Update README.md
Browse files# Small Vlasiator Dataset for Machine Learning Studies
The data is stored in [Zarr](https://zarr.dev).
It can be downloaded to a local `data_small` directory with:
```
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="deinal/spacecast-data",
repo_type="dataset",
local_dir="data_small"
)
```
This will yield a local `data_small` folder that can be used with [spacecast](https://github.com/fmihpc/spacecast):
```
data/
├── 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](https://github.com/mllam/mllam-data-prep):
```
mllam_data_prep data_small/vlasiator_run.yaml
```
This produces training-ready Zarr stores 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/vlasiator_config.yaml --name simple --levels 1 --coarsen-factor 5 --plot
python -m neural_lam.create_graph --config_path data/vlasiator_config.yaml --name multiscale --levels 3 --coarsen-factor 5 --plot
python -m neural_lam.create_graph --config_path data/vlasiator_config.yaml --name hierarchical --levels 3 --coarsen-factor 5 --hierarchical --plot
```