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Update DvsMult preliminary files for HP2020 (#704)
* Update DvsMult preliminary files to avoid wrong comparisons * Remove files that contain final results, keeping macros as examples
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‎Analyses/ALICE_D2H_vs_mult_pp13/AliHFSystErrToYaml.C‎

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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <string>
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#include <vector>
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using std::cout;
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using std::endl;
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#include "TCanvas.h"
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#include "TDirectoryFile.h"
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#include "TF1.h"
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#include "TFile.h"
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#include "TGraphErrors.h"
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#include "TH1F.h"
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#include "THnSparse.h"
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#include "TLatex.h"
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#include "TLegend.h"
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#include "TList.h"
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#include "TPad.h"
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#include "TROOT.h"
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#include "TSystem.h"
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#include "AliHFSystErr.h"
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void AliHFSystErrToYaml(TString inputrootfile, TString outputyamlfile, Bool_t isLc = kFALSE){
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/*
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const Int_t nPtBins = 5;
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Float_t PtBins[nPtBins+1] = {2., 4., 6., 8., 12., 24.};
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Float_t midPtBins[nPtBins] = {3., 5., 7., 10., 18.};
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*/
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const Int_t nPtBins = 6;
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Float_t PtBins[nPtBins+1] = {1., 2., 4., 6., 8., 12., 24.};
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Float_t midPtBins[nPtBins] = {1.5, 2.9, 5., 7., 10., 18.};
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TFile* fInput = new TFile(inputrootfile.Data(),"read");
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AliHFSystErr* oSystErr = (AliHFSystErr*)fInput->Get("AliHFSystErr");
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ofstream fOutYaml (outputyamlfile.Data());
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fOutYaml <<endl <<endl;
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fOutYaml << "#Extracted automatically from AliHFSystErrObject of file: " << inputrootfile << endl;
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// yield
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fOutYaml <<"names: [\"yield\", \"cut\", \"track\", \"ptshape\", \"feeddown_mult\", \"trigger\", \"multiplicity_weights\", \"multiplicity_interval\", \"branching_ratio\", \"sigmav0\"]" <<endl <<endl;
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fOutYaml <<"yield:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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fOutYaml <<" - [0., 0., " <<oSystErr->GetRawYieldErr(midPtBins[ipt]) <<", " <<oSystErr->GetRawYieldErr(midPtBins[ipt]) <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"cut:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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//fOutYaml <<" - [0., 0., " <<oSystErr->GetCutsEffErr(midPtBins[ipt]) <<", " <<oSystErr->GetCutsEffErr(midPtBins[ipt]) <<"]" <<endl;
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fOutYaml <<" - [0., 0., " << TMath::Sqrt( oSystErr->GetCutsEffErr(midPtBins[ipt]) * oSystErr->GetCutsEffErr(midPtBins[ipt]) + oSystErr->GetPIDEffErr(midPtBins[ipt]) * oSystErr->GetPIDEffErr(midPtBins[ipt])) <<", " << TMath::Sqrt( oSystErr->GetCutsEffErr(midPtBins[ipt]) * oSystErr->GetCutsEffErr(midPtBins[ipt]) + oSystErr->GetPIDEffErr(midPtBins[ipt]) * oSystErr->GetPIDEffErr(midPtBins[ipt])) <<"]" <<endl;
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}
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fOutYaml <<endl;
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/*
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// PID ---> Included in cut as for ML!
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fOutYaml <<"PID:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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fOutYaml <<" - [0., 0., " <<oSystErr->GetPIDEffErr(midPtBins[ipt]) <<", " <<oSystErr->GetPIDEffErr(midPtBins[ipt]) <<"]" <<endl;
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}
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fOutYaml <<endl;
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*/
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fOutYaml <<"track:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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fOutYaml <<" - [0., 0., " <<oSystErr->GetTrackingEffErr(midPtBins[ipt]) <<", " <<oSystErr->GetTrackingEffErr(midPtBins[ipt]) <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"ptshape:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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fOutYaml <<" - [0., 0., " <<oSystErr->GetMCPtShapeErr(midPtBins[ipt]) <<", " <<oSystErr->GetMCPtShapeErr(midPtBins[ipt]) <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"feeddown_mult:" <<endl;
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//TString fpromptname = "gFcConservative";
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//if(isLc) fpromptname = "gFcCorrConservative";
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//TGraphAsymmErrors* fPrompt = (TGraphAsymmErrors*)fInput->Get(fpromptname.Data());
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for(int ipt=0; ipt<nPtBins; ipt++){
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//double errYhigh = fPrompt->GetErrorYhigh(ipt+1);
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//double errYlow = fPrompt->GetErrorYlow(ipt+1);
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//NOT PART OF ALIHFSYSTERR, TO BE FILLED IN BY HAND
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fOutYaml <<" - [0., 0., " <<0. <<", " <<0. <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"trigger:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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//NOT PART OF ALIHFSYSTERR, TO BE FILLED IN BY HAND
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fOutYaml <<" - [0., 0., " <<0. <<", " <<0. <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"multiplicity_weights:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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//NOT PART OF ALIHFSYSTERR, TO BE FILLED IN BY HAND
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fOutYaml <<" - [0., 0., " <<0. <<", " <<0. <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"multiplicity_interval:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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//NOT PART OF ALIHFSYSTERR, TO BE FILLED IN BY HAND
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fOutYaml <<" - [0., 0., " <<0. <<", " <<0. <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"branching_ratio:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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if(!isLc) fOutYaml <<" - [0., 0., " <<0.0129 <<", " <<0.0129 <<"]" <<endl;
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else fOutYaml <<" - [0., 0., " <<0.053 <<", " <<0.053 <<"]" <<endl;
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//fOutYaml <<" - [0., 0., " <<oSystErr->GetBRErr() <<", " <<oSystErr->GetBRErr() <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml <<"sigmav0:" <<endl;
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for(int ipt=0; ipt<nPtBins; ipt++){
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fOutYaml <<" - [0., 0., " <<0.05 <<", " <<0.05 <<"]" <<endl;
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//fOutYaml <<" - [0., 0., " <<oSystErr->GetNormErr() <<", " <<oSystErr->GetNormErr() <<"]" <<endl;
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}
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fOutYaml <<endl;
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fOutYaml.close();
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}

‎Analyses/ALICE_D2H_vs_mult_pp13/README.md‎

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# For files with final results, see [AN repo](https://gitlab.cern.ch/D_meson_analyses/d2h-vs-mult-pp13/-/tree/master/)
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## NB: plot_final_results_*.py will only work in AN note repository. Shown here as example
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Since this analysis ended up being combined with a non-MLHEP D0 and Lc -> pKpi analysis, the final macros were developed + maintained in the Analysis Note repository. The working versions of these macros can be found [here](https://gitlab.cern.ch/D_meson_analyses/d2h-vs-mult-pp13/-/tree/master/macros_final) (calculation of the observables, systematic propagation, Lc averaging, ...), and [here](https://gitlab.cern.ch/D_meson_analyses/d2h-vs-mult-pp13/-/tree/master/HP20Preliminaries) (plotting in ALICE Preliminary layout).
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**The functions used are the same as in MachineLearningHEP/machine_learning_hep/utilities_plot.py**

‎Analyses/ALICE_D2H_vs_mult_pp13/average_pkpi_pk0s.py‎

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#############################################################################
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## © Copyright CERN 2018. All rights not expressly granted are reserved. ##
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## Author: [email protected] ##
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## This program is free software: you can redistribute it and/or modify it ##
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## under the terms of the GNU General Public License as published by the ##
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## Free Software Foundation, either version 3 of the License, or (at your ##
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## option) any later version. This program is distributed in the hope that ##
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## it will be useful, but WITHOUT ANY WARRANTY; without even the implied ##
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## warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. ##
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## See the GNU General Public License for more details. ##
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## You should have received a copy of the GNU General Public License ##
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## along with this program. if not, see <https://www.gnu.org/licenses/>. ##
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#############################################################################
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"""
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main script for doing final stage analysis
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NB: Duplicate of macro in AN Note repository. Will not work here!
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Just as an example how functions can be used
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"""
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import os
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# pylint: disable=import-error, no-name-in-module, unused-import
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import yaml
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from ROOT import TFile, gStyle, gROOT, TH1F, TGraphAsymmErrors, TH1
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from ROOT import kBlue, kAzure, kOrange, kGreen, kBlack, kRed, kWhite
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from ROOT import Double, TCanvas, gPad
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from machine_learning_hep.utilities_plot import Errors
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from machine_learning_hep.utilities_plot import average_pkpi_pk0s
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def extract_histo(histo_name, path):
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in_file = TFile.Open(path, "READ")
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histo = in_file.Get(histo_name)
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if isinstance(histo, TH1):
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histo.SetDirectory(0)
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return histo
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def make_standard_save_path(prefix, filepath):
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folder_plots = f"{filepath}"
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if not os.path.exists(folder_plots):
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print("creating folder ", folder_plots)
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os.makedirs(folder_plots)
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return f"{folder_plots}/{prefix}.eps"
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#############################################################################
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############################# Input arguments ##############################
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#############################################################################
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SIGMAV0 = 57.8e9
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BRPKPI = 0.0623
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BRPK0S = 0.0109
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PTBINS = 6
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FILE_PKPI = "inputfiles_HP20/finalcrossLcpKpippMBvspt_ntrklmult4.root"
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FILE_PK0S = "inputfiles_HP20/finalcrossLcpK0sppMBvspt_ntrklmult4.root"
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FILE_PK0S2 = "inputfiles_HP20/FeeddownSyst_NbNbx2_LcpK0sppMBvspt_ntrkl.root"
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FILE_OUT = "inputfiles_HP20/finalcrossLcAverageMBvspt_ntrklmult4.root"
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ERROR_PKPI = "syst_HP20/syst_forLcaverage/LcpKpipp/errors_histoSigmaCorr_4.yaml"
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ERROR_PK0S = "syst_HP20/syst_forLcaverage/LcpK0spp/errors_histoSigmaCorr_4.yaml"
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ERROR_OUT = "syst_HP20/syst_forLcaverage/errors_avg_histoSigmaCorr_4.yaml"
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gROOT.SetBatch(True)
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#############################################################################
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################################# Average ###################################
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#############################################################################
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HISTO_PKPI = extract_histo("histoSigmaCorr_rebin", FILE_PKPI)
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HISTO_PKPI.SetName(f"histoSigmaCorr_pKpi")
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HISTO_PKPI.Scale(1./BRPKPI)
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HISTO_PK0S = extract_histo("histoSigmaCorr", FILE_PK0S)
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HISTO_PK0S.SetName(f"histoSigmaCorr_pK0s")
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HISTO_PK0S.Scale(1./BRPK0S)
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HISTO_PK0S.Scale(1./SIGMAV0)
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GRFD_PKPI = extract_histo("gFcCorrConservative", FILE_PKPI)
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GRFD_PK0S = extract_histo("gNbCorrConservative", FILE_PK0S2)
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DICTEXTRA_PKPI = {}
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HISTOEFF_PKPI = extract_histo("hDirectEffpt", FILE_PKPI)
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ERROREFF_PKPI = []
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for i in range(HISTOEFF_PKPI.GetNbinsX()):
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print(HISTOEFF_PKPI.GetBinCenter(i+1))
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RELERREFF = HISTOEFF_PKPI.GetBinError(i+1) / HISTOEFF_PKPI.GetBinContent(i+1)
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if i == 0:
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ERROREFF_PKPI.append([0, 0, 99, 99])
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else:
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ERROREFF_PKPI.append([0, 0, RELERREFF, RELERREFF])
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DICTEXTRA_PKPI["statunceff"] = ERROREFF_PKPI
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ERRSPKPI = Errors(PTBINS)
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ERRSPKPI.read(ERROR_PKPI, DICTEXTRA_PKPI)
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DICTEXTRA_PK0S = {}
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HISTOEFF_PK0S = extract_histo("hDirectEffpt", FILE_PK0S)
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ERROREFF_PK0S = []
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for i in range(HISTOEFF_PK0S.GetNbinsX()):
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RELERREFF = HISTOEFF_PK0S.GetBinError(i+1) / HISTOEFF_PK0S.GetBinContent(i+1)
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ERROREFF_PK0S.append([0, 0, RELERREFF, RELERREFF])
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DICTEXTRA_PK0S["statunceff"] = ERROREFF_PK0S
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ERRSPK0S = Errors(PTBINS)
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ERRSPK0S.read(ERROR_PK0S, DICTEXTRA_PK0S)
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MATCHPKPI = [-99, 1, 2, 3, 4, 5]
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MATCHPK0S = [1, 2, 3, 4, 5, 6]
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MATCHPKPIGR = [-99, 2, 3, 4, 5, 6] #Empty bin 0-1 still in fprompt tgraph
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MATCHPK0SGR = [1, 2, 3, 4, 5, 6]
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AVGCORRYIELD, AVGSTATUNC, AVGFPROMPT, \
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AVGFPROMPTLOW, AVGFPROMPTHIGH, AVGERROR = average_pkpi_pk0s(HISTO_PKPI, HISTO_PK0S,
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GRFD_PKPI, GRFD_PK0S,
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ERRSPKPI, ERRSPK0S,
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MATCHPKPI, MATCHPK0S,
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MATCHPKPIGR, MATCHPK0SGR)
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HISTAVG = HISTO_PK0S.Clone("histoSigmaCorr_average")
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GRFDAVG = TGraphAsymmErrors(PTBINS)
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for ipt in range(PTBINS):
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HISTAVG.SetBinContent(ipt+1, AVGCORRYIELD[ipt])
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HISTAVG.SetBinError(ipt+1, AVGSTATUNC[ipt])
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GRFDAVG.SetPoint(ipt+1, GRFD_PK0S.GetX()[ipt+1], AVGFPROMPT[ipt])
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GRFDAVG.SetPointError(ipt+1, GRFD_PK0S.GetEXlow()[ipt+1], GRFD_PK0S.GetEXhigh()[ipt+1], \
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AVGFPROMPTLOW[ipt], AVGFPROMPTHIGH[ipt])
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AVGERROR.print()
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print("\n\n Store above in", ERROR_OUT)
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print("\n\n\nStoring ROOT objects in", FILE_OUT)
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FOUT = TFile(FILE_OUT, "RECREATE")
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FOUT.cd()
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HISTAVG.Write("histoSigmaCorr_average")
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HISTO_PKPI.Write("histoSigmaCorr_pKpi")
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HISTO_PK0S.Write("histoSigmaCorr_pK0s")
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GRFDAVG.Write("gFcCorrConservative_average")
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GRFD_PKPI.Write("gFcCorrConservative_pKpi")
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GRFD_PK0S.Write("gNbCorrConservative_pK0s")
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FOUT.Close()

Analyses/ALICE_D2H_vs_mult_pp13/data/errors/Dspp/MBvspt_ntrkl/errors_histoSigmaCorr_1.yaml renamed to Analyses/ALICE_D2H_vs_mult_pp13/data/EXAMPLE_errors_histoSigmaCorr_0.yaml

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# Taken from https://indico.cern.ch/event/855807/contributions/3601563/attachments/1928380/3193129/HF_171019_Ds_MLHEP.pdf
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# Updated by Luuk on 20/10 after discussion after HF approval
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# MULTIPLICITY 0 < ntrkl < inf
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#
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#Systematics in this dictionary. feeddown_Nb will be read from inputfile
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#Example file for storing systematic errors
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names: ["yield", "cut", "pid", "track", "ptshape", "feeddown_mult", "feeddown_mult_spectra", "trigger", "multiplicity_weights", "multiplicity_interval", "branching_ratio", "sigmav0"]
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yield:
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- [0., 0., 0.035, 0.035]
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- [0., 0., 0.035, 0.035]
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#Conservative guess from trigger experts
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sigmav0:
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- [0., 0., 0.05, 0.05]
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- [0., 0., 0.05, 0.05]
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- [0., 0., 0.05, 0.05]
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- [0., 0., 0.05, 0.05]
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- [0., 0., 0.05, 0.05]
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#StatUncEff and Feeddown_NB are added automatically in final_results_* macros

‎Analyses/ALICE_D2H_vs_mult_pp13/data/PreliminaryQM19/D0forLc/finalcrossD0ppMBvspt_ntrklmult0.root‎

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‎Analyses/ALICE_D2H_vs_mult_pp13/data/PreliminaryQM19/D0forLc/finalcrossD0ppMBvspt_ntrklmult1.root‎

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‎Analyses/ALICE_D2H_vs_mult_pp13/data/PreliminaryQM19/D0forLc/finalcrossD0ppMBvspt_ntrklmult2.root‎

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‎Analyses/ALICE_D2H_vs_mult_pp13/data/PreliminaryQM19/LcforLc19/finalcrossLcpKpippMBvspt_ntrklmult0.root‎

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‎Analyses/ALICE_D2H_vs_mult_pp13/data/PreliminaryQM19/LcforLc3059/finalcrossLcpKpippMBvspt_ntrklmult0.root‎

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