def calc2(MS, R0=None, R1=None):
if R0 == None:
R0 = numpy.arange(0.05, 1.0, 0.05)
if R1 == None:
R1 = numpy.arange(0.05, 1.1, 0.05)
LLmax = float('-inf')
Mfit = copy.deepcopy(MS.M)
print('Turning debugging OFF')
fitglobals.debugoff()
f = open('data.txt', 'w')
f.write('# A0 A1 LL\n')
for A0 in R0:
for A1 in R1:
# Mfit.P.A = numpy.matrix([[A0, 0], [0,A1]])
MS.setParams(A0, A1, Mfit)
LL = fitEKF.ekf(MS.Data, Mfit)
if LL > LLmax:
A0max = A0
A1max = A1
LLmax = LL
f.write('%s %s %s\n' % (A0, A1, LL))
print A0, A1, LL
f.write('\n')
print ""
f.close()
print 'Max Like =', LLmax, '@ (A0,A1) =', [A0max, A1max]
simParams = MS.getParams()
LLsim = MS.loglike(simParams[0], simParams[1])
print 'Sim Like =', LLsim, '@ (A0, A1) =', simParams[0], simParams[1]
def __init__(self):
fitglobals.debugoff()
P = noise.NoiseParams()
P.tstop = 20
P.dt = 0.1
A0 = 0.5
A1 = 0.1
P.A = numpy.matrix([[A0, 0], [0, A1]])
# P.B, next line define noise injected to each component, uncorrelated
P.B = numpy.matrix([[0.5, 0], [0, 0.4]])
P.InitialCov = numpy.matrix([[1, 0], [0, 1]])
elist = numpy.arange(0.1, 20, 0.1).tolist()
elist2 = numpy.arange(0.05, 20.0, 0.05).tolist()
O1 = models.ObserveState0(P, 5)
O2 = models.ObserveStateSum(P, 6)
# O1.Times.set([2,4,6,8,10,12,14,16,18,20])
# O2.Times.set([3,6,9,12,15,18])
O1.Times.set(elist)
O2.Times.set(elist)
O1.sigma = 0.001
O2.sigma = 0.0001
Obs = models.ObservationModel(P, 5, [O1, O2])
Sys = models.DecayModel(P, 0, 2)
# Sys.Injection.set([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
Sys.Injection.set(elist2)
Initial = numpy.matrix([[1.0], [2.0]])
self.M = models.Model(Sys, Obs, P, Initial)
self.sim(False)
from neuron import h
import fitglobals
fitglobals.debugoff()
h.load_file('mulfit.hoc')
h.load_file('eonerunmlf.hoc')
import nrnbfilt
h.load_file('ch3_11p.ses')
h('objref nb')
h.nb = h.List("PythonObject").o(0)
