def add_synchans(model, container):
    synchans=[]
    #2D array to store all the synapses.  Rows=num synapse types, columns=num comps
    #at the end they are concatenated into a dictionary
    for key in model.SYNAPSE_TYPES:
        synchans.append([])
    allkeys = sorted(model.SYNAPSE_TYPES)

    comp_list = moose.wildcardFind(container + '/#[TYPE=Compartment]')
    for comp in comp_list:
        #create each type of synchan in each compartment.  Add to 2D array
        for key in DendSynChans(model):
            keynum = allkeys.index(key)
            Gbar = model.SYNAPSE_TYPES[key].Gbar
            Gbarvar=model.SYNAPSE_TYPES[key].var
            synchans[keynum].append(addoneSynChan(key,comp,Gbar, model.calYN, Gbarvar))
        
        for key in SpineSynChans(model):
            keynum = allkeys.index(key)
            Gbar = model.SYNAPSE_TYPES[key].Gbar
            Gbarvar=model.SYNAPSE_TYPES[key].var
            for spcomp in moose.wildcardFind(comp.path + '/#[ISA=Compartment]'):
                if NAME_HEAD in spcomp.path:
                    synchans[keynum].append(addoneSynChan(key,spcomp,Gbar, model.calYN, Gbarvar))
                   
    allsynchans={key:synchans[keynum]
                 for keynum, key in enumerate(sorted(model.SYNAPSE_TYPES))}

    return allsynchans

def main():
        #solver = "gsl"  # Pick any of gsl, gssa, ee..
        solver = "gssa"  # Pick any of gsl, gssa, ee..
	mfile = '../../Genesis_files/Repressillator.g'
	runtime = 6000.0
	if ( len( sys.argv ) >= 2 ):
                solver = sys.argv[1]
	modelId = moose.loadModel( mfile, 'model', solver )
        # Increase volume so that the stochastic solver gssa 
        # gives an interesting output
        compt = moose.element( '/model/kinetics' )
        compt.volume = 1e-19 
        dt = moose.element( '/clock' ).dt

	moose.reinit()
	moose.start( runtime ) 

	# Display all plots.
        img = mpimg.imread( 'repressillatorOsc.png' )
        fig = plt.figure( figsize=(12, 10 ) )
        png = fig.add_subplot( 211 )
        imgplot = plt.imshow( img )
        ax = fig.add_subplot( 212 )
	x = moose.wildcardFind( '/model/#graphs/conc#/#' )
        plt.ylabel( 'Conc (mM)' )
        plt.xlabel( 'Time (seconds)' )
	for x in moose.wildcardFind( '/model/#graphs/conc#/#' ):
            t = numpy.arange( 0, x.vector.size, 1 ) * dt
            pylab.plot( t, x.vector, label=x.name )
        pylab.legend()
        pylab.show()

def updateDisplay():
    makeYmodel()
    tabvec = moose.wildcardFind( '/model/graphs/plot#' )
    moose.element( '/model/elec/' ).name = 'Y'
    vecYdend = moose.wildcardFind( '/model/Y/soma,/model/Y/dend#' )
    vecYbranch1 = moose.wildcardFind( '/model/Y/branch1#' )
    vecYbranch2 = moose.wildcardFind( '/model/Y/branch2#' )
    moose.reinit()
    dt = interval1
    currtime = 0.0
    for i in lines:
        moose.start( dt )
        currtime += dt
        #print "############## NumDendData = ", len( vecYdend )
        i.YdendLines.set_ydata( [v.Vm*1000 for v in vecYdend] )
        i.Ybranch1Lines.set_ydata( [v.Vm*1000 for v in vecYbranch1] )
        i.Ybranch2Lines.set_ydata( [v.Vm*1000 for v in vecYbranch2] )
        dt = interval2

    moose.start( runtime - currtime )

    #print "############## len (tabvec)  = ", len( tabvec[0].vector )
    for i, tab in zip( tplot, tabvec ):
        i.set_ydata( tab.vector * 1000 )
    
    moose.delete( '/model' )
    moose.delete( '/library' )

def main():
    global synSpineList 
    global synDendList 
    numpy.random.seed( 1234 )
    rdes = buildRdesigneur()
    for i in elecFileNames:
        print(i)
        rdes.cellProtoList = [ ['./cells/' + i, 'elec'] ]
        rdes.buildModel( '/model' )
        rdes.soma.inject = inject
        assert( moose.exists( '/model' ) )
        synSpineList = moose.wildcardFind( "/model/elec/#head#/glu,/model/elec/#head#/NMDA" )
        temp = set( moose.wildcardFind( "/model/elec/#/glu,/model/elec/#/NMDA" ) )
        synDendList = list( temp - set( synSpineList ) )
        print("[INFO] reinitialzing")
        moose.reinit()
        buildPlots( rdes )
        # Run for baseline, tetanus, and post-tetanic settling time 
        t1 = time.time()
        moose.start( runtime )
        print('runtime = ', runtime, '; real time = ', time.time() - t1)

        saveAndClearPlots( "bigElec" )
        moose.delete( '/model' )
        rdes.elecid = moose.element( '/' )

def main():
                simdt = 0.1
                plotdt = 0.1
                runtime = 100.0

		makeModel()

		# Schedule the whole lot
		moose.setClock( 4, simdt ) # for the computational objects
		moose.setClock( 5, simdt ) # for the computational objects
		moose.setClock( 8, plotdt ) # for the plots
		moose.useClock( 4, '/model/compt#/ksolve#', 'init' )
		moose.useClock( 5, '/model/compt#/ksolve#', 'process' )
		moose.useClock( 8, '/model/graphs/#', 'process' )

		moose.reinit()
		moose.start( runtime ) # Run the model for 100 seconds.
		for x in moose.wildcardFind( '/model/compt#/#[ISA=PoolBase]' ):
                    print x.name, x.conc

		# Iterate through all plots, dump their contents to data.plot.
		for x in moose.wildcardFind( '/model/graphs/conc#' ):
				#x.xplot( 'scriptKineticModel.plot', x.name )
				t = numpy.linspace( 0, runtime, x.vector.size ) # sec
				pylab.plot( t, x.vector, label=x.name )
		pylab.legend()
		pylab.show()


		quit()

    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 transformNMDAR( path ):
    for i in moose.wildcardFind( path + "/##/#NMDA#[ISA!=NMDAChan]" ):
        chanpath = i.path
        pa = i.parent
        i.name = '_temp'
        if ( chanpath[-3:] == "[0]" ):
            chanpath = chanpath[:-3]
        nmdar = moose.NMDAChan( chanpath )
        sh = moose.SimpleSynHandler( chanpath + '/sh' )
        moose.connect( sh, 'activationOut', nmdar, 'activation' )
        sh.numSynapses = 1
        sh.synapse[0].weight = 1
        nmdar.Ek = i.Ek
        nmdar.tau1 = i.tau1
        nmdar.tau2 = i.tau2
        nmdar.Gbar = i.Gbar
        nmdar.CMg = 12
        nmdar.KMg_A = 1.0 / 0.28
        nmdar.KMg_B = 1.0 / 62
        nmdar.temperature = 300
        nmdar.extCa = 1.5
        nmdar.intCa = 0.00008
        nmdar.intCaScale = 1
        nmdar.intCaOffset = 0.00008
        nmdar.condFraction = 0.02
        moose.delete( i )
        moose.connect( pa, 'channel', nmdar, 'channel' )
        caconc = moose.wildcardFind( pa.path + '/#[ISA=CaConcBase]' )
        if ( len( caconc ) < 1 ):
            print('no caconcs found on ', pa.path)
        else:
            moose.connect( nmdar, 'ICaOut', caconc[0], 'current' )
            moose.connect( caconc[0], 'concOut', nmdar, 'assignIntCa' )

def writeCompt(modelpath, cremodel_):
    # getting all the compartments
    compts = moose.wildcardFind(modelpath + '/##[ISA=ChemCompt]')
    groupInfo = {}
    for compt in compts:
        comptName = convertSpecialChar(compt.name)
        # converting m3 to litre
        size = compt.volume * pow(10, 3)
        ndim = compt.numDimensions
        c1 = cremodel_.createCompartment()
        c1.setId(str(idBeginWith(comptName +
                                 "_" +
                                 str(compt.getId().value) +
                                 "_" +
                                 str(compt.getDataIndex()) +
                                 "_")))
        c1.setName(comptName)
        c1.setConstant(True)
        c1.setSize(size)
        c1.setSpatialDimensions(ndim)
        c1.setUnits('volume')
        #For each compartment get groups information along
        for grp in moose.wildcardFind(compt.path+'/##[TYPE=Neutral]'):
            grp_cmpt = findGroup_compt(grp.parent)
            try:
                value = groupInfo[moose.element(grp)]
            except KeyError:
                # Grp is not present
                groupInfo[moose.element(grp)] = []


    if compts:
        return True,groupInfo
    else:
        return False,groupInfo

def poolMerge(comptA,comptB,poolNotcopiedyet):

    aCmptGrp = moose.wildcardFind(comptA.path+'/#[TYPE=Neutral]')
    aCmptGrp = aCmptGrp +(moose.element(comptA.path),)

    bCmptGrp = moose.wildcardFind(comptB.path+'/#[TYPE=Neutral]')
    bCmptGrp = bCmptGrp +(moose.element(comptB.path),)

    objA = moose.element(comptA.path).parent.name
    objB = moose.element(comptB.path).parent.name
    for bpath in bCmptGrp:
        grp_cmpt = ((bpath.path).replace(objB,objA)).replace('[0]','')
        if moose.exists(grp_cmpt) :
            if moose.element(grp_cmpt).className != bpath.className:
                grp_cmpt = grp_cmpt+'_grp'
                bpath.name = bpath.name+"_grp"
                l = moose.Neutral(grp_cmpt)
        else:
            #moose.Neutral(grp_cmpt)
            src = bpath
            srcpath = (bpath.parent).path
            des = srcpath.replace(objB,objA)
            moose.copy(bpath,moose.element(des))

        apath = moose.element(bpath.path.replace(objB,objA))

        bpoollist = moose.wildcardFind(bpath.path+'/#[ISA=PoolBase]')
        apoollist = moose.wildcardFind(apath.path+'/#[ISA=PoolBase]')
        for bpool in bpoollist:
            if bpool.name not in [apool.name for apool in apoollist]:
                copied = copy_deleteUnlyingPoolObj(bpool,apath)
                if copied == False:
                    #hold it for later, this pool may be under enzyme, as cplx
                    poolNotcopiedyet.append(bpool)

    def installCellFromProtos( self ):
        if self.stealCellFromLibrary:
            moose.move( self.elecid, self.model )
            if self.elecid.name != 'elec':
                self.elecid.name = 'elec'
        else:
            moose.copy( self.elecid, self.model, 'elec' )
            self.elecid = moose.element( self.model.path + '/elec' )
            self.elecid.buildSegmentTree() # rebuild: copy has happened.
        if hasattr( self, 'chemid' ):
            self.validateChem()
            if self.stealCellFromLibrary:
                moose.move( self.chemid, self.model )
                if self.chemid.name != 'chem':
                    self.chemid.name = 'chem'
            else:
                moose.copy( self.chemid, self.model, 'chem' )
                self.chemid = moose.element( self.model.path + '/chem' )

        ep = self.elecid.path
        somaList = moose.wildcardFind( ep + '/#oma#[ISA=CompartmentBase]' )
        if len( somaList ) == 0:
            somaList = moose.wildcardFind( ep + '/#[ISA=CompartmentBase]' )
        if len( somaList ) == 0:
            raise BuildError( "installCellFromProto: No soma found" )
        maxdia = 0.0
        for i in somaList:
            if ( i.diameter > maxdia ):
                self.soma = i

def loadModel(filename, chanProto, chanDistrib, passiveDistrib):
    """Load the model and insert channels """
    global modelName
    global nchans, ncompts

    # Load in the swc file.
    modelName = "elec"
    cellProto = [ ( filename, modelName ) ]
    rdes = rd.rdesigneur( cellProto = cellProto
            , combineSegments = True
            , passiveDistrib = passiveDistrib
            , chanProto = chanProto
            , chanDistrib = chanDistrib
            )

    rdes.buildModel('/model')

    compts = moose.wildcardFind( "/model/%s/#[ISA=CompartmentBase]"%modelName )
    setupStimuls( compts[0] )

    for compt in compts:
        vtab = moose.Table( '%s/vm' % compt.path )
        moose.connect( vtab, 'requestOut', compt, 'getVm' )
        _records[compt.path] = vtab

    nchans  = len(set([x.path for x in
        moose.wildcardFind('/model/elec/##[TYPE=ZombieHHChannel]')])
        )
    _logger.info("Total channels: %s" % nchans)
    return _records

def showVisualization():
    makeModel()
    elec = moose.element( '/model/elec' )
    elec.setSpineAndPsdMesh( moose.element('/model/chem/spine'), moose.element('/model/chem/psd') )

    eHead = moose.wildcardFind( '/model/elec/#head#' )
    oldDia = [ i.diameter for i in eHead ]
    graphs = moose.Neutral( '/graphs' )
    #makePlot( 'psd_x', moose.vec( '/model/chem/psd/x' ), 'getN' )
    #makePlot( 'head_x', moose.vec( '/model/chem/spine/x' ), 'getN' )
    makePlot( 'dend_x', moose.vec( '/model/chem/dend/x' ), 'getN' )
    dendZ = makePlot( 'dend_z', moose.vec( '/model/chem/dend/z' ), 'getN' )
    makePlot( 'head_z', moose.vec( '/model/chem/spine/z' ), 'getN' )
    psdZ = makePlot( 'psd_z', moose.vec( '/model/chem/psd/z' ), 'getN' )
    diaTab = makePlot( 'headDia', eHead, 'getDiameter' )
    # print diaTab[0].vector[-1]
    # return
    dendrite = moose.element("/model/elec/dend")
    dendrites = [dendrite.path + "/" + str(i) for i in range(len(dendZ))]
    # print dendrites
    moose.reinit()

    spineHeads = moose.wildcardFind( '/model/elec/#head#')
    # print moose.wildcardFind( '/model/elec/##')

    # print "dendZ", readValues(dendZ)
    # print dendrite

    app = QtGui.QApplication(sys.argv)
    viewer = create_viewer("/model/elec", dendrite, dendZ, diaTab, psdZ)
    viewer.showMaximized()
    viewer.start()
    return app.exec_()

def spinetabs(model,neuron,comps='all'):
    if not moose.exists(DATA_NAME):
        moose.Neutral(DATA_NAME)
    #creates tables of calcium and vm for spines
    spcatab = defaultdict(list)
    spvmtab = defaultdict(list)
    for typenum,neurtype in enumerate(neuron.keys()):
        if type(comps)==str and comps in {'*', 'all'}:
            spineHeads=[moose.wildcardFind(neurtype+'/##/#head#[ISA=CompartmentBase]')]
        else:
            spineHeads=[moose.wildcardFind(neurtype+'/'+c+'/#head#[ISA=CompartmentBase]') for c in comps]
        for spinelist in spineHeads:
            for spinenum,spine in enumerate(spinelist):
                compname = spine.parent.name
                sp_num=spine.name.split(NAME_HEAD)[0]
                spvmtab[typenum].append(moose.Table(vm_table_path(neurtype, spine=sp_num, comp=compname)))
                log.debug('{} {} {}',spinenum, spine.path, spvmtab[typenum][-1].path)
                moose.connect(spvmtab[typenum][-1], 'requestOut', spine, 'getVm')
                if model.calYN:
                    for child in spine.children:
                        if child.className == "CaConc" or  child.className == "ZombieCaConc" :
                            spcatab[typenum].append(moose.Table(DATA_NAME+'/%s_%s%s'% (neurtype,sp_num,compname)+child.name))
                            spcal = moose.element(spine.path+'/'+child.name)
                            moose.connect(spcatab[typenum][-1], 'requestOut', spcal, 'getCa')
                        elif child.className == 'DifShell':
                            spcatab[typenum].append(moose.Table(DATA_NAME+'/%s_%s%s'% (neurtype,sp_num,compname)+child.name))
                            spcal = moose.element(spine.path+'/'+child.name)
                            moose.connect(spcatab[typenum][-1], 'requestOut', spcal, 'getC')
    return spcatab,spvmtab

def deliverStim(currTime):
	global injectionCurrent	
	global spineVm
	global somaVm
	if numpy.fabs( currTime - baselineTime ) < frameRunTime/2.0 :
		#start
		eList = moose.wildcardFind( '/model/elec/#soma#' )
		assert( len(eList) > 0 )
		eList[0].inject = injectionCurrent
		#print "1. injected current = ", injectionCurrent
		injectionCurrent += deltaCurrent
		#print "del stim first ", moose.element('/clock').currentTime
	if numpy.fabs( currTime - baselineTime - currPulseTime) < frameRunTime/2.0 :
		#end
		eList = moose.wildcardFind( '/model/elec/#soma#' )
		assert( len(eList) > 0 )
		eList[0].inject = 0.0
		#print "2. injected current = ", injectionCurrent
		#print "del stim second ", moose.element('/clock').currentTime
	if runtime - currTime < frameRunTime * 2.0 :
		#print "3. reinit-ing"
		somaVm.append( moose.element( '/graphs/VmTab' ).vector )
		spineVm.append( moose.element( '/graphs/eSpineVmTab' ).vector )
		iList.append(injectionCurrent)
		if injectionCurrent < maxCurrent :
			moose.reinit()	

def dumpPlots( fname ):
    if ( os.path.exists( fname ) ):
        os.remove( fname )
    for x in moose.wildcardFind( '/graphs/#[ISA=Table]' ):
        moose.element( x[0] ).xplot( fname, x[0].name )
    for x in moose.wildcardFind( '/graphs/elec/#[ISA=Table]' ):
        moose.element( x[0] ).xplot( fname, x[0].name + '_e' )

def read_prototype(celltype, cdict):
    """Read the cell prototype file for the specified class. The
    channel properties are updated using values in cdict."""
    filename = '%s/%s.p' % (config.modelSettings.protodir, celltype)
    logger.debug('Reading prototype file %s' % (filename))
    adjust_chanlib(cdict)
    cellpath = '%s/%s' % (config.modelSettings.libpath, celltype)
    if moose.exists(cellpath):
        return moose.element(cellpath)
    for handler in logger.handlers:
        handler.flush()
    proto = moose.loadModel(filename, cellpath)
    # If prototype files do not have absolute compartment positions,
    # set the compartment postions to origin. This will avoid
    # incorrect assignemnt of position when the x/y/z values in
    # prototype file is just to for setting the compartment length.
    if not config.modelSettings.morph_has_postion:
        for comp in moose.wildcardFind('%s/#[TYPE=Compartment]' % (proto.path)):
            comp.x = 0.0
            comp.y = 0.0
            comp.z = 0.0
    leveldict = read_keyvals('%s/%s.levels' % (config.modelSettings.protodir, celltype))
    depths = read_keyvals('%s/%s.depths' % (config.modelSettings.protodir, celltype))
    depthdict = {}
    for level, depthset in list(depths.items()):
        if len(depthset) != 1:
            raise Exception('Depth set must have only one entry.')
        depthdict[level] = depthset.pop()
    assign_depths(proto, depthdict, leveldict)
    config.logger.debug('Read %s with %d compartments' % (celltype, len(moose.wildcardFind('%s/#[TYPE=Compartment]' % (proto.path)))))
    return proto

def setCompartmentSolver(modelRoot,solver):
	compts = moose.wildcardFind(modelRoot+'/##[ISA=ChemCompt]')
	for compt in compts:
		if ( solver == 'gsl' ) or (solver == 'Runge Kutta'):
			ksolve = moose.Ksolve( compt.path+'/ksolve' )
		if ( solver == 'gssa' ) or (solver == 'Gillespie'):
			ksolve = moose.Gsolve( compt.path+'/gsolve' )
		if ( solver != 'ee' ):
			stoich = moose.Stoich( compt.path+'/stoich' )
			stoich.compartment = compt
			stoich.ksolve = ksolve
			if moose.exists(compt.path):
				stoich.path = compt.path+"/##"
	stoichList = moose.wildcardFind(modelRoot+'/##[ISA=Stoich]')
	if len( stoichList ) == 2:
		stoichList[1].buildXreacs( stoichList[0] )
	if len( stoichList ) == 3:
		stoichList[1].buildXreacs (stoichList [0])
		stoichList[1].buildXreacs (stoichList [2])

	for i in stoichList:
		i.filterXreacs()
	
	for x in moose.wildcardFind( modelRoot+'/data/graph#/#' ):
		x.tick = 18

    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 main():
    global synSpineList 
    global synDendList 
    numpy.random.seed( 1234 )
    rdes = buildRdesigneur( )
    for i in elecFileNames:
        print(i)
        rdes.cellProtoList = [ ['./cells/' + i, 'elec'] ]
        rdes.buildModel( )
        assert( moose.exists( '/model' ) )
        synSpineList = moose.wildcardFind( "/model/elec/#head#/glu,/model/elec/#head#/NMDA" )
        temp = set( moose.wildcardFind( "/model/elec/#/glu,/model/elec/#/NMDA" ) )

        synDendList = list( temp - set( synSpineList ) )
        moose.reinit()
        buildPlots( rdes )
        # Run for baseline, tetanus, and post-tetanic settling time 
        t1 = time.time()
        probeStimulus( baselineTime )
        tetanicStimulus( tetTime )
        probeStimulus( postTetTime )
        print(('real time = ', time.time() - t1))

        printPsd( i + ".fig5" )
        saveAndClearPlots( i + ".fig5" )
        moose.delete( '/model' )
        rdes.elecid = moose.element( '/' )

def transformNMDAR(path):
    for i in moose.wildcardFind(path + "/##/#NMDA#[ISA!=NMDAChan]"):
        chanpath = i.path
        pa = i.parent
        i.name = "_temp"
        if chanpath[-3:] == "[0]":
            chanpath = chanpath[:-3]
        nmdar = moose.NMDAChan(chanpath)
        sh = moose.SimpleSynHandler(chanpath + "/sh")
        moose.connect(sh, "activationOut", nmdar, "activation")
        sh.numSynapses = 1
        sh.synapse[0].weight = 1
        nmdar.Ek = i.Ek
        nmdar.tau1 = i.tau1
        nmdar.tau2 = i.tau2
        nmdar.Gbar = i.Gbar
        nmdar.CMg = 12
        nmdar.KMg_A = 1.0 / 0.28
        nmdar.KMg_B = 1.0 / 62
        nmdar.temperature = 300
        nmdar.extCa = 1.5
        nmdar.intCa = 0.00008
        nmdar.intCaScale = 1
        nmdar.intCaOffset = 0.00008
        nmdar.condFraction = 0.02
        moose.delete(i)
        moose.connect(pa, "channel", nmdar, "channel")
        caconc = moose.wildcardFind(pa.path + "/#[ISA=CaConcBase]")
        if len(caconc) < 1:
            print("no caconcs found on ", pa.path)
        else:
            moose.connect(nmdar, "ICaOut", caconc[0], "current")
            moose.connect(caconc[0], "concOut", nmdar, "assignIntCa")