aQC studies

Content

Files

1. runList.txt contains the path of the QC objects that need to be analyzed. The path to put is the one found in the mail [QA] Daily report from MonALISA
2. config_data.yml, config_MC.yml are configuration files used to download data and MC root files.
3. run_list_data.txt, run_list_.txt are the files containing the run list of downloaded data and MC.

Macros

1. QC_readFiles.C reads the QC file downloaded by the download-QC-from-grid.py script and saves a simple root file with the histograms of local board occupancy and 44/all tracks efficiency for a selected run. It saves also the png of these histograms in the images folder. It needs in input the period, the apass, and the run number, passed as strings, e.g. QC_readfiles.C("LHC23zd", "apass4", "538932"). It can be executed by the script runReady.py.

2. QC_Comparison.C reads the root files produced by QC_readFile.C macro and obtain the ratios of histograms from apass4 and apass1. It saves the png of these ratios in the folder ratio-images. It needs in input the period and the run number, passes as strings, e.g. QC_comparison.C("LHC23zd", "538932"). It can be executed by the script runComparison.py.

3. Data_MC_comparison_run.C reads the ROOT files produced by download-from-hyperloop-data and download-from-hyperloop-MC, and generates a PDF (saved in the run folder) containing the distributions of η, φ, and pT for both Data and MC. This includes distributions with basic event selections and with basic event selections + pT selections. Additionally, ratios between MC and Data are also produced. It needs in input the MC_production name anchored to dataset, the period and the pass of data, and the run number, passes as strings (except the run number that is an integer), e.g. Data_MC_comparison_run.C("LHC24g3", "PbPb2023", "apass4", 544122). It can be executed by the script script_macro_data_MC_comparison_runlist.py for a set of runs. Note: selections that allow marking a run's quality as "Good" or "Bad" have been added to the macro and will generate different lists in the period folder (a "Good" and a "Bad" lists will be created for all the different ways used to flag a run as good/bad from the MC/data point of view).

4. Data_MC_comparison_fullperiod.C reads the ROOT files for the entire period under investigation (that has to be downloaded manually from HY) and generates a PDF containing the distributions of η, φ, and pT for both Data and MC. This includes distributions with basic event selections and with basic event selections + pT selections. Additionally, ratios between MC and Data are also produced. It needs in input the MC_production name anchored to dataset, the period and the pass of data, passes as strings, e.g. Data_MC_comparison_fullperiod.C("LHC24g3", "PbPb2023", "apass4").

5. Creation_histogram_runquality.C reads goodRuns.txt and badRuns.txt lists produced from Data_MC_comparison_run.Cand creates a PDF file with pie charts illustrating the MC/data run quality. It needs in input the MC_production name anchored to dataset, the period and the pass of data, passes as strings, e.g. Creation_histogram_runquality.C("LHC24g3", "PbPb2023", "apass4").

6. Data_MC_comparison_run_cbt_muon.C reads the ROOT files produced by download-from-hyperloop-data and download-from-hyperloop-MC, and generates a PDF (saved in the run folder) containing the distributions of η, φ, and pT for both Data and MC for only CBT_MUON run list. This includes distributions with basic event selections and with basic event selections + pT selections. Additionally, ratios between MC and Data are also produced. It needs in input the MC_production name anchored to dataset, the period and the pass of data, and the run number, passes as strings (except the run number that is an integer), e.g. Data_MC_comparison_run_cbt_muon.C("LHC24g3", "PbPb2023", "apass4", 544122). It can be executed by the script script_macro_data_MC_comparison_runlist_cbt_muon.py for a set of runs. Note: selections that allow marking a run's quality as "Good" or "Bad" have been added to the macro and will generate two lists in the period folder called goodRuns_thresholds_sigmas_cbt_muon.txt and badRuns_thresholds_sigmas_cbt_muon.txt.

7. Creation_histogram_runquality_cbt_muon.C reads goodRuns_thresholds_sigmas_cbt_muon.txt and badRuns_thresholds_sigmas_cbt_muon.txt lists produced from Data_MC_comparison_run_cbt_muon.Cand creates a PDF file with pie charts illustrating the MC/data run quality. It needs in input the MC_production name anchored to dataset, the period and the pass of data, passes as strings, e.g. Creation_histogram_runquality.C("LHC24g3", "PbPb2023", "apass4").

Scripts

1. download-QC-from-grid.py downloads the QC_fullrun.root files from the grid whose path is listed in runList.txt file. Run it using python3 download-QC-from-grid.py.
2. runRead.py is a script that runs QC_readFiles.C macro for all the runs listed in runList.txt file. Run it using python3 runRead.py.
3. runComparison.py is a script that runs QC_comparions.C for all the runs listed in runList.txt file. Run it using python3 runComparison.py.
4. writeAnalyzedRuns.py read a txt file with the runs that have been analyzed: to create this file copy the runs fro the QA - Daily report mail. Periods need to be separated by a blank line. The file needs to start with a black line. The output file out.txt contains the list of analyzed runs in each period.
5. download-data-mc.py is a script that reads a txt file containing a list of runs (eg runs.txt). All the runs needs to be processed by the same pass for data, and to belong to the same production for MC. The name of the file, the pass and the MC production needs to be passed as input, as explained by running python3 download-data-mc.py --help.
6. download-from-hyperloop.py is a script provided by Lush Miscelletti to download the data run-by-run after a hyperloop hyperTrain has run over the data. The file also need to have the config.yml file where you insert the list of runs that you want to download, together with the path you fetch from hyperloop. Moreover, you need to modify the output_dir_name to the path where you want to save the data and that's it. To launch the script (inside ALICE-O2 environment) execute: python download-from-hyperloop.py config.yml --download
7. download-from-hyperloop-data.py and download-from-hyperloop-MC.py are similar to download-from-hyperloop.py with the exception that they run over Data and MC separately (for this reason there are a config_data.yml and a config_MC.yml file) in order to save ROOT files with a different name and to produce two different runs_list (respectively run_list_data.txt and run_list_MC.txt). Remember to modify the output_dir_name to the path where you want to save the Data (and MC) outputs, together with the run_list and the path you fetch from hyperloop in the config_data.yml (config_MC.yml). To launch the script (inside ALICE-O2 environment) execute: python download-from-hyperloop-data.py config_data.yml --download and/or python download-from-hyperloop-MC.py config_MC.yml --download.
8. common_run_list.py is a script that reads the run_list_data.txt and run_list_MC.txt and create a common_run_list.txt. This is useful to perform the comparison between Data and MC only for common runs.
9. script_macro_data_MC_comparison_runlist.py is a script to run the macro Data_MC_comparison_run.C for all the runs in common_run_list.txt. Remember to modify the macro_dir line to point to the folder that contains the Data_MC_comparison_run.C macro.
10. common_run_list_cbt_muon.py is a script that reads the run_list_data.txt and run_list_MC.txt and create a common_run_list_cbt_muon.txt. This is useful to perform the comparison between Data and MC only for common runs in the CBT_MUON run list.
11. script_macro_data_MC_comparison_runlist_cbt_muon.py is a script to run the macro Data_MC_comparison_run_cbt_muon.C for all the runs in common_run_list_cbt_muon.txt. Remember to modify the macro_dir line to point to the folder that contains the Data_MC_comparison_run_cbt_muon.C macro.

Workflow for comparison between passes

The scripts to make the comparison between passes need to be run in the following order because each of them takes as input the output of the previous:

1. download-QC-from-grid.py
2. runRead.py
3. runComparison.py

Workflow for data/MC comparison at the A02D level

Note: it is important to respect the order of following the operations since the output of each step is used the the steps that follows.

1. modify the config_data.yml and config_MC.yml with the desired output_dir_name, run_list and alien_input_path.
2. run the download-from-hyperloop-data.py and download-from-hyperloop-MC.py scripts with python download-from-hyperloop-data.py config_data.yml --download and/or python download-from-hyperloop-MC.py config_MC.yml --download.
3. create a common_run list running the common_run_list.py script with python common_run_list.py
4. run the script_macro_data_MC_comparison_runlist.py script to compare the distributions of η, φ, and pT between Data and MC python script_macro_data_MC_comparison_runlist.py
5. if you would like to compare Data and MC over the full period, download the merged outputs from HY and run the Data_MC_comparison_fullperiod.C macro.

Workflow for data/MC comparison at the A02D level (only CBT_MUON runs)

Note: it is important to respect the order of following the operations since the output of each step is used the the steps that follows.

1. modify the config_data.yml and config_MC.yml with the desired output_dir_name, run_list and alien_input_path.
2. run the download-from-hyperloop-data.py and download-from-hyperloop-MC.py scripts with python download-from-hyperloop-data.py config_data.yml --download and/or python download-from-hyperloop-MC.py config_MC.yml --download.
3. create a common_run list running the common_run_list_cbt_muon.py script with python common_run_list_cbt_muon.py
4. run the script_macro_data_MC_comparison_runlist_cbt_muon.py script to compare the distributions of η, φ, and pT between Data and MC python script_macro_data_MC_comparison_runlist_cbt_muon.py
5. run the macro Creation_histogram_runquality_cbt_muon.C to create a PDF with pie charts illustrating the MC/data run quality.