def buildChanProto( self ):
for i in self.chanProtoList:
if len(i) == 1:
chanName = self.parseChanName( i[0] )
else:
chanName = i[1]
j = [i[0], chanName]
if not self.checkAndBuildProto( "chan", j, [], ["xml"] ):
cm = ChannelML( {'temperature': self.temperature} )
cm.readChannelMLFromFile( i[0] )
if ( len( i ) == 2 ):
chan = moose.element( '/library/' + chanName )
chan.name = i[1]
def buildChanProto(self):
for i in self.chanProtoList:
if len(i) == 1:
chanName = self.parseChanName(i[0])
else:
chanName = i[1]
j = [i[0], chanName]
if not self.checkAndBuildProto("chan", j, [], ["xml"]):
cm = ChannelML({'temperature': self.temperature})
cm.readChannelMLFromFile(i[0])
if (len(i) == 2):
chan = moose.element('/library/' + chanName)
chan.name = i[1]
def main():
cm = ChannelML( {'temperature': 32 })
cm.readChannelMLFromFile( 'CA1_migliore_reference/hd.xml' )
cm.readChannelMLFromFile( 'CA1_migliore_reference/kap.xml' )
cm.readChannelMLFromFile( 'CA1_migliore_reference/kad.xml' )
cm.readChannelMLFromFile( 'CA1_migliore_reference/kdr.xml' )
cm.readChannelMLFromFile( 'CA1_migliore_reference/na3.xml' )
cm.readChannelMLFromFile( 'CA1_migliore_reference/nax.xml' )
if ( len( sys.argv ) < 2 ):
print("Usage: ", sys.argv[0], " filename")
return
# filename = "./Bhavika_swcplusnmlfiles/preliminarily corrected nmlfiles/ascoli+buzsaki/valid/" + sys.argv[1]
filename = sys.argv[1]
moose.Neutral( '/model' )
# Load in the swc file.
cell = moose.loadModel( filename, '/model/ca1' )
for i in moose.wildcardFind( '/library/##' ):
i.tick = -1
chanDistrib = [ \
"EM", "#", "-58e-3", \
"initVm", "#", "-65e-3", \
"RM", "#", "2.8", \
"CM", "#", "0.01", \
"RA", "#", "1.5", \
"RA", "#axon#", "0.5", \
"hd", "#dend#,#apical#", "5e-2*(1+(r*3e4))", \
"kdr", "#", "100", \
"na3", "#soma#,#dend#,#apical#", "250", \
"nax", "#axon#", "1250", \
"kap", "#axon#,#soma#", "300", \
"kap", "#dend#,#apical#", "150*(1+sign(100-r*1e6)) * (1+(r*1e4))", \
"kad", "#dend#,#apical#", "150*(1+sign(r*1e6-100))*(1+r*1e4)", \
]
moose.showfields( cell[0] )
cell[0].channelDistribution = chanDistrib
cell[0].parseChanDistrib()
for i in range( 8 ):
moose.setClock( i, simdt )
hsolve = moose.HSolve( '/model/ca1/hsolve' )
hsolve.dt = simdt
hsolve.target = '/model/ca1/soma'
'''
'''
moose.reinit()
makePlot( cell[0] )
# Now we set up the display
moose.le( '/model/ca1/soma' )
soma = moose.element( '/model/ca1/soma' )
kap = moose.element( '/model/ca1/soma/kap' )
graphs = moose.Neutral( '/graphs' )
vtab = moose.Table( '/graphs/vtab' )
moose.connect( vtab, 'requestOut', soma, 'getVm' )
kaptab = moose.Table( '/graphs/kaptab' )
moose.connect( kaptab, 'requestOut', kap, 'getGk' )
compts = moose.wildcardFind( "/model/ca1/#[ISA=CompartmentBase]" )
'''
for i in compts:
if moose.exists( i.path + '/Na' ):
print i.path, moose.element( i.path + '/Na' ).Gbar, \
moose.element( i.path + '/K_DR' ).Gbar, \
i.Rm, i.Ra, i.Cm
'''
'''
Na = moose.wildcardFind( '/model/ca1/#/Na#' )
print Na
Na2 = []
for i in compts:
if ( moose.exists( i.path + '/NaF2' ) ):
Na2.append( moose.element( i.path + '/NaF2' ) )
if ( moose.exists( i.path + '/NaPF_SS' ) ):
Na2.append( moose.element( i.path + '/NaPF_SS' ) )
ecomptPath = map( lambda x : x.path, compts )
print "Na placed in ", len( Na ), len( Na2 ), " out of ", len( compts ), " compts."
'''
compts[0].inject = inject
ecomptPath = [x.path for x in compts]
# Graphics stuff here.
app = QtGui.QApplication(sys.argv)
morphology = moogli.read_morphology_from_moose(name = "", path = "/model/ca1")
morphology.create_group( "group_all", ecomptPath, -0.08, 0.02, \
[0.0, 0.0, 1.0, 1.0], [1.0, 0.0, 0.0, 0.1] )
viewer = moogli.DynamicMorphologyViewerWidget(morphology)
def callback( morphology, viewer ):
moose.start( frameRunTime )
Vm = [moose.element( x ).Vm for x in compts]
morphology.set_color( "group_all", Vm )
currTime = moose.element( '/clock' ).currentTime
#print currTime, compts[0].Vm
if ( currTime < runtime ):
return True
return False
viewer.set_callback( callback, idletime = 0 )
viewer.showMaximized()
viewer.show()
app.exec_()
t = numpy.arange( 0, runtime, vtab.dt )
fig = plt.figure()
p1 = fig.add_subplot(311)
p2 = fig.add_subplot(312)
p2.plot( t, vtab.vector, label = 'Vm Soma' )
p2.legend()
p3 = fig.add_subplot(313)
p3.plot( t, kaptab.vector, label = 'kap Soma' )
p3.legend()
plt.show()
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"]
moose.Neutral('/library') # creates /library in MOOSE tree; elif present, wraps
_logger.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
_logger.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:
_logger.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):
_logger.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):
_logger.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:
_logger.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
_logger.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:
_logger.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:
_logger.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
_logger.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():
moose_utils.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
_logger.info("Finished loading into library, cell: %s " % cellname)
return {cellname:(self.segDict,self.cableDict)}
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][0][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][0][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 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][0][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][0][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 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"]
moose.Neutral(
'/library') # creates /library in MOOSE tree; elif present, wraps
_logger.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
_logger.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:
_logger.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):
_logger.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):
_logger.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)
moosechannel = moose.Neutral('/library/' + mechanismname)
moosechannelval = moose.Mstring(moosechannel.path +
'/vReset')
moosecomp.vReset = moosechannelval.value
moosechannelval = moose.Mstring(moosechannel.path +
'/thresh')
moosecomp.thresh = moosechannelval.value
moosechannelval = moose.Mstring(moosechannel.path +
'/refracT')
moosecomp.refractoryPeriod = 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:
_logger.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
_logger.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:
_logger.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:
_logger.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
_logger.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(
):
moose_utils.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
_logger.info("Finished loading into library, cell: %s " % cellname)
return {cellname: (self.segDict, self.cableDict)}
def createProjection(self, projection, Efactor, Tfactor):
projectionname = projection.attrib["name"]
_logger.info("Setting %s" % 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][0][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][0][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,
)