def get_waveforms_amplitude(self, cluster_id):
nt = self.get_cluster_templates(cluster_id)
# Find the template with the highest number of spikes belonging
# to the selected cluster.
template_id = np.argmax(nt)
# Find the masked template waveform amplitude.
mw = self.templates[[template_id]]
mm = get_masks(mw)
mw = mw[0, ...]
mm = mm[0, ...]
assert mw.ndim == 2
assert mw.ndim == 2
return get_waveform_amplitude(mm, mw)
def get_waveforms_amplitude(self, cluster_id):
nt = self.get_cluster_templates(cluster_id)
# Find the template with the highest number of spikes belonging
# to the selected cluster.
template_id = np.argmax(nt)
# Find the masked template waveform amplitude.
mw = self.templates[[template_id]]
mm = self.template_masks[[template_id]]
mw = mw[0, ...]
mm = mm[0, ...]
assert mw.ndim == 2
assert mw.ndim == 2
return get_waveform_amplitude(mm, mw)
def _get_waveforms(self, cluster_id):
"""Return a selection of waveforms for a cluster."""
pos = self.model.channel_positions
spike_ids = self.selector.select_spikes(
[cluster_id],
self.n_spikes_waveforms,
self.batch_size_waveforms,
)
data = self.model.all_waveforms[spike_ids]
mm = self._get_mean_masks(cluster_id)
mw = np.mean(data, axis=0)
amp = get_waveform_amplitude(mm, mw)
masks = self._get_masks(cluster_id)
# Find the best channels.
channel_ids = np.argsort(amp)[::-1]
return Bunch(
data=data[..., channel_ids],
channel_ids=channel_ids,
channel_positions=pos[channel_ids],
masks=masks[:, channel_ids],
)
def get_waveforms_amplitude(self, cluster_id):
mm = self.get_mean_masks(cluster_id)
mw = self.get_mean_waveforms(cluster_id)
assert mw.ndim == 2
return get_waveform_amplitude(mm, mw)
def get_waveforms_amplitude(self, cluster_id):
mm = self.get_mean_masks(cluster_id)
mw = self.get_mean_waveforms(cluster_id)
assert mw.ndim == 2
return get_waveform_amplitude(mm, mw)