Reference

Command Line Interface

bdf --help
bdf validate data/sample.bdf.csv
bdf clean data/sample.bdf.csv --out cleaned.bdf.csv --assume-bdf
bdf convert raw/vendor.csv --to output.bdf.csv
bdf detect raw/vendor.csv
bdf plot data/sample.bdf.csv --assume-bdf --save plot.png
bdf meta-jsonld data/sample.bdf.csv --title "My dataset" --description "..." --creator "Name|ORCID|Affiliation"

Python API

bdf.read(path, *[, plugin, normalize, ...])	Read path (local file or URL) to BDF-canonical form, returning (df, metadata).
bdf.save(df, pathlike, *[, metadata, ...])	Save a BDF table to a CSV/parquet/IPC/JSON/ndjson/xlsx artifact.
bdf.normalize(df, *[, normalizer, validate, ...])	Map vendor columns to BDF canonical names with unit conversion and dtype casting.
bdf.validate(obj, *[, report, ...])	Validate a BDF DataFrame, a local file path, an HTTP/HTTPS URL, or a dataset id.
bdf.clean(df, *[, time_fix, outlier, ...])	Clean a BDF-normalized table.
bdf.plot(df, *[, xdata, ydata, yydata, ...])	Publication-style BDF plot:
bdf.explore(df, *[, xdata, ydata, yydata, ...])	Interactive plotting entry point.
bdf.detect(path)	Resolve (plugin_id, Plugin) for path (local file or URL).
bdf.ingest(source, *[, out_dir, format, ...])	Convert raw vendor files to BDF and validate existing BDF artifacts.
bdf.templates(*names[, root, overwrite])	Create template sidecar files with REQUIRED/OPTIONAL placeholders.
bdf.datasets([registry_path])	Return dataset IDs from the registry.
bdf.load_registry([path])	Load datasets registry.
bdf.get_entry(registry, entry_id)	Return the entry dict with matching id (case-insensitive).
bdf.build_registry(sources[, registry_dir, ...])
bdf.search(query[, registry_dir, limit])
bdf.sparql(query[, registry_dir])