| --- |
| license: cc-by-4.0 |
| tags: |
| - space |
| - plasma |
| - physics |
| size_categories: |
| - 100K<n<1M |
| pretty_name: Vlasiator Dataset for Machine Learning Studies |
| citation: | |
| @misc{vlasiator2025mldata, |
| title = {Vlasiator Dataset for Machine Learning Studies}, |
| author = {Zaitsev, Ivan and Holmberg, Daniel and Alho, Markku and Bouri, Ioanna and |
| Franssila, Fanni and Jeong, Haewon and Palmroth, Minna and Roos, Teemu}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/deinal/spacecast-data}, |
| doi = {10.57967/hf/7027}, |
| } |
| --- |
| |
| # Vlasiator Dataset for Machine Learning Studies |
|
|
| The data is stored in [Zarr](https://zarr.dev). |
|
|
| It can be downloaded to a local `data` directory with: |
| ``` |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="deinal/spacecast-data", |
| repo_type="dataset", |
| local_dir="data" |
| ) |
| ``` |
|
|
| This will yield a local `data` folder that can be used with [spacecast](https://github.com/fmihpc/spacecast): |
| ``` |
| data/ |
| ├── graph/ - Directory containing graphs for training |
| ├── run_1.zarr/ - Vlasiator run 1 with ρ = 0.5 cm⁻³ solar wind |
| ├── run_2.zarr/ - Vlasiator run 2 with ρ = 1.0 cm⁻³ solar wind |
| ├── run_3.zarr/ - Vlasiator run 3 with ρ = 1.5 cm⁻³ solar wind |
| ├── run_4.zarr/ - Vlasiator run 4 with ρ = 2.0 cm⁻³ solar wind |
| ├── static.zarr/ - Static features x, z, r coordinates |
| ├── vlasiator_config.yaml - Configuration file for neural-lam |
| ├── vlasiator_run_1.yaml - Configuration file for datastore 1, referred to from vlasiator_config.yaml |
| ├── vlasiator_run_2.yaml - Configuration file for datastore 2, referred to from vlasiator_config.yaml |
| ├── vlasiator_run_3.yaml - Configuration file for datastore 3, referred to from vlasiator_config.yaml |
| └── vlasiator_run_4.yaml - Configuration file for datastore 4, referred to from vlasiator_config.yaml |
| ``` |
|
|
| Preprocess the runs with [mllam-data-prep](https://github.com/mllam/mllam-data-prep), run: |
| ``` |
| mllam_data_prep data/vlasiator_run_1.yaml |
| mllam_data_prep data/vlasiator_run_2.yaml |
| mllam_data_prep data/vlasiator_run_3.yaml |
| mllam_data_prep data/vlasiator_run_4.yaml |
| ``` |
| This produces training-ready Zarr stores in the data 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 |
| ``` |
|
|
| ## Citation |
|
|
| ``` |
| @misc{vlasiator2025mldata, |
| title = {Vlasiator Dataset for Machine Learning Studies}, |
| author = {Zaitsev, Ivan and Holmberg, Daniel and Alho, Markku and Bouri, Ioanna and |
| Franssila, Fanni and Jeong, Haewon and Palmroth, Minna and Roos, Teemu}, |
| year = {2025}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/datasets/deinal/spacecast-data}, |
| doi = {10.57967/hf/7027}, |
| } |
| ``` |
