def remap(ids, source='Allen', dest='Beryl', output='acronym'):
br = BrainRegions()
_, inds = ismember(ids, br.id[br.mappings[source]])
ids = br.id[br.mappings[dest][inds]]
if output == 'id':
return br.id[br.mappings[dest][inds]]
elif output == 'acronym':
return br.get(br.id[br.mappings[dest][inds]])['acronym']
# Check if data is available for this probe
if probe not in clusters.keys():
continue
# Check if histology is available for this probe
if not hasattr(clusters[probe], 'acronym'):
continue
# Check if cluster metrics are available
if 'metrics' not in clusters[probe]:
continue
# Get list of brain regions
if ATLAS == 'beryl-atlas':
mapped_br = br.get(ids=remap(clusters[probe]['atlas_id']))
clusters_regions = mapped_br['acronym']
elif ATLAS == 'allen-atlas':
clusters_regions = combine_layers_cortex(
clusters[probe]['acronym'])
# Get list of neurons that pass QC
if INCL_NEURONS == 'pass-QC':
clusters_pass = np.where(
clusters[probe]['metrics']['label'] == 1)[0]
elif INCL_NEURONS == 'all':
clusters_pass = np.arange(clusters[probe]['metrics'].shape[0])
# Decode per brain region
for r, region in enumerate(np.unique(clusters_regions)):
'x': np.array([ch['x'] for ch in channels]) / 1e6,
'y': np.array([ch['y'] for ch in channels]) / 1e6,
'z': np.array([ch['z'] for ch in channels]) / 1e6,
'axial_um': np.array([ch['axial'] for ch in channels]),
'lateral_um': np.array([ch['lateral'] for ch in channels])
}
else:
print(
f'No histology or ephys aligned trajectory for session: {eid} and '
f'probe: {probe_label}, no channels available')
chans = None
if chans is not None:
r = BrainRegions()
chans['acronym'] = r.get(ids=chans['atlas_id']).acronym
chans['rgb'] = r.get(ids=chans['atlas_id']).rgb
cluster_brain_region = chans['acronym'][cluster_chans]
cluster_colour = chans['rgb'][cluster_chans]
cluster_xyz = np.c_[chans['x'], chans['y'], chans['z']][cluster_chans]
regions, idx, n_clust = np.unique(cluster_brain_region,
return_counts=True,
return_index=True)
region_cols = cluster_colour[idx, :]
fig, ax = plt.subplots()
ax.bar(x=np.arange(len(regions)),
height=n_clust,
tick_label=regions,
color=region_cols / 255)
ax.set_xlabel('Brain region acronym')