Python ObserveState0 Examples

Example #1

File: mStructures.py Project: nrnhines/bfilt

 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)

Example #2

File: mStructures.py Project: nrnhines/bfilt

 def __init__(self):
     P = noise.NoiseParams()
     P.tstop = 20
     P.dt = 0.1
     P.A = numpy.matrix(0.5)
     P.B = numpy.matrix([0.1])
     P.InitialCov = numpy.matrix(1)
     O1 = models.ObserveState0(P, 5)
     elist = numpy.arange(0.1, 20, 0.1).tolist()
     elist2 = numpy.arange(0.05, 20.0, 0.05)
     # O1.Times.set([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
     O1.Times.set([2, 4, 6, 8, 10, 12, 14, 16, 18, 20])
     # O1.Times.set(elist)
     O1.sigma = 0.001
     Obs = models.ObservationModel(P, 5, [O1])
     Sys = models.DecayModel(P, 0, 1)
     # Sys.Injection.set(elist2)
     Sys.Injection.set([
         1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
         20
     ])
     self.M = models.Model(Sys, Obs, P, numpy.matrix(2))
     self.sim(False)

Example #3

File: runpas.py Project: nrnhines/bfilt

import noise
import models
import fitEKF
import numpy
import fitglobals

fitglobals.debugon()

P = noise.NoiseParams()
P.tstop = 20
P.dt = 0.1
P.A = numpy.matrix(0.5)
P.B = numpy.matrix([0.5, 0.4, 0.3])
P.InitialCov = numpy.matrix(1)
O1 = models.ObserveState0(P,5)
O2 = models.ObserveState0(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.sigma = 0.001
O2.sigma = 0.0001
Obs = models.ObservationModel(P,5,[O1,O2])
#Sys = models.DecayModel(P,0,3)
Sys = models.NeuronModel(P,0,3)
Sys.Injection.set([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
M = models.Model(Sys,Obs,P)

# Turn noise off and simulate
P.B = numpy.matrix([0.,0.,0.])
# Not needed: change(P)
M.Obs.C[0].sigma = 0