def test_stft_extractor():
stim = AudioStim(join(AUDIO_DIR, 'barber.wav'), onset=4.2)
ext = STFTAudioExtractor(frame_size=1.,
spectrogram=False,
freq_bins=[(100, 300), (300, 3000),
(3000, 20000)])
result = ext.transform(stim)
df = result.to_df()
assert df.shape == (557, 7)
assert df['onset'][0] == 4.2
ext = STFTAudioExtractor(frame_size=1., spectrogram=False, freq_bins=5)
result = ext.transform(stim)
df = result.to_df(timing=False, object_id=False)
assert df.shape == (557, 5)
assert '0_1102' in df.columns
def test_stft_extractor():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'barber.wav'))
ext = STFTAudioExtractor(frame_size=1., spectrogram=False,
freq_bins=[(100, 300), (300, 3000), (3000, 20000)])
result = ext.transform(stim)
df = result.to_df()
assert df.shape == (557, 5)
def test_convert_to_long():
audio_dir = join(get_test_data_path(), 'audio')
stim = AudioStim(join(audio_dir, 'barber.wav'))
ext = STFTAudioExtractor(frame_size=1., spectrogram=False,
freq_bins=[(100, 300), (300, 3000), (3000, 20000)])
timeline = ext.transform(stim)
long_timeline = to_long_format(timeline)
assert long_timeline.shape == (timeline.to_df().shape[0] * 3, 4)
assert 'feature' in long_timeline.columns
assert 'value' in long_timeline.columns
assert '100_300' not in long_timeline.columns
timeline = ExtractorResult.merge_features([timeline])
long_timeline = to_long_format(timeline)
assert 'feature' in long_timeline.columns
assert 'extractor' in long_timeline.columns
assert '100_300' not in long_timeline.columns
# peep_spectrum.py from pliers.extractors import STFTAudioExtractor import matplotlib snd_0 = 'snd/peep-I-neu-chk.wav' snd_1 = 'snd/peep-I-hap-tlk.mp3' snds = [snd_0, snd_1] # result will generate spectrogram ext = STFTAudioExtractor(freq_bins=10, spectrogram=True) result = ext.transform(snds) print(result[0]) print(result[1])