Notebooks ========= Worked examples as `marimo `_ notebooks --- **plain Python files** with ``@app.cell`` decorators, so they diff and review like source rather than like JSON, and they run three ways: .. code-block:: bash pip install 'vdjtools[examples]' marimo edit examples/signature_features.py # interactive marimo run examples/signature_features.py # read-only app python examples/signature_features.py # plain script; prints, no UI Each one bootstraps its own data from Hugging Face (`isalgo/airr_benchmark `_) and caches it, so a fresh ``pip install`` is enough --- no local paths and no pre-staged files. Drop a copy under ``./data_dump/`` (gitignored) and it is used instead of downloading. Signatures ---------- .. raw:: html

Signature features

The vsig half block by block: what each column measures, how it is transformed, and which ones survive a shallow library.

Signature pipeline (mirpy)

A folder of AIRR TSVs to one table joined with your metadata, in one command --- both halves at once. The end-to-end recipe.

Scaling a cohort

Coverage-standardised diversity across samples of wildly different depth, and why the standardisation is what makes them comparable.
Analysis -------- .. raw:: html

Aging

78 donors aged 0-103, three ways: streamed cohort statistics, coverage-standardised iNEXT diversity, and repertoire divergence by MDS.

Pre-processing

The three filtering axes, and what each one does to a real cohort.

Overlap and similarity

Pairwise repertoire overlap, the metrics that behave under depth variation, and the ones that do not.

Model workshop

Build, check and compare V(D)J generation models; score sequences; read the training log.