def plot_performance(fig_num):
num_evaluations = int(num_examples/update_interval)
time_steps = range(0, num_evaluations)
performance = np.zeros(num_evaluations)
fig = b2.figure(fig_num, figsize = (5, 5))
fig_num += 1
ax = fig.add_subplot(111)
im2, = ax.plot(time_steps, performance) #my_cmap
b2.ylim(ymax = 100)
b2.title('Classification performance')
fig.canvas.draw()
return im2, performance, fig_num, fig
def plot_performance(fig_num):
num_evaluations = int(num_examples/update_interval)
time_steps = range(0, num_evaluations)
performance = np.zeros(num_evaluations)
fig = b2.figure(fig_num, figsize = (5, 5))
fig_num += 1
ax = fig.add_subplot(111)
im2, = ax.plot(time_steps, performance) #my_cmap
b2.ylim(ymax = 100)
b2.title('Classification performance')
fig.canvas.draw()
return im2, performance, fig_num, fig
def visualise_connectivity(S):
Ns = len(S.source)
Nt = len(S.target)
b.figure(figsize=(10, 4))
b.subplot(121)
b.plot(b.zeros(Ns), b.arange(Ns), 'ok', ms=10)
b.plot(b.ones(Nt), b.arange(Nt), 'ok', ms=10)
for i, j in zip(S.i, S.j):
b.plot([0, 1], [i, j], '-k')
b.xticks([0, 1], ['Source', 'Target'])
b.ylabel('Neuron index')
b.xlim(-0.1, 1.1)
b.ylim(-1, max(Ns, Nt))
b.subplot(122)
b.plot(S.i, S.j, 'ok')
b.xlim(-1, Ns)
b.ylim(-1, Nt)
b.xlabel('Source neuron index')
b.ylabel('Target neuron index')
leaveout_steps = int(conv_width / b2.defaultclock.dt)
# leaveout_steps = 10
b2.figure()
for trace in range(N_traces):
net = run_model(net=net)
r1 = net['r1']
r2 = net['r2']
r0 = net['r0']
rI = net['rI']
# ri1 = net['ri1']
# ri2 = net['ri2']
b2.plot(
r1.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
r2.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz)
ymin, ymax = b2.ylim()
xmin, xmax = b2.xlim()
b2.ylim([min(xmin, ymin), max(xmax, ymax)])
b2.xlim([min(xmin, ymin), max(xmax, ymax)])
b2.figure()
b2.plot(r1.t[:-leaveout_steps] / b2.ms,
r1.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='1')
b2.plot(r2.t[:-leaveout_steps] / b2.ms,
r2.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='2')
b2.plot(r0.t[:-leaveout_steps] / b2.ms,
r0.smooth_rate(width=conv_width)[:-leaveout_steps] / b2.Hz,
label='0')
b2.plot(rI.t[:-leaveout_steps] / b2.ms,
