metadata
license: cc-by-4.0
tags:
- space
- plasma
- physics
size_categories:
- 1K<n<10K
Small Vlasiator Dataset for Machine Learning Studies
The data is stored in Zarr format.
It can be downloaded to a local data_small directory with:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="deinal/spacecast-data-small",
repo_type="dataset",
local_dir="data_small"
)
This will yield a local data_small folder that can be used with spacecast:
data_small/
├── graph/ - Directory containing graphs for training
├── run.zarr/ - Vlasiator run with ρ = 1.0 cm⁻³ solar wind
├── static.zarr/ - Static features x, z, r coordinates
├── vlasiator_run.zarr - Preprocessed Vlasiator run
├── vlasiator_config.yaml - Configuration file for neural-lam
└── vlasiator_run.yaml - Configuration file for the datastore, referred to from vlasiator_config.yaml
The run was preprocessed with mllam-data-prep:
mllam_data_prep data_small/vlasiator_run.yaml
This produces a training-ready Zarr store in the data_small directory.
Simple, multiscale, and hierarchical graphs are included already, but can be created using the following commands:
python -m neural_lam.create_graph --config_path data_small/vlasiator_config.yaml --name simple --levels 1 --coarsen-factor 5 --plot
python -m neural_lam.create_graph --config_path data_small/vlasiator_config.yaml --name multiscale --levels 3 --coarsen-factor 5 --plot
python -m neural_lam.create_graph --config_path data_small/vlasiator_config.yaml --name hierarchical --levels 3 --coarsen-factor 5 --hierarchical --plot