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
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 extract_spikes(traces,
interval,
sample_rate=None,
spike_times=None,
spike_clusters=None,
cluster_groups=None,
all_masks=None,
n_samples_waveforms=None):
cluster_groups = cluster_groups or {}
sr = sample_rate
ns = n_samples_waveforms
if not isinstance(ns, tuple):
ns = (ns // 2, ns // 2)
offset_samples = ns[0]
wave_len = ns[0] + ns[1]
# Find spikes.
a, b = spike_times.searchsorted(interval)
st = spike_times[a:b]
sc = spike_clusters[a:b]
m = all_masks[a:b]
n = len(st)
assert len(sc) == n
assert m.shape[0] == n
# Extract waveforms.
spikes = []
for i in range(n):
b = Bunch()
# Find the start of the waveform in the extracted traces.
sample_start = int(round((st[i] - interval[0]) * sr))
sample_start -= offset_samples
o = _extract_wave(traces, sample_start, m[i], wave_len)
if o is None: # pragma: no cover
logger.debug("Unable to extract spike %d.", i)
continue
b.waveforms, b.channels = o
# Masks on unmasked channels.
b.masks = m[i, b.channels]
b.spike_time = st[i]
b.spike_cluster = sc[i]
b.cluster_group = cluster_groups.get(b.spike_cluster, None)
b.offset_samples = offset_samples
spikes.append(b)
return spikes
def extract_spikes(traces, interval, sample_rate=None,
spike_times=None, spike_clusters=None,
all_masks=None,
n_samples_waveforms=None):
sr = sample_rate
ns = n_samples_waveforms
if not isinstance(ns, tuple):
ns = (ns // 2, ns // 2)
offset_samples = ns[0]
wave_len = ns[0] + ns[1]
# Find spikes.
a, b = spike_times.searchsorted(interval)
st = spike_times[a:b]
sc = spike_clusters[a:b]
m = all_masks[a:b]
n = len(st)
assert len(sc) == n
assert m.shape[0] == n
# Extract waveforms.
spikes = []
for i in range(n):
b = Bunch()
# Find the start of the waveform in the extracted traces.
sample_start = int(round((st[i] - interval[0]) * sr))
sample_start -= offset_samples
o = _extract_wave(traces, sample_start, m[i], wave_len)
if o is None: # pragma: no cover
logger.debug("Unable to extract spike %d.", i)
continue
b.waveforms, b.channels = o
# Masks on unmasked channels.
b.masks = m[i, b.channels]
b.spike_time = st[i]
b.spike_cluster = sc[i]
b.offset_samples = offset_samples
spikes.append(b)
return spikes
