# Main function
Vars = {'inv_mass': 0}
parser = argparse.ArgumentParser(description='Arguments')
parser.add_argument('--mc', action='store_true', help='flag for MC outputs')
parser.add_argument('inputfile', metavar='text', help='input root file name')
parser.add_argument('inputdir', metavar='text', help='task directory name')
parser.add_argument('inputlist', metavar='text', help='task list name')
args = parser.parse_args()
if args.mc:
print('MC validation not yet implemented. Exit')
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(
from utils.AnalysisUtils import ComputeRatioDiffBins #pylint: disable=wrong-import-position,import-error
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle #pylint: disable=wrong-import-position,import-error
inDir = 'inputdir'
inFileNames = ['file1.root', 'file2.root']
histoNames = ['hAccEff', 'hAccEff']
colors = [kRed + 1, kAzure + 4]
markers = [kFullSquare, kFullCircle]
legNames = ['title1', 'title2']
outDir = 'outputdir'
outSuffix = 'suffix'
showUnc = True
SetGlobalStyle(padleftmargin=0.16,
padtopmargin=0.05,
padbottommargin=0.14,
titleoffsety=1.5,
titlesize=0.05,
labelsize=0.045)
hEffPrompt, hEffFD, hEffPromptRatio, hEffFDRatio = ([] for _ in range(4))
leg = TLegend(0.2, 0.18, 0.9, 0.33)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextSize(0.04)
for iFile, inFileName in enumerate(inFileNames):
inFileName = join(inDir, inFileName)
inFile = TFile.Open(inFileName)
hEffPrompt.append(inFile.Get(f'{histoNames[iFile]}Prompt'))
hEffFD.append(inFile.Get(f'{histoNames[iFile]}FD'))
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)
BR_dsPhitoKKpi = 0.0224 # (PDG 2020)
BR_BtoDs = np.array([0.09, 0.117, 0.93,
0.011]) # B+, B0, Bs, Lb (PDG 2020)
FF_btoB = np.array([0.344, 0.344, 0.115, 0.198
]) # B+, B0, Bs, Lb (PDG 2020 from ppbar collisions)
Ds_from_B = np.inner(BR_BtoDs, FF_btoB)
cooked_factor = Ds_from_B * BR_dsPhitoKKpi
n_bins = 1001
bin_width = 0.05 # GeV
bin_lims = [i * bin_width for i in range(0, n_bins + 1)]
hFONLLCentral = TH1D(
'hDsPhipitoKkpifromBpred_central_corr',
';#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'))
hFONLLMin = TH1D(
'hDsPhipitoKkpifromBpred_min_corr',
';#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'))
hFONLLMax = TH1D(
'hDsPhipitoKkpifromBpred_max_corr',
';#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'))
fonll_df = pd.read_csv("fonll/FONLL_DfromB_pp5_y05_toCook.txt",
sep=" ",
header=14).astype('float64')
for pt, cen, low, up in zip(fonll_df['pt'].to_numpy(),
fonll_df['central'].to_numpy(),
fonll_df['min'].to_numpy(),
fonll_df['max'].to_numpy()):
hFONLLCentral.Fill(pt, cen * cooked_factor)
hFONLLMin.Fill(pt, low * cooked_factor)
hFONLLMax.Fill(pt, up * cooked_factor)
for iBin in range(hFONLLCentral.GetNbinsX()):
hFONLLCentral.SetBinError(iBin + 1, 1.e-3)
hFONLLMin.SetBinError(iBin + 1, 1.e-3)
hFONLLMax.SetBinError(iBin + 1, 1.e-3)
stdFONLLfile = TFile.Open(
"fonll/feeddown/D0DplusDstarPredictions_502TeV_y05_noYShift_all_191017_BDShapeCorrected.root"
)
hStdFONLLCentral = stdFONLLfile.Get('hDsPhipitoKkpifromBpred_central_corr')
hStdFONLLMin = stdFONLLfile.Get('hDsPhipitoKkpifromBpred_min_corr')
hStdFONLLMax = stdFONLLfile.Get('hDsPhipitoKkpifromBpred_max_corr')
hFONLLPromptCentral = stdFONLLfile.Get('hDsPhipitoKkpipred_central')
hFONLLPromptMin = stdFONLLfile.Get('hDsPhipitoKkpipred_min')
hFONLLPromptMax = stdFONLLfile.Get('hDsPhipitoKkpipred_max')
hStdFONLLCentral.SetDirectory(0)
hStdFONLLMin.SetDirectory(0)
hStdFONLLMax.SetDirectory(0)
hFONLLPromptCentral.SetDirectory(0)
hFONLLPromptMin.SetDirectory(0)
hFONLLPromptMax.SetDirectory(0)
hStdFONLLCentral.SetStats(0)
hStdFONLLMin.SetStats(0)
hStdFONLLMax.SetStats(0)
hFONLLPromptCentral.SetStats(0)
hFONLLPromptMin.SetStats(0)
hFONLLPromptMax.SetStats(0)
stdFONLLfile.Close()
outFile = TFile(
'fonll/feeddown/NonPromptDsPredictions_502TeV_y05_cooked.root',
'recreate')
hFONLLCentral.Write()
hFONLLMin.Write()
hFONLLMax.Write()
hFONLLPromptCentral.Write()
hFONLLPromptMin.Write()
hFONLLPromptMax.Write()
outFile.Close()
hFONLL = [hFONLLCentral, hFONLLMin, hFONLLMax]
hStdFONLL = [hStdFONLLCentral, hStdFONLLMin, hStdFONLLMax]
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, hStdFONLL, 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'hStdFONLL{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'hRatioCookedOverStd{label}')
histo_ratio.Divide(histo, histo_std)
histo_ratio.GetYaxis().SetTitle('Cooked FONLL / "Std" FONLL')
print(
f'\n"STD" FONLL {label} integral (ub): {histo_std.Integral("width"):.4e}'
)
print(
f'Cooked FONLL {label} integral (ub): {histo.Integral("width"):.4e}\n'
)
canv.Divide(2, 1)
canv.cd(1).DrawFrame(
pt_min, 1e-7, pt_max, 2.,
';#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'Cooked FONLL {label}', 'p')
leg.AddEntry(histo_std, f'"Std" FONLL {label}', 'p')
gPad.SetLogy()
leg.Draw()
hframe_ratio = canv.cd(2).DrawFrame(
pt_min, 0., pt_max, 3.,
';#it{p}_{T} (GeV/#it{c}); Cooked FONLL / "Std" FONLL')
hframe_ratio.GetYaxis().SetDecimals()
SetObjectStyle(histo_ratio, color=kAzure + 4, markerstyle=kFullSquare)
histo_ratio.Draw('same')
canv.Update()
canv.SaveAs(f'CompFONLL_CookedVsStd_{label}.pdf')
input('Press enter to exit')
'''
Script for the computation of the data-driven fraction ratio
run: python ComputeDataDrivenFracRatio.py config.yml
'''
import os
import argparse
import numpy as np
import yaml
from ROOT import TCanvas, TFile, TLine, TLegend, TGraphAsymmErrors # pylint: disable=import-error,no-name-in-module
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle, GetROOTColor #pylint: disable=wrong-import-position,import-error,no-name-in-module
SetGlobalStyle(padbottommargin=0.14, padleftmargin=0.16,
padtopmargin=0.035, titleoffsety=1.4, maxdigits=3)
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('config', metavar='text', default='config.yml',
help='yaml config file')
args = parser.parse_args()
with open(args.config, 'r') as ymlCfg: # pylint: disable=unspecified-encoding
cfg = yaml.load(ymlCfg, yaml.FullLoader)
inFileNames, colors, correl, labels, titles = ([] for _ in range(5))
cfgDen = cfg['inputs']['denominator']
inFileNames.append(cfgDen['file'])
colors.append(cfgDen['color'])
labels.append(cfgDen['label'])
titles.append(cfgDen['title'])
correl.append(None)
cfgNum = cfg['inputs']['numerator']
args = parser.parse_args()
with open(args.cfgFileName, 'r') as ymlCfgFile:
inputCfg = yaml.load(ymlCfgFile, yaml.FullLoader)
inKineFileName = inputCfg['inKineFileName']
Bhadrons = inputCfg['pdgCodeB']
FFbtoB = inputCfg['FFbtoB']
Dhadrons = inputCfg['pdgCodeD']
BRDhadrons = inputCfg['BRD']
partPlusAntiPart = inputCfg['partPlusAntiPart']
outFileName = inputCfg['outFileName']
kineDf = LoadDfFromRootOrParquet(inKineFileName, None, 'fTreeDecays')
SetGlobalStyle(padleftmargin=0.14,
padbottommargin=0.12,
titleoffsety=1.3,
optstat=0)
Bcolors = [kRed + 1, kAzure + 4, kOrange + 7, kGreen + 2]
Bnames = {
511: 'B^{+}',
521: 'B^{0}',
531: 'B_{s}^{0}',
5122: '#Lambda_{b}^{0}'
}
Dnames = {
411: 'D^{+}',
421: 'D^{0}',
431: 'D_{s}^{+}',
4122: '#Lambda_{c}^{+}'
}
(uncEffAcc / effAcc)**2)
gCrossSectionSyst[systSource].SetPointError(iPt, 0.4,
relSyst * crossSec)
if args.system == 'pp':
gCrossSectionSystLumi = TGraphErrors(1)
gCrossSectionSystLumi.SetName('gCrossSectionSystLumi')
gCrossSectionSystLumi.SetTitle('Luminosity syst. unc.;;')
gCrossSectionSystLumi.SetPoint(0, 1., 1.)
gCrossSectionSystLumi.SetPointError(0, 0.4, lumiUnc)
SetObjectStyle(gCrossSectionSystLumi,
color=GetROOTColor('kBlue'),
fillstyle=0)
gROOT.SetBatch(args.batch)
SetGlobalStyle(padleftmargin=0.18, padbottommargin=0.14)
cCrossSec = TCanvas(f'c{histoName}', '', 700, 800)
cCrossSec.SetLogy()
hCrossSection.Draw()
gCrossSectionSyst['Tot'].Draw('2')
legFrac = TLegend(0.2, 0.84, 0.4, 0.94)
legFrac.SetBorderSize(0)
legFrac.SetFillStyle(0)
legFrac.SetTextSize(0.045)
legFrac.AddEntry(hPromptFrac, 'Prompt', 'p')
legFrac.AddEntry(hFDFrac, 'Non-prompt', 'p')
legEff = legFrac.Clone('legEff')
legEff.SetY1(0.2)
parser.add_argument('multDepFileName',
metavar='text',
default='multDepFile.root',
help='root file containing B/D multiplicity dependence')
parser.add_argument('outFileName',
metavar='text',
default='outFile.root',
help='output root file')
parser.add_argument('--multFactor',
type=float,
default=1.,
help='value of multiplicity wrt to MB')
args = parser.parse_args()
# set global style
SetGlobalStyle(padleftmargin=0.18, padbottommargin=0.14, titleoffsety=1.7)
inFileFrac = TFile.Open(args.fracFileName)
gFrac = inFileFrac.Get('gFcConservative')
if not gFrac:
print('ERROR: graph with prompt fraction not present in input file! Exit')
sys.exit()
inFileFrac.Close()
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
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')
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')
hCrossSecFD.append(
inFileCrossSec.Get(
f"{crossSecCfg['histonames']['feeddown']}_central_corr"))
hCrossSecFD.append(
inFileCrossSec.Get(
f"{crossSecCfg['histonames']['feeddown']}_min_corr"))
hCrossSecFD.append(
inFileCrossSec.Get(
f"{crossSecCfg['histonames']['feeddown']}_max_corr"))
if compareToNb:
sigmaMB = cutSetCfg['theorydriven']['sigmaMB']
SetGlobalStyle(padleftmargin=0.15,
padtopmargin=0.08,
titleoffsetx=1.,
titleoffsety=1.4,
opttitle=1,
palette=kRainBow,
maxdigits=2)
legDistr = TLegend(0.45, 0.69, 0.75, 0.89)
legDistr.SetFillStyle(0)
legDistr.SetBorderSize(0)
legDistr.SetTextSize(0.045)
legEff = TLegend(0.2, 0.2, 0.4, 0.4)
legEff.SetFillStyle(0)
legEff.SetBorderSize(0)
legEff.SetTextSize(0.045)
legFrac = TLegend(0.2, 0.79, 0.4, 0.89)
'''
Script for plotting the RMS and shift of the raw-yield multi-trial distributions vs pT
run: python PlotMultiTrialRMSvsPt MultiTrial.root RawYieldsDefault.root Output.root
'''
import sys
import argparse
from ROOT import TCanvas, TFile, TGaxis, gPad, TMath, TLegend # pylint: disable=import-error,no-name-in-module
from ROOT import kBlack, kRed, kBlue # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle #pylint: disable=wrong-import-position,import-error
# set global style
SetGlobalStyle(padleftmargin=0.14,
padrightmargin=0.14,
padbottommargin=0.14,
titleoffsety=1.2,
padticky=0)
# load inputs
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)
'''
import sys
import argparse
import yaml
import numpy as np
from ROOT import TFile, TCanvas, TH2F, TLegend # pylint: disable=import-error,no-name-in-module
from ROOT import kRed, kAzure, kRainBow, kFullCircle, kFullSquare, kFullDiamond # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.DfUtils import FilterBitDf, LoadDfFromRootOrParquet #pylint: disable=wrong-import-position,import-error
from utils.AnalysisUtils import ApplyHistoEntriesToColumn #pylint: disable=wrong-import-position,import-error
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle #pylint: disable=wrong-import-position,import-error
SetGlobalStyle(palette=kRainBow,
padleftmargin=0.14,
padrightmargin=0.14,
padbottommargin=0.14,
titleoffsety=1.4)
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('cfgFileName',
metavar='text',
default='cfgFileName.yml',
help='config file name with root input files')
parser.add_argument('cutSetFileName',
metavar='text',
default='cutSetFileName.yml',
help='input file with cut set')
parser.add_argument('outFileName',
metavar='text',
default='outFileName.root',
'EffAccFD': '(Acc#times#font[152]{e})_{FD}',
'fPrompt': '#it{f}_{ prompt}^{ fc}',
'fFD': '#it{f}_{ FD}^{ fc}'
}
varsName4Tuple = ':'.join(cutVars) + ':PtMin:PtMax:S:B:' + ':'.join(
estNames.keys())
tSignif = TNtuple('tSignif', 'tSignif', varsName4Tuple)
totSets = 1
for cutRange in cutRanges:
totSets *= len(cutRange)
print(f'Total number of sets per pT bin: {totSets}')
SetGlobalStyle(padleftmargin=0.2,
padrightmargin=0.2,
padbottommargin=0.15,
titleoffset=1.4,
titleoffsety=1.8,
palette=kRainBow)
cSignifVsRest, hSignifVsRest, cEstimVsCut, hEstimVsCut = [], [], [], []
for iPt, (ptMin, ptMax) in enumerate(zip(ptMins, ptMaxs)):
if not inputCfg['infiles']['background']['isMC']:
outDirFitSB.cd()
outDirFitSBPt.append(
TDirectoryFile(f'pT{ptMin}-{ptMax}', f'pT{ptMin}-{ptMax}'))
outDirFitSBPt[iPt].Write()
outDirPlots.cd()
outDirPlotsPt.append(
TDirectoryFile(f'pT{ptMin}-{ptMax}', f'pT{ptMin}-{ptMax}'))
outDirPlotsPt[iPt].Write()
run: python ComputeDataDrivenFracVsMultSyst.py config.yml
'''
import sys
import os
import argparse
import numpy as np
import yaml
from ROOT import TCanvas, TFile, TH1F, TGraphErrors, TLine, TBox, TLatex, TLegend, kRainBow, kRed, kBlack # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle, DivideCanvas #pylint: disable=wrong-import-position,import-error,no-name-in-module
SetGlobalStyle(padbottommargin=0.2,
padleftmargin=0.2,
padtopmargin=0.1,
opttitle=1,
titleoffset=1.6,
maxdigits=3)
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('config',
metavar='text',
default='config.yml',
help='yaml config file')
args = parser.parse_args()
with open(args.config, 'r') as ymlCfg: # pylint: disable=unspecified-encoding
cfg = yaml.load(ymlCfg, yaml.FullLoader)
multClasses = cfg['inputs'].keys()
if 'MB' not in multClasses:
'''
import sys
import argparse
import yaml
from ROOT import TCanvas, TFile, TLine, TLegend # pylint: disable=import-error,no-name-in-module
from ROOT import kBlack, kBlue, kOrange, kGreen, kRed, kAzure # pylint: disable=import-error,no-name-in-module
from ROOT import kFullTriangleUp, kFullTriangleDown, kFullCross, kFullCircle, kFullSquare, kFullDiamond, kFullStar # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle #pylint: disable=wrong-import-position,import-error
parser = argparse.ArgumentParser(description='Arguments')
parser.add_argument('cfgFileName', metavar='text', default='config_ptshape_syst.yml')
args = parser.parse_args()
SetGlobalStyle(padleftmargin=0.15, padbottommargin=0.14, titlesize=0.045, labelsize=0.04, titleoffsety=1.4)
modelNames = ['pythia', 'fonll', 'tamu', 'phsd', 'mc@shq', 'catania', 'lido']
colors = {'pythia': kBlack, 'fonll': kBlue+1, 'tamu': kOrange+7, 'phsd': kGreen+2,
'catania': kRed+2, 'mc@shq': kAzure+4, 'lido': kRed}
markers = {'pythia': kFullCircle, 'fonll': kFullSquare, 'tamu': kFullCross, 'phsd': kFullDiamond,
'catania': kFullTriangleUp, 'mc@shq': kFullTriangleDown, 'lido': kFullStar}
legNames = {'pythia': 'Pythia', 'fonll': 'FONLL', 'tamu': 'FONLL #times TAMU', 'phsd': 'FONLL #times PHSD',
'catania': 'FONLL #times Catania', 'mc@shq': 'FONLL #times MC@sHQ', 'lido': 'LIDO'}
histoNames = {'pythia': 'hPtGen', 'fonll': 'hPtFONLL', 'tamu': 'hPtFONLLtimesTAMU', 'phsd': 'hPtFONLLtimesPHSD',
'catania': 'hPtFONLLtimesCatania', 'mc@shq': 'hPtFONLLtimesGossiaux', 'lido': 'hPtFONLLtimesLIDO'}
histoWeightNames = {'fonll': 'hPtWeightsFONLL', 'tamu': 'hPtWeightsFONLLtimesTAMU', 'phsd': 'hPtWeightsFONLLtimesPHSD',
'catania': 'hPtWeightsFONLLtimesCatania', 'mc@shq': 'hPtWeightsFONLLtimesGossiaux',
'lido': 'hPtWeightsFONLLtimesLIDO'}
leg = TLegend(0.2, 0.2, 0.5, 0.4)
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')
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(
'hNtrklWeightsCandInMass')
hNtrklWeightsCandInMass.Divide(hNtrklCandInMass['MC'])
SetGlobalStyle(padbottommargin=0.12,
padleftmargin=0.14,
padtopmargin=0.075,
titleoffsety=1.4,
opttitle=1)
lNtrkl = TLegend(0.2, 0.8, 0.7, 0.9)
lNtrkl.SetTextSize(0.045)
lNtrkl.SetBorderSize(0)
lNtrkl.SetFillStyle(0)
lNtrkl.AddEntry(
hNtrkl['data'],
f'data, mean = {hNtrkl["data"].GetMean():0.1f}, RMS = {hNtrkl["data"].GetRMS():0.1f}',
'pl')
lNtrkl.AddEntry(
hNtrkl['MC'],
f'MC, mean = {hNtrkl["MC"].GetMean():0.1f}, RMS = {hNtrkl["MC"].GetRMS():0.1f}',
'pl')
inputfilenames = ['file1.root', 'file2.root']
outputdir = 'outputdir'
outputsuffix = 'suffix'
signalhistonames = ['hRawYields', 'hRawYields']
bkghistonames = ['hRawYieldsBkg', 'hRawYieldsBkg']
SoverBhistonames = ['hRawYieldsSoverB', 'hRawYieldsSoverB']
signifhistonames = ['hRawYieldsSignificance', 'hRawYieldsSignificance']
evhistonames = ['hEvForNorm', 'hEvForNorm']
colors = [kRed + 1, kAzure + 4]
markers = [kFullSquare, kFullCircle]
legendnames = ['title1', 'title2']
SetGlobalStyle(padleftmargin=0.18,
padtopmargin=0.05,
padbottommargin=0.14,
titleoffsety=1.8,
titlesize=0.045,
labelsize=0.04,
maxdigits=2)
hSignal, hRatioSignal, hBackground, hRatioBkg, hSoverB, hSignif, hRatioSignif, hEv = [], [], [], [], [], [], [], []
leg = TLegend(0.5, 0.73, 0.8, 0.93)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextSize(0.04)
for iFile, filename in enumerate(inputfilenames):
inputfile = TFile(f'{inputdir}/{filename}')
hSignal.append(inputfile.Get(signalhistonames[iFile]))
hBackground.append(inputfile.Get(bkghistonames[iFile]))
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()
elif args.centClass == 'kpp5TeVPrompt':
cent = 'pp5TeVPrompt'
elif args.centClass == 'kpp13TeVFD':
cent = 'pp13TeVFD'
elif args.centClass == 'kpp13TeVPrompt':
cent = 'pp13TeVPrompt'
else:
print(f"ERROR: cent class \'{args.centClass}\' is not supported! Exit")
sys.exit()
with open(args.fitConfigFileName, 'r') as ymlfitConfigFile:
fitConfig = yaml.load(ymlfitConfigFile, yaml.FullLoader)
gROOT.SetBatch(args.batch)
SetGlobalStyle(padleftmargin=0.14,
padbottommargin=0.12,
padtopmargin=0.12,
opttitle=1)
ptMins = fitConfig[cent]['PtMin']
ptMaxs = fitConfig[cent]['PtMax']
fixSigma = fitConfig[cent]['FixSigma']
fixMean = fitConfig[cent]['FixMean']
if 'EnableRef' not in fitConfig[cent]:
enableRef = False
else:
enableRef = fitConfig[cent]['EnableRef']
if not isinstance(fixSigma, list):
fixSigma = [fixSigma for _ in ptMins]
if not isinstance(fixMean, list):
fixMean = [fixMean for _ in ptMins]
ptLims = list(ptMins)
':PtMin:PtMax:ParCutMin:ParCutMax:EffAccPromptError:EffAccFDError:SError:BError'
':SignifError:SoverBError:' + ':'.join(estNames.keys()))
tSignif = TNtuple('tSignif', 'tSignif', varsName4Tuple)
totSets = [1 for _ in enumerate(ptMaxs)]
for iPt, _ in enumerate(ptMaxs):
for cutRange in cutRanges[iPt]:
totSets[iPt] *= len(cutRange)
print(f'Total number of sets per pT bin: {totSets}')
SetGlobalStyle(padleftmargin=0.12,
padrightmargin=0.2,
padbottommargin=0.15,
padtopmargin=0.075,
titleoffset=1.,
palette=kRainBow,
titlesize=0.06,
labelsize=0.055,
maxdigits=4)
cSignifVsRest, hSignifVsRest, cEstimVsCut, hEstimVsCut = [], [], [], []
counter = 0
for iPt, (ptMin, ptMax) in enumerate(zip(ptMins, ptMaxs)):
# reshuffle bkg and take only a fraction of it, seed fixed for reproducibility
dfBkgPt = dfBkg_tot.query(
f'{ptMin} < pt_cand and pt_cand < {ptMax}').sample(
frac=fractionstokeep[iPt], random_state=42).reset_index(drop=True)
outDirFitSB.cd()
outDirFitSBPt.append(
import ctypes
import numpy as np
import yaml
from ROOT import AliHFInvMassFitter, AliVertexingHFUtils # pylint: disable=import-error,no-name-in-module
from ROOT import TFile, TCanvas, TH1F, TF1, TVirtualFitter, TDatabasePDG, TLegend # pylint: disable=import-error,no-name-in-module
from ROOT import kGreen, kAzure, kRed, kRainBow, kFullCircle, kFullSquare, kFullDiamond # pylint: disable=import-error,no-name-in-module
from ROOT import kFullCross, kOpenCircle, kOpenSquare, kOpenDiamond, kOpenCross # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.StyleFormatter import SetObjectStyle, SetGlobalStyle, DivideCanvas #pylint: disable=wrong-import-position,import-error
from utils.FitUtils import VoigtFunc, ExpoPowLaw #pylint: disable=wrong-import-position,import-error
# set global style
SetGlobalStyle(padrightmargin=0.1,
padleftmargin=0.18,
padbottommargin=0.14,
padtopmargin=0.075,
titleoffsety=1.8,
palette=kRainBow,
maxdigits=2,
opttitle=1)
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('cfgFileNameMC',
metavar='text',
default='cfgFileNameMC.yml',
help='config file name with MC root input files')
parser.add_argument('cfgFileNameData',
metavar='text',
default='cfgFileNameData.yml',
help='config file name with data root input files')
parser.add_argument('outputPath',
metavar='text',
'''
import sys
import argparse
import os
import numpy as np
import yaml
from PIL import Image
from ROOT import TFile, TCanvas, TDirectoryFile, TLegend # pylint: disable=import-error,no-name-in-module
from ROOT import kBlue, kRed, kFullCircle, kOpenCircle # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.TaskFileLoader import LoadSparseFromTask #pylint: disable=wrong-import-position,import-error
from utils.StyleFormatter import SetObjectStyle, SetGlobalStyle #pylint: disable=wrong-import-position,import-error
# main function
SetGlobalStyle()
PARSER = argparse.ArgumentParser(description='Arguments to pass')
PARSER.add_argument('cfgFileName',
metavar='text',
default='cfgFileName.yml',
help='config file name with root input files')
PARSER.add_argument('cutSetFileName',
metavar='text',
default='cutSetFileName.yml',
help='input file with cut set')
PARSER.add_argument('outFileName',
metavar='text',
default='outFileName.root',
help='output root file name')
ARGS = PARSER.parse_args()
print('ERROR: you should enable the comparison for either D+ or Ds! Exit')
sys.exit()
elif args.Dplus and args.Ds:
print('ERROR: you cannot enable the comparison for both D+ and Ds! Exit')
sys.exit()
if args.Dplus:
BR = 0.0898
mesonName = 'D^{+}'
elif args.Ds:
BR = 0.0227
mesonName = 'D_{s}^{+}'
SetGlobalStyle(padleftmargin=0.18,
padbottommargin=0.14,
titleoffsety=1.6,
maxdigits=2,
optstat=0)
if args.prompt:
infilePrompt = TFile.Open(args.prompt)
hCrossSectionPrompt = infilePrompt.Get('hCrossSection')
if args.syst:
gCrossSectionPrompt = infilePrompt.Get('gCrossSectionSystTot')
if not hCrossSectionPrompt:
hCrossSectionPrompt = infilePrompt.Get('histoSigmaCorr')
hCrossSectionPrompt.Scale(1.e-6 / BR)
hCrossSectionPrompt.SetStats(0)
if args.syst:
gCrossSectionPrompt = infilePrompt.Get('gSigmaCorr')
gCrossSectionPrompt.RemovePoint(0)
cent = 'Cent6080'
elif args.centClass == 'kpp5TeVPrompt':
cent = 'pp5TeVPrompt'
elif args.centClass == 'kpp5TeVFD':
cent = 'pp5TeVFD'
elif args.centClass == 'kpp13TeVPrompt':
cent = 'pp13TeVPrompt'
elif args.centClass == 'kpp13TeVFD':
cent = 'pp13TeVFD'
else:
print(f'ERROR: centrality {args.centClass} is not supported! Exit')
exit()
gROOT.SetBatch(args.batch)
SetGlobalStyle(padleftmargin=0.14,
padbottommargin=0.12,
titlesize=0.045,
labelsize=0.04)
ptMins = fitConfig[cent]['PtMin']
ptMaxs = fitConfig[cent]['PtMax']
ptLims = list(ptMins)
nPtBins = len(ptMins)
ptLims.append(ptMaxs[-1])
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'))
'''
python script for the pT extrapolation of data-driven pp references
run: python ExtrapolateDataDrivenCrossSection.py configFile.yml
'''
import sys
import argparse
import numpy as np
import yaml
from ROOT import TFile, TF1, TCanvas, TH1F, TLegend, TGraphAsymmErrors # pylint: disable=import-error,no-name-in-module
sys.path.append('..')
from utils.AnalysisUtils import ComputeRatioDiffBins
from utils.StyleFormatter import SetGlobalStyle, SetObjectStyle, GetROOTColor, GetROOTMarker
SetGlobalStyle(padbottommargin=0.13)
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('cfgFileName',
metavar='text',
default='cfgFileName.yml',
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')
# load published result
if inputCfg['PublishedResult']['plotPublished']:
hDRaaStat, gDRaaSyst = ReadHepDataROOT(
inputCfg['PublishedResult']['filenameHepData'],
inputCfg['PublishedResult']['tableRaa'])
hDCorrYieldStat, gDCorrYieldSyst = ReadHepDataROOT(
inputCfg['PublishedResult']['filenameHepData'],
inputCfg['PublishedResult']['tableCorrYield'])
SetGraphStyle(gDRaaSyst, kBlack, kWhite, kFullSquare, 1)
SetGraphStyle(gDCorrYieldSyst, kBlack, kWhite, kFullSquare, 1)
SetGraphStyle(hDRaaStat, kBlack, kWhite, kFullSquare, 1)
SetGraphStyle(hDCorrYieldStat, kBlack, kWhite, kFullSquare, 1)
# plots
SetGlobalStyle(padleftmargin=0.14, titlesize=0.045, labelsize=0.04)
legTAMU, legPHSD, legGossiaux, legCatania = ({
'CorrYield':
TLegend(0.45, 0.7, 0.65, 0.85),
'Raa':
TLegend(0.45, 0.7, 0.65, 0.85),
'Signif':
TLegend(0.45, 0.75, 0.65, 0.85)
} for iDic in range(4))
for iLeg in legTAMU:
legTAMU[iLeg].SetFillStyle(0)
legTAMU[iLeg].SetTextSize(0.045)
legPHSD[iLeg].SetFillStyle(0)
legPHSD[iLeg].SetTextSize(0.045)
import os
import sys
import argparse
import yaml
from ROOT import TCanvas, TLegend, TDatabasePDG, TH1F, TH2F, TF1, TLine, TGraph, TVirtualFitter, TLatex # pylint: disable=import-error,no-name-in-module
from ROOT import kFullCircle, kFullSquare, kRainBow, kRed, kAzure, kGray, kBlack # pylint: disable=import-error,no-name-in-module
from ROOT import RooStats # pylint: disable=import-error,no-name-in-module
sys.path.append('../..')
from utils.TaskFileLoader import LoadSingleSparseFromTask #pylint: disable=wrong-import-position,import-error
from utils.DfUtils import LoadDfFromRootOrParquet #pylint: disable=wrong-import-position,import-error
from utils.StyleFormatter import SetObjectStyle, SetGlobalStyle, DivideCanvas #pylint: disable=wrong-import-position,import-error
SetGlobalStyle(palette=kRainBow,
padbottommargin=0.14,
padrightmargin=0.14,
padleftmargin=0.14,
padtopmargin=0.075,
titleoffsety=1.4,
opttitle=1)
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('cfgFileName',
metavar='text',
default='cfgFileName.yml',
help='config file name with root input files')
parser.add_argument('cutSetFileName',
metavar='text',
default='cutset.yml',
help='config file name with root input files')
parser.add_argument('outputPath',
metavar='text',
