#! /usr/bin/python
import lems.api as lems
model = lems.Model()
model.add(lems.Dimension('voltage', m=1, l=3, t=-3, i=-1))
model.add(lems.Dimension('time', t=1))
model.add(lems.Dimension('capacitance', m=-1, l=-2, t=4, i=2))
model.add(lems.Unit('milliVolt', 'mV', 'voltage', -3))
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)
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
#!/usr/bin/env python3
import lems.api as lems
from lems.base.util import validate_lems
model = lems.Model()
model.add(lems.Dimension(name="time", t=1))
model.add(lems.Unit(name="second", symbol="s", dimension="time", power=1))
model.add(
lems.Unit(name="milli second", symbol="ms", dimension="time", power=-3))
lorenz = lems.ComponentType(
name="lorenz1963",
description=
"The Lorenz system is a simplified model for atomspheric convection, derived from the Navier Stokes equations"
)
model.add(lorenz)
lorenz.add(
lems.Parameter(name="sigma",
dimension="none",
description="Prandtl Number"))
lorenz.add(
lems.Parameter(name="beta",
dimension="none",
description="Also named b elsewhere"))
lorenz.add(
lems.Parameter(
name="rho",
dimension="none",