def text_test_dir(text_test_dir):
transcription_path = os.path.join(text_test_dir, 'test_text_transcription.txt')
with pytest.raises(DelimiterError):
load_discourse_transcription('test',
transcription_path," ",[],
trans_delimiter = ',')
c = load_discourse_transcription('test',transcription_path)
assert(sorted(c.lexicon.inventory) == sorted(['#','a','b','c','d']))
def text_test_dir(text_test_dir):
transcription_path = os.path.join(text_test_dir,
'test_text_transcription.txt')
with pytest.raises(DelimiterError):
load_discourse_transcription('test',
transcription_path,
" ", [],
trans_delimiter=',')
c = load_discourse_transcription('test', transcription_path)
assert (sorted(c.lexicon.inventory) == sorted(['#', 'a', 'b', 'c', 'd']))
def test_load_transcription_morpheme(text_test_dir):
transcription_morphemes_path = os.path.join(text_test_dir, 'test_text_transcription_morpheme_boundaries.txt')
ats = inspect_discourse_transcription(transcription_morphemes_path)
ats[0].morph_delimiters = set('-=')
c = load_discourse_transcription('test',transcription_morphemes_path, ats)
assert(c.lexicon['cab'].frequency == 2)
assert(str(c.lexicon['cab'].transcription) == 'c.a-b')
def test_load_transcription_morpheme(text_test_dir):
transcription_morphemes_path = os.path.join(
text_test_dir, 'test_text_transcription_morpheme_boundaries.txt')
ats = inspect_discourse_transcription(transcription_morphemes_path)
ats[0].morph_delimiters = set('-=')
c = load_discourse_transcription('test', transcription_morphemes_path, ats)
assert (c.lexicon['cab'].frequency == 2)
assert (str(c.lexicon['cab'].transcription) == 'c.a-b')
def test_export_transcription(export_test_dir, unspecified_test_corpus):
d = generate_discourse(unspecified_test_corpus)
export_path = os.path.join(export_test_dir, 'test_export_transcription.txt')
export_discourse_transcription(d, export_path, single_line = False)
d2 = load_discourse_transcription('test', export_path)
words = sorted([x for x in unspecified_test_corpus], key = lambda x: x.transcription)
words2 = sorted([x for x in d2.lexicon], key = lambda x: x.transcription)
for i,w in enumerate(words):
w2 = words2[i]
assert(w.transcription == w2.transcription)
assert(w.frequency == w2.frequency)
def run(self):
time.sleep(0.1)
textType = self.kwargs.pop('text_type')
isDirectory = self.kwargs.pop('isDirectory')
logging.info('Importing {} corpus named {}'.format(
textType, self.kwargs['corpus_name']))
logging.info('Path: '.format(self.kwargs['path']))
log_annotation_types(self.kwargs['annotation_types'])
try:
if textType == 'spelling':
if isDirectory:
corpus = load_directory_spelling(**self.kwargs)
else:
corpus = load_discourse_spelling(**self.kwargs)
elif textType == 'transcription':
if isDirectory:
corpus = load_directory_transcription(**self.kwargs)
else:
corpus = load_discourse_transcription(**self.kwargs)
elif textType == 'ilg':
if isDirectory:
corpus = load_directory_ilg(**self.kwargs)
else:
corpus = load_discourse_ilg(**self.kwargs)
elif textType == 'textgrid':
if isDirectory:
corpus = load_directory_textgrid(**self.kwargs)
else:
corpus = load_discourse_textgrid(**self.kwargs)
elif textType == 'csv':
corpus = load_corpus_csv(**self.kwargs)
elif textType in ['buckeye', 'timit']:
self.kwargs['dialect'] = textType
if isDirectory:
corpus = load_directory_multiple_files(**self.kwargs)
else:
corpus = load_discourse_multiple_files(**self.kwargs)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
time.sleep(0.1)
self.finishedCancelling.emit()
return
self.dataReady.emit(corpus)
def run(self):
time.sleep(0.1)
textType = self.kwargs.pop('text_type')
isDirectory = self.kwargs.pop('isDirectory')
logging.info('Importing {} corpus named {}'.format(textType, self.kwargs['corpus_name']))
logging.info('Path: '.format(self.kwargs['path']))
log_annotation_types(self.kwargs['annotation_types'])
try:
if textType == 'spelling':
if isDirectory:
corpus = load_directory_spelling(**self.kwargs)
else:
corpus = load_discourse_spelling(**self.kwargs)
elif textType == 'transcription':
if isDirectory:
corpus = load_directory_transcription(**self.kwargs)
else:
corpus = load_discourse_transcription(**self.kwargs)
elif textType == 'ilg':
if isDirectory:
corpus = load_directory_ilg(**self.kwargs)
else:
corpus = load_discourse_ilg(**self.kwargs)
elif textType == 'textgrid':
if isDirectory:
corpus = load_directory_textgrid(**self.kwargs)
else:
corpus = load_discourse_textgrid(**self.kwargs)
elif textType == 'csv':
corpus = load_corpus_csv(**self.kwargs)
elif textType in ['buckeye', 'timit']:
self.kwargs['dialect'] = textType
if isDirectory:
corpus = load_directory_multiple_files(**self.kwargs)
else:
corpus = load_discourse_multiple_files(**self.kwargs)
except PCTError as e:
self.errorEncountered.emit(e)
return
except Exception as e:
e = PCTPythonError(e)
self.errorEncountered.emit(e)
return
if self.stopped:
time.sleep(0.1)
self.finishedCancelling.emit()
return
self.dataReady.emit(corpus)
def test_export_transcription(export_test_dir, unspecified_test_corpus):
d = generate_discourse(unspecified_test_corpus)
export_path = os.path.join(export_test_dir,
'test_export_transcription.txt')
export_discourse_transcription(d, export_path, single_line=False)
d2 = load_discourse_transcription('test', export_path)
words = sorted([x for x in unspecified_test_corpus],
key=lambda x: x.transcription)
words2 = sorted([x for x in d2.lexicon], key=lambda x: x.transcription)
for i, w in enumerate(words):
w2 = words2[i]
assert (w.transcription == w2.transcription)
assert (w.frequency == w2.frequency)
