def test_dss0(n_bad_chans):
"""Test dss0.
Find the linear combinations of multichannel data that
maximize repeatability over trials. Data are time * channel * trials.
Uses dss0().
`n_bad_chans` set the values of the first corresponding number of channels
to zero.
"""
n_samples = 300
data, source = create_data(n_samples=n_samples, n_bad_chans=n_bad_chans)
# apply DSS to clean them
c0, _ = tscov(data)
c1, _ = tscov(np.mean(data, 2))
[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)
z = fold(np.dot(unfold(data), todss), epoch_size=n_samples)
best_comp = np.mean(z[:, 0, :], -1)
scale = np.ptp(best_comp) / np.ptp(source)
assert_allclose(np.abs(best_comp), np.abs(np.squeeze(source)) * scale,
atol=1e-6) # use abs as DSS component might be flipped
def test_dss0(n_bad_chans):
"""Test dss0.
Find the linear combinations of multichannel data that
maximize repeatability over trials. Data are time * channel * trials.
Uses dss0().
`n_bad_chans` set the values of the first corresponding number of channels
to zero.
"""
# create synthetic data
n_samples = 100 * 3
n_chans = 30
n_trials = 100
noise_dim = 20 # dimensionality of noise
# source
source = np.hstack(
(np.zeros((n_samples // 3, )),
np.sin(2 * np.pi * np.arange(n_samples // 3) / (n_samples / 3)).T,
np.zeros((n_samples // 3, ))))[np.newaxis].T
s = source * np.random.randn(1, n_chans) # 300 * 30
s = s[:, :, np.newaxis]
s = np.tile(s, (1, 1, 100))
# set first `n_bad_chans` to zero
s[:, :n_bad_chans] = 0.
# noise
noise = np.dot(unfold(np.random.randn(n_samples, noise_dim, n_trials)),
np.random.randn(noise_dim, n_chans))
noise = fold(noise, n_samples)
# mix signal and noise
SNR = 0.1
data = noise / rms(noise.flatten()) + SNR * s / rms(s.flatten())
# apply DSS to clean them
c0, _ = tscov(data)
c1, _ = tscov(np.mean(data, 2))
[todss, _, pwr0, pwr1] = dss.dss0(c0, c1)
z = fold(np.dot(unfold(data), todss), epoch_size=n_samples)
best_comp = np.mean(z[:, 0, :], -1)
scale = np.ptp(best_comp) / np.ptp(source)
assert_allclose(np.abs(best_comp),
np.abs(np.squeeze(source)) * scale,
atol=1e-6) # use abs as DSS component might be flipped
SNR = 0.1 data = noise / rms(noise.flatten()) + SNR * s / rms(s.flatten()) ############################################################################### # Apply DSS to clean them # ----------------------------------------------------------------------------- # Compute original and biased covariance matrices c0, _ = tscov(data) # In this case the biased covariance is simply the covariance of the mean over # trials c1, _ = tscov(np.mean(data, 2)) # Apply DSS [todss, _, pwr0, pwr1] = dss.dss0(c0, c1) z = fold(np.dot(unfold(data), todss), epoch_size=n_samples) # Find best components best_comp = np.mean(z[:, 0, :], -1) ############################################################################### # Plot results # ----------------------------------------------------------------------------- f, (ax1, ax2, ax3) = plt.subplots(3, 1) ax1.plot(source, label='source') ax2.plot(np.mean(data, 2), label='data') ax3.plot(best_comp, label='recovered') plt.legend() plt.show()
