tracealign 0.2.0

Textual Reuse, Alignment, and Collation Engine — pairwise philological alignment with pluggable language packs

TRACE

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).

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Highlights

• 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.

Installation

pip install tracealign

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

From source

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.

Verifying the install

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).

Quick start — pairwise

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

Quick start — multi-witness (v0.2)

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.

Documentation

Section	What it covers
Installation	pip / from source / dev setup / docs build
Usage	Tokenize, pairwise align, multi-witness align, work with the result, custom lexica, I/O
Details	Tokenizer pipeline, scoring tiers, pairwise DP algorithm, multi-witness POA pipeline
FAQ	Common questions about scope, language packs, performance, multi-witness semantics
Contributing	Development workflow, TDD discipline, branch model

Project status

Current PyPI release	0.1.3 (v0.2.0 in flight on feature/v0.2-multi-witness)
Roadmap	docs/ROADMAP.md — ten-stage long-term vision
v0.1 design spec	docs/superpowers/specs/2026-04-28-trace-v0.1-design.md
v0.2 design spec	docs/superpowers/specs/2026-05-21-trace-v0.2-multi-witness-design.md
Released stages	1 (pairwise + Hebrew pack)
In progress	2 (master alignment graph / multi-witness)
Future sub-projects	Geniza anchor detection · Text-reuse · Apparatus / critical edition · Cross-tradition Hexapla · Stemmatic reconstruction · Allusion detection · Citation graphs · Reception history

Citation

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.

License

MIT © 2026 Benjamin Schnabel.