def run(
self,
signal_list,
sampling_freq_list,
event_times_list,
wavelet="haar",
num_coefficients_kept=10,
normalize=True,
**kwargs
):
# features, feature_times for each trial
features_list = []
feature_times_list = []
for signal, sampling_freq, event_times in zip(signal_list, sampling_freq_list, event_times_list):
features, feature_times, _, _ = generate_spike_windows(signal, sampling_freq, event_times, **kwargs)
features_list.append(features)
feature_times_list.append(feature_times)
# unify features before getting wavelet_coefficients
total_len = 0
for features in features_list:
total_len += len(features)
unified_features = numpy.empty((total_len,) + features_list[0].shape[1:])
begin = 0
end = 0
unpacking_index = {}
for i, features in enumerate(features_list):
end = begin + len(features)
unified_features[begin:end] = features
unpacking_index[i] = [begin, end]
begin = end
wavelet_coefficients = get_wavelet_coefficients(unified_features, wavelet, num_coefficients_kept)
if normalize:
means = numpy.average(wavelet_coefficients, axis=0)
stds = numpy.std(wavelet_coefficients, axis=0)
wavelet_coefficients = (wavelet_coefficients - means) / stds
# deunify wavelet_coefficients before returning them
wavelet_coefficients_list = []
for i in range(len(features_list)):
begin, end = unpacking_index[i]
wavelet_coefficients_list.append(wavelet_coefficients[begin:end])
return [wavelet_coefficients_list, feature_times_list]
def run(self, signal, sampling_freq, event_times, **kwargs):
return generate_spike_windows(signal, sampling_freq, event_times,
**kwargs)[:2]
