def get_background_features(self):
k = max(1, int(self.n_spikes // self.n_spikes_background_features))
spike_ids = slice(None, None, k)
b = Bunch()
b.data = self.all_features[spike_ids]
m = self.get_feature_lim()
b.data = _normalize(b.data.copy(), -m, +m)
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
def get_features(self, cluster_id, load_all=False):
# Overriden to take into account the sparse structure.
# Only keep spikes belonging to the features spike ids.
if self.features_spike_ids is not None:
# All spikes
spike_ids = self._select_spikes(cluster_id)
spike_ids = np.intersect1d(spike_ids, self.features_spike_ids)
# Relative indices of the spikes in the self.features_spike_ids
# array, necessary to load features from all_features which only
# contains the subset of the spikes.
spike_ids_rel = _index_of(spike_ids, self.features_spike_ids)
else:
spike_ids = self._select_spikes(cluster_id,
self.n_spikes_features
if not load_all else None)
spike_ids_rel = spike_ids
st = self.spike_templates[spike_ids]
nc = self.n_channels
nfpc = self.n_features_per_channel
ns = len(spike_ids)
f = _densify(spike_ids_rel, self.all_features,
self.features_ind[st, :], self.n_channels)
f = np.transpose(f, (0, 2, 1))
assert f.shape == (ns, nc, nfpc)
b = Bunch()
# Normalize features.
m = self.get_feature_lim()
f = _normalize(f, -m, m)
b.data = f
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
def get_features(self, cluster_id, load_all=False):
# Overriden to take into account the sparse structure.
# Only keep spikes belonging to the features spike ids.
if self.features_spike_ids is not None:
# All spikes
spike_ids = self._select_spikes(cluster_id)
spike_ids = np.intersect1d(spike_ids, self.features_spike_ids)
# Relative indices of the spikes in the self.features_spike_ids
# array, necessary to load features from all_features which only
# contains the subset of the spikes.
spike_ids_rel = _index_of(spike_ids, self.features_spike_ids)
else:
spike_ids = self._select_spikes(
cluster_id, self.n_spikes_features if not load_all else None)
spike_ids_rel = spike_ids
st = self.spike_templates[spike_ids]
nc = self.n_channels
nfpc = self.n_features_per_channel
ns = len(spike_ids)
f = _densify(spike_ids_rel, self.all_features,
self.features_ind[st, :], self.n_channels)
f = np.transpose(f, (0, 2, 1))
assert f.shape == (ns, nc, nfpc)
b = Bunch()
# Normalize features.
m = self.get_feature_lim()
f = _normalize(f, -m, m)
b.data = f
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
def _select_data(self, cluster_id, arr, n_max=None):
spike_ids = self._select_spikes(cluster_id, n_max)
b = Bunch()
b.data = arr[spike_ids]
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
def _select_data(self, cluster_id, arr, n_max=None):
spike_ids = self._select_spikes(cluster_id, n_max)
b = Bunch()
b.data = arr[spike_ids]
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
def get_background_features(self):
k = max(1, int(self.n_spikes // self.n_spikes_background_features))
spike_ids = slice(None, None, k)
b = Bunch()
b.data = self.all_features[spike_ids]
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
def get_background_features(self):
k = max(1, int(self.n_spikes // self.n_spikes_background_features))
spike_ids = slice(None, None, k)
b = Bunch()
b.data = self.all_features[spike_ids]
b.spike_ids = spike_ids
b.spike_clusters = self.spike_clusters[spike_ids]
b.masks = self.all_masks[spike_ids]
return b
