def test_complex_text_stim(self):
text_dir = join(_get_test_data_path(), 'text')
stim = ComplexTextStim(join(text_dir, 'complex_stim_no_header.txt'),
columns='ot', default_duration=0.2)
self.assertEquals(len(stim.elements), 4)
self.assertEquals(stim.elements[2].onset, 34)
self.assertEquals(stim.elements[2].duration, 0.2)
stim = ComplexTextStim(join(text_dir, 'complex_stim_with_header.txt'))
self.assertEquals(len(stim.elements), 4)
self.assertEquals(stim.elements[2].duration, 0.1)
def test_text_extractor(self):
stim = ComplexTextStim(join(TEXT_DIR, 'sample_text.txt'),
columns='to', default_duration=1)
td = DictionaryExtractor(join(TEXT_DIR, 'test_lexical_dictionary.txt'),
variables=['length', 'frequency'])
self.assertEquals(td.data.shape, (7, 2))
timeline = stim.extract([td])
df = TimelineExporter.timeline_to_df(timeline)
self.assertEquals(df.shape, (12, 4))
self.assertEquals(df.iloc[9, 3], 10.6)
def test_complex_stim_from_text():
textfile = join(get_test_data_path(), 'text', 'scandal.txt')
text = open(textfile).read().strip()
stim = ComplexTextStim.from_text(text)
target = ['To', 'Sherlock', 'Holmes']
assert [w.text for w in stim.elements[:3]] == target
assert len(stim.elements) == 231
stim = ComplexTextStim.from_text(text, unit='sent')
# Custom tokenizer
stim = ComplexTextStim.from_text(text, tokenizer='(\w+)')
assert len(stim.elements) == 209
def test_complex_stim_from_text(self):
textfile = join(_get_test_data_path(), 'text', 'scandal.txt')
text = open(textfile).read().strip()
stim = ComplexTextStim.from_text(text)
target = ['To', 'Sherlock', 'Holmes']
self.assertEquals([w.text for w in stim.elements[:3]], target)
self.assertEquals(len(stim.elements), 231)
stim = ComplexTextStim.from_text(text, unit='sent')
# Custom tokenizer
stim = ComplexTextStim.from_text(text, tokenizer='(\w+)')
self.assertEquals(len(stim.elements), 209)
def test_complex_stim_from_text():
textfile = join(_get_test_data_path(), 'text', 'scandal.txt')
text = open(textfile).read().strip()
stim = ComplexTextStim.from_text(text)
target = ['To', 'Sherlock', 'Holmes']
assert [w.text for w in stim.elements[:3]] == target
assert len(stim.elements) == 231
stim = ComplexTextStim.from_text(text, unit='sent')
# Custom tokenizer
stim = ComplexTextStim.from_text(text, tokenizer='(\w+)')
assert len(stim.elements) == 209
def test_complex_text_stim():
text_dir = join(_get_test_data_path(), 'text')
stim = ComplexTextStim(join(text_dir, 'complex_stim_no_header.txt'),
columns='ot',
default_duration=0.2)
assert len(stim.elements) == 4
assert stim.elements[2].onset == 34
assert stim.elements[2].duration == 0.2
stim = ComplexTextStim(join(text_dir, 'complex_stim_with_header.txt'))
assert len(stim.elements) == 4
assert stim.elements[2].duration == 0.1
def test_complex_stim_from_text(self):
textfile = join(_get_test_data_path(), 'text', 'scandal.txt')
text = open(textfile).read().strip()
stim = ComplexTextStim.from_text(text)
target = ['To', 'Sherlock', 'Holmes']
self.assertEquals([w.text for w in stim.elements[:3]], target)
self.assertEquals(len(stim.elements), 231)
stim = ComplexTextStim.from_text(text, unit='sent')
# Custom tokenizer
stim = ComplexTextStim.from_text(text, tokenizer='(\w+)')
self.assertEquals(len(stim.elements), 209)
def test_text_extractor():
stim = ComplexTextStim(join(TEXT_DIR, 'sample_text.txt'),
columns='to', default_duration=1)
td = DictionaryExtractor(join(TEXT_DIR, 'test_lexical_dictionary.txt'),
variables=['length', 'frequency'])
assert td.data.shape == (7, 2)
timeline = stim.extract([td])
df = timeline.to_df()
assert np.isnan(df.iloc[0, 3])
assert df.shape == (12, 4)
target = df.query('name=="frequency" & onset==5')['value'].values
assert target == 10.6
def test_complex_stim_from_srt():
srtfile = join(_get_test_data_path(), 'text', 'wonderful.srt')
textfile = join(_get_test_data_path(), 'text', 'wonderful.txt')
df = pd.read_csv(textfile, sep='\t')
target = df["text"].tolist()
srt_stim = ComplexTextStim(srtfile)
texts = [sent.text.decode('UTF-8') for sent in srt_stim.elements]
assert texts == target
def test_predefined_dictionary_extractor():
text = """enormous chunks of ice that have been frozen for thousands of
years are breaking apart and melting away"""
stim = ComplexTextStim.from_text(text)
td = PredefinedDictionaryExtractor(['aoa/Freq_pm', 'affect/V.Mean.Sum'])
timeline = stim.extract([td])
df = TimelineExporter.timeline_to_df(timeline)
assert df.shape == (36, 4)
valid_rows = df.query('name == "affect_V.Mean.Sum"').dropna()
assert len(valid_rows) == 3
def test_indicoAPI_extractor():
srtfile = join(_get_test_data_path(), 'text', 'wonderful.srt')
srt_stim = ComplexTextStim(srtfile)
if 'INDICO_APP_KEY' in os.environ:
ext = IndicoAPIExtractor(api_key=os.environ['INDICO_APP_KEY'],model = 'emotion')
output = ext.apply(srt_stim)
outdfKeys = set(output.to_df()['name'])
outdfKeysCheck = set([
'emotion_anger',
'emotion_fear',
'emotion_joy',
'emotion_sadness',
'emotion_surprise'])
assert outdfKeys == outdfKeysCheck
def __init__(self, filename, transcription, **kwargs):
if isinstance(transcription, six.string_types):
transcription = ComplexTextStim(transcription, **kwargs)
self.transcription = transcription
super(TranscribedAudioStim, self).__init__(filename)
def test_part_of_speech_extractor():
stim = ComplexTextStim(join(TEXT_DIR, 'complex_stim_with_header.txt'))
tl = stim.extract([PartOfSpeechExtractor()])
df = tl.to_df()
assert df.iloc[1, 3] == 'NN'
assert df.shape == (4, 4)
def test_part_of_speech_extractor(self):
stim = ComplexTextStim(join(TEXT_DIR, 'complex_stim_with_header.txt'))
tl = stim.extract([PartOfSpeechExtractor()])
df = tl.to_df()
self.assertEquals(df.iloc[1, 3], 'NN')
self.assertEquals(df.shape, (4, 4))
def test_part_of_speech_extractor(self):
stim = ComplexTextStim(join(TEXT_DIR, 'complex_stim_with_header.txt'))
tl = stim.extract([PartOfSpeechExtractor()]).to_df()
self.assertEquals(tl.iloc[1, 3], 'NN')
self.assertEquals(tl.shape, (4, 4))
def _convert(self, audio):
import speech_recognition as sr
with sr.AudioFile(audio.filename) as source:
clip = self.recognizer.record(source)
text = getattr(self.recognizer, self.recognize_method)(clip, self.api_key)
return ComplexTextStim.from_text(text=text)
