Datasets:

aschachner
/

cy-database

cicy_id int64	h11 int64	h12 int64	chi int64	lcs_row_index int64	gv_shard_id int64	gv_row_index int64	has_gv bool
31	0	0	0	0	null	null	false
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End of preview.

`cy-database` — Calabi–Yau geometries for string-compactification workflows

Precomputed Calabi–Yau threefold data, organised as a family of sub-datasets, accessed through the stringforge infrastructure package. Each sub-dataset covers one class of Calabi–Yau constructions and is keyed by a class-specific identifier system.

This top-level card describes the conventions, layout, and loading interface that are common to all sub-datasets. Each sub-dataset has its own card with construction-specific details.

Available sub-datasets

Sub-dataset	Construction class	Identifier	Status	Card
`tdf/`	Trilayer, double favourable toric hypersurfaces from the Kreuzer–Skarke list	`(ks_id, triang_id)`	Available	TDF card
`cicy/`	Complete-intersection Calabi–Yau threefolds	`cicy_id`	Available	CICY card
`kklt/`	Curated KKLT index over `tdf/` (one-face-divisor conifold classes)	`(ks_id, coni_class_id, coni_id)`	Available	KKLT card

Quick start

pip install stringforge

from stringforge import CYDatabase, TDFDatabase, CICYDatabase, LCSDatabase, KKLTDatabase

# 1.  Pure I/O on the TDF sub-dataset (no JAXVacua import)
db = TDFDatabase()                                # downloads catalogue only (~10 MB)
df = db.query(h11=2, has_conifolds=True)          # catalogue-level filter, no shard I/O

# 2.  Same as 1, but in mirror convention and producing JAXVacua model objects
lcs = LCSDatabase(dataset="tdf")                  # mirror-convention wrapper
tree = lcs.load(                                  # returns a jaxvacua.lcs.lcs_tree
    ks_id=int(df.iloc[0]["ks_id"]),
    triang_id=int(df.iloc[0]["triang_id"]),
    h12=int(df.iloc[0]["h12"]),                   # h12 in mirror convention
    include_gv=True,
    include_conifolds=True,
)

# 3.  Plain CICY access
cicy = CICYDatabase()
cicy_df = cicy.query(h11=3)

# 4.  Curated KKLT index (logical links into TDF; no geometry duplication)
kklt = KKLTDatabase()
polys = kklt.query_polytopes(Q_min=100)

The LCSDatabase class is the recommended entry point for JAXVacua workflows: it operates in the mirror convention used by lcs_tree / FluxVacuaFinder and exposes load_model(...) to construct a fully initialised FluxVacuaFinder in one call.

Repository layout

aschachner/cy-database/
    README.md                       ← this file (umbrella card)

    tdf/                            ← Trilayer, Double Favourable toric models
        README.md                   ← TDF card
        catalog.parquet
        conifold_catalog.parquet
        schema.json
        manifest.json
        lcs_data/h11_{N}/
        gv/h11_{N}/
        conifolds/h11_{N}/
        polytope/
        extra/

    cicy/                           ← Complete-intersection threefolds
        README.md                   ← CICY card
        catalog.parquet
        schema.json
        manifest.json
        lcs_data/h11_{N}/
        gv/

    kklt/                           ← Curated KKLT index over tdf/
        README.md                   ← KKLT card
        catalog.parquet                       ← polytope-grain
        conifold_class_catalog.parquet        ← class-grain
        conifold_catalog.parquet              ← conifold-grain (with TDF link)
        schema.json
        gv/h11_{N}/

Shared design

All sub-datasets follow the same conventions.

Format

Apache Parquet shards for all bulk data.
One small catalog.parquet per sub-dataset, serving as the lazy-download entry point. KKLT additionally carries a class-grain and a conifold-grain catalogue.

Lazy, on-demand access

The stringforge.cy_io.CYDatabase class downloads only the files required by a given query:

db.query(...) → catalogue only (~10 MB).
db.load(...) → catalogue + the specific shard(s) needed for one model.
db.load_batch(...) → catalogue + shards for a batch of models.

Constructing a database object performs no network access; the first query downloads the catalogue, and only subsequent model-loading steps download shard files.

Cache modes

cache_mode="persistent" (default): shards are cached in memory (LRU) and on disk. Optimal for repeated access.
cache_mode="none": shards are downloaded, the requested row is read, and the file is deleted immediately. Ideal for scanning millions of models without filling local disk.

Offline mode

For HPC clusters without outbound network access, set offline=True. All data is served from the local cache; any missing shard raises FileNotFoundError instead of triggering a network call. The common pattern is to warm the cache on a login node and replay on worker nodes.

Schema versioning

Each sub-dataset carries a schema.json file with an integer schema_version. CYDatabase checks it against the client library's stringforge.SCHEMA_VERSION and raises a clear SchemaVersionError on incompatibility.

Bucketing by $h^{1,1}$

Where row sizes scale strongly with $h^{1,1}$ (e.g. triple intersection tensors are $O(h^3)$), data is sub-bucketed by $h^{1,1}$ (directories named h11_{N}/). This keeps small-$h^{1,1}$ users from downloading large-$h^{1,1}$ data, and vice versa. Flat splits (small, uniform rows) are not bucketed.

Adaptive shard sizing

For large buckets (e.g. conifolds at high $h^{1,1}$ with millions of rows), shard sizes are chosen adaptively to target ≈ 30 files per bucket, clamped to $[500,; 50,000]$ rows. See each sub-dataset's card for specifics.

Mirror convention

Catalogues store h11 and h12 in catalogue convention (typically small h11, large h12). JAXVacua works in the mirror convention (the two are swapped). Use stringforge.lcs_database.LCSDatabase — which inherits from CYDatabase — when working with mirror-convention models; it transparently swaps the two columns at the boundary.

Loading without `stringforge`

All sub-datasets are plain Parquet and can be read with any compatible tool:

import pandas as pd
from huggingface_hub import hf_hub_download

catalog_path = hf_hub_download(
    repo_id="aschachner/cy-database",
    filename="tdf/catalog.parquet",
    repo_type="dataset",
)
catalog = pd.read_parquet(catalog_path)

Each catalogue contains (shard_id, row_index) pointers into the sub-dataset's data splits, so you can resolve individual rows by path construction. See each sub-dataset card for the catalogue schema and resolved path templates.

Versioning and rebuilds

Builds are incremental: unchanged models (tracked by SHA-256 hash of their source files) are skipped, and only new or changed models are appended. Major layout changes bump SCHEMA_VERSION in stringforge/cy_io.py.

Scope and limitations

Data is precomputed; not all fields are present for every model. Use has_gv=True, has_conifolds=True, etc. to filter in query().
Catalogues use pandas nullable Int64 for optional shard pointers; consumers that do not support nullable integers should cast or drop missing rows.
Sub-datasets are independent: different constructions live in separate top-level directories and use different identifier schemes.

Citation

If you use this dataset, please cite:

@article{XXX
}

Licence

GPL-3.0, matching the stringforge and jaxvacua libraries.

Contact

Issues, questions, and contributions: https://github.com/AndreasSchachner/stringforge/issues.

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cy-database — Calabi–Yau geometries for string-compactification workflows