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)
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)
print('... finished in %.4f seconds\n'%(clock()-t0))
## Finish preparing plots
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)
modelArgs_ic['ics'] = {'w': 0.0}
modelArgs_ic['pars'] = {'k': pestData_par['pars_sol']['k']}
modelArgs_ic['name'] = 'test_model_ic'
testModel_ic = embed(Generator.Vode_ODEsystem(modelArgs_ic))
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)
print('... finished in %.4f seconds\n'%(clock()-t0))
## Finish preparing plots
## 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)
print("... finished in %.4f seconds\n" % (clock() - t0))
## Finish preparing plots
print("\nPreparing plots")
figure()
disp_dt = 0.05
plotData_orig = HH_test_model.sample("orig", ["v"], disp_dt, precise=True)
origleg = "v initial"
