def builder(myList):
# sets list of all skeleton IDs
mySkels = myList
myAnnotations = NeuronObjectData.getMyAnnotations(217284386)
myAnnotations2 = NeuronObjectData.getMyAnnotations(3552044)
myAnnotations3 = merge_dicts(myAnnotations, myAnnotations2)
# creates a dictionary with key-value pairs of int(skelID) and and str(neuron name)
myNames = NeuronObjectData.getLookUpTableSkID_Name(217284386)
myNames2 = NeuronObjectData.getLookUpTableSkID_Name(3552044)
# creates empty list to be filled with all instances
myNeurons = []
# creates list of of all instances while setting each instance's name and skeletonID to be an element from mySkls
for i in mySkels:
x = AscendingNeuron(i)
myNeurons.append(x)
# converts skeletonID attribute to string for use as dictionary key then adds all available synapses from
# dictionary of synapses and all available annotations from dictionary of annotations
for elem in myNeurons:
y = elem.skeletonID
z = str(y)
if z in myAnnotations3:
elem.annotations = myAnnotations3[z]
p = elem.annotations
if "Descending Neuron" in p:
elem.classification = "Descending"
elem.status = "Descending Neuron"
elif "Ascending Neuron" in p:
elem.classification = "Ascending"
elem.status = "Ascending Neuron"
elif "Putative_Ascending" in p:
elem.classification = 'Ascending'
elem.status = "Putative_Ascending"
if "merges larger DN" in p:
elem.status = "Merges into larger DN"
elif "merges existing DN" in p:
elem.status = "Merges into existing DN"
elif 'Halted' in p or 'Halted Neuron' in p:
elem.status = 'Halted'
elif 'FindSoma' in p:
elem.status = 'FindSoma'
if y in myNames:
elem.neuronName = myNames[y]
if y in myNames2:
elem.neuronName = myNames2[y]
removeAnno = re.compile("Merged: Google")
newAnno = [
ann for ann in elem.annotations if not removeAnno.search(ann)
]
elem.annotations = newAnno
return myNeurons
def builder():
dnskidList = NeuronObjectData.getListOfSkID_int(2998068)
putative_skidlist = NeuronObjectData.getListOfSkID_int(217431412)
skidList = []
for i in dnskidList:
skidList.append(i)
for i in putative_skidlist:
if i not in skidList:
skidList.append(i)
mySet = DnAutoseg_set(np.array(DnAutosegClass.builder(skidList)))
return mySet
def builder():
ascending_skidlist = NeuronObjectData.getListOfSkID_int(217284386)
putative_skidlist = NeuronObjectData.getListOfSkID_int(3552044)
skidList = []
for i in ascending_skidlist:
skidList.append(i)
for i in putative_skidlist:
if i not in skidList:
skidList.append(i)
mySet = AscendingSet(np.array(AscendingNeuronClass.builder(skidList)))
return mySet
def correctAnnotations(self):
mySet = NeuronObjectData.removeSomaless(self)
rightHem = []
leftHem = []
for item in mySet:
if item.soma[0] < 500000:
rightHem.append(item.skeletonID + 1)
elif item.soma[0] > 550000:
leftHem.append(item.skeletonID + 1)
responseOne = requests.post(
'https://neuropil.janelia.org/tracing/fafb/v14/{}/annotations/remove'
.format(project_id),
auth=config.CatmaidApiTokenAuth(token),
data={
'entity_ids': rightHem,
'annotation_ids': [1223085]
})
responseOne = json.loads(responseOne.content)
print(responseOne)
responseTwo = requests.post(
'https://neuropil.janelia.org/tracing/fafb/v14-seg-li-190805.0/{}/annotations/remove'
.format(project_id),
auth=config.CatmaidApiTokenAuth(token),
data={
'entity_ids': leftHem,
'annotation_ids': [1167304]
})
responseTwo = json.loads(responseTwo.content)
print(responseTwo)
return
def getNeuronName(self):
y = self.skeletonID
z = str(y)
myNames = NeuronObjectData.getLookUpTableSkID_Name()
if y in myNames:
self.neuronName = myNames[y]
return self.neuronName
def getAnnotations(self):
myAnnotations = NeuronObjectData.getMyAnnotations()
y = self.skeletonID
z = str(y)
if z in myAnnotations:
self.annotations = myAnnotations[z]
return self.annotations
def getSkeletonNodes(self):
self.skeletonNodes = NeuronObjectData.getAllNodes(self)
return self.skeletonNodes
def builder(myList):
# sets list of all skeleton IDs
mySkels = myList
myAnnotations = NeuronObjectData.getMyAnnotations(2998068)
myAnnotations2 = NeuronObjectData.getMyAnnotations(217431412)
myAnnotations3 = merge_dicts(myAnnotations, myAnnotations2)
# creates a dictionary with key-value pairs of int(skelID) and and str(neuron name)
myNames = NeuronObjectData.getLookUpTableSkID_Name(2998068)
myNames2 = NeuronObjectData.getLookUpTableSkID_Name(217431412)
myNames3 = merge_dicts(myNames, myNames2)
# creates empty list to be filled with all instances
myNeurons = []
# creates list of of all instances while setting each instance's name and skeletonID to be an element from mySkls
for i in mySkels:
x = DescendingNeuron(i)
myNeurons.append(x)
# converts skeletonID attribute to string for use as dictionary key then adds all available synapses from
# dictionary of synapses and all available annotations from dictionary of annotations
for elem in myNeurons:
y = elem.skeletonID
z = str(y)
if z in myAnnotations3:
elem.annotations = myAnnotations3[z]
p = elem.annotations
#DNp10 Non-Visual Input ClusterA
DNTypeReg = re.compile(
r'^.*DN[a-z][0-9](?!Interneuron)(?!Exploration)(?!presynaptic)(?!postsynaptic)(?!.*Non-Visual)'
)
DNNewType = re.compile(r'DN[a-z] type')
DNPutative = re.compile(r'putative_DN[a-z]$')
idType = list(filter(DNTypeReg.match, p))
newType = list(filter(DNNewType.match, p))
putType = list(filter(DNPutative.match, p))
if len(idType) != 0:
elem.classification = str(idType[0])
elif len(newType) != 0:
elem.classification = str(newType[0])
elif len(putType) != 0:
elem.classification = str(putType[0])
if "putative_Descending" in p:
elem.classification = "putative_Descending"
elif "DNa" in p:
elem.dnType = "DNa"
elif "DNb" in p:
elem.dnType = "DNb"
elif "DNc" in p:
elem.dnType = "DNc"
elif "DNd" in p:
elem.dnType = "DNd"
elif "DNg" in p:
elem.dnType = "DNg"
elif "DNp" in p:
elem.dnType = "DNp"
elif "DNx" in p:
elem.dnType = "DNx"
if "Typed DN" in p:
elem.ID = "Typed DN"
elif "miscellaneous DNa" in p:
elem.ID = "Miscellaneous DNa"
elif "miscellaneous DNp" in p:
elem.ID = "Miscellaneous DNp"
elif "miscellaneous DNg" in p:
elem.ID = "Miscellaneous DNg"
if elem.ID is None:
if "Identified DN" in p:
elem.ID = "Identified DN"
elif "Unidentified DN" in p:
elem.ID = "Unidentified DN"
if 'RIGHT HEMISPHERE' in p:
elem.hemisphere = "RIGHT HEMISPHERE"
elif 'LEFT HEMISPHERE' in p:
elem.hemisphere = "LEFT HEMISPHERE"
elif 'midLine' in p:
elem.hemisphere = "midLine"
elif 'FindSoma' in p:
elem.hemisphere = 'FindSoma'
if elem.hemisphere is not None:
elem.status = "Complete"
if "duplicate - manual" in p:
elem.status = "duplicate - manual"
elif "duplicate - auto" in p:
elem.status = "duplicate - auto"
elif "check FC" in p:
elem.status = "Check FC"
elif 'Putative_Ascending' in p:
elem.status = 'Putative_Ascending'
elif 'Halted' in p or 'Halted Neuron' in p:
elem.status = 'Halted'
elif 'unclear FW' in p:
elem.status = 'unclear FW'
elif 'no FW soma' in p:
elem.status = 'no FW soma'
elif 'FindSoma' in p:
elem.status = 'FindSoma'
if 'check DT' in p:
elem.dtSide = 'check DT'
elif 'double DT' in p:
elem.dtSide = "double DT"
elif 'left DT' in p:
elem.dtSide = 'Left'
elif 'right DT' in p:
elem.dtSide = 'Right'
if y in myNames3:
elem.neuronName = myNames3[y]
removeAnno = re.compile("Merged: Google")
newAnno = [
ann for ann in elem.annotations if not removeAnno.search(ann)
]
elem.annotations = newAnno
for neuron in myNeurons:
classif = str(neuron.classification)
neuron.classification = classif.replace("_", " ")
return myNeurons
def getSomaNode(self):
self.soma = NeuronObjectData.getSoma(self)
return self.soma