def review_status(request, project_id=None):
""" Return the review status for each skeleton in the request
as a value between 0 and 100 (integers). """
skeleton_ids = set(int(v) for k,v in request.POST.iteritems() if k.startswith('skeleton_ids['))
user_ids = set(int(v) for k,v in request.POST.iteritems() if k.startswith('user_ids['))
status = get_review_status(skeleton_ids, user_ids)
return HttpResponse(json.dumps(status))
def _evaluate(project_id, user_id, start_date, end_date, max_gap, min_nodes):
# Obtain neurons that are fully reviewed at the moment
# and to which the user contributed nodes within the date range.
# 1. Find out skeleton_ids towards which the user contributed
# within the date range
ts = Treenode.objects.filter(
user_id=user_id,
creation_time__range = (start_date, end_date)) \
.values_list('skeleton') \
.annotate(Count('skeleton'))
# Pick only skeletons for which the user contributed at least min_nodes
skeleton_ids = set(skid for skid, count in ts if count > min_nodes)
if not skeleton_ids:
return None
# Find the subset of fully reviewed (union without evaluated user) skeletons
review_status = get_review_status(skeleton_ids)
not_fully_reviewed = set()
for skid, status in review_status.iteritems():
if status != 100:
not_fully_reviewed.add(skid)
skeleton_ids = skeleton_ids - not_fully_reviewed
if not skeleton_ids:
return None
# Get review information and organize it by skeleton ID and treenode ID
reviews = defaultdict(lambda: defaultdict(list))
for r in Review.objects.filter(skeleton_id__in=skeleton_ids):
reviews[r.skeleton_id][r.treenode_id].append(r)
# Sort all reviews of all treenodes by review time, most recent first
for skid, tid_to_rs in reviews.iteritems():
for tid, rs in tid_to_rs.iteritems():
rs.sort(key=lambda r: r.review_time)
rs.reverse()
relations = dict(Relation.objects.filter(project_id=project_id, relation_name__in=['presynaptic_to', 'postsynaptic_to']).values_list('relation_name', 'id'))
# 2. Load each fully reviewed skeleton one at a time
evaluations = {skid: _evaluate_arbor(user_id, skid, tree, reviews[skid], relations, max_gap) \
for skid, tree in lazy_load_trees(skeleton_ids, ('location', 'creation_time', 'user_id', 'editor_id', 'edition_time'))}
# 3. Extract evaluations for the user_id over time
# Each evaluation contains an instance of EpochOps namedtuple, with members:
# 'review_date_range', 'creation_date_range', 'user_node_counts',
# 'splits', 'merges', 'appended', 'node_count'
# The X axis is the last (user) creation date within the review epoch
# The Y axis is multiple, and includes:
# * skeleton_id
# * reviewer_id
# * time of the last node created by the user_id in skeleton_id
# * nodes contributed by the user that were reviewed within the epoch
# * number of nodes missed by the user (which were added by the reviewer)
# * splits onto the user's nodes
# * merges onto the user's nodes
# * additions by the reviewer onto nodes of this user (another form of merges)
# * total number of presynaptic relations of skeleton_id
# * total number of postsynaptic relations of skeleton_id
# * number of presynaptic_to relations created by the reviewer within the review period onto treenodes created by user_id
# * number of postsynaptic_to relations created by the reviewer within the review period onto treenodes created by user_id
# * newer_synapses: number of synapses created by someone else onto treenodes created by user_id, after the creation of the treenode
d = []
for skid, arbor_epoch_ops in evaluations.iteritems():
for epoch_ops in arbor_epoch_ops:
if 0 == epoch_ops.user_node_counts[user_id]:
# user did not contribute at all to this chunk
continue
appended = epoch_ops.appended[user_id]
print appended
d.append({'skeleton_id': skid,
'reviewer_id': epoch_ops.reviewer_id,
'timepoint': epoch_ops.creation_date_range[user_id]['end'].strftime('%Y-%m-%d'),
'n_created_nodes': epoch_ops.user_node_counts[user_id],
'n_nodes': epoch_ops.node_count,
'n_missed_nodes': sum(appended),
'n_splits': epoch_ops.splits[user_id],
'n_merges': epoch_ops.merges[user_id] + len(appended),
'n_pre': epoch_ops.n_pre,
'n_post': epoch_ops.n_post,
'reviewer_n_pre': epoch_ops.reviewer_n_pre.get(user_id, 0),
'reviewer_n_post': epoch_ops.reviewer_n_post.get(user_id, 0),
'newer_pre': epoch_ops.newer_pre_count.get(user_id, 0),
'newer_post': epoch_ops.newer_post_count.get(user_id, 0)})
return d
def _evaluate(project_id, user_id, start_date, end_date, max_gap,
min_nodes) -> Optional[List[Dict]]:
# Obtain neurons that are fully reviewed at the moment
# and to which the user contributed nodes within the date range.
# 1. Find out skeleton_ids towards which the user contributed
# within the date range
ts = Treenode.objects.filter(
user_id=user_id,
creation_time__range = (start_date, end_date)) \
.values_list('skeleton') \
.annotate(Count('skeleton'))
# Pick only skeletons for which the user contributed at least min_nodes
skeleton_ids = set(skid for skid, count in ts if count > min_nodes)
if not skeleton_ids:
return None
# Find the subset of fully reviewed (union without evaluated user) skeletons
review_status = get_review_status(skeleton_ids)
not_fully_reviewed = set()
for skid, status in review_status.items():
if status[0] != status[1]:
not_fully_reviewed.add(skid)
skeleton_ids = skeleton_ids - not_fully_reviewed
if not skeleton_ids:
return None
# Get review information and organize it by skeleton ID and treenode ID
reviews = defaultdict(lambda: defaultdict(list)) # type: DefaultDict
for r in Review.objects.filter(skeleton_id__in=skeleton_ids):
reviews[r.skeleton_id][r.treenode_id].append(r)
# Sort all reviews of all treenodes by review time, most recent first
for skid, tid_to_rs in reviews.items():
for tid, rs in tid_to_rs.items():
rs.sort(key=lambda r: r.review_time)
rs.reverse()
relations = dict(
Relation.objects.filter(project_id=project_id,
relation_name__in=[
'presynaptic_to', 'postsynaptic_to'
]).values_list('relation_name', 'id'))
# 2. Load each fully reviewed skeleton one at a time
evaluations = {skid: _evaluate_arbor(user_id, skid, tree, reviews[skid], relations, max_gap) \
for skid, tree in lazy_load_trees(skeleton_ids, ('location_x', 'location_y', 'location_z', \
'creation_time', 'user_id', 'editor_id', \
'edition_time'))}
# 3. Extract evaluations for the user_id over time
# Each evaluation contains an instance of EpochOps namedtuple, with members:
# 'review_date_range', 'creation_date_range', 'user_node_counts',
# 'splits', 'merges', 'appended', 'node_count'
# The X axis is the last (user) creation date within the review epoch
# The Y axis is multiple, and includes:
# * skeleton_id
# * reviewer_id
# * time of the last node created by the user_id in skeleton_id
# * nodes contributed by the user that were reviewed within the epoch
# * number of nodes missed by the user (which were added by the reviewer)
# * splits onto the user's nodes
# * merges onto the user's nodes
# * additions by the reviewer onto nodes of this user (another form of merges)
# * total number of presynaptic relations of skeleton_id
# * total number of postsynaptic relations of skeleton_id
# * number of presynaptic_to relations created by the reviewer within the review period onto treenodes created by user_id
# * number of postsynaptic_to relations created by the reviewer within the review period onto treenodes created by user_id
# * newer_synapses: number of synapses created by someone else onto treenodes created by user_id, after the creation of the treenode
d = []
for skid, arbor_epoch_ops in evaluations.items():
for epoch_ops in arbor_epoch_ops:
if 0 == epoch_ops.user_node_counts[user_id]:
# user did not contribute at all to this chunk
continue
appended = epoch_ops.appended[user_id]
d.append({
'skeleton_id':
skid,
'reviewer_id':
epoch_ops.reviewer_id,
'timepoint':
epoch_ops.creation_date_range[user_id]['end'].strftime(
'%Y-%m-%d'),
'n_created_nodes':
epoch_ops.user_node_counts[user_id],
'n_nodes':
epoch_ops.node_count,
'n_missed_nodes':
sum(appended),
'n_splits':
epoch_ops.splits[user_id],
'n_merges':
epoch_ops.merges[user_id] + len(appended),
'n_pre':
epoch_ops.n_pre,
'n_post':
epoch_ops.n_post,
'reviewer_n_pre':
epoch_ops.reviewer_n_pre.get(user_id, 0),
'reviewer_n_post':
epoch_ops.reviewer_n_post.get(user_id, 0),
'newer_pre':
epoch_ops.newer_pre_count.get(user_id, 0),
'newer_post':
epoch_ops.newer_post_count.get(user_id, 0)
})
return d