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pyaegean

A specialist Python toolkit for Ancient Greek — alphabetic Greek and the Aegean syllabic scripts (Linear A, Linear B, Cypriot, and Cypro-Minoan). pyaegean focuses narrowly and deeply on Greek and the Aegean world: a script-agnostic corpus data layer, the analytical methods from the Linear A Research Workbench, translation, and a pluggable multi-provider AI layer.

Status: v0.7.0 (alpha). The script-agnostic core, Linear A, Linear B (Mycenaean Greek), the Cypriot syllabary (Arcado-Cypriot Greek), and the undeciphered Cypro-Minoan script complete the Aegean set — each deciphered script with a sign inventory, transliteration, and a Greek-reading bridge; Cypro-Minoan, undeciphered, ships its sign inventory only. The Greek NLP track is a full pipeline — including an opt-in Perseus AGDT treebank backend (attested lemmas + gold POS/morphology), a generalizing averaged-perceptron POS tagger (use_tagger; ~84% on unseen forms), a generalizing lemmatizer (use_lemmatizer; edit-trees) plus a neural seq2seq lemmatizer (use_neural_lemmatizer; 76.3% on unseen forms), LSJ glossing, a dependency parser, a benchmark harness, and a neutral out-of-AGDT (PROIEL) evaluator — and the multi-provider AI layer + hybrid translation are implemented, over a corpus data layer with a lossless JSON round-trip (to_json/from_json) and a compound query(), plus EpiDoc/CSV/Parquet export. Analytical and generative output on the undeciphered Linear A material is exploratory — see Data & Provenance.

New here?

• Never used Python? Start with Getting Started — it walks you from "I have nothing installed" to your first result, no prior programming assumed.
• Want to learn by doing? The Tutorial answers two real research questions end to end — one in Linear A, one in Greek.
• Something not working? See the FAQ & Troubleshooting.

Quick start

import aegean

corpus = aegean.load("lineara")          # 1,721 inscriptions, bundled, offline
print(len(corpus))                       # 1721

ht = corpus.filter(site="Haghia Triada") # filter by metadata (site name)
df = corpus.to_dataframe(level="word")   # pandas-native, one row per word

from aegean.analysis import balance_check, word_matches_sign_pattern
checks = balance_check(corpus.get("HT13"))          # KU-RO accounting reconciliation
hits = [w for w, _ in corpus.word_frequencies()
        if word_matches_sign_pattern(w, "KU-*-RO")] # wildcard sign search

from aegean import greek
greek.betacode_to_unicode("mh=nin")          # 'μῆνιν'
greek.syllabify("ἄνθρωπος")                  # ['ἄν', 'θρω', 'πος']
greek.accentuation("λόγος").classification    # 'paroxytone'

What's here

Module	What it does
aegean.core	Script-agnostic model: Corpus, Document, Token, Sign, SignInventory, Numeral, the Script plugin registry, provenance, a lossless JSON round-trip, and a compound query()
Linear A	Bundled 1,721-inscription corpus, 84-sign inventory, sign→sound map, transliteration
Linear B	Mycenaean Greek: 211-sign Unicode inventory, transliteration, a Greek-reading bridge (po-me → ποιμήν), accounting, bring-your-own EpiDoc corpus
Cypriot	Arcado-Cypriot Greek: 55-sign Unicode syllabary, transliteration, a Greek-reading bridge (pa-si-le-u-se → βασιλεύς)
Cypro-Minoan	Undeciphered Bronze Age Cyprus: 99-sign Unicode inventory + sign-sequence tokenization (no phonetics or bridge — the script is undeciphered)
Analysis	Accounting reconciliation, sign-pattern search, phonetic distance/alignment, morphology clustering, collocation stats, query engine, structure detection
Greek NLP	Beta Code↔Unicode, tokenize, syllabify, accent & prosody, metrical scansion, reconstructed IPA, POS tagging, morphological analysis, lemmatize; opt-in Perseus-treebank lemmas/POS (use_treebank), a generalizing POS tagger (use_tagger; ~84% on unseen forms) and lemmatizer (use_lemmatizer; edit-trees), a neural seq2seq lemmatizer (use_neural_lemmatizer; 76.3% on unseen forms), LSJ glossing (use_lsj), a dependency parser (use_parser), and a benchmark harness
aegean.io	Export adapters: EpiDoc (TEI) write — the inverse of the bring-your-own reader — plus CSV and Parquet
Geography	aegean.geo: corpus → geopandas GeoDataFrame (per-inscription or per-site points) from a bundled Aegean gazetteer, for mapping/spatial analysis
AI Layer	Multi-provider clients (Anthropic/OpenAI/Grok/Gemini), grounding, caching, exploratory-labeled capabilities, hybrid translation
Data & Provenance	Bundled data, download-to-cache, citation/licensing

The API reference documents every public module, class, and function, generated from the docstrings and type hints — complementing the guides above.

Install

pip install pyaegean            # core + Linear A + Greek
pip install "pyaegean[ai]"      # + Anthropic / OpenAI / Grok / Gemini clients
pip install "pyaegean[all]"     # everything

See Installation for the full extras matrix, and Development to build from source and run the test suite.

Roadmap

Shipped (through v0.3): the script-agnostic core and bundled Linear A corpus; the multi-provider AI layer and hybrid translation; and a deep Greek NLP pipeline — treebank lemmas/POS, LSJ glossing, a dependency parser, generalizing perceptron POS tagging (~84% on unseen forms), edit-tree and neural seq2seq lemmatization (76.3% on unseen forms), and a benchmark harness. v0.4 adds Linear B (Mycenaean Greek: a Unicode-built sign inventory, transliteration, a Greek-reading bridge, and accounting) and the Cypriot syllabary (Arcado-Cypriot Greek). v0.5 adds Cypro-Minoan (the undeciphered Bronze Age script of Cyprus; sign inventory only), completing the Aegean set, and a neutral out-of-AGDT evaluator (the PROIEL treebank) backing the Greek-NLP numbers. v0.6 adds the corpus data layer's lossless JSON round-trip (to_json/from_json) and a compound query(); v0.7 adds aegean.io export adapters — EpiDoc (TEI), CSV, and Parquet — completing the EpiDoc read+write round-trip. Next: a context-aware lemmatizer and the workbench bridge toward v1.0 stable.

License

Apache-2.0. Corpus data is GORILA (Godart & Olivier 1976–1985) via mwenge/lineara.xyz; facsimile imagery © École Française d'Athènes (referenced, not redistributed). See Data & Provenance.