checklevel=2,
ics=xic,
name='testODEsol'
)
testODE = Generator.Vode_ODEsystem(testDSargs)
genInfoEntry = makeGenInfoEntry(testODE, ['testODEsol'])
genInfoDict = makeGenInfo([genInfoEntry])
modelArgs_par = {'genInfo': genInfoDict,
'name': 'test_model_par',
'ics': xic}
testModel_par = Model.Model(modelArgs_par)
pest_par = LMpest(freeParams=['k'],
depVarNames=['w'],
testModel=testModel_par,
goalTraj=goaltraj,
trange=trange
)
start_time = time.clock()
pestData_par = pest_par.run(verbose=True)
bestFitModel_par = pestData_par['sys_fit']
print '... finished in %.4f seconds\n' % (time.clock()-start_time)
## Initial condition estimation
print "Estimating initial condition for w (assuming k is correct)"
print "Goal value is w(0) = ", xic['w'], " ..."
modelArgs_ic = {'genInfo': genInfoDict,
'name': 'test_model_ic',
'ics': {'w': 0.0}}
vl = s.testModel.pars['vl']
gl_adjust = 30 * (0.0001 / gl**2 + 0.0001 / (gl - 0.15)**2)
vl_adjust = 0.001 * (vl + 66)**4
sp_adjust = 20 * (evtime - HH_spike_t)**2
vec = vl_adjust + gl_adjust + sp_adjust + \
1/(1+10*sp_adjust) * (s.goalTraj(tmesh,s.depVarNames).toarray() - \
s.testModel(s.trajname, tmesh, s.depVarNames).toarray())
adjusted = sum(vec**2.0)
## print vl, gl, vl_adjust, gl_adjust, sp_adjust
print "adjusted error = ", adjusted, "\n"
return vec
pest_pars = LMpest(freeParams=['vl', 'gl'],
depVarNames=['v'],
testModel=HH_test_model,
goalTraj=goaltraj,
trange=[0, 15],
residual_fn=residual_fn)
start = clock()
pestData_par = pest_pars.run(parDict={
'ftol': 1e-4,
'xtol': 1e-4,
'args': (HH_spike_t, )
},
verbose=True)
print '... finished in %.3f seconds\n' % (clock() - start)
## Finish preparing plots
print '\nPreparing plots'
disp_dt = 0.05
# make test system
est_pars_test = {'h': 0.3, 'gl': 38, 'vl': -35, 'gnap': 2.8}
pars_test = copy(pars)
pars_test.update(est_pars_test)
testDS = makeTestDS('ref', pars_test, icdict, tdomain)
testModel = embed(testDS, icdict)
est_parnames = ['gl', 'vl']
# parameter estimation
print 'Starting parameter estimation'
print 'Goal pars are gl = ', est_pars_ref['gl'], ' vl = ', est_pars_ref['vl']
pest_pars = LMpest(
freeParams=est_parnames,
depVarNames=['v'],
testModel=testModel,
goalTraj=reftraj,
trange=tdomain,
)
start = clock()
pestData_par = pest_pars.run(parDict={
'ftol': 3e-3,
'xtol': 1e-3
},
tmesh=tmesh,
verbose=True)
print ' ... finished in %.3f seconds.\n' % (clock() - start)
## Finish preparing plots
print '\nPreparing plots'
## for t in tmesh])
## print gna, gl, gna_adjust, gl_adjust, sp_adjust
## print "adjusted error = ", adjusted, "\n"
## return gna_adjust + gl_adjust + sp_adjust + \
## 8/(1+sp_adjust) * array([s.goalTraj(t,s.depVarNames)[0] - \
## s.testModel(s.trajname,
## t,
## s.depVarNames)[0] \
## for t in tmesh])
return s.goalTraj(tmesh,s.depVarNames).toarray() - \
s.testModel(s.trajname, tmesh, s.depVarNames).toarray()
pest_pars = LMpest(freeParams=['gna', 'gl'],
depVarNames=['v'],
testModel=HH_test_model,
goalTraj=goaltraj,
trange=[0, 20],
residual_fn=residual_fn,
usePsyco=True)
t0 = clock()
pestData_par = pest_pars.run(parDict={
'ftol': 1e-4,
'xtol': 1e-4,
'args': (HH_spike_t, )
},
verbose=True)
print '... finished in %.4f seconds\n' % (clock() - t0)
## Finish preparing plots
print '\nPreparing plots'
