def create_LIF():
neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile('cells_channels/LIF.morph.xml')
libcell = moose.Neuron('/library/LIF')
LIFCellid = moose.copy(libcell, moose.Neutral('/cells'), 'IF1')
LIFCell = moose.LeakyIaF(LIFCellid)
return LIFCell
def createPopulations(self):
"""
Create population dictionary.
"""
populations = self.network.findall(".//{" + nmu.nml_ns + "}population")
if not populations:
utils.dump("WARN", [
"No population find in model",
"Searching in namespace {}".format(nmu.nml_ns)
],
frame=inspect.currentframe())
for population in populations:
cellname = population.attrib["cell_type"]
populationName = population.attrib["name"]
utils.dump("INFO",
"Loading population `{0}`".format(populationName))
# if cell does not exist in library load it from xml file
if not moose.exists(self.libraryPath + '/' + cellname):
utils.dump(
"DEBUG", "Searching in subdirectories for cell types" +
" in `{0}.xml` and `{0}.morph.xml` ".format(cellname))
mmlR = MorphML.MorphML(self.nml_params)
model_filenames = (cellname + '.xml', cellname + '.morph.xml')
success = False
for modelFile in model_filenames:
model_path = nmu.find_first_file(modelFile, self.modelDir)
if model_path is not None:
cellDict = mmlR.readMorphMLFromFile(model_path)
success = True
break
if not success:
raise IOError(
'For cell {0}: files {1} not found under {2}.'.format(
cellname, model_filenames, self.modelDir))
self.cellSegmentDict.update(cellDict)
if cellname == 'LIF':
cellid = moose.LeakyIaF(self.libraryPath + '/' + cellname)
else:
# added cells as a Neuron class.
cellid = moose.Neuron(self.libraryPath + '/' + cellname)
self.populationDict[populationName] = (cellname, {})
for instance in population.findall(".//{" + nmu.nml_ns +
"}instance"):
instanceid = instance.attrib['id']
location = instance.find('./{' + nmu.nml_ns + '}location')
rotationnote = instance.find('./{' + nmu.meta_ns + '}notes')
if rotationnote is not None:
# the text in rotationnote is zrotation=xxxxxxx
zrotation = float(rotationnote.text.split('=')[1])
else:
zrotation = 0
if cellname == 'LIF':
cell = moose.LeakyIaF(cellid)
self.populationDict[populationName][1][int(
instanceid)] = cell
else:
# No Cell class in MOOSE anymore! :( addded Neuron class -
# Chaitanya
cell = moose.Neuron(cellid)
self.populationDict[populationName][1][int(
instanceid)] = cell
x = float(location.attrib['x']) * self.length_factor
y = float(location.attrib['y']) * self.length_factor
z = float(location.attrib['z']) * self.length_factor
self.translate_rotate(cell, x, y, z, zrotation)
def readMorphML(self, cell, params={}, lengthUnits="micrometer"):
"""
returns {cellName:segDict}
where segDict = { segid1 : [ segname
, (proximalx,proximaly,proximalz)
, (distalx,distaly,distalz),diameter,length,[potential_syn1, ... ] ]
, ... }
segname is "<name>_<segid>" because 1) guarantees uniqueness,
2) later scripts obtain segid from the compartment's name!
"""
debug.printDebug("DEBUG", "Entered function readMorphML")
if lengthUnits in ['micrometer', 'micron']:
self.length_factor = 1e-6
else:
self.length_factor = 1.0
cellName = cell.attrib["name"]
if cellName == 'LIF':
self.mooseCell = moose.LeakyIaF(self.cellPath + '/' + cellName)
self.segDict = {}
else:
# using moose Neuron class - in previous version 'Cell' class
# Chaitanya.
self.mooseCell = moose.Neuron(self.cellPath + '/' + cellName)
self.cellDictBySegmentId[cellName] = [self.mooseCell, {}]
self.cellDictByCableId[cellName] = [self.mooseCell, {}]
self.segDict = {}
# load morphology and connections between compartments Many neurons
# exported from NEURON have multiple segments in a section Combine
# those segments into one Compartment / section assume segments of
# a compartment/section are in increasing order and assume all
# segments of a compartment/section have the same cableid findall()
# returns elements in document order:
self.running_cableid = ''
running_segid = ''
segments = cell.findall(".//{" + self.mml + "}segment")
segmentstotal = len(segments)
for segnum, segment in enumerate(segments):
self.addSegment(cellName, segnum, segment)
# load cablegroups into a dictionary
self.cablegroupsDict = {}
# Two ways of specifying cablegroups in neuroml 1.x
# <cablegroup>s with list of <cable>s
cablegroups = cell.findall(".//{" + self.mml + "}cablegroup")
for cablegroup in cablegroups:
cablegroupname = cablegroup.attrib['name']
self.cablegroupsDict[cablegroupname] = []
for cable in cablegroup.findall(".//{" + self.mml + "}cable"):
cableid = cable.attrib['id']
self.cablegroupsDict[cablegroupname].append(cableid)
# <cable>s with list of <meta:group>s
cables = cell.findall(".//{" + self.mml + "}cable")
for cable in cables:
cableid = cable.attrib['id']
cablegroups = cable.findall(".//{" + self.meta + "}group")
for cablegroup in cablegroups:
cablegroupname = cablegroup.text
if cablegroupname in list(self.cablegroupsDict.keys()):
self.cablegroupsDict[cablegroupname].append(cableid)
else:
self.cablegroupsDict[cablegroupname] = [cableid]
# Add bioPhysics to the cell
self.addBiophysics(cell, cellName)
# load connectivity / synapses into the compartments
connectivity = cell.find(".//{" + self.neuroml + "}connectivity")
if connectivity is not None:
self.addConnectivity(cell, cellName, connectivity)
return {cellName: self.segDict}