return numeric_to_traj([varray], self._trajname, ['v'],
indepvar=tmesh)
pest_context = context([ (spike_interface, int_spike_iface),
(geom_interface, int_geom_iface) ])
## Parameter estimation
print('Estimating pars gna and vl for fit to non-identical HH cell')
print('Goal values are gna =', par_args_HH_goal['gna'], ', gl =', \
par_args_HH_goal['gl'], ' ...')
pest_pars = LMpest(freeParams=['gna', 'gl'],
testModel=HH_test_model,
context=pest_context,
verbose_level=2
)
# In case finite difference stepsize needs adjusting
pest_pars.fn.eps=1e-5
pest_context.set_weights({spike_interface: {sp_feat: 10},
geom_interface: {geom_feat: 0.2}})
t0=clock()
pestData_par_phase1 = pest_pars.run(parDict={'ftol':1e-5,
'xtol':1e-6
},
verbose=True)
return numeric_to_traj([varray], self._trajname, ['v'],
indepvar=tmesh)
pest_context = context([ (spike_interface, int_spike_iface),
(geom_interface, int_geom_iface) ])
## Parameter estimation
print('Estimating pars gna and vl for fit to non-identical HH cell')
print('Goal values are gna =', par_args_HH_goal['gna'], ', gl =', \
par_args_HH_goal['gl'], ' ...')
pest_pars = LMpest(freeParams=['gna', 'gl'],
testModel=HH_test_model,
context=pest_context,
verbose_level=2,
usePsyco=False
)
# In case finite difference stepsize needs adjusting
pest_pars.fn.eps=1e-5
pest_context.set_weights({spike_interface: {sp_feat: 10},
geom_interface: {geom_feat: 0.2}})
t0=clock()
pestData_par_phase1 = pest_pars.run(parDict={'ftol':1e-5,
'xtol':1e-6
},
verbose=True)
def postprocess_test_traj(self, traj):
# use tmesh of data points only (may not be the same mesh as was used by
# this model traj, that's why we have to resample
varray = traj(tmesh)["v"]
return numeric_to_traj([varray], self._trajname, ["v"], indepvar=tmesh)
pest_context = context([(spike_interface, int_spike_iface), (geom_interface, int_geom_iface)])
## Parameter estimation
print("Estimating pars gna and vl for fit to non-identical HH cell")
print("Goal values are gna =", par_args_HH_goal["gna"], ", gl =", par_args_HH_goal["gl"], " ...")
pest_pars = LMpest(
freeParams=["gna", "gl"], testModel=HH_test_model, context=pest_context, verbose_level=2, usePsyco=False
)
# In case finite difference stepsize needs adjusting
pest_pars.fn.eps = 1e-5
pest_context.set_weights({spike_interface: {sp_feat: 10}, geom_interface: {geom_feat: 0.2}})
t0 = clock()
pestData_par_phase1 = pest_pars.run(parDict={"ftol": 1e-5, "xtol": 1e-6}, verbose=True)
HH_test_model.set(pars=pestData_par_phase1["pars_sol"])
pest_context.set_weights({spike_interface: {sp_feat: 5}, geom_interface: {geom_feat: 0.7}})
pestData_par = pest_pars.run(parDict={"ftol": 1e-5, "xtol": 1e-6}, verbose=True)
class ext_iface(extModelInterface):
# holds the data (external from the model)
pass
class int_iface(intModelInterface):
# holds the test model
pass
pest_data_interface_w = ext_iface(reftraj,
pest_condition_w)
c = context([ (pest_data_interface_w, int_iface) ])
testModel_par = embed(Generator.Vode_ODEsystem(testDSargs))
pest_pars = LMpest(freeParams=['k'],
testModel=testModel_par,
context=c
)
start_time = time.clock()
pestData_par = pest_pars.run(parDict={'ftol': ftol,
'xtol':1e-3},
verbose=True)
print('... finished in %.4f seconds\n' % (time.clock()-start_time))
bestFitModel_par = pestData_par['sys_sol']
## Initial condition estimation
print("Estimating initial condition for w (assuming k is correct)")
print("Goal value is w(0) = ", xic['w'], " ...")
modelArgs_ic = copy(testDSargs)
# embed the reference trajectory and measurement condition/features
# into an instance of the external interface
pest_data_interface = ext_iface(reftraj, pest_condition)
# generate a context for the model between the external interface
# instance and the internal interface class (not an instance yet)
c = context([(pest_data_interface, int_iface)])
# specify which parameters we will fit
est_parnames = ['gl', 'vl']
# parameter estimation
print('Starting Least Squares parameter estimation')
print('Goal pars are gl = ', est_pars_ref['gl'], ' vl = ', est_pars_ref['vl'])
pest_pars = LMpest(freeParams=est_parnames, testModel=testModel, context=c)
start = clock()
pestData_par = pest_pars.run(parDict={
'ftol': ftol,
'xtol': 1e-3
},
verbose=True)
print(' ... finished in %.3f seconds.\n' % (clock() - start))
# Prepare plots
print('\nPreparing plots')
disp_dt = 0.05
##plotData_goal = reftraj.sample(['v'], disp_dt)
goalleg = "v original"
