merkmal 0.1.1

Standalone phonological feature systems for historical linguistics

merkmal

merkmal is a standalone Python package for manipulating phonological features. Zero runtime dependencies, Python 3.12+.

It provides:

• bundled phonological feature datasets
• pluggable feature systems (9 built-in)
• feature geometry and distance functions (Clements & Hume 1995)
• tonal geometry (Yip/Bao)
• query and analysis helpers for graphemes and feature sets
• UPA transcription support

Installation

Install from PyPI:

pip install merkmal

Development install:

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

Run checks:

ruff check .
mypy src
pytest -q

Quick start

import merkmal

# Built-in systems
print(merkmal.list_systems())
# ['descriptive', 'broad', 'distinctive', 'pbase-hc', 'pbase-jfh',
#  'pbase-spe', 'pbase-uftc', 'phoible', 'classfeat']

# Basic grapheme lookup
print(merkmal.get_features("p"))
# frozenset({'consonant', 'voiceless', 'bilabial', 'stop'})

# Predefined sound classes
print(merkmal.get_class_features("V"))
# frozenset({'vowel'})

# Distance
print(merkmal.distance("a", "e"))
print(merkmal.distance("p", "b", system="classfeat"))

Systems

System	Type	Features	Distance
descriptive	categorical	articulatory	geometry-weighted
broad	categorical	simplified	geometry-weighted
distinctive	privative	Clements & Hume	geometry-weighted
pbase-hc, -jfh, -spe, -uftc	multi-state	4 theoretical families	geometry-weighted
phoible	binary	37 features	Hamming
classfeat	hybrid	sound classes + continuous	trained weights

All systems implement the same FeatureSystem protocol. Distances, queries, matrices, and natural class derivation work across all of them.

Working with systems

You can use the lazy default registry through top-level helpers, or work with a specific system object.

import merkmal

descriptive = merkmal.get_system("descriptive")
distinctive = merkmal.get_system("distinctive")
pbase = merkmal.get_system("pbase-hc")

print(descriptive.grapheme_to_features("a"))
print(distinctive.grapheme_to_features("a"))
print(pbase.grapheme_to_representation("a"))

Exact reverse lookup is available when a native representation maps directly to a known grapheme.

descriptive = merkmal.get_system("descriptive")

grapheme = descriptive.features_to_grapheme(
    frozenset({"consonant", "voiced", "bilabial", "stop"})
)
print(grapheme)
# 'b'

Feature queries

Use features_to_graphemes(...) to find all graphemes matching a feature set. Matching is partial by default.

import merkmal

vowels = merkmal.features_to_graphemes(frozenset({"vowel"}))
print(vowels[:10])

# Exact matching
features = merkmal.get_features("a")
print(merkmal.features_to_graphemes(features, exact=True))

Natural classes and matrices

import merkmal

# Shared features of a segment set
print(merkmal.derive_class_features(["p", "t", "k"]))
# frozenset({'consonant', 'voiceless', 'stop'})

# Minimal distinguishing matrix
matrix = merkmal.minimal_matrix(["t", "d", "s"])
print(merkmal.tabulate_matrix(matrix))

grapheme | continuant | voiced
---------+------------+-------
t        | False      | False
d        | False      | True
s        | True       | False

Distance

import merkmal

print(merkmal.distance("a", "e"))
print(merkmal.distance("a", "u"))
print(merkmal.distance("p", "b"))
print(merkmal.distance("t", "d", system="pbase-hc"))

You can also supply a precomputed nested dictionary:

precomputed = {"a": {"e": 1.5, "u": 2.0}, "p": {"b": 0.5}}
print(merkmal.distance("a", "e", precomputed=precomputed))

Multi-state systems (P-base)

P-base-derived systems expose multi-state values (+, -, n, ., o, x) through FeatureState.

import merkmal

rep = merkmal.get_representation("a", system="pbase-hc")
print(rep.values["syllabic"])
# FeatureState.POSITIVE

The bundled P-base table is derived, not verbatim. Duplicate rows with conflicting values have the conflicting cells downgraded to . (FeatureState.DOT). The P-base data retains its own attribution and license notice in src/merkmal/data/pbase/.

Custom datasets

from merkmal import create_registry, load_dataset

dataset = load_dataset(directory="my_feature_data")
registry = create_registry(dataset=dataset)
system = registry.get_system("descriptive")
print(system.grapheme_to_features("k"))

Expected files in my_feature_data/: sounds.tsv, classes.tsv, features.tsv.

Documentation

See the tutorials for worked examples covering phonological features, typology, historical linguistics, cognate detection, and UPA transcription.

License

MIT. See LICENSE.