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 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 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
