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 assert_ltl(ltl, node, molecule, time):
logger_.debug("Running a LTL (%s) on given node" % ltl.name)
field = ltl.field
vec = node['%s_table' % field].vector
N = len(vec)
dt = time / N
startN, stopN = int(ltl.start/dt), int(ltl.stop/dt)
data = vec[startN:stopN]
if len(data) == 0:
mu.warn([ "Ignoring test"
, "Probably simulation time is not enough" ]
)
return None
func = np.vectorize(ltl.test_func)
res = func(data, ltl.value)
witness = startN + np.flatnonzero(res)
time_witness = witness * dt
value_witness = np.take(vec, witness)
if len(witness) == 0: print("\t Passed")
else:
outfile = "%s_%s.witness" % (molecule, ltl.name)
witness_mat = np.vstack([time_witness, value_witness]).T
print("\tFailed. Witness is printed below (time, value)")
print(witness_mat)
print("NOTICE: These witness are also saved to file: %s" % outfile)
np.savetxt(outfile, witness_mat, delimiter=',')
def add_reaction( reacPath ):
if moose.exists( reacPath ):
mu.warn( 'Reaction %s already exists' % reacPath )
return moose.element( reacPath )
else:
r = moose.Reac( reacPath )
return r
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 num_subunits( name ):
m = re.search( r'x(?P<xs>\d)y(?P<ys>\d)', name )
try:
xs, ys = int(m.group('xs')), int(m.group( 'ys' ))
except Exception as e:
mu.warn( 'Failed to get subunit numbers in %s' % name )
raise e
return xs + ys
def mooseReadNML2(modelpath, verbose=False):
"""Read NeuroML model (version 2) and return reader object.
"""
global nml2Import_
if not nml2Import_:
mu.warn(nml2ImportError_)
raise RuntimeError("Could not load NML2 support.")
reader = _neuroml2.NML2Reader(verbose=verbose)
reader.read(modelpath)
return reader
def mooseReadNML2( modelpath, verbose = False ):
"""Read NeuroML model (version 2) and return reader object.
"""
global nml2Import_
if not nml2Import_:
mu.warn( nml2ImportError_ )
raise RuntimeError( "Could not load NML2 support." )
reader = _neuroml2.NML2Reader( verbose = verbose )
reader.read( modelpath )
return reader
def loadModel(filename, modelpath, solverclass="gsl"):
"""loadModel: Load model from a file to a specified path.
Parameters
----------
filename: str
model description file.
modelpath: str
moose path for the top level element of the model to be created.
method: str
solver type to be used for simulating the model.
TODO: Link to detailed description of solvers?
Returns
-------
object
moose.element if succcessful else None.
"""
if not os.path.isfile(os.path.realpath(filename)):
mu.warn("Model file '%s' does not exists or is not readable." %
filename)
return None
extension = os.path.splitext(filename)[1]
if extension in [".swc", ".p"]:
return _moose.loadModelInternal(filename, modelpath, "Neutral")
if extension in [".g", ".cspace"]:
# only if genesis or cspace file and method != ee then only
# mooseAddChemSolver is called.
ret = _moose.loadModelInternal(filename, modelpath, "ee")
sc = solverclass.lower()
if sc in ["gssa", "gillespie", "stochastic", "gsolve"]:
method = "gssa"
elif sc in [
"gsl", "runge kutta", "deterministic", "ksolve", "rungekutta",
"rk5", "rkf", "rk"
]:
method = "gsl"
elif sc in ["exponential euler", "exponentialeuler", "neutral"]:
method = "ee"
else:
method = "ee"
if method != 'ee':
_chemUtil.add_Delete_ChemicalSolver.mooseAddChemSolver(
modelpath, method)
return ret
else:
logger_.error("Unknown model extenstion '%s'" % extension)
return None
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 loadModel(filename, modelpath, solverclass="gsl"):
"""loadModel: Load model from a file to a specified path.
Parameters
----------
filename: str
model description file.
modelpath: str
moose path for the top level element of the model to be created.
method: str
solver type to be used for simulating the model.
TODO: Link to detailed description of solvers?
Returns
-------
object
moose.element if succcessful else None.
"""
if not os.path.isfile( os.path.realpath(filename) ):
mu.warn( "Model file '%s' does not exists or is not readable." % filename )
return None
extension = os.path.splitext(filename)[1]
if extension in [".swc", ".p"]:
return _moose.loadModelInternal(filename, modelpath, "Neutral" )
if extension in [".g", ".cspace"]:
# only if genesis or cspace file and method != ee then only
# mooseAddChemSolver is called.
ret = _moose.loadModelInternal(filename, modelpath, "ee")
sc = solverclass.lower()
if sc in ["gssa","gillespie","stochastic","gsolve"]:
method = "gssa"
elif sc in ["gsl","runge kutta","deterministic","ksolve","rungekutta","rk5","rkf","rk"]:
method = "gsl"
elif sc in ["exponential euler","exponentialeuler","neutral"]:
method = "ee"
else:
method = "ee"
if method != 'ee':
chemError = _chemUtil.add_Delete_ChemicalSolver.mooseAddChemSolver(modelpath, method)
return ret
else:
mu.error( "Unknown model extenstion '%s'" % extension)
return None
def mooseWriteNML2( outfile ):
mu.warn( "Writing to NML2 is not supported yet" )
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)}
