WELCOME TO SCINT !!#

Image of four minions going happily to code

Rodrigo Álvarez-Garrote, Andrés de la Torre Rojo, Sergio Manthey Corchado, Laura Pérez-Molina

The Sensor Calibration Interface for Neutrino Team (SCINT) is a python library to analyze experimental data acquired by the Neutrino Group.

You can navigate through the documentation using the table of contents below, and you can search for specific keywords using the search tab placed at left side.

CONTENTS#

Warning

🚧 This project is still under development. Please, contact the authors for more information.🚧

SUMMARY#

For a quick summary or just as a reminder follow the next steps:

You need to have git, python (>=3.7) and pip3 installed.

  1. Make sure you have access to data to analyze:

  • [RECOMENDED] Configure VSCode SSH conection and work from gaeuidc1.ciemat.es (you will have access to the data in /pc/choozdsk01/DATA/SCINT/folders)

  • Run the usual command to connect via SSH through the terminal: ssh AFS_USER@gaeuidc1.ciemat.es

  1. Clone the repository into a local directory and create your branch:

    git clone https://github.com/CIEMAT-Neutrino/SCINT.git
cd SCINT
git checkout -b <your_branch_name>

  2. Install packages needed for the library to run ⚠️ TIME CONSUMING STEP - JUST FIRST TIME ⚠️:

source setup.sh

It will ask for confirmation to make sure you are running from SCINT folder and to link the data folder with data to run in the tutorial mode.

  1. To run the macros:

    cd srcs/macros
python3 XXmacro.py (--flags input)

If you want to have a personal folder to store your test files locally you can create a test folder (it won’t be synchronized with the repository). Otherwise, you can create a folder for your custom scripts and add them to the .gitignore file:

mkdir <your_folder_name>
echo "<your_folder_name/*>" >> .gitignore

Preferred work-flow

  • Store raw data in some share space, for example:

    • /pc/choozdsk01/DATA/SCINT/your_folder/raw/*

    • /pnfs/ciemat.es/neutrinos/LAB/your_folder/raw/* —> used for backup but not during the analysis

  • Save the processed data in the generated folder when running 00Raw2Np.py (.../your_folder/npy/*)

  • Whenever you are done, compress the needed files for the analysis and store them in pnfs (it would be helpful for the group to include a README file)

INDICES AND TABLES#