nInputs = len(incoming)
nOutputs = len(outgoing)
outgoingIds = [oc.getId() for oc in outgoing]
incomingIds = [ic.getId() for ic in incoming]
# get node connections, out/in synapse number, synapse id
# get branch id
if branchTable.has_key(nd):
branch = branchTable[nd]
else:
branch = 0
# get area
ndArea = abs(
AreaCalculations.area(nd.getData().getPathIterator(None)) *
calibration.pixelWidth * calibration.pixelHeight)
ndLayerIndex = nd.getLayer().getParent().indexOf(nd.getLayer()) + 1
# save a line of node profile data
# ['neuron', 'neurite', 'areatreeId', 'nodeId', 'branch', 'layer', 'x', 'y', 'z', 'area', 'nInputs', 'nOutputs', 'input', 'output']
nodeData = [
neurite.getParent().getTitle(),
neurite.getTitle(),
areatree.getId(),
nd.getId(), branch, ndLayerIndex, x, y, z,
nd.getNormalAngle(),
nd.getCrossRadius(),
nd.getFlatRadius(), nInputs, nOutputs, incomingIds, outgoingIds
]
foundNeuriteNodes.append(nodeData)
continue
areatrees = neurite.findChildrenOfTypeR("areatree")
for areatree in areatrees:
areatree = areatree.getObject()
root = areatree.getRoot()
if root is None:
continue
nEmpty = 0
nNodes = 0
layerset = areatree.getLayerSet()
calibration = layerset.getCalibration()
affine = areatree.getAffineTransform()
for nd in root.getSubtreeNodes():
nNodes += 1
ndArea = abs( AreaCalculations.area(nd.getData().getPathIterator(None)) )
if ndArea < 1:
nEmpty += 1
fp = array([nd.getX(), nd.getY()], 'f')
affine.transform(fp, 0, fp, 0, 1)
x = fp[0]
y = fp[1]
notDrawnCo.append(Coordinate(x, y, nd.getLayer(), nd))
notDrawn.append([nd.getLayer().getParent().indexOf(nd.getLayer()) + 1, neurite.getTitle(), nd.getId()])
if nEmpty:
print neurite.getTitle(), 'has ', nEmpty, '/', nNodes, 'nodes not drawn (area too small).'
outfile = open('notDrawn.csv','wb')
writer = csv.writer(outfile)
writer.writerows(notDrawn)
outfile.close()
d = []
for tnd in ndIn:
d.append(MeasurePathDistance(areatree, nd, tnd).getDistance())
ndOutDistance[nd] = min(d)
# input synapses
for nd, d in ndInDistance.iteritems():
# get node coordinates, from tut on web
fp = array([nd.getX(), nd.getY()], 'f')
affine.transform(fp, 0, fp, 0, 1)
x = fp[0] * calibration.pixelWidth
y = fp[1] * calibration.pixelHeight
z = nd.getLayer().getZ() * calibration.pixelWidth # a TrakEM2 oddity
# get area
ndArea = abs( AreaCalculations.area( nd.getData().getPathIterator(None) ) * calibration.pixelWidth * calibration.pixelHeight )
ndLayerIndex = nd.getLayer().getParent().indexOf(nd.getLayer()) + 1
# save a line of node profile data
# ['neuron', 'neurite', 'areatreeId', 'nodeId', 'synapseDirection', 'layer', 'x', 'y', 'z', 'area', 'distance']
nodeData = [neurite.getParent().getTitle(), neurite.getTitle(), areatree.getId(), nd.getId(), 'incoming', ndLayerIndex, x, y, z, ndArea, d]
foundNeuriteNodes.append(nodeData)
# output synapses
for nd, d in ndOutDistance.iteritems():
# get node coordinates, from tut on web
fp = array([nd.getX(), nd.getY()], 'f')
affine.transform(fp, 0, fp, 0, 1)
x = fp[0] * calibration.pixelWidth
y = fp[1] * calibration.pixelHeight
z = nd.getLayer().getZ() * calibration.pixelWidth # a TrakEM2 oddity
outputNeurites = [str(tnTable[t].getTitle()) for t in outs[nd]]
if nd in ins:
inputNeurites = [str(tnTable[t].getTitle()) for t in ins[nd]]
# get node coordinates, from tut on web
fp = array([nd.getX(), nd.getY()], 'f')
affine.transform(fp, 0, fp, 0, 1)
x = fp[0] * calibration.pixelWidth
y = fp[1] * calibration.pixelHeight
z = nd.getLayer().getZ() * calibration.pixelWidth # a TrakEM2 oddity
# get branch id
if branchTable.has_key(nd):
branch = branchTable[nd]
else:
branch = 0
# get area
ndArea = abs( AreaCalculations.area( nd.getData().getPathIterator(None) ) * calibration.pixelWidth * calibration.pixelHeight )
ndLayerIndex = nd.getLayer().getParent().indexOf(nd.getLayer()) + 1
# save a line of node profile data
# ['neuron', 'neurite', 'areatreeId', 'nodeId', 'branch', 'layer', 'x', 'y', 'z', 'area', 'nSynapseInputs', 'nSynapseOutputs', 'inputNeurites', 'outputNeurites']
nodeData = [neurite.getParent().getTitle(), neurite.getTitle(), areatree.getId(), nd.getId(), branch, ndLayerIndex, x, y, z, ndArea, nAutoIn, nAutoOut, inputNeurites, outputNeurites]
foundNeuriteNodes.append(nodeData)
outfile = open('neuritesConnectors01.csv','wb')
writer = csv.writer(outfile)
writer.writerows(foundNeuriteNodes)
outfile.close()
projectRoot = project.getRootProjectThing()
output = [['lobe', 'Z', 'area']]
arealists = projectRoot.findChildrenOfTypeR("area_list")
for arealist in arealists:
if re.match(r".*lobe", arealist.getTitle()) is None:
continue
arealist = arealist.getObject()
layerset = arealist.getLayerSet()
calibration = layerset.getCalibration()
layers = arealist.getLayerRange()
for layer in layers:
area = arealist.getArea(layer)
if area is None:
A = 0
else:
A = abs(
AreaCalculations.area(area.getPathIterator(None)) *
calibration.pixelWidth * calibration.pixelHeight)
output.append(
[arealist.getTitle(),
layer.getZ() * calibration.pixelWidth, A])
print 'writing ...'
outfile = open('area.lobes.csv', 'wb')
writer = csv.writer(outfile)
writer.writerows(output)
outfile.close()
print 'done.'
import re
import csv
project = Project.getProjects().get(0)
projectRoot = project.getRootProjectThing()
output = [['lobe', 'Z','area']]
arealists = projectRoot.findChildrenOfTypeR("area_list")
for arealist in arealists:
if re.match(r".*lobe", arealist.getTitle()) is None:
continue
arealist = arealist.getObject()
layerset = arealist.getLayerSet()
calibration = layerset.getCalibration()
layers = arealist.getLayerRange()
for layer in layers:
area = arealist.getArea(layer)
if area is None:
A = 0
else:
A = abs( AreaCalculations.area(area.getPathIterator(None) ) * calibration.pixelWidth * calibration.pixelHeight )
output.append([arealist.getTitle(), layer.getZ()*calibration.pixelWidth, A])
print 'writing ...'
outfile = open('area.lobes.csv','wb')
writer = csv.writer(outfile)
writer.writerows(output)
outfile.close()
print 'done.'
