def extract(self, extractors, merge_events=True):
timeline = Timeline()
for ext in extractors:
events = ext.apply(self)
for ev in events:
timeline.add_event(ev, merge=merge_events)
return timeline
def extract(self, extractors, merge_events=True):
timeline = Timeline()
for ext in extractors:
events = ext.apply(self)
for ev in events:
timeline.add_event(ev, merge=merge_events)
return timeline
def extract(self, extractors):
timeline = Timeline()
audio_exts, text_exts = [], []
for ext in extractors:
if ext.target.__name__ == 'AudioStim':
audio_exts.append(ext)
elif ext.target.__name__ == 'ComplexTextStim':
text_exts.append(ext)
audio_tl = super(TranscribedAudioStim, self).extract(audio_exts)
timeline.merge(audio_tl)
text_tl = self.transcription.extract(text_exts)
timeline.merge(text_tl)
return timeline
def extract(self, extractors, merge_events=True, **kwargs):
period = 1. / self.fps
timeline = Timeline(period=period)
for ext in extractors:
# For VideoExtractors, pass the entire stim
if ext.target.__name__ == self.__class__.__name__:
events = ext.apply(self, **kwargs)
for ev in events:
timeline.add_event(ev, merge=merge_events)
# Otherwise, for images, loop over frames
else:
c = 0
for frame in self:
if frame.data is not None:
event = Event(onset=c * period)
event.add_value(ext.apply(frame))
timeline.add_event(event, merge=merge_events)
c += 1
return timeline
def test_dummy_code_timeline(self):
data = [{'A': 12.0, 'B': 'abc'}, { 'A': 7, 'B': 'def'}, { 'C': 40 }]
events = [Event(values=[Value(None, None, x)], duration=1) for x in data]
tl = Timeline(events=events, period=1)
self.assertEqual(tl.to_df().shape, (5, 4))
tl_dummy = tl.dummy_code()
self.assertEqual(tl_dummy.to_df().shape, (7, 4))
tl = Timeline(events=events, period=1)
tl_dummy = tl.dummy_code(string_only=False)
self.assertEqual(tl_dummy.to_df().shape, (9, 4))
def extract(self, extractors):
timeline = Timeline()
audio_exts, text_exts = [], []
for ext in extractors:
if ext.target.__name__ in ['AudioStim', 'TranscribedAudioStim']:
audio_exts.append(ext)
elif ext.target.__name__ == 'ComplexTextStim':
text_exts.append(ext)
audio_tl = super(TranscribedAudioStim, self).extract(audio_exts)
timeline.merge(audio_tl)
text_tl = self.transcription.extract(text_exts)
timeline.merge(text_tl)
return timeline
def extract(self, extractors, merge_events=True):
timeline = Timeline()
# Extractors can either take ComplexTextStim input, in which case we
# pass the current instance, or TextStim input, in which case we loop
# over all elements.
for ext in extractors:
if ext.target.__name__ == self.__class__.__name__:
events = ext.apply(self)
for ev in events:
timeline.add_event(ev, merge=merge_events)
else:
for elem in self.elements:
# If no onset is available, index with order
onset = elem.onset or elem.order
event = Event(onset=onset, values=[ext.apply(elem)])
timeline.add_event(event, merge=merge_events)
return timeline
def extract(self, extractors, merge_events=True, **kwargs):
period = 1. / self.fps
timeline = Timeline(period=period)
for ext in extractors:
# For VideoExtractors, pass the entire stim
if ext.target.__name__ == self.__class__.__name__:
events = ext.apply(self, **kwargs)
for ev in events:
timeline.add_event(ev, merge=merge_events)
# Otherwise, for images, loop over frames
else:
c = 0
for frame in self:
if frame.data is not None:
event = Event(onset=c * period)
event.add_value(ext.apply(frame))
timeline.add_event(event, merge=merge_events)
c += 1
return timeline