def parse_mtree(self):
if self.lang != 'en':
raise Exception(
"MetricalTree parsing only works currently for English text.")
import metricaltree as mtree
mtree.set_paths(self.dir_mtree)
wordtoks = self.wordtokens()
toks = [wtok.token for wtok in wordtoks]
pauses = mtree.pause_splitter_tokens(toks)
#sents = [sent for pause in pauses for sent in pause]
sents = []
for pause in pauses:
sents.extend(mtree.split_sentences_from_tokens(pause))
parser = mtree.return_parser(self.dir_mtree)
trees = list(parser.lex_parse_sents(sents, verbose=False))
stats = parser.get_stats(trees, arto=True, format_pandas=False)
assert len(stats) == len(wordtoks)
sents = []
sent = []
sent_id = None
for wTok, wStat in zip(wordtoks, stats):
if sent_id != wStat['sidx']:
sent_id = wStat['sidx']
if sent: sents += [sent]
sent = []
sent += [wTok]
#for k,v in wStat.items():
# setattr(wTok,k,v)
if not hasattr(wTok, 'feats'): wTok.feats = {}
for k, v in list(wStat.items()):
if k in mtree.INFO_DO_NOT_STORE: continue
wTok.feats[k] = v
if sent: sents += [sent]
assert len(sents) == len(trees)
from Sentence import Sentence
for sent, tree in zip(sents, trees):
sentobj = Sentence(sent, tree)
self._sentences += [sentobj]
# create a normalized stress per line
import numpy as np
for line in self.lines():
wtoks = line.children
# norm mean
stresses = [
wtok.feats['norm_mean'] for wtok in wtoks
if not np.isnan(wtok.feats['norm_mean'])
]
max_stress = float(max(stresses))
min_stress = float(min(stresses))
for wtok in wtoks:
wtok.feats['norm_mean_line'] = (
wtok.feats['norm_mean'] - min_stress) / (
max_stress - min_stress) if max_stress else np.nan
# mean
stresses = [
wtok.feats['mean'] for wtok in wtoks
if not np.isnan(wtok.feats['mean'])
]
min_stress = float(min(stresses))
diff = 1.0 - min_stress
for wtok in wtoks:
wtok.feats['mean_line'] = wtok.feats['mean'] + diff
def parse_mtree(self):
if self.lang!='en': raise Exception("MetricalTree parsing only works currently for English text.")
import metricaltree as mtree
mtree.set_paths(self.dir_mtree)
wordtoks = self.wordtokens()
toks = [wtok.token for wtok in wordtoks]
pauses = mtree.pause_splitter_tokens(toks)
#sents = [sent for pause in pauses for sent in pause]
sents=[]
for pause in pauses:
sents.extend(mtree.split_sentences_from_tokens(pause))
parser = mtree.return_parser(self.dir_mtree)
trees = list(parser.lex_parse_sents(sents, verbose=False))
stats = parser.get_stats(trees,arto=True,format_pandas=False)
assert len(stats)==len(wordtoks)
sents = []
sent = []
sent_id=None
for wTok,wStat in zip(wordtoks,stats):
if sent_id!=wStat['sidx']:
sent_id=wStat['sidx']
if sent: sents+=[sent]
sent=[]
sent+=[wTok]
#for k,v in wStat.items():
# setattr(wTok,k,v)
if not hasattr(wTok,'feats'): wTok.feats={}
for k,v in wStat.items():
if k in mtree.INFO_DO_NOT_STORE: continue
wTok.feats[k]=v
if sent: sents+=[sent]
assert len(sents) == len(trees)
from Sentence import Sentence
for sent,tree in zip(sents,trees):
sentobj = Sentence(sent, tree)
self._sentences+=[sentobj]
# create a normalized stress per line
import numpy as np
for line in self.lines():
wtoks = line.children
# norm mean
stresses = [wtok.feats['norm_mean'] for wtok in wtoks if not np.isnan(wtok.feats['norm_mean'])]
max_stress = float(max(stresses))
min_stress = float(min(stresses))
for wtok in wtoks:
wtok.feats['norm_mean_line']=(wtok.feats['norm_mean']-min_stress)/(max_stress-min_stress) if max_stress else np.nan
# mean
stresses = [wtok.feats['mean'] for wtok in wtoks if not np.isnan(wtok.feats['mean'])]
min_stress = float(min(stresses))
diff = 1.0 - min_stress
for wtok in wtoks:
wtok.feats['mean_line']=wtok.feats['mean'] + diff
