e = GaussianEnv(zeta,gamma,eta,L,N,-2.0,-2.0,4.0,4.0,sigma,order) neuron = PoissonNeuron(np.random.normal(0.0,1.0,N*N),alpha*np.identity(N*N),phi,N) lifneuron = IFNeuron(np.zeros(N),dt,1) code = PoissonPlasticCode() video = VideoSink((N,N),filename,rate = 25,byteorder = "Y8") print "Now sampling and plotting...\n" delete_them = [] gr = lambda x : (grayscale(x,-6.0,6.0)) vgr = np.vectorize(gr) for i in range(0,nframes): plot = e.samplestep(dt) p = vgr(plot) spi = neuron.spike(plot,dt) video.run(p.astype(np.uint8)) print spi # f = plt.figure() # plt.imshow(plot,cmap = cm.Greys_r,extent=[-2,2,-2,2], vmin=-3, vmax = 3) # plt.imshow(p.astype(np.uint8),cmap=cm.Greys_r, vmin = 0, vmax = 255) # filename = prefix+str('%05d' % i) + '.png' # delete_them.append(filename) # plt.savefig(filename, dpi = 100) # print 'Wrote plot to ', filename # plt.close(f) #plt.colorbar() video.close()
from gaussianenv import GaussianEnv emat = GaussianEnv(1.0,1.0,1.0,1.0,1,0.0,0.0,1.0,1.0,0.00001,2) eou = GaussianEnv(1.0,1.0,1.0,1.0,1,0.0,0.0,1.0,1.0,0.00001,1) ts = arange(0.0,4.0,0.01) smat1 = [] sou1 = [] for i in ts: smat1.append(emat.samplestep(0.01)) sou1.append(eou.samplestep(0.01)) smat1 = array(smat1).ravel() sou1 = array(sou1).ravel() emat.reset() eou.reset() smat2 = [] sou2 = [] for i in ts: smat2.append(emat.samplestep(0.01)) sou2.append(eou.samplestep(0.01)) smat2 = array(smat2).ravel() sou2 = array(sou2).ravel() emat.reset()