rx-data 0.1.5

Project with lists of LFNs and utilities needed to download filteres ntuples

$R_X$ 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.

Installation

To install this project run:

pip install rx_data

# The line below will upgrade it, in case new samples are available, the list of LFNs is part of the
# project itself
pip install --upgrade rx_data

The download would require a grid proxy, which can be made with:

. /cvmfs/lhcb.cern.ch/lib/LbEnv

# This will create a 100 hours long proxy
lhcb-proxy-init -v 100:00

Listing available triggers

In order to see what triggers are present in the current version of the ntuples do:

list_triggers -v v1

# And this will save them to a yaml file
list_triggers -v v1 -o triggers.yaml

Downloading the ntuples

For this, run:

download_rx_data -m 5 -p /path/to/downloaded/.data -v v1 -d -t triggers.yaml

which will use 5 threads to download the ntuples associated to the triggers in triggers.yaml and version v1 to the specified path.

IMPORTANT:

• In order to prevent deleting the data, save it in a hiden folder, e.g. one starting with a period. Above it is .data.
• This path is optional, one can export DOWNLOAD_NTUPPATH and the path will be picked up

Potential problems: The download happens through XROOTD, which will try to pick a kerberos token. If authentication problems happen, do:

which kinit

and make sure that your kinit does not come from a virtual environment but it is the one in the LHCb stack or the native one.

Organizing paths

Building directory structure

All the ntuples will be downloaded in a single directory. In order to group them by sample and trigger run:

make_tree_structure -i /path/to/downloaded/.data/v1 -o /path/to/directory/structure

this will not make a copy of the ntuples, it will only create symbolic links to them.

Making YAML with files list

If instead one does:

make_tree_structure -i /path/to/downloaded/.data/v1 -f samples.yaml

the links won't be made, instead a YAML file will be created with the list of files for each sample and trigger.

Lists from files in the grid

If instead of taking the downloaded files, one wants the ones in the grid, one can do:

make_tree_structure -v v4 -f samples.yaml

where v4 is the version of the JSON files holding the LFNs. In case one needs the old naming, used in Run1 and Run2 one would run:

make_tree_structure -v v4 -f samples.yaml -n old

This will likely drop samples that have no old naming, because they were not used in the past.

Dropping triggers

The YAML outputs of the commands above will be very large and not all of it will be needed. One can drop triggers by:

# This will dump a list of triggers to triggers.yaml
# You can optionally remove not needed triggers
list_triggers -v v4 -o triggers.yaml

# This will use those triggers only to make samples.yaml
make_tree_structure -v v4 -f samples.yaml -t triggers.yaml

Samples naming

The samples were named after the DecFiles names for the samples and:

• Replacing certain special charactes as shown here
• Adding a _SS suffix for split sim samples. I.e. samples where the photon converts into an electron pair.

A useful guide showing the correspondence between event type and name is here

Accessing ntuples

If the ntuples are stored in a directory where each tuple is accompanied by a friend tree, a preliminary step that attaches all friend trees is needed. This is done by RDFGetter as shown below:

from rx_data.rdf_getter     import RDFGetter

# This is where the directories with the samples are
RDFGetter.samples_dir = '/publicfs/ucas/user/campoverde/Data/RX_run3/v4/NO_q2_bdt_mass_Q2_central_VR_v1'

# 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_MagUp_24c2', trigger='Hlt2RD_BuToKpMuMu_MVA')
rdf = gtr.get_rdf()

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