Installation

Install from PyPI with pip:

pip install flpq-data

Requires Python 3.11–3.13; see the Install page for details.

What’s inside

FLPQ_Data bundles 113 labeled directed graphs across eight families — C alias analysis, RDF/OWL datasets, Java points-to, field-sensitive aliasing, context-sensitive data-flow, data provenance, name resolution (stack graphs), and biological graphs from UniProt — together with the context-free grammars used to query them. Each graph ships as a per-label MatrixMarket archive; see the Graphs page for statistics and download links.

Examples

>>> import flpq_data
>>> flpq_data.GRAPHS[:3]
['skos', 'wc', 'generations']

>>> bzip_path = flpq_data.download_graph("bzip")
>>> bzip = flpq_data.graph_from_mtx_dir(bzip_path / "graph")

For developers

To work on FLPQ_Data itself, set up the development environment (uv):

uv sync --all-groups

and run the main local checks that CI enforces — tests, quality checks, and the docs build:

uv run pytest
uv run pre-commit run --all-files
uv run make -C docs html

The full developer guide — development setup, quality checks, the test pipeline, docs build and deployment, the package release process, and contribution guidelines — is on the Developer page.

How to contribute new data?

Create an Issue corresponding to the “CONTRIBUTE NEW DATA” issue template — one or more validated archives (one graph each), links to them, and the motivation. The maintainers upload and merge the data into the dataset; the full contribution guide is on the Contributing data page.

Citation

If you use FLPQ_Data in your work, please cite it — see the Citation section on the About page for BibTeX entries.

Licensing

The code (package source, documentation, and tooling) is licensed under Apache-2.0 (LICENSE.txt). The dataset (graphs, grammars, and benchmarks) is licensed under CC-BY 4.0 (LICENSE-DATA.txt).