def LPLC2_LP_Filter(self):
# myGroup = createGroupByAnnotation(self, 'LPLC2')
# gets all skeletonNodes for each skeleton in the set
self.getAllSkeletonNodes()
LPnodesAvg = {}
for i in self:
if i.curColor is None:
JN.autoSaveJSONColorGrad(self)
# tests each node in a given skeleton for whether or not it is within the LP (based on a curve built from the points: (191994, 239560), (218895, 239680), (286360,230800), (318592,216920), (332166,209480) run through "mycurvefit.com"
for x in self:
LPnodes = []
for i in x.skeletonNodes:
if -16938980000 + (332993.6 + 16938980000) / (
1.0 + (i[2] / 379562.8)**25.65271) <= i[1]:
LPnodes.append(i)
xCount = 0
yCount = 0
zCount = 0
divisor = len(LPnodes)
if divisor == 0:
print(x.skeletonID)
divisor = 1
for i in LPnodes:
xCount += i[0]
yCount += i[1]
zCount += i[2]
xAvg = xCount / divisor
yAvg = yCount / divisor
zAvg = zCount / divisor
LPnodesAvg[x.skeletonID] = [xAvg, yAvg, zAvg, x.curColor]
return LPnodesAvg
def LC4_LO_Filter(self):
if self[0].skeletonNodes is None:
# gets all skeletonNodes for each skeleton in the set
self.getAllSkeletonNodes()
LOnodesAvg = {}
for i in self:
if i.curColor is None:
JN.autoSaveJSONColorGrad(self)
# tests each node in a given skeleton for whether or not it is within the LP (based on a curve built from
# the points: (191994, 239560), (218895, 239680), (286360,230800), (318592,216920), (332166,209480) run
# through "mycurvefit.com"
for x in self:
LOtnid = []
LOtnid = GN.getDownStreamNodes(skeletonID=x.skeletonID,
tag='startOfLobula')
LOnodes = []
for tnid in LOtnid:
if tnid in x.skeletonNodes:
LOnodes.append(x.skeletonNodes[tnid])
xCount = 0
yCount = 0
zCount = 0
divisor = len(LOnodes)
if divisor == 0:
print(x.skeletonID)
divisor = 1
for i in LOnodes:
xCount += i[0]
yCount += i[1]
zCount += i[2]
xAvg = xCount / divisor
yAvg = yCount / divisor
zAvg = zCount / divisor
LOnodesAvg[x.skeletonID] = [xAvg, yAvg, zAvg, x.curColor]
return LOnodesAvg
def colorByGradient(self):
for i in self:
if i.curColor is None:
JN.autoSaveJSONColorGrad(self)
return
# e2c.makeCSV(myDNp02Set, 'saveGeneralSpecificAnnotations') myDNp04Set = CN.builder(6958818) DNp04LC4 = CN.createGroupByAnnotation(myDNp04Set, 'putative LC4 neuron') DNp04LPLC2 = CN.createGroupByAnnotation(myDNp04Set, 'LPLC2') sorted04LC4 = CN.sortBySynH2L(DNp04LC4) sorted04LPLC2 = CN.sortBySynH2L(DNp04LPLC2) # e2c.makeCSV(myDNp04Set, 'saveGeneralSpecificAnnotations') myDNp11Set = CN.builder(5349447) # print(myDNp02Set) DNp11LC4 = CN.createGroupByAnnotation(myDNp11Set, 'putative LC4 neuron') sorted11LC4 = CN.sortBySynH2L(DNp11LC4) JN.autoSaveJSONColorGrad(DNp04LC4) JN.autoSaveJSONColorGrad(DNp04LPLC2) JN.autoSaveJSONColorGrad(DNp02LC4) JN.autoSaveJSONColorGrad(DNp11LC4) sorted02LC4.colorByGradient() sorted04LC4.colorByGradient() sorted11LC4.colorByGradient() e2c.makeCSV(sorted02LC4, 'saveSKIDwithColor') e2c.makeCSV(sorted04LC4, 'saveSKIDwithColor') e2c.makeCSV(sorted11LC4, 'saveSKIDwithColor')