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End of preview. Expand in Data Studio
SII Stellarator Configuration Dataset
Wenyang Li | PhD student, Shanghai Innovation Institute
AI-Driven Controlled Fusion Simulation, Control & Design Lab
Email: lwydsg@mail.nankai.edu.cn | Phone: +86-156-2003-5216. License: CC BY 4.0.
Overview
Stellarator boundary configurations and recorded physics evaluations, covering NFP 1-5. This replaces the earlier 158,685-row release. The original data came from proxima-fusion/constellaration. New sources include sBO/CMA, traditional AL, earlier BO, DP, ExLLM and FusionLeague evaluations. Incremental maintenance also collects reviewed benchmark physics evaluations.
Collection updated (UTC, per-file snapshots): 2026-09-28; schema: stellarator-1.1.
| Directory | Configurations | Parquet shards |
|---|---|---|
main/ |
2,775,699 | 56 |
failed/ |
103,564 | 3 |
336,344 main rows have all nine metrics.
The main table retains successful evaluations. Failed records are stored separately.
Fields and Quality
Flat column names are retained. Missing values are null.
| Fields | Type and meaning |
|---|---|
configuration_id, schema_version |
Strings: canonical configuration SHA-256 and format version |
boundary.n_field_periods |
int64: NFP |
boundary.r_cos, boundary.z_sin |
Nested float64 lists: stellarator-symmetric Fourier boundary |
metrics.* |
Nine nullable float64 diagnostics listed below |
evaluation.success |
Nullable bool; null means unknown, not failure |
evaluation.error, .fidelity, .solver_version |
Nullable strings: original error and recorded evaluation settings |
evaluation.feasibility |
Nullable float64: source score, not a universal constraint definition |
source.name, .path, .record |
Strings: source family, relative file path, record locator |
quality.iota_representation |
signed, absolute_only or unknown |
quality.metric_count, .flags |
Count of finite metrics and list of quality/transformation flags |
The .field abbreviations in a row retain that row's prefix, e.g. source.path.
Metric suffix after metrics. |
Meaning |
|---|---|
aspect_ratio |
Aspect ratio |
edge_rotational_transform_over_n_field_periods |
Edge iota divided by NFP |
average_triangularity |
Average triangularity |
max_elongation |
Maximum elongation |
edge_magnetic_mirror_ratio |
Edge magnetic mirror ratio |
vacuum_well |
Vacuum magnetic well |
minimum_normalized_magnetic_gradient_scale_length |
L_delta_b |
qi |
QI diagnostic, not log10(QI) |
flux_compression_in_regions_of_bad_curvature |
Bad-curvature flux compression |
- Deduplication: exact NFP plus canonical Fourier coefficients. Prefer successful, then unknown, then failed evaluations; within a status prefer more complete metrics, then deterministic source order.
- Boundary: usually 5x9; higher-order matrices such as 8x15 are also retained. For
(M, 2N+1), rows arem=0..M-1, columnsn=-N..N. Smaller supported grids are zero-padded; higher modes are preserved. Coefficients are not normalized or approximately rounded for deduplication. - Use limits: metric completeness does not establish physical feasibility or comparable solver fidelity.
Read the Data
import pyarrow.dataset as ds
data = ds.dataset("main", format="parquet")
complete = data.to_table(filter=(ds.field("quality.metric_count") == 9)
& (ds.field("evaluation.success") == True))
For signed-iota training also require quality.iota_representation == "signed";
for raw-QI-only studies exclude qi_reconstructed_from_log10 in quality.flags.
Maintain the Dataset
FusionLeague/datasets/stellarator/ is the editing source: main/, failed/, these two READMEs and tools/.
Edit here first; the external HF clone is only a publication copy. Tools and audit archives are not uploaded.
Step 1: update the source dataset on Linux.
Start from the FusionLeague/ repository root:
cd datasets/stellarator && python tools/update_dataset.py
If dependencies are missing, run python -m pip install -r tools/requirements.txt once, then retry.
Wait for a successful update before copying; do not update data while copying. Add --dry-run to inspect sources only.
- Sources: automatically scan
methods/for realqi/qi_frontierevaluation journals; skip quadratic/examples and duplicate run exports. Collectbenchmarks/qi_design/datasubmissions only when review, result hash, submission identity and point inputs agree. Running or unapproved results are deferred. No solvers or surrogate inference run. - Incremental merge: index the current dataset on the first run, then remember completed log positions. Read new complete records, defer unfinished final lines, and apply the same exact deduplication/whole-record preference above. Unchanged sources are skipped; changed log prefixes are re-imported without deleting historical configurations. Revoked reviews do not automatically retract previously collected data.
- Output: without winning additions/replacements, leave Parquet and READMEs unchanged. Otherwise export and fully verify a sorted dataset, replace
main/andfailed/, and refresh only the statistics blocks in both READMEs. Ingestion is incremental, but changed datasets are fully re-exported and verified. - State and recovery:
tools/.update-state/holds the SQLite index, cursors, lock, latest summary, reports and previous published dataset. Only one updater may run. After an error/interruption, keep this state and rerun the same command to recover; do not manually remove its lock or edit/replace indexed Parquet files. Do not read/copy/push during installation or after an unfinished update. - Resources and audit: first indexing takes longer; allow space for the index, staged output and previous dataset in addition to current data. The latest
last_update.jsonpoints to the report/verification/quarantine; invalid records are also kept in the indexissuestable. Review notices and quality/provenance before publication. Earlier publication backups are cleaned after a successful update; this is not a full version archive.
The original full-source builder remains in tools/build_dataset.py for separate rebuilds; routine
updates do not rescan all historical optimization directories. Original snapshot audits remain at
../_archive/stellarator-20260927/releases/20260923-full-v1/. Local fixture tests:
python -m unittest discover -s tools -p 'test_*.py'.
Publish to Hugging Face
One-time setup (skip if already cloned). Install Git LFS and run from the parent of FusionLeague/, placing the two repositories side by side:
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/SII-AI4Fusion/SII-Stellarator-Configuration-Dataset
git -C SII-Stellarator-Configuration-Dataset lfs install --local
Step 2: copy the update. Start from FusionLeague/datasets/stellarator/, where Step 1 finished. The HF clone should be on main with no uncommitted changes; replace only its data and READMEs:
cd ../../../SII-Stellarator-Configuration-Dataset &&
GIT_LFS_SKIP_SMUDGE=1 git pull --ff-only origin main &&
git rm -r --ignore-unmatch -- main failed &&
cp -r ../FusionLeague/datasets/stellarator/{main,failed,README.md,README.zh.md} . &&
git add main failed README.md README.zh.md &&
git diff --cached --stat
Step 3: review and push. Continue from the HF clone; skip if nothing changed, and resolve any error before continuing:
git commit -m "Update consolidated stellarator dataset" &&
git lfs fsck HEAD &&
git push origin main
Commands use Bash and the repository's existing Parquet LFS configuration. Only data and READMEs are copied, not tools or archives. Review data for public release and keep HF tokens out of documentation. Commit first, check LFS, then push only after success; old versions remain in commit history.
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