def apply(self, stim):
data = self._stft(stim)
events = []
time_bins = np.arange(0., stim.duration - self.frame_size,
self.hop_size)
if isinstance(self.freq_bins, int):
bins = []
bin_size = data.shape[1] / self.freq_bins
for i in range(self.freq_bins):
bins.append((i * bin_size, (i + 1) * bin_size))
self.freq_bins = bins
for i, tb in enumerate(time_bins):
ev = Event(onset=tb, duration=self.frame_size)
value_data = {}
for fb in self.freq_bins:
label = '%d_%d' % fb
start, stop = fb
val = data[i, start:stop].mean()
if np.isinf(val):
val = 0.
value_data[label] = val
ev.add_value(Value(stim, self, value_data))
events.append(ev)
return events
def apply(self, stim):
data = self._stft(stim)
events = []
time_bins = np.arange(0., stim.duration-self.frame_size, self.hop_size)
if isinstance(self.freq_bins, int):
bins = []
bin_size = data.shape[1] / self.freq_bins
for i in range(self.freq_bins):
bins.append((i*bin_size, (i+1)*bin_size))
self.freq_bins = bins
for i, tb in enumerate(time_bins):
ev = Event(onset=tb, duration=self.frame_size)
value_data = {}
for fb in self.freq_bins:
label = '%d_%d' % fb
start, stop = fb
val = data[i, start:stop].mean()
if np.isinf(val):
val = 0.
value_data[label] = val
ev.add_value(Value(stim, self, value_data))
events.append(ev)
return events
def apply(self, stim):
events = []
time_bins = np.arange(0., stim.duration, 1.)
for i, tb in enumerate(time_bins):
ev = Event(onset=tb, duration=1000)
ev.add_value(Value(stim, self, {'second': i}))
return events
def apply(self, stim):
events = []
time_bins = np.arange(0., stim.duration, 1.)
for i, tb in enumerate(time_bins):
ev = Event(onset=tb, duration=1000)
ev.add_value(Value(stim, self, {'second': i}))
return events
def apply(self, stim, show=False):
events = []
for i, f in enumerate(stim):
img = f.data
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if i == 0:
last_frame = img
total_flow = 0
flow = cv2.calcOpticalFlowFarneback(
last_frame, img, 0.5, 3, 15, 3, 5, 1.2, 0)
flow = np.sqrt((flow ** 2).sum(2))
if show:
cv2.imshow('frame', flow.astype('int8'))
cv2.waitKey(1)
last_frame = img
total_flow = flow.sum()
value = Value(stim, self, {'total_flow': total_flow})
event = Event(onset=f.onset, duration=f.duration, values=[value])
events.append(event)
return events
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 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 apply(self, stim):
amps = stim.data
sampling_rate = stim.sampling_rate
elements = stim.transcription.elements
events = []
for i, el in enumerate(elements):
onset = sampling_rate * el.onset
duration = sampling_rate * el.duration
r_onset = np.round(onset).astype(int)
r_offset = np.round(onset + duration).astype(int)
if not r_offset <= amps.shape[0]:
raise Exception('Block ends after data.')
mean_amplitude = np.mean(amps[r_onset:r_offset])
amplitude_data = {'mean_amplitude': mean_amplitude}
ev = Event(onset=onset, duration=duration)
ev.add_value(Value(stim, self, amplitude_data))
events.append(ev)
return events
def apply(self, text):
tokens = [token.text for token in text]
scores = self.model(tokens)
events = []
for i, w in enumerate(text):
if type(scores[i]) == float:
values = [Value(text, self, {self.name: scores[i]})]
elif type(scores[i]) == dict:
values = []
for k in scores[i].keys():
values.append(
Value(text, self, {self.name + '_' + k: scores[i][k]}))
# parse dictionary into list if getting dict back
event = Event(onset=w.onset, duration=w.duration, values=values)
events.append(event)
return events
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
