigmahist_std)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double'), meanhist_double)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double'), sigmahist_double)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double_neighbour'),
meanhist_double_neighbour)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double_neighbour'),
sigmahist_double_neighbour)
objarr.append(meanhist)
objarr.append(sigmahist)
objarr.append(meanhist_std)
objarr.append(sigmahist_std)
objarr.append(meanhist_double)
objarr.append(sigmahist_double)
objarr.append(meanhist_double_neighbour)
objarr.append(sigmahist_double_neighbour)
for objs in objarr:
objs.SetMarkerColor(2)
objs.SetMarkerStyle(20)
objs.SetMarkerSize(1)
normgraph.SetName('normgraph')
meangraph.SetName('meangraph')
sigmagraph.SetName('sigmagraph')
chisquaregraph.SetName('chisquare')
mean_corrgraph.SetName('mean_corr')
normgraph.SetTitle('Normalization; Channel; Normalization')
meangraph.SetTitle('Mean; Channel; DAC Value (a.u.)')
sigmagraph.SetTitle('Sigma; Channel; DAC Value (a.u.)')
chisquaregraph.SetTitle('Chisquared/NDF_gr; Channel; Chisquared/NDF ')
ROOT.gStyle.SetOptFit(1111)
stack = THStack('a', ';DAC Value (a.u.); Occupancy')
f_GlobalData_Map.Add(ROOT.TObjString("stack"), stack)
fitfuncs = []
fitparams = []
gr1 = []
for pixel in range(0, channels):
gr1.append(
TH1D(
str(pixel).zfill(3),
str(pixel + 1).zfill(3) + ';DAC Value (a.u.); Occupancy ', 256,
0.5, 256.5))
f_GlobalData_Map.Add(ROOT.TObjString(str(pixel).zfill(3)), gr1[pixel])
#gr2.append(TH1F('th1f_'+str(pixel).zfill(3),str(pixel+1).zfill(3)+';DAC Value (a.u.); Occupancy',256,0.5,256.5))
color = pixel % 8 + 1
formating_th1(gr1[pixel], color)
if (meas_type == 0):
fitfuncs.append(
TF1('gauss' + str(pixel + 1).zfill(3), 'gaus(0)', 0, 256))
fitfuncs[pixel].SetNpx(256)
fitfuncs[pixel].SetLineColor(color)
f_GlobalData_Map.Add(
ROOT.TObjString('gauss' + str(pixel).zfill(3)),
fitfuncs[pixel])
#Here we read the data and fill the histogram
for event in tree:
eventstr = []
for counter, vals in enumerate(tree.AR_MPA):
#eventstr.append(vals)
channelcounts.Fill(counter, tree.THRESHOLD, vals)
if (counter < channels):
gr1[counter].Fill(tree.THRESHOLD, vals)
#if tree.THRESHOLD%20==0 and tree.REPETITION==0:
#print eventstr
#print ("Threshold %d Repetion %d" % (tree.THRESHOLD,tree.REPETITION))
#print tree.AR_MPA
#now we make a small analysis of the curves fitting different functions to it:
print "Finished Reading the Tree\n Normalization of Histograms\n"
for pixel in range(0, channels):
#gr1[pixel].ResetStats()
for j in range(0, gr1[pixel].GetXaxis().GetNbins() + 1):
gr1[pixel].SetBinError(
gr1[pixel].GetBin(j),
TMath.Sqrt(gr1[pixel].GetBinContent(gr1[pixel].GetBin(j))))
#if(pixel==0):
#gr1[pixel].Print("all")
#gr1[pixel].Sumw2(ROOT.kTRUE)
gr1[pixel].Scale(1 / arr[1])
#if(pixel==0):
#gr1[pixel].Print("all")
stack.Add(gr1[pixel])
#first create a THStack with histograms:
iterator = stack.GetHists()
if (meas_type == 0):
for idx, it in enumerate(iterator):
fitparams.append([])
if (it.Integral() > 0):
if (idx < channels):
#fitfuncs.append(TF1('combined'+str(idx),combined, 0,256,5))
#fitfuncs.append(TF1('combined_same_mean'+str(idx),combined_mean, 0,256,4))
#fitfuncs.append(TF1('double_gauss'+str(idx),'gaus(0)+gaus(3)',0,256))
#fitfuncs.append(TF1('gauss'+str(idx),'gaus(0)',0,256))
#fitfuncs.append(TF1('double_gauss_same_mean'+str(idx),double_gauss, 0,256,5))
#print it.GetName(), idx
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean()+1,it.GetRMS(),it.GetMean()-1,it.GetRMS());
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean(),it.GetRMS()*0.1,it.GetRMS()*0.1);
#print ("Channels %f\t%f\t%f\n" % (it.GetMaximum(),it.GetMean(),it.GetRMS()))
fitfuncs[idx].SetParameters(it.GetMaximum(), it.GetMean(),
it.GetRMS())
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),it.GetMean(),10*it.GetRMS());
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),10*it.GetRMS());
#it.Fit(fitfuncs[idx],'lr0 rob=0.95','same',0,256)
#it.Fit(fitfuncs[idx],'lr0q ','',0,256)
it.Fit(fitfuncs[idx], 'r0q ', '', 0, 256)
fitparams[idx].append(fitfuncs[idx].GetParameter(0))
fitparams[idx].append(fitfuncs[idx].GetParameter(1))
fitparams[idx].append(fitfuncs[idx].GetParameter(2))
fitparams[idx].append(fitfuncs[idx].GetParError(0))
fitparams[idx].append(fitfuncs[idx].GetParError(1))
fitparams[idx].append(fitfuncs[idx].GetParError(2))
if (fitfuncs[idx].GetNDF() > 0):
fitparams[idx].append(fitfuncs[idx].GetChisquare() /
fitfuncs[idx].GetNDF())
else:
for kk in range(0, 7):
fitparams[idx].append(0)
#print "fitparamarray"
fitarray = np.array(fitparams)
## print fitarray
for pointno, it in enumerate(fitarray):
if (fitarray[pointno][0] > 0):
normgraph.SetPoint(pointno, pointno + 1, fitarray[pointno][0])
normgraph.SetPointError(pointno, 0, fitarray[pointno][3])
meangraph.SetPoint(pointno, pointno + 1, fitarray[pointno][1])
meangraph.SetPointError(pointno, 0, fitarray[pointno][4])
meanhist.Fill(fitarray[pointno][1])
sigmagraph.SetPoint(pointno, pointno + 1, fitarray[pointno][2])
sigmagraph.SetPointError(pointno, 0, fitarray[pointno][5])
sigmahist.Fill(fitarray[pointno][2])
chisquaregraph.SetPoint(pointno, pointno + 1,
fitarray[pointno][6])
chisquaregraph.SetPointError(pointno, 0, 0)
## iterator.ls()
# Map the data to the pixel layout:
tmp_objarr = []
tmp_objarr.extend(
[meanhist_std, meanhist_double, meanhist_double_neighbour])
tmp_objarr.extend(
[sigmahist_std, sigmahist_double, sigmahist_double_neighbour])
for i in tmp_objarr:
print str(i.GetName())
fill2d(fitarray[:, 0], mapsa_mat, objarr2d[0])
fill2d(fitarray[:, 1], mapsa_mat, objarr2d[1])
fill2d(fitarray[:, 2], mapsa_mat, objarr2d[2])
fill2d(fitarray[:, 6], mapsa_mat, objarr2d[3])
fill1d_edges(objarr2d[1], tmp_objarr[0:3])
fill1d_edges(objarr2d[2], tmp_objarr[3:])
g.cd(str(outfile))
mapsa_fitter_inst.Make_dirs()
mapsa_fitter_inst.Set_run_no(outfile)
if (meas_type == 1):
for idx, it in enumerate(iterator):
if (it.Integral() > 0):
if (idx < channels):
mapsa_fitter_inst.Find_signal(it, idx, 0.0025, 3)
g.cd()
#g.mkdir(str(outfile)+"/Channels")
#g.cd(str(outfile)+"/Channels")
#iterator.Write()
g.cd(str(outfile))
g.mkdir(str(outfile) + "/Overview")
## iterator.First().Print("all")
Maximum = TMath.Power(10, (round(TMath.Log10(stack.GetMaximum())) - 1))
#Minimum = TMath.Power(10,(round(TMath.Log10(stack.GetMinimum()))+1))
Minimum = .1
ROOT.gStyle.SetLabelSize(0.06, "xyz")
ROOT.gStyle.SetTitleSize(0.06, "xyz")
ROOT.gStyle.SetTitleOffset(1.2, "y")
ROOT.gStyle.SetTitleOffset(.825, "x")
ROOT.gStyle.SetPadGridX(1)
ROOT.gStyle.SetPadGridY(1)
ROOT.gStyle.SetOptStat(0)
# ROOT.gStyle.SetPadLeftMargin(.2);
# ROOT.gStyle.SetPadRightMargin(.1);
c1.cd(1)
stack.Draw("nostack hist e1 x0")
stack.GetXaxis().SetRangeUser(0, 256)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLogy()
c2.cd(0)
stack.Draw("nostack hist e1 x0")
#if(outfile.Contains("SR_90_on_top")):
#stack.GetXaxis().SetRangeUser(0,256)
#else:
#stack.GetXaxis().SetRangeUser(0,100)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
ROOT.gPad.SetLogy()
ROOT.gPad.Update()
#for idx, it in enumerate(fitfuncs):
## if idx>0 and idx<7:
#c1.cd(1)
#fitfuncs[idx].Draw("same")
#c2.cd(0)
#fitfuncs[idx].DrawCopy("psame")
## it.SetLineColor(idx%9+1)
## it.Draw("same")
#g.cd(str(outfile)+"/Channels")
#it.Write("HitMap_py_"+str(idx+1)+"_fit")
g.cd(str(outfile) + "/Overview")
c1.cd(2)
chisquaregraph.Draw("ap")
c1.cd(3)
normgraph.Draw("ap")
c1.cd(4)
sigmagraph.Draw("ap")
sigmagraph.GetYaxis().SetRangeUser(0, 5)
sigmagraph.GetXaxis().SetRangeUser(0, channels + 1)
c2.cd(2)
sigmagraph.Draw("ap")
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
c1.cd(5)
meangraph.Draw("ap")
c1.cd(6)
channelcounts.Draw("colz")
channelcounts.GetXaxis().SetRangeUser(0, channels + 1)
# c2.cd(3)
c3.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy = channelcounts.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy"),copy)
#if(outfile.Contains("SR_90_on_top")):
#copy.SetMaximum(100)
#copy.SetMinimum(1)
copy.GetYaxis().SetTitle("DAC Value (a.u.)")
c4.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy1"),copy1)
copy1.GetZaxis().SetTitle("Sigma (a.u.)")
copy1.GetZaxis().SetTitleOffset(1.2)
ROOT.gPad.SetRightMargin(.2)
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c5.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
#copy1 = chisquare.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1.GetZaxis().SetTitle("sigma (a.u.)")
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c1.Update()
c1.Modified()
c2.Update()
c2.Modified()
c3.Update()
c3.Modified()
c4.Update()
c4.Modified()
c5.Update()
c5.Modified()
## c1.SaveAs("double_gauss_same_mean.pdf")
## time.sleep(2)
g.cd(str(outfile) + "/Overview")
#for objs in objarr:
#objs.Write(objs.GetName())
#norm_2d.GetZaxis().SetRangeUser(1E5,2E6)
#mean_2d.GetZaxis().SetRangeUser(54,64)
## norm_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3))-2)), TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3)))-1)))
## mean_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(mean_2d.mean_2d.GetStdDev(3)))-1))-5,TMath.Power(10,(round(TMath.Log10(mean_2d.GetStdDev(3)))-1))+5)
#sigma_2d.GetZaxis().SetRangeUser(0,5)
#chisquare.GetZaxis().SetRangeUser(0,10000 )
#for objs in objarr2d:
#objs.Write(objs.GetName())
#c1.Write("c1")
#outfile1=outfile+TString(".pdf")
#c2.SaveAs(str(outfile1))
#c2.Write("c2")
#c3.SaveAs("c3"+str(outfile1))
#c3.Write("c3")
#c4.SaveAs("c4"+str(outfile1))
#c4.Write("c4")
## while (TObject(iterator.Next())):
## print iterator.Next().Title()
#stack.Write("stack")
#g.cd(str(outfile))
#channelcounts.Write(str(outfile))
#f.Close()
c1.Close()
c2.Close()
c3.Close()
c4.Close()
c5.Close()
f_GlobalData_Map.DeleteAll()
f.Close()
def makeDiTauStack(outDir,
inFile,
rootDir,
s,
labelX,
units="GeV",
left=False,
channel="",
json="Golden",
log=False,
dndm=False,
doRatio=False,
year=2017,
sign='OS',
LTcut=0.,
cat='mmtt',
wait='wait'):
tdrstyle.setTDRStyle()
writeExtraText = True # if extra text
extraText = "Preliminary" # default extra text is "Preliminary"
lumi_sqrtS = "13 TeV"
if json == "Golden": lumi_13TeV = channel + " 41.8 fb^{-1}, 2017"
iPeriod = 4 # 1=7TeV, 2=8TeV, 3=7+8TeV, 7=7+8+13TeV
xR = 0.65 #legend parameters
xR = 0.2 #legend parameters
H = 600
W = 600
H_ref = 600
W_ref = 600
# references for T, B, L, R
T = 0.08 * H_ref
B = 0.12 * H_ref
L = 0.16 * W_ref
R = 0.04 * W_ref
#margins for inbetween the pads in a ratio plot
B_ratio = 0.1 * H_ref
T_ratio = 0.03 * H_ref
#margin required for lebal on bottom of raito plot
B_ratio_label = 0.3 * H_ref
c = TCanvas('c1', 'c1', 50, 50, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
if not doRatio:
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.cd()
ratioPad = TPad("pad2", "", 0.0, 0.0, 1.0, 0.29)
plotPad = TPad("pad1", "", 0.0016, 0.291, 1.0, 1.0)
if doRatio:
plotPad.SetTicks(0, 0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B_ratio / H)
plotPad.SetFillColor(0)
plotPad.SetBottomMargin(0)
ratioPad.SetLeftMargin(L / W)
ratioPad.SetRightMargin(R / W)
ratioPad.SetTopMargin(T_ratio / H)
ratioPad.SetTopMargin(0.007)
ratioPad.SetBottomMargin(B_ratio_label / H)
ratioPad.SetGridy(1)
ratioPad.SetFillColor(4000)
else:
plotPad = TPad("pad1", "", 0.0, 0.03, 1.0, 1.0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B / H)
plotPad.Draw()
plotPad.cd()
print("In makeStack inFile={0:s}".format(inFile))
f = TFile(inFile)
groups = ['data', 'Reducible', 'Rare', 'ZZ4L', 'Signal']
histo = {}
colors = {
'data': 0,
'Reducible': kMagenta - 10,
'Rare': kBlue - 8,
'ZZ4L': kAzure - 9,
'Signal': kRed
}
hs = THStack("hs", "")
for group in groups:
if len(cat) == 4:
histo[group] = f.Get("h{0:s}_{1:s}_Mtt".format(group, cat))
else:
histo[group] = f.Get("h{0:s}_ee{1:s}_Mtt".format(group, cat))
histo2 = f.Get("h{0:s}_mm{1:s}_Mtt".format(group, cat))
histo[group].Add(histo2)
if dndm: convertToDNDM(histo[group])
if group == 'data':
applyDATAStyle(histo[group])
else:
applyStyle(histo[group], colors[group], 1, 1001)
if group != 'data': hs.Add(histo[group])
try:
hMax = 1.2 * max(histo['data'].GetMaximum(), hs.GetMaximum())
except KeyError:
hMax = 1.2 * hs.GetMaximum()
hs.SetMaximum(hMax)
hs.Draw("HIST")
try:
histo['data'].Draw("e,SAME")
except KeyError:
pass
if doRatio:
hs.GetXaxis().SetLabelSize(0)
else:
if units != "":
hs.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
hs.GetXaxis().SetTitle(labelX)
hs.GetYaxis().SetTitle("Events")
hs.GetYaxis().SetTitleOffset(1)
if dndm: hs.GetYaxis().SetTitle("dN/d" + labelX)
c.cd()
if doRatio:
data2 = histo['data'].Clone("data")
mc = histo['Rare']
mc.Add(histo['Reducible'])
mc.Add(histo['ZZ4L'])
xmin = mc.GetXaxis().GetXmin()
xmax = mc.GetXaxis().GetXmax()
line = TLine(xmin, 1.0, xmax, 1.0)
line.SetLineWidth(1)
line.SetLineColor(kBlack)
ratioPad.Draw()
ratioPad.cd()
data2.Divide(data2, mc)
data2.SetMarkerStyle(20)
data2.SetTitleSize(0.12, "Y")
data2.SetTitleOffset(0.40, "Y")
data2.SetTitleSize(0.12, "X")
data2.SetLabelSize(0.10, "X")
data2.SetLabelSize(0.08, "Y")
data2.GetYaxis().SetRangeUser(0.62, 1.38)
data2.GetYaxis().SetNdivisions(305)
data2.GetYaxis().SetTitle("Obs/Exp ")
if units != "":
data2.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
data2.GetXaxis().SetTitle(labelX)
data2.Draw("P")
line.Draw()
c.cd()
plotPad.cd()
l = TLegend(xR, 0.55, xR + 0.28, 0.9)
for group in groups:
try:
l.AddEntry(histo[group], group, "F")
except KeyError:
continue
l.SetBorderSize(0)
l.SetFillColor(0)
l.SetFillStyle(0)
l.SetTextSize(0.04)
l.Draw()
lTex1 = TLatex(120., 0.97 * hMax, 'Preliminary {0:d}'.format(year))
lTex1.SetTextSize(0.04)
lTex1.Draw()
signText = 'Same Sign'
if sign == 'OS': signText = 'Opposite Sign'
lTex2 = TLatex(120., 0.90 * hMax, '{0:s}'.format(signText))
lTex2.SetTextSize(0.04)
lTex2.Draw()
if len(cat) == 4:
lt = {
'eeet': '#it{l}#it{l}#it{e}#tau',
'eemt': '#it{l}#it{l}#mu#tau',
'eett': '#it{l}#it{l}#tau#tau',
'mmet': '#mu#mu#it{e}#tau',
'mmmt': '#mu#mu#mu#tau',
'mmtt': '#mu#mu#tau#tau'
}
else:
lt = {
'et': '#it{l}#it{l}#it{e}#tau',
'mt': '#it{l}#it{l}#mu#tau',
'tt': '#it{l}#it{l}#tau#tau'
}
lTex3 = TLatex(120., 0.83 * hMax,
'{0:s} H_LT > {1:d} '.format(lt[cat], int(LTcut)))
lTex3.SetTextSize(0.04)
lTex3.Draw()
plotPad.Draw()
#CMS_lumi(plotPad,4,11)
outFileBase = "Stack_{0:d}_{1:s}_{2:s}_LT{3:02d}".format(
year, cat, sign, int(LTcut))
c.SaveAs("{0:s}.png".format(outFileBase))
c.SaveAs("{0:s}.root".format(outFileBase))
if wait == 'wait':
raw_input()
else:
import time
time.sleep(5.)
def plot(var, cut, norm=False, nm1=False):
### Preliminary Operations ###
doBinned = False
if options.mode == "binned": doBinned = True
fileRead = os.path.exists("combinedCards_" + options.name +
"/fitDiagnostics_" + options.file + ".root")
treeRead = not any(
x == cut
for x in ['0l', '1e', '1m', '2e', '2m', '1e1m', 'Gen', 'Trigger'
]) #(var in variable.keys()) # Read from tree
#signal definition
if fileRead:
sign = ['ttDM_MChi1_MPhi200_scalar',
'tDM_MChi1_MPhi200_scalar'] #for postfit plot
if not fileRead and not options.limit:
sign = ['ttDM_MChi1_MPhi100_scalar',
'tDM_MChi1_MPhi100_scalar'] #for normal plotting
#bkg definition
if fileRead or options.limit:
back = [
"QCD", "DYJetsToNuNu_HT", "DYJetsToLL_HT", "VV", "ST",
"WJetsToLNu_HT", "TTbarSL"
] #for postfit or limit
if (cut).find('>250') or (cut).startswith('AH'): #for hadronic selections
back = [
"QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarV",
"TTbar2L", "TTbar1L", "DYJetsToNuNu_HT"
]
if fileRead or options.limit:
back = [
"QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarSL",
"DYJetsToNuNu_HT"
] #for postfit or limit
binLow = ""
binHigh = ""
binName = ""
if "binned" in cut:
binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1]
binHigh = cut[cut.find("HighVal") + 7:]
binName = "bin_" + binLow + "_" + binHigh
cut = cut[:cut.find("binned")]
useformula = False
if 'formula' in variable[var]:
useformula = True
channel = cut
plotdir = cut
plotname = var
weight = "eventWeightLumi" #*(2.2/35.9)
isBlind = BLIND and ('SR' in channel or 'ps' in channel)
if fileRead:
isBlind = False
options.saveplots = True
RESIDUAL = True
elif isBlind:
RATIO = 0
SIGNAL = 20
else:
RATIO = 4
SIGNAL = 1
RESIDUAL = False
showSignal = True #('SR' in channel)
cutSplit = cut.split()
for s in cutSplit:
if s in selection.keys():
plotdir = s
cut = cut.replace(s, selection[s])
if not binLow == "":
cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh
#if treeRead and cut in selection: cut = cut.replace(cut, selection[cut])
# Determine Primary Dataset
pd = []
if any(w in cut
for w in ['1l', '1m', '2m', 'isWtoMN', 'isZtoMM', 'isTtoEM']):
pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x]
if any(w in cut for w in ['1l', '1e', '2e', 'isWtoEN', 'isZtoEE']):
pd += [x for x in sample['data_obs']['files'] if 'SingleElectron' in x]
if any(w in cut for w in ['0l', 'isZtoNN']):
pd += [x for x in sample['data_obs']['files'] if 'MET' in x]
if len(pd) == 0:
raw_input("Warning: Primary Dataset not recognized, continue?")
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
print " cut :", weight
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
if fileRead:
var = 'MET_pt'
if channel.startswith('SL'): var = 'MET_sign'
if channel.endswith('ZR'): var = 'FakeMET_pt'
plotname = var
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
if doBinned:
bins = np.array([])
if 'bins' in variable[var].keys():
bins = np.array(variable[var]['bins'])
else:
binsize = (variable[var]['max'] -
variable[var]['min']) / variable[var]['nbins']
bins = np.arange(variable[var]['min'],
variable[var]['max'] + binsize, binsize)
bins = np.append(bins, 10000)
for i in range(0, len(bins) - 1):
rbin = str(bins[i]) + "_" + str(bins[i + 1])
fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not any(
t in s for t in ['data', 'tDM']
) else "rootfiles_" + options.name + "/" + channel + "bin_" + rbin + ".root"
histName = "shapes_fit_b/" + channel + "bin_" + rbin + "/" + s if not any(
t in s for t in ['data', 'tDM']) else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if 'data' not in s: hist[s].SetMarkerSize(0)
if tmphist:
hist[s].SetBinContent(i + 1, tmphist.GetBinContent(1))
hist[s].SetBinError(i + 1, tmphist.GetBinError(1))
else:
hist[s].SetBinContent(i + 1, 0.)
hist[s].SetBinError(i + 1, 0.)
else:
fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not s == 'data_obs' else "rootfiles_" + options.name + "/" + channel + binName + ".root"
histName = "shapes_fit_b/" + channel + "/" + s if not s == 'data_obs' else s
file[s] = TFile(fileName, "READ")
tmphist = file[s].Get(histName)
if tmphist == None:
tmphist = hist[back[0]].Clone(s)
tmphist.Reset("MICES")
print "Histogram", histName, "not found in file", fileName
if s == 'data_obs': hist[s] = tmphist
else:
hist[s] = hist['data_obs'].Clone(s)
hist[s].SetMarkerSize(0)
for i in range(tmphist.GetNbinsX() + 1):
hist[s].SetBinContent(i + 1,
tmphist.GetBinContent(i + 1))
elif 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 not binLow == "":
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('log' if variable[var]['log'] else ''), 1, float(binLow),
float(binHigh))
elif binLow == "" and variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('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()
redFactorString = ""
redFactorValue = ""
#if isBlind and 'data' in s:
if isBlind and 'data' in s and options.limit:
redFactorString = " && Entry$ % 15 == 1"
#if isBlind and 'data' not in s:
if isBlind and 'data' not in s and options.limit:
redFactorValue = " / 15"
cutstring = "(" + weight + redFactorValue + ")" + (
"*(" + cut + redFactorString + ")" if len(cut) > 0 else "")
if '-' in s:
cutstring = cutstring.replace(
cut, cut + "&& nBQuarks==" + s.split('-')[1][0])
if useformula == True:
tree[s].Project(s, variable[var]['formula'], cutstring)
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)
if hist[s].Integral() < 0: hist[s].Scale(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 'WJetsToLNu' in s and 'SL' in channel and 'WR' in channel: hist[s].Scale(1.30)
#if 'TTbar' in s and 'SL' in channel and 'TR' in channel: hist[s].Scale(0.91)
hist['BkgSum'] = hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s], 1)
if fileRead:
hist['PreFit'] = hist['BkgSum'].Clone("PreFit")
if doBinned:
for i in range(0, len(bins) - 1):
rbin = str(bins[i]) + "_" + str(bins[i + 1])
tmphist = file[back[0]].Get("shapes_prefit/" + channel +
"bin_" + rbin + "/" +
"total_background")
if tmphist:
hist['PreFit'].SetBinContent(i + 1,
tmphist.GetBinContent(1))
else:
hist['PreFit'].SetBinContent(i + 1, 0.)
else:
tmphist = file[back[0]].Get("shapes_prefit/" + channel + "/" +
"total_background")
for i in range(tmphist.GetNbinsX() + 1):
hist['PreFit'].SetBinContent(i + 1,
tmphist.GetBinContent(i + 1))
addOverflow(hist['PreFit'], False)
hist['PreFit'].SetLineStyle(2)
hist['PreFit'].SetLineColor(617) #923
hist['PreFit'].SetLineWidth(3)
hist['PreFit'].SetFillStyle(0)
hist['BkgSum'].SetFillStyle(3002)
hist['BkgSum'].SetFillColor(1)
# 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[0]].SetMarkerStyle(20)
hist[data[0]].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 norm:
for i, s in enumerate(sign):
hist[s].Scale(hist['BkgSum'].Integral() / hist[s].Integral())
# 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 SIGNAL > 1:
if not var == "Events":
for i, s in enumerate(sign):
hist[s].Scale(SIGNAL)
# Create stack
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 = TLegend(0.45, 0.63, 0.93, 0.92)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetNColumns(3)
leg.SetTextFont(42)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if 'PreFit' not in hist:
leg.AddEntry(hist['BkgSum'], sample['BkgSum']['label'], "f")
else:
leg.AddEntry(hist['BkgSum'], 'MC unc.', "l")
leg.AddEntry(hist['PreFit'], sample['PreFit']['label'], "l")
if showSignal:
for i, s in enumerate(sign):
if SIGNAL > 1:
if sample[s]['plot']:
leg.AddEntry(hist[s],
'%s (x%d)' % (sample[s]['label'], SIGNAL),
"l")
else:
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "l")
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:
if RESIDUAL:
c1.Divide(1, 3)
setFitTopPad(c1.GetPad(1), RATIO)
setFitBotPad(c1.GetPad(2), RATIO)
setFitResPad(c1.GetPad(3), RATIO)
else:
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 = ("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:
graph = fixData(hist[data[0]], USEGARWOOD)
graph.Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if 'PreFit' in hist: hist['PreFit'].Draw("SAME, HIST")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist[data[0]].GetBinContent(hist[data[0]].GetMaximumBin()) +
hist[data[0]].GetBinError(hist[data[0]].GetMaximumBin())))
if len(sign) > 0 and 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[0]].GetMinimum()), 5.e-1) if log else 0.)
if log:
#bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3)
bkg.GetYaxis().SetMoreLogLabels(True)
else:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3)
leg.Draw()
if fileRead and 'SR' in channel:
drawCMS(LUMI / 15., "Preliminary")
else:
drawCMS(LUMI, "Preliminary")
drawRegion(channel, True)
drawAnalysis("DM" + channel[:2])
drawOverflow()
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 / Bkg")
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))
if RESIDUAL: setFitBotStyle(err)
else: setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist[data[0]].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 RESIDUAL: setFitBotStyle(res)
else: setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
if 'PreFit' in hist:
respre = hist[data[0]].Clone("ResiduesPreFit")
respre.Divide(hist['PreFit'])
respre.SetLineStyle(2)
respre.SetLineColor(617) #923
respre.SetLineWidth(3)
respre.SetFillStyle(0)
respre.Draw("SAME, HIST")
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 RATIO and RESIDUAL:
c1.cd(3)
c1.SetGrid(1, 0)
resFit = hist[data[0]].Clone("Residues")
resFit.Reset("MICES")
resFit.SetTitle("")
#resFit.GetYaxis().SetTitle("Residuals")
resFit.GetYaxis().SetTitle(
"#frac{Data - Bkg}{#sqrt{#sigma_{Data}^{2}+#sigma_{Bkg}^{2}}}")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
resFit.SetBinContent(
i, (hist[data[0]].GetBinContent(i) -
hist['BkgSum'].GetBinContent(i)) / (math.sqrt(
math.pow(hist['BkgSum'].GetBinError(i), 2) +
math.pow(hist[data[0]].GetBinError(i), 2))))
setFitResStyle(resFit)
resFit.SetLineColor(15)
resFit.SetFillColor(15)
resFit.SetFillStyle(1001)
resFit.Draw("HIST")
resFitLine = resFit.Clone("resFitLine")
resFitLine.SetLineWidth(1)
resFitLine.SetFillStyle(0)
resFitLine.Draw("SAME, HIST")
c1.Update()
if gROOT.IsBatch(
) and options.saveplots: # and (treeRead and channel in selection.keys()):
AddString = ""
if not os.path.exists("plots_" + options.name + "/" + plotdir):
os.makedirs("plots_" + options.name + "/" + plotdir)
if fileRead:
if RESIDUAL: AddString = "_PostFit_Residual"
else: AddString = "_PostFit"
#c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+AddString+".png")
c1.Print("plots_" + options.name + "/" + plotdir + "/" + plotname +
binName + AddString + ".pdf")
# Print table
printTable(hist, sign)
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if gROOT.IsBatch() and not fileRead and (
var == 'MET_pt' or
(channel.startswith('SL') and var == 'MET_sign') or
(channel.endswith('ZR') and var == 'FakeMET_pt')):
saveHist(hist, channel + binName)
pad2.Draw()
noData = False
oneLine = TF1("oneline", "1", -9e9, 9e9)
oneLine.SetLineColor(kBlack)
oneLine.SetLineWidth(1)
oneLine.SetLineStyle(2)
maxVal = stack.GetMaximum()
if not noData:
maxVal = max(rebinnedData.GetMaximum(), maxVal)
minVal = 0
# minVal = max(stack.GetStack()[0].GetMinimum(),1)
stack.SetMaximum(1.25 * maxVal)
stack.SetMinimum(minVal)
errorband = stack.GetStack().Last().Clone("error")
errorband.Sumw2()
errorband.SetLineColor(kBlack)
errorband.SetFillColor(kBlack)
errorband.SetFillStyle(3245)
errorband.SetMarkerSize(0)
legend.AddEntry(rebinnedData, "Data", 'pe')
legend.AddEntry(errorband, "Uncertainty", "f")
for ih in rebinnedHist:
legend.AddEntry(rebinnedHist[ih], ih, 'f')
inMCTTbar_list[i].SetFillColor(kAzure - 2)
inMCTTbar_list[i].SetLineColor(kBlack)
hs.Add(inMCTTbar_list[i])
#MC DY hist
inMCDY_list[i].Scale(norm) #normalize MC
inMCDY_list[i].SetFillColor(kOrange - 3)
inMCDY_list[i].SetLineColor(kBlack)
hs.Add(inMCDY_list[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.3 * max(hs.GetMaximum(), inDATA_list[i].GetMaximum()))
inDATA_list[i].SetMaximum(
1.3 * max(hs.GetMaximum(), inDATA_list[i].GetMaximum()))
hs.Draw("histo")
inDATA_list[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle(inDATA_list[i].GetTitle())
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
def main():
extension = "pdf"
luminosity = 44307.4
ymin = 0
ymax = 0.024
data_set_names = [
(["SZee_all_susy_2l_0jets.root",
"SZmumu_all_susy_2l_0jets.root"], "Z#rightarrowll", 860 + 0, 23),
(["ZZ_1in3_susy_2l_0jets.root"], "ZZ#rightarrowll#nu#nu", 416 + 3, 43),
(["WZ_1in3_susy_2l_0jets.root"], "WZ#rightarrowl#null", 416 - 8, 33),
(["WW_1in3_susy_2l_0jets.root"], "WW#rightarrowl#nul#nu", 416 + 0, 20),
([
"ttbar_1in3_susy_2l_0jets.root", "top_1in3_susy_2l_0jets.root",
"antitop_1in3_susy_2l_0jets.root"
], "Top", 800 + 4, 21),
]
DATA_dir = os.path.join(os.environ["HOME_DIRECTORY"], "Data", "SR_SUSY")
## Creating the plots
gROOT.SetStyle("ATLAS")
gROOT.ForceStyle()
gStyle.SetErrorX(0.5)
canvas = TCanvas("canvas", 'A basic canvas', 800, 600)
# canvas.SetLogy()
## Adding in the legend
leg = Plotting.Create_Legend(0.60, 0.60, 0.95, 0.95, ncols=1)
## Creating the stack
stack = THStack("stack", "stack")
stack.SetMinimum(ymin)
stack.SetMaximum(ymax)
## Cycling through the different datasets
for datasets, label, colour, style in data_set_names:
## Creating the total histogram which will be filled
myhist = TH1D(label, label, 50, 0, 150)
myhist.SetStats(True)
myhist.StatOverflows(True)
for dataset in datasets:
## The root file is opened
rootfile_name = os.path.join(DATA_dir, dataset)
print(rootfile_name)
root_file = TFile.Open(rootfile_name, 'read')
tree = root_file.Get("mt2_Truth")
## Creating the current histogram which will be filled
thishist = TH1D(label, label, 50, 0, 150)
thishist.SetStats(True)
thishist.StatOverflows(True)
## Drawing the tree and saving the hist to the matrix
execution = "mt2>>{}".format(label)
tree.Draw(execution, "", "goff")
thishist.SetDirectory(0)
myhist.Add(thishist)
## We are dont with the file
root_file.Close()
del root_file
## Changing the properties of the histogram
myhist.Scale(1 / myhist.Integral("width"))
myhist.SetLineColor(colour)
myhist.SetMarkerColor(colour)
myhist.SetMarkerStyle(style)
myhist.SetMarkerSize(1.5)
## Adding the legend entry
leg.AddEntry(myhist, label, "p")
## Adding the object to the stack
stack.Add(myhist)
del myhist
## Drawing the stack on the currrent canvas
stack.Draw("NOSTACK HIST P")
leg.Draw()
## Setting axis labels
stack.GetXaxis().SetTitle("m_{T2} (True #it{E}_{T}^{miss}) [GeV]")
stack.GetYaxis().SetTitle("Normalised Distributions")
## Moving axis tick marks
stack.GetYaxis().SetMaxDigits(3)
stack.GetXaxis().SetLabelOffset(0.017)
## Drawing all text
left = 0.2
size = 1.0
shift = 0.06
pos = 0.88
Plotting.Draw_ATLASLabel(left, pos, "Simulation", scale=1.0)
pos -= shift
Plotting.Draw_Text(left, pos, "work in progress", scale=size)
pos -= shift
Plotting.Draw_Lumi(left, pos, 0, scale=size)
pos -= shift
## Updating the canvas
canvas.Update()
out_file = "true_strans.{}".format(extension)
canvas.Print(out_file)
del canvas
return 0
def plotDataVSMC(DataHist,
MCHist,
xtitle,
legendNames,
DataName,
destination,
year,
ytitle_bottom='Data/MC',
ylog=False):
GeneralSettings()
Canv = TCanvas("Canv" + destination, "Canv" + destination, 1000, 1000)
#Set Histogram Styles
for i, h in enumerate(MCHist):
h.SetFillColor(
TColor.GetColor(GetStackColorTauPOGbyName(legendNames[i])))
h.SetLineColor(
TColor.GetColor(GetStackColorTauPOGbyName(legendNames[i])))
DataHist.SetMarkerColor(ROOT.kBlack)
DataHist.SetLineColor(ROOT.kBlack)
DataHist.SetMarkerStyle(20)
#Add all MC samples to use in ratios
totBkgr = MCHist[0].Clone("totBkgr")
for h in MCHist[1:]:
totBkgr.Add(h)
DataOverMC = DataHist.Clone("DataOverMC")
DataOverMC.Divide(totBkgr)
#Errors
predStatError = totBkgr.Clone("PredictedStatError")
predSystError = totBkgr.Clone("PredictedSystError")
predTotError = totBkgr.Clone("PredictedTotalError")
for b in xrange(predSystError.GetNbinsX() + 1):
predSystError.SetBinError(b, 0.3 * totBkgr.GetBinContent(b))
syst = predSystError.GetBinError(b)
stat = predStatError.GetBinError(b)
predTotError.SetBinError(b, np.sqrt(stat * stat + syst * syst))
#predTotError.SetFillStyle(3013)
#predTotError.SetFillColor(ROOT.kGray+2)
#predTotError.SetMarkerStyle(0)
#predTotError.Draw("E2 Same")
#First pad
plotpad = TPad("plotpad", "plotpad", 0, .3, 1, 0.98)
plotpad.SetBottomMargin(0.025)
plotpad.Draw()
plotpad.cd()
#Create Stack (Change with most logical ordering)
hs = THStack("hs", "hs")
for h in MCHist:
hs.Add(h)
hs.Draw(
"EHist"
) #Draw before using GetHistogram, see https://root-forum.cern.ch/t/thstack-gethistogram-null-pointer-error/12892/4
title = " ; ; Events / " + str(MCHist[0].GetBinWidth(1)) + " GeV"
hs.SetTitle(title)
# hs.GetHistogram().GetXaxis().SetTickLength(0)
hs.GetHistogram().GetXaxis().SetLabelOffset(9999999)
hs.GetHistogram().SetMaximum(1)
#Set range
overallMin = GetOverallMinimum(MCHist + [DataHist])
overallMax = GetOverallMaximum([totBkgr] + [DataHist])
if ylog:
plotpad.SetLogy()
hs.SetMinimum(0.3 * overallMin)
hs.SetMaximum(10 * overallMax)
else:
hs.SetMinimum(0.5 * overallMin)
hs.SetMaximum(1.7 * overallMax)
DataHist.Draw("EPSame")
predTotError.SetFillStyle(3013)
predTotError.SetFillColor(ROOT.kGray + 2)
predTotError.SetMarkerStyle(0)
predTotError.Draw("E2 Same")
#Create Legend
legend = TLegend(0.7, .7, .9, .9)
for h, n in zip(MCHist, legendNames):
legend.AddEntry(h, n)
legend.AddEntry(DataHist, DataName)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw()
#Return to canvas
Canv.cd()
#Second pad
ratiopad = TPad("ratiopad", "ratiopad", 0, 0.05, 1, .3)
ratiopad.SetTopMargin(0.05)
ratiopad.SetBottomMargin(0.25)
ratiopad.Draw()
ratiopad.cd()
#Errors
StatErrorRatio = predStatError.Clone("StatErrorRatio")
StatErrorRatio.SetFillStyle(1001)
StatErrorRatio.SetFillColor(TColor.GetColor('#6EF9F5'))
TotErrorRatio = predTotError.Clone("SystErrorRatio")
TotErrorRatio.SetFillColor(TColor.GetColor('#63E2C6'))
TotErrorRatio.SetFillStyle(1001)
for b in xrange(StatErrorRatio.GetNbinsX() + 1):
if (StatErrorRatio.GetBinContent(b) != 0):
StatErrorRatio.SetBinError(
b,
StatErrorRatio.GetBinError(b) /
StatErrorRatio.GetBinContent(b))
TotErrorRatio.SetBinError(
b,
TotErrorRatio.GetBinError(b) / TotErrorRatio.GetBinContent(b))
StatErrorRatio.SetBinContent(b, 1.)
TotErrorRatio.SetBinContent(b, 1.)
else:
StatErrorRatio.SetBinContent(b, 0)
TotErrorRatio.SetBinContent(b, 0)
#Set Style for bottom plot
DataOverMC.SetTitle(";" + xtitle + "; " + ytitle_bottom)
DataOverMC.GetXaxis().SetTitleSize(.12)
DataOverMC.GetYaxis().SetTitleSize(.12)
DataOverMC.GetYaxis().SetTitleOffset(.6)
DataOverMC.GetXaxis().SetLabelSize(.12)
DataOverMC.GetYaxis().SetLabelSize(.12)
DataOverMC.SetMinimum(0.3)
DataOverMC.SetMaximum(1.7)
DataOverMC.Draw("EP")
TotErrorRatio.Draw("E2 same")
StatErrorRatio.Draw("E2 same")
DataOverMC.Draw("EPsame")
#Draw a guide for the eye
line = TLine(DataOverMC.GetXaxis().GetXmin(), 1,
DataOverMC.GetXaxis().GetXmax(), 1)
line.SetLineColor(ROOT.kRed)
line.SetLineWidth(1)
line.SetLineStyle(1)
line.Draw("Same")
#Create Legend
legend_bottom = TLegend(0.2, .8, .9, .9)
legend_bottom.SetNColumns(3)
legend_bottom.AddEntry(DataOverMC, "Obs./Pred.")
legend_bottom.AddEntry(StatErrorRatio, "Stat. Unc.")
legend_bottom.AddEntry(TotErrorRatio, "Tot. Unc.")
legend_bottom.SetFillStyle(0)
legend_bottom.SetBorderSize(0)
legend_bottom.Draw()
#Throw CMs lumi at it
cl.CMS_lumi(Canv, 4, 11, year, 'Preliminary', True)
print 'save'
#Save everything
savePlots(Canv, destination)
print 'saved'
latex.SetTextFont(42)
latex.SetTextAlign(31)
latex.SetTextSize(lumiTextSize * t)
posX_ = 0
posY_ = 1 - t - relPosY * (1 - t - b)
latex.SetTextFont(cmsTextFont)
latex.SetTextSize(0.8 * cmsTextSize * t)
latex.SetTextAlign(align_)
extraTextSize = extraOverCmsTextSize * cmsTextSize
latex.SetTextFont(extraTextFont)
latex.SetTextAlign(align_)
latex.SetTextSize(0.8 * extraTextSize * t)
hs.SetMaximum(1.6 * hs.GetMaximum())
fPads1.cd()
hs.Draw("HIST")
hs.GetYaxis().SetTitle("Events/bin")
hs.GetYaxis().SetTitleSize(0.04)
hs.GetYaxis().SetTickLength(0.02)
hs.GetYaxis().SetTitleOffset(1.2)
hs.GetXaxis().SetTitleOffset(1.3)
hs.GetXaxis().SetLabelSize(0.0)
hs.GetXaxis().SetTitle("M_{jj} [GeV]")
bin_width_signal = ['500', '600', '700', '1000', 'inf']
bin_width_control = ['200', '300', '400', '500']
hs.Print("all")
x1 = array('d', (4, 4))
def draw(hist,
fit,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
setHistStyle(hist['BkgSum'], 1.1 if ratio else 1.0)
# Create stack
bkg = THStack(
"Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";" +
hist['BkgSum'].GetYaxis().GetTitle())
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.69, 0.86 - 0.04 * n, 0.95, 0.86)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
### data/bkg ratio and systematics
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("data / bkg")
if fit == "prefit":
err.SetFillColor(ROOT.kOrange - 2)
elif fit == "postfit":
err.SetFillColor(ROOT.kBlue)
err.SetFillStyle(3001)
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))
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))
# Legend
leg1 = TLegend(0.12, 0.45, 0.25, 0.5)
leg1.SetBorderSize(0)
leg1.SetFillStyle(0)
leg1.SetFillColor(0)
leg1.SetTextSize(0.05)
leg1.AddEntry(err, "systematic uncertainty (" + fit + ")", "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
gStyle.SetOptStat(0)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.08)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
# hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
pass
bkg.SetMaximum((2. if log else 1.1) * max(
bkg.GetMaximum(), hist['data_obs'].GetMaximum() +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 1.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
setHistStyle(bkg, 1.1 if ratio else 1.0)
leg.Draw()
# drawCMS(lumi, "Preliminary",True)
drawCMS(lumi, "Private", True)
drawRegion(channel)
drawAnalysis(channel)
drawFit(fit)
if ratio:
c1.cd(2)
setBotStyle(err, 3, True)
setBotStyle(res)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
res.Draw("SAME, PE0")
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
leg1.Draw()
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, leg1, err, errLine, res]
pad = c1.cd(1)
pad.SetPad(0, 0.3, 1, 1)
pad.SetTopMargin(0.1)
pad.SetBottomMargin(0.005)
t = pad.GetTopMargin()
# prepare the 2nd pad
pad = c1.cd(2)
pad.SetPad(0, 0.0, 1, 0.3)
pad.SetTopMargin(0.06)
pad.SetBottomMargin(0.4)
pad.SetTickx(1)
pad.SetTicky(1)
c1.cd(1)
hs.SetMaximum(hs.GetMaximum() * 5)
hs.SetMinimum(0.1)
gPad.SetLogy()
hs.Draw("Hist")
h_bkg.Draw("e2 same")
h_data.Draw("same")
for ihist in reversed(templates[5:8]):
print 'overlaying, ', ihist.GetName()
ihist.Draw("ehist same")
xTitle = h_top.GetXaxis().GetTitle()
yTitle = h_top.GetYaxis().GetTitle()
setTitle(hs, xTitle)
def plotStack():
inputfile = TFile(filename, "read")
if inputfile.IsZombie():
print("inputfile is Zombie")
sys.exit()
# loop over features
for feature, values in features.items():
file = open(
"%s%s_%s_isNorm%s_wtStat%s_isBlind%s.txt" %
(outputdir, feature, plotname, normalization, showStats, blind),
"w")
file.write(
"\\begin{table}[]\n\\resizebox{!}{.33\\paperheight}{\n \\begin{tabular}{|l|l|l|}\n\\hline\nProcess & Yield & Entries \\\\ \\hline \n"
)
# set up legend
legend = TLegend(0.2, 0.6, 0.7, 0.88)
legend.SetHeader("%i %s" % (year, region))
legend.SetNColumns(4)
legend.SetBorderSize(0)
c, pad1, pad2 = createCanvasPads()
hstack = THStack("hstack", "hstack")
hstack.SetTitle(
"#scale[0.9]{#font[22]{CMS} #font[12]{Preliminary} %i at %.2f fb^{-1}(13TeV)}"
% (year, luminosity[year]))
histName = "TTH_" + feature # assuming TTH is always there
if not inputfile.GetListOfKeys().Contains(histName):
print("%s doesn't have histogram %s, please use another hist " %
(inputfile, histName))
sys.exit()
h0 = inputfile.Get(histName)
h_totalsig = h0.Clone("h_totalsig")
h_totalsig.SetDirectory(0)
h_totalsig.Reset()
h_totalsig.SetMarkerStyle(20)
h_totalsig.Sumw2()
h_totalbkg = h0.Clone("h_totalbkg")
h_totalbkg.SetDirectory(0)
h_totalbkg.Reset()
h_totalbkg.SetMarkerStyle(20)
h_totalbkg.Sumw2()
h_totalmc = h0.Clone("h_totalmc")
h_totalmc.SetDirectory(0)
h_totalmc.Reset()
h_totalmc.SetLineColor(kBlack)
h_totalmc.SetFillColor(kGray + 3)
h_totalmc.SetFillStyle(3001)
h_totalmc.SetTitle("")
#h_totalmc.SetMinimum(0.8)
#h_totalmc.SetMaximum(1.35)
h_totalmc.Sumw2()
h_totalmc.SetStats(0)
h_dataobs = h0.Clone("h_dataobs")
h_dataobs.SetDirectory(0)
h_dataobs.Reset()
h_dataobs.SetMarkerStyle(20)
# loop over samples
for sample in Samples:
hist = h_totalmc.Clone(sample)
hist.SetDirectory(0)
hist.Reset()
if sample not in Process:
print("sample %s is not in Process " % sample)
continue
# loop over data:
if sample == "Data" or sample == "data":
for p in Process[sample]:
if p not in sampleName:
print("process %s is not in sampleName " % s)
continue
hist_name = p + "_" + feature
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(inputfile, hist_name))
continue
h1 = inputfile.Get(hist_name).Clone(hist_name)
h1.SetDirectory(0)
h_dataobs.Add(h1)
error = Double(0)
h1.IntegralAndError(0, h1.GetNbinsX(), error)
if not blind:
file.write(
"%s & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
# loop over mc
# loop over signal
elif sample in Signals:
for p in Process[sample]:
if p not in sampleName:
print("process %s is not in sampleName " % s)
continue
hist_name = p + "_" + feature
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(filename, hist_name))
continue
h1 = inputfile.Get(hist_name).Clone(hist_name)
h1.SetDirectory(0)
if p == "FakeSub" and sample == "Fakes":
hist.Add(h1, -1)
h_totalsig.Add(h1, -1)
h_totalmc.Add(h1, -1)
else:
hist.Add(h1)
h_totalsig.Add(h1)
h_totalmc.Add(h1)
error = Double(0)
h1.IntegralAndError(0, h1.GetNbinsX(), error)
if h1.Integral() < 0.05 or h1.GetEntries() < 100:
file.write(
"\\textcolor{red}{%s} & %.2f +/- %.2f & %i \\\\ \\hline \n"
% (p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
else:
file.write(
"%s & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
if sample == "TH" and tH != 1:
hist.Scale(tH)
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist, "%s * %i" % (sample, tH), "f")
else:
hstack.Add(hist)
legend.AddEntry(hist, sample, "f")
# create required parts
# loop over bkg
else:
for p in Process[sample]:
if p not in sampleName:
print("process %s is not in sampleName " % p)
continue
hist_name = p + "_" + feature
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(filename, hist_name))
continue
h1 = inputfile.Get(hist_name).Clone(hist_name)
h1.SetDirectory(0)
if p == "FakeSub" and sample == "Fakes":
hist.Add(h1, -1)
h_totalmc.Add(h1, -1)
h_totalbkg.Add(h1, -1)
else:
hist.Add(h1)
h_totalmc.Add(h1)
h_totalbkg.Add(h1)
error = Double(0)
h1.IntegralAndError(0, h1.GetNbinsX(), error)
if h1.Integral() < 0.05 or h1.GetEntries() < 100:
file.write(
"\\textcolor{red}{%s} & %.2f +/- %.2f & %i \\\\ \\hline \n"
% (p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
else:
file.write(
"%s & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(p.replace('_', '\\_'), h1.Integral(), error,
h1.GetEntries()))
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
if sample == "TH" and tH != 1:
hist.Scale(tH)
hist.SetFillColor(Color[sample])
hist.SetLineColor(kBlack)
hist.SetFillStyle(Style[sample])
hstack.Add(hist)
legend.AddEntry(hist, "%s * %i" % (sample, tH), "f")
else:
hstack.Add(hist)
legend.AddEntry(hist, sample, "f")
error = Double(0)
h_totalsig.IntegralAndError(0, h_totalsig.GetNbinsX(), error)
file.write("signal & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(h_totalsig.Integral(), error, h_totalsig.GetEntries()))
error = Double(0)
h_totalbkg.IntegralAndError(0, h_totalbkg.GetNbinsX(), error)
file.write("bkg & %.2f +/- %.2f & %i \\\\ \\hline \n" %
(h_totalbkg.Integral(), error, h_totalbkg.GetEntries()))
# create required parts
if blind:
h_sqrtB = createSqrt(h_totalbkg)
h_MCerr = createTotalMCErr(h_sqrtB, values["xlabel"])
h_ratio = createRatio(h_totalsig, h_sqrtB, values["xlabel"],
normalization)
else:
h_MCerr = createTotalMCErr(h_totalmc, feature)
h_ratio = createRatio(h_dataobs, h_totalmc, values["xlabel"],
normalization)
legend.AddEntry(h_dataobs, "observed", "lep")
legend.AddEntry(h_totalmc, "Uncertainty", "f")
# draw everything
pad1.cd()
if values["logy"] == 1:
pad1.SetLogy()
maximum = h_dataobs.GetMaximum()
upperbound = 2. * maximum
lowerbound = -maximum / 40.
if values["logy"] == 1:
upperbound = 1000 * maximum
lowerbound = 0.1
hstack.SetMinimum(lowerbound)
hstack.SetMaximum(upperbound)
hstack.Draw("HISTY")
# Adjust y-axis settings
y = hstack.GetYaxis()
y.SetTitle("Events ")
y.SetTitleSize(25)
y.SetTitleFont(43)
y.SetTitleOffset(1.55)
y.SetLabelFont(43)
y.SetLabelSize(20)
nbins = h_ratio.GetNbinsX()
#hstack.GetXaxis().SetRange(0, nbins+1)
hstack.GetXaxis().SetRangeUser(values["min"], values["max"])
h_totalmc.Draw("e2same")
if not blind:
h_dataobs.Draw("same")
legend.Draw("same")
pad2.cd()
if blind:
h_ratio.SetMinimum(0.)
#maximum = h_ratio.GetMaximum()
#upperbound = 1.5*maximum
#h_ratio.SetMaximum(upperbound)
h_ratio.SetMaximum(3.)
h_ratio.GetXaxis().SetRangeUser(values["min"], values["max"])
h_ratio.Draw("")
else:
h_MCerr.SetMinimum(0.5)
h_MCerr.SetMaximum(1.8)
h_MCerr.GetXaxis().SetRangeUser(values["min"], values["max"])
h_MCerr.Draw("e2")
h_ratio.Draw("same")
c.SaveAs(
"%s%s_%s_isNorm%s_wtStat%s_isBlind%s_stack.png" %
(outputdir, feature, plotname, normalization, showStats, blind))
file.write("\\end{tabular}\n}\n\\end{table}\n")
file.close()
inputfile.Close()
def calcAndDrawSignificance(SignalHist,
BkgrHist,
xtitle,
legendNames,
DataName,
destination,
year,
ylog=False,
customLabels=None,
extraText=None,
scaleSignalToBkgr=False,
DivideByLine=None):
GeneralSettings()
#Make sure the code works for single backgrounds
if not isinstance(BkgrHist, (list, )):
BkgrHist = [BkgrHist]
if not isinstance(SignalHist, (list, )):
SignalHist = [SignalHist]
BkgrHist, legendNames = orderHist(BkgrHist, legendNames, True)
#Add all backgrounds
totBkgr = BkgrHist[0].Clone("TotBkgr")
for h in BkgrHist[1:]:
totBkgr.Add(h)
#Normalize signal to background if needed
if scaleSignalToBkgr:
for h in SignalHist:
h.scale(totBkgr.GetSumOfWeights() / h.GetSumOfWeights)
#Define a canvas
Canv = TCanvas("Canv" + destination, "Canv" + destination, 1500, 1000)
#Set Histogram Styles
for h, n in zip(BkgrHist, legendNames):
h.SetFillColor(TColor.GetColor(GetStackColorTauPOGbyName(n)))
h.SetLineColor(TColor.GetColor(GetStackColorTauPOGbyName(n)))
for i, h in enumerate(SignalHist):
h.SetMarkerColor(TColor.GetColor(GetLineColor(i)))
h.SetLineColor(TColor.GetColor(GetLineColor(i)))
h.SetMarkerStyle(GetMarker(i))
list_of_significance_hists = []
for i, sh in enumerate(SignalHist):
#Calculate significance
total = totBkgr.Clone("Total")
total.Add(sh)
sqrt_total = total.Clone('Sqrt_Total')
for xbin in xrange(
1,
total.GetSize() - 1
): #GetSize returns nbins + 2 (for overflow and underflow bin)
sqrt_x = np.sqrt(total.GetBinContent(xbin))
sqrt_total.SetBinContent(xbin, sqrt_x)
sqrt_total.SetBinError(xbin,
0.5 * total.GetBinError(xbin) / sqrt_x)
significance = sh.Clone('Signal' + str(i))
significance.Divide(sqrt_total)
list_of_significance_hists.append(significance)
#First pad
plotpad = TPad("plotpad", "plotpad", 0, .3, 1, 0.98)
plotpad.SetBottomMargin(0.025)
plotpad.Draw()
plotpad.cd()
#Create Stack (Change with most logical ordering)
hs = THStack("hs", "hs")
for h in BkgrHist:
hs.Add(h)
hs.Draw(
"EHist"
) #Draw before using GetHistogram, see https://root-forum.cern.ch/t/thstack-gethistogram-null-pointer-error/12892/4
title = " ; ; Events"
hs.SetTitle(title)
# hs.GetHistogram().GetXaxis().SetTickLength(0)
hs.GetHistogram().GetXaxis().SetLabelOffset(9999999)
#hs.GetHistogram().SetMaximum(1)
#Set range
overallMin = GetOverallMinimum(BkgrHist + SignalHist, True)
overallMax = GetOverallMaximum([totBkgr] + SignalHist)
if ylog:
if overallMin == 0.:
overallMin = 0.1
ymin = 0.3 * overallMin
ymax = 30 * overallMax
plotpad.SetLogy()
else:
ymin = 0.7 * overallMin
ymax = 1.3 * overallMax
hs.SetMinimum(ymin)
hs.SetMaximum(ymax)
for h in SignalHist:
h.Draw("EPSame")
#Create Legend
legend = TLegend(0.7, .7, .9, .9)
for h, n in zip(BkgrHist, legendNames):
legend.AddEntry(h, n)
for h, n in zip(SignalHist, DataName):
legend.AddEntry(h, n)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw()
#Draw lines if needed
if DivideByLine is not None:
tdrStyle_Left_Margin = 0.16
tdrStyle_Right_Margin = 0.02
plot_size_hor = 1 - tdrStyle_Left_Margin - tdrStyle_Right_Margin
#Option one, user provides the number of divisions and we divide equally
if isinstance(DivideByLine[0], int):
x_pos = np.linspace(totBkgr.GetXaxis().GetXmin(),
totBkgr.GetXaxis().GetXmax(),
DivideByLine[0] + 1)
#Option two, user provides the specific boundaries
if isinstance(DivideByLine[0], (list, )):
x_pos = DivideByLine[0]
#Draw the lines
lines = []
for i, x in enumerate(x_pos[1:-1]):
lines.append(TLine(x, ymin, x, ymax))
lines[i].SetLineColor(ROOT.kRed)
lines[i].SetLineStyle(10)
for line in lines:
line.Draw('same')
#Add extra text
for i, name in enumerate(DivideByLine[1]):
x = ((x_pos[i + 1] + x_pos[i]) /
(2 * (x_pos[-1] - x_pos[0]) /
plot_size_hor)) + tdrStyle_Left_Margin
extraText.append(extraTextFormat(name, x, 0.1, None, 22))
#Draw extra text if needed
if extraText is not None:
DrawExtraText(plotpad, extraText)
#Return to canvas
Canv.cd()
#Second pad
ratiopad = TPad("ratiopad", "ratiopad", 0, 0.05, 1, .3)
ratiopad.SetTopMargin(0.05)
ratiopad.SetBottomMargin(0.25)
ratiopad.Draw()
ratiopad.cd()
#print list_of_significance_hists[0].GetMaximum(), list_of_significance_hists[1].GetMaximum(), list_of_significance_hists[2].GetMaximum()
overallMin = GetOverallMinimum(list_of_significance_hists)
overallMax = GetOverallMaximum(list_of_significance_hists)
significance = list_of_significance_hists[0]
#Prepare lines before changing maximum
lines_bottom = []
for i, sig in enumerate(list_of_significance_hists):
lines_bottom.append(
TLine(sig.GetXaxis().GetXmin(), sig.GetMaximum(),
sig.GetXaxis().GetXmax(), sig.GetMaximum()))
lines_bottom[i].SetLineColor(TColor.GetColor(GetLineColor(i)))
lines_bottom[i].SetLineStyle(3)
#Set Style for bottom plot
significance.SetTitle(";" + xtitle + "; S/#sqrt{S+B}")
significance.GetXaxis().SetTitleSize(.12)
significance.GetYaxis().SetTitleSize(.12)
significance.GetYaxis().SetTitleOffset(.6)
significance.GetXaxis().SetLabelSize(.12)
significance.GetYaxis().SetLabelSize(.12)
significance.SetMinimum(0.)
significance.SetMaximum(1.3 * overallMax)
significance.Draw("EP")
for sig in list_of_significance_hists[1:]:
sig.Draw('EPSame')
for line in lines_bottom:
line.Draw("same")
#Set custom x labels
xaxis = significance.GetXaxis()
if customLabels != None:
number_of_bins = significance.GetNbinsX()
if number_of_bins != len(customLabels):
if DivideByLine is not None:
for i in range(number_of_bins):
xaxis.SetBinLabel(
i + 1, customLabels[i % len(customLabels)]
) #Only works when DivideByLine[0] is an integer
else:
print 'Please provide ' + str(
number_of_bins) + ' labels instead of ' + str(
len(customLabels))
return
else:
for i, label in zip(range(number_of_bins), customLabels):
xaxis.SetBinLabel(i + 1, label)
#Throw CMs lumi at it
cl.CMS_lumi(Canv, 4, 11, year, 'Simulation Preliminary', True)
#Save everything
savePlots(Canv, destination)
ROOT.SetOwnership(
Canv, False
) #https://root-forum.cern.ch/t/tlatex-crashing-in-pyroot-after-many-uses/21638/4
return
def draw(hist, data, back, sign, snorm=1, ratio=0, poisson=False, log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Some style
for i, s in enumerate(data):
hist[s].SetMarkerStyle(21)
hist[s].SetMarkerSize(1.25)
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
# Poisson error bars for data
if poisson:
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(
i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L,
U - N)
res_graph.SetPoint(i,
hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if samples[x]['plot']])
for i, s in enumerate(sign):
if 'sublabel' in samples[s]: n += 1
if 'subsublabel' in samples[s]: n += 1
#leg = TLegend(0.68, 0.9-0.05*n, 0.93, 0.9)
leg = TLegend(0.68 - 0.05, 0.9 - 0.05 * n, 0.93, 0.9) #DCMS
leg.SetTextSize(0.03) #DCMS
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("Signal x-sec=%.0f pb" % (1 * snorm))
if len(data) > 0:
leg.AddEntry(hist[data[0]], samples[data[0]]['label'], "ple1")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], samples[s]['label'], "f")
for i, s in enumerate(sign):
leg.AddEntry(hist[s], samples[s]['label'], "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[-1].GetXaxis().GetTitle(), 1000,
800 if ratio else 700)
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)
if log:
c1.GetPad(bool(ratio)).SetLogy()
#c1.GetPad(bool(ratio)).SetLogx()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if poisson: data_graph.Draw("SAME, PE")
elif len(data) > 0: hist['data_obs'].Draw("SAME, PE")
for i, s in enumerate(sign):
if samples[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
# Determine range
if 'data_obs' in hist:
bkg.SetMaximum((2.5 if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(
hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
else:
bkg.SetMaximum(bkg.GetMaximum() * (2.5 if log else 1.2))
bkg.SetMinimum(5.e-1 if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
leg.Draw()
#drawCMS(LUMI, "Preliminary")
#drawRegion(channel)
#drawAnalysis("LL")
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 / Bkg")
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(2)
errLine.SetFillStyle(0)
errLine.SetLineColor(2) #L#
errLine.SetLineStyle(2) #L#
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'data_obs' in hist:
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))
setBotStyle(res)
if poisson: res_graph.Draw("SAME, PE0")
else: res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
else:
res = None
c1.Update()
# return list of objects created by the draw() function
return [
c1, bkg, leg, err if ratio else None, errLine if ratio else None,
res if ratio else None, data_graph if poisson else None,
res_graph if poisson else None
]
def createPlots_(plot, compounddetectorname):
"""Cumulative material budget from simulation.
Internal function that will produce a cumulative profile of the
material budget inferred from the simulation starting from the
single detectors that compose the tracker. It will iterate over
all existing detectors contained in the DETECTORS
dictionary. The function will automatically skip non-existent
detectors.
"""
theDirname = "Figures"
hist_X0_detectors = OrderedDict()
if plot not in plots.keys():
print("Error: chosen plot name not known %s" % plot)
return
# We need to keep the file content alive for the lifetime of the
# full function....
subDetectorFiles = []
hist_X0_elements = OrderedDict()
prof_X0_elements = OrderedDict()
for subDetector, color in DETECTORS.iteritems():
subDetectorFilename = "matbdg_%s.root" % subDetector
if not checkFile_(subDetectorFilename):
print("Error opening file: %s" % subDetectorFilename)
continue
subDetectorFiles.append(TFile(subDetectorFilename))
subDetectorFile = subDetectorFiles[-1]
print("Opening file: %s" % subDetectorFilename)
prof_X0_XXX = subDetectorFile.Get("%d" % plots[plot].plotNumber)
hist_X0_detectors[subDetector] = prof_X0_XXX.ProjectionX()
# category profiles
for label, [num, color, leg] in hist_label_to_num.iteritems():
prof_X0_elements[label] = subDetectorFile.Get(
"%d" % (num + plots[plot].plotNumber))
hist_X0_elements[label] = assignOrAddIfExists_(
hist_X0_elements.setdefault(label, None),
prof_X0_elements[label])
cumulative_matbdg = TH1D(
"CumulativeSimulMatBdg", "CumulativeSimulMatBdg",
hist_X0_detectors["BeamPipe"].GetNbinsX(),
hist_X0_detectors["BeamPipe"].GetXaxis().GetXmin(),
hist_X0_detectors["BeamPipe"].GetXaxis().GetXmax())
cumulative_matbdg.SetDirectory(0)
# colors
for det, color in DETECTORS.iteritems():
setColorIfExists_(hist_X0_detectors, det, color)
for label, [num, color, leg] in hist_label_to_num.iteritems():
hist_X0_elements[label].SetFillColor(color)
# First Plot: BeamPipe + Tracker + ECAL + HCal + HGCal + MB + MGNT
# stack
stackTitle_SubDetectors = "Material Budget;%s;%s" % (plots[plot].abscissa,
plots[plot].ordinate)
stack_X0_SubDetectors = THStack("stack_X0", stackTitle_SubDetectors)
for det, histo in hist_X0_detectors.iteritems():
stack_X0_SubDetectors.Add(histo)
cumulative_matbdg.Add(histo, 1)
# canvas
can_SubDetectors = TCanvas("can_SubDetectors", "can_SubDetectors", 800,
800)
#can_SubDetectors.Range(0,0,25,25)
can_SubDetectors.SetFillColor(kWhite)
# Draw
stack_X0_SubDetectors.SetMinimum(plots[plot].ymin)
stack_X0_SubDetectors.SetMaximum(plots[plot].ymax)
stack_X0_SubDetectors.Draw("HIST")
#stack_X0_SubDetectors.GetXaxis().SetLimits(plots[plot].xmin, plots[plot].xmax)
# Legenda
theLegend_SubDetectors = TLegend(0.130, 0.7, 0.93,
0.90) #(0.180,0.8,0.98,0.90)
theLegend_SubDetectors.SetNColumns(2)
theLegend_SubDetectors.SetFillColor(0)
theLegend_SubDetectors.SetFillStyle(0)
theLegend_SubDetectors.SetBorderSize(0)
for det, histo in hist_X0_detectors.iteritems():
theLegend_SubDetectors.AddEntry(histo, det, "f")
theLegend_SubDetectors.Draw()
# text
text_SubDetectors = TPaveText(0.130, 0.627, 0.352, 0.687,
"NDC") #(0.180,0.727,0.402,0.787,"NDC")
text_SubDetectors.SetFillColor(0)
text_SubDetectors.SetBorderSize(0)
text_SubDetectors.AddText("CMS Simulation")
text_SubDetectors.SetTextAlign(11)
text_SubDetectors.Draw()
# Store
can_SubDetectors.Update()
if not checkFile_(theDirname):
os.mkdir(theDirname)
can_SubDetectors.SaveAs("%s/MaterialBdg_%s_%s.pdf" %
(theDirname, compounddetectorname, plot))
can_SubDetectors.SaveAs("%s/MaterialBdg_%s_%s.png" %
(theDirname, compounddetectorname, plot))
can_SubDetectors.SaveAs("%s/MaterialBdg_%s_%s.root" %
(theDirname, compounddetectorname, plot))
if plot == "x_vs_eta" or plot == "l_vs_eta":
canname = "MBCan_1D_%s_%s_total" % (compounddetectorname, plot)
can2 = TCanvas(canname, canname, 800, 800)
can2.Range(0, 0, 25, 25)
can2.SetFillColor(kWhite)
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
#title = TPaveLabel(.11,.95,.35,.99,"Total accumulated material budget","brndc")
stack_X0_SubDetectors.GetStack().Last().SetMarkerStyle(34)
stack_X0_SubDetectors.GetStack().Last().GetXaxis().SetRangeUser(
1.0, 3.5)
stack_X0_SubDetectors.GetStack().Last().Draw()
stack_X0_SubDetectors.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zplus.pdf" %
(theDirname, compounddetectorname, plot))
can2.SaveAs("%s/%s_%s_total_Zplus.png" %
(theDirname, compounddetectorname, plot))
stack_X0_SubDetectors.GetStack().Last().GetXaxis().SetRangeUser(
-3.5, -1.0)
stack_X0_SubDetectors.GetStack().Last().Draw()
stack_X0_SubDetectors.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zminus.pdf" %
(theDirname, compounddetectorname, plot))
can2.SaveAs("%s/%s_%s_total_Zminus.png" %
(theDirname, compounddetectorname, plot))
#Also print them to give them exact numbers
etavalues = []
matbudginX0 = []
matbudginIntLen = []
for binx in range(
0,
stack_X0_SubDetectors.GetStack().Last().GetXaxis().GetNbins()):
bincontent = stack_X0_SubDetectors.GetStack().Last().GetBinContent(
binx)
if bincontent == 0: continue
etavalues.append(
stack_X0_SubDetectors.GetStack().Last().GetBinCenter(binx))
if plot == "x_vs_eta":
matbudginX0.append(bincontent)
d1 = {'Eta': etavalues, 'MatBudInX0': matbudginX0}
df1 = pd.DataFrame(data=d1).round(2)
df1.to_csv(
r'/afs/cern.ch/work/a/apsallid/CMS/PFCalStudies/CMS-HGCAL/matbudV10fromVertexToBackofHGCal/CMSSW_11_0_X_2019-06-04-2300/src/Validation/Geometry/test/EtavsMatBudinXo.txt',
sep=' ',
index=False,
header=False)
#print df1
if plot == "l_vs_eta":
matbudginIntLen.append(bincontent)
d2 = {'Eta': etavalues, 'MatBudInIntLen': matbudginIntLen}
df2 = pd.DataFrame(data=d2).round(2)
df2.to_csv(
r'/afs/cern.ch/work/a/apsallid/CMS/PFCalStudies/CMS-HGCAL/matbudV10fromVertexToBackofHGCal/CMSSW_11_0_X_2019-06-04-2300/src/Validation/Geometry/test/EtavsMatBudInIntLen.txt',
sep=' ',
index=False,
header=False)
#print df2
return cumulative_matbdg
def Direct_Estimator(var, cut, year):
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
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])
# 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)
LepBDTMCTTbar_list[i].SetFillColor(kMagenta + 3)
LepBDTMCTTbar_list[i].SetLineColor(kBlack)
hs.Add(LepBDTMCTTbar_list[i])
#MC DY hist
LepBDTMCDY_list[i].Scale(norm) # MC normalization
LepBDTMCDY_list[i].SetFillColor(kMagenta - 6)
LepBDTMCDY_list[i].SetLineColor(kBlack)
hs.Add(LepBDTMCDY_list[i])
#upper plot pad
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.Draw()
pad1.cd()
hs.SetMaximum(1.6 * max(hs.GetMaximum(), LepBDTDATA_list[i].GetMaximum()))
LepBDTDATA_list[i].SetMaximum(
1.6 * max(hs.GetMaximum(), LepBDTDATA_list[i].GetMaximum()))
hs.SetMinimum(10) #EF for ele BDT
hs.Draw("histo")
LepBDTDATA_list[i].Draw("sameEP")
hs.SetTitle("")
hs.GetXaxis().SetTitle("BDT score")
hs.GetXaxis().SetRangeUser(0.75, 1) #EF for ele BDT
hs.GetXaxis().SetLabelFont(43)
hs.GetXaxis().SetLabelSize(15)
hs.GetYaxis().SetTitleSize(20)
hs.GetYaxis().SetTitleFont(43)
hs.GetYaxis().SetTitleOffset(1.8)
def plot(var, cut, nm1=False):
### Preliminary Operations ###
treeRead = True if not FILE else False # Read from tree
channel = cut
isBlind = BLIND
showSignal = False if 'SB' in cut or 'TR' in cut else True
# Determine explicit cut
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if k in cut: cut = cut.replace(k, alias[k])
# Determine Primary Dataset
pd = []
if "isSingleMuonPhotonTrigger" in cut:
pd = [x for x in sample['data_obs']['files'] if "MuonEG" in x]
elif "isJPsiTrigger" in cut:
pd = [x for x in sample['data_obs']['files'] if "Charmonium" in x]
else:
print "Cannot determine Primary Dataset."
exit()
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " dataset:", pd
print " cut :", cut
if isBlind and "SR" in channel and var in ["H_mass"]:
cut += " && ( isMC ? 1 : !(H_mass > 86 && H_mass < 96) && !(H_mass > 120 && H_mass < 130) )"
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
cutstring = "(eventWeightLumi)" + ("*(" + cut +
")" if len(cut) > 0 else "")
### Create and fill MC histograms ###
for i, s in enumerate(data + back + sign):
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events;" +
('logx' if variable[var]['logx'] else '') +
('logy' if variable[var]['logy'] else ''), variable[var]['nbins'],
variable[var]['min'], variable[var]['max'])
hist[s].Sumw2()
tree[s] = TChain("Events")
for j, ss in enumerate(sample[s]['files']):
if s in data and not ss in pd: continue
if YEAR == 2016 and not ('Run2016' in ss or 'Summer16' in ss):
continue
if YEAR == 2017 and not ('Run2017' in ss or 'Fall17' in ss):
continue
if YEAR == 2018 and not ('Run2018' in ss or 'Autumn18' in ss):
continue
for f in os.listdir(NTUPLEDIR + '/' + ss):
tree[s].Add(NTUPLEDIR + '/' + ss + '/' + f)
tree[s].Project(s, var, cutstring)
if not tree[s].GetTree() == None:
hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
# jobs = []
# queue = multiprocessing.Queue()
# for i, s in enumerate(data+back+sign):
# for j, ss in enumerate(sample[s]['files']):
# if s in data and not ss in pd: continue
# if YEAR == 2016 and not ('Run2016' in ss or 'Summer16' in ss): continue
# if YEAR == 2017 and not ('Run2017' in ss or 'Fall17' in ss): continue
# if YEAR == 2018 and not ('Run2018' in ss or 'Autumn18' in ss): continue
# if treeRead: # Project from tree
## hist[s] = loopProject(s, ss, variable[var], cutstring, True)
# p = multiprocessing.Process(target=parallelProject, args=(queue, s, ss, variable[var], cutstring, ))
# jobs.append(p)
# p.start()
# else: # Histogram written to file
# hist[s] = readhist(FILE, s, var, cut)
#
# # Wait for all jobs to finish
# for job in jobs:
# h = queue.get()
# if not h.GetOption() in hist: hist[h.GetOption()] = h
# else: hist[h.GetOption()].Add(h)
# for job in jobs:
# job.join()
# Histogram style
for i, s in enumerate(data + back + sign):
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'] if not options.norm else 0)
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
hist[s].SetLineWidth(sample[s]['linewidth'])
### Create Bkg Sum histogram ###
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())
for i, s in enumerate(sign):
hist[s].Scale(hist[data[0]].Integral() / hist[s].Integral())
# Create data and Bkg sum histograms
# if 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
# Create stack
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() * 0.04)
# --- 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)
logX, logY = "logx" in hist['BkgSum'].GetZaxis().GetTitle(
), "logy" in hist['BkgSum'].GetZaxis().GetTitle()
if logY: c1.GetPad(bool(RATIO)).SetLogy()
if logX: c1.GetPad(bool(RATIO)).SetLogx()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
#data_graph.Draw("SAME, PE")
# if showSignal:
# smagn = 1. #if treeRead else 1.e2 #if logY else 1.e2
for i, s in enumerate(sign):
if sample[s]['plot']: hist[s].Draw("SAME, HIST")
# 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.SetMaximum((5. if logY 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 logY else 0.)
if logY:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
#if logY: bkg.SetMinimum(1)
leg.Draw()
drawCMS(LUMI[YEAR], "Preliminary")
if channel in aliasNames: drawRegion(aliasNames[channel], True)
#drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
#if len(sign) > 0:
# if channel.startswith('X') and len(sign)>0: drawNorm(0.9-0.05*(leg.GetNRows()+1), "#sigma(X) = %.1f pb" % 1.)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
if logX: c1.GetPad(2).SetLogx()
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
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))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 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 var in ["H_mass"]:
c1.cd(bool(RATIO))
boxZ = drawBox(XZMIN, hist['data_obs'].GetMinimum(), XZMAX,
hist['data_obs'].GetMaximum() / 1.30, "Z")
boxH = drawBox(XHMIN, hist['data_obs'].GetMinimum(), XHMAX,
hist['data_obs'].GetMaximum() / 1.30, "H")
c1.Update()
if True: #gROOT.IsBatch():
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
c1.Print("plots/" + channel + "/" + varname + ".png")
c1.Print("plots/" + channel + "/" + varname + ".pdf")
# Print table
printTable(hist, sign)
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
