<jupyter_start><jupyter_text>Simulation data with latent splines<jupyter_code>F = generate_latent_splines(1000, 2)
print(F.shape)
for j in range(2):
    plt.plot(F[:, j])
plt.show()
loading = np.random.normal(0, 1, [2, 15])
log_covariance = np.matmul(F, loading)
print(log_covariance.shape)
cov_series = create_covariance_series(log_covariance, 5)
print(cov_series.shape)
for i in range(5):
    plt.subplot(1, 5, i + 1)
    plt.imshow(cov_series[i * 200, :, :], cmap='jet')
    plt.xticks([])
    plt.yticks([])
    plt.colorbar(fraction=0.046, pad=0.04)
plt.show()
ts = sample_time_series(cov_series)
print(ts.shape)
for j in range(5):
    plt.plot(ts[:, j], alpha=0.5)
plt.show()<jupyter_output><empty_output>
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