def iter_cognates(dataset, column='Segments', method='turchin', threshold=0.5, **kw):
"""
Compute cognates automatically for a given dataset.
"""
if method == 'turchin':
for row in dataset.objects['FormTable']:
sounds = ''.join(lingpy.tokens2class(row[column], 'dolgo'))
if sounds.startswith('V'):
sounds = 'H' + sounds
sounds = '-'.join([s for s in sounds if s != 'V'][:2])
cogid = slug(row['Parameter_ID']) + '-' + sounds
if '0' not in sounds:
yield dict(
Form_ID=row['ID'],
Form=row['Value'],
Cognateset_ID=cogid,
Cognate_Detection_Method='CMM')
if method in ['sca', 'lexstat']:
lex = _cldf2lexstat(dataset)
if method == 'lexstat':
lex.get_scorer(**kw)
lex.cluster(method=method, threshold=threshold, ref='cogid')
for k in lex:
yield Cognate(
Form_ID=lex[k, 'lid'],
Form=lex[k, 'value'],
Cognateset_ID=lex[k, 'cogid'],
Cognate_Detection_Method=method + '-t{0:.2f}'.format(threshold))
def toASJP(w):
if w == u'ũ': return 'u'
if w == u'XXX': return ''
w = w.replace('\"', '').replace('-', '') #.replace(' ','')
wA = ''.join(lp.tokens2class(w.split(), 'asjp'))
wAA = cleanASJP(
wA.replace(u'0', '').replace(u'I', u'3').replace(u'H', u'N'))
return ''.join([x for x in wAA if x in sounds])
def find_bad_tokens(wordlist):
"""Collect which bad symbols appear in which forms."""
bad_tokens = {}
for k, segments, form_id in wordlist.iter_rows('tokens', "reference"):
classes = lingpy.tokens2class(segments, 'dolgo')
for token, cls in zip(segments, classes):
if cls == "0":
bad_tokens.setdefault(token, []).append(form_id)
return bad_tokens
def ipa2sca(ipa):
"""Convert an IPA string into a SCA token string.
This function tries to preserve the len of the token string.
"""
sca_list = [
t for x in tokenize_word_reversibly(ipa)
for t, char in itertools.zip_longest(tokens2class(x, 'sca'), "0")
]
assert len(''.join(sca_list)) == len(ipa)
return ''.join(sca_list)
def iter_cognates(dataset, column='Segments', method='turchin', threshold=0.5, **kw):
"""
Compute cognates automatically for a given dataset.
:param dataset: Either a `LexibankWriter` instance or a `pycldf.Dataset`.
"""
forms = dataset.objects['FormTable'] \
if hasattr(dataset, 'objects') else list(dataset['FormTable'])
if method == 'turchin':
for row in forms:
sounds = ''.join(lingpy.tokens2class(row[column], 'dolgo'))
if sounds.startswith('V'):
sounds = 'H' + sounds
sounds = '-'.join([s for s in sounds if s != 'V'][:2])
cogid = slug(row['Parameter_ID']) + '-' + sounds
if '0' not in sounds:
yield dict(
Form_ID=row['ID'],
Form=row['Value'],
Cognateset_ID=cogid,
Cognate_Detection_Method='CMM')
if method in ['sca', 'lexstat']:
try:
lex = _cldf2lexstat(dataset)
except ValueError:
return
if method == 'lexstat':
lex.get_scorer(**kw)
lex.cluster(method=method, threshold=threshold, ref='cogid')
for k in lex:
yield dict(
Form_ID=lex[k, 'lid'],
Form=lex[k, 'value'],
Cognateset_ID=lex[k, 'cogid'],
Cognate_Detection_Method=method + '-t{0:.2f}'.format(threshold))
def get_wordlist(
self,
doculect='base',
profile=False,
ref='crossid',
lexstat=True,
threshold=0.4):
"""
Return a classical wordlist from the data.
"""
if profile:
profile = segments.Tokenizer(profile)
tokenize = lambda x: profile('^' + x + '$', column='IPA').split() # noqa: E731
else:
tokenize = lingpy.ipa2tokens
D = {
0: [
'doculect',
'concept',
'concept_in_source',
'concept_type',
'form',
'tokens',
'occurrences',
'word_forms',
'gloss_forms',
'phrase_example',
'gloss_example',
'references',
]
}
idx = 1
for ctype in ['lexicon', 'grammar']:
concepts = self.get_concepts(ctype=ctype)
concordance = self._concordances[ctype]
for concept, entries in concepts.items():
for form, lid, cis, freq in entries:
# retrieve the concordance
pidx, sA, sB = concordance[form, concept, cis, lid][0]
txt = self[pidx].phrase
gls = self[pidx].gloss
word, fgls = self[pidx, sA]
tokens = tokenize(form)
references = ' '.join(
['{0}:{1}:{2}'.format(a, b, c)
for a, b, c in concordance[form, concept, cis, lid]])
# check tokens
try:
lingpy.tokens2class(tokens, 'sca')
check = True
except: # noqa: E722, # pragma: no cover
check = False
if concept.strip() and check:
D[idx] = [
doculect if self.monolingual else lid,
concept,
cis,
ctype,
form,
tokens,
freq,
word,
fgls,
txt,
gls,
references]
idx += 1
else:
print('[!] Problem with "{0}" / [{1}] [{2}] / {3} {4} {5}'.format(
concept,
form,
tokens,
pidx,
sA,
sB,
))
wl = lingpy.Wordlist(D)
if lexstat:
wl = lingpy.LexStat(D)
wl.cluster(method='sca', threshold=threshold, ref=ref)
else:
wl.add_entries('cog', 'concept,form', lambda x, y: x[y[0]] + '-' + x[y[1]])
wl.renumber('cog', ref)
return wl
def parse_chinese_morphemes(seq, context=False):
"""
Parse a Chinese syllable and return its basic structure.
"""
# get the tokens
if isinstance(seq, list):
tokens = [s for s in seq]
else:
tokens = lingpy.ipa2tokens(seq, merge_vowels=False)
# get the sound classes according to the art-model
arts = [int(x) for x in lingpy.tokens2class(tokens, _art, cldf=True)]
# get the pro-string
prostring = lingpy.prosodic_string(arts)
# parse the zip of tokens and arts
I, M, N, C, T = '', '', '', '', ''
ini = False
med = False
nuc = False
cod = False
ton = False
triples = [('?', '?', '?')] + list(zip(tokens, arts,
prostring)) + [('?', '?', '?')]
for i in range(1,
len(triples) -
1): #enumerate(triples[1:-1]): #zip(tokens,arts,prostring):
t, c, p = triples[i]
_t, _c, _p = triples[i - 1]
t_, c_, p_ = triples[i + 1]
# check for initial entry first
if p == 'A' and _t == '?':
# now, if we have a j-sound and a vowel follows, we go directly to
# medial environment
if t[0] in 'jɥw':
med = True
ini, nuc, cod, ton = False, False, False, False
else:
ini = True
med, nuc, doc, ton = False, False, False, False
# check for initial vowel
elif p == 'X' and _t == '?':
if t[0] in 'iuy' and c_ == '7':
med = True
ini, nuc, cod, ton = False, False, False, False
else:
nuc = True
ini, med, cod, ton = False, False, False, False
# check for medial after initial
elif p == 'C':
med = True
ini, nuc, cod, ton = False, False, False, False
# check for vowel medial
elif p == 'X' and p_ == 'Y':
# if we have a medial vowel, we classify it as medial
if t in 'iyu':
med = True
ini, nuc, cod, ton = False, False, False, False
else:
nuc = True
ini, med, cod, ton = False, False, False, False
# check for vowel without medial
elif p == 'X' or p == 'Y':
if p_ in 'LTY' or p_ == '?':
nuc = True
ini, med, cod, ton = False, False, False, False
elif p == 'Y':
nuc = True
ini, med, cod, ton = 4 * [False]
else:
cod = True
ini, med, nuc, ton = 4 * [False]
# check for consonant
elif p == 'L':
cod = True
ini, med, nuc, ton = 4 * [False]
# check for tone
elif p == 'T':
ton = True
ini, med, nuc, cod = 4 * [False]
if ini:
I += t
elif med:
M += t
elif nuc:
N += t
elif cod:
C += t
else:
T += t
# bad conversion for output, but makes what it is supposed to do
out = [I, M, N, C, T]
tf = lambda x: x if x else '-'
out = [tf(x) for x in out]
# transform tones to normal letters
tones = dict(zip('¹²³⁴⁵⁶⁷⁸⁹⁰₁₂₃₄₅₆₇₈₉₀', '1234567890123456789'))
# now, if context is wanted, we'll yield that
ic = '1' if [x for x in I if x in 'bdgmnŋȵɳɴ'] else '0'
mc = '1' if [m for m in M + N if m in 'ijyɥ'] else '0'
cc = '1' if C in 'ptkʔ' else '0'
tc = ''.join([tones.get(x, x) for x in T])
IC = '/'.join(['I', ic, mc, cc, tc]) if I else ''
MC = '/'.join(['M', ic, mc, cc, tc]) if M else ''
NC = '/'.join(['N', ic, mc, cc, tc]) if N else ''
CC = '/'.join(['C', ic, mc, cc, tc]) if C else ''
TC = '/'.join(['T', ic, mc, cc, tc]) if T else ''
if context:
return out, [x for x in [IC, MC, NC, CC, TC] if x]
def to_asjp(segments):
return lingpy.tokens2class(segments, model, cldf=False)
