def readMorphMLFromFile(self, filename, params={}):
"""
specify params for this MorphML file as a dict:
presently combineSegments and createPotentialSynapses are implemented.
See readMorphML().
See also nml_params in __init__().
returns { cellname1 : (segDict,cableDict), ... }
see readMorphML(...) for segDict and cableDict
"""
mu.info("Reading morphology from %s" % filename)
try:
tree = ET.parse(filename)
except Exception as e:
mu.error("Failed to load morphology from file %s" % filename)
neuroml_element = tree.getroot()
cellsDict = {}
for cell in neuroml_element.findall('.//{' + self.neuroml + '}cell'):
if 'lengthUnits' in neuroml_element.attrib:
lengthUnits = neuroml_element.attrib['lengthUnits']
else:
lengthUnits = 'micrometer'
cellDict = self.readMorphML(cell, params, lengthUnits)
cellsDict.update(cellDict)
return cellsDict
def run( nogui = True ):
global SCRIPT_DIR
filename = os.path.join(SCRIPT_DIR, 'test_files/passiveCell.nml' )
mu.info('Loading: %s' % filename )
nml = moose.mooseReadNML2( filename )
if not nml:
mu.warn( "Failed to parse NML2 file" )
return
assert nml, "Expecting NML2 object"
msoma = nml.getComp(nml.doc.networks[0].populations[0].id,0,0)
data = moose.Neutral('/data')
pg = nml.getInput('pulseGen1')
inj = moose.Table('%s/pulse' % (data.path))
moose.connect(inj, 'requestOut', pg, 'getOutputValue')
vm = moose.Table('%s/Vm' % (data.path))
moose.connect(vm, 'requestOut', msoma, 'getVm')
simtime = 150e-3
moose.reinit()
moose.start(simtime)
print("Finished simulation!")
t = np.linspace(0, simtime, len(vm.vector))
yvec = vm.vector
injvec = inj.vector * 1e12
m1, u1 = np.mean( yvec ), np.std( yvec )
m2, u2 = np.mean( injvec ), np.std( injvec )
assert np.isclose( m1, -0.0456943 ), m1
assert np.isclose( u1, 0.0121968 ), u1
assert np.isclose( m2, 26.64890 ), m2
assert np.isclose( u2, 37.70607574 ), u2
quit( 0 )
def calculateRateFn(self, ratefn, vmin, vmax, tablen=3000, vShift='0mV'):
"""Returns A / B table from ngate."""
tab = np.linspace(vmin, vmax, tablen)
if self._is_standard_nml_rate(ratefn):
midpoint, rate, scale = map(
SI, (ratefn.midpoint, ratefn.rate, ratefn.scale))
return self.rate_fn_map[ratefn.type](tab, rate, scale, midpoint)
else:
for ct in self.doc.ComponentType:
if ratefn.type == ct.name:
mu.info("Using %s to evaluate rate" % ct.name)
rate = []
for v in tab:
vals = pynml.evaluate_component(
ct,
req_variables={
'v': '%sV' % v,
'vShift': vShift,
'temperature': self._getTemperature()
})
'''mu.info vals'''
if 'x' in vals:
rate.append(vals['x'])
if 't' in vals:
rate.append(vals['t'])
if 'r' in vals:
rate.append(vals['r'])
return np.array(rate)
def test_nml2(nogui=True):
global SCRIPT_DIR
filename = SCRIPT_DIR / 'test_files' / 'passiveCell.nml'
mu.info('Loading: %s' % filename)
nml = moose.readNML2(filename)
if not nml:
mu.warn("Failed to parse NML2 file")
return
assert nml, "Expecting NML2 object"
msoma = nml.getComp(nml.doc.networks[0].populations[0].id, 0, 0)
data = moose.Neutral('/data')
pg = nml.getInput('pulseGen1')
inj = moose.Table('%s/pulse' % (data.path))
moose.connect(inj, 'requestOut', pg, 'getOutputValue')
vm = moose.Table('%s/Vm' % (data.path))
moose.connect(vm, 'requestOut', msoma, 'getVm')
simtime = 150e-3
moose.reinit()
moose.start(simtime)
print("Finished simulation!")
yvec = vm.vector
injvec = inj.vector * 1e12
m1, u1 = np.mean(yvec), np.std(yvec)
m2, u2 = np.mean(injvec), np.std(injvec)
assert np.isclose(m1, -0.0456943), m1
assert np.isclose(u1, 0.0121968), u1
assert np.isclose(m2, 26.64890), m2
assert np.isclose(u2, 37.70607574), u2
def importChannelsToCell(self, nmlcell, moosecell, membrane_properties):
sg_to_segments = self._cell_to_sg[nmlcell]
for chdens in membrane_properties.channel_densities + membrane_properties.channel_density_v_shifts:
segments = getSegments(nmlcell, chdens, sg_to_segments)
condDensity = SI(chdens.cond_density)
erev = SI(chdens.erev)
try:
ionChannel = self.id_to_ionChannel[chdens.ion_channel]
except KeyError:
mu.info('No channel with id', chdens.ion_channel)
continue
if self.verbose:
mu.info(
'Setting density of channel %s in %s to %s; erev=%s (passive: %s)'
% (chdens.id, segments, condDensity, erev,
self.isPassiveChan(ionChannel)))
if self.isPassiveChan(ionChannel):
for seg in segments:
comp = self.nml_to_moose[seg]
setRm(self.nml_to_moose[seg], condDensity)
setEk(self.nml_to_moose[seg], erev)
else:
for seg in segments:
self.copyChannel(chdens, self.nml_to_moose[seg],
condDensity, erev)
'''moose.le(self.nml_to_moose[seg])
def copyChannel(self, chdens, comp, condDensity, erev):
"""Copy moose prototype for `chdens` condutcance density to `comp`
compartment.
"""
proto_chan = None
if chdens.ion_channel in self.proto_chans:
proto_chan = self.proto_chans[chdens.ion_channel]
else:
for innerReader in self.includes.values():
if chdens.ionChannel in innerReader.proto_chans:
proto_chan = innerReader.proto_chans[chdens.ion_channel]
break
if not proto_chan:
raise Exception('No prototype channel for %s referred to by %s' %
(chdens.ion_channel, chdens.id))
if self.verbose:
mu.info('Copying %s to %s, %s; erev=%s' %
(chdens.id, comp, condDensity, erev))
orig = chdens.id
chid = moose.copy(proto_chan, comp, chdens.id)
chan = moose.element(chid)
for p in self.paths_to_chan_elements.keys():
pp = p.replace('%s/' % chdens.ion_channel, '%s/' % orig)
self.paths_to_chan_elements[pp] = self.paths_to_chan_elements[
p].replace('%s/' % chdens.ion_channel, '%s/' % orig)
#mu.info(self.paths_to_chan_elements)
chan.Gbar = sarea(comp) * condDensity
chan.Ek = erev
moose.connect(chan, 'channel', comp, 'channel')
return chan
def importMembraneProperties(self, nmlcell, moosecell, mp):
"""Create the membrane properties from nmlcell in moosecell"""
if self.verbose:
mu.info('Importing membrane properties')
self.importCapacitances(nmlcell, moosecell, mp.specific_capacitances)
self.importChannelsToCell(nmlcell, moosecell, mp)
self.importInitMembPotential(nmlcell, moosecell, mp)
def read(self, filename, symmetric=True):
filename = os.path.realpath(filename)
self.doc = nml.loaders.read_neuroml2_file(filename,
include_includes=True,
verbose=self.verbose)
if self.verbose:
mu.info('Parsed NeuroML2 file: %s' % filename)
self.filename = filename
if len(self.doc.networks) >= 1:
self.network = self.doc.networks[0]
moose.celsius = self._getTemperature()
self.importConcentrationModels(self.doc)
self.importIonChannels(self.doc)
self.importInputs(self.doc)
for cell in self.doc.cells:
self.createCellPrototype(cell, symmetric=symmetric)
if len(self.doc.networks) >= 1:
self.createPopulations()
self.createInputs()
mu.info("Read all from %s" % filename)
def stimulus_text():
stimtext = [ 'load_file("stdrun.hoc")' ]
mu.info(" Default sim time is 0.1 second. Change it in script.")
#stimtext.append('dt=%s' % plot_dt_)
stimtext.append('tstop=%s' % 100)
stimtext.append('cvode.active(1)')
stimtext.append('finitialize()')
stimtext.append('run()')
stimtext.append("\n")
stimtext = "\n".join(stimtext)
return stimtext
def createPopulations(self):
for pop in self.network.populations:
mpop = moose.Neutral('%s/%s' % (self.lib.path, pop.id))
self.cells_in_populations[pop.id] = {}
for i in range(pop.size):
mu.info("Creating %s/%s instances of %s under %s" %
(i, pop.size, pop.component, mpop))
self.pop_to_cell_type[pop.id] = pop.component
chid = moose.copy(self.proto_cells[pop.component], mpop,
'%s' % (i))
self.cells_in_populations[pop.id][i] = chid
def modelInfo(path : str = '/##', **kwargs) -> str:
"""Generate the list of all available moose-elements in model
"""
mu.info(f"Couting elements in model under {path}")
msg = []
types = [ "Table", "Table2", "Compartment", "Pool", "BufPool", "Enz", "Reac" ]
for t in types:
paths = moose.wildcardFind("{}[TYPE={}]".format(path, t))
if len(paths) > 0:
msg.append("{:>20} : {}".format(t, len(paths)))
return "\n".join(msg)
def importBiophysics(self, nmlcell, moosecell):
"""Create the biophysical components in moose Neuron `moosecell`
according to NeuroML2 cell `nmlcell`."""
bp = nmlcell.biophysical_properties
if bp is None:
mu.info('Warning: %s in %s has no biophysical properties' %
(nmlcell.id, self.filename))
return
self.importMembraneProperties(nmlcell, moosecell,
bp.membrane_properties)
self.importIntracellularProperties(nmlcell, moosecell,
bp.intracellular_properties)
def mooseReadNML2( modelpath ):
"""Read NeuroML model (version 2).
"""
global nml2Import_
if nml2Import_:
reader = _neuroml2.NML2Reader( )
reader.read( modelpath )
return reader
else:
mu.info( nml2ImportError_ )
mu.warn( "Could not load NML2 support. Doing nothing" )
return False
def importIonChannels(self, doc, vmin=-150e-3, vmax=100e-3, vdivs=5000):
if self.verbose:
mu.info(self.filename, 'Importing the ion channels')
for chan in doc.ion_channel + doc.ion_channel_hhs:
if chan.type == 'ionChannelHH':
mchan = self.createHHChannel(chan)
elif self.isPassiveChan(chan):
mchan = self.createPassiveChannel(chan)
else:
mchan = self.createHHChannel(chan)
self.id_to_ionChannel[chan.id] = chan
self.nml_to_moose[chan] = mchan
self.proto_chans[chan.id] = mchan
if self.verbose:
mu.info(self.filename, 'Created ion channel', mchan.path,
'for', chan.type, chan.id)
def to_neuron(path, **kwargs):
moose.reinit()
mooseCompts = moose.wildcardFind('%s/##[TYPE=Compartment]' % path)
zombiles = moose.wildcardFind('%s/##[TYPE=ZombieCompartment]'% path)
compts = set(mooseCompts).union(set(zombiles))
headerText = []
comptText = []
for c in compts:
comptText.append(create_section_in_neuron(c))
connectionText = []
for c in compts:
connectionText.append(connect_neuron_sections(c))
pulsetext = []
for stim in moose.wildcardFind('%s/##[TYPE=PulseGen]' % path):
pulsetext.append(insert_pulsegen(stim))
recordText, tableList = [], []
text = []
text.append('objref rect')
text.append('rect = new Vector()')
text.append('rect.record(&t)')
recordText.append("\n".join(text))
for i, table in enumerate(moose.wildcardFind('%s/##[TYPE=Table]' % path)):
text, tableName = insert_record(i, table)
recordText.append(text)
tableList.append(tableName)
stimtext = stimulus_text()
plottext = plot_text(tableList)
outfile = kwargs.get('outfile', 'moose_to_neuron.hoc')
mu.info("Writing neuron model to %s" % outfile)
with open(outfile, "w") as f:
f.writelines(headerText)
f.writelines(comptText)
f.writelines(connectionText)
f.writelines(recordText)
f.writelines(pulsetext)
f.writelines(stimtext)
f.writelines(plottext)
def createDecayingPoolConcentrationModel(self, concModel):
"""Create prototype for concentration model"""
if concModel.name is not None:
name = concModel.name
else:
name = concModel.id
ca = moose.CaConc('%s/%s' % (self.lib.path, id))
mu.info('11111', concModel.restingConc)
mu.info('2222', concModel.decayConstant)
mu.info('33333', concModel.shellThickness)
ca.CaBasal = SI(concModel.restingConc)
ca.tau = SI(concModel.decayConstant)
ca.thick = SI(concModel.shellThickness)
ca.B = 5.2e-6 # B = 5.2e-6/(Ad) where A is the area of the shell and d is thickness - must divide by shell volume when copying
self.proto_pools[concModel.id] = ca
self.nml_to_moose[concModel.id] = ca
self.moose_to_nml[ca] = concModel
logger.debug('Created moose element: %s for nml conc %s' %
(ca.path, concModel.id))
def main():
print(dir(mu))
mu.info('Hellow')
def createPassiveChannel(self, chan):
mchan = moose.Leakage('%s/%s' % (self.lib.path, chan.id))
if self.verbose:
mu.info(self.filename, 'Created', mchan.path, 'for', chan.id)
return mchan
def createHHChannel(self, chan, vmin=-150e-3, vmax=100e-3, vdivs=5000):
mchan = moose.HHChannel('%s/%s' % (self.lib.path, chan.id))
mgates = map(moose.element, (mchan.gateX, mchan.gateY, mchan.gateZ))
assert (len(chan.gate_hh_rates) <= 3
) # We handle only up to 3 gates in HHCHannel
if self.verbose:
mu.info('== Creating channel: %s (%s) -> %s (%s)' %
(chan.id, chan.gate_hh_rates, mchan, mgates))
all_gates = chan.gates + chan.gate_hh_rates
for ngate, mgate in zip(all_gates, mgates):
if mgate.name.endswith('X'):
mchan.Xpower = ngate.instances
elif mgate.name.endswith('Y'):
mchan.Ypower = ngate.instances
elif mgate.name.endswith('Z'):
mchan.Zpower = ngate.instance
mgate.min = vmin
mgate.max = vmax
mgate.divs = vdivs
# I saw only examples of GateHHRates in
# HH-channels, the meaning of forwardRate and
# reverseRate and steadyState are not clear in the
# classes GateHHRatesInf, GateHHRatesTau and in
# FateHHTauInf the meaning of timeCourse and
# steady state is not obvious. Is the last one
# refering to tau_inf and m_inf??
fwd = ngate.forward_rate
rev = ngate.reverse_rate
self.paths_to_chan_elements[
'%s/%s' %
(chan.id, ngate.id)] = '%s/%s' % (chan.id, mgate.name)
q10_scale = 1
if ngate.q10_settings:
if ngate.q10_settings.type == 'q10Fixed':
q10_scale = float(ngate.q10_settings.fixed_q10)
elif ngate.q10_settings.type == 'q10ExpTemp':
q10_scale = math.pow(
float(ngate.q10_settings.q10_factor),
(self._getTemperature() -
SI(ngate.q10_settings.experimental_temp)) / 10)
#mu.info('Q10: %s; %s; %s; %s'%(ngate.q10_settings.q10_factor, self._getTemperature(),SI(ngate.q10_settings.experimental_temp),q10_scale))
else:
raise Exception(
'Unknown Q10 scaling type %s: %s' %
(ngate.q10_settings.type, ngate.q10_settings))
if self.verbose:
mu.info(
' === Gate: %s; %s; %s; %s; %s; scale=%s' %
(ngate.id, mgate.path, mchan.Xpower, fwd, rev, q10_scale))
if (fwd is not None) and (rev is not None):
alpha = self.calculateRateFn(fwd, vmin, vmax, vdivs)
beta = self.calculateRateFn(rev, vmin, vmax, vdivs)
mgate.tableA = q10_scale * (alpha)
mgate.tableB = q10_scale * (alpha + beta)
# Assuming the meaning of the elements in GateHHTauInf ...
if hasattr(ngate,'time_course') and hasattr(ngate,'steady_state') \
and (ngate.time_course is not None) and (ngate.steady_state is not None):
tau = ngate.time_course
inf = ngate.steady_state
tau = self.calculateRateFn(tau, vmin, vmax, vdivs)
inf = self.calculateRateFn(inf, vmin, vmax, vdivs)
mgate.tableA = q10_scale * (inf / tau)
mgate.tableB = q10_scale * (1 / tau)
if hasattr(ngate, 'steady_state') and (
ngate.time_course is None) and (ngate.steady_state
is not None):
inf = ngate.steady_state
tau = 1 / (alpha + beta)
if (inf is not None):
inf = self.calculateRateFn(inf, vmin, vmax, vdivs)
mgate.tableA = q10_scale * (inf / tau)
mgate.tableB = q10_scale * (1 / tau)
if self.verbose:
mu.info(self.filename, '== Created', mchan.path, 'for', chan.id)
return mchan
def readMorphML(self, cell, params={}, lengthUnits="micrometer"):
"""
returns cellDict = { cellname: (segDict, cableDict) } # note: single cell only
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!
and cableDict = { cablegroupname : [campartment1name, compartment2name, ... ], ... }.
params is dict which can contain, combineSegments and/or createPotentialSynapses,
both boolean.
"""
if lengthUnits in ['micrometer', 'micron']:
self.length_factor = 1e-6
else:
self.length_factor = 1.0
cellname = cell.attrib["name"]
# creates /library in MOOSE tree; elif present, wraps
if not moose.exists('/library'):
moose.Neutral('/library')
mu.info("Loading cell %s into /library ." % cellname)
#~ moosecell = moose.Cell('/library/'+cellname)
#using moose Neuron class - in previous version 'Cell' class Chaitanya
moosecell = moose.Neuron('/library/' + cellname)
self.cellDictBySegmentId[cellname] = [moosecell, {}]
self.cellDictByCableId[cellname] = [moosecell, {}]
self.segDict = {}
if 'combineSegments' in params:
self.combineSegments = params['combineSegments']
else:
self.combineSegments = False
if 'createPotentialSynapses' in params:
self.createPotentialSynapses = params['createPotentialSynapses']
else:
self.createPotentialSynapses = False
mu.info("readMorphML using combineSegments = %s" %
self.combineSegments)
###############################################
#### load cablegroups into a dictionary
self.cablegroupsDict = {}
self.cablegroupsInhomoparamsDict = {}
## 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] = []
self.cablegroupsInhomoparamsDict[cablegroupname] = []
for cable in cablegroup.findall(".//{" + self.mml + "}cable"):
cableid = cable.attrib['id']
self.cablegroupsDict[cablegroupname].append(cableid)
# parse inhomogenous_params
for inhomogeneous_param in cablegroup.findall(
".//{" + self.mml + "}inhomogeneous_param"):
metric = inhomogeneous_param.find(".//{" + self.mml +
"}metric")
if metric.text == 'Path Length from root':
inhomoparamname = inhomogeneous_param.attrib['name']
inhomoparamvar = inhomogeneous_param.attrib['variable']
self.cablegroupsInhomoparamsDict[cablegroupname].append(\
(inhomoparamname,inhomoparamvar))
else:
mu.warning('Only "Path Length from root" metric is '
' supported currently, ignoring %s ' %
metric.text)
## <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 self.cablegroupsDict:
self.cablegroupsDict[cablegroupname].append(cableid)
else:
self.cablegroupsDict[cablegroupname] = [cableid]
###################################################
## load all mechanisms in this cell into /library for later copying
## set which compartments have integrate_and_fire mechanism
self.intFireCableIds = {
} # dict with keys as Compartments/cableIds which are IntFire
# with mechanismnames as values
for mechanism in cell.findall(".//{" + self.bio + "}mechanism"):
mechanismname = mechanism.attrib["name"]
passive = False
if "passive_conductance" in mechanism.attrib:
if mechanism.attrib['passive_conductance'] in [
"true", 'True', 'TRUE'
]:
passive = True
if not passive:
## if channel does not exist in library load it from xml file
if not moose.exists("/library/" + mechanismname):
mu.info("Loading mechanism %s into library." %
mechanismname)
cmlR = ChannelML(self.nml_params)
model_filename = mechanismname + '.xml'
model_path = neuroml_utils.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))
## set those compartments to be LIF for which
## any integrate_and_fire parameter is set
if not moose.exists("/library/" + mechanismname):
mu.warn("Mechanism doesn't exist: %s " % mechanismname)
moose.le('/library')
moosemech = moose.element("/library/" + mechanismname)
if moose.exists(moosemech.path + "/integrate_and_fire"):
mooseIaF = moose.element(
moosemech.path + "/integrate_and_fire") # Mstring
if mooseIaF.value in ['true', 'True', 'TRUE']:
mech_params = mechanism.findall(".//{" + self.bio +
"}parameter")
for parameter in mech_params:
parametername = parameter.attrib['name']
## check for the integrate_and_fire parameters
if parametername in [
'threshold', 't_refrac', 'v_reset',
'g_refrac'
]:
for group in parameter.findall(".//{" +
self.bio +
"}group"):
cablegroupname = group.text
if cablegroupname == 'all':
self.intFireCableIds = {
'all': mechanismname
}
break
else:
for cableid in self.cablegroupsDict[
cablegroupname]:
## only one intfire mechanism is allowed in a cable
## the last one parsed will override others
self.intFireCableIds[
cableid] = mechanismname
if 'all' in self.intFireCableIds:
break
############################################################
#### load morphology and connections between compartments
## Many neurons exported from NEURON have multiple segments in a section
## If self.combineSegments = True,
## then combine those segments into one Compartment / section
## for combining, 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:
running_cableid = ''
running_segid = ''
running_comp = None
running_diameter = 0.0
running_dia_nums = 0
segments = cell.findall(".//{" + self.mml + "}segment")
segmentstotal = len(segments)
for segnum, segment in enumerate(segments):
segmentname = segment.attrib['name']
## cable is an optional attribute. WARNING: Here I assume it is always present.
cableid = segment.attrib['cable']
segmentid = segment.attrib['id']
## if old cableid still running AND self.combineSegments == True,
## then don't start a new compartment, skip to next segment
if cableid == running_cableid and self.combineSegments:
self.cellDictBySegmentId[cellname][1][segmentid] = running_comp
proximal = segment.find('./{' + self.mml + '}proximal')
if proximal is not None:
running_diameter += float(
proximal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
distal = segment.find('./{' + self.mml + '}distal')
if distal is not None:
running_diameter += float(
distal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
## if (self.combineSegments and new cableid starts) or if not self.combineSegments,
## then start a new compartment
else:
## Create a new compartment
## the moose "hsolve" method assumes compartments to be
## asymmetric compartments and symmetrizes them
## but that is not what we want when translating
## from Neuron which has only symcompartments -- so be careful!
## Check if integrate_and_fire mechanism is present,
## if so use LIF instead of Compartment
moosecompname = segmentname + '_' + segmentid # just segmentname is NOT unique
# eg: mitral bbmit exported from NEURON
moosecomppath = moosecell.path + '/' + moosecompname
mechanismname = None
if 'all' in self.intFireCableIds:
mechanismname = self.intFireCableIds['all']
if cableid in self.intFireCableIds:
mechanismname = self.intFireCableIds[cableid]
if mechanismname is not None: # this cableid is an intfire
# create LIF (subclass of Compartment) and set to default values
moosecomp = moose.LIF(moosecomppath)
mname = '/library/' + mechanismname
moosechannel = moose.element(mname) if moose.exists(
mname) else moose.Neutral(mname)
# Mstring values are 'string'; make sure to convert them to
# float else it will seg-fault with python3+
moosechannelval = moose.Mstring(moosechannel.path +
'/vReset')
moosecomp.vReset = float(moosechannelval.value)
moosechannelval = moose.Mstring(moosechannel.path +
'/thresh')
moosecomp.thresh = float(moosechannelval.value)
moosechannelval = moose.Mstring(moosechannel.path +
'/refracT')
moosecomp.refractoryPeriod = eval(moosechannelval.value)
## refracG is currently not supported by moose.LIF
## when you implement it, check if refracG or g_refrac
## is a conductance density or a conductance, I think the former
#moosechannelval = moose.Mstring(moosechannel.path+'/refracG')
else:
moosecomp = moose.Compartment(moosecomppath)
self.cellDictBySegmentId[cellname][1][segmentid] = moosecomp
## cables are grouped and mechanism densities are set for cablegroups later.
## hence I will need to refer to segment according to which cable it belongs to.
## if combineSegments is False, there can be multiple segments per cable,
## so make array of compartments for cellDictByCableId[cellname][1][cableid]
if cableid in self.cellDictByCableId[cellname][1]:
self.cellDictByCableId[cellname][1][cableid].append(
moosecomp)
else:
self.cellDictByCableId[cellname][1][cableid] = [moosecomp]
running_cableid = cableid
running_segid = segmentid
running_comp = moosecomp
running_diameter = 0.0
running_dia_nums = 0
if 'parent' in segment.attrib:
parentid = segment.attrib[
'parent'] # I assume the parent is created before the child
# so that I can immediately connect the child.
parent = self.cellDictBySegmentId[cellname][1][parentid]
## It is always assumed that axial of parent is connected to raxial of moosesegment
## THIS IS WHAT GENESIS readcell() DOES!!! UNLIKE NEURON!
## THIS IS IRRESPECTIVE OF WHETHER PROXIMAL x,y,z OF PARENT = PROXIMAL x,y,z OF CHILD.
## THIS IS ALSO IRRESPECTIVE OF fraction_along_parent SPECIFIED IN CABLE!
## THUS THERE WILL BE NUMERICAL DIFFERENCES BETWEEN MOOSE/GENESIS and NEURON.
## moosesegment sends Ra and Vm to parent, parent sends only Vm
## actually for symmetric compartment, both parent and moosesegment require each other's Ra/2,
## but axial and raxial just serve to distinguish ends.
moose.connect(parent, 'axial', moosecomp, 'raxial')
else:
parent = None
proximal = segment.find('./{' + self.mml + '}proximal')
if proximal is None: # If proximal tag is not present,
# then parent attribute MUST be present in the segment tag!
## if proximal is not present, then
## by default the distal end of the parent is the proximal end of the child
moosecomp.x0 = parent.x
moosecomp.y0 = parent.y
moosecomp.z0 = parent.z
else:
moosecomp.x0 = float(
proximal.attrib["x"]) * self.length_factor
moosecomp.y0 = float(
proximal.attrib["y"]) * self.length_factor
moosecomp.z0 = float(
proximal.attrib["z"]) * self.length_factor
running_diameter += float(
proximal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
distal = segment.find('./{' + self.mml + '}distal')
if distal is not None:
running_diameter += float(
distal.attrib["diameter"]) * self.length_factor
running_dia_nums += 1
## finished creating new compartment
## Update the end position, diameter and length, and segDict of this comp/cable/section
## with each segment that is part of this cable (assumes contiguous segments in xml).
## This ensures that we don't have to do any 'closing ceremonies',
## if a new cable is encoutered in next iteration.
if distal is not None:
running_comp.x = float(distal.attrib["x"]) * self.length_factor
running_comp.y = float(distal.attrib["y"]) * self.length_factor
running_comp.z = float(distal.attrib["z"]) * self.length_factor
## Set the compartment diameter as the average diameter of all the segments in this section
running_comp.diameter = running_diameter / float(running_dia_nums)
## Set the compartment length
running_comp.length = math.sqrt((running_comp.x-running_comp.x0)**2+\
(running_comp.y-running_comp.y0)**2+(running_comp.z-running_comp.z0)**2)
## NeuroML specs say that if (x0,y0,z0)=(x,y,z), then round compartment e.g. soma.
## In Moose set length = dia to give same surface area as sphere of dia.
if running_comp.length == 0.0:
running_comp.length = running_comp.diameter
## Set the segDict
## the empty list at the end below will get populated
## with the potential synapses on this segment, in function set_compartment_param(..)
self.segDict[running_segid] = [running_comp.name,\
(running_comp.x0,running_comp.y0,running_comp.z0),\
(running_comp.x,running_comp.y,running_comp.z),\
running_comp.diameter,running_comp.length,[]]
if neuroml_utils.neuroml_debug:
mu.info('Set up compartment/section %s' % running_comp.name)
###############################################
#### load biophysics into the compartments
biophysics = cell.find(".//{" + self.neuroml + "}biophysics")
if biophysics is not None:
## see pg 219 (sec 13.2) of Book of Genesis for Physiological Units
if biophysics.attrib["units"] == 'Physiological Units':
CMfactor = 1e-2 # F/m^2 from microF/cm^2
Cfactor = 1e-6 # F from microF
RAfactor = 1e1 # Ohm*m from KOhm*cm
RMfactor = 1e-1 # Ohm*m^2 from KOhm*cm^2
Rfactor = 1e-3 # Ohm from KOhm
Efactor = 1e-3 # V from mV
Gfactor = 1e1 # S/m^2 from mS/cm^2
Ifactor = 1e-6 # A from microA
Tfactor = 1e-3 # s from ms
else:
CMfactor = 1.0
Cfactor = 1.0
RAfactor = 1.0
RMfactor = 1.0
Rfactor = 1.0
Efactor = 1.0
Gfactor = 1.0
Ifactor = 1.0
Tfactor = 1.0
spec_capacitance = cell.find(".//{" + self.bio +
"}spec_capacitance")
for parameter in spec_capacitance.findall(".//{" + self.bio +
"}parameter"):
self.set_group_compartment_param(cell, cellname, parameter,\
'CM', float(parameter.attrib["value"])*CMfactor, self.bio)
spec_axial_resitance = cell.find(".//{" + self.bio +
"}spec_axial_resistance")
for parameter in spec_axial_resitance.findall(".//{" + self.bio +
"}parameter"):
self.set_group_compartment_param(cell, cellname, parameter,\
'RA', float(parameter.attrib["value"])*RAfactor, self.bio)
init_memb_potential = cell.find(".//{" + self.bio +
"}init_memb_potential")
for parameter in init_memb_potential.findall(".//{" + self.bio +
"}parameter"):
self.set_group_compartment_param(cell, cellname, parameter,\
'initVm', float(parameter.attrib["value"])*Efactor, self.bio)
chan_distrib = [
] # the list for moose to parse inhomogeneous params (filled below)
for mechanism in cell.findall(".//{" + self.bio + "}mechanism"):
mechanismname = mechanism.attrib["name"]
passive = False
if "passive_conductance" in mechanism.attrib:
if mechanism.attrib['passive_conductance'] in [
"true", 'True', 'TRUE'
]:
passive = True
mu.info("Loading mechanism %s " % mechanismname)
## ONLY creates channel if at least one parameter (like gmax) is specified in the xml
## Neuroml does not allow you to specify all default values.
## However, granule cell example in neuroconstruct has Ca ion pool without
## a parameter, applying default values to all compartments!
mech_params = mechanism.findall(".//{" + self.bio +
"}parameter")
## if no params, apply all default values to all compartments
if len(mech_params) == 0:
for compartment_list in self.cellDictByCableId[cellname][
1].values():
for compartment in compartment_list:
self.set_compartment_param(compartment, None,
'default',
mechanismname)
## if params are present, apply params to specified cable/compartment groups
for parameter in mech_params:
parametername = parameter.attrib['name']
if passive:
if parametername in ['gmax']:
self.set_group_compartment_param(cell, cellname, parameter,\
'RM', RMfactor*1.0/float(parameter.attrib["value"]), self.bio)
elif parametername in ['e', 'erev']:
self.set_group_compartment_param(cell, cellname, parameter,\
'Em', Efactor*float(parameter.attrib["value"]), self.bio)
elif parametername in ['inject']:
self.set_group_compartment_param(cell, cellname, parameter,\
'inject', Ifactor*float(parameter.attrib["value"]), self.bio)
else:
mu.warning([
"Yo programmer of MorphML! You didn't",
" implement parameter %s " % parametername,
" in mechanism %s " % mechanismname
])
else:
if parametername in ['gmax']:
gmaxval = float(
eval(parameter.attrib["value"],
{"__builtins__": None}, {}))
self.set_group_compartment_param(cell, cellname, parameter,\
'Gbar', Gfactor*gmaxval, self.bio, mechanismname)
elif parametername in ['e', 'erev']:
self.set_group_compartment_param(cell, cellname, parameter,\
'Ek', Efactor*float(parameter.attrib["value"]), self.bio, mechanismname)
elif parametername in [
'depth'
]: # has to be type Ion Concentration!
self.set_group_compartment_param(cell, cellname, parameter,\
'thick', self.length_factor*float(parameter.attrib["value"]),\
self.bio, mechanismname)
elif parametername in ['v_reset']:
self.set_group_compartment_param(cell, cellname, parameter,\
'v_reset', Efactor*float(parameter.attrib["value"]),\
self.bio, mechanismname)
elif parametername in ['threshold']:
self.set_group_compartment_param(cell, cellname, parameter,\
'threshold', Efactor*float(parameter.attrib["value"]),\
self.bio, mechanismname)
elif parametername in ['t_refrac']:
self.set_group_compartment_param(cell, cellname, parameter,\
't_refrac', Tfactor*float(parameter.attrib["value"]),\
self.bio, mechanismname)
else:
mu.warning([
"Yo programmer of MorphML import! You didn't",
" implement parameter %s " % parametername,
" in mechanism %s " % mechanismname
])
## variable parameters:
## varying with:
## p, g, L, len, dia
## p: path distance from soma, measured along dendrite, in metres.
## g: geometrical distance from soma, in metres.
## L: electrotonic distance (# of lambdas) from soma, along dend. No units.
## len: length of compartment, in metres.
## dia: for diameter of compartment, in metres.
var_params = mechanism.findall(".//{" + self.bio +
"}variable_parameter")
if len(var_params) > 0:
## if variable params are present
## and use MOOSE to apply the variable formula
for parameter in var_params:
parametername = parameter.attrib['name']
cablegroupstr4moose = ""
## the neuroml spec says there should be a single group in a variable_parameter
## of course user can always have multiple variable_parameter tags,
## if user wants multiple groups conforming to neuroml specs.
group = parameter.find(".//{" + self.bio + "}group")
cablegroupname = group.text
if cablegroupname == 'all':
cablegroupstr4moose = "#"
else:
for cableid in self.cablegroupsDict[
cablegroupname]:
for compartment in self.cellDictByCableId[
cellname][1][cableid]:
cablegroupstr4moose += "#" + compartment.name + "#,"
if cablegroupstr4moose[-1] == ',':
cablegroupstr4moose = cablegroupstr4moose[:
-1] # remove last comma
inhomo_value = parameter.find(".//{" + self.bio +
"}inhomogeneous_value")
inhomo_value_name = inhomo_value.attrib['param_name']
inhomo_value_value = inhomo_value.attrib['value']
if parametername == 'gmax':
inhomo_eqn = '(' + inhomo_value_value + ')*' + str(
Gfactor)
# careful about physiol vs SI units
else:
inhomo_eqn = inhomo_value_value
mu.warning('Physiol. vs SI units translation not'
' implemented for parameter ' +
parametername + 'in channel ' +
mechanismname) + '. Use SI units'
'or ask for implementation.'
chan_distrib.extend(
(mechanismname, cablegroupstr4moose, parametername,
inhomo_eqn, ""))
# use extend, not append, moose wants it this way
## get mooose to parse the variable parameter gmax channel distributions
#pu.info("Some channel parameters distributed as per "+str(chan_distrib))
moosecell.channelDistribution = chan_distrib
#### Connect the Ca pools and channels
#### Am connecting these at the very end so that all channels and pools have been created
#### Note: this function is in moose.utils not moose.neuroml.utils !
for compartment_list in self.cellDictByCableId[cellname][1].values(
):
mu.connect_CaConc(compartment_list,\
self.temperature+neuroml_utils.ZeroCKelvin) # temperature should be in Kelvin for Nernst
##########################################################
#### load connectivity / synapses into the compartments
connectivity = cell.find(".//{" + self.neuroml + "}connectivity")
if connectivity is not None:
for potential_syn_loc in cell.findall(".//{" + self.nml +
"}potential_syn_loc"):
if 'synapse_direction' in potential_syn_loc.attrib:
if potential_syn_loc.attrib['synapse_direction'] in [
'post', 'preAndOrPost'
]:
self.set_group_compartment_param(cell, cellname, potential_syn_loc,\
'synapse_type', potential_syn_loc.attrib['synapse_type'],\
self.nml, mechanismname='synapse')
if potential_syn_loc.attrib['synapse_direction'] in [
'pre', 'preAndOrPost'
]:
self.set_group_compartment_param(cell, cellname, potential_syn_loc,\
'spikegen_type', potential_syn_loc.attrib['synapse_type'],\
self.nml, mechanismname='spikegen')
##########################################################
#### annotate each compartment with the cablegroups it belongs to
self.cableDict = {}
for cablegroupname in self.cablegroupsDict:
comp_list = []
for cableid in self.cablegroupsDict[cablegroupname]:
for compartment in self.cellDictByCableId[cellname][1][
cableid]:
cableStringPath = compartment.path + '/cable_groups'
cableString = moose.Mstring(cableStringPath)
if cableString.value == '':
cableString.value += cablegroupname
else:
cableString.value += ',' + cablegroupname
comp_list.append(compartment.name)
self.cableDict[cablegroupname] = comp_list
mu.info("Finished loading into library, cell: %s " % cellname)
return {cellname: (self.segDict, self.cableDict)}
def readNeuroMLFromFile(self, filename: Path, params={}, cellsDict={}):
"""
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.
"""
mu.info("Loading neuroml file %s " % filename)
moose.Neutral(
"/library") # creates /library in MOOSE tree; elif present, wraps
assert filename.exists(), f'{filename} does not exists or not readable'
tree = ET.parse(str(filename))
root_element = tree.getroot()
# if model_path is given in params, use it else use the directory of NML
# as model_dir.
self.model_dir: Path = params.get("model_dir",
filename.parent.resolve())
if "lengthUnits" in list(root_element.attrib.keys()):
self.lengthUnits = root_element.attrib["lengthUnits"]
else:
self.lengthUnits = "micrometer"
## lots of gymnastics to check if temperature meta tag is present
self.temperature = (
MNU.CELSIUS_default
) # gets replaced below if tag for temperature is present
self.temperature_default = True
for meta_property in root_element.findall(".//{" + MNU.meta_ns +
"}property"):
## tag can be an attrib or an element
if "tag" in list(meta_property.attrib.keys()): # tag is an attrib
tagname = meta_property.attrib["tag"]
if "temperature" in tagname:
self.temperature = float(meta_property.attrib["value"])
self.temperature_default = False
else: # tag is a separate element
tag = meta_property.find(".//{" + MNU.meta_ns + "}tag")
tagname = tag.text
if "temperature" in tagname:
## value can be a tag or an element
if "value" in list(
tag.attrib.keys()): # value is an attrib
self.temperature = float(tag.attrib["value"])
self.temperature_default = False
else: # value is a separate element
self.temperature = float(
tag.find(".//{" + MNU.meta_ns + "}value").text)
self.temperature_default = False
if self.temperature_default:
mu.info("Using default temperature of %s degree Celsius" %
self.temperature)
self.nml_params = {
"temperature": self.temperature,
"model_dir": str(self.model_dir),
}
mu.info("Loading channels and synapses into MOOSE /library ...")
cmlR = ChannelML(self.nml_params)
for channels in root_element.findall(".//{" + MNU.neuroml_ns +
"}channels"):
self.channelUnits = channels.attrib["units"]
for channel in channels.findall(".//{" + MNU.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=params,
units=self.channelUnits)
for synapse in channels.findall(".//{" + MNU.cml_ns +
"}synapse_type"):
cmlR.readSynapseML(synapse, units=self.channelUnits)
for ionConc in channels.findall(".//{" + MNU.cml_ns +
"}ion_concentration"):
cmlR.readIonConcML(ionConc, units=self.channelUnits)
mu.info("Loading cell definitions into MOOSE /library ...")
mmlR = MorphML(self.nml_params)
self.cellsDict = cellsDict
for cells in root_element.findall(".//{" + MNU.neuroml_ns + "}cells"):
for cell in cells.findall(".//{" + MNU.neuroml_ns + "}cell"):
cellDict = mmlR.readMorphML(cell,
params=params,
lengthUnits=self.lengthUnits)
self.cellsDict.update(cellDict)
## check if there are populations in this NML files,
## if not, it's a MorphML or ChannelML file, not NetworkML, so skip.
if (root_element.find(".//{" + MNU.neuroml_ns + "}populations") is None
and root_element.find(".//{" + MNU.nml_ns + "}populations") is
None):
return (self.cellsDict, "no populations (L3 NetworkML) found.")
else:
mu.info("Loading individual cells into MOOSE root ... ")
nmlR = NetworkML(self.nml_params)
return nmlR.readNetworkML(
root_element,
self.cellsDict,
params=params,
lengthUnits=self.lengthUnits,
)
## cellsDict = { cellname: (segDict, cableDict), ... } # multiple cells
## 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!
## and cableDict = { cablegroupname : [campartment1name, compartment2name, ... ], ... }
self.cellsDict = nmlR.cellSegmentDict
def main( ):
print( dir( mu ) )
mu.info( 'Hellow' )
def readNeuroMLFromFile(self, filename, params={}, cellsDict={}):
"""
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.
"""
mu.info("Loading neuroml file %s " % filename)
moose.Neutral('/library') # creates /library in MOOSE tree; elif present, wraps
tree = ET.parse(filename)
root_element = tree.getroot()
# if model_path is given in params, use it else use the directory of NML
# as model_dir.
self.model_dir = params.get('model_dir', path.dirname(path.abspath(filename)))
if 'lengthUnits' in list(root_element.attrib.keys()):
self.lengthUnits = root_element.attrib['lengthUnits']
else:
self.lengthUnits = 'micrometer'
## lots of gymnastics to check if temperature meta tag is present
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'):
## tag can be an attrib or an element
if 'tag' in list(meta_property.attrib.keys()): # tag is an attrib
tagname = meta_property.attrib['tag']
if 'temperature' in tagname:
self.temperature = float(meta_property.attrib['value'])
self.temperature_default = False
else: # tag is a separate element
tag = meta_property.find('.//{'+meta_ns+'}tag')
tagname = tag.text
if 'temperature' in tagname:
## value can be a tag or an element
if 'value' in list(tag.attrib.keys()): # value is an attrib
self.temperature = float(tag.attrib['value'])
self.temperature_default = False
else: # value is a separate element
self.temperature = float(tag.find('.//{'+meta_ns+'}value').text)
self.temperature_default = False
if self.temperature_default:
mu.info("Using default temperature of %s degree Celsius" % self.temperature)
self.nml_params = {
'temperature':self.temperature,
'model_dir':self.model_dir,
}
mu.debug("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=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)
mu.debug("Loading cell definitions into MOOSE /library ...")
mmlR = MorphML(self.nml_params)
self.cellsDict = cellsDict
for cells in root_element.findall('.//{'+neuroml_ns+'}cells'):
for cell in cells.findall('.//{'+neuroml_ns+'}cell'):
cellDict = mmlR.readMorphML(cell,params=params,lengthUnits=self.lengthUnits)
self.cellsDict.update(cellDict)
## check if there are populations in this NML files,
## if not, it's a MorphML or ChannelML file, not NetworkML, so skip.
if root_element.find('.//{'+neuroml_ns+'}populations') is None \
and root_element.find('.//{'+nml_ns+'}populations') is None:
return (self.cellsDict,'no populations (L3 NetworkML) found.')
else:
mu.debug("Loading individual cells into MOOSE root ... ")
nmlR = NetworkML(self.nml_params)
return nmlR.readNetworkML(root_element,self.cellsDict,\
params=params,lengthUnits=self.lengthUnits)
## cellsDict = { cellname: (segDict, cableDict), ... } # multiple cells
## 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!
## and cableDict = { cablegroupname : [campartment1name, compartment2name, ... ], ... }
self.cellsDict = nmlR.cellSegmentDict
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':
# this reader converts to SI
mu.info("Comparment %s inject %s A." % (compartment.name, value))
compartment.inject = value
elif name == 'v_reset':
compartment.vReset = value # compartment is a moose.LIF instance (intfire)
elif name == 'threshold':
compartment.thresh = value # compartment is a moose.LIF instance (intfire)
elif name == 't_refrac':
compartment.refractoryPeriod = value # compartment is a moose.LIF instance (intfire)
elif name == 'g_refrac':
mu.info("SORRY, current moose.LIF doesn't support g_refrac.")
elif mechanismname == 'synapse': # synapse being added to the compartment
## these are potential locations, we do not actually make synapses,
## unless the user has explicitly asked for it
if self.createPotentialSynapses:
syn_name = value
if not moose.exists(compartment.path + '/' + syn_name):
make_new_synapse(syn_name, compartment, syn_name,
self.nml_params)
## I assume below that compartment name has _segid at its end
segid = compartment.name.split('_')[
-1] # get segment id from compartment name
self.segDict[segid][5].append(value)
elif mechanismname == '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
## previous were mechanism that don't need a ChannelML definition
## including integrate_and_fire (I ignore the ChannelML definition)
## thus integrate_and_fire mechanism default values cannot be used
## i.e. nothing needed in /library, but below mechanisms need.
elif mechanismname is not None:
## if mechanism is not present in compartment, deep copy from library
## all mechanisms have been loaded into the library earlier
if not moose.exists(compartment.path + '/' + mechanismname):
neutralObj = moose.element(
"/library/" + mechanismname) # gives error if not present
if 'CaConc' == neutralObj.className: # Ion concentration pool
libcaconc = moose.CaConc("/library/" + mechanismname)
## deep copies the library caconc under the compartment
caconc = moose.copy(libcaconc, compartment, mechanismname)
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("/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.className: ## 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.element(compartment.path + '/' +
mechanismname)
if 'CaConc' == neutralObj.className: # Ion concentration pool
caconc = moose.CaConc(
compartment.path + '/' +
mechanismname) # wraps existing channel
channel = None
elif 'HHChannel2D' == neutralObj.className: ## HHChannel2D
channel = moose.HHChannel2D(
compartment.path + '/' +
mechanismname) # wraps existing channel
elif 'HHChannel' == neutralObj.className: ## HHChannel
channel = moose.HHChannel(
compartment.path + '/' +
mechanismname) # wraps existing channel
if name == 'Gbar':
if channel is None: # if CaConc, neuroConstruct uses gbar for thickness or phi
## 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 = mu.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
elif name == 'thick': # thick seems to be NEURON's extension to NeuroML level 2.
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.
## OR based on the Mstring phi under CaConc path.
if neuroml_utils.neuroml_debug:
mu.info("Setting %s for comparment %s to %s" %
(name, compartment.path, value))
