def build_lems_for_model(src):
model = lems.Model()
model.add(lems.Dimension('time', t=1))
# model.add(lems.Dimension('au'))
# primary element of the model is a mass model component
mass = lems.ComponentType(src.name, extends="baseCellMembPot")
model.add(mass)
######### Adding v is required to ease mapping to NEURON...
mass.dynamics.add(lems.StateVariable(name="v", dimension="voltage", exposure="v"))
mass.add(lems.Attachments(name="synapses",type_="basePointCurrentDL"))
for input in src.input:
mass.dynamics.add(lems.DerivedVariable(name=input,
dimension='none',
exposure=input,
select='synapses[*]/I',
reduce='add'))
mass.add(lems.Exposure(input, 'none'))
for key, val in src.const.items():
mass.add(lems.Parameter(key, 'none')) # TODO units
mass.add(lems.Constant(name="MSEC", dimension="time", value="1ms"))
mass.add(lems.Constant(name="PI", dimension="none", value="3.14159265359"))
states = []
der_vars = []
# for key in src.param:
# mass.add(lems.Parameter(key, 'au')) # TODO units
for key, val in src.auxex:
val = val.replace('**', '^')
mass.dynamics.add(lems.DerivedVariable(key, value=val))
for key in src.obsrv:
name_dv = key.replace('(','_').replace(')','').replace(' - ','_min_')
mass.dynamics.add(lems.DerivedVariable(name_dv, value=key, exposure=name_dv))
mass.add(lems.Exposure(name_dv, 'none'))
for src_svar in src.state_space:
name = src_svar.name
ddt = src_svar.drift.replace('**', '^')
mass.dynamics.add(lems.StateVariable(name, 'none', name))
mass.dynamics.add(lems.TimeDerivative(name, '(%s)/MSEC'%ddt))
mass.add(lems.Exposure(name, 'none'))
''' On condition is not need on the model but NeuroML requires its definition -->
<OnCondition test="r .lt. 0">
<EventOut port="spike"/>
</OnCondition>'''
oc = lems.OnCondition(test='v .gt. 0')
oc.actions.append(lems.EventOut(port='spike'))
mass.dynamics.add(oc)
return model
def channelpedia_xml_to_neuroml2(cpd_xml, nml2_file_name, unknowns=""):
info = 'Automatic conversion of Channelpedia XML file to NeuroML2\n'+\
'Uses: https://github.com/OpenSourceBrain/BlueBrainProjectShowcase/blob/master/Channelpedia/ChannelpediaToNeuroML2.py'
print(info)
root = ET.fromstring(cpd_xml)
channel_id = 'Channelpedia_%s_%s' % (root.attrib['ModelName'].replace(
"/", "_").replace(" ", "_").replace(".", "_"), root.attrib['ModelID'])
doc = neuroml.NeuroMLDocument()
metadata = osb.metadata.RDF(info)
ion = root.findall('Ion')[0]
chan = neuroml.IonChannelHH(
id=channel_id,
conductance='10pS',
species=ion.attrib['Name'],
notes=
"This is an automated conversion to NeuroML 2 of an ion channel model from Channelpedia. "
+
"\nThe original channel model file can be found at: http://channelpedia.epfl.ch/ionchannels/%s"
% root.attrib['ID'] +
"\n\nConversion scripts at https://github.com/OpenSourceBrain/BlueBrainProjectShowcase"
)
chan.annotation = neuroml.Annotation()
model_url_template = 'http://channelpedia.epfl.ch/ionchannels/%s/hhmodels/%s.xml'
desc = osb.metadata.Description(channel_id)
metadata.descriptions.append(desc)
osb.metadata.add_simple_qualifier(desc, \
'bqmodel', \
'isDerivedFrom', \
model_url_template%(root.attrib['ID'], root.attrib['ModelID']), \
"Channelpedia channel ID: %s, ModelID: %s; direct link to original XML model" % (root.attrib['ID'], root.attrib['ModelID']))
channel_url_template = 'http://channelpedia.epfl.ch/ionchannels/%s'
osb.metadata.add_simple_qualifier(desc, \
'bqmodel', \
'isDescribedBy', \
channel_url_template%(root.attrib['ID']), \
"Channelpedia channel ID: %s; link to main page for channel" % (root.attrib['ID']))
for reference in root.findall('Reference'):
pmid = reference.attrib['PubmedID']
#metadata = update_metadata(chan, metadata, channel_id, "http://identifiers.org/pubmed/%s"%pmid)
ref_info = reference.text
osb.metadata.add_simple_qualifier(desc, \
'bqmodel', \
'isDescribedBy', \
osb.resources.PUBMED_URL_TEMPLATE % (pmid), \
("PubMed ID: %s is referenced in original XML\n"+\
" %s") % (pmid, ref_info))
for environment in root.findall('Environment'):
for animal in environment.findall('Animal'):
species = animal.attrib['Name'].lower()
if species:
if species in osb.resources.KNOWN_SPECIES:
known_id = osb.resources.KNOWN_SPECIES[species]
osb.metadata.add_simple_qualifier(desc, \
'bqbiol', \
'hasTaxon', \
osb.resources.NCBI_TAXONOMY_URL_TEMPLATE % known_id, \
"Known species: %s; taxonomy id: %s" % (species, known_id))
else:
print("Unknown species: %s" % species)
unknowns += "Unknown species: %s\n" % species
for cell_type_el in environment.findall('CellType'):
cell_type = cell_type_el.text.strip().lower()
if cell_type:
if cell_type in osb.resources.KNOWN_CELL_TYPES:
known_id = osb.resources.KNOWN_CELL_TYPES[cell_type]
osb.metadata.add_simple_qualifier(desc, \
'bqbiol', \
'isPartOf', \
osb.resources.NEUROLEX_URL_TEMPLATE % known_id, \
"Known cell type: %s; taxonomy id: %s" % (cell_type, known_id))
else:
print("Unknown cell_type: %s" % cell_type)
unknowns += "Unknown cell_type: %s\n" % cell_type
print("Currently unknown: <<<%s>>>" % unknowns)
comp_types = {}
for gate in root.findall('Gates'):
eq_type = gate.attrib['EqType']
gate_name = gate.attrib['Name']
target = chan.gates
if eq_type == '1':
g = neuroml.GateHHUndetermined(id=gate_name,
type='gateHHtauInf',
instances=int(
float(gate.attrib['Power'])))
elif eq_type == '2':
g = neuroml.GateHHUndetermined(id=gate_name,
type='gateHHrates',
instances=int(
float(gate.attrib['Power'])))
for inf in gate.findall('Inf_Alpha'):
equation = check_equation(inf.findall('Equation')[0].text)
if eq_type == '1':
new_comp_type = "%s_%s_%s" % (channel_id, gate_name, 'inf')
g.steady_state = neuroml.HHVariable(type=new_comp_type)
comp_type = lems.ComponentType(
new_comp_type, extends="baseVoltageDepVariable")
comp_type.add(lems.Constant('TIME_SCALE', '1 ms', 'time'))
comp_type.add(lems.Constant('VOLT_SCALE', '1 mV', 'voltage'))
comp_type.dynamics.add(
lems.DerivedVariable(name='V',
dimension="none",
value="v / VOLT_SCALE"))
comp_type.dynamics.add(
lems.DerivedVariable(name='x',
dimension="none",
value="%s" % equation,
exposure="x"))
comp_types[new_comp_type] = comp_type
elif eq_type == '2':
new_comp_type = "%s_%s_%s" % (channel_id, gate_name, 'alpha')
g.forward_rate = neuroml.HHRate(type=new_comp_type)
comp_type = lems.ComponentType(new_comp_type,
extends="baseVoltageDepRate")
comp_type.add(lems.Constant('TIME_SCALE', '1 ms', 'time'))
comp_type.add(lems.Constant('VOLT_SCALE', '1 mV', 'voltage'))
comp_type.dynamics.add(
lems.DerivedVariable(name='V',
dimension="none",
value="v / VOLT_SCALE"))
comp_type.dynamics.add(
lems.DerivedVariable(name='r',
dimension="per_time",
value="%s / TIME_SCALE" % equation,
exposure="r"))
comp_types[new_comp_type] = comp_type
for tau in gate.findall('Tau_Beta'):
equation = check_equation(tau.findall('Equation')[0].text)
if eq_type == '1':
new_comp_type = "%s_%s_tau" % (channel_id, gate_name)
g.time_course = neuroml.HHTime(type=new_comp_type)
comp_type = lems.ComponentType(new_comp_type,
extends="baseVoltageDepTime")
comp_type.add(lems.Constant('TIME_SCALE', '1 ms', 'time'))
comp_type.add(lems.Constant('VOLT_SCALE', '1 mV', 'voltage'))
comp_type.dynamics.add(
lems.DerivedVariable(name='V',
dimension="none",
value="v / VOLT_SCALE"))
comp_type.dynamics.add(
lems.DerivedVariable(name='t',
dimension="time",
value="(%s) * TIME_SCALE" % equation,
exposure="t"))
comp_types[new_comp_type] = comp_type
elif eq_type == '2':
new_comp_type = "%s_%s_%s" % (channel_id, gate_name, 'beta')
g.reverse_rate = neuroml.HHRate(type=new_comp_type)
comp_type = lems.ComponentType(new_comp_type,
extends="baseVoltageDepRate")
comp_type.add(lems.Constant('TIME_SCALE', '1 ms', 'time'))
comp_type.add(lems.Constant('VOLT_SCALE', '1 mV', 'voltage'))
comp_type.dynamics.add(
lems.DerivedVariable(name='V',
dimension="none",
value="v / VOLT_SCALE"))
comp_type.dynamics.add(
lems.DerivedVariable(name='r',
dimension="per_time",
value="%s / TIME_SCALE" % equation,
exposure="r"))
comp_types[new_comp_type] = comp_type
target.append(g)
doc.ion_channel_hhs.append(chan)
doc.id = channel_id
writers.NeuroMLWriter.write(doc, nml2_file_name)
print("Written NeuroML 2 channel file to: " + nml2_file_name)
for comp_type_name in comp_types.keys():
comp_type = comp_types[comp_type_name]
ct_xml = comp_type.toxml()
# Quick & dirty pretty printing..
ct_xml = ct_xml.replace('<Const', '\n <Const')
ct_xml = ct_xml.replace('<Dyna', '\n <Dyna')
ct_xml = ct_xml.replace('</Dyna', '\n </Dyna')
ct_xml = ct_xml.replace('<Deriv', '\n <Deriv')
ct_xml = ct_xml.replace('</Compone', '\n </Compone')
# print("Adding definition for %s:\n%s\n"%(comp_type_name, ct_xml))
nml2_file = open(nml2_file_name, 'r')
orig = nml2_file.read()
new_contents = orig.replace("</neuroml>",
"\n %s\n\n</neuroml>" % ct_xml)
nml2_file.close()
nml2_file = open(nml2_file_name, 'w')
nml2_file.write(new_contents)
nml2_file.close()
print("Inserting metadata...")
nml2_file = open(nml2_file_name, 'r')
orig = nml2_file.read()
new_contents = orig.replace(
"<annotation/>", "\n <annotation>\n%s </annotation>\n" %
metadata.to_xml(" "))
nml2_file.close()
nml2_file = open(nml2_file_name, 'w')
nml2_file.write(new_contents)
nml2_file.close()
###### Validate the NeuroML ######
from neuroml.utils import validate_neuroml2
validate_neuroml2(nml2_file_name)
return unknowns
model.add(lems.Unit('milliSecond', 'ms', 'time', -3))
model.add(lems.Unit('microFarad', 'uF', 'capacitance', -12))
iaf1 = lems.ComponentType('iaf1')
model.add(iaf1)
iaf1.add(lems.Parameter('threshold', 'voltage'))
iaf1.add(lems.Parameter('reset', 'voltage'))
iaf1.add(lems.Parameter('refractoryPeriod', 'time'))
iaf1.add(lems.Parameter('capacitance', 'capacitance'))
iaf1.add(lems.Exposure('vexp', 'voltage'))
dp = lems.DerivedParameter('range', 'threshold - reset', 'voltage')
iaf1.add(dp)
iaf1.dynamics.add(lems.StateVariable('v','voltage', 'vexp'))
iaf1.dynamics.add(lems.DerivedVariable('v2',dimension='voltage', value='v*2'))
cdv = lems.ConditionalDerivedVariable('v_abs','voltage')
cdv.add(lems.Case('v .geq. 0','v'))
cdv.add(lems.Case('v .lt. 0','-1*v'))
iaf1.dynamics.add(cdv)
model.add(lems.Component('celltype_a', iaf1.name))
model.add(lems.Component('celltype_b', iaf1.name, threshold="20mV"))
fn = '/tmp/model.xml'
model.export_to_file(fn)
print("----------------------------------------------")
print(open(fn,'r').read())
print("----------------------------------------------")
def mdf_to_neuroml(graph, save_to=None, format=None, run_duration_sec=2):
print("Converting graph: %s to NeuroML" % (graph.id))
net = neuromllite.Network(id=graph.id)
net.notes = "NeuroMLlite export of {} graph: {}".format(
format if format else "MDF",
graph.id,
)
model = lems.Model()
lems_definitions = "%s_lems_definitions.xml" % graph.id
for node in graph.nodes:
print(" Node: %s" % node.id)
node_comp_type = "%s__definition" % node.id
node_comp = "%s__instance" % node.id
# Create the ComponentType which defines behaviour of the general class
ct = lems.ComponentType(node_comp_type, extends="baseCellMembPotDL")
ct.add(lems.Attachments("only_input_port", "basePointCurrentDL"))
ct.dynamics.add(
lems.DerivedVariable(name="V",
dimension="none",
value="0",
exposure="V"))
model.add(ct)
# Define the Component - an instance of the ComponentType
comp = lems.Component(node_comp, node_comp_type)
model.add(comp)
cell = neuromllite.Cell(id=node_comp,
lems_source_file=lems_definitions)
net.cells.append(cell)
pop = neuromllite.Population(
id=node.id,
size=1,
component=cell.id,
properties={
"color": "0.2 0.2 0.2",
"radius": 3
},
)
net.populations.append(pop)
if len(node.input_ports) > 1:
raise Exception(
"Currently only max 1 input port supported in NeuroML...")
for ip in node.input_ports:
ct.add(lems.Exposure(ip.id, "none"))
ct.dynamics.add(
lems.DerivedVariable(
name=ip.id,
dimension="none",
select="only_input_port[*]/I",
reduce="add",
exposure=ip.id,
))
on_start = None
for p in node.parameters:
print("Converting %s" % p)
if p.value is not None:
try:
v_num = float(p.value)
ct.add(lems.Parameter(p.id, "none"))
comp.parameters[p.id] = v_num
print(comp.parameters[p.id])
except Exception as e:
ct.add(lems.Exposure(p.id, "none"))
dv = lems.DerivedVariable(
name=p.id,
dimension="none",
value="%s" % (p.value),
exposure=p.id,
)
ct.dynamics.add(dv)
elif p.function is not None:
ct.add(lems.Exposure(p.id, "none"))
func_info = mdf_functions[p.function]
expr = func_info["expression_string"]
expr2 = substitute_args(expr, p.args)
for arg in p.args:
expr += ";{}={}".format(arg, p.args[arg])
dv = lems.DerivedVariable(name=p.id,
dimension="none",
value="%s" % (expr2),
exposure=p.id)
ct.dynamics.add(dv)
else:
ct.add(lems.Exposure(p.id, "none"))
ct.dynamics.add(
lems.StateVariable(name=p.id,
dimension="none",
exposure=p.id))
if p.default_initial_value:
if on_start is None:
on_start = lems.OnStart()
ct.dynamics.add(on_start)
sa = lems.StateAssignment(
variable=p.id,
value=str(evaluate_expr(p.default_initial_value)))
on_start.actions.append(sa)
if p.time_derivative:
td = lems.TimeDerivative(variable=p.id,
value=p.time_derivative)
ct.dynamics.add(td)
if len(node.output_ports) > 1:
raise Exception(
"Currently only max 1 output port supported in NeuroML...")
for op in node.output_ports:
ct.add(lems.Exposure(op.id, "none"))
ct.dynamics.add(
lems.DerivedVariable(name=op.id,
dimension="none",
value=op.value,
exposure=op.id))
only_output_port = "only_output_port"
ct.add(lems.Exposure(only_output_port, "none"))
ct.dynamics.add(
lems.DerivedVariable(
name=only_output_port,
dimension="none",
value=op.id,
exposure=only_output_port,
))
if len(graph.edges) > 0:
model.add(
lems.Include(
os.path.join(os.path.dirname(__file__),
"syn_definitions.xml")))
rsDL = neuromllite.Synapse(id="rsDL",
lems_source_file=lems_definitions)
net.synapses.append(rsDL)
# syn_id = 'silentSyn'
# silentSynDL = neuromllite.Synapse(id=syn_id, lems_source_file=lems_definitions)
for edge in graph.edges:
print(f" Edge: {edge.id} connects {edge.sender} to {edge.receiver}")
ssyn_id = "silentSyn_proj_%s" % edge.id
ssyn_id = "silentSyn_proj_%s" % edge.id
# ssyn_id = 'silentSynX'
silentDLin = neuromllite.Synapse(id=ssyn_id,
lems_source_file=lems_definitions)
model.add(lems.Component(ssyn_id, "silentRateSynapseDL"))
net.synapses.append(silentDLin)
net.projections.append(
neuromllite.Projection(
id="proj_%s" % edge.id,
presynaptic=edge.sender,
postsynaptic=edge.receiver,
synapse=rsDL.id,
pre_synapse=silentDLin.id,
type="continuousProjection",
weight=1,
random_connectivity=neuromllite.RandomConnectivity(
probability=1),
))
# Much more todo...
model.export_to_file(lems_definitions)
print("Nml net: %s" % net)
if save_to:
new_file = net.to_json_file(save_to)
print("Saved NML to: %s" % save_to)
################################################################################
### Build Simulation object & save as JSON
simtime = 1000 * run_duration_sec
dt = 0.1
sim = neuromllite.Simulation(
id="Sim%s" % net.id,
network=new_file,
duration=simtime,
dt=dt,
seed=123,
recordVariables={"OUTPUT": {
"all": "*"
}},
)
recordVariables = {}
for node in graph.nodes:
for ip in node.input_ports:
if not ip.id in recordVariables:
recordVariables[ip.id] = {}
recordVariables[ip.id][node.id] = 0
for p in node.parameters:
if p.is_stateful():
if not p.id in recordVariables:
recordVariables[p.id] = {}
recordVariables[p.id][node.id] = 0
for op in node.output_ports:
if not op.id in recordVariables:
recordVariables[op.id] = {}
recordVariables[op.id][node.id] = 0
sim.recordVariables = recordVariables
if save_to:
sf = sim.to_json_file()
print("Saved Simulation to: %s" % sf)
return net, sim