def _compute_correlograms(self, cluster_ids):
# Keep spikes belonging to the selected clusters.
ind = np.in1d(self.spike_clusters, cluster_ids)
st = self.spike_times[ind]
sc = self.spike_clusters[ind]
# Take excerpts of the spikes.
n_spikes_total = len(st)
st = get_excerpts(st,
excerpt_size=self.excerpt_size,
n_excerpts=self.n_excerpts)
sc = get_excerpts(sc,
excerpt_size=self.excerpt_size,
n_excerpts=self.n_excerpts)
n_spikes_exerpts = len(st)
logger.log(
5,
"Computing correlograms for clusters %s (%d/%d spikes).",
', '.join(map(str, cluster_ids)),
n_spikes_exerpts,
n_spikes_total,
)
# Compute all pairwise correlograms.
ccg = correlograms(
st,
sc,
cluster_ids=cluster_ids,
sample_rate=self.sample_rate,
bin_size=self.bin_size,
window_size=self.window_size,
)
return ccg
def _compute_correlograms(self, cluster_ids):
# Keep spikes belonging to the selected clusters.
ind = np.in1d(self.spike_clusters, cluster_ids)
st = self.spike_times[ind]
sc = self.spike_clusters[ind]
# Take excerpts of the spikes.
n_spikes_total = len(st)
st = get_excerpts(st, excerpt_size=self.excerpt_size,
n_excerpts=self.n_excerpts)
sc = get_excerpts(sc, excerpt_size=self.excerpt_size,
n_excerpts=self.n_excerpts)
n_spikes_exerpts = len(st)
logger.log(5, "Computing correlograms for clusters %s (%d/%d spikes).",
', '.join(map(str, cluster_ids)),
n_spikes_exerpts, n_spikes_total,
)
# Compute all pairwise correlograms.
ccg = correlograms(st, sc,
cluster_ids=cluster_ids,
sample_rate=self.sample_rate,
bin_size=self.bin_size,
window_size=self.window_size,
)
return ccg
def _get_correlograms(self, cluster_ids, bin_size, window_size):
spike_ids = self.selector.select_spikes(cluster_ids, 100000)
st = self.model.spike_times[spike_ids]
sc = self.supervisor.clustering.spike_clusters[spike_ids]
return correlograms(
st,
sc,
sample_rate=self.model.sample_rate,
cluster_ids=cluster_ids,
bin_size=bin_size,
window_size=window_size,
)