def make_new_synapse(self, syn_name, postcomp, syn_name_full):
## if channel does not exist in library load it from xml file
if not moose.exists('/library/'+syn_name):
cmlR = ChannelML(self.nml_params)
cmlR.readChannelMLFromFile(syn_name+'.xml')
#~ synid = self.context.deepCopy(self.context.pathToId('/library/'+syn_name),postcomp.id,syn_name_full)
syn = moose.SynChan(synid)
#### connect the post compartment to the synapse
if syn.getField('mgblock')=='True': # If NMDA synapse based on mgblock, connect to mgblock
mgblock = moose.Mg_block(syn.path+'/mgblock')
moose.connect(postcomp,"channel", mgblock, "channel")
else: # if SynChan or even NMDAChan, connect normally
moose.connect(postcomp,"channel", syn, "channel")
def make_new_synapse(self, syn_name, postcomp, syn_name_full):
## if channel does not exist in library load it from xml file
if not moose.exists('/library/'+syn_name):
cmlR = ChannelML(self.nml_params)
cmlR.readChannelMLFromFile(syn_name+'.xml')
## deep copies the library synapse to an instance under postcomp named as <arg3>
synid = moose.copy(moose.Neutral('/library/'+syn_name),postcomp,syn_name_full)
syn = moose.SynChan(synid)
childmgblock = utils.get_child_Mstring(syn,'mgblock')
#### connect the post compartment to the synapse
if childmgblock.value=='True': # If NMDA synapse based on mgblock, connect to mgblock
mgblock = moose.Mg_block(syn.path+'/mgblock')
moose.connect(postcomp,"channel", mgblock, "channel")
else: # if SynChan or even NMDAChan, connect normally
moose.connect(postcomp,"channel", syn, "channel")
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().
Returns (populationDict,projectionDict),
see doc string of NetworkML.readNetworkML() for details.
"""
print "Loading neuroml file ... ", filename
moose.Neutral('/library') # creates /library in MOOSE tree; elif present, wraps
tree = ET.parse(filename)
root_element = tree.getroot()
self.model_dir = path.dirname( path.abspath( filename ) )
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,
'model_dir':self.model_dir,
}
#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 ... "
print self.cellsDict
nmlR = NetworkML(self.nml_params)
print self.cellsDict
return nmlR.readNetworkML(root_element,self.cellsDict,params=params,lengthUnits=self.lengthUnits)
def set_compartment_param(self, compartment, name, value, mechanismname):
""" Set the param for the compartment depending on name and mechanismname. """
if name == 'CM':
compartment.Cm = value*math.pi*compartment.diameter*compartment.length
elif name == 'RM':
compartment.Rm = value/(math.pi*compartment.diameter*compartment.length)
elif name == 'RA':
compartment.Ra = value*compartment.length/(math.pi*(compartment.diameter/2.0)**2)
elif name == 'Em':
compartment.Em = value
elif name == 'initVm':
compartment.initVm = value
elif name == 'inject':
print compartment.name, 'inject', value, 'A.'
compartment.inject = value
elif mechanismname is 'synapse': # synapse being added to the compartment
## these are potential locations, we do not actually make synapses.
#synapse = self.context.deepCopy(self.context.pathToId('/library/'+value),\
# self.context.pathToId(compartment.path),value) # value contains name of synapse i.e. synapse_type
#moose.connect(compartment,"channel", synapse, "channel")
## I assume below that compartment name has _segid at its end
segid = string.split(compartment.name,'_')[-1] # get segment id from compartment name
self.segDict[segid][5].append(value)
elif mechanismname is 'spikegen': # spikegen being added to the compartment
## these are potential locations, we do not actually make the spikegens.
## spikegens for different synapses can have different thresholds,
## hence include synapse_type in its name
## value contains name of synapse i.e. synapse_type
#spikegen = moose.SpikeGen(compartment.path+'/'+value+'_spikegen')
#moose.connect(compartment,"VmSrc",spikegen,"Vm")
pass
elif mechanismname is not None:
## if mechanism is not present in compartment, deep copy from library
if not moose.exists(compartment.path+'/'+mechanismname):
## if channel does not exist in library load it from xml file
if not moose.exists("/library/"+mechanismname):
cmlR = ChannelML(self.nml_params)
model_filename = mechanismname+'.xml'
model_path = find_first_file(model_filename,self.model_dir)
if model_path is not None:
cmlR.readChannelMLFromFile(model_path)
else:
raise IOError(
'For mechanism {0}: files {1} not found under {2}.'.format(
mechanismname, model_filename, self.model_dir
)
)
neutralObj = moose.Neutral("/library/"+mechanismname)
if 'CaConc' == neutralObj.class_: # Ion concentration pool
libcaconc = moose.CaConc("/library/"+mechanismname)
## deep copies the library caconc under the compartment
channel = moose.copy(libcaconc,compartment,mechanismname)
channel = moose.CaConc(channel)
## CaConc connections are made later using connect_CaConc()
## Later, when calling connect_CaConc,
## B is set for caconc based on thickness of Ca shell and compartment l and dia.
elif 'HHChannel2D' == neutralObj.class_ : ## HHChannel2D
libchannel = moose.HHChannel2D("/library/"+mechanismname)
## deep copies the library channel under the compartment
channel = moose.copy(libchannel,compartment,mechanismname)
channel = moose.HHChannel2D(channel)
moose.connect(channel,'channel',compartment,'channel')
elif 'HHChannel' == neutralObj.class_ : ## HHChannel
libchannel = moose.HHChannel("/library/"+mechanismname)
## deep copies the library channel under the compartment
channel = moose.copy(libchannel,compartment,mechanismname)
channel = moose.HHChannel(channel)
moose.connect(channel,'channel',compartment,'channel')
## if mechanism is present in compartment, just wrap it
else:
neutralObj = moose.Neutral(compartment.path+'/'+mechanismname)
if 'CaConc' == neutralObj.class_: # Ion concentration pool
caconc = moose.CaConc(compartment.path+'/'+mechanismname) # wraps existing channel
elif 'HHChannel2D' == neutralObj.class_ : ## HHChannel2D
channel = moose.HHChannel2D(compartment.path+'/'+mechanismname) # wraps existing channel
elif 'HHChannel' == neutralObj.class_ : ## HHChannel
channel = moose.HHChannel(compartment.path+'/'+mechanismname) # wraps existing channel
if name == 'Gbar':
channel.Gbar = value*math.pi*compartment.diameter*compartment.length
elif name == 'Ek':
channel.Ek = value
elif name == 'thick':
caconc.thick = value ## JUST THIS WILL NOT DO - HAVE TO SET B based on this thick!
## Later, when calling connect_CaConc,
## B is set for caconc based on thickness of Ca shell and compartment l and dia.
if neuroml_debug: print "Setting ",name," for ",compartment.path," value ",value
def set_compartment_param(self, compartment, name, value, mechName):
""" Set the param for the compartment depending on name and mechName. """
if name == 'CM':
compartment.Cm = value *math.pi*compartment.diameter*compartment.length
elif name == 'RM':
compartment.Rm = value/(math.pi*compartment.diameter*compartment.length)
elif name == 'RA':
compartment.Ra = value * compartment.length / \
(math.pi*(compartment.diameter/2.0)**2)
elif name == 'Em':
compartment.Em = value
elif name == 'initVm':
compartment.initVm = value
elif name == 'inject':
msg = " {0} inject {1} A.".format(compartment.name, value)
debug.printDebug("INFO", msg)
compartment.inject = value
elif mechName is 'synapse':
# synapse being added to the compartment
# these are potential locations, we do not actually make synapses.
# I assume below that compartment name has _segid at its end
# get segment id from compartment name
segid = moose_methods.getCompartmentId(compartment.name)
self.segDict[segid][5].append(value)
# spikegen being added to the compartment
elif mechName is 'spikegen':
# these are potential locations, we do not actually make the
# spikegens. spikegens for different synapses can have different
# thresholds, hence include synapse_type in its name value contains
# name of synapse i.e. synapse_type
#spikegen = moose.SpikeGen(compartment.path+'/'+value+'_spikegen')
#moose.connect(compartment,"VmSrc",spikegen,"Vm")
pass
elif mechName is not None:
# if mechanism is not present in compartment, deep copy from library
if not moose.exists(compartment.path+'/'+mechName):
# if channel does not exist in library load it from xml file
if not moose.exists(self.libraryPath+"/"+mechName):
cmlR = ChannelML(self.nml_params)
model_filename = mechName+'.xml'
model_path = neuroml_utils.find_first_file(
model_filename
, self.model_dir
)
if model_path is not None:
cmlR.readChannelMLFromFile(model_path)
else:
msg = 'Mechanism {0}: files {1} not found under {2}'\
.format( mechName
, model_filename
, self.model_dir
)
debug.printDebug("ERROR"
, msg
, frame = inspect.currentframe()
)
sys.exit(0)
neutralObj = moose.Neutral(self.libraryPath+"/"+mechName)
# Ion concentration pool
if 'CaConc' == neutralObj.className:
libcaconc = moose.CaConc(self.libraryPath+"/"+mechName)
# deep copies the library caconc under the compartment
caconc = moose.copy(libcaconc,compartment,mechName)
caconc = moose.CaConc(caconc)
# CaConc connections are made later using connect_CaConc()
# Later, when calling connect_CaConc, B is set for caconc
# based on thickness of Ca shell and compartment l and dia
# OR based on the Mstring phi under CaConc path.
channel = None
elif 'HHChannel2D' == neutralObj.className : ## HHChannel2D
libchannel = moose.HHChannel2D(self.libraryPath+"/"+mechName)
## deep copies the library channel under the compartment
channel = moose.copy(libchannel,compartment,mechName)
channel = moose.HHChannel2D(channel)
moose.connect(channel,'channel',compartment,'channel')
elif 'HHChannel' == neutralObj.className : ## HHChannel
libchannel = moose.HHChannel(self.libraryPath+"/"+mechName)
# deep copies the library channel under the compartment
channel = moose.copy(libchannel,compartment,mechName)
channel = moose.HHChannel(channel)
moose.connect(channel,'channel',compartment,'channel')
# if mechanism is present in compartment, just wrap it
else:
neutralObj = moose.Neutral(compartment.path+'/'+mechName)
# Ion concentration pool
if 'CaConc' == neutralObj.className:
# wraps existing channel
caconc = moose.CaConc(compartment.path+'/'+mechName)
channel = None
elif 'HHChannel2D' == neutralObj.className: ## HHChannel2D
# wraps existing channel
channel = moose.HHChannel2D(compartment.path+'/'+mechName)
elif 'HHChannel' == neutralObj.className : ## HHChannel
# wraps existing channel
channel = moose.HHChannel(compartment.path+'/'+mechName)
if name == 'Gbar':
# if CaConc, neuroConstruct uses gbar for thickness or phi
if channel is None:
# If child Mstring 'phi' is present, set gbar as phi BUT,
# value has been multiplied by Gfactor as a Gbar, SI or
# physiological not known here, ignoring Gbar for CaConc,
# instead of passing units here
child = moose_utils.get_child_Mstring(caconc,'phi')
if child is not None:
#child.value = value
pass
else:
#caconc.thick = value
pass
else: # if ion channel, usual Gbar
channel.Gbar = value * math.pi * compartment.diameter \
* compartment.length
elif name == 'Ek':
channel.Ek = value
# thick seems to be NEURON's extension to NeuroML level 2.
elif name == 'thick':
# JUST THIS WILL NOT DO - HAVE TO SET B based on this thick!
caconc.thick = value
# Later, when calling connect_CaConc, B is set for caconc based
# on thickness of Ca shell and compartment l and dia. OR based
# on the Mstring phi under CaConc path.
if neuroml_utils.neuroml_debug:
msg = "Setting {0} for {1} value {2}".format(name, compartment.path
, value
)
debug.printDebug("DEBUG", msg, frame=inspect.currentframe())
def createProjections(self):
self.projectionDict={}
projections = self.network.find(".//{"+nml_ns+"}projections")
if projections is not None:
if projections.attrib["units"] == 'Physiological Units': # see pg 219 (sec 13.2) of Book of Genesis
Efactor = 1e-3 # V from mV
Tfactor = 1e-3 # s from ms
else:
Efactor = 1.0
Tfactor = 1.0
for projection in self.network.findall(".//{"+nml_ns+"}projection"):
projectionname = projection.attrib["name"]
print "setting",projectionname
source = projection.attrib["source"]
target = projection.attrib["target"]
self.projectionDict[projectionname] = (source,target,[])
for syn_props in projection.findall(".//{"+nml_ns+"}synapse_props"):
syn_name = syn_props.attrib['synapse_type']
## if synapse does not exist in library load it from xml file
if not moose.exists("/library/"+syn_name):
cmlR = ChannelML(self.nml_params)
model_filename = syn_name+'.xml'
model_path = find_first_file(model_filename,self.model_dir)
if model_path is not None:
cmlR.readChannelMLFromFile(model_path)
else:
raise IOError(
'For mechanism {0}: files {1} not found under {2}.'.format(
mechanismname, model_filename, self.model_dir
)
)
weight = float(syn_props.attrib['weight'])
threshold = float(syn_props.attrib['threshold'])*Efactor
if 'prop_delay' in syn_props.attrib:
prop_delay = float(syn_props.attrib['prop_delay'])*Tfactor
elif 'internal_delay' in syn_props.attrib:
prop_delay = float(syn_props.attrib['internal_delay'])*Tfactor
else: prop_delay = 0.0
for connection in projection.findall(".//{"+nml_ns+"}connection"):
pre_cell_id = connection.attrib['pre_cell_id']
post_cell_id = connection.attrib['post_cell_id']
if 'file' not in pre_cell_id:
# source could be 'mitrals', self.populationDict[source][0] would be 'mitral'
pre_cell_name = self.populationDict[source][0]
if 'pre_segment_id' in connection.attrib:
pre_segment_id = connection.attrib['pre_segment_id']
else: pre_segment_id = "0" # assume default segment 0, usually soma
pre_segment_path = self.populationDict[source][1][int(pre_cell_id)].path+'/'+\
self.cellSegmentDict[pre_cell_name][pre_segment_id][0]
else:
# I've removed extra excitation provided via files, so below comment doesn't apply.
# 'file[+<glomnum>]_<filenumber>' # glomnum is
# for mitral_granule extra excitation from unmodelled sisters.
pre_segment_path = pre_cell_id+'_'+connection.attrib['pre_segment_id']
# target could be 'PGs', self.populationDict[target][0] would be 'PG'
post_cell_name = self.populationDict[target][0]
if 'post_segment_id' in connection.attrib:
post_segment_id = connection.attrib['post_segment_id']
else: post_segment_id = "0" # assume default segment 0, usually soma
post_segment_path = self.populationDict[target][1][int(post_cell_id)].path+'/'+\
self.cellSegmentDict[post_cell_name][post_segment_id][0]
self.projectionDict[projectionname][2].append((syn_name, pre_segment_path, post_segment_path))
properties = connection.findall('./{'+nml_ns+'}properties')
if len(properties)==0:
self.connect(syn_name, pre_segment_path, post_segment_path, weight, threshold, prop_delay)
else:
for props in properties:
synapse_type = props.attrib['synapse_type']
if syn_name in synapse_type:
weight_override = float(props.attrib['weight'])
if 'internal_delay' in props.attrib:
delay_override = float(props.attrib['internal_delay'])
else: delay_override = prop_delay
if weight_override != 0.0:
self.connect(syn_name, pre_segment_path, post_segment_path,\
weight_override, threshold, delay_override)
def set_compartment_param(self, compartment, name, value, mechName):
""" Set the param for the compartment depending on name and mechName. """
if name == 'CM':
compartment.Cm = value * math.pi * compartment.diameter * compartment.length
elif name == 'RM':
compartment.Rm = value / (math.pi * compartment.diameter *
compartment.length)
elif name == 'RA':
compartment.Ra = value * compartment.length / \
(math.pi*(compartment.diameter/2.0)**2)
elif name == 'Em':
compartment.Em = value
elif name == 'initVm':
compartment.initVm = value
elif name == 'inject':
msg = " {0} inject {1} A.".format(compartment.name, value)
debug.printDebug("INFO", msg)
compartment.inject = value
elif mechName is 'synapse':
# synapse being added to the compartment
# these are potential locations, we do not actually make synapses.
# I assume below that compartment name has _segid at its end
# get segment id from compartment name
segid = moose_methods.getCompartmentId(compartment.name)
self.segDict[segid][5].append(value)
# spikegen being added to the compartment
elif mechName is 'spikegen':
# these are potential locations, we do not actually make the
# spikegens. spikegens for different synapses can have different
# thresholds, hence include synapse_type in its name value contains
# name of synapse i.e. synapse_type
#spikegen = moose.SpikeGen(compartment.path+'/'+value+'_spikegen')
#moose.connect(compartment,"VmSrc",spikegen,"Vm")
pass
elif mechName is not None:
# if mechanism is not present in compartment, deep copy from library
if not moose.exists(compartment.path + '/' + mechName):
# if channel does not exist in library load it from xml file
if not moose.exists(self.libraryPath + "/" + mechName):
cmlR = ChannelML(self.nml_params)
model_filename = mechName + '.xml'
model_path = neuroml_utils.find_first_file(
model_filename, self.model_dir)
if model_path is not None:
cmlR.readChannelMLFromFile(model_path)
else:
msg = 'Mechanism {0}: files {1} not found under {2}'\
.format( mechName
, model_filename
, self.model_dir
)
debug.printDebug("ERROR",
msg,
frame=inspect.currentframe())
sys.exit(0)
neutralObj = moose.Neutral(self.libraryPath + "/" + mechName)
# Ion concentration pool
if 'CaConc' == neutralObj.className:
libcaconc = moose.CaConc(self.libraryPath + "/" + mechName)
# deep copies the library caconc under the compartment
caconc = moose.copy(libcaconc, compartment, mechName)
caconc = moose.CaConc(caconc)
# CaConc connections are made later using connect_CaConc()
# Later, when calling connect_CaConc, B is set for caconc
# based on thickness of Ca shell and compartment l and dia
# OR based on the Mstring phi under CaConc path.
channel = None
elif 'HHChannel2D' == neutralObj.className: ## HHChannel2D
libchannel = moose.HHChannel2D(self.libraryPath + "/" +
mechName)
## deep copies the library channel under the compartment
channel = moose.copy(libchannel, compartment, mechName)
channel = moose.HHChannel2D(channel)
moose.connect(channel, 'channel', compartment, 'channel')
elif 'HHChannel' == neutralObj.className: ## HHChannel
libchannel = moose.HHChannel(self.libraryPath + "/" +
mechName)
# deep copies the library channel under the compartment
channel = moose.copy(libchannel, compartment, mechName)
channel = moose.HHChannel(channel)
moose.connect(channel, 'channel', compartment, 'channel')
# if mechanism is present in compartment, just wrap it
else:
neutralObj = moose.Neutral(compartment.path + '/' + mechName)
# Ion concentration pool
if 'CaConc' == neutralObj.className:
# wraps existing channel
caconc = moose.CaConc(compartment.path + '/' + mechName)
channel = None
elif 'HHChannel2D' == neutralObj.className: ## HHChannel2D
# wraps existing channel
channel = moose.HHChannel2D(compartment.path + '/' +
mechName)
elif 'HHChannel' == neutralObj.className: ## HHChannel
# wraps existing channel
channel = moose.HHChannel(compartment.path + '/' +
mechName)
if name == 'Gbar':
# if CaConc, neuroConstruct uses gbar for thickness or phi
if channel is None:
# If child Mstring 'phi' is present, set gbar as phi BUT,
# value has been multiplied by Gfactor as a Gbar, SI or
# physiological not known here, ignoring Gbar for CaConc,
# instead of passing units here
child = moose_utils.get_child_Mstring(caconc, 'phi')
if child is not None:
#child.value = value
pass
else:
#caconc.thick = value
pass
else: # if ion channel, usual Gbar
channel.Gbar = value * math.pi * compartment.diameter \
* compartment.length
elif name == 'Ek':
channel.Ek = value
# thick seems to be NEURON's extension to NeuroML level 2.
elif name == 'thick':
# JUST THIS WILL NOT DO - HAVE TO SET B based on this thick!
caconc.thick = value
# Later, when calling connect_CaConc, B is set for caconc based
# on thickness of Ca shell and compartment l and dia. OR based
# on the Mstring phi under CaConc path.
if neuroml_utils.neuroml_debug:
msg = "Setting {0} for {1} value {2}".format(
name, compartment.path, value)
debug.printDebug("DEBUG", msg, frame=inspect.currentframe())
