def Stack(can, reverse=False):
if can.GetPrimitive('pad_top'):
return Stack(can.GetPrimitive('pad_top'), reverse=reverse)
from ROOT import TH1, THStack
stack = THStack('stack', 'stack')
xaxislabel, yaxislabel = '', ''
binlabels = []
if reverse:
the_primitives = reversed(can.GetListOfPrimitives())
else:
the_primitives = can.GetListOfPrimitives()
for i in the_primitives:
if issubclass(type(i), TH1):
stack.Add(i)
if not xaxislabel: xaxislabel = i.GetXaxis().GetTitle()
if not yaxislabel: yaxislabel = i.GetYaxis().GetTitle()
if not binlabels and i.GetXaxis().GetBinLabel(1):
for j in range(i.GetNbinsX()):
binlabels.append(i.GetXaxis().GetBinLabel(j + 1))
can.Clear()
tobject_collector.append(stack)
can.cd()
stack.Draw('hist')
stack.GetXaxis().SetTitle(xaxislabel)
stack.GetYaxis().SetTitle(yaxislabel)
if binlabels:
for i in range(stack.GetXaxis().GetNbins()):
stack.GetXaxis().SetBinLabel(i + 1, binlabels[i])
can.Modified()
can.Update()
can.RedrawAxis()
return
def getStack(self, lumi, name, var, nbin, xmin, xmax, cut, options,
xlabel):
hs = THStack(name, "")
for b in self.blocks:
AuxName = "auxStack_block_" + name + "_" + b.name
haux = b.getTH1F(lumi, AuxName, var, nbin, xmin, xmax, cut,
options, xlabel)
haux.SetFillColor(b.color)
hs.Add(haux)
del haux
can_aux = TCanvas("can_%s_%s" % (name, b.name))
can_aux.cd()
hs.Draw()
del can_aux
if xmax != xmin:
hs.GetXaxis().SetTitle(xlabel)
b = int((xmax - xmin) / nbin)
ylabel = "Events / " + str(b) + " GeV"
else:
ylabel = "# events"
hs.GetYaxis().SetTitle(ylabel)
return hs
def __draw_stack(self, regions, th1d_dict, output_name):
canvas = TCanvas("canvas", 'A basic canvas', 1000, 600)
stack = THStack("stack", "stack")
leg = Plotting.Create_Legend(0.60, 0.60, 0.95, 0.95)
for region in regions:
hist = th1d_dict[region]
hist.SetLineColor(1)
hist.SetLineWidth(1)
hist.SetMarkerColor(self.__get_colour(region))
hist.SetMarkerStyle(0)
hist.SetFillColor(self.__get_colour(region))
leg.AddEntry(hist, self.__get_label(region), "f")
stack.Add(hist)
del hist
stack.Draw("HIST")
stack.GetYaxis().SetTitle("Events")
stack.GetXaxis().SetTitle("True #it{E_{T}^{miss}} [GeV]")
leg.Draw()
canvas.Print(output_name)
def plot_calibrations(group_data, voltage, cal_pars_list, stacked=False):
group_name = str(group_data[0].run_number)
run = group_data[2]
name = str(run.run_number)
hists_and_lines = []
for cal_pars in cal_pars_list:
pars = parameters.gain_pars[cal_pars]
run.calibrate_energies(voltage, pars)
hist = make_hist(name, run.calibrated_energies, 0, 3500)
K_line = TLine(1460.820, 0, 1460.820, 1000000)
K_line.SetLineColor(632)
Tl_line = TLine(2614.511, 0, 2614.511, 1000000)
Tl_line.SetLineColor(632)
hists_and_lines.append([hist, K_line, Tl_line])
if stacked:
print('Plotting stacked calibrated histograms.')
run_canvas = TCanvas()
run_canvas.SetLogy()
colors = [808, 397, 436, 424, 628, 852, 800, 863, 403, 797]
color_index = 0
hs = THStack('hs', 'Group %s Histograms' % group_name)
for entry in hists_and_lines:
title = str(run.run_number)
entry[0].SetLineColor(colors[color_index])
color_index += 1
hs.Add(entry[0])
hs.Draw('nostack')
K_line = TLine(1460.820, 0, 1460.820, 1000000)
K_line.SetLineColor(632)
K_line.Draw()
Tl_line = TLine(2614.511, 0, 2614.511, 1000000)
Tl_line.SetLineColor(632)
Tl_line.Draw()
run_canvas.BuildLegend()
path = 'output/group_' + str(group_data[0].run_number) + '/'
run_canvas.Print((path + str(group_data[0].run_number) +
'_stacked_calibrated_histogram.pdf['))
run_canvas.Print((path + str(group_data[0].run_number) +
'_stacked_calibrated_histogram.pdf'))
run_canvas.Print((path + str(group_data[0].run_number) +
'_stacked_calibrated_histogram.pdf]'))
else:
print('Plotting calibrated energy histogram.')
run_canvas = TCanvas("run_canvas", "run canvas")
run_canvas.Divide(3, int(len(cal_pars_list) / 2))
canvas_index = 1
for entry in hists_and_lines:
pad = run_canvas.cd(canvas_index)
pad.SetLogy()
entry[0].Draw()
entry[1].Draw()
entry[2].Draw()
pad.Update()
canvas_index += 1
path = 'output/group_' + group_name + '/'
run_canvas.Print((path + name + '_calibrated_histograms.pdf['))
run_canvas.Print((path + name + '_calibrated_histograms.pdf'))
run_canvas.Print((path + name + '_calibrated_histograms.pdf]'))
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 drawAppMVAOutputPlots(region, method, isMC):
MCstr = ""
if isMC:
MCstr = "BsMC12"
Region = region[:1].upper() + region[
1:] #capitalize first letter for retrieving file names
HistName = "ApplicationOutput" + MCstr + Region + method
CvsName = "canvasMVA_" + MCstr + method + region
canvas = TCanvas(CvsName, CvsName)
stackBDT = THStack(HistName, HistName)
histos = []
appFiles = []
canvas.Draw()
# note: TMVApp file name structure: "TMVApp"+Region+method+sample+".root"
# "BsMC12TMVApp"+Region+method+sample+".root"
appFile = TFile(MCstr + "TMVApp" + Region + method + ".root")
histo = appFile.Get("MVA_" + method).Clone(HistName)
histo.Scale(1 / histo.GetEntries())
stackBDT.Add(histo)
for sample in range(3):
appFiles.append(
TFile(MCstr + "TMVApp" + Region + method + str(sample) + ".root"))
histos.append(appFiles[sample].Get("MVA_" + method).Clone("HistName" +
str(sample)))
histos[sample].SetLineColor(2 * sample)
histos[sample].Scale(1 / histos[sample].GetEntries())
stackBDT.Add(histos[sample])
stackBDT.Draw("nostack")
if isMC:
applicationBDTLegend = TLegend(0.2, 0.7, 0.5, 0.9, "", "brNDC")
#applicationBDTLegend.SetHeader("Bs MC "+region.split("BsMC")[1])
applicationBDTLegend.SetHeader("Bs MC " + Region)
else:
applicationBDTLegend = TLegend(0.55, 0.7, 0.85, 0.9, "", "brNDC")
applicationBDTLegend.SetHeader(Region)
applicationBDTLegend.AddEntry(histo, "Full sample", "l")
applicationBDTLegend.AddEntry(histos[0],
"Trained on 0, tested on 1, applied on 2",
"l")
applicationBDTLegend.AddEntry(histos[1],
"Trained on 1, tested on 2, applied on 0",
"l")
applicationBDTLegend.AddEntry(histos[2],
"Trained on 2, tested on 0, applied on 1",
"l")
applicationBDTLegend.Draw("same")
applicationBDTLegend.SetFillColor(0)
applicationBDTLegend.SetLineColor(0)
canvas.SaveAs(figuresDir + "Application" + method + "Output_" + MCstr +
region + ".pdf")
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)
class TreeStack(object):
def __init__(self, name):
'''Name is the name you want for your treestack.'''
self.name = name
self.trees = dict()
self.styles = dict()
self.treenames = []
self.hists = []
self.histsum = None
self.stack = THStack('_'.join([self.name, 'stack']), 'title')
def add(self, name, tree, style=None):
'''Add a new tree with name "name".
Your tree should have the method Project.
You may implement tree weighting in this function.
style should behave like in tools.style
'''
self.trees[name] = tree
self.styles[name] = style
self.treenames.append(name)
def hname(self, name):
return '_'.join([self.name, name])
def project(self, var, cut, bins, xmin, xmax):
'''Fill the histograms with all trees,
using:
var : variable to plot
cut : cut to use
These variables will be passed to the Project method of your trees.
bins, xmin, xmax defins the histogram binning.
'''
for name in self.treenames:
tree = self.trees[name]
hname = self.hname(name)
hist = TH1F(hname, hname, bins, xmin, xmax)
self.hists.append(hist)
self.styles[name].formatHisto(hist)
tree.Project(hname, var, cut)
for hist in self.hists:
if self.histsum is None:
self.histsum = hist.Clone(self.hname('sum'))
else:
self.histsum.Add(hist)
self.stack.Add(hist)
def draw(self):
'''Draw the stack. Call project first.'''
self.stack.Draw()
def study_cal_eff():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/data_nn50.root", 'read')
t_data = f_data.data
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_noise=lumical",
"../trees_5gev_e/lucas_geocuts_noise_cal.root")
t_mc.AddFriend("lumical_eff=lumical",
"../trees_5gev_e/lucas_geocuts_noise_cal_eff.root")
c = TCanvas()
hs = THStack("hs", "title")
t_data.Draw("energy*(layer > 1)>>h_data50(200, 0, 50)",
"energy*(layer > 1) > 0")
h_data50 = gROOT.FindObject("h_data50")
h_data50.SetTitle("Data")
h_data50.SetLineWidth(2)
h_data50.SetLineColor(1)
hs.Add(h_data50, "histo")
t_mc.Draw("cal_energy_new/0.0885>>h_mc(200, 0, 50)",
"cal_energy_new/0.0885 > 0", "", 100000)
h_mc = gROOT.FindObject("h_mc")
h_mc.SetTitle("MC smearing and cal eff")
h_mc.SetLineWidth(2)
h_mc.SetLineColor(3)
h_mc.Scale(t_data.GetEntries() / 100000) #t_mc.GetEntries())
hs.Add(h_mc, "histo")
t_mc.Draw("cal_energy_smeared7/0.0885>>h_mc7(200, 0, 50)",
"cal_energy_smeared7/0.0885 > 0", "", 100000)
h_mc7 = gROOT.FindObject("h_mc7")
h_mc7.SetTitle("MC smearing=0.7*#sigma")
h_mc7.SetLineWidth(2)
h_mc7.SetLineColor(2)
h_mc7.Scale(t_data.GetEntries() / 100000) #t_mc.GetEntries())
hs.Add(h_mc7, "histo")
print(h_mc7.GetEntries())
hs.Draw("nostack")
hs.SetTitle(
"Deposited energy in the pads of calorimeter;E, [MIP]; N_{hits}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
def plotstuff2(cut1,cut2,cutname):
events.Draw("eleClT-posClT>>h1(200,-10,10)",cut1,"goff")
events.Draw("eleClT-posClT>>h2(200,-10,10)",cut2,"goff")
hs = THStack("hs",cutname);
hs.Add(gDirectory.Get("h1"))
hs.Add(gDirectory.Get("h2"))
hs.Draw("nostack")
leg = TLegend(0.6,0.75,0.9,0.9)
leg.AddEntry(gDirectory.Get("h1"),"after base cuts")
leg.AddEntry(gDirectory.Get("h2"),"after vertexing cuts")
leg.Draw()
gDirectory.Get("h1").SetLineColor(1)
gDirectory.Get("h2").SetLineColor(2)
hs.GetXaxis().SetTitle("tEle-tPos [ns]")
hs.GetYaxis().SetTitle("events/(0.1 ns)")
c.Print(sys.argv[1]+".pdf")
def histo_creation(lumi2017_param, variable_param, binHist_param,
minHist_param, maxHist_param, titleX_param, titleY_param):
histo = []
legend = TLegend(0.5, 0.5, 0.9, 0.9)
canvas = TCanvas()
if (lumi2017_param):
legend.SetHeader("41.53 fb-1", "C")
canvas.SetName(variable_param + "2017")
else:
legend.SetHeader("35.9 fb-1", "C")
canvas.SetName(variable_param + "2016")
for i in range(len(MC_sample)):
foo = TH1F("essaie{}".format(i), "essaie{}".format(i), binHist_param,
minHist_param, maxHist_param)
rootfile[i].Get('events').Project("essaie{}".format(i), variable_param,
"weight")
histo.append(foo)
histo[i].SetFillColor(color[i])
histo[i].Scale(luminosity2017 * 1000 * cross_sec[i] / eventsN0[i])
if (not lumi2017_param):
histo[i].Scale(lumiRatio)
print(histo[i].GetEntries() * lumiRatio * luminosity2017 * 1000 *
cross_sec[i] / eventsN0[i])
histo[i].Draw()
legend.AddEntry(histo[i], MC_name[i], "f")
stack = THStack(variable_param, "")
for i in range(len(MC_sample)):
stack.Add(histo[len(MC_sample) - (i + 1)])
stack.Draw("HIST")
stack.GetXaxis().SetTitle(titleX_param)
stack.GetYaxis().SetTitle(titleY_param)
legend.Draw()
if (lumi2017_param):
canvas.SaveAs(localisation + "/results/MC_" + variable_param +
"2017.png")
else:
canvas.SaveAs(localisation + "/results/MC_" + variable_param +
"2016.png")
canvas.Write()
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 study_trigger():
# Import data and MC files:
f_data = TFile.Open("../trees_5gev_e/.root", 'read')
t_data = f_data.data
f_mc = TFile.Open("../trees_5gev_e/lucas.root", 'read')
t_mc = f_mc.lumical
t_mc.AddFriend("lumical_tr_clusters=lumical", "../trees_5gev_e/.root")
c = TCanvas()
t_data.Draw("tr1_n_clusters>>h_data(6, 0, 6)")
h_data = gROOT.FindObject("h_data")
h_data.SetTitle("Data")
h_data.SetLineWidth(2)
h_data.SetLineColor(1)
t_mc.Draw("tr1_n_hits>>h_mc(6, 0, 6)")
h_mc = gROOT.FindObject("h_mc")
h_mc.SetTitle("MC")
h_mc.SetLineWidth(2)
h_mc.SetLineColor(2)
h_mc.Scale(t_data.GetEntries() / t_mc.GetEntries())
t_mc.Draw("tr1_n_clusters>>h_mc2(6, 0, 6)", "n_triggers == 3")
h_mc2 = gROOT.FindObject("h_mc2")
h_mc2.SetTitle("MC w/ trigger")
h_mc2.SetLineWidth(2)
h_mc2.SetLineColor(4)
h_mc2.Scale(t_data.GetEntries() / t_mc.GetEntries())
hs = THStack("hs", "title")
hs.Add(h_data, "histo")
hs.Add(h_mc, "histo")
hs.Add(h_mc2, "histo")
hs.Draw("nostack")
hs.SetTitle(
"Number of clusters in the 1st tracker;N_{particles}; N_{events}")
c.SetGridx()
c.SetGridy()
c.BuildLegend()
c.Update()
input("wait")
def timing(data, leg_entries):
""" Plot different timing delays after receiving the trigger. """
stack = THStack('stack', 'Counts per BX')
leg = TLegend(.8, .5, 1., .9)
for idx, list in enumerate(data):
h = TH1F('', '', 100, 0, 220)
h.SetFillColor(idx+2)
leg.AddEntry(h, leg_entries[idx], 'f')
for val in list:
h.Fill(val)
stack.Add(h)
canvas = TCanvas()
stack.Draw()
stack.GetXaxis().SetTitle('BX')
stack.GetYaxis().SetTitle('Counts')
leg.Draw()
canvas.SaveAs('timing.pdf')
def plotstuff3(cuts,cutname):
hs = THStack("hs",cutname);
hs.Add(gDirectory.Get("h1"))
leg = TLegend(0.6,0.75,0.9,0.9)
events.Draw("eleClT-posClT>>h0(200,-10,10)",cuts[0],"goff")
leg.AddEntry(gDirectory.Get("h0"),"after base cuts")
hs.Add(gDirectory.Get("h0"))
gDirectory.Get("h0").SetLineColor(1)
#gDirectory.Get("h1").SetTitle(cutname)
for i in range(1,len(cuts)):
events.Draw("eleClT-posClT>>h{0}(100,-10,10)".format(i),makeCutString(cuts[0:i+1]),"goff")
leg.AddEntry(gDirectory.Get("h{0}".format(i)),"after {0} cut".format(cutnames[i]))
hs.Add(gDirectory.Get("h{0}".format(i)))
gDirectory.Get("h{0}".format(i)).SetLineColor(i+1)
hs.Draw("nostack")
leg.Draw()
hs.GetXaxis().SetTitle("tEle-tPos [ns]")
hs.GetYaxis().SetTitle("events/(0.1 ns)")
c.Print(sys.argv[1]+".pdf")
class StackedHisto(object):
def __init__(self, name, title):
self._name = name
self._title = title
self._hstack = THStack(self._name, self._title)
def addHisto(histo):
self._hstack.Add(histo.histogram)
@property
def name(self):
return self._name
@property
def title(self):
return self._title
def create_canvas(self):
self._canvas = TCanvas(self._name, self._name)
gStyle.SetCanvasBorderMode(0)
gStyle.SetPadBorderMode(0)
self._canvas.cd()
def draw(self):
# create canvas to draw histogram
if not getattr(self, '_canvas', None):
self.create_canvas()
self._hstack.Draw('nostack')
def save_as(self, output_directory, format="png"):
try:
self._canvas.Print(
"%s/%s.%s" % (output_directory, self._name, format), format)
except AttributeError:
print("You have to draw histograms before saving.")
pass
def set_stats_style(self):
pass
def plotstuff(cut1,cut2,cut3,cutname):
events.Draw("eleClT-posClT>>h1(200,-10,10)",cut1,"goff")
events.Draw("eleClT-posClT>>h2(200,-10,10)",cut2,"goff")
events.Draw("eleClT-posClT>>h3(200,-10,10)",cut3,"goff")
hs = THStack("hs","Effect of "+cutname+" cut");
hs.Add(gDirectory.Get("h1"))
hs.Add(gDirectory.Get("h2"))
hs.Add(gDirectory.Get("h3"))
hs.Draw("nostack")
leg = TLegend(0.6,0.75,0.9,0.9)
leg.AddEntry(gDirectory.Get("h1"),"passed all but this cut")
leg.AddEntry(gDirectory.Get("h2"),"passed all cuts")
leg.AddEntry(gDirectory.Get("h3"),"failed this cut")
leg.Draw()
gDirectory.Get("h1").SetLineColor(1)
gDirectory.Get("h2").SetLineColor(4)
gDirectory.Get("h3").SetLineColor(2)
hs.GetXaxis().SetTitle("tEle-tPos [ns]")
hs.GetYaxis().SetTitle("events/(0.1 ns)")
c.Print(sys.argv[1]+".pdf")
def plotHistograms(histname,
filenames,
labels,
canvas,
legend,
normalize=False):
assert (len(filenames) == len(labels))
TH1.SetDefaultSumw2()
# get histograms from the files
histograms = []
for fname in filenames:
f = TFile.Open(fname, "READ")
h = f.Get(histname)
h.SetDirectory(0)
histograms.append(h)
f.Close()
# plot
canvas.cd()
hs = THStack()
hs.Draw()
norm = 1.
for i, (hist, label) in enumerate(zip(histograms, labels)):
hist.SetStats(0)
hist.SetLineColor(i + 1)
hist.SetMarkerColor(i + 1)
# normalize to the first histogram if needed
if normalize:
if i == 0:
norm = hist.Integral()
else:
norm_i = hist.Integral()
hist.Scale(norm / norm_i)
hs.Add(hist)
legend.AddEntry(hist, label)
return hs
def Stack(can,reverse=False) :
if can.GetPrimitive('pad_top') :
return Stack(can.GetPrimitive('pad_top'),reverse=reverse)
from ROOT import TH1,THStack
stack = THStack('stack','stack')
xaxislabel,yaxislabel = '',''
binlabels = []
if reverse :
the_primitives = reversed(can.GetListOfPrimitives())
else :
the_primitives = can.GetListOfPrimitives()
for i in the_primitives :
if issubclass(type(i),TH1) :
stack.Add(i)
if not xaxislabel : xaxislabel = i.GetXaxis().GetTitle()
if not yaxislabel : yaxislabel = i.GetYaxis().GetTitle()
if not binlabels and i.GetXaxis().GetBinLabel(1) :
for j in range(i.GetNbinsX()) :
binlabels.append(i.GetXaxis().GetBinLabel(j+1))
# The original objects are cleared from the histogram:
can.Clear()
tobject_collector.append(stack)
# Set draw option of underlying histograms for automatically nice legends
for hist in stack.GetHists() :
hist.SetOption('f')
can.cd()
stack.Draw('hist')
stack.GetXaxis().SetTitle(xaxislabel)
stack.GetYaxis().SetTitle(yaxislabel)
if binlabels :
for i in range(stack.GetXaxis().GetNbins()) :
stack.GetXaxis().SetBinLabel(i+1,binlabels[i])
can.RedrawAxis()
can.Modified()
can.Update()
return
hist_met_ = fin.Get('h_summary_' + iprocess)
hist_met_.Sumw2()
hist_met_.SetDirectory(0)
TH1.AddDirectory(0)
# gStyle.SetPalette(53)
hist_met_.SetFillColor(color[i])
#hist_met_.SetLineColor(color[i])
bkgStack.Add(hist_met_, 'hist')
legend.AddEntry(hist_met_, processesLegend[i], "f")
i = i + 1
c = TCanvas()
c.SetLogy(1)
bkgStack.Draw()
j = 0
for iregion in region:
bkgStack.GetXaxis().SetBinLabel(j + 1, iregion)
j = j + 1
legend.Draw()
latex = TLatex(0.15, 0.85, 'CMS')
latex.SetTextSize(0.036)
latex.SetTextAlign(12)
latex.SetNDC(kTRUE)
latex.SetTextFont(61)
latex.Draw()
latex1 = TLatex(0.13, 0.82, 'Preliminary')
latex1.SetTextSize(0.036)
def plot(path, leg_title, ecms, xmax, ymax, runNolow, runNoup, mode):
try:
f_data = TFile(path[0])
f_incMC1 = TFile(path[1])
f_incMC2 = TFile(path[2])
f_sigMC1 = TFile(path[3])
f_sigMC2 = TFile(path[4])
t_data = f_data.Get('save')
t_incMC1 = f_incMC1.Get('save')
t_incMC2 = f_incMC2.Get('save')
t_sigMC1 = f_sigMC1.Get('save')
t_sigMC2 = f_sigMC2.Get('save')
entries_data = t_data.GetEntries()
entries_incMC1 = t_incMC1.GetEntries()
entries_incMC2 = t_incMC2.GetEntries()
entries_sigMC1 = t_sigMC1.GetEntries()
entries_sigMC2 = t_sigMC2.GetEntries()
logging.info('data entries :' + str(entries_data))
logging.info('inclusive MC(open charm) entries :' +
str(entries_incMC1))
logging.info('inclusive MC(qqbar) entries :' + str(entries_incMC2))
logging.info('signal MC(D1(2420)) entries :' + str(entries_sigMC1))
logging.info('signal MC(psi(3770)) entries :' + str(entries_sigMC2))
except:
logging.error('File paths are invalid!')
sys.exit()
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
xmin = 0.4
xbins = 80
content = (xmax - xmin) / xbins * 1000
ytitle = 'Events/%.1f MeV' % content
xtitle = 'M(#pi^{+}_{0}#pi^{-}_{0})(GeV)'
h_data = TH1F('data', 'data', xbins, xmin, float(xmax))
h_incMC1 = TH1F('incMC1', 'inclusive MC: open charm', xbins, xmin,
float(xmax))
h_incMC2 = TH1F('incMC2', 'inclusive MC: qqbar', xbins, xmin, float(xmax))
h_sigMC1 = TH1F('sigMC1', 'signal MC: D1(2420)', xbins, xmin, float(xmax))
h_sigMC2 = TH1F('sigMC2', 'signal MC: psi(3770)', xbins, xmin, float(xmax))
set_histo_style(h_data, h_incMC1, h_incMC2, h_sigMC1, h_sigMC2, xtitle,
ytitle, ymax)
rm_pipi_fill(t_data, t_incMC1, t_incMC2, t_sigMC1, t_sigMC2, h_data,
h_incMC1, h_incMC2, h_sigMC1, h_sigMC2, runNolow, runNoup)
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
h_sigMC1.Scale(scale_factor(ecms, 'D1_2420'))
h_sigMC2.Scale(scale_factor(ecms, 'psipp'))
h_incMC1.Scale(scale_factor(ecms, 'DD'))
h_incMC2.Scale(scale_factor(ecms, 'qq'))
h_data.Draw('E1')
h_incMC1.SetFillColor(ROOT.kBlue)
h_incMC2.SetFillColor(ROOT.kYellow)
h_sigMC1.SetFillColor(ROOT.kGreen)
h_sigMC2.SetFillColor(ROOT.kRed)
hs = THStack('hs', 'Stacked')
hs.Add(h_sigMC1)
hs.Add(h_sigMC2)
hs.Add(h_incMC2)
hs.Add(h_incMC1)
hs.Draw('same')
h_data.Draw('sameE1')
legend = TLegend(0.55, 0.6, 0.8, 0.75)
set_legend(legend, h_data, h_incMC1, h_incMC2, h_sigMC1, h_sigMC2,
leg_title)
legend.Draw()
mbc.SaveAs('./figs/m_pipi_' + str(ecms) + '_' + mode + '.pdf')
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))
mc_sum.GetXaxis().SetLabelFont(63)
mc_sum.GetXaxis().SetLabelSize(14) # labels will be 14 pixels
data.GetYaxis().SetLabelFont(63)
data.GetYaxis().SetLabelSize(14) # labels will be 14 pixels
mc_sum.GetYaxis().SetLabelFont(63)
mc_sum.GetYaxis().SetLabelSize(14) # labels will be 14 pixels
if args.ratio:
mc_sum.GetXaxis().SetLabelOffset(999)
mc_sum.GetXaxis().SetLabelSize(0)
data.GetXaxis().SetLabelOffset(999)
data.GetXaxis().SetLabelSize(0)
mc_sum.Draw("e2")
hs.Draw("same")
mc_sum.Draw("same e2")
if not args.no_data:
data.SetLineWidth(3)
data.Draw("same P")
leg.Draw("same")
#left_title = TPaveText(0.1, 0.9, 0.4, 0.94, "brNDC")
#left_title.AddText("CMS preliminary at 13 TeV")
#left_title.SetTextFont(1)
left_title = TPaveText(0.15, 0.82, 0.25, 0.88, "brNDC")
left_title.AddText("CMS")
left_title.SetTextFont(1)
def plotter(plotdir,
plot,
xTitle,
logY,
channels=['VV'],
includeData=False,
scaleSignal=0,
UserRange=[None, None, None, None],
initPath=''):
# channelTex={'WPWP':'W^{+}W^{+}', 'WPWM':'W^{+}W^{-}','WMWM':'W^{-}W^{-}','WPZ':'W^{+}Z','WMZ':'W^{-}Z','ZZ':'ZZ'}
channelTex = {'ZZ': 'ZZ'}
# plotstyle=[(1,1),(1,2),(2,1),(2,2),(4,1),(4,2)]
plotstyle = [(1, 1)]
# 0 1 2 3 4 5 6
# Backgrounds=['QCD', 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'TT', 'WW', 'WZ', 'ZZ']
# BGColors= [rt.kAzure+7, rt.kRed-4, rt.kOrange-2, rt.kGreen+2, rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
# BGTeX= ['QCD', 'W+JetsToQQ', 'Z+JetsToQQ', 'TTbar'], 'WW', 'WZ', 'ZZ']
#stackOrder= [4,5,6,2,1,3,0]
Backgrounds = [
'QCD'
] # 'WJetsToQQ_HT600ToInf', 'ZJetsToQQ_HT600ToInf', 'ZZ'] 'TT', 'WW', 'WZ', 'ZZ']
BGColors = [
rt.kAzure + 7
] #, rt.kOrange-2, rt.kGreen+2], rt.kOrange+7, rt.kBlue+1, rt.kMagenta+2]
BGTeX = ['QCD'] #
stackOrder = [0]
PreSelection = [
'nocuts', 'common', 'corrections', 'cleaner', 'softdropmassCorr',
'AK4pfidfilter', 'AK8pfidfilter', 'invMAk8sel', 'detaAk8sel',
'AK8N2sel'
]
Selection = [
'preselection', 'softdropAK8sel', 'tau21sel', 'deltaR48', 'VVRegion',
'AK4N2sel', 'OpSignsel', 'detaAk4sel', 'invMAk4sel_1p0', 'Kin_AK8'
]
cutnames = [
'cleaner', 'AK8N2sel', 'invMAk8sel', 'detaAk8sel', 'softdropAK8sel',
'tau21sel', 'AK4cleaner', 'AK4N2sel', 'OpSignsel', 'detaAk4sel',
'invMAk4sel_1p0'
]
cuts = {
'cleaner':
'#splitline{p_{T-AK8} > 200 GeV, |#eta_{AK8}| < 2.5}{p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0}',
'AK8N2sel': 'N_{AK8} #geq 2',
'invMAk8sel': 'M_{jj-AK8} > 1050 GeV',
'detaAk8sel': '|#Delta#eta_{jj-AK8}|<1.3',
'softdropAK8sel': '65 GeV <M_{SD}< 105 GeV',
'tau21sel': '0 #leq #tau_{2}/#tau_{1}<0.45',
# 'AK4cleaner':'p_{T-AK4} > 30 GeV, |#eta_{AK4}| < 5.0',
'AK4cleaner': '',
'AK4N2sel': 'N_{AK4} #geq 2',
'OpSignsel': '#eta_{1-AK4} #eta_{2-AK4} < 0',
'detaAk4sel': '|#Delta#eta_{jj-AK4}| > 3.0',
'invMAk4sel_1p0': 'M_{jj-AK4} > 1.0 TeV'
}
VV = ('VV' in channels)
seperate = (not VV)
if VV:
#channels=["WPWP","WPWM","WMWM","WPZ","WMZ","ZZ"]
channels = ["ZZ"]
plottitle = plotdir + '_' + plot
lumi = 36.814
xLabelSize = 18.
yLabelSize = 18.
xTitleSize = 20.
yTitleSize = 22.
xTitleOffset = 4.
yTitleOffset = 1.3
printout = False
Portrait = True
cutname = False
ratio = includeData
if ('highbin' in plot):
binning = 'dijetbinning'
else:
binning = 'default'
if (Portrait):
canvX = 600
canvY = 600
else:
canvX = 900
canvY = 675
if (plotdir in PreSelection):
region = 'PreSelection'
initPath = ''
referenceHistPath = 'detaAk8sel/N_pv'
if (PreSelection.index(plotdir) < 4 and ('softdrop' in plot)):
return 'SofdropMass not filled yet!'
else:
region = 'SignalRegion'
referenceHistPath = 'tau21sel/N_pv'
referenceHistPath = plotdir + '/' + plot
if (initPath == ''):
path = '/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/%s' % region
else:
path = initPath
outputPath = path.replace(
'/nfs/dust/cms/user/loemkerj/bachelor/CMSSW_10_2_16/src/UHH2/aQGCVVjjhadronic/SignalRegion',
'plots/')
if (plotdir in PreSelection):
CutNumber = PreSelection.index(plotdir)
else:
CutNumber = Selection.index(plotdir)
outputPath = outputPath + '/%02i_%s' % (CutNumber, plotdir) + '/'
if (printout):
print('InputPath:', path)
print('OutputPath:', outputPath)
#check if OutputPath exists - and if not create it!
if not os.path.exists(outputPath):
os.makedirs(outputPath)
# path='/home/albrec/Master/signal/'
scaleVV = (scaleSignal != 0)
VVScale = scaleSignal
if (UserRange[2] == None or UserRange[3] == None):
YRangeUser = False
Ymin = UserRange[2]
Ymax = UserRange[3]
else:
YRangeUser = True
Ymin = UserRange[2]
Ymax = UserRange[3]
if (UserRange[0] == None or UserRange[1] == None):
XRangeUser = False
Xmin = UserRange[0]
Xmax = UserRange[1]
else:
XRangeUser = True
Xmin = UserRange[0]
Xmax = UserRange[1]
# YRangeUser=False
# Ymin=0.11
# Ymax=9*10**3
# XRangeUser=False
# Xmin=0
# Xmax=6000.
gROOT.ProcessLine("gErrorIgnoreLevel = 2001;")
SFiles = []
for i in range(len(channels)):
SFiles.append(
TFile(
path +
"/uhh2.AnalysisModuleRunner.MC.MC_aQGC_%sjj_hadronic_2016v3.root"
% channels[i]))
#uhh2.AnalysisModuleRunner.MC.MC_aQGC_ZZjj_hadronic_2016v3.root
##Open Files to get BackgroundHist:
BFiles = []
for i in range(len(Backgrounds)):
BFiles.append(
TFile(path +
"/uhh2.AnalysisModuleRunner.MC.MC_%s.root" % Backgrounds[i]))
#Open File to get DataHist:
# DataFile = TFile(path+"/uhh2.AnalysisModuleRunner.Data.DATA.root")
#gROOT.ProcessLine( "gErrorIgnoreLevel = 0;")
if (includeData == True):
#calculate QCDscale with Integrals from the following Histogram:
# referenceHistPath = 'tau21sel/N_AK4'
# referenceHistPath = 'detaAk8sel/N_pv'
# referenceHistPath = 'tau21sel/met_pt_over_sumptAK8_2'
QCDscale = float(DataFile.Get(referenceHistPath).Integral())
QCDNorm = 1
for i in range(len(BFiles)):
if ('QCD' in BFiles[i].GetName()):
QCDNorm = float(BFiles[i].Get(referenceHistPath).Integral())
else:
QCDscale -= float(BFiles[i].Get(referenceHistPath).Integral())
QCDscale /= QCDNorm
else:
QCDscale = 1.0
if (printout):
print('using QCDscale:', QCDscale)
SHists = []
for i in range(len(channels)):
SHists.append(SFiles[i].Get(plotdir + '/' + plot))
BHists = []
for i in range(len(BFiles)):
BHists.append(BFiles[i].Get(plotdir + '/' + plot))
if ('QCD' in BFiles[i].GetName()):
BHists[-1].Scale(QCDscale)
#if(includeData):
# DataHist=DataFile.Get(plotdir+'/'+plot)
if (binning == 'dijetbinning'):
fitbinning = array('d')
binwidth = 200
NBins = (14000 / binwidth) - ((1040 / binwidth) + 1)
NBins = int(NBins)
for i in range(NBins + 1):
fitbinning.append(1050 + i * binwidth)
for i in range(len(channels)):
SHists[i] = SHists[i].Rebin(NBins, "new binning", fitbinning)
for i in range(len(Backgrounds)):
BHists[i] = BHists[i].Rebin(NBins, "new binning", fitbinning)
if (includeData):
DataHist = DataHist.Rebin(NBins, "new binning", fitbinning)
canv = TCanvas(plottitle, plottitle, canvX, canvY)
yplot = 0.7
yratio = 0.3
ymax = 1.0
xmax = 1.0
xmin = 0.0
if (ratio):
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot, xmax, ymax)
ratiopad = TPad("ratiopad", "Ratio", xmin, ymax - yplot - yratio, xmax,
ymax - yplot)
else:
plotpad = TPad("plotpad", "Plot", xmin, ymax - yplot - yratio, xmax,
ymax)
plotpad.SetTopMargin(0.08)
plotpad.SetLeftMargin(0.1)
plotpad.SetRightMargin(0.05)
plotpad.SetTicks()
plotpad.Draw()
if (ratio):
plotpad.SetBottomMargin(0.016)
ratiopad.SetTopMargin(0.016)
ratiopad.SetBottomMargin(0.35)
ratiopad.SetLeftMargin(0.1)
ratiopad.SetRightMargin(0.05)
ratiopad.SetTicks()
ratiopad.Draw()
else:
plotpad.SetBottomMargin(0.1)
if (logY):
plotpad.SetLogy()
canv.SetLogy()
if ('-logX' in xTitle):
plotpad.SetLogx()
if (ratio):
ratiopad.SetLogx()
canv.SetLogx()
drawOptions = "HE"
stack = THStack(plottitle, plottitle)
BHist = THStack(plottitle, plottitle)
# for i in range(len(Backgrounds)):
for i in stackOrder:
# BHists[i].SetFillColor(BGColors[i])
BHists[i].SetLineColor(BGColors[i])
BHist.Add(BHists[i], 'Hist')
BHist.SetTitle(plottitle)
BHistErr = BHists[0].Clone()
for i in range(1, len(Backgrounds)):
BHistErr.Add(BHists[i])
BHistErr.SetFillStyle(3204)
BHistErr.SetFillColor(rt.kGray + 2)
BHistErr.SetLineColor(1)
BGMax = BHist.GetMaximum()
SIGMax = 0
#if(VV): #new
# SIGMax=VVsum.GetMaximum()
#else:
for i in range(len(channels)):
tmpmax = SHists[i].GetMaximum()
if (tmpmax > SIGMax):
SIGMax = tmpmax
if (scaleVV):
SIGMax = SIGMax * VVScale
if (logY):
MAX = 0.9 * float(10**(magnitude(max(BGMax, SIGMax)) + 1))
MIN = float(10**(magnitude(max(BGMax, SIGMax)) - 5))
MIN += float(10**(magnitude(MIN)))
legendMIN = math.log(max(BGMax, SIGMax)) / math.log(MAX)
else:
MAX = (1.0 / 0.8) * max(BGMax, SIGMax)
legendMIN = 0.7
MIN = 0.
legendMIN = (legendMIN * 0.7) + 0.3 - 0.016
legend = TLegend(0.5, 0.75, 0.85, 0.89)
legend.SetFillStyle(0)
legend.SetTextSize(0.02)
legend.SetMargin(0.4)
legend.SetNColumns(2)
legend.SetColumnSeparation(0.3)
if (includeData):
DataHist.SetMarkerStyle(8)
DataHist.SetLineColor(1)
DataHist.SetTitle(plottitle)
if VV:
for i in range(len(channels)):
if (i == 0):
VVsum = SHists[i].Clone()
else:
VVsum.Add(SHists[i])
legentry = 'VVjj'
if (scaleVV):
VVsum.Scale(VVScale)
legentry += ' *%0.f' % VVScale
VVsum.SetLineColor(1)
VVsum.SetLineStyle(plotstyle[0][1])
VVsum.SetLineWidth(2)
legend.AddEntry(VVsum, legentry)
else:
for i in range(len(channels)):
SHists[i].SetLineColor(plotstyle[i][0])
SHists[i].SetLineStyle(plotstyle[i][1])
SHists[i].SetLineWidth(2)
legentry = "%sjj" % channelTex[channels[i]]
if (scaleVV):
SHists[i].Scale(VVScale)
legentry += ' *%.2E' % VVScale
legend.AddEntry(SHists[i], legentry)
for i in stackOrder:
legend.AddEntry(BHists[i], BGTeX[i], "f")
legend.AddEntry(BHistErr, "stat. Uncertainty", "f")
if (includeData):
legend.AddEntry(DataHist, "Data", "lep")
canv.SetTitle(plottitle)
BHistErr.GetYaxis().SetTitle('Events')
BHistErr.GetYaxis().SetRangeUser(MIN, MAX)
BHistErr.GetYaxis().SetTitleFont(43)
BHistErr.GetYaxis().SetTitleSize(yTitleSize)
BHistErr.GetYaxis().SetTitleOffset(yTitleOffset)
BHistErr.GetYaxis().SetLabelFont(43)
BHistErr.GetYaxis().SetLabelSize(yLabelSize)
if (ratio):
BHistErr.GetXaxis().SetTitleSize(0.0)
BHistErr.GetXaxis().SetLabelSize(0.0)
else:
BHistErr.GetXaxis().SetTitle(xTitle)
BHistErr.GetXaxis().SetTitleFont(43)
BHistErr.GetXaxis().SetTitleSize(xTitleSize)
# BHistErr.GetXaxis().SetTitleOffset(xTitleOffset)
BHistErr.GetXaxis().SetTitleOffset(1.2)
BHistErr.GetXaxis().SetLabelFont(43)
BHistErr.GetXaxis().SetLabelSize(xLabelSize)
# BHistErr.GetXaxis().SetTickLength(0.08)
# BHistErr.GetXaxis().SetNdivisions(506)
if (YRangeUser):
BHistErr.GetYaxis().SetRangeUser(Ymin, Ymax)
if (XRangeUser):
BHistErr.GetXaxis().SetRangeUser(Xmin, Xmax)
plotpad.cd()
BHistErr.Draw("E2")
BHist.Draw("HistSAME")
BHistErr.Draw("E2SAME")
if (VV):
VVsum.Draw("SAME" + drawOptions)
elif ('-noSig' not in xTitle):
for i in range(len(channels)):
SHists[i].Draw("SAME" + drawOptions)
if (includeData):
DataHist.Draw("APE1SAME")
plotpad.RedrawAxis()
if (ratio):
ratiopad.cd()
if (includeData):
ratioHist = DataHist.Clone()
else:
ratioHist = BHistErr.Clone()
ratioHist.SetLineColor(rt.kBlack)
# ratioHist.Sumw2()
ratioHist.SetStats(0)
ratioHist.Divide(BHistErr)
ratioHist.SetMarkerStyle(21)
ratioHist.SetMarkerSize(0.7)
#Yaxis
ratioHist.GetYaxis().SetRangeUser(0.3, 1.7)
ratioHist.GetYaxis().SetTitle("Data/BG")
ratioHist.GetYaxis().CenterTitle()
ratioHist.GetYaxis().SetTitleFont(43)
ratioHist.GetYaxis().SetTitleSize(yTitleSize)
ratioHist.GetYaxis().SetTitleOffset(yTitleOffset)
ratioHist.GetYaxis().SetLabelFont(43)
ratioHist.GetYaxis().SetLabelSize(yLabelSize)
ratioHist.GetYaxis().SetNdivisions(506)
#Xaxis
ratioHist.GetXaxis().SetTitle(xTitle)
ratioHist.GetXaxis().SetTitleFont(43)
ratioHist.GetXaxis().SetTitleSize(xTitleSize)
ratioHist.GetXaxis().SetTitleOffset(xTitleOffset)
ratioHist.GetXaxis().SetLabelFont(43)
ratioHist.GetXaxis().SetLabelSize(xLabelSize)
ratioHist.GetXaxis().SetTickLength(0.08)
ratioHist.GetXaxis().SetNdivisions(506)
# if(YRangeUser):
# ratioHist.GetYaxis().SetRangeUser(Ymin,Ymax)
if (XRangeUser):
ratioHist.GetXaxis().SetRangeUser(Xmin, Xmax)
ratioXMin = Xmin
ratioXMax = Xmax
else:
ratioXMin = ratioHist.GetXaxis().GetXmin()
ratioXMax = ratioHist.GetXaxis().GetXmax()
ratioHist.Draw("ep")
zeropercent = TLine(ratioXMin, 1, ratioXMax, 1)
zeropercent.Draw()
plus10percent = TLine(ratioXMin, 1.1, ratioXMax, 1.1)
plus10percent.SetLineStyle(rt.kDashed)
plus10percent.Draw()
minus10percent = TLine(ratioXMin, 0.9, ratioXMax, 0.9)
minus10percent.SetLineStyle(rt.kDashed)
minus10percent.Draw()
canv.cd()
gPad.RedrawAxis()
legend.Draw()
latex = TLatex()
latex.SetNDC(kTRUE)
latex.SetTextSize(20)
latex.DrawLatex(0.69, 0.953, "%.2f fb^{-1} (13 TeV)" % lumi)
latex.DrawLatex(0.1, 0.953, "private work")
lastcut = 'nocuts'
for cut in cutnames:
if cut in plotdir:
lastcut = cut
if (not (lastcut == 'nocuts') and cutname):
# latex.SetTextSize(0.03)
latex.SetTextSize(15)
for l in range(cutnames.index(lastcut) + 1):
latex.DrawLatex(0.12, 0.8 - l * 0.04, cuts[cutnames[l]])
canv.Update()
canv.Print(outputPath + '/%s_%s.pdf' % (plotdir, plot))
#prevents memory leak in Canvas Creation/Deletion
#see: https://root.cern.ch/root/roottalk/roottalk04/2484.html
gSystem.ProcessEvents()
if (ratio):
del ratiopad
del plotpad, canv
# gc.collect()
return 'done!'
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 createCompoundPlots(detector, plot):
"""Produce the requested plot for the specified detector.
Function that will plot the requested @plot for the specified
@detector. The specified detector could either be a real
detector or a compound one. The list of available plots are the
keys of plots dictionary (imported from plot_utils.
"""
theDirname = 'Images'
if not checkFile_(theDirname):
os.mkdir(theDirname)
goodToGo, theDetectorFilename = paramsGood_(detector, plot)
if not goodToGo:
return
theDetectorFile = TFile(theDetectorFilename)
#
# get TProfiles
prof_X0_elements = OrderedDict()
hist_X0_elements = OrderedDict()
for label, [num, color, leg] in hist_label_to_num.iteritems():
#print label, num, color, leg
prof_X0_elements[label] = theDetectorFile.Get(
"%d" % (num + plots[plot].plotNumber))
hist_X0_elements[label] = prof_X0_elements[label].ProjectionX()
hist_X0_elements[label].SetFillColor(color)
hist_X0_elements[label].SetLineColor(kBlack)
files = []
if detector in COMPOUNDS.keys():
for subDetector in COMPOUNDS[detector][1:]:
subDetectorFilename = "matbdg_%s.root" % subDetector
# open file
if not checkFile_(subDetectorFilename):
continue
subDetectorFile = TFile(subDetectorFilename)
files.append(subDetectorFile)
print("*** Open file... %s" % subDetectorFilename)
# subdetector 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].Add(
prof_X0_elements[label].ProjectionX(
"B_%s" % prof_X0_elements[label].GetName()), +1.000)
# stack
stackTitle = "Material Budget %s;%s;%s" % (detector, plots[plot].abscissa,
plots[plot].ordinate)
stack_X0 = THStack("stack_X0", stackTitle)
for label, [num, color, leg] in hist_label_to_num.iteritems():
stack_X0.Add(hist_X0_elements[label])
# canvas
canname = "MBCan_1D_%s_%s" % (detector, plot)
can = TCanvas(canname, canname, 800, 800)
can.Range(0, 0, 25, 25)
can.SetFillColor(kWhite)
gStyle.SetOptStat(0)
gStyle.SetOptTitle(1)
# Draw
stack_X0.Draw("HIST")
stack_X0.GetYaxis().SetTitleOffset(1.15)
# Legenda
theLegend = TLegend(0.40, 0.65, 0.60, 0.89)
if plot == "x_vs_phi" or plot == "l_vs_phi":
theLegend = TLegend(0.65, 0.30, 0.89, 0.70)
if plot == "x_vs_R" or plot == "l_vs_R":
theLegend = TLegend(0.75, 0.60, 0.95, 0.90)
for label, [num, color, leg] in hist_label_to_num.iteritems():
theLegend.AddEntry(hist_X0_elements[label], leg, "f")
theLegend.Draw()
# Store
can.Update()
can.SaveAs("%s/%s_%s.pdf" % (theDirname, detector, plot))
can.SaveAs("%s/%s_%s.png" % (theDirname, detector, plot))
#Let's also save the total accumulated budget vs eta since muon id relies
#on adequate calorimeter thickness
if plot == "x_vs_eta" or plot == "l_vs_eta":
canname = "MBCan_1D_%s_%s_total" % (detector, 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.GetStack().Last().GetXaxis().SetRangeUser(0., 3.)
stack_X0.GetStack().Last().Draw()
stack_X0.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zplus.pdf" % (theDirname, detector, plot))
can2.SaveAs("%s/%s_%s_total_Zplus.png" % (theDirname, detector, plot))
stack_X0.GetStack().Last().GetXaxis().SetRangeUser(-3., 0.)
stack_X0.GetStack().Last().Draw()
stack_X0.GetYaxis().SetTitleOffset(1.15)
can2.Update()
can2.Modified()
can2.SaveAs("%s/%s_%s_total_Zminus.pdf" % (theDirname, detector, plot))
can2.SaveAs("%s/%s_%s_total_Zminus.png" % (theDirname, detector, plot))
def createPlotsReco_(reco_file, label, debug=False):
"""Cumulative material budget from reconstruction.
Internal function that will produce a cumulative profile of the
material budget in the reconstruction starting from the single
detectors that compose the tracker. It will iterate over all
existing detectors contained in the sDETS dictionary. The
function will automatically stop everytime it encounters a
non-existent detector, until no more detectors are left to
try. For this reason the keys in the sDETS dictionary can be as
inclusive as possible.
"""
cumulative_matbdg = None
sPREF = ["Original_RadLen_vs_Eta_", "RadLen_vs_Eta_"]
c = TCanvas("c", "c", 1024, 1024)
diffs = []
if not checkFile_(reco_file):
print("Error: missing file %s" % reco_file)
raise RuntimeError
file = TFile(reco_file)
prefix = "/DQMData/Run 1/Tracking/Run summary/RecoMaterial/"
for s in sPREF:
hs = THStack("hs", "")
histos = []
for det, color in sDETS.iteritems():
layer_number = 0
while True:
layer_number += 1
name = "%s%s%s%d" % (prefix, s, det, layer_number)
prof = file.Get(name)
# If we miss an object, since we are incrementally
# searching for consecutive layers, we may safely
# assume that there are no additional layers and skip
# to the next detector.
if not prof:
if debug:
print("Missing profile %s" % name)
break
else:
histos.append(prof.ProjectionX("_px", "hist"))
diffs.append(histos[-1])
histos[-1].SetFillColor(color + layer_number)
histos[-1].SetLineColor(color + layer_number + 1)
name = "CumulativeRecoMatBdg_%s" % s
if s == "RadLen_vs_Eta_":
cumulative_matbdg = TH1D(name, name, histos[0].GetNbinsX(),
histos[0].GetXaxis().GetXmin(),
histos[0].GetXaxis().GetXmax())
cumulative_matbdg.SetDirectory(0)
for h in histos:
hs.Add(h)
if cumulative_matbdg:
cumulative_matbdg.Add(h, 1.)
hs.Draw()
hs.GetYaxis().SetTitle("RadLen")
c.Update()
c.Modified()
c.SaveAs("%sstacked_%s.png" % (s, label))
hs = THStack("diff", "")
for d in range(0, len(diffs) / 2):
diffs[d + len(diffs) / 2].Add(diffs[d], -1.)
hs.Add(diffs[d + len(diffs) / 2])
hs.Draw()
hs.GetYaxis().SetTitle("RadLen")
c.Update()
c.Modified()
c.SaveAs("RadLen_difference_%s.png" % label)
return cumulative_matbdg
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)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
hs.GetYaxis().SetTitle("Events")
gStyle.SetOptStat(0)
def show(self, outDir):
if len(self.mc) == 0:
print '%s has no MC!' % self.name
return
htype = self.mc[0].ClassName()
if htype.find('TH2') >= 0:
print 'Skipping TH2'
return
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gROOT.SetBatch(1)
canvas = TCanvas('c_' + self.name, 'C', 800, 800)
canvas.cd()
t1 = TPad("t1", "t1", 0.0, 0.20, 1.0, 1.0)
t1.SetBottomMargin(0)
t1.Draw()
t1.cd()
self._garbageList.append(t1)
frame = None
# Decide which backgrounds are visible
maxint = max([x.Integral() for x in self.mc])
hists_to_add = [h for h in self.mc if h.Integral() > 0.005 * maxint]
# Make the legend with the correct size
leg = TLegend(0.75, 0.74 - 0.04 * max(len(hists_to_add) - 2, 0), .89,
0.89)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetTextFont(43)
leg.SetTextSize(20)
nlegCols = 0
maxY = 1.0
if self.data is not None:
leg.AddEntry(self.data, self.data.GetTitle(), 'p')
frame = self.dataH.Clone('frame')
self._garbageList.append(frame)
maxY = self.dataH.GetMaximum()
frame.Reset('ICE')
elif self.dataH is not None:
leg.AddEntry(self.dataH, self.dataH.GetTitle(), 'p')
frame = self.dataH.Clone('frame')
self._garbageList.append(frame)
maxY = self.dataH.GetMaximum()
frame.Reset('ICE')
# Add the legend entries for the visible backgrounds
for h in sorted(hists_to_add, key=lambda x: x.Integral(),
reverse=True):
leg.AddEntry(h, h.GetTitle(), 'f')
nlegCols = nlegCols + 1
# Build the stack to plot from all backgrounds
totalMC = None
stack = THStack('mc', 'mc')
for h in sorted(self.mc, key=lambda x: x.Integral()):
stack.Add(h, 'hist')
if totalMC is None:
totalMC = h.Clone('totalmc')
self._garbageList.append(totalMC)
totalMC.SetDirectory(0)
else:
totalMC.Add(h)
if totalMC is not None:
maxY = max(totalMC.GetMaximum(), maxY)
if frame is None:
frame = totalMC.Clone('frame')
frame.Reset('ICE')
self._garbageList.append(frame)
if self.data is not None or self.dataH is not None:
nlegCols = nlegCols + 1
if nlegCols == 0:
print '%s is empty' % self.name
return
frame.GetYaxis().SetTitleSize(0.045)
frame.GetYaxis().SetLabelSize(0.04)
frame.GetYaxis().SetRangeUser(0.5, 1.2 * maxY)
frame.GetYaxis().SetNoExponent()
frame.SetDirectory(0)
frame.Draw()
frame.GetYaxis().SetTitleOffset(1.6)
stack.Draw('hist same')
if self.data is not None:
self.data.Draw('P')
elif self.dataH is not None:
self.dataH.Draw('e1same')
# leg.SetNColumns(nlegCols)
leg.Draw()
# redrawBorder(t1)
## Draw CMS Preliminary label
CMS_lumi(pad=t1, iPeriod=2, iPosX=0, extraText='') #Work in Progress')
if self.normalizedToData:
txt = TLatex()
txt.SetNDC(True)
txt.SetTextFont(42)
txt.SetTextColor(ROOT.kGray + 1)
txt.SetTextSize(0.035)
txt.SetTextAngle(90)
txt.SetTextAlign(12)
txt.DrawLatex(0.05, 0.05, '#it{Normalized to data}')
if totalMC is None or (self.data is None and self.dataH is None):
t1.SetPad(0, 0, 1, 1)
t1.SetBottomMargin(0.12)
else:
canvas.cd()
t2 = TPad("t2", "t2", 0.0, 0.0, 1.0, 0.2)
self._garbageList.append(t2)
t2.SetTopMargin(0)
t2.SetBottomMargin(0.4)
t2.SetGridy()
t2.Draw()
t2.cd()
ratioframe = self.dataH.Clone('ratioframe')
ratioframe.Reset('ICE')
ratioframe.Draw()
ratioframe.GetYaxis().SetRangeUser(self.ratiorange[0],
self.ratiorange[1])
ratioframe.GetYaxis().SetTitle('Obs./Exp.')
ratioframe.GetYaxis().SetNdivisions(5)
ratioframe.GetYaxis().SetLabelSize(0.15)
ratioframe.GetXaxis().SetLabelSize(0.15)
ratioframe.GetYaxis().SetTitleSize(0.18)
ratioframe.GetXaxis().SetLabelSize(0.18)
ratioframe.GetXaxis().SetTitleSize(0.18)
ratioframe.GetYaxis().SetTitleOffset(0.4)
ratioframe.GetXaxis().SetTitleOffset(0.9)
gr = ROOT.TGraphAsymmErrors()
gr.SetName("data2bkg")
gr.SetMarkerStyle(self.dataH.GetMarkerStyle())
gr.SetMarkerSize(0.7 * self.dataH.GetMarkerSize())
gr.SetMarkerColor(self.dataH.GetMarkerColor())
gr.SetLineColor(self.dataH.GetLineColor())
gr.SetLineWidth(self.dataH.GetLineWidth())
bkgUncGr = ROOT.TGraphErrors()
bkgUncGr.SetName('bkgunc')
bkgUncGr.SetMarkerColor(920)
bkgUncGr.SetMarkerStyle(1)
bkgUncGr.SetLineColor(920)
bkgUncGr.SetFillColor(920)
bkgUncGr.SetFillStyle(3001)
for xbin in xrange(1, self.dataH.GetXaxis().GetNbins() + 1):
x = self.dataH.GetXaxis().GetBinCenter(xbin)
dx = self.dataH.GetXaxis().GetBinWidth(xbin)
dataCts = self.dataH.GetBinContent(xbin)
if self.data:
data_err_low = self.data.GetErrorYlow(
xbin - 1) #get errors from the graph
data_err_up = self.data.GetErrorYhigh(xbin - 1)
else:
data_err_low = self.dataH.GetBinError(xbin)
data_err_up = data_err_low
bkgCts = totalMC.GetBinContent(xbin)
bkgCts_err = totalMC.GetBinError(xbin)
if bkgCts == 0: continue
errLo = math.sqrt(
math.pow(data_err_low * bkgCts, 2) +
math.pow(dataCts * bkgCts_err, 2)) / math.pow(bkgCts, 2)
errHi = math.sqrt(
math.pow(data_err_up * bkgCts, 2) +
math.pow(dataCts * bkgCts_err, 2)) / math.pow(bkgCts, 2)
np = gr.GetN()
gr.SetPoint(np, x, dataCts / bkgCts)
gr.SetPointError(np, 0, 0, errLo, errHi)
bkgUncGr.SetPoint(np, x, 1)
bkgUncGr.SetPointError(np, dx, bkgCts_err / bkgCts)
bkgUncGr.Draw('2')
gr.Draw('p')
# redrawBorder(t2)
canvas.cd()
canvas.Modified()
canvas.Update()
for ext in self.plotformats:
canvas.SaveAs(os.path.join(outDir, self.name + '.' + ext))
if self.savelog:
t1.cd()
t1.SetLogy()
frame.GetYaxis().SetRangeUser(70, 2 * maxY)
# frame.GetYaxis().SetRangeUser(1000,10*maxY)
canvas.cd()
for ext in self.plotformats:
canvas.SaveAs(os.path.join(outDir, self.name + '_log.' + ext))
tex3.SetLineWidth(2)
cc = TCanvas(var, var, 800, 750)
# cc.SetLogy()
pad1 = TPad("p1", "p1", 0, 0.25, 1, 1, 0, 0)
pad1.SetMargin(0.15, 0.03, 0, 0.01)
pad2 = TPad("p2", "p2", 0, 0, 1, 0.25, 0, 0)
pad2.SetMargin(0.15, 0.03, 0.3, 0.01)
pad2.SetGrid()
pad1.Draw()
pad2.Draw()
pad1.cd()
pad1.SetLogy()
hs.SetMinimum(0.1)
hs.Draw("HIST")
# hs.GetXaxis().SetTitle(TITLE[var])
hs.GetYaxis().SetTitle("Events")
hs.GetYaxis().SetTitleOffset(0.8)
hs.GetYaxis().SetTitleSize(0.05)
data_hist.Draw("same" "LPE")
sig_hist.Draw("same" "LPE")
leg.Draw()
tex1.Draw()
tex2.Draw()
tex3.Draw()
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
pad2.cd()
ratio_hist.SetMarkerColor(4)
