Textual Reuse, Alignment, and Collation Engine — a Python library for philological alignment with pluggable language packs. Pairwise (v0.1) and simultaneous multi-witness (v0.2) alignment.

TRACE is designed for textual criticism, manuscript witness comparison, and the creation of digital synopses and critical editions. The core is language-agnostic; the first shipped language pack covers Biblical and Rabbinic Hebrew (hbo).

• Tokenizer pipeline with editorial-marker awareness ([reconstructed], ⟦deletion⟧, 〈insertion〉, (expanded), lacunae).
• Tiered scoring returning (score, reason) per token pair — EXACT, NIQQUD_STRIPPED, PLENE_DEFECTIVE, ABBREVIATION, ORTHOGRAPHIC, INSERTION, OMISSION, NO_MATCH.
• Pairwise aligner — semi-global Needleman–Wunsch with affine gap penalties (Gotoh) and a multi-token abbreviation lookahead (ר"י ↔ רבי ישמעאל).
• Multi-witness aligner (v0.2) — N witnesses aligned simultaneously into a canonical variant graph (DAG) plus a derived aligned table view, via pairwise distances → UPGMA guide tree → POA-based progressive merge. Determinism is pinned by a permutation-invariance property test; correctness by a lossless-reconstruction property test.
• Hebrew language pack with niqqud strip, plene/defective skeleton matching, gershayim/maqqef tokenizer hooks, and a seed lexicon of rabbinic abbreviations (extendable via Lexica.merge()).
• I/O for plain text, JSON (round-trip for both pairwise and multi-witness results), eScriptorium exports (with bbox + line metadata), and TEI XML (<tei:w> mode + flow-text fallback).
• Reproducible — every AlignmentResult / MultiAlignmentResult carries trace_version and language_pack_version in its params.

pip install tracealign

Requires Python 3.10, 3.11, or 3.12. Pulls pydantic, numpy, lxml, and rapidfuzz.

git clone https://github.com/bsesic/trace.git
cd trace
pip install -e ".[dev]"

The dev extra adds pytest and flake8 (the project's quality gates). For documentation contributions, use pip install -e ".[docs]" to add Sphinx, furo, and myst-parser.

python -c "import tracealign; print(tracealign.__version__, tracealign.list_languages())"

Should print the current version and ['hbo'] (the Hebrew language pack registers itself on import).

import tracealign

w1 = tracealign.tokenize("שלום עולם רַבִּי דויד ר\"י אמר", lang="hbo", seq_label="W1")
w2 = tracealign.tokenize("שלום עולם רבי דוד רבי ישמעאל אמר", lang="hbo", seq_label="W2")

result = tracealign.align(w1, w2, lang="hbo")

print(f"total score: {result.total_score:.2f}")
print(f"summary: {dict(result.summary)}")
for m in result.matches:
    a = m.token_a.text if m.token_a else "—"
    b = m.token_b.text if m.token_b else "—"
    print(f"  {a:>10} ↔ {b:<10}  {m.reason.value:<18} {m.score:.2f}")

Output (abridged):

total score: 0.91
summary: {EXACT: 3, NIQQUD_STRIPPED: 1, PLENE_DEFECTIVE: 1, ABBREVIATION: 1}
       שלום ↔ שלום        exact              1.00
       עולם ↔ עולם        exact              1.00
      רַבִּי ↔ רבי         niqqud_stripped    0.95
       דויד ↔ דוד          plene_defective    0.85
        ר"י ↔ רבי          abbreviation       0.85   (primary)
        ר"י ↔ ישמעאל       abbreviation       0.00   (continuation)
        אמר ↔ אמר          exact              1.00

import tracealign

witnesses = {
    "W1": tracealign.tokenize("שלום עולם רַבִּי דויד אמר",  lang="hbo", seq_label="W1"),
    "W2": tracealign.tokenize("שלום עולם רבי דוד אמר",       lang="hbo", seq_label="W2"),
    "W3": tracealign.tokenize("שלום עולם ר\"י אמר",          lang="hbo", seq_label="W3"),
    "W4": tracealign.tokenize("שלום עולם רבי דוד אמר טוב",   lang="hbo", seq_label="W4"),
}

result = tracealign.align_multi(witnesses, lang="hbo")

print(result.guide_tree.format_text())
print(result.table.format_text())

for node in result.graph.variants():
    readings = {wid: t.text for wid, t in node.tokens.items()}
    print(node.id, readings)

The MultiAlignmentResult exposes a canonical VariantGraph (DAG with witness trails), a derived AlignedTable (re-anchorable to any witness for presentation), a GuideTree (UPGMA-built, carrying the original distance matrix — useful for downstream stemmatic work), and the same reproducibility-aware params snapshot the pairwise aligner produces.

JSON persistence works the same way as the pairwise aligner, in its own module:

from tracealign.io import multi_result as mr_io

mr_io.dump(result, "alignment.json")
restored = mr_io.load("alignment.json")

See the documentation for the full API, more usage examples, the algorithm details, FAQs, and the design rationale.

If you use TRACE in academic work, please cite via the Zenodo concept DOI (always resolves to the latest archived release) or pick a specific version DOI from the Zenodo record. A CITATION.cff is at the repo root — GitHub's "Cite this repository" button generates APA / BibTeX / RIS automatically from it.

MIT © 2026 Benjamin Schnabel.