def loadGran98NeuroML_L123(filename):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
    somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
    somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
    somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
    ## Am not able to plot KDr gating variable X when running under hsolve
    #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')

    print "Reinit MOOSE ... "
    resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')

    print "Running ... "
    moose.start(runtime)
    tvec = arange(0.0,runtime*2.0,plotdt)
    tvec = tvec[ : somaVm.vector.size ]
    plot(tvec,somaVm.vector)
    title('Soma Vm')
    xlabel('time (s)')
    ylabel('Voltage (V)')
    figure()
    plot(tvec,somaCa.vector)
    title('Soma Ca')
    xlabel('time (s)')
    ylabel('Ca conc (mol/m^3)')
    figure()
    plot(tvec,somaIKCa.vector)
    title('soma KCa current')
    xlabel('time (s)')
    ylabel('KCa current (A)')
    print "Showing plots ..."
    show()

def runSTGNeuroML_L123(filename):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma1_path = populationDict['AB_PD'][1][0].path+'/Soma_0'
    soma1Vm = setupTable('somaVm',moose.Compartment(soma1_path),'Vm')
    soma2_path = populationDict['LP'][1][0].path+'/Soma_0'
    soma2Vm = setupTable('somaVm',moose.Compartment(soma2_path),'Vm')
    soma3_path = populationDict['PY'][1][0].path+'/Soma_0'
    soma3Vm = setupTable('somaVm',moose.Compartment(soma3_path),'Vm')
    #somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/CaPool_STG'),'Ca')
    #somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/KCa_STG'),'Ik')
    #somaCaE = setupTable('somaCaE',moose.HHChannel(soma_path+'/CaT_STG/nernst'),'E')
    ## Am not able to plot KDr gating variable X when running under hsolve
    #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')

    print "Reinit MOOSE ... "
    resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')

    print "Running ... "
    moose.start(runtime)
    tvec = arange(0.0,runtime+2*plotdt,plotdt)
    tvec = tvec[ : soma1Vm.vec.size ]
    plot(tvec,soma1Vm.vec,label='AB_PD',color='g',linestyle='dashed')
    plot(tvec,soma2Vm.vec,label='LP',color='r',linestyle='solid')
    plot(tvec,soma3Vm.vec,label='PY',color='b',linestyle='dashed')
    legend()
    title('Soma Vm')
    xlabel('time (s)')
    ylabel('Voltage (V)')
    print "Showing plots ..."
    show()

    def _loadElec( self, efile, elecname ):
        if ( efile[ len( efile ) - 2:] == ".p" ):
            self.elecid = moose.loadModel( efile, '/library/' + elecname)[0]
            print self.elecid
        elif ( efile[ len( efile ) - 4:] == ".swc" ):
            self.elecid = moose.loadModel( efile, '/library/' + elecname)[0]
        else:
            nm = NeuroML()
            print "in _loadElec, combineSegments = ", self.combineSegments
            nm.readNeuroMLFromFile( efile, \
                    params = {'combineSegments': self.combineSegments, \
                    'createPotentialSynapses': True } )
            if moose.exists( '/cells' ):
                kids = moose.wildcardFind( '/cells/#' )
            else:
                kids = moose.wildcardFind( '/library/#[ISA=Neuron],/library/#[TYPE=Neutral]' )
                if ( kids[0].name == 'spine' ):
                    kids = kids[1:]

            assert( len( kids ) > 0 )
            self.elecid = kids[0]
            temp = moose.wildcardFind( self.elecid.path + '/#[ISA=CompartmentBase]' )

        transformNMDAR( self.elecid.path )
        kids = moose.wildcardFind( '/library/##[0]' )
        for i in kids:
            i.tick = -1

def loadGran98NeuroML_L123(filename, nogui=False):
    neuromlR = NeuroML()
    populationDict, projectionDict = neuromlR.readNeuroMLFromFile(filename)
    soma_path = populationDict["Gran"][1][0].path + "/Soma_0"
    somaVm = setupTable("somaVm", moose.Compartment(soma_path), "Vm")
    somaCa = setupTable("somaCa", moose.CaConc(soma_path + "/Gran_CaPool_98"), "Ca")
    somaIKCa = setupTable("somaIKCa", moose.HHChannel(soma_path + "/Gran_KCa_98"), "Gk")

    soma = moose.Compartment(soma_path)
    print ("Reinit MOOSE ... ")
    resetSim(["/elec", "/cells"], simdt, plotdt, simmethod="ee")  # from moose.utils
    print ("Running ... ")
    moose.start(runtime)
    print ("Finished simulation of %s seconds" % runtime)

    if not nogui:
        from pylab import *

        tvec = arange(0.0, runtime, plotdt)
        plot(tvec, somaVm.vector[1:])
        title("Soma Vm")
        xlabel("time (s)")
        ylabel("Voltage (V)")
        figure()
        plot(tvec, somaCa.vector[1:])
        title("Soma Ca")
        xlabel("time (s)")
        ylabel("Ca conc (mol/m^3)")
        figure()
        plot(tvec, somaIKCa.vector[1:])
        title("KCa current (A)")
        xlabel("time (s)")
        ylabel("")
        print "Showing plots ..."
        show()

def loadGran98NeuroML_L123(filename):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
    somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
    somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
    somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
    #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
    soma = moose.Compartment(soma_path)
    print("Reinit MOOSE ... ")
    resetSim(['/elec','/cells'],simdt,plotdt,simmethod='ee') # from moose.utils
    print("Running ... ")
    moose.start(runtime)
    tvec = arange(0.0,runtime,plotdt)
    plot(tvec,somaVm.vector[1:])
    title('Soma Vm')
    xlabel('time (s)')
    ylabel('Voltage (V)')
    figure()
    plot(tvec,somaCa.vector[1:])
    title('Soma Ca')
    xlabel('time (s)')
    ylabel('Ca conc (mol/m^3)')
    figure()
    plot(tvec,somaIKCa.vector[1:])
    title('KCa current (A)')
    xlabel('time (s)')
    ylabel('')
    print("Showing plots ...")
    show()

    def loadXMLModel(self, filename, target):
        """Load a model in some XML format. 

        Looks inside the XML to figure out if this is a neuroML or an
        SBML file and calls the corresponding loader functions.

        Currently only SBML and neuroML are support. In future 9ml
        support will be provided as the specification becomes stable.

        """
        with open(filename, 'r') as xmlfile:
            for line in xmlfile:
                self._xmlreader.feed(line)
                if self._saxhandler.model_type is not None:
                    break
        ret = self._saxhandler.model_type
        self._saxhandler.model_type = None
        self._xmlreader.reset()
        if ret == MooseHandler.type_neuroml:
            #self._context.loadModel(filename, target)
            neuromlR = NeuroML()
            populationDict, projectionDict = neuromlR.readNeuroMLFromFile(filename)
        elif ret == MooseHandler.type_sbml:
            print 'Unsupported in GUI Mode'
        return ret

    def _loadElec( self, efile, elecname, combineSegments ):
        library = moose.Neutral( '/library' )
        if ( efile[ len( efile ) - 2:] == ".p" ):
            self.elecid = moose.loadModel( efile, self.model.path + '/' + elecname )
        else:
            nm = NeuroML()
            nm.readNeuroMLFromFile( efile, \
                    params = {'combineSegments': combineSegments, \
                    'createPotentialSynapses': True } )
            if moose.exists( '/cells' ):
                kids = moose.wildcardFind( '/cells/#' )
            else:
                kids = moose.wildcardFind( '/library/#[ISA=Neuron],/library/#[TYPE=Neutral]' )
                if ( kids[0].name == 'spine' ):
                    kids = kids[1:]

            assert( len( kids ) > 0 )
            self.elecid = kids[0]
            temp = moose.wildcardFind( self.elecid.path + '/#[ISA=CompartmentBase]' )
            moose.move( self.elecid, self.model )
            self.elecid.name = elecname

        self._transformNMDAR( self.elecid.path )
        kids = moose.wildcardFind( '/library/##[0]' )
        for i in kids:
            i.tick = -1

def loadGran98NeuroML_L123(filename):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma_path = populationDict['CA1group'][1][0].path+'/Seg0_soma_0_0'
    somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
    soma = moose.Compartment(soma_path)
    moose.reinit()
    moose.start(runtime)
    tvec = np.arange(0.0,runtime,simdt)
    res =  count.spike_train_simple_stat( somaVm.vector )
    return res['number of spikes']

def loadGran98NeuroML_L123(filename):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
    somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
    somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
    somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
    #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
    soma = moose.Compartment(soma_path)
    print "Reinit MOOSE ... "
    resetSim(['/elec','/cells'],simdt,plotdt,simmethod='ee') # from moose.utils
    print "Running ... "
    moose.start(runtime)
    print 'Finished simulation for', runtime, 'seconds'

def loadModel(filename):
    global soma_, cellSpikeTable_
    neuromlR = NeuroML()
    neuromlR.readNeuroMLFromFile(filename)
    libcell = moose.Neuron('/library/CA1soma')
    CA1Cellid = moose.copy(libcell,moose.Neutral('/cells'),'CA1')
    CA1Cell = moose.Neuron(CA1Cellid)

    spikeGen = moose.SpikeGen(CA1Cell.path+'/spikeGen')
    spikeGen.threshold = -30e-3 # V
    soma_ = moose.Compartment(CA1Cell.path+'/Seg0_soma_0_0')
    soma_.inject = 0 # by default the cell has a current injection
    moose.connect(soma_,'VmOut',spikeGen,'Vm')
    table_path = moose.Neutral(CA1Cell.path+'/data').path
    cellSpikeTable_ = moose.Table(table_path+'/spikesTable')
    moose.connect(spikeGen,'spikeOut', cellSpikeTable_,'input')

def loadGran98NeuroML_L123(filename,params):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename,params=params)
    print "Number of compartments =",\
        len(moose.Neuron(populationDict['CA1group'][1][0].path).children)
    soma_path = populationDict['CA1group'][1][0].path+'/Seg0_soma_0_0'
    somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
    #somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
    #somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
    #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
    soma = moose.Compartment(soma_path)
    
    print "Reinit MOOSE ... "
    resetSim(['/elec','/cells'],simdt,plotdt,simmethod='hsolve') # from moose.utils
    print "Running ... "
    moose.start(runtime)
    tvec = arange(0.0,runtime,simdt)

def loadGran98NeuroML_L123(filename):
    neuromlR = NeuroML()
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    # readNeuroMLFromFile returns populationDict = { 'populationname1':(cellname,{int(instanceid1):moosecell, ... }) , ... }
    #    and projectionDict = { 'projectionname1':(source,target,[(syn_name1,pre_seg_path,post_seg_path),...]) , ... }
    soma_path = populationDict['Gran'][1][0].path+'/Soma_0'
    
    somaVm = setupTable('somaVm',moose.Compartment(soma_path),'Vm')
    somaCa = setupTable('somaCa',moose.CaConc(soma_path+'/Gran_CaPool_98'),'Ca')
    somaIKCa = setupTable('somaIKCa',moose.HHChannel(soma_path+'/Gran_KCa_98'),'Gk')
    #KDrX = setupTable('ChanX',moose.HHChannel(soma_path+'/Gran_KDr_98'),'X')
    soma = moose.Compartment(soma_path)
    
    print "Reinit MOOSE ... "
    resetSim(['/elec','/cells'],simdt,plotdt,simmethod='ee') # from moose.utils
    print "Running ... "
    moose.start(runtime)
    
    # plotting
    tvec = arange(0.0,runtime,plotdt)
    plot(tvec,somaVm.vector[1:])
    title('Soma Vm')
    xlabel('time (s)')
    ylabel('Voltage (V)')
    figure()
    plot(tvec,somaCa.vector[1:])
    title('Soma Ca')
    xlabel('time (s)')
    ylabel('Ca conc (mol/m^3)')
    figure()
    plot(tvec,somaIKCa.vector[1:])
    title('KCa current (A)')
    xlabel('time (s)')
    ylabel('')
    print "Showing plots ..."
    show()

def loadSTGNeuroML_L123(filename):
    neuromlR = NeuroML()
    ## readNeuroMLFromFile below returns:
    # This returns
    # populationDict = {
    #     'populationname1':('cellName',{('instanceid1'):moosecell, ... }) 
    #     , ... 
    #     }
    # (cellName and instanceid are strings, mooosecell is a moose.Neuron object instance)
    # and
    # projectionDict = { 
    #     'projName1':('source','target',[('syn_name1','pre_seg_path','post_seg_path')
    #     ,...]) 
    #     , ... 
    #     }
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma1_path = populationDict['AB_PD'][1][0].path+'/Soma_0'
    soma1Vm = setupTable('somaVm',moose.Compartment(soma1_path),'Vm')
    soma2_path = populationDict['LP'][1][0].path+'/Soma_0'
    soma2Vm = setupTable('somaVm',moose.Compartment(soma2_path),'Vm')
    soma3_path = populationDict['PY'][1][0].path+'/Soma_0'
    soma3Vm = setupTable('somaVm',moose.Compartment(soma3_path),'Vm')

    # monitor channel current
    channel_path = soma1_path + '/KCa_STG'
    channel_Ik = setupTable('KCa_Ik',moose.element(channel_path),'Ik')
    # monitor Ca
    capool_path = soma1_path + '/CaPool_STG'
    capool_Ca = setupTable('CaPool_Ca',moose.element(capool_path),'Ca')

    # monitor synaptic current
    soma2 = moose.element(soma2_path)
    print "Children of",soma2_path,"are:"
    for child in soma2.children:
        print child.className, child.path
    if graded_syn:
        syn_path = soma2_path+'/DoubExpSyn_Ach__cells-0-_AB_PD_0-0-_Soma_0'
        syn = moose.element(syn_path)
    else:
        syn_path = soma2_path+'/DoubExpSyn_Ach'
        syn = moose.element(syn_path)
    syn_Ik = setupTable('DoubExpSyn_Ach_Ik',syn,'Ik')

    print "Reinit MOOSE ... "
    resetSim(['/elec',cells_path], simdt, plotdt, simmethod='ee')

    print "Running ... "
    moose.start(runtime)
    tvec = arange(0.0,runtime+2*plotdt,plotdt)
    tvec = tvec[ : soma1Vm.vector.size ]
    
    figure(facecolor='w')
    plot(tvec,soma1Vm.vector,label='AB_PD',color='g',linestyle='solid')
    plot(tvec,soma2Vm.vector,label='LP',color='r',linestyle='solid')
    plot(tvec,soma3Vm.vector,label='PY',color='b',linestyle='solid')
    legend()
    title('Soma Vm')
    xlabel('time (s)')
    ylabel('Voltage (V)')

    figure(facecolor='w')
    plot(tvec,channel_Ik.vector,color='b',linestyle='solid')
    title('KCa current; Ca conc')
    xlabel('time (s)')
    ylabel('Ik (Amp)')
    twinx()
    plot(tvec,capool_Ca.vector,color='r',linestyle='solid')
    ylabel('Ca (mol/m^3)')

    figure(facecolor='w')
    plot(tvec,syn_Ik.vector,color='b',linestyle='solid')    
    title('Ach syn current in '+soma2_path)
    xlabel('time (s)')
    ylabel('Isyn (S)')
    print "Showing plots ..."
    show()

def loadRunSTGNeuroML_L123(filename):
    'Loads and runs the pyloric rhythm generator from NeuroML files.'
    # for graded synapses, else NeuroML event-based are used
    from load_synapses import load_synapses
    moose.Neutral('/library')
    # set graded to False to use event based synapses
    #  if False, neuroml event-based synapses get searched for and loaded
    # True to load graded synapses
    graded_syn = True
    #graded_syn = False
    if graded_syn:
        load_synapses()

    neuromlR = NeuroML()
    ## readNeuroMLFromFile below returns:
    # This returns
    # populationDict = {
    #     'populationname1':('cellName',{('instanceid1'):moosecell, ... }) 
    #     , ... 
    #     }
    # (cellName and instanceid are strings, mooosecell is a moose.Neuron object instance)
    # and
    # projectionDict = { 
    #     'projName1':('source','target',[('syn_name1','pre_seg_path','post_seg_path')
    #     ,...]) 
    #     , ... 
    #     }
    populationDict, projectionDict = \
        neuromlR.readNeuroMLFromFile(filename)
    soma1_path = populationDict['AB_PD'][1][0].path+'/Soma_0'
    soma1Vm = setupTable('somaVm',moose.Compartment(soma1_path),'Vm')
    soma2_path = populationDict['LP'][1][0].path+'/Soma_0'
    soma2Vm = setupTable('somaVm',moose.Compartment(soma2_path),'Vm')
    soma3_path = populationDict['PY'][1][0].path+'/Soma_0'
    soma3Vm = setupTable('somaVm',moose.Compartment(soma3_path),'Vm')

    # monitor channel current
    channel_path = soma1_path + '/KCa_STG'
    channel_Ik = setupTable('KCa_Ik',moose.element(channel_path),'Ik')
    # monitor Ca
    capool_path = soma1_path + '/CaPool_STG'
    capool_Ca = setupTable('CaPool_Ca',moose.element(capool_path),'Ca')

    # monitor synaptic current
    soma2 = moose.element(soma2_path)
    print "Children of",soma2_path,"are:"
    for child in soma2.children:
        print child.className, child.path
    if graded_syn:
        syn_path = soma2_path+'/DoubExpSyn_Ach__cells-0-_AB_PD_0-0-_Soma_0'
        syn = moose.element(syn_path)
    else:
        syn_path = soma2_path+'/DoubExpSyn_Ach'
        syn = moose.element(syn_path)
    syn_Ik = setupTable('DoubExpSyn_Ach_Ik',syn,'Ik')

    print "Reinit MOOSE ... "
    resetSim(['/elec',cells_path], simdt, plotdt, simmethod='hsolve')

    print "Using graded synapses? = ", graded_syn
    print "Running model filename = ",filename," ... "
    moose.start(runtime)
    tvec = np.arange(0.0,runtime+2*plotdt,plotdt)
    tvec = tvec[ : soma1Vm.vector.size ]
    
    fig = plt.figure(facecolor='w',figsize=(10,6))
    axA = plt.subplot2grid((3,2),(0,0),rowspan=3,colspan=1,frameon=False)
    img = plt.imread( 'STG.png' )
    imgplot = axA.imshow( img )
    for tick in axA.get_xticklines():
        tick.set_visible(False)
    for tick in axA.get_yticklines():
        tick.set_visible(False)
    axA.set_xticklabels([])
    axA.set_yticklabels([])
    ax = plt.subplot2grid((3,2),(0,1),rowspan=1,colspan=1)
    ax.plot(tvec,soma1Vm.vector*1000,label='AB_PD',color='g',linestyle='solid')
    ax.set_xticklabels([])
    ax.set_ylabel('AB_PD (mV)')
    ax = plt.subplot2grid((3,2),(1,1),rowspan=1,colspan=1)
    ax.plot(tvec,soma2Vm.vector*1000,label='LP',color='r',linestyle='solid')
    ax.set_xticklabels([])
    ax.set_ylabel('LP (mV)')
    ax = plt.subplot2grid((3,2),(2,1),rowspan=1,colspan=1)
    ax.plot(tvec,soma3Vm.vector*1000,label='PY',color='b',linestyle='solid')
    ax.set_ylabel('PY (mV)')
    ax.set_xlabel('time (s)')
    fig.tight_layout()

    fig = plt.figure(facecolor='w')
    plt.plot(tvec,soma2Vm.vector*1000,label='LP',color='r',linestyle='solid')
    plt.plot(tvec,soma3Vm.vector*1000,label='PY',color='b',linestyle='solid')
    plt.legend()
    plt.xlabel('time (s)')
    plt.ylabel('Soma Vm (mV)')

    plt.figure(facecolor='w')
    plt.plot(tvec,channel_Ik.vector,color='b',linestyle='solid')
    plt.title('KCa current; Ca conc')
    plt.xlabel('time (s)')
#.........这里部分代码省略.........

import sys
sys.path.append('.')
import moose
from moose.utils import *

from moose.neuroml.NeuroML import NeuroML

from pylab import *

SIMDT = 25e-6 # s
PLOTDT = 25e-6 # s
RUNTIME = 1.0 # s

injectmax = 2e-12 # Amperes

neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile('cells_channels/CA1soma.morph.xml')
libcell = moose.Neuron('/library/CA1soma')
CA1Cellid = moose.copy(libcell,moose.Neutral('/cells'),'CA1')
CA1Cell = moose.Neuron(CA1Cellid)
#printCellTree(CA1Cell)

## edge-detect the spikes using spike-gen (table does not have edge detect)
spikeGen = moose.SpikeGen(CA1Cell.path+'/spikeGen')
spikeGen.threshold = -30e-3 # V
CA1CellSoma = moose.Compartment(CA1Cell.path+'/Seg0_soma_0_0')
CA1CellSoma.inject = 0 # by default the cell has a current injection
moose.connect(CA1CellSoma,'VmOut',spikeGen,'Vm')
## save spikes in table
table_path = moose.Neutral(CA1Cell.path+'/data').path
CA1CellSpikesTable = moose.Table(table_path+'/spikesTable')

import sys
sys.path.append('.')
import moose
from moose.utils import *

from moose.neuroml.NeuroML import NeuroML

from pylab import *

SIMDT = 25e-6 # s
PLOTDT = 25e-6 # s
RUNTIME = 2.0 # s

injectmax = 20e-12 # Amperes

neuromlR = NeuroML()
neuromlR.readNeuroMLFromFile('cells_channels/Granule_98.morph.xml')
libcell = moose.Neuron('/library/Granule_98')
granCellid = moose.copy(libcell,moose.Neutral('/cells'),'granCell')
granCell = moose.Neuron(granCellid)

## edge-detect the spikes using spike-gen (table does not have edge detect)
spikeGen = moose.SpikeGen(granCell.path+'/spikeGen')
spikeGen.threshold = 0e-3 # V
granCellSoma = moose.Compartment(granCell.path+'/Soma_0')
moose.connect(granCellSoma,'VmOut',spikeGen,'Vm')
## save spikes in table
table_path = moose.Neutral(granCell.path+'/data').path
granCellSpikesTable = moose.Table(table_path+'/spikesTable')
moose.connect(spikeGen,'spikeOut',granCellSpikesTable,'input')