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 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 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 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 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 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")
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 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
def ClusterSizeOneTrouble(self):
RootDirectory = 'FTClusterMonitor'
for files in self.ListOfFiles:
tfile = TFile.Open(files)
OutFileMantisse = files[:files.find(
'-histos')] + "/" + RootDirectory + "_" + time.strftime(
'%y-%m-%d_%H-%M', time.localtime())
self.CreateDir(OutFileMantisse)
tdir = tfile.GetDirectory(RootDirectory)
for type in {"x_position", "Channel"}:
All = tdir.Get("Cluster_" + type + "_resolution_1Ch")
Cell0 = tdir.Get("Cluster_" + type + "_resolution_1Ch_Cell0")
Cell127 = tdir.Get("Cluster_" + type +
"_resolution_1Ch_Cell127")
Cell63 = tdir.Get("Cluster_" + type + "_resolution_1Ch_Cell63")
Cell64 = tdir.Get("Cluster_" + type + "_resolution_1Ch_Cell64")
CellNeighbour = tdir.Get(
"Cluster_" + type + "_resolution_1Ch_WithNeighbouringCell")
All.SetLineColor(4)
Cell0.SetLineColor(2)
Cell127.SetLineColor(7)
Cell63.SetLineColor(6)
Cell64.SetLineColor(40)
CellNeighbour.SetLineColor(28)
# Standard drawing
ClusterMoniSizeOneTroublecvs = TCanvas(
"ClusterSizeOneTroublecvs", "ClusterSizeOneTrouble")
ClusterMoniSizeOneTroublecvs.SetGrid()
ClusterMoniSizeOneTroublecvs.cd()
All.Draw("H")
Cell0.Draw("HSAME")
Cell127.Draw("HSAME")
Cell63.Draw("HSAME")
Cell64.Draw("HSAME")
CellNeighbour.Draw("HSAME")
Leg = TLegend(0.11, 0.7, 0.4, 0.89)
Leg.AddEntry(All, "All Cells", "l")
Leg.AddEntry(Cell63, "Cell 63", "l")
Leg.AddEntry(Cell64, "Cell 64", "l")
Leg.AddEntry(Cell0, "Cell 0", "l")
Leg.AddEntry(Cell127, "Cell 127", "l")
Leg.AddEntry(CellNeighbour, "Cells with left/right neighbour",
"l")
Leg.Draw()
ClusterMoniSizeOneTroublecvs.SaveAs(
OutFileMantisse + "/" + (OutFileMantisse.split('/')[0]) +
"_" + All.GetName() + self.OutFileType)
# Stack drawing
Cell0.SetFillColor(2)
Cell127.SetFillColor(7)
Cell63.SetFillColor(6)
Cell64.SetFillColor(40)
CellNeighbour.SetFillColor(28)
HistoStack = THStack("HistoStack", "Stacked 1D histograms")
HistoStack.Add(Cell63)
HistoStack.Add(Cell64)
HistoStack.Add(Cell0)
HistoStack.Add(Cell127)
HistoStack.Add(CellNeighbour)
ClusterMoniSizeOneTroubleStackcvs = TCanvas(
"ClusterSizeOneTroubleStackcvs",
"ClusterSizeOneTroubleStack")
ClusterMoniSizeOneTroubleStackcvs.SetGrid()
ClusterMoniSizeOneTroubleStackcvs.cd()
HistoStack.Draw("H")
HistoStack.GetXaxis().SetTitle(All.GetXaxis().GetTitle())
HistoStack.GetYaxis().SetTitle(All.GetYaxis().GetTitle())
#ClusterMoniSizeOneTroubleStackcvs.Update()
All.Draw("HSAME")
LegStack = TLegend(0.11, 0.7, 0.4, 0.89)
LegStack.AddEntry(All, "All Cells", "l")
LegStack.AddEntry(Cell63, "Cell 63", "f")
LegStack.AddEntry(Cell64, "Cell 64", "f")
LegStack.AddEntry(Cell0, "Cell 0", "l=f")
LegStack.AddEntry(Cell127, "Cell 127", "f")
LegStack.AddEntry(CellNeighbour,
"Cells with left/right neighbour", "f")
LegStack.Draw()
ClusterMoniSizeOneTroubleStackcvs.SaveAs(
OutFileMantisse + "/" + (OutFileMantisse.split('/')[0]) +
"_" + All.GetName() + "_Stack" + self.OutFileType)
# Details on cells with neighbours
Cell1to4 = tdir.Get("Cluster_Channel_resolution_1Ch_Cell1to4")
Cell59to62 = tdir.Get("Cluster_Channel_resolution_1Ch_Cell59to62")
Cell65to68 = tdir.Get("Cluster_Channel_resolution_1Ch_Cell65to68")
Cell122to126 = tdir.Get(
"Cluster_Channel_resolution_1Ch_Cell122to126")
CellAllOthers = tdir.Get(
"Cluster_Channel_resolution_1Ch_AllOtherNeighbors")
Cell1to4.SetLineColor(2)
Cell1to4.SetFillColor(2)
Cell59to62.SetLineColor(3)
Cell59to62.SetFillColor(3)
Cell65to68.SetLineColor(4)
Cell65to68.SetFillColor(4)
Cell122to126.SetLineColor(5)
Cell122to126.SetFillColor(5)
CellAllOthers.SetLineColor(6)
CellAllOthers.SetFillColor(6)
NeighbourStack = THStack("NeighbourStack", "Stacked 1D histograms")
NeighbourStack.Add(Cell1to4)
NeighbourStack.Add(Cell59to62)
NeighbourStack.Add(Cell65to68)
NeighbourStack.Add(Cell122to126)
NeighbourStack.Add(CellAllOthers)
ClusterMoniSizeOneTroubleNeighbourhoodcvs = TCanvas(
"ClusterSizeOneTroubleNeighbourhoodcvs",
"ClusterSizeOneTroubleNeighbourhood")
ClusterMoniSizeOneTroubleNeighbourhoodcvs.SetGrid()
ClusterMoniSizeOneTroubleNeighbourhoodcvs.cd()
NeighbourStack.Draw("nostack")
NeighbourStack.GetXaxis().SetTitle(Cell1to4.GetXaxis().GetTitle())
NeighbourStack.GetYaxis().SetTitle(Cell1to4.GetYaxis().GetTitle())
CellNeighbour.Draw("SAME")
ClusterMoniSizeOneTroubleNeighbourhoodcvs.SaveAs(
OutFileMantisse + "/" + (OutFileMantisse.split('/')[0]) +
"_NeighboringStudy_Stack" + self.OutFileType)
# Details on cells with/without Cluster size=4 neighbours
#CellWithCl4Neighbour = tdir.Get("Cluster_Channel_resolution_1Ch_Cell1to4")
CellWithCl4Neighbour = tdir.Get(
"Cluster_Channel_resolution_1Ch_withSize4neigh")
#CellWithoutCl4Neighbour = tdir.Get("Cluster_Channel_resolution_1Ch_Cell1to4")
CellWithoutCl4Neighbour = tdir.Get(
"Cluster_Channel_resolution_1Ch_withoutSize4neigh")
CellWithCl4Neighbour.SetLineColor(2)
CellWithCl4Neighbour.SetFillColor(2)
CellWithoutCl4Neighbour.SetLineColor(3)
CellWithoutCl4Neighbour.SetFillColor(3)
Clus4NeighbourStack = THStack("Clus4NeighbourStack",
"St histograms")
Clus4NeighbourStack.Add(CellWithCl4Neighbour)
Clus4NeighbourStack.Add(CellWithoutCl4Neighbour)
Clus4TroubleNeighbourhoodcvs = TCanvas(
"Clus4TroubleNeighbourhoodcvs", "Clus4TroubleNeighbourhoodcvs")
Clus4TroubleNeighbourhoodcvs.SetGrid()
Clus4TroubleNeighbourhoodcvs.cd()
Clus4NeighbourStack.Draw("H")
Clus4NeighbourStack.GetXaxis().SetTitle(
CellWithCl4Neighbour.GetXaxis().GetTitle())
Clus4NeighbourStack.GetYaxis().SetTitle(
CellWithCl4Neighbour.GetYaxis().GetTitle())
All.Draw("HSAME")
LegClus4Size = TLegend(0.11, 0.7, 0.4, 0.89)
LegClus4Size.AddEntry(All, "All Cells", "l")
LegClus4Size.AddEntry(CellWithCl4Neighbour,
"With size 4 neighbour ", "f")
LegClus4Size.AddEntry(CellWithoutCl4Neighbour,
"Without size 4 neighbour ", "f")
LegClus4Size.Draw()
Clus4TroubleNeighbourhoodcvs.SaveAs(
OutFileMantisse + "/" + (OutFileMantisse.split('/')[0]) +
"_CluS4NeighboringStudy_Stack" + self.OutFileType)
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
errorband.SetMarkerSize(0)
legend.AddEntry(rebinnedData, "Data", 'pe')
legend.AddEntry(errorband, "Uncertainty", "f")
for ih in rebinnedHist:
legend.AddEntry(rebinnedHist[ih], ih, 'f')
pad1.cd()
stack.Draw('HIST')
rebinnedData.Draw('E,X0,SAME')
legend.Draw("same")
stack.GetXaxis().SetTitle('')
stack.GetXaxis().SetLabelSize(0)
stack.GetYaxis().SetLabelSize(gStyle.GetLabelSize() /
(1. - padRatio + padOverlap))
stack.GetYaxis().SetTitleSize(gStyle.GetTitleSize() /
(1. - padRatio + padOverlap))
stack.GetYaxis().SetTitleOffset(gStyle.GetTitleYOffset() *
(1. - padRatio + padOverlap))
#stack.SetTitle(';;<Events/GeV>')# '%rebin)
#CMS_lumi.channelText = (channelText+"\\n"+regionText)
#if postfitPlots: CMS_lumi.channelText =channelText+"\\n "+regionText+"\\n "+chi2Text
CMS_lumi.channelText = "#splitline{%s}{%s}" % (channelText, regionText)
if postfitPlots:
CMS_lumi.channelText = "#splitline{%s}{%s}" % (channelText + ";" +
regionText, chi2Text)
CMS_lumi.writeChannelText = True
chic0.Project('fromchic0', 'raw_ge')
chic1.Project('fromchic1', 'raw_ge')
chic2.Project('fromchic2', 'raw_ge')
fromchic0.Scale(214404 / 9997)
fromchic1.Scale(214404 / 10399)
fromchic2.Scale(214404 / 10177)
hs.Add(fromchic0)
hs.Add(fromchic1)
hs.Add(fromchic2)
mbc.cd()
hs.Draw()
hs.GetXaxis().SetTitle('E_{#gamma} (GeV)')
hs.GetYaxis().SetTitle(ytitle)
hs.GetYaxis().SetTitleOffset(1.5)
#f1 = TFile('run/chic2incl/hist_inclusiveMC/chic2incl_psip_mc_event_merged_ncostag.root')
#h_E = f1.Get('h_gam1_E')
#h_E.Draw('same')
legend = TLegend(0.5, 0.6, 0.72, 0.8)
legend.AddEntry('fromchic0', 'E_{#gamma} in process chic0')
legend.AddEntry('fromchic1', 'E_{#gamma} in process chic0')
legend.AddEntry('fromchic2', 'E_{#gamma} in process chic0')
#legend.SetNColums(1)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.Draw()
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 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))
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))
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)
ratio_hist.SetMarkerSize(1)
ratio_hist.SetMarkerStyle(ROOT.kFullCircle)
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))
y1 = array('d', (0, 1.1 * hs.GetMaximum()))
g1 = TGraph(2, x1, y1)
g1.SetLineColor(1)
g1.SetLineStyle(2)
tmp_histogram_sublist[-1].SetLineColor(kBlack)
tmp_histogram_sublist[-1].SetMarkerColor(flavour_color)
#tmp_histogram_sublist[-1].SetStats(kTRUE)
#tmp_histogram_sublist[-1].Draw()
#tmp_canvas_single.Write()
#tmp_canvas_single.SaveAs(histogram_name+"_"+flavour_name+'_Canvas.pdf')
tmp_stack.Add(tmp_histogram_sublist[-1])
tmp_legend.AddEntry(tmp_histogram_sublist[-1],flavour_name,'f')
tmp_process_sublist.append(tmp_histogram_sublist)
stack_process_sublist.append(tmp_stack)
tmp_canvas=TCanvas(histogram_name+'_'+process_name.split('.')[-1]+'_'+'CanvasStack','')
#tmp_canvas.cd()
#tmp_histogram_sublist[-1].Draw('histo')
tmp_stack.Draw('histo')
tmp_stack.GetXaxis().SetTitle(histogram_xaxis)
tmp_stack.GetYaxis().SetTitle(histogram_yaxis)
tmp_stack.SetTitle(histogram_description)
tmp_legend.Draw()
#tmp_canvas.BuildLegend(0.2,0.2,0.8,0.8)#tmp_legend.Draw()
#autolegend=tmp_canvas.BuildLegend()
stackcanvas_process_sublist.append(tmp_canvas)
# outfile.Cd()
if histogram_name in ["subTrackNPixelHits","subTrackMomentum","subTrackJetDistVal","subTrackDecayLenVal","subTrackSip3dVal","subTrackSip3dSig","subJetNSecondaryVertices","subCSV"]:
tmp_canvas.SetLogy()
tmp_canvas.Write()
if histogram_name in ["subTrackNPixelHits","subTrackMomentum","subTrackJetDistVal","subTrackDecayLenVal","subTrackSip3dVal","subTrackSip3dSig","subFlightDistance3dSig","subVertexMassJTC","subJetNSecondaryVertices","subVertexNTracks","subCSV"]:
tmp_canvas.SaveAs(histogram_name+'_'+process_name.split('.')[-1]+'_'+'CanvasStack.pdf')
#tmp_stack.Write()
input_histos.append(tmp_process_sublist)
output_histos.append(stack_process_sublist)
output_canvases.append(stackcanvas_process_sublist)
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 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 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 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
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)
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 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
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)
pad1.SetLogy()
# legend
legend = TLegend(0.1, 0.75, 0.3, 0.9) #left alignment
#legend = TLegend(0.7,0.75,0.9,0.9) #right alignment
legend.AddEntry(LepBDTDATA_list[i], "Data", "p")
legend.AddEntry(LepBDTMCDY_list[i], "DY MC", "f")
legend.AddEntry(LepBDTMCTTbar_list[i], "t#bar{t} MC", "f")
def createCompoundPlots(detector, plot, geometry):
"""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.
"""
setTDRStyle()
theDirname = 'Images'
if not checkFile_(theDirname):
os.mkdir(theDirname)
goodToGo, theDetectorFilename = paramsGood_(detector, plot, geometry)
if not goodToGo:
return
hist_X0_elements = OrderedDict()
# stack
stackTitle = "%s;%s;%s" % (detector,
plots[plot].abscissa,
plots[plot].ordinate)
stack_X0 = THStack("stack_X0", stackTitle);
theLegend = TLegend(0.50, 0.70, 0.70, 0.90);
def setRanges(h):
legendSpace = 1. + 0.3 # 30%
minY = h.GetYaxis().GetXmin()
maxY = h.GetBinContent(h.GetMaximumBin()) * legendSpace
h.GetYaxis().SetRangeUser(minY, maxY)
for label, [num, color, leg] in six.iteritems(hist_label_to_num):
# We don't want the sum to be added as part of the stack
if label is 'SUM':
continue
hist_X0_elements[label] = get1DHisto_(detector,
num + plots[plot].plotNumber,
geometry)
hist_X0_elements[label].SetFillColor(color)
hist_X0_elements[label].SetLineColor(kBlack)
stack_X0.Add(hist_X0_elements[label])
if hist_X0_elements[label].Integral() > 0.: theLegend.AddEntry(hist_X0_elements[label], leg, "f")
# canvas
canname = "MBCan_1D_%s_%s" % (detector, plot)
can = TCanvas(canname, canname, 800, 800)
can.Range(0,0,25,25)
gStyle.SetOptTitle(0)
# Draw
setRanges(stack_X0.GetStack().Last())
stack_X0.Draw("HIST");
stack_X0.GetXaxis().SetLabelSize(0.035)
stack_X0.GetYaxis().SetLabelSize(0.035)
theLegend.Draw();
cmsMark = TLatex()
cmsMark.SetNDC();
cmsMark.SetTextAngle(0);
cmsMark.SetTextColor(kBlack);
cmsMark.SetTextFont(61)
cmsMark.SetTextSize(5e-2)
cmsMark.SetTextAlign(11)
cmsMark.DrawLatex(0.16,0.86,"CMS")
simuMark = TLatex()
simuMark.SetNDC();
simuMark.SetTextAngle(0);
simuMark.SetTextColor(kBlack);
simuMark.SetTextSize(3e-2)
simuMark.SetTextAlign(11)
simuMark.DrawLatex(0.16,0.82,"#font[52]{Simulation Internal}")
# Store
can.Update();
can.SaveAs( "%s/%s_%s_%s.pdf"
% (theDirname, detector, plot, geometry))
can.SaveAs( "%s/%s_%s_%s.png"
% (theDirname, detector, plot, geometry))
