def make_multiinstantiate(self, special_properties, name, parameters):
"""
Adds ComponentType with MultiInstantiate in order to make
a population of neurons.
Parameters
----------
special_properties : dict
all variables to be defined in MultiInstantiate
name : str
MultiInstantiate component name
parameters : dict
all extra parameters needed
"""
PARAM_SUBSCRIPT = "_p"
self._model_namespace["ct_populationname"] = name+"Multi"
multi_ct = lems.ComponentType(self._model_namespace["ct_populationname"], extends=BASE_POPULATION)
structure = lems.Structure()
multi_ins = lems.MultiInstantiate(component_type=name,
number="N")
param_dict = {}
# number of neruons
multi_ct.add(lems.Parameter(name="N", dimension="none"))
# other parameters
for sp in special_properties:
if special_properties[sp] is None:
multi_ct.add(lems.Parameter(name=sp+PARAM_SUBSCRIPT, dimension=self._all_params_unit[sp]))
multi_ins.add(lems.Assign(property=sp, value=sp+PARAM_SUBSCRIPT))
param_dict[sp] = parameters[sp]
else:
# multi_ct.add(lems.Parameter(name=sp, dimension=self._all_params_unit[sp]))
# check if there are some units in equations
equation = special_properties[sp]
# add spaces around brackets to prevent mismatching
equation = re.sub("\(", " ( ", equation)
equation = re.sub("\)", " ) ", equation)
for i in get_identifiers(equation):
# iterator is a special case
if i == "i":
regexp_noletter = "[^a-zA-Z0-9]"
equation = re.sub("{re}i{re}".format(re=regexp_noletter),
" {} ".format(INDEX), equation)
# here it's assumed that we don't use Netwton in neuron models
elif i in name_to_unit and i != "N":
const_i = i+'const'
multi_ct.add(lems.Constant(name=const_i, symbol=const_i,
dimension=self._all_params_unit[sp], value="1"+i))
equation = re.sub(i, const_i, equation)
multi_ins.add(lems.Assign(property=sp, value=equation))
structure.add(multi_ins)
multi_ct.structure = structure
self._model.add(multi_ct)
param_dict = dict([(k+"_p", v) for k, v in param_dict.items()])
param_dict["N"] = self._nr_of_neurons
self._model_namespace["populationname"] = self._model_namespace["ct_populationname"] + "pop"
self._model_namespace["networkname"] = self._model_namespace["ct_populationname"] + "Net"
self.add_population(self._model_namespace["networkname"],
self._model_namespace["populationname"],
self._model_namespace["ct_populationname"],
**param_dict)
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
description="Also named b elsewhere"))
lorenz.add(
lems.Parameter(
name="rho",
dimension="none",
description="Related to the Rayleigh number, also named r elsewhere"))
lorenz.add(lems.Parameter(name="x0", dimension="none"))
lorenz.add(lems.Parameter(name="y0", dimension="none"))
lorenz.add(lems.Parameter(name="z0", dimension="none"))
lorenz.add(lems.Exposure(name="x", dimension="none"))
lorenz.add(lems.Exposure(name="y", dimension="none"))
lorenz.add(lems.Exposure(name="z", dimension="none"))
lorenz.add(lems.Constant(name="sec", value="1s", dimension="time"))
lorenz.dynamics.add(
lems.StateVariable(name="x", dimension="none", exposure="x"))
lorenz.dynamics.add(
lems.StateVariable(name="y", dimension="none", exposure="y"))
lorenz.dynamics.add(
lems.StateVariable(name="z", dimension="none", exposure="z"))
lorenz.dynamics.add(
lems.TimeDerivative(variable="x", value="( sigma * (y - x)) / sec"))
lorenz.dynamics.add(
lems.TimeDerivative(variable="y", value="( rho * x - y - x * z ) / sec"))
lorenz.dynamics.add(
lems.TimeDerivative(variable="z", value="( x * y - beta * z) / sec"))