makeprov 0.1.1

An RDF provenance tracking library for simple Python workflows

makeprov: Pythonic Provenance Tracking

This library provides a way to track file provenance in Python workflows using RDF and PROV (W3C Provenance) semantics. It supports defining input/output files via decorators and automatically generates provenance datasets.

Features

• Use decorators to define rules for workflows.
• Automatically generate RDF-based provenance metadata.
• Handles input and output streams.
• Integrates with Python's type hints for easy configuration.
• Outputs provenance data in TRIG format.

Installation

You can install the module directly from PyPI:

pip install makeprov

Usage

Here’s an example of how to use this package in your Python scripts:

from makeprov import rule, InFile, OutFile, build

@rule()
def process_data(
    input_file: InFile = InFile('input.txt'), 
    output_file: OutFile = OutFile('output.txt')
):
    with input_file.open('r') as infile, output_file.open('w') as outfile:
        data = infile.read()
        outfile.write(data.upper())

if __name__ == '__main__':
    process_data()

    # or as a command line interface
    import defopt
    defopt.run(process_data)

    # or as a workflow graph that automatically (re)generates all dependencies
    from makeprov import build
    build('output.txt')

You can execute example.py via the CLI like so:

python example.py build-all

# Or set configuration through the CLI
python example.py build-all --conf='{"base_iri": "http://mybaseiri.org/", "prov_dir": "my_prov_directory"}' --force --input_file input.txt --output_file final_output.txt

# Or set configuration through a TOML file
python example.py build-all --conf=@my_config.toml

Complex CSV-to-RDF Workflow

For a more involved scenario, see complex_example.py. It creates multiple CSV files, aggregates their contents, and emits an RDF graph that is both serialized to disk and embedded into the provenance dataset because the function returns an rdflib.Graph.

@rule()
def export_totals_graph(
    totals_csv: InFile = InFile("data/region_totals.csv"),
    graph_ttl: OutFile = OutFile("data/region_totals.ttl"),
) -> Graph:
    graph = Graph()
    graph.bind("sales", SALES)

    with totals_csv.open("r", newline="") as handle:
        for row in csv.DictReader(handle):
            region_key = row["region"].lower().replace(" ", "-")
            subject = SALES[f"region/{region_key}"]

            graph.add((subject, RDF.type, SALES.RegionTotal))
            graph.add((subject, SALES.regionName, Literal(row["region"])))
            graph.add((subject, SALES.totalUnits, Literal(row["total_units"], datatype=XSD.integer)))
            graph.add((subject, SALES.totalRevenue, Literal(row["total_revenue"], datatype=XSD.decimal)))

    with graph_ttl.open("w") as handle:
        handle.write(graph.serialize(format="turtle"))

    return graph

Run the entire workflow, including CSV generation and RDF export, with:

python complex_example.py build-sales-report

Configuration

You can customize the provenance tracking with the following options:

• base_iri (str): Base IRI for new resources
• prov_dir (str): Directory for writing PROV .trig files
• force (bool): Force running of dependencies
• dry_run (bool): Only check workflow, don't run anything

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.