def ApplyBinShiftCorrection(self, hist):
"""
Apply bin-shift correction to the input spectrum using an iterative procedure
@param hist: Input spectrum
@return: Bin-shift corrected spectrum
"""
h = deepcopy(hist)
h.SetName("htemp")
# Bin shift correction performed in model specturm * pt
for i in range(1, h.GetNbinsX()+1):
pt = h.GetBinCenter(i)
h.SetBinContent(i, h.GetBinContent(i)*pt)
h.SetBinError(i, h.GetBinError(i)*pt)
result = TGraphErrors(h)
for i in range(0, result.GetN()):
result.GetEX()[i] = 0.
fitfun = TF1("fitfun","([0]*(1.+x/[1])^(-[2])*x)-[3]",0.15,100.0)
fitfun.SetParameter(0,1000)
fitfun.SetParameter(1,1)
fitfun.SetParameter(2,5)
fitfun.FixParameter(3,0)
h.Fit(fitfun,"")
self.__StableFit(h, fitfun, True)
# Iterative approach:
# - Use model to get the mean of the function inside the bin
# - Get the X where the mean is found
# - Use the new coordinate (x,y) for the next iteration of the fit
# for now 10 iterations fixed
for k in range(1, 11):
for i in range(1, h.GetNbinsX()+1):
y = fitfun.Integral(h.GetBinLowEdge(i), h.GetBinUpEdge(i)) / h.GetBinWidth(i)
result.GetX()[i-1] = self.FindX(y, fitfun, h.GetBinLowEdge(i), h.GetBinUpEdge(i))
self.__StableFit(result, fitfun, False)
# Undo multiplication with pt
for i in range(0, result.GetN()):
pt = result.GetX()[i]
result.GetY()[i] /= pt
result.GetEY()[i] /= pt
#remove points that are 0
while result.GetY()[0] < 1.e-99:
result.RemovePoint(0)
bval = 0
for mybin in range(0, result.GetN()+1):
if result.GetY()[bin] < 1.e-99:
bval = mybin
break
while result.RemovePoint(bval) > 0:
continue
return result
def initGraph(self):
TGraphErrors.__init__(self, len(self.x) )
i = 0
for i in range(0, len(self.x) ):
# print i, self.x[i], self.y[i]
self.SetPoint(i, self.x[i], self.y[i])
errx = 0
erry = 0
if len(self.errx)>i:
errx = self.errx[i]
if len(self.erry)>i:
erry = self.erry[i]
self.SetPointError(i, errx, erry)
def ApplyBinShiftCorrectionGeneral(self, hist, fit):
"""
Alternative method for bin shift correction:
- Apply user-default model for bin-shift correction
- don't multiply by pt
:param hist: Input spectrum for the bin shift correction
:param fit: Model for the bin-shift correction
:type hist: TH1
:type fit: TF1
:return: The bin-shift corrected spectrum as graph
:rtype: TGraphErrors
"""
h = deepcopy(hist)
hist.SetName("htemp")
result = TGraphErrors(h);
for i in range(0, result.GetN()):
result.GetEX()[i] = 0.
y = 0
#for now 10 iterations fixes
for k in range(0, 10):
for i in range(1, h.GetNbinsX()+1):
y = fit.Integral(h.GetBinLowEdge(i),h.GetBinUpEdge(i)) / h.GetBinWidth(i)
x = self.FindX(y, fit, h.GetBinLowEdge(i), h.GetBinUpEdge(i))
result.GetX()[i-1] = x
# remove points that are 0
while result.GetY()[0] < 1.e-99:
result.RemovePoint(0)
mybin = 0
for biniter in range(0, result.GetN()):
if result.GetY()[biniter] < 1.e-99:
mybin = biniter
break
while result.RemovePoint(mybin) > 0:
continue
return result
bp_tail_mean = RooRealVar("bp_tail_mean", "bp_tail_mean", 0, -10, 10)
bp_tail_sigma = RooRealVar("bp_tail_sigma", "bp_tail_sigma", 9, 0, 15)
bp_tail_gaussian = RooGaussModel("bp_tail_gaussian", "bp_tail_gaussian",
sp_bp_residuals, bp_tail_mean, bp_tail_sigma)
bp_core_fraction = RooRealVar("bp_core_fraction", "Fraction of Core", .8, 0, 1)
resolution_model = RooAddModel("resolution_model", "resolution_model",
RooArgList(bp_core_gaussian, bp_tail_gaussian),
RooArgList(bp_core_fraction))
canvas.Print("sp_residuals.pdf[")
h_sp_residuals_bp_vs_p_5hit.Draw("colz")
canvas.Print("sp_residuals.pdf(")
g_bp_pos_resolution_vs_p_5hit = TGraphErrors()
for bin in xrange(1, h_sp_residuals_bp_vs_p_5hit.GetXaxis().GetNbins()):
projection = h_sp_residuals_bp_vs_p_5hit.ProjectionY("", bin, bin)
if projection.GetEntries() < 100: continue
sp_bp_residuals_data = RooDataHist("sp_bp_residuals_data",
"Bend Plane Residuals Data",
RooArgList(sp_bp_residuals), projection)
sp_bp_residuals_plot = sp_bp_residuals.frame()
sp_bp_residuals_data.plotOn(sp_bp_residuals_plot)
resolution_model.fitTo(sp_bp_residuals_data)
resolution_model.plotOn(sp_bp_residuals_plot)
#skip bins with too few events
if (projectionHist.Integral() > 20):
tmpDeltaTMean = projectionHist.GetMean()
tmpDeltaTMeanErr = projectionHist.GetMeanError()
dTMean.append(tmpDeltaTMean)
dTMeanErr.append(tmpDeltaTMeanErr)
xArr = array.array('f', x)
xErrArr = array.array('f', xErr)
yArr = array.array('f', dTMean)
yErrArr = array.array('f', dTMeanErr)
effGraph = TGraphErrors(nbins, xArr, yArr, xErrArr, yErrArr)
effGraph.Draw("APE")
effGraph.SetTitle("")
effGraph.GetXaxis().SetTitle("X [mm]")
effGraph.GetXaxis().SetTitleSize(0.05)
effGraph.GetXaxis().SetTitleOffset(0.90)
effGraph.GetXaxis().SetLabelSize(0.03)
#effGraph.GetXaxis().SetRangeUser(23.0,25.0)
effGraph.GetYaxis().SetTitle("Mean #Delta t [ns]")
effGraph.GetYaxis().SetTitleSize(0.05)
effGraph.GetYaxis().SetTitleOffset(0.92)
effGraph.GetYaxis().SetLabelSize(0.03)
effGraph.GetYaxis().SetRangeUser(-2, 2)
title = TLatex()
title.SetTextSize(0.05)
def mistagSFtopEMu(year_, ana_):
if year_ == 2017:
dir = "monohbb.v06.00.05.2017_NCU/withSingleTop/" + ana_ + "/"
if year_ == 2018:
dir = "monohbb.v06.00.05.2018_NCU/withSingleTop/" + ana_ + "/"
print "Top Electron Region"
print " "
openfile1 = TFile(dir + "TopE.root") #
topmatchTopE = openfile1.Get("h_TopMatch")
WmatchTopE = openfile1.Get("h_Wmatch")
unmatchTopE = openfile1.Get("h_unmatch")
wjetsTopE = openfile1.Get("h_sumWJets")
dibosonTopE = openfile1.Get("h_sumDiboson")
unsubtractedDataTopE = openfile1.Get("h_Data")
failMCsubtractTopE = openfile1.Get("h_ttFailed")
passMCsubtractTopE = openfile1.Get("h_ttPassed")
subtractedDataTopE = openfile1.Get("SubtractedData")
datafailTopE = openfile1.Get("SubtractedDataFailed")
datapassTopE = openfile1.Get("SubtractedDataPassed") #
totaldataTopE = openfile1.Get("h_totaldata")
totalMCtopE = openfile1.Get("h_tt")
passedTopEdataBfrSubtract = openfile1.Get("h_Data_Passed")
wjetsTopEpassed = openfile1.Get("h_sumWJetsPassed")
dibosonTopEpassed = openfile1.Get("h_sumDibosonPassed")
failedTopEdataBfrSubtract = openfile1.Get("h_Data_Failed")
wjetsTopEfailed = openfile1.Get("h_sumWJetsFailed")
dibosonTopEfailed = openfile1.Get("h_sumDibosonFailed")
stTopE = openfile1.Get("h_sumST")
stTopEpassed = openfile1.Get("h_sumSTPassed")
stTopEfailed = openfile1.Get("h_sumSTFailed")
print "Top Muon Region"
print " "
openfile2 = TFile(dir + "TopMu.root") #
topmatchTopMu = openfile2.Get("h_TopMatch")
WmatchTopMu = openfile2.Get("h_Wmatch")
unmatchTopMu = openfile2.Get("h_unmatch")
wjetsTopMu = openfile2.Get("h_sumWJets")
dibosonTopMu = openfile2.Get("h_sumDiboson")
unsubtractedDataTopMu = openfile2.Get("h_Data")
failMCsubtractTopMu = openfile2.Get("h_ttFailed")
passMCsubtractTopMu = openfile2.Get("h_ttPassed")
subtractedDataTopMu = openfile2.Get("SubtractedData")
datafailTopMu = openfile2.Get("SubtractedDataFailed")
datapassTopMu = openfile2.Get("SubtractedDataPassed") #
totaldataTopMu = openfile2.Get("h_totaldata")
totalMCtopMu = openfile2.Get("h_tt")
passedTopMudataBfrSubtract = openfile2.Get("h_Data_Passed")
wjetsTopMupassed = openfile2.Get("h_sumWJetsPassed")
dibosonTopMupassed = openfile2.Get("h_sumDibosonPassed")
failedTopMudataBfrSubtract = openfile2.Get("h_Data_Failed")
wjetsTopMufailed = openfile2.Get("h_sumWJetsFailed")
dibosonTopMufailed = openfile2.Get("h_sumDibosonFailed")
stTopMu = openfile2.Get("h_sumST")
stTopMupassed = openfile2.Get("h_sumSTPassed")
stTopMufailed = openfile2.Get("h_sumSTFailed")
print "get histograms from root files: done"
print " "
SubtractedDataPassedTopE = datapassTopE.Clone("SubtractedDataPassedTopE")
SubtractedDataPassedTopMu = datapassTopMu.Clone(
"SubtractedDataPassedTopMu")
#merge histogram"
print "merge histograms"
print " "
topmatchMerge = topmatchTopE + topmatchTopMu
WmatchMerge = WmatchTopE + WmatchTopMu
unmatchMerge = unmatchTopE + unmatchTopMu
wjetsMerge = wjetsTopE + wjetsTopMu
stMerge = stTopE + stTopMu
dibosonMerge = dibosonTopE + dibosonTopMu
unsubtractedDataMerge = unsubtractedDataTopE + unsubtractedDataTopMu
failMCsubtractMerge = failMCsubtractTopE.Clone("failMCsubtractMerge")
failMCsubtractMerge = failMCsubtractMerge + failMCsubtractTopMu
passMCsubtractMerge = passMCsubtractTopE.Clone("passMCsubtractMerge")
passMCsubtractMerge = passMCsubtractMerge + passMCsubtractTopMu
subtractedDataMerge = subtractedDataTopE + subtractedDataTopMu
ttData_fraction = arr.array('d')
ttData_error = arr.array('d')
totaldataMerge = totaldataTopE + totaldataTopMu
totaldata = totaldataMerge.Integral()
totaldataMerge.Rebin(14)
datafailMerge = datafailTopE + datafailTopMu
faildata = datafailMerge.Integral()
datafailMerge.Rebin(14)
datafailMerge.Sumw2()
datafailMerge.Divide(totaldataMerge)
frac_ttData_fail = datafailMerge.Integral()
ttData_fraction.append(frac_ttData_fail)
ttData_error.append(datafailMerge.GetBinError(1))
datapassMerge = datapassTopE + datapassTopMu
passdata = datapassMerge.Integral()
datapassMerge.Rebin(14)
datapassMerge.Sumw2()
datapassMerge.Divide(totaldataMerge)
frac_ttData_pass = datapassMerge.Integral()
ttData_fraction.append(frac_ttData_pass)
ttData_error.append(datapassMerge.GetBinError(1))
ttMC_fraction = arr.array('d')
ttMC_error = arr.array('d')
totalMCmerge = totalMCtopE + totalMCtopMu
totalMCmerge.Rebin(14)
MCfailTopE = failMCsubtractTopE.Clone("MCfailTopE")
MCfailTopMu = failMCsubtractTopMu.Clone("MCfailTopMu")
MCfailMerge = MCfailTopE + MCfailTopMu
MCfailMerge.Rebin(14)
MCfailMerge.Sumw2()
MCfailMerge.Divide(totalMCmerge)
frac_Failed_fin = MCfailMerge.Integral()
ttMC_fraction.append(frac_Failed_fin)
ttMC_error.append(MCfailMerge.GetBinError(1))
MCpassTopE = passMCsubtractTopE.Clone("MCpassTopE")
MCpassTopMu = passMCsubtractTopMu.Clone("MCpassTopMu")
MCpassMerge = MCpassTopE + MCpassTopMu
MCpassMerge.Rebin(14)
MCpassMerge.Sumw2()
MCpassMerge.Divide(totalMCmerge)
frac_Passed_fin = MCpassMerge.Integral()
ttMC_fraction.append(frac_Passed_fin)
ttMC_error.append(MCpassMerge.GetBinError(1))
#print "\nttMC_fraction:", ttMC_fraction
#print "ttMC_error:", ttMC_error
sfMerge = datapassMerge.Clone("sfMerge")
sfMerge.Sumw2()
sfMerge.Divide(MCpassMerge)
stacklist = []
stacklist.append(dibosonMerge)
stacklist.append(stMerge)
stacklist.append(wjetsMerge)
stacklist.append(unmatchMerge)
stacklist.append(WmatchMerge)
stacklist.append(topmatchMerge)
print "merge histograms: done"
print " "
print "draw histograms"
print " "
#----------------------- canvas 1 -----------------------#
c1 = TCanvas("c1", "", 800, 900) #width-height
c1.SetTopMargin(0.4)
c1.SetBottomMargin(0.05)
c1.SetRightMargin(0.1)
c1.SetLeftMargin(0.15)
gStyle.SetOptStat(0)
binvalues1 = []
for i in range(14):
binvalue = unsubtractedDataMerge.GetBinContent(i)
binvalues1.append(binvalue)
totalmax = max(binvalues1) + 100
padMain = TPad("padMain", "", 0.0, 0.25, 1.0, 0.97)
padMain.SetTopMargin(0.4)
padMain.SetRightMargin(0.05)
padMain.SetLeftMargin(0.17)
padMain.SetBottomMargin(0.03)
padMain.SetTopMargin(0.1)
padRatio = TPad("padRatio", "", 0.0, 0.0, 1.0, 0.25)
padRatio.SetRightMargin(0.05)
padRatio.SetLeftMargin(0.17)
padRatio.SetTopMargin(0.05)
padRatio.SetBottomMargin(0.3)
padMain.Draw()
padRatio.Draw()
padMain.cd()
gPad.GetUymax()
leg1 = myLegend(coordinate=[0.45, 0.57, 0.65, 0.6])
unsubtractedDataMerge.SetMaximum(totalmax)
unsubtractedDataMerge.SetLineColor(1)
unsubtractedDataMerge.SetMarkerStyle(20)
unsubtractedDataMerge.SetMarkerSize(1.5)
unsubtractedDataMerge.GetXaxis().SetLabelSize(0)
unsubtractedDataMerge.GetXaxis().SetTitleSize(0)
unsubtractedDataMerge.GetXaxis().SetTitle("DDB")
unsubtractedDataMerge.GetYaxis().SetTitle("Events/Bin")
leg1.AddEntry(unsubtractedDataMerge, "Data", "lep")
unsubtractedDataMerge.Draw("e1")
stackhisto = myStack(colorlist_=[627, 800, 854, 813, 822, 821],
backgroundlist_=stacklist,
legendname_=[
"Diboson", "Single Top", "W+Jets",
"Top (unmtch.)", "Top (W-mtch.)", "Top (mtch.)"
])
stackhisto[0].Draw("histsame")
unsubtractedDataMerge.Draw("e1same")
stackhisto[1].Draw()
leg1.Draw()
lt = TLatex()
lt.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
if ana_ == "Inclusive":
lt.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt.DrawLatexNDC(0.17, 0.92,
"#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
padRatio.cd()
gPad.GetUymax()
h_totalBkg = topmatchMerge.Clone("h_totalBkg")
h_totalBkg = h_totalBkg + WmatchMerge + unmatchMerge + wjetsMerge + dibosonMerge
ratio = dataPredRatio(data_=unsubtractedDataMerge, totalBkg_=h_totalBkg)
ratio.SetLineColor(1)
ratio.SetLineWidth(3)
ratio.SetMarkerSize(1.5)
ratio.GetXaxis().SetLabelSize(0.13)
ratio.GetXaxis().SetTitleOffset(1)
ratio.GetXaxis().SetTitleSize(0.13)
ratio.GetXaxis().SetTickLength(0.1)
ratio.GetYaxis().SetLabelSize(0.12)
ratio.GetYaxis().SetTitleOffset(0.5)
ratio.GetYaxis().SetTitleSize(0.13)
ratio.GetYaxis().SetNdivisions(405)
ratio.GetYaxis().SetTitle("#frac{Data-Pred}{Pred}")
ratio.GetXaxis().SetTitle("DDB")
ratio.Draw("e1")
c1.SaveAs(dir + "Merge_all.pdf") #
#----------------------- canvas 2 -----------------------#
c2 = myCanvas(c="c2")
gPad.GetUymax()
leg2 = myLegend()
binvalues2 = []
for i in range(14):
binvalue = subtractedDataMerge.GetBinContent(i)
binvalues2.append(binvalue)
ttmax = max(binvalues2) + 50
failMCsubtractMerge.SetMaximum(ttmax)
failMCsubtractMerge.SetFillColor(821)
failMCsubtractMerge.SetLineColor(821) #922
failMCsubtractMerge.GetXaxis().SetTitle("DDB")
failMCsubtractMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(failMCsubtractMerge, "t#bar{t}", "f")
passMCsubtractMerge.SetFillColor(622)
passMCsubtractMerge.SetLineColor(622)
#passMCsubtractMerge.GetXaxis().SetTitle("DDB")
#passMCsubtractMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(passMCsubtractMerge, "t#bar{t} mistag", "f")
subtractedDataMerge.SetLineColor(1)
subtractedDataMerge.SetMarkerStyle(20)
subtractedDataMerge.SetMarkerSize(1.5)
#subtractedDataMerge.GetXaxis().SetTitle("DDB")
#subtractedDataMerge.GetYaxis().SetTitle("Events/Bin")
leg2.AddEntry(subtractedDataMerge, "Subtracted Data", "lep")
failMCsubtractMerge.Draw("hist")
passMCsubtractMerge.Draw("histsame")
subtractedDataMerge.Draw("e1same")
leg2.Draw()
lt2 = TLatex()
if ana_ == "Inclusive":
lt2.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt2.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt2.DrawLatexNDC(0.23, 0.85,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt2.DrawLatexNDC(0.23, 0.8, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt2.DrawLatexNDC(0.23, 0.75, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt2.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt2.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
c2.SaveAs(dir + "Merged_subtrac.pdf") #
#----------------------- canvas 3 -----------------------#
c3 = myCanvas(c="c3", size=[700, 900])
pad1 = TPad("pad1", "", 0.01, 0.25, 0.93, 1.0)
pad1.SetTopMargin(0.1)
pad1.SetRightMargin(0.05)
pad1.SetLeftMargin(0.17)
pad1.SetBottomMargin(0.05)
pad2 = TPad("pad2", "", 0.0, 0.0, 0.375, 0.24)
pad2.SetTopMargin(0.0)
pad2.SetRightMargin(0.0)
pad2.SetLeftMargin(0.0)
pad2.SetBottomMargin(0.0)
pad3 = TPad("pad3", "", 0.38, 0.025, 0.94, 0.25)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin(0.0)
pad2.SetLeftMargin(0.45)
pad2.SetBottomMargin(0.2)
pad1.Draw()
pad2.Draw()
pad3.Draw()
#* Pad 1 *#
pad1.cd()
leg3 = myLegend(coordinate=[0.65, 0.4, 0.75, 0.5])
xaxisname = arr.array('d', [1, 2])
zero1 = np.zeros(2)
gPad.Modified()
gPad.SetGridy()
gr1 = TGraphErrors(2, xaxisname, ttMC_fraction, zero1, ttMC_error)
gr1.SetTitle("t#bar{t}")
gr1.SetLineColor(870)
gr1.SetLineWidth(3)
gr1.SetMarkerStyle(20)
gr1.SetMarkerColor(870)
leg3.AddEntry(gr1, "t#bar{t}", "lep")
gr2 = TGraphErrors(2, xaxisname, ttData_fraction, zero1, ttData_error)
gr2.SetTitle("t#bar{t} Data")
gr2.SetLineColor(1)
gr2.SetLineWidth(2)
gr2.SetMarkerStyle(20)
gr2.SetMarkerColor(1)
leg3.AddEntry(gr2, "t#bar{t} Data", "lep")
mg = TMultiGraph("mg", "")
mg.Add(gr1)
mg.Add(gr2)
mg.GetHistogram().SetMaximum(1.5)
mg.GetHistogram().SetMinimum(0)
mg.GetYaxis().SetTitle("Fraction")
mg.GetXaxis().SetLimits(0, 3)
mg.GetXaxis().SetTickLength(0.03)
mg.GetXaxis().SetNdivisions(103)
mg.GetXaxis().ChangeLabel(2, -1, -1, -1, -1, -1, "Fail")
mg.GetXaxis().ChangeLabel(1, -1, 0)
mg.GetXaxis().ChangeLabel(-1, -1, 0)
mg.GetXaxis().ChangeLabel(3, -1, -1, -1, -1, -1, "Pass")
mg.Draw("AP")
leg3.Draw()
lt3 = TLatex()
if ana_ == "Inclusive":
lt3.DrawLatexNDC(0.17, 0.92, "#scale[0.7]{#bf{" + ana_ + "}}")
if ana_ == "PT-200-350" or ana_ == "PT-350-500" or ana_ == "PT-500-2000":
words = ana_.split("-")
if words[2] == "2000":
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-Inf GeV}}")
else:
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{p_{T} " + words[1] + "-" +
words[2] + " GeV}}")
else:
words = ana_.split("-")
lt3.DrawLatexNDC(
0.17, 0.92, "#scale[0.7]{#bf{" + words[0] + " " + words[1] + "-" +
words[2] + " GeV}}")
lt3.DrawLatexNDC(0.19, 0.855,
"#scale[0.8]{CMS} #scale[0.65]{#bf{#it{Internal}}}")
lt3.DrawLatexNDC(0.19, 0.805, "#scale[0.7]{#bf{t#bar{t} CR (e+#mu)}}")
lt3.DrawLatexNDC(0.19, 0.755, "#scale[0.5]{#bf{2-prong (bq) enriched}}")
if year_ == 2017:
lt3.DrawLatexNDC(0.71, 0.92, "#scale[0.7]{#bf{41.5 fb^{-1} (13 TeV)}}")
if year_ == 2018:
lt3.DrawLatexNDC(0.71, 0.92,
"#scale[0.7]{#bf{58.827 fb^{-1} (13 TeV)}}")
lt3.Draw()
pad1.Update()
#* Pad 2 *#
pad2.cd()
MCpassMerge.SetFillColor(0)
MCpassMerge.SetLineColor(870)
MCpassMerge.SetLineWidth(3)
MCpassMerge.SetMarkerColor(870)
MCpassMerge.SetMarkerStyle(20)
MCpassMerge.GetYaxis().SetTitle("Fraction")
MCpassMerge.GetYaxis().SetTitleSize(0.09)
MCpassMerge.GetYaxis().SetLabelSize(0.1)
MCpassMerge.GetYaxis().SetNdivisions(404)
MCpassMerge.SetMaximum(0.3)
MCpassMerge.SetMinimum(0.0)
MCpassMerge.GetXaxis().SetTitle("")
MCpassMerge.GetXaxis().SetLabelOffset(0.02)
MCpassMerge.GetXaxis().SetLabelSize(0.09)
MCpassMerge.GetXaxis().SetNdivisions(104)
MCpassMerge.GetXaxis().ChangeLabel(2, -1, -1, -1, -1, -1, "Pass")
MCpassMerge.GetXaxis().ChangeLabel(1, -1, 0)
MCpassMerge.GetXaxis().ChangeLabel(-1, -1, 0)
datapassMerge.SetFillColor(0)
datapassMerge.SetLineColor(1)
datapassMerge.SetLineWidth(2)
datapassMerge.SetMarkerColor(1)
datapassMerge.SetMarkerStyle(20)
MCpassMerge.Draw("e1")
datapassMerge.Draw("e1histsame")
#* Pad 3 *#
pad3.cd()
SF = sfMerge.Integral()
print "******"
print "mistag SF:", SF
SFfinal = round(SF, 3)
SFtext = "SF = " + str(SFfinal)
mistagSFmax = SF + 0.2
mistagSFmin = SF - 0.2
sfMerge.SetLineColor(797)
sfMerge.SetMarkerColor(797)
sfMerge.SetLineWidth(3)
sfMerge.SetMaximum(mistagSFmax)
sfMerge.SetMinimum(mistagSFmin)
sfMerge.GetXaxis().SetTitle(" ")
sfMerge.GetXaxis().SetLabelOffset(999)
sfMerge.GetXaxis().SetLabelSize(0)
sfMerge.GetXaxis().SetTickLength(0)
sfMerge.GetYaxis().SetLabelSize(0.1)
sfMerge.GetYaxis().SetNdivisions(404)
sfMerge.GetYaxis().SetTitle(" ")
sfMerge.Draw("e1hist")
pt = TPaveText(0.21, 0.72, 0.31, 0.8, "brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(42)
pt.SetTextSize(0.1)
pt.AddText(SFtext)
pt.Draw()
c3.SaveAs(dir + "Merge_SF.pdf") #
passedBfrSubtactDataMerge = (passedTopEdataBfrSubtract +
passedTopMudataBfrSubtract).Integral()
failedBfrSubtractDataMerge = (failedTopEdataBfrSubtract +
failedTopMudataBfrSubtract).Integral()
passbackground = (wjetsTopEpassed + wjetsTopMupassed + dibosonTopEpassed +
dibosonTopMupassed + stTopEpassed +
stTopMupassed).Integral()
failbackground = (wjetsTopEfailed + wjetsTopMufailed + dibosonTopEfailed +
dibosonTopMufailed + stTopEfailed +
stTopMufailed).Integral()
totalbackground = (wjetsMerge + dibosonMerge + stMerge).Integral()
#get the statistical uncertainty#
dx = ttData_error[1]
print "data efficiency error", dx
dy = ttMC_error[1]
print "MC efficiency error", dy
x = datapassMerge.Integral()
y = MCpassMerge.Integral()
statUnc = TMath.Sqrt(((dx**2) / (y**2)) + ((x**2) * (dy**2) / (y**4)))
#print "statistical Uncertainty in Top (e+muon) CR", statUnc
#print " "
print "relative statistical Uncertainty in Top (e+muon) CR", statUnc / SF * 100, " %"
print " "
print "DDB Mistag SF and stat: ", round(SF, 3), " +- ", round(statUnc,
3), " (stat)"
#print "theoretical statistical uncertainty of data efficiency", TMath.Sqrt((x*(1-x))/(subtractedDataMerge.Integral()))
#print "theoretical statistical uncertainty of MC efficiency", TMath.Sqrt((y*(1-y))/(totalMCmerge.Integral()))
#print " "
header = ["Process", "Number of Events", "Top (e+muon)"]
row1 = [
" ", "DDB mistag SF",
str(SFfinal) + " +- " + str(round(statUnc, 3)) + " (stat)"
]
row2 = ["tt MC", "Pass (not normalized)", ""]
row3 = [
" ", "Pass (normalized)",
str(round(passMCsubtractMerge.Integral(), 2))
]
row4 = [" ", "Fail (not normalized)", ""]
row5 = [
" ", "Fail (normalized)",
str(round(failMCsubtractMerge.Integral(), 2))
]
row6 = [" ", "Total (not normalized)", ""]
row7 = [" ", "Total (normalized)", str(round(totalMCmerge.Integral(), 2))]
inforMC = [row2, row3, row4, row5, row6, row7]
row8 = [
"tt DATA", "Pass (before subtraction)",
str(round(passedBfrSubtactDataMerge, 2))
]
row9 = [" ", "Pass (after subtraction)", str(round(passdata, 2))]
row10 = [
" ", "Fail (before subtraction)",
str(round(failedBfrSubtractDataMerge, 2))
]
row11 = [" ", "Fail (after subtraction)", str(round(faildata, 2))]
row12 = [
" ", "Total (before subtraction)",
str(round(unsubtractedDataMerge.Integral(), 2))
]
row13 = [" ", "Total (after subtraction)", str(round(totaldata, 2))]
inforDATA = [row8, row9, row10, row11, row12, row13]
row14 = ["Background", "Pass (normalized)", str(round(passbackground, 2))]
row15 = [" ", "Fail (normalized)", str(round(failbackground, 2))]
row16 = [" ", "Total (normalized)", str(round(totalbackground, 2))]
inforBKG = [row14, row15, row16]
DDB_mistagSF.makeTable(header, row1, inforMC, inforDATA, inforBKG)
def runGlobalFit(country, active, totalPopulation, symptomaticFraction,
transmissionProbability, recoveryRate, doSmearing, doFit):
tStart = 0
tStop = 9 + 6 + 12 + 42 + 61 + 40 + 50 + 200
timeList = [
9, 9 + 6, 9 + 6 + 12, 9 + 6 + 12 + 42, 9 + 6 + 12 + 42 + 61,
9 + 6 + 12 + 42 + 61 + 40, 9 + 6 + 12 + 42 + 61 + 40 + 50, tStop
]
ntuple = [
tStart, tStop, totalPopulation, symptomaticFraction,
transmissionProbability, recoveryRate
]
################
# Active cases #
################
myCanvActive = TCanvas('myCanvActive_' + country,
'Active cases ' + country)
xValues = [
i for i in range(len(active.keys())) if i >= tStart and i <= tStop
]
yValues = [
active[k] for i, k in enumerate(sorted(active.keys()))
if i >= tStart and i <= tStop
]
erryValues = assignErrors(yValues)
myGraphActive = TGraphErrors()
myGraphActive.SetMarkerStyle(20)
for i in range(len(xValues)):
myGraphActive.SetPoint(myGraphActive.GetN(), xValues[i], yValues[i])
myGraphActive.SetPointError(myGraphActive.GetN() - 1, 0, erryValues[i])
myGraphActive.Draw('APE1')
myGraphActive.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphActive.GetHistogram().GetYaxis().SetTitle(
'Active cases affected by CoViD-19')
ntuple.extend([
0, xValues, yValues, erryValues, timeList, 1894, 16052, 0.435, 0.438,
0.308, 0.216, 0.054, 0.435 / 2.5, 0.435 / 1.5, 0.435
]) # 0.0133, 0.175, 0.217, 0.393
if doFit == True:
###################
# Build the model #
###################
evActive = evolution([ntuple[11], ntuple[12], ntuple[13]], tStart,
timeList[0], totalPopulation, recoveryRate,
symptomaticFraction, transmissionProbability)
evolutions = [
evolution([0, 0, ntuple[14 + i]], timeList[i], timeList[i + 1], 0,
recoveryRate, symptomaticFraction,
transmissionProbability) for i in range(3)
]
evolutions.extend([
evolution([0, 0, ntuple[14 + 3]], timeList[3], timeList[4], 0,
0.035, symptomaticFraction, transmissionProbability / 8)
])
evolutions.extend([
evolution([0, 0, ntuple[14 + 4]], timeList[4], timeList[5], 0,
recoveryRate, symptomaticFraction,
transmissionProbability / 2.5)
])
evolutions.extend([
evolution([0, 0, ntuple[14 + 5]], timeList[5], timeList[6], 0,
recoveryRate, symptomaticFraction,
transmissionProbability / 1.5)
])
evolutions.extend([
evolution([0, 0, ntuple[14 + 6]], timeList[6], timeList[7], 0,
recoveryRate, symptomaticFraction,
transmissionProbability)
])
istat, parValues, parNames = evActive.runGlobalOptimization(
evolutions, xValues, yValues, erryValues, [0, 1, 2, 3, 4, 5],
doSmearing)
evActive.evolveGlobal(evolutions, evolutions[-1].tStop, parValues,
True)
if doSmearing == True:
evActive.smearing()
evActive.setFitFun(evActive.evolveLookUpWrapper, tStart, tStop,
parNames, parValues)
evActiveGraphN = evActive.getGraphN()
evActiveGraphN.Draw('PL same')
statActive = evActive.addStats(parNames, parValues)
print(
'==> Active cases, history active cases * dt, p-infected, Carrying capacity, total population alive',
evActive.evolveGlobal(evolutions, tStop, parValues), 'at day',
tStop)
print(
'==> Percentage population with antibodies',
round(100. *
evActive.totalInfectedGlobal(evolutions, tStop, parValues) /
totalPopulation), '% at day', tStop)
now = TLine(
len(active) - 1, 0,
len(active) - 1, evActive.fitFun.Eval(len(active) - 1))
now.SetLineColor(4)
now.SetLineWidth(2)
now.Draw('same')
willbe = TLine(evActive.fitFun.GetMaximumX(), 0,
evActive.fitFun.GetMaximumX(),
evActive.fitFun.GetMaximum())
willbe.SetLineColor(6)
willbe.SetLineWidth(2)
willbe.Draw('same')
myCanvActiveR0 = TCanvas('myCanvActiveR0_' + country, 'R0 ' + country)
evActiveGraphR0 = evActive.getGraphR0(evActiveGraphN)
evActiveGraphR0.Draw('APL')
evActiveGraphR0.GetHistogram().GetXaxis().SetTitle('Time (days)')
evActiveGraphR0.GetHistogram().GetYaxis().SetTitle('R')
myCanvActiveR0.SetGrid()
myCanvActiveR0.Modified()
myCanvActiveR0.Update()
myCanvActive.SetGrid()
myCanvActive.Modified()
myCanvActive.Update()
if doFit == False:
return [ntuple, myCanvActive, myGraphActive]
return [
ntuple, myCanvActive, myGraphActive, evActive, evActiveGraphN,
statActive, now, willbe, myCanvActiveR0, evActiveGraphR0
]
trackEfficiency
), "Purity= ", 100 - np.mean(correctedTrackAmbiguity), "%"
print "Errors: Eff. Error=", np.std(trackEfficiency) / math.sqrt(
NLoops), "Purity Err", np.std(correctedTrackAmbiguity) / math.sqrt(
NLoops), "%"
_Efficiency.append(np.mean(trackEfficiency))
_Purity.append(100 - np.mean(correctedTrackAmbiguity))
_NumberOfProtons.append(nMeanProton)
_EfficiencyErr.append(np.std(trackEfficiency) / math.sqrt(NLoops))
_PurityErr.append(np.std(correctedTrackAmbiguity) / math.sqrt(NLoops))
_NumberOfProtonsErr.append(0.0)
canvas1 = TCanvas('c1', "mycanvas", 200, 10, 700, 500)
effGraph = TGraphErrors(len(_NumberOfProtons), _NumberOfProtons, _Efficiency,
_NumberOfProtonsErr, _EfficiencyErr)
effGraph.SetMarkerStyle(2)
effGraph.SetMarkerSize(2)
effGraph.GetXaxis().SetTitle("Mean N Proton")
effGraph.GetYaxis().SetTitle("Efficiency/Purity (%)")
effGraph.SetTitle("Efficiency")
purGraph = TGraphErrors(len(_NumberOfProtons), _NumberOfProtons, _Purity,
_NumberOfProtonsErr, _PurityErr)
purGraph.SetMarkerStyle(5)
purGraph.SetLineColor(2)
purGraph.SetMarkerColor(2)
purGraph.SetMarkerSize(2)
purGraph.GetXaxis().SetTitle("Mean N Proton")
purGraph.GetYaxis().SetTitle("Purity")
purGraph.SetTitle("Purity")
def main(argv):
#Usage controls from OptionParser
parser_usage = "outputfile.root"
parser = OptionParser(usage=parser_usage)
(options, args) = parser.parse_args(argv)
if (len(args) != 1):
parser.print_help()
return
#Get files, add to another dictionary
run_dict = get_run_dict()
f_list = {}
for runnum in run_dict:
print "Run: " + str(runnum)
filesmatching = glob.glob(base_directory + "*_" + str(runnum) +
"*.root")
if (len(filesmatching) != 1):
print "ERROR FINDING FILE: " + base_directory + "hd_root_" + str(
runnum) + "*.root"
print "exiting..."
return
f = TFile.Open(filesmatching[0])
f_list[runnum] = f
rocid_arr = array('i',
[31, 32, 34, 35, 37, 38, 40, 41
]) #These are the rocids associated with BCAL fADC250s
slot_arr = array(
'i',
[3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16
]) #These are the slots used for BCAL fADC250s, common to all crates
channel_arr = array(
'i',
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
]) #These are the channels used on each slot, common to all slots
if (year == "2015"):
rocid_arr = array(
'i', [31, 32, 34, 35
]) #These are the rocids associated with BCAL fADC250s
h_layer1_US_RMS_arr = []
h_layer2_US_RMS_arr = []
h_layer3_US_RMS_arr = []
h_layer4_US_RMS_arr = []
h_global_US_RMS_arr = []
h_layer1_DS_RMS_arr = []
h_layer2_DS_RMS_arr = []
h_layer3_DS_RMS_arr = []
h_layer4_DS_RMS_arr = []
h_global_DS_RMS_arr = []
h_global_all_RMS_arr = []
#A whole big bunch of arrays for TGraphErrors
mean_layer1_US_RMS = array('d', [])
mean_layer2_US_RMS = array('d', [])
mean_layer3_US_RMS = array('d', [])
mean_layer4_US_RMS = array('d', [])
mean_global_US_RMS = array('d', [])
mean_layer1_DS_RMS = array('d', [])
mean_layer2_DS_RMS = array('d', [])
mean_layer3_DS_RMS = array('d', [])
mean_layer4_DS_RMS = array('d', [])
mean_global_DS_RMS = array('d', [])
mean_global_all_RMS = array('d', [])
mean_err_layer1_US_RMS = array('d', [])
mean_err_layer2_US_RMS = array('d', [])
mean_err_layer3_US_RMS = array('d', [])
mean_err_layer4_US_RMS = array('d', [])
mean_err_global_US_RMS = array('d', [])
mean_err_layer1_DS_RMS = array('d', [])
mean_err_layer2_DS_RMS = array('d', [])
mean_err_layer3_DS_RMS = array('d', [])
mean_err_layer4_DS_RMS = array('d', [])
mean_err_global_DS_RMS = array('d', [])
mean_err_global_all_RMS = array('d', [])
bias_arr = array('d', [])
bias_err_arr = array('d', [])
mean_layer1_diff_RMS = array('d', [])
mean_layer2_diff_RMS = array('d', [])
mean_layer3_diff_RMS = array('d', [])
mean_layer4_diff_RMS = array('d', [])
mean_global_diff_RMS = array('d', [])
mean_err_layer1_diff_RMS = array('d', [])
mean_err_layer2_diff_RMS = array('d', [])
mean_err_layer3_diff_RMS = array('d', [])
mean_err_layer4_diff_RMS = array('d', [])
mean_err_global_diff_RMS = array('d', [])
#Loop over all files
for curr_run in f_list:
print "Runnum: " + str(curr_run)
print "bias V: " + str(run_dict[curr_run])
curr_file = f_list[curr_run]
curr_bias = run_dict[curr_run]
#Create histograms
hist_min = 0.
hist_max = 4.
h_layer1_US_RMS = TH1F("h_layer1_US_RMS_bias" + str(curr_bias),
"RMS of US Layer 1 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer2_US_RMS = TH1F("h_layer2_US_RMS_bias" + str(curr_bias),
"RMS of US Layer 2 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer3_US_RMS = TH1F("h_layer3_US_RMS_bias" + str(curr_bias),
"RMS of US Layer 3 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer4_US_RMS = TH1F("h_layer4_US_RMS_bias" + str(curr_bias),
"RMS of US Layer 4 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_global_US_RMS = TH1F("h_global_US_RMS_bias" + str(curr_bias),
"RMS of All US BCAL Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer1_DS_RMS = TH1F("h_layer1_DS_RMS_bias" + str(curr_bias),
"RMS of DS Layer 1 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer2_DS_RMS = TH1F("h_layer2_DS_RMS_bias" + str(curr_bias),
"RMS of DS Layer 2 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer3_DS_RMS = TH1F("h_layer3_DS_RMS_bias" + str(curr_bias),
"RMS of DS Layer 3 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_layer4_DS_RMS = TH1F("h_layer4_DS_RMS_bias" + str(curr_bias),
"RMS of DS Layer 4 Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_global_DS_RMS = TH1F("h_global_DS_RMS_bias" + str(curr_bias),
"RMS of All DS BCAL Channels;RMS (ADC units)",
1000, hist_min, hist_max)
h_global_all_RMS = TH1F("h_global_all_RMS_bias" + str(curr_bias),
"RMS of All BCAL Channels;RMS (ADC units)",
1000, hist_min, hist_max)
#Loop over all channels
for rocid in rocid_arr:
for slot in slot_arr:
for channel in channel_arr:
hist = get_hist_from_rocid_slot_channel(
curr_file, rocid, slot, channel)
quadrant = getquadrant(rocid)
layer = getlayer(slot, channel)
is_downstream = getend(slot, channel)
# if(quadrant==3): continue #Skip quadrant 3, it has LEDs firing (in 2019 at least)
if (quadrant == 3 or quadrant == 2 or quadrant == 4):
continue #Skip quadrant 3, it has LEDs firing (in 2019 at least)
#Fill appropriate histograms
if (layer == 1 and is_downstream == 0):
h_layer1_US_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (layer == 2 and is_downstream == 0):
h_layer2_US_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (layer == 3 and is_downstream == 0):
h_layer3_US_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (layer == 4 and is_downstream == 0):
h_layer4_US_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (is_downstream == 0):
h_global_US_RMS.Fill(hist.GetRMS())
if (layer == 1 and is_downstream == 1):
h_layer1_DS_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (layer == 2 and is_downstream == 1):
h_layer2_DS_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (layer == 3 and is_downstream == 1):
h_layer3_DS_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (layer == 4 and is_downstream == 1):
h_layer4_DS_RMS.Fill(hist.GetRMS() / sqrt(layer))
if (is_downstream == 1):
h_global_DS_RMS.Fill(hist.GetRMS())
h_global_all_RMS.Fill(hist.GetRMS())
#End of file: add histograms to list
h_layer1_US_RMS_arr.append(h_layer1_US_RMS)
h_layer2_US_RMS_arr.append(h_layer2_US_RMS)
h_layer3_US_RMS_arr.append(h_layer3_US_RMS)
h_layer4_US_RMS_arr.append(h_layer4_US_RMS)
h_global_US_RMS_arr.append(h_global_US_RMS)
h_layer1_DS_RMS_arr.append(h_layer1_DS_RMS)
h_layer2_DS_RMS_arr.append(h_layer2_DS_RMS)
h_layer3_DS_RMS_arr.append(h_layer3_DS_RMS)
h_layer4_DS_RMS_arr.append(h_layer4_DS_RMS)
h_global_DS_RMS_arr.append(h_global_DS_RMS)
h_global_all_RMS_arr.append(h_global_all_RMS)
mean_layer1_US_RMS.append(h_layer1_US_RMS.GetMean())
mean_layer2_US_RMS.append(h_layer2_US_RMS.GetMean())
mean_layer3_US_RMS.append(h_layer3_US_RMS.GetMean())
mean_layer4_US_RMS.append(h_layer4_US_RMS.GetMean())
mean_global_US_RMS.append(h_global_US_RMS.GetMean())
mean_layer1_DS_RMS.append(h_layer1_DS_RMS.GetMean())
mean_layer2_DS_RMS.append(h_layer2_DS_RMS.GetMean())
mean_layer3_DS_RMS.append(h_layer3_DS_RMS.GetMean())
mean_layer4_DS_RMS.append(h_layer4_DS_RMS.GetMean())
mean_global_DS_RMS.append(h_global_DS_RMS.GetMean())
mean_global_all_RMS.append(h_global_all_RMS.GetMean())
mean_err_layer1_US_RMS.append(h_layer1_US_RMS.GetRMS())
mean_err_layer2_US_RMS.append(h_layer2_US_RMS.GetRMS())
mean_err_layer3_US_RMS.append(h_layer3_US_RMS.GetRMS())
mean_err_layer4_US_RMS.append(h_layer4_US_RMS.GetRMS())
mean_err_global_US_RMS.append(h_global_US_RMS.GetRMS())
mean_err_layer1_DS_RMS.append(h_layer1_DS_RMS.GetRMS())
mean_err_layer2_DS_RMS.append(h_layer2_DS_RMS.GetRMS())
mean_err_layer3_DS_RMS.append(h_layer3_DS_RMS.GetRMS())
mean_err_layer4_DS_RMS.append(h_layer4_DS_RMS.GetRMS())
mean_err_global_DS_RMS.append(h_global_DS_RMS.GetRMS())
mean_err_global_all_RMS.append(h_global_all_RMS.GetRMS())
bias_arr.append(curr_bias)
bias_err_arr.append(0)
mean_layer1_diff_RMS.append(h_layer1_DS_RMS.GetMean() -
h_layer1_US_RMS.GetMean())
mean_layer2_diff_RMS.append(h_layer2_DS_RMS.GetMean() -
h_layer2_US_RMS.GetMean())
mean_layer3_diff_RMS.append(h_layer3_DS_RMS.GetMean() -
h_layer3_US_RMS.GetMean())
mean_layer4_diff_RMS.append(h_layer4_DS_RMS.GetMean() -
h_layer4_US_RMS.GetMean())
mean_global_diff_RMS.append(h_global_DS_RMS.GetMean() -
h_global_US_RMS.GetMean())
mean_err_layer1_diff_RMS.append(
sqrt(h_layer1_DS_RMS.GetRMS()**2 + h_layer1_US_RMS.GetRMS()**2))
mean_err_layer2_diff_RMS.append(
sqrt(h_layer2_DS_RMS.GetRMS()**2 + h_layer2_US_RMS.GetRMS()**2))
mean_err_layer3_diff_RMS.append(
sqrt(h_layer3_DS_RMS.GetRMS()**2 + h_layer3_US_RMS.GetRMS()**2))
mean_err_layer4_diff_RMS.append(
sqrt(h_layer4_DS_RMS.GetRMS()**2 + h_layer4_US_RMS.GetRMS()**2))
mean_err_global_diff_RMS.append(
sqrt(h_global_DS_RMS.GetRMS()**2 + h_global_US_RMS.GetRMS()**2))
gr_layer1_US_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer1_US_RMS, bias_err_arr,
mean_err_layer1_US_RMS)
gr_layer2_US_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer2_US_RMS, bias_err_arr,
mean_err_layer2_US_RMS)
gr_layer3_US_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer3_US_RMS, bias_err_arr,
mean_err_layer3_US_RMS)
gr_layer4_US_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer4_US_RMS, bias_err_arr,
mean_err_layer4_US_RMS)
gr_global_US_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_global_US_RMS, bias_err_arr,
mean_err_global_US_RMS)
gr_layer1_DS_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer1_DS_RMS, bias_err_arr,
mean_err_layer1_DS_RMS)
gr_layer2_DS_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer2_DS_RMS, bias_err_arr,
mean_err_layer2_DS_RMS)
gr_layer3_DS_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer3_DS_RMS, bias_err_arr,
mean_err_layer3_DS_RMS)
gr_layer4_DS_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer4_DS_RMS, bias_err_arr,
mean_err_layer4_DS_RMS)
gr_global_DS_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_global_DS_RMS, bias_err_arr,
mean_err_global_DS_RMS)
gr_global_all_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_global_all_RMS, bias_err_arr,
mean_err_global_DS_RMS)
gr_layer1_diff_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer1_diff_RMS, bias_err_arr,
mean_err_layer1_diff_RMS)
gr_layer2_diff_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer2_diff_RMS, bias_err_arr,
mean_err_layer2_diff_RMS)
gr_layer3_diff_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer3_diff_RMS, bias_err_arr,
mean_err_layer3_diff_RMS)
gr_layer4_diff_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_layer4_diff_RMS, bias_err_arr,
mean_err_layer4_diff_RMS)
gr_global_diff_RMS = TGraphErrors(len(bias_arr), bias_arr,
mean_global_diff_RMS, bias_err_arr,
mean_err_global_diff_RMS)
if (subtract_floor_term):
gr_layer1_US_RMS = remove_floor_term_from_gr(gr_layer1_US_RMS)
gr_layer2_US_RMS = remove_floor_term_from_gr(gr_layer2_US_RMS)
gr_layer3_US_RMS = remove_floor_term_from_gr(gr_layer3_US_RMS)
gr_layer4_US_RMS = remove_floor_term_from_gr(gr_layer4_US_RMS)
gr_global_US_RMS = remove_floor_term_from_gr(gr_global_US_RMS)
gr_layer1_DS_RMS = remove_floor_term_from_gr(gr_layer1_DS_RMS)
gr_layer2_DS_RMS = remove_floor_term_from_gr(gr_layer2_DS_RMS)
gr_layer3_DS_RMS = remove_floor_term_from_gr(gr_layer3_DS_RMS)
gr_layer4_DS_RMS = remove_floor_term_from_gr(gr_layer4_DS_RMS)
gr_global_DS_RMS = remove_floor_term_from_gr(gr_global_DS_RMS)
gr_global_all_RMS = remove_floor_term_from_gr(gr_global_all_RMS)
gr_layer1_diff_RMS = remove_floor_term_from_gr_diff(
gr_layer1_US_RMS, gr_layer1_DS_RMS)
gr_layer2_diff_RMS = remove_floor_term_from_gr_diff(
gr_layer2_US_RMS, gr_layer2_DS_RMS)
gr_layer3_diff_RMS = remove_floor_term_from_gr_diff(
gr_layer3_US_RMS, gr_layer3_DS_RMS)
gr_layer4_diff_RMS = remove_floor_term_from_gr_diff(
gr_layer4_US_RMS, gr_layer4_DS_RMS)
gr_global_diff_RMS = remove_floor_term_from_gr_diff(
gr_global_US_RMS, gr_global_DS_RMS)
gr_layer1_US_RMS.SetNameTitle(
"gr_layer1_US_RMS",
"Layer 1 Upstream;Bias (V);Pedestal Width (ADC units)")
gr_layer2_US_RMS.SetNameTitle(
"gr_layer2_US_RMS",
"Layer 2 Upstream;Bias (V);Pedestal Width (ADC units)")
gr_layer3_US_RMS.SetNameTitle(
"gr_layer3_US_RMS",
"Layer 3 Upstream;Bias (V);Pedestal Width (ADC units)")
gr_layer4_US_RMS.SetNameTitle(
"gr_layer4_US_RMS",
"Layer 4 Upstream;Bias (V);Pedestal Width (ADC units)")
gr_global_US_RMS.SetNameTitle(
"gr_global_US_RMS", "ALL Upstream;Bias (V);Pedestal Width (ADC units)")
gr_layer1_DS_RMS.SetNameTitle(
"gr_layer1_DS_RMS",
"Layer 1 Downstream;Bias (V);Pedestal Width (ADC units)")
gr_layer2_DS_RMS.SetNameTitle(
"gr_layer2_DS_RMS",
"Layer 2 Downstream;Bias (V);Pedestal Width (ADC units)")
gr_layer3_DS_RMS.SetNameTitle(
"gr_layer3_DS_RMS",
"Layer 3 Downstream;Bias (V);Pedestal Width (ADC units)")
gr_layer4_DS_RMS.SetNameTitle(
"gr_layer4_DS_RMS",
"Layer 4 Downstream;Bias (V);Pedestal Width (ADC units)")
gr_global_DS_RMS.SetNameTitle(
"gr_global_DS_RMS",
"ALL Downstream;Bias (V);Pedestal Width (ADC units)")
gr_global_all_RMS.SetNameTitle(
"gr_global_all_RMS",
"ALL Channels;Bias (V);Pedestal Width (ADC units)")
gr_layer1_diff_RMS.SetNameTitle(
"gr_layer1_diff_RMS",
"Layer 1 Downstream - Upstream;Bias (V);Pedestal Width Difference (ADC units)"
)
gr_layer2_diff_RMS.SetNameTitle(
"gr_layer2_diff_RMS",
"Layer 2 Downstream - Upstream;Bias (V);Pedestal Width Difference (ADC units)"
)
gr_layer3_diff_RMS.SetNameTitle(
"gr_layer3_diff_RMS",
"Layer 3 Downstream - Upstream;Bias (V);Pedestal Width Difference (ADC units)"
)
gr_layer4_diff_RMS.SetNameTitle(
"gr_layer4_diff_RMS",
"Layer 4 Downstream - Upstream;Bias (V);Pedestal Width Difference (ADC units)"
)
gr_global_diff_RMS.SetNameTitle(
"gr_global_diff_RMS",
"ALL Downstream - Upstream;Bias (V);Pedestal Width Difference (ADC units)"
)
#Save results to file
f_out = TFile(argv[0], "RECREATE")
f_out.cd()
gr_layer1_US_RMS.Write()
gr_layer2_US_RMS.Write()
gr_layer3_US_RMS.Write()
gr_layer4_US_RMS.Write()
gr_global_US_RMS.Write()
gr_layer1_DS_RMS.Write()
gr_layer2_DS_RMS.Write()
gr_layer3_DS_RMS.Write()
gr_layer4_DS_RMS.Write()
gr_global_DS_RMS.Write()
gr_global_all_RMS.Write()
gr_layer1_diff_RMS.Write()
gr_layer2_diff_RMS.Write()
gr_layer3_diff_RMS.Write()
gr_layer4_diff_RMS.Write()
gr_global_diff_RMS.Write()
for i in range(0, len(h_layer1_US_RMS_arr)):
h_layer1_US_RMS_arr[i].Write()
h_layer2_US_RMS_arr[i].Write()
h_layer3_US_RMS_arr[i].Write()
h_layer4_US_RMS_arr[i].Write()
h_global_US_RMS_arr[i].Write()
h_layer1_DS_RMS_arr[i].Write()
h_layer2_DS_RMS_arr[i].Write()
h_layer3_DS_RMS_arr[i].Write()
h_layer4_DS_RMS_arr[i].Write()
h_global_DS_RMS_arr[i].Write()
h_global_all_RMS_arr[i].Write()
f_out.Close()
print("Done")
return
signal[m].plotOn(frame_signal, RooFit.LineColor(color_scheme[j]), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
#drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
# ts_wrongmass[i].Write()
# rootfile2[i].Close()
# this part for different plot
c2 = TCanvas("c2", "mean value of inv.m. vs top mass", 100, 100, 500, 500)
x = []
x.append(166.5)
x.append(169.5)
x.append(172.5)
x.append(173.5)
x.append(175.5)
x.append(178.5)
graph1 = TGraphErrors(6)
graph2 = TGraphErrors(6)
graph1err = TGraphErrors(6)
graph2err = TGraphErrors(6)
y = [0.] * 6
z = [0.] * 6
yerr = [0.] * 6
zerr = [0.] * 6
for i in range(0, 6):
y[i] = ts_correct[i].GetMean()
z[i] = ts_correctmass[i].GetMean()
yerr[i] = ts_correct[i].GetMeanError()
zerr[i] = ts_correctmass[i].GetMeanError()
graph1.SetPoint(i, x[i], y[i])
##
## \macro_image
## \macro_code
##
## \author Wim Lavrijsen
from ROOT import TCanvas, TGraphErrors
from ROOT import gROOT
from array import array
c1 = TCanvas('c1', 'A Simple Graph with error bars', 200, 10, 700, 500)
c1.SetGrid()
c1.GetFrame().SetFillColor(21)
c1.GetFrame().SetBorderSize(12)
n = 10
x = array('f', [-0.22, 0.05, 0.25, 0.35, 0.5, 0.61, 0.7, 0.85, 0.89, 0.95])
ex = array('f', [0.05, 0.1, 0.07, 0.07, 0.04, 0.05, 0.06, 0.07, 0.08, 0.05])
y = array('f', [1, 2.9, 5.6, 7.4, 9.0, 9.6, 8.7, 6.3, 4.5, 1])
ey = array('f', [0.8, 0.7, 0.6, 0.5, 0.4, 0.4, 0.5, 0.6, 0.7, 0.8])
gr = TGraphErrors(n, x, y, ex, ey)
gr.SetTitle('TGraphErrors Example')
gr.SetMarkerColor(4)
gr.SetMarkerStyle(21)
gr.Draw('ALP')
c1.Update()
c1.Draw()
c1.Print("TGrpah.png")
def makeBeamspotPlots(name, beamspots):
if len(beamspots) == 0:
print "No beamspot"
nPoints = len(beamspots)
print "Found", nPoints
x = TGraphErrors(nPoints)
x.SetName(name + "_x")
x.SetTitle(name + " x; lb; x [mm]")
y = TGraphErrors(nPoints)
y.SetName(name + "_y")
y.SetTitle(name + " y; lb; y [mm]")
z = TGraphErrors(nPoints)
z.SetName(name + "_z")
z.SetTitle(name + " z; lb; z [mm]")
sx = TGraphErrors(nPoints)
sx.SetName(name + "_sx")
sx.SetTitle(name + " sx; lb; #sigma(x) [mm]")
sy = TGraphErrors(nPoints)
sy.SetName(name + "_sy")
sy.SetTitle(name + " sy; lb; #sigma(y) [mm]")
sz = TGraphErrors(nPoints)
sz.SetName(name + "_sz")
sz.SetTitle(name + " sz; lb; #sigma(z) [mm]")
ax = TGraphErrors(nPoints)
ax.SetName(name + "_ax")
ax.SetTitle(name + " ax; lb; slope x-z [rad]")
ay = TGraphErrors(nPoints)
ay.SetName(name + "_ay")
ay.SetTitle(name + " ay; lb; slope y-z [rad]")
k = TGraphErrors(nPoints)
k.SetName(name + "_k")
k.SetTitle(name + " k; lb; k")
rhoxy = TGraphErrors(nPoints)
rhoxy.SetName(name + "_rhoxy")
rhoxy.SetTitle(name + " rhoxy; lb; #rho_{xy}")
nEvents = TGraphErrors(nPoints)
nEvents.SetName(name + "_nEvents")
nEvents.SetTitle(name + " nEvents; lb; nEvents")
graphs.extend([x, y, z, sx, sy, sz, ax, ay, k, rhoxy, nEvents])
point = 0
for evt, bs in beamspots:
xmid = evt.lbStart + 0.5 * evt.lumiRange
xerr = 0.5 * evt.lumiRange
x.SetPoint(point, xmid, bs.xc)
y.SetPoint(point, xmid, bs.yc)
z.SetPoint(point, xmid, bs.z)
sx.SetPoint(point, xmid, bs.sx)
sy.SetPoint(point, xmid, bs.sy)
sz.SetPoint(point, xmid, bs.sz)
ax.SetPoint(point, xmid, bs.ax)
ay.SetPoint(point, xmid, bs.ay)
k.SetPoint(point, xmid, bs.k)
rhoxy.SetPoint(point, xmid, bs.rhoxy)
nEvents.SetPoint(point, xmid, evt.nEvents)
x.SetPointError(point, xerr, bs.Exc)
y.SetPointError(point, xerr, bs.Eyc)
z.SetPointError(point, xerr, bs.Ez)
sx.SetPointError(point, xerr, bs.Esx)
sy.SetPointError(point, xerr, bs.Esy)
sz.SetPointError(point, xerr, bs.Esz)
ax.SetPointError(point, xerr, bs.Eax)
ay.SetPointError(point, xerr, bs.Eay)
k.SetPointError(point, xerr, bs.Ek)
rhoxy.SetPointError(point, xerr, bs.Erhoxy)
nEvents.SetPointError(point, xerr, sqrt(evt.nEvents))
point = point + 1
return (x, y, z, sx, sy, sz, ax, ay, k, rhoxy, nEvents)
import numpy as np
from xlrd import open_workbook
from ROOT import TCanvas, TH1F, TGraph, TGraphErrors, TH1D, TLegend, TFile, TDirectory, gROOT, gStyle
gStyle.SetOptFit(1111)
totalNbOfFiles = 388;
filenamelist = open("filenamelist.txt");
#prepare histograms and root file
rootfile = TFile("Histograms1.root","recreate");
hist_spec_list = []
hist_deri_list = []
hist_comptonedge = TH1F("comptonedge_stable","",40,680,720)
gComptonVsRun = TGraphErrors()
gChi2VsRun = TGraph()
filecount = 0;
for i in range(0,totalNbOfFiles,1):
filename = filenamelist.readline().rstrip("\r\n")
#filename = [s[0:12] for s in filename]
print filename
hist_spec_name = filename + "_spec" + str(i)
hist_spec = TH1F(hist_spec_name,"",4096,0,4096)
hist_spec_list.append(hist_spec)
hist_deri_name = filename + "_deri" + str(i)
hist_deri = TH1F(hist_deri_name,"",4096,0,4096)
hist_deri_list.append(hist_deri)
wb = open_workbook(filename)
totalCnts = 0
for sheet in wb.sheets():
number_of_rows = sheet.nrows
def make_tgrapherrors(name,
title,
color=1,
marker=20,
marker_size=1,
width=1,
asym_err=False,
style=1,
x=None,
y=None):
if (x and y) is None:
gr = TGraphErrors() if not asym_err else TGraphAsymmErrors()
else:
gr = TGraphErrors(len(x), array(x, 'd'), array(
y, 'd')) if not asym_err else TGraphAsymmErrors(
len(x), array(x, 'd'), array(y), 'd')
gr.SetTitle(title)
gr.SetName(name)
gr.SetMarkerStyle(marker)
gr.SetMarkerColor(color)
gr.SetLineColor(color)
gr.SetMarkerSize(marker_size)
gr.SetLineWidth(width)
gr.SetLineStyle(style)
return gr
tren_phi_er.append(0)
asy_er.append(y_err)
# create fit function for asy
fasy = TF1("fasy", "[0]*sin(x*3.1415926/180.0)", 0.0, 361.0)
fasy.SetParameter(0, 1)
c1 = TCanvas("c1", "canvas", 800, 800)
gStyle.SetOptStat(0000)
c1.Divide(2, 2)
c1.cd(1)
h_pos_asym.SetTitle("Counts with Pos. Helicity; #phi_{trento}; Counts")
h_pos_asym.Draw()
c1.cd(2)
h_neg_asym.SetTitle("Counts with Neg. Helicity; #phi_{trento}; Counts")
h_neg_asym.Draw()
c1.cd(3)
h_p_copy.SetTitle("Pos.-Neg. Helicity; #phi_{trento}; Counts")
h_p_copy.Draw()
c1.cd(4)
g_asy = TGraphErrors(len(tren_phi), tren_phi, asy, tren_phi_er, asy_er)
g_asy.SetTitle("Measured Asym; #phi_{trento}; Asym")
g_asy.Fit("fasy")
g_asy.Draw("AP")
fasy.Draw("same")
c1.SaveAs("phi_asy.pdf")
def signal(category):
interPar = True
n = len(genPoints)
cColor = color[category] if category in color else 4
nBtag = category.count('b')
isAH = False #relict from using Alberto's more complex script
if not os.path.exists(PLOTDIR + "MC_signal_" + YEAR):
os.makedirs(PLOTDIR + "MC_signal_" + YEAR)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar("jpt_1", "jet1 pt", 0., 13000., "GeV")
jj_deltaEta = RooRealVar("jj_deltaEta_widejet", "", 0., 5.)
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4.)
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4.)
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500.)
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8.)
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8.)
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8.)
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8.)
jmuonpt_1 = RooRealVar("jmuonpt_1", "j1 muon pt", 0., 13000.)
jmuonpt_2 = RooRealVar("jmuonpt_2", "j2 muon pt", 0., 13000.)
nmuons = RooRealVar("nmuons", "n_{#mu}", -1., 10.)
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500", "", -1., 1.)
HLT_PFJet500 = RooRealVar("HLT_PFJet500", "", -1., 1.)
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID", "", -1., 1.)
HLT_PFHT900 = RooRealVar("HLT_PFHT900", "", -1., 1.)
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550", "", -1., 1.)
HLT_PFJet550 = RooRealVar("HLT_PFJet550", "", -1., 1.)
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID", "", -1., 1.)
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050", "", -1., 1.)
HLT_DoublePFJets100_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets100_CaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets200_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets200_CaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets350_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets350_CaloBTagDeepCSV_p71", "", -1., 1.)
HLT_DoublePFJets40_CaloBTagDeepCSV_p71 = RooRealVar(
"HLT_DoublePFJets40_CaloBTagDeepCSV_p71", "", -1., 1.)
weight = RooRealVar("eventWeightLumi", "", -1.e9, 1.e9)
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass)
variables.add(
RooArgSet(j1_pt, jj_deltaEta, jbtag_WP_1, jbtag_WP_2, fatjetmass_1,
fatjetmass_2, jnmuons_1, jnmuons_2, weight))
variables.add(
RooArgSet(nmuons, nelectrons, jid_1, jid_2, jmuonpt_1, jmuonpt_2))
variables.add(
RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID,
HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550,
HLT_CaloJet550_NoJetID, HLT_PFHT1050))
variables.add(
RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71,
HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71,
HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71,
HLT_DoublePFJets200_CaloBTagDeepCSV_p71,
HLT_DoublePFJets350_CaloBTagDeepCSV_p71,
HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax())
if VARBINS:
binsXmass = RooBinning(len(abins) - 1, abins)
X_mass.setBinning(binsXmass)
plot_binning = RooBinning(
int((X_mass.getMax() - X_mass.getMin()) / 100), X_mass.getMin(),
X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100),
X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
X_mass.setBinning(plot_binning, "PLOT")
#X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/10))
#binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
#X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
if BTAGGING == 'semimedium':
SRcut = aliasSM[category]
#SRcut = aliasSM[category+"_vetoAK8"]
else:
SRcut = alias[category].format(WP=working_points[BTAGGING])
#SRcut = alias[category+"_vetoAK8"].format(WP=working_points[BTAGGING])
if ADDSELECTION: SRcut += SELECTIONS[options.selection]
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
valpha2 = {}
vslope2 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_scale_jes",
"Variation of the resonance position with the jet energy scale", 0.02,
-1., 1.) #0.001
smean_jes = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p1_jes",
"Change of the resonance position with the jet energy scale", 0., -10,
10)
xsigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_scale_jer",
"Variation of the resonance width with the jet energy resolution",
0.10, -1., 1.)
ssigma_jer = RooRealVar(
"CMS" + YEAR + "_sig_" + category + "_p2_jer",
"Change of the resonance width with the jet energy resolution", 0.,
-10, 10)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
for m in massPoints:
signalMass = "%s_M%d" % (stype, m)
signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m)
sampleName = "bstar_M{}".format(m)
signalColor = sample[sampleName][
'linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m,
m * 0.96, m * 1.05)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)",
RooArgList(vmean[m], xmean_jes, smean_jes))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma",
m * 0.0233, m * 0.019, m * 0.025)
ssigma[m] = RooFormulaVar(
signalName + "_sigma", "@0*(1+@1*@2)",
RooArgList(vsigma[m], xsigma_jer, ssigma_jer))
valpha1[m] = RooRealVar(
signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28
) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0",
RooArgList(valpha1[m]))
#vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail
vslope1[m] = RooRealVar(signalName + "_vslope1",
"Crystal Ball slope 1", 13., 10.,
20.) # slope of the power tail
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0",
RooArgList(vslope1[m]))
valpha2[m] = RooRealVar(signalName + "_valpha2",
"Crystal Ball alpha 2", 1.)
valpha2[m].setConstant(True)
salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0",
RooArgList(valpha2[m]))
#vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail
## FIXME test FIXME
vslope2_estimation = -5.88111436852 + m * 0.00728809389442 + m * m * (
-1.65059568762e-06) + m * m * m * (1.25128996309e-10)
vslope2[m] = RooRealVar(signalName + "_vslope2",
"Crystal Ball slope 2", vslope2_estimation,
vslope2_estimation * 0.9, vslope2_estimation *
1.1) # slope of the higher power tail
## FIXME end FIXME
sslope2[m] = RooFormulaVar(
signalName + "_slope2", "@0",
RooArgList(vslope2[m])) # slope of the higher power tail
signal[m] = RooDoubleCrystalBall(signalName,
"m_{%s'} = %d GeV" % ('X', m), X_mass,
smean[m], ssigma[m], salpha1[m],
sslope1[m], salpha2[m], sslope2[m])
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName + "_yield", "signalYield", 50,
0., 1.e15)
signalNorm[m] = RooRealVar(signalName + "_norm", "signalNorm", 1., 0.,
1.e15)
signalXS[m] = RooRealVar(signalName + "_xs", "signalXS", 1., 0., 1.e15)
signalExt[m] = RooExtendPdf(signalName + "_ext", "extended p.d.f",
signal[m], signalYield[m])
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
if YEAR == 'run2':
pd = sample[sampleName]['files']
if len(pd) > 3:
print "multiple files given than years for a single masspoint:", pd
sys.exit()
for ss in pd:
if not '2016' in ss and not '2017' in ss and not '2018' in ss:
print "unknown year given in:", ss
sys.exit()
else:
pd = [x for x in sample[sampleName]['files'] if YEAR in x]
if len(pd) > 1:
print "multiple files given for a single masspoint/year:", pd
sys.exit()
for ss in pd:
if options.unskimmed:
j = 0
while True:
if os.path.exists(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j)):
treeSign[m].Add(NTUPLEDIR + ss + "/" + ss +
"_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
if os.path.exists(NTUPLEDIR + ss + ".root"):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
else:
print "found no file for sample:", ss
if treeSign[m].GetEntries() <= 0.:
if VERBOSE:
print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
#setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar("eventWeightLumi*BTagAK4Weight_deepJet"), RooFit.Import(treeSign[m]))
setSignal[m] = RooDataSet("setSignal_" + signalName, "setSignal",
variables, RooFit.Cut(SRcut),
RooFit.WeightVar(weight),
RooFit.Import(treeSign[m]))
if VERBOSE:
print " - Dataset with", setSignal[m].sumEntries(
), "events loaded"
# FIT
entries = setSignal[m].sumEntries()
if entries < 0. or entries != entries: entries = 0
signalYield[m].setVal(entries)
# Instead of eventWeightLumi
#signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) )
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1),
RooFit.Extended(True),
RooFit.SumW2Error(True),
RooFit.PrintLevel(-1))
if VERBOSE:
print "********** Fit result [", m, "] **", category, "*" * 40, "\n", frSignal[
m].Print(), "\n", "*" * 80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass + "_" + category, stype + category, X_mass,
signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist"
# Remove HVT cross sections
#xs = getCrossSection(stype, channel, m)
xs = 1.
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(
massArg, RooFit.NormSet(massArg),
RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if boundaryFactor < 0. or boundaryFactor != boundaryFactor:
boundaryFactor = 0
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[
m].getVal(), "/", boundaryFactor, "events"
signalNorm[m].setVal(boundaryFactor * signalYield[m].getVal() /
signalXS[m].getVal()
) # here normalize to sigma(X) x Br = 1 [fb]
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
valpha2[m].setConstant(True)
vslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
### FIXME FIXME just for a test FIXME FIXME
#print
#print
#print "slope2 fit results:"
#print
#y_vals = []
#for m in genPoints:
# y_vals.append(vslope2[m].getVal())
#print "m =", genPoints
#print "y =", y_vals
#sys.exit()
### FIXME FIXME test end FIXME FIXME
# ====== CONTROL PLOT ======
color_scheme = [
636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635,
634, 633, 632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633,
632, 633, 636, 635, 634, 633, 632, 633, 636, 635, 634, 633, 632, 633
]
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for j, m in enumerate(genPoints):
if m in signalExt.keys():
#print "color:",(j%9)+1
#print "signalNorm[m].getVal() =", signalNorm[m].getVal()
#print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent
signal[m].plotOn(
frame_signal, RooFit.LineColor(color_scheme[j]),
RooFit.Normalization(signalNorm[m].getVal(),
RooAbsReal.NumEvent),
RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
#drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR + "MC_bstar_" + YEAR + "/" + stype + "_" +
category + "_Signal.pdf")
c_signal.SaveAs(PLOTDIR + "MC_bstar_" + YEAR + "/" + stype + "_" +
category + "_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
if gnorm.GetMaximum() < signalNorm[m].getVal():
gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
#gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast()))
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0,
min(vmean[m].getError(), vmean[m].getVal() * 0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(
n, 0, min(vsigma[m].getError(), vsigma[m].getVal() * 0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(
n, 0, min(valpha1[m].getError(), valpha1[m].getVal() * 0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(
n, 0, min(vslope1[m].getError(), vslope1[m].getVal() * 0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(
n, 0, min(valpha2[m].getError(), valpha2[m].getVal() * 0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(
n, 0, min(vslope2[m].getError(), vslope2[m].getVal() * 0.10))
#tmpVar = w.var(var+"_"+signalString)
#print m, tmpVar.getVal(), tmpVar.getError()
n = n + 1
gmean.Fit(fmean, "Q0", "SAME")
gsigma.Fit(fsigma, "Q0", "SAME")
galpha1.Fit(falpha1, "Q0", "SAME")
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
# gnorm.Fit(fnorm, "Q0", "", 700, 5000)
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
#gnorm.Fit(fnorm, "Q", "SAME", 700, 6000)
gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently
for m in massPoints:
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1. - spline / sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
#jmean = gmean.Eval(m)
#jsigma = gsigma.Eval(m)
#jalpha1 = galpha1.Eval(m)
#jslope1 = gslope1.Eval(m)
#jalpha2 = galpha2.Eval(m)
#jslope2 = gslope2.Eval(m)
jmean = gmean.Eval(m, 0, "S")
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(
0) + fmean.GetParameter(1) * m + fmean.GetParameter(2) * m * m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(
1) * m + fsigma.GetParameter(2) * m * m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(
1) * m + falpha1.GetParameter(2) * m * m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(
1) * m + fslope1.GetParameter(2) * m * m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(
1) * m + falpha2.GetParameter(2) * m * m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(
1) * m + fslope2.GetParameter(2) * m * m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1 == 0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
if not jalpha2 == 0: valpha2[m].setVal(jalpha2)
islope2.SetPoint(islope2.GetN(), m, jslope2)
if jslope2 > 0: vslope2[m].setVal(jslope2)
#### newly introduced, not yet sure if helpful:
vmean[m].removeError()
vsigma[m].removeError()
valpha1[m].removeError()
valpha2[m].removeError()
vslope1[m].removeError()
vslope2[m].removeError()
#signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit
#c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200
#c1.Divide(2, 3)
c1 = TCanvas("c1", "Crystal Ball", 1800, 800)
c1.Divide(3, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(category)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(category)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(category)
galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(category)
gslope1.GetYaxis().SetRangeUser(0.,
150.) #adjusted upper limit from 125 to 60
if True: #isAH:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(category)
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR + "MC_bstar_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.pdf")
c1.Print(PLOTDIR + "MC_bstar_" + YEAR + "/" + stype + "_" + category +
"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0] - 100, genPoints[-1] + 100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum() * 1.25)
#drawCMS(-1, "Simulation Preliminary", year=YEAR)
#drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawCMS(-1, "", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR + "MC_bstar_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.pdf")
c2.Print(PLOTDIR + "MC_bstar_" + YEAR + "/" + stype + "_" + category +
"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("bstar_" + YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m],
RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR + "MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR + "MC_signal_%s_%s.root" % (
YEAR, category), "saved successfully"
def main(network_name, do_weights, general_weights, electron_weights,
muon_weights, data_files, no_true_comp, nbins, veto_all_jets,
require_jets, WorkingP_List, Independant_Variable_List,
Histogram_Variable_List, Profile_List, TH2D_List):
TH1.SetDefaultSumw2()
## First we check that you havent vetoed and requested jets
if veto_all_jets and require_jets:
print("\n\n\nYOU CANT ASK FOR NONE YET SOME JETS\n\n\n")
return 0
for data_dir, data_set_name in data_files:
OUTPUT_dir = os.path.join(os.environ["HOME_DIRECTORY"], "Output")
DATA_dir = os.path.join(os.environ["HOME_DIRECTORY"], "Data")
data_base_dir = os.path.join(DATA_dir, data_dir)
print("\n\n" + data_set_name)
"""
____ ____ _____ ____ _ ____ _ _____ ___ ___ _ _
| _ \ | _ \ | ____| | _ \ / \ | _ \ / \ |_ _| |_ _| / _ \ | \ | |
| |_) | | |_) | | _| | |_) | / _ \ | |_) | / _ \ | | | | | | | | | \| |
| __/ | _ < | |___ | __/ / ___ \ | _ < / ___ \ | | | | | |_| | | |\ |
|_| |_| \_\ |_____| |_| /_/ \_\ |_| \_\ /_/ \_\ |_| |___| \___/ |_| \_|
"""
## The list of matrices
Indp_output = [None for var in Independant_Variable_List]
TH1D_output = [[None for wp in WorkingP_List]
for var in Histogram_Variable_List]
Tail_output = [[None for wp in WorkingP_List]
for var in Histogram_Variable_List]
Prof_output = [[None for wp in WorkingP_List] for var in Profile_List]
TH2D_output = [[None for wp in WorkingP_List] for var in TH2D_List]
## Open the root file
root_file = TFile.Open(os.path.join(data_base_dir, data_set_name))
## Loading the tree containing the working point information
wpt_tree = root_file.Get("wpt_tree")
## Making the wpt_tree and the other trees friends so that they can be compared
wpt_tree.AddFriend(network_name, root_file)
wpt_tree.AddFriend("alt_tree", root_file)
wpt_tree.AddFriend("var_tree", root_file)
## Creating a string of all the weights to be applied
event_weights = []
if "SR" in data_dir: event_weights += general_weights
if "ee" in data_dir: event_weights += electron_weights
if "mumu" in data_dir: event_weights += muon_weights
if veto_all_jets:
event_weights += ["(var_tree.Jets_Loose_SumET==0)"]
if require_jets:
event_weights += ["(var_tree.Jets_Loose_SumET>0)"]
if do_weights: weight_string = " * ".join(event_weights)
else: weight_string = ""
if len(weight_string):
print("Events are weighted using: {}".format(weight_string))
## The strings to call up the Truth Values
if no_true_comp:
True_Et = "0"
True_Ex = "0"
True_Ey = "0"
True_Phi = "0"
else:
True_Et = "WP_Truth_ET"
True_Ex = "WP_Truth_X"
True_Ey = "WP_Truth_Y"
True_Phi = "WP_Truth_Phi"
"""
____ ____ _ __ __ ___ _ _ ____
| _ \ | _ \ / \ \ \ / / |_ _| | \ | | / ___|
| | | | | |_) | / _ \ \ \ /\ / / | | | \| | | | _
| |_| | | _ < / ___ \ \ V V / | | | |\ | | |_| |
|____/ |_| \_\ /_/ \_\ \_/\_/ |___| |_| \_| \____|
"""
## Before the workingpoint loop, the independant variables are drawn
for v, var in enumerate(Independant_Variable_List):
print(" -- {}".format(var.name))
## Creating the histogram which will be filled
hist_name = var.name
myhist = TH1D(hist_name, hist_name, var.nbins, var.xmin, var.xmax)
myhist.SetStats(True)
myhist.StatOverflows(True)
## Get Maths Function from variable
maths_string = var.tree + "." + var.branch
## Drawing the tree and saving the hist to the matrix
execution = "{}>>{}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
Indp_output[v] = myhist
## First we select the working point and create the correct strings
for w, wp in enumerate(WorkingP_List):
print(" -- {}:".format(wp.name))
Rec_Et = wp.Et
Rec_Ex = wp.Ex
Rec_Ey = wp.Ey
Rec_Phi = wp.Phi
if wp.tree == "ann_tree":
Rec_Et = network_name + "." + Rec_Et
Rec_Ex = network_name + "." + Rec_Ex
Rec_Ey = network_name + "." + Rec_Ey
rec_and_truth_vars = [
Rec_Et, Rec_Ex, Rec_Ey, Rec_Phi, True_Et, True_Ex, True_Ey,
True_Phi
]
## Drawing the 1D histograms
for v, var in enumerate(Histogram_Variable_List):
print(" -- -- {}".format(var.name))
## Creating the histogram which will be filled
hist_name = "{}_{}".format(var.name, wp.name)
myhist = TH1D(hist_name, hist_name, var.nbins, var.xmin,
var.xmax)
myhist.SetStats(True)
myhist.StatOverflows(True)
## Individual special plots
if var.name == "XY":
## Plot the X histogram
maths_string = Evaluation.TH1D_Maths_String(
"X", *rec_and_truth_vars)
execution = "{} >> {}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Add the y histogram
maths_string = Evaluation.TH1D_Maths_String(
"Y", *rec_and_truth_vars)
execution = "{} >> +{}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
else:
## Get Maths Function from variable
maths_string = Evaluation.TH1D_Maths_String(
var.name, *rec_and_truth_vars)
## Drawing the tree and saving the hist to the matrix
execution = "{} >> {}".format(maths_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
TH1D_output[v][w] = myhist
## Drawing the Profiles
for v, (vx, vy) in enumerate(Profile_List):
print(" -- -- {} vs {}".format(vx.name, vy.name))
## Creating the profile which will be filled
hist_name = "{}_vs_{}_{}".format(vx.name, vy.name, wp.name)
myhist = TProfile(hist_name, hist_name, vx.nbins, vx.xmin,
vx.xmax)
if vy.reso: myhist.SetErrorOption('s')
## The x variable is called from its branch in the correct tree
x_string = vx.tree + "." + vx.branch
## Individual special plots
if vy.name == "XY":
y_string = Evaluation.TH1D_Maths_String(
"X", *rec_and_truth_vars)
execution = "{}:{} >> {}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
y_string = Evaluation.TH1D_Maths_String(
"Y", *rec_and_truth_vars)
execution = "{}:{} >>+{}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
elif vy.name == "AZ":
z_x = "(alt_tree.ll_px)"
z_y = "(alt_tree.ll_py)"
z_pt = "(alt_tree.ll_pt)"
x_string = z_pt
y_string = "({rx}*{zx} + {ry}*{zy})/{zpt}".format(
rx=Rec_Ex, ry=Rec_Ey, zx=z_x, zy=z_y, zpt=z_pt)
execution = "{}:{} >> {}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
else:
y_string = Evaluation.TH1D_Maths_String(
vy.name, *rec_and_truth_vars)
execution = "{}:{} >> {}".format(y_string, x_string,
hist_name)
wpt_tree.Draw(execution, weight_string, "goff prof")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
Prof_output[v][w] = myhist
if wp.name not in ["Network", "Tight"]:
continue
## Drawing the TH2Ds
for v, (vx, vy) in enumerate(TH2D_List):
print(" -- -- {} vs {}".format(vx.name, vy.name))
## Creating the profile which will be filled
hist_name = "2D_{}_vs_{}_{}".format(vx.name, vy.name, wp.name)
myhist = TH2D(hist_name, hist_name, vx.nbins2d, vx.xmin2d,
vx.xmax2d, vy.nbins2d, vy.xmin2d, vy.xmax2d)
x_string = Evaluation.TH1D_Maths_String(
vx.name, *rec_and_truth_vars)
y_string = Evaluation.TH1D_Maths_String(
vy.name, *rec_and_truth_vars)
## The x variable is called from its branch in the correct tree
if x_string is None:
x_string = vx.tree + "." + vx.branch
execution = "{}:{} >> {}".format(y_string, x_string, hist_name)
wpt_tree.Draw(execution, weight_string, "goff")
## Saving the Histogram to the Matrix
myhist.SetDirectory(0)
TH2D_output[v][w] = myhist
root_file.Close()
"""
_____ ____ ___ _____ ___ _ _ ____
| ____| | _ \ |_ _| |_ _| |_ _| | \ | | / ___|
| _| | | | | | | | | | | | \| | | | _
| |___ | |_| | | | | | | | | |\ | | |_| |
|_____| |____/ |___| |_| |___| |_| \_| \____|
"""
## We now go through the 1D histograms and make them include overflow and underflow
for v, var in enumerate(Histogram_Variable_List):
for w, wp in enumerate(WorkingP_List):
## Include the overflow
last_bin = TH1D_output[v][w].GetBinContent(nbins)
overflow = TH1D_output[v][w].GetBinContent(nbins + 1)
TH1D_output[v][w].SetBinContent(nbins, last_bin + overflow)
TH1D_output[v][w].SetBinContent(nbins + 1, 0)
## Include the underflow
first_bin = TH1D_output[v][w].GetBinContent(1)
underflow = TH1D_output[v][w].GetBinContent(0)
TH1D_output[v][w].SetBinContent(1, first_bin + underflow)
TH1D_output[v][w].SetBinContent(0, 0)
## We create a tail distrobution if it is requested
if var.tail:
tail_temp = TH1D_output[v][w].GetCumulative(False)
tail_temp.Scale(1 / tail_temp.GetBinContent(1))
Tail_output[v][w] = tail_temp
## We go through the resolution profiles and replace their entries with the RMSE for each bin
for v, (vx, vy) in enumerate(Profile_List):
if not vy.reso:
continue
for w, wp in enumerate(WorkingP_List):
old_prof = Prof_output[v][w]
bin_width = old_prof.GetBinWidth(1)
name = "{}_vs_{}_{}_res".format(vx.name, vy.name, wp.name)
new_prof = TGraphErrors(vx.nbins)
new_prof.SetName(name)
new_prof.SetTitle(name)
new_prof.SetLineWidth(2)
for b_idx in range(vx.nbins):
new_prof.SetPoint(b_idx, old_prof.GetBinCenter(b_idx + 1),
old_prof.GetBinError(b_idx + 1))
new_prof.SetPointError(b_idx, bin_width / 2, 0)
Prof_output[v][w] = new_prof
"""
____ _ __ __ ___ _ _ ____
/ ___| / \ \ \ / / |_ _| | \ | | / ___|
\___ \ / _ \ \ \ / / | | | \| | | | _
___) | / ___ \ \ V / | | | |\ | | |_| |
|____/ /_/ \_\ \_/ |___| |_| \_| \____|
"""
## Creating the output directory
output_dir = os.path.join(OUTPUT_dir, network_name, data_set_name[:-5])
if veto_all_jets:
output_dir = output_dir + "_NOJETS"
if require_jets:
output_dir = output_dir + "_SOMEJETS"
## Check that the file can be saved
if not os.path.exists(output_dir):
os.system("mkdir -p " + output_dir)
## We create an output file for the histograms
output_file = TFile(os.path.join(output_dir, "histograms.root"),
"update")
gFile = output_file
## We save the independants
for v, var in enumerate(Independant_Variable_List):
Indp_output[v].Write("", TObject.kOverwrite)
## We save the TH1Ds and tails
for v, var in enumerate(Histogram_Variable_List):
for w in range(len(WorkingP_List)):
TH1D_output[v][w].Write("", TObject.kOverwrite)
if var.tail:
Tail_output[v][w].Write("", TObject.kOverwrite)
## We save the profiles
for v in range(len(Profile_List)):
for w in range(len(WorkingP_List)):
Prof_output[v][w].Write("", TObject.kOverwrite)
## We save the TH2Ds
for v in range(len(TH2D_List)):
for w in range(len(WorkingP_List[:2])):
TH2D_output[v][w].Write("", TObject.kOverwrite)
output_file.Close()
return 0
def makeBeamspotCutPlots(name, points):
nPoints = len(points)
x = TGraphErrors(nPoints)
x.SetName(name)
x.SetTitle(name + " x; lb; x [mm]")
y = TGraphErrors(nPoints)
y.SetName(name)
y.SetTitle(name + " y; lb; y [mm]")
z = TGraphErrors(nPoints)
z.SetName(name)
z.SetTitle(name + " z; lb; z [mm]")
sx = TGraphErrors(nPoints)
sx.SetName(name)
sx.SetTitle(name + " sx; lb; #sigma(x) [mm]")
sy = TGraphErrors(nPoints)
sy.SetName(name)
sy.SetTitle(name + " sy; lb; #sigma(y) [mm]")
sz = TGraphErrors(nPoints)
sz.SetName(name)
sz.SetTitle(name + " sz; lb; #sigma(z) [mm]")
xrms = TGraphErrors(nPoints)
xrms.SetName(name + "_rms")
xrms.SetTitle(name + "_rms" + " x")
yrms = TGraphErrors(nPoints)
yrms.SetName(name + "_rms")
yrms.SetTitle(name + "_rms" + " y")
zrms = TGraphErrors(nPoints)
zrms.SetName(name + "_rms")
zrms.SetTitle(name + "_rms" + " z")
sxrms = TGraphErrors(nPoints)
sxrms.SetName(name + "_rms")
sxrms.SetTitle(name + "_rms" + " sx")
syrms = TGraphErrors(nPoints)
syrms.SetName(name + "_rms")
syrms.SetTitle(name + "_rms" + " sy")
szrms = TGraphErrors(nPoints)
szrms.SetName(name + "_rms")
szrms.SetTitle(name + "_rms" + " sz")
graphs.extend([x, y, z, sx, sy, sz, xrms, yrms, zrms, sxrms, syrms, szrms])
print len(points)
for i in range(len(points)):
xval = points[i][0]
yvals = points[i][1]
xerr = 0
x.SetPoint(i, xval, yvals[0][0])
y.SetPoint(i, xval, yvals[1][0])
z.SetPoint(i, xval, yvals[2][0])
sx.SetPoint(i, xval, yvals[3][0])
sy.SetPoint(i, xval, yvals[4][0])
sz.SetPoint(i, xval, yvals[5][0])
x.SetPointError(i, xerr, yvals[0][1])
y.SetPointError(i, xerr, yvals[1][1])
z.SetPointError(i, xerr, yvals[2][1])
sx.SetPointError(i, xerr, yvals[3][1])
sy.SetPointError(i, xerr, yvals[4][1])
sz.SetPointError(i, xerr, yvals[5][1])
xrms.SetPoint(i, xval, yvals[0][2])
yrms.SetPoint(i, xval, yvals[1][2])
zrms.SetPoint(i, xval, yvals[2][2])
sxrms.SetPoint(i, xval, yvals[3][2])
syrms.SetPoint(i, xval, yvals[4][2])
szrms.SetPoint(i, xval, yvals[5][2])
xrms.SetPointError(i, xerr, yvals[0][3])
yrms.SetPointError(i, xerr, yvals[1][3])
zrms.SetPointError(i, xerr, yvals[2][3])
sxrms.SetPointError(i, xerr, yvals[3][3])
syrms.SetPointError(i, xerr, yvals[4][3])
szrms.SetPointError(i, xerr, yvals[5][3])
c = TCanvas("cx")
c.Divide(3, 1)
c.cd(1)
x.Draw("ap")
c.cd(2)
y.Draw("ap+Y+")
c.cd(3)
yaxis_xrms = TGaxis(-1, 0.2, 1, 0.2, -1, 2, 510, "+R")
yaxis_xrms.ImportAxisAttributes(y.GetHistogram().GetYaxis())
#g = TMultiGraph()
#g.Add(x,"aXp")
#g.Add(y,"aY*")
#g.Draw("a")
# graphs.append(g)
# aa = y.GetHistogram()
#x.Draw("ap")
yaxis_xrms.Draw()
#axis.PaintAxis(0,0.5,0.1,0.6,0.4,1.4,510,"+R")
#y.Draw("pY+sames")
#b.Draw("sames")
c.Modified()
c.Update()
#c.Print()
canvases.append(c)
def plot( self, plotoptions, opt="?" ):
vx= array( "d", self.aostand.getPointsCenter() )
values= self.values
sterrs= self.sterrs
if "m" in opt:
print "AnalysisObservable::plot: use errors from error matrix"
sterrs= array( "d", self.aostand.getErrors( "m" ) )
syerrs= self.syerrs
npoints= len(vx)
if "xshift" in plotoptions:
for i in range(npoints):
vx[i]+= plotoptions["xshift"]
vex= array( "d", npoints*[0.0] )
tgest= TGraphErrors( npoints, vx, values, vex, sterrs )
toterrs= np.sqrt( np.add( np.square( sterrs ), np.square( syerrs ) ) )
tgesy= TGraphErrors( npoints, vx, values, vex, toterrs )
tgesy.SetMarkerStyle( plotoptions["markerStyle"] )
tgesy.SetMarkerSize( plotoptions["markerSize"] )
drawas= plotoptions["drawas"] if "drawas" in plotoptions else "p"
tgesy.SetName( self.obs )
if "fillcolor" in plotoptions:
tgesy.SetFillColor(plotoptions["fillcolor"])
tgest.SetFillColor(plotoptions["fillcolor"])
if "s" in opt:
tgesy.Draw( "psame" )
else:
if "title" in plotoptions:
tgesy.SetTitle( plotoptions["title"] )
else:
tgesy.SetTitle( self.obs )
tgesy.SetMinimum( plotoptions["ymin"] )
tgesy.SetMaximum( plotoptions["ymax"] )
xaxis= tgesy.GetXaxis()
xaxis.SetLimits( plotoptions["xmin"], plotoptions["xmax"] )
if "xlabel" in plotoptions:
xaxis.SetTitle( plotoptions["xlabel"] )
if "ylabel" in plotoptions:
tgesy.GetYaxis().SetTitle( plotoptions["ylabel"] )
tgesy.Draw( "a"+drawas )
optlogx= plotoptions["logx"] if "logx" in plotoptions else 0
gPad.SetLogx( optlogx )
optlogy= plotoptions["logy"] if "logy" in plotoptions else 0
gPad.SetLogy( optlogy )
tgest.Draw( "same"+drawas )
return tgest, tgesy
def main(argv):
print(len(argv))
if len(argv) == 5:
#print ('The script is called: '+str(script))
print('Number of M bins is: ' + argv[0])
print('Number of t bins is: ' + argv[1])
print('Number of bootstrap samples is:' + argv[2])
print('Directory for bootstrap results for different M bins is: ' +
argv[3])
print(
'Intensity results from fitting the original data are written in: '
+ argv[4])
else:
sys.exit(
"You need to give 5 args..., usage: python script.py Nmassbins Ntbins NBootstrapsamples Bindir fitresultfile"
)
NumBins, NumtBins, kBootstrapPoints, Binfolderdir, Fitresultfile = argv
NumBins = int(NumBins)
NumtBins = int(NumtBins)
kBootstrapPoints = int(kBootstrapPoints)
## Output file to save the histograms in
file_out = TFile('Drawing_Bootstrap_errors_moments.root', 'Recreate')
with open(Fitresultfile, 'r') as f:
t = f.read()
l = t.splitlines()
amps = l[0]
amps = amps.split()[
2::2] # start at 3rd element and take every other element
N_amps = (len(l[0].split()) -
2) / 2 #first two arguments in the line are M and t
print(amps)
print(N_amps)
if N_amps < 1:
sys.exit("You should specify at least one moment")
## column number for M value and each of the amplitude intensities and
##corresponding errors
column_m = 0
column_t = 1
colomn_moments = np.arange(2, 2 * N_amps + 1, 2)
colomn_moments_err = np.arange(3, 2 * N_amps + 2, 2)
mass_bins = np.zeros(NumBins)
## Arrays of intensities from fitting the original data in different M bins
orig_moment = np.zeros((N_amps, NumtBins, NumBins))
##Reading uncertainties from MINUIT
orig_moment_err = np.zeros((N_amps, NumtBins, NumBins))
## Asigning values to arrays of moments from fitting the original fit amplitude calculation in
##different M and t bins by reading from a file
token_origdata_fitresults = open(Fitresultfile, 'r')
linestoken_orig = token_origdata_fitresults.readlines()
j = 0
fit_res_colomns = 0
for x in linestoken_orig[1:]:
fit_res_colomns = len(x.split())
mass_bins[j / NumtBins] = float(x.split()[column_m])
for Moms in range(0, N_amps):
orig_moment[Moms][int(j % NumtBins)][int(j / NumtBins)] = float(
x.split()[colomn_moments[Moms]])
orig_moment_err[Moms][int(j % NumtBins)][j / NumtBins] = float(
x.split()[colomn_moments_err[Moms]])
j = j + 1
if j != NumBins * NumtBins:
sys.exit(
"The total number of bins are different from the one provided")
## Arrays of values for given moment from
##fitting different bootstrapping samples
moments = np.zeros((N_amps, kBootstrapPoints))
#uncertainty on the given moment for each M and t bin
std_moments = np.zeros((N_amps, NumtBins, NumBins))
## Histograms of moments for given mass and t bin and moment from different bootstraping samples
h1_list_moments = []
i_Mbin = 0
for Mb in range(0, NumBins):
h1_list_moments.append([])
for tb in range(0, NumtBins):
h1_list_moments[i_Mbin].append([])
i_Mbin = i_Mbin + 1
#Defining histograms for moments from different bootstraping samples for a given aplitude and M bin
for Bin in range(0, NumBins):
#N_bins=int(300*orig_all_moments[Bin])
N_bins = int(80000)
x_min = int(-500)
for Bint in range(0, NumtBins):
for Moms in range(0, N_amps):
x_max = int(3 * orig_moment[Moms][Bint][Bin])
if Moms == 11 and Bint == 0 and Bin == 5:
x_max = 1000000
h1_list_moments[Bin][Bint].append(
TH1F(
'h1_boot_' + str(amps[Moms]) +
'_Mbin' + str(Bin + 1) + '_tbin' + str(Bint + 1),
str(amps[Moms]), N_bins, x_min, x_max))
## Assigning values to arrays of values for given moment from fitting different bootstrapping samples
## by reading the values from files of each of the M and t bins
##(number of lines in the .txt file corresponds to number of bootstraping samples + 1)
for bin in range(0, NumBins):
for bint in range(0, NumtBins):
token = open(
Binfolderdir + '/bin_' + str(bin) + '_' + str(bint) +
'/etapi_fit.txt', 'r')
linestoken = token.readlines()
i = 0
#Looping through lines corresponding to results from fitting different bottstraping samples
for x in linestoken[1:]: #First line has the names of arguments
if len(x.split()) != fit_res_colomns:
sys.exit(
"Fit results and bootstraping results have different number of moments"
)
for Moms in range(0, N_amps):
moments[Moms][i] = x.split()[colomn_moments[Moms]]
h1_list_moments[bin][bint][Moms].Fill(moments[Moms][i])
i = i + 1
token.close()
for Moms in range(0, N_amps):
std_moments[Moms][bint][bin] = moments[Moms].std()
print('I am here')
##The graphs of intensities of different Moms
error_M = np.zeros(NumBins)
grerr_list_moment = []
t_bin = 0
for Moms in range(0, N_amps):
grerr_list_moment.append(
TGraphErrors(NumBins, mass_bins, orig_moment[Moms][t_bin], error_M,
std_moments[Moms][t_bin]))
c1 = TCanvas('c1', 'A Simple Graph with error bars', 200, 10, 700, 500)
c1.Divide(3, 2)
c1.SetGrid()
c1.GetFrame().SetFillColor(21)
c1.GetFrame().SetBorderSize(12)
for Moms in range(0, N_amps):
grerr_list_moment[Moms].SetMarkerSize(.5)
grerr_list_moment[Moms].SetMarkerStyle(20)
grerr_list_moment[Moms].SetName(amps[Moms])
grerr_list_moment[Moms].SetTitle(amps[Moms])
grerr_list_moment[Moms].SetMaximum(1.2 * np.amax(orig_moment[Moms]))
grerr_list_moment[Moms].SetMinimum(0.8 * np.amin(orig_moment[Moms]))
grerr_list_moment[Moms].Draw('AP')
grerr_list_moment[Moms].SetName(amps[Moms])
grerr_list_moment[Moms].Write()
c1.Print('Plots/Moment_' + amps[Moms] + '_boot.pdf')
file_out.Write()
file_out.Close()
def comparePxcone( filename="sjm91_all.root", optKind="emin", optRate="R2" ):
pr097vals= dict()
pr097st= dict()
pr097sy= dict()
arrays= ascii2arrays( "pr097-pxcone"+optKind+".txt" )
pr097pts= arrays[0]
pr097vals["R2"]= arrays[1]
pr097st["R2"]= arrays[2]
pr097sy["R2"]= arrays[3]
pr097vals["R3"]= arrays[4]
pr097st["R3"]= arrays[5]
pr097sy["R3"]= arrays[6]
pr097vals["R4"]= arrays[7]
pr097st["R4"]= arrays[8]
pr097sy["R4"]= arrays[9]
npr097pts= len( pr097pts )
vexpr097= array( "d", npr097pts*[0.0] )
pr097vals= np.divide( pr097vals[optRate], 100.0 )
pr097st= np.divide( pr097st[optRate], 100.0 )
pr097sy= np.divide( pr097sy[optRate], 100.0 )
pr097tot= np.sqrt( np.add( np.square( pr097st ), np.square( pr097sy ) ) )
pr408vals= dict()
pr408st= dict()
pr408sy= dict()
arrays= ascii2arrays( "pr408-pxcone"+optKind+"91.txt" )
pr408pts= arrays[0]
pr408vals["R2"]= arrays[1]
pr408st["R2"]= arrays[2]
pr408sy["R2"]= arrays[3]
pr408vals["R3"]= arrays[4]
pr408st["R3"]= arrays[5]
pr408sy["R3"]= arrays[6]
pr408vals["R4"]= arrays[7]
pr408st["R4"]= arrays[8]
pr408sy["R4"]= arrays[9]
npr408pts= len( pr408pts )
vexpr408= array( "d", npr408pts*[0.0] )
pr408vals= pr408vals[optRate]
pr408st= np.divide( pr408st[optRate], 100.0 )
pr408sy= np.divide( pr408sy[optRate] , 100.0 )
pr408tot= np.sqrt( np.add( np.square( pr408st ), np.square( pr408sy ) ) )
f= TFile( filename )
aopxcone= createAnalysisObservable( f, "pxcone"+optKind+optRate )
xmax= { "R": 1.7, "emin": 27.0 }
ymax= { "R2": 1.1, "R3": 0.35, "R4": 0.18 }
xlabel= { "R": "R [rad.]", "emin": "E_{min} [GeV]" }
ylabel= { "R2": "2-jet rate", "R3": "3-jet rate", "R4": "4-jet rate" }
plotoptions= { "xmin": 0.0, "xmax": xmax[optKind], "ymin": 0.0, "ymax": ymax[optRate],
"markerStyle": 20, "markerSize": 0.8,
"xlabel": xlabel[optKind], "ylabel": ylabel[optRate],
"title": "Cone "+optKind+" "+filename }
aopxcone.plot( plotoptions )
xshift= { "R": 0.02, "emin": 0.2 }
pr097pts= np.add( pr097pts, -xshift[optKind] )
tgepr097= TGraphErrors( npr097pts, pr097pts, pr097vals, vexpr097, pr097tot )
tgepr097.SetMarkerStyle( 24 )
tgepr097.SetMarkerSize( 1.0 )
tgepr097.SetName( "pr097" )
tgepr097.Draw( "psame" )
pr408pts= np.add( pr408pts, xshift[optKind] )
tgepr408= TGraphErrors( npr408pts, pr408pts, pr408vals, vexpr408, pr408tot )
tgepr408.SetMarkerStyle( 29 )
tgepr408.SetMarkerSize( 1.0 )
tgepr408.SetName( "pr408" )
tgepr408.Draw( "psame" )
tl= TLegend( 0.7, 0.5, 0.9, 0.7 )
tl.AddEntry( "pr097", "OPAL PR097", "ep" )
tl.AddEntry( "pr408", "OPAL PR408", "ep" )
tl.AddEntry( "pxcone"+optKind+optRate, filename, "ep" )
tl.Draw()
return
vis.Draw()
c3.Update()
c3.Print(vis_pdf) # save to pdf
# Save graph to file
OutputFilevis = '%svisibility_voltage_plot_%s.root' % (OutputPath, scan)
f3 = TFile(OutputFilevis, "RECREATE")
vis.Write()
f3.Close()
# Voltage plots
# Draw graphs
voltage_pdf1 = '%sscan%s_peak1_voltage.pdf' % (PDFFilePath, scan)
voltage_pdf2 = '%sscan%s_peak2_voltage.pdf' % (PDFFilePath, scan)
voltage_pdf3 = '%sscan%s_peak3_voltage.pdf' % (PDFFilePath, scan)
c2 = TCanvas("c2", "Peak VS Voltage", 200, 10, 700, 500)
graph1v = TGraphErrors(n_points, voltage_sq, peak1_array, voltage_error, error1_array)
graph1v.SetTitle("peak 1")
graph1v.GetXaxis().SetTitle("Voltage^2 (V^2)")
graph1v.GetYaxis().SetTitle("Peaks Amp (mV)")
graph1v.Draw()
c2.Update()
c2.Print(voltage_pdf1)
c2.Clear()
graph2v = TGraphErrors(n_points, voltage_sq, peak2_array, voltage_error, error2_array)
graph2v.SetTitle("peak 2")
graph2v.GetXaxis().SetTitle("Voltage^2 (V^2)")
graph2v.GetYaxis().SetTitle("Peaks Amp (mV)")
graph2v.Draw()
c2.Update()
c2.Print(voltage_pdf2)
def compareDurhamjetrates( filename="sjm91_all.root",
datafilename="/home/iwsatlas1/skluth/Downloads/JRTMC/share/NEW/data.dat",
donkersfilename="donkers-durhamjets91.txt" ):
f= TFile( filename )
R2ao= createAnalysisObservable( f, "durhamycutfjR2" )
R3ao= createAnalysisObservable( f, "durhamycutfjR3" )
plotoptions= { "xmin": 0.0005, "xmax": 0.5, "ymin": 0.0, "ymax": 1.05, "markerStyle": 20,
"markerSize": 0.75, "title": "Durham R2 and R3 "+filename,
"xlabel": "y_{cut}", "ylabel": "Jet rates", "logx": 1 }
R2tgest, R2tgesy= R2ao.plot( plotoptions )
plotoptions["markerStyle"]= 21
R3tgest, R3tgesy= R3ao.plot( plotoptions, "s" )
arrays= ascii2arrays( datafilename )
ycutpoints= arrays[0]
R2values= np.divide( arrays[1], 100.0 )
R2sterrs= np.divide( arrays[2], 100.0 )
R2syerrs= np.divide( arrays[3], 100.0 )
R3values= np.divide( arrays[4], 100.0 )
R3sterrs= np.divide( arrays[5], 100.0 )
R3syerrs= np.divide( arrays[6], 100.0 )
R2errs= np.sqrt( np.add( np.square( R2sterrs ), np.square( R2syerrs ) ) )
R3errs= np.sqrt( np.add( np.square( R3sterrs ), np.square( R3syerrs ) ) )
n= len(ycutpoints)
xerrs= array( "d", n*[0.0] )
R2datatge= TGraphErrors( n, ycutpoints, R2values, xerrs, R2errs )
R2datatge.SetMarkerStyle( 24 )
R2datatge.SetMarkerSize( 0.75 )
R2datatge.SetName( "R2datatge" )
R2datatge.Draw( "psame" )
R3datatge= TGraphErrors( n, ycutpoints, R3values, xerrs, R3errs )
R3datatge.SetMarkerStyle( 25 )
R3datatge.SetMarkerSize( 0.75 )
R3datatge.SetName( "R3datatge" )
R3datatge.Draw( "psame" )
legend= TLegend( 0.6, 0.6, 0.9, 0.9 )
R2tgesy.SetName( "R2tgesy" )
legend.AddEntry( "R2tgesy", "OPAL R2", "pe" )
R3tgesy.SetName( "R3tgesy" )
legend.AddEntry( "R3tgesy", "OPAL R3", "pe" )
legend.AddEntry( "R2datatge", "Andrii R2", "pe" )
legend.AddEntry( "R3datatge", "Andrii R3", "pe" )
if donkersfilename:
dkarrays= ascii2arrays( donkersfilename )
dkycutpoints= np.power( 10.0, dkarrays[0] )
dkR2values= dkarrays[1]
dkR2sterrs= np.divide( dkarrays[2], 100.0 )
dkR2syerrs= np.divide( dkarrays[3], 100.0 )
dkR3values= dkarrays[4]
dkR3sterrs= np.divide( dkarrays[5], 100.0 )
dkR3syerrs= np.divide( dkarrays[6], 100.0 )
dkR2errs= np.sqrt( np.add( np.square( dkR2sterrs ), np.square( dkR2syerrs ) ) )
dkR3errs= np.sqrt( np.add( np.square( dkR3sterrs ), np.square( dkR3syerrs ) ) )
dkn= len( dkycutpoints )
dkxerrs= array( "d", dkn*[0.0] )
dkR2datatge= TGraphErrors( dkn, dkycutpoints, dkR2values, dkxerrs, dkR2errs )
dkR2datatge.SetMarkerStyle( 26 )
dkR2datatge.SetMarkerSize( 0.75 )
dkR2datatge.SetName( "dkR2datatge" )
dkR2datatge.Draw( "psame" )
dkR3datatge= TGraphErrors( dkn, dkycutpoints, dkR3values, dkxerrs, dkR3errs )
dkR3datatge.SetMarkerStyle( 27 )
dkR3datatge.SetMarkerSize( 0.75 )
dkR3datatge.SetName( "dkR3datatge" );
dkR3datatge.Draw( "psame" )
legend.AddEntry( "dkR2datatge", "Donkers R2", "pe" )
legend.AddEntry( "dkR3datatge", "Donkers R3", "pe" )
legend.Draw()
return
data_y = np.array([6 ,1 ,10 ,12 ,6 ,13 ,23 ,22 ,15 ,21 ,23 ,26 ,36 ,25 ,27 ,35 ,40 ,44 ,66 ,81, 75 , 57 ,48 ,45 ,46 ,41 ,35 ,36 ,53 ,32 ,40 ,37 ,38 ,31 ,36 ,44 ,42 ,37 ,32 ,32, 43 ,44 ,35 ,33 ,33 ,39 ,29 ,41 ,32 ,44 ,26 ,39 ,29 ,35 ,32 ,21 ,21 ,15 ,25 ,15], dtype=np.float)
sigma_x = np.array(nPoints*[0], dtype=np.float)
sigma_y = np.array(np.sqrt(data_y), dtype=np.float)
pi=np.pi
L=TF1("L","[0]*([1]/2)/(pi*((x-[2])**2+([1]/2)**2))",0.6,1.2)
Pol=TF1("Pol","[0]*x**2+[1]*x+[2]",0,3)
#Fiting data with 6 parameters-------------------------------------------------------
gStyle.SetOptFit(111) # superimpose fit results
c1=TCanvas("c1","Daten",200,10,700,500) #make nice Canvas
c1.SetGrid()
gr=TGraphErrors(nPoints,data_x,data_y,sigma_x,sigma_y)
gr.SetTitle("6 Parameter Fit")
gr.Draw("AP");
Tmp=TF1("Tmp","[0]*x**2+[1]*x+[2]+[3]*([4]/2)/(3.141*((x-[5])**2+([4]/2)**2))",0,3)
Tmp.SetParameter(3,90)
Tmp.SetParameter(4,0.9)
Tmp.SetParameter(5,0.1)
gr.Fit(Tmp)
#c1.Update()
gr.Draw('AP')
fitrp = TVirtualFitter.GetFitter()
nPar = fitrp.GetNumberTotalParameters()
covmat = TMatrixD(nPar, nPar,fitrp.GetCovarianceMatrix())
print('The Covariance Matrix is: ')
def fit():
mbc = TCanvas('mbc', 'mbc', 800, 600)
set_canvas_style(mbc)
mbc.cd()
ipoint, geexs = 0, TGraphErrors(0)
path = './txts/xs_total_' + patch + '_plot.txt'
xs_max = 0
from tools.data_base import ECMS
for line in open(path):
try:
if '#' in line: continue
fargs = map(float, line.strip().split())
ecms, xs, xserr = fargs[0], fargs[1], fargs[2]
geexs.Set(ipoint + 1)
geexs.SetPoint(ipoint, ECMS(int(ecms*1000)), xs)
geexs.SetPointError(ipoint, 0.0, xserr)
if xs > xs_max: xs_max = xs
ipoint += 1
except:
'''
'''
xtitle = '#sqrt{s}(GeV)'
ytitle = '#sigma(e^{+}e^{-}#rightarrow#pi^{+}#pi^{-}D^{+}D^{-})(pb)'
if 'BW_4230_a' not in components and 'PHSP_a' not in components: set_graph_style(geexs, xtitle, ytitle, xmin = -15., xmax = xs_max * 1.7)
else: set_graph_style(geexs, xtitle, ytitle, xmin = -5., xmax = xs_max * 1.7)
geexs.Draw('ap')
tfunc = construct_A2()
geexs.Fit(tfunc, 'E')
tfunc.Draw('same')
mbc.Update()
from tools.Resonances import Resonances
start_param_pos = 0
param = {}
for component in components:
par = []
len_param = len(Resonances[component]['init']) - len(Resonances[component]['constant'])
for i in range(start_param_pos, start_param_pos + len_param):
par.append(tfunc.GetParameter(i))
for const in Resonances[component]['constant']:
if const == 'mass': par.insert(0, Resonances[component]['init']['mass'])
if const == 'width': par.insert(1, Resonances[component]['init']['width'])
if const == 'BrGam': par.insert(2, Resonances[component]['init']['BrGam'])
if const == 'phase': par.insert(3, Resonances[component]['init']['phase'])
start_param_pos += len_param
param[component] = par
used = []
VarsManager = locals()
for i, icomponent in enumerate(components):
for j, jcomponent in enumerate(components):
if (icomponent, jcomponent) in used or (jcomponent, icomponent) in used: continue
used.append((icomponent, jcomponent))
color = 1 + i + j
if color == 5: color += 1 # not yellow
if icomponent == jcomponent and 'BW' in icomponent and 'BW' in jcomponent:
par = param[icomponent]
VarsManager['f_' + icomponent + '_' + jcomponent] = TF1(icomponent, BW_BW, xmin, xmax, len(par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetParameters(array('d', par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineColor(color)
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineStyle(ROOT.kSolid)
VarsManager['f_' + icomponent + '_' + jcomponent].Draw('same')
if icomponent == jcomponent and 'PHSP' in icomponent and 'PHSP' in jcomponent:
par = param[icomponent]
VarsManager['f_' + icomponent + '_' + jcomponent] = TF1(icomponent, PHSP_PHSP, xmin, xmax, len(par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetParameters(array('d', par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineColor(color)
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineStyle(ROOT.kSolid)
VarsManager['f_' + icomponent + '_' + jcomponent].Draw('same')
if icomponent != jcomponent and 'BW' in icomponent and 'BW' in jcomponent:
param_i = param[icomponent]
param_j = param[jcomponent]
par = param_i + param_j
VarsManager['f_' + icomponent + '_' + jcomponent] = TF1(icomponent + '_' + str(jcomponent), BW1_BW2, xmin, xmax, len(par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetParameters(array('d', par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineColor(color)
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineStyle(ROOT.kDashed)
VarsManager['f_' + icomponent + '_' + jcomponent].Draw('same')
if 'BW' in icomponent and 'PHSP' in jcomponent:
param_i = param[icomponent]
param_j = param[jcomponent]
par = param_i + param_j
VarsManager['f_' + icomponent + '_' + jcomponent] = TF1(icomponent + '_' + jcomponent, BW_PHSP, xmin, xmax, len(par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetParameters(array('d', par));
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineColor(color)
VarsManager['f_' + icomponent + '_' + jcomponent].SetLineStyle(4)
VarsManager['f_' + icomponent + '_' + jcomponent].Draw('same')
funcs = []
names = []
for key, value in VarsManager.items():
if 'f_' in key:
names.append(value.GetName())
funcs.append(value)
mbc.Update()
chi2 = tfunc.GetChisquare()
ndf = tfunc.GetNDF()
line = '#chi^{2}/ndf = ' + str(round(chi2, 1)) + '/' + str(round(ndf, 1)) + ' = ' + str(round(chi2/ndf, 1))
if not os.path.exists('./txts/'):
os.makedirs('./txts/')
label = ''
for component in components:
label += '_' + component
with open('./txts/likelihood' + label + '.txt', 'w') as f:
f.write(str(chi2) + '\n' + str(ndf))
with open('./txts/params' + label + '.txt', 'w') as f:
for key, value in par_index.items():
out = key + ' ' + str(tfunc.GetParameter(value)) + '\n'
f.write(out)
if 'BW_4230_a' not in components and 'PHSP_a' not in components: legend = TLegend(0.45, 0.6, 0.65, 0.88)
else: legend = TLegend(0.2, 0.6, 0.5, 0.88)
set_legend(legend, tfunc, names, funcs, line)
legend.Draw()
if not os.path.exists('./figs/'):
os.makedirs('./figs/')
mbc.SaveAs('./figs/xs' + label + '.pdf')
raw_input('Enter anything to end...')
def plot_data_histogram(fits, name):
return
test_canvas = TCanvas("TestCanvas", "Ds Fit", 0, 0, 800, 575)
gStyle.SetPadBorderMode(0)
gStyle.SetFrameBorderMode(0)
test_canvas.Divide(1, 2, 0, 0)
upper_pad = test_canvas.GetPad(1)
lower_pad = test_canvas.GetPad(2)
low, high = 0.05, 0.95
upper_pad.SetPad(low, 0.4, high, high)
lower_pad.SetPad(low, low, high, 0.4)
test_canvas.cd(1)
ROOT.IABstyles.canvas_style(test_canvas, 0.25, 0.05, 0.02, 0.15, 0, 0)
h_Mjj = TH1D("h_Mjj", "Mass Spectrum", 100, 0.2, 12)
h_Mjj.GetYaxis().SetTitle("num. events")
h_Mjj.GetXaxis().SetTitle("M [Tev/c^{-2}]")
ROOT.IABstyles.h1_style(h_Mjj, ROOT.IABstyles.lWidth // 2,
ROOT.IABstyles.Scolor, 1, 0, 0, -1111.0, -1111.0,
508, 508, 8, ROOT.IABstyles.Scolor, 0.1, 0)
h_Mjj.GetYaxis().SetRangeUser(0.1, 1e6)
h_Mjj.GetXaxis().SetRangeUser(1, 10)
h_Mjj.GetXaxis().SetTitleOffset(1)
h_Mjj.GetYaxis().SetTitleOffset(1.1)
upper_pad.SetLogy(1)
upper_pad.SetLogx(1)
lower_pad.SetLogx(1)
gr = TGraphErrors(fits.num_bins, array("d", fits.xmiddle),
array("d", fits.data), array("d", fits.xwidth),
array("d", fits.errors))
ROOT.IABstyles.h1_style(gr, ROOT.IABstyles.lWidth // 2,
ROOT.IABstyles.Scolor, 1, 0, 0, -1111, -1111, 505,
505, 8, ROOT.IABstyles.Scolor, 0.1, 0)
grFit = TGraph(fits.num_bins, array("d", fits.xmiddle),
array("d", fits.data_fits))
ROOT.IABstyles.h1_style(grFit, ROOT.IABstyles.lWidth // 2, 632, 1, 0, 0,
-1111, -1111, 505, 505, 8, 632, 0.1, 0)
h_Mjj.Draw("axis")
gr.Draw("P")
grFit.Draw("c")
test_canvas.Update()
gPad.SetBottomMargin(1e-5)
test_canvas.cd(2)
gPad.SetTopMargin(1e-5)
h2 = TH1D("h2", "", 100, 0.2, 12)
h2.GetXaxis().SetRangeUser(1, 10)
h2.GetYaxis().SetRangeUser(-10, 10)
h2.SetStats(False) # don't show stats box
h2.Draw("axis")
sig_values = [(data - theory) / theory if
(data != 0 and theory != 0) else -100
for data, theory in zip(fits.data, fits.data_fits)]
sig = TGraph(fits.num_bins, array("d", fits.xmiddle),
array("d", sig_values))
#ROOT.IABstyles.h1_style(sig, ROOT.IABstyles.lWidth/2, 632, 1, 0, 0, -1111, -1111, 505, 505, 8, 632, 0.1, 0)
ROOT.IABstyles.h1_style(gr, ROOT.IABstyles.lWidth // 2,
ROOT.IABstyles.Scolor, 1, 0, 0, -1111, -1111, 505,
505, 8, ROOT.IABstyles.Scolor, 0.1, 0)
sig.SetMarkerStyle(22) # triangle
sig.SetMarkerColor(2) # red
sig.SetMarkerSize(0.8)
sig.Draw("P")
#lower_pad.Draw()
label = ROOT.TText()
label.SetNDC()
label.SetTextSize(0.03)
label.DrawText(0.5, 0.7, "{0}GeV q*".format(MASS_FILE))
# test_canvas.SaveAs("output_qstar.pdf")
test_canvas.SaveAs("plots/{0}/{2}/plot-{1}-{2}-hist.png".format(
CLUSTER_ID, name, MASS_FILE))
def makePlot1D(filepath, foutname, plottitle='', masstitle=''):
br = 1 if 'Resonant' in plottitle else 0.68
limits = parseLimitFiles2D(filepath, br)
xaxis = []
xseclist = []
xsecerr = []
cent = []
obs = []
up1 = []
up2 = []
down1 = []
down2 = []
maxval = 0
minval = 999
for m in sorted(limits):
l = limits[m]
xaxis.append(m)
xseclist.append(l.xsec)
xsecerr.append(l.xsec * .2)
cent.append(l.cent)
up1.append(l.up1 - l.cent)
up2.append(l.up2 - l.cent)
down1.append(l.cent - l.down1)
down2.append(l.cent - l.down2)
obs.append(l.obs)
maxval = max(maxval, l.up2)
minval = min(minval, l.down2)
N = len(xaxis)
up1Sigma = array('f', up1)
up2Sigma = array('f', up2)
down1Sigma = array('f', down1)
down2Sigma = array('f', down2)
cent = array('f', cent)
obs = array('f', obs)
xarray = array('f', xaxis)
xsecarray = array('f', xseclist)
xsecerrarray = array('f', xsecerr)
zeros = array('f', [0 for i in xrange(N)])
graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray)
graphCent = TGraph(N, xarray, cent)
graphObs = TGraph(N, xarray, obs)
graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma,
up1Sigma)
graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma,
up2Sigma)
c = TCanvas('c', 'c', 700, 600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]')
graph2Sigma.GetYaxis().SetTitle(
'95% C.L. upper limit [#sigma/#sigma_{theory}]')
c2 = root.kOrange
c1 = root.kGreen + 1
graph2Sigma.SetLineColor(c2)
graph1Sigma.SetLineColor(c1)
graph2Sigma.SetFillColor(c2)
graph1Sigma.SetFillColor(c1)
graph2Sigma.SetMinimum(0.5 * minval)
graph2Sigma.SetMaximum(10 * maxval)
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graphObs.SetMarkerStyle(20)
graphObs.SetMarkerSize(1)
graphObs.SetMarkerColor(1)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55, 0.7, 0.9, 0.9)
leg.AddEntry(graphCent, 'Expected', 'L')
if not BLIND:
leg.AddEntry(graphObs, 'Observed', 'Lp')
leg.AddEntry(graph1Sigma, '1 std. dev.', 'F')
leg.AddEntry(graph2Sigma, '2 std. dev.', 'F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same Lp')
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
coupling = '0.1' if 'Resonant' in plottitle else '0.25'
graphXsec.SetLineColor(2)
graphXsec.SetLineWidth(2)
graphXsec.SetLineStyle(2)
graphXsec.SetFillColor(2)
graphXsec.SetFillStyle(3005)
graphXsec.Draw('same L3')
'''
if not scale:
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l')
else:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l')
'''
if not BLIND:
findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1])
findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1])
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextSize(0.8 * c.GetTopMargin())
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.15, 0.94, "CMS")
label.SetTextFont(52)
label.SetTextSize(0.6 * c.GetTopMargin())
# label.DrawLatex(0.25,0.94,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.7 * c.GetTopMargin())
label.DrawLatex(0.19, 0.83, plottitle)
if 'Resonant' in plottitle:
label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling)
label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25")
label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV")
label.SetTextSize(0.6 * c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi))
c.SaveAs(foutname + '.pdf')
c.SaveAs(foutname + '.png')
def closureTest_plots(filename):
f = BareRootFile(filename)
tree = f.Get('closureTest')
gStyle.SetStatX(0.9)
gStyle.SetStatY(0.9)
gStyle.SetStatW(0.2)
gStyle.SetStatH(0.14)
hist1 = TH1F('hist1', '', 55, 0.9, 2.0)
hist2 = TH1F('hist2', '', 55, 0.9, 2.0)
condition = ('fit_overlapDiff>=0 && temp_overlapDiff>=0 && '
'fit_chisq/fit_dof<=2 && temp_chisq/temp_dof<=2')
tree.Draw('temp_chisq/temp_dof>>hist1', condition)
tree.Draw('fit_chisq/fit_dof>>hist2', condition)
for i, hist in [(0, hist1), (1, hist2)]:
hist.SetLineColor(colors[i])
hist.SetLineWidth(3)
maxi = max(hist1.GetMaximum(), hist2.GetMaximum()) * 1.1
plot = SingleHistBase(hist1,
'hist_numberPerChisq',
fill=None,
workinprogress=wip)
gStyle.SetOptStat(10)
plot._xtitle = '#chi^{2}/d.o.f.'
plot._ytitle = 'number of toys'
plot.yrange(0.0, maxi)
leg = TLegend(0.65, 0.75, 0.89, 0.85)
leg.SetBorderSize(0)
leg.AddEntry(hist1, 'DG fit', 'L')
leg.AddEntry(hist2, 'SupDG fit', 'L')
plot.draw()
hist2.Draw('SAMEH')
leg.Draw()
plot.save_pdf()
plot.Close()
hist3 = TH2F('hist3', '', 31, -0.05, 3.05, 31, -0.05, 3.05)
tree.Draw('100*temp_overlapDiff:100*fit_overlapDiff>>hist3', condition)
one = TF1('one', 'x', -10.0, 10.0)
one.SetLineColor(1)
plot = SingleHistBase(hist3,
'correctionDGvsSupDG',
fill=None,
workinprogress=wip)
plot.add(one)
plot._drawoption = 'BOX'
plot._xtitle = 'correction [%] from SupDG fit'
plot._ytitle = 'correction [%] from DG fit'
plot._above = True
plot.draw()
plot.save_pdf()
plot.Close()
hist4 = TH2F('hist4', '', 22, 0.95, 1.5, 22, 0.95, 1.5)
tree.Draw('temp_chisq/temp_dof:fit_chisq/fit_dof>>hist4', condition)
plot = SingleHistBase(hist4,
'chisqDGvsSupDG',
fill=None,
workinprogress=wip)
plot.add(one)
plot._drawoption = 'BOX'
plot._xtitle = '#chi^{2}/d.o.f. of SupDG fit'
plot._ytitle = '#chi^{2}/d.o.f. of DG fit'
plot._above = True
plot.draw()
plot.save_pdf()
plot.Close()
gStyle.SetStatX(0.9)
gStyle.SetStatY(0.83)
gStyle.SetStatW(0.3)
gStyle.SetStatH(0.08)
#bins_csq = [(0.0, 1.1), (1.1, 1.3), (1.3, 1.6), (1.6, 2.0)]
#bins_cor = [(0.0, 0.5), (0.5, 1.0), (1.0, 1.5), (1.5, 2.0), (2.0, 2.5)]
#bins_csq = [(1.07, 1.13), (1.06, 1.11), (1.3, 1.5), (1.08, 1.17), (1.1, 1.2)]
#bins_cor = [(0.3, 0.8), (0.9, 1.2), (0.1, 0.5), (0.4, 0.7), (0.3, 1.1)]
bins_csq = [(0.99, 1.06), (0.98, 1.07), (0.99, 1.10), (1.02, 1.06),
(1.00, 1.06), (1.00, 1.04)]
bins_cor = [(0.9, 1.9), (0.4, 1.6), (0.6, 1.5), (0.6, 1.9), (1.0, 1.3),
(0.3, 1.5)]
means = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
meane = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
rmses = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
rmser = {
mod: [[0.0 for __ in bins_cor] for __ in bins_csq]
for mod in ('DG', 'SupDG')
}
#for i, (csq_lo, csq_hi) in enumerate(bins_csq):
# for j, (cor_lo, cor_hi) in enumerate(bins_cor):
for i, ((csq_lo, csq_hi), (cor_lo,
cor_hi)) in enumerate(zip(bins_csq, bins_cor)):
j = i
name = 'hist_{{0}}_{0}csq{1}_{2}cor{3}' \
.format(csq_lo, csq_hi, cor_lo, cor_hi)
fields = '100*({0}_overlapDiff-toy_overlapDiff)>>hist'
condition = (
'100*{{0}}_overlapDiff>={0} && 100*{{0}}_overlapDiff<{1} && '
'{{0}}_chisq/{{0}}_dof>={2} && {{0}}_chisq/{{0}}_dof<{3} && '
'fit_overlapDiff>=0 && temp_overlapDiff>=0 && '
'fit_chisq/fit_dof<=2 && temp_chisq/temp_dof<=2').format(
cor_lo, cor_hi, csq_lo, csq_hi)
xtitle = 'correction [%] from {0} fit #minus true correction [%]'
line1 = '{0} < correction < {1}'.format(cor_lo, cor_hi)
line2 = '{0} < #chi^{{2}}/d.o.f. < {1}'.format(csq_lo, csq_hi)
gStyle.SetOptStat(2210)
for prefix, modname in (('temp', 'DG'), ('fit', 'SupDG')):
hist = TH1F('hist', '', 41, -2.05, 2.05)
hist.StatOverflows()
tree.Draw(fields.format(prefix), condition.format(prefix))
plot = SingleHistBase(hist,
name.format(modname),
fill=None,
workinprogress=wip)
gStyle.SetOptStat(2210)
plot._xtitle = xtitle.format(modname)
plot._ytitle = 'number of toys'
plot.xrange(-2.05, 2.05)
plot.yrange(0.0, plot._graph.GetMaximum() * 1.2)
pave = plot.add_pave(0.6, 0.83, 0.9, 0.9, border=True)
pave(line1)
pave(line2)
plot.draw()
plot.save_pdf()
means[modname][i][j] = plot._graph.GetMean()
meane[modname][i][j] = plot._graph.GetMeanError()
rmses[modname][i][j] = plot._graph.GetRMS()
rmser[modname][i][j] = plot._graph.GetRMSError()
plot.Close()
multi = TMultiGraph('multi', '')
for k, modname in enumerate(('DG', 'SupDG')):
for i, (csq_lo, csq_hi) in enumerate(bins_csq):
xval = array(
'd',
[j - 0.35 + k * 0.4 + i * 0.1 for j in range(len(bins_cor))])
xerr = array('d', [0.0] * len(bins_cor))
yval = array('d', means[modname][i])
yerr = array('d', rmses[modname][i])
graph = TGraphErrors(len(bins_cor), xval, yval, xerr, yerr)
graph.SetName('graph{0}{1}'.format(k, i))
graph.SetMarkerStyle(22 + k)
graph.SetMarkerColor(colors[i])
graph.SetLineColor(colors[i])
multi.Add(graph)
minvalues = [
means[mod][i][j] - rmses[mod][i][j] for j in range(len(bins_cor))
for i in range(len(bins_csq)) for mod in ('DG', 'SupDG')
]
maxvalues = [
means[mod][i][j] + rmses[mod][i][j] for j in range(len(bins_cor))
for i in range(len(bins_csq)) for mod in ('DG', 'SupDG')
]
mini, maxi = min(minvalues), max(maxvalues)
mini, maxi = mini - 0.1 * (maxi - mini), maxi + 0.3 * (maxi - mini)
hist = TH2F('axishist', '', len(bins_cor), -0.5,
len(bins_cor) - 0.5, 100, mini, maxi)
for j, (cor_lo, cor_hi) in enumerate(bins_cor):
hist.GetXaxis().SetBinLabel(j + 1,
'{0}% #minus {1}%'.format(cor_lo, cor_hi))
leg1 = TLegend(0.53, 0.78, 0.63, 0.86)
leg1.SetBorderSize(0)
dgmarker = TMarker(0.0, 0.0, 22)
supdgmarker = TMarker(0.0, 0.0, 23)
leg1.AddEntry(dgmarker, 'DG', 'P')
leg1.AddEntry(supdgmarker, 'SupDG', 'P')
leg2 = TLegend(0.65, 0.75, 0.89, 0.89)
leg2.SetBorderSize(0)
csqmarker = TMarker(0.0, 0.0, 1)
for i, (csq_lo, csq_hi) in enumerate(bins_csq):
title = '{0} < #chi^{{2}}/d.o.f. < {1}'.format(csq_lo, csq_hi)
entry = leg2.AddEntry(csqmarker, title, 'L')
entry.SetMarkerColor(colors[i])
entry.SetLineColor(colors[i])
zero = TF1('zero', '0.0', -1.0, 10.0)
zero.SetLineColor(1)
zero.SetLineStyle(2)
plot = SingleHistBase(hist,
name='differenceDoubleDifferential',
fill=None,
workinprogress=wip)
plot._xtitle = 'correction from fit'
plot._ytitle = 'correction [%] from fit #minus true correction [%]'
plot.xrange(-0.5, len(bins_cor) - 0.5)
plot.yrange(mini, maxi)
plot._drawoption = 'AXIS'
plot.draw()
plot.xaxis().SetNdivisions(len(bins_cor), False)
plot.xaxis().SetLabelSize(0.03)
zero.Draw('SAME')
multi.Draw('P')
leg1.Draw()
leg2.Draw()
plot.save_pdf()
plot.Close()
bcid = (41, 281, 872, 1783, 2063)
DGcor1 = array('d', [0.809, 0.392, 0.846, 0.731, 0.497])
DGerr1 = array('d', [0.548, 0.567, 0.984, 0.984, 1.018])
DGcor2 = array('d', [1.145, 0.799, 1.58, 1.465, 1.281])
DGerr2 = array('d', [0.432, 0.395, 0.656, 0.656, 0.649])
DGxval = array('d', [j - 0.1 for j in range(5)])
SupDGcor1 = array('d', [0.823, 0.761, 1.458, 0.986, 1.012])
SupDGerr1 = array('d', [0.513, 0.513, 0.499, 0.513, 0.499])
SupDGcor2 = array('d', [0.978, 0.916, 1.532, 1.141, 1.086])
SupDGerr2 = array('d', [0.489, 0.489, 0.493, 0.489, 0.493])
SupDGxval = array('d', [j + 0.1 for j in range(5)])
xerr = array('d', [0.0] * 5)
for fill, values in [
(4266, {
'DGcor': [1.021, 1.057, 0.968, 1.084, 1.114],
'DGerr': [(e**2 + 0.74**2)**0.5
for e in (0.118, 0.124, 0.119, 0.117, 0.119)],
'SupDGcor': [1.402, 1.411, 1.164, 1.549, 1.589],
'SupDGerr': [(e**2 + 0.45**2)**0.5
for e in (0.106, 0.110, 0.108, 0.106, 0.115)],
'bcids': [51, 771, 1631, 2211, 2674]
}),
(4954, {
'DGcor': [0.799, 0.398, 0.845, 0.724, 0.502],
'DGerr': [(e**2 + 0.79**2)**0.5
for e in (0.137, 0.124, 0.122, 0.130, 0.116)],
'SupDGcor': [0.794, 0.694, 1.642, 0.983, 0.993],
'SupDGerr': [(e**2 + 0.50**2)**0.5
for e in (0.126, 0.112, 0.186, 0.102, 0.144)],
'bcids': [41, 281, 872, 1783, 2063]
}),
(4937, {
'DGcor': [0.649, 0.494, 0.575, 0.527, 0.602],
'DGerr':
[(e**2 + 0.85**2)**0.5 for e in (0.127, 0.115, 0.120, 0.125)],
'SupDGcor': [0.377, 0.611, 1.137, 0.453, 1.840],
'SupDGerr': [(e**2 + 0.56**2)**0.5
for e in (0.100, 0.105, 0.288, 0.161, 0.207)],
'bcids': [81, 875, 1610, 1690, 1730]
}),
(6016, {
'DGcor': [0.146, 0.394, 0.377, 0.488, 0.184],
'DGerr': [(e**2 + 1.15**2)**0.5
for e in (0.110, 0.114, 0.118, 0.123, 0.109)],
'SupDGcor': [0.760, 0.953, 1.048, 0.847, 0.373],
'SupDGerr': [(e**2 + 0.79**2)**0.5
for e in (0.219, 0.094, 0.189, 0.098, 0.169)],
'bcids': [41, 281, 872, 1783, 2063]
})
]:
bcid = values['bcids']
DGcor = array('d', values['DGcor'])
DGerr = array('d', values['DGerr'])
DGxvl = array('d', [j - 0.1 for j in range(len(bcid))])
SupDGcor = array('d', values['SupDGcor'])
SupDGerr = array('d', values['SupDGerr'])
SupDGxvl = array('d', [j + 0.1 for j in range(len(bcid))])
xerr = array('d', [0.0] * len(bcid))
maxi = max(max([v + e for v, e in zip(DGcor, DGerr)]),
max([v + e for v, e in zip(SupDGcor, SupDGerr)]))
mini = min(min([v - e for v, e in zip(DGcor, DGerr)]),
min([v - e for v, e in zip(SupDGcor, SupDGerr)]))
maxi, mini = maxi + 0.2 * (maxi - mini), mini - 0.1 * (maxi - mini)
graphDG = TGraphErrors(len(bcid), DGxvl, DGcor, xerr, DGerr)
graphDG.SetName('graphDG')
graphSupDG = TGraphErrors(len(bcid), SupDGxvl, SupDGcor, xerr,
SupDGerr)
graphSupDG.SetName('graphSupDG')
multi = TMultiGraph('multi', '')
for i, graph in [(0, graphDG), (1, graphSupDG)]:
graph.SetMarkerStyle(22 + i)
graph.SetMarkerColor(colors[i])
graph.SetLineColor(colors[i])
multi.Add(graph)
leg = TLegend(0.15, 0.80, 0.5, 0.83)
leg.SetNColumns(2)
leg.SetBorderSize(0)
leg.AddEntry(graphDG, 'DG fit', 'PL')
leg.AddEntry(graphSupDG, 'SupDG fit', 'PL')
axishist = TH2F('axishist', '', len(bcid), -0.5,
len(bcid) - 0.5, 100, mini, maxi)
for i, bx in enumerate(bcid):
axishist.GetXaxis().SetBinLabel(i + 1, '{0}'.format(bx))
plot = SingleHistBase(axishist,
name='Fill{0}biased'.format(fill),
fill=fill,
workinprogress=wip)
plot._xtitle = 'BCID'
plot._ytitle = 'correction [%] from fit'
plot.xrange(-0.5, len(bcid) - 0.5)
plot.yrange(mini, maxi)
plot._drawoption = 'AXIS'
plot.draw()
plot.xaxis().SetNdivisions(len(bcid), False)
plot.xaxis().SetLabelSize(0.03)
multi.Draw('P')
leg.Draw()
plot.save_pdf()
plot.Close()
print
print 'Fill', fill
for bx, cor, err in zip(bcid, SupDGcor, SupDGerr):
print 'BCID {0}:\t{1:.2f} +- {2:.2f}'.format(bx, cor, err)
print
#linearizzazione
intensity = [1 / x**0.5 for x in intensity]
intensity_error = [
0.5 * i**3 * old_err for i, old_err in zip(intensity, intensity_error)
]
out_intensity = "intensity.out"
with open(out_intensity, "w") as out_file:
for dist, intens, int_err in zip(prefissi, intensity, intensity_error):
line = dist + " " + str(intens) + " " + "0" + " " + str(int_err) + "\n"
out_file.write(line)
can = TCanvas("dist", "dist")
can.SetFillColor(0)
intensity_graph = TGraphErrors(out_intensity)
intensity_graph.SetTitle()
line = TF1("line", "pol1")
intensity_graph.SetMarkerStyle(8)
intensity_graph.Fit("line")
intensity_graph.GetYaxis().SetDecimals()
intensity_graph.GetYaxis().SetTitle("I^{-1/2} #[]{n_events^{-1/2}}")
intensity_graph.GetYaxis().SetDecimals()
intensity_graph.GetXaxis().SetTitle("distance #[]{mm}")
intensity_graph.Draw("ap")
r = intensity_graph.GetCorrelationFactor() #correlation coefficient=?
print(r)
#r = 0.826 #correlation coefficient=?
npoints = 6
ndf = npoints - 2
t = r * sqrt(ndf / (1 - r**2))
def analyzeData(country, total, active, recovered, deaths, tStart, tStop,
totalPopulation, symptomaticFraction, transmissionProbability,
recoveryRate, doSmearing, doFit):
ntuple = [
tStart, tStop, totalPopulation, symptomaticFraction,
transmissionProbability, recoveryRate
]
farFromMax = 0.95
growthRate = 0.13
carryingCapacity = 4e5
################
# Active cases #
################
myCanvActive = TCanvas('myCanvActive_' + country,
'Active cases ' + country)
xValues = [
i for i in range(len(active.keys())) if i >= tStart and i <= tStop
]
yValues = [
active[k] for i, k in enumerate(sorted(active.keys()))
if i >= tStart and i <= tStop
]
erryValues = assignErrors(yValues)
myGraphActive = TGraphErrors()
myGraphActive.SetMarkerStyle(20)
for i in range(len(xValues)):
myGraphActive.SetPoint(myGraphActive.GetN(), xValues[i], yValues[i])
myGraphActive.SetPointError(myGraphActive.GetN() - 1, 0, erryValues[i])
myGraphActive.Draw('APE1')
myGraphActive.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphActive.GetHistogram().GetYaxis().SetTitle(
'Active cases affected by CoViD-19')
historyActive = 0.
for i, k in enumerate(sorted(active.keys())):
if i < tStart:
historyActive += active[k]
ntuple.extend([historyActive, xValues, yValues, erryValues])
print('==> History active cases:', historyActive)
###################
# Build the model #
###################
evActive = evolution([yValues[0], carryingCapacity, growthRate], tStart,
tStop, totalPopulation, recoveryRate,
symptomaticFraction, transmissionProbability,
historyActive)
if doFit == True:
evActive.runOptimization(xValues, yValues, erryValues, [], doSmearing)
evActive.evolve(evActive.tStop, evActive.parValues, True)
if doSmearing == True:
evActive.smearing()
evActiveGraphN = evActive.getGraphN()
evActiveGraphN.Draw('PL same')
statActive = evActive.addStats(evActive.parNames, evActive.parValues)
print(
'==> Active cases, history active cases, p-infected, Carrying capacity, total population alive',
evActive.evolve(tStop, evActive.parValues), 'at day', tStop)
print('==> Percentage population with antibodies',
round(100. * evActive.totalInfected(tStop) / totalPopulation),
'% at day', tStop, '(herd immunity at', evActive.herdImmunity(),
'%)')
print('==> Doubling time:', round(log(2.) / evActive.parValues[2], 1),
'days')
now = TLine(
len(active) - 1, 0,
len(active) - 1, evActive.fitFun.Eval(len(active) - 1))
now = TLine(len(active) - 1, 0, len(active) - 1, 1)
now.SetLineColor(4)
now.SetLineWidth(2)
now.Draw('same')
willbe = TLine(evActive.fitFun.GetMaximumX(), 0,
evActive.fitFun.GetMaximumX(),
evActive.fitFun.GetMaximum())
willbe.SetLineColor(6)
willbe.SetLineWidth(2)
willbe.Draw('same')
myCanvActiveR0 = TCanvas('myCanvActiveR0_' + country, 'R0 ' + country)
evActiveGraphR0 = evActive.getGraphR0(evActiveGraphN)
evActiveGraphR0.Draw('APL')
evActiveGraphR0.GetHistogram().GetXaxis().SetTitle('Time (days)')
evActiveGraphR0.GetHistogram().GetYaxis().SetTitle('R')
myCanvActiveR0.SetGrid()
myCanvActiveR0.Modified()
myCanvActiveR0.Update()
myCanvActive.SetGrid()
myCanvActive.Modified()
myCanvActive.Update()
#################
# CONTROL PLOTS #
#################
###############
# Total cases #
###############
myCanvTotal = TCanvas('myCanvTotal_' + country, 'Total cases ' + country)
myGraphTotal = TGraphErrors()
myGraphTotal.SetMarkerStyle(20)
for k in sorted(total.keys()):
myGraphTotal.SetPoint(myGraphTotal.GetN(), myGraphTotal.GetN(),
total[k])
myGraphTotal.SetPointError(myGraphTotal.GetN() - 1, 0, sqrt(total[k]))
myGraphTotal.Draw('APE1')
myGraphTotal.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphTotal.GetHistogram().GetYaxis().SetTitle(
'Total cases affected by CoViD-19')
myCanvTotal.SetGrid()
myCanvTotal.Modified()
myCanvTotal.Update()
##########
# Deaths #
##########
myCanvDeaths = TCanvas('myCanvDeaths_' + country, 'Deaths ' + country)
myGraphDeaths = TGraphErrors()
myGraphDeaths.SetMarkerStyle(20)
for k in sorted(deaths.keys()):
myGraphDeaths.SetPoint(myGraphDeaths.GetN(), myGraphDeaths.GetN(),
deaths[k])
myGraphDeaths.SetPointError(myGraphDeaths.GetN() - 1, 0,
sqrt(deaths[k]))
myGraphDeaths.Draw('APE1')
myGraphDeaths.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphDeaths.GetHistogram().GetYaxis().SetTitle('Total deaths')
myCanvDeaths.SetGrid()
myCanvDeaths.Modified()
myCanvDeaths.Update()
########################
# Ratio deaths / total #
########################
myCanvRatio01 = TCanvas('myCanvRatio01_' + country, 'Ratio ' + country)
myGraphRatio01 = TGraphErrors()
myGraphRatio01.SetMarkerStyle(20)
for k in sorted(deaths.keys()):
myGraphRatio01.SetPoint(myGraphRatio01.GetN(), myGraphRatio01.GetN(),
deaths[k] / total[k])
myGraphRatio01.SetPointError(
myGraphRatio01.GetN() - 1, 0,
myGraphRatio01.GetY()[myGraphRatio01.GetN() - 1] *
sqrt(deaths[k] / (deaths[k] * deaths[k]) + total[k] /
(total[k] * total[k])))
myGraphRatio01.Draw('APE1')
myGraphRatio01.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphRatio01.GetHistogram().GetYaxis().SetTitle(
'Total deaths / Total cases')
myCanvRatio01.SetGrid()
myCanvRatio01.Modified()
myCanvRatio01.Update()
############################################
# Ratio delta(deaths + recovered) / active #
############################################
myCanvRatio02 = TCanvas('myCanvRatio02_' + country, 'Ratio ' + country)
myGraphRatio02 = TGraphErrors()
myGraphRatio02.SetMarkerStyle(20)
sortedKeys = sorted(deaths.keys())
for i, k in enumerate(sortedKeys[1:]):
numerator = abs(deaths[k] - deaths[sortedKeys[i]] + recovered[k] -
recovered[sortedKeys[i]])
denominator = active[k]
myGraphRatio02.SetPoint(
myGraphRatio02.GetN(),
myGraphRatio02.GetN() + 1,
numerator / denominator if denominator != 0 else 0)
myGraphRatio02.SetPointError(
myGraphRatio02.GetN() - 1, 0,
(myGraphRatio02.GetY()[myGraphRatio02.GetN() - 1] *
sqrt(numerator / pow(numerator, 2) +
denominator / pow(denominator, 2)))
if denominator != 0 else 0)
myGraphRatio02.Draw('AP')
myGraphRatio02.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphRatio02.GetHistogram().GetYaxis().SetTitle(
'#Delta Recovered (alive + dead) / Active cases')
myCanvRatio02.SetGrid()
myCanvRatio02.Modified()
myCanvRatio02.Update()
######################################
# Ratio delta(deaths) / delta(total) #
######################################
myCanvRatio03 = TCanvas('myCanvRatio03_' + country, 'Ratio ' + country)
myGraphRatio03 = TGraphErrors()
myGraphRatio03.SetMarkerStyle(20)
sortedKeys = sorted(deaths.keys())
for i, k in enumerate(sortedKeys[1:]):
numerator = abs(deaths[k] - deaths[sortedKeys[i]])
denominator = total[k] - total[sortedKeys[i]]
myGraphRatio03.SetPoint(
myGraphRatio03.GetN(),
myGraphRatio03.GetN() + 1,
numerator / denominator if denominator != 0 else 0)
myGraphRatio03.SetPointError(
myGraphRatio03.GetN() - 1, 0,
(myGraphRatio03.GetY()[myGraphRatio03.GetN() - 1] *
sqrt(numerator / pow(numerator, 2) +
denominator / pow(denominator, 2)))
if denominator != 0 else 0)
myGraphRatio03.Draw('AP')
myGraphRatio03.GetHistogram().GetXaxis().SetTitle('Time (days)')
myGraphRatio03.GetHistogram().GetYaxis().SetTitle(
'#Delta deaths / #Delta total')
myCanvRatio03.SetGrid()
myCanvRatio03.Modified()
myCanvRatio03.Update()
if doFit == False:
return [
ntuple, myCanvTotal, myGraphTotal, myCanvActive, myGraphActive,
myCanvDeaths, myGraphDeaths, myCanvRatio01, myGraphRatio01,
myCanvRatio02, myGraphRatio02, myCanvRatio03, myGraphRatio03
]
return [
ntuple, myCanvTotal, myGraphTotal, myCanvActive, myGraphActive,
evActive, evActiveGraphN, statActive, now, willbe, myCanvActiveR0,
evActiveGraphR0, myCanvDeaths, myGraphDeaths, myCanvRatio01,
myGraphRatio01, myCanvRatio02, myGraphRatio02, myCanvRatio03,
myGraphRatio03
]
def plotDistributionComparisonPlot(cfg):
multiGraph = TMultiGraph()
multiGraph.SetName("triggerRateMultiGraph")
tfiles = []
histograms = []
canvas = TCanvas("canvas", "canvas", 800, 800)
'''Contains the legend'''
legend = TLegend(0.3, 0.7, 0.90, 0.9)
'''Maximum value container, used to scale histograms'''
maximumY = float("-inf")
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.05) # Upper and lower plot are joined
#pad1.SetBottomMargin(0) # Upper and lower plot are joined
pad1.SetGridx() # Vertical grid
pad1.Draw() # Draw the upper pad: pad1
pad1.cd() # pad1 becomes the current pad
for histogramFileNameAndTitle in cfg.plots:
tfile = TFile(histogramFileNameAndTitle[0])
tfiles.append(tfile)
histogram = tfile.Get(histogramFileNameAndTitle[1])
histograms.append(histogram)
if histogram.ClassName() == "TH1F":
histogram.SetStats(0) # No statistics on upper plot
maximumY = histogram.GetMaximum(
) if histogram.GetMaximum() > maximumY else maximumY
legend.AddEntry(histogram, histogramFileNameAndTitle[2], "l")
# histograms[0] settings
histograms[0].SetMarkerColor(4)
histograms[0].SetLineColor(4)
histograms[0].SetLineWidth(1)
# Y axis histograms[0] plot settings
histograms[0].GetYaxis().SetTitleSize(20)
histograms[0].GetYaxis().SetTitleFont(43)
histograms[0].GetYaxis().SetTitleOffset(1.55)
#histograms[0].Scale(1./histograms[0].GetEntries())
if histograms[0].ClassName() == "TH1F":
histograms[0].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[0].Draw(
"SAME APE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[0])
if getattr(cfg, "xRange", None) is not None:
histograms[0].GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "xAxisLabel", None) is not None:
histograms[0].GetXaxis().SetTitle(cfg.xAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yAxisLabel", None) is not None:
histograms[0].GetYaxis().SetTitle(cfg.yAxisLabel)
gPad.RedrawAxis()
if getattr(cfg, "yRange", None) is not None:
histograms[0].GetYaxis().SetRangeUser(cfg.yRange[0], cfg.yRange[1])
gPad.RedrawAxis()
else:
maximumY *= 1.1
histograms[0].GetYaxis().SetRangeUser(1e-6, maximumY)
if getattr(cfg, "logY", False):
canvas.SetLogy()
# histograms[1] settings
histograms[1].SetMarkerColor(2)
histograms[1].SetLineColor(2)
histograms[1].SetLineWidth(1)
#histograms[1].Scale(1./histograms[1].GetEntries())
if histograms[1].ClassName() == "TH1F":
histograms[1].Draw(
"SAME HIST") # Draw histograms[1] on top of histograms[0]
else:
histograms[1].Draw(
"SAME PE") # Draw histograms[1] on top of histograms[0]
#multiGraph.Add(histograms[1])
#if multiGraph.GetListOfGraphs() != None:
# multiGraph.Draw("SAME PE")
# Do not draw the Y axis label on the upper plot and redraw a small
# axis instead, in order to avoid the first label (0) to be clipped.
#histograms[0].GetYaxis().SetLabelSize(0.)
#axis = TGaxis( 0, 20, 0, maximumY, 20, maximumY, 510,"")
#axis.SetLabelFont(43) # Absolute font size in pixel (precision 3)
#axis.SetLabelSize(15)
#axis.Draw()
# Adding a small text with the chi-squared
chiSquared = 0
if (histograms[0].ClassName() == "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
numberOfBins = histograms[0].GetN()
numberOfDegreesOfFreedom = numberOfBins
else:
numberOfBins = histograms[0].GetNbinsX()
numberOfDegreesOfFreedom = numberOfBins
for x in xrange(
1, numberOfBins + 1
): # numberOfBins contains last bin, numberOfBins+1 contains the overflow (latter excluded), underflow also excluded
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName() == "TGraphErrors"):
binContent0 = histograms[0].GetY()[x - 1]
else:
binContent0 = histograms[0].GetBinContent(x)
if (histograms[1].ClassName()
== "TGraph") or (histograms[1].ClassName() == "TGraphErrors"):
binContent1 = histograms[1].GetY()[x - 1]
else:
binContent1 = histograms[1].GetBinContent(x)
bin0ErrorSquared = binContent0
bin1ErrorSquared = binContent1
#bin1ErrorSquared = 0
if (binContent0 == 0) and (binContent1 == 0):
numberOfDegreesOfFreedom -= 1 #No data means one less degree of freedom
else:
binDifferenceSquared = (binContent0 - binContent1)**2
chiSquaredTerm = binDifferenceSquared / (bin0ErrorSquared +
bin1ErrorSquared)
chiSquared += chiSquaredTerm
if chiSquaredTerm > chiSquaredWarningThreshold:
if (histograms[0].ClassName()
== "TGraph") or (histograms[0].ClassName()
== "TGraphErrors"):
print "Bin", x, "-", histograms[0].GetX()[
x - 1], "has a CS=", chiSquaredTerm
else:
print "Bin", x, "-", histograms[0].GetBinCenter(
x), "has a CS=", chiSquaredTerm
chiSquareLabel = TPaveText(0.7, 0.6, 0.9, 0.4)
chiSquareLabel.AddText("#chi^{2}/ndf = " + str(chiSquared) + "/" +
str(numberOfDegreesOfFreedom) + " = " +
str(chiSquared / numberOfDegreesOfFreedom))
chiSquareLabel.Draw()
print "FINAL CS IS", format(
chiSquared,
".2f") + "/" + str(numberOfDegreesOfFreedom) + " = " + format(
chiSquared / numberOfDegreesOfFreedom, ".2f")
legend.SetHeader(
"#chi^{2}/ndf = " + format(chiSquared, ".2f") + "/" +
str(numberOfDegreesOfFreedom) + " = " +
format(chiSquared / numberOfDegreesOfFreedom, ".2f"), "C")
legend.Draw()
# lower plot will be in pad
canvas.cd() # Go back to the main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0)
pad2.SetBottomMargin(0.2)
pad2.SetGridx() # vertical grid
pad2.Draw()
pad2.cd() # pad2 becomes the current pad
pad2.SetGridy()
# Define the ratio plot
ratioPlot = TGraphErrors(histograms[0])
ratioPlot.SetName("ratioPlot")
graph_histo0 = TGraphErrors(histograms[0])
graph_histo1 = TGraphErrors(histograms[1])
ratioPlot.SetLineColor(1)
ratioPlot.SetMinimum(0.6) # Define Y ..
ratioPlot.SetMaximum(1.5) # .. range
#ratioPlot.Sumw2()
#ratioPlot.SetStats(0) # No statistics on lower plot
#Dividing point by point
for index in xrange(0, ratioPlot.GetN()):
if graph_histo1.GetY()[index] == 0:
ratioPlot.GetY()[index] = 0
ratioPlot.GetEY()[index] = 0
else:
ratioPlot.GetY()[index] /= graph_histo1.GetY()[index]
ratioPlot.GetEY()[index] = sqrt(
((graph_histo1.GetY()[index])**2 *
(graph_histo0.GetEY()[index])**2 +
(graph_histo0.GetY()[index])**2 *
(graph_histo1.GetEY()[index])**2) /
(graph_histo1.GetY()[index])**4)
ratioPlot.SetMarkerStyle(21)
if getattr(cfg, "xRange", None) is not None:
ratioPlot.GetXaxis().SetRangeUser(cfg.xRange[0], cfg.xRange[1])
gPad.RedrawAxis()
if getattr(cfg, "yRangeRatio", None) is not None:
ratioPlot.GetYaxis().SetRangeUser(cfg.yRangeRatio[0],
cfg.yRangeRatio[1])
gPad.RedrawAxis()
ratioPlot.Draw("APE") # Draw the ratio plot
line0 = TLine(ratioPlot.GetXaxis().GetXmin(), 1,
ratioPlot.GetXaxis().GetXmax(), 1)
line0.SetLineColor(2)
line0.SetLineWidth(2)
line0.SetLineStyle(2)
line0.Draw()
# Ratio plot (ratioPlot) settings
ratioPlot.SetTitle("") # Remove the ratio title
# Y axis ratio plot settings
ratioPlot.GetYaxis().SetTitle("Ratio #frac{blue}{red}")
ratioPlot.GetYaxis().SetNdivisions(505)
ratioPlot.GetYaxis().SetTitleSize(20)
ratioPlot.GetYaxis().SetTitleFont(43)
ratioPlot.GetYaxis().SetTitleOffset(1.55)
ratioPlot.GetYaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetYaxis().SetLabelSize(15)
# X axis ratio plot settings
ratioPlot.GetXaxis().SetTitleSize(20)
ratioPlot.GetXaxis().SetTitleFont(43)
ratioPlot.GetXaxis().SetTitleOffset(4.)
ratioPlot.GetXaxis().SetLabelFont(
43) # Absolute font size in pixel (precision 3)
ratioPlot.GetXaxis().SetLabelSize(15)
xRangeBinning = getattr(cfg, "simplifiedRatioPlotXRangeBinning", None)
if xRangeBinning is not None:
simplifiedRatioPlot = TGraphErrors(len(xRangeBinning) - 1)
simplifiedRatioPlot.SetName("simplifiedRatioPlot")
ratioPlotIndex = 0
for idx in xrange(0, simplifiedRatioPlot.GetN()):
yAverage = 0.
yMax = float("-inf")
yMin = float("+inf")
nPoints = 0.
simplifiedRatioPlot.GetX()[idx] = (xRangeBinning[idx] +
xRangeBinning[idx + 1]) / 2.
simplifiedRatioPlot.GetEX()[idx] = (xRangeBinning[idx + 1] -
xRangeBinning[idx]) / 2.
while (ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx]):
ratioPlotIndex += 1
while ((ratioPlotIndex < ratioPlot.GetN()) and
(ratioPlot.GetX()[ratioPlotIndex] < xRangeBinning[idx + 1])
and
(ratioPlot.GetX()[ratioPlotIndex] >= xRangeBinning[idx])):
yAverage += ratioPlot.GetY()[ratioPlotIndex]
if (yMax < ratioPlot.GetY()[ratioPlotIndex] +
ratioPlot.GetEY()[ratioPlotIndex]):
yMax = ratioPlot.GetY()[ratioPlotIndex] + ratioPlot.GetEY(
)[ratioPlotIndex]
if (yMin > ratioPlot.GetY()[ratioPlotIndex] -
ratioPlot.GetEY()[ratioPlotIndex]):
yMin = ratioPlot.GetY()[ratioPlotIndex] - ratioPlot.GetEY(
)[ratioPlotIndex]
nPoints += 1.
ratioPlotIndex += 1
simplifiedRatioPlot.GetY()[idx] = yAverage / nPoints
simplifiedRatioPlot.GetEY()[idx] = (yMax - yMin) / 2.
saveFile = TFile(cfg.saveFileName, "RECREATE")
saveFile.cd()
canvas.Write()
histograms[0].Write()
histograms[1].Write()
if multiGraph.GetListOfGraphs() != None:
multiGraph.Write()
ratioPlot.Write()
if xRangeBinning is not None:
simplifiedRatioPlot.Write()
saveFile.Close()
for tfile in tfiles:
tfile.Close()
def plot_2(var, cuts):
for s in attr:
if ct_dep == 0:
c1 = TCanvas("c1", "Signals", 800, 800)
c1.cd()
c1.SetGrid()
#gStyle.SetTitleFontSize(8.1)
if s in ('elf', 'muf', 'chm', 'cm', 'nm'):
c1.SetLogx()
for cc in channel:
hist[cc][s].Draw('colz same')
hist_CHS[cc][s].Draw('colz same')
legend = TLegend(0.90, 0.90, 0.99, 0.99)
#legend.SetHeader('Samples')
for cc in channel:
legend.AddEntry(hist[cc][s], cc)
legend.AddEntry(hist_CHS[cc][s], cc + 'CHS')
legend.Draw()
c1.Print(path1 + s + var +
cuts.replace('(', '_').replace(')', '_').replace(
'&&', 'A').replace('>', 'LG').replace('<', 'LS').
replace('=', 'EQ').replace('.', 'P').replace('-', 'N').
replace('Jet', 'J').replace('GenBquark', 'GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')
elif ct_dep == 1:
eac0 = str(entries_after_cut['ct0'][s])
c1 = TCanvas("c1", "Signals", 800, 800)
c1.SetGrid()
c1.cd()
c1.SetLogx()
#gr = TGraph( len_of_lt , x , yy['sgn'][s] )
gr = TGraphErrors(len_of_lt, x, yy['sgn'][s], ex, ey['sgn'][s])
gr.SetMarkerSize(1.5)
gr.SetMarkerStyle(21)
gr.SetLineColor(4)
gr.SetLineWidth(4)
gr.SetTitle(
'averaged ' + s + ' cut: ' +
cuts.replace('(', '_').replace(')', '_').replace('&&', ',').
replace('Jet', 'J').replace('GenBquark', 'GBQ') + '[entries:' +
eac0 + ']')
gr.GetXaxis().SetTitle('decaying length (mm)')
gr.GetYaxis().SetTitle('mean frequency')
gr.SetName('sgn')
gr.Draw('ACP') # '' sets up the scattering style
gr1 = TGraphErrors(len_of_lt, x, yy['qcd'][s], ex, ey['qcd'][s])
gr1.SetMarkerSize(1.0)
gr1.SetMarkerStyle(2)
gr1.SetLineColor(2)
gr1.SetLineWidth(2)
gr1.SetName('qcd')
#gr1.SetTitle('averaged ' + s)
#gr1.GetXaxis().SetTitle('decaying length (mm)')
#gr1.GetYaxis().SetTitle('mean frequency')
gr1.Draw('CP') # '' sets up the scattering style
legend = TLegend(0.89, 0.89, 0.99, 0.99)
legend.AddEntry('qcd', legendb)
legend.AddEntry('sgn', legends)
legend.Draw()
c1.Print(path1 + 'mean_' + s + var +
cuts.replace('(', '_').replace(')', '_').replace(
'&&', 'A').replace('>', 'LG').replace('<', 'LS').
replace('=', 'EQ').replace('.', 'P').replace('-', 'N').
replace('Jet', 'J').replace('GenBquark', 'GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')
pdfGraph = None
if plotPDF :
# plot PDF asymmetry in time bins
if blind or not pdfBlindVals :
vals = pdfVals
valsTot = pdfValsTot if plotComp else pdfVals
else :
vals = pdfBlindVals
valsTot = pdfBlindValsTot if plotComp else pdfBlindVals
pdfArr = array( 'd', [ ( pVals[frac] - mVals[frac] ) / ( pValsTot[frac] + mValsTot[frac] )\
for pVals, mVals, pValsTot, mValsTot in zip( vals['plus'], vals['minus'], valsTot['plus'], valsTot['minus'] )\
for frac in timeFracs ] )
for it in range( len(timeFracs) + 1 ) : pdfArr.append( pdfArr[it] )
pdfErrArr = array( 'd', [ 0. ] * len(pdfArr) )
pdfGraph = Graph( len(timeArrPdf), timeArrPdf, pdfArr, timeErrArrPdf, pdfErrArr )
pdfGraph.SetName('pdf')
pdfIntGraph = None
if plotPDFInt :
# plot integrated-PDF asymmetry in time bins
if blind or not pdfIntBlindVals :
intVals = pdfIntVals
else :
intVals = pdfIntBlindVals
pdfIntArr = array( 'd', [ 2. * pVal / ( pVal + mVal ) - 1. for pVal, mVal in zip( intVals['plus'], intVals['minus'] ) ] )
pdfIntArr.append( pdfIntArr[0] )
pdfIntErrArr = array( 'd', [ 0. ] * len(pdfIntArr) )
pdfIntGraph = Graph( len(timeArr), timeArr, pdfIntArr, timeErrArr, pdfIntErrArr )
pdfIntGraph.SetName('pdfInt')