def testIssue(self):
elm = sample.sampleelementType()
elm.ValueAmount = "100"
elm.ValueAmount.currencyID = "abc"
sam = sample.samplerootelement()
sam.sampleelement.append(elm)
bds = pyxb.utils.domutils.BindingDOMSupport()
bds.setDefaultNamespace(sample.Namespace)
bds.declareNamespace(xsi)
samdom = sam.toDOM(bds)
bds.addAttribute(samdom.documentElement, xsi.createExpandedName("schemaLocation"), "c:\sample.xsd")
xmld = samdom.toprettyxml(encoding="utf-8")
self.assertEqual(self.Expectedd, xmld)
def testIssue (self):
elm = sample.sampleelementType()
elm.ValueAmount = '100'
elm.ValueAmount.currencyID = 'abc'
sam = sample.samplerootelement()
sam.sampleelement.append(elm)
bds = pyxb.utils.domutils.BindingDOMSupport()
bds.setDefaultNamespace(sample.Namespace)
bds.declareNamespace(xsi)
samdom = sam.toDOM(bds)
bds.addAttribute(samdom.documentElement, xsi.createExpandedName('schemaLocation'), "c:\sample.xsd")
xmld = samdom.toprettyxml(encoding = "utf-8")
self.assertEqual(self.Expectedd, xmld)
def testIssue (self):
elm = sample.sampleelementType()
elm.ValueAmount = '100'
elm.ValueAmount.currencyID = 'abc'
sam = sample.samplerootelement()
sam.sampleelement.append(elm)
bds = pyxb.utils.domutils.BindingDOMSupport()
bds.setDefaultNamespace(sample.Namespace)
bds.declareNamespace(xsi)
samdom = sam.toDOM(bds)
bds.addAttribute(samdom.documentElement, xsi.createExpandedName('schemaLocation'), "c:\sample.xsd")
# xsi is probably not referenced elsewhere, so add the XMLNS declaration too
bds.addXMLNSDeclaration(samdom.documentElement, xsi)
xmld = samdom.toprettyxml(encoding = "utf-8")
self.assertEqual(self.Expectedd, xmld)
def to_xml(self, encoding='utf-8'):
"""
Get an XML document from this instance and its values.
:param encoding: Character encoding to declare the XML document with. Default: utf-8
:type encoding: basestring
:return: The complete XML document of brreg:Melding
:rtype: unicode
"""
bds = pyxb.utils.domutils.BindingDOMSupport()
bds.setDefaultNamespace(melding.Namespace)
bds.declareNamespace(xsi)
elem_dom = self.element.toDOM(bds)
bds.addAttribute(elem_dom.documentElement,
xsi.createExpandedName('schemaLocation'),
self.schemaLocation)
bds.addXMLNSDeclaration(elem_dom.documentElement, xsi)
return elem_dom.toxml(encoding=encoding)
def Export_NeuroML (network_file, neuroML_file ):
"""Reads .net file and exports it to NeuroML (Level3) format
@param *network_file (.net) network model file name
@param *neuroML_file neuroML file name
@return Finished reading file if the process goes well"""
filename = network_file
if os.path.isfile ( filename )==False:
print('\nERROR: unable to read neurons _network_ data file')
os._exit(0)
return None
bds = pyxb.utils.domutils.BindingDOMSupport()
bds.declareNamespace(xsi)
network = ml.neuroml()
py_net.Read_Network(network_file)
t = time.clock()
network.properties
network.length_units='micrometer'
network.populations = net.Populations()
network.cells = ml.Level3Cells()
xsi.createExpandedName('schemaLocation')
network.channels = cml.ChannelML()
network.channels.units = meta.Units( "Physiological Units")
# Channels
print('writing channels')
for sy in range(1):
network.channels.synapse_type.append(cml.SynapseType())
sy = 0 # synapse_type Dual Exponential
network.channels.synapse_type[sy].name = 'Exp2Syn'
network.channels.synapse_type[sy].status= meta.Status()
network.channels.synapse_type[sy].status.value_ = 'stable'
network.channels.synapse_type[sy].doub_exp_syn = cml.DoubleExponentialSynapse()
network.channels.synapse_type[sy].doub_exp_syn.max_conductance = bio.ConductanceValue()
network.channels.synapse_type[sy].doub_exp_syn.reversal_potential = bio.VoltageValue()
network.channels.synapse_type[sy].doub_exp_syn.decay_time = bio.TimeConstantValue(1.0)
network.channels.synapse_type[sy].doub_exp_syn.rise_time = bio.TimeConstantValueIncZero()
network.channels.synapse_type[sy].doub_exp_syn.max_conductance = 0.001
network.channels.synapse_type[sy].doub_exp_syn.reversal_potential = 0
network.channels.synapse_type[sy].doub_exp_syn.decay_time = 5.0
network.channels.synapse_type[sy].doub_exp_syn.rise_time = 1.0
for ch in range (2):
network.channels.channel_type.append(cml.ChannelType())
ch = 0 # channel_type Passive conductance
network.channels.channel_type[ch].name = 'pas'
network.channels.channel_type[ch].status= meta.Status()
network.channels.channel_type[ch].status.value_ = 'stable'
network.channels.channel_type[ch].current_voltage_relation = cml.CurrentVoltageRelation()
network.channels.channel_type[ch].current_voltage_relation.cond_law = 'ohmic'
network.channels.channel_type[ch].current_voltage_relation.ion = 'non_specific'
network.channels.channel_type[ch].current_voltage_relation.default_erev = -70.0
network.channels.channel_type[ch].current_voltage_relation.default_gmax = 0.001
ch = 1 # channel-type Integrate and fire
network.channels.channel_type[ch].name = 'IF'
network.channels.channel_type[ch].status= meta.Status()
network.channels.channel_type[ch].status.value_ = 'stable'
network.channels.channel_type[ch].current_voltage_relation = cml.CurrentVoltageRelation()
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire = cml.IntegrateAndFire()
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.threshold = bio.VoltageValue()
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.t_refrac = bio.TimeValue()
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.v_reset = bio.VoltageValue()
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.g_refrac = bio.ConductanceValue()
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.threshold = -50.0
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.t_refrac = 5.0
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.v_reset = -70.0
network.channels.channel_type[ch].current_voltage_relation.integrate_and_fire.g_refrac = 100.0
for pop in range(py_net.total_populations):
# Cells
print('writing cells')
network.cells.cell.append(ml.Level3Cell())
network.cells.cell[pop].name = 'Cell_' + str(pop)
network.cells.cell[pop].notes = meta.Notes()
network.cells.cell[pop].notes = 'Cell type created in NeuralSyns simulation environment'
network.cells.cell[pop].segments = mml.CTD_ANON_3()
network.cells.cell[pop].cables = mml.CTD_ANON_4()
network.cells.cell[pop].biophysics = ml.Level3Biophysics()
network.cells.cell[pop].biophysics.units="Physiological Units"
network.cells.cell[pop].connectivity = net.Level3Connectivity()
for fdm in range (py_net.population[pop].n_fdomains):
# Cables
network.cells.cell[pop].cables.cable.append(mml.Cable())
network.cells.cell[pop].cables.cable[fdm].id = py_net.population[pop].fdomain[fdm].fdm_ID
network.cells.cell[pop].cables.cable[fdm].name = str('group_' + py_net.population[pop].fdomain[fdm].label)
network.cells.cell[pop].cables.cable[fdm].group = meta.Group()
network.cells.cell[pop].cables.cable[fdm].group.append('group_' + py_net.population[pop].fdomain[fdm].label)
for pop in range(py_net.total_populations):
for fdm in range (py_net.population[pop].n_fdomains):
# Fdomains (Segments)
network.cells.cell[pop].segments.segment.append(mml.Segment())
network.cells.cell[pop].segments.segment[fdm].id = py_net.population[pop].fdomain[fdm].fdm_ID
network.cells.cell[pop].segments.segment[fdm].name = py_net.population[pop].fdomain[fdm].label
network.cells.cell[pop].segments.segment[fdm].proximal = meta.Point()
network.cells.cell[pop].segments.segment[fdm].distal = meta.Point()
if py_net.population[pop].fdomain[fdm].fdm_ID == 0:
network.cells.cell[pop].segments.segment[fdm].proximal.x = py_net.population[pop].fdomain[fdm].position.x
network.cells.cell[pop].segments.segment[fdm].proximal.y = py_net.population[pop].fdomain[fdm].position.y
network.cells.cell[pop].segments.segment[fdm].proximal.z = py_net.population[pop].fdomain[fdm].position.z
network.cells.cell[pop].segments.segment[fdm].proximal.diameter = float(py_net.population[pop].fdomain[fdm].position.r) * 2.0
network.cells.cell[pop].segments.segment[fdm].distal.x = py_net.population[pop].fdomain[fdm].position.x
network.cells.cell[pop].segments.segment[fdm].distal.y = py_net.population[pop].fdomain[fdm].position.y
network.cells.cell[pop].segments.segment[fdm].distal.z = py_net.population[pop].fdomain[fdm].position.z
network.cells.cell[pop].segments.segment[fdm].distal.diameter = float(py_net.population[pop].fdomain[fdm].position.r) * 2.0
else :
network.cells.cell[pop].segments.segment[fdm].parent = py_net.population[pop].fdomain[fdm].parent_fdomain
network.cells.cell[pop].segments.segment[fdm].proximal.x = py_net.population[pop].fdomain[py_net.population[pop].fdomain[fdm].parent_fdomain].position.x
network.cells.cell[pop].segments.segment[fdm].proximal.y = py_net.population[pop].fdomain[py_net.population[pop].fdomain[fdm].parent_fdomain].position.y
network.cells.cell[pop].segments.segment[fdm].proximal.z = py_net.population[pop].fdomain[py_net.population[pop].fdomain[fdm].parent_fdomain].position.z
network.cells.cell[pop].segments.segment[fdm].proximal.diameter = float(py_net.population[pop].fdomain[py_net.population[pop].fdomain[fdm].parent_fdomain].position.r) * 2
network.cells.cell[pop].segments.segment[fdm].distal.x = py_net.population[pop].fdomain[fdm].position.x
network.cells.cell[pop].segments.segment[fdm].distal.y = py_net.population[pop].fdomain[fdm].position.y
network.cells.cell[pop].segments.segment[fdm].distal.z = py_net.population[pop].fdomain[fdm].position.z
network.cells.cell[pop].segments.segment[fdm].distal.diameter = float(py_net.population[pop].fdomain[fdm].position.r) * 2.0
network.cells.cell[pop].segments.segment[fdm].cable = network.cells.cell[pop].cables.cable[fdm].id
# Biophysics
print('writing biophysics')
for m in range(2):
network.cells.cell[pop].biophysics.mechanism.append(bio.Mechanism())
network.cells.cell[pop].biophysics.mechanism[m].type = bio.MechanismType('Channel Mechanism')
m = 0 # IF model
network.cells.cell[pop].biophysics.mechanism[m].name = network.channels.channel_type[1].name
network.cells.cell[pop].biophysics.mechanism[m].passive_conductance = 'true'
network.cells.cell[pop].biophysics.mechanism[m].parameter.append(bio.NamedParameter())
network.cells.cell[pop].biophysics.mechanism[m].parameter[0].group.append( network.cells.cell[pop].cables.cable[0].name )
network.cells.cell[pop].biophysics.mechanism[m].parameter[0].name = 'V_rest'
network.cells.cell[pop].biophysics.mechanism[m].parameter[0].value_ = -70.0
network.cells.cell[pop].biophysics.mechanism[m].parameter.append(bio.NamedParameter())
network.cells.cell[pop].biophysics.mechanism[m].parameter[1].group.append( network.cells.cell[pop].cables.cable[0].name )
network.cells.cell[pop].biophysics.mechanism[m].parameter[1].name = 'gmax'
network.cells.cell[pop].biophysics.mechanism[m].parameter[1].value_ = 0.3
m = 1 # passive model
network.cells.cell[pop].biophysics.mechanism[m].name = network.channels.channel_type[0].name
network.cells.cell[pop].biophysics.mechanism[m].passive_conductance = 'true'
network.cells.cell[pop].biophysics.mechanism[m].parameter.append(bio.NamedParameter())
for fdm in range (0,py_net.population[pop].n_fdomains) :
network.cells.cell[pop].biophysics.mechanism[m].parameter[0].group.append( network.cells.cell[pop].cables.cable[fdm].name )
network.cells.cell[pop].biophysics.mechanism[m].parameter[0].name = 'e'
network.cells.cell[pop].biophysics.mechanism[m].parameter[0].value_ = 0.001
network.cells.cell[pop].biophysics.mechanism[m].parameter.append(bio.NamedParameter())
for fdm in range (0,py_net.population[pop].n_fdomains) :
network.cells.cell[pop].biophysics.mechanism[m].parameter[1].group.append( network.cells.cell[pop].cables.cable[fdm].name )
network.cells.cell[pop].biophysics.mechanism[m].parameter[1].name = 'gmax'
network.cells.cell[pop].biophysics.mechanism[m].parameter[1].value_ = 0.3
# Capacitance and axial resistance
network.cells.cell[pop].biophysics.spec_capacitance = bio.SpecCapacitance()
network.cells.cell[pop].biophysics.spec_capacitance.parameter.append(bio.UnnamedParameter())
network.cells.cell[pop].biophysics.spec_capacitance.parameter[0].value_ = 1.0 # default_value
network.cells.cell[pop].biophysics.spec_capacitance.parameter[0].group.append('all')
network.cells.cell[pop].biophysics.spec_axial_resistance = bio.SpecAxialResistance()
for fdm in range (py_net.population[pop].n_fdomains):
network.cells.cell[pop].biophysics.spec_axial_resistance.parameter.append(bio.UnnamedParameter())
network.cells.cell[pop].biophysics.spec_axial_resistance.parameter[fdm].value_ = 0.1 #default_value
network.cells.cell[pop].biophysics.spec_axial_resistance.parameter[fdm].group.append( network.cells.cell[pop].cables.cable[fdm].name )
#connections
#default (connections between any segment will be available in neuroconstruct)
for popi in range(py_net.total_populations):
network.cells.cell[popi].connectivity.potential_syn_loc.append(net.PotentialSynLoc())
network.cells.cell[popi].connectivity.potential_syn_loc[0].group_.append('all')
network.cells.cell[popi].connectivity.potential_syn_loc[0].synapse_type = network.channels.synapse_type[0].name
for pop in range(py_net.total_populations):
# Populations
print('writing populations')
network.populations.population.append(net.Population())
network.populations.population[pop].name = py_net.population[pop].label
network.populations.population[pop].cell_type_ = network.cells.cell[pop].name
network.populations.population[pop].instances = net.Instances()
network.populations.population[pop].instances.size = py_net.population[pop].n_neurons
for nrn in range (py_net.population[pop].n_neurons):
network.populations.population[pop].instances.instance.append(net.CellInstance())
network.populations.population[pop].instances.instance[nrn].id = nrn
network.populations.population[pop].instances.instance[nrn].location = meta.Point3D()
network.populations.population[pop].instances.instance[nrn].location.x = py_net.neuron[py_net.population[pop].first_neuron+nrn].position.x
network.populations.population[pop].instances.instance[nrn].location.y = py_net.neuron[py_net.population[pop].first_neuron+nrn].position.y
network.populations.population[pop].instances.instance[nrn].location.z = py_net.neuron[py_net.population[pop].first_neuron+nrn].position.z
# Connectivity (projections and connections)
print('writing synapses')
if py_net.total_synapses != 0:
network.projections = net.Projections()
network.projections.units = meta.Units('Physiological Units')
pairs = 0
for popi in range (py_net.total_populations):
#total_connections = 0
#total_connections = py_net.population[popi].n_neurons * py_net.neuron[py_net.population[popi].first_neuron].n_synapses
for popj in range (py_net.total_populations):
if py_net.connection[popi][popj]!=0:
pop_source = popi
pop_target = popj
print(popi, popj)
network.projections.projection.append(net.Projection())
network.projections.projection[pairs].name = py_net.connection[pop_source][pop_target].label
network.projections.projection[pairs].source_ = py_net.population[pop_source].label
network.projections.projection[pairs].target_ = str(py_net.population[pop_target].label)
network.projections.projection[pairs].synapse_props.append( net.GlobalSynapticProperties())
network.projections.projection[pairs].synapse_props[0].synapse_type_ = network.channels.synapse_type[0].name
network.projections.projection[pairs].synapse_props[0].internal_delay = py_net.neuron[py_net.population[pop_source].first_neuron].synapse[0].delay
network.projections.projection[pairs].synapse_props[0].weight = py_net.neuron[py_net.population[pop_source].first_neuron].synapse[0].weight
network.projections.projection[pairs].connections = net.Connections()
c = 0
post_cell = []
pre_cell = []
for nrn in range (py_net.population[pop_source].first_neuron,py_net.population[pop_source].first_neuron + py_net.population[pop_source].n_neurons):
for syn in range(py_net.neuron[nrn].n_synapses):
p_target = py_net.neuron[py_net.neuron[nrn].synapse[syn].target_neuron].population
post_cell_id = py_net.neuron[nrn].synapse[syn].target_neuron - py_net.population[p_target].first_neuron
if (p_target == pop_target) :
post_cell.append(int(post_cell_id))
pre_cell.append(nrn)
c = c + 1
network.projections.projection[pairs].connections.size = c
for conn in range (c):
network.projections.projection[pairs].connections.connection.append(net.Connection())
network.projections.projection[pairs].connections.connection[conn].properties_.append(net.LocalSynapticProperties())
network.projections.projection[pairs].connections.connection[conn].id = conn
network.projections.projection[pairs].connections.connection[conn].pre_cell_id = pre_cell[conn] - py_net.population[pop_source].first_neuron
network.projections.projection[pairs].connections.connection[conn].pre_segment_id = 0 # source segment is always soma
network.projections.projection[pairs].connections.connection[conn].post_cell_id = post_cell[conn]
network.projections.projection[pairs].connections.connection[conn].post_segment_id = py_net.connection[pop_source][pop_target].fdomain
network.projections.projection[pairs].connections.connection[conn].post_fraction_along = meta.ZeroToOne()
network.projections.projection[pairs].connections.connection[conn].post_fraction_along = 0.5
pairs = pairs + 1
#Inputs
network.inputs = net.Inputs()
network.inputs.units = meta.Units('SI Units')
network.inputs.input.append(net.Input())
network.inputs.input[0].name = 'RandomInput'
network.inputs.input[0].target = net.InputTarget()
network.inputs.input[0].target.population = network.populations.population[0].name
network.inputs.input[0].target.sites = net.InputSites()
network.inputs.input[0].target.sites.size = 1
network.inputs.input[0].target.sites.site.append(net.InputSite())
network.inputs.input[0].target.sites.site[0].cell_id = net.CellIdInNetwork()
network.inputs.input[0].target.sites.site[0].cell_id = 0
network.inputs.input[0].random_stim = net.RandomStim()
network.inputs.input[0].random_stim.frequency = bio.FrequencyValue()
network.inputs.input[0].random_stim.frequency = 10
network.inputs.input[0].random_stim.synaptic_mechanism = network.channels.synapse_type[0].name
print('Writing your xml network')
file(neuroML_file, 'w').write(network.toxml("utf-8"))
print "\n\nFinished writing file. Elapsed time = ", time.clock()-t, 'secs.'
