def test_merge_extractor_results_by_features():
np.random.seed(100)
image_dir = join(get_test_data_path(), 'image')
stim = ImageStim(join(image_dir, 'apple.jpg'))
# Merge results for static Stims (no onsets)
extractors = [BrightnessExtractor(), VibranceExtractor()]
results = [e.extract(stim) for e in extractors]
df = ExtractorResult.merge_features(results)
de = DummyExtractor()
de_names = ['Extractor1', 'Extractor2', 'Extractor3']
results = [de.extract(stim, name) for name in de_names]
df = ExtractorResult.merge_features(results)
assert df.shape == (177, 10)
assert df.columns.levels[1].unique().tolist() == ['duration', 0, 1, 2, '']
assert df.columns.levels[0].unique().tolist() == de_names + ['onset', 'stim']
def test_merge_extractor_results_by_stims():
image_dir = join(get_test_data_path(), 'image')
stim1 = ImageStim(join(image_dir, 'apple.jpg'))
stim2 = ImageStim(join(image_dir, 'obama.jpg'))
de = DummyExtractor()
results = [de.extract(stim1), de.extract(stim2)]
df = ExtractorResult.merge_stims(results)
assert df.shape == (200, 5)
assert df.columns.tolist() == ['onset', 'duration', 0, 1, 2]
assert set(df.index.levels[1].unique()) == set(['obama.jpg', 'apple.jpg'])
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,
bins=[(100, 300), (300, 3000), (3000, 20000)])
timeline = ext.extract(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
