FORMAT.md

metadata

language:
  - en
  - zh
configs:
  - config_name: default
    data_files:
      - split: train
        path: main/*.parquet
  - config_name: failed
    data_files:
      - split: train
        path: failed/*.parquet

SII Stellarator Configuration Dataset: Consolidated Format

Schema version: stellarator-1.1. This card describes the new offline export, not a claim that the existing Hugging Face dataset has already been replaced. See report.json for this particular export's actual counts and inventory.json for its source file inventory. Upload and source-license review are separate steps.

One Row, One Configuration

The primary data is main/*.parquet. It includes successful evaluations even when physical constraints are violated, plus explicitly labelled unknown-status partial evaluations. failed/*.parquet contains explicit solver failures with identifiable boundaries. These are not train/test splits. The HF train label above is only a loader convention; no evaluation holdout has been defined.

Identical NFP and numerical Fourier coefficients produce one record across both groups. Prefer successful over unknown over failed status, then more finite metrics, then deterministic source ordering. Repeated evaluations are not kept as separate samples. A selected row is kept whole; missing fields are never filled from a different evaluation of the same boundary.

Boundary

Column	Type	Meaning
configuration_id	string	SHA-256 of canonical NFP, mode grid and float64 coefficients
schema_version	string	stellarator-1.1
boundary.n_field_periods	int64	Positive integer NFP; different NFP means different configuration
boundary.r_cos	list[list[float64]]	Radial cosine coefficients; usually 5 by 9
boundary.z_sin	list[list[float64]]	Vertical sine coefficients; same shape as r_cos

For shape (M, 2N+1), rows are poloidal modes m=0..M-1 and columns are toroidal indices n=-N..N. Standard records use 5 by 9; DP also uses 8 by 15. r_cos[0, n<0] and z_sin[0, n<=0] must be zero under the source convention. Only stellarator-symmetric boundaries are represented; nonzero r_sin or z_cos are rejected, not discarded silently. Coefficients are never rescaled by major radius or standardized as model features. Recognized smaller centered odd-width arrays are padded to at least 5 by 9; larger arrays retain every coefficient, with a higher_fourier_modes_preserved flag. Flat length-45 arrays are reshaped row-major. Negative zero becomes zero. All other float64 values are retained exactly, with no tolerance/rounding.

For the standard 5-by-9 grid, the ID hashes little-endian signed int64 NFP followed by float64 values in row-major r_cos, then row-major z_sin order (struct.pack('<q90d', ...)). This also makes JSON formatting and equivalent zero padding up to the standard grid irrelevant to deduplication. For a larger grid, the prefix is b'higher-modes\x00', followed by NFP/int64, row count/int32 and column count/int32, then all coefficients in the same order. No higher coefficient is rounded or discarded to match the standard grid.

Nine Physical Metrics

Every field is nullable float64. Null means unavailable or nonfinite, not zero.

Column	Meaning / legacy pool name
metrics.aspect_ratio	Aspect ratio / aspect_ratio
metrics.edge_rotational_transform_over_n_field_periods	Edge iota divided by NFP / iota_nfp
metrics.average_triangularity	Mean triangularity / triangularity
metrics.max_elongation	Maximum elongation / elongation
metrics.edge_magnetic_mirror_ratio	Edge magnetic mirror ratio / mirror
metrics.vacuum_well	Vacuum well / vac_well
metrics.minimum_normalized_magnetic_gradient_scale_length	L_delta_b / l_delta_b
metrics.qi	QI diagnostic, not log10(QI)
metrics.flux_compression_in_regions_of_bad_curvature	Bad-curvature flux compression / flux_comp

Source metric conventions are retained. Signed iota is not turned into an absolute value. Historical BO data that already lost its sign remains positive/absolute-only and is labelled accordingly. Its saved qi_log10 is converted using 10**value; this cannot undo historical clipping and is marked qi_reconstructed_from_log10. No predicted metric, current constraint score or newly computed physics value is substituted for a missing original diagnostic.

Evaluation, Source and Quality

Column	Type / interpretation
evaluation.success	Nullable bool; null means original status cannot be established
evaluation.error	Nullable string; original error or explicit original forward-model failure flag
evaluation.fidelity	Nullable string; recorded fidelity only
evaluation.solver_version	Nullable string; recorded execution version, otherwise recorded configuration version
evaluation.feasibility	Nullable float64; source score, not a unified feasibility definition
source.name	Source family selected by the importer
source.path	Relative source file path, without local machine root
source.record	Parquet zero-based row; JSONL one-based line; nested objects add path components
quality.iota_representation	signed, absolute_only or unknown
quality.metric_count	Number of finite values among the nine metrics
quality.flags	List of transformation, invalid-value or status-inference labels

Missing solver versions, fidelity and timestamps are not inferred from today's code. Pool success may be inferred from its documented source filtering; the flag success_inferred_from_source distinguishes this from an explicit result status. Complete unlabelled historical measurements can have success_inferred_from_complete_metrics. A partial record with no reliable status uses evaluation_status_unknown. Invalid scalar metrics carry nonfinite_or_invalid:<metric>. Unknown iota convention must not be treated as proof that its sign was preserved. Metric completeness does not establish fidelity or physical feasibility.

higher_fourier_modes_preserved requires downstream models to support variable mode grids. legacy_boundary_text_precision identifies ExLLM arrays stored as rounded printed numbers; missing digits cannot be reconstructed. DP raw constraint values are retained, but its scaled objective is not assumed to equal raw L_delta_b (scaled_target_not_used_as_raw_ldb).

Loading and Filtering

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))
# Request just the original 12 columns for compatible downstream consumers.
core = complete.select([name for name in complete.column_names
                        if name.startswith(('boundary.', 'metrics.'))])

For signed-iota training, additionally require quality.iota_representation == 'signed'. For raw-QI-only studies, exclude records carrying the reconstruction flag. failed is for failure analysis, not a source of zero-valued physics labels. No common feasibility <= threshold filter is guaranteed valid across all sources.

Coverage and Review

The builder recognizes cleaned/raw original Parquet, historical and current optimization pools/CMA/sBO records, legacy BO, traditional AL final results, DP SQLite/NPZ evaluations, ExLLM stored measurements and FusionLeague QI/QI-frontier evaluation records. It excludes leaderboard references, quadratic tests, surrogate predictions, latent-only points and opaque binary model or optimizer state. Unsupported formats are not interpreted as physical labels.

Input files are individually bounded snapshots, not one global freeze. Malformed or unusable records are indexed in quarantine.jsonl without fabricating a row; all source files should be retained for investigation. report.json lists duplicate counts, missing data, selected source distribution, read statistics and shard checksums. Check those reports before publication. Scientific comparability and licenses across sources require review; this export does not grant new permissions.