def runPF(nparticle):
env_rng = np.random.mtrand.RandomState()
env = ge.GaussianEnv(gamma=gamma,eta=eta,zeta=zeta,
x0=0.0,y0=.0,L=1.0,N=1,order=1,
sigma=0.1,Lx=1.0,Ly=1.0,randomstate=env_rng)
env.reset(np.array([0.0]))
#code is the population of neurons, plastic poisson neurons
code_rng = np.random.mtrand.RandomState()
code = pn.PoissonPlasticCode(A=alpha,phi=phi,tau=tau,
thetas=np.arange(-10.0,10.0,0.15),
dm=dm,randomstate=code_rng,alpha=alpha)
#s is the stimulus, sps holds the spikes, rates the rates of
# each neuron and particles give the position of the particles
# weights gives the weights associated with each particle
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
results = pf.particle_filter(code,env,timewindow=timewindow,dt=dt,
nparticles=nparticle,mode = 'Silent')
print "ping"
return [nparticle,results[4]]
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
if gaussian:
[mg,varg,spsg,sg,mseg] = pf.gaussian_filter(code,env,timewindow=timewindow,dt=dt,mode = 'v')
env_rng.seed(12345)
code_rng.seed(67890)
env.reset(np.array([0.0]))
code.reset()
[mp,varp,spsp,sp,msep,parts,ws] = pf.particle_filter(code, env, timewindow=timewindow,
dt=dt, nparticles=nparticles,
mode='v', testf=(lambda x:x))
if gaussian:
print "MSE of gaussian filter %f"% mseg
print "MSE of particle filter %f"% msep
if plotting:
#matplotlib.rcParams['font.size']=10
plt.close()
plt.figure()
ax1 = plt.gcf().add_subplot(2,1,1)
times = np.arange(0.0,dt*timewindow,dt)
if gaussian:
ax1.plot(times,sg,'r',label='Signal')
