Python SDK¶

Use the Goodomics Python SDK to record run, sample, metric, and file context from Python code.

Import the SDK¶

from goodomics import run

The lightweight goodomics-core distribution should expose the same goodomics import package and command-line entry point for users who only need SDK, parser, report generation, and pipeline integration features.

For constructor arguments, class attributes, and method details, see the Python SDK API reference.

Record run context¶

from goodomics import run

with run(
    "rnaseq-batch-042",
    project="rnaseq-core",
    analysis_type_id="rna_sequencing",
    method_id="nf-core/rnaseq",
    method_version="3.18",
) as ctx:
    ctx.log_metric("S1", "pct_mapped", 97.2, unit="percent")
    ctx.metric("duplication_rate", 0.18)
    ctx.file("multiqc_report.html")

Logged SDK metrics are written to the DuckDB analytical store as generic metric records, not to the SQL catalog database. The context manager records the run and sample catalog metadata in SQLite, then flushes metric observations to the project DuckDB store when the block exits successfully.

Pipeline integration¶

The SDK should stay lightweight enough to use from Nextflow, Snakemake, WDL, shell workflows, notebooks, and internal Python pipelines.

Custom parsers¶

Use the SDK-style custom parser API when you need to read a lab-specific table or dataframe and emit structured Goodomics records. Parsers describe how to read data; Goodomics handles ingestion and persistence.

Context over control

Goodomics sits after whatever system already runs the workflow. It records and reviews outputs; it should not become the workflow orchestrator.