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
After Change
self.freq_bins = bins
features = ["%d_%d" % fb for fb in self.freq_bins]
index = [tb for tb in time_bins]values = np.zeros((len(index), len(features)))
for i, fb in enumerate(self.freq_bins):
start, stop = fb
values[:, i] = data[:, start:stop].mean(1)
values = np.nan_to_num(values)
return ExtractorResult(values, stim, self, features=features,
onsets=index, durations=self.hop_size)
