A Primer on Automatic Inference of Sound Correspondence Patterns (2): Initial Experiments with Alignments from the Tableaux Phonétiques des Patois Suisses Romands

README.md

A Primer on Automatic Inference of Sound Correspondence Patterns (2): Initial Experiments with Alignments from the Tableaux Phonétiques des Patois Suisses Romands

To run the script provided here, make sure to download the data from Zenodo, and unpack the folder multiple.zip. Then cd into the folder, and run the script as follows:

$ python to_wordlist.py

To install all requirements, just type:

$ pip install lingpy

to_wordlist.py

from lingpy import *
from glob import glob
import re
import tqdm
from lingpy.align.sca import normalize_alignment

files = glob('msa/*.msa')

selection = {'Boudry': '46',
 'Cerneux-Péquignot': '53',
 'Champéry': '18',
 'Courtedoux': '62',
 'Courtepin': '41',
 'Côte-aux-Fées': '50',
 'Dompierre': '42',
 'Evolène': '30',
 'Grimentz': '31',
 'Hermance': '36',
 'Lourtier': '22',
 'L’Auberson': '3',
 'Ormont-Dessus': '15',
 'Plagne': '56',
 }

D = {0: [
    'doculect',
    'language_id',
    'concept',
    'latin',
    'french',
    'form',
    'tokens',
    'alignment',
    'cogid'
    ]}

idx, cogid = 1, 1

converter = {
        '_': '+'
        }

for f in tqdm.tqdm(files):

    msa = MSA(f)
    if msa.dataset == 'French':
        french, latin = re.findall(
                r'\*(.*?)\*', 
                msa.seq_id
                )
        for taxon, alm in zip(msa.taxa, 
                msa.alignment):
            taxon_id = selection.get(taxon, '')
            if taxon_id:
                tokens = [converter.get(
                    x, 
                    x
                    ) for x in alm if x != '-']
                form = ''.join(tokens)
                D[idx] = [
                        taxon,
                        taxon_id,
                        french,
                        latin,
                        french,
                        form,
                        tokens,
                        alm,
                        cogid
                        ]
                idx += 1
        cogid += 1

alms = Alignments(D, ref='cogid')
for cogid, msa in alms.msa['cogid'].items():
    for idx, alm in zip(
            msa['ID'],
            normalize_alignment(
                msa['alignment']
                )
            ):
        alms[idx, 'alignment'] = alm

alms.output(
        'tsv', 
        filename='tppsr-bdpa'
        )

tppsr-bdap.tsv