def execute(self):
# if no clusters to merge: do nothing
if len(self.spikes_to_split) == 0:
return
clusters = self.dh.clusters.copy()
# array with clusters to split
clusters_to_split = np.unique(self._old_clusters[self.spikes_to_split])
clusters_to_split.sort()
nclusters_to_split = len(clusters_to_split)
# create nclusters_to_split new clusters
nc = self.dh.new_cluster()
new_clusters = np.arange(nc, nc + nclusters_to_split)
# invalidate the cross correlograms of the clusters to merge
self.sdh.invalidate(clusters_to_split)
for cluster, new_cluster in zip(clusters_to_split, new_clusters):
# spikes which are in the current cluster
spikes_in_cluster = np.nonzero(self._old_clusters == cluster)[0]
# the spikes in the current cluster to be split
spikes_in_cluster_to_split = np.in1d(spikes_in_cluster, self.spikes_to_split)
spikes_in_cluster_to_split = spikes_in_cluster[spikes_in_cluster_to_split]
# assign the new cluster to the split spikes
clusters[spikes_in_cluster_to_split] = new_cluster
# record the list of newly created clusters
self.new_clusters = new_clusters
self.clusters_to_split = clusters_to_split
# get clusters info
clusters_info = get_clusters_info(clusters)
nclusters = len(clusters_info)
for clusteridx, info in clusters_info.iteritems():
# if the cluster has not been changed
if clusteridx not in new_clusters:
info['color'] = self.dh.clusters_info['clusters_info'][clusteridx]['color']
info['groupidx'] = self.dh.clusters_info['clusters_info'][clusteridx]['groupidx']
# group of new cluster = group of corresponding old cluster
for old_clusteridx, new_clusteridx in zip(clusters_to_split, new_clusters):
clusters_info[new_clusteridx]['groupidx'] = self._old_clusters_info[old_clusteridx]['groupidx']
# update
self.dh.nclusters = nclusters
self.dh.clusters = clusters
self.dh.clusters_info['clusters_info'] = clusters_info
# Update correlation matrix.
tasks.TASKS.correlation_matrix_queue.process(self.dh)
def execute(self):
# if no clusters to merge: do nothing
if len(self.clusters_to_merge) == 0:
return
# invalidate the cross correlograms of the clusters to merge
self.sdh.invalidate(self.clusters_to_merge)
# get the index of the new cluster
self.new_cluster = self.dh.new_cluster()
# copy and update the clusters array
clusters = self.dh.clusters.copy()
# update the clusters array
ind = np.in1d(clusters, self.clusters_to_merge)
# if old cluster in clusters to merge, then assign to new cluster
clusters[ind] = self.new_cluster
# get clusters info
clusters_info = get_clusters_info(clusters)
nclusters = len(clusters_info)
colors = np.zeros(nclusters, dtype=np.int32)
groups = np.zeros(nclusters, dtype=np.int32)
new_color = self.dh.clusters_info['clusters_info'][self.clusters_to_merge[0]]['color']
new_group = self.dh.clusters_info['clusters_info'][self.clusters_to_merge[0]]['groupidx']
for clusteridx, info in clusters_info.iteritems():
# if the cluster has not been changed
if clusteridx != self.new_cluster and clusteridx not in self.clusters_to_merge:
# new color = old color
info['color'] = self.dh.clusters_info['clusters_info'][clusteridx]['color']
info['groupidx'] = self.dh.clusters_info['clusters_info'][clusteridx]['groupidx']
# otherwise, set the new color
else:
info['color'] = new_color
info['groupidx'] = new_group
# update
self.dh.nclusters = nclusters
self.dh.clusters = clusters
self.dh.clusters_info['clusters_info'] = clusters_info
# Update correlation matrix.
tasks.TASKS.correlation_matrix_queue.process(self.dh)