def test_sample_by_variance():
pytest.importorskip("scipy")
dt = 0.001
t = np.arange(0.0, 1.0, dt) + dt
spikes = np.zeros((len(t), 4))
spikes[::, 1] = 1.0 / dt
spikes[::10, 2] = 1.0 / dt
spikes[::100, 3] = 1.0 / dt
t_sampled, spikes_sampled = sample_by_variance(t, spikes, num=1, filter_width=0.001)
assert (t_sampled == t).all()
assert (spikes_sampled == spikes[:, [2]]).all()
t_sampled, spikes_sampled = sample_by_variance(t, spikes, num=1, filter_width=0.1)
assert (t_sampled == t).all()
assert (spikes_sampled == spikes[:, [3]]).all()
t_sampled, spikes_sampled = sample_by_variance(t, spikes, num=2, filter_width=0.1)
assert (t_sampled == t).all()
assert (spikes_sampled == spikes[:, [3, 2]]).all()
t_sampled, spikes_sampled = sample_by_variance(t, spikes, num=20, filter_width=0.1)
assert (t_sampled == t).all()
assert (spikes_sampled == spikes[:, [3, 2, 1, 0]]).all()
plt.plot(t, xy[:, 0], label="X")
plt.plot(t, xy[:, 1], label="Y")
plt.legend()
plt.show()
plt.figure(figsize=(16, 6))
rates = rates_kernel(t, place_spikes).T
im = plt.imshow(rates,
origin='upper',
aspect='auto',
interpolation='none',
extent=[np.min(t), np.max(t), 0, rates.shape[0]])
plt.show()
t_sample, place_spikes_sample = sample_by_variance(t,
place_spikes,
num=250,
filter_width=0.5)
t_cycle_idxs = np.where(np.logical_and(t_sample >= 4.5, t_sample <= 10))[0]
plt.figure(figsize=(16, 6))
rates = rates_kernel(t_sample, place_spikes_sample, tau=0.25).T
peaks = np.argmax(rates, axis=1)
sorted_peaks = np.argsort(peaks)
plt.subplot(211)
plt.plot(t[t_cycle_idxs], xy[t_cycle_idxs, 0], label="X")
plt.plot(t[t_cycle_idxs], xy[t_cycle_idxs, 1], label="Y")
plt.subplot(212)
t = np.linspace(0, tstop, int(tstop / dt))
xy = np.load("xy.npy")
xy_reader = np.load("xy_reader.npy")
xy_reader_spikes = np.load("xy_reader_spikes.npy")
plt.figure(figsize=(16, 6))
rates = rates_kernel(t, xy_reader_spikes).T
im = plt.imshow(rates,
origin='upper',
aspect='auto',
interpolation='none',
extent=[np.min(t), np.max(t), 0, rates.shape[0]])
plt.show()
t_sample, xy_reader_spikes_sample = sample_by_variance(t,
xy_reader_spikes,
num=250,
filter_width=0.1)
t_cycle_idxs = np.where(np.logical_and(t_sample >= 4.5, t_sample <= 10))[0]
plt.figure(figsize=(16, 6))
rates = rates_kernel(t_sample, xy_reader_spikes_sample, tau=0.1).T
peaks = np.argmax(rates, axis=1)
sorted_peaks = np.argsort(peaks)
plt.subplot(311)
plt.plot(t[t_cycle_idxs], xy[t_cycle_idxs, 0], label="X")
plt.plot(t[t_cycle_idxs], xy[t_cycle_idxs, 1], label="Y")
plt.legend()
plt.subplot(312)
plt.ylabel("Position")
plt.show()
plt.figure(figsize=(16, 6))
plt.title("Training Error")
plt.plot(sim.trange()[t_train_idxs], np.mean(sim.data[p_place_error][t_train_idxs], axis=1),
alpha=0.8, label="Mean place error")
plt.xlabel("Time (s)")
plt.ylabel("Output")
plt.legend()
plt.show()
plt.figure(figsize=(16, 6))
plt.title("Place Reader Spikes")
plot_spikes(
*merge(
*cluster(
*sample_by_variance(
sim.trange(), sim.data[p_place_reader_spikes],
num=500, filter_width=.02),
filter_width=.002),
num=50))
plt.show()