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
"""
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",