[TOC]

$R_X$ data

This repository contains:

• Versioned lists of LFNs
• Utilities to download them and link them into a tree structure

for all the $R_X$ like analyses. For instructions on how to:

• Produce new ntuples with friend trees
• Downloading filtered ntuples from the grid
• Merging data ntuples
• Copying ntuples from cluster to laptop
• Outdated instructions that hasn't been removed yet

Check this.

Below are the instructions on how to access data from EOS.

Installation

To install this project run:

pip install git+ssh://git@gitlab.cern.ch:7999/rx_run3/rx_data.git

The code below assumes that all the data is in ANADIR. If you want to use the data in EOS do:

export ANADIR=/eos/lhcb/wg/RD/RX_run3

preferably in ~/.bashrc.

Accessing ntuples

Once

from rx_data.rdf_getter     import RDFGetter

# This picks one sample for a given trigger
# The sample accepts wildcards, e.g. `DATA_24_MagUp_24c*` for all the periods
gtr = RDFGetter(
    sample   ='DATA_24_Mag*_24c*',
    analysis = 'rx',                    # This is the default, could be nopid
    tree     = 'DecayTree'              # This is the default, could be MCDecayTre
    trigger  ='Hlt2RD_BuToKpMuMu_MVA')

# If False (default) will return a single dataframe for the sample
rdf = gtr.get_rdf(per_file=False)

# If True, will return a dictionary with an entry per file. They key is the full path of the ROOT file
d_rdf = gtr.get_rdf(per_file=True)

The way this class will find the paths to the ntuples is by using the DATADIR environment variable. This variable will point to a path $DATADIR/samples/ with the YAML files mentioned above.

In the case of the MVA friend trees the branches added would be mva.mva_cmb and mva.mva_prc.

Thus, one can easily extend the ntuples with extra branches without remaking them.

Checking what samples exist

For this run:

check_sample_stats -p rx

which will print something like:

Where the rows represent samples and the columns represent the friend trees. The numbers are the number of ntuples.

Multithreading

Multithreading with ROOT dataframes at the moment is dangerous and should be done only in a few places. To turn this on run:

nthreads = 3 # Or any reasonable number
with RDFGetter.multithreading(nthreads=nthreads):
    gtr = RDFGetter(sample=sample, trigger='Hlt2RD_BuToKpEE_MVA')
    rdf = gtr.get_rdf()

    process_rdf(rdf)

• Once outside the manager, multithreading will be off.
• One can use nthreads=1 to turn off mulithreading
• Negative or zero threads will raise exception.

Unique identifiers

In order to get a string that fully identifies the underlying sample, i.e. a hash, do:

gtr = RDFGetter(sample='DATA_24_Mag*_24c*', trigger='Hlt2RD_BuToKpMuMu_MVA')
uid = gtr.get_uid()

Excluding datasets

One can also exclude a certain type of friend trees with:

from rx_data.rdf_getter     import RDFGetter

wih RDFGetter.exclude_friends(names=['mva']):
    gtr = RDFGetter(sample='DATA_24_Mag*_24c*', trigger='Hlt2RD_BuToKpMuMu_MVA')
    rdf = gtr.get_rdf(per_file=False)

that should leave the MVA branches out of the dataframe.

Defining custom columns

Given that this RDFGetter can be used across multiple modules, the safest way to add extra columns is by specifying their definitions once at the beggining of the process (i.e. the initializer function called within the main function). This is done with:

from rx_data.rdf_getter     import RDFGetter

RDFGetter.custom_columns(columns = d_def)

If custom columns are defined in more than one place in the code, the function will raise an exception, thus ensuring a unique definition for all dataframes.

Accessing metadata

Information on the ntuples can be accessed through the metadata instance of the TStringObj class, which is stored in the ROOT files. This information can be dumped in a YAML file for easy access with:

dump_metadata -f root://x509up_u12477@eoslhcb.cern.ch//eos/lhcb/grid/user/lhcb/user/a/acampove/2025_02/1044184/1044184991/data_24_magdown_turbo_24c2_Hlt2RD_BuToKpEE_MVA_4df98a7f32.root

which will produce metadata.yaml.

Run1/2 samples

For now these samples are only in the UCAS cluster and only the rare electron signal has been made available through:

from rx_data.rdf_getter12 import RDFGetter12

gtr = RDFGetter12(
    sample ='Bu_Kee_eq_btosllball05_DPC', # BuKee
    trigger='Hlt2RD_BuToKpEE_MVA',        # This will be the eTOS trigger
    dset   ='2018')                       # Can be any year in Run1/2 or all for the full sample

rdf = gtr.get_rdf()

this dataframe has had the full selection applied, except for the MVA, q2 and mass cuts.

Cuts can be added with:

from rx_data.rdf_getter12 import RDFGetter12

d_sel   = {
    'bdt' : 'mva_cmb > 0.5 & mva_prc > 0.5',
    'q2'  : 'q2_track > 14300000'}

with RDFGetter12.add_selection(d_sel = d_sel):
    gtr = RDFGetter12(
        sample =sample,
        trigger=trigger,
        dset   =dset)

    rdf = gtr.get_rdf()