ELaborative Particle Analysis from Satellite Observations (EL-PASO)

EL-PASO is a Python framework designed to streamline the download, processing, and saving of satellite particle observation data.

Its primary purpose is to prepare and standardize particle data for use in radiation belt modeling.

Features

• Format Flexibility: Capable of handling different input formats including cdf, netcdf, h5, ascii, and json
• Integrated Processing: Provides a comprehensive set of functions for common particle data analysis tasks
• Supports Metadata: Stores all processing and metadata alongside the data, ensuring full traceability and reproducibility.
• Standardized output files: Saving processed data in different standards (e.g. PRBEM) to enable easy loading and sharing of processed data

Full documentation can be viewed here.

Available processing scripts

• Arase
  • MEPe
  • XEP real-time
  • PWE density
• GOES-R
  • MPS-High real-time
  • MPS-High
• ESA
  • NGRM satellites
• POES
  • MEPED (electrons)
  • TED (electrons)
• PROBA-V
  • EPT (electrons and protons)
• Van Allen Probes
  • HOPE (electrons) and ECT-combined
  • EMFISIS and EFW density

Installation Guide

Installation

Step 1: Clone the Repository

Begin by cloning the EL-PASO repository and navigating into its directory.

git clone https://github.com/GFZ/EL_PASO.git
cd EL_PASO

Step 2: Set up a Python Virtual Environment

It is highly recommended to use a virtual environment to manage dependencies.

uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install .

The custom setup.py script will automatically download and compile the IRBEM Fortran library during this step.

Verifying the Installation

You can validate your installation by running the minimal example located in examples:

python examples/minimal_example.py

Acknowledgements

This work has been funded by the German Research Foundation (NFDI4Earth, DFG project no. 460036893, https://www.nfdi4earth.de/). The authors acknowledge the work of Mátyás Szabó-Roberts who led the foundation for the EL-PASO framework.

The thank the authors of the IRBEM library for providing their code.