def createPopulations(self):
self.populationDict = {}
for population in self.network.findall(".//{"+nml_ns+"}population"):
cellname = population.attrib["cell_type"]
populationname = population.attrib["name"]
print "loading", populationname
## if channel does not exist in library load it from xml file
if not self.context.exists('/library/'+cellname):
mmlR = MorphML(self.nml_params)
cellDict = mmlR.readMorphMLFromFile(cellname+'.morph.xml')
self.cellSegmentDict.update(cellDict)
libcell = moose.Cell('/library/'+cellname)
self.populationDict[populationname] = (cellname,{})
for instance in population.findall(".//{"+nml_ns+"}instance"):
instanceid = instance.attrib['id']
location = instance.find('./{'+nml_ns+'}location')
rotationnote = instance.find('./{'+meta_ns+'}notes')
if rotationnote is not None:
## the text in rotationnote is zrotation=xxxxxxx
zrotation = float(string.split(rotationnote.text,'=')[1])
else:
zrotation = 0
## deep copies the library cell to an instance
cell = moose.Cell(libcell,"/"+populationname+"_"+instanceid)
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 createPopulations(self):
self.populationDict = {}
for population in self.network.findall(".//{" + nml_ns +
"}population"):
cellname = population.attrib["cell_type"]
populationname = population.attrib["name"]
print "loading", populationname
## if channel does not exist in library load it from xml file
if not self.context.exists('/library/' + cellname):
mmlR = MorphML(self.nml_params)
cellDict = mmlR.readMorphMLFromFile(cellname + '.morph.xml')
self.cellSegmentDict.update(cellDict)
libcell = moose.Cell('/library/' + cellname)
self.populationDict[populationname] = (cellname, {})
for instance in population.findall(".//{" + nml_ns + "}instance"):
instanceid = instance.attrib['id']
location = instance.find('./{' + nml_ns + '}location')
rotationnote = instance.find('./{' + meta_ns + '}notes')
if rotationnote is not None:
## the text in rotationnote is zrotation=xxxxxxx
zrotation = float(string.split(rotationnote.text, '=')[1])
else:
zrotation = 0
## deep copies the library cell to an instance
cell = moose.Cell(libcell,
"/" + populationname + "_" + instanceid)
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 readNeuroMLFromFile(self,filename,params={}):
"""
For the format of params required to tweak what cells are loaded,
refer to the doc string of NetworkML.readNetworkMLFromFile().
"""
print "Loading neuroml file ... ", filename
tree = ET.parse(filename)
root_element = tree.getroot()
self.lengthUnits = root_element.attrib['lengthUnits']
self.temperature = CELSIUS_default # gets replaced below if tag for temperature is present
self.temperature_default = True
for meta_property in root_element.findall('.//{'+meta_ns+'}property'):
tagname = meta_property.attrib['tag']
if 'temperature' in tagname:
self.temperature = float(meta_property.attrib['value'])
self.temperature_default = False
if self.temperature_default:
print "Using default temperature of", self.temperature,"degrees Celsius."
self.nml_params = {'temperature':self.temperature}
#print "Loading channels and synapses into MOOSE /library ..."
cmlR = ChannelML(self.nml_params)
for channels in root_element.findall('.//{'+neuroml_ns+'}channels'):
self.channelUnits = channels.attrib['units']
for channel in channels.findall('.//{'+cml_ns+'}channel_type'):
## ideally I should read in extra params
## from within the channel_type element and put those in also.
## Global params should override local ones.
cmlR.readChannelML(channel,params={},units=self.channelUnits)
for synapse in channels.findall('.//{'+cml_ns+'}synapse_type'):
cmlR.readSynapseML(synapse,units=self.channelUnits)
for ionConc in channels.findall('.//{'+cml_ns+'}ion_concentration'):
cmlR.readIonConcML(ionConc,units=self.channelUnits)
#print "Loading cell definitions into MOOSE /library ..."
mmlR = MorphML(self.nml_params)
self.cellsDict = {}
for cells in root_element.findall('.//{'+neuroml_ns+'}cells'):
for cell in cells.findall('.//{'+neuroml_ns+'}cell'):
cellDict = mmlR.readMorphML(cell,params={},lengthUnits=self.lengthUnits)
self.cellsDict.update(cellDict)
#print "Loading individual cells into MOOSE root ... "
nmlR = NetworkML(self.nml_params)
self.populationDict, self.projectionDict = \
nmlR.readNetworkML(root_element,self.cellsDict,params=params,lengthUnits=self.lengthUnits)
def readNeuroMLFromFile(self,filename,params={}):
"""
For the format of params required to tweak what cells are loaded,
refer to the doc string of NetworkML.readNetworkMLFromFile().
"""
tree = ET.parse(filename)
root_element = tree.getroot()
self.length_units = root_element.attrib['length_units']
print filename
mmlR = MorphML()
self.cellsList = []
for cells in root_element.findall('.//{'+self.neuroml_ns+'}cells'):
print 'found a cell'
for cell in cells.findall('.//{'+self.neuroml_ns+'}cell'):
cellList = mmlR.readMorphML(cell,params={},length_units=self.length_units)
self.cellsList.extend(cellList)
return self.cellsList
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)
