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skeletonexport.py
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skeletonexport.py
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# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import json
import logging
import networkx as nx
import pytz
import six
from functools import partial
from collections import defaultdict, deque
from math import sqrt
from datetime import datetime
from django.core.serializers.json import DjangoJSONEncoder
from django.db import connection
from django.http import HttpResponse, JsonResponse
from rest_framework.decorators import api_view
from catmaid.models import UserRole, ClassInstance, Treenode, \
TreenodeClassInstance, ConnectorClassInstance, Review
from catmaid.control import export_NeuroML_Level3
from catmaid.control.authentication import requires_user_role
from catmaid.control.common import get_relation_to_id_map
from catmaid.control.review import get_treenodes_to_reviews, \
get_treenodes_to_reviews_with_time
from psycopg2.extras import DateTimeTZRange
from catmaid.control.tree_util import edge_count_to_root, partition
# Python 2 and 3 compatible map iterator
from six.moves import map
try:
from exportneuroml import neuroml_single_cell, neuroml_network
except ImportError:
logging.getLogger(__name__).warn("NeuroML module could not be loaded.")
def default(obj):
"""Default JSON serializer."""
if isinstance(obj, DateTimeTZRange):
l_bound = "[" if obj.lower_inc else "("
u_bound = "]" if obj.upper_inc else ")"
return "{}{},{}{}".format(l_bound, obj.lower, obj.upper, u_bound)
elif isinstance(obj, datetime):
return str(obj)
raise TypeError('Not sure how to serialize object of type %s: %s' % (type(obj), obj,))
def get_treenodes_qs(project_id=None, skeleton_id=None, with_labels=True):
treenode_qs = Treenode.objects.filter(skeleton_id=skeleton_id)
if with_labels:
labels_qs = TreenodeClassInstance.objects.filter(
relation__relation_name='labeled_as',
treenode__skeleton_id=skeleton_id).select_related('treenode', 'class_instance')
labelconnector_qs = ConnectorClassInstance.objects.filter(
relation__relation_name='labeled_as',
connector__treenodeconnector__treenode__skeleton_id=skeleton_id).select_related('connector', 'class_instance')
else:
labels_qs = []
labelconnector_qs = []
return treenode_qs, labels_qs, labelconnector_qs
def get_swc_string(treenodes_qs, linearize_ids=False):
all_rows = []
for tn in treenodes_qs:
swc_row = [tn.id]
swc_row.append(0)
swc_row.append(tn.location_x)
swc_row.append(tn.location_y)
swc_row.append(tn.location_z)
swc_row.append(max(tn.radius, 0))
swc_row.append(-1 if tn.parent_id is None else tn.parent_id)
all_rows.append(swc_row)
if linearize_ids:
# Find successors for each node
successors = defaultdict(list)
root = None
for tn in all_rows:
node, parent = tn[0], tn[6]
if parent == -1:
root = node
else:
successors[parent].append(node)
# Map each node to a new incremental ID
id_map = dict()
working_set = deque([root])
count = 1
while working_set:
node = working_set.popleft()
id_map[node] = count
count += 1
working_set.extend(successors[node])
# Replace each original ID with the mapped ID
for tn in all_rows:
tn[0] = id_map[tn[0]]
tn[6] = id_map[tn[6]] if tn[6] != -1 else -1
# Sort based on node ID
all_rows.sort(key=lambda tn: tn[0])
result = ""
for row in all_rows:
result += " ".join(map(str, row)) + "\n"
return result
def export_skeleton_response(request, project_id=None, skeleton_id=None, format=None):
treenode_qs, labels_qs, labelconnector_qs = get_treenodes_qs(project_id, skeleton_id)
# Make sure we export in consistent order
treenode_qs = treenode_qs.order_by('id')
if format == 'swc':
linearize_ids = request.GET.get('linearize_ids', 'false') == 'true'
return HttpResponse(get_swc_string(treenode_qs, linearize_ids), content_type='text/plain')
elif format == 'json':
return JsonResponse(treenode_qs)
else:
raise Exception("Unknown format ('%s') in export_skeleton_response" % (format,))
@api_view(['GET'])
@requires_user_role(UserRole.Browse)
def compact_skeleton_detail(request, project_id=None, skeleton_id=None):
"""Get a compact treenode representation of a skeleton, optionally with the
history of individual nodes and connectors.
Returns, in JSON, [[nodes], [connectors], {nodeID: [tags]}], with
connectors and tags being empty when 0 == with_connectors and 0 ==
with_tags, respectively.
Each element in the [nodes] array has the following form:
[id, parent_id, user_id, location_x, location_y, location_z, radius, confidence].
Each element in the [connectors] array has the following form, with the
third element representing the connector link as 0 = presynaptic, 1 =
postsynaptic, 2 = gap junction, -1 = other:
[treenode_id, connector_id, 0|1|2|-1, location_x, location_y, location_z]
If history data is requested, each row contains a validity interval. Note
that for the live table entry (the currently valid version), there are
special semantics for this interval: The upper bound is older than or the
same as the lower bound. This is done to encode the information of this row
being the most recent version and including the original creation time at
the same time, plus it requires less queries on the back-end to retireve
data. This requires the client to do slightly more work, but unfortunately
the original creation time is needed for data that was created without
history tables enabled.
---
parameters:
- name: with_connectors
description: |
Whether linked connectors should be returned.
required: false
type: boolean
defaultValue: "false"
paramType: form
- name: with_tags
description: |
Whether tags should be returned.
required: false
type: boolean
defaultValue: "false"
paramType: form
- name: with_history
description: |
Whether history information should be returned for each treenode and connector.
required: false
type: boolean
defaultValue: "false"
paramType: form
- name: with_merge_history
description: |
Whether the history of arbors merged into the requested skeleton should be returned. Only used if history is returned.
required: false
type: boolean
defaultValue: "false"
paramType: form
type:
- type: array
items:
type: string
required: true
"""
# Sanitize
project_id = int(project_id)
skeleton_id = int(skeleton_id)
with_connectors = request.GET.get("with_connectors", "false") == "true"
with_tags = request.GET.get("with_tags", "false")
with_history = request.GET.get("with_history", "false") == "true"
with_merge_history = request.GET.get("with_merge_history", "false") == "true"
result = _compact_skeleton(project_id, skeleton_id, with_connectors,
with_tags, with_history, with_merge_history)
return JsonResponse(result, safe=False,
json_dumps_params={
'separators': (',', ':'),
'default': default
})
@requires_user_role(UserRole.Browse)
def compact_skeleton(request, project_id=None, skeleton_id=None, with_connectors=None, with_tags=None):
"""Get a compact treenode representation of a skeleton, optionally with the
history of individual nodes and connectors. This does exactly the same as
compact_skeleton_detail(), but provides a slightly different interface. This
is done to provide backward compatibility, because many external tools still
use this endpoint.
"""
# Sanitize
project_id = int(project_id)
skeleton_id = int(skeleton_id)
with_connectors = int(with_connectors) != 0
with_tags = int(with_tags) != 0
with_history = request.GET.get("with_history", "false") == "true"
# Indicate if history of merged in skeletons should also be included if
# history is returned. Ignored if history is not retrieved.
with_merge_history = request.GET.get("with_merge_history", "false") == "true"
result = _compact_skeleton(project_id, skeleton_id, with_connectors,
with_tags, with_history, with_merge_history)
return JsonResponse(result, safe=False,
json_dumps_params={
'separators': (',', ':'),
'default': default
})
def _compact_skeleton(project_id, skeleton_id, with_connectors=True, with_tags=True, with_history=False, with_merge_history=True):
"""Get a compact treenode representation of a skeleton, optionally with the
history of individual nodes and connectors. Note this function is
performance critical!
Returns, in JSON, [[nodes], [connectors], {nodeID: [tags]}], with
connectors and tags being empty when 0 == with_connectors and 0 ==
with_tags, respectively.
If history data is requested, each row contains a validity interval. Note
that for the live table entry (the currently valid version), there are
special semantics for this interval: The upper bound is older than or the
same as the lower bound. This is done to encode the information of this row
being the most recent version and including the original creation time at
the same time, plus it requires less queries on the back-end to retireve
data. This requires the client to do slightly more work, but unfortunately
the original creation time is needed for data that was created without
history tables enabled.
"""
cursor = connection.cursor()
if not with_history:
cursor.execute('''
SELECT id, parent_id, user_id,
location_x, location_y, location_z,
radius, confidence
FROM treenode
WHERE skeleton_id = %s
''', (skeleton_id,))
nodes = tuple(cursor.fetchall())
else:
params = {
'skeleton_id': skeleton_id
}
# Get present and historic nodes. If a historic validity range is empty
# (e.g. due to a change in the same transaction), the edition time is
# taken for both start and end validity, because this is what actually
# happened.
query = '''
SELECT
treenode.id,
treenode.parent_id,
treenode.user_id,
treenode.location_x,
treenode.location_y,
treenode.location_z,
treenode.radius,
treenode.confidence,
treenode.edition_time,
treenode.creation_time
FROM treenode
WHERE treenode.skeleton_id = %(skeleton_id)s
UNION ALL
SELECT
treenode__history.id,
treenode__history.parent_id,
treenode__history.user_id,
treenode__history.location_x,
treenode__history.location_y,
treenode__history.location_z,
treenode__history.radius,
treenode__history.confidence,
COALESCE(lower(treenode__history.sys_period), treenode__history.edition_time),
COALESCE(upper(treenode__history.sys_period), treenode__history.edition_time)
FROM treenode__history
WHERE treenode__history.skeleton_id = %(skeleton_id)s
'''
if with_merge_history:
query = '''
{}
UNION ALL
SELECT
th.id,
th.parent_id,
th.user_id,
th.location_x,
th.location_y,
th.location_z,
th.radius,
th.confidence,
COALESCE(lower(th.sys_period), th.edition_time),
COALESCE(upper(th.sys_period), th.edition_time)
FROM treenode__history th
JOIN treenode t
ON th.id = t.id
AND t.skeleton_id = %(skeleton_id)s
AND th.skeleton_id <> t.skeleton_id
'''.format(query)
cursor.execute(query, params)
nodes = tuple(cursor.fetchall())
if 0 == len(nodes):
# Check if the skeleton exists
if 0 == ClassInstance.objects.filter(pk=skeleton_id).count():
raise Exception("Skeleton #%s doesn't exist" % skeleton_id)
# Otherwise returns an empty list of nodes
connectors = ()
tags = defaultdict(list)
if with_connectors or with_tags:
# postgres is caching this query
cursor.execute("SELECT relation_name, id FROM relation WHERE project_id=%s" % project_id)
relations = dict(cursor.fetchall())
if with_connectors:
# Fetch all connectors with their partner treenode IDs
pre = relations['presynaptic_to']
post = relations['postsynaptic_to']
gj = relations.get('gapjunction_with', -1)
relation_index = {pre: 0, post: 1, gj: 2}
if not with_history:
cursor.execute('''
SELECT tc.treenode_id, tc.connector_id, tc.relation_id,
c.location_x, c.location_y, c.location_z
FROM treenode_connector tc,
connector c
WHERE tc.skeleton_id = %s
AND tc.connector_id = c.id
AND (tc.relation_id = %s OR tc.relation_id = %s OR tc.relation_id = %s)
''', (skeleton_id, pre, post, gj))
connectors = tuple((row[0], row[1], relation_index.get(row[2], -1), row[3], row[4], row[5]) for row in cursor.fetchall())
else:
params = {
'skeleton_id': skeleton_id,
'pre': pre,
'post': post,
'gj': gj
}
# Get present and historic connectors. If a historic validity range
# is empty (e.g. due to a change in the same transaction), the
# edition time is taken for both start and end validity, because
# this is what actually happened.
query = '''
SELECT links.treenode_id, links.connector_id, links.relation_id,
c.location_x, c.location_y, c.location_z,
links.valid_from, links.valid_to
FROM (
SELECT tc.treenode_id, tc.connector_id, tc.relation_id,
tc.edition_time, tc.creation_time
FROM treenode_connector tc
WHERE tc.skeleton_id = %(skeleton_id)s
UNION ALL
SELECT tc.treenode_id, tc.connector_id, tc.relation_id,
COALESCE(lower(tc.sys_period), tc.edition_time),
COALESCE(upper(tc.sys_period), tc.edition_time)
FROM treenode_connector__history tc
WHERE tc.skeleton_id = %(skeleton_id)s
{}
) links(treenode_id, connector_id, relation_id, valid_from, valid_to)
JOIN connector__with_history c
ON links.connector_id = c.id
WHERE (links.relation_id = %(pre)s OR links.relation_id = %(post)s OR links.relation_id = %(gj)s)
'''
if with_merge_history:
query = query.format('''
UNION ALL
SELECT tch.treenode_id, tch.connector_id, tch.relation_id,
COALESCE(lower(tch.sys_period), tch.edition_time),
COALESCE(upper(tch.sys_period), tch.edition_time)
FROM treenode_connector__history tch
JOIN treenode_connector tc
ON tc.id = tch.id
AND tc.skeleton_id = %(skeleton_id)s
AND tch.skeleton_id <> tc.skeleton_id
''')
else:
query = query.format('')
cursor.execute(query, params)
connectors = tuple((row[0], row[1], relation_index.get(row[2], -1), row[3], row[4], row[5], row[6], row[7]) for row in cursor.fetchall())
if with_tags:
history_suffix = '__with_history' if with_history else ''
t_history_query = ', tci.edition_time' if with_history else ''
# Fetch all node tags
cursor.execute('''
SELECT c.name, tci.treenode_id
{0}
FROM treenode{1} t,
treenode_class_instance{1} tci,
class_instance{1} c
WHERE t.skeleton_id = %s
AND t.id = tci.treenode_id
AND tci.relation_id = %s
AND c.id = tci.class_instance_id
'''.format(t_history_query, history_suffix), (skeleton_id, relations['labeled_as']))
for row in cursor.fetchall():
tags[row[0]].append(row[1])
return [nodes, connectors, tags]
def _compact_arbor(project_id=None, skeleton_id=None, with_nodes=None,
with_connectors=None, with_tags=None, with_time=None):
"""
Performance-critical function. Do not edit unless to improve performance.
Returns, in JSON, [[nodes], [connections], {nodeID: [tags]}],
with connections being empty when 0 == with_connectors,
and the dict of node tags being empty 0 == with_tags, respectively.
The difference between this function and the compact_skeleton function is that
the connections contain the whole chain from the skeleton of interest to the
partner skeleton:
[treenode_id, confidence,
connector_id,
confidence, treenode_id, skeleton_id,
relation_id, relation_id]
where the first 2 values are from the given skeleton_id,
then the connector_id,
then the next 3 values are from the partner skeleton,
and finally the two relations: first for the given skeleton_id and then for the other skeleton.
The relation_id is 0 for pre and 1 for post. If <with_time> is truthy, each
row will also contain both the creation time and edition time as last
elements.
"""
# Sanitize
project_id = int(project_id)
skeleton_id = int(skeleton_id)
with_nodes = int(with_nodes)
with_connectors = int(with_connectors)
with_tags = int(with_tags)
cursor = connection.cursor()
nodes = ()
connectors = []
tags = defaultdict(list)
if 0 != with_nodes:
if with_time:
extra_fields = ', EXTRACT(EPOCH FROM creation_time), EXTRACT(EPOCH FROM edition_time)'
else:
extra_fields = ''
cursor.execute('''
SELECT id, parent_id, user_id,
location_x, location_y, location_z,
radius, confidence{}
FROM treenode
WHERE skeleton_id = %s
'''.format(extra_fields), (skeleton_id,))
nodes = tuple(cursor.fetchall())
if 0 == len(nodes):
# Check if the skeleton exists
if 0 == ClassInstance.objects.filter(pk=skeleton_id).count():
raise Exception("Skeleton #%s doesn't exist" % skeleton_id)
# Otherwise returns an empty list of nodes
if 0 != with_connectors or 0 != with_tags:
# postgres is caching this query
cursor.execute("SELECT relation_name, id FROM relation WHERE project_id=%s" % project_id)
relations = dict(cursor.fetchall())
if 0 != with_connectors:
# Fetch all inputs and outputs
pre = relations['presynaptic_to']
post = relations['postsynaptic_to']
cursor.execute('''
SELECT tc1.treenode_id, tc1.confidence,
tc1.connector_id,
tc2.confidence, tc2.treenode_id, tc2.skeleton_id,
tc1.relation_id, tc2.relation_id
FROM treenode_connector tc1,
treenode_connector tc2
WHERE tc1.skeleton_id = %s
AND tc1.id != tc2.id
AND tc1.connector_id = tc2.connector_id
AND (tc1.relation_id = %s OR tc1.relation_id = %s)
''' % (skeleton_id, pre, post))
for row in cursor.fetchall():
# Ignore all other kinds of relation pairs (there shouldn't be any)
if row[6] == pre and row[7] == post:
connectors.append((row[0], row[1], row[2], row[3], row[4], row[5], 0, 1))
elif row[6] == post and row[7] == pre:
connectors.append((row[0], row[1], row[2], row[3], row[4], row[5], 1, 0))
if 0 != with_tags:
# Fetch all node tags
cursor.execute('''
SELECT c.name, tci.treenode_id
FROM treenode t,
treenode_class_instance tci,
class_instance c
WHERE t.skeleton_id = %s
AND t.id = tci.treenode_id
AND tci.relation_id = %s
AND c.id = tci.class_instance_id
''' % (skeleton_id, relations['labeled_as']))
for row in cursor.fetchall():
tags[row[0]].append(row[1])
return nodes, connectors, tags
@requires_user_role(UserRole.Browse)
def compact_arbor(request, project_id=None, skeleton_id=None, with_nodes=None, with_connectors=None, with_tags=None):
with_time = request.GET.get("with_time", "false") == "true"
nodes, connectors, tags = _compact_arbor(project_id, skeleton_id,
with_nodes, with_connectors, with_tags, with_time)
return HttpResponse(json.dumps((nodes, connectors, tags), separators=(',', ':')))
def _treenode_time_bins(skeleton_id=None):
""" Return a map of time bins (minutes) vs. list of nodes. """
minutes = defaultdict(list)
epoch = datetime.utcfromtimestamp(0).replace(tzinfo=pytz.utc)
for row in Treenode.objects.filter(skeleton_id=int(skeleton_id)).values_list('id', 'creation_time'):
minutes[int((row[1] - epoch).total_seconds() / 60)].append(row[0])
return minutes
@requires_user_role([UserRole.Browse])
def treenode_time_bins(request, project_id=None, skeleton_id=None):
minutes = _treenode_time_bins(skeleton_id)
return HttpResponse(json.dumps(minutes, separators=(',', ':')))
@requires_user_role([UserRole.Browse])
def compact_arbor_with_minutes(request, project_id=None, skeleton_id=None, with_nodes=None, with_connectors=None, with_tags=None):
nodes, connectors, tags = _compact_arbor(project_id, skeleton_id,
with_nodes, with_connectors, with_tags)
minutes = _treenode_time_bins(skeleton_id)
return HttpResponse(json.dumps((nodes, connectors, tags, minutes), separators=(',', ':')))
# DEPRECATED. Will be removed.
def _skeleton_for_3d_viewer(skeleton_id, project_id, with_connectors=True, lean=0, all_field=False):
""" with_connectors: when False, connectors are not returned
lean: when not zero, both connectors and tags are returned as empty arrays. """
skeleton_id = int(skeleton_id) # sanitize
cursor = connection.cursor()
# Fetch the neuron name
cursor.execute(
'''SELECT name
FROM class_instance ci,
class_instance_class_instance cici
WHERE cici.class_instance_a = %s
AND cici.class_instance_b = ci.id
''' % skeleton_id)
row = cursor.fetchone()
if not row:
# Check that the skeleton exists
cursor.execute('''SELECT id FROM class_instance WHERE id=%s''' % skeleton_id)
if not cursor.fetchone():
raise Exception("Skeleton #%s doesn't exist!" % skeleton_id)
else:
raise Exception("No neuron found for skeleton #%s" % skeleton_id)
name = row[0]
if all_field:
added_fields = ', creation_time, edition_time'
else:
added_fields = ''
# Fetch all nodes, with their tags if any
cursor.execute(
'''SELECT id, parent_id, user_id, location_x, location_y, location_z, radius, confidence %s
FROM treenode
WHERE skeleton_id = %s
''' % (added_fields, skeleton_id) )
# array of properties: id, parent_id, user_id, x, y, z, radius, confidence
nodes = tuple(cursor.fetchall())
tags = defaultdict(list) # node ID vs list of tags
connectors = []
# Get all reviews for this skeleton
if all_field:
reviews = get_treenodes_to_reviews_with_time(skeleton_ids=[skeleton_id])
else:
reviews = get_treenodes_to_reviews(skeleton_ids=[skeleton_id])
if 0 == lean: # meaning not lean
# Text tags
cursor.execute("SELECT id FROM relation WHERE project_id=%s AND relation_name='labeled_as'" % int(project_id))
labeled_as = cursor.fetchall()[0][0]
cursor.execute(
''' SELECT treenode_class_instance.treenode_id, class_instance.name
FROM treenode, class_instance, treenode_class_instance
WHERE treenode.skeleton_id = %s
AND treenode.id = treenode_class_instance.treenode_id
AND treenode_class_instance.class_instance_id = class_instance.id
AND treenode_class_instance.relation_id = %s
''' % (skeleton_id, labeled_as))
for row in cursor.fetchall():
tags[row[1]].append(row[0])
if with_connectors:
if all_field:
added_fields = ', c.creation_time'
else:
added_fields = ''
# Fetch all connectors with their partner treenode IDs
cursor.execute(
''' SELECT tc.treenode_id, tc.connector_id, r.relation_name,
c.location_x, c.location_y, c.location_z %s
FROM treenode_connector tc,
connector c,
relation r
WHERE tc.skeleton_id = %s
AND tc.connector_id = c.id
AND tc.relation_id = r.id
''' % (added_fields, skeleton_id) )
# Above, purposefully ignoring connector tags. Would require a left outer join on the inner join of connector_class_instance and class_instance, and frankly connector tags are pointless in the 3d viewer.
# List of (treenode_id, connector_id, relation_id, x, y, z)n with relation_id replaced by 0 (presynaptic) or 1 (postsynaptic)
# 'presynaptic_to' has an 'r' at position 1:
for row in cursor.fetchall():
x, y, z = map(float, (row[3], row[4], row[5]))
connectors.append((row[0],
row[1],
0 if 'r' == row[2][1] else 1,
x, y, z,
row[6] if all_field else None))
return name, nodes, tags, connectors, reviews
return name, nodes, tags, connectors, reviews
# DEPRECATED. Will be removed.
@requires_user_role([UserRole.Annotate, UserRole.Browse])
def skeleton_for_3d_viewer(request, project_id=None, skeleton_id=None):
return HttpResponse(json.dumps(_skeleton_for_3d_viewer(skeleton_id, project_id, with_connectors=request.POST.get('with_connectors', True), lean=int(request.POST.get('lean', 0)), all_field=request.POST.get('all_fields', False)), separators=(',', ':')))
# DEPRECATED. Will be removed.
@requires_user_role([UserRole.Annotate, UserRole.Browse])
def skeleton_with_metadata(request, project_id=None, skeleton_id=None):
def default(obj):
"""Default JSON serializer."""
import calendar, datetime
if isinstance(obj, datetime.datetime):
if obj.utcoffset() is not None:
obj = obj - obj.utcoffset()
millis = int(
calendar.timegm(obj.timetuple()) * 1000 +
obj.microsecond / 1000
)
return millis
return HttpResponse(json.dumps(_skeleton_for_3d_viewer(skeleton_id, project_id, \
with_connectors=True, lean=0, all_field=True), separators=(',', ':'), default=default))
def _measure_skeletons(skeleton_ids):
if not skeleton_ids:
raise Exception("Must provide the ID of at least one skeleton.")
skids_string = ",".join(map(str, skeleton_ids))
cursor = connection.cursor()
cursor.execute('''
SELECT id, parent_id, skeleton_id, location_x, location_y, location_z
FROM treenode
WHERE skeleton_id IN (%s)
''' % skids_string)
# TODO should be all done with numpy,
# TODO by partitioning the skeleton into sequences of x,y,z representing the slabs
# TODO and then convolving them.
class Skeleton():
def __init__(self):
self.nodes = {}
self.raw_cable = 0
self.smooth_cable = 0
self.principal_branch_cable = 0
self.n_ends = 0
self.n_branch = 0
self.n_pre = 0
self.n_post = 0
class Node():
def __init__(self, parent_id, x, y, z):
self.parent_id = parent_id
self.x = x
self.y = y
self.z = z
self.wx = x # weighted average of itself and neighbors
self.wy = y
self.wz = z
self.children = {} # node ID vs distance
skeletons = defaultdict(dict) # skeleton ID vs (node ID vs Node)
for row in cursor.fetchall():
skeleton = skeletons.get(row[2])
if not skeleton:
skeleton = Skeleton()
skeletons[row[2]] = skeleton
skeleton.nodes[row[0]] = Node(row[1], row[3], row[4], row[5])
for skeleton in six.itervalues(skeletons):
nodes = skeleton.nodes
tree = nx.DiGraph()
root = None
# Accumulate children
for nodeID, node in six.iteritems(nodes):
if not node.parent_id:
root = nodeID
continue
tree.add_edge(node.parent_id, nodeID)
parent = nodes[node.parent_id]
distance = sqrt( pow(node.x - parent.x, 2)
+ pow(node.y - parent.y, 2)
+ pow(node.z - parent.z, 2))
parent.children[nodeID] = distance
# Measure raw cable, given that we have the parent already
skeleton.raw_cable += distance
# Utilize accumulated children and the distances to them
for nodeID, node in six.iteritems(nodes):
# Count end nodes and branch nodes
n_children = len(node.children)
if not node.parent_id:
if 1 == n_children:
skeleton.n_ends += 1
continue
if n_children > 2:
skeleton.n_branch += 1
continue
# Else, if 2 == n_children, the root node is in the middle of the skeleton, being a slab node
elif 0 == n_children:
skeleton.n_ends += 1
continue
elif n_children > 1:
skeleton.n_branch += 1
continue
# Compute weighted position for slab nodes only
# (root, branch and end nodes do not move)
oids = node.children.copy()
if node.parent_id:
oids[node.parent_id] = skeleton.nodes[node.parent_id].children[nodeID]
sum_distances = sum(six.itervalues(oids))
wx, wy, wz = 0, 0, 0
for oid, distance in six.iteritems(oids):
other = skeleton.nodes[oid]
w = distance / sum_distances if sum_distances != 0 else 0
wx += other.x * w
wy += other.y * w
wz += other.z * w
node.wx = node.x * 0.4 + wx * 0.6
node.wy = node.y * 0.4 + wy * 0.6
node.wz = node.z * 0.4 + wz * 0.6
# Find out nodes that belong to the principal branch
principal_branch_nodes = set(sorted(partition(tree, root), key=len)[-1])
# Compute smoothed cable length, also for principal branch
for nodeID, node in six.iteritems(nodes):
if not node.parent_id:
# root node
continue
parent = nodes[node.parent_id]
length = sqrt( pow(node.wx - parent.wx, 2)
+ pow(node.wy - parent.wy, 2)
+ pow(node.wz - parent.wz, 2))
skeleton.smooth_cable += length
if nodeID in principal_branch_nodes:
skeleton.principal_branch_cable += length
# Count inputs
cursor.execute('''
SELECT tc.skeleton_id, count(tc.skeleton_id)
FROM treenode_connector tc,
relation r
WHERE tc.skeleton_id IN (%s)
AND tc.relation_id = r.id
AND r.relation_name = 'postsynaptic_to'
GROUP BY tc.skeleton_id
''' % skids_string)
for row in cursor.fetchall():
skeletons[row[0]].n_pre = row[1]
# Count outputs
cursor.execute('''
SELECT tc1.skeleton_id, count(tc1.skeleton_id)
FROM treenode_connector tc1,
treenode_connector tc2,
relation r1,
relation r2
WHERE tc1.skeleton_id IN (%s)
AND tc1.connector_id = tc2.connector_id
AND tc1.relation_id = r1.id
AND r1.relation_name = 'presynaptic_to'
AND tc2.relation_id = r2.id
AND r2.relation_name = 'postsynaptic_to'
GROUP BY tc1.skeleton_id
''' % skids_string)
for row in cursor.fetchall():
skeletons[row[0]].n_post = row[1]
return skeletons
@requires_user_role([UserRole.Annotate, UserRole.Browse])
def measure_skeletons(request, project_id=None):
skeleton_ids = tuple(int(v) for k,v in six.iteritems(request.POST) if k.startswith('skeleton_ids['))
def asRow(skid, sk):
return (skid, int(sk.raw_cable), int(sk.smooth_cable), sk.n_pre, sk.n_post, len(sk.nodes), sk.n_branch, sk.n_ends, sk.principal_branch_cable)
return HttpResponse(json.dumps([asRow(skid, sk) for skid, sk in _measure_skeletons(skeleton_ids).iteritems()]))
def _skeleton_neuroml_cell(skeleton_id, preID, postID):
skeleton_id = int(skeleton_id) # sanitize
cursor = connection.cursor()
cursor.execute('''
SELECT id, parent_id, location_x, location_y, location_z, radius
FROM treenode
WHERE skeleton_id = %s
''' % skeleton_id)
nodes = {row[0]: (row[1], (row[2], row[3], row[4]), row[5]) for row in cursor.fetchall()}
cursor.execute('''
SELECT tc.treenode_id, tc.connector_id, tc.relation_id
FROM treenode_connector tc
WHERE tc.skeleton_id = %s
AND (tc.relation_id = %s OR tc.relation_id = %s)
''' % (skeleton_id, preID, postID))
pre = defaultdict(list) # treenode ID vs list of connector ID
post = defaultdict(list)
for row in cursor.fetchall():
if row[2] == preID:
pre[row[0]].append(row[1])
else:
post[row[0]].append(row[1])
return neuroml_single_cell(skeleton_id, nodes, pre, post)
@requires_user_role(UserRole.Browse)
def skeletons_neuroml(request, project_id=None):
""" Export a list of skeletons each as a Cell in NeuroML. """
project_id = int(project_id) # sanitize
skeleton_ids = tuple(int(v) for k,v in six.iteritems(request.POST) if k.startswith('skids['))
cursor = connection.cursor()
relations = get_relation_to_id_map(project_id, ('presynaptic_to', 'postsynaptic_to'), cursor)
preID = relations['presynaptic_to']
postID = relations['postsynaptic_to']
# TODO could certainly fetch all nodes and synapses in one single query and then split them up.
cells = (_skeleton_neuroml_cell(skeleton_id, preID, postID) for skeleton_id in skeleton_ids)
response = HttpResponse(content_type='text/txt')
response['Content-Disposition'] = 'attachment; filename="data.neuroml"'
neuroml_network(cells, response)
return response
@requires_user_role(UserRole.Browse)
def export_neuroml_level3_v181(request, project_id=None):
"""Export the NeuroML Level 3 version 1.8.1 representation of one or more skeletons.
Considers synapses among the requested skeletons only. """
skeleton_ids = tuple(int(v) for v in request.POST.getlist('skids[]'))
mode = int(request.POST.get('mode'))
skeleton_strings = ",".join(map(str, skeleton_ids))
cursor = connection.cursor()
relations = get_relation_to_id_map(project_id, ('presynaptic_to', 'postsynaptic_to'), cursor)
presynaptic_to = relations['presynaptic_to']
postsynaptic_to = relations['postsynaptic_to']
cursor.execute('''
SELECT cici.class_instance_a, ci.name
FROM class_instance_class_instance cici,
class_instance ci,
relation r
WHERE cici.class_instance_a IN (%s)
AND cici.class_instance_b = ci.id
AND cici.relation_id = r.id
AND r.relation_name = 'model_of'
''' % skeleton_strings)
neuron_names = dict(cursor.fetchall())
skeleton_query = '''
SELECT id, parent_id, location_x, location_y, location_z,
radius, skeleton_id
FROM treenode
WHERE skeleton_id IN (%s)
ORDER BY skeleton_id
''' % skeleton_strings
if 0 == mode:
cursor.execute('''
SELECT treenode_id, connector_id, relation_id, skeleton_id
FROM treenode_connector
WHERE skeleton_id IN (%s)
AND (relation_id = %s OR relation_id = %s)
''' % (skeleton_strings, presynaptic_to, postsynaptic_to))
# Dictionary of connector ID vs map of relation_id vs list of treenode IDs
connectors = defaultdict(partial(defaultdict, list))
for row in cursor.fetchall():
connectors[row[1]][row[2]].append((row[0], row[3]))
# Dictionary of presynaptic skeleton ID vs map of postsynaptic skeleton ID vs list of tuples with presynaptic treenode ID and postsynaptic treenode ID.
connections = defaultdict(partial(defaultdict, list))
for connectorID, m in six.iteritems(connectors):
for pre_treenodeID, skID1 in m[presynaptic_to]:
for post_treenodeID, skID2 in m[postsynaptic_to]:
connections[skID1][skID2].append((pre_treenodeID, post_treenodeID))
cursor.execute(skeleton_query)
generator = export_NeuroML_Level3.exportMutual(neuron_names, cursor.fetchall(), connections)
else:
if len(skeleton_ids) > 1:
raise Exception("Expected a single skeleton for mode %s!" % mode)
input_ids = tuple(int(v) for v in request.POST.getlist('inputs[]', []))
input_strings = ",".join(map(str, input_ids))
if 2 == mode:
constraint = "AND tc2.skeleton_id IN (%s)" % input_strings
elif 1 == mode:
constraint = ""
else:
raise Exception("Unknown mode %s" % mode)
cursor.execute('''
SELECT tc2.skeleton_id, tc1.treenode_id
FROM treenode_connector tc1,
treenode_connector tc2
WHERE tc1.skeleton_id = %s
AND tc1.connector_id = tc2.connector_id
AND tc1.treenode_id != tc2.treenode_id
AND tc1.relation_id = %s
AND tc2.relation_id = %s
%s
''' % (skeleton_strings, postsynaptic_to, presynaptic_to, constraint))
# Dictionary of skeleton ID vs list of treenode IDs at which the neuron receives inputs