def testNeuroMeshMultiscale():
        useHsolve = 1
        runtime = 0.5
        if useHsolve:
	    elecDt = 50e-6
        else:
	    elecDt = 10e-6
	chemDt = 0.005
	ePlotDt = 0.5e-3
	cPlotDt = 0.005
	plotName = 'nm.plot'

	makeNeuroMeshModel()
	print "after model is completely done"
	for i in moose.wildcardFind( '/model/chem/#/#/#/transloc#' ):
		print i[0].name, i[0].Kf, i[0].Kb, i[0].kf, i[0].kb

	makeChemPlots()
	makeElecPlots()
	makeCaPlots()
	moose.setClock( 0, elecDt )
	moose.setClock( 1, elecDt )
	moose.setClock( 2, elecDt )
	moose.setClock( 4, chemDt )
	moose.setClock( 5, chemDt )
	moose.setClock( 6, chemDt )
	moose.setClock( 7, cPlotDt )
	moose.setClock( 8, ePlotDt )
        if useHsolve:
	    hsolve = moose.HSolve( '/model/elec/hsolve' )
	    moose.useClock( 1, '/model/elec/hsolve', 'process' )
	    hsolve.dt = elecDt
	    hsolve.target = '/model/elec/compt'
	    moose.reinit()
        else:
	    moose.useClock( 0, '/model/elec/##[ISA=Compartment]', 'init' )
	    moose.useClock( 1, '/model/elec/##[ISA=Compartment]', 'process' )
	    moose.useClock( 2, '/model/elec/##[ISA=ChanBase],/model/##[ISA=SynBase],/model/##[ISA=CaConc]','process')

	moose.useClock( 1, '/model/elec/##[ISA=SpikeGen]', 'process' )
	moose.useClock( 2, '/model/##[ISA=SynBase],/model/##[ISA=CaConc]','process')
	#moose.useClock( 5, '/model/chem/##[ISA=PoolBase],/model/##[ISA=ReacBase],/model/##[ISA=EnzBase]', 'process' )
	#moose.useClock( 4, '/model/chem/##[ISA=Adaptor]', 'process' )
	moose.useClock( 4, '/model/chem/#/dsolve', 'process' )
	moose.useClock( 4, '/model/chem/#/ksolve', 'init' )
	moose.useClock( 5, '/model/chem/#/ksolve', 'process' )
	moose.useClock( 6, '/model/chem/spine/adaptCa', 'process' )
	moose.useClock( 6, '/model/chem/dend/DEND/adaptCa', 'process' )
	moose.useClock( 7, '/graphs/chem/#', 'process' )
	moose.useClock( 8, '/graphs/elec/#,/graphs/ca/#', 'process' )
        moose.element( '/model/elec/soma' ).inject = 2e-10
        moose.element( '/model/chem/psd/Ca' ).concInit = 0.001
        moose.element( '/model/chem/spine/Ca' ).concInit = 0.002
        moose.element( '/model/chem/dend/DEND/Ca' ).concInit = 0.003
	moose.reinit()

	moose.start( runtime )
#        moose.element( '/model/elec/soma' ).inject = 0
#	moose.start( 0.25 )
        makeGraphics( cPlotDt, ePlotDt )

 def make_neuron_spike(nrnidx,I=1e-7,duration=1e-3):
     """ Inject a brief current pulse to
     make a neuron spike
     """
     network.vec[nrnidx].inject = I
     moose.start(duration)
     network.vec[nrnidx].inject = 0.

def run(nogui):
    
    reader = NML2Reader(verbose=True)

    filename = 'test_files/NML2_SingleCompHHCell.nml'
    print('Loading: %s'%filename)
    reader.read(filename, symmetric=True)
    
    
    msoma = reader.getComp(reader.doc.networks[0].populations[0].id,0,0)
    print(msoma)
    
    
    data = moose.Neutral('/data')
    
    pg = reader.getInput('pulseGen1')
    
    inj = moose.Table('%s/pulse' % (data.path))
    moose.connect(inj, 'requestOut', pg, 'getOutputValue')
    
    
    vm = moose.Table('%s/Vm' % (data.path))
    moose.connect(vm, 'requestOut', msoma, 'getVm')
    
    simdt = 1e-6
    plotdt = 1e-4
    simtime = 300e-3
    #moose.showmsg( '/clock' )
    for i in range(8):
        moose.setClock( i, simdt )
    moose.setClock( 8, plotdt )
    moose.reinit()
    moose.start(simtime)
    
    print("Finished simulation!")
    
    t = np.linspace(0, simtime, len(vm.vector))
    
    if not nogui:
        import matplotlib.pyplot as plt

        vfile = open('moose_v_hh.dat','w')

        for i in range(len(t)):
            vfile.write('%s\t%s\n'%(t[i],vm.vector[i]))
        vfile.close()
        plt.subplot(211)
        plt.plot(t, vm.vector * 1e3, label='Vm (mV)')
        plt.legend()
        plt.title('Vm')
        plt.subplot(212)
        plt.title('Input')
        plt.plot(t, inj.vector * 1e9, label='injected (nA)')
        #plt.plot(t, gK.vector * 1e6, label='K')
        #plt.plot(t, gNa.vector * 1e6, label='Na')
        plt.legend()
        plt.figure()
        test_channel_gates()
        plt.show()
        plt.close()

def singleCompt( name, params ):
    mod = moose.copy( '/library/' + name + '/' + name, '/model' )
    A = moose.element( mod.path + '/A' )
    Z = moose.element( mod.path + '/Z' )
    Z.nInit = 1
    Ca = moose.element( mod.path + '/Ca' )
    CaStim = moose.element( Ca.path + '/CaStim' )
    runtime = params['preStimTime'] + params['stimWidth'] + params['postStimTime'] 
    steptime = 100

    CaStim.expr += ' + x2 * (t > ' + str( runtime ) + ' ) * ( t < ' + str( runtime + steptime ) +  ' )'
    print(CaStim.expr)
    tab = moose.Table2( '/model/' + name + '/Atab' )
    #for i in range( 10, 19 ):
        #moose.setClock( i, 0.01 )
    ampl = moose.element( mod.path + '/ampl' )
    phase = moose.element( mod.path + '/phase' )
    moose.connect( tab, 'requestOut', A, 'getN' )
    ampl.nInit = params['stimAmplitude'] * 1
    phase.nInit = params['preStimTime']

    ksolve = moose.Ksolve( mod.path + '/ksolve' )
    stoich = moose.Stoich( mod.path + '/stoich' )
    stoich.compartment = mod
    stoich.ksolve = ksolve
    stoich.path = mod.path + '/##'
    runtime += 2 * steptime

    moose.reinit()
    moose.start( runtime )
    t = np.arange( 0, runtime + 1e-9, tab.dt )
    return name, t, tab.vector

def run_LIF():
	## reset and run the simulation
	print("Reinit MOOSE.")
	## from moose_utils.py sets clocks and resets
	resetSim(['/cells[0]'], SIMDT, PLOTDT, simmethod='ee')
	print("Running now...")
	moose.start(RUNTIME)

def main():
    """ This example illustrates loading, running of an SBML model defined in XML format.\n
	The model 00001-sbml-l3v1.xml is taken from l3v1 SBML testcase.\n
	Plots are setup.\n
	Model is run for 20sec.\n
	As a general rule we created model under '/path/model' and plots under '/path/graphs'.\n
    """

    mfile =  os.path.join( script_dir, 'chem_models/00001-sbml-l3v1.xml')
    runtime = 20.0
        
    # Loading the sbml file into MOOSE, models are loaded in path/model
    sbmlId = moose.readSBML(mfile,'sbml')
    

    s1 = moose.element('/sbml/model/compartment/S1')
    s2= moose.element('/sbml/model/compartment/S2')
                      
    # Creating MOOSE Table, Table2 is for the chemical model
    graphs = moose.Neutral( '/sbml/graphs' )
    outputs1 = moose.Table2 ( '/sbml/graphs/concS1')
    outputs2 = moose.Table2 ( '/sbml/graphs/concS2')

    # connect up the tables
    moose.connect( outputs1,'requestOut', s1, 'getConc' );
    moose.connect( outputs2,'requestOut', s2, 'getConc' );

        
    # Reset and Run
    moose.reinit()
    moose.start(runtime)

def run_sim_parallel(passive=True, solver='hsolve'):
    data_info_list = []
    model_info_list = []
    for jj, ti in enumerate(intervals):
        for ii, st in enumerate(stim_order):
            experiment_name = 'expt_%d_%d' % (jj, ii)
            dinfo, minfo = setup_experiment(experiment_name, st, tonset, ti, passive=passive, solver=solver)
            data_info_list.append(dinfo)
            model_info_list.append(minfo)
    mutils.setDefaultDt(elecdt=simdt)
    mutils.assignDefaultTicks()
    moose.reinit()
    moose.start(tstop)
    print('$$$$$$$$$$$', moose.element('/clock'    ).currentTime)
    axes_vm = fig.add_subplot(111)
    # axes_vm_out = fig.add_subplot(121)
    # axes_vm_in = fig.add_subplot(122, sharex=axes_vm_out, sharey=axes_vm_out)
    ################
    # axes_vm = fig.add_subplot(311)
    # axes_nmda = fig.add_subplot(312)
    # axes_ampa = fig.add_subplot(313)
    for jj, ti in enumerate(intervals):
        for ii, st in enumerate(stim_order):
            dinfo = data_info_list[jj * len(stim_order) + ii]
            print('Interval=', ti, 'Stim order=', st)
            print('dinfo:', dinfo)
            print(dinfo['soma_vm'])
            print(dinfo['soma_vm'].vector)
            v = dinfo['soma_vm'].vector
            t = np.linspace(0, tstop, len(v))
            print('num points=', len(t), 't0=', t[0], 't_last=', t[-1], 'v0=', v[0], 'v_last=', v[-1])

 def resetAndStartSimulation(self):
     """TODO this should provide a clean scheduling through all kinds
     of simulation or default scheduling should be implemented in MOOSE
     itself. We need to define a policy for handling scheduling. It can
     be pushed to the plugin-developers who should have knowledge of
     the scheduling criteria for their domain."""
     settings = config.MooseSetting()
     try:
         simdt_kinetics = float(settings[config.KEY_KINETICS_SIMDT])
     except ValueError:
         simdt_kinetics = 0.1
     try:
         simdt_electrical = float(settings[config.KEY_ELECTRICAL_SIMDT])
     except ValueError:
         simdt_electrical = 0.25e-4
     try:
         plotdt_kinetics = float(settings[config.KEY_KINETICS_PLOTDT])
     except ValueError:
         plotdt_kinetics = 0.1
     try:
         plotdt_electrical = float(settings[config.KEY_ELECTRICAL_PLOTDT])
     except ValueError:
         plotdt_electrical = 0.25e-3
     try:
         simtime = float(settings[config.KEY_SIMTIME])
     except ValueError:
         simtime = 1.0
     moose.reinit()
     view = self.plugin.getRunView()
     moose.start(simtime)
     if view.getCentralWidget().plotAll:
         view.getCentralWidget().plotAllData()
     self.setCurrentView('run')        

 def continueSimulation(self):
     """TODO implement this somewhere else"""
     try:
         simtime = float(config.MooseSetting()[config.KEY_SIMTIME])
     except ValueError:
         simtime = 1.0
     moose.start(simtime)      

def main():
    """
    A toy compartmental neuronal + chemical model that causes bad things
    to happen to the hsolver, as of 28 May 2013. Hopefully this will
    become irrelevant soon.
    """
    fineDt = 1e-5
    coarseDt = 5e-5
    make_spiny_compt()
    make_plots()
    for i in range( 8 ):
        moose.setClock( i, fineDt )
    moose.setClock( 8, coarseDt )
    moose.reinit()
    moose.start( 0.1 )
    display_plots( 'instab.plot' )
    # make Hsolver and rerun
    hsolve = moose.HSolve( '/n/hsolve' )
    for i in range( 8 ):
        moose.setClock( i, coarseDt )
    hsolve.dt = coarseDt
    hsolve.target = '/n/compt'
    moose.reinit()
    moose.start( 0.1 )
    display_plots( 'h_instab.plot' )
    pylab.show()

def test_crossing_single():
    """This function creates an ematrix of two PulseGen elements and
    another ematrix of two Table elements.

    The two pulsegen elements have same amplitude but opposite phase.

    Table[0] is connected to PulseGen[1] and Table[1] to Pulsegen[0].

    In the plot you should see two square pulses of opposite phase.

    """
    size = 2
    pg = moose.PulseGen('pulsegen', size)
    for ix, ii in enumerate(pg.vec):
        pulse = moose.element(ii)
        pulse.delay[0] = 1.0
        pulse.width[0] = 2.0
        pulse.level[0] = (-1)**ix
    tab = moose.Table('table', size)
    moose.connect(tab.vec[0], 'requestOut', pg.vec[1], 'getOutputValue', 'Single')
    moose.connect(tab.vec[1], 'requestOut', pg.vec[0], 'getOutputValue', 'Single')
    print 'Neighbors:'
    for t in tab.vec:
        print t.path
        for n in moose.element(t).neighbors['requestOut']:
            print 'requestOut <-', n.path
    moose.setClock(0, 0.1)
    moose.useClock(0, '/##', 'process')
    moose.start(5)
    for ii in tab.vec:
        t = moose.Table(ii).vector
        print len(t)
        pylab.plot(t)
    pylab.show()

def main():
		makeModel()
                '''
                '''
		ksolve = moose.Ksolve( '/model/compartment/ksolve' )
		stoich = moose.Stoich( '/model/compartment/stoich' )
		stoich.compartment = moose.element( '/model/compartment' )
		stoich.ksolve = ksolve
		stoich.path = "/model/compartment/##"
		#solver.method = "rk5"
		#mesh = moose.element( "/model/compartment/mesh" )
		#moose.connect( mesh, "remesh", solver, "remesh" )
                '''
		moose.setClock( 5, 1.0 ) # clock for the solver
		moose.useClock( 5, '/model/compartment/ksolve', 'process' )
                '''

		moose.reinit()
		moose.start( 100.0 ) # Run the model for 100 seconds.
                func = moose.element( '/model/compartment/d/func' )
                if useY:
                    func.expr = "-y0 + 10*y1"
                else:
                    func.expr = "-x0 + 10*x1"
		moose.start( 100.0 ) # Run the model for 100 seconds.
                #moose.showfields( '/model/compartment/d' )
                #moose.showfields( '/model/compartment/d/func' )
                print func.x.value
                print moose.element( '/model/compartment/b' ).n

		# Iterate through all plots, dump their contents to data.plot.
		displayPlots()

		quit()

def main():
	"""
	This snippet shows the use of several objects.
	This snippet sets up a StimulusTable to control a RandSpike which
	sends its outputs to two places: to a SimpleSynHandler on an IntFire,
	which is used to monitor spike arrival, and to various Stats objects.
    Each of these are recorded and plotted.
	The StimulusTable has a sine-wave waveform.
	"""
        make_model()

        moose.reinit()
        moose.start( runtime )
        plots = moose.element( '/plots' )
        plot1 = moose.element( '/plot1' )
        plot2 = moose.element( '/plot2' )
        plotf = moose.element( '/plotf' )
        t = [i * dt for i in range( plot1.vector.size )]
        pylab.plot( t, plots.vector, label='stimulus' )
        pylab.plot( t, plot1.vector, label='spike rate mean' )
        pylab.plot( t, plot2.vector, label='Vm mean' )
        pylab.plot( t, plotf.vector, label='Vm' )
        pylab.legend()
        pylab.show()

	'''

def testCubeMultiscale( useSolver ):
    elecDt = 10e-6
    chemDt = 1e-4
    plotDt = 5e-4
    plotName = 'mc.plot'
    if ( useSolver ):
        elecDt = 50e-6
        chemDt = 2e-3
        plotName = 'mcs.plot'
    makeCubeMultiscale()

    makeChemPlots()
    makeElecPlots()
    moose.setClock( 0, elecDt )
    moose.setClock( 1, elecDt )
    moose.setClock( 2, elecDt )
    moose.setClock( 5, chemDt )
    moose.setClock( 6, chemDt )
    moose.setClock( 7, plotDt )
    moose.setClock( 8, plotDt )
    moose.useClock( 0, '/model/elec/##[ISA=Compartment]', 'init' )
    moose.useClock( 1, '/model/elec/##[ISA=Compartment],/model/elec/##[ISA=SpikeGen]', 'process' )
    moose.useClock( 2, '/model/elec/##[ISA=SynBase],/model/elec/##[ISA=ChanBase],/model/elec/##[ISA=CaConc]','process')
    moose.useClock( 5, '/model/##[ISA=ReacBase],/model/##[ISA=EnzBase]', 'process' )
    moose.useClock( 6, '/model/##[ISA=PoolBase],/model/chem/##[ISA=Adaptor]', 'process' )
    moose.useClock( 7, '/graphs/#', 'process' )
    moose.useClock( 8, '/graphs/elec/#', 'process' )
    if ( useSolver ):
        makeSolvers( elecDt )
    moose.reinit()
    moose.start( 1.0 )
    dumpPlots( plotName )

def loadAndRun(solver=True):
    simtime = 500e-3
    model = moose.loadModel('../data/h10.CNG.swc', '/cell')
    comp = moose.element('/cell/apical_e_177_0')
    soma = moose.element('/cell/soma')
    for i in range(10):
        moose.setClock(i, dt)
    if solver:
        solver = moose.HSolve('/cell/solver')
        solver.target = soma.path
        solver.dt = dt
    stim = moose.PulseGen('/cell/stim')
    stim.delay[0] = 50e-3
    stim.delay[1] = 1e9
    stim.level[0] = 1e-9
    stim.width[0] = 2e-3    
    moose.connect(stim, 'output', comp, 'injectMsg')
    tab = moose.Table('/cell/Vm')
    moose.connect(tab, 'requestOut', comp, 'getVm')
    tab_soma = moose.Table('/cell/Vm_soma')
    moose.connect(tab_soma, 'requestOut', soma, 'getVm')
    moose.reinit()
    print('[INFO] Running for %s' % simtime)
    moose.start(simtime )
    vec = tab_soma.vector 
    moose.delete( '/cell' )
    return vec

def run_single_channel(channelname, Gbar, simtime, simdt=testutils.SIMDT, plotdt=testutils.PLOTDT):
    testId = uuid.uuid4().int
    container = moose.Neutral('test%d' % (testId))
    model_container = moose.Neutral('%s/model' % (container.path))
    data_container = moose.Neutral('%s/data' % (container.path))
    params = testutils.setup_single_compartment(
        model_container, data_container,
        channelbase.prototypes[channelname],
        Gbar)
    vm_data = params['Vm']
    gk_data = params['Gk']
    ik_data = params['Ik']
    testutils.setup_clocks(simdt, plotdt)
    testutils.assign_clocks(model_container, data_container)
    moose.reinit()
    print 'Starting simulation', testId, 'for', simtime, 's'
    moose.start(simtime)
    print 'Finished simulation'
    vm_file = 'data/%s_Vm.dat' % (channelname)
    gk_file = 'data/%s_Gk.dat' % (channelname)
    ik_file = 'data/%s_Ik.dat' % (channelname)
    tseries = np.array(range(len(vm_data.vec))) * simdt
    print 'Vm:', len(vm_data.vec), 'Gk', len(gk_data.vec), 'Ik', len(ik_data.vec)
    data = np.c_[tseries, vm_data.vec]
    np.savetxt(vm_file, data)
    print 'Saved Vm in', vm_file
    print len(gk_data.vec), len(vm_data.vec)
    data = np.c_[tseries, gk_data.vec]
    np.savetxt(gk_file, data)
    print 'Saved Gk in', gk_file
    data = np.c_[tseries, ik_data.vec]
    np.savetxt(ik_file, data)
    print 'Saved Gk in', ik_file
    return params

def main():
        """
        This example illustrates loading, and running a kinetic model 
        for a bistable positive feedback system, defined in kkit format. 
        This is based on Bhalla, Ram and Iyengar, Science 2002.

        The core of this model is a positive feedback loop comprising of
        the MAPK cascade, PLA2, and PKC. It receives PDGF and Ca2+ as 
        inputs.

        This model is quite a large one and due to some stiffness in its
        equations, it runs somewhat slowly. 

        The simulation illustrated here shows how the model starts out in
        a state of low activity. It is induced to 'turn on' when a 
        a PDGF stimulus is given for 400 seconds. 
        After it has settled to the new 'on' state, model is made to 
        'turn off'
        by setting the system calcium levels to zero for a while. This
        is a somewhat unphysiological manipulation!
        """
        solver = "gsl"  # Pick any of gsl, gssa, ee..
        #solver = "gssa"  # Pick any of gsl, gssa, ee..
	mfile = '../../genesis/acc35.g'
	runtime = 2000.0
	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 = 5e-19 

	moose.reinit()
	moose.start( 500 ) 
        moose.element( '/model/kinetics/PDGFR/PDGF' ).concInit = 0.0001
	moose.start( 400 ) 
        moose.element( '/model/kinetics/PDGFR/PDGF' ).concInit = 0.0
	moose.start( 2000 ) 
        moose.element( '/model/kinetics/Ca' ).concInit = 0.0
	moose.start( 500 ) 
        moose.element( '/model/kinetics/Ca' ).concInit = 0.00008
	moose.start( 2000 ) 

	# Display all plots.
        img = mpimg.imread( 'mapkFB.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#/#' )
        t = numpy.arange( 0, x[0].vector.size, 1 ) * x[0].dt
        ax.plot( t, x[0].vector, 'b-', label=x[0].name )
        ax.plot( t, x[1].vector, 'c-', label=x[1].name )
        ax.plot( t, x[2].vector, 'r-', label=x[2].name )
        ax.plot( t, x[3].vector, 'm-', label=x[3].name )
        plt.ylabel( 'Conc (mM)' )
        plt.xlabel( 'Time (seconds)' )
        pylab.legend()
        pylab.show()

def run_model_fig7(chans_1, chans_2, chans_3):
    to_run = simtime
    delta_t = 0.25 * tau
    for ii in range(0, len(chans_1), 2):
        print(ii)
        print('-----------------')
        chans_1[ii].Gk = 1 / Rm
        chans_1[ii+1].Gk = 1 / Rm
        chans_2[-ii-1].Gk = 1 / Rm
        chans_2[-ii-2].Gk = 1 / Rm
        for chan in chans_3:
            chan.Gk = 0.25 / Rm
        
        for chan in chans_1:
            print(chan.Gk, end=' ')
        print()
        for chan in chans_2:
            print(chan.Gk, end=' ')
        print()
        moose.start(delta_t)
        for chan in chans_1:
            chan.Gk = 0.0
        for chan in chans_2:
            chan.Gk = 0.0
        to_run = to_run - delta_t
    for chan in chans_3:
        chan.Gk = 0.0
    moose.start(to_run)

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 test():
    global finish_all_
    os.environ['MOOSE_STREAMER_ADDRESS'] = 'http://127.0.0.1:%d'%port_
    done = mp.Value('d', 0)
    q = mp.Queue()
    client = mp.Process(target=socket_client, args=(q, done))
    client.start()

    time.sleep(0.1)

    print( '[INFO] Socket client is running now' )
    ts = models.simple_model_a()
    moose.reinit()
    time.sleep(0.1)
    # If TCP socket is created, some delay is often neccessary before start. Don't
    # know why. probably some latency in a fresh TCP socket. A TCP guru can
    # tell.
    moose.start(50)
    print( 'MOOSE is done' )

    time.sleep(0.5)
    done.value = 1
    res = q.get()
    client.join()

    if not res:
        raise RuntimeWarning('Nothing was streamed')
    for k in res:
        a = res[k][1::2]
        b = moose.element(k).vector
        print(k, len(a), len(b))
        assert( (a==b).all())
    print( 'Test 1 passed' )