Producing quantified predictions of magnetospheric region for spacecraft in the near-Mercury environment based on MESSENGER observations

A software repository to determine the likelihood of observing solar wind, magnetosheath, and magnetosphere regions at any position in the near-Mercury environment.

Clone this repository

HTTPS

git clone https://github.com/daraghhollman/bepi-region-prediction.git
cd bepi-region-prediction

SSH

git clone git@github.com:daraghhollman/bepi-region-prediction.git
cd bepi-region-prediction

Setup

Some initial setup is required before this repository can be used:

• Downloading a MESSENGER bow shock and magnetopause crossing list
• Downloading BepiColombo SPICE kernels

This is all handled automatically by running the following scripts:

Tip

For portability, we strongly recommend and use uv to manage dependencies and versions. If you do not wish to use uv, dependencies can be found within pyproject.toml and a requirements.txt file can be made with pip compile pyproject.toml -o requirements.txt.

uv run python src/setup/init.py

Note

Note that while we use BepiColombo SPICE kernels as examples throughout this work, the outputs are applicable to any spacecraft in Mercury's magnetospheric environment, including MESSENGER.

Creating maps of relative region occurrence

# Creates a dataset containing MESSENGER region observations at a 20 minutes time cadence.
uv run python src/determine_messenger_regions.py

# Bins the above observations spatially and determines a probabilitiy and uncertainty for each bin.
uv run python src/create_probability_maps.py

Examples

Before runnning the included examples, please ensure you have downloaded the probability map file from the Releases section, or run the above scripts to create the maps locally.

mkdir resources

wget https://github.com/daraghhollman/bepi-region-prediction/releases/download/v1.1.0/region_probability_maps.nc -P resources/

The two examples included in the publication can be found under src/figure-creation/, however, we instead recommend to first look at the examples directory: src/examples/, which includes worked examples in Python notebooks.