def convert_hhgate(gate):
"""Convert a MOOSE gate into GateHHRates in NeuroML"""
hh_rates = neuroml.GateHHRates(id=gate.id_.value, name=gate.name)
alpha = gate.tableA.copy()
beta = gate.tableB - alpha
vrange = np.linspace(gate.min, gate.max, len(alpha))
afn, ap = hhfit.find_ratefn(vrange, alpha)
bfn, bp = hhfit.find_ratefn(vrange, beta)
if afn is None:
raise Exception('could not find a fitting function for `alpha`')
if bfn is None:
raise Exception('could not find a fitting function for `alpha`')
afn_type = fn_rate_map[afn]
afn_component_type = None
if afn_type is None:
afn_type, afn_component_type = define_component_type(afn)
hh_rates.forward_rate = neuroml.HHRate(type=afn_type,
midpoint='%gmV' % (ap[2]),
scale='%gmV' % (ap[1]),
rate='%gper_ms' % (ap[0]))
bfn_type = fn_rate_map[bfn]
bfn_component_type = None
if bfn_type is None:
bfn_type, bfn_component_type = define_component_type(bfn)
hh_rates.reverse_rate = neuroml.HHRate(type=bfn_type,
midpoint='%gmV' % (bp[2]),
scale='%gmV' % (bp[1]),
rate='%gper_ms' % (bp[0]))
return hh_rates, afn_component_type, bfn_component_type
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
Generating a Hodgkin-Huxley Ion Channel and writing it to NeuroML
"""
import neuroml
import neuroml.writers as writers
chan = neuroml.IonChannelHH(id='na',
conductance='10pS',
species='na',
notes="This is an example voltage-gated Na channel")
m_gate = neuroml.GateHHRates(id='m',instances='3')
h_gate = neuroml.GateHHRates(id='h',instances='1')
m_gate.forward_rate = neuroml.HHRate(type="HHExpRate",
rate="0.07per_ms",
midpoint="-65mV",
scale="-20mV")
m_gate.reverse_rate = neuroml.HHRate(type="HHSigmoidRate",
rate="1per_ms",
midpoint="-35mV",
scale="10mV")
h_gate.forward_rate = neuroml.HHRate(type="HHExpLinearRate",
rate="0.1per_ms",
midpoint="-55mV",
scale="10mV")
h_gate.reverse_rate = neuroml.HHRate(type="HHExpRate",
rate="0.125per_ms",
midpoint="-65mV",
def generate_channel_nml2(self,bio_params,channel_params,model_params):
"""
Generates NeuroML2 file from ion channel parameters.
:param bio_params: Biological parameters
:param channel_params: Channel parameters
:param model_params: NeuroML2 file parameters
:return: NeuroML2 file
"""
unknowns = ''
info = 'This is an ion channel model NeuroML2 file generated by ChannelWorm: https://github.com/openworm/ChannelWorm'
channel_id='ChannelWorm_%s_%s_%s'%(model_params['channel_name'],model_params['channel_id'],model_params['model_id'])
doc = neuroml.NeuroMLDocument()
metadata = osb.metadata.RDF(info)
desc = osb.metadata.Description(channel_id)
metadata.descriptions.append(desc)
osb.metadata.add_simple_qualifier(desc,
'bqmodel',
'isDerivedFrom',
"ChannelWorm channel Name: %s channel ID: %s, ModelID: %s"
%(model_params['channel_name'],model_params['channel_id'],model_params['model_id']))
for reference in model_params['references']:
DOI = reference['doi']
pmid = reference['PMID']
ref_info = reference['citation']
osb.metadata.add_simple_qualifier(desc,
'bqmodel',
'isDescribedBy',
osb.resources.PUBMED_URL_TEMPLATE % (pmid),
("DOI: %s, PubMed ID: %s \n"+
" %s") % (DOI, pmid, ref_info))
species = 'caenorhabditis elegans'
if osb.resources.KNOWN_SPECIES.has_key(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
cell_type = bio_params['cell_type']
if osb.resources.KNOWN_CELL_TYPES.has_key(cell_type):
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
# for contributor in model_params['contributors']:
# metadata.descriptions.append('Curator: %s (%s)'%(contributor['name'],contributor['email']))
print("Currently unknown: <<<%s>>>"%unknowns)
ion = bio_params['ion_type']
unit = dict(zip(bio_params['channel_params'],bio_params['unit_chan_params']))
chan = neuroml.IonChannelHH(id=channel_id,
conductance='10pS',
species=ion)
chan.annotation = neuroml.Annotation()
target = chan.gate_hh_tau_infs
for gate in bio_params['gate_params']:
gate_name = gate
instances = bio_params['gate_params'][gate_name]['power']
g_type='HHSigmoidVariable'
if gate_name == 'vda':
g = neuroml.GateHHTauInf(id=gate_name, instances=instances)
v_half = str(channel_params['v_half_a']) + ' ' + str(unit['v_half_a'])
k = str(channel_params['k_a']) + ' ' + str(unit['k_a'])
g.steady_state = neuroml.HHRate(midpoint=v_half,scale=k,rate=1,type=g_type)
if 'T_a' in channel_params:
T = str(channel_params['T_a']) + ' ' + str(unit['T_a'])
t_type="fixedTimeCourse"
g.time_course = neuroml.HHTime(tau=T, type=t_type)
target.append(g)
elif gate_name == 'vdi':
g = neuroml.GateHHTauInf(id=gate_name, instances=instances)
v_half = str(channel_params['v_half_i']) + ' ' + str(unit['v_half_i'])
k = str(channel_params['k_i']) + ' ' + str(unit['k_i'])
g.steadyState = neuroml.HHTime(midpoint=v_half,scale=k,rate=1,type=g_type)
if 'T_i' in channel_params:
T = str(channel_params['T_i']) + ' ' + str(unit['T_i'])
t_type="fixedTimeCourse"
g.timeCourse = neuroml.HHTime(tau=T, type=t_type)
target.append(g)
# TODO: Consider ion dependent activation/inactivation
nml2_file_name = model_params['file_name']
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)
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()
myValidator = validators.Validator()
myValidator.validate_nml2(nml2_file_name)
return nml2_file