HiggsDNA

Higgs to diphoton nanoAOD framework

Installation

The installation procedure consists in the following steps:

1. Clone this repository

git clone --recursive https://gitlab.cern.ch/HiggsDNA-project/HiggsDNA  
cd HiggsDNA  

2. Install dependencies

The necessary dependencies (listed in environment.yml) can be installed manually, but the suggested way is to create a [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/mana
ge-environments.html) by running:

conda env create -f environment.yml  

the conda env can become pretty large (multiple GB), so you may want to specify an installation location before running the above step with

mkdir <some_path_where_you_have_more_disk_space>/.conda/pkgs
chmod 755 -R <some_path_where_you_have_more_disk_space>/.conda/pkgs
conda config --prepend pkg_dirs <some_path_where_you_have_more_disk_space>/.conda/pkgs

Then activate the environment with

conda activate higgs-dna

** lxplus-specific notes ** If you are running on lxplus you may run into permission errors, which can be fixed with:

chmod 755 -R /afs/cern.ch/user/<your_username_first_initial>/<your_username>/.conda

You may also want to increase your disk quota at this link, otherwise you may run out of space while installing your conda environment.

Please note that the field python>=3.6 will create an environment with the most recent stable version of Python. Change it to suite your needs (but still matching the requirement of Python>=3.6).

One additional package, correctionlib, must be installed via pip, rather than conda. Run

setup.sh

to install this script.

3. Install higgs_dna

Install with:

pip install -e .  

If you notice issues with the conda pack command for creating the tarball, try updating and cleaning your environment with (after running conda activate higgs-dna):

conda env update --file environment.yml --prune

Development Guidelines and Contribution

Here we summarize some general guidelines for developers and contributors.

Printing Useful Analysis Info

We use the Python logging facility together with rich (for pretty printing) to provide useful analysis information. At the time of writing, two levels of information are supported: INFO and DEBUG.

All we have to do to print information from the code we are developing consists in:

• if not already present in the submodule, add the lines

import logging
logger = logging.getLogger(__name__)

• type logger.info(message) or logger.debug(message) depending on which level we want our message to be displayed

Note: due to the way the logging package works, if the level is set to INFO only INFO messages are printed; if the level is set to DEBUG, both DEBUG and INFO messages are printed.

Raising Detailed Exceptions

Raising detailed exceptions is important. Whenever it is possible, let's try to raise exception with detailed messages and possible solutions for the user to fix their code.

Tests

A battery of tests based on unittests is available. Before sending a PR, it is suggested to check that the new code didn't break anything by running:

python -m unittest -v

Please note that this is good practice even if CI is available. It is indeed a waste of time and resources to trigger a build if there is something clearly wrong that can be spotted by simply running the above mentioned command.