sparse_eps[x,y] = sparse_eps[x-1,y]
max1 = np.max(sparse_eps)
max2 = np.max(dense_eps)
max3 = np.max(particle_eps)
maxtotal = np.max([max1,max2,max3])
min1 = np.min(sparse_eps)
min2 = np.min(dense_eps)
min3 = np.min(particle_eps)
mintotal = np.min([min1,min2,min3])
ax1 = ppl.subplot2grid((3,2),[0,0],rowspan=3)
ax4 = ppl.subplot2grid((3,2),[2,1])
ax2 = ppl.subplot2grid((3,2),[0,1])
ax3 = ppl.subplot2grid((3,2),[1,1])
x = np.arange(-2.0,2.0,0.01)
x_density = np.exp(-x**2/(2*0.25))/np.sqrt(np.pi/2)
alpha=0.5
font = {'size':10}
plt.rc('font',**font)
tuning1 = 0.7*np.exp(-(x-0.8)**2/(2*alpha**2))
tuning2 = 0.7*np.exp(-(x+0.8)**2/(2*alpha**2))
for i in range(N):
ou[i] = ou[i-1]*(1.0-gamma*dt) + sqdt*numpy.random.normal()
return ou
if __name__=="__main__":
ou = make_OU(100000,0.001,0.5,1.0)
spike_1 = generate_spike_train(ou,0.001,0.8,1.0, 1.0)
spike_times_1 = numpy.where(spike_1==1)
spike_2 = generate_spike_train(ou,0.001,0.8,0.2, 1.0)
spike_times_2 = numpy.where(spike_2==1)
font = {'size':18}
plt.rc('font',**font)
ax1 = ppl.subplot2grid((2,4),[0,0],colspan=3)
ax2 = ppl.subplot2grid((2,4),[1,0],colspan=3)
ax3 = ppl.subplot2grid((2,4),[0,3],sharey=ax1)
ax4 = ppl.subplot2grid((2,4),[1,3],sharey=ax2, sharex=ax3)
times = 0.001*numpy.array(range(ou.size))
ppl.plot(times,ou,ax=ax1)
ppl.plot(times[spike_times_1],numpy.ones_like(spike_times_1).ravel(),'o',ax=ax1)
ppl.plot(times,ou,ax=ax2)
ppl.plot(times[spike_times_2],numpy.ones_like(spike_times_2).ravel(),'o',ax=ax2)
ax2.set_xlabel('Time [s]')
ax2.set_ylabel('Position [cm]')
ax1.set_ylabel('Position [cm]')
