def skeleton_statistics(request, project_id=None, skeleton_id=None):
p = get_object_or_404(Project, pk=project_id)
skel = Skeleton( skeleton_id = skeleton_id, project_id = project_id )
const_time = skel.measure_construction_time()
construction_time = '{0} minutes {1} seconds'.format( const_time / 60, const_time % 60)
return HttpResponse(json.dumps({
'node_count': skel.node_count(),
'input_count': skel.input_count(),
'output_count': skel.output_count(),
'presynaptic_sites': skel.presynaptic_sites_count(),
'postsynaptic_sites': skel.postsynaptic_sites_count(),
'cable_length': int(skel.cable_length()),
'measure_construction_time': construction_time,
'percentage_reviewed': "%.2f" % skel.percentage_reviewed() }), content_type='text/json')
def skeleton_info(request, project_id=None, skeleton_id=None):
# This function can take as much as 15 seconds for a mid-sized arbor
# Problems in the generated SQL:
# 1. Many repetitions of the query: SELECT ... FROM "relation" WHERE "relation"."project_id" = 4. Originates in one call per connected skeleton, in Skeleton._fetch_upstream_skeletons and _fetch_downstream_skeletons
# 2. Usage of WHERE project_id = 4, despite IDs being unique. Everywhere.
# 3. Lots of calls to queries similar to: SELECT ... FROM "class_instance" WHERE "class_instance"."id" = 17054183
p = get_object_or_404(Project, pk=project_id)
synaptic_count_high_pass = int( request.POST.get( 'threshold', 10 ) )
skeleton = Skeleton( skeleton_id, project_id )
data = {
'incoming': {},
'outgoing': {}
}
for skeleton_id_upstream, synaptic_count in skeleton.upstream_skeletons.items():
if synaptic_count >= synaptic_count_high_pass:
tmp_skeleton = Skeleton( skeleton_id_upstream )
data['incoming'][skeleton_id_upstream] = {
'synaptic_count': synaptic_count,
'skeleton_id': skeleton_id_upstream,
'percentage_reviewed': '%i' % tmp_skeleton.percentage_reviewed(),
'node_count': tmp_skeleton.node_count(),
'name': '{0} / skeleton {1}'.format( tmp_skeleton.neuron.name, skeleton_id_upstream)
}
for skeleton_id_downstream, synaptic_count in skeleton.downstream_skeletons.items():
if synaptic_count >= synaptic_count_high_pass:
tmp_skeleton = Skeleton( skeleton_id_downstream )
data['outgoing'][skeleton_id_downstream] = {
'synaptic_count': synaptic_count,
'skeleton_id': skeleton_id_downstream,
'percentage_reviewed': '%i' % tmp_skeleton.percentage_reviewed(),
'node_count': tmp_skeleton.node_count(),
'name': '{0} / skeleton {1}'.format( tmp_skeleton.neuron.name, skeleton_id_downstream)
}
result = {
'incoming': list(reversed(sorted(data['incoming'].values(), key=itemgetter('synaptic_count')))),
'outgoing': list(reversed(sorted(data['outgoing'].values(), key=itemgetter('synaptic_count'))))
}
json_return = json.dumps(result, sort_keys=True, indent=4)
return HttpResponse(json_return, content_type='text/json')