legFrac = TLegend(0.2, 0.79, 0.4, 0.89)
legFrac.SetFillStyle(0)
legFrac.SetBorderSize(0)
legFrac.SetTextSize(0.045)
latInfo = TLatex()
latInfo.SetNDC()
latInfo.SetTextSize(0.045)
latInfo.SetTextFont(42)
latInfo.SetTextColor(1)
hCorrYieldPrompt = hRawYields[0].Clone('hCorrYieldPrompt')
hCorrYieldPrompt.SetTitle(';#it{p}_{T} (GeV/#it{c}); #it{N}_{prompt}')
SetObjectStyle(hCorrYieldPrompt,
color=kRed + 1,
fillcolor=kRed + 1,
markerstyle=kFullCircle)
hCorrYieldFD = hRawYields[0].Clone('hCorrYieldFD')
hCorrYieldFD.SetTitle(';#it{p}_{T} (GeV/#it{c}); #it{N}_{non-prompt}')
SetObjectStyle(hCorrYieldFD,
color=kAzure + 4,
fillcolor=kAzure + 4,
markerstyle=kFullSquare)
hCovCorrYields = [[
hRawYields[0].Clone('hCovPromptPrompt'),
hRawYields[0].Clone('hCovPromptFD')
], [hRawYields[0].Clone('hCovFDPrompt'), hRawYields[0].Clone('hCovFDFD')]]
for iRow, row in enumerate(hCovCorrYields):
for iCol, hCov in enumerate(row):
cPtDFromB[D].DrawFrame(
0., 1.e-7, 50.05, 2.,
';d#it{p}_{T} (GeV/#it{c});d#sigma/d#it{p}_{T} #times BR (#mub GeV^{-1} #it{c});'
)
cPtDFromB[D].SetLogy()
for iB, (B, FFb) in enumerate(zip(Bhadrons, FFbtoB)):
kineDfSelDSelB = kineDfSelDAcc.query(f'pdgB == {B}')
hPtDFromB[D][B] = TH1F(f'hPt{D}From{B}', '', 1001, 0., 50.05)
for pt in kineDfSelDSelB['ptD'].to_numpy():
hPtDFromB[D][B].Fill(pt)
hPtDFromB[D][B].Sumw2()
hPtDFromB[D][B].Scale(1.e-6 * BRBhadronsToD[B][D] * FFb * norm * acc *
BRD / hPtDFromB[D][B].Integral())
SetObjectStyle(hPtDFromB[D][B],
linecolor=Bcolors[iB],
markercolor=Bcolors[iB],
linewidth=1,
markerstyle=0)
hPtDFromB[D][B].Draw('same')
leg.AddEntry(hPtDFromB[D][B], f'{Bnames[B]} #rightarrow {Dnames[D]}',
'le')
hPtDFromB[D]['sum'] = hPtDFromB[D][B].Clone(f'hPt{D}FromAllB')
SetObjectStyle(hPtDFromB[D]['sum'],
linecolor=kBlack,
markercolor=kBlack,
linewidth=1,
markerstyle=0)
hPtDFromB[D]['sum'].Reset()
for B in Bhadrons:
hPtDFromB[D]['sum'].Add(hPtDFromB[D][B])
hEffPrompt = TH1F('hEffPrompt', ';#it{p}_{T} (GeV/#it{c});Efficiency', nPtBins,
np.asarray(ptLims, 'd'))
hEffFD = TH1F('hEffFD', ';#it{p}_{T} (GeV/#it{c});Efficiency', nPtBins,
np.asarray(ptLims, 'd'))
hYieldPromptGen = TH1F('hYieldPromptGen',
';#it{p}_{T} (GeV/#it{c}); # Generated MC', nPtBins,
np.asarray(ptLims, 'd'))
hYieldFDGen = TH1F('hYieldFDGen', ';#it{p}_{T} (GeV/#it{c}); # Generated MC',
nPtBins, np.asarray(ptLims, 'd'))
hYieldPromptReco = TH1F('hYieldPromptReco',
';#it{p}_{T} (GeV/#it{c}); # Reco MC', nPtBins,
np.asarray(ptLims, 'd'))
hYieldFDReco = TH1F('hYieldFDReco', ';#it{p}_{T} (GeV/#it{c}); # Reco MC',
nPtBins, np.asarray(ptLims, 'd'))
SetObjectStyle(hEffPrompt, color=kRed + 1, markerstyle=kFullCircle)
SetObjectStyle(hEffFD,
color=kAzure + 4,
markerstyle=kOpenSquare,
markersize=1.5,
linewidh=2,
linestyle=7)
SetObjectStyle(hYieldPromptGen, color=kRed + 1, markerstyle=kFullCircle)
SetObjectStyle(hYieldFDGen,
color=kAzure + 4,
markerstyle=kOpenSquare,
markersize=1.5,
linewidh=2,
linestyle=7)
SetObjectStyle(hYieldPromptReco, color=kRed + 1, markerstyle=kFullCircle)
SetObjectStyle(hYieldFDReco,
for iParCut, (ParCutMin,
ParCutMax) in enumerate(zip(ParCutMins, ParCutMaxs)):
if len(varNames) == 1:
for est in estNames:
minVar = cutVars[varNames[0]]['min'][iPt] - cutVars[
varNames[0]]['step'][iPt] / 2
maxVar = cutVars[varNames[0]]['max'][iPt] + cutVars[
varNames[0]]['step'][iPt] / 2
nBinsVar = int(
(maxVar - minVar) / cutVars[varNames[0]]['step'][iPt])
hEstimVsCut[iPt][est] = TH1F(
f'h{est}VsCut_pT{ptMin}-{ptMax}_{ParCutsName}{ParCutMin}-{ParCutMax}',
f';{varNames[0]};{estNames[est]}', nBinsVar, minVar,
maxVar)
SetObjectStyle(hEstimVsCut[iPt][est],
color=kBlack,
marker=kFullCircle,
linewidth=1)
elif len(varNames) == 2:
for est in estNames:
minVar0 = cutVars[varNames[0]]['min'][iPt] - cutVars[
varNames[0]]['step'][iPt] / 2
minVar1 = cutVars[varNames[1]]['min'][iPt] - cutVars[
varNames[1]]['step'][iPt] / 2
maxVar0 = cutVars[varNames[0]]['max'][iPt] + cutVars[
varNames[0]]['step'][iPt] / 2
maxVar1 = cutVars[varNames[1]]['max'][iPt] + cutVars[
varNames[1]]['step'][iPt] / 2
nBinsVar0 = int(
(maxVar0 - minVar0) / cutVars[varNames[0]]['step'][iPt])
nBinsVar1 = int(
(maxVar1 - minVar1) / cutVars[varNames[1]]['step'][iPt])
hFracPrompt, hFracNonPrompt, hRatioFracPrompt, hRatioFracNonPrompt = ([] for _ in range(4))
gFracNonPrompt, gRatioFracNonPrompt = ([] for _ in range(2))
hFracNonPromptRawYieldSys, hFracNonPromptCutSetsSys, \
hFracNonPromptMultWeightsSys, hFracNonPromptPtWeightsSys = ([] for _ in range(4))
hRatioFracNonPromptRawYieldSys, hRatioFracNonPromptCutSetsSys, \
hRatioFracNonPromptMultWeightsSys, hRatioFracNonPromptPtWeightsSys = ([] for _ in range(4))
for iFile, fileName in enumerate(inFileNames):
inFile = TFile.Open(fileName)
hFracPrompt.append(inFile.Get('hPromptFracCorr'))
hFracPrompt[iFile].SetDirectory(0)
hFracPrompt[iFile].SetName(f'hFracPrompt_{labels[iFile]}')
SetObjectStyle(hFracPrompt[iFile], color=GetROOTColor(colors[iFile]))
hFracNonPrompt.append(inFile.Get('hFDFracCorr'))
hFracNonPrompt[iFile].SetDirectory(0)
hFracNonPrompt[iFile].SetName(f'hFracNonPrompt_{labels[iFile]}')
SetObjectStyle(hFracNonPrompt[iFile], color=GetROOTColor(colors[iFile]))
hFracNonPromptRawYieldSys.append(
hFracNonPrompt[iFile].Clone(f'hFracNonPromptRawYieldSys_{labels[iFile]}')
)
hFracNonPromptCutSetsSys.append(
hFracNonPrompt[iFile].Clone(f'hFracNonPromptCutSetsSys_{labels[iFile]}')
)
hFracNonPromptMultWeightsSys.append(
hFracNonPrompt[iFile].Clone(f'hFracNonPromptMultWeightsSys_{labels[iFile]}')
)
systColors = {
'YieldExtr': 'kAzure+4',
'SelEff': 'kRed',
'TrEff': 'kBlue+1',
'PIDEff': 'kOrange+7',
'PtShape': 'kRed',
'FD': 'kAzure+4',
'Tot': 'kBlack'
}
gCrossSectionSyst = {}
for systSource in systGetter:
gCrossSectionSyst[systSource] = TGraphErrors(1)
gCrossSectionSyst[systSource].SetName(f'g{histoName}Syst{systSource}')
gCrossSectionSyst[systSource].SetTitle(axisTitle)
SetObjectStyle(gCrossSectionSyst[systSource],
color=GetROOTColor(systColors[systSource]),
fillstyle=0)
hPromptFrac = hEffAccPrompt.Clone('hPromptFrac')
hFDFrac = hEffAccFD.Clone('hFDFrac')
hPromptFrac.SetTitle(';#it{p}_{T} (GeV/#it{c}); #it{f}_{prompt}')
hFDFrac.SetTitle(';#it{p}_{T} (GeV/#it{c}); #it{f}_{FD}')
for iPt in range(hCrossSection.GetNbinsX()):
ptMin = hRawYields.GetBinLowEdge(iPt + 1)
ptMax = ptMin + hRawYields.GetBinWidth(iPt + 1)
ptCent = hRawYields.GetBinCenter(iPt + 1)
rawYield = hRawYields.GetBinContent(iPt + 1)
rawYieldUnc = hRawYields.GetBinError(iPt + 1)
effAccPrompt = hEffAccPrompt.GetBinContent(iPt + 1)
effAccFD = hEffAccFD.GetBinContent(iPt + 1)
#output histos
nbins = len(cutVars['Pt']['max'])
xbins = cutVars['Pt']['min'] + list(
set(cutVars['Pt']['max']) - set(cutVars['Pt']['min']))
for iVar, VartoDraw in enumerate(VarDrawList):
hProject.append(
TH1F(
f'hProject{VartoDraw}', f'{VartoDraw} over p'
'_{T}'
f'; p'
'_{T}'
' [GeV/c] ;'
f'{VartoDraw}', nbins, np.asarray(xbins, float)))
SetObjectStyle(hProject[iVar],
color=kGreen - iVar,
markerstyle=kOpenCrossX,
markersize=1.5,
linewidh=2,
linestyle=7)
for iPt, _ in enumerate(cutVars['Pt']['min']):
dfSignifSel = dfSignif.query(selToApply[iPt])
hProject[iVar].SetBinContent(iPt + 1, dfSignifSel[f'{VartoDraw}'])
if VartoDraw in ('EffAccFD', 'EffAccPrompt'):
hProject[iVar].SetBinError(iPt + 1,
dfSignifSel[f'{VartoDraw}Error'])
if VartoDraw == 'S':
hProject[iVar].SetBinError(
iPt + 1, dfSignifSel['S'] / dfSignifSel['Signif'])
if VartoDraw == 'B':
hProject[iVar].SetBinError(iPt + 1, np.sqrt(dfSignifSel['B']))
TProject.cd(iVar + 1)
hProject[iVar].DrawCopy()
hSignif[iFile].SetDirectory(0)
hEv[iFile].SetDirectory(0)
hSignal[iFile].Scale(1. / hEv[iFile].GetBinContent(1))
hBackground[iFile].Scale(1. / hEv[iFile].GetBinContent(1))
hSignif[iFile].Scale(1. / math.sqrt(hEv[iFile].GetBinContent(1)))
hRatioSignal.append(ComputeRatioDiffBins(hSignal[iFile], hSignal[0]))
hRatioSignal[iFile].SetDirectory(0)
hRatioSignal[iFile].SetName(f'hRatioSignal{iFile}')
hRatioBkg.append(ComputeRatioDiffBins(hBackground[iFile], hBackground[0]))
hRatioBkg[iFile].SetDirectory(0)
hRatioBkg[iFile].SetName(f'hRatioBkg{iFile}')
hRatioSignif.append(ComputeRatioDiffBins(hSignif[iFile], hSignif[0]))
hRatioSignif[iFile].SetDirectory(0)
hRatioSignif[iFile].SetName(f'hRatioSignif{iFile}')
SetObjectStyle(hSignal[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile])
SetObjectStyle(hBackground[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile])
SetObjectStyle(hSoverB[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile])
SetObjectStyle(hSignif[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile])
SetObjectStyle(hRatioSignal[iFile],
linecolor=colors[iFile],
leg = TLegend(0.45, 0.6, 0.85, 0.9)
leg.SetTextSize(0.04)
hMass = []
cMassMLSel = TCanvas('cMassMLSel', '', 800, 800)
cMassMLSel.cd().DrawFrame(hMassVsML.GetYaxis().GetBinLowEdge(1), 0., \
hMassVsML.GetYaxis().GetBinUpEdge(nMassbins), hMassVsML.ProjectionY().GetMaximum()*1.5*args.rebin, \
';#it{M}(KK#pi) (GeV/#it{c}^{2}); Counts')
for iBin, MLbin in enumerate(
range(MLoutBinMin, MLoutBinMax + MLoutStepBin, MLoutStepBin)):
MLoutBinMax = sparseBkg.GetAxis(13).SetRange(MLbin, nMLbins + 1)
hMass.append(sparseBkg.Projection(0))
SetObjectStyle(hMass[iBin],
color=kRed - 5 + iBin,
markerstyle=kFullCircle,
linewidth=2)
if args.rebin:
hMass[iBin].Rebin(args.rebin)
hMass[iBin].Draw('Esame')
leg.AddEntry(
hMass[iBin], 'ML output > {:0.4f}'.format(
sparseBkg.GetAxis(13).GetBinLowEdge(MLbin)), 'p')
leg.Draw()
cMassVsML.SaveAs('{0}_MassVsML.pdf'.format(args.outFileName))
cMassMLSel.SaveAs('{0}_MassDiffSel.pdf'.format(args.outFileName))
if six.PY2:
raw_input('Press Enter to exit')
MLbinmin = sPIDNsigma.GetAxis(1).FindBin(MLmin[iPt] * 1.0001)
MLbinmax = sPIDNsigma.GetAxis(1).FindBin(MLmax[iPt] * 0.9999)
if det != 'Comb':
sparse = sPIDNsigma
else:
sparse = sPIDNsigmaComb
sparse.GetAxis(0).SetRange(ptbinmin, ptbinmax)
hNsigma[det][spe][prong]['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)] = \
sparse.Projection(axes[det][spe][prong])
hNsigma[det][spe][prong]['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)].SetName(\
'hNsigma{0}_{1}_Prong{2}_Pt{3:.0f}_{4:.0f}'.format(det, spe, prong, ptmin, ptmax))
SetObjectStyle(hNsigma[det][spe][prong]['Pt{:.0f}_{:.0f}'.format(\
ptmin, ptmax)], color=kBlue, linealpha=0.25, fillalpha=0.25, markeralpha=1, markerstyle=kFullCircle, markersize=0.5, linewidth=1)
sparse.GetAxis(1).SetRange(MLbinmin, MLbinmax)
hNsigmaSel[det][spe][prong]['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)] = \
sparse.Projection(axes[det][spe][prong])
hNsigmaSel[det][spe][prong]['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)].SetName(\
'hNsigmaSel{0}_{1}_Prong{2}_Pt{3:.0f}_{4:.0f}'.format(det, spe, prong, ptmin, ptmax))
SetObjectStyle(hNsigmaSel[det][spe][prong]['Pt{:.0f}_{:.0f}'.format(\
ptmin, ptmax)], color=kRed, linealpha=0.25, fillalpha=0.25, markeralpha=1, markerstyle=kOpenCircle, markersize=0.5, linewidth=1)
sparse.GetAxis(0).SetRange(-1, -1)
sparse.GetAxis(1).SetRange(-1, -1)
#2D distributions (TPC vs TOF)
hNsigma['TPCTOF'], hNsigmaSel['TPCTOF'] = {}, {}
def main(): #pylint: disable=too-many-statements
"""
main function of script
"""
SetGlobalStyle(padleftmargin=0.18,
padbottommargin=0.14,
titleoffsety=1.6,
maxdigits=2,
optstat=0)
n_bins = 1001
bin_width = 0.05 # GeV
bin_lims = [i * bin_width for i in range(0, n_bins + 1)]
hFONLLBtoDCentral = TH1D(
'hDfromBpred_central',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBtoDMin = TH1D(
'hDfromBpred_min',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBtoDMax = TH1D(
'hDfromBpred_max',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBCentral = TH1D(
'hBpred_central',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBMin = TH1D(
'hBpred_min',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
hFONLLBMax = TH1D(
'hBpred_max',
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (pb GeV^{-1} #it{c})',
n_bins, np.asarray(bin_lims, 'd'))
fonllBtoD_df = pd.read_csv("fonll/FONLL_DfromB_pp5_y05_toCook.txt",
sep=" ",
header=14).astype('float64')
fonllB_df = pd.read_csv("fonll/FONLL_B_pp5_y05.txt", sep=" ",
header=13).astype('float64')
for pt, cen, low, up in zip(fonllBtoD_df['pt'].to_numpy(),
fonllBtoD_df['central'].to_numpy(),
fonllBtoD_df['min'].to_numpy(),
fonllBtoD_df['max'].to_numpy()):
hFONLLBtoDCentral.Fill(pt, cen)
hFONLLBtoDMin.Fill(pt, low)
hFONLLBtoDMax.Fill(pt, up)
for pt, cen, low, up in zip(fonllB_df['pt'].to_numpy(),
fonllB_df['central'].to_numpy(),
fonllB_df['min'].to_numpy(),
fonllB_df['max'].to_numpy()):
hFONLLBCentral.Fill(pt, cen)
hFONLLBMin.Fill(pt, low)
hFONLLBMax.Fill(pt, up)
for iBin in range(hFONLLBtoDCentral.GetNbinsX()):
hFONLLBtoDCentral.SetBinError(iBin + 1, 1.e-3)
hFONLLBtoDMin.SetBinError(iBin + 1, 1.e-3)
hFONLLBtoDMax.SetBinError(iBin + 1, 1.e-3)
hFONLLBCentral.SetBinError(iBin + 1, 1.e-3)
hFONLLBMin.SetBinError(iBin + 1, 1.e-3)
hFONLLBMax.SetBinError(iBin + 1, 1.e-3)
hFONLL = [hFONLLBtoDCentral, hFONLLBtoDMin, hFONLLBtoDMax]
hBFONLL = [hFONLLBCentral, hFONLLBMin, hFONLLBMax]
labels = ['Central', 'Min', 'Max']
canvas = []
for label in labels:
canvas.append(TCanvas(f'cComp{label}', '', 2000, 1000))
for (histo, histo_std, label, canv) in zip(hFONLL, hBFONLL, labels,
canvas):
pt_lims = np.array(histo.GetXaxis().GetXbins(), 'd')
pt_min = list(pt_lims)[0]
pt_max = list(pt_lims)[-1]
histo_std = histo_std.Rebin(histo.GetNbinsX(), f'hBFONLL{label}',
pt_lims)
histo.Rebin(5)
histo_std.Rebin(5)
histo.Scale(1.e-6)
histo_std.Scale(1.e-6)
histo_ratio = histo.Clone(f'hRatioDfromBOverB{label}')
histo_ratio.Divide(histo, histo_std)
histo_ratio.GetYaxis().SetTitle('FONLL D from B / FONLL B')
print(
f'\nFONLL B {label} integral (ub): {histo_std.Integral("width"):.4e}'
)
print(
f'FONLL B from D {label} integral (ub): {histo.Integral("width"):.4e}\n'
)
canv.Divide(2, 1)
canv.cd(1).DrawFrame(
pt_min, 1e-4, pt_max, 100.,
';#it{p}_{T} (GeV/#it{c});#frac{d#sigma}{d#it{p}_{T}} (#mub GeV^{-1} #it{c})'
)
leg = TLegend(0.4, 0.7, 0.8, 0.9)
leg.SetTextSize(0.045)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
SetObjectStyle(histo, color=kAzure + 4, markerstyle=kFullSquare)
SetObjectStyle(histo_std, color=kRed, markerstyle=kFullCircle)
histo.Draw('same')
histo_std.Draw('same')
leg.AddEntry(histo, f'FONLL D from B {label}', 'p')
leg.AddEntry(histo_std, f'FONLL B {label}', 'p')
gPad.SetLogy()
leg.Draw()
hframe_ratio = canv.cd(2).DrawFrame(
pt_min, 0., pt_max, 3.5,
';#it{p}_{T} (GeV/#it{c}); FONLL D from B / FONLL B')
hframe_ratio.GetYaxis().SetDecimals()
SetObjectStyle(histo_ratio, color=kAzure + 4, markerstyle=kFullSquare)
histo_ratio.Draw('same')
canv.Update()
canv.SaveAs(f'CompFONLL_DfromBvsB_{label}.pdf')
input('Press enter to exit')
def main(): #pylint: disable=too-many-statements
SetGlobalStyle(padleftmargin=0.18, padbottommargin=0.14, titleoffsety=1.6, maxdigits=2, optstat=0)
inFileFONLL = TFile.Open('fonll/feeddown/DmesonLcPredictions_502TeV_y05_FFee_BRpythia8_SepContr_PDG2020.root')
hFONLLPromptD0 = inFileFONLL.Get('hD0Kpipred_max')
hFONLLPromptDplus = inFileFONLL.Get('hDpluskpipipred_max')
hFONLLPromptDs = inFileFONLL.Get('hDsPhipitoKkpipred_max')
hFONLLNonPromptD0 = inFileFONLL.Get('hD0KpifromBpred_central_corr')
hFONLLNonPromptDplus = inFileFONLL.Get('hDpluskpipifromBpred_central_corr')
hFONLLNonPromptDs = inFileFONLL.Get('hDsPhipitoKkpifromBpred_central_corr')
binLims = np.array([0., 1., 2., 3., 4., 5., 6., 7., 8., 10., 12., 16., 24.], 'd')
nBins = 12
hFONLLPromptD0 = hFONLLPromptD0.Rebin(nBins, 'hFONLLPromptD0', binLims)
hFONLLPromptDplus = hFONLLPromptDplus.Rebin(nBins, 'hFONLLPromptDplus', binLims)
hFONLLPromptDs = hFONLLPromptDs.Rebin(nBins, 'hFONLLPromptDs', binLims)
hFONLLNonPromptD0 = hFONLLNonPromptD0.Rebin(nBins, 'hFONLLNonPromptD0', binLims)
hFONLLNonPromptDplus = hFONLLNonPromptDplus.Rebin(nBins, 'hFONLLNonPromptDplus', binLims)
hFONLLNonPromptDs = hFONLLNonPromptDs.Rebin(nBins, 'hFONLLNonPromptDs', binLims)
hFONLLNPfracD0 = hFONLLPromptD0.Clone('hFONLLNPfracD0')
hFONLLNPfracD0.Divide(hFONLLNonPromptD0, hFONLLNPfracD0)
hFONLLNPfracDplus = hFONLLPromptDplus.Clone('hFONLLNPfracDplus')
hFONLLNPfracDplus.Divide(hFONLLNonPromptDplus, hFONLLNPfracDplus)
hFONLLNPfracDs = hFONLLPromptDs.Clone('hFONLLNPfracDs')
hFONLLNPfracDs.Divide(hFONLLNonPromptDs, hFONLLNPfracDs)
SetObjectStyle(hFONLLNPfracD0, color=kRed, linewitdh=2, markerstyle=0, fillstyle=0)
SetObjectStyle(hFONLLNPfracDplus, color=kGreen, linewitdh=2, markerstyle=0, fillstyle=0)
SetObjectStyle(hFONLLNPfracDs, color=kOrange, linewitdh=2, markerstyle=0, fillstyle=0)
mesonList = ['D0', 'D+', 'Ds']
histoList = [hFONLLNPfracD0, hFONLLNPfracDplus, hFONLLNPfracDs]
print(*binLims, sep=' ')
for meson, histo in zip(mesonList, histoList):
valueList = []
for iBin in range(1, nBins + 1):
valueList.append(f'{histo.GetBinContent(iBin):.3f}')
print(meson)
print(*valueList, sep=' ')
legMeson = TLegend(0.65, 0.6, 0.85, 0.8)
legMeson.SetTextSize(0.04)
legMeson.SetFillStyle(0)
legMeson.AddEntry(hFONLLNPfracD0, 'D^{0}', 'l')
legMeson.AddEntry(hFONLLNPfracDplus, 'D^{+}', 'l')
legMeson.AddEntry(hFONLLNPfracDs, 'D_{s}^{+}', 'l')
cFrac = TCanvas('cFrac', '', 500, 500)
hFrame = cFrac.DrawFrame(0., 0.01, 24., 1.05, ';#it{p}_{T} (GeV/#it{c}); #it{f}_{non-prompt}')
hFrame.GetYaxis().SetDecimals()
hFrame.Draw('axis same')
hFONLLNPfracD0.Draw('same')
hFONLLNPfracDplus.Draw('same')
hFONLLNPfracDs.Draw('same')
legMeson.Draw('same')
cFrac.Update()
TFile('./NaturalNonPromptFrac_FONLL_FFee_BRpythia8.root', 'recreate')
hFONLLNPfracD0.Write()
hFONLLNPfracDplus.Write()
hFONLLNPfracDs.Write()
cFrac.Write()
input('Press enter to exit')
def comp_fit_pars(do_ratio=False, meson='Ds'): #pylint: disable-msg=too-many-statements,too-many-locals
inputdir = '../../AnalysisNonPromptDpp2017/Dplus/outputs/rawyields'
input_files = ['RawYieldsDplus_pp5TeV_prompt_central.root', 'RawYieldsDplus_pp5TeV_FD_central_freesigma.root']
input_files_MC = ['RawYieldsDplusMC_pp5TeV_prompt_central.root', 'RawYieldsDplusMC_pp5TeV_FD_central.root']
colors = [kOrange+7, kAzure+2, kRed+1, kAzure+4]
markers = [kOpenCircle, kOpenSquare, kFullCircle, kFullSquare]
legendnames = ['MC - prompt enhanced', 'MC - FD enhanced', 'data - prompt enhanced', 'data - FD enhanced']
suffix = 'CompMCData'
min_pt = 2.
max_pt = 16.
SetGlobalStyle(padleftmargin=0.18, padtopmargin=0.05, padbottommargin=0.14,
titleoffsety=1.6, titlesize=0.045, labelsize=0.04)
hMean, hSigma = [], []
input_files = input_files_MC + input_files
if meson == 'Ds':
massD = TDatabasePDG.Instance().GetParticle(431).Mass()
elif meson == 'Dplus':
massD = TDatabasePDG.Instance().GetParticle(411).Mass()
lineMass = TLine(min_pt, massD, max_pt, massD)
lineMass.SetLineWidth(2)
lineMass.SetLineColor(kBlack)
lineMass.SetLineStyle(9)
legSigma = TLegend(0.2, 0.78, 0.8, 0.93)
legSigma.SetFillStyle(0)
legSigma.SetBorderSize(0)
legSigma.SetTextSize(0.04)
legMean = TLegend(0.4, 0.73, 0.7, 0.93)
legMean.SetFillStyle(0)
legMean.SetBorderSize(0)
legMean.SetTextSize(0.04)
legMean.AddEntry(lineMass, "PDG", 'l')
for file_path, color, marker, legend_name in zip(input_files, colors, markers, legendnames):
input_file = TFile(f'{inputdir}/{file_path}')
histo_mean = input_file.Get('hRawYieldsMean')
histo_sigma = input_file.Get('hRawYieldsSigma')
histo_mean.SetDirectory(0)
histo_sigma.SetDirectory(0)
SetObjectStyle(histo_mean, linecolor=color, markercolor=color, markerstyle=marker)
SetObjectStyle(histo_sigma, linecolor=color, markercolor=color, markerstyle=marker)
legMean.AddEntry(histo_mean, legend_name, 'p')
legSigma.AddEntry(histo_sigma, legend_name, 'p')
hMean.append(histo_mean)
hSigma.append(histo_sigma)
if do_ratio:
mean_num_list = list(hMean)
mean_num_list.pop(0)
mean_den_hist = hMean[0]
mean_ratio_list = []
for histo in mean_num_list:
mean_ratio_list.append(ComputeRatioDiffBins(histo, mean_den_hist))
cMeanRatio = TCanvas('cMeanRatio', '', 800, 800)
cMeanRatio.DrawFrame(min_pt, 0.99, max_pt, 1.01, ';#it{p}_{T} (GeV/#it{c}); peak mean / peak mean MC')
lineRatio = TLine(min_pt, 1., max_pt, 1.)
lineRatio.SetLineWidth(2)
lineRatio.SetLineColor(kBlack)
lineRatio.SetLineStyle(9)
legMeanRatio = TLegend(0.4, 0.73, 0.7, 0.93)
legMeanRatio.SetFillStyle(0)
legMeanRatio.SetBorderSize(0)
legMeanRatio.SetTextSize(0.04)
for hist, color, marker, legend_name in zip(mean_ratio_list, colors[1:],
markers[1:], legendnames[1:]):
SetObjectStyle(hist, linecolor=color, markercolor=color, markerstyle=marker)
hist.Draw('same')
legMeanRatio.AddEntry(hist, legend_name, 'p')
lineRatio.Draw()
legMeanRatio.Draw()
sigma_num_list = list(hSigma)
sigma_num_list.pop(0)
sigma_den_hist = hSigma[0]
sigma_ratio_list = []
for histo in sigma_num_list:
sigma_ratio_list.append(ComputeRatioDiffBins(histo, sigma_den_hist))
cSigmaRatio = TCanvas('cSigmaRatio', '', 800, 800)
hFrameSigma = cSigmaRatio.DrawFrame(min_pt, 0.6, max_pt, 1.8,
';#it{p}_{T} (GeV/#it{c}); peak width / peak width MC')
hFrameSigma.GetYaxis().SetDecimals()
legSigmaRatio = TLegend(0.4, 0.73, 0.7, 0.93)
legSigmaRatio.SetFillStyle(0)
legSigmaRatio.SetBorderSize(0)
legSigmaRatio.SetTextSize(0.04)
for hist, color, marker, legend_name in zip(sigma_ratio_list, colors[1:],
markers[1:], legendnames[1:]):
SetObjectStyle(hist, linecolor=color, markercolor=color, markerstyle=marker)
hist.Draw('same')
legSigmaRatio.AddEntry(hist, legend_name, 'p')
lineRatio.Draw()
legSigmaRatio.Draw()
cMeanRatio.SaveAs(f'{inputdir}/MeanRatio_{suffix}.pdf')
cSigmaRatio.SaveAs(f'{inputdir}/SigmaRatio_{suffix}.pdf')
cMean = TCanvas('cMean', '', 800, 800)
hFrameMean = cMean.DrawFrame(min_pt, massD*0.995, max_pt, massD*1.01,
';#it{p}_{T} (GeV/#it{c}); peak mean (GeV/#it{c}^{2})')
hFrameMean.GetYaxis().SetDecimals()
lineMass.Draw("same")
for histo_mean in hMean:
histo_mean.Draw('same')
legMean.Draw()
cSigma = TCanvas('cSigma', '', 800, 800)
cSigma.DrawFrame(min_pt, 0., max_pt, 0.025, ';#it{p}_{T} (GeV/#it{c}); peak width (GeV/#it{c}^{2})')
for histo_sigma in hSigma:
histo_sigma.Draw('same')
legSigma.Draw()
cMean.SaveAs(f'{inputdir}/Mean_{suffix}.pdf')
cSigma.SaveAs(f'{inputdir}/Sigma_{suffix}.pdf')
input('Press enter to exit')
parser = argparse.ArgumentParser(description='Arguments')
parser.add_argument('inFileNameMultiTrial',
metavar='text',
default='MultiTrial.root')
parser.add_argument('inFileNameRawYields',
metavar='text',
default='RawYieldsDefault.root')
parser.add_argument('outFileName', metavar='text', default='Output.root')
args = parser.parse_args()
inFileRaw = TFile.Open(args.inFileNameRawYields)
hRawYields = inFileRaw.Get('hRawYields')
hSoverB = inFileRaw.Get('hRawYieldsSoverB')
hRawYields.SetDirectory(0)
hSoverB.SetDirectory(0)
SetObjectStyle(hRawYields, color=kBlack, fillstyle=0, linewidth=3)
SetObjectStyle(hSoverB, color=kRed + 1, fillstyle=0, linewidth=3)
inFileRaw.Close()
nPtBins = hRawYields.GetNbinsX()
hRawYieldDistr = []
hRMS = hRawYields.Clone('hRMS')
hRMS.Reset()
hMeanShift = hRawYields.Clone('hMeanShift')
hMeanShift.Reset()
hSyst = hRawYields.Clone('hSyst')
hSyst.Reset()
inFileMT = TFile.Open(args.inFileNameMultiTrial)
for iPt in range(1, nPtBins + 1):
ptMin = hRawYields.GetBinLowEdge(iPt)
outDir = inputCfg['outputfile']['directory']
outSuffix = inputCfg['outputfile']['suffix']
inFilePtWeightsName = inputCfg['inputs']['ptweightsfile']
inFilePtWeights = TFile.Open(inFilePtWeightsName)
enableBweights = inputCfg['inputs']['enableBweights']
hPtDistrD, hPtWeightsD, hPtDistrB, hPtWeightsB, hEffPrompt, \
hEffFD, hEffPromptRatio, hEffFDRatio = ({} for _ in range(8))
for model in modelNames:
if model == 'pythia' or inputCfg['inputs']['shapes'][model]['enabled']:
if model != 'pythia':
hPtDistrD[model] = inFilePtWeights.Get(f'{histoNames[model]}Dcent')
hPtWeightsD[model] = inFilePtWeights.Get(f'{histoWeightNames[model]}Dcent')
SetObjectStyle(hPtWeightsD[model], linewidth=2, linecolor=colors[model],
markerstyle=markers[model], markercolor=colors[model])
else:
hPtDistrD[model] = inFilePtWeights.Get(f'{histoNames[model]}D')
SetObjectStyle(hPtDistrD[model], linewidth=2, linecolor=colors[model],
markerstyle=markers[model], markercolor=colors[model])
if enableBweights:
if model != 'pythia':
hPtDistrB[model] = inFilePtWeights.Get(f'{histoNames[model]}Bcent')
hPtWeightsB[model] = inFilePtWeights.Get(f'{histoWeightNames[model]}Bcent')
SetObjectStyle(hPtWeightsB[model], linewidth=2, linecolor=colors[model],
markerstyle=markers[model], markercolor=colors[model])
else:
hPtDistrB[model] = inFilePtWeights.Get(f'{histoNames[model]}B')
SetObjectStyle(hPtDistrB[model], linewidth=2, linecolor=colors[model],
markerstyle=markers[model], markercolor=colors[model])
inFileEff = TFile.Open(inputCfg['inputs']['shapes'][model]['efffile'])
hMass.append(
infile.Get('hPromptMass_{0:.0f}_{1:.0f}'.format(
ptMin * 10, ptMax * 10)))
hMass[iPt].Add(
infile.Get('hFDMass_{0:.0f}_{1:.0f}'.format(
ptMin * 10, ptMax * 10)))
if secPeak:
hMass[iPt].Add(
infile.Get('hPromptSecPeakMass_{0:.0f}_{1:.0f}'.format(
ptMin * 10, ptMax * 10)))
hMass[iPt].Add(
infile.Get('hFDSecPeakMass_{0:.0f}_{1:.0f}'.format(
ptMin * 10, ptMax * 10)))
hMass[iPt].SetDirectory(0)
hMass[iPt].Sumw2()
SetObjectStyle(hMass[iPt], color=kBlack, markerstyle=kFullCircle)
hEv = infile.Get('hEvForNorm')
hEv.SetDirectory(0)
hEv.Sumw2()
SetObjectStyle(hEv, color=kBlack, markerstyle=kFullCircle)
infile.Close()
hSigmaToFix = None
if sum(fixSigma) > 0:
infileSigma = TFile.Open(fitConfig[cent]['SigmaFile'])
if not infileSigma:
print(f'ERROR: file "{infileSigma}" cannot be opened! Exit!')
sys.exit()
hSigmaToFix = infileSigma.Get('hRawYieldsSigma')
hSigmaToFix.SetDirectory(0)
hTmp = inList.FindObject('hSPDMultVsCentCandInMass')
hNtrklCandInMass[inpt].Add(
hTmp.ProjectionY('hSPDMultCandInMass', binCentMin,
binCentMax).Clone())
hNtrkl[inpt].SetName(f'hNtrkl{inpt}')
hNtrklCand[inpt].SetName(f'hNtrklCand{inpt}')
hNtrklCandInMass[inpt].SetName(f'hNtrklCandInMass{inpt}')
hNtrkl[inpt].Sumw2()
hNtrklCand[inpt].Sumw2()
hNtrklCandInMass[inpt].Sumw2()
hNtrkl[inpt].Scale(1. / hNtrkl[inpt].Integral())
hNtrklCand[inpt].Scale(1. / hNtrklCand[inpt].Integral())
hNtrklCandInMass[inpt].Scale(1. / hNtrklCandInMass[inpt].Integral())
SetObjectStyle(hNtrkl[inpt],
fillstyle=0,
color=colors[inpt],
markerstyle=markers[inpt])
SetObjectStyle(hNtrklCand[inpt],
fillstyle=0,
color=colors[inpt],
markerstyle=markers[inpt])
SetObjectStyle(hNtrklCandInMass[inpt],
fillstyle=0,
color=colors[inpt],
markerstyle=markers[inpt])
hNtrklWeights = hNtrkl['data'].Clone('hNtrklWeights')
hNtrklWeights.Divide(hNtrkl['MC'])
hNtrklWeightsCand = hNtrklCand['data'].Clone('hNtrklWeightsCand')
hNtrklWeightsCand.Divide(hNtrklCand['MC'])
hNtrklWeightsCandInMass = hNtrklCandInMass['data'].Clone(
hMassKKSigRegionMC, hMassKKSigRegionData, hSideBandsMC, hSideBandsData, hMassKKwoPhiMC, hMassKKwoPhiData, \
fMassKKwoPhiMC, fMassKKwoPhiData, hMassKKBkgSubMC, hMassKKBkgSubData, fMassKKMC, fMassKKData, \
hConfIntMassKKMC, hConfIntMassKKData = ([] for _ in range(14))
hMeanMC = TH1F('hMeanMC', ';#it{p}_{T} (GeV/#it{c}); mean (GeV/#it{c}^{2})',
nPtBinsMC - 1, np.array(ptLims, 'f'))
hMeanData = TH1F('hMeanData',
';#it{p}_{T} (GeV/#it{c}); mean (GeV/#it{c}^{2})',
nPtBinsMC - 1, np.array(ptLims, 'f'))
hFWHMMC = TH1F('hFWHMMC', ';#it{p}_{T} (GeV/#it{c}); sigma (GeV/#it{c}^{2})',
nPtBinsMC - 1, np.array(ptLims, 'f'))
hFWHMData = TH1F('hFWHMData',
';#it{p}_{T} (GeV/#it{c}); sigma (GeV/#it{c}^{2})',
nPtBinsMC - 1, np.array(ptLims, 'f'))
SetObjectStyle(hMeanMC, color=kRed + 1, markerstyle=kFullSquare)
SetObjectStyle(hMeanData, color=kAzure + 4, markerstyle=kFullCircle)
SetObjectStyle(hFWHMMC, color=kRed + 1, markerstyle=kFullSquare)
SetObjectStyle(hFWHMData, color=kAzure + 4, markerstyle=kFullCircle)
hEffMC = {
5:
TH1F('hEffMC05', ';#it{p}_{T} (GeV/#it{c}); efficiency', nPtBinsMC - 1,
np.array(ptLims, 'f')),
10:
TH1F('hEffMC10', ';#it{p}_{T} (GeV/#it{c}); efficiency', nPtBinsMC - 1,
np.array(ptLims, 'f')),
15:
TH1F('hEffMC15', ';#it{p}_{T} (GeV/#it{c}); efficiency', nPtBinsMC - 1,
np.array(ptLims, 'f')),
20:
TH1F('hEffMC20', ';#it{p}_{T} (GeV/#it{c}); efficiency', nPtBinsMC - 1,
def main(): #pylint: disable=too-many-locals,too-many-statements
"""
Main function of the script
"""
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('cfgFileName',
metavar='text',
default='cfgFileName.yml',
help='config file name')
parser.add_argument('outFileDir',
metavar='text',
default='./',
help='output file directory')
args = parser.parse_args()
with open(args.cfgFileName, 'r') as ymlCfgFile:
inputCfg = yaml.load(ymlCfgFile, yaml.FullLoader)
#Load data
dfPromptP6 = LoadDfFromRootOrParquet(inputCfg['input']['prompt_files'][0])
dfPromptP8 = LoadDfFromRootOrParquet(inputCfg['input']['prompt_files'][1])
dfFDP6 = LoadDfFromRootOrParquet(inputCfg['input']['fd_files'][0])
dfFDP8 = LoadDfFromRootOrParquet(inputCfg['input']['fd_files'][1])
#Select pt bin
ptMin = inputCfg['pt_bin'][0]
ptMax = inputCfg['pt_bin'][1]
dfPromptP6 = dfPromptP6.query(f'{ptMin} < pt_cand < {ptMax}')
dfPromptP8 = dfPromptP8.query(f'{ptMin} < pt_cand < {ptMax}')
dfFDP6 = dfFDP6.query(f'{ptMin} < pt_cand < {ptMax}')
dfFDP8 = dfFDP8.query(f'{ptMin} < pt_cand < {ptMax}')
SetGlobalStyle(padbottommargin=0.14,
padleftmargin=0.18,
padrightmargin=0.06,
titleoffsety=1.6)
varTitle = inputCfg['scan_variable']['title']
nBins = inputCfg['scan_variable']['histo_bins']
binLims = inputCfg['scan_variable']['histo_lims']
varName = inputCfg['scan_variable']['name']
hPromptP6 = TH1F('hPromptP6', f';{varTitle};Counts', nBins, binLims[0],
binLims[1])
hPromptP8 = TH1F('hPromptP8', f';{varTitle};Counts', nBins, binLims[0],
binLims[1])
hFDP6 = TH1F('hFDP6', f';{varTitle};Counts', nBins, binLims[0], binLims[1])
hFDP8 = TH1F('hFDP8', f';{varTitle};Counts', nBins, binLims[0], binLims[1])
scaleFactor = inputCfg['scan_variable']['rescale_factor']
dfPromptP6[varName] = dfPromptP6[varName] * scaleFactor
dfPromptP8[varName] = dfPromptP8[varName] * scaleFactor
dfFDP6[varName] = dfFDP6[varName] * scaleFactor
dfFDP8[varName] = dfFDP8[varName] * scaleFactor
for value in dfPromptP6[varName].to_numpy():
hPromptP6.Fill(value)
for value in dfPromptP8[varName].to_numpy():
hPromptP8.Fill(value)
for value in dfFDP6[varName].to_numpy():
hFDP6.Fill(value)
for value in dfFDP8[varName].to_numpy():
hFDP8.Fill(value)
SetObjectStyle(hPromptP6, color=kAzure + 4, marker=kFullCircle)
SetObjectStyle(hPromptP8, color=kRed + 1, marker=kFullCircle)
SetObjectStyle(hFDP6, color=kAzure + 4, marker=kFullCircle)
SetObjectStyle(hFDP8, color=kRed + 1, marker=kFullCircle)
hPromptP6.GetXaxis().SetNdivisions(505)
hFDP6.GetXaxis().SetNdivisions(505)
hPromptP8.GetXaxis().SetNdivisions(505)
hFDP8.GetXaxis().SetNdivisions(505)
scanRange = inputCfg['scan_variable']['scan_range']
scanStep = inputCfg['scan_variable']['scan_step']
nEffBins = round((scanRange[1] - scanRange[0]) / scanStep)
hEffPromptP6 = TH1F('hEffPromptP6', f';{varTitle} >;Efficiency', nEffBins,
scanRange[0], scanRange[1])
hEffPromptP8 = TH1F('hEffPromptP8', f';{varTitle} >;Efficiency', nEffBins,
scanRange[0], scanRange[1])
hEffFDP6 = TH1F('hEffFDP6', f';{varTitle} >;Efficiency', nEffBins,
scanRange[0], scanRange[1])
hEffFDP8 = TH1F('hEffFDP8', f';{varTitle} >;Efficiency', nEffBins,
scanRange[0], scanRange[1])
SetObjectStyle(hEffPromptP6, color=kAzure + 4, marker=kFullCircle)
SetObjectStyle(hEffPromptP8, color=kRed + 1, marker=kFullCircle)
SetObjectStyle(hEffFDP6, color=kAzure + 4, marker=kFullCircle)
SetObjectStyle(hEffFDP8, color=kRed + 1, marker=kFullCircle)
effPromptP6, effPromptP8, effFDP6, effFDP8 = ([] for _ in range(4))
effPromptUncP6, effPromptUncP8, effFDUncP6, effFDUncP8 = (
[] for _ in range(4))
labelsConf = inputCfg['legend']['conf_labels']
legPrompt = TLegend(0.25, 0.2, 0.6, 0.4)
legPrompt.SetBorderSize(0)
legPrompt.SetFillStyle(0)
legPrompt.SetHeader('Prompt')
legPrompt.AddEntry(hEffPromptP6, labelsConf[0], 'p')
legPrompt.AddEntry(hEffPromptP8, labelsConf[1], 'p')
legFD = TLegend(0.25, 0.2, 0.65, 0.4)
legFD.SetBorderSize(0)
legFD.SetFillStyle(0)
legFD.SetHeader('Non-prompt')
legFD.AddEntry(hEffPromptP6, labelsConf[0], 'p')
legFD.AddEntry(hEffPromptP8, labelsConf[1], 'p')
for iBin, cut in enumerate(np.arange(scanRange[0], scanRange[1],
scanStep)):
dfPromptP6Sel = dfPromptP6.query(f'{varName} > {cut}')
dfPromptP8Sel = dfPromptP8.query(f'{varName} > {cut}')
dfFDP6Sel = dfFDP6.query(f'{varName} > {cut}')
dfFDP8Sel = dfFDP8.query(f'{varName} > {cut}')
effPromptP6.append(float(len(dfPromptP6Sel) / len(dfPromptP6)))
effPromptP8.append(float(len(dfPromptP8Sel) / len(dfPromptP8)))
effFDP6.append(float(len(dfFDP6Sel) / len(dfFDP6)))
effFDP8.append(float(len(dfFDP8Sel) / len(dfFDP8)))
effPromptUncP6.append(
np.sqrt(effPromptP6[-1] * (1 - effPromptP6[-1]) / len(dfPromptP6)))
effPromptUncP8.append(
np.sqrt(effPromptP8[-1] * (1 - effPromptP8[-1]) / len(dfPromptP8)))
effFDUncP6.append(
np.sqrt(effFDP6[-1] * (1 - effFDP6[-1]) / len(dfFDP6)))
effFDUncP8.append(
np.sqrt(effFDP8[-1] * (1 - effFDP8[-1]) / len(dfFDP8)))
hEffPromptP6.SetBinContent(iBin + 1, effPromptP6[-1])
hEffPromptP8.SetBinContent(iBin + 1, effPromptP8[-1])
hEffFDP6.SetBinContent(iBin + 1, effFDP6[-1])
hEffFDP8.SetBinContent(iBin + 1, effFDP8[-1])
hEffPromptP6.SetBinError(iBin + 1, effPromptUncP6[-1])
hEffPromptP8.SetBinError(iBin + 1, effPromptUncP8[-1])
hEffFDP6.SetBinError(iBin + 1, effFDUncP6[-1])
hEffFDP8.SetBinError(iBin + 1, effFDUncP8[-1])
hEffPromptP6.GetXaxis().SetNdivisions(505)
hEffFDP6.GetXaxis().SetNdivisions(505)
hEffPromptP8.GetXaxis().SetNdivisions(505)
hEffFDP8.GetXaxis().SetNdivisions(505)
hEffPromptRatio = hEffPromptP8.Clone('hEffPromptRatio')
hEffPromptRatio.Divide(hEffPromptP6)
hEffPromptRatio.GetYaxis().SetTitle(
f'Prompt eff ratio {labelsConf[1]} / {labelsConf[0]}')
hEffFDRatio = hEffFDP8.Clone('hEffFDRatio')
hEffFDRatio.Divide(hEffFDP6)
hEffFDRatio.GetYaxis().SetTitle(
f'Non-prompt eff ratio {labelsConf[1]} / {labelsConf[0]}')
hEffPromptRatio.GetXaxis().SetNdivisions(505)
hEffFDRatio.GetXaxis().SetNdivisions(505)
cDistributions = TCanvas('cDistributions', '', 1920, 1080)
cDistributions.Divide(2, 1)
cDistributions.cd(1).SetLogy()
hPromptP8.Draw('e')
hPromptP6.Draw('esame')
legPrompt.Draw()
cDistributions.cd(2).SetLogy()
hFDP8.Draw('e')
hFDP6.Draw('esame')
legFD.Draw()
cEfficiency = TCanvas('cEfficiency', '', 1920, 1080)
cEfficiency.Divide(2, 1)
cEfficiency.cd(1).SetLogy()
hEffPromptP6.Draw('e')
hEffPromptP8.Draw('esame')
legPrompt.Draw()
cEfficiency.cd(2).SetLogy()
hEffFDP6.Draw('e')
hEffFDP8.Draw('esame')
legFD.Draw()
cEfficiencyRatio = TCanvas('cEfficiencyRatio', '', 1920, 1080)
cEfficiencyRatio.Divide(2, 1)
cEfficiencyRatio.cd(1)
hEffPromptRatio.Draw('e')
cEfficiencyRatio.cd(2)
hEffFDRatio.Draw('e')
tag = f'{labelsConf[0]}Vs{labelsConf[1]}_pT{ptMin}_{ptMax}'
cDistributions.SaveAs(f'{args.outFileDir}/{varName}_Distr_{tag}.pdf')
cEfficiency.SaveAs(f'{args.outFileDir}/{varName}_CutEff_{tag}.pdf')
cEfficiencyRatio.SaveAs(
f'{args.outFileDir}/{varName}_CutEffRatio_{tag}.pdf')
print('Press any key to exit!')
input()
hCrossSectionPrompt.Scale(1.e-6 / BR)
hCrossSectionPrompt.SetStats(0)
if args.syst:
gCrossSectionPrompt = infilePrompt.Get('gSigmaCorr')
gCrossSectionPrompt.RemovePoint(0)
ScaleGraph(gCrossSectionPrompt, 1.e-6 / BR)
for iPt in range(hCrossSectionPrompt.GetNbinsX()):
gCrossSectionPrompt.SetPointEXhigh(
iPt,
hCrossSectionPrompt.GetBinWidth(iPt + 1) / 2.5)
gCrossSectionPrompt.SetPointEXlow(
iPt,
hCrossSectionPrompt.GetBinWidth(iPt + 1) / 2.5)
hCrossSectionPrompt.SetName('hCrossSectionPrompt')
hCrossSectionPrompt.SetDirectory(0)
SetObjectStyle(hCrossSectionPrompt, color=kBlack, markerstyle=kFullCircle)
if args.syst:
SetObjectStyle(gCrossSectionPrompt, color=kBlack, fillstyle=0)
gCrossSectionPrompt.SetName('gCrossSectionPrompt')
infilePrompt.Close()
ptLimitsPrompt = np.array(hCrossSectionPrompt.GetXaxis().GetXbins(), 'd')
ptMinPrompt = list(ptLimitsPrompt)[0]
ptMaxPrompt = list(ptLimitsPrompt)[-1]
ptMin = ptMinPrompt
ptMax = ptMaxPrompt
sigmaMin = hCrossSectionPrompt.GetMinimum() * 0.2
sigmaMax = hCrossSectionPrompt.GetMaximum() * 5
infileFONLL = TFile.Open(args.FONLLFileName)
if args.Dplus:
sFracRatioVsMult, multFactor = [], []
tunes = [0, 3, 7, 8, 9]
for tune in tunes:
fRatioVsMult = uproot.open(
args.multDepFileName)[f'hfnonprompt_ratioMB_meson_tune{tune}']
bins = fRatioVsMult.axis(0).edges()
multCent = [(bins[iBin] + bins[iBin + 1]) / 2
for iBin in range(len(bins) - 1)]
sFracRatioVsMult.append(
InterpolatedUnivariateSpline(multCent, fRatioVsMult.values()))
multFactor.append(sFracRatioVsMult[-1](args.multFactor))
gFracFONLLUnc, gFracMultUnc, gFracTotUnc = (TGraphAsymmErrors(0)
for _ in range(3))
SetObjectStyle(gFracFONLLUnc, color=kAzure + 4, fillalpha=0.2)
SetObjectStyle(gFracMultUnc, color=kGreen + 2, fillalpha=0.2)
SetObjectStyle(gFracTotUnc, color=kRed + 1, fillalpha=0.2)
ptMin, ptMax = -1., 1.
for iPt in range(gFrac.GetN() - 1):
promptFrac, pT = ctypes.c_double(), ctypes.c_double()
gFrac.GetPoint(iPt + 1, pT, promptFrac)
FONLLUncLow = gFrac.GetErrorYlow(iPt + 1)
FONLLUncHigh = gFrac.GetErrorYhigh(iPt + 1)
ptUncLow = gFrac.GetErrorXlow(iPt + 1)
ptUncHigh = gFrac.GetErrorXhigh(iPt + 1)
nonPromptFrac = 1 - promptFrac.value
nonPromptFracLow = nonPromptFrac * min(multFactor)
nonPromptFracHigh = nonPromptFrac * max(multFactor)
help='config file name with root input files')
args = parser.parse_args()
with open(args.cfgFileName, 'r') as ymlCutSetFile:
inCfg = yaml.load(ymlCutSetFile, yaml.FullLoader)
inFile = TFile.Open(inCfg['infilename'])
hCrossSec = inFile.Get('hCrossSection')
hCrossSec.SetName('hCrossSecMeas')
hCrossSec.SetDirectory(0)
gCrossSec = inFile.Get('gCrossSectionSystTot')
gCrossSec.SetName('gCrossSectionSystTotMeas')
gCrossSecLumi = inFile.Get('gCrossSectionSystLumi')
systErr = inFile.Get('AliHFSystErr')
SetObjectStyle(hCrossSec,
color=GetROOTColor('kBlack'),
markerstyle=GetROOTMarker('kFullSquare'),
markersize=0.8)
SetObjectStyle(gCrossSec, color=GetROOTColor('kBlack'), fillstyle=0)
inFile.Close()
ptMaxMeas = hCrossSec.GetXaxis().GetXmax()
ptLimsMeas = [
hCrossSec.GetBinLowEdge(iPt) for iPt in range(1,
hCrossSec.GetNbinsX() + 1)
]
ptLimsMeas.append(ptMaxMeas)
ptMinMeas = ptLimsMeas[0]
colors = {
'cent': GetROOTColor('kBlack'),
'min': GetROOTColor('kRed+1'),
hVisibleCrossSectionStat = TH1F(f'h{graphName}', utilStr, 1, 0.5, 1.5)
gVisCrossSecUncorrSyst, gVisCrossSecCorrSyst, gVisCrossSecTotSyst, gVisCrossSecSystWoTrFDBRAndLumi, \
gVisCrossSecSystTracking, gVisCrossSecSystFD, gVisCrossSecSystLumi, gVisCrossSecSystBR = \
(TGraphAsymmErrors(0) for _ in range(8))
gVisCrossSecUncorrSyst.SetNameTitle(f'g{graphName}UncorrSys', utilStr)
gVisCrossSecCorrSyst.SetNameTitle(f'g{graphName}PtCorrSys', utilStr)
gVisCrossSecTotSyst.SetNameTitle(f'g{graphName}TotSys', utilStr)
gVisCrossSecSystWoTrFDBRAndLumi.SetNameTitle(f'g{graphName}SysWoTrFDBRAndLumi', utilStr)
gVisCrossSecSystTracking.SetNameTitle('gVisCrossSecSysTracking', utilStr)
gVisCrossSecSystFD.SetNameTitle(f'g{graphName}SysFD', utilStr)
gVisCrossSecSystLumi.SetNameTitle(f'g{graphName}SysLumi', utilStr)
gVisCrossSecSystBR.SetNameTitle(f'g{graphName}SysBR', utilStr)
SetObjectStyle(hVisibleCrossSectionStat, color=kBlack, markerstyle=kFullCircle)
SetObjectStyle(gVisCrossSecUncorrSyst, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecCorrSyst, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecTotSyst, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecSystLumi, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecSystBR, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecSystFD, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecSystTracking, color=kBlack, fillstyle=0)
SetObjectStyle(gVisCrossSecSystWoTrFDBRAndLumi, color=kBlack, fillstyle=0)
hVisibleCrossSectionStat.SetBinContent(1, visCrossSec)
hVisibleCrossSectionStat.SetBinError(1, statUnc)
gVisCrossSecUncorrSyst.SetPoint(0, 1., visCrossSec)
gVisCrossSecCorrSyst.SetPoint(0, 1., visCrossSec)
gVisCrossSecTotSyst.SetPoint(0, 1., visCrossSec)
gVisCrossSecSystWoTrFDBRAndLumi.SetPoint(0, 1., visCrossSec)
legAbs.AddEntry(gRelVar, 'considered', 'p')
legAbs.AddEntry(gRelVarOutliers, 'outliers', 'p')
if doRelVar:
cRelVar = TCanvas('cRelVar', '', 800, 800)
minVals = np.min(relVar, axis=0)
maxVals = np.max(relVar, axis=0)
hFrameRel = cRelVar.DrawFrame(
minVals[0] - 0.5 * abs(minVals[0]), minVals[1] - 0.5 * abs(minVals[1]),
maxVals[0] + 0.5 * abs(maxVals[0]), maxVals[1] + 0.5 * abs(maxVals[1]),
f";relative variation of {varName['var1']};relative variation of {varName['var2']}"
)
hFrameRel.GetYaxis().SetDecimals()
hFrameRel.GetYaxis().SetNdivisions(505)
hFrameRel.GetXaxis().SetNdivisions(505)
SetObjectStyle(gRelVar, color=kAzure + 4, markerstyle=kFullCircle)
gRelVar.Draw('P')
if removeOutliersRel:
SetObjectStyle(gRelVarOutliers,
color=kRed + 1,
markerstyle=kFullCircle)
gRelVarOutliers.Draw('P')
legRel.Draw()
cRelVar.SaveAs(f'{outFileName}_RelativeVariation.pdf')
if doAbsVar:
cAbsVar = TCanvas('cAbsVar', '', 800, 800)
minVals = np.min(absVar, axis=0)
maxVals = np.max(absVar, axis=0)
hFrameAbs = cAbsVar.DrawFrame(
minVals[0] - 0.5 * abs(minVals[0]), minVals[1] - 0.5 * abs(minVals[1]),
maxVals[0] + 0.5 * abs(maxVals[0]), maxVals[1] + 0.5 * abs(maxVals[1]),
leg.SetFillStyle(0)
leg2D = TLegend(0.18, 0.17, 0.52, 0.4)
leg2D.SetTextSize(0.05)
leg2D.SetFillStyle(0)
for iVar, axis in enumerate(topovars):
hVars[f'Pt{ptmin:.0f}_{ptmax:.0f}'][
topovars[axis]] = sparseReco['RecoPrompt'].Projection(axis)
hVars[f'Pt{ptmin:.0f}_{ptmax:.0f}'][topovars[axis]].SetName(
f'hVarsSel_{topovars[axis]}_Pt{ptmin:.0f}_{ptmax:.0f}')
SetObjectStyle(hVars[f'Pt{ptmin:.0f}_{ptmax:.0f}'][topovars[axis]],
color=kBlue,
linealpha=0.25,
fillalpha=0.25,
markeralpha=1,
markerstyle=kFullCircle,
markersize=0.5,
linewidth=1)
hVarsVsPt[f'Pt{ptmin:.0f}_{ptmax:.0f}'][
topovars[axis]] = sparseReco['RecoPrompt'].Projection(axis, 1)
hVarsVsPt[f'Pt{ptmin:.0f}_{ptmax:.0f}'][topovars[axis]].SetName(
f'hVarsSel_{topovars[axis]}_Pt{ptmin:.0f}_{ptmax:.0f}')
SetObjectStyle(hVarsVsPt[f'Pt{ptmin:.0f}_{ptmax:.0f}'][topovars[axis]],
color=kBlue,
linealpha=0.25,
fillalpha=0.25,
markeralpha=1,
markerstyle=kFullCircle,
markersize=0.3,
sys.exit()
SetGlobalStyle(padleftmargin=0.15,
padtopmargin=0.05,
titlesize=0.045,
labelsize=0.04)
hMassTask, hMassTreeCreator, cComparison = ({} for iDic in range(3))
infile = TFile.Open(args.inputfile)
listTask = infile.Get('{0}/{1}'.format(args.inputdir, args.inputlist))
hEv = listTask.FindObject('hNEvents')
sparse = listTask.FindObject('fnSparse')
for iVar in Vars:
hMassTask[iVar] = sparse.Projection(Vars[iVar])
SetObjectStyle(hMassTask[iVar], color=kBlack, markerstyle=kFullCircle)
dfD = uproot.open(args.inputfile)['PWGHF_TreeCreator/tree_Ds'].arrays(
library='pd') #tree_event_char
dfEv = uproot.open(
args.inputfile)['PWGHF_TreeCreator/tree_event_char'].arrays(library='pd')
dfEvSel = dfEv.query('is_ev_rej==0')
dfMerged = pd.merge(dfD, dfEvSel, on=['run_number', 'ev_id'])
isSel = FidAccSel(dfMerged['pt_cand'].to_numpy(), dfMerged.y_cand.to_numpy())
dfMerged['fidacc'] = isSel
dfMergedSel = dfMerged.query('fidacc==True')
leg = TLegend(0.4, 0.6, 0.8, 0.8)
for counter, iVar in enumerate(Vars):
styleMarkerPrompt = [
kFullDiamond, kFullSquare, kFullStar, kFullTriangleUp, kFullCross
]
styleMarkerFD = [
kOpenDiamond, kOpenSquare, kOpenStar, kOpenTriangleUp, kOpenCross
]
hEffPrompt, hEffFD = [], []
for iReso, fileName, in enumerate(inputFile):
infile = TFile.Open(fileName)
hEffPrompt.append(infile.Get('hAccEffPrompt'))
hEffFD.append(infile.Get('hAccEffFD'))
hEffPrompt[iReso].SetDirectory(0)
hEffFD[iReso].SetDirectory(0)
SetObjectStyle(hEffPrompt[iReso],
color=colorMarkerPrompt[iReso],
markerstyle=styleMarkerPrompt[iReso])
SetObjectStyle(hEffFD[iReso],
color=colorMarkerFD[iReso],
markerstyle=styleMarkerFD[iReso],
markersize=1.5,
linewidh=2,
linestyle=7)
ptMin = hEffPrompt[0].GetBinLowEdge(1)
ptMax = hEffPrompt[0].GetBinLowEdge(
hEffPrompt[0].GetNbinsX()) + hEffPrompt[0].GetBinWidth(
hEffPrompt[0].GetNbinsX())
cEffPrompt = TCanvas('cEffPrompt', '', 800, 800)
cEffPrompt.DrawFrame(ptMin, 1.e-5, ptMax, 1.,
cRho00VsPromptFrac = TCanvas('cRho00VsPromptFrac', '', 1200, 400)
cRho00VsNonPromptFrac = TCanvas('cRho00VsNonPromptFrac', '', 1200, 400)
DivideCanvas(cRho00VsPromptFrac, nPtBins)
DivideCanvas(cRho00VsNonPromptFrac, nPtBins)
gRho00VsPromptFrac, gRho00VsNonPromptFrac, fRho00VsPromptFrac, fRho00VsNonPromptFrac = (
[] for _ in range(4))
ciRho00VsPromptFrac, ciRho00VsNonPromptFrac = [], []
ptLims = ptMins.copy()
ptLims.append(ptMaxs[-1])
ptLims = np.array(ptLims, 'f')
hRho00Charm = TH1D('hRho00Charm', ';#it{p}_{T} (GeV/#it{c});#it{#rho}_{00}',
nPtBins, ptLims)
SetObjectStyle(hRho00Charm,
color=GetROOTColor('kRed+1'),
marker=GetROOTMarker('kFullSquare'))
hRho00Beauty = TH1D('hRho00Beauty', ';#it{p}_{T} (GeV/#it{c});#it{#rho}_{00}',
nPtBins, ptLims)
SetObjectStyle(hRho00Beauty,
color=GetROOTColor('kAzure+4'),
marker=GetROOTMarker('kFullCircle'))
lat = TLatex()
lat.SetNDC()
lat.SetTextFont(42)
lat.SetTextSize(0.045)
lat.SetTextColor(GetROOTColor('kBlack'))
for iPt, (ptMin, ptMax) in enumerate(zip(ptMins, ptMaxs)):
inFile = TFile.Open(inFileName)
hEffPrompt.append(inFile.Get(f'{histoNames[iFile]}Prompt'))
hEffFD.append(inFile.Get(f'{histoNames[iFile]}FD'))
hEffPrompt[iFile].SetDirectory(0)
hEffFD[iFile].SetDirectory(0)
hEffPrompt[iFile].GetYaxis().SetRangeUser(1e-4, 1.)
hEffFD[iFile].GetYaxis().SetRangeUser(1e-4, 1.)
hEffPromptRatio.append(
ComputeRatioDiffBins(hEffPrompt[iFile], hEffPrompt[0], 'B'))
hEffPromptRatio[iFile].SetDirectory(0)
hEffPromptRatio[iFile].SetName(f'hEffPromptRatio{iFile}')
hEffFDRatio.append(ComputeRatioDiffBins(hEffFD[iFile], hEffFD[0], 'B'))
hEffFDRatio[iFile].SetDirectory(0)
hEffFDRatio[iFile].SetName(f'hEffFDRatio{iFile}')
SetObjectStyle(hEffPrompt[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile])
SetObjectStyle(hEffFD[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile],
linestyle=1)
SetObjectStyle(hEffPromptRatio[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile],
linestyle=1)
SetObjectStyle(hEffFDRatio[iFile],
linecolor=colors[iFile],
markercolor=colors[iFile],
markerstyle=markers[iFile])
leg = TLegend(0.18, 0.76, 0.52, 0.89)
leg.SetTextSize(0.05)
leg.SetFillStyle(0)
leg2D = TLegend(0.18, 0.17, 0.52, 0.4)
leg2D.SetTextSize(0.05)
leg2D.SetFillStyle(0)
for iVar, axis in enumerate(topovars):
hVars['Pt{:.0f}_{:.0f}'.format(
ptmin, ptmax)][topovars[axis]] = sparseReco['RecoPrompt'].Projection(axis)
hVars['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)][topovars[axis]].SetName(
'hVarsSel_{:s}_Pt{:.0f}_{:.0f}'.format(topovars[axis], ptmin, ptmax))
SetObjectStyle(hVars['Pt{:.0f}_{:.0f}'.format(
ptmin, ptmax)][topovars[axis]], color=kBlue, linealpha=0.25, fillalpha=0.25, markeralpha=1, markerstyle=kFullCircle, markersize=0.5, linewidth=1)
hVarsVsPt['Pt{:.0f}_{:.0f}'.format(
ptmin, ptmax)][topovars[axis]] = sparseReco['RecoPrompt'].Projection(axis, 1)
hVarsVsPt['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)][topovars[axis]].SetName(
'hVarsSel_{:s}_Pt{:.0f}_{:.0f}'.format(topovars[axis], ptmin, ptmax))
SetObjectStyle(hVarsVsPt['Pt{:.0f}_{:.0f}'.format(
ptmin, ptmax)][topovars[axis]], color=kBlue, linealpha=0.25, fillalpha=0.25, markeralpha=1, markerstyle=kFullCircle, markersize=0.3, linewidth=1)
hVarsVsML['Pt{:.0f}_{:.0f}'.format(
ptmin, ptmax)][topovars[axis]] = sparseReco['RecoPrompt'].Projection(axis, 13)
hVarsVsML['Pt{:.0f}_{:.0f}'.format(ptmin, ptmax)][topovars[axis]].SetName(
'hVarsSel_{:s}_Pt{:.0f}_{:.0f}'.format(topovars[axis], ptmin, ptmax))
SetObjectStyle(hVarsVsML['Pt{:.0f}_{:.0f}'.format(
ptmin, ptmax)][topovars[axis]], color=kBlue, linealpha=0.25, fillalpha=0.25, markeralpha=1, markerstyle=kFullCircle, markersize=0.3, linewidth=1)
