def draw(hist,
fit,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
setHistStyle(hist['BkgSum'], 1.1 if ratio else 1.0)
# Create stack
bkg = THStack(
"Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";" +
hist['BkgSum'].GetYaxis().GetTitle())
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.69, 0.86 - 0.04 * n, 0.95, 0.86)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
### data/bkg ratio and systematics
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("data / bkg")
if fit == "prefit":
err.SetFillColor(ROOT.kOrange - 2)
elif fit == "postfit":
err.SetFillColor(ROOT.kBlue)
err.SetFillStyle(3001)
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
# Legend
leg1 = TLegend(0.12, 0.45, 0.25, 0.5)
leg1.SetBorderSize(0)
leg1.SetFillStyle(0)
leg1.SetFillColor(0)
leg1.SetTextSize(0.05)
leg1.AddEntry(err, "systematic uncertainty (" + fit + ")", "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
gStyle.SetOptStat(0)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.08)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
# hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
pass
bkg.SetMaximum((2. if log else 1.1) * max(
bkg.GetMaximum(), hist['data_obs'].GetMaximum() +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 1.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
setHistStyle(bkg, 1.1 if ratio else 1.0)
leg.Draw()
# drawCMS(lumi, "Preliminary",True)
drawCMS(lumi, "Private", True)
drawRegion(channel)
drawAnalysis(channel)
drawFit(fit)
if ratio:
c1.cd(2)
setBotStyle(err, 3, True)
setBotStyle(res)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
res.Draw("SAME, PE0")
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
leg1.Draw()
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, leg1, err, errLine, res]
def myStack(fname_, region_, isSR, prefitbackgroundlist_, legendname_,
colorlist_, regionName_, dirName_, isMerged, pad1ymax_):
nbins = 4
edges = arr.array('f')
openfile = TFile(fname_)
print " "
print "*************************"
print region_, "for", dirName_
print " "
prefit_path = "shapes_prefit/" + region_ + "/"
postfit_path = "shapes_fit_b/" + region_ + "/"
#get the histograms from prefit directory
print "get histograms from", prefit_path
print " "
backgroundlist_ = []
for j in prefitbackgroundlist_:
jh = openfile.Get(prefit_path + j)
backgroundlist_.append(jh)
if isSR: signal_ = openfile.Get(prefit_path + "signal")
edges = arr.array('f')
for i in range(nbins):
low = backgroundlist_[0].GetXaxis().GetBinLowEdge(i + 1)
edges.append(low)
up = backgroundlist_[0].GetXaxis().GetBinUpEdge(nbins)
edges.append(up)
data = openfile.Get(prefit_path + "data")
data_ = TH1F("data_", "", nbins, edges)
nPoints = data.GetN()
for i in range(nPoints):
x = ROOT.Double(0)
y = ROOT.Double(0)
data.GetPoint(i, x, y)
k = data_.FindFixBin(x)
data_.SetBinContent(k, y)
data_.SetBinError(i + 1, data.GetErrorY(i))
prefit_ = openfile.Get(prefit_path + "total_background")
#get the histogram from post fit directory
print "get histograms from", postfit_path
print " "
postfit_ = openfile.Get(postfit_path + "total_background")
# draw the histograms #
leg = myLegend(ncol=2)
pad = myPad()
pad[1].cd()
gPad.SetLogy()
data_.SetLineColor(1)
data_.SetLineWidth(3)
data_.SetMarkerStyle(20)
data_.SetMarkerColor(1)
data_.SetMarkerSize(1.5)
data_.GetXaxis().SetLabelSize(0)
data_.GetYaxis().SetLabelSize(0.05)
data_.GetYaxis().SetTitleOffset(1.2)
data_.GetYaxis().SetTitleSize(0.05)
data_.GetYaxis().SetNdivisions(510)
data_.SetMaximum(pad1ymax_)
data_.GetYaxis().SetTitle("Events/GeV")
data_.Draw("e1")
hs = THStack("hs", "")
for j in range(len(colorlist_)):
h = backgroundlist_[j]
h.SetFillColor(colorlist_[j])
h.SetLineColor(colorlist_[j])
hs.Add(h, "")
leg.AddEntry(h, legendname_[j], "f")
hs.Draw("histsame")
postfit_.SetLineColor(634)
postfit_.SetLineWidth(4)
leg.AddEntry(postfit_, "Post-fit", "l")
postfit_.Draw("histsame")
if isSR:
signal_.SetLineColor(416)
signal_.SetLineWidth(4)
signal_.SetMarkerStyle(21)
#signal_.SetMarkerColor(824)
leg.AddEntry(signal_, "Signal", "l")
signal_.Draw("histsame")
leg.AddEntry(data_, "Data", "lep")
data_.Draw("e1same")
leg.Draw()
drawE = drawenergy()
for i in drawE:
i.Draw()
pt4 = TPaveText(0.2577181, 0.815, 0.5580537, 0.875, "brNDC")
pt4.SetBorderSize(0)
pt4.SetTextAlign(12)
pt4.SetFillStyle(0)
pt4.SetTextFont(52)
pt4.AddText(regionName_)
pt4.Draw()
pad[2].cd()
gPad.GetUymax()
leg1 = myLegend(coordinate=[0.5, 0.80, 0.87, 0.95])
leg1.SetTextSize(0.1)
prefithist = dataPredRatio(data_=data_, totalBkg_=prefit_)
prefithist.SetLineColor(1)
prefithist.SetLineWidth(3)
prefithist.SetMarkerSize(1.5)
prefithist.GetXaxis().SetLabelSize(0.13)
prefithist.GetXaxis().SetTitleOffset(1)
prefithist.GetXaxis().SetTitleSize(0.13)
prefithist.GetXaxis().SetTickLength(0.1)
prefithist.GetYaxis().SetLabelSize(0.12)
prefithist.GetYaxis().SetRangeUser(-0.5, 0.5)
prefithist.GetYaxis().SetTitleOffset(0.5)
prefithist.GetYaxis().SetTitleSize(0.13)
prefithist.GetYaxis().SetNdivisions(505)
prefithist.GetYaxis().SetTitle("#frac{Data-Pred}{Pred}")
prefithist.GetXaxis().SetTitle("Recoil (GeV)")
leg1.AddEntry(prefithist, "Prefit", "lep")
prefithist.Draw("e1")
postfithist = dataPredRatio(data_=data_, totalBkg_=postfit_)
postfithist.SetLineColor(634)
postfithist.SetLineWidth(3)
postfithist.SetMarkerSize(1.5)
postfithist.SetMarkerColor(634)
postfithist.GetYaxis().SetTitle("#frac{Data-Pred}{Pred}")
postfithist.GetXaxis().SetTitle("Recoil (GeV)")
leg1.AddEntry(postfithist, "Postfit", "lep")
postfithist.Draw("e1same")
leg1.Draw()
if not os.path.exists(dirName_):
os.mkdir(dirName_)
pad[0].Modified()
pad[0].Update()
if isMerged:
pad[0].SaveAs(dirName_ + region_ + "_merged_2017.pdf")
pad[0].SaveAs(dirName_ + region_ + "_merged_2017.png")
if not isMerged:
pad[0].SaveAs(dirName_ + region_ + "_resolved_2017.pdf")
pad[0].SaveAs(dirName_ + region_ + "_resolved_2017.png")
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
for i in range(0, N_hist):
#printHistName = "nJets"
#printHistName = "sigTop"
printHistName = "leptauDR"
hnames = sig1samples[sig1samples.keys()[0]]["hname"][i].split("_")
if hnames[1] == printHistName:
string0 = "%s \n" % hnames
fNevt.write(string0)
if hnames[1] == printHistName:
print hnames[1], " ", hnames[2], " ", hnames[3]
hs = THStack()
l = TLegend(0.15, 0.71, 0.89, 0.87)
l.SetNColumns(4)
l.SetTextSize(0.04)
l.SetLineColor(0)
l.SetFillColor(0)
ntotalbkg = 0
k = 0
for fname in bkgsamples.keys():
h_tmp = bkgsamples[fname]["file"].Get(bkgsamples[fname]["hname"][i])
nbins = h_tmp.GetNbinsX()
h_tmp.AddBinContent(nbins, h_tmp.GetBinContent(nbins + 1)) #overflow
h_tmp.SetFillColor(bkgsamples[fname]["col"])
scale = lumi / (bkgsamples[fname]["total"] /
bkgsamples[fname]["xsection"])
def plot_hist(histlist,
labellist,
norm,
log,
overflow,
filename,
options,
scaling=[]):
canv = TCanvas("c1", "c1", 800, 600)
canv.SetTopMargin(10)
canv.SetRightMargin(100)
if log: gPad.SetLogy()
histstack = THStack("stack", histlist[0].GetTitle())
legend = TLegend(0.76, 0.88 - 0.08 * len(histlist), 0.91, 0.88, '', 'NDC')
colorlist = [4, 8, 2, 6, 1]
if len(scaling) > 1 and scaling[1] == 0: scaling = []
if options: options += " NOSTACK"
else: options = "NOSTACK"
maximum = 0
for i in range(len(histlist)):
entries = histlist[i].GetEntries()
mean = histlist[i].GetMean()
histlist[i].SetLineColorAlpha(colorlist[i], 0.65)
histlist[i].SetLineWidth(3)
nbins = histlist[i].GetNbinsX()
legend.AddEntry(
histlist[i], "#splitline{" + labellist[i] +
"}{#splitline{%d entries}{mean=%.2f}}" % (entries, mean), "l")
if overflow:
histlist[i].SetBinContent(
nbins, histlist[i].GetBinContent(nbins) +
histlist[i].GetBinContent(nbins + 1)) #overflow
histlist[i].SetBinContent(1, histlist[i].GetBinContent(0) +
histlist[i].GetBinContent(1)) #underflow
if entries and norm: histlist[i].Scale(1. / entries)
if histlist[i].GetMaximum() > maximum:
maximum = histlist[i].GetMaximum()
histstack.Add(histlist[i])
histstack.SetMaximum(maximum * 1.4)
histstack.Draw(options)
histstack.GetXaxis().SetTitle(histlist[0].GetXaxis().GetTitle())
histstack.GetYaxis().SetTitle(histlist[0].GetYaxis().GetTitle())
xlimit = [
histlist[0].GetBinLowEdge(1),
histlist[0].GetBinLowEdge(histlist[0].GetNbinsX()) +
histlist[0].GetBinWidth(histlist[0].GetNbinsX())
]
if len(scaling) != 0:
top_axis = TGaxis(xlimit[0], maximum * 1.4, xlimit[1], maximum * 1.4,
(xlimit[0] - scaling[0]) / scaling[1],
(xlimit[1] - scaling[0]) / scaling[1], 510, "-")
top_axis.SetTitle("normalized scale")
top_axis.SetTickSize(0)
top_axis.Draw("SAME")
legend.SetTextSize(0.02)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
legend.Draw("SAME")
canv.SaveAs(filename)
if log: gPad.Clear()
canv.Clear()
del canv
def plotStack(name,histo,index,indexName,histoIntLumi,lumiBool,resStack,resCanvas,resLegend,recovBool = True,compBool = False,approvedPlots = False):
# name: Mainly an index of the output. Also used to define TAxis Title
# histo: dict of histograms or TProfile to be displayed
# index: list of keys of histo to be displayed
# indexName: dict of namesof index used for the TLegend
# histoIntLumi : integrated lumi with the same x binning as histo
# lumiBool : display results in term of lumi and not percent
# resStack, resCanvas, resLegend: dict of (stacks, canvas, legend) outputs
# recovBool: display the recoverable histograms (referenced as %%%__recov in histo
# compBool: this is a 2 yearTag plots. Write all numbers in TLegend and not only last bin (that is not meaningful in yearTag)
# unit is the main unit. unitAux is the complementary one used only the TLegend
if (lumiBool):
unit = "pb^{-1}"
unitAux = "%%"
else:
unit = "%%"
unitAux = "pb^{-1}"
nameSplitted = name.split("--") # Assume that the name is "Veto/defect (y axis) - Year/Run (x axis)- Dataset name"
xAxisTitle = nameSplitted[1]
if unit == "%%":
yAxisTitle = "Lost luminosity due to %s [%%]"%(nameSplitted[0])
else:
yAxisTitle = "Lost luminosity due to %s [%s]"%(nameSplitted[0],unit)
legendHeader = nameSplitted[2]
resCanvas[name] = TCanvas(name,"%s - %s"%(yAxisTitle,xAxisTitle),200, 10, 1150, 500)
resCanvas[name].SetLeftMargin(0.08)
resCanvas[name].SetRightMargin(0.28)
resCanvas[name].SetGridy(1)
resStack[name] = THStack("%s_stack"%name,"")
resLegend[name] = MakeLegend(0.725,0.8,0.98,0.95) # Y1 will be redefined later according tp the number of entries
first = True
totalIneff = 0.
totalIneff1 = 0.
nBinsX = histoIntLumi.GetNbinsX()
totalIntegratedLumi = histoIntLumi.GetBinContent(nBinsX)
if lumiBool:
auxScaleFactor = 100./totalIntegratedLumi
else:
auxScaleFactor = totalIntegratedLumi/100.
for iIndex in sorted(index,reverse=True):
if first: # Create a recoverable histograms just in case of
resStack["%s__recov"%name] = MakeTH1("h1_%s__recovTotal"%(name),"Recoverable","",-0.5,-0.5+nBinsX,nBinsX,1)
resStack["%s__recov"%name].SetMarkerStyle(23)
first = False
iIndexName = iIndex.split("_")[0]
# Fill histo["%s_toStack"] the main histo
# and histo["%s_aux"] the complementary one used only for TLegend
if (histo[iIndex]).IsA().InheritsFrom("TProfile"):
histo['%s_toStack'%iIndex] = histo[iIndex].ProjectionX()
histo['%s_toStack'%iIndex].SetFillColor(histo[iIndex].GetFillColor())
else:
histo['%s_toStack'%iIndex] = histo[iIndex]
if lumiBool:
histo['%s_toStack'%iIndex].Multiply(histo['%s_toStack'%iIndex],histoIntLumi,0.01)
resStack[name].Add(histo['%s_toStack'%iIndex])
entryNb = 0
for iIndex in sorted(index): # Reverse order to have the TLegend ordered as the stacks
iIndexName = iIndex.split("__")[0] # Trick needed to get the defect name for the recoverable defect histogram
baseEntry = "%s"%(strLumi(histo['%s_toStack'%iIndex].GetBinContent(nBinsX),unit))
auxEntry = "%s"%(strLumi(histo['%s_toStack'%iIndex].GetBinContent(nBinsX)*auxScaleFactor,unitAux))
if (recovBool and "%s__recov"%iIndex in histo.keys() and histo["%s__recov"%iIndex].GetBinContent(nBinsX) != 0.):
baseEntryRecov = "%s"%(strLumi(histo["%s__recov"%iIndex].GetBinContent(nBinsX),unit))
entry = "#splitline{%s}{%s(recov:%s) / %s}"%(indexName[iIndexName],baseEntry,baseEntryRecov,auxEntry) # Second part of Legend to fix
for iBin in range(nBinsX+1):
resStack["%s__recov"%name].Fill(iBin-1,histo["%s__recov"%iIndex].GetBinContent(iBin))
else:
entry = "#splitline{%s}{%s / %s}"%(indexName[iIndex],baseEntry,auxEntry)
if (compBool): # This is a >=2 yearTag histograms
if histo[iIndex].GetNbinsX() == 2:
entry = "#splitline{%s}{%s / %s }"%(indexName[iIndexName],strLumi(histo[iIndex].GetBinContent(1),unit),strLumi(histo[iIndex].GetBinContent(2),unit))
elif histo[iIndex].GetNbinsX() == 3:
entry = "#splitline{%s}{%s / %s / %s}"%(indexName[iIndexName],strLumi(histo[iIndex].GetBinContent(1),unit),strLumi(histo[iIndex].GetBinContent(2),unit),strLumi(histo[iIndex].GetBinContent(3),unit))
else:
entry = "%s"%(indexName[iIndexName])
if (histo[iIndex].GetMaximum() != 0):
resLegend[name].AddEntry(histo[iIndex],entry,"f")
entryNb = entryNb+1
totalIneff = totalIneff + histo['%s_toStack'%iIndex].GetBinContent(nBinsX)
if compBool:
totalIneff1 = totalIneff1 + histo['%s_toStack'%iIndex].GetBinContent(1)
mx = resStack[name].GetMaximum()*1.2
resStack[name].SetMaximum(mx)
resStack[name].Draw("hist")
resStack[name].GetXaxis().SetTitle("%s"%xAxisTitle)
resStack[name].GetYaxis().SetTitle("%s"%yAxisTitle)
resStack[name].GetYaxis().SetTitleOffset(0.7)
if resStack[name].GetMaximum()>10.:
resCanvas[name].SetLogy(1)
if compBool:
resLegend[name].SetHeader("#splitline{%s}{Total loss: %s / %s}"%(legendHeader,strLumi(totalIneff1,unit),strLumi(totalIneff,unit)))
else:
totalIneffAux = totalIneff*auxScaleFactor
if (approvedPlots):
resLegend[name].SetHeader("#splitline{%s}{Total loss: %s / %s}"%(legendHeader,strLumi(totalIneff,unit),strLumi(totalIneffAux,unitAux)))
else:
resLegend[name].SetHeader("#splitline{%s (%s)}{Total loss: %s / %s}"%(legendHeader,strLumi(totalIntegratedLumi,"pb"),strLumi(totalIneff,unit),strLumi(totalIneffAux,unitAux)))
if resStack["%s__recov"%name].GetEntries() != 0.:
resStack["%s__recov"%name].SetMarkerStyle(20)
resStack["%s__recov"%name].SetMarkerColor(kAzure+8)
resStack["%s__recov"%name].Draw("PSAME HIST")
resLegend[name].AddEntry(resStack["%s__recov"%name],"#splitline{Recoverable}{total: %.2f%%}"%(resStack["%s__recov"%name].GetBinContent(nBinsX)),"p")
entryNb = entryNb + 1
resLegend[name].SetY1(max(0.83-entryNb*0.12,0.05))
resLegend[name].Draw()
resCanvas[name].Update()
return totalIneff # totalIneff is used only with the savePage1 option in DeMoStatus
def DrawHisto(self,
doSF=False,
name='',
chan='',
level='',
rebin=1,
log=True):
self.SetChanAndLevel('', level)
self.SetMode('SF' if doSF else 'OF')
if name == '':
name = 'DYDD_%s_%s' % ('SF' if doSF else 'OF', self.GetLevel())
hData = self.GetDYhisto(chan, level, True)
hMC = self.GetDYhisto(chan, level, False)
hemu = self.GetDYhisto('ElMu', level, True)
hData.SetMarkerStyle(20)
hData.SetMarkerColor(1)
hData.SetLineColor(1)
hData.SetMarkerSize(1.3)
hMC.SetFillStyle(1000)
hMC.SetLineColor(kAzure - 8)
hMC.SetFillColor(kAzure - 8)
hemu.SetFillStyle(1000)
hemu.SetLineColor(kGray)
hemu.SetFillColor(kGray)
hData.Rebin(rebin)
hemu.Rebin(rebin)
hMC.Rebin(rebin)
ke, kee = self.GetKfactor(elname)
km, kme = self.GetKfactor(muname)
hemu.Scale(0.5 * (ke if chan == 'ElE' else km))
hs = THStack()
hs.Add(hemu)
hs.Add(hMC)
lab = 'e^{#pm}e^{#mp}' if chan == 'ElEl' else '#mu^{#pm}#mu^{#mp}'
rlab = 'ee' if chan == 'ElEl' else '#mu#mu'
processes = [
'Z/#gamma*#rightarrow %s' % (lab),
'0.5 k_{%s} #times Data(e#mu)' % rlab
]
colors = {processes[0]: kAzure - 9, processes[1]: kGray}
s = Stack(doRatio=False)
s.SetXtitle(size=0.06, offset=0.8, nDiv=510, labSize=0.05)
s.SetTextCMS(cmstex='CMS', x=0.13, y=0.88, s=0.06)
s.SetTextCMSmode(texcmsMode='Preliminary', x=0.225, y=0.875, s=0.052)
s.SetLumi(304.32)
s.SetTextLumi(texlumi='%2.1f pb^{-1} (5.02 TeV)',
texlumiX=0.68,
texlumiY=0.95,
texlumiS=0.045)
s.SetDataHisto(hData)
s.SetColors(colors)
s.SetStack(hs)
s.SetOutName('DYestimate_%s%s' % (chan, level) +
('_log' if log else ''))
s.SetOutPath(self.outpath)
plotMinimum = 0.5 if log else 0
plotMaxScale = 10 if log else 1.3
s.SetLogY(log)
s.SetPlotMaxScale(plotMaxScale)
s.SetPlotMinimum(plotMinimum)
maximum = hData.GetMaximum() * (5 if log else 1.2)
s.AddLine(76, 0.5, 76, maximum)
s.AddLine(106, 0.5, 106, maximum)
s.processes = processes
s.SetLegendPos(x0=0.60, y0=0.55, x1=0.93, y1=0.91, size=0.042, ncol=1)
s.DrawStack('m_{%s} (GeV)' % rlab)
def makeDiTauStack(outDir,
inFile,
rootDir,
s,
labelX,
units="GeV",
left=False,
channel="",
json="Golden",
log=False,
dndm=False,
doRatio=False,
year=2017,
sign='OS',
LTcut=0.,
cat='mmtt',
wait='wait'):
tdrstyle.setTDRStyle()
writeExtraText = True # if extra text
extraText = "Preliminary" # default extra text is "Preliminary"
lumi_sqrtS = "13 TeV"
if json == "Golden": lumi_13TeV = channel + " 41.8 fb^{-1}, 2017"
iPeriod = 4 # 1=7TeV, 2=8TeV, 3=7+8TeV, 7=7+8+13TeV
xR = 0.65 #legend parameters
xR = 0.2 #legend parameters
H = 600
W = 600
H_ref = 600
W_ref = 600
# references for T, B, L, R
T = 0.08 * H_ref
B = 0.12 * H_ref
L = 0.16 * W_ref
R = 0.04 * W_ref
#margins for inbetween the pads in a ratio plot
B_ratio = 0.1 * H_ref
T_ratio = 0.03 * H_ref
#margin required for lebal on bottom of raito plot
B_ratio_label = 0.3 * H_ref
c = TCanvas('c1', 'c1', 50, 50, W, H)
c.SetFillColor(0)
c.SetBorderMode(0)
c.SetFrameFillStyle(0)
c.SetFrameBorderMode(0)
if not doRatio:
c.SetLeftMargin(L / W)
c.SetRightMargin(R / W)
c.SetTopMargin(T / H)
c.SetBottomMargin(B / H)
c.cd()
ratioPad = TPad("pad2", "", 0.0, 0.0, 1.0, 0.29)
plotPad = TPad("pad1", "", 0.0016, 0.291, 1.0, 1.0)
if doRatio:
plotPad.SetTicks(0, 0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B_ratio / H)
plotPad.SetFillColor(0)
plotPad.SetBottomMargin(0)
ratioPad.SetLeftMargin(L / W)
ratioPad.SetRightMargin(R / W)
ratioPad.SetTopMargin(T_ratio / H)
ratioPad.SetTopMargin(0.007)
ratioPad.SetBottomMargin(B_ratio_label / H)
ratioPad.SetGridy(1)
ratioPad.SetFillColor(4000)
else:
plotPad = TPad("pad1", "", 0.0, 0.03, 1.0, 1.0)
plotPad.SetLeftMargin(L / W)
plotPad.SetRightMargin(R / W)
plotPad.SetTopMargin(T / H)
plotPad.SetBottomMargin(B / H)
plotPad.Draw()
plotPad.cd()
print("In makeStack inFile={0:s}".format(inFile))
f = TFile(inFile)
groups = ['data', 'Reducible', 'Rare', 'ZZ4L', 'Signal']
histo = {}
colors = {
'data': 0,
'Reducible': kMagenta - 10,
'Rare': kBlue - 8,
'ZZ4L': kAzure - 9,
'Signal': kRed
}
hs = THStack("hs", "")
for group in groups:
if len(cat) == 4:
histo[group] = f.Get("h{0:s}_{1:s}_Mtt".format(group, cat))
else:
histo[group] = f.Get("h{0:s}_ee{1:s}_Mtt".format(group, cat))
histo2 = f.Get("h{0:s}_mm{1:s}_Mtt".format(group, cat))
histo[group].Add(histo2)
if dndm: convertToDNDM(histo[group])
if group == 'data':
applyDATAStyle(histo[group])
else:
applyStyle(histo[group], colors[group], 1, 1001)
if group != 'data': hs.Add(histo[group])
try:
hMax = 1.2 * max(histo['data'].GetMaximum(), hs.GetMaximum())
except KeyError:
hMax = 1.2 * hs.GetMaximum()
hs.SetMaximum(hMax)
hs.Draw("HIST")
try:
histo['data'].Draw("e,SAME")
except KeyError:
pass
if doRatio:
hs.GetXaxis().SetLabelSize(0)
else:
if units != "":
hs.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
hs.GetXaxis().SetTitle(labelX)
hs.GetYaxis().SetTitle("Events")
hs.GetYaxis().SetTitleOffset(1)
if dndm: hs.GetYaxis().SetTitle("dN/d" + labelX)
c.cd()
if doRatio:
data2 = histo['data'].Clone("data")
mc = histo['Rare']
mc.Add(histo['Reducible'])
mc.Add(histo['ZZ4L'])
xmin = mc.GetXaxis().GetXmin()
xmax = mc.GetXaxis().GetXmax()
line = TLine(xmin, 1.0, xmax, 1.0)
line.SetLineWidth(1)
line.SetLineColor(kBlack)
ratioPad.Draw()
ratioPad.cd()
data2.Divide(data2, mc)
data2.SetMarkerStyle(20)
data2.SetTitleSize(0.12, "Y")
data2.SetTitleOffset(0.40, "Y")
data2.SetTitleSize(0.12, "X")
data2.SetLabelSize(0.10, "X")
data2.SetLabelSize(0.08, "Y")
data2.GetYaxis().SetRangeUser(0.62, 1.38)
data2.GetYaxis().SetNdivisions(305)
data2.GetYaxis().SetTitle("Obs/Exp ")
if units != "":
data2.GetXaxis().SetTitle(labelX + " [" + units + "]")
else:
data2.GetXaxis().SetTitle(labelX)
data2.Draw("P")
line.Draw()
c.cd()
plotPad.cd()
l = TLegend(xR, 0.55, xR + 0.28, 0.9)
for group in groups:
try:
l.AddEntry(histo[group], group, "F")
except KeyError:
continue
l.SetBorderSize(0)
l.SetFillColor(0)
l.SetFillStyle(0)
l.SetTextSize(0.04)
l.Draw()
lTex1 = TLatex(120., 0.97 * hMax, 'Preliminary {0:d}'.format(year))
lTex1.SetTextSize(0.04)
lTex1.Draw()
signText = 'Same Sign'
if sign == 'OS': signText = 'Opposite Sign'
lTex2 = TLatex(120., 0.90 * hMax, '{0:s}'.format(signText))
lTex2.SetTextSize(0.04)
lTex2.Draw()
if len(cat) == 4:
lt = {
'eeet': '#it{l}#it{l}#it{e}#tau',
'eemt': '#it{l}#it{l}#mu#tau',
'eett': '#it{l}#it{l}#tau#tau',
'mmet': '#mu#mu#it{e}#tau',
'mmmt': '#mu#mu#mu#tau',
'mmtt': '#mu#mu#tau#tau'
}
else:
lt = {
'et': '#it{l}#it{l}#it{e}#tau',
'mt': '#it{l}#it{l}#mu#tau',
'tt': '#it{l}#it{l}#tau#tau'
}
lTex3 = TLatex(120., 0.83 * hMax,
'{0:s} H_LT > {1:d} '.format(lt[cat], int(LTcut)))
lTex3.SetTextSize(0.04)
lTex3.Draw()
plotPad.Draw()
#CMS_lumi(plotPad,4,11)
outFileBase = "Stack_{0:d}_{1:s}_{2:s}_LT{3:02d}".format(
year, cat, sign, int(LTcut))
c.SaveAs("{0:s}.png".format(outFileBase))
c.SaveAs("{0:s}.root".format(outFileBase))
if wait == 'wait':
raw_input()
else:
import time
time.sleep(5.)
def print_single_min(vec_beg, vec_end, name, text):
total_beg = vec_beg[0].Clone()
c = 0
for v in vec_beg:
if c != 0:
total_beg.Add(vec_beg[c])
c = c + 1
norm = 1/total_beg.Integral()
total_beg.Scale(norm)
if 'buff' in name:
legb = TLegend(0.25,0.93,0.90,0.60)
legb.SetNColumns(5)
else:
legb = TLegend(0.60,0.80,0.90,0.60)
legb.SetNColumns(2)
legb.SetLineColor(0)
legb.SetFillStyle(0)
legb.SetShadowColor(0)
legb.SetBorderSize(0)
if 'buff' in name:
lege = TLegend(0.25,0.93,0.90,0.60)
lege.SetNColumns(5)
else:
lege = TLegend(0.22,0.90,0.52,0.70)
lege.SetNColumns(2)
lege.SetLineColor(0)
lege.SetFillStyle(0)
lege.SetShadowColor(0)
lege.SetBorderSize(0)
#vec_beg = sorted(vec_beg, key=lambda x: x.Integral(), reverse=True)
stack1 = THStack("stack1","stack1")
for v in vec_beg:
if v.Integral() != 0:
v.Scale(norm)
legb.AddEntry(v, v.GetTitle()[4:], "f")
stack1.Add(v)
total_end = vec_end[0].Clone()
c = 0
for v in vec_end:
if c != 0:
total_end.Add(vec_end[c])
c = c + 1
norm = 1/total_end.Integral()
total_end.Scale(norm)
stack2 = THStack("stack2","stack2")
for v in vec_end:
if v.Integral() != 0:
v.Scale(norm)
lege.AddEntry(v, v.GetTitle()[4:], "f")
stack2.Add(v)
total_beg.SetLineColor(kRed)
total_beg.SetLineWidth(1)
total_end.SetLineColor(kRed)
total_end.SetLineWidth(1)
total_beg.GetXaxis().SetTitle("Minutes after stable beams declared [min]")
total_end.GetXaxis().SetTitle("Minutes before stable beams ended [min]")
if '0_4' in name:
total_beg.GetYaxis().SetTitle("Arbitrary units / 3 mins")
total_end.GetYaxis().SetTitle("Arbitrary units / 3 mins")
else:
total_beg.GetYaxis().SetTitle("Arbitrary units / 5 mins")
total_end.GetYaxis().SetTitle("Arbitrary units / 5 mins")
c4 = TCanvas( 'c4', 'c4',1000, 1200)
c4.Divide(1,2)
c4.cd(1)
total_beg.Draw("HIST")
stack1.Draw("PFC PLC SAME HIST")
total_beg.Draw("SAMEHIST")
AtlasStyle.ATLAS_LABEL(0.18,.96, 1, "Internal")
AtlasStyle.myText(0.40, 0.96, kBlack, text)
legb.Draw()
c4.cd(2)
total_end.Draw("HIST")
stack2.Draw("PFC PLC SAME HIST")
total_end.Draw("SAMEHIST")
AtlasStyle.ATLAS_LABEL(0.18,.96, 1, "Internal")
AtlasStyle.myText(0.40, 0.96, kBlack, text)
lege.Draw()
c4.Update()
c4.Print("plots/"+name+".pdf")
class Plot_Multi_TH1:
def __init__(self,filepath,filename,tree,list_variable,weight,list_cut,list_legend,list_color,name,bins,xmin,xmax,title,xlabel,ylabel,option='hist',logx=False,logy=False,legend_pos=[0.5,0.5,0.9,0.85],stack=False,norm=False):
self.filepath = filepath
self.filename = filename
self.tree = tree
self.weight = weight
self.name = name
self.bins = bins
self.xmin = xmin
self.xmax = xmax
self.title = title
self.xlabel = xlabel
self.ylabel = ylabel
self.legend_pos = legend_pos
self.logx = logx
self.logy = logy
self.option = option
self.stack = stack
self.norm = norm
if not isinstance(list_cut,list):
list_cut = [list_cut]
if not isinstance(list_variable,list):
list_variable = [list_variable]
if not isinstance(list_legend,list):
list_legend = [list_legend]
if len(list_variable) == 1 and len(list_cut) > 1:
logging.debug('\tOnly one variable but several cuts')
self.list_variable = list_variable*len(list_cut)
self.list_cut = list_cut
elif len(list_variable) > 1 and len(list_cut) == 1:
logging.debug('\tOnly one cut but several variables')
self.list_cut = list_cut*len(list_variable)
self.list_variable = list_variable
elif len(list_variable) == 1 and len(list_cut) == 1:
logging.warning('\tWhy do you even need to stack ?')
elif len(list_variable) != len(list_cut):
raise RuntimeError('Inconsistent number of variables and cuts')
print (len(list_variable))
print (len(list_cut))
else:
self.list_variable = list_variable
self.list_cut = list_cut
if len(list_legend) != max(len(list_variable),len(list_cut)):
raise RuntimeError('Inconsistent number of legends compared to variables and cuts')
else:
self.list_legend = list_legend
if len(list_color) != len(list_legend):
raise RuntimeError('Inconsistent number of colors compared to legends')
else:
self.list_color = list_color
for i in range(len(self.list_color)):
if isinstance(self.list_color[i],str):
self.list_color[i] = ROOT.TColor.GetColor(self.list_color[i])
def MakeHisto(self):
self.list_obj = []
for i in range(0,len(self.list_variable)):
instance = Plot_TH1(filepath = self.filepath,
filename = self.filename,
tree = self.tree,
variable = self.list_variable[i],
weight = self.weight,
cut = self.list_cut[i],
name = self.name,
bins = self.bins,
xmin = self.xmin,
xmax = self.xmax,
title = self.title,
xlabel = self.xlabel,
ylabel = self.ylabel,
logx = self.logx,
logy = self.logy)
instance.MakeHisto()
self.list_obj.append(copy.deepcopy(instance.histo))
def PlotOnCanvas(self,pdf_name):
tdrstyle.setTDRStyle()
# Canvas #
canvas = TCanvas("c", "c", 600, 600)
canvas.SetTopMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetRightMargin(0.05)
canvas.SetLeftMargin(0.15)
if self.title == '':
canvas.SetTopMargin(0.05)
if self.logx:
canvas.SetLogx()
if self.logy:
canvas.SetLogy()
self.list_obj[0].SetMinimum(10)
# Norm #
if self.norm:
for obj in self.list_obj:
if obj.Integral() != 0:
obj.Scale(1./obj.Integral())
# Axes #
maxY = max([h.GetMaximum() for h in self.list_obj])
if self.logy:
maxY *= 2
else:
maxY *= 1.1
self.list_obj[0].SetMaximum(maxY)
self.list_obj[0].GetXaxis().SetLabelSize(0.03)
self.list_obj[0].GetXaxis().SetTitleSize(0.05)
if len(self.xlabel) > 30:
self.list_obj[0].GetXaxis().SetTitleSize(0.035)
if len(self.xlabel) > 60:
self.list_obj[0].GetXaxis().SetTitleSize(0.03)
if "frac" in self.xlabel:
self.list_obj[0].GetXaxis().SetTitleSize(0.03)
self.list_obj[0].GetXaxis().SetTitleOffset(1.9)
else:
self.list_obj[0].GetXaxis().SetTitleOffset(1.6)
self.list_obj[0].GetYaxis().SetTitleOffset(1.5)
self.list_obj[0].GetYaxis().SetLabelSize(0.03)
self.list_obj[0].GetYaxis().SetTitleSize(0.05)
self.list_obj[0].GetYaxis().SetNdivisions(10)
if len(self.ylabel) > 30:
self.list_obj[0].GetYaxis().SetTitleSize(0.035)
if len(self.ylabel) > 60:
self.list_obj[0].GetYaxis().SetTitleSize(0.03)
# Stacking #
if self.stack:
self.stack_hist = THStack("hs","") # Needs to be in self, otherwise destroyed at end of function
opt = self.option
for col,obj in zip(self.list_color,self.list_obj):
obj.SetFillColor(col)
obj.SetLineColor(col)
self.stack_hist.Add(obj)
obj.Draw(opt)
if not "same" in opt :
opt += " same"
self.stack_hist.Draw(self.option+" same")
maxY = self.stack_hist.GetMaximum()
if self.logy:
maxY *= 10
else:
maxY *= 1.1
self.list_obj[0].SetMaximum(maxY)
# Regular plotting
else:
opt = self.option
for col,obj in zip(self.list_color,self.list_obj):
obj.SetLineColor(col)
obj.SetLineWidth(2)
obj.Draw(opt)
if not "same" in opt :
opt += " same"
# Legend #
legend = TLegend(*self.legend_pos)
for leg,obj in zip(self.list_legend,self.list_obj):
legend.AddEntry(obj,leg,"f" if self.stack else "l")
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.Draw()
ROOT.SetOwnership(legend,0) # Otherwise goes out of scope and not printed
return canvas,self.filename
def PlotOnCanvas(self,pdf_name):
tdrstyle.setTDRStyle()
# Canvas #
canvas = TCanvas("c", "c", 600, 600)
canvas.SetTopMargin(0.15)
canvas.SetBottomMargin(0.15)
canvas.SetRightMargin(0.05)
canvas.SetLeftMargin(0.15)
if self.title == '':
canvas.SetTopMargin(0.05)
if self.logx:
canvas.SetLogx()
if self.logy:
canvas.SetLogy()
self.list_obj[0].SetMinimum(10)
# Norm #
if self.norm:
for obj in self.list_obj:
if obj.Integral() != 0:
obj.Scale(1./obj.Integral())
# Axes #
maxY = max([h.GetMaximum() for h in self.list_obj])
if self.logy:
maxY *= 2
else:
maxY *= 1.1
self.list_obj[0].SetMaximum(maxY)
self.list_obj[0].GetXaxis().SetLabelSize(0.03)
self.list_obj[0].GetXaxis().SetTitleSize(0.05)
if len(self.xlabel) > 30:
self.list_obj[0].GetXaxis().SetTitleSize(0.035)
if len(self.xlabel) > 60:
self.list_obj[0].GetXaxis().SetTitleSize(0.03)
if "frac" in self.xlabel:
self.list_obj[0].GetXaxis().SetTitleSize(0.03)
self.list_obj[0].GetXaxis().SetTitleOffset(1.9)
else:
self.list_obj[0].GetXaxis().SetTitleOffset(1.6)
self.list_obj[0].GetYaxis().SetTitleOffset(1.5)
self.list_obj[0].GetYaxis().SetLabelSize(0.03)
self.list_obj[0].GetYaxis().SetTitleSize(0.05)
self.list_obj[0].GetYaxis().SetNdivisions(10)
if len(self.ylabel) > 30:
self.list_obj[0].GetYaxis().SetTitleSize(0.035)
if len(self.ylabel) > 60:
self.list_obj[0].GetYaxis().SetTitleSize(0.03)
# Stacking #
if self.stack:
self.stack_hist = THStack("hs","") # Needs to be in self, otherwise destroyed at end of function
opt = self.option
for col,obj in zip(self.list_color,self.list_obj):
obj.SetFillColor(col)
obj.SetLineColor(col)
self.stack_hist.Add(obj)
obj.Draw(opt)
if not "same" in opt :
opt += " same"
self.stack_hist.Draw(self.option+" same")
maxY = self.stack_hist.GetMaximum()
if self.logy:
maxY *= 10
else:
maxY *= 1.1
self.list_obj[0].SetMaximum(maxY)
# Regular plotting
else:
opt = self.option
for col,obj in zip(self.list_color,self.list_obj):
obj.SetLineColor(col)
obj.SetLineWidth(2)
obj.Draw(opt)
if not "same" in opt :
opt += " same"
# Legend #
legend = TLegend(*self.legend_pos)
for leg,obj in zip(self.list_legend,self.list_obj):
legend.AddEntry(obj,leg,"f" if self.stack else "l")
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.Draw()
ROOT.SetOwnership(legend,0) # Otherwise goes out of scope and not printed
return canvas,self.filename
myfile = TFile.Open(plotDirectory + "myZJets_Prefit.root", "recreate")
for ih in summedHist:
rebinnedHist[ih] = summedHist[ih].Rebin(len(binning) - 1, "", binning)
rebinnedHist[ih].SetLineColor(template_category[ih])
rebinnedHist[ih].SetFillColor(template_category[ih])
rebinnedHist[ih].Write()
rebinnedData = data_obs.Rebin(len(binning) - 1, "", binning)
rebinnedData.Write()
myfile.Close()
sys.exit()
else:
if postfitPlots:
rebinnedHist['myZJets'].Scale(ZJetSF)
stack = THStack()
stack.Add(rebinnedHist['myBackground'])
stack.Add(rebinnedHist['myZJets'])
canvasRatio = TCanvas('c1Ratio', 'c1Ratio', W, H)
canvasRatio.SetFillColor(0)
canvasRatio.SetBorderMode(0)
canvasRatio.SetFrameFillStyle(0)
canvasRatio.SetFrameBorderMode(0)
canvasRatio.SetLeftMargin(L / W)
canvasRatio.SetRightMargin(R / W)
canvasRatio.SetTopMargin(T / H)
canvasRatio.SetBottomMargin(B / H)
canvasRatio.SetTickx(0)
canvasRatio.SetTicky(0)
canvasRatio.Draw()
def draw(hist,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
#if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
data_graph.Draw("SAME, PE")
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((2. if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
#if log: bkg.SetMinimum(1)
leg.Draw()
drawCMS(lumi, "Preliminary")
drawRegion(channel)
drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
if len(sign) > 0:
if channel.startswith('X') and len(sign) > 0:
drawNorm(0.9 - 0.04 * (n + 1),
"#sigma(X) #times B(X #rightarrow Vh) = %.1f pb" % snorm)
#elif "SR" in channel: drawNorm(0.9-0.04*(n+1), "DM+bb/tt, scaled by %.0f" % snorm, "m_{#chi}=1 GeV, scalar mediator")
elif "SR" in channel:
drawNorm(0.9 - 0.04 * (n + 1), "DM+bb/tt, m_{#chi}=1 GeV",
"scalar mediator")
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
#res.Draw("SAME, PE0")
res_graph.Draw("SAME, PE0")
# if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot
# drawRatio(hist['data_obs'], hist['BkgSum'])
# drawKolmogorov(hist['data_obs'], hist['BkgSum'])
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, err, errLine, res, data_graph, res_graph]
def plot(path, leg_title, ecms, xmin, xmax, xbins, runNolow, runNoup, ymax):
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)
content = (xmax - xmin) / xbins * 1000
ytitle = 'Events/%.1f MeV' % content
xtitle = 'M(D^{+}#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_Dpi0_' + str(ecms) + '.pdf')
for ch in [
'STHct_j3b3_01', 'STHct_j4b2_01', 'STHct_j4b3_01', 'STHut_j3b3_01',
'STHut_j4b2_01', 'STHut_j4b3_01'
]:
gen = False
bdt_data = TH1F('bdt_data', ch + ' BDT Score', 20, -0.3, 0.3)
bdt_sig = TH1F('bdt_sig', '', 20, -0.3, 0.3)
bdt_sig_gen = TH1F('bdt_sig_gen', '', 20, -0.3, 0.3)
keras_data = TH1F('keras_data', ch + ' Keras Score', 20, 0, 1)
keras_sig = TH1F('keras_sig', '', 20, 0, 1)
keras_sig_gen = TH1F('keras_sig_gen', '', 20, 0, 1)
hs_bdt_bkg = THStack()
hs_keras_bkg = THStack()
shape_file = TFile.Open(
'/home/minerva1993/fcnc/analysis_2017/limitSetting/limitSettingInput/shape_'
+ tmva_version + '_' + ch + '.root')
########################################
bdt_ttbb = shape_file.Get('bdt_ttbb')
bdt_ttbj = shape_file.Get('bdt_ttbj')
bdt_ttcc = shape_file.Get('bdt_ttcc')
bdt_ttLF = shape_file.Get('bdt_ttLF')
bdt_singletop = shape_file.Get('bdt_singletop')
bdt_others = shape_file.Get('bdt_others')
bdt_data = shape_file.Get('bdt_data_obs')
bdt_sig = shape_file.Get('bdt_sig')
def make_plot_all_rods(error_dict, rod_dict, name):
leg = TLegend(0,0,0,0)
if 'lock' in name:
leg = TLegend(0.18,0.85,0.50,0.55)
else:
leg = TLegend(0.18,0.85,0.40,0.55)
leg.SetLineColor(0)
leg.SetFillStyle(0)
leg.SetShadowColor(0)
leg.SetBorderSize(0)
leg.SetNColumns(3)
gStyle.SetLegendTextSize(0.045)
v_hists = []
#for e,c in zip(error_bits, error_colors):
for e in error_bits:
h = TH1F('h'+e,'h'+e, len(rod_dict), 0, len(rod_dict))
h.SetFillStyle(1001)
h.SetLineWidth(0)
v_hists.append(h)
v_hists[-1].SetDirectory(0)
if bin(int('0x'+e, 16))[2:].zfill(4) == '0111':
leg.AddEntry(v_hists[-1],'GOL 3',"f");
elif bin(int('0x'+e, 16))[2:].zfill(4) == '1011':
leg.AddEntry(v_hists[-1],'GOL 2',"f");
elif bin(int('0x'+e, 16))[2:].zfill(4) == '1101':
leg.AddEntry(v_hists[-1],'GOL 1',"f");
elif bin(int('0x'+e, 16))[2:].zfill(4) == '1110':
leg.AddEntry(v_hists[-1],'GOL 0',"f");
else:
leg.AddEntry(v_hists[-1],bin(int('0x'+e, 16))[2:].zfill(4),"f");
h = leg.GetY2()-leg.GetY1();
w = leg.GetX2()-leg.GetX1()*.6;
leg.SetMargin(leg.GetNColumns()*h/(leg.GetNRows()*w))
for key,val in error_dict.items():
idx_rod = 0
for i, key2 in enumerate(rod_dict):
if key2 == key[:8]:
idx_rod = i
v_hists[int(key[11:12], 16)].Fill(idx_rod, val)
stack = THStack("stack","stack")
for hist in v_hists:
stack.Add(hist)
if 'buff' in name:
c1 = TCanvas( 'c1', 'c1', 2000, 500)
else:
c1 = TCanvas( 'c1', 'c1', 1000, 500)
h1 = TH1F('h_1','h_1', len(rod_dict), 0, len(rod_dict))
for i, key in enumerate(rod_dict):
h1.GetXaxis().SetBinLabel(i+1,key)
h1.SetBinContent(i+1,rod_dict[key])
h1.GetXaxis().LabelsOption("v")
h1.GetXaxis().SetTitle("ROD")
h1.GetXaxis().SetTitleOffset(2.2)
h1.GetYaxis().SetTitle("# of rocketio errors")
h1.SetLineColor(kRed)
h1.SetLineWidth(1)
leg.AddEntry(h1,'total',"l");
c1.SetBottomMargin(0.23)
h1.GetXaxis().SetTitle("ROD")
h1.Draw("HIST")
stack.Draw("PFC PLC SAME HIST")
h1.Draw("SAMEHIST")
AtlasStyle.ATLAS_LABEL(0.19,.88, 1, "Internal")
leg.Draw()
c1.Update()
c1.Print("plots/"+name +".pdf")
c1.Clear()
SipMCTTbar.append(histoMCTTbar_input.Get('Z_ExtraEl_SIP_MC_TTbar_hist'))
SipMCTTbar.append(histoMCTTbar_input.Get('Z_ExtraEl_SIP_MC_TTbar_hist_EB'))
SipMCTTbar.append(histoMCTTbar_input.Get('Z_ExtraEl_SIP_MC_TTbar_hist_EE'))
SipMCTTbar.append(histoMCTTbar_input.Get('Z_ExtraMu_SIP_MC_TTbar_hist'))
SipMCTTbar.append(histoMCTTbar_input.Get('Z_ExtraMu_SIP_MC_TTbar_hist_MB'))
SipMCTTbar.append(histoMCTTbar_input.Get('Z_ExtraMu_SIP_MC_TTbar_hist_ME'))
# ******************************
# do DATA vs MC comparison plots
# ******************************
for i in range(len(SipDATA)):
canvas = TCanvas("canvas", "canvas", 800, 800)
hs = THStack("hs", "")
norm = 1 # Normalize to MC cross-section
#norm = SipDATA[i].Integral() / (SipMCTTbar[i].Integral() + SipMCDY[i].Integral()) # Normalize MC to data
#DATA hist
SipDATA[i].SetMarkerStyle(20)
SipDATA[i].SetMarkerSize(0.6)
#MC TTbar hist
SipMCTTbar[i].Scale(norm) # MC normalization
SipMCTTbar[i].SetFillColor(kAzure - 3)
SipMCTTbar[i].SetLineColor(kBlack)
hs.Add(SipMCTTbar[i])
#MC DY hist
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():
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)
# Draw
stack_X0.Draw("HIST")
# Legenda
theLegend = TLegend(0.70, 0.70, 0.89, 0.89)
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))
i = 0 #counter for histos lists
for key in keyList :
# debug
# print key, massFitDATA_dict[key]
# print key, widthFitDATA_dict[key]
# print key, massFitMC_dict[key]
# print key, widthFitMC_dict[key]
if (ZTree and massFitDATA_dict[key]==0) : continue
canvas = TCanvas("canvas","canvas",800,800)
hs = THStack("hs","")
norm = 1 # normalize to MC xsection
# norm = ZMass_DATA[i].Integral() / (ZMass_MC_TTJets[i].Integral() + ZMass_MC_DY[i].Integral()) #normalize MC to data
#DATA hist
ZMass_DATA[i].SetMarkerStyle(20)
ZMass_DATA[i].SetMarkerSize(0.6)
#TTJets MC hist
ZMass_MC_TTJets[i].Scale(norm) #normalize MC
ZMass_MC_TTJets[i].SetFillColor(3)
ZMass_MC_TTJets[i].SetLineColor(kBlack)
hs.Add(ZMass_MC_TTJets[i])
def createPlots_(plot):
"""Cumulative material budget from simulation.
Internal function that will produce a cumulative profile of the
material budget inferred from the simulation starting from the
single detectors that compose the tracker. It will iterate over
all existing detectors contained in the DETECTORS
dictionary. The function will automatically skip non-existent
detectors.
"""
IBs = ["InnerServices", "Phase2PixelBarrel", "TIB", "TIDF", "TIDB"]
theDirname = "Figures"
hist_X0_detectors = OrderedDict()
if plot not in plots.keys():
print("Error: chosen plot name not known %s" % plot)
return
hist_X0_IB = None
# 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)
# Merge together the "inner barrel detectors".
if subDetector in IBs:
hist_X0_IB = assignOrAddIfExists_(hist_X0_IB, prof_X0_XXX)
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_IB.GetNbinsX(),
hist_X0_IB.GetXaxis().GetXmin(),
hist_X0_IB.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 + Pixel + TIB/TID + TOB + TEC + Outside
# stack
stackTitle_SubDetectors = "Tracker Material Budget;%s;%s" % (
plots[plot].abscissa, plots[plot].ordinate)
stack_X0_SubDetectors = THStack("stack_X0", stackTitle_SubDetectors)
for det, histo in 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(0.180, 0.8, 0.98, 0.92)
theLegend_SubDetectors.SetNColumns(3)
theLegend_SubDetectors.SetFillColor(0)
theLegend_SubDetectors.SetFillStyle(0)
theLegend_SubDetectors.SetBorderSize(0)
for det, histo in hist_X0_detectors.iteritems():
theLegend_SubDetectors.AddEntry(histo, det, "f")
theLegend_SubDetectors.Draw()
# text
text_SubDetectors = TPaveText(0.180, 0.727, 0.402, 0.787, "NDC")
text_SubDetectors.SetFillColor(0)
text_SubDetectors.SetBorderSize(0)
text_SubDetectors.AddText("CMS Simulation")
text_SubDetectors.SetTextAlign(11)
text_SubDetectors.Draw()
# Store
can_SubDetectors.Update()
if not checkFile_(theDirname):
os.mkdir(theDirname)
can_SubDetectors.SaveAs("%s/Tracker_SubDetectors_%s.pdf" %
(theDirname, plot))
can_SubDetectors.SaveAs("%s/Tracker_SubDetectors_%s.root" %
(theDirname, plot))
# Second Plot: BeamPipe + SEN + ELE + CAB + COL + SUP + OTH/AIR +
# Outside stack
stackTitle_Materials = "Tracker Material Budget;%s;%s" % (
plots[plot].abscissa, plots[plot].ordinate)
stack_X0_Materials = THStack("stack_X0", stackTitle_Materials)
stack_X0_Materials.Add(hist_X0_detectors["BeamPipe"])
for label, [num, color, leg] in hist_label_to_num.iteritems():
stack_X0_Materials.Add(hist_X0_elements[label])
# canvas
can_Materials = TCanvas("can_Materials", "can_Materials", 800, 800)
can_Materials.Range(0, 0, 25, 25)
can_Materials.SetFillColor(kWhite)
# Draw
stack_X0_Materials.SetMinimum(plots[plot].ymin)
stack_X0_Materials.SetMaximum(plots[plot].ymax)
stack_X0_Materials.Draw("HIST")
stack_X0_Materials.GetXaxis().SetLimits(plots[plot].xmin, plots[plot].xmax)
# Legenda
theLegend_Materials = TLegend(0.180, 0.8, 0.95, 0.92)
theLegend_Materials.SetNColumns(3)
theLegend_Materials.SetFillColor(0)
theLegend_Materials.SetBorderSize(0)
theLegend_Materials.AddEntry(hist_X0_detectors["BeamPipe"], "Beam Pipe",
"f")
for label, [num, color, leg] in hist_label_to_num.iteritems():
theLegend_Materials.AddEntry(hist_X0_elements[label], leg, "f")
theLegend_Materials.Draw()
# text
text_Materials = TPaveText(0.180, 0.727, 0.402, 0.787, "NDC")
text_Materials.SetFillColor(0)
text_Materials.SetBorderSize(0)
text_Materials.AddText("CMS Simulation")
text_Materials.SetTextAlign(11)
text_Materials.Draw()
# Store
can_Materials.Update()
can_Materials.SaveAs("%s/Tracker_Materials_%s.pdf" % (theDirname, plot))
can_Materials.SaveAs("%s/Tracker_Materials_%s.root" % (theDirname, plot))
return cumulative_matbdg
# FILTER EVENTS
# configure total rate hist
totalRateHist.SetMarkerStyle(22)
totalRateHist.SetMarkerSize(1.2)
if (last_lb - first_lb) > 600:
totalRateHist.SetMarkerSize(0.6)
totalRateHist.SetMarkerColor(ROOT.kBlack)
totalRateHist.SetLineColor(ROOT.kBlack)
for bin in range(1, totalRateHist.GetNbinsX() + 1):
totalRateHist.SetBinError(bin, 0) # no appreciable errors on rate
# add total rpvll event hist entry to legend
l1.AddEntry(totalRateHist, "Overall RPVLL Events", "lp")
# initialize stack for all filter rate hists
hs1 = THStack("hs1", "")
# add individual filter rate hists to stack
for i, hist in enumerate(rateHists):
hist.SetFillColor(colors[i % len(colors)])
hist.SetLineColor(colors[i % len(colors)])
if i >= len(colors):
hist.SetFillStyle(3004)
hs1.Add(hist)
# add individual event entries to legend
l1.AddEntry(hist, filterNames_mAug[i], "f")
# draw individual filter stack + overall rate hist
hs1.Draw("HIST")
totalRateHist.Draw("pSAME")
## configure axes
cAll = TCanvas("cAllPosts", "All Posts", 600, 600)
hAllPosts.Draw("histo")
cEmojis = TCanvas("cEmojis", "Emojis Relative", 600, 600)
colour = 1
leg = TLegend(0.6, 0.7, 0.9, 0.9)
leg.SetHeader("Emoticonos: uso relativo")
gStyle.SetFillStyle(0)
gStyle.SetLineWidth(0)
gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
hs = THStack("hs", "")
for h in histos:
h.Divide(hAllPosts)
h.SetLineColor(1)
h.SetFillColor(colour)
h.SetLineWidth(1)
for binn in range(1, 8):
h.GetXaxis().SetBinLabel(binn, binlabels[binn-1])
leg.AddEntry(h, h.GetTitle(), "f")
hs.Add(h)
colour += 1
hs.Draw()
leg.Draw()
def plot(path, ecms, xmin, xmax, num_charm):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('data entries :' + str(entries_data))
except:
logging.error(path[0] + 'is invalid!')
sys.exit()
try:
f_side1 = TFile(path[1])
t_side1 = f_side1.Get('save')
entries_side1 = t_side1.GetEntries()
logging.info('data(side1) entries :' + str(entries_side1))
except:
logging.error(path[1] + ' is invalid!')
sys.exit()
try:
f_side2 = TFile(path[2])
t_side2 = f_side2.Get('save')
entries_side2 = t_side2.GetEntries()
logging.info('data(side2) entries :' + str(entries_side2))
except:
logging.error(path[2] + ' is invalid!')
sys.exit()
try:
f_side3 = TFile(path[3])
t_side3 = f_side3.Get('save')
entries_side3 = t_side3.GetEntries()
logging.info('data(side3) entries :' + str(entries_side3))
except:
logging.error(path[3] + ' is invalid!')
sys.exit()
try:
f_side4 = TFile(path[4])
t_side4 = f_side4.Get('save')
entries_side4 = t_side4.GetEntries()
logging.info('data(side4) entries :' + str(entries_side4))
except:
logging.error(path[4] + ' is invalid!')
sys.exit()
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
xbins = 100
ytitle = 'Eentries'
xtitle = 'E(OtherTrks) (GeV)'
h_data = TH1F('data', 'data', xbins, xmin, xmax)
set_histo_style(h_data, xtitle, ytitle, 1, -1)
E_othertrks_fill(t_data, h_data, num_charm)
h_side1 = TH1F('side1', 'side1', xbins, xmin, xmax)
set_histo_style(h_side1, xtitle, ytitle, 3, 3004)
E_othertrks_fill(t_side1, h_side1, num_charm)
h_side2 = TH1F('side2', 'side2', xbins, xmin, xmax)
set_histo_style(h_side2, xtitle, ytitle, 3, 3004)
E_othertrks_fill(t_side2, h_side2, num_charm)
h_side3 = TH1F('side3', 'side3', xbins, xmin, xmax)
set_histo_style(h_side3, xtitle, ytitle, 3, 3004)
E_othertrks_fill(t_side3, h_side3, num_charm)
h_side4 = TH1F('side4', 'side4', xbins, xmin, xmax)
set_histo_style(h_side4, xtitle, ytitle, 3, 3004)
E_othertrks_fill(t_side4, h_side4, num_charm)
h_side1.Add(h_side2)
h_side1.Scale(0.5)
h_side3.Add(h_side4)
h_side3.Scale(0.25)
h_side1.Add(h_side3, -1)
h_data.Draw('E1')
hs = THStack('hs', 'Stacked')
hs.Add(h_side1)
hs.Draw('same')
h_data.Draw('sameE1')
legend = TLegend(0.5, 0.6, 0.8, 0.85)
leg_title = str(ecms) + ' MeV'
set_legend(legend, h_data, h_side1, leg_title)
legend.Draw()
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/E_othertrks_' + str(ecms) + '_' + str(num_charm) +
'.pdf')
raw_input('Enter anything to end...')
legend = TLegend(0.5, 0.7, 0.9, 0.9)
drawOpt = 'HE1'
rf_stack = []
rf_sup = []
maxY = 0
for file in inputFiles:
if (file in tofill):
rf_stack.append(TFile(path + file, 'READ'))
else:
rf_sup.append(TFile(path + file, 'READ'))
stack = THStack('stack', plottitle)
hist_counter = 0
for rf in rf_stack:
if (rebin):
hist_oldbin = rf.Get(dir + '/' + histname)
hist = hist_oldbin.Rebin(nBins, "new binning", xbins)
else:
hist = rf.Get(dir + '/' + histname)
hist.SetFillColor(867 + hist_counter)
hist.SetLineColor(867 + hist_counter)
hist.SetTitle('stack')
stack.Add(hist)
hist_counter = hist_counter + 1
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 read_file(arr, meas_type, mapsa_fitter_inst, path):
if not os.path.isfile(str(path + arr[0])):
print "Root file not found at", str(path + arr[0])
#sys.exit(1)
return
f = TFile(str(path + arr[0]), 'READ')
if (f.IsZombie()):
print "Error opening file"
return
else:
print "Reading File ", arr[0]
#f.ls()
tree = f.Get('tree')
f_GlobalData_Map = ROOT.TMap()
f_GlobalData_Map.Add(ROOT.TObjString("tree"), tree)
#tree.Print()
outfile = TString(arr[0])
outfile.ReplaceAll(".root", "")
outfile.ReplaceAll(" ", "")
# print outfile
if (meas_type == 0):
outfile = "pedestal"
if (not g.FindKey(str(outfile))):
g.mkdir(str(outfile))
g.cd(str(outfile))
else:
return
channels = 288
if (arr[2] == 'inv'):
channels = 96
mapsa_mat = [[1, 0, 0], [1, 0, 0]]
elif (arr[2] == 'norm'):
channels = 288
mapsa_mat = [[1, 1, 1], [1, 0, 1]]
#print "channels", channels
#print "mapsa_mat", mapsa_mat
c1 = TCanvas('c1', 'Pixel Monitor ', 700, 900)
c2 = TCanvas('c2', 'Pixel Monitor ', 500, 500)
c3 = TCanvas('c3', 'Pixel Monitor ', 1280, 720)
c4 = TCanvas('c4', 'Pixel Monitor ', 500, 500)
c5 = TCanvas('c5', 'Pixel Monitor ', 500, 500)
f_GlobalData_Map.Add(ROOT.TObjString("c1"), c1)
f_GlobalData_Map.Add(ROOT.TObjString("c2"), c2)
f_GlobalData_Map.Add(ROOT.TObjString("c3"), c3)
f_GlobalData_Map.Add(ROOT.TObjString("c4"), c4)
f_GlobalData_Map.Add(ROOT.TObjString("c5"), c5)
# c2.Divide(2,1)
#c2.cd(0)
c1.Divide(3, 2)
for i in range(1, 7):
c1.cd(i)
ROOT.gPad.SetGridx()
ROOT.gPad.SetGridy()
# channelcounts = TH2I('HitMap','Counts; Channel; DAC Value (1.456 mV)', 288, .5,288.5,256, .5, 256.5)
channelcounts = TH2I('HitMap', 'Counts; Channel; DAC Value (a.u.)', 288,
.5, 288.5, 256, .5, 256.5)
channelcounts_norm = TH2F('HitMap_norm',
'Occupancy ; Channel; DAC Value (a.u.)', 288, .5,
288.5, 256, .5, 256.5)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts)
f_GlobalData_Map.Add(ROOT.TObjString("HitMap"), channelcounts_norm)
norm_2d = TH2F('Norm2D', 'Normalization; Column; Row', 48, .5, 48.5, 6, .5,
6.5)
mean_2d = TH2F('Mean2D', 'Mean; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
sigma_2d = TH2F('Sigma2D', 'Sigma; Column; Row', 48, .5, 48.5, 6, .5, 6.5)
chisquare = TH2F('Chisquare2D', 'Chisquare; Column; Row', 48, .5, 48.5, 6,
.5, 6.5)
f_GlobalData_Map.Add(ROOT.TObjString("Norm2D"), norm_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Mean2D"), mean_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Sigma2D"), sigma_2d)
f_GlobalData_Map.Add(ROOT.TObjString("Chisquare2D"), chisquare)
objarr2d = []
objarr2d.append(norm_2d)
objarr2d.append(mean_2d)
objarr2d.append(sigma_2d)
objarr2d.append(chisquare)
normgraph = TGraphErrors()
meangraph = TGraphErrors()
sigmagraph = TGraphErrors()
chisquaregraph = TGraphErrors()
mean_corrgraph = TGraphErrors()
f_GlobalData_Map.Add(ROOT.TObjString("normgraph "), normgraph)
f_GlobalData_Map.Add(ROOT.TObjString("meangraph "), meangraph)
f_GlobalData_Map.Add(ROOT.TObjString("sigmagraph "), sigmagraph)
f_GlobalData_Map.Add(ROOT.TObjString("chisquaregraph"), chisquaregraph)
f_GlobalData_Map.Add(ROOT.TObjString("mean_corrgraph"), mean_corrgraph)
meanhist = TH1F('meanhist', 'Mean DAC; DAC Value (a.u.); counts', 2560, 0,
255)
sigmahist = TH1F('sigmahist', 'Sigma DAC; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_std = TH1F('meanhist_std',
'Mean DAC Standard; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_std = TH1F('sigmahist_std',
'Sigma DAC Standard; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double = TH1F('meanhist_double',
'Mean DAC Double; DAC Value (a.u.); counts', 2560,
0, 255)
sigmahist_double = TH1F('sigmahist_double',
'Sigma DAC Double; DAC Value (a.u.); counts', 100,
0, 10)
meanhist_double_neighbour = TH1F(
'meanhist_double_neighbour',
'Mean DAC Double Neighbour; DAC Value (a.u.); counts', 2560, 0, 255)
sigmahist_double_neighbour = TH1F(
'sigmahist_double_neighbour',
'Sigma DAC Double Neighbour; DAC Value (a.u.); counts', 100, 0, 10)
objarr = []
objarr.append(normgraph)
objarr.append(meangraph)
objarr.append(sigmagraph)
objarr.append(chisquaregraph)
objarr.append(mean_corrgraph)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist'), meanhist)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist'), sigmahist)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_std'), meanhist_std)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_std'), sigmahist_std)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double'), meanhist_double)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double'), sigmahist_double)
f_GlobalData_Map.Add(ROOT.TObjString('meanhist_double_neighbour'),
meanhist_double_neighbour)
f_GlobalData_Map.Add(ROOT.TObjString('sigmahist_double_neighbour'),
sigmahist_double_neighbour)
objarr.append(meanhist)
objarr.append(sigmahist)
objarr.append(meanhist_std)
objarr.append(sigmahist_std)
objarr.append(meanhist_double)
objarr.append(sigmahist_double)
objarr.append(meanhist_double_neighbour)
objarr.append(sigmahist_double_neighbour)
for objs in objarr:
objs.SetMarkerColor(2)
objs.SetMarkerStyle(20)
objs.SetMarkerSize(1)
normgraph.SetName('normgraph')
meangraph.SetName('meangraph')
sigmagraph.SetName('sigmagraph')
chisquaregraph.SetName('chisquare')
mean_corrgraph.SetName('mean_corr')
normgraph.SetTitle('Normalization; Channel; Normalization')
meangraph.SetTitle('Mean; Channel; DAC Value (a.u.)')
sigmagraph.SetTitle('Sigma; Channel; DAC Value (a.u.)')
chisquaregraph.SetTitle('Chisquared/NDF_gr; Channel; Chisquared/NDF ')
ROOT.gStyle.SetOptFit(1111)
stack = THStack('a', ';DAC Value (a.u.); Occupancy')
f_GlobalData_Map.Add(ROOT.TObjString("stack"), stack)
fitfuncs = []
fitparams = []
gr1 = []
for pixel in range(0, channels):
gr1.append(
TH1D(
str(pixel).zfill(3),
str(pixel + 1).zfill(3) + ';DAC Value (a.u.); Occupancy ', 256,
0.5, 256.5))
f_GlobalData_Map.Add(ROOT.TObjString(str(pixel).zfill(3)), gr1[pixel])
#gr2.append(TH1F('th1f_'+str(pixel).zfill(3),str(pixel+1).zfill(3)+';DAC Value (a.u.); Occupancy',256,0.5,256.5))
color = pixel % 8 + 1
formating_th1(gr1[pixel], color)
if (meas_type == 0):
fitfuncs.append(
TF1('gauss' + str(pixel + 1).zfill(3), 'gaus(0)', 0, 256))
fitfuncs[pixel].SetNpx(256)
fitfuncs[pixel].SetLineColor(color)
f_GlobalData_Map.Add(
ROOT.TObjString('gauss' + str(pixel).zfill(3)),
fitfuncs[pixel])
#Here we read the data and fill the histogram
for event in tree:
eventstr = []
for counter, vals in enumerate(tree.AR_MPA):
#eventstr.append(vals)
channelcounts.Fill(counter, tree.THRESHOLD, vals)
if (counter < channels):
gr1[counter].Fill(tree.THRESHOLD, vals)
#if tree.THRESHOLD%20==0 and tree.REPETITION==0:
#print eventstr
#print ("Threshold %d Repetion %d" % (tree.THRESHOLD,tree.REPETITION))
#print tree.AR_MPA
#now we make a small analysis of the curves fitting different functions to it:
print "Finished Reading the Tree\n Normalization of Histograms\n"
for pixel in range(0, channels):
#gr1[pixel].ResetStats()
for j in range(0, gr1[pixel].GetXaxis().GetNbins() + 1):
gr1[pixel].SetBinError(
gr1[pixel].GetBin(j),
TMath.Sqrt(gr1[pixel].GetBinContent(gr1[pixel].GetBin(j))))
#if(pixel==0):
#gr1[pixel].Print("all")
#gr1[pixel].Sumw2(ROOT.kTRUE)
gr1[pixel].Scale(1 / arr[1])
#if(pixel==0):
#gr1[pixel].Print("all")
stack.Add(gr1[pixel])
#first create a THStack with histograms:
iterator = stack.GetHists()
if (meas_type == 0):
for idx, it in enumerate(iterator):
fitparams.append([])
if (it.Integral() > 0):
if (idx < channels):
#fitfuncs.append(TF1('combined'+str(idx),combined, 0,256,5))
#fitfuncs.append(TF1('combined_same_mean'+str(idx),combined_mean, 0,256,4))
#fitfuncs.append(TF1('double_gauss'+str(idx),'gaus(0)+gaus(3)',0,256))
#fitfuncs.append(TF1('gauss'+str(idx),'gaus(0)',0,256))
#fitfuncs.append(TF1('double_gauss_same_mean'+str(idx),double_gauss, 0,256,5))
#print it.GetName(), idx
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean()+1,it.GetRMS(),it.GetMean()-1,it.GetRMS());
#fitfuncs[idx].SetParameters(it.GetMaximum(),it.GetMean(),it.GetRMS()*0.1,it.GetRMS()*0.1);
#print ("Channels %f\t%f\t%f\n" % (it.GetMaximum(),it.GetMean(),it.GetRMS()))
fitfuncs[idx].SetParameters(it.GetMaximum(), it.GetMean(),
it.GetRMS())
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),it.GetMean(),10*it.GetRMS());
#fitfuncs[idx].SetParameters(0.999*it.GetMaximum(),it.GetMean(),.7*it.GetRMS(),0.001*it.GetMaximum(),10*it.GetRMS());
#it.Fit(fitfuncs[idx],'lr0 rob=0.95','same',0,256)
#it.Fit(fitfuncs[idx],'lr0q ','',0,256)
it.Fit(fitfuncs[idx], 'r0q ', '', 0, 256)
fitparams[idx].append(fitfuncs[idx].GetParameter(0))
fitparams[idx].append(fitfuncs[idx].GetParameter(1))
fitparams[idx].append(fitfuncs[idx].GetParameter(2))
fitparams[idx].append(fitfuncs[idx].GetParError(0))
fitparams[idx].append(fitfuncs[idx].GetParError(1))
fitparams[idx].append(fitfuncs[idx].GetParError(2))
if (fitfuncs[idx].GetNDF() > 0):
fitparams[idx].append(fitfuncs[idx].GetChisquare() /
fitfuncs[idx].GetNDF())
else:
for kk in range(0, 7):
fitparams[idx].append(0)
#print "fitparamarray"
fitarray = np.array(fitparams)
## print fitarray
for pointno, it in enumerate(fitarray):
if (fitarray[pointno][0] > 0):
normgraph.SetPoint(pointno, pointno + 1, fitarray[pointno][0])
normgraph.SetPointError(pointno, 0, fitarray[pointno][3])
meangraph.SetPoint(pointno, pointno + 1, fitarray[pointno][1])
meangraph.SetPointError(pointno, 0, fitarray[pointno][4])
meanhist.Fill(fitarray[pointno][1])
sigmagraph.SetPoint(pointno, pointno + 1, fitarray[pointno][2])
sigmagraph.SetPointError(pointno, 0, fitarray[pointno][5])
sigmahist.Fill(fitarray[pointno][2])
chisquaregraph.SetPoint(pointno, pointno + 1,
fitarray[pointno][6])
chisquaregraph.SetPointError(pointno, 0, 0)
## iterator.ls()
# Map the data to the pixel layout:
tmp_objarr = []
tmp_objarr.extend(
[meanhist_std, meanhist_double, meanhist_double_neighbour])
tmp_objarr.extend(
[sigmahist_std, sigmahist_double, sigmahist_double_neighbour])
for i in tmp_objarr:
print str(i.GetName())
fill2d(fitarray[:, 0], mapsa_mat, objarr2d[0])
fill2d(fitarray[:, 1], mapsa_mat, objarr2d[1])
fill2d(fitarray[:, 2], mapsa_mat, objarr2d[2])
fill2d(fitarray[:, 6], mapsa_mat, objarr2d[3])
fill1d_edges(objarr2d[1], tmp_objarr[0:3])
fill1d_edges(objarr2d[2], tmp_objarr[3:])
g.cd(str(outfile))
mapsa_fitter_inst.Make_dirs()
mapsa_fitter_inst.Set_run_no(outfile)
if (meas_type == 1):
for idx, it in enumerate(iterator):
if (it.Integral() > 0):
if (idx < channels):
mapsa_fitter_inst.Find_signal(it, idx, 0.0025, 3)
g.cd()
#g.mkdir(str(outfile)+"/Channels")
#g.cd(str(outfile)+"/Channels")
#iterator.Write()
g.cd(str(outfile))
g.mkdir(str(outfile) + "/Overview")
## iterator.First().Print("all")
Maximum = TMath.Power(10, (round(TMath.Log10(stack.GetMaximum())) - 1))
#Minimum = TMath.Power(10,(round(TMath.Log10(stack.GetMinimum()))+1))
Minimum = .1
ROOT.gStyle.SetLabelSize(0.06, "xyz")
ROOT.gStyle.SetTitleSize(0.06, "xyz")
ROOT.gStyle.SetTitleOffset(1.2, "y")
ROOT.gStyle.SetTitleOffset(.825, "x")
ROOT.gStyle.SetPadGridX(1)
ROOT.gStyle.SetPadGridY(1)
ROOT.gStyle.SetOptStat(0)
# ROOT.gStyle.SetPadLeftMargin(.2);
# ROOT.gStyle.SetPadRightMargin(.1);
c1.cd(1)
stack.Draw("nostack hist e1 x0")
stack.GetXaxis().SetRangeUser(0, 256)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLogy()
c2.cd(0)
stack.Draw("nostack hist e1 x0")
#if(outfile.Contains("SR_90_on_top")):
#stack.GetXaxis().SetRangeUser(0,256)
#else:
#stack.GetXaxis().SetRangeUser(0,100)
stack.SetMinimum(Minimum)
stack.SetMaximum(Maximum)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
ROOT.gPad.SetLogy()
ROOT.gPad.Update()
#for idx, it in enumerate(fitfuncs):
## if idx>0 and idx<7:
#c1.cd(1)
#fitfuncs[idx].Draw("same")
#c2.cd(0)
#fitfuncs[idx].DrawCopy("psame")
## it.SetLineColor(idx%9+1)
## it.Draw("same")
#g.cd(str(outfile)+"/Channels")
#it.Write("HitMap_py_"+str(idx+1)+"_fit")
g.cd(str(outfile) + "/Overview")
c1.cd(2)
chisquaregraph.Draw("ap")
c1.cd(3)
normgraph.Draw("ap")
c1.cd(4)
sigmagraph.Draw("ap")
sigmagraph.GetYaxis().SetRangeUser(0, 5)
sigmagraph.GetXaxis().SetRangeUser(0, channels + 1)
c2.cd(2)
sigmagraph.Draw("ap")
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetRightMargin(.05)
c1.cd(5)
meangraph.Draw("ap")
c1.cd(6)
channelcounts.Draw("colz")
channelcounts.GetXaxis().SetRangeUser(0, channels + 1)
# c2.cd(3)
c3.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy = channelcounts.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy"),copy)
#if(outfile.Contains("SR_90_on_top")):
#copy.SetMaximum(100)
#copy.SetMinimum(1)
copy.GetYaxis().SetTitle("DAC Value (a.u.)")
c4.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy1"),copy1)
copy1.GetZaxis().SetTitle("Sigma (a.u.)")
copy1.GetZaxis().SetTitleOffset(1.2)
ROOT.gPad.SetRightMargin(.2)
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c5.cd(0)
ROOT.gStyle.SetOptStat(0)
ROOT.gPad.SetRightMargin(.15)
ROOT.gPad.SetLeftMargin(.15)
ROOT.gPad.SetGrid(0)
#copy1 = chisquare.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1 = sigma_2d.DrawCopy("colz")
#f_GlobalData_Map.Add(ROOT.TObjString("copy2"),copy1)
copy1.GetZaxis().SetTitle("sigma (a.u.)")
if (arr[2] == 'inv'):
copy1.GetXaxis().SetRangeUser(.5, 16.5)
copy1.SetMaximum(5)
copy1.SetMinimum(0)
c1.Update()
c1.Modified()
c2.Update()
c2.Modified()
c3.Update()
c3.Modified()
c4.Update()
c4.Modified()
c5.Update()
c5.Modified()
## c1.SaveAs("double_gauss_same_mean.pdf")
## time.sleep(2)
g.cd(str(outfile) + "/Overview")
#for objs in objarr:
#objs.Write(objs.GetName())
#norm_2d.GetZaxis().SetRangeUser(1E5,2E6)
#mean_2d.GetZaxis().SetRangeUser(54,64)
## norm_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3))-2)), TMath.Power(10,(round(TMath.Log10(norm_2d.GetStdDev(3)))-1)))
## mean_2d.GetZaxis().SetRangeUser(TMath.Power(10,(round(TMath.Log10(mean_2d.mean_2d.GetStdDev(3)))-1))-5,TMath.Power(10,(round(TMath.Log10(mean_2d.GetStdDev(3)))-1))+5)
#sigma_2d.GetZaxis().SetRangeUser(0,5)
#chisquare.GetZaxis().SetRangeUser(0,10000 )
#for objs in objarr2d:
#objs.Write(objs.GetName())
#c1.Write("c1")
#outfile1=outfile+TString(".pdf")
#c2.SaveAs(str(outfile1))
#c2.Write("c2")
#c3.SaveAs("c3"+str(outfile1))
#c3.Write("c3")
#c4.SaveAs("c4"+str(outfile1))
#c4.Write("c4")
## while (TObject(iterator.Next())):
## print iterator.Next().Title()
#stack.Write("stack")
#g.cd(str(outfile))
#channelcounts.Write(str(outfile))
#f.Close()
c1.Close()
c2.Close()
c3.Close()
c4.Close()
c5.Close()
f_GlobalData_Map.DeleteAll()
f.Close()
def MakeOneDHist(pathToDir, distribution):
numFittingSamples = 0
HeaderLabel = TPaveLabel(header_x_left, header_y_bottom, header_x_right,
header_y_top, HeaderText, "NDC")
HeaderLabel.SetTextAlign(32)
HeaderLabel.SetBorderSize(0)
HeaderLabel.SetFillColor(0)
HeaderLabel.SetFillStyle(0)
LumiLabel = TPaveLabel(topLeft_x_left, topLeft_y_bottom, topLeft_x_right,
topLeft_y_top, LumiText, "NDC")
LumiLabel.SetBorderSize(0)
LumiLabel.SetFillColor(0)
LumiLabel.SetFillStyle(0)
NormLabel = TPaveLabel()
NormLabel.SetDrawOption("NDC")
NormLabel.SetX1NDC(topLeft_x_left)
NormLabel.SetX2NDC(topLeft_x_right)
NormLabel.SetBorderSize(0)
NormLabel.SetFillColor(0)
NormLabel.SetFillStyle(0)
NormText = ""
if arguments.normalizeToUnitArea:
NormText = "Scaled to unit area"
elif arguments.normalizeToData:
NormText = "MC scaled to data"
NormLabel.SetLabel(NormText)
YieldsLabel = TPaveText(0.39, 0.7, 0.59, 0.9, "NDC")
YieldsLabel.SetBorderSize(0)
YieldsLabel.SetFillColor(0)
YieldsLabel.SetFillStyle(0)
YieldsLabel.SetTextAlign(12)
RatiosLabel = TPaveText()
RatiosLabel.SetDrawOption("NDC")
RatiosLabel.SetBorderSize(0)
RatiosLabel.SetFillColor(0)
RatiosLabel.SetFillStyle(0)
RatiosLabel.SetTextAlign(32)
Legend = TLegend()
Legend.SetBorderSize(0)
Legend.SetFillColor(0)
Legend.SetFillStyle(0)
fittingIntegral = 0
scaleFactor = 1
HistogramsToFit = []
TargetDataset = distribution['target_dataset']
FittingLegendEntries = []
DataLegendEntries = []
FittingHistogramDatasets = []
Stack_list = []
Stack_list.append(THStack("stack_before", distribution['name']))
Stack_list.append(THStack("stack_after", distribution['name']))
fileName = condor_dir + "/" + distribution['target_dataset'] + ".root"
if not os.path.exists(fileName):
return
inputFile = TFile(fileName)
if inputFile.IsZombie() or not inputFile.GetNkeys():
return
Target = inputFile.Get("OSUAnalysis/" + distribution['channel'] + "/" +
distribution['name']).Clone()
Target.SetDirectory(0)
inputFile.Close()
Target.SetMarkerStyle(20)
Target.SetMarkerSize(0.8)
Target.SetFillStyle(0)
Target.SetLineColor(colors[TargetDataset])
Target.SetLineStyle(1)
Target.SetLineWidth(2)
targetIntegral = Target.Integral()
if (arguments.normalizeToUnitArea and Target.Integral() > 0):
Target.Scale(1. / Target.Integral())
if arguments.rebinFactor:
RebinFactor = int(arguments.rebinFactor)
#don't rebin histograms which will have less than 5 bins or any gen-matching histograms
if Target.GetNbinsX() >= RebinFactor * 5 and Target.GetName().find(
"GenMatch") is -1:
Target.Rebin(RebinFactor)
### formatting target histogram and adding to legend
legendIndex = 0
Legend.AddEntry(Target, labels[TargetDataset], "LEP")
legendIndex = legendIndex + 1
if not outputFile.Get("OSUAnalysis"):
outputFile.mkdir("OSUAnalysis")
if not outputFile.Get("OSUAnalysis/" + distribution['channel']):
outputFile.Get("OSUAnalysis").mkdir(distribution['channel'])
for sample in distribution[
'datasets']: # loop over different samples requested to be fit
dataset_file = "%s/%s.root" % (condor_dir, sample)
inputFile = TFile(dataset_file)
HistogramObj = inputFile.Get(pathToDir + "/" +
distribution['channel'] + "/" +
distribution['name'])
if not HistogramObj:
print "WARNING: Could not find histogram " + pathToDir + "/" + distribution[
'channel'] + "/" + distribution[
'name'] + " in file " + dataset_file + ". Will skip it and continue."
continue
Histogram = HistogramObj.Clone()
Histogram.SetDirectory(0)
inputFile.Close()
if arguments.rebinFactor:
RebinFactor = int(arguments.rebinFactor)
#don't rebin histograms which will have less than 5 bins or any gen-matching histograms
if Histogram.GetNbinsX() >= RebinFactor * 5 and Histogram.GetName(
).find("GenMatch") is -1:
Histogram.Rebin(RebinFactor)
xAxisLabel = Histogram.GetXaxis().GetTitle()
unitBeginIndex = xAxisLabel.find("[")
unitEndIndex = xAxisLabel.find("]")
if unitBeginIndex is not -1 and unitEndIndex is not -1: #x axis has a unit
yAxisLabel = "Entries / " + str(Histogram.GetXaxis().GetBinWidth(
1)) + " " + xAxisLabel[unitBeginIndex + 1:unitEndIndex]
else:
yAxisLabel = "Entries per bin (" + str(
Histogram.GetXaxis().GetBinWidth(1)) + " width)"
if not arguments.makeFancy:
histoTitle = Histogram.GetTitle()
else:
histoTitle = ""
legLabel = labels[sample]
if (arguments.printYields):
yieldHist = Histogram.Integral()
legLabel = legLabel + " (%.1f)" % yieldHist
FittingLegendEntries.append(legLabel)
if (types[sample] == "bgMC"):
numFittingSamples += 1
fittingIntegral += Histogram.Integral()
Histogram.SetLineStyle(1)
if (arguments.noStack):
Histogram.SetFillStyle(0)
Histogram.SetLineColor(colors[sample])
Histogram.SetLineWidth(2)
else:
Histogram.SetFillStyle(1001)
Histogram.SetFillColor(colors[sample])
Histogram.SetLineColor(1)
Histogram.SetLineWidth(1)
elif (types[sample] == "signalMC"):
numFittingSamples += 1
Histogram.SetFillStyle(0)
Histogram.SetLineColor(colors[sample])
Histogram.SetLineStyle(1)
Histogram.SetLineWidth(2)
if (arguments.normalizeToUnitArea and Histogram.Integral() > 0):
Histogram.Scale(1. / Histogram.Integral())
HistogramsToFit.append(Histogram)
FittingHistogramDatasets.append(sample)
#scaling histograms as per user's specifications
if targetIntegral > 0 and fittingIntegral > 0:
scaleFactor = targetIntegral / fittingIntegral
for fittingHist in HistogramsToFit:
if arguments.normalizeToData:
fittingHist.Scale(scaleFactor)
if arguments.normalizeToUnitArea and not arguments.noStack and fittingIntegral > 0:
fittingHist.Scale(1. / fittingIntegral)
elif arguments.normalizeToUnitArea and arguments.noStack and fittingHist.Integral(
) > 0:
fittingHist.Scale(1. / fittingHist.Integral())
def fitf(x, par):
xBin = HistogramsToFit[0].FindBin(x[0])
value = 0.0
for i in range(0, len(HistogramsToFit)):
value += par[i] * HistogramsToFit[i].GetBinContent(xBin) + par[
i +
len(HistogramsToFit)] * HistogramsToFit[i].GetBinError(xBin)
return value
lowerLimit = Target.GetBinLowEdge(1)
upperLimit = Target.GetBinLowEdge(Target.GetNbinsX()) + Target.GetBinWidth(
Target.GetNbinsX())
if 'lowerLimit' in distribution:
lowerLimit = distribution['lowerLimit']
if 'upperLimit' in distribution:
upperLimit = distribution['upperLimit']
func = TF1("fit", fitf, lowerLimit, upperLimit, 2 * len(HistogramsToFit))
for i in range(0, len(HistogramsToFit)):
if 'fixed_datasets' in distribution and distribution['datasets'][
i] in distribution['fixed_datasets']:
func.FixParameter(i, 1.0)
else:
func.SetParameter(i, 1.0)
# func.SetParLimits (i, 0.0, 1.0e2) # comment this out so we don't have to pre-normalize the QCD input sample
func.SetParName(i, labels[FittingHistogramDatasets[i]])
shiftedScaleFactors = []
if arguments.parametricErrors:
# loop over all input histograms and shift them +- 1 sigma
for i in range(0, len(HistogramsToFit)):
sfs = []
# -1 => -1 sigma, +1 => +1 sigma
for j in [-1, 1]:
# loop over the parameters holding the errors for each dataset, fixing all to 0
for k in range(len(HistogramsToFit), 2 * len(HistogramsToFit)):
func.FixParameter(k, 0)
# fix the error of the dataset of interest to +-1
func.FixParameter(i + len(HistogramsToFit), j)
# perform new fit
for k in range(0, distribution['iterations'] - 1):
if j == -1:
print "Scale down " + labels[FittingHistogramDatasets[
i]] + " iteration " + str(k + 1) + "..."
if j == 1:
print "Scale up " + labels[FittingHistogramDatasets[
i]] + " iteration " + str(k + 1) + "..."
Target.Fit("fit", "QEMR0")
Target.Fit("fit", "VEMR0")
# save the new scale factors for each dataset
for k in range(0, len(HistogramsToFit)):
sfs.append(func.GetParameter(k))
shiftedScaleFactors.append(sfs)
# reset the parameters with the errors of each dataset to 0
for i in range(len(HistogramsToFit), 2 * len(HistogramsToFit)):
func.FixParameter(i, 0)
# do the fit to get the central values
for i in range(0, distribution['iterations'] - 1):
print "Iteration " + str(i + 1) + "..."
Target.Fit("fit", "QEMR0")
Target.Fit("fit", "VEMR0")
if arguments.parametricErrors:
# make a list of the largest errors on each contribution by shifting any other contribution
parErrors = []
# loop over all the datasets
for i in range(0, len(HistogramsToFit)):
centralValue = func.GetParameter(i)
maxError = 0
# find the maximum deviation from the central value and save that
for shiftedScaleFactor in shiftedScaleFactors[i]:
currentError = abs(shiftedScaleFactor - centralValue)
if currentError > maxError:
maxError = currentError
parErrors.append(maxError)
finalMax = 0
if not arguments.noStack:
for fittingHist in HistogramsToFit:
finalMax += fittingHist.GetMaximum()
else:
for fittingHist in HistogramsToFit:
if (fittingHist.GetMaximum() > finalMax):
finalMax = fittingHist.GetMaximum()
if (Target.GetMaximum() > finalMax):
finalMax = Target.GetMaximum()
Target.SetMaximum(1.1 * finalMax)
Target.SetMinimum(0.0001)
Canvas = TCanvas(distribution['name'] + "_FitFunction")
Canvas.cd(1)
Target.Draw()
func.Draw("same")
outputFile.cd("OSUAnalysis/" + distribution['channel'])
Canvas.Write()
if arguments.savePDFs:
if histogram == input_histograms[0]:
Canvas.Print(pdfFileName + "(", "pdf")
else:
Canvas.Print(pdfFileName, "pdf")
Target.SetStats(0)
### formatting bgMC histograms and adding to legend
legendIndex = numFittingSamples - 1
for Histogram in reversed(HistogramsToFit):
if (arguments.noStack):
Legend.AddEntry(Histogram, FittingLegendEntries[legendIndex], "L")
else:
Legend.AddEntry(Histogram, FittingLegendEntries[legendIndex], "F")
legendIndex = legendIndex - 1
### Drawing histograms to canvas
makeRatioPlots = arguments.makeRatioPlots
makeDiffPlots = arguments.makeDiffPlots
yAxisMin = 0.0001
if arguments.setYMin:
yAxisMin = float(arguments.setYMin)
### Draw everything to the canvases !!!!
for i in range(0, 2): # 0 => before, 1 => after
integrals = []
ratios = []
errors = []
if i == 1:
# loop over each dataset, saving it's yield and the errors on it
for j in range(0, len(HistogramsToFit)):
integrals.append(HistogramsToFit[j].Integral())
HistogramsToFit[j].Scale(func.GetParameter(j))
ratios.append(func.GetParameter(j))
errors.append(func.GetParError(j))
for fittingHist in HistogramsToFit:
if not arguments.noStack:
Stack_list[i].Add(fittingHist)
#creating the histogram to represent the statistical errors on the stack
if not arguments.noStack:
ErrorHisto = HistogramsToFit[0].Clone("errors")
ErrorHisto.SetFillStyle(3001)
ErrorHisto.SetFillColor(13)
ErrorHisto.SetLineWidth(0)
if i == 1:
Legend.AddEntry(ErrorHisto, "Stat. Errors", "F")
for Histogram in HistogramsToFit:
if Histogram is not HistogramsToFit[0]:
ErrorHisto.Add(Histogram)
if i == 0:
Canvas = TCanvas(distribution['name'] + "_Before")
if i == 1:
Canvas = TCanvas(distribution['name'] + "_After")
if makeRatioPlots or makeDiffPlots:
Canvas.SetFillStyle(0)
Canvas.Divide(1, 2)
Canvas.cd(1)
gPad.SetPad(0, 0.25, 1, 1)
gPad.SetMargin(0.15, 0.05, 0.01, 0.07)
gPad.SetFillStyle(0)
gPad.Update()
gPad.Draw()
if arguments.setLogY:
gPad.SetLogy()
Canvas.cd(2)
gPad.SetPad(0, 0, 1, 0.25)
# format: gPad.SetMargin(l,r,b,t)
gPad.SetMargin(0.15, 0.05, 0.4, 0.01)
gPad.SetFillStyle(0)
gPad.SetGridy(1)
gPad.Update()
gPad.Draw()
Canvas.cd(1)
### finding the maximum value of anything going on the canvas, so we know how to set the y-axis
finalMax = 0
if numFittingSamples is not 0 and not arguments.noStack:
finalMax = ErrorHisto.GetMaximum() + ErrorHisto.GetBinError(
ErrorHisto.GetMaximumBin())
else:
for bgMCHist in HistogramsToFit:
if (bgMCHist.GetMaximum() > finalMax):
finalMax = bgMCHist.GetMaximum()
if (Target.GetMaximum() > finalMax):
finalMax = Target.GetMaximum() + Target.GetBinError(
Target.GetMaximumBin())
finalMax = 1.15 * finalMax
if arguments.setYMax:
finalMax = float(arguments.setYMax)
if not arguments.noStack: # draw stacked background samples
Stack_list[i].SetTitle(histoTitle)
Stack_list[i].Draw("HIST")
Stack_list[i].GetXaxis().SetTitle(xAxisLabel)
Stack_list[i].GetYaxis().SetTitle(yAxisLabel)
Stack_list[i].SetMaximum(finalMax)
Stack_list[i].SetMinimum(yAxisMin)
if makeRatioPlots or makeDiffPlots:
Stack_list[i].GetHistogram().GetXaxis().SetLabelSize(0)
#draw shaded error bands
ErrorHisto.Draw("A E2 SAME")
else: #draw the unstacked backgrounds
HistogramsToFit[0].SetTitle(histoTitle)
HistogramsToFit[0].Draw("HIST")
HistogramsToFit[0].GetXaxis().SetTitle(xAxisLabel)
HistogramsToFit[0].GetYaxis().SetTitle(yAxisLabel)
HistogramsToFit[0].SetMaximum(finalMax)
HistogramsToFit[0].SetMinimum(yAxisMin)
for bgMCHist in HistogramsToFit:
bgMCHist.Draw("A HIST SAME")
Target.Draw("A E X0 SAME")
#legend coordinates, empirically determined :-)
x_left = 0.6761745
x_right = 0.9328859
x_width = x_right - x_left
y_max = 0.9
entry_height = 0.05
if (numFittingSamples is not 0): #then draw the data & bgMC legend
numExtraEntries = 2 # count the target and (lack of) title
Legend.SetX1NDC(x_left)
numExtraEntries = numExtraEntries + 1 # count the stat. errors entry
Legend.SetY1NDC(y_max - entry_height *
(numExtraEntries + numFittingSamples))
Legend.SetX2NDC(x_right)
Legend.SetY2NDC(y_max)
Legend.Draw()
RatiosLabel.SetX1NDC(x_left - 0.1)
RatiosLabel.SetX2NDC(x_right)
RatiosLabel.SetY2NDC(Legend.GetY1NDC() - 0.1)
RatiosLabel.SetY1NDC(RatiosLabel.GetY2NDC() - entry_height *
(numFittingSamples))
# Deciding which text labels to draw and drawing them
drawLumiLabel = False
drawNormLabel = False
offsetNormLabel = False
drawHeaderLabel = False
if not arguments.normalizeToUnitArea: #don't draw the lumi label if there's no data and it's scaled to unit area
drawLumiLabel = True
# move the normalization label down before drawing if we drew the lumi. label
offsetNormLabel = True
if arguments.normalizeToUnitArea or arguments.normalizeToData:
drawNormLabel = True
if arguments.makeFancy:
drawHeaderLabel = True
drawLumiLabel = False
# now that flags are set, draw the appropriate labels
if drawLumiLabel:
LumiLabel.Draw()
if drawNormLabel:
if offsetNormLabel:
NormLabel.SetY1NDC(topLeft_y_bottom - topLeft_y_offset)
NormLabel.SetY2NDC(topLeft_y_top - topLeft_y_offset)
else:
NormLabel.SetY1NDC(topLeft_y_bottom)
NormLabel.SetY2NDC(topLeft_y_top)
NormLabel.Draw()
if drawHeaderLabel:
HeaderLabel.Draw()
YieldsLabel.Clear()
mcYield = Stack_list[i].GetStack().Last().Integral()
dataYield = Target.Integral()
if i == 0:
YieldsLabel.AddText("Before Fit to Data")
if i == 1:
YieldsLabel.AddText("After Fit to Data")
YieldsLabel.AddText("data yield: " + '%.1f' % dataYield)
YieldsLabel.AddText("bkgd yield: " + '%.1f' % mcYield)
YieldsLabel.AddText("data/bkgd: " + '%.2f' % (dataYield / mcYield))
if i == 1:
for j in range(0, len(FittingLegendEntries)):
if abs(ratios[j] - 1) < 0.001 and abs(
errors[j]
) < 0.001: #then it probably was held fixed
continue
if arguments.showFittedYields:
yield_ = ratios[j] * integrals[j]
yielderror_ = errors[j] * yield_
text = FittingLegendEntries[
j] + " yield: " + '%.0f' % yield_ + ' #pm %.0f' % yielderror_
else:
text = FittingLegendEntries[
j] + " ratio: " + '%.2f' % ratios[
j] + ' #pm %.2f' % errors[j]
text = text + " (fit)"
if arguments.parametricErrors:
yield_ = ratios[j] * integrals[j]
yieldParError_ = parErrors[j] * yield_
if arguments.showFittedYields:
text += ' #pm %.2f' % yieldParError_
else:
text += ' #pm %.2f' % parErrors[j]
text = text + " (sys)"
RatiosLabel.AddText(text)
YieldsLabel.Draw()
RatiosLabel.Draw()
# drawing the ratio or difference plot if requested
if (makeRatioPlots or makeDiffPlots):
Canvas.cd(2)
BgSum = Stack_list[i].GetStack().Last()
if makeRatioPlots:
if arguments.ratioRelErrMax:
Comparison = ratioHistogram(Target, BgSum,
arguments.ratioRelErrMax)
else:
Comparison = ratioHistogram(Target, BgSum)
elif makeDiffPlots:
Comparison = Target.Clone("diff")
Comparison.Add(BgSum, -1)
Comparison.SetTitle("")
Comparison.GetYaxis().SetTitle("Data-Bkgd")
Comparison.GetXaxis().SetTitle(xAxisLabel)
Comparison.GetYaxis().CenterTitle()
Comparison.GetYaxis().SetTitleSize(0.1)
Comparison.GetYaxis().SetTitleOffset(0.5)
Comparison.GetXaxis().SetTitleSize(0.15)
Comparison.GetYaxis().SetLabelSize(0.1)
Comparison.GetXaxis().SetLabelSize(0.15)
if makeRatioPlots:
RatioYRange = 1.15
if arguments.ratioYRange:
RatioYRange = float(arguments.ratioYRange)
Comparison.GetYaxis().SetRangeUser(-1 * RatioYRange,
RatioYRange)
elif makeDiffPlots:
YMax = Comparison.GetMaximum()
YMin = Comparison.GetMinimum()
if YMax <= 0 and YMin <= 0:
Comparison.GetYaxis().SetRangeUser(-1.2 * YMin, 0)
elif YMax >= 0 and YMin >= 0:
Comparison.GetYaxis().SetRangeUser(0, 1.2 * YMax)
else: #axis crosses y=0
if abs(YMax) > abs(YMin):
Comparison.GetYaxis().SetRangeUser(
-1.2 * YMax, 1.2 * YMax)
else:
Comparison.GetYaxis().SetRangeUser(
-1.2 * YMin, 1.2 * YMin)
Comparison.GetYaxis().SetNdivisions(205)
Comparison.Draw("E0")
if i == 0:
Canvas.Write(distribution['name'] + "_Before")
if arguments.savePDFs:
pathToDirString = plainTextString(pathToDir)
Canvas.SaveAs(condor_dir + "/fitting_histogram_pdfs/" +
pathToDirString + "/" + distribution['name'] +
"_Before.pdf")
if i == 1:
Canvas.Write(distribution['name'] + "_After")
if arguments.savePDFs:
pathToDirString = plainTextString(pathToDir)
Canvas.SaveAs(condor_dir + "/fitting_histogram_pdfs/" +
pathToDirString + "/" + distribution['name'] +
"_After.pdf")
def main():
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i", "--inputfile", dest="inputfile")
parser.add_option("-N", "--multiplicity", dest="N", type="int", default=3)
parser.add_option("-x", "--exclusive", action="store_true",\
dest="isExclusive", default=False)
parser.add_option("-l", "--label", dest="label", type="string", default="")
(options, args) = parser.parse_args()
N = options.N
isExclusive = options.isExclusive
label_text = options.label
if isExclusive and not (N == 2 or N == 3):
parser.error("Exclusive plot only for N =2 or 3")
import configurations as config
from ROOT import TFile, TCanvas, THStack, TLegend, TPaveText, gStyle
from ModelParser import ModelKey
gStyle.SetPadTopMargin(0.05)
gStyle.SetPadRightMargin(0.05)
suffix = ""
if not isExclusive:
suffix = "up"
sm_files = []
for model in config.sm_models:
f = TFile("%s/%s.root" % (config.sm_dir, model), "READ")
sm_files.append(f)
bh_weights = []
bh_files = []
from BHXsec import BHXsec
xsec = BHXsec()
for model in config.bh_showcase:
f = TFile("%s/%s.root" % (config.bh_dir, model), "READ")
bh_files.append(f)
h = f.Get("plotsNoCut/ST")
nEvents = h.GetEntries()
bh_weights.append(
xsec.get(model) / nEvents * config.integrated_luminosity)
c = TCanvas("ST_Mul%d%s" % (N, suffix), "ST_Mul%d%s" % (N, suffix), 500,
500)
hs = THStack()
infile = TFile(options.inputfile, "READ")
hBkg = infile.Get("Background_N%d%s" % (N, suffix))
gBkg = infile.Get("BackgroundGraph_N%d%s" % (N, suffix))
hData = infile.Get("Data_N%d%s" % (N, suffix))
hBkg = infile.Get("Background_N%d%s" % (N, suffix))
hBkg.SetMarkerSize(0)
hBkg_ = hBkg.Clone("BkgLine")
hBkg.SetFillColor(33)
hBkg.SetLineColor(33)
hBkg_.SetLineWidth(3)
hBkg_.SetLineColor(862)
hs.Add(hBkg, "e3")
legend = TLegend(0.3326613, 0.6419492, 0.9294355, 0.9216102)
legend.SetTextSize(0.02966102)
legend.SetTextFont(42)
legend.SetFillColor(0)
legend.SetLineColor(0)
if isExclusive:
legend.SetHeader("N = %d" % N)
else:
legend.SetHeader("N #geq %d" % N)
legend.AddEntry(hData, "Data", "p")
legend.AddEntry(hBkg_, "Background", "l")
legend.AddEntry(hBkg, "Uncertainty", "f")
legend_sm = TLegend(0.6471774, 0.7669492, 0.8508065, 0.8771186)
legend_sm.SetTextSize(0.02966102)
legend_sm.SetTextFont(42)
legend_sm.SetFillColor(0)
legend_sm.SetLineColor(0)
for i, f in enumerate(bh_files):
h = f.Get("plotsN%d%s/ST" % (N, suffix))
h.Rebin(config.rebin)
h.Scale(bh_weights[i])
# Add background
for ibin in range(h.GetNbinsX()):
h.SetBinContent(ibin+1,\
h.GetBinContent(ibin+1)\
+ hBkg.GetBinContent(ibin+1))
h.SetLineWidth(2)
h.SetLineStyle(i + 2)
hs.Add(h, "hist")
model = ModelKey(config.bh_showcase[i])
bh_legend = "M_{D} = %.1f TeV, M_{BH}^{ min} = %.1f TeV, n = %d" % (\
model.parameter["MD"],
model.parameter["M"],
model.parameter["n"])
legend.AddEntry(h, bh_legend, "l")
if isExclusive:
for i, f in enumerate(sm_files):
h = f.Get("plotsN%d%s/ST" % (N, suffix))
h.Rebin(config.rebin)
h.Scale(config.integrated_luminosity)
h.SetFillColor(config.sm_colors[i])
h.SetLineColor(config.sm_colors[i])
hs.Add(h, "hist")
legend_sm.AddEntry(h, config.sm_models[i], "f")
#hs.Add(hData, "e")
hs.Draw("nostack")
c.SetLogy(1)
hs.GetXaxis().SetTitle("S_{T} (GeV)")
hs.GetYaxis().SetTitle(hData.GetYaxis().GetTitle())
hs.GetYaxis().SetTitleOffset(1.2)
ibin = 0
if isExclusive:
hs.GetXaxis().SetRangeUser(config.fit_range[0], config.maxST)
ibin = hData.FindBin(config.fit_range[0])
else:
hs.GetXaxis().SetRangeUser(config.norm_range[0], config.maxST)
ibin = hData.FindBin(config.norm_range[0])
from Styles import formatUncertainty
formatUncertainty(gBkg)
gBkg.Draw("LX")
hData.Draw("esame")
hs.SetMinimum(5e-2)
if isExclusive:
hs.SetMaximum(hData.GetBinContent(ibin) * 20)
else:
#hs.SetMaximum(4e4)
hs.SetMaximum(hData.GetBinContent(ibin) * 20)
legend.Draw("plain")
if isExclusive:
legend_sm.Draw("plain")
if isExclusive:
cmslabel = TPaveText(0.5544355, 0.5127119, 0.8991935, 0.6292373,
"brNDC")
else:
cmslabel = TPaveText(0.1955645, 0.1631356, 0.5403226, 0.279661,
"brNDC")
cmslabel.AddText(config.cmsTitle)
cmslabel.AddText(config.cmsSubtitle)
cmslabel.SetFillColor(0)
cmslabel.Draw("plain")
label = TPaveText(0.6891129, 0.8644068, 0.9435484, 0.9258475, "brNDC")
label.SetFillColor(0)
label.SetTextSize(0.0529661)
label.AddText(label_text)
label.Draw("plain")
c.Update()
raw_input("Press Enter to continue...")
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 plot(path, pt_title, ecms, xmin, xmax, xbins, ymax):
try:
f_data = TFile(path[0])
t_data = f_data.Get('save')
entries_data = t_data.GetEntries()
logging.info('data entries :' + str(entries_data))
except:
logging.error(path[0] + ' is invalid!')
sys.exit()
try:
f_DDPIPI = TFile(path[1])
t_DDPIPI = f_DDPIPI.Get('save')
entries_DDPIPI = t_DDPIPI.GetEntries()
logging.info('DDPIPI entries :' + str(entries_DDPIPI))
except:
logging.error(path[1] + ' is invalid!')
sys.exit()
try:
f_DDPI = TFile(path[2])
t_DDPI = f_DDPI.Get('save')
entries_DDPI = t_DDPI.GetEntries()
logging.info('DDPI entries :' + str(entries_DDPI))
except:
logging.error(path[2] + ' is invalid!')
sys.exit()
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
content = (xmax - xmin) / xbins * 1000
ytitle = 'Events/%.1f MeV' % content
xtitle = 'RM(D^{+}#pi_{0}^{+}#pi_{0}^{-})(GeV)'
h_data = TH1F('data', 'data', xbins, xmin, float(xmax))
set_histo_style(h_data, xtitle, ytitle, 1, False)
rm_Dpipi_fill(t_data, h_data)
h_DDPIPI = TH1F('DDPIPI', 'DDPIPI', xbins, xmin, float(xmax))
set_histo_style(h_DDPIPI, xtitle, ytitle, 2, True)
rm_Dpipi_fill(t_DDPIPI, h_DDPIPI)
h_DDPI = TH1F('DDPI', 'DDPI', xbins, xmin, float(xmax))
set_histo_style(h_DDPI, xtitle, ytitle, 3, True)
rm_Dpipi_fill(t_DDPI, h_DDPI)
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
F_DDPIPI = scale(ecms, 'DDPIPI')
F_DDPI = scale(ecms, 'DDPI')
h_DDPIPI.Scale(F_DDPIPI)
h_DDPI.Scale(F_DDPI)
h_data.GetYaxis().SetRangeUser(0, ymax)
h_data.Draw('E1')
hs = THStack('hs', 'Stacked')
hs.Add(h_DDPIPI)
hs.Add(h_DDPI)
hs.Draw('same')
h_data.Draw('sameE1')
if ecms > 4320: legend = TLegend(0.2, 0.25, 0.5, 0.55)
if ecms < 4320: legend = TLegend(0.2, 0.2, 0.55, 0.4)
set_legend(legend, h_data, h_DDPIPI, h_DDPI, pt_title, ecms)
legend.Draw()
mbc.SaveAs('./figs/rm_Dpipi_' + str(ecms) + '_raw_sideband.pdf')
raw_input('Enter anything to end...')