def renumber_clusters(clusters, cluster_info):
clusters_unique = get_array(get_indices(cluster_info))
nclusters = len(clusters_unique)
assert np.array_equal(clusters_unique, np.unique(clusters))
clusters_array = get_array(clusters)
groups = get_array(cluster_info['group'])
colors = get_array(cluster_info['color'])
groups_unique = np.unique(groups)
# Reorder clusters according to the group.
clusters_unique_reordered = np.hstack(
[sorted(clusters_unique[groups == group]) for group in groups_unique])
# WARNING: there's a +2 offset to avoid conflicts with the old convention
# cluster 0 = noise, cluster 1 = MUA.
clusters_renumbered = reorder(clusters_array,
clusters_unique_reordered) + 2
cluster_permutation = reorder(clusters_unique_reordered, clusters_unique)
# Reorder cluster info.
groups_reordered = groups[cluster_permutation]
colors_reordered = colors[cluster_permutation]
# Recreate new cluster info.
cluster_info_reordered = pd.DataFrame(
{
'color': colors_reordered,
'group': groups_reordered
},
dtype=np.int32,
index=(np.arange(nclusters) + 2))
return clusters_renumbered, cluster_info_reordered
def kwa_to_json(kwa_dict):
"""Convert a KWA dictionary to JSON.
cluster_colors and group_colors are pandas.Series objects."""
kwa_full = {}
kwa_full['shanks'] = []
for shank, kwa in kwa_dict['shanks'].iteritems():
cluster_colors = kwa['cluster_colors']
group_colors = kwa['group_colors']
clusters = get_indices(cluster_colors)
groups = get_indices(group_colors)
kwa_shank = dict(
clusters=[{'cluster': str(cluster), 'color': str(cluster_colors[cluster])}
for cluster in clusters],
groups_of_clusters=[{'group': str(group), 'color': str(group_colors[group])}
for group in groups],
shank_index=shank
)
kwa_full['shanks'].append(kwa_shank)
return json.dumps(kwa_full, indent=4)
def get_next_cluster(self, cluster):
cluster_groups = self.get_cluster_groups('all')
group = get_array(self.get_cluster_groups(cluster))
clusters = get_indices(cluster_groups)
cluster_groups = get_array(cluster_groups)
samegroup = (cluster_groups == group) & (clusters > cluster)
i = np.nonzero(samegroup)[0]
if len(i) > 0:
return clusters[i[0]]
else:
return cluster
def get_next_cluster(self, cluster):
cluster_groups = self.get_cluster_groups('all')
group = self.get_cluster_groups(cluster)
clusters = get_indices(cluster_groups)
cluster_groups = get_array(cluster_groups)
samegroup = (cluster_groups == group) & (clusters > cluster)
i = np.nonzero(samegroup)[0]
if len(i) > 0:
return clusters[i[0]]
else:
return cluster
def renumber_clusters(clusters, cluster_info):
clusters_unique = get_array(get_indices(cluster_info))
nclusters = len(clusters_unique)
assert np.array_equal(clusters_unique, np.unique(clusters))
clusters_array = get_array(clusters)
groups = get_array(cluster_info["group"])
colors = get_array(cluster_info["color"])
groups_unique = np.unique(groups)
# Reorder clusters according to the group.
clusters_unique_reordered = np.hstack([sorted(clusters_unique[groups == group]) for group in groups_unique])
# WARNING: there's a +2 offset to avoid conflicts with the old convention
# cluster 0 = noise, cluster 1 = MUA.
clusters_renumbered = reorder(clusters_array, clusters_unique_reordered) + 2
cluster_permutation = reorder(clusters_unique_reordered, clusters_unique)
# Reorder cluster info.
groups_reordered = groups[cluster_permutation]
colors_reordered = colors[cluster_permutation]
# Recreate new cluster info.
cluster_info_reordered = pd.DataFrame(
{"color": colors_reordered, "group": groups_reordered}, dtype=np.int32, index=(np.arange(nclusters) + 2)
)
return clusters_renumbered, cluster_info_reordered
def get_new_group(self):
groups = get_indices(self.group_names).values
if len(groups) > 0:
return groups.max() + 1
else:
return 0
def get_spikes(self, clusters=None):
if clusters is None:
clusters = self.clusters_selected
return get_indices(self.get_clusters(clusters=clusters))
def get_new_group(self):
groups = get_indices(self.group_names).values
if len(groups) > 0:
return groups.max() + 1
else:
return 0
def get_spikes(self, clusters=None):
if clusters is None:
clusters = self.clusters_selected
return get_indices(self.get_clusters(clusters=clusters))
def save(self, renumber=False):
# Report progress.
self.report_progress_save(1, 6)
self.update_cluster_info()
self.update_group_info()
# Renumber internal variables, knowing that in this case the file
# will be automatically reloaded right afterwards.
if renumber:
self.renumber()
self.clusters = self.clusters_renumbered
self.cluster_info = self.cluster_info_renumbered
self._update_data()
# Update the changes in the HDF5 tables.
self.spike_table.cols.cluster_manual[:] = get_array(self.clusters)
# Report progress.
self.report_progress_save(2, 6)
# Update the clusters table.
# --------------------------
# Add/remove rows to match the new number of clusters.
self._update_table_size(self.clusters_table,
len(self.get_clusters_unique()))
self.clusters_table.cols.cluster[:] = self.get_clusters_unique()
self.clusters_table.cols.group[:] = np.array(self.cluster_info['group'])
# Report progress.
self.report_progress_save(3, 6)
# Update the group table.
# -----------------------
# Add/remove rows to match the new number of clusters.
groups = get_array(get_indices(self.group_info))
self._update_table_size(
self.groups_table,
len(groups), )
self.groups_table.cols.group[:] = groups
self.groups_table.cols.name[:] = list(self.group_info['name'])
# Commit the changes on disk.
self.kwik.flush()
# Report progress.
self.report_progress_save(4, 6)
# Save the CLU file.
# ------------------
save_clusters(self.filename_clu,
convert_to_clu(self.clusters, self.cluster_info['group']))
# Report progress.
self.report_progress_save(5, 6)
# Update the KWA file.
# --------------------
kwa={}
kwa['shanks'] = {
shank: dict(
cluster_colors=self.cluster_info['color'],
group_colors=self.group_info['color'],
) for shank in self.shanks
}
write_kwa(self.filename_kwa, kwa)
# Report progress.
self.report_progress_save(6, 6)
