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}}
testModel_ic = Model.Model(modelArgs_ic)
pest_ic = LMpest(freeParams=['w'],
depVarNames=['w'],
testModel=testModel_ic,
goalTraj=goaltraj,
# 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'
disp_dt = 0.05
##plotData_goal = reftraj.sample(['v'], disp_dt)
goalleg = "v original"
plotData_par = testModel.sample('par_est', ['v'], disp_dt, precise=True)
pylab.ylabel('v')
pylab.xlabel('t')
##goalline=plot(plotData_par['t'], plotData_goal['v'])
goal_v = reftraj(tmesh, 'v').toarray()
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,
usePsyco=True
)
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
plotData_goal = goaltraj.sample(['v'], disp_dt, precise=True)
goalleg = "v original"
plotData_par = HH_test_model.sample('par_est', ['v'], disp_dt, precise=True)
pylab.ylabel('v')
pylab.xlabel('t')
goalline=plot(plotData_goal['t'], plotData_goal['v'])
estline = plot(plotData_par['t'], plotData_par['v'])
## 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
plotData_goal = goaltraj.sample(['v'], disp_dt, precise=True)
goalleg = "v original"
plotData_par = HH_test_model.sample('par_est', ['v'], disp_dt, precise=True)
pylab.ylabel('v')
pylab.xlabel('t')
goalline = plot(plotData_goal['t'], plotData_goal['v'])
estline = plot(plotData_par['t'], plotData_par['v'])
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'
disp_dt = 0.05
##plotData_goal = reftraj.sample(['v'], disp_dt)
goalleg = "v original"
plotData_par = testModel.sample('par_est', ['v'], disp_dt, precise=True)
pylab.ylabel('v')
pylab.xlabel('t')
##goalline=plot(plotData_par['t'], plotData_goal['v'])
goal_v = reftraj(tmesh, 'v').toarray()
goalline = plot(tmesh, goal_v, 'ok')