def create_data(n_samples=100 * 3, n_chans=30, n_trials=100, noise_dim=20,
n_bad_chans=1, SNR=.1, show=False):
"""Create synthetic data.
Parameters
----------
n_samples : int
[description], by default 100*3
n_chans : int
[description], by default 30
n_trials : int
[description], by default 100
noise_dim : int
Dimensionality of noise, by default 20
n_bad_chans : int
[description], by default 1
Returns
-------
data : ndarray, shape=(n_samples, n_chans, n_trials)
source : ndarray, shape=(n_samples,)
"""
# 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
data = noise / rms(noise.flatten()) + SNR * s / rms(s.flatten())
if show:
f, ax = plt.subplots(3)
ax[0].plot(source[:, 0], label='source')
ax[1].plot(noise[:, 1, 0], label='noise')
ax[2].plot(data[:, 1, 0], label='mixture')
ax[0].legend()
ax[1].legend()
ax[2].legend()
plt.show()
return data, source
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
def create_data(n_times,
n_chans=10,
n_trials=20,
freq=12,
sfreq=250,
noise_dim=8,
SNR=.8,
t0=100,
show=False):
"""Create synthetic data.
Returns
-------
noisy_data: array, shape=(n_times, n_channels, n_trials)
Simulated data with oscillatory component strting at t0.
"""
# source
source = np.sin(2 * np.pi * freq * np.arange(n_times - t0) / sfreq)[None].T
s = source * np.random.randn(1, n_chans)
s = s[:, :, np.newaxis]
s = np.tile(s, (1, 1, n_trials))
signal = np.zeros((n_times, n_chans, n_trials))
signal[t0:, :, :] = s
# noise
noise = np.dot(unfold(np.random.randn(n_times, noise_dim, n_trials)),
np.random.randn(noise_dim, n_chans))
noise = fold(noise, n_times)
# mix signal and noise
signal = SNR * signal / rms(signal.flatten())
noise = noise / rms(noise.flatten())
noisy_data = signal + noise
if show:
f, ax = plt.subplots(3)
ax[0].plot(signal[:, 0, 0], label='source')
ax[1].plot(noise[:, 1, 0], label='noise')
ax[2].plot(noisy_data[:, 1, 0], label='mixture')
ax[0].legend()
ax[1].legend()
ax[2].legend()
plt.show()
return noisy_data, signal
def _stim_data(n_times, n_chans, n_trials, noise_dim, SNR=1, t0=100):
"""Create synthetic data."""
# source
source = np.sin(2 * np.pi * np.linspace(0, .5, n_times - t0))[np.newaxis].T
s = source * np.random.randn(1, n_chans)
s = s[:, :, np.newaxis]
s = np.tile(s, (1, 1, n_trials))
signal = np.zeros((n_times, n_chans, n_trials))
signal[t0:, :, :] = s
# noise
noise = np.dot(unfold(np.random.randn(n_times, noise_dim, n_trials)),
np.random.randn(noise_dim, n_chans))
noise = fold(noise, n_times)
# mix signal and noise
signal = SNR * signal / rms(signal.flatten())
noise = noise / rms(noise.flatten())
noisy_data = signal + noise
return noisy_data, signal
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))
# 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
# -----------------------------------------------------------------------------
# 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)
# source
source = np.sin(2 * np.pi * target * np.arange(n_times - t0) / sfreq)[None].T
s = source * np.random.randn(1, n_chans)
s = s[:, :, np.newaxis]
s = np.tile(s, (1, 1, n_trials))
signal = np.zeros((n_times, n_chans, n_trials))
signal[t0:, :, :] = s
# noise
noise = np.dot(unfold(np.random.randn(n_times, noise_dim, n_trials)),
np.random.randn(noise_dim, n_chans))
noise = fold(noise, n_times)
# mix signal and noise
signal = SNR * signal / rms(signal.flatten())
noise = noise / rms(noise.flatten())
data = signal + noise
# Plot
f, ax = plt.subplots(3)
ax[0].plot(signal[:, 0, 0], c='C0', label='source')
ax[1].plot(noise[:, 1, 0], c='C1', label='noise')
ax[2].plot(data[:, 1, 0], c='C2', label='mixture')
ax[0].legend()
ax[1].legend()
ax[2].legend()
###############################################################################
# Enhance oscillatory activity using RESS
# -----------------------------------------------------------------------------
